Indicadores desagregados por territorio histórico

Fin de la Pobreza

1.1.1 Proporción de la población que vive por debajo del umbral internacional de pobreza, desglosada por sexo, edad, situación laboral y ubicación geográfica (urbana o rural)
1.2.1 Proporción de la población que vive por debajo del umbral nacional de pobreza, desglosada por sexo y edad
1.2.2 Proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza, en todas sus dimensiones, con arreglo a las definiciones nacionales
1.3.1 Proporción de la población cubierta por sistemas o niveles mínimos de protección social, desglosada por sexo, distinguiendo entre los niños, los desempleados, los ancianos, las personas con discapacidad, las mujeres embarazadas, los recién nacidos, las víctimas de accidentes de trabajo, los pobres y los vulnerables
1.4.1 Proporción de la población que vive en hogares con acceso a los servicios básicos
1.4.2 Proporción del total de la población adulta con derechos seguros de tenencia de la tierra: a) que posee documentación reconocida legalmente al respecto y b) considera seguros sus derechos, desglosada por sexo y tipo de tenencia
1.5.1 Número de personas muertas, desaparecidas y afectadas directamente atribuido a desastres por cada 100.000 habitantes
1.5.2 Pérdidas económicas directas atribuidas a los desastres en relación con el producto interno bruto (PIB) mundial
1.5.3 Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030
1.5.4 Proporción de gobiernos locales que adoptan y aplican estrategias locales de reducción del riesgo de desastres en consonancia con las estrategias nacionales de reducción del riesgo de desastres
1.a.1 Total de las subvenciones de asistencia oficial para el desarrollo de todos los donantes que se centran en la reducción de la pobreza como porcentaje del ingreso nacional bruto del país receptor
1.a.2 Proporción del gasto público total que se dedica a servicios esenciales (educación, salud y protección social)
1.b.1 Gasto público social en favor de los pobres
Hambre Cero

Salud y Bienestar

Educación de Calidad

Igualdad de Género

Agua Limpia y Saneamiento

Energía Asequible y No Contaminante

Trabajo decente y crecimiento económico

Industria, Innovación e Infraestructura

Reducción de las Desigualdades

Ciudades y comunidades sostenibles

Producción y Consumo Responsables

Acción por el Clima

Vida Submarina

Vida de Ecosistemas Terrestres

Paz, justicia e instituciones fuertes

Alianzas para Lograr los Objetivos

Objetivo OCECA

[{"number"=>"1.1.1", "slug"=>"1-1-1", "name"=>"Proporción de la población que vive por debajo del umbral internacional de pobreza, desglosada por sexo, edad, situación laboral y ubicación geográfica (urbana o rural)", "url"=>"/site/es/1-1-1/", "sort"=>"010101", "goal_number"=>"1", "target_number"=>"1.1", "global"=>{"name"=>"Proporción de la población que vive por debajo del umbral internacional de pobreza, desglosada por sexo, edad, situación laboral y ubicación geográfica (urbana o rural)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[{"unit"=>"Porcentaje", "minimum"=>0, "maximum"=>5}], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Proporción de la población que vive por debajo del umbral internacional de la pobreza", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción de la población que vive por debajo del umbral internacional de pobreza, desglosada por sexo, edad, situación laboral y ubicación geográfica (urbana o rural)", "indicator_number"=>"1.1.1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Meta:</b> Erradicar la pobreza extrema", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Bienestar, Juventud y Reto Demográfico", "periodicity"=>"Bienal", "url"=>"https://www.euskadi.eus/encuesta-de-pobreza-y-desigualdades-sociales-epds/web01-s2enple/es/", "url_text"=>"Encuesta de pobreza y desigualdades sociales (EPDS)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "x_axis_label"=>"", "indicador_disponible"=>"Proporción de la población que vive por debajo del umbral internacional de la pobreza", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.1- De aquí a 2030, erradicar para todas las personas y en todo el mundo la pobreza extrema (actualmente se considera que sufren pobreza extrema las personas que viven con menos de 1,25 dólares de los Estados Unidos al día)", "definicion"=>"Proporción de personas cuyo ingreso neto equivalente, después de las transferencias sociales,  es inferior al umbral internacional de pobreza, establecido en 2,15 dólares estadounidenses  en paridad de poder adquisitivo (PPA) de 2017.", "formula"=>"\n$$PP_{UIP}^{t} = \\frac{P_{UIP}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$P_{UIP}^{t} =$ población con ingresos neto equivalente inferior al umbral internacional de la pobreza, en el año $t$\n\n$P^{t} =$ población total en el año $t$\n", "desagregacion"=>"", "periodicidad"=>"Bienal", "observaciones"=>"\nPara tener en cuenta el impacto de las diferencias en el tamaño y la composición del hogar, el ingreso disponible de un hogar se divide por el número de miembros del hogar convertidos en adultos  equivalentes (unidades de consumo). \nEl número de unidades de consumo de un hogar se calcula utilizando la escala OCDE modificada, que asigna un peso de 1 a la primera persona de 14 o más años, un peso de 0,5 al resto de personas de 14 o más años y un peso de 0,3 a las personas de menos de 14 años.", "justificacion_global"=>"Las líneas de pobreza varían según el poder adquisitivo de los países y \npresentan un fuerte gradiente económico, de modo que los países más ricos \ntienden a adoptar estándares de vida más altos para definir la pobreza. \n\nSin embargo, para medir de forma consistente la pobreza absoluta global en \ntérminos de consumo, es necesario tratar a dos personas con el mismo poder \nadquisitivo sobre bienes básicos de la misma manera —ambas son pobres o \nno pobres—, incluso si viven en países diferentes.\n\nDesde el Informe sobre el Desarrollo Mundial de 1990, el Banco Mundial se ha \npropuesto aplicar un estándar común para medir la pobreza extrema, basado \nen lo que significa la pobreza en los países más pobres del mundo. El \nbienestar de las personas que viven en diferentes países puede medirse en \nuna escala común ajustando las diferencias en el poder adquisitivo de las \nmonedas. \n\nEl estándar comúnmente utilizado de 1 dólar al día, medido a precios \ninternacionales de 1985 y ajustado a la moneda local utilizando tipos \nde cambio de paridad de poder adquisitivo (PPA), se eligió para el \nInforme sobre el Desarrollo Mundial de 1990 porque era típico de las \nlíneas de pobreza en los países de bajos ingresos de la época. A medida \nque evolucionan las diferencias en el costo de vida a nivel mundial, la \n línea de pobreza internacional debe actualizarse periódicamente \nutilizando nuevos datos de precios PPA para reflejar estos cambios. \nEl último cambio se produjo en septiembre de 2022, cuando el Banco \nMundial adoptó 2,15 dólares como línea de pobreza internacional \nutilizando la PPA de 2017.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01a.pdf\">Metadatos 1-1-1 (1).pdf</a> (solo en inglés)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01b.pdf\">Metadatos 1-1-1 (2).pdf</a> (solo en inglés)\n", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas, pero aporta\ninformación similar.\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.1.1&seriesCode=SI_POV_DAY1&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAGE%20%7C%20ALLAREA%20%7C%20BOTHSEX\">Proporción de población por debajo del umbral internacional de pobreza (%) SI_POV_DAY1</a> UNSTATS", "informacion_interes"=>"<a href=\"https://pip.worldbank.org/home\">Plataforma de Pobreza y Desigualdad del Banco Mundial</a>", "en"=>{"indicador_disponible"=>"Proporción de la población que vive por debajo del umbral internacional de la pobreza", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.1- De aquí a 2030, erradicar para todas las personas y en todo el mundo la pobreza extrema (actualmente se considera que sufren pobreza extrema las personas que viven con menos de 1,25 dólares de los Estados Unidos al día)", "definicion"=>"Proportion of people whose net equivalent income, after social transfers,  is below the international poverty line of USD2.15  in purchasing power parity (PPP) of 2017.", "formula"=>"\n$$PP_{IPL}^{t} = \\frac{P_{IPL}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$P_{IPL}^{t} =$ population with net equivalent income below the international poverty line, in year $t$\n\n$P^{t} =$ total population in year $t$\n", "desagregacion"=>nil, "periodicidad"=>"Bienal", "observaciones"=>"\nTo account for the impact of differences in household size and composition,  a household's disposable income is divided by the number of household members converted to adult  equivalents (units of consumption).\nThe number of consumption units in a household  is calculated using the modified OECD scale, which assigns a weight of 1 to the  first person aged 14 or over, a weight of 0.5 for other people aged 14 or over  and a weight of 0.3 for people under 14 years of age.", "justificacion_global"=>"Poverty lines vary according to the purchasing power of countries and \nhave a strong economic gradient, so that the richest countries \ntend to adopt higher standards of living to define poverty.\n\nHowever, to consistently measure global absolute poverty in \nterms of consumption, it is necessary to treat two people with the same \npurchasing power over basic goods in the same way —both are poor or not poor— \neven if they live in different countries.\n\nSince the 1990 World Development Report, the World Bank has \nproposed to apply a common standard to measure extreme poverty, based on \non what poverty means in the poorest countries of the world. The \nwell-being of people living in different countries can be measured in \na common scale adjusting for differences in the purchasing power of the \ncurrencies.\n\nThe commonly used standard of USD1 a day, measured at 1985 prices \nand adjusted to the local currency using rates \nof purchasing power parity (PPP), was chosen for the \nWorld Development Report of 1990 because it was typical of the \npoverty lines in low-income countries at the time. Bespoke \nthat differences in the cost of living are evolving worldwide, the \nInternational Poverty Line must be updated regularly \nusing new PPP price data to reflect these changes. \nThe last change occurred in September 2022, when the World Bank \nadopted USD2.15 as international poverty line \nusing the 2017 PPP.\n\nSource: United Nations Statistics Division\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01a.pdf\">Metadata 1-1-1 (1).pdf</a>\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01b.pdf\">Metadata 1-1-1 (2).pdf</a>\n", "comparabilidad"=>"The available indicator does not comply with the metadata of the United Nations indicator, but provides \nsimilar information.\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.1.1&seriesCode=SI_POV_DAY1&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAGE%20%7C%20ALLAREA%20%7C%20BOTHSEX\">Proportion of population below the international poverty line (%) SI_POV_DAY1</a> UNSTATS", "informacion_interes"=>"<a href=\"https://pip.worldbank.org/home\">World Bank Poverty and Inequality Platform </a>"}, "eu"=>{"indicador_disponible"=>"Proporción de la población que vive por debajo del umbral internacional de la pobreza", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.1- De aquí a 2030, erradicar para todas las personas y en todo el mundo la pobreza extrema (actualmente se considera que sufren pobreza extrema las personas que viven con menos de 1,25 dólares de los Estados Unidos al día)", "definicion"=>"Transferentzia sozialen ondoren, diru-sarrera garbi baliokidea pobreziaren  nazioarteko atalasea baino txikiagoa duten pertsonen proportzioa. Muga hori  AEBetako 2,15 dolarretan dago ezarrita, 2017ko erosteko ahalmenaren parekotasunean.  ", "formula"=>"\n$$PP_{UIP}^{t} = \\frac{P_{UIP}^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$P_{UIP}^{t} =$ pobreziaren nazioarteko atalasea baino diru-sarrera garbi baliokide \ntxikiagoa duten biztanleak, $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urtean\n", "desagregacion"=>nil, "periodicidad"=>"Bienal", "observaciones"=>"\nEtxearen tamainan eta osaeran dauden desberdintasunen eragina kontuan hartzeko,  etxeko diru-sarrera erabilgarria zatitu egiten da heldu baliokide bihurtutako etxeko kideen artean (kontsumo-unitateak). \nEtxeko kontsumo-unitateen kopurua kalkulatzeko, ELGA eskala aldatua erabiltzen da.  Eskala horrek 1 pisua esleitzen dio 14 urte edo gehiagoko lehen pertsonari, 0,5 pisua 14 urte edo  gehiagoko gainerako pertsonei eta 0,3 pisua 14 urtetik beherako pertsonei.", "justificacion_global"=>"Pobrezia-lerroak aldatu egiten dira herrialdeen erosahalmenaren arabera, eta gradiente ekonomiko \nesanguratsua dute. Hori dela eta, herrialde aberatsenek bizi-estandar altuagoak ezarri ohi dituzte \npobrezia zehazteko. \n\nHala ere, pobrezia absolutu globala kontsumoaren arabera neurtu nahi bada, beharrezkoa da oinarrizko \nondasunen gainean erosahalmen berdina duten bi pertsona berdin tratatzea –biak pobreak edo biak ez-pobreak–, \nbaita herrialde desberdinetan bizi badira ere. \n\nMundu Mailako Garapenari buruzko 1990eko Txostena egin zenetik, Munduko Bankuak estandar komun bat ezarri \nnahi izan du muturreko pobrezia neurtzeko, pobreziak munduko herrialde pobreenetan zer esan nahi duen \noinarri hartuta. Herrialde desberdinetan bizi diren pertsonen ongizatea eskala komun batean neur daiteke, \nmoneten erosahalmenean dauden aldeak doituz. \n\nNormalean erabiltzen den estandarra eguneko dolar 1ekoa da, 1985eko nazioarteko prezioen arabera neurtua \neta tokiko dibisara egokitua, erosahalmenaren paritatearen (EAP) aldaketa-tasak erabiliz. Estandar hori \nMundu Mailako Garapenari buruzko 1990eko Txostenerako aukeratu zen, garai hartan diru-sarrera baxuko \nherrialdeetako pobrezia-lerroetan ohikoa zelako. Mundu-mailan bizitzaren kostuan dauden aldeak aldatu \nahala, nazioarteko pobrezia-lerroa eguneratu egin behar da, EAP prezioen datu berriak erabilita, aldaketa \nhoriek islatzek aldera. Azken aldaketa 2022ko irailean izan zen. Munduko Bankuak 2,15 dolar ezarri zituen \nnazioarteko pobrezia-lerro gisa, 2017ko EAP baliatuta.  \n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01a.pdf\">Metadatuak 1-1-1 (1).pdf</a> (ingelesez bakarrik)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01b.pdf\">Metadatuak 1-1-1 (2).pdf</a> (ingelesez bakarrik)\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina \nantzeko informazioa ematen du. \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.1.1&seriesCode=SI_POV_DAY1&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAGE%20%7C%20ALLAREA%20%7C%20BOTHSEX\">Nazioarteko pobrezia-atalasearen azpitik dagoen biztanleriaren proportzioa (%) SI_POV_DAY1</a> UNSTATS", "informacion_interes"=>"<a href=\"https://pip.worldbank.org/home\">Munduko Bankuaren Pobreziaren eta Desberdintasunaren Plataforma</a>"}, "national_metadata_updated_date"=>"2025-03-15", "national_data_updated_date"=>"2025-05-08", "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.1: By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.1.1: Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SI_POV_EMP1 - Employed population below international poverty line [1.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.3.1, 8.2.1, 8.3.1, 8.5.1, 8.5.2, 10.4.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The proportion of the employed population below the international poverty line of US$2.15 per day, also referred to as the working poverty rate, is defined as the share of employed persons living in households with per-capita consumption or income that is below the international poverty line of US$2.15.</p>\n<p><strong>Concepts:</strong></p>\n<p>Employment: All persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit.</p>\n<p>Poverty Line: Threshold below which individuals in the reference population are considered poor and above which they are considered non-poor. The threshold is generally defined as the per-capita monetary requirements an individual needs to afford the purchase of a basic bundle of goods and services. For the purpose of this indicator, an absolute international poverty line of US$2.15 per day is used.</p>\n<p>Household in poverty: Households are defined as poor if their income or consumption expenditure is below the poverty line taking into account the number of household members and composition (e.g., number of adults and children).</p>\n<p>Working poor: Employed persons living in households that are classified as poor, that is, that have income or consumption levels below the poverty line used for measurement.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The series is disaggregated by sex and age, for which there are no standard international classifications. The age groups refer to all persons (aged 15+), youth (aged 15-24) and adults (aged 25+).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The preferred data source is a household survey with variables that can reliably identify both the poverty status of households and the economic activity of the household&#x2019;s members. Examples include household income and expenditure surveys (HIES), living standards measurement surveys (LSMS) with employment modules, or labour force surveys (LFS) that collect information on household income. Such surveys offer the benefit of allowing the employment status and income (or consumption expenditure) variables to be derived from the same sampled households ideally for the same observation period.</p>\n<p>Employment estimates derived from a household survey other than a labour force survey may, however, not be the most robust due to questionnaire design. Similarly, a labour force survey may not be the best instrument for collecting household income or consumption expenditure data, although an attached income module can be designed to achieve statistically reliable results, including ensuring an overlap in the observation period between household income (or consumption expenditure) and employment status. </p>\n<p>Another possibility is to combine data from a household income and expenditure survey and from a separate labour force survey when the respondent households can be matched and consistency in the long observation period between the surveys can be obtained.</p>", "COLL_METHOD__GLOBAL"=>"<p>The ILO processes national household survey microdatasets in line with internationally-agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians (ICLS). </p>", "FREQ_COLL__GLOBAL"=>"<p>Continuous</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are updated once per year (in November or December).</p>", "DATA_SOURCE__GLOBAL"=>"<p>Mainly National Statistical Offices.</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "INST_MANDATE__GLOBAL"=>"<p>The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians (ICLS). It also compiles and produces labour statistics with the goal of disseminating internationally-comparable datasets and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.</p>", "RATIONALE__GLOBAL"=>"<p>In order to eradicate poverty, we must understand the root causes of poverty. The working poverty rate reveals the proportion of the employed population living in poverty despite being employed, implying that their employment-related incomes are not sufficient to lift them and their families out of poverty and ensure decent living conditions. The adequacy of earnings is a fundamental aspect of job quality, and these deficits in job quality could be keeping workers and their families in poverty.</p>\n<p>The proportion of working poor in total employment (that is, the working poverty rate) combines data on household income or consumption with labour force framework variables measured at the individual level and sheds light on the relationship between employment and household poverty.</p>", "REC_USE_LIM__GLOBAL"=>"<p>At the country level, comparisons over time may be affected by such factors as changes in survey types or data collection methods. The use of Purchasing Power Parity (PPP) rather than market exchange rates ensures that differences in price levels across countries are taken into account. However, it cannot be categorically asserted that two people in two different countries, living below US$2.15 a day at PPP, face the same degree of deprivation or have the same degree of need.</p>\n<p>Poverty in the context of this indicator is a concept that is applied to households, and not to individuals, based on the assumption that households pool their income. This assumption may not always be true.</p>\n<p>Moreover, the poverty status of a household is a function of the wage and other employment-related income secured by those household members in employment, income derived from asset ownership, plus any other available income such as transfer payments and the number of household members. Whether a worker is counted as working poor therefore depends on his or her own income, the income of other household members and the number of household members who need to be supported. It is thus often valuable to study household structure in relation to working poverty. </p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">W</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">k</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">g</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">v</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">E</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">U</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">$</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>2</mn>\n        <mo>.</mo>\n        <mn>15</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">y</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The ILO engages in annual consultations with Member States through the ILOSTAT questionnaire and related Statistics Reporting System (StaRS). National data providers receive a link to the portal where they can review all national SDG data available on ILOSTAT.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Through the ILO Harmonized Microdata initiative, the ILO strives to produce internationally comparable labour statistics based on the indicator concepts and definitions adopted by the International Conference of Labour Statisticians (ICLS).</p>", "IMPUTATION__GLOBAL"=>"<p>&#x2022; Estimates are produced for countries and years for which no direct working poverty estimates are available based on household survey estimates, but for which total poverty estimates are available in the World Bank&#x2019;s PovcalNet database. This is carried out through a multivariate regression model described in &#x201C;Employment and economic class in the developing world&#x201D; (Kapsos and Bourmpoula, 2013), available at <a href=\"http://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_216451.pdf\">http://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_216451.pdf</a>.</p>\n<p>&#x2022; Following the step described directly above, missing data at the national level are estimated through a multivariate regression model for the purpose of producing global and regional estimates.</p>", "REG_AGG__GLOBAL"=>"<p>The ILO produces global and regional estimates of employment by economic class (and thus, of working poverty rates) using the ILO&#x2019;s Employment by Class model. These estimates are part of the ILO Estimates and Projections series, analysed in the ILO&apos;s World Employment and Social Outlook reports. For more information, on the model used to derive these estimates, refer to the ILO paper &#x201C;Employment and economic class in the developing world&#x201D; (Kapsos and Bourmpoula, 2013), available at <a href=\"http://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_216451.pdf\">http://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_216451.pdf</a>.</p>", "DOC_METHOD__GLOBAL"=>"<p>National poverty estimates will differ from this SDG indicator. This SDG indicator uses the international poverty line of US$2.15 at purchasing power parity. For further information, see: Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (ILO) <a href=\"https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The processes of compilation, production, and publication of data, including its quality control, are carried out following the methodological framework and standards established by the ILO Department of Statistics, in compliance with the information technology and management standards of the ILO.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination on ILOSTAT. These checks consist of data and metadata revision of all the relevant inputs applying protocols to ensure that international comparability and time-series consistency are maintained. For the resulting modelled estimates, both statistical and judgmental assessments of the output data are carried out.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The final assessment of the quality of information is carried out by the Data Production and Analysis Unit of the ILO Department of Statistics. If any issues encountered cannot be clarified, the respective information is not published. </p>", "COVERAGE__GLOBAL"=>"<p>Data may differ from those published on ILOSTAT due to different update schedules and stricter criteria applied for inclusion in the SDG database.</p>\n<p><strong>Data availability:</strong></p>\n<p>Data for this indicator is available for 86 countries and territories. </p>\n<p><strong>Time series:</strong></p>\n<p>This submission covers country data from 2000 to 2022. Global and regional aggregates are available from 2000 to 2024.</p>\n<p> </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The working poverty rate (proportion of employed persons living in poverty) is disaggregated by sex and age (youth and adults).</p>", "COMPARABILITY__GLOBAL"=>"<p>National poverty estimates will differ from this SDG indicator. This SDG indicator uses the international poverty line, currently set at US$2.15 at purchasing power parity. For further information, see: Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (ILO) <a href=\"https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm</a> </p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>ILOSTAT portal: https://ilostat.ilo.org </li>\n  <li>Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (ILO) <a href=\"https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm</a> </li>\n  <li>ILOSTAT (<a href=\"https://ilostat.ilo.org/\">https://ilostat.ilo.org/</a>).<ul>\n      <li>ILOSTAT&#x2019;s topic page on working poverty (https://ilostat.ilo.org/topics/working-poverty/)</li>\n    </ul>\n  </li>\n  <li>Employment and economic class in the developing world (Kapsos and Bourmpoula, 2013) <a href=\"http://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_216451.pdf\">http://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_216451.pdf</a></li>\n  <li>Decent Work Indicators Manual <a href=\"http://www.ilo.org/wcmsp5/groups/public/---dgreports/---integration/documents/publication/wcms_229374.pdf\">http://www.ilo.org/wcmsp5/groups/public/---dgreports/---integration/documents/publication/wcms_229374.pdf</a> (second version, page 70). </li>\n</ul>", "indicator_sort_order"=>"01-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"1.2.1", "slug"=>"1-2-1", "name"=>"Proporción de la población que vive por debajo del umbral nacional de pobreza, desglosada por sexo y edad", "url"=>"/site/es/1-2-1/", "sort"=>"010201", "goal_number"=>"1", "target_number"=>"1.2", "global"=>{"name"=>"Proporción de la población que vive por debajo del umbral nacional de pobreza, desglosada por sexo y edad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"related indicators", "embedded_feature_title"=>"", "embedded_feature_url"=>"https://eustat-des.github.io/site/embeded/1-2-1", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Proporción de la población que vive por debajo del umbral de la pobreza", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción de la población que vive por debajo del umbral nacional de pobreza, desglosada por sexo y edad", "indicator_number"=>"1.2.1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Meta:</b> Reducir a la mitad la población que vive en la pobreza en todas sus dimensiones", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Bienestar, Juventud y Reto Demográfico", "periodicity"=>"Bienal", "url"=>"https://www.euskadi.eus/encuesta-de-pobreza-y-desigualdades-sociales-epds/web01-s2enple/es/", "url_text"=>"Encuesta de pobreza y desigualdades sociales (EPDS)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Proporción de la población que vive por debajo del umbral de la pobreza", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"Proporción de personas con ingresos por unidad de consumo por debajo del  60% de la mediana de la C.A. de Euskadi (escala OCDE modificada)", "formula"=>"\n$$PP_{RPR}^{t} = \\frac{P_{RPR}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$P_{RPR}^{t} =$ población con ingresos por debajo del 60% de la mediana de los ingresos por unidad de consumo (escala OCDE modificada) en el año $t$\n\n$P^{t} =$ población total en el año $t$\n", "desagregacion"=>"Sexo", "periodicidad"=>"Bienal", "observaciones"=>"El número de unidades de consumo de un hogar se calcula utilizando la escala OCDE modificada, que asigna un peso de 1 a la primera persona de 14 o más años, un peso de 0,5 al resto de personas de 14 o más años y un peso de 0,3 a las personas de menos de 14 años.", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl indicador de riesgo de pobreza relativa (At-Risk-of-Poverty Rate en inglés) o pobreza monetaria\nes uno de los indicadores clave para hacer el seguimiento de la pobreza en la Unión Europea.\n\nEste indicador no mide ni la riqueza ni la pobreza, sino bajos ingresos en comparación con otros \nresidentes del territorio, lo que no necesariamente implica un bajo nivel de vida.\n\nLa tasa de riesgo de pobreza es la proporción de personas\n con una renta disponible equivalente (después de transferencias sociales) inferior al \numbral de riesgo de pobreza, que está fijada por convención en la UE en el \n60 % de la mediana de la renta disponible equivalente después de transferencias \nsociales en un territorio.\n\nPara tener en cuenta el impacto de las diferencias en el tamaño y la composición del hogar,\nel ingreso disponible de un hogar (ingreso total, después de impuestos y otras deducciones, que está \ndisponible para gastar o ahorrar) se divide por el número de miembros del hogar convertidos en adultos \nequivalentes (unidades de consumo). En la UE se aplica un factor de equivalencia calculado según \nla escala modificada de la OCDE propuesta en 1994.\n\n  Fuente: Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_01_20/default/table?lang=en&category=sdg.sdg_01\">Personas en riesgo de pobreza monetaria tras las transferencias sociales - Encuestas EU-SILC y ECHP(sdg_01_20)</a> Eurostat", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas. Para el cálculo  de la tasa de pobreza de la Comunidad Autónoma de Euskadi y sexo, se aplica  el umbral de la pobreza de la Comunidad Autónoma.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-02-01.pdf\">Metadatos 1-2-1.pdf</a> (solo en inglés)\n\n<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_01_20_esmsip2.htm\">Metadatos Eurostat (sdg_01_20)</a> (solo en inglés)\n", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-15", "en"=>{"indicador_disponible"=>"Proporción de la población que vive por debajo del umbral de la pobreza", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"Proportion of people with income per consumption unit below 60% of the median of  the Basque Autonomous Community (modified OECD scale)", "formula"=>"\n$$PP_{RPR}^{t} = \\frac{P_{RPR}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$P_{RPR}^{t} =$ population with income below 60% of median income per unit of consumption (modified OECD scale) in year $t$\n\n$P^{t} =$ total population in year $t$\n", "desagregacion"=>"Sex", "periodicidad"=>"Bienal", "observaciones"=>"The number of consumption units in a household  is calculated using the modified OECD scale, which assigns a weight of 1 to the  first person aged 14 or over, a weight of 0.5 for other people aged 14 or over  and a weight of 0.3 for people under 14 years of age.", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe At-Risk-of-Poverty Rate or monetary poverty is one of the key indicators \nfor monitoring poverty in the European Union.\n\nThis indicator measures neither wealth nor poverty, but low income in comparison with other residents of the territory, \nwhich does not necessarily imply a low standard of living.\n\nThe at-risk-of-poverty rate is the proportion of people \nwith an equivalent disposable income (after social transfers) below the \nat-risk-of-poverty threshold, which is set by convention in the EU in the \n60% of the median equivalent disposable income after social transfers \nin a territory.\n\nTo account for the impact of differences in household size and composition, \na household's disposable income (total income, after taxes and other deductions, which is \navailable to spend or save) is divided by the number of household members converted into adult \nequivalents (units of consumption). In the EU, an equivalence factor calculated according to \nthe modified OECD scale proposed in 1994 is applied.\n\nSource: Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_01_20/default/table?lang=en&category=sdg.sdg_01\">People at risk of monetary poverty after social transfers - EU-SILC and ECHP surveys(sdg_01_20)</a> Eurostat", "comparabilidad"=>"\nThe available indicator complies with the metadata of the United Nations indicator. For the calculation  of the poverty rate of the Autonomous Community of the Basque Country and sex, the poverty threshold   of the Autonomous Community applies.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-02-01.pdf\">Metadata 1-2-1.pdf</a>\n\n<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_01_20_esmsip2.htm\">Metadata Eurostat (sdg_01_20)</a>\n", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Proporción de la población que vive por debajo del umbral de la pobreza", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"EAEko medianaren % 60tik beherako kontsumo-unitateko diru-sarrerak  dituzten pertsonen proportzioa (ELGA eskala aldatua) ", "formula"=>"\n$$PP_{RPR}^{t} = \\frac{P_{RPR}^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$P_{RPR}^{t} =$ diru-sarreren medianaren % 60tik beherako kontsumo-unitateko diru-sarrerak dituzten biztanleak $t$ urtean (ELGA eskala aldatua)\n\n$P^{t} =$ biztanleria $t$ urtean\n", "desagregacion"=>"Sexua", "periodicidad"=>"Bienal", "observaciones"=>"Etxeko kontsumo-unitateen kopurua kalkulatzeko, ELGA eskala aldatua erabiltzen da.  Eskala horrek 1 pisua esleitzen dio 14 urte edo gehiagoko lehen pertsonari, 0,5 pisua 14 urte edo  gehiagoko gainerako pertsonei eta 0,3 pisua 14 urtetik beherako pertsonei.  ", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEuropar Batasunean pobreziaren jarraipena egiteko adierazle gakoetako bat pobrezia erlatiboaren arriskuaren adierazlea \n(At-Risk-of-Poverty Rate, ingelesez) edo diru-pobrezia izenekoa da. \n\nAdierazle horrek ez du neurtzen ez aberastasuna ez pobrezia, baizik eta diru-sarrera baxuak, lurraldeko beste biztanle \nbatzuekin alderatuta, nahiz eta horrek ez duen zertan bizi-maila baxua adierazi. \n\nPobrezia-arriskuaren tasa honela zehazten da: pobrezia-arriskuaren atalasea baino errenta baliokide eskuragarri \nbaxuagoa duten pertsonen proportzioa (transferentzia sozialen ostean). EBn atalase hori hitzarmen bidez zehazten \nda: errenta baliokide eskuragarriaren medianaren % 60, lurralde bateko transferentzia sozialen ostean. \n\nAldeek etxe baten tamainan eta osaeran duten eragina kontuan hartzeko, etxe baten diru-sarrera eskuragarria \n(diru-sarrerak guztira, gastatzeko edo aurrezteko moduan daudenak, zergak eta beste kenkari batzuk aplikatu ondoren) \nzatitu egin behar da heldu bihurtu diren etxeko kide baliokideen arabera (kontsumo-unitateak). EBn aplikatzen den \nbaliokidetasun-faktorea ELGAren eskala aldatuaren arabera kalkulatzen da (1994an proposatua). \n\n\nIturria: Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_01_20/default/table?lang=en&category=sdg.sdg_01\">Gizarte-transferentzien ondoren diru-pobreziaren arriskuan dauden pertsonak - EU-SILC eta ECHP inkestak(sdg_01_20)</a> Eurostat", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu. EAEko pobrezia-tasa  eta sexua kalkulatzeko, autonomia-erkidegoko pobrezia-atalasea aplikatzen da. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-02-01.pdf\">Metadatuak 1-2-1.pdf</a> (ingelesez bakarrik)\n\n<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_01_20_esmsip2.htm\">Metadatuak Eurostat (sdg_01_20)</a> (ingelesez bakarrik)\n", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.2: By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.2.1: Proportion of population living below the national poverty line, by sex and age</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SI_POV_NAHC - Proportion of population living below the national poverty line [1.2.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.1.1: Proportion of population below the international poverty line, by sex, age, employment status and geographical location (urban/rural)</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The national poverty rate is the percentage of the total population living below the national poverty line. The rural poverty rate is the percentage of the rural population living below the national poverty line (or in cases where a separate, rural poverty line is used, the rural poverty line). Urban poverty rate is the percentage of the urban population living below the national poverty line (or in cases where a separate, urban poverty line is used, the urban poverty line). </p>\n<p><strong>Concepts:</strong></p>\n<p>In assessing poverty in a given country, and how best to reduce poverty according to national definitions, one naturally focuses on a poverty line that is considered appropriate for that country. Poverty lines across countries vary in terms of their purchasing power, and they have a strong economic gradient, such that richer countries tend to adopt higher standards of living in defining poverty. Within a country, the cost of living is typically higher in urban areas than in rural areas. Some countries may have separate urban and rural poverty lines to represent different purchasing powers. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%). The unit of measures is the proportion of the population.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>National poverty estimates are typically produced and owned by country governments (e.g., National Statistic Office), and sometimes with technical assistance from the World Bank and UNDP. Upon release of the national poverty estimates by the government, the Global Poverty Working Group of the World Bank assesses the methodology used by the government, validates the estimates with raw data whenever possible, and consults the country economists for publishing. Accepted estimates, along with metadata, will be published in the WDI database as well as the Poverty and Inequality Platform of the World Bank. </p>\n<p>Another source is World Bank&#x2019;s Poverty Assessments. The World Bank periodically prepares poverty assessments of countries in which it has an active program, in close collaboration with national institutions, other development agencies, and civil society groups, including poor people&#x2019;s organizations. Poverty assessments report the extent and causes of poverty and propose strategies to reduce it. The poverty assessments are the best available source of information on poverty estimates using national poverty lines. They often include separate assessments of urban and rural poverty. </p>", "COLL_METHOD__GLOBAL"=>"<p>Source collection is ongoing by the Global Poverty Working Group of the World Bank. </p>", "FREQ_COLL__GLOBAL"=>"<p>The schedule of source collection is determined by the country governments. Some are annual, and most others are less frequent. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The data are published in the World Development Indicators (WDI) and are updated every April and October.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistic Offices</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Bank &#x2013; Global Poverty Working Group</p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>Monitoring national poverty is important for country-specific development agendas. National poverty lines are used to make more accurate estimates of poverty consistent with the country&#x2019;s specific economic and social circumstances, and are not intended for international comparisons of poverty rates. </p>", "REC_USE_LIM__GLOBAL"=>"<p>National poverty estimates are derived from household survey data. Caveats and limitations inherent to survey data applying to the construction of indicator 1.1.1 apply here as well. </p>\n<p>To be useful for poverty estimates, surveys must be nationally representative. They must also include enough information to compute a comprehensive estimate of total household consumption or income (including consumption or income from own production) and to construct a correctly weighted distribution of consumption or income per person.</p>\n<p>Consumption is the preferred welfare indicator for a number of reasons<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>. Income is generally more difficult to measure accurately. For example, the poor who work in the informal sector may not receive or report monetary wages; self-employed workers often experience irregular income flows; and many people in rural areas depend on idiosyncratic, agricultural incomes. Moreover, consumption accords better with the idea of the standard of living than income, which can vary over time even if the actual standard of living does not. Thus, whenever possible, consumption-based welfare indicators are used to estimate the poverty measures reported here. But consumption data are not always available. For instance in Latin America and the Caribbean, the vast majority of countries collect primarily income data. In those cases there is little choice but to use income data.</p>\n<p>Consumption is measured by using household survey questions on food and nonfood expenditures as well as food consumed from the household&#x2019;s own production, which is particularly important in the poorest developing countries. This information is collected either through recall questions using lists of consumption items or through diaries in which respondents record all expenditures daily. But these methods do not always provide equivalent information, and depending on the approach used, consumption can be underestimated or overestimated. Different surveys use different recall or reference periods. Depending on the true flow of expenditures, the rate of spending reported is sensitive to the length of reporting period. The longer the reference period, the more likely respondents will fail to recall certain expenses&#x2014;especially food items&#x2014;thus resulting in underestimation of true expenditure.</p>\n<p>Best-practice surveys administer detailed lists of specific consumption items. These individual items collected through the questionnaires are aggregated afterwards. But many surveys use questionnaires in which respondents are asked to report expenditures for broad categories of goods. In other words, specific consumption items are implicitly aggregated by virtue of the questionnaire design. This shortens the interview, reducing the cost of the survey. A shorter questionnaire is also thought to reduce the likelihood of fatigue for both respondents and interviewers, which can lead to reporting errors. However, there is also evidence that less detailed coverage of specific items in the questionnaire can lead to underestimation of actual household consumption. The reuse of questionnaires may cause new consumption goods to be omitted, leading to further underreporting.</p>\n<p>Invariably some sampled households do not participate in surveys because they refuse to do so or because nobody is at home. This is often referred to as &#x201C;unit nonresponse&#x201D; and is distinct from &#x201C;item nonresponse,&#x201D; which occurs when some of the sampled respondents participate but refuse to answer certain questions, such as those pertaining to consumption or income. To the extent that survey nonresponse is random, there is no concern regarding biases in survey-based inferences; the sample will still be representative of the population. However, households with different incomes are not equally likely to respond. Relatively rich households may be less likely to participate because of the high opportunity cost of their time or because of concerns about intrusion in their affairs. It is conceivable that the poorest can likewise be underrepresented; some are homeless and hard to reach in standard household survey designs, and some may be physically or socially isolated and thus less easily interviewed. If nonresponse systematically increases with income, surveys will tend to overestimate poverty. But if compliance tends to be lower for both the very poor and the very rich, there will be potentially offsetting effects on the measured incidence of poverty.</p>\n<p>Even if survey data were entirely accurate and comprehensive, the measure of poverty obtained could still fail to capture important aspects of individual welfare. For example, using household consumption measures ignores potential inequalities within households. Thus, consumption- or income-based poverty measures are informative but should not be interpreted as a sufficient statistic for assessing the quality of people&#x2019;s lives. The national poverty rate, a &#x201C;headcount&#x201D; measure, is one of the most commonly calculated measures of poverty. Yet it has the drawback that it does not capture income inequality among the poor or the depth of poverty. For instance, it fails to account for the fact that some people may be living just below the poverty line, while others experience far greater shortfalls. Policymakers seeking to make the largest possible impact on the headcount measure might be tempted to direct their poverty alleviation resources to those closest to the poverty line (and therefore least poor).</p>\n<p>Issues may also arise when comparing poverty measures within countries when urban and rural poverty lines represent different purchasing powers. For example, the cost of living is typically higher in urban than in rural areas. One reason is that food staples tend to be more expensive in urban areas. So the urban monetary poverty line should be higher than the rural poverty line. But it is not always clear that the difference between urban and rural poverty lines found in practice reflects only differences in the cost of living. In some countries the urban poverty line in common use has a higher real value&#x2014;meaning that it allows the purchase of more commodities for consumption&#x2014;than does the rural poverty line. Sometimes the difference has been so large as to imply that the incidence of poverty is greater in urban than in rural areas, even though the reverse is found when adjustments are made only for differences in the cost of living. As with international comparisons, when the real value of the poverty line varies it is not clear how meaningful such urban-rural comparisons are.</p>\n<p>Lastly, these income/consumption based poverty indicators do not fully reflect the other dimensions of poverty such as inequality, vulnerability, and lack of voice and power of the poor.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> For a discussion on reasons consumption is preferred, check: Deaton, Angus (2003). &#x201C;Household Surveys, Consumption, and the Measurement of Poverty&#x201D;. Economic Systems Research, Vol. 15, No. 2, June 2003 <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>The formula for calculating the proportion of the total, urban and rural population living below the national poverty line, or headcount index, is as follows:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <mn>0</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n      <mrow>\n        <mi>N</mi>\n      </mrow>\n    </mfrac>\n    <mrow>\n      <munderover>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi>i</mi>\n          <mo>=</mo>\n          <mn>1</mn>\n        </mrow>\n        <mrow>\n          <mi>N</mi>\n        </mrow>\n      </munderover>\n      <mrow>\n        <mi>I</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>y</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <mi>z</mi>\n          </mrow>\n        </mfenced>\n        <mo>=</mo>\n        <mfrac>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>p</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>Where <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mo>.</mo>\n      </mrow>\n    </mfenced>\n  </math> is an indicator function that takes on a value of 1 if the bracketed expression is true, and 0 otherwise. If individual consumption or income <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>y</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n  </math> is less than the national poverty line<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>&amp;nbsp;</mi>\n    <mi>z</mi>\n  </math> (for example, in absolute terms the line could be the price of a consumption bundle or in relative terms a percentage of the income distribution), then <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mo>.</mo>\n      </mrow>\n    </mfenced>\n  </math> is equal to 1 and the individual is counted as poor. <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>N</mi>\n      </mrow>\n      <mrow>\n        <mi>p</mi>\n      </mrow>\n    </msub>\n  </math> is the total, urban or rural number of poor.<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>&amp;nbsp;</mi>\n    <mi>N</mi>\n  </math> is the total, urban or rural population. </p>\n<p>Consumption or income data are gathered from nationally representative household surveys, which contain detailed responses to questions regarding spending habits and sources of income. Consumption, including consumption from own production, or income is calculated for the entire household. In some cases, an &#x201C;effective&#x201D; household size is calculated from the actual household size to reflect assumed efficiencies in consumption; adjustments may also be made to reflect the number of children in a household. The number of people in those households is aggregated to estimate the number of poor persons. </p>\n<p>National poverty rates use a country specific poverty line, reflecting the country&#x2019;s economic and social circumstances. In some case, the national poverty line is adjusted for different areas (such as urban and rural) within the country, to account for differences in prices or the availability of goods and services. Typically the urban poverty line is set higher than the rural poverty line; reflecting the relatively higher costs of living in urban areas.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Validation takes place by National Statistical Offices, often in collaboration with the World Bank. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Adjustsments are made to the national poverty rates from EU-SILC, in the sense that the national poverty rate reported by Eurostat for year x is reported as year x-1 here. The reason is that the income data used by EU-SILC refers to year x-1 (indiviuals in year x are asked about their income the prior year).</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values in consumption of particular items are counted as zero. This is a standard practice in processing survey data. If the consumption is not reported, it is taken as zero consumption, and thus the consumption expenditure is zero. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Because national poverty lines are country-specific. There is no aggregation at the regional or global level. </p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries may refer to the report &#x201C;On the Construction of a Consumption Aggregate for Inequality and Poverty Analysis&#x201D;. The report is available here:</p>\n<p>ahttps://documents.worldbank.org/en/publication/documents-reports/documentdetail/099225003092220001/p1694340e80f9a00a09b20042de5a9cd47e</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The quality of the estimates is managed through the World Bank&#x2019;s Global Poverty Working Group. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The poverty estimates released by the World Bank are quality checked by members of the Global Poverty Working Group and often with members of the relevant National Statistical Offices.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data availability depends on the availability of household surveys and analysis of survey data. Data for total poverty are available for more than 150 countries.</p>\n<p><strong>Time series:</strong></p>\n<p>Data are available spanningover 30 years. Because the effort and capacity of collecting and analysing survey data are different for each country, the length of the time series for each country varies greatly. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Currently no disaggregation is made.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>National poverty estimates is a different concept from international poverty estimates. National poverty rate is defined at country-specific poverty lines in local currencies, which are different in real terms across countries and different from the $2.15-a-day international poverty line. Thus, national poverty rates cannot be compared across countries or with the $2.15-a-day poverty rate.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>Poverty and Inequality Platform</p>\n<p><a href=\"http://pip.worldbank.org/\">http://pip.worldbank.org/</a></p>\n<p><strong>References:</strong></p>\n<p>Deaton, Angus. 2003. &#x201C;Household Surveys, Consumption, and the Measurement of Poverty&#x201D;. Economic Systems Research, Vol. 15, No. 2, June 2003</p>\n<p>Deaton, Angus; Zaidi, Salman. 2002. Guidelines for Constructing Consumption Aggregates for Welfare Analysis. LSMS Working Paper; No. 135. World Bank. </p>\n<p>World Bank 2008. <em>Poverty data: A supplement to World Development Indicators 2008</em>. Washington, DC. </p>", "indicator_sort_order"=>"01-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"embed", "label"=>"related indicators"}]}, {"number"=>"1.2.2", "slug"=>"1-2-2", "name"=>"Proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza, en todas sus dimensiones, con arreglo a las definiciones nacionales", "url"=>"/site/es/1-2-2/", "sort"=>"010202", "goal_number"=>"1", "target_number"=>"1.2", "global"=>{"name"=>"Proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza, en todas sus dimensiones, con arreglo a las definiciones nacionales"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"Los datos a partir del 2022 del Indicador de pobreza y exclusion AROPE se calculan con la nueva definicion de la tasa (metodología Eurostat del año 2021), mientras que los datos anteriores corresponden a la definición antigua.", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"related indicators", "embedded_feature_title"=>"", "embedded_feature_url"=>"https://eustat-des.github.io/site/embeded/1-2-2", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_series_breaks"=>[{"series"=>"", "unit"=>"", "label_content"=>"Ruptura de la serie", "value"=>6.5}], "graph_target_lines"=>[{"series"=>"", "unit"=>"", "label_content"=>"Objetivo del indicador para 2030", "value"=>11.3}], "graph_title"=>"Tasa AROPE. Proporción de personas en riesgo de pobreza o exclusión social", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza, en todas sus dimensiones, con arreglo a las definiciones nacionales", "indicator_number"=>"1.2.2", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Meta:</b> Reducir a la mitad la población que vive en la pobreza en todas sus dimensiones", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Bienestar, Juventud y Reto Demográfico", "periodicity"=>"Bienal", "url"=>"https://www.euskadi.eus/encuesta-de-pobreza-y-desigualdades-sociales-epds/web01-s2enple/es/", "url_text"=>"Encuesta de pobreza y desigualdades sociales (EPDS)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Tasa AROPE. Proporción de personas en riesgo de pobreza o exclusión social", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"Proporción de personas que cumplen al menos uno de los tres criterios del riesgo de pobreza   o exclusión social: estar en riesgo de pobreza relativa, en situación de carencia material y social  severa o viviendo en hogares con baja intensidad de trabajo", "formula"=>"\n$$T_{AROPE}^{t} = \\frac{P_{AROPE}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$P_{AROPE}^{t} =$ población en riesgo de pobreza o exclusión social el año $t$\n\n$P^{t} =$ población total en el año $t$\n", "desagregacion"=>"Dimensiones de la pobreza: riesgo de pobreza; carencia material y social severa; \nbaja intensidad de trabajo\n\nSexo\n", "observaciones"=>"El indicador mide la proporción de personas que cumplen al menos uno de los siguientes criterios:\n\n<b>Personas en riesgo de pobreza relativa:</b> personas con ingresos por unidad de consumo por\ndebajo del 60% de la mediana (escala OCDE modificada).\n\nEl número de unidades de consumo de un hogar se calcula utilizando la escala OCDE modificada, \nque asigna un peso de 1 a la primera  persona de 14 o más años, un peso de 0,5 al resto de personas de \n14 o más años y un peso de 0,3 a las personas de menos de 14 años.\n\n<b>Personas en situación de carencia material y social severa:</b> personas que padecen al menos \nsiete de la siguiente lista de trece limitaciones (siete definidas a nivel de hogar y seis a nivel de persona):\n\nA nivel de hogar:\n 1. No puede permitirse ir de vacaciones al menos una semana al año.\n 2. No puede permitirse una comida de carne, pollo o pescado al menos cada dos días.\n 3. No puede permitirse mantener la vivienda con una temperatura adecuada.\n 4. No tiene capacidad para afrontar gastos imprevistos.\n 5. Ha tenido retrasos en el pago de gastos relacionados con la vivienda principal \n(hipoteca o alquiler, recibos de gas, comunidad, etc) o en\ncompras a plazos en los últimos 12 meses.\n 6. No puede permitirse disponer de un automóvil.\n 7. No puede sustituir muebles estropeados o viejos.\n\nA nivel de persona:\n 8. No puede permitirse sustituir ropa estropeada por otra nueva.\n 9. No puede permitirse tener dos pares de zapatos en buenas condiciones.\n 10. No puede permitirse reunirse con amigos/familia para comer o tomar algo al menos una vez al mes.\n 11. No puede permitirse participar regularmente en actividades de ocio.\n 12. No puede permitirse gastar una pequeña cantidad de dinero en sí mismo.\n 13. No puede permitirse conexión a internet.\n\nEn el caso de menores de 16 años no se dispone de los seis conceptos enumerados anteriormente a \nnivel de persona. Para estos menores los valores de esos elementos se imputan a partir de los valores\nrecogidos para los miembros de su hogar con 16 o más años.\n\n<b>Baja intensidad en el empleo:</b> son los hogares en los que sus miembros en edad de trabajar \n(personas de 18 a 64 años, excluyendo los estudiantes de 18 a 24 años, los jubilados o retirados, \nasí como las personas inactivas entre 60 y 64 cuya fuente principal de ingresos del hogar sean las pensiones)\nlo hicieron menos del 20% del total de su potencial de trabajo durante el año de referencia.\nEsta variable no se aplica en el caso de las personas de 65 y más años.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "periodicidad"=>"Bienal", "justificacion_global"=>"\nTradicionalmente se ha definido la pobreza como la falta de dinero. \nSin embargo, la pobreza hay que entenderla de manera mucho más amplia.\nUna persona pobre puede sufrir múltiples desventajas al mismo tiempo; por ejemplo, puede \ntener mala salud o desnutrición, falta de agua potable o electricidad, mala calidad del \ntrabajo o poca escolaridad. Centrarse en un solo factor, como el ingreso, no es \nsuficiente para captar la verdadera realidad de la pobreza.\n\n\nEn la Unión Europea, la población en riesgo de pobreza o exclusión social, \nabreviado como AROPE, \ncorresponde a la suma de personas que están en riesgo de pobreza (pobreza de ingresos), \ngravemente desfavorecidas material y socialmente, o que viven en un hogar con una \nintensidad laboral muy baja. \nLas personas se incluyen sólo una vez incluso si se encuentran en más de una de las \nsituaciones mencionadas anteriormente. La tasa AROPE es la proporción de la población total \nque está en riesgo de pobreza o exclusión social.\n\nLa tasa AROPE es el principal indicador para hacer el seguimiento del\nobjetivo de pobreza y exclusión social de la UE para 2030 y fue el indicador principal \npara hacer el seguimiento del objetivo de pobreza de la Estrategia UE 2020.\n\nEl <a href=\"https://ec.europa.eu/social/main.jsp?catId=1606&langId=es\">Pilar \nEuropeo de Derechos Sociales</a> propone tres objetivos a nivel \nde la UE que deben alcanzarse de aquí a 2030 en los ámbitos del empleo, \nlas capacidades y la protección social. La pobreza y la exclusión social \nes uno de los objetivos. El número de personas en riesgo de pobreza o exclusión\nsocial debería reducirse en al menos 15 millones de aquí a 2030, y de ellas, \nal menos 5 millones deberían ser niños.\n\nFuente: División de Estadísticas de las Naciones Unidas, Eurostat\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-02-02.pdf\">Metadatos 1-2-2.pdf (solo en inglés)</a>\n\n<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_01_10_esmsip2.htm\">Metadatos Eurostat (sdg_01_10)</a> (solo en inglés)\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\"> Personas en riesgo de pobreza o exclusión social por edad y sexo (ilc_peps01n)</a> Eurostat", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Tasa AROPE. Proporción de personas en riesgo de pobreza o exclusión social", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"\nProportion of people who meet at least one of the three criteria for risk of poverty  or social exclusion (being at risk of relative poverty, in a situation of material and social deprivation  or living in a household with low work intensity)", "formula"=>"\n$$T_{AROPE}^{t} = \\frac{P_{AROPE}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$P_{AROPE}^{t} =$ population at risk of poverty or social exclusion in year $t$\n\n$P^{t} =$ total population in year $t$\n", "desagregacion"=>"Dimensions of poverty: risk of poverty; severe material and social deprivation; \nlow work intensity\n\nSex\n", "observaciones"=>"The indicator measures the proportion of people who meet at least one of the following criteria:\n\n<b>People at risk of relative poverty:</b> people with income per unit of consumption\nbelow 60% of the median (modified OECD scale).\n\nThe number of consumption units in a household \nis calculated using the modified OECD scale, which assigns a weight of 1 to the \nfirst person aged 14 or over, a weight of 0.5 for other people aged 14 or over \nand a weight of 0.3 for people under 14 years of age.\n\n<b>People in a situation of severe material and social deprivation:</b> people who suffer from at least \nseven from the following list of thirteen limitations (seven defined at the household level and six at the individual level):\n\n At the household level:\n 1. You can't afford to go on vacation for at least one week a year.\n 2. You cannot afford a meal of meat, chicken or fish at least every other day.\n 3. You cannot afford to keep the home at an adequate temperature.\n 4. You do not have the capacity to face unforeseen expenses.\n 5. You have had delays in the payment of expenses related to the main home (mortgage or rent, gas bills, community...) \n or in installment purchases in the last 12 months.\n 6. You can't afford a car.\n 7. You cannot replace damaged or old furniture.\n\nAt the individual level:\n 8. You cannot afford to replace damaged clothes with new ones.\n 9. You can't afford to have two pairs of shoes in good condition.\n 10. You can't afford to meet friends/family for food or drinks at least once a month.\n 11. You cannot afford to regularly participate in leisure activities.\n 12. You can't afford to spend a small amount of money on yourself.\n 13. Cannot afford internet connection.\n\nIn the case of minors under 16 years of age, the six concepts listed above are not available at the individual level.\nFor these minors, the values of these elements are imputed from the values collected for the members of their household aged 16 or over.\n\n<b>Low work intensity:</b> these are households in which their working age members (people from 18 to 64 years old, excluding \nstudents from 18 to 24 years old, retirees or retirees, as well as inactive people between 60 and 64 whose main source of household income \nare pensions) worked less than 20% of their total work potential during the reference year.\nThis variable does not apply for people aged 65 and over.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "periodicidad"=>"Bienal", "justificacion_global"=>"\nTraditionally, poverty has been defined as a lack of money. \nHowever, poverty must be understood much more broadly. \nA poor person can suffer multiple disadvantages at the same time; For example, you can \nsuffer from poor health or malnutrition, lack of drinking water or electricity, poor quality of \nwork or little schooling. Focusing on a single factor, such as income, is not \nenough to capture the true reality of poverty.\n\nIn the European Union, the population at risk of poverty or social exclusion, \nabbreviated as AROPE, corresponds to the sum of people who are at risk of poverty (income poverty), \nseverely disadvantaged materially and socially, or living in a household with a \nvery low work intensity.\nPeople are included only once, even if they are in more than one of the \nsituations mentioned above. The AROPE rate is the proportion of the total population \nwho are at risk of poverty or social exclusion.\n\nThe AROPE rate is the main indicator for monitoring the \nEU poverty and social exclusion target for 2030 and was the leading indicator \nto monitor the poverty target of the EU 2020 strategy.\n\n\nThe <a href=\"https://employment-social-affairs.ec.europa.eu/european-pillar-social-rights-20-principles_en\">\nEuropean Pillar of Social Rights</a> proposes three objectives at the level of EU\nto be achieved by 2030 in the areas of employment, skills and social protection.\nPoverty and social exclusion is one of the objectives. The number of people at risk of poverty or exclusion\nshould be reduced by at least 15 million by 2030, and of these, at least 5 million should be children.\n\nSource: United Nations Statistics Division, Eurostat\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-02-02.pdf\">Metadata 1-2-2.pdf</a>\n\n<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_01_10_esmsip2.htm\">Metadata Eurostat (sdg_01_10)</a>\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\"> People at risk of poverty or social exclusion by age and sex (ilc_peps01n)</a> Eurostat", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Tasa AROPE. Proporción de personas en riesgo de pobreza o exclusión social", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"Pobreziako edo gizarte-bazterketako arriskuaren hiru irizpideetako bat gutxienez  betetzen duten pertsonen proportzioa: pobrezia erlatiboko arriskuan egotea,  gabezia material eta sozial larriko egoeran egotea, edo lan-intentsitate txikiko  etxe batean bizitzea", "formula"=>"\n$$T_{AROPE}^{t} = \\frac{P_{AROPE}^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$P_{AROPE}^{t} =$ pobreziako edo gizarte-bazterketako arrisku erlatiboan dagoen biztanleria $t$ urtean\n\n$P^{t} =$ biztanleria osoa $t$ urtean\n", "desagregacion"=>"Pobreziaren dimentsioak: pobrezia-arriskua; gabezia material eta sozial larria; \nlan-intentsitate txikia  \n\nSexua\n", "observaciones"=>"Adierazleak irizpide hauetako bat gutxienez betetzen duten pertsonen proportzioa neurtzen du: \n\n<b>Pobrezia erlatiboaren arriskuan dauden pertsonak:</b> kontsumo-unitate bakoitzeko diru-sarrerak \nmedianaren % 60tik behera dituzten pertsonak (ELGA eskala aldatua). \n\nEtxeko kontsumo-unitateen kopurua kalkulatzeko, ELGA eskala aldatua erabiltzen da. \nEskala horrek 1 pisua esleitzen dio 14 urte edo gehiagoko lehen pertsonari, 0,5 pisua 14 urte edo \ngehiagoko gainerako pertsonei eta 0,3 pisua 14 urtetik beherako pertsonei. \n\n<b>Gabezia material eta sozial larria duten pertsonak:</b> ondoko hamahiru egoeretatik gutxienez \nzazpi pairatzen dituzten pertsonak; zazpi egoera etxeari dagokizkonak dira eta sei pertsonari dagozkionak: \n\nEtxeari dagozkionak: \n 1. Ezin du urtean gutxienez astebetez oporretara joan. \n 2. Ezin du gutxienez bi egunetik behin haragi-, oilasko- edo arrain-otordurik egin.\n 3. Ezin du etxebizitza tenperatura egokian mantendu.\n 4. Ez du ustekabeko gastuei aurre egiteko gaitasunik.\n 5. Atzerapenak izan ditu etxebizitza nagusiarekin lotutako gastuen ordainketan \n(hipoteka edo alokairua, gas-ordainagiriak, komunitatea, etab.) edo epeka egindako \nerosketetan azken 12 hilabeteetan.\n 6. Ezin du kotxerik eduki.\n 7. Ezin ditu ordezkatu hondatutako altzariak edo zaharrak.\n\nPertsonari dagozkionak:\n 8. Ezin du hondatutako arroparen ordez berria erosi.\n 9. Ezin ditu egoera onean dauden bi zapata pare eduki.\n 10. Ezin du zerbait jateko edo edateko lagunekin/familiarekin elkartu hilean behin gutxienez.\n 11. Ezin du aldizka aisialdiko jardueretan parte hartu.\n 12. Ezin du bere buruarentzako gauzetan diru kopuru txiki bat gastatu.\n 13. Ezin du Interneteko konexiorik ordaindu.\n\n16 urtetik beherakoen kasuan, pertsonari dagozkion azken sei kontzeptuak ez daude eskuragarri. \nAdingabe horien kasuan, elementu horien balioak 16 urte edo gehiago dituzten etxeko kideentzat \njasotako balioetatik abiatuta egozten dira. \n\n<b>Lan-intentsitate txikia:</b> etxe horietako lan egiteko adinean dauden kideek beren lan-potentzial osoaren % 20 \nbaino gutxiago lan egin zuten erreferentziako urtean; lan egiteko adinean dauden kidetzat hartzen dira 18 eta 64 \nurteko arteko pertsonak (salbu eta 18 eta 24 urte bitarteko ikasleak, erretiratuak, eta 60-64 urte bitarteko \npertsona ez-aktiboak, horien etxeko diru-sarreren iturri nagusia pentsioak badira). Aldagai hori ez da aplikatzen \n65 urte edo gehiagoko pertsonen kasuan. \n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "periodicidad"=>"Bienal", "justificacion_global"=>"\nTradizionalki, pobrezia diru-faltarekin lotu izan da. Hala ere, pobreziak askoz eremu zabalagoa hartzen du. Pertsona \npobre batek desabantaila ugari jasan ditzake aldi berean; esaterako, osasun txarra edo desnutrizioa, edateko urik edo \nelektrizitaterik eza, lanaren kalitate eskasa edo eskolaratze baxua. Faktore bakarra hartzea (diru-sarrera kasu) ez \nda aski pobreziaren benetako errealitatea ezagutzeko. \n\nEuropar Batasunean, pobreziako edo gizarte-bazterketako arriskuan dagoen biztanleria (AROPE adierazlea) honako hauen \nbatura da: pobrezia-arriskuan (diru-sarreren pobrezian) dauden pertsonak, materialki eta sozialki larriki kaltetutako \npertsonak eta lan-intentsitate oso baxuko etxe batean bizi diren pertsonak. Pertsonak behin bakarrik zenbatzen dira, \naipatu egoera batean baino gehiagotan egon arren. AROPE tasa pobreziako edo gizarte-bazterketako arriskuan dauden \nbiztanle guztien proportzioa da. \n\nAROPA tasa adierazle nagusia da EBko pobreziaren eta gizarte-bazterketaren 2030erako helburuaren jarraipena egiteko. \nHalaber, adierazle nagusia izan zen EB 2020 Estrategiaren pobreziaren helburuaren jarraipena egiteko. \n\n<a href=\"https://ec.europa.eu/social/main.jsp?catId=1606&langId=es\"Gizarte Eskubideen Europako Zutabeak hiru helburu \nproposatzen ditu EB mailan, hemendik eta 2030era lortu beharrekoak enpleguaren, gaitasunen eta gizarte-babesaren \neremuetan. Pobrezia eta gizarte-bazterketa helburu horien artean sartzen dira. Pobreziako eta gizarte-bazterketako \narriskuan dauden pertsonen kopurua gutxienez 15 milioi inguru murriztu beharko litzateke 2030era bitartean, eta \nhorietatik gutxienez 5 milioi haurrak izan beharko lirateke.\n\n\nIturria: Nazio Batuen Estatistika Sekzioa, Eurostat\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-02-02.pdf\">Metadatuak 1-2-2.pdf</a> (ingelesez bakarrik) \n\n<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_01_10_esmsip2.htm\">Eurostat Metadatuak (sdg_01_10)</a> (ingelesez bakarrik)\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">Pobrezia edo gizarte-bazterketa arriskuan dauden pertsonak, adinaren eta sexuaren arabera (ilc_peps01n)</a> Eurostat", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.2: By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.2.2: Proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SD_MDP_ANDI - Average proportion of deprivations for people multidimensionally poor [1.2.2]</p>\n<p>SD_MDP_ANDIHH - Average share of weighted deprivations of total households (intensity) (%) [1.2.2]</p>\n<p>SD_MDP_CSMP - Proportion of children living in child-specific multidimensional poverty [1.2.2]</p>\n<p>SD_MDP_MUHC - Proportion of population living in multidimensional poverty [1.2.2]</p>\n<p>SD_MDP_MUHHC - Proportion of households living in multidimensional poverty [1.2.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>The World Bank, United Nations Children&#x2019;s Fund (UNICEF), United Nations Development Programme (UNDP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>The World Bank, United Nations Children&#x2019;s Fund (UNICEF), United Nations Development Programme (UNDP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The following five series are used to monitor the SDG 1.2.2. </p>\n<p>1) Official multidimensional poverty headcount, by sex, and age (% of population)</p>\n<ul>\n  <li>The percentage of people who are multidimensionally poor</li>\n</ul>\n<p>2) Average share of weighted deprivations (intensity) for total population</p>\n<ul>\n  <li>The average share of weighted dimensions in which poor people are deprived among total population </li>\n</ul>\n<p>3) Official multidimensional poverty headcount (% of total households)</p>\n<ul>\n  <li>The percentage of households who are multidimensionally poor</li>\n</ul>\n<p>4) Average share of weighted deprivations (intensity) for total households</p>\n<ul>\n  <li>The average share of weighted dimensions in which poor people are deprived among total households </li>\n</ul>\n<p>5) Multidimensional deprivation for children (% of population under 18)</p>\n<ul>\n  <li>The percentage of children who are simultaneously deprived in multiple material dimensions </li>\n</ul>\n<p><strong>Concepts:</strong></p>\n<p>The design of a measure of multidimensional poverty is different in each country, but regardless of the exact methodology selected, it still follows a similar process to define the features of the measure, which include: i) the purpose of the measure; ii) the unit of identification (most frequently either the household or the individuals); iii) the dimensions and respective indicators that delimit which deprivations should be measured; iv) the methodology for developing the measure (including deprivation cut-offs, weights, and poverty cut-offs).</p>\n<p>The most commonly used method is the Alkire Foster (AF) methodology which identifies dimensions, typically health, education and living standards and several indicators in each dimension. The unit of analysis could be either the individual or the household. The individuals or households are considered as multidimensionally poor if they are deprived in multiple dimensions, exceeding certain thresholds. </p>\n<p>EU Member States, Island, Norway, Albania, Kosovo, North Macedonia, Montenegro and Turkey have a different approach to measure the multidimensional poverty using the concept of &quot;people at risk of poverty or social exclusion&quot; (AROPE) calculated by EUROSTAT using the data from EU statistics on income and living conditions (EU-SILC). AROPE consists of three components, and individuals are considered as &quot;at risk of poverty or social exclusion&quot; if they are &quot;at risk of poverty&quot; or &quot;severely materially and socially deprived&quot; or &quot;living in a household with a very low work intensity&quot;. <sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></p>\n<p>There is a multidimensional poverty measure specifically designed for children. A child is considered multidimensionally poor if s/he is simultaneously deprived in multiple dimensions. It identifies the dimensions of poverty and the indicators under each dimension, and has a similar structure to the AF methodology. However, it is different in that it focuses on the life-cycle of children, creating different sets of dimensions and indicators for different age groups (e.g., for ages 0-4, 5-11, 12-14, 15-17 years), and conducts analyses separately for each age group. In the global SDG database, the multidimensional poverty headcount (%) for the overall 0-17 age group has been used for countries reporting individual measures of child multidimensional poverty.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> For more information please see Eurostat&#x2019;s definitions <a href=\"https://ec.europa.eu/eurostat/statistics-explained/index.php?title=EU_statistics_on_income_and_living_conditions_(EU-SILC)_methodology_-_people_at_risk_of_poverty_or_social_exclusion\">https://ec.europa.eu/eurostat/statistics-explained/index.php?title=EU_statistics_on_income_and_living_conditions_(EU-SILC)_methodology_-_people_at_risk_of_poverty_or_social_exclusion</a>. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data sources used for calculating indicators differ from survey to survey in each country. For details, please refer to the official documentation through the links listed at the end.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data collection methods used for calculating indicators differ from survey to survey in each country. For details, please refer to the official documentation through the links listed at the end.</p>", "FREQ_COLL__GLOBAL"=>"<p>The timing of the data collection differs from survey to survey in each country. For details, please refer to the official documentation through the links listed at the end.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>EU countries and some Latin American countries conduct the survey and produce multidimensional indicators every year, but most of the developing countries have published multidimensional measurement only once or a few times in the last 10 years. For these countries, it is difficult to state definitely when the next data is available.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Following is the list of national data providers responsible for producing the data at the national level.</p>\n<p><strong>Table 1: List of national data providers</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Country</strong></p>\n      </td>\n      <td>\n        <p><strong>Source</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Afghanistan</p>\n      </td>\n      <td>\n        <p>National Statistics and Information Authority (NSIA)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Albania</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Angola</p>\n      </td>\n      <td>\n        <p>National Statistics Institute (INE) of Angola</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Armenia</p>\n      </td>\n      <td>\n        <p>Statistical Committee of Republic of Armenia</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Austria</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Belgium</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Belize</p>\n      </td>\n      <td>\n        <p>Statistical Institute of Belize</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Bhutan</p>\n      </td>\n      <td>\n        <p>National Statistics Bureau</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Bulgaria</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Burundi</p>\n      </td>\n      <td>\n        <p>Burundi Institute of Statistics and Economic Studies</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Chile</p>\n      </td>\n      <td>\n        <p>Ministerio de Desarrollo Social</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Colombia</p>\n      </td>\n      <td>\n        <p>National Administrative Department of Statistics (DANE)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Costa Rica</p>\n      </td>\n      <td>\n        <p>The National Institute of Statistics and Census of Costa Rica</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Croatia</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Cyprus</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Czechia</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denmark</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Dominican Republic</p>\n      </td>\n      <td>\n        <p>Ministry of Economy, Planning and Development</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ecuador</p>\n      </td>\n      <td>\n        <p>National Institute of Statistics and Census (INEC), Ministry of Social Development Coordination and National Secretary of Planning and Development</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Egypt</p>\n      </td>\n      <td>\n        <p>The Ministry of Social Solidarity (MoSS), the Central Agency for Public Mobilization and Statistics (CAPMAS)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>El Salvador</p>\n      </td>\n      <td>\n        <p>Secretar&#xED;a T&#xE9;cnica y de Planificaci&#xF3;n Presidencia</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Estonia</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Finland</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>France</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Germany</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ghana</p>\n      </td>\n      <td>\n        <p>Ghana Statistical Service, National Development Planning Commission</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Greece</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Guatemala</p>\n      </td>\n      <td>\n        <p>Ministry of Social Development</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Guinea</p>\n      </td>\n      <td>\n        <p>INSTITUT NATIONAL DE LA STATISTIQUE</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Guinea Bissau</p>\n      </td>\n      <td>\n        <p>La Direction Generale du Plan, Instituto Nacional de Estat&#xED;stica (INE)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Honduras</p>\n      </td>\n      <td>\n        <p>Secretar&#xED;a de Coordinaci&#xF3;n General de Gobierno, Instituto Nacional de Estad&#xED;stica INE</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Hungary</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Iceland</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>India</p>\n      </td>\n      <td>\n        <p>NITI Aayog</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ireland</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Italy</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Kosovo</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Kyrgyz</p>\n      </td>\n      <td>\n        <p>National Statistical Committee of the Kyrgyz Republic</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Laos</p>\n      </td>\n      <td>\n        <p>Lao Statistics Bureau</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Latvia</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Lebanon</p>\n      </td>\n      <td>\n        <p>Central Administration of Statistics</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Lesotho</p>\n      </td>\n      <td>\n        <p>Bureau of Statistics</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Lithuania</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Luxembourg</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Malawi</p>\n      </td>\n      <td>\n        <p>National Statistical Office</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Malaysia</p>\n      </td>\n      <td>\n        <p>Department of Statistics Malaysia</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Maldives</p>\n      </td>\n      <td>\n        <p>National Bureau of Statistics (NBS)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Mali</p>\n      </td>\n      <td>\n        <p>Institut National de la Statistique (INSTAT), La Cellule Technique de Coordination du Cadre Strat&#xE9;gique de Lutte contre la Pauvret&#xE9; (CT-CSCLP)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Malta</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Mexico</p>\n      </td>\n      <td>\n        <p>Consejo Nacional de Evaluacion de la Politica de Desarrollo Social (CONEVAL)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Montenegro</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Morocco</p>\n      </td>\n      <td>\n        <p>The High Commission of Planning</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Mozambique</p>\n      </td>\n      <td>\n        <p>Ministry of Economics and Finance - Directorate of Economic and Financial Studies</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Namibia</p>\n      </td>\n      <td>\n        <p>Namibia Statistics Agency (NSA)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Nepal</p>\n      </td>\n      <td>\n        <p>National Planning Commission</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Netherlands</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Nigeria</p>\n      </td>\n      <td>\n        <p>National Bureau of Statistics</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>North Macedonia</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Norway</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Pakistan</p>\n      </td>\n      <td>\n        <p>Ministry of Planning Development &amp; Reform</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Palestine</p>\n      </td>\n      <td>\n        <p>The Palestinian Central Bureau of Statistics (PCBS)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Panama</p>\n      </td>\n      <td>\n        <p>(2017)</p>\n        <p>Ministry of Social Development</p>\n        <p>(2018)</p>\n        <p>Ministry of Economy and Finance</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Philippines</p>\n      </td>\n      <td>\n        <p>Philippine Statistics Authority</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Poland</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Romania</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Rwanda</p>\n      </td>\n      <td>\n        <p>National Institute of Statistics of Rwanda</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Saint Lucia</p>\n      </td>\n      <td>\n        <p>The Central Statistical Office of Saint Lucia</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Samoa</p>\n      </td>\n      <td>\n        <p>Samoa Bureau of Statistics</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>S&#xE3;o Tom&#xE9;<em> and Pr&#xED;ncipe</em></p>\n      </td>\n      <td>\n        <p>Ministry of Economy and International Cooperation</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Serbia</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Seychelles</p>\n      </td>\n      <td>\n        <p>National Bureau of Statistics</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Slovakia</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Slovenia</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>South Africa</p>\n      </td>\n      <td>\n        <p>Statistics South Africa</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Spain</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Sri Lanka</p>\n      </td>\n      <td>\n        <p>Department of Census and Statistics</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Suriname</p>\n      </td>\n      <td>\n        <p>Ministry of Labor, Employment Opportunity and Youth Affair</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Sweden</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Thailand</p>\n      </td>\n      <td>\n        <p>National Economic and Social Development Council (NESDC)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Tonga</p>\n      </td>\n      <td>\n        <p>Tonga Statistics Department</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Turkey</p>\n      </td>\n      <td>\n        <p>EUROSTAT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Uganda</p>\n      </td>\n      <td>\n        <p>Uganda Bureau of Statistics</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Uruguay</p>\n      </td>\n      <td>\n        <p>The Government of Uruguay&apos;s National Institute of Statistics (INE) </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Vietnam</p>\n      </td>\n      <td>\n        <p>General Statistics Office</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Zambia</p>\n      </td>\n      <td>\n        <p>Ministry of National Development Planning</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Zimbabwe</p>\n      </td>\n      <td>\n        <p>Zimbabwe National Statistics Agency (ZIMSTAT)</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "COMPILING_ORG__GLOBAL"=>"<p>The World Bank, United Nations Children&#x2019;s Fund (UNICEF), and United Nations Development Programme (UNDP)</p>", "INST_MANDATE__GLOBAL"=>"<p>The UN Statistical Commission has adopted <a href=\"https://unstats.un.org/unsd/statcom/49th-session/documents/BG-Item-3a-IAEG-SDGs-DataFlowsGuidelines-E.pdf\">Guidelines on Data Flows and Global Data Reporting for the SDGs</a>, which aim to establish efficient and transparent mechanisms for reporting on SDG data from national to international levels. The guidelines define a framework for national and international agencies to work together to improve the transmission and validation of SDG data at the global level. </p>\n<p>The Statistical Commission sets these guidelines under the overarching Fundamental Principles of Official Statistics and the Principles Governing International Statistical Activities, emphasizing in particular the principles of transparency, collaboration and communication, and professional and ethical standards. </p>\n<p>The guidelines mandate that SDG indicators be based on data produced and owned by national statistical systems, and that national statistical offices (NSOs) play a central coordinating role in the reporting process. The guidelines outline the roles and responsibilities of entities involved in the compilation of SDG data for global reporting, including NSOs, other national institutions, and international organizations. </p>\n<p>At the national level, the NSO, as coordinator of the National Statistical System, is expected to identify a national data provider for each indicator and liaise between national entities and international custodian agencies. For SDG Indicator 1.2.2, the data provider would be the national entity that is leading the development and monitoring of a measure of national multidimensional poverty recognized as official by the government. </p>\n<p>At the global level, custodian agencies are mandated to compile national SDG indicator data, to harmonize it to ensure quality, international comparability and the computation of regional aggregates, and to report (upload) the data to the Global SDG Indicator Database. In many instances, custodian agencies also support the methodological development of indicators and provide technical assistance to under-resourced national statistical systems. Custodian agencies are expected to publish a timeline of data collection activities, to ensure transparency and sufficient time for NSOs and national data providers to respond to requests for SDG data. </p>\n<p>SDG 1.2.2 is different from other SDG indicators in two important ways. Firstly, it is nationally defined and not a uniform measure across countries, and therefore it is not internationally comparable. Secondly, its custodians are NSOs and not international agencies. Because of these characteristics, UNDP, UNICEF and the World Bank collaborate as special partner agencies to provide a platform for compiling national SDG 1.2.2 data and reporting it to the global SDG database, a function typically performed by custodian agencies. While the special partner agencies strive to ensure that reported data is official and of good quality, they do not perform any harmonization or other processing of the data. The Guidelines on Data Flows and Global Data Reporting for the SDGs also require that national metadata be submitted at the same time as SDG data, to ensure accuracy and international comparability. The variety of methodologies for SDG Indicator 1.2.2 increases the relevance of national metadata as an instrument to ensure high quality and the accuracy of reported data. The three agencies also have extensive portfolios of technical assistance and capacity support to countries for the development of their national measures of multidimensional poverty. </p>", "RATIONALE__GLOBAL"=>"<p>Poverty has traditionally been defined as the lack of money. However, the poor themselves consider their experience of poverty much more broadly. A person who is poor can suffer multiple disadvantages at the same time &#x2013; for example, they may have poor health or malnutrition, a lack of clean water or electricity, poor quality of work or little schooling. Focusing on one factor alone, such as income, is not enough to capture the true reality of poverty. Therefore, multidimensional poverty measures described above have been developed to create a more comprehensive picture by looking at multiple dimensions such as health, education, living standards. Official multidimensional poverty headcount (% population), official multidimensional poverty headcount (% of total households) and multidimensional deprivation for children (% of population under 18) are all about the headcount ratio trying to capture how many people, households, or children in the entire pool are regarded as multidimensionally poor. On the other hand, average share of weighted deprivation tries to capture the depth of multidimensional poverty. For instance, if there are 18 indicators to capture different dimensions of poverty, the person who is deprived in 5 indicators, and the person who is deprived in 15 indicators are considered to be both multidimensionally poor. However, the &apos;intensity&apos; of the poverty is different between these two people, which is captured by the average share of weighted deprivation.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The compiled data of SDG 1.2.2 is not intended to be comparable across countries due to national definitions. For instance, key parameters to calculate the measure such as the number of indicators, the weight allocated to each indicator etc, are tailored to the country specific context. </p>", "DATA_COMP__GLOBAL"=>"<p>The measurement of poverty involves two crucial steps: (1) identification &#x2013; identifying who is poor, and (2) aggregation &#x2013; compiling the individual&#x2019;s information into a summary measure. There are different ways to perform these two steps. All measures currently being estimated by countries or multilateral organizations use the counting approach. Therefore, what follows relates only to counting approaches, even if other non-counting methodologies have been developed by experts. </p>\n<p>The identification and aggregation of the multidimensionally poor involves the following steps:</p>\n<ol>\n  <li>Define the set of relevant dimensions of poverty, and for each of these define a set of indicators.</li>\n  <li>For each dimension, determine the criteria to assess deprivation based on the indicators.</li>\n  <li>For each indicator, define a satisfaction threshold, such that a person (or household) with an achievement below the threshold will be identified as deprived in that indicator.</li>\n  <li>For each indicator, compare each person&#x2019;s (or household&#x2019;s) achievement with the satisfaction threshold and create a variable that assumes, for example, the value 1 if the person is deprived in that indicator and 0 otherwise, and then classify them as either deprived or not in that indicator. </li>\n  <li>For each individual (or household), sum up the number of deprivations. In the summation, each indicator can be weighted differently or equally. Typically, if there are more indicators in one dimension than in others, indicator weights are adjusted to ensure equal weights across dimensions, but this need not be the case.</li>\n  <li>Define a poverty cut-off, such that a person exceeding the cut-off will be identified and counted (aggregated) as poor. </li>\n  <li>Aggregate up across individuals (or households) to obtain a measurement of multidimensional poverty for the country or region of interest. </li>\n</ol>\n<p>To illustrate this method, suppose a hypothetical society with five people, where multidimensional poverty is measured based on four indicators: per capita household income, years of schooling, access to sanitation, and access to source of water. The deprivation thresholds for these indicators are, respectively: 400 monetary units (e.g. dollars, pesos, shillings), 5 years of schooling for adults, having access to improved sanitation, and having access to improved sources of water. In this example, the four indicators are weighted equally<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>, and the multidimensional poverty cut-off is two out of the four indicators. That is, the person would be considered poor if she is deprived in at least two out of the four indicators. Table 2 presents the individuals&#x2019; achievements in each of the four relevant indicators, and the deprivation cut-offs are shown in the bottom row. The achievements falling below the deprivation thresholds are highlighted in red. Table 3 shows the deprivation status of all individuals in the four indicators. Column (5) shows the sum of deprivations. Comparing this sum with the poverty cut-off (as mentioned above, two out of four) the individuals can be classified as poor and non-poor, as shown in column (6). </p>\n<p><strong>Table 2. Individual achievements in the variables selected to define multidimensional poverty</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Individual</strong></p>\n      </td>\n      <td>\n        <p><strong>Income</strong></p>\n        <p><strong>(in dollars)</strong></p>\n      </td>\n      <td>\n        <p><strong>Schooling</strong></p>\n        <p><strong>(in years of education)</strong></p>\n      </td>\n      <td>\n        <p><strong>Improved Sanitation </strong></p>\n      </td>\n      <td>\n        <p><strong>Improved Water</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>100</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>200</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>No </p>\n      </td>\n      <td>\n        <p>Yes </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>350</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>Yes </p>\n      </td>\n      <td>\n        <p>Yes </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>500</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>600</p>\n      </td>\n      <td>\n        <p>6</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>Yes </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Deprivation cut-offs</p>\n      </td>\n      <td>\n        <p>400</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>Yes </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Note: Please note that the water and sanitation indicators are binary variables where a value of 1 corresponds to having access to an improved sanitation or water source, and is 0 otherwise.</p>\n<p><strong>Table 3: Deprivation status, deprivation score and poverty status</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td rowspan=\"2\">\n        <p><strong>Individual</strong></p>\n      </td>\n      <td colspan=\"4\">\n        <p><strong>Deprived in&#x2026;</strong></p>\n      </td>\n      <td rowspan=\"2\">\n        <p><strong>Sum of Deprivations </strong></p>\n      </td>\n      <td rowspan=\"2\">\n        <p><strong>Poor (at least two out of four)</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Income</p>\n      </td>\n      <td>\n        <p>Schooling</p>\n      </td>\n      <td>\n        <p>Sanitation</p>\n      </td>\n      <td>\n        <p>Water</p>\n      </td>\n    </tr>\n    <tr>\n      <td></td>\n      <td>\n        <p>(1)</p>\n      </td>\n      <td>\n        <p>(2)</p>\n      </td>\n      <td>\n        <p>(3)</p>\n      </td>\n      <td>\n        <p>(4)</p>\n      </td>\n      <td>\n        <p>(5)</p>\n      </td>\n      <td>\n        <p>(6)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>The last step involves aggregating the information across individuals. The most common summary measure is the headcount ratio or incidence of poverty. The headcount ratio is the proportion of the total population classed as poor. In the example above, the incidence of multidimensional poverty is 60 percent (<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mn>3</mn>\n      </mrow>\n      <mrow>\n        <mn>5</mn>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math>). All empirical examples discussed in this section use the headcount ratio as the core measure of multidimensional poverty. On one hand, this measure is very intuitive and can be disaggregated by population sub-groups. On the other hand, it cannot be broken down by the contributions of each different indicator and it is not sensitive to the number of deprivations experienced by the poor. Because of these limitations, some methodologies propose other summary measures in addition to the headcount ratio. For the purpose of reporting on SDG Indicator 1.2.2, countries only need to compute the headcount ratio. </p>\n<ol>\n  <li>Unmet Basic Needs</li>\n</ol>\n<p>The measures of Unmet Basic Needs (UBN), which proliferated in Latin America in the 1980s, are a direct application of the counting approach.<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup> These measures often use census data to produce detailed maps of poverty and can also be estimated using household surveys. They identify the poor using the counting approach as described above, following all the steps mentioned, and aggregate the information across households and people using incidence ratios. Most generally, the share of households or individuals with unmet basic needs is presented for different poverty cut-offs &#x2013; that is, the proportion of households and people with one or more unmet basic need, the proportion of households and people with two or more unmet basic needs, and so on. The basic needs considered in these measures usually include (Feres and Mancero, 2001): access to housing that meets minimum housing standards, access to basic services that guarantee minimum sanitary conditions, access to basic education, and economic capacity to achieve minimum consumption levels. When these measures are estimated using census data, they can be highly disaggregated geographically, which makes it possible to construct detailed maps of poverty at district, municipality and even census ratio levels. Because of this property, maps of unmet basic needs have sometimes been used to allocate resources across areas. </p>\n<ol>\n  <li>Multidimensional Poverty Measurement in Mexico</li>\n</ol>\n<p>The counting approach has been used to assess the number of people that are deprived simultaneously in income and in some non-monetary dimensions.<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup> Early applications can be found in Ireland, and more recently, in the United Kingdom for measuring child poverty.<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup> But the first country to develop an official and permanent measure of multidimensional poverty in the developing world was Mexico. The National Council for Evaluation of Social Development Policy (CONEVAL) led that process. In Mexico, multidimensional poverty is measured in the space of economic well-being and social rights, at the individual level:</p>\n<p>&#x201C;A person is considered to be multidimensionally poor when the exercise of at least one of her social rights is not guaranteed and if she also has an income that is insufficient to buy the goods and services required to fully satisfy her needs.&#x201D; (<a href=\"https://www.coneval.org.mx/rw/resource/coneval/med_pobreza/MPMMPingles100903.pdf\">CONEVAL, 2010</a>) </p>\n<p><strong>Table 4: Dimensions and indicators of the measure of multidimensional poverty of Mexico</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Type of Dimension</strong></p>\n      </td>\n      <td>\n        <p><strong>Dimension</strong></p>\n      </td>\n      <td>\n        <p><strong>Indicator</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Economic well-being</p>\n      </td>\n      <td>\n        <p>Economic well-being</p>\n      </td>\n      <td>\n        <p>Income per capita</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"6\">\n        <p>Social rights</p>\n      </td>\n      <td>\n        <p>Education</p>\n      </td>\n      <td>\n        <p>Educational gap (meeting a minimum level of education for their age cohort)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Health</p>\n      </td>\n      <td>\n        <p>Enrolled in the Social Health Protection System</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Social security</p>\n      </td>\n      <td>\n        <p>Access to social security</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Housing </p>\n      </td>\n      <td>\n        <p>Quality and spaces of dwelling (floor, roof, walls, and overcrowding)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Services in the dwelling</p>\n      </td>\n      <td>\n        <p>Access to basic services in dwelling (water, drainage, electricity, cooking fuel)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Food</p>\n      </td>\n      <td>\n        <p>Food security</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>All persons whose income per capita is insufficient to cover necessary goods and services are considered deprived in economic well-being. For social rights, each of the six indicators in Table is generated as a binary variable, with 1 representing deprivation, and 0 otherwise. In the cases in which there is more than one indicator, that is, for housing and access to services in the dwelling, the individual is classified as deprived if she fails to meet the threshold for any single indicator within the dimension. The social deprivation index is then defined as the sum of these six indicators associated with social deprivation. The six dimensions are equally weighted, as all human rights are considered equally important. The social deprivation index thus takes a value between zero (the person is not deprived in any of the six social rights indicators) and six (the individual is deprived in all of them).</p>\n<p>The classification of the population according to this method is illustrated in Figure 1. The vertical axis represents the space of economic well-being, measured by per capita household income. The horizontal axis represents the space of social rights. In this axis, individuals at the origin have a social deprivation index of six, individuals placed more to the right have fewer deprivations. The deprivation cutoff in the space of social rights is one, and individuals to the left of this threshold or on this threshold are considered to be deprived in social rights. People are divided into four groups (CONEVAL 2010, p. 32):</p>\n<ol>\n  <li><em>Multidimensionally poor</em>. People with an income below the economic well-being threshold and with one or more unfulfilled social rights.</li>\n  <li><em>Vulnerable due to social deprivation</em>. Socially deprived people with an income higher than the economic well-being threshold.</li>\n  <li><em>Vulnerable due to income</em>. Population with no social deprivations and with an income below the economic well-being threshold.</li>\n  <li><em>Not multidimensionally poor and not vulnerable</em>. Population with an income higher than the economic well-being threshold and with no social deprivations. </li>\n</ol>\n<p><strong> </strong><strong>Figure 1: Identification of the multidimensionally poor in Mexico</strong></p>\n<p><img src=\"data:image/png;base64,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\"></p>\n<p>Source: Adapted of CONEVAL (2010).</p>\n<p>Among the multidimensionally poor, those in extreme poverty are also identified, by considering a lower economic well-being threshold (the minimum economic well-being threshold)<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup> and a higher deprivation threshold of three of more social deprivations. </p>\n<p>In terms of aggregation, Mexico produces several categories of summary measures. The core measure is the headcount ratio, that is, the proportion of people who are multidimensionally poor (i.e. the proportion of people in group I in Figure 1). In addition, other headcount measures are also reported, such as the proportion of people deprived in economic well-being, the proportion deprived in each of the social rights, and the proportion showing one or more social deprivations. The depth of poverty is computed separately with respect to economic well-being and social deprivations. The depth of poverty in terms of economic well-being is the average gap between the well-being threshold and the income of poor people.<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup> This measure is reported for groups I and III in Figure 1. The depth of poverty in terms of social deprivations is the average proportion of deprivations among those suffering at least one deprivation. This measure is reported for groups I and II in Figure 1. Finally, the intensity of poverty corresponds to the product of the headcount ratio and the depth of poverty.<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup> This measure is computed for the multidimensionally poor (group I) and the socially deprived (group II).</p>\n<p>In 2015, Vietnam launched their official multidimensional poverty index, following an approach similar to the one adopted in Mexico but using the household as the unit of analysis. A multidimensionally poor household is a household (1) whose monthly average income per capita is at or below income-based poverty line, OR (2) whose monthly average income per capita is above income-based poverty line but below minimum living standard AND is deprived on at least 3 indices for measuring deprivation of access to basic social services. Ten indicators are included in the list of basic social services. These are (1) adult education, (2) child school attendance, (3) accessibility to health care services, (4) health insurance, (5) quality of house, (6) housing area per capita, (7) drinking water supply, (8) hygienic toilet/latrine, (9) use of telecommunication services, and (10) assets for information accessibility.<sup><sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup></sup></p>\n<ol>\n  <li>At Risk of Poverty or Social Exclusion</li>\n</ol>\n<p>The &#x201C;at-risk-of-poverty or social exclusion&#x201D; rate, <a href=\"https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:At_risk_of_poverty_or_social_exclusion_(AROPE)\">AROPE</a>, is the main indicator to monitor the EU 2030 target on poverty and social exclusion, aiming at reducing the number of people at risk of poverty or social exclusion by at least 15 million, out of them, at least 5 million should be children. It also was the headline indicator to monitor the <a href=\"https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:EU_2020_Strategy\">EU 2020 Strategy</a> poverty target. It is defined as the proportion of people (or number of persons) that are either at risk of (monetary) poverty, or are living in a household with very low work intensity, or are severely materially and socially deprived. In other words, AROPE considers three dimensions/indicators, and the individual is at risk of poverty or social exclusion if she is deprived in at least one of those components. </p>\n<p>An individual is <a href=\"https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:At-risk-of-poverty_rate\">at-risk-of-poverty</a> if:</p>\n<ol>\n  <li>She has an equivalized disposable income (after social transfers) below the at-risk-of-poverty threshold, which is defined as the 60 percent of the national median equivalized disposable income after social transfers. </li>\n  <li>Lives in a household with <a href=\"https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Persons_living_in_households_with_low_work_intensity\">very low work intensity</a>, defined as &#x201C;people from 0-64 years living in households where the adults (those aged 18-64, but excluding students aged 18-24 and people who are retired according to their self-defined current economic status or who receive any pension (except survivors pension), as well as people in the age bracket 60-64 who are inactive and living in a household where the main income is pensions) worked a working time equal or less than 20% of their total combined work-time potential during the previous year&#x201D;. </li>\n  <li>Is <a href=\"https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Severe_material_and_social_deprivation_rate_(SMSD)&amp;stable=0&amp;redirect=no\">severely materially and socially deprived</a>, that is if she or her household cannot afford at least seven of the following 13 items<sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup>:</li>\n</ol>\n<p>List of items at household level:</p>\n<ul>\n  <li>Capacity to face unexpected expenses</li>\n  <li>Capacity to afford paying for one week annual holiday away from home</li>\n  <li>Capacity to being confronted with payment arrears (on mortgage or rental payments, utility bills, hire purchase instalments or other loan payments)</li>\n  <li>Capacity to afford a meal with meat, chicken, fish or vegetarian equivalent every second day</li>\n  <li>Ability to keep home adequately</li>\n  <li>Have access to a car/van for personal use</li>\n  <li>Replacing worn-out furniture</li>\n</ul>\n<p>List of items at individual level:</p>\n<ul>\n  <li>Having internet connection</li>\n  <li>Replacing worn-out clothes by some new ones</li>\n  <li>Having two pairs of properly fitting shoes (including a pair of all-weather shoes)</li>\n  <li>Spending a small amount of money each week on him/herself</li>\n  <li>Having regular leisure activities</li>\n  <li>Getting together with friends/family for a drink/meal at least once a month</li>\n</ul>\n<p>The information on the individuals at risk of poverty and social exclusion is aggregated in the form of an incidence rate, the proportion of individuals in the total population that are identified as being at risk of poverty or social exclusion. People are included only once even if they are in more than one situation (AROPE components mentioned above).</p>\n<p>The construction of AROPE follows the same steps outlined above that are used in the UBN or mixed (CONEVAL) experiences. In addition, as in the two other highlighted cases, the three dimensions are equally weighted. However, while CONEVAL takes as deprived in social rights as those suffering from at least one deprivation in any indicator within this dimension, AROPE requires that within material and social deprivation at least seven deprivation items out of 13 are needed for establishing severe material and social deprivation.</p>\n<ol>\n  <li>Alkire-Foster Approach to Multidimensional Poverty </li>\n</ol>\n<p>Alkire and Foster presented a family of multidimensional poverty measures based on the counting approach, which has captured global attention and is being widely adopted by countries. The first and most well-known application is the UNDP-OPHI Multidimensional Poverty Index (MPI) at the global level, which has been published since 2011. Since then, many countries have followed their guidance in what is known as &#x201C;the MPI approach.&#x201D; </p>\n<p>The Alkire-Foster family of measures follows the five steps of counting approaches described above and the two stages of identification and aggregation: (1) there is a first cut-off for each deprivation-specific threshold, and (2) there is second cut-off at the aggregation stage to determine whether the person (or household) is multidimensionally poor based on the deprivation score. Differential weights are sometimes used at the aggregation stage, but they are not mandatory. This results in an estimate of the incidence or prevalence of poverty, which is usually referred as H.</p>\n<p>An innovation introduced by the Alkire-Foster family of measures is that it is possible to account simultaneously for both the incidence of poverty (H), as well as its intensity (A).<sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup> The intensity of poverty &#x2013; also called breadth of poverty &#x2013; is defined as the average proportion of the relevant multidimensional poverty indicators (weighted or not) in which the poor are deprived. When using categorical variables, it is possible to estimate an adjusted headcount ratio (<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>M</mi>\n      </mrow>\n      <mrow>\n        <mn>0</mn>\n      </mrow>\n    </msub>\n  </math> or MPI), where </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>M</mi>\n      </mrow>\n      <mrow>\n        <mn>0</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi>H</mi>\n    <mo>&#xD7;</mo>\n    <mi>A</mi>\n  </math><em>.</em></p>\n<p>The adjusted headcount ratio, just like the other measures described in this note, can be disaggregated by population subgroups (e.g. geographic area, ethnicity), and it can be broken down by dimension or indicator. For more details on the methodology, see Alkire et al. (2015). </p>\n<p>The Alkire-Foster approach can be seen as a general framework to measure multidimensional poverty that can be tailored to very different contexts. Many of the existing permanent national statistics of multidimensional poverty are based on the global MPI, but with substantial modifications in terms of dimensions, indicators, and thresholds.<sup><a href=\"#footnote-13\" id=\"footnote-ref-13\">[12]</a></sup> Since 2018, the World Bank regularly presents multidimensional poverty measures across countries using the headcount ratio (H), as is done by UNDP-OPHI measure, albeit with differences in the selection of parameters, some of the indicators, and sources of data. In addition to the headcount ratio, the 2018 Poverty and Shared Prosperity report, where the World Bank introduced this multidimensional measure, presents estimates of global poverty using the adjusted headcount ratio of the Alkire-Foster family as well as the distribution-sensitive multidimensional poverty measure, proposed in Datt (2018). </p>\n<ol>\n  <li>Child Poverty</li>\n</ol>\n<p>Children experience and suffer poverty differently than adults (UNICEF, 2019). Their needs are also different, for example in terms of nutrition or education. However, children are often invisible in poverty estimates. That is why the SDG 1.2.2 explicitly mentions children and why countries should establish a child-specific measure of poverty. The European Conference of Statisticians (2020) recommends that countries &#x201C;develop child-specific and life-cycle adapted multidimensional poverty measures&#x201D; (Recommendation 29). </p>\n<p>If child-specific poverty measures are not developed, there is a risk of misinterpreting the evolving situation of children and consequently misinterpreting the impact of policies and external shocks. It is possible that while the situation of children in a given household deteriorates, that household becomes &#x201C;non-poor&#x201D; due to indicators that matter only for adults. In such a case, despite the fact that these children are worse-off than they were before, they would no longer be counted as poor. </p>\n<p>Over 70 low- and middle-income countries which have carried out child poverty analyses based on a child-specific measure of child poverty use the child as the unit of analysis. These countries are in all regions of the developing world, (e.g. Argentina, Armenia, Brazil, Egypt, Ethiopia, Mexico, Sierra Leone, Uganda, and Zambia), as well as in the European Union.</p>\n<p>Estimating multidimensional child poverty follows the same steps as the other examples mentioned above: the relevant dimensions are identified, criteria to assess deprivation in each dimension are established, and deprived children in each dimension are identified. A threshold is then specified concerning the minimum number of dimensions in which a child must be deprived to be considered poor, and children above or below this threshold are then counted. Moreover, the percentage (and number) of children deprived in exactly one, exactly two, exactly three, et cetera, deprivations are reported and analyzed, as well as the overlaps or simultaneous deprivations. This makes it possible to measure the incidence, the breadth, and the severity of poverty in a simple and integrated way.</p>\n<p>For child poverty, the selection of dimensions should be based on child rights. However, not all rights constitute child poverty, as explained in the Guidelines on Human Rights and Poverty from the Office of the High Commissioner for Human Rights. According to the Conference of European Statisticians: &#x201C;Deprivation measures need to be based upon a clear and explicit theory or normative definition of poverty in order to ensure that each indicator is a valid measure, i.e. that <strong>it measures poverty and not some other related (or unrelated) concept such as wellbeing [sic] or happiness</strong>&#x201D; (Recommendation 28 (a), emphasis added).</p>\n<p>As in the case of CONEVAL (explicitly) and UBN (implicitly), no differential weights should be applied across dimensions because they are rights. All rights are equally important and cannot be substituted. This is not just emanating from the human rights approach, but it is also the case with capabilities approach, as stated by Dixon and Nussbaum (2012): &#x201C;A Capabilities Approach is generally committed to the equal protection of rights for all up to a certain threshold. Any trade-off that leaves some people below this threshold will thus be a clear failure of basic justice under a Capabilities Approach&#x201D; (Children&#x2019;s Rights and a Capabilities Approach: The Question of Special Priority, p. 554, Public Law and Legal Theory Working Paper No. 384.)</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> Decanq and Lugo (2013) explore and explain various approaches to setting weights. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> This approach was proposed in several publications before being adopted widely in Latin America. See, among others: ILO (1978), Morris (1978) and Streeten et al. (1981). <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> Early examples of analyses using this approach include, for instance, Beccaria and Minuj&#xED;n (1985), Minujin, A. (1995), and Erikson, R (1989). <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> In Ireland, since 1997 &#x201C;consistent poverty&#x201D; is defined as the proportion of people who are both income-poor and cannot afford at least two of the set of items considered essential for a basic standard of living (previously 8, now 11 items are considered as essential). Since 2010, the United Kingdom applies a similar definition for one of its four policy targets on child poverty, combining low income and material deprivation (The Child Poverty Unit, 2014). <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> The economic well-being threshold was defined with reference to a basket of basic goods and services. The minimum economic well-being threshold is the minimum required income to acquire enough food to ensure adequate nutrition. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> Foster, Greer and Thorbecke (1976). <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> Following Alkire and Foster (2007). <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> Vietnam General Statistics Office. <a href=\"https://www.gso.gov.vn/en/metadata/2019/10/explanation-of-terminology-content-and-methodology-of-some-statistical-indicators-on-living-standard/\">https://www.gso.gov.vn/en/metadata/2019/10/explanation-of-terminology-content-and-methodology-of-some-statistical-indicators-on-living-standard/</a> <a href=\"#footnote-ref-10\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> In 2021, the AROPE indicator was modified in line with the new EU 2030 target so that the severe material deprivation component includes social deprivation. The low work intensity component was also revised to better account for the social exclusion situation of those in the working age. During 2010-2020, under the EU 2020 target, the households were regarded as severely materially deprived if she can not afford at least four of the following nine items; 1) to pay the rent, mortgage or utility bills, 2) to keep the home adequately warm, 3) to face unexpected expenses, 4) to eat meat or proteins regularly, 5) to go on holiday, 6) a television set, 7) a washing machine, 8) a car, 9) a telephone. <a href=\"#footnote-ref-11\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> The formula developed by Datt and featured in the 2018 Poverty and Shared Prosperity report by the World Bank (2018), also allows for a combination of incidence and breadth of poverty. There are several other formulae which allow this combination. <a href=\"#footnote-ref-12\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-13\">12</sup><p> For information on these measures, visit the website of the Multidimensional Poverty Peer Network (MPPN), <a href=\"http://www.mppn.org\">www.mppn.org</a>. The MPPN was launched in 2013 to provide support to policy makers who are implementing a Multidimensional Poverty Index (MPI) or are exploring the possibility of developing multidimensional measures of poverty. <a href=\"#footnote-ref-13\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>The data has been validated by a three-stage approach to ensure its accuracy. First, the data is entered by World Bank staff assigned to each country, typically in consultation with the country NSO and/or country official documents. That data is sent to UNICEF and UNDP country officers for the validation. After integrating inputs from these three agencies, the data is sent to the SDGs focal point for each country for their final approval. For countries where the World Bank does not have any country offices, such as for OECD and EU countries, the World Bank collected the information based on data source available online, and sent it directly to the official counterparts of each country for verification.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>The treatment of missing values differs from survey to survey. For details, please refer to the official documentation through the links listed at the end.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>No estimation by international agencies has been implemented for missing values in this data.</p>", "REG_AGG__GLOBAL"=>"<p>Since the data for indicator 1.2.2 are based on the national definitions of poverty &#x2013; and consequently the indicators and thresholds used to produce them are different, as described in the &#x201C;comments and limitations&#x201D; section, data are not comparable across countries. Thus, regional and global aggregates are not produced.</p>", "DOC_METHOD__GLOBAL"=>"<p>A successful measure of multidimensional poverty should be rigorous, institutionalized, sustainable, and useful. Such a measure generates credible and relevant information, and it is established as an official permanent statistic alongside traditional ones such as the income or expenditure poverty headcount and poverty gaps. As with other indicators, it is important that a clear and transparent system be in place for the regular updating of the measurement. This implies that the responsibility for these updates is assigned to an official entity and that associated costs are incorporated in the government&#x2019;s budget. Ideally, a multidimensional poverty measure could be used actively to guide policy-making (e.g. policies coordination, targeting, and policy evaluation). </p>\n<p>To make such a measure institutional and useful, it is fundamental for the government to own the process. Having the support of high-level representatives within the government, such as the president or prime minister, or ministers, grants additional legitimacy to the process and may facilitate the adoption of the measure by other levels of government and stakeholders. In addition, a high-level official may be able to bring other relevant actors into the design process and work on the institutionalization of the measure. The active participation of different ministries in the discussions and decisions throughout the process of design, namely the selection of indicators, respective cut-offs, and weights, is essential to ensure that the final measure meets the needs of policy makers in a specific country context.</p>\n<p>To make a measure long-lasting, rather than specific to a particular administration, it is useful to build consensus and a shared sense of legitimacy around the measure that transcends individual political actors. This requires that the process of developing the measure is perceived as credible, transparent, and non-partisan. Engaging key stakeholders, such as academics, opinion leaders, the opposition, and civil society representatives throughout the process is highly desirable. This should include wide consultations with the public, for example through nationally representative surveys to capture the national consensus about the minima required to satisfy different dimensions. In addition, it is important to have a well-designed communication strategy to explain the concept and the process to these different actors, allowing for channels for them to participate in the discussions about the design of the measure. Some countries have opted for involving a poverty committee that gathers experts and representatives from different sectors of society in the decision process of designing the measure. </p>\n<p>More specifically, the design of a measure of multidimensional poverty generally involves a technical process, complemented and supported by a political process. If both technical and political committees are set up, it is useful to agree on: (1) a plan of activities and timeline; (2) a schedule of regular interactions to ensure good communication; and (3) a documentation system that keeps track of all decisions and respective rationales. However, political interference in the technical process should be avoided, as recommended by the UNSD National Quality Assurance Frameworks Manual for Official Statistics.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The data has been validated by a three-stage approach to assure its accuracy. First, the data is entered by World Bank staff assigned to each country, typically in consultation with the country NSO and/or country official documents. That data is sent to UNICEF and UNDP country officers for the validation. After integrating inputs from these three agencies, the data is sent to the SDGs focal point for each country for their final approval. For countries where the World Bank does not have any country offices, such as for OECD and EU countries, the World Bank collected the information based on data source available online, and sent it directly to the official counterparts of each country for verification.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Initially, the data has been input by poverty economists, which has been checked carefully together with the metadata information by the central team for monitoring SDGs 1.2.2 in the World Bank. Then data has been sent to the UNDP and UNICEF for further verification.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>As the custodians of the data are countries, the partner agencies do not conduct any quality assessment on the data itself other than ensuring that the data corresponds to those numbers officially published.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Level of disaggregation:</strong></p>\n<p>Official multidimensional poverty headcount (% population) is disaggregated by sex and age. The age band for official multidimensional poverty headcount for children is mostly 0-17, but some countries have different age definition for children, such as 0-15 in El Salvador. Geographically it is disaggregated by urban and rural areas.</p>\n<p><strong>Years of Reporting:</strong></p>\n<p>Years of reporting in the SDG 1.2.2 indicators are those when the source survey has been conducted except for the AROPE. When the survey year is split into two years, the first year has been reported. In AROPE, the reference period for all dimensions along with the indicators is disseminated as well as variables related to the materially deprived items in question in the survey year, except for age, income, variables on arrears, work intensity of the household, country of birth. As far as age is concerned, depending on the EU-SILC question, age can refer to two different moments in time: (i) age at the end of the income reference period; (ii) age at the date of interview. The age at the end of the income reference period is considered as the main age (e.g. it is used to define the statistical population, sample person, etc.). For income, the income reference period is a fixed 12-month period (such as the previous calendar or tax year). Variables on arrears refer to the last 12 months, while work intensity of the household refers to the number of months that all working age household members have been working during the income reference year. </p>\n<p><strong>Data Availability:</strong></p>\n<p>So far, 87 countries&apos; multidimensional poverty measurements were reported and confirmed by SDG focal points. However, the availability of the multidimensional poverty indicator over time differs greatly from country to country. The following table 5 shows the years in which data is available for a country (the coloured boxes). The star mark indicates that data on multidimensional deprivation for children is available. </p>\n<p>Table 5: Headcount data availability for countries</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Country</strong></p>\n      </td>\n      <td>\n        <p><strong>2013</strong></p>\n      </td>\n      <td>\n        <p><strong>2014</strong></p>\n      </td>\n      <td>\n        <p><strong>2015</strong></p>\n      </td>\n      <td>\n        <p><strong>2016</strong></p>\n      </td>\n      <td>\n        <p><strong>2017</strong></p>\n      </td>\n      <td>\n        <p><strong>2018</strong></p>\n      </td>\n      <td>\n        <p><strong>2019</strong></p>\n      </td>\n      <td>\n        <p><strong>2020</strong></p>\n      </td>\n      <td>\n        <p><strong>2021</strong></p>\n      </td>\n      <td>\n        <p><strong>2022</strong></p>\n      </td>\n      <td>\n        <p><strong>2023</strong></p>\n      </td>\n      <td>\n        <p><strong>2024</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Afghanistan</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p> PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p> PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Albania</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p> PH</p>\n      </td>\n      <td>\n        <p> PH</p>\n      </td>\n      <td>\n        <p> PH</p>\n      </td>\n      <td>\n        <p> PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Angola</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p> PH *</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Armenia</p>\n      </td>\n      <td>\n        <p>PH *</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH *</p>\n      </td>\n      <td>\n        <p>PH *</p>\n      </td>\n      <td>\n        <p>PH *</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Austria</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Belgium</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH </p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Belize</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Bhutan</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Bulgaria</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Burundi</p>\n      </td>\n      <td>\n        <p>PH *</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Chile</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Colombia</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Costa Rica</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Croatia</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Cyprus</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Czechia</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denmark</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Dominican Republic</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ecuador</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Egypt</p>\n      </td>\n      <td></td>\n      <td>\n        <p>*</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>El Salvador</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Estonia</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Finland</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>France</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Germany</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ghana</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>*</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Greece</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Guatemala</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Guinea</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Guinea Bissau</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH *</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Honduras</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Hungary</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Iceland</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>India</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ireland</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Italy</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Kosovo</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Kyrgyz</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Laos</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Latvia</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Lebanon</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Lesotho</p>\n      </td>\n      <td></td>\n      <td>\n        <p>*</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Lithuania</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Luxembourg</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Malawi</p>\n      </td>\n      <td>\n        <p>*</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH *</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Malaysia</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Maldives</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Mali</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>*</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Malta</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Mexico</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Montenegro</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Morocco</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Mozambique</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Namibia</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Nepal</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Netherlands</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Nigeria</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>North Macedonia</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Norway</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Pakistan</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Palestine</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Panama</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Paraguay</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Philippines</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Poland</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Romania</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Rwanda</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Saint Lucia</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Samoa</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>S&#xE3;o Tom&#xE9; and Pr&#xED;ncipe</p>\n      </td>\n      <td></td>\n      <td>\n        <p>*</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Serbia</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Seychelles</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Slovakia</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Slovenia</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>South Africa</p>\n      </td>\n      <td></td>\n      <td>\n        <p>*</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Spain</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Sri Lanka</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Suriname</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Sweden</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH </p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Thailand</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH </p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Tonga</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Turkey</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Uganda</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Uruguay</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Vietnam</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td>\n        <p>PH</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Zambia</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>*</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Zimbabwe</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>*</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p> PH</p>\n      </td>\n      <td>\n        <p>Poverty headcount data available</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>*</p>\n      </td>\n      <td>\n        <p>Multidimensional deprivation for children available</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "COMPARABILITY__GLOBAL"=>"<p><strong>Comparability:</strong></p>\n<p>As it was mentioned in section 4, the compiled data of SDG 1.2.2 are not intended to be comparable across countries due to national definitions. It is quite common that countries use a different number of dimensions and a variety of indicators depending on the country context. As SDG 1.2.2 explicitly says multidimensional poverty should be estimated in each country according to national definitions, this lack of comparability is not an issue.</p>\n<p><strong>Sources of discrepancies:</strong></p>\n<p>Given there is no custodian agency to estimate internationally comparable levels of multidimensional poverty, there are no, <em>stricto sensu</em>, challenges in terms of discrepancies. Nevertheless, sometimes agencies do calculate multidimensional poverty, using common and comparable dimensions, indicators, and thresholds for different types of reports or analyses. In these cases, it has to be remembered that these are not official (i.e. government sanctioned and approved) estimates. Most importantly, they should not be used to replace nationally owned estimates.</p>", "OTHER_DOC__GLOBAL"=>"<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Country</strong></p>\n      </td>\n      <td>\n        <p>Reference</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Afghanistan</strong></p>\n      </td>\n      <td>\n        <p>(2016)</p>\n        <p>Official publication: <a href=\"https://www.mppn.org/wp-content/uploads/2019/03/AFG_2019_vs9_online.pdf\">Afghanistan Multidimensional Poverty Index 2016-2017</a> </p>\n        <p>(2019) </p>\n        <p>Official publication: <a href=\"https://microdatalib.worldbank.org/index.php/catalog/12377/related-materials\">Income and Expenditure &amp; Labor Force Survey 2019-2020</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Albania</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a> </p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Angola</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.unicef.org/esa/sites/unicef.org.esa/files/2019-01/UNICEF-Angola-2018-A-Multidimensional-Analysis-of-Child-Poverty.pdf\">Childhood in Angola - A Multidimensional Analysis of Child Poverty</a>/ </p>\n        <p><a href=\"https://ophi.org.uk/wp-content/uploads/Angola_PM_2020.pdf\">Pobreza Multidimensional em Angola</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Armenia</strong></p>\n      </td>\n      <td>\n        <p>(2010-2017)<br>Official publication: <a href=\"https://www.armstat.am/en/?nid=82&amp;id=2095\">Social Snapshot and Poverty in Armenia: Statistical and analytical report, 2018</a> </p>\n        <p>Methodological documentation: <a href=\"http://documents.worldbank.org/curated/en/111701504028830403/The-many-faces-of-deprivation-a-multidimensional-approach-to-poverty-in-Armenia\">The Many Faces of Deprivation: A Multidimensional Approach to Poverty in Armenia</a> <br>(2018)<br>Official publication: <a href=\"https://armstat.am/am/?nid=82&amp;id=2217\">Social Snapshot and Poverty in Armenia, 2019</a> </p>\n        <p>Methodological documentation: <a href=\"http://documents.worldbank.org/curated/en/111701504028830403/The-many-faces-of-deprivation-a-multidimensional-approach-to-poverty-in-Armenia\">The Many Faces of Deprivation: A Multidimensional Approach to Poverty in Armenia</a> </p>\n        <p>(2019)</p>\n        <p>Official publication: <a href=\"https://armstat.am/en/?nid=82&amp;id=2438\">Social Snapshot and Poverty in Armenia, 2021</a></p>\n        <p>Methodological documentation: <a href=\"http://documents.worldbank.org/curated/en/111701504028830403/The-many-faces-of-deprivation-a-multidimensional-approach-to-poverty-in-Armenia\">The Many Faces of Deprivation: A Multidimensional Approach to Poverty in Armenia</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Austria</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a> </p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Belgium</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a> </p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Belize</strong></p>\n      </td>\n      <td>\n        <p>(2021)</p>\n        <p>Official publication: <a href=\"https://sib.org.bz/wp-content/uploads/MPI_Infographic_v03.pdf\">Multidimensional Poverty in Belize</a> </p>\n        <p>(2023)</p>\n        <p>Official publication: <a href=\"https://sib.org.bz/statistics/poverty-statistics/\">Multidimensional Poverty in Belize</a></p>\n        <p>(2024)</p>\n        <p>Official publication: <a href=\"https://sib.org.bz/statistics/poverty-statistics/\">Multidimensional Poverty in Belize</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Bhutan</strong></p>\n      </td>\n      <td>\n        <p>(2010)</p>\n        <p>Official publication: CHILD POVERTY IN BHUTAN: Insights from Multidimensional Child Poverty Index and Qualitative Interviews with Poor Children</p>\n        <p>(2012, 2017)</p>\n        <p>Official publication: <a href=\"https://ophi.org.uk/wp-content/uploads/Bhutan_2017_vs5_23Dec_online.pdf\">Bhutan Multidimensional Poverty Index 2017</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Bulgaria</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a> </p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Burundi</strong></p>\n      </td>\n      <td>\n        <p>Official publication: </p>\n        <p><a href=\"https://www.ilo.org/surveyLib/index.php/catalog/2153/download/18083\">Rapport de l&#x2019;enqu&#xEA;te modulaire sur les conditions de vie des m&#xE9;nages 2013/2014</a> /</p>\n        <p><a href=\"https://www.unicef.org/esa/sites/unicef.org.esa/files/2018-09/UNICEF-Burundi-2017-Child-Poverty.pdf\">La Pauvret&#xE9; des Enfants au Burundi</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Chile</strong></p>\n      </td>\n      <td>\n        <p>(2011 and 2013)<br>Official publication: <a href=\"http://www.desarrollosocialyfamilia.gob.cl/pdf/upload/Libro_IDS_2015_final.pdf\">Informe de desarrollo social 2015</a><br>(2015 and 2017)<br>Official publication: <a href=\"http://observatorio.ministeriodesarrollosocial.gob.cl/storage/docs/casen/2017/Resultados_pobreza_Casen_2017.pdf\">http://observatorio.ministeriodesarrollosocial.gob.cl/storage/docs/casen/2017/Resultados_pobreza_Casen_2017.pdf</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Colombia</strong></p>\n      </td>\n      <td>\n        <p>(2010)</p>\n        <p>Official publication: <a href=\"https://www.dane.gov.co/files/investigaciones/condiciones_vida/pobreza/2018/bt_pobreza_multidimensional_18.pdf\">Pobreza multidimensional en Colombia</a></p>\n        <p>(2011-2020)</p>\n        <p>Official publication: <a href=\"https://www.dane.gov.co/files/investigaciones/condiciones_vida/pobreza/2020/presentacion-rueda-de-prensa-pobreza-multidimensional-20.pdf\">Pobreza Multidimensional</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Costa Rica</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://admin.inec.cr/sites/default/files/2022-10/reenaho2022.pdf\">Encuesta Nacional de Hogares Julio 2022 RESULTADOS GENERALES</a> <br><br></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Croatia</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a> </p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Cyprus</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a> </p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Czechia</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Denmark</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Dominican Republic</strong></p>\n      </td>\n      <td>\n        <p>(2010-2016)</p>\n        <p>Official publication: <a href=\"https://mepyd.gob.do/wp-content/uploads/drive/UAAES/Publicaciones/El%20indice%20de%20Pobreza%20Multidimensional%20para%20America%20Latina.pdf\">The <br>Multidimensional Poverty Index for Latin America (MPI-LA): an application for the Dominican Republic 2000-2016</a>. </p>\n        <p>(2017-2019)</p>\n        <p>Official publication: <a href=\"https://mepyd.gob.do/sisdom\">Sistema de Indicadores Sociales de la Rep&#xFA;blica Dominicana SISDOM 19</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Ecuador</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.ecuadorencifras.gob.ec/documentos/web-inec/POBREZA/2019/Diciembre-2019/Tabulados%20IPM-dic%2019.xlsx\">National Employment, Underemployment and Unemployment Survey (ENEMDU) 2019</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Egypt</strong></p>\n      </td>\n      <td>\n        <p>(2014)</p>\n        <p>Official publication: <a href=\"https://www.unicef.org/egypt/reports/understanding-child-multidimensional-poverty-egypt\">Understanding Multidimensional Poverty in Egypt</a> </p>\n        <p>(2022)</p>\n        <p>Official publication: <a href=\"https://www.unescwa.org/sites/default/files/pubs/pdf/multidimensional-poverty-egypt-english_0.pdf\">Multidimensional poverty in Egypt: An in-depth analysis</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>El Salvador</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://cepei.org/wp-content/uploads/2020/01/Informe_ODS-1.pdf\">INFORME EL SALVADOR 2019</a></p>\n        <p>Methodological documentation: <a href=\"https://www.cepal.org/sites/default/files/presentations/08-10-el_salvador-medicion-multidimencional-pobreza.pdf\">EHMP 2016 El Salvador</a>/ <a href=\"https://www.transparencia.gob.sv/institutions/capres/documents/292084/download#:~:text=La%20intensidad%20de%20la%20pobreza,saneamiento%20(83.8%20%25)%20y%20las\">Informe MMP 2017</a>. </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Estonia</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Finland</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>France</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Germany</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Ghana</strong></p>\n      </td>\n      <td>\n        <p>(2010)</p>\n        <p>Official publication: <a href=\"https://www.undp.org/content/dam/ghana/docs/Doc/Inclgro/Non-Monetary%20Poverty%20in%20Ghana%20(24-10-13).pdf\">Non-Monetary Poverty in Ghana</a> </p>\n        <p>(2011, 2016, 2018)</p>\n        <p>Official publication: <a href=\"https://www.gh.undp.org/content/dam/ghana/docs/Reports/UNDP_GH_MPI_Report_2020.pdf\">Ghana Multidimensional Poverty Index (MPI) report 2020</a></p>\n        <p>(2017)</p>\n        <p>Official publication: <a href=\"https://www.unicef.org/ghana/media/2676/file/Multi-Dimensional%20Child%20Poverty%20Report.pdf\">Multi-Dimensional Child Poverty in Ghana</a> </p>\n        <p>(2023)</p>\n        <p>Official publication: <a href=\"https://statsghana.gov.gh/gssmain/fileUpload/pressrelease/2023_Q1-Q4_MPI_Report_Bulletin.pdf\">Ghana 2023 Annual Household Income and Expenditure Survey</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Greece</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Guatemala</strong></p>\n      </td>\n      <td>\n        <p>Official publication: </p>\n        <p><a href=\"https://www.mintrabajo.gob.gt/images/Servicios/DGT/ComisionNacionalSalario/InformacionGeneral/MIDES/Estad%C3%ADsticas_Indic%C3%A9_de_Pobreza_Multidimensional_2014.xlsx\">https://www.mintrabajo.gob.gt/images/Servicios/DGT/ComisionNacionalSalario/InformacionGeneral/MIDES/Estad%C3%ADsticas_Indic%C3%A9_de_Pobreza_Multidimensional_2014.xlsx</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Guinea</strong></p>\n      </td>\n      <td>\n        <p>Official publication : </p>\n        <p><a href=\"http://www.stat-guinee.org/images/Documents/Publications/INS/rapports_enquetes/RGPH3/RGPH3_rapport_pauvrete.pdf\">RECENSEMENT GENERAL DE LA POPULATION ET DE L&#x2019;HABITATION</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Guinea Bissau</strong></p>\n      </td>\n      <td>\n        <p>(2010, 2014)</p>\n        <p>Official publication: <a href=\"https://uprdoc.ohchr.org/uprweb/downloadfile.aspx?filename=7579&amp;file=Annexe7https://uprdoc.ohchr.org/uprweb/downloadfile.aspx?filename=7579&amp;file=Annexe7\">PAUVRETE MULTIDIMENSIONNELLE ET PRIVATIONS MULTIPLES DES ENFANTS EN GUINEE-BISSAU</a><br></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Honduras</strong></p>\n      </td>\n      <td>\n        <p>Official publication : </p>\n        <p><a href=\"https://mppn.org/wp-content/uploads/2019/10/IPM_SINTESIS_SERIE_12_16_Final.pdf\">Multidimensional Poverty Index 2012- 2016</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Hungary</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Iceland</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>India</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.niti.gov.in/sites/default/files/2023-08/India-National-Multidimentional-Poverty-Index-2023.pdf\">India National Multidimensional Poverty Index- A Progress Review 2023</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Ireland</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Italy</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Kosovo</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Kyrgyz</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://sustainabledevelopment-kyrgyzstan.github.io\">KR National SDG Reporting Platform</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Laos</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://thedocs.worldbank.org/en/doc/923031603135932002-0070022020/original/LaoPDRPovertyProfileReportENG.pdf\">Poverty profile in Lao PDR</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Latvia</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Lebanon</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"http://www.cas.gov.lb/images/PDFs/Poverty/Lebanon%20MPI%202019%20Report%20%20EN.pdf\">Multidimensional Poverty Index for Lebanon</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Lesotho</strong></p>\n      </td>\n      <td>\n        <p>Official publication:</p>\n        <p><a href=\"https://lesotho.un.org/sites/default/files/2019-10/Lesotho%20child%20poverty_main%20report_4%20Oct.pdf\">Child Poverty in Lesotho: Understanding the Extent of Multiple Overlapping Deprivation</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Lithuania</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Luxembourg</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Malawi</strong></p>\n      </td>\n      <td>\n        <p>(2013)</p>\n        <p>Official publication: <a href=\"https://www.unicef.org/esa/sites/unicef.org.esa/files/2018-09/UNICEF-Malawi-2016-Child-Poverty.PDF\">Child Poverty in Malawi</a></p>\n        <p>(2016)</p>\n        <p>Official publication: <a href=\"https://www.unicef.org/esa/sites/unicef.org.esa/files/2019-01/UNICEF-Malawi-2018-Child-Poverty-in-Malawi.pdf\">Child Poverty in Malawi</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Malaysia</strong></p>\n      </td>\n      <td>\n        <p>(2014, 2016)</p>\n        <p>Official publication: <a href=\"https://www.talentcorp.com.my/clients/TalentCorp_2016_7A6571AE-D9D0-4175-B35D-99EC514F2D24/contentms/img/publication/Mid-Term%20Review%20of%2011th%20Malaysia%20Plan.pdf\">Mid-term Review of the Eleventh Malaysia Plan, 2016&#x2013;2020: New Priorities and Emphases:</a> </p>\n        <p>(2019)</p>\n        <p>Official publication: </p>\n        <p><a href=\"https://newss.statistics.gov.my/newss-portalx/ep/epFreeDownloadContentSearch.seam?cid=158397\">https://newss.statistics.gov.my/newss-portalx/ep/epFreeDownloadContentSearch.seam?cid=158397</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Maldives</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"http://statisticsmaldives.gov.mv/nbs/wp-content/uploads/2020/06/Multidimensional-Poverty-in-Maldives-2020_4th-june.pdf\">National Multidimensional Poverty in Maldives 2020</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Mali</strong></p>\n      </td>\n      <td>\n        <p>(2015)</p>\n        <p>Official publication : <a href=\"https://www.unicef.org/mali/rapports/privation-multidimensionnelle-et-pauvret%C3%A9-des-enfants-au-mali\">Privation multidimensionnelle et pauvret&#xE9; des enfants au Mali</a></p>\n        <p>(2016)</p>\n        <p>Official publication : <a href=\"https://www.instat-mali.org/laravel-filemanager/files/shares/eq/rap-ind16-17_eq.pdf\">La pauvret&#xE9; &#xE0; plusieurs dimensions au Mali</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Malta</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Mexico</strong></p>\n      </td>\n      <td>\n        <p>(2010, 2012, 2014)<br>Official publication: <a href=\"https://www.coneval.org.mx/Medicion/MP/Paginas/Pobreza-2018.aspx\">https://www.coneval.org.mx/Medicion/MP/Paginas/Pobreza-2018.aspx</a></p>\n        <p>Methodological documentation: https://www.coneval.org.mx/Informes/Coordinacion/Publicaciones%20oficiales/MEDICION_MULTIDIMENSIONAL_SEGUNDA_EDICION.pdf<br><br>(2016, 2018, 2020)<br>Official publication: <a href=\"https://www.coneval.org.mx/Medicion/MP/Paginas/Pobreza_2020.aspx\">https://www.coneval.org.mx/Medicion/MP/Paginas/Pobreza_2020.aspx</a></p>\n        <p>Methodological documentation: <a href=\"https://www.coneval.org.mx/InformesPublicaciones/InformesPublicaciones/Documents/Metodologia-medicion-multidimensional-3er-edicion.pdf\">https://www.coneval.org.mx/InformesPublicaciones/InformesPublicaciones/Documents/Metodologia-medicion-multidimensional-3er-edicion.pdf</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Montenegro</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Morocco</strong></p>\n      </td>\n      <td>\n        <p>(2011)<br>Official publication: <a href=\"https://www.hcp.ma/Les-Cahiers-du-Plan-N-43-Mars-Avril-2013_a1248.html\">Principaux r&#xE9;sultats de l&#x2019;Enqu&#xEA;te nationale sur l&#x2019;anthropom&#xE9;trie 2011</a><strong> </strong><br>(2014)<br>Official publication: <a href=\"https://www.hcp.ma/file/198688/\">Principaux r&#xE9;sultats de la cartographie de la pauvret&#xE9; multidimensionnelle 2004 - 2014 : Paysage territorial et dynamique</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Mozambique</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.wider.unu.edu/sites/default/files/Final_QUARTA%20AVALIA%C3%87AO%20NACIONAL%20DA%20POBREZA_2016-10-26_2.pdf\">Poverty and Well-being in Mozambique: Fourth National Poverty Assessment (IOF 2014/2015) </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Namibia</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.unicef.org/esa/media/9041/file/UNICEF-Namibia-Multidimensional-Poverty-Index-2021.pd\">Namibia Multidimensional Poverty Index (MPI) report 2021</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Nepal</strong></p>\n      </td>\n      <td>\n        <p>(2011)</p>\n        <p>Official publication: <a href=\"https://www.npc.gov.np/images/category/Nepal_MPI.pdf\">Nepal Multidimensional Poverty Index 2018</a> <br>(2014,2019)</p>\n        <p>Official publication: <a href=\"https://mppn.org/wp-content/uploads/2021/08/MPI_Report_2021_for_web.pdf\">Nepal Multidimensional Poverty Index </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Netherlands</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Nigeria</strong></p>\n      </td>\n      <td>\n        <p>(2017) </p>\n        <p>Official publication: <a href=\"https://hdr.undp.org/content/national-human-development-report-2018-nigeria\">National Human Development Report 2018</a></p>\n        <p>(2021)</p>\n        <p>Official publication: <a href=\"https://nigerianstat.gov.ng/elibrary/read/1241254\"><strong> </strong>Nigeria Multidimensional Poverty Index</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>North Macedonia</strong></p>\n      </td>\n      <td>\n        <p>(2010)</p>\n        <p>Official publication: <a href=\"http://www.stat.gov.mk/Publikacii/2.4.15.01.pdf\">Survey on Income and Living Conditions, 2012</a></p>\n        <p>(2011-2013)<br>Official publication: <a href=\"http://www.stat.gov.mk/Publikacii/2.4.15.13.pdf)\">Survey on Income and Living Conditions, 2013</a><br>(2014-2016)<br>Official publication: <a href=\"http://www.stat.gov.mk/Publikacii/2.4.17.13.pdf\">Survey on Income and Living Conditions, 2016</a><br>(2017)<br>Official publication: <a href=\"http://www.stat.gov.mk/Publikacii/2.4.18.13.pdf\">Survey on Income and Living Conditions, 2017</a> <br>(2018)<br>Official publication: <a href=\"http://makstat.stat.gov.mk/PXWeb/pxweb/en/MakStat/MakStat__ZivotenStandard__LaekenIndikatorSiromastija/425_ZivStd_Mk_LaekenAROPE_ml.px/?rxid=46ee0f64-2992-4b45-a2d9-c\">http://makstat.stat.gov.mk/PXWeb/pxweb/en/MakStat/MakStat__ZivotenStandard__LaekenIndikatorSiromastija/425_ZivStd_Mk_LaekenAROPE_ml.px/?rxid=46ee0f64-2992-4b45-a2d9-c</a></p>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p>Methodological documentation: <a href=\"http://www.stat.gov.mk/MetodoloskiObjasSoop_en.aspx?id=115&amp;rbrObl=13\">Laeken Poverty Indicators</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Norway</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Pakistan</strong></p>\n      </td>\n      <td>\n        <p>(2010, 2012, 2014)</p>\n        <p>Official publication: <a href=\"https://www.undp.org/content/dam/pakistan/docs/MPI/Multidimensional%20Poverty%20in%20Pakistan.pdf\">Multidimensional Poverty in Pakistan</a></p>\n        <p>(2019)</p>\n        <p>Official publication: <a href=\"https://pc.gov.pk/uploads/report/UC_MPI_Report.pdf\">Multidimensional Poverty Index Report 2019-20</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Palestine</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://mppn.org/wp-content/uploads/2020/06/book2524-Palestine-28-48.pdf\">Multi-dimensional Poverty Profile in Palestine, 2017</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Panama</strong></p>\n      </td>\n      <td>\n        <p>(2017)</p>\n        <p>Official publication: <a href=\"https://www.mides.gob.pa/wp-content/uploads/2017/06/Informe-del-%c3%8dndice-de-Pobreza-Multidimensional-de-Panam%c3%a1-2017.pdf\">Panama Multidimensional Poverty Index</a></p>\n        <p>(2018)</p>\n        <p>Official publication: <a href=\"https://www.mides.gob.pa/wp-content/uploads/2018/09/MEF_DAES-Informe-del-IPM-de-ni%c3%b1os-ni%c3%b1as-y-adolescentes-a%c3%b1o-2018.pdf\">Multidimensional Poverty Index of Boys, Girlsand Adolescents in Panama - IPM-NNA</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Paraguay</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.ine.gov.py/Publicaciones/Biblioteca/documento/8e39_BOLETIN_TECNICO_IPM_2020.pdf\">Multidimensional poverty index</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Philippines</strong></p>\n      </td>\n      <td>\n        <p>Official document: <a href=\"https://psa.gov.ph/poverty-press-releases/nid/136930\">Philippine Statistics Authority press release</a> </p>\n        <p>Methodological documentation: <a href=\"https://psa.gov.ph/sites/default/files/mpi%20technical%20notes.pdf\">Technical notes on the estimation of the MPI based on the initial methodology</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Poland</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Romania</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Rwanda</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.mppn.org/wp-content/uploads/2018/12/EICV5_Thematic-Report_Multidimensional-Poverty-Index_MPI.pdf\">Rwanda Multidimensional Poverty Index Report, 2018</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Saint Lucia</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.stats.gov.lc/wp-content/uploads/2019/01/Saint-Lucia-National-Report-of-Living-Conditions-2016-Final_December-2018.pdf\">Saint Lucia National Report of Living Conditions 2016</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Samoa</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://sbs.gov.ws/documents/social/mpi/Samoa_MPI_Report_2022.pdf\">Samoa Multidimensional Poverty Index</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>S&#xE3;o Tom&#xE9; and Pr&#xED;ncipe</strong></p>\n      </td>\n      <td>\n        <p>Official publication: </p>\n        <p><a href=\"https://www.academia.edu/24458392/Analyse_de_la_situation_des_enfants_et_des_femmes_%C3%A0_S%C3%A3o_Tom%C3%A9-et-Principe_en_2015\">Analyse de la situation des enfants et des femmes &#xE0; S&#xE3;o Tom&#xE9;-et-Principe en 2015</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Serbia</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Seychelles</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.nbs.gov.sc/downloads/social-statistics/multidimensional-poverty-index/2018\">https://www.nbs.gov.sc/downloads/social-statistics/multidimensional-poverty-index/2018</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Slovakia</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Slovenia</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>South Africa</strong></p>\n      </td>\n      <td>\n        <p>(2011)</p>\n        <p>Official publication: <a href=\"http://www.statssa.gov.za/publications/Report-03-10-08/Report-03-10-082014.pdf\">The South African MPI</a> </p>\n        <p>(2016)</p>\n        <p>Official publication: <a href=\"http://documents.worldbank.org/curated/en/530481521735906534/pdf/124521-REV-OUO-South-Africa-Poverty-and-Inequality-Assessment-Report-2018-FINAL-WEB.pdf\">Overcoming Poverty and Inequality in South Africa</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Spain</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Sri Lanka</strong></p>\n      </td>\n      <td>\n        <p>(2016)</p>\n        <p>Official publication: <a href=\"http://www.statistics.gov.lk/Resource/en/Poverty/GMPI_Bulletin2019.pdf\">Global Multidimensional Poverty for Sri Lanka</a></p>\n        <p>(2019)</p>\n        <p>Official publication: <a href=\"http://www.statistics.gov.lk/Poverty/StaticalInformation/MultidimensionalPovertyinSriLanka-2019\">Multidimensional Poverty in Sri Lanka</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Suriname</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://statistics-suriname.org/wp-content/uploads/2023/10/Methods-and-techniques-to-determine-and-combat-Poverty-in-Suriname_English-version.pdf\">Methods and techniques to determine and combat poverty in Suriname</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Sweden</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Thailand</strong></p>\n      </td>\n      <td>\n        <p>(2015)</p>\n        <p>Official publication: <a href=\"https://www.unicef.org/thailand/sites/unicef.org.thailand/files/2019-09/unicef%20Thailand%20Child%20MPI%20Report-TH-for%20web%20v02_0.pdf\">Thailand Child Poverty Report</a></p>\n        <p>(2017)</p>\n        <p>Official publication: <a href=\"http://social.nesdc.go.th/social/Portals/0/Documents/%e0%b8%a3%e0%b8%a7%e0%b8%a1%20NMPI%2007102019%20(1630)_2305.pdf\">http://social.nesdc.go.th/social/Portals/0/Documents/%e0%b8%a3%e0%b8%a7%e0%b8%a1%20NMPI%2007102019%20(1630)_2305.pdf</a></p>\n        <p>Methodological documentation:</p>\n        <p><a href=\"http://www.nso.go.th/sites/2014en/Pages/survey/Social/Household/The-2017-Household-Socio-Economic-Survey.aspx\">http://www.nso.go.th/sites/2014en/Pages/survey/Social/Household/The-2017-Household-Socio-Economic-Survey.aspx</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Tonga</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://tongastats.gov.to/statistics/social-statistics/poverty-in-tonga/#:~:text=According%20to%20the%20latest%20data,from%2033%25%20to%2028%25.\">Assessing progress towards the reductio of poverty in Tonga 2021</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Turkey</strong></p>\n      </td>\n      <td>\n        <p> Official publication: <a href=\"https://ec.europa.eu/eurostat/databrowser/view/ilc_peps01n/default/table?lang=en\">People at risk of poverty or social exclusion by age and sex &#x2013; EU 2030 target </a></p>\n        <p><a href=\"https://ec.europa.eu/eurostat/documents/3217494/9940483/KS-02-19-165-EN-N.pdf\">Sustainable development in the European Union </a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Uganda</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.ubos.org/release-of-the-multi-poverty-dimensional-index-report-2022\">Multidimensional Poverty Index Report</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Uruguay</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.gub.uy/instituto-nacional-estadistica/comunicacion/publicaciones/pobreza-multidimensional-2024\">Pobreza Multidimensional 2024</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Vietnam</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.gso.gov.vn/en/px-web/?pxid=E1144&amp;theme=Health%2C%20Culture%2C%20Sport%20and%20Living%20standard\">https://www.gso.gov.vn/en/px-web/?pxid=E1144&amp;theme=Health%2C%20Culture%2C%20Sport%20and%20Living%20standard</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Zambia</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.unicef.org/zambia/reports/child-poverty-zambia-report-2018\">Child Poverty in Zambia</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Zimbabwe</strong></p>\n      </td>\n      <td>\n        <p>Official publication: <a href=\"https://www.unicef.org/esa/media/10241/file/UNICEF-Zimbabwe-MODA-Child-Poverty-Report-2021.pdf\">Child Poverty in Zimbabwe</a></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>References:</strong></p>\n<p>Alkire, Sabina and James Foster (2007): &#x201C;Counting and multidimensional poverty measurement&#x201D;, Working Paper N&#xBA; 7 and No 32 (revised), Oxford Poverty and Human Development Initiative.</p>\n<p>Alkire, S., Roche, J. M., Ballon, P., Foster, J., Santos, M. E., &amp; Seth, S. (2015). <em>Multidimensional poverty measurement and analysis</em>. Oxford University Press, USA.</p>\n<p>Beccaria, L. and Minuj&#xED;n, A. (1985) &#x201C;Alternative methods for measuring the evolution of poverty&#x201D; Proceedings of the 45th Session, ISI</p>\n<p>CONEVAL (2010). <em>Methodology for Multidimensional Poverty Measurement in Mexico</em>. Consejo Nacional de Evaluaci&#xF3;n de la Pol&#xED;tica de Desarrollo Social, Mexico City.</p>\n<p>Datt, G. (2017) &#x201C;Distribution-sensitive multidimensional poverty measures with an application to India&#x201D;, Monash Business School, Department of Economics, Discussion Paper number 6.</p>\n<p>Decancq, K. and M. A. Lugo. (2013). &#x201C;Weights in multidimensional Indices of well-being: an overview&#x201D;. <em>Econometric Reviews 32</em> (1): 7-34.</p>\n<p>Dixon, R., and M. Nussbaum (2012) &#x201C;Children&#x2019;s rights and a capabilities approach: The question of special priority&#x201D;, 97 Cornell Law Review. Volume 97, number 37: 549-593.</p>\n<p>Erikson, R (1989) &#x2018;Descriptions of Inequality: The Swedish Approach to Welfare Research&#x2019;, UNU WIDER Working Paper 67</p>\n<p>Feres, J. C., &amp; Mancero, X. (2001). <em>El m&#xE9;todo de las necesidades b&#xE1;sicas insatisfechas (NBI) y sus aplicaciones en Am&#xE9;rica Latina</em>. Cepal.</p>\n<p>Foster, James, Joel Greer and Erik Thorbecke (1984), &#x201C;A class of decomposable poverty measures&#x201D;, Econometrica, vol. 52, N&#xBA; 3</p>\n<p>Gordon, D. (2006). The concept and measurement of poverty. <em>Poverty and Social Exclusion in Britain. The Millennium Survey, Policy Press, Bristol</em>, 29-69.</p>\n<p>ILO (1976) Employment, Growth and Basic Needs: A One-World Problem, Geneva.</p>\n<p>Minujin, A. (1995) &#x201C;Squeezed: the middle class in Latin America&#x201D; Environment and Urbanization, Vol. 7, No. 2</p>\n<p>Morris, Morris D. (1978). &#x2018;A physical quality of life index&#x201D;. Urban Ecology, 3(3): 225&#x2013;240.</p>\n<p>Narayan, D. (2000). <em>Voices of the poor: Can anyone hear us?</em>. World Bank.</p>\n<p>Streeten, Paul, Shahid Javed Burki, Mahbub Ul Haq, Norman Hicks and Frances Stewart (1981). First Things First: Meeting Basic Human Needs in the Developing Countries. World Bank.</p>\n<p>The Child Poverty Unit (2014). Child Poverty Act 2010, http://www.legislation.gov.uk/ukpga/2010/9/contents,</p>\n<p>UNICEF (2019) Measuring and monitoring child poverty: Position paper https://data.unicef.org/resources/measuring-and-monitoring-child-poverty/</p>\n<p>United Nations Economic Commission for Europe (2020) Poverty measurement: Guide to data disaggregation, ECE/CES/2020/9: Conference of European Statisticians: Geneva.</p>\n<p>World Bank (2017). <em>Monitoring Global Poverty: Report of the Commission on Global Poverty</em>. Washington, DC: World Bank. </p>\n<p>World Bank.2018. <em>Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle</em>. Washington, D.C: World Bank Group.</p>\n<p>World Bank, UNDP and UNICEF 2021. A Roadmap for Countries Measuring Multidimensional Poverty. Washington, DC: World Bank. License: Creative Commons Attribution CC BY 3.0 IGO.</p>", "indicator_sort_order"=>"01-02-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"embed", "label"=>"related indicators"}]}, {"number"=>"1.3.1", "slug"=>"1-3-1", "name"=>"Proporción de la población cubierta por sistemas o niveles mínimos de protección social, desglosada por sexo, distinguiendo entre los niños, los desempleados, los ancianos, las personas con discapacidad, las mujeres embarazadas, los recién nacidos, las víctimas de accidentes de trabajo, los pobres y los vulnerables", "url"=>"/site/es/1-3-1/", "sort"=>"010301", "goal_number"=>"1", "target_number"=>"1.3", "global"=>{"name"=>"Proporción de la población cubierta por sistemas o niveles mínimos de protección social, desglosada por sexo, distinguiendo entre los niños, los desempleados, los ancianos, las personas con discapacidad, las mujeres embarazadas, los recién nacidos, las víctimas de accidentes de trabajo, los pobres y los vulnerables"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población cubierta por sistemas o niveles mínimos de protección social, desglosada por sexo, distinguiendo entre los niños, los desempleados, los ancianos, las personas con discapacidad, las mujeres embarazadas, los recién nacidos, las víctimas de accidentes de trabajo, los pobres y los vulnerables", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población cubierta por sistemas o niveles mínimos de protección social, desglosada por sexo, distinguiendo entre los niños, los desempleados, los ancianos, las personas con discapacidad, las mujeres embarazadas, los recién nacidos, las víctimas de accidentes de trabajo, los pobres y los vulnerables", "indicator_number"=>"1.3.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Aumento", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_57/opt_1/ti_encuesta-de-poblacion-en-relacion-con-la-actividad/temas.html", "url_text"=>"Encuesta de población en relación con la actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_268/opt_1/ti_censo-de-poblacion-y-viviendas-estructura-de-la-poblacion/temas.html", "url_text"=>"Censo de población y viviendas. Estructura de la población", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Ministerio de Inclusión, Seguridad Social y Migraciones", "periodicity"=>"Anual", "url"=>"https://www.seg-social.es/wps/portal/wss/internet/EstadisticasPresupuestosEstudios/Estadisticas/c9bd51e5-79bc-44f2-9618-14d6094eb585", "url_text"=>"Estadística de bases de cotización de la seguridad social", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de la población cubierta por sistemas o niveles mínimos de protección social, desglosada por sexo, distinguiendo entre los desempleados y la población que ha superado la edad legal de jubilación", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.3- Implementar a nivel nacional sistemas y medidas apropiados de protección social para todos, incluidos niveles mínimos, y, de aquí a 2030, lograr una amplia cobertura de las personas pobres y vulnerables", "definicion"=>"\nEl indicador refleja la proporción de personas efectivamente cubiertas por un sistema de protección social. Los datos se presentan en diferentes series para distinguir \nentre los distintos componentes del sistema de protección social. Para cada caso, la cobertura se expresa como porcentaje de la población respectiva.\n\nSeries disponibles: \n\n<b> - Proporción de personas desempleadas que reciben prestaciones por desempleo:</b> \nRelación entre beneficiarios de prestaciones por desempleo y número de desempleados\n\n<b> - Proporción de la población que supera la edad legal de jubilación y recibe una pensión:</b> \nRelación entre personas mayores de la edad legal de jubilación que reciben una pensión (contributiva o no contributiva) y personas mayores de la edad legal de jubilación \n", "formula"=>"\n<b>Proporción de personas desempleadas que reciben prestaciones por desempleo</b>\n\n$$PP_{desempleo}^{t} = \\frac{P_{prestación\\, desempleo}^{t}}{P_{desempleo}^{t}} \\cdot 100$$\n\ndonde:\n\n$P_{prestación\\, desempleo}^{t} =$ población perceptora de prestaciones por desempleo en el año $t$\n\n$P_{desempleo}^{t} =$ población desempleada en el año $t$\n\n<br>\n\n<b>Proporción de la población que supera la edad legal de jubilación y recibe una pensión</b>\n\n$$PP_{jubilación}^{t} = \\frac{P_{pensionista\\,edad\\, jubilación}^{t}}{P_{edad\\, jubilación}^{t}} \\cdot 100$$\n\ndonde:\n\n$P_{pensionista\\,edad\\, jubilación} =$ población que supera la edad legal de jubilación y recibe una pensión en el año $t$\n\n$PP_{edad\\, jubilación}^{t} =$ población que supera la edad legal de jubilación a 1 de julio del año $t$\n", "desagregacion"=>"Sexo \n\nTerritorio histórico\n", "observaciones"=>"La serie \"Proporción de personas desempleadas que reciben prestaciones por desempleo\" debe ser tomada con cautela y no se debe interpretar como la proporción de personas desempleadas que perciben una prestación, ya que un elevado volumen de trabajadores fijos dicontinuos puede dar lugar a una elevada tasa de cobertura, incluso superior a 100", "periodicidad"=>"Anual", "justificacion_global"=>"\nEl indicador de Naciones Unidas pretende reflejar la proporción de personas\nefectivamente cubiertas por un sistema de protección social,  incluidos los\nniveles mínimos de protección social. También refleja los principales\ncomponentes de la protección social (prestaciones infantiles y de maternidad,\napoyo a las personas desempleadas, personas con discapacidad, víctimas  de\naccidentes laborales y personas mayores). La cobertura efectiva de la\nprotección social se mide por el número  de personas que contribuyen\nactivamente a un plan de seguro social o reciben prestaciones (contributivas o\nno contributivas).\n\nEl acceso a al menos un nivel básico de protección social\ndurante todo el ciclo de vida es un derecho humano. El principio de\nuniversalidad de la protección social evidencia la importancia de los sistemas\nde protección social  para garantizar condiciones de vida dignas a toda la\npoblación, durante toda su vida. La proporción de la población  cubierta por\nsistemas de protección social proporciona una indicación de hasta qué punto se\nlogra la universalidad  y, por tanto, de cuán seguras son las condiciones de\nvida de la población.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.3.1&seriesCode=SI_COV_CHLD&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\"> Proporción de niños/hogares que reciben prestaciones en efectivo por hijo/familia, por sexo(%) SI_COV_CHLD</a> UNSTATS", "comparabilidad"=>"El indicador disponible en la C.A de Euskadi cumple con los metadatos de Naciones Unidas para las series analizadas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-03-01a.pdf\">Metadatos 1-3-1 (1).pdf</a> (solo en inglés)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-03-01b.pdf\">Metadatos 1-3-1 (2).pdf</a> (solo en inglés)\n", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de la población cubierta por sistemas o niveles mínimos de protección social, desglosada por sexo, distinguiendo entre los desempleados y la población que ha superado la edad legal de jubilación", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.3- Implementar a nivel nacional sistemas y medidas apropiados de protección social para todos, incluidos niveles mínimos, y, de aquí a 2030, lograr una amplia cobertura de las personas pobres y vulnerables", "definicion"=>"\nThe indicator reflects the proportion of people actually covered by a social protection system. \nThe data are presented in different series to distinguish between the different components \nof the social protection system. For each case, coverage is expressed as a percentage \nof the respective population.  \n\n\nAvailable series: \n\n<b>- Proportion of unemployed people receiving unemployment benefits:</b> \nRatio of unemployment benefit recipients to the number of unemployed\n\n<b>- Proportion of the population that exceeds the legal retirement age and receives a pension:</b> \nRatio of persons over the legal retirement age who receive a pension (contributory or non-contributory) \nto persons over the legal retirement age \n", "formula"=>"\n<b>Proportion of unemployed people receiving unemployment benefits</b>\n\n$$PP_{unemployed}^{t} = \\frac{P_{unemployment\\, benefit}^{t}}{P_{unemployment}^{t}} \\cdot 100$$\n\nwhere:\n\n$P_{unemployment\\, benefit}^{t} =$ Population receiving unemployment benefits in year $t$\n\n$P_{unemployed}^{t} =$ unemployed population in year $t$\n\n<br>\n\n<b>Proportion of the population that exceeds the legal retirement age and receives a pension</b>\n\n$$PP_{retired}^{t} = \\frac{P_{pension\\,retirement\\, age}^{t}}{P_{retirement\\, age}^{t}} \\cdot 100$$\n\nwhere:\n\n$P_{pension\\,retirement\\, age} =$ population that exceeds the legal retirement age and receives a pension in year $t$\n\n$PP_{retirement\\, age}^{t} =$ Population over the legal retirement age on 1 July $t$\n", "desagregacion"=>"Sex \n\nProvince\n", "observaciones"=>"The series \"Proportion of unemployed persons receiving unemployment benefits\" should be taken with caution and should not be interpreted as the proportion of unemployed persons who receive a benefit, since a high volume of permanent discontinuous workers can lead to a high coverage rate, even above 100.", "periodicidad"=>"Anual", "justificacion_global"=>"\nThe United Nations indicator aims to reflect the proportion of people \neffectively covered by a social protection system, including \nminimum levels of social protection. It also reflects the main \ncomponents of social protection (child and maternity benefits, \nsupport for the unemployed, people with disabilities, victims of \noccupational accidents and the elderly). Effective coverage of the \nSocial protection is measured by the number of people who contribute \nactively to a social insurance plan or receive benefits (contributory or \nnon-contributory).\n\nAccess to at least a basic level of social protection \nthroughout the life cycle is a human right. The principle of \nThe universality of social protection shows the importance of the social protection \nsystems to guarantee decent living conditions for all the population, throughout their lives. \nThe proportion of the population covered by social protection systems provides \nan indication of the extent to which universality is achieved, and therefore of how safe \nthe living conditions of the population are.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.3.1&seriesCode=SI_COV_CHLD&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\"> Proportion of children/households receiving cash benefits per child/family, by sex (%) SI_COV_CHLD</a> UNSTATS", "comparabilidad"=>"The indicator available in the Basque Country complies with  the United Nations metadata for the series analysed.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-03-01a.pdf\">Metadata 1-3-1 (1).pdf</a>\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-03-01b.pdf\">Metadata 1-3-1 (2).pdf</a>\n", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Proporción de la población cubierta por sistemas o niveles mínimos de protección social, desglosada por sexo, distinguiendo entre los desempleados y la población que ha superado la edad legal de jubilación", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.3- Implementar a nivel nacional sistemas y medidas apropiados de protección social para todos, incluidos niveles mínimos, y, de aquí a 2030, lograr una amplia cobertura de las personas pobres y vulnerables", "definicion"=>"\nGizarte-babeseko sistema batek benetan estaltzen dituen pertsonen proportzioa islatzen du adierazleak.\nDatuak serie desberdinetan aurkezten dira, gizarte-babeseko sistemaren osagaiak bereizteko. Kasu bakoitzerako, \nestaldura dagokion biztanleriaren ehuneko gisa adierazten da.\n\nEskuragarri dauden serieak:  \n\n<b> - Langabezia-prestazioak jasotzen dituzten langabeen proportzioa:</b> \nLangabezia-prestazioen onuradunen eta langabe-kopuruaren arteko harremana \n\n<b> - Erretirorako legezko adina gainditu eta pentsioa jasotzen duten biztanleen proportzioa:</b> \nErretirorako legezko adinetik gora izan eta pentsio bat (kontributiboa edo ez kontributiboa) jasotzen \nduten pertsonen, eta erretirorako legezko adinetik gorako guztien arteko harremana \n", "formula"=>"\n<b>Langabezia-prestazioak jasotzen dituzten langabeen proportzioa</b>\n\n$$PP_{langabezia}^{t} = \\frac{P_{langabezia\\, prestazioa}^{t}}{P_{langabezia}^{t}} \\cdot 100$$\n\nnon:\n\n$P_{langabezia\\, prestazioa}^{t} =$ langabezia-prestazioak jasotzen dituzten biztanleak $t$ urtean\n\n$P_{langabezia}^{t} =$ langabetuak $t$ urtean\n\n<br>\n\n<b>Erretirorako legezko adinetik gora izan eta pentsioa jasotzen duten biztanleen proportzioa</b>\n\n$$PP_{erretiroa}^{t} = \\frac{P_{pentsioduna \\, erretiro\\, adina}^{t}}{P_{erretiro\\, adina}^{t}} \\cdot 100$$\n\nnon:\n\n$P_{pentsioduna \\, erretiro\\, adina} =$ erretirorako legezko adinetik gora izan eta pentsioa jasotzen duten biztanleak $t$ urtean\n\n$PP_{erretiro\\, adina}^{t} =$ erretirorako legezko adinetik gora duen biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "observaciones"=>"\n\"Langabezia-prestazioak jasotzen dituzten langabeen proportzioa\" seriea tentuz hartu behar da, \neta ez da interpretatu behar prestazio bat jasotzen duten langabeen proportzio gisa; izan ere, \naldizkako langile finkoen bolumen handiak estaldura-tasa handia eragin dezake, baita 100 baino \nhandiagoa ere.\n", "periodicidad"=>"Anual", "justificacion_global"=>"\nNazio Batuen adierazleak gizarte-babeseko sistema batek benetan estaltzen dituen pertsonen proportzioa \nadierazten du, gizarte-babeseko gutxieneko mailak barne. Halaber, gizarte-babesaren osagai nagusiak \nislatzen ditu (haurren eta amatasunaren prestazioak nahiz langabeentzako, desgaitasunen bat duten pertsonentzako, \nlan-istripuen biktimentzako eta adineko pertsonentzako laguntzak). Gizarte-babesaren benetako estaldura neurtzeko, \ngizarte-aseguruko plan batean aktiboki parte hartzen duten edo prestazioak (kotizaziopekoak nahiz ez) jasotzen \ndituzten pertsonen kopurua zenbatzen da. \n\nBizitzako ziklo osoan zehar gutxienez gizarte-babeseko oinarrizko maila eskuratzea giza eskubide bat da. \nGizarte-babesaren unibertsaltasunaren printzipioak agerian jartzen du gizarte-babeseko sistemak zeinen \ngarrantzitsuak diren biztanleria osoari bizi-baldintza duinak bermatzeko beren bizitza osoan zehar. \nGizarte-babeseko sistemek estalitako biztanleriaren proportzioak adierazten du zein puntura arte lortzen den \nunibertsaltasuna eta, beraz, zein seguruak diren biztanleriaren bizi-baldintzak. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.3.1&seriesCode=SI_COV_CHLD&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\"> Haur edo familiako prestazioak eskudirutan jasotzen dituzten haurren/etxebizitzen proportzioa, sexuaren arabera (%) SI_COV_CHLD</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu aztertutako serieetarako.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-03-01a.pdf\">Metadatuak 1-3-1 (1).pdf</a> (ingelesez bakarrik)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-03-01b.pdf\">Metadatuak 1-3-1 (2).pdf</a> (ingelesez bakarrik)\n", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.3: Implement nationally appropriate social protection systems and measures for all, including floors, and by 2030 achieve substantial coverage of the poor and the vulnerable</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.3.1: Proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-injury victims and the poor and the vulnerable</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SI_COV_SOCINS - Proportion of population covered by social insurance programs [1.3.1]</p>\n<p>SI_COV_SOCAST - Proportion of population covered by social assistance programs [1.3.1]</p>\n<p>SI_COV_LMKT - Proportion of population covered by labour market programs [1.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.a.1 Proportion of resources allocated by the government directly to poverty reduction programs </p>\n<p>1.a.2. Proportion of total government spending on essential services (education, health and social protection) </p>\n<p>1.b.1. Proportion of government recurrent and capital spending in sectors that disproportionatly benefit women, the poor and vulnerable groups.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank (WB)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank (WB)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Coverage of social protection and labor (SPL) is the percentage of population participating in social insurance, social assistance, and labor market programs. Estimates include both direct and indirect beneficiaries (number of individuals who live in a household where at least one member participates in a social protection program).</p>\n<p><strong>Concepts:</strong></p>\n<p>CoverageCoverage is estimated by three social protection areasareas: social insurance, social assistance and labor market programs. The indicator is calculated for the entire population and for the poorest quintile, which is generated usingusingper capita incomeincome or consumption including transfers. </p>\n<p>The source of the indicator is the Atlas of Social Protection: Indicators of Resilience and Equity (ASPIRE). ASPIRE is the World Bank&apos;s premier compilation of indicators to analyze the scope and performance of social protection programs. Developed by the Social Protection and Jobs (SPJ) Global Practice, ASPIRE provides indicators for 130+ countries on social assistance, social insurance and labor market programs based on both program-level administrative data and national household survey data. ASPIRE is an ongoing project that aims to improve SPL data quality, comparability, and availability to better inform SPL policies and programs. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%). Beneficiaries as percent of total population and the poorest quintile.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The World Bank&#x2019;s classification of social protection and labor market programs includes 3 areas and 12 program categories, as follows: </p>\n<ol>\n  <li>Social insurance: Contributory pensions and othero social insurance;</li>\n  <li>Labor market: Active andand passive labor market programs;</li>\n  <li>Social assistance: Unconditional cash transfers, conditionalc cash transfers, socials pensions (non-contributory), foodf and in-kind transfers, schools feeding, publicp works, workfare and direct job creation, feef waivers and targeted subsidies, and othero social assistance.</li>\n</ol>", "SOURCE_TYPE__GLOBAL"=>"<p>Data are based on official national representative household surveys produced by national statistical offices. Data source is ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank (see www.worldbank.org/aspire/).</p>", "COLL_METHOD__GLOBAL"=>"<p>Unit-record data of nationally representative household surveys are collected by national statistical offices (NSOs) and provided to the World Bank (WB) for analytical purposes. The ASPIRE (Atlas of Social Protection &#x2013; Indicators of Resilience and Equity) team harmonizes social protection information captured by these household surveys to make the analysis reasonably comparable across countries and over time.</p>\n<p>The ASPIRE harmonization methodology for household survey data rests on the following three steps:</p>\n<p>1. <em>Identification and classification of social protection and labor (SPL) programs</em></p>\n<p>Household surveys are carefully reviewed to identify SPL program information. Once this information is identified, two levels of analysis are implemented: first, variables are created for each of the country specific programs found in the survey; second, program variables are aggregated and harmonized into 12 SPL program categories, and 2 private transfer categories. The country specific programs included into these SPL categories are documented in detail and validated with World Bank country task teams leaders in close coordination with national counterparts.</p>\n<p>ToTo generate the indicators, the following variables are also used: household identification number, location (urban/rural), household size, welfare aggregate (income or consumption), household weight, and two poverty lines: a relative poverty line, thatthat defines the poorest 20% of the welfare distribution, and the international poverty line of PPP $22.15 per day.</p>\n<p><em>2. Welfare aggregates</em></p>\n<p>Households are ranked in quintiles of percapita welfare (income or consumption). ASPIRE makes special efforts to include the most updated welfare aggregates officially agreed with NSOsNSOs and harmonized by the World Bank&#x2019;s Global Monitoring Database initiative, led by the Poverty and Equity Global Practice. These welfare aggregates are comparable across countries and across years for global poverty monitoring and welfare measurement. </p>\n<p>3. <em>PPP conversions</em></p>\n<p>All monetary variables (transfer amounts) and welfare aggregates are deflated to 2017 values and then expressed in 2017 purchasing power parity (PPP) terms. To this effect, the private consumption PPP conversion factor is used.</p>\n<p>Once the information is harmonized performance indicators are generated using ADePT social protection software developed by the World BankBank.</p>", "FREQ_COLL__GLOBAL"=>"<p>Ongoing process </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Ongoing process</p>", "DATA_SOURCE__GLOBAL"=>"<p>World Bank</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Bank</p>", "INST_MANDATE__GLOBAL"=>"<p>The World Bank supports social protection and labor (SPL) systems in client countries as central part of its mission to reduce poverty through sustainable and inclusive growth. The World Bank&#x2019;s SPL strategy lays out ways to deepen itsits involvement, capacity, knowledge and impact in SPL. In this context ASPIRE (Atlas of Social Protection &#x2013; Indicators of Resilience and Equity) is the main World Bank tool to track the outcomes of the SPL strategy. </p>", "RATIONALE__GLOBAL"=>"<p>ASPIRE (Atlas of Social Protection &#x2013; Indicators of Resilience and Equity) coverage indicators refer to the &#x2018;effective&#x2019; coverage definition, measuring the direct and indirect beneficiaries who are receiving social protection benefits at the time when nationally representative household survey data are collected. Effective coverage is directly relevant to SDG 1 of ending poverty in all its forms. </p>\n<p>ASPIRE indicators do not include individuals who have benefits guaranteed but are not receiving them at the time when the survey is administered &#x2013; for example, people who actively contribute to old age pensions and are entitled to the benefits when reaching retirement age.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Information on country social protection and labor (SPL) programs included in ASPIRE (Atlas of Social Protection &#x2013; Indicators of Resilience and Equity) is limited to what is captured in the respective national representative household survey and does not necessarily represent the universe of programs existing in the country. In addition, the periodicity in which household surveys are produced by national statistical offices vary across countries, therefore, ASPIRE does not include balance panels for regional aggregates.</p>\n<p>Despite the above limitations, household surveys have the unique advantage of allowing analysis of program impact on household welfare. HenceHence, ASPIRE indicators based on household surveys provide an approximate measure of SPLSPL systems performance.</p>", "DATA_COMP__GLOBAL"=>"<p>ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity estimatesestimates social protection and labor (SPL) coverage using nationalrepresentativenational household surveys. Coverage is estimated as follows:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>o</mi>\n    <mi>v</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>a</mi>\n    <mi>g</mi>\n    <mi>e</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>S</mi>\n        <mi>P</mi>\n        <mi>L</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>f</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>i</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>q</mi>\n        <mi>u</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>q</mi>\n        <mi>u</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mi>x</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>100</mn>\n  </math></p>\n<p>ASPIRE SPL coverage indicators are based on a first level analysis of original household survey data (with no imputations) and a unified methodology that does not necessarily reflect country-specific knowledge or in depth country analysis relying on different data sources, such as administrative program level data..</p>", "DATA_VALIDATION__GLOBAL"=>"<p>ASPIRE (Atlas of Social Protection &#x2013; Indicators of Resilience and Equity) uses nationally representative household survey data from national statistics offices (NSOs) to estimate social protection and labor (SPL) performance indicators. NSOs follow their own validation processes to ensure quality. The ASPIRE team relies on these curated microdatamicro and on the validation and harmonization processes done by the World Bank&#x2019;s Poverty and Equity practice.... Indicators are validated and cleared by the NSOs when required by these institutions before publication.</p>", "ADJUSTMENT__GLOBAL"=>"<p>For regional and global comparisons, monetary variables and welfare aggregates are deflated to 2017 values and then converted to international purchasing power parity (PPP) values as explained above (see 3.b. Data Collection method). </p>", "IMPUTATION__GLOBAL"=>"<ol>\n  <li><strong>Country level</strong></li>\n</ol>\n<p>Indicators at the country level are estimated using each of the social protection variables captured in the survey. Surveys may include two variables for the same program: a binary variable indicating program participation, and a continuous variable with transfer amounts. If the number of positive observations in the two variables is not the same, missing values are imputed as follows: </p>\n<ol>\n  <li>If continuous variable &lt; binary variable: missing values are replaced by the mean transfer amount if there is a corresponding positive answer in the binary variable</li>\n  <li>If binary variable &lt; continuous variable: missing values are replaced by &#x201C;1&#x201D; if there is a corresponding tranfer value in the continuous variable.</li>\n</ol>\n<p>Indicators are not reported in cases where the aggregated variable for a social protection area (social assistance, social insurance or labor market programs) has positive observations only for 60 or fewer households. </p>\n<ol>\n  <li><strong>Regional level</strong></li>\n</ol>\n<p>No imputations are performed at the country nor regional levels.</p>", "REG_AGG__GLOBAL"=>"<p>The regional and global aggregates consist of simple and population-weighted averages</p>\n<p>of country level indicators. For this, ASPIRE uses the most recent surveysurvey by country within the last 10 years. </p>", "DOC_METHOD__GLOBAL"=>"<p>ASPIRE (Atlas of Social Protection &#x2013; Indicators of Resilience and Equity) uses national representative household surveys conducted by the national statistics offices (NSOs). These institutions have their own methodologies for the collection and compilation of the data.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The raw data that ASPIRE (Atlas of Social Protection &#x2013; Indicators of Resilience and Equity) uses to estimate social protection and labor (SPL) performance indicators are already validated and curated by the NSOs. Microdata with harmonized welfares aggregates are produced and validated by the World Bank&#x2019;s Poverty and Equity Practice based on their own standards. Furthermore, ASPIRE has an internal quality management protocol including a documented workflow, methodological guidances and a specialized software (ADePT) to automate the generation of performance indicators and minimize human errors. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>To ensure quality of its indicators, the ASPIRE (Atlas of Social Protection &#x2013; Indicators of Resilience and Equity) team conducts internal validations, trend comparison, and outlier analysis to detect errors and inconsistencies.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The ASPIRE (Atlas of Social Protection &#x2013; Indicators of Resilience and Equity) team peer reviews indicators internally. Indicators are also validated in consultation with World Bank&#x2019;s Task Team Leaders, specialists and country counterparts.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data Availability </strong></p>\n<p>ASPIRE (Atlas of Social Protection &#x2013; Indicators of Resilience and Equity) includes 500+ nationally representative household surveys from 130+ countries. Country data represent all regions of the world: East Asia &amp; Pacific, Europe &amp; Central Asia, Latin America &amp; the Caribbean, Middle East &amp; North Africa, Sub-Saharan Africa and South Asia.</p>\n<p>ASPIRE makes a constant effort to update indicators as soon as new country surveys become available. Depending on the availability of surveys, time trends can be estimated for some countries.</p>\n<p> </p>\n<p>Indicators are disaggregated by per capita income/consumption quintiles including transfers. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>While efforts are made to ensure consistency between ASPIRE (Atlas of Social Protection &#x2013; Indicators of Resilience and Equity) indicators and World Bank&apos;s regional and country reports/national estimates, there may still be cases where ASPIRE performance indicators differ from official WB country reports/national estimates given methodological differences.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.worldbank.org\">www.worldbank.org</a></p>\n<p><strong>References:</strong></p>\n<p>ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank (<a href=\"http://www.worldbank.org/aspire\">www.worldbank.org/aspire</a>). </p>", "indicator_sort_order"=>"01-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"1.4.1", "slug"=>"1-4-1", "name"=>"Proporción de la población que vive en hogares con acceso a los servicios básicos", "url"=>"/site/es/1-4-1/", "sort"=>"010401", "goal_number"=>"1", "target_number"=>"1.4", "global"=>{"name"=>"Proporción de la población que vive en hogares con acceso a los servicios básicos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[{"field"=>"Servicios básicos", "value"=>"BSRVH2O"}, {"field"=>"Servicios básicos", "value"=>"BSRVSAN"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de viviendas principales con acceso a servicios básicos (abastecimiento de agua corriente y baño)", "graph_type"=>"line", "indicator_name"=>"Proporción de la población que vive en hogares con acceso a los servicios básicos", "indicator_number"=>"1.4.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Aumento", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_25/opt_1/ti_censos-de-poblacion-y-viviendas/temas.html", "url_text"=>"Censos de población y viviendas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Proporción de viviendas principales con acceso a servicios básicos (abastecimiento de agua corriente y baño)", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.4- De aquí a 2030, garantizar que todos los hombres y mujeres, en particular los pobres y los vulnerables, tengan los mismos derechos a los recursos económicos y acceso a los servicios básicos, la propiedad y el control de la tierra y otros bienes, la herencia, los recursos naturales, las nuevas tecnologías apropiadas y los servicios financieros, incluida la microfinanciación", "definicion"=>"\nSeries disponibles: \n\n<b> - Proporción de viviendas con agua corriente:</b>\nProporción de viviendas principales con abastecimiento de agua corriente\n\n<b> - Proporción de viviendas con baño:</b>\nProporción de viviendas principales con baño\n", "formula"=>"\n<b>Proporción de viviendas con agua corriente</b>\n\n$$PVP_{agua\\, corriente}^{t} = \\frac{VP_{agua\\, corriente}^{t}}{VP^{t}} \\cdot 100$$\n\ndonde:\n\n$VP_{agua\\, corriente}^{t} =$ viviendas principales con abastecimiento de agua corriente en el año $t$\n\n$VP^{t} =$ viviendas principales en el año $t$\n\n<br>\n\n<b>Proporción de viviendas con baño</b>\n\n$$PVP_{baño}^{t} = \\frac{VP_{baño}^{t}}{VP^{t}} \\cdot 100$$\n\ndonde:\n\n$VP_{baño}^{t} =$ viviendas principales con baño en el año $t$\n\n$VP^{t} =$ viviendas principales en el año $t$\n", "desagregacion"=>"\nTerritorio histórico/Comarca/Municipio", "observaciones"=>"Vivienda principal es aquella vivienda que es utilizada toda o la mayor parte del año como  residencia habitual por una o más personas", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl indicador de Naciones Unidas se basa en 9 componentes. La proporción de la población \nque vive en hogares con acceso a servicios básicos se define como la proporción de la población \nque utiliza sistemas de prestación de servicios públicos que satisfacen las necesidades \nhumanas básicas, incluidos agua potable, saneamiento, \nhigiene, energía, movilidad, recogida de residuos, atención sanitaria, educación y tecnologías \nde la información.\n\nEstos componentes se capturan en varios indicadores independientes de los ODS, \nlo que significa que los conceptos y definiciones del indicador 1.4.1 de los ODS se derivarán \nde estos indicadores específicos de los ODS o serán los mismos que ellos.\n\nEntre los diferentes aspectos de la pobreza, este indicador se centra en el acceso a \nservicios básicos. Proporcionar acceso a servicios básicos como agua potable, servicios \nde saneamiento e higiene, energía \ny movilidad sostenibles, vivienda, educación, atención sanitaria, etc., ayuda a mejorar \nla calidad de vida de los pobres.\n\nLa falta de prestación de servicios básicos y la falta de empoderamiento y participación \nde los gobiernos locales en la prestación de servicios básicos socavan el crecimiento económico \ny la calidad de vida en cualquier comunidad. Unos sistemas adecuados de prestación de servicios \nbásicos promueven mejoras socioeconómicas y ayudan a lograr el crecimiento económico, la \ninclusión social, la reducción de la pobreza y la igualdad.\n\nMás específicamente, la mejora de los servicios básicos puede ayudar a aumentar el bienestar \ny la productividad de las comunidades, crear empleos, ahorrar tiempo y esfuerzo humano en \nel transporte de agua, apoyar la seguridad alimentaria, mejorar el uso de la energía, producir \nproductos básicos esenciales, mejorar la salud (haciendo que los servicios médicos atención \nsanitaria, disponibilidad de agua potable o recogida de residuos sólidos) o mejorar el nivel \nde educación.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.4.1&seriesCode=SP_ACS_BSRVH2O&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=RURAL\">Proporción de población que utiliza servicios básicos de agua potable, por ubicación (%) SP_ACS_BSRVH2O</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.4.1&seriesCode=SP_ACS_BSRVSAN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">\nProporción de población que utiliza servicios básicos de saneamiento, por ubicación (%) SP_ACS_BSRVSAN</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible en la C.A de Euskadi cumple con  los metadatos de Naciones Unidas para las series analizadas. Otras series se presentan en otros indicadores de desarrollo sostenible de esta plataforma (3-8-1, 4-1-1, 7-1-2, 9-1-1, 9-c-1, 11-2-1, 11-6-1)", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-04-01.pdf\">Metadatos 1-4-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de viviendas principales con acceso a servicios básicos (abastecimiento de agua corriente y baño)", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.4- De aquí a 2030, garantizar que todos los hombres y mujeres, en particular los pobres y los vulnerables, tengan los mismos derechos a los recursos económicos y acceso a los servicios básicos, la propiedad y el control de la tierra y otros bienes, la herencia, los recursos naturales, las nuevas tecnologías apropiadas y los servicios financieros, incluida la microfinanciación", "definicion"=>"\nAvailable series: \n\n<b>- Proportion of homes with running water:</b> \nProportion of main family dwellings with running water supply \n\n<b>- Proportion of homes with a bathroom:</b> \nProportion of main family dwellings with a bathroom \n", "formula"=>"\n<b>Proportion of homes with running water</b>\n\n$$PVP_{running\\, water}^{t} = \\frac{VP_{running\\, water}^{t}}{VP^{t}} \\cdot 100$$\n\nwhere:\n\n$VP_{running\\, water}^{t} =$ main family dwellings with running water supply in year $t$\n\n$VP^{t} =$ main family dwellings in year $t$\n\n<br>\n\n<b>Proportion of homes with a bathroom</b>\n\n$$PVP_{bathroom}^{t} = \\frac{VP_{bathroom}^{t}}{VP^{t}} \\cdot 100$$\n\nwhere:\n\n$VP_{bathroom}^{t} =$ main family dwellings with a bathroom in year $t$\n\n$VP^{t} =$ main family dwellings in year $t$\n", "desagregacion"=>"\nProvince/County/Municipalities", "observaciones"=>"A main family dwelling is a home that is used all or most of the year  as a habitual residence by one or more persons.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe United Nations indicator is based on nine components. The proportion of \nthe population living in households with access to basic services is defined \nas the proportion of the population using public service delivery systems that meet \nbasic human needs, including safe drinking water, sanitation, hygiene, energy, \nmobility, waste collection, healthcare, education, and information technology.\n\nThese components are captured in various standalone indicators of the SDGs, \nwhich means that the concepts and definitions of SDG indicator 1.4.1 will \nbe derived from or are the same as those of these specific SDG indicators.\n\nAmong the different aspects of poverty, this indicator focuses on access to basic services. \nProviding access to basic services such as safe drinking water, sanitation and hygiene \nservices, sustainable energy and mobility, housing, education, healthcare, etc., helps improve \nthe quality of life of the poor.\n\nThe lack of basic service provision and the lack of empowerment and participation \nof local governments in the provision of basic services undermine economic growth \nand the quality of life in any community. Adequate basic service delivery systems \npromote socioeconomic improvements and help achieve economic growth, social inclusion, \npoverty reduction, and equality.\n\nMore specifically, improved basic services can help to raise well-being and productivity \nof communities, create jobs, save time and human effort in transporting water, \nsupport food security, better use of energy, production of essential commodities, \nimprove health (by making medical care, clean water or solid waste collection available) \nor enhance the level of education.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.4.1&seriesCode=SP_ACS_BSRVH2O&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=RURAL\">Proportion of population using basic drinking water services, by location (%) SP_ACS_BSRVH2O</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.4.1&seriesCode=SP_ACS_BSRVSAN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">\nProportion of population using basic sanitation services, by location (%) SP_ACS_BSRVSAN</a> UNSTATS\n", "comparabilidad"=>"The indicator available in the Basque Country complies with the United Nations metadata for the series analyzed. Other series are presented in other sustainable development indicators on this platform. (3-8-1, 4-1-1, 7-1-2, 9-1-1, 9-c-1, 11-2-1, 11-6-1)", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-04-01.pdf\">Metadata 1-4-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Proporción de viviendas principales con acceso a servicios básicos (abastecimiento de agua corriente y baño)", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.4- De aquí a 2030, garantizar que todos los hombres y mujeres, en particular los pobres y los vulnerables, tengan los mismos derechos a los recursos económicos y acceso a los servicios básicos, la propiedad y el control de la tierra y otros bienes, la herencia, los recursos naturales, las nuevas tecnologías apropiadas y los servicios financieros, incluida la microfinanciación", "definicion"=>"\nEskuragarri dauden serieak: \n\n<b> - Txorrotako ura duten etxebizitzen proportzioa:</b> \nUr-hornidura duten etxebizitza nagusien proportzioa\n\n<b> - Bainugela duten etxebizitzen proportzioa:</b> \nBainugela duten etxebizitza nagusien proportzioa\n", "formula"=>"\n<b>Txorrotako ura duten etxebizitzen proportzioa</b>\n\n$$PVP_{txorrotako\\, ura}^{t} = \\frac{VP_{txorrotako\\, ura}^{t}}{VP^{t}} \\cdot 100$$\n\nnon:\n\n$VP_{txorrotako\\, ura}^{t} =$ ur-hornidura duten etxebizitza nagusiak $t$ urtean\n\n$VP^{t} =$ etxebizitza nagusiak $t$ urtean\n\n<br>\n\n<b>Bainugela duten etxebizitzen proportzioa</b>\n\n$$PVP_{bainugela}^{t} = \\frac{VP_{bainugela}^{t}}{VP^{t}} \\cdot 100$$\n\nnon:\n\n$VP_{bainugela}^{t} =$ bainugela duten etxebizitza nagusiak $t$ urtean\n\n$VP^{t} =$ etxebizitza nagusiak $t$ urtean\n", "desagregacion"=>"\nLurralde historikoa/Eskualdea/Udalerria", "observaciones"=>"Etxebizitza nagusia da urte osoan edo urte gehienean pertsona batek edo gehiagok  ohiko bizileku gisa erabiltzen duten etxea.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nNazio Batuen adierazlea 9 osagaitan oinarritzen da. Oinarrizko zerbitzuak eskura dituzten etxeetan bizi \ndiren biztanleen proportzioa honela definitzen da: oinarrizko giza beharrizanak asetzeko zerbitzu publikoko \nsistemak erabiltzen dituzten biztanleen proportzioa, zerbitzu horien barruan sartzen direlarik edateko ura, \nsaneamendua, higienea, energia, mugikortasuna, hondakinen bilketa, arreta sanitarioa, hezkuntza eta \ninformazioaren teknologiak. \n\nOsagai horiek GJHetatik independenteak diren hainbat adierazletan jasotzen dira; ondorioz, GJHen 1.4.1 \nadierazleko kontzeptuak eta definizioak GJHen adierazle espezifiko hauetatik eratorriko dira, edo horien \nberdinak izango dira. \n\nPobreziaren alderdi desberdinen artean, oinarrizko zerbitzuetarako sarbidea neurtzen du adierazle honek. \nOinarrizko zerbitzuetarako sarbidea emateak (besteak beste, edateko ura, saneamendu- eta higiene-zerbitzuak, \nenergia eta mugikortasun jasangarriak, etxebizitza, hezkuntza, arreta sanitarioa eta abar eskuratzeko) \npobreen bizi-kalitatea hobetzen laguntzen du. \n\nOinarrizko zerbitzuen prestaziorik ez badago eta tokiko gobernuek oinarrizko zerbitzuak emateko orduan \nparte hartzen ez badute edo horretarako ahaldunduta ez badaude, edozein komunitatetako hazkunde ekonomikoa \neta bizi-kalitatea kaltetu egiten dira.  Oinarrizko zerbitzuak emateko sistema egokiek hobekuntza sozioekonomikoak \nsustatzen dituzte, eta hazkunde ekonomikoa, inklusio soziala, pobreziaren murrizketa eta berdintasuna lortzen \nlaguntzen dute. \n\nZehatzago, oinarrizko zerbitzuak hobetzea lagungarria izan daiteke komunitateen ongizatea eta produktibitatea \nareagotzeko, enpleguak sortzeko, uraren garraioan denbora eta giza ahalegina aurrezteko, elikagaien segurtasuna \nbabesteko, energiaren erabilera hobetzeko, oinarrizko funtsezko produktuak ekoizteko, osasuna hobetzeko (arreta \nsanitarioko zerbitzu medikoak, edateko uraren erabilgarritasuna edo hondakin solidoen bilketa) edo hezkuntza \nmaila hobetzeko. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.4.1&seriesCode=SP_ACS_BSRVH2O&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=RURAL\">Edateko uraren oinarrizko zerbitzuak erabiltzen dituzten biztanleen proportzioa, kokapenaren arabera (%) SP_ACS_BSRVH2O</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.4.1&seriesCode=SP_ACS_BSRVSAN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">\nOinarrizko saneamendu-zerbitzuak erabiltzen dituzten biztanleen proportzioa, kokapenaren arabera (%) SP_ACS_BSRVSAN</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu aztertutako serieetarako. Beste serie batzuk plataforma honetako garapen jasangarriaren beste adierazle batzuetan aurkezten dira (3-8-1, 4-1-1, 7-1-2, 9-1-1, 9-c-1, 11-2-1, 11-6-1)", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-04-01.pdf\">Metadatuak 1-4-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.4: By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinance</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.4.1: Proportion of population living in households with access to basic services</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-07-18", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>SDG global targets 1.2, 2.2, 3.2, 3.7, 3.8, 3.9, 4.1, 4.a, 5.4, 5.b, 6.1, 6.2, 7.1, 7.2, 9.1 and 11.2.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>\n<p>WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (1.4.1a, b and c)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The proportion of population living in households with access to basic services is defined as the proportion of population using public service provision systems that meet basic human needs including drinking water, sanitation, hygiene, energy, mobility, waste collection, health care, education and information technologies. The basic services indicator is therefore based on 9 components. These components are captured in various standalone indicators of the SDGs, which means that the concepts and definitions of SDG indicator 1.4.1 will be derived from or are the same as those of these specific SDG indicators. </p>\n<p><strong>Concepts:</strong></p>\n<p>The term<strong> &#x2018;access to basic services&#x2019;</strong> implies that sufficient and affordable service is reliably available with adequate quality.</p>\n<ol>\n  <li><strong>Access to Basic Drinking Water Services</strong> refers to the use of drinking water from an improved source with a collection time of not more than 30 minutes for a round trip, including queuing. &#x2018;Improved&#x2019; drinking water sources include the following:: piped water, boreholes or tube wells, protected dug wells, protected springs, rainwater, water kiosks, and packaged or delivered water. This definition is based on the WHO/UNICEF Joint Monitoring Programme (JMP) drinking water ladder and is the foundation for SDG indicator 6.1.1 - <em>Proportion of population using safely managed drinking water services<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>.</em> </li>\n  <li><strong>Access to Basic Sanitation Services</strong> refers to the use of improved facilities that are not shared with other households. An &#x2018;improved sanitation facility&#x2019; is defined as one designed to hygienically separate human excreta from human contact. Improved sanitation facilities include wet sanitation technologies such as flush or pour flush toilets connected to sewer systems, septic tanks or pit latrines; and dry sanitation technologies such as dry pit latrines with slabs (constructed from materials that are durable and easy to clean), ventilated improved pit (VIP) latrines, pit latrines with a slab, composting toilets and container-based sanitation. If a household uses a flush or pour flush toilet but does not know where it is flushed to, the sanitation facility is considered to be improved since the household may not be aware about whether it flushes to a sewer, septic tank or pit latrine. This definition is based on the JMP sanitation ladder and is the foundation for SDG indicator 6.2.1a - <em>Proportion of population using safely managed sanitation services <sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>.</em> </li>\n  <li><strong>Access to Basic Hygiene Facilities</strong> refers to availability of a handwashing facility with soap and water at home. Handwashing facilities may be located within the dwelling, yard or plot. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents. This definition is based on the JMP hygiene ladder and is the foundation for SDG indicator 6.2.1b - <em>Proportion of population with handwashing facilities with soap and water available at home<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup>.</em> </li>\n</ol>\n<p>For many low and middle-income countries, achieving universal access to basic drinking water, sanitation and hygiene remains a high priority, which will help them achieve access to &#x2018;safely managed services&#x2019;, the target for SDG targets 6.1 and 6.2. </p>\n<ol>\n  <li><strong>Access to clean fuels and technology</strong> refers to use of fuels and technology that are defined by the emission rate targets and specific fuel recommendations (i.e., against unprocessed coal and kerosene) included in the normative guidance WHO guidelines for indoor air quality: household fuel combustion. This component will be captured through SDG 7.1.2 - <em>Percentage of population with primary reliance on clean fuels and technology.</em></li>\n  <li><strong>Access to Basic Mobility </strong>refers to having convenient access to transport in a rural context (SDG 9.1.1) or having convenient access to public transport in an urban context (SDG 11.2.1). </li>\n</ol>\n<ul>\n  <li><strong><em>Access to mobility rural context</em></strong></li>\n</ul>\n<p>To eradicate poverty, communities need to be connected to socio-economic opportunities by roads that are passable all season and attract reliable and affordable public transport services. In many areas, safe footpaths, footbridges and waterways may be required in conjunction with, or as an alternative, to roads. For reasons of simplification, specific emphasis was given to roads in this definition (based on the Rural Access Index - RAI - percentage of the population &lt;2km from an all-season road (equivalent to a walk of 20-25 mins)<sup><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></sup>)<sup><sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup></sup> since road transport reflects accessibility for the great majority of people in rural contexts. In those situations where another mode, such as water transport is dominant the definition will be modified and contextualized to reflect and capture those aspects. </p>\n<p>Access to mobility has shown some of the largest impacts on poverty reduction and has a strong correlation to educational, economic and health outcomes (&#x201C;transport as an enabler&#x201D;). </p>\n<p>RAI is the most widely accepted metric for tracking access to transport in rural areas and has been included in the SDGs as SDG indicator 9.1.1 - <em>Proportion of the rural population who live within 2 km of an all-season road.</em> This component will be therefore captured through SDG 9.1.1.</p>\n<p>The existing RAI methodology relies on household level survey data &#x2013; however, is currently being revised into a GIS-based index that exploits advances in digital technology with the aim to create a more accurate and cost-effective tool. </p>\n<ul>\n  <li><strong><em>Access to mobility urban context</em></strong></li>\n</ul>\n<p>The urban context of access to transport is measured utilizing the methodology of SDG 11.2.1 &#x2013;<em>Proportion of the population that has convenient access to public transport by sex, age and persons with disabilities</em>. </p>\n<p>The metadata methodology<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup> is available (UN-Habitat being the custodian agency). City delimitation is conducted to identify the urban area which will act as the spatial analysis scope as inventory of available public stops in the service areas is collected. Identification of population served by available street network allows for measurement 500m and/or 1km walkable distance to nearest stop (&#x201C;service area&#x201D;). We know that measuring spatial access is not sufficient and does not address the temporal dimension associated with the availability of public transport. Complementary to the above, other parameters of tracking the transport target related to street density/no. of intersections, affordability, or quality in terms of safety, travel time, universal access, are all tracked. </p>\n<ol>\n  <li><strong>Access to Basic Waste Collection Services </strong>refers to the access that the population have to a reliable waste collection service, including both formal municipal and informal sector services. This is connected to and will be captured through SDG Indicator 11.6.1 - <em><u>Proportion of municipal solid waste collected and managed in controlled facilities out of total municipal waste generated, by cities</u></em>. A &#x2018;collection service&#x2019; may be &#x2018;door to door&#x2019; or by deposit into a community container. &#x2018;Collection&#x2019; includes collection for recycling as well as for treatment and disposal (includes e.g., collection of recyclables by itinerant waste buyers). &#x2018;Reliable&#x2019; means regular - frequency will depend on local conditions and on any pre-separation of the waste. For example, both mixed waste and organic waste are often collected daily in tropical climates for public health reasons, and generally at least weekly; source-separated dry recyclables may be collected less frequently.</li>\n  <li><strong>Access to Basic Health Care Services</strong> refers to access to services that cover in and out-of-area emergency services, in-patient hospital and physician care, outpatient medical services, laboratory and radiology services, and preventive health services. Basic health care services also extend to access to limited treatment of mental illness and substance abuse in accordance with minimum standards prescribed by local and national ministries of health. This is connected to and will be measured through SDG indicator 3.8.1 &#x2013; <em>Coverage of essential health services</em>.</li>\n  <li>Access<strong> to Basic Education</strong> refers to access to education services that provides all learners with capabilities they require to become economically productive, develop sustainable livelihoods, contribute to peaceful and democratic societies and enhance individual well-being. This is connected to and will be captured through SDG 4.1.1 - <em>Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sex</em>. </li>\n  <li><strong>Access to Basic Information Services</strong> refers to having a broadband internet access. Broadband is defined as technologies that deliver advertised download speeds of at least 256 kbit/s. Connecting the 50% of the world that is still offline means, in large part, ensuring that everyone, everywhere is able to access an internet that is affordable. The main types of broadband services are: 1) Fixed (wired) broadband network, such as DSL, cable modem, high speed leased lines, fibre to-the-home/building, powerline and other fixed (wired) broadband; 2) Terrestrial fixed (wireless) broadband network, such as WiMAX, fixed CDMA; 3) Satellite broadband network (via a satellite connection); 4) Mobile broadband network (at least 3G, e.g. UMTS) via a handset and 5) Mobile broadband network (at least 3G, e.g. UMTS) via a card (e.g. integrated SIM card in a computer) or USB modem. This is connected to and will be captured through SDG 9.c.1 - <em>Proportion of population covered by a mobile network, by technology</em>.</li>\n</ol><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> https://unstats.un.org/sdgs/metadata/files/Metadata-06-01-01.docx <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01a.docx <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01b.docx <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> https://www.ssatp.org/sites/ssatp/files/publications/HTML/Gender-RG/index.html <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> <a href=\"http://www.worldbank.org/en/topic/transport/brief/connections-note-23\">http://www.worldbank.org/en/topic/transport/brief/connections-note-23</a> <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> https://unstats.un.org/sdgs/metadata/files/Metadata-11-02-01.pdf <a href=\"#footnote-ref-7\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Proportion of population</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The main sources of data for this indicator remain censuses and household surveys (including DHS, MICS, LSMS)and administrative data. Other datasets could also be used, such as compilations by international or regional initiatives (e.g., Eurostat), studies conducted by research institutes, or technical advice received during country consultations. </p>\n<p>The data sources used for each of the constituent measures are described in more detail in the reference metadata. </p>", "COLL_METHOD__GLOBAL"=>"<p>National data for each of the constituent measures are compiled by the relevant custodian agencies. See reference metadata for information on data collection methods for each of the constituent measures.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data for constituent measures are collected at intervals of between 2 and 5 years. See reference metadata for information on the data collection calendar for each constituent measure. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Every 2-5 years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The main data source for the generation of indicators are national statistics offices; ministries of water, health, education, and environment; regulators of drinking water service providers.</p>\n<p>UN-Habitat and various supporting agencies such as WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), UNEP, World Bank, AfDB, IDB, EBRD and ADB and bilateral donors (JICA, GIZ, etc.) provide the estimates for the indicators.</p>", "COMPILING_ORG__GLOBAL"=>"<p>National statistical offices and relevant ministries lead the compilation and reporting at a national level with support from custodian agencies. Global and regional reporting is led by UN-Habitat. The collection of the data is supported by collaborative efforts of several international institutions (UN-Habitat, WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP),UNEP, World Bank, AfDB, IDB, EBRD and ADB) and bilateral donors (JICA, GIZ, etc.).</p>", "INST_MANDATE__GLOBAL"=>"<p>This is described in the reference metadata for each of the constituent measures used to report on this indicator</p>", "RATIONALE__GLOBAL"=>"<p>Poverty has many dimensions. It is not only a lack of material well-being but also a lack of opportunities to live a tolerable life. The international extreme poverty line was updated in 2015 to 1.90 USD per day using 2011 purchasing power parity (World Bank, 2015). Living under the extreme poverty line often encompasses deprivations of safe drinking water, proper sanitation, access to modern energy, sustainable mobility to economic resources, information technology, healthcare, education, etc. Poverty is also a manifestation of hunger and malnutrition, limited access to education and other basic services, social discrimination and exclusion as well as the lack of participation in decision-making. In other words, poverty is multidimensional and covers many aspects of life ranging from access to opportunities, livelihoods and means of survival. </p>\n<p>Among the different aspects of poverty, this indicator focuses on &#x2018;access to basic services. Providing access to basic services such as safe drinking water, sanitation and hygiene services, sustainable energy and mobility, housing, education, healthcare etc, helps to improve the quality of life of the poor. The lack of basic services provision and the lack of empowerment and involvement of local governments in basic service delivery undermine the economic growth and quality of life in any community. Adequate basic service delivery systems promote socio-economic improvements and help to achieve economic growth, social inclusion, poverty reduction and equality. More specifically, improved basic services can help to raise well-being and productivity of communities, create jobs, save time and human effort in transporting water, support food security, better use of energy, production of essential commodities, improve health (by making medical care, clean water or solid waste collection available) or enhance the level of education. </p>\n<p>In the Quito implementation plan for the New Urban Agenda (NUA) adopted in the Habitat III conference, Member States commit to &#x201C;promoting equitable and affordable access to sustainable basic physical and social infrastructure for all, without discrimination, including affordable serviced land, housing, modern and renewable energy, safe drinking water and sanitation, safe, nutritious and adequate food, waste disposal, sustainable mobility, health care and family planning, education, culture, and information and communications technologies&#x201D;. They further commit to &#x201C;ensuring that these services are responsive to the rights and needs of women, children and youth, older persons and persons with disabilities, migrants, indigenous peoples and local communities, as appropriate, and to those of others in vulnerable situations&#x201D;.</p>\n<p>Basic service delivery must move towards a demand-driven approach, which is appropriate for the local needs &#x2013; and hence able to respond to the concept of &#x201C;Access for all&#x201D; &#x2013; as stated in the NUA. Basic services are fundamental to improving living standards. Governments have the responsibility for their provision. This indicator will measure levels of accessibility to basic services and guide the efforts of governments for provision of equitable basic services for all to eradicate poverty.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Different local characteristics of what constitutes &#x201C;basic services&#x201D; around the world by some concerned authorities and stakeholders compelled the team to work on modules and global guides for this indicator. This draws on definitions available for many other SDG indicators. For example, elements of basic services are measured under indicators 3.8.1 (health), 4.1.1 (education), 6.1.1 (drinking water), 6.2.1 (sanitation and hygiene), 7.1.1 (energy), 11.2.1 (public transport), etc. </p>\n<p>Finally, many countries still have limited capacities for data management, data collection and monitoring, and continue to struggle with limited data. This means that complementarity in data reporting in a few exceptions is needed to ensure that both national and global figures achieve consistencies in the final reported data for access to basic services.</p>\n<p>See the original reference metadata for each of the measures for more details.</p>", "DATA_COMP__GLOBAL"=>"<p>This indicator is a combination of various components of basic services which on their own are mostly existing as standalone indicators of the SDGs. As a result, the team of experts advised and agreed that these should be presented as a dashboard. Their metadata provide the specific methodologies for computing each of the constituent measures used to report on this indicator. </p>\n<p><strong>Data presentation</strong></p>\n<p>Individual components of access to basic services will be computed separately from various data sources over the years. However, the dashboard is configured to display the most recent data points, but with the possibility to visualize data for earlier years through a drilled down access. </p>\n<p>Data will be presented or visualized as a dashboard but with the possibility to map it out through various visualization tools such as spider web and stellar charts of the achievement of access to different basic services in a country through plotting the various components of the indicators. In this way, policy makers can be informed of most needed intervention areas for any region and country. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>For different measures, national authorities are consulted on the estimates generated from national data sources through country consultation process facilitated by the custodian agencies. See the original reference metadata for each of the measures for more details.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>&#x2022; <strong>At country level</strong></p>\n<p>Treatment of missing values varies among different measures and is provided in relevant metadata for each individual indicator. </p>\n<p>&#x2022; <strong>At regional and global levels</strong></p>\n<p>Treatment of missing values varies among different measures and is provided in relevant metadata for each individual indicator. </p>", "REG_AGG__GLOBAL"=>"<p>Aggregation methods for each measure are presented in relevant metadata for each individual indicator.</p>", "DOC_METHOD__GLOBAL"=>"<p>Custodian agencies have provided technical guidance for national authorities on the collection and analysis of data required to report on each indicator. Countries are expected to present this data in dashboards they developed. Examples of easy-to-use tools for presenting the data as a dashboard will be provided to countries via the national statistical systems/offices. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Original data quality management is managed by the custodian agencies for each indicator that is presented under the 1.4.1 dashboard.</p>\n<p> </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Original data quality assurance is managed by the custodian agencies for each indicator that is presented under the 1.4.1 dashboard. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See quality assurance.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data for a large set of indicators such as drinking water, sanitation and hygiene, energy and information are readily available and already included in different international household survey frameworks. Refinement of definitions of different types of basic services and inclusion of the newly developed survey items in the existing household surveys was completed. Data compilation has shown that more than 143 countries have data at the national level.</p>\n<p><strong>Time series:</strong></p>\n<p>Time series data are produced for the periods running from 1990 to present. This is available based on the richness of the data sources for each indicator.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregation by geographic location (urban/rural, sub-national regions, etc.) and by socioeconomic characteristics (wealth, education, ethnicity, etc.) is possible in a growing number of indicators and countries (see further details in metadata for each indicator). However, the dashboard does not provide disaggregated data for each individual indicator.</p>", "COMPARABILITY__GLOBAL"=>"<p>See further details in metadata for each indicator.</p>", "OTHER_DOC__GLOBAL"=>"<ol>\n  <li>World Bank, 2015 The International Poverty Line, <a href=\"http://www.worldbank.org/en/programs/icp/brief/poverty-line\">http://www.worldbank.org/en/programs/icp/brief/poverty-line</a> </li>\n  <li>WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) </li>\n</ol>\n<p>JMP Website: https://www.washdata.org/</p>\n<p>JMP Data: <a href=\"https://washdata.org/data\">https://washdata.org/data</a></p>\n<p>JMP Reports: <a href=\"https://washdata.org/reports\">https://washdata.org/reports</a></p>\n<p>JMP Methods: <a href=\"https://washdata.org/monitoring/methods\">https://washdata.org/monitoring/methods</a></p>\n<p>JMP Methodology: 2017 update and SDG baselines</p>\n<p><a href=\"https://washdata.org/report/jmp-methodology-2017-update\">https://washdata.org/report/jmp-methodology-2017-update</a></p>\n<p>JMP Core questions on water, sanitation and hygiene for household surveys: <a href=\"https://washdata.org/report/jmp-2018-core-questions-household-surveys\">https://washdata.org/report/jmp-2018-core-questions-household-surveys</a></p>\n<ol>\n  <li>UNDP 2016 Technical Notes Calculating the Human Development Indices, https://hdr.undp.org/sites/default/files/2021-22_HDR/hdr2021-22_technical_notes.pdf </li>\n  <li>The World Bank Group, ESMAP, 2015 Beyond Connections Energy Access Redefined <a href=\"http://www.worldbank.org/en/topic/energy/publication/energy-access-redefined\">http://www.worldbank.org/en/topic/energy/publication/energy-access-redefined</a> </li>\n  <li>ITU, 2015 ICT Indicators for the SDG Monitoring Framework , <a href=\"http://www.itu.int/en/ITU-D/Statistics/Documents/intlcoop/sdgs/ITU-ICT-technical-information-sheets-for-the-SDG-indicators.pdf\">http://www.itu.int/en/ITU-D/Statistics/Documents/intlcoop/sdgs/ITU-ICT-technical-information-sheets-for-the-SDG-indicators.pdf</a> </li>\n  <li>Wilson et al - Wasteaware ISWM indicators - doi10.1016j.wasman.2014.10.006 - January 2015, https://eprints.whiterose.ac.uk/85319/9/Wilson_et_al_Supplementary_information_Wasteaware_ISWM_Benchmark_Indicators_User_Manual_FINAL.pdf</li>\n</ol>\n<h1>Gender and Transport Resource Guide. <a href=\"https://www.ssatp.org/sites/ssatp/files/publications/HTML/Gender-RG/index.html\">https://www.ssatp.org/sites/ssatp/files/publications/HTML/Gender-RG/index.html</a></h1>\n<p>Transport brief: World Bank. <a href=\"https://www.worldbank.org/en/topic/transport\">https://www.worldbank.org/en/topic/transport</a> </p>\n<p><strong>Table 1. Links to methodologies for Indicator 1.4.1 components.</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Component</p>\n      </td>\n      <td>\n        <p>Measured by:</p>\n      </td>\n      <td>\n        <p>Link to methodology</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Basic drinking water services</p>\n      </td>\n      <td>\n        <p>Proportion of population with access to an improved source with collection time of not more than 30 minutes for a roundtrip including queuing (Part of SDG 6.1.1)</p>\n      </td>\n      <td>\n        <p><a href=\"https://washdata.org/monitoring/drinking-water\">https://washdata.org/monitoring/drinking-water</a> </p>\n        <p><a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-01-01.pdf\">https://unstats.un.org/sdgs/metadata/files/Metadata-06-01-01.pdf</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Basic sanitation services</p>\n      </td>\n      <td>\n        <p>Proportion of population using improved facilities which are not shared with other households (Part of SDG 6.2.1a)</p>\n      </td>\n      <td>\n        <p><a href=\"https://washdata.org/monitoring/sanitation\">https://washdata.org/monitoring/sanitation</a> </p>\n        <p>https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01a.docx</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Basic hygiene services</p>\n      </td>\n      <td>\n        <p>Proportion of population with a handwashing facility with soap and water available at home (SDG 6.2.1b)</p>\n      </td>\n      <td>\n        <p><a href=\"https://washdata.org/monitoring/hygiene\">https://washdata.org/monitoring/hygiene</a> </p>\n        <p>https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01b.docx</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Waste collection</p>\n      </td>\n      <td>\n        <p>11.6.1 Proportion of municipal solid waste collected and managed in controlled facilities out of total municipal waste generated, by cities</p>\n      </td>\n      <td>\n        <p><a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-06-01.pdf\">https://unstats.un.org/sdgs/metadata/files/Metadata-11-06-01.pdf</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Mobility and transport</p>\n      </td>\n      <td>\n        <p>9.1.1 Proportion of the rural population who live within 2 km of an all-season road </p>\n        <p>11.2.1 Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities</p>\n      </td>\n      <td>\n        <p><a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-01-01.pdf\">https://unstats.un.org/sdgs/metadata/files/Metadata-09-01-01.pdf</a> </p>\n        <p><a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-02-01.pdf\">https://unstats.un.org/sdgs/metadata/files/Metadata-11-02-01.pdf</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Modern energy</p>\n      </td>\n      <td>\n        <p>7.1.2 Percentage of population with primary reliance on clean fuels and technology</p>\n      </td>\n      <td>\n        <p><a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-01-02.pdf\">https://unstats.un.org/sdgs/metadata/files/Metadata-07-01-02.pdf</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>ICT</p>\n      </td>\n      <td>\n        <p>9.c.1 Proportion of population covered by a mobile network, by technology</p>\n      </td>\n      <td>\n        <p><a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-0C-01.pdf\">https://unstats.un.org/sdgs/metadata/files/Metadata-09-0C-01.pdf</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Education</p>\n      </td>\n      <td>\n        <p>4.1.1 Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sex</p>\n      </td>\n      <td>\n        <p><a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-01-01A.pdf\">https://unstats.un.org/sdgs/metadata/files/Metadata-04-01-01A.pdf</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Health</p>\n      </td>\n      <td>\n        <p>3.8.1 Coverage of essential health services<strong><em> </em></strong></p>\n      </td>\n      <td>\n        <p><a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf\">https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf</a> </p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "indicator_sort_order"=>"01-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"1.4.2", "slug"=>"1-4-2", "name"=>"Proporción del total de la población adulta con derechos seguros de tenencia de la tierra: a) que posee documentación reconocida legalmente al respecto y b) considera seguros sus derechos, desglosada por sexo y tipo de tenencia", "url"=>"/site/es/1-4-2/", "sort"=>"010402", "goal_number"=>"1", "target_number"=>"1.4", "global"=>{"name"=>"Proporción del total de la población adulta con derechos seguros de tenencia de la tierra: a) que posee documentación reconocida legalmente al respecto y b) considera seguros sus derechos, desglosada por sexo y tipo de tenencia"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción del total de la población adulta con derechos seguros de tenencia de la tierra: a) que posee documentación reconocida legalmente al respecto y b) considera seguros sus derechos, desglosada por sexo y tipo de tenencia", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción del total de la población adulta con derechos seguros de tenencia de la tierra: a) que posee documentación reconocida legalmente al respecto y b) considera seguros sus derechos, desglosada por sexo y tipo de tenencia", "indicator_number"=>"1.4.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Los sistemas de tenencia se enfrentan a una presión cada vez mayor, ya que la \ncreciente población mundial requiere seguridad alimentaria y la urbanización, \nla degradación ambiental y el clima afectan el uso de la tierra y la productividad. \n\nMuchos problemas de tenencia también surgen debido a una gobernanza deficiente de la tierra, \ndisputas debido a la adquisición de tierras o inversiones a gran escala en tierras, \ne intentos de abordar los problemas de tenencia asociados con dualismos en los regímenes de \ntenencia. \n\nLa gobernanza responsable de la tenencia de la tierra está inextricablemente vinculada \ncon el acceso y la gestión de otros recursos naturales, como los bosques, el agua, \nla pesca y los recursos minerales. La gobernanza de la tenencia es un elemento crucial \npara determinar si las personas, las comunidades y otros adquieren derechos, y sus \nobligaciones asociadas, para usar y controlar la tierra y los recursos naturales, y \nde qué manera. \n\nEl reconocimiento legal de la tenencia grupal o la adopción de una administración de la \ntierra \"adecuada para el propósito\" y su uso para reconocer los límites exteriores de \nla tierra en posesión de acuerdos comunales o consuetudinarios han recibido cada vez \nmás atención de los gobiernos en el pasado reciente.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-04-02.pdf\">Metadatos 1-4-2.pdf</a> (solo en inglés)\n", "dato_global"=>"", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nLand tenure systems are facing increasing pressure as the growing global population \ndemands food security, and urbanization, environmental degradation, and climate change \naffect land use and productivity.\n\nMany tenure problems also arise due to poor land governance, disputes over land \nacquisition or large-scale land investments, and attempts to address tenure problems \nassociated with dualisms in land tenure regimes.\n\nResponsible governance of land tenure is inextricably linked with the access to \nand management of other natural resources, such as forests, water, fisheries, and \nmineral resources. Tenure governance is a crucial element in determining whether and \nhow individuals, communities, and others acquire rights, and their associated \nobligations, to use and control land and natural resources.\n\nThe legal recognition of group tenure or the adoption of \"fit-for-purpose\" land \nadministration and its use to recognize the outer boundaries of land held under \ncommunal or customary arrangements have received increasing attention from governments \nin the recent past.\n \nSource: United Nations Statistics Division  \n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-04-02.pdf\">Metadata 1-4-2.pdf</a>\n", "dato_global"=>nil}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Edukitze-sistemek geroz eta presio handiagoa dute, mundu-mailako populazioa geroz eta handiagoa izanik \nbeharrezkoak baitira elikagaien segurtasuna eta urbanizazioa. Gainera, ingurumenaren narriadurak eta \nklimak eragina dute lurraren eta produktibitatearen erabileran. \n\nEdukitze-arazo askoren atzean daude, era berean, lurraren gobernantza eskasa, lurrak erostearen ondoriozko \ngatazkak edo lurren inbertsio handiak, bai eta edukitze araubideetako dualismoekin lotutako edukitze-arazoak \njorratzeko saiakerak ere. \n\nLurraren edukitzaren gobernantza arduratsua nahitaez dago lotuta beste baliabide natural batzuk eskuratu \neta kudeatzeko moduarekin (besteak beste basoak, ura, arrantza edo baliabide mineralak). Edukitzaren gobernantza \nfuntsezko elementua da, batetik, zehazteko ea pertsonek, komunitateek eta bestelakoek eskubideak eta horiekin \nlotutako betebeharrak bereganatzen dituzten lurra eta baliabide naturalak erabili eta kontrolatzeko orduan, eta, \nbestetik, hori guzti hori nola gauzatzen den zehazteko. \n\nIragan hurbilean gobernuek geroz eta arreta gehiagoz aztertu dituzte hainbat eremu; besteak beste, taldeko \nedukitza legez aitortzea, lurra \"helbururako egokia\" den gisan administratzea, eta hori auzo edo ohiturazko \nakordioen arabera jabetzako lurren kanpoko mugak zehazteko erabiltzea. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-04-02.pdf\">Metadatuak 1-4-2.pdf</a> (ingelesez bakarrik)\n", "dato_global"=>nil}, "national_metadata_updated_date"=>"2025-03-15", "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.4: By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinance</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.4.2: Proportion of total adult population with secure tenure rights to land, (a) with legally recognized documentation, and (b) who perceive their rights to land as secure, by sex and type of tenure</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SP_LGL_LNDDOC - Proportion of people with legally recognized documentation of their rights to land out of total adult population [1.4.2]</p>\n<p>SP_LGL_LNDSEC - Proportion of people who perceive their rights to land as secure out of total adult population [1.4.2]</p>\n<p>SP_LGL_LNDSTR - Proportion of people with secure tenure rights to land out of total adult population [1.4.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>This indicator is Goal 1, and is also particularly related to Goal 5, 5.a.1 (access to agricultural land) and 5.a.2 (legal framework for land governance). Tenure security also matters for Goal 2, Target 2.3 (2.3.1 and 2.3.2 addressing smallholder farmers; Target 2.4 (2.4.1 on agricultural area), to Goal 11, to indicator 11.1.1 (access to affordable housing/upgrading slums) and indicator 11.3.1 (sustainable urbanization/settlement planning). Land tenure also influences access to fisheries and is thus key to achieving Goal 14, indicator 14.b.1 to provide access to small-scale fishers and marine resources, and to Goal 15 on the sustainable use of land and natural resources (indicator 15.1.1 on forest area). Similarly, land is a significant source of conflict, and thus also matters for Goal 16 for promoting peace and inclusive societies and institutions (indicator 16.3.3 on dispute resolution mechanisms).</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UN-Habitat and World Bank</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UN-Habitat and World Bank</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Indicator 1.4.2 measures the relevant part of Target 1.4 (ensure men and women have equal rights to economic resources, as well as access to &#x2026;, ownership of and control over land and other forms of property, inheritance, natural resources). It measures the results of policies that aim to strengthen tenure security for all, including women and other vulnerable groups.</p>\n<p>Indicator 1.4.2 covers (a) all types of land use (such as residential, commercial, agricultural, forestry, grazing, wetlands based on standard land-use classification) in both rural and urban areas; and (b) all land tenure types as recognized at the country level, such as freehold, leasehold, public land, customary land. An individual can hold land in his/her own name, jointly with other individuals, as a member of a household, or collectively as member of group<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>, cooperative or other type of association. </p>\n<p><strong>Secure tenure rights</strong>: comprised of two sub-components: (i) legally recognized documentation and (ii) perception of the security of tenure, which are both necessary to provide a full measurement of tenure security. </p>\n<p><strong>Legally recognized documentation</strong>: Legal documentation of rights refers to the recording and publication of information on the nature and location of land, rights and right holders in a form that is recognized by government, and is therefore official. For purposes of computing SDG Indicator 1.4.2, the country specific metadata will define what documentation on land rights will be counted as legally recognized (see next section for rationale).</p>\n<p><strong>Perceived security of tenure</strong>: Perception of tenure security refers to an individual&#x2019;s perception of the likelihood of involuntary loss of land, such as disagreement of the ownership rights over land or ability to use it, regardless of the formal status and can be more optimistic or pessimistic. Although those without land rights&#x2019; documentation may frequently be perceived to be under threat, and those with documentation perceived as protected, there may be situations where documented land rights alone are insufficient to guarantee tenure security. Conversely, even without legally recognized documentation, individuals may feel themselves to be protected against eviction or dispossession. Therefore, capturing and analysing these diverse ranges of situations will enable a more comprehensive understanding of land tenure security, based on a country specific context.</p>\n<p>For purposes of constructing the indicator (see next section for rationale), we define perceptions of tenure to be secure if:</p>\n<ol>\n  <li>The landholder does not report a fear of involuntary loss of the land within the next five years due to, for example, intra-family, community or external threats and </li>\n  <li>The landholder reports having the right to bequeath the land. </li>\n</ol>\n<p><strong><em>Total adult population:</em></strong> A country&#x2019;s adult population<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> is measured by census data or through surveys using an adequate sampling frame.</p>\n<p><strong>Interpretation:</strong></p>\n<p>One motivation that makes the indicator actionable is that, in many developing countries, the gap between data on the availability of documentation and on perception of tenure security can be large. For example, tenure may be perceived as secure, even though rights are not formally documented, as in the case of customary systems and trusted local land governance arrangements. Or, the opposite, tenure may be perceived as insecure even when there is a high level of formal documentation of rights. The latter situation can be caused by various factors, including limited trust in land administration services, possible duplicated documents, high cost of having state institutions protecting such rights.</p>\n<p>Reporting on perceived security will provide important information on people&#x2019;s satisfaction with the institutional quality of service, transparency, appropriateness, accessibility and affordability of land administration services and justice systems.</p>\n<p><strong>Concepts:</strong></p>\n<p>The concepts below are based on the &#x201C;Voluntary Guidelines for the Responsible Governance of Tenure of Land, Forests and Fisheries in the Context of National Food Security&#x201D; (shorthand VGGT), which were endorsed by the United Nations World Committee on World Food Security in 2012 and therefore considered an internationally accepted standard. Other international frameworks using these concepts are the African Union Agenda on Land as laid out in the 2009 Framework and Guidelines on Land Policy in Africa and the 2014 Nairobi Action Plan on Large-Scale Land-Based Investments.</p>\n<p><strong>Tenure</strong>: How people, communities and others gain access to land and natural resources (including fisheries and forests) is defined and regulated by societies through systems of tenure. These tenure systems determine who can use which resources, for how long, and under what conditions. Tenure systems may be based on written policies and laws, as well as on unwritten customs and practices. No tenure right, including private ownership, is absolute. All tenure rights are limited by the rights of others and by the measures taken by states for public purposes (VGGT, 2012).</p>\n<p><strong>Tenure typology</strong>: A tenure typology is country specific and refers to categories of tenure rights, for example customary, leasehold, public and freehold. Rights can be held collectively, jointly or individually and may cover one or more elements of the bundle of rights (the right of possession, of control, of exclusion, of enjoyment and of disposition).</p>\n<p><strong>Land governance</strong>: Rules, processes and structures through which decisions are made regarding access to and the use (and transfer) of land, how those decisions are implemented and the way that conflicting interests in land are managed. States provide legal recognition for tenure rights through policies, law and land administration services, and define the categories of rights that are considered official.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> <em>Group rights</em> include shared or collective rights, and examples include the ejido in Mexico, indigenous territories in Honduras, perpetual DUAT for rural communities in Mozambique. Collective rights occur in a situation where holders of rights to land and natural resources are clearly defined as a collective group and have the right to exclude third parties from the enjoyment of those rights. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> Country specific legal definition of an &#x2018;adult&#x2019; will be applied. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The data sources used are census, multi-topic household surveys conducted by national statistical Organizations and, depending on availability, administrative data on land tenure reported by national land institutions (in most cases land registries and cadastres).</p>\n<p><em>Household surveys and census</em></p>\n<p>Household surveys and census that have been implemented by national statistical agencies, are a key source of information for computing the indicator.</p>\n<p><em>Censuses: </em>These provide a complete enumeration of all the populations of the country at a specific time. In many recent censuses, questions on household characteristics, including short modules on security of tenure, are collected. So far, 41 countries have carried out a census in which questions on land tenure were included. Options for expanding land-related questions in the upcoming agricultural census are being discussed together with FAO (custodians of 5.a.1).</p>\n<p><em>Household-level consumption/expenditure surveys:</em> To provide aggregate information on levels of consumption, prices and, often, estimates of GDP, many countries conduct this type of survey. As one of the key assets, this often includes questions on how residential land is accessed but rarely goes beyond this in terms of the type of documents held or the gender of rights holders. Elaborated housing modules are often included, and which already contain some questions on tenure status of the dwelling and documentation held. In consultation with the NSO, these modules will be fine-tuned to fully cover the essential land questions identified for 1.4.2.</p>\n<p><em>Multi-topic household surveys:</em> Building on the need to generate reliable poverty estimates and understand the factors that lead households to fall into poverty or escape from it in developing countries, these surveys include a roster of household members and, where agriculture is a main source of livelihood, a detailed agricultural module that in many cases obtains information on tenure status, ownership, and production at plot level. The essential questions for 1.4.2 as well as 5.a.1 have been included in the <em>Living Standard Measurement Surveys</em> approach, which includes individual surveys and puts much emphasis on measuring intra household dynamics through direct reporting.</p>\n<p><em>Demographic and Health Surveys (DHS):</em> Responding to a need for more frequent and reliable information on population and health, especially in developing countries, these types of surveys provide nationally representative data on a wide range of areas including fertility, family planning, maternal and child health, gender, HIV/AIDS, malaria, and nutrition. A standard questionnaire, regularly revised to incorporate newly emerging issues, is administrated at the household and individual level. It is a nationally representative survey. In a majority of DHS surveys, people eligible for individual interviews include women of reproductive age (15-49) and men age 15-49, 15-54, or 15-59.<strong> </strong>The individual questionnaires in the latest version (round 7) includes questions on whether respondents own land, if they have formal ownership documents, and if their name is included on these documents.</p>\n<p><em>Multiple Indicator Cluster Surveys (MICS):</em> Surveys implemented by NSOs under the program developed by the United Nations Children&apos;s Fund (UNICEF) to provide internationally comparable, statistically rigorous data on the situation of children and women. They cover topics such as health, education, child protection, and water and sanitation. The survey design follows closely that of DHS questions and modules. This facilitates cross-country comparisons of estimates obtained using DHS data with those obtained using MICS data. In addition to the household questionnaire, there are questionnaires for women of reproductive ages (15-49), men aged between 15 and 49 and children (aged 0-5 and aged 5-17). The household questionnaire includes questions on ownership of land that can be used for agriculture by any member of the household, and on the size of the agricultural land owned by the household members. Also, there are questions about ownership/rental of dwelling where the household lives.</p>\n<p>Discussions are ongoing with the teams in charge of DHS and MICS, specifically on expanding questions on land in their standardized and nationally representative surveys, in order to cover all data requirements for 1.4.2.</p>\n<p><em>Urban Inequity Surveys (UIS): </em>These specialized surveys were designed by UN-Habitat as household surveys to monitor and assess water and sanitation service coverage and other topics on urban inequities, including tenure. More recently, these surveys have been expanded to cover both rural and urban areas. The upcoming UIS surveys will be reviewed to ensure that the data requirements for SDG 1.4.2 are covered.</p>\n<p>Administrative data</p>\n<p>Production of land records and maps is a core function of public land registries, with legally recognized documentation being the output. Reporting on the information contained in these land records ((i) names of people holding rights, (ii) type of rights and (iii) location) is not difficult in principle if records are kept in a computerized format. Using household surveys, this land information can be cross-checked against survey information with respect to quality and coverage. In the case of registered communal or group rights, identifying the group members who gain tenure security through its registration is equally possible.</p>\n<p>The country specific metadata will include a description of the structure of the land information data base, available information and approach for routine SDG reporting.</p>", "COLL_METHOD__GLOBAL"=>"<p>The custodians of 1.4.2 together with FAO and UN Women, custodians of 5. a.1<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup>, developed a standardized, consolidated and succinct survey instrument with essential questions as data collection requirements are partly similar (<a href=\"https://gltn.net/download/measuring-individuals-rights-to-land-an-integrated-approach-to-data-collection-for-sdg-indicators-1-4-2-and-5-a-1-english/?wpdmdl=16316&amp;refresh=5efb342458df61593521188\">https://gltn.net/download/measuring-individuals-rights-to-land-an-integrated-approach-to-data-collection-for-sdg-indicators-1-4-2-and-5-a-1-english/?wpdmdl=16316&amp;refresh=5efb342458df61593521188</a>). The standardization of indicator definitions improves data comparability across countries. The scope and capacity for standardized data collection, analysis and reporting across national statistics offices (NSOs) is expected to rise with progressive data collection and implementation of the methodology.</p>\n<p>The <a href=\"http://documents.worldbank.org/curated/en/812621505371556739/Land-tenure-module-essential-questions-for-data-collection-for-1-4-2-and-5-a-1)\">module</a> is made available to NSOs for integration in survey instruments already in place, and will be used by other international household survey programs working with NSOs (such as Living Standard Measurement Survey (LSMS) and UIS). The module can be used by any other complementary survey instrument implemented by other actors, using a data collection protocol that meets SDG 1.4.2 requirements, while the data produced are approved and reported by NSO to the custodians. In addition, both the USAID and the Millennium Challenge Cooperation (MCC), have agreed to incorporate the essential questions from 5.a.1 and 1.4.2 into future land impact evaluations and has already done so for upcoming ones. The Property Rights Index initiative has integrated the SDG questions into its data collection tools on perceptions of tenure security. This range of efforts will further expand data availability and leverage efforts by NSOs to report on this indicator.</p>\n<p>Country-specific metadata will be elaborated that provides an inventory of the tenure types and type of documents in use, identifies which documents are legally recognized as evidence of land rights with images of each document, and elaborates on the correspondence between the two types of data sets (survey data and administrative data). This instrument will ensure consistency of definitions across countries. These country specific metadata will also be used for customizing surveys.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> Indicator title 5.a.1: (a) Proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and (b) Share of women among owners or rights-bearers of agricultural land, by type of tenure. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "FREQ_COLL__GLOBAL"=>"<p>Data collection will be the responsibility of national agencies. Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and Living Standard Measurement Survey (LSMS)-type surveys are conducted in a cycle of about three years, while census data is available every 10 years. Administrative data can be reported on an annual basis where land information systems are fully electronic, with the accompanying population data made available from censuses or inter-censual projections.</p>\n<p>Via the EGMs conducted, the custodians have been able to put together a network of NSOs and land administration institutions to link to national statistics offices (NSOs) and their regional representations, and to provide administrative data. The World Bank, UN-Habitat, the Global Donor Working Group on Land (<a href=\"https://www.donorplatform.org/\">GDWGL</a>), Global Land Tool Network (<a href=\"http://www.gltn.net/index.php/land-tools/gltn-land-tools/global-land-indicators-initiative-glii\">GLTN</a>)/ Global Land Indicator Initiative (<a href=\"http://www.gltn.net/index.php/land-tools/gltn-land-tools/global-land-indicators-initiative-glii\">GLII</a>) and other partners will support capacity strengthening at regional and country level for data providers and reporting mechanisms, and promote understanding of this indicator at all levels. Concerted investments are ongoing to expand data availability by integrating the consolidated land data module with essential questions in upcoming surveys, as already indicated above. </p>\n<p>A capacity assessment<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup> on the preparedness and ability of NSOs to report on indicator 1.4.2 indicator was conducted by the custodians, with support of GLTN/GLII. The findings show NSOs agree to build on existing national survey systems and are ready to coordinate with land agencies to generate data and report on this indicator. Capacity needs were also identified and being used to develop a country capacity development strategy for NSOs, jointly with FAO and UN Women. The custodians of 1.4.2 and 5.a.1 have agreed to work closely with country and regional statistical agencies and global partners to support for country data collection, analysis and reporting. Similar capacity building support will be developed for land agencies to set up gender disaggregated electronic reporting systems.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> Reports received from 17 countries: Bhutan, Bangladesh, Cameroon, Tunisia, Tanzania, Senegal, Uganda, Mauritius, Colombia, Japan, Slovenia, Sweden, Jamaica, Singapore, Madagascar, Niger and India. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "REL_CAL_POLICY__GLOBAL"=>"<p>No fixed releases; depends on release of relevant survey data.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National data providers: </p>\n<ul>\n  <li>Statistical agencies &#x2013; surveys</li>\n  <li>Government administrative sources /registries, cadastres</li>\n</ul>\n<p>Compilation &amp; reporting at the global level: </p>\n<ul>\n  <li>UN-Habitat - United Nations Human Settlements Programme</li>\n  <li>World Bank </li>\n</ul>\n<p>Development of methodology and data collection tools was done with support of national statistics offices (NSOs) (Colombia, India, Jamaica, Tanzania, Uganda, Cameroon, the United States, the Africa Centre for Statistics/UNECA) and land agencies (Belgium, Brazil, Colombia, Republic of Korea, Mexico, Netherlands, Romania, Spain, United Arab Emirates and Uganda) and regional organizations of land agencies (registries, cadastres, ministries responsible for land) through international Expert Group Meetings. </p>\n<p>The data collection tool was developed in coordination with FAO and UN Women/EDGE to harmonize instruments for 1.4.2 and 5.a.1. </p>\n<p>The development of this SDG indicator is supported by the Global Donor Working Group on Land (<a href=\"https://www.donorplatform.org/\">GDWGL</a>). This is a network of 24 bi- and multilateral donors and international organizations committed to improving land governance worldwide and which collectively represents virtually all global donor assistance in the land sector: the Global Land Tool Network (<a href=\"http://www.gltn.net/index.php/land-tools/gltn-land-tools/global-land-indicators-initiative-glii\">GLTN</a>) and the Global Land Indicator Initiative (<a href=\"http://www.gltn.net/index.php/land-tools/gltn-land-tools/global-land-indicators-initiative-glii\">GLII</a>), a network of over 70 CSOs, NGOs, professional organizations, research and training organizations; the International Land Coalition (<a href=\"http://www.landcoalition.org/\">ILC</a>), an alliance of more than 200 intergovernmental and civil society organizations working on land; and the African Union/UNECA/AfDB <a href=\"https://www.uneca.org/lpi\">Land Policy Initiative</a>.</p>", "COMPILING_ORG__GLOBAL"=>"<ul>\n  <li>UN-Habitat - United Nations Human Settlements Programme</li>\n  <li>World Bank </li>\n</ul>", "INST_MANDATE__GLOBAL"=>"<p>No set of rules or instructions available.</p>", "RATIONALE__GLOBAL"=>"<p>Tenure systems increasingly face stress as the world&#x2019;s growing population requires food security, and as urbanization, environmental degradation and climate affect land use and productivity. Many tenure problems also arise because of weak land governance, disputes due to land acquisition or large-scale land-based investments, and attempts to address tenure problems associated with dualisms to tenure regimes. Responsible governance of tenure of land is inextricably linked with access to and management of other natural resources, such as forests, water, fisheries and mineral resources. The governance of tenure is a crucial element in determining if and how people, communities and others acquire rights, and their associated obligations, to use and control land and natural resources. Legal recognition to group tenure or adopting a &#x2018;fit for purpose&#x2019; land administration and using these to recognize outer boundaries of land held under communal or customary arrangements have increasingly received government attention in the recent past. </p>\n<p>Increasing demand for pro-poor land reforms has created the need for a core set of land indicators that have national application and global comparability, and culminated in SDG 1.4.2<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup>. Regular reporting on indicator 1.4.2 will provide an impetus to improve the availability of data from surveys as well as regularity of reporting on land administration service delivery to people by registries and other line agencies. Indicator 1.4.2 thus measures gender disaggregated progress in tenure security.</p>\n<p>All forms of tenure should provide people with a degree of tenure security, with states protecting legitimate tenure rights, ensuring that people are not arbitrarily evicted and that their legitimate tenure rights are not otherwise extinguished or infringed. Perceptions of tenure security matter because they influence the way that land is used. Sources of perceived insecurity may include contestation from within households, families, communities or as a result of the actions of governments or private land claimants. Secure tenure rights for women require particular attention and could be affected by a number of factors, including intra-household power relations, community level inequalities, or different tenure regimes, and which can be cross tabulated against other factors of difference to ensure that women are no left behind. If measured at the individual level, the right to bequeath is another proxy of perception of tenure security. Women&#x2019;s ability to influence intergenerational land transfers is an important aspect of female empowerment (and one way in which this indicator links with indicator 5.a.1).</p>\n<p>&#x201C;Legally recognized documentation&#x201D; and &#x201C;perception of tenure security&#x201D; are two complementary parts of this indicator and which reflects several insights, namely (i) land is a key asset that is essential for poverty reduction, human rights and equality of opportunity including by gender; (ii) secure land tenure creates incentives for investment in land, allows land to be transferred, and creates the institutional precondition for use of land as collateral to access finance for economic activity; (iii) there is a need to complement formal measures of tenure security with perception-based measures.</p>\n<p>This indicator will inform policy and allow for the assessment of specific outcomes and practical priorities for further improvements of tenure security at the country level. Regular reporting on the two components of Indicator 1.4.2 will:</p>\n<ul>\n  <li>provide incentives for governments to improve performance on progress with responsible land governance</li>\n  <li>inform governments and non-state actors to what extent countries&#x2019; legal and institutional frameworks recognize and support different land-tenure categories</li>\n  <li>provide information on implementation capacity to protect such rights in practice, as well as progress </li>\n  <li>identify the scope for additional action required at the country level as well as at a subnational level or for certain categories, geographic entities or ecosystems, and</li>\n  <li>provide for equity between men and women in land rights. </li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> This need for data led to a collaboration between UN-Habitat, the Millennium Challenge Corporation and the World Bank in 2012, facilitated by the Global Land Tool Network, to develop a set of core land indicators to measure tenure security globally and at country level; the process saw the start of the Global Land Indicators Initiative (GLII), a platform used by the global land community to underscore the need for tenure security through evidence-based policymaking through more and better data. <a href=\"#footnote-ref-6\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>In 2016, a total of 116 countries reported having electronic land information systems in place. Countries with paper-based systems will have more difficulties with reporting on administrative data and household surveys will be the main source of data for this indicator in these countries. The expansion of digitization of records and land data management is one way to facilitate the ease of reporting administrative data for this indicator. Coverage may, however, be geographically skewed, for example towards urban or specific rural regions where cadastral coverage is concentrated, and therefore sub-national dimensions should be properly considered and conveyed in narrative reporting by specific countries to accompany the headline data.</p>\n<p>In federal countries with decentralized land registry systems and no centralized reporting yet, data reporting systems for aggregation will be put in place. For countries where the land administration system does not yet collect information on gender, and gender disaggregation cannot be computed using other core data (social security numbers, ID etc), land agencies are encouraged to start expanding this by recording also the gender of owners/users of newly registered land.</p>\n<p>Most of the national household surveys&#x2019; target samples are sufficiently large to provide the statistical power for disaggregation by sex and tenure type at rural /urban and sub-national levels. Inferring the extent to which the adult population is tenure secure based on the existing web of surveys, will require the use of a standardized set of questions so that surveys can be combined. However, even nationally representative surveys tend to cover certain segments of the population (those living in agricultural areas, families in which there are women of reproductive age, official urban areas etc.). Even when all the existing surveys are aggregated, there may be pockets of the population that are not captured by the surveys and for which there is thus no data on tenure security. This may include families living in areas that are too far or costly to reach, like forest areas.</p>\n<p>Household surveys generally collect household-level data from proxy respondents. Family members who are not the head or the most knowledgeable person in their households are not interviewed, as is also noted in the methodological note for the IAEG-SDG Secretariat for Indicator 5.a.1. This approach is problematic for measuring tenure rights and security due to the introduction of non-random measurement errors<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup>. For instance, proxy reporting by one member of the household tends to incorrectly assign rights and misjudge and underestimate both women&#x2019;s and men&#x2019;s rights and use of land. Indicator 1.4.2 should therefore be based on self-reported rather than proxy data. If not all household members are surveyed, only those surveyed should be reported, estimating the global adult population based on the smaller sample enumerated. This lack of information affects only the numerators of the indicator; it has no bearing on the denominator which should always be the total adult population. In other words, the indicator reports and tracks the proportion of the population for which there is self-reported data stating that they are tenure secure. People for whom there is no information cannot be assumed to be tenure secure and therefore are not counted in the numerator. National statistics offices (NSOs) should report the data collected from household surveys as individual level data that corresponds to the respondent and is not extrapolated to the rest of his/her household. Any limitations in the representativeness of this data should be clearly noted in the country specific metadata submitted with the reporting, including who was included in the enumeration.</p>\n<p>Data will still be used for countries that do not yet have survey instruments in place that survey individuals, while capacity for expanding sampling and individual self-reporting by NSOs is expanded progressively through Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Living Standard Measurement Survey (LSMS) and other type of surveys in coordination with FAO and UN-Women. Addressing this challenge will require combined efforts. Custodians of the land rights indicators1.4.2 and 5.a.1, and relevant stakeholders from the land sector, will work with custodians from other SDG indicators also require surveying of individuals, and in particular the NSOs, to identify effective approaches to start filling the void on self-reported data. NSOs need to be supported to collect data by interviewing individual adult household member. The custodians will leverage the work of the UN - Evidence and Data for Gender Equality <a href=\"https://unstats.un.org/edge/\">EDGE project</a><u><sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup></u>, in particular, which is the most advanced in using and testing gender sensitive methodologies and approaches. They have found the approach feasible and have developed training materials and data collection instruments suitable for this effort.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> Findings from the Methodological Experiment on Measuring Asset Ownership from A Gender Perspective (MEXA) experiment revealed that data from proxy respondents yield different estimates than self-reported data, with variations by asset, by type of ownership and by the sex of the owner. For instance, the study found that self-reported data increase both women&#x2019;s and men&#x2019;s reported ownership of agricultural land in Uganda. Such increase is greater for men (15 percentage points) than for women (10 percentage points), and is less pronounced when we consider documented ownership (+7 percentage points for men and +2 percentage points for women) (Kilic and Moylan, 20160. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> <a href=\"https://unstats.un.org/edge/\">https://unstats.un.org/edge/</a> <a href=\"#footnote-ref-8\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>Indicator 1.4.2 is composed of two parts: (A) measures the incidence of adults with legally recognized documentation over land among the total adult population; while (B) focuses on the incidence of adults who report having perceived secure rights to land among the adult population. Part (A) and part (B) provide two complementary data sets on security of tenure rights, needed for measuring the indicator.</p>\n<p>Part (A): <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi>P</mi>\n        <mi>e</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>A</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>t</mi>\n        <mo>)</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>g</mi>\n        <mi>n</mi>\n        <mi>i</mi>\n        <mi>z</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>d</mi>\n        <mi>o</mi>\n        <mi>c</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n    </mfrac>\n  </math> X 100</p>\n<p>Part (B): <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi>P</mi>\n        <mi>e</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>a</mi>\n            <mi>d</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>t</mi>\n          </mrow>\n        </mfenced>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>i</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>i</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>g</mi>\n        <mi>h</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>c</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n    </mfrac>\n  </math> x 100</p>\n<p>Part A will be computed using national census data or household survey data generated by the national statistical system and/or administrative data generated by land agency (depending on data availability)<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup>.</p>\n<p>Part B will be computed using national census data or household survey data that feature the perception questions globally agreed through the EGMs and standardized in the module with the list of essential questions.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> The decision on data source will be taken at the specific country level. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>Computing of indicator by custodians based on survey data released by national statistics offices (NSOs) and/ or administrative data submitted by government agency.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Only use of raw (but cleaned and quality check applied) data released by national statistics offices (NSOs) or government agency; computed by statistical staff world bank.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Standard quality criteria are met.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>This indicator was reclassified from Tier III to <strong>Tier II </strong>during the 6<sup>th</sup> Meeting of IAEG-SDG. An internationally established methodology exists but data is not regularly produced by countries. Administrative data are routinely produced by land administration institutions. The 116 countries reporting having electronic land information systems, can generate the required data at a low cost on a routine basis, and at high levels of disaggregation, once the queries for the SDG dashboard are put in place.</p>\n<p>Nationally representative multi-topic household<strong> </strong>surveys have collected land related data in many countries.<strong> </strong>These provide information, separately for residential and non-residential land, on (i) the share of individuals with legally documented rights; and (ii) the share of individuals who perceive their rights to be secure. Nationally representative household surveys will also provide data on two other key elements, namely (i) reported type of documentation and (ii) perception of tenure security by tenure type and other disaggregations discussed above.</p>\n<p><strong>Time series:</strong></p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator will be disaggregated by sex and type of tenure, using the standards developed by the working group on data disaggregation, which is a subgroup of the Inter-Agency Expert Group on SDGs<sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup>.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> <a href=\"https://unstats.un.org/sdgs/files/meetings/iaeg-sdgs-meeting-05/12_14.%20Data%20disaggregation_plenary.pdf\">https://unstats.un.org/sdgs/files/meetings/iaeg-sdgs-meeting-05/12_14.%20Data%20disaggregation_plenary.pdf</a> <a href=\"#footnote-ref-10\">&#x2191;</a></p></div></div>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p>Kilic, T., and Moylan, H. (2016). &#x201C;Methodological experiment on measuring asset ownership from a gender perspective (MEXA): <a href=\"http://siteresources.worldbank.org/INTLSMS/Resources/3358986-1423600559701/MEXA_Technical_Report.pdf\">technical report</a>.&#x201D; Washington, DC: World Bank</p>\n<p><strong>Selected Land policy normative documents </strong></p>\n<p>Africa Union, African Development bank and United Nations Economic Commission for Africa (1999). <em>Land Policy in Africa: A Framework to Strengthen Land Rights, Enhance Productivity and Secure Livelihoods. </em>Available at: <a href=\"https://www.uneca.org/publications/framework-and-guidelines-landpolicy-africa\">https://www.uneca.org/publications/framework-and-guidelines-landpolicy-africa</a> </p>\n<p>Africa Union, African Development bank and United Nations Economic Commission for Africa (2014). <em>Guiding Principles on Large-Scale Land-Based Investment in Africa</em>. Nairobi. Available at: <a href=\"https://www.uneca.org/sites/default/files/PublicationFiles/guiding_principles_eng_rev_era_size.pdf\">https://www.uneca.org/sites/default/files/PublicationFiles/guiding_principles_eng_rev_era_size.pdf</a> </p>\n<p>Food and Agriculture Organization of the United Nations (2012). <em>Voluntary Guidelines on the Responsible Governance of Tenure of Land, Fisheries and Forests in the Context of National Food Security</em>. Available at: <a href=\"http://www.fao.org/docrep/016/i2801e/i2801e.pdf\">http://www.fao.org/docrep/016/i2801e/i2801e.pdf</a> </p>\n<p><strong>Proceedings EGMs for SDG 1.4.2 </strong></p>\n<p>Expert Group Meetings on methodology development using survey data: <a href=\"https://gltn.net/home/download/international-expert-group-meeting-on-land-tenure-security-to-develop-a-set-of-household-survey-questions-for-monitoring-sdg-indicator-1-4-2/?wpdmdl=111\">https://gltn.net/home/download/international-expert-group-meeting-on-land-tenure-security-to-develop-a-set-of-household-survey-questions-for-monitoring-sdg-indicator-1-4-2/?wpdmdl=111</a></p>\n<p>Expert Group Meetings on methodology development using administrative data (<a href=\"http://documents.worldbank.org/curated/en/482991505367111149/pdf/119691-WP-P095390-PUBLIC-SDGEGMproceedingsuseofadministrativedatalandagencies.pdf\">http://documents.worldbank.org/curated/en/482991505367111149/pdf/119691-WP-P095390-PUBLIC-SDGEGMproceedingsuseofadministrativedatalandagencies.pdf</a>) </p>\n<p>Consolidated essential questions land module for 1.4.2 and 5.a.1 (FAO, UN-Habitat, UN Women, World Bank). Module for individual interviewing under preparation; Version for household surveys with proxy respondents; available at: <a href=\"http://documents.worldbank.org/curated/en/812621505371556739/Land-tenure-module-essential-questions-for-data-collection-for-1-4-2-and-5-a-1\">http://documents.worldbank.org/curated/en/812621505371556739/Land-tenure-module-essential-questions-for-data-collection-for-1-4-2-and-5-a-1</a>).</p>\n<p>ANNEX:</p>\n<p>Full methodology development narrative (including list of pilot countries, data and other results from pilot studies)</p>\n<p>METHODOLOGICAL DEVELOPMENT</p>\n<p>Global consultations on the methodological developments of this indicator were conducted with a diverse range of participants and partners. The custodian agencies, working directly with NSOs and land agencies, developed tools and capacity development packs, followed by computation of data points for relevant variables for this indicator for several countries on a pilot basis, using existing data sources from nationally representative surveys and census and, in exceptional cases, rigorous impact evaluations without national coverage.</p>\n<p>Methodology development and piloting results</p>\n<p><strong>Formulas and combining different elements</strong></p>\n<p> The process used for methodology development are presented above. As discussed in detail there, indicator 1.4.2 comprises two parts: (A) measures the incidence of adults with legally recognized documentation over land among the total adult population; while (B) focuses on the incidence of adults who report having perceived secure rights to land among the adult population. Part (A) and part (B) provide two complementary data sets on security of tenure rights, needed for measuring the indicator.</p>\n<p><strong>Part (A):</strong></p>\n<p> <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">A</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mo>)</mo>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">w</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">z</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">d</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n      </mrow>\n    </mfrac>\n  </math> X 100</p>\n<p><strong>Part (B):</strong></p>\n<p> <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi mathvariant=\"normal\">a</mi>\n            <mi mathvariant=\"normal\">d</mi>\n            <mi mathvariant=\"normal\">u</mi>\n            <mi mathvariant=\"normal\">l</mi>\n            <mi mathvariant=\"normal\">t</mi>\n          </mrow>\n        </mfenced>\n        <mi mathvariant=\"normal\">w</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n      </mrow>\n    </mfrac>\n  </math> X 100</p>\n<p>The computation formula has built in system for computing the individual components of this indicator.</p>\n<ol>\n  <li>Where survey data are collected separately for agricultural and residential land, double counting is avoided by adjusting for households that access both types of land simultaneously. </li>\n  <li>Strata title: cases where a residence is in an apartment building, the rights to the residency are counted as rights to the land. </li>\n  <li>For purposes of retrospective data collection, parcels that are already affected by a dispute are also included in the reporting below on fear for involuntary loss of land.</li>\n</ol>\n<p>As required by the indicator definition, any component can be disaggregated by gender and tenure type.</p>\n<p>The national censuses or household surveys by the national statistical system were used to assess the number of people to access any land either through individual or joint ownership or via rental. Gender was calculated from surveys or calculated by land agencies using administrative data.</p>\n<p>More detailed technical issues, e.g. ways to deal with proxy reporting by one member of the household on and when and how administrative data can be used are explained in the draft meta data for SDG 1.4.2.</p>\n<p>Piloting results</p>\n<p>Results from applying the methodology to select data are summarized in Table 1. They demonstrate not only the viability of the methodology, including the scope for how survey and administrative data to complement each other in a useful way, as well as the ability to derive a meaningful and actionable indicator. Rather than discussing substantive implications and actionability at country level, we focus on cross-cutting and data issues, illustrating in particular how different data sources can usefully complement each other.</p>\n<p>Table 1: Selected countries with data on indicator 1.4.2</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Country/Region</p>\n      </td>\n      <td>\n        <p>Data Source(s)</p>\n      </td>\n      <td>\n        <p>Year</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Land access via</p>\n      </td>\n      <td>\n        <p>Formal</p>\n      </td>\n      <td>\n        <p>Perceived</p>\n      </td>\n      <td>\n        <p>Index</p>\n      </td>\n      <td>\n        <p>Gender</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\"></td>\n      <td></td>\n      <td>\n        <p>Ownership</p>\n      </td>\n      <td>\n        <p>Rental</p>\n      </td>\n      <td>\n        <p>Document</p>\n      </td>\n      <td>\n        <p>Security</p>\n      </td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Africa</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Benin</p>\n      </td>\n      <td>\n        <p>INSAE, MCC &amp; Admin</p>\n      </td>\n      <td>\n        <p>2011</p>\n      </td>\n      <td>\n        <p>0.809</p>\n      </td>\n      <td>\n        <p>0.047</p>\n      </td>\n      <td>\n        <p>0.113</p>\n      </td>\n      <td>\n        <p>0.903</p>\n      </td>\n      <td>\n        <p>0.51</p>\n      </td>\n      <td>\n        <p>0.123</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Lesotho</p>\n      </td>\n      <td>\n        <p>MCC</p>\n      </td>\n      <td>\n        <p>2013</p>\n      </td>\n      <td>\n        <p>0.914</p>\n      </td>\n      <td>\n        <p>0.029</p>\n      </td>\n      <td>\n        <p>0.611</p>\n      </td>\n      <td>\n        <p>0.929</p>\n      </td>\n      <td>\n        <p>0.77</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Mozambique</p>\n      </td>\n      <td>\n        <p>INE</p>\n      </td>\n      <td>\n        <p>2011</p>\n      </td>\n      <td>\n        <p>0.882</p>\n      </td>\n      <td>\n        <p>0.033</p>\n      </td>\n      <td>\n        <p>0.498</p>\n      </td>\n      <td>\n        <p>0.811</p>\n      </td>\n      <td>\n        <p>0.65</p>\n      </td>\n      <td>\n        <p>0.112</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Malawi</p>\n      </td>\n      <td>\n        <p>NBS</p>\n      </td>\n      <td>\n        <p>2015</p>\n      </td>\n      <td>\n        <p>0.868</p>\n      </td>\n      <td>\n        <p>0.023</p>\n      </td>\n      <td>\n        <p>0.019</p>\n      </td>\n      <td>\n        <p>0.697</p>\n      </td>\n      <td>\n        <p>0.36</p>\n      </td>\n      <td>\n        <p>0.226</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Nigeria</p>\n      </td>\n      <td>\n        <p>NBS</p>\n      </td>\n      <td>\n        <p>2013</p>\n      </td>\n      <td>\n        <p>0.741</p>\n      </td>\n      <td>\n        <p>0.025</p>\n      </td>\n      <td>\n        <p>0.021</p>\n      </td>\n      <td>\n        <p>0.741</p>\n      </td>\n      <td>\n        <p>0.38</p>\n      </td>\n      <td>\n        <p>0.162</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Rwanda</p>\n      </td>\n      <td>\n        <p>LSMS-ISA &amp; admin data</p>\n      </td>\n      <td>\n        <p>2015</p>\n      </td>\n      <td>\n        <p>0.886</p>\n      </td>\n      <td>\n        <p>0.002</p>\n      </td>\n      <td>\n        <p>0.858</p>\n      </td>\n      <td>\n        <p>0.969</p>\n      </td>\n      <td>\n        <p>0.91</p>\n      </td>\n      <td>\n        <p>0.864</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Tanzania</p>\n      </td>\n      <td>\n        <p>LSMS-ISA</p>\n      </td>\n      <td>\n        <p>2013</p>\n      </td>\n      <td>\n        <p>0.839</p>\n      </td>\n      <td>\n        <p>0.123</p>\n      </td>\n      <td>\n        <p>0.250</p>\n      </td>\n      <td>\n        <p>0.960</p>\n      </td>\n      <td>\n        <p>0.61</p>\n      </td>\n      <td>\n        <p>0.339</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Uganda</p>\n      </td>\n      <td>\n        <p>LSMS-ISA</p>\n      </td>\n      <td>\n        <p>2014</p>\n      </td>\n      <td>\n        <p>0.902</p>\n      </td>\n      <td>\n        <p>0.080</p>\n      </td>\n      <td>\n        <p>0.080</p>\n      </td>\n      <td>\n        <p>0.919</p>\n      </td>\n      <td>\n        <p>0.50</p>\n      </td>\n      <td>\n        <p>0.525</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Asia</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Korea, Rep.</p>\n      </td>\n      <td>\n        <p>Census &amp; Admin</p>\n      </td>\n      <td>\n        <p>2016</p>\n      </td>\n      <td>\n        <p>0.723</p>\n      </td>\n      <td>\n        <p>0.237</p>\n      </td>\n      <td>\n        <p>1.000</p>\n      </td>\n      <td>\n        <p>0.960</p>\n      </td>\n      <td>\n        <p>0.98</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Mongolia</p>\n      </td>\n      <td>\n        <p>MCC-SHPS</p>\n      </td>\n      <td>\n        <p>2012</p>\n      </td>\n      <td>\n        <p>0.809</p>\n      </td>\n      <td>\n        <p>0.163</p>\n      </td>\n      <td>\n        <p>0.654</p>\n      </td>\n      <td>\n        <p>0.966</p>\n      </td>\n      <td>\n        <p>0.81</p>\n      </td>\n      <td>\n        <p>0.268</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Americas</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Costa Rica</p>\n      </td>\n      <td>\n        <p>Census &amp; Admin</p>\n      </td>\n      <td>\n        <p>2011</p>\n      </td>\n      <td>\n        <p>0.699</p>\n      </td>\n      <td>\n        <p>0.279</p>\n      </td>\n      <td>\n        <p>1.000</p>\n      </td>\n      <td>\n        <p>0.978</p>\n      </td>\n      <td>\n        <p>0.99</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Europe</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Belgium</p>\n      </td>\n      <td>\n        <p>Census &amp; Admin</p>\n      </td>\n      <td>\n        <p>2011</p>\n      </td>\n      <td>\n        <p>0.628</p>\n      </td>\n      <td>\n        <p>0.362</p>\n      </td>\n      <td>\n        <p>0.948</p>\n      </td>\n      <td>\n        <p>0.939</p>\n      </td>\n      <td>\n        <p>0.94</p>\n      </td>\n      <td>\n        <p>0.543</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Netherlands</p>\n      </td>\n      <td>\n        <p>Census &amp; Admin</p>\n      </td>\n      <td>\n        <p>2011</p>\n      </td>\n      <td>\n        <p>0.539</p>\n      </td>\n      <td>\n        <p>0.429</p>\n      </td>\n      <td>\n        <p>1.000</p>\n      </td>\n      <td>\n        <p>0.968</p>\n      </td>\n      <td>\n        <p>0.98</p>\n      </td>\n      <td>\n        <p>0.640</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Oceania</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>New Zealand</p>\n      </td>\n      <td>\n        <p>Census &amp; Admin</p>\n      </td>\n      <td>\n        <p>2013</p>\n      </td>\n      <td>\n        <p>0.607</p>\n      </td>\n      <td>\n        <p>0.327</p>\n      </td>\n      <td>\n        <p>0.990</p>\n      </td>\n      <td>\n        <p>0.925</p>\n      </td>\n      <td>\n        <p>0.96</p>\n      </td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n<p>Source: SDG Indicator 1.4.2 Global Database</p>\n<p>Selected data comments</p>\n<p>The data on formal documentation of land rights of the indicator are self-reported from household surveys for most low-income countries or from land agencies&#x2019; records. Ranging from less about 2% in Malawi and Nigeria to full coverage in Costa Rica, the Netherlands, and Korea, there is enormous variation in this part of the indicator across countries.</p>\n<p>Data on perceived tenure security, which is from survey data and self-reported for all except the European countries and Costa Rica. In the latter case, we used the share of the population who, according to the population census or household surveys, either report owning or renting land or their residence to represent the share of the population who enjoy legally recognized documentation and by implication whose tenure is legally secure.</p>\n<p><strong>Benin</strong>: Administrative data indicate that the population have received individual documents based on the Plan Foncier Rural, but also suggest that, with about 12% of documents registered in their name, women have not benefited to the extent that may be expected. </p>\n<p><strong>Costa Rica</strong>: Administrative records suggest that in Costa Rica, all land is covered with land records. But census data indicate that some 2.5% of the population still suffers from precarious tenure, highlighting that even in cases where administrative data are available, they need to be linked to population-based evidence to give a fuller picture. </p>\n<p><strong>Malawi</strong>: Although the Government is engaged in an ambitious effort to digitize available records that would provide a basis for better land administration and reporting, only information from household survey data is available. The survey data point to high levels of tenure insecurity that are mostly gender related.</p>\n<p><strong>Nigeria</strong>: As a federal country, several states in Nigeria have administrative data that are of sufficient quality for reporting for the pilot. Data suggest that insecurity is high and, in many cases, is caused by the state due to expropriation. </p>\n<p><strong>Rwanda</strong>: A representative survey is available and points towards high levels of tenure security. However, information on the gender distribution of legally recognized documentation can be more reliably obtained from administrative data that show that more than 86% of women have land registered in their name either individually or jointly and perception of tenure security is high. </p>\n<p><strong>The Netherlands</strong>: This case shows that administrative data can be gender-disaggregated, and that in an advanced economy the share of individuals accessing land through various forms of institutions is high.</p>\n<p>The Questionnaire Module with essential question for reporting on SDG 1.4.2</p>\n<p>The module with essential questions for reporting on 1.4.2 is discussed in detail below. The module is developed by the UN Habitat and the World Bank, together with FAO and UN Women, with inputs from other stakeholders through GDWGL and GLII, and supported by the Living Standard Measurement Survey (LSMS) team. </p>\n<p>The results of the EDGE project and other recent evidence suggest that individual level data collection is preferred to potential proxy respondents (where feasible).</p>\n<p>Because of the scalability benefits of collecting data for both indicators simultaneously, the module is designed to provide the data required to compute indicators 1.4.2 as well as 5.a.1. Only the essential questions for indicator computation are included, however, the module may be expanded upon as needed by NSOs to address a wider range of land tenure issues relevant at the country level.</p>\n<p>The module example, appended to this note is designed as a household level questionnaire in which a full roster of parcels is collected at the household level and the module is then implemented for each parcel, where the respondent is the most knowledgeable household member for the given parcel.</p>\n<p>The module incorporates lessons learnt from methodological experiments1, as well as from implementation at national scale by the national Statistical Office of Malawi in its 2016/17 Integrated Household Survey (IHS4). The IHS4 interviewed 12,480 cross-sectional households across 780 EAs, and in parallel, revisited a national sub-sample of 2,516 households that had been previously interviewed in 2010 and 2013. As part of the IHS4 panel component, the survey administered up to 4 adult individual interviews per household. The modules asked separately questions regarding (i) reported ownership, (ii) economic ownership, (iii) documented ownership, and rights to (iv) sell, (v) bequeath, (vi) use as collateral, (vii) rent out, and (viii) make improvements/ invest.</p>\n<p>Indicator 1.4.2 considers two aspects of tenure security: documentation and perception. Only documentation that is official, and therefore provides legally protected tenure rights, is considered under indicator 1.4.2. That is combined with perception of tenure security, which is captured through the respondent-estimated probability of involuntary loss of land rights in the next five-year period and the reported right to bequeath.</p>\n<p>While the module has been carefully designed to be as universal as possible to maintain comparability of the computed indicator across time and space, certain questions, marked in the questionnaire, will require customization at the country level. Customization cannot be avoided in full due to the varying legal systems and land tenure arrangements across countries. Collection of metadata, including the identification of legally recognized documentation in the particular country context must take place prior to implementation of the module.</p>\n<p>The Questionnaire Module</p>\n<p>The questionnaire module assumes a survey that has households as the unit of enumeration and analysis, and where a household roster is used to identify household members and collected basic information on their demographics including age and gender. In this process, each household member is assigned a unique identifier (HHID). In the Annex, the questions are color-coded to identify those required for indicator 1.4.2 only, for indicator 5.a.1 only, for both indicators, and those included for disaggregation or other analytical purposes. In what follows, practical issues for implementation are discussed, and explanatory notes on each individual question presented.</p>\n<p><strong>Scalability &amp; Up-Take</strong></p>\n<p>While it is to be expected that the module will be usually implemented in conjunction with a larger survey operation, nothing prevents users to implement it independently. Implementing the module in the context of multi-topic surveys will increase its analytical value as, beyond generating an indicator for the SDG monitoring process, countries would be in a position to explore how land tenure issues relate to other development outcomes, including other SDG goals. The custodians foresee implementing the module as part of Living Standard Measurement Study (LSMS) surveys and the Urban Inequality Survey (UIS), and will be discussed with the USAID-funded Demographic and Health Surveys (DHS) programme, and UNICEF Multiple Indicator Cluster Surveys (MICS). Any nationally representative sample survey can of course become a vehicle for implementing the module. The custodians envisage working with National Statistical Offices to engage in dissemination and capacity development, as integrating the module in national statistical programs is the only viable way to ensure sustainability of the data collection process and ownership of the results by countries.</p>\n<p><strong>Implementation Method</strong></p>\n<p>The questionnaire module has been designed for paper assisted personal interviewing (PAPI) implementation to have the widest reach. However, an electronic version of the questionnaire will be created by the custodians for use in computer assisted personal interviewing. The application will be created using the World Bank&#x2019;s open access CAPI platform, Survey Solutions (solutions.worldbank. org), and will be made publicly available. The CAPI application can be customized from the base module as necessary. Implementation of the module via CAPI is recommended, as this can minimize data entry errors, allow for more immediate data review and analysis, and enable quick use of photo aids (which can improve data quality).</p>\n<p><strong>Before Going to the Field: Collecting Metadata</strong></p>\n<p>In this context, metadata refers to the classification of land documentation into legally recognized and unrecognized types as defined for indicator 1.4.2. The metadata will vary by country and will therefore, need to be released along with the computation of the indicator for transparency, and update in the case of changes in the regulatory frameworks. The metadata will identify which types of documentation are legally recognized, and therefore, what constitutes secure tenure. Questions on unrecognized and/or informal documentation can be asked separately, but is not considered in the computation of Indicator 1.4.2.</p>\n<p><strong>Question-by-Question Guidance</strong></p>\n<p>The implementation of the questionnaire included in the Annex is fairly intuitive, yet it is recommended that prior to its implementation, adequate training is provided and an enumerator manual is produced to guide data collection, including with images of the range of tenure related documentation in use by land holders. Detailed explanatory notes on each question are found below, which can be used to develop such manuals. Where customization is necessary, this is indicated. Annex I also indicates skip patterns (indicated by the arrow sign &#x2018;&gt;&gt;&#x2019;).</p>\n<p><strong>Guidance for sample questionnaire annex 1 (household survey with parcel roster)</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>RESPONDENT ID:</p>\n      </td>\n      <td>\n        <p>The respondent ID is the ID of the person responding for the respective parcel, recorded from the household roster. The respondent should be the most knowledgeable household member for each parcel. Therefore, the respondent may differ for each parcel. </p>\n        <p>The optimal respondent should be identified through a discussion amongst the enumerator and all adult members of the household (or as many as possible) prior to beginning the module. During this meeting, the full roster of parcels should be recorded and the optimal respondent identified for each.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q1:</p>\n      </td>\n      <td>\n        <p>The roster of parcels should contain all parcels used by, owned by, or occupied by any household member(s) at the time of the interview. Alternatively, a single set date could be identified for a given survey. This option is especially applicable in when fieldwork is conducted over an extended period of time (such as a 12-month rolling fieldwork design). The first parcel listed should be the parcel on which the household resides.</p>\n        <p>The parcel name must be unique to each parcel, as it will be used to refer to the specific parcel throughout the remainder of the module. In the case of panel surveys, or surveys with multiple visits, parcel names referring to a crop grown, for example, should be avoided as that may change over time.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q2:</p>\n      </td>\n      <td>\n        <p>Parcel area has been included in the module to allow for disaggregation of the indicator (for example, for smallholder farmers only). Farmer estimation of parcel area should be collected for all parcels. Additionally, GPS measurement of parcels is strongly advised, wherever feasible. Recent evidence points to systematic bias in farmer estimates of land area1.</p>\n        <p>Land area units must be customized for the country context.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q3:</p>\n      </td>\n      <td>\n        <p>Parcel acquisition type is used as a filter question for the following questions, allowing for maximum efficiency in skipping questions where possible. Response code to be reviewed in light of the country context.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q4:</p>\n      </td>\n      <td>\n        <p>The tenure system of the parcel is used to disaggregate indicator 1.4.2. Response codes to be reviewed in light of the country context.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q5:</p>\n      </td>\n      <td>\n        <p>The primary use of the current parcel is used to disaggregate indicator 1.4.2, and to identify land subject to indicator 5.a.1, which pertains to agricultural land. In some cases, such as when land is rented out, the actual use may not be known, hence the inclusion of the &#x201C;Don&#x2019;t Know&#x201D; response. However, wherever possible, the actual use of the land, rather than current ownership or use arrangements, should be recorded.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q6:</p>\n      </td>\n      <td>\n        <p>Question 6 identifies the owner(s) or use right holder(s) of the parcel, as reported by the respondent. Multiple household members may be listed, as joint ownership/use right holding is common.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q7:</p>\n      </td>\n      <td>\n        <p>This module only seeks to identify the possession of documents that are pre-determined to be legally recognized in the given context. Question 7, therefore, asks about the possession of documents from a specific government agency(ies). Examples of relevant documents are embedded in the question to provide context to the respondent and to clarify that documents other than title deeds are relevant.</p>\n        <p>The government agency(ies) and example documents embedded in the question must be customized for the country context. Refer to the section above on metadata for guidance on determining what is to be classified as legally recognized.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q8:</p>\n      </td>\n      <td>\n        <p>If the response to Question 7 is &#x201C;yes&#x201D;, question 8 is answered to record the specific type of documents held by the household, and which members are named on each. Codes must be customized at country level to include all legally recognized documents (as determined through the pre-survey preparation of metadata). Rental contracts of some form should be included, as long as rights are legally protected. </p>\n        <p>To minimize errors in naming and classifying documents, a photo aid containing an image of all legally recognized documents should be constructed and shown to the respondent. The integration of visual aids (e.g. a photo of an actual document of the reproduction of a facsimile) is most easily done in a CAPI application, but can also be integrated in traditional PAPI interviews.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q9:</p>\n      </td>\n      <td>\n        <p>The right to sell the parcel is captured in questions 9 and 10. Question 9 is a filter question, asking if any household member has the right to sell the parcel, either alone or jointly. That is, if any household member has the right to sell (or believes they have the right to sell) whether that be alone or with the approval/signature/etc. of another person either within or outside the household, the respond should be &#x201C;yes&#x201D;. This question is skipped for parcels acquired through short-term rentals (&lt;3 years) and sharecropping-in. Questions on the right to sell are used for computation of indicator 5.a.1 only.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q10:</p>\n      </td>\n      <td>\n        <p>List the ID codes of the household members that have the right to sell the parcel. If there are any external members that have the right to sell, enter the code accordingly. This question is skipped for parcels acquired through short-term rentals (&lt;3 years) and sharecropping-in.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q11:</p>\n      </td>\n      <td>\n        <p>The right to bequeath the parcel is captured in questions 10 and 11. Question 10 is a filter question, asking if any household member has the right to bequeath the parcel, either alone or jointly. That is, if any household member has the right to bequeath (or believes they have the right to bequeath) whether that be alone or with the approval/signature/etc. of another person either within or outside the household, the respond should be &#x201C;yes&#x201D;. This question is skipped for parcels acquired through short-term rentals (&lt;3 years) and sharecropping-in. Here, bequeath is defined as the ability to transfer rights to the parcel either in life or in death.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q12:</p>\n      </td>\n      <td>\n        <p>List the ID codes of the household members that have the right to bequeath the parcel. If there are any external members that have the right to bequeath, enter the code accordingly. This question is skipped for parcels acquired through short-term rentals (&lt;3 years) and sharecropping-in.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q13:</p>\n      </td>\n      <td>\n        <p>Question 13 identifies the likelihood of involuntarily losing ownership/use rights to the parcel in the next five years. Responses are made on a scale from 1 to 7, with 1 being not at all likely and 7 being extremely likely. </p>\n        <p>This question is asked about each owner/use right holder separately that was identified in Question 6 (but asked all to the same parcel-level respondent). This formulation of the question allows for the observance of intra-household insecurity, for example involuntary transfer of rights from female to male household members. For parcels acquired through short-term rental (&lt;3 years), the question will be asked for likelihood of involuntary loss in the remaining duration of the contract.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<ol>\n  <li>G. Carletto, S. Gourlay, S. Murray, and A. Zezza (2016), Land Area Measurement in Household Surveys, Washington DC, The World Bank.</li>\n</ol>", "indicator_sort_order"=>"01-04-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"1.5.1", "slug"=>"1-5-1", "name"=>"Número de personas muertas, desaparecidas y afectadas directamente atribuido a desastres por cada 100.000 habitantes", "url"=>"/site/es/1-5-1/", "sort"=>"010501", "goal_number"=>"1", "target_number"=>"1.5", "global"=>{"name"=>"Número de personas muertas, desaparecidas y afectadas directamente atribuido a desastres por cada 100.000 habitantes"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[{"unit"=>"Por 100.000 habitantes", "minimum"=>0, "maximum"=>5}], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de personas muertas directamente atribuido a desastres por cada 100.000 habitantes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de personas muertas, desaparecidas y afectadas directamente atribuido a desastres por cada 100.000 habitantes", "indicator_number"=>"1.5.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_28/opt_0/tipo_1/ti_defunciones/temas.html", "url_text"=>"Estadística de defunciones", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Número de personas muertas directamente atribuido a desastres por cada 100.000 habitantes", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.5- De aquí a 2030, fomentar la resiliencia de los pobres y las personas que se encuentran en situaciones de vulnerabilidad y reducir su exposición y vulnerabilidad a los fenómenos extremos relacionados con el clima y otras perturbaciones y desastres económicos, sociales y ambientales", "definicion"=>"Defunciones atribuidas a desastres naturales por cada 100.000 habitantes", "formula"=>"\n$$TM_{desastres}^{t} = \\frac{D_{desastres}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$D_{desastres}^{t} =$ defunciones atribuidas a desastres naturales (códigos X30-X39 de la CIE-10) en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico/Comarca/Municipio\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue \nadoptado por los Estados miembros de las Naciones Unidas en marzo de 2015 \ncomo una política global de reducción del riesgo de desastres.\n\nEntre sus metas se encuentran:\n\nMeta A: Reducir sustancialmente la mortalidad global por desastres para \n2030, con el objetivo de reducir el promedio de mortalidad global por cada 100.000 habitantes entre \n2020-2030 en comparación con 2005-2015\n\nMeta B: Reducir sustancialmente el número de personas afectadas\na nivel mundial para 2030 , con el objetivo de reducir la cifra promedio \nmundial por cada 100.000 habitantes \nentre 2020 y 2030 en comparación con el período 2005-2015.\n\nLa consecución de sus metas contribuirá al desarrollo sostenible y fortalecerá la \nresiliencia económica, social, sanitaria y ambiental.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-01.pdf\">Metadatos 1-5-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"Número de personas muertas directamente atribuido a desastres por cada 100.000 habitantes", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.5- De aquí a 2030, fomentar la resiliencia de los pobres y las personas que se encuentran en situaciones de vulnerabilidad y reducir su exposición y vulnerabilidad a los fenómenos extremos relacionados con el clima y otras perturbaciones y desastres económicos, sociales y ambientales", "definicion"=>"Deaths attributed to natural disasters per 100.000 inhabitants", "formula"=>"\n$$TM_{disasters}^{t} = \\frac{D_{disasters}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$D_{disasters}^{t} =$ deaths attributed to natural disasters (codes X30-X39 of the ICD-10) in year $t$\n\n$P^{t} =$ population as of 1 July of year $t$\n", "desagregacion"=>"Sex\n\nProvince/County/Municipality\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted \nby UN Member States in March 2015 as a global policy of disaster risk \nreduction. \n\nIts targets include: \n\nTarget A: Substantially reduce global disaster mortality by 2030, aiming \nto lower average per 100,000 global mortality between 2020-2030 compared \nwith 2005-2015. \n\nTarget B: Substantially reduce the  number of affected people globally by \n2030, aiming to lower the average global figure per 100,000 between 2020-2030 \ncompared with 2005-2015. \n\nAchieving its targets will contribute to sustainable development and strengthen \neconomic, social, health, and environmental resilience. \n\nSource: United Nations Statistics Division\n", "comparabilidad"=>"The available indicator partially complies with the United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-01.pdf\">Metadata 1-5-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Número de personas muertas directamente atribuido a desastres por cada 100.000 habitantes", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.5- De aquí a 2030, fomentar la resiliencia de los pobres y las personas que se encuentran en situaciones de vulnerabilidad y reducir su exposición y vulnerabilidad a los fenómenos extremos relacionados con el clima y otras perturbaciones y desastres económicos, sociales y ambientales", "definicion"=>"Hondamendi naturalei egotzitako heriotzak 100.000 biztanleko", "formula"=>"\n$$TM_{hondamendiak}^{t} = \\frac{D_{hondamendiak}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$D_{hondamendiak}^{t} =$ hondamendi naturalei egotzitako heriotzak (GNS-10eko X30-X39 kodeak) $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa/Eskualdea/Udalerria\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Nazio Batuen estatu-kideek 2015eko martxoan ezarri zuten Hondamendien Arriskua Murrizteko 2015-2030 aldiko \nSendaiko Esparrua hondamendien arriskua murrizteko politika global gisa. \n\nHona hemen haren xedeetako batzuk:\n\nA xedea: Hondamendien ondoriozko heriotza-tasa orokorra nabarmen murriztea 2030erako, hartara 2020 eta 2030 \nartean 100.000 biztanleko heriotza-tasaren batezbestekoa gutxitzeko, 2005-2015 aldiaren aldean. \n\nB xedea: Mundu-mailan hondamendiek erasandako pertsonen kopuru orokorra nabarmen murriztea 2030erako, hartara \n2020 eta 2030 artean 100.000 biztanleko munduko batezbestekoa gutxitzeko, 2005-2015 aldiaren aldean. \n\nXedeak lortuz gero, garapen jasangarria sustatu eta erresilientzia indartuko da ekonomia, gizarte, osasun \nnahiz ingurumenean. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa  \n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen metadatuak.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-01.pdf\">Metadatuak 1-5-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_DSR_AFFCT - Number of people affected by disaster [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_DAFF - Number of directly affected persons attributed to disasters per 100,000 population [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_IJILN - Number of injured or ill people attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_MISS - Number of missing persons due to disaster [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_MMHN - Number of deaths and missing persons attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_MORT - Number of deaths due to disaster [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_MTMP - Number of deaths and missing persons attributed to disasters per 100,000 population [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_PDAN - Number of people whose damaged dwellings were attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_PDLN - Number of people whose livelihoods were disrupted or destroyed, attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_PDYN - Number of people whose destroyed dwellings were attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.5.1, 13.1.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator measures the number of people who died, went missing or were directly affected by disasters per 100,000 population. </p>\n<p><strong>Concepts:</strong></p>\n<p><strong>Disasters</strong>: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Death</strong>: The number of people who died during the disaster, or directly after, as a direct result of the hazardous event.</p>\n<p><strong>Missing persons</strong>: The number of people whose whereabouts is unknown since the hazardous event. It includes people who are presumed dead, for whom there is no physical evidence such as a body, and for which an official/legal report has been filed with competent authorities.</p>\n<p><strong>Disaster-affected persons</strong>: People who are affected, either directly or indirectly, by a hazardous event. Directly affected are those who have suffered injury, illness or other health effects. Indirectly affected are people who have suffered consequences, other than or in addition to direct effects (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Injured or ill persons</strong>: People suffering from a new or exacerbated physical or psychological harm, trauma or an illness as a result of a disaster.</p>\n<p><strong>Livelihood</strong>: The capacities, productive assets (both living and material) and activities required for securing a means of living, on a sustainable basis, with dignity. </p>\n<p><strong>People whose damaged or destroyed dwellings were attributed to disasters</strong>: The estimated number of inhabitants previously living in the dwellings (houses, or housing units) damaged or destroyed. These inhabitants are considered affected by the fact that their dwellings were damaged (asset property damage), and because in many cases they would be included in those evacuated, displaced, or relocated. The categories of <em>evacuated, displaced, or relocated</em> should not be included in the indicators.</p>\n<p><strong>Houses damaged</strong>: Houses (housing units) with minor damage, not structural or architectural, and which may continue to be habitable, although they may require repair and/or cleaning.</p>\n<p><strong>Houses destroyed</strong>: Houses (housing units) levelled, buried, collapsed, washed away or damaged to the extent that they are no longer habitable, or must be rebuilt.</p>\n<p><strong>Notes</strong>: </p>\n<p>1) The data on number of deaths and number of missing/presumed dead are mutually exclusive, so no-one should be double counted.</p>\n<p>2) It&#x2019;s important to remember that disasters are not natural, they result from human choices.</p>\n<p>Detailed definitions, concepts, including composition and calculation for each of the data series, can be found in the SFM Technical Guidance (see below the Reference section)</p>", "UNIT_MEASURE__GLOBAL"=>"<p>For VC_DSR_MTMP - Number of deaths and missing persons attributed to disasters per 100,000 population; and VC_DSR_DAFF - Number of directly affected persons attributed to disasters per 100,000 population: ratio</p>\n<p>For other data series: Number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Data sources and collection method:</strong></p>\n<p>Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are reported by national Sendai Framework focal points in the Sendai Framework Monitor (SFM) and national disaster loss database: DesInventar-Sendai. Data are consisted of official, national reporting exclusively. Number of deaths attributed to disasters, number of missing persons attributed to disasters, number of injured or ill people attributed to disasters, number of people whose damaged dwellings were attributed to disasters, number of people whose destroyed dwellings were attributed to disasters, and number of people whose livelihoods were disrupted or destroyed, attributed to disasters are reported in SFM and DesInventar-Sendai. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are reported in Sendai Framework Monitor (SFM) on an ongoing basis, and snapshotted once every year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released once a year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OIEWG) report, endorsed by the United Nations General Assembly in Resolution A/RES/71/276, requested the UNDRR to undertake technical work and provide technical guidance to develop minimum standards and metadata, the methodologies, and the global monitoring and measurements of the SFM global indicators.</p>\n<p>This indicator is recommended by the OIEWG for the measurement of global Targets A and B of the Sendai Framework, which were endorsed by the UN General Assembly in its Resolution A/RES/71/276, <em>Report of the open-ended intergovernmental</em> <em>expert working group on indicators and terminology relating to disaster risk</em>.</p>", "RATIONALE__GLOBAL"=>"<p>The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. Among the global targets, &#x201C;Target A: Substantially reduce global disaster mortality by 2030, aiming to lower average per 100,000 global mortality between 2020-2030 compared with 2005-2015&#x201D; and &#x201C;Target B: Substantially reduce the number of affected people globally by 2030, aiming to lower the average global figure per 100,000 between 2020-2030 compared with 2005-2015&#x201D; will contribute to sustainable development and strengthen economic, social, health and environmental resilience. The economic, environmental and social perspectives would include poverty eradication, urban resilience, and climate change adaptation.</p>\n<p>The open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OIEWG) established by the General Assembly (resolution 69/284) has developed a set of indicators to measure global progress in the implementation of the Sendai Framework, which was endorsed by the United Nations General Assembly (UNGA) (OIEWG report A/71/644). The relevant global indicators for the Sendai Framework will be used to report for this indicator. </p>\n<p>Disaster loss, mortality and affected populations are greatly influenced by large-scale catastrophic events, as well as a high number of small-scale hazardous events. UNDRR recommends Member States to report the data by event in DesInventar-Sendai, and per the minimum reporting requirement of the Sendai Framework Monitor (SFM) using the Technical Guidance (see Reference and Documentation section), so complementary analysis can be done on the regional and global scale.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The Sendai Framework Monitoring (SFM) System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States report through the system since 2018. The data for SDG indicators are compiled and reported by UNDRR.</p>\n<p>Proxy, alternative and additional indicators:</p>\n<p>In most cases international data sources only record events that surpass some threshold of impact and use secondary data sources which usually have non uniform or even inconsistent methodologies, producing heterogeneous datasets</p>", "DATA_COMP__GLOBAL"=>"<p>Related indicators as of December 2017</p>\n<p>For death and missing perons:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mo>(</mo>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n          </mrow>\n          <mrow>\n            <mn>2</mn>\n            <mi>a</mi>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n          </mrow>\n          <mrow>\n            <mn>3</mn>\n            <mi>a</mi>\n          </mrow>\n        </msub>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n  </math></p>\n<p>Where:</p>\n<p>A<sub>1</sub>: Number of deaths and missing persions attributed to disasters per 100 000; corresponding to Sendai Framework Indicator A-1.</p>\n<p>A<sub>2a</sub>: Number of deaths attributed to disasters; </p>\n<p>A<sub>3a</sub>: Number of missing persons attributed to disasters; and </p>\n<p>Population: Represented population.</p>\n<p>* Detailed methodologies can be found in the Technical Guidance (see below the Reference section)</p>\n<p>For number of disaster-affected person:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>B</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mo>(</mo>\n        <msub>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n          <mrow>\n            <mn>2</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n          <mrow>\n            <mn>3</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n          <mrow>\n            <mn>4</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n          <mrow>\n            <mn>5</mn>\n          </mrow>\n        </msub>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n  </math></p>\n<p>Where:</p>\n<p>B<sub>1</sub>: Number of directly affected people attributed to disasters, per 100,000 population; corresponding to Sendai Framework Indicator B-1.</p>\n<p>B<sub>2</sub>: Number of injured or ill people attributed to disasters; corresponding to Sendai Framework Indicator B-2.</p>\n<p>B<sub>3</sub>: Number of people whose damaged dwellings were attributed to disasters; corresponding to Sendai Framework Indicator B-3.</p>\n<p>B<sub>4</sub>: Number of people whose destroyed dwellings were attributed to disasters; corresponding to Sendai Framework Indicator B-4.</p>\n<p>B<sub>5</sub>: Number of people whose livelihoods were disrupted or destroyed, attributed to disasters; corresponding to Sendai Framework Indicator B-5.</p>\n<p>Population: Represented population.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are validation by UNDRR and national focal points.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction</li>\n  <li>ADPC Disaster and Climate Resilience e-Learning: An orientation to using the online Sendai Framework Monitor, https://courses.adpc.net/courses/course-v1:UNISDR+SFM001+2019Y1/about</li>\n</ul>\n<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>2005-2023</p>\n<p><strong>Time series:</strong></p>\n<p>Annual</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Number of deaths attributed to disasters; </p>\n<p>Number of missing persons attributed to disasters; and </p>\n<p>Number of directly affected people attributed to disasters. </p>\n<p> [Optional Disaggregation]:</p>\n<p>Hazard types</p>\n<p>Geography (Administrative Unit)</p>\n<p>Sex</p>\n<p>Age (3 categories)</p>\n<p>Disability</p>\n<p>Income</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Official SDG Metadata URL: </strong><a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-01.pdf\">https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-01.pdf</a></p>\n<p><strong>Internationally agreed methodology and guideline URL: </strong></p>\n<p><strong>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017)</strong></p>\n<p><a href=\"https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf\">https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf</a></p>\n<p>Sendai Framework Monitor</p>\n<p><a href=\"https://sendaimonitor.undrr.org/\">https://sendaimonitor.undrr.org/</a></p>\n<p><br>DesInventar-Sendai</p>\n<p><a href=\"https://www.desinventar.net/\">https://www.desinventar.net/</a></p>\n<p><strong>Other references:</strong></p>\n<p><strong>Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG</strong>). Endorsed by UNGA on 2nd February 2017. Available at: <a href=\"https://www.preventionweb.net/publications/view/51748\">https://www.preventionweb.net/publications/view/51748</a></p>", "indicator_sort_order"=>"01-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"1.5.2", "slug"=>"1-5-2", "name"=>"Pérdidas económicas directas atribuidas a los desastres en relación con el producto interno bruto (PIB) mundial", "url"=>"/site/es/1-5-2/", "sort"=>"010502", "goal_number"=>"1", "target_number"=>"1.5", "global"=>{"name"=>"Pérdidas económicas directas atribuidas a los desastres en relación con el producto interno bruto (PIB) mundial"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Indemnizaciones por daños personales, pérdidas pecuniarias y daños de bienes pagadas y/o provisionadas en proporción al PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Pérdidas económicas directas atribuidas a los desastres en relación con el producto interno bruto (PIB) mundial", "indicator_number"=>"1.5.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Consorcio de Compensación de Seguros", "periodicity"=>"Anual", "url"=>"https://www.consorseguros.es/noticias/-/asset_publisher/ya2OdYGgbjgX/content/publicacion-de-la-estadistica-de-riesgos-extraordinarios-1971-2023-", "url_text"=>"Estadística de riesgos extraordinarios", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_10/opt_1/ti_cuentas-economicas/temas.html", "url_text"=>"Cuentas económicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Mensual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176802&menu=ultiDatos&idp=1254735976607", "url_text"=>"Índice de precios de consumo", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Indemnizaciones por daños personales, pérdidas pecuniarias y daños de bienes pagadas y/o provisionadas en proporción al PIB", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.5- De aquí a 2030, fomentar la resiliencia de los pobres y las personas que se encuentran en situaciones de vulnerabilidad y reducir su exposición y vulnerabilidad a los fenómenos extremos relacionados con el clima y otras perturbaciones y desastres económicos, sociales y ambientales", "definicion"=>"Proporción de indemnizaciones por daños personales, pérdidas pecuniarias  y daños de bienes pagadas y/o provisionadas (pendientes de liquidación o pago)  en relación al PIB a precios corrientes", "formula"=>"$$INDEMPIB^{t} = \\frac{(IDP^{t}+IPP^{t}+IDB^{t})/IUIPC_{refT}^{t}}{PIB^{t}} \\cdot 100 \\quad \\mathrm t=1,\\dots,\\mathrm T$$\n\n$$IUIPC_{refT}^{T} = 1$$\n\n$$IUIPC_{refT}^{t-1} = IUIPC_{refT}^{t} \\cdot \\frac{(100+TIPC_{dic}^{t})}{100} \\quad \\mathrm t=\\mathrm T,\\mathrm T-1,\\dots,\\mathrm 2$$\n\ndonde:\n\n$IDP^{t}$ = indemnizaciones por daños personales pagadas y/o provisionadas en el año $t$ a precios del año $T$\n\n$IPP^{t}$ = indemnizaciones por pérdidas pecuniarias pagadas y/o provisionadas en el año $t$ a precios del año $T$\n\n$IDB^{t}$ = indemnizaciones por daños de bienes pagadas y/o provisionadas en el año $t$ a precios del año $T$\n\n$TIPC_{dic}^{t}$ = tasa de variación anual del IPC nacional en el mes de diciembre del año $t$ \n\n$PIB^{t}$ = producto interior bruto en términos corrientes en el año $t$\n", "desagregacion"=>"Territorio histórico", "observaciones"=>"Las indemnizaciones se asignan al lugar y año de ocurrencia del siniestro,  no incluyendo los siniestros ocurridos en el extranjero", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue \nadoptado por los Estados miembros de las Naciones Unidas en marzo de 2015 \ncomo una política global de reducción del riesgo de desastres.\n\nEntre sus metas se encuentra la Meta C: Reducir las pérdidas económicas directas \npor desastres en relación con el producto interno bruto (PIB) mundial para 2030.\n\nLa consecución de sus metas contribuirá al desarrollo sostenible y fortalecerá la \nresiliencia económica, social, sanitaria y ambiental.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible no cumple con los metadatos de Naciones Unidas,  pero aporta información similar", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-02.pdf\">Metadatos 1-5-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Indemnizaciones por daños personales, pérdidas pecuniarias y daños de bienes pagadas y/o provisionadas en proporción al PIB", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.5- De aquí a 2030, fomentar la resiliencia de los pobres y las personas que se encuentran en situaciones de vulnerabilidad y reducir su exposición y vulnerabilidad a los fenómenos extremos relacionados con el clima y otras perturbaciones y desastres económicos, sociales y ambientales", "definicion"=>"\nProportion of compensation for personal injury, pecuniary loss and  property damage paid and/or provisioned (pending settlement or payment)  in relation to GDP at current prices", "formula"=>"$$INDEMPIB^{t} = \\frac{(IDP^{t}+IPP^{t}+IDB^{t})/IUIPC_{refT}^{t}}{PIB^{t}} \\cdot 100 \\quad \\mathrm t=1,\\dots,\\mathrm T$$\n\n$$IUIPC_{refT}^{T} = 1$$\n\n$$IUIPC_{refT}^{t-1} = IUIPC_{refT}^{t} \\cdot \\frac{(100+TIPC_{dic}^{t})}{100} \\quad \\mathrm t=\\mathrm T,\\mathrm T-1,\\dots,\\mathrm 2$$\n\nwhere:\n\n$IDP^{t} =$ Compensation for personal injuries paid and/or provisioned in year $t$ at prices of year $T$\n\n$IPP^{t} =$ Compensation for pecuniary losses paid and/or provisioned in year $t$ at prices of year $T$\n\n$IDB^{t} =$ Compensation for property damage paid and/or provisioned in year $t$ at prices of year $T$\n\n$TIPC_{dic}^{t} =$ annual variation rate of the national CPI in the month of December of year $t$ \n\n$PIB^{t} =$ gross domestic product at current prices in year $t$\n", "desagregacion"=>"Province", "observaciones"=>"Compensation is assigned to the place and year of occurrence of the claim,  not including claims occurring abroad.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted \nby United Nations Member States in March 2015 as a global disaster risk \nreduction policy.\n\nAmong its goals is Goal C: Reduce direct economic losses from disasters \nrelative to global gross domestic product (GDP) by 2030.\n\nAchieving its goals will contribute to sustainable development and strengthen \neconomic, social, health, and environmental resilience.\n\nSource: United Nations Statistics Division\n", "comparabilidad"=>"The available indicator does not comply with UN metadata, but provides similar information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-02.pdf\">Metadata 1-5-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Indemnizaciones por daños personales, pérdidas pecuniarias y daños de bienes pagadas y/o provisionadas en proporción al PIB", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.5- De aquí a 2030, fomentar la resiliencia de los pobres y las personas que se encuentran en situaciones de vulnerabilidad y reducir su exposición y vulnerabilidad a los fenómenos extremos relacionados con el clima y otras perturbaciones y desastres económicos, sociales y ambientales", "definicion"=>"Kalte pertsonalengatiko, diru galerengatiko eta ondasunen kalteengatiko kalte-ordainen proportzioa  BPGrekiko, prezio arruntetan. Kontuan hartzen dira bai ordaindutako kalte-ordainak, baita hornitutakoak  ere (likidatzeko edo ordaintzeko daudenak). ", "formula"=>"$$INDEMPIB^{t} = \\frac{(IDP^{t}+IPP^{t}+IDB^{t})/IUIPC_{refT}^{t}}{PIB^{t}} \\cdot 100 \\quad \\mathrm t=1,\\dots,\\mathrm T$$\n\n$$IUIPC_{refT}^{T} = 1$$\n\n$$IUIPC_{refT}^{t-1} = IUIPC_{refT}^{t} \\cdot \\frac{(100+TIPC_{abe}^{t})}{100} \\quad \\mathrm t=\\mathrm T,\\mathrm T-1,\\dots,\\mathrm 2$$\n\nnon:\n\n$IDP^{t}$ = kalte pertsonalengatik ordaindutako edota hornitutako kalte-ordainak, $T$ urteko prezioetan, $t$ urtean\n\n$IPP^{t}$ = diru galerengatik ordaindutako edota hornitutako kalte-ordainak, $T$ urteko prezioetan, $t$ urtean\n\n$IDB^{t}$ = ondasunetan izandako kalteengatik ordaindutako edota hornitutako kalte-ordainak, $T$ urteko prezioetan, $t$ urtean\n\n$TIPC_{abe}^{t}$ = KPI nazionalaren urteko aldakuntza-tasa $t$ urteko abenduan\n\n$PIB^{t}$ = barne-produktu gordina, uneko prezioetan $t$ urtean\n", "desagregacion"=>"Lurralde historikoa", "observaciones"=>"Kalte-ordainak ezbeharra gertatu den lekuari eta urteari esleituko zaizkie, atzerrian  gertatutako ezbeharrak kontuan hartu gabe.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Nazio Batuen estatu-kideek 2015eko martxoan ezarri zuten Hondamendien Arriskua Murrizteko 2015-2030 \naldiko Sendaiko Esparrua hondamendien arriskua murrizteko politika global gisa. \n\nXedeen artean dago C xedea: Hondamendien ondoriozko galera ekonomiko zuzenak murriztea, munduko \nbarne-produktu gordinarekin (BPG) lotuta, 2030erako. \n\nXedeak lortuz gero, garapen jasangarria sustatu eta erresilientzia indartuko da ekonomia, gizarte, \nosasun nahiz ingurumenean. \n\nIturria: Nazio Batuen Estatistika Sekzioa  \n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen metadatuak betetzen, baina  antzeko informazioa ematen du  ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-02.pdf\">Metadatuak 1-5-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_DSR_GDPLS - Direct economic loss attributed to disasters [1.5.2,11.5.2]</p>\n<p>VC_DSR_LSGP - Direct economic loss attributed to disasters relative to GDP [1.5.2, 11.5.2]</p>\n<p>VC_DSR_AGLH - Direct agriculture loss attributed to disasters [1.5.2, 11.5.2]</p>\n<p>VC_DSR_HOLH - Direct economic loss in the housing sector attributed to disasters, by hazard type [1.5.2, 11.5.2]</p>\n<p>VC_DSR_CILN - Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters [1.5.2, 11.5.2]</p>\n<p>VC_DSR_CHLN - Direct economic loss to cultural heritage damaged or destroyed attributed to disasters [1.5.2, 11.5.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.5.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator measures the ratio of direct economic loss attributed to disasters in relation to gross domestic product (GDP).</p>\n<p><strong>Concepts:</strong></p>\n<p>Disasters: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Economic Loss:</strong> Total economic impact that consists of direct economic loss and indirect economic loss.</p>\n<p><strong>Direct economic loss:</strong> The monetary value of total or partial destruction of physical assets existing in the affected area. Direct economic loss is nearly equivalent to physical damage.</p>\n<p><strong>Indirect economic loss:</strong> A decline in economic value added as a consequence of direct economic loss and/or human and environmental impacts.</p>\n<p><em><u>Annotations:</u></em></p>\n<p><em>Examples of physical assets that are the basis for calculating direct economic loss include homes, schools, hospitals, commercial and governmental buildings, transport, energy, telecommunications infrastructures and other infrastructure; business assets and industrial plants; production such as crops, livestock and production infrastructure. They may also encompass environmental assets and cultural heritage. Direct economic losses usually happen during the event or within the first few hours after the event and are often assessed soon after the event to estimate recovery cost and claim insurance payments. These are tangible and relatively easy to measure.</em></p>\n<p>Detailed definitions, concepts, including composition and calculation for each of the data series, can be found in the SFM Technical Guidance (see below the Reference section)</p>", "UNIT_MEASURE__GLOBAL"=>"<p>For VC_DSR_LSGP - Direct economic loss attributed to disasters relative to GDP (%): per cent (%).</p>\n<p>For other data series: current United States Dollar.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are reported by national Sendai Framework focal points in the Sendai Framework Monitor (SFM) and national disaster loss database: DesInventar-Sendai. Data are consisted of official, national reporting exclusively. Direct agricultural loss attributed to disasters, direct economic loss to all other damaged or destroyed productive assets attributed to disasters, direct economic loss in the housing sector attributed to disasters, direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters, and direct economic loss to cultural heritage damaged or destroyed attributed to disasters are reported in SFM and DesInventar-Sendai.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are reported in Sendai Framework Monitor (SFM) on an ongoing basis, and snapshotted once every year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released once a year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OIEWG) report, endorsed by the United Nations General Assembly in Resolution A/RES/71/276, requested the UNDRR to undertake technical work and provide technical guidance to develop minimum standards and metadata, the methodologies, and the global monitoring and measurements of the SFM global indicators.</p>\n<p>This indicator is recommended by the OIEWG for the measurement of global Target C of the Sendai Framework, which were endorsed by the UN General Assembly in its Resolution A/RES/71/276, <em>Report of the open-ended intergovernmental</em> <em>expert working group on indicators and terminology relating to disaster risk</em>.</p>", "RATIONALE__GLOBAL"=>"<p>The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. Among the global targets, &#x201C;Target C: Reduce direct disaster economic loss in relation to global GDP by 2030&#x201D; will contribute to sustainable development and strengthen economic, social, health and environmental resilience. The economic, environmental and social perspectives would include poverty eradication, urban resilience, and climate change adaptation.</p>\n<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology relating to disaster risk reduction (OIEWG) established by the United Nations General Assembly (UNGA) (resolution 69/284) has developed a set of indicators to measure global progress in the implementation of the Sendai Framework, which was endorsed by the UNGA (OIEWG <a href=\"http://www.preventionweb.net/publications/view/51748\">report A/71/644</a>). The relevant global indicators for the Sendai Framework will be used to report for this indicator.</p>\n<p>Direct economic losses are significantly influenced by both large-scale catastrophic events. In the meantime, a high number of small-scale hazardous events also impose heavy impacts on economies especially in vulnerable environments. UNDRR recommends Member States to report the data by event in DesInventar-Sendai, and per the minimum reporting requirement of the Sendai Framework Monitor (SFM) using the Technical Guidance (see Reference and Documentation section), so complementary analysis can be done on the regional and global scale.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The Sendai Framework Monitoring (SFM) System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States report through the system since March 2018. The data for SDG indicators are compiled and reported by UNDRR.</p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mn>2</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mn>3</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mn>4</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mn>5</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mn>6</mn>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mi>G</mi>\n        <mi>D</mi>\n        <mi>P</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where:</p>\n<p>C<sub>1</sub>: Direct economic loss attributed to disasters in relation to gross domestic product; corresponding to Sendai Framework Indicator C-1.</p>\n<p>C<sub>2</sub>:Direct agricultural loss attributed to disasters; corresponding to Sendai Framework Indicator C-2. Agriculture is understood to include the crops, livestock, fisheries, apiculture, aquaculture and forest sectors as well as associated facilities and infrastructure.</p>\n<p>C<sub>3</sub>: Direct economic loss to all other damaged or destroyed productive assets attributed to disasters; corresponding to Sendai Framework Indicator C-3. Productive assets would be disaggregated by economic sector, including services, according to standard international classifications. Member States would report against those economic sectors relevant to their economies.</p>\n<p>C<sub>4</sub>: Direct economic loss in the housing sector attributed to disasters; corresponding to Sendai Framework Indicator C-4. Data would be disaggregated according to damaged and destroyed dwellings.</p>\n<p>C<sub>5</sub>: Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters; corresponding to Sendai Framework Indicator C-5. The decision regarding those elements of critical infrastructure to be included in the calculation will be left to the Member States. Protective infrastructure and green infrastructure should be included where relevant.</p>\n<p>C<sub>6</sub>: Direct economic loss to cultural heritage damaged or destroyed attributed to disasters; corresponding to Sendai Framework Indicator C-6.</p>\n<p>GDP: national gross domestic product, current United States Dollar.</p>\n<p>* Detailed methodologies can be found in the Technical Guidance (see below the Reference section)</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are validation by UNDRR and national focal points.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction</li>\n  <li>ADPC Disaster and Climate Resilience e-Learning: An orientation to using the online Sendai Framework Monitor, https://courses.adpc.net/courses/course-v1:UNISDR+SFM001+2019Y1/about</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>2005-2023</p>\n<p><strong>Time series:</strong></p>\n<p>Annual</p>\n<p><strong>Disaggregation:</strong></p>\n<ul>\n  <li>Direct agricultural loss attributed to disasters.</li>\n  <li>Direct economic loss to all other damaged or destroyed productive assets attributed to disasters.</li>\n  <li>Direct economic loss in the housing sector attributed to disasters.</li>\n  <li>Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters.</li>\n  <li>Direct economic loss to cultural heritage damaged or destroyed attributed to disasters.</li>\n</ul>\n<p><strong>Desirable Disaggregation:</strong></p>\n<p>For direct agricultural loss attributed to disasters:</p>\n<ul>\n  <li>By loss of aquaculture production area affected</li>\n  <li>By loss of crops damaged or destroyed attributed to disasters</li>\n  <li>By loss of fisheries production area affected</li>\n  <li>By loss of forests affected/destroyed by disasters</li>\n  <li>By loss of livestock attributed to disasters</li>\n  <li>By loss of agricultural assets area affected</li>\n  <li>By loss of agricultural stock affected</li>\n  <li>By hazard types</li>\n  <li>By geography region (administrative unit)</li>\n</ul>\n<p>For direct economic loss to all other damaged or destroyed productive assets attributed to disasters:</p>\n<ul>\n  <li>By productive asset types</li>\n  <li>By hazard types</li>\n  <li>By geography region (administrative unit)</li>\n</ul>\n<p>For direct economic loss in the housing sector attributed to disasters:</p>\n<ul>\n  <li>By housing sectors</li>\n  <li>By hazard types</li>\n  <li>By geography region (administrative unit)</li>\n</ul>\n<p>For direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters:</p>\n<ul>\n  <li>By loss of health facilities</li>\n  <li>By loss of education facilities</li>\n  <li>By loss of other facilities</li>\n  <li>By hazard types</li>\n  <li>By geography region (administrative unit)</li>\n</ul>\n<p>For direct economic loss to cultural heritage damaged or destroyed attributed to disasters:</p>\n<ul>\n  <li>By number of buildings, monuments and fixed infrastructures of cultural heritage assets</li>\n  <li>By number of mobile cultural heritage assets (such as artworks) damaged</li>\n  <li>By number of mobile cultural heritage assets destroyed</li>\n  <li>By cost of rehabilitation or reconstruction</li>\n  <li>By cost of rehabilitation</li>\n  <li>By acquisition cost, if available</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Internationally agreed methodology and guideline URL:</strong></p>\n<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017)</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf\">https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf</a></p>\n<p>Sendai Framework Monitor</p>\n<p><a href=\"https://sendaimonitor.undrr.org/\">https://sendaimonitor.undrr.org/</a></p>\n<p><br>DesInventar-Sendai</p>\n<p><a href=\"https://www.desinventar.net/\">https://www.desinventar.net/</a></p>\n<p><strong>Other references:</strong></p>\n<ul>\n  <li>Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG). Endorsed by UNGA on 2<sup>nd</sup> February 2017. Available at:</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/publications/view/51748\">https://www.preventionweb.net/publications/view/51748</a></p>\n<p><strong>Country examples:</strong></p>\n<ul>\n  <li><strong>Proxy, alternative and additional indicators:</strong> In most cases international data sources only record events that surpass some threshold of impact and use secondary data sources which usually have non uniform or even inconsistent methodologies, producing heterogeneous datasets.</li>\n</ul>", "indicator_sort_order"=>"01-05-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"1.5.3", "slug"=>"1-5-3", "name"=>"Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030", "url"=>"/site/es/1-5-3/", "sort"=>"010503", "goal_number"=>"1", "target_number"=>"1.5", "global"=>{"name"=>"Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>true, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030", "indicator_number"=>"1.5.3", "national_geographical_coverage"=>"", "page_content"=>"La C.A. de Euskadi cuenta con leyes, estrategias y planes orientados a la prevención y respuesta a los desastres, \nque involucran tanto a los distintos Departamentos del Gobierno Vasco como a las Administraciones Locales:\n\n<a href=\"https://www.euskadi.eus/plan-proteccion-civil-euskadi/web01-a2blarri/es/\" target=\"_blank\"> Plan de Protección Civil de Euskadi, Larrialdiei Aurregiteko Bidea-LABI</a>: el marco para la protección civil en la C.A. de Euskadi\n\n<a href=\"https://www.euskadi.eus/directrices-de-ordenacion-territorial-dot/web01-a3lurral/es/\" target=\"_blank\"> Planes de Ordenación del Territorio</a>: recoge el objetivo de limitar los usos del suelo en función de las vulnerabilidades existentes \ny una utilización más correcta del mismo para evitar el incremento del riesgo.\n\n<a href=\"https://www.euskadi.eus/documentacion/2015/estrategia-vasca-de-cambio-climatico-2050/web01-a2ingkli/es/\" target=\"_blank\">  Estrategia vasca de cambio climático 2050</a>: recoge como objetivos a largo plazo la reducción de las emisiones de gases de efecto invernadero en un 80% para 2050, así como el aumento de la resiliencia del territorio vasco para hacer frente a los efectos esperados por el cambio de clima.\n", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nEl Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue adoptado por los Estados Miembros de las \nNaciones Unidas en marzo de 2015 como una política global de reducción del riesgo de desastres. \n\nEl resultado esperado del Marco de Sendai es lograr “La reducción sustancial del riesgo de desastres y las \npérdidas en vidas, medios de subsistencia y salud y en los activos económicos, físicos, sociales, culturales \ny ambientales de las personas, las empresas, las comunidades y los países”. Entre las metas globales del \nMarco de Sendai, la “Meta E: Aumentar sustancialmente el número de países con estrategias nacionales y \nlocales de reducción del riesgo de desastres para 2020” tiene como objetivo mejorar \nel progreso global y la cobertura de las estrategias y políticas nacionales y locales de \nreducción del riesgo de desastres. \n\nLos objetivos de los planes, estrategias y políticas nacionales \nde reducción del riesgo de desastres son prevenir nuevos riesgos de desastres y reducir los \nexistentes mediante la implementación de medidas económicas, estructurales, legales, \nsociales, de salud, culturales, educativas, ambientales, tecnológicas, políticas e \ninstitucionales integradas e inclusivas que prevengan y reduzcan la exposición a peligros y \nla vulnerabilidad a los desastres, aumenten la preparación para la respuesta y la recuperación y, \nde esa manera, fortalezcan la resiliencia. \n\nEl indicador creará un puente entre los ODS y el Marco de Sendai para la Reducción del \nRiesgo de Desastres. Un número cada vez mayor de gobiernos nacionales que adopten e \nimplementen estrategias nacionales y locales de reducción del riesgo de desastres, como \nlo exige el Marco de Sendai, contribuirá al desarrollo sostenible desde las perspectivas \neconómica, ambiental y social.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.5.3&seriesCode=SG_DSR_SFDRR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Número de países que informaron tener una Estrategia Nacional de RRD alineada con el Marco de Sendai SG_DSR_SFDRR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-03.pdf\">Metadatos 1-5-3.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in \nMarch 2015 as a global policy of disaster risk reduction. \n\nThe expected outcome of the Sendai Framework \nis to realize “The substantial reduction of disaster risk and losses in lives, \nlivelihoods and health and in the economic, physical, social, cultural \nand environmental assets of persons, businesses, communities and \ncountries”. Among the Sendai Framework global targets, “Target E: Substantially \nincrease the number of countries with national and local disaster risk reduction \nstrategies by 2020” aims to enhance the global progress and coverage of national \nand local disaster risk reduction strategies and policies. \n\nThe objectives of the national DRR plans, strategies and policies are to prevent \nnew and reduce existing disaster risk through the implementation of integrated \nand inclusive economic, structural, legal, social, health, cultural, educational, \nenvironmental, technological, political and institutional measures that prevent and \nreduce hazard exposure and vulnerability to disaster, increase preparedness for \nresponse and recovery, and thus strengthen resilience. \n\nThe indicator will build bridge between the SDGs and the Sendai Framework for Disaster \nRisk Reduction (DRR). Increasing number of national governments that adopt and \nimplement national and local DRR strategies, which the Sendai Framework calls for, \nwill contribute to sustainable development from economic, environmental and social perspectives.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.5.3&seriesCode=SG_DSR_SFDRR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Number of countries that reported having a National DRR Strategy aligned with the Sendai Framework SG_DSR_SFDRR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-03.pdf\">Metadata 1-5-3.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>nil, "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nNazio Batuen estatu-kideek 2015eko martxoan ezarri zuten Hondamendien Arriskua Murrizteko 2015-2030 aldiko \nSendaiko Esparrua hondamendien arriskua murrizteko politika global gisa. \n\nSendaiko Esparrutik espero den emaitza da “nabarmen murriztea hondamendien arriskua eta galerak bizitzetan, \nbizirauteko bitartekoetan, osasunean, jarduera ekonomikoetan, fisikoetan, sozialetan, kulturaletan eta \ningurumenekoetan, pertsona, enpresa, komunitate eta herrialdeetan”. Sendaiko Esparruko xede orokorren artean \nE xedea dago: Hondamendien arriskua murrizteko nazioko eta tokiko estrategiak dituzten herrialdeen kopurua \nnabarmen areagotzea 2020rako. Hartara, hondamendien arriskua murrizteko nazioko eta tokiko estrategia eta \npolitiken estaldura eta aurrerapen globala hobetu nahi dira. \n\nHondamendien arriskua murrizteko plan, estrategia eta politika nazionalen helburuak dira hondamendien arrisku \nberriak prebenitzea eta daudenak murriztea, horretarako neurri integratu eta inklusiboak ezarriz ekonomian, \negituran, legean, gizartean, osasunean, kulturan, hezkuntzan, ingurumenean, teknologietan, politiketan eta \ninstituzioetan, hartara hondamendien aurreko arriskua eta zaurgarritasuna prebenitu eta murrizteko, egoera \nhorri erantzun eta hartatik berreskuratzeko prestaketa-lana areagotzeko, eta, beraz, erresilientzia indartzeko. \n\nAdierazleak zubi-lana egingo du GJHen eta Hondamendien Arriskuak Murrizteko Sendaiko Esparruaren artean. \nHondamendien arriskua murrizteko nazioko eta tokiko estrategiak ezartzen dituzten gobernu nazionalak zenbat \neta gehiago izan (halaxe ezartzen du Sendaiko Esparruak), orduan eta gehiago sustatuko da garapen jasangarria, \nekonomiaren, ingurumenaren eta gizartearen ikuspegitik. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa  \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.5.3&seriesCode=SG_DSR_SFDRR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Sendaiko esparruarekin bat datorren HAM (Hondamendi Arriskua Murrizteko) Estrategia Nazionala dutela jakinarazi duten herrialdeen kopurua SG_DSR_SFDRR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-03.pdf\">Metadatuak 1-5-3.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>nil, "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.5.3: Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015&#x2013;2030</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_DSR_SFDRR - Countries that reported having a National DRR Strategy which is aligned to the Sendai Framework to a certain extent (1 = YES; 0 = NO) [1.5.3, 11.b.1, 13.1.2]</p>\n<p>SG_DSR_LEGREG - Countries with legislative and/or regulatory provisions been made for managing disaster risk (1 = YES; 0 = NO) [1.5.3,11.b.1,13.1.2]</p>\n<p>SG_DSR_LGRGSR - Score of adoption and implementation of national DRR strategies in line with the Sendai Framework [1.5.3, 11.b.1, 13.1.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.b.1, 13.1.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator captures the number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015&#x2013;2030, and the coverage score for the level of implementation which Member States will report their status in the Sendai Framework Monitor (SFM).</p>\n<p><strong>Concepts:</strong></p>\n<p><strong>Disasters</strong>: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Disaster risk reduction strategies</strong>: define goals and objectives across different timescales and with concrete targets, indicators and time frames. In line with the Sendai Framework for Disaster Risk Reduction 2015-2030, the strategies should be aimed at preventing the creation of disaster risk, the reduction of existing risk and the strengthening of economic, social, health and environmental resilience.</p>\n<p><strong>Notes: </strong></p>\n<p>[a] An open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction established by the General Assembly (resolution 69/284) is developing a set of indicators to measure global progress in the implementation of the Sendai Framework. These indicators will eventually reflect the agreements on the Sendai Framework indicators.</p>\n<p>Detailed definitions, concepts, including composition and calculation for each of the data series, can be found in the SFM Technical Guidance (see below the Reference section)</p>", "UNIT_MEASURE__GLOBAL"=>"<p>SG_DSR_LGRGSR: index</p>\n<p>SG_DSR_SFDRR: number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are reported by national Sendai Framework focal points in the Sendai Framework Monitor (SFM). </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are reported in Sendai Framework Monitor (SFM) on an ongoing basis, and snapshotted once every year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released once a year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The coordinating lead institution chairing the National Disaster Risk Reduction (DRR) platform which is comprised of special purpose agencies including national disaster agencies, civil protection agencies, and meteorological agencies.</p>\n<p>The coordinating lead institution chairing the National DRR platform which is comprised of special purpose agencies including national disaster agencies, civil protection agencies, and meteorological agencies.</p>\n<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OIEWG) report, endorsed by the United Nations General Assembly in Resolution A/RES/71/276, requested the UNDRR to undertake technical work and provide technical guidance to develop minimum standards and metadata, the methodologies, and the global monitoring and measurements of the SFM global indicators.</p>\n<p>This indicator is recommended by the OIEWG for the measurement of global Target E of the Sendai Framework, which were endorsed by the UN General Assembly in its Resolution A/RES/71/276, <em>Report of the open-ended intergovernmental</em> <em>expert working group on indicators and terminology relating to disaster risk</em>.</p>", "RATIONALE__GLOBAL"=>"<p>The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. The expected outcome of the Sendai Framework is to realize &#x201C;The substantial reduction of disaster risk and losses in lives, livelihoods and health and in the economic, physical, social, cultural and environmental assets of persons, businesses, communities and countries&#x201D;. Among the Sendai Framework global targets, &#x201C;Target E: Substantially increase the number of countries with national and local disaster risk reduction strategies by 2020&#x201D; aims to enhance the global progress and coverage of national and local disaster risk reduction strategies and policies. The objectives of the national DRR plans, strategies and policies are to prevent new and reduce existing disaster risk through the implementation of integrated and inclusive economic, structural, legal, social, health, cultural, educational, environmental, technological, political and institutional measures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness for response and recovery, and thus strengthen resilience. </p>\n<p>The indicator will build bridge between the SDGs and the Sendai Framework for Disaster Risk Reduction (DRR). Increasing number of national governments that adopt and implement national and local DRR strategies, which the Sendai Framework calls for, will contribute to sustainable development from economic, environmental and social perspectives.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The Sendai Framework Monitoring (SFM) System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States report through the system since March 2018. The data for SDG indicators are compiled and reported by UNDRR.</p>\n<p>To the deliberations of the OEIWG as well as the IAEG-SDG, UNDRR proposed computation methodologies that allow the monitoring of improvement in national DRR strategies. </p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <msubsup>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>j</mi>\n              <mo>=</mo>\n              <mn>1</mn>\n            </mrow>\n            <mrow>\n              <mn>10</mn>\n            </mrow>\n          </msubsup>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>K</mi>\n                <mi>E</mi>\n              </mrow>\n              <mrow>\n                <mi>j</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mn>10</mn>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where:</p>\n<p>E<sub>1</sub>: National DRR strategy progress score; corresponding to Sendai Framework Indicator E-1.</p>\n<p>KE<sub>j</sub>: the level of achievement of the DRR national strategy Key Element j in the country.</p>\n<p>Member States will assess the level of implementation for ten key elements of the national DRR strategy, and enter key elements scores in the Sendai Framework Monitor. The national DRR strategy progress score E<sub>1</sub> would be calculated as the arithmetic average across ten national DRR strategy key elements (KE<sub>j</sub>).</p>\n<p>The national DRR strategy progress score will benchmark according to the following categories:</p>\n<ul>\n  <li>Comprehensive implementation: E<sub>1</sub> is higher than 0.75;</li>\n  <li>Substantial implementation, additional progress required: E<sub>1</sub> is higher than 0.5, but less than or equal to 0.75;</li>\n  <li>Moderate implementation, neither comprehensive nor substantial: E<sub>1</sub> is higher than 0.25, but less than or equal to 0.5;</li>\n  <li>Limited implementation: E<sub>1</sub> is higher than 0 but less than or equal to 0.25,</li>\n  <li>No national DRR strategy: If there is no implementation of national DRR strategy, or no existence of such plans, the score will be 0.</li>\n</ul>\n<p><strong>Note: </strong></p>\n<p>For the complete computation methodology, refer to the Technical Guidance, which provides a full detailed methodology for the indicator and sub-components.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are validation by UNDRR and national focal points.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction</li>\n  <li>ADPC Disaster and Climate Resilience e-Learning: An orientation to using the online Sendai Framework Monitor, https://courses.adpc.net/courses/course-v1:UNISDR+SFM001+2019Y1/about</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>2005-2023</p>\n<p><strong>Time series:</strong></p>\n<p>Annual</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Not applicable</p>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Internationally agreed methodology and guideline URL:</strong></p>\n<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017)</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf\">https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf</a></p>\n<p>Sendai Framework Monitor</p>\n<p><a href=\"https://sendaimonitor.undrr.org/\">https://sendaimonitor.undrr.org/</a></p>\n<p><strong>Other references:</strong></p>\n<ul>\n  <li>Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG). Endorsed by UNGA on 2<sup>nd</sup> February 2017. Available at:</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/publications/view/51748\">https://www.preventionweb.net/publications/view/51748</a></p>", "indicator_sort_order"=>"01-05-03", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"1.5.4", "slug"=>"1-5-4", "name"=>"Proporción de gobiernos locales que adoptan y aplican estrategias locales de reducción del riesgo de desastres en consonancia con las estrategias nacionales de reducción del riesgo de desastres", "url"=>"/site/es/1-5-4/", "sort"=>"010504", "goal_number"=>"1", "target_number"=>"1.5", "global"=>{"name"=>"Proporción de gobiernos locales que adoptan y aplican estrategias locales de reducción del riesgo de desastres en consonancia con las estrategias nacionales de reducción del riesgo de desastres"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"Los municipios que disponen de un plan (municipal o territorial) de emergencias tienen un valor de 100%", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de gobiernos locales que adoptan y aplican estrategias locales de reducción del riesgo de desastres en consonancia con las estrategias nacionales de reducción del riesgo de desastres", "indicator_number"=>"1.5.4", "national_geographical_coverage"=>"", "page_content"=>"En la C.A. de Euskadi, se dispone de un plan de emergencias a nivel autonómico y tres planes de emergencias territoriales, uno por cada territorio histórico. Por tanto, el 100% de los municipios se encuentran bajo el paraguas de un plan de emergencias territorial", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Seguridad", "periodicity"=>"Anual", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.5- De aquí a 2030, fomentar la resiliencia de los pobres y las personas que se encuentran en situaciones de vulnerabilidad y reducir su exposición y vulnerabilidad a los fenómenos extremos relacionados con el clima y otras perturbaciones y desastres económicos, sociales y ambientales", "definicion"=>"Porcentaje de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres mediante planes territoriales de emergencias y planes municipales de emergencias", "formula"=>"<b>Porcentaje de municipios que disponen de un plan municipal de emergencias</b>\n\n$$PMUN_{RRD\\, municipal}^{t} = \\frac{MUN_{RRD\\, municipal}^{t}}{MUN^{t}} \\cdot 100$$\n\ndonde:\n\n$MUN_{RRD\\, municipal}^{t} =$ número de gobiernos locales con planes municipales de emergencias en el año $t$\n\n$MUN^{t} =$ número de gobiernos locales en el año $t$\n\n <br>\n\n<b>Porcentaje de municipios cubiertos por un plan territorial de emergencias</b>\n\n$$PMUN_{RRD\\, territorial}^{t} = \\frac{MUN_{RRD\\, territorial}^{t}}{MUN^{t}} \\cdot 100$$\n\ndonde:\n\n$MUN_{RRD\\, territorial}^{t} =$ número de gobiernos locales con planes territoriales de emergencias en el año $t$\n\n$MUN^{t} =$ número de gobiernos locales en el año $t$\n", "desagregacion"=>"Territorio histórico/Comarca/Municipio", "observaciones"=>"\nEn Euskadi, según determina el Plan de Protección Civil de Euskadi – LABI, deben elaborar \ny aprobar planes municipales de emergencia (PEM) los municipios con población superior a 20.000 habitantes. \n\nPara los municipios de más de 5.000 habitantes, en coherencia con la normativa estatal reguladora de las \nBases del Régimen Local, esta directriz es recomendatoria. En 2024, el 96% de los municipios de 5.000 a 20.000 habitantes \ny el 25% de los municipios de 1.000 a 5.000 habitantes disponen de Plan de Emergencia Municipal homologado.\n", "periodicidad"=>"Anual", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue adoptado por los \nEstados Miembros de las Naciones Unidas en marzo de 2015 como una política global de \nreducción del riesgo de desastres. El resultado esperado del Marco de Sendai es lograr \n“la reducción sustancial del riesgo de desastres y de las pérdidas en vidas, medios de \nsubsistencia y salud y en los activos económicos, físicos, sociales, culturales y \nambientales de las personas, las empresas, las comunidades y los países”. \n\nEntre las metas globales del Marco de Sendai, la “Meta E: Aumentar sustancialmente \nel número de países con estrategias nacionales y locales de reducción del riesgo \nde desastres para 2020” tiene por objeto mejorar el progreso y la cobertura globales \nde las estrategias y políticas nacionales y locales de reducción del riesgo de desastres.\n\nLos objetivos de los planes, estrategias y políticas nacionales de reducción del riesgo \nde desastres son prevenir nuevos riesgos de desastres y reducir los existentes mediante \nla implementación de medidas económicas, estructurales, legales, sociales, de salud, culturales, \neducativas, ambientales, tecnológicas, políticas e institucionales integradas e inclusivas que \nprevengan y reduzcan la exposición a los peligros y la vulnerabilidad a los desastres, aumenten \nla preparación para la respuesta y la recuperación y, de ese modo, fortalezcan la resiliencia. \n\nAumentar la proporción de gobiernos locales que adoptan e implementan estrategias locales de \nreducción del riesgo de desastres, como lo exige el Marco de Sendai, contribuirá al desarrollo \nsostenible y fortalecerá la resiliencia económica, social, sanitaria y ambiental. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-04.pdf\">Metadatos 1-5-4.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"Proporción de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.5- De aquí a 2030, fomentar la resiliencia de los pobres y las personas que se encuentran en situaciones de vulnerabilidad y reducir su exposición y vulnerabilidad a los fenómenos extremos relacionados con el clima y otras perturbaciones y desastres económicos, sociales y ambientales", "definicion"=>"Percentage of local governments that adopt and implement local disaster risk  reduction strategies through territorial emergency plans and municipal emergency plans", "formula"=>"<b>Percentage of municipalities that have a municipal emergency plan</b>\n\n$$PMUN_{DRR\\, municipal}^{t} = \\frac{MUN_{DRR\\, municipal}^{t}}{MUN^{t}} \\cdot 100$$\n\nwhere:\n\n$MUN_{DRR\\, municipal}^{t} =$ number of local governments with municipal emergency plans in the year $t$\n\n$MUN^{t} =$ number of local governments in the year $t$\n\n <br>\n\n<b>Percentage of municipalities covered by a territorial emergency plan</b>\n\n$$PMUN_{DRR\\, territorial}^{t} = \\frac{MUN_{DRR\\, territorial}^{t}}{MUN^{t}} \\cdot 100$$\n\nwhere:\n\n$MUN_{DDR\\, territorial}^{t} =$ number of local governments with territorial emergency plans in the year $t$\n\n$MUN^{t} =$ number of local governments in the year $t$\n", "desagregacion"=>"Province/County/Municipality", "observaciones"=>"\nIn the Basque Country, according to the Basque Civil Protection Plan (LABI), municipalities \nwith a population of over 20,000 must prepare and approve municipal emergency plans (PEM). \n\nFor municipalities with more than 5,000 inhabitants, in accordance with the state regulations \ngoverning the Bases of the Local Government, this guideline is recommendatory. In 2024, 96% of \nmunicipalities with 5,000 to 20,000 inhabitants and 25% of municipalities with 1,000 to 5,000 \ninhabitants have an approved Municipal Emergency Plan.\n", "periodicidad"=>"Anual", "justificacion_global"=>"The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in \nMarch 2015 as a global policy of disaster risk reduction. The expected outcome of the Sendai Framework \nis to realize “The substantial reduction of disaster risk and losses in lives, livelihoods \nand health and in the economic, physical, social, cultural and environmental assets of persons, \nbusinesses, communities and countries”.\n\nAmong the Sendai Framework global targets, “Target E: Substantially increase the number of \ncountries with national and local disaster risk reduction strategies by 2020” aims to enhance \nthe global progress and coverage of national and local disaster risk reduction strategies and policies.\n\nThe objectives of the national DRR plans, strategies and policies are to prevent new and reduce \nexisting disaster risk through the implementation of integrated and inclusive economic, structural, \nlegal, social, health, cultural, educational, environmental, technological, political and institutional \nmeasures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness \nfor response and recovery, and thus strengthen resilience.\n\nIncreasing the proportion of local governments that adopt and implement local disaster risk reduction \nstrategies, which the Sendai Framework calls for, will contribute to sustainable development and \nstrengthen economic, social, health and environmental resilience.\n\nSource: United Nations Statistics Division\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-04.pdf\">Metadata 1-5-4.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Proporción de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.5- De aquí a 2030, fomentar la resiliencia de los pobres y las personas que se encuentran en situaciones de vulnerabilidad y reducir su exposición y vulnerabilidad a los fenómenos extremos relacionados con el clima y otras perturbaciones y desastres económicos, sociales y ambientales", "definicion"=>"Larrialdietako lurralde-planen eta larrialdietako udal-planen bidez hondamendi-arriskua  murrizteko tokiko estrategiak hartzen eta ezartzen dituzten tokiko gobernuen ehunekoa   ", "formula"=>"<b>Larrialdietarako udal-plana duten udalerrien ehunekoa</b>\n\n$$PMUN_{udal\\, HAM}^{t} = \\frac{MUN_{udal\\, HAM}^{t}}{MUN^{t}} \\cdot 100$$\n\nnon:\n\n$MUN_{udal\\, HAM}^{t} =$ larrialdietarako udal-planak dituzten udalerrien kopurua, $t$ urtean\n\n$MUN^{t} =$ udalerriak $t$ urtean\n\n <br>\n\n<b>Larrialdietarako lurralde-plan batek estalitako udalerrien ehunekoa</b>\n\n$$PMUN_{lurralde\\, HAM}^{t} = \\frac{MUN_{lurralde\\, HAM}^{t}}{MUN^{t}} \\cdot 100$$\n\nnon:\n\n$MUN_{lurralde\\, HAM}^{t} =$ larrialdietarako lurralde-planak dituzten udalerrien kopurua, $t$ urtean\n\n$MUN^{t} =$ udalerriak $t$ urtean\n", "desagregacion"=>"Lurralde historikoa/Eskualdea/Udalerria", "observaciones"=>"\nEuskadin, Euskadiko Babes Zibileko Planak (LABI) ezarritakoaren arabera, 20.000 biztanletik gorako \nudalerriek larrialdietarako udal-planak egin eta onartu behar dituzte.\n\n5.000 biztanletik gorako udalerrietan, Toki Araubidearen Oinarriak arautzen dituen Estatuko araudiarekin \nbat etorriz, jarraibide hau gomendagarria da. 2024an, 5.000 eta 20.000 biztanle arteko udalerrien % 96k \neta 1.000 eta 5.000 biztanle arteko udalerrien % 25ek Udal Larrialdi Plan homologatua dute.\n", "periodicidad"=>"Anual", "justificacion_global"=>"Nazio Batuen estatu-kideek 2015eko martxoan ezarri zuten Hondamendien Arriskua Murrizteko 2015-2030 aldiko \nSendaiko Esparrua hondamendien arriskua murrizteko politika global gisa. Sendaiko Esparrutik espero den \nemaitza da “nabarmen murriztea hondamendien arriskua eta galerak bizitzetan, bizirauteko bitartekoetan, \nosasunean, jarduera ekonomikoetan, fisikoetan, sozialetan, kulturaletan eta ingurumenekoetan, pertsona, \nenpresa, komunitate eta herrialdeetan”. \n\nSendaiko Esparruko xede orokorren artean E xedea dago: Hondamendien arriskua murrizteko nazioko eta tokiko \nestrategiak dituzten herrialdeen kopurua nabarmen areagotzea 2020rako. Hartara, hondamendien arriskua murrizteko \nnazioko eta tokiko estrategia eta politiken estaldura orokorra eta aurrerapena hobetu nahi dira. \n\nHondamendien arriskua murrizteko plan, estrategia eta politika nazionalen helburuak dira hondamendien arrisku \nberriak prebenitzea eta daudenak murriztea, horretarako neurri integratu eta inklusiboak ezarriz ekonomian, \negituran, legean, gizartean, osasunean, kulturan, hezkuntzan, ingurumenean, teknologietan, politiketan eta \ninstituzioetan, hartara hondamendien aurreko arriskua eta zaurgarritasuna prebenitu eta murrizteko, egoera \nhorri erantzun eta horretatik berreskuratzeko prestaketa-lana areagotzeko, eta, beraz, erresilientzia indartzeko. \n\nHondamendien arriskua murrizteko tokiko estrategiak ezartzen dituzten tokiko gobernuak zenbat eta gehiago izan \n(halaxe ezartzen du Sendaiko Esparruak), orduan eta gehiago sustatuko da garapen jasangarria, eta erresilientzia \nindartuko da ekonomiaren, osasunaren, ingurumenaren eta gizartearen ikuspegitik. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa  \n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-04.pdf\">Metadatuak 1-5-4.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere </p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.5.4: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_DSR_SILS - Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies [1.5.4, 11.b.2, 13.1.3]</p>\n<p>SG_DSR_SILN - Number of local governments that adopt and implement local DRR strategies in line with national strategies [1.5.4, 11.b.2, 13.1.3]</p>\n<p>SG_GOV_LOGV - Number of local governments [1.5.4, 11.b.2, 13.1.3]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.b.2, 13.1.3</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator captures the percentage of local governments that adopt and implement local disaster risk reduction strategies in line with national strategies.</p>\n<p><strong>Concepts:</strong></p>\n<p><strong>Disasters</strong>: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Disaster risk reduction strategies</strong>: define goals and objectives across different timescales and with concrete targets, indicators and time frames. In line with the Sendai Framework for Disaster Risk Reduction 2015-2030, the strategies should be aimed at preventing the creation of disaster risk, the reduction of existing risk and the strengthening of economic, social, health and environmental resilience.</p>\n<p><strong>Local Government</strong>: Form of sub-national public administration with responsibility for disaster risk reduction &#x2013; to be determined by countries for the purposes of monitoring Sendai Framework Target E.</p>\n<p><strong>Notes: </strong></p>\n<p>[a] An open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction established by the General Assembly (resolution 69/284) is developing a set of indicators to measure global progress in the implementation of the Sendai Framework. These indicators will eventually reflect the agreements on the Sendai Framework indicators.</p>\n<p>Detailed definitions, concepts, including composition and calculation for each of the data series, can be found in the SFM Technical Guidance (see below the Reference section)</p>", "UNIT_MEASURE__GLOBAL"=>"<p>SG_DSR_SILS: Percent (%) </p>\n<p>SG_DSR_SILN: Number </p>\n<p>SG_GOV_LOGV: Number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are reported by national Sendai Framework focal points in the Sendai Framework Monitor (SFM). </p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are reported in Sendai Framework Monitor (SFM) on an ongoing basis, and snapshotted once every year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released once a year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Sendai Framework Focal Points usually represent the coordinating lead institution chairing the National Disaster Risk Reduction (DRR) platform which is comprised of special purpose agencies including national disaster agencies, civil protection agencies, and meteorological agencies.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OIEWG) report, endorsed by the United Nations General Assembly in Resolution A/RES/71/276, requested the UNDRR to undertake technical work and provide technical guidance to develop minimum standards and metadata, the methodologies, and the global monitoring and measurements of the SFM global indicators.</p>\n<p>This indicator is recommended by the OIEWG for the measurement of global Target E of the Sendai Framework, which were endorsed by the UN General Assembly in its Resolution A/RES/71/276, Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk.</p>", "RATIONALE__GLOBAL"=>"<p>The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. The expected outcome of the Sendai Framework is to realize &#x201C;The substantial reduction of disaster risk and losses in lives, livelihoods and health and in the economic, physical, social, cultural and environmental assets of persons, businesses, communities and countries&#x201D;. Among the Sendai Framework global targets, &#x201C;Target E: Substantially increase the number of countries with national and local disaster risk reduction strategies by 2020&#x201D; aims to enhance the global progress and coverage of national and local disaster risk reduction strategies and policies. The objectives of the national DRR plans, strategies and policies are to prevent new and reduce existing disaster risk through the implementation of integrated and inclusive economic, structural, legal, social, health, cultural, educational, environmental, technological, political and institutional measures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness for response and recovery, and thus strengthen resilience. </p>\n<p>Increasing the proportion of local governments that adopt and implement local disaster risk reduction strategies, which the Sendai Framework calls for, will contribute to sustainable development and strengthen economic, social, health and environmental resilience. Their economic, environmental and social perspectives would include poverty eradication, urban resilience, and climate change adaptation.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The Sendai Framework Monitoring (SFM) System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States report through the system since March 2018. The data for SDG indicators are compiled and reported by UNDRR.</p>\n<p>To the deliberations of the OEIWG as well as the IAEG-SDG, UNDRR proposed computation methodologies that allow the monitoring of improvement in national DRR strategies. </p>", "DATA_COMP__GLOBAL"=>"<p>Member States count the number of local governments that adopt and implement local DRR strategies in line with the national strategy and express it as a percentage of the total number of local governments in the country.</p>\n<p>Local governments are determined by the reporting country for this indicator, considering sub-national public administrations with responsibility to develop local disaster risk reduction strategies. It is recommended that countries report on progress made by the lowest level of government accorded the mandate for disaster risk reduction, as the Sendai Framework promotes the adoption and implementation of local disaster risk reduction strategies in every local authority.</p>\n<p>Each Member State will calculate the ratio of the number of local governments with local DRR strategies in line with national strategies and the total number of local governments.</p>\n<p>Global Average will then be calculated as below through arithmetic average of the data from each Member State.</p>\n<p>For the complete computation methodology, refer to the Technical Guidance, which provides a full detailed methodology for the indicator.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are validation by UNDRR and national focal points.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction</li>\n  <li>ADPC Disaster and Climate Resilience e-Learning: An orientation to using the online Sendai Framework Monitor, https://courses.adpc.net/courses/course-v1:UNISDR+SFM001+2019Y1/about</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>2005-2023</p>\n<p><strong>Time series:</strong></p>\n<p>Annual</p>\n<p><strong>Disaggregation:</strong></p>\n<p>By local government (applying sub-national administrative unit)</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Internationally agreed methodology and guideline URL:</strong></p>\n<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017)</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf\">https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf</a></p>\n<p>Sendai Framework Monitor</p>\n<p><a href=\"https://sendaimonitor.undrr.org/\">https://sendaimonitor.undrr.org/</a></p>\n<p><strong>Other references:</strong></p>\n<ul>\n  <li>Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG). Endorsed by UNGA on 2<sup>nd</sup> February 2017. Available at:</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/publications/view/51748\">https://www.preventionweb.net/publications/view/51748</a></p>", "indicator_sort_order"=>"01-05-04", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"1.a.1", "slug"=>"1-a-1", "name"=>"Total de las subvenciones de asistencia oficial para el desarrollo de todos los donantes que se centran en la reducción de la pobreza como porcentaje del ingreso nacional bruto del país receptor", "url"=>"/site/es/1-a-1/", "sort"=>"01aa01", "goal_number"=>"1", "target_number"=>"1.a", "global"=>{"name"=>"Total de las subvenciones de asistencia oficial para el desarrollo de todos los donantes que se centran en la reducción de la pobreza como porcentaje del ingreso nacional bruto del país receptor"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Total de las subvenciones de asistencia oficial para el desarrollo de todos los donantes que se centran en la reducción de la pobreza como porcentaje del ingreso nacional bruto del país receptor", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Total de las subvenciones de asistencia oficial para el desarrollo de todos los donantes que se centran en la reducción de la pobreza como porcentaje del ingreso nacional bruto del país receptor", "indicator_number"=>"1.a.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notapplicable", "reporting_status"=>"notapplicable", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Los flujos totales de Ayuda Oficial al Desarrollo (AOD) a los países en desarrollo \ncuantifican el esfuerzo público (excluidos los flujos no concesionales y \nlos créditos a la exportación) que todos los donantes realizan para el desarrollo \neconómico y el bienestar de los países en desarrollo. \n\nDentro de la AOD, los servicios sociales básicos y la ayuda alimentaria \npara el desarrollo se centran en la mitigación de la pobreza en los países en desarrollo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-0a-01.pdf\">Metadatos 1-a-1.pdf</a> (solo en inglés)", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.1&seriesCode=DC_ODA_POVG&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Subvenciones de asistencia oficial al desarrollo para la reducción de la pobreza (porcentaje del INB) DC_ODA_POVG</a> UNSTATS", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nTotal Official Development Assistance (ODA) flows to developing countries quantify \nthe public effort (excluding non-concessional flows and export credits) that all donors \nmake for the economic development and well-being of developing countries. \n\nWithin ODA, basic social services and food aid for development focus on poverty alleviation \nin developing countries.\n\nSource: United Nations Statistics Division\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-0a-01.pdf\">Metadata 1-a-1.pdf</a>", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.1&seriesCode=DC_ODA_POVG&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Official development assistance grants for poverty reduction (percentage of GNI) DC_ODA_POVG</a> UNSTATS"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Garapen-bidean dauden herrialdeetarako Garapenerako Laguntza Ofizialaren (GLO) guztizko fluxuetan \nzenbatesten da emaile guztiek garapen-bidean dauden herrialdeen garapen ekonomikorako eta ongizaterako \negiten duten ahalegin publikoa (emakidakoak ez diren fluxuak eta esportaziorako kredituak kenduta). \n\nGLOren barruan, oinarrizko gizarte-zerbitzuek eta garapenerako elikadura-laguntzak garapen-herrialdeetan \npobrezia arintzea dute helburu. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-0a-01.pdf\">Metadatuak 1-a-1.pdf</a> (ingelesez bakarrik)", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.1&seriesCode=DC_ODA_POVG&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Garapenerako laguntza ofizialeko dirulaguntzak, pobrezia murrizteko (INBren ehunekoa) DC_ODA_POVG</a> UNSTATS"}, "national_metadata_updated_date"=>"2025-03-15", "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.a: Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programmes and policies to end poverty in all its dimensions</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.a.1: Total official development assistance grants from all donors that focus on poverty reduction as a share of the recipient country&#x2019;s gross national income</p>", "META_LAST_UPDATE__GLOBAL"=>"2020-04-14", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>OECD</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>OECD</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Total official development assistance (ODA) grants from all donors that focus on poverty reduction as a share of the recipient country&#x2019;s gross national income. </p>\n<p>The OECD/Development Assistance Committee (DAC) defines ODA as &#x201C;flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are i) provided by official agencies, including state and local governments, or by their executive agencies; and ii) each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent). (See <a href=\"http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm\">http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm</a>).</p>\n<p>Poverty reduction items can be defined as ODA to basic social services (basic health, basic education, basic water and sanitation, population programmes and reproductive health) and developmental food aid (see here: <a href=\"http://www.oecd.org/dac/stats/purposecodessectorclassification.htm\">http://www.oecd.org/dac/stats/purposecodessectorclassification.htm</a>).</p>\n<p><strong>Concepts:</strong></p>\n<p>The OECD/Development Assistance Committee (DAC) defines ODA as &#x201C;flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are i) provided by official agencies, including state and local governments, or by their executive agencies; and ii) each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent). (See <a href=\"http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm\">http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm</a>).</p>\n<p>Basic social services and development food aid, which focus on poverty reduction, are defined using the following OECD Creditor Reporting System purpose codes, which identify the sector the activity is intended to target: </p>\n<ul>\n  <li>Basic Education (CRS codes 112xx)</li>\n  <li>Basic Health (CRS codes (122xx)</li>\n  <li>Water Supply and Sanitation (CRS codes 140xx)</li>\n  <li>Multisector aid for basic social services (CRS code 16050)</li>\n  <li>Development Food Aid (CRS code 52010)</li>\n</ul>\n<p>The detailed list of CRS purpose codes and their definitions are available here: <a href=\"http://www.oecd.org/dac/stats/purposecodessectorclassification.htm\">http://www.oecd.org/dac/stats/purposecodessectorclassification.htm</a></p>", "SOURCE_TYPE__GLOBAL"=>"<p>The OECD/DAC has been collecting data on official and private resource flows, from 1960 at an aggregate level, and 1973 at an activity level through the Creditor Reporting System (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements). </p>\n<p>The data are reported by donors according to the same standards and methodologies (see here: <a href=\"http://www.oecd.org/dac/stats/methodology.htm\">http://www.oecd.org/dac/stats/methodology.htm</a>). </p>\n<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.)</p>", "COLL_METHOD__GLOBAL"=>"<p>A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc.</p>\n<p>The OECD prepares and sends a questionnaire on aid flows (at an activity level and aggregate level) to the national statistical reporter every year. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data collection is annual. Detailed 2019 flows will be published in December 2020.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Detailed 2019 flows will be published in December 2020.</p>", "DATA_SOURCE__GLOBAL"=>"<p>A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc.</p>", "COMPILING_ORG__GLOBAL"=>"<p>OECD, Development Cooperation Directorate.</p>", "RATIONALE__GLOBAL"=>"<p>Total ODA flows to developing countries quantify the public effort (excluding non- concessional flows and export credits), that all donors provide for the economic development and welfare of developing countries. Within ODA, basic social services and development food aid focus on poverty alleviation in developing countries.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data in the Creditor Reporting System (i.e. at an activity level), are available from 1973 onwards. However, the data coverage is considered complete since 1995 for commitments and 2002 for disbursements.</p>", "DATA_COMP__GLOBAL"=>"<p><u>From a donor country&#x2019;s perspective</u>: The sum of bilateral ODA grants by donor that focus on poverty reduction as a share of the donor country&#x2019;s gross national income. </p>\n<p><u>From a recipient country&#x2019;s perspective</u>: The sum of total ODA grants from all donors (i.e. DAC donors, multilateral organisations and other bilateral providers of development cooperation) that focus on poverty reduction as a share of the developing country&#x2019;s gross national income. </p>", "IMPUTATION__GLOBAL"=>"<p>Due to high quality of reporting, no estimates are produced for missing data.</p>\n<p>&#x2022; <strong>At country level</strong></p>\n<p>Due to high quality of reporting, no estimates are produced for missing data.</p>\n<p>&#x2022; <strong>At regional and global levels</strong></p>\n<p>Due to high quality of reporting, no estimates are produced for missing data.</p>", "REG_AGG__GLOBAL"=>"<p>Global, regional and country figures are based on the sum of ODA grant flows for poverty reduction.</p>", "DOC_METHOD__GLOBAL"=>"<p>The DAC statistical Reporting Directives govern the reporting of DAC statistics, and are reviewed and agreed by the DAC Working Party of Development Finance Statistics, see: <a href=\"https://one.oecd.org/document/DCD/DAC/STAT(2018)9/FINAL/en/pdf\">https://one.oecd.org/document/DCD/DAC/STAT(2018)9/FINAL/en/pdf</a></p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The OECD/DAC Secretariat is responsible for verifying and validating data submissions from providers of development cooperation, as well as publishing the data.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are published on an annual basis in December for flows in the previous year.</p>\n<p>Detailed 2019 flows will be published in December 2020.</p>\n<p>Provisional data classification: Tier I</p>\n<p><strong>Time series:</strong></p>\n<p>The OECD/DAC has been collecting data on official and private resource flows, from 1960 at an aggregate level, and 1973 at an activity level through the Creditor Reporting System (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements). </p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator can be disaggregated by donor, by recipient country, by type of finance, by type of aid, by sub-sector, by policy marker (e.g. gender), etc.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<p>See all links here:<strong> </strong><a href=\"http://www.oecd.org/dac/stats/methodology.htm\"><strong>http://www.oecd.org/dac/stats/methodology.htm</strong></a></p>\n<p><strong>References: </strong></p>\n<p>See all links here:<strong> </strong><a href=\"http://www.oecd.org/dac/stats/methodology.htm\"><strong>http://www.oecd.org/dac/stats/methodology.htm</strong></a></p>", "indicator_sort_order"=>"01-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"1.a.2", "slug"=>"1-a-2", "name"=>"Proporción del gasto público total que se dedica a servicios esenciales (educación, salud y protección social)", "url"=>"/site/es/1-a-2/", "sort"=>"01aa02", "goal_number"=>"1", "target_number"=>"1.a", "global"=>{"name"=>"Proporción del gasto público total que se dedica a servicios esenciales (educación, salud y protección social)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Servicios esenciales", "value"=>"Salud"}, {"field"=>"Servicios esenciales", "value"=>"Protección social"}, {"field"=>"Servicios esenciales", "value"=>"Educación"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"related indicators", "embedded_feature_title"=>"", "embedded_feature_url"=>"https://eustat-des.github.io/site/embeded/1-a-2", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"Servicios esenciales", "graph_title"=>"Proporción del gasto público total que se dedica a servicios esenciales (educación, salud y protección social)", "graph_type"=>"bar", "indicator_name"=>"Proporción del gasto público total que se dedica a servicios esenciales (educación, salud y protección social)", "indicator_number"=>"1.a.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Bienal", "url"=>"https://www.eustat.eus/estadisticas/tema_302/opt_0/ti_cuenta-de-la-educacion/temas.html", "url_text"=>"Cuenta de la Educación", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_149/opt_1/ti_cuentas-de-las-administraciones-publicas/temas.html", "url_text"=>"Cuenta de las Administraciones Públicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Bienal", "url"=>"https://www.eustat.eus/estadisticas/tema_121/opt_0/ti_cuenta-de-la-salud/temas.html", "url_text"=>"Cuenta de la Salud", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_94/opt_0/ti_cuenta-de-la-proteccion-social/temas.html", "url_text"=>"Cuenta de la Protección Social", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_151/opt_1/ti_estadisticas-presupuestarias-del-sector-publico/temas.html", "url_text"=>"Estadística presupuestaria del sector público", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "x_axis_label"=>"", "indicador_disponible"=>"Proporción del gasto público total que se dedica a servicios esenciales (educación, salud y protección social)", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.a- Garantizar una movilización significativa de recursos procedentes de diversas fuentes, incluso mediante la mejora de la cooperación para el desarrollo, a fin de proporcionar medios suficientes y previsibles a los países en desarrollo, en particular los países menos adelantados, para que implementen programas y políticas encaminados a poner fin a la pobreza en todas sus dimensiones", "definicion"=>"Proporción del gasto de las administraciones públicas en la C.A. de Euskadi que se dedica a servicios\nesenciales: educación, salud y protección social (grupos funcionales 09, 07 y 10 de la Clasificación \nde Funciones del Gobierno -COFOG-, utilizada a nivel \ninternacional para clasificar los propósitos de las actividades gubernamentales)\n", "formula"=>"\n$$PE_{t} = \\frac{TE_{t} + TH_{t} + TS_{t}}{TG_{t}} \\cdot 100$$\n\ndonde:\n\n$PE_{t}$ = gasto del gobierno en servicios esenciales (educación, salud y protección social) como porcentaje del gasto total del gobierno en el año fiscal $t$\n\n$TE_{t}$ = gasto total del gobierno en educación en el año fiscal $t$\n\n$TH_{t}$ = gasto total del gobierno en salud en el año fiscal $t$\n\n$TS_{t}$ = gasto total del gobierno en protección social en el año fiscal $t$\n\n$TG_{t}$ = gasto total del gobierno en el año fiscal $t$\n", "desagregacion"=>"Servicios esenciales: educación; salud; protección social\n", "observaciones"=>"\nEl indicador se calcula para el gobierno general, que incluye diferentes unidades\ngubernamentales: gobierno central, gobierno autonómico, gobiernos forales y gobiernos locales.\n\nLa Clasificación de Funciones del Gobierno (COFOG), desarrollada por la \nOrganización para la Cooperación y el Desarrollo Económicos (OCDE) y publicada \npor la División de Estadística de las Naciones Unidas, estructura el gasto público\nen 10 grupos funcionales:\n\n - 01 Servicios públicos generales\n - 02 Defensa\n - 03 Orden público y seguridad\n - 04 Asuntos económicos\n - 05 Protección del medio ambiente\n - 06 Vivienda y servicios comunitarios\n - 07 Salud\n - 08 Ocio, cultura y religión\n - 09 Educación\n - 10 Protección social\n", "periodicidad"=>"Anual", "justificacion_global"=>"El indicador se utiliza para evaluar el gasto del gobierno en servicios \nesenciales (educación, salud y protección social) en relación con el monto del \ngasto gubernamental total. Dado que proviene de un marco internacional, \nel Manual de estadísticas de finanzas públicas (MEFP 2014), proporciona a los \nanalistas un conjunto de datos comparable entre países y garantiza el establecimiento \nde conclusiones analíticas sólidas para respaldar el seguimiento de los ODS \nutilizando datos fiscales.\n\nUna alta proporción del gasto público en servicios esenciales (educación, \nsalud y protección social) demuestra una alta prioridad gubernamental para \nestas funciones de gasto.\n\nPara informar este indicador, se considera que el gobierno general es el \nnivel más apropiado de cobertura institucional, ya que abarcará las unidades \ngubernamentales pertinentes, incluidos los gobiernos locales, ya que en muchos \npaíses se debe considerar la descentralización de estas categorías de gasto. \nUn país puede tener un gobierno central; varios gobiernos estatales, \nprovinciales o regionales; y muchos gobiernos locales, y el MEFP 2014 recomienda \nque se compilen estadísticas para todas esas unidades de gobierno general.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible en la C.A de Euskadi utiliza la clasificación COFOG,  que no cumple con los metadatos del indicador de Naciones Unidas pero  aporta información similar. El indicador mundial utiliza el  manual de estadísticas  de finanzas gubernamentales (MEFP) donde el concepto de gasto se aplica de manera  ligeramente diferente.", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.2&seriesCode=SG_XPD_ESSRV&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProporción del gasto público total en servicios esenciales (%) SG_XPD_ESSRV</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.2&seriesCode=SG_XPD_PROT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProporción del gasto público total en servicios esenciales, protección social (%) SG_XPD_PROT</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.2&seriesCode=SG_XPD_EDUC&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProporción del gasto público total en servicios esenciales, educación (%) SG_XPD_EDUC</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.2&seriesCode=SG_XPD_HLTH&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProporción del gasto público total en servicios esenciales, salud (%) SG_XPD_HLTH</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-0a-02a.pdf\">Metadatos 1-a-2 (1).pdf</a> (solo en inglés)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-0a-02b.pdf\">Metadatos 1-a-2 (2).pdf</a> (solo en inglés)\n", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción del gasto público total que se dedica a servicios esenciales (educación, salud y protección social)", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.a- Garantizar una movilización significativa de recursos procedentes de diversas fuentes, incluso mediante la mejora de la cooperación para el desarrollo, a fin de proporcionar medios suficientes y previsibles a los países en desarrollo, en particular los países menos adelantados, para que implementen programas y políticas encaminados a poner fin a la pobreza en todas sus dimensiones", "definicion"=>"\nProportion of public administration spending in the Basque Country dedicated \nto essential services: education, health, and social protection (functional groups 09, \n07, and 10 of the Classification of Government Functions (COFOG), used internationally to \nclassify the purposes of government activities)\n", "formula"=>"\n$$PE_{t} = \\frac{TE_{t} + TH_{t} + TS_{t}}{TG_{t}} \\cdot 100$$\n\ndonde:\n\n$PE_{t}$ = Government expenditure on essential services (education, health, and social protection) as a percentage of total government expenditure in the fiscal year $t$\n\n$TE_{t}$ = total government expenditure on education in the fiscal year $t$\n\n$TH_{t}$ = total government expenditure on health in the fiscal year $t$\n\n$TS_{t}$ = total government expenditure on social protection in the fiscal year $t$\n\n$TG_{t}$ = total government expenditure in the fiscal year $t$\n", "desagregacion"=>"Essential services: education, health, social protection  \n", "observaciones"=>"\nThe indicator is calculated for the general government, which includes \ndifferent governmental units: central government, regional government, regional governments, \nand local governments.\n\nThe Classification of Functions of Government (COFOG), developed by the Organization for Economic Cooperation and Development (OECD) and published by the United Nations Statistics Division, structures public expenditure into 10 functional groups:\n\n - 01 General public services\n - 02 Defence\n - 03 Public order and safety\n - 04 Economic affairs\n - 05 Environmental protection\n - 06 Housing and community services\n - 07 Health\n - 08 Leisure, culture and religion\n - 09 Education\n - 10 Social Protection\n", "periodicidad"=>"Anual", "justificacion_global"=>"The indicator is used to assess government's expenditure on essential services \n(education, health and social protection) relative to the amount of total government \nspending. Since it comes from an international framework, the GFSM 2014, it provides \nanalysts with a cross-country comparable dataset and ensures establishing robust \nanalytical findings to support SDG monitoring using fiscal data.\n\nA high proportion of government expenditure on essential services (education, health and social\nprotection) demonstrates a high government priority for these functions of spending.\n\nFor reporting this indicator, general government is considered the most appropriate \nlevel of institutional coverage as it will encompass relevant government units, \nincluding local governments, since in many countries descentralization of these \ncategories of expense are to be considered. A country may have one central \ngovernment; several state, provincial, or regional governments; and many local \ngovernments, and the GFSM 2014 recommends that statistics should be compiled for all \nsuch general government units.\n\nSource: United Nations Statistics Division\n", "comparabilidad"=>"The indicator available in the Basque Country uses the COFOG classification, which does not  comply with the metadata of the United Nations indicator but provides similar information.  The global indicator uses the Manual of Government Finance Statistics (GFSM), where the concept  of expenditure is applied slightly differently.", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.2&seriesCode=SG_XPD_ESSRV&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProportion of total public expenditure on essential services (%) SG_XPD_ESSRV</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.2&seriesCode=SG_XPD_PROT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProportion of total public expenditure on essential services, social protection (%) SG_XPD_PROT</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.2&seriesCode=SG_XPD_EDUC&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProportion of total public expenditure on essential services, education (%) SG_XPD_EDUC</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.2&seriesCode=SG_XPD_HLTH&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProportion of total public expenditure on essential services, health (%) SG_XPD_HLTH</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-0a-02a.pdf\">Metadata 1-a-2 (1).pdf</a>\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-0a-02b.pdf\">Metadata 1-a-2 (2).pdf</a>\n", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción del gasto público total que se dedica a servicios esenciales (educación, salud y protección social)", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.a- Garantizar una movilización significativa de recursos procedentes de diversas fuentes, incluso mediante la mejora de la cooperación para el desarrollo, a fin de proporcionar medios suficientes y previsibles a los países en desarrollo, en particular los países menos adelantados, para que implementen programas y políticas encaminados a poner fin a la pobreza en todas sus dimensiones", "definicion"=>"Funtsezko zerbitzuetara (hezkuntza, osasuna eta gizarte-babesa) bideratzen den EAEko \nherri-administrazioen gastuaren proportzioa (gobernu-jardueren helburuak sailkatzeko \nnazioartean erabiltzen den Gobernuaren Funtzioen Sailkapeneko 09, 07 eta 10 funtzio-taldeak)   \n", "formula"=>"\n$$PE_{t} = \\frac{TE_{t} + TH_{t} + TS_{t}}{TG_{t}} \\cdot 100$$\n\nnon:\n\n$PE_{t}$ = gobernuaren gastua funtsezko zerbitzuetan (hezkuntza, osasuna eta gizarte-babesa), gobernuaren $t$ urte fiskaleko gastu osoaren ehuneko gisa \n\n$TE_{t}$ = gobernuak hezkuntzan egiten duen guztizko gastua, $t$ urte fiskalean\n\n$TH_{t}$ = gobernuak osasunean egiten duen guztizko gastua, $t$ urte fiskalean\n\n$TS_{t}$ = gobernuak gizarte-babesean egiten duen guztizko gastua, $t$ urte fiskalean\n\n$TG_{t}$ = gobernuaren guztizko gastua, $t$ urte fiskalean\n", "desagregacion"=>"Funtsezko zerbitzuak: hezkuntza; osasuna; gizarte-babesa\n", "observaciones"=>"\nAdierazlea gobernu orokorrerako kalkulatzen da, gobernu-unitate desberdinak barne hartzen dituena: \ngobernu zentrala, gobernu autonomikoa, foru gobernuak eta tokiko gobernuak.\n\nGobernuaren Funtzioen Sailkapenak (COFOG), Ekonomia Lankidetza eta Garapenerako Erakundeak (ELGA) \ngaratu eta Nazio Batuen Estatistika Atalak argitaratutakoak, gastu publikoa 10 talde funtzionaletan \negituratzen du:\n\n- 01 Zerbitzu publiko orokorrak\n- 02 Defentsa\n- 03 Ordena publikoa eta segurtasuna\n- 04 Gai ekonomikoak\n- 05 Ingurumena babestea\n- 06 Etxebizitza eta zerbitzu komunitarioak\n- 07 Osasuna\n- 08 Aisia, kultura eta erlijioa\n- 09 Hezkuntza\n- 10 Gizarte-babesa\n", "periodicidad"=>"Anual", "justificacion_global"=>"Adierazlea gobernuak oinarrizko zerbitzuetan egiten duen gastua ebaluatzeko erabiltzen da (hezkuntza, \nosasuna eta gizarte-babesa), gobernuaren gastu osoaren zenbatekoarekin lotuta. Nazioarteko esparrutik \ndatorrenez, finantza publikoen estatistiken eskuliburuak (2014) analistei datu batzuk eskaintzen dizkie, \nherrialdeen artean alderatzeko modukoak. Halaber, ondorio analitiko solidoak ezarri ahal izatea bermatzen \ndu, GJHen jarraipena babesteko, zerga-datuak kontuan hartuta. \n\nOinarrizko zerbitzuetan gastu publikoa altua bada (hezkuntza, osasuna eta gizarte-babesa), gobernuek \ngastuaren eginkizun horiei lehentasun altua ematen dietela esan nahi du. \n\nAdierazle horren berri emateko, kontuan hartzen da gobernu orokorra dela estaldura instituzionaleko \nmailarik egokiena, gobernu-unitate egokiak hartzen baititu bere baitan, tokiko gobernuak barne. Izan ere, \nherrialde askotan gastuaren kategoria hauek deszentralizatuta daude, hau da, herrialde batek gobernu zentral \nbat eduki dezake, estatu, probintzia edo eskualdeko hainbat gobernu, eta tokiko gobernu asko. 2014ko eskuliburuak, \nberaz, gobernu orokorraren unitate horientzat guztientzat estatistikak batu daitezela gomendatzen du. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "comparabilidad"=>"Euskal Autonomia Erkidegoan eskuragarri dagoen adierazleak COFOG sailkapena erabiltzen du.  Sailkapen horrek ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina antzeko  informazioa ematen du. Munduko adierazleak gobernu finantzen estatistiken eskuliburua  (GFEE) erabiltzen du, non gastuaren kontzeptua pixka bat desberdin aplikatzen den.  ", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.2&seriesCode=SG_XPD_ESSRV&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nFuntsezko zerbitzuetako guztizko gastu publikoaren proportzioa (%) SG_XPD_ESSRV</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.2&seriesCode=SG_XPD_PROT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nFuntsezko zerbitzuetako guztizko gastu publikoaren proportzioa, gizarte-babesa (%) SG_XPD_PROT</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.2&seriesCode=SG_XPD_EDUC&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nFuntsezko zerbitzuetako guztizko gastu publikoaren proportzioa, hezkuntza (%) SG_XPD_EDUC</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=1.a.2&seriesCode=SG_XPD_HLTH&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nFuntsezko zerbitzuetako guztizko gastu publikoaren proportzioa, osasuna (%) SG_XPD_HLTH</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-0a-02a.pdf\">Metadatuak 1-a-2 (1).pdf</a> (ingelesez bakarrik)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-0a-02b.pdf\">Metadatuak 1-a-2 (2).pdf</a> (ingelesez bakarrik)\n", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.a: Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programmes and policies to end poverty in all its dimensions</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.a.2: Proportion of total government spending on essential services (education, health and social protection)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SD_XPD_ESED - Proportion of total government spending on essential services, education [UIS methodology] [1.a.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-06-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>4.5.3, 4.5.4, 4.5.5, 4.5.6, 4.b.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Institute for Statistics (UNESCO-UIS)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Institute for Statistics (UNESCO-UIS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>As agreed by the Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs), data for the education component only of indicator 1.a.2 are provided by two custodian agencies (co-custodians IMF and UNESCO-UIS) to provide maximum country coverage. Therefore, the Global SDG Indicators Database includes two distinct data series for the education component, supplied by these different data providers (the co-custodians). Each provider employs unique data collection and calculation methods. Consequently, variations may arise when comparing the datasets: SG_XPD_EDUC (provided by IMF as part of a set that contains all components of indicator 1.a.2 and the total) and SD_XPD_ESED (provided by UNESCO-UIS for the education component only). To gain insight into the methodologies used by each provider, please review the additional metadata file associated with indicator 1.a.2.</strong></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Definition:</strong></p>\n<p>Total general (local, regional and central) government expenditure on education (current, capital, and transfers), expressed as a percentage of total general government expenditure on all sectors (including health, education, social services, etc.). It includes expenditure funded by transfers from international sources to the government.</p>\n<p><strong>Concepts:</strong></p>\n<p>Government expenditure on education covers educational expenditure by all levels of government (local, regional, central) on the formal education system, from early childhood to tertiary education, in both public and private instructional and non-instructional institutions within the borders of a country.</p>\n<p>Expenditure on education includes expenditure on core educational goods and services, such as teaching staff, school buildings, or school books and teaching materials, and peripheral educational goods and services such as ancillary services, general administration and other activities.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percentage. This indicator is the total general government expenditure on education, expressed as a percentage of total general government expenditure on all sectors. </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The formal education system comprises the levels of education defined in the 2011 revision of the <em>International Standard Classification of Education (ISCED 2011)</em>. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data on government spending come from government budgetary documents, financial reports, and official statistics provided by government agencies responsible for finance and budgeting.</p>", "COLL_METHOD__GLOBAL"=>"<p>The UIS compiles government spending data through:</p>\n<ul>\n  <li>UIS Formal Education Survey: <ul>\n      <li>Numerator: data provided by countries responding to the annual UIS survey on formal education including the UNESCO-OECD-Eurostat (UOE) data collection. </li>\n      <li>Denominator: data on total general government expenditure (all sectors) are extracted from the International Monetary Fund&apos;s World Economic Outlook database and are updated annually.</li>\n    </ul>\n  </li>\n  <li>UIS Dynamic Template (numerator and denominator): data provided by countries or data obtained from the national official documents.</li>\n</ul>", "FREQ_COLL__GLOBAL"=>"<ol>\n  <li>Annual UIS (usually launched the 4th quarter every year) and UOE survey (usually launched in June every year). </li>\n  <li>Data mining is conducted periodically to correspond to the UIS data release schedule</li>\n</ol>", "REL_CAL_POLICY__GLOBAL"=>"<p>Biannual UIS data release (February and September).</p>", "DATA_SOURCE__GLOBAL"=>"<p>Ministries of Finance, Ministries of Education, National Statistical Offices.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNESCO Institute for Statistics, OECD, Eurostat.</p>", "INST_MANDATE__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The Institute produces internationally comparable data and methodologies in the fields of education, science, culture and communication for countries at all stages of development.</p>\n<p>The <a href=\"https://www.moe.gov.bn/DocumentDownloads/Education%202030/Education2030.pdf\"><u>Education 2030 Framework for Action &#xA7;100</u></a> has clearly stated that: &#x201C;<em>In recognition of the importance of harmonization of monitoring and reporting, the UIS will remain the official source of cross-nationally comparable data on education. It will continue to produce international monitoring indicators based on its annual education survey and on other data sources that guarantee international comparability for more than 200 countries and territories. In addition to collecting data, the UIS will work with partners to develop new indicators, statistical approaches and monitoring tools to better assess progress across the targets related to UNESCO&#x2019;s mandate, working in coordination with the SDG-Education 2030 SC&#x201D;.</em></p>", "RATIONALE__GLOBAL"=>"<p>The indicator is used to assess a government&apos;s emphasis on education relative to other sectors. The indicator shows how much of a priority education is for a given government, over time or in comparison with other countries.</p>", "REC_USE_LIM__GLOBAL"=>"<p>A high proportion of government expenditure on education demonstrates a high government priority for education relative to other public investments. The Education 2030 Framework for Action has endorsed a benchmark for this indicator, which encourages countries to allocate at least 15% to 20% of their public expenditure to education.</p>\n<p>While the indicator allows for cross-country comparisons, differences in government structures, budgeting practices, and definitions may limit the comparability of data between countries. Variations in how countries categorize and report spending on education can introduce biases into comparative analyses.</p>\n<p>The indicator does not consider the demographic profile of a country&apos;s population, such as age distribution or socioeconomic status. Countries with younger populations may naturally allocate a higher proportion of spending to education, regardless of government priorities, while countries with aging populations may prioritize other essential services, such as healthcare or social security.</p>", "DATA_COMP__GLOBAL"=>"<p>Total government expenditure for all levels of education combined expressed as a percentage of total general government expenditure (all sectors).</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>X</mi>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>T</mi>\n        <msub>\n          <mrow>\n            <mi>X</mi>\n            <mi>E</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>T</mi>\n            <mi>P</mi>\n            <mi>X</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>X</mi>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> = total general government expenditure on education (all levels of education combined) as a percentage of total government expenditure in financial year <em>t</em></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>T</mi>\n        <mi>X</mi>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> = total general government expenditure on education (all levels of education combined) in financial year <em>t</em></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>T</mi>\n        <mi>P</mi>\n        <mi>X</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> = total general government expenditure in financial year <em>t</em></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The UNESCO Institute for Statistics shares all indicator values and notes on methodology with National Statistical Offices, Ministries of Education, or other relevant agencies in individual countries for their review, feedback and validation before the publication of the data. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Data should cover formal education only and should follow common definitions. </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>No imputations are made by data compiler.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Data gaps are filled with latest available value.</p>", "REG_AGG__GLOBAL"=>"<p>Median with coverage equal or higher to 50% of countries.</p>", "DOC_METHOD__GLOBAL"=>"<p>The UIS has elaborated guidance for the countries on the methodology that should be used to calculate this indicator based on the Survey of Formal Education and its manual. The standardized Dynamic template containts instructions for its completion. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The UIS maintains a global database used to produce this indicator and defines the protocols and standards for data reporting by countries. For transparency purposes, the inclusion of a data point in the database is completed by following a protocol and is reviewed by UIS technical focal points to ensure consistency and overall data quality, based on objective criteria to ensure that only the most recent and reliable information are included in the database.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) prioritizes the accuracy and reliability of its government spending data on education. The UIS validates with countries the indicator values compiled through the UIS Formal Education Survey and Dynamic Template. These tools allow countries to contribute data directly, while the UIS cross-references it with total government expenditure figures obtained from the International Monetary Fund (IMF) World Economic Outlook database.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The indicator should be produced based on consistent and actual data on total government expenditures on education and total government expenditures on all sectors combined. Criteria for quality assessment include: data sources must include proper documentation; data values must be representative at the national population level and, if not, should be footnoted; data are plausible and based on trends and consistency with previously published/reported values for the indicator.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>125 countries with at least one data point for the period 2000-2023.</p>\n<p><strong>Time series:</strong></p>\n<p>2000-2023 in the SDG Global database.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>None. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies</strong></p>\n<p>The data is derived from different sources and may be subject to differences in national definitions of expenditure types. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://sdg4-data.uis.unesco.org/\">http://sdg4-data.uis.unesco.org/</a> </p>\n<p><strong>References:</strong></p>\n<p>UIS Instructional Manual: Survey of Formal Education </p>\n<p><a href=\"https://uis.unesco.org/sites/default/files/questionnaires/UIS_ED_M_2024_EN.pdf\">https://uis.unesco.org/sites/default/files/questionnaires/UIS_ED_M_2024_EN.pdf</a> </p>\n<p>UOE data collection on formal education: Manual on concepts, definitions and classifications</p>\n<p><a href=\"https://www.oecd.org/statistics/data-collection/UOE-Manual.pdf\">https://www.oecd.org/statistics/data-collection/UOE-Manual.pdf</a> </p>\n<p>UIS Questionnaire on Educational Expenditure (ISCED 0-8)</p>\n<p><a href=\"http://uis.unesco.org/en/uis-questionnaires\">http://uis.unesco.org/en/uis-questionnaires</a></p>\n<p>UIS dynamic templates : </p>\n<p><a href=\"https://tcg.uis.unesco.org/wp-content/uploads/sites/4/2022/11/WG_EMIS_2_Dynamic-Templates.pdf\">https://tcg.uis.unesco.org/wp-content/uploads/sites/4/2022/11/WG_EMIS_2_Dynamic-Templates.pdf</a> </p>\n<p>IMF World Economic Outlook</p>\n<p><a href=\"https://www.imf.org/en/Publications/WEO\">https://www.imf.org/en/Publications/WEO</a></p>", "indicator_sort_order"=>"01-0a-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"embed", "label"=>"related indicators"}]}, {"number"=>"1.b.1", "slug"=>"1-b-1", "name"=>"Gasto público social en favor de los pobres", "url"=>"/site/es/1-b-1/", "sort"=>"01bb01", "goal_number"=>"1", "target_number"=>"1.b", "global"=>{"name"=>"Gasto público social en favor de los pobres"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>"", "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Gasto público social en favor de los pobres", "indicator_number"=>"1.b.1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "sources"=>"", "standalone"=>false, "tags"=>[], "x_axis_label"=>"", "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El indicador mide en qué medida el gasto público en tres áreas clave que son fundamentales para la \nerradicación de la pobreza, incluida la salud, la educación y otras transferencias directas, se asigna \ndirectamente a individuos u hogares en situación de pobreza monetaria según la definición nacional. \n\nEl indicador mide si el gasto público está dirigido a los pobres monetarios. El gasto social a favor de los \npobres se define si la proporción de los gastos gubernamentales en servicios sociales es mayor que la proporción de \nla población, medida al nivel determinado por la definición nacional de pobreza de ingresos/consumo (en consonancia \ncon el ODS 1.2.1). Por ejemplo, si la proporción del gasto público recibido por los pobres excede (cae por debajo) \nde la proporción de pobres según la definición de las definiciones nacionales, los gastos públicos pueden \ninterpretarse como pro-pobres (no pro-pobres). Esta es una medida sólida del compromiso financiero que asumen \nlos gobiernos para dirigir sus servicios y transferencias a los grupos pobres de la sociedad, reforzando las estrategias \nde desarrollo a favor de los pobres. \n\nLos futuros desarrollos de la metodología y las mejoras en la disponibilidad de datos pueden \npermitir ampliar este indicador a otros grupos vulnerables, como las mujeres y los niños.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-0b-01.pdf\">Metadatos 1-b-1.pdf</a> (solo en inglés)", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The indicator measures the extent to which public spending in three key areas which \nare critical for poverty eradication, including health, education, and other direct \ntransfers is directly allocated to individuals or households in the monetary poor \nas per the national definition. \n\nThe indicator measures if public spending is targeting the monetary poor. Pro-poor \nsocial spending is defined if the proportion of government expenditures on social \nservices is higher than the proportion of the population, measured at the level determined \nby national definition of income/consumption poverty (consistent with SDG 1.2.1). For instance, \nif the proportion of public spending received by the poor exceeds (falls below) the proportion \nof poor as defined by national definitions, public expenditures can be interpreted as pro-poor \n(not pro-poor). This is a strong measurement of the financial commitment governments make to \ntarget their services and transfers on the poor groups of society, reinforcing propoor \ndevelopment strategies. \n\nFurther developments of the methodology and improvements in data availability may allow to expand\nthis indicator to other vulnerable groups, such as women and children.\n\nSource: United Nations Statistics Division\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-0b-01.pdf\">Metadata 1-b-1.pdf</a>"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Adierazle honek neurtzen du pobrezia ezabatzeko funtsezkoak diren hiru arlo gakotan (osasuna, hezkuntza \neta beste transferentzia zuzen batzuk) zein neurritan esleitzen zaien gastu publikoa zuzenean definizio \nnazionalaren arabera diru-pobreziako egoeran dauden norbanako edo etxeei. \n\nGastu publikoa diruz pobre direnei zuzentzen zaien neurtzen du adierazleak. Pobreen aldeko gastu soziala \nzehazteko kontuan hartzen da ea oinarrizko zerbitzuen gobernu-gastuen proportzioa handiagoa den biztanleriaren \nproportzioa baino, diru-sarreren eta kontsumoaren araberako pobreziaren definizio nazionalak zehaztutako \nmaila kontuan hartuta (1.2.1 GJHrekin bat). Adibidez, pobreek jasotako gastu publikoaren proportzioak gainditu \negiten badu pobreen proportzioa, definizio nazionalek emandako definizioaren arabera, gastu publikoak pobreen \naldekotzat jo daitezke; proportzio hori azpitik geratzen bada, bestalde, ez da pobreen aldekotzat jotzen. \nAdierazle sendoa da, beraz, gobernuek beren zerbitzuak eta transferentziak gizarteko talde pobreei zuzentzeko \norduan hartzen duten finantza-konpromisoa neurtzeko, pobreen aldeko garapen-estrategiak indartuz. \n\nEtorkizunean metodologia garatu eta datu eskuragarriak hobetu ahala, adierazle hau beste talde zaurgarri \nbatzuetara hedatu ahalko da, hala nola emakume eta haurrengana. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-0b-01.pdf\">Metadatuak 1-b-1.pdf</a> (ingelesez bakarrik)"}, "national_metadata_updated_date"=>"2025-03-09", "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.b: Create sound policy frameworks at the national, regional and international levels, based on pro-poor and gender-sensitive development strategies, to support accelerated investment in poverty eradication actions</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.b.1: Pro-poor public social spending</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_XPD_PR - Proportion of government spending which benefits the poorest 20 percent [1.b.1]</p>\n<p>SG_XPD_PR_SOC - Proportion of government spending on social transfers which benefits the poorest 20 percent [1.b.1]</p>\n<p>SG_XPD_PR_HLTH - Proportion of government spending on health which benefits the poorest 20 percent [1.b.1]</p>\n<p>SG_XPD_PR_EDUC - Proportion of government spending on education which benefits the poorest 20 percent [1.b.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>10.4.2, 1.2.1, 1.a.2, 1.1.1, 1.2.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&#x2019;s Fund (UNICEF), Save the Children</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&#x2019;s Fund (UNICEF), Save the Children</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of government spending on social transfers, health, and education, which benefits the poorest 20%.</p>\n<p><strong>Concepts:</strong></p>\n<p><u>Public spending:</u> Expenditures by governments on social transfers (cash, near-cash, and/or in-kind), health, and education. If data only exists for some sectors, these can still be reported (indicated with an explanation in a footnote).</p>\n<p><u>Poorest 20%:</u> Bottom quintile of income/consumption/physical asset-based distribution. Footnotes should indicate what kind of data was used.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>This indicator requires fiscal or budgetary or administrative data on social expenditures and subsidy expenditure as well as a nationally representative micro-data set (for instance income/expenditure survey or household budget survey).</p>", "COLL_METHOD__GLOBAL"=>"<p>Nationally representative micro-data sets are often collected and hosted by the national statistics agency. Fiscal or budgetary or administrative data is occasionally available in unabridged summaries with enough detail at the program or policy level for the estimation of the indicator. More often, however, budgetary and administrative data is kept by the agency executing the program. The validation process requires consultation with each of the ministries and agencies responsible for executing programmatic expenditures.</p>", "FREQ_COLL__GLOBAL"=>"<p>Source data collection follows the update cycle for country-specific micro-data sets as well as the audit cycle for fiscal year revenues and expenditures.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>We are planning annual updates of the dataset in March.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Ultimately the data providers are national-level statistical agencies for the micro-data sets and national-level fiscal agencies and bodies for the budgetary and administrative data.</p>\n<p>Where a country produces its own 1.b.1 indicator it will take precedence over estimates produced by other institutions, subject to meeting the reporting requirements below. For all other countries, estimates and indicators produced by the WBG, the Commitment to Equity Institute, UNICEF. Save the Children, or other istitutoins will be considered.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNICEF and Save the Children are the custodians of the compilation and reporting procedures for this indicator across national participants and contributing organizations. The custodian agencies collaborate with the World Bank, the CEQ Institute at University of Tulane, and other partners in collating and processing data.</p>", "INST_MANDATE__GLOBAL"=>"<p>Data compilation on the pro-poor social spending are the responsibilities undertaken by UNICEF and Save the Children in view of their mandate to assess policies and government efforts on behalf of families and children (UN Convention on the Rights of the Child, Article 4).</p>", "RATIONALE__GLOBAL"=>"<p>The indicator measures the extent to which public spending in three key areas which are critical for poverty eradication, including health, education, and other social transfers is directly allocated to individuals or households in the poorest 20%.</p>\n<p>Pro-poor social spending is defined if the proportion of government expenditures on social services is (a) equal or larger than 20%.</p>\n<p>This is a strong measurement of the financial commitment governments make to target their services and transfers on the poor groups of society, reinforcing pro-poor development strategies.</p>\n<p>In line with the SDG guidelines on data disaggregation, this indicator (where possible) should be disaggregated by gender, children, location (urban/rural), and political and administrative units.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Feasibility: The indicator can be estimated for any country for which (a) a micro-data set detailing incomes or expenditures and services utilization (i.e. education, health, and cash transfers receipts) at the individual or household level exists and (b) a set of fiscal, administrative, or budgetary records detailing public expenditures at the program level is available.</p>\n<p>Suitability/relevance: The indicator provides an estimate how well public resources are allocated to sectors which disproportionally benefit the poor. This reflects the financial consequences of policy frameworks, which are based on pro-poor development strategies, which allows to measure progress on the SDG target 1.b.</p>\n<p>Relationship with other SDGs: The indicator could be compared with the one under SDG 10 on equity of fiscal policy. Countries should be encouraged to collect and analyse the data within a single process to create synergy and avoid unnecessary duplication.</p>\n<p>Limitations: The indicator does not take into effect the consequences of revenue-related fiscal activities, such as taxes or contributions to public insurance systems, on the poor.</p>", "DATA_COMP__GLOBAL"=>"<p><u>Population in the poorest 20% of households</u> can be derived directly from a nationally representative micro-data set (an Income and Expenditure Survey, for example).</p>\n<p><u>Public spending on social services</u> can be directly derived from budget administrative data.</p>\n<p>A fiscal incidence analysis is required to estimate the benefit the poor individuals or households (depending on underlying survey data) are receiving from those services. The incidence analysis measures the monetised value of in-kind transfers in education and health services at average government costs. In addition, this indicator includes cash and near cash transfers in the definition of social services (conditional and unconditional cash transfers, school feeding programmes etc.). The procedures are described in detailed in the CEQ Handbook, Meerman, Jacob (1979), Selowsky, Marcelo (1979), and many other ones.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Every year, UNICEF validates the latest data on all the SDG indicators UNICEF is custodian for. The validation process involves sharing the latest data (and metadata) of the indicators with SDG focal persons in each country, via the UNICEF Country Offices. Governments respond in writing accepting, rejecting or providing an alternative indicator.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>The indicator cannot be calculated when no nationally representative micro-data set and/or country-level fiscal, budgetary, and administrative data are available. Budget and administrative data exists for every fiscal system but is not always public.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>SDG reporting regions will be used when at least half of the countries in a given region report data.</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>This indicator can be calculated based on the current state of household surveys micro-data and budget administrative data.</li>\n  <li>The methodology is the traditional Benefit Incidence Analysis wich has been used for many years in many countries an contexts.</li>\n  <li>A detailed description of the methodology can be found in Lustig, Nora (ed). 2018. <u>CEQ Handbook: Estimating the Impact of Fiscal Policy on Inequality and Poverty</u>, CEQ Institute at Tulane University and Brookings Institution Press, Meerman, Jacob Public Expenditures in Malaysia: Who Benefits and Why? (New York: Oxford University Press, 1979), Selowsky, Marcelo (1979) Who Benefits from Government Expenditure? (New York: Oxford University Press), and many others.</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>UNICEF and Save the Children will coordinate with data providers on the quality of their respective indicators</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UNICEF and Save the Children will seek collaboration with the UN Regional Economic Commissions, the UN Department of Economic and Social Affairs, the International Monetary Fund, The World Bank, and Regional Development Banks to provide quality assurance and international comparability.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Submissions to the SDG Indicator Database will indicate whether information has been prepared by the official government agencies, Save the Children, UNICEF, the World Bank, the Commitment to Equity Institute, or any other agency.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Directly comparable data from fiscal incidence analyses for at least one sector (health, education, transfers) exist currently for 85 countries globally (covering 72% of the global population), with data for 59 countries (27% of population) available for 2015 or later. </p>\n<p>Including further available data for individual sectors (not directly comparable across countries, but still included in SDG reporting in line with 2.a), data on pro-poor spending currently exist for 151 countries (covering 94% of the global population), with data for 133 countries (73% of population) for 2015 or later. </p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Region</strong></p>\n      </td>\n      <td>\n        <p><strong>Number of countries (2015 or later)</strong></p>\n      </td>\n      <td>\n        <p><strong>Population coverage (2015 or later)</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Australia and New Zealand</p>\n      </td>\n      <td>\n        <p>1 (1)</p>\n      </td>\n      <td>\n        <p>84% (84%)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Central Asia and Southern Asia</p>\n      </td>\n      <td>\n        <p>13 (13)</p>\n      </td>\n      <td>\n        <p>100% (100%)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Eastern Asia and South-eastern Asia</p>\n      </td>\n      <td>\n        <p>11 (10)</p>\n      </td>\n      <td>\n        <p>91% (30%)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Latin America and the Caribbean</p>\n      </td>\n      <td>\n        <p>22 (19)</p>\n      </td>\n      <td>\n        <p>97% (90%)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Northern America and Europe</p>\n      </td>\n      <td>\n        <p>40 (39)</p>\n      </td>\n      <td>\n        <p>96% (96%)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Oceania excluding Australia and New Zealand</p>\n      </td>\n      <td>\n        <p>7 (3)</p>\n      </td>\n      <td>\n        <p>90% (3%)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Sub-Saharan Africa</p>\n      </td>\n      <td>\n        <p>42 (39)</p>\n      </td>\n      <td>\n        <p>94% (88%)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Western Asia and Northern Africa </p>\n      </td>\n      <td>\n        <p>15 (9)</p>\n      </td>\n      <td>\n        <p>82% (49%)</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Time series:</strong></p>\n<p>Comparable estimates for this iindicator are for the most part available for single country/year pairs only.The limitation to producing more frequent time series is the availability of more frequent household surveys. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator can be disaggregated by should be disaggregated by gender, children, location (urban/rural), and political and administrative units, where data is available.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p>UNICEF, Save the Children (2023). Pro-poor public social spending: concepts, reporting status, challenges, and forward steps. <u>https://data.unicef.org/resources/measuring-pro-poor-public-social-spending-challenges-and-opportunities-for-achieving-the-sdgs/</u>.<u> </u></p>\n<p>Lustig, Nora (ed). 2018. CEQ Handbook: Estimating the Impact of Fiscal Policy on Inequality and Poverty, CEQ Institute at Tulane University and Brookings Institution Press. <a href=\"http://commitmentoequity.org/publications-ceq-handbook\">commitmentoequity.org/publications-ceq-handbook</a>.</p>\n<p>Meerman, Jacob Public Expenditures in Malaysia: Who Benefits and Why? (New York: Oxford University Press, 1979).</p>\n<p>Selowsky, Marcelo (1979) Who Benefits from Government Expenditure? (New York: Oxford University Press).</p>", "indicator_sort_order"=>"01-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.1.1", "slug"=>"2-1-1", "name"=>"Prevalencia de la subalimentación", "url"=>"/site/es/2-1-1/", "sort"=>"020101", "goal_number"=>"2", "target_number"=>"2.1", "global"=>{"name"=>"Prevalencia de la subalimentación"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Prevalencia de la subalimentación", "indicator_number"=>"2.1.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"La prevalencia de la desnutrición o sub-alimentación\nes una estimación de la proporción de la población cuyo consumo habitual de \nalimentos es insuficiente para proporcionar la niveles de energía dietética \nnecesarios para mantener una vida normal, activa y saludable. Se expresa como \nporcentaje.\n\nEl indicador ha sido utilizado por la FAO para hacer el seguimiento de la meta de la \nCumbre Mundial sobre la Alimentación y la meta 1C de los ODM, a nivel nacional, \nregional y global, desde 1999. \n\nPermite hacer el seguimiento de las tendencias en el grado de \ninsuficiencia de energía alimentaria en una población a lo largo del tiempo, generadas \ncomo es resultado de la combinación de cambios en la disponibilidad general de alimentos,\nen la capacidad de los hogares para acceder a ellos y en las características \nsociodemográficas de la población, así como de diferencias entre países y regiones\nen un momento dado.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.1.1&seriesCode=SN_ITK_DEFC&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Prevalencia de desnutrición (%) SN_ITK_DEFC</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-01-01.pdf\">Metadatos 2-1-1.pdf (solo en inglés)</a>", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "descripcion_global"=>"The prevalence of undernourishment (PoU) is an estimate of the\nproportion of the population whose habitual food consumption is insufficient \nto provide the dietary energy levels that are required to maintain a normal \nactive and healthy life. It is expressed as a percentage.\n", "justificacion_global"=>"\nThe indicator has been used by FAO to monitor the World Food Summit Target \nand the MDG Target 1C, at national, regional and global level, since 1999.\n\nIt allows monitoring trends in the extent of dietary energy inadequacy \nin a population over time, generated as a result of the combination of changes \nin the overall availability of food, in the households’ ability to access it, \nand in the socio-demographic characteristics of the population, as well as \ndifferences across countries and regions in any given moment in time.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.1.1&seriesCode=SN_ITK_DEFC&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\n Prevalence of undernourishment (%) SN_ITK_DEFC</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-01-01.pdf\">Metadata 2-1-1.pdf </a>"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "descripcion_global"=>"Desnutrizioaren edo azpielikaduraren nagusitasunak neurtzen du biztanleriaren zein proportziotan \nden elikagaien ohiko kontsumoa urriegia bizitza normal, aktibo eta osasuntsua edukitzeko beharrezkoak \ndiren energia dietetikoko mailak emateko. Ehunekotan adierazten da. \n", "justificacion_global"=>"FAOk adierazle hau erabili izan du Elikadurari buruzko Munduko Biltzarreko xedearen eta Milurteko \nGarapen Helburuen 1C xedearen jarraipena egiteko, nazioan, eskualdean eta maila orokorrean, 1999tik. \n\nAukera ematen du joeren jarraipena egiteko biztanleria baten elikagai-energiaren urritasun mailan, \nepe luzera, hainbat faktoretan egondako aldaketen ondorioz: elikagaien erabilgarritasun orokorrean, \netxeek elikagaiak eskuratzeko duten gaitasunean, eta biztanleriaren ezaugarri soziodemografikoetan. \nHalaber, herrialde eta eskualdeen arteko aldeak aztertzen ditu une jakin batean. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.1.1&seriesCode=SN_ITK_DEFC&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Desnutrizioaren prebalentzia (%) SN_ITK_DEFC</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-01-01.pdf\">Metadatuak 2-1-1.pdf (ingelesez bakarrik)</a>"}, "national_metadata_updated_date"=>"2025-03-17", "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.1: By 2030, end hunger and ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year round</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.1.1: Prevalence of undernourishment</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SN_ITK_DEFC - Prevalence of undernourishment [2.1.1]</p>\n<p>SN_ITK_DEFCN - Number of undernourish people [2.1.1] </p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>2.1.2, 2.2.1, 2.2.2, 2.2.3 </p>\n<p>Comments: </p>\n<p>Links with Target 2.2, to the extent that hunger may lead to malnutrition, and Target 2.2 may not be achieved if Target 2.1 is not achieved.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition: </strong></p>\n<p>The prevalence of undernourishment (PoU) (French: pourcentage de sous-alimentation; Spanish: porcentaje de sub-alimentaci&#xF3;n; Italian: prevalenza di sotto-alimentazione) is an estimate of the proportion of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. It is expressed as a percentage.</p>\n<p><strong>Concepts: </strong></p>\n<p>Undernourishment is defined as the condition by which a person has access, on a regular basis, to the amount of food that are insufficient to provide the energy required for conducting a normal, healthy and active life, given his or her own dietary energy requirements. </p>\n<p>Though strictly related, &#x201C;undernourishment&#x201D; as defined here is different from the physical conditions of &#x201C;malnutrition&#x201D; and &#x201C;undernutrition&#x201D; as it refers to the condition of insufficient intake of food, rather than to the outcome in terms of nutritional status. In French, Spanish and Italian the difference is marked by the use of the terms alimentation, alimentaci&#xF3;n, or alimentazione, instead of nutrition, nutrici&#xF3;n or nutrizione, in the name of the indicator. A more appropriate expression in English that would render the precise meaning of the indicator might have been &#x201C;prevalence of under-feeding&#x201D; but by now the term &#x201C;undernourishment&#x201D; has long been associated with the indicator. </p>\n<p>While the undernourishment condition applies to individuals, due to conceptual and data-related considerations, the indicator can only be referred to a population, or group of individuals. The prevalence of undernourishment is thus an estimate of the percentage of individuals in a group that are in that condition, but it does not allow for the identification of which individuals in the group are, in fact, undernourished. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Prevalence of undernourishment: Percent (%) Number of undernourished people: Millions (of people) </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The construction of the regional and global estimates, as well as estimates for specific groups, such as Least Developed Countries, Land Locked Developing countries, Small Island Developing States, Developed Regions, and Developing Regions, of this indicator follows the UN M49 Standard.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The ideal source of data to estimate the PoU would be a carefully designed and skillfully conducted individual dietary intake survey, in which actual daily food consumption, together with heights and weights for each surveyed individual, are repeatedly measured on a sample that is representative of the target population. Due to their cost, however, such surveys are rare.</p>\n<p>In principle, a well-designed household survey that collects information on food acquisitions might be sufficient to inform a reliable estimate of the Prevalence of Undernourishment (PoU) in a population, at a reasonable cost and with the necessary periodicity to inform the SDG monitoring process, provided that: </p>\n<ol>\n  <li>All sources of food consumption for all members of the households are properly accounted for, including, in particular, food that is consumed away from home; </li>\n  <li>Sufficient information is available to convert the data on food consumption or on food expenditures into their contribution to dietary energy intake; </li>\n  <li>The proper methods to compute the PoU are used, to control for excess variability in the estimated levels of habitual food consumption across households, allowing for the presence on normal variability in the distribution of food consumption across individuals, induced by the differences in energy requirements of the members of the population. </li>\n</ol>\n<p>Examples of surveys that could be considered for this purpose include surveys conducted to compute economic statistics and conduct poverty assessments, such as Household Income and Expenditure Surveys, Household Budget Surveys and Living Standard Measurement Surveys. </p>\n<p>In practice, however, it is often impossible, and not advisable, to rely only on data collected through a household survey, as the information needed to estimate the four parameters of the PoU model is either missing or imprecise. </p>\n<p>Household Survey food consumption data often must be integrated by </p>\n<p>a) Data on the demographic structure of the population of interest by sex and age; </p>\n<p>b) Data or information on the median height of individuals in each sex and age class; </p>\n<p>c) Data on the distribution of physical activity levels in the population; </p>\n<p>d) Alternative data on the total amounts of food available for human consumption, to correct for biases in the estimate of the national average daily dietary energy consumption in the population. </p>\n<p>Data for a), b) and c) could be available through the same multipurpose survey that provides food consumption data, but are more likely available from other sources, such as National Demographic and Health Surveys (for a) and b)) and Time Use Surveys (for c)). </p>\n<p>Correcting for bias in the estimated average daily dietary energy consumption might need to be based on alternative sources on food consumption, such as aggregate food supply and utilization accounts and food balance sheets. </p>\n<p>To inform its estimate of PoU at national, regional and global level, in addition to all household surveys for which it is possible to obtain micro data on food consumption, FAO relies on: </p>\n<p>a) UN Population Division&#x2019;s World Population Prospects (https://esa.un.org/unpd/wpp/Download/Standard/Population/), which provide updated estimates of the structures of the national population by sex and age every two years for most countries in the world; </p>\n<p>b) FAO Food Balance Sheets (http://www.fao.org/faostat/en/#data), which provides updated estimates of the national availability of food every year for most countries in the world.</p>\n<p>Micro data from household surveys that collect food consumption data are sourced by FAO directly through the National Statistical Agencies&#x2019; websites, or through specific bilateral agreements. </p>", "COLL_METHOD__GLOBAL"=>"<p>Official information on food commodity production, trade and utilization used by FAO to compile Food Balance Sheets is provided mainly by Statistical Units of the Ministry of Agriculture. FAO sends out a data collection questionnaire every year to an identified focal point. </p>\n<p>Microdata of household surveys are generally owned and provided by National Statistical Agencies. When available, data is sourced by FAO directly through the NSA&#x2019; website. In several cases, when microdata is not available in the public domain, bilateral agreements have been signed, usually in the contexts of technical assistance and capacity development programs. </p>\n<p>Data on the population size and structure for all monitored countries is obtained from the UN Population Division&#x2019;s World Population Prospects. </p>", "FREQ_COLL__GLOBAL"=>"<p>Continuing</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released each year alongside the <em>State of Food Security and Nutrition in the World</em> report, usually in mid-July. </p>", "DATA_SOURCE__GLOBAL"=>"<p>Given the various data sources, national data providers vary. Official information on food commodity production, trade and utilization used by FAO to compile Food Balance Sheets is provided mainly by Statistical Units of the Ministry of Agriculture. Microdata of household surveys are generally owned and provided by National Statistical Agencies.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations, Statistics Division, Food Security and Nutrition Statistics Team</p>", "INST_MANDATE__GLOBAL"=>"<p>The Office of the Chief Statistician of FAO manages the Interdepartmental Working Group on SDG indicators under the FAO custodianship and identifies a focal point for each of them. The team leader of the Food Security and Nutrition Statistics Team of the Statistics Division is formally appointed as the focal person for the collection, processing, and dissemination of statistics for this indicator. </p>", "RATIONALE__GLOBAL"=>"<p>The indicator has been used by FAO to monitor the World Food Summit Target and the MDG Target 1C, at national, regional and global level, since 1999. It allows monitoring trends in the extent of dietary energy inadequacy in a population over time, generated as a result of the combination of changes in the overall availability of food, in the households&#x2019; ability to access it, and in the socio-demographic characteristics of the population, as well as differences across countries and regions in any given moment in time. </p>\n<p>The parametric approach adopted by FAO allows obtaining reliable estimated for relatively large population groups. As it reflects a severe condition of lack of food, it is fully consistent with the spirit of a Goal that aims at reducing hunger.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Over the years, the parametric approach informing the computation of the PoU has been criticized, based on the presumptions that undernourishment should be assessed necessarily starting at the individual level, by comparing individual energy requirements with individual energy intakes. According to such a view, the prevalence of undernourishment could be simply computed by counting the number of individuals in a representative sample of the population that is classified as undernourished, based on a comparison of individual habitual food consumption and requirements. </p>\n<p>Unfortunately, such an approach is not feasible for two reasons: first, due to the cost of individual dietary intake surveys, individual food consumption is measured only in a few countries, every several years, on relatively small samples; moreover, individual energy requirements are practically unobservable with standard data collection methods (to the point that observed habitual energy consumption of individuals in a healthy status is still the preferred way to infer individual energy requirements). This means that even if it were possible to obtain accurate observations of the individual dietary energy consumption, this would be insufficient to infer on the undernourishment condition at individual level, unless integrated by the observation on the physical status (body mass index) and of its dynamic over time, of the same individual. </p>\n<p>The model-based approach to estimate the PoU developed by FAO integrates information that is available with sufficient regularity from different sources for most countries in the world, in a theoretically consistent way, thus providing what is still one of the most reliable tools to monitor progress towards reducing global hunger. </p>\n<p>Further specific consideration </p>\n<p>1. Feasibility </p>\n<p>Estimation of PoU at national level has been feasible for most countries in the world since 1999. In the worst case scenario, when no data on food consumption was available from a recent household survey, the model-based estimate of the PoU is informed by an estimate of mean level of dietary energy consumption (DEC) from Food Balance Sheets (FBS), an indirect estimate of the coefficient of variation (CV) based on information on the country&#x2019;s GDP, Gini coefficient of Income, an index of the relative price of food, or other indicators of development such as country&#x2019;s Under 5 Mortality Rate, and an estimate of the Minimum Dietary Energy Requirement (MDER) based on the UN Population Division&#x2019;s World Population Prospects data. </p>\n<p>2. Reliability </p>\n<p>Reliability mostly depends on the quality of the data used to inform the estimation of the model&#x2019;s parameters. </p>\n<p>DEC could be estimated either from survey data or from food balances. Neither source is devoid of problems. When comparing estimates of national DEC from FBS and from surveys, differences are frequently noted. </p>\n<p>DEC estimates from survey data can be affected by systematic measurement errors due to under-reporting of food consumption, or to incomplete recording of all food consumption sources. Recent research shows that a negative bias of up to more than 850 kcal can be induced on the estimated daily per capita caloric consumption can be induced by the type of food consumption module chosen to capture the data at the household level. (See De Weerdt et al., 2015, Table 2, <a href=\"https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf\">https://feb.kuleuven.be/drc/licos/publications/dp/DP%20365%20Complete.pdf</a>). A detailed analysis of a recent Household Budget Survey in Brazil revealed how food provided for free through the school meals program and consumed by children while at school, had not been accounted among the sources of household food consumption, accounting for a downward bias of the average per capita daily dietary energy consumption of 674 kcal. (See Borlizzi, Cafiero &amp; Del Grossi, forthcoming.) </p>\n<p>DEC estimates from Food Balance Sheets can also be affected by errors, though it is difficult to establish the direction of induced bias. As average food availability is a residual in the FBS method, any errors in reported production, trade, and stocks might affect the estimates of national food availability. Moreover, errors might be induced by the difficulty in properly accounting for all forms of food commodity utilization. To the extent that all these errors are uncorrelated, though, the impact on the estimated average food consumption will be lower than each of the errors, considered separately, might imply. Nevertheless, considering how problematic it is to precisely account for variations in national reserves of food commodities, for which official data may be unreliable, it is recognized that the estimated annual stock variation is prone to considerable uncertainty that would be transferred to the estimated DEC in each given year. </p>\n<p>To limit the impact of such errors, FAO has traditionally presented estimates of PoU at national level as three-year averages, on the presumption that errors induced by imprecise recording of stocks variations in each single year might be highly reduced when considering an average over three consecutive years. </p>\n<p>Survey data are the only source to estimate the CV. As described in the section of metadata on the method of computation, unless obtained from high quality individual dietary intake surveys, data needs to be treated to reduce the likely upward bias in the estimates of the CV that would be induced by the spurious variability due to errors in measuring individual habitual dietary energy intake. </p>\n<p>3. Comparability </p>\n<p>If the same method of computation is used, comparability across time and space is relatively high, with the only potential cause of inhomogeneity found in the different quality of the background data. </p>\n<p>4. Limitations </p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. Even though it is not possible to compute theoretical Margins of Error (MoE) for PoU estimates, these would very likely exceed plus or minus 2.5% in most cases. For this reason, FAO publishes national level PoU estimates only when they are larger than 2.5%. This also suggests that 2.5% is the lowest feasible target that can be set for the PoU indicator, a value that is unsatisfactorily large when the ambition is to fully eradicate the scourge of hunger. </p>\n<p>If no survey is available that collects food consumption data and that is representative at subnational level, the indicator can only be computed at national level.&quot;</p>", "DATA_COMP__GLOBAL"=>"<p>To compute an estimate of the prevalence of undernourishment in a population, the probability distribution of habitual dietary energy intake levels (expressed in kcal per person per day) for the average individual is modelled as a parametric probability density function (pdf), f(x).</p>\n<p>The indicator is obtained as the cumulative probability that the habitual dietary energy intake (x) is below the minimum dietary energy requirements (MDER) (i.e. the lowest limit of the range of energy requirements for the population&#x2019;s representative average individual) as in the formula below: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>P</mi>\n    <mi>o</mi>\n    <mi>U</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mrow>\n      <msubsup>\n        <mo stretchy=\"false\">&#x222B;</mo>\n        <mrow>\n          <mi>x</mi>\n          <mo>&amp;lt;</mo>\n          <mi>M</mi>\n          <mi>D</mi>\n          <mi>E</mi>\n          <mi>R</mi>\n        </mrow>\n        <mrow>\n          <mi>&amp;nbsp;</mi>\n        </mrow>\n      </msubsup>\n      <mrow>\n        <mi>f</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>x</mi>\n            <mo>|</mo>\n            <mi>&#x3B8;</mi>\n          </mrow>\n        </mfenced>\n        <mi>d</mi>\n        <mi>x</mi>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>where &#x3B8; is a vector of parameters that characterizes the pdf. The distribution is assumed to be lognormal, and thus fully characterized by only two parameters: the mean dietary energy consumption (DEC), and its coefficient of variation (CV). </p>\n<p>A custom R function is available from the Statistics Division at FAO to compute the PoU, given the three parameters DEC, CV, and MDER. </p>\n<p>Different data sources can be used to estimate the different parameters of the model. </p>\n<p><u>DEC </u></p>\n<p>Ideally, data on food consumption should come from nationally representative household surveys (such as Living Standard Measurement Surveys or Household Incomes and Expenditure Surveys). However, only very few countries conduct such surveys on an annual basis. Thus, in FAO&#x2019;s PoU estimates for global monitoring, DEC values are estimated from the dietary energy supply (DES) reported in the Food Balance Sheets (FBS), compiled by FAO for most countries in the world (<a href=\"https://www.fao.org/faostat/en/#data/FBS\">https://www.fao.org/faostat/en/#data/FBS</a>). </p>\n<p><u>CV </u></p>\n<p>When reliable data on food consumption are available from aforementioned nationally representative household surveys, the CV due to income (CV|y) that describes the distribution of average daily dietary energy requirement in the population can be estimated directly.</p>\n<p>When no suitable survey data are available, FIES data collected by FAO since 2014 are used to project the changes in the CV|y from 2015 (or from the year of the last food consumption survey) up to 2019, based on a smoothed (three-year moving average) trend in severe food insecurity.</p>\n<p>Since 2014, FIES data provide evidence on recent changes in the extent of severe food insecurity that might closely reflect changes in the PoU. To the extent that such changes in PoU are not explained by changes in average food supplies, they can thus be used to infer the likely changes in the CV|y that might have occurred in the most recent year. Analysis of the combined set of historic PoU estimates reveals that, on average, and once differences in DEC and MDER have been controlled for, the CV|y explains about one-third of the differences in PoU across time and space. For each country for which FIES data are available, the CV|y is estimated by the amount that would generate one-third of a percentage point change in the PoU for each observed percentage point change in the prevalence of severe food insecurity. For all other countries, the CV|y is kept constant at the estimated 2017 value.</p>\n<p>In the FAO PoU parametric approach, the CV due to body weight and lifestyle, a.k.a. CV due to requirement (CV|r), represents the variability of the distribution of dietary energy requirements of a hypothetical average individual representative of a healthy population, which is also equal to the CV of the distribution of dietary energy intakes of a hypothetical average individual if the population is perfectly nourished. The distribution of dietary energy requirements of a hypothetical average individual can be assumed to be normal, thus its variability can be estimated if at least two percentiles and their values are known. As a result, given that we are interested in deriving the theoretical distribution of dietary energy requirements for healthy hypothetical average individuals to estimate the CV|r, the MDER and the average dietary energy requirement (ADER) can be used to approximate the 1st percentile and the 50th percentile of the distribution of energy requirements of the hypothetical average individual as they are built on the same principles of a weighted average from sex-age-physiological status groups. Therefore, the value of CV|r is derived as the inverse cumulative standard normal distribution of the difference between the MDER and the ADER. Similar to the MDER, the ADER is estimated using the average of the minimum and the maximum values of the PAL category &#x2018;Active or moderately active lifestyle&#x2019;.</p>\n<p>The total CV is then obtained as the geometric mean of the CV|y and the CV|r:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>V</mi>\n    <mo>=</mo>\n    <msqrt>\n      <mfenced separators=\"|\">\n        <mrow>\n          <msup>\n            <mrow>\n              <mi>C</mi>\n              <mi>V</mi>\n              <mo>|</mo>\n              <mi>y</mi>\n            </mrow>\n            <mrow>\n              <mn>2</mn>\n            </mrow>\n          </msup>\n        </mrow>\n      </mfenced>\n      <mo>+</mo>\n      <mfenced separators=\"|\">\n        <mrow>\n          <msup>\n            <mrow>\n              <mi>C</mi>\n              <mi>V</mi>\n              <mo>|</mo>\n              <mi>r</mi>\n            </mrow>\n            <mrow>\n              <mn>2</mn>\n            </mrow>\n          </msup>\n        </mrow>\n      </mfenced>\n    </msqrt>\n  </math></p>\n<p><strong>Challenges and limitations:</strong> While formally the state of being undernourished or not is a condition that applies to individuals, given the data usually available on a large scale, it is impossible to reliably identify which individuals in a certain group are actually undernourished. Through the statistical model described above, the indicator can only be computed with reference to a population or a group of individuals for which a representative sample is available. The prevalence of undernourishment is thus an estimate of the percentage of individuals in that group that are in such condition and cannot be further disaggregated.</p>\n<p>Due to the probabilistic nature of the inference and the margins of uncertainty associated with estimates of each of the parameters in the model, the precision of the PoU estimates is generally low. While it is not possible to formally compute margins of error around PoU estimates, these are expected to likely exceed 5 percent in most cases. For this reason, FAO does not consider PoU estimates that result to be lower than 2.5 percent as sufficiently reliable to be reported.</p>\n<p><u>MDER </u></p>\n<p>Human energy requirements for an individual in a given sex/age class are determined on the basis of normative requirements for basic metabolic rate (BMR) per kilogram of body mass, multiplied by the ideal weights that a healthy person of that sex/age class may have, given his or her height, and then multiplied by a coefficient of physical activity level (PAL) to take into account physical activity. Given that both healthy BMIs and PALs vary among active and healthy individuals of the same sex and age, a <em>range </em>of energy requirements applies to each sex and age group of the population. The MDER for the average individual in the population, which is the parameter used in the PoU formula, is obtained as the weighted average of the lower bounds of the energy requirement ranges for each sex and age group, using the shares of the population in each sex and age group as weights.</p>\n<p>Information on the population structure by sex and age is available for most countries in the world and for each year from the UN Department of Economic and Social Affairs (DESA) Population Prospects, revised every two years.</p>\n<p>Information on the median height in each sex and age group for a given country is derived from a recent demographic and health survey (DHS) or from other surveys that collect anthropometry data on children and adults. Even if such surveys do not refer to the same year for which the PoU is estimated, the impact of possible small intervening changes in median heights over the years on PoU estimates is expected to be negligible.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>There are no formal country consultations. Data validation is internal to FAO. This indicator has been in existence since 1999. FAO has produced it to inform the World Food Summit target and the MDG target 1.C without country consultations. Upon request, FAO has provided countries with details on the data used in their specific case. </p>", "ADJUSTMENT__GLOBAL"=>"<p>None </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level </strong></p>\n<p>When no data on food consumption is available from a recent household survey, the model-based estimate of the PoU is informed by an estimate of DEC from Food Balance Sheets, an indirect estimate of CV based on information on the country&#x2019;s GDP, Gini coefficient of Income, an index of the relative price of food, or other indicators of development such as country&#x2019;s Under 5 Mortality Rate, and an estimate of the MDER based on the UN Population Division&#x2019;s World Population Prospects data. </p>\n<p>See the section on method of computation for details. </p>\n<p><strong>&#x2022; At regional and global levels </strong></p>\n<p>Missing values for individual countries are implicitly imputed to be equal to the population weighted average of the estimated values of the countries present in the same subregion or region.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates of the PoU are computed as: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>U</mi>\n      </mrow>\n      <mrow>\n        <mi>R</mi>\n        <mi>E</mi>\n        <mi>G</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>P</mi>\n                <mi>o</mi>\n                <mi>U</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n            <mi>&amp;nbsp;</mi>\n            <mo>&#xD7;</mo>\n            <mi>&amp;nbsp;</mi>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>N</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where PoU<sub>i</sub> are the values of PoU estimated for all countries (i) in the aggregate for which available data allow to compute a reliable estimate, and N<sub>i</sub> the corresponding population size. </p>", "DOC_METHOD__GLOBAL"=>"<p>The main three sources of data at national level are: </p>\n<p>a) Official reports on the production, trade and utilization of the major food crop and livestock productions. </p>\n<p>b) Household survey data on food consumption </p>\n<p>c) Demographic characteristics of the national population </p>\n<p>Data sources for agricultural production are usually national surveys that are conducted by the Ministry of Agricultural/Livestock and/or the National Statistical Office. The surveys are usually annual, and in the absence of direct measurements, use information on areas/animal numbers and crop yields/carcass weights to calculate crop or livestock product quantities. Agricultural censuses, which FAO recommends conducting every ten years, may complement these surveys by providing more updated measured data on crops and livestock, and thus enable more precise projections/revisions. </p>\n<p>The data source for agricultural and food trade is almost exclusively the national customs office (with few exceptions where data may be obtained from the Central Bank). Countries often prepare these trade reports following international standard formats (commodity/country classifications, units of measurement, trading partner detail). While such trade data may be considered quite reliable, being the result of direct measurement/reporting by/to the customs office, issues of unreported border trade (and animal movement), misclassification of commodities, confidentiality, time-lag, to name a few, may necessitate some data analysis and validation (often by referring to &#x2018;mirror&#x2019; trade statistics to cross-check quantities and values). </p>\n<p>Data on the utilization of primary and processed crops and livestock may be obtained through specialized surveys (supplemented by research) through the national agri-food industry system. Utilizations of interest here are those quantities destined for, among others, animal feed, for industrial uses (e.g. biofuel production), for national/enterprise/farm stocks, for seed (sowing for the successive agricultural cycle) &#x2013; to enable as accurate an assessment as possible of the quantities destined/available for potential human consumption. </p>\n<p>These datasets (production, trade and utilizations), once cross-checked and validated, form the basis for the compilation of the Food Balance Sheets (FBS). The FBS are an accounting framework whereby supply (production + imports + stock withdrawals) should equal utilization (export + food processing + feed + seed + industrial use, etc.). It should be noted that, within the FBS framework, post-harvest/slaughter losses (up to the retail level) are considered as utilization, and thus a component in the balancing of the FBS. The FBS framework provides a snapshot of the agricultural supply situation at the national level, and allows for a cross-referenced structure whereby data, official or estimated/imputed, may be further analyzed and validated (e.g. animal numbers may result as being under-reported/estimated). </p>\n<p>The main result of the compilation of the FBS is the calculation of the Dietary Energy Supply (DES) in kilocalories per person (based on population figures) in a given year (quantities resulting as available for human consumption are converted into their caloric equivalents by using appropriate nutritive conversion factors by commodity). The DES, in the absence of direct consumption data from household surveys, is one of the key components in the calculation of the Prevalence of Undernourishment (PoU). FAO is presently embarking on a more focused program of providing FBS capacity to countries, including an updated compilation tool. </p>\n<p>FAO obtains crop/livestock primary/processed production data, and principal utilization thereof, through country-tailored questionnaires that are dispatched to all countries annually. Official country trade statistics are obtained annually through bulk downloads of the United Nations trade database (countries are expected to report to UNSD annually). In some cases, when available, national FBS data are also used. These datasets are then validated and form inputs in the country FBS which FAO compiles. It should be noted that when data are not officially reported/available (as is frequently the case with commodity utilization data), and hence it is necessary to resort to imputations to fill the data gaps. </p>\n<p>The new FBS Guidelines for national compilation (completed recently in collaboration with the Global Strategy) and new compilation tool (R-based &#x2018;shiny&#x2019; application). </p>\n<p>Detail on FBS methodology: <a href=\"http://www.fao.org/economic/ess/fbs/ess-fbs02/en/\">http://www.fao.org/economic/ess/fbs/ess-fbs02/en/</a>. </p>\n<p>The FBS Handbook shown here should not be confused with the recently completed FBS Guidelines. The Handbook is of a more technical nature and explains the methodology followed by FAO in compiling country FBS. The Guidelines on the other hand, while based on the Handbook, provide countries with a more revised and practical guidance and recommendations for compilation at the national level. </p>\n<p>Some FBS background text also available on FAOSTAT: <a href=\"http://www.fao.org/faostat/en/#data/FBS\">http://www.fao.org/faostat/en/#data/FBS</a>. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>ESS conducts trend analysis of the newly updated indicator with other relevant indicators. Meanwhile, preliminary estimates of each round of the update are circulated among regional offices for review. Because of their knowledge of their regions and countries, they often provide invaluable inputs to the revisions and finalization of the update. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>FBS capacity development programme in cooperation with the Global Strategy (more details may be provided if required); capacity development in cooperation with the ESS Food Security team as a PoU/FBS package (financed by projects); and direct FBS capacity development based on specific direct country requests.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>High</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Since 2017 FAO has reported separate estimates of PoU for 160 countries. </p>\n<p>While country-level estimates are presented as three-year averages, regional and global estimates are yearly estimates. </p>\n<p><strong>Time series: </strong></p>\n<p>2000 - current </p>\n<p><strong>Disaggregation: </strong></p>\n<p>Due to reliance on national Food Balance Sheets data to estimate mean caloric consumption levels in the population, the global monitoring of MDG Target 1C and of the WFS target has been based on estimates of the PoU at national level only. </p>\n<p>In principle, the indicator can be computed for any specific population group, provided sufficient accurate information exists to characterize the model&#x2019;s parameters for that specific group, that is, if data on the group&#x2019;s food consumption levels, age/gender structure and &#x2013; possibly &#x2013; physical activity levels, exist. </p>\n<p>The scope for disaggregation thus crucially depends on the availability of surveys designed to be representative at the level of sub national population groups. Given prevailing practice in the design of national household surveys, sufficient reliable information is seldom available for disaggregation beyond the level of macro area of residence (urban-rural) and of the main Provinces/Divisions in a country. To the extent that most of the used surveys are designed to accurately capture the distribution of income, inference can be drawn on the PoU in different income classes of the population. Gender disaggregation is limited by the possibility to identify and group households by gender-related information (such as sex of the head of the household, or male/female ratio).</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies: </strong></p>\n<p>Many countries have produced and reported on estimates of the Prevalence of Undernourishment, including in their national MDG Reports, but almost invariably using a different methodology than the one developed by FAO, which makes national figures not comparable to those reported by FAO for global monitoring. </p>\n<p>The most common approach used in preparing national reports has been to calculate the percentage of households for which the average per capita daily dietary energy consumption is found to be below thresholds based on daily Recommended Dietary Intake, usually set at 2,100 kcal, based on household survey data. In some cases, also lower thresholds of around 1,400 kcal have been used, probably as a reaction to the fact that percentages of households reporting average daily consumption of less than 2,100 kcal per capita were implausibly high estimates of the prevalence of undernourishment. </p>\n<p>Almost without exception, no consideration related to the presence of excess variability in the dietary energy consumption data is made, and the reports reveal limited or no progress in the reduction of PoU over time. </p>\n<p>As discussed in the section on the method of computation, the results obtained through these alternative methods are highly unreliable and almost certainly biased toward overestimation. It is therefore advisable that a concerted effort is made to advocate for use of the FAO methods also in preparation of national reports. FAO stands ready to provide all necessary technical support.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<p><a href=\"https://www.fao.org/food-agriculture-statistics/statistical-domains/food-security-and-nutrition/en/\">https://www.fao.org/food-agriculture-statistics/statistical-domains/food-security-and-nutrition/en/</a> </p>\n<p><strong>References: </strong></p>\n<p><a href=\"http://www.fao.org/docrep/012/w0931e/w0931e16.pdf\">http://www.fao.org/docrep/012/w0931e/w0931e16.pdf</a></p>\n<p><a href=\"http://www.fao.org/docrep/005/Y4249E/y4249e06.htm#bm06\">http://www.fao.org/docrep/005/Y4249E/y4249e06.htm#bm06</a></p>\n<p><a href=\"http://www.fao.org/3/a-i4060e.pdf\">http://www.fao.org/3/a-i4060e.pdf</a></p>\n<p><a href=\"http://www.fao.org/3/a-i4046e.pdf\">http://www.fao.org/3/a-i4046e.pdf</a></p>", "indicator_sort_order"=>"02-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.1.2", "slug"=>"2-1-2", "name"=>"Prevalencia de la inseguridad alimentaria moderada o grave entre la población, según la escala de experiencia de inseguridad alimentaria", "url"=>"/site/es/2-1-2/", "sort"=>"020102", "goal_number"=>"2", "target_number"=>"2.1", "global"=>{"name"=>"Prevalencia de la inseguridad alimentaria moderada o grave entre la población, según la escala de experiencia de inseguridad alimentaria"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Prevalencia de la inseguridad alimentaria moderada o grave entre la población, según la escala de experiencia de inseguridad alimentaria", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Prevalencia de la inseguridad alimentaria moderada o grave entre la población, según la escala de experiencia de inseguridad alimentaria", "indicator_number"=>"2.1.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Bienestar, Juventud y Reto Demográfico", "periodicity"=>"Bienal", "url"=>"https://www.euskadi.eus/encuesta-de-pobreza-y-desigualdades-sociales-epds/web01-s2enple/es/", "url_text"=>"Encuesta de pobreza y desigualdades sociales (EPDS)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Prevalencia de la inseguridad alimentaria grave o muy grave", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.1- De aquí a 2030, poner fin al hambre y asegurar el acceso de todas las personas, en particular los pobres y las personas en situaciones de vulnerabilidad, incluidos los niños menores de 1 año, a una alimentación sana, nutritiva y suficiente durante todo el año", "definicion"=>"Porcentaje de la población que ha sufrido durante los últimos doce meses alguno de los problemas de inseguridad alimentaria de la Escala de Seguridad Alimentaria (FSS)", "formula"=>"\n$$T_{FSS}^{t} = \\frac{P_{FSS}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$P_{FSS}^{t} =$ población que ha sufrido alguna situación de inseguridad alimentaria \ngrave o muy grave en los últimos 12 meses en el año $t$\n\n$P^{t} =$ población total en el año $t$\n", "desagregacion"=>"", "observaciones"=>"El indicador mide la proporción de personas que experimentan alguna situación de\ninseguridad alimentaria según el siguiente cuestionario:\n\n - A- Se les agotan los alimentos que compran y no disponen de dinero para conseguir más\n - B- No pueden conseguir una alimentación equilibrada y variada\n - C- ¿Han recortado la cantidad de comida o incluso se han saltado algunas comidas porque no disponían de dinero suficiente para alimentación?\n - E- ¿Han comido menos de lo que hubiesen querido porque no disponían de suficiente dinero para comprar alimentos?\n - F- ¿Han tenido ustedes hambre pero no comieron porque no pudieron conseguir comida suficiente?\n", "periodicidad"=>"Bienal", "justificacion_global"=>"\nLa inseguridad alimentaria en niveles moderados de gravedad suele estar asociada \na la incapacidad de comer regularmente dietas saludables y equilibradas. Por tanto, \nuna prevalencia elevada de inseguridad alimentaria en niveles moderados puede \nconsiderarse un predictor de diversas formas de problemas de salud relacionados \ncon la dieta en la población, asociados a la deficiencia de micronutrientes y a \ndietas desequilibradas. \n\nLos niveles graves de inseguridad alimentaria, por otra parte, implican una alta \nprobabilidad de reducción de la ingesta de alimentos y, por tanto, pueden dar lugar a \nformas más graves de desnutrición, incluido el hambre.\n\nLos cuestionarios breves como la Escala de experiencia de inseguridad alimentaria \nson muy fáciles de administrar y \ntienen un coste limitado, lo que constituye una de las principales ventajas de su uso.\n\nFuente: División de Estadísticas de las Naciones Unidas, Eurostat\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-01-02.pdf\">Metadatos 2-1-2.pdf (solo en inglés)</a>", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.1.2&seriesCode=AG_PRD_FIESMS&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAGE%20%7C%20ALLAREA%20%7C%20BOTHSEX\"> Prevalencia de inseguridad alimentaria moderada o severa en la población (%) AG_PRD_FIESMS</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos de Naciones Unidas pero aporta información similar.", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-17", "en"=>{"indicador_disponible"=>"Prevalencia de la inseguridad alimentaria grave o muy grave", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.1- De aquí a 2030, poner fin al hambre y asegurar el acceso de todas las personas, en particular los pobres y las personas en situaciones de vulnerabilidad, incluidos los niños menores de 1 año, a una alimentación sana, nutritiva y suficiente durante todo el año", "definicion"=>"Percentage of the population experiencing any of the food insecurity problems  listed in the Food Security Scale (FSS) during the past twelve months", "formula"=>"\n$$T_{FSS}^{t} = \\frac{P_{FSS}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$P_{FSS}^{t} =$ Population that has suffered a severe or very severe food insecurity \nsituation in the last 12 months of the year $t$\n\n$P^{t} =$ total population in the year $t$\n", "desagregacion"=>nil, "observaciones"=>"The indicator measures the proportion of people experiencing some form of food \ninsecurity according to the following questionnaire:\n\n - A- They run out of food they buy and have no money to buy more.\n - B- They cannot achieve a balanced and varied diet\n - C- Have you cut back on the amount of food you ate or even skipped meals because\n      you didn't have enough money for food?\n - E- Have you eaten less than you would have liked because you didn't have enough money to buy food?\n - F- Have you ever been hungry but didn't eat because you couldn't get enough food?\n", "periodicidad"=>"Bienal", "justificacion_global"=>"\nFood insecurity at moderate levels of severity is typically associated with the inability \nto regularly eat healthy, balanced diets. As such, high prevalence of food insecurity at \nmoderate levels can be considered a predictor of various forms of diet-related health \nconditions in the population, associated with micronutrient deficiency and unbalanced diets. \n\nSevere levels of food insecurity, on the other hand, imply a high probability of reduced \nfood intake and therefore can lead to more severe forms of undernutrition, including hunger.\n\nShort questionnaires like the FIES are very easy to administer at limited cost, which is \none of the main advantages of their use.\n\nSource: United Nations Statistics Division, Eurostat\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-01-02.pdf\">Metadata 2-1-2.pdf</a>", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.1.2&seriesCode=AG_PRD_FIESMS&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAGE%20%7C%20ALLAREA%20%7C%20BOTHSEX\"> Prevalence of moderate or severe food insecurity in the population (%) AG_PRD_FIESMS</a> UNSTATS", "comparabilidad"=>"\nThe available indicator does not comply with United Nations metadata but provides similar information.", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Prevalencia de la inseguridad alimentaria grave o muy grave", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.1- De aquí a 2030, poner fin al hambre y asegurar el acceso de todas las personas, en particular los pobres y las personas en situaciones de vulnerabilidad, incluidos los niños menores de 1 año, a una alimentación sana, nutritiva y suficiente durante todo el año", "definicion"=>"Azken hamabi hilabeteetan Elikagaien Segurtasuneko Eskalako (ESE) elikaduraren segurtasun-gabeziako  arazoren bat izan duten biztanleen ehunekoa  ", "formula"=>"\n$$T_{FSS}^{t} = \\frac{P_{FSS}^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$P_{FSS}^{t} =$ azken 12 hilabeteetan elikaduraren segurtasun-gabeziako egoera larri \nedo oso larriren bat jasan duten biztanleak, $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>"Adierazleak elikaduraren segurtasun-gabeziaren bat duten pertsonen proportzioa neurtzen du, \ngaldetegi honen arabera: \n\n- A- Erosten dituzten elikagaiak agortzen zaizkie eta ez dute dirurik gehiago lortzeko \n- B- Ezin dute elikadura orekatua eta askotarikoa eskuratu \n- C- Janari kantitatea murriztu dute edo otordu batzuk ere saltatu dituzte elikatzeko diru nahikorik ez zutelako \n- E- Nahi baino gutxiago jan dute elikagaiak erosteko nahikoa diru ez zeukatelako \n- F- Gose izan dira, baina ez dute jan, nahikoa janari lortu ezin izan dutelako  \n", "periodicidad"=>"Bienal", "justificacion_global"=>"\nLarritasun ertaineko mailetan elikagai-segurtasunik ez badago, ezinezkoa izan ohi da modu osasuntsu eta \norekatuan maiz jatea. Ondorioz, larritasun ertaineko mailetan elikagai segurtasunik eza nagusitzen bada, \nhainbat osasun-arazo aurreikus daitezke, besteak beste biztanleen dietarekin, mikronutrienteen urritasunarekin \nedo dieta desorekatuekin lotuta. \n\nElikagaien segurtasunik ezeko maila larriek, bestalde, aukera handiak eragiten dituzte jandako elikagaien \nkopurua murrizteko, eta, beraz, desnutrizio-forma larriak eragin ditzakete, gosea barne. \n\nElikagaien segurtasunik ezari buruzko esperientziaren eskala bezalako galdetegi laburrak oso erabilerrazak \ndira, eta kostu mugatua dute, horiek izanik dituzten abantaila nagusietakoak. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa, Eurostat \n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-01-02.pdf\">Metadatuak 2-1-2.pdf (ingelesez bakarrik)</a>", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.1.2&seriesCode=AG_PRD_FIESMS&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAGE%20%7C%20ALLAREA%20%7C%20BOTHSEX\"> Elikagadura segurtasun-gabezia ertain edo larriaren prebalentzia biztanlerian (%) AG_PRD_FIESMS</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen metadatuak betetzen, baina antzeko informazioa ematen du.", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.1: By 2030, end hunger and ensure access by all people, in particular the poor and people in vulnerable situations, including infants, to safe, nutritious and sufficient food all year round</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.1.2: Prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>AG_PRD_FIESMS - Prevalence of moderate or severe food insecurity in the population [2.1.2]</p>\n<p>AG_PRD_FIESMSN - Population in moderate or severe food insecurity (number) [2.1.2]</p>\n<p>AG_PRD_FIESS - Prevalence of severe food insecurity in the population [2.1.2]</p>\n<p>AG_PRD_FIESSN - Population in severe food insecurity (number) [2.1.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>2.1.1, 2.2.1, 2.2.2, 2.2.3</p>\n<p>Comments: </p>\n<p>Links with Target 2.2, to the extent that hunger may lead to malnutrition, and Target 2.2 may not be achieved if Target 2.1 is not achieved.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organisation of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organisation of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator measures the percentage of individuals in the population who have experienced food insecurity at moderate or severe levels during the reference period. The severity of food insecurity, defined as a latent trait, is measured on the Food Insecurity Experience Scale global reference scale, a measurement standard established by FAO through the application of the Food Insecurity Experience Scale in more than 140 countries worldwide, starting in 2014.</p>\n<p><strong>Concepts:</strong></p>\n<p>Extensive research over more than 25 years has demonstrated that the inability to access food results in a series of experiences and conditions that are fairly common across cultures and socio-economic contexts and that range from being concerned about the ability to obtain enough food, to the need to compromise on the quality or the diversity of food consumed, to being forced to reduce the intake of food by cutting portion sizes or skipping meals, up to the extreme condition of feeling hungry and not having means to access any food for a whole day. Typical conditions like these form the basis of an experience-based food insecurity measurement scale. When analysed through sound statistical methods rooted in Item Response Theory, data collected through such scales provide the basis to compute theoretically consistent, cross country comparable measures of the prevalence of food insecurity. The severity of the food insecurity condition as measured by this indicator thus directly reflects the extent of households&#x2019; or individuals&#x2019; inability to regularly access the food they need.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Prevalence of food insecurity: Percent (%) </p>\n<p>Number of food insecure people: Millions (of people) </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The construction of the regional and global estimates, as well as estimates for specific groups, such as Least Developed Countries, Land Locked Developing countries, Small Island Developing States, Developed Regions, and Developing Regions, of this indicator follows the UN M49 Standard.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data can be collected using the Food Insecurity Experience Scale survey module (FIES-SM) developed by FAO, or any other experience-based food security scale questionnaires, including:</p>\n<ul>\n  <li>the Household Food Security Survey Module (HFSSM) developed by the Economic Research Service of the US Department of Agriculture, and used in the US and Canada, </li>\n  <li>the Latin American and Caribbean Food Security Scale (or Escala Latinoamericana y Caribe&#xF1;a de Seguridad Alimentaria &#x2013; ELCSA), used in Guatemala and tested in several other Spanish speaking countries in Latin America, </li>\n  <li>the Mexican Food Security Scale (or Escala Mexicana de Seguridad Alimentaria, - EMSA), an adaptation of the ELCSA used in Mexico, </li>\n  <li>the Brazilian Food Insecurity Scale (Escala Brasileira de medida de la Inseguran&#xE7;a Alimentar &#x2013; EBIA) used in Brazil, or </li>\n  <li>the Household Food Insecurity Access Scale (HFIAS), </li>\n</ul>\n<p>or any adaptation of the above that can be calibrated against the global FIES.</p>\n<p>Two versions of the FIES-SM are available for use in surveys of individuals or households respectively, and the difference stands in whether respondents are asked to report only on their individual experiences, or also on that of other member of the household.</p>\n<p>The current FIES-SM module include eight questions as in the table below. </p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"2\">\n        <p>GLOBAL FOOD INSECURITY EXPERIENCE SCALE</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Now I would like to ask you some questions about food. </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q1. During the last 12 MONTHS, was there a time when you (or any other adult in the household) were worried you would not have enough food to eat because of a lack of money or other resources?</p>\n      </td>\n      <td>\n        <p>0 No</p>\n        <p>1 Yes</p>\n        <p>98 Don&#x2019;t Know</p>\n        <p>99 Refused</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q2. Still thinking about the last 12 MONTHS, was there a time when you (or any other adult in the household) were unable to eat healthy and nutritious food because of a lack of money or other resources? </p>\n      </td>\n      <td>\n        <p>0 No</p>\n        <p>1 Yes</p>\n        <p>98 Don&#x2019;t Know</p>\n        <p>99 Refused</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q3. And was there a time when you (or any other adult in the household) ate only a few kinds of foods because of a lack of money or other resources? </p>\n      </td>\n      <td>\n        <p>0 No</p>\n        <p>1 Yes</p>\n        <p>98 Don&#x2019;t Know</p>\n        <p>99 Refused</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q4. Was there a time when you (or any other adult in the household) had to skip a meal because there was not enough money or other resources to get food?</p>\n      </td>\n      <td>\n        <p>0 No</p>\n        <p>1 Yes</p>\n        <p>98 Don&#x2019;t Know</p>\n        <p>99 Refused</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q5. Still thinking about the last 12 MONTHS, was there a time when you (or any other adult in the household) ate less than you thought you should because of a lack of money or other resources?</p>\n      </td>\n      <td>\n        <p>0 No</p>\n        <p>1 Yes</p>\n        <p>98 Don&#x2019;t Know</p>\n        <p>99 Refused</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q6. And was there a time when your household ran out of food because of a lack of money or other resources?</p>\n      </td>\n      <td>\n        <p>0 No</p>\n        <p>1 Yes</p>\n        <p>98 Don&#x2019;t Know</p>\n        <p>99 Refused</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q7. Was there a time when you (or any other adult in the household) were hungry but did not eat because there was not enough money or other resources for food?</p>\n      </td>\n      <td>\n        <p>0 No</p>\n        <p>1 Yes</p>\n        <p>98 Don&#x2019;t Know</p>\n        <p>99 Refused</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Q8. Finally, was there a time when you (or any other adult in the household) went without eating for a whole day because of a lack of money or other resources?</p>\n      </td>\n      <td>\n        <p>0 No</p>\n        <p>1 Yes</p>\n        <p>98 Don&#x2019;t Know</p>\n        <p>99 Refused</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>The questions should be adapted and administered in the respondents&#x2019; preferred language and enumerators instructed to make sure that respondents recognize the reference period and the qualifier according to which experiences should be reported only when due to &#x201C;lack of money or other resources&#x201D; and not, for example, for reasons related to health or other cultural habits (such as fasting for religious credos).</p>\n<p>The FIES-SM can be included in virtually any telephone-based or personal interview-based survey of the population, though face to face interview is preferred. </p>\n<p>Since 2014, the individual referenced FIES-SM is applied to nationally representative samples of the population aged 15 or more in all countries covered by the Gallup World Poll (more than 140 countries every year, covering 90% of the world population). In most countries, samples include about 1000 individuals (with larger samples of 3000 individuals in India and 5000 in mainland China).</p>\n<p>Additionally to the GWP, in 2020 FAO collected data in 20 countries through Geopoll&#xAE; with the specific objective of assessing food insecurity during the COVID-19 pandemic. The countries covered were: Afghanistan, Burkina Faso, Cameroon, Central African Republic, Chad, Democratic Republic of the Congo, El Salvador, Ethiopia, Guatemala, Haiti, Iraq, Liberia, Mozambique, Myanmar, Niger, Nigeria, Sierra Leone, Somalia, South Africa and Zimbabwe. For all these countries, the 2020 assessment was based on Geopoll data.</p>\n<p>Other national surveys exist that already collect FIES compatible data. </p>\n<p>For Afghanistan, Angola, Armenia, Botswana, Burkina Faso, Cabo Verde, Canada, Chile, Costa Rica, Ecuador, Fiji, Ghana, Greece, Grenada, Honduras, Indonesia, Israel, Kazakhstan, Kenya, Kiribati, Kyrgyzstan, Lesotho, Malawi, Mauritania, Mexico, Morocco, Namibia, Niger, Nigeria, Palestine, Philippines, Republic of Korea, Russian Federation, Saint Lucia, Samoa, Senegal, Seychelles, Sierra Leone, South Sudan, Sudan, Tonga, Uganda, United Republic of Tanzania, United States of America, Vanuatu, Viet Nam and Zambia, national government survey data were used to calculate the prevalence estimates of food insecurity by applying FAO&#x2019;s statistical methods to adjust national results to the same global reference standard, covering approximately a quarter of the world population. Countries are considered for the year/years when national data are available, informing the regional and subregional aggregates assuming a constant trend in the period 2014&#x2013;2020, or integrating the remaining years with GWP or Geopoll data in case they were compatible. Exceptions to this rule are: Armenia, Botswana, Burkina Faso, Chile, Costa Rica, Ecuador, Ghana, Honduras, Indonesia, Israel, Malawi, Namibia, Niger, Nigeria, Sierra Leone, Uganda and Zambia. In these cases, the following procedure was followed:</p>\n<ul>\n  <li>Use national data collected in one year to inform the corresponding year.</li>\n  <li>For the remaining years, apply the smoothed trend coming from the data collected by FAO through the Gallup&#xA9; World Poll to the national data to describe evolution over time. Smoothed trend is computed by taking the mean of the rates of change between consecutive three-year averages.</li>\n</ul>\n<p>The motivation behind this procedure was the strong evidence found in support of the trend suggested by data collected by FAO (for instance, evolution of poverty, extreme poverty, employment, food inflation, among others), allowing to provide a more updated description of the trend in the period 2014&#x2013;2020.</p>\n<p>In Indonesia, Kazakhstan, Kyrgyzstan, Mauritania, Nicaragua, Paraguay, Rwanda, Seychelles, Sudan and United Republic of Tanzania, due to lack of data in 2020, the corresponding subregional trend between 2019 and 2020 was used to inform 2020.</p>\n<p><u>Obtaining internationally comparable data for global monitoring:</u></p>\n<p>To ensure comparability of the FImod+sev and FIsev indicators computed for different populations, universal thresholds are defined on the FIES global reference scale and converted into corresponding values on the &#x201C;local&#x201D; scales obtained as a result of application of the Rasch model on any specific population, through a process of &#x201C;equating&#x201D;.</p>\n<p>Equating is a form of standardization of the metric based on identification of the subset of items that can be considered common to the global FIES and the specific scale used for measurement in each context. The severity levels associated with the common items are used as anchoring points to adjust the global FIES thresholds to the local scales. The standardization process ensures that the mean and standard deviation of the set of common items is the same when measured on the global FIES or on the national scale. Compatibility with the global FIES and the possibility to compile this indicator requires that at least four of the eight FIES items are identified as common.</p>\n<p>The Statistics Division at FAO has developed the RM.weights package under R, which provides routines for estimating the parameters of the Rasch model using conditional maximum likelihood, with the possibility to allow for the complex survey design.</p>", "COLL_METHOD__GLOBAL"=>"<p>Face-to-face and telephone interviews within national surveys.</p>", "FREQ_COLL__GLOBAL"=>"<p>Continuing</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released each year alongside the <em>State of Food Security and Nutrition in the World</em> report, usually in mid-July. </p>", "DATA_SOURCE__GLOBAL"=>"<p>National data providers will be the National Statistical Authorities that are responsible for the survey in which the FIES or similar scale is included. FAO will provide data for countries where the FIES or compatible module is not included in any national survey.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Organization(s) responsible for compilation and reporting on this indicator at the global level: Food and Agriculture Organization of the United Nations, Statistics Division, Food Security and Nutrition Statistics Team.</p>", "INST_MANDATE__GLOBAL"=>"<p>The Office of the Chief Statistician of FAO manages the Interdepartmental Working Group on SDG indicators under the FAO custodianship, and identifies a focal point for each of them. The team leader of the Food Security and Nutrition Statistics Team of the Statistics Division is formally appointed as the focal person for the collection, processing, and dissemination of statistics for this indicator. </p>", "RATIONALE__GLOBAL"=>"<p>Food insecurity at moderate levels of severity is typically associated with the inability to regularly eat healthy, balanced diets. As such, high prevalence of food insecurity at moderate levels can be considered a predictor of various forms of diet-related health conditions in the population, associated with micronutrient deficiency and unbalanced diets. Severe levels of food insecurity, on the other hand, imply a high probability of reduced food intake and therefore can lead to more severe forms of undernutrition, including hunger.</p>\n<p>Short questionnaires like the FIES are very easy to administer at limited cost, which is one of the main advantages of their use. The ability to precisely determine the food insecurity status of specific individuals or households, however, is limited by the small number of questions, a reason why assignment of individual respondents to food insecurity classes is best done in probability terms, thus ensuring that estimates of prevalence rates in a population are sufficiently reliable even when based on relatively small sample sizes. </p>\n<p>As with any statistical assessment, reliability and precision crucially depend on the quality of the survey design and implementation. One major advantage of the analytic treatment of the data through the Rasch model-based methods is that it permits testing the quality of the data collected and evaluating the likely margin of uncertainty around estimated prevalence rates, which should always be reported.</p>", "REC_USE_LIM__GLOBAL"=>"<p>An average of less than three minutes of survey time is estimated to collect FIES data in a well-conducted face-to-face survey, which should make it possible to include the FIES-SM in a nationally representative survey in every country in the world, at a very reasonable cost. FAO provides versions of the FIES-SM adapted and translated in each of the more than 200 languages and dialects used in the Gallup World Poll.</p>\n<p>When used in the Gallup World Poll, with sample sizes of only about 1000 individuals, the width of confidence intervals rarely exceeds 20% of the measured prevalence (that is, prevalence rates of around 50% are estimated with margins of errors of plus or minus 5%). Obviously, confidence intervals are likely to be much smaller when national prevalence rates are estimated using larger samples.</p>\n<p>Compared to other proposed non-official indicators of household food insecurity, the FIES based approach has the advantage that food insecurity prevalence rates are directly comparable across population groups and countries. Even if they use similar labels (such as &#x201C;mild&#x201D;, &#x201C;moderate&#x201D; and &#x201C;severe&#x201D; food insecurity) other approaches have yet to demonstrate the formal comparability of the thresholds used for classification, due to lack of the definition of a proper statistical model that links the values of the &#x201C;indexes&#x201D; or &#x201C;scores&#x201D; used for classification, to the severity of food insecurity. For this reason, care should be taken when comparing the results obtained with the FIES with those obtained with these other indicators, even if, unfortunately, similar labels are used to describe them.</p>", "DATA_COMP__GLOBAL"=>"<p>Data at the individual or household level is collected by applying an experience-based food security scale questionnaire within a survey. The food security survey module collects answers to questions asking respondents to report the occurrence of several typical experiences and conditions associated with food insecurity. The data is analysed using the Rasch model (also known as one-parameter logistic model, 1-PL), which postulates that the probability of observing an affirmative answer by respondent i to question j, is a logistic function of the distance, on an underlying scale of severity, between the position of the respondent, <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>a</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n  </math>, and that of the item, <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>b</mi>\n    <msub>\n      <mrow></mrow>\n      <mrow>\n        <mi>j</mi>\n      </mrow>\n    </msub>\n  </math>.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>P</mi>\n    <mi>r</mi>\n    <mi>o</mi>\n    <mi>b</mi>\n    <mfenced open=\"{\" close=\"}\" separators=\"|\">\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>X</mi>\n          </mrow>\n          <mrow>\n            <mi>i</mi>\n            <mo>,</mo>\n            <mi>j</mi>\n          </mrow>\n        </msub>\n        <mo>=</mo>\n        <mi>Y</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfenced>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">exp</mi>\n          </mrow>\n          <mo>&#x2061;</mo>\n          <mrow>\n            <mfenced separators=\"|\">\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>a</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>i</mi>\n                  </mrow>\n                </msub>\n                <mo>-</mo>\n                <msub>\n                  <mrow>\n                    <mi>b</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>j</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfenced>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n        <mo>+</mo>\n        <mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">exp</mi>\n          </mrow>\n          <mo>&#x2061;</mo>\n          <mrow>\n            <mfenced separators=\"|\">\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>a</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>j</mi>\n                  </mrow>\n                </msub>\n                <mo>-</mo>\n                <msub>\n                  <mrow>\n                    <mi>b</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>j</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfenced>\n          </mrow>\n        </mrow>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Parameters <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>a</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n  </math> and <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>b</mi>\n      </mrow>\n      <mrow>\n        <mi>j</mi>\n      </mrow>\n    </msub>\n  </math> can be estimated using maximum likelihood procedures. Parameters <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>a</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n  </math>, in particular, are interpreted as a measure of the severity of the food security condition for each respondent and are used to classify them into classes of food insecurity.</p>\n<p>The FIES considers the three classes of (a) food security or mild food insecurity; b) moderate or severe food insecurity, and (c) severe food insecurity, and estimates the probability of being moderately or severely food insecure (<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>p</mi>\n      </mrow>\n      <mrow>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mo>+</mo>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>v</mi>\n      </mrow>\n    </msub>\n  </math>) and the probability of being severely food insecure (<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>p</mi>\n      </mrow>\n      <mrow>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>v</mi>\n      </mrow>\n    </msub>\n  </math>) for each respondent, with <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mn>0</mn>\n    <mo>&amp;lt;</mo>\n    <msub>\n      <mrow>\n        <mi>p</mi>\n      </mrow>\n      <mrow>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>v</mi>\n      </mrow>\n    </msub>\n    <mo>&amp;lt;</mo>\n    <msub>\n      <mrow>\n        <mi>p</mi>\n      </mrow>\n      <mrow>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mo>+</mo>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>v</mi>\n      </mrow>\n    </msub>\n    <mo>&amp;lt;</mo>\n    <mn>1</mn>\n  </math>. The probability of being food secure or mildly food insecure can be obtained as <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>p</mi>\n      </mrow>\n      <mrow>\n        <mi>f</mi>\n        <mi>s</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mn>1</mn>\n    <mo>-</mo>\n    <msub>\n      <mrow>\n        <mi>p</mi>\n      </mrow>\n      <mrow>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mo>+</mo>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>v</mi>\n      </mrow>\n    </msub>\n  </math>.</p>\n<p>Given a representative sample, the prevalence of food insecurity at moderate or severe levels (FImod+sev), and at severe levels (FIsev) in the population are computed as the weighted sum of the probability of belonging to the moderate or severe food insecurity class, and to the severe food insecurity class, respectively, of all individual or household respondents in a sample:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>(</mo>\n    <mn>1</mn>\n    <mo>)</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>F</mi>\n    <msub>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mo>+</mo>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>v</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mrow>\n      <msub>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi>i</mi>\n        </mrow>\n      </msub>\n      <mrow>\n        <msub>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>p</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <mi>m</mi>\n            <mi>o</mi>\n            <mi>d</mi>\n            <mo>+</mo>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>v</mi>\n          </mrow>\n        </msub>\n        <mo>&#xD7;</mo>\n        <msub>\n          <mrow>\n            <mi>w</mi>\n          </mrow>\n          <mrow>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>and </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>(</mo>\n    <mn>2</mn>\n    <mo>)</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>F</mi>\n    <msub>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>v</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mrow>\n      <msub>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi>i</mi>\n        </mrow>\n      </msub>\n      <mrow>\n        <msub>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>p</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>v</mi>\n          </mrow>\n        </msub>\n        <mo>&#xD7;</mo>\n        <msub>\n          <mrow>\n            <mi>w</mi>\n          </mrow>\n          <mrow>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>where <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>w</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n  </math> are post-stratification weights that indicate the proportion of individual or households in the national population represented by each element in the sample.</p>\n<p>It is important to note that if <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>w</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n  </math> are individual sampling weights, then the prevalence of food insecurity refers to the total population of individuals, while if they are household weights, the prevalence refers to the population of households. For the calculation of the indicator 2.1.2, objective is to produce a prevalence of individuals. This implies that:</p>\n<p>if a survey is at household level, and provides household sampling weights, they should be transformed to individual sampling weights by multiplying the weights by the household size. This individual weighting system can then be used to calculate the individual prevalence rates in formulas (1) and (2)</p>\n<p>If the survey includes only adults, then the adult weights applied to the probabilities in formulas (1) and (2) provide the adult prevalence rates (<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>F</mi>\n    <msup>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mi>A</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n      </mrow>\n    </msup>\n  </math>). In this case, to calculate the prevalence in the total population, then the proportion of children who live in households where at least one adult is food insecure must also be calculated. This can be done by dividing the adult weights by the number of adults in the household and multiplying those approximate household weights by the number of children in the household. Once the approximate child weights are obtained, the prevalence of food insecurity of children who live in households where at least one adult is food insecure (<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>F</mi>\n    <msup>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mi>C</mi>\n        <mi>h</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>d</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n      </mrow>\n    </msup>\n  </math>) can be calculated by applying these weights to the probabilities of food insecurity in formulas (1) and (2). The prevalence of food insecurity in the total population is finally calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>F</mi>\n    <msub>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mo>+</mo>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>v</mi>\n      </mrow>\n    </msub>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>F</mi>\n        <msubsup>\n          <mrow>\n            <mi>I</mi>\n          </mrow>\n          <mrow>\n            <mi>m</mi>\n            <mi>o</mi>\n            <mi>d</mi>\n            <mo>+</mo>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>v</mi>\n          </mrow>\n          <mrow>\n            <mi>A</mi>\n            <mi>d</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>t</mi>\n            <mi>s</mi>\n          </mrow>\n        </msubsup>\n        <mo>&#xD7;</mo>\n        <msup>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>A</mi>\n            <mi>d</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>t</mi>\n            <mi>s</mi>\n          </mrow>\n        </msup>\n        <mo>+</mo>\n        <mi>F</mi>\n        <msubsup>\n          <mrow>\n            <mi>I</mi>\n          </mrow>\n          <mrow>\n            <mi>m</mi>\n            <mi>o</mi>\n            <mi>d</mi>\n            <mo>+</mo>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>v</mi>\n          </mrow>\n          <mrow>\n            <mi>C</mi>\n            <mi>h</mi>\n            <mi>i</mi>\n            <mi>l</mi>\n            <mi>d</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n          </mrow>\n        </msubsup>\n        <mo>&#xD7;</mo>\n        <msup>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>C</mi>\n            <mi>h</mi>\n            <mi>i</mi>\n            <mi>l</mi>\n            <mi>d</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n          </mrow>\n        </msup>\n      </mrow>\n      <mrow>\n        <msup>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>A</mi>\n            <mi>d</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>t</mi>\n            <mi>s</mi>\n          </mrow>\n        </msup>\n        <mo>+</mo>\n        <msup>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>C</mi>\n            <mi>h</mi>\n            <mi>i</mi>\n            <mi>l</mi>\n            <mi>d</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n          </mrow>\n        </msup>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>and </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>F</mi>\n    <msub>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>v</mi>\n      </mrow>\n    </msub>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>F</mi>\n        <msubsup>\n          <mrow>\n            <mi>I</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>v</mi>\n          </mrow>\n          <mrow>\n            <mi>A</mi>\n            <mi>d</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>t</mi>\n            <mi>s</mi>\n          </mrow>\n        </msubsup>\n        <mo>&#xD7;</mo>\n        <msup>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>A</mi>\n            <mi>d</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>t</mi>\n            <mi>s</mi>\n          </mrow>\n        </msup>\n        <mo>+</mo>\n        <mi>F</mi>\n        <msubsup>\n          <mrow>\n            <mi>I</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>v</mi>\n          </mrow>\n          <mrow>\n            <mi>C</mi>\n            <mi>h</mi>\n            <mi>i</mi>\n            <mi>l</mi>\n            <mi>d</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n          </mrow>\n        </msubsup>\n        <mo>&#xD7;</mo>\n        <msup>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>C</mi>\n            <mi>h</mi>\n            <mi>i</mi>\n            <mi>l</mi>\n            <mi>d</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n          </mrow>\n        </msup>\n      </mrow>\n      <mrow>\n        <msup>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>A</mi>\n            <mi>d</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>t</mi>\n            <mi>s</mi>\n          </mrow>\n        </msup>\n        <mo>+</mo>\n        <msup>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>C</mi>\n            <mi>h</mi>\n            <mi>i</mi>\n            <mi>l</mi>\n            <mi>d</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n          </mrow>\n        </msup>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msup>\n      <mrow>\n        <mi>N</mi>\n      </mrow>\n      <mrow>\n        <mi>A</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n      </mrow>\n    </msup>\n  </math> and <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msup>\n      <mrow>\n        <mi>N</mi>\n      </mrow>\n      <mrow>\n        <mi>C</mi>\n        <mi>h</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>d</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n      </mrow>\n    </msup>\n  </math> are the adult and children populations in the country.</p>\n<p>When applied to the country total population, the prevalence of food insecurity in the total population provides the number of individuals who live in food insecure households (or in households where at least one adult is food insecure) in a country, at different levels of severity (<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>N</mi>\n      </mrow>\n      <mrow>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mo>+</mo>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>v</mi>\n      </mrow>\n    </msub>\n  </math> and <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>N</mi>\n      </mrow>\n      <mrow>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>v</mi>\n      </mrow>\n    </msub>\n  </math>). In the database, the number of food insecure people are expressed in thousands. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>For data collected by FAO through the Gallup World Poll or other service providers, the country results have been shared with all national statistical offices through an email communication sent by the FAO Chief Statistician, requesting feedback, and published only if they did not refuse to.</p>", "ADJUSTMENT__GLOBAL"=>"<p>International calibration of food insecurity thresholds is performed to ensure national and sub-national results are comparable.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>The indicator is not computed if no country data are available.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Missing values for individual countries are implicitly imputed to be equal to the population weighted average of the estimated values of the countries present in the same region.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates of the prevalence of moderate or severe food insecurity (FI) based on FIES are computed as: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>F</mi>\n    <msub>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mi>R</mi>\n        <mi>E</mi>\n        <mi>G</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mo>(</mo>\n    <mi>F</mi>\n    <msub>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n    <mo>&#xD7;</mo>\n    <msub>\n      <mrow>\n        <mi>N</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n    <mo>)</mo>\n    <mo>/</mo>\n    <msub>\n      <mrow>\n        <mi>N</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n  </math></p>\n<p>where <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>F</mi>\n    <msub>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n  </math> are the values of FI estimated for all countries in the regions for which available data allow to compute a reliable estimate, and <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>N</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n  </math> the corresponding population size. </p>", "DOC_METHOD__GLOBAL"=>"<p>Experience-based food security scales data are collected through population surveys (either household or individual surveys) using questionnaires/modules that are adapted to the country language and condition.</p>\n<p>Examples are provided below: </p>\n<p>U.S.A.: Household Food Security Survey Module (<a href=\"https://www.ers.usda.gov/media/8271/hh2012.pdf\"><u>https://www.ers.usda.gov/media/8271/hh2012.pdf</u></a>)</p>\n<p>Brazil: Escala Brasileira de Inseguran&#xE7;a Alimentar (<u><a href=\"http://biblioteca.ibge.gov.br/visualizacao/livros/liv91984.pdf\">http://biblioteca.ibge.gov.br/visualizacao/livros/liv91984.pdf</a></u>, Quadro 5, page 30)</p>\n<p>Mexico: Escala Mexicana de Seguridad Alimentaria (<a href=\"https://www.coneval.org.mx/Evaluacion/ECNCH/Documents/CIESAS_alimentacion.pdf\">https://www.coneval.org.mx/Evaluacion/ECNCH/Documents/CIESAS_alimentacion.pdf</a>)</p>\n<p>Guatemala: Escala Latino Americana y Caribena de Seguridad Alimentaria (<u><a href=\"http://www.ine.gob.gt/sistema/uploads/2015/12/11/DDrIEuLOPuEcXTcLXab1yOkiOV2HQreq.pdf\">http://www.ine.gob.gt/sistema/uploads/2015/12/11/DDrIEuLOPuEcXTcLXab1yOkiOV2HQreq.pdf</a></u>, pagina 3)</p>\n<p>FAO &#x2013; Food Insecurity Experience Scale (<a href=\"http://www.fao.org/3/a-bl404e.pdf\"><u>http://www.fao.org/3/a-bl404e.pdf</u></a>) </p>\n<p>Inclusion of the FIES survey module in a questionnaire is a simple matter of adapting the questions to the local language by following guidelines provided in the following documents.</p>\n<p><a href=\"http://www.fao.org/3/a-be898e.pdf\"><u>http://www.fao.org/3/a-be898e.pdf</u></a></p>\n<p><a href=\"http://www.fao.org/3/a-be898f.pdf\"><u>http://www.fao.org/3/a-be898f.pdf</u></a></p>\n<p><a href=\"http://www.fao.org/3/a-be898s.pdf\"><u>http://www.fao.org/3/a-be898s.pdf</u></a></p>\n<p><a href=\"http://www.fao.org/3/a-be898r.pdf\"><u>http://www.fao.org/3/a-be898r.pdf</u></a></p>\n<p><a href=\"http://www.fao.org/3/a-be898a.pdf\"><u>http://www.fao.org/3/a-be898a.pdf</u></a></p>\n<p><a href=\"http://www.fao.org/3/a-be898c.pdf\"><u>http://www.fao.org/3/a-be898c.pdf</u></a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>ESS conducts trend analysis of the newly updated indicator with other relevant indicators. Meanwhile, preliminary estimates of each round of the update are circulated among regional offices for review. Because of their knowledge of their regions and countries, they often provide invaluable inputs to the revisions and finalization of the update. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>FIES data are validated through testing of adherence to the Rasch model assumption of equal discrimination of the items and absence of residual correlation and measurement of Rasch reliability indexes. Such a test would reveal whether the data is of sufficient quality to produce reliable estimates of the prevalence of food insecurity according to the FIES standard. </p>\n<p>Then, item severity parameters are compared with the FIES global reference standard to verify the possibility of calibrating the measures against such standard and thus produce estimates of the prevalence of food insecurity that can be considered comparable across countries.</p>\n<p>Relevant material is available at <a href=\"http://www.fao.org/3/a-i4830e.pdf\"><u>http://www.fao.org/3/a-i4830e.pdf</u></a>, <a href=\"http://www.fao.org/3/b-i4830s.pdf\"><u>http://www.fao.org/3/b-i4830s.pdf</u></a>, <a href=\"http://www.fao.org/3/c-i4830f.pdf\"><u>http://www.fao.org/3/c-i4830f.pdf</u></a> and <a href=\"http://www.fao.org/3/a-i3946e.pdf\"><u>http://www.fao.org/3/a-i3946e.pdf</u></a>.</p>\n<p>When the estimates are based on official national data, the data used to compile the indicator is obtained directly from the microdata dissemination websites of countries, when available (e.g. USA), or by direct request to the national statistical offices responsible for data collection (e.g. Canada).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>High. For the vast majority of countries, the quality assurance steps provide indication of high quality and reliable data.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data for 2014-2020 are available from FAO for more than 140 countries, areas and territories included in the Gallup World Poll. </p>\n<p>Regional and sub regional aggregates are computed for all regions, with the exceptions of the Caribbean and the Middle Africa regions (as less than 50% of the regional population up to 2019 was covered). Both regions can be estimated only for 2020. Data have been subject to a country consultation process and only results validated by national statistical offices are published at country level.</p>\n<p><strong>Time series:</strong></p>\n<p>Only the 3-year average (2014-2016, 2015-17, 2016-18, 2017-19 and 2018-20) is provided for country level data. Annual values are provided for regional aggregates.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>As the FIES or any other compatible experience-based food security questionnaire is applied through surveys, the prevalence of food insecurity can be measured in any population group for which the survey used to collect data is representative.</p>\n<p>If applied at household level, disaggregation is thus possible based on household characteristics such as location, household income, composition (including for example presence and number of small children, members with disabilities, elderly members, etc.), sex, age and education of the household head, etc. If applied at the individual level, proper disaggregation of the prevalence of food insecurity by sex is possible as the prevalence of food insecurity among male and among female members of the same population group can be measured independently.</p>\n<p>When producing disaggregated statistics, attention must be devoted to verifying the validity of the application by estimating the Rasch model with the data from each specific subpopulation group and, if necessary, perform the appropriate equating of the measure before comparing results.</p>\n<p>It is good practice to associate a measure of variability (margins of error or upper and lower bound) when disaggregated data are produced.</p>\n<p>At the moment, disaggregated statistics by gender of the respondent are provided.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>In the few cases where indicators of food insecurity based on experience-based food security scales have been reported by countries (U.S., Canada, Mexico, Guatemala and Brazil), these have been based on nationally set thresholds that do not correspond to the international thresholds proposed by the FIES. See Annex I and Table A3 in http://www.fao.org/3/i4830e.pdf for a description of the differences. In the future, it is desirable that country would start reporting prevalence estimates using also the internationally set thresholds for moderate or severe and severe levels, in addition to those based on national thresholds.</p>\n<p>FAO is ready to provide assistance on the analytic methods needed to estimate prevalence based on the FIES global reference thresholds.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong><a href=\"http://www.fao.org/in-action/Voices-of-the-Hungry/\">http://www.fao.org/in-action/Voices-of-the-Hungry/</a> </p>\n<p><a href=\"http://www.fao.org/3/i4830e.pdf\">http://www.fao.org/3/i4830e.pdf</a></p>", "indicator_sort_order"=>"02-01-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.2.1", "slug"=>"2-2-1", "name"=>"Prevalencia del retraso del crecimiento (estatura para la edad, desviación típica < -2 de la mediana de los patrones de crecimiento infantil de la Organización Mundial de la Salud (OMS)) entre los niños menores de 5 años", "url"=>"/site/es/2-2-1/", "sort"=>"020201", "goal_number"=>"2", "target_number"=>"2.2", "global"=>{"name"=>"Prevalencia del retraso del crecimiento (estatura para la edad, desviación típica < -2 de la mediana de los patrones de crecimiento infantil de la Organización Mundial de la Salud (OMS)) entre los niños menores de 5 años"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Prevalencia del retraso del crecimiento (estatura para la edad, desviación típica < -2 de la mediana de los patrones de crecimiento infantil de la Organización Mundial de la Salud (OMS)) entre los niños menores de 5 años", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Prevalencia del retraso del crecimiento (estatura para la edad, desviación típica < -2 de la mediana de los patrones de crecimiento infantil de la Organización Mundial de la Salud (OMS)) entre los niños menores de 5 años", "indicator_number"=>"2.2.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"El crecimiento infantil refleja el estado nutricional del niño. El retraso del \ncrecimiento infantil se refiere a un niño que es demasiado bajo para su edad \ny es el resultado de la desnutrición crónica o recurrente.\n\nEl retraso del crecimiento es un factor de riesgo que contribuye a la \nmortalidad infantil y también es un indicador de desigualdades en el \ndesarrollo humano. Los niños con retraso del crecimiento no logran alcanzar su \npotencial físico y cognitivo. El retraso del crecimiento infantil es uno \nde los indicadores de las metas de nutrición de la Asamblea Mundial de la Salud.\n\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.2.1&seriesCode=SH_STA_STNT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y\">Proporción de niños con retraso del crecimiento moderado o grave (%) SH_STA_STNT</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-02-01.pdf\">Metadatos 2-2-1.pdf (solo en inglés)</a>", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"\nChild growth reflects child nutritional status. Child stunting \nrefers to a child who is too short for his or her age and is the result \nof chronic or recurrent malnutrition.\n\nStunting is a contributing risk factor to child mortality and is also a \nmarker of inequalities in human development. Stunted children fail to \nreach their physical and cognitive potential. Child stunting is one of \nthe World Health Assembly nutrition target indicators.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.2.1&seriesCode=SH_STA_STNT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y\">Proportion of children moderately or severely stunted (%) SH_STA_STNT</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-02-01.pdf\">Metadata 2-2-1.pdf</a>"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"Haurren hazkuntzak haurraren egoera nutrizionala islatzen du. Haurren hazkuntzan atzerapena dagoela \nesaten da haurra baxuegia bada bere adinerako. Egoera hori desnutrizio kroniko edo errepikakorraren \nemaitza izaten da. \n\nHazkuntzan atzerapena egotea arrisku-faktore bat da, haurren hilkortasunean eragiten duena. Halaber, \ngiza garapenean dauden desberdintasunen adierazlea ere bada. Hazkuntzan atzerapena duten haurrek ez \ndute lortzen beren potentzial fisiko eta kognitibora iristerik. Haurren hazkuntzako atzerapena da \nOsasunaren Munduko Asanbladaren nutrizio-xedeen adierazleetako bat. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.2.1&seriesCode=SH_STA_STNT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y\">Hazkunde atzerapen ertaina edo larria duten haurren proportzioa (%) SH_STA_STNT</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-02-01.pdf\">Metadatuak 2-2-1.pdf (ingelesez bakarrik)</a>"}, "national_metadata_updated_date"=>"2025-03-09", "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition, and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.2: by 2030 end all forms of malnutrition, including achieving by 2025 the internationally agreed targets on stunting and wasting in children under five years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women, and older persons</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.2.1: Prevalence of stunting (height for age &lt;-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_STA_STNT - Proportion of children moderately or severely stunted [2.2.1]</p>\n<p>SH_STA_STNTN - Children moderately or severely stunted (number) [2.2.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Good nutrition lays the foundation for achieving many of the SDGs with improvements in nutrition directly supporting the achievement of SDG3 (ensuring healthy lives), while also playing a role in ending poverty (SDG1), ensuring quality education (SDG4), achieving gender equality (SDG5), promoting economic growth (SDG8), and reducing inequalities (SDG10). In this way, nutrition is the lifeblood of sustainable development, and drives the changes needed for a more sustainable and prosperous future.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>\n<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>\n<p>World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Prevalence of stunting (height-for-age &lt;-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age. </p>\n<p> (French: pourcentage de retard de croissance (i.e., longueur/taille pour l&apos;&#xE2;ge &lt;-2 &#xE9;carts types par rapport &#xE0; la m&#xE9;diane des normes de croissance de l&apos;enfant de l&apos;Organisation Mondiale de la Sant&#xE9; (OMS)) chez les enfants de moins de cinq ans; Spanish: porcentaje de retraso del crecimiento (i.e., longitud/estatura para la edad &lt; -2 desviaciones est&#xE1;ndar de la mediana de los est&#xE1;ndares de crecimiento infantil de la Organizaci&#xF3;n Mundial de la Salud (OMS)) en los ni&#xF1;os y ni&#xF1;as menores de cinco a&#xF1;os de edad )</p>\n<p><strong>Concepts: </strong></p>\n<p>The UNICEF/WHO/World Bank Joint Malnutrition Estimates (JME) working group generates modelled estimates for 205 countries and territories utilizing primary data sources (e.g., household surveys). The g<u>lobal SDG Indicators Database only contains modelled estimates.</u> Primary data sources can be found at data.unicef.org/nutrition/malnutrition.html, <a href=\"https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb\">https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb</a>, and http://datatopics.worldbank.org/child-malnutrition.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Proportion</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The WHO Multicentre Growth Reference Study (MGRS) (<a href=\"https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study\">WHO 2006</a>) generated a growth standard for assessing the growth and development of infants and young children around the world. The MGRS collected primary growth data and related information from children from widely different ethnic backgrounds and cultural settings (Brazil, Ghana, India, Norway, Oman, and the USA). The resulting growth standard can be applied to all children everywhere, regardless of ethnicity, socioeconomic status and type of feeding. The indicator refers to those moderately or severely stunted, that is with a z-score below -2 standard deviations for height-for-age from the median of the growth standard. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>For the majority of countries, nationally representative household surveys constitute the primary data source used to generate the JME modelled estimates. For a limited number of countries, data from administrative (routine) and surveillance systems are also used as a primary data source for generation of the JME modelled estimates if sufficient population coverage is documented (about 80%). For all types of primary data sources, the child&#x2019;s height/length and date of birth as well as date of measurement (to generate age in days) have to be collected following recommended standard measuring techniques (<a href=\"https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/\">WHO/UNICEF 2019</a>). </p>", "COLL_METHOD__GLOBAL"=>"<p>UNICEF, WHO and the World Bank group jointly review new data sources to update the country level estimates. Each agency uses their existing mechanisms for obtaining data.</p>\n<p>For UNICEF, the cadre of dedicated data and monitoring specialists working at national, regional and international levels in 190 countries routinely provide technical support for the collection and analysis of nutrition data. UNICEF also relies on a data source catalogue that is regularly updated using data sources from catalogues of other international organizations and national statistics offices. This data collection is done in close collaboration with UNICEF regional offices with the purpose of ensuring that UNICEF global databases contain updated and internationally comparable data. The regional office staff work with country offices and local counterparts to ensure that all relevant data are shared.</p>\n<p>WHO data gathering strongly relies on the organization&#x2019;s structure and network established over the past 30 years, since the creation of its global database, the WHO Global Database on Child Growth and Malnutrition, in the late 1980&#x2019;s (<a href=\"https://pubmed.ncbi.nlm.nih.gov/15542535/https:/pubmed.ncbi.nlm.nih.gov/15542535/\">de Onis et al. 2004</a>). </p>\n<p>The World Bank Group provides estimates available through the Living Standard Measurement Surveys (LSMS) which usually requires re-analysis of datasets given that the LSMS reports often do not tabulate the stunting data. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data collection is carried out by the three-agency group throughout the year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The UNICEF-WHO-WB Joint Child Malnutrition (JME) group releases country, regional and worldwide estimates at the end of March every other year so that data are available for the SDG report and database. The JME group also maintains a database of primary data sources (e.g., household surveys) that is used to generate the JME modelled estimates.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Most data sources used are nationally representative household surveys, such as Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and National Nutrition Surveys (NNS). Some data come from other sources (administrative, sentinel systems). Data providers vary and most commonly are ministries of health, national offices of statistics or national institutes of nutrition. </p>", "COMPILING_ORG__GLOBAL"=>"<p>UNICEF, WHO and the World Bank group</p>", "INST_MANDATE__GLOBAL"=>"<p>UNICEF is responsible for global monitoring and reporting on the wellbeing of children. UNICEF actively supports countries in data collection and analysis for reporting on child malnutrition indicators primarily through high-quality MICS surveys, as well as providing technical and financial support to other surveys. UNICEF not only supports household surveys but also works with global partners to define technical standards for the collection and analysis of anthropometric data. UNICEF also compiles statistics on child nutrition with the goal of making internationally comparable estimates and databases publicly available. In-depth analyses of the data on child malnutrition, which are included in relevant data-driven publications, including in its flagship publication, <em>The State of the World&#x2019;s Children,</em> and the <em>Child Nutrition Report</em> are also conducted by UNICEF.</p>\n<p>WHO has an established role in the monitoring of child growth and malnutrition since the late 1980&#x2019;s and had the mandate to develop the WHO Child Growth Standards, launched in 2006, and adopted by more than 160 countries. WHO published several peer-reviewed articles with regional and global estimates until 2012, when they joined forces with UNICEF and the World Bank, with the objective of harmonizing child malnutrition estimates. WHO has the mandate to monitor and report progress on the six global nutrition targets, endorsed in 2012 by the World Health Assembly, amongst them, three on child malnutrition, namely stunting, overweight and wasting (SDG 2.2.1, 2.2.2 (1) and 2.2.2 (2)). </p>", "RATIONALE__GLOBAL"=>"<p>Child growth is an internationally accepted outcome reflecting child nutritional status. Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Survey estimates have uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error, etc.). The JME modelled estimates for stunting take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age data from household surveys supports compilation of a time series that is comparable across countries and over time. </p>\n<p>The JME working group carefully utilizes all available national data sources, and documents all the steps taken to infer about country trends based on the national data sources. The estimation method (McClain et al 2018) is based on and closely aligned to country data. The approach smooths and fits a trend line across the national data points. The basis of the estimates are nationally representative household surveys as well as data from administrative and surveillance systems. However, as surveys are conducted infrequently (e.g., less frequently than every 3 years) in some countries, models produce a complete time series with estimates available in the same years for all countries. This allows for comparable assessment of progress; for example, all countries can be assessed using the same baseline year. For any individual country, an increase in the availability of primary data points can result in more robust and accurate modelled estimates.</p>", "DATA_COMP__GLOBAL"=>"<p>National estimates from primary sources (e.g., from household surveys) used to generate the JME country modelled estimates are based on standardized methodology using the WHO Child Growth Standards as described in <em>Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old (</em><a href=\"https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/\">WHO/UNICEF 2019</a>) and WHO Anthro Survey Analyser (<a href=\"https://www.who.int/tools/child-growth-standards/software\">WHO, 2019</a>). The JME country modelled estimates are generated using smoothing techniques and covariates (<a href=\"https://pubmed.ncbi.nlm.nih.gov/30430613/\">McLain et al. 2018</a>) applied to quality-assured national data to derive trends and up-to-date estimates. Worldwide and regional estimates are derived from the average of country stunting estimates from JME modelling weighted by the countries&#x2019; under-five population estimates (UNPD-WPP latest available edition). The current methodology used by JME has been published in 2024 (<a href=\"https://data.unicef.org/resources/jme-standard-methodology/\">JME Methods 2024</a>).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>UNICEF, WHO and the World Bank undertake a joint review for each potential primary data source used to generate the JME modelled estimates. The group conducts a review when (at minimum) a final report with full methodological details and results are available, as well as (ideally) a data quality assessment flagging potential limitations. When the raw data are available, they are analysed using the Anthro Survey Analyzer software to produce a standard set of results and data quality outputs against which the review is conducted. Comments are documented in a standard review template that includes methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., weight and height distributions, percentage of cases that were flagged as implausible according to the WHO Child Growth Standards) and malnutrition prevalence estimates generated by standard, recommended methodology. These estimates are compared against the reported values, as well as against those from other data sources already included in the JME database, to assess the plausibility of the trend before including the new point. Reports that are preliminary, or that lack key details on methodology or results, cannot be reviewed and are left pending until full information is available. </p>\n<p>The methods used to generate the JME country modelled estimates for stunting and overweight were cross-validated to ensure estimates produced by the method are closely aligned to national data points. The methodology used to model these estimates was reviewed through a technical consultation with experts and country representatives of national statistics offices as well as IAEG-SDGs Members in 2019 (UNICEF/WHO/World Bank, 2019). Country consultations with SDG 2.2 focal points are also held every two years before finalizing and disseminating each edition of the JME global, regional and country estimates. The purpose of the country consultations is to ensure the estimates include all recent and relevant primary data sources and to engage with and receive feedback from national governments on the estimates. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Adjustments to reported values are made in cases where raw data are not available for re-analysis and it is known from the report that the estimates were derived based on indicators that do not adhere to the standard definition used for monitoring of the SDGs (e.g., they are based on different growth references, etc.). The three types of adjustments that have been applied to the JME country database include adjustments to standardize for: (i) area of residence, specifically for data sources that were only nationally representative at the rural level; (ii) growth reference, specifically for data sources that used the 1977 NCHS/WHO Growth Reference instead of the 2006 WHO Growth Standards to generate the child malnutrition estimates; and (iii) age, specifically for data sources that did not include the full 0&#x2013;59-month age group (e.g., data sources reporting on 2&#x2013;4-year-olds). These three types of adjustments are described further in this section. </p>\n<p><strong>i. Adjustment from national rural to national </strong></p>\n<p>A number of surveys cover only rural areas, and, while they have been sampled to be nationally representative for the rural parts of the country, they did not sample any urban areas. Given that malnutrition prevalence generally varies between urban and rural areas (i.e., stunting prevalence was reported to be two times higher in rural areas compared to urban areas at the global level (5)), a rural-only survey would not be comparable with a national survey representative of both urban and rural areas. To improve comparability of the rural-only data sources for the specific country, it is necessary to account for urban populations in estimates from these surveys. </p>\n<p>The adjustment method used by the JME group is to apply the relative proportions of malnutrition prevalence for each urban and rural area from the closest survey in the country&#x2019;s JME database that includes disaggregated estimates by area of residence, to the survey that covers only rural areas. This is done under the assumption that the urban:rural population ratio remains the same as the survey with the disaggregations available (e.g., the proportion of children living in rural areas in the country is the same in the survey year used for the adjustment as in the survey year being adjusted) and also that relative prevalence of malnutrition across urban-rural areas in the survey with the missing data is the same as in the survey with full information used for the adjustment. </p>\n<p><strong>ii. Adjustment to use the 2006 WHO Growth Standard (converted estimates):</strong></p>\n<p>The indicators of stunting, wasting and overweight used to track SDG Target 2.2 require a standard deviation (SD) score (z-score) to be calculated for each child who is measured for a data source; and the z-score requires a growth reference against which it can be calculated. Prior to the release of the WHO Child Growth Standards in 2006, the 1977 NCHS/WHO reference was recommended for international comparisons. The WHO Growth Standard results in estimates of stunting and wasting prevalence that are higher as well as estimates of overweight that are lower than estimates generated using the NCHS/WHO growth reference (6). It was therefore necessary to account for these differences and standardize estimates across data sources. As such, data sources published prior to the release of the new growth standard in 2006 had to be re-analysed using the 2006 growth standards to obtain comparable estimates across time and location. When raw data were not available, a standard algorithm was applied to convert estimates from surveys based on the NCHS reference to estimates based on the WHO Growth Standards (7). </p>\n<p><strong>iii. Age-adjustment </strong></p>\n<p>A limited number of surveys in the JME country database of primary sources that do not have microdata report on age groups that do not cover the entire 0&#x2013;59-month age range in the standard definition for stunting, wasting and overweight. Adjustment for age is needed as malnutrition prevalence can vary by sub-age group. For example, stunting prevalence among children 24&#x2013;59 months old in recent surveys with age-disaggregation were more than two times higher than the stunting prevalence among children 0&#x2013;5 months old (8). Surveys that omit part of the full age range might thus not be comparable with a survey that did cover all children aged 0&#x2013;59-months. Age adjustment can thus help to properly assess the country trend. Similar to the adjustment for rural-only surveys, the proportion of children with malnutrition in the two sub-age groups is assumed to be the same in the survey years in question.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Please refer to <a href=\"https://pubmed.ncbi.nlm.nih.gov/30430613/\">McLain et al. 2018</a> for technical details of the methods used to address missing values. Based on these methods, the JME country modelled estimates are produced from 2000 until the year before the year of publication (e.g., until 2024 for the JME 2025 edition), and are used to generate regional and worldwide aggregates. For countries without any primary input data meeting inclusion criteria, the JME country modelled estimates were produced solely for generation of regional and worldwide aggregates, and were not released to the public</p>\n<ul>\n  <li><strong>At regional and worldwide levels</strong></li>\n</ul>\n<p>There are no missing data for the generation of worldwide and regional estimates as modelled estimates are produced for all countries, including those with and those without primary data in the JME country database. </p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates are available for the following classifications: UN, SDG, UNICEF, WHO, The World Bank regions and income groups.</p>", "DOC_METHOD__GLOBAL"=>"<p>Methods and guidance: </p>\n<p><a href=\"https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/\">Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years of age (WHO/UNICEF, 2019)</a> </p>\n<p><a href=\"https://worldhealthorg.sharepoint.com/sites/ws-jme/Shared%20Documents/Global%20Reporting/UNSD%20SDG/2025/METADATA_SUBMISSION/The%20UNICEF-WHO-World%20Bank%20Joint%20Child%20Malnutrition%20Estimates%20(JME)%20standard%20methodology\">The UNICEF-WHO-World Bank Joint Child Malnutrition Estimates (JME) standard methodology (2024)</a></p>\n<p>Analysis tool: <a href=\"https://worldhealthorg.shinyapps.io/anthro/\">WHO Anthro Survey Analyser (shinyapps.io)</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The JME working group, which was formed in 2011 with representatives from UNICEF, WHO and the World Bank, is responsible for management of the processes used to develop regular updates of the JME estimates. This includes the regular update of the country database of surveys used to generate the JME modelled estimates. Regular communication with regional and country teams allows the JME working group to secure microdata for re-analysis according to the standard method and discuss potential data quality issues. The JME working group also continuously reviews methods and considers and tests different methodologies to improve the estimates as necessary. Additionally, the Technical Expert Advisory Group on Nutrition Monitoring (TEAM), jointly established by UNICEF and WHO, provides advice on nutrition monitoring methods and processes, including on the JME.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The quality criteria established in the 2019 UNICEF/WHO guidance (<a href=\"https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/\">WHO/UNICEF, 2019</a>) were used to update the JME primary data source review form. The JME review form is used to abstract key information including methodological details (e.g., sampling procedures, description of anthropometry equipment), data quality outputs (e.g., response rates, weight and height distributions, percentage of cases that were flagged as having implausible anthropometry outcomes according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from each primary data source (e.g., household survey) under review. One JME working group member fills in the review form for each data source and when information is missing or further details are required, the country teams are contacted. Once all information is available and the JME primary data source review form is completed, each data source is reviewed by the three agencies (UNICEF, WHO, WB) which form the JME working group. This allows for a thorough and efficient standard joint review of each data source by the three agencies prior to inclusion in the JME country database of primary sources (e.g., household surveys) that are used to generate the JME country modelled estimates. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data consistency and quality checks described above are conducted for each potential primary data source (e.g., household survey) before inclusion in the JME country database of primary sources that are used to generate the JME modelled estimates. Cross-validation exercises are performed for the modelled estimates to ensure the method generates estimates that are aligned to national data points. Country consultations with SDG 2.2 focal points held every other year also provide an opportunity to ensure the estimates include all recent and relevant country data. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The JME modelled country estimates from 2000 to 2024 for stunting were released for 163 countries that had at least one primary data source (e.g., from household survey) included in the JME November 2024 database. </p>\n<p><strong>Time series:</strong></p>\n<p>At country level, JME modelled estimates from 2000 to the year before the JME release are presented for countries with at least one data point (e.g., from survey/surveillance) included in the joint database of primary data sources. Data sources years range from 1983 to the year before the JME release.. Worldwide and regional annual estimates are also available from 2000 to the year before the JME release. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Country, regional and worldwide JME modelled estimates refer to the age group of children under 5 years. . JME modelled estimates are available by sex. A disaggregated dataset of national primary sources with sub national and stratified estimates (e.g., sex, age groups, wealth, mothers&apos; education, residence) is also available.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>For the survey estimates included in the JME joint database of primary sources, re-analysis based on standardized methodology using the WHO Child Growth Standards as described in <em>Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old</em> (<a href=\"https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/\">WHO/UNICEF 2019</a>) and WHO Anthro Survey Analyser (<a href=\"https://www.who.int/tools/child-growth-standards/software\">WHO, 2019</a>) is applied whenever microdata are available to enhance comparability across the time series. Country teams are encouraged to use the WHO Anthro Survey Analyser (<a href=\"https://www.who.int/tools/child-growth-standards/software\">WHO, 2019</a>) to undertake survey analysis and harmonize with the global standard analysis methods. </p>\n<p>For the inclusion of survey estimates into the JME database, the inter-agency group applies a set of survey quality assessment criteria. When there is insufficient documentation, the survey is not included until information becomes available. </p>\n<p>Discrepancies between results from standardised methodology and those reported may occur for various reasons; for example, the use of different standards for z-score calculations, imputation of the day of birth when missing, the use of rounded age in months, and the use of different flagging systems for data exclusion. For surveys based on the previous NCHS/WHO references, and for which raw data are not available, a method for converting the z-scores to be based on the WHO Child Growth Standards is applied (<a href=\"http://www.biomedcentral.com/1471-2431/8/19\">Yang and de Onis, 2008</a>). In addition, when surveys do not cover the age interval 0-59 month, or are only representative of the rural areas, an adjustment based on other surveys for the same country, is performed. Any adjustment or conversion is transparently stated in the annotated joint data set.</p>\n<p>The JME country modelled estimates, which are based on smoothing techniques and covariates, as described elsewhere (<a href=\"https://pubmed.ncbi.nlm.nih.gov/30430613/\">McLain et al. 2018</a>), vary from estimates from primary data sources such as household surveys, but in most cases the 95 per cent confidence bounds of the modelled estimates for a given country in a given year fall within the 95 per cent confidence bounds of the estimate from the primary source for the corresponding country and year(s).</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>data.unicef.org/nutrition/malnutrition.html;</p>\n<p>https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb; http://datatopics.worldbank.org/child-malnutrition;</p>\n<p><strong>References:</strong></p>\n<p>de Onis M, Bl&#xF6;ssner M, Borghi E, et al. (2004), Methodology for estimating regional and global trends of childhood malnutrition. Int J Epidemiol, 33(6):1260-70. &lt;<a href=\"https://pubmed.ncbi.nlm.nih.gov/15542535/\">https://pubmed.ncbi.nlm.nih.gov/15542535/</a>&gt;</p>\n<p>de Onis, M., Onyango, A., Borghi, E., Garza, C., and Yang, H. (2006). Comparison of the World Health Organization (WHO) Child Growth Standards and the National Center for Health Statistics/WHO international growth reference: Implications for child health pro&#xAD;grammes. Public Health Nutrition, 9(7), 942-947. doi:10.1017/PHN20062005 &lt;<a href=\"https://www.who.int/childgrowth/publications/Comparison_implications.pdf\">https://www.who.int/childgrowth/publications/Comparison_implications.pdf</a>&gt;</p>\n<p>McLain A, Frongillo E, Feng J, Borghi E (2018). Prediction intervals for penalized longitudinal models with multi-source summary measures: an application to childhood malnutrition. Stat Med; 38(6):1002-1012; doi: 10.1002/sim.8024. Epub 2018 Nov 14. &lt;<a href=\"https://pubmed.ncbi.nlm.nih.gov/30430613/\">https://pubmed.ncbi.nlm.nih.gov/30430613/</a>&gt;</p>\n<p>United Nations Children&#x2019;s Fund (UNICEF), World Health Organization, International Bank for Reconstruction and Development/The World Bank (2019). Meeting report on Technical Consultation on a Country-level model for SDG2.2. December 2019.</p>\n<p>WHO (2006). WHO Multicentre Growth Reference Study (MGRS) &lt;<a href=\"https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study\">https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study</a>&gt;</p>\n<p>World Health Organization and United Nations Children&#x2019;s Fund (2019). Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: </p>\n<p>World Health Organization and the United Nations Children&#x2019;s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. &lt;<a href=\"https://www.who.int/nutrition/publications/anthropometry-data-quality-report\">https://www.who.int/nutrition/publications/anthropometry-data-quality-report</a>&gt;</p>\n<p>WHO. WHO Anthro Survey Analyser (2019). Available at <a href=\"https://www.who.int/tools/child-growth-standards/software\">https://www.who.int/tools/child-growth-standards/software</a>. </p>\n<p>Yang H and de Onis M (2008). <a href=\"http://www.who.int/entity/nutgrowthdb/publications/algorithms/en/index.html\">Algorithms for converting estimates of child malnutrition based on the NCHS reference into estimates based on the WHO Child Growth Standards</a>. BMC Pediatrics 2008, 8:19 (05 May 2008) &lt;<a href=\"http://www.biomedcentral.com/1471-2431/8/19\">http://www.biomedcentral.com/1471-2431/8/19</a>&gt;.</p>\n<p>The UNICEF-WHO-World Bank Joint Child Malnutrition Estimates (JME) standard methodology New York: the United Nations Children&#x2019;s Fund (UNICEF), the World Health Organization and the World Bank, 2024. Licence: CC BY-NC-SA 3.0 IGO, <a href=\"https://data.unicef.org/resources/jme-standard-methodology/\">https://data.unicef.org/resources/jme-standard-methodology/</a> , https://iris.who.int/bitstream/handle/10665/379080/9789240100190-eng.pdf?sequence=1</p>", "indicator_sort_order"=>"02-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.2.2", "slug"=>"2-2-2", "name"=>"Prevalencia de la malnutrición (peso para la estatura, desviación típica > +2 o < -2 de la mediana de los patrones de crecimiento infantil de la OMS) entre los niños menores de 5 años, desglosada por tipo (emaciación y sobrepeso)", "url"=>"/site/es/2-2-2/", "sort"=>"020202", "goal_number"=>"2", "target_number"=>"2.2", "global"=>{"name"=>"Prevalencia de la malnutrición (peso para la estatura, desviación típica > +2 o < -2 de la mediana de los patrones de crecimiento infantil de la OMS) entre los niños menores de 5 años, desglosada por tipo (emaciación y sobrepeso)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Proporción de menores entre 2 y 17 años con obesidad, sobrepeso o peso insuficiente", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Prevalencia de la malnutrición (peso para la estatura, desviación típica > +2 o < -2 de la mediana de los patrones de crecimiento infantil de la OMS) entre los niños menores de 5 años, desglosada por tipo (emaciación y sobrepeso)", "indicator_number"=>"2.2.2", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Salud", "periodicity"=>"Quinquenal", "url"=>"https://www.euskadi.eus/encuesta-salud/inicio/", "url_text"=>"Encuesta de salud", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Proporción de menores entre 2 y 17 años con obesidad, sobrepeso o peso insuficiente", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.2- De aquí a 2030, poner fin a todas las formas de malnutrición, incluso logrando, a más tardar en 2025, las metas convenidas internacionalmente sobre el retraso del crecimiento y la emaciación de los niños menores de 5 años, y abordar las necesidades de nutrición de las adolescentes, las mujeres embarazadas y lactantes y las personas de edad", "definicion"=>"Proporción de menores entre 2 y 17 años con malnutrición (obesidad, sobrepeso o peso insuficiente)\n", "formula"=>"\n$$PPM_{2-17}^{t} = \\frac{PM_{2-17}^{t}}{P_{2-17}^{t}} \\cdot 100$$\n\ndonde:\n\n$PPM_{2-17}^{t} =$ población entre 2 y 17 años con malnutrición (obesidad, sobrepeso o peso insuficiente) en el año $t$\n\n$P_{2-17}^{t} =$ población entre 2 y 17 años en el año $t$\n", "desagregacion"=>"Sexo", "observaciones"=>"", "periodicidad"=>"Quinquenal", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Está aceptado internacionalmente que el crecimiento infantil \nrefleja el estado nutricional del niño. \n\nEl sobrepeso infantil se refiere a un niño con un peso excesivo para su \nestatura. Esta forma de malnutrición se debe a un gasto calórico insuficiente para la \ncantidad de alimentos consumidos y aumenta el riesgo de enfermedades no transmisibles \nen etapas posteriores de la vida. El sobrepeso infantil es uno de los indicadores de nutrición de la Asamblea Mundial de la Salud.\n\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.2.2&seriesCode=SN_STA_OVWGT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y\">Proporción de niños con sobrepeso moderado o grave (%) SN_STA_OVWGT</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas al no utilizar la misma forma de cálculo del sobrepeso, ni el mismo grupo de edad.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-02-02a.pdf\">Metadatos 2-2-2 (1).pdf</a> (solo en inglés)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-02-02b.pdf\">Metadatos 2-2-2 (2).pdf</a> (solo en inglés)\n", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Proporción de menores entre 2 y 17 años con obesidad, sobrepeso o peso insuficiente", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.2- De aquí a 2030, poner fin a todas las formas de malnutrición, incluso logrando, a más tardar en 2025, las metas convenidas internacionalmente sobre el retraso del crecimiento y la emaciación de los niños menores de 5 años, y abordar las necesidades de nutrición de las adolescentes, las mujeres embarazadas y lactantes y las personas de edad", "definicion"=>"Population between 2 and 17 years old with malnutrition \n(obese, overweight, or underweight)\n", "formula"=>"\n$$PPM_{2-17}^{t} = \\frac{PM_{2-17}^{t}}{P_{2-17}^{t}} \\cdot 100$$\n\nwhere:\n\n$PPM_{2-17}^{t} =$ population between 2 and 17 years old with malnutrition \n(obese, overweight, or underweight) in the year $t$\n\n$P_{2-17}^{t} =$ population between 2 and 17 years old in the year $t$\n", "desagregacion"=>"Sex", "observaciones"=>"", "periodicidad"=>"Quinquenal", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nChild growth is an internationally accepted outcome area reflecting child \nnutritional status. Child overweight refers to a child who is too heavy \nfor his or her height. This form of malnutrition results from expending too \nfew calories for the amount of food consumed and increases the risk of noncommunicable \ndiseases later in life. Child overweight is one of the World Health Assembly \nnutrition target indicators.\n\nTherefore, the United Nations indicator refers to child growth and measures the \nprevalence of overweight (weight-for-height >+2 standard deviations from the median \nof the World Health Organization child growth standards) among children under \n5 years of age.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.2.2&seriesCode=SN_STA_OVWGT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y\">Proportion of children moderately or severely overweight (%) SN_STA_OVWGT</a> UNSTATS", "comparabilidad"=>"The available indicator does not comply with the metadata of the United Nations  indicator because it does not use the same method of calculating overweight or  the same age group.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-02-02a.pdf\">Metadata 2-2-2 (1).pdf</a>\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-02-02b.pdf\">Metadata 2-2-2 (2).pdf</a>\n", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Proporción de menores entre 2 y 17 años con obesidad, sobrepeso o peso insuficiente", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.2- De aquí a 2030, poner fin a todas las formas de malnutrición, incluso logrando, a más tardar en 2025, las metas convenidas internacionalmente sobre el retraso del crecimiento y la emaciación de los niños menores de 5 años, y abordar las necesidades de nutrición de las adolescentes, las mujeres embarazadas y lactantes y las personas de edad", "definicion"=>"Malnutrizioa duten 2 eta 17 urte bitarteko adingabeen proportzioa (obesitatea, gehiegizko pisua edo pisu gutxiegi) \n", "formula"=>"\n$$PPM_{2-17}^{t} = \\frac{PM_{2-17}^{t}}{P_{2-17}^{t}} \\cdot 100$$\n\nnon:\n\n$PPM_{2-17}^{t} =$ malnutrizioak duten 2 eta 17 urte bitarteko biztanleak $t$ urtean\n\n$P_{2-17}^{t} =$ 2 eta 17 urte bitarteko biztanleak $t$ urtean\n", "desagregacion"=>"Sexua", "observaciones"=>"", "periodicidad"=>"Quinquenal", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Haurren hazkuntzak haurraren egoera nutrizionala islatzen duela nazioartean onartutako baieztapena da. \n\nHaurren gehiegizko pisua haur batek bere altuerarako pisu gehiegi duela adierazteko erabiltzen da. \nMalnutrizio horren arabera, egiten den gastu kalorikoa ez da aski kontsumitutako elikagai-kopururako. \nHorrek bizitzako ondorengo etapetan gaixotasun ez kutsakorrak izateko arriskua areagotzen du. Haurren \ngehiegizko pisua da Osasunaren Munduko Asanbladaren nutrizio-adierazleetako bat. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.2.2&seriesCode=SN_STA_OVWGT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y\">Gehiegizko pisu ertaina edo larria duten haurren proportzioa (%) SN_STA_OVWGT</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen adierazlearen metadatuak betetzen,  ez baitu gehiegizko pisua kalkulatzeko modu bera erabiltzen, ezta adin-talde bera ere.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-02-02a.pdf\">Metadatuak 2-2-2 (1).pdf</a> (ingelesez bakarrik)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-02-02b.pdf\">Metadatuak 2-2-2 (2).pdf</a> (ingelesez bakarrik)\n", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition, and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.2: by 2030 end all forms of malnutrition, including achieving by 2025 the internationally agreed targets on stunting and wasting in children under five years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women, and older persons</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.2.2: Prevalence of malnutrition (weight for height &gt;+2 or &lt;-2 standard deviation from the median of the WHO Child Growth Standards) among children under 5 years of age, by type (wasting and overweight)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_STA_WAST - Proportion of children moderately or severely wasted [2.2.2]</p>\n<p>SH_STA_WASTN - Children moderately or severely wasted (number) [2.2.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Good nutrition lays the foundation for achieving many of the SDGs with improvements in nutrition directly supporting the achievement of SDG3 (ensuring healthy lives), while also playing a role in ending poverty (SDG1), ensuring quality education (SDG4), achieving gender equality (SDG5), promoting economic growth (SDG8), and reducing inequalities (SDG10). In this way, nutrition is the lifeblood of sustainable development, and drives the changes needed for a more sustainable and prosperous future.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>\n<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>\n<p>World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Prevalence of wasting (weight for height &lt;-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age.</p>\n<p>(French: pourcentage de &#xE9;maciation (i.e. poids pour longueur/taille &lt; -2 &#xE9;carts-types par rapport &#xE0; la m&#xE9;diane des normes de croissance de l&apos;enfant de l&apos;Organisation Mondiale de la Sant&#xE9; (OMS)) chez les enfants de moins de cinq ans; Spanish: porcentaje de emaciaci&#xF3;n (i.e. peso para longitud/estatura &lt; -2 desviaciones est&#xE1;ndar de la mediana de los est&#xE1;ndares de crecimiento infantil de la Organizaci&#xF3;n Mundial de la Salud (OMS)) en ni&#xF1;os y ni&#xF1;as menores de cinco a&#xF1;os de edad.)</p>\n<p><strong>Concepts:</strong></p>\n<p>The official SDG indicator is wasting as assessed using weight-for-height. Wasting can however also be assessed with mid-upper-arm circumference (MUAC). Estimates of wasting based on MUAC are not considered for the JME joint database. In addition, while wasting constitutes the major form of moderate acute malnutrition (MAM), there are acutely malnourished children who would not be picked up with weight-for-height or MUAC, namely those presenting with bilateral pitting oedema (characterized by swollen feet, face and limbs). For surveys that report wasting including oedema cases, these are included in the prevalence of low weight-for-height in the JME database unless raw data are available for re-analysis. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Proportion</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The WHO Multicentre Growth Reference Study (MGRS) (<a href=\"https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study\">WHO 2006</a>) generated a growth standard for assessing the growth and development of infants and young children around the world. The MGRS collected primary growth data and related information from children from widely different ethnic backgrounds and cultural settings (Brazil, Ghana, India, Norway, Oman, and the USA). The resulting growth standard can be applied to all children everywhere, regardless of ethnicity, socioeconomic status and type of feeding. The indicator refers to those moderately or severely wasted, that is with a z-score below -2 standard deviations from the median weight-for-length/height of the growth standard. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>For the majority of countries, nationally representative household surveys constitute the data source. For a limited number of countries data from administrative (routine) and surveillance systems is used if sufficient population coverage is documented (about 80%). For all data sources, the child&#x2019;s length/height and weight measurements have to be collected following recommended standard measuring techniques (<a href=\"https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/\">WHO/UNICEF 2019</a>).</p>", "COLL_METHOD__GLOBAL"=>"<p>UNICEF, WHO and the World Bank group jointly review new data sources to update the country level estimates. Each agency uses their existing mechanisms for obtaining data.</p>\n<p></p>\n<p>For UNICEF, the cadre of dedicated data and monitoring specialists working at national, regional and international levels in 190 countries routinely provide technical support for the collection and analysis of nutrition data. UNICEF also relies on a data source catalogue that is regularly updated using data sources from catalogues of other international organizations and national statistics offices. This data collection is done in close collaboration with UNICEF regional offices with the purpose of ensuring that UNICEF global databases contain updated and internationally comparable data. The regional office staff work with country offices and local counterparts to ensure all relevant data are shared.</p>\n<p>WHO data gathering strongly relies on the organization&#x2019;s structure and network established over the past 30 years, since the creation of its global database, the WHO Global Database on Child Growth and Malnutrition, in the late 1980&#x2019;s (<a href=\"https://pubmed.ncbi.nlm.nih.gov/15542535/\">de Onis et al. 2004</a>).</p>\n<p>The World Bank Group provides estimates available through the Living Standard Measurement Surveys (LSMS) which usually requires re-analysis of datasets given that the LSMS reports often do not tabulate the child malnutrition data.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data collection is carried out by the three-agency group regularly throughout the year so that data are available for the SDG report and database.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The UNICEF-WHO-WB Joint Child Malnutrition (JME) group releases country, regional and worldwide estimates at the end of March every other year so that data are available for the SDG report and database. The JME group also maintains a database of primary data sources (e.g., household surveys), that is used to generate the JME global and regional estimates.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Most data sources are nationally representative household surveys, such as Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and National Nutrition Surveys (NNS). Some data come from other sources (e.g., administrative, sentinel systems or national information systems).</p>\n<p>Data providers vary and most commonly are ministries of health, national offices of statistics or national institutes of nutrition.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNICEF, WHO and the World Bank group</p>", "INST_MANDATE__GLOBAL"=>"<p>UNICEF is responsible for global monitoring and reporting on the wellbeing of children. UNICEF actively supports countries in data collection and analysis for reporting on child malnutrition indicators primarily through high-quality MICS surveys, as well as providing technical and financial support to other surveys. UNICEF not only supports household surveys but also works with global partners to define technical standards for the collection and analysis of anthropometric data. UNICEF also compiles statistics on child nutrition with the goal of making internationally comparable estimates and databases publicly available. In-depth analyses of the data on child malnutrition, which are included in relevant data-driven publications, including in its flagship publication, <em>The State of the World&#x2019;s Children,</em> and the <em>Child Nutrition Report</em> are also conducted by UNICEF.</p>\n<p>WHO has an established role in the monitoring of child growth and malnutrition since the late 1980&#x2019;s and had the mandate to develop the WHO Child Growth Standards, launched in 2006, and adopted by more than 160 countries. WHO published several peer-reviewed articles with regional and global estimates until 2012, when they joined forces with UNICEF and the World Bank, with the objective of harmonizing child malnutrition estimates. WHO has the mandate to monitor and report progress on the six global nutrition targets, endorsed in 2012 by the World Health Assembly, amongst them, three on child malnutrition, namely stunting, overweight and wasting (SDG 2.2.1, 2.2.2 (1) and 2.2.2 (2)).</p>", "RATIONALE__GLOBAL"=>"<p>Child growth is an internationally accepted outcome reflecting child nutritional status and well-being. Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible. Child wasting is one of the World Health Assembly nutrition target indicators.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Survey estimates have uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age data<u> </u>from household surveys supports compilation of a time series that is comparable across countries and over time. </p>\n<p>Surveys are carried out in a specific period of the year, usually over a few months. This indicator can be affected by seasonality; factors related to food availability (e.g., pre-harvest periods), disease (e.g., rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts can impact wasting. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided for country-level.</p>", "DATA_COMP__GLOBAL"=>"<p>Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described in <em>Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old</em> (<a href=\"https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/\">WHO/UNICEF 2019</a>) and WHO Anthro Survey Analyser (<a href=\"https://www.who.int/tools/child-growth-standards/software\">WHO, 2019</a>). The current methodology used by JME has been published in 2024 (<a href=\"https://data.unicef.org/resources/jme-standard-methodology/\">JME Methods 2024</a>).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>UNICEF, WHO and the World Bank undertake a joint review for each potential data source (e.g., household survey). The group conducts a review when (at minimum) a final report with full methodological details and results is available, as well as (ideally) a data quality assessment flagging potential limitations. When the raw data are available, they are analysed using the Anthro Survey Analyzer software to produce a standard set of results and data quality outputs against which the review is conducted. Comments are documented in a standard review template that includes methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., weight and height distributions, percentage of cases that were flagged as implausible according to the WHO Child Growth Standards) and malnutrition prevalence estimates generated by standard, recommended methodology. These estimates are compared against the reported values, as well as against those from other data sources already included in the JME database, to assess the plausibility of the trend before including the new point. Reports that are preliminary, or that lack key details on methodology or results, cannot be reviewed and are left pending until full information is available. Country consultations with SDG 2.2 focal points are also held every two years before finalizing and disseminating each edition of the JME estimates. The purpose of the country consultations is to ensure the wasting estimates include all recent and relevant country data and to engage with and receive feedback from national governments on the estimates. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Adjustments to reported values are made in cases where raw data are not available for re-analysis and it is known from the report that the estimates were derived based on indicators that do not adhere to the standard definition used for monitoring of the SDGs (e.g., they are based on different growth references). The three types of adjustments that have been applied to the JME country dataset include adjustments to standardize for: (i) area of residence, specifically for data sources that were only nationally representative at the rural level; (ii) growth reference, specifically for data sources that used the 1977 NCHS/WHO Growth Reference instead of the 2006 WHO Growth Standards to generate the child malnutrition estimates; and (iii) age, specifically for data sources that did not include the full 0&#x2013;59-month age group (e.g., data sources reporting on 2&#x2013;4-year-olds). These three types of adjustments are described further in this section.</p>\n<p><strong>i. Adjustment from national rural to national</strong></p>\n<p>A number of surveys cover only rural areas, and, while they have been sampled to be nationally representative for the rural parts of the country, they did not sample any urban areas. Given that malnutrition prevalence generally varies between urban and rural areas (i.e., stunting prevalence was reported to be two times higher in rural areas compared to urban areas at the global level (5)), a rural-only survey would not be comparable with a national survey that are representative of both urban and rural areas. To improve comparability of the rural-only data sources for the specific country, it is necessary to account for urban populations in estimates from these surveys. </p>\n<p>The adjustment method used by the JME group is to apply the relative proportions of malnutrition prevalence for each urban and rural area from the closest survey in the country&#x2019;s JME dataset that includes disaggregated estimates by area of residence, to the survey that covers only rural areas. This is done under the assumption that the urban:rural population ratio remains the same as the survey with the disaggregations available (e.g., the proportion of children living in rural areas in the country is the same in the survey year used for the adjustment as in the survey year being adjusted) and also that relative prevalence of malnutrition across urban-rural areas in the survey with the missing data is the same as in the survey with full information used for the adjustment. </p>\n<p><strong>ii. Adjustment to use the 2006 WHO Growth Standard (converted estimates):</strong></p>\n<p>The indicators of stunting, wasting and overweight used to track SDG Target 2.2 require a standard deviation (SD) score (z-score) to be calculated for each child who is measured for a data source; and the z-score requires a growth reference against which it can be calculated. Prior to the release of the WHO Child Growth Standards in 2006, the 1977 NCHS/WHO reference was recommended for international comparisons. The WHO Growth Standard results in estimates of stunting and wasting prevalence that are higher as well as estimates of overweight that are lower than estimates generated using the NCHS/WHO growth reference (6). It was therefore necessary to account for these differences and standardize estimates across data sources. As such, data sources published prior to the release of the new growth standard in 2006 had to be re-analysed using the 2006 growth standards to obtain comparable estimates across time and location. When raw data were not available, a standard algorithm was applied to convert estimates from surveys based on the NCHS reference to estimates based on the WHO Growth Standards (7). </p>\n<p><strong>iii. Age-adjustment</strong></p>\n<p>A limited number of surveys in the JME country database of primary sources that do not have microdata report on age groups that do not cover the entire 0&#x2013;59-month age range in the standard definition for stunting, wasting and overweight. Adjustment for age is needed as malnutrition prevalence can vary by sub-age group. For example, stunting prevalence among children 24&#x2013;59 months old in recent surveys with age-disaggregation were more than two times higher than the stunting prevalence among children 0&#x2013;5 months old (8). Surveys that omit part of the full age range might thus not be comparable with a survey that did cover all children aged 0&#x2013;59-months. Age adjustment can thus help to properly assess the country trend. Similar to the adjustment for rural-only surveys, the proportion of children with malnutrition in the two sub-age groups is assumed to be the same in the survey years in question.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>No imputation methodology is applied to derive estimates for countries or years where no data are available.</p>\n<ul>\n  <li><strong>At regional and worldwide levels</strong></li>\n</ul>\n<p>Countries and years are treated as missing randomly following a multilevel modelling approach (<a href=\"https://pubmed.ncbi.nlm.nih.gov/15542535/\">de Onis et al. 2004</a>).</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates are available for the following classifications: UN, SDG, UNICEF, WHO, The World Bank regions and income groups.</p>", "DOC_METHOD__GLOBAL"=>"<p>Methods and guidance: </p>\n<p><a href=\"https://www.who.int/publications/i/item/9789241515559\">Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years of age (WHO/UNICEF, 2019)</a> </p>\n<p><a href=\"file:///C:\\Users\\KWELKEMA\\AppData\\Local\\Microsoft\\Windows\\INetCache\\Content.Outlook\\A1DL2518\\The%20UNICEF-WHO-World%20Bank%20Joint%20Child%20Malnutrition%20Estimates%20(JME)%20standard%20methodology\">The UNICEF-WHO-World Bank Joint Child Malnutrition Estimates (JME) standard methodology (2024)</a></p>\n<p>Analysis tool: <a href=\"https://worldhealthorg.shinyapps.io/anthro/\">WHO Anthro Survey Analyser (shinyapps.io)</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The JME working group, which was formed in 2011 with representatives from UNICEF, WHO and the World Bank, is responsible for management of the processes used to develop regular updates of the JME estimates. This includes the regular update of the country database of surveys used to generate the JME global estimates, for which regular communication with regional and country teams allows the JME working group to secure microdata for re-analysis according to the standard method. The JME working group also continuously reviews methods and considers and tests different methodologies to improve the estimates as necessary. Additionally, the Technical Expert Advisory Group on Nutrition Monitoring (TEAM), jointly established by UNICEF and WHO, provides advice on nutrition monitoring methods and processes, including on the JME.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The quality criteria established in the 2019 UNICEF/WHO guidance (<a href=\"https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/\">WHO/UNICEF, 2019</a>) were used to update the JME primary data source review form. The JME review form is used to abstract key information including methodological details (e.g., sampling procedures, description of anthropometry equipment), data quality outputs (e.g., response rates, weight and height distributions, percentage of cases that were flagged as having implausible anthropometry outcomes according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from each primary data source (e.g., household survey) under review. One JME working group member fills in the review form for each data source and when information is missing or further details are required, the country teams are contacted. Once all information is available and the JME primary data source review form is completed, each data source is reviewed by the three agencies which form the JME working group. This allows for a thorough and efficient standard joint review of each data source by the three agencies which form the JME working group prior to inclusion in the JME country database of primary sources (e.g., household surveys). </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data consistency and quality checks described above are conducted for each potential primary data source (e.g., household survey) before inclusion in the JME country database of primary sources. Country consultations with SDG 2.2 focal points also provide an overall evaluation of the estimates and help to ensure that all recent and relevant country data are included.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p> The JME global and regional estimates from 2000 to 2024 for wasting were released for 161 countries that had at least one primary data source (e.g., from household survey) included in the 2025 JME country database.</p>\n<p><strong>Time series:</strong></p>\n<p>At country level, data are provided from the year 2000. Data source years for the full JME database range from 1983 to the year before the JME release. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Worldwide and regional estimates refer to the age group of children under 5 years, sexes combined. A disaggregated dataset of national primary sources with sub national and stratified estimates (e.g., sex, age groups, wealth, mothers&apos; education, residence) is available.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>For the survey estimates included in the JME joint database, re-analysis based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old (<a href=\"https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/\">WHO/UNICEF 2019</a>) and WHO Anthro Survey Analyser (<a href=\"https://www.who.int/tools/child-growth-standards/software\">WHO, 2019</a>) is applied whenever microdata are available, for enhancing comparability across the time series. Country teams are encouraged to use the WHO Anthro Survey Analyser (<a href=\"https://www.who.int/tools/child-growth-standards/software\">WHO, 2019</a>) to undertake survey analysis and harmonize with the global standard analysis methods. </p>\n<p>For the inclusion of survey estimates into the JME database, the inter-agency group applies a set of survey quality assessment criteria. When there is insufficient documentation, the survey is not included until information becomes available. </p>\n<p>Discrepancies between results from the standard methodology and those reported may occur for various reasons, for example, the use of different standards for z-score calculations, imputation of the day of birth when missing, the use of rounded age in months, the use of different flagging systems for data exclusion. For surveys based on the previous NCHS/WHO references, and for which raw data are not available, a method for converting the z-scores to be based on the WHO Child Growth Standards is applied (<a href=\"http://www.biomedcentral.com/1471-2431/8/19\">Yang and de Onis, 2008</a>).). In addition, when surveys do not cover the age interval 0-59 months, or are only representative of the rural areas, an adjustment based on other surveys for the same country, is performed. Any adjustment or conversion is transparently stated in the annotated joint data set.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>data.unicef.org/nutrition/malnutrition.html; </p>\n<p>https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb; http://datatopics.worldbank.org/child-malnutrition;</p>\n<p><strong>References:</strong></p>\n<p>de Onis M, Bl&#xF6;ssner M, Borghi E, et al. (2004), Methodology for estimating regional and global trends of childhood malnutrition. Int J Epidemiol, 33(6):1260-70. &lt;<a href=\"https://pubmed.ncbi.nlm.nih.gov/15542535/\">https://pubmed.ncbi.nlm.nih.gov/15542535/</a>&gt;</p>\n<p>de Onis, M., Onyango, A., Borghi, E., Garza, C., and Yang, H. (2006). Comparison of the World Health Organization (WHO) Child Growth Standards and the National Center for Health Statistics/WHO international growth reference: Implications for child health pro&#xAD;grammes. Public Health Nutrition, 9(7), 942-947. doi:10.1017/PHN20062005 &lt;<a href=\"https://www.who.int/childgrowth/publications/Comparison_implications.pdf\">https://www.who.int/childgrowth/publications/Comparison_implications.pdf</a>&gt;</p>\n<p>United Nations Children&#x2019;s Fund, World Health Organization, The World Bank (2012). UNICEFWHO-World Bank Joint Child Malnutrition Estimates. (UNICEF, New York; WHO, Geneva; The World Bank, Washington, DC; 2012). &lt;<a href=\"https://www.who.int/docs/default-source/child-growth/jme-brochure2012.pdf?sfvrsn=ca20d895_2\">https://www.who.int/docs/default-source/child-growth/jme-brochure2012.pdf?sfvrsn=ca20d895_2</a>&gt;</p>\n<p>WHO (2006). WHO Multicentre Growth Reference Study (MGRS) &lt;<a href=\"https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study\">https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study</a>&gt;</p>\n<p>World Health Organization and United Nations Children&#x2019;s Fund (2019). Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children&#x2019;s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. &lt;<a href=\"https://www.who.int/nutrition/publications/anthropometry-data-quality-report\">https://www.who.int/nutrition/publications/anthropometry-data-quality-report</a>&gt;</p>\n<p>WHO. WHO Anthro Survey Analyser (2019). Available at <a href=\"https://www.who.int/tools/child-growth-standards/software\">https://www.who.int/tools/child-growth-standards/software</a>. </p>\n<p>Yang H and de Onis M (2008). <a href=\"http://www.who.int/entity/nutgrowthdb/publications/algorithms/en/index.html\">Algorithms for converting estimates of child malnutrition based on the NCHS reference into estimates based on the WHO Child Growth Standards</a>. BMC Pediatrics 2008, 8:19 (05 May 2008) &lt;<a href=\"http://www.biomedcentral.com/1471-2431/8/19\">http://www.biomedcentral.com/1471-2431/8/19</a>&gt;.</p>\n<p>The UNICEF-WHO-World Bank Joint Child Malnutrition Estimates (JME) standard methodology New York: the United Nations Children&#x2019;s Fund (UNICEF), the World Health Organization and the World Bank, 2024. Licence: CC BY-NC-SA 3.0 IGO, <a href=\"https://data.unicef.org/resources/jme-standard-methodology/\">https://data.unicef.org/resources/jme-standard-methodology/</a> , https://iris.who.int/bitstream/handle/10665/379080/9789240100190-eng.pdf?sequence=1</p>", "indicator_sort_order"=>"02-02-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.2.3", "slug"=>"2-2-3", "name"=>"Prevalencia de la anemia en las mujeres de entre 15 y 49 años, desglosada por embarazo (porcentaje)", "url"=>"/site/es/2-2-3/", "sort"=>"020203", "goal_number"=>"2", "target_number"=>"2.2", "global"=>{"name"=>"Prevalencia de la anemia en las mujeres de entre 15 y 49 años, desglosada por embarazo (porcentaje)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Prevalencia de la anemia en las mujeres de entre 15 y 49 años, desglosada por embarazo (porcentaje)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Prevalencia de la anemia en las mujeres de entre 15 y 49 años, desglosada por embarazo (porcentaje)", "indicator_number"=>"2.2.3", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"La anemia tiene una prevalencia muy alta a nivel mundial y afecta desproporcionadamente \na niños y mujeres en edad reproductiva. Afecta negativamente el desarrollo cognitivo \ny motor y la capacidad de trabajo, y entre las mujeres embarazadas la anemia por deficiencia \nde hierro se asocia con resultados reproductivos adversos, incluidos partos prematuros, \nbebés con bajo peso al nacer y disminución de las reservas de hierro del bebé, \nlo que puede conducir a un desarrollo reducido. \n\nLa deficiencia de hierro se considera la causa más común de anemia, pero existen \notras causas nutricionales y no nutricionales. Las concentraciones de hemoglobina \nen sangre se ven afectadas por muchos factores, entre ellos la altitud (metros sobre el \nnivel del mar), el tabaquismo, el trimestre de embarazo, la edad y el sexo. \n\nLa anemia puede evaluarse midiendo la hemoglobina en sangre y, cuando se usa en \ncombinación con otros indicadores del estado del hierro, la hemoglobina en sangre \nproporciona información sobre la gravedad de la deficiencia de hierro. La prevalencia \nde anemia en la población se utiliza para clasificar la importancia del problema \npara la salud pública.\n\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.2.3&seriesCode=SH_STA_ANEM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-49%20%7C%20FEMALE\">Proporción de mujeres de 15 a 49 años con anemia (%) SH_STA_ANEM</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-02-03.pdf\">Metadatos 2-2-3.pdf (solo en inglés)</a>", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"\nAnaemia is highly prevalent globally, disproportionately affecting \nchildren and women of reproductive age. It negatively affects cognitive \nand motor development and work capacity, and among pregnant women iron \ndeficiency anaemia is associated with adverse reproductive outcomes, including \npreterm delivery, low-birth-weight infants, and decreased iron stores for the \nbaby, which may lead to impaired development.\n\nIron deficiency is considered the most common cause of anaemia, but there \nare other nutritional and non-nutritional causes. Blood haemoglobin concentrations \nare affected by many factors, including altitude (metres above sea level), \nsmoking, trimester of pregnancy, age and sex.\n\nAnaemia can be assessed by measuring blood haemoglobin, and when used in \ncombination with other indicators of iron status, blood haemoglobin provides \ninformation about the severity of iron deficiency. The anaemia prevalence for \nthe population is used to classify the public health significance of the problem.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.2.3&seriesCode=SH_STA_ANEM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-49%20%7C%20FEMALE\">Proportion of women aged 15-49 years with anaemia (%) SH_STA_ANEM</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-02-03.pdf\">Metadata 2-2-3.pdf</a>"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"Anemiak nagusitasun oso altua du mundu-mailan, eta haurrei eta ugalkortasun-adinean dauden emakumeei \nneurrigabeki eragiten die. Eragin negatiboa du garapen kognitibo eta motorrean eta lanerako gaitasunean, \neta, gainera, haurdun dauden emakumeen artean burdin-faltaren ondoriozko anemia lotuta egoten da \nugalketa-emaitza negatiboekin, horren barruan sartuta erditze goiztiarrak, jaiotzean pisu gutxi duten \nhaurrak eta haurraren burdin-erreserben murrizketa, horrek garapen murriztua eragin dezakeelarik. \n\nBurdin gutxi izatea da anemiaren arrazoi ohikoenetakoa baina badaude beste kausa batzuk ere, nutrizioaren \narlokoak eta hortik kanpokoak. Odoleko hemoglobina-kontzentrazioek faktore askoren eragina dute, hala nola \naltitudea (itsasoaren mailaren gainetik dauden metroak), tabakismoa, haurdunaldiko hiruhilekoa, adina eta sexua. \n\nAnemia odoleko hemoglobina neurtuta ebaluatu daiteke eta, burdinaren egoeraren beste adierazle batzuekin \nbatera erabiltzen denean, odoleko hemoglobinak burdinaren urritasunaren larritasunari buruzko informazioa \nematen du. Biztanleriaren artean anemiak duen nagusitasuna erabiltzen da osasun publikorako arazoaren garrantzia \nsailkatzeko. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.2.3&seriesCode=SH_STA_ANEM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-49%20%7C%20FEMALE\">Anemia duten 15-49 urteko emakumeen proportzioa (%) SH_STA_ANEM</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-02-03.pdf\">Metadatuak 2-2-3.pdf</a> (ingelesez bakarrik)"}, "national_metadata_updated_date"=>"2025-03-09", "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.2: By 2030, end all forms of malnutrition, including achieving, by 2025, the internationally agreed targets on stunting and wasting in children under 5 years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women and older persons</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.2.3: Prevalence of anaemia in women aged 15 to 49 years, by pregnancy status (percentage)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_STA_ANEM - Proportion of women aged 15-49 years with anaemia [2.2.3]</p>\n<p>SH_STA_ANEM_NPRG - Proportion of women aged 15-49 years with anaemia, non-pregnant [2.2.3]</p>\n<p>SH_STA_ANEM_PREG - Proportion of women aged 15-49 years with anaemia, pregnant [2.2.3]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Anaemia is estimated to contribute to 17% lower productivity in heavy manual labour and 5% lower productivity in other manual labour (Goal 1 End poverty in all its forms everywhere); during pregnancy, it increases the risk of maternal and perinatal mortality and contributes to low birth-weight infants (Goal 3. Good health and well-being); it also limits cognitive development, children who have adequate iron have more energy to participate in classroom exercises, and they are more mentally prepared to master the material (Goal 4. Quality education); anaemia rates in females are much higher than males &#x2014; while anaemia rates decrease for males by the end of puberty, they remain high for females through reproductive years due to menstruation, thus reducing anaemia contributes to boosting females&#x2019; relative academic performance and worker productivity and helps achieve gender equality (Goal 5. Gender equality).</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Percentage of women aged 15&#x2212;49 years with a haemoglobin concentration less than 120 g/L for non-pregnant women and lactating women, and less than 110 g/L for pregnant women, adjusted for altitude and smoking.</p>\n<p><strong>Concepts:</strong></p>\n<p>Anaemia: condition in which the concentration of blood haemoglobin concentration falls below established cut-off values.</p>\n<p>Iron deficiency: state in which there is insufficient iron to maintain the normal physiological function of blood, brain and muscles (ICD-11, 5B5K.0 iron deficiency)</p>\n<p>Iron deficiency anaemia: (ICD-11, 3A00, iron deficiency anaemia)</p>\n<p>Blood haemoglobin concentration: concentration of haemoglobin in whole blood</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>WHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Vitamin and Mineral Nutrition Information System. Geneva, World Health Organization, 2011 (WHO/NMH/NHD/MNM/11.1)(<a href=\"http://www.who.int/vmnis/indicators/haemoglobin.pdf\">http://www.who.int/vmnis/indicators/haemoglobin.pdf</a>, accessed [4 March 2021).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The preferable source of data is population-based surveys. Data from surveillance systems may be used under some conditions, but recorded diagnoses are typically underestimated. Data are from the Micronutrients Database of the WHO Vitamin and Mineral Nutrition Information System (VMNIS) (https://www.who.int/teams/nutrition-and-food-safety/databases/vitamin-and-mineral-nutrition-information-system This database compiles and summarizes data on the micronutrient status of populations from various other sources, including data collected from the scientific literature and through collaborators, including WHO regional and country offices, United Nations organizations, ministries of health, research and academic institutions, and nongovernmental organizations. In addition, anonymized individual-level data are obtained from multi-country surveys, including demographic and health surveys, multiple indicator cluster surveys, reproductive health surveys and malaria indicator surveys.</p>", "COLL_METHOD__GLOBAL"=>"<p>The anaemia status of women is assessed using blood haemoglobin concentrations. In surveys, blood haemoglobin concentrations are typically measured using the direct cyanmethemoglobin method in a laboratory or with a portable, battery-operated, haemoglobin photometer in the field that uses the azide-methaemoglobin method.</p>\n<p>A PubMed search was carried out for relevant search terms related to anaemia, haemoglobin and iron status, searching for studies published after 1 January 1990. In addition to indexed articles, many reports of national and international agencies were identified and accessed through requests to each corresponding organization. Data are also collected during the country validation process, described below, and from publicly available individual-level survey data. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data on anaemia are continuously being collected from survey report and manuscripts and entered into the WHO Micronutrients Database.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>There is no fixed date in which the new round of anaemia estimates will be generated; however, estimates are generally generated every three to five years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>There are two main data sources of survey data for anaemia: 1) reports generated by countries or implementing partners and 2) published manuscripts. Occasionally, Member States, regional offices, the international community or colleagues managing other databases within WHO provide reports directly to staff responsible for maintaining the WHO Micronutrients Database. If data meet the eligibility criteria, they are entered into the database. Reports and publications are primarily requested and collected from: </p>\n<ul>\n  <li>Ministries of Health through WHO regional and country offices, </li>\n  <li>National research and academic institutions,</li>\n  <li>Nongovernmental organizations, and</li>\n  <li>Organizations of the <a href=\"http://www.unsceb.org/directory\">United Nations</a> system. </li>\n</ul>", "COMPILING_ORG__GLOBAL"=>"<p>WHO compiles the data fed into the Micronutrients Database of the WHO Vitamin and Mineral Information System (VMNIS).</p>", "INST_MANDATE__GLOBAL"=>"<p>The Vitamin and Mineral Nutrition Information System (VMNIS), formerly known as the Micronutrient Deficiency Information System (MDIS), was established in 1991 following a request by the World Health Assembly to strengthen surveillance of micronutrient deficiencies at the global level. Part of WHO&apos;s mandate is to assess the micronutrient status of populations, monitor and evaluate the impact of strategies for the prevention and control of micronutrient malnutrition, and to track related trends over time.</p>", "RATIONALE__GLOBAL"=>"<p>Anaemia is highly prevalent globally, disproportionately affecting children and women of reproductive age. It negatively affects cognitive and motor development and work capacity, and among pregnant women iron deficiency anaemia is associated with adverse reproductive outcomes, including preterm delivery, low-birth-weight infants, and decreased iron stores for the baby, which may lead to impaired development. Iron deficiency is considered the most common cause of anaemia, but there are other nutritional and non-nutritional causes. Blood haemoglobin concentrations are affected by many factors, including altitude (metres above sea level), smoking, trimester of pregnancy, age and sex. Anaemia can be assessed by measuring blood haemoglobin, and when used in combination with other indicators of iron status, blood haemoglobin provides information about the severity of iron deficiency. The anaemia prevalence for the population is used to classify the public health significance of the problem.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Despite the extensive data search, data for blood haemoglobin concentrations are still limited, compared to other nutritional indicators such as child anthropometry (1, 24); this was especially true in the high-income countries of the WHO European Region. As a result, the estimates may not capture the full variation across countries and regions, tending to &#x201C;shrink&#x201D; towards global means when data are sparse. </p>\n<p>Estimates may differ from those reported by countries.</p>", "DATA_COMP__GLOBAL"=>"<p>Prevalence of anaemia and/or mean haemoglobin in women of reproductive age were obtained from 408 population-representative data sources from 124 countries worldwide. Data collected from 1995 to 2020 were used. Adjustment of data on blood haemoglobin concentrations for altitude and smoking was carried out whenever possible. Biologically implausible haemoglobin values (&lt;25 g/L or &gt;200 g/L) were excluded. A Bayesian hierarchical mixture model was used to estimate haemoglobin distributions and systematically addressed missing data, non-linear time trends, and representativeness of data sources. </p>\n<p>Full details on statistical methods may be found <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4547326/\">here</a>: Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995&#x2013;2011: a systematic analysis of population-representative data (Stevens et al, 2013). </p>\n<p>Briefly, the model calculates estimates for each country and year, informed by data from that country and year themselves, if available, and by data from other years in the same country and in other countries with data for similar time periods, especially countries in the same region. The model borrows data, to a greater extent, when data are non-existent or weakly informative, and to a lesser degree for data-rich countries and regions. The resulting estimates are also informed by covariates that help predict blood haemoglobin concentrations (e.g. socio-demographic index, meat supply (kcal/capita), mean BMI for women and log of under-five mortality for children). The uncertainty ranges (credibility intervals) reflect the major sources of uncertainty, including sampling error, non-sampling error due to issues in sample design/measurement, and uncertainty from making estimates for countries and years without data.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Once survey data are compiled and the Bayesian hierarchical mixture model is run to generate anaemia estimates, countries are sent a memorandum to provide a background to the estimates and explain the process. Information on the survey data used to generate the estimates for that country, estimates for the years 2000, 2005, 2010, 2015, and 2019, and the resulting plots for each country are provided along with an explanation of the methodology used in generating the estimates. Countries are requested to provide feedback within six weeks.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Data on mean haemoglobin and anaemia prevalence from high-altitude countries that were not adjusted for altitude when published were adjusted for altitude by WHO, as described in Stevens et al (2013). The Bayesian hierarchical mixture internally adjusts summary statistics computed with non-standard haemoglobin cut-offs to match the standard WHO cut-offs listed above. </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>A Bayesian hierarchical mixture model was used to estimate haemoglobin distributions and systematically addressed missing data, non-linear time trends, and representativeness of data sources. The full description of the methodology for country and region estimates can be found at Supplement to: Stevens GA, Finucane MM, De-Regil LM, et al. Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995&#x2013;2011: a systematic analysis of population-representative data. Lancet Glob Health 2013; 1: e16&#x2013;25. Available at https://www.thelancet.com/cms/10.1016/S2214-109X(13)70001-9/attachment/e073f9da-1330-4a1d-a1a0-67caf08c11bf/mmc1.pdf.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Global and regional prevalence estimates were calculated as population-weighted averages of the constituent countries (see treatment of missing values at country level).</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional prevalence estimates were calculated as population-weighted averages of the constituent countries (see methodology for deriving country-level estimates above). </p>", "DOC_METHOD__GLOBAL"=>"<p>This indicator is part of the Global Nutrition Monitoring Framework (GNMF), for which operational guidance is offered to countries &#x2013; the Global nutrition monitoring framework: Operational guidance for tracking progress in meeting targets for 2025 available at https://www.who.int/publications/i/item/9789241513609 in the six UN official languages.</p>\n<p>WHO in collaboration with UNICEF, the US Centers for Disease Control and Prevention and Nutrition International updated a Micronutrient Survey Manual, containing details about conducting and national nutrition survey and reporting results.<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></p>\n<p> </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p>Centers for Disease Control and Prevention, World Health Organization, Nutrition International, UNICEF. Micronutrient survey manual. Geneva: World Health Organization; 2020. Licence: CC BY-NC-SA 3.0 IGO. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "QUALITY_MGMNT__GLOBAL"=>"<p>All surveys included in the database pass through inclusion criteria described below. Data also follows the five WHO Data principles<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> WHO data principles. https://www.who.int/data/principles <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "QUALITY_ASSURE__GLOBAL"=>"<p>Survey data provided in peer-reviewed publications or survey reports are screened for inclusion in the WHO Micronutrients Database. Eligibility criteria to the Micronutrients database include: details of the sampling method are provided; the sample was representative of at least the 1st administrative level (e.g. state, province, canton, oblast); the sample was population-based, household-based, or facility-based (i.e., for pregnant women, newborns, and preschool and school-age children); the sample was cross-sectional or was the baseline assessment in an intervention programme; and the study used standard, validated data collection techniques and laboratory methodology. If there are particular concerns regarding the reported data, attempts are made to discuss these concerns with a country representative.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data from the Micronutrients database passes an additional screening to be included into the estimates if a facility-based sampling scheme was used in order to exclude data where these would not be representative of the general population. The general threshold for inclusion was 80% affiliation of the target population with the facility. For studies of children sampled from primary care physician rosters or well-child visits, we included the data if national coverage of the third dose of DTP vaccine exceeded 80%. For women sampled from obstetric care providers, data were included if the coverage of at least one ANC care was greater than 80%. For school-based sampling of adolescents, the completion rate of lower secondary school for girls was required to be greater than 80%.</p>\n<p>We excluded data if migrants comprised more than 40% of the population in the country, and the data source only covered nationals. Quality checks (e.g. implausible values that are not in according with life, ) are done when data is entered into the database, and when data is compiled for producing the estimates.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Prevalence of anaemia and/or mean haemoglobin in women of reproductive age were obtained from 408 population-representative data sources from 124 countries worldwide. Data collected from 1995 to 2020 were used.</p>\n<p> </p>\n<p><strong>Time series:</strong></p>\n<p>Estimates for 2000 to 2019 were derived in the latest exercise.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Anaemia prevalence data are generally reported disaggregated by age, sex, income, geographic region (within country) and 1<sup>st</sup> administrative level within a country. When producing estimates of anaemia for the purpose of contributing to the monitoring of SDGs, estimates are produced for women of reproductive age (15-49 years) by pregnancy status (pregnant or non-nonpregnant) for each country. Data are then aggregated by WHO or UN region and for the global level.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Data conform to the standard WHO definition of anaemia.</p>", "OTHER_DOC__GLOBAL"=>"<p>&#x2022; WHO Global Anaemia estimates, 2021 Edition. Global anaemia estimates in women of reproductive age, by pregnancy status, and in children aged 6-59 months. Geneva: World Health Organization; 2021 (Available at https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children)$</p>\n<p>&#x2022; WHO Micronutrients database. Vitamin and Mineral Nutrition Information System (VMNIS). Geneva: World Health Organization; 2021 (Available at https://www.who.int/teams/nutrition-and-food-safety/databases/vitamin-and-mineral-nutrition-information-system)</p>\n<p>&#x2022; WHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Vitamin and Mineral Nutrition Information System. Geneva, World Health Organization, 2011 (WHO/NMH/NHD/MNM/11.1) (Available at http://www.who.int/vmnis/indicators/haemoglobin.pdf)</p>\n<p>&#x2022; Stevens GA, Finucane MM, De-Regil LM, Paciorek CJ, Flaxman SR , Branca F, Pe&#xF1;a-Rosas JP, Bhutta ZA, Ezzati M, Nutrition Impact Model Study Group (Anaemia). Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995-2011: a systematic analysis of population-representative data. Lancet Glob Health. 2013 Jul;1(1):e16-25. doi: 10.1016/S2214-109X(13)70001-9. Epub 2013 Jun 25.</p>\n<p>&#x2022; WHO. Comprehensive Implementation Plan on Maternal, Infant and Young Child Nutrition. Geneva: World Health Organization; 2014. (Available at https://apps.who.int/iris/bitstream/handle/10665/113048/WHO_NMH_NHD_14.1_eng.pdf)</p>\n<p>&#x2022; WHO. Global nutrition targets 2025: anaemia policy brief (WHO/NMH/NHD/14.4). Geneva: World Health (Available at https://www.who.int/publications/i/item/WHO-NMH-NHD-14.4) Organization; 2014.</p>\n<p>&#x2022; Global anaemia reduction efforts among women of reproductive age: impact, achievement of targets and the way forward for optimizing efforts. Geneva: World Health Organization; 2020. Licence: CC BY-NCSA 3.0 IGO. (Available at https://www.who.int/publications/i/item/9789240012202) </p>\n<p>&#x2022; Nutritional anaemias: tools for effective prevention and control. Geneva: World Health Organization; 2017. Licence: CC BY-NC-SA 3.0 IGO (Available at http://apps.who.int/iris/bitstream/handle/10665/259425/9789241513067-eng.pdf) </p>\n<p>&#x2022; Every Woman Every Child. Global strategy for women&apos;s, children&apos;s and adolescents&apos; health. New York: United Nations; 2015. (Available at https://www.who.int/life-course/partners/global-strategy/globalstrategyreport2016-2030-lowres.pdf</p>", "indicator_sort_order"=>"02-02-03", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.2.4", "slug"=>"2-2-4", "name"=>"Prevalence of minimum dietary diversity, by population group (children aged 6 to 23.9 months and non-pregnant women aged 15 to 49 years)", "url"=>"/site/es/2-2-4/", "sort"=>"020204", "goal_number"=>"2", "target_number"=>"2.2", "global"=>{}, "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.2: By 2030, end all forms of malnutrition, including achieving, by 2025, the internationally agreed targets on stunting and wasting in children under 5 years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women and older persons</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.2.4: Prevalence of minimum dietary diversity, by population group (children aged 6 to 23.9 months and nonpregnant women aged 15 to 49 years)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_MDD_WMN_NPRG - Prevalence of minimum dietary diversity among non-pregnant women aged 15-49 years [2.2.4]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Healthy diets are fundamental for achieving SDG 2 and a prerequisite for reaching many other SDGs including SDG 3 (ensuring healthy lives), playing a role in ending poverty (SDG 1), ensuring quality education (SDG 4), achieving gender equality (SDG 5), promoting economic growth (SDG 8), and reducing inequalities (SDG 10). Unhealthy diets are the leading cause of poor health and non-communicable disease worldwide and so minimum dietary diversity is also strongly linked to SDG target 3.4, which aims to reduce premature mortality from non-communicable diseases by one third by 2030.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Percentage of non-pregnant women aged 15-49 years who consumed foods or beverages from at least five out of ten defined food groups during the preceding 24 hours.</p>\n<p><strong>Concepts:</strong></p>\n<ul>\n  <li><strong>Dietary diversity: </strong>Minimum Dietary Diversity for Women (MDD-W) is a population-level food group-based indicator that captures dietary diversity, a key characteristic of healthy diets.</li>\n  <li><strong>Food groups: </strong>FAO has defined ten mutually exclusive food groups (1). See section 4.c. for the food group descriptions.</li>\n  <li><strong>Dichotomous indicator: </strong>Achieving MDD-W is defined as the consumption of at least five out of ten food groups.</li>\n  <li><strong>Nutrient adequacy: </strong>MDD-W has been validated as an indicator for a minimally acceptable level of adequacy for 11 micronutrients (2&#x2013;4). Achievement of MDD-W therefore signals better micronutrient intake.</li>\n  <li><strong>Non-quantitative:</strong> No data is collected on intake quantities during the questionnaire administration; a simple yes/no response is recorded as to whether any foods or beverages from a food group was consumed. However, foods or beverages usually consumed in trivial quantities (under 15 grams) are excluded from the food list in the questionnaire.</li>\n</ul>", "UNIT_MEASURE__GLOBAL"=>"<p>Proportion</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Population-based nationally representative household surveys, such as the Demographic and Health Surveys (DHS), Gallup World Poll (GWP), Living Standards Measurement Surveys (LSMS), and Multiple Indicator Cluster Surveys (MICS), and Standardized Monitoring and Assessment of Relief and Transitions (SMART) surveys, are the primary source of country-level MDD-W data. Other data sources include national nutrition and health surveys and nationally representative quantitative dietary intake surveys using 24-hour recalls.</p>", "COLL_METHOD__GLOBAL"=>"<p>Surveys predominantly assess MDD-W by collecting data on the intake of food groups among non-pregnant women aged 15-49 years through a face-to-face or telephone-based interviewer-administered non-quantitative food list-based 24-hour recall of dietary intake as recommended by FAO in <a href=\"https://www.fao.org/3/cb3434en/cb3434en.pdf\">&#x201C;MDD-W: An updated guide to measurement - from collection to action</a>.&#x201D;</p>\n<p>Below, an example MDD-W questionnaire from the Tanzania DHS 2022. All country-specific DHS questionnaires can be found on the <a href=\"https://dhsprogram.com/pubs/pdf/DHSQ8/DHS8_Womans_QRE_EN_14Feb2023_DHSQ8.pdf\">DHS Program</a> website. All country-specific GWP questionnaires can be found on the <a href=\"https://www.dietquality.org/tools\">Global Diet Quality Project</a> website. Questionnaires from national nutrition and health surveys are usually available in final reports.</p>\n<p>For the DHS, as an example, MDD-W data are collected among 10,000 to 40,000 non-pregnant women. The response rate among women aged 15-49 years is 95% or higher for all available DHS. For the GWP, MDD-W data are collected among approximately 200-600 women in each survey round in a country.</p>\n<p>Nationally representative data have been collected at various times of the year/seasons between countries. However, to ensure the comparability of MDD-W estimates within countries and to mitigate biased inferences of change over time, FAO recommends that repeated surveys carry out data collection in the same time period as the previous survey (5).</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>NO.</p>\n      </td>\n      <td>\n        <p>QUESTIONS AND FILTERS</p>\n      </td>\n      <td colspan=\"4\">\n        <p>CODING CATEGORIES</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"16\">\n        <p>643</p>\n      </td>\n      <td>\n        <p>Now I&#x2019;d like to ask you about foods and drinks that you consumed yesterday during the day or night, whether you ate or drank it at home or somewhere else. Please think about snacks and small meals as well as main meals.</p>\n        <p>I will ask you about different foods and drinks, and I would like to know whether you ate a food even if it was combined with other foods.</p>\n        <p>Please do not answer &#x2018;yes&#x2019; for any food or ingredient only used in a small amount to add flavor to a dish.</p>\n        <p>a) Ugali, porridge, rice, pasta, bread, chapati, kitumbua, or maize?</p>\n      </td>\n      <td>\n        <p>a) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>YES 1</p>\n      </td>\n      <td>\n        <p>NO 2</p>\n      </td>\n      <td>\n        <p>DK 8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>b) Orange flesh sweet potato or carrots?</p>\n      </td>\n      <td>\n        <p>b) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>c) Cassava, cassava ugali, makopa, green banana, Irish potato, white-flesh sweet potato?</p>\n      </td>\n      <td>\n        <p>c) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>d1) Chinese cabbage, cabbage, amaranth leaves, cowpea leaves, or cassava leaves?</p>\n      </td>\n      <td>\n        <p>d) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>d2) Nightshade leaves, spider flower, jute mallow, sweet potato leaves, or pumpkin leaves?</p>\n      </td>\n      <td>\n        <p>d) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>e) Any other vegetables such as, cabbage, tomato, African eggplant, eggplant, sweet pepper,</p>\n      </td>\n      <td>\n        <p>e) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>f) Mango, papaya, or passionfruit?</p>\n      </td>\n      <td>\n        <p>f) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>g1) Any other fruits such as, bananas, lemons, tangerines, pineapple, avocado, or grapes?</p>\n      </td>\n      <td>\n        <p>g) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>g2) Pear, apple, watermelon, baobab, guava, or jackfruit?</p>\n      </td>\n      <td>\n        <p>g) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>h) Liver, kidney, intestine, heart, or gizzard?</p>\n      </td>\n      <td>\n        <p>h) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>i) Sausages or canned meat?</p>\n      </td>\n      <td>\n        <p>i) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>j) Any other meat, such as beef, mutton, goat, or</p>\n      </td>\n      <td>\n        <p>j) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>k) Eggs?</p>\n      </td>\n      <td>\n        <p>k) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>l) Fresh fish, dried small fish, dried small tilapia, seafood, shrimp, or octopus?</p>\n      </td>\n      <td>\n        <p>l) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>m) Beans, green peas, green gram, cowpeas, pigeon peas, peanut, groundnuts or makande?</p>\n      </td>\n      <td>\n        <p>m) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>n) Pumpkin seeds, kashata, cashews, peanuts, or peanut paste?</p>\n      </td>\n      <td>\n        <p>n) . . . . . . . . . . . . .</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "FREQ_COLL__GLOBAL"=>"<p>The data collection calendar varies according to the source of the data. To illustrate, data collection through the DHS is carried out approximately every five years for over 90 countries, while GWP and other data collection efforts are currently on an ad-hoc basis, also for over 90 countries.</p>\n<p>The DHS Program was expected to collect and release nationally representative MDD-W estimates for 17 UN Member States in 2025. The Global Diet Quality Project is expected to collect and release nationally representative MDD-W estimates from the GWP for an additional seven UN Member States in 2025.</p>\n<p>Data from the abovementioned sources on MDD-W are continuously collated and compiled from national statistics offices, survey reports, and data collection platforms.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>There is currently no fixed date in which new rounds of MDD-W estimates will be released; however,</p>\n<p>regional estimates are expected to be generated every year, while county-level estimates depend on the cadence of data collection.</p>", "DATA_SOURCE__GLOBAL"=>"<p>MDD-W estimates from the DHS are usually jointly published by ICF and ministries of health and/or national statistics offices. </p>\n<p>MDD-W estimates from the GWP are published by the Global Diet Quality Project.</p>\n<p>MDD-W estimates from the LSMS are published by the World Bank.</p>\n<p>MDD-W estimates from the MICS are usually jointly published by UNICEF and ministries of health and/or national statistics offices. </p>", "COMPILING_ORG__GLOBAL"=>"<ul>\n  <li>At country level</li>\n</ul>\n<p>DHS Program, Global Diet Quality Project, World Bank, UNICEF, and national statistics offices.</p>\n<ul>\n  <li>At regional and global levels</li>\n</ul>\n<p>FAO</p>", "INST_MANDATE__GLOBAL"=>"<p>FAO&#x2019;s mandate is to improve nutrition, increase agricultural productivity, raise the standard of living in rural populations and contribute to global economic growth. Therefore, FAO&#x2019;s work on the collection, collation, and harmonization of statistical information on food and diet represents a core element of the Organization&#x2019;s mandate. As stated in Article I of the Constitution of FAO, &#x201C;The Organization shall collect, analyse, interpret and disseminate information relating to nutrition, food and agriculture.&#x201D; Hence, from its inception, FAO has strived to maintain the best possible capacity to collect, process, validate, harmonize, and analyse incoming data and generate accurate and timely information. Improving the quality, transparency, and access to FAO&#x2019;s statistical data is an important priority.</p>\n<p>Furthermore, FAO&#x2019;s Strategic Framework 2022-31 seeks to support the 2030 Agenda through the transformation to more efficient, inclusive, resilient and sustainable agrifood systems for better production, better nutrition, a better environment, and a better life, leaving no one behind. Under the pillar of &#x201C;better nutrition,&#x201D; FAO&#x2019;s mission is to end hunger, achieve food security and improved nutrition in all its forms, including promoting nutritious food and increasing access to healthy diets. In-depth analyses of MDD-W have previously been included in flagship publications, such as <em>The State of Food Security and Nutrition in the World </em>(6).</p>", "RATIONALE__GLOBAL"=>"<p>Dietary diversity is a fundamental characteristic of healthy diets. No single food or food group provides the multitude of nutrients and other bioactive compounds necessary for optimal nutrition, growth, and long-term health. Eating a wide variety of foods therefore increases the likelihood that a diet will provide all the nutrients required by an individual. Diets that lack diversity increase the risk of micronutrient deficiencies, particularly for women who have relatively higher nutrient requirements, which can compromise health. Dietary diversity is therefore a long-standing public health principle widely advocated in food-based dietary guidelines (7), the World Health Organization&#x2019;s (WHO) <a href=\"https://www.who.int/news-room/fact-sheets/detail/healthy-diet\">&#x2018;Healthy Diet&#x2019;</a> fact sheet, FAO and WHO&#x2019;s guiding principles for <a href=\"https://www.fao.org/3/ca6640en/ca6640en.pdf\">&#x2018;Sustainable healthy diets&#x2019;</a>, and UNICEF&#x2019;s <a href=\"https://www.unicef.org/media/113291/file/UNICEF%20Conceptual%20Framework.pdf\">&#x2018;Conceptual Framework on Maternal and Child Nutrition&#x2019;</a>.</p>\n<p>While quantitative dietary assessment methods provide the best measure of the healthfulness of diets, these are often labour-intense, costly and require significant capacity to carry out. As a result, they are not routinely carried out in most countries. The MDD-W questionnaire was developed in response to a need for a quick, low-cost method that captures some information of the healthfulness of diets. It also responded to the need for as an easy-to-understand indicator for advocacy and decision-making purposes, i.e. the percentage of women meeting a minimally acceptable dietary diversity.</p>\n<p>The basic interpretation of MDD-W is: &#x201C;X% of women achieved minimum dietary diversity,</p>\n<p>and they are more likely to have higher (more adequate) micronutrient intakes than the 100-X% of</p>\n<p>women who did not.&#x201D; MDD-W should not be interpreted as an indicator of overall diet quality, or ofindividual-level dietary diversity. There is no universal cut-off that denotes levels of severity or acceptability of MDD-W prevalence. Since 2016, FAO has provided guidance on how to collect, analyse, present, and interpret MDD-W data. The latest FAO guidance &#x2018;<a href=\"https://www.fao.org/3/cb3434en/cb3434en.pdf\">Minimum Dietary Diversity for Women: An updated guide to measurement - from collection to action</a>&#x2019; was published in 2021.</p>\n<p> </p>", "REC_USE_LIM__GLOBAL"=>"<p>As household surveys are the primary source of data on MDD-W, the estimates come with levels of uncertainty due to both sampling and non-sampling error (e.g., potential omission or intrusion of food item examples on food lists, recall biases).</p>", "DATA_COMP__GLOBAL"=>"<p>The MDD-W indicator is calculated in two steps. The first step is to construct a food group diversity score summing the ten defined food groups. The ten defined food groups are:</p>\n<ol>\n  <li>Grains, white roots and tubers, and plantains;</li>\n  <li>Pulses (beans, peas and lentils);</li>\n  <li>Nuts and seeds;</li>\n  <li>Milk and milk products;</li>\n  <li>Meat, poultry, and fish;</li>\n  <li>Eggs;</li>\n  <li>Dark green leafy vegetables;</li>\n  <li>Other vitamin A-rich fruits and vegetables;</li>\n  <li>Other vegetables; and</li>\n  <li>Other fruits.</li>\n</ol>\n<p>Each individual begins with a score of 0. For each of the ten food groups, add one point if any of the foods or beverages included as an example under the food group was consumed.</p>\n<p>The second step is to calculate the MDD-W prevalence as follows:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mo>&#x2265;</mo>\n        <mn>15</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">A</mi>\n        <mi mathvariant=\"bold\">N</mi>\n        <mi mathvariant=\"bold\">D</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">w</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mo>&amp;lt;</mo>\n        <mn>50</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">A</mi>\n        <mi mathvariant=\"bold\">N</mi>\n        <mi mathvariant=\"bold\">D</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mo>&#x2265;</mo>\n        <mn>5</mn>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mo>&#x2265;</mo>\n        <mn>15</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">A</mi>\n        <mi mathvariant=\"bold\">N</mi>\n        <mi mathvariant=\"bold\">D</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">w</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mo>&amp;lt;</mo>\n        <mn>50</mn>\n      </mrow>\n    </mfrac>\n  </math> &#xD7; 100</p>", "DATA_VALIDATION__GLOBAL"=>"<p>FAO reviews newly available data against a set of quality assessment criteria. These criteria include:</p>\n<ul>\n  <li>National representativeness: Sufficient documentation should be available to assess sampling at various stages such as methodology to select primary sampling units, develop household listing and selection of households. The documents should allow for determination of household and individual response rate.</li>\n  <li>Plausible time trends: Country level data are reviewed for plausible time trends. In case of outliers FAO country offices are contacted to get additional information to explain available data/trends. </li>\n  <li>Adherence to standard questions and calculations: Survey questionnaires are reviewed to confirm adherence to global guidance in terms of methods and questions used to assess MDD-W. Only estimates based on non-quantitative 24-hour recall of a standard list of foods and beverages (with no major omissions or intrusions) or non-quantitative and quantitative open 24-hour recalls are allowed.</li>\n</ul>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li>At country level </li>\n</ul>\n<p>There is no imputation for countries with no data for MDD-W</p>\n<ul>\n  <li>At regional and global levels </li>\n</ul>\n<p>There is no imputation for individual countries with missing data. Global and regional aggregates for this indicator are based on countries with available data.</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates are calculated as population weighted averages of the prevalence of MDD-W in each country over a specific time-period, using the total population size of a country from the United Nations Population Division World Population Prospects as weights.</p>\n<p>Regional aggregates are available for the following classifications: UN (M49), SDG, and The World Bank income groups. As a rule, regional aggregates are only displayed if available data represents at least 40percent of the region&#x2019;s countries or total population size.</p>", "DOC_METHOD__GLOBAL"=>"<p><a href=\"https://www.fao.org/3/cb3434en/cb3434en.pdf\">Minimum Dietary Diversity for Women: An updated guide to measurement - from collection to action</a></p>\n<p><a href=\"https://www.fao.org/3/cc9229en/cc9229en.pdf\">Minimum Dietary Diversity for Women: Frequently Asked Questions</a></p>\n<p><a href=\"https://elearning.fao.org/course/view.php?id=909\">Minimum Dietary Diversity for Women: eLearning course</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>FAO is responsible for publishing nationally representative, weighted estimates of MDD-W from non-quantitative dietary surveys through the Food and Diet Domain on <a href=\"https://www.fao.org/faostat/en/#data/MDDW\">FAOSTAT</a>. For this purpose, FAO reanalyses microdata on food group intake according to the standard FAO methodology and cross-checks MDD-W statistics against final country reports. FAO collaborates with implementing organizations, such as DHS, to clarify and resolve any potential discrepancies. Furthermore, FAO collaborates with the Global Diet Quality Project to develop and review country-specific food lists to facilitate accurate MDD-W estimates.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>FAO review key information from primary data sources including methodological survey details (e.g., sampling framework, exclusion areas, MDD-W questionnaire), data quality outputs (e.g., response rates, missing data), and the and the MDD-W prevalence estimates from each primary data source (e.g.,</p>\n<p>household survey) under review. When information is missing or further details are required, the country-level data compilers (i.e., national statistics offices) are contacted.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data consistency and quality checks described above are conducted for each potential primary data source before inclusion in the database that are used to generate regional and global data on MDD-W. FAO collaborates with its regional and country offices throughout the year to ensure all recent and relevant data are included in the country-level database.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Minimum dietary diversity data for non-pregnant women aged 15-49 years are available for over 30 countries in Africa (001), 15 countries in the Americas (019), over 20 countries in Asia (142), and 6 countries in Europe (151).</p>\n<p><strong>Time series:</strong></p>\n<p>Country-level data for minimum dietary diversity is available from 2016 onwards and is updated annually to ensure most recent data are reflected in the database.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregated country level data are usually available for the DHS and national nutrition and health surveys by age of woman (15-19, 20-29, 30-39, 40-49 years), area of residence (rural, urban), administrative regions (e.g., zone, province, region), level of woman&#x2019;s education, and wealth quintile. For the GWP, data are often disaggregated by area of residence (rural, urban).</p>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p>1. Food and Agriculture Organization of the United Nations. Minimum Dietary Diversity for Women. An updated guide for measurement: from collection to action. Rome: FAO; 2021. 158 p. </p>\n<p>2. Martin-Prev&#xE9;l Y, Arimond M, Allemand P, Wiesmann D, Ballard TJ, Deitchler M, et al. Development of a Dichotomous Indicator for Population-Level Assessment of Dietary Diversity in Women of Reproductive Age. Curr Dev Nutr [Internet]. 2017;1(12):cdn.117.001701. Available from: http://cdn.nutrition.org/lookup/doi/10.3945/cdn.117.001701</p>\n<p>3. Verger EO, Eymard-Duvernay S, Bahya-Batinda D, Hanley-Cook GT, Argaw A, Becquey E, et al. Defining a Dichotomous Indicator for Population-Level Assessment of Dietary Diversity Among Pregnant Adolescent Girls and Women: A Secondary Analysis of Quantitative 24-h Recalls from Rural Settings in Bangladesh, Burkina Faso, India, and Nepal. Curr Dev Nutr. 2024;8(1). </p>\n<p>4. Hanley-Cook GT, Hoogerwerf S, Parraguez JP, Gie SM, Holmes BA. Minimum dietary diversity for adolescents: Multi-country analysis to define food group thresholds predicting micronutrient adequacy among girls and boys aged 10-19 years. Curr Dev Nutr [Internet]. 2024;8(3):102097. Available from: https://doi.org/10.1016/j.cdnut.2024.102097</p>\n<p>5. Hanley-Cook G, Argaw A, de Kok B, Toe LC, Dailey-Chwalib&#xF3;g T, Ou&#xE9;draogo M, et al. Seasonality and Day-to-Day Variability of Dietary Diversity: Longitudinal Study of Pregnant Women Enrolled in a Randomized Controlled Efficacy Trial in Rural Burkina Faso. J Nutr. 2022;152(9):2145&#x2013;54. </p>\n<p>6. FAO, IFAD, UNICEF, WFP, WHO. The State of Food Security and Nutrition in the World 2020. Transforming food systems for affordable healthy diets. Rome, Italy; 2020. </p>\n<p>7. Herforth A, Arimond M, &#xC1;lvarez-S&#xE1;nchez C, Coates J, Christianson K, Muehlhoff E. A Global Review of Food-Based Dietary Guidelines. Adv Nutr. 2019;10(4):590&#x2013;605. </p>", "indicator_number"=>"2.2.4", "reporting_status"=>"notstarted", "data_non_statistical"=>true, "graph_type"=>"line", "indicator_sort_order"=>"02-02-04", "indicator_name"=>"Prevalence of minimum dietary diversity, by population group (children aged 6 to 23.9 months and non-pregnant women aged 15 to 49 years)", "graph_title"=>"Prevalence of minimum dietary diversity, by population group (children aged 6 to 23.9 months and non-pregnant women aged 15 to 49 years)", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.2.E1", "slug"=>"2-2-E1", "name"=>"Tasa de obesidad de la población adulta según el índice de masa corporal (IMC) (Indicador UE sdg_02_10)", "url"=>"/site/es/2-2-E1/", "sort"=>"0202E1", "goal_number"=>"2", "target_number"=>"2.2", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Tasa de obesidad de la población adulta según el índice de masa corporal (IMC) (Indicador UE sdg_02_10)", "indicator_number"=>"2.2.E1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Salud", "periodicity"=>"Quinquenal", "url"=>"https://www.euskadi.eus/encuesta-salud/inicio/", "url_text"=>"Encuesta de salud", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}, {"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Quinquenal", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176783&menu=resultados&idp=1254735573175", "url_text"=>"Encuesta Nacional de Salud de España", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}, {"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Quinquenal", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176784&menu=resultados&idp=1254735573175", "url_text"=>"Encuesta Europea de Salud", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Tasa de obesidad de la población adulta según el índice de masa corporal (IMC) (Indicador UE sdg_02_10)", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.2- De aquí a 2030, poner fin a todas las formas de malnutrición, incluso logrando, a más tardar en 2025, las metas convenidas internacionalmente sobre el retraso del crecimiento y la emaciación de los niños menores de 5 años, y abordar las necesidades de nutrición de las adolescentes, las mujeres embarazadas y lactantes y las personas de edad", "definicion"=>"\nEl indicador mide la proporción de personas obesas en función de su índice de masa corporal (IMC). \nEl IMC se define como el peso en kilos dividido por el cuadrado de la altura en metros.\n\nSe considera obesas a las personas de 18 años o más con un IMC igual o superior a 30.\n", "formula"=>"\n$$PPO_{>18}^{t} = \\frac{PO_{>18}^{t}}{P_{>18}^{t}} \\cdot 100$$\n\ndonde:\n\n$PPO_{>18}^{t} =$ población adulta con obesidad (IMC>=30) en el año $t$\n\n$P_{>18}^{t} =$ población adulta en el año $t$\n", "desagregacion"=>"Sexo", "observaciones"=>"", "periodicidad"=>"Irregular / Aperiódica", "justificacion_global"=>"\nAunque la UE ha superado en gran medida los problemas del hambre, están surgiendo nuevos desafíos\nrelacionados con la nutrición, como el aumento de la obesidad.\n\nPor ello, el indicador europeo mide la obesidad en la edad adulta, y se utiliza para \nhacer el seguimiento del progreso hacia el ODS 2\n(poner fin al hambre y la malnutrición) y el ODS 3 (buena salud y bienestar), que están\nincluidos en las prioridades de la Comisión Europea en el marco del \"Acuerdo Verde Europeo\", \n\"Una economía que funcione para las personas\" y \"Promoción de nuestro modo de vida europeo\".\n\nLa política alimentaria de la UE incluye iniciativas de nutrición y trabaja con los Estados \nmiembros de la UE en diversas iniciativas de salud pública en el área de la nutrición y \nla actividad física para promover un estilo de vida saludable y equilibrado.\n\nFuente: Eurostat\n", "dato_global"=>"\n<a href=\"https://ec.europa.eu/eurostat/databrowser/bookmark/4a2b0f6f-97a9-496c-9336-19b95f81ddc1?lang=en\">Tasa de obesidad por índice de masa corporal (IMC) (sdg_02)</a> Eurostat <br>", "comparabilidad"=>"La tasa de obesidad de la población adulta es comparable con el indicador europeo.", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_02_10_esmsip2.htm\">Metadatos sdg_02_10</a> (solo en inglés)\n", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-17", "en"=>{"indicador_disponible"=>"Tasa de obesidad de la población adulta según el índice de masa corporal (IMC) (Indicador UE sdg_02_10)", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.2- De aquí a 2030, poner fin a todas las formas de malnutrición, incluso logrando, a más tardar en 2025, las metas convenidas internacionalmente sobre el retraso del crecimiento y la emaciación de los niños menores de 5 años, y abordar las necesidades de nutrición de las adolescentes, las mujeres embarazadas y lactantes y las personas de edad", "definicion"=>"\nThe indicator measures the share of obese people based on their body mass index (BMI). \nBMI is defined as the weight in kilos divided by the square of the height in meters. \nPeople aged 18 years or over are considered obese with a BMI equal or greater than 30.\n", "formula"=>"\n$$PPO_{>18}^{t} = \\frac{PO_{>18}^{t}}{P_{>18}^{t}} \\cdot 100$$\n\nwhere:\n\n$PPO_{>18}^{t} =$ adult population with obesity (BMI>=30) in the year $t$\n\n$P_{>18}^{t} =$ adult population in the year $t$\n", "desagregacion"=>"Sex", "observaciones"=>"", "periodicidad"=>"Irregular / Aperiódica", "justificacion_global"=>"\nAlthough the EU has largely overcome hunger problems, new nutrition-related \nchallenges are emerging, such as rising obesity.\n\nTherefore, the European indicator measures obesity in adulthood and is used \nto monitor progress towards SDG 2 (ending hunger and malnutrition) and SDG 3 \n(good health and well-being), which are included in the European Commission's \npriorities within the framework of the \"European Green Deal,\" \"An economy that \nworks for people,\" and \"Promoting our European way of life.\"\n\nThe EU's food policy includes nutrition initiatives and works with EU Member \nStates on various public health initiatives in the areas of nutrition and physical \nactivity to promote a healthy and balanced lifestyle.\n\nSource: Eurostat\n", "dato_global"=>"\n<a href=\"https://ec.europa.eu/eurostat/databrowser/bookmark/4a2b0f6f-97a9-496c-9336-19b95f81ddc1?lang=en\">Obesity rate by body mass index (BMI) (sdg_02)</a> Eurostat <br>", "comparabilidad"=>"The obesity rate among the adult population is comparable to the European indicator.", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_02_10_esmsip2.htm\">Metadata sdg_02_10</a>\n", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Tasa de obesidad de la población adulta según el índice de masa corporal (IMC) (Indicador UE sdg_02_10)", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.2- De aquí a 2030, poner fin a todas las formas de malnutrición, incluso logrando, a más tardar en 2025, las metas convenidas internacionalmente sobre el retraso del crecimiento y la emaciación de los niños menores de 5 años, y abordar las necesidades de nutrición de las adolescentes, las mujeres embarazadas y lactantes y las personas de edad", "definicion"=>"Adierazleak obesitatea duten pertsonen proportzioa neurtzen du, gorputz-masaren indizearen (GMI) \narabera. GMIa honela definitzen da: pisua kilotan, zati garaieraren karratua, metrotan.\n\nObesitatea duten pertsonatzat hartzen dira 30 edo gehiagoko GMIa duten 18 urtetik gorakoak. \n", "formula"=>"\n$$PPO_{>18}^{t} = \\frac{PO_{>18}^{t}}{P_{>18}^{t}} \\cdot 100$$\n\nnon:\n\n$PPO_{>18}^{t} =$ obesitatea duten helduak (GMI >30) $t$ urtean \n\n$P_{>18}^{t} =$ 18 urte edo gehiagoko biztanleak $t$ urtean \n", "desagregacion"=>"Sexua", "observaciones"=>"", "periodicidad"=>"Irregular / Aperiódica", "justificacion_global"=>"\nNahiz eta EBk neurri handi batean gosearen arazoak gainditu dituen, nutrizioarekin lotutako erronka \nberriak ari dira agertzen, besteak beste obesitatearen igoera. \n\nHorregatik, adierazle europarrak helduaroko obesitatea neurtzen du, eta 2. GJHra (goseari eta malnutrizioari \namaiera jartzea) eta 3. GJHra (osasun ona eta ongizatea) heltzeko aurrerapenaren jarraipena egiteko \nerabiltzen da. Helburu horiek Europako Batzordearen lehentasunen artean sartzen dira, “Akordio Berde Europarra”, \n“Pertsonentzat funtzionatzen duen ekonomia” eta “Gure bizimodu europarraren sustapena” izenekoen esparruan. \n\nEBko elikadura-politikan nutrizio-ekimenak sartzen dira, eta EBko estatu-kideekin lan egiten da osasun \npublikoko hainbat ekimenetan, nutrizioaren eta jarduera fisikoaren arloan, bizimodu osasuntsu eta orekatua \nsustatzeko.\n\n\nIturria: Eurostat \n", "dato_global"=>"\n<a href=\"https://ec.europa.eu/eurostat/databrowser/bookmark/4a2b0f6f-97a9-496c-9336-19b95f81ddc1?lang=en\">Obesitate-tasa, gorputz-masaren indizearen arabera (GMI) (sdg_02)</a> Eurostat <br>", "comparabilidad"=>"Helduen obesitate-tasa Europako adierazlearekin aldera daiteke.", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_02_10_esmsip2.htm\">Metadatuak sdg_02_10</a> (ingelesez bakarrik)\n", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "indicator_sort_order"=>"02-02-E1", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.3.1", "slug"=>"2-3-1", "name"=>"Volumen de producción por unidad de trabajo desglosado por tamaño y tipo de explotación (agropecuaria/ganadera/forestal)", "url"=>"/site/es/2-3-1/", "sort"=>"020301", "goal_number"=>"2", "target_number"=>"2.3", "global"=>{"name"=>"Volumen de producción por unidad de trabajo desglosado por tamaño y tipo de explotación (agropecuaria/ganadera/forestal)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Volumen de producción por unidad de trabajo desglosado por tamaño", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Volumen de producción por unidad de trabajo desglosado por tamaño y tipo de explotación (agropecuaria/ganadera/forestal)", "indicator_number"=>"2.3.1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Meta:</b> duplicar la productividad agrícola y los ingresos de los pequeños productores de alimentos", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Alimentación, Desarrollo Rural, Agricultura y Pesca", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/ricav-agroganaderia/web01-a2estadi/es/", "url_text"=>"Red de Información Contable Agraria Vasca (RICAV)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Volumen de producción por unidad de trabajo desglosado por tamaño", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.3- De aquí a 2030, duplicar la productividad agrícola y los ingresos de los productores de alimentos en pequeña escala, en particular las mujeres, los pueblos indígenas, los agricultores familiares, los ganaderos y los pescadores, entre otras cosas mediante un acceso seguro y equitativo a las tierras, a otros recursos e insumos de producción y a los conocimientos, los servicios financieros, los mercados y las oportunidades para añadir valor y obtener empleos no agrícolas ", "definicion"=>"\nProducción total de cultivos y sus derivados, ganado, productos ganaderos y otras producciones agrícolas, \nexpresada por Unidad de Trabajo Anual (UTA), tanto en explotaciones pequeñas como grandes.\n\nUna explotación se considera pequeña si cumple ambas condiciones:\n\n - Opera una cantidad de tierra que se encuentra dentro del 40% inferior de la \ndistribución acumulada del tamaño de las explotaciones agrícolas en la Comunidad \nAutónoma de Euskadi (medida en hectáreas).\n\n - Obtiene un ingreso anual de las actividades agrícolas que se encuentra \ndentro del 40% inferior de la distribución acumulada de los ingresos económicos \nagrícolas por unidad de producción en la Comunidad Autónoma de Euskadi.\n\nSi una explotación no cumple alguna de estas condiciones, se considera grande.\n", "formula"=>"\n$$PUT^{t} = \\frac{SE131^{t}}{SE010^{t}}$$\n\ndonde:\n\n$SE131^{t}$ = Producción total de cultivos y derivados, ganado, productos ganaderos \n  y otras producciones de las explotaciones agrícolas en el año $t$\n\n$SE010^{t}$ = Total de la mano de obra en las explotaciones agrícolas \n  en el año $t$, expresada en Unidades de Trabajo Anual (UTA), donde 1 UTA equivale a una \n  persona trabajando a tiempo completo durante un año\n", "desagregacion"=>"Territorio histórico", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nLa Agenda 2030 para el Desarrollo Sostenible ha hecho hincapié en la importancia de mejorar \nla productividad de los pequeños productores de alimentos, ya que estos desempeñan un \npapel importante en la producción mundial de alimentos. El indicador monitorea los avances \nen esta área, donde el objetivo es duplicar la productividad para el año 2030.\n\nLa mejora de la productividad laboral en las unidades de producción en pequeña escala también \ntiene implicaciones en la reducción de la pobreza, ya que los pequeños productores de \nalimentos suelen ser pobres y con frecuencia se encuentran en condiciones \ncercanas a la subsistencia.\n\nFuente: División de Estadísticas de las Naciones Unidas, Eurostat\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-03-01.pdf\">Metadatos 2-3-1.pdf (solo en inglés)</a>", "dato_global"=>"", "comparabilidad"=>"El indicador disponible no cumple con los metadatos de Naciones Unidas pero aporta información similar.", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-17", "en"=>{"indicador_disponible"=>"Volumen de producción por unidad de trabajo desglosado por tamaño", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.3- De aquí a 2030, duplicar la productividad agrícola y los ingresos de los productores de alimentos en pequeña escala, en particular las mujeres, los pueblos indígenas, los agricultores familiares, los ganaderos y los pescadores, entre otras cosas mediante un acceso seguro y equitativo a las tierras, a otros recursos e insumos de producción y a los conocimientos, los servicios financieros, los mercados y las oportunidades para añadir valor y obtener empleos no agrícolas ", "definicion"=>"Total production of crops and derivatives, livestock, livestock products, and other production \nof agricultural holdings, whether small or large, expressed per Annual Work Unit (AWU).\n\nAn agricultural holding is considered small when it meets both conditions:\n - It operates an amount of land that falls within the first two quintiles (the bottom 40%) \n   of the cumulative distribution of land size at the level of the Autonomous Community of \n   the Basque Country (measured in hectares).\n - Obtains an annual income from agricultural activities that falls within the first two \n   quintiles (the bottom 40%) of the cumulative distribution of agricultural income per unit \n   of production at the level of the Autonomous Community of the Basque Country.\n\nIf an agricultural holding does not meet any of these conditions, it is considered large.\n", "formula"=>"\n$$PUT^{t} = \\frac{SE131^{t}}{SE010^{t}}$$\n\nwhere:\n\n$SE131^{t}$ = Total production of crops and derivatives, livestock, livestock products, \nand other production from agricultural holdings (small or large) in the year $t$\n\n$SE010^{t}$ = Total labor force on agricultural holdings (small or large) in the year $t$, \nexpressed in Annual Work Units (AWU), where 1 AWU is equivalent to one person working full-time \nfor one year.\n", "desagregacion"=>"Province", "observaciones"=>nil, "periodicidad"=>"Anual", "justificacion_global"=>"\nThe 2030 Sustainable Development Agenda has emphasized the importance of enhancing \nproductivity of small-scale food producers, as these producers play an important role \nin the global production of food. The indicator monitors progress in this area, where \nthe target is to double productivity by year 2030.\n\nThe enhancement of labour productivity in small-scale production units also has \nimplications on poverty reduction, as small-scale food producers are often poor, \nand are frequently found to be close to subsistence conditions.\n\nSource: United Nations Statistics Division, Eurostat\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-03-01.pdf\">Metadata 2-3-1.pdf</a>", "dato_global"=>nil, "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information.", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Volumen de producción por unidad de trabajo desglosado por tamaño", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.3- De aquí a 2030, duplicar la productividad agrícola y los ingresos de los productores de alimentos en pequeña escala, en particular las mujeres, los pueblos indígenas, los agricultores familiares, los ganaderos y los pescadores, entre otras cosas mediante un acceso seguro y equitativo a las tierras, a otros recursos e insumos de producción y a los conocimientos, los servicios financieros, los mercados y las oportunidades para añadir valor y obtener empleos no agrícolas ", "definicion"=>"Laboreen eta horien deribatuen, abereen, abeltzaintzako produktuen eta nekazaritzako \nbeste ekoizpen batzuen guztizko ekoizpena ustiategi txikietan zein handietan, Urteko \nLan Unitatearen (NLU) arabera adierazita. \n\n\nUstiategia txikitzat hartzen da ondoko bi baldintzak betetzen baditu:\n - Erabiltzen duen lur-kantitatea (hektareatan neurtuta) Euskal Autonomia Erkidegoko \n   nekazaritza-ustiategien tamainaren banaketa metatuaren % 40 baxuenaren barruan dago. \n - Nekazaritzako jardueren urteko diru-sarrera Euskal Autonomia Erkidegoko nekazaritzako \n   diru-sarreren banaketa metatuaren % 40 txikienaren barruan dago.\n\nUstiategiak baldintza horietakoren bat betetzen ez badu, handitzat joko da.\n", "formula"=>"\n$$PUT^{t} = \\frac{SE131^{t}}{SE010^{t}}$$\n\nnon:\n\n$SE131^{t}$ = laboreen eta horien deribatuen, abereen, abeltzaintzako produktuen eta nekazaritzako \nbeste ekoizpen batzuen guztizko ekoizpena ustiategi txikietan zein handietan $t$ urtean\n\n$SE010^{t}$ = nekazaritzako ustiategi txiki zein handien eskulana, guztira, $t$ urtean, Urteko \n  Lan Unitateetan (NLU) adierazita. Bertan, NLU 1 da urte batean lanaldi osoan lan egiten duen \n  pertsona baten baliokidea\n", "desagregacion"=>"Lurralde historikoa", "observaciones"=>nil, "periodicidad"=>"Anual", "justificacion_global"=>"\nGarapen Jasangarrirako 2030eko Agendak nabarmendu duenez, garrantzitsua da elikagaien ekoizle txikien \nproduktibitatea hobetzea, horiek berebiziko esangura baitute elikagaiak mundu-mailan ekoizteko orduan. \nAdierazleak arlo honetako aurrerapenak ikuskatzen ditu, 2030. urterako produktibitatea bikoizteko asmoz. \n\nEskala txikiko ekoizpen-unitateetan lan-produktibitatea hobetzeak, halaber, eragina dauka pobrezia \nmurrizteko orduan, elikagaien ekoizle txikiak pobreak izan ohi direlako eta, askotan, bizirauteko baldintza \nurrietan daudelako. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa, Eurostat \n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-03-01.pdf\">Metadatuak 2-3-1.pdf (ingelesez bakarrik)</a>", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen metadatuak betetzen, baina antzeko informazioa ematen du.  ", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists and fishers, including through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employment</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.3.1: Volume of production per labour unit by classes of farming/pastoral/forestry enterprise size</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>PD_AGR_SSFP - Productivity of small-scale food producers [2.3.1]</p>\n<p>PD_AGR_LSFP - Productivity of large-scale food producers [2.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>SDG indicator 2.3.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Volume of agricultural production of small-scale food producer in crop, livestock, fisheries, and forestry activities per number of days worked. The indicator is computed as a <em>ratio of annual output to the number of working days in one year</em>. As the indicator is referred to a set of production units &#x2013; those of a small scale &#x2014; the denominator needs to summarize information on the entire production undertaken in each unit. This requires that volumes of production are reported in a common numeraire, given that it is impossible to sum up physical units. The most convenient numeraire for aggregating products in the numerator is a vector of constant prices. When measured at different points in time, as required by the monitoring of the SDG indicators, changes in constant values represent aggregated volume changes. </p>\n<p>FAO proposes to define small-scale food producers as producers who: </p>\n<ul>\n  <li>operate an amount of land falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of land size at national level (measured in hectares); and </li>\n  <li>operate a number of livestock falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of the number of livestock per production unit at national level (measured in Tropical Livestock Units &#x2013; TLUs); and </li>\n  <li>obtain an annual economic revenue from agricultural activities falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of economic revenues from agricultural activities per production unit at national level (measured in Purchasing Power Parity Dollars) not exceeding 34,387 Purchasing Power Parity Dollars.</li>\n</ul>\n<p><strong>Concepts:</strong></p>\n<ul>\n  <li>The following concepts are adopted for the computation of indicators 2.3.1:</li>\n  <li>Small-scale food producers are defined as those falling in the intersection of the bottom 40 percent of the cumulative distribution of land, livestock and revenues.</li>\n  <li>Tropical Livestock Units are a conversion scale used for standardization and measurement of the number of livestock heads. One TLU is the metabolic weight equivalent of one cattle in North America. The complete list of conversion factors can be found in the Guidelines for the preparation of livestock sector Reviews </li>\n  <li>The concept of productivity is standardized by OECD&#x2019;s Manual for Measuring Productivity. This defines productivity as &#x201C;a ratio of a volume measure of outputs to a volume measure of input use.&#x201D; More information on possible definitions can be found in &#x201C;Productivity and Efficiency Measurement in Agriculture: Literature Review and Gaps Analysis&#x201D;.</li>\n</ul>", "UNIT_MEASURE__GLOBAL"=>"<p>Constant PPP USD 2017.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Given that indicator 2.3.1 is measured on a target population of producers &#x2013; those considered as small-scale &#x2013; the ideal data source for measuring it is a single survey that collects all the information required with reference to individual production units. The most appropriate data source for collecting information on total value of agricultural production and on labour input adopted on the agricultural holding would be agricultural surveys. Other possibilities to be explored in absence of an agricultural surveys are: </p>\n<ol>\n  <li>household surveys integrated with an agricultural module, </li>\n  <li>agricultural censuses, </li>\n  <li>administrative data.</li>\n</ol>", "COLL_METHOD__GLOBAL"=>"<p>The target population of indicator 2.3.1. are small-scale producers for which the best data sources are agricultural surveys. These contain information on agricultural production, economic variables and labour input. However, agricultural surveys are not conducted in a systematic way, so they may be scattered in long time periods. FAO promotes the Agricultural Integrated Surveys (AGRIS) which collects data in a yearly basis for different modules, e.g. agricultural production. </p>\n<p>Currently, the indicator is produced mainly using the Living Standards Measurement Study (LSMS) of the World Bank. Some countries contain an Integrated Surveys of Agriculture (LSMS-ISA). These surveys include information such as farm size, disaggregation by geographic areas, type of activities and type of households, values of output, values of production costs and number of work hours in different activities. Such surveys have data relevant to the computation of the indicators.</p>\n<p>FAO, along with the World Bank and IFAD are compiling harmonized indicators of rural livelihoods with information on micro-level household data the LSMS surveys and other household surveys publicly available in the initiative called RuLIS (Rural Livelihoods Information System) which includes the indicators disaggregated by gender, rural areas, urban areas, income quintiles and income percentage that comes from agriculture.</p>\n<p>Some of the datasets utilized to do the computation of the indicator 2.3.1. can be seen in Annex 1 of the document &#x201C;Methodology for Computing and Monitoring the Sustainable Development Goal Indicators 2.3.1 and 2.3.2.&#x201D; available in <a href=\"http://www.fao.org/3/ca3043en/CA3043EN.pdf\">http://www.fao.org/3/ca3043en/CA3043EN.pdf</a> and Annex 1 of the document &#x201C;Rural Livelihoods Information System (RuLIS). Technical notes on concepts and definitions used for the indicators derived from household surveys&#x201D; available in <a href=\"http://www.fao.org/3/ca2813en/CA2813EN.pdf\">http://www.fao.org/3/ca2813en/CA2813EN.pdf</a>. </p>", "FREQ_COLL__GLOBAL"=>"<p>The data collection calendar depends on the frequency of surveys required to compute the indicators. FAO is engaging with countries to include the questions needed to measure the indicator into their existing national surveys, i.e., household-based surveys, agricultural surveys and censuses through capacity development activities at national/ regional levels and provision of technical assistance needed to compute the indicator.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The data release depends highly on the frequency of surveys required to compute the indicators. </p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices or other institutions involved in agricultural surveys, such as dedicated statistics offices of the Ministry of Agriculture. </p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agricultural Organization of the United Nations (FAO)</p>", "INST_MANDATE__GLOBAL"=>"<p>Article I of the FAO constitution requires that the Organization collect, analyses, interpret and disseminate information relating to nutrition, food and agriculture. <a href=\"http://www.fao.org/3/K8024E/K8024E.pdf\">http://www.fao.org/3/K8024E/K8024E.pdf</a>. </p>", "RATIONALE__GLOBAL"=>"<p>The 2030 Sustainable Development Agenda has emphasized the importance of enhancing productivity of small-scale food producers, as these producers play an important role in the global production of food. The indicator monitors progress in this area, where the target is to double productivity by year 2030. </p>\n<p>The enhancement of labour productivity in small-scale production units also has implications on poverty reduction, as small-scale food producers are often poor, and are frequently found to be close to subsistence conditions.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Given the approved methodology, the computation of the indicator requires survey microdata collected at the farm level on a wide range of variables &#x2013; including all elements that allow computing revenues and costs of the enterprise, together with labour input and the availability of land and livestock &#x2013; referred to the same production unit. Such type of surveys are seldom collected at the national level. For this reason, the availability of data for the indicator is altogether limited. In some countries, data can be obtained from household surveys reporting details on agricultural production. These data sources have to be considered as second-best solution, given that their sampling is focused on households and not on food production units. While in many countries there is a considerable degree of overlap between the population of food producers and households, this is still a partial overlap, which can undermine the accuracy of the computation.</p>", "DATA_COMP__GLOBAL"=>"<p><strong>Computation Method:</strong></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">S</mi>\n    <mi mathvariant=\"normal\">D</mi>\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mn>2</mn>\n    <mo>.</mo>\n    <mn>3</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mo>=</mo>\n    <msubsup>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mn>2</mn>\n        <mo>.</mo>\n        <mn>3</mn>\n        <mo>.</mo>\n        <mn>1</mn>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msubsup>\n    <mo>=</mo>\n    <mrow>\n      <mrow>\n        <mrow>\n          <munderover>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>j</mi>\n              <mo>=</mo>\n              <mn>1</mn>\n            </mrow>\n            <mrow>\n              <mi>n</mi>\n            </mrow>\n          </munderover>\n          <mrow>\n            <mo>(</mo>\n            <mfrac>\n              <mrow>\n                <mrow>\n                  <munder>\n                    <mo stretchy=\"false\">&#x2211;</mo>\n                    <mrow>\n                      <mi>i</mi>\n                    </mrow>\n                  </munder>\n                  <mrow>\n                    <msubsup>\n                      <mrow>\n                        <mi>V</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>i</mi>\n                        <mi>j</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>t</mi>\n                      </mrow>\n                    </msubsup>\n                    <msubsup>\n                      <mrow>\n                        <mi>p</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>i</mi>\n                        <mi>j</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>t</mi>\n                      </mrow>\n                    </msubsup>\n                  </mrow>\n                </mrow>\n              </mrow>\n              <mrow>\n                <msubsup>\n                  <mrow>\n                    <mi>L</mi>\n                    <mi>d</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>j</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>t</mi>\n                  </mrow>\n                </msubsup>\n              </mrow>\n            </mfrac>\n            <mo>)</mo>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mo>/</mo>\n      <mrow>\n        <mi>n</mi>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>where: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mi>V</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mi>j</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msubsup>\n  </math> is the physical volume of agricultural product i sold by the small-scale food producer j during year t; </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mi>p</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mi>j</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msubsup>\n  </math> is the constant sale price received by the small-scale food producer j for the agricultural product i during same year t; </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mi>L</mi>\n        <mi>d</mi>\n      </mrow>\n      <mrow>\n        <mi>j</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msubsup>\n  </math> is the number of labour days utilized by the small-scale food producer j during year t; </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n  </math>is the number of small-scale food producers. </p>\n<p>As the indicator is referred to a set of production units &#x2013; those of a small scale &#x2014; the denominator needs to summarize information on the entire production undertaken in each unit. This requires that volumes of production are reported in a common numeraire, given that it is impossible to sum up physical units. The most convenient numeraire for aggregating products in the numerator is a vector of constant prices. When measured at different points in time, as required by the monitoring of the SDG indicators, changes in constant values represent aggregated volume changes. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>FAO is responsible to check the syntaxes used in the computation of the indicator as well as the questions.</p>", "ADJUSTMENT__GLOBAL"=>"<p>The productivity of small-scale food producers per labour day in the dataset is in local currency units (LCU). For each country and year, the LCU labour value of production has to be converted into PPP 2017 USD. The process first consists on accounting for inflation in the currency, for which the Consumer Price Index (CPI) of each country is used; once deflated, it is converted into PPP 2017 USD, which allows for a homogenous standard of the indicator. SDG 2.3 not only focuses on small-scale farmers, but also on women and people with indigenous status. The indicator (which is at the household level) is then calculated disaggregated by sex of the household head or producer (depending on whether a household or an agricultural survey was used), that is, whether the household head or producer is female or male headed.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Variables employed in the computation are subject to outlier detection, through Median Absolute Deviations and other approaches, on a case by case basis. </p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>No imputation of data is made at the regional and global level. </p>", "REG_AGG__GLOBAL"=>"<p>No regional or global aggregates can be computed, given the limited availability of data. </p>", "DOC_METHOD__GLOBAL"=>"<p>Countries can rely on the methodology paper available at <a href=\"http://www.fao.org/3/ca3043en/ca3043en.pdf\">http://www.fao.org/3/ca3043en/ca3043en.pdf</a> and the eLearning available at <a href=\"https://elearning.fao.org/course/view.php?id=483\">https://elearning.fao.org/course/view.php?id=483</a> .</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Logical and arithmetic control of reporting data is carried out.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The microdata of surveys utilized in the computation are publicly available, hence their quality rests with the producers. The quality of the calculation was checked with a number of colleagues, and with two independent peer-reviewers of the RuLIS project.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Qualitative assessment has been performed on the final estimations of the indicator, which was updated this year and compared with 2019 results. PPP conversion factors are retrieved from the World Bank and are constantly updated, which results in a change of conversion factors and therefore a slight modification in the results on indicator 2.3.1. from 2019 to 2021.</p>\n<p>Some countries have data that needs to be assessed further, either checks on the raw data and/or the processing of data by the RuLIS team.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data is available for over 40 countries, including all 27 EU countries, 12 countries in Africa and two countries each in Asia and the Americas.</p>\n<p><strong>Time series:</strong></p>\n<p>A maximum of three data points is available for some countries.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Indicator 2.3.1 must be disaggregated by classes of farming/pastoral/forestry enterprise size. The overall SDG Target 2.3 requires specific focus on women, indigenous peoples, family farmers, pastoralists and fishers. For this reason, the indicator must be disaggregated by sex, type of enterprise and by community of reference.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>Note on the proposed &#x201C;Methodology for Computing and Monitoring the Sustainable Development Goal Indicator 2.3.1 and 2.3.2&#x201D;, Office of the Chief Statistician and Statistics Division, FAO, Rome <a href=\"https://www.fao.org/publications/card/en/c/CA3043EN/\">https://www.fao.org/publications/card/en/c/CA3043EN/</a></li>\n  <li>Defining Small Scale Food producers to Monitor Target 2.3 of the 2030 Agenda for Sustainable Development. FAO Statistics Division Working Paper available at <a href=\"http://www.fao.org/3/a-i6858e.pdf\">http://www.fao.org/3/a-i6858e.pdf</a> </li>\n</ul>", "indicator_sort_order"=>"02-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.3.2", "slug"=>"2-3-2", "name"=>"Media de ingresos de los productores de alimentos en pequeña escala, desglosada por sexo y condición indígena", "url"=>"/site/es/2-3-2/", "sort"=>"020302", "goal_number"=>"2", "target_number"=>"2.3", "global"=>{"name"=>"Media de ingresos de los productores de alimentos en pequeña escala, desglosada por sexo y condición indígena"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Media de ingresos de los productores de alimentos en pequeña escala", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Media de ingresos de los productores de alimentos en pequeña escala, desglosada por sexo y condición indígena", "indicator_number"=>"2.3.2", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Meta:</b> duplicar la productividad agrícola y los ingresos de los pequeños productores de alimentos", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Alimentación, Desarrollo Rural, Agricultura y Pesca", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/ricav-agroganaderia/web01-a2estadi/es/", "url_text"=>"Red de Información Contable Agraria Vasca (RICAV)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Media de ingresos de los productores de alimentos en pequeña escala", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.3- De aquí a 2030, duplicar la productividad agrícola y los ingresos de los productores de alimentos en pequeña escala, en particular las mujeres, los pueblos indígenas, los agricultores familiares, los ganaderos y los pescadores, entre otras cosas mediante un acceso seguro y equitativo a las tierras, a otros recursos e insumos de producción y a los conocimientos, los servicios financieros, los mercados y las oportunidades para añadir valor y obtener empleos no agrícolas ", "definicion"=>"Ingreso Bruto de la Explotación de las explotaciones agrícolas, pequeñas y grandes.\n\nUna explotación se considera pequeña cuando cumple ambas condiciones:\n - Opera una cantidad de tierra que se encuentra en los dos primeros quintiles \n   (el 40% inferior) de la distribución acumulada del tamaño de la tierra a nivel de la \n   Comunidad Autónoma de Euskadi (medida en hectáreas).\n - Obtiene un ingreso económico anual de las actividades agrícolas que se encuentra en los \n   dos primeros quintiles (el 40% inferior) de la distribución acumulada de los ingresos \n   económicos agrícolas por unidad de producción a nivel de la Comunidad Autónoma de Euskadi.\n\nSi una explotación no cumple alguna de estas condiciones, se considera grande.\n", "formula"=>"\n$$IBM^{t} = SE131^{t} - SE275^{t} - SE365^{t}$$\n\ndonde:\n\n$SE131^{t}$ = producción total de cultivos y derivados, ganado y productos ganaderos y \notras producciones en el año $t$\n\n$SE275^{t}$ = costes específicos de cultivos y ganado como los costes generales en el año $t$\n\n$SE365^{t}$ = remuneración de los factores de producción (trabajo, tierra y capital) que no \nson propiedad del agricultor (salarios, arrendamientos e intereses pagados) en el año $t$\n", "desagregacion"=>"Territorio histórico", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nLa Agenda 2030 para el Desarrollo Sostenible ha hecho hincapié en la importancia de mejorar los \ningresos de los pequeños productores de alimentos, ya que estos desempeñan un papel importante \nen la producción mundial de alimentos. El indicador monitorea los avances en esta área, donde \nel objetivo es duplicar los ingresos para el año 2030.\n\nLa mejora de los ingresos de las unidades de producción en pequeña escala también tiene \nimplicaciones en la reducción de la pobreza, ya que los pequeños productores de alimentos \nsuelen ser pobres y con frecuencia se encuentran cerca de las condiciones de subsistencia.\n\nFuente: División de Estadísticas de las Naciones Unidas, Eurostat\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-03-02.pdf\">Metadatos 2-3-2.pdf (solo en inglés)</a>", "dato_global"=>"", "comparabilidad"=>"El indicador disponible no cumple con los metadatos de Naciones Unidas pero aporta información similar.", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Media de ingresos de los productores de alimentos en pequeña escala", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.3- De aquí a 2030, duplicar la productividad agrícola y los ingresos de los productores de alimentos en pequeña escala, en particular las mujeres, los pueblos indígenas, los agricultores familiares, los ganaderos y los pescadores, entre otras cosas mediante un acceso seguro y equitativo a las tierras, a otros recursos e insumos de producción y a los conocimientos, los servicios financieros, los mercados y las oportunidades para añadir valor y obtener empleos no agrícolas ", "definicion"=>"Gross Operating Income of small and large agricultural holdings.\n\nAn agricultural holding is considered small when it meets both conditions:\n - It operates an amount of land that falls within the first two quintiles (the bottom 40%) \n   of the cumulative distribution of land size at the level of the Autonomous Community of \n   the Basque Country (measured in hectares).\n - Obtains an annual income from agricultural activities that falls within the first two \n   quintiles (the bottom 40%) of the cumulative distribution of agricultural income per unit \n   of production at the level of the Autonomous Community of the Basque Country.\n\nIf an agricultural holding does not meet any of these conditions, it is considered large.\n", "formula"=>"\n$$IBM^{t} = SE131^{t} - SE275^{t} - SE365^{t}$$\n\nwhere:\n\n$SE131^{t}$ = Total production of crops and derivatives, livestock and livestock products, \nand other production in the year $t$\n\n$SE275^{t}$ = specific costs of crops and livestock as well as general costs in the year $t$\n\n$SE365^{t}$ = Compensation for factors of production (labor, land, and capital) that are not \nowned by the farmer (wages, rent, and interest paid) in the year $t$\n", "desagregacion"=>"Province", "observaciones"=>nil, "periodicidad"=>"Anual", "justificacion_global"=>"\nThe 2030 Sustainable Development Agenda has emphasized the importance of enhancing \nproductivity of small-scale food producers, as these producers play an important role \nin the global production of food. The indicator monitors progress in this area, where \nthe target is to double productivity by year 2030.\n\nThe enhancement of labour productivity in small-scale production units also has \nimplications on poverty reduction, as small-scale food producers are often poor, \nand are frequently found to be close to subsistence conditions.\n\nSource: United Nations Statistics Division, Eurostat\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-03-02.pdf\">Metadata 2-3-2.pdf</a>", "dato_global"=>nil, "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information.", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Media de ingresos de los productores de alimentos en pequeña escala", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.3- De aquí a 2030, duplicar la productividad agrícola y los ingresos de los productores de alimentos en pequeña escala, en particular las mujeres, los pueblos indígenas, los agricultores familiares, los ganaderos y los pescadores, entre otras cosas mediante un acceso seguro y equitativo a las tierras, a otros recursos e insumos de producción y a los conocimientos, los servicios financieros, los mercados y las oportunidades para añadir valor y obtener empleos no agrícolas ", "definicion"=>"Nekazaritzako ustiategi txiki eta handien diru-sarrera gordina.\n\nUstiategia txikitzat hartzen da ondoko bi baldintzak betetzen baditu:\n - Erabiltzen duen lur-kantitatea (hektareatan neurtuta) Euskal Autonomia Erkidegoko \n   nekazaritza-ustiategien tamainaren banaketa metatuaren % 40 baxuenaren barruan dago. \n - Nekazaritzako jardueren urteko diru-sarrera Euskal Autonomia Erkidegoko nekazaritzako \n   diru-sarreren banaketa metatuaren % 40 txikienaren barruan dago.\n\nUstiategiak baldintza horietakoren bat betetzen ez badu, handitzat joko da.\n", "formula"=>"\n$$IBM^{t} = SE131^{t} - SE275^{t} - SE365^{t}$$\n\nnon:\n\n$SE131^{t}$ = laboreen eta horien deribatuen, abereen, abeltzaintzako produktuen eta nekazaritzako \nbeste ekoizpen batzuen guztizko ekoizpena $t$ urtean\n\n$SE275^{t}$ = laboreen eta aziendaren kostu espezifikoak, hala nola kostu orokorrak $t$ urtean\n\n$SE365^{t}$ = nekazariaren jabetzakoak ez diren ekoizpen-faktoreen ordainketa (lana, lurra eta \nkapitala), hau da, soldatak, errentamenduak eta ordaindutako interesak $t$ urtean\n", "desagregacion"=>"Lurralde historikoa", "observaciones"=>nil, "periodicidad"=>"Anual", "justificacion_global"=>"\nGarapen Jasangarrirako 2030eko Agendak nabarmendu duenez, garrantzitsua da elikagaien ekoizle txikien \ndiru-sarrerak hobetzea, horiek berebiziko esangura baitute elikagaiak mundu-mailan ekoizteko orduan. \nAdierazleak arlo honetako aurrerapenak ikuskatzen ditu, 2030. urterako diru-sarrerak bikoizteko asmoz. \n\nEskala txikiko ekoizpen-unitateetan diru-sarrerak hobetzeak, halaber, eragina dauka pobrezia murrizteko \norduan, elikagaien ekoizle txikiak pobreak izan ohi direlako eta, askotan, bizirauteko baldintza urrietan \ndaudelako. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa, Eurostat \n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-03-02.pdf\">Metadatuak 2-3-2.pdf (ingelesez bakarrik)</a>", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen metadatuak betetzen, baina antzeko informazioa ematen du.", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists and fishers, including through secure and equal access to land, other productive resources and inputs, knowledge, financial services, markets and opportunities for value addition and non-farm employment</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.3.2: Average income of small-scale food producers, by sex and indigenous status</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SI_AGR_SSFP - Average income of small-scale food producers [2.3.2]</p>\n<p>SI_AGR_SSFP - Average income of small-scale food producers [2.3.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>SDG indicator 2.3.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>SDG indicator 2.3.2 measures income from on-farm production activities, which is related to the production of food and agricultural products. This includes income from crop production, livestock production, fisheries and aquaculture production, and from forestry production. </p>\n<p>The indicator is computed as <em>annual income</em>. </p>\n<p>FAO proposes to define small-scale food producers as producers who: </p>\n<ul>\n  <li>operate an amount of land falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of land size at national level (measured in hectares); and </li>\n  <li>operate a number of livestock falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of the number of livestock per production unit at national level (measured in Tropical Livestock Units &#x2013; TLUs); and </li>\n  <li>obtain an annual economic revenue from agricultural activities falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of economic revenues from agricultural activities per production unit at national level (measured in Purchasing Power Parity Dollars) not exceeding 34,387 Purchasing Power Parity Dollars.</li>\n</ul>\n<p><strong>Concepts:</strong></p>\n<p>The following concepts are adopted for the computation of indicators 2.3.2:</p>\n<ul>\n  <li>Small-scale food producers are defined as those falling in the intersection of the bottom 40 percent of the cumulative distribution of land, livestock and revenues.</li>\n  <li>Tropical Livestock Units are a conversion scale used for standardization and measurement of the number of livestock heads. One TLU is the metabolic weight equivalent of one cattle in North America. The complete list of conversion factors can be found in the Guidelines for the preparation of livestock sector Reviews </li>\n  <li>The computation of income is based on the resolution adopted by the 17th International Conference of Labour Statisticians (ICLS). Income should be computed by deducting from revenues the operating costs and the depreciation of assets.</li>\n</ul>", "UNIT_MEASURE__GLOBAL"=>"<p>Constant PPP 2017 USD. </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Given that indicator 2.3.2 is measured on a target population of producers &#x2013; those considered as small-scale &#x2013; the ideal data source for measuring them is a single survey that collects all the information required with reference to individual production units. The most appropriate data source for collecting information on agricultural production and the associated costs are agricultural surveys. Other possibilities to be explored in absence of an agricultural surveys are: </p>\n<ol>\n  <li>household surveys integrated with an agricultural module, </li>\n  <li>agricultural censuses, </li>\n  <li>administrative data.</li>\n</ol>", "COLL_METHOD__GLOBAL"=>"<p>The target population of indicator 2.3.2 are small-scale producers for which the best data sources are agricultural surveys. These contain information on agricultural production, economic variables and labour input. However, agricultural surveys are not conducted in a systematic way, so they may be scattered in long time periods. FAO promotes the Agricultural Integrated Surveys (AGRIS) which collects data in a yearly basis for different modules, e.g. agricultural production. </p>\n<p>Currently, the indicator is produced mainly using the Living Standards Measurement Study (LSMS) of the World Bank. Some countries contain an Integrated Surveys of Agriculture (LSMS-ISA). These surveys include information such as farm size, disaggregation by geographic areas, type of activities and type of households, values of output, values of production costs and number of work hours in different activities. Such surveys have data relevant to the computation of the indicators.</p>\n<p>FAO, along with the World Bank and IFAD are compiling harmonized indicators of rural livelihoods with information on micro-level household data the LSMS surveys and other household surveys publicly available in the initiative called RuLIS (Rural Livelihoods Information System) which includes the indicators disaggregated by gender, rural areas, urban areas, income quintiles and income percentage that comes from agriculture.</p>\n<p>Some of the datasets utilized to do the computation of the indicator 2.3.2. can be seen in Annex 1 of the document &#x201C;Methodology for Computing and Monitoring the Sustainable Development Goal Indicators 2.3.1 and 2.3.2.&#x201D; available in <a href=\"http://www.fao.org/3/ca3043en/CA3043EN.pdf\">http://www.fao.org/3/ca3043en/CA3043EN.pdf</a> and Annex 1 of the document &#x201C;Rural Livelihoods Information System (RuLIS). Technical notes on concepts and definitions used for the indicators derived from household surveys&#x201D; available in <a href=\"http://www.fao.org/3/ca2813en/CA2813EN.pdf\">http://www.fao.org/3/ca2813en/CA2813EN.pdf</a>. </p>", "FREQ_COLL__GLOBAL"=>"<p>The data collection calendar depends on the frequency of surveys required to compute the indicators. FAO is engaging with countries to include the questions needed to measure the indicator into their existing national surveys, i.e., household-based surveys, agricultural surveys and censuses through capacity development activities at national/ regional levels and provision of technical assistance needed to compute the indicator. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The data release depends highly on the frequency of surveys required to compute the indicators.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices or other institutions involved in agricultural surveys, such as dedicated statistics offices of the Ministry of Agriculture. </p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agricultural Organization of the United Nations (FAO)</p>", "INST_MANDATE__GLOBAL"=>"<p>Article I of the FAO constitution requires that the Organization collect, analyses, interpret and disseminate information relating to nutrition, food and agriculture. <a href=\"http://www.fao.org/3/K8024E/K8024E.pdf\">http://www.fao.org/3/K8024E/K8024E.pdf</a>. </p>", "RATIONALE__GLOBAL"=>"<p>The 2030 Sustainable Development Agenda has emphasized the importance of enhancing income of small-scale food producers, as these producers play an important role in the global production of food. The indicator monitors progress in this area, where the target is to double income by year 2030. </p>\n<p>The enhancement of income of small-scale production units also has implications on poverty reduction, as small-scale food producers are often poor, and are frequently found to be close to subsistence conditions.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Given the approved methodology, the computation of the indicator requires survey microdata collected at the farm level on a wide range of variables &#x2013; including all elements allowing to compute revenues and costs of the enterprise together with labour input and the availability of land and livestock &#x2013; referred to the same production unit. Such type of surveys are seldom collected at the national level. For this reason, the availability of data for the indicator is altogether limited. In some countries, data can be obtained from household surveys reporting details on agricultural production. These data sources have to be considered as second-best solution, given that their sampling is focused on households and not on food production units. While in many countries there is a considerable degree of overlap between the population of food producers and households, this is still a partial overlap, which can undermine the accuracy of the computation. </p>", "DATA_COMP__GLOBAL"=>"<p>Given i agricultural activities, including crops, livestock, fisheries and forestry activities, and j [1,&#x2026;,n] small scale food producers defined as in the first section as a subset of all N [1,&#x2026;,k] food producers, the SDG indicator 2.3.2 must be computed using the following formula: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">S</mi>\n    <mi mathvariant=\"normal\">D</mi>\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mn>2</mn>\n    <mo>.</mo>\n    <mn>3</mn>\n    <mo>.</mo>\n    <mn>2</mn>\n    <mo>=</mo>\n    <msubsup>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mn>2</mn>\n        <mo>.</mo>\n        <mn>3</mn>\n        <mo>.</mo>\n        <mn>2</mn>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msubsup>\n    <mo>=</mo>\n    <mrow>\n      <mrow>\n        <mrow>\n          <munderover>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>j</mi>\n              <mo>=</mo>\n              <mn>1</mn>\n            </mrow>\n            <mrow>\n              <mi>n</mi>\n            </mrow>\n          </munderover>\n          <mrow>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mrow>\n                  <munder>\n                    <mo stretchy=\"false\">&#x2211;</mo>\n                    <mrow>\n                      <mi>i</mi>\n                    </mrow>\n                  </munder>\n                  <mrow>\n                    <msubsup>\n                      <mrow>\n                        <mo>(</mo>\n                        <mi>V</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>i</mi>\n                        <mi>j</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>t</mi>\n                      </mrow>\n                    </msubsup>\n                    <msubsup>\n                      <mrow>\n                        <mi>p</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>i</mi>\n                        <mi>j</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>t</mi>\n                      </mrow>\n                    </msubsup>\n                  </mrow>\n                </mrow>\n                <mo>-</mo>\n                <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n                <msubsup>\n                  <mrow>\n                    <mi>C</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>i</mi>\n                    <mi>j</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>t</mi>\n                  </mrow>\n                </msubsup>\n              </mrow>\n            </mfenced>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mo>/</mo>\n      <mrow>\n        <mi>n</mi>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>where:</p>\n<ul>\n  <li><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n      <msubsup>\n        <mrow>\n          <mi>V</mi>\n        </mrow>\n        <mrow>\n          <mi>i</mi>\n          <mi>j</mi>\n        </mrow>\n        <mrow>\n          <mi>t</mi>\n        </mrow>\n      </msubsup>\n    </math> is the physical volume of agricultural product i sold by the small scale food producer j during year t; </li>\n  <li><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n      <msubsup>\n        <mrow>\n          <mi>p</mi>\n        </mrow>\n        <mrow>\n          <mi>i</mi>\n          <mi>j</mi>\n        </mrow>\n        <mrow>\n          <mi>t</mi>\n        </mrow>\n      </msubsup>\n    </math> is the constant sale price received by the small scale food producer j for the agricultural product i during year t; </li>\n  <li><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n      <msubsup>\n        <mrow>\n          <mi>C</mi>\n        </mrow>\n        <mrow>\n          <mi>i</mi>\n          <mi>j</mi>\n        </mrow>\n        <mrow>\n          <mi>t</mi>\n        </mrow>\n      </msubsup>\n    </math> is the production cost of agricultural product i supported by the small scale food producer j during year t; </li>\n  <li><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n      <mi>n</mi>\n      <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    </math>is the number of small-scale food producer. </li>\n</ul>\n<p>In details, physical volumes <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mi>V</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mi>k</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msubsup>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n  </math>are derived, for each k producer, from the following items:</p>\n<ul>\n  <li>Crop revenues: crop sold, crop for own consumption, crop used as feed, crop saved for seed, crop stored, crop used for by-products, crop given as gift, crop used for paying labour, crop used for paying rent, crop used for paying inputs, crop given out in sharecropping agreement (sharecrop out), crop wasted. Similar criteria apply for the computation of revenues from tree crops and forestry products. </li>\n  <li>Livestock revenues: livestock sold (alive), livestock gifts given away (component can only be kept if stock variation is possible to construct), livestock by-/products sold, livestock products self-consumed, livestock by-products self-used (also a cost in crop, for example dung used as fertilisers), livestock by-/products pay away, livestock by-/products credit away. </li>\n  <li>Forestry revenues: products sold, forestry products for own consumption, forestry products stored, forestry products used for paying labour, forestry products used for paying rent, forestry products used for paying inputs, forestry products given out in sharecropping agreement, Forestry products wasted.</li>\n  <li>Fisheries revenues: captured fresh fish sold, captured processed fish sold, captured fresh fish for own consumption, captured processed fish for own consumption, traded fresh fish sold, traded processed fish sold.</li>\n</ul>\n<p>Production costs <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mi>C</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mi>j</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msubsup>\n  </math> are meant to include operating costs. These comprise all variable costs (payments in cash and kind of agricultural inputs as fertiliser, seeds, and occasional labour) and fixed costs (hired labour, land rent and technical assistance costs). </p>\n<p>In more details, costs <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mi>C</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mi>j</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msubsup>\n  </math> generally include the following items: </p>\n<ul>\n  <li>Costs of crop activities: inputs paid in cash, land rent, technical assistance/extension costs, crop saved for seed, crop used for paying labour, crop used for paying rent, crop used for paying inputs, crop given out in sharecropping agreement (sharecrop out), crop wasted, crop used for producing by-products, total value of input purchased, including those reimbursed in kind </li>\n  <li>Costs of livestock activities: livestock bought, livestock additional expenditures, crop used as feed, technical assistance/extension costs for livestock, </li>\n  <li>Costs of forestry activities: input costs (seedlings, fertilisers, hired labour, etc.), machine rental costs, land rental costs, other related costs. </li>\n  <li>Costs of fisheries and aquaculture activities: fishing gear expenditures, hired labour expenditures, trading activities, fresh fish purchases, processed fish purchases, other related costs </li>\n</ul>\n<p>To obtain comparable results across countries in the case of income, values must necessarily be expressed in International Dollars at Purchasing Power Parity (PPP $), based on the conversion provided by the World Bank International Comparison Project. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>FAO is responsible to check the syntaxes used in the computation of the indicator as well as the questions.</p>", "ADJUSTMENT__GLOBAL"=>"<p>The Average income of small-scale food producers in constant PPP 2011 USD is in the dataset in local currency units (LCU). For each country and year, the LCU labour value of production has to be converted into PPP 2011 USD. The process first consists on accounting for inflation in the currency, for which the Consumer Price Index (CPI) of each country is used; once deflated, it is converted into PPP 2011 USD, which allows for a homogenous standard of the indicator. SDG 2.3 not only focuses on small-scale farmers, but also on women and people with indigenous status. The indicator (which is at the household level) is then calculated disaggregated by sex of the household head or producer (depending on whether a household or an agricultural survey was used), that is, whether the household head or producer is female or male headed.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Variables employed in the computation are subject to outlier detection, through Median Absolute Deviations and other approaches, on a case by case basis. </p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>No imputation of data is made at the regional and global level. </p>", "REG_AGG__GLOBAL"=>"<p>No regional or global aggregates can be computed, given the limited availability of data. </p>", "DOC_METHOD__GLOBAL"=>"<p>Countries can rely on the methodology paper available at <a href=\"http://www.fao.org/3/ca3043en/ca3043en.pdf\">http://www.fao.org/3/ca3043en/ca3043en.pdf</a> and the eLearning available at <a href=\"https://elearning.fao.org/course/view.php?id=483\">https://elearning.fao.org/course/view.php?id=483</a> .</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Logical and arithmetic control of reporting data is carried out.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The microdata of surveys utilized in the computation are publicly available, hence their quality rests with the producers. The quality of the calculation was checked with a number of colleagues, and with two independent peer-reviewers of the RuLIS project.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Qualitative assessment has been performed on the final estimations of the indicator, which was updated this year and compared with previous results. PPP conversion factors are retrieved from the World Bank and are constantly updated, which results in a change of conversion factors and therefore a slight modification in the results on indicator 2.3.2. from 2019 to 2021.</p>\n<p>Some countries have data that needs to be assessed further, either checks on the raw data and/or the processing of data by the RuLIS team.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data availability is currently limited (though growing) around the world, and most of the available data points derive from suitable surveys in countries in Africa, Asia and Latin America. The limited data availability does not yet allow for producing regional and global aggregates.</p>\n<p><strong>Time series:</strong></p>\n<p>By 2030</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Indicator 2.3.2 must be disaggregated by classes of farming/pastoral/forestry enterprise size. The overall SDG Target 2.3 requires specific focus on women, indigenous peoples, family farmers, pastoralists and fishers. For this reason, the indicator must be disaggregated by <em>sex, type of enterprise and by community of reference</em>.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p>Note on the proposed &#x201C;Methodology for Computing and Monitoring the sustainable Development Goal Indicator 2.3.1 and 2.3.2&#x201D;, Office of the Chief Statistician and Statistics Division, FAO, Rome <a href=\"https://www.fao.org/3/ca3043en/CA3043EN.pdf\">https://www.fao.org/3/ca3043en/CA3043EN.pdf</a> </p>\n<p><em>Defining Small Scale Food producers to Monitor Target 2.3 of the 2030 Agenda for Sustainable Development</em>. FAO Statistics Division Working Paper available at <a href=\"http://www.fao.org/3/a-i6858e.pdf\">http://www.fao.org/3/a-i6858e.pdf</a> </p>", "indicator_sort_order"=>"02-03-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.4.1", "slug"=>"2-4-1", "name"=>"Proporción de la superficie agrícola en que se practica una agricultura productiva y sostenible", "url"=>"/site/es/2-4-1/", "sort"=>"020401", "goal_number"=>"2", "target_number"=>"2.4", "global"=>{"name"=>"Proporción de la superficie agrícola en que se practica una agricultura productiva y sostenible"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Proporción de la superficie agrícola en que se practica una agricultura productiva y sostenible", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción de la superficie agrícola en que se practica una agricultura productiva y sostenible", "indicator_number"=>"2.4.1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Agricultura, Pesca y Alimentación", "periodicity"=>"Anual", "url"=>"https://www.mapa.gob.es/es/estadistica/temas/estadisticas-agrarias/economia/red-contable-recan/", "url_text"=>"Red contable agraria nacional", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Proporción de la superficie agrícola en que se practica una agricultura productiva y sostenible", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.4- De aquí a 2030, asegurar la sostenibilidad de los sistemas de producción de alimentos y aplicar prácticas agrícolas resilientes que aumenten la productividad y la producción, contribuyan al mantenimiento de los ecosistemas, fortalezcan la capacidad de adaptación al cambio climático, los fenómenos meteorológicos extremos, las sequías, las inundaciones y otros desastres, y mejoren progresivamente la calidad de la tierra y el suelo", "definicion"=>"\nProporción de la superficie agrícola donde se ha alcanzado un nivel deseable o aceptable de:\n - valor de la producción agrícola por hectárea\n - ingresos agrícolas netos\n", "formula"=>"\n<b>Proporción de la superficie agrícola donde se ha alcanzado un nivel deseable o aceptable de producción por hectárea</b>\n\n$$PSANR_{\\text{deseable o aceptable}}^{t} = \\frac{SANR_{\\text{deseable}}^{t} + SANR_{\\text{aceptable}}^{t}}{SANR_{\\text{deseable}}^{t} + SANR_{\\text{aceptable}}^{t} + SANR_{\\text{insostenible}}^{t}}$$\n\ndonde:\n\n$SANR_{\\text{deseable}}^{t}$ = superficie agrícola correspondiente a aquellas explotaciones donde su producción por \nhectárea es mayor o igual que 2/3 del percentil 90 de la producción por hectárea de las explotaciones de su mismo \nterritorio y orientación técnico-económica (agricultura, ganadería o mixta) en el año $t$\n\n$SANR_{\\text{aceptable}}^{t}$ = superficie agrícola correspondiente a aquellas explotaciones donde su producción \npor hectárea es menor que 2/3 pero mayor o igual que 1/3 del percentil 90 de la producción por hectárea de las \nexplotaciones de su mismo territorio y orientación técnico-económica (agricultura, ganadería o mixta) en el año $t$\n\n$SANR_{\\text{insostenible}}^{t}$ = superficie agrícola correspondiente a aquellas explotaciones donde su producción \npor hectárea es menor que 1/3 del percentil 90 de la producción por hectárea de las explotaciones de su mismo \nterritorio y orientación técnico-económica (agricultura, ganadería o mixta) en el año $t$\n\n<br>\n\n<b>Proporción de la superficie agrícola donde se ha alcanzado un nivel deseable o aceptable de ingresos netos</b>\n\n$$PSANR_{\\text{deseable o aceptable}}^{t} = \n  \\frac{SANR_{\\text{deseable}}^{t} + SANR_{\\text{aceptable}}^{t}}\n  {SANR_{\\text{deseable}}^{t} + SANR_{\\text{aceptable}}^{t} + SANR_{\\text{insostenible}}^{t}}$$\n\n\ndonde:\n\n$SANR_{\\text{deseable}}^t$ = superficie agrícola correspondiente a aquellas explotaciones donde sus ingresos netos han sido positivos en los años $t$, $t-1$ y $t-2$\n  \n$SANR_{\\text{aceptable}}^t$ = superficie agrícola correspondiente a aquellas explotaciones donde sus ingresos netos han sido positivos en al menos uno de los años $t$, $t-1$ y $t-2$, pero no en todos ellos\n  \n$SANR_{\\text{insostenible}}^t$ = superficie agrícola correspondiente a aquellas explotaciones donde sus ingresos netos no han sido positivos en ninguno de los años $t$, $t-1$ y $t-2$\n", "desagregacion"=>"", "observaciones"=>"\nA la hora de interpretar la serie \"Proporción de la superficie agrícola donde se ha alcanzado un nivel deseable o  aceptable de producción por hectárea\" se debe tener en cuenta que es un indicador relativo, ya que  el nivel de productividad de una explotación no depende únicamente de cómo es su producción por hectárea,  sino también de cómo es la producción por hectárea de las explotaciones de su mismo territorio y orientación  técnico-económica (agricultura, ganadería o mixta).\nA la hora de interpretar la serie \"Proporción de la superficie agrícola donde se ha alcanzado un nivel deseable o aceptable de ingresos netos\" se debe tener en cuenta que es un indicador absoluto, ya que el  nivel de rentabilidad de una explotación depende exclusivamente de sus ingresos netos en los últimos tres años.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLos enfoques para definir la agricultura sostenible varían \nen términos de su cobertura de las tres dimensiones principales de la \nsostenibilidad, es decir, económica, ambiental y social, y en términos \nde la escala que se utiliza para evaluar la sostenibilidad, es decir, desde \nescalas de campo y granja hasta escalas nacional y global. Algunos enfoques\nconsideran diferentes características de la sostenibilidad, por ejemplo, \nsi las prácticas actuales son económicamente viables, respetuosas con el \nmedio ambiente y socialmente deseables. Otros enfoques se centran en prácticas\nparticulares como la agricultura orgánica, regenerativa o de bajos insumos y \npueden equipararlas con la agricultura sostenible.\n\n\nEl indicador 2.4.1 de los ODS se basa en un conjunto de subindicadores \nque cubren las tres dimensiones de la sostenibilidad:\n - Valor de la producción agrícola por hectárea \n - Ingresos agrícolas netos \n - Mecanismos de mitigación de riesgos\n - Prevalencia de la degradación del suelo \n - Variación en la disponibilidad de agua \n - Gestión de fertilizantes\n - Gestión de pesticidas \n - Uso de prácticas que apoyan la agrobiodiversidad \n - Salarios en la agricultura \n - Escala de experiencia de inseguridad alimentaria\n - Derechos de tenencia seguros de la tierra\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.4.1&seriesCode=AG_LND_SUST_PRXCSS&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Progreso hacia una agricultura productiva y sostenible, puntaje de estado actual AG_LND_SUST_PRXCSS</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-04-01.pdf\">Metadatos 2-4-1 (1).pdf</a> (solo en inglés)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-04-01proxy.pdf\">Metadatos 2-4-1 (2).pdf</a> (solo en inglés)\n", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de la superficie agrícola en que se practica una agricultura productiva y sostenible", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.4- De aquí a 2030, asegurar la sostenibilidad de los sistemas de producción de alimentos y aplicar prácticas agrícolas resilientes que aumenten la productividad y la producción, contribuyan al mantenimiento de los ecosistemas, fortalezcan la capacidad de adaptación al cambio climático, los fenómenos meteorológicos extremos, las sequías, las inundaciones y otros desastres, y mejoren progresivamente la calidad de la tierra y el suelo", "definicion"=>"\nProportion of agricultural land with a desirable or acceptable level of:\n- production per hectare\n- net income\n", "formula"=>"\n<b>Proportion of agricultural area where a desirable or acceptable level of production per hectare has been achieved</b>\n\n$$PSANR_{\\text{desirable or acceptable}}^{t} = \\frac{SANR_{\\text{desirable}}^{t} + SANR_{\\text{acceptable}}^{t}}{SANR_{\\text{desirable}}^{t} + SANR_{\\text{acceptable}}^{t} + SANR_{\\text{unsustainable}}^{t}}$$\n\nwhere:\n\n$SANR_{\\text{desirable}}^{t}$ = Agricultural area corresponding to those agricultural holdings where \ntheir production per hectare is greater than or equal to 2/3 of the 90th percentile of the production \nper hectare of agricultural holdings in the same territory and technical-economic orientation (agriculture, \nlivestock, or mixed) in the year $t$\n\n$SANR_{\\text{acceptable}}^{t}$ = Agricultural area corresponding to those agricultural holdings where \ntheir production per hectare is less than two-thirds but greater than or equal to one-third of the 90th \npercentile of the production per hectare of agricultutal holdings in the same territory and with a \ntechnical-economic orientation (agriculture, livestock, or mixed) in the year $t$\n\n$SANR_{\\text{unsustainable}}^{t}$ = Agricultural area corresponding to those agricultural holdings where \ntheir production per hectare is less than 1/3 of the 90th percentile of the production per hectare of \nagricultural holdings in the same territory and technical-economic orientation (agriculture, livestock, \nor mixed) in the year $t$\n\n<br>\n\n<b>Proportion of agricultural area where a desirable or acceptable level of net income has been achieved</b>\n\n$$PSANR_{\\text{desirable or acceptable}}^{t} = \n  \\frac{SANR_{\\text{desirable}}^{t} + SANR_{\\text{acceptable}}^{t}}\n  {SANR_{\\text{desirable}}^{t} + SANR_{\\text{acceptable}}^{t} + SANR_{\\text{unsustainable}}^{t}}$$\n\n\nwhere:\n\n$SANR_{\\text{desirable}}^t$ = Agricultural area corresponding to those agricultural holdings where their net income has been positive in the years $t$, $t-1$ and $t-2$\n  \n$SANR_{\\text{acceptable}}^t$ = Agricultural area corresponding to those agricultural holdings where their net income has been positive in at least one of the years $t$, $t-1$ and $t-2$, but not in all of them\n  \n$SANR_{\\text{unsustainable}}^t$ = Agricultural area corresponding to those agricultural holdings where their net income has not been positive in any of the years $t$, $t-1$ and $t-2$\n", "desagregacion"=>nil, "observaciones"=>"\nWhen interpreting the series \"Proportion of agricultural land with a desirable or acceptable level  of production per hectare\", it should be noted that it is a relative indicator, since the productivity level  of an agricultural holding depends not only on its production per hectare, but also on the production per hectare  of agricultural holdings in the same territory and with the same technical and economic orientation  (agriculture, livestock, or mixed).\nWhen interpreting the series \"Proportion of agricultural land with a desirable or acceptable level  of net income\", it should be noted that it is an absolute indicator, since the profitability of an agricultural  holding depends exclusively on its net income over the past three years.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe approaches to framing and defining sustainable agriculture vary in terms of their coverage of \nthe three primary dimensions of sustainability, i.e. economic, environmental and social, and in \nterms of the scale that is used to assess sustainability, i.e. from field and farm scales, to national \nand global scales. Some approaches consider different features of sustainability, for example whether \ncurrent practices are economically feasible, environmentally friendly and socially desirable. Other \napproaches focus on particular practices such as organic, regenerative or low-input agriculture and \ncan equate these with sustainable agriculture.\n\n\nSDG indicator 2.4.1 is based on a set of sub-indicators that cover the three dimensions of sustainability:\n - Farm output value per hectare \n - Net farm income \n - Risk mitigation mechanisms\n - Prevalence of soil degradation \n - Variation in water availability \n - Management of fertilizers\n - Management of pesticides \n - Use of agro-biodiversity-supportive practices \n - Wage rate in agriculture \n - Food Insecurity Experience Scale\n - Secure tenure rights to land\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.4.1&seriesCode=AG_LND_SUST_PRXCSS&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Progress towards productive and sustainable agriculture, current status score AG_LND_SUST_PRXCSS</a> UNSTATS\n", "comparabilidad"=>"The available indicator partially complies with the United Nations indicator metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-04-01.pdf\">Metadata 2-4-1 (1).pdf</a>\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-04-01proxy.pdf\">Metadata 2-4-1 (2).pdf</a>\n", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Proporción de la superficie agrícola en que se practica una agricultura productiva y sostenible", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.4- De aquí a 2030, asegurar la sostenibilidad de los sistemas de producción de alimentos y aplicar prácticas agrícolas resilientes que aumenten la productividad y la producción, contribuyan al mantenimiento de los ecosistemas, fortalezcan la capacidad de adaptación al cambio climático, los fenómenos meteorológicos extremos, las sequías, las inundaciones y otros desastres, y mejoren progresivamente la calidad de la tierra y el suelo", "definicion"=>"\nOndoko aspektuetan maila desiragarria edo onargarria lortu duen nekazaritza-azaleraren proportzioa:\n - nekazaritza-ekoizpenaren balioa hektareako\n - nekazaritzako diru-sarrera garbiak\n", "formula"=>"\n<b>Hektareako ekoizpen-maila desiragarria edo onargarria lortu duen nekazaritza-azaleraren proportzioa</b>\n\n$$PSANR_{\\text{desiragarria edo onargarria}}^{t} = \\frac{SANR_{\\text{desiragarria}}^{t} + SANR_{\\text{onargarria}}^{t}}{SANR_{\\text{desiragarria}}^{t} + SANR_{\\text{onargarria}}^{t} + SANR_{\\text{eutsiezina}}^{t}}$$\n\nnon:\n\n$SANR_{\\text{desiragarria}}^{t}$ = ondoko baldintza betetzen duten ustiapenen nekazaritza-azalera $t$ urtean: lurralde bereko eta orientazio tekniko-ekonomiko bereko (nekazaritza, \nabeltzaintza edo mistoa) ustiategien hektareako ekoizpenaren 90 pertzentilaren 2/3 edo handiagoa izatea \n\n$SANR_{\\text{onargarria}}^{t}$ = ondoko baldintza betetzen duten ustiapenen nekazaritza-azalera $t$ urtean: lurralde bereko eta orientazio tekniko-ekonomiko bereko (nekazaritza, \nabeltzaintza edo mistoa) ustiategien hektareako ekoizpenaren 90 pertzentilaren 2/3 baino txikiagoa baina 1/3 edo handiagoa izatea\n\n$SANR_{\\text{eutsiezina}}^{t}$ = ondoko baldintza betetzen duten ustiapenen nekazaritza-azalera $t$ urtean: lurralde bereko eta orientazio tekniko-ekonomiko bereko (nekazaritza, \nabeltzaintza edo mistoa) ustiategien hektareako ekoizpenaren 90 pertzentilaren 1/3 edo txikiagoa izatea\n\n<br>\n\n<b>Diru-sarrera garbien maila desiragarria edo onargarria lortu duen nekazaritza-azaleraren proportzioa</b>\n\n$$PSANR_{\\text{desiragarria edo onargarria}}^{t} = \n  \\frac{SANR_{\\text{desiragarria}}^{t} + SANR_{\\text{onargarria}}^{t}}\n  {SANR_{\\text{desiragarria}}^{t} + SANR_{\\text{onargarria}}^{t} + SANR_{\\text{eutsiezina}}^{t}}$$ \n\nnon:\n\n$SANR_{\\text{desiragarria}}^t$ = $t$, $t-1$ eta $t-2$ urteetan diru-sarrera garbi positiboak izan dituzten ustiategien nekazaritza-azalera \n  \n$SANR_{\\text{onargarria}}^t$ = $t$, $t-1$ eta $t-2$ urteetako batean gutxienez, baina ez guztietan, diru-sarrera garbi positiboak izan dituzten ustiategien nekazaritza-azalera \n  \n$SANR_{\\text{eutsiezin}}^t$ = ez $t$, ez $t-1$ eta ez $t-2$ urtean ere diru-sarrera garbi positiborik izan ez duten ustiategien nekazaritza-azalera \n", "desagregacion"=>nil, "observaciones"=>"\n\"Hektareako ekoizpen-maila desiragarria edo onargarria lortu duen nekazaritza-azaleraren proportzioa\"  seriea interpretatzeko orduan, kontuan hartu behar da adierazle erlatiboa dela; izan ere, ustiategi  baten produktibitate-maila ez dago soilik hektareako duen ekoizpenaren mende, lurralde bereko  eta orientazio tekniko-ekonomiko bereko (nekazaritza, abeltzaintza edo mistoa) ustiategien hektareako  ekoizpenaren mende ere badago.\n\"Diru-sarrera garbien maila desiragarria edo onargarria lortu duen nekazaritza-azaleraren proportzioa\"  seriea interpretatzeko orduan, kontuan hartu behar da adierazle absolutua dela, ustiategi baten  errentagarritasun-maila azken hiru urteetan izan dituen diru-sarrera garbien mende baitago soilik.  ", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nNekazaritza jasangarria zehazteko ikuspegiak aldatu egiten dira, jasangarritasunaren hiru dimentsio nagusien \nestaldurari erreparatuz gero (ekonomia, ingurumena eta gizartea) eta jasangarritasuna ebaluatzeko erabiltzen \nden eskala kontuan hartuz gero (hau da, landa eta baserrien eskaletatik eskala nazional eta globaletara). \nIkuspegi batzuek jasangarritasuneko ezaugarri desberdinak jorratzen dituzte; esaterako, ea egungo praktikak \nekonomikoki bideragarriak diren, ingurumena zaintzen duten, eta gizartean desiragarriak diren. Beste ikuspegi \nbatzuek praktika partikularrak lantzen dituzte, esate baterako nekazaritza organikoa, birsortzailea edo intsumo \nbaxukoa, eta horiek nekazaritza jasangarriarekin alderatu ditzakete. \n\nGJHen 2.4.1 adierazlea jasangarritasuneko hiru dimentsioak estaltzen dituzten azpi-adierazleen multzo batean oinarritzen da:\n - Nekazaritza-ekoizpenaren balioa hektareako\n - Nekazaritzako diru-sarrera garbiak\n - Arriskuak arintzeko mekanismoak\n - Lurzoruaren narriaduraren nagusitasuna\n - Aldakuntza uraren erabilgarritasunean \n - Ongarrien kudeaketa\n - Pestiziden kudeaketa\n - Nekazaritzako biodibertsitatea babesten duten praktiken erabilera \n - Nekazaritzako soldatak \n - Elikagaien segurtasunik ezaren esperientziaren eskala \n Lurraren edukitze seguruen eskubideak\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.4.1&seriesCode=AG_LND_SUST_PRXCSS&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Nekazaritza produktibo eta jasangarri bateranzko aurrerapena, egungo egoeraren puntuazioa AG_LND_SUST_PRXCSS</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen adierazlearen metadatuak.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-04-01.pdf\">Metadatuak 2-4-1 (1).pdf</a> (ingelesez bakarrik)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-04-01proxy.pdf\">Metadatuak 2-4-1 (2).pdf</a> (ingelesez bakarrik)\n", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively improve land and soil quality</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.4.1: Proportion of agricultural area under productive and sustainable agriculture</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>AG_LND_SUST_PRXTS - [PROXY] Progress toward productive and sustainable agriculture, trend score [2.4.1]</p>\n<p>AG_LND_SUST_PRXCSS - [PROXY] Progress toward productive and sustainable agriculture, current status score [2.4.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>It links to: </p>\n<p>Indicator 2.3.1: Productivity of small-scale food producers</p>\n<p>Indicator 2.3.2: Average income of small-scale food producers, by sex and indigenous status</p>\n<p>Indicator 6.4.2: Level of water stress: Agriculture component of water stress</p>\n<p>Indicator 8.3.1: Informal employment in agriculture</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p>Over the past 30 years, the definition and measurement of sustainable agriculture has been much debated. According to the 2030 Agenda for Sustainable Development, the performance of all sectors, including agriculture, must be assessed against the three dimensions of sustainability: economic, social and environmental. Until recently, there has been no internationally agreed method to measure sustainable agriculture. The SDG process created the opportunity to develop a commonly accepted measurement method. SDG target 2.4 requires that by 2030, countries &#x201C;ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme weather, drought, flooding and other disasters and that progressively improve land and soil quality&#x201D;. During a meeting in December 2022, the Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs), which governs the overall SDG monitoring process, endorsed the new methodology relating to SDG indicator 2.4.1, which operationalizes an internationally agreed definition of sustainable agriculture.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>For each country, scores are assigned to each sub-indicator based on the applicable method described in Annexes 1 and 2, and the average score determines the classification of the country into one of five bands with respect to the <u>trend towards</u> productive and sustainable agriculture as well as <u>status with respect to</u> productive and sustainable agriculture, as follows:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Score</p>\n      </td>\n      <td>\n        <p>Trend towards productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1 &#x2013;&lt; 1.5</p>\n      </td>\n      <td>\n        <p>Band 1: Deterioration away from productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.5 &#x2013;&lt; 2.5</p>\n      </td>\n      <td>\n        <p>Band 2: Slight deterioration from productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.5 &#x2013;&lt; 3.5</p>\n      </td>\n      <td>\n        <p>Band 3: No improvement towards productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3.5 &#x2013;&lt; 4.5</p>\n      </td>\n      <td>\n        <p>Band 4: Slight improvement towards productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.5 &#x2013; 5</p>\n      </td>\n      <td>\n        <p>Band 5: Improvement towards productive and sustainable agriculture</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Score</p>\n      </td>\n      <td>\n        <p>Current status with respect to productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1 &#x2013;&lt; 1.5</p>\n      </td>\n      <td>\n        <p>Band 1: Very far from achieving productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.5 &#x2013;&lt; 2.5</p>\n      </td>\n      <td>\n        <p>Band 2: Far from achieving productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.5 &#x2013;&lt; 3.5</p>\n      </td>\n      <td>\n        <p>Band 3: At a moderate distance to achieving productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3.5 &#x2013;&lt; 4.5</p>\n      </td>\n      <td>\n        <p>Band 4: Close to achieving productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.5 &#x2013; 5</p>\n      </td>\n      <td>\n        <p>Band 5: Productive and sustainable agriculture already achieved</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p> </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The land area classification is the FAO Land Use Classification, as implemented in the FAO Land Use, Irrigation and Agricultural Practices Questionnaire (<a href=\"http://www.fao.org/faostat/en/#data/RL/metadata\">http://www.fao.org/faostat/en/#data/RL/metadata</a>). It is consistent with the classification of the Census of Agriculture and the System of Environmental and Economic Accounts (SEEA).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The SDG 2.4.1. sub-indicators allow for monitoring seven distinct themes, using national statistics available either directly in countries, or sourced as default from existing UN databases, mostly from FAOSTAT (Table 1). The annual <em>Questionnaire on Land Use, Irrigation and Agricultural Practices</em>, which collects national data on land use (primarily focusing on agriculture, forestry, aquaculture and fisheries), irrigation and agricultural practices, SDG indicator 6.4.2 (based on responses to the AQUASTAT Questionnaire) and SDG indicator 8.3.1 form the basis of data compilation for deriving this indicator. </p>\n<p>The choice of the seven sub-indicators proxies for SDG 2.4.1 is based on recent FAO work (Progress Towards Monitoring Sustainable Agriculture, <a href=\"https://www.fao.org/documents/card/en/c/cb4549en\">Tubiello</a> et al., 2021). Information may be complemented with statistics from national statistical yearbooks and other official publications and information from governmental data portals. </p>", "COLL_METHOD__GLOBAL"=>"<p>Data for the 7 sub-indicators measures are collected and analysed directly at national level. FAO Questionnaires on Land Use, Irrigation and Agricultural Practices and AQUASTAT, are dispatched annually to relevant national entities. The measure based on SDG 8.3.1 is prepared by International Labour Organization (ILO) in close consultation with national governments. </p>\n<p>The list of the relevant FAO Questionnaires and their purpose are as follows:</p>\n<p><strong>Land Use, Irrigation and Agricultural Practices:</strong> Data on land use (primarily focusing on agriculture, forestry, aquaculture and fisheries), irrigation and agricultural practices.</p>\n<p><strong>Crop and Livestock Production and Utilization: </strong>Data on primary crop production data, primary crop utilization data, area harvested, live animals number data, primary livestock production and loss data, oils utilization data, selected derived agricultural commodities production data.</p>\n<p><strong>Fertilizers: </strong>Data on production, agricultural use and other uses of fertilizers (both chemical and organic)</p>\n<p><strong>AQUASTAT: </strong>Data on water withdrawals by sectors and by sources, wastewater and irrigated areas.</p>\n<p><strong>Prices Received by Farmers: Primary Crop and Livestock Products: </strong>Data on agricultural producer prices for primary crops and livestock.</p>", "FREQ_COLL__GLOBAL"=>"<p>FAO Questionnaires Dispatch Dates:</p>\n<p>Land Use, Irrigation and Agricultural Practices: October </p>\n<p>Crop and Livestock Production and Utilization: May</p>\n<p>Fertilizers: October</p>\n<p>AQUASTAT: May</p>\n<p>Prices Received by Farmers: Primary Crop and Livestock Products: May</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Annual data dissemination schedules are as follows:</p>\n<p><strong>Land Use, Irrigation and Agricultural Practices: </strong>June 30</p>\n<p><strong>Crop and Livestock Production and Utilization: </strong>December 23</p>\n<p><strong>Fertilizers: </strong>June 30</p>\n<p><strong>AQUASTAT: </strong>January </p>\n<p><strong>Prices Received by Farmers: Primary Crop and Livestock Products: </strong>December </p>\n<p>Data for SDG 8.3.1 are released annually by the ILO</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are provided by various governmental sources serving as official focal points. The institutions responsible for data collection at national level vary according to countries, including Ministry of Agriculture, Ministry of Water, Ministry of Environment, other relevant line Ministries and the National Statistics Office (NSO). </p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations</p>", "INST_MANDATE__GLOBAL"=>"<p>Article I of the FAO constitution requires that the Organization collect, analyses, interpret and disseminate information relating to nutrition, food and agriculture <a href=\"http://www.fao.org/3/K8024E/K8024E.pdf\">http://www.fao.org/3/K8024E/K8024E.pdf</a>.</p>", "RATIONALE__GLOBAL"=>"<p>The SDG 2.4.1 Proxy offers a simplified methodology for monitoring progress on SDG 2.4.1 &#x2018;&#x2019;Proportion of agricultural area under productive and sustainable agriculture&#x2019;&#x2019; based on national level statistic (Tubiello et al., 2021). The SDG 2.4.1 Proxy consists of seven sub-indicators computable from existing national statistics, with a default option to source data from FAOSTAT. A set of simple rules to assess status and trend of each sub-indicator and determine aggregate scores is also provided, based on the UN Global SDG Progress Chart and the FAO SDG Progress Report. The 7 sub-indicators cover relevant socio-economic and environmental dimensions of sustainability and are based on readily available statistics already collected by FAO from member countries, thus easing the SDG data collection burden on national entities. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The scoring system for the indicator scores allows for a current status and trend overview for each of the measures which comprise the indicator, and the overall status and trend towards productive and sustainable agriculture. Therefore, in the absence of sufficient data to produce the indicator, status and trend assessment of available sub-indicators is possible. </p>", "DATA_COMP__GLOBAL"=>"<p>The 7 measures are assessed both in terms of the direction and consistency of their trend and in terms of their current status according to the system-wide methodology adopted for the <a href=\"https://unstats.un.org/sdgs/report/2021/progress-chart/\">global SDG Progress Chart</a>, and also by FAO itself for its <a href=\"https://www.fao.org/sdg-progress-report/2021/en/\">SDG Progress Report</a>. Of the 7 indicators, only one has a clearly defined numerical target, whereas a further 3 have a conventionally or scientifically established upper bound, which, however, cannot serve as a normative target for the purpose of this progress assessment, given that countries that lie below this upper bound should not necessary strive to reach the upper bound.</p>\n<p>Therefore, the four main progress assessment methods, considering the trend and the current status for indicators with and without a numerical target, are as follows:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Trend assessment for indicators with a numerical target: <u>Ratio actual vs. required (CR)</u></p>\n      </td>\n      <td>\n        <p>Trend assessment for indicators without a numerical target: <u>actual growth (CAGR) compared to baseline</u></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Status assessment for indicator with a numerical target: <u>distance to the target</u></p>\n      </td>\n      <td>\n        <p>Status assessment for indicators without a numerical target: <u>quintile distribution</u></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>The compound annual growth rate (CAGR) for is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n        <mi>A</mi>\n        <mi>G</mi>\n        <mi>R</mi>\n      </mrow>\n      <mrow>\n        <mi>a</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <msup>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mfrac>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>x</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>t</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>x</mi>\n                  </mrow>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>t</mi>\n                      </mrow>\n                      <mrow>\n                        <mn>0</mn>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mn>1</mn>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n            <mo>-</mo>\n            <msub>\n              <mrow>\n                <mi>t</mi>\n              </mrow>\n              <mrow>\n                <mn>0</mn>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </msup>\n    <mo>-</mo>\n    <mn>1</mn>\n  </math></p>\n<p>where t<sub>0</sub> (2015) is the beginning of the assessment period. The ratio of actual vs. target growth rate (CR) is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>R</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>C</mi>\n        <mi>A</mi>\n        <mi>G</mi>\n        <msub>\n          <mrow>\n            <mi>R</mi>\n          </mrow>\n          <mrow>\n            <mi>a</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mi>C</mi>\n        <mi>A</mi>\n        <mi>G</mi>\n        <msub>\n          <mrow>\n            <mi>R</mi>\n          </mrow>\n          <mrow>\n            <mi>r</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msup>\n          <mrow>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mfrac>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>x</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>t</mi>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>x</mi>\n                      </mrow>\n                      <mrow>\n                        <msub>\n                          <mrow>\n                            <mi>t</mi>\n                          </mrow>\n                          <mrow>\n                            <mn>0</mn>\n                          </mrow>\n                        </msub>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                </mfrac>\n              </mrow>\n            </mfenced>\n          </mrow>\n          <mrow>\n            <mfrac>\n              <mrow>\n                <mn>1</mn>\n              </mrow>\n              <mrow>\n                <mi>t</mi>\n                <mo>-</mo>\n                <msub>\n                  <mrow>\n                    <mi>t</mi>\n                  </mrow>\n                  <mrow>\n                    <mn>0</mn>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </msup>\n        <mo>-</mo>\n        <mn>1</mn>\n      </mrow>\n      <mrow>\n        <msup>\n          <mrow>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mfrac>\n                  <mrow>\n                    <msup>\n                      <mrow>\n                        <mi>x</mi>\n                      </mrow>\n                      <mrow>\n                        <mi mathvariant=\"normal\">*</mi>\n                      </mrow>\n                    </msup>\n                  </mrow>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>x</mi>\n                      </mrow>\n                      <mrow>\n                        <msub>\n                          <mrow>\n                            <mi>t</mi>\n                          </mrow>\n                          <mrow>\n                            <mn>0</mn>\n                          </mrow>\n                        </msub>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                </mfrac>\n              </mrow>\n            </mfenced>\n          </mrow>\n          <mrow>\n            <mfrac>\n              <mrow>\n                <mn>1</mn>\n              </mrow>\n              <mrow>\n                <mn>2030</mn>\n                <mo>-</mo>\n                <msub>\n                  <mrow>\n                    <mi>t</mi>\n                  </mrow>\n                  <mrow>\n                    <mn>0</mn>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </msup>\n        <mo>-</mo>\n        <mn>1</mn>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>A full methodological note for each of the 7 measures and the two different assessment approaches can be found in the Annex 2.</p>\n<p><strong>Translation of progress assessment into a country score:</strong></p>\n<ol>\n  <li><strong>Example of country results</strong></li>\n</ol>\n<p>Country results are disseminated through a set of complementary modalities, including an aggregate score, a dashboard based on traffic-light colours, and a full dataset of absolute values for each of the 7 sub-indicators. The global SDG database will only disseminate aggregate country scores for current status and trend toward productive and sustainable agriculture. More granular scores at the level of the 7-sub-indicators, along with complementary dashboards and visualizations can be accessed through FAO&#x2019;s dedicated shinyapp here: <a href=\"https://foodandagricultureorganization.shinyapps.io/SDG_241_PROXY/\">https://foodandagricultureorganization.shinyapps.io/SDG_241_PROXY/</a></p>\n<ol>\n  <li><strong>Aggregate single-country score</strong></li>\n</ol>\n<p>For each country, scores assigned to each sub-indicator based on the applicable method described in Annexes 2 and 3 are averaged, and the average score determines the classification of the country into one of five bands with respect to the <u>trend towards</u> productive and sustainable agriculture as well as <u>status with respect to</u> productive and sustainable agriculture, as follows:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Score</p>\n      </td>\n      <td>\n        <p>Trend towards productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1 &#x2013;&lt; 1.5</p>\n      </td>\n      <td>\n        <p>Band 1: Deterioration away from productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.5 &#x2013;&lt; 2.5</p>\n      </td>\n      <td>\n        <p>Band 2: Slight deterioration from productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.5 &#x2013;&lt; 3.5</p>\n      </td>\n      <td>\n        <p>Band 3: No improvement towards productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3.5 &#x2013;&lt; 4.5</p>\n      </td>\n      <td>\n        <p>Band 4: Slight improvement towards productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.5 &#x2013; 5</p>\n      </td>\n      <td>\n        <p>Band 5: Improvement towards productive and sustainable agriculture</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Score</p>\n      </td>\n      <td>\n        <p>Current status with respect to productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1 &#x2013;&lt; 1.5</p>\n      </td>\n      <td>\n        <p>Band 1: Very far from achieving productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.5 &#x2013;&lt; 2.5</p>\n      </td>\n      <td>\n        <p>Band 2: Far from achieving productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.5 &#x2013;&lt; 3.5</p>\n      </td>\n      <td>\n        <p>Band 3: At a moderate distance to achieving productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3.5 &#x2013;&lt; 4.5</p>\n      </td>\n      <td>\n        <p>Band 4: Close to achieving productive and sustainable agriculture</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.5 &#x2013; 5</p>\n      </td>\n      <td>\n        <p>Band 5: Productive and sustainable agriculture already achieved</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>The two conditions for proceeding to the calculation (if not met, no score is calculated) are:</p>\n<ol>\n  <li>A minimum of 4 out of 7 sub-indicator are available for the country</li>\n  <li>A minimum of 1 sub-indicator for social &amp; economic dimension and 2 sub-indicators for the environmental dimension</li>\n  <li><strong>Single country dashboard</strong></li>\n</ol>\n<p>For additional insight into the situation of a particular country, it is possible to display a dashboard of results for its trend and current status with respect to productive and sustainable agriculture. In the example below, we can see that the country is making slight or good progress towards a number of sub- indicators, yet it is still far or very far from the target for most indicators.</p>\n<p>By applying the scoring system, the country will be categorized into Band 4 with respect to trend and into Band 2 with respect to Current Status. Therefore, the country is making &#x201C;slight improvement towards productive and sustainable agriculture&#x201D;, even though it is still &#x201C;far from achieving productive and sustainable agriculture&#x201D;.</p>\n<p>Table 2. Country level dashboard example</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Proposed Proxy measure</strong></p>\n      </td>\n      <td>\n        <p><strong>Trend</strong></p>\n      </td>\n      <td>\n        <p><strong>Current status</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Gross production value per hectare</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Gross output diversification</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Agricultural value added per worker</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Nitrogen use efficiency </p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Agriculture component of water stress</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>GHG emissions intensity in agriculture</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Informal employment in agriculture </p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Average score</p>\n      </td>\n      <td>\n        <p>3.3</p>\n      </td>\n      <td>\n        <p>2.4</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "DATA_VALIDATION__GLOBAL"=>"<p>Of the 7 sub-indicators, two are components of SDG indicators (8.3.1 and 6.4.2) and are considered official data. The other six sub-indicators are based on either official data provided by the country to FAO or estimated by FAO as part of its mandate on food and agriculture statistics. The entire set of country values pertaining to the six metrics based on FAO estimates are shared with National Statistical Offices by the FAO Chief Statistician, and considered validated unless the country objects to their publication. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>Imputation methods of the sub-indicators are domain-specific and are applied at country level. Estimates by FAO are produced by a variety of methods, such as imputation, interpolation, modelling, etc. For reporting of the sub-indicators within SDG 2.4.1, carry-forward, linear interpolation, and carry-backwards routines are applied to the underlying input data.</p>\n<ol>\n  <li>At the country level, in order to compute scores the following conditions need to both apply:</li>\n  <li>At least 4 sub-indicators are available for the country, of which:</li>\n  <li>At least 1 covers the socio-economic dimension and at least 2 cover the environmental dimension.</li>\n</ol>\n<p>Country aggregate scores are calculated as a simple average across the indicators.</p>\n<p>(ii) There is no additional treatment of missing values at the regional level.</p>", "REG_AGG__GLOBAL"=>"<p>At the regional level, scores are calculated using a weighted average of the country scores, with agricultural land as the weighting variable. Missing countries or those that do not meet the criteria above are not included in the aggregates, and the implicit assumption is that these countries perform the same as the neighbouring countries in the region.</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries compile the data through annual submissions to the following FAO Questionnaires:</p>\n<p>Land Use, Irrigation and Agricultural Practices, Crop and Livestock Production and Utilization, Fertilizers, AQUASTAT, and Prices Received by Farmers: Primary Crop and Livestock Products, as well as undertaking the well-established processes to report on SDG indicators 6.4.2 and 8.3.1. Underlying sources of data from countries include agricultural censuses and surveys.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The underlying data collected from FAO undergo rigorous quality assurance quality control (QAQC) procedures. These include the checking of totals, visual inspection of updated data and revisions vs previously disseminated data, and comparisons with alternative data sources.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>FAO is responsible for the quality of the internal statistical processes used to compile the published datasets. The FAO Statistics Quality Assurance Framework (SQAF), available at: http://www.fao.org/docrep/019/i3664e/i3664e.pdf, provides the necessary principles, guidelines and tools to carry out quality assessments. FAO performs an internal bi-annual survey (FAO Quality Assessment and Planning Survey) designed to gather information on all of FAO&#x2019;s statistical activities, notably to assess the extent to which quality standards are being implemented with a view to increasing compliance with the quality dimensions of SQAF, documenting best practices and prepare quality improvement plans, where necessary. Domain-specific quality assurance activities are carried out systematically (e.g. quality reviews, self-assessments, compliance monitoring).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The responsible officer conducts a self-assessment of the calculation process and its outputs on the basis of the FAO Statistics Quality Assurance Framework (SQAF). The SQAF considers the following principles: relevance, accuracy and reliability, timelessness and punctuality, coherence and comparability, and accessibility and clarity.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability: </strong>The measures are established and widely available (&#x201C;Tier I&#x201D;-type) indicators that FAO has disseminated for many years through FAOSTAT and AQUASTAT (seven indicators have a country coverage that is higher than 80%, while the informal employment in agriculture indicator for rural areas currently has a country coverage slightly over 50%).</p>\n<p><strong>Time series: </strong>2015 to T &#x2013; 2, where T is the current calendar year.</p>\n<p><strong>Disaggregation: </strong>Data for the 7 measures are collected and analysed directly at national level.</p>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable since FAO shall compile indicators for all countries.</p>", "OTHER_DOC__GLOBAL"=>"<p>Tubiello, F.N., Wanner, N., Asprooth, L., Mueller, M, Ignaciuk, A., Khan, A. A. &amp; Rosero Moncayo, J., 2021. Measuring progress towards sustainable agriculture. FAO Statistics Working Paper 21-24. Rome, FAO. https://doi.org/10.4060/cb4549en FAO. 1988. Report of the FAO Council, 94th Session, 1988. FAO, Rome, Italy</p>\n<h1>Annex 1: Description of the sub-indicators</h1>\n<p><strong>1. Gross production value per hectare </strong></p>\n<p>Formula:</p>\n<p>Gross production value per hectare = <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi>G</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>s</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>P</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>V</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>A</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n      </mrow>\n      <mrow>\n        <mi>A</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>L</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n      </mrow>\n    </mfrac>\n  </math> </p>\n<p><em>Numerator </em>(Gross Production Value Agriculture):<em> </em> Value of gross production has been compiled by multiplying gross production in physical terms by output prices at farm gate. Thus, value of production measures production in monetary terms at the farm gate level. Since intermediate uses within the agricultural sector (seed and feed) have not been subtracted from production data, this value of production aggregate refers to the notion of &quot;gross production&quot;. </p>\n<p><em>Denominator (Agriculture Land)</em>: Land used for cultivation of crops and animal husbandry. The total of area under &apos;&apos;Cropland&apos;&apos; and &apos;&apos;Permanent meadows and pastures.&apos;&apos; </p>\n<p><em>Unit of measure:</em> <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>2014</mn>\n        <mo>-</mo>\n        <mn>2016</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">$</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mn>1000</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p><em>Data sources</em>:</p>\n<p>Numerator: FAOSTAT Value of Agricultural Production Domain <a href=\"https://www.fao.org/faostat/en/#data/QV\">https://www.fao.org/faostat/en/#data/QV</a></p>\n<p>Denominator: FAOSTAT Land Use Domain </p>\n<p><a href=\"https://www.fao.org/faostat/en/#data/RL\">https://www.fao.org/faostat/en/#data/RL</a></p>\n<p><strong>2. Gross output diversification </strong></p>\n<p>Formula:</p>\n<p>Gross output diversification = <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mn>1</mn>\n    <mo>-</mo>\n    <mi>&amp;nbsp;</mi>\n    <mrow>\n      <mo stretchy=\"false\">&#x2211;</mo>\n      <mrow>\n        <mo>(</mo>\n        <msup>\n          <mrow>\n            <mfrac>\n              <mrow>\n                <mi>G</mi>\n                <mi>r</mi>\n                <mi>o</mi>\n                <mi>s</mi>\n                <mi>s</mi>\n                <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n                <mi>P</mi>\n                <mi>r</mi>\n                <mi>o</mi>\n                <mi>d</mi>\n                <mi>u</mi>\n                <mi>c</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <msub>\n                  <mrow>\n                    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n                    <mi>V</mi>\n                    <mi>a</mi>\n                    <mi>l</mi>\n                    <mi>u</mi>\n                    <mi>e</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>c</mi>\n                    <mi>i</mi>\n                    <mi>t</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n              <mrow>\n                <mi>G</mi>\n                <mi>r</mi>\n                <mi>o</mi>\n                <mi>s</mi>\n                <mi>s</mi>\n                <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n                <mi>P</mi>\n                <mi>r</mi>\n                <mi>o</mi>\n                <mi>d</mi>\n                <mi>u</mi>\n                <mi>c</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n                <mi>V</mi>\n                <mi>a</mi>\n                <mi>l</mi>\n                <mi>u</mi>\n                <msub>\n                  <mrow>\n                    <mi>e</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>i</mi>\n                    <mi>t</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n            <mo>)</mo>\n          </mrow>\n          <mrow>\n            <mn>2</mn>\n          </mrow>\n        </msup>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>C= crop</p>\n<p>I = country </p>\n<p>t = year</p>\n<p><em>Unit of measure:</em> unitless</p>\n<p><em>Data source:</em></p>\n<p>FAOSTAT Value of Agricultural Production Domain</p>\n<p><a href=\"https://www.fao.org/faostat/en/#data/QV\">https://www.fao.org/faostat/en/#data/QV</a></p>\n<p><strong>3. Agricultural value added per worker </strong></p>\n<p><strong> </strong></p>\n<p>Formula: </p>\n<p>Agricultural value added per worker<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">V</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mo>,</mo>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">N</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>This indicator provides information on the output of the agricultural sector by worker engaged. It is a measure of agricultural productivity. The data on the value added in agriculture, forestry and fisheries is extracted from FAOSTAT and then divided by the number of people employed in agriculture (in broad sense) extracted from ILOSTAT for a given year in a given country.</p>\n<p><em>Unit of measure:</em> US$ (2015 prices) per worker</p>\n<p><em>Data source</em>:</p>\n<p>FAOSTAT Employment Indicators: Agriculture Domain</p>\n<p><a href=\"http://www.fao.org/faostat/en/#data/OE\"><u>http://www.fao.org/faostat/en/#data/OE</u></a></p>\n<p><strong>4. Cropland nitrogen use efficiency</strong></p>\n<p>Formula: The nutrient budget (NB) is calculated as the sum of inputs: mineral fertilizers (MF) multiplied by the fraction of fertilizer applied to cropland (CF), manure applied to soils (MAS), nitrogen deposition (ND), and biological fixation (BF), and seed (SD) minus outputs: crop removal (CR).</p>\n<p>Thus: the Nutrient Budget (NB) for country <em>i</em> for nutrient <em>j</em> for year <em>y</em> is calculated as:</p>\n<p> NB<sub>i,j,y</sub> = sum(MF<sub>i,j,y</sub> x CF<sub>i,j,y</sub>, MAS<sub>i,j,y</sub> , ND<sub>i,j,y</sub> , BF<sub>i,j,y</sub> , SD<sub>i,j,y</sub>) &#x2013; CR<sub>i,j,y</sub></p>\n<p>The Nutrient Use Efficiency (NUE) for country I for nutrient j for year y is calculated as:</p>\n<p>NUE<sub>i,j,y</sub> = Cri,<sub>j,y</sub>/sum(MF<sub>i,j,y</sub> x CF<sub>i,j,y</sub>, MAS<sub>i,j,y</sub> , ND<sub>i,j,y</sub> , BF<sub>i,j,y</sub> ,SD<sub>i,j,y</sub>)</p>\n<p><em>Unit of measure</em>: %</p>\n<p>&#x201C;A global reference database in FAOSTAT of cropland nutrient budgets and nutrient use efficiency: nitrogen, phosphorus and potassium, 1961&#x2013;2020&#x201D;</p>\n<p>Ludemann, C. (Creator), Wanner, N. (Creator), Chivenge, P. (Creator), Dobermann, A. (Creator), Einarsson, R. (Creator), Grassini, P. (Creator), Gruere, A. (Creator), Jackson, K. (Creator), Lassaletta, L. (Creator), Maggi, F. (Creator), Obli-Laryea, G. (Creator), van Ittersum, M. (Creator), Vishwakarma, S. (Creator), Zhang, X. (Creator) &amp; Tubiello, F. N. (Creator), 2 Jun 2023</p>\n<p>DOI: 10.5061/dryad.hx3ffbgkh</p>\n<p><em>Data sources</em>:</p>\n<p>Mineral fertilizers: </p>\n<p>Data: &#x201C;Fertilizers by Nutrient&#x201D; domain in FAOSTAT</p>\n<p><a href=\"http://fenix.fao.org/faostat/internal/en/#data/RFN\">http://fenix.fao.org/faostat/internal/en/#data/RFN</a></p>\n<p>Coefficients: The cropland fraction estimates were derived from 4 exisiting datasets</p>\n<p>Zou, T., et. al. Global trends of cropland phosphorus use and sustainability challenges. Nature (2022).</p>\n<p>Manure applied to soils</p>\n<p>Data: &#x201C;Manure applied to Soils&#x201D; domain in FAOSTAT</p>\n<p><a href=\"http://fenix.fao.org/faostat/internal/en/#data/GU\">http://fenix.fao.org/faostat/internal/en/#data/GU</a></p>\n<p>Coefficients: OECD Secretariat 1997, USA (Midwest Plan Service 1985) and Europe (Levington Agriculture 1997) and from Sheldrick et al (2003). Statistics Netherlands (2012). </p>\n<p>Atmospheric Deposition: </p>\n<p>Data: Vishwakarma, Srishti et al. (2022), Quantifying nitrogen deposition inputs to cropland: A national scale dataset from 1961 to 2020, Dryad, Dataset.</p>\n<p>Crop Removal: </p>\n<p>Data: Primary Crops under the domain &#x201C;Crops and livestock products&#x201D;</p>\n<p><a href=\"https://www.fao.org/faostat/en/#data/QCL\">https://www.fao.org/faostat/en/#data/QCL</a></p>\n<p>Coefficients: Ludemann et al (2022) Global data on crop nutrient concentration and harvest indices</p>\n<p><a href=\"https://doi.org/10.5061/dryad.n2z34tn0x\">https://doi.org/10.5061/dryad.n2z34tn0x</a></p>\n<p>Biological Fixation : </p>\n<p>Data : Primary Crops under the domain &#x201C;Crops and livestock products&#x201D;</p>\n<p><a href=\"https://www.fao.org/faostat/en/#data/QCL\">https://www.fao.org/faostat/en/#data/QCL</a></p>\n<p>Methods: Peoples et al. (2021) and Herridge et al. (2022).</p>\n<p><strong>5. Agriculture component of water stress</strong></p>\n<p>Formula:</p>\n<p>Agriculture component of water stress = <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">F</mi>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">R</mi>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">R</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mo>-</mo>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">E</mi>\n        <mi mathvariant=\"normal\">F</mi>\n        <mi mathvariant=\"normal\">R</mi>\n        <mo>)</mo>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n      </mrow>\n    </mfrac>\n    <mi>*</mi>\n    <mn>100</mn>\n    <mi>%</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n  </math></p>\n<p>TFWW: the total freshwater withdrawn (km3 /year (109 m3 /year))</p>\n<p>TRWR: the difference between the total renewable freshwater resources km3 /year (109 m3 /year))</p>\n<p>EFR: the environmental flow requirements (km3 /year (109 m3 /year))</p>\n<p>While for the overall SDG indicator 6.4.2., values below 25% are considered safe (no stress), whereas values over 25% are classified into four different levels of severity, for the agriculture component of the indicator, adjusted thresholds have been determined at 70 percent of these conventional thresholds at aggregate national level, considering that globally, agriculture is responsible for 70 percent of all water withdrawals. Therefore, a water stress level for the agriculture component of below 17.5% is considered safe, a level of between 17.5% and 35% is considered to be low stress, and so on. </p>\n<p><em>Unit of measure:</em> Percentage</p>\n<p><em>Data source:</em> <a href=\"https://www.fao.org/sustainable-development-goals/indicators/642/en/\">https://www.fao.org/sustainable-development-goals/indicators/642/en/</a></p>\n<p>https://unstats.un.org/sdgs/dataportal</p>\n<p><strong>6. GHG emissions intensity in agriculture</strong></p>\n<p>Formula<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>:</mo>\n  </math></p>\n<p>Green House Gas Emissions Intensity <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>E</mi>\n        <mi>m</mi>\n        <mi>i</mi>\n        <mi>s</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>F</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>g</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>V</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>A</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>P</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n    <mi>*</mi>\n    <mn>100</mn>\n    <mi>%</mi>\n  </math> </p>\n<p><em>Numerator</em> (Farm gate emissions): Emissions from drained organic soils, cultivation of histosols, inorganic N fertilizers, crop residues, manure deposited on pasture, range and paddock, manure applied to soils, manure management, enteric fermentation, prescribed burning of savanna, burning crop residues, rice cultivation, and on-farm energy use.</p>\n<p><em>Denominator </em>(Value of Agricultural Production): Value of gross production has been compiled by multiplying gross production in physical terms by output prices at farm gate.</p>\n<p><em>Unit of measure:</em> kg CO<sub>2</sub> equivalent per constant 2014-2016 USD</p>\n<p><em>Data source:</em></p>\n<p>FAOSTAT Climate Change: Agrifood system emissions Emissions totals domain</p>\n<p><a href=\"https://www.fao.org/faostat/en/#data/GT\">https://www.fao.org/faostat/en/#data/GT</a></p>\n<p>FAOSTAT Value of Agricultural Production Domain</p>\n<p><a href=\"https://www.fao.org/faostat/en/#data/QV\">https://www.fao.org/faostat/en/#data/QV</a></p>\n<p><strong>7. Informal employment in agriculture </strong></p>\n<p>SDG Indicator 8.3.1 Proportion of informal employment in total employment, disaggregated by the agricultural sector</p>\n<p>Informal employment comprises persons who in their main or secondary jobs were in one of the following categories: - Own-account workers, employers and members of producers&#x2019; cooperatives employed in their own informal sector enterprises (the characteristics of the enterprise determine the informal nature of their jobs) - Own-account workers engaged in the production of goods exclusively for own final use by their household (e.g. subsistence farming) - Contributing family workers, regardless of whether they work in formal or informal sector enterprises (they usually do not have explicit, written contracts of employment, and are not subject to labour legislation, social security regulations, collective agreements, etc., which determines the informal nature of their jobs) - Employees holding informal jobs, whether employed by formal sector enterprises, informal sector enterprises, or as paid domestic workers by households (employees are considered to have informal jobs if their employment relationship is, in law or in practice, not subject to national labour legislation, income taxation, social protection or entitlement to certain employment benefits) For the purpose of classifying persons into formal or informal employment for this indicator, only the characteristics of the main job are considered. </p>\n<p><em>Unit of measure:</em> Percentage</p>\n<p><em>Data sources: ILO Stat</em></p>\n<p><a href=\"https://ilostat.ilo.org/topics/informality/\">https://ilostat.ilo.org/topics/informality/</a></p>\n<h1>Annex 2: Methods for assessing the current status</h1>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Dimension</strong></p>\n      </td>\n      <td>\n        <p><strong>2.4.1 sub-indicator theme</strong></p>\n      </td>\n      <td>\n        <p><strong>Proposed Proxy measure</strong></p>\n      </td>\n      <td>\n        <p><strong>Numerical target</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Economic</p>\n      </td>\n      <td>\n        <p>Land productivity</p>\n      </td>\n      <td>\n        <p>Gross production value per hectare</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Economic</p>\n      </td>\n      <td>\n        <p> Resilience </p>\n      </td>\n      <td>\n        <p>Gross output diversification</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Economic</p>\n      </td>\n      <td></td>\n      <td>\n        <p>Agricultural value added per worker (link to 2.3.2)</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Environment</p>\n      </td>\n      <td>\n        <p>Soil quality</p>\n      </td>\n      <td>\n        <p>Nitrogen use efficiency</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Environment</p>\n      </td>\n      <td>\n        <p>Water availability</p>\n      </td>\n      <td>\n        <p>Agriculture component of water stress (6.4.2 disaggregation)</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Environment</p>\n      </td>\n      <td>\n        <p>[No equivalent theme]</p>\n      </td>\n      <td>\n        <p>GHG emissions intensity</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Social</p>\n      </td>\n      <td>\n        <p>Decent employment</p>\n      </td>\n      <td>\n        <p>Proportion of informal employment in agriculture</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<ul>\n  <li>\n    <ol>\n      <li><strong>Indicators with a numerical target</strong></li>\n    </ol>\n  </li>\n</ul>\n<p>The current distance to the target is calculated only when a numerical target exists, as follows:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>d</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mfenced open=\"{\" separators=\"|\">\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <msup>\n                  <mrow>\n                    <mi>x</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>*</mi>\n                  </mrow>\n                </msup>\n                <mo>-</mo>\n                <msub>\n                  <mrow>\n                    <mi>x</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>i</mi>\n                    <mi>t</mi>\n                  </mrow>\n                </msub>\n                <mo>,</mo>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>w</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>d</mi>\n                <mi>e</mi>\n                <mi>s</mi>\n                <mi>i</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>d</mi>\n                <mi>i</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>c</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>i</mi>\n                <mi>s</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>a</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>c</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>a</mi>\n                <mi>s</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>v</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>m</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <msup>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>x</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>i</mi>\n                        <mi>t</mi>\n                      </mrow>\n                    </msub>\n                    <mo>-</mo>\n                    <mi>x</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>*</mi>\n                  </mrow>\n                </msup>\n                <mo>,</mo>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>w</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>d</mi>\n                <mi>e</mi>\n                <mi>s</mi>\n                <mi>i</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>d</mi>\n                <mi>i</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>c</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>i</mi>\n                <mi>s</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>a</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>d</mi>\n                <mi>e</mi>\n                <mi>c</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>a</mi>\n                <mi>s</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>v</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>m</mi>\n                <mi>e</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p>Here <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>x</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> denotes the numerical value of the generic indicator for country <em>i</em> in year <em>t</em>; while <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msup>\n      <mrow>\n        <mi>x</mi>\n      </mrow>\n      <mrow>\n        <mi>*</mi>\n      </mrow>\n    </msup>\n  </math> is the target value of the generic indicator (to be reached by 2030). This distance measure is 0 for indicators having already reached the target at the time of the assessment.</p>\n<ol>\n  <li><strong>SDG indicator 6.4.2, agriculture component</strong></li>\n</ol>\n<p>For this indicator, thresholds have been determined that are set at 70 percent, the conventional thresholds for the severity levels of water stress at aggregate national level (as per metadata of SDG indicator 6.4.2), considering that globally, agriculture is responsible for 70 percent of all water withdrawals. The current distance to the target for the agriculture component of SDG indicator 6.4.2 is therefore calculated as follows: <em>Where </em>x is the level of water stress attributable to agriculture</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Bounds</strong></p>\n      </td>\n      <td>\n        <p><strong>Color</strong></p>\n      </td>\n      <td>\n        <p><strong>Meaning</strong></p>\n      </td>\n      <td>\n        <p><strong>Score</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mi>x</mi>\n            <mo>&#x2264;</mo>\n          </math><strong> 17.5 percent</strong></p>\n      </td>\n      <td>\n        <p>Dark green</p>\n      </td>\n      <td>\n        <p>Target already met</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mn>17</mn>\n            <mo>.</mo>\n            <mn>5</mn>\n            <mo>&amp;lt;</mo>\n            <mi>x</mi>\n            <mo>&#x2264;</mo>\n            <mn>35</mn>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>c</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n          </math></p>\n      </td>\n      <td>\n        <p>Light green</p>\n      </td>\n      <td>\n        <p>Close to the target</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mn>35</mn>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>c</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mo>&amp;lt;</mo>\n            <mi>x</mi>\n            <mo>&#x2264;</mo>\n            <mn>52</mn>\n            <mo>.</mo>\n            <mn>5</mn>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>c</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n          </math></p>\n      </td>\n      <td>\n        <p>Yellow</p>\n      </td>\n      <td>\n        <p>Moderate distance to the target</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mn>52</mn>\n            <mo>.</mo>\n            <mn>5</mn>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>c</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mo>&amp;lt;</mo>\n            <mi>x</mi>\n            <mo>&#x2264;</mo>\n            <mn>70</mn>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>c</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n          </math></p>\n      </td>\n      <td>\n        <p>Orange</p>\n      </td>\n      <td>\n        <p>Far from the target</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mi>x</mi>\n            <mo>&amp;gt;</mo>\n            <mn>70</mn>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>c</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n          </math></p>\n      </td>\n      <td>\n        <p>Red</p>\n      </td>\n      <td>\n        <p>Very far from the target</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>None</p>\n      </td>\n      <td>\n        <p>Grey</p>\n      </td>\n      <td>\n        <p>Missing data</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<ol>\n  <li><strong>Nitrogen Use Efficiency</strong></li>\n</ol>\n<p>For the cropland nitrogen use efficiency (NUE), the desired range is between 50% to 90%, based on a scientifically determined optimal target between 65% and 80%<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>. The assessment of the current status (last available data) will be conducted by calculating the distance to the target as shown below. The cropland NUE value <em>x</em> for country <em>i</em> in year <em>t </em>will be assessed as follows:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Bounds</strong></p>\n      </td>\n      <td>\n        <p><strong>Color</strong></p>\n      </td>\n      <td>\n        <p><strong>Meaning</strong></p>\n      </td>\n      <td>\n        <p><strong>Score</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mn>50</mn>\n            <mi>%</mi>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mo>,</mo>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <mn>90</mn>\n            <mi>%</mi>\n          </math></p>\n      </td>\n      <td>\n        <p>Dark green</p>\n      </td>\n      <td>\n        <p>Target already met</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mn>45</mn>\n            <mi>%</mi>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mo>,</mo>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <mn>50</mn>\n            <mi>%</mi>\n          </math></p>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mn>90</mn>\n            <mi>%</mi>\n            <mo>&amp;lt;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mo>,</mo>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <mn>95</mn>\n            <mi>%</mi>\n          </math></p>\n      </td>\n      <td>\n        <p>Light green</p>\n      </td>\n      <td>\n        <p>Close to the target</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mn>40</mn>\n            <mi>%</mi>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mo>,</mo>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <mn>45</mn>\n            <mi>%</mi>\n          </math></p>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mn>95</mn>\n            <mi>%</mi>\n            <mo>&amp;lt;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mo>,</mo>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <mn>100</mn>\n            <mi>%</mi>\n          </math></p>\n      </td>\n      <td>\n        <p>Yellow</p>\n      </td>\n      <td>\n        <p>Moderate distance to the target</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mn>35</mn>\n            <mi>%</mi>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mo>,</mo>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <mn>40</mn>\n            <mi>%</mi>\n          </math></p>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mn>100</mn>\n            <mi>%</mi>\n            <mo>&amp;lt;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mo>,</mo>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <mn>105</mn>\n            <mi>%</mi>\n          </math></p>\n      </td>\n      <td>\n        <p>Orange</p>\n      </td>\n      <td>\n        <p>Far from target</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mo>,</mo>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <mn>35</mn>\n            <mi>%</mi>\n          </math></p>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mo>,</mo>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;gt;</mo>\n            <mn>105</mn>\n            <mi>%</mi>\n          </math></p>\n      </td>\n      <td>\n        <p>Red</p>\n      </td>\n      <td>\n        <p>Very far from target</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>None</p>\n      </td>\n      <td>\n        <p>Grey</p>\n      </td>\n      <td>\n        <p>Missing data</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>1.2 Indicators without a numerical target</strong></p>\n<p>All the other six proxy measures will be treated as indicators without a numerical target, for which the distance to the target cannot be calculated. For analytical purposes, it is useful however to provide a summary picture that describes the current worldwide distribution of the indicator. For this reason, each country will be associated to the corresponding quintile. The quintiles divide the entire distribution of countries into five equal groups, according to their indicator value: the first quintile contains the bottom fifth of the countries on the indicators scale (i.e. the 20 % of the countries with the lowest value), the second quintile represents the second fifth (from 20 % to 40 %) and so on; finally the fifth quintile represents the top 20 % countries, i.e. those with the highest values for the indicator. A country&#x2019;s quintile categorization will earn it a corresponding score for the purposes of calculating its overall progress towards productive and sustainable agriculture, depending on the normative direction: </p>\n<p><strong>With an increasing normative direction </strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Quintile</strong></p>\n      </td>\n      <td>\n        <p><strong>Color</strong></p>\n      </td>\n      <td>\n        <p>Meaning</p>\n      </td>\n      <td>\n        <p><strong>Score</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>80</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>100</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n      <td>\n        <p>Dark green</p>\n      </td>\n      <td>\n        <p>Best performers</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>60</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>80</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n      <td>\n        <p>Light green</p>\n      </td>\n      <td>\n        <p>Above median performers</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>40</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>60</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n      <td>\n        <p>Yellow</p>\n      </td>\n      <td>\n        <p>Median performers</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>20</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>40</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n      <td>\n        <p>Orange</p>\n      </td>\n      <td>\n        <p>Below median performers</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>0</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>20</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n      <td>\n        <p>Red</p>\n      </td>\n      <td>\n        <p>Worst performers</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>None</strong></p>\n      </td>\n      <td>\n        <p>Grey</p>\n      </td>\n      <td>\n        <p>Missing data</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>With a decreasing normative direction</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Quintile</strong></p>\n      </td>\n      <td>\n        <p><strong>Color</strong></p>\n      </td>\n      <td>\n        <p>Meaning</p>\n      </td>\n      <td>\n        <p><strong>Score</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>0</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>20</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n      <td>\n        <p>Dark green</p>\n      </td>\n      <td>\n        <p>Best performers</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>20</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>40</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n      <td>\n        <p>Light green</p>\n      </td>\n      <td>\n        <p>Above median performers</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>40</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>60</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n      <td>\n        <p>Yellow</p>\n      </td>\n      <td>\n        <p>Median performers</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>60</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>80</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n      <td>\n        <p>Orange</p>\n      </td>\n      <td>\n        <p>Below median performers</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>80</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <msub>\n              <mrow>\n                <mi>x</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>q</mi>\n              </mrow>\n              <mrow>\n                <mn>100</mn>\n                <mi>%</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n      <td>\n        <p>Red</p>\n      </td>\n      <td>\n        <p>Worst performers</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>None</strong></p>\n      </td>\n      <td>\n        <p>Grey</p>\n      </td>\n      <td>\n        <p>Missing data</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<h1>Annex 3: Method for assessing trend</h1>\n<p>The method to assess the trend distinguishes between indicators underpinning targets with and without a numerical yardstick. </p>\n<p><strong>2.1 Indicators with a numerical target </strong></p>\n<p>For indicators with a fixed numerical target, the trend is assessed by comparing the actual growth since the baseline year, with the growth required to achieve the target. Assuming a geometrical growth over time, the trend is assessed with the following mathematical expression<sup><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></sup></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>R</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>C</mi>\n        <mi>A</mi>\n        <mi>G</mi>\n        <msub>\n          <mrow>\n            <mi>R</mi>\n          </mrow>\n          <mrow>\n            <mi>a</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mi>C</mi>\n        <mi>A</mi>\n        <mi>G</mi>\n        <msub>\n          <mrow>\n            <mi>R</mi>\n          </mrow>\n          <mrow>\n            <mi>r</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msup>\n          <mrow>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mfrac>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>x</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>t</mi>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>x</mi>\n                      </mrow>\n                      <mrow>\n                        <msub>\n                          <mrow>\n                            <mi>t</mi>\n                          </mrow>\n                          <mrow>\n                            <mn>0</mn>\n                          </mrow>\n                        </msub>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                </mfrac>\n              </mrow>\n            </mfenced>\n          </mrow>\n          <mrow>\n            <mfrac>\n              <mrow>\n                <mn>1</mn>\n              </mrow>\n              <mrow>\n                <mi>t</mi>\n                <mo>-</mo>\n                <msub>\n                  <mrow>\n                    <mi>t</mi>\n                  </mrow>\n                  <mrow>\n                    <mn>0</mn>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </msup>\n        <mo>-</mo>\n        <mn>1</mn>\n      </mrow>\n      <mrow>\n        <msup>\n          <mrow>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mfrac>\n                  <mrow>\n                    <msup>\n                      <mrow>\n                        <mi>x</mi>\n                      </mrow>\n                      <mrow>\n                        <mi mathvariant=\"normal\">*</mi>\n                      </mrow>\n                    </msup>\n                  </mrow>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>x</mi>\n                      </mrow>\n                      <mrow>\n                        <msub>\n                          <mrow>\n                            <mi>t</mi>\n                          </mrow>\n                          <mrow>\n                            <mn>0</mn>\n                          </mrow>\n                        </msub>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                </mfrac>\n              </mrow>\n            </mfenced>\n          </mrow>\n          <mrow>\n            <mfrac>\n              <mrow>\n                <mn>1</mn>\n              </mrow>\n              <mrow>\n                <mn>2030</mn>\n                <mo>-</mo>\n                <msub>\n                  <mrow>\n                    <mi>t</mi>\n                  </mrow>\n                  <mrow>\n                    <mn>0</mn>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </msup>\n        <mo>-</mo>\n        <mn>1</mn>\n      </mrow>\n    </mfrac>\n  </math>:</p>\n<p>Against the following thresholds and categories as included in the technical annex of the global SDG Progress Chart:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Level or ratio CR</strong></p>\n      </td>\n      <td>\n        <p><strong>Color</strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>Assessment category</strong></p>\n      </td>\n      <td>\n        <p><strong>Score</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mi mathvariant=\"normal\">x</mi>\n            <mo>&#x2264;</mo>\n            <msup>\n              <mrow>\n                <mi mathvariant=\"normal\">x</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">*</mi>\n              </mrow>\n            </msup>\n          </math> </p>\n      </td>\n      <td colspan=\"2\">\n        <p>Dark green</p>\n      </td>\n      <td>\n        <p>Target already met </p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mi mathvariant=\"normal\">C</mi>\n            <mi mathvariant=\"normal\">R</mi>\n            <mo>&#x2265;</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>95</mn>\n          </math></p>\n      </td>\n      <td colspan=\"2\">\n        <p>Light green</p>\n      </td>\n      <td>\n        <p>On-track to achieve the target</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>5</mn>\n            <mo>&amp;lt;</mo>\n            <mi>C</mi>\n            <mi>R</mi>\n            <mo>&amp;lt;</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>95</mn>\n          </math></p>\n      </td>\n      <td colspan=\"2\">\n        <p>Yellow</p>\n      </td>\n      <td>\n        <p>On-path, but too slow to achieve the target</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mo>-</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>10</mn>\n            <mo>&#x2264;</mo>\n            <mi>C</mi>\n            <mi>R</mi>\n            <mo>&#x2264;</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>5</mn>\n          </math></p>\n      </td>\n      <td colspan=\"2\">\n        <p>Orange</p>\n      </td>\n      <td>\n        <p>No improvement (stagnation) since baseline</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mi>C</mi>\n            <mi>R</mi>\n            <mo>&amp;lt;</mo>\n            <mo>-</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>10</mn>\n          </math></p>\n      </td>\n      <td colspan=\"2\">\n        <p>Red</p>\n      </td>\n      <td>\n        <p>Deterioration/movement away from the target (&lt;&lt;)</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Missing data</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Grey</p>\n      </td>\n      <td>\n        <p>None</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>2.2 Indicators without a numerical target (applies to all the other indicators)</strong></p>\n<p>For indicators without a set numerical target, which is the case for most of the suggested indicators in this proposal, it is only possible to assess the actual growth (<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>A</mi>\n    <mi>G</mi>\n    <msub>\n      <mrow>\n        <mi>R</mi>\n      </mrow>\n      <mrow>\n        <mi>a</mi>\n      </mrow>\n    </msub>\n  </math> in the expression above) against two sets of thresholds and categories, which depend on the normative direction of the indicator.</p>\n<p>Therefore,</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n        <mi>A</mi>\n        <mi>G</mi>\n        <mi>R</mi>\n      </mrow>\n      <mrow>\n        <mi>a</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <msup>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mfrac>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>x</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>t</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>x</mi>\n                  </mrow>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>t</mi>\n                      </mrow>\n                      <mrow>\n                        <mn>0</mn>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mn>1</mn>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n            <mo>-</mo>\n            <msub>\n              <mrow>\n                <mi>t</mi>\n              </mrow>\n              <mrow>\n                <mn>0</mn>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </msup>\n    <mo>-</mo>\n    <mn>1</mn>\n  </math></p>\n<p>Different criteria can be used to assess the CAGR, depending on the sign of the normative direction and also on the fact that for some indicators a situation that remains unchanged over time (not increase or not decrease) can be judged positively.</p>\n<p><strong>Thresholds and categories when a positive outcome corresponds to an <u>increase</u> of the indicator</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Levels of actual growth rate</strong></p>\n      </td>\n      <td>\n        <p><strong>Color</strong></p>\n      </td>\n      <td>\n        <p><strong>Assessment category</strong></p>\n      </td>\n      <td>\n        <p><strong>Score</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>C</mi>\n                <mi>A</mi>\n                <mi>G</mi>\n                <mi>R</mi>\n              </mrow>\n              <mrow>\n                <mi>a</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;gt;</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>01</mn>\n          </math></p>\n      </td>\n      <td>\n        <p>Dark green</p>\n      </td>\n      <td>\n        <p>Improvement since baseline-year (&gt;&gt;)</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mn>0</mn>\n                <mo>.</mo>\n                <mn>005</mn>\n                <mo>&amp;lt;</mo>\n                <mi>C</mi>\n                <mi>A</mi>\n                <mi>G</mi>\n                <mi>R</mi>\n              </mrow>\n              <mrow>\n                <mi>a</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>01</mn>\n          </math></p>\n      </td>\n      <td>\n        <p>Light green</p>\n      </td>\n      <td>\n        <p>Slight improvement since baseline-year (&gt;)</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mo>-</mo>\n                <mn>0</mn>\n                <mo>.</mo>\n                <mn>005</mn>\n                <mo>&#x2264;</mo>\n                <mi>C</mi>\n                <mi>A</mi>\n                <mi>G</mi>\n                <mi>R</mi>\n              </mrow>\n              <mrow>\n                <mi>a</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>005</mn>\n          </math></p>\n      </td>\n      <td>\n        <p>Yellow</p>\n      </td>\n      <td>\n        <p>No improvement since baseline-year (=)</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mo>-</mo>\n                <mn>0</mn>\n                <mo>.</mo>\n                <mn>01</mn>\n                <mo>&#x2264;</mo>\n                <mi>C</mi>\n                <mi>A</mi>\n                <mi>G</mi>\n                <mi>R</mi>\n              </mrow>\n              <mrow>\n                <mi>a</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <mo>-</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>005</mn>\n          </math></p>\n      </td>\n      <td>\n        <p>Orange</p>\n      </td>\n      <td>\n        <p>Slight deterioration since baseline-year (&lt;)</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>C</mi>\n                <mi>A</mi>\n                <mi>G</mi>\n                <mi>R</mi>\n              </mrow>\n              <mrow>\n                <mi>a</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <mo>-</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>01</mn>\n          </math></p>\n      </td>\n      <td>\n        <p>Red</p>\n      </td>\n      <td>\n        <p>Deterioration since baseline-year (&lt;&lt;)</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Missing data</p>\n      </td>\n      <td>\n        <p>Grey</p>\n      </td>\n      <td>\n        <p>None</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Thresholds and categories when a positive outcome corresponds to a <u>decrease</u> of the indicator</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Levels of actual growth rate</strong></p>\n      </td>\n      <td>\n        <p><strong>Color</strong></p>\n      </td>\n      <td>\n        <p><strong>Assessment category</strong></p>\n      </td>\n      <td>\n        <p><strong>Score</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>C</mi>\n                <mi>A</mi>\n                <mi>G</mi>\n                <mi>R</mi>\n              </mrow>\n              <mrow>\n                <mi>a</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <mo>-</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>01</mn>\n          </math></p>\n      </td>\n      <td>\n        <p>Dark green</p>\n      </td>\n      <td>\n        <p>Improvement since baseline-year (&gt;&gt;)</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mo>-</mo>\n                <mn>0</mn>\n                <mo>.</mo>\n                <mn>01</mn>\n                <mo>&#x2264;</mo>\n                <mi>C</mi>\n                <mi>A</mi>\n                <mi>G</mi>\n                <mi>R</mi>\n              </mrow>\n              <mrow>\n                <mi>a</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <mo>-</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>005</mn>\n          </math></p>\n      </td>\n      <td>\n        <p>Light green</p>\n      </td>\n      <td>\n        <p>Slight improvement since baseline-year (&gt;)</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mo>-</mo>\n                <mn>0</mn>\n                <mo>.</mo>\n                <mn>005</mn>\n                <mo>&#x2264;</mo>\n                <mi>C</mi>\n                <mi>A</mi>\n                <mi>G</mi>\n                <mi>R</mi>\n              </mrow>\n              <mrow>\n                <mi>a</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>005</mn>\n          </math></p>\n      </td>\n      <td>\n        <p>Yellow</p>\n      </td>\n      <td>\n        <p>No improvement since baseline-year (=)</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mn>0</mn>\n                <mo>.</mo>\n                <mn>005</mn>\n                <mo>&amp;lt;</mo>\n                <mi>C</mi>\n                <mi>A</mi>\n                <mi>G</mi>\n                <mi>R</mi>\n              </mrow>\n              <mrow>\n                <mi>a</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2264;</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>01</mn>\n          </math></p>\n      </td>\n      <td>\n        <p>Orange</p>\n      </td>\n      <td>\n        <p>Slight deterioration since baseline-year (&lt;)</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>C</mi>\n                <mi>A</mi>\n                <mi>G</mi>\n                <mi>R</mi>\n              </mrow>\n              <mrow>\n                <mi>a</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;gt;</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>01</mn>\n          </math></p>\n      </td>\n      <td>\n        <p>Red</p>\n      </td>\n      <td>\n        <p>Deterioration since baseline-year (&lt;&lt;)</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Missing data</p>\n      </td>\n      <td>\n        <p>Grey</p>\n      </td>\n      <td>\n        <p>None</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Countries that have already reached the maximum score for the current status assessment are assigned the maximum score for the trend assessment. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p>Ludemann <em>et al</em>., 2023, in press <a href=\"https://essd.copernicus.org/preprints/essd-2023-206/essd-2023-206.pdf\">https://essd.copernicus.org/preprints/essd-2023-206/essd-2023-206.pdf</a> <a 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"proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Consejo de Agricultura y Alimentación Ecológica de Euskadi", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/produccion-ecologica/web01-a2estadi/es/", "url_text"=>"Estadística de la producción ecológica", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Ekolurra.png?raw=true"}, {"organisation"=>"Departamento de Alimentación, Desarrollo Rural, Agricultura y Pesca", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/web01-a1estadi/es/", "url_text"=>"Estadística sobre la distribución general de tierras y sobre las producciones agrarias", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}, {"organisation"=>"Ministerio de Agricultura, Pesca y Alimentación", "periodicity"=>"Anual", "url"=>"https://www.mapa.gob.es/es/estadistica/temas/estadisticas-agrarias/agricultura/esyrce/", "url_text"=>"Encuesta de superficies y rendimientos de cultivo (ESYRCE)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Superficie dedicada a la agricultura ecológica (Indicador UE sdg_02_40)", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.4- De aquí a 2030, asegurar la sostenibilidad de los sistemas de producción de alimentos y aplicar prácticas agrícolas resilientes que aumenten la productividad y la producción, contribuyan al mantenimiento de los ecosistemas, fortalezcan la capacidad de adaptación al cambio climático, los fenómenos meteorológicos extremos, las sequías, las inundaciones y otros desastres, y mejoren progresivamente la calidad de la tierra y el suelo", "definicion"=>"Proporción de la superficie agrícola (cultivos y prados y pastizales) destinada a la producción ecológica", "formula"=>"$$PSA_{ecológica}^{t} = \\frac{SA_{ecológica}^{t}}{SA^{t}} \\cdot 100$$\n\ndonde:\n\n$SA_{ecológica}^t =$ superficie agrícola destinada a la producción ecológica en el año $t$\n\n$SA^{t} =$ superficie agrícola (cultivos, prados y pastizales) en el año $t$\n", "desagregacion"=>"Territorio histórico/Comarca/Municipio", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Si bien la agricultura orgánica no se menciona explícitamente en la \nAgenda 2030, la UE considera la agricultura orgánica como un método de \nproducción que pone el mayor énfasis en la protección del medio ambiente \ny la vida silvestre y, con respecto a la producción ganadera, en consideraciones \nde bienestar animal. Evita o reduce en gran medida el uso de insumos químicos \nsintéticos como fertilizantes, pesticidas, aditivos y productos médicos. \nEstá prohibida la producción de organismos genéticamente modificados (OGM) y \nsu uso en la alimentación animal.\n\nEl indicador \"Superficie dedicada a la agricultura ecológica\" forma parte del \nconjunto de indicadores de los Objetivos de Desarrollo Sostenible (ODS) \nde la UE, y está incluido en las Prioridades de la Comisión Europea bajo el Acuerdo Verde Europeo. \nEl indicador mide la proporción del área agrícola utilizada (SAU) total ocupada por la \nagricultura orgánica (áreas existentes cultivadas orgánicamente y áreas en proceso de conversión).\n\nFuente: Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_02_40/default/table?lang=en&category=sdg.sdg_02\">Superficie dedicada a la agricultura ecológica (sdg_02_40)</a> Eurostat", "comparabilidad"=>"El indicador disponible es comparable con el indicador europeo.\n", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_02_40_esmsip2.htm\">Metadatos Eurostat (sdg_02_40)</a> (solo en inglés)\n", "national_data_updated_date"=>"2025-05-26", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Superficie dedicada a la agricultura ecológica (Indicador UE sdg_02_40)", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.4- De aquí a 2030, asegurar la sostenibilidad de los sistemas de producción de alimentos y aplicar prácticas agrícolas resilientes que aumenten la productividad y la producción, contribuyan al mantenimiento de los ecosistemas, fortalezcan la capacidad de adaptación al cambio climático, los fenómenos meteorológicos extremos, las sequías, las inundaciones y otros desastres, y mejoren progresivamente la calidad de la tierra y el suelo", "definicion"=>"Proportion of agricultural land (crops and meadows and pastures) devoted to organic production", "formula"=>"$$PSA_{organic}^{t} = \\frac{SA_{organic}^{t}}{SA^{t}} \\cdot 100$$\n\nwhere:\n\n$SA_{organic}^t =$ agricultural area used for organic production in the year $t$\n\n$SA^{t} =$ agricultural area (crops, meadows and pastures) in the year $t$\n", "desagregacion"=>"Province/County/Municipality", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAlthough organic farming is not explicitly mentioned in the 2030 Agenda, the EU considers organic \nfarming to be a production method that places the greatest emphasis on environmental and wildlife \nprotection and, with respect to livestock production, on animal welfare considerations. It avoids \nor greatly reduces the use of synthetic chemical inputs such as fertilizers, pesticides, additives, \nand medical products. The production of genetically modified organisms (GMOs) and their use in animal \nfeed are prohibited.\n\nThe \"Area under organic farming\" indicator is part of the EU Sustainable Development Goals (SDG) \nindicator set and is included in the European Commission's Priorities under the European Green Deal. \nThe indicator measures the proportion of the total utilized agricultural area (UAA) occupied by organic \nfarming (existing organically farmed areas and areas in the process of conversion). \n\nSource: Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_02_40/default/table?lang=en&category=sdg.sdg_02\">Area under organic farming (sdg_02_40)</a> Eurostat", "comparabilidad"=>"The available indicator is comparable with the European indicator.\n", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_02_40_esmsip2.htm\">Metadata Eurostat (sdg_02_40)</a>\n", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Superficie dedicada a la agricultura ecológica (Indicador UE sdg_02_40)", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.4- De aquí a 2030, asegurar la sostenibilidad de los sistemas de producción de alimentos y aplicar prácticas agrícolas resilientes que aumenten la productividad y la producción, contribuyan al mantenimiento de los ecosistemas, fortalezcan la capacidad de adaptación al cambio climático, los fenómenos meteorológicos extremos, las sequías, las inundaciones y otros desastres, y mejoren progresivamente la calidad de la tierra y el suelo", "definicion"=>"Ekoizpen ekologikora bideratutako nekazaritza-azaleraren proportzioa (labore-lurrak, belardiak eta larreak)", "formula"=>"$$PSA_{ekologikoa}^{t} = \\frac{SA_{ekologikoa}^{t}}{SA^{t}} \\cdot 100$$\n\nnon:\n\n$SA_{ekologikoa}^t =$ ekoizpen ekologikora bideratutako nekazaritza-azalera $t$ urtean\n\n$SA^{t} =$ nekazaritza-azalera (labore-lurrak, belardiak eta larreak) $t$ urtean\n", "desagregacion"=>"Lurralde historikoa/Eskualdea/Udalerria", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Nahiz eta nekazaritza organikoa ez den berariaz aipatzen 2030eko Agendan, EBren ustez nekazaritza organikoa \ningurumena eta basoko bizitza babesteari arreta berezia ematen dion ekoizpen-metodo bat eta, abeltzaintza-ekoizpenari \ndagokionez, animalien ongizatea babesten du. Neurri handi batean, intsumo kimiko sintetikoen erabilera saihestu edo \nmurrizten du, besteak beste ongarri, pestizida, gehigarri edo produktu medikoena. Genetikoki aldatutako organismoak \n(GAO) ekoiztea eta horiek elikagaien elikaduran erabiltzea debekatuta dago. \n\n“Nekazaritza ekologikora bideratutako azalera” adierazlea EBko Garapen Jasangarriko Helburuen adierazle-multzoaren \nparte da, eta Europako Batzordearen lehentasunetan jasotzen da, Europako Akordio Berdearen pean. Adierazle horrek \nnekazaritza organikoak okupatutako nekazaritza-arlo erabiliaren proportzioa neurtzen du (organikoki landutako arloak \neta bihurketa-prozesuan dauden arloak). \n\n\nIturria: Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_02_40/default/table?lang=en&category=sdg.sdg_02\">Nekazaritza ekologikora bideratutako azalera (sdg_02_40)</a> Eurostat", "comparabilidad"=>"EAEn eskuragarri dagoen adierazlea Europako adierazlearekin aldera daiteke.\n", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_02_40_esmsip2.htm\">Metadatuak Eurostat (sdg_02_40)</a> (ingelesez bakarrik)\n", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "indicator_sort_order"=>"02-04-E1", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"2.4.E2", "slug"=>"2-4-E2", "name"=>"Emisiones de amoníaco de la agricultura y ganadería (Indicador UE sdg_02_60)", "url"=>"/site/es/2-4-E2/", "sort"=>"0204E2", "goal_number"=>"2", "target_number"=>"2.4", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Emisiones de amoníaco de la agricultura y ganadería (Indicador UE sdg_02_60)", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Emisiones de amoníaco de la agricultura y ganadería (Indicador UE sdg_02_60)", "indicator_number"=>"2.4.E2", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/web01-s2ekono/es/contenidos/informacion/estatistika_ing_090226/es_def/index.shtml", "url_text"=>"Inventario de Emisiones de contaminantes a la atmósfera", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Emisiones de amoníaco de la agricultura y ganadería (Indicador UE sdg_02_60)", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.4- De aquí a 2030, asegurar la sostenibilidad de los sistemas de producción de alimentos y aplicar prácticas agrícolas resilientes que aumenten la productividad y la producción, contribuyan al mantenimiento de los ecosistemas, fortalezcan la capacidad de adaptación al cambio climático, los fenómenos meteorológicos extremos, las sequías, las inundaciones y otros desastres, y mejoren progresivamente la calidad de la tierra y el suelo", "definicion"=>"\nSeries disponibles: \n\n<b> - Emisiones de amoníaco (NH3) de la agricultura y ganadería</b>\n\n<b> - Emisiones de amoníaco (NH3) de la agricultura y ganadería por hectárea:</b> \nEmisiones de amoníaco (NH3) de la agricultura y ganadería por cada hectárea destinada a tierras de cultivo, prados y pastizales\n", "formula"=>"\n<b> Emisiones de amoníaco (NH3) de la agricultura y ganadería</b>\n\n$$ENH3_{agricultura\\, y\\, ganadería}^{t} = ENH3_{agricultura}^{t} + ENH3_{ganadería}^{t}$$\n\ndonde: \n\n$ENH3_{agricultura}^{t}$ = emisiones de NH3 de la agricultura en el año $t$\n\n$ENH3_{ganadería}^{t}$ = emisiones de NH3 de la ganadería en el año $t$\n\n<br>\n\n<b>Emisiones de amoníaco (NH3) de la agricultura y ganadería por hectárea</b>\n\n$$ENH3S_{agricultura\\, y\\, ganadería}^{t} = \\frac{ENH3_{agricultura}^{t} + ENH3_{ganadería}^{t}}{S_{tierras\\, de\\, cultivo}^{t} + S_{prados\\, y\\, pastizales}^{t}}$$\n\ndonde:\n\n$ENH3_{agricultura}^{t}$ = emisiones de NH3 de la agricultura en el año $t$\n\n$ENH3_{ganadería}^{t}$ = emisiones de NH3 de la ganadería en el año $t$\n\n$S_{tierras\\, de\\, cultivo}^{t}$ = superficie destinada a tierras de cultivo en el año $t$\n\n$S_{prados\\, y\\, pastizales}^{t}$ = superficie destinada a prados y pastizales en el año $t$\n", "desagregacion"=>"Actividad agrícola: agricultura, ganadería", "observaciones"=>"\nLas emisiones de amoníaco asignadas a la agricultura son las \ncorrespondientes a las actividades con códigos NFR (Nomenclature for Reporting): \n3Da1, 3Da2a, 3Da2b, 3Da2c, 3Da3, 3Da4, 3Db, 3Dc, 3Dd, 3De, 3Df, 3F y 3I.\n\nLas emisiones de amoníaco asignadas a la ganadería son las correspondientes a \nlas actividades con códigos NFR (Nomenclature for Reporting): 3B1a, 3B1b, \n3B2, 3B3, 3B4a, 3B4d, 3B4e, 3B4f, 3B4gi, 3B4gii, 3B4giii, 3B4giv y 3B4h.\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador forma parte del conjunto de indicadores de los Objetivos de Desarrollo Sostenible (ODS) de la UE.\nSe utiliza para hacer el seguimiento del progreso hacia el ODS 2 sobre poner fin al hambre y la desnutrición, y está \nintegrado en las prioridades de la Comisión Europea en el marco del Acuerdo Verde Europeo\n\nEl indicador mide las emisiones de amoniaco ($NH_3$) como resultado de \nla producción agrícola. Estas emisiones resultan del manejo del estiércol, \nlas aplicaciones de fertilizantes nitrogenados inorgánicos y estiércol animal \naplicado al suelo, así como la orina y el estiércol depositados por los animales en pastoreo.\n\nEl amoníaco ($NH_3$) es un gas incoloro, de olor acre y corrosivo que se produce por la descomposición \nde la materia orgánica vegetal y de los excrementos de humanos y animales. Cuando se libera a la atmósfera, \nel amoníaco contribuye al nivel de contaminación del aire. El sector agrícola sigue siendo el mayor contribuyente \na las emisiones de amoníaco en la UE.\n\nLas medidas implementadas en el marco de la Política Agrícola Común (PAC) y la \nDirectiva Marco del Agua han llevado a la UE a reducir las emisiones de \namoníaco procedentes de la agricultura desde la década de 1990. En concreto, las \nmedidas para cambiar las prácticas agrícolas provocaron reducciones en el uso de \nfertilizantes nitrogenados y una reducción de la densidad ganadera por hectárea en toda la UE-15.\n\nLa Directiva sobre Compromisos Nacionales de Reducción de Emisiones (Directiva NEC) \nestablece compromisos nacionales de reducción de emisiones para los Estados miembros y la\nUE para cinco importantes contaminantes del aire, incluido el amoníaco.\n\nFuente: Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_02_60/default/table?lang=en&category=sdg.sdg_02\"> Emisiones de amoníaco de la agricultura (sdg_02_60)</a> Eurostat", "comparabilidad"=>"El indicador disponible en la C.A. de Euskadi es comparable con el indicador europeo.", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_02_60_esmsip2.htm\">Metadata sdg_20_60</a> (solo en inglés)<br>", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Emisiones de amoníaco de la agricultura y ganadería (Indicador UE sdg_02_60)", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.4- De aquí a 2030, asegurar la sostenibilidad de los sistemas de producción de alimentos y aplicar prácticas agrícolas resilientes que aumenten la productividad y la producción, contribuyan al mantenimiento de los ecosistemas, fortalezcan la capacidad de adaptación al cambio climático, los fenómenos meteorológicos extremos, las sequías, las inundaciones y otros desastres, y mejoren progresivamente la calidad de la tierra y el suelo", "definicion"=>"\nAvailable series: \n\n<b> - Ammonia (NH3) emissions from agriculture and livestock</b>\n\n<b> - Ammonia (NH3) emissions from agriculture and livestock per hectare:</b> \nAmmonia (NH3) emissions from agriculture and livestock farming per hectare of cropland, meadows and pastures \n", "formula"=>"\n<b> Ammonia (NH3) emissions from agriculture and livestock</b>\n\n$$ENH3_{agriculture\\, and\\, livestock}^{t} = ENH3_{agriculture}^{t} + ENH3_{livestock}^{t}$$\n\nwhere: \n\n$ENH3_{agriculture}^{t}$ = NH3 emmisions from agriculture in the year $t$ \n\n$ENH3_{livestock}^{t}$ = NH3 emmisions from livestock in the year $t$ \n\n<br>\n\n<b>Ammonia (NH3) emissions from agriculture and livestock per hectare</b>\n\n$$ENH3S_{agriculture\\, and\\, livestock}^{t} = \\frac{ENH3_{agriculture}^{t} + ENH3_{livestock}^{t}}{S_{cropland}^{t} + S_{meadows\\, and\\, pastures}^{t}}$$\n\nwhere:\n\n$ENH3_{agriculture}^{t}$ = NH3 emmisions from agriculture in the year $t$ \n\n$ENH3_{livestock}^{t}$ = NH3 emmisions from livestock in the year $t$ \n\n$S_{cropland}^{t}$ = croplands area in year $t$ \n\n$S_{meadows\\, and\\, pastures}^{t}$ = meadows and pastures area in year $t$ \n", "desagregacion"=>"Agricultural activity: agriculture, livestock", "observaciones"=>"\nAmmonia emissions assigned to agriculture are those corresponding to activities with NFR \n(Nomenclature for Reporting) codes: 3Da1, 3Da2a,3Da2b,3Da2c, 3Da3, 3Da4, 3Db, 3Dc, 3Dd, 3De, \n3Df, 3F and 3I.\n\nThe ammonia emissions assigned to livestock are those corresponding to activities with NFR \n(Nomenclature for Reporting) codes: 3B1a, 3B1b, 3B2, 3B3, 3B4a, 3B4d, 3B4e, 3B4f, 3B4gi, \n3B4gii, 3B4giii, 3B4giv and 3B4h.\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The indicator is part of the EU Sustainable Development Goals (SDG) indicator set. \nIt is used to monitor progress towards SDG 2 on ending hunger and malnutrition and is \nintegrated into the European Commission's priorities within the framework of the European Green Deal.\n\nThe indicator measures ammonia (NH3) emissions as a result of agricultural production. \nThese emissions result from manure management, applications of inorganic nitrogen fertilisers \nand animal manure applied to soil, as well as urine and dung deposited by grazing animals.\n\nAmmonia (NH3) is a colorless, pungent-smelling, corrosive gas produced by the decomposition \nof plant organic matter and human and animal excrement. When released into the atmosphere, \nammonia contributes to air pollution. The agricultural sector remains the largest contributor \nto ammonia emissions in the EU.\n\nMeasures implemented within the framework of the Common Agricultural Policy (CAP) and the Water \nFramework Directive have led the EU to reduce ammonia emissions from agriculture since the 1990s. \nSpecifically, measures to change agricultural practices led to reductions in the use of nitrogen \nfertilizers and a decrease in livestock density per hectare across the EU-15.\n\nThe National Emission Reduction Commitments Directive (NEC Directive) sets national emission \nreduction commitments for Member States and the EU for five major air pollutants, including ammonia.\n\nSource: Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_02_60/default/table?lang=en&category=sdg.sdg_02\"> Ammonia emissions from agriculture (sdg_02_60)</a> Eurostat", "comparabilidad"=>"The indicator available in the Basque Country is comparable with the European indicator.", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_02_60_esmsip2.htm\">Metadata sdg_20_60</a> <br>", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Emisiones de amoníaco de la agricultura y ganadería (Indicador UE sdg_02_60)", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.4- De aquí a 2030, asegurar la sostenibilidad de los sistemas de producción de alimentos y aplicar prácticas agrícolas resilientes que aumenten la productividad y la producción, contribuyan al mantenimiento de los ecosistemas, fortalezcan la capacidad de adaptación al cambio climático, los fenómenos meteorológicos extremos, las sequías, las inundaciones y otros desastres, y mejoren progresivamente la calidad de la tierra y el suelo", "definicion"=>"\nAdierazle honek bi serie ditu: \n\n<b> - Nekazaritzaren eta abeltzaintzaren amoniako-isuriak (NH3)</b>\n\n<b> - Nekazaritzaren eta abeltzaintzaren amoniako-isuriak (NH3) hektareako:</b> \nNekazaritzaren eta abeltzaintzaren amoniako-isuriak (NH3) laborantzarako, belardietarako \neta larreetarako hektarea bakoitzeko \n", "formula"=>"\n<b> Nekazaritzaren eta abeltzaintzaren amoniako-isuriak (NH3)</b>\n\n$$ENH3_{nekazaritza\\, eta\\, abeltzaintza}^{t} = ENH3_{nekazaritza}^{t} + ENH3_{abeltzaintza}^{t}$$\n\nnon: \n\n$ENH3_{nekazaritza}^{t}$ = nekazaritzaren amoniako-isuriak $t$ urtean\n\n$ENH3_{abeltzaintza}^{t}$ = abeltzaintzaren amoniako-isuriak $t$ urtean\n\n<br>\n\n<b>Nekazaritzaren eta abeltzaintzaren amoniako-isuriak (NH3) hektareako</b>\n\n$$ENH3S_{nekazaritza\\, eta\\, abeltzaintza}^{t} = \\frac{ENH3_{nekazaritza}^{t} + ENH3_{abeltzaintza}^{t}}{S_{labore-lurrak}^{t} + S_{belardiak\\, eta\\, larreak}^{t}}$$\n\nnon:\n\n$ENH3_{nekazaritza}^{t}$ = nekazaritzaren amoniako-isuriak $t$ urtean\n\n$ENH3_{abeltzaintza}^{t}$ = abeltzaintzaren amoniako-isuriak $t$ urtean\n\n$S_{labore-lurrak}^{t}$ = labore-lurren azalera $t$ urtean\n\n$S_{belardiak\\, eta\\, larreak}^{t}$ = belardi eta larreen azalera $t$ urtean\n", "desagregacion"=>"Jarduera: nekazaritza; abeltzaintza", "observaciones"=>"\nNekazaritzari esleitutako amoniako-isuriak ondoko NFR (Nomenclature for Reporting) kodeak dituzten \njarduerei dagozkienak dira: 3Da1, 3Da2a, 3Da2b, 3Da2c, 3Da3, 3Da4, 3Db, 3Dc, 3Dd, 3De, 3Df, 3F eta 3I.\n\nAbeltzaintzari esleitutako amoniako-isuriak ondoko NFR (Nomenclature for Reporting) kodeak dituzten \njarduerei dagozkienak dira: 3B1a, 3B1b, 3B2, 3B3, 3B4a, 3B4d, 3B4e, 3B4f, 3B4gi, 3B4gii, 3B4giii, 3B4giv eta 3B4h.\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Adierazlea EBko Garapen Jasangarriko Helburuen (GJH) adierazleen parte da. Goseari eta desnutrizioari amaiera jartzeko \n2. GJHrako aurrerapenaren jarraipena egiteko erabiltzen da, eta Europako Batzordearen lehentasunetan sartzen da, Europako \nAkordio Berdearen esparruan. \n\nAdierazleak nekazaritzako ekoizpenaren ondoriozko amoniako-emisioak neurtzen ditu ($NH_3$). Emisio hauen atzean daude \nsimaurraren erabilera, ongarri nitrogenatu inorganikoen aplikazioak, lurrera aplikatutako animalia-simaurra, eta animaliek \nlarreetan utzitako txiza eta simaurra. \n\nAmoniakoa ($NH_3$) kolorerik gabeko gasa da, akre usainekoa eta korrosiboa. Material organiko begetala eta gizakien eta \nanimalien gorozkiak deskonposatzean sortzen da. Atmosferara isurtzen denean, amoniakoak airearen kutsadura-maila igotzen \ndu. Nekazaritzaren sektorea da oraindik ere EBn amoniako-emisioen ekarpen gehien egiten duena. \n\nNekazaritza Politika Erkidearen (NPE) eta Uraren Zuzentarau Esparruaren barnean ezarritako neurrien ondorioz, EBk murriztu \negin ditu nekazaritzatik datozen amoniako-emisioak, 1990eko hamarkadaz geroztik. Zehazki, nekazaritzako praktikak aldatzeko \nneurriek murrizketak eragin zituzten ongarri nitrogenatuen erabileran eta, murrizketak, halaber, hektareako abeltzaintza-dentsitatean, \nEB-15 osoan. \n\nEmisioak Murrizteko Konpromiso Nazionalei buruzko Zuzentarauak (NEC Zuzentaraua) emisioak murrizteko konpromiso nazionalak \nezartzen dizkie estatu-kideei eta EBri, airearen bost kutsatzaile handiri begira (amoniakoa barne). \n\n\nIturria: Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_02_60/default/table?lang=en&category=sdg.sdg_02\"> Nekazaritzaren amoniako-isuriak (sdg_02_60)</a> Eurostat", "comparabilidad"=>"Euskal Autonomia Erkidegoan eskuragarri dagoen adierazlea Europako adierazlearekin aldera daiteke.", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_02_60_esmsip2.htm\">Metadatuak sdg_20_60</a> (ingelesez bakarrik)<br>", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "indicator_sort_order"=>"02-04-E2", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.5.1", "slug"=>"2-5-1", "name"=>"Número de: a) recursos genéticos vegetales y b) animales para la alimentación y la agricultura preservados en instalaciones de conservación a medio y largo plazo", "url"=>"/site/es/2-5-1/", "sort"=>"020501", "goal_number"=>"2", "target_number"=>"2.5", "global"=>{"name"=>"Número de: a) recursos genéticos vegetales y b) animales para la alimentación y la agricultura preservados en instalaciones de conservación a medio y largo plazo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de: a) recursos genéticos vegetales y b) animales para la alimentación y la agricultura preservados en instalaciones de conservación a medio y largo plazo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de: a) recursos genéticos vegetales y b) animales para la alimentación y la agricultura preservados en instalaciones de conservación a medio y largo plazo", "indicator_number"=>"2.5.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"Los recursos genéticos para la alimentación y la agricultura constituyen \nlos elementos básicos de la seguridad alimentaria y, directa o indirectamente, \nsustentan los medios de vida de todas las personas de la Tierra. \n\nComo la conservación \ny la accesibilidad a estos recursos son de vital importancia, se han establecido \ninstalaciones de conservación a mediano o largo plazo (bancos de genes) para preservar \ny hacer accesibles estos recursos y la información asociada a ellos para la cría y \nla investigación a nivel nacional, regional y mundial. Los inventarios de los fondos \nde los bancos de genes proporcionan una medida dinámica de la diversidad vegetal y \nanimal existente y su nivel de conservación. Los datos pertinentes a este indicador \nfacilitan el seguimiento de la diversidad asegurada y accesible a través de los \nbancos de genes y apoyan la elaboración y actualización de estrategias para la \nconservación y el uso sostenible de los recursos genéticos. \n\nEl indicador está relacionado con un marco de seguimiento aprobado por la Comisión \nde Recursos Genéticos para la Alimentación y la Agricultura de la FAO en el \nque se describe el estado y las tendencias de los recursos genéticos vegetales \ny animales mediante indicadores acordados a nivel mundial y evaluaciones periódicas \nimpulsadas por los países. \n\nEl número de materiales conservados en condiciones de almacenamiento a mediano o \nlargo plazo proporciona una medición indirecta de la diversidad genética total, \nque se gestiona para asegurar su uso futuro. En términos generales, las \nvariaciones positivas se aproximan a un aumento de la agrobiodiversidad \nasegurada, mientras que las variaciones negativas a una pérdida de la misma.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-05-01a.pdf\">Metadatos 2-5-1(1).pdf</a> (solo en inglés) \n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-05-01b.pdf\">Metadatos 2-5-1(2).pdf</a> (solo en inglés) \n", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "descripcion_global"=>"\nGenetic resources for food and agriculture provide the building blocks of food \nsecurity and, directly or indirectly, support the livelihoods of every person on earth.\n\nAs the conservation and accessibility to these resources are of vital importance, \nmedium- or long- term conservation facilities (genebanks) to preserve and make these \nresources and their associated information accessible for breeding and research have \nbeen established at country, regional and global levels. Inventories of genebank holdings \nprovide a dynamic measure of the existing plant and animal diversity and its level of preservation. \nData relevant to this indicator facilitate the monitoring of diversity secured and accessible \nthrough genebanks and support the development and updating of strategies for the conservation \nand sustainable use of genetic resources.\n\nThe indicator is related to a monitoring framework endorsed by the FAO Commission on Genetic \nResources for Food and Agriculture in which the status and trends of plant and animal \ngenetic resources are described through globally agreed indicators and regular \ncountry-driven assessments.\n\nThe number of materials conserved under medium- or long-term storage conditions provides \nan indirect measurement of the total genetic diversity, which are managed to secure for future \nuse. Overall, positive variations are therefore approximated to an increase in the agro-biodiversity \nsecured, while negative variations to a loss of it.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-05-01a.pdf\">Metadata 2-5-1(1).pdf</a> \n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-05-01b.pdf\">Metadata 2-5-1(2).pdf</a> "}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"Elikadurarako eta nekazaritzarako baliabide genetikoak elikadura-segurtasunaren oinarrizko elementuak \ndira, eta, zuzenean edo zeharka, Lurreko pertsona guztien bizibideak sostengatzen dituzte.\n\nBaliabide horien kontserbazioak eta irisgarritasunak berebiziko garrantzia dutenez, epe ertain edo \nluzerako kontserbazio-instalazioak ezarri dira (gene-bankuak) baliabide horiek eta horiei lotutako \ninformazioa babesteko eta eskuragarri jartzeko, nazio, eskualde eta mundu mailako hazkuntzarako \neta ikerketarako. Gene-bankuen hondoen inbentarioek landare- eta animalia-aniztasunaren eta \nkontserbazio-mailaren neurri dinamikoa ematen dute. Adierazle horri dagozkion datuek ziurtatutako \neta gene-bankuen bidez eskuragarri dagoen aniztasunaren jarraipena errazten dute, eta baliabide \ngenetikoak kontserbatzeko eta modu jasangarrian erabiltzeko estrategiak egiten eta eguneratzen \nlaguntzen dute.\n\nAdierazlea FAOko Elikadura eta Nekazaritzarako Baliabide Genetikoen Batzordeak onartutako \njarraipen-esparru batekin lotuta dago. Esparru horretan, landareen eta animalien baliabide \ngenetikoen egoera eta joerak deskribatzen dira, mundu mailan adostutako adierazleen eta herrialdeek \nbultzatutako aldizkako ebaluazioen bidez.\n\nEpe ertain edo luzerako biltegiratze-baldintzetan kontserbatutako materialen kopuruak dibertsitate \ngenetiko osoaren zeharkako neurketa bat ematen du, eta aniztasun hori etorkizunean erabiliko dela \nbermatzeko kudeatzen da. Oro har, aldakuntza positiboak agrobiodibertsitate aseguratua handitzera \nhurbiltzen dira, eta aldakuntza negatiboak, berriz, galera batera.\n\nIturria: Nazio Batuen Estatistika Sekzioa  \n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-05-01a.pdf\">Metadatuak 2-5-1(1).pdf</a> (ingelesez bakarrik) \n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-05-01b.pdf\">Metadatuak 2-5-1(2).pdf</a> (ingelesez bakarrik)\n"}, "national_metadata_updated_date"=>"2025-05-20", "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.5: By 2020, maintain the genetic diversity of seeds, cultivated plants and farmed and domesticated animals and their related wild species, including through soundly managed and diversified seed and plant banks at the national, regional and international levels, and promote access to and fair and equitable sharing of benefits arising from the utilization of genetic resources and associated traditional knowledge, as internationally agreed</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.5.1: Number of (a) plant and (b) animal genetic resources for food and agriculture secured in either medium- or long-term conservation facilities</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_GRF_ANIMRCNTN - Number of local breeds for which sufficient genetic resources are stored for reconstitution [2.5.1]</p>\n<p>ER_GRF_ANIMKPT - Number of local breeds kept in the country [2.5.1]</p>\n<p>ER_GRF_ANIMRCNTN_TRB - Number of transboundary breeds for which sufficient genetic resources are stored for reconstitution [2.5.1]</p>\n<p>ER_GRF_ANIMKPT_TRB - Number of transboundary breeds (including extinct ones) [2.5.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 2.5.1a on plant genetic resources and 2.5.2 on animal genetic resources</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The conservation of plant and animal genetic resources for food and agriculture (GRFA) in medium- or long-term conservation facilities (<em>ex situ in vito</em>, i.e. in genebanks) represents a trusted means of conserving genetic resources worldwide.</p>\n<p>The measure of trends in <em>ex situ</em> conserved materials provides a partial assessment of the extent to which we are managing to maintain genetic diversity available for future use and thus protected from any permanent loss of genetic diversity which may occur in the natural habitat, i.e. <em>in situ</em>/on-farm.</p>\n<p>The two components of the indicator 2.5.1, plant (a) and animal (b) GRFA, are separately counted.</p>\n<p><em>Animal genetic resources</em> </p>\n<p>The animal component is calculated as the number of local (i.e. being reported to exist only in one country) and transboundary (i.e. being reported to exist in more than one country) breeds with material stored within a genebank collection with an amount of genetic material which is required to reconstitute the breed in case of extinction (further information on &#x201C;sufficient material stored to reconstitute a breed&#x201D; can be found in the Guidelines on Cryoconservation of Animal Genetic Resources, FAO, 2012, accessible at <a href=\"http://www.fao.org/docrep/016/i3017e/i3017e00.htm\">http://www.fao.org/docrep/016/i3017e/i3017e00.htm</a>). The guidelines have been endorsed by the FAO Commission on Genetic Resources for Food and Agriculture at its Thirteenth Regular Session (http://www.fao.org/docrep/meeting/024/mc192e.pdf).</p>\n<p><strong>Concepts:</strong></p>\n<p><em>Animal genetic resources</em></p>\n<p>Breed: A breed is either a sub-specific group of domestic livestock with definable and identifiable external characteristics that enable it to be separated by visual appraisal from other similarly defined groups within the same species, or a group for which geographical and/or cultural separation from phenotypically similar groups has led to acceptance of its separate identity. </p>\n<p>Medium- or long-term conservation facilities: Biological diversity is often conserved <em>ex situ</em>, outside its natural habitat, in facilities called genebanks. In the case of domestic animal diversity, <em>ex situ</em> conservation includes both the maintenance of live animals (<em>in vivo</em>) e.g. in zoos and cryoconservation (<em>in vitro</em>). </p>\n<p>Cryoconservation is the collection and deep-freezing of semen, ova, embryos or tissues for potential future use in breeding or regenerating animals.</p>\n<p>The indicator covers materials under <em>ex situ in vitro</em> conservation.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of local breeds and number of transboundary breeds</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>International standards and classifications used have been endorsed by the FAO Commission on Genetic Resources for Food and Agriculture at its Thirteenth Regular Session (<a href=\"http://www.fao.org/docrep/meeting/024/mc192e.pdf\">http://www.fao.org/docrep/meeting/024/mc192e.pdf</a>).</p>", "SOURCE_TYPE__GLOBAL"=>"<p><em>Animal genetic resources</em></p>\n<p>National Coordinators for Management of Animal Genetic Resources, nominated by their respective government, provide data to the Domestic Animal Diversity Information System (DAD-IS) (<a href=\"http://dad.fao.org/\">http://dad.fao.org/</a>). DAD-IS allows countries the storage of data on animal genetic resources being secured in genebank facilities as needed for the indicator.</p>", "COLL_METHOD__GLOBAL"=>"<p>The indicator is related to a monitoring framework endorsed by the FAO Commission on Genetic Resources for Food and Agriculture in which the status and trends of plant and animal genetic resources are described through globally agreed indicators and regular country-driven assessments. Officially appointed National Focal Points /National Coordinators report directly to FAO, using a format agreed by the FAO Commission on Genetic Resources for Food and Agriculture.</p>\n<p>Sessions of the intergovernmental technical working groups on plant and on animal genetic resources for food and agriculture allow for formal consultation processes. </p>", "FREQ_COLL__GLOBAL"=>"<p><em>Animal genetic resources</em> </p>\n<p>Data in DAD-IS can be updated throughout the whole year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p><em>Animal genetic resources</em></p>\n<p>The SDG reports and tools are published at least once a year (March) and up to a maximum of four times per year (March, May, September, December) according to an internationally agreed calendar. The date of last update is displayed below each figure or table.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The officially nominated National Focal Points / National Coordinators. For information by country see for animal genetic resources <a href=\"http://www.fao.org/dad-is/national-coordinators/en/\">http://www.fao.org/dad-is/national-coordinators/en/</a>.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "INST_MANDATE__GLOBAL"=>"<p>The National Coordinators for Management of Animal Genetic Resources are responsible for the provision of national data the indicator is based on. Their Terms of Reference have been endorsed by the Commission on Genetics Resources for Food and Agriculture and are described in more detail in: <em>Developing the institutional framework for the management of animal genetic resources</em>.</p>\n<p>FAO Animal Production and Health Guidelines. No. 6. Rome. (Accessible at <a href=\"http://www.fao.org/3/ba0054e/ba0054e00.pdf\">http://www.fao.org/3/ba0054e/ba0054e00.pdf</a>). </p>", "RATIONALE__GLOBAL"=>"<p>Genetic resources for food and agriculture provide the building blocks of food security and, directly or indirectly, support the livelihoods of every person on earth. As the conservation and accessibility to these resources are of vital importance, medium- or long- term conservation facilities (genebanks) to preserve and make these resources and their associated information accessible for breeding and research have been established at country levels. Inventories of genebank holdings provide a dynamic measure of the existing plant and animal diversity and its level of preservation. Data relevant to this indicator facilitate the monitoring of diversity secured and accessible through genebanks and support the development and updating of strategies for the conservation and sustainable use of genetic resources.</p>\n<p>The indicator is related to a monitoring framework endorsed by the FAO Commission on Genetic Resources for Food and Agriculture in which the status and trends of plant and animal genetic resources are described through globally agreed indicators and regular country-driven assessments. </p>\n<p>The number of materials conserved under medium- or long-term storage conditions provides an indirect measurement of the genetic diversity, which are managed to secure for future use. Overall, positive variations are therefore approximated to an increase in the agro-biodiversity secured, while negative variations to a loss of it. </p>", "REC_USE_LIM__GLOBAL"=>"<p><em>Animal genetic resources</em></p>\n<p>Information on cryo-conserved material in the Domestic Animal Diversity Information System DAD-IS needs to be updated on a regular base.</p>", "DATA_COMP__GLOBAL"=>"<p><em>Animal genetic resources</em></p>\n<p>For the animal component the indicator is calculated as the number of local breeds and transboundary breeds with enough genetic material stored within genebank collections allowing to reconstitute the breed in case of extinction (based on the Guidelines on Cryoconservation of animal genetic resources, FAO, 2012, <a href=\"http://www.fao.org/docrep/016/i3017e/i3017e00.htm\">http://www.fao.org/docrep/016/i3017e/i3017e00.htm</a>). Numbers for local and transboundary breeds are presented separately. To decide whether the material stored is sufficient on regional or global levels the numbers provided to DAD-IS for each type of material (e.g. semen samples, embryos, somatic cells) conserved within the framework of a cryconservation programme, as well as the number of the respective male and female donor animals, must be summed across the countries belonging to the respective region of interest.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>There is no validation process in place.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>For animals, for a given breed, if no data are provided for a respective year, it is assumed that the storage status remains the same as for the last year for which data have been reported. In this case the nature of data is considered to be estimated. </p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Missing values are treated as such and not replaced by estimates. </p>", "REG_AGG__GLOBAL"=>"<p>Aggregates are the sum of country values.</p>", "DOC_METHOD__GLOBAL"=>"<p>For the animal component the National Coordinators for the Management of Animal Genetic Resources provide the type of material (e.g. semen samples, embryos, somatic cells) cryo-conserved within the framework of a cryoconservation programme, as well as the number of the respective male and female donors to the Domestic Animal Diversity Information System DAD-IS. FAO provides internationally endorsed guidelines on the definition of &#x201C;sufficient&#x201D; material (see FAO. 2012. Cryo-conservation of animal genetic resources. FAO Animal Production and Health Guidelines No. 12. Rome. (available at <a href=\"http://www.fao.org/docrep/016/i3017e/i3017e00.pdf\">http://www.fao.org/docrep/016/i3017e/i3017e00.pdf</a>)</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>FAO provides regular training to National Coordinators related to data collection and entering data into the official system, DAD-IS. The indicator itself is automatically calculated in DAD-IS. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>FAO is responsible for the quality of the internal statistical processes used to compile the published datasets.</p>\n<p>FAO. 2012. Cryo-conservation of animal genetic resources. FAO Animal Production and Health Guidelines No. 12. Rome. (available at <a href=\"http://www.fao.org/docrep/016/i3017e/i3017e00.pdf\">http://www.fao.org/docrep/016/i3017e/i3017e00.pdf</a>)</p>\n<p>Boes, J., Boettcher, P. &amp; Honkatukia, M., eds. 2023. Innovations in cryoconservation of animal genetic resources &#x2013; Practical guide. FAO Animal Production and Health Guidelines, No. 33. Rome. (available at <a href=\"https://doi.org/10.4060/cc3078en\" target=\"_blank\">https://doi.org/10.4060/cc3078en</a>)</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>At least each second year FAO is organizing a global National Coordinators&#x2019; Workshops to assess and discuss the collection of data the indicator is based on. The indicators itself is automatically calculated in DAD-IS. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p><em>Animal genetic resources</em></p>\n<p>The analysis of country reports to FAO provided by 128 countries in 2014 for the preparation of &#x2018;The Second Report on the State of the World&#x2019;s Animal Genetic Resources for Food and Agriculture&#x2019; provided a first baseline with regard to the number of national breed populations where sufficient material is stored. Information on cryoconserved material is made available to DAD-IS for approximately 50% of breeds. </p>\n<p><strong>Time series:</strong></p>\n<p><em>Animal genetic resources</em></p>\n<p>DAD-IS data are available since 2000 .</p>\n<p><strong>Disaggregation:</strong></p>\n<p>A geographic disaggregation (national, regional, global) is made. Grouping by sex, age etc. is not applicable.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There are no internationally estimated data. Data on this indicator are all produced by countries. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong><em>Animal genetic resources</em></strong></p>\n<p>Preparation of the First Report on the State of the World&apos;s Animal Genetic Resources</p>\n<p>Guidelines for the Development of Country Reports. Annex 2. Working definitions for use in developing country reports and providing supporting data.</p>\n<p><a href=\"http://www.fao.org/docrep/004/y1100m/y1100m03.htm\">http://www.fao.org/docrep/004/y1100m/y1100m03.htm</a></p>\n<p>Guidelines on Cryoconservation of Animal Genetic Resources, FAO, 2012, accessible at <a href=\"http://www.fao.org/docrep/016/i3017e/i3017e00.htm\">http://www.fao.org/docrep/016/i3017e/i3017e00.htm</a></p>\n<p>National Coordinator for Management of Animal Genetic Resources. </p>\n<p><a href=\"http://dad.fao.org/cgi-bin/EfabisWeb.cgi?sid=-1,contacts\">http://dad.fao.org/cgi-bin/EfabisWeb.cgi?sid=-1,contacts</a></p>\n<p><br>Status of Animal Genetic Resources &#x2013; 2022.</p>\n<p>https://www.fao.org/3/cc3705en/cc3705en.pdf</p>\n<p>Guidelines on In vivo Conservation of Animal Genetic Resources, FAO, 2013. <a href=\"http://www.fao.org/docrep/018/i3327e/i3327e.pdf\">http://www.fao.org/docrep/018/i3327e/i3327e.pdf</a></p>\n<p>The Second Report on the State of the World&#x2019;s Animal Genetic Resources for Food and Agriculture.</p>\n<p><a href=\"http://www.fao.org/3/a-i4787e.pdf\">http://www.fao.org/3/a-i4787e.pdf</a></p>\n<p>Boes, J., Boettcher, P. &amp; Honkatukia, M., eds. 2023. Innovations in cryoconservation of animal genetic resources &#x2013; Practical guide. FAO Animal Production and Health Guidelines, No. 33. Rome. (available at <a href=\"https://doi.org/10.4060/cc3078en\" target=\"_blank\">https://doi.org/10.4060/cc3078en</a>)</p>", "indicator_sort_order"=>"02-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.5.2", "slug"=>"2-5-2", "name"=>"Proporción de razas y variedades locales consideradas en riesgo de extinción", "url"=>"/site/es/2-5-2/", "sort"=>"020502", "goal_number"=>"2", "target_number"=>"2.5", "global"=>{"name"=>"Proporción de razas y variedades locales consideradas en riesgo de extinción"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de razas y variedades locales consideradas en riesgo de extinción", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de razas y variedades locales consideradas en riesgo de extinción", "indicator_number"=>"2.5.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"El indicador tiene un vínculo directo con la biodiversidad, ya que los \nrecursos genéticos animales o ganaderos representan una parte integral de los \necosistemas agrícolas y la biodiversidad como tal. Además, existen vínculos \nindirectos con la “malnutrición”: los recursos genéticos animales para la \nalimentación y la agricultura son una parte esencial de la base biológica \nde la seguridad alimentaria mundial y contribuyen a los medios de vida de \nmás de mil millones de personas. \n\nUna base de recursos diversa es fundamental para la supervivencia y el \nbienestar humanos y una contribución a la erradicación del hambre: los \nrecursos genéticos animales son cruciales para la adaptación a las condiciones \nsocioeconómicas y ambientales cambiantes, incluido el cambio climático. \n\nSon la materia prima del criador de animales y uno de los insumos más \nesenciales del agricultor. Son esenciales para la producción agrícola \nsostenible. Ningún aumento del porcentaje de razas en peligro o en vías \nde extinción está directamente relacionado con “detener la pérdida de biodiversidad”.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-05-02.pdf\">Metadatos 2-5-2.pdf</a> (solo en inglés) ", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "descripcion_global"=>"\nThe indicator has a direct link to “biodiversity” as animal or livestock \ngenetic resources represent an integral part of agricultural ecosystems and \nbiodiversity as such. Further there are indirect links to “malnutrition”: \nAnimal genetic resources for food and agriculture are an essential part of \nthe biological basis for world food security, and contribute to the livelihoods \nof over a thousand million people.\n\nA diverse resource base is critical for human survival and well-being, and a \ncontribution to the eradication of hunger: animal genetic resources are crucial \nin adapting to changing socio-economic and environmental conditions, including \nclimate change.\n\nThey are the animal breeder’s raw material and amongst the farmer’s most essential \ninputs. They are essential for sustainable agricultural production. No increase of \nthe percentage of breeds being at risk or being extinct is directly related to “halt \nthe loss of biodiversity”.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-05-02.pdf\">Metadata 2-5-2.pdf</a>"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"Adierazleak lotura zuzena du biodibertsitatearekin; izan ere, animalia- edo abeltzaintza-baliabide \ngenetikoak nekazaritza-ekosistemen eta biodibertsitatearen zati integral bat dira. Gainera, \nzeharkako loturak daude \"malnutrizioarekin\": elikadurarako eta nekazaritzarako animalien \nbaliabide genetikoak munduko elikadura-segurtasunaren oinarri biologikoaren funtsezko zati dira, \neta mila milioi pertsona baino gehiagoren bizi-baliabideei laguntzen diete.\n\nAskotariko baliabideen oinarri bat funtsezkoa da gizakien biziraupenerako eta ongizaterako, \neta gosea desagerrarazten laguntzen du: animalien baliabide genetikoak funtsezkoak dira baldintza \nsozioekonomiko eta ingurumen-baldintza aldakorretara egokitzeko, klima-aldaketa barne.\n\nAnimalia-hazlearen lehengaia eta nekazariaren intsumo funtsezkoenetako bat dira. Funtsezkoak dira \nnekazaritza-ekoizpen iraunkorrerako. Arriskuan edo desagertzeko bidean dauden arrazen ehunekoaren \nigoerarik ez dago zuzenean lotuta \"biodibertsitatearen galera geldiaraztearekin\".\n\nIturria: Nazio Batuen Estatistika Seskzioa    \n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-05-02.pdf\">Metadatuak 2-5-2.pdf</a> (ingelesez bakarrik) "}, "national_metadata_updated_date"=>"2025-05-20", "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.5: By 2020, maintain the genetic diversity of seeds, cultivated plants and farmed and domesticated animals and their related wild species, including through soundly managed and diversified seed and plant banks at the national, regional and international levels, and promote access to and fair and equitable sharing of benefits arising from the utilization of genetic resources and associated traditional knowledge, as internationally agreed</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.5.2: Proportion of local and transboundary breeds classified as being at risk of extinction</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_RSK_LBREDS - Proportion of local breeds classified as being at risk of extinction as a share of local breeds with known level of extinction risk (%) [2.5.2]</p>\n<p>ER_NOEX_LBREDN - Number of local breeds (not extinct) [2.5.2]</p>\n<p>ER_UNK_LBREDN - Number of local breeds with unknown risk status [2.5.2]</p>\n<p>ER_NOEX_TRBBREDN - Number of transboundary breeds (not extinct) [2.5.2]</p>\n<p>ER_UNK_TRBBREDN - Number of transboundary breeds with unknown risk status [2.5.2]</p>\n<p>ER_RSK_TRBBREDS - Proportion of transboundary breeds classified as being at risk of extinction as a share of local breeds with known level of extinction risk [2.5.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 2.5.1b on animal genetic resources</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition: </strong></p>\n<p>The indicator presents the percentage of local and transboundary livestock breeds among local and transboundary breeds with known risk status classified as being at risk of extinction at a certain moment in time, as well as the trends for this percentage.</p>\n<p><strong>Concepts: </strong></p>\n<p>A similar indicator was originally proposed for the Target 15.5, and it serves also as an indicator for the Aichi Target 13 &#x201C;Genetic Diversity of Terrestrial Domesticated Animals&#x201D; under the Convention on Biological Diversity (CBD). It is described on the webpage of the Biodiversity Indicators Partnership (BIP), a network of organizations, which have come together to provide the most up-to date biodiversity information possible for tracking progress towards the Aichi Targets (http://www.bipindicators.net/domesticatedanimals). Further, it is presented in the Global Biodiversity Outlook 4, page 91 (see <a href=\"https://www.cbd.int/gbo/gbo4/publication/gbo4-en.pdf\">https://www.cbd.int/gbo/gbo4/publication/gbo4-en.pdf</a> ) which is an output of the processes under the CBD.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>International standards and classifications used have been endorsed by the FAO Commission on Genetic Resources for Food and Agriculture and are provided in more detail in: FAO. 2013. In vivo conservation of animal genetic resources (accessible at <a href=\"http://www.fao.org/3/a-i3327e.pdf\">http://www.fao.org/3/a-i3327e.pdf</a>).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>DAD-IS is the Domestic Animal Diversity Information System maintained and developed by FAO (<a href=\"http://www.fao.org/dad-is/en/\">http://www.fao.org/dad-is/en/</a>). It provides access to searchable databases of breed-related information and photos and links to other online resources on livestock diversity. It allows to analyze the diversity of livestock breeds on national, regional and global levels including the status of breeds regarding their risk of extinction. DAD-IS currently contains data from 182 countries and 39 species. It contains information on more than 8,800 mammalian and avian breeds, among those about 7,700 are considered local (i.e. reported to occur in only one country) and 1,100 transboundary.</p>", "COLL_METHOD__GLOBAL"=>"<p>Livestock census on breed level or data derived from national herdbooks or national surveys. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data entry into DAD-IS is possible year round.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The indicator is updated in the first quarter of each year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The data are provided by the National Coordinators for the Management of Animal Genetic Resources (NCs). The NC is officially nominated by the country (usually by the Ministry of Agriculture). FAO provides the password for entering/updating the country&#x2019;s data within the global data information system DAD-IS directly to the NC, after having received the official nomination letter.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "INST_MANDATE__GLOBAL"=>"<p>The National Coordinators for Management of Animal Genetic Resources are responsible for the provision of national data the indicator is based on. Their Terms of Reference have been endorsed by the Commission on Genetics Resources for Food and Agriculture and are described in more detail in: <em>Developing the institutional framework for the management of animal genetic resources</em>.</p>\n<p>FAO Animal Production and Health Guidelines. No. 6. Rome. (Accessible at <a href=\"http://www.fao.org/3/ba0054e/ba0054e00.pdf\">http://www.fao.org/3/ba0054e/ba0054e00.pdf</a>). </p>", "RATIONALE__GLOBAL"=>"<p>The indicator has a direct link to &#x201C;biodiversity&#x201D; as animal or livestock genetic resources represent an integral part of agricultural ecosystems and biodiversity as such. Further there are indirect links to &#x201C;malnutrition&#x201D;: Animal genetic resources for food and agriculture are an essential part of the biological basis for world food security, and contribute to the livelihoods of over a thousand million people. A diverse resource base is critical for human survival and well-being, and a contribution to the eradication of hunger: animal genetic resources are crucial in adapting to changing socio-economic and environmental conditions, including climate change. They are the animal breeder&#x2019;s raw material and amongst the farmer&#x2019;s most essential inputs. They are essential for sustainable agricultural production. </p>\n<p>No increase of the percentage of breeds being at risk or being extinct is directly related to &#x201C;halt the loss of biodiversity&#x201D;.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Breed-related information remains far from complete. Across the world, when excluding extinct breeds, 61 percent of local breeds and 35 percent of transboundary breeds are classified as of unknown status because of missing population data or lack of recent updates. </p>\n<p>Generally, data collection should be possible in all countries. Updating of population size data at least each 10 years is needed for the definition of the risk classes.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is based on the data contained in FAO&#x2019;s Global Databank for Animal Genetic Resources DAD-IS (<a href=\"http://dad.fao.org/\">http://dad.fao.org/</a>). Risk classes are defined based on population sizes of breeds reported to DAD-IS. The risk class is considered to be &#x201C;unknown&#x201D; if (i) no population sizes are reported or (ii) the most recent population size reported refers to a year more than 10- years before the year of calculation (10 year cut off point). </p>\n<p> Species are assigned to two groups. The first group comprises species that have high reproductive capacity, such as pigs, rabbits, guinea pigs and avian species, and the second comprises species that</p>\n<p>have low reproductive capacity, i.e. those belonging to the taxonomical families Bovidae, Equidae, Camelidae and Cervidae.</p>\n<p>The risk status categories are defined as follows (see also FAO. 2013. In vivo conservation of animal genetic resources. FAO Animal Production and Health Guidelines. No. 14. Rome. Accessible at <a href=\"http://www.fao.org/docrep/018/i3327e/i3327e.pdf\">http://www.fao.org/docrep/018/i3327e/i3327e.pdf</a>): </p>\n<p><strong><em>Extinct. </em></strong>A breed is categorized as extinct when there are no breeding males or breeding females remaining and any cryoconserved genetic material that may be available is insufficient for breed reconstitution.</p>\n<p><strong><em>Cryoconserved only. </em></strong>Breeds that have no living male or female animals remaining, but for which there is sufficient cryopreserved material to allow for reconstitution of the breed, are assigned to the category cryoconserved only. The ability to reconstitute an otherwise extinct breed depends on the amount of and type of stored germplasm. Requirements differ greatly according to species. Guidance on what constitutes &#x201C;sufficient cryopreserved material&#x201D; is provided in the FAO guidelines <em>Cryoconservation of animal genetic resources </em>(FAO, 2012).</p>\n<p><strong><em>Critical. </em></strong>A breed is categorized as critical if:</p>\n<p><strong>&#x2022; </strong>the total number of breeding females is less than or equal to 100 (300 for species with low reproductive capacity); or</p>\n<p><strong>&#x2022; </strong>the overall population size is less than or equal to 80 (240) and the population trend is increasing and the proportion of females being bred to males of the same breed is greater than 80 percent (i.e. cross-breeding is equal to or less than 20 percent); or</p>\n<p><strong>&#x2022; </strong>the overall population size is less than or equal to 120 (360) and the population trend is stable or decreasing; or</p>\n<p><strong>&#x2022; </strong>the total number of breeding males is less than or equal to five (i.e. <em>&#x394;F </em>is 3 percent or greater).</p>\n<p>If the population trend is unknown, then it is assumed to be stable. Breeds for which demographic characteristics suggest a critical risk of extinction, but that have active conservation programmes (including cryoconservation) in place, or populations that are maintained by commercial companies or research institutions are considered to be &#x201C;critical-maintained&#x201D; for reporting purposes.</p>\n<p><strong><em>Endangered. </em></strong>A breed is categorized as endangered if:</p>\n<p><strong>&#x2022; </strong>the total number of breeding females is greater than 100 (300 for species with low reproductive capacity) and less than or equal to 1 000 (3 000); or</p>\n<p><strong>&#x2022; </strong>the overall population size is greater than 80 (240) and less than 800 (2 400) and increasing in size and the percentage of females being bred to males of the same breed is above 80 percent; or</p>\n<p><strong>&#x2022; </strong>the overall population size is greater than 120 (360) and less than or equal to 1 200 (3 600) and the trend is stable or decreasing; or</p>\n<p><strong>&#x2022; </strong>the total number of breeding males is less than or equal to 20 and greater than five (i.e. <em>&#x394;F </em>is between 1 and 3 percent).</p>\n<p>Once again, if the population trend is unknown, then it is assumed to be stable. Endangered breeds will be assigned to the subcategory &#x201C;endangered-maintained&#x201D; if active conservation programmes are in place or if their populations are maintained by commercial companies or research institutions.</p>\n<p><strong><em>Vulnerable. </em></strong>A breed is categorized as vulnerable if:</p>\n<p><strong>&#x2022; </strong>the total number of breeding females is between 1 000 and 2 000 (3 000 and 6 000 for species with low reproductive capacity); or</p>\n<p><strong>&#x2022; </strong>the overall population size is greater than 800 (2 400) and less than or equal to 1 600 (4 800) and increasing and the percentage of females being bred to males of the same breed is greater than 80 percent; or</p>\n<p><strong>&#x2022; </strong>the overall population size is greater than 1 200 (3 600) and less than or equal to 2 400 (7 200) but stable or decreasing; or</p>\n<p><strong>&#x2022; </strong>the total number of breeding males is between 20 and 35 (i.e. the <em>&#x394;F </em>is between 0.5 and 1 percent).</p>\n<p>Unreported population trends are assumed to be stable.</p>\n<p><strong><em>Not at risk. </em></strong>A breed is categorized as not at risk if the population status is known and the breed does not fall in the critical or endangered categories (including the respective subcategories) or the vulnerable category.</p>\n<p> </p>\n<p><strong><em>Unknown. </em></strong>This category is self-explanatory and calls for action. A population survey is needed; the breed could be critical, endangered or vulnerable.</p>\n<p>A breed is considered to be <strong>at risk</strong> if it has been classified as either critical, critical-maintained, endangered, endangered-maintained or vulnerable. </p>\n<p>For transboundary breeds the risk status for the national breed population in one country may be &quot;at risk&quot; but at the regional or global levels the numbers combined across countries may result in &#x201C;not at risk&#x201D;. The risk status on national level is not of relevance as the total population size (the sum over all existing national population sizes) is the only relevant figure for the calculation of the overall risk of extinction of a transboundary breed. Therefore, from a statistical point of view, it is necessary to consider the population size data from all countries where animals of a particular transboundary breed are reported. </p>\n<p>To determine the risk status for transboundary breeds, the total population size for each transboundary breed is the sum over all national populations (note: for local breeds, the total population size is equal to the national population size, as a local breed consists of only one national population). </p>\n<p>The indicator is presented separately for local breeds and transboundary breeds using the national populations size and the total population size, respectively, and calculated as follows:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Risk status of breeds</strong></p>\n      </td>\n      <td>\n        <p><strong>Number</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>At risk</p>\n      </td>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>n</mi>\n              </mrow>\n              <mrow>\n                <mi>R</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Not at risk</p>\n      </td>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>n</mi>\n              </mrow>\n              <mrow>\n                <mi>N</mi>\n                <mi>R</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Unknown</p>\n      </td>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>n</mi>\n              </mrow>\n              <mrow>\n                <mi>U</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>All risk classes</p>\n      </td>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mi>n</mi>\n            <mo>=</mo>\n            <msub>\n              <mrow>\n                <mi>n</mi>\n              </mrow>\n              <mrow>\n                <mi>R</mi>\n              </mrow>\n            </msub>\n            <mo>+</mo>\n            <msub>\n              <mrow>\n                <mi>n</mi>\n              </mrow>\n              <mrow>\n                <mi>N</mi>\n                <mi>R</mi>\n              </mrow>\n            </msub>\n            <mo>+</mo>\n            <msub>\n              <mrow>\n                <mi>n</mi>\n              </mrow>\n              <mrow>\n                <mi>U</mi>\n              </mrow>\n            </msub>\n          </math></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>SDG indicator for country i: </strong><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi mathvariant=\"bold-italic\">p</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"bold-italic\">i</mi>\n      </mrow>\n    </msub>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>p</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>n</mi>\n          </mrow>\n          <mrow>\n            <mi>R</mi>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>n</mi>\n          </mrow>\n          <mrow>\n            <mi>R</mi>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>n</mi>\n          </mrow>\n          <mrow>\n            <mi>N</mi>\n            <mi>R</mi>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>Consistency of data uploaded for computation of risk status is automatically checked by DAD-IS (e.g. number of females not exceeding total population size).</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>At breed level</strong></p>\n<p>If no population data are provided for a respective year, it is assumed that the risk status remains the same as for the last year for which population data have been reported. In this case the nature of data is considered to be estimated. However, if the most recent reporting refers to a year more than 10- years before, the risk status is considered &#x201C;unknown&#x201D;. </p>\n<p><strong>&#x2022; At country level</strong></p>\n<p>Country information is considered to be missing if 100% percent of a country&#x2019;s local or transboundary breeds do have risk status &#x201C;unknown&#x201D;, respectively. If 100% of a country&#x2019;s breed risk status values are estimates (see above), the nature of country data is also considered to be an estimate.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>See aggregation rules under 4.g</p>", "REG_AGG__GLOBAL"=>"<p>Aggregated SDG indicator Pj for k countries (with at least one local or transboundary breed with known risk status) in region j with total number of local or transboundary breeds not extinct and with known status in k countries: <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>N</mi>\n    <mo>=</mo>\n    <mrow>\n      <munderover>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi>i</mi>\n          <mo>=</mo>\n          <mn>1</mn>\n        </mrow>\n        <mrow>\n          <mi>k</mi>\n        </mrow>\n      </munderover>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>n</mi>\n          </mrow>\n          <mrow>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mrow>\n  </math> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <mi>j</mi>\n      </mrow>\n    </msub>\n  </math>=<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mrow>\n      <munderover>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi>i</mi>\n          <mo>=</mo>\n          <mn>1</mn>\n        </mrow>\n        <mrow>\n          <mi>k</mi>\n        </mrow>\n      </munderover>\n      <mrow>\n        <mo>(</mo>\n        <msub>\n          <mrow>\n            <mi>p</mi>\n          </mrow>\n          <mrow>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mrow>\n    <mo>&#x2219;</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>n</mi>\n          </mrow>\n          <mrow>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mi>N</mi>\n      </mrow>\n    </mfrac>\n    <mo>)</mo>\n  </math></p>\n<p>Regional and global results are only reported if more than 50% of the countries within the respective region or globally are not missing.</p>", "DOC_METHOD__GLOBAL"=>"<p>Livestock census on breed level or data derived from national herdbooks or national surveys. </p>\n<p>FAO. 2011. Surveying and monitoring of animal genetic resources. FAO Animal Production and Health Guidelines. No. 7. Rome. (available at <a href=\"http://www.fao.org/docrep/014/ba0055e/ba0055e00.htm\">http://www.fao.org/docrep/014/ba0055e/ba0055e00.htm</a>)</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>FAO provides regular training to National Coordinators related to data collection and entering data into the official system, DAD-IS. The indicators itself is automatically calculated in DAD-IS. </p>\n<p>When uploading the data to the DAD-IS, the data consistency is automatically checked and the national coordinators are informed of any unexpected increases or decreases in population size. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Described in section 7 of FAO. 2011. Surveying and monitoring of animal genetic resources. FAO Animal Production and Health Guidelines. No. 7. Rome. (available at <a href=\"http://www.fao.org/docrep/014/ba0055e/ba0055e00.htm\">http://www.fao.org/docrep/014/ba0055e/ba0055e00.htm</a>) </p>\n<p>The guidelines were presented to and endorsed by the Commission on Genetic Resources for Food and Agriculture at its Thirteenth Regular Session in July 2011. </p>\n<p>FAO is responsible for the quality of the internal statistical processes used to compile the published datasets.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Each second year, FAO is organizing a global National Coordinators&#x2019; Workshops to assess and discuss the collection of data the indicator is based on. The indicators itself is automatically calculated in DAD-IS. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are publicly available through DAD-IS (see <a href=\"http://dad.fao.org/\">http://dad.fao.org/</a>).</p>\n<p><strong>Time series:</strong></p>\n<p>DAD-IS data are available since 2000.</p>\n<p><strong>Disaggregation: </strong></p>\n<p>Data are available by country.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://dad.fao.org/\">http://dad.fao.org/</a> </p>\n<p><strong>References: </strong></p>\n<p>FAO. 2013. In vivo conservation of animal genetic resources.</p>\n<p>FAO Animal Production and Health Guidelines. No. 14. Rome. Accessible at <a href=\"http://www.fao.org/docrep/018/i3327e/i3327e.pdf\">http://www.fao.org/docrep/018/i3327e/i3327e.pdf</a></p>", "indicator_sort_order"=>"02-05-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.a.1", "slug"=>"2-a-1", "name"=>"Índice de orientación agrícola para el gasto público", "url"=>"/site/es/2-a-1/", "sort"=>"02aa01", "goal_number"=>"2", "target_number"=>"2.a", "global"=>{"name"=>"Índice de orientación agrícola para el gasto público"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[{"field"=>"Territorio histórico", "value"=>"ES21"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Valor añadido de la Agricultura como porcentaje del PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Índice de orientación agrícola para el gasto público", "indicator_number"=>"2.a.1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_10/opt_1/ti_cuentas-economicas/temas.html", "url_text"=>"Cuentas económicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_489/opt_0/ti_pib-municipal/temas.html", "url_text"=>"PIB Municipal", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Valor añadido de la Agricultura como porcentaje del PIB", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.a- Aumentar, incluso mediante una mayor cooperación internacional, las inversiones en infraestructura rural, investigación y servicios de extensión agrícola, desarrollo tecnológico y bancos de genes de plantas y ganado a fin de mejorar la capacidad de producción agropecuaria en los países en desarrollo, particularmente en los países menos adelantados", "definicion"=>"\nValor Añadido Bruto (VAB) del sector de la agricultura, ganadería y pesca respecto \nal VAB del total de los sectores, a precios corrientes.\n\nEl valor agregado agrícola y el PIB se basan en el Sistema Europeo de Cuentas (SEC-2010). La agricultura se refiere \nal sector de agricultura, silvicultura, pesca y caza (Sección A de la CNAE 2009 o CIIU Rev4)\n", "formula"=>"\n$$PVAB_{agricultura}^{t} = \\frac{VAB_{agricultura}^{t}}{PIB^{t}} \\cdot 100$$\n\ndonde:\n\n$VAB_{agricultura}^{t} =$ valor añadido bruto del sector de la agricultura, ganadería y pesca a precios corrientes en el año $t$\n\n$PIB^{t} =$  producto interior bruto a precios corrientes en el año $t$\n", "desagregacion"=>"Territorio histórico/Comarca/Municipio\n", "observaciones"=>"Los datos difundidos en el PIB Municipal se elaboran mediante estimaciones realizadas en base a la metodología establecida en el Sistema Europeo de Cuentas (SEC-2010).", "periodicidad"=>"Anual", "justificacion_global"=>"Un índice de orientación agrícola (IOA) superior a 1 refleja una mayor orientación hacia \nel sector agrícola, que recibe una mayor proporción del gasto público en relación con su contribución \nal valor añadido económico. \n\nUn IOA inferior a 1 refleja una menor orientación hacia la agricultura, mientras que un IOA igual a 1 refleja neutralidad \nen la orientación de un gobierno hacia el sector agrícola. El gasto público en agricultura incluye el gasto en políticas \ny programas sectoriales; mejora del suelo y control de la degradación del suelo; riego y embalses para uso agrícola; \ngestión de la salud animal, investigación ganadera y capacitación en cría de animales; investigación biológica marina y de \nagua dulce; forestación y otros proyectos forestales; etc. \n\nEl gasto en estas actividades agrícolas ayuda a aumentar la eficiencia del sector, la productividad y \nel crecimiento de los ingresos al aumentar el capital físico o humano y/o reducir las restricciones presupuestarias \nintertemporales. \n\nSin embargo, el sector privado normalmente invierte poco en estas actividades debido a la presencia de fallas del mercado \n(por ejemplo, la naturaleza de bien público de la investigación y el desarrollo; las externalidades positivas \nde las condiciones mejoradas del suelo y el agua; la falta de acceso a crédito competitivo debido a la \ninformación asimétrica entre productores e instituciones financieras, etc.). \n\nDe manera similar, el alto \nriesgo que enfrentan los productores agrícolas, en particular los pequeños agricultores que no pueden \nprotegerse contra el riesgo, a menudo requiere la intervención del gobierno en términos de redistribución \ndel ingreso para apoyar a los pequeños agricultores en dificultades luego de pérdidas de cosechas y \nganado debido a plagas, sequías, inundaciones, fallas de infraestructura o cambios severos de precios.\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.a.1&seriesCode=AG_PRD_AGVAS&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Valor añadido de la agricultura como porcentaje del PIB (%) AG_PRD_AGVAS</a> UNSTATS", "comparabilidad"=>"El indicador disponible es una de las series del indicador de Naciones Unidas, pero no es el indicador principal.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-0A-01.pdf\">Metadatos 2-a-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-21", "national_metadata_updated_date"=>"2025-03-11", "en"=>{"indicador_disponible"=>"Valor añadido de la Agricultura como porcentaje del PIB", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.a- Aumentar, incluso mediante una mayor cooperación internacional, las inversiones en infraestructura rural, investigación y servicios de extensión agrícola, desarrollo tecnológico y bancos de genes de plantas y ganado a fin de mejorar la capacidad de producción agropecuaria en los países en desarrollo, particularmente en los países menos adelantados", "definicion"=>"\nGross Value Added (VAB) of agriculture, livestock and fishing with respect to the GVA \nof the total sectors, at current prices.\n\nAgricultural value added and GDP are based on the European System of Accounts (ESA-2010). \nAgriculture refers to the agriculture, forestry, fishing, and hunting sectors (Section A of \nthe CNAE 2009 or ISIC Rev4).\n", "formula"=>"\n$$PVAB_{agriculture}^{t} = \\frac{VAB_{agriculture}^{t}}{PIB^{t}} \\cdot 100$$\n\nwhere:\n\n$VAB_{agriculture}^{t} =$ gross added value of the agriculture, livestock and fishing sector at current prices in the year $t$\n\n$PIB^{t} =$ gross domestic product at current prices in the year $t$\n", "desagregacion"=>"Province/County/Municipality\n", "observaciones"=>"The data published in the Municipal GDP are compiled using estimates based on the methodology established in the European System of Accounts (ESA-2010).", "periodicidad"=>"Anual", "justificacion_global"=>"\nAn Agriculture Orientation Index (AOI) greater than 1 reflects a higher orientation towards the agriculture \nsector, which receives a higher share of government spending relative to its contribution to economic \nvalue-added.\n\nAn AOI less than 1 reflects a lower orientation to agriculture, while an AOI equal to 1 \nreflects neutrality in a government’s orientation to the agriculture sector. \n\nGovernment spending in agriculture includes spending on sector policies and programs; soil improvement \nand soil degradation control; irrigation and reservoirs for agricultural use; animal health management, \nlivestock research and training in animal husbandry; marine/freshwater biological research; afforestation \nand other forestry projects; etc.\n\nSpending in these agricultural activities helps to increase sector efficiency, productivity and income \ngrowth by increasing physical or human capital and/or reducing inter-temporal budget constraints. \n\nHowever, the private sector typically under-invests in these activities due to the presence of market \nfailure (e.g. the public good nature of research and development; the positive externalities from \nimproved soil and water conditions; lack of access to competitive credit due to asymmetric information \nbetween producers and financial institutions, etc.). \n\nSimilarly, the high risk faced by agricultural producers, particular smallholders unable to hedge \nagainst risk, often requires government intervention in terms of income redistribution to support \nsmallholders in distress following crop failures and livestock loss from pests, droughts, floods, \ninfrastructure failure, or severe price changes.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.a.1&seriesCode=AG_PRD_AGVAS&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Agricultural value added as a percentage of GDP (%) AG_PRD_AGVAS</a> UNSTATS", "comparabilidad"=>"The available indicator is one of the series of the United Nations indicator, but it is not the main indicator.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-0A-01.pdf\">Metadata 2-a-1.pdf</a>", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Valor añadido de la Agricultura como porcentaje del PIB", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.a- Aumentar, incluso mediante una mayor cooperación internacional, las inversiones en infraestructura rural, investigación y servicios de extensión agrícola, desarrollo tecnológico y bancos de genes de plantas y ganado a fin de mejorar la capacidad de producción agropecuaria en los países en desarrollo, particularmente en los países menos adelantados", "definicion"=>"Nekazaritza, abeltzaintza eta arrantza sektorearen balio erantsi gordina (BEG) sektore guztien BEGarekiko, \nuneko prezioetan.\n\nNekazaritzako balio erantsia eta BPGa Europako Kontu Sisteman (KSE-2010) oinarritzen dira. Nekazaritza terminoa \nnekazaritza, basogintza, arrantza eta ehizaren sektoreari dagokio (EJSN 2009ren edo CIIU Ver4ren A atala)\n", "formula"=>"\n$$PVAB_{nekazaritza}^{t} = \\frac{VAB_{nekazaritza}^{t}}{PIB^{t}} \\cdot 100$$\n\nnon:\n\n$VAB_{nekazaritza}^{t} =$ nekazaritza, abeltzaintza eta arrantza sektorearen balio erantsi gordina, uneko prezioetan $t$ urtean\n\n$PIB^{t} =$  barne-produktu gordina, uneko prezioetan $t$ urtean\n", "desagregacion"=>"Lurralde historikoa/Eskualdea/Udalerria\n", "observaciones"=>"Udal BPGan argitaratutako datuak Kontuen Europako Sisteman (KSE-2010) ezarritako metodologian oinarrituta egindako zenbatespenen bidez egiten dira.", "periodicidad"=>"Anual", "justificacion_global"=>"Nekazaritza-orientazioko indizea (NOI) 1 baino altuagoa bada, nekazaritzaren sektorerako orientazioa handiagoa da, hau da, \ngastu publiko handiagoa jasotzen du, balio erantsi ekonomikoari egiten dion ekarpenari lotuta. \n\nNOI 1 baino baxuagoa bada, nekazaritzarako orientazioa baxuagoa da. Eta NOI 1en parekoa bada, neutraltasuna islatzen da \ngobernu batek nekazaritzaren sektorerako duen orientazioan. Nekazaritzako gastu publikoaren barruan sartzen dira sektoreko \npolitika eta programen gaineko gastua; lurzoruaren narriaduraren kontrola eta lurzoruaren hobekuntza; nekazaritzarako \nurtegiak eta ureztatze-sistemak; animalien osasunaren kudeaketa, abeltzaintzaren ikerketa eta animaliak hazteko trebakuntza; \nitsasoko eta ur gezako ikerketa biologikoa; basogintza eta beste baso-proiektu batzuk; eta abar. \n\nNekazaritzako jarduera hauetan egiten den gastua lagungarria izango da sektorearen eraginkortasuna, produktibitatea eta \ndiru-sarreren hazkundea areagotzeko, giza kapitala edo kapital fisikoa handitu egiten delako edota denborarteko \naurrekontu-mugak murriztu. \n\nHala ere, sektore pribatuak normalean gutxi inbertitzen du jarduera hauetan, merkatuan hutsuneak daudelako (adibidez, \nikerketaren eta garapenaren ondasun publikoaren izaera; lurzoruaren eta uraren baldintza hobetuen kanpotasun positiboak; \nkreditu lehiakorrerako sarbiderik eza, ekoizle eta finantza-instituzioen artean informazio asimetrikoa dagoelako, eta abar).\n\nEra berean, nekazaritzako ekoizleek eta, bereziki, arriskuaren aurka babestu ezin diren nekazari txikiek gainditu behar \nduten arrisku altuaren ondorioz sarritan gobernuaren esku hartzea behar da, diru-sarrera birbanatzeko, eta, hala, zailtasunak \ndituzten nekazari gazteak babesteko, besteak beste uzta galdu dutenean, abereak izurriteen biktima izan direnean, lehorteetan, \nuholdeetan, azpiegitura-hutsuneetan eta prezio-aldaketa handiak daudenean. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.a.1&seriesCode=AG_PRD_AGVAS&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Nekazaritzaren balio erantsia BPGren ehuneko gisa (%) AG_PRD_AGVAS</a> UNSTATS", "comparabilidad"=>"Eskuragarri dagoen adierazlea Nazio Batuen adierazlearen serieetako bat da, baina ez da adierazle nagusia.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-0A-01.pdf\">Metadatuak 2-a-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.a: Increase investment, including through enhanced international cooperation, in rural infrastructure, agricultural research and extension services, technology development and plant and livestock gene banks in order to enhance agricultural productive capacity in developing countries, in particular least developed countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.a.1: The agriculture orientation index for government expenditures</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Primary series:</p>\n<p>AG_PRD_AGVAS - Agriculture value added share of GDP [2.a.1]</p>\n<p>Complementary series: </p>\n<p>AG_PRD_ORTIND - Agriculture orientation index for government expenditures [2.a.1]</p>\n<p>AG_XPD_AGSGB - Agriculture share of Government Expenditure [2.a.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicators 17.1.1 and 17.1.2 also apply IMF GFS methodology.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The Agriculture Orientation Index (AOI) for Government Expenditures is defined as the Agriculture share of Government Expenditure, divided by the Agriculture value added share of GDP, where Agriculture refers to the agriculture, forestry, fishing and hunting sector. The measure is a currency-free index, calculated as the ratio of these two shares. National governments are requested to compile Government Expenditures according to the Government Finance Statistics (GFS) and the Classification of the Functions of Government (COFOG), and Agriculture value added share of GDP according to the System of National Accounts (SNA).</p>\n<p><strong>Concepts:</strong></p>\n<p>Agriculture refers to the agriculture, forestry, fishing and hunting sector, or Division A of ISIC Rev 4 (equal to Division A+B of ISIC Rev 3.2). </p>\n<p>Government Expenditure comprise all expense and acquisition of non-financial assets associated with supporting a particular sector, as defined in the Government Finance Statistics Manual (GFSM) 2014 developed by the International Monetary Fund (IMF). NOTE: Transactions in assets and liabilities, such as loans by general government units (disbursement and repayment), are excluded when compiling COFOG data for GFS reporting purposes.</p>\n<p>Government Expenditure are classified according to the Classification of the Functions of Government (COFOG), a classification developed by the Organisation for Economic Co-operation and Development (OECD) and published by the United Nations Statistical Division (UNSD). </p>\n<p>Agriculture value-added and GDP are based on the System of National Accounts (SNA). </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Index </p>\n<p>See 4.c. Method of computation, below.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The Classification of the Functions of Government (COFOG) is a detailed classification of the functions, or socioeconomic objectives, that general government units aim to achieve through various kinds of expenditure. Functions are classified using a three-level scheme, consistent with the International Standard Industrial Classification of All Economic Activities (ISIC), Rev.4. In particular, the scheme includes: </p>\n<ol>\n  <li>10 first-level, or two digit, categories, referred to as divisions, including Economic Affairs (04) and Environmental Protection (05); </li>\n  <li>within each division, 2 or more 3-digit three-digit categories, referred to as groups, such as Agriculture, Forestry, Fishing, and Hunting (042) and Protection of Biodiversity and Landscapes (054); and </li>\n  <li>within each group, one or more four-digit categories, referred to as classes, such as Agriculture (0421), Forestry (0422) and Fishing and hunting (0423), as well as related Research and Development (0482), covering the administration and operation of government agencies engaged in applied research and experimental development related to the sector, including that undertaken by nongovernment bodies, such as research institutes and universities funded by government grants and subsidies.</li>\n</ol>\n<p>The International Monetary Fund (IMF) questionnaire on Government Finance Statistics (GFS) collects annual data on the first two levels (divisions and groups). The FAO questionnaire aims at collecting information on classes, as well as a breakdown of the related expenditure in recurrent and capital expenditures. The three classification levels and the contents of each class are described in the GFSM 2014, accessible at https://www.imf.org/external/np/sta/gfsm/. </p>\n<p>FAOSTAT geographic classification is used to aggregate indicators across country groups (<a href=\"http://www.fao.org/faostat/en/#definitions\">http://www.fao.org/faostat/en/#definitions</a>).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data on government expenditures is collected from countries through an annual questionnaire administered by FAO. These data are not affected by sampling error, given that countries typically compile the questionnaires administered by FAO on the basis of their financial and accounting systems, using administrative information on government expenditures based on the availability and comprehensiveness of source data. For some countries that do not report directly data to FAO, key expenditure aggregates needed to calculate Indicator 2.a.1 are obtained either from the IMF GFS database, from other regional organizations, or from official national governmental websites. </p>\n<p>Data on agriculture value-added and GDP are retrieved from the UN Statistics Division, which provides national accounts estimates for 220 countries and territories.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data for the denominator are annually collected from countries using the FAO questionnaire on Government Expenditure on Agriculture (GEA), developed in collaboration with the IMF., For countries with missing information, data is supplemented with data collected by the IMF, regional organizations or published on official national governmental websites. The official counterpart(s) at country level are, depending on the country, from the national statistics office, the ministry of finance (or other central planning agency), or the ministry of agriculture. Validation and consultation were conducted through various FAO commissions and committees, including its two agricultural statistics commissions in Africa and the Asia and Pacific, its Committee on Agriculture and Livestock Statistics in Latin America and the Caribbean, and its Committee on Agriculture. </p>", "FREQ_COLL__GLOBAL"=>"<p>The t-1 reference year data collection cycle for Government Expenditure on Agriculture (GEA) will start in March/April of year t. Due to the time required to collect, compile and publish national data, countries may experience delays in reporting timely data. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>As the COFOG data is largely compiled annually, this indicator is released every year in March, covering data up to reference year t-2 (for the countries for which data collection, compilation, release is more timely).</p>\n<p> </p>", "DATA_SOURCE__GLOBAL"=>"<p>Ministry of Finance, Central Planning Agency, Central Banks, National Statistics Office, and/or Ministry of Agriculture.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "INST_MANDATE__GLOBAL"=>"<p>Article I of the FAO Constitution requires the Organization to &quot;collect, analyse, interpret and disseminate information relating to nutrition, food and agriculture.&quot; (http://www.fao.org/docrep/x5584e/x5584e00.htm). Member countries reaffirmed this mandate in 2000. Within the FAO&apos;s statistical program of work, member countries endorsed the development of an investment statistics domain, including ongoing work on government expenditure on agriculture, during meetings of three statutory bodies: the Asia and Pacific Commission on Agricultural Statistics (APCAS) held in Vietnam in February 2014; the African Commission on Agricultural Statistics (AFCAS) held in Morocco in December 2013; and the IICA working group on agricultural and livestock statistics for Latin America and the Caribbean, held in Trinidad and Tobago in June 2013. </p>", "RATIONALE__GLOBAL"=>"<p>An Agriculture Orientation Index (AOI) greater than 1 reflects a higher orientation towards the agriculture sector, which receives a higher share of government spending relative to its contribution to economic value-added. An AOI less than 1 reflects a lower orientation to agriculture, while an AOI equal to 1 reflects neutrality in a government&#x2019;s orientation to the agriculture sector.</p>\n<p>Government spending in agriculture includes spending on sector policies and programs; soil improvement and soil degradation control; irrigation and reservoirs for agricultural use; animal health management, livestock research and training in animal husbandry; marine/freshwater biological research; afforestation and other forestry projects; etc.</p>\n<p>Spending in these agricultural activities helps to increase sector efficiency, productivity and income growth by increasing physical or human capital and/or reducing inter-temporal budget constraints. </p>\n<p>However, the private sector typically under-invests in these activities due to the presence of market failure (e.g. the public good nature of research and development; the positive externalities from improved soil and water conditions; lack of access to competitive credit due to asymmetric information between producers and financial institutions, etc.). Similarly, the high risk faced by agricultural producers, particular smallholders unable to hedge against risk, often requires government intervention in terms of income redistribution to support smallholders in distress following crop failures and livestock loss from pests, droughts, floods, infrastructure failure, or severe price changes. </p>\n<p>Government spending in agriculture is essential to address these market failures and the periodic need for income redistribution. This leads to several potential indicators for the SDGs, which include: a) the level of Government Expenditure on Agriculture (GEA); b) the Agriculture share of Government Expenditure, and c) the AOI for Government Expenditures.</p>\n<p>An indicator that measures GEA levels fails to take into account the size of an economy. If two countries, A and B, have the same level of GEA, and the same agriculture contribution to GDP, but country A&#x2019;s economy is 10 times that of country B, setting the same target levels for GEA fails to take economic size into account. </p>\n<p>An indicator that measures the Agriculture share of Government Expenditure fails to take into account the relative contributions of the agricultural sector to a country&#x2019;s GDP. Consider two countries with the same economic size, C and D, where agriculture contributes 2 percent to C&#x2019;s GDP, and 10 per cent to country D&#x2019;s GDP. If total Government Expenditures were equal in both countries, C would experience greater relative investment in Agriculture than D. If total Government Expenditures differed, the result could be magnified or diluted.</p>\n<p>The AOI index takes into account a country&#x2019;s economic size, Agriculture&#x2019;s contribution to GDP, and the total amount of Government Expenditure. While the indicator does not allow setting of a universal and achievable target, it is useful to interpret the AOI in combination with its numerator and denominator separately: the Agriculture share of Government Expenditure and the Agriculture value-added Share of GDP.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Since the numerator of this data is based on financial and accounting systems and administrative sources, there is no confidence interval or standard error associated with government expenditure data. For the denominator, national accounts data typically do not provide any standard error or confidence interval information.</p>\n<p>The key limitation with this indicator is that Consolidated General Government expenditure &#x2013; the best measure for cross-country comparisons &#x2013; is not available for all reporting countries. While most advanced economies &#x2013; and many emerging market economies &#x2013; do report these data, many smaller and/or low-income economies either do not have significant fiscal interventions in agriculture at the state/provincial and local/municipal levels; or do not have adequate source data to compile meaningful general government estimates for each subsector, as relevant. Given that in several countries, significant intervention in agriculture is implemented by sub-national governments, the Indicator 2.a.1 is calculated using the highest level of government available for the reporting country. For some countries, such as India, where the general government sector is defined for fiscal policy purposes as budgetary central government plus state government, the Indicator will take this into account.</p>\n<p>Annex I lists the reporting countries, their M49 code, the latest year for which data are available and the level of government for which data has been reported. The level of government notation used is as follows: GG: Consolidated General Government; CG Consolidated Central Government (excluding Social Security Funds): CGI: Consolidated Central Government (including Social Security Funds); BA: Budgetary Central Government.</p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>A</mi>\n    <mi>O</mi>\n    <mi>I</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>A</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>S</mi>\n        <mi>h</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>G</mi>\n        <mi>o</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>n</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>E</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>A</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>S</mi>\n        <mi>h</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>G</mi>\n        <mi>D</mi>\n        <mi>P</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>A</mi>\n    <mi>g</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>u</mi>\n    <mi>l</mi>\n    <mi>t</mi>\n    <mi>u</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>S</mi>\n    <mi>h</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>G</mi>\n    <mi>o</mi>\n    <mi>v</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>n</mi>\n    <mi>m</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>t</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>E</mi>\n    <mi>x</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>u</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>G</mi>\n        <mi>o</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>n</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>E</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>A</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>G</mi>\n        <mi>o</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>n</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>E</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Agriculture refers to COFOG category 042 (agriculture, forestry, fishing and hunting); and</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>A</mi>\n    <mi>g</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>u</mi>\n    <mi>l</mi>\n    <mi>t</mi>\n    <mi>u</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>v</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>u</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mi>d</mi>\n    <mi>d</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>S</mi>\n    <mi>h</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>G</mi>\n    <mi>D</mi>\n    <mi>P</mi>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>A</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n      </mrow>\n      <mrow>\n        <mi>G</mi>\n        <mi>D</mi>\n        <mi>P</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Agriculture refers to the Division A of ISIC Rev 4 (agriculture, forestry, fishing and hunting), equal to Division A+B of ISIC Rev 3.2.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Countries are asked to validate and update historical questionnaire data that pre-populates their questionnaire. FAO validates data against the historical series, as well as data submitted to IMF, regional organizations and from country&apos;s websites.</p>", "ADJUSTMENT__GLOBAL"=>"<p>FAO revises data only when historical revisions or missing historical data are provided by countries, the IMF or regional organizations or when they become available through the national authorities&#x2019; websites. For example, prefilled questionnaires are sent out with reported data for t-2 through t-5, which countries are asked to review, revise where needed, and - to the extent possible &#x2013; fill-in missing information. Conversion of values into millions is done as well.</p>", "IMPUTATION__GLOBAL"=>"<h5><strong>At country level</strong></h5>\n<p>Missing values of government expenditure in agriculture were forecasted using trends in GDP and 3 to 5 year moving averages of the share of agriculture in total expenditure. Forecasted values are employed to compute regional and global aggregates, but not presented at the national level.</p>\n<h5><strong>At regional and global levels</strong></h5>\n<p>Regional and global aggregates of were based on a mixture of data directly reported by countries (to FAO or IMF) and forecasts of missing values. For time series period, regional and global aggregates are computed on the basis of based on data as reported by countries and interpolations of missing values.</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional estimates are compiled by first separately summing across countries the four individual components of the index: government expenditure on agriculture, total government expenditure, agriculture value-added, and GDP. These are added only for those countries in a region (or globally) for which all components are available, and the index is then calculated for this larger region.</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries are requested to reference the IMF&apos;s Government Finance Statistics Manual (GFSM 2014), particularly Chapter 6 - Annex: Classification of the Functions of Government and Chapter 2 &#x2013; Institutional Units and Sectors, available at <a href=\"https://www.imf.org/external/np/sta/gfsm\">https://www.imf.org/external/np/sta/gfsm</a>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Comparisons of key aggregates reported in both the FAO GEA and IMF GFS questionnaires are periodically conducted in order to ensure consistency.<br></p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The FAO Statistics Quality Assurance Framework is available at: <a href=\"http://www.fao.org/docrep/019/i3664e/i3664e.pdf\">http://www.fao.org/docrep/019/i3664e/i3664e.pdf</a> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The quality of the data may vary considerably among countries, as not all of them apply the COFOG classification. In such cases, FAO seeks to validate reported aggregates against fiscal data published by national authorities&apos; websites. Since 2012, the FAO Statistics Division also fields a detailed annual questionnaire on Government Expenditure on Agriculture that is pre-populated with key major aggregates reported to the IMF or identified by FAO. Where reported details diverge significantly from the pre-populated aggregates, queries are sent to national counterparts, to ensure the methodological quality, objectivity and reliability of the data submitted by countries.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are reported for the highest level of government available (Consolidated general government, consolidated central government or budgetary central government) and are available for about 100 countries on a regular basis. In some cases (for example, India and Pakistan), data may reflect the general government sector as per national norm. That is, budgetary central government combined with state government. </p>\n<p><strong>Time series:</strong></p>\n<p>From 2001 forward</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Since this indicator is based on national accounts data and total government expenditures, it does not allow for disaggregation by demographic characteristics or geographic location. However, where countries report expenditure data for the consolidated general government and it subsectors, disaggregation by level of government is possible.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>When in-country compilation errors are identified and FAO has modified government expenditure data reported by countries, or where errors are found in comparison with the IMF GFS COFOG data or fiscal data published on national authorities&apos; websites after querying to national respondents, there may be some difference between data reported by FAO and unrevised national figures.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>www.fao.org</p>\n<p><strong>References:</strong></p>\n<ul>\n  <li>FAOSTAT domain of Government Expenditure on Agriculture http://www.fao.org/faostat/en/#data/IG;</li>\n  <li>IMF Government Finance Statistics Manual 2014 <br>https://www.imf.org/external/np/sta/gfsm/.</li>\n</ul>\n<p><strong>2.a.1 metadata ANNEX I: Highest Level of Government Available &#x2013; last updated 01 March 2022</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Latest year</strong></p>\n      </td>\n      <td>\n        <p><strong>M49 code</strong></p>\n      </td>\n      <td>\n        <p><strong>Area</strong></p>\n      </td>\n      <td>\n        <p><strong>Level of government </strong></p>\n      </td>\n      <td>\n        <p><strong>Latest year</strong></p>\n      </td>\n      <td>\n        <p><strong>M49 code</strong></p>\n      </td>\n      <td>\n        <p><strong>Area</strong></p>\n      </td>\n      <td>\n        <p><strong>Level of government </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2017</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>Afghanistan</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>214</p>\n      </td>\n      <td>\n        <p>Dominican Republic</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n      <td>\n        <p>Albania</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>218</p>\n      </td>\n      <td>\n        <p>Ecuador</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2018</p>\n      </td>\n      <td>\n        <p>12</p>\n      </td>\n      <td>\n        <p>Algeria</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>818</p>\n      </td>\n      <td>\n        <p>Egypt</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>24</p>\n      </td>\n      <td>\n        <p>Angola</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>222</p>\n      </td>\n      <td>\n        <p>El Salvador</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>28</p>\n      </td>\n      <td>\n        <p>Antigua and Barbuda</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>226</p>\n      </td>\n      <td>\n        <p>Equatorial Guinea</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>32</p>\n      </td>\n      <td>\n        <p>Argentina</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>233</p>\n      </td>\n      <td>\n        <p>Estonia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>51</p>\n      </td>\n      <td>\n        <p>Armenia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2018</p>\n      </td>\n      <td>\n        <p>748</p>\n      </td>\n      <td>\n        <p>Eswatini</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>36</p>\n      </td>\n      <td>\n        <p>Australia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>231</p>\n      </td>\n      <td>\n        <p>Ethiopia</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>40</p>\n      </td>\n      <td>\n        <p>Austria</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>242</p>\n      </td>\n      <td>\n        <p>Fiji</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>31</p>\n      </td>\n      <td>\n        <p>Azerbaijan</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>246</p>\n      </td>\n      <td>\n        <p>Finland</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>44</p>\n      </td>\n      <td>\n        <p>Bahamas</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>250</p>\n      </td>\n      <td>\n        <p>France</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>48</p>\n      </td>\n      <td>\n        <p>Bahrain</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>270</p>\n      </td>\n      <td>\n        <p>Gambia</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2016</p>\n      </td>\n      <td>\n        <p>50</p>\n      </td>\n      <td>\n        <p>Bangladesh</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>268</p>\n      </td>\n      <td>\n        <p>Georgia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2005</p>\n      </td>\n      <td>\n        <p>52</p>\n      </td>\n      <td>\n        <p>Barbados</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>276</p>\n      </td>\n      <td>\n        <p>Germany</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>112</p>\n      </td>\n      <td>\n        <p>Belarus</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>288</p>\n      </td>\n      <td>\n        <p>Ghana</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>56</p>\n      </td>\n      <td>\n        <p>Belgium</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>300</p>\n      </td>\n      <td>\n        <p>Greece</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>84</p>\n      </td>\n      <td>\n        <p>Belize</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>308</p>\n      </td>\n      <td>\n        <p>Grenada</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>204</p>\n      </td>\n      <td>\n        <p>Benin</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>320</p>\n      </td>\n      <td>\n        <p>Guatemala</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>64</p>\n      </td>\n      <td>\n        <p>Bhutan</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>324</p>\n      </td>\n      <td>\n        <p>Guinea</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2014</p>\n      </td>\n      <td>\n        <p>68</p>\n      </td>\n      <td>\n        <p>Bolivia (Plurinational State of)</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2017</p>\n      </td>\n      <td>\n        <p>624</p>\n      </td>\n      <td>\n        <p>Guinea-Bissau</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>72</p>\n      </td>\n      <td>\n        <p>Botswana</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>328</p>\n      </td>\n      <td>\n        <p>Guyana</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>76</p>\n      </td>\n      <td>\n        <p>Brazil</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>340</p>\n      </td>\n      <td>\n        <p>Honduras</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>100</p>\n      </td>\n      <td>\n        <p>Bulgaria</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>348</p>\n      </td>\n      <td>\n        <p>Hungary</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>854</p>\n      </td>\n      <td>\n        <p>Burkina Faso</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>352</p>\n      </td>\n      <td>\n        <p>Iceland</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>108</p>\n      </td>\n      <td>\n        <p>Burundi</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>356</p>\n      </td>\n      <td>\n        <p>India</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>132</p>\n      </td>\n      <td>\n        <p>Cabo Verde</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>360</p>\n      </td>\n      <td>\n        <p>Indonesia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>124</p>\n      </td>\n      <td>\n        <p>Canada</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2009</p>\n      </td>\n      <td>\n        <p>364</p>\n      </td>\n      <td>\n        <p>Iran (Islamic Republic of)</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>140</p>\n      </td>\n      <td>\n        <p>Central African Republic</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>372</p>\n      </td>\n      <td>\n        <p>Ireland</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>152</p>\n      </td>\n      <td>\n        <p>Chile</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>376</p>\n      </td>\n      <td>\n        <p>Israel</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>156</p>\n      </td>\n      <td>\n        <p>China</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>380</p>\n      </td>\n      <td>\n        <p>Italy</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>344</p>\n      </td>\n      <td>\n        <p>China, Hong Kong SAR</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>388</p>\n      </td>\n      <td>\n        <p>Jamaica</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>170</p>\n      </td>\n      <td>\n        <p>Colombia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>392</p>\n      </td>\n      <td>\n        <p>Japan</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2018</p>\n      </td>\n      <td>\n        <p>178</p>\n      </td>\n      <td>\n        <p>Congo</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>400</p>\n      </td>\n      <td>\n        <p>Jordan</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>184</p>\n      </td>\n      <td>\n        <p>Cook Islands</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>398</p>\n      </td>\n      <td>\n        <p>Kazakhstan</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>188</p>\n      </td>\n      <td>\n        <p>Costa Rica</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>404</p>\n      </td>\n      <td>\n        <p>Kenya</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>384</p>\n      </td>\n      <td>\n        <p>C&#xF4;te d&apos;Ivoire</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>412</p>\n      </td>\n      <td>\n        <p>Kosovo (Serbia)</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>191</p>\n      </td>\n      <td>\n        <p>Croatia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>414</p>\n      </td>\n      <td>\n        <p>Kuwait</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>192</p>\n      </td>\n      <td>\n        <p>Cuba</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>417</p>\n      </td>\n      <td>\n        <p>Kyrgyzstan</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>196</p>\n      </td>\n      <td>\n        <p>Cyprus</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>418</p>\n      </td>\n      <td>\n        <p>Lao PDR</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>203</p>\n      </td>\n      <td>\n        <p>Czechia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>428</p>\n      </td>\n      <td>\n        <p>Latvia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>180</p>\n      </td>\n      <td>\n        <p>Dem. Rep. of the Congo</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>422</p>\n      </td>\n      <td>\n        <p>Lebanon</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>208</p>\n      </td>\n      <td>\n        <p>Denmark</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>426</p>\n      </td>\n      <td>\n        <p>Lesotho</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>212</p>\n      </td>\n      <td>\n        <p>Dominica</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>430</p>\n      </td>\n      <td>\n        <p>Liberia</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Latest year</strong></p>\n      </td>\n      <td>\n        <p><strong>M49 code</strong></p>\n      </td>\n      <td>\n        <p><strong>Area</strong></p>\n      </td>\n      <td>\n        <p><strong>Level of government </strong></p>\n      </td>\n      <td>\n        <p><strong>Latest year</strong></p>\n      </td>\n      <td>\n        <p><strong>M49 code</strong></p>\n      </td>\n      <td>\n        <p><strong>Area</strong></p>\n      </td>\n      <td>\n        <p><strong>Level of government </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>440</p>\n      </td>\n      <td>\n        <p>Lithuania</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>662</p>\n      </td>\n      <td>\n        <p>Saint Lucia</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>442</p>\n      </td>\n      <td>\n        <p>Luxembourg</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>670</p>\n      </td>\n      <td>\n        <p>Saint Vincent and the Grenadines</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>450</p>\n      </td>\n      <td>\n        <p>Madagascar</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>882</p>\n      </td>\n      <td>\n        <p>Samoa</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>454</p>\n      </td>\n      <td>\n        <p>Malawi</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>678</p>\n      </td>\n      <td>\n        <p>Sao Tome and Principe</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>458</p>\n      </td>\n      <td>\n        <p>Malaysia</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>682</p>\n      </td>\n      <td>\n        <p>Saudi Arabia</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2018</p>\n      </td>\n      <td>\n        <p>462</p>\n      </td>\n      <td>\n        <p>Maldives</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>686</p>\n      </td>\n      <td>\n        <p>Senegal</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>466</p>\n      </td>\n      <td>\n        <p>Mali</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>688</p>\n      </td>\n      <td>\n        <p>Serbia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>470</p>\n      </td>\n      <td>\n        <p>Malta</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>690</p>\n      </td>\n      <td>\n        <p>Seychelles</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2018</p>\n      </td>\n      <td>\n        <p>584</p>\n      </td>\n      <td>\n        <p>Marshall Islands</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>694</p>\n      </td>\n      <td>\n        <p>Sierra Leone</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>478</p>\n      </td>\n      <td>\n        <p>Mauritania</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>702</p>\n      </td>\n      <td>\n        <p>Singapore</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>480</p>\n      </td>\n      <td>\n        <p>Mauritius</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>703</p>\n      </td>\n      <td>\n        <p>Slovakia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>484</p>\n      </td>\n      <td>\n        <p>Mexico</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>705</p>\n      </td>\n      <td>\n        <p>Slovenia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>583</p>\n      </td>\n      <td>\n        <p>Micronesia (Federated States of)</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>90</p>\n      </td>\n      <td>\n        <p>Solomon Islands</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>496</p>\n      </td>\n      <td>\n        <p>Mongolia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>706</p>\n      </td>\n      <td>\n        <p>Somalia</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2015</p>\n      </td>\n      <td>\n        <p>499</p>\n      </td>\n      <td>\n        <p>Montenegro</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>710</p>\n      </td>\n      <td>\n        <p>South Africa</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>504</p>\n      </td>\n      <td>\n        <p>Morocco</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>728</p>\n      </td>\n      <td>\n        <p>South Sudan</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>508</p>\n      </td>\n      <td>\n        <p>Mozambique</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>724</p>\n      </td>\n      <td>\n        <p>Spain</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>104</p>\n      </td>\n      <td>\n        <p>Myanmar</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>144</p>\n      </td>\n      <td>\n        <p>Sri Lanka</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>516</p>\n      </td>\n      <td>\n        <p>Namibia</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>275</p>\n      </td>\n      <td>\n        <p>State of Palestine</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>524</p>\n      </td>\n      <td>\n        <p>Nepal</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2018</p>\n      </td>\n      <td>\n        <p>729</p>\n      </td>\n      <td>\n        <p>Sudan</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>528</p>\n      </td>\n      <td>\n        <p>Netherlands</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>740</p>\n      </td>\n      <td>\n        <p>Suriname</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>554</p>\n      </td>\n      <td>\n        <p>New Zealand</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>752</p>\n      </td>\n      <td>\n        <p>Sweden</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>558</p>\n      </td>\n      <td>\n        <p>Nicaragua</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>756</p>\n      </td>\n      <td>\n        <p>Switzerland</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>562</p>\n      </td>\n      <td>\n        <p>Niger</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>762</p>\n      </td>\n      <td>\n        <p>Tajikistan</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>566</p>\n      </td>\n      <td>\n        <p>Nigeria</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>764</p>\n      </td>\n      <td>\n        <p>Thailand</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>807</p>\n      </td>\n      <td>\n        <p>North Macedonia</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>626</p>\n      </td>\n      <td>\n        <p>Timor-Leste</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>578</p>\n      </td>\n      <td>\n        <p>Norway</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2018</p>\n      </td>\n      <td>\n        <p>768</p>\n      </td>\n      <td>\n        <p>Togo</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>512</p>\n      </td>\n      <td>\n        <p>Oman</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>780</p>\n      </td>\n      <td>\n        <p>Trinidad and Tobago</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>586</p>\n      </td>\n      <td>\n        <p>Pakistan</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2017</p>\n      </td>\n      <td>\n        <p>788</p>\n      </td>\n      <td>\n        <p>Tunisia</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2018</p>\n      </td>\n      <td>\n        <p>585</p>\n      </td>\n      <td>\n        <p>Palau</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>792</p>\n      </td>\n      <td>\n        <p>Turkey</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2018</p>\n      </td>\n      <td>\n        <p>591</p>\n      </td>\n      <td>\n        <p>Panama</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>800</p>\n      </td>\n      <td>\n        <p>Uganda</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>598</p>\n      </td>\n      <td>\n        <p>Papua New Guinea</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>804</p>\n      </td>\n      <td>\n        <p>Ukraine</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>600</p>\n      </td>\n      <td>\n        <p>Paraguay</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>784</p>\n      </td>\n      <td>\n        <p>United Arab Emirates</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>604</p>\n      </td>\n      <td>\n        <p>Peru</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>826</p>\n      </td>\n      <td>\n        <p>UK of Great Britain and Northern Ireland</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>608</p>\n      </td>\n      <td>\n        <p>Philippines</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>834</p>\n      </td>\n      <td>\n        <p>United Republic of Tanzania</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>616</p>\n      </td>\n      <td>\n        <p>Poland</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>840</p>\n      </td>\n      <td>\n        <p>United States of America</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>620</p>\n      </td>\n      <td>\n        <p>Portugal</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>858</p>\n      </td>\n      <td>\n        <p>Uruguay</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2005</p>\n      </td>\n      <td>\n        <p>634</p>\n      </td>\n      <td>\n        <p>Qatar</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>860</p>\n      </td>\n      <td>\n        <p>Uzbekistan</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>410</p>\n      </td>\n      <td>\n        <p>Republic of Korea</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>548</p>\n      </td>\n      <td>\n        <p>Vanuatu</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>498</p>\n      </td>\n      <td>\n        <p>Republic of Moldova</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2014</p>\n      </td>\n      <td>\n        <p>862</p>\n      </td>\n      <td>\n        <p>Venezuela (Bolivarian Republic of)</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>642</p>\n      </td>\n      <td>\n        <p>Romania</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>704</p>\n      </td>\n      <td>\n        <p>Viet Nam</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>643</p>\n      </td>\n      <td>\n        <p>Russian Federation</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2014</p>\n      </td>\n      <td>\n        <p>887</p>\n      </td>\n      <td>\n        <p>Yemen</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>646</p>\n      </td>\n      <td>\n        <p>Rwanda</p>\n      </td>\n      <td>\n        <p>GG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>894</p>\n      </td>\n      <td>\n        <p>Zambia</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2019</p>\n      </td>\n      <td>\n        <p>659</p>\n      </td>\n      <td>\n        <p>Saint Kitts and Nevis</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n      <td>\n        <p>716</p>\n      </td>\n      <td>\n        <p>Zimbabwe</p>\n      </td>\n      <td>\n        <p>BA</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "indicator_sort_order"=>"02-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"2.a.2", "slug"=>"2-a-2", "name"=>"Total de corrientes oficiales de recursos (asistencia oficial para el desarrollo más otras corrientes oficiales) destinado al sector agrícola", "url"=>"/site/es/2-a-2/", "sort"=>"02aa02", "goal_number"=>"2", "target_number"=>"2.a", "global"=>{"name"=>"Total de corrientes oficiales de recursos (asistencia oficial para el desarrollo más otras corrientes oficiales) destinado al sector agrícola"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Total de corrientes oficiales de recursos (asistencia oficial para el desarrollo más otras corrientes oficiales) destinado al sector agrícola", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Total de corrientes oficiales de recursos (asistencia oficial para el desarrollo más otras corrientes oficiales) destinado al sector agrícola", "indicator_number"=>"2.a.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"Los flujos totales de Ayuda Oficial al Desarrollo (AOD) y otros \nflujos oficiales (OFO) a los países en desarrollo cuantifican \nel esfuerzo público (excluidos los créditos a las exportaciones) que los \ndonantes proporcionan a los países en desarrollo para la agricultura.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-0a-02.pdf\">Metadatos 2-a-2.pdf (solo en inglés)</a>", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"\nTotal ODA and OOF flows to developing countries quantify the public \neffort (excluding export credits) that donors provide to developing \ncountries for agriculture.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-0a-02.pdf\">Metadata 2-a-2.pdf</a>"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"Garapen-bidean dauden herrialdetara egiten diren Garapenerako Laguntza Ofizialaren (GLO) fluxuek eta beste \nfluxu ofizial batzuek (BFO) zenbatesten dute emaileek garapen-bidean dauden herrialdeei nekazaritzarako ematen \ndieten ahalegin publikoa (esportazioetarako kredituak kanpo). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-0a-02.pdf\">Metadatuak 2-a-2.pdf</a> (ingelesez bakarrik) "}, "national_metadata_updated_date"=>"2025-03-11", "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.a: Increase investment, including through enhanced international cooperation, in rural infrastructure, agricultural research and extension services, technology development and plant and livestock gene banks in order to enhance agricultural productive capacity in developing countries, in particular least developed countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.a.2: Total official flows (official development assistance plus other official flows) to the agriculture sector</p>", "META_LAST_UPDATE__GLOBAL"=>"2017-07-09", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Other ODA indicators</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Gross disbursements of total ODA and other official flows from all donors to the agriculture sector.</p>\n<p><strong>Concepts:</strong></p>\n<p>ODA: The DAC defines ODA as &#x201C;those flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are </p>\n<ol>\n  <li>provided by official agencies, including state and local governments, or by their executive agencies; and </li>\n  <li>each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and</li>\n</ol>\n<p>is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent). (See <a href=\"http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm\">http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm</a>)</p>\n<p>Other official flows (OOF): Other official flows (excluding officially supported export credits) are defined as transactions by the official sector which do not meet the conditions for eligibility as ODA, either because they are not primarily aimed at development, or because they are not sufficiently concessional.</p>\n<p>(See <a href=\"http://www.oecd.org/dac/stats/documentupload/DCDDAC(2016)3FINAL.pdf\">http://www.oecd.org/dac/stats/documentupload/DCDDAC(2016)3FINAL.pdf</a>, Para 24).</p>\n<p>The agriculture sector is as defined by the DAC and comprises all CRS sector codes in the 311 series (see here: http://www.oecd.org/dac/stats/purposecodessectorclassification.htm)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The OECD/DAC has been collecting data on official and private resource flows from 1960 at an aggregate level and 1973 at an activity level through the Creditor Reporting System (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements). </p>\n<p>The data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm). </p>\n<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.</p>", "COLL_METHOD__GLOBAL"=>"<p>A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are published on an annual basis in December for flows in the previous year.</p>\n<p>Detailed 2015 flows will be published in December 2016.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>December 2016. </p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.</p>", "COMPILING_ORG__GLOBAL"=>"<p>OECD</p>", "RATIONALE__GLOBAL"=>"<p>Total ODA and OOF flows to developing countries quantify the public effort (excluding export credits) that donors provide to developing countries for agriculture.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data in the Creditor Reporting System are available from 1973. However, the data coverage is considered complete since 1995 for commitments at an activity level and 2002 for disbursements.</p>", "DATA_COMP__GLOBAL"=>"<p>The sum of ODA and OOF flows from all donors to developing countries in the agriculture sector.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Due to high quality of reporting, no estimates are produced for missing data.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional figures are based on the sum of ODA and OOF flows to the agriculture sector.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>On a recipient basis for all developing countries eligible for ODA.</p>\n<p><strong>Time series:</strong></p>\n<p>Data available since 1973 on an annual (calendar) basis</p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator can be disaggregated by type of flow (ODA or OOF), by donor, recipient country, type of finance, type of aid (project agriculture sub-sector) etc.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>www.oecd.org/dac/stats</p>\n<p><strong>References:</strong></p>\n<p>See all links here: <a href=\"http://www.oecd.org/dac/stats/methodology.htm\">http://www.oecd.org/dac/stats/methodology.htm</a></p>", "indicator_sort_order"=>"02-0a-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.b.1", "slug"=>"2-b-1", "name"=>"Subsidios a la exportación de productos agropecuarios", "url"=>"/site/es/2-b-1/", "sort"=>"02bb01", "goal_number"=>"2", "target_number"=>"2.b", "global"=>{"name"=>"Subsidios a la exportación de productos agropecuarios"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Subsidios a la exportación de productos agropecuarios", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Subsidios a la exportación de productos agropecuarios", "indicator_number"=>"2.b.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"El propósito de este indicador es proporcionar información detallada \nsobre el nivel de subvenciones a la exportación aplicadas anualmente por \nproducto o grupo de productos, según lo notificado por los Miembros de la OMC.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-0b-01.pdf\">Metadatos 2-b-1.pdf (solo en inglés)</a>", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"\nThe purpose of this indicator is to give detailed information on \nthe level of export subsidies applied annually per product or group \nof products, as notified by WTO Members.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-0b-01.pdf\">Metadata 2-b-1.pdf</a>"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "justificacion_global"=>"Adierazle honen asmoa da produktuko edo produktu-taldeko urtero aplikatutako esportazioetarako \ndirulaguntzen mailari buruzko informazio zehatza ematea, Merkataritzako Mundu Erakundeko kideek \njakinarazitakoaren arabera. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-0b-01.pdf\">Metadatuak 2-b-1.pdf</a> (ingelesez bakarrik) "}, "national_metadata_updated_date"=>"2025-03-11", "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.b: Correct and prevent trade restrictions and distortions in world agricultural markets, including through the parallel elimination of all forms of agricultural export subsidies and all export measures with equivalent effect, in accordance with the mandate of the Doha Development Round</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.b.1: Agricultural export subsidies</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>AG_PRD_XSUBDY - Agricultural export subsidies [2.b.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Targets 17.10 and 17.11</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>The World Trade Organization (WTO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>The World Trade Organization (WTO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Agricultural export subsidies are defined as export subsidies budgetary outlays and quantities as notified by WTO Members in Tables ES:1 and supporting Tables ES:2 (following templates in document G/AG/2 dated 30 June 1995).</p>\n<p>Data cover:</p>\n<p>&#x2022; Notifications by WTO Members with export subsidy reduction commitments included in part IV of their Schedules;</p>\n<p>&#x2022; Notifications of export subsidies applied by developing WTO country Members pursuant to the provisions of article 9.4 of the Agreement on Agriculture.</p>\n<p>Notifications by WTO Members indicating the absence of use of export subsidies are not included in the indicator series. The dataset therefore does not differentiate between WTO Members not having provided the notifications on export subsidies and WTO Members having notified the absence of use of such export subsidies. </p>\n<p>Budgetary outlays and quantities are expressed in a currency (national or other) and in quantity units as per Member&apos;s notification practices. For Members with export subsidy reduction commitments included in part IV of their Schedules, the currency used in the notifications is similar to the one used in the Schedules.</p>\n<p>Data are available by country and by products or groups of products, according to Members&apos; schedules for Members with export subsidy reduction commitments included in part IV of their Schedules and according to Member&apos;s notification practices in the case of developing country Members using export subsidies under the provisions of article 9.4 of the Agreement on Agriculture.&quot;</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Millions of current United States dollars</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The sources of data are WTO Members&apos; notifications in their Table ES:1 and supporting table ES:2 notifications, pursuant to the notification requirements and formats adopted by the WTO Committee on Agriculture and contained in document G/AG/2.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are received by the WTO through WTO members&apos; notifications. Data collection methods at the national level are not known by the WTO </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are collected as they are received through WTO Members&apos; notification submissions. All WTO Members are required to provide annual notifications on their use of export subsidies, which are to be submitted no later than 120 days or 30 days (see WTO document G/AG/2 for detailed rules) following the end of the year in question.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Cf. above</p>", "DATA_SOURCE__GLOBAL"=>"<p>WTO Members</p>\n<p>The WTO is receiving WTO Members notifications and compiling the information contained in these notifications to report on this indicator.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Information on institutions producing the data at national level not available at the WTO </p>", "INST_MANDATE__GLOBAL"=>"<p>WTO Agreement on Agriculture and WTO Decision G/AG/2</p>", "RATIONALE__GLOBAL"=>"<p>The purpose of this indicator is to give detailed information on the level of export subsidies applied annually per product or group of products, as notified by WTO Members.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The quality of the indicator depends on WTO Members&apos; timeliness and accuracy of their notifications.</p>", "DATA_COMP__GLOBAL"=>"<p>The country level data come directly from Members&apos; notifications to the WTO and are not subject to any computation by the WTO. Each WTO Member collects data following his own national practice to prepare his notification.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Not applicable</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Values are missing when a WTO Member has not submitted their notification. Missing values cannot be estimated.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Not applicable </p>", "REG_AGG__GLOBAL"=>"<p>The WTO does not calculate regional aggregates.</p>\n<p>An overall global indicator measuring the total annual applied export subsidies budgetary outlays is calculated by summing all the available data after having converted them into a single currency (US$). </p>", "DOC_METHOD__GLOBAL"=>"<p>Detailed guidelines available to countries to fill-in their notifications can be found in https://www.wto.org/english/tratop_e/agric_e/ag_notif_e.pdf</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Agriculture information management tool to permit on-line notification by WTO Members and facilitate retrieval of notifications. Available on https://agims.wto.org/</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are available on a WTO Member country by country basis, based on WTO Members&apos; notifications.</p>\n<p>Contrary to the information for Members with export subsidy reduction commitments that is available for all notified years, information for developing country Members using export subsidies, pursuant to the provisions of article 9.4 of the Agreement on Agriculture is available only for the years during which these export subsidies were used.</p>\n<p>The WTO Secretariat closely monitors Members&apos; compliance with their notification requirements, including on export subsidies. </p>\n<p>The table listing the notifications on export subsidies received from WTO Members can be found under section 2.4 in the WTO document series <a href=\"https://docs.wto.org/dol2festaff/Pages/FE_Search/FE_S_S006.aspx?MetaCollection=WTO&amp;SymbolList=G%2fAG%2fGEN%2f86%2f*&amp;Language=ENGLISH&amp;SearchPage=FE_S_S001&amp;languageUIChanged=true\">G/AG/GEN/86</a> .</p>\n<p><strong>Time series:</strong></p>\n<p>Since 1995 </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator gives country and product based information on the level of applied export subsidies, both in terms of budgetary outlays and quantities.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The WTO does not estimate data. Only data contained in WTO Members&apos; notifications are used. Therefore, there is no difference between country produced data and data available at the WTO.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.wto.org\">www.wto.org</a> </p>\n<p><strong>References:</strong></p>\n<p><a href=\"https://www.wto.org/english/tratop_e/agric_e/transparency_toolkit_e.htm\">https://www.wto.org/english/tratop_e/agric_e/transparency_toolkit_e.htm</a> </p>", "indicator_sort_order"=>"02-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"2.c.1", "slug"=>"2-c-1", "name"=>"Indicador de anomalías en los precios de los alimentos", "url"=>"/site/es/2-c-1/", "sort"=>"02cc01", "goal_number"=>"2", "target_number"=>"2.c", "global"=>{"name"=>"Indicador de anomalías en los precios de los alimentos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Índice de precios de los alimentos", "graph_titles"=>[], "graph_type"=>"bar", "indicator_name"=>"Indicador de anomalías en los precios de los alimentos", "indicator_number"=>"2.c.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Meta:</b> Limitar la extrema volatilidad de los precios de los alimentos", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Mensual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176802&menu=ultiDatos&idp=1254735976607", "url_text"=>"Índice de precios de consumo", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Índice de precios de los alimentos", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.c Adoptar medidas para asegurar el buen funcionamiento de los mercados de productos básicos alimentarios y sus derivados y facilitar el acceso oportuno a la información sobre los mercados, incluso sobre las reservas de alimentos, a fin de ayudar a limitar la extrema volatilidad de los precios de los alimentos", "definicion"=>"\nEste indicador se compone de dos series temporales:\n\n<b> - Indicador de anomalías en los precios de los alimentos</b>\n<b> - Índice de precios de consumo de los alimentos</b>\n", "formula"=>"\n<b>Indicador de anomalías en los precios de los alimentos</b>\n\n$$IFPA^{t} = \\frac{1}{12} \\cdot \\sum_{m=1}^{12} IFPA_{m}^{t}$$\n\ndonde:\n\n$IFPA_{m}^{t} $= indicador de anomalías en los precios de los alimentos del mes $m$ del año $t$ \n\n\n<br>\n\n<b>Índice de precios de consumo de los alimentos</b>\n\n$$IPC_{alimentos}^{t} = \\frac{1}{12} \\cdot \\sum_{m=1}^{12} IPC_{alimentos, m}^{t}$$\n\ndonde:\n\n$IPC_{alimentos, m}^{t}$ = índice de precios de consumo de los alimentos del mes $m$ del año $t$\n", "desagregacion"=>"Productos: alimentos (todos), cereales y derivados, pan, huevos, leche\naceites y grasas, frutas frescas y legumbres y hortalizas frescas\n\nTerritorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"\nLa serie \"Índice de precios de consumo de los alimentos\" está calculada utilizando una  metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador de anomalías de los precios de los alimentos (IFPA) \nidentifica los precios de mercado que son anormalmente altos. La IFPA \nse basa en una tasa de crecimiento compuesta ponderada que representa el crecimiento \nde los precios tanto dentro del año como a lo largo del año.\n\nEl indicador evalúa directamente \nel crecimiento de los precios durante un mes particular durante muchos años, teniendo \nen cuenta la estacionalidad en los mercados agrícolas y la inflación, lo que permite \nresponder a la pregunta de si un cambio en el precio es anormal o no para un período determinado.\n\nEl indicador es sólo una guía para comprender la dinámica del mercado. \nComo tal, no se puede confiar en él como único elemento para determinar si el \nprecio de un alimento en un mercado particular en un momento dado es anormalmente\n alto o bajo debido a los efectos directos de las políticas locales. Los resultados \ndeben sopesarse con otra información disponible sobre los fundamentos del mercado, \nel contexto macroeconómico y los shocks externos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.c.1&seriesCode=AG_FPA_HMFP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ABNORMAL\">Proporción de países que registran precios de los alimentos anormalmente altos o moderadamente altos, según el Indicador de anomalías de los precios de los alimentos (%) AG_FPA_HMFP</a> UNSTATS", "comparabilidad"=>"La serie \"Indicador de anomalías de los precios de los alimentos\" cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-0C-01.pdf\">MetadatOS 2-c-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-13", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Índice de precios de los alimentos", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.c Adoptar medidas para asegurar el buen funcionamiento de los mercados de productos básicos alimentarios y sus derivados y facilitar el acceso oportuno a la información sobre los mercados, incluso sobre las reservas de alimentos, a fin de ayudar a limitar la extrema volatilidad de los precios de los alimentos", "definicion"=>"This indicator is composed of two time series:\n\n<b>- Indicator of Food Price Anomalies (IFPA)</b> \n<b>- Consumer Food Price Index</b> \n", "formula"=>"\n<b>Indicator of Food Price Anomalies (IFPA)</b> \n\n$$IFPA^{t} = \\frac{1}{12} \\cdot \\sum_{m=1}^{12} IFPA_{m}^{t}$$ \n\nwhere:\n\n$IFPA_{m}^{t}$ = food price anomaly indicator for month $m$ of year $t$ \n\n<br>\n\n<b>Consumer Food Price Index</b> \n\n$$IPC_{food}^{t} = \\frac{1}{12} \\cdot \\sum_{m=1}^{12} IPC_{food,m}^{t}$$ \n\nwhere: \n\n$IPC_{food,m}^{t}$ = consumer price index for food in month $m$ of year $t$ \n", "desagregacion"=>"Products: food (all), cereals and derivatives, bread, eggs, milk, oils and fats, fresh fruits and vegetables\n\nProvince\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe Food Price Anomaly Indicator (FPAI) identifies abnormally high market prices. \nThe FPIAI is based on a weighted compound growth rate that represents both intra- \nand year-over-year price growth.\n\nThe indicator directly assesses price growth during a particular month over many years, \ntaking into account seasonality in agricultural markets and inflation, thereby answering \nthe question of whether a price change is abnormal for a given period.\n\nThe indicator is only a guide to understanding market dynamics. As such, it cannot be relied \nupon as the sole indicator to determine whether the price of a food product in a particular \nmarket at a given time is abnormally high or low due to the direct effects of local policies. \nThe results must be weighed against other available information on market fundamentals, the \nmacroeconomic context, and external shocks.\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.c.1&seriesCode=AG_FPA_HMFP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ABNORMAL\">Proportion of countries experiencing abnormally high or moderately high food prices, according to the Food Price Anomaly Indicator (%) AG_FPA_HMFP</a> UNSTATS", "comparabilidad"=>"The available indicator does not comply with the UN indicator metadata, but provides  complementary information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-0C-01.pdf\">Metadata 2-c-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Índice de precios de los alimentos", "objetivo_global"=>"2- Poner fin al hambre, lograr la seguridad alimentaria y la mejora de la nutrición y promover la agricultura sostenible", "meta_global"=>"2.c Adoptar medidas para asegurar el buen funcionamiento de los mercados de productos básicos alimentarios y sus derivados y facilitar el acceso oportuno a la información sobre los mercados, incluso sobre las reservas de alimentos, a fin de ayudar a limitar la extrema volatilidad de los precios de los alimentos", "definicion"=>"Adierazle honek bi serie ditu:\n\n <b> - Elikagaien prezioen anomalien adierazlea</b> \n <b> - Elikagaien kontsumo-prezioen indizea</b> \n", "formula"=>"\n<b>Elikagaien prezioen anomalien adierazlea</b>\n\n$$IFPA^{t} = \\frac{1}{12} \\cdot \\sum_{m=1}^{12} IFPA_{m}^{t}$$\n\nnon:\n\n$IFPA_{m}^{t} $= elikagaien prezioen anomalien adierazlea $t$ urteko $m$ hilean  \n\n<br>\n\n<b>Elikagaien kontsumo-prezioen indizea</b>\n\n$$IPC_ {elikagaiak}^{t} =\\frac{1}{12} \\cdot \\sum_{m=1}^{12} IPC_{elikagaiak,m}^{t}$$\n\nnon:\n\n$IPC_{elikagaiak, m}^{t}$= elikagaien kontsumo-prezioen indizea $t$ urteko $m$ hilean  \n", "desagregacion"=>"Produktuak: elikagaiak (guztiak); zerealak eta deribatuak; ogia; arrautzak; esnea; olio eta koipeak; fruta freskoak; lekaleak eta barazki freskoak\n\nLurralde historikoa\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"\n\"Elikagaien kontsumo-prezioen indizea\" seriea autonomia-erkidegoetako estatistika-organo nagusien  arteko metodologia harmonizatua erabiliz kalkulatu da. ", "justificacion_global"=>"Elikagaien prezioen anomalien adierazleak (EPAA) anormalki altuak diren merkatuko prezioak identifikatzen ditu. \nEPAA hazkunde-tasa konposatu haztatu batean oinarritzen da. Tasa horrek urtean zehar eta urte barruan prezioen \nigoera zein izan den adierazten du. \n\nAdierazleak zuzenean ebaluatzen du prezioen hazkundea urte askotan zehar hilabete jakin batez, kontuan hartuta \nnekazaritzako merkatuen urtarokotasuna eta inflazioa. Hala, prezioan egondako aldaketa bat aldi jakin baterako \nanormala den ala ez zehaztu dezake. \n\nAdierazlea merkatuaren dinamika ulertzeko gida bat baino ez da. Horrenbestez, ezin da elementu bakar gisa erabili \nzehazteko ea elikagai batek une jakin batean merkatu zehatz batean duen prezioa anormalki altua edo baxua den, tokiko \npolitiken ondorio zuzenen eraginez. Emaitzak bestelako datuekin aztertu behar dira, merkatuaren oinarriak, testuinguru \nmakroekonomikoa eta kanpoko shockak kontuan hartuta. \n\n\nIturria: Nazio Batuen Estatistika Saila \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=2.c.1&seriesCode=AG_FPA_HMFP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ABNORMAL\">Elikagaien prezio anormalki altuak edo nahiko altuak erregistratzen dituzten herrialdeen proportzioa, elikagaien prezioen anomalien adierazlearen arabera (%) AG_FPA_HMFP</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina  informazio osagarria ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-02-0C-01.pdf\">Metadatuak 2-c-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 2.c: Adopt measures to ensure the proper functioning of food commodity markets and their derivatives and facilitate timely access to market information, including on food reserves, in order to help limit extreme food price volatility</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 2.c.1: Indicator of food price anomalies</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>AG_FPA_CFPI - Indicator of Food Price Anomalies (IFPA), by Consumer Food Price Index [2.c.1]</p>\n<p>AG_FPA_COMM - Indicator of Food Price Anomalies (IFPA) [2.c.1]</p>\n<p>AG_FPA_HMFP - Proportion of countries recording abnormally high or moderately high food prices, according to the Indicator of Food Price Anomalies [2.c.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator of food price anomalies (IFPA) identifies market prices that are abnormally high. The IFPA relies on a weighted compound growth rate that accounts for both within year and across year price growth. The indicator directly evaluates growth in prices over a particular month over many years, taking into account seasonality in agricultural markets and inflation, allowing to answer the question of whether or not a change in price is abnormal for any particular period.</p>\n<p><strong>Concepts:</strong></p>\n<p>The indicator of price anomalies (IFPA) relies on two compound growth rates (CGR&#x2019;s), a quarterly compound growth rate (CQGR) and an annual compound growth rate (CAGR). A CGR is a geometric mean<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> that assumes that a random variable grows at a steady rate, compounded over a specific period of time. Because it assumes a steady rate of growth the CGR smoothes the effect of volatility of price changes. The CGR is the growth in any random variable from time period <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n      <mrow>\n        <mi>A</mi>\n      </mrow>\n    </msub>\n  </math> to <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n      <mrow>\n        <mi>B</mi>\n      </mrow>\n    </msub>\n  </math>, raised to the power of one over the length of the period of time being considered.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n        <mi>X</mi>\n        <mi>G</mi>\n        <mi>R</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <msup>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mfrac>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>P</mi>\n                  </mrow>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>t</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>B</mi>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                </msub>\n              </mrow>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>P</mi>\n                  </mrow>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>t</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>A</mi>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mn>1</mn>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>t</mi>\n              </mrow>\n              <mrow>\n                <mi>B</mi>\n              </mrow>\n            </msub>\n            <mo>-</mo>\n            <msub>\n              <mrow>\n                <mi>t</mi>\n              </mrow>\n              <mrow>\n                <mi>A</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </msup>\n    <mo>-</mo>\n    <mn>1</mn>\n  </math> (1)</p>\n<p>where:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n        <mi>X</mi>\n        <mi>G</mi>\n        <mi>R</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> is the quarterly or annual compound growth rate in month t</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n          <mrow>\n            <mi>A</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </msub>\n  </math>is the price at the beginning of the period</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </msub>\n  </math> is the price at the end of the period, </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n      <mrow>\n        <mi>B</mi>\n      </mrow>\n    </msub>\n    <mo>-</mo>\n    <msub>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n      <mrow>\n        <mi>A</mi>\n      </mrow>\n    </msub>\n  </math> is the time in months between periods <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>A</mi>\n  </math> and <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>B</mi>\n  </math>.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> A geometric mean is a type of average, which indicates the typical value of a set of numbers by using the product of their values as opposed to the arithmetic mean which relies on their sum (<a href=\"https://en.wikipedia.org/wiki/Geometric_mean\">Wikipedia, 2017</a>) <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Index and Percent.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>FAO relies on official domestic price data that it compiles in the Food Price Monitoring and Analysis (<a href=\"http://www.fao.org/giews/food-prices/tool/public/#/home\">FPMA</a>) tool to calculate and monitor the indicator. Five cereal products will be monitored: maize &amp; maize products, wheat &amp; wheat flour, rice, sorghum and millet. While diets across the world have become more diversified with increasing incomes, cereals still account for 45 percent of a person&#x2019;s daily caloric intake, making this commodity group the most important in terms of its contribution to caloric intake, particularly for low-income populations (FAOSTAT, 2017). For the purpose of a more comprehensive coverage at the global level, FAO also calculates IFPA on countries&#x2019; officially reported food price indices as reported in <a href=\"http://www.fao.org/faostat/en/#data/CP\">FAOSTAT</a>, which facilitates cross country comparisons as it uses a national level food basket covering all the most important commodities consumed. While the basket differs from country to country, this approach is more reflective of national and global trends as countries have predefined the commodities that have the most impact on local consumers. This approach also facilitates the implementation of the indicator as countries will not be asked to create a new index or modify existing methodologies.</p>\n<p>For the Food CPI, the FAOSTAT monthly CPI &amp; Food CPI database was based on the ILO CPI data until December 2014. In 2014, IMF-ILO-FAO agreed to transfer global CPI data compilation from ILO to IMF. Upon agreement, CPIs for all items and its subcomponents originates from the International Monetary Fund (IMF), and the UN Statistics Division (UNSD) for countries not covered by the IMF. However, due to a limited time coverage from IMF and UNSD for a number of countries, the Organisation for Economic Co-operation and Development (OECD), the European statistics (EUROSTAT), the Latin America and the Caribbean statistics (CEPALSTAT), Central Bank of Western African States (BCEAO), Eastern Caribbean Central Bank (ECCB) and national statistical office website data are used for missing historical data from IMF and UNSD food CPI. The FAO CPI dataset for all items (or general CPI) and the Food CPI, consists of a complete and consistent set of time series from January 2000 onwards. It further contains regional and global food CPIs compiled by FAO using population weights to aggregate across countries. </p>", "COLL_METHOD__GLOBAL"=>"<p>Food commodity prices are collected from webpages, newsletters or emails from national agencies responsible for collecting and disseminating food prices. Food Price Indices are collected from <a href=\"http://www.fao.org/faostat/en/#data/CP\">FAOSTAT</a> (please refer to the 3.a. Data sources). </p>", "FREQ_COLL__GLOBAL"=>"<p>Food commodity prices in the Food Price Monitoring and Analysis (<a href=\"http://www.fao.org/giews/food-prices/tool/public/#/home\">FPMA</a>) tool are updated monthly. Food Price Indices in <a href=\"http://www.fao.org/faostat/en/#data/CP\">FAOSTAT</a> are updated quarterly.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>During the second quarter of each year</p>", "DATA_SOURCE__GLOBAL"=>"<p>The sources of the price information are numerous and are listed for each price series in the FPMA tool at <a href=\"https://fpma.apps.fao.org/giews/food-prices/tool/public/#/home\">https://fpma.apps.fao.org/giews/food-prices/tool/public/#/home</a>. </p>\n<p>For the Food Price Indices, the source is FAOSTAT <a href=\"http://www.fao.org/faostat/en/#data/CP\">http://www.fao.org/faostat/en/#data/CP</a>. </p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "INST_MANDATE__GLOBAL"=>"<p>Article I of the FAO constitution requires that the Organization collect, analyses, interpret and disseminate information relating to nutrition, food and agriculture <a href=\"http://www.fao.org/3/K8024E/K8024E.pdf\">http://www.fao.org/3/K8024E/K8024E.pdf</a>. </p>", "RATIONALE__GLOBAL"=>"<p>The thresholds for the <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n      </mrow>\n    </msub>\n  </math> are expressed as the normalized difference of the compound growth rate of prices from their historical mean for the predefined period of time. And three ranges are established: 1) a less than half a standard deviation difference from the mean is considered normal; 2) a difference that is half but less than one standard deviation is considered moderately high; 3) a difference from the historical mean that is at least one standard deviation greater than the mean is considered abnormally high. </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mtable>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>5</mn>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>I</mi>\n                <mi>F</mi>\n                <mi>P</mi>\n                <mi>A</mi>\n              </mrow>\n              <mrow>\n                <mi>y</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <mn>1</mn>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>M</mi>\n            <mi>o</mi>\n            <mi>d</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>e</mi>\n            <mi>l</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>H</mi>\n            <mi>i</mi>\n            <mi>g</mi>\n            <mi>h</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <msub>\n              <mrow>\n                <mi>I</mi>\n                <mi>F</mi>\n                <mi>P</mi>\n                <mi>A</mi>\n              </mrow>\n              <mrow>\n                <mi>y</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2265;</mo>\n            <mn>1</mn>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>A</mi>\n            <mi>b</mi>\n            <mi>n</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>m</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>l</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>H</mi>\n            <mi>i</mi>\n            <mi>g</mi>\n            <mi>h</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mo>-</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>5</mn>\n            <mo>&#x2264;</mo>\n            <msub>\n              <mrow>\n                <mi>I</mi>\n                <mi>F</mi>\n                <mi>P</mi>\n                <mi>A</mi>\n              </mrow>\n              <mrow>\n                <mi>y</mi>\n              </mrow>\n            </msub>\n            <mo>&amp;lt;</mo>\n            <mn>0</mn>\n            <mo>.</mo>\n            <mn>5</mn>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>N</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>m</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n    </mtable>\n  </math></p>\n<p>We use one standard deviation as the relevant threshold since we want to minimize the probability of missing a significant market event. Events that deviate by more than one standard deviation from their historical distribution have a low probability of occurring and thus are easier to identify as abnormally high prices.</p>", "REC_USE_LIM__GLOBAL"=>"<p>It is appropriate to caution the reader that the indicator is just a guide to understanding market dynamics. As such, one cannot rely on it as the sole element to determine whether a food price in a particular market at a given time is abnormally high or low due to the direct effects of local policies. Results must be weighed with other available information on market fundamentals, macroeconomic context and external shocks. The main challenge in implementing the indicator is data availability and data quality. The calculation of the indicator requires an uninterrupted monthly price series (i.e. if more than 3 consecutive months of data are missing the series may be dropped) of at least 5 years, which include the year being analysed and the 4 preceding years to generate averages and standard deviations. Finally, the indicator is calculated on real price terms to net out the effects of inflation and compare prices in constant money terms over time. However, if food items&#x2019; contribution to CPI is high, it induces downward bias in food real price &#x2013; i.e., it underestimates the extent of the price increase (nominal prices or a non-food CPI could be used).</p>", "DATA_COMP__GLOBAL"=>"<p>Mathematically the IFPA for a particular year <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>y</mi>\n  </math> in month <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>t</mi>\n    <mi>&amp;nbsp;</mi>\n  </math> is calculated as the weighted sum of the quarterly indicator of food price anomalies <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mo>(</mo>\n        <mi>Q</mi>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow></mrow>\n    </msubsup>\n  </math>), and the annual indicator of food price anomalies<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>&amp;nbsp;</mi>\n    <msubsup>\n      <mrow>\n        <mo>(</mo>\n        <mi>A</mi>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow></mrow>\n    </msubsup>\n    <mo>)</mo>\n  </math>.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced separators=\"|\">\n      <mrow>\n        <mfrac>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>C</mi>\n                <mi>X</mi>\n                <mi>G</mi>\n                <mi>R</mi>\n              </mrow>\n              <mrow>\n                <mi>y</mi>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n            <mo>-</mo>\n            <msub>\n              <mrow>\n                <mover accent=\"false\">\n                  <mrow>\n                    <mi>W</mi>\n                    <mo>_</mo>\n                    <mi>C</mi>\n                    <mi>X</mi>\n                    <mi>G</mi>\n                    <mi>R</mi>\n                  </mrow>\n                  <mo>&#xAF;</mo>\n                </mover>\n              </mrow>\n              <mrow>\n                <mi>t</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mover accent=\"true\">\n                  <mrow>\n                    <mi>&#x3C3;</mi>\n                  </mrow>\n                  <mo>^</mo>\n                </mover>\n              </mrow>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>W</mi>\n                    <mo>_</mo>\n                    <mi>C</mi>\n                    <mi>X</mi>\n                    <mi>G</mi>\n                    <mi>R</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>t</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfenced>\n    <mo>=</mo>\n    <msubsup>\n      <mrow>\n        <mi>X</mi>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow></mrow>\n    </msubsup>\n  </math> (2)</p>\n<p>Where:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n        <mi>X</mi>\n        <mi>G</mi>\n        <mi>R</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> is either the quarterly or annual compound growth rate in month <em>t </em>for year <em>y</em></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mover accent=\"false\">\n          <mrow>\n            <mi>W</mi>\n            <mo>_</mo>\n            <mi>C</mi>\n            <mi>X</mi>\n            <mi>G</mi>\n            <mi>R</mi>\n          </mrow>\n          <mo>&#xAF;</mo>\n        </mover>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> is the weighted average of either the quarterly or annual compound growth rate for month <em>t</em> across years <em>y</em></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mover accent=\"true\">\n          <mrow>\n            <mi>&#x3C3;</mi>\n          </mrow>\n          <mo>^</mo>\n        </mover>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>W</mi>\n            <mo>_</mo>\n            <mi>C</mi>\n            <mi>X</mi>\n            <mi>G</mi>\n            <mi>R</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </msub>\n  </math> is the weighted standard deviation of either the quarterly or annual compound growth rate for month <em>t</em> over years <em>y</em>, </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mi>X</mi>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow></mrow>\n    </msubsup>\n  </math> is either the quarterly or annual indicator of a price anomaly in month <em>t</em> for year <em>y</em>. </p>\n<p>Then IFPA is defined as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi>&#x3B3;</mi>\n    <msubsup>\n      <mrow>\n        <mi>Q</mi>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow></mrow>\n    </msubsup>\n    <mo>+</mo>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mn>1</mn>\n        <mo>-</mo>\n        <mi>&#x3B3;</mi>\n      </mrow>\n    </mfenced>\n    <msubsup>\n      <mrow>\n        <mi>A</mi>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow></mrow>\n    </msubsup>\n  </math> (3)</p>\n<p>Where: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> is the indicator of food price anomalies in year <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n  </math>and month <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>t</mi>\n  </math> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mi>Q</mi>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow></mrow>\n    </msubsup>\n  </math> is the quarterly indicator of food price anomalies in year <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n  </math>and month <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>t</mi>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mi>A</mi>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow></mrow>\n    </msubsup>\n  </math> is the annual indicator of food price anomalies in year <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n  </math>and month <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>t</mi>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>&#x3B3;</mi>\n  </math> is a weight with a value of 0.4.</p>\n<p>The weight <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>&#x3B3;</mi>\n  </math> establishes the relative importance of quarterly (<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mi>Q</mi>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow></mrow>\n    </msubsup>\n  </math>) anomalies to the year-on-year price variations (<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mi>A</mi>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow></mrow>\n    </msubsup>\n    <mo>)</mo>\n  </math>. The weight <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>&#x3B3;</mi>\n  </math> is set to 0.4, giving a weight of 0.6--<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced separators=\"|\">\n      <mrow>\n        <mn>1</mn>\n        <mo>-</mo>\n        <mi>&#x3B3;</mi>\n      </mrow>\n    </mfenced>\n  </math>-- to abnormal price growth from year-to-year. This is done to better capture the price level relative to its seasonal trends, which is measured to the price level a year earlier. SDG indicator 2.c.1 is then calculated as the arithmetic mean over <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>t</mi>\n  </math> months of the <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> as follows:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </mfrac>\n    <mrow>\n      <munderover>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi>i</mi>\n          <mo>=</mo>\n          <mn>1</mn>\n        </mrow>\n        <mrow>\n          <mi>t</mi>\n        </mrow>\n      </munderover>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>I</mi>\n            <mi>F</mi>\n            <mi>P</mi>\n            <mi>A</mi>\n          </mrow>\n          <mrow>\n            <mi>y</mi>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mrow>\n  </math> (4)</p>\n<p>Where: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n      </mrow>\n    </msub>\n  </math> is the annual indicator of food price anomalies in year <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>y</mi>\n  </math> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>I</mi>\n        <mi>F</mi>\n        <mi>P</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>y</mi>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> is the indicator of food price anomalies in year <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n  </math>and month <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>t</mi>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>t</mi>\n  </math> is the number of months in a year</p>", "DATA_VALIDATION__GLOBAL"=>"<p> Not applicable</p>", "ADJUSTMENT__GLOBAL"=>"<p> Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>For the domestic food commodity prices, the data is republished data harvested from national governmental organizations without imputation of missing values. For the purpose of the indicator, if more than 3 consecutive months of data are missing or if less than 5 years are available the series may be dropped from monitoring.</p>\n<p>For the food price index in <a href=\"http://www.fao.org/faostat/en/#data/CP\">FAOSTAT</a>, the data is republished data harvested from other international organizations without imputation of missing values. For the purpose of the indicator, if more than 3 consecutive months of data are missing or if less than 5 years are available the series may be dropped from monitoring.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Not Applicable</p>", "REG_AGG__GLOBAL"=>"<p><strong>Consumer Food Price Index</strong>: Results are organized on a regional basis but IFPA values are not aggregated as such. The unit of the indicator provided for each region represents instead the proportion of countries recording abnormally high or moderately high food prices in each region.</p>\n<p><strong>Five key commodities (maize, rice, wheat, sorghum, millet)</strong>: Results are <u>not</u> organized on a regional basis but at country level. This is because the commodities and food baskets monitored across countries are not sufficiently homogenous to aggregate into one price index. However, if a majority of countries within a region presents abnormally high prices, either for a particular commodity or the food price index, this region is qualified as a region suffering from high prices. </p>\n<p><strong>Sources of discrepancies:</strong></p>\n<p>FAO relies on the Food Price Indices as reported in FAOSTAT as well as on available official domestic food price data that it compiles in the Food Price Monitoring and Analysis (FPMA) tool to calculate the indicator. The FPMA database brings together price series for main food commodities (mainly cereal products) in selected markets in countries around the world. As a result, the indicator estimated by FAO can differ from the indicator estimated at country level, as it may be calculated on prices for a different market or commodity. </p>", "DOC_METHOD__GLOBAL"=>"<p>An interactive e-learning course is available on <a href=\"https://elearning.fao.org/course/view.php?id=362\">SDG Indicator 2.c.1 &#x2013; Food price anomalies</a> to complement countries&#x2019; efforts in monitoring the 2030 Agenda and broaden the subject&#x2019;s understanding. The course covers basic concepts related to market functioning, prices determination and price volatility and explains how to calculate the indicator and use the online Food Price Monitoring and Analysis (FPMA) tool to interpret indicator results, at national and international level. Besides in English, the online version of this course is also available in <a href=\"https://elearning.fao.org/pluginfile.php/485928/course/section/1889/SDG-2C1_RU_WIN32_CDROM.zip\" target=\"_blank\">Russian</a>, French and Spanish.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>FAO is responsible for the quality of the internal statistical processes used to compile the published datasets. The FAO Statistics Quality Assurance Framework (SQAF), available at: <a href=\"http://www.fao.org/docrep/019/i3664e/i3664e.pdf\">http://www.fao.org/docrep/019/i3664e/i3664e.pdf</a>, provides the necessary principles, guidelines and tools to carry out quality assessments. FAO is performing an internal bi-annual survey (FAO Quality Assessment and Planning Survey) designed to gather information on all of FAO&#x2019;s statistical activities, notably to assess the extent to which quality standards are being implemented with a view to increasing compliance with the quality dimensions of SQAF, documenting best practices and prepare quality improvement plans, where necessary. Domain-specific quality assurance activities are carried out systematically (e.g. quality reviews, self-assessments, compliance monitoring). </p>", "QUALITY_ASSURE__GLOBAL"=>"<ul>\n  <li>The indicator is calculated on food price data, which is gathered from official sources, same as for the food price index published in FAOSTAT. To ensure the correct calculation of the indicator, the process for the calculation of the indicator relies on an automated system. </li>\n  <li>On request, countries are supported by FAO to implement the indicator and interpret the results. In addition, training is provided in the country upon request. </li>\n</ul>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The responsible officer conducts a self-assessment of the calculation process and its outputs on the basis of the FAO Statistics Quality Assurance Framework (SQAF). The SQAF considers the following principles: relevance, accuracy and reliability, timelessness and punctuality, coherence and comparability, and accessibility and clarity. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>IFPA on commodity prices is available for about two fifths of countries, while IFPA on Food CPI is available for almost all countries.</p>\n<p><strong>Time series:</strong></p>\n<p>IFPA on commodity prices is available annually from 2015, while IFPA on Food CPI is available annually since 2010.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Type of product, level of price anomaly.</p>", "COMPARABILITY__GLOBAL"=>"<p>FAO relies on the Food Price Indices as reported in FAOSTAT as well as on available official domestic food price data that it compiles in the Food Price Monitoring and Analysis (FPMA) tool to calculate the indicator. The FPMA database brings together price series for main food commodities (mainly cereal products) in selected markets in countries around the world. As a result, the indicator estimated by FAO can differ from the indicator estimated at country level, as it may be calculated on prices for a different market or commodity. When food products that are most relevant to the country differ from the five commodities that FAO calculates, countries are strongly encouraged to produce the IFPA of those food items and monitor their price volatilities. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.fao.org/giews/food-prices/research/en/\">http://www.fao.org/giews/food-prices/research/en/</a></p>\n<p><strong>References:</strong></p>\n<p><a href=\"https://fpma.apps.fao.org/giews/food-prices/tool/public/#/home\">https://fpma.apps.fao.org/giews/food-prices/tool/public/#/home</a></p>", "indicator_sort_order"=>"02-0c-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.1.1", "slug"=>"3-1-1", "name"=>"Tasa de mortalidad materna", "url"=>"/site/es/3-1-1/", "sort"=>"030101", "goal_number"=>"3", "target_number"=>"3.1", "global"=>{"name"=>"Tasa de mortalidad materna"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[{"series"=>"", "unit"=>"", "label_content"=>"Objetivo del indicador para 2030", "value"=>70}], "graph_title"=>"Tasa de mortalidad materna", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de mortalidad materna", "indicator_number"=>"3.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Meta:</b> Reducir a menos de 70 por cada 100.000 nacidos vivos", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_28/opt_0/tipo_1/ti_defunciones/temas.html", "url_text"=>"Estadística de defunciones", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tasa de mortalidad materna", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.1- De aquí a 2030, reducir la tasa mundial de mortalidad materna a menos de 70 por cada 100.000 nacidos vivos", "definicion"=>"Defunciones de mujeres atribuidas a embarazo, parto y puerperio por cada 100.000 nacimientos", "formula"=>"\n$$TM_{materna}^{t} = \\frac{D_{materna}^{t}}{N^{t}} \\cdot 100.000$$\n\ndonde:\n\n$D_{materna}^{t} =$ defunciones de mujeres atribuidas a embarazo, parto y puerperio (códigos O00-O99 de la CIE-10) en el año $t$\n\n$N^{t} =$ nacimientos en el año $t$\n", "desagregacion"=>"Territorio histórico", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa tasa de mortalidad materna se define como el número de muertes maternas durante un tiempo determinado\npor cada 100.000 nacidos vivos durante el mismo período. Representa el riesgo de muerte materna en relación\nal número de nacidos vivos y esencialmente captura el riesgo de muerte en un solo embarazo (representado por un\núnico nacido vivo).\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.1.1&seriesCode=SH_STA_MORT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=FEMALE\">Tasa de mortalidad materna (%) SH_STA_MORT</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-01-01.pdf\">Metadatos 3-1-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Tasa de mortalidad materna", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.1- De aquí a 2030, reducir la tasa mundial de mortalidad materna a menos de 70 por cada 100.000 nacidos vivos", "definicion"=>"Deaths of women attributed to pregnancy, childbirth and the puerperium per 100.000 births", "formula"=>"\n$$TM_{maternal}^{t} = \\frac{D_{maternal}^{t}}{N^{t}} \\cdot 100.000$$\n\nwhere:\n\n$D_{maternal}^{t} =$ deaths of women attributed to pregnancy, childbirth and the puerperium (codes O00-O99 of the ICD-10)\nin year $t$\n\n$N^{t} =$ births in year $t$\n", "desagregacion"=>"Province", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe maternal mortality ratio (MMR) is defined as the number of maternal deaths \nduring a given time period per 100,000 live births during the same time period. \nIt depicts the risk of maternal death relative to the number of live births \nand essentially captures the risk of death in a single pregnancy (proxied by a \nsingle live birth).\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.1.1&seriesCode=SH_STA_MORT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=FEMALE\">Maternal mortality ratio (%) SH_STA_MORT</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-01-01.pdf\">Metadata 3-1-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Tasa de mortalidad materna", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.1- De aquí a 2030, reducir la tasa mundial de mortalidad materna a menos de 70 por cada 100.000 nacidos vivos", "definicion"=>"Haurdunaldiari, erditzeari eta erdiberrialdiari egotzitako emakumeen heriotzak 100.000 jaiotzako", "formula"=>"\n$$TM_{amatasuna}^{t} = \\frac{D_{amatasuna}^{t}}{N^{t}} \\cdot 100.000$$\n\nnon:\n\n$D_{amatasuna}^{t} =$ haurdunaldiari, erditzeari eta erdiberrialdiari egotzitako emakumeen heriotzak (GNS-10eko O00-O99 kodeak) $t$ urtean\n\n$N^{t} =$ jaiotzak $t$ urtean\n", "desagregacion"=>"Lurralde historikoa", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAmen heriotza-tasa denbora jakin batez bizirik irten diren 100.000 umeko hil diren amen kopurua da. Amaren heriotza-arriskua \nadierazten du, bizirik jaio diren umeen kopuruari lotuta, eta, funtsean, haurdunaldi bakar bateko heriotza-arriskua hartzen \ndu (bizirik jaiotako haur bakar baten arabera irudikatua). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.1.1&seriesCode=SH_STA_MORT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=FEMALE\">Amen heriotza-tasa (%) SH_STA_MORT</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-01-01.pdf\">Metadatuak 3-1-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.1.1: Maternal mortality ratio</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_STA_MORT - Maternal mortality ratio [3.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>3.1.2: Proportion of births attended by skilled health personnel</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO). Department of Sexual and Reproductive Health and Research.</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO). Department of Sexual and Reproductive Health and Research.</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The maternal mortality ratio (MMR) is defined as the number of maternal deaths during a given time period per 100,000 live births during the same time period. It depicts the risk of maternal death relative to the number of live births and essentially captures the risk of death in a single pregnancy (proxied by a single live birth).</p>\n<p><strong>Concepts:</strong></p>\n<p>In the <em>International statistical classification of diseases and related health problems (ICD) </em>WHO defines the following:</p>\n<p><strong>Maternal death</strong>: The death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management (from direct or indirect obstetric death), but not from unintentional or incidental causes.</p>\n<p>A death occurring during pregnancy, childbirth and puerperium (also known as a <strong>pregnancy-related death</strong>): The death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the cause of death.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Maternal deaths per 100,000 live births</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Maternal deaths are classified according to the <em>International statistical classification of diseases and related health problems</em> (ICD) definition. The specific codes used under ICD-10 (the 10<sup>th</sup> revision of the ICD) to define a maternal death are: O00-O96; O98, O99 and A34.</p>\n<p>ICD-11 (the 11th revision of the ICD) was adopted by the World Health Assembly in May 2019 and comes into effect on 1<sup>st</sup> January 2022. Further information is available at: <a href=\"http://www.who.int/classifications/icd/en/\">www.who.int/classifications/icd/en/</a> The coding rules related to maternal mortality are being edited to fully match the new structure of ICD-11, but without changing the resulting statistics. The ICD-11 rules can be accessed in the reference guide of ICD-11, at <a href=\"https://icd.who.int\">https://icd.who.int</a> . Forthcoming releases from 2022 onwards will transition to use ICD-11 coding. Care has been taken to ensure that the definition of maternal death used for international comparison of mortality statistics remains stable over time, but the word &#x201C;unintentional&#x201D; has been used in the ICD-11 definition in place of the word &#x201C;accidental&#x201D; which was previously used, in ICD-10.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Please see Sections 3.1 and 3.2 of the report: <a href=\"https://www.who.int/publications/i/item/9789240068759\">Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division.</a> Geneva: World Health Organization; 2023.</p>", "COLL_METHOD__GLOBAL"=>"<p>The United Nations Maternal Mortality Estimation Inter-Agency Group (UN MMEIG) &#x2013; comprising WHO, UNICEF, UNFPA, the World Bank Group and the United Nations Population Division (UNDESA/Population Division) maintains an input database consisting of maternal mortality data from civil registration, population-based surveys, surveillance systems, censuses, and other specialized studies/surveys. This database is updated before the release of every new round of estimates and is used to calculate the proportion of maternal deaths (PM) among women of reproductive age (WRA). The maternal mortality ratio (MMR) is then calculated as MMR = PM(D/B); where &quot;D&quot; is the number of all-cause deaths among women WRA and &quot;B&quot; is the number of live births. The number of live births is based upon the World Population Prospects published by UNDESA/Population Division. </p>\n<p>Statistical modelling is undertaken to generate comparable country, regional, and global level estimates. Adjustments are made according to the data source type (See Section 4e below). The analysis accounts for stochastic errors, sampling error in the data source, errors during data collection and processing, and other random error. The model&apos;s fit is assessed by cross-validation.</p>", "FREQ_COLL__GLOBAL"=>"<p>The input datasets are updated prior to each new publication round of the maternal mortality ratio (MMR) estimates. Source data are collected by countries, typically annually for civil registration and vital statistics (CRVS) sources, every 3-5 years for specialized reviews, every 5-7 years for population-based surveys, and every 10 years for censuses. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The maternal mortality estimates are updated approximately every 2-3 years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National-level data providers are typically statistical offices, specialized epidemiology monitoring authorities and/or Ministry of Health.</p>", "COMPILING_ORG__GLOBAL"=>"<p>The United Nations Maternal Mortality Estimation Inter-Agency Group (UN MMEIG) &#x2013; comprising WHO, UNICEF, UNFPA, the World Bank Group and the United Nations Population Division (UNDESA/Population Division) of the Department of Economic and Social Affairs.</p>\n<p> </p>", "INST_MANDATE__GLOBAL"=>"<p>World Health Organization (WHO) is the custodian UN agency for the maternal mortality ratio.</p>", "RATIONALE__GLOBAL"=>"<p>All maternal mortality indicators include a point-estimate and an 80% uncertainty interval (UI). Both point-estimates and 80% UIs should be taken into account when assessing estimates.</p>\n<p>For example: &#x201C;The estimated 2020 global MMR is 223 (UI 202 to 255).&#x201D;</p>\n<p>This means:</p>\n<ul>\n  <li>The point-estimate is 223 and the 80% uncertainty interval ranges 202 to 255.</li>\n  <li>There is a 50% chance that the true 2020 global MMR lies above 223, and a 50% chance that the true value lies below 223.</li>\n  <li>There is an 80% chance that the true 2020 global MMR lies between 202 and 255.</li>\n  <li>There is still a 10% chance that the true 2020 global MMR lies above 255, and a 10% chance that the true value lies below 202.</li>\n</ul>\n<p>Other accurate interpretations include:</p>\n<ul>\n  <li>We are 90% certain that the true 2020 global MMR is at least 202.</li>\n  <li>We are 90% certain that the true 2020 global MMR is 255 or less.</li>\n</ul>\n<p>The amount of data available for estimating an indicator and the quality of that data determine the width of an indicator&#x2019;s UI. As data availability and quality improve, the certainty increases that an indicator&#x2019;s true value lies close to the point-estimate.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The extent of maternal mortality in a population is essentially the combination of two factors:</p>\n<ol>\n  <li>The risk of death in a single pregnancy or a single live birth.</li>\n  <li>The fertility level (i.e. the number of pregnancies or births that are experienced by women of reproductive age).</li>\n</ol>\n<p>The maternal mortality ratio (MMR) is defined as the number of maternal deaths during a given time period per 100 000 live births during the same time period. It depicts the risk of maternal death relative to the number of live births and essentially captures (i) above.</p>\n<p>By contrast, the maternal mortality rate (MMRate) is calculated as the number of maternal deaths divided by person-years lived by women of reproductive age. The MMRate captures both the risk of maternal death per pregnancy or per total birth (live birth or stillbirth), and the level of fertility in the population. </p>\n<p>In addition to the MMR and the MMRate, it is possible to calculate the adult lifetime risk of maternal mortality for women in the population. An alternative measure of maternal mortality, the proportion of deaths among women of reproductive age that are due to maternal causes (PM), is calculated as the number of maternal deaths divided by the total deaths among women aged 15&#x2013;49 years.</p>", "DATA_COMP__GLOBAL"=>"<p>The maternal mortality ratio (MMR) can be calculated by dividing recorded (or estimated) maternal deaths by total recorded (or estimated) live births in the same period and multiplying by 100 000. Measurement requires information on pregnancy status, timing of death (during pregnancy, childbirth, or within 42 days of termination of pregnancy), and cause of death.</p>\n<p>The MMR can be calculated directly from data collected through vital registration systems, household surveys or other sources. There are often data quality problems, particularly related to the underreporting and misclassification of maternal deaths. Therefore, data are often adjusted in order to take these data quality issues into account. Some countries undertake these adjustments or corrections as part of specialized/confidential enquiries or administrative efforts embedded within maternal mortality monitoring programmes.</p>\n<p><strong>Bayesian maternal mortality estimation model (the BMat model):</strong></p>\n<p>Estimation and projection of maternal mortality indicators are undertaken using the BMat model. This model is intended to ensure that the MMR estimation approach is consistent across all countries but remains flexible in that it is based on covariate-driven trends to inform estimates in countries or country-periods with limited information; captures observed trends in countries with longer time series of observations; and takes into account the differences in stochastic and sampling errors across observations.</p>\n<p>The model is summarized as follows:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">log</mi>\n      </mrow>\n      <mo>&#x2061;</mo>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>E</mi>\n            <mi>P</mi>\n            <msup>\n              <mrow>\n                <mi>M</mi>\n              </mrow>\n              <mrow>\n                <mi>N</mi>\n                <mi>A</mi>\n              </mrow>\n            </msup>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mrow>\n    <mo>=</mo>\n    <msub>\n      <mrow>\n        <mi>b</mi>\n      </mrow>\n      <mrow>\n        <mn>0</mn>\n      </mrow>\n    </msub>\n    <mo>+</mo>\n    <msub>\n      <mrow>\n        <mi>b</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">log</mi>\n      </mrow>\n      <mo>&#x2061;</mo>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>G</mi>\n            <mi>D</mi>\n            <mi>P</mi>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mrow>\n    <mo>+</mo>\n    <msub>\n      <mrow>\n        <mi>b</mi>\n      </mrow>\n      <mrow>\n        <mn>2</mn>\n      </mrow>\n    </msub>\n    <mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">log</mi>\n      </mrow>\n      <mo>&#x2061;</mo>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>G</mi>\n            <mi>F</mi>\n            <mi>R</mi>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mrow>\n    <mo>+</mo>\n    <msub>\n      <mrow>\n        <mi>b</mi>\n      </mrow>\n      <mrow>\n        <mn>3</mn>\n      </mrow>\n    </msub>\n    <mi>S</mi>\n    <mi>B</mi>\n    <mi>A</mi>\n    <mo>+</mo>\n    <msub>\n      <mrow>\n        <mi>&#x3B3;</mi>\n      </mrow>\n      <mrow>\n        <mi>j</mi>\n      </mrow>\n    </msub>\n    <mo>+</mo>\n    <msub>\n      <mrow>\n        <mi>&#x3C6;</mi>\n      </mrow>\n      <mrow>\n        <mi>k</mi>\n      </mrow>\n    </msub>\n  </math></p>\n<p>Where:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>E</mi>\n    <mi>P</mi>\n    <msup>\n      <mrow>\n        <mi>M</mi>\n      </mrow>\n      <mrow>\n        <mi>N</mi>\n        <mi>A</mi>\n      </mrow>\n    </msup>\n  </math>= the expected proportion of non-HIV-related deaths to women aged 15&#x2013;49 years that are due to maternal causes [NA = non-HIV; formerly it referred to &#x201C;non-AIDS&#x201D;]</p>\n<p><em>GDP </em>= gross domestic product per capita (in 2011 PPP US dollars)</p>\n<p><em>GFR </em>= general fertility rate (live births per woman aged 15&#x2013;49 years)</p>\n<p><em>SBA </em>= proportion of births attended by skilled health personnel</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>&#x3B3;</mi>\n      </mrow>\n      <mrow>\n        <mi>j</mi>\n      </mrow>\n    </msub>\n  </math><em> </em>= random intercept term for country j</p>\n<p><em>&#x3C6;k </em>= random intercept term for region k.</p>\n<p>For countries with data available on maternal mortality, the expected proportion of non-HIV-related maternal deaths was based on country and regional random effects, whereas for countries with no data available, predictions were derived using regional random effects only. </p>\n<p>The resulting estimates of the <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>E</mi>\n    <mi>P</mi>\n    <msup>\n      <mrow>\n        <mi>M</mi>\n      </mrow>\n      <mrow>\n        <mi>N</mi>\n        <mi>A</mi>\n      </mrow>\n    </msup>\n  </math> were used to obtain the expected non-HIV MMR through the following relationship:</p>\n<p><em>Expected non-HIV MMR =EPM<sup>NA</sup>*(1-a)*E/B</em></p>\n<p>Where: </p>\n<p><em>a</em> = the proportion of HIV-related deaths among all deaths to women aged 15&#x2013;49 years</p>\n<p>E = the total number of deaths to women of reproductive age</p>\n<p>B = the number of births.</p>\n<p><strong>Estimation of HIV-related indirect maternal deaths:</strong></p>\n<p>For countries with generalized HIV epidemics and high HIV prevalence, HIV/AIDS is a leading cause of death during pregnancy and post-delivery. There is also some evidence from community studies that women with HIV infection have a higher risk of maternal death, although this may be offset by lower fertility. If HIV is prevalent, there will also be more incidental HIV deaths among pregnant and postpartum women. When estimating maternal mortality in these countries, it is, thus, important to differentiate between incidental HIV deaths (non-maternal deaths) and HIV-related indirect maternal deaths (maternal deaths caused by the aggravating effects of pregnancy on HIV) among HIV-positive pregnant and postpartum women who have died (i.e. among all HIV-related deaths occurring during pregnancy, childbirth and puerperium).</p>\n<p>The number of HIV-related indirect<strong><em> </em></strong>maternal deaths <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msup>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mi>H</mi>\n        <mi>I</mi>\n        <mi>V</mi>\n      </mrow>\n    </msup>\n  </math>, is estimated by:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msup>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mi>H</mi>\n        <mi>I</mi>\n        <mi>V</mi>\n      </mrow>\n    </msup>\n    <mo>=</mo>\n    <mi>a</mi>\n    <mo>&#x2219;</mo>\n    <mi>E</mi>\n    <mo>&#x2219;</mo>\n    <mi>v</mi>\n    <mo>&#x2219;</mo>\n    <mi>u</mi>\n  </math></p>\n<p>Where: </p>\n<p>a*E = the total number of HIV-related deaths among all deaths to women aged 15&#x2013;49.</p>\n<p><em>v = </em>is the proportion of HIV-related deaths to women aged 15&#x2013;49 that occur during pregnancy. The value of <em>v </em>can be computed as follows: <em>v </em>= <em>c k GFR / [</em>1 + <em>c</em>(<em>k-</em>1) <em>GFR] </em>where<strong> </strong>GFR is the general fertility rate, and where<em> c </em>is the average exposure time (in years) to the risk of pregnancy-related mortality per live birth (set equal to 1 for this analysis), and where <em>k </em>is the relative risk of dying from AIDS for a pregnant versus a non-pregnant woman (reflecting both the decreased fertility of HIV-positive women and the increased mortality risk of HIV-positive pregnant women). The value of <em>k </em>was set at 0.3.</p>\n<p>u = is the fraction of pregnancy-related AIDS deaths assumed to be indirect maternal deaths. The United Nations Maternal Mortality Estimation Inter-Agency Group (UN MMEIG)/TAG reviewed available study data on AIDS deaths among pregnant women and recommended using <em>u = </em>0.3.</p>\n<p>For observed PMs, we assumed that the total reported maternal deaths are a combination of the proportion of reported non-HIV-related maternal deaths and the proportion of reported HIV-related (indirect) maternal deaths, where the latter is given by a*v for observations with a &#x201C;pregnancy-related death&#x201D; definition and a*v*u for observations with a &#x201C;maternal death&#x201D; definition.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Estimates are reviewed with Member States through a World Health Organization (WHO) country consultation process and SDG focal points. In 2001, the WHO Executive Board endorsed a resolution (EB. 107.R8) seeking to &#x201C;establish a technical consultation process bringing together personnel and perspectives from Member States in different WHO regions&#x201D;. A key objective of this consultation process is &#x201C;to ensure that each Member State is consulted on the best data to be used&#x201D;. Since the process is an integral step in the overall estimation strategy, it is described here in brief.</p>\n<p>The country consultation process entails an exchange between WHO and technical focal person(s) in each country. It is carried out prior to the publication of estimates. During the consultation period, WHO invites focal person(s) to review input data sources, methods for estimation and the preliminary estimates. Focal person(s) are encouraged to submit additional data that may not have been taken into account in the preliminary estimates.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Full details on adjustments and formulas are published/available here:</p>\n<p>(1) Peterson E, Chou D, Gemmill A, Moller AB, Say L, Alkema L. Estimating maternal mortality using vital registration data: a Bayesian hierarchical bivariate random walk model to estimate sensitivity and specificity of reporting for population-periods without validation data. 2019 (<a href=\"https://arxiv.org/abs/1909.08578\">https://arxiv.org/abs/1909.08578</a>) </p>\n<p>(2) Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. Geneva: World Health Organization; 2023.2019 (<a href=\"https://www.who.int/reproductivehealth/publications/maternal-mortality-2000-2017/en/\">https://www.who.int/reproductivehealth/publications/maternal-mortality-2000-2017/en/</a>). </p>\n<p>To summarise the key adjustments in brief:</p>\n<ul>\n  <li>Adjustments for variation in definitions of the input data:</li>\n</ul>\n<p>Previous studies found incidental or accidental deaths (comprise 10% of pregnancy-related deaths (excluding HIV-related deaths) in sub-Saharan African countries, and 15% in other low- and middle-income countries. Adjustments are applied to pregnancy-related deaths to account for these non-maternal deaths.</p>\n<ul>\n  <li>Adjustment for crisis years:</li>\n</ul>\n<p>The proportion of pregnancy-related deaths among the deaths attributable to mortality shock from crisis is assumed to be equal to the proportion of women in the population who are pregnant or postpartum at the time of the crisis. The proportion of pregnant women in the population is set equal to the general fertility rate, based on the assumption of a one-year period associated with a live birth. Additional uncertainty is added to the estimates of crisis years.</p>\n<ul>\n  <li>Adjustment for age distribution in population-based surveys:</li>\n</ul>\n<p>Population-based surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) obtain information by interviewing respondents about the survival of their siblings. This approach, commonly referred to as the direct sisterhood method. Given the study design (based on sisters of respondents), the population exposed to risk may be atypical of the population at large. Therefore, we compute an age-standardized value of PM, based on the female population of households at the time of the survey.</p>\n<ul>\n  <li>Adjustment for underreporting (unregistered) and misclassification in civil registration and vital statistics (CRVS) systems:</li>\n</ul>\n<p>Underreporting and misclassification in CRVS systems are accounted for with specialized studies. Model estimated country-year specific adjustment factors are obtained and applied to CRVS data.</p>\n<ul>\n  <li>Adjustment for under-reporting in non-CRVS and non-specialised sources:</li>\n</ul>\n<p>It is widely believed that some form of upward adjustment is required for data sources that are not CRVS or specialised studies, to account for deaths early in pregnancy that might not have been captured. Therefore, an upwards adjustment of 10% was applied to maternal deaths that were not obtained from CRVS systems or specialized studies.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Missing values are treated at the country-level. This is done as follows. There is no treatment of missing values at the regional level.</p>\n<p><strong>Predictor variable data:</strong></p>\n<p>Complete and comparable predictor data is obtained by constructing time series estimates for predictor variables (covariates).</p>\n<ul>\n  <li>Gross domestic product (GDP) per capita, measured in purchasing power parity (PPP) equivalent international dollars using 2017 as the baseline</li>\n  <li>General fertility rate (GFR) </li>\n  <li>Skilled birth attendant (SBA) </li>\n</ul>\n<p><strong>Response variable data:</strong></p>\n<p>All-cause deaths for WRA, used to denominate maternal deaths in the statistic PM, are imputed when missing and in some cases overwritten. </p>\n<ul>\n  <li>Estimated all-cause deaths from by UNDP&#x2019;s World Population Prospects 2022 lifetables were used to impute and overwrite all-cause deaths in specialized studies in which the search went beyond registration systems.</li>\n  <li>Civil Registration and Vital Statistics (CRVS) reported all-cause deaths were used to impute missing all-cause deaths in specialized studies in which the search was within registration systems.</li>\n  <li>Estimated all-cause deaths from by UNDP&#x2019;s World Population Prospects 2022 Estimates were used to impute missing all-cause deaths in miscellaneous studies.</li>\n</ul>", "REG_AGG__GLOBAL"=>"<p>Regional aggregations are calculated by aggregating the national-level estimates. The size of a country is determined by the live births estimated by World Population Prospects. Aggregations are currently made for each of the UN Agencies that comprise the UN MMEIG.</p>", "DOC_METHOD__GLOBAL"=>"<p>The methodology used by countries to compile the data depends on the source input type (CRVS, specialised study etc). Useful references include:</p>\n<ul>\n  <li>WHO Civil Registration and Vital Statistics (CRVS) Tools &amp; Resources: <a href=\"https://www.who.int/data/data-collection-tools/civil-registration-and-vital-statistics-(crvs)\">https://www.who.int/data/data-collection-tools/civil-registration-and-vital-statistics-(crvs)</a> </li>\n  <li>World Health Organization. (&#x200E;2013)&#x200E;. WHO guidance for measuring maternal mortality from a census. World Health Organization. <a href=\"https://apps.who.int/iris/handle/10665/87982\">https://apps.who.int/iris/handle/10665/87982</a></li>\n  <li>World Health Organization. (&#x200E;2004)&#x200E;. Beyond the numbers : reviewing maternal deaths and complications to make pregnancy safer. World Health Organization. <a href=\"https://apps.who.int/iris/handle/10665/42984\">https://apps.who.int/iris/handle/10665/42984</a> </li>\n  <li>World Health Organization. (2022). Maternal mortality measurement: guidance to improve national reporting. World Health Organization. <a href=\"https://www.who.int/publications/i/item/9789240052376\">https://www.who.int/publications/i/item/9789240052376</a> </li>\n  <li>World Health Organization. (2022). Certification of deaths during pregnancy, childbirth, or the puerperium where confirmed or suspected COVID-19 is a cause of death. World Health Organization. <a href=\"https://www.who.int/publications/i/item/9789240049314\">https://www.who.int/publications/i/item/9789240049314</a> </li>\n</ul>\n<p>Support and guidance to national authorities may also be requested from the WHO Secretariat.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>For information on data quality management, assurance, and assessment processes at WHO, please refer to: <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a> </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>For information on data quality management, assurance, and assessment processes at WHO, please refer to: <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>For information on data quality management, assurance, and assessment processes at WHO, please refer to: <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a> </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data Availability</strong></p>\n<p>Data availability is presented by country with the country profiles, please see here: <a href=\"https://www.who.int/data/gho/data/themes/maternal-and-reproductive-health/maternal-mortality-country-profiles\">https://www.who.int/data/gho/data/themes/maternal-and-reproductive-health/maternal-mortality-country-profiles</a> </p>\n<p><a href=\"https://www.who.int/publications/i/item/9789240068759\">https://www.who.int/publications/i/item/9789240068759</a></p>\n<p><strong>Disaggregation:</strong></p>\n<p>Current maternal mortality ratio (MMR) estimates are reported at national, regional, and global levels. Countries and territories included in the analyses are WHO Member States with populations over 100 000, plus two territories (Puerto Rico, and the occupied Palestinian Territory, including east Jerusalem).</p>\n<p>The <strong>time series</strong> available is currently 2000 to 2020.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The maternal mortality ratio is defined as the number of maternal deaths divided by live births. However, to account for potential incompleteness of death recording in various data sources, the United Nations Maternal Mortality Estimation Inter-Agency Group (UN MMEIG) first computes the fraction of deaths due to maternal causes from original data sources (referred to as the &#x201C;proportion maternal&#x201D;, or PM), and then applies that fraction to WHO estimates of total deaths among women of reproductive age to obtain an estimate of the number of maternal deaths. </p>\n<p>In other words, the following fraction is first computed from country data sources:</p>\n<p>PM= Number of maternal deaths 15-49/All female deaths at ages 15-49 </p>\n<p>and then the PM is used to compute the MMR as follows:</p>\n<p>MMR=PM &#xD7; (All female deaths at ages 15-49/Number of live births)</p>\n<p>Where the estimate of all deaths at ages 15-49 in the second equation is derived from WHO Global Health Estimates life tables, and the number of live births is from the World Population Prospects 2019.</p>\n<p>With this as background, a few reasons that MMEIG estimates may differ from national statistics are as follows:</p>\n<ol>\n  <li>Civil registration and vital statistics systems are not always complete (i.e., they do not always capture 100% of all deaths) and completeness may change over time. The MMEIG estimation approach attempts to correct for this by using the above approach, which involves first computing the PM.</li>\n  <li>The MMEIG often applies adjustment factors to the PM computed from original data to account for measurement issues (such as how the country defined &#x201C;maternal&#x201D; deaths; misclassification; or incompleteness). </li>\n  <li>The MMEIG uses the standardized series of live births from the United Nations Population Division, as published in World Population Prospects 2022, in the denominator of the MMR equation. To better inform the WPP, countries should discuss discrepancies directly with the United Nations Population Division: the contact address is population@un.org; this email address is monitored regularly, and messages are dispatched to the appropriate analysts for each country or concern. </li>\n  <li>Statistically speaking, maternal deaths are a relatively rare event, which can lead to noisy time trends in data over time. As the goal of the MMEIG estimates is to track long term progress in reducing maternal mortality, the estimation process involves some smoothing to generate a curve that better captures changes in underlying risk</li>\n</ol>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> <a href=\"https://www.who.int/publications/i/item/9789240068759\">https://www.who.int/publications/i/item/9789240068759</a></p>\n<p><strong>References:</strong></p>\n<ol>\n  <li>Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. Geneva: World Health Organization; 2023.</li>\n  <li>Peterson E, Chou D, Gemmill A, Moller AB, Say L, Alkema L. Estimating maternal mortality using vital registration data: a Bayesian hierarchical bivariate random walk model to estimate sensitivity and specificity of reporting for population-periods without validation data. 2019 (<a href=\"https://arxiv.org/abs/1909.08578\">https://arxiv.org/abs/1909.08578</a>). </li>\n</ol>", "indicator_sort_order"=>"03-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.1.2", "slug"=>"3-1-2", "name"=>"Proporción de partos atendidos por personal sanitario especializado", "url"=>"/site/es/3-1-2/", "sort"=>"030102", "goal_number"=>"3", "target_number"=>"3.1", "global"=>{"name"=>"Proporción de partos atendidos por personal sanitario especializado"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Proporción de partos atendidos por personal sanitario especializado", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de partos atendidos por personal sanitario especializado", "indicator_number"=>"3.1.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_48/opt_1/ti_estadistica-de-nacimientos/temas.html", "url_text"=>"Estadística de nacimientos", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de partos atendidos por personal sanitario especializado", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.1- De aquí a 2030, reducir la tasa mundial de mortalidad materna a menos de 70 por cada 100.000 nacidos vivos", "definicion"=>"Proporción de partos atendidos por personal sanitario especializado", "formula"=>"\n$$PPAR_{atendidos}^{t} = \\frac{PAR_{atendidos}^{t}}{PAR^{t}} \\cdot 100$$\n\ndonde:\n\n$PAR_{atendidos}^{t} =$ partos atendidos por personal sanitario especializado en el año $t$\n\n$PAR^{t} =$ partos en el año $t$\n", "desagregacion"=>"Territorio histórico", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Proporción de partos atendidos por personal sanitario cualificado (generalmente médicos, enfermeras o matronas, pero puede\nreferirse a otros profesionales de la salud que brindan atención al parto) es la proporción de partos atendidos\npor personal sanitario cualificado.\n\nSegún la definición actual estos son \nprofesionales de la salud maternal o neonatal (MNH) educados, capacitados y regulados según estándares a nivel nacional e internacional. Son competentes para:\n\n- (i) proporcionar y promover la calidad basada en evidencia y derechos humanos,\natención socioculturalmente sensible y digna a las mujeres y recién nacidos\n\n- (ii) facilitar los procesos fisiológicos\ndurante el trabajo de parto y el parto para garantizar una experiencia de parto limpia y positiva\n\n- (iii) identificar y\natender o derivar mujeres y/o recién nacidos con complicaciones\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.1.2&seriesCode=SH_STA_BRTC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de partos atendidos por personal sanitario especializado (%) SH_STA_BRTC</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-01-02.pdf\">Metadatos 3-1-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Proporción de partos atendidos por personal sanitario especializado", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.1- De aquí a 2030, reducir la tasa mundial de mortalidad materna a menos de 70 por cada 100.000 nacidos vivos", "definicion"=>"Proportion of births attended by skilled health personnel", "formula"=>"\n$$PPAR_{attended}^{t} = \\frac{PAR_{attended}^{t}}{PAR^{t}} \\cdot 100$$\n\nwhere:\n\n$PAR_{attended}^{t} =$ births attended by skilled health personnel in year $t$\n\n$PAR^{t} =$ births in year $t$\n", "desagregacion"=>"Province", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Proportion of births attended by skilled health personnel (generally doctors, \nnurses or midwives but can refer to other health professionals providing childbirth \ncare) is the proportion of childbirths attended by skilled health personnel. According to \nthe current definition these are competent maternal and newborn health (MNH) professionals \neducated, trained and regulated to national and international standards. They are competent to: \n\n- (i) provide and promote evidence-based, human-rights based, quality, socio-culturally sensitive \nand dignified care to women and newborns\n- (ii) facilitate physiological processes during labour and delivery to ensure a clean and \npositive childbirth experience\n- (iii) identify and manage or refer women and/or newborns with complications\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.1.2&seriesCode=SH_STA_BRTC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of births attended by skilled health personnel (%) SH_STA_BRTC</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-01-02.pdf\">Metadata 3-1-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Proporción de partos atendidos por personal sanitario especializado", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.1- De aquí a 2030, reducir la tasa mundial de mortalidad materna a menos de 70 por cada 100.000 nacidos vivos", "definicion"=>"Osasun-langile espezializatuek artatutako erditzeen proportzioa", "formula"=>"\n$$PPAR_{artatutakoak}^{t} = \\frac{PAR_{artatutakoak}^{t}}{PAR^{t}} \\cdot 100$$\n\nnon:\n\n$PAR_{artatutakoak}^{t} =$ osasun-langile espezializatuek artatutako erditzeak $t$ urtean\n\n$PAR^{t} =$ erditzeak $t$ urtean\n", "desagregacion"=>"Lurralde historikoa", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Kualifikatutako osasun-langileek (oro har, medikuek, erizainek edo emaginek, baina baita erditzeetan laguntzen duten \nbestelako osasun-profesionalek ere) artatutako erditzeen proportzioa neurtzeko, kualifikatutako osasun-langileek \nartatutako erditzeen proportzioa kalkulatzen da. \n\nEgungo definizioaren arabera, nazioko eta nazioarteko estandarrei jarraikiz hezi, trebatu eta araututako osasun-profesionalak \ndira, amen edo jaioberrien arloan. Beren eskumenen artean sartzen dira: \n\n- (i) emakumeei eta jaio berriei ebidentzian, giza eskubideetan eta kalitatean oinarritutako, eta soziokulturalki \nsentikorra den arreta duina ematea eta sustatzea \n\n- (ii) erditzeko prozesu fisiologikoak eta erditzea bera erraztea, erditze-esperientzia garbia eta positiboa \nizan dadin \n\n- (iii) konplikazioak dituzten amak edo jaio berriak identifikatu eta artatu edo bideratzea \n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.1.2&seriesCode=SH_STA_BRTC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Osasun-langile espezializatuek artatutako erditzeen proportzioa (%) SH_STA_BRTC</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-01-02.pdf\">Metadatuak 3-1-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.1.2: Proportion of births attended by skilled health personnel </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_STA_BRTC - Proportion of births attended by skilled health personnel [3.1.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Related to Target 3.1 on reducing maternal mortality, 3.2 on reducing neonatal mortality and 3.8 on achieving universal health coverage (coverage of essential health services)</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&#x2019;s Fund (UNICEF) and World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&#x2019;s Fund (UNICEF) and World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of births attended by skilled health personnel (generally doctors, nurses or midwives but can refer to other health professionals providing childbirth care) is the proportion of childbirths attended by skilled health personnel. According to the current definition<sup><a href=\"#endnote-2\" id=\"endnote-ref-2\">[1]</a></sup> these are competent maternal and newborn health (MNH) professionals educated, trained and regulated to national and/or international standards. They are competent to: (i) provide and promote evidence-based, human-rights based, quality, socio-culturally sensitive and dignified care to women and newborns; (ii) facilitate physiological processes during labour and delivery to ensure a clean and positive childbirth experience; and (iii) identify and manage or refer women and/or newborns with complications. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"endnote-2\">1</sup><p> Definition of skilled health personnel providing care during childbirth 2018 joint statement by WHO, UNFPA, UNICEF, ICM, ICN, FIGO and IPA. <a href=\"https://www.who.int/reproductivehealth/publications/statement-competent-mnh-professionals/en/\">https://www.who.int/reproductivehealth/publications/statement-competent-mnh-professionals/en/</a>. <a href=\"#endnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>This indicator is reported in proportion (or percentage (%))</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>An important aspect of this indicator is the reporting of categories or occupational titles of health providers at country level. Standard categories for the indicator include doctor, nurse and midwife. However, some additional categories are currently being reported by some countries. When that is the case, a process of verification is conducted in which the competency level of other categories of health care providers is assessed with national sources and in communication with national counterparts. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>National-level household surveys are the main data sources used to collect data for skilled health personnel providing childbirth care. These surveys include Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS) and other national surveys based on similar methodologies. In these surveys the respondent is asked about the last live birth(s) and who helped during delivery for a period up to five years before the interview.</p>\n<p>Surveys are generally undertaken every three to five years. </p>\n<p>Population-based surveys are the preferred data source in countries with a low utilization of childbirth services, where private sector data are excluded from routine data collection, and/or with weak health information systems. </p>\n<p>Routine service/facility records are a more common data source in countries where a high proportion of births occur in health facilities and are therefore recorded. These data can be used to track the indicator on an annual basis.</p>", "COLL_METHOD__GLOBAL"=>"<p>UNICEF and WHO maintain a joint database on SDG 3.1.2: &#x201C;Proportion of births attended by skilled health personnel&#x201D; and collaborate to ensure quality and consistency of data sources. </p>\n<p> </p>\n<p>As part of the data harmonization process and interaction with countries, an annual country consultation is conducted by UNICEF. During the country consultation, SDG country focal points are contacted for updating and verifying values included in the database and for obtaining new data sources. New data sources are reviewed and assessed jointly with WHO. As part of the process, the national categories or occupational titles of skilled health personnel are verified. The reported data for some countries may include additional categories of trained personnel beyond doctor, nurse and midwife. </p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>UNICEF/WHO joint database is updated on an annual basis. However, not all countries report new data on an annual basis. Countries reporting data from household surveys, may report a new value every three-five years, according to their data collection schedule. Data reported from routine administrative sources are regularly available on an annual basis. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Country reported data and global and regional estimates are published annually; in April by UNICEF in the data website <a href=\"http://www.data.unicef.org\">www.data.unicef.org</a><sup><a href=\"#endnote-3\" id=\"endnote-ref-3\">[2]</a></sup> and by the World Health Organization in May in the World Health Statistics Report (<a href=\"http://www.who.int/whosis/whostat/en/\" target=\"_blank\">http://www.who.int/whosis/whostat/en/</a>) and the WHO Global Health Observatory (<a href=\"https://www.who.int/data/gho\">https://www.who.int/data/gho</a>). UNICEF also reports this indicator in the State of the World&#x2019;s Children report which is on a bi-annual reporting schedule (https://www.unicef.org/reports/state-of-worlds-children). </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"endnote-3\">2</sup><p> Joint UNICEF/WHO database of skilled health personnel, based on population-based national household survey data and routine health systems. <a href=\"https://data.unicef.org/topic/maternal-health/delivery-care/#\">https://data.unicef.org/topic/maternal-health/delivery-care/#</a>. <a href=\"#endnote-ref-3\">&#x2191;</a></p></div></div>", "DATA_SOURCE__GLOBAL"=>"<p>Ministries of Health and National Statistical Offices, either through household surveys or routine sources. </p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Children&#x2019;s Fund (UNICEF) and World Health Organization (WHO). </p>", "INST_MANDATE__GLOBAL"=>"<p>UNICEF and WHO are co-custodians for the compilation and reporting of this indicator. </p>", "RATIONALE__GLOBAL"=>"<p>Having a skilled health care provider at the time of childbirth is an important lifesaving intervention for both women and newborns. Not having access to this key assistance is detrimental to women&apos;s and newborns&#x2019; health because it could cause adverse health outcomes such as the death of the women and/or the newborns or long lasting morbidity. Achieving universal coverage is therefore essential for reducing maternal and newborn mortality and morbidity. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Births attended by skilled health personnel is an indicator of health care utilization. It is a measure of the health system&#x2019;s functioning and potential to provide adequate coverage for childbirth. On its own, however, this indicator does not provide insight into the availability or accessibility of services, for example in cases where emergency care is needed. Neither does this indicator capture the quality of care received.</p>\n<p>Data collection and data interpretation in many countries is challenged by lack of guidelines, standardization of professional titles and functions of the health care provider, and in some countries by task-shifting. In addition, many countries have found that there are large gaps between international standards and the competencies of existing health care professionals providing childbirth care. Lack of training and an enabling environment often hinder evidence-based management of common obstetric and neonatal complications.</p>", "DATA_COMP__GLOBAL"=>"<p>Numerator: </p>\n<p>Number of births attended by skilled health personnel (doctor, nurse or midwife) trained in providing quality childbirth care, including giving the necessary support and care in the immediate postpartum period </p>\n<p>Denominator: The total number of live births in the same period. </p>\n<p>Births attended by skilled health personnel = (number of births attended by skilled health personnel)/(total number of live births) x 100.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>As part of the data harmonization process, an annual country consultation is conducted by UNICEF. Country inputs are reviewed and assessed jointly with WHO. During the process, SDG country focal points are contacted for updating and verifying values included in the database and obtaining new sources of data. The national categories of skilled health personnel are verified, and the estimates for some countries may include additional categories of trained personnel beyond doctor, nurse, and midwife. This process serves as validation of the reported values. </p>\n<p>Furthermore, with regards to data obtained from surveys, the validity of such data depends on the correct identification by the women of the credentials of the person attending the childbirth, which may not be obvious in certain countries.</p>", "ADJUSTMENT__GLOBAL"=>"<p>In cases where reporting of skilled categories or occupational titles is not consistent with previous years or with categories considered skilled at country level, reported values may be adjusted. When this is done, the process is consulted with countries. </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>There is no treatment of missing values at country level. If a value is missing for a given year, then there is no reporting of that value. </p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>For the calculation of the regional and global aggregates, modelled annualized country level estimates are used. </p>", "REG_AGG__GLOBAL"=>"<p>Regional and global estimates are calculated using weighted averages. Annual number of live births from United Nations Population Division, World Population Prospects<sup><a href=\"#endnote-4\" id=\"endnote-ref-4\">[3]</a></sup> are used as the weighting indicator. Regional values are calculated for a reference year using modelled annualized country level times series estimates.<strong> </strong>The time series are calculated using a Bayesian hierarchical time series AR(1) model with region- and country-specific intercepts and slopes.<strong> </strong></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"endnote-4\">3</sup><p> United Nations Population Division, World Population Prospects. <a href=\"https://population.un.org/wpp/Download/Standard/Population/\">https://population.un.org/wpp/Download/Standard/Population/</a>. <a href=\"#endnote-ref-4\">&#x2191;</a></p></div></div>", "DOC_METHOD__GLOBAL"=>"<p>Definition of skilled health personnel varies between countries. The proportion of births attended by skilled health personnel is calculated as the number of births attended by skilled health personnel (doctors, nurses or midwives) expressed as a proportion of the number of live births in the same period.</p>\n<p>In household surveys, such as DHS, MICS and RHS, the respondent is asked about the most recent birth(s) and who helped during childbirth for a period up to five years before the interview. For consistency of reporting, survey customization teams in country are encouraged to review categories or occupational title of health care providers reported on the previous surveys and ensure comparability. Service/facility records could be used where a high proportion of births occur in health facilities and are recorded. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data are reported to UNICEF on an annual basis during the country consultation. Values are reviewed and assessed to make sure that reported indicator complies with standard definition and methodology. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p> </p>\n<p>As part of the data harmonization process an annual country consultation process is conducted by UNICEF. Country inputs are reviewed and assessed jointly with WHO. During the process, SDG country focal points are contacted for updating and verifying values included in the data bases and obtaining new sources of data. The national categories of skilled health personnel are verified, and the reported data for some countries may include additional categories of trained personnel beyond doctor, nurse, and midwife.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data included in the database is verified through an annual country consultation process and data harmonization process conducted by the two custodian agencies: UNICEF and WHO. All values are also assessed for consistency in terms of standard definition, representativeness, source of information, and quality. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability: </strong></p>\n<p>Data are available for 192 countries. </p>\n<p>The lag between the reference year and actual production of data series depends on the availability of the household survey for each country. </p>\n<p><strong>Time series:</strong></p>\n<p>2000-2024</p>\n<p><strong>Disaggregation: </strong></p>\n<p>For this indicator, when data are reported from household surveys, disaggregation is available for various socio-economic characteristics including age of the mother, residence (urban/rural), household wealth (quintiles), education level of the mother, maternal age, geographic regions. When data are reported from administrative sources, disaggregation is more limited and tend to include only residence. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong> </p>\n<p>Discrepancies are possible if there are national figures compiled at the health facility level. These would differ from the global figures, which are typically based on survey data collected at the household level. </p>\n<p>In terms of survey data, some survey reports may present a total percentage of births attended by a skilled health professional that does not conform to the definition (e.g., total includes provider that is not considered skilled, such as a community health worker). In that case, the proportion of childbirths by a physician, nurse, or a midwife are totalled, consulted with the country and included in the global database as the SDG estimate. </p>\n<p>In some countries where the indicator on skilled health personnel is not actively reported, birth in a health facility (institutional births) is used as a proxy indicator. This is frequent in countries in the Latin America region, in European and Central Asian regions, where the proportion of births attended by health professionals is very high. Nonetheless, it should be noted that institutional births may underestimate the percentage of births assisted by skilled health professionals, particularly in cases were home births - assisted by skilled health professionals - are prevalent. </p>", "OTHER_DOC__GLOBAL"=>"<p> </p>\n<p><strong>References:</strong> </p>", "indicator_sort_order"=>"03-01-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.2.1", "slug"=>"3-2-1", "name"=>"Tasa de mortalidad de niños menores de 5 años", "url"=>"/site/es/3-2-1/", "sort"=>"030201", "goal_number"=>"3", "target_number"=>"3.2", "global"=>{"name"=>"Tasa de mortalidad de niños menores de 5 años"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[{"series"=>"", "unit"=>"", "label_content"=>"Objetivo del indicador para 2030", "value"=>25}], "graph_title"=>"Tasa de mortalidad de niños menores de 5 años", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de mortalidad de niños menores de 5 años", "indicator_number"=>"3.2.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Meta:</b> Reducir al menos a 25 por cada 1.000 nacidos vivos", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_28/opt_0/tipo_1/ti_defunciones/temas.html", "url_text"=>"Estadística de defunciones", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tasa de mortalidad de niños menores de 5 años", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.2- De aquí a 2030, poner fin a las muertes evitables de recién nacidos y de niños menores de 5 años, logrando que todos los países intenten reducir la mortalidad neonatal al menos a 12 por cada 1.000 nacidos vivos y la mortalidad de los niños menores de 5 años al menos a 25 por cada 1.000 nacidos vivos", "definicion"=>"Defunciones de menores de 5 años por cada 1.000 nacimientos", "formula"=>"\n$$TM_{0-4}^{t} = \\frac{D_{0-4}^{t}}{N^{t}} \\cdot 1.000$$\n\ndonde:\n\n$D_{0-4}^{t} =$ defunciones de menores de 5 años cumplidos de vida en el año $t$\n\n$N^{t} =$ nacimientos en el año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Las tasas de mortalidad entre los niños pequeños son un indicador clave de resultados para la salud y el bienestar infantil y,\nen términos más generales, para el desarrollo social y económico. Es un indicador de salud pública muy seguido\nporque refleja el acceso de los niños y las comunidades a intervenciones sanitarias básicas como\nvacunación, tratamiento médico de enfermedades infecciosas y nutrición adecuada.\n\nLa tasa de mortalidad de menores de cinco años es la probabilidad de que un niño nacido en un año o período específico muera antes\nalcanzar la edad de 5 años, si está sujeto a las tasas de mortalidad específicas por edad de ese período, expresadas como defunciones\npor 1.000 nacidos vivos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.2.1&seriesCode=SH_DYN_MORT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y%20%7C%20BOTHSEX\">Tasa de mortalidad de menores de cinco años, por sexo (muertes por cada 1.000 nacidos vivos) SH_DYN_MORT</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-02-01.pdf\">Metadatos 3-2-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Tasa de mortalidad de niños menores de 5 años", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.2- De aquí a 2030, poner fin a las muertes evitables de recién nacidos y de niños menores de 5 años, logrando que todos los países intenten reducir la mortalidad neonatal al menos a 12 por cada 1.000 nacidos vivos y la mortalidad de los niños menores de 5 años al menos a 25 por cada 1.000 nacidos vivos", "definicion"=>"Deaths of children under 5 years old per 1.000 births", "formula"=>"\n$$TM_{0-4}^{t} = \\frac{D_{0-4}^{t}}{N^{t}} \\cdot 1.000$$\n\nwhere:\n\n$D_{0-4}^{t} =$ deaths of children under 5 years of age in year $t$\n\n$N^{t} =$  births in year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nMortality rates among young children are a key output indicator for child \nhealth and well-being, and, more broadly, for social and economic development. \nIt is a closely watched public health indicator because it reflects the access \nof children and communities to basic health interventions such as vaccination, \nmedical treatment of infectious diseases and adequate nutrition.\n\nThe under-five mortality rate is the probability of a child born in a specific \nyear or period dying before reaching the age of 5 years, if subject to age-specific \nmortality rates of that period, expressed as deaths per 1000 live births.\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.2.1&seriesCode=SH_DYN_MORT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y%20%7C%20BOTHSEX\">Under-five mortality rate, by sex (deaths per 1,000 live births) SH_DYN_MORT</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-02-01.pdf\">Metadata 3-2-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Tasa de mortalidad de niños menores de 5 años", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.2- De aquí a 2030, poner fin a las muertes evitables de recién nacidos y de niños menores de 5 años, logrando que todos los países intenten reducir la mortalidad neonatal al menos a 12 por cada 1.000 nacidos vivos y la mortalidad de los niños menores de 5 años al menos a 25 por cada 1.000 nacidos vivos", "definicion"=>"5 urtetik beherakoen heriotzak 1.000 jaiotzako", "formula"=>"\n$$TM_{0-4}^{t} = \\frac{D_{0-4}^{t}}{N^{t}} \\cdot 1.000$$\n\nnon:\n\n$D_{0-4}^{t} =$ 5 urtetik beherakoen heriotzak $t$ urtean\n\n$N^{t} =$  jaiotzak $t$ urtean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Haur txikien arteko heriotza-tasak adierazle gakoak dira haurren osasuna eta ongizatea neurtzeko eta, \noro har, garapen sozial eta ekonomikoa aztertzeko. Osasun publikoko adierazle oso jarraitua da, haurrek \neta komunitateek oinarrizko esku-hartze sanitarioetara duten sarbidea islatzen duelako, besteak beste \ntxertaketa, infekzio-gaixotasunen tratamendu medikoa edo nutrizio egokia eskuratzeko orduan. \n\nBost urtetik beherakoen heriotza-tasa urte eta aldi jakin batean jaiotako haur bat 5 urteak bete baino \nlehen hiltzeko arriskua da, baldin eta aldi horretan adinaren araberako heriotza tasa espezifikoen pean \nbadago (bizirik jaiotako 1.000 umeko heriotzen arabera adierazia). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.2.1&seriesCode=SH_DYN_MORT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y%20%7C%20BOTHSEX\">5 urtetik beherakoen heriotza-tasa, sexuaren arabera (heriotzak bizirik jaiotako 1.000 haur bakoitzeko) SH_DYN_MORT</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-02-01.pdf\">Metadatuak 3-2-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.2.1: Under&#x2011;5 mortality rate</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_DYN_IMRT - Infant mortality rate [3.2.1]</p>\n<p>SH_DYN_IMRTN - Infant deaths (number) [3.2.1]</p>\n<p>SH_DYN_MORT - Under-five mortality rate [3.2.1]</p>\n<p>SH_DYN_MORTN - Under-five deaths (number) [3.2.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>3.2.2: Neonatal mortality rate</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The under-five mortality rate is the probability of a child born in a specific year or period dying before reaching the age of 5 years, if subject to age-specific mortality rates of that period, expressed as deaths per 1000 live births.</p>\n<p> </p>\n<p><strong>Concepts:</strong></p>\n<p>The under-five mortality rate as defined here is, strictly speaking, not a rate (i.e. the number of deaths divided by the number of population at risk during a certain period of time), but a probability of death derived from a life table and expressed as a rate per 1000 live births. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number (SH_DYN_MORTN); Deaths per 1,000 live births (SH_DYN_MORT)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Nationally representative estimates of child mortality can be derived from several different sources, including civil registration and sample surveys. Demographic surveillance sites and hospital data are excluded as they are not nationally representative. The preferred source of data is a civil registration system that records births and deaths on a continuous basis. If registration is complete and the system functions efficiently, the resulting estimates will be accurate and timely. However, many countries do not have well-functioning vital registration systems. In such cases, household surveys, such as the UNICEF-supported Multiple Indicator Cluster Surveys (MICS), the USAID-supported Demographic and Health Surveys (DHS) and periodic population censuses have become the primary sources of data on under-five mortality. These surveys ask women about the survival of their children, and it is these reports that provide the basis of child mortality estimates for a majority of low- and middle-income countries. These data are subject to sampling and non-sampling errors, which might be substantial. </p>\n<p> </p>\n<p><strong>Civil registration </strong></p>\n<p>Civil registration is the preferred data source for under-five, infant and neonatal mortality estimation. The calculation of the under-five and infant mortality rates from civil registration data is derived from a standard period abridged life table using available data on the number of deaths and mid-year populations. For civil registration data), initially annual observations were constructed for all observation years in a country. </p>\n<p> </p>\n<p><strong>Population census and household survey data </strong></p>\n<p>Most survey data come in one of two forms: the full birth history (FBH), whereby women are asked for the date of birth of each of their children, whether the child is still alive, and if not, the age at death; and the summary birth history (SBH), whereby women are asked only about the number of their children ever born and the number that have died (or equivalently the number still alive). </p>", "COLL_METHOD__GLOBAL"=>"<p>For under-five mortality, UNICEF and the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) compile data from all available data sources, including household surveys, censuses, and vital registration data. UNICEF and the UN IGME compile these data whenever they are available publicly and then conduct data quality assessment. UNICEF also collects data through UNICEF country offices by reaching national counterpart(s). The UN IGME also collects vital registration data reported by Ministries of Health or other relevant agencies to WHO. </p>\n<p> </p>\n<p>To increase the transparency of the estimation process, the UN IGME has developed a child mortality web portal, https://childmortality.org/, which includes all available data and shows estimates for each country. Once the new estimates are finalized, the web portal will be updated to reflect all available data and the new estimates. </p>", "FREQ_COLL__GLOBAL"=>"<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) underlying database is continuously updated whenever new empirical data become available. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>A new round of United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates is released annually, usually in the 3<sup>rd</sup> or 4<sup>th</sup> quarter. </p>", "DATA_SOURCE__GLOBAL"=>"<p>The National Statistical Office or the Ministry of Health is the typical provider of data for generating under-five mortality estimates at the national level. </p>\n<p> </p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Children&#x2019;s Fund (UNICEF)</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), led by the United Nations Children&#x2019;s Fund (UNICEF) and including members from the World Health Organization (WHO), the World Bank Group and the United Nations Population Division, was established in 2004 to advance the work on monitoring progress towards the achievement of child survival goals and to augment country capacity to collect high quality data on and produce timely estimates of child mortality. Every year, the UN IGME estimates levels and trends in under-5 mortality at the global, regional and country level and provides an assessment of current progress towards the SDG targets.</p>", "RATIONALE__GLOBAL"=>"<p>Mortality rates among young children are a key output indicator for child health and well-being, and, more broadly, for social and economic development. It is a closely watched public health indicator because it reflects the access of children and communities to basic health interventions such as vaccination, medical treatment of infectious diseases and adequate nutrition. </p>", "REC_USE_LIM__GLOBAL"=>"<p>A civil registration system that continuously records all births and deaths in a population is the preferred source of high-quality underlying data on under-five mortality but these systems are not well developed in many low- and middle-income countries. Instead, household surveys and population censuses are the primary sources of underlying data in these countries. </p>\n<p>The reliance on multiple data sources, i.e. surveys and census conducted several years apart and producing retrospective time series, can result in disparate mortality rates from different sources, sometimes referring to the same time period. Available data also suffer from sampling and nonsampling errors, including misreporting of age and sex, survivor selection bias, underreporting of child deaths, and recall errors as data are collected retrospectively. Further misclassifications can also impact the accuracy of data, for example early neonatal deaths may be classified as stillbirths. Thus, simply comparing two country data points from different sources and drawing a line between them is not a technically sound way to assess levels and trends. Given varying levels of data quality across different sources, this sort of trend assessment will provide misleading results. Hence, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) fits a statistical model to these data that takes into account these various data sources to produce annualized estimates. </p>\n<p>It is important to keep these challenges in mind when looking at available country data and also when discrepancies between country data and the UN IGME estimates are being discussed. The following</p>\n<p>points are important to highlight:</p>\n<ul>\n  <li>The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates of child mortality. Thus, UN IGME estimates are derived from country data. Notably, UN IGME assesses the quality of underlying data sources and adjusts data when necessary.</li>\n  <li>National estimates may refer to an earlier calendar year than the UN IGME estimates. This is particularly the case where estimates from the most recent national survey are used as the national estimate, since the survey estimates derived from a birth history are retrospective and typically refer to a period before the year of the survey, which may be several years behind the target year for the UN IGME estimates. National estimates may also use a different combination of data sources, or different projection or calculation methods.</li>\n  <li>In the absence of error-free data, there will always be uncertainty around data and estimates. To allow for added comparability, the UN IGME generates estimates with uncertainty bounds. When discussing the UN IGME estimates, it&#x2019;s important to look at the uncertainty ranges, which might be fairly wide in the case of some countries.</li>\n</ul>", "DATA_COMP__GLOBAL"=>"<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are derived from nationally representative data from censuses, surveys or vital registration systems. The UN IGME does not use any covariates to derive its estimates. It only applies a curve fitting method to good-quality empirical data to derive trend estimates after data quality assessment. In most cases, the UN IGME estimates are close to the underlying data. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates. The UN IGME applies the Bayesian B-splines bias-reduction model to empirical data to derive trend estimates of under-five mortality for all countries. See references for details. </p>\n<p> </p>\n<p>For the underlying data mentioned above, the most frequently used methods are as follows: </p>\n<p> </p>\n<p>Civil registration: The under-five mortality rate can be derived from a standard period abridged life table using the age-specific deaths and mid-year population counts from civil registration data to calculate death rates, which are then converted into age-specific probabilities of dying. </p>\n<p> </p>\n<p>Census and surveys: An indirect method is used based on a summary birth history, a series of questions asked of each woman of reproductive age as to how many children she has ever given birth to and how many are still alive. The Brass method and model life tables are then used to obtain an estimate of under-five and infant mortality rates. Censuses often include questions on household deaths in the last 12 months, which can also be used to calculate mortality estimates. </p>\n<p> </p>\n<p>Surveys: A direct method is used based on a full birth history, a series of detailed questions on each child a woman has given birth to during her lifetime. Neonatal, post-neonatal, infant, child and under-five mortality estimates can be derived from the full birth history. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) conducts an annual country consultation whereby the draft UN IGME estimates, empirical data used to derive the estimates, and notes on methodology are sent to National Statistical Offices and to Ministries of Health or other relevant agencies for review. National Statistical Offices, Ministries of Health or other relevant agencies have the opportunity to provide feedback or comments on estimates and methods, as well as supply additional empirical data during this consultation. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Direct estimates from survey data are adjusted in high prevalence HIV settings for under-reporting of under-five mortality due to &#x2018;missing mothers,&#x2019; i.e. women who have died from HIV/AIDS and cannot report on the mortality experience of their children. Furthermore, United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are also adjusted to capture rapidly changing mortality rates due to HIV/AIDS and crises/disasters that are not well captured in survey data. </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are based on underlying empirical data. If the empirical data refer to an earlier reference period than the end year of the period the estimates are reported, the UN IGME extrapolates the estimates to the common end year. The UN IGME does not use any covariates to derive the estimates.</p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>To construct aggregate estimates of under-five mortality before 1990, regional averages of mortality rates were used for country-years with missing information and weighted by the respective population in the country-year. </p>", "REG_AGG__GLOBAL"=>"<p>Global and regional estimates of under-five mortality rates are derived using the aggregated number of country-specific under-five deaths for a specific region or globally estimated by the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) using a birth-week cohort approach and aggregated country-specific births from the United Nations Population Division. </p>", "DOC_METHOD__GLOBAL"=>"<p>Detailed methodological descriptions can be found at <a href=\"https://childmortality.org/methods\">https://childmortality.org/methods</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) applies a standard estimation method across all countries in the interest of comparability. This method aims to estimate a smooth trend curve of age-specific mortality rates, accounting for potential outliers and biases in data sources and averaging over the possibly many disparate data sources for a country. A more detailed description of the different phases of the statistical production process is available in the annual UN IGME report and at <a href=\"https://childmortality.org/methods\">https://childmortality.org/methods</a>.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality is assured by applying standard statistical and demographic methods to all input data and conducting regular data quality assessments. Countries are also consulted on the draft estimates during the annual country consultation process. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) aims to produce transparent, timely and accurate annual estimates of under-five mortality. Data quality is critical to that end. The UN IGME assesses data quality using both internal and external validity checks and does not include data sources with substantial non-sampling errors or omissions as underlying empirical data in its statistical model.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>This indicator is available for all countries from 1990 (or earlier depending on the availability of empirical data for each country before 1990) to the most recent target reference year, typically one or two years behind the current calendar year. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregation is available by sex, age (neonatal, infant, child) and wealth quintile.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong> </p>\n<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are based on nationally representative data. Countries may use a single source as their official estimate or apply methods different from the UN IGME methods to derive official national estimates. The differences between the UN IGME estimates and national official estimates are usually not large if empirical data are of high quality.</p>\n<p> </p>\n<p>Many countries lack a single source of high-quality data covering the last several decades, instead relying on multiple data sources to estimate mortality. Data from different sources require different calculation methods and may suffer from different errors, for example random errors in sample surveys or systematic errors due to misreporting. As a result, different surveys often yield widely different estimates of under-five mortality for a given time period and available data collected by countries are often inconsistent across sources. It is important to analyse, reconcile and evaluate all data sources simultaneously for each country.</p>\n<p>Each new survey or data point must be examined in the context of all other sources, including previous data, and with respect to any sampling or non-sampling errors that may be present (such as misreporting of age and survivor selection bias; underreporting of child deaths is also common). The UN IGME assesses the quality of underlying data sources and adjusts data when necessary. Furthermore, the latest data produced by countries often are not current estimates but refer to an earlier reference period. Thus, the UN IGME also extrapolates estimates to a common reference year. </p>\n<p>In order to reconcile these differences and take better account of the systematic biases associated with the various types of data inputs, the UN IGME has developed an estimation method to fit a smoothed trend curve to a set of observations and to extrapolate that trend to a defined time point. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates of child mortality. In the absence of error-free data, there will always be uncertainty around data and estimates. To allow for added comparability, the UN IGME generates such estimates with uncertainty bounds. Applying a consistent methodology also allows for comparisons between countries, despite the varied number and types of data sources. The UN IGME applies a common methodology across countries and uses original empirical data from each country but does not report figures produced by individual countries using other methods, which would not be comparable to other country estimates.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p>All data sources, estimates and detailed methods are documented on the website <a href=\"https://childmortality.org/\">https://childmortality.org/</a></p>\n<p> </p>\n<p><strong>References:</strong> </p>\n<p>United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Levels and trends in child mortality. Report 2024. New York: UNICEF, 2025. Available at <a href=\"https://childmortality.org/wp-content/uploads/2025/03/UNIGME-2024-Child-Mortality-Report.pdf\">https://childmortality.org/wp-content/uploads/2025/03/UNIGME-2024-Child-Mortality-Report.pdf</a></p>\n<p>United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Subnational Under-five and Neonatal Mortality Estimates, 2000&#x2013;2021. New York: UNICEF, 2023. Available at <a href=\"https://childmortality.org/wp-content/uploads/2023/10/UN-IGME_Subnational_U5MR_and_NMR_2000-2021-1.pdf\">https://childmortality.org/wp-content/uploads/2023/10/UN-IGME_Subnational_U5MR_and_NMR_2000-2021-1.pdf</a></p>\n<p>Alkema L, New JR. Global estimation of child mortality using a Bayesian B-spline bias-reduction method. The Annals of Applied Statistics. 2014; 8(4): 2122&#x2013;2149. Available at: <a href=\"https://arxiv.org/abs/1309.1602\">https://arxiv.org/abs/1309.1602</a> </p>\n<p> </p>\n<p>Alkema L, Chao F, You D, Pedersen J, Sawyer CC. National, regional, and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: a systematic assessment. The Lancet Global Health. 2014; 2(9): e521&#x2013;e530. </p>\n<p> </p>\n<p>Pedersen J, Liu J. Child Mortality Estimation: Appropriate Time Periods for Child Mortality Estimates from Full Birth Histories. Plos Medicine. 2012;9(8). Available at: <a href=\"http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001289\">http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001289</a></p>\n<p> </p>\n<p>Silva R. Child Mortality Estimation: Consistency of Under-Five Mortality Rate Estimates Using Full Birth Histories and Summary Birth Histories. Plos Medicine. 2012;9(8). Available at: <a href=\"http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001296\">http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001296</a></p>\n<p> </p>\n<p>Walker N, Hill K, Zhao FM. Child Mortality Estimation: Methods Used to Adjust for Bias due to AIDS in Estimating Trends in Under-Five Mortality. Plos Medicine. 2012;9(8). Available at: <a href=\"http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001296\">http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001296</a></p>", "indicator_sort_order"=>"03-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.2.2", "slug"=>"3-2-2", "name"=>"Tasa de mortalidad neonatal", "url"=>"/site/es/3-2-2/", "sort"=>"030202", "goal_number"=>"3", "target_number"=>"3.2", "global"=>{"name"=>"Tasa de mortalidad neonatal"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[{"series"=>"", "unit"=>"", "label_content"=>"Objetivo del indicador para 2030", "value"=>12}], "graph_title"=>"Tasa de mortalidad neonatal", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de mortalidad neonatal", "indicator_number"=>"3.2.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Meta:</b> Reducir al menos a 12 por cada 1.000 nacidos vivos", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_28/opt_0/tipo_1/ti_defunciones/temas.html", "url_text"=>"Estadística de defunciones", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tasa de mortalidad neonatal", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.2- De aquí a 2030, poner fin a las muertes evitables de recién nacidos y de niños menores de 5 años, logrando que todos los países intenten reducir la mortalidad neonatal al menos a 12 por cada 1.000 nacidos vivos y la mortalidad de los niños menores de 5 años al menos a 25 por cada 1.000 nacidos vivos", "definicion"=>"Defunciones de menores de 28 días por cada 1.000 nacimientos", "formula"=>"\n$$TM_{neonatal}^{t} = \\frac{D_{neonatal}^{t}}{N^{t}} \\cdot 1.000$$\n\ndonde:\n\n$D_{neonatal}^{t} =$ defunciones de menores de 28 días cumplidos de vida en el año $t$\n\n$N^{t} =$ nacimientos en el año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Las tasas de mortalidad entre los niños pequeños son un indicador clave de resultados para la salud y el bienestar infantil y,\nen términos más generales, para el desarrollo social y económico. Es un indicador de salud pública muy seguido\nporque refleja el acceso de los niños y las comunidades a intervenciones sanitarias básicas como\nvacunación, tratamiento médico de enfermedades infecciosas y nutrición adecuada.\n\nLa tasa de mortalidad neonatal es la probabilidad de que un niño nacido en un año o período específico muera durante\nlos primeros 28 días completos de vida, si están sujetos a las tasas de mortalidad específicas por edad de ese período, expresadas por\n1.000 nacidos vivos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.2.2&seriesCode=SH_DYN_NMRT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C1M%20%7C%20BOTHSEX\">Tasa de mortalidad neonatal (muertes por cada 1.000 nacidos vivos) SH_DYN_NMRT</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-02-02.pdf\">Metadatos 3-2-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Tasa de mortalidad neonatal", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.2- De aquí a 2030, poner fin a las muertes evitables de recién nacidos y de niños menores de 5 años, logrando que todos los países intenten reducir la mortalidad neonatal al menos a 12 por cada 1.000 nacidos vivos y la mortalidad de los niños menores de 5 años al menos a 25 por cada 1.000 nacidos vivos", "definicion"=>"Deaths of infants under 28 days old per 1.000 births", "formula"=>"\n$$TM_{neonatal}^{t} = \\frac{D_{neonatal}^{t}}{N^{t}} \\cdot 1.000$$\n\nwhere:\n\n$D_{neonatal}^{t} =$ deaths of children under 28 days old in year $t$\n\n$N^{t} =$ births in year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nMortality rates among young children are a key output indicator for child \nhealth and well-being, and, more broadly, for social and economic development. \nIt is a closely watched public health indicator because it reflects the access \nof children and communities to basic health interventions such as vaccination, \nmedical treatment of infectious diseases and adequate nutrition.\n\nThe neonatal mortality rate is the probability that a child born in a specific \nyear or period will die during the first 28 completed days of life, if subject \nto age-specific mortality rates of that period, expressed per 1000 live births.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.2.2&seriesCode=SH_DYN_NMRT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C1M%20%7C%20BOTHSEX\">Neonatal mortality rate (deaths per 1,000 live births) SH_DYN_NMRT</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-02-02.pdf\">Metadata 3-2-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Tasa de mortalidad neonatal", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.2- De aquí a 2030, poner fin a las muertes evitables de recién nacidos y de niños menores de 5 años, logrando que todos los países intenten reducir la mortalidad neonatal al menos a 12 por cada 1.000 nacidos vivos y la mortalidad de los niños menores de 5 años al menos a 25 por cada 1.000 nacidos vivos", "definicion"=>"28 egun baino gutxiagoko haurren heriotzak, 1.000 jaiotzako", "formula"=>"\n$$TM_{jaioberriak}^{t} = \\frac{D_{jaioberriak}^{t}}{N^{t}} \\cdot 1.000$$\n\nnon:\n\n$D_{jaioberriak}^{t} =$ 28 egun baino gutxiagoko haurren heriotzak $t$ urtean \n\n$N^{t} =$ jaiotzak $t$ urtean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Haur txikien arteko heriotza-tasak adierazle gakoak dira haurren osasuna eta ongizatea neurtzeko eta, \noro har, garapen sozial eta ekonomikoa aztertzeko. Osasun publikoko adierazle oso jarraitua da, haurrek \neta komunitateek oinarrizko esku-hartze sanitarioetara duten sarbidea islatzen duelako, besteak beste \ntxertaketa, infekzio-gaixotasunen tratamendu medikoa edo nutrizio egokia eskuratzeko orduan. \n\nJaio berrien heriotza-tasa urte eta aldi jakin batean jaio berri bat bere bizitzako lehen 28 egun osoetan \nhiltzeko arriskua da, baldin eta aldi horretan adinaren araberako heriotza-tasa espezifikoen pean badago \n(bizirik jaiotako 1.000 umeko heriotzen arabera adierazia). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.2.2&seriesCode=SH_DYN_NMRT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C1M%20%7C%20BOTHSEX\">Jaioberrien heriotza-tasa (heriotzak bizirik jaiotako 1.000 haurreko) SH_DYN_NMRT</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-02-02.pdf\">Metadatuak 3-2-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.2.2: Neonatal mortality rate </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_DYN_NMRT - Neonatal mortality rate [3.2.2]</p>\n<p>SH_DYN_NMRTN - Neonatal deaths (number) [3.2.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>3.2.1: Under-five mortality rate</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>The neonatal mortality rate is the probability that a child born in a specific year or period will die during the first 28 completed days of life, if subject to age-specific mortality rates of that period, expressed per 1000 live births. </p>\n<p> </p>\n<p>Neonatal deaths (deaths among live births during the first 28 completed days of life) may be subdivided into early neonatal deaths, occurring during the first 7 days of life, and late neonatal deaths, occurring after the 7th day but before the 28th completed day of life. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number (SH_DYN_NMRTN); Deaths per 1,000 live births (SH_DYN_NMRT)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Nationally representative estimates of child mortality can be derived from several different sources, including civil registration and sample surveys. Demographic surveillance sites and hospital data are excluded as they are not nationally representative. The preferred source of data is a civil registration system that records births and deaths on a continuous basis. If registration is complete and the system functions efficiently, the resulting estimates will be accurate and timely. However, many countries do not have well-functioning vital registration systems. In such cases household surveys, such as the UNICEF-supported Multiple Indicator Cluster Surveys (MICS), the USAID-supported Demographic and Health Surveys (DHS) and periodic population censuses have become the primary sources of data on under-five and neonatal mortality. These surveys ask women about the survival of their children, and it is these reports that provide the basis of child mortality estimates for a majority of low- and middle-income countries. These data are subject to sampling and non-sampling errors, which might be substantial. </p>\n<p> </p>\n<p><strong>Civil registration </strong></p>\n<p>Civil registration is the preferred data source for under-five, infant and neonatal mortality estimation. Neonatal mortality rates are calculated using the number of neonatal deaths and the number of live births over a period. For civil registration data, initially annual observations were constructed for all observation years in a country. </p>\n<p> </p>\n<p><strong>Population census and household survey data </strong></p>\n<p>The majority of survey data comes from the full birth history (FBH), whereby women are asked for the date of birth of each of their children, whether the child is still alive, and if not the age at death. </p>", "COLL_METHOD__GLOBAL"=>"<p>For neonatal mortality, UNICEF and the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) compile data from all available data sources, including household surveys, censuses, and vital registration data. UNICEF and the UN IGME compile these data whenever they are available publicly and then conduct data quality assessment. UNICEF also collects data through UNICEF country offices by reaching national counterpart(s). The UN IGME also collects vital registration data reported by Ministries of Health or other relevant agencies to WHO. </p>\n<p> </p>\n<p>To increase the transparency of the estimation process, the UN IGME has developed a child mortality web portal, <a href=\"https://childmortality.org/\">https://childmortality.org/</a>, which includes all available data and shows estimates for each country. Once the new estimates are finalized, the web portal will be updated to reflect all available data and the new estimates. </p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) underlying database is continuously updated whenever new empirical data become available. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>A new round of United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates is released annually, usually in the 3<sup>rd</sup> or 4<sup>th</sup> quarter. </p>", "DATA_SOURCE__GLOBAL"=>"<p>The National Statistical Office or the Ministry of Health is the typical provider of data for generating neonatal mortality estimates at the national level. </p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), led by the United Nations Children&#x2019;s Fund (UNICEF) and including members from the World Health Organization (WHO), the World Bank Group and the United Nations Population Division, was established in 2004 to advance the work on monitoring progress towards the achievement of child survival goals and to augment country capacity to collect high quality data on and produce timely estimates of child mortality. Every year, the UN IGME estimates levels and trends in neonatal mortality at the global, regional and country level and provides an assessment of current progress towards the SDG targets.</p>", "RATIONALE__GLOBAL"=>"<p>Mortality rates among young children are a key output indicator for child health and well-being, and, more broadly, for social and economic development. It is a closely watched public health indicator because it reflects the access of children and communities to basic health interventions such as vaccination, medical treatment of infectious diseases and adequate nutrition. </p>", "REC_USE_LIM__GLOBAL"=>"<p>A civil registration system that continuously records all births and deaths in a population is the preferred source of high-quality underlying data on under-five mortality but these systems are not well developed in many low- and middle-income countries. Instead, household surveys and population censuses are the primary sources of underlying data in these countries. </p>\n<p>The reliance on multiple data sources, i.e. surveys and census conducted several years apart and producing retrospective time series, can result in disparate mortality rates from different sources, sometimes referring to the same time period. Available data also suffer from sampling and nonsampling errors, including misreporting of age and sex, survivor selection bias, underreporting of child deaths, and recall errors as data are collected retrospectively. Further misclassifications can also impact the accuracy of data, for example, early neonatal deaths may be classified as stillbirths. Thus, simply comparing two country data points from different sources and drawing a line between them is not a technically sound way to assess levels and trends. Given varying levels of data quality across different sources, this sort of trend assessment will provide misleading results. Hence, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) fits a statistical model to these data that takes into account these various data sources to produce annualized estimates. </p>\n<p>It is important to keep these challenges in mind when looking at available country data and also when discrepancies between country data and the UN IGME estimates are being discussed. The following</p>\n<p>points are important to highlight:</p>\n<ul>\n  <li>The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates of child mortality. Thus, UN IGME estimates are derived from country data. Notably, UN IGME assesses the quality of underlying data sources and adjusts data when necessary.</li>\n  <li>National estimates may refer to an earlier calendar year than the UN IGME estimates. This is particularly the case where estimates from the most recent national survey are used as the national estimate, since the survey estimates derived from a birth history are retrospective and typically refer to a period before the year of the survey, which may be several years behind the target year for the UN IGME estimates. National estimates may also use a different combination of data sources, or different projection or calculation methods.</li>\n  <li>In the absence of error-free data, there will always be uncertainty around data and estimates. To allow for added comparability, the UN IGME generates estimates with uncertainty bounds. When discussing the UN IGME estimates, it&#x2019;s important to look at the uncertainty ranges, which might be fairly wide in the case of some countries.</li>\n</ul>", "DATA_COMP__GLOBAL"=>"<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are derived from nationally representative data from censuses, surveys or vital registration systems. The UN IGME does not use any covariates to derive its estimates (except in the case of neonatal mortality estimation, which incorporates the relatively more data-rich under-five mortality rate estimates in the modelling). It only applies a curve fitting method to good-quality empirical data to derive trend estimates after data quality assessment. In most cases, the UN IGME estimates are close to the underlying data. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates. The UN IGME produces neonatal mortality rate (NMR) estimates with a Bayesian spline regression model, which models the ratio of neonatal mortality rate / (under-five mortality rate - neonatal mortality rate). Estimates of NMR are obtained by recombining the estimates of the ratio with the UN IGME-estimated under-five mortality rate. See the references for details. </p>\n<p> </p>\n<p>For the underlying data mentioned above, the most frequently used methods are as follows: </p>\n<p> </p>\n<p>Civil registration: The neonatal mortality rate can be calculated from the number of children who died during the first 28 days of life and the number of live births. </p>\n<p> </p>\n<p>Censuses and surveys: Censuses and surveys often include questions on household deaths in the last 12 months, which can be used to calculate mortality estimates. </p>\n<p> </p>\n<p>Surveys: A direct method is used based on a full birth history, a series of detailed questions on each child a woman has given birth to during her lifetime. Neonatal, post-neonatal, infant, child and under-five mortality estimates can be derived from the full birth history. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) conducts an annual country consultation whereby the draft UN IGME estimates, empirical data used to derive the estimates, and notes on methodology are sent to National Statistical Offices and to Ministries of Health or other relevant agencies for review. National Statistical Offices, Ministries of Health or other relevant agencies have the opportunity to provide feedback or comments on estimates and methods, as well as supply additional empirical data during this consultation. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Direct estimates from survey data are adjusted in high prevalence HIV settings for under-reporting of under-five mortality due to &#x2018;missing mothers,&#x2019; i.e. women who have died from HIV/AIDS and cannot report on the mortality experience of their children. Furthermore, United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are also adjusted to capture rapidly changing mortality rates due to HIV/AIDS and crises/disasters that are not well captured in survey data. </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p> </p>\n<p>United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are based on underlying empirical data. If the empirical data refer to an earlier reference period than the end year of the period the estimates are reported, the UN IGME extrapolates the estimates to the common end year. The UN IGME does not use any covariates to derive the estimates (except in the case of neonatal mortality estimation, which incorporates the relatively more data-rich under-five mortality rate estimates in the modelling). </p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p> </p>\n<p>To construct aggregate estimates of neonatal mortality before 1990, regional averages of mortality rates were used for country-years with missing information and weighted by the respective population in the country-year. </p>", "REG_AGG__GLOBAL"=>"<p>Global and regional estimates of neonatal mortality rates are derived using the aggregated number of country-specific neonatal deaths for a specific region or globally estimated by the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) using a birth-week cohort approach and aggregated country-specific births from the United Nations Population Division. </p>", "DOC_METHOD__GLOBAL"=>"<p>Detailed methodological descriptions can be found at <a href=\"https://childmortality.org/methods\">https://childmortality.org/methods</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) applies a standard estimation method across all countries in the interest of comparability. This method aims to estimate a smooth trend curve of age-specific mortality rates, accounting for potential outliers and biases in data sources and averaging over the possibly many disparate data sources for a country. A more detailed description of the different phases of the statistical production process is available in the annual UN IGME report and at <a href=\"https://childmortality.org/methods\">https://childmortality.org/methods</a>.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality is assured by applying standard statistical and demographic methods to all input data and conducting regular data quality assessments. Countries are also consulted on the draft estimates during the annual country consultation process. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) aims to produce transparent, timely and accurate annual estimates of under-five mortality. Data quality is critical to that end. The UN IGME assesses data quality using both internal and external validity checks and does not include data sources with substantial non-sampling errors or omissions as underlying empirical data in its statistical model.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability: </strong></p>\n<p>This indicator is available for all countries from 1990 (or earlier depending on the availability of empirical data for each country before 1990) to the most recent target reference year, typically one or two years behind the current calendar year. </p>\n<p><strong>Disaggregation: </strong></p>\n<p>Due to data limitations, neonatal mortality rates are not estimated for any conventional disaggregation at this time.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong> </p>\n<p>The United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) estimates are based on nationally representative data. Countries may use a single source as their official estimate or apply methods different from the UN IGME methods to derive official national estimates. The differences between the UN IGME estimates and national official estimates are usually not large if empirical data are of high quality. </p>\n<p> </p>\n<p>Many countries lack a single source of high-quality data covering the last several decades, instead relying on multiple data sources to estimate mortality. Data from different sources require different calculation methods and may suffer from different errors, for example random errors in sample surveys or systematic errors due to misreporting. As a result, different surveys often yield widely different estimates of under-five mortality for a given time period and available data collected by countries are often inconsistent across sources. It is important to analyse, reconcile and evaluate all data sources simultaneously for each country. </p>\n<p>Each new survey or data point must be examined in the context of all other sources, including previous data, and with respect to any sampling or non-sampling errors that may be present (such as misreporting of age and survivor selection bias; underreporting of child deaths is also common). The UN IGME assesses the quality of underlying data sources and adjusts data when necessary. Furthermore, the latest data produced by countries often are not current estimates but refer to an earlier reference period. Thus, the UN IGME also extrapolates estimates to a common reference year. </p>\n<p>In order to reconcile these differences and take better account of the systematic biases associated with the various types of data inputs, the UN IGME has developed an estimation method to fit a smoothed trend curve to a set of observations and to extrapolate that trend to a defined time point. The UN IGME aims to minimize the errors for each estimate, harmonize trends over time and produce up-to-date and properly assessed estimates of child mortality. In the absence of error-free data, there will always be uncertainty around data and estimates. To allow for added comparability, the UN IGME generates such estimates with uncertainty bounds. Applying a consistent methodology also allows for comparisons between countries, despite the varied number and types of data sources. The UN IGME applies a common methodology across countries and uses original empirical data from each country but does not report figures produced by individual countries using other methods, which would not be comparable to other country estimates. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p>All data sources, estimates and detailed methods are documented on the website <a href=\"https://childmortality.org\">https://childmortality.org</a>.</p>\n<p> </p>\n<p><strong>References:</strong> </p>\n<p>United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Levels and trends in child mortality. Report 2024. New York: UNICEF, 2025. Available at <a href=\"https://childmortality.org/wp-content/uploads/2025/03/UNIGME-2024-Child-Mortality-Report.pdf\">https://childmortality.org/wp-content/uploads/2025/03/UNIGME-2024-Child-Mortality-Report.pdf</a></p>\n<p>United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Subnational Under-five and Neonatal Mortality Estimates, 2000&#x2013;2021. New York: UNICEF, 2023. Available at <a href=\"https://childmortality.org/wp-content/uploads/2023/10/UN-IGME_Subnational_U5MR_and_NMR_2000-2021-1.pdf\">https://childmortality.org/wp-content/uploads/2023/10/UN-IGME_Subnational_U5MR_and_NMR_2000-2021-1.pdf</a> </p>\n<p> </p>\n<p>Alexander, M. and L. Alkema, Global Estimation of Neonatal Mortality using a Bayesian Hierarchical Splines Regression Model Demographic Research, vol. 38, 2018, pp. 335&#x2013;372. </p>\n<p> </p>\n<p>Alkema L, New JR. Global estimation of child mortality using a Bayesian B-spline bias-reduction method. The Annals of Applied Statistics. 2014; 8(4): 2122&#x2013;2149. Available at: <a href=\"https://arxiv.org/abs/1309.1602\">https://arxiv.org/abs/1309.1602</a> </p>\n<p> </p>\n<p>Alkema L, Chao F, You D, Pedersen J, Sawyer CC. National, regional, and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: a systematic assessment. The Lancet Global Health. 2014; 2(9): e521&#x2013;e530. </p>\n<p> </p>\n<p>Pedersen J, Liu J. Child Mortality Estimation: Appropriate Time Periods for Child Mortality Estimates from Full Birth Histories. Plos Medicine. 2012;9(8). Available at: <a href=\"http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001289 \">http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001289</a></p>\n<p> </p>\n<p>Silva R. Child Mortality Estimation: Consistency of Under-Five Mortality Rate Estimates Using Full Birth Histories and Summary Birth Histories. Plos Medicine. 2012;9(8). Available at: <a href=\"http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001296\">http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001296</a> </p>\n<p> </p>\n<p>Walker N, Hill K, Zhao FM. Child Mortality Estimation: Methods Used to Adjust for Bias due to AIDS in Estimating Trends in Under-Five Mortality. Plos Medicine. 2012;9(8). Available at: <a href=\"http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001298 \">http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001298</a></p>", "indicator_sort_order"=>"03-02-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.3.1", "slug"=>"3-3-1", "name"=>"Número de nuevas infecciones por el VIH por cada 1.000 habitantes no infectados, desglosado por sexo, edad y poblaciones clave", "url"=>"/site/es/3-3-1/", "sort"=>"030301", "goal_number"=>"3", "target_number"=>"3.3", "global"=>{"name"=>"Número de nuevas infecciones por el VIH por cada 1.000 habitantes no infectados, desglosado por sexo, edad y poblaciones clave"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Número de nuevas infecciones por el VIH por cada 1.000 habitantes no infectados, desglosado por sexo, edad y poblaciones clave", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de nuevas infecciones por el VIH por cada 1.000 habitantes no infectados, desglosado por sexo, edad y poblaciones clave", "indicator_number"=>"3.3.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Sanidad", "periodicity"=>"Anual", "url"=>"https://cne.isciii.es/es/servicios/departamento-enfermedades-transmisibles/enfermedades-a-z", "url_text"=>"Estadística de enfermedades de declaración obligatoria", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Número de infecciones por el VIH por cada 1.000 habitantes, desglosado por sexo", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.3-  De aquí a 2030, poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas y combatir la hepatitis, las enfermedades transmitidas por el agua y otras enfermedades transmisibles", "definicion"=>"Nuevos diagnósticos de VIH notificados al Sistema de Información sobre Nuevos Diagnósticos de VIH  (SINIVIH) por cada 1.000 habitantes", "formula"=>"\n$$TND_{VIH}^{t} = \\frac{ND_{VIH}^{t}}{P^{t}} \\cdot 1.000$$\n\ndonde:\n\n$ND_{VIH}^{t} =$ nuevos diagnósticos de VIH notificados al Sistema de Información sobre Nuevos Diagnósticos de VIH (SINIVIH) en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Sexo", "periodicidad"=>"Anual", "observaciones"=>"\nEste indicador no tiene en cuenta el lugar de origen de la infección, es decir, se consideran tanto infecciones \nautóctonas   (infecciones producidas en el territorio nacional) como infecciones no autóctonas (infecciones producidas \nen el extranjero). \n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa tasa de incidencia proporciona una medida del progreso hacia la prevención de la transmisión del VIH.\nAunque otros indicadores también son muy importantes para la epidemia del VIH, la incidencia del VIH refleja el éxito en\nprogramas de prevención y, hasta cierto punto, programas de tratamiento exitosos, ya que también conducirán\na reducir la incidencia del VIH.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.1&seriesCode=SH_HIV_INCD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C15Y%20%7C%20BOTHSEX\">Número de nuevas infecciones por VIH por cada 1.000 habitantes no infectados, por sexo y edad (por cada 1.000 habitantes no infectados) SH_HIV_INCD</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple estrictamente con los metadatos de Naciones Unidas  al no cuantificar la tasa sobre la población no infectada, sino por toda la población.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-01.pdf\">Metadatos 3-3-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Número de infecciones por el VIH por cada 1.000 habitantes, desglosado por sexo", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.3-  De aquí a 2030, poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas y combatir la hepatitis, las enfermedades transmitidas por el agua y otras enfermedades transmisibles", "definicion"=>"New HIV diagnoses reported to the Information System on New HIV Diagnoses (SINIVIH) per 1,000 inhabitants", "formula"=>"\n$$TND_{VIH}^{t} = \\frac{ND_{VIH}^{t}}{P^{t}} \\cdot 1.000$$\n\nwhere:\n\n$ND_{VIH}^{t} =$ New HIV diagnoses reported to the Information System on New HIV Diagnoses (SINIVIH) in year $t$\n\n$P^{t} =$ population on July 1 of year $t$\n", "desagregacion"=>"Sex", "periodicidad"=>"Anual", "observaciones"=>"\nThis indicator does not take into account the place of origin of the infection, that is, both \nautochthonous infections (infections produced in the national territory) and non-autochthonous \ninfections (infections produced abroad) are considered.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe incidence rate provides a measure of progress toward preventing onward transmission of HIV. \nAlthough other indicators are also very important to the HIV epidemic, HIV incidence reflects \nsuccess in prevention programmes and, to some extent, successful treatment programmes, as those \nwill also lead to lower HIV incidence. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.1&seriesCode=SH_HIV_INCD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C15Y%20%7C%20BOTHSEX\"> Number of new HIV infections per 1,000 uninfected population, by sex and age (per 1,000 uninfected population) SH_HIV_INCD</a> UNSTATS", "comparabilidad"=>"The available indicator does not strictly comply with the United Nations metadata, as it does  not quantify the rate for the uninfected population, but rather for the entire population.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-01.pdf\">Metadata 3-3-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Número de infecciones por el VIH por cada 1.000 habitantes, desglosado por sexo", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.3-  De aquí a 2030, poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas y combatir la hepatitis, las enfermedades transmitidas por el agua y otras enfermedades transmisibles", "definicion"=>"GIB diagnostiko berriei buruzko informazio-sistemari (SINIVIH) jakinarazitako GIBaren diagnostiko berriak 1.000  biztanleko", "formula"=>"\n$$TND_{VIH}^{t} = \\frac{ND_{VIH}^{t}}{P^{t}} \\cdot 1.000$$\n\nnon:\n\n$ND_{VIH}^{t} =$ GIB diagnostiko berriei buruzko informazio-sistemari (SINIVIH) jakinarazitako GIBaren diagnostiko berriak $t$ urtean\n\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean \n", "desagregacion"=>"Sexua", "periodicidad"=>"Anual", "observaciones"=>"\nAdierazle horrek ez du kontuan hartzen infekzioaren sorlekua, hau da, infekzio autoktonoak (lurralde nazionalean \nsortutako infekzioak) eta infekzio ez-autoktonoak (atzerrian sortutako infekzioak) hartzen dira kontuan.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nIntzidentzia-tasak GIBaren transmisioaren prebentziorako aurrerapen-maila adierazten du. Nahiz eta beste \nadierazle batzuk ere oso garrantzitsuak diren GIBaren pandemiarako, GIBaren intzidentziak prebentzio-programetako \narrakasta adierazten du eta, neurri batean, tratamendu-programa arrakastatsuak ere, GIBaren intzidentzia murrizten \nlagunduko baitute. \n\nIturria: Nazio Batuen Estatistika Sekzioa   \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.1&seriesCode=SH_HIV_INCD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C15Y%20%7C%20BOTHSEX\">GIBaren infekzio berrien kopurua GIBik ez duten 1.000 biztanleko, sexuaren eta adinaren arabera banatuta (infektatu gabeko 1.000 biztanleko) SH_HIV_INCD</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu zorrotz betetzen Nazio Batuen metadatuak, ez baitu kuantifikatzen  infektatu gabeko biztanleriaren gaineko tasa, populazio osoarena baizik.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-01.pdf\">Metadatuak 3-3-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.3.1: Number of new HIV infections per 1,000 uninfected population, by sex, age and key populations </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_HIV_INCD - Number of new HIV infections per 1,000 uninfected population [3.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Achieving this target will positively impact multiple SDG goals and by reaching other goals will improve countries ability to reduce new HIV infections. The goals that are linked to HIV include goals 1 through 8, 10, 11, 16 and 17.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>The Joint United Nations Programme on HIV/AIDS (UNAIDS)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>The Joint United Nations Programme on HIV/AIDS (UNAIDS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The number of new HIV infections per 1,000 uninfected population, by sex, age and key populations as defined as the number of new HIV infections per 1,000 persons among the uninfected population.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of newly infected people per 1,000 uninfected population.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Spectrum modelling is used for the data presented here which incorporates programme data, surveillance data, survey data and region-specific assumptions about the HIV epidemic. Alternative methods of measures include household or key population surveys with HIV incidence-testing, or routine surveillance among key populations.</p>\n<p>The model development is guided by the UNAIDS Reference Group on Estimates, Modelling and Projections provides technical guidance on the development of the HIV component of the Spectrum software (<a href=\"http://www.epidem.org\">www.epidem.org</a>). The Spectrum software is developed by Avenir Health (www.avenirhealth.org)&#x2014;which includes a module, the Estimates and Projections Package, which is developed by the East-West Center (www.eastwestcenter.org).</p>", "COLL_METHOD__GLOBAL"=>"<p>Country teams use UNAIDS-supported Spectrum software to develop estimates annually. The country teams are comprised primarily of national epidemiologists, demographers, monitoring and evaluation specialists and technical partners. The model incorporates data that are collected through programme information systems, surveillance and surveys.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data sources are compiled all year long. The spectrum models are created in the first three months of every year and finalized by May.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released every year in July</p>", "DATA_SOURCE__GLOBAL"=>"<p>The estimates are produced by a team of national experts consisting of ministry of health, national AIDS advisory groups and development partners. The results are signed off on by senior managers at the ministries of health.</p>", "COMPILING_ORG__GLOBAL"=>"<p>After the data review process, the national experts share their results with UNAIDS who compiles the data for all countries and calculates regional and global estimates.</p>", "INST_MANDATE__GLOBAL"=>"<p>The UN Political Declarations on HIV/AIDS (from 2001, 2011, 2016 and 2021) have mandated for UNAIDS to support countries to produce these data and for UNAIDS to report on the status of the Global HIV epidemic annually as well as through the UN Secretary General.</p>", "RATIONALE__GLOBAL"=>"<p>The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Although other indicators are also very important to the HIV epidemic, HIV incidence reflects success in prevention programmes and, to some extent, successful treatment programmes, as those will also lead to lower HIV incidence.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The methods and limitations for estimating HIV incidence vary based on the data and surveillance systems available in countries. </p>\n<ul>\n  <li>Countries with high HIV prevalence in the general population have relatively strong surveillance systems with household surveys contributing to the information required to estimate incidence. In epidemics concentrated in key populations, the surveillance systems for key hard-to-reach populations are often not comparable over time due to changing survey and sampling methods. The estimated size of key populations, a critical input to the Spectrum model for concentrated epidemics, can also lead to important under or over estimation of HIV incidence in concentrated epidemics.</li>\n  <li>In many countries trends in recent new infections rely on prevalence data from routine antenatal clinic testing. If those data are biased because women with known positive HIV status are not captured when calculating prevalence, or women found to be negative at initial antenatal care visit are retested later in the pregnancy, the derived incidence trends might be biased. While some limitations of the models are reflected in the uncertainty bounds the measurement biases and the uncertainty caused by these biases are not easily quantified and are thus not included. </li>\n  <li>Although HIV prevalence and incidence among children appears to be reasonably robust in generalized epidemics, estimating the pediatric HIV epidemic in concentrated epidemics remains a challenge because no robust measures of fertility exist among key populations living with HIV. </li>\n  <li>Currently UNAIDS only supports the HIV estimates development in countries with populations greater than 250,000. This is primarily due to support capacity at UNAIDS. </li>\n</ul>", "DATA_COMP__GLOBAL"=>"<p>Longitudinal data on individuals newly infected with HIV would be the most accurate source of data to measure HIV incidence, however these data are rarely available for representative populations. Special diagnostic tests in surveys or from health facilities can also be used to obtain data on HIV incidence but these require very large samples to accurately estimate HIV incidence and the latter are also rarely representative. HIV incidence is thus modelled using the Spectrum software. The software incorporates data on HIV prevalence, the number of people on treatment, demographics and other relevant indicators to estimate historical HIV incidence, among other indicators. A full description of the model is available in peer-reviewed articles and in the most recent UNAIDS Global AIDS Update Reports. <a href=\"https://onlinelibrary.wiley.com/toc/17582652/2021/24/S5\">https://onlinelibrary.wiley.com/toc/17582652/2021/24/S5</a></p>\n<p><a href=\"https://www.unaids.org/en/resources/documents/2024/global-aids-update-2024\">2024 global AIDS report &#x2014; The Urgency of Now: AIDS at a Crossroads | UNAIDS</a></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The HIV incidence estimates are created by country teams and are signed off on by ministry of health managers, including a clear statement that these data will be provided for SDG reporting. The SDG focal point in country is copied on the requests for clearance. UNAIDS reviews the input data and results to ensure quality before requesting clearance and compiling to regional and global values. </p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made to the estimates. </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level: </strong></p>\n<p>Estimates are not collected from countries with populations &lt; 250,000 according to the latest world population prospects estimates. In addition, no estimates are available for 8 countries with very small HIV epidemics who do not produce estimates.</p>\n<p>For some countries in which the estimates were not finalized at the time of publication, the country-specific values are presented as NA.</p>\n<p><strong>&#x2022; At regional and global levels: </strong></p>\n<p>The countries with populations &lt; 250,000 and the 8 countries that do not produce estimates are not included in regional or global level estimates. For countries in which the estimates were not finalized at the time of publication, the unofficial best estimates are included in the regional and global values.</p>", "REG_AGG__GLOBAL"=>"<p>Available for the World, the SDG regional groupings, Least Developed Countries, Landlocked Developing Countries and Small Island Developing States.</p>", "DOC_METHOD__GLOBAL"=>"<p>A description of the methodology is available from the latest Global AIDS Update reports in the methods annex. Resources are also available at HIVtools.unaids.org.</p>\n<p>Countries are provided with capacity building workshops on the methods every other year. In addition, they are supported by in-country UNAIDS advisers in roughly 45 countries. Where no in-country specialists are available, remote assistance is provided. Training videos and documentation are also available at: HIVtools.unaids.org</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Development of methods is overseen by an external reference group of experts (www.epidem.org). The actual files are reviewed by UNAIDS global experts to ensure consistency between countries.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Countries are fully involved in the development of the estimates. The final values are reviewed for quality by UNAIDS and approved by senior managers at national Ministries of Health.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Results are routinely compared to empirical evidence when available. These empirical data include research studies, household surveys with incidence measurement, and longitudinal HIV surveillance sites when available. If inconsistencies are found modifications are considered for the models. Methods are also published in peer-reviewed journals every two years. See links to publications at <a href=\"http://www.epidem.org\">www.epidem.org</a>.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability: </strong></p>\n<p>172 countries in 2024. Data are available by age and sex, however there are methodological challenges in estimating incidence among key populations.</p>\n<p><strong>Time series:</strong></p>\n<p>2000 -2023</p>\n<p><strong>Disaggregation: </strong></p>\n<p>General population, Age groups (0-14, 15-24, 15-49, 50+ years, All ages), sex (male, female, both). Key population data are currently not available as methods are being developed. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies: </strong></p>\n<p>These variations will differ by country.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>unaids.org</p>\n<p><strong>References:</strong></p>\n<p>More information on the estimates process, tools and tutorial videos on the methods</p>\n<p><a href=\"https://hivtools.unaids.org/\">https://hivtools.unaids.org/</a></p>\n<p>Journal Supplement on methods:</p>\n<p><a href=\"https://onlinelibrary.wiley.com/journal/17582652\">Journal of the International AIDS Society - Wiley Online Library</a></p>\n<p>UNAIDS Global AIDS Monitoring </p>\n<p>https://www.unaids.org/en/global-aids-monitoring</p>\n<p>Political Declaration on HIV and AIDS: Ending inequalities</p>\n<p>https://www.unaids.org/en/resources/documents/2021/2021_political-declaration-on-hiv-and-aids</p>\n<p>UNAIDS website for access to data </p>\n<p>http://aidsinfo.unaids.org/</p>\n<p>UNAIDS website for downloading files used to create incidence estimates https://www.unaids.org/en/dataanalysis/datatools/spectrum-epp</p>\n<p>Consolidated guidelines on person-centred HIV strategic information: strengthening routine data for impact: <a href=\"https://www.who.int/publications/i/item/9789240055315\">https://www.who.int/publications/i/item/9789240055315</a></p>", "indicator_sort_order"=>"03-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.3.2", "slug"=>"3-3-2", "name"=>"Incidencia de la tuberculosis por cada 100.000 habitantes", "url"=>"/site/es/3-3-2/", "sort"=>"030302", "goal_number"=>"3", "target_number"=>"3.3", "global"=>{"name"=>"Incidencia de la tuberculosis por cada 100.000 habitantes"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Incidencia de la tuberculosis por cada 100.000 habitantes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Incidencia de la tuberculosis por cada 100.000 habitantes", "indicator_number"=>"3.3.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Sanidad", "periodicity"=>"Anual", "url"=>"https://cne.isciii.es/es/servicios/departamento-enfermedades-transmisibles/enfermedades-a-z", "url_text"=>"Estadística de enfermedades de declaración obligatoria", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Incidencia de la tuberculosis por cada 100.000 habitantes", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.3-  De aquí a 2030, poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas y combatir la hepatitis, las enfermedades transmitidas por el agua y otras enfermedades transmisibles", "definicion"=>"Casos autóctonos de tuberculosis notificados a la Red Nacional de Vigilancia Epidemiológica (RENAVE) por cada 100.000 habitantes.", "formula"=>"\n$$TCA_{tuberculosis}^{t} = \\frac{CA_{tuberculosis}^{t}}{P^{t}} \\cdot 100.000$$\n\ndonde:\n\n$CA_{tuberculosis}^{t} =$ casos autóctonos de tuberculosis notificados a la Red Nacional de Vigilancia Epidemiológica (RENAVE) en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Sexo", "periodicidad"=>"Anual", "observaciones"=>"Los casos autóctonos son aquellos en los que la enfermedad se ha contraído en el territorio nacional.", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nTras dos años de consultas, la OMS aprobó una estrategia mundial contra la tuberculosis después de 2015,\nen mayo de 2014. Conocida como Estrategia Fin a la Tuberculosis, cubre el período 2016-2035. El objetivo general\nes “poner fin a la epidemia mundial de tuberculosis” y, en consecuencia, contiene objetivos ambiciosos para reducir la tuberculosis. \nLas objetivos de muertes y casos de tuberculosis están fijados para 2030 (reducción del 80% en la tasa de incidencia en \ncomparación con el nivel de 2015) y 2035 (reducción del 90% en la tasa de incidencia), en el contexto de los ODS.\n\nLa incidencia de tuberculosis (TB) se define como el número estimado de casos nuevos y recurrentes de \ntuberculosis (toda forma de tuberculosis, incluidos los casos en personas que viven con el VIH) que surgen en \nun año determinado. Generalmente se expresa como tasa por 100.000 habitantes.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.2&seriesCode=SH_TBS_INCD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Incidencia de tuberculosis (por 100.000 habitantes) SH_TBS_INCD</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-02.pdf\">Metadatos 3-3-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Incidencia de la tuberculosis por cada 100.000 habitantes", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.3-  De aquí a 2030, poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas y combatir la hepatitis, las enfermedades transmitidas por el agua y otras enfermedades transmisibles", "definicion"=>"Autochthonous cases of tuberculosis notified to the National Epidemiological Surveillance Network (RENAVE) per 100,000 inhabitants", "formula"=>"\n$$TCA_{tuberculosis}^{t} = \\frac{CA_{tuberculosis}^{t}}{P^{t}} \\cdot 100.000$$\n\nwhere:\n\n$CA_{tuberculosis}^{t} =$ autochthonous cases of tuberculosis notified to the National Epidemiological Surveillance Network (RENAVE) in year $t$\n\n$P^{t} =$  population on July 1 of year $t$\n", "desagregacion"=>"Sex", "periodicidad"=>"Anual", "observaciones"=>"Autochthonous cases are those in which the disease has been contracted in the national territory", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nFollowing two years of consultations, a post-2015 global tuberculosis strategy was endorsed \nby the World Health Assembly in May 2014. Known as the End TB Strategy, it covers the period \n2016-2035. The overall goal is to “End the global tuberculosis epidemic”, and correspondingly \nambitious targets for reductions in tuberculosis deaths and cases are set for 2030 (80% reduction \nin incidence rate compared with the level of 2015) and 2035 (90% reduction in incidence rate), \nin the context of the SDGs. \n\nTuberculosis (TB) incidence is defined as the estimated number of new and relapse TB cases (all \nforms of TB, including cases in people living with HIV) arising in a given year. It is usually \nexpressed as a rate per 100 000 population. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.2&seriesCode=SH_TBS_INCD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Tuberculosis incidence (per 100,000 population) SH_TBS_INCD</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-02.pdf\">Metadata 3-3-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Incidencia de la tuberculosis por cada 100.000 habitantes", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.3-  De aquí a 2030, poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas y combatir la hepatitis, las enfermedades transmitidas por el agua y otras enfermedades transmisibles", "definicion"=>"Zaintza Epidemiologikoko Sare Nazionalari (RENAVE) jakinarazitako tuberkulosi-kasu autoktonoak 100.000 biztanleko", "formula"=>"\n$$TCA_{tuberkulosia}^{t} = \\frac{CA_{tuberkulosia}^{t}}{P^{t}} \\cdot 100.000$$\n\nnon:\n\n$CA_{tuberkulosia}^{t} =$ Zaintza Epidemiologikoko Sare Nazionalari (RENAVE) jakinarazitako tuberkulosi-kasu autoktonoak $t$ urtean \n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean \n", "desagregacion"=>"Sexua", "periodicidad"=>"Anual", "observaciones"=>"Kasu autoktonoak dira gaixotasuna lurralde nazionalean harrapatu dutenak", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nBi urteko kontsulten ostean, OMEk 2015aren ondoko tuberkulosiaren aurkako munduko estrategia onartu zuen \n2014ko maiatzean. “Tuberkulosiaren Amaiera” izeneko estrategiak 2016-2035 aldia hartzen du. Helburu nagusia \nda “amaiera ematea tuberkulosiaren munduko epidemiari” eta, horretarako, tuberkulosia murrizteko helburu \nhandizaleak jasotzen ditu. Tuberkulosiaren kasuen eta heriotzen helburuak 2030erako (intzidentzia-tasa % 80 \nmurriztea 2015eko mailekin alderatuta) eta 2035erako (intzidentzia-tasa % 90 murriztea) ezartzen dira, GJHen \ntestuinguruan. \n\nTuberkulosiaren intzidentzia urte jakin batean sortzen diren tuberkulosiaren kasu berrien eta errepikakorren \nzenbatekoa da (tuberkulosiaren forma guztiak, GIBarekin bizi diren pertsonak barne). Oro har, 100.000 biztanleko \ntasa gisa adierazten da. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.2&seriesCode=SH_TBS_INCD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Tuberkulosiaren intzidentzia (100.000 biztanleko) SH_TBS_INCD</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-02.pdf\">Metadatuak 3-3-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.3.2: Tuberculosis incidence per 100,000 population</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_TBS_INCD - Tuberculosis incidence [3.3.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicators associated with tuberculosis incidence: numbers: 1.1.1, 1.3.1, 2.1.1, 3.3.1, 3.4.1, 3.5.2, 3.a.1, 3.8.1, 3.8.2, 7.1.2, 8.1.1, 10.1.1, 11.1.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Tuberculosis (TB) incidence is defined as the estimated number of new and relapse TB cases (all forms of TB, including cases in people living with HIV) arising in a given year. It is usually expressed as a rate per 100 000 population.</p>\n<h3>Concepts:</h3>\n<p>Direct measurement requires high-quality surveillance systems in which underreporting is negligible, and strong health systems so that under-diagnosis is also negligible; otherwise, indirect estimates are produced, using either a) notification data combined with estimates of levels of underreporting and under-diagnosis, b) inventory studies combined with capture-recapture modelling, c) population-based surveys of the prevalence of TB disease or d) dynamic models fitted to monthly/quarterly notification data. Dynamic models are only used for selected countries in which major drops in TB case notifications from 2020 onwards, compared with pre-2020 trends, suggest major reductions in access to TB diagnosis and treatment during the COVID-19 pandemic.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of incident cases per year per 100,000 population.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Consolidated guidance on tuberculosis data generation and use: module 1: tuberculosis surveillance. Geneva: World Health Organization; 2024 (<a href=\"https://www.who.int/publications/i/item/9789240075290\">https://www.who.int/publications/i/item/9789240075290</a>). </p>", "SOURCE_TYPE__GLOBAL"=>"<p>Details about data sources and methods are available in annex 2 and the technical appendix on methods used by WHO to estimate the global burden of tuberculosis disease published alongside the most recent WHO Global Tuberculosis Report at <a href=\"https://www.who.int/teams/global-tuberculosis-programme/data\"><u>https://www.who.int/teams/global-</u></a> <a href=\"https://www.who.int/teams/global-tuberculosis-programme/data\"><u>tuberculosis-programme/data</u></a></p>", "COLL_METHOD__GLOBAL"=>"<p>National Tuberculosis (TB) Programmes report their annual TB data to WHO every year between April and June using a standardized web-based data reporting system maintained at WHO. The system includes real-time checks for data consistency. Estimates of TB burden are prepared in June-July and shared with countries for review in July-August; revisions are made based on feedback received. In selected countries with new survey data, estimates are updated separately during the year. The final set of estimates is reviewed in WHO before publication in October, for compliance with specific international standards and harmonization of breakdowns for age and sex groups.</p>", "FREQ_COLL__GLOBAL"=>"<p>April-June each year</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>October each year</p>", "DATA_SOURCE__GLOBAL"=>"<p>National TB Programmes, Ministries of Health</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "INST_MANDATE__GLOBAL"=>"<p>Several World Health Organization resolutions endorsed by Member States at different World Health Assemblies have given the World Health Organization responsibility for monitoring the burden of TB globally and reporting on the response:</p>\n<p>Global strategy and targets for tuberculosis prevention, care and control after 2015, World Health Organization, 67th World Health Assembly, Resolutions and decisions, Resolution WHA 67.11, Geneva, Switzerland, 2014. </p>\n<p><a href=\"https://apps.who.int/gb/ebwha/pdf_files/WHA67-REC1/A67_2014_REC1-en.pdf#page=25\">https://apps.who.int/gb/ebwha/pdf_files/WHA67-REC1/A67_2014_REC1-en.pdf#page=25</a></p>\n<p>Prevention and control of multidrug-resistant tuberculosis and extensively drug-resistant tuberculosis, World Health Organization, 62nd World Health Assembly, Resolutions and decisions, Resolution WHA 62.15, Geneva, Switzerland, 2009. </p>\n<p><a href=\"https://apps.who.int/gb/ebwha/pdf_files/WHA62-REC1/WHA62_REC1-en-P2.pdf#page=25\">https://apps.who.int/gb/ebwha/pdf_files/WHA62-REC1/WHA62_REC1-en-P2.pdf#page=25</a></p>\n<p>Tuberculosis control: progress and long-term planning</p>\n<p>World Health Organization. 60th World Health Assembly. Resolutions and decisions.</p>\n<p>Resolution WHA 60.19. Geneva, Switzerland: WHO; 2007. </p>\n<p><a href=\"https://apps.who.int/gb/ebwha/pdf_files/WHASSA_WHA60-Rec1/E/WHASS1_WHA60REC1-en.pdf#page=67\">https://apps.who.int/gb/ebwha/pdf_files/WHASSA_WHA60-Rec1/E/WHASS1_WHA60REC1-en.pdf#page=67</a></p>\n<p>Sustainable financing for tuberculosis prevention and control</p>\n<p>World Health Organization. 58th World Health Assembly. Resolutions and decisions.</p>\n<p>Resolution WHA 58.14. Geneva, Switzerland: WHO; 2005. <a href=\"https://apps.who.int/gb/ebwha/pdf_files/WHA58-REC1/english/A58_2005_REC1-en.pdf#page=96\">https://apps.who.int/gb/ebwha/pdf_files/WHA58-REC1/english/A58_2005_REC1-en.pdf#page=96</a></p>\n<p>Stop Tuberculosis Initiative</p>\n<p>World Health Organization. 53rd World Health Assembly. Resolutions and decisions.</p>\n<p>Resolution WHA 53.1. Geneva, Switzerland: WHO; 2000. <a href=\"https://apps.who.int/gb/ebwha/pdf_files/WHA53-REC1/WHA53-2000-REC1-eng.pdf#page=18\">https://apps.who.int/gb/ebwha/pdf_files/WHA53-REC1/WHA53-2000-REC1-eng.pdf#page=18</a></p>\n<p>Tuberculosis control programme</p>\n<p>World Health Organization. 44th World Health Assembly. Resolutions and decisions.</p>\n<p>Resolution WHA44.8. Geneva, Switzerland: WHO, 1991.</p>\n<p>Two United Nations high-level meetings on TB also requested WHO to continue for monitoring the burden of TB globally and reporting on the response:</p>\n<p>Resolution 78/5: Political declaration of the high-level meeting of the General Assembly on the fight against tuberculosis. New York: United Nations; 2023 (https://undocs.org/A/RES/78/5).</p>\n<p>Resolution 73/3: Political declaration of the high-level meeting of the General Assembly on the fight against tuberculosis. New York: United Nations; 2018 (<a href=\"https://undocs.org/A/RES/73/3\">https://undocs.org/A/RES/73/3</a>). </p>", "RATIONALE__GLOBAL"=>"<p>Following two years of consultations, a post-2015 global tuberculosis strategy was endorsed by the World Health Assembly in May 2014. Known as the End TB Strategy, it covers the period 2016-2035. The overall goal is to &#x201C;End the global tuberculosis epidemic&#x201D;, and correspondingly ambitious targets for reductions in tuberculosis deaths and cases are set for 2030 (80% reduction in incidence rate compared with the level of 2015) and 2035 (90% reduction in incidence rate), in the context of the SDGs.</p>\n<p>The tuberculosis incidence rate was selected as an indicator for measuring reductions in TB disease burden. Although this indicator was estimated with considerable uncertainty in most countries in 2014, notifications of cases to national authorities provide a good proxy if there is limited under-reporting of detected cases and limited under- or over-diagnosis of cases.</p>", "REC_USE_LIM__GLOBAL"=>"<p>TB incidence has been used for over a century as a main indicator of TB burden, along with TB mortality. The indicator allows comparisons over time and between countries. Improvement in the quality of TB surveillance data result in reduced uncertainty about indicator values.</p>", "DATA_COMP__GLOBAL"=>"<p>Estimates of TB incidence are produced through a consultative and analytical process led by WHO and are published annually. These estimates are derived from annual case notifications, assessments of the quality and coverage of TB notification data, national surveys of the prevalence of TB disease, national inventory studies and information from death (vital) registration systems.</p>\n<p>For the period 2000-2019, estimates of incidence for each country were derived using one or more of the following approaches, depending on available data: (i) incidence = case notifications/estimated proportion of cases notified; (ii) capture-recapture modelling, (iii) estimated duration of untreated TB, applied to prevalence estimates where available.</p>\n<p>For 2020 and subsequent years, these methods were retained for most countries. However, for countries with large absolute reductions in the reported number of people newly diagnosed with TB from 2020 onwards, relative to pre-2020 trends, which suggested major disruptions to access to TB diagnosis and treatment during the COVID-19 pandemic, dynamic models were used in replacement of the methods used for 2000-2019. </p>\n<p>Uncertainty bounds are provided in addition to best estimates.</p>\n<p> Details are provided in the technical appendix on methods used by WHO to estimate the global burden of tuberculosis disease published alongside the most recent WHO global tuberculosis report at <a href=\"https://www.who.int/teams/global-tuberculosis-programme/data\">https://www.who.int/teams/global-tuberculosis-</a> <a href=\"https://www.who.int/teams/global-tuberculosis-programme/data\">programme/data.</a></p>", "DATA_VALIDATION__GLOBAL"=>"<p>Estimates of TB burden are prepared in June-July and shared with countries for review. In selected countries with new survey data, estimates are updated separately during the year. All estimates are communicated in July-August and revisions are made based on feedback. The final set of estimates is reviewed in WHO before publication in October, for compliance with specific international standards and harmonization of breakdowns for age and sex groups.</p>", "ADJUSTMENT__GLOBAL"=>"<p>The final set of estimates is reviewed in WHO before publication in October, for compliance with specific international standards and harmonization of breakdowns for age and sex groups.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Details are provided in the technical appendix of each WHO Global Tuberculosis Report at https:<a href=\"http://www.who.int/teams/global-tuberculosis-programme/data\">//www.who</a>.i<a href=\"http://www.who.int/teams/global-tuberculosis-programme/data\">nt/teams/global-tuberculosis-programme/data</a></p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Details are provided in the technical appendix of each WHO Global Tuberculosis Report at </p>\n<p>https:<a href=\"http://www.who.int/teams/global-tuberculosis-programme/data\">//ww</a>w<a href=\"http://www.who.int/teams/global-tuberculosis-programme/data\">.wh</a>o<a href=\"http://www.who.int/teams/global-tuberculosis-programme/data\">.int/teams/global-tuberculosis-programme/data</a></p>", "REG_AGG__GLOBAL"=>"<p>Country estimates of case counts are aggregated. Uncertainty is propagated assuming independence of country estimates.</p>", "DOC_METHOD__GLOBAL"=>"<p>Consolidated guidance on tuberculosis data generation and use: module 1: tuberculosis surveillance. Geneva: World Health Organization; 2024 (<a href=\"https://www.who.int/publications/i/item/9789240075290\">https://www.who.int/publications/i/item/9789240075290</a>).</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>All health statistics published by WHO undergo a systematic internal review process from the Data Division, including TB burden statistics. External review of specific statistics is conducted in various ways, including through country consultations and reviews by technical review bodies such as the WHO Global Task Force on TB Impact Measurement (https://www.who.int/groups/global-task-force-on-tb-impact-measurement/). </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The underlying TB data reported by WHO member states is carefully checked for completeness and internal consistency. Additional data sources are used in the process of disease burden estimation, including survey results, according to methods published in WHO documents mentioned in previous sections and cited in section 7.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>TB surveillance data are assessed systematically through &#x2018;epidemiological reviews&#x2019;, which provide data quality scores used to update plans for strengthening TB surveillance and are used to inform estimates for the burden of TB. In addition, the data are reviewed internally for consistency. Data and estimates are published in the form of country profiles, which are published following their review by countries, as mentioned in previous sections and cited in section 7. Results are published in detail in publicly available annual global TB reports.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>All countries</p>\n<p><strong>Time series:</strong></p>\n<p>2000 onwards</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator is disaggregated by country, sex and age group and five risk factors.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Population denominators may differ between national sources and United Nations Population Division (UNPD). WHO uses UNPD population estimates.</p>", "OTHER_DOC__GLOBAL"=>"<h3>URL:</h3>\n<p><a href=\"https://www.who.int/teams/global-tuberculosis-programme/data\"><u>https://www.who.int/teams/global-tuberculosis-programme/data</u></a></p>\n<h3>References:</h3>\n<p>The latest WHO Global Tuberculosis Report: <a href=\"https://www.who.int/teams/global-tuberculosis-programme/data\"><u>https://www.who.int/teams/global-tuberculosis-</u></a> <a href=\"https://www.who.int/teams/global-tuberculosis-programme/data\"><u>programme/data</u></a>).</p>\n<p>Consolidated guidance on tuberculosis data generation and use: module 1: tuberculosis surveillance. Geneva: World Health Organization; 2024 (<a href=\"https://www.who.int/publications/i/item/9789240075290\">https://www.who.int/publications/i/item/9789240075290</a>). </p>\n<p>World Health Assembly governing body documentation: official records. Geneva: World Health Organization <a href=\"http://apps.who.int/gb/or/\">(ht</a>t<a href=\"http://apps.who.int/gb/or/\">ps://apps.who.int/gb/or/ </a>).</p>", "indicator_sort_order"=>"03-03-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.3.3", "slug"=>"3-3-3", "name"=>"Incidencia de la malaria por cada 1.000 habitantes", "url"=>"/site/es/3-3-3/", "sort"=>"030303", "goal_number"=>"3", "target_number"=>"3.3", "global"=>{"name"=>"Incidencia de la malaria por cada 1.000 habitantes"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Incidencia de la malaria por cada 1.000 habitantes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Incidencia de la malaria por cada 1.000 habitantes", "indicator_number"=>"3.3.3", "national_geographical_coverage"=>"", "page_content"=>"<b>No aplicable:</b> La Comunidad Autónoma de Euskadi no es actualmente zona de transmisión de la malaria", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Sanidad", "periodicity"=>"Anual", "url"=>"https://cne.isciii.es/es/servicios/departamento-enfermedades-transmisibles/enfermedades-a-z", "url_text"=>"Estadística de enfermedades de declaración obligatoria", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Incidencia de la malaria por cada 1.000 habitantes", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.3-  De aquí a 2030, poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas y combatir la hepatitis, las enfermedades transmitidas por el agua y otras enfermedades transmisibles", "definicion"=>"Casos autóctonos de malaria notificados a la Red Nacional de Vigilancia Epidemiológica (RENAVE) por cada 1.000 habitantes.", "formula"=>"\n$$TCA_{malaria}^{t} = \\frac{CA_{malaria}^{t}}{P^{t}} \\cdot 1.000$$\n\ndonde:\n\n$CA_{malaria}^{t} =$ casos autóctonos de malaria notificados a la Red Nacional de Vigilancia Epidemiológica (RENAVE) en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Sexo", "periodicidad"=>"Anual", "observaciones"=>"Los casos autóctonos son aquellos en los que la enfermedad se ha contraído en el territorio nacional.", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl objetivo es medir las tendencias en la morbilidad de la malaria e identificar lugares donde el riesgo de \nenfermedad es mayor.  Con esta información, los programas pueden responder a tendencias inusuales, como epidemias, y dar \nrecursos a las poblaciones más necesitadas. Estos datos también sirven para informar la asignación global de recursos \npara la malaria, cuando se definen los criterios de elegibilidad para la financiación del Fondo Mundial. \n\nLa incidencia de malaria se define como el número de nuevos casos de malaria por cada 1.000 personas \nen riesgo cada año. Un caso de malaria se define como la aparición de infección por malaria en una persona \nen la que la presencia de parásitos de la malaria en la sangre se ha confirmado mediante una prueba de diagnóstico.  \nLa población considerada es la población en riesgo de padecer la enfermedad. \n\nFuente: División de Estadísticas de las Naciones Unidas \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.3&seriesCode=SH_STA_MALR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Incidencia de malaria por 1.000 habitantes en riesgo (por 1.000 habitantes) SH_STA_MALR</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-03.pdf\">Metadatos 3-3-3.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Incidencia de la malaria por cada 1.000 habitantes", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.3-  De aquí a 2030, poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas y combatir la hepatitis, las enfermedades transmitidas por el agua y otras enfermedades transmisibles", "definicion"=>"Autochthonous cases of malaria notified to the National Epidemiological Surveillance Network (RENAVE) per 1,000 inhabitants.", "formula"=>"\n$$TCA_{malaria}^{t} = \\frac{CA_{malaria}^{t}}{P^{t}} \\cdot 1.000$$\n\nwhere:\n\n$CA_{malaria}^{t} =$ autochthonous cases of malaria notified to the National Epidemiological Surveillance Network (RENAVE) in year $t$\n\n$P^{t} =$ population on July 1 of year $t$\n", "desagregacion"=>"Sex", "periodicidad"=>"Anual", "observaciones"=>"Autochthonous cases are those in which the disease has been contracted in the national territory.", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe objective is to measure trends in malaria morbidity and to identify locations where \nthe risk of disease is highest.  With this information, programmes can respond to unusual \ntrends, such as epidemics, and direct resources to the populations most in need. These data \nalso serves to inform global resource allocation for malaria such as when defining eligibility \ncriteria for Global Fund finance.\n\nIncidence of malaria is defined as the number of new cases of malaria per 1,000 people at risk \neach year. A case of malaria is defined as the occurrence of malaria infection in a person in \nwhom the presence of malaria parasites in the blood has been confirmed by a diagnostic test. \nThe population considered is the population at risk of the disease. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.3&seriesCode=SH_STA_MALR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Malaria incidence per 1,000 population at risk SH_STA_MALR</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-03.pdf\">Metadata 3-3-3.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Incidencia de la malaria por cada 1.000 habitantes", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.3-  De aquí a 2030, poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas y combatir la hepatitis, las enfermedades transmitidas por el agua y otras enfermedades transmisibles", "definicion"=>"Zaintza Epidemiologikoko Sare Nazionalari (RENAVE) jakinarazitako malaria kasu autoktonoak, 1.000 biztanleko.", "formula"=>"\n$$TCA_{malaria}^{t} = \\frac{CA_{malaria}^{t}}{P^{t}} \\cdot 1.000$$\n\nnon:\n\n$CA_{malaria}^{t} =$ Zaintza Epidemiologikoko Sare Nazionalari (RENAVE) jakinarazitako malaria kasu autoktonoak $t$ urtean \n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean \n", "desagregacion"=>"Sexua", "periodicidad"=>"Anual", "observaciones"=>"Kasu autoktonoak dira gaixotasuna lurralde nazionalean harrapatu dutenak.", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nHelburua da malariaren erikortasuneko joerak neurtzea eta gaixotasun-arrisku handiagoa duten lekuak identifikatzea.  \nInformazio horri esker, programek ezohiko joerei erantzun diezaiekete, besteak beste epidemiei, eta baliabideak eman \nbeharrizan gehien duten biztanleei. Datu horiek lagungarriak dira malariarako baliabideen esleipen orokorraren berri \nemateko, baldin eta Munduko Funtsaren finantzaketarako hautagarritasun-irizpideak zehazten badira. \n\nMalaria-intzidentzia urte bakoitzean arriskuan dauden 1.000 pertsonako malaria-kasu berrien kopurua da. Malaria-kasu \nbat da malariaren ondoriozko infekzio bat agertzea pertsona batengan, diagnostiko-proba baten bidez berretsi denean \nodolean malariaren parasitoak daudela. Kontuan hartu den biztanleria gaixotasuna jasateko arriskuan dagoen biztanleria \nda.\n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.3&seriesCode=SH_STA_MALR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Malariaren intzidentzia, arriskuan dauden 1.000 biztanleko (1.000 biztanleko) SH_STA_MALR</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-03.pdf\">Metadatuak 3-3-3.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.3.3: Malaria incidence per 1,000 population</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_STA_MALR - Malaria incidence per 1,000 population at risk [3.3.3]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Global Malaria Programme at World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Global Malaria Programme at World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>Incidence of malaria is defined as the number of new cases of malaria per 1,000 people at risk each year. </p>\n<p><strong>Concepts:</strong></p>\n<p>A case of malaria is defined as the occurrence of malaria infection in a person in whom the presence of malaria parasites in the blood has been confirmed by a diagnostic test. The population considered is the population at risk of the disease.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Cases per 1000 population at risk. </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>N.A.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Cases reported by the NMCP are obtained from each country surveillance system. This includes among others information on the number of suspected cases, number of tested cases, number of positive cases by method of detection and by species as well as number of health facilities that report those cases. This information is summarized in a DHIS2 application developed for this purpose. Data for representative household surveys are publicly available and included National Demographic Household Surveys (DHS) or Malaria Indicator Survey (MIS). </p>", "COLL_METHOD__GLOBAL"=>"<p>The official counterpart for each country is the National Malaria Control Program at the Ministry of Health.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data is collected every year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data is released yearly.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The National Malaria Control Program is the responsible to collect the information at each country.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Strategic Information for Response Unit of the Global Malaria Control Programme is the responsible to compile and process all the relevant information. National estimates for some countries are estimated in collaboration with the Malaria Atlas Project (MAP) which has been designated a WHO collaborating centre in geospatial disease modelling.</p>", "INST_MANDATE__GLOBAL"=>"<p>The Global technical strategy and targets for malaria 2016&#x2013;2030 was adopted by The 68 World Health Assembly (<a href=\"https://apps.who.int/iris/bitstream/handle/10665/253469/A68_R1_REC1-en.pdf?sequence=1&amp;isAllowed=y\">https://apps.who.int/iris/bitstream/handle/10665/253469/A68_R1_REC1-en.pdf?sequence=1&amp;isAllowed=y</a>). The Assembly requested WHO to monitor the progress toward the GTS milestones and targets. The World Malaria Report is the process by which the GTS is monitored by country, WHO region and globally.</p>", "RATIONALE__GLOBAL"=>"<p>To measure trends in malaria morbidity and to identify locations where the risk of disease is highest. With this information, programmes can respond to unusual trends, such as epidemics, and direct resources to the populations most in need. These data also serve to inform global resource allocation for malaria such as when defining eligibility criteria for Global Fund finance.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The estimated incidence can differ from the incidence reported by a Ministry of Health which can be affected by:</p>\n<ul>\n  <li>The completeness of reporting: the number of reported cases can be lower than the estimated cases if the percentage of health facilities reporting in a month is less than 100%</li>\n  <li>The extent of malaria diagnostic testing (the number of slides examined or RDTs performed)</li>\n  <li>The use of private health facilities which are usually not included in reporting systems.</li>\n  <li>The indicator is estimated only where malaria transmission occurs.</li>\n</ul>", "DATA_COMP__GLOBAL"=>"<p>Malaria incidence (1) is expressed as the number of new cases per 1000 population per year with the population of a country derived from projections made by the UN Population Division and the total proportion at risk estimated by a country&#x2019;s National Malaria Control Programme. More specifically, the country estimates what is the total proportion of the population at risk of malaria and then, for each year, the total population at risk is estimated as the UN Population for that year, times the proportion of the population at risk at baseline. The same proportion of the population at risk is used for the entire time series to ensure comparability of estimates through time. </p>\n<p>For each country or area, the number of malaria cases was estimated by one of the three methods described below.</p>\n<p><strong>Method 1:</strong></p>\n<p>Method 1 was used for countries and areas outside the World Health Organization (WHO) African Region, and for low transmission countries and areas in the African Region as follows: Afghanistan, Bangladesh, the Bolivarian Republic of Venezuela, Botswana, Brazil, Cambodia, Colombia, the Dominican Republic (until 2020), Eritrea, Ethiopia, French Guiana (until 2020), the Gambia, Guatemala (until 2020), Guyana, Haiti, Honduras (until 2020), India, Indonesia, the Lao People&#x2019;s Democratic Republic, Madagascar, Mauritania, Myanmar, Namibia, Nepal (until 2020), Nicaragua, Pakistan, Panama (until 2020), Papua New Guinea, Peru, the Philippines, the Plurinational State of Bolivia, Rwanda, Senegal, Solomon Islands, Timor-Leste (until 2016), Vanuatu, Viet Nam (until 2020), Yemen and Zimbabwe. Estimates were made by adjusting the number of reported malaria cases for completeness of reporting, the likelihood that presumed cases were parasite positive, and the extent of health service use. The procedure, which is described in the World malaria report 2008 (1), combines national data annually reported by national malaria programmes (NMPs) (i.e. reported cases, reporting completeness and test positivity rates) with data obtained from nationally representative household surveys on health service use among children aged under 5 years, which was assumed to be representative of the service use in all ages. Briefly:</p>\n<p>T = (a + (c &#xD7; e))/d &#xD7; (1 + f/g + (1 &#x2212; g &#x2212; f)/2/g) </p>\n<p>where: </p>\n<p>a is malaria cases confirmed in the public sector </p>\n<p>c is presumed cases (not tested but treated as malaria) </p>\n<p>d is reporting completeness </p>\n<p>e is test positivity rate (malaria positive fraction) = a/b, where b is suspected cases tested </p>\n<p>f is the fraction seeking treatment in the private sector </p>\n<p>g is the fraction seeking treatment in the public sector </p>\n<p>Factor to adjust for those not seeking treatment: (1 &#x2013; g &#x2013; f) </p>\n<p>Cases in the public sector: (a + (c &#xD7; e))/d </p>\n<p>Cases in the private sector: (a + (c &#xD7; e))/d &#xD7; f/g </p>\n<p>To estimate the uncertainty around the number of cases, the test positivity rate was assumed to have a normal distribution centred on the test positivity rate value and standard deviation &#x2013; defined as 0.244 &#xD7; e0.5547 and truncated to be in the range 0, 1. Reporting completeness (d) was assumed to have one of three distributions, depending on the value reported by the NMP. If the value was reported as a range greater than 80%, the distribution was assumed to be triangular, with limits of 0.8 and 1.0, and the peak at 0.95. If the reporting completeness was reported as a value and was more than 80%, a beta distribution was assumed, with a mean value of the reported value (maximum of 95%) and confidence intervals (CIs) of 5% around the mean value. If the value or range was more than 50% but less than or equal to 80%, the distribution was assumed to be rectangular, with limits of 0.5 and 0.8, and the peak at 0.8. Finally, if the value or range was less than or equal to 50%, the distribution was assumed to be triangular, with limits of 0 and 0.5, and the peak at 0.5 (2). The fraction of children brought for care in the public sector and in the private sector was assumed to have a beta distribution, with the mean value being the estimated value in the survey and the standard deviation being calculated from the range of the estimated 95% CIs. The fraction of children not brought for care was assumed to have a rectangular distribution, with the lower limit being 0 and the upper limit calculated as 1 minus the proportion that were brought for care in the public and private sectors. The three distributions (fraction seeking treatment in the public sector, fraction seeking treatment in the private sector only and fraction not seeking treatment) were constrained to add up to 1.</p>\n<p>Sector-specific care seeking fractions were linearly interpolated between the years that had a survey and were extrapolated for the years before the first or after the last survey. The parameters used to propagate uncertainty around these fractions were also imputed in a similar way or, if there was no value for any year in the country or area, were imputed as a mixture of the distributions of the region for that year. CIs were obtained from 10 000 draws of the convoluted distributions. The data were analysed using R statistical software, using the convdistr R package to propagate uncertainty and manage distributions (2). </p>\n<p>For India, the values were obtained at subnational level using the same methodology. An additional adjustment was applied in several states in India between 2020 and 2022, to control for the reductions in reported testing rates associated with disruptions in health services related to the COVID-19 pandemic. The states with reductions in testing rates below those expected (defined as a change in testing rates of more than 10% observed between 2018 and 2019) in 2020 were Bihar, Chandigarh, Chhattisgarh, Dadra and Nagar Haveli, Delhi, Goa, Jharkhand, Karnataka, Puducherry, Punjab, Uttar Pradesh, Uttarakhand and West Bengal. In 2021, the states with reductions in testing rates were Assam, Chandigarh, Chhattisgarh, Daman and Diu, Delhi, Goa, Himachal Pradesh, Karnataka, Kerala, Manipur, Puducherry, Punjab, Uttar Pradesh, Uttarakhand and West Bengal. In 2022, cases were corrected for the states of Assam, Bihar, Chandigarh, Chhattisgarh, Delhi, Gujarat, Himachal Pradesh, Manipur, Puducherry, Punjab, Sikkim and West Bengal. In these states, the excess number of indigenous cases expected in the absence of diagnostic disruptions was calculated by estimating the number of additional tests that would have been conducted if testing rates were similar to those observed in 2019, then applying the test positivity ratio observed in 2019 (or in 2020 for Delhi and Jharkhand, or in 2021 and 2022 for Delhi and Puducherry) to this number. The malaria burden in countries outside the WHO African Region was affected by the COVID-19 pandemic in different ways. In several countries, the movement disruptions led to transmission reductions; in other cases, testing rates remained unchanged. This made it challenging to apply a single source of data for correction to all countries, considering also that it was difficult to relate the reported data to the essential health services (EHS) response. No adjustment for private sector treatment seeking was made for the following countries and areas because they report cases from the private and public sector together: Bangladesh, the Bolivarian Republic of Venezuela, Botswana, Brazil, Colombia, the Dominican Republic, French Guiana, Guatemala, Guyana, Haiti, Honduras, Indonesia (since 2017), Myanmar (since 2013), Nepal (since 2019), Nicaragua, Panama, Peru, the Plurinational State of Bolivia and Rwanda. For Senegal and Yemen, reported cases from last year were used, adjusting for the changes in population at risk values, and then these data were used to estimate the number of cases.</p>\n<p><strong>Method 2: </strong></p>\n<p>Method 2 was used for high transmission countries in the WHO African Region and for countries in the Eastern Mediterranean Region in which the quality of surveillance data did not permit a robust estimate from the number of reported cases. These countries were Angola, Benin, Burkina Faso, Burundi, Cameroon, the Central African Republic, Chad, the Congo, C&#xF4;te d&#x2019;Ivoire, the Democratic Republic of the Congo, Equatorial Guinea, Gabon, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, Malawi, Mali, Mozambique, the Niger, Nigeria, Sierra Leone, Somalia, South Sudan, the Sudan, Togo, Uganda, the United Republic of Tanzania and Zambia. In this method, estimates of the number of malaria cases were derived from information on parasite prevalence obtained from household surveys. </p>\n<p>First, data on parasite prevalence from almost 60 000 survey records were assembled within a spatiotemporal Bayesian geostatistical model, together with environmental and sociodemographic covariates, and data distribution on interventions such as insecticide-treated mosquito nets (ITNs), antimalarial drugs and indoor residual spraying (IRS) (3) that are updated yearly to review the model. The geospatial model enabled predictions of Plasmodium falciparum prevalence in children aged 2&#x2013;10 years, at a resolution of 5 &#xD7; 5 k 2 m, throughout all malaria endemic WHO African Region countries for each year from 2000 to 2020. Second, an ensemble model was developed to predict malaria incidence as a function of parasite prevalence (4). The model was then applied to the estimated parasite prevalence, to obtain estimates of the malaria case incidence at 5 &#xD7; 5 km2 resolution for each year from 2000 to 2021.1 Data for each 5 &#xD7; 5 km2 area were then aggregated within country and regional boundaries, to obtain both national and regional estimates of malaria cases (5). </p>\n<p>Between 2020 and 2022, additional cases estimated using this method were added to account for the disruptions in malaria prevention, diagnostic and treatment services as a result of the COVID-19 pandemic and other events that occurred during this period. Disruption information was reported per country and was obtained from the national pulse surveys on continuity of EHS during the COVID-19 pandemic conducted by WHO (first round in May&#x2013;July 2020, second in January&#x2013;March 2021 and third in November&#x2013;December 2021) (6-8), and extended into 2022. The medium, minimum and maximum (with a limit of 50%) values of the ranges provided by countries to define disruptions were used to quantify the percentage of malaria service disruptions. This information was integrated into the estimates by applying an approach previously used for assessing the impacts of interventions on malaria burden through the creation of counterfactual burden estimates for scenarios with varying levels of intervention coverage. It was assumed that COVID-19-related disruptions to health care manifested themselves as reduced treatment seeking for malaria and thus reduced effective treatment with an antimalarial drug. The counterfactual estimates were then aligned, per country, with the estimates from the pulse surveys to produce a set of COVID-19-adjusted estimates for 2020, 2021 and 2022. For countries for which the estimates with the updated spatiotemporal model were considerably different from previous estimates without addition of new data or evidence that explained the drastic changes estimated by the model (Burkina Faso, Gabon, Guinea, Mali, the Niger, Nigeria, Somalia, the Sudan and Uganda), the case series published in the World malaria report 2023 (10) were used until 2022, adjusting for the changes in population-at-risk values. The values for 2023 were estimated by applying the change rate between the cases estimated using the spatiotemporal model of incidence between 2022 and 2023 and adjusting for population changes between these 2 years.</p>\n<p><strong>Method 3:</strong></p>\n<p>For most of the elimination countries and countries at the stage of prevention of reintroduction, the number of indigenous and introduced cases registered by NMPs are reported without further adjustments (6). The countries in this category were Algeria, Argentina, Armenia, Azerbaijan, Belize, Bhutan, Cabo Verde, China, the Comoros, Costa Rica, the Democratic People&#x2019;s Republic of Korea, Djibouti, the Dominican Republic (since 2021), Ecuador, Egypt, El Salvador, Eswatini, French Guiana (since 2021), Georgia, Guatemala (since 2021), Honduras (since 2021), Iraq, the Islamic Republic of Iran, Kazakhstan, Kyrgyzstan, Malaysia, Mexico, Morocco, Nepal (since 2021), Oman, Panama (since 2021), Paraguay, the Republic of Korea, Sao Tome and Principe, Saudi Arabia, South Africa, Sri Lanka, Suriname, the Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste (since 2017), T&#xFC;rkiye, Turkmenistan, the United Arab Emirates, Uzbekistan and Viet Nam (since 2021).</p>\n<p>Country-specific adjustments</p>\n<p>For some years, information for certain countries was not available or could not be used because it was of poor quality. For countries in this situation, the number of cases was imputed from other years when the quality of the data was better (adjusting for population growth), as follows: for Afghanistan, values for 2000&#x2013;2001 were imputed from 2002&#x2013;2003; and for Bangladesh, values for 2001&#x2013;2005 were imputed from 2006&#x2013;2008. For Ethiopia, values for 2000&#x2013;2019 were taken from a mixed distribution between values from Method 1 and Method 2 (50% from each method). For the Gambia, values for 2000&#x2013;2010 were imputed from 2011&#x2013;2013; for Haiti, values for 2000&#x2013;2005, 2009 and 2010 were imputed from 2006&#x2013;2008; for Indonesia, values for 2000&#x2013;2003 and 2007&#x2013;2009 were imputed from 2004&#x2013;2006; and for Mauritania, values for 2000&#x2013;2010 were imputed from a mixture of Method 1 and Method 2, starting with 100% values from Method 2 for 2001&#x2013;2002, with that percentage decreasing to 10% of Method 1 in 2010. For Myanmar, values for 2000&#x2013;2005 were imputed from 2007&#x2013;2009; and for Namibia, values for 2000 were imputed from 2001&#x2013;2003 and values for 2012 were imputed from 2011 and 2013. For Pakistan, values for 2000 were imputed from 2001&#x2013;2003; and for Papua New Guinea, values for 2012 were imputed from 2009&#x2013;2011. For Rwanda, values for 2000&#x2013;2006 were imputed from a mixture of Method 1 and Method 2, starting with 100% values from Method 2 in 2000, with that percentage decreasing to 10% in 2006. For Senegal, values for 2000&#x2013;2006 were imputed from a mixture of Method 1 and Method 2, with 90% of Method 2 in 2000, decreasing to 10% of Method 2 in 2006. For Thailand, values for 2000 were imputed from 2001&#x2013;2003; for Timor-Leste, values for 2000&#x2013;2001 were imputed from 2002&#x2013;2004; and for Zimbabwe, values for 2000&#x2013;2006 were imputed from 2007&#x2013;2009.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Burden estimates presented in the World Malaria Report are sent to the countries via regional offices for consultation and approval.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not Applicable</p>", "IMPUTATION__GLOBAL"=>"<p>(i) <strong>At country level </strong></p>\n<p>For missing values of the parameters (test positivity rate and reporting completeness) a distribution based on a mixture of the distribution of the available values is used, if any value exists for the country or from the region otherwise. Values for health seeking behaviour parameters are imputed by linear interpolation of the values when the surveys were made or extrapolation of the first or last survey. When no reported data is available the number of cases is interpolated taking into account the population growth. </p>\n<p> </p>\n<p>(ii) <strong>At regional and global levels </strong></p>\n<p>Not Applicable </p>", "REG_AGG__GLOBAL"=>"<p>Number of cases are aggregated by region, and uncertainty obtained from the aggregation of each country&#x2019;s distribution. Population at risk is aggregated without any further adjustment. Estimation at global level is obtained from aggregation of the regional values.</p>", "DOC_METHOD__GLOBAL"=>"<p>Information is provided by each country&#x2019;s NMCP using a DHIS 2 application created specifically for this purpose. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Burden estimates are first reviewed internally by GMP and WHO regional and country offices. These are then shared to country for validation. Final approval is received from the WHO division of Data, Analytics.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>We perform internal validation for outliers and completeness and raise queries to countries through the regional offices for clarification. When necessary, we rely on data quality assessment information from external sources such as partners working in malaria monitoring and evaluation. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>We perform internal validation for outliers and completeness and raise queries to countries through the regional offices for clarification. When necessary, we rely on data quality assessment information from external sources such as partners working in malaria monitoring and evaluation. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability: </strong></p>\n<p>109 countries </p>\n<p> </p>\n<p><strong>Time series: </strong></p>\n<p>Annually since 2000 </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator is estimated at country level. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong> </p>\n<p>The estimated incidence can differ from the incidence reported by a Ministry of Health which can be affected by: </p>\n<ul>\n  <li>The completeness of reporting: the number of reported cases can be lower than the estimated cases if the percentage of health facilities reporting in a month is less than 100% </li>\n  <li>The extent of malaria diagnostic testing (the number of slides examined or RDTs performed) </li>\n  <li>The use of private health facilities which are usually not included in reporting systems. </li>\n</ul>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p><a href=\"https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2024\">https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2024</a></p>\n<p><strong>References:</strong> </p>\n<p>1. World malaria report 2008. Geneva: World Health Organization; 2008 (<a href=\"https://apps.who.int/iris/handle/10665/43939\">https://apps.who.int/iris/handle/10665/43939</a>).</p>\n<p>2. The R Project for statistical computing [website]. Vienna: R Foundation for Statistical Computing; 2023 (https://www.R-project.org/). </p>\n<p>3. Weiss DJ, Mappin B, Dalrymple U, Bhatt S, Cameron E, Hay SI et al. Re-examining environmental correlates of Plasmodium falciparum malaria endemicity: a data-intensive variable selection approach. Malar J. 2015;14:68 (<a href=\"https://doi.org/10.1186/s12936-015-0574-x\">https://doi.org/10.1186/s12936-015-0574-x</a>). </p>\n<p>4. Cameron E, Battle KE, Bhatt S, Weiss DJ, Bisanzio D, Mappin B et al. Defining the relationship between infection prevalence and clinical incidence of Plasmodium falciparum malaria. Nat Commun. 2015;6:8170 (https://doi.org/10.1038/ncomms9170). </p>\n<p>5. Malaria Atlas Project [website]. 2023 (<a href=\"https://malariaatlas.org\">https://malariaatlas.org</a>). </p>\n<p>6. Pulse survey on continuity of essential health services during the COVID-19 pandemic: interim report, 27 August 2020. Geneva: World Health Organization; 2020 (<a href=\"https://iris.who.int/handle/10665/334048\">https://iris.who.int/handle/10665/334048</a>).</p>\n<p>7. Second round of the national pulse survey on continuity of essential health services during the COVID-19 pandemic: January&#x2212;March 2021. Geneva: World Health Organization; 2021 (https://iris.who.int/handle/10665/340937).</p>\n<p>8. Third round of the global pulse survey on continuity of essential health services during the COVID-19 pandemic: November&#x2013;December 2021. Geneva: World Health Organization; 2022 (https://iris.who.int/handle/10665/351527).</p>", "indicator_sort_order"=>"03-03-03", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.3.4", "slug"=>"3-3-4", "name"=>"Incidencia de la hepatitis B por cada 100.000 habitantes", "url"=>"/site/es/3-3-4/", "sort"=>"030304", "goal_number"=>"3", "target_number"=>"3.3", "global"=>{"name"=>"Incidencia de la hepatitis B por cada 100.000 habitantes"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Incidencia de la hepatitis B por cada 100.000 habitantes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Incidencia de la hepatitis B por cada 100.000 habitantes", "indicator_number"=>"3.3.4", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Sanidad", "periodicity"=>"Anual", "url"=>"https://cne.isciii.es/es/servicios/departamento-enfermedades-transmisibles/enfermedades-a-z", "url_text"=>"Estadística de enfermedades de declaración obligatoria", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Incidencia de la hepatitis B por cada 100.000 habitantes", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.3-  De aquí a 2030, poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas y combatir la hepatitis, las enfermedades transmitidas por el agua y otras enfermedades transmisibles", "definicion"=>"Casos autóctonos de hepatitis B notificados a la Red Nacional de Vigilancia Epidemiológica (RENAVE) por cada 100.000 habitantes", "formula"=>"\n$$TCA_{hepatitis\\, B}^{t} = \\frac{CA_{hepatitis\\, B}^{t}}{P^{t}} \\cdot 100.000$$\n\ndonde:\n\n$CA_{hepatitis\\, B}^{t} =$ casos autóctonos de hepatitis B notificados a la Red Nacional de Vigilancia Epidemiológica (RENAVE) en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Sexo", "periodicidad"=>"Anual", "observaciones"=>"Los casos autóctonos son aquellos en los que la enfermedad se ha contraído en el territorio nacional.", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl propósito es describir la reducción de las infecciones crónicas por hepatitis B. La mayor parte de la carga \nde enfermedad por infección por VHB proviene de infecciones adquiridas antes de los 5 años de edad. Por lo tanto, \nla prevención de la infección por VHB se centra en niños menores de 5 años. Las Naciones Unidas seleccionaron \nel acumulado de la incidencia de infección crónica por VHB a los 5 años de edad como indicador del objetivo de \n“combatir la hepatitis”. Este indicador se mide indirectamente a través de la proporción de niños 5 años de edad \nque han desarrollado una infección crónica por VHB (es decir, la proporción que da positivo en una \nprueba marcador de infección llamado antígeno de superficie de la hepatitis B [HBsAg]).\n\nEste indicador se mide indirectamente a través de la proporción de niños de 5 años que han desarrollado una \ninfección crónica por VHB (es decir, la proporción que da positivo para un marcador de infección llamado \nantígeno de superficie de la hepatitis B [HBsAg]). \n\nFuente: División de Estadísticas de las Naciones Unidas \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.4&seriesCode=SH_HAP_HBSAG&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y\">Prevalencia del antígeno de superficie de la hepatitis B (HBsAg) (%) SH_HAP_HBSAG</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos de Naciones Unidas, pero aporta información similar", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-04.pdf\">Metadatos 3-3-4.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Incidencia de la hepatitis B por cada 100.000 habitantes", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.3-  De aquí a 2030, poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas y combatir la hepatitis, las enfermedades transmitidas por el agua y otras enfermedades transmisibles", "definicion"=>"Autochthonous cases of hepatitis B notified to the National Epidemiological Surveillance Network (RENAVE) per 100,000 inhabitants", "formula"=>"\n$$TCA_{hepatitis B}^{t} = \\frac{CA_{hepatitis B}^{t}}{P^{t}} \\cdot 100.000$$\n\nwhere:\n\n$CA_{hepatitis B}^{t} =$ autochthonous cases of hepatitis B notified to the National Epidemiological Surveillance Network (RENAVE) in year $t$\n\n$P^{t} =$ population on July 1 of year $t$\n", "desagregacion"=>"Sex", "periodicidad"=>"Anual", "observaciones"=>"Autochthonous cases are those in which the disease has been contracted in the national territory.", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe purpose is to describe the reduction in chronic hepatitis b infections. Most of the burden of disease \nfrom HBV infection comes from infections acquired before the age of 5 years. Therefore, prevention of \nHBV infection focuses on children under 5 years of age. The United Nations selected the cumulative \nincidence of chronic HBV infection at 5 years of age as an indicator of the Sustainable Development Goal \ntarget for “combating hepatitis”. This indicator is measured indirectly through the proportion of children \n5 years of age who have developed chronic HBV infection (i.e. the proportion that tests positive for a \nmarker of infection called hepatitis B surface antigen [HBsAg]).\n\nThis indicator is measured indirectly through the proportion of children 5 years of age who have \ndeveloped chronic HBV infection (i.e. the proportion that tests positive for a marker of infection called \nhepatitis B surface antigen [HBsAg]). \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.4&seriesCode=SH_HAP_HBSAG&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y\">Prevalence of the Hepatitis B surface antigen (HBsAg) (%) SH_HAP_HBSAG</a> UNSTATS", "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-04.pdf\">Metadata 3-3-4.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Incidencia de la hepatitis B por cada 100.000 habitantes", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.3-  De aquí a 2030, poner fin a las epidemias del SIDA, la tuberculosis, la malaria y las enfermedades tropicales desatendidas y combatir la hepatitis, las enfermedades transmitidas por el agua y otras enfermedades transmisibles", "definicion"=>"Zaintza Epidemiologikoko Sare Nazionalari (RENAVE) jakinarazitako B hepatitisaren kasu autoktonoak, 100.000 biztanleko", "formula"=>"\n$$TCA_{B\\, hepatitisa}^{t} = \\frac{CA_{B\\, hepatitisa}^{t}}{P^{t}} \\cdot 100.000$$\n\nnon:\n\n$CA_{B\\, hepatitisa}^{t} =$ Zaintza Epidemiologikoko Sare Nazionalari (RENAVE) jakinarazitako B hepatitisaren kasu autoktonoak $t$ urtean \n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean \n", "desagregacion"=>"Sexua", "periodicidad"=>"Anual", "observaciones"=>"Kasu autoktonoak dira gaixotasuna lurralde nazionalean harrapatu dutenak.", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAsmoa da B hepatitisaren ziozko infekzio kronikoen murrizketa deskribatzea. GIBaren ondoriozko infekzioaren \ngaixotasun-karga handiena 5 urteak bete baino lehen hartutako infekzioetatik dator. Ondorioz, GIBaren infekzioaren \nprebentzioa 5 urtetik beherako haurrengan lantzen da bereziki. Nazio Batuek 5 urtekoen GIBaren ondoriozko infekzio \nkronikoen intzidentziaren metaketa aukeratu zuten “hepatitisari aurre egiteko” helburuaren adierazle gisa. Adierazle \nhori zuzenean neurtzen da GIBaren ziozko infekzio kronikoa garatu duten 5 urteko haurren proportzioaren bidez \n(hau da, hepatitisaren azalera-antigenoa izeneko infekzio-proba markatzaile batean positibo eman dutenen proportzioa \n[HBsAg]).\n\nAdierazle hori zuzenean neurtzen da GIBaren ziozko infekzio kronikoa garatu duten 5 urteko haurren proportzioaren \nbidez (hau da, hepatitisaren azalera-antigenoa izeneko infekzio proba markatzaile batean positibo eman dutenen \nproportzioa [HBsAg]). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.4&seriesCode=SH_HAP_HBSAG&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y\">B hepatitisaren gainazaleko antigenoaren prebalentzia (HBsAg) (%) SH_HAP_HBSAG</a> UNSTATS", "comparabilidad"=>"Eskuragarri dagoen adierazleak ez ditu Nazio Batuen metadatuak betetzen, baina antzeko informazioa ematen du", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-04.pdf\">Metadatuak 3-3-4.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.3.4: Hepatitis B incidence per 100,000 population </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Not applicable</p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2021-04-01</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>This indicator is measured indirectly through the proportion of children 5 years of age who have developed chronic HBV infection (i.e. the proportion that tests positive for a marker of infection called hepatitis B surface antigen [HBsAg]).<sup>1</sup> </p>\n<p> </p>\n<p>Hepatitis B surface antigen: a protein from the virus&#x2019;s coat. A positive test for HBsAg indicates active HBV infection. The immune response to HBsAg provides the basis for immunity against HBV, and HBsAg is the main component of HepB.<sup>2</sup> </p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>It is not possible, on clinical grounds, to differentiate hepatitis B from hepatitis caused by other viral agents, hence, laboratory confirmation of the diagnosis is essential. The Hepatitis B surface antigen is the most common hepatitis B test. The presence of HBsAg in serum indicates that the patient has contracted HBV infection. The measurement of HBsAg levels have been standardized in IU/ml. The test is used to identify those at risk of spreading the disease. HBsAg, an HBV viral coat antigen, is produced in large quantities in infected-cell cytoplasm and continues to be produced in patients with chronic, active HBV infection. Documented HBsAg positivity in serum for 6 or more months suggests chronic HBV with a low likelihood of subsequent spontaneous resolution. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Prevalence of the Hepatitis b surface antigen in children under five years of age (proportion with chronic infection)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>A systematic search on articles published between Jan 1, 1965, and Oct 30, 2018. in the databases Embase, PubMed, Global Index Medicus, Popline, and Web of Science. </p>\n<p> </p>\n<p>Following full text review, we extracted data from each study using the following variables: study characteristics (study and sample collection dates, study locations i.e., city, subnational [an area, region, state, or province in a country], or national level), participant characteristics (age range, sex, year, and population group), and prevalence of the HBV marker, type of laboratory tests, and number of participants the HBV marker prevalence was based on.</p>\n<p> </p>\n<p>Data of eligible articles were entered into a Microsoft EXCEL&#xAE; and/or Distiller databank by two reviewers independently. Information was extracted for author name, year, age, gender, marker, laboratory test used, number of individuals tested, prevalence of each marker when reported, the population group (general population, HCWs, or blood donors) and whether the data reported was for a city, sub-national (an area, region, state or province in a country) or national level, GDP per capita. In addition to HBsAg, HBeAg was recorded, as available for individuals when HBsAg was also reported. In order to record information on methodological quality and study bias resulting from non-representativeness, an additional variable was used: samples likely to be representative for the country/area specified were coded as 0 and others, e.g. convenience samples in certain communities or tribes in the country were assigned a 1, supplemented by additional information. The risk of bias/non-representativeness information was applied if the population was neither HCW nor blood donor (see description below).3 In the following, variables extracted from the studies and assumptions made are described in detail: </p>\n<ol>\n  <li>Author, Date </li>\n  <li>Year start/end of study conduct: Year of study begin and end was extracted. If this information was not available from the studies, we used the commonly used assumption that the study was conducted two years prior to the year of publication (e.g. author, 2000, year of study conduct: 1998). </li>\n  <li>Sex: Sex-specific values were extracted. If only an overall (all) estimate was provided, the share of females in the study was specified in the column additional information. </li>\n  <li>Age start/end: The most specific age-group provided by the data was extracted. If the age-group on which the parameter value was based on was not available, assumptions were made based on the context of the study. Therefore, the following was applied in case of missing information on age-groups in the study population: </li>\n  <li>If the study was conducted in the general population without further specification and if only one prevalence estimate is provided, the age-group was considered to be 0-85 years. Subsequently, if the beginning and last age-group is missing, the lower value of the youngest age-group is 1 year, the upper value of the oldest age-groups is 85 years. </li>\n  <li>If the study was conducted among adult populations but no age-range is provided, the age-group is considered to be 17-65 years. </li>\n  <li>If the study was conducted among pupils but no age-range is provided, the age-group is considered to be 5-15 years. </li>\n  <li>If the study was conducted among pregnant women but no age-range is provided, the age-group is considered to be 15-49 years (reproductive age). </li>\n  <li>If the study was conducted among blood donors but no age-range is provided, the age-group is considered to be 17-65 years. </li>\n  <li>If the study was conducted among army recruits or soldiers but no age-range is provided, the age-group is considered to be 18-45 years. </li>\n  <li>If the study was conducted among the working population but no age-range is provided, the age-group is considered to be 16-65 years. </li>\n  <li>HBsAg Prevalence: The most specific prevalence estimate provided by the data was extracted (defined by age-/sex-/year-prevalence). Separate lines for each marker were used in the data extraction file (e.g. one for HBeAg and one line for HBsAg, even if the study group/publication was the same) </li>\n  <li>HBeAg Prevalence (optional marker): The most specific prevalence estimate (defined by age-/sex-/year-prevalence) of HBeAg among HBsAg-positive individuals was extracted and, if applicable was calculated to reflect prevalence among HBsAg carriers. </li>\n  <li>anti-HBc Prevalence (optional marker): The most specific prevalence estimate provided by the data was extracted (defined by age-/sex-/year-prevalence). </li>\n  <li>Laboratory method: Testing immune response markers of HBV infection began in the 1970s by counter-immuno-electrophoresis technique (CIEP). Since then, different detection methods have been developed (RIA, EIA, &#x2026;). The most applied method in prevalence studies is the ELISA (enzyme-linked immunosorbent assay). Five categories were established to record the method/test used for prevalence detection in the studies: ELI new (ELISA -2, -3, EIA, &#x2026;), EIA old (CMIA, CIEP, RPHA), NAT (qPCR/real-time PCR, nested PCR, multiplex PCR), other (e.g. RIA); Unknown/not specified. </li>\n  <li>Country: Country names were recorded according to www.who.int and, for additional analysis purpose, were grouped according to the six WHO regions: the African Region, the Region of the Americas, the Eastern Mediterranean Region, the European Region, the South East-Asia Region and the Western Pacific Region. </li>\n  <li>Sample size of individuals blood drawn from; of individuals involved in analyses/bases for parameter estimate: As a quality indicator of the study, we distinguished the effective sample size, i.e. the number of individuals involved in the analysis/on which the parameter estimate is based on, from the number of individuals from which blood was drawn from (separate column) and the initially calculated/planed sample size (separate column). </li>\n  <li>Population: Although focus was on the general population, two additional groups were included and specified. These include: HCW and blood donor (plus subgroups unspecified, paid, unpaid/voluntary). If in this column &#x201C;population&#x201D; was specified as HCW or blood donor and not as general population, the risk of bias column (following) remains empty. </li>\n  <li>Level: Information is provided if the study was conducted on a national, sub-national, city level or if the level was not further specified (four categories). </li>\n  <li>Study Location: This free-text variable specifies the city/area within the country where the included study was conducted. The variables/columns Level and Study Location were additionally included following the WHO Meeting on Impact of Hepatitis B Vaccination at WHO, Geneva, in March 2014. </li>\n</ol>\n<p> </p>\n<p>Additional data from other sources than the eligible studies: </p>\n<ol>\n  <li>Year of vaccine introduction in the entire country: data is derived from official reports by WHO Member States and unless otherwise stated, data is reported annually through the WHO/UNICEF joint reporting process. <a href=\"http://www.who.int/entity/immunization/monitoring_surveillance/data/year_vaccine_introduction.xls?ua=1\">http://www.who.int/entity/immunization/monitoring_surveillance/data/year_vaccine_introduction.xls?ua=1</a> </li>\n  <li>Period when the study was conducted: pre- vaccination or post vaccination. This is determined according the year of introduction in the whole country. </li>\n  <li>Coverage estimates series: data is obtained from WUENIC: <a href=\"http://apps.who.int/immunization_monitoring/globalsummary/timeseries/tswucoveragebcg.html\" target=\"_blank\">http://apps.who.int/immunization_monitoring/globalsummary/timeseries/tswucoveragebcg.html</a> </li>\n  <li>GDP per capita was used form UN data that compiles information from the World Bank Source <a href=\"http://data.un.org/Data.aspx?q=GDP&amp;d=SNAAMA&amp;f=grID%3a101%3bcurrID%3aUSD%3bpcFlag%3a1\" target=\"_blank\">http://data.un.org/Data.aspx?q=GDP&amp;d=SNAAMA&amp;f=grID%3a101%3bcurrID%3aUSD%3bpcFlag%3a1</a> ), </li>\n  <li>Longitude and latitude data (source: <a href=\"http://www.google.com/\" target=\"_blank\">www.google.com</a>). </li>\n  <li>Population structure and size data for each country was from the UN population division: </li>\n</ol>\n<p><a href=\"http://www.un.org/en/development/desa/population/\" target=\"_blank\">http://www.un.org/en/development/desa/population/</a> </p>", "COLL_METHOD__GLOBAL"=>"<p>WHO provides Member States the opportunity to review and comment on data as part of the so called country consultation process. Member States receive an annex with their country specific estimates, the serosurveys used to inform the mathematical model and the summary of the methodology. They are provided with sufficient time to provide any additional study to be screened according to the inclusion and inclusion criteria. </p>", "FREQ_COLL__GLOBAL"=>"<p>The systematic review of published serosurveys and model estimates are updated on an annual basis. Planned for the last quarter of 2019. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Second quarter of each year</p>", "DATA_SOURCE__GLOBAL"=>"<p>World Health Organization </p>", "COMPILING_ORG__GLOBAL"=>"<p>World Health Organization </p>", "RATIONALE__GLOBAL"=>"<p>The purpose is to describe the reduction in chronic hepatitis b infections. Most of the burden of disease from HBV infection comes from infections acquired before the age of 5 years. Therefore, prevention of HBV infection focuses on children under 5 years of age. The United Nations selected the cumulative incidence of chronic HBV infection at 5 years of age as an indicator of the Sustainable Development Goal target for &#x201C;combating hepatitis&#x201D;. This indicator is measured indirectly through the proportion of children 5 years of age who have developed chronic HBV infection (i.e. the proportion that tests positive for a marker of infection called hepatitis B surface antigen [HBsAg]). </p>", "REC_USE_LIM__GLOBAL"=>"<p>The main Limitations of the analysis is that despite the thorough and in-depth literature search and access, there are fewer data on post vaccination studies than pre- vaccination studies. The model is largely informed by pre-vaccination studies in adults. </p>\n<p>The quality of studies and data was assessed by reviewing representativeness of sampling. Bias factor is a dichotomous variable. </p>\n<p>Potential important biases included geographical representation of the data points. Also, studies were from many different sources such as blood donors and pregnant women. The former possibly having a lower proportion of Hep B prevalence than the general population as donor questionnaires often exclude individuals with risk factors for blood-borne diseases and the pregnant women possibly having a higher prevalence as were in studies to see the effect of a birth dose of vaccine to prevent vertical transmission. As the proportion of studies and size of studies that were from blood donors was significantly greater than those on pregnant women, we may presume that our estimates of prevalence of pre- vaccination may be on the low side. </p>", "DATA_COMP__GLOBAL"=>"<p>The data was modelled using a Bayesian logistic regression looking at the proportion of individuals that tested positive for HBsAg in each study, weighting each study by its size and using a conditional autoregressive (CAR) model accounting for spatial and economic correlations between similar countries. This model uses data from well sampled countries to estimate prevalence in more data poor countries with effects such as sex, age and vaccination status, these are also informed by the geographic and countries GDP proximity to other countries (CAR model). Under the assumption that countries that are close together economically and/or geographically will have more similar prevalence due to similar social structure and health care capabilities. </p>\n<p>The response variable in the model was the prevalence of Hepatitis surface antigen (HBsAg) with the explanatory variables being age (three categories, under 5, juvenile (5-15) and adult (16+), split using the average age of participants in the study), sex (proportion female in the study), study bias (e.g. a high fraction of study participants from indigenous populations), 3 dose vaccine coverage, birth dose of the vaccine and country of study. The coverage of routine 3 dose vaccination and birth dose vaccination in each study was calculated by cross referencing the year of and age of participants in each study with the corresponding WHO-UNICEF vaccine coverage estimates for that country. The WHO-UNICEF estimates are annual data for the country as a whole, and did not contain information on vaccine efficacy which was not used in the analysis as no data on this was obtained. The vaccine efficacy would be implicitly estimated in the analysis as we see vaccination having a variable effect across time and space across the studies. The coverage of routine 3 dose vaccination and birth dose vaccination in each study was calculated by cross referencing the year of and age of participants in each study with the corresponding WHO-UNICEF vaccine coverage estimates for that country. The coverage of routine 3 dose vaccination and birth dose vaccination in each study was calculated by cross referencing the year of and age of participants in each study with the corresponding WHO-UNICEF vaccine coverage estimates for that country. More explicitly, the model uses the ages and timing of the study to calculate the years across which the participants are born, so if the if there was an age group range of 10-15 in a study that was undertaken in 2015, the birth years would be from 2000-2005, we then average the vaccination coverage from the WHO-UNICEF estimates across those 5 years assuming that each age was evenly represented in that age group in the study. The same process was used for the 3 dose and birth dose vaccination. </p>\n<p> </p>\n<p>The general logistic model equation is described below, </p>\n<p>Yi ~Binomial (&#x3C0;i, Ni), log&#x3C0;i1&#x2212;&#x3C0;i= &#x3B2;0+ &#x2211;j=1p&#x3B2;jxij+ui </p>\n<p> </p>\n<p>Where &#x3B2;j are the fixed effects of the explanatory variables xii. With the spatial random effects described by </p>\n<p>ui~ N(u&#x2212;i,&#x3C3;2u/ni)</p>\n<p> , </p>\n<p>where, </p>\n<p>u&#x2212;i= &#x2211;j &#x2208; neigh(i)wiuj/ni</p>\n<p>Where ni is the number of neighbours for country i and weights wi, are 1. </p>\n<p> </p>\n<p>The model was simulated in the Bayesian statistical package WinBUGS, and data manipulation and model initialisation run from R (3.3.1) using R2WinBUGS. The model considers the parameters of age, sex, study bias (e.g. a high fraction of study participants from indigenous populations), vaccine coverage, birth dose of the vaccine and country of study. </p>\n<p>The model uses the CAR-normal function, in WinBUGS, to model the spatial and economic autocorrelation related to neighbouring countries. For each country that had prevalence data, a weighted central position was calculated using the size and location of each study. For those countries with no data, we used the population centroid. In a novel approach, we considered 3 dimensions in the country adjacency matrix; we used the usual geographic dimensions, latitude and longitude and also combined these with the natural log of the country&#x2019;s GDP per capita. This was to measure not only geographic but also the developmental proximity of countries. The adjacency matrix for the geo-economic distance gives a score between each country to every other country. Those countries which are close geographically and economically would have a low score and those further apart either geographically or economically would have a high score/distance. Therefore, those countries that are more alike will have a low score and those countries which are alike would have a high score. </p>\n<p>The way we proportioned the geographic and economic distance to produce the adjacency matrix was then explored, this is because geographic distance may be more or less important than economic similarities. Thus, by creating a number of different adjacency matrices (not definitive) we could select the most suitable matrix that explains reality best. We normalised the geographic and GDP distance and then calculated the distance between these two normalised figures. This creates a smoothed Gaussian surface that is dependent on both spatial proximity and GDP per-capita proximity. We compared ratios of, 1:0, 1:1, 2:1, 1:2 (Geographic:GDP). </p>\n<p>For each different adjacency matrix, we also had to select a neighbourhood distance, i.e. over what distance can a country be effected by another. Thus, we also varied the radius of distance from which to select neighbours for the neighbourhood network, we used the maximum minimum distance, twice the maximum minimum and three times the maximum minimum, thus varying the number of neighbours each country would have. </p>\n<p> </p>\n<p>Finally, to decide the magnitude of the effect one country has on another in the neighbourhood network we varied the weights of pairs of countries in the adjacency matrix, using either a neutral weighting of 1, so that each neighbour has an equal effect on each other (not dependent on the distance in the network), or decaying weights over distance with 1/distance, and 1/distance2, where the closer the country is the greater the effect it has on another country. The outcome of these 36 different combinations led to minimum DIC (Deviance Information Criterion) being found for a ratio of 1:2 (Geographic:GDP), the neighbourhood networks minimum distance being twice the maximum minimum distance and an even weighting of 1/distance for each adjacent country. </p>\n<p>This model structure produces estimates for all fixed effects and also individual country level risk, this provides information on which are significantly at greater or lower risk to the average risk. </p>\n<p>All parameters were given un-informative priors. Simulations were run with 3 MCMC chains with 50,000 burn in iterations and each parameter estimated from 1000 samples taken from a thinned 250,000 iterations to produce the posterior distribution. Convergence was attained, with r&#x302; values all very close to 1.000. Due to the Bayesian framework and WinBUGS software it was possible to gain estimates for countries where we had no data on prevalence, using their GDP and geographic proximity to inform this estimate. Those countries with the largest number of studies provided the estimates with the tightest confidence intervals and those with few or no data were less well defined, often producing a log normal distributed posterior distribution, giving estimates with long tails. </p>\n<p>Posterior distributions of parameters were inspected for convergence and to check for covariance between parameters. Where necessary parameters were centred and scaled to N (0, 1) to aid parameter convergence and remover covariance. This was done for the sex parameter, which was entered as the proportion of the sample that was female; this was seen to co-vary with the intercept and bias parameters before re-centring and scaling. However, the covariance of routine vaccination and birth dose persisted even after re-centring. This is in part unsurprising as there a few instances where birth dose is administered without the routine vaccination. Here we tried to reduce this interaction of the terms by transforming the birth dose data. We modelled birth dose using only data where the birth dose was greater than 60, 70, 80 &amp; 90% respectively, we also modelled birth dose to the square, thus increasing the effect of high birth doses over smaller doses. Model selection dependent on which one both reduced the covariance between the parameters and returned the lowest DIC score. </p>\n<p>Model validation was conducted using 90% of randomly selected data against the remaining 10%, and by comparing model estimates of prevalence against observed data (Figure 3). Figure 4 shows the average prevalence in each country from all the studies plotted against the models estimate. Figure 5 shows the marginal and joint posterior distributions for the fitted parameters. Table 1 gives the estimated parameter values with associated credible intervals. </p>\n<p>During the validation exercise (in which countries were consulted over their estimates) it was pointed out that China had undertaken three very large-scale population-based serological surveys in order to establish baseline prevalence and progress towards HBV elimination. There were a large number of other surveys from China, that are less representative than these three nationwide surveys. We conducted a sensitivity analysis by restricting the data from China to the three nationally representative surveys. The effect of this change in input data was that the effect of vaccination was more distinct, but the estimated age effects (change in prevalence in children under 5, or juveniles (children 5-15 years)) were no longer significantly different from zero (see Table 2 and Figure 6). The deviance was significantly reduced, suggesting a much better fitting model (Table 2), albeit on a somewhat reduced dataset. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Estimates are provided for the 194 WHO Member States and grouped accordingly to the six WHO regions. We also provide estimates according to income classification and follow UN Regional Groupings and Compositions as much as possible. </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>All values represent the best estimates for the hepatitis B surface antigen indicator and aim to facilitate comparability across countries and over time. The estimates are not always the same as the official national estimates, because of the use of different methodologies and data sources. Estimates are provided for 194 WHO Member States. The analysis was carried out for the age groups 0-5 years and for the general population. Due to scarcity of data from some countries, the estimates are more robust at global and regional level than at country level, therefore, we suggest countries focus on the 95% Credible Intervals and not only on the reported point estimates. </p>\n<p> </p>\n<p>A thorough and robust literature review was undertaken to find studies across the 194 WHO Member States and across age groups and vaccination status. We updated the systematic review by Schweitzer et al, 2015 that included a systematic search on articles published between Jan 1, 1965, and Oct 23, 2013. We updated the systematic search to include articles published between Oct 23, 2013, and October 30, 2018 in the databases Embase, PubMed, Global Index Medicus, Popline, and Web of Science. </p>\n<p> </p>\n<p>For each country that had prevalence data, a weighted central position was calculated using the size and location of each study. For those countries with no data, we used the population centroid. Please see detailed explanation above. </p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p>Same as above </p>", "REG_AGG__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong> </p>\n<p>The estimates are not always the same as the official national estimates, because of the use of different methodologies and data sources. The study selection criteria were similar to (Schweitzer, et al., 2015). Observational studies on chronic HBV infection seroprevalence (HBsAg prevalence), done in the general population or among blood donors, health-care workers (HCWs), and pregnant women were considered for inclusion in this systematic review. Studies were excluded if they were systematic reviews or meta-analyses, surveillance reports, case studies, letters or correspondence, or did not contain HBsAg seroprevalence data. Studies were also excluded if they exclusively reported prevalence estimates for high-risk population groups (e.g., migrants and refugees). </p>\n<p>Country estimates may come from selected serosurveys. </p>", "DOC_METHOD__GLOBAL"=>"<p>Non applicable. Estimates come from the mathematical model. </p>\n<p> </p>\n<p>Gather checklist of information that should be included in new reports of global health estimates. Gather promotes best practices in reporting health estimates. A range of health indicators are used to monitor population health and guide resource allocation throughout the world. But the lack of data for some regions and differing measurement methods present challenges that are often addressed by using statistical modelling techniques to generate coherent estimates based on often disparate sources of data. <a href=\"http://gather-statement.org/\" target=\"_blank\"><u>http://gather-statement.org/</u></a> </p>", "QUALITY_ASSURE__GLOBAL"=>"<p><strong>Quality assurance</strong> </p>\n<ul>\n  <li>WHO&#x2019;s estimates use a methodology reviewed by the Immunization and Vaccines Related Implementation Research Advisory Committee (IVIR-AC) and presented to the Strategic Advisory Group of Experts (SAGE). These estimates have been documented following the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). </li>\n</ul>\n<p> </p>\n<ul>\n  <li>WHO provided Member States the opportunity to review and comment on data and estimates as part of the so called country consultation process. </li>\n</ul>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability: </strong></p>\n<p>Estimates are available for 194 Member States and for the six WHO Regions, as well as at global level. </p>\n<p><strong>Time series:</strong></p>\n<p>Estimates are available for pre- vaccine era, 2015 and 2018 and 2020 </p>\n<p><strong>Disaggregation: </strong></p>\n<p>age groups (i.e. under five years of age, 5 years and older (although these estimates are not reported) and the general population); sex/gender if possible. Although the data for the latter is scarce. In addition, data at national, regional and global level. </p>\n<p> </p>", "COMPARABILITY__GLOBAL"=>"<p>This dataset represents the best estimates for the hepatitis B surface antigen indicator and aims to facilitate comparability across countries and over time. The estimates are not always the same as the official national estimates, because of the use of different methodologies and data sources e.g. special populations or populations at risk are not included in the hepatitis b seroprevalence model. Estimates are provided for 194 WHO Member States. The conditional autoregressive model uses data from well sampled countries to estimate prevalence in more data-poor countries taking account of effects such as sex, age and vaccination status. Due to scarcity of data from some countries, the estimates are more robust at global and regional level than at country level, therefore focus should be on the 95% Credible Intervals and not only on the reported point estimates.</p>\n<p><strong>Sources of discrepancies:</strong></p>\n<p>Inclusion or exclusion criteria of the type of seroprevalence studies. Observational studies on chronic HBV infection seroprevalence (HBsAg prevalence), done in the general population or among blood donors, health-care workers (HCWs), and pregnant women were considered for inclusion. Studies were excluded if they were systematic reviews or meta-analyses, surveillance reports, case studies, letters or correspondence, or did not contain HBsAg seroprevalence data. Studies were also excluded if they exclusively reported prevalence estimates for high-risk population groups (e.g., migrants and refugees).</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Serosurveys are available for each member states and reference provided for each data point.</strong> </p>\n<p><strong>URL: </strong><a href=\"http://whohbsagdashboard.com/#global-strategies\" target=\"_blank\"><strong><u>http://whohbsagdashboard.com/#global-strategies</u></strong></a><strong> </strong> </p>", "indicator_sort_order"=>"03-03-04", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.3.5", "slug"=>"3-3-5", "name"=>"Número de personas que requieren intervenciones contra enfermedades tropicales desatendidas", "url"=>"/site/es/3-3-5/", "sort"=>"030305", "goal_number"=>"3", "target_number"=>"3.3", "global"=>{"name"=>"Número de personas que requieren intervenciones contra enfermedades tropicales desatendidas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de personas que requieren intervenciones contra enfermedades tropicales desatendidas", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de personas que requieren intervenciones contra enfermedades tropicales desatendidas", "indicator_number"=>"3.3.5", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nSe prevé que el número medio anual de personas que requieren tratamiento y \natención por enfermedades tropicales desatendidas (ETD) disminuya hacia \nel “fin de las ETD” en 2030 (meta 3.3), a \nmedida que se erradiquen, eliminen o controlen las ETD. Se prevé que el número \nde personas que requieren otras intervenciones contra las ETD (por ejemplo, \ngestión de vectores, salud pública veterinaria, agua, saneamiento e higiene) se \nmantenga más allá de 2030 y, por lo tanto, se abordará en el contexto de \notras metas e indicadores, a saber, la cobertura sanitaria universal (CSU) y \nel acceso universal al agua y al saneamiento. Esta cifra no debe interpretarse \ncomo el número de personas en riesgo de contraer ETD. De hecho, es un subconjunto \ndel número mayor de personas en riesgo. \n\nEl tratamiento masivo se limita a quienes viven en distritos por encima de \nun nivel umbral de prevalencia; no incluye a todas las personas que viven \nen distritos con algún riesgo de infección. El tratamiento y la atención \nindividual están destinados a quienes están o ya han estado infectados; \nno incluye a todos los contactos y otras personas en riesgo de infección. \n\nEsta cifra puede interpretarse mejor como el número de personas con un nivel \nde riesgo que requiere intervención médica, es decir, tratamiento y atención \npara las ETD.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.5&seriesCode=SH_TRP_INTVN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Número de personas que requieren intervenciones contra enfermedades tropicales desatendidas (número) SH_TRP_INTVN</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-05.pdf\">Metadatos 3-3-5.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2025-03-12", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe average annual number of people requiring treatment and care for NTDs is the number that is \nexpected to decrease toward “the end of NTDs” by 2030 (target 3.3), as NTDs are eradicated, eliminated \nor controlled. The number of people requiring other interventions against NTDs (e.g. vector \nmanagement, veterinary public health, water, sanitation and hygiene) are expected to be maintained \nbeyond 2030 and are therefore to be addressed in the context of other targets and indicators, namely \nUniversal Health Coverage (UHC) and universal access to water and sanitation.\n\nThis number should not be interpreted as the number of people at risk for NTDs. It is in fact a subset of \nthe larger number of people at risk. Mass treatment is limited to those living in districts above a \nthreshold level of prevalence; it does not include all people living in districts with any risk of infection. \nIndividual treatment and care is for those who are or have already been infected; it does not include all \ncontacts and others at risk of infection.\n\nThis number can better be interpreted as the number of people \nat a level of risk requiring medical intervention – that is, treatment and care for NTDs.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.5&seriesCode=SH_TRP_INTVN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Number of people requiring treatment and care for Neglected Tropical Diseases (number) SH_TRP_INTVN</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-05.pdf\">Metadata 3-3-5.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nArtatu gabeko gaixotasun tropikalen (AGGT) ziozko arreta eta tratamendua behar duten pertsonen urteko batez besteko \nkopurua 2030an “AGGTen amaierara” hurbiltzea espero da (3.3 xedea), AGGTak desagertu, ezabatu edo kontrolatu ahala. \nAGGTen aurkako beste esku-hartze batzuk behar dituzten pertsonen kopurua (adibidez, bektoreen kudeaketa, albaitaritza-osasun \npublikoa, ura, saneamendua eta higienea) 2030az harago mantentzea espero da, eta, beraz, beste xede eta adierazle \nbatzuen testuinguruan jorratuko da, esaterako osasun-estaldura unibertsala (OEU) eta uraren eta saneamenduaren sarbide \nunibertsala bezalakoetan. Zenbateko hori ez da AGGTa hartzeko arriskuan dauden pertsonen kopurutzat jo behar. Berez, \narriskuan dauden pertsonen kopuru handiaren azpimultzo bat da. \n\nTratamendu masiboa nagusitasun-atalaren mailaren gainetik dagoen barrutietan bizi direnengana mugatzen da; ez ditu \nbarne hartzen infekzio-arriskuren bat duten barrutietan bizi diren pertsona guztiak. Banakako arreta eta tratamendua \ninfektatuta dauden edo egon diren pertsonei zuzentzen zaie; ez dira sartzen infekzio-arriskua duten beste pertsona \nbatzuk edo kontaktuak. \n\nZenbateko hori esku-hartze mediko bat, hau da, AGGTen tratamendu edo arretaren bat behar duten arrisku-mailako pertsonen \nkopuru bezala interpretatu daiteke. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.3.5&seriesCode=SH_TRP_INTVN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Artatu gabeko gaixotasun tropikalen aurkako esku-hartzeak behar dituzten pertsonen kopurua (kopurua) SH_TRP_INTVN</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-03-05.pdf\">Metadatuak 3-3-5.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.3.5: Number of people requiring interventions against neglected tropical diseases </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_TRP_INTVN - Number of people requiring interventions against neglected tropical diseases [3.3.5]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>NTDs are formally recognized as targets for global action in SDG target 3.3, which calls to &#x201C;end the epidemics of ... neglected tropical diseases&#x201D; by 2030, as part of Goal 3 (Ensure healthy lives and ensure well-being for all at all ages). Successful interventions against NTDs contribute to meeting other SDGs, such as alleviating poverty (Goal 1) and hunger (Goal 2), enabling people to pursue an education (Goal 4) and lead productive working lives (Goal 8) and promoting equality, for example with regard to gender (Goals 5 and 10). Progress towards other Goals can accelerate the achievement of NTD goals. For example, wider provision of clean water, sanitation and hygiene (WASH) (Goal 6) is believed to help to eliminate or control NTDs; the availability of resilient infrastructure (Goal 9) should facilitate delivery of medicines and outreach to remote communities; the goals of sustainable cities (Goal 11) and climate action (Goal 13) can support the environmental management necessary for control of disease vectors. Attaining all SDGs and NTD goals is founded on strong global partnerships (Goal 17).</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Number of people requiring treatment and care for any one of the neglected tropical diseases (NTDs) targeted by the WHO NTD Roadmap and World Health Assembly resolutions and reported to WHO.</p>\n<p><strong>Concepts:</strong></p>\n<p>Treatment and care is broadly defined to allow for preventive, curative, surgical or rehabilitative treatment and care. In particular, it includes both: </p>\n<p>1) Average annual number of people requiring mass treatment known as preventive chemotherapy (PC) for at least one PC-NTD; and</p>\n<p>2) Number of new cases requiring individual treatment and care for other NTDs.</p>\n<p>Other key interventions against NTDs (e.g. vector management, veterinary public health, water, sanitation and hygiene) are to be addressed in the context of other targets and indicators, namely Universal Health Coverage (UHC) and universal access to water and sanitation.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of people</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Description:</strong></p>\n<p>The number of people requiring treatment and care for NTDs is measured by existing country systems, and reported through joint request and reporting forms for donated medicines, the WHO Integrated Data Platform, and other reports to WHO.</p>\n<p><u>https://www.who.int/teams/control-of-neglected-tropical-diseases/data-platforms-and-tools</u></p>\n<p>Country data are published via the WHO Global Health Observatory. </p>\n<p><u>https://www.who.int/data/gho/data/themes/neglected-tropical-diseases</u></p>", "COLL_METHOD__GLOBAL"=>"<p><strong>NTDs requiring preventive chemotherapy (PC-NTDs)</strong></p>\n<p>As part of global efforts to accelerate expansion of preventive chemotherapy for elimination and control of lymphatic filariasis (LF), schistosomiasis (SCH) and soil-transmitted helminthiases (STH), WHO facilitates the supply of the following medicines donated by the pharmaceutical industry: diethylcarbamazine citrate, albendazole, mebendazole, and praziquantel. WHO also collaborates to supply ivermectin for onchocerciasis (ONCHO) and LF elimination programmes, and azithromycin for trachoma (TRA) through the Trachoma Elimination Monitoring Form.</p>\n<p>A joint mechanism and a set of forms have been developed to facilitate the process of application, review and reporting as well as to improve coordination and integration among different programmes.</p>\n<p>Joint Request for Selected PC Medicines (JRSM) &#x2013; designed to assist countries in quantifying the number of tablets of the relevant medicines required to reach the planned target population and districts in a coordinated and integrated manner against multiple diseases during the year for which medicines are requested.</p>\n<p>Joint Reporting Form (JRF) &#x2013; designed to assist countries in reporting annual progress on integrated and coordinated distribution of medicines across PC-NTDs in the reporting year in a standardized format.</p>\n<p>PC Epidemiological Data Reporting Form (EPIRF) &#x2013; designed to standardize national reporting of epidemiological data on LF, ONCHO, soil-transmitted helminthiases and SCH. National authorities are encouraged to complete this form and submit it to WHO on a yearly basis, together with the JRF.</p>\n<p>The reports generated in the JRSM and in the JRF (SUMMARY worksheets) must be printed and signed by the NTD coordinator or a Ministry of Health representative to formally endorse the country&#x2019;s request for these medicines and the reported annual progress of the national programme(s). The date of signature must also be included. Once signatures have been obtained, the scanned copies of the two worksheets, together with the full Excel versions of the JRSM, the JRF and the EPIRF can be jointly submitted to WHO.</p>\n<p>The forms are submitted to the WHO Representative of the concerned WHO Country office with electronic copies to <a href=\"mailto:PC_JointForms@who.int\"><em>PC_JointForms@who.int</em></a> and the concerned Regional focal point. The relevant submission deadline depends on the time of planned implementation dates as follows: </p>\n<ul>\n  <li>the final report should be submitted within 3 months after the last round was implemented and no later than 31 March of the next implementation year;</li>\n  <li>to ensure the medicines are delivered on time, the request for PC medicines should be submitted at least 9 months before the first date of MDA planned in the calendar year of the request.</li>\n</ul>\n<p><a href=\"https://www.who.int/teams/control-of-neglected-tropical-diseases/interventions/strategies/preventive-chemotherapy/joint-application-package\">https://www.who.int/teams/control-of-neglected-tropical-diseases/interventions/strategies/preventive-chemotherapy/joint-application-package</a></p>\n<p><strong>NTDs requiring individual diagnosis and treatment</strong></p>\n<p>Countries are invited to report on Buruli ulcer, Chagas disease, leprosy, the leishmaniases, mycetoma, rabies, snakebite envenoming and yaws cases using Excel templates or directly into the WHO integrated data platform (<a href=\"https://extranet.who.int/dhis2\" target=\"_blank\"><u>https://extranet.who.int/dhis2</u></a>). </p>\n<p>Cases of human African trypanosomiasis (HAT) and other key HAT indicators are reported at village level by national sleeping sickness control programmes through annual reports and entered in the Atlas of HAT (<a href=\"https://www.who.int/publications/i/item/1476-072X-8-15\">https://www.who.int/publications/i/item/1476-072X-8-15</a>), but annual cases aggregated at country level are also entered in the WHO integrated data platform.</p>\n<p>In 2023, WHO/NTD introduced the Global NTD Annual Reporting Form (GNARF) to enable WHO Member States to annually submit streamlined, consolidated data on roadmap indicators for relevant NTDs, aiming to support monitoring progress towards the roadmap targets and facilitate integration of NTD data into national health information systems. The process involves four key stages: Member States accessing the WHO Country Portal (<a href=\"https://countryportal.who.int/\">https://countryportal.who.int/</a>) to download and complete the GNARF with support from WHO Country Offices; joint validation of reported data by regional WHO offices and headquarters; Member States revising submissions based on feedback received; and the eventual publication of validated data in annual global reporting on NTDs and relevant dashboards, providing crucial information for guiding strategies in combating neglected tropical diseases.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data for the reporting year is being collected and reported during first 3 quarters of the next year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data reported for the preceding year is released during the last quarter of the year</p>", "DATA_SOURCE__GLOBAL"=>"<p>National NTD programmes within Ministries of Health</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "INST_MANDATE__GLOBAL"=>"<p>A process of data reporting by national NTD programmes implemented according to the WHO Data Sharing Policy on use and sharing of data collected in Member States by the World Health Organization (WHO) outside the context of public health emergencies (<a href=\"https://www.who.int/about/policies/publishing/data-policy\">https://www.who.int/about/policies/publishing/data-policy</a>). The department of control of Neglected tropical diseases at WHO is then responsible for processing and disseminating the statistics for this indicator.</p>", "RATIONALE__GLOBAL"=>"<p>The average annual number of people requiring treatment and care for NTDs is the number that is expected to decrease toward &#x201C;the end of NTDs&#x201D; by 2030 (target 3.3), as NTDs are eradicated, eliminated or controlled. The number of people requiring other interventions against NTDs (e.g. vector management, veterinary public health, water, sanitation and hygiene) are expected to be maintained beyond 2030 and are therefore to be addressed in the context of other targets and indicators, namely Universal Health Coverage (UHC) and universal access to water and sanitation.</p>\n<p>This number should not be interpreted as the number of people at risk for NTDs. It is in fact a subset of the larger number of people at risk. Mass treatment is limited to those living in districts above a threshold level of prevalence; it does not include all people living in districts with any risk of infection. Individual treatment and care is for those who are or have already been infected; it does not include all contacts and others at risk of infection. This number can better be interpreted as the number of people at a level of risk requiring medical intervention &#x2013; that is, treatment and care for NTDs.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Country reports may not be perfectly comparable over time. Improved surveillance and case-finding may lead to an apparent increase in the number of people known to require treatment and care. Some further estimation may be required to adjust for changes in surveillance and case-finding. Missing country reports may need to be imputed for some diseases in some years.</p>", "DATA_COMP__GLOBAL"=>"<p>Some estimation is required to aggregate data across interventions and diseases. There is an established methodology that has been tested and an agreed international standard. [<a href=\"https://apps.who.int/iris/bitstream/handle/10665/241869/WER8702.PDF\">https://apps.who.int/iris/bitstream/handle/10665/241869/WER8702.PDF</a>]</p>\n<p>1) Average annual number of people requiring mass treatment known as PC for at least one PC-NTD (lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiases and trachoma). People may require PC for more than one PC-NTD. The number of people requiring PC is compared across the PC-NTDs, by age group and implementation unit (e.g. district). The largest number of people requiring PC is retained for each age group in each implementation unit. The total is considered to be a conservative estimate of the number of people requiring PC for at least one PC-NTD. Prevalence surveys determine when an NTD has been eliminated or controlled and PC can be stopped or reduced in frequency, such that the average annual number of people requiring PC is reduced.</p>\n<p>2) Number of new cases requiring individual treatment and care for other NTDs: The number of new cases is based on country reports, whenever available, of new and known cases of Buruli ulcer, dengue, dracunculiasis, echinococcosis, human African trypanosomiasis (HAT), leprosy, the leishmaniases, rabies and yaws. Where the number of people requiring and requesting surgery for PC-NTDs (e.g. trichiasis or hydrocele surgery) is reported, it can be added here. Similarly, new cases requiring and requesting rehabilitation (e.g. leprosy or lymphoedema) can be added whenever available.</p>\n<p>Populations referred to under 1) and 2) may overlap; the sum would overestimate the total number of people requiring treatment and care. The maximum of 1) or 2) is therefore retained at the lowest common implementation unit and summed to get conservative country, regional and global aggregates. By 2030, improved co-endemicity data and models will validate the trends obtained using this simplified approach.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data is jointly validated by the three levels of the organization &#x2013; countries, regions and global.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>We do not impute missing values for countries that have never reported data for any NTD. For countries that have reported data in the past, we impute missing values only for those NTDs that have been reported in the past but that have not been reported in the current year.</p>\n<p>For reproducibility, we employ multiple imputation techniques using the freely available Amelia package in R. We impute 100 complete datasets using all available cross-sectional data (countries and years), applying a square root transformation to exclude negative values of incidence, as well as categorical variables denoting regions and income groups, and allowing for country-specific linear time effects. We aggregate across diseases and extract the mean and 2.5th and 97.5th centile values to report best estimates and uncertainty intervals for each country.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Using the 100 imputed datasets, we aggregate across diseases and regions, extract the mean and 2.5th and 97.5th centile values to report best estimates and uncertainty intervals at the regional and global levels.</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional estimates are simple aggregates of the country values, with no particular weighting. There is no further adjustment for global and regional estimates.</p>", "DOC_METHOD__GLOBAL"=>"<p>This indicator is based on national-level data reported to WHO by its Member States and disseminated via the WHO Global Health Observatory (<u>https://www.who.int/data/gho/data/themes/neglected-tropical-diseases</u>) and PC Data Portal (<a href=\"https://www.who.int/data/preventive-chemotherapy/\">https://www.who.int/data/preventive-chemotherapy/</a>). Some adjustment is required to aggregate country-reported data on individual neglected tropical diseases across all NTDs included in this indicator. There is an established methodology to standardize this aggregation: <a href=\"https://apps.who.int/iris/bitstream/handle/10665/241869/WER8702.PDF\">https://apps.who.int/iris/bitstream/handle/10665/241869/WER8702.PDF</a></p>\n<p>For NTDs requiring preventive chemotherapy, a joint reporting mechanism and set of reporting forms have been developed to facilitate the process of requesting donated medicines and reporting progress as well as to improve coordination and integration among programmes. More information is available here,<u> https://www.who.int/teams/control-of-neglected-tropical-diseases/interventions/strategies/preventive-chemotherapy/joint-application-package</u></p>\n<p>For the other NTDs, the number of new cases should be reported by the health facilities to the national level in order to compile them. If active case search activities are organized (e.g. for integrated skin NTDs, human African trypanosomiasis, etc.), the country must ensure that the number of new cases detected through these activities are also reported, either through the health facilities or directly to the national level. A strong health information system is essential for countries to be able to collect, compile and analyse good quality information on these NTDs.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>A framework for monitoring and evaluating progress of the road map for neglected tropical diseases guides activities involving the development of standards, tools and methods for generating, collecting, compiling, analysing, using and disseminating high-quality data on NTDs. At WHO, the department of control of neglected tropical diseases is responsible for curating and generating the statistics on NTDs, which will be checked and validated internally by the Division of Data and Analytics before publication and dissemination.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>A user guide and video tutorial for the joint reporting mechanism and set of reporting forms are available here:<u> https://www.who.int/teams/control-of-neglected-tropical-diseases/interventions/strategies/preventive-chemotherapy/joint-application-package</u></p>\n<p>Details about individual NTD data are available via:<u> https://www.who.int/data/gho/data/themes/neglected-tropical-diseases</u>. For NTDs requiring preventive chemotherapy, reports are signed by the NTD coordinator or a Ministry of Health representative to formally endorse the country&#x2019;s request for medicines (when applicable) and data. They are submitted to the WHO Representative of the concerned WHO Country office.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>A data quality review toolkit has been developed by WHO to provide a multi-pronged approach that ensures a comprehensive and holistic review of the quality of health facility data. WHO has also developed a field manual to guide national NTD programmes in using tools to improve data quality and information, through coverage evaluation surveys, data quality assessments and a supervisors&#x2019; coverage tool (<a href=\"https://apps.who.int/iris/bitstream/handle/10665/329376/9789241516464-eng.pdf\">https://apps.who.int/iris/bitstream/handle/10665/329376/9789241516464-eng.pdf</a>).</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are currently being reported by WHO Member States, with good coverage of all regions.</p>\n<p><strong>Time series:</strong></p>\n<p>2010-2023</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregation by disease is required; ending the epidemic of NTDs requires a reduction in the number of people requiring interventions for each NTD.</p>\n<p>Disaggregation by age is required for PC: preschool-aged children (1-4 years), school-aged (5-14 years) and adults (= 15 years+).</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Countries do not typically aggregate their data across NTDs, but if they applied the aggregation method as described above, they would obtain the same number. The only exceptions would be countries with one or more missing values for individual NTDs. In these exceptional cases, internationally estimated aggregates will be higher than country produced aggregates that assume missing values are nil. We present best estimates with uncertainty intervals to highlight those missing values that have a significant impact on country aggregates, until such time that missing values are reported.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><u>https://www.who.int/teams/control-of-neglected-tropical-diseases/overview</u> </p>\n<p><strong>References:</strong></p>\n<p>Global report on neglected tropical diseases 2024. Geneva: World Health Organization; 2024</p>\n<p>(https://www.who.int/teams/control-of-neglected-tropical-diseases/global-report-on-neglected-tropical-diseases-2024, accessed 8 February 2025).</p>\n<p>Global report on neglected tropical diseases 2023. Geneva: World Health Organization; 2023</p>\n<p>(<a href=\"https://www.who.int/publications/i/item/9789240067295\">https://www.who.int/publications/i/item/9789240067295</a>, accessed 8 February 2023).</p>\n<p>Ending the neglect to attain the Sustainable Development Goals: A road map for neglected tropical diseases 2021&#x2013;2030. Geneva: World Health Organization; 2021 (<a href=\"https://www.who.int/publications/i/item/9789240010352\">https://www.who.int/publications/i/item/9789240010352</a>, accessed 8 February 2023).</p>\n<p>A compendium of indicators for monitoring and evaluating progress of the road map for neglected tropical diseases 2021&#x2013;2030,</p>\n<p>(<a href=\"https://iris.who.int/handle/10665/365511\">https://iris.who.int/handle/10665/365511</a>, accessed 8 February 2024).</p>", "indicator_sort_order"=>"03-03-05", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.4.1", "slug"=>"3-4-1", "name"=>"Tasa de mortalidad atribuida a las enfermedades cardiovasculares, el cáncer, la diabetes o las enfermedades respiratorias crónicas", "url"=>"/site/es/3-4-1/", "sort"=>"030401", "goal_number"=>"3", "target_number"=>"3.4", "global"=>{"name"=>"Tasa de mortalidad atribuida a las enfermedades cardiovasculares, el cáncer, la diabetes o las enfermedades respiratorias crónicas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[{"series"=>"Tasa de mortalidad prematura (entre 30 y 70 años)", "unit"=>"", "label_content"=>"Objetivo del indicador para 2030", "value"=>7.2}], "graph_title"=>"Tasa de mortalidad atribuida a las enfermedades cardiovasculares, el cáncer, la diabetes o las enfermedades respiratorias crónicas", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Tasa de mortalidad atribuida a las enfermedades cardiovasculares, el cáncer, la diabetes o las enfermedades respiratorias crónicas", "indicator_number"=>"3.4.1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Meta:</b> Reducir en un tercio la mortalidad prematura por enfermedades no transmisibles", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_28/opt_0/tipo_1/ti_defunciones/temas.html", "url_text"=>"Estadística de defunciones", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Tasa de mortalidad atribuida a las enfermedades cardiovasculares, el cáncer, la diabetes o las enfermedades respiratorias crónicas", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.4-  De aquí a 2030, reducir en un tercio la mortalidad prematura por enfermedades no transmisibles mediante su prevención y tratamiento, y promover la salud mental y el bienestar", "definicion"=>"\nEste indicador se compone de dos series temporales:\n\n <b> - Tasa de mortalidad prematura (entre 30 y 70 años) atribuida a las enfermedades no transmisibles</b>: probabilidad de morir entre los 30 y los 70 años por enfermedades cardiovasculares, cáncer, \ndiabetes o enfermedades respiratorias crónicas.\n\n <b> - Tasa de mortalidad atribuida a las enfermedades cardiovasculares, el cáncer, la diabetes o las enfermedades respiratorias \ncrónicas por cada 100.000 habitantes</b>: defunciones atribuidas a las enfermedades cardiovasculares, el cáncer, la diabetes o las enfermedades respiratorias \ncrónicas por cada 100.000 habitantes\n", "formula"=>"\n<b>Tasa de mortalidad prematura atribuida a las enfermedades no transmisibles</b>\n\n$$^{5}M_x = \\frac{DENT_{x,x+5}^{t}}{PENT_{x,x+5}^{t}}$$\n\ndonde:\n\n$DENT_{x,x+5}^{t} =$ defunciones atribuidas a las 4 enfermedades no transmisibles\nentre la edad exacta $x$ y la edad exacta $x+5$ en el año $t$\n\n$P^{t} =$ población entre la edad exacta $x$ y la edad exacta $x+5$ del año $t$\n\n\n<b>Tasa de mortalidad atribuida a las enfermedades cardiovasculares, el cáncer, la diabetes o las enfermedades respiratorias \ncrónicas por cada 100.000 habitantes</b>\n\n\n$$TM_{ENT}^{t} = \\frac{D_{ENT}^{t}}{P^{t}} \\cdot 100.000$$\n\n\ndonde:\n\n$D_{ENT}^{t} =$ defunciones atribuidas a enfermedades no transmisibles en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"\nEnfermedad no transmisible (ENT): enfermedades cardiovasculares, el cáncer, la diabetes o las enfermedades respiratorias \ncrónicas\n\nSexo\n", "periodicidad"=>"Anual", "observaciones"=>"\nSe utilizan los siguientes códigos de la Clasificación Internacional de Enfermedades para cada una de las enfermedades no transmisibles:\n- enfermedades cardiovasculares (códigos I00-I99 de la CIE-10) \n- cáncer (códigos C00-C97 de la CIE-10)\n- diabetes (códigos E10-E14 de la CIE-10)\n- enfermedades respiratorias crónicas (códigos J30-J98 de la CIE-10) \n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa morbilidad por enfermedades no transmisibles (ENT) entre los adultos está aumentando rápidamente a nivel mundial debido\n al envejecimiento y las transiciones epidemiológicas. Las enfermedades cardiovasculares, el cáncer, la diabetes y las\n enfermedades respiratorias crónicas son las cuatro causas principales de la carga de ENT. Medir el riesgo de morir \npor estas cuatro causas principales es importante para evaluar el alcance de la carga de mortalidad prematura por ENT en \nuna población, suponiendo que experimentaría las tasas de mortalidad actuales a cualquier edad y no moriría por ninguna\n otra causa de muerte (por ejemplo, lesiones o VIH/SIDA).\n\nLa tasa de mortalidad atribuida a enfermedades cardiovasculares, cáncer, diabetes o \nenfermedades respiratorias crónicas es la probabilidad de morir entre los 30 y los 70 años \npor enfermedades cardiovasculares, cáncer, diabetes\no enfermedades respiratorias crónicas, definida como el porcentaje de personas de 30 años que morirían antes de cumplir \n70 años por enfermedades cardiovasculares, cáncer, diabetes o enfermedades respiratorias crónicas, suponiendo que experimentaría \nlas tasas de mortalidad actuales a cualquier edad y no moriría por ninguna otra causa de muerte (por ejemplo, lesiones o VIH/SIDA).\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.4.1&seriesCode=SH_DTH_NCOM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=30-70%20%7C%20BOTHSEX\">Tasa de mortalidad atribuida a enfermedades cardiovasculares, cáncer, diabetes o enfermedades respiratorias crónicas (probabilidad) SH_DTH_NCOM</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-04-01.pdf\">Metadatos 3-4-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Tasa de mortalidad atribuida a las enfermedades cardiovasculares, el cáncer, la diabetes o las enfermedades respiratorias crónicas", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.4-  De aquí a 2030, reducir en un tercio la mortalidad prematura por enfermedades no transmisibles mediante su prevención y tratamiento, y promover la salud mental y el bienestar", "definicion"=>"\nThis indicator is composed of two time series:\n\n  <b>- Premature mortality rate (between ages 30 and 70) attributed to non-communicable diseases:</b>  \n probability of dying between ages 30 and 70 from cardiovascular disease, cancer, diabetes, or \n chronic respiratory disease.\n\n  <b>- Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory \n disease per 100,000 inhabitants:</b> Deaths attributed to cardiovascular disease, cancer, diabetes \n or chronic respiratory disease per 100,000 inhabitants. \n", "formula"=>"\n<b>Premature mortality rate attributed to non-communicable diseases</b>\n\n$$^{5}M_x = \\frac{DENT_{x,x+5}^{t}}{PENT_{x,x+5}^{t}}$$\n\nwhere:\n\n$DENT_{x,x+5}^{t} =$ Deaths attributed to the 4 non-communicable diseases\nbetween exact age $x$ and exact age $x+5$ in year $t$\n\n$P^{t} =$ population between exact age $x$ and exact age $x+5$ in year $t$\n\n\n<b>Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory \ndisease per 100,000 inhabitants</b>\n\n\n$$TM_{ENT}^{t} = \\frac{D_{ENT}^{t}}{P^{t}} \\cdot 100.000$$\n\n\nwhere:\n\n$D_{ENT}^{t} =$ Deaths attributed to non-communicable diseases in year $t$ \n\n$P^{t} =$ population as of 1 July of year $t$ \n", "desagregacion"=>"\nNon-communicable disease (NCD): cardiovascular diseases, cancer, diabetes, or chronic respiratory disease\n\nSex\n", "periodicidad"=>"Anual", "observaciones"=>"\nThe following International Classification of Diseases codes are used for each of the non-communicable diseases:\n- cardiovascular diseases (ICD-10 codes I00-I99) \n- cancer (ICD-10 codes C00-C97)\n- diabetes (ICD-10 codes E10-E14)\n- chronic respiratory disease (ICD-10 codes J30-J98) \n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nMorbidity from noncommunicable diseases (NCDs) among adults is increasing rapidly globally due to aging \nand epidemiological transitions. Cardiovascular disease, cancer, diabetes, and chronic respiratory \ndiseases are the four leading causes of the NCD burden. Measuring the risk of dying from these four \nleading causes is important for assessing the extent of the premature NCD mortality burden in a population, \nassuming they would experience current mortality rates at any age and would not die from any other cause \nof death (e.g., injuries or HIV/AIDS).\n\nThe mortality rate attributed to cardiovascular disease, cancer, diabetes, or chronic respiratory disease \nis the probability of dying between ages 30 and 70 from cardiovascular disease, cancer, diabetes, or \nchronic respiratory disease, defined as the percentage of 30-year-olds who would die before reaching age 70\nfrom cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming they would \nexperience current mortality rates at any age and would not die from any other cause of death (e.g., \ninjuries or HIV/AIDS). \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.4.1&seriesCode=SH_DTH_NCOM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=30-70%20%7C%20BOTHSEX\">Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability) SH_DTH_NCOM</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-04-01.pdf\">Metadata 3-4-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Tasa de mortalidad atribuida a las enfermedades cardiovasculares, el cáncer, la diabetes o las enfermedades respiratorias crónicas", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.4-  De aquí a 2030, reducir en un tercio la mortalidad prematura por enfermedades no transmisibles mediante su prevención y tratamiento, y promover la salud mental y el bienestar", "definicion"=>"\nAdierazle honek bi denborazko serie ditu: \n\n <b> - Transmititu ezin diren gaixotasunei egotzitako heriotza goiztiarren (30-70 urtekoen) tasa:</b>  gaixotasun kardiobaskularren, \nminbiziaren, diabetesaren edo arnas gaixotasun kronikoen ondorioz 30 eta 70 urte bitartean hiltzeko probabilitatea. \n\n <b> - Gaixotasun kardiobaskularrei, minbiziari, diabetesari edo arnas gaixotasun kronikoei egotzitako heriotza-tasa 100.000 \nbiztanleko:</b> gaixotasun kardiobaskularrei, minbiziari, diabetesari edo arnas gaixotasun kronikoei egotzitako \nheriotzak 100.000 biztanleko \n", "formula"=>"\n<b>Transmititu ezin diren gaixotasunei egotzitako heriotza goiztiarren tasa</b>\n\n$$^{5}M_x = \\frac{DENT_{x,x+5}^{t}}{PENT_{x,x+5}^{t}}$$\n\nnon:\n\n$DENT_{x,x+5}^{t} =$ $x$ adin zehatzaren eta $x+5$ adin zehatzaren artean transmititu ezin diren 4 gaixotasunei \negotzitako heriotzak $t$ urtean\n\n$P^{t} =$ $x$ adin zehatzaren eta $x+5$ adin zehatzaren arteko biztanleria $t$ urtean\n\n\n<b>Gaixotasun kardiobaskularrei, minbiziari, diabetesari edo arnas gaixotasun kronikoei egotzitako heriotza-tasa \n100.000 biztanleko</b>\n\n\n$$TM_{ENT}^{t} = \\frac{D_{ENT}^{t}}{P^{t}} \\cdot 100.000$$\n\n\nnon:\n\n$D_{ENT}^{t} =$ transmititu ezin diren gaixotasunei egotzitako heriotzak $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"\nGaixotasun ez-transmitigarria (GET): gaixotasun kardiobaskularrak; minbizia; diabetesa; arnas gaixotasun \nkronikoak\n\nSexua\n", "periodicidad"=>"Anual", "observaciones"=>"\nGaixotasunen Nazioarteko Sailkapenaren kode hauek erabiltzen dira transmititu ezin den gaixotasun bakoitzerako: \n- gaixotasun kardiobaskularrak (GNS-10eko I00-I99 kodeak)\n- minbizia (GNS-10eko C00-C97 kodeak)\n- diabetesa (GNS-10eko E10-E14 kodeak)\n- arnas gaixotasun kronikoak (GNS-10eko J30-J98 kodeak)  \n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nHelduen artean, gaixotasun ez-kutsakorren (GEK) ziozko erikortasuna azkar handitzen ari da mundu-mailan, zahartzearen \neta trantsizio epidemiologikoen ondorioz. Gaixotasun kardiobaskularrak, minbizia, diabetesa eta arnas-gaixotasun \nkronikoak dira GEK kargaren lau arrazoi nagusiak. Lau arrazoi nagusi hauen ondorioz hiltzeko arriskua neurtzea \ngarrantzitsua da biztanleria baten GEK bidez goiz hiltzeko kargaren irismena ebaluatzeko, suposatuz edozein adinetan \negungo heriotza-tasak esperimentatuko lituzkeela eta ez litzatekeela beste heriotza-arrazoi baten zioz hilko (esaterako, \nlesioek edo GIB/hiesak eraginda). \n\nGaixotasun kardiobaskularrei, minbiziari, diabetesari eta arnasketa-gaixotasun kronikoei lotutako heriotza-tasa da 30 \neta 70 urte bitartean gaixotasun kardiobaskularren, minbiziaren, diabetesaren edo arnasketa-gaixotasun kronikoen ondorioz \nhiltzeko arriskua, hau da, gaixotasun kardiobaskularren, minbiziaren, diabetesaren edo arnasketa-gaixotasun kronikoen \nondorioz 70 urteak bete baino lehen hilko diren 30 urteko pertsonen ehunekoa, suposatuz egungo heriotza-tasak esperimentatuko \ndituela edozein adinetan eta ez dela hilko beste ezein heriotza-arrazoiren ondorioz (esaterako, lesioek edo GIB/ihesak \neraginda). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.4.1&seriesCode=SH_DTH_NCOM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=30-70%20%7C%20BOTHSEX\">Gaixotasun kardiobaskularrei, minbiziari, diabetesari edo arnas gaixotasun kronikoei egotzitako heriotza-tasa (probabilitatea) SH_DTH_NCOM</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-04-01.pdf\">Metadatuak 3-4-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_DTH_NCOM - Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease [3.4.1]</p>\n<p>SH_DTH_NCD - Deaths attributed non-communicable diseases (number) [3.4.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>\n<p> </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions: </strong></p>\n<p> Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease is defined as theprobability of dying between the ages of 30 and 70 years from cardiovascular diseases, cancer, diabetes or chronic respiratory diseases, defined as the percent of 30-year-old-people who would die before their 70th birthday from cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS). This indicator is calculated using life table methods (see further details in section 3.3). </p>\n<p><strong>Concepts:</strong></p>\n<p>Probability of dying: The likelihood that an individual would die between two ages given current mortality rates at each age in between, calculated using life table methods. </p>\n<p> </p>\n<p>Life table: A table showing the mortality experience of a hypothetical group of infants born at the same time and subject throughout their lifetime to a set of age-specific mortality rates. </p>\n<p> </p>\n<p>Cardiovascular disease, cancer, diabetes or chronic respiratory diseases: ICD-10 underlying causes of death I00-I99, COO-C97, E10-E14 and J30-J98, or ICD-11 underlying causes of death 8B00-8B2Z, BA00-BE2Z, 2A00-2F9Z, 5A10-5A2Y, CA20-CA2Z, CA60-CA8Z, CA00-CA0Z, CB00-CB0Z, CB20-CB2Z, CB40, CB41, CB60-CB64, CB7Z. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The four noncommunicable causes of death are defined in terms of the International Classification of Diseases, Tenth Revision (ICD-10) or the International Classification of Diseases, 11th Revision (ICD-11) (See 2.a)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The preferred data source is death registration systems with complete coverage and medical certification of cause of death. Other possible data sources include household surveys with verbal autopsy, and sample or sentinel registration systems. </p>", "COLL_METHOD__GLOBAL"=>"<p>WHO conducts a formal country consultation process before releasing its cause-of-death estimates. </p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>WHO annually requests tabulated death registration data (including all causes of death) from Member States. Countries may submit annual cause-of-death statistics to WHO on an ongoing basis. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Every 2-3 years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National statistics offices and/or ministries of health. </p>", "COMPILING_ORG__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "INST_MANDATE__GLOBAL"=>"<p>According to Article 64 of its constitution, WHO is mandated to request each Member State to provide statistics on mortality. Furthermore, the WHO Nomenclature Regulations of 1967 affirms the importance of compiling and publishing statistics of mortality and morbidity in comparable form. Member States started to report mortality data to WHO since the early fifties and this reporting activity is continuing until today. </p>", "RATIONALE__GLOBAL"=>"<p>Disease burden from non-communicable diseases (NCDs) is rapidly increasing globally due to the increasing population of adults and older adults and declining mortality from competing communicable diseases. Cardiovascular diseases, cancer, diabetes and chronic respiratory diseases are the four main causes of NCD burden. Measuring the risk of dying from these four major causes is important to assess the extent of burden from premature mortality due to NCDs in a population. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Cause of death estimates have large uncertainty ranges for some causes and some regions. Data gaps and limitations in high-mortality regions reinforce the need for caution when interpreting global comparative cause of death assessments, as well as the need for increased investment in population health measurement systems. The use of verbal autopsy methods in sample registration systems, demographic surveillance systems and household surveys provides some information on causes of death in populations without well-functioning death registration systems, but there remain considerable challenges in the validation and interpretation of such data, and in the assessment of uncertainty associated with diagnoses of underlying cause of death. In countries with high-quality death registration systems, deaths certified to non-communicable diseases increased during the COVID-19 pandemic. These are likely a combination of true increases and misclassification of COVID-19 deaths. NCD deaths may also have been misclassified to COVID-19. In countries with weak surveillance systems, little is known about changes in NCD mortality during the pandemic. Estimates for are therefore particularly uncertain during these years.</p>", "DATA_COMP__GLOBAL"=>"<p>The methods used for the analysis of causes of death depend on the type of data available from countries: </p>\n<p> </p>\n<p>For countries with a high-quality vital registration system including information on cause of death, the vital registration that member states submit to the WHO Mortality Database were used, with adjustments where necessary, e.g. for under-reporting of deaths, unknown age and sex, and ill-defined causes of death. </p>\n<p> </p>\n<p>For countries without high-quality death registration data, cause of death estimates are calculated using other data, including household surveys with verbal autopsy, sample or sentinel registration systems, special studies and surveillance systems. In most cases, these data sources are combined in a modelling framework. </p>\n<p> </p>\n<p>The probability of dying between ages 30 and 70 years from the four main NCDs was estimated using age-specific death rates of the combined four main NCD categories. Using the life table method, the risk of death between the exact ages of 30 and 70, from any of the four causes and in the absence of other causes of death, was calculated using the equation provided in the document below. </p>\n<p> </p>\n<p>Formulas to (1) calculate age-specific mortality rate for each five-year age group between 30 and 70, (2) translate the 5-year death rate into the probability of death in each 5-year age range, and (3) calculate the probability of death from age 30 to age 70, independent of other causes of death, can be found on page 6 of this document: </p>\n<p> </p>\n<p>NCD Global Monitoring Framework: Indicator Definitions and Specifications. Geneva: World Health Organization, 2014 (https://www.who.int/publications/m/item/noncommunicable-diseases-global-monitoring-framework-indicator-definitions-and-specifications). </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The number of deaths were country-consulted with country designated focal points (usually at the Ministry of Health or National Statistics Office) as part of the full set of causes of death prior to the release.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Deaths of unknown sex were redistributed pro-rata within cause-age groups of known sexes, and then deaths of unknown age were redistributed pro-rata within cause-sex groups of known ages.</p>\n<p>4.f. Treatment of missing values (i) at country level and (ii) at regional level (IMPUTATION) </p>\n<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p> </p>\n<p>For countries with high-quality cause-of-death statistics, interpolation/extrapolation was done for missing country-years; for countries with only low-quality or no data on causes of death, modelling was used. Complete methodology may be found here: </p>\n<p>WHO methods and data sources for country-level causes of death, 2000&#x2013;2021 (https://www.who.int/data/global-health-estimates) </p>\n<p> </p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p> </p>\n<p>Not applicable </p>", "REG_AGG__GLOBAL"=>"<p>Aggregation of estimates of deaths by cause, age and sex by country, and aggregation of population by age, sex and country as denominator where needed. </p>", "DOC_METHOD__GLOBAL"=>"<p>The cause of death categories follow the definitions in terms of the International Classification of Diseases, Tenth Revision (ICD-10) or the International Classification of Diseases, 11th Revision (ICD-11). Please see Annex Table A of the WHO methods and data sources for country-level causes of death, 2000&#x2013;2021 ( (https://www.who.int/data/global-health-estimates)</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The World Health Organization (WHO) established a Reference Group on Health Statistics in 2013 to provide advice on population health statistics to the organization with a focus on methodological and data issues related to the measurement of mortality and cause-of-death patterns. The group facilitated interaction between multilateral development institutions and other independent academic groups with WHO expert groups in specific subject areas including methods to the estimation on causes of death.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The data principles of the World Health Organization (WHO) provide a foundation for continually reaffirming trust in WHO&#x2019;s information and evidence on public health. The five principles are designed to provide a framework for data governance for the organization. The principles are intended primarily for use by WHO staff across all parts of the organization in order to help define the values and standards that govern how data that flows into, across and out of the organization is collected, processed, shared and used. These principles are made publicly available so that they may be used and referred to by Member States and non-state actors collaborating with the organization.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>All statements and claims made officially by WHO headquarters about population-level (country, regional, global) estimates of health status (e.g. mortality, incidence, prevalence, burden of disease), are cleared by the Department of Data and Analytics (DNA) through the executive clearance process. This includes the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement. GATHER promotes best practices in reporting health estimates using a checklist of 18 items that should be reported every time new global health estimates are published, including descriptions of input data and estimation methods. Developed by a working group convened by the World Health Organization, the guidelines aim to define and promote good practice in reporting health estimates.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability: </strong></p>\n<p>Almost 70 countries currently provide WHO with regular high-quality data on mortality by age, sex and causes of death, and another 58 countries submit data of lower quality. However, comprehensive cause-of-death estimates are calculated by WHO systematically for all of its Member States (with a certain population threshold).</p>\n<p><strong>Time series:</strong></p>\n<p>2000-2021</p>\n<p><strong>Disaggregation: </strong></p>\n<p>Sex </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>In countries with high quality vital registration systems, point estimates sometimes differ primarily for two reasons: 1) WHO redistributes deaths with ill-defined cause of death; and 2) WHO corrects for incomplete death registration. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p> </p>\n<p><a href=\"http://www.who.int/gho/en/\">http://www.who.int/gho/en/</a> </p>\n<p> </p>\n<p><strong>References:</strong> </p>\n<p> </p>\n<p>NCD Global Monitoring Framework: Indicator Definitions and Specifications. Geneva: World Health Organization, 2014 ( https://www.who.int/publications/m/item/noncommunicable-diseases-global-monitoring-framework-indicator-definitions-and-specifications) </p>\n<p> </p>\n<p> </p>\n<p>WHO methods and data sources for global causes of death, 2000&#x2013;2021 </p>\n<p>( https://www.who.int/data/global-health-estimates) </p>\n<p>World Health Assembly Resolution, WHA66.10 (2014): Follow-up to the Political Declaration of the High-level Meeting of the General Assembly on the Prevention and Control of Non-communicable Diseases. Including Appendix 2: Comprehensive global monitoring framework, including 25 indicators, and a set of nine voluntary global targets for the prevention and control of noncommunicable diseases. (<a href=\"http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R10-en.pdf?ua=1\">http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R10-en.pdf?ua=1</a>) </p>\n<p> </p>\n<p>WHO Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020 (<a href=\"http://apps.who.int/iris/bitstream/10665/94384/1/9789241506236_eng.pdf?ua=1\">http://apps.who.int/iris/bitstream/10665/94384/1/9789241506236_eng.pdf?ua=1</a>) </p>\n<p> </p>", "indicator_sort_order"=>"03-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.4.2", "slug"=>"3-4-2", "name"=>"Tasa de mortalidad por suicidio", "url"=>"/site/es/3-4-2/", "sort"=>"030402", "goal_number"=>"3", "target_number"=>"3.4", "global"=>{"name"=>"Tasa de mortalidad por suicidio"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de mortalidad por suicidio", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de mortalidad por suicidio", "indicator_number"=>"3.4.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_28/opt_0/tipo_1/ti_defunciones/temas.html", "url_text"=>"Estadística de defunciones", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tasa de mortalidad por suicidio", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.4-  De aquí a 2030, reducir en un tercio la mortalidad prematura por enfermedades no transmisibles mediante su prevención y tratamiento, y promover la salud mental y el bienestar", "definicion"=>"Defunciones atribuidas a suicidio por cada 100.000 habitantes.", "formula"=>"\n$$TM_{suicidio }^{t} = \\frac{D_{suicidio }^{t}}{P^{t}} \\cdot 100.000$$\n\ndonde:\n\n$D_{suicidio }^{t} =$ defunciones atribuidas a suicidio (códigos X60-X84 de la CIE-10) en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Sexo \n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLos trastornos mentales se dan en todas las regiones y culturas del mundo. Los más frecuentes  son la depresión y la ansiedad, que se estima que afectan a casi 1 de cada 10 personas. En su peor\n forma, la depresión puede conducir al suicidio. En 2019, se estima que hubo más de 700.000 muertes \npor suicidio en todo el mundo.\nLa tasa de mortalidad por suicidio se define como el número de muertes por suicidio en un año,  dividido por la población y multiplicado por 100 000. Los suicidios se definen en términos de la\n Clasificación Internacional de Enfermedades, Décima Revisión (CIE-10)\n\nFuente: División de Estadísticas de las Naciones Unidas", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.4.2&seriesCode=SH_STA_SCIDE&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Tasa de mortalidad por suicidio, por sexo (muertes por cada 100.000 habitantes) SH_STA_SCIDE</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-04-02.pdf\">Metadatos 3-4-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Tasa de mortalidad por suicidio", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.4-  De aquí a 2030, reducir en un tercio la mortalidad prematura por enfermedades no transmisibles mediante su prevención y tratamiento, y promover la salud mental y el bienestar", "definicion"=>"Deaths attributed to suicide per 100.000 inhabitants.", "formula"=>"\n$$TM_{suicide }^{t} = \\frac{D_{suicide }^{t}}{P^{t}} \\cdot 100.000$$\n\nwhere:\n\n$D_{suicide }^{t} =$ deaths attributed to suicide (codes X60-X84 of theCIE-10) in year $t$\n\n$P^{t} =$ population as of 1 July of year $t$\n", "desagregacion"=>"Sex \n\nProvince\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nMental disorders occur in all regions and cultures of the world. The most prevalent  of these disorders are depression and anxiety, which are estimated to affect nearly  1 in 10 people. At its worst, depression can lead to suicide. In 2019, it is estimated  that there were more than 700,000 deaths by suicide worldwide.\nSuicide mortality rate is defined as the number of suicide deaths in a year, divided  by the population, and multiplied by 100,000. Suicides are defined in terms of the  International Classification of Diseases, Tenth Revision (ICD-10).", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.4.2&seriesCode=SH_STA_SCIDE&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Suicide mortality rate, by sex (deaths per 100,000 population) SH_STA_SCIDE</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-04-02.pdf\">Metadata 3-4-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Tasa de mortalidad por suicidio", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.4-  De aquí a 2030, reducir en un tercio la mortalidad prematura por enfermedades no transmisibles mediante su prevención y tratamiento, y promover la salud mental y el bienestar", "definicion"=>"Suizidioari egotzitako heriotzak 100.000 biztanleko", "formula"=>"\n$$TM_{suizidioa}^{t} = \\frac{D_{suizidioa}^{t}}{P^{t}} \\cdot 100.000$$\n\nnon:\n\n$D_{suizidioa}^{t} =$ suizidioari egotzitako heriotzak (GNS-10eko X60-X84 kodeak) $t$ urtean \n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nNahasmendu mentalak munduko eskualde eta kultura guztietan daude. Ohikoenak depresioa eta antsietatea dira; 10  pertsonatik ia 1i eragiten diotela kalkulatzen da. Formarik txarrenean, depresioak suizidioa eragin dezake. 2019an,  mundu osoan suizidioaren ziozko 700.000 heriotza baino gehiago egon zirela kalkulatzen da. \nSuizidioaren ziozko heriotza-tasa urtebetean suizidioaren ondorioz egondako heriotza-kopurua da, zati biztanleria  osoa eta bider 100.000. Suizidioak Gaixotasunen Nazioarteko Sailkapenaren arabera zehazten dira, Hamargarren  Berrikuspena kontuan hartuta (GNS-10). \n\nIturria: Nazio Batuen Estatistika Sekzioa ", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.4.2&seriesCode=SH_STA_SCIDE&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Suizidioaren ondoriozko heriotza-tasa, sexuaren arabera (heriotzak 100.000 biztanleko) SH_STA_SCIDE</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-04-02.pdf\">Metadatuak 3-4-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.4.2: Suicide mortality rate</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_STA_SCIDE - Suicide mortality rate [3.4.2]</p>\n<p>SH_STA_SCIDEN - Number of deaths attributed to suicide [3.4.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions: </strong></p>\n<p>Suicide mortality rate is defined as the number of suicide deaths in a year, divided by the population, and multiplied by 100,000. </p>\n<p><strong>Concepts:</strong></p>", "UNIT_MEASURE__GLOBAL"=>"<p>Deaths per 100,000 population </p>\n<p>Number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Suicides are defined in terms of the International Classification of Diseases, Tenth Revision (ICD-10) (See 3.a)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The preferred data source is death registration systems with complete coverage and medical certification of cause of death, coded using the international classification of diseases (ICD). The ICD-10 codes for suicide are: X60-X84, Y87.0. Other possible data sources include household surveys with verbal autopsy, sample or sentinel registration systems, special studies and surveillance systems. </p>", "COLL_METHOD__GLOBAL"=>"<p>WHO conducts a formal country consultation process before releasing its cause-of-death estimates. </p>", "FREQ_COLL__GLOBAL"=>"<p>WHO annually requests tabulated death registration data (including all causes of death) from Member States. Countries may submit annual cause-of-death statistics to WHO on an ongoing basis. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Every 2-3 years</p>", "DATA_SOURCE__GLOBAL"=>"<p>National statistics offices and/or ministries of health. </p>", "COMPILING_ORG__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "INST_MANDATE__GLOBAL"=>"<p>According to Article 64 of its constitution, WHO is mandated to request each Member State to provide statistics on mortality. Furthermore, the WHO Nomenclature Regulations of 1967 affirms the importance of compiling and publishing statistics of mortality and morbidity in comparable form. Member States started to report mortality data to WHO since the early fifties and this reporting activity is continuing until today. </p>", "RATIONALE__GLOBAL"=>"<p>Mental disorders occur in all regions and cultures of the world. The most prevalent of these disorders are depression and anxiety, which are estimated to affect nearly 1 in 10 people. At its worst, depression can lead to suicide. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The complete recording of suicide deaths in death-registration systems requires good linkages with coronial and police systems, but can be seriously impeded by stigma, social and legal considerations, and delays in determining cause of death. Less than one half of WHO Member States have well-functioning death-registration systems that record causes of death. </p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>S</mi>\n    <mi>u</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>i</mi>\n    <mi>d</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>m</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>r</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>100</mn>\n        <mo>,</mo>\n        <mn>000</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfenced>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>u</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>i</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>y</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mi>M</mi>\n        <mi>i</mi>\n        <mi>d</mi>\n        <mo>-</mo>\n        <mi>y</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>a</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>y</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n  </math></p>\n<p> </p>\n<p>The methods used for the analysis of causes of death depend on the type of data available from countries: </p>\n<p> </p>\n<p>For countries with a high-quality vital registration system including information on cause of death, the vital registration that member states submit to the WHO Mortality Database were used, with adjustments where necessary, e.g. for under-reporting of deaths. </p>\n<p> </p>\n<p>For countries without high-quality death registration data, cause of death estimates are calculated using other data, including household surveys with verbal autopsy, sample or sentinel registration systems, special studies and surveillance systems. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The number of suicide deaths were country-consulted with country designated focal points (usually at the Ministry of Health or National Statistics Office) as part of the full set of causes of death prior to the release.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Deaths of unknown sex were redistributed pro-rata within cause-age groups of known sexes, and then deaths of unknown age were redistributed pro-rata within cause-sex groups of known ages. </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level: </strong></li>\n</ul>\n<p> </p>\n<p>For countries with high-quality cause-of-death statistics, interpolation/extrapolation was done for missing country-years; for countries with only low-quality or no data on causes of death, modelling was used. Complete methodology may be found here: </p>\n<p>WHO methods and data sources for global causes of death, 2000&#x2013;2021 (https://www.who.int/data/global-health-estimates) </p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p> </p>\n<p>Not applicable </p>", "REG_AGG__GLOBAL"=>"<p>Country estimates of number of deaths by cause, along with corresponding population estimates, are summed to obtain regional and global aggregates. </p>", "DOC_METHOD__GLOBAL"=>"<p>The cause of death categories (including suicides) follow the definitions in terms of the International Classification of Diseases, Tenth Revision (ICD-10). Please see Annex Table A of the WHO methods and data sources for global causes of death, 2000&#x2013;2021 (https://www.who.int/data/global-health-estimates)</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The World Health Organization (WHO) established a Reference Group on Health Statistics in 2013 to provide advice on population health statistics to the organization with a focus on methodological and data issues related to the measurement of mortality and cause-of-death patterns. The group facilitated interaction between multilateral development institutions and other independent academic groups with WHO expert groups in specific subject areas including methods to the estimation on causes of death.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The data principles of the World Health Organization (WHO) provide a foundation for continually reaffirming trust in WHO&#x2019;s information and evidence on public health. The five principles are designed to provide a framework for data governance for the organization. The principles are intended primarily for use by WHO staff across all parts of the organization in order to help define the values and standards that govern how data that flows into, across and out of the organization is collected, processed, shared and used. These principles are made publicly available so that they may be used and referred to by Member States and non-state actors collaborating with the organization.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>All statements and claims made officially by WHO headquarters about population-level (country, regional, global) estimates of health status (e.g. mortality, incidence, prevalence, burden of disease), are cleared by the Department of Data and Analytics (DNA) through the executive clearance process. This includes the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement. GATHER promotes best practices in reporting health estimates using a checklist of 18 items that should be reported every time new global health estimates are published, including descriptions of input data and estimation methods. Developed by a working group convened by the World Health Organization, the guidelines aim to define and promote good practice in reporting health estimates.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Almost 70 countries currently provide WHO with regular high-quality data on mortality by age, sex and causes of death, and another 58 countries submit data of lower quality. However, comprehensive cause-of-death estimates are calculated by WHO systematically for all of its Member States (with a certain population threshold) every 3 years. </p>\n<p><strong>Time series:</strong></p>\n<p>From 2000 to 2021</p>\n<p><strong>Disaggregation: </strong></p>\n<p>Sex, age group </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>In countries with high quality vital registration systems, point estimates sometimes differ primarily for two reasons: 1) WHO redistributes deaths with ill-defined cause of death (i.e. injuries of unknown intent, ICD codes Y10-Y34 and Y872) to suicide; and 2) WHO corrects for incomplete death registration. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p> </p>\n<p><a href=\"http://www.who.int/gho/en/\">http://www.who.int/gho/en/</a> </p>\n<p> </p>\n<p><strong>References:</strong> </p>\n<p>WHO methods and data sources for global causes of death, 2000&#x2013;2021 </p>\n<p>( https://www.who.int/data/global-health-estimates) </p>\n<p> </p>\n<p>World Health Assembly Resolution WHA66.8 (2013): Comprehensive mental health action plan 2013&#x2013;2020, including Appendix 1: Indicators for Measuring Progress Towards Defined Targets of the Comprehensive Mental Health Action Plan 2013-2020 (<a href=\"http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R8-en.pdf?ua=1\">http://apps.who.int/gb/ebwha/pdf_files/WHA66/A66_R8-en.pdf?ua=1</a>) </p>\n<p> </p>", "indicator_sort_order"=>"03-04-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.4.E1", "slug"=>"3-4-E1", "name"=>"Tasa de participación en los programas de cribado poblacional de cáncer (Indicador Gobierno Vasco)", "url"=>"/site/es/3-4-E1/", "sort"=>"0304E1", "goal_number"=>"3", "target_number"=>"3.4", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[{"series"=>"Cáncer colorrectal", "unit"=>"", "label_content"=>"Recomendación Guía Europea", "value"=>65}], "graph_title"=>"Tasa de participación en los programas de cribado poblacional de cáncer (Indicador Gobierno Vasco)", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Tasa de participación en los programas de cribado poblacional de cáncer (Indicador Gobierno Vasco)", "indicator_number"=>"3.4.E1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Salud", "periodicity"=>"Anual", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicator_sort_order"=>"03-04-E1", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.4.E2", "slug"=>"3-4-E2", "name"=>"Esperanza de vida y esperanza de vida en buena salud (Indicador Gobierno Vasco)", "url"=>"/site/es/3-4-E2/", "sort"=>"0304E2", "goal_number"=>"3", "target_number"=>"3.4", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Esperanza de vida y esperanza de vida en buena salud (Indicador Gobierno Vasco)", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Esperanza de vida y esperanza de vida en buena salud (Indicador Gobierno Vasco)", "indicator_number"=>"3.4.E2", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_169/opt_0/ti_indicadores-demograficos-anuales/temas.html", "url_text"=>"Indicadores demográficos anuales", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicator_sort_order"=>"03-04-E2", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.5.1", "slug"=>"3-5-1", "name"=>"Cobertura de los tratamientos(farmacológicos y psicosociales y servicios de rehabilitación y postratamiento) de trastornos por abuso de sustancias adictivas", "url"=>"/site/es/3-5-1/", "sort"=>"030501", "goal_number"=>"3", "target_number"=>"3.5", "global"=>{"name"=>"Cobertura de los tratamientos(farmacológicos y psicosociales y servicios de rehabilitación y postratamiento) de trastornos por abuso de sustancias adictivas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Sustancia adictiva", "value"=>"Todas las sustancias adictivas"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de admisiones a tratamiento por abuso o dependencia de alcohol y drogas ilegales por cada 100.000 habitantes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Cobertura de los tratamientos(farmacológicos y psicosociales y servicios de rehabilitación y postratamiento) de trastornos por abuso de sustancias adictivas", "indicator_number"=>"3.5.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> No evaluable", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Salud", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/sistema-de-informacion-de-toxicomanias/web01-a3regepi/es/", "url_text"=>"Registro de información sobre toxicomanías", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Número de admisiones a tratamiento por abuso o dependencia de alcohol y drogas ilegales por cada 100.000 habitantes", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.5- Fortalecer la prevención y el tratamiento del abuso de sustancias adictivas, incluido el uso indebido de estupefacientes y el consumo nocivo de alcohol", "definicion"=>"Número de admisiones a tratamiento ambulatorio por abuso o dependencia de sustancias adictivas  en un centro de tratamiento por primera vez en un año determinado por cada 100.000 habitantes", "formula"=>"\n$$TAT_{sustancias\\, adictivas}^{t} = \\frac{AT_{sustancias\\, adictivas}^{t}}{P^{t}} \\cdot 100.000$$\n\ndonde:\n\n$AT_{sustancias\\, adictivas}^{t} =$ número de admisiones a tratamiento ambulatorio por abuso o dependencia de sustancias adictivas en un centro de tratamiento por primera vez en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Sustancias adictivas: drogas ilegales, alcohol\n\nSexo \n", "periodicidad"=>"Anual", "observaciones"=>"\nSe puede producir cierta sobrestimación del indicador a nivel estatal, dado que una misma \npersona ha podido ser notificada en dos o más comunidades diferentes y no es posible su \nidentificación por motivos de confidencialidad.\n\nTratamiento: cualquier intervención realizada por profesionales cualificados para eliminar, \nreducir o controlar el consumo de drogas.\n\nAmbulatorio: la persona no pernocta en el centro. Este criterio no se aplica a los centros penitenciarios.\n\nDrogas ilegales: no se consideran drogas ilegales el alcohol y la nicotina (tabaco).\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEste indicador de Naciones Unidas mide la cobertura de las intervenciones de tratamiento para los trastornos por consumo \nde sustancias y se define como el número de personas que recibieron tratamiento en un año \ndividido por el número total de personas con trastornos por consumo de sustancias en el mismo año. \nEste indicador se desglosa en dos grandes grupos de sustancias psicoactivas: (1) drogas, (2) alcohol y otras sustancias psicoactivas.\n\nSegún datos de la Oficina de las Naciones Unidas contra la Droga y el Delito (ONUDD) y la Organización Mundial de la Salud (OMS), \n alrededor de 296 millones de personas de entre 15 y 64 años en todo el mundo consumieron una droga ilegal al menos una vez en 2021, \nalrededor de 2.300 millones de personas beben alcohol en la actualidad, unos 35 millones de personas sufren trastornos por consumo \nde drogas y 289 millones por consumo de alcohol.\n\nLos trastornos por consumo de sustancias son afecciones de salud graves que representan una carga significativa para \nlas personas afectadas, sus familias y comunidades. Los trastornos por consumo de sustancias no tratados generan costos sustanciales\npara la sociedad, incluida la pérdida de productividad, el aumento del gasto en atención médica y los costos relacionados con \nla justicia penal, el bienestar social y otras consecuencias sociales. \n\nEl fortalecimiento de los servicios de tratamiento \nimplica  proporcionar acceso a un conjunto integral de intervenciones basadas en evidencia (establecidas en las normas y \ndirectrices internacionales) que deberían estar disponibles para todos los grupos de población que las necesiten. \nEl indicador indicará en qué medida se dispone de una variedad de intervenciones basadas en evidencia para el tratamiento\nde los trastornos por consumo de sustancias y la población necesitada tiene acceso a ellas a nivel nacional, regional y mundial.\n\nAunque existe un tratamiento eficaz, solo una pequeña cantidad de personas con trastornos por consumo de sustancias\n lo recibe. Por ejemplo, se estima que, a nivel mundial, una de cada siete personas con trastornos por consumo de drogas\ntiene acceso a servicios de tratamiento de drogas o se le proporcionan (Informe mundial sobre drogas 2019). Los datos del \nATLAS de la OMS sobre el uso de sustancias mostraron que, en 2014, solo el 11,9 % (de los 103 países que respondieron) informó \nde una cobertura alta (40 % o más) para la dependencia del alcohol. El indicador 3.5.1 de los ODS es crucial para medir el progreso\nhacia el fortalecimiento del tratamiento del abuso de sustancias en todo el mundo, tal como se formula en la Meta 3.5.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.5.1&seriesCode=SH_SUD_TREAT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20DRUG_TOTAL\">Cobertura de intervenciones de tratamiento (farmacológicas, psicosociales y de rehabilitación y servicios de seguimiento) para trastornos por consumo de sustancias (%) SH_SUD_TREAT</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de  Naciones Unidas, pero aporta información complementaria.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-05-01.pdf\">Metadatos 3-5-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"Número de admisiones a tratamiento por abuso o dependencia de alcohol y drogas ilegales por cada 100.000 habitantes", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.5- Fortalecer la prevención y el tratamiento del abuso de sustancias adictivas, incluido el uso indebido de estupefacientes y el consumo nocivo de alcohol", "definicion"=>"\nNumber of first-time outpatient treatment admissions for addictive substance abuse or dependence  at a treatment center in a given year per 100,000 population", "formula"=>"\n$$TAT_{addictive\\, substances}^{t} = \\frac{AT_{addictive\\, substances}^{t}}{P^{t}} \\cdot 100.000$$\n\nwhere:\n\n$AT_{addictive\\, substances}^{t} =$ Number of admissions to outpatient treatment for addictive substance abuse or \ndependence at a treatment center for the first time in the year $t$\n\n$P^{t} =$ population on July 1 of year $t$\n", "desagregacion"=>"Addictive substances: illegal drugs, alcohol\n\nSex \n", "periodicidad"=>"Anual", "observaciones"=>"\nThere may be some overestimation of the indicator at the state level, given that the same person may \nhave been notified in two or more different communities and their identification is not possible for \nconfidentiality reasons.\n\nTreatment: Any intervention performed by qualified professionals to eliminate, reduce, or control drug use.\n\nOutpatient: the person does not spend the night at the facility. This criterion does not apply to penitentiary centers.\n\nIllegal drugs: Alcohol and nicotine (tobacco) are not considered illegal drugs.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThis United Nations indicator measures the coverage of treatment interventions for substance \nuse disorders is defined as the number of people who received treatment in a year divided by \nthe total number of people with substance use disorders in the same year. This indicator is \ndisaggregated by two broad groups of psychoactive substances: (1) drugs, (2) alcohol and other \npsychoactive substances.\n\nAccording to United Nations Office on Drugs and Crime (UNODC) and World Health Organization (WHO) \ndata, around 296 million people aged 15 to 64 years worldwide used an illicit drug at least once in \n2021, about 2.3 billion people are current drinkers of alcohol, some 35 million of people suffer \nfrom drug use disorders and 289 million from alcohol use disorders.\n\nSubstance use disorders are serious health conditions that present a significant burden for affected \nindividuals, their families and communities. Untreated substance use disorders trigger substantial costs \nto society including lost productivity, increased health care expenditure, and costs related to criminal \njustice, social welfare, and other social consequences.\n\nStrengthening treatment services entails providing access to a comprehensive set of evidence-based \ninterventions (-laid down in the international standards and guidelines) that should be available \nto all population groups in need. The indicator will inform the extent to which a range of evidence-based\ninterventions for treatment of substance use disorder are available and are accessed by the population \nin need at country, regional and global level.\n\nEven though effective treatment exists, only a small amount of people with substance use disorders \nreceive it. For instance, it is estimated that globally one out of 7 people with drug use disorders \nhave access to or provided drug treatment services (World Drug Report 2019). WHO ATLAS-Substance \nUse data showed that in 2014 only 11.9 % (out of 103 responding) countries reported high coverage (40% \nor more) for alcohol dependence. SDG indicator 3.5.1 is crucial for measurement the progress \ntowards strengthening the treatment of substance abuse worldwide as formulated in the Target 3.5. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.5.1&seriesCode=SH_SUD_TREAT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20DRUG_TOTAL\">Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disorders (%) SH_SUD_TREAT</a> UNSTATS", "comparabilidad"=>"The available indicator does not meet the UN indicator metadata requirements, but provides  complementary information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-05-01.pdf\">Metadata 3-5-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Número de admisiones a tratamiento por abuso o dependencia de alcohol y drogas ilegales por cada 100.000 habitantes", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.5- Fortalecer la prevención y el tratamiento del abuso de sustancias adictivas, incluido el uso indebido de estupefacientes y el consumo nocivo de alcohol", "definicion"=>"Urte jakin batean substantzia adiktiboen abusu edo mendekotasunagatik tratamendu-zentro batean tratamendu anbulatorioan lehen aldiz    artatutakoen kopurua, 100.000 biztanleko", "formula"=>"\n$$TAT_{substantzia\\, adiktiboak}^{t} = \\frac{AT_{substantzia\\, adiktiboak}^{t}}{P^{t}} \\cdot 100.000$$\n\nnon: \n\n$AT_{substantzia\\, adiktiboak}^{t} =$ substantzia adiktiboen abusu edo mendekotasunagatik tratamendu-zentro batean tratamendu anbulatorioan lehen aldiz \nartatutakoen kopurua $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Substantia adiktiboak: legez kanpoko drogak; alkohola\n\nSexua \n", "periodicidad"=>"Anual", "observaciones"=>"\nAdierazlea neurriz kanpo kalkula daiteke estatuan, pertsona bera bi erkidego edo gehiagotan zenbatu \nahal izan delako, eta konfidentzialtasun-arrazoiengatik ezin delako identifikatu.\n\nTratamendua: droga-kontsumoa desagerrarazteko, murrizteko edo kontrolatzeko profesional kualifikatuek \negindako edozein esku-hartze.\n\nAnbulatorioa: pertsonak ez du gaua zentroan igarotzen. Irizpide hori ez zaie aplikatzen espetxeei.\n\nLegez kanpoko drogak: alkohola eta nikotina (tabakoa) ez dira legez kanpoko drogatzat hartzen.  \n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nNazio Batuen adierazle honek substantzien kontsumoaren ondoriozko nahasmenduetarako tratamenduen esku-hartzeen estaldura \nneurtzen du, eta honela definitzen da: urtebetean tratamendua jaso duten pertsonen kopurua zati urte berean substantzien \nkontsumoaren ondoriozko nahasmenduak izan dituzten pertsonen kopurua. Adierazle hau substantzia psikoaktiboen bi talde \nhanditan banatzen da: (1) drogak, (2) alkohola eta beste substantzia psikoaktibo batzuk. \n\nDrogen eta Delituen aurkako Nazio Batuen Bulegoaren (DDNBB) eta Osasunaren Mundu Erakundearen (OME) datuen arabera, \nmundu osoan 15 eta 64 urte bitarteko 296 milioi pertsona inguruk hartu zuten legez kanpoko gutxienez drogaren bat \n2021ean, 2.300 pertsona inguruk edaten dute alkohola gaur egun, 35 milioi pertsona inguruk dute drogen kontsumoaren \nziozko nahasmenduren bat eta 289 milioi pertsona inguruk alkoholaren kontsumoaren ziozko nahasmenduren bat. \n\nSubstantzien kontsumoaren ziozko nahasmenduak osasun-afekzio larriak dira, halakoak jasaten dituzten pertsonentzat, \nberen familientzat eta komunitateentzat karga handia eragiten dutenak. Substantzien kontsumoaren ondoriozko nahasmenduak \ntratatzen ez badira, kostu handiak eragiten zaizkio gizarteari, besteak beste produktibitatea galtzea, arreta medikoko \ngastua igotzea edo zigor-justiziarekin, gizarte-ongizatearekin eta beste ondorio sozial batzuekin lotutako kostuak \nsortzea. \n\nTratamendu-zerbitzuak indartzeak zera esan nahi du: ebidentzian oinarrituta biztanle guztien esku egon beharko luketen \nesku-hartzeen multzo osoa eskuratzeko aukera izatea (nazioarteko arau eta zuzentarauetan ezarritakoaren arabera). \nAdierazle honek zehaztuko du zein neurritan dauden substantzien kontsumoaren ondoriozko nahasmenduak tratatzeko \nebidentzian oinarritutako esku-hartzeak, eta zein neurritan duen biztanleriak horiek eskuratzeko aukera, nazioan, \neskualdean eta munduan. \n\nNahiz eta badagoen tratamendu eraginkor bat, substantzien kontsumoaren ziozko nahasmenduak dituzten pertsona gutxi \nbatzuek baino ez dute jasotzen. Adibidez, kalkulatzen da mundu-mailan drogen kontsumoaren ziozko nahasmenduak dituzten \nzazpi pertsonatik batek drogen tratamendurako zerbitzuak dituela eskura edo halakoak ematen zaizkiola (drogei buruzko \n2019ko txostena, mundu-mailakoa). OMEren substantzien erabilerari buruzko ATLASeko datuek agerian utzi zuten 2014an \nsoilik % 11,9k (erantzun zuten 103 herrialdeetatik) eman zutela alkoholaren mendekotasunerako estaldura altuaren berri \n(% 40 edo gehiago). GJHen 3.5.1 adierazlea funtsezkoa da mundu osoan substantzien gehiegikeriaren tratamendua indartzeko \nbidean egondako aurrerapena neurtzeko, 3.5 xedean zehazten den gisa. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.5.1&seriesCode=SH_SUD_TREAT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20DRUG_TOTAL\">substantzien kontsumoagatiko nahasmenduetarako tratamendu-esku-hartzeen estaldura (farmakologikoak, psikosozialak eta errehabilitaziokoak, eta jarraipen-zerbitzuak) (%) SH_SUD_TREAT</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina informazio  osagarria ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-05-01.pdf\">Metadatuak 3-5-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.5: Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcohol</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.5.1: Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disorders </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_SUD_ALCOL - Alcohol use disorders, 12-month prevalence (%) [3.5.1]</p>\n<p>SH_SUD_TREAT - Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disorders (%) [3.5.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-05-24", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>3.5.2: Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO) </p>\n<p>United Nations Office on Drugs and Crime (UNODC) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO) </p>\n<p>United Nations Office on Drugs and Crime (UNODC) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>The coverage of treatment interventions for substance use disorders is defined as the number of people who received treatment in a year divided by the total number of people with substance use disorders in the same year. This indicator is disaggregated by two broad groups of psychoactive substances: (1) drugs, (2) alcohol and other psychoactive substances. </p>\n<p> </p>\n<p>Whenever possible, this indicator is additionally disaggregated by type of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services). The indicator is accompanied and can be analysed together with contextual information such as prevalence of alcohol and drug use disorders and availability coverage, i.e. Service Capacity Index for Substance Use Disorders (SCI-SUD) <sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> that reflects the capacity of national health systems to provide treatment for substance (alcohol, drugs and other psychoactive substances) use disorders, in terms of the proportion (%) of available health system elements in a given country from a theoretical maximum. </p>\n<p> </p>\n<p><strong>Concepts: </strong></p>\n<p>The central concept of &#x201C;substance abuse&#x201D; in the SDG health target 3.5 implies the non-medical, hazardous, harmful or dependent pattern of use of psychoactive substances that, when taken in or administered into one&apos;s system, affect mental processes, e.g. perception, consciousness, cognition or affect. The concept of &#x201C;substance use disorders&#x201D; includes both &#x201C;drugs use disorders&#x201D; and &#x201C;alcohol use disorders&#x201D; according to the <a href=\"https://icd.who.int/browse10/2016/en\" target=\"_blank\">International Classification of Diseases (ICD-10 and ICD-11)</a><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>. </p>\n<p> </p>\n<p>The term &#x201C;drugs&#x201D; refers to controlled psychoactive substances as scheduled by the three <a href=\"https://www.unodc.org/unodc/en/commissions/CND/conventions.html\" target=\"_blank\">Drug Control Conventions (1961, 1971 and 1988)</a>, substances controlled under national legislations, new psychoactive substances (NPS) and some other that are not controlled under the Conventions, but may pose a public health threat. &#x201C;Alcohol&#x201D; refers to ethanol - a psychoactive substance with dependence producing properties that is consumed in ethanol-based alcoholic beverages or their surrogates. </p>\n<p> </p>\n<p>People with substance use disorders are those with harmful patterns of substance use or substance dependence. Harmful pattern of substance use is defined in the ICD-11 as a pattern of use of substances that has caused damage to a person&#x2019;s physical or mental health or has resulted in behaviour leading to harm to the health of others. According to ICD-11, dependence arises from repeated or continuous use of psychoactive substances. The characteristic feature is a strong internal drive to use psychoactive substance, which is manifested by impaired ability to control use, increasing priority given to use over other activities and persistence of use despite harm or negative consequences. </p>\n<p> </p>\n<p>Within this context treatment interventions for substance use disorders include any structured intervention that is aimed specifically to a) reduce substance use and cravings for substance use; b) improve health, well-being and social functioning of the affected individual, and c) prevent future harms by decreasing the risk of complications and relapse. These may include pharmacological treatment, psychosocial interventions and rehabilitation and aftercare. All evidence-based used for treatment of substance use disorders are well defined in WHO and UNODC related documents. Though hazardous substance use is not included in the concept of &#x201C;substance use disorder&#x201D;, such patterns of substance use are important targets for prevention interventions in health systems, and such interventions are included in the overall scope of comprehensive health system responses to &#x201C;substance abuse&#x201D; as defined in SDG 3.5.1 indicator. </p>\n<p> </p>\n<p><u>Pharmacological treatment</u> refers to evidence-based interventions that include administration of pharmacological agents or medicines in the context of different treatment modalities and interventions, including withdrawal management; treatment of alcohol use disorders with baclofen, naltrexone, acamprosate and disulfiram; management of opioid dependence with opioid agonists (methadone, buprenorphine) and antagonists (naltrexone); and prevention and management of opioid overdose with naloxone (WHO/UNODC International Standards for the treatment of drug use disorders<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup>, 2020 and WHO Mental Health Gap Action Programme (mhGAP) guideline for mental, neurological and substance use disorders, 2023<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup> ).</p>\n<p> </p>\n<p><u>Psychosocial interventions</u> refer to programs that address motivational, behavioral, psychological, social, and environmental factors related to substance use and have been shown to improve quality of life and well-being, reduce psychoactive substance use, promote abstinence and prevent relapse. For different substance use disorders, the evidence from clinical trials supports the effectiveness of treatment planning, screening and brief intervention (SBI), counselling, peer support groups, cognitive behavioral therapy (CBT), motivational interviewing (MI), community reinforcement approach (CRA), motivational enhancement therapy (MET), family therapy (FT) modalities, contingency management (CM), counselling, insight-oriented treatments, housing and employment support among others. (UNODC/WHO International Standards for the Treatment of Drug Use Disorders, 2020 and WHO Mental Health Gap Action Programme (mhGAP) guideline for mental, neurological and substance use disorders, 2023). </p>\n<p> </p>\n<p><u>Rehabilitation and aftercare</u> (Recovery Management and Social Support) refers to interventions that are based on scientific evidence and focused on the process of rehabilitation, recovery and social reintegration. (UNODC/WHO International Standards for the Treatment of Drug Use Disorders, 2020 and WHO Mental Health Gap Action Programme (mhGAP) guideline for mental, neurological and substance use disorders, 2023). </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> <a href=\"https://onlinelibrary.wiley.com/doi/10.1002/mpr.1950\">https://onlinelibrary.wiley.com/doi/10.1002/mpr.1950</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> <a href=\"https://icd.who.int/browse/2024-01/mms/en#1676588433\">https://icd.who.int/browse/2024-01/mms/en#1676588433</a> <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> <a href=\"https://iris.who.int/handle/10665/331635\">https://iris.who.int/handle/10665/331635</a> <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> <a href=\"https://iris.who.int/handle/10665/374250\">https://iris.who.int/handle/10665/374250</a> <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The International Statistical Classification of Diseases and Related Health Problems (ICD)<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup> is used to define substance use disorders, both for drugs and alcohol. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> See <a href=\"https://www.who.int/standards/classifications/classification-of-diseases\">https://www.who.int/standards/classifications/classification-of-diseases</a> <a href=\"#footnote-ref-6\">&#x2191;</a></p></div></div>", "SOURCE_TYPE__GLOBAL"=>"<p>Numerator (people who received treatment):</p>\n<p>Treatment registries are the main source of data for the number of people receiving treatment. They are expected to cover the entire national territory and be linked to all relevant agencies providing treatment services. </p>\n<p>Denominator (people with substance use disorders):</p>\n<p>To estimate the number of people with drug use disorders, the sources include: </p>\n<ul>\n  <li>Household surveys </li>\n  <li>Surveys among people using substances &#x2013; using for instance respondent driven sampling </li>\n  <li>Indirect methods such as capture/recapture or multiplier benchmark method </li>\n</ul>\n<p>Surveys should be nationally representative, with a sample size sufficiently large to capture relevant events and compute needed disaggregation, and they should be based on a solid sample design. The use of indirect questions for network scale-up methods in household surveys is encouraged. </p>\n<p>When data at the national level are not available, estimates on the number of people with drugs use disorders produced by the Institute for Health Metrics and Evaluation (IHME), and published through the Global Burden of Disease (GBD) study<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup>, can be used for the denominator of the indicator. </p>\n<p>To estimate the number of people with alcohol use disorders, preferred data sources are population-based surveys targeting the adult population (15+ years) and using standardized diagnostic instruments. International surveys such as World Health Survey (WHS), WHO STEPwise approach to surveillance (STEPS), Gender, Alcohol, and Culture: An International Study (GENACIS), and The European Cancer Anaemia Survey (ECAS) represent good practices. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> https://www.healthdata.org/research-analysis/gbd <a href=\"#footnote-ref-7\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p>WHO and UNODC use well-established data collection tools and procedures to gather available statistics from member states: </p>\n<ul>\n  <li>UNODC Annual Report Questionnaire ; </li>\n  <li>WHO Global Survey on Progress with attainment of SDG Health Target 3.5; </li>\n</ul>\n<p><strong>Drugs: </strong></p>\n<ul>\n  <li>Data on people with drug use disorders and the number of people in treatment are collected through a standardised questionnaire sent to countries, the Annual Report Questionnaire (ARQ). This questionnaire provides specific definitions of data to be collected and it collects a set of metadata to identify possible discrepancies from standard definitions and to assess overall data quality (e.g. sample size, target population, agency responsible for the data collection, etc.). At the national level, countries are required to have standardized treatment reporting system. </li>\n  <li>A revised ARQ is currently in use since 2021, with data being collected through the newly developed Data eXchange Platform (DXP). Data on drug use disorders and treatment, with the relevant disaggregations are collected through this tool. The revision of the questionnaire took into consideration the disaggregation and data quality needs emerging from the SDG data exercise. </li>\n  <li>Countries are requested to nominate national focal points to ensure technical supervision at country level </li>\n  <li>Automated and substantive validation procedures are in place to assess data consistency and compliance with standards </li>\n  <li>When data from national official sources are missing or not complying with methodological standards, data from other sources are also considered and processed by using the same quality assurance procedures. </li>\n</ul>\n<p><strong>Alcohol and other substances: </strong></p>\n<ul>\n  <li>Data on prevalence of alcohol and other substance use disorders, the number of people in treatment as well as on development of treatment systems for substance use disorders are collected through the periodical WHO global surveys addressed to the national focal points officially nominated by the Ministries of Health.</li>\n  <li> These focal points provide national government statistics and data or links or contacts through which the data can be accessed. </li>\n  <li>In addition, data are accessed from country-specific industry data sources in the public domain and other databases as well as systematic literature reviews. </li>\n  <li>WHO global surveillance activities include population-based national surveys that allows to generate country data used for estimation of the number of people with substance use disorders in populations (such as World Mental Health Survey and STEPS surveys) </li>\n  <li>Data on service utilization and contextual information are being collected by periodical WHO Global Survey on Progress with SDG Health Target 3.5 and through specific activities such as service mapping surveys implemented in collaboration with UNODC.</li>\n  <li>The collected, collated and analysed data is included in the process of country consultations. </li>\n</ul>\n<p>After the validation process, the data are sent to national focal points for their review before publication. </p>", "FREQ_COLL__GLOBAL"=>"<p>Countries are encouraged to conduct general population surveys on substance use regularly, but at least every four-five years. Also, countries are encouraged to use less costly alternatives to estimate the number of people with substance use disorders and service utilization, taking advantage of the availability of administrative data through the use of indirect estimation methods. Collection of data from countries is planned on annual or biannual basis. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data on relevant SDG indicators are collected, compiled and sent back to countries for data review annually by UNODC, and every 3-4 years by WHO. Data are then reported to United Nations Statistics Division (UNSD) through the regular reporting channels annually. </p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are collected through national focal points. Data providers vary by country and they can be institutions such as Drug Control Agencies, National Drug Observatories, Ministries of Health and/or National Statistical Offices. </p>", "COMPILING_ORG__GLOBAL"=>"<p>Data will be compiled by the co-custodians for this indicator (UNODC and WHO). </p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) is a global leader in the fight against illicit drugs, transnational organized crime, terrorism and corruption, and is the guardian of most of the related conventions, particularly:</p>\n<ul>\n  <li>The United Nations Convention against Transnational Organized Crime and its three protocols (against trafficking in persons, smuggling of migrants and trafficking in firearms)</li>\n  <li>The United Nations Convention against Corruption</li>\n  <li>The international drug control conventions</li>\n</ul>\n<p>UNODC is specifically mandated by the three <a href=\"https://www.unodc.org/documents/commissions/CND/Int_Drug_Control_Conventions/Ebook/The_International_Drug_Control_Conventions_E.pdf\">international drug conventions</a> to collect drug-related data on annual basis from Member States through the Annual Report Questionnaire (ARQ)<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup>. In addition, the Convention on Narcotic Drugs (CND) has overseen the process for the latest revision of the ARQ and endorsed the questionnaire in its 63<sup>rd</sup> session in March 2020<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup>. </p>\n<p>The World Health Organization (WHO) is a specialized agency of the United Nations responsible for international public health. WHO&#x2019;s activities are implemented in accordance with the mission set out in the Organization&#x2019;s Thirteen General Programme of Work: to promote health, keep the world safe and serve the vulnerable. It is structured around three interconnected strategic priorities (to ensure healthy lives and well-being for all at all ages: achieving universal health coverage, addressing health emergencies and promoting healthier populations) and WHO&#x2019;s six core functions: (1) Providing leadership on matters critical to health and engaging in partnerships where joint action is needed; (2) shaping the research agenda and stimulating the generation, translation and dissemination of valuable knowledge; (3) setting norms and standards and promoting and monitoring their implementation; (4) articulating ethical and evidence-based policy options; (5) providing technical support, catalysing change, and building sustainable institutional capacity; and (6) monitoring the health situation and assessing health trends.</p>\n<p>WHO is one of the four treaty bodies to the international drug control conventions. As part of the United Nations system, WHO&#x2019;s role under the conventions is to protect individuals and societies from harm due to drug use and to promote public health interventions to reduce harm. WHO focuses on prevention of substance use (including all psychoactive substances), treatment of substance use disorders (including both harmful pattern of use and dependence), monitoring trends in substance use and its health consequences, prevention and management of associated health and social conditions and public health problems in order to reduce the health and social burden attributable to substance use. In addition to the international drug conventions, WHO &#x2018;s work related to psychoactive substances is guided by the WHO Constitution and by the Organization&#x2019;s governing bodies (chiefly through resolutions of the World Health Assembly and WHO&#x2019;s regional committees), such as the WHO Global Alcohol Action Plan 2022&#x2013;2030 endorsed by the Seventy-fifth World Health Assembly in May 2022 to effectively implement the global strategy to reduce the harmful use of alcohol as a public health priority.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> See <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/arq.html\">https://www.unodc.org/unodc/en/data-and-analysis/arq.html</a> <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> See <a href=\"https://documents-dds-ny.un.org/doc/UNDOC/GEN/V19/117/68/PDF/V1911768.pdf?OpenElement\">https://documents-dds-ny.un.org/doc/UNDOC/GEN/V19/117/68/PDF/V1911768.pdf?OpenElement</a> <a href=\"#footnote-ref-9\">&#x2191;</a></p></div></div>", "RATIONALE__GLOBAL"=>"<p>According to United Nations Office on Drugs and Crime (UNODC) and World Health Organization (WHO) data, around 296 million people aged 15 to 64 years worldwide used an illicit drug at least once in 2021, about 2.3 billion people are current drinkers of alcohol, some 35 million of people suffer from drug use disorders and 289 million from alcohol use disorders. </p>\n<p> </p>\n<p>Substance use disorders are serious health conditions that present a significant burden for affected individuals, their families and communities. Untreated substance use disorders trigger substantial costs to society including lost productivity, increased health care expenditure, and costs related to criminal justice, social welfare, and other social consequences. Strengthening treatment services entails providing access to a comprehensive set of evidence-based interventions (-laid down in the international standards and guidelines) that should be available to all population groups in need. The indicator will inform the extent to which a range of evidence-based interventions for treatment of substance use disorder are available and are accessed by the population in need at country, regional and global level. </p>\n<p> </p>\n<p>Even though effective treatment exists, only a small amount of people with substance use disorders receive it. For instance, it is estimated that globally one out of 7 people with drug use disorders have access to or provided drug treatment services (World Drug Report 2019). <a href=\"https://www.who.int/gho/substance_abuse/en/\" target=\"_blank\">WHO ATLAS-Substance Use</a> data showed that in 2014 only 11.9 % (out of 103 responding) countries reported high coverage (40% or more) for alcohol dependence. SDG indicator 3.5.1 is crucial for measurement the progress towards strengthening the treatment of substance abuse worldwide as formulated in the Target 3.5. </p>\n<p> </p>", "REC_USE_LIM__GLOBAL"=>"<p>The two main challenges in terms of computing the SDG 3.5.1 indicator are the limited availability of household surveys on substance use and the under-reporting of use among survey respondents. </p>\n<p>Data reported from household surveys are one of the sources of information on of the number of people with substance use disorders. There are issues of under-reporting for certain psychoactive substances, in countries where stigma is associated to substance use and when a considerable proportion of the drug or alcohol using population is institutionalized, homeless or unreachable by population-based surveys. Additionally, being a relatively rare event, household surveys on substance use disorders require a large sample and can be costly. In order to address these issues, additional approaches (e.g. scale up methods) are increasingly used in household surveys to address undercount issues. These can be used in conjunction with special studies and/or additional information, in order to obtain reasonable estimates via indirect methods, such as benchmark/multiplier or capture-recapture methods.</p>\n<p>Given these challenges, often national officially produced estimates on the number of people with drug use disorders are not available. In this context, additional sources are considered, such as the estimates produced by the Institute for Health Metrics and Evaluation (IHME), and published through the Global Burden of Disease (GBD) study. Data on treatment of drug use disorders is more widely available at the national level, as it relies on administrative records. </p>\n<p> </p>\n<p>An additional step in data validation and country capacity building for monitoring treatment coverage for substance use disorders will be implemented during the next couple of years for in-depth data generation in a sample of countries from different regions and representing different levels of health system development. A rapid assessment tool for in-depth data generation is in the process of development by WHO. </p>\n<p> </p>\n<p>The indicator stresses on type, availability and coverage of services but does not necessarily provide information on the actual quality of the interventions/services provided. To address this, the proposed treatment indicator is accompanied with contextual information on availability coverage produced by WHO and using Service Capacity Index for Substance Use Disorders (SCI-SUD)<sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup> that reflects the capacity of national health systems to provide treatment for alcohol and drug use disorders, in terms of the proportion (%) of available service elements in a given country from a theoretical maximum. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> https://onlinelibrary.wiley.com/doi/10.1002/mpr.1950 <a href=\"#footnote-ref-10\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>The indicator will be computed by dividing the number of people receiving treatment services at least once in a year by the total number of people with substance use disorders (SUD) in the same year: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n        <mi>o</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n      </mrow>\n      <mrow>\n        <mi>S</mi>\n        <mi>U</mi>\n        <mi>D</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">U</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">w</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">U</mi>\n        <mi mathvariant=\"normal\">D</mi>\n      </mrow>\n    </mfrac>\n    <mi>X</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mn>100</mn>\n  </math></p>\n<p> </p>\n<p>Where: SUD &#x2013; Substance use disorders </p>", "DATA_VALIDATION__GLOBAL"=>"<p><strong>Drugs</strong></p>\n<p>Data on people in treatment for SUD and people with SUD collected through the ARQ and other sources go through a thorough validation process that involves identification of outliers, consistency with previous reported data, consistency with data reported by other countries, direct communication with technical counterparts providing data through the DXP, as well as exploring other sources of data. In addition, once a year data available through the ARQ and other sources are shared with ARQ Focal Points for their review in a pre-publication process related to UNODC&#x2019;s flagship publication, the World Drug Report. Finally, data compiled for the SDG indicator 3.5.1 are shared with SDG Focal Points for their feedback and review, during the SDG pre-publication process before submission to UNSD every March. All feedback received by Member States related to these data are incorporated. </p>\n<p><strong>Alcohol and other psychoactive substances</strong></p>\n<p>WHO Global Survey on Progress with SDG Health Target 3.5 collects data from WHO focal points in Ministries of Health, nominated by their governments to participate in the survey. The WHO&apos;s LimeSurvey platform is used to collect information. Respondents are encouraged to contact and consult additional experts from the following areas: (1) persons in charge of or involved in alcohol/drug control in the Ministry of Health, Ministry of Justice or other ministry, or the most senior government official in charge of alcohol control or alcohol-related conditions, or drug demand reduction programmes; (2) the head of a prominent non-governmental organization dedicated to alcohol/drug control; (3) a health professional (e.g., medical doctor, nurse, pharmacist, social worker, psychologist) who specialized in alcohol-related conditions and conditions due to other SU; (4) a faculty member of a public health or other relevant university department; (5) a police or other law enforcement officer; (6) a person at the Ministry of Finance, tax agency or statistical office; (7) a researcher, civil servant, or faculty member with expertise in treatment systems for SUD and treatment/service coverage. Adjusted for comparability, country summaries with data points are validated through the established network of WHO focal points to ensure data accuracy prior to their publication. Data validation processes include checking of internal consistency, identification of outliers and checking consistency with previously reported data, and discrepancies in data are communicated to the focal points for clarification. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Adjustments may take place to fit the age group requirements of the indicator (people 15 years of age and older), depending on the national data available. </p>", "IMPUTATION__GLOBAL"=>"<p><strong>At country level </strong></p>\n<p>For drug use disorders, data will be reported for countries where information is available for both numerator and denominator, either via data reported by the government or produced by other sources mentioned in this document. No data estimates will be published at the national level. </p>\n<p> </p>\n<p>For alcohol, when information on service utilization is missing in a country, several approaches are used to produce estimates based on all available contextual service capacity data in the country and regionally. Link to be established between service availability and service utilization to get rough understanding on number of people who might be using services for countries where no direct information on number of people using services is available. </p>\n<p><strong> </strong></p>\n<p><strong>At regional and global level </strong></p>\n<p>Sub-regional and regional aggregates are produced when enough data at the country level are available (a minimum number of countries and a minimum percentage of population coverage). When data are available, sub-regional estimates are created first and then aggregated at regional level. The global level is computed as aggregation of regional estimates. </p>\n<p><strong>With regards to the contextual information related to the .Service Capacity Index</strong></p>\n<p>For countries that do not submit any data or have levels of missingness deemed as very high through WHO Global Survey on Progress with SDG Health Target 3.5, WHO employs multivariate imputation by chained equations method (van Buuren, 2018) to impute SCI-SUD assuming that the data are missing at random (MAR) and using the predictive mean matching method (Krupchanka et al., 2022). </p>", "REG_AGG__GLOBAL"=>"<p> Regional and global aggregations are produced for the indicator on substance use disorders related to &#x201C;Any drug&#x201D;. After data are validated by Member State, estimates by year and sub-region are produced. These are later aggregated to the regional and global levels. </p>\n<p>Data aggregation is also possible for contextual information for the indicator (i.e. Service Capacity Index), both at global and regional levels.</p>", "DOC_METHOD__GLOBAL"=>"<p>UNODC has published a series of methodological guidelines on several issues related to the drug problem, entitled &#x201C;Global Assessment Program (GAP)&#x201D;. These guidelines consist of 8 modules, covering different aspects of monitoring the drug situation including setting up drug information systems, estimating drug prevalence using indirect methods, setting up treatment monitoring and reporting systems, etc. The modules can be found at: <a href=\"https://www.unodc.org/unodc/en/GAP/\">https://www.unodc.org/unodc/en/GAP/</a>. It is planned to update these guidelines in the near future. </p>\n<p> </p>\n<p>As part of the Annual Report Questionnaire (ARQ) review process, UNODC is planning to enhance its capacity building tools by complementing regional and national capacity building activities with: </p>\n<ul>\n  <li>E-learning training modules with incorporated training curricula </li>\n  <li>Creating methodological guidelines and tools on drug-related issues, including drug use disorders and treatment </li>\n  <li>promoting national coordination mechanisms on drugs data, including national drug observatories </li>\n</ul>\n<p>WHO has published series of documents on alcohol monitoring in populations (e.g. International Guide for Monitoring Alcohol Consumption and Related Harm), and established a <a href=\"https://www.who.int/substance_abuse/activities/gisah/en/\" target=\"_blank\">Global Information System on Alcohol and Health (GISAH)</a> that provides easy and rapid access to a wide range of alcohol-related health indicators. It is an essential tool for assessing and monitoring the health situation and trends related to alcohol consumption, alcohol-related harm, and policy responses in countries. Data on prevalence of alcohol use disorders at national, regional and global levels have been regularly reported by WHO and included among the key indicators in periodic WHO publications such as global status reports on alcohol and health. GISAH is a further development of the Global Alcohol Database which has been built since 1997 by the WHO Department of Mental Health and Substance Use. The main purpose of GISAH is to serve WHO Member States and governmental and nongovernmental organizations by making alcohol-related health data available. These data can help to analyse the state of the health situation related to alcohol in a country, a WHO region or sub-region, or the world. <a href=\"https://www.who.int/substance_abuse/activities/gisah_indicatorbook.pdf?ua=1\" target=\"_blank\">The Indicator Code Book</a> has been prepared to assist countries in collecting the data. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The prepublication processes conducted for most UNODC and WHO data collections as described in the Validation section above allow to properly manage the quality of the data submitted. In particular, as data for the SDG indicator 3.5.1 are shared with different Focal Points (from the drugs and the SDG sides), often representing different institutions within the national system, this allows for consolidating a national figure on the indicator and other key drug-demand indicators, such as people in treatment and people with alcohol/drug use disorders. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>At UNODC, quality assurance measures are in place to collect, process and disseminate statistical data. They build on the &#x2018;Principles governing international statistical activities&#x2019; and regulate the collection, processing, publication and dissemination of data. </p>\n<p> </p>\n<p>All data for SDG indicators as compiled by the Office are sent to countries (through the relevant national focal points) for their review before statistical data are officially released by UNODC. When countries provide feedback/comments on the data, a technical discussion is conducted to identify a common position. </p>\n<p> </p>\n<p>At WHO quality assurance measures are in place for producing the health statistics that include the main indicators on alcohol consumption and its health consequences. WHO Technical Advisory Group on Alcohol and Drug Epidemiology provides technical advice and input to WHO activities on monitoring alcohol consumption and treatment capacity for substance use disorders in its Member States. </p>\n<p> </p>\n<p>Data compilation is to be performed centrally by WHO and UNODC based on data collected from countries that later will be validated through official focal points. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>As for certain regions there could be different data published at the international level on the indicators used for the calculation of the indicator, i.e., people in treatment for alcohol/drug use disorders and people with alcohol/drug use disorders, there is an exercise of data exchange taking place with key actors, including WHO regional offices and other regional organizations such as the European Monitoring Center for Drugs and Drug Addiction (EMCDDA). This exercise, together with the validation and prepublication processes described before, allows to produce the most accurate data as possible. It is important to note that in cases the production of these indicators at the national level, especially for the estimation of people with alcohol/drug use disorders, can differ in definition from country to country depending on the methodology used. While UNODC and WHO strives to publish data that is as comparable as possible, cases that differ from the standard definitions are clearly identified in the corresponding footnotes. Ideally, this would require additional activities that would allow in-depth data collection in selected countries: a future direction for strengthening the indicator.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data on treatment of people with alcohol/drug use disorders is widely available countries as it relies on administrative data. While data on the estimated number of people with drug use disorders can be scarce in certain regions as it relies in data from surveys or indirect methods, external sources such as the ones mentioned in this document can provide a coverage for almost all countries in the world. As a consequence, data on this indicator is available for over 100 countries globally. The indicator can also be disaggregated by gender and substance based on the data available. </p>\n<p>Data on prevalence of alcohol use disorders are currently available for 188 Member States (for 2012, 2016, 2019) and validated through the process of country consultation. Data are regularly updated and presented through WHO Global Health Observatory. For utilization of treatment by people with alcohol use disorders, data are currently available for at least 30 countries and further data collection is ongoing </p>\n<p>For contextual information on treatment services, WHO has collected data from 145 countries for 2019 and extended to all countries using multiple imputation (described above). New round of data collection is taking place in 2023-2024.. </p>\n<p><strong>Time series: </strong></p>\n<p>During 2015-2021, data are available for over XX countries for at least two years for both numerator and denominator necessary for the calculation of the SDG indicator on drug use disorders. For the alcohol, data on denominator are available for a long period since establishment of GISAH in 1997 and the indicator has been tentatively calculated for at least 30 countries in 2019, with contextual comparable information available for 188 countries. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Given the policy importance, the indicator will be disaggregated to provide data for drugs and alcohol. Depending on data availability, it will be additionally disaggregated by following: </p>\n<ul>\n  <li>by treatment interventions (pharmacological, psychosocial, rehabilitation and aftercare) </li>\n  <li>by sex </li>\n  <li>by age groups </li>\n</ul>\n<p>In relation to drug use disorders, the following types of drugs should be considered: </p>\n<ul>\n  <li>cannabis (including herb and resin) </li>\n  <li>opioids (opium, heroin, medicinal products containing opioids and other opioids)), </li>\n  <li>cocaine type, </li>\n  <li>amphetamines (amphetamine, methamphetamine, medicinal products containing ATS), </li>\n  <li>ecstasy-type substances, </li>\n  <li>sedatives and tranquilizers, </li>\n  <li>hallucinogens </li>\n  <li>solvents and inhalants </li>\n  <li>new psychoactive substances (NPS) </li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Given the heterogeneity of national data collection systems, there is potential for discrepancies related either to the differences in recording the number of people in treatment and for people with substance use disorders. For this purpose, the Annual Report Questionnaire (ARQ) as well as the relevant WHO data collection tools have recently been improved to allow for countries to specify the nature of the data reported and to enable UNODC and WHO to assess the accuracy and comparability of data. </p>\n<p> </p>\n<p>Apart from evaluating the consistency of data and addressing data discrepancies by using additional sources, UNODC and WHO are in continuous communication and discusses technical issues with reporting countries in order to minimize discrepancies and inconsistency of data. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URLs:</strong> </p>\n<p><a href=\"https://www.who.int/data/gho/data/themes/resources-for-substance-use-disorders\">https://www.who.int/data/gho/data/themes/resources-for-substance-use-disorders</a> </p>\n<p><a href=\"https://wdr.unodc.org/wdr2019/\" target=\"_blank\"><u>https://wdr.unodc.org/wdr2019/</u></a> </p>\n<p><a href=\"https://www.who.int/data/global-health-estimates\">https://www.who.int/data/global-health-estimates</a> <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5608813/\" target=\"_blank\"><u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5608813/</u></a> </p>\n<p><a href=\"https://icd.who.int/browse10/2016/en\" target=\"_blank\"><u>https://icd.who.int/browse10/2016/en</u></a> </p>\n<p><a href=\"https://icd.who.int/en\">https://icd.who.int/en</a><u> </u></p>\n<p><u>https://www.unodc.org/unodc/en/commissions/CND/conventions.html</u> </p>\n<p><a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2395571/\" target=\"_blank\"><u>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2395571/</u></a> </p>\n<p><a href=\"https://apps.who.int/iris/bitstream/handle/10665/258734/9789241564052-eng.pdf\">https://apps.who.int/iris/bitstream/handle/10665/258734/9789241564052-eng.pdf</a> </p>\n<p><u>https://www.unodc.org/unodc/en/GAP/</u> </p>\n<p><a href=\"https://www.unodc.org/documents/pakistan/Survey_Report_Final_2013.pdf\" target=\"_blank\"><u>https://www.unodc.org/documents/pakistan/Survey_Report_Final_2013.pdf</u></a> </p>\n<p><a href=\"https://www.unodc.org/documents/data-and-analysis/statistics/Drugs/Drug_Use_Survey_Nigeria_2019_BOOK.pdf\" target=\"_blank\"><u>https://www.unodc.org/documents/data-and-analysis/statistics/Drugs/Drug_Use_Survey_Nigeria_2019_BOOK.pdf</u></a> </p>\n<p><a href=\"https://www.who.int/data/gho/data/themes/global-information-system-on-alcohol-and-health\">https://www.who.int/data/gho/data/themes/global-information-system-on-alcohol-and-health</a> </p>\n<p><a href=\"https://apps.who.int/gho/data/node.main.GISAH\">https://apps.who.int/gho/data/node.main.GISAH</a> </p>\n<p><a href=\"https://onlinelibrary.wiley.com/doi/10.1002/mpr.1950\">https://onlinelibrary.wiley.com/doi/10.1002/mpr.1950</a> </p>", "indicator_sort_order"=>"03-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.5.2", "slug"=>"3-5-2", "name"=>"Consumo de alcohol per cápita (a partir de los 15 años de edad) durante un año civil en litros de alcohol puro", "url"=>"/site/es/3-5-2/", "sort"=>"030502", "goal_number"=>"3", "target_number"=>"3.5", "global"=>{"name"=>"Consumo de alcohol per cápita (a partir de los 15 años de edad) durante un año civil en litros de alcohol puro"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Consumo de alcohol per cápita (a partir de los 15 años de edad) durante un año civil en litros de alcohol puro", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Consumo de alcohol per cápita (a partir de los 15 años de edad) durante un año civil en litros de alcohol puro", "indicator_number"=>"3.5.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Hacienda", "periodicity"=>"Anual", "url"=>"https://sede.agenciatributaria.gob.es/Sede/datosabiertos/catalogo/hacienda/Informes_anuales_de_Recaudacion_Tributaria.shtml", "url_text"=>"Informes anuales de recaudación tributaria", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_237/opt_1/ti_encuesta-de-gasto-familiar/temas.html", "url_text"=>"Encuesta de gasto familiar", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Consumo anual de alcohol en litros de alcohol puro por persona de 15 y más años", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.5- Fortalecer la prevención y el tratamiento del abuso de sustancias adictivas, incluido el uso indebido de estupefacientes y el consumo nocivo de alcohol", "definicion"=>"\nConsumo anual de alcohol registrado en litros de alcohol puro (bebidas destiladas, vino y cerveza) por persona de 15 y más años", "formula"=>"\n$$ECAR^{t} = \\frac{CAR_{bebidas\\, destiladas}^{t}+CAR_{vino}^{t}+CAR_{cerveza}^{t}}{P_{15+}^{t}}$$\n\ndonde:\n\n$CAR_{bebidas\\, destiladas}^{t} =$ consumo de alcohol registrado en bebidas destiladas en el año $t$\n\n$CAR_{vino}^{t} =$ consumo de alcohol registrado en vino en el año $t$\n\n$CAR_{cerveza}^{t} =$ consumo de alcohol registrado en cerveza en el año $t$\n\n$P_{15+}^{t} =$ población de 15 y más años a 1 de julio del año $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl indicador de Naciones Unidas mide el uso nocivo de alcohol, definido \nsegún el contexto nacional como el consumo de alcohol \nper cápita (personas de 15 años o más) en un año calendario en litros de alcohol puro.\n\nEl consumo de alcohol puede tener un impacto no sólo en la incidencia de enfermedades, lesiones y \notras condiciones de salud, sino también en la evolución de los trastornos y sus consecuencias en las personas.\n\nEl consumo de alcohol se ha identificado como una causa componente de más de 200 enfermedades, lesiones y otros problemas\nde salud. El consumo de alcohol per cápita es ampliamente aceptado como el mejor indicador posible de la exposición al\nalcohol en las poblaciones y el indicador clave para la estimación de la carga de enfermedades atribuibles al alcohol y\nlas muertes atribuibles al alcohol.\n\nSu correcta interpretación requiere el uso de indicadores poblacionales adicionales, como la prevalencia del consumo de \nalcohol, y, como resultado, estimula el desarrollo de sistemas nacionales de vigilancia del alcohol y la salud que \ninvolucran contribuciones de una amplia gama de partes interesadas, incluidos los sectores de producción y comercio de alcohol.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.5.2&seriesCode=SH_ALC_CONSPT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20BOTHSEX\">Consumo de alcohol per cápita (mayores de 15 años) en un año calendario (litros de alcohol puro) SH_ALC_CONSPT</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de  Naciones Unidas, pero aporta información similar.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-05-02.pdf\">Metadatos 3-5-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Consumo anual de alcohol en litros de alcohol puro por persona de 15 y más años", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.5- Fortalecer la prevención y el tratamiento del abuso de sustancias adictivas, incluido el uso indebido de estupefacientes y el consumo nocivo de alcohol", "definicion"=>"\nAnnual registered alcohol consumption in litres of pure alcohol (distilled spirits, wine and beer) per person aged 15 and over", "formula"=>"\n$$ECAR^{t} = \\frac{CAR_{distilled\\, spirits}^{t}+CAR_{wine}^{t}+CAR_{beer}^{t}}{P_{15+}^{t}}$$\n\nwhere:\n\n$CAR_{distilled\\, spirits}^{t} =$ alcohol consumption recorded in distilled beverages in year $t$\n\n$CAR_{wine}^{t} =$ alcohol consumption recorded in wine in year $t$\n\n$CAR_{beer}^{t} =$ alcohol consumption recorded in beer in year $t$\n\n$P_{15+}^{t} =$ population aged 15 and over on July 1 of year $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nUnited Nations indicator measures the harmful use of alcohol, defined according to the national context \nas alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol.\n\nAlcohol consumption can have an impact not only on the incidence of diseases, injuries and other health \nconditions, but also on the course of disorders and their outcomes in individuals. \n\nAlcohol consumption has been identified as a component cause for more than 200 diseases, injuries and other health \nconditions. Per capita alcohol consumption is widely accepted as the best possible indicator of alcohol \nexposure in populations and the key indicator for estimation of alcohol-attributable disease burden and \nalcohol-attributable deaths. \n\nIts correct interpretation requires the use of additional population-based indicators such as prevalence \nof drinking, and, as a result, stimulates development of national monitoring systems on alcohol and health \ninvolving contributions from a wide range of stakeholders, including alcohol production and trade sectors. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.5.2&seriesCode=SH_ALC_CONSPT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20BOTHSEX\"> Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol SH_ALC_CONSPT</a> UNSTATS", "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-05-02.pdf\">Metadata 3-5-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Consumo anual de alcohol en litros de alcohol puro por persona de 15 y más años", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.5- Fortalecer la prevención y el tratamiento del abuso de sustancias adictivas, incluido el uso indebido de estupefacientes y el consumo nocivo de alcohol", "definicion"=>"\nUrteko erregistratutako alkohola-kontsumoa alkohol puruaren litrotan (edari destilatuak, ardoa eta garagardoa), 15 urte eta gehiagoko pertsona bakoitzeko", "formula"=>"\n$$ECAR^{t} = \\frac{CAR_{edari\\, destilatuak}^{t}+CAR_{ardoa}^{t}+CAR_{garagardoa}^{t}}{P_{15+}^{t}}$$\n\nnon:\n\n$CAR_{edari\\, destilatuak}^{t} =$ edari destilatuetan erregistratutako alkohol-kontsumoa $t$ urtean\n\n$CAR_{ardoa}^{t} =$ ardoan erregistratutako alkohol-kontsumoa $t$ urtean\n\n$CAR_{garagardoa}^{t} =$ garagardoan erregistratutako alkohol-kontsumoa $t$ urtean\n\n$P_{15+}^{t} =$ 15 urteko eta gehiagoko biztanleak $t$ urteko uztailaren 1ean\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nNazio Batuen adierazleak alkoholaren erabilera kaltegarria neurtzen du, testuinguru nazionalaren arabera biztanleko \nalkohol-kontsumo gisa zehaztua (15 urte edo gehiagoko pertsonak) egutegiko urtebetean, alkohol puruko litrotan. \n\nAlkoholaren kontsumoak eragina izan dezake ez soilik gaixotasunen, lesioen eta beste osasun-arazo batzuen intzidentzian, \nbaizik eta baita nahasmenduen bilakaeran eta pertsonen gaineko ondorioetan ere. \n\nAlkoholaren kontsumoa 200dik gora gaixotasun, lesio edo bestelako osasun-arazoren arrazoitzat jo da. Biztanleko \nalkohol-kontsumoa biztanleen artean alkoholaren esposizioa ondoen zehazten duen adierazletzat jotzen da. Halaber, \nfuntsezko adierazlea da alkoholari egotzi ahal zaizkion gaixotasunen karga eta alkoholari egotzi ahal zaizkion \nheriotzak kalkulatzeko. \n\nZuzen interpretatzeko, biztanleria-adierazle osagarriak erabili behar dira, besteak beste alkohol-kontsumoaren \nnagusitasuna. Ondorioz, alkohola eta osasuna zaintzeko sistema nazionalen garapena sustatzen da, interesa duten \nalderdi askoren ekarpenak inplikatuz (alkohola ekoitzi eta merkaturatzen duten sektoreak barne). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.5.2&seriesCode=SH_ALC_CONSPT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20BOTHSEX\">Alkohol-kontsumoa per capita (15 urtetik gorakoak) egutegiko urte batean (alkohol puruaren litroak) SH_ALC_CONSPT</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen adierazlearen metadatuak betetzen, baina  antzeko informazioa ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-05-02.pdf\">Metadatuak 3-5-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.5: Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcohol</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.5.2: Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Not applicable</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Goal 8; Targets 3.4, 3.6 </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Harmful use of alcohol, defined according to the national context as alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol. </p>\n<p> </p>\n<p>Total alcohol per capita (15+ years) consumption (APC) is defined as the total (sum of three-year average recorded APC and unrecorded APC adjusted for tourist consumption) amount of pure alcohol consumed per adult (15+ years), in a calendar year, in litres of pure alcohol. Recorded alcohol consumption refers to official statistics at country level (production, import, export, and sales or taxation data), while the unrecorded alcohol consumption refers to alcohol which is not taxed and is outside the usual system of governmental control, such as home or informally produced alcohol (legal or illegal), smuggled alcohol, surrogate alcohol (which is alcohol not intended for human consumption), or alcohol obtained through cross-border shopping (which is recorded in a different jurisdiction). Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries. Positive figures denote alcohol consumption of outbound tourists being greater than alcohol consumption by inbound tourists, negative numbers the opposite. Tourist consumption is based on UN statistics, and data are provided by the Institute for Health Metrics and Evaluation. </p>\n<p><strong>Concepts:</strong></p>\n<p>Recorded alcohol per capita (15+) consumption of pure alcohol is calculated as the sum of beverage-specific alcohol consumption of pure alcohol (beer, wine, spirits, other) from different sources. The first priority in the decision tree is given to government national statistics; second are country-specific alcohol industry statistics in the public domain based on interviews or fieldwork (GlobalData (formerly Canadean), International Wine and Spirit Research (IWSR), Wine Institute; historically World Drink Trends) or data from the International Organisation of Vine and Wine (OIV); third is the Food and Agriculture Organization of the United Nations&apos; statistical database (FAOSTAT), and fourth is data from alcohol industry statistics in the public domain based on desk review. </p>\n<p>For countries where the data source is FAOSTAT, the unrecorded consumption may be included in the recorded consumption. As for the beverage-specific categories, beer includes malt beers, wine includes wine made from grapes and vermouth, spirits include all distilled beverages, and other includes one or several other alcoholic beverages, such as fermented beverages made from sorghum, maize, millet, rice, or cider, fruit wine, fortified wine, etc. For unrecorded APC, the first priority in the decision tree is given to nationally representative empirical data; these are often general population surveys in countries where alcohol is legal. Second are specific empirical investigations, and third is expert opinion supported by periodic survey of experts at country level using modified Delphi-technique. </p>\n<p> </p>\n<p>For recorded APC, if beverage volumes are not available in litres of pure alcohol, they are transformed into litres of pure alcohol. The alcohol content (% alcohol by volume) is considered to be as follows: beer (barley beer 5%), wine (grape wine 12%; must of grape 9%, vermouth 16%), spirits (distilled spirits 40%; spirit-like 30%), and other (sorghum, millet, maize beers 5%; cider 5%; fortified wine 17% and 18%; fermented wheat and fermented rice 9%; other fermented beverages 9%).</p>\n<p>Unrecorded APC is estimated using a regression analysis. Fractional response random intercepts regression models, which account for clustering of data points within countries, are used to estimate what percentage of total APC is due to unrecorded APC. Univariate models are fitted for alcohol consumption statistics and other predictors.</p>\n<p> </p>\n<p>The litres of alcohol consumed by tourists (15 years of age and older) in a country are based on the number of tourists who visited a country, the average amount of time they spent in the country, and how much these people drink on average in their countries of origin (estimated based on per capita consumption of recorded and unrecorded alcohol). Furthermore, tourist alcohol consumption also accounts for the inhabitants of a country consuming alcohol while visiting other countries (based on the average time spent outside of their country (for all people 15 years and older) and the amount of alcohol consumed in their country of origin). These estimations assume the following: (1) that people drink the same amounts of alcohol when they are tourists as they do in their home countries, and (2) that global tourist consumption is equal to 0 (and thus tourist consumption can be either net negative or positive). </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Litres of pure alcohol </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SRC_TYPE_COLL_METHOD__GLOBAL"=>"<p>3.a. Data sources (SOURCE_TYPE) </p>\n<p>Recorded: Government statistics or, alternatively, alcohol industry statistics in the public domain, FAOSTAT. </p>\n<p> </p>\n<p>Unrecorded: Nationally representative empirical data or, alternatively, specific empirical investigations, expert opinion. </p>\n<p>Tourist: UN tourist statistics</p>", "COLL_METHOD__GLOBAL"=>"<p>The Global Survey on Alcohol and Health is conducted periodically in collaboration with all six WHO regional offices. National counterparts or focal points in all WHO Member States are officially nominated by the respective ministries of health. They are provided with the online survey data collection tool for completion. Where online completion is not feasible, a hard copy of the tool is forwarded to those who requested it. The survey submissions are checked and whenever information is incomplete or in need of clarification, the questionnaire is returned to the focal point or national counterpart in the country concerned for revision. Amendments to the survey responses are resubmitted by e-mail or electronically. Data submitted from countries is triangulated with data from key industry-supported data providers at annual meetings organized by WHO with an objective to identify discrepancies and solutions. Estimates for key indicators, such as APC, are compiled into country profiles which are sent to the focal point or national counterpart in the country for validation and endorsement. </p>", "FREQ_COLL__GLOBAL"=>"<p>Ongoing updates from data sources on the web. The next WHO global surveys on alcohol and health involving data collection from WHO Member States are in 2022 and 2025. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Annually </p>", "DATA_SOURCE__GLOBAL"=>"<p>Ministries of Health; National statistical bureau/agencies (data on alcohol production and trade/sales); National monitoring centres on alcohol and drug use; National academic and monitoring centres concerned with population-based surveys of risk factors to health. </p>", "COMPILING_ORG__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "INST_MANDATE__GLOBAL"=>"<p>Monitoring public health risks and generate, collate, compile and disseminate reliable information on the health impact of alcohol, drugs and addictive behaviours as well as health policy and health system responses. </p>", "RATIONALE__GLOBAL"=>"<p>Alcohol consumption can have an impact not only on the incidence of diseases, injuries and other health conditions, but also on the course of disorders and their outcomes in individuals. Alcohol consumption has been identified as a component cause for more than 200 diseases, injuries and other health conditions. Per capita alcohol consumption is widely accepted as the best possible indicator of alcohol exposure in populations and the key indicator for estimation of alcohol-attributable disease burden and alcohol-attributable deaths. Its correct interpretation requires the use of additional population-based indicators such as prevalence of drinking, and, as a result, stimulates development of national monitoring systems on alcohol and health involving contributions from a wide range of stakeholders, including alcohol production and trade sectors. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator is feasible and suitable for monitoring purposes as evidenced by availability of data from 190 countries and inclusion of this indicator in global, regional and national monitoring frameworks. This is the key indicator for alcohol exposure in populations. The data available (based on production, import, export, and sales or taxation) do not enable the disaggregation of alcohol per capita consumption (APC) by sex or age; to this end, other data sources, such as survey data, are needed. The estimation of unrecorded APC remains a challenge, and triangulation of data from different sources as well as Delphi-techniques are used for increasing validity of estimates. In recent time, the number of research activities focused on improvement of the estimates of unrecorded alcohol consumption as well as their geographical coverage have increased substantially. As a result, it leads to a more accurate assessment of the total amount of alcohol consumed per person per year in a given country. </p>", "DATA_COMP__GLOBAL"=>"<p>Numerator: The sum of the amount of recorded alcohol consumed per capita (15+ years), average during three calendar years, in litres of pure alcohol, and the amount of three-year average unrecorded alcohol per capita consumption (15+ years), during a calendar year, in litres of pure alcohol, adjusted for tourist consumption. </p>\n<p> </p>\n<p>Denominator: Midyear resident population (15+ years) for the same calendar year, UN World Population Prospects, medium variant. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>Estimates are sent to focal points or national counterparts in the country through WHO Regional Offices for validation and endorsement. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>At country level </strong></p>\n<p>The values of missing countries (e.g. Monaco, San Marino) are that small that they would not affect global or regional figures. </p>\n<p><strong>At regional and global levels </strong></p>\n<p>The values of missing countries (e.g. Monaco, San Marino) are that small that they would not affect global or regional figures.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates are population weighted averages from country values (weighted by population of inhabitants 15+ years of the respective countries). </p>\n<p> </p>", "DOC_METHOD__GLOBAL"=>"<p>Global Status Report on Alcohol and Health 2018 (<a href=\"https://www.who.int/publications/i/item/9789241565639\">https://www.who.int/publications/i/item/9789241565639</a>). </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Steering Committee of Global Information System on Alcohol and Health; Technical Advisory Group on Alcohol and Drug Epidemiology. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Statistics clearance by Data, Analytics and Delivery for Impact Unit. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data, Analytics and Delivery for Impact Unit.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Global, by WHO and SDG regions, by World Bank income groups, by country. The data are available for 190 WHO Member States. </p>\n<p><strong>Time series:</strong></p>\n<p>Recorded alcohol per capita consumption since 1960s, and total alcohol per capita consumption since 2000. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Sex, age. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Population estimates, alcohol content by volume across different alcoholic beverage categories, age distributions, requirements for survey data used in producing the estimates, estimates of unrecorded alcohol consumption. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p><a href=\"https://apps.who.int/gho/data/node.gisah.GISAH?showonly=GISAH\">https://apps.who.int/gho/data/node.gisah.GISAH?showonly=GISAH</a> </p>\n<p> </p>\n<p><strong>References:</strong> </p>\n<p><a href=\"https://apps.who.int/gho/data/node.gisah.GISAH?showonly=GISAH\">https://apps.who.int/gho/data/node.gisah.GISAH?showonly=GISAH</a></p>\n<p> </p>\n<p><a href=\"https://www.who.int/data/gho/data/themes/global-information-system-on-alcohol-and-health\">https://www.who.int/data/gho/data/themes/global-information-system-on-alcohol-and-health</a></p>\n<p><a href=\"http://www.who.int/substance_abuse/publications/global_alcohol_report/en/\">http://www.who.int/substance_abuse/publications/global_alcohol_report/en/</a> </p>", "indicator_sort_order"=>"03-05-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.6.1", "slug"=>"3-6-1", "name"=>"Tasa de mortalidad por lesiones debidas a accidentes de tráfico", "url"=>"/site/es/3-6-1/", "sort"=>"030601", "goal_number"=>"3", "target_number"=>"3.6", "global"=>{"name"=>"Tasa de mortalidad por lesiones debidas a accidentes de tráfico"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[{"series"=>"", "unit"=>"", "label_content"=>"Objetivo del indicador para 2030", "value"=>1.75}], "graph_title"=>"Tasa de mortalidad por lesiones debidas a accidentes de tráfico", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Tasa de mortalidad por lesiones debidas a accidentes de tráfico", "indicator_number"=>"3.6.1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Meta:</b> Reducir a la mitad el número de muertes", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_28/opt_0/tipo_1/ti_defunciones/temas.html", "url_text"=>"Estadística de defunciones", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tasa de mortalidad por lesiones debidas a accidentes de tráfico", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.6- De aquí a 2020, reducir a la mitad el número de muertes y lesiones causadas por accidentes de tráfico en el mundo", "definicion"=>"Defunciones atribuidas a accidentes de tráfico por cada 100.000 habitantes", "formula"=>"\n$$TM_{\\text{accidentes tráfico}}^{t} = \\frac{D_{\\text{accidentes tráfico}}^{t}}{P^{t}} \\cdot 100.000$$\n\ndonde:\n$D_{\\text{accidentes tráfico}}^{t} =$ defunciones atribuidas a accidentes de tráfico (lista extensa de códigos de la CIE-10, que se puede consultar en la metodología de la Estadística de defunciones según la causa de muerte) en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"\nA partir de 2016 se incluyen los accidentes de tráfico de vehículos sin motor, los accidentes de transporte \nno especificados como debidos o no a tráfico y las víctimas de accidente de tráfico en las que en el momento \ndel accidente estaban subiendo o bajando del vehículo\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Las lesiones causadas por el tránsito siguen siendo un importante problema de salud pública, en particular \nen los países de ingresos bajos y medios.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.6.1&seriesCode=SH_STA_TRAF&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Tasa de mortalidad por lesiones causadas por accidentes de tránsito, por sexo (por cada 100.000 habitantes) SH_STA_TRAF</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-06-01.pdf\">Metadatos 3-6-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Tasa de mortalidad por lesiones debidas a accidentes de tráfico", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.6- De aquí a 2020, reducir a la mitad el número de muertes y lesiones causadas por accidentes de tráfico en el mundo", "definicion"=>"Deaths attributed to traffic accidents per 100,000 inhabitants", "formula"=>"\n$$TM_{\\text{traffic accidents}}^{t} = \\frac{D_{\\text{traffic accidents}}^{t}}{P^{t}} \\cdot 100.000$$\n\nwhere: \n\n$D_{\\text{traffic accidents}}^{t} =$ Deaths attributed to traffic accidents (extensive list of ICD-10 codes, which can be consulted in the methodology of the Statistics of deaths according to the cause of death) in the year $t$\n\n$P^{t} =$ population on July 1 of year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "periodicidad"=>"Anual", "observaciones"=>"\nSince 2016, these include traffic accidents involving non-motorized vehicles, transport accidents not specified as due to traffic or not, and traffic accident victims who were getting into or out of the vehicle at the time of the accident.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Road traffic injuries remain a major public health problem, particularly in low- and middle-income countries. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.6.1&seriesCode=SH_STA_TRAF&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Death rate due to road traffic injuries, by sex (per 100,000 population) SH_STA_TRAF</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-06-01.pdf\">Metadata 3-6-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Tasa de mortalidad por lesiones debidas a accidentes de tráfico", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.6- De aquí a 2020, reducir a la mitad el número de muertes y lesiones causadas por accidentes de tráfico en el mundo", "definicion"=>"Zirkulazio-istripuei egotzitako heriotzak 100.000 biztanleko", "formula"=>"\n$$TM_{\\text{zirkulazio-istripuak}}^{t} = \\frac{D_{\\text{zirkulazio-istripuak}}^{t}}{P^{t}} \\cdot 100.000$$\n\nnon: \n\n$D_{\\text{zirkulazio-istripuak}}^{t} =$ zirkulazio-istripuei egotzitako heriotzak $t$ urtean (GNS-10en kode-zerrenda luzea, \nzeina kontsultagai baitago heriotza-kausaren araberako heriotzen estatistikaren metodologian) \n\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean \n", "desagregacion"=>"Sexua \n\nLurralde historikoa \n", "periodicidad"=>"Anual", "observaciones"=>"\n2016tik aurrera, hauek gehitu dira: motorrik gabeko ibilgailuen trafiko-istripuak, trafikoaren ondoriozkotzat \njotzen ez diren garraio-istripuak eta istripuaren unean ibilgailutik igotzen edo jaisten ari ziren trafiko-istripuen \nbiktimak\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Bidaiek eragindako lesioak oraindik ere osasun publikoko arazo handia dira, bereziki diru sarrera baxuak eta \nertainak dituzten herrialdeetan. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.6.1&seriesCode=SH_STA_TRAF&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Zirkulazio-istripuek eragindako lesioen ondoriozko heriotza-tasa, sexuaren arabera (100.000 biztanleko) SH_STA_TRAF</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-06-01.pdf\">Metadatuak 3-6-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.6: By 2020, halve the number of global deaths and injuries from road traffic accidents</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.6.1: Death rate due to road traffic injuries</p>\n<p>0.d. Series (ERIES_DESCR)</p>\n<p>SH_STA_TRAF - Death rate due to road traffic injuries [3.6.1]</p>\n<p>SH_STA_TRAFN - Number of deaths rate due to road traffic injuries [3.6.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>3.5, 11.2 </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>For World Health Organization and the health sector, the actual interpretation of the definition of a road traffic fatality is revealed in the recording or coding of information in medical records by health personnel. This recording is based on the International Classification of Diseases (World Health Organization 1994), which covers road traffic deaths under Chapter XX: External Causes of Morbidity and Mortality, in a section on Transport Accidents (V01&#x2013;V99). According to ICD-10: &#x201C;Road traffic deaths are fatalities caused by injuries sustained in traffic accidents involving one or more moving vehicles on public highway&#x201D;. ICD-10 provides a detailed classification of road traffic injuries and deaths, with specific codes depending on the type of vehicle involved, the role of the injured person, and the circumstances of the accident. Common codes include:</p>\n<ul>\n  <li>V01&#x2013;V09: Pedestrians injured in transport accidents</li>\n  <li>V10&#x2013;V19: Cyclists injured in transport accidents</li>\n  <li>V20&#x2013;V29: Motorcycle riders injured in transport accidents</li>\n  <li>V30&#x2013;V39: Occupants of three-wheeled motor vehicles injured in transport accidents</li>\n  <li>V40&#x2013;V49: Car occupants injured in transport accidents</li>\n  <li>V50&#x2013;V59: Occupants of pick-up trucks or vans injured in transport accidents</li>\n  <li>V60&#x2013;V69: Occupants of heavy transport vehicles injured in transport accidents</li>\n  <li>V70&#x2013;V79: Bus occupants injured in transport accidents</li>\n  <li>V80&#x2013;V89: Other land transport accidents (e.g., animal-drawn vehicles, streetcars) </li>\n</ul>\n<p>Death rate due to road traffic injuries as defined as the number of road traffic fatal injury deaths per 100,000 population. </p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p> </p>\n<p>Numerator: Number of deaths due to road traffic crashes </p>\n<p> </p>\n<p>Absolute figure indicating the number of people who die as result of a road traffic crash. </p>\n<p> </p>\n<p>Denominator: Population (number of people by country) </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Per 100,000 population </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Road injuries are defined in terms of the International Classification of Diseases, Tenth Revision (ICD-10) (See Annex A of the WHO methods and data sources for global causes of death, 2000&#x2013;2021) </p>\n<p>For countries where the ICD-10 has four characters, the codes to be filtered are as follows:</p>\n<p>V011:V019, V021:V029, V031:V039, V041:V049,V061:V069, V092 ,V093 ,V103:V109, V113:V119, V123:V129, V133:V139, V143:V149, V154:V159, V164:V169,V174:V179, V184:V189, V194:V199, V203:V209, V213:V219, V223:V229, V233:V239, V243:V249, V253:V259,V263:V269, V273:V279, V283:V289, V294:V299, V304:V309, V314:V319,V324:V329, V334:V339, V344:V349,V354:V359, V364:V369, V374:V379, V384:V389, V394:V399, V404:V409, V414:V419, V424:V429, V434:V439,V444:V449, V454:V459, V464:V469, V474:V479, V484:V489, V494:V499, V504:V509, V514:V519, V524:V529,V534:V539, V544:V549, V554:V559, V564:V569, V574:V579, V584:V589, V594:V599, V604:V609, V614:V619,V624:V629, V634:V639, V644:V649, V654:V659, V664:V669, V674:V679, V684:V689, V694:V699, V704:V709,V714:V719, V724:V729, V734:V739, V744:V749, V754:V759, V764:V769, V774:V779, V784:V789, V794:V799,V803:V805, V811, V821, V828, V829, V830:V833, V840:V843, V850:V853, V860:V863, V870:V879, V892, V893, V899, V99, Y850, X594;</p>", "SOURCE_TYPE__GLOBAL"=>"<p>For the road traffic deaths, we have two sources of data. Data from Global Status Report on Road Safety survey and Vital registration or certificate deaths data that WHO receive every year from member states (ministries of health). </p>\n<p> </p>\n<p>For the population, we used data from the United Nations/Department of Economic and Social Affairs/ Population division. </p>", "COLL_METHOD__GLOBAL"=>"<p>The methodology for collecting data involved engaging multiple sectors and stakeholders within each country. WHO Regional Advisors established regional networks in collaboration with WHO Regional Data Focal Points (RDFPs) and government-appointed National Data Focal Points (NDFPs), who were trained in the project methodology.</p>\n<p>As representatives of their respective ministries, NDFPs were tasked with identifying up to ten National Data Collaborators (NDCs)&#x2014;road safety experts from various sectors such as health, police, transport, non-governmental organizations, and academia. They also facilitated a consensus meeting among these collaborators.</p>\n<p>Each expert initially responded to the questionnaire based on their specific expertise. During the consensus meeting led by the NDFPs, participants reviewed and discussed all responses, ultimately agreeing on a finalized dataset that most accurately reflected their country&#x2019;s situation at that time. This consolidated information was then submitted to the World Health Organization.</p>\n<p>Further details can be found in the Global Status Report on Road Safety 2023 and the WHO Methods and Data Sources for Global Causes of Death, 2000&#x2013;2021.</p>", "FREQ_COLL__GLOBAL"=>"<p>WHO annually requests tabulated death registration data, including statistics on all causes of death, by sending an official letter to all focal points. Member States can provide these annual cause-of-death statistics to WHO on a continuous basis.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data for the Global Status Report on Road Safety is collected every two or three years, with the most recent report published in 2023.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The road traffic deaths data were provided nationally by mainly three ministries, namely, ministry of health, ministry of interior and ministry of transport.</p>", "COMPILING_ORG__GLOBAL"=>"<p>WHO is the organization responsible for compilation and reporting on this indicator at the global level.</p>", "INST_MANDATE__GLOBAL"=>"<p>According to Article 64 of its constitution, WHO is mandated to request each Member State to provide statistics on mortality. Furthermore, the WHO Nomenclature Regulations of 1967 affirms the importance of compiling and publishing statistics of mortality and morbidity in comparable form. Member States started to report mortality data to WHO since the early fifties and this reporting activity is continues till date. </p>", "RATIONALE__GLOBAL"=>"<p>Road traffic crashes are a major public health issue due to their significant contribution to global morbidity and mortality, causing 1.19 million deaths annually and leaving millions injured or disabled. These crashes disproportionately affect young people, especially in low- and middle-income countries. In 2021, road traffic injuries became the leading cause of death for individuals aged 5 to 29 and the 14th leading cause of death across all age groups. The economic and social consequences are severe, particularly in developing countries, where over 90% of road casualties occur.</p>", "REC_USE_LIM__GLOBAL"=>"<p>There are no vital registration data for all countries to make a comparison against the data received on the survey. Also, WHO cannot collect road traffic data every year using this methodology outlined in the Global status report. </p>", "DATA_COMP__GLOBAL"=>"<p>The methods used for the analysis of causes of death depend on the type of data available from countries: </p>\n<p> </p>\n<p>For countries with a high-quality vital registration system including information on cause of death, the vital registration that member states submit to the WHO Mortality Database were used, with adjustments where necessary, e.g. for under-reporting of deaths, unknown age and sex, and ill-defined causes of deaths. </p>\n<p> </p>\n<p>For countries without high-quality death registration data, cause of death estimates are calculated using other data, including household surveys with verbal autopsy, sample or sentinel registration systems, special studies.</p>\n<p>Death rate due to road traffic injuries as defined as the number of road traffic fatal injury deaths per 100,000 population. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The number of deaths due to road injury were country consulted with country designated focal points (usually at the Ministry of Health or National Statistics Office) as part of the full set of causes of death prior to the release.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Deaths of unknown sex were redistributed pro-rata within cause-age groups of known sexes, and then deaths of unknown age were redistributed pro-rata within cause-sex groups of known ages.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li>At country level </li>\n</ul>\n<p> </p>\n<p>For countries with high-quality cause-of-death statistics, interpolation/extrapolation was done for missing country-years; for countries with only low-quality or no data on causes of death, modelling was used. Complete methodology may be found here: </p>\n<p>WHO methods and data sources for global causes of death, 2000-2021 (<a href=\"https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2021_cod_methods.pdf?sfvrsn=dca346b7_1\">https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2021_cod_methods.pdf?sfvrsn=dca346b7_1</a> </p>\n<p> </p>\n<ul>\n  <li>At regional and global levels </li>\n</ul>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Country estimates of number of deaths by cause are summed to obtain regional and global aggregates.</p>", "DOC_METHOD__GLOBAL"=>"<p>The cause of death categories (including road injury) follow the definitions in terms of the International Classification of Diseases, Tenth Revision (ICD-10). Please see Annex Table A of the WHO methods and data sources for global causes of death, 2000&#x2013;2021 </p>\n<p><a href=\"https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2021_cod_methods.pdf?sfvrsn=dca346b7_1\">https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2021_cod_methods.pdf?sfvrsn=dca346b7_1</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The World Health Organization (WHO) established a Reference Group on Health Statistics in 2013 to provide advice on population health statistics to WHO with a focus on methodological and data issues related to the measurement of mortality and cause-of-death patterns. The group facilitated interaction between multilateral development institutions and other independent academic groups with WHO expert groups in specific subject areas including methods to the estimation on causes of death.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The data principles of the World Health Organization (WHO) provide a foundation for continually reaffirming trust in WHO&#x2019;s information and evidence on public health. The five principles are designed to provide a framework for data governance for WHO. The principles are intended primarily for use by WHO staff across all parts of the Organization in order to help define the values and standards that govern how data that flows into, across and out of WHO is collected, processed, shared and used. These principles are made publicly available so that they may be used and referred to by Member States and non-state actors collaborating with WHO.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>All statements and claims made officially by WHO headquarters about population-level (country, regional, global) estimates of health status (e.g. mortality, incidence, prevalence, burden of disease), are cleared by the Department of Data and Analytics (DNA) through the executive clearance process. This includes the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement. GATHER promotes best practices in reporting health estimates using a checklist of 18 items that should be reported every time new global health estimates are published, including descriptions of input data and estimation methods. Developed by a working group convened by the World Health Organization, the guidelines aim to define and promote good practice in reporting health estimates.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Almost 70 countries currently provide WHO with regular high-quality data on mortality by age, sex and causes of death, and another 58 countries submit data of lower quality. However, comprehensive cause-of-death estimates are calculated by WHO systematically for all of its Member States (with a certain population threshold) every 3 years. </p>\n<p><strong>Time series:</strong></p>\n<p>From 2000 to 2021</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Sex, age group </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>WHO&apos;s estimation of road traffic rates is, in many countries, different from the official estimates for the reasons described above that relate to our methodology. </p>\n<p> </p>\n<p>There are also differences in the data used for the population between the national data and the estimates produced by the United Nations Population Division. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p> </p>\n<p><a href=\"http://www.who.int/violence_injury_prevention\">http://www.who.int/violence_injury_prevention</a> </p>\n<p> </p>\n<p><strong>References:</strong> </p>\n<p>Global status report on road safety 2023</p>\n<p>https://iris.who.int/bitstream/handle/10665/375016/9789240086517-eng.pdf?sequence=1</p>\n<p>WHO methods and data sources for global causes of death, 2000&#x2013;2021 </p>\n<p>https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2021_cod_methods.pdf?sfvrsn=dca346b7_1 </p>\n<p>(<a href=\"https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf\">https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf</a>) </p>", "indicator_sort_order"=>"03-06-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.7.1", "slug"=>"3-7-1", "name"=>"Proporción de mujeres en edad de procrear (entre 15 y 49 años) que cubren sus necesidades de planificación familiar con métodos modernos", "url"=>"/site/es/3-7-1/", "sort"=>"030701", "goal_number"=>"3", "target_number"=>"3.7", "global"=>{"name"=>"Proporción de mujeres en edad de procrear (entre 15 y 49 años) que cubren sus necesidades de planificación familiar con métodos modernos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de mujeres en edad de procrear (entre 18 y 49 años) que cubren sus necesidades de planificación familiar con métodos modernos", "graph_titles"=>[], "graph_type"=>"bar", "indicator_name"=>"Proporción de mujeres en edad de procrear (entre 15 y 49 años) que cubren sus necesidades de planificación familiar con métodos modernos", "indicator_number"=>"3.7.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Anual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736177006&menu=ultiDatos&idp=1254735573002", "url_text"=>"Encuesta de fecundidad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de mujeres en edad de procrear (entre 18 y 49 años) que cubren sus necesidades de planificación familiar con métodos modernos", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.7- De aquí a 2030, garantizar el acceso universal a los servicios de salud sexual y reproductiva, incluidos los de planificación familiar, información y educación, y la integración de la salud reproductiva en las estrategias y los programas nacionales", "definicion"=>"Proporción de mujeres entre 18 y 49 años, casadas con un hombre o con pareja masculina, que utilizan métodos  anticonceptivos modernos respecto a aquellas que no desean quedarse embarazadas, es decir, que utilizan métodos  anticonceptivos o tienen una necesidad insatisfecha de planificación familiar", "formula"=>"\n$$PM_{18-49,anticonceptivos\\, modernos}^{t} = \\frac{M_{18-49,anticonceptivos\\, modernos}^{t}}{M_{18-49,anticonceptivos}^{t}+M_{18-49,insatisfecha}^{t}} \\cdot 100$$\n\ndonde:\n\n$M_{18-49,anticonceptivos\\, modernos}^{t} =$ mujeres entre 18 y 49 años, casadas con un hombre o con pareja masculina, que utilizan métodos anticonceptivos modernos en el año $t$\n\n$M_{18-49,anticonceptivos}^{t} =$ mujeres entre 18 y 49 años, casadas con un hombre o con pareja masculina, que utilizan \nmétodos anticonceptivos en el año $t$\n\n$M_{18-49,insatisfecha}^{t} =$ mujeres entre 18 y 49 años, casadas con un hombre o con pareja masculina, que tienen una necesidad insatisfecha de planificación familiar en el año $t$\n", "desagregacion"=>"", "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl indicador mide el porcentaje de mujeres en edad reproductiva (15-49 años) que actualmente \nutilizan un \nmétodo moderno de anticoncepción entre aquellas que desean no tener hijos (adicionales) o \nposponer el próximo embarazo. El indicador también se conoce como la demanda de \nplanificación familiar satisfecha con métodos modernos.\n\nLa proporción de la demanda de planificación familiar satisfecha con métodos modernos es útil \npara evaluar los niveles generales de cobertura de los programas y servicios de planificación familiar. \nEl acceso a un medio eficaz para prevenir el embarazo y su uso ayudan a que las mujeres y sus parejas \nejerzan su derecho a decidir libre y responsablemente el número y el espaciamiento de sus hijos y a disponer \nde la información, la educación y los medios para hacerlo. Satisfacer la demanda de planificación familiar \ncon métodos modernos también contribuye a la salud materna e infantil al prevenir los embarazos no deseados \ny los embarazos con poco espaciamiento, que tienen mayor riesgo de malos resultados obstétricos.\n\nLos niveles de demanda de planificación familiar satisfecha con métodos modernos del 75 por ciento o más se \nconsideran generalmente altos, y los valores del 50 por ciento o menos se consideran generalmente como muy bajos. \nEl indicador no tiene un valor numérico global \"objetivo\" que se deba alcanzar para 2030. Si nos fijamos en los \nvalores más altos del indicador, en 22 países que representan regiones como Europa y América del Norte, América Latina \ny el Caribe y Asia oriental y sudoriental, más del 85 por ciento de las mujeres que quieren evitar el \nembarazo están usando un método anticonceptivo moderno, pero en ningún país esta estimación supera el 91 por ciento. \n\nFuente: División de Estadísticas de las Naciones Unidas \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.7.1&seriesCode=SH_FPL_MTMM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-49%20%7C%20FEMALE\"> Proporción de mujeres en edad reproductiva (de 15 a 49 años) que tienen satisfechas sus necesidades de planificación familiar con métodos modernos (% de mujeres de 15 a 49 años) SH_FPL_MTMM</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple estrictamente con los metadatos del indicador de Naciones Unidas porque utiliza un grupo de edad diferente, pero aporta información similar.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-07-01.pdf\">Metadatos 3-7-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de mujeres en edad de procrear (entre 18 y 49 años) que cubren sus necesidades de planificación familiar con métodos modernos", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.7- De aquí a 2030, garantizar el acceso universal a los servicios de salud sexual y reproductiva, incluidos los de planificación familiar, información y educación, y la integración de la salud reproductiva en las estrategias y los programas nacionales", "definicion"=>"Proportion of women aged 18–49 years married to a man or with a male partner, who use  modern methods of contraception with respect to those who do not wish to become pregnant,  i.e. who use methods of contraception or who have an unmet need for family planning", "formula"=>"\n$$PM_{18-49,modern\\, contraception}^{t} = \\frac{M_{18-49,modern\\, contraception}^{t}}{M_{18-49,contraception}^{t}+M_{18-49,unmet}^{t}} \\cdot 100$$\n\nwhere:\n\n$M_{18-49,modern\\, contraception}^{t} =$ women aged 18–49 years married to a man or with a male partner, who use modern methods of contraception in year $t$\n\n$M_{18-49,contraception}^{t} =$ women aged 18–49 years married to a man or with a male partner, who use methods of contraception in year $t$\n\n$M_{18-49,unmet}^{t} =$ women aged 18–49 years married to a man or with a male partner, who have an unmet need for family planning in year $t$\n", "desagregacion"=>"", "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe indicator measures the percentage of women of reproductive age (15-49 years) currently \nusing a modern method of contraception among those who desire either to have no (additional) \nchildren or to postpone the next pregnancy. The indicator is also referred to as the demand \nfor family planning satisfied with modern methods.\n\nThe proportion of demand for family planning satisfied with modern methods is useful in \nassessing overall levels of coverage for family planning programmes and services. Access \nto and use of an effective means to prevent pregnancy helps enable women and their partners \nto exercise their rights to decide freely and responsibly the number and spacing of their \nchildren and to have the information, education and means to do so. Meeting demand for family \nplanning with modern methods also contributes to maternal and child health by preventing \nunintended pregnancies and closely spaced pregnancies, which are at higher risk for poor \nobstetrical outcomes.\n\nLevels of demand for family planning satisfied with modern methods of 75 percent or more are \ngenerally considered high, and values of 50 percent or less are generally considered as very low. \nThe indicator has no global numerical ‘target’ value set to be achieved by 2030. Looking at the \nhighest values of the indicator, in 22 countries representing regions such as Europe and Northern \nAmerica, Latin America and the Caribbean and Eastern and South-Eastern Asia, more than 85 percent \nof women who want to avoid pregnancy are using a modern contraceptive method but for no country \nis this estimate above 91 percent. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.7.1&seriesCode=SH_FPL_MTMM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-49%20%7C%20FEMALE\"> Proportion of women of reproductive age (aged 15-49 years) who have their need for family planning satisfied with modern methods (% of women aged 15-49 years) SH_FPL_MTMM</a> UNSTATS", "comparabilidad"=>"The available indicator does not strictly comply with the metadata of the UN indicator because it uses a different age group, but provides similar information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-07-01.pdf\">Metadata 3-7-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Proporción de mujeres en edad de procrear (entre 18 y 49 años) que cubren sus necesidades de planificación familiar con métodos modernos", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.7- De aquí a 2030, garantizar el acceso universal a los servicios de salud sexual y reproductiva, incluidos los de planificación familiar, información y educación, y la integración de la salud reproductiva en las estrategias y los programas nacionales", "definicion"=>"Gizon batekin ezkonduta dauden edo gizonezko bikotekidea duten 18 eta 49 urte bitarteko emakumeen artean, metodo  antikontzeptibo modernoak erabiltzen dituztenen proportzioa, haurdun geratu nahi ez dutenekiko, hau da, metodo  antikontzeptiboak erabiltzen dituzten edo familia-plangintzaren premia asegabea dutenekiko", "formula"=>"\n$$PM_{18-49\\, antikontzeptibo\\, modernoak}^{t} = \\frac{M_{18-49\\, antikontzeptibo\\, modernoak}^{t}}{M_{18-49\\, antikontzeptiboak}^{t}+M_{18-49\\, asegabea}^{t}} \\cdot 100$$\n\nnon:\n\n$M_{18-49\\, antikontzeptibo\\, modernoak}^{t} =$ gizon batekin ezkonduta dauden edo gizonezko bikotekidea duten \n18 eta 49 urte bitarteko emakumeak, $t$ urtean metodo antikontzeptibo modernoak erabiltzen dituztenak \n\n$M_{18-49\\, antikontzeptiboak}^{t} =$ gizon batekin ezkonduta dauden edo gizonezko bikotekidea duten \n18 eta 49 urte bitarteko emakumeak, $t$ urtean metodo antikontzeptiboak erabiltzen dituztenak\n\n$M_{18-49\\, asegabea}^{t} =$ gizon batekin ezkonduta dauden edo gizonezko bikotekidea duten \n18 eta 49 urte bitarteko emakumeak, $t$ urtean familia-plangintzaren premia asegabea dutenak\n", "desagregacion"=>"", "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nGaur egun seme-alabarik (edo seme-alaba gehiago) izan nahi ez duten edo hurrengo haurdunaldia atzeratu nahi duten \npertsonen artean metodo antikontzeptibo modernoa erabiltzen duten ugalketa-adineko (15-49 urte) emakumeen ehunekoa \nneurtzen du adierazleak. Halaber, metodo modernoak erabiliz asetako familia-plangintzaren eskari gisa ere ezagutzen da. \n\nMetodo modernoak erabiliz asetako familia-plangintzaren eskariaren proportzioa erabilgarria da familia planifikatzeko \nprograma eta zerbitzuen estaldura-maila orokorrak ebaluatzeko. Haurdunaldia prebenitzeko bitarteko eraginkor baterako \nsarbidea izateak eta halakoak erabiltzeak lagundu egiten dute emakumeek eta beren bikotekideek askatasunez eta arduraz \nerabaki dezaten zenbat seme-alaba izango dituzten, eta noiz, eta, halaber, horretarako informazioa, hezkuntza eta \nbitartekoak eskaintzen dituzte. Metodo modernoak erabiliz familia-plangintzaren eskaria asetzea lagungarria da, era \nberean, amaren eta haurren osasuna mantentzeko, desio ez diren haurdunaldiak edo denbora-tarte gutxiko haurdunaldiak \nsaihesten baitira, emaitza obstetriko txarrak izateko arrisku altuagoa dutenak, alegia. \n\nMetodo modernoak erabiliz asetako familia-plangintzaren eskari-mailak ehuneko 75 edo altuagoak badira, oro har altutzat \njotzen dira, eta, aldiz, ehuneko 50 edo baxuagok badira, oro har oso baxutzat jotzen dira. Adierazleak ez dauka 2030erako \nlortu behar den balio numeriko orokorrik. Adierazlearen balio altuenak aztertzen baditugu, Europa eta Ipar Amerika, \nAmerika Latina eta Karibe eta Asia ekialdeko eta hego-ekialdeko eskualdeak ordezkatzen dituzten 22 herrialdetan, \nhaurdunaldia saihestu nahi duten emakumeen % 85 baino gehiago metodo antikontzeptibo modernoak erabiltzen ari dira, baina \nherrialde batek ere ez du ehuneko 91tik gorako kopururik. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.7.1&seriesCode=SH_FPL_MTMM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-49%20%7C%20FEMALE\"> Familia-plangintzako premiak metodo modernoekin aseta dituzten ugalketa-adinean daude emakumeen proportzioa (15-49 urteko emakumeen %) SH_FPL_MTMM</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu zorrotz betetzen Nazio Batuen adierazlearen metadatuak, adin-talde desberdina erabiltzen duelako, baina antzeko informazioa ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-07-01.pdf\">Metadatuak 3-7-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.7: By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.7.1: Proportion of women of reproductive age (aged 15&#x2013;49 years) who have their need for family planning satisfied with modern methods</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_FPL_MTMM - Proportion of women of reproductive age (aged 15-49 years) who have their need for family planning satisfied with modern methods [3.7.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>This indicator is linked to Target 3.8 (Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all) because the provision of family planning information and methods to all individuals who want to prevent pregnancy is an important component of achieving universal health coverage.</p>\n<p>This indicator is also linked to Target 5.6 (Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferences) because meeting the demand for family planning is facilitated by increasing access to sexual and reproductive health-care services, and also improves sexual and reproductive health and the ability to exercise reproductive rights.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Population Division, Department of Economic and Social Affairs (DESA)</p>\n<p>United Nations Population Fund (UNFPA)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Population Division, Department of Economic and Social Affairs (DESA)</p>\n<p>United Nations Population Fund (UNFPA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The percentage of women of reproductive age (15-49 years) currently using a modern method of contraception among those who desire either to have no (additional) children or to postpone the next pregnancy. The indicator is also referred to as the demand for family planning satisfied with modern methods.</p>\n<p><strong>Concepts:</strong></p>\n<p>The percentage of women of reproductive age (15-49 years) who have their need for family planning satisfied with modern methods is also referred to as the proportion of demand satisfied by modern methods. The components of the indicator are contraceptive prevalence (any method and modern methods) and unmet need for family planning.</p>\n<p>Contraceptive prevalence is the percentage of women who are currently using, or whose partner is currently using, at least one method of contraception, regardless of the method used.</p>\n<p>For analytical purposes, contraceptive methods are often classified as either modern or traditional. Modern methods of contraception include female and male sterilization, the intra-uterine device (IUD), the implant, injectables, oral contraceptive pills, male and female condoms, vaginal barrier methods (including the diaphragm, cervical cap and spermicidal foam, jelly, cream and sponge), lactational amenorrhea method (LAM), emergency contraception and other modern methods not reported separately (e.g., the contraceptive patch or vaginal ring). Traditional methods of contraception include rhythm (e.g., fertility awareness-based methods, periodic abstinence), withdrawal and other traditional methods not reported separately.</p>\n<p>Unmet need for family planning is defined as the percentage of women of reproductive age who want to stop or delay childbearing but are not using any method of contraception. The standard definition of unmet need for family planning includes women who are fecund and sexually active in the numerator, and who report not wanting any (more) children, or who report wanting to delay the birth of their next child for at least two years or are undecided about the timing of the next birth, but who are not using any method of contraception. The numerator also includes pregnant women whose pregnancies were unwanted or mistimed at the time of conception; and postpartum amenorrheic women who are not using family planning and whose last birth was unwanted or mistimed. Further information on the operational definition of the unmet need for family planning, as well as survey questions and statistical programs needed to derive the indicator, can be found at the following website of the USAID Demographic and Health Surveys Program: <a href=\"http://measuredhs.com/Topics/Unmet-Need.cfm\">http://measuredhs.com/Topics/Unmet-Need.cfm</a>.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The classification of contraceptive methods is presented in World Health Organization Department of Reproductive Health and Research (WHO/RHR) and Johns Hopkins Bloomberg School of Public Health/Center for Communication Programs (CCP) (2018).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>This indicator is calculated from nationally-representative household survey data. Multi-country survey programmes that include relevant data for this indicator are: Contraceptive Prevalence Surveys (CPS), Demographic and Health Surveys (DHS), Fertility and Family Surveys (FFS), Reproductive Health Surveys (RHS), Multiple Indicator Cluster Surveys (MICS), Performance Monitoring and Accountability 2020 surveys (PMA), World Fertility Surveys (WFS), other international survey programmes and national surveys.</p>\n<p>For information on the source of each estimate, see United Nations, Department of Economic and Social Affairs, Population Division (2024). World Contraceptive Use 2024. (https://www.un.org/development/desa/pd/data/world-contraceptive-use)</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are compiled based on systematic searches of websites of international survey programmes, survey databases (e.g., the Integrated Household Survey Network (IHSN) database), websites of national statistical offices, SDG national reporting platforms and ad hoc queries in addition to utilization of the country-specific information from UNFPA country offices.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are compiled in the period from February to May.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Updated data compilations on the indicator are released by the Population Division biennially in July as a comprehensive compilation of data and model-based annual estimates and projections up to 2030 at the national, regional and global level. See:</p>\n<p>United Nations, Department of Economic and Social Affairs, Population Division (2024). World Contraceptive Use 2024. New York: United Nations. (https://www.un.org/development/desa/pd/data/world-contraceptive-use)</p>\n<p>United Nations, Department of Economic and Social Affairs, Population Division (2024). Estimates and Projections of Family Planning Indicators 2024. New York: United Nations. (https://www.un.org/development/desa/pd/data/family-planning-indicators)</p>\n<p>The data are also available in the interactive data portal of the Population Division (https://population.un.org/dataportal/home)</p>", "DATA_SOURCE__GLOBAL"=>"<p>Survey data are obtained from national household surveys that are internationally coordinated&#x2014;such as the Demographic and Health Surveys (DHS), the Reproductive Health Surveys (RHS), and the Multiple Indicator Cluster Surveys (MICS), Gender and Generation Surveys (GGS)&#x2014;and other nationally-sponsored surveys.</p>", "COMPILING_ORG__GLOBAL"=>"<p>This indicator is produced at the global level by the Population Division, Department of Economic and Social Affairs, United Nations in collaboration with the United Nations Population Fund (UNFPA).</p>", "INST_MANDATE__GLOBAL"=>"<p>The Population Division of the Department of Economic and Social Affairs conducts demographic research in the area of population and development and assists countries in developing their capacity to produce and analyse population data and information. The Population Division compiles global datasets of family planning indicators and provides analysis of levels and trends in contraceptive use and the need for family planning. The Population Division monitors progress in ensuring universal access to sexual and reproductive health-care services, as called for in the 2030 Agenda for Sustainable Development, and is the custodian agency for Sustainable Development Goal (SDG) indicator 3.7.1.</p>", "RATIONALE__GLOBAL"=>"<p>The proportion of demand for family planning satisfied with modern methods is useful in assessing overall levels of coverage for family planning programmes and services. Access to and use of an effective means to prevent pregnancy helps enable women and their partners to exercise their rights to decide freely and responsibly the number and spacing of their children and to have the information, education and means to do so. Meeting demand for family planning with modern methods also contributes to maternal and child health by preventing unintended pregnancies and closely spaced pregnancies, which are at higher risk for poor obstetrical outcomes. </p>\n<p>Levels of demand for family planning satisfied with modern methods of 75 percent or more are generally considered high, and values of 50 percent or less are generally considered as very low. The indicator has no global numerical &#x2018;target&#x2019; value set to be achieved by 2030. Looking at the highest values of the indicator, in 22 countries representing regions such as Europe and Northern America, Latin America and the Caribbean and Eastern and South-Eastern Asia, more than 85 percent of women who want to avoid pregnancy are using a modern contraceptive method but for no country is this estimate above 91 percent. </p>\n<p>Even in these countries, specific sub-populations (for example, adolescents or the poor) can still face barriers of access to family planning information and services. It should also be recognized that reaching 100 percent may not be a necessary or even desirable outcome with respect to reproductive rights. Some women may prefer to use a traditional method, even while having access to a full range of modern methods and being aware of the typical differences in effectiveness of methods in preventing pregnancies. Other women might have ambivalent preferences regarding their next pregnancy which may influence their contraceptive choice.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Differences in the survey design and implementation, as well as differences in the way survey questionnaires are formulated and administered can affect the comparability of the data. The most common differences relate to the range of contraceptive methods included and the characteristics (age, sex, marital or union status) of the persons for whom contraceptive prevalence is estimated (base population). The time frame used to assess contraceptive prevalence can also vary. In most surveys there is no definition of what is meant by &#x201C;currently using&#x201D; a method of contraception.</p>\n<p> </p>\n<p>In some surveys, the lack of probing questions, asked to ensure that the respondent understands the meaning of the different contraceptive methods, can result in an underestimation of contraceptive prevalence, in particular for traditional methods. Sampling variability can also be an issue, especially when contraceptive prevalence is measured for a specific subgroup (by age-group, level of educational attainment, place of residence, etc.) or when analysing trends over time. </p>\n<p> </p>\n<p>When data on all women aged 15 to 49 are not available, information for married or in-union women is reported. Examples of other base populations that are sometimes presented when data on all women of reproductive age are not available are: married or in-union women aged 15-44, sexually active women (irrespective of marital status), or ever-married women. Notes in the data set indicate any differences between the data presented and the standard definitions of contraceptive prevalence or unmet need for family planning or where data pertain to populations that are not representative of women of reproductive age.</p>", "DATA_COMP__GLOBAL"=>"<p>The numerator is the number of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method (CP<sub>Mod</sub>). The denominator is the total demand for family planning (the sum of the number of women using any contraceptive method (CP<sub>Any</sub>) and the number of women with unmet need for family planning (UMN)). The quotient is then multiplied by 100 to arrive at the percentage of women (aged 15 to 49 years) who have their need for family planning satisfied with modern methods (NS<sub>Mod</sub>).</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>N</mi>\n        <mi>S</mi>\n      </mrow>\n      <mrow>\n        <mi>M</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msubsup>\n          <mrow>\n            <mi>C</mi>\n            <mi>P</mi>\n          </mrow>\n          <mrow>\n            <mi>M</mi>\n            <mi>o</mi>\n            <mi>d</mi>\n          </mrow>\n          <mrow></mrow>\n        </msubsup>\n      </mrow>\n      <mrow>\n        <mi>U</mi>\n        <mi>M</mi>\n        <mi>N</mi>\n        <msubsup>\n          <mrow>\n            <mo>+</mo>\n            <mi>C</mi>\n            <mi>P</mi>\n          </mrow>\n          <mrow>\n            <mi>A</mi>\n            <mi>n</mi>\n            <mi>y</mi>\n          </mrow>\n          <mrow></mrow>\n        </msubsup>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>For surveys with microdata sets, the indicators are calculated following the definitions and concepts described above. These results are compared with the indicators published in survey reports, SDG national reporting platforms, or obtained from ad hoc queries. In some cases of discrepancies, the results are consulted with the national institutions that conducted the survey.</p>\n<p>For model-based estimates and projections, out-of-sample validation methods are described in Kantorov&#xE1; et al (2020).</p>", "ADJUSTMENT__GLOBAL"=>"<p>Generally, there is no discrepancy between data presented and data published in survey reports. However, some published national data have been adjusted by the Population Division to improve comparability. Notes are used in the data set to indicate when adjustments were made and where data differed from standard definitions. Surveys might differ in the classification of modern and traditional methods. To improve comparability of data over time and across countries, method classifications used in some survey are adjusted to follow the classification described above. </p>\n<p>The global indicator represents all women of reproductive age. Some survey estimates represent women who are married or in a union and this is indicated in a note.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>There is no attempt to provide estimates for individual countries or areas when country or area data are not available.</p>\n<p>For analytical and comparative purposes, country-level model-based estimates and projections are generated using a Bayesian hierarchical model (see references below).</p>\n<ul>\n  <li><strong>At regional and global levels</strong> </li>\n</ul>\n<p>In order to generate regional and global estimates for any given reference year, the Population Division/DESA uses a Bayesian hierarchical model, described in detail in:</p>\n<p>Alkema et al (2013) and Kantorov&#xE1; et al (2020).</p>\n<p>Country-level, model-based estimates are only used for computing regional and global averages and are not used for global SDG reporting of trends at the country level. However, the model-based estimates are recommended to be used for analytical and comparative purposes. Since the model takes into account the relationship of family planning indicators - contraceptive use of any, modern and traditional methods, unmet need for family planning &#x2013; the information from surveys that only provide data on contraceptive use (and have no information on unmet need for family planning) is considered as well. The model is providing estimates of the indicator for countries and years without direct survey data by extrapolating underlying trends determined using data across all countries. The model implicitly weights observations from other countries such that higher weights are given to observations from more similar countries. The fewer the number of observations for the country of interest, the more its estimates are driven by the experience of other countries, whereas for countries with many observations the results are determined to a greater extent by those empirical observations.</p>", "REG_AGG__GLOBAL"=>"<p>The Bayesian hierarchical model is used to generate regional and global estimates and projections of the indicator. Aggregate estimates and projections are weighted averages of the model-based country estimates, using the number of women aged 15-49 for the reference year in each country. The number of women aged 15-49 are taken from United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024. Numbers of women who are married or in a union are taken from United Nations, Department of Economic and Social Affairs, Population Division (2024). Estimates and Projections of Women of Reproductive Age Who Are Married or in a Union: 2022 Revision. New York: United Nations, which are estimates and projections based on data from United Nations, Department of Economic and Social Affairs, Population Division (2019). World Marriage Data 2019. </p>\n<p>Details of the methodology are described in Kantorov&#xE1; et al (2020) and United Nations, Department of Economic and Social Affairs, Population Division (2024).</p>", "DOC_METHOD__GLOBAL"=>"<p>E-Learning video for SDG indicator 3.7.1 on the website of the Population Division (<a href=\"https://www.un.org/development/desa/pd/file/10712\">https://www.un.org/development/desa/pd/file/10712</a>)</p>\n<p>Information on the operational definitions and calculations of family planning indicators from surveys, as well as survey questions and statistical programs needed to derive the indicator, can be found at the website of the USAID Demographic and Health Surveys Program: https://dhsprogram.com/topics/Family-Planning.cfm and the website of UNICEF MICS: <a href=\"https://mics.unicef.org/\">https://mics.unicef.org/</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Detailed guidelines are established for data compilation, data checking, and the production of model-based estimates and projections. Data compilations and model-based estimates and projections of family planning indicators are compliant with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) (http://gather-statement.org/).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data for the percentage of women of reproductive age (15-49 years) who have their need for family planning satisfied with modern methods are available for 143 countries or areas for the 2000-2024 time period. For 113 countries or areas, there are at least two available data points.</p>\n<p>Table 1: The regional breakdown of data availability is as follows:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Region</strong></p>\n      </td>\n      <td>\n        <p><strong>At least one data point</strong></p>\n      </td>\n      <td>\n        <p><strong>At least two data points</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>WORLD</em></p>\n      </td>\n      <td>\n        <p><em>143</em></p>\n      </td>\n      <td>\n        <p><em>113</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Central and Southern Asia</em></p>\n      </td>\n      <td>\n        <p><em>14</em></p>\n      </td>\n      <td>\n        <p><em>10</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Central Asia</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Southern Asia</p>\n      </td>\n      <td>\n        <p>9</p>\n      </td>\n      <td>\n        <p>6</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Eastern and South-Eastern Asia</em></p>\n      </td>\n      <td>\n        <p><em>13</em></p>\n      </td>\n      <td>\n        <p><em>10</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Eastern Asia</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>South-Eastern Asia</p>\n      </td>\n      <td>\n        <p>9</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Europe and Northern America</em></p>\n      </td>\n      <td>\n        <p><em>17</em></p>\n      </td>\n      <td>\n        <p><em>12</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Eastern Europe</p>\n      </td>\n      <td>\n        <p>6</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Northern Europe</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Southern Europe</p>\n      </td>\n      <td>\n        <p>6</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Western Europe</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Northern America</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Latin America and the Caribbean</em></p>\n      </td>\n      <td>\n        <p><em>25</em></p>\n      </td>\n      <td>\n        <p><em>20</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Caribbean</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Central America</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>South America</p>\n      </td>\n      <td>\n        <p>9</p>\n      </td>\n      <td>\n        <p>7</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Northern Africa and Western Asia</em></p>\n      </td>\n      <td>\n        <p><em>18</em></p>\n      </td>\n      <td>\n        <p><em>15</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Northern Africa</p>\n      </td>\n      <td>\n        <p>6</p>\n      </td>\n      <td>\n        <p>6</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Western Asia</p>\n      </td>\n      <td>\n        <p>12</p>\n      </td>\n      <td>\n        <p>9</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Oceania (excluding Australia and New Zealand)</em></p>\n      </td>\n      <td>\n        <p><em>10</em></p>\n      </td>\n      <td>\n        <p><em>6</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Melanesia</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Micronesia</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Polynesia</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Sub-Saharan Africa</em></p>\n      </td>\n      <td>\n        <p><em>46</em></p>\n      </td>\n      <td>\n        <p><em>40</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Eastern Africa</p>\n      </td>\n      <td>\n        <p>16</p>\n      </td>\n      <td>\n        <p>14</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Middle Africa</p>\n      </td>\n      <td>\n        <p>9</p>\n      </td>\n      <td>\n        <p>7</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Southern Africa</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Western Africa</p>\n      </td>\n      <td>\n        <p>16</p>\n      </td>\n      <td>\n        <p>15</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Landlocked developing countries (LLDCs)</em></p>\n      </td>\n      <td>\n        <p><em>32</em></p>\n      </td>\n      <td>\n        <p><em>27</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Least developed countries (LDCs)</em></p>\n      </td>\n      <td>\n        <p><em>44</em></p>\n      </td>\n      <td>\n        <p><em>40</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Small island developing States (SIDS)</em></p>\n      </td>\n      <td>\n        <p><em>28</em></p>\n      </td>\n      <td>\n        <p><em>20</em></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Time series:</strong></p>\n<p>Not applicable</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Age, marital status, geographic location, socioeconomic status and other categories, depending on the data source and number of observations.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Generally, there is no discrepancy between data presented and data published in survey reports. However, some published national data have been adjusted by the Population Division to improve comparability. Notes are used in the data set to indicate when adjustments were made and where data differed from standard definitions. Surveys might differ in the classification of modern and traditional methods. To improve comparability of data over time and across countries, method classifications used in some surveys are adjusted to follow the classification described above.</p>\n<p>The global indicator represents all women of reproductive age. Some survey estimates represent women who are married or in a union and this is indicated in a note. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://www.un.org/development/desa/pd/\">https://www.un.org/development/desa/pd/</a>; <a href=\"https://population.un.org/dataportal/home\">https://population.un.org/dataportal/home</a>; <a href=\"https://www.unfpa.org/data\">https://www.unfpa.org/data</a> </p>\n<p><strong>References:</strong></p>\n<p>Alkema, L., Kantorova, V., Menozzi, C., &amp; Biddlecom, A. (2013). National, regional, and global rates and trends in contraceptive prevalence and unmet need for family planning between 1990 and 2015: a systematic and comprehensive analysis. <em>The Lancet, 381</em>(9878), 1642-1652.</p>\n<p>Bradley, S. E. K., Croft, T. N., Fishel, J. D., &amp; Westoff, C. F. (2012). Revising Unmet Need for Family Planning: DHS Analytical Studies No. 25. ICF International, Calverton, Maryland. <a href=\"http://dhsprogram.com/pubs/pdf/AS25/AS25%5b12June2012%5d.pdf\" target=\"_blank\">http://dhsprogram.com/pubs/pdf/AS25/AS25[12June2012].pdf</a></p>\n<p>Every Woman Every Child (2016). Commitments to Every Woman Every Child&#x2019;s Global Strategy for Women&#x2019;s Children&#x2019;s and Adolescents&#x2019; Health (2016-2030), <a href=\"https://www.everywomaneverychild.org/global-strategy/\">https://www.everywomaneverychild.org/global-strategy/</a></p>\n<p>Every Woman Every Child (2020). United Nations EWEC 2020 Progress Report &#x2013; Protect the Progress: Rise, Refocus, Recover. <a href=\"https://protect.everywomaneverychild.org/\">https://protect.everywomaneverychild.org/</a></p>\n<p>Kantorov&#xE1; V., M. C. Wheldon, P. Ueffing., A. N. Z. Dasgupta (2020). Estimating progress towards meeting women&#x2019;s contraceptive needs in 185 countries: A Bayesian hierarchical modelling study. PLoS Medicine 17(2):e1003026. </p>\n<p>United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024. (<a href=\"https://population.un.org/wpp/\">https://population.un.org/wpp/</a>)</p>\n<p>United Nations, Department of Economic and Social Affairs, Population Division (2019). World Marriage Data 2019. (<a href=\"https://www.un.org/development/desa/pd/data/world-marriage-data\">https://www.un.org/development/desa/pd/data/world-marriage-data</a>) </p>\n<p>United Nations, Department of Economic and Social Affairs, Population Division (2024). Estimates and Projections of Women of Reproductive Age Who Are Married or in a Union: 2024 Revision. New York: United Nations.</p>\n<p>United Nations, Department of Economic and Social Affairs, Population Division (2024). World Contraceptive Use 2024. See also methodology with technical details available at</p>\n<p>(<a href=\"https://www.un.org/development/desa/pd/data/world-contraceptive-use\">https://www.un.org/development/desa/pd/data/world-contraceptive-use</a>) </p>\n<p>United Nations, Department of Economic and Social Affairs, Population Division (2024). Estimates and Projections of Family Planning Indicators 2024. New York: United Nations. (<a href=\"https://www.un.org/development/desa/pd/data/family-planning-indicators\">https://www.un.org/development/desa/pd/data/family-planning-indicators</a>)</p>\n<p>United Nations Department of Economic and Social Affairs, Population Division (2022). World Family Planning 2022: Meeting the changing needs for family planning: Contraceptive use by age and method. (<a href=\"https://www.un.org/development/desa/pd/content/family-planning-0\">https://www.un.org/development/desa/pd/content/family-planning-0</a>)</p>\n<p>United Nations Department of Economic and Social Affairs, Population Division (2020). E-Learning for SDG indicator 3.7.1. (<a href=\"https://www.un.org/development/desa/pd/content/family-planning-0\">https://www.un.org/development/desa/pd/content/family-planning-0</a>) </p>\n<p>United Nations, Department of Economic and Social Affairs, Population Division (2024). World Contraceptive Use 2024 and Estimates and Projections of Family Planning Indicators 2024. Methodology report. UN DESA/POP/2022/DC/NO. 5. ( https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/undesa_pd_2024_methodology-report_world_contraceptive_use.pdf</p>\n<p>)</p>\n<p>World Health Organization Department of Reproductive Health and Research (WHO/RHR) and Johns Hopkins Bloomberg School of Public Health/Center for Communication Programs (CCP), Knowledge for Health Project. Family Planning: A Global Handbook for Providers (2018 update). Baltimore and Geneva: CCP and WHO, 2018. (<a href=\"https://www.who.int/reproductivehealth/publications/fp-global-handbook/en/\">https://www.who.int/reproductivehealth/publications/fp-global-handbook/en/</a>)</p>\n<p>World Health Organization (n.d.). Family planning/contraception methods. <a href=\"https://www.who.int/news-room/fact-sheets/detail/family-planning-contraception\">https://www.who.int/news-room/fact-sheets/detail/family-planning-contraception</a>. Accessed: 27 February2024</p>\n<p>World Health Organization (n.d.). World Health Statistics . <a href=\"https://www.who.int/data/gho/publications/world-health-statistics\">https://www.who.int/data/gho/publications/world-health-statistics</a><u> </u>Accessed: 27 February2024</p>", "indicator_sort_order"=>"03-07-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.7.2", "slug"=>"3-7-2", "name"=>"Tasa de fecundidad de las adolescentes (entre 10 y 14 años y entre 15 y 19 años) por cada 1.000 mujeres de ese grupo de edad", "url"=>"/site/es/3-7-2/", "sort"=>"030702", "goal_number"=>"3", "target_number"=>"3.7", "global"=>{"name"=>"Tasa de fecundidad de las adolescentes (entre 10 y 14 años y entre 15 y 19 años) por cada 1.000 mujeres de ese grupo de edad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Edad", "value"=>"15-19 años"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de fecundidad de las adolescentes (entre 10 y 14 años y entre 15 y 19 años) por cada 1.000 mujeres de ese grupo de edad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de fecundidad de las adolescentes (entre 10 y 14 años y entre 15 y 19 años) por cada 1.000 mujeres de ese grupo de edad", "indicator_number"=>"3.7.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_48/opt_1/ti_estadistica-de-nacimientos/temas.html", "url_text"=>"Estadística de nacimientos", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Tasa de fecundidad de las adolescentes (entre 10 y 14 años y entre 15 y 19 años) por cada 1.000 mujeres de ese grupo de edad", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.7- De aquí a 2030, garantizar el acceso universal a los servicios de salud sexual y reproductiva, incluidos los de planificación familiar, información y educación, y la integración de la salud reproductiva en las estrategias y los programas nacionales", "definicion"=>"\nNacimientos de madres entre 10 y 14 años y entre 15 y 19 años por cada 1.000 mujeres de ese grupo de edad", "formula"=>"\n$$TF_{grupo\\, de\\, edad}^{t} = \\frac{N_{grupo\\, de\\, edad}^{t}}{P_{mujeres\\, grupo\\, de\\, edad}^{t}} \\cdot 1.000$$\n\ndonde:\n\n$N_{grupo\\, de\\, edad}^{t} =$ nacimientos de madres en un grupo de edad en el año $t$\n\n$P_{mujeres\\, grupo\\, de\\, edad}^{t} =$ mujeres en el grupo de edad correspondiente a 1 de julio del año $t$\n", "desagregacion"=>"Grupo de edad: 10-14 años y 15-19 años", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nReducir la fecundidad adolescente y abordar los múltiples factores subyacentes son esenciales para \nmejorar la salud sexual y reproductiva y el bienestar social y económico de los adolescentes. \nExiste un amplio consenso en la literatura sobre el hecho de que las mujeres que quedan embarazadas y \ndan a luz muy temprano en su vida reproductiva están sujetas a mayores riesgos de complicaciones o \nincluso de muerte durante el embarazo y el parto y sus hijos también son más vulnerables. Por lo tanto, \nprevenir los nacimientos muy tempranos en la vida de una mujer es una medida importante para mejorar \nla salud materna y reducir la mortalidad infantil.\n\nAdemás, las mujeres que tienen hijos a una edad temprana experimentan menores oportunidades de progreso \nsocioeconómico, en particular porque las madres jóvenes tienen menos probabilidades de completar su educación\ny, si necesitan trabajar, pueden encontrar especialmente difícil combinar las responsabilidades familiares y laborales. \nLa tasa de natalidad adolescente también proporciona evidencia indirecta sobre el acceso a los servicios de salud \npertinentes, ya que los jóvenes, y en particular las mujeres adolescentes solteras, a menudo experimentan \ndificultades para acceder a los servicios de salud sexual y reproductiva. \n\nFuente: División de Estadísticas de las Naciones Unidas \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.7.2&seriesCode=SP_DYN_ADKL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=10-14%20%7C%20FEMALE\">Tasa de natalidad en adolescentes (por cada 1.000 mujeres de 10 a 14 años) SP_DYN_ADKL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.7.2&seriesCode=SP_DYN_ADKL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-19%20%7C%20FEMALE\">Tasa de natalidad en adolescentes (por cada 1.000 mujeres de 15 a 19 años) SP_DYN_ADKL</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-07-02.pdf\">Metadatos 3-7-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Tasa de fecundidad de las adolescentes (entre 10 y 14 años y entre 15 y 19 años) por cada 1.000 mujeres de ese grupo de edad", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.7- De aquí a 2030, garantizar el acceso universal a los servicios de salud sexual y reproductiva, incluidos los de planificación familiar, información y educación, y la integración de la salud reproductiva en las estrategias y los programas nacionales", "definicion"=>"\nBirths to mothers aged 10-14 years and 15-19 year per 1.000 women in these age groups", "formula"=>"\n$$TF_{age\\, group}^{t} = \\frac{N_{age\\, group}^{t}}{P_{women\\, age\\, group}^{t}} \\cdot 1.000$$\n\nwhere:\n\n$N_{age\\, group}^{t} =$ births to mothers in age group in year $t$\n\n$P_{women\\, age\\, group}^{t} =$ women in age group as of 1 July in year $t$\n", "desagregacion"=>"Age group: 10-14 years and 15-19 years", "periodicidad"=>"Anual", "observaciones"=>nil, "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nReducing adolescent fertility and addressing the multiple factors underlying it are essential \nfor improving sexual and reproductive health and the social and economic well-being of adolescents. \nThere is substantial agreement in the literature that women who become pregnant and give birth \nvery early in their reproductive lives are subject to higher risks of complications or even death \nduring pregnancy and birth and their children are also more vulnerable. Therefore, preventing births \nvery early in a woman’s life is an important measure to improve maternal health and reduce infant \nmortality.\n\nFurthermore, women having children at an early age experience reduced opportunities for socioeconomic \nadvancement, particularly because young mothers are less likely to complete their education and, if they \nneed to work, may find it especially difficult to combine family and work responsibilities. The \nadolescent birth rate also provides indirect evidence on access to pertinent health services since young \npeople, and in particular unmarried adolescent women, often experience difficulties in access to sexual \nand reproductive health services. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.7.2&seriesCode=SP_DYN_ADKL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=10-14%20%7C%20FEMALE\">Adolescent birth rate (per 1,000 women aged 10-14 years) SP_DYN_ADKL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.7.2&seriesCode=SP_DYN_ADKL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-19%20%7C%20FEMALE\">Adolescent birth rate (per 1,000 women aged 15-19) SP_DYN_ADKL</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-07-02.pdf\">Metadata 3-7-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Tasa de fecundidad de las adolescentes (entre 10 y 14 años y entre 15 y 19 años) por cada 1.000 mujeres de ese grupo de edad", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.7- De aquí a 2030, garantizar el acceso universal a los servicios de salud sexual y reproductiva, incluidos los de planificación familiar, información y educación, y la integración de la salud reproductiva en las estrategias y los programas nacionales", "definicion"=>"\n10 eta 14 urte bitartean eta 15 eta 19 urte bitartean ama izandakoak, adin-talde horretako 1.000 emakumeko  ", "formula"=>"\n$$TF_{adin-taldea}^{t} = \\frac{N_{adin-taldea}^{t}}{P_{emakumeak\\, adin-taldea}^{t}} \\cdot 1.000$$\n\nnon:\n\n$N_{adin-taldea}^{t} =$ adin-talde batean ama izandakoak $t$ urtean\n\n$P_{emakumeak\\, adin-taldea}^{t} =$ adin-talde bateko emakumeak $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Adin-taldea: 10-14 urte; 15-19 urte", "periodicidad"=>"Anual", "observaciones"=>nil, "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nNerabeen ugalkortasuna murriztea eta horren azpian dauden askotariko faktoreei heltzea funtsezkoa da nerabeen \nsexu- eta ugalketa-osasuna eta ongizate sozial eta ekonomikoa hobetzeko. Literaturan oro har onartuta dago \nugalketa-bizitzan oso goiz haurdun geratzen diren eta erditzen diren emakumeek haurdunaldian eta erditzean \nkonplikazioak izateko edo hiltzeko arrisku handiagoa dutela eta beren seme-alabak ere ahulagoak direla. Beraz, \nemakume baten bizitzan jaiotza oso goiztiarrak prebenitzea neurri garrantzitsua da amaren osasuna hobetzeko eta \nhaurren heriotza-tasa murrizteko. \n\nGainera, adin goiztiarrean seme-alabak dituzten emakumeek aurrerapen sozioekonomikorako aukera gutxiago izaten dituzte, \nbereziki ama gazteek beren hezkuntza osatzeko aukera gutxiago dituztelako eta, lan egin behar badute, bereziki zaila \nizan daitekeelako familiako eta laneko erantzukizunak uztartzea. Nerabeen jaiotza-tasak ere zeharkako ebidentzia ematen \ndu osasun-zerbitzu egokien sarbideari buruz; izan ere, gazteek, eta, bereziki, emakume nerabe ezkongabeek askotan \nzailtasunak izaten dituzte sexu- eta ugalketa-osasuneko zerbitzuak eskuratzeko. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.7.2&seriesCode=SP_DYN_ADKL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=10-14%20%7C%20FEMALE\">Jaiotza-tasa nerabeetan (10-14 urteko 1.000 emakumeko) SP_DYN_ADKL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.7.2&seriesCode=SP_DYN_ADKL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-19%20%7C%20FEMALE\">Jaiotza-tasa nerabeetan (15-19 urteko 1.000 emakumeko) SP_DYN_ADKL</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-07-02.pdf\">Metadatuak 3-7-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.7: By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.7.2: Adolescent birth rate (aged 10&#x2013;14 years; aged 15&#x2013;19 years) per 1,000 women in that age group</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SP_DYN_ADKL - Adolescent birth rate (per 1,000 women aged 15-19 and 10-14 years) [3.7.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator is linked to Target 5.6 (Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferences) because reductions in adolescent childbearing that are brought about by increasing access to sexual and reproductive health-care services are also reflective of improvements in sexual and reproductive health and the exercise of reproductive rights per se. This indicator is linked to Target 17.19 (By 2030 build on existing initiatives to develop measurements of progress on sustainable development that complement gross domestic product and support statistical capacity-building in developing countries) because the estimates of the adolescent birth rate are based in part on birth registration and census data. Strengthened civil registration and vital statistics systems reaching universal coverage of birth registration and conducting at least one census every 10 years are directly relevant for measuring progress on Target 3.7.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Population Division, Department of Economic and Social Affairs (DESA) </p>\n<p>United Nations Population Fund (UNFPA)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Population Division, Department of Economic and Social Affairs (DESA) </p>\n<p>United Nations Population Fund (UNFPA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition: </strong></p>\n<p>Annual number of births to females aged 10-14 or 15-19 years per 1,000 females in the respective age group. </p>\n<p><strong>Concepts: </strong></p>\n<p>The adolescent birth rate represents the level of childbearing among females in the particular age group. The adolescent birth rate among women aged 15-19 years is also referred to as the age-specific fertility rate for women aged 15-19.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Annual number of births to females aged 10-14 or 15-19 years per 1,000 females in the respective age group.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not Applicable.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Civil registration is the preferred data source. Census and household survey are alternate sources when there is no reliable civil registration. </p>\n<p>Data on births by age of mother are obtained from civil registration systems covering 90 percent or more of all live births, supplemented eventually by census or survey estimates for periods when registration data are not available. For the numerator, the figures reported by National Statistical Offices (NSOs) to the United Nations Statistics Division (UNSD) have first priority. When they are not available or present problems, use is made of data from the regional statistical units or directly from NSOs. For the denominator, first priority is given to the latest revision of World Population Prospects (WPP) produced by the Population Division, Department of Economic and Social Affairs, United Nations. In cases where the numerator does not cover the complete de facto population, an alternative appropriate population estimate is used if available. When either the numerator or denominator is missing, the direct estimate of the rate produced by the NSO is used. Information on sources is provided at the cell level. When the numerator and denominator come from two different sources, they are listed in that order. </p>\n<p>In countries and areas lacking a civil registration system or where the coverage of that system is lower than 90 percent of all live births, the adolescent birth rate is obtained from household survey data and census data. Registration data regarded as less than 90 percent complete are exceptionally used for countries and areas where the alternative sources present problems of compatibility and registration data can provide an assessment of trends. In countries and areas with multiple survey programmes, large sample surveys conducted on an annual or biennial basis are given precedence when they exist. </p>\n<p>For information on the source of each estimate, see United Nations, Department of Economic and Social Affairs, Population Division: DemoData: Data Browser (online database of empirical demographic data and selected tabulations), <a href=\"https://popdiv.dfs.un.org/demodata/web/#!\">https://popdiv.dfs.un.org/demodata/web/#!#%2Fhome</a>. </p>", "COLL_METHOD__GLOBAL"=>"<p>For civil registration data, data on births or the adolescent birth rate are obtained from country-reported data from the United Nations Statistics Division or regional Statistics Divisions or statistical units (ESCWA, ESCAP, CARICOM, SPC). The population figures are obtained from the last revision of the United Nations Population Division World Population Prospects and only exceptionally from other sources. Survey data are obtained from national household surveys that are internationally coordinated&#x2014;such as the Demographic and Health Surveys (DHS), the Reproductive Health Surveys (RHS), and the Multiple Indicator Cluster Surveys (MICS)&#x2014;and other nationally-sponsored surveys. Other national surveys conducted as part of the European Fertility and Family Surveys (FFS), or the Pan-Arab Project for Family Health (PAPFAM) may be considered as well. The data are taken from published survey reports or, in exceptional cases, other published analytical reports. Whenever the estimates are available in the survey report, they are directly taken from it. If clarification is needed, contact is made with the survey sponsors or authoring organization, which occasionally may supply corrected or adjusted estimates in response. In other cases, if microdata is available, estimates are produced by the Population Division based on national data. </p>\n<p>For census data, the estimates are preferably obtained directly from census reports. In such cases, adjusted rates are only used when reported by the National Statistical Office (NSO). In other cases, the adolescent birth rate is computed from tables on births in the preceding 12 months by age of mother, and census population distribution by sex and age. </p>\n<p>In addition to obtaining data and estimates directly from the websites of NSOs, the following databases and websites are utilized: the DHS (<a href=\"http://api.dhsprogram.com/#/index.html\">http://api.dhsprogram.com/#/index.html</a>), Demographic Yearbook database of the Statistics Division of the Department of Economic and Social Affairs (DESA) of the United Nations Secretariat (<a href=\"http://data.un.org/\">http://data.un.org/</a>), internal databases of the Population Division of DESA of the United Nations Secretariat (see latest public release here: <a href=\"https://population.un.org/wpp/assets/Files/WPP2024_Data_Sources.pdf\">https://population.un.org/wpp/assets/Files/WPP2024_Data_Sources.pdf</a>), Eurostat (<a href=\"https://ec.europa.eu/eurostat/data/database\">https://ec.europa.eu/eurostat/data/database</a>), the Human Fertility Database (<a href=\"http://www.humanfertility.org\">http://www.humanfertility.org</a>), the Human Fertility Collection (<a href=\"http://www.fertilitydata.org\">http://www.fertilitydata.org</a>), and the MICS (<a href=\"http://mics.unicef.org/\">http://mics.unicef.org/</a>). Survey databases (e.g., the Integrated Household Survey Network (IHSN) database) are also consulted in addition to searches for data on websites of National Statistical Offices and ad hoc queries.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are compiled and updated on a regular basis.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Updated data on the adolescent birth rate for SDG monitoring are released by the Population Division annually. The next release is expected in 2026.</p>", "DATA_SOURCE__GLOBAL"=>"<p>For civil registration data, data on births or the adolescent birth rate are obtained from country-reported data from the United Nations Statistics Division (UNSD) or regional Statistics Divisions or statistical units (ESCWA, ESCAP, CARICOM, SPC). The population figures are obtained from the last revision of the United Nations Population Division World Population Prospects and only exceptionally from other sources. Survey data are obtained from national household surveys that are internationally coordinated&#x2014;such as the Demographic and Health Surveys (DHS), the Reproductive Health Surveys (RHS), and the Multiple Indicator Cluster Surveys (MICS)&#x2014;and other nationally-sponsored surveys. Data from censuses are obtained from country-reported data from the UNSD or regional Statistics Divisions or statistical units (ESCWA, ESCAP, CARICOM, SPC) or directly from census reports.</p>", "COMPILING_ORG__GLOBAL"=>"<p>This indicator is produced at the global level by the Population Division, Department of Economic and Social Affairs, United Nations in collaboration with the United Nations Population Fund (UNFPA).</p>", "INST_MANDATE__GLOBAL"=>"<p>The Population Division of the Department of Economic and Social Affairs provides the international community with timely and accessible population data and analysis of population trends and development outcomes for all countries and areas of the world. It is the custodian agency for Sustainable Development Goal (SDG) Indicator 3.7.2.</p>", "RATIONALE__GLOBAL"=>"<p>Reducing adolescent fertility and addressing the multiple factors underlying it are essential for improving sexual and reproductive health and the social and economic well-being of adolescents. There is substantial agreement in the literature that women who become pregnant and give birth very early in their reproductive lives are subject to higher risks of complications or even death during pregnancy and birth and their children are also more vulnerable. Therefore, preventing births very early in a woman&#x2019;s life is an important measure to improve maternal health and reduce infant mortality. Furthermore, women having children at an early age experience reduced opportunities for socio- economic advancement, particularly because young mothers are less likely to complete their education and, if they need to work, may find it especially difficult to combine family and work responsibilities. The adolescent birth rate also provides indirect evidence on access to pertinent health services since young people, and in particular unmarried adolescent women, often experience difficulties in access to sexual and reproductive health services.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Discrepancies between estimates obtained from different national data are common. For civil registration, rates are subject to limitations which depend on the completeness of birth registration, the treatment of infants born alive but die before registration or within the first 24 hours of life, the quality of the reported information relating to age of the mother, and the inclusion of births from previous periods. The population estimates may suffer from limitations connected to age misreporting and coverage. </p>\n<p>For survey and census data, both the numerator and denominator come from the same population. The main limitations concern age misreporting, the omission of births, misreporting the date of birth of the child, and, in the case of surveys, sampling size and variability. </p>\n<p>With respect to estimates of the adolescent birth rate among females aged 10-14 years, comparative evidence suggests that a very small proportion of births in this age group occur to females below age 12. Other evidence based on retrospective birth history data from surveys indicates that women aged 15-19 years are less likely to report first births before age 15 than women from the same birth cohort when asked five years later at ages 20&#x2013;24 years. </p>\n<p>The adolescent birth rate is commonly reported as the age-specific fertility rate for ages 15-19 years in the context of calculation of total fertility estimates. It has also been called adolescent fertility rate. A related measure is the proportion of adolescent fertility measured as the percentage of total fertility contributed by women aged 15-19.</p>", "DATA_COMP__GLOBAL"=>"<p>The adolescent birth rate is computed as a ratio. The numerator is the number of live births to women aged 15-19 years, and the denominator an estimate of exposure to childbearing by women aged 15-19 years. The computation is the same for the age group 10-14 years. The numerator and the denominator are calculated differently for civil registration, survey and census data.</p>\n<p>Computation formula: Adolescent Birth Rate (15-19) = (number of births to women ages 15-19/mid-year population of women ages 15-19) * 1,000 </p>\n<p>In the case of civil registration data, the numerator is the registered number of live births born to women aged 15-19 years during a given year, and the denominator is the estimated or enumerated population of women aged 15-19 years.</p>\n<p>In the case of survey data, the numerator is the number of live births obtained from retrospective birth histories of the interviewed women who were 15-19 years of age at the time of the births during a reference period before the interview, and the denominator is person-years lived between the ages of 15 and 19 years by the interviewed women during the same reference period. The reported observation year corresponds to the middle of the reference period. For some surveys without data on retrospective birth histories, computation of the adolescent birth rate is based on the date of last birth or the number of births in the 12 months preceding the survey. </p>\n<p>With census data, the adolescent birth rate is computed on the basis of the date of last birth or the number of births in the 12 months preceding the enumeration. The census provides both the numerator and the denominator for the rates. In some cases, the rates based on censuses are adjusted for under- registration based on indirect methods of estimation. For some countries and areas with no other reliable data, the own-children method of indirect estimation provides estimates of the adolescent birth rate for a number of years before the census.</p>\n<p>Whenever data are available, adolescent fertility at ages 10-14 years are also computed. </p>\n<p>For a thorough treatment of the different methods of computation, see Handbook on the Collection of Fertility and Mortality Data, United Nations Publication, Sales No. E.03.XVII.11, (<a href=\"https://unstats.un.org/unsd/demographic/standmeth/handbooks/Handbook_Fertility_Mortality.pdf\">https://unstats.un.org/unsd/demographic/standmeth/handbooks/Handbook_Fertility_Mortality.pdf</a>). In direct methods of estimation are analyzed in Manual X: Indirect Techniques for Demographic Estimation, United Nations Publication, Sales No. E.83.XIII.2. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The Population Division maintains an online database on of empirical demographic data and selected tabulations (including fertility rates) from different sources including estimates produced by National Statistical Offices (NSOs) and regional statistical units. Data since the last round of SDG reporting are updated from various sources on a regular basis. Newly available demographic data are subjected to quality analyses and evaluated by examining the consistency in the patterns, levels and trends of the data within and across countries and regions. The Population Division has often reached out to the United Population Fund (UNFPA) to request its country offices to assist in obtaining data that might be available but not yet published. Also, through the UNFPA, data that the Population Division deems questionable are verified by the NSO or other relevant government agency. The process of data compilation and selection for SDG reporting is available at: <a href=\"https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/unpd-egm-fer-2020-10-backgroundpaper_newtitle_final_oct9.pdf\">https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/unpd-egm-fer-2020-10-backgroundpaper_newtitle_final_oct9.pdf</a>. </p>", "ADJUSTMENT__GLOBAL"=>"<p>When data are available from civil registration systems covering 90 percent or more of all live births, the adolescent birth rate is calculated by dividing the annual number of live births to females aged 19 years or younger (10-14 and 15-19 age groups) by the female population in the pertinent age group taken from the latest revision of World Population Prospects produced by the Population Division. The adolescent birth rate from other sources is not adjusted.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>There is no attempt to provide estimates for individual countries and areas when country or area data are not available.</p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p>The regional or global aggregates of the adolescent birth rate for the age group 15-19 years are from the latest revision of World Population Prospects produced by the Population Division. In cases where data are missing or assessed as unreliable, estimates for individual countries and areas are generated either through expert-based opinion, reviewing and weighting each observation analytically, or, in more recent years, using automated statistical methods, or by using a bias-adjusted data model to control for systematic biases between different types of data. See United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024: Methodology of the United Nations population estimates and projections (UN DESA/POP/2024/DC/NO. 10), available at: <a href=\"https://population.un.org/wpp/assets/Files/WPP2024_Methodology-Report_Final.pdf\">https://population.un.org/wpp/assets/Files/WPP2024_Methodology-Report_Final.pdf</a>. </p>", "REG_AGG__GLOBAL"=>"<p>The adolescent birth rates reported for global and regional aggregates are based on the average of estimated adolescent birth rates by one-year time interval published in United Nations, Department of Economic and Social Affairs, Population Division (2024), World Population Prospects 2024 (<a href=\"https://population.un.org/wpp/\">https://population.un.org/wpp/</a>) </p>\n<p>The age-specific fertility rates for global and regional aggregates from World Population Prospects (WPP) are based on population reconstruction at the country level and provide a best estimate based on all the available demographic information. WPP considers potentially as many types and sources of empirical estimates as possible (including retrospective birth histories, direct and indirect fertility estimates), and the final estimates are derived to ensure as much internal consistency as possible with all other demographic components and intercensal cohorts enumerated in successive censuses.</p>", "DOC_METHOD__GLOBAL"=>"<p>Handbook on the Collection of Fertility and Mortality Data, United Nations Publication (ST/ESA/STAT/SER.F/92), available at: <a href=\"https://unstats.un.org/unsd/demographic/standmeth/handbooks/Handbook_Fertility_Mortality.pdf\">https://unstats.un.org/unsd/demographic/standmeth/handbooks/Handbook_Fertility_Mortality.pdf</a>. </p>\n<p>Manual X: Indirect Techniques for Demographic Estimation, United Nations Publication, Sales No. E.83.XIII.2, available at: <a href=\"https://www.un.org/en/development/desa/population/publications/pdf/mortality/Manual_X.pdf\">https://www.un.org/en/development/desa/population/publications/pdf/mortality/Manual_X.pdf</a>.</p>\n<p>Indicator and Monitoring Framework for the Global Strategy for Women&#x2019;s, Children&#x2019;s and Adolescents&#x2019; Health (2016-2030), available at: <a href=\"https://platform.who.int/docs/default-source/mca-documents/rmncah/global-strategy/gs-indicator-and-monitoring-framework.pdf\">https://platform.who.int/docs/default-source/mca-documents/rmncah/global-strategy/gs-indicator-and-monitoring-framework.pdf</a>.</p>\n<p>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The Population Division maintains an online database of empirical demographic data and selected tabulations (including fertility rates) from different sources including estimates produced by National Statistical Offices and regional statistical units. Data since the last round of SDG reporting are continuously updated from various sources: DemoData: Data Browser (online database of empirical demographic data and selected tabulations), <a href=\"https://popdiv.dfs.un.org/demodata/web/#!\">https://popdiv.dfs.un.org/demodata/web/#!#%2Fhome</a>.</p>\n<p>With each revision of World Population Prospects (WPP), the Population Division carries out a re-estimation of historical demographic trends for countries and areas of the world. These demographic estimates are based on the most recently available data sources, such as censuses, demographic surveys, registries of vital events, population registers and various other sources. Newly available demographic data and information are subjected to quality analyses and evaluated by examining the consistency in the patterns, levels and trends of the data across countries and regions. The description of the guidelines for managing the quality of the data and process is available at: <a href=\"https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/unpd-egm-fer-2020-10-backgroundpaper_newtitle_final_oct9.pdf\">https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/unpd-egm-fer-2020-10-backgroundpaper_newtitle_final_oct9.pdf</a>;</p>\n<p> <a href=\"https://population.un.org/wpp/assets/Files/WPP2024_Methodology-Report_Final.pdf\">https://population.un.org/wpp/assets/Files/WPP2024_Methodology-Report_Final.pdf</a>. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>See 4.d and 4.i.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See 4.d and 4.i.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability: </strong></p>\n<p>Data selected for the adolescent birth rate for women aged 10-14 years and 15-19 years are available for 223 and 230 out of 237 countries and areas, respectively, for the 2000-2024 time period. </p>\n<p>In the 10-14 age group, 14 countries and areas have no data: three in Latin America and the Caribbean (British Virgin Islands, Falkland Islands, and Sint Maarten), three in Europe and Northern America (Holy See, Isle of Man, and Monaco), three in Oceania (Solomon Islands, Tokelau, and Vanuatu), two in Northern Africa and Western Asia (Syrian Arab Republic, and Western Sahara), two in Sub-Sharan Africa (Equatorial Guinea and Saint Helena) and one in Eastern and South-Eastern Asia (China). For this age group, only four countries have just one data point: one in Europe and Northern America (Channel Islands &#x2013; Guernsey), one in Eastern and South-Eastern Asia (Democratic Republic of Korea), one in Western Asia and Northern Africa (Lebanon), and one in Latin America and the Caribbean (Saint Kitts and Nevis). </p>\n<p>In the 15-19 age group, seven countries and areas have no data: three in Europe and Northern America (Holy See, Isle of Man, Monaco), two in Latin America and the Caribbean (Falkland Islands and Sint Maarten), one in Northern Africa and Western Asia (Western Sahara), and one in Sub-Saharan Africa (Saint Helena). Among the 230 countries and areas with available data for this age group, only three have just one data point: one in Oceania (Tokelau), one in Europe and Northern America (Channel Islands &#x2013; Guernsey), and one in Western Asia and Northern Africa (Lebanon). </p>\n<p>Table 1: The regional breakdown of data availability is as follows:</p>\n<table>\n  <tbody>\n    <tr>\n      <td></td>\n      <td colspan=\"2\">\n        <p><strong>At least one data point between 2000 and 2024</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>World and SDG regions</strong> </p>\n      </td>\n      <td>\n        <p><strong>ABR women aged 10-14 years</strong> </p>\n      </td>\n      <td>\n        <p><strong>ABR women aged 15-19 years</strong> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>WORLD </p>\n      </td>\n      <td>\n        <p>223</p>\n      </td>\n      <td>\n        <p>230</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Europe and Northern America </p>\n      </td>\n      <td>\n        <p>52</p>\n      </td>\n      <td>\n        <p>52</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Latin America and the Caribbean </p>\n      </td>\n      <td>\n        <p>47</p>\n      </td>\n      <td>\n        <p>48</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Central Asia and Southern Asia </p>\n      </td>\n      <td>\n        <p>14</p>\n      </td>\n      <td>\n        <p>14</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Eastern Asia and South-eastern Asia </p>\n      </td>\n      <td>\n        <p>18</p>\n      </td>\n      <td>\n        <p>19</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Northern Africa and Western Asia </p>\n      </td>\n      <td>\n        <p>23</p>\n      </td>\n      <td>\n        <p>24</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Sub-Saharan Africa </p>\n      </td>\n      <td>\n        <p>49</p>\n      </td>\n      <td>\n        <p>50</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Oceania excluding Australia and New Zealand </p>\n      </td>\n      <td>\n        <p>18</p>\n      </td>\n      <td>\n        <p>21</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Australia and New Zealand </p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Landlocked developing countries (LLDCs) </p>\n      </td>\n      <td>\n        <p>32</p>\n      </td>\n      <td>\n        <p>32</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Least Developed Countries (LDCs) </p>\n      </td>\n      <td>\n        <p>43</p>\n      </td>\n      <td>\n        <p>44</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Small island developing States (SIDS) </p>\n      </td>\n      <td>\n        <p>54</p>\n      </td>\n      <td>\n        <p>57</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><br></p>\n<p><strong>Time series: </strong></p>\n<p>See Table 1 above. </p>\n<p><strong>Disaggregation: </strong></p>\n<p>Age, education, number of living children, marital status, socioeconomic status, geographic location and other categories, depending on the data source and number of observations.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies: </strong></p>\n<p>Estimates based on civil registration are only provided when the country reports at least 90 percent coverage and when there is reasonable agreement between civil registration estimates and survey estimates. Small discrepancies might arise due to different denominators or the inclusion of births to women under 15 years of age. Survey estimates are only provided when there is no reliable civil registration. There might be discrepancies on the dating and the actual figure if a different reference period is being used. In particular, many surveys report rates both for a three-year and a five-year reference period. For countries and areas where data are scarce, reference periods located more than five years before the survey might be used.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://www.un.org/development/desa/pd/\">https://www.un.org/development/desa/pd/</a>;</p>\n<p><a href=\"https://www.unfpa.org/data\">https://www.unfpa.org/data</a>; </p>\n<p>Expert group meeting on the evaluation of adolescent fertility data and estimates, available at: <a href=\"https://www.un.org/development/desa/pd/events/EGM-on-the-evaluation-of-adolescent-fertility-data-estimates-for-SDG-reporting\">https://www.un.org/development/desa/pd/events/EGM-on-the-evaluation-of-adolescent-fertility-data-estimates-for-SDG-reporting</a></p>\n<p><strong>References: </strong></p>\n<p>United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024: Methodology of the United Nations population estimates and projections (UN DESA/POP/2024/DC/NO. 10), available at: <a href=\"https://population.un.org/wpp/assets/Files/WPP2024_Methodology-Report_Final.pdf\">https://population.un.org/wpp/assets/Files/WPP2024_Methodology-Report_Final.pdf</a>.</p>\n<p>United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, available at <a href=\"https://population.un.org/wpp/\">https://population.un.org/wpp/</a>. </p>\n<p>Handbook on the Collection of Fertility and Mortality Data, United Nations Publication (ST/ESA/STAT/SER.F/92), available at: <a href=\"https://unstats.un.org/unsd/demographic/standmeth/handbooks/Handbook_Fertility_Mortality.pdf\">https://unstats.un.org/unsd/demographic/standmeth/handbooks/Handbook_Fertility_Mortality.pdf</a>.</p>\n<p>Manual X: Indirect Techniques for Demographic Estimation, United Nations Publication, Sales No. E.83.XIII.2, available at: <a href=\"https://www.un.org/en/development/desa/population/publications/pdf/mortality/Manual_X.pdf\">https://www.un.org/en/development/desa/population/publications/pdf/mortality/Manual_X.pdf</a></p>\n<p>Indicator and Monitoring Framework for the Global Strategy for Women&#x2019;s, Children&#x2019;s and Adolescents&#x2019; Health (2016-2030), available at: <a href=\"https://platform.who.int/docs/default-source/mca-documents/rmncah/global-strategy/gs-indicator-and-monitoring-framework.pdf\">https://platform.who.int/docs/default-source/mca-documents/rmncah/global-strategy/gs-indicator-and-monitoring-framework.pdf</a>. </p>", "indicator_sort_order"=>"03-07-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.8.1", "slug"=>"3-8-1", "name"=>"Cobertura de los servicios de salud esenciales", "url"=>"/site/es/3-8-1/", "sort"=>"030801", "goal_number"=>"3", "target_number"=>"3.8", "global"=>{"name"=>"Cobertura de los servicios de salud esenciales"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[{"unit"=>"Porcentaje", "minimum"=>0, "maximum"=>10}], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de personas de 16 y más años con necesidad insatisfecha de atención médica", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Cobertura de los servicios de salud esenciales", "indicator_number"=>"3.8.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Anual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176807&menu=ultiDatos&idp=1254735976608", "url_text"=>"Encuesta de condiciones de vida", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de personas de 16 y más años con necesidad insatisfecha de atención médica", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.8- Lograr la cobertura sanitaria universal, incluida la protección contra los riesgos financieros, el acceso a servicios de salud esenciales de calidad y el acceso a medicamentos y vacunas inocuos, eficaces, asequibles y de calidad para todos", "definicion"=>"Proporción de personas de 16 años y más que informa sobre necesidades insatisfechas de atención médica debido a alguna de  las siguientes razones: Razones financieras, lista de espera y demasiado lejos para viajar", "formula"=>"\n$$PPNIAM_{16+}^{t} = \\frac{PNIAM_{16+}^{t}}{P_{16+}^{t}} \\cdot 100$$\n\ndonde:\n\n$PNIAM_{16+}^{t} =$ población de 16 y más años que indica necesidades insatisfechas de atención médica en el año $t$ \n$P_{16+}^{t} =$ población de 16 y más años en el año $t$ \n", "desagregacion"=>"Sexo", "periodicidad"=>"Anual", "observaciones"=>"\nLas necesidades insatisfechas autoinformadas se refieren a la propia evaluación de una persona  sobre si necesitaba un examen o tratamiento médico (excluido el cuidado dental), pero no lo tuvo  o no lo buscó.", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl indicador mide la cobertura de servicios de salud esenciales (definida como la cobertura promedio de servicios esenciales \nbasada en intervenciones trazadoras que incluyen salud reproductiva, materna, neonatal \ne infantil, enfermedades infecciosas, enfermedades no transmisibles y capacidad y acceso a los servicios, \nentre la población general y la más desfavorecida).\n\nLa meta 3.8 se define como “Lograr la cobertura sanitaria universal, incluida la protección contra los riesgos financieros, \nel acceso a servicios esenciales de salud de calidad y el acceso a medicamentos y vacunas esenciales seguros, eficaces, \nde calidad y asequibles para todos”. El objetivo es que todas las personas y comunidades reciban los servicios de \nsalud de calidad que necesitan (incluidos medicamentos y otros productos sanitarios), sin dificultades financieras.\n\nSe han elegido dos indicadores para hacer el seguimiento de la meta 3.8 en el marco de los ODS. El indicador 3.8.1 es para\nla cobertura de servicios de salud y el indicador 3.8.2 se centra en los gastos de salud en relación con el\npresupuesto de un hogar para identificar las dificultades financieras causadas por los pagos directos de atención médica. \nEn conjunto, los indicadores 3.8.1 y 3.8.2 tienen por objeto captar las dimensiones de cobertura de servicios y \nprotección financiera, respectivamente, de la meta 3.8. Estos dos indicadores siempre deben monitorearse conjuntamente.\n\nLos países proporcionan muchos servicios esenciales para la protección, promoción, prevención, tratamiento y atención de \nla salud. Los indicadores de cobertura de servicios (definidos como las personas que reciben el servicio que necesitan) \nson la mejor manera de seguir el progreso en la prestación de servicios en el marco de la cobertura sanitaria universal (CSU).\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.8.1&seriesCode=SH_ACS_UNHC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Índice de cobertura de servicios de cobertura sanitaria universal (CSU) SH_ACS_UNHC</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas, pero aporta información complementaria.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf\">Metadatos 3-8-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de personas de 16 y más años con necesidad insatisfecha de atención médica", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.8- Lograr la cobertura sanitaria universal, incluida la protección contra los riesgos financieros, el acceso a servicios de salud esenciales de calidad y el acceso a medicamentos y vacunas inocuos, eficaces, asequibles y de calidad para todos", "definicion"=>"Proportion of people aged 16 and over who report having unsatisfied medical care needs for any of  the following reasons: Financial reasons, waiting lists and too far to travel", "formula"=>"\n$$PPNIAM_{16+}^{t} = \\frac{PNIAM_{16+}^{t}}{P_{16+}^{t}} \\cdot 100$$\n\nwhere:\n\n$PNIAM_{16+}^{t} =$ people aged 16 and over who report having unsatisfied medical care needs in year $t$ \n$P_{16+}^{t} =$ people aged 16 and over in year $t$\n", "desagregacion"=>"Sex", "periodicidad"=>"Anual", "observaciones"=>"\nSelf-reported unsatisfied needs refer to one individual’s own assessment as to whether he or she needed  a medical examination or treatment (excluding dental care), but did not receive or seek it.", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe indicator measures coverage of essential health services (defined as the average coverage of essential \nservices based on tracer interventions that include reproductive, maternal, newborn and child health, \ninfectious diseases, non-communicable diseases and service capacity and access, among the general and the most \ndisadvantaged population).\n\nTarget 3.8 is defined as “Achieve universal health coverage, including financial risk protection, access to \nquality essential health-care services and access to safe, effective, quality and affordable essential \nmedicines and vaccines for all”. The objective is for all people and communities to receive the quality \nhealth services they need (including medicines and other health products), without financial hardship. \n\nTwo indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1 is for \nhealth service coverage and indicator 3.8.2 focuses on health expenditures in relation to a household’s \nbudget to identify financial hardship caused by direct health care payments. Taken together, indicators \n3.8.1 and 3.8.2 are meant to capture the service coverage and financial protection dimensions, \nrespectively, of target 3.8. These two indicators should be always monitored jointly. \n\nCountries provide many essential services for health protection, promotion, prevention, treatment and \ncare. Indicators of service coverage – defined as people receiving the service they need – are the best \nway to track progress in providing services under universal health coverage (UHC). \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.8.1&seriesCode=SH_ACS_UNHC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Universal health coverage (UHC) service coverage index SH_ACS_UNHC</a> UNSTATS", "comparabilidad"=>"The available indicator does not comply with the metadata of the United Nations indicator, but provides complementary information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf\">Metadata 3-8-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Proporción de personas de 16 y más años con necesidad insatisfecha de atención médica", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.8- Lograr la cobertura sanitaria universal, incluida la protección contra los riesgos financieros, el acceso a servicios de salud esenciales de calidad y el acceso a medicamentos y vacunas inocuos, eficaces, asequibles y de calidad para todos", "definicion"=>"Ondoko arrazoiren bategatik arreta medikoaren premiak ase gabe dituztela dioten 16 urteko eta gehiagoko pertsonen proportzioa:  Finantza-arrazoiak, itxaron-zerrenda eta bidaiatzeko urrunegi egotea", "formula"=>"\n$$PPNIAM_{16+}^{t} = \\frac{PNIAM_{16+}^{t}}{P_{16+}^{t}} \\cdot 100$$\n\nnon:\n\n$PNIAM_{16+}^{t} =$ arreta medikoaren premiak ase gabe dituztela dioten 16 urteko eta gehiagoko pertsonak $t$ urtean \n\n$P_{16+}^{t} =$ 16 urteko eta gehiagoko biztanleria $t$ urtean \n", "desagregacion"=>"Sexua", "periodicidad"=>"Anual", "observaciones"=>"\nAse gabeko behar autoinformatuak esan nahi du pertsonak berak ebaluatzen duela azterketa edo  tratamendu medikurik behar duen (hortzen zainketa izan ezik), baina ez duen lortu edo ez duen bilatu ", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAdierazleak oinarrizko osasun-zerbitzuen estaldura neurtzen du (oinarrizko zerbitzuen batez besteko estaldura \ngisa definitzen da, ugalketa-osasuna, amen, jaioberrien eta haurren osasuna, gaixotasun infekziosoak, gaixotasun \nez-kutsakorrak eta zerbitzuen gaitasuna eta sarbidea barne hartzen dituzten esku-hartze trazatzaileetan oinarrituta, \nbiztanleria orokorraren eta behartsuenaren artean). \n\n3.8 xedea honela definitzen da: \"Osasun-estaldura unibertsala lortzea, finantza-arriskuen aurkako babesa, kalitatezko \noinarrizko osasun-zerbitzuetarako sarbidea eta oinarrizko sendagai eta txerto seguruak, eraginkorrak, kalitatezkoak \neta guztiontzat eskuragarriak eskuratzeko bidea barne\". Helburua da pertsona eta komunitate guztiek behar dituzten \nkalitatezko osasun-zerbitzuak jasotzea (sendagaiak eta bestelako osasun-produktuak barne), finantza-zailtasunik gabe. \n\nBi adierazle aukeratu dira 3.8 xedearen jarraipena egiteko GJHen esparruan. 3.8.1 adierazlea osasun-zerbitzuak \nestaltzeko da, eta 3.8.2 adierazlea osasun-gastuetan zentratzen da, familia baten aurrekontua oinarri hartuta, arreta \nmedikoaren zuzeneko ordainketek eragindako finantza-zailtasunak identifikatze aldera. Oro har, 3.8.1 eta 3.8.2 adierazleen \nhelburua 3.8 xedeko zerbitzuen estalduraren eta finantza-babesaren dimentsioak atzematea da. Bi adierazle horiek batera \nmonitorizatu behar dira beti. \n\nHerrialdeek oinarrizko zerbitzu asko ematen dituzte osasuna babesteko, sustatzeko, prebenitzeko, tratatzeko eta artatzeko. \nZerbitzuen estalduraren adierazleak (behar duten zerbitzua jasotzen duten pertsonak bezala definituak) dira osasun-estaldura \nunibertsalaren (OEU) esparruan ematen diren zerbitzuen jarraipena egiteko modurik onena. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.8.1&seriesCode=SH_ACS_UNHC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Osasun-estaldura unibertsaleko (OEU) zerbitzuen estaldura-indizea SH_ACS_UNHC</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina informazio osagarria ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf\">Metadatuak 3-8-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.8.1: Coverage of essential health services </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_ACS_UNHC - Universal health coverage (UHC) service coverage index [3.8.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>The universal health coverage (UHC) service coverage index is designed to summarize existing indicators of health service coverage to ensure consistency with the SDGs and other global initiatives and reduce duplication and reporting burden. </p>\n<p>Indicator 3.8.1 should always be interpreted together with the other SDG UHC indicator, 3.8.2, which measures financial protection.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO) </p>\n<p> </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>Coverage of essential health services (defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population). </p>\n<p> </p>\n<p> </p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>The index of health service coverage is computed as the geometric means of 14 tracer indicators. The 14 indicators are listed below and detailed metadata for each of the components is given in Annex 1. The tracer indicators are as follows, organized by four broad categories of service coverage: </p>\n<p> </p>\n<p>I. Reproductive, maternal, newborn and child health </p>\n<p>1. <u>Family planning</u>: Percentage of women of reproductive age (15&#x2212;49 years) who are married or in-union who have their need for family planning satisfied with modern methods </p>\n<p>2. <u>Pregnancy care</u>: Percentage of women aged 15-49 years with a live birth in a given time period who received antenatal care four or more times </p>\n<p>3. <u>Child immunization</u>: Percentage of infants receiving three doses of diphtheria-tetanus-pertussis containing vaccine </p>\n<p>4. <u>Child treatment</u>: Percentage of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing due to a problem in the chest and not due to a blocked nose only) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider</p>\n<p> </p>\n<p>II. Infectious diseases </p>\n<p>5. <u>Tuberculosis</u>: Percentage of incident TB cases that are detected and treated </p>\n<p>6. <u>HIV/AIDS</u>: Percentage of adults and children living with HIV currently receiving antiretroviral therapy </p>\n<p>7. <u>Malaria</u>: Percentage of population in malaria-endemic areas who slept under an insecticide-treated net the previous night [only for countries with high malaria burden] </p>\n<p>8. <u>Water, sanitation and hygiene</u>: Percentage of population using at least basic sanitation services. </p>\n<p> </p>\n<p>III. Noncommunicable diseases </p>\n<p>9. <u>Hypertension</u>: Prevalence of treatment (taking medicine) for hypertension among adults aged 30-79 years with hypertension (age-standardized estimate) (%)</p>\n<p>10. <u>Diabetes</u>: Age-standardized mean fasting plasma glucose (mmol/L) for adults aged 18 years and older </p>\n<p>11. <u>Tobacco</u>: Age-standardized prevalence of adults &gt;=15 years currently using any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis (SDG indicator 3.a.1, metadata available <a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0a-01.pdf\" target=\"_blank\"><u>here</u></a>) </p>\n<p> </p>\n<p>IV. Service capacity and access </p>\n<p>12. <u>Hospital access</u>: Hospital beds density, relative to a maximum threshold of 18 per 10,000 population </p>\n<p>13. <u>Health workforce</u>: Health professionals (physicians, psychiatrists, and surgeons) per capita, relative to maximum thresholds for each cadre (partial overlap with SDG indicator 3.c.1, see metadata <a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0C-01.pdf\" target=\"_blank\"><u>here</u></a>) </p>\n<p>14. <u>Health security</u>: International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of 13 core capacities that have been attained (SDG indicator 3.d.1, see metadata <a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0D-01.pdf\" target=\"_blank\"><u>here</u></a>) </p>\n<p> </p>", "UNIT_MEASURE__GLOBAL"=>"<p>The indicator is an index reported on a unitless scale of 0 to 100.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Many of the tracer indicators of health service coverage are measured by household surveys. However, administrative data, facility data, facility surveys, and sentinel surveillance systems are utilized for certain indicators. Underlying data sources for each of the 14 tracer indicators are explained in more detail in Annex 1. </p>\n<p> </p>\n<p>In terms of values used to compute the index, values are taken from existing published sources. This includes assembled data sets and estimates from various UN agencies. This is summarized in the above link. </p>", "COLL_METHOD__GLOBAL"=>"<p>The mechanisms for collecting data from countries vary across the 14 tracer indicators, however in many cases a UN agency or interagency group has assembled and analysed relevant national data sources and then conducted a formal country consultation with country governments to review or produce comparable country estimates. For the universal health coverage (UHC) service coverage index, once this existing information on the 14 tracer indicators is collated, WHO conducts a country consultation with nominated focal points from national governments to review inputs and the calculation of the index. WHO does not undertake new estimation activities to produce tracer indicator values for the service coverage index; rather, the index is designed to make use of existing and well-established indicator data series to reduce reporting burden. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data collection varies from every 1 to 5 years across tracer indicators. For example, country data on immunizations and HIV treatment are reported annually, whereas household surveys to collect information on child treatment may occur every 3-5 years, depending on the country. More details about individual tracer indicators are available in Annex 1. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The first release of baseline values for the universal health coverage (UHC) service coverage index took place in December 2017. Updates are released every two years. </p>", "DATA_SOURCE__GLOBAL"=>"<p>In most cases, Ministries of Health and National Statistical Offices oversee data collection and reporting for health service coverage indicators. </p>", "COMPILING_ORG__GLOBAL"=>"<p>The World Health Organization, drawing on inputs from other international agencies such as UNICEF, UNAIDS, UN DESA, OECD, Eurostat, World Bank Group. </p>", "INST_MANDATE__GLOBAL"=>"<p>WHO support for monitoring the service coverage dimension of Universal Health Coverage (UHC) (target 3.8, indicator 3.8.1 specifically) is underpinned by Resolution WHA69 that requests the Secretariat to track progress towards achieving UHC as part of the SDG 2030 agenda for Sustainable Development.</p>", "RATIONALE__GLOBAL"=>"<p>Target 3.8 is defined as &#x201C;Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all&#x201D;. The objective is for all people and communities to receive the quality health services they need (including medicines and other health products), without financial hardship. Two indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1 is for health service coverage and indicator 3.8.2 focuses on health expenditures in relation to a household&#x2019;s budget to identify financial hardship caused by direct health care payments. Taken together, indicators 3.8.1 and 3.8.2 are meant to capture the service coverage and financial protection dimensions, respectively, of target 3.8. These two indicators should be always monitored jointly. </p>\n<p> </p>\n<p>Countries provide many essential services for health protection, promotion, prevention, treatment and care. Indicators of service coverage &#x2013; defined as people receiving the service they need &#x2013; are the best way to track progress in providing services under universal health coverage (UHC). Since a single health service indicator does not suffice for monitoring UHC, an index is constructed from 14 tracer indicators selected based on epidemiological and statistical criteria. This includes several indicators that are already included in other SDG targets, thereby minimizing the data collection and reporting burden. The index is reported on a unitless scale of 0 to 100, with 100 being the optimal value. </p>", "REC_USE_LIM__GLOBAL"=>"<p>These tracer indicators are meant to be indicative of service coverage, not a complete or exhaustive list of health services and interventions that are required for universal health coverage. The 14 tracer indicators were selected because they are well-established, with available data widely reported by countries (or expected to become widely available soon). Therefore, the index can be computed with existing data sources and does not require initiating new data collection efforts solely to inform the index. </p>", "DATA_COMP__GLOBAL"=>"<p>The index is computed with geometric means, based on the methods used for the Human Development Index. The calculation of the 3.8.1 indicator requires first standardizing the 14 tracer indicators so that they can be combined into the index, and then computing the index from those values. </p>\n<p> </p>\n<p>The 14 tracer indicators are first all placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement, e.g., the percentage of infants who have been immunized ranges from 0 to 100 percent. However, for a few indicators, conversion and/or rescaling is required to obtain appropriate values from 0 to 100, as follows: </p>\n<p><u>Conversion</u></p>\n<p>The prevalence of tobacco use is converted into prevalence of tobacco non-use, so that an increase means an improvement.</p>\n<p><u>Rescaling</u></p>\n<ul>\n  <li>Rescaling based on a non-zero minimum to obtain finer resolution (this &#x201C;stretches&#x201D; the distribution across countries): prevalence of non-use of tobacco is rescaled using a minimum value of 30%, which indicate a realistic range of prevalence levels for the indicator. </li>\n</ul>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>b</mi>\n    <mi>a</mi>\n    <mi>c</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>n</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>u</mi>\n    <mi>s</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi>X</mi>\n    <mo>-</mo>\n    <mn>30</mn>\n    <mo>)</mo>\n    <mo>/</mo>\n    <mo>(</mo>\n    <mn>100</mn>\n    <mo>-</mo>\n    <mn>30</mn>\n    <mo>)</mo>\n    <mi>*</mi>\n    <mn>100</mn>\n  </math></p>\n<ul>\n  <li>Rescaling for a continuous measure: mean fasting plasma glucose, which is a continuous measure (units of mmol/L), is converted to a scale of 0 to 100 using the minimum theoretical biological risk (5.1 mmol/L) and observed maximum across countries (7.4 mmol/L). </li>\n</ul>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>v</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>u</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mn>7</mn>\n    <mo>.</mo>\n    <mn>4</mn>\n    <mi>&amp;nbsp;</mi>\n    <mo>-</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>g</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>v</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>u</mi>\n    <mi>e</mi>\n    <mo>)</mo>\n    <mi>&amp;nbsp;</mi>\n    <mo>/</mo>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mn>7</mn>\n    <mo>.</mo>\n    <mn>4</mn>\n    <mo>-</mo>\n    <mn>5</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mo>)</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>*</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>100</mn>\n  </math> </p>\n<p> </p>\n<p> </p>\n<ul>\n  <li>Maximum thresholds for rate indicators: hospital bed density and health workforce density are both capped at maximum thresholds, and values above this threshold are held constant at 100. These thresholds are based on minimum values observed across OECD countries (2015 edition of OECD Health Statistics Database). </li>\n</ul>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>h</mi>\n    <mi>o</mi>\n    <mi>s</mi>\n    <mi>p</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>b</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>10</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>m</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>m</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>g</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>v</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>u</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>/</mo>\n    <mi>&amp;nbsp;</mi>\n    <mn>18</mn>\n    <mi>*</mi>\n    <mn>100</mn>\n    <mo>)</mo>\n  </math> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>h</mi>\n    <mi>y</mi>\n    <mi>s</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>i</mi>\n    <mi>a</mi>\n    <mi>n</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>1</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>m</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>m</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>g</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>v</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>u</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>/</mo>\n    <mi>&amp;nbsp;</mi>\n    <mn>0</mn>\n    <mo>.</mo>\n    <mn>9</mn>\n    <mi>*</mi>\n    <mn>100</mn>\n    <mo>)</mo>\n  </math> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>s</mi>\n    <mi>y</mi>\n    <mi>c</mi>\n    <mi>h</mi>\n    <mi>i</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>m</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>m</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>g</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>v</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>u</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>/</mo>\n    <mi>&amp;nbsp;</mi>\n    <mn>1</mn>\n    <mi>*</mi>\n    <mn>100</mn>\n    <mo>)</mo>\n  </math> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>s</mi>\n    <mi>u</mi>\n    <mi>r</mi>\n    <mi>g</mi>\n    <mi>e</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>m</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>m</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>g</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>v</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>u</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>/</mo>\n    <mi>&amp;nbsp;</mi>\n    <mn>14</mn>\n    <mi>*</mi>\n    <mn>100</mn>\n    <mo>)</mo>\n  </math> </p>\n<p> </p>\n<p>Once all tracer indicator values are on a scale of 0 to 100, geometric means are computed within each of the four health service areas, and then a geometric mean is taken of those four values. If the value of a tracer indicator happens to be zero or beyond 100, it is set to 1 (out of 100) or 100 (out of 100) respectively before computing the geometric mean. The following diagram illustrates the calculations. </p>\n<p> <img src=\"data:image/png;base64,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\"> </p>\n<p> </p>\n<p>Note that in countries with low malaria burden, the tracer indicator for use of insecticide-treated nets is dropped from the calculation. </p>\n<p> </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The data obtained to calculate the index have typically already been checked for quality through separated processes. However, a quality assessment is performed before consulting countries (i.e. detection of important outliers or substantial difference between last update and next update for the same year). The index estimates are included in a consultation to obtain country&#x2019;s feedback. Data are revised as needed for antenatal care coverage and hospital beds densities. The revision of all the other indicators should follow the reporting mechanism already in place. </p>\n<p>Information on the validation of the index construction can be found in the following paper: <a href=\"https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(17)30472-2/fulltext\">https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(17)30472-2/fulltext</a></p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>The starting point for computing the index is to assemble existing information for each tracer indicator. In many cases, this involves using country time series that have been produced or collated by UN agencies in consultation with country governments (e.g., immunization coverage, access to sanitation, HIV treatment coverage, etc). Some of these published time series involve mathematical modelling to reconcile multiple data sources or impute missing values, and these details are summarized in Annex 1. </p>\n<p> </p>\n<p>After assembling these inputs, there are still missing values for some country-years for some indicators. Calculating the universal health coverage (UHC) service coverage index requires values for each tracer indicator for a country, so some imputation is necessary to fill these data gaps. The current approach involves a simple imputation algorithm. For each indicator: </p>\n<ul>\n  <li>If a country has missing values between two years with values, linear interpolation is used to fill missing values for the intervening years </li>\n  <li>If a country has historical years with values, but no current value, constant extrapolation is used to fill missing values to the current year </li>\n  <li>If a country has no values, a value is imputed. For pneumonia care-seeking and density of surgeons, a regression is fit to impute missing values (see Annex 1 for details). For all other indicators, a regional median is calculated to impute missing values. By default, regions are based on UN SDG subregions. However, when there are not enough countries within UN SDG subregions with available data, other groupings can be used.</li>\n</ul>\n<p> </p>\n<p>Given the timing and distribution of various health surveys and other data collection mechanisms, countries do not collect and report on all 14 tracer indicators of health service coverage on an annual basis. In addition, monitoring at country level is most suitably done at broader time intervals, e.g., every 5 years, to allow for new data collection across indicators. Therefore, the extent to which imputation has been used to fill missing information should be communicated along with the index value. </p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p>Any needed imputation is done at country level. These country values can then be used to compute regional and global values. </p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates use United Nations population estimates at the country level to compute a weighted average of country values for the index. This is justified because universal health coverage (UHC) is a property of countries, and the index of essential services is a summary measure of access to essential services for each country&#x2019;s population. United Nations population estimates at country level are used to ensure consistency and comparability of estimates within countries and between countries over time. </p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See 4.d Validation</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Summarizing data availability for the universal health coverage (UHC) service coverage index is not straightforward, as different data sources are used across the 14 tracer indicators. Additionally, for many indicators comparable estimates have been produced, in many cases drawing on different types of underlying data sources to inform the estimates while also using projections to impute missing values. </p>\n<p><strong>Time series:</strong></p>\n<p>A baseline value for the UHC service coverage index for 2015 across 183 countries was published in late 2017. As part of this process, data sources going back to 2000 were assembled. In 2019, UHC service coverage index were estimates for the years: 2000, 2005, 2010, 2015 and 2017. From 2021, the index is estimated every two years for all countries (i.e. 194 WHO member states).</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Equity is central to the definition of UHC, and therefore the UHC service coverage index should be used to communicate information about inequalities in service coverage within countries. This can be done by presenting the index separately for the national population vs disadvantaged populations to highlight differences between them. </p>\n<p> </p>\n<p>For countries, geographic location is likely the most feasible dimension for sub-national disaggregation based on average coverage levels measured with existing data sources. To do this, the UHC index can be computed separately by, e.g., province or urban vs rural residence, which would allow for subnational comparisons of service coverage. Currently, the most readily available data for disaggregation on other dimensions of inequality, such as household wealth, is for indicators of coverage within the reproductive, maternal, newborn and child health services category. Inequality observed in this dimension can be used as a proxy to understand differences in service coverage across key inequality dimensions. This approach should be replaced with full disaggregation of all 14 tracer indicators once data are available to do so. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The service coverage index draws on existing, publicly available data and estimates for tracer indicators. These numbers have already been through a country consultation process (e.g., for immunization coverage), or are taken directly from country reported data.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong><a href=\"https://www.who.int/health-topics/universal-health-coverage\">https://www.who.int/health-topics/universal-health-coverage</a></p>\n<p> </p>\n<p><strong>References: </strong><a href=\"https://www.who.int/publications/i/item/tracking-universal-health-coverage\">https://www.who.int/publications/i/item/tracking-universal-health-coverage</a> </p>\n<p><a href=\"http://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(17)30472-2.pdf\" target=\"_blank\"><u>http://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(17)30472-2.pdf</u></a> </p>\n<p><a href=\"https://www.who.int/health-topics/universal-health-coverage\">https://www.who.int/health-topics/universal-health-coverage</a></p>\n<p>For historical development of methods, see: </p>\n<p><a href=\"https://www.who.int/publications/i/item/9789241565264\">https://www.who.int/publications/i/item/9789241565264</a> </p>\n<p><a href=\"https://www.who.int/publications/i/item/monitoring-progress-towards-universal-health-coverage-at-country-and-global-levels-framework-measures-and-targets\">https://www.who.int/publications/i/item/monitoring-progress-towards-universal-health-coverage-at-country-and-global-levels-framework-measures-and-targets</a> </p>\n<p><a href=\"http://collections.plos.org/uhc2014\" target=\"_blank\"><u>http://collections.plos.org/uhc2014</u></a> </p>\n<p> </p>\n<p>Annex 1: Metadata for tracer indicators used to measure the coverage of essential health services for monitoring SDG indicator 3.8.1.</p>\n<p>Please send any comments or queries to: uhc_stats@who.int</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Family planning</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Percentage of women of reproductive age (15&#x2212;49 years) who are married or in-union who have their need for family planning satisfied with modern methods.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Number of women aged 15-49 who are married or in-union who are currently using, or whose partner is currently using a modern method of contraception </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>Number of women aged 15-49 who are married or in-union with a need for family planning </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Population-based health surveys</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>Household surveys include a series of questions to measure the modern contraceptive prevalence rate and need for family planning. The number of women with a need for family planning is defined as the sum of the number of women of reproductive age (15&#x2013;49 years) who are married or in a union and who are currently using, or whose sexual partner is currently using, at least one contraceptive method (modern or traditional), and the number of women of reproductive age with an unmet need for family planning. Unmet need for family planning is the proportion of women of reproductive age (15&#x2013;49 years) either married or in a consensual union, who are fecund and sexually active but who are not using any method of contraception (modern or traditional), and report not wanting any more children or wanting to delay the birth of their next child for at least two years. Included are:</p>\n        <ol>\n          <li>all pregnant women (married or in a consensual union) whose pregnancies were unwanted or mistimed at the time of conception;</li>\n          <li>all postpartum amenorrhoeic women (married or in consensual union) who are not using family planning and whose last birth was unwanted or mistimed;</li>\n          <li>all fecund women (married or in consensual union) who are neither pregnant nor postpartum amenorrhoeic, and who either do not want any more children (want to limit family size), or who wish to postpone the birth of a child for at least two years or do not know when or if they want another child (want to space births), but are not using any contraceptive method.</li>\n        </ol>\n        <p>Modern methods include female and male sterilization, the intra-uterine device (IUD), the implant, injectables, oral contraceptive pills, male and female condoms, vaginal barrier methods (including the diaphragm, cervical cap and spermicidal foam, jelly, cream and sponge), lactational amenorrhea method (LAM), emergency contraception and other modern methods not reported separately.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>The United Nations Population Division produces a systematic and comprehensive series of annual estimates and projections of the proportion of need for family planning among women of reproductive age (15-49) satisfied with modern methods. A Bayesian hierarchical model is applied to a comprehensive global dataset of a country-specific data to generate the estimates and projections. The model accounts for differences by data source, sample population, and survey questions. </p>\n        <p>See here for details: </p>\n        <p><a href=\"https://www.un.org/development/desa/pd/data/family-planning-indicators\">https://www.un.org/development/desa/pd/data/family-planning-indicators</a> </p>\n        <p>Data compilation of country-specific survey data in World Contraceptive Use: </p>\n        <p><a href=\"https://www.un.org/development/desa/pd/node/3285\">https://www.un.org/development/desa/pd/node/3285</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Pregnancy care</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Percentage of women aged 15-49 years with a live birth in a given time period who received antenatal care four or more times </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Number of women aged 15&#x2212;49 years with a live birth in a given time period who received antenatal care four or more times</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>Total number of women aged 15&#x2212;49 years with a live birth in the same period.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Household surveys and routine facility information systems.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>Data on four or more antenatal care visits is based on questions that ask if and how many times the health of the woman was checked during pregnancy. Household surveys that can generate this indicator include DHS, MICS, RHS and other surveys based on similar methodologies. Service/facility reporting systems can be used where the coverage is high, usually in higher income countries.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>WHO maintains a data base on coverage of antenatal care: <a href=\"http://apps.who.int/gho/data/node.main.ANTENATALCARECOVERAGE4\"><u>http://apps.who.int/gho/data/node.main.ANTENATALCARECOVERAGE4</u></a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p>Ideally this indicator would be replaced with a more comprehensive measure of pregnancy care, for example the proportion of women who have a skilled provider attend the birth or an institutional delivery. A challenge in measuring skilled attendance at birth is determining which providers are &#x201C;skilled&#x201D;. </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Child immunization</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Percentage of infants receiving three doses of diphtheria-tetanus-pertussis containing vaccine</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Children 1 year of age who have received three doses of diphtheria-tetanus-pertussis containing vaccine</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>All children 1 year of age</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Household surveys and facility information systems.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>For survey data, the vaccination status of children aged 12&#x2013;23 months is collected from child health cards or, if there is no card, from recall by the care-taker. For administrative data, the total number of doses administered to the target population is extracted.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>Together, WHO and UNICEF derive estimates of DTP3 coverage based on data officially reported to WHO and UNICEF by Member States, as well as data reported in the published and grey literature. They also consult with local experts - primarily national EPI managers and WHO regional office staff - for additional information regarding the performance of specific local immunization services. Based on the available data, consideration of potential biases, and contributions from local experts, WHO/UNICEF determine the most likely true level of immunization coverage. </p>\n        <p>For details, see here: </p>\n        <p> <a href=\"https://www.who.int/teams/immunization-vaccines-and-biologicals/immunization-analysis-and-insights/global-monitoring/immunization-coverage/who-unicef-estimates-of-national-immunization-coverage\">https://www.who.int/teams/immunization-vaccines-and-biologicals/immunization-analysis-and-insights/global-monitoring/immunization-coverage/who-unicef-estimates-of-national-immunization-coverage</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p>There is variability in national vaccine schedules across countries. Given this, one option for monitoring full child immunization is to monitor the fraction of children receiving vaccines included in their country&#x2019;s national schedule. A second option, which may be more comparable across countries and time, is to monitor DTP3 coverage as a proxy for full child immunization. Diphtheria-tetanus-pertussis containing vaccine often includes other vaccines, e.g., against Hepatitis B and Haemophilus influenza type B, and is a reasonable measure of the extent to which there is a robust vaccine delivery platform within a country. </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Child treatment </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Percentage of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing <strong>due to a problem in the chest</strong> <strong>and not due to a blocked nose only</strong>) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Number of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing <strong>due to a problem in the chest</strong> <strong>and not due to a blocked nose only</strong>) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>Number of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing <strong>due to a problem in the chest</strong> <strong>and not due to a blocked nose only</strong>) in the 2 weeks preceding the survey </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Household surveys</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>The indicator is captured by household surveys including DHS, MICS and other national population-based surveys and is intended for use in high under-5 mortality settings to monitor efforts to reduce mortality from acute respiratory infections (including pneumonia) which are a leading cause of death for children under the age of 5 years. The Child Health Accountability Tracking Technical Advisory Group (CHAT TAG), convened by WHO and UNICEF, has ratified this indicator and is working to standardize its use across household surveys.</p>\n        <p>WHO/UNICEF maintains a database of country-level observations from household surveys that can be accessed here: <a href=\"https://data.unicef.org/topic/child-health/pneumonia/\">https://data.unicef.org/topic/child-health/pneumonia/</a> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>UNICEF and WHO maintain a data base on this indicator and work on ensuring that values presented are comparable, using the same indicator definition.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p>This indicator is not typically measured in higher income countries with well-established health systems. </p>\n        <p>For countries without observed data, coverage was estimated from a regression that predicts coverage of care-seeking for symptoms of acute respiratory infection (on the logit scale), obtained from the WHO data base described above, as a function of the log of the estimated under-five all-causes mortality rate, which can be found here: <a href=\"https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates\">https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates</a></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Tuberculosis treatment</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Percentage of incidence TB cases that are detected and treated in a given year</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Number of new and relapse cases detected and treated in a given year </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>Number of new and relapse cases in the same year</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Facility information systems, surveillance systems, population-based health surveys with TB diagnostic testing, TB register and related quarterly reporting system (or electronic TB registers)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>This indicator requires two main inputs:</p>\n        <p>(1) The number of new and relapse TB cases diagnosed and treated in national TB control programmes and notified to WHO in a given year.</p>\n        <p>(2) The number of incident TB cases for the same year, typically estimated by WHO.</p>\n        <p>The final indicator = (1)/(2) </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>Estimates of TB incidence are produced through a consultative and analytical process led by WHO and are published annually. These estimates are based on annual case notifications, assessments of the quality and coverage of TB notification data, national surveys of the prevalence of TB disease and information from death (vital) registration systems. Estimates of incidence for each country are derived, using one or more of the following approaches depending on available data:</p>\n        <p>1. incidence = case notifications/estimated proportion of cases detected;</p>\n        <p>2. incidence = prevalence/duration of condition;</p>\n        <p>3. incidence = deaths/proportion of incident cases that die. </p>\n        <p>Dynamic and statistical models were introduced to produce estimates for 2020 and 2021 that account for the major disruptions to the provision of and access to TB diagnostic and treatment services that have occurred in the context of the coronavirus (COVID-19) pandemic.</p>\n        <p>These estimates of TB incidence are combined with country-reported data on the number of cases detected and treated, and the percentage of cases successfully treated, as described above.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p>To compute the indicator using WHO estimates, one can access necessary files here: <a href=\"http://www.who.int/tb/country/data/download/en/\"><u>http://www.who.int/tb/country/data/download/en/</u></a>, and compute the indicator as = c_cdr </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>HIV treatment</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Percentage of adults and children living with HIV currently receiving antiretroviral therapy (ART)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Number of adults and children who are currently receiving ART at the end of the reporting period</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>Number of adults and children living with HIV during the same period</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Facility reporting systems, sentinel surveillance sites, population-based surveys</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>Numerator: The numerator is generated by counting the number of adults and children who received ART at the end of the reporting period. Data can be collected from facility-based ART registers or drug supply management systems. These are then tallied and transferred to cross sectional monthly or quarterly reports which will then be aggregated for national totals. Patients receiving ART in the private sector and public sector should be included in the numerator.</p>\n        <p>Denominator: Data on the number of people with HIV infection may come from epidemic models and population-based surveys or, as is common in sub-Saharan Africa, surveillance systems based on antenatal care clinics.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>Estimates of antiretroviral treatment coverage among people living with HIV for 2000-2018 are derived as part of the 2019 UNAIDS&apos; estimation round. </p>\n        <p>To estimate the number of people living with HIV across time in high burden countries, UNAIDS in collaboration with countries use an epidemic model (Spectrum) that combines surveillance data on prevalence with the current number of patients receiving ART and assumptions about the natural history of HIV disease progression. </p>\n        <p>Since ART is now recommended for all individuals living with HIV, monitoring ART coverage is less complicated than before, when only those with a certain level of disease severity were eligible to receive ART.</p>\n        <p>Estimates of ART coverage can be found here: <a href=\"https://www.who.int/data/gho/data/indicators/indicator-details/GHO/estimated-antiretroviral-therapy-coverage-among-people-living-with-hiv-(-)\">https://www.who.int/data/gho/data/indicators/indicator-details/GHO/estimated-antiretroviral-therapy-coverage-among-people-living-with-hiv-(-)</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p>Comparable estimates of ART coverage in high income countries, in particular time trends, are not always available.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Malaria prevention</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Percentage of population in malaria-endemic areas who slept under an ITN the previous night.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Number of people in malaria-endemic areas who slept under an ITN.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>Total number of people in malaria endemic areas.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Data on household access and use of ITNs come from nationally representative household surveys such as Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and Malaria Indicator Surveys. Data on the number of ITNs delivered by manufacturers to countries are compiled by Milliner Global Associates, and data on the number of ITNs distributed within countries are reported by National Malaria Control Programs.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>Many recent national surveys report the number of ITNs observed in each respondent household. Ownership rates can be converted to the proportion of people sleeping under an ITN using a linear relationship between access and use that has been derived from 62 surveys that collect information on both indicators. </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>Mathematical models can be used to combine data from household surveys on access and use with information on ITN deliveries from manufacturers and ITN distribution by national malaria programmes to produce annual estimates of ITN coverage. WHO uses this approach in collaboration with the Malaria Atlas Project. Methodological details can be found in pages 122-123 of the World Malaria Report 2021: <a href=\"https://www.who.int/publications/i/item/9789240040496\">https://www.who.int/publications/i/item/9789240040496</a>.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p>WHO produces comparable ITN coverage estimates for 40 of the 47 malaria endemic countries or areas of sub-Saharan Africa. The islands of </p>\n        <p>Mayotte (for which no ITN delivery or distribution data </p>\n        <p>were available) and Cabo Verde (which does not distribute </p>\n        <p>ITNs) were excluded, as were the low transmission </p>\n        <p>countries of Eswatini, Namibia, Sao Tome and Principe, </p>\n        <p>and South Africa, for which ITNs comprise a small </p>\n        <p>proportion of vector control. Analyses were limited to </p>\n        <p>populations categorized by NMPs as being at risk. For other countries, ITN coverage is not included in the UHC service coverage index due to data limitations. </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Water, sanitation and hygiene</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Percentage of population using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Number of people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush toilets connected to piped sewer systems, septic tanks or pit latrines; pit latrines with slabs (including ventilated pit latrines), and composting toilets</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>Total population</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Population-based household surveys and censuses</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>Data on improved sanitation facilities are routinely collected in household surveys and censuses. These data sources may also collect information on sharing of sanitation facilities are shared among two or more households, and on emptying of on-site sanitation facilities. Household-level responses, weighted by household size, are used to compute population coverage.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) is responsible for SDG reporting on drinking water, sanitation and hygiene (WASH) and has produced regular estimates of coverage of the population using at least basic sanitation services since 2000. The JMP assembles, reviews and assesses national data collected by statistics offices and other relevant institutions including sectoral authorities. Linear regression is used to provide estimates of the population using improved sanitation facilities, as well as the proportion practising open defecation. Regressions are also made to estimate the population using improved sanitation facilities connected to sewers and septic tanks; these are constrained to not exceed the estimates for total improved facilities. The proportion of the population sharing sewered and non-sewered sanitation facilities is estimated by making a linear regression on all available data on sharing from household surveys and censuses. Basic sanitation services are calculated by multiplying the proportion of the population using improved sanitation facilities by the proportion of improved sanitation facilities which are not shared among two or more households. Separate estimates are made for urban and rural areas, and national estimates are generated as weighted averages of the two, using population data from the most recent report of the United Nations Population Division. The most recent household survey or census available for most countries was typically conducted two to six years ago. The JMP extrapolates regressions for two years beyond the last available data point. Beyond this point the estimates remain unchanged for up to four years unless coverage is below 0.5 per cent or above 99.5 per cent, in which case the line is extended indefinitely. For more information see <a href=\"https://washdata.org/monitoring/methods/estimation-methods\">https://washdata.org/monitoring/methods/estimation-methods</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p>The SDG global indicator of &#x201C;proportion of population using safely managed sanitation services&#x201D; (SDG 6.2.1a) is an expanded version of the MDG indicator, which additionally considers safe management of excreta along the entire sanitation chain, including treatment and disposal This indicator is not used for UHC monitoring due to lower data availability. </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Prevention of cardiovascular disease</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Prevalence of treatment (taking medicine) for hypertension among adults aged 30-79 years with hypertension (age-standardized estimate) (%)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Number of adults aged 30-79 years who took medication for hypertension</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>Number of adults aged 30-79 years with hypertension (defined as having systolic blood pressure &#x2265; 140 mmHg, diastolic blood pressure &#x2265; 90 mmHg, or taking medication for hypertension)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Population-based surveys and surveillance systems</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>Data sources recording measured blood pressure are used (self-reported data are excluded). If multiple blood pressure readings are taken per participant, the first reading is dropped and the remaining readings are averaged. Whether medication is taken for hypertension may be assessed using questions worded as variations of &#x201C;Are you currently taking any medicines, tablets, or pills for high blood pressure?&#x201D; or &#x201C;In the past 2 weeks, have you taken any drugs (medication) for raised blood pressure prescribed by a doctor or other health worker?&#x201D; In studies that gather information on prescribed medicines, survey information may be used to establish that the purpose of taking a blood pressure-lowering drug was specifically to treat hypertension.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>Full details of input and data methods are available at: NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. The Lancet S0140-6736(21)01330-1 (https://www.thelancet.com/article/S0140-6736(21)01330-1/fulltext). A total of 1,201 population-based studies that included measured blood pressure and data on blood pressure treatment in 104 million individuals aged 30&#x2013;79 years were used to estimate trends in hypertension and hypertension diagnosis, treatment and control from 1990 to 2019. Age-standardized estimates are produced by applying the crude estimates to the WHO Standard Population.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Management of diabetes</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Age-standardized mean fasting plasma glucose for adults aged 18 years and older</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Population-based surveys and surveillance systems</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>Fasting plasma glucose (FPG) levels are determined by taking a blood sample from participants who have fasted for at least 8 hours. Other related biomarkers, such as hemoglobin A1c (HbA1c), were used to help calculate estimates (see below).</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>For producing comparable national estimates, data observations based on mean FPG, oral glucose tolerance test (OGTT), HbA1c, or combinations therein, are all converted to mean FPG. A Bayesian hierarchical model is then fitted to these data to calculate age-sex-year-country specific prevalences, which accounts for national vs. subnational data sources, urban vs. rural data sources, and allows for variation in prevalence across age and sex. Age-standardized estimates are then produced by applying the crude estimates to the WHO Standard Population. Methodological details can be found here: <a href=\"https://www.who.int/diabetes/global-report/en/\"><u>https://www.who.int/diabetes/global-report/en/</u></a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p>An individual&#x2019;s FPG may be low because of effective treatment with glucose-lowering medication, or because the individual is not diabetic as a result of health promotion activities or other factors such as genetics. Mean FPG is thus a proxy for both effective promotion of healthy diets and behaviors and effective treatment of diabetes. </p>\n        <p>The above estimates are done separately for men and women; for the UHC tracer indicator a simple average of values for men and women is computed. The indicator, which is a continuous measure (units of mmol/L), is converted to a scale of 0 to 100 using the minimum theoretical biological risk (5.1 mmol/L) and observed maximum across countries (7.41 mmol/L). </p>\n        <p>rescaled value = (7.41 - original value) / (7.41-5.1) * 100</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Tobacco control</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Age-standardized percentage of the population aged 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Estimated number of adults 15 years and older who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>Total number of adults 15 years and older</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Household surveys</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), &#x201C;e-cigars&#x201D;, &#x201C;e-hookahs&#x201D;, JUUL and &#x201C;e-pipes&#x201D;.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>A statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966&#x2013;976 (2015). Once the age-and-sex-specific prevalence rates from national surveys were compiled into a dataset, the model was fit to calculate trend estimates from the year 2000 to 2025. The model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. Depending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is &#x201C;borrowed&#x201D; from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. Estimates for countries with irregular surveys or many data gaps will have large uncertainty ranges, and such results should be interpreted with caution.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p>Prevalence of tobacco non-use is computed as 1 minus the prevalence of tobacco use. The indicator is then rescaled based on a non-zero minimum to obtain finer resolution : rescaled tobacco non-use = (X-30)/(100-30)*100.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Hospital access</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Hospital beds per capita, relative to a maximum threshold of 18 per 10,000 population</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Number of hospital beds (should exclude labor and delivery beds)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>Total population</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Administrative systems / Health facility reporting system</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>Country administrative systems are used to total the number of hospital beds, which are divided by the total estimated population, and multiplied by 10,000. </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>Using available data, the indicator is computed relative to a threshold value of 18 hospital beds per 10,000 population. This threshold is below the observed OECD high income country minimum (since year 2000) of 20 per 10,000 (OECD Health Statistics database, 2015 edition) and tends to correspond to an inpatient hospital admission rate of around 5 per 100 per year. This indicator is designed to capture low levels of hospital capacity; the maximum threshold is used because very high hospital bed densities are not necessary an efficient use of resources. The indicator is computed as follows, using country data on hospital bed density (<em>x</em>), which results in values ranging from 0 to 100:</p>\n        <ul>\n          <li>Country with a hospital bed density <em>x</em> &lt; 18 per 10,000 per year, the indicator = <em>x</em> /18*100. </li>\n          <li>Country with a hospital bed density <em>x</em> &gt;= 18 per 10,000 per year, the indicator = 100.</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p>This indicator is used as proxy for the full coverage of inpatient care services. An alternative indicator could be hospital in-patient admission rate, relative to a maximum threshold. However, that indicator is currently not reported widely across regions, in particular the African Region. In countries where both hospital beds per capita and in-patient admission rates are available, they are highly correlated.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Health workforce</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>Health professionals (physicians, psychiatrists, and surgeons) per capita, relative to maximum thresholds for each cadre</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Number of physicians, psychiatrists and surgeons</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>Total population</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>National Health Workforce Accounts. This includes reported data from Member States based on national registry of health workers, ideally coupled with regular assessment of completeness using census data, labour force surveys, professional association registers, or facility censuses.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>The classification of health workers is based on criteria for vocational education and training, regulation of health professions, and activities and tasks of jobs, i.e., a framework for categorizing key workforce variables according to shared characteristics. The WHO framework largely draws on the latest revisions to the internationally standardized classification systems of the International Labour Organization (International Standard Classification of Occupations), United Nations Educational, Scientific and Cultural Organization (International Standard Classification of Education), and the United Nations Statistics Division (International Standard Industrial Classification of All Economic Activities). Methodological details can be found here: <a href=\"https://www.who.int/activities/improving-health-workforce-data-and-evidence\">https://www.who.int/activities/improving-health-workforce-data-and-evidence</a></p>\n        <p>Health workforce data can be accessed on the NHWA data portal: <a href=\"https://apps.who.int/nhwaportal/\">https://apps.who.int/nhwaportal/</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>Using available data, the indicator is computed by first rescaling, separately, health worker density ratios for each of the three cadres (physicians, psychiatrists and surgeons) relative to the minimum observed values across OECD countries since 2000 (OECD Health Statistics database, 2015 edition), which are as follows: physicians = 0.9 per 1000, psychiatrists = 1 per 100,000, and surgeons = 14 per 100,000. This rescaling is done in the same way as that for the hospital bed density indicator described above, resulting in indicator values that range from 0 to 100 for each of the three cadres. For example, using country data on physicians per 1000 population (<em>x</em>), the cadre-specific indicator would be computed as:</p>\n        <ul>\n          <li>Country with <em>x</em> &lt; 0.9 per 1000 per year, the cadre-specific indicator = <em>x</em> /0.9*100. </li>\n          <li>Country with <em>x</em> &gt;= 0.9 per 1000 per year, the cadre-specific indicator = 100.</li>\n        </ul>\n        <p>As a final step, the geometric mean of the three cadre-specific indicator values is computed to obtain the final indicator of health workforce density.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p>Due to major challenges measuring coverage in all health areas, which leaves major gaps for important areas such as routine medical exams, treatment for mental illnesses, emergency care and surgical procedure, proxies are used. Physician, psychiatrist and surgeon densities are used as proxies for the full coverage of outpatient care, mental health care and emergency/surgical care services, respectively. It should be noted that those measures are difficult to interpret because the optimal level for those indicators is unknown and they do not relate to a specific need for services. Despite this fact, low levels for these indicators are indicative of poor access to and use of essential health services.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Tracer area</p>\n      </td>\n      <td>\n        <p>Health security</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator definition</p>\n      </td>\n      <td>\n        <p>International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of all core capacities that have been attained at a specific point in time. </p>\n        <p>The second edition SPAR tool has been expanded from 13 to 15 capacities. The 15 core capacities are (1) Policy, legal and normative instruments to implement IHR; (2) IHR Coordination and National Focal Point Functions; (3) Financing; (4) Laboratory; (5) Surveillance; (6) Human resources; (7) Health emergency management (8) Health Service Provision; (9) Infection Prevention and Control; (10) Risk communication and community engagement; (11) Points of entry and border health; (12) Zoonotic diseases; (13) Food safety; (14) Chemical events; (15) Radiation emergencies. </p>\n        <p>The 13 core capacities of the first edition of the IHR State Parties Annual Assessment and Reporting Tool are (1) Legislation and financing; (2) IHR Coordination and National Focal Point Functions; (3) Zoonotic events and the Human-Animal Health Interface; (4) Food safety; (5) Laboratory; (6) Surveillance; (7) Human resources; (8) National Health Emergency Framework; (9) Health Service Provision; (10) Risk communication; (11) Points of entry; (12) Chemical events; (13) Radiation emergencies. </p>\n        <p>Both SPAR questionnaires (1st and 2nd editions) use a five-level scoring with indicators based on five cumulative levels to measure the implementation status for each capacity. For each indicator, the reporting State Party is asked to select which of the five levels best describes the State Party&apos;s current status. To move to the next level, all capacities described in previous levels should be in place for each indicator.</p>\n        <p>For the years 2010 to 2017, Member States used the IHR monitoring questionnaire. The questionnaire is divided into thirteen sections, one for each of the eight core capacities, PoE and four hazards. Individual questions are grouped by components and indicators in the questionnaires. States Parties can provide additional information on the questions in the comment boxes. Responses to the questions include marking one appropriate value (Yes, No, or Not Known) or the appropriate percentages. For statistical purposes, the &quot;Not Known&quot; value will be computed as a &quot;No&quot; value. The IHR monitoring questionnaire includes the following: IHR01. National legislation, policy and financing; IHR02. Coordination and National Focal Point communications; IHR03. Surveillance; IHR04. Response; IHR05. Preparedness; IHR06. Risk communication; IHR07. Human resources; IHR08. Laboratory; IHR09. Points of entry; IHR10. Zoonotic events; IHR11. Food safety; IHR12. Chemical events; IHR13. Radio nuclear emergencies.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Numerator</p>\n      </td>\n      <td>\n        <p>Number of attributes attained</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Denominator</p>\n      </td>\n      <td>\n        <p>Total number of attributes</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Main data sources</p>\n      </td>\n      <td>\n        <p>Key informant survey</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of measurement</p>\n      </td>\n      <td>\n        <p>Key informants report on attainment of a set of attributes for each of the core capacities using a standard WHO instrument. This instrument is based on a self-assessment and self-reporting by the State Party. There are three datasets based on the different tools to collect data for SPAR. For the period 2010 to 2017, the questionnaire, known as the IHR monitoring questionnaire, is divided into thirteen sections, one for each of the eight core capacities, PoE and four hazards and information on the status of implementation for each capacity. The IHR monitoring questionnaire ( 2010 to 2017) was replaced by the IHR State Parties Self-Assessment Tool &#x2013; SPAR, published in July 2018 also known as SPAR 1st edition. The States Parties used the questionnaire from the 2018 &#x2013; 2020 SPAR reporting cycle. The current questionnaire replaced the SPAR 1st edition and was used by the Member States for 2021. Under each capacity, the indicators were either retained, replaced or added. Historical trends based on the data for similar capacity titles may be taken with caution.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Method of estimation</p>\n      </td>\n      <td>\n        <p>The score of each indicator level is classified as a percentage of performance along the &#x201C;1 to 5&#x201D; scale. e.g. for a country selecting level 3 for indicator 2.1, the indicator level will be expressed as: 3/5*100=60% CAPACITY LEVEL The level of the capacity is expressed as the average of all indicators. e.g. for a country selecting level 3 for indicator 2.1 and level 4 for indicator 2.2. Indicator level for 2.1 will be expressed as: 3/5*100=60%, indicator level for 2.2 will be expressed as: 4/5*100=80% and capacity level for 2 will be expressed as: (60+80)/2=70%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UHC-related notes</p>\n      </td>\n      <td>\n        <p>Countries began reporting IHR core capacity attainment to WHO for the year 2010. The earliest available IHR score for each country is used for all years 2000-2009.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "indicator_sort_order"=>"03-08-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.8.2", "slug"=>"3-8-2", "name"=>"Proporción de la población con grandes gastos sanitarios por hogar como porcentaje del total de gastos o ingresos de los hogares", "url"=>"/site/es/3-8-2/", "sort"=>"030802", "goal_number"=>"3", "target_number"=>"3.8", "global"=>{"name"=>"Proporción de la población con grandes gastos sanitarios por hogar como porcentaje del total de gastos o ingresos de los hogares"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población con grandes gastos sanitarios por hogar como porcentaje del total de gastos o ingresos de los hogares", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población con grandes gastos sanitarios por hogar como porcentaje del total de gastos o ingresos de los hogares", "indicator_number"=>"3.8.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_237/opt_1/ti_encuesta-de-gasto-familiar/temas.html", "url_text"=>"Encuesta de gasto familiar", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de la población con grandes gastos sanitarios por hogar como porcentaje del total de gasto del hogar", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.8- Lograr la cobertura sanitaria universal, incluida la protección contra los riesgos financieros, el acceso a servicios de salud esenciales de calidad y el acceso a medicamentos y vacunas inocuos, eficaces, asequibles y de calidad para todos", "definicion"=>"Proporción de personas que viven en hogares con un gasto sanitario que representa más del 10% o 25 % del gasto total del hogar", "formula"=>"\n$$PPGGS_{\\gt x\\%}^{t} = \\frac{PGGS_{\\gt x\\%}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$PGGS_{\\gt x\\%}^{t} =$ población que vive en hogares con grandes gastos sanitarios, superiores a $x\\%$ del gasto total del hogar, en el año $t$\n\n$P^{t} =$ población total en el año $t$\n", "desagregacion"=>"Umbral de gasto: 10% y 25% del gasto total del hogar", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl indicador mide la proporción de la población con un elevado gasto familiar en salud como porcentaje del gasto o ingreso \ntotal del hogar. Se utilizan dos umbrales para definir el “gran gasto familiar en salud”: superior al 10% y superior \nal 25% del gasto o ingreso total del hogar.\n\nLa meta 3.8 se refiere a la cobertura sanitaria universal (CSU) y se define como “Lograr la cobertura \nsanitaria universal, incluida la protección contra los riesgos financieros, el acceso a servicios \nde salud esenciales de calidad y el acceso a medicamentos y vacunas esenciales seguros, eficaces, de calidad \ny asequibles para todos”. \n\nLa preocupación es que todas las personas y comunidades reciban los servicios sanitarios\nde calidad que necesitan (incluidos los medicamentos y otros productos sanitarios) sin dificultades financieras. \nLas dificultades financieras son una consecuencia clave de los mecanismos inadecuados de protección\ncontra los riesgos financieros y pueden experimentarse en cualquier país, independientemente del nivel de ingresos\n y el tipo de sistema sanitario.  \n\nEl indicador 3.8.2 trata de identificar a las personas con gastos de \nbolsillo en salud que exceden su capacidad de pago, lo que podría llevar a recortar el gasto en otras necesidades \nbásicas como la educación, la alimentación, la vivienda y los servicios públicos. \n\nReducir las dificultades financieras en materia de salud es importante en la \nagenda de desarrollo mundial, así como una prioridad del \nsector sanitario de muchos países en todas las regiones.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.8.2&seriesCode=SH_XPD_EARN25&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proporción de la población con grandes gastos domésticos en salud (superiores al 25%) como porcentaje del gasto o ingreso total del hogar (%) SH_XPD_EARN25</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.8.2&seriesCode=SH_XPD_EARN10&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proporción de la población con grandes gastos domésticos en salud (superiores al 10%) como porcentaje del gasto o ingreso total del hogar (%) SH_XPD_EARN10</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf\">Metadatos 3-8-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Proporción de la población con grandes gastos sanitarios por hogar como porcentaje del total de gasto del hogar", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.8- Lograr la cobertura sanitaria universal, incluida la protección contra los riesgos financieros, el acceso a servicios de salud esenciales de calidad y el acceso a medicamentos y vacunas inocuos, eficaces, asequibles y de calidad para todos", "definicion"=>"Proportion of people who live in households with large expenditure on health that exceeds 10% or 25% of total household expenditure", "formula"=>"\n$$PPGGS_{\\gt x\\%}^{t} = \\frac{PGGS_{\\gt x\\%}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$PGGS_{\\gt x\\%}^{t} =$ population who live in households with large expenditures on health, greater than $x\\%$ of total household expenditure, in year,  $t$\n\n$P^{t} =$ total population in year $t$\n", "desagregacion"=>"Spending threshold: 10% and 25% of total household expenditure", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe indicator measures the proportion of the population with large household expenditure on health as a \nshare of total household expenditure or income. Two thresholds are used to define “large household \nexpenditure on health”: greater than 10% and greater than 25% of total household expenditure or income.\n\nTarget 3.8 is about universal health coverage (UHC) and is defined as “Achieve universal health coverage, \nincluding financial risk protection, access to quality essential health-care services and access to safe, \neffective, quality and affordable essential medicines and vaccines for all”. \n\nThe concern is with all people and communities receiving the quality health services they need (including \nmedicines and other health products) without financial hardship. Financial hardship is a key consequence \nof inadequate financial risk protection mechanisms and can be experienced in any country, regardless of the \nincome level and type of health system.\n\nIndicator 3.8.2 is about identifying people with out-of-pocket health spending on health exceeding their \nability to pay, which might lead to cutting spending on other basic needs such as education, food, housing \nand utilities.\n\nReducing financial hardship in health is important on the global development agenda as well as a priority \nof the health sector of many countries across all regions. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.8.2&seriesCode=SH_XPD_EARN25&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proportion of population with large household expenditures on health (greater than 25%) as a share of total household expenditure or income (%) SH_XPD_EARN25</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.8.2&seriesCode=SH_XPD_EARN10&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proportion of population with large household expenditures on health (greater than 10%) as a share of total household expenditure or income (%) SH_XPD_EARN10</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf\">Metadata 3-8-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Proporción de la población con grandes gastos sanitarios por hogar como porcentaje del total de gasto del hogar", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.8- Lograr la cobertura sanitaria universal, incluida la protección contra los riesgos financieros, el acceso a servicios de salud esenciales de calidad y el acceso a medicamentos y vacunas inocuos, eficaces, asequibles y de calidad para todos", "definicion"=>"Etxeko guztizko gastuaren % 10 edo % 25 baino gehiago osasun-gastuetara bideratzen den etxeetan bizi diren pertsonen proportzioa", "formula"=>"\n$$PPGGS_{\\gt x\\%}^{t} = \\frac{PGGS_{\\gt x\\%}^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$PGGS_{\\gt x\\%}^{t} =$ osasun-gastu handiak (etxeko guztizko gastuaren $%\\x$etik gorakoak) dituzten etxeetan bizi den biztanleria $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urtean\n", "desagregacion"=>"Gastuaren atalasea: Familia-gastuaren %10; %25", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAdierazleak familia-osasunean gastu handia duen biztanleriaren proportzioa neurtzen du, etxekoen gastu edo diru-sarrera \nosoaren ehuneko gisa. Bi atalase erabiltzen dira \"osasun-arloko familia-gastu handia\" definitzeko: etxekoen guztizko \ngastuaren edo diru-sarreraren % 10etik gorakoa eta % 25etik gorakoa. \n\n3.8 xedea osasun-estaldura unibertsalari (OEU) buruzkoa da, eta honela definitzen da: \"Osasun-estaldura unibertsala \nlortzea, finantza-arriskuen aurkako babesa, kalitatezko oinarrizko osasun-zerbitzuetarako sarbidea eta oinarrizko \nsendagai eta txerto seguruak, eraginkorrak, kalitatezkoak eta guztiontzat eskuragarriak eskuratzeko bidea barne\". \n\nAsmoa da pertsona eta komunitate guztiek behar dituzten kalitatezko osasun-zerbitzuak jasotzea (sendagaiak eta bestelako \nosasun-produktuak barne), finantza-zailtasunik gabe. Finantza-zailtasunak finantza-arriskuen aurkako babes-mekanismo \ndesegokien funtsezko ondorio dira, eta edozein herrialdetan izan daitezke, diru-sarreren maila eta osasun-sistemaren \nmota edozein izanda ere. \n\n3.8.2 adierazlearen bidez, ordaintzeko gaitasuna gainditzen duten osasun-arloko poltsikoko gastuak dituzten pertsonak \nidentifikatu nahi dira, horrek oinarrizko beste premia batzuetako gastua murriztea ekar bailezake, hala nola hezkuntzakoa, \nelikadurakoa, etxebizitzakoa eta zerbitzu publikoetakoa. \n\nOsasunaren arloko finantza-zailtasunak murriztea garrantzitsua da munduko garapen-agendan. Halaber, herrialde askotako \nosasun-sektoreak eskualde guztietan duen lehentasuna ere bada. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.8.2&seriesCode=SH_XPD_EARN25&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Etxeko osasun-gastu handiak ( %25etik gorakoak) dituzten biztanleen proportzioa, etxeko gastu edo diru-sarrera osoaren portzentaje gisa (%) SH_XPD_EARN25</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.8.2&seriesCode=SH_XPD_EARN10&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Etxeko osasun-gastu handiak ( %10etik gorakoak) dituzten biztanleen proportzioa, etxeko gastu edo diru-sarrera osoaren portzentaje gisa (%) SH_XPD_EARN10</a> UNSTATS\n", "comparabilidad"=>"Eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf\">Metadatuak 3-8-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.8.2: Proportion of population with large household expenditures on health as a share of total household expenditure or income</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Applies to all series (SH_XPD_EARN25 and SH_XPD_EARN10)</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-05-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>SDG indicators: 3.8.1; 1.1.1 and 1.2.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO) and the World Bank</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of the population with large household expenditure on health as a share of total household expenditure or income. Two thresholds are used to define &#x201C;large household expenditure on health&#x201D;: greater than 10% and greater than 25% of total household expenditure or income.</p>\n<p> </p>\n<p><strong>Concepts:</strong></p>\n<p>Indicator 3.8.2 is defined as the &#x201C;Proportion of the population with large household expenditure on health as a share of total household expenditure or income&#x201D;. In effect, it is based on a ratio exceeding a threshold. The two main concepts of interest behind this ratio are household expenditure on health (numerator) and total household consumption expenditure or, when unavailable, income (denominator). </p>\n<p><strong><em>Numerator</em></strong></p>\n<p>Household expenditure on health is defined as any expenditure incurred at the time of service use to get any type of care (promotive, preventive, curative, rehabilitative, palliative or long-term care), including all medicines, vaccines and other pharmaceutical preparations, as well as all health products, <em>from any type of provider and for all members of the household</em>. These health expenditures are characterized by direct payments that are financed by a household&#x2019;s income (including remittances), savings or loans <strong>but do not include any third-party payer reimbursement. </strong>They are labelled Out-Of-Pocket (OOP) payments in the classification of health care financing schemes (HF) of the International Classification for Health Accounts (ICHA). They are the most inequitable source of funding for the health system as they are solely based on the willingness and ability to pay of the household; they only grant access to the health services and health products individuals can pay for, without any solidarity between the healthy and the sick beyond the household<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup>, the rich and the poor; they represent a barrier to access for those people who are unable to find the economic resources need to pay out of their own pocket. </p>\n<p>The components of household expenditure on health should be consistent with division 06 on the health of the UN Classification of Individual Consumption According to Purpose (COICOP) on medicines and medical products (06.1), outpatient care services (06.2), inpatient care services (06.3) and other health services (06.4)<sup><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></sup>.</p>\n<p>Further information on definitions and classifications of health expenditures should be consistent with the <a href=\"http://www.who.int/health-accounts/methodology/en/\"><u>International Classification for Health Accounts</u></a><u> (ICHA)</u> and its family of classifications (for example, by type of provider). </p>\n<p><strong><em>Denominator</em></strong></p>\n<p>Expenditure on household consumption and household income are both monetary welfare measures. Household consumption is a function of permanent income, which is a measure of a household&#x2019;s long-term economic resources that determine living standards. Consumption is generally defined as the sum of the monetary values of all items consumed by the household on a domestic account during a common reference period<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup>. It includes monetary expenditures on food and non-food non-durable goods and services consumed as well as the imputed values of goods and services that are not purchased but procured otherwise for consumption (value of in-kind consumption); the value use of durables, and the value use of owner-occupied housing. Information on household consumption is usually collected in household surveys that may use different approaches to measure &#x2018;consumption&#x2019; depending on whether items refer to durable or non-durable goods and/or are directly produced by households.</p>\n<p>The most relevant measure of income is disposable income, as it is close to the maximum available to the household for consumption expenditure during the accounting period. Disposable income is defined as total income less direct taxes (net of refunds), compulsory fees and fines. Total income is generally composed of income from employment, property income, income from household production of services for own consumption, transfers received in cash and goods, and transfers received as services<sup><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></sup>. </p>\n<p>Income is more difficult to measure accurately due to its greater variability over time. Consumption is less variable over time and easier to measure. Therefore, it is recommended that whenever there is information on household consumption and income, the former is used (see the &#x201C;comments and limitations&#x201D; section to learn more about the sensitivity of 3.8.2 to the income/expenditure choice in the denominator). Statistics on 3.8.2 currently produced by WHO and the World Bank predominantly rely on consumption (see the section on data sources). </p>\n<p><strong><em>Thresholds</em></strong></p>\n<p>Two thresholds are used for global reporting to identify large household expenditure on health as a share of total household consumption or income<u>: </u><strong><u>a lower threshold of 10% (3.8.2_10) and a higher threshold of 25% (3.8.2_25)</u></strong>. With these two thresholds, the indicator measures financial hardship (see the section on comments and limitations). </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> <a href=\"http://www.oecd-ilibrary.org/social-issues-migration-health/a-system-of-health-accounts/classification-of-health-care-financing-schemes-icha-hf_9789264116016-9-en\">http://www.oecd-ilibrary.org/social-issues-migration-health/a-system-of-health-accounts/classification-of-health-care-financing-schemes-icha-hf_9789264116016-9-en</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> Agenda item 3(l) available at <a href=\"https://unstats.un.org/unsd/statcom/49th-session/documents/\">https://unstats.un.org/unsd/statcom/49th-session/documents/</a>; <a href=\"http://unstats.un.org/unsd/cr/registry/regcs.asp?Cl=5&amp;Lg=1&amp;Co=06.1\">http://unstats.un.org/unsd/cr/registry/regcs.asp?Cl=5&amp;Lg=1&amp;Co=06.1</a> <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099225003092220001/p1694340e80f9a00a09b20042de5a9cd47e <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> <a href=\"http://www.ilo.org/public/english/bureau/stat/download/17thicls/r2hies.pdf\">http://www.ilo.org/public/english/bureau/stat/download/17thicls/r2hies.pdf</a> <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) (proportion of people)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><em>For the definition of health expenditures (numerator) </em></p>\n<ul>\n  <li><a href=\"http://www.oecd-ilibrary.org/social-issues-migration-health/a-system-of-health-accounts/classification-of-health-care-financing-schemes-icha-hf_9789264116016-9-en\"><u>http://www.oecd-ilibrary.org/social-issues-migration-health/a-system-of-health-accounts/classification-of-health-care-financing-schemes-icha-hf_9789264116016-9-en</u></a><u> </u></li>\n</ul>\n<p><em>For the components of health expenditures (numerator)</em></p>\n<ul>\n  <li>division 06 of the UN Classification of Individual Consumption According to Purpose (COICOP) <a href=\"https://unstats.un.org/unsd/class/revisions/coicop_revision.asp\"><u>https://unstats.un.org/unsd/class/revisions/coicop_revision.asp</u></a>; </li>\n</ul>\n<p><em>For the components of household total consumption (preferred denominator)</em></p>\n<p>UN Classification of Individual Consumption According to Purpose (COICOP) <a href=\"https://unstats.un.org/unsd/class/revisions/coicop_revision.asp\"><u>https://unstats.un.org/unsd/class/revisions/coicop_revision.asp</u></a>; </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The recommended data sources for the monitoring of the &#x201C;Proportion of the population with large household expenditure on health as a share of total household expenditure or income&#x201D; are household surveys with information on both household consumption expenditure on health and total household consumption expenditures, which are routinely conducted by national statistical offices. Household budget surveys (HBS) and household income and expenditure surveys (HIES) typically collect these as they are primarily undertaken to provide inputs to the calculation of consumer price indices or the compilation of national accounts. Another potential source of information is socio-economic or living standards surveys; however, some of these surveys may not collect information on total household consumption expenditures &#x2013; for example, when a country measures poverty using income as the welfare indicator<sup><sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup></sup>. The most important criterion for selecting a data source to measure SDG indicator 3.8.2 is the availability of both household consumption expenditure on health and total household consumption expenditures.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> <a href=\"http://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01a.pdf\">http://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01a.pdf</a> <a href=\"#footnote-ref-6\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p>The World Health Organizaiton (WHO) and the World Bank contact Ministries of Health and/or National statistical offices for two purposes: a) request access to the household survey microdata in order to produce SDG indicator 3.8.2; b) request estimates produced by the country itself. </p>\n<p>A) The first type of request is done by each organization separately. WHO obtains access to the household survey microdata from national statistical offices through its regional offices or country offices. The access request is often part of technical assistance programs on health financing issues.</p>\n<p>The World Bank also typically receives data from National Statistical Offices (NSOs) directly. In other cases, it uses NSO data received indirectly. For example, it receives data from Eurostat and LIS (Luxembourg Income Study), which provide the World Bank NSO data in its original form or harmonized for comparability. The Universidad Nacional de La Plata, Argentina and the World Bank jointly maintain the SEDLAC (Socio-Economic Database for Latin American and Caribbean) database that includes harmonized statistics on poverty and other distributional and social variables from 24 Latin American and Caribbean countries, based on microdata from household surveys conducted by NSOs. Data is obtained through country-specific programs, including technical assistance programs and joint analytical and capacity-building activities. The World Bank has relationships with NSOs on work programs involving statistical systems and data analysis. Poverty economists from the World Bank typically engage with NSOs broadly on poverty measurement and analysis as part of technical assistance activities. </p>\n<p>The World Health Organization and the World Bank regularly undertake training events on the measurement of lack of financial protection coverage to produce SDG 3.8.2 indicator. This type of activity involves participants from the Ministry of Health as well as from the National Statistical Office. </p>\n<p>All the country-year estimates produced by both organizations are assembled in a joint database following a quality assessment process (see section 4.j). Such estimates are included in a country consultation conducted to give an opportunity to i) review the estimates, the data sources and the methods used for computation; ii) provide information about additional data sources; iii) build a mutual understanding of the strengths and weaknesses of available data and ensure broad ownership of the results; and iv) request estimates produced by the country as further explained hereafter.</p>\n<p>B) Estimates produced by each country are requested through a country consultation conducted by the World Health Organization. Following the WHO Executive Board resolution (EB107.R8), this process starts with WHO sending a formal request to ministries of health to nominate a focal point for the consultation. WHO sends draft estimates and methodological descriptions to them, copying countries&#x2019; focal points for SDG reporting where nominated at the request of the UN Statistics Division. Codes are available to reproduce the estimates shared. The focal points then send to WHO their comments, often including new data or revised country estimates that are used to update the country estimates. Estimates produced by the countries are subject to the same quality assessment process and included in the joint database if they are not flagged in consumption or the health budget share (see section 4.j).</p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>A country consultation on SDG 3.8.2 estimates is typically conducted between January and March every two years.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>SDG 3.8.2 estimates at country, regional and global levels are released every two years either on December 12 (Universal Health Coverage day) or in September (UN General Assembly). </p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices in collaboration with Ministries of Health. See 3.a Data sources for further details. </p>", "COMPILING_ORG__GLOBAL"=>"<p>The World Health Organization and the World Bank.</p>", "INST_MANDATE__GLOBAL"=>"<p>WHO support for monitoring the financial protection dimension of Universal Health Coverage (target 3.8, indicator 3.8.2 specifically) is underpinned by Resolution <a href=\"https://cdn.who.int/media/docs/default-source/health-financing/sustainable-health-financing-universal-coverage-and-social-health-insurance.pdf?sfvrsn=f8358323_3\">WHA58.33</a> on sustainable health financing, universal coverage and social health insurance.</p>", "RATIONALE__GLOBAL"=>"<p>Target 3.8 is about universal health coverage (UHC) and is defined as &#x201C;Achieve universal health coverage, including <em>financial risk protection</em>, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all&#x201D;. The concern is with all people and communities receiving the quality health services they need (including medicines and other health products) without financial hardship. Financial hardship is a key consequence of inadequate financial risk protection mechanisms and can be experienced in any country, regardless of the income level and type of health system. Indicator 3.8.2 is about identifying people with out-of-pocket health spending on health exceeding their ability to pay, which might lead to cutting spending on other basic needs such as education, food, housing and utilities. Reducing financial hardship in health is important on the global development agenda as well as a priority of the health sector of many countries across all regions.</p>", "REC_USE_LIM__GLOBAL"=>"<p>It is feasible to monitor indicator 3.8.2 on a regular basis using the same household survey data that is used to monitor SDG targets 1.1 and 1.2 on poverty<sup><sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></sup>. These surveys are also regularly conducted for other purposes, such as calculating weights for the Consumer Price Index. These surveys are typically undertaken by National Statistical Offices (NSOs). Thus, monitoring the proportion of the population with large household expenditures on health as a share of total household consumption or income does not add any additional data collection burden so long as the health expenditure component of the household non-food consumption data can be identified. While this is an advantage, indicator 3.8.2 suffers from the same challenges of timeliness, frequency, data quality and comparability of surveys as SDG indicator 1.1.1. However, indicator 3.8.2 has its own conceptual and empirical limitations.</p>\n<p>First, challenges to track out-of-pocket health spending (numerator): indicator 3.8.2 attempts to identify financial hardship that individuals face when using their income, savings or taking loans to pay for health care. However, most household surveys fail to identify the source of funding used by a household that is reporting health expenditure. In countries where there is no retrospective reimbursement of household spending on health, this is not a problem. If a household does report any expenditure on health, it would be because it will not be reimbursed by any third-party payer. It is, therefore, consistent with the definition given for direct health care payments (the numerator). For those countries, on the other hand, where there is retrospective reimbursement &#x2013; for example, via a contributory health insurance scheme - the amount reported by a household on health expenditures might be totally or partially reimbursed at some later point, perhaps outside the recall period of the household survey. </p>\n<p>Clearly, more work is needed to ensure that survey instruments gather information on the sources of funding used by the household to pay for health care or that the household survey instrument always specifies that health expenditures should be net of any reimbursement. The survey instrument and sample design should also be carefully reviewed to minimize measurement errors due to both non-sampling errors such as very short or very long recall periods precluding proper data collection of all health care components (overnight stay, medicines, etc.); or sampling errors such as over-sample of areas with a particularly low burden of disease.</p>\n<p>Second, the sensitivity of the indicator to the choice of the welfare metric for disaggregation (consumption or income in the denominator): in the current definition of indicator 3.8.2, large health expenditures can be identified by comparing how much household spend on health to either household income or total household expenditure. Expenditure is the recommended measure of a household&#x2019;s resources (see concept section), but recent empirical work has demonstrated that while statistics on 3.8.2 at the country level are fairly robust to such choice, their disaggregation by income group is pretty sensitive to it. Income-based measures show a greater concentration of the proportion of the population with large household expenditure on health among the poor than expenditure-based measures (see Chapter 2 in the WHO and World Bank 2017 report on tracking universal health coverage as well as Wagstaff et al. 2018).</p>\n<p>Third, cut-off values to identify large health expenditures: indicator 3.8.2. relies on a single cut-off point to identify what constitutes &#x2018;large health expenditure as a share of total household expenditure or income&#x2019;. People just below such threshold are not taken into account, which is always the problem with measures based on cut-offs. This is simply avoided by plotting the cumulative distribution function of the health expenditure ratio behind 3.8.2. By doing so, it is possible to identify for any threshold the proportion of the population that is devoting any share of its household&#x2019;s budget to health. </p>\n<p>Fourth, there are other indicators used to measure financial hardship, all based on the same data sources. The current definition of SDG indicator 3.8.2 is based on methodologies dating back to the 1990s developed in collaboration with academics at the World Bank and the World Health Organization. It corresponds to an indicator of the incidence of catastrophic health spending using a budget share approach (see references). In addition to SDG indicator 3.8.2, WHO also defines large health expenditure in relation to non-subsistence spending<sup><sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup>,<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup>,<sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup>,</sup> and both WHO and the World Bank use indicators of impoverishing health spending to assess to what extent OOP health spending deters efforts to &#x201C;End poverty in all its form everywhere&#x201D; (SDG 1). </p>\n<p>Fifth, SDG indicator 3.8.2. needs to be tracked jointly with SDG indicator 3.8.1, as well as indicators of barriers to access. Two indicators have been chosen to monitor target 3.8 on Universal Health Coverage within the SDG framework. SDG indicator 3.8.1 is for the health service coverage dimension of universal health coverage (UHC), and SDG indicator 3.8.2 tracks the financial protection dimensions. These two indicators should always be monitored jointly. Indeed, some of the people seeking care face barriers to access related to financial constraints, acceptability issues, unavailability of services, or accessibility. Those unable to overcome such barriers (financial and non-financial ones) will not report any spending on health, which will tend to reduce SDG indicator 3.8.2 rates. When this happens, SDG indicator 3.8.1 levels should also be low as the tracer indicators of service coverage should reflect that large fractions of the population are unable to get the services they need. But specific indicators on barriers to access ought to be tracked to understand which type of barriers is precluding access to needed services. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> <a href=\"http://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01a.pdf\">http://unstats.un.org/sdgs/metadata/files/Metadata-01-01-01a.pdf</a> <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> Chapter 2 in &#x201C;Tracking universal health coverage: 2017 global monitoring report&#x201D;, World Health Organization and International Bank for Reconstruction and Development/ The World Bank; 2017; <a href=\"http://www.who.int/healthinfo/indicators/2015/en/\">http://www.who.int/healthinfo/indicators/2015/en/</a> ; <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p>Xu, K., Evans, D. B., Carrin, G., Aguilar-Rivera, A. M., Musgrove, P., and Evans, T. (2007), &#x201C;Protecting Households From Catastrophic Health Spending,&#x201D; <em>Health Affairs</em>, 26, 972&#x2013;983. Xu, K., Evans, D., Kawabata, K., Zeramdini, R., Klavus, J., and Murray, C. (2003), &#x201C;Households Catastrophic Health Expenditure: A Multi-Country Analysis,&#x201D; <em>The Lancet</em>, 326, 111&#x2013;117. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> <a href=\"http://www.euro.who.int/en/health-topics/Health-systems/health-systems-financing/publications/clusters/universal-health-coverage-financial-protection\">http://www.euro.who.int/en/health-topics/Health-systems/health-systems-financing/publications/clusters/universal-health-coverage-financial-protection</a>;<a href=\"http://applications.emro.who.int/dsaf/EMROPUB_2016_EN_19169.pdf?ua=1\">http://applications.emro.who.int/dsaf/EMROPUB_2016_EN_19169.pdf?ua=1</a> ; <a href=\"http://apps.searo.who.int/uhc\">http://apps.searo.who.int/uhc</a><a href=\"http://www.paho.org/hq/index.php?option=com_content&amp;view=article&amp;id=11065%3A2015-universal-health-coverage-latin-america-caribbean&amp;catid=3316%3Apublications&amp;Itemid=3562&amp;lang=en\">http://www.paho.org/hq/index.php?option=com_content&amp;view=article&amp;id=11065%3A2015-universal-health-coverage-latin-america-caribbean&amp;catid=3316%3Apublications&amp;Itemid=3562&amp;lang=en</a> <a href=\"#footnote-ref-10\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>Population weighted average number of people with large household expenditure on health as a share of total household expenditure or income</p>\n<p> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>m</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n            <msub>\n              <mrow>\n                <mi>&#x3C9;</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n            <mn>1</mn>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mfrac>\n                  <mrow>\n                    <mi>h</mi>\n                    <mi>e</mi>\n                    <mi>a</mi>\n                    <mi>l</mi>\n                    <mi>t</mi>\n                    <mi>h</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>e</mi>\n                    <mi>x</mi>\n                    <mi>p</mi>\n                    <mi>e</mi>\n                    <mi>n</mi>\n                    <mi>d</mi>\n                    <mi>i</mi>\n                    <mi>t</mi>\n                    <mi>u</mi>\n                    <mi>r</mi>\n                    <mi>e</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>o</mi>\n                    <mi>f</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>t</mi>\n                    <mi>h</mi>\n                    <mi>e</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>h</mi>\n                    <mi>o</mi>\n                    <mi>u</mi>\n                    <mi>s</mi>\n                    <mi>e</mi>\n                    <mi>h</mi>\n                    <mi>o</mi>\n                    <mi>l</mi>\n                    <mi>d</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>i</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>t</mi>\n                    <mi>o</mi>\n                    <mi>t</mi>\n                    <mi>a</mi>\n                    <mi>l</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>e</mi>\n                    <mi>x</mi>\n                    <mi>p</mi>\n                    <mi>e</mi>\n                    <mi>n</mi>\n                    <mi>d</mi>\n                    <mi>i</mi>\n                    <mi>t</mi>\n                    <mi>u</mi>\n                    <mi>r</mi>\n                    <mi>e</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>o</mi>\n                    <mi>f</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>t</mi>\n                    <mi>h</mi>\n                    <mi>e</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>h</mi>\n                    <mi>o</mi>\n                    <mi>u</mi>\n                    <mi>s</mi>\n                    <mi>e</mi>\n                    <mi>h</mi>\n                    <mi>o</mi>\n                    <mi>l</mi>\n                    <mi>d</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>i</mi>\n                  </mrow>\n                </mfrac>\n                <mo>&amp;gt;</mo>\n                <mi>&#x3C4;</mi>\n              </mrow>\n            </mfenced>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>m</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n        <msub>\n          <mrow>\n            <mi>&#x3C9;</mi>\n          </mrow>\n          <mrow>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where <em>i </em>denotes a household,<em> 1()</em> is the indicator function that takes on the value 1 if the bracketed expression is true, and 0 otherwise, <em>m<sub>i</sub></em> corresponds to the number of household members of <em>i</em>,<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi>&#x3C9;</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n  </math> corresponds to the sampling weight of household <em>i</em>, <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">&#x3C4;</mi>\n  </math> is a threshold identifying large household expenditure on health as a share of total household consumption or income (i.e., 10% and 25%). </p>\n<p>Household health expenditure and household expenditure or income are defined as explained in the 2.a Definitions and concepts section. For more information about the methodology, please refer to Wagstaff et al. (2018) and Chapter 2 in the WHO and World Bank 2017 report on tracking universal health coverage.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The microdata obtained by WHO is requested to National Statistical Offices with the denominator (household total consumption expenditure) already constructed following their own guidelines and follows those guidelines when the denominator is not provided. WHO generates the numerator (household total health spending) following the definitions and classifications described in 2.a and 2.c. </p>\n<p>The microdata obtained by the World Bank is provided by country governments and typically includes the denominator and the numerator already constructed. Sometimes, the World Bank has to construct the welfare aggregate or adjust the aggregate provided by the country.</p>\n<p>The microdata obtained by both institutions to track SDG indicator 3.8.2 has typically already been checked for quality to track other important indicators (e.g. SDG indicator 1.1.1). A quality assessment is performed before consulting countries on SDG 3.8.2 estimates (see section 4.k).</p>\n<p>The estimates produced by both organizations are included in a consultation to obtain the country&#x2019;s feedback and revise as needed.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>At the country level, no imputation is attempted to produce estimates. The proportion of the population with large household expenditure on health as a share of total household expenditure or income is estimated for all years for which a nationally representative survey on the household budget, household income and expenditure, socio-economic conditions or living standards is available with information on both total household expenditure or income and total household expenditure on health. When there are multiple surveys over time for the same country from different collections, a preference is given to estimates produced based on the same type of survey. A series of tests is performed to retain the best performing series (see 4.k).</p>\n<p><strong>&#x2022; At regional levels</strong></p>\n<p>Because surveys are not conducted yearly in most countries, SDG 3.8.2 estimates across countries are computed for different years. To compute regional and global aggregates for a common reference year (i.e. every five years between 2000 and 2015; every two years from 2015.), survey-based country estimates are &#x201C;lined-up&#x201D; using one of the following different methods depending upon the availability of information for that country around or at the reference year (T*): In countries for which there is an observed incidence rate of the SDG indicator 3.8.2 in the reference year T*, this point is used. When there are at least two observed incidence rates of the SDG indicator 3.8.2 around the reference over a 5-year window around the reference year [T*&#x2013;5; T*+5], linear interpolation is used to project the value of SDG indicator 3.8.2 in the reference year. If these conditions are not met but there are at least two observed incidences rates of the SDG indicator 3.8.2, a multilevel model is estimated using the aggregate share of out-of-pocket health spending over total consumption expenditure as the explanatory variable if that information is available. If such information is not available or there aren&#x2019;t two incidence rates of the SDG indicator 3.8.2, the incidence rate is imputed in the reference year with the median incidence in that year among countries within the same income group (low, lower-middle, upper-middle, or high) as classified by the World Bank. If such classification is missing, the regional median value of the SDG indicator 3.8.2 at the 10% threshold is used. The regional classification used for the imputation is M49 level 1. The country estimates for the reference year are then aggregated up to the regional and global levels to compute the &#x201C;Total population with household expenditures on health greater than 10% of total household income or expenditure&#x201D; in millions. The proportion of the total population at the global and regional levels is then calculated by expressing these numbers as a share of the relevant population, equivalent to taking a population-weighted average of the relevant country rates. For more information, pleas consult the <a href=\"https://www.who.int/data/gho/indicator-metadata-registry/imr-details/4844\">WHO Global Health Observatory metadata registry (https://www.who.int/data/gho/indicator-metadata-registry/imr-details/4844).</a></p>\n<p>The aggregate proportion of the population with large household expenditure on health as a share of total household expenditure or income for a region corresponds to the total number of people across all the countries in that region with such large expenditures divided by the total number of people in that region. </p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates correspond to population-weighted averages of the &#x201C;lined-up&#x201D; country estimates (see 4.f).</p>\n<p>The World Bank and the World Health Organization use their own regional grouping in addition to the regional breakdown used for SDG reporting. </p>", "DOC_METHOD__GLOBAL"=>"<p>All documentation needed to compile the data at the national level is shared with nominated focal points every two years. It can be requested by National Statistical Offices as well as Ministries of Health along with Stata codes, to <a href=\"mailto:uhc_stats@who.int\">uhc_stats@who.int</a>, subject: package to produce SDG indicator 3.8.2. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The quality of the estimates is managed through WHO Health Financing and Economics unit and the World Bank Health, Nutrition and Population Global Practice, Global Engagement Unit</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The estimates released by the World Health Organization and the World Bank are quality checked by members of the WHO Health Financing and Economics unit and the World Bank Health, Nutrition and Population Global Practice, Global Engagement Unit and submitted to a country consultation composed of members of the relevant National Statistical Offices and Ministry of health every two years.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The World Health Organization and the World Bank generate indicator 3.8.2 following the methods, validation and treatment of missing values described in sections 4.c. to 4.d. Both institutions combine estimates at the meso-level. Eligibility of the estimates included in a joint global database at a country level and used to produce regional and global estimates is based on the following quality assessment:</p>\n<p><em>For the denominator of the health expenditure ratio</em></p>\n<ul>\n  <li>Compare the average monthly total household per capita consumption or income in a benchmark source with the average monthly value estimated from the survey. The comparison is based on the ratio of both averages (benchmark source to the survey-based estimate). If the ratio is greater than 20% (when both averages are based on consumption) or 30% (when the benchmark source estimate is based on income and the survey-based one on consumption), the survey point is identified as an outlier in terms of consumption per capita and flagged for possible exclusion. Both averages are expressed in interntional dollars. The source for the benchmark average is either the Poverty and Inequality Platform<sup><sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup></sup> (already expressed in international dollars), or derived from the World Development Indicators (WDI)<sup><sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup></sup> andcomputed as the household final consumption expenditures in constant international dollars divided by the total population. The average estimated from the survey is available in local nominal currency units. It is converted into internation dollars using purchasing power parities (PPP) for private consumption and consumer index prices. PPP data are downloadable from the World Bank&#x2019;s (WDI) data website14 and the Poverty and Inequality Platform (PIP). Data on CPIs is also downloadable from the Poverty and Inequality Platform (PIP). PIP is the preferred data source for both CPIs and PPPs. </li>\n  <li>Compare the poverty headcount estimated from the survey using international poverty lines with the poverty incidence reported in Poverty and Inequality Platform at the same poverty lines (benchmark value). When the absolute difference between the benchmark value and the survey-based estimate exceeds 10 percentage points, the survey-based point is identified as an outlier to track poverty using international poverty lines and flagged for possible exclusion. An extreme and moderate poverty line are used for this assessment. The latest value of international extreme poverty line is $2.15 per day per capita using 2017 purchasing power parities (PPPs) for private consumption and replaces the $1.90 poverty line based on 2011 PPPs. The latest value of the moderate international poverty line is $3.65 per person per day is based on 2017 PPPs which replaces the $3.20 poverty line based on 2011 PPPs. It corresponds to the typical standard used to assess national poverty levels in lower-middle-income countries. For more information about the latest purchasing power parity revision (PPP), please consult https://www.worldbank.org/en/news/factsheet/2022/05/02/fact-sheet-an-adjustment-to-global-poverty-lines</li>\n</ul>\n<p><em>For the numerator of the health expenditure ratio</em></p>\n<ul>\n  <li>Compare the average health expenditure ratio in the survey to a benchmark average health budget share. The latter is constructed from national health accounts data as the ratio of the aggregate measure of household out-of-pocket expenditures to the final consumption expenditure of households and profit institutions serving households, both in current local currency. When the absolute difference exceeds 5 percentage points, the survey point is identified as an outlier in terms of household budget share spent on health and flagged for possible exclusion. The macro-indicators are available from the <a href=\"https://apps.who.int/nha/database\"><u>Global Health Expenditure Database</u></a> (GHED)<sup><sup><a href=\"#footnote-13\" id=\"footnote-ref-13\">[12]</a></sup></sup>. </li>\n</ul>\n<p>These benchmarks are also used to decide which estimates to accept between two estimates for those countries and the years for which both institutions have the same data source. For a survey-based estimate of SDG indicator 3.8.2 to be included in the joint database and, therefore, in the country consultation conducted every two years previously described, it cannot be an outlier in consumption, nor in terms of the health budget share. </p>\n<p>Estimates produced by the countries and shared through the country consultation are subject to the same quality assurance process. They are included in the joint database if they are not flagged neither in consumption nor in the health budget share.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> <a href=\"https://pip.worldbank.org/home\">https://pip.worldbank.org/home</a> <a href=\"#footnote-ref-11\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> <a href=\"https://datacatalog.worldbank.org/dataset/world-development-indicators\">https://datacatalog.worldbank.org/dataset/world-development-indicators</a> <a href=\"#footnote-ref-12\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-13\">12</sup><p> <a href=\"https://apps.who.int/nha/database\">https://apps.who.int/nha/database</a> <a href=\"#footnote-ref-13\">&#x2191;</a></p></div></div>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The number of countries or territories with SDG 3.8.2 data increases over time as more surveys become available.. For more information and to get the latest updates, please use WHO and World Bank dedicated data portals: </p>\n<p><a href=\"https://www.who.int/data/gho/data/themes/topics/financial-protection\">https://www.who.int/data/gho/data/themes/topics/financial-protection</a> and</p>\n<p><a href=\"https://datatopics.worldbank.org/universal-health-coverage/\">https://datatopics.worldbank.org/universal-health-coverage/</a> </p>\n<p><strong>Time series:</strong></p>\n<p>The frequency of such data is similar to the frequency of the data used to produce SDG indicator 1.1.1. It varies across countries but on average, this ranges from an annual 1-year basis to 3 to 5 years.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The following disaggregation is possible in so far as the survey has been designed to provide representative estimates and/or there are enough observations collected at such level:</p>\n<ul>\n  <li>Geographic location (rural/urban)</li>\n  <li>Sex of the head of the household (male/female); </li>\n  <li>Age and sex of the head of the household (below 60 years old/ 60 years or older; male/female);</li>\n  <li>Age composition of the household based on the following grouping: &#x201C;Adults only (20-59 years old)&#x201D; - households that consist of members aged between 20 and 59 years old; &#x201C;Adults with children and adolescents (below 60 years old members)&#x201D; - households that consist of members aged below 60 only as follows: at least one member below 20 years old AND at least one member aged between 20 and 59 years old; &#x201C;Multigenerational households (all ages)&#x201D; - households that include at least one person below 20 years old AND at least one person aged between 20 and 59 years old AND at least one person &gt;= 60 years old; &#x201C;Adults with older persons (from 20 years old)&#x201D; - households that consist of members aged &gt;=20 only as follows: at least one person aged between 20 and 59 years old AND at least one person &gt;= 60 years old; &#x201C;Only older adults (&gt;=60 years old)&#x201D; - households that consist of members aged &gt;=60 years old only; &#x201C;Only members below 20 years old&#x201D; - households that consist of members aged below 20 years old only.</li>\n  <li>Geographic location (rural/urban)</li>\n  <li>Other possible disaggregation are possible such as by quintiles of the household welfare measures (total household consumption expenditure or income). See section 4.b on comments and limitations for the sensitivity of the disaggregation to the choice of the welfare measure.</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Country-level estimates are all based on nationally representative surveys with information on both household total expenditure or income and household expenditure on health (see data sources). In most cases, such data come from non-standard household surveys, and ex-post-standardization processes can be designed to increase the degree of comparability across countries. For instance, regional teams from the World Bank produce standardized versions of raw datasets following common regional proceduressuch as the Eastern Europe and Central Asia poverty harmonized datasets (ECAPOV<sup><sup><a href=\"#footnote-14\" id=\"footnote-ref-14\">[13]</a></sup></sup>); the Survey based Harmonized Indicators (SHIP) collection results from a poverty program on harmonized household surveys in the World Bank&#x2019;s African region, while the Standardized Household Economic Survey (SHES) collection was developed by the World Bank for the international comparison program. The Luxembourg income study (LIS) datasets result from an effort to harmonize datasets from many high and middle-income countries<sup><sup><a href=\"#footnote-15\" id=\"footnote-ref-15\">[14]</a></sup></sup>. </p>\n<p>In some cases, the raw data is accessible to produce country-level estimates. In some countries, both raw data and standardized versions are available; in some countries, only the standardized version is available. When multiple versions of the same survey are available, the estimate which performed best in a series of quality assurance tests is retained (see collection process). When a standardized version of a nationally designed survey instrument is chosen, there are differences between expenditure variables generated using the raw data and the expenditure variables generated using the harmonization procedures, which might result in the different estimated incidence of the population with large household expenditure on health as a share of household total expenditure or income.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-14\">13</sup><p> https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.15/2016/Wshp/Session_A._LEAD_PRESENTATION_WB_ENG.pdf <a href=\"#footnote-ref-14\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-15\">14</sup><p> http://www.lisdatacenter.org/ <a href=\"#footnote-ref-15\">&#x2191;</a></p></div></div>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><u>https://www.who.int/data/gho/data/themes/topics/financial-protection</u>; <a href=\"http://datatopics.worldbank.org/universal-health-coverage/\"><u>http://datatopics.worldbank.org/universal-health-coverage/</u></a> </p>\n<p><strong>References:</strong></p>\n<p><em>Global monitoring reports (e.g. 2015, 2017, 2019, 2021)</em></p>\n<p><a href=\"https://www.who.int/teams/health-systems-governance-and-financing/global-monitoring-report\">https://www.who.int/teams/health-systems-governance-and-financing/global-monitoring-report</a> </p>\n<p><em>Methodology:</em></p>\n<ul>\n  <li>Chapter 2 on Financial protection in &#x201C;Tracking universal health coverage: 2017 global monitoring report&#x201D;, World Health Organization and International Bank for Reconstruction and Development/ The World Bank; 2017; </li>\n  <li>Wagstaff, A., Flores, G., Hsu J., Smitz, M-F., Chepynoga, K., Buisman, L.R., van Wilgenburg, K. and Eozenou, P., (2018), &#x201C;Progress on catastrophic health spending in 133 countries: a retrospective observational study&#x201D;, the Lancet Global Health, volume 6, issue 2, e169-e179. </li>\n</ul>\n<p><a href=\"http://dx.doi.org/10.1016/S2214-109X(17)30429-1\"><u>http://dx.doi.org/10.1016/S2214-109X(17)30429-1</u></a></p>\n<ul>\n  <li>Chapter 18 of &#x201C;Analyzing health equity using household survey data&#x201D;. Washington, DC: World Bank Group; 2008, <a href=\"http://www.worldbank.org/en/topic/health/publication/analyzing-health-equity-using-household-survey-data\"><u>http://www.worldbank.org/en/topic/health/publication/analyzing-health-equity-using-household-survey-data</u></a> </li>\n</ul>", "indicator_sort_order"=>"03-08-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.9.1", "slug"=>"3-9-1", "name"=>"Tasa de mortalidad atribuida a la contaminación de los hogares y del aire ambiente", "url"=>"/site/es/3-9-1/", "sort"=>"030901", "goal_number"=>"3", "target_number"=>"3.9", "global"=>{"name"=>"Tasa de mortalidad atribuida a la contaminación de los hogares y del aire ambiente"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de mortalidad atribuida a la contaminación de los hogares y del aire ambiente", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de mortalidad atribuida a la contaminación de los hogares y del aire ambiente", "indicator_number"=>"3.9.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nComo parte de un proyecto más amplio para evaluar los principales factores \nde riesgo para la salud, se evaluó la mortalidad resultante de la exposición \na la contaminación del aire ambiental (exterior) y la contaminación del aire \ndoméstico (interior) causada por el uso de combustibles contaminantes para cocinar. \n\nLa contaminación del aire ambiental es el resultado de las emisiones de la \nactividad industrial, los hogares, los automóviles y los camiones, que son \nmezclas complejas de contaminantes del aire, muchos de los cuales son nocivos \npara la salud. De todos estos contaminantes, las partículas finas son las que \ntienen el mayor efecto sobre la salud humana. \n\nPor combustibles contaminantes se entiende la madera, el carbón, el \nestiércol animal, el carbón vegetal y los desechos de los cultivos, así \ncomo el queroseno. La contaminación del aire es el mayor riesgo ambiental \npara la salud. La mayor parte de la carga recae sobre las poblaciones de \nlos países de ingresos bajos y medios.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-09-01.pdf\">Metadatos 3-9-1.pdf</a> (solo en inglés)", "dato_global"=>"", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nAs part of a broader project to assess major risk factors to health, the mortality \nresulting from exposure to ambient (outdoor) air pollution and household (indoor) \nair pollution from polluting fuel use for cooking was assessed. \n\nAmbient air pollution results from emissions from industrial activity, households, \ncars and trucks which are complex mixtures of air pollutants, many of which are harmful \nto health. Of all of these pollutants, fine particulate matter has the greatest effect \non human health.\n\nBy polluting fuels is understood as wood, coal, animal dung, charcoal, and crop wastes, \nas well as kerosene. Air pollution is the biggest environmental risk to health. The \nmajority of the burden is borne by the populations in low and middle-income countries. \n\nSource: United Nations Statistics Division\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-09-01.pdf\">Metadata 3-9-1.pdf</a>", "dato_global"=>nil, "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nOsasunerako arrisku-faktore nagusiak ebaluatzeko proiektu zabalago baten barruan, ingurumen-airearen (kanpokoa) \nkutsadurarekiko esposizioaren ondoriozko hilkortasuna eta janaria prestatzeko erregai kutsatzaileen erabilerak \neragindako etxeko airearen kutsadura (barnekoa) ebaluatu ziren. \n\nIngurumen-airearen kutsadura industria-jardueraren, etxeen, automobilen eta kamioien emisioen emaitza da. Horiek \nairearen kutsatzaileen nahasketa konplexuak dira, eta horietako asko osasunerako kaltegarriak dira. Kutsatzaile \nhorien guztien artean, partikula finek dute eraginik handiena gizakiaren osasunean. \n\nErregai kutsatzaileak dira zura, ikatza, animalia-simaurra, landare-ikatza eta laboreen hondakinak, bai eta kerosenoa \nere. Airearen kutsadura da osasunerako ingurumen arriskurik handiena. Kargaren zatirik handiena diru-sarrera txikiko \neta ertaineko herrialdeetako biztanleei dagokie. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-09-01.pdf\">Metadatuak 3-9-1.pdf</a> (ingelesez bakarrik)", "dato_global"=>nil, "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.9.1: Mortality rate attributed to household and ambient air pollution </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_AAP_ASMORT - Age-standardized mortality rate attributed to ambient air pollution [3.9.1]</p>\n<p>SH_HAP_ASMORT - Age-standardized mortality rate attributed to household air pollution [3.9.1]</p>\n<p>SH_STA_ASAIRP - Age-standardized mortality rate attributed to household and ambient air pollution [3.9.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.6.2: Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted)</p>\n<p>7.1.2: Proportion of population with primary reliance on clean fuels and technology</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The mortality rate attributable to the joint effects of household and ambient air pollution can be expressed as: crude death rate or age-standardized death rate. Crude rates are calculated by dividing the brut number of deaths by the total population (or indicated if a different population group is used, e.g. children under 5 years), while the age-standardized rates adjust for differences in the age distribution of the population by applying the observed age-specific mortality rates for each population to a standard population.</p>\n<p>Evidence from epidemiological studies have shown that exposure to air pollution is linked, among others, to the important underlying causes of death taken into account in this estimate:</p>\n<p>- Acute lower respiratory infections (estimated in all age groups; ICD-10: J09-J22, P23, U04 );</p>\n<p>- Cerebrovascular diseases (stroke) in adults (estimated above 25 years; ICD-10: I60-I69);</p>\n<p>- Ischaemic heart diseases (IHD) in adults (estimated above 25 years; ICD-10: I20-I25);</p>\n<p>- Chronic obstructive pulmonary disease (COPD) in adults (estimated above 25 years; ICD-10: J40-J44); and</p>\n<p>- Lung cancer in adults (estimated above 25 years; ICD-10: C33-C34).</p>\n<p><strong>Concepts:</strong></p>\n<p>The mortality resulting from the exposure to ambient (outdoor) air pollution and household (indoor) air pollution from polluting fuels used for cooking and/or heating was assessed. Ambient air pollution results from emissions from industrial activity, households, cars and trucks which are complex mixtures of air pollutants, many of which are harmful to health. Of all these pollutants, fine particulate matter has the greatest effect on human health. By polluting fuels is understood kerosene, wood, coal, animal dung, charcoal, and crop wastes.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Deaths per 100,000 population</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>A. Exposure:</p>\n<ul>\n  <li>Household air pollution: Indicator 7.1.2 was used as exposure indicator Ambient air pollution: Annual mean concentration of particulate matter of less than 2.5 &#xB5;m was used as exposure indicator for ambient air pollution. The data is modelled according to methods described for Indicator 11.6.2.</li>\n</ul>\n<p>B. Exposure-response function: </p>\n<p>The integrated exposure-response functions (IER) developed for the Global Burden of Disease (GBD) project 2010 and 2013 (Burnett et al, 2014 and Forouzanfar et al, 2015) were used. These IERs were updated using the most recent epidemiological evidence identified through a systematic search of studies on particulate matter and mortality, for the five outcomes of interest. </p>\n<p>The exposure-response function captures the magnitude of the death risks due to the exposure to air pollution by integrating epidemiological evidence from four sources of PM: ambient air pollution, household air pollution, active smoking, and second-hand smoking; and excluding the possible effects of other risk factors on the outcomes of interest. Due that, it is possible to assess the attributable burden due to household and ambient air pollution using the same IERs. </p>\n<p>The IER has recently been included and is available for download in the AirQ+ software tool for health risk assessment of air pollution, version 2.2 (released in March the 14<sup>th</sup>, 2023).</p>\n<p>C. Background health burden: The total number of deaths by country, disease, sex and age group have been developed by the World Health Organization&#x2019;s (WHO 2019b) Global Health Estimates (GHE).</p>", "COLL_METHOD__GLOBAL"=>"<p>A. Exposure:</p>\n<ul>\n  <li>Household air pollution: As reported for Indicator 7.1.2 </li>\n  <li>Ambient air pollution: As reported for Indicator 11.6.2.</li>\n</ul>\n<p>B. Exposure-response function: </p>\n<p>Modelled by the WHO Air Quality and Health Unit with input from epidemiological studies on particulate matter and mortality, collected through a systematic search. </p>\n<p>C. Background health burden: collected from the WHO Global Health Estimates (GHE).</p>", "FREQ_COLL__GLOBAL"=>"<p>Not applicable</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Not applicable</p>", "DATA_SOURCE__GLOBAL"=>"<p>WHO Global Health Estimates</p>\n<p>Global Burden of Disease project</p>\n<p>WHO as a custodial agency of the SDG 11.6.2</p>\n<p>WHO as a custodial agency of the SDG 7.1.2</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>As part of a broader project to assess major risk factors to health, the mortality resulting from exposure to ambient (outdoor) air pollution and household (indoor) air pollution from polluting fuel use for cooking was assessed. Ambient air pollution results from emissions from industrial activity, households, cars and trucks which are complex mixtures of air pollutants, many of which are harmful to health. Of all of these pollutants, fine particulate matter has the greatest effect on human health. By polluting fuels is understood as wood, coal, animal dung, charcoal, and crop wastes, as well as kerosene.</p>\n<p>Air pollution is the biggest environmental risk to health. The majority of the burden is borne by the populations in low and middle-income countries.</p>", "REC_USE_LIM__GLOBAL"=>"<p>An approximation of the combined effects of risk factors (i.e., ambient and household air pollution) is possible if independence and little correlation between risk factors with impacts on the same diseases can be assumed (Ezzati et al, 2003). In the case of air pollution, however, there are some limitations to estimate the joint effects: limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions (Lim et al, 2012; Smith et al, 2014). In several regions, however, household air pollution remains mainly a rural issue, while ambient air pollution is predominantly an urban problem. Also, in some continents, many countries are relatively unaffected by household air pollution, while ambient air pollution is a major concern. If assuming independence and little correlation, a rough estimate of the total impact can be calculated, which is less than the sum of the impact of the two risk factors.</p>\n<p>On the other hand, as the IER function integrates epidemiological evidence from four sources of PM (i.e., ambient air pollution, household air pollution, active smoking and second-hand smoking), some assumptions are assumed. Specifically, the relative risk at any concentration is independent of the source of PM2.5, and only dependent on the magnitude of the total exposure from all sources together (Burnett et al, 2020).</p>", "DATA_COMP__GLOBAL"=>"<p>Attributable mortality is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed, proportion of population relying primarily on polluting fuels for cooking).</p>\n<p>This allows calculation of the &apos;population attributable fraction&apos; (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in that case of both the annual mean concentration of particulate matter and exposure to polluting fuels for cooking).</p>\n<p>Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths), gives the total number of deaths that results from exposure to that particular risk factor (in the example given above, to ambient and household air pollution).</p>\n<p>To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003).</p>\n<p>The mortality associated with household and ambient air pollution was estimated based on the calculation of the joint population attributable fractions assuming independently distributed exposures and independent hazards as described in (Ezzati et al, 2003).</p>\n<p>The joint population attributable fraction (PAF) were calculated using the following formula:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>P</mi>\n    <mi>A</mi>\n    <mi>F</mi>\n    <mo>=</mo>\n    <mn>1</mn>\n    <mo>-</mo>\n    <mi>P</mi>\n    <mi>R</mi>\n    <mi>O</mi>\n    <mi>D</mi>\n    <mi>U</mi>\n    <mi>C</mi>\n    <mi>T</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mn>1</mn>\n    <mo>-</mo>\n    <mi>P</mi>\n    <mi>A</mi>\n    <mi>F</mi>\n    <mi>i</mi>\n    <mo>)</mo>\n  </math></p>\n<p>Where PAFi is PAF of individual risk factors.</p>\n<p>The PAF for ambient air pollution and the PAF for household air pollution were assessed separately, based on the Comparative Risk Assessment (Ezzati et al, 2002) and expert groups for the Global Burden of Disease (GBD) 2010 study (Lim et al, 2012; Smith et al, 2014).</p>\n<p>For exposure to ambient air pollution, annual mean estimates of particulate matter of a diameter of less than 2.5 um (PM25) were modelled as described in (Shaddick et al, 2018; Shaddick et al, 2021)), or for Indicator 11.6.2.</p>\n<p>For exposure to household air pollution, the proportion of population with primary reliance on polluting fuels use for cooking was modelled (see Indicator 7.1.2 [polluting fuels use=1-clean fuels use]). Details on the model are published in (Bonjour et al, 2013).</p>\n<p>The integrated exposure-response functions (IER) developed for the GBD 2010 and 2013 (Burnett et al, 2014 and Forouzanfar et al, 2015) were used. These IERs were updated using the most recent epidemiological evidence identified through a systematic search of studies on particulate matter and mortality for the five outcomes of interest.</p>\n<p>The percentage of the population exposed to a specific risk factor (here ambient air pollution, i.e. PM2.5) was provided by country and by increment of 1 &#xB5;g/m3; relative risks were calculated for each PM2.5 increment, based on the IER. The counterfactual concentration was selected to be between 2.4 and 5.9 &#xB5;g/m<sup>3</sup>, as described elsewhere (Cohen et al, 2017). The country population attributable fraction for ALRI, COPD, IHD, stroke and lung cancer were calculated using the following formula: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>P</mi>\n    <mi>A</mi>\n    <mi>F</mi>\n    <mo>=</mo>\n    <mi>S</mi>\n    <mi>U</mi>\n    <mi>M</mi>\n    <mo>(</mo>\n    <mi>P</mi>\n    <mi>i</mi>\n    <mo>(</mo>\n    <mi>R</mi>\n    <mi>R</mi>\n    <mo>-</mo>\n    <mn>1</mn>\n    <mo>)</mo>\n    <mo>/</mo>\n    <mo>(</mo>\n    <mi>S</mi>\n    <mi>U</mi>\n    <mi>M</mi>\n    <mo>(</mo>\n    <mi>R</mi>\n    <mi>R</mi>\n    <mo>-</mo>\n    <mn>1</mn>\n    <mo>)</mo>\n    <mo>+</mo>\n    <mn>1</mn>\n    <mo>)</mo>\n  </math></p>\n<p>Where i is the level of PM2.5 in ug/m<sup>3</sup>, and Pi is the percentage of the population exposed to that level of air pollution, and RR is the relative risk.</p>\n<p>The calculations for household air pollution are similar and are explained in detail elsewhere (WHO 2014a).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Not applicable</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Countries with no data are reported as blank.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Countries with no data are not considered to estimate the regional and global averages.</p>", "REG_AGG__GLOBAL"=>"<p>Number of deaths by country are summed and divided by the population of countries included in the region (regional aggregates) or by the total population (global aggregates).</p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data is available by country, sex, disease and age.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The data is available by country, by sex, by disease, and by age.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Underlying differences between country produced and internationally estimated data may due to :</p>\n<p>- Different exposure data (annual mean concentration of particulate matter of less than 2.5 &#xB5;m of diameter, proportion of population using clean fuels and technology for cooking)</p>\n<p>- Different exposure-risk estimates</p>\n<p>- Different underlying mortality data</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>https://www.who.int/data/gho/data/themes/air-pollution</p>\n<p><strong>References:</strong></p>\n<p>Bonjour S, Adair-Rohani H, Wolf J, Bruce NG, Mehta S, Pr&#xFC;ss-Ust&#xFC;n A, Lahiff M, Rehfuess EA, Mishra V, Smith KR. (2013). Solid fuel use for household cooking: country and regional estimates for 1980-2010. Environ Health Perspect. 121(7):784-90. doi: 10.1289/ehp.1205987. </p>\n<p>Burnett RT, Pope CA 3rd, Ezzati M, Olives C, Lim SS, Mehta S, Shin HH, Singh G, Hubbell B, Brauer M, Anderson HR, Smith KR, Balmes JR, Bruce NG, Kan H, Laden F, Pr&#xFC;ss-Ust&#xFC;n A, Turner MC, Gapstur SM, Diver WR, Cohen A. (2014). An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. Environ Health Perspect. 122(4):397-403. doi: 10.1289/ehp.1307049. </p>\n<p>Burnett R, Cohen A. (2020). Relative Risk Functions for Estimating Excess Mortality Attributable to Outdoor PM2.5 Air Pollution: Evolution and State-of-the-Art. Atmosphere, 11, 589. <a href=\"https://doi.org/10.3390/atmos11060589\">https://doi.org/10.3390/atmos11060589</a></p>\n<p>Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, Balakrishnan K, Brunekreef B, Dandona L, Dandona R, Feigin V, Freedman G, Hubbell B, Jobling A, Kan H, Knibbs L, Liu Y, Martin R, Morawska L, Pope CA 3rd, Shin H, Straif K, Shaddick G, Thomas M, van Dingenen R, van Donkelaar A, Vos T, Murray CJL, Forouzanfar MH. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet. 389(10082):1907-1918. doi: 10.1016/S0140-6736(17)30505-6. </p>\n<p>Ezzati M, Hoorn SV, Rodgers A, Lopez AD, Mathers CD, Murray CJ. (2003). Comparative Risk Assessment Collaborating Group. Estimates of global and regional potential health gains from reducing multiple major risk factors. Lancet. 362(9380):271-80. doi: 10.1016/s0140-6736(03)13968-2. </p>\n<p>Forouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S, Brauer M, Burnett R, Casey D, Coates MM, Cohen A, Delwiche K, Estep K, Frostad JJ, Astha KC, Kyu HH, Moradi-Lakeh M, Ng M, Slepak EL, Thomas BA, Wagner J, Aasvang GM, Abbafati C, Abbasoglu Ozgoren A, Abd-Allah F, Abera SF, Aboyans V, Abraham B, Abraham JP, Abubakar I, Abu-Rmeileh NM, Aburto TC, Achoki T, Adelekan A, Adofo K, Adou AK, Adsuar JC, Afshin A, Agardh EE, Al Khabouri MJ, Al Lami FH, Alam SS, Alasfoor D, Albittar MI, Alegretti MA, Aleman AV, Alemu ZA, Alfonso-Cristancho R, Alhabib S, Ali R, Ali MK, Alla F, Allebeck P, Allen PJ, Alsharif U, Alvarez E, Alvis-Guzman N, Amankwaa AA, Amare AT, Ameh EA, Ameli O, Amini H, Ammar W, Anderson BO, Antonio CA, Anwari P, Argeseanu Cunningham S, Arnl&#xF6;v J, Arsenijevic VS, Artaman A, Asghar RJ, Assadi R, Atkins LS, Atkinson C, Avila MA, Awuah B, Badawi A, Bahit MC, Bakfalouni T, Balakrishnan K, Balalla S, Balu RK, Banerjee A, Barber RM, Barker-Collo SL, Barquera S, Barregard L, Barrero LH, Barrientos-Gutierrez T, Basto-Abreu AC, Basu A, Basu S, Basulaiman MO, Batis Ruvalcaba C, Beardsley J, Bedi N, Bekele T, Bell ML, Benjet C, Bennett DA, Benzian H, Bernab&#xE9; E, Beyene TJ, Bhala N, Bhalla A, Bhutta ZA, Bikbov B, Bin Abdulhak AA, Blore JD, Blyth FM, Bohensky MA, Bora Ba&#x15F;ara B, Borges G, Bornstein NM, Bose D, Boufous S, Bourne RR, Brainin M, Brazinova A, Breitborde NJ, Brenner H, Briggs AD, Broday DM, Brooks PM, Bruce NG, Brugha TS, Brunekreef B, Buchbinder R, Bui LN, Bukhman G, Bulloch AG, Burch M, Burney PG, Campos-Nonato IR, Campuzano JC, Cantoral AJ, Caravanos J, C&#xE1;rdenas R, Cardis E, Carpenter DO, Caso V, Casta&#xF1;eda-Orjuela CA, Castro RE, Catal&#xE1;-L&#xF3;pez F, Cavalleri F, &#xC7;avlin A, Chadha VK, Chang JC, Charlson FJ, Chen H, Chen W, Chen Z, Chiang PP, Chimed-Ochir O, Chowdhury R, Christophi CA, Chuang TW, Chugh SS, Cirillo M, Cla&#xDF;en TK, Colistro V, Colomar M, Colquhoun SM, Contreras AG, Cooper C, Cooperrider K, Cooper LT, Coresh J, Courville KJ, Criqui MH, Cuevas-Nasu L, Damsere-Derry J, Danawi H, Dandona L, Dandona R, Dargan PI, Davis A, Davitoiu DV, Dayama A, de Castro EF, De la Cruz-G&#xF3;ngora V, De Leo D, de Lima G, Degenhardt L, del Pozo-Cruz B, Dellavalle RP, Deribe K, Derrett S, Des Jarlais DC, Dessalegn M, deVeber GA, Devries KM, Dharmaratne SD, Dherani MK, Dicker D, Ding EL, Dokova K, Dorsey ER, Driscoll TR, Duan L, Durrani AM, Ebel BE, Ellenbogen RG, Elshrek YM, Endres M, Ermakov SP, Erskine HE, Eshrati B, Esteghamati A, Fahimi S, Faraon EJ, Farzadfar F, Fay DF, Feigin VL, Feigl AB, Fereshtehnejad SM, Ferrari AJ, Ferri CP, Flaxman AD, Fleming TD, Foigt N, Foreman KJ, Paleo UF, Franklin RC, Gabbe B, Gaffikin L, Gakidou E, Gamkrelidze A, Gankp&#xE9; FG, Gansevoort RT, Garc&#xED;a-Guerra FA, Gasana E, Geleijnse JM, Gessner BD, Gething P, Gibney KB, Gillum RF, Ginawi IA, Giroud M, Giussani G, Goenka S, Goginashvili K, Gomez Dantes H, Gona P, Gonzalez de Cosio T, Gonz&#xE1;lez-Castell D, Gotay CC, Goto A, Gouda HN, Guerrant RL, Gugnani HC, Guillemin F, Gunnell D, Gupta R, Gupta R, Guti&#xE9;rrez RA, Hafezi-Nejad N, Hagan H, Hagstromer M, Halasa YA, Hamadeh RR, Hammami M, Hankey GJ, Hao Y, Harb HL, Haregu TN, Haro JM, Havmoeller R, Hay SI, Hedayati MT, Heredia-Pi IB, Hernandez L, Heuton KR, Heydarpour P, Hijar M, Hoek HW, Hoffman HJ, Hornberger JC, Hosgood HD, Hoy DG, Hsairi M, Hu G, Hu H, Huang C, Huang JJ, Hubbell BJ, Huiart L, Husseini A, Iannarone ML, Iburg KM, Idrisov BT, Ikeda N, Innos K, Inoue M, Islami F, Ismayilova S, Jacobsen KH, Jansen HA, Jarvis DL, Jassal SK, Jauregui A, Jayaraman S, Jeemon P, Jensen PN, Jha V, Jiang F, Jiang G, Jiang Y, Jonas JB, Juel K, Kan H, Kany Roseline SS, Karam NE, Karch A, Karema CK, Karthikeyan G, Kaul A, Kawakami N, Kazi DS, Kemp AH, Kengne AP, Keren A, Khader YS, Khalifa SE, Khan EA, Khang YH, Khatibzadeh S, Khonelidze I, Kieling C, Kim D, Kim S, Kim Y, Kimokoti RW, Kinfu Y, Kinge JM, Kissela BM, Kivipelto M, Knibbs LD, Knudsen AK, Kokubo Y, Kose MR, Kosen S, Kraemer A, Kravchenko M, Krishnaswami S, Kromhout H, Ku T, Kuate Defo B, Kucuk Bicer B, Kuipers EJ, Kulkarni C, Kulkarni VS, Kumar GA, Kwan GF, Lai T, Lakshmana Balaji A, Lalloo R, Lallukka T, Lam H, Lan Q, Lansingh VC, Larson HJ, Larsson A, Laryea DO, Lavados PM, Lawrynowicz AE, Leasher JL, Lee JT, Leigh J, Leung R, Levi M, Li Y, Li Y, Liang J, Liang X, Lim SS, Lindsay MP, Lipshultz SE, Liu S, Liu Y, Lloyd BK, Logroscino G, London SJ, Lopez N, Lortet-Tieulent J, Lotufo PA, Lozano R, Lunevicius R, Ma J, Ma S, Machado VM, MacIntyre MF, Magis-Rodriguez C, Mahdi AA, Majdan M, Malekzadeh R, Mangalam S, Mapoma CC, Marape M, Marcenes W, Margolis DJ, Margono C, Marks GB, Martin RV, Marzan MB, Mashal MT, Masiye F, Mason-Jones AJ, Matsushita K, Matzopoulos R, Mayosi BM, Mazorodze TT, McKay AC, McKee M, McLain A, Meaney PA, Medina C, Mehndiratta MM, Mejia-Rodriguez F, Mekonnen W, Melaku YA, Meltzer M, Memish ZA, Mendoza W, Mensah GA, Meretoja A, Mhimbira FA, Micha R, Miller TR, Mills EJ, Misganaw A, Mishra S, Mohamed Ibrahim N, Mohammad KA, Mokdad AH, Mola GL, Monasta L, Monta&#xF1;ez Hernandez JC, Montico M, Moore AR, Morawska L, Mori R, Moschandreas J, Moturi WN, Mozaffarian D, Mueller UO, Mukaigawara M, Mullany EC, Murthy KS, Naghavi M, Nahas Z, Naheed A, Naidoo KS, Naldi L, Nand D, Nangia V, Narayan KM, Nash D, Neal B, Nejjari C, Neupane SP, Newton CR, Ngalesoni FN, Ngirabega Jde D, Nguyen G, Nguyen NT, Nieuwenhuijsen MJ, Nisar MI, Nogueira JR, Nolla JM, Nolte S, Norheim OF, Norman RE, Norrving B, Nyakarahuka L, Oh IH, Ohkubo T, Olusanya BO, Omer SB, Opio JN, Orozco R, Pagcatipunan RS Jr, Pain AW, Pandian JD, Panelo CI, Papachristou C, Park EK, Parry CD, Paternina Caicedo AJ, Patten SB, Paul VK, Pavlin BI, Pearce N, Pedraza LS, Pedroza A, Pejin Stokic L, Pekericli A, Pereira DM, Perez-Padilla R, Perez-Ruiz F, Perico N, Perry SA, Pervaiz A, Pesudovs K, Peterson CB, Petzold M, Phillips MR, Phua HP, Plass D, Poenaru D, Polanczyk GV, Polinder S, Pond CD, Pope CA, Pope D, Popova S, Pourmalek F, Powles J, Prabhakaran D, Prasad NM, Qato DM, Quezada AD, Quistberg DA, Racap&#xE9; L, Rafay A, Rahimi K, Rahimi-Movaghar V, Rahman SU, Raju M, Rakovac I, Rana SM, Rao M, Razavi H, Reddy KS, Refaat AH, Rehm J, Remuzzi G, Ribeiro AL, Riccio PM, Richardson L, Riederer A, Robinson M, Roca A, Rodriguez A, Rojas-Rueda D, Romieu I, Ronfani L, Room R, Roy N, Ruhago GM, Rushton L, Sabin N, Sacco RL, Saha S, Sahathevan R, Sahraian MA, Salomon JA, Salvo D, Sampson UK, Sanabria JR, Sanchez LM, S&#xE1;nchez-Pimienta TG, Sanchez-Riera L, Sandar L, Santos IS, Sapkota A, Satpathy M, Saunders JE, Sawhney M, Saylan MI, Scarborough P, Schmidt JC, Schneider IJ, Sch&#xF6;ttker B, Schwebel DC, Scott JG, Seedat S, Sepanlou SG, Serdar B, Servan-Mori EE, Shaddick G, Shahraz S, Levy TS, Shangguan S, She J, Sheikhbahaei S, Shibuya K, Shin HH, Shinohara Y, Shiri R, Shishani K, Shiue I, Sigfusdottir ID, Silberberg DH, Simard EP, Sindi S, Singh A, Singh GM, Singh JA, Skirbekk V, Sliwa K, Soljak M, Soneji S, S&#xF8;reide K, Soshnikov S, Sposato LA, Sreeramareddy CT, Stapelberg NJ, Stathopoulou V, Steckling N, Stein DJ, Stein MB, Stephens N, St&#xF6;ckl H, Straif K, Stroumpoulis K, Sturua L, Sunguya BF, Swaminathan S, Swaroop M, Sykes BL, Tabb KM, Takahashi K, Talongwa RT, Tandon N, Tanne D, Tanner M, Tavakkoli M, Te Ao BJ, Teixeira CM, T&#xE9;llez Rojo MM, Terkawi AS, Texcalac-Sangrador JL, Thackway SV, Thomson B, Thorne-Lyman AL, Thrift AG, Thurston GD, Tillmann T, Tobollik M, Tonelli M, Topouzis F, Towbin JA, Toyoshima H, Traebert J, Tran BX, Trasande L, Trillini M, Trujillo U, Dimbuene ZT, Tsilimbaris M, Tuzcu EM, Uchendu US, Ukwaja KN, Uzun SB, van de Vijver S, Van Dingenen R, van Gool CH, van Os J, Varakin YY, Vasankari TJ, Vasconcelos AM, Vavilala MS, Veerman LJ, Velasquez-Melendez G, Venketasubramanian N, Vijayakumar L, Villalpando S, Violante FS, Vlassov VV, Vollset SE, Wagner GR, Waller SG, Wallin MT, Wan X, Wang H, Wang J, Wang L, Wang W, Wang Y, Warouw TS, Watts CH, Weichenthal S, Weiderpass E, Weintraub RG, Werdecker A, Wessells KR, Westerman R, Whiteford HA, Wilkinson JD, Williams HC, Williams TN, Woldeyohannes SM, Wolfe CD, Wong JQ, Woolf AD, Wright JL, Wurtz B, Xu G, Yan LL, Yang G, Yano Y, Ye P, Yenesew M, Yent&#xFC;r GK, Yip P, Yonemoto N, Yoon SJ, Younis MZ, Younoussi Z, Yu C, Zaki ME, Zhao Y, Zheng Y, Zhou M, Zhu J, Zhu S, Zou X, Zunt JR, Lopez AD, Vos T, Murray CJ. (2015). Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 386(10010):2287-323. doi: 10.1016/S0140-6736(15)00128-2. </p>\n<p>Shaddick G, Thomas ML, Green A, Brauer M, van Donkelaar A, Burnett R, Chang HH, Cohen A, Van Dingenen R, Dora C, Gumy S, Liu Y, Martin R, Waller LA, West J, Zidek JV, Pr&#xFC;ss-Ust&#xFC;n A. (2018). Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution. Journal of the Royal Statistical Society. Series C (Applied Statistics), 67(1), 231&#x2013;253. <a href=\"http://www.jstor.org/stable/44682225\">http://www.jstor.org/stable/44682225</a></p>\n<p>Shaddick G, Salter JM, Peuch VH, Ruggeri G, Thomas ML, Mudu P, Tarasova O, Baklanov A, Gumy S. (2021). Global Air Quality: An Inter-Disciplinary Approach to Exposure Assessment for Burden of Disease Analyses. Atmosphere, 12, 48. https://doi.org/10.3390/atmos12010048</p>\n<p>Smith KR, Bruce N, Balakrishnan K, Adair-Rohani H, Balmes J, Chafe Z, Dherani M, Hosgood HD, Mehta S, Pope D, Rehfuess E; HAP CRA Risk Expert Group. (2014). Millions dead: how do we know and what does it mean? Methods used in the comparative risk assessment of household air pollution. Annu Rev Public Health. 35:185-206. doi: 10.1146/annurev-publhealth-032013-182356</p>\n<p>WHO (2014a). Methods description for the burden of disease attributable to household air pollution. Access at: <a href=\"http://www.who.int/phe/health_topics/outdoorair/database/HAP_BoD_methods_March2014.pdf?ua=1\">http://www.who.int/phe/health_topics/outdoorair/database/HAP_BoD_methods_March2014.pdf?ua=1</a> </p>\n<p>WHO (2019b). Global Health Estimates 2019: Deaths by Cause, Age and Sex, by Country, 2000-2019 (provisional estimates). Geneva, World Health Organization, 2019. </p>", "indicator_sort_order"=>"03-09-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.9.2", "slug"=>"3-9-2", "name"=>"Tasa de mortalidad atribuida al agua insalubre, el saneamiento deficiente y la falta de higiene (exposición a servicios insalubres de agua, saneamiento e higiene para todos (WASH))", "url"=>"/site/es/3-9-2/", "sort"=>"030902", "goal_number"=>"3", "target_number"=>"3.9", "global"=>{"name"=>"Tasa de mortalidad atribuida al agua insalubre, el saneamiento deficiente y la falta de higiene (exposición a servicios insalubres de agua, saneamiento e higiene para todos (WASH))"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de mortalidad atribuida al agua insalubre, el saneamiento deficiente y la falta de higiene (exposición a servicios insalubres de agua, saneamiento e higiene para todos (WASH))", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de mortalidad atribuida al agua insalubre, el saneamiento deficiente y la falta de higiene (exposición a servicios insalubres de agua, saneamiento e higiene para todos (WASH))", "indicator_number"=>"3.9.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nEl indicador expresa el número de muertes causadas por la falta de agua, \nsaneamiento e higiene (con especial atención a los servicios de agua, saneamiento \ne higiene) que podrían evitarse mejorando esos servicios y prácticas. \n\nSe basa tanto en la prestación de servicios de agua, saneamiento e higiene en \nel país como en los resultados sanitarios relacionados, por lo que proporciona \ninformación importante sobre las enfermedades reales causadas por los riesgos \nmedidos en las metas 6.1 y 6.2.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-09-02.pdf\">Metadatos 3-9-2.pdf</a> (solo en inglés)", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.9.2&seriesCode=SH_STA_WASHARI&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Tasa de mortalidad atribuida al agua insalubre, el saneamiento inseguro y la falta de higiene por diarrea, infecciones por nematodos intestinales, desnutrición e infecciones respiratorias agudas (muertes por cada 100.000 habitantes) SH_STA_WASHARI</a> UNSTATS", "informacion_interes"=>"", "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2025-03-12", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe indicator expresses the number of deaths from inadequate water, sanitation and \nhygiene (with focus on WASH services) which could be prevented by improving those \nservices and practices. \n\nIt is based on both the WASH service provision in the country, as well as the related \nhealth outcomes, and therefore provides important information on the actual disease \ncaused by the risks measured in targets 6.1 and 6.2.\n\nSource: United Nations Statistics Division\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-09-02.pdf\">Metadata 3-9-2.pdf</a>", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.9.2&seriesCode=SH_STA_WASHARI&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene from diarrhoea, intestinal nematode infections, malnutrition and acute respiratory infections (deaths per 100,000 population) SH_STA_WASHARI</a> UNSTATS", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nAdierazle honek zehazten du zenbat pertsona hil diren urik, saneamendurik eta higienerik ez izateagatik \n(arreta berezia jarriz ur-, saneamendu- eta higiene-zerbitzuetan). Zerbitzu eta praktika horiek hobetuta \nsaihestu litekeen egoera da hori. \n\nHerrialdeko ur-, saneamendu- eta higiene-zerbitzuetan eta horiekin lotutako osasun emaitzetan oinarritzen da, \neta, beraz, 6.1 eta 6.2 xedeetan neurtutako arriskuek eragindako gaixotasun errealei buruzko informazio \ngarrantzitsua ematen du. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-09-02.pdf\">Metadatuak 3-9-2.pdf</a> (ingelesez bakarrik)", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.9.2&seriesCode=SH_STA_WASHARI&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Higiene eza, hesteetako nematodoek eragindako infekzioak, desnutrizioa eta arnas infekzio akutuengatik, ur osasungaitzari, saneamendu ez-seguruari eta beherakoari egotzitako heriotza-tasa (heriotzak 100.000 biztanleko) SH_STA_WASHARI</a> UNSTATS", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.9.2: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services) </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Not applicable</p>", "META_LAST_UPDATE__GLOBAL"=>"2022-07-07", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 7.1.2: Proportion of population with primary reliance on clean fuels and technology </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>The mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services) as defined as the number of deaths from unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe WASH services) in a year, divided by the population, and multiplied by 100,000. </p>\n<p> </p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>Deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services, expressed per 100,000 population; The included diseases are diarrhoea (GHE code 110 which includes ICD-10 codes A00, A01, A03, A04, A06-A09), acute respiratory infections (GHE code 380 which includes ICD-10 codes H65-H66, J00-J22, P23, and U04) intestinal nematode infections (GHE codes 340, 350 and 360 which include ICD-10 codes B76-B77, and B79) and protein-energy malnutrition (GHE code 550 which includes ICD-10 codes E40-E46). </p>\n<p> </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Mortality rate (deaths per 100,000 population)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data is compiled mainly from country and other databases directly. To maximize the data for robust estimates, as well as to reduce duplication of data collection to avoid further data reporting burden on countries, complementary data are used from various databases (please refer to section 4.c. for specific data sources). </p>", "COLL_METHOD__GLOBAL"=>"<p>WHO conducts a formal country consultation process before releasing its cause-of-death estimates. </p>", "FREQ_COLL__GLOBAL"=>"<p>Ongoing </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>2022, second quarter </p>", "DATA_SOURCE__GLOBAL"=>"<p>National statistics offices, Various line ministries and databases covering civil registration with complete coverage and medical certification of cause of death. </p>", "COMPILING_ORG__GLOBAL"=>"<p>WHO </p>", "INST_MANDATE__GLOBAL"=>"<p>The World Health Organization (WHO) is the Custodian Agency or co-Custodian Agency for reporting on several SDG indicators, including indicator 3.9.2, the mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services).</p>", "RATIONALE__GLOBAL"=>"<p>The indicator expresses the number of deaths from inadequate water, sanitation and hygiene (with focus on WASH services) which could be prevented by improving those services and practices. It is based on both the WASH service provision in the country, as well as the related health outcomes, and therefore provides important information on the actual disease caused by the risks measured in targets 6.1 and 6.2. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Data rely on (a) statistics on WASH services (6.1 and 6.2), which are well assessed in almost all countries, and (b) data on deaths. Data on deaths are also widely available from countries from death registration data or sample registration systems, which are certainly feasible systems. Such data are crucial for improving health and reducing preventable deaths in countries. The main limitation is that not all countries do have such registration systems to date, and data need to be completed with other type of information. </p>", "DATA_COMP__GLOBAL"=>"<p><u>4.c.i. Model</u></p>\n<p>&apos;WHO estimation of health impacts from environmental risks is based on comparative risk assessment (CRA) methods, which are used extensively in burden of disease assessments (Ezzati et al., 2002). This approach estimates the proportional reduction in disease or death that would occur if exposures were reduced to an alternative baseline level bearing a minimum risk (also referred to as theoretical minimum risk), while other conditions remain unchanged. The CRA methodology combines data on exposure, disease burden and the exposure-response relationship to estimate the burden of disease associated with that exposure (Ezzati et al., 2002). For each risk factor (unsafe water, sanitation, or hygiene), the population attributable fraction (PAF) is estimated by comparing current exposure distributions to a counterfactual distribution, for each exposure level, sex and age group:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">A</mi>\n    <mi mathvariant=\"normal\">F</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <munderover>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi mathvariant=\"normal\">i</mi>\n              <mo>=</mo>\n              <mn>1</mn>\n            </mrow>\n            <mrow>\n              <mi mathvariant=\"normal\">n</mi>\n            </mrow>\n          </munderover>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">p</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">i</mi>\n              </mrow>\n            </msub>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mo>(</mo>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">R</mi>\n                <mi mathvariant=\"normal\">R</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">i</mi>\n              </mrow>\n            </msub>\n            <mo>-</mo>\n            <mn>1</mn>\n            <mo>)</mo>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mrow>\n          <munderover>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi mathvariant=\"normal\">i</mi>\n              <mo>=</mo>\n              <mn>1</mn>\n            </mrow>\n            <mrow>\n              <mi mathvariant=\"normal\">n</mi>\n            </mrow>\n          </munderover>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">p</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">i</mi>\n              </mrow>\n            </msub>\n            <mfenced separators=\"|\">\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi mathvariant=\"normal\">R</mi>\n                    <mi mathvariant=\"normal\">R</mi>\n                  </mrow>\n                  <mrow>\n                    <mi mathvariant=\"normal\">i</mi>\n                  </mrow>\n                </msub>\n                <mo>-</mo>\n                <mn>1</mn>\n              </mrow>\n            </mfenced>\n            <mo>+</mo>\n            <mn>1</mn>\n          </mrow>\n        </mrow>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where <em>pi</em> and <em>RR<sub>i</sub></em> are the proportion of the exposed population and the relative risk at exposure level <em>i</em>, respectively, and <em>n</em> is the total number of exposure levels. The joint burden of exposure to unsafe water, sanitation and hygiene was estimated by the following formula (6):</p>\n<p> <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">A</mi>\n    <mi mathvariant=\"normal\">F</mi>\n    <mo>=</mo>\n    <mn>1</mn>\n    <mo>-</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mrow>\n      <munderover>\n        <mo stretchy=\"false\">&#x220F;</mo>\n        <mrow>\n          <mi mathvariant=\"normal\">r</mi>\n          <mo>=</mo>\n          <mn>1</mn>\n        </mrow>\n        <mrow>\n          <mi mathvariant=\"normal\">R</mi>\n        </mrow>\n      </munderover>\n      <mrow>\n        <mo>(</mo>\n        <mn>1</mn>\n        <mo>-</mo>\n        <msub>\n          <mrow>\n            <mi mathvariant=\"normal\">P</mi>\n            <mi mathvariant=\"normal\">A</mi>\n            <mi mathvariant=\"normal\">F</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">r</mi>\n          </mrow>\n        </msub>\n        <mo>)</mo>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>Where <em>r </em>is the individual risk factor, and <em>R</em> the total of risk factors accounted for in the cluster. Additional details on the methods of estimation are available from various publications (1,7). </p>\n<p>This methodology has been used extensively to calculate the health gains from improvements in water supply, as well as sanitation and hygiene and had been published in various documents (Clasen et al., 2014; Pr&#xFC;ss-Ust&#xFC;n et al., 2014; Pr&#xFC;ss-Ust&#xFC;n et al., 2019)</p>\n<p>The following four types of data are required to produce estimates for indicator 3.9.2:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Data type </p>\n      </td>\n      <td>\n        <p>Source</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Population </p>\n        <p>Country level population figures </p>\n      </td>\n      <td>\n        <p><u>UN Population Division. </u><a href=\"https://population.un.org/wpp/\">https://population.un.org/wpp/</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Exposure </p>\n        <p>The necessary water indicators include </p>\n        <ul>\n          <li>safely managed drinking water services; </li>\n          <li>basic drinking water services; </li>\n          <li>population using surface water, unimproved drinking water sources, or limited drinking water services; </li>\n          <li>population practising household water treatment with filtration, chlorination, or solar disinfection. </li>\n        </ul>\n        <p>The necessary sanitation indicators include </p>\n        <ul>\n          <li>basic sanitation services with sewer connections; </li>\n          <li>basic sanitation services without sewer connections; </li>\n          <li>open defecation, unimproved sanitation facilities, or limited sanitation services</li>\n        </ul>\n        <p>One hygiene indicator is used: </p>\n        <ul>\n          <li>population practising handwashing with soap and water after potential faecal contacts.</li>\n        </ul>\n      </td>\n      <td>\n        <p>Many of these data are available in the global database maintained by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, and several are SDG indicators. Where countries lack data for one or more indicators, missing values are imputed using multi-level logistic modelling (Wolf et al, 2013; Pr&#xFC;ss-Ust&#xFC;n et al., 2014; Pr&#xFC;ss-Ust&#xFC;n et al., 2019)</p>\n        <p><a href=\"http://www.washdata.org\">www.washdata.org</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Diseaase burden </p>\n        <p>The total number of deaths and DALYs caused by diarrhoeal disease per year.</p>\n      </td>\n      <td>\n        <p>WHO Global Health Observatory (GHO) <a href=\"https://www\">https://www</a>.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Exposure-response relationship</p>\n        <p>The relative risk, which links exposure with disease. </p>\n      </td>\n      <td>\n        <p>The calculation uses the exposure-response relationship for drinking water and diarrhoea calculated as part of the most recent systematic review of water and sanitation intervention studies and impacts on diarrhoea </p>\n        <p>(Wolf, J, 2022, under review).</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p> </p>", "DATA_VALIDATION__GLOBAL"=>"<p>Draft estimates are reviewed with Member States through a WHO country consultation process and SDG focal points every time new data are generated. In addition, the methods and data are published in a peer-reviewed journal. 2016 estimates were published in 2019 (see 4.c.), and the manuscript for the 2019 estimates presently being submitted is currently under development, with plans for submission to a peer-reviewed journal by April 2022. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>For population data and disease burden envelopes, complete datasets are available, so there are no issues with missing data at the country level. For exposure data, many of these data are available in the global database maintained by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene, and several are SDG indicators. Where data are lacking for one or more required indicators, missing values are imputed using multi-level logistic modelling (Wolf et al, 2013). </p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p> </p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Country estimates of number of deaths by cause are summed to obtain regional and global aggregates. Populations published by the UNPD&#x2019;s World Population Prospects are aggregated to regional and global levels. The mortality rate is then calculated at the regional and global levels. </p>\n<p> </p>", "DOC_METHOD__GLOBAL"=>"<p>Data for this indicator are not routinely collected by countries. Rather, they are modelled using Comparative Risk Assessment methods<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> (For further information please see section 4.c.). However, while countries do not routinely collect these data to feed into the global figures for indicator 3.9.2, there have been a small number of requests for technical assistance from WHO country offices for support in the country-level calculation of WASH-attributable disease burden. A country tool is in development to enable countries to calculate the estimated burden of disease associated with WASH for their own country, and this will be available later this year. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Pr&#xFC;ss-Ust&#xFC;n A, Wolf J, Bartram J, Clasen T, Cumming O, Freeman MC, Gordon B, Hunter PR, Medlicott K, Johnston R. Burden of disease from inadequate water, sanitation and hygiene for selected adverse health outcomes: an updated analysis with a focus on low- and middle-income countries. International journal of hygiene and environmental health. 2019 Jun 1; 222(5): 765-77. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "QUALITY_MGMNT__GLOBAL"=>"<p>For information on data quality management, assurance, and assessment processes at WHO, please refer to: <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a></p>", "QUALITY_ASSURE__GLOBAL"=>"<p>For information on data quality management, assurance, and assessment processes at WHO, please refer to: <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a></p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>For information on data quality management, assurance, and assessment processes at WHO, please refer to: <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a></p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are available for 183 UN Member States, and can be accessed through the WHO Global Health Observatory: https://apps.who.int/gho/data/view.main.INADEQUATEWSHv?lang=en</p>\n<p> </p>\n<p><strong>Time series:</strong></p>\n<p>Previous rounds of estimates have been published with reference years of 2012, 2015, and 2016. As there have been changes in methods for diarrhoea, they have limited comparability. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>National, regional and global data are available at the total population; disaggregated into male and female populations; and for the population under age five.</p>\n<p> </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>WHO is required by World Health Assembly resolution to consult on all WHO statistics, and seek feedback from countries on data about countries and territories. Before publishing, all estimates undergo country consultations. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p> </p>\n<p>WHO indicator definition <a href=\"https://www.who.int/data/gho/indicator-metadata-registry/imr-details/2260\">https://www.who.int/data/gho/indicator-metadata-registry/imr-details/2260</a> </p>\n<p>WHO methods and data sources for global causes of death, 2000&#x2013;2012 <a href=\"https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf\">https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf</a></p>\n<p> </p>\n<p><strong>References:</strong> </p>\n<p>Clasen, T., Pr&#xFC;ss-Ust&#xFC;n, A., Mathers, C. D., Cumming, O., Cairncross, S., &amp; Colford, J. M. (2014). Estimating the impact of unsafe water, sanitation and hygiene on the global burden of disease: evolving and alternative methods. Trop Med Int Health, 19(8), 884-893. <a href=\"https://doi.org/10.1111/tmi.12330\">https://doi.org/10.1111/tmi.12330</a></p>\n<p>Ezzati, M., Lopez, A. D., Rodgers, A., Vander Hoorn, S., Murray, C. J., &amp; Group, C. R. A. C. (2002). Selected major risk factors and global and regional burden of disease. Lancet, 360(9343), 1347-1360. <a href=\"https://doi.org/10.1016/S0140-6736(02)11403-6\">https://doi.org/10.1016/S0140-6736(02)11403-6</a></p>\n<p>&apos;Pr&#xFC;ss-Ust&#xFC;n, A., Bartram, J., Clasen, T., Colford, J. M., Cumming, O., Curtis, V., . . . Cairncross, S. (2014). Burden of disease from inadequate water, sanitation and hygiene in low- and middle-income settings: a retrospective analysis of data from 145 countries. Trop Med Int Health, 19(8), 894-905. <a href=\"https://doi.org/10.1111/tmi.12329\">https://doi.org/10.1111/tmi.12329</a></p>\n<p>Pr&#xFC;ss-Ust&#xFC;n A, Wolf J, Bartram J, Clasen T, Cumming O, Freeman MC, Gordon B, Hunter PR, Medlicott K, Johnston R. (2019) Burden of disease from inadequate water, sanitation and hygiene for selected adverse health outcomes: an updated analysis with a focus on low- and middle-income countries. International journal of hygiene and environmental health. 222(5): 765-77.</p>\n<p><a href=\"https://doi.org/10.1016/j.ijheh.2019.05.004\" target=\"_blank\">https://doi.org/10.1016/j.ijheh.2019.05.004</a></p>\n<p>&apos;WHO (2014). Preventing diarrhoea through better water, sanitation and hygiene: exposures and impacts in low- and middle-income countries. <a href=\"https://www.who.int/publications/i/item/9789241564823\">https://www.who.int/publications/i/item/9789241564823</a> </p>\n<p>Wolf, J., Bonjour, S., &amp; Pr&#xFC;ss-Ust&#xFC;n, A. (2013). An exploration of multilevel modeling for estimating access to drinking-water and sanitation. <em>Journal of Water and Health</em>, <em>11</em>(1), 64-77</p>\n<p><a href=\"https://doi.org/10.2166/wh.2012.107\" target=\"_blank\">https://doi.org/10.2166/wh.2012.107</a></p>\n<p> </p>\n<p> </p>\n<p>. </p>", "indicator_sort_order"=>"03-09-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.9.3", "slug"=>"3-9-3", "name"=>"Tasa de mortalidad atribuida a intoxicaciones involuntarias", "url"=>"/site/es/3-9-3/", "sort"=>"030903", "goal_number"=>"3", "target_number"=>"3.9", "global"=>{"name"=>"Tasa de mortalidad atribuida a intoxicaciones involuntarias"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de mortalidad atribuida a intoxicaciones involuntarias", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de mortalidad atribuida a intoxicaciones involuntarias", "indicator_number"=>"3.9.3", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_28/opt_0/tipo_1/ti_defunciones/temas.html", "url_text"=>"Estadística de defunciones", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tasa de mortalidad atribuida a intoxicaciones involuntarias", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.9- De aquí a 2030, reducir considerablemente el número de muertes y enfermedades causadas por productos químicos peligrosos y por la polución y contaminación del aire, el agua y el suelo", "definicion"=>"Defunciones atribuidas a intoxicaciones involuntarias por cada 100.000 habitantes", "formula"=>"\n$$TM_{\\text{intoxicaciones involuntarias}}^{t} = \\frac{D_{\\text{intoxicaciones involuntarias}}^{t}}{P^{t}} \\cdot 100.000$$\n\ndonde:\n\n$D_{\\text{intoxicaciones involuntarias}}^{t} =$ defunciones atribuidas a intoxicaciones involuntarias (códigos X40, X43, X44 y X46-X49 de la CIE-10) en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La medición de la tasa de mortalidad por envenenamientos no intencionales proporciona una indicación del \ngrado de gestión inadecuada de los productos químicos peligrosos y la contaminación, y de la eficacia del sistema de \nsalud de un país.\n\nLa tasa de mortalidad atribuida a envenenamiento no intencional se define como el número de muertes por envenenamiento \nno intencional en un año, dividido por la población y multiplicado por 100.000.Incluye los códigos de la \nClasificación Internacional de Enfermedades, Décima Revisión (CIE-10) X40, X43, X46-X48 y X49.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.9.3&seriesCode=SH_STA_POISN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\"> Tasa de mortalidad atribuida a intoxicaciones no intencionales, por sexo (muertes por cada 100.000 habitantes) SH_STA_POISN</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-09-03.pdf\">Metadatos 3-9-3.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Tasa de mortalidad atribuida a intoxicaciones involuntarias", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.9- De aquí a 2030, reducir considerablemente el número de muertes y enfermedades causadas por productos químicos peligrosos y por la polución y contaminación del aire, el agua y el suelo", "definicion"=>"Deaths attributed to unintentional poisoning per 100.000 inhabitants", "formula"=>"\n$$TM_{\\text{unintentional poisoning}}^{t} = \\frac{D_{\\text{unintentional poisoning}}^{t}}{P^{t}} \\cdot 100.000$$\n\nwhere:\n\n$D_{\\text{unintentional poisoning}}^{t} =$ deaths attributed to unintentional poisoning (codes X40, X43, X44 y X46-X49 of the CIE-10) in year $t$\n\n$P^{t} =$ population as of 1 July of year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The measure of mortality rate from unintentional poisonings provides an indication of the extent \nof inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s \nhealth system. It includes codes X40, X43, X46-X48, X49 of the International Classification of Diseases, \nTenth Revision (ICD-10). \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.9.3&seriesCode=SH_STA_POISN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\"> Mortality rate attributed to unintentional poisonings, by sex (deaths per 100,000 population) SH_STA_POISN</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-09-03.pdf\">Metadata 3-9-3.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Tasa de mortalidad atribuida a intoxicaciones involuntarias", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.9- De aquí a 2030, reducir considerablemente el número de muertes y enfermedades causadas por productos químicos peligrosos y por la polución y contaminación del aire, el agua y el suelo", "definicion"=>"Nahi gabeko intoxikazioei egotzitako heriotzak 100.000 biztanleko", "formula"=>"\n$$TM_{\\text{nahi gabeko intoxikazioak}}^{t} = \\frac{D_{\\text{nahi gabeko intoxikazioak}}^{t}}{P^{t}} \\cdot 100.000$$\n\nnon:\n\n$D_{\\text{nahi gabeko intoxikazioak}}^{t} =$ nahi gabeko intoxikazioei egotzitako heriotzak (GNS-10eko X40, X43, X44 y X46-X49 kodeak) $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Nahita egin ez diren pozoitzeen ondoriozko heriotza-tasaren neurketak produktu kimiko arriskutsuen eta \nkutsaduraren kudeaketa-maila desegokia adierazten du, bai eta herrialde bateko osasun-sistemaren eraginkortasuna ere. \n\nNahita egin ez den pozoitzeari egotzitako heriotza-tasa honela definitzen da: nahita egin ez den pozoitzeak eragindako \nheriotzen kopurua urtebetean, zati biztanleria eta bider 100.000. Gaixotasunen Nazioarteko Sailkapenaren kodeak biltzen \nditu: X40, X43, X46-X48 eta X49, Hamargarren Berrikuspena (GNS-10). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.9.3&seriesCode=SH_STA_POISN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\"> Nahigabeko intoxikazioei egotzitako heriotza-tasa, sexuaren arabera (heriotzak 100.000 biztanleko) SH_STA_POISN</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-09-03.pdf\">Metadatuak 3-9-3.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.9.3: Mortality rate attributed to unintentional poisoning</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_STA_POISN - Mortality rate attributed to unintentional poisonings [3.9.3]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 7.1.2: Proportion of population with primary reliance on clean fuels and technology </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>The mortality rate attributed to unintentional poisoning as defined as the number of deaths of unintentional poisonings in a year, divided by the population, and multiplied by 100,000. </p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>Mortality rate in the country from unintentional poisonings per year. The International Classification of Diseases, Tenth Revision (ICD-10) codes corresponding to the indicator includes X40, X43, X46-X48, X49. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Rate per 100,000 population </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Poisonings are defined in terms of the International Classification of Diseases, Tenth Revision (ICD-10) (See 2.a).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data inputs to the estimate include cause-of-death data, of which the preferred data source is death registration systems with complete coverage and medical certification of cause of death.</p>", "COLL_METHOD__GLOBAL"=>"<p>WHO collects data directly from country sources, and following established method, estimates are shared with countries to receive their feedback before publication. See Indicator 6.1 for more details. </p>", "FREQ_COLL__GLOBAL"=>"<p>WHO sends an e-mail twice annually requesting tabulated death registration data (including all causes of death) from Member States. Countries may submit annual cause-of-death statistics to WHO on an ongoing basis.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Every 1-2 years</p>", "DATA_SOURCE__GLOBAL"=>"<p>National statistics offices, various line ministries and databases covering civil registration with complete coverage and medical certification of cause of death. </p>", "COMPILING_ORG__GLOBAL"=>"<p>World Health Organization (WHO) </p>", "INST_MANDATE__GLOBAL"=>"<p>According to Article 64 of its constitution, WHO is mandated to request each Member State to provide statistics on mortality. Furthermore, the WHO Nomenclature Regulations of 1967 affirms the importance of compiling and publishing statistics of mortality and morbidity in comparable form. Member States started to report mortality data to WHO since the early fifties and this reporting activity is continuing until today. </p>", "RATIONALE__GLOBAL"=>"<p>The measure of mortality rate from unintentional poisonings provides an indication of the extent of inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country&#x2019;s health system.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data on deaths are widely available from countries from death registration data or sample registration systems, which are feasible systems, but good quality data are not yet available in all countries. Such data are crucial for improving health and reducing preventable deaths in countries. For countries that do not have such registration systems, data need to be completed with other types of information. </p>", "DATA_COMP__GLOBAL"=>"<p>The methods with agreed international standards have been developed, reviewed and published in various documents. </p>\n<p>For countries with a high-quality vital registration system including information on cause of death, the vital registration that member states submit to the WHO Mortality Database were used, with adjustments where necessary, e.g. for under-reporting of deaths, unknown age and sex, and ill-defined causes of deaths. </p>\n<p> </p>\n<p>For countries without high-quality death registration data, cause of death estimates are calculated using other data, including household surveys with verbal autopsy, sample or sentinel registration systems, special studies. Complete methodology may be found here: </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The number of deaths were country consulted with country designated focal points (usually at the Ministry of Health or National Statistics Office) as part of the full set of causes of death prior to the release.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Deaths of unknown sex were redistributed pro-rata within cause-age groups of known sexes, and then deaths of unknown age were redistributed pro-rata within cause-sex groups of known ages.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>For countries with high-quality cause-of-death statistics, interpolation/extrapolation was done for missing country-years; for countries with only low-quality or no data on causes of death, modelling was used. Complete methodology may be found here: </p>\n<p>WHO methods and data sources for country-level causes of death 2000-2021 (https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2021_cod_methods.pdf)</p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p>Not applicable </p>", "REG_AGG__GLOBAL"=>"<p>Country estimates of number of deaths by cause are summed to obtain regional and global aggregates. </p>", "DOC_METHOD__GLOBAL"=>"<p>The cause of death categories (including unintentional poisoning follow the definitions in terms of the International Classification of Diseases, Tenth Revision (ICD-10). Please see Annex Table A of the WHO methods and data sources for country-level causes of death 2000-2021 (https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2021_cod_methods.pdf)</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The World Health Organization (WHO) established a Reference Group on Health Statistics in 2013 to provide advice to it on population health statistics with a focus on methodological and data issues related to the measurement of mortality and cause-of-death patterns. The group facilitated interaction between multilateral development institutions and other independent academic groups with WHO expert groups in specific subject areas including methods to the estimation on causes of death.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The data principles of the World Health Organization (WHO) provide a foundation for continually reaffirming trust in its information and evidence on public health. The five principles are designed to provide a framework for data governance for the organization. The principles are intended primarily for use by all staff in order to help define the values and standards that govern how data that flows into, across and out of the organization is collected, processed, shared and used. These principles are made publicly available so that they may be used and referred to by Member States and non-state actors collaborating with the organization.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>All statements and claims made officially by WHO headquarters about population-level (country, regional, global) estimates of health status (e.g. mortality, incidence, prevalence, burden of disease), are cleared by the Department of Data and Analytics (DNA) through the executive clearance process. These include the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement. GATHER promotes best practices in reporting health estimates using a checklist of 18 items that should be reported every time new global health estimates are published, including descriptions of input data and estimation methods. Developed by a working group convened by the World Health Organization, the guidelines aim to define and promote good practice in reporting health estimates.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Almost 70 countries currently provide WHO with regular high-quality data on mortality by age, sex and causes of death, and another 58 countries submit data of lower quality. However, comprehensive cause-of-death estimates are calculated by WHO systematically for all of its Member States (with a certain population threshold) every 1-2 years. </p>\n<p><strong>Time series:</strong></p>\n<p>From 2000 to 2021</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data can be disaggregated by age group, sex and disease. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>WHO is required by World Health Assembly resolution to consult on all its statistics, and seek feedback from countries on data about countries and territories before publishing all estimates. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p><a href=\"https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates\">https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates</a></p>\n<p> </p>\n<p><strong>References:</strong> </p>\n<p>WHO indicator definition (http://apps.who.int/gho/data/node.imr.SDGPOISON?lang=en) </p>\n<p> </p>\n<p>WHO methods and data sources for country-level causes of death 2000-2021 (https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2021_cod_methods.pdf)</p>", "indicator_sort_order"=>"03-09-03", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.a.1", "slug"=>"3-a-1", "name"=>"Prevalencia del consumo actual de tabaco a partir de los 15 años de edad (edades ajustadas)", "url"=>"/site/es/3-a-1/", "sort"=>"03aa01", "goal_number"=>"3", "target_number"=>"3.a", "global"=>{"name"=>"Prevalencia del consumo actual de tabaco a partir de los 15 años de edad (edades ajustadas)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de personas de 15 y más años que fuman a diario", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Prevalencia del consumo actual de tabaco a partir de los 15 años de edad (edades ajustadas)", "indicator_number"=>"3.a.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Salud", "periodicity"=>"Quinquenal", "url"=>"https://www.euskadi.eus/encuesta-salud/inicio/", "url_text"=>"Encuesta de salud", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Proporción de personas de 15 y más años que fuman a diario", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.a- Fortalecer la aplicación del Convenio Marco de la Organización Mundial de la Salud para el Control del Tabaco en todos los países, según proceda", "definicion"=>"Proporción de personas de 15 y más años que fuman a diario", "formula"=>"\n$$PPFD_{15+}^{t} = \\frac{PFD_{15+}^{t}}{P_{15+}^{t}} \\cdot 100$$\n\ndonde:\n\n$PFD_{15+}^{t} =$ población de 15 y más años que fuma a diario en el año $t$\n\n$P_{15y+}^{t} =$ población de 15 y más años en el año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "periodicidad"=>"Quinquenal", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl indicador de Naciones Unidas se define como el porcentaje de la población de 15 años o más que \nactualmente consume algún producto de tabaco (tabaco fumado y/o sin humo) de manera diaria o no diaria.\n\nEl consumo de tabaco es una de las principales causas de enfermedad y muerte por enfermedades no transmisibles (ENT). \nNo se ha demostrado que el consumo de tabaco o la exposición al humo de tabaco ajeno sean seguros. Todos los consumidores \ndiarios y no diarios de tabaco corren el riesgo de sufrir diversos problemas de salud a lo largo de la vida, \nincluidas las ENT.\n\nReducir la prevalencia del consumo actual de tabaco contribuirá en gran medida a reducir la mortalidad prematura por \nENT (meta 3.4).\n\nEl seguimiento sistemático y regular de este indicador es necesario para permitir un seguimiento y \nuna evaluación precisos del impacto de la aplicación del Convenio Marco de la OMS para el Control del Tabaco \n(CMCT de la OMS) o de las políticas de control del tabaco en los países que aún no son Partes en el CMCT de la OMS, \na lo largo del tiempo.\n\nLos niveles de prevalencia del consumo de tabaco son un indicador adecuado de la aplicación \nde la meta 3.a de los ODS “Fortalecer la aplicación del Convenio Marco de la Organización Mundial de la Salud para el \nControl del Tabaco en todos los países, según proceda”. \n\nFuente: División de Estadísticas de las Naciones Unidas \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.a.1&seriesCode=SH_PRV_SMOK&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20BOTHSEX\"> Prevalencia estandarizada por edad del consumo actual de tabaco entre personas de 15 años o más, por sexo (%) SH_PRV_SMOK</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple estrictamente con los metadatos de Naciones Unidas pero aporta información similar.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0a-01.pdf\">Metadatos 3-a-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de personas de 15 y más años que fuman a diario", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.a- Fortalecer la aplicación del Convenio Marco de la Organización Mundial de la Salud para el Control del Tabaco en todos los países, según proceda", "definicion"=>"Proportion of people aged 15 and over who smoke daily", "formula"=>"\n$$PPFD_{15+}^{t} = \\frac{PFD_{15+}^{t}}{P_{15+}^{t}} \\cdot 100$$\n\nwhere:\n\n$PFD_{15+}^{t} =$ population aged 15 and over who smoke daily in the year $t$\n$P_{15y+}^{t} =$ population aged 15 and over in year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "periodicidad"=>"Quinquenal", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe United Nations indicator is defined as the percentage of the population \naged 15 years and over who currently use any tobacco product (smoked and/or \nsmokeless tobacco) on a daily or non-daily basis.\n\nTobacco use is a major contributor to illness and death from non-communicable \ndiseases (NCDs). There is no proven safe level of tobacco use or of second-hand \nsmoke exposure. All daily and non-daily users of tobacco are at risk of a variety \nof poor health outcomes across the life-course, including NCDs.\n\nReducing the prevalence of current tobacco use will make a large contribution to \nreducing premature mortality from NCDs (Target 3.4).\n\nRoutine and regular monitoring of this indicator is necessary to enable accurate \nmonitoring and evaluation of the impact of implementation of the WHO Framework \nConvention on Tobacco Control (WHO FCTC), or tobacco control policies in the \ncountries that are not yet Parties to the WHO FCTC, over time.\n\nTobacco use prevalence levels are an appropriate indicator of implementation \nof SDG Target 3.a “Strengthen the implementation of the World Health Organization \nFramework Convention on Tobacco Control in all countries, as appropriate”. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.a.1&seriesCode=SH_PRV_SMOK&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20BOTHSEX\"> Age-standardized prevalence of current tobacco use among persons aged 15 years and older, by sex (%) SH_PRV_SMOK</a> UNSTATS", "comparabilidad"=>"The available indicator does not strictly comply with the United Nations metadata  but provides similar information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0a-01.pdf\">Metadata 3-a-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Proporción de personas de 15 y más años que fuman a diario", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.a- Fortalecer la aplicación del Convenio Marco de la Organización Mundial de la Salud para el Control del Tabaco en todos los países, según proceda", "definicion"=>"Egunero erretzen duten 15 urte eta gehiagoko pertsonen proportzioa", "formula"=>"\n$$PPFD_{15+}^{t} = \\frac{PFD_{15+}^{t}}{P_{15+}^{t}} \\cdot 100$$\n\nnon:\n\n$PFD_{15+}^{t} =$ egunero erretzen duten 15 urte eta gehiagoko biztanleak $t$ urtean\n\n$P_{15y+}^{t} =$ 15 urte eta gehiagoko biztanleak $t$ urtean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "periodicidad"=>"Quinquenal", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nNazio Batuen adierazlea gaur egun egunero edo ez-egunero tabako-produkturen bat (erretako tabakoa edo kerik gabea) \nkontsumitzen duten 15 urte edo gehiagoko biztanleen ehunekoa da. \n\nTabakoaren kontsumoa gaixotasun ez-kutsakorren (GEZ) ziozko heriotza eta gaixotasunerako arrazoi nagusietako bat da. \nEz da frogatu tabakoa kontsumitzea eta besteren tabakoaren kearen pean egotea seguruak direnik. Eguneroko eta ez-eguneroko \ntabako kontsumitzaile guztiek dute arriskua bizitzan zehar hainbat osasun-arazo izateko, GEZak barne. \n\nTabako-kontsumoaren egungo nagusitasuna murrizteak nabarmen lagunduko du GEZen bidezko hilkortasun goiztiarra murrizten \n(3.4 xedea). \n\nAdierazle honen jarraipen sistematiko eta erregularra egitea beharrezkoa da Tabakoa Kontrolatzeko OMEren Esparru \nHitzarmena (OMEren TKEH) edo oraindik OMEren TKEHren parte ez diren herrialdeetako tabakoa kontrolatzeko politikak \naplikatzearen eraginaren jarraipen eta ebaluazio zehatzak egin ahal izateko denboran zehar. \n\nTabakoaren kontsumoaren nagusitasun-mailak adierazle egokia dira GJHen 3.a xedearen aplikazioa neurtzeko: “Tabakoa \nKontrolatzeko Osasunaren Mundu Erakundearen Esparru Hitzarmenaren aplikazioa indartzea herrialde guztietan, bidezko \neran”. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.a.1&seriesCode=SH_PRV_SMOK&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20BOTHSEX\"> 15 urte edo gehiagoko pertsonen egungo tabako-kontsumoaren prebalentzia estandarizatua adinaren eta sexuaren arabera (%) SH_PRV_SMOK</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu zorrotz betetzen Nazio Batuen adierazlearen metadatuak, baina antzeko informazioa eskaintzen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0a-01.pdf\">Metadatuak 3-a-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.a: Strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriate</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.a.1: Age-standardized prevalence of current tobacco use among persons aged 15 years and older</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Not applicable </p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2021-12-06</p>", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>3.4.1 Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization; Secretariat of the WHO Framework Convention on Tobacco Control (co-custodians) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization; Secretariat of the WHO Framework Convention on Tobacco Control (co-custodians) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition: </strong> </p>\n<p>The indicator is defined as the percentage of the population aged 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. </p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>Tobacco use means use of smoked and/or smokeless tobacco products. &#x201C;Current use&#x201D; means use within the previous 30 days at the time of the survey, whether daily or non-daily use. </p>\n<p> </p>\n<p>Tobacco products means products entirely or partly made of the leaf tobacco as raw material intended for human consumption through smoking, sucking, chewing or sniffing. </p>\n<p>&#x201C;Smoked tobacco products&#x201D; include cigarettes, cigarillos, cigars, cheroots, bidis, pipes, shisha (water pipes), roll-your-own tobacco, kretek, heated tobacco products and any other form of tobacco that is consumed by smoking. </p>\n<p> </p>\n<p>&quot;Smokeless tobacco product&quot; includes moist snuff, creamy snuff, dry snuff, plug, dissolvables, gul, loose leaf, red tooth powder, snus, chimo, gutkha, khaini, gudakhu, zarda, quiwam, dohra, tuibur, nasway, naas, naswar, shammah, toombak, paan (betel quid with tobacco), iq&#x2019;mik, mishri, tapkeer, tombol and any other tobacco product that consumed by sniffing, holding in the mouth or chewing. </p>\n<p> </p>\n<p>Prevalence estimates have been &#x201C;age-standardized&#x201D; to make them comparable across all countries no matter the demographic profile of the country. This is done by applying each country&#x2019;s age-and-sex specific prevalence rates to the WHO Standard Population. The resulting rates are hypothetical numbers which are only meaningful when comparing rates obtained for one country </p>\n<p>with those obtained for another country. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Proportion (per cent) </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>&#x201C;Tobacco products&#x201D; are defined in Article 1 (f) of the WHO FCTC, see <a href=\"https://www.who.int/fctc/text_download/en/\">https://www.who.int/fctc/text_download/en/</a>. Heated tobacco products are classified as tobacco products in decision FCTC/CIO8(22), see <a href=\"https://www.who.int/fctc/cop/sessions/cop8/FCTC__COP8(22).pdf\">https://www.who.int/fctc/cop/sessions/cop8/FCTC__COP8(22).pdf</a> </p>\n<p>WHO Standard population is used for age-standardisation, see <a href=\"https://www.who.int/healthinfo/paper31.pdf\">https://www.who.int/healthinfo/paper31.pdf</a> </p>\n<p>World Population Prospects (population aged 15 years or more per country) is used in the denominator of the indicator, see <a href=\"https://population.un.org/wpp/\">https://population.un.org/wpp/</a> </p>", "SOURCE_TYPE__GLOBAL"=>"<p>Prevalence rates by age-by-sex from national representative population surveys conducted since 1990: </p>\n<p>&#x2022; officially recognized by the national health authority; </p>\n<p>&#x2022; of randomly selected participants representative of the general population; and </p>\n<p>&#x2022; reporting at least one indicator measuring current tobacco use, daily tobacco use, current tobacco smoking, daily tobacco smoking, current cigarette smoking or daily cigarette smoking. </p>\n<p> </p>\n<p>Official survey reports are gathered from Member States by one or more of the following methods: </p>\n<p>&#x2022; reporting system of the WHO FCTC on the progress in implementation of the Convention; </p>\n<p>&#x2022; review of surveys conducted under the aegis of the Global Tobacco Surveillance System; </p>\n<p>&#x2022; review of other surveys conducted in collaboration with WHO such as STEPwise surveys and World Health Surveys; </p>\n<p>&#x2022; scanning of international surveillance databases such as those of the Demographic and Health Survey (DHS), Multiple Indicator Cluster Survey (MICS) and the World Bank Living Standards Measurement Survey (LSMS); and </p>\n<p>&#x2022; identification and review of country-specific surveys that are not part of international surveillance systems. </p>", "COLL_METHOD__GLOBAL"=>"<p>Reports either downloaded from websites, submitted through the WHO FCTC reporting platform or emailed by national counterparts. WHO shares and makes public the methodologies for its estimates through the WHO global report on trends in tobacco use 2000-2025 and the WHO Report on the Global Tobacco Epidemic. The WHO estimates undergo country consultation prior to publication. </p>", "FREQ_COLL__GLOBAL"=>"<p>Continual data collection. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Biennial release via the WHO Global Report on Trends in Tobacco Use 2000-2025, the WHO Global Health Observatory and the Global Progress Report on Implementation of the WHO FCTC.</p>", "DATA_SOURCE__GLOBAL"=>"<p>WHO Member States, Parties to the WHO FCTC. </p>", "COMPILING_ORG__GLOBAL"=>"<p>WHO Tobacco Free Initiative; Secretariat of the WHO Framework Convention on Tobacco Control and the Protocol to Eliminate Illicit Trade in Tobacco Products. </p>", "INST_MANDATE__GLOBAL"=>"<p>The WHO Framework Convention on Tobacco Control (WHO FCTC) was adopted by the World Health Assembly on 21 May 2003 (Resolution 56.1) and entered into force on 27 February 2005. In 2010, Conference of the Parties adopted Decision FCTC/COP4(16), which requests the Convention Secretariat, in cooperation with competent authorities within WHO, in particular the Tobacco Free Initiative, to further standardize definitions and indicators and facilitate regular review of progress in implementation of the Convention. See <a href=\"https://apps.who.int/gb/fctc/PDF/cop4/FCTC_COP4_DIV6-en.pdf\">https://apps.who.int/gb/fctc/PDF/cop4/FCTC_COP4_DIV6-en.pdf</a> </p>", "RATIONALE__GLOBAL"=>"<p>Tobacco use is a major contributor to illness and death from non-communicable diseases (NCDs). There is no proven safe level of tobacco use or of second-hand smoke exposure. All daily and non-daily users of tobacco are at risk of a variety of poor health outcomes across the life-course, including NCDs. Reducing the prevalence of current tobacco use will make a large contribution to reducing premature mortality from NCDs (Target 3.4). Routine and regular monitoring of this indicator is necessary to enable accurate monitoring and evaluation of the impact of implementation of the WHO Framework Convention on Tobacco Control (WHO FCTC), or tobacco control policies in the countries that are not yet Parties to the WHO FCTC, over time. Tobacco use prevalence levels are an appropriate indicator of implementation of SDG Target 3.a &#x201C;Strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriate&#x201D;. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Raw data collected through nationally representative population-based surveys in the countries are used to calculate comparable estimates for this indicator. Information from subnational surveys are not used. </p>\n<p> </p>\n<p>In some countries, all tobacco use and tobacco smoking may be equivalent, but for many countries where other forms of tobacco are also being consumed, smoking rates will be lower than tobacco use rates to some degree. </p>\n<p> </p>\n<p>The comparability, quality and frequency of household surveys affects the accuracy and quality of the estimates. Non-comparability of data can arise from the use of different survey instruments, sampling and analysis methods, and indicator definitions across Member States. Surveys may cover a variety of age ranges (not always 15+) and be repeated at irregular intervals. Surveys may include a variety of different tobacco products, or sometimes only one product such as cigarettes, based on the country&#x2019;s perception of which products are important to monitor. Unless both smoked and smokeless products are monitored simultaneously, tobacco use prevalence will be underreported. Countries have begun to monitor use of e-cigarettes and other emerging products, which may confound countries&#x2019; definitions of tobacco use. The definition of current use may not always be restricted to the 30 days prior to the survey. In addition, surveys ask people to self-report their tobacco use, which can lead to under-reporting of tobacco use. </p>\n<p> </p>\n<p>There is no standard protocol used across Member States to ask people about their tobacco use. WHO&#x2019;s Tobacco Questions for Surveys (TQS) have been adopted in many surveys, which helps improve comparability of indicators across countries. </p>", "DATA_COMP__GLOBAL"=>"<p>A statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966&#x2013;976 (2015). Once the age-and-sex-specific prevalence rates from national surveys are compiled into a dataset, the model is fit to calculate trend estimates from the year 2000 to 2030. The model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. Depending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. To fill data gaps, information is &#x201C;borrowed&#x201D; from countries in the same UN subregion. </p>\n<p>The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. </p>\n<p>Estimates for countries with irregular surveys or many data gaps will have large uncertainty ranges, and such results should be interpreted with caution. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The results of the modelling described in the <em>Method of Computation</em> are compared with the input data to assure a good model fit. The results and input data are shared with countries via the tobacco control focal point for country consultation prior to publication in the biennial reports <em>WHO global report on trends in tobacco use 2000-2025 </em>and<em> WHO Report on the Global Tobacco Epidemic</em>. During country consultation, sometimes additional data are made available to WHO by the country for the purposes of modelling indicator 3.a.1.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Except for adjustments made during modelling as described in the <em>Method of Computation</em>, no other adjustments are made.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#xF0B7; At country level </strong></p>\n<p>For countries with less than two national surveys completed in different years since 1990, no estimate is calculated, since no trend can be determined. For countries with data from two or more national surveys, data gaps, if any, are filled as described in the <em>Method of Computation</em>. </p>\n<p> </p>\n<p><strong>&#xF0B7; At regional and global levels </strong></p>\n<p>Countries where no estimate can be calculated are included in regional and global averages by assuming their prevalence rates for men and women are equal to the average rates for men and women seen in the UN subregion1 in which they are located. Where fewer than 50% of a UN subregion&#x2019;s population was surveyed, UN subregions are grouped with neighbouring subregions until at least 50% of the grouped population has contributed data to the region&#x2019;s average rates. </p>", "REG_AGG__GLOBAL"=>"<p>Average prevalence rates for regions are calculated by population-weighting the age-specific prevalence rates in countries, then age-standardizing the age-specific average rates of the region. </p>", "DOC_METHOD__GLOBAL"=>"<p>Countries use a variety of population-based survey protocols to monitor tobacco use at national level. Examples of internationally supported protocols include Tobacco Questions for Surveys (<a href=\"https://www.gtssacademy.org/survey-tools/tqs/\">https://www.gtssacademy.org/survey-tools/tqs/</a>); the Global Adult Tobacco Survey (<a href=\"https://www.gtssacademy.org/survey-tools/gats/\">https://www.gtssacademy.org/survey-tools/gats/</a>); the WHO STEPS survey (<a href=\"https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps\">https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps</a>); the World Health Survey (<a href=\"https://www.who.int/data/data-collection-tools/world-health-survey-plus\">https://www.who.int/data/data-collection-tools/world-health-survey-plus</a>); the Multiple Indicator Cluster survey (<a href=\"https://mics.unicef.org/tools\">https://mics.unicef.org/tools</a>); and the Demographic and Health Survey (<a href=\"https://www.dhsprogram.com/Methodology/index.cfm\">https://www.dhsprogram.com/Methodology/index.cfm</a>). Sampling for national representativeness is the preserve of National Statistics Offices. Survey data submitted by the WHO FCT Parties biennially to the Convention Secretariat via the WHO FCTC Reporting Instrument (<a href=\"https://fctc.who.int/who-fctc/reporting/reporting-instrument\">https://fctc.who.int/who-fctc/reporting/reporting-instrument</a>) are shared with WHO. Additional data are obtained by WHO through contact with tobacco focal points at the Ministries of Health or by searching in the public domain.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Clearance of statistical methods and publications through WHO Division of Data, Analytics, and Delivery for Impact. Adherence to GATHER guidelines (<a href=\"http://gather-statement.org/\">http://gather-statement.org/</a>) is required for clearance. Data, estimates and metadata are published through the Global Health Observatory.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The survey data reported by WHO member states and by Parties to the WHO-FCTC is checked against published reports and for internal consistency. Modelling results, together with input data, are shared with tobacco surveillance and policy experts in WHO Regions prior to being shared with tobacco focal points in Ministries of Health. The pertinent WHO collaborating centre also reviews the results prior to publication. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Availability depends on each country&#x2019;s schedule for publishing their nationally representative population survey results. WHO calculates estimates every two years.</p>\n<p><strong>Time series:</strong></p>\n<p>The indicator is calculated for all countries from 2000 to the current year. Where the current year is later than the most recent national survey year, projections are made according to the <em>Method of Computation</em> described above. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Sex </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong> </p>\n<p>WHO estimates differ from national estimates in that they are (i) age-standardised to improve international comparability and (ii) calculated using one standard method for all countries. Infrequent surveys or unavailability of recent surveys lead to more reliance on modelling. As the data set for each country improves over time with addition of new surveys, recent estimates may seem inconsistent with earlier estimates. WHO estimates undergo country consultation prior to release. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong> </p>\n<p><a href=\"http://www.who.int/gho/en/\" target=\"_blank\"><u>http://www.who.int/gho/en/</u></a> </p>\n<p><a href=\"http://apps.who.int/fctc/implementation/database/\" target=\"_blank\"><u>http://apps.who.int/fctc/implementation/database/</u></a> </p>\n<p><strong>Notes:</strong> </p>\n<p><sup>1</sup> For a listing of countries by UN region, please refer to World Population Prospects, published by the UN Department of Economic and Social Affairs. For the purposes of tobacco use analysis, the following adjustments were made: (i) Eastern Africa subregion was divided into two regions: Eastern Africa Islands and Remainder of Eastern Africa; (ii) Armenia, Azerbaijan, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Tajikistan, Uzbekistan and Turkmenistan were classified with Eastern Europe, (iii); Cyprus, Israel and Turkey were classified with Southern Europe, and (iv) Melanesia, Micronesia and Polynesia subregions were combined into one subregion.</p>", "indicator_sort_order"=>"03-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.b.1", "slug"=>"3-b-1", "name"=>"Proporción de la población inmunizada con todas las vacunas incluidas en cada programa nacional", "url"=>"/site/es/3-b-1/", "sort"=>"03bb01", "goal_number"=>"3", "target_number"=>"3.b", "global"=>{"name"=>"Proporción de la población inmunizada con todas las vacunas incluidas en cada programa nacional"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población inmunizada con todas las vacunas incluidas en cada programa nacional", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población inmunizada con todas las vacunas incluidas en cada programa nacional", "indicator_number"=>"3.b.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Sanidad", "periodicity"=>"Anual", "url"=>"https://pestadistico.inteligenciadegestion.sanidad.gob.es/publicoSNS/S/sivamin", "url_text"=>"Estadística de Vacunaciones Sistemáticas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de la población inmunizada con todas las vacunas incluidas en cada programa nacional", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.b- Apoyar las actividades de investigación y desarrollo de vacunas y medicamentos contra las enfermedades transmisibles y no transmisibles que afectan primordialmente a los países en desarrollo y facilitar el acceso a medicamentos y vacunas esenciales asequibles de conformidad con la Declaración relativa al Acuerdo sobre los Aspectos de los Derechos de Propiedad Intelectual Relacionados con el Comercio y la Salud Pública, en la que se afirma el derecho de los países en desarrollo a utilizar al máximo las disposiciones del Acuerdo sobre los Aspectos de los Derechos de Propiedad Intelectual Relacionados con el Comercio respecto a la flexibilidad para proteger la salud pública y, en particular, proporcionar acceso a los medicamentos para todos", "definicion"=>"Proporción de la población inmunizada con todas las vacunas incluidas en cada programa nacional", "formula"=>"\n$$PPV_{\\text{vacuna}}^{t} = \\frac{PV_{\\text{vacuna}}^{\\text{cohorte}\\, t - c}}{P^{\\text{cohorte}\\, t - c}} \\cdot 100$$\n\ndonde:\n\n$PV_{\\text{vacuna}}^{\\text{cohorte}\\, t - c}=$ es la población vacunada con el tipo de vacuna especificado, \nperteneciente a la cohorte nacida en $t-c$\n\n$P^{\\text{cohorte}\\, t - c}=$ es la población total de la cohorte nacida en $t-c$\n\n${c}=$ el intervalo de años que define la cohorte específica para cada vacuna (c=1 o cohorte nacida en t-1 para DTPa y VNC, c=5 para SRP y c=13 para VPH)\n", "desagregacion"=>"Tipo de vacuna: vacunación de recuerdo contra difteria, tétanos y tosferina en la población infantil (DTPa); vacunación de recuerdo \ncontra neumococo en la población infantil (VNC); segunda dosis de la vacuna frente a sarampión, rubeola y parotiditis en la población \ninfantil (SRP); segunda dosis de la vacuna frente al virus del papiloma humano (VPH) en adolescentes.\n\nSexo\n", "periodicidad"=>"Anual", "observaciones"=>"\nLos resultados facilitados se obtienen utilizando las definiciones propuestas por la \nPonencia de Programa y Registro de Vacunaciones y acordadas por la Comisión de Salud \nPública en octubre de 2017. Desde entonces, las coberturas de vacunación se calculan \npor cohortes de nacimiento de la población.\n\nNo se incluyen las vacunas adquiridas en oficinas de farmacia ni las \nadministradas por el sector sanitario privado.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Este indicador tiene por objeto medir el acceso a las vacunas, incluidas las vacunas recientemente \ndisponibles o las que se utilizan de forma insuficiente, a nivel nacional.\n\nEn las últimas décadas, todos los países \nhan añadido numerosas vacunas nuevas y que se utilizan de forma insuficiente a sus calendarios nacionales de \nvacunación y hay varias vacunas en la fase final de desarrollo que se introducirán de aquí a 2030. Para supervisar el \ncontrol de enfermedades y el impacto de las vacunas, es importante medir la cobertura de cada vacuna del calendario nacional \nde vacunación. La medición directa de la proporción de la población cubierta con todas las vacunas del programa solo es posible \nsi el país cuenta con un registro electrónico nacional de vacunación que funcione bien y permita estimar \nfácilmente la cobertura por cohorte.\n\nSe miden las siguientes vacunas:\n\n - Cobertura de la vacuna DTP (tercera dosis): porcentaje de lactantes supervivientes que recibieron \nlas 3 dosis de la vacuna contra la difteria, el tétanos y la tos ferina en un año determinado.\n\n - Cobertura de la vacuna contra el sarampión (segunda dosis): porcentaje de niños que recibieron dos dosis de la vacuna contra el sarampión según el calendario recomendado a nivel nacional a través de los servicios de vacunación de rutina\n en un año determinado.\n\n - Cobertura de la vacuna antineumocócica conjugada (última dosis del calendario): porcentaje de lactantes \nsupervivientes que recibieron las dosis de vacuna antineumocócica conjugada recomendadas a nivel nacional en un año determinado.\n\n - Cobertura de la vacuna contra el VPH (última dosis del calendario): porcentaje de niñas de 15 años que recibieron \nlas dosis recomendadas de la vacuna contra el VPH. Actualmente se utiliza el desempeño del programa en el año \ncalendario anterior en función del grupo de edad objetivo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.1&seriesCode=SH_ACS_DTP3&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proporción de la población objetivo que recibió 3 dosis de la vacuna contra la difteria, el tétanos y la tos ferina (DTP3) (%) SH_ACS_DTP3</a> UNSTATS \n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.1&seriesCode=SH_ACS_MCV2&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proporción de la población objetivo que recibió la segunda dosis de la vacuna contra el sarampión (MCV2) (%) SH_ACS_MCV2</a> UNSTATS \n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.1&seriesCode=SH_ACS_PCV3&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proporción de la población objetivo que recibió una tercera dosis de la vacuna antineumocócica conjugada (PCV3) (%) SH_ACS_PCV3</a> UNSTATS \n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.1&seriesCode=SH_ACS_HPV&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de la población objetivo que recibió la dosis final de la vacuna contra el virus del papiloma humano (VPH) (%) SH_ACS_HPV</a> UNSTATS \n", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0b-01.pdf\">Metadatos 3-b-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de la población inmunizada con todas las vacunas incluidas en cada programa nacional", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.b- Apoyar las actividades de investigación y desarrollo de vacunas y medicamentos contra las enfermedades transmisibles y no transmisibles que afectan primordialmente a los países en desarrollo y facilitar el acceso a medicamentos y vacunas esenciales asequibles de conformidad con la Declaración relativa al Acuerdo sobre los Aspectos de los Derechos de Propiedad Intelectual Relacionados con el Comercio y la Salud Pública, en la que se afirma el derecho de los países en desarrollo a utilizar al máximo las disposiciones del Acuerdo sobre los Aspectos de los Derechos de Propiedad Intelectual Relacionados con el Comercio respecto a la flexibilidad para proteger la salud pública y, en particular, proporcionar acceso a los medicamentos para todos", "definicion"=>"Proportion of the target population covered by all vaccines included in their national programme", "formula"=>"\n$$PPV_{\\text{vaccine}}^{t} = \\frac{PV_{\\text{vaccine}}^{\\text{cohort}\\, t - c}}{P^{\\text{cohort}\\, t - c}} \\cdot 100$$\n\nwhere:\n\n$PV_{\\text{vaccine}}^{\\text{cohort}\\, t - c}=$ population vaccinated with the specified type of vaccine, \nbelonging to the cohort born in $t-c$\n\n$P^{\\text{cohort}\\, t - c}=$ total population of the cohort born in $t-c$\n\n${c}=$ interval of years that defines the specific cohort for each vaccine (c=1 or cohort born at t-1 for DPTa and VNC, c=5 for SRP and c=13 for VPH)\n", "desagregacion"=>"Vaccine type: \n- booster vaccination against diphtheria, tetanus, and whooping cough in children (DPTa) \n- booster vaccination against pneumococcus in the pediatric population (VNC)  \n- second dose of the measles, mumps, and rubella vaccine in children (SRP) \n- second dose of the human papillomavirus (HPV) vaccine in adolescents \n\nSex\n", "periodicidad"=>"Anual", "observaciones"=>"\nThe results are obtained using the definitions proposed by the Vaccination Registry and \nProgram Report and agreed by the Public Health Commission in October 2017. Since then, \nvaccination coverage has been calculated by population birth cohorts. \n\nVaccines purchased in pharmacies or administered by the private health sector are not included.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"This indicator aims to measure access to vaccines, including the newly available or \nunderutilized vaccines, at the national level.\n\nIn the past decades all countries added numerous new and underutilised vaccines in \ntheir national immunization schedule and there are several vaccines under final stage of \ndevelopment to be introduced by 2030. For monitoring diseases control and impact of vaccines \nit is important to measure coverage from each vaccine in national immunization schedule. \nDirect measurement for proportion of population covered with all vaccines in the programme \nis only feasible if the country has a wellfunctioning national electronic immunization registry \nallowing coverage by cohort to be easily estimated. \n\nFollowing vaccines are measured:\n\n - Coverage of DTP containing vaccine (3rd dose): Percentage of surviving infants who \nreceived the 3 doses of diphtheria and tetanus toxoid with pertussis containing vaccine \nin a given year.\n\n - Coverage of Measles containing vaccine (2nd dose): Percentage of children who received \ntwo dose of measles containing vaccine according to nationally recommended schedule through \nroutine immunization services in a given year. \n\n - Coverage of Pneumococcal conjugate vaccine (last dose in the schedule): Percentage of \nsurviving infants who received the nationally recommended doses of pneumococcal conjugate \nvaccine in a given year. \n\n - Coverage of HPV vaccine (last dose in the schedule): Percentage of 15 years old girls \nwho received the recommended doses of HPV vaccine. Currently performance of the programme \nin the previous calendar year based on target age group is used. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.1&seriesCode=SH_ACS_DTP3&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proportion of the target population who received 3 doses of diphtheria-tetanus-pertussis (DTP3) vaccine (%) SH_ACS_DTP3</a> UNSTATS \n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.1&seriesCode=SH_ACS_MCV2&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proportion of the target population who received measles-containing-vaccine second-dose (MCV2) (%) SH_ACS_MCV2</a> UNSTATS \n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.1&seriesCode=SH_ACS_PCV3&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proportion of the target population who received a 3rd dose of pneumococcal conjugate (PCV3) vaccine (%) SH_ACS_PCV3</a> UNSTATS \n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.1&seriesCode=SH_ACS_HPV&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of the target population who received the final dose of human papillomavirus (HPV) vaccine (%) SH_ACS_HPV</a> UNSTATS \n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0b-01.pdf\">Metadata 3-b-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de la población inmunizada con todas las vacunas incluidas en cada programa nacional", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.b- Apoyar las actividades de investigación y desarrollo de vacunas y medicamentos contra las enfermedades transmisibles y no transmisibles que afectan primordialmente a los países en desarrollo y facilitar el acceso a medicamentos y vacunas esenciales asequibles de conformidad con la Declaración relativa al Acuerdo sobre los Aspectos de los Derechos de Propiedad Intelectual Relacionados con el Comercio y la Salud Pública, en la que se afirma el derecho de los países en desarrollo a utilizar al máximo las disposiciones del Acuerdo sobre los Aspectos de los Derechos de Propiedad Intelectual Relacionados con el Comercio respecto a la flexibilidad para proteger la salud pública y, en particular, proporcionar acceso a los medicamentos para todos", "definicion"=>"Programa nazional bakoitzean sartutako txerto guztiekin immunizatutako biztanleriaren proportzioa", "formula"=>"\n$$PPV_{\\text{txertoa}}^{t} = \\frac{PV_{\\text{txertoa}}^{\\text{kohortea}\\, t - c}}{P^{\\text{kohortea}\\, t - c}} \\cdot 100$$\n\nnon:\n\n$PV_{\\text{txertoa}}^{\\text{kohortea}\\, t - c}=$ zehaztutako txerto motaren bidez txertatutako biztanleria, $t-c$-an jaiotako kohorteari dagokiona\n\n$P^{\\text{kohortea}\\, t - c}=$ $t-c$-an jaiotako kohortearen biztanleria osoa\n\n${c}=$ txerto bakoitzerako kohorte espezifikoa definitzen duen urte-tartea (c = 1 edo t-1ean jaiotako kohortea DTPa eta VNCrako, c = 5 SRPrako eta c = 13 VPHrako)\n", "desagregacion"=>"Txerto mota: difteriaren, tetanosaren eta kukutxeztularen aurkako oroitzapeneko txertaketa haurren artean (DTPa); pneumokokoaren aurkako \noroitzapenezko txertaketa haurren artean (VNC); elgorriaren, errubeolaren eta parotiditisaren (SRP) aurkako txertoaren bigarren \ndosia haurren artean; giza papilomaren birusaren (VPH) aurkako txertoaren bigarren dosia nerabeetan\n\nSexua\n", "periodicidad"=>"Anual", "observaciones"=>"\nEmandako emaitzak lortzeko, Txertaketen Programaren eta Erregistroaren Ponentziak proposatutako \neta Osasun Publikoaren Batzordeak 2017ko urrian adostutako definizioak erabiltzen dira. Ordutik, \nbiztanleriaren jaiotza-kohorteen arabera kalkulatzen dira txertaketa-estaldurak.\n\nEz dira sartzen farmazia-bulegoetan erositako txertoak, ezta osasun-sektore pribatuak emandakoak \nere. \n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAdierazle honen helburua da txertoen sarbidea neurtzea, horren barruan sartuta oraintsu eskuragarri jarritako txertoak \nedo aski erabiltzen ez direnak, Estatu mailan. \n\nAzken hamarkadetan, herrialde guztiek aski ez erabiltzen diren edo berriak diren txerto ugari gehitu dituzte beren \ntxertaketa-egutegi nazionaletara, eta hainbat txerto daude azken garapen-fasean, 2030era bitartean egutegian sartzeko. \nGaixotasunen kontrola eta txertoen eragina ikuskatzeko, garrantzitsua da txertaketa-egutegi nazionaleko txerto \nbakoitzaren estaldura neurtzea. Programako txerto guztiekin estaltzen den biztanleria-zatia zuzenean neurtzea posible da, \nsoilik baldin eta herrialdeak txertaketen erregistro elektroniko nazionala badu, erregistro horrek ondo funtzionatzen \nbadu, eta kohorteen araberako estaldura aise kalkulatzeko aukera ematen badu. \n\nTxerto hauek neurtzen dira: \n\n- DTP txertoaren estaldura (hirugarren dosia): bizirik iraun zuten eta difteria, tetanos eta kukutxeztularen aurkako \ntxertoaren 3 dosiak urte jakin batean jaso zituzten eta bularreko haurren ehunekoa. \n\n- Elgorriaren aurkako txertoaren estaldura (bigarren dosia): urte jakin batean elgorriaren aurkako bi dosi jaso zituzten \nhaurren ehunekoa, Estatu mailan gomendatutako egutegiaren arabera, errutinazko txertaketa-zerbitzuen bidez. \n\n- Txerto antineumokoziko bateratuaren estaldura (egutegiko azken dosia): bizirik iraun zuten eta Estatu-mailan \ngomendatutako txerto antineumokoziko bateratuaren dosia jaso zuten bularreko haurren ehunekoa, urte jakin batean. \n\n- GPBren aurkako txertoaren estaldura (egutegiko azken dosia): GPBren aurkako txertoaren dosi gomendatuak jaso zituzten \n15 urteko neskatoen ehunekoa. Gaur egun, aurreko egutegiko programaren garapena erabiltzen da, xedeko adin-taldearen \narabera. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.1&seriesCode=SH_ACS_DTP3&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Difteriaren, tetanosaren eta kukutxeztularen aurkako txertoaren (DTP3) 3 dosi jaso dituen xede-biztanleriaren proportzioa (%) SH_ACS_DTP3</a> UNSTATS \n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.1&seriesCode=SH_ACS_MCV2&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Elgorriaren aurkako txertoaren bigarren dosia jaso duen xede-biztanleriaren proportzioa (MCV2) (%) SH_ACS_MCV2</a> UNSTATS \n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.1&seriesCode=SH_ACS_PCV3&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Txerto antipneumokoziko konjugatuaren hirugarren dosi bat jaso duen xede-biztanleriaren proportzioa (PCV3) (%) SH_ACS_PCV3</a> UNSTATS \n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.1&seriesCode=SH_ACS_HPV&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Giza papilomaren birusaren aurkako txertoaren azken dosia jaso duen xede-biztanleriaren proportzioa (%) SH_ACS_HPV</a> UNSTATS \n", "comparabilidad"=>"Eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0b-01.pdf\">Metadatuak 3-b-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.b: Support the research and development of vaccines and medicines for the communicable and non&#x2011;communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.b.1: Proportion of the target population covered by all vaccines included in their national programme </p>", "META_LAST_UPDATE__GLOBAL"=>"2023-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Target 3.8 Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all. Indicator 3.8.1: Coverage of essential health services (defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population) </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO), United Nations Children&#x2019;s Fund (UNICEF) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO), United Nations Children&#x2019;s Fund (UNICEF) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p><em>Coverage of DTP containing vaccine (3<sup>rd</sup> dose):</em> Percentage of surviving infants who received the 3 doses of diphtheria and tetanus toxoid with pertussis containing vaccine in a given year. </p>\n<p> </p>\n<p>Coverage of Measles containing vaccine (2<sup>nd</sup> dose): Percentage of children who received two dose of measles containing vaccine according to nationally recommended schedule through routine immunization services in a given year. </p>\n<p> </p>\n<p>Coverage of Pneumococcal conjugate vaccine (last dose in the schedule): Percentage of surviving infants who received the nationally recommended doses of pneumococcal conjugate vaccine in a given year. </p>\n<p> </p>\n<p>Coverage of HPV vaccine (last dose in the schedule):<strong> </strong>Percentage of 15 years old girls who received the recommended doses of HPV vaccine. Currently performance of the programme in the previous calendar year based on target age group is used. </p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>In accordance with its mandate to provide guidance to Member States on health policy matters, WHO provides global vaccine and immunization recommendations for diseases that have an international public health impact. National programmes adapt the recommendations and develop national immunization schedules, based on local disease epidemiology and national health priorities. National immunization schedules and number of recommended vaccines vary between countries, with only DTP polio and measles containing vaccines being used in all countries. </p>\n<p> </p>\n<p>The target population for given vaccine is defined based on recommended age for administration. The primary vaccination series of most vaccines are administered in the first two years of life. </p>\n<p> </p>\n<p><em>Coverage of DTP containing vaccine</em> measure the overall system strength to deliver infant vaccination </p>\n<p>Coverage of Measles containing vaccine ability to deliver vaccines beyond first year of life through routine immunization services. </p>\n<p>Coverage of Pneumococcal conjugate vaccine: adaptation of new vaccines for children </p>\n<p>Coverage of HPV vaccine: life course vaccination<strong> </strong> </p>\n<p> </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent</p>", "SOURCE_TYPE__GLOBAL"=>"<p>National Health Information Systems or National Immunization systems </p>\n<p>National immunization registries </p>\n<p>High quality household surveys with immunization module (e.g. Demographic and Health Surveys (DHS), Multiple-Indicator Health Surveys (MICS), other national surveys) </p>", "COLL_METHOD__GLOBAL"=>"<p>Annual data collection through established mechanism. Since 1998, in an effort to strengthen collaboration and minimize the reporting burden, WHO and UNICEF jointly collect information through a standard questionnaire (the Joint Reporting Form) sent to all Member States <a href=\"http://www.who.int/immunization/monitoring_surveillance/routine/reporting/en/\" target=\"_blank\"><u>http://www.who.int/immunization/monitoring_surveillance/routine/reporting/en/</u></a> </p>", "FREQ_COLL__GLOBAL"=>"<p>Annual data collection March-May each year. Country consultation June each year </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>15 July each year for time series 1980 &#x2013; release year -1. (on 17 July 2023 estimates from 1980-2022) </p>", "DATA_SOURCE__GLOBAL"=>"<p>Ministries of Health, Immunization programmes, DHS and MICS websites </p>", "COMPILING_ORG__GLOBAL"=>"<p>WHO and UNICEF</p>", "RATIONALE__GLOBAL"=>"<p>This indicator aims to measure access to vaccines, including the newly available or underutilized vaccines, at the national level. In the past decades all countries added numerous new and underutilised vaccines in their national immunization schedule and there are several vaccines under final stage of development to be introduced by 2030. For monitoring diseases control and impact of vaccines it is important to measure coverage from each vaccine in national immunization schedule. A system is already in place to monitor immunization coverage for all national programmes, however direct measurement for proportion of population covered with all vaccines in the programme is only feasible if the country has a well-functioning national electronic immunization registry allowing coverage by cohort to be easily estimated. While countries will develop and strengthen immunization registries there is a need for an alternative measurement.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The rational to select a set of vaccines reflects the ability of immunization programmes to deliver vaccines over the life cycle and to adapt new vaccines. Coverage for other WHO recommended vaccines are also available and can be provided. </p>\n<p> </p>\n<p>Given that HPV vaccine is relatively new and vaccination schedule varies from countries to country coverage estimate will be made for girls vaccinated by ag 15 and at the moment data is limited to very few countries therefore reporting will start later. </p>", "DATA_COMP__GLOBAL"=>"<p>WHO and UNICEF jointly developed a methodology to estimate national immunization coverage form selected vaccines in 2000, and this approach has been refined and reviewed by expert committees over time. The methodology was published and reference is available under the reference section. Estimates time series for WHO recommended vaccines produced and published annually since 2001. </p>\n<p>The methodology uses data reported by national authorities from countries administrative systems as well as data from immunization or multi indicator household surveys. The WHO/UNICEF estimates of national immunization coverage have been assessed using the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) checklist. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>WHO and UNICEF encourage countries to review and comment on the draft coverage estimates shared following the draft production. In past years, regional or sub-regional consultations have been held during May/June to go through select country data and estimates. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>The first data point is the first reporting year after vaccine introduction. When country data are not available interpolation is used between 2 data points and extrapolation from the latest available data point. </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p>Any needed imputation is done at country level. These country values are then used to compute regional and global estimates. </p>", "REG_AGG__GLOBAL"=>"<p>Weighted average of the country-level coverage rates where the weights are the country target population sizes based on World Population Prospects: 2022 revision from the UN Population Division. All Member States from the region are included. For HPV 15 year old girls are used for calculation weighted average. </p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Coverage data for different vaccines are collected annually and reviewed by WHO and UNICEF inter agency expert group and estimates made for each country and each year. Data are published both on WHO and UNICEF web sites. </p>\n<p><a href=\"http://www.who.int/immunization/%20monitoring_surveillance/routine/coverage/en/index4.html\" target=\"_blank\"><u>http://www.who.int/immunization/ monitoring_surveillance/routine/coverage/en/index4.html</u></a><u> </u><a href=\"http://www.data.unicef.org/child-health/immunization\" target=\"_blank\"><u>http://www.data.unicef.org/child-health/immunization</u></a> </p>\n<p> </p>\n<p>Coverage for 2021 (in %)</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>DTP3 </p>\n      </td>\n      <td>\n        <p>MCV2 </p>\n      </td>\n      <td>\n        <p>PCV3 </p>\n      </td>\n      <td>\n        <p>HPV </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Global </p>\n      </td>\n      <td>\n        <p>81</p>\n      </td>\n      <td>\n        <p>71</p>\n      </td>\n      <td>\n        <p>51</p>\n      </td>\n      <td>\n        <p>12</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Australia and New Zealand </p>\n      </td>\n      <td>\n        <p>94</p>\n      </td>\n      <td>\n        <p>92</p>\n      </td>\n      <td>\n        <p>96</p>\n      </td>\n      <td>\n        <p>63</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Central Asia and Southern Asia </p>\n      </td>\n      <td>\n        <p>86</p>\n      </td>\n      <td>\n        <p>83</p>\n      </td>\n      <td>\n        <p>45</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Eastern Asia and South-eastern Asia </p>\n      </td>\n      <td>\n        <p>84</p>\n      </td>\n      <td>\n        <p>83</p>\n      </td>\n      <td>\n        <p>14</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Latin America &amp; the Caribbean </p>\n      </td>\n      <td>\n        <p>75</p>\n      </td>\n      <td>\n        <p>68</p>\n      </td>\n      <td>\n        <p>70</p>\n      </td>\n      <td>\n        <p>32</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Northern America and Europe </p>\n      </td>\n      <td>\n        <p>93</p>\n      </td>\n      <td>\n        <p>91</p>\n      </td>\n      <td>\n        <p>80</p>\n      </td>\n      <td>\n        <p>37</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Oceania </p>\n      </td>\n      <td>\n        <p>70</p>\n      </td>\n      <td>\n        <p>63</p>\n      </td>\n      <td>\n        <p>70</p>\n      </td>\n      <td>\n        <p>35</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Sub-Saharan Africa </p>\n      </td>\n      <td>\n        <p>70</p>\n      </td>\n      <td>\n        <p>40</p>\n      </td>\n      <td>\n        <p>64</p>\n      </td>\n      <td>\n        <p>20</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Western Asia and Northern Africa (M49) </p>\n      </td>\n      <td>\n        <p>88</p>\n      </td>\n      <td>\n        <p>83</p>\n      </td>\n      <td>\n        <p>56</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p> </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Geographical location, i.e. regional and national and potentially subnational estimates </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Countries often relay on administrative coverage data, while WHO and UNICEF review and assess data from different sources including administrative systems and surveys. Differences between country produced and international estimates are mainly due to differences between coverage estimates from administrative system and survey results. </p>\n<p> </p>\n<p>In case the vaccine is not included in national immunization schedule the coverage from private sector vaccine delivery will not be reflected. </p>", "OTHER_DOC__GLOBAL"=>"<p>Burton A, Monasch R, Lautenbach B, Gacic-Dobo M, Neill M, Karimov R, Wolfson L, Jones G, Birmingham M. WHO and UNICEF estimates of national infant immunization coverage: methods and processes. Bull World Health Organ. 2009;87(7):535-41.Available at: <a href=\"http://www.who.int/bulletin/volumes/87/7/08-053819/en/\" target=\"_blank\"><u>http://www.who.int/bulletin/volumes/87/7/08-053819/en/</u></a> </p>\n<p> </p>\n<p> Burton A, Kowalski R, Gacic-Dobo M, Karimov R, Brown D. A Formal Representation of the WHO and UNICEF Estimates of National Immunization Coverage: A Computational Logic Approach. PLoS ONE 2012;7(10): e47806. doi:10.1371/journal.pone.0047806. Available at: <a href=\"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3485034/pdf/pone.0047806.pdf\" target=\"_blank\"><u>http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3485034/pdf/pone.0047806.pdf</u></a> </p>\n<p> </p>\n<p>Brown D, Burton A, Gacic-Dobo M, Karimov R. An Introduction to the Grade of Confidence in the WHO and UNICEF Estimates of National Immunization Coverage. The Open Public Health Journal 2013, 6, 73-76. Available at: <a href=\"http://www.benthamscience.com/open/tophj/articles/V006/73TOPHJ.pdf\" target=\"_blank\"><u>http://www.benthamscience.com/open/tophj/articles/V006/73TOPHJ.pdf</u></a> </p>\n<p> </p>\n<p>Brown, David &amp; Burton, Anthony &amp; Gacic-Dobo, Marta. An examination of a recall bias adjustment applied to survey-based coverage estimates for multi-dose vaccines. 2015. 10.13140/RG.2.1.2086.2883. </p>\n<p>Danovaro-Holliday MC, Gacic-Dobo M, Diallo MS <em>et al.</em> Compliance of WHO and UNICEF estimates of national immunization coverage (WUENIC) with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) criteria. <em>Gates Open Res</em> 2021, 5:77 Available at: <a href=\"https://doi.org/10.12688/gatesopenres.13258.1\">https://doi.org/10.12688/gatesopenres.13258.1</a></p>", "indicator_sort_order"=>"03-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.b.2", "slug"=>"3-b-2", "name"=>"Total neto de asistencia oficial para el desarrollo destinado a los sectores de la investigación médica y la atención sanitaria básica", "url"=>"/site/es/3-b-2/", "sort"=>"03bb02", "goal_number"=>"3", "target_number"=>"3.b", "global"=>{"name"=>"Total neto de asistencia oficial para el desarrollo destinado a los sectores de la investigación médica y la atención sanitaria básica"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Total neto de asistencia oficial para el desarrollo destinado a los sectores de la investigación médica y la atención sanitaria básica", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Total neto de asistencia oficial para el desarrollo destinado a los sectores de la investigación médica y la atención sanitaria básica", "indicator_number"=>"3.b.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nLos flujos totales de ayuda oficial al desarrollo (AOD) a los países en desarrollo \ncuantifican el esfuerzo público que los donantes proporcionan a los países en \ndesarrollo para la investigación médica y la salud básica.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.2&seriesCode=DC_TOF_HLTHNT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Asistencia oficial total para el desarrollo destinada a los sectores de investigación médica y salud básica, desembolso neto, por países receptores (millones de dólares de los Estados Unidos constantes de 2022) DC_TOF_HLTHNT</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.2&seriesCode=DC_TOF_HLTHL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Asistencia oficial total para el desarrollo destinada a los sectores de investigación médica y salud básica, desembolso bruto, por países receptores (millones de dólares de los Estados Unidos constantes de 2022) DC_TOF_HLTHL</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0b-02.pdf\">Metadatos 3-b-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nTotal ODA flows to developing countries quantify the public effort that donors \nprovide to developing countries for medical research and basic health\n\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.2&seriesCode=DC_TOF_HLTHNT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Total official development assistance to medical research and basic health sectors, net disbursement, by recipient countries (millions of constant 2022 United States dollars) DC_TOF_HLTHNT</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.2&seriesCode=DC_TOF_HLTHL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Total official development assistance to medical research and basic health sectors, gross disbursement, by recipient countries (millions of constant 2022 United States dollars) DC_TOF_HLTHL</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0b-02.pdf\">Metadata 3-b-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nGarapen-bidean dauden herrialdeei ematen zaien garapenerako laguntza ofizialaren (GLO) guztizko fluxuek zenbatesten dute \nemaileek garapen-bidean dauden herrialdeei ematen dieten ahalegin publikoa ikerketa medikorako eta oinarrizko osasunerako. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.2&seriesCode=DC_TOF_HLTHNT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Ikerketa medikorako eta oinarrizko osasun sektorerako garapenerako laguntza ofiziala, ordainketa garbia, herrialde hartzaileen arabera (2022ko Estatu Batuetako dolar konstante milioiak) DC_TOF_HLTHNT</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.b.2&seriesCode=DC_TOF_HLTHL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Ikerketa medikorako eta oinarrizko osasun sektorerako garapenerako laguntza ofiziala, ordainketa gordina, herrialde hartzaileen arabera (2022ko Estatu Batuetako dolar konstante milioiak) DC_TOF_HLTHL</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0b-02.pdf\">Metadatuak 3-b-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.b: Support the research and development of vaccines and medicines for the communicable and non&#x2011;communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.b.2: Total net official development assistance to medical research and basic health sectors</p>", "META_LAST_UPDATE__GLOBAL"=>"2017-07-09", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Other ODA indicators </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>Gross disbursements of total ODA from all donors to medical research and basic health sectors. </p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>ODA: The DAC defines ODA as &#x201C;those flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are </p>\n<ol>\n  <li>provided by official agencies, including state and local governments, or by their executive agencies; and </li>\n  <li>each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and </li>\n</ol>\n<p>is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent). (See <a href=\"http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm\" target=\"_blank\"><u>http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm</u></a>) </p>\n<p> </p>\n<p>Medical research and basic health sectors are as defined by the DAC. Medical research refers to CRS sector code 12182 and basic health covers all codes in the 122 series (see here: <a href=\"http://www.oecd.org/dac/stats/purposecodessectorclassification.htm\" target=\"_blank\"><u>http://www.oecd.org/dac/stats/purposecodessectorclassification.htm</u></a>) </p>\n<p> </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The OECD/DAC has been collecting data on official and private resource flows from 1960 at an aggregate level and 1973 at an activity level through the Creditor Reporting System (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements). </p>\n<p> </p>\n<p>The data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm). </p>\n<p> </p>\n<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc. </p>", "COLL_METHOD__GLOBAL"=>"<p>A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are published on an annual basis in December for flows in the previous year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Detailed 2015 flows will be published in December 2016. </p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc. </p>", "COMPILING_ORG__GLOBAL"=>"<p>OECD </p>", "RATIONALE__GLOBAL"=>"<p>Total ODA flows to developing countries quantify the public effort that donors provide to developing countries for medical research and basic health. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Data in the Creditor Reporting System are available from 1973. However, the data coverage is considered complete from 1995 for commitments at an activity level and 2002 for disbursements. </p>", "DATA_COMP__GLOBAL"=>"<p>The sum of ODA flows from all donors to developing countries for medical research and basic health.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p> </p>\n<p>Due to high quality of reporting, no estimates are produced for missing data. </p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p> </p>\n<p>Not applicable. </p>", "REG_AGG__GLOBAL"=>"<p>Global and regional figures are based on the sum of ODA flows to medical research and basic health. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>On a recipient basis for all developing countries eligible for ODA. </p>\n<p><strong>Time series:</strong></p>\n<p>Data available since 1973 on an annual (calendar) basis </p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator can be disaggregated by donor, recipient country, type of finance, type of aid, health sub-sector, etc. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p> </p>\n<p><a href=\"http://www.oecd.org/dac/stats\" target=\"_blank\"><u>www.oecd.org/dac/stats</u></a> </p>\n<p> </p>\n<p><strong>References:</strong> </p>\n<p> </p>\n<p>See all links here: <a href=\"http://www.oecd.org/dac/stats/methodology.htm\" target=\"_blank\"><u>http://www.oecd.org/dac/stats/methodology.htm</u></a> </p>", "indicator_sort_order"=>"03-0b-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.b.3", "slug"=>"3-b-3", "name"=>"Proporción de centros de salud que disponen de un conjunto básico de medicamentos esenciales asequibles de manera sostenible", "url"=>"/site/es/3-b-3/", "sort"=>"03bb03", "goal_number"=>"3", "target_number"=>"3.b", "global"=>{"name"=>"Proporción de centros de salud que disponen de un conjunto básico de medicamentos esenciales asequibles de manera sostenible"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de centros de salud que disponen de un conjunto básico de medicamentos esenciales asequibles de manera sostenible", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de centros de salud que disponen de un conjunto básico de medicamentos esenciales asequibles de manera sostenible", "indicator_number"=>"3.b.3", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nLa medición y el seguimiento del acceso a los medicamentos esenciales son \nde alta prioridad para la agenda mundial de desarrollo, dado que el \nacceso es una parte integral del movimiento de cobertura sanitaria \nuniversal y un elemento indispensable de la prestación de atención \nsanitaria de calidad. \n\nEl acceso a los medicamentos es un concepto multidimensional \ncompuesto que se compone de la disponibilidad de medicamentos y \nla asequibilidad de sus precios. La información sobre estas \ndos dimensiones se ha recopilado y analizado desde la 54.ª Asamblea \nMundial de la Salud en 2001, cuando los Estados Miembros adoptaron \nla Estrategia de la OMS sobre medicamentos (resolución WHA54.11). \n\nEsta resolución condujo al lanzamiento del proyecto conjunto sobre \nprecios y disponibilidad de medicamentos por parte de la OMS y la \norganización no gubernamental internacional Health Action International \n(HAI/WHO), así como a una propuesta de metodología HAI/OMS para recopilar \ndatos y medir los componentes del acceso a los medicamentos. \n\nHasta el día de hoy, esta metodología se ha implementado ampliamente \npara producir análisis útiles de la disponibilidad y asequibilidad de los \nmedicamentos, sin embargo, las dos dimensiones se han evaluado por separado.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0b-03.pdf\">Metadatos 3-b-3.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2025-03-12", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nMeasurement and monitoring of access to essential medicines are of high \npriority for the global development agenda given access is an integral part \nof the Universal Health Coverage movement and an indispensable element of \nthe delivery of quality health care. \n\nAccess to medicines is a composite multidimensional concept that is composed \nof the availability of medicines and the affordability of their prices. Information \non these two dimensions has been collected and analysed since the 54th World Health \nAssembly in 2001, when Member States adopted the WHO Medicines Strategy (resolution \nWHA54.11).\n\nThis resolution led to the launch of the joint project on Medicine Prices and \nAvailability by WHO and the international non-governmental organization Health \nAction International (HAI/WHO), as well as a proposed HAI/WHO methodology for \ncollecting data and measuring components of access to medicines. \n\nTo this day, this methodology has been widely implemented to produce useful \nanalyses of availability and affordability of medicines, however the two dimensions \nhave been evaluated separately.\n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0b-03.pdf\">Metadata 3-b-3.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nOinarrizko sendagaien eskuragarritasunaren neurketa eta jarraipena lehentasun handikoak dira munduko garapen-agendarako; \nizan ere, eskuragarritasun hori ezinbesteko elementua da osasun-estaldura unibertsalaren mugimenduaren zati integrala \neta kalitatezko osasun-arreta emateko. \n\nSendagaien eskuragarritasuna dimentsio anitzeko kontzeptu konposatua da. Sendagaien erabilgarritasunaz eta haien prezioen \neskuragarritasunaz dihardu. Bi dimentsio horiei buruzko informazioa Osasunaren 54. Mundu Asanbladaz geroztik batu eta \naztertu da. 2001eko Asanbladan, izan ere, estatu-kideek sendagaiei buruzko OMEren estrategia ezarri zuten (WHA54.11 \nebazpena). \n\nEbazpen horren ondorioz, OMEk eta Health Action International (HAI/WHO) izeneko nazioarteko gobernuz kanpoko erakundeak \nsendagaien prezioei eta eskuragarritasunari buruzko proiektu bateratua abiarazi zuten, bai eta HAI/OME metodologia \nproposatu ere sendagaien eskuragarritasunaren osagaiak neurtu eta datuak batzeko. \n\nGaur egunera arte, metodologia hau leku askotan ezarri da sendagaien erabilgarritasunaren eta eskuragarritasunaren \nazterketa erabilgarriak egiteko. Hala ere, bi dimentsioak bereizita ebaluatu dira. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0b-03.pdf\">Metadatuak 3-b-3.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.b: Support the research and development of vaccines and medicines for the communicable and non&#x2011;communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.b.3: Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis </p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2019-01-01</p>", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>3.b.1- Proportion of the target population covered by all vaccines included in their national programme</p>\n<p>3.b.2- Total net official development assistance to medical research and basic health sectors</p>\n<p>3.8.1-Coverage of essential health services (defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, new born and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population)</p>\n<p>3.8.2-Proportion of population with large household expenditures on health as a share of total household expenditure or income</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis.</p>\n<p>The indicator is a multidimensional index reported as a proportion (%) of health facilities that have a defined core set of quality-assured medicines that are available and affordable relative to the total number of surveyed health facilities at national level.</p>\n<p><strong>Concepts:</strong></p>\n<p>Indicator 3.b.3 is defined as the &#x201C;Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis&#x201D;.This indicator is based on the proportion of facilities (pharmacies, hospitals, clinics,primary care centers, public/private, etc.) where core essential medicines from the identified set are available for purchase and their prices are affordable, compared to the total number of facilities surveyed.</p>\n<p>There are several core concepts that are used for measuring indicator 3.b.3:</p>\n<ol>\n  <li>Availability of medicine</li>\n  <li>Affordability of medicine</li>\n</ol>\n<p><strong>&#x2192;</strong>to define affordability, additional concepts are used:</p>\n<ul>\n  <li>Daily dose treatment of the medicine</li>\n  <li>National poverty line</li>\n  <li>Wage of the lowest paid unskilled government worker</li>\n</ul>\n<ol>\n  <li>Core set of relevant essential medicines (defined on a global level)</li>\n</ol>\n<p><strong>&#x2192;</strong>to apply a core set of relevant essential medicines defined on a global level to all countries, an additional concept is used:</p>\n<ul>\n  <li>global burden of disease</li>\n</ul>\n<p>1)A medicine is <u>available</u> in a facility when it is found in this facility by the interviewer on the day of data collection. Availability is measured as a binary variable with 1=medicine is available and 0=otherwise.</p>\n<p>2) A medicine is <u>affordable </u>when no extra daily wages (EDW) are needed for the lowest paid unskilled government sector worker (LPGW wage) to purchase a monthly dose treatment of this medicine after fulfilling basic needs represented by the national poverty line (NPL). Affordability is measured as a ratio of 1) the sum of the NPL and the price per daily dose of treatment of the medicine (DDD), over 2) the LPGW salary. This measures the number of extra daily wages needed to cover the cost of the medicines in the core set and that can vary between 0 and infinity.</p>\n<p><em>2.a) Daily dose of treatment (DDD)</em> is an average maintenance dose per day for a medicine used for its main indication in adults.<sup>2</sup> DDDs allow comparisons of medicine use despite differences in strength, quantity or pack size.</p>\n<p><em>2.b) National poverty line (NLP)</em> is the benchmark for estimating poverty indicators that are consistent with the country&apos;s specific economic and social circumstances. NPLs reflect local perceptions of the level and composition of consumption or income needed to be non-poor.</p>\n<p>2.c) W<em>age of the lowest paid unskilled government worker (LPGW </em>is a minimum living wage that employees are entitled to receive to ensure overcome of poverty and reduction of inequalities.</p>\n<p>In other words, affordability of a medicine identifies how many (if any) extra daily wages are needed for an individual who earns the LPGW wage to be able to purchase a medicine. The computed EDW ratio aims to indicate whether the LPGW wage is enough for the individual who earns the lowest possible income to cover 1) the daily expenditures for food and non-food items used to define (relative or absolute) poverty using national standards (NPL) and 2) the daily needs for a medicine (DDD). This ratio then requires transformation into a binary variable where medicine is affordable when zero extra daily wages are required to purchase it and not affordable otherwise. </p>\n<p>3)The <u>core set of relevant essential medicines </u>is a list of 32 tracer essential medicines for acute and chronic, communicable and non-communicable diseases in the primary health care setting.</p>\n<p>This basket of medicines has been selected from the 2017 WHO Model List of Essential Medicines and used in primary health care. By definition, essential medicines are those that satisfy the priority health care needs of the population and are selected for inclusion on the Model List based on due consideration of disease prevalence, evidence of efficacy and safety, and consideration of cost and cost-effectiveness.</p>\n<p>These medicines are listed in <em>table 1 </em>of <u>Annex 1</u>, where a detailed justification for including each medicine is also provided, as well as online references for the relevant treatment guidelines and sections in the WHO List of Essential Medicines.</p>\n<p>This list of medicines is intended as a global reference. However, to address regional and country specificities in terms of medicine needs, the medicines in this basket are weighted according to the regional burden of disease.</p>\n<p>3.a) The <em>global burden of disease</em> is an assessment of the health of the world&apos;s population. More specifically, disease burden provides information on the global and regional estimates of premature mortality, disability and loss of health for causes. The summary measure used to give an indication of the burden of disease is the disability adjusted life years (DALYs), which represent a person&#x2019;s loss of the equivalent of one year of full health. This metric incorporates years of life lost due to death and years of life lost through living in states of less than full health (or disability).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The indicator relies on three data sources that have been used by countries to collect information on medicine prices and availability:</p>\n<ol>\n  <li>Health Action International Project supported by the WHO <strong>[HAI/WHO]</strong></li>\n  <li>The Service Availability and Readiness Assessment survey <strong>[SARA]</strong></li>\n  <li>The WHO Medicines Price and Availability Monitoring mobile application <strong>[EMP MedMon]</strong></li>\n</ol>\n<p>Health Action International Project supported by WHO <strong>[HAI/WHO]</strong> provides data from national and sub-national surveys that have used the WHO/HAI methodology, Measuring Medicine Prices, Availability and Affordability and Price Components. The database is available at the following link: <a href=\"http://haiweb.org/what-we-do/price-availability-affordability/price-availability-data/\"><u>http://haiweb.org/what-we-do/price-availability-affordability/price-availability-data/</u></a> </p>\n<p>The Service Availability and Readiness Assessment <strong>[SARA] </strong>is a health facility assessment tool designed to assess and monitor availability and readiness of the services provided in the health sector and to generate evidence to support the planning and managing of a health system. </p>\n<p>The WHO Medicines Price and Availability Monitoring mobile application <strong>[EMP MedMon] </strong>can be<strong> </strong>considered as an updated version of the HAI/WHO tool for collecting data on medicine prices and availability. This data collection tool was created based on the two previously mentioned existing and well-established methodologies. This application is used at facility level to collect information on availability and price of the agreed-upon core basket of medicines. </p>\n<p>The EMP MedMon is easier to use, faster to conduct and consumes much fewer resources for collecting data. It also allows for a modular approach to defining the basket, which is highly useful and convenient for the purposes of this indicator.</p>\n<p>In order to compute historical data points prior to 2018, data from HAI/WHO is used. To compute current and future data points, SARA and EMP MedMon are recommended</p>", "COLL_METHOD__GLOBAL"=>"<p><u>Availability and affordability of medicines</u></p>\n<p>WHO obtains SARA survey data on availability and affordability from the countries&#x2019; Ministries of Health (MoH). HAI/WHO historical data collected at the facility level is available from HAI by request, as publicly available HAI/WHO data on the HAI website has already aggregated at the country level. The EMP MedMon data on availability and medicine prices is collected in collaboration between WHO and Ministries of Health of the countries.</p>\n<p><u>NPLs, LPGW wages, DALYs:</u></p>\n<p>National poverty reports consistently provide information on the <em>NPLs </em>in local currency units. The updated and recalculated NPLs are also published by the countries in these poverty reports. The <em>wage of the LPGW </em>is published in the ILOSTAT database. Information regarding the <em>regional burden of diseases (DALYs) </em>is publicly available and published by WHO.</p>", "FREQ_COLL__GLOBAL"=>"<p>SARA &amp; HAI/WHO: Data collection activities have often been conducted using funds from international donors.</p>\n<p>EMP MedMon: Data collection activities have been conducted using funds from international donors, but WHO is currently testing a sustainable regular monitoring mechanism through the integration of similar data collection during government inspection of health facilities or using country-determined sentinel monitoring sites.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Based on historical data points, the first release of the SDG indicator 3.b.3 results is planned for the summer of 2019. Subsequently, updated values will be calculated and published on an annual basis.</p>", "DATA_SOURCE__GLOBAL"=>"<p>SARA, HAI/WHO, EMP MedMon<strong>: </strong>Data is collected by the countries&#x2019; Ministries of Health (MOH), often with the support of the WHO country office. Data is then validated by MoH-based statisticians and shared with WHO by request.</p>", "COMPILING_ORG__GLOBAL"=>"<p>The World Health Organization</p>", "RATIONALE__GLOBAL"=>"<p>Measurement and monitoring of access to essential medicines are of high priority for the global development agenda given access is an integral part of the Universal Health Coverage movement and an indispensable element of the delivery of quality health care. Access to medicines is a composite multidimensional concept that is composed of the availability of medicines and the affordability of their prices. Information on these two dimensions has been collected and analysed since the 54<sup>th</sup> World Health Assembly in 2001, when Member States adopted the WHO Medicines Strategy (resolution WHA54.11). This resolution led to the launch of the joint project on Medicine Prices and Availability by WHO and the international non-governmental organization Health Action International (HAI/WHO), as well as a proposed HAI/WHO methodology for collecting data and measuring components of access to medicines. To this day, this methodology has been widely implemented to produce useful analyses of availability and affordability of medicines, however the two dimensions have been evaluated separately.</p>\n<p>While the above approach has provided an overview of the countries&#x2019; performance and progress on improving the affordability and availability of medicines, it has not allowed evaluation of overall access to medicines.</p>\n<p>This evaluation is in turn essential as country&#x2019;s success in ensuring one of the dimensions (e.g. availability) does not necessarily indicate the realization of the other (e.g. affordability) and vice versa. For example, a country may focus its policy efforts on ensuring the availability of a core set of essential medicines in the event of low capacity of local production and/or challenges associated with geographic location. As a result of the proposed policies, medicines may become available but their prices may not be affordable. The opposite situation is also possible, as lowering prices of medicines to increase affordability may be too restrictive for some pharmaceutical producers and lead to a decreased supply. Therefore, given the multidimensionality of access to medicines, it is necessary to evaluate both affordability and availability of medicines at the same time.</p>\n<p>The proposed methodology for indicator 3.b.3 allows the combination of both dimensions into a single indicator to evaluate the availability and affordability of medicines simultaneously. This methodology also allows for disaggregation so that each dimension can be analysed separately and the main driver of poor performance of the overall index can be properly identified.</p>\n<p>Monitoring the core set of relevant essential medicines is based on the WHO Model List of Essential Medicines (EML). The 2017 WHO EML contains 433 medications deemed essential for addressing the most important public health needs globally. The current index is computed based on a subset of 32 tracer essential medicines for the treatment, prevention and management of acute and chronic, communicable and non-communicable diseases in a primary health care setting.</p>", "REC_USE_LIM__GLOBAL"=>"<ol>\n  <li>On basket of tracer essential medicines:<ol>\n      <li>Although it is possible to regularly monitor all 400+ medicines on the current WHO Model List of Essential Medicines, indicator 3.b.3 requires a specific subset of this list. Over the years, several baskets of medicines have been defined for different purposes and used to conduct data collection and monitor price and availability. This core set of medicines does not replace the other existing baskets, and WHO teams and partners are encouraged and committed to continue ad hoc monitoring through other existing channels. Throughout the process of identifying the core set of medicines, one area of focus has been to balance the selection of the tracer medicines for primary health care with the size of the basket itself. The proposed basket represents a balanced approach to allow that relevant tracer medicines for primary health care are monitored yet ensuring a practical and feasible data collection and analysis. The 32 medicines listed in the basket are meant to be indicative of the access to medicines for primary health care but do not serve as a complete or exhaustive list.</li>\n      <li>As mentioned above, each medicine in the basket is weighted according to the regional Disability Adjusted Life Years (DALYs) for relevant disease from the WHO Global health estimates. Regional estimates are less sensitive to country-by-country variability of data quality, they sufficiently illustrate the disease distribution across countries in the region and work well due simplicity and comparability. Hence, regional weights for medicines are used to establish the associated country weights. However, this diminishes the specificity of the basket to the national context.</li>\n    </ol>\n  </li>\n  <li>On the measurement of medicines&#x2019; availability:<ol>\n      <li>The proposed approach for measuring the availability of medicines is based on the presence of the medicine on the day that the interviewer visits the facility and does not account for temporary and/or planned stock outs. The 32 medicines identified for the analysis should always be available in the facilities considering that in some (mainly rural) areas, the facility may be very difficult to reach and individuals may not have resources to travel on a daily basis. Moreover, in this proposed methodology the price of the medicine does not take into consideration the so-called indirect costs, which normally include transportation and other costs to reach the facility. Thus, the proposed measure for availability presents some limitations.</li>\n    </ol>\n  </li>\n</ol>\n<p>Furthermore, given the data collection occurs at the facility level and does not monitor quantities of any given medicine, an overall analysis of the available medicines compared to the national needs is not possible.</p>\n<ol>\n  <li>On the measurement of medicines&#x2019; affordability:<ol>\n      <li>Affordability of a medicine is often measured as the capacity of the population of a given country to pay for this medicine either ex-ante (usually based on income) or ex-post (usually based on reported expenditures). The latter would mainly require data collected at the individual level and from household surveys. However, information on medicine expenditures in these surveys is not always collected and when collected, is not done so consistently and regularly across the countries. In addition, there is usually a large amount of missing data.</li>\n    </ol>\n  </li>\n</ol>\n<p>The ex-ante approach is suggested for the purposes of this indicator as it is measured at the facility level. Ex-ante analysis requires identifying a reference person or group of people for the measurement. The lowest paid unskilled government worker is suggested to serve as the reference for this indicator. In other words, if a medicine is identified as being affordable for the individual who receives the LPGW wage, it will most likely be affordable for all other individuals affiliated with that economic group and higher. This obviously does not account for people employed in the unofficial labour market.</p>\n<p>The proposed methodology is an adjusted HAI/WHO methodology. The HAI/WHO approach suggests computing the affordability of medicine prices as the number of daily wages that are required for the lowest paid unskilled government worker (LPGW) to purchase a daily dose of a medicine (DDD). This approach is straightforward and also refers to the capacity of the reference individual to pay for the medicines. However, no threshold was identified to distinguish the maximum number of daily wages that an individual must spend on a medicine in order to still be able to afford it.</p>\n<ul>\n  <li>\n    <ol>\n      <li>Information on minimum LPGW wage is available by the International Labour Organization (ILO) for 155 countries. When information is missing or when information has not been updated recently, the alternative measure suggested is to be taken from the World Development Indicators data on &#x201C;minimum wage for a 19-year old worker or an apprentice&#x201D;, which is often used as an alternative in ILO reports.</li>\n      <li>The proposed indicator, being measured at the facility level, does not account for potential reimbursement schemes/insurance coverage present at the national level. Information about insurance or other forms of cost-coverage schemes at the national level is not readily available and would require standardization to allow for comparison across countries and income levels of the population. However, as demonstrated by the OECD in its Health at a Glance report in 2015, in 31 high- and middle-income countries the out-of-pocket (OOP) expenditures on pharmaceuticals as a share of all OOP on health varies from 64 to 16%.</li>\n    </ol>\n  </li>\n</ul>\n<p>Moreover, there are other SDG indicators, such as 3.8.1 and 3.8.2 that capture coverage of essential health services as well as financial protection from health expenditures net of reimbursement, including expenditures for medicines.</p>\n<ol>\n  <li>Other dimensions on access to medicines (quality)<ol>\n      <li>The quality of the product is another equally important dimension of access to medicines. Currently, there is no systematic and publicly available data collection on quality of a single medicine or in a single country. WHO has, however, contributed to enhanced access to quality health products through different programmes such as regulatory systems strengthening and prequalification.</li>\n    </ol>\n  </li>\n</ol>\n<p>A national regulatory authority (NRA) plays a key role in assuring the quality, safety, and efficacy of medical products until they reach the patient/consumer, as well as ensuring the relevance and accuracy of product information. Hence, stable, well-functioning and integrated regulatory systems are an essential component of a health system and contribute to better public health outcomes. NRA maturity and WHO prequalification of medicines can be considered as a proxy for ensuring that medicines in a country are of assured quality. The NRA maturity level is assessed using the WHO National Regulatory Authority Global Benchmarking Tool (WHO NRA GBT). After the evaluations, countries are assigned one of five levels of maturity, with a score of maturity level three representing the minimum acceptable regulatory capacity and maturity level five representing the highest level of functioning.</p>\n<p>The importance of transparency and the disclosure of the results of assessments amongst regulators (from ML 3 up) are taken into consideration. However, the information on country-specific NRA maturity level is not currently publicly available and WHO is working to address this limitation through recent discussions on WHO Listed Authorities (WLA).</p>\n<ol>\n  <li>Other comments:<ol>\n      <li>The &#x201C;sustainability&#x201D; dimension in this indicator can be measured only when more than one-time series of computations is available for a specific country so that a trend (tendency of a series of data points to move in a certain direction over time) can be identified.</li>\n    </ol>\n  </li>\n</ol>\n<p>The proposed methodology takes advantage of recognized standards and data collection methods, proposing a recombination of dimensions to allow measurement of affordability of a core set of relevant essential medicines for communicable and non-communicable diseases.</p>", "DATA_COMP__GLOBAL"=>"<p>The index is computed as a ratio of the health facilities with available and affordable medicines for primary health care over the total number of the surveyed health facilities:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>S</mi>\n        <mi>D</mi>\n        <mi>G</mi>\n      </mrow>\n      <mrow>\n        <mn>3</mn>\n        <mo>.</mo>\n        <mi>b</mi>\n        <mo>.</mo>\n        <mn>3</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>F</mi>\n        <mi>a</mi>\n        <mi>c</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>v</mi>\n        <mi>a</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>b</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>f</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>d</mi>\n        <mi>a</mi>\n        <mi>b</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>b</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>k</mi>\n        <mi>e</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>n</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>S</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>y</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>F</mi>\n        <mi>a</mi>\n        <mi>c</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>n</mi>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>For this indicator, the following variables are considered for a multidimensional understanding of the components of access to medicines:</p>\n<ul>\n  <li>A core set of relevant essential medicines for primary healthcare </li>\n  <li>Regional burden of disease</li>\n  <li>Availability of a medicine</li>\n  <li>Price of a medicine</li>\n  <li>Treatment courses for each medicine (number of units per treatment &amp; duration of treatment)</li>\n  <li>National poverty line and lowest-paid unskilled government worker (LPGW) wage</li>\n  <li>Proxy for quality of the core set of relevant essential medicines.</li>\n</ul>\n<p>The index is measured for each facility separately. Then a proportion of facilities that have accessible medicines is computed. The following <strong>steps</strong> must be taken to compute the index at the <u>facility level</u>:</p>\n<ol>\n  <li>Review and selection of the core basket of medicines for primary health care</li>\n  <li>Estimate weights for the defined medicines based on regional burden of disease</li>\n  <li>Measure the two dimensions of the access to medicine<ul>\n      <li>\n        <ul>\n          <li>\n            <ul>\n              <li>\n                <ol>\n                  <li>Availability</li>\n                  <li>Affordability</li>\n                </ol>\n              </li>\n            </ul>\n          </li>\n        </ul>\n      </li>\n    </ul>\n  </li>\n  <li>Combine the two dimensions on availability and affordability (access to medicines)</li>\n  <li>Apply weights to the medicine in the basket according to the regional prevalence of the diseases that are cured, treated, and controlled by these medicines</li>\n  <li>Identify whether a facility has a core set of relevant essential medicines available and affordable </li>\n</ol>\n<p>The next two steps are calculated at the <u>country level</u> across all the surveyed facilities:</p>\n<ol>\n  <li>Calculate the indicator as the proportion of facilities with accessible medicines in the country</li>\n  <li>Consideration of the quality of the accessible medicines in the country using a proxy</li>\n</ol>\n<p>Below is a more detailed procedure of the index computation.</p>\n<p>Step 1: Review and selection of the core basket of medicines for primary health care </p>\n<p>For some of the disease categories captured by the proposed basket of medicines, a therapeutic category of medicine has been specified (e.g. statins, beta blockers, corticosteroids, etc.) and a specific medicine must be identified for monitoring. For example, beclomethasone is used to treat non-communicable respiratory disease and if it is not supplied in a particular country for some policy or market reason, an alternative corticosteroid inhaler must be included in the analysis. In other cases, more than one medicine should be included in the basket per disease category. This will require a preliminary review of the basket before starting the data collection process.</p>\n<p>Step 2: Estimate weights for the defined medicines based on regional burden of disease</p>\n<p>The following points must be considered when computing medicines&#x2019; weights:</p>\n<ol>\n  <li>Equal weights are assigned to medicines that are used to treat, cure, and control the same disease(s) (e.g. gliclazide (or other sulfonylurea), metformin and insulin regular are assigned equal weights according to the diabetes disease burden).</li>\n  <li>For a medicine indicated for multiple diseases, DALYs values for each disease are summed.</li>\n  <li>For a medicine used for treating conditions for children (four medicines from the list) sum of DALYs is computed for males and females at the age between 0 and 14 years. </li>\n  <li>For some of the medicines which cannot be assigned to a specific disease (e.g. paracetamol) the weight is computed as <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n      <mfrac>\n        <mrow>\n          <mn>1</mn>\n        </mrow>\n        <mrow>\n          <mi>T</mi>\n        </mrow>\n      </mfrac>\n    </math> (where T is a total number of medicines in the surveyed basket) assuming equal use of the medicine relative to other medicines in the core list. </li>\n  <li>For medicines not in the list but &#x201C;suggested for monitoring&#x201D; by the country, weight is computed as <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n      <mn>0</mn>\n      <mo>.</mo>\n      <mn>5</mn>\n      <mi>*</mi>\n      <mfrac>\n        <mrow>\n          <mn>1</mn>\n        </mrow>\n        <mrow>\n          <mi>T</mi>\n        </mrow>\n      </mfrac>\n      <mi>&amp;nbsp;</mi>\n    </math> assuming a minor relevance of these medicines for this indicator and to avoid major issues in inter-country comparison. </li>\n</ol>\n<p>To estimate the weight for each medicine, the following <u>steps</u> have to be undertaken:</p>\n<ul>\n  <li>\n    <ol>\n      <li>Assign each medicine in the basket to one or several disease(s) that are treated/cured/controlled by that medicine (<em>Annex 1 table 2</em>) </li>\n      <li>Assign to each disease the corresponding DALYs<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup> (if several diseases are treated with the same medicine, compute sum of these DALYs accordingly) [<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n          <msub>\n            <mrow>\n              <mi>&amp;nbsp;</mi>\n              <mi>D</mi>\n              <mi>A</mi>\n              <mi>L</mi>\n              <mi>Y</mi>\n              <mi>s</mi>\n            </mrow>\n            <mrow>\n              <mi>M</mi>\n              <mi>i</mi>\n            </mrow>\n          </msub>\n        </math> ]</li>\n      <li>Compute total sum of the DALYs per medicine [ <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n          <msub>\n            <mrow>\n              <mrow>\n                <munderover>\n                  <mo stretchy=\"false\">&#x2211;</mo>\n                  <mrow>\n                    <mi>i</mi>\n                    <mo>=</mo>\n                    <mn>1</mn>\n                  </mrow>\n                  <mrow>\n                    <mn>32</mn>\n                  </mrow>\n                </munderover>\n                <mrow>\n                  <mi>D</mi>\n                  <mi>A</mi>\n                  <mi>L</mi>\n                  <mi>Y</mi>\n                  <mi>s</mi>\n                </mrow>\n              </mrow>\n            </mrow>\n            <mrow>\n              <mi>M</mi>\n              <mi>i</mi>\n            </mrow>\n          </msub>\n        </math> ]</li>\n      <li>Compute weight of each medicine as a proportion of the medicine specific DALYs to the total sum of DALYs in the basket [<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n          <mi>&amp;nbsp;</mi>\n          <msub>\n            <mrow>\n              <mi>W</mi>\n            </mrow>\n            <mrow>\n              <mi>M</mi>\n              <mi>i</mi>\n            </mrow>\n          </msub>\n        </math> ]:</li>\n    </ol>\n  </li>\n</ul>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>W</mi>\n      </mrow>\n      <mrow>\n        <mi>M</mi>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>D</mi>\n            <mi>A</mi>\n            <mi>L</mi>\n            <mi>Y</mi>\n            <mi>s</mi>\n          </mrow>\n          <mrow>\n            <mi>M</mi>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mrow>\n              <munderover>\n                <mo stretchy=\"false\">&#x2211;</mo>\n                <mrow>\n                  <mi>i</mi>\n                  <mo>=</mo>\n                  <mn>1</mn>\n                </mrow>\n                <mrow>\n                  <mn>32</mn>\n                </mrow>\n              </munderover>\n              <mrow>\n                <mi>D</mi>\n                <mi>A</mi>\n                <mi>L</mi>\n                <mi>Y</mi>\n                <mi>s</mi>\n              </mrow>\n            </mrow>\n          </mrow>\n          <mrow>\n            <mi>M</mi>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>As an example, the weights computed across regions for year 2015 are represented in<u> Annex 2 table 2.1 and 2.2</u>. </p>\n<p>Step 3: Measure the two dimensions of access to medicine</p>\n<p><em>Availability</em> and <em>affordability</em> of medicines must be measured and transformed (when necessary) into the format of a binary variable. </p>\n<ol>\n  <li><em>Availability</em> is measured as a binary variable coded as &#x201C;1&#x201D; when the medicine is in the facility on the day of the survey and coded as &#x201C;0&#x201D; otherwise. This approach is currently used in the HAI/WHO methodology.<sup><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></sup></li>\n  <li><em>Affordability</em> is computed following these <u>steps</u>:</li>\n</ol>\n<p>3.1 Compute daily price per dose of treatment for each medicine (price per DDD) in the selected basket of medicines </p>\n<p>WHO treatment guidelines provide the needed information to compute DDD. </p>\n<p>DDD of a medicine is defined using the following formula:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>p</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>D</mi>\n    <mi>D</mi>\n    <mi>D</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>M</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>m</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mi>h</mi>\n          </mrow>\n        </mfenced>\n        <mi>*</mi>\n        <mi>U</mi>\n        <mi>n</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mo>(</mo>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mn>365</mn>\n        <mo>/</mo>\n        <mn>12</mn>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where:</p>\n<ul>\n  <li>Units per treatments are tablets/vials or other forms that are needed for an individual with the average severity of the disease per one course of treatment of a duration of one month (365 days per year / 12 months per year = 30.42 days given 30 or 31 day per month), and</li>\n  <li>Medicine prices are calculated per unit (per tablet/vial/other form) requiring adjustments for gram or milligram according to the potency. </li>\n</ul>\n<p>This ratio varies between &#x201C;0&#x201D; and infinity and is measured in local currency units per day [LCU/d].</p>\n<p>Information on the number of units per treatment is specified in <u>Annex 3</u>. The price per DDD can be measured in per day or per month. </p>\n<p>3.2 Define National poverty line (NPL) and minimum wage of the LPGW for the analysed country</p>\n<p><u>National poverty line (NLP):</u> countries periodically recalculate and update their poverty lines based on new survey data and publish this information in their national reports on poverty. To adjust the latest available NPLs to the relevant year of analysis (when needed) information on the Consumer Price Index (CPI) in the analysed country has to be used to account for deflation/inflation. </p>\n<p>National poverty reports consistently provide information on the NPLs in local currency units but often refer to different recall periods from country to country (NPL can be measured per day, per month or per year). For consistency, NPL has to be adjusted to be measured per day [LCU/d].</p>\n<p><u>The wage of the lowest paid unskilled government worker (LPGW):</u> is estimated and published in the ILOSTAT database. For countries with the latest available data collected in a year different from the year of analysis, LPGW wage is actualised using the CPI conversion factor. </p>\n<p>ILO provides information on the minimum LPGW wages in local currency units per month. LPGW wage has to be adjusted to be measured per day as well [LCU/d].</p>\n<p>The NPL and LPGW wage can be measured in per day or per month.</p>\n<p>3.3 Compute extra daily wages (EDW) </p>\n<p>First, the LPGW wage is compared to the NPL and if it is lower, medicine is considered unaffordable. In this case, only medicines with a price equal to zero will be considered affordable. </p>\n<p>Next, the affordability is measured via the number of extra daily wages (EDW) that are needed for the LPGW to pay for one-month course of treatment using the formula below. In particular, the number of extra daily wages can be computed using the following formula:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>E</mi>\n    <mi>x</mi>\n    <mi>t</mi>\n    <mi>r</mi>\n    <mi>a</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>d</mi>\n    <mi>a</mi>\n    <mi>i</mi>\n    <mi>l</mi>\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>w</mi>\n    <mi>a</mi>\n    <mi>g</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>E</mi>\n        <mi>D</mi>\n        <mi>W</mi>\n      </mrow>\n    </mfenced>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>P</mi>\n        <mi>L</mi>\n        <mo>+</mo>\n        <mi>p</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>D</mi>\n        <mi>D</mi>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mi>d</mi>\n        <mi>a</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>a</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>L</mi>\n        <mi>P</mi>\n        <mi>G</mi>\n        <mi>W</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>3.4 Transform EDW variable into a binary format</p>\n<p>Following the definition, medicine is considered to be affordable when the sum of NPL and price of a daily dose of the treatment <u>is equal to or less</u> than the minimum daily wage of the LPGW:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced open=\"{\" separators=\"|\">\n      <mrow>\n        <mi>&amp;nbsp;</mi>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>i</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>E</mi>\n                <mi>D</mi>\n                <mi>W</mi>\n                <mi>&amp;nbsp;</mi>\n                <mo>&#x2264;</mo>\n                <mn>1</mn>\n                <mo>,</mo>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>a</mi>\n                <mi>f</mi>\n                <mi>f</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>d</mi>\n                <mi>a</mi>\n                <mi>b</mi>\n                <mi>i</mi>\n                <mi>l</mi>\n                <mi>i</mi>\n                <mi>t</mi>\n                <mi>y</mi>\n                <mo>=</mo>\n                <mn>1</mn>\n                <mo>,</mo>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>o</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>w</mi>\n                <mi>i</mi>\n                <mi>s</mi>\n                <mi>e</mi>\n                <mo>,</mo>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>a</mi>\n                <mi>f</mi>\n                <mi>f</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>d</mi>\n                <mi>a</mi>\n                <mi>b</mi>\n                <mi>i</mi>\n                <mi>l</mi>\n                <mi>i</mi>\n                <mi>t</mi>\n                <mi>y</mi>\n                <mo>=</mo>\n                <mn>0</mn>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p>Hence, the affordability of medicines is also measured as a binary variable that is coded as &#x201C;1&#x201D; when the medicine is affordable and &#x201C;0&#x201D; otherwise.</p>\n<p><u>When the price of the medicine is 0</u>, there is no need for the above-mentioned computations and the medicine is considered affordable (i.e. &#x201C;1&#x201D;). If all medicines in the country are provided free of charge, all medicines are directly marked as affordable and further computation of the index depends on the availability of these medicines.</p>\n<p>Step 4: Combine the two dimensions on availability and affordability<strong> (access to medicines)</strong></p>\n<p>In this step, the two dimensions of access to medicines (availability and affordability) are combined into a multidimensional index. </p>\n<p>The construction of a multidimensional index is based on the union identification approach<sup><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></sup> proposed by S. Alkire and G. Robles.</p>\n<p>The combination of the dimensions of medicines can be built in matrix form: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msubsup>\n      <mrow>\n        <mi>g</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mi>j</mi>\n      </mrow>\n      <mrow>\n        <mi>o</mi>\n      </mrow>\n    </msubsup>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfenced open=\"[\" close=\"]\" separators=\"|\">\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <msub>\n                <mrow>\n                  <mi>x</mi>\n                </mrow>\n                <mrow>\n                  <mn>11</mn>\n                </mrow>\n              </msub>\n            </mtd>\n            <mtd>\n              <mo>&#x2026;</mo>\n            </mtd>\n            <mtd>\n              <msub>\n                <mrow>\n                  <mi>x</mi>\n                </mrow>\n                <mrow>\n                  <mn>1</mn>\n                  <mi>d</mi>\n                </mrow>\n              </msub>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mo>&#x2026;</mo>\n            </mtd>\n            <mtd>\n              <mo>&#x2026;</mo>\n            </mtd>\n            <mtd>\n              <mo>&#x2026;</mo>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <msub>\n                <mrow>\n                  <mi>x</mi>\n                </mrow>\n                <mrow>\n                  <mi>n</mi>\n                  <mn>1</mn>\n                </mrow>\n              </msub>\n            </mtd>\n            <mtd>\n              <mo>&#x2026;</mo>\n            </mtd>\n            <mtd>\n              <msub>\n                <mrow>\n                  <mi>x</mi>\n                </mrow>\n                <mrow>\n                  <mi>n</mi>\n                  <mi>d</mi>\n                </mrow>\n              </msub>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p>This matrix contains performance for n objects of analysis (specified in rows) in d dimensions (specified in columns). The performance of any object <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">i</mi>\n  </math> in all <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">d</mi>\n  </math> dimensions is represented by the d-dimensional vector <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">x</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">i</mi>\n        <mo>.</mo>\n      </mrow>\n    </msub>\n  </math> for all <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">i</mi>\n    <mo>=</mo>\n    <mn>1</mn>\n    <mo>,</mo>\n    <mo>&#x2026;</mo>\n    <mo>,</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">n</mi>\n  </math>. The performance in any dimension <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">j</mi>\n  </math> for all <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">n</mi>\n  </math> objects are represented by the n-dimensional vector <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">x</mi>\n      </mrow>\n      <mrow>\n        <mo>.</mo>\n        <mi mathvariant=\"normal\">j</mi>\n      </mrow>\n    </msub>\n  </math> for all <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">j</mi>\n    <mo>=</mo>\n    <mn>1</mn>\n    <mo>,</mo>\n    <mo>&#x2026;</mo>\n    <mo>,</mo>\n    <mi mathvariant=\"normal\">d</mi>\n  </math>. Overall, an index should be computed via two main steps: identification and aggregation. An example of how to combine the 2 dimensions can be found in Annex 4.</p>\n<p>Step 5: Apply weights to the medicine in the basket according to the regional prevalence of the diseases that are cured/treated/controlled by these medicines</p>\n<p>After identifying the access variable, medicines in the basket have to be weighted according to the prevalence of the disease(s) that these medicines are used to cure/treat/control using the weights identified in step 2 and provided in Annex 2, tables 2.1 and 2.2.<strong> </strong>This is performed by multiplying the access variable with the medicine weights: </p>\n<p><strong>Figure 1.</strong> Achievement matrix of weighted access to medicine</p>\n<p><img src=\"data:image/png;base64,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\"></p>\n<p>Step 6: Identify whether a facility has a core set of relevant essential medicines available and affordable</p>\n<p>The following computations must be undertaken in this step: </p>\n<p>6.1 Calculate proportion of medicines that are accessible (both available and affordable) in each facility</p>\n<p>Because medicines are weighted, the proportion is computed as a weighted sum of medicines that are both available and affordable (accessible) in each facility using the following formula: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>A</mi>\n    <mi>c</mi>\n    <mi>c</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>s</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mrow>\n      <munderover>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi>i</mi>\n          <mo>=</mo>\n          <mn>1</mn>\n        </mrow>\n        <mrow>\n          <mi>n</mi>\n        </mrow>\n      </munderover>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>w</mi>\n          </mrow>\n          <mrow>\n            <mi>m</mi>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>This variable is then transformed into a percentage and varies from 0 to 100. </p>\n<p>The computed number of accessible medicines accounts for the importance of the analysed medicines in the country. In particular, if a medicine with a higher weight (for example hypertension) is not accessible, the index will be sensitive to this and will demonstrate the lack of access. On the contrary, if a medicine has a low weight (i.e. approaching zero, such as antimalarial medication in a non-endemic country) and is not accessible, the index will not be affected. </p>\n<p>6.2 Mark facilities that have 80% or more of available and affordable medicines</p>\n<p>The computed variable &#x201C;access&#x201D; is then transformed into the binary format identifying facilities that have the core basket of essential medicines available and affordable versus facilities that do not. A threshold of 80% is applied in order to transform the &#x201C;access&#x201D; variable into a binary format. In particular, at least 80% of all the medicines surveyed in a facility have to be both available and affordable. The transformation is made using the following formula:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced open=\"{\" separators=\"|\">\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>i</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <msub>\n                  <mrow>\n                    <mi>A</mi>\n                    <mi>c</mi>\n                    <mi>c</mi>\n                    <mi>e</mi>\n                    <mi>s</mi>\n                    <mi>s</mi>\n                  </mrow>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>f</mi>\n                        <mi>a</mi>\n                        <mi>c</mi>\n                        <mi>i</mi>\n                        <mi>l</mi>\n                        <mi>i</mi>\n                        <mi>t</mi>\n                        <mi>y</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>i</mi>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                </msub>\n                <mo>&#x2265;</mo>\n                <mn>80</mn>\n                <mi>%</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>F</mi>\n                <mi>a</mi>\n                <mi>c</mi>\n                <mi>i</mi>\n                <mi>l</mi>\n                <mi>i</mi>\n                <mi>t</mi>\n                <mi>y</mi>\n                <mo>=</mo>\n                <mn>1</mn>\n                <mo>,</mo>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mn>0</mn>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>o</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>w</mi>\n                <mi>i</mi>\n                <mi>s</mi>\n                <mi>e</mi>\n                <mo>,</mo>\n                <mi>&amp;nbsp;</mi>\n                <mi>F</mi>\n                <mi>a</mi>\n                <mi>c</mi>\n                <mi>i</mi>\n                <mi>l</mi>\n                <mi>i</mi>\n                <mi>t</mi>\n                <mi>y</mi>\n                <mo>=</mo>\n                <mn>0</mn>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p>This threshold is agreed upon and adopted by the WHO Global Action Plan on Non-Communicable Diseases and used as a reference in this proposed methodology. </p>\n<p>Step 7: Calculate the indicator as the proportion of facilities with accessible medicines in the country</p>\n<p>The proportion of facilities that have reached the 80% threshold is calculated out of the total number of surveyed facilities in a selected country using the following formula:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>S</mi>\n        <mi>D</mi>\n        <mi>G</mi>\n      </mrow>\n      <mrow>\n        <mn>3</mn>\n        <mo>.</mo>\n        <mi>b</mi>\n        <mo>.</mo>\n        <mn>3</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>F</mi>\n        <mi>a</mi>\n        <mi>c</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>v</mi>\n        <mi>a</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>b</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>f</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>d</mi>\n        <mi>a</mi>\n        <mi>b</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>b</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>k</mi>\n        <mi>e</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>n</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>S</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>y</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>F</mi>\n        <mi>a</mi>\n        <mi>c</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>n</mi>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>The computed indicator is a proportion that will then be converted into a percentage between 0-100%.</p>\n<p>Step 8: Consideration of quality of the accessible medicines in the country using a proxy </p>\n<p>The country level of medicine regulatory capacity assessed using the WHO NRA GBT is used as a proxy of the quality of the accessible medicines. The countries with a WHO Listed Authority (WLA corresponding to maturity level 3 and above) will be flagged to indicate the assured quality component.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> DALYs for a disease are calculated as the sum of the <em>Years of Life Lost (YLL)</em> due to premature mortality in the population and the <em>Years Lost due to Disability (YLD)</em> for people living with the health condition or its consequences (DALYs YLL + YLD). That is why DALYs allow &#x201C;calculating&#x201D; consequences both from acute diseases (mortality) and from chronic diseases (disability and life with disease). <a href=\"http://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html\">http://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> <a href=\"http://www.who.int/medicines/areas/access/OMS_Medicine_prices.pdf\">http://www.who.int/medicines/areas/access/OMS_Medicine_prices.pdf</a> <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> <a href=\"https://www.ophi.org.uk/wp-content/uploads/OPHIRP046a.pdf\">https://www.ophi.org.uk/wp-content/uploads/OPHIRP046a.pdf</a> <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>Treatment of missing values has already been partially addressed. In particular, when a medicine is not available, its price cannot be collected. For this reason, missing price values are considered as the medicine not being available and therefore not accessible (access = 0).</p>\n<p>Observing missing values for availability and affordability simultaneously indicates that these medicines are not provided at all in the surveyed facility. For example, in some countries medicines for in-patient care (mostly in injectable forms) are provided only in hospitals. In this case, the procedure for computing the indicator is the same except that:</p>\n<ol>\n  <li>Medicines that are used for inpatient care are excluded from the analysis of the data collected in pharmacies and other non-tertiary health care facilities, and</li>\n  <li>Two different versions of weights are applied to the list of medicines for hospitals and for pharmacies.</li>\n</ol>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>When computing regional or global aggregates of indicator 3.b.3, it is possible to accommodate missing values from countries resulting from a lack of data collection for a given country in a given year. In order to calculate a regionally aggregated 3.b.3 indicator, a 5-year period of data collection will be used as a reference to identify the available indicators for all the countries in the region. If during the defined 5-year period, one country of the region does not have even one indicator result, this country will not be included in the regional aggregate. The missing values from the countries can only be imputed when at least one data point exists for the given country in such a 5-year period.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates can be computed using national population size of a country as a proxy for the country weights in the region or globally. This is justified because medicines must be available and affordable for every individual in the population.</p>\n<p>To compute the regional indicator, the weighted average of the country indicators (using either the actual national indicator when available for the specific year of calculation, or the imputed value that corresponds to the year closest to the year of calculation) is used.</p>", "DOC_METHOD__GLOBAL"=>"<p>The HAI/WHO manual on measuring medicine prices, availability, affordability and price components describes the methodology as well as the guidelines for the data collection procedure and analysis of the availability and affordability of medicines on the facility and national level:</p>\n<p><a href=\"http://www.who.int/medicines/areas/access/medicines_prices08/en/\" target=\"_blank\"><u>http://www.who.int/medicines/areas/access/medicines_prices08/en/</u></a></p>\n<p><a href=\"http://www.who.int/healthinfo/systems/SARA_Reference_Manual_Full.pdf\" target=\"_blank\"><u>http://www.who.int/healthinfo/systems/SARA_Reference_Manual_Full.pdf</u></a></p>\n<p><a href=\"http://www.who.int/medicines/areas/policy/monitoring/empmedmon\" target=\"_blank\"><u>http://www.who.int/medicines/areas/policy/monitoring/empmedmon</u></a></p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality control can be performed based on the median availability and median consumer price ratio of selected generic medicines listed on the Global Health Observatory (GHO). The quality of the key components of this indicator (i.e. availability, prices, etc.) can be assured for data collected using any of the three mechanisms listed above when cross-referenced with the GHO values.</p>\n<p>For future data collection, quality will be based on the analysis of the sample size and the number of medicines captured in the basket.</p>\n<p>Countries will collect and share data with the WHO Secretariat. WHO will subsequently compute the indicator and return to the countries for validation. By request, WHO will also provide all background materials and training for data collection and indicator computation.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>SARA<strong>: </strong>21 national surveys are currently available from 2010 to 2017 for a total of 13 countries. Two- and three-year trends are available for six countries; the other seven countries only have one data point. 67% of the SDG basket of relevant essential medicines is covered by such surveys. These data will be used to test quality on the availability dimension only.</p>\n<p>HAI/WHO:<strong> </strong>Historical data points are available for 55 countries (28%) of all WHO Member States. The highest number of countries captured by the surveys is in the SEARO region (59%) and the smallest is in EURO region (15%). More than 60% of the medicines from the defined SDG indicator basket are captured in the HAI/WHO historical data surveys.</p>\n<p><strong>Table 1. </strong>Number of countries captured by the surveys across regions</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong><u>WHO Region</u></strong></p>\n      </td>\n      <td>\n        <p><strong><u>2001-2005</u></strong></p>\n      </td>\n      <td>\n        <p><strong><u>2005-2010</u></strong></p>\n      </td>\n      <td>\n        <p><strong><u>2010-2015</u></strong></p>\n      </td>\n      <td>\n        <p><strong><u>Total</u></strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>African Region</em></p>\n      </td>\n      <td>\n        <p>14</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p><strong>21</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Region of the Americas</em></p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>7</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p><strong>11</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Eastern Mediterranean Region</em></p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p><strong>16</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>European Region</em></p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p><strong>10</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>South-East Asia Region</em></p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p><strong>8</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Western Pacific Region</em></p>\n      </td>\n      <td>\n        <p>6</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p><strong>10</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Total</strong></p>\n      </td>\n      <td>\n        <p><strong>41</strong></p>\n      </td>\n      <td>\n        <p><strong>23</strong></p>\n      </td>\n      <td>\n        <p><strong>12</strong></p>\n      </td>\n      <td>\n        <p><strong>76</strong></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>HAI/WHO surveys were conducted more than once in some of the countries for a total of 76 surveys. </p>\n<p>EMP MedMon: In 2016 the design of the EMP MedMon tool for data collection was finalised. Since then, several pilot surveys have been conducted to test the tool. The first pilot survey was conducted across 19 countries using a basket of medicines that captures around 60% of the one currently proposed. The second pilot used a basket adjusted for the purposes of capturing non-communicable diseases only. These pilots have demonstrated that this tool is flexible and can be easily manipulated to include specialized modules of medicines for future data collection.</p>\n<p><strong>Time series:</strong></p>\n<p>Existing data has been historically collected based on available funding. The majority of existing surveys have been collected thus far using the <strong>HAI/WHO</strong> data collection tool. Most of the existing data points are from 2000 &#x2013; 2005. </p>\n<p><strong>Table 2. </strong>Number of surveys and % of medicines from the defined basket </p>\n<p>that are captured by HAI/WHO surveys</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong><u>2001-2005</u></strong></p>\n      </td>\n      <td>\n        <p><strong><u>2005-2010</u></strong></p>\n      </td>\n      <td>\n        <p><strong><u>2010-2015</u></strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Total number of surveys (n)</em></p>\n      </td>\n      <td>\n        <p>41</p>\n      </td>\n      <td>\n        <p>23</p>\n      </td>\n      <td>\n        <p>12</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><em>Medicines captured in the surveys (%)</em></p>\n      </td>\n      <td>\n        <p>49.8%</p>\n      </td>\n      <td>\n        <p>66.3%</p>\n      </td>\n      <td>\n        <p>72.9%</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>The distribution of these 76 surveys across WHO regions is represented in<strong> Table 3. </strong></p>\n<p><strong>Table 3. </strong>Number of HAI/WHO surveys across regions</p>\n<p><img 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\"></p>\n<p>Overall 21 SARA surveys were conducted over the period from 2010 to 2017. 17 surveys were conducted between 2010 and 2015 and 4 surveys after 2015. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The proposed indicator will allow for the following disaggregation: </p>\n<ol>\n  <li>public/private/mission sectors facilities (managing authority)</li>\n  <li>geography &#x2013; rural/urban areas</li>\n  <li>therapeutic group</li>\n  <li>facility type (pharmacy/hospital)</li>\n  <li>medicine.</li>\n</ol>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Data can be received from three data sources: SARA, HAI/WHO, and the EMP MedMon. These data collection methods demonstrate the following discrepancies:</p>\n<ol>\n  <li>Sampling of the facilities to be surveyed,</li>\n  <li>Size of the sampling of the facilities to be surveyed, and</li>\n  <li>Questions asked at facility level to capture availability (i.e. SARA considers potentially available expired medicines as well).</li>\n</ol>\n<p>WHO will use any of these three data sources available for the year of calculation as a compromise between the limitations that these discrepancies pose to the proposed methodology and the need to overcome data availability issues in order to start reporting on this critical indicator. In the unlikely case that data is available through more than one data source for a specific country, WHO will rely on the source with a larger sample size and a higher percentage of medicines from the defined core list captured by the survey.</p>", "OTHER_DOC__GLOBAL"=>"<ol>\n  <li>World Health Organization and Health Action International, <em>Measuring medicine prices, availability, affordability and price components, 2<sup>nd</sup> Edition</em> (Switzerland, 2008), available from <a href=\"http://www.who.int/medicines/areas/access/OMS_Medicine_prices.pdf\" target=\"_blank\"><u>http://www.who.int/medicines/areas/access/OMS_Medicine_prices.pdf</u></a></li>\n  <li>&#x201C;Defined Daily Dose: Definition and general considerations&#x201D; (WHO Collaborating Centre for Drug Statistics methodology, 07 February 2018),<strong> </strong><a href=\"https://www.whocc.no/ddd/definition_and_general_considera/\" target=\"_blank\"><u>https://www.whocc.no/ddd/definition_and_general_considera/</u></a></li>\n  <li>&#x201C;How to define a minimum wage?&#x201D; (International Labour Organization, 2018),<a href=\"https://www.ilo.org/global/topics/wages/minimum-wages/definition/lang--en/index.htm\" target=\"_blank\"><u>https://www.ilo.org/global/topics/wages/minimum-wages/definition/lang--en/index.htm</u></a></li>\n  <li>World Health Organization, <em>The Global Burden of Disease: 2004 Update</em> (Switzerland, 2008), available from<a href=\"http://www.who.int/healthinfo/global_burden_disease/2004_report_update/en/\" target=\"_blank\"><u>http://www.who.int/healthinfo/global_burden_disease/2004_report_update/en/</u></a></li>\n  <li>&#x201C;WHO Global Benchmarking Tool (GBT) for evaluation of national regulatory systems&#x201D; (WHO Essential medicines and health products, 2018), available from<a href=\"http://www.who.int/medicines/regulation/benchmarking_tool/en/\">http://www.who.int/medicines/regulation/benchmarking_tool/en/</a>.</li>\n  <li>&#x201C;Disease burden and mortality estimates&#x201D; (WHO Health statistics and information systems, 2018), available from<strong> </strong><a href=\"http://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html\">http://www.who.int/healthinfo/global_burden_disease/estimates/en/index1.html</a>.</li>\n  <li>Alkire, S. and Robles, G. (2016). &#x201C;Measuring multidimensional poverty: Dashboards, Union identification, and the Multidimensional Poverty Index (MPI).&#x201D; OPHI Research in Progress 46a, University of Oxford.</li>\n  <li>&#x201C;Essential Medicines&#x201D; (WHO Global Health Observatory data repository, 2016), available from <a href=\"http://apps.who.int/gho/data/node.main.487\" target=\"_blank\"><u>http://apps.who.int/gho/data/node.main.487</u></a>.</li>\n  <li>Health at a Glance 2017: OECD Indicators, OECD (2017). OECD Publishing, Paris <a href=\"https://doi.org/10.1787/health_glance-2017-en\" target=\"_blank\"><u>https://doi.org/10.1787/health_glance-2017-en</u></a><u>.</u></li>\n</ol>\n<table>\n  <thead>\n    <tr>\n      <th>\n        <p><strong><u>Medicine</u></strong></p>\n      </th>\n      <th>\n        <p><strong><u>Category (Therapeutic group)</u></strong></p>\n      </th>\n      <th>\n        <p><strong><u>Justification</u></strong></p>\n      </th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>\n        <p>Salbutamol (100 mcg/dose inhaler)</p>\n      </td>\n      <td>\n        <p>NCD - Respiratory</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Salbutamol, a short acting beta-2 agonist, is recommended for prophylaxis and the first-line treatment of bronchospasm in asthma and COPD. It is recommended for all patients with acute severe asthma. </p>\n        <p><strong>Treatment References: </strong><a href=\"http://www.who.int/ncds/management/pen_tools/en/\"><u>WHO PEN 5.b</u></a><strong>, </strong><u>WHO </u><a href=\"http://apps.who.int/iris/bitstream/handle/10665/76173/9789241548397_eng.pdf\"><u>Guidelines for primary health care in low-resource settings</u></a><strong> </strong></p>\n        <p><strong>More information in WHO EML 2017 Section Reference: </strong>25.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Beclometasone (100 mcg/dose inhaler) or other corticosteroid inhaler</p>\n        <p>Alternatives would include, but not be limited to, budesonide, fluticasone, ciclesonide. Refer to ATC group R03BA - </p>\n      </td>\n      <td>\n        <p>NCD - Respiratory</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Inhaled corticosteroids are indicated for maintenance treatment of asthma symptoms by reducing inflammation and reducing airways hyper-responsiveness. These do not provide symptomatic relief in acute asthma. Beclometasone is a representative antiasthmatic in the WHO EML. </p>\n        <p><strong>Treatment References: </strong><a href=\"http://www.who.int/ncds/management/pen_tools/en/\"><u>WHO PEN 5.b</u></a><strong>, </strong><u>WHO </u><a href=\"http://apps.who.int/iris/bitstream/handle/10665/76173/9789241548397_eng.pdf\"><u>Guidelines for primary health care in low-resource settings</u></a><strong> </strong></p>\n        <p><strong>More information in WHO EML 2017 Section Reference: </strong>25.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Gliclazide (80 mg cap/tab) or other sulfonylurea</p>\n        <p>Alternatives would include but not be limited to glibenclamide, glimepiride. Refer to ATC group A10BB </p>\n      </td>\n      <td>\n        <p>NCD - Diabetes</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Second generation sulfonylureas (SFUs) increase the release of insulin from the pancreas to relieve the hyperglycaemia associated with diabetes. SFUs are useful in patients unable to tolerate metformin, or not adequately controlled on metformin. These are among the main therapies for most patients with type 2 diabetes, but contraindicated for patients with type 1 diabetes. However, it should be noted that glibenclamide has associated with higher levels of hypoglycaemia compared with gliclazide. Gliclazide is the representative sulfonylurea in the WHO EML.</p>\n        <p><strong>Treatment References: </strong><a href=\"http://www.who.int/ncds/management/pen_tools/en/\"><u>WHO PEN 5.b</u></a><strong>, </strong><u>WHO </u><a href=\"http://apps.who.int/iris/bitstream/handle/10665/76173/9789241548397_eng.pdf\"><u>Guidelines for primary health care in low-resource settings</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference: </strong>18.5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Metformin (500 mg cap/tab, 850 mg cap/tab or 1 g cap/tab) </p>\n      </td>\n      <td>\n        <p>NCD - Diabetes</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Metformin, an oral anti-diabetic medicine, can be used in patients with type 2 diabetes as a monotherapy or in combination with sulfonylureas.</p>\n        <p><strong>Treatment References: </strong><a href=\"http://www.who.int/ncds/management/pen_tools/en/\"><u>WHO PEN 5.b</u></a><strong>, </strong><u>WHO </u><a href=\"http://apps.who.int/iris/bitstream/handle/10665/76173/9789241548397_eng.pdf\"><u>Guidelines for primary health care in low-resource settings</u></a><strong> </strong></p>\n        <p><strong>More information in WHO EML 2017 Section Reference: </strong>18.5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Insulin regular, soluble (100 IU/ml injection)</p>\n      </td>\n      <td>\n        <p>NCD - Diabetes</p>\n      </td>\n      <td>\n        <p><strong>Rationale: </strong>Regular human insulin, a rapid acting insulin, is necessary for all patients with type 1 and more than 10% of patients with type 2 diabetes. It is currently more affordable to health systems than other long-acting or analogue insulins.</p>\n        <p><strong>Treatment References: </strong><a href=\"http://www.who.int/ncds/management/pen_tools/en/\"><u>WHO PEN 5.b</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference: </strong>18.5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Two of the following antihypertensive:</p>\n        <ol>\n          <li>Amlodipine (5 mg cap/tab)</li>\n          <li>Enalapril (5 mg cap/tab) or other angiotensin converting enzyme inhibitor (ACEI). Refer to ATC group C09AA.</li>\n          <li>Hydrochlorothiazide (25 mg cap/tab) or Chlorthalidone (25 mg cap/tab) </li>\n          <li>Bisoprolol (5 mg cap/tab) or alternative betablocker (atenolol or carvedilol or metoprolol only) </li>\n        </ol>\n      </td>\n      <td>\n        <p>NCD - Cardiovascular</p>\n      </td>\n      <td>\n        <p><strong>Rationale: </strong></p>\n        <p>Calcium channel blockers (CBB) are among the first-line treatment options for patients with hypertension. Amlodipine is the representative CCB in the WHO EML. </p>\n        <p>ACEIs are among first-line treatment options for patients with hypertension. ACEIs are also used in the management of heart failure. Enalapril is the representative ACEI in the WHO EML.</p>\n        <p>Thiazide diuretics are among the first-line treatment options for patients with hypertension. Thiazides are also used as the management of heart failure. Hydrochlorothiazide is the representative thiazide diuretic in the WHO EML.</p>\n        <p>Beta-blockers are among the recommended treatment options for patients with hypertension, angina, cardiac arrhythmias or heart failure. Bisoprolol is the representative beta-blocker in the WHO EML. </p>\n        <p><strong>Treatment References: </strong><a href=\"http://www.who.int/ncds/management/pen_tools/en/\"><u>WHO PEN 5.b</u></a><u>, WHO </u><a href=\"http://apps.who.int/iris/bitstream/handle/10665/76173/9789241548397_eng.pdf\"><u>Guidelines for primary health care in low-resource settings</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference: </strong>12.3, 12.4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Simvastatin (20 mg cap/tab) or other statin. Refer to ATC group C10AA.</p>\n      </td>\n      <td>\n        <p>NCD - Cardiovascular</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Statins, lipid-lowering medicines, are used to reduce the risk of coronary heart disease, including fatal and non-fatal myocardial infarction and stroke. Simvastatin is the representative statin in the WHO EML.</p>\n        <p><strong>Treatment References: </strong><a href=\"http://www.who.int/ncds/management/pen_tools/en/\"><u>WHO PEN 5.b</u></a><strong>, </strong><u>WHO </u><a href=\"http://apps.who.int/iris/bitstream/handle/10665/76173/9789241548397_eng.pdf\"><u>Guidelines for primary health care in low-resource settings</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference: </strong>12.6</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Acetylsalicylic acid (aspirin) (100 mg cap/tab)</p>\n      </td>\n      <td>\n        <p>NCD &#x2013; Cardiovascular </p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Aspirin, an anti-platelet medication, is recommended for preventing a first stroke, has an important role in preventing recurrent strokes, and can reduce the severity of an ischemic stroke. Low-dose aspirin has numerous therapeutic indications including anti-platelet therapy and can be used to reduce the risk of cardiovascular disease. </p>\n        <p><strong>Treatment References: </strong><a href=\"http://www.who.int/ncds/management/pen_tools/en/\"><u>WHO PEN 5.b</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference: </strong>12.5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Furosemide 40 mg tablet</p>\n      </td>\n      <td>\n        <p>NCD - Cardiovascular</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Furosemide is a loop diuretic used in the treatment of oedema, congestive heart failure, and kidney disease. </p>\n        <p><strong>Treatment References: WHO PEN 5.b </strong></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 12.4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Morphine (10mg tablet)</p>\n      </td>\n      <td>\n        <p>Palliative care</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Morphine, an opioid analgesic, is the first-choice opioid for treatment of strong pain, including cancer pain. It is also recommended as a preoperative medication and sedation for short-term procedures. </p>\n        <p><strong>Treatment References: </strong></p>\n        <p><a href=\"http://apps.who.int/medicinedocs/en/d/Jh2929e/7.html\"><u>WHO Model Prescribing Information: Drugs Used in Anaesthesia</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 2.2,1.3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Paracetamol (any strength)</p>\n      </td>\n      <td>\n        <p>Pain and Palliative Care</p>\n      </td>\n      <td>\n        <p><strong>Rationale: </strong>Paracetamol, also referred to as acetaminophen or APAP, is an analgesic and antipyretic that is used widely as a first-line treatment for mild to moderate pain and fever. It is also often found in combinations with other medications to treat a cold or for severe pain. In particular, it is the preferred analgesic for pregnant women.</p>\n        <p><strong>Treatment References:</strong> <a href=\"http://apps.who.int/medicinedocs/en/d/Jh2929e/7.html\"><u>WHO Model Prescribing Information: Drugs Used in Anaesthesia</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference: </strong>2.1, 7.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Fluoxetine (20 mg cap/tab) or other selective serotonin reuptake inhibitor (SSRI)</p>\n      </td>\n      <td>\n        <p>CNS</p>\n      </td>\n      <td>\n        <p><strong>Rationale: </strong>SSRIs are among the most widely used drugs in the treatment of depressive disorders. Fluoxetine is recommended for use in depressive disorders and can be used to treat patients over 8 years old.</p>\n        <p>SSRIs should be used as part of a comprehensive management plan. </p>\n        <p><strong>Treatment References:</strong></p>\n        <p><a href=\"http://www.who.int/mental_health/mhgap/evidence/depression/en/\"><u>Evidence-based recommendations for management of depression in non-specialized health settings</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference: </strong>24.2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Phenytoin (100mg Tablet) or Carbamazepine (200 mg cap/tab)</p>\n      </td>\n      <td>\n        <p>CNS</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Carbamazepine and phenytoin are anticonvulsant/antiepileptic medicines used in the management of generalized and partial seizures and neuropathic pain.</p>\n        <p><strong>Treatment References: </strong></p>\n        <p><a href=\"http://www.who.int/mental_health/mhgap/evidence/epilepsy/en/\"><u>Evidence-based recommendations for management of epilepsy and seizures in non-specialized health settings</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Gentamicin (40 mg/mL in 2mL vial)</p>\n      </td>\n      <td>\n        <p>Anti-infective</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Gentamicin, an aminoglycoside antibiotic, is used for the systemic treatment of susceptible infections. It is classified as an ACCESS antibiotic in the WHO EML, signifying that it should widely available, affordable, and quality assured. It is the first-line treatment for community acquired pneumonia, complicated severe malnutrition, and neonatal sepsis, and second-line treatment for gonorrhoeae.</p>\n        <p><strong>Treatment References: </strong></p>\n        <p><a href=\"http://apps.who.int/medicinedocs/en/d/Js5406e/16.19.html\"><u>WHO Model Prescribing Information: Drugs used in Bacterial Infections</u></a><u> </u></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 6.2.2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Amoxicillin (500mg cap/tab)</p>\n      </td>\n      <td>\n        <p>Anti-infective</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Amoxicillin, a beta-lactam antibiotic, is used to treat a wide range of susceptible infections. It is classified as an ACCESS antibiotic in the WHO EML, signifying that it should widely available, affordable, and quality assured. It is the first-line treatment for specific infectious syndromes, including community acquired pneumonia, neonatal sepsis, lower urinary tract infections, and the second-line treatment for acute bacterial meningitis.</p>\n        <p><strong>Treatment References: </strong></p>\n        <p><a href=\"http://apps.who.int/medicinedocs/en/d/Js5406e/16.1.html\"><u>WHO Model Prescribing Information: Drugs used in Bacterial Infections</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 6.2.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ceftriaxone (1g/vial Injection)</p>\n      </td>\n      <td>\n        <p>Anti-infective</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Ceftriaxone, a third generation cephalosporin, is used for the systemic treatment of susceptible infections. It is classified as a WATCH in the WHO EML, signifying it higher resistance potential and recommendation for only a specific, limited number of indications. It is the first-line treatment for specific infectious syndromes including severe community acquired pneumonia, acute bacterial meningitis, and gonorrhoeae.</p>\n        <p><strong>Treatment References:</strong></p>\n        <p><a href=\"http://apps.who.int/medicinedocs/en/d/Js5406e/16.11.html\"><u>WHO Model Prescribing Information: Drugs used in Bacterial Infections</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 6.2.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Procaine benzylpenicillin (1G = 1MU Injection) or Benzathine benzylpenicillin (900mg=1.2 MIU or 1.44g = 2.4MIU) injection</p>\n      </td>\n      <td>\n        <p>Anti-infective</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Procaine benzylpenicillin, a beta-lactam antibiotic, is used to treat syphilis in adults and children. It is classified as an ACCESS antibiotic in the WHO EML, signifying that it should widely available, affordable, and quality assured.</p>\n        <p><strong>Treatment References: </strong></p>\n        <p><a href=\"http://apps.who.int/medicinedocs/en/d/Js5406e/16.6.html\"><u>WHO Model Prescribing Information: Drugs used in Bacterial Infections</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 6.2.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>One of the following contraceptives:</p>\n        <ol>\n          <li>Ethinylestradiol + levonorgestrel: tablet 30 mcg + 150 mcg (or alternative combined oral contraceptive)</li>\n          <li>Levonorgestrel 30 microgram tablet. </li>\n          <li>Medroxyprogesterone acetate injection IM 150 mg/mL or SC 104 mg/0.65mL</li>\n          <li>Progesterone-releasing implant (etonogestrel 68 mg or levonorgestrel 150 mg)</li>\n          <li>Levonorgestrel 750 mcg or 1.5 mg tablet</li>\n        </ol>\n      </td>\n      <td>\n        <p>MCH</p>\n      </td>\n      <td>\n        <p><strong>Rationale: </strong>Promotion of family planning &#x2013; and ensuring access to preferred contraceptive methods for women and couples &#x2013; is essential to securing the well-being and autonomy of women, while supporting the health and development of communities. Access to contraceptives can reduce infant and maternal mortality rates associated with closely spaced and ill-timed pregnancies. Additionally, contraceptives have be included on the WHO EML since its inception and are also listed as life-saving commodities by the UN Commission on Life-Saving Commodities for Women and Children. </p>\n        <p><strong>Treatment References: </strong><a href=\"http://apps.who.int/iris/bitstream/handle/10665/181468/9789241549158_eng.pdf;jsessionid=0B133D3AD9912240BE31344EAC19187C?sequence=1\"><u>Medical eligibility criteria for contraceptive use</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference: </strong>18.3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Oral rehydration (salts 1 litre)</p>\n      </td>\n      <td>\n        <p>MCH </p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Oral rehydration salts (ORS), solutions containing sodium, potassium, citrate, and glucose, are used to replace fluid and electrolytes orally. ORS is used to treat acute diarrhoea in children to prevent or treat dehydration. </p>\n        <p><strong>Treatment References: </strong></p>\n        <p><a href=\"http://www.who.int/maternal_child_adolescent/documents/a85500/en/\"><u>Diarrhoea treatment guidelines including new recommendations for the use of ORS and zinc supplementation for clinic-based healthcare workers</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 26.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Zinc sulphate (20mg dispersible tablet)</p>\n      </td>\n      <td>\n        <p>MCH </p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Zinc supplements are recommended to reduce the severity and duration of acute diarrhoea. If given for 10 to 14 days, zinc also reduces the incidence of new episodes of diarrhoea in the 2 to 3 months following treatment.</p>\n        <p><strong>Treatment References: </strong></p>\n        <p><a href=\"http://www.who.int/maternal_child_adolescent/documents/a85500/en/\"><u>Diarrhoea treatment guidelines including new recommendations for the use of ORS and zinc supplementation for clinic-based healthcare workers</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 17.5.2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Oxytocin (5iu or 10iu injection)</p>\n      </td>\n      <td>\n        <p>MCH </p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Oxytocin, a peptide hormone, is used for the prevention and treatment of postpartum and post-abortion haemorrhage in emergency situations. It is the recommended that all women giving birth should be offered uterotonic drugs, such as oxytocin, during the third stage of labour for the prevention of PPH.</p>\n        <p><strong>Treatment References: </strong><a href=\"http://apps.who.int/iris/bitstream/handle/10665/75411/9789241548502_eng.pdf\"><u>WHO Recommendations for the Prevention and Treatment of Postpartum Haemorrhage</u></a><u>, </u><a href=\"https://www.unfpa.org/sites/default/files/pub-pdf/Key%20Data%20and%20Findings%20Maternal%20Health%20Medicines-FINAL.pdf\"><u>UNFPA Medicines for Maternal Health</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 22.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Magnesium sulphate 50% 10ml Injection</p>\n      </td>\n      <td>\n        <p>MCH </p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Magnesium sulfate, an anticonvulsant, is used in the management and prevention of recurrent seizures in eclampsia and pre-eclampsia.</p>\n        <p><strong>Treatment References: </strong></p>\n        <p><a href=\"https://extranet.who.int/rhl/topics/preconception-pregnancy-childbirth-and-postpartum-care/medical-problems-during-pregnancy/who-recommendation-magnesium-sulfate-prevention-eclampsia-women-severe-pre-eclampsia\"><u>WHO recommendation on magnesium sulfate for the prevention of eclampsia in women with severe pre-eclampsia</u></a><u>, </u><a href=\"https://www.unfpa.org/sites/default/files/pub-pdf/Key%20Data%20and%20Findings%20Maternal%20Health%20Medicines-FINAL.pdf\"><u>UNFPA Medicines for Maternal Health</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Folic acid</p>\n      </td>\n      <td>\n        <p>MCH </p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Single-agent folic acid is important for the prevention of neural tube defects and should be taken periconceptionally and in first trimester of pregnancy. </p>\n        <p><strong>Treatment References: </strong><a href=\"http://www.who.int/elena/titles/folate_periconceptional/en/\"><u>WHO recommendation on periconceptional folic acid supplementation to prevent neural tube defects</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 10.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artemisinin-based combination therapy (ACT) for treatment of uncomplicated <em>P. falciparum</em> malaria.</p>\n        <p>One of the following:</p>\n        <ol>\n          <li>Artemether+lumefantrine (20/120 mg cap/tab)</li>\n          <li>Artesunate+amodiaquine (any strength)</li>\n          <li>Artesunate+mefloquine (any strength)</li>\n          <li>Dihydroartemisinin+piperaquine (any strength)</li>\n          <li>Artesunate+sulfadoxine-pyrimethamine (50 mg+500mg/25mg)</li>\n        </ol>\n      </td>\n      <td>\n        <p>Anti-malarial</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> WHO Guidelines recommend treating adults and children with uncomplicated <em>P. falciparum</em> malaria with artemisinin-based combination therapy (strong recommendation, high-quality evidence). </p>\n        <p><strong>Treatment References: </strong><a href=\"http://apps.who.int/iris/bitstream/handle/10665/162441/9789241549127_eng.pdf\"><u>WHO Guidelines for the Treatment of Malaria</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 6.5.3.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artesunate (60 mg injection or 100 mg rectal dose form)</p>\n      </td>\n      <td>\n        <p>Anti-malarial</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> IM or rectal artesunate is recommended pre-referral treatment of suspected cases of severe malaria pending transfer to a higher level facility.</p>\n        <p><strong>Treatment References: </strong><a href=\"http://apps.who.int/iris/bitstream/handle/10665/162441/9789241549127_eng.pdf\"><u>WHO Guidelines for the Treatment of Malaria</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 6.5.3.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Combination anti-retroviral therapy for first line treatment of HIV </p>\n        <p>One of the following combinations individually for concomitant use or in fixed-dose combination:</p>\n        <p>1. Efavirenz (400 mg or 600 mg) + Emtricitabine (200 mg) + Tenofovir disoproxil fumarate (300 mg)</p>\n        <p>2. Efavirenz (400 mg or 600 mg) + Lamivudine (300 mg) + Tenofovir disoproxil fumarate (300 mg)</p>\n      </td>\n      <td>\n        <p>Antiretroviral</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Efavirenz/Emtricitabine/Tenofovir is the preferred fixed-dose combination antiretroviral therapies for treatment of HIV in adults, pregnant or breastfeeding women, and adolescents. </p>\n        <p><strong>Treatment References: </strong><a href=\"http://apps.who.int/iris/bitstream/handle/10665/208825/9789241549684_eng.pdf\"><u>WHO Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 6.4.2.4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ibuprofen (200mg tablet)</p>\n      </td>\n      <td>\n        <p>Pain and Palliative Care</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Ibuprofen, a non-steroidal anti-inflammatory drug, is a first choice medicine in the treatment of mild pain. </p>\n        <p><strong>Treatment References: </strong><a href=\"file:///C:\\Users\\nanneic\\AppData\\Local\\Microsoft\\Windows\\Temporary%20Internet%20Files\\Content.Outlook\\HUJABWIN\\Treatment%20References:%20http:\\apps.who.int\\iris\\bitstream\\handle\\10665\\44540\\9789241548120_Guidelines.pdf;jsessionid=6AF4E039A5576A8C77618EBA7AD07D68%3fsequence=1\"><u>WHO Guidelines on the pharmacological treatment of persisting pain in children with medical illnesses</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 2.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Chlorhexidine </p>\n        <p>Solution or gel: 7.1% (digluconate) delivering 4% chlorhexidine</p>\n      </td>\n      <td>\n        <p>Neonatal care</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> A recommended antiseptic that should be applied to the umbilical cord in cases of unclean delivery, and if the traditional practices in place increase the risk of cord infection</p>\n        <p><strong>Treatment References: </strong><a href=\"http://www.who.int/selection_medicines/committees/subcommittee/2/chlorhexidine.pdf?ua=1\"><u>Review of the available evidence on 4% chlorhexidine solution for umbilical cord care</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 29.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ready-to-use therapeutic food (RUTF),</p>\n        <p>paste or spread (1 sachet = 92 g [500 Kcal]) </p>\n        <p>or</p>\n        <p>biscuit (28.4g, 500 kcal per 100g)</p>\n      </td>\n      <td>\n        <p>Nutrition</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Energy-dense, micronutrient enhanced pastes used in therapeutic feeding for the community-based management of children who are suffering from uncomplicated severe acute malnutrition and who retain an appetite. Is provided as the therapeutic food in the rehabilitation phase (following F-75 in the stabilization phase)</p>\n        <p><strong>Treatment References:</strong> <a href=\"http://apps.who.int/iris/bitstream/handle/10665/95584/9789241506328_eng.pdf?sequence=1\"><u>WHO Guideline: Updates on the management of severe acute malnutrition in infants and children. 2013</u></a></p>\n        <p><strong>More information in WHO EML 2017:</strong> Not currently included</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Isoniazid + pyrazinamide + rifampicin (50 mg + 150 mg + 75 mg)</p>\n      </td>\n      <td>\n        <p>Antituberculosis</p>\n      </td>\n      <td>\n        <p><strong>Rationale: </strong>Isoniazid + pyrazinamide + rifampicin is recommended as fixed-dose combination therapy for the intensive phase of treatment of drug-susceptible tuberculosis in children.</p>\n        <p><strong>Treatment References: </strong><u><a href=\"http://apps.who.int/medicinedocs/documents/s21535en/s21535en.pdf\">Guidance for national tuberculosis programmes on the</a></u></p>\n        <p><u><a href=\"http://apps.who.int/medicinedocs/documents/s21535en/s21535en.pdf\">management of tuberculosis in children, 2014</a></u></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 6.2.4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Erythropoiesis - stimulating agents.</p>\n        <p>One of the following:</p>\n        <ol>\n          <li>Epoetin alfa (2,000 IU/mL)</li>\n          <li>Darbepoetin alfa (100 mcg/mL)</li>\n        </ol>\n      </td>\n      <td>\n        <p>Chronic kidney disease</p>\n      </td>\n      <td>\n        <p><strong>Rationale: </strong>Erythropoiesis-stimulating agents are recommended for treatment of anaemia of chronic kidney disease in children, young</p>\n        <p>people and adult patients with chronic renal disease requiring dialysis.</p>\n        <p><strong>Treatment References: </strong><u><a href=\"https://www.who.int/selection_medicines/committees/expert/21/applications/s10_erythropoietins_add.pdf?ua=1\">WHO EML 2016-2017 - Application for erythropoietin-stimulating agents </a></u></p>\n        <p><u><a href=\"https://www.who.int/selection_medicines/committees/expert/21/applications/s10_erythropoietins_add.pdf?ua=1\">(erythropoietin type blood factors)</a></u></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 10.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"3\">\n        <p><strong>Suggested for monitoring (optional for countries) *</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>One of the following: </p>\n        <ol>\n          <li>Epinephrine injection 1 mg (as hydrochloride or hydrogen tartrate) in 1- mL ampoule </li>\n          <li>Dexamethasone injection 4 mg/ mL in 1- mL ampoule (as disodium phosphate salt)</li>\n        </ol>\n      </td>\n      <td>\n        <p>Antiallergics and medicine used in anaphylaxis </p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong> Epinephrine (adrenaline) is the first line treatment for a severe allergic reaction. During anaphylactic shock, it must be administered through an intramuscular injection. </p>\n        <p>Dexamethasone is a corticosteroid that prevents almost all symptoms of inflammation associated with allergy. It can also be used during emergency anaphylactic shock.</p>\n        <p><strong>Treatment References: </strong><a href=\"http://www.who.int/selection_medicines/committees/expert/19/applications/Histamine_3_AC_R.pdf\"><u>WHO Antiallergics and Medicine Use in Anaphylaxis</u></a><strong> </strong></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Fluconazole (50 mg cap/tab) and</li>\n          <li>Nystatin (tablet 500 000 IU)</li>\n        </ol>\n      </td>\n      <td>\n        <p>Anti-fungal drugs</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong></p>\n        <p>Nystatin is an antifungal polyene antibiotic that is effective against infections caused by a wide range of yeasts and yeasts-like fungi. It is used for the treatment of oral, oesophageal and intestinal candidosis.</p>\n        <p>Fluconazole is an orally active imidazole antifungal agent with activity against dermatophytes, yeasts, and other pathogenic fungi.</p>\n        <p>It is widely used in the treatment of serious gastrointestinal and systemic mycoses as well as in the management of superficial infections. Fluconazole is also used to prevent fungal infections in immunocompromised patients.</p>\n        <p><strong>Treatment References</strong>: <a href=\"http://www.who.int/selection_medicines/list/WMF2008.pdf?ua=1\"><u>WHO Model Formulary 2008</u></a></p>\n        <p><a href=\"http://apps.who.int/medicinedocs/en/d/Js2215e/9.13.html\"><u>WHO Model Prescribing Information</u></a></p>\n        <p><a href=\"http://apps.who.int/iris/bitstream/handle/10665/37143/9241401052.pdf?sequence=1&amp;isAllowed=y\"><u>Drugs used in sexually transmitted diseases</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 6.3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Levothyroxine (tablet 50 micrograms)</p>\n      </td>\n      <td>\n        <p>Thyroid hormones</p>\n      </td>\n      <td>\n        <p><strong>Rationale:</strong></p>\n        <p>Levothyroxine is used for the management of hypothyroidism, diffuse non-toxic goitre, Hashimoto thyroiditis and thyroid cancer.</p>\n        <p><strong>Treatment References</strong>: <a href=\"http://www.who.int/selection_medicines/list/WMF2008.pdf?ua=1\"><u>WHO Model Formulary 2008</u></a></p>\n        <p><strong>More information in WHO EML 2017 Section Reference:</strong> 18.8</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Annex 1: Basket of core set of relevant essential medicines for primary health care and related disease category </p>\n<p><strong>Table 1. Basket of core set of relevant essential medicines for primary health care</strong></p>\n<p><strong>*</strong> These additional medicines were suggested for monitoring during the consultations with WHO regional advisers and WHO Member States, however they do not represent major burden of disease in countries and cannot be weighted according to the same procedure as the mandatory list. </p>\n<p><strong>Table 2. Diseases treated with the medicines in the core list</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Medicine name </strong></p>\n      </td>\n      <td>\n        <p><strong>Affiliated disease (code of the diseases according to the ICD-11 classification) </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Salbutamol </p>\n      </td>\n      <td>\n        <p><strong>&#x2192;</strong> Asthma (1190)</p>\n        <p><strong>&#x2192;</strong> Chronic obstructive pulmonary disease (1180)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Beclometasone or other corticosteroid inhaler</p>\n      </td>\n      <td>\n        <p><strong>&#x2192;</strong> Asthma (1190)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Gliclazide or other sulfonylurea</p>\n      </td>\n      <td rowspan=\"3\">\n        <p><strong>&#x2192;</strong> Diabetes mellitus (800)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Metformin </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Insulin regular, soluble</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Amlodipine </p>\n      </td>\n      <td>\n        <p><strong>&#x2192;</strong> Hypertensive heart disease (1120)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Enalapril or other angiotensin converting enzyme inhibitor </p>\n      </td>\n      <td rowspan=\"2\">\n        <p><strong>&#x2192;</strong> Hypertensive heart disease (1120)</p>\n        <p><strong>&#x2192; </strong>Cardiomyopathy, myocarditis, endocarditis (1150)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Hydrochlorothiazide or Chlorthalidone</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Bisoprolol or alternative betablocker (atenolol or carvedilol or metoprolol only) </p>\n      </td>\n      <td>\n        <p><strong>&#x2192;</strong> Hypertensive heart disease (1120)</p>\n        <p><strong>&#x2192;</strong> Ischaemic heart disease (1130)</p>\n        <p><strong>&#x2192; </strong>Other circulatory diseases (1160)</p>\n        <p><strong>&#x2192; </strong>Cardiomyopathy, myocarditis, endocarditis (1150)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Furosemide </p>\n      </td>\n      <td>\n        <p><strong>&#x2192; </strong>Cardiomyopathy, myocarditis, endocarditis (1150)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Simvastatin or other statin </p>\n      </td>\n      <td>\n        <p><strong>&#x2192;</strong> Ischaemic heart disease (1130)</p>\n        <p><strong>&#x2192;</strong> Stroke (1140)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Acetylsalicylic acid (aspirin)</p>\n      </td>\n      <td>\n        <p><strong>&#x2192;</strong> Ischaemic heart disease (1130)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Morphine</p>\n      </td>\n      <td>\n        <p><strong>&#x2192; </strong>Malignant neoplasms (610)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Paracetamol</p>\n      </td>\n      <td>\n        <p><strong>&#x2192; </strong>weight =<strong> </strong>1/T</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ibuprofen</p>\n      </td>\n      <td>\n        <p><strong>&#x2192; </strong>weight =<strong> </strong>1/T</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Fluoxetine or other selective serotonin reuptake inhibitor </p>\n      </td>\n      <td>\n        <p><strong>&#x2192;</strong> Depressive disorders (830)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Phenytoin or Carbamazepine </p>\n      </td>\n      <td>\n        <p><strong>&#x2192;</strong> Epilepsy (970)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Gentamicin</p>\n      </td>\n      <td>\n        <p><strong>&#x2192; </strong>Lower respiratory infections (390)</p>\n        <p><strong>&#x2192; </strong>Infectious and parasitic diseases (20)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Amoxicillin</p>\n      </td>\n      <td rowspan=\"3\">\n        <p><strong>&#x2192; </strong>Infectious and parasitic diseases (20)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ceftriaxone</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Procaine benzylpenicillin or Benzathine benzylpenicillin</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ethinylestradiol + levonorgestrel (or alternative combined oral contraceptive)</p>\n      </td>\n      <td rowspan=\"4\">\n        <p><strong>&#x2192; </strong>Maternal conditions (420)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Medroxyprogesterone acetate injection </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Progesterone-releasing implant (etonogestrel or levonorgestrel)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Levonorgestrel</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Oral rehydration</p>\n      </td>\n      <td rowspan=\"2\">\n        <p><strong>&#x2192; </strong>Diarrhoeal diseases (110)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Zinc sulphate</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Oxytocin</p>\n      </td>\n      <td>\n        <p><strong>&#x2192; </strong>Maternal conditions (420)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Magnesium sulphate</p>\n      </td>\n      <td>\n        <p><strong>&#x2192; </strong>Epilepsy (970)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Folic acid</p>\n      </td>\n      <td>\n        <p><strong>&#x2192; </strong>Iron-deficiency anaemia (580)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artemether+lumefantrine</p>\n      </td>\n      <td rowspan=\"6\">\n        <p><strong>&#x2192; </strong>Malaria (220)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artesunate+amodiaquine</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artesunate+mefloquine</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Dihydroartemisinin+piperaquine</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artesunate+sulfadoxine-pyrimethamine</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artesunate</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Efavirenz + Emtricitabine + Tenofovir disoproxil fumarate</p>\n      </td>\n      <td rowspan=\"2\">\n        <p><strong>&#x2192; </strong>HIV/AIDS (100)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Efavirenz + Lamivudine + Tenofovir disoproxil fumarate </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Chlorhexidine</p>\n      </td>\n      <td>\n        <p><strong>&#x2192; </strong>Neonatal sepsis and infections (520)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ready-to-use therapeutic food (RUTF)</p>\n      </td>\n      <td>\n        <p><strong>&#x2192; </strong>Nutritional deficiencies (540)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Isoniazid + pyrazinamide + rifampicin</p>\n      </td>\n      <td>\n        <p><strong>&#x2192;</strong> Tuberculosis (30)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Erythropoiesis - stimulating agents</p>\n      </td>\n      <td>\n        <p><strong>&#x2192; </strong>Other chronic kidney disease (1273)</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Suggested for monitoring (optional)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Epinephrine or Dexamethasone </p>\n      </td>\n      <td rowspan=\"4\">\n        <p><strong>&#x2192; </strong>weight =<strong> 0.5*(</strong>1/T)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Fluconazole </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Nystatin</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Levothyroxine</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Annex 2. Calculation of weights</p>\n<p>Weights are region-specific, and the sum of the weights assigned to medicines in the basket is always equal to &#x201C;1&#x201D; in a given region. Since some of the medicines are weighted not according to the DALYs but according to the formula in points iii. and iv. above, the weights have to be normalized so that their sum is equal to &#x201C;1&#x201D;.</p>\n<p>WHO regional data on disease burden is computed and published for 5-year intervals (e.g. 2000, 2005, 2010 and 2015 for now). As a result, for data points falling between the reference years for which DALY estimates are available the closest reference year is used to calculate medicines&#x2019; weights (either previous or following) (<em>Figure 1</em>). </p>\n<p>Figure 2.1. Selection of data year for computing medicine weights</p>\n<p><img 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\"></p>\n<p>Two versions of weights are computed: one capturing 32 medicines (excluding optional medicines) and the other capturing 36 medicines (including optional medicines). For countries where the distribution of specific medicines is calculated only in specialized facilities (for example injectable medicines are provided only in hospitals), WHO suggests computing two versions of weights (1 &#x2013; for pharmacies and other non-tertiary health care facilities based on a shorter list of medicines that exclude the mentioned medicines and 2 &#x2013; for hospitals that includes the full list of medicines).</p>\n<p><img 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\"></p>\n<p><img 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\"></p>\n<p>.</p>\n<p>Annex 3: Basket of core set of relevant essential medicines for primary health care: number of units and duration per treatment</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong><u>Medicine</u></strong></p>\n      </td>\n      <td>\n        <p><strong><u>Dose</u></strong></p>\n      </td>\n      <td>\n        <p><strong><u>Duration</u></strong></p>\n      </td>\n      <td>\n        <p><strong><u>Units</u></strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Salbutamol </p>\n      </td>\n      <td>\n        <p>100 mcg/dose inhaler</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Beclometasone </p>\n      </td>\n      <td>\n        <p>100 mcg/dose inhaler</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>60</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Gliclazide </p>\n      </td>\n      <td>\n        <p>80 mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Metformin </p>\n      </td>\n      <td>\n        <p>500 mg cap/tab <strong><u>OR</u></strong> 850 mg cap/tab OR 1 g cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>90</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Insulin regular, soluble </p>\n      </td>\n      <td>\n        <p>100 IU/ml injection</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>90</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Amlodipine </p>\n      </td>\n      <td>\n        <p>5 mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Enalapril </p>\n      </td>\n      <td>\n        <p>5 mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Hydrochlorothiazide </p>\n      </td>\n      <td>\n        <p>25 mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Chlorthalidone </p>\n      </td>\n      <td>\n        <p>25 mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>15</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Bisoprolol </p>\n      </td>\n      <td>\n        <p>5 mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Simvastatin </p>\n      </td>\n      <td>\n        <p>20 mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Acetylsalicylic acid (aspirin)</p>\n      </td>\n      <td>\n        <p>100 mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Morphine </p>\n      </td>\n      <td>\n        <p>10mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>180</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Paracetamol </p>\n      </td>\n      <td>\n        <p>500 mg tab/cap</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>180</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Fluoxetine </p>\n      </td>\n      <td>\n        <p>20 mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Phenytoin </p>\n      </td>\n      <td>\n        <p>100mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>90</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Carbamazepine </p>\n      </td>\n      <td>\n        <p>200 mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>150</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Gentamicin </p>\n      </td>\n      <td>\n        <p>40 mg/mL in 2mL vial</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>15</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Amoxicillin for adults </p>\n      </td>\n      <td>\n        <p>500mg cap/tab</p>\n      </td>\n      <td>\n        <p>7</p>\n      </td>\n      <td>\n        <p>21</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ceftriaxone </p>\n      </td>\n      <td>\n        <p>1g/vial Injection</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Procaine benzylpenicillin </p>\n      </td>\n      <td>\n        <p>1G = 1MU Injection</p>\n      </td>\n      <td>\n        <p>10</p>\n      </td>\n      <td>\n        <p>10</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Benzathine benzylpenicillin </p>\n      </td>\n      <td>\n        <p>900mg=1.2 MIU <strong><u>OR</u></strong> 1.44g = 2.4MIU injection</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1 or 2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ethinylestradiol + levonorgestrel</p>\n      </td>\n      <td>\n        <p>30 mcg cap/tab + 150 mcg cap/tab</p>\n      </td>\n      <td>\n        <p>28</p>\n      </td>\n      <td>\n        <p>21</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Levonorgestrel</p>\n      </td>\n      <td>\n        <p>30 mcg cap/tab</p>\n      </td>\n      <td>\n        <p>28</p>\n      </td>\n      <td>\n        <p>28</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Medroxyprogesterone acetate injection </p>\n      </td>\n      <td>\n        <p>IM 150 mg/mL <strong><u>OR</u></strong> SC 104 mg/0.65mL</p>\n      </td>\n      <td>\n        <p>84</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Progesterone-releasing implant: Etonogestrel OR Levonorgestrel</p>\n      </td>\n      <td>\n        <p>Etonogestrel 68 mg OR Levonorgestrel 150 mg</p>\n      </td>\n      <td>\n        <p>3 or 5 years</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Levonorgestrel </p>\n      </td>\n      <td>\n        <p>750 mcg <strong><u>OR</u></strong> 1.5 mg tablet</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2 or 1 </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Oral rehydration salts</p>\n      </td>\n      <td>\n        <p>1 litre</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Zinc sulphate </p>\n      </td>\n      <td>\n        <p>20mg dispersible tablet</p>\n      </td>\n      <td>\n        <p>14</p>\n      </td>\n      <td>\n        <p>14</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Oxytocin </p>\n      </td>\n      <td>\n        <p>5iu or 10iu injection</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Magnesium sulphate </p>\n      </td>\n      <td>\n        <p>50% 10ml Injection</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Folic acid</p>\n      </td>\n      <td>\n        <p>400 mcg tablet</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artemether+lumefantrine </p>\n      </td>\n      <td>\n        <p>20/120 mg cap/tab</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>24</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artesunate+amodiaquine </p>\n      </td>\n      <td>\n        <p>100 mg + 270 mg</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>6</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artesunate+mefloquine </p>\n      </td>\n      <td>\n        <p>100 mg + 220 mg</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>6</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Dihydroartemisinin+piperaquine </p>\n      </td>\n      <td>\n        <p>40 mg + 320 mg</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>9</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artesunate+sulfadoxine-pyrimethamine </p>\n      </td>\n      <td>\n        <p>200 mg + 1500mg + 75mg</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>3 + 1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artesunate </p>\n      </td>\n      <td>\n        <p>60 mg injection <strong><u>OR</u></strong> 100 mg rectal dose form</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Efavirenz + Emtricitabine + Tenofovir disoproxil fumarate</p>\n      </td>\n      <td>\n        <p> 400 mg OR 600 mg + 200 mg + 300 mg</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Efavirenz + Lamivudine + Tenofovir disoproxil fumarate</p>\n      </td>\n      <td>\n        <p>400 mg or 600 mg + 300 mg + 300 mg</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ibuprofen for adults </p>\n      </td>\n      <td>\n        <p>200mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>60</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Furosemide </p>\n      </td>\n      <td>\n        <p>40 mg cap/tab</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Epinephrine</p>\n      </td>\n      <td>\n        <p>1 mg injection</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>0.5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Dexamethasone</p>\n      </td>\n      <td>\n        <p>injection 4 mg/ mL in 1- mL ampoule (as disodium phosphate salt)</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Fluconazole </p>\n      </td>\n      <td>\n        <p>50 mg cap/tab (depending on indication)</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Nystatin </p>\n      </td>\n      <td>\n        <p>tablet 500 000 IU</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Levothyroxine </p>\n      </td>\n      <td>\n        <p>tablet 50 micrograms</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>60</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Chlorhexidine</p>\n      </td>\n      <td>\n        <p>Solution or gel: 7.1% (digluconate) delivering 4% chlorhexidine</p>\n      </td>\n      <td>\n        <p>7</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Ready-to-use therapeutic food (RUTF)</p>\n      </td>\n      <td>\n        <p>paste or spread (1 sachet = 92 g [500 Kcal]) <strong><u>OR</u></strong></p>\n        <p>biscuit (28.4g, 500 kcal per 100g)</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>150 - 220 kcal/kg per day</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Isoniazid + pyrazinamide + rifampicin</p>\n      </td>\n      <td>\n        <p>50 mg + 150 mg + 75 mg</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>30 (60, 90 or 120)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Epoetin alfa</p>\n      </td>\n      <td>\n        <p>2,000 IU/mL</p>\n      </td>\n      <td>\n        <p>12</p>\n      </td>\n      <td>\n        <p>50 units/kg</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Annex 4 &#x2013; Combination of availability and affordability</p>\n<p>As an example, consider a simplified case of access to a basket of three medicines (<em>Figure 2</em>). In the matrix:</p>\n<ul>\n  <li>&#x201C;1&#x201D; indicates that a medicine is available or is affordable. </li>\n  <li>&#x201C;0&#x201D; indicates that a medicine is not available or not affordable. In other words, &#x201C;0&#x201D; in the matrix indicates that the dimension is deprived. </li>\n  <li>&#x201C;.&#x201D; indicates cases when medicine is not available and consequently affordability of medicine is not measured. In other words, information on prices cannot be collected when a medicine is not found by the interviewer in the facility. </li>\n</ul>\n<p><strong>Figure 4.1.</strong> Achievement matrix on access to medicine (two dimensions)</p>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAdEAAAEzCAIAAADhLa5FAAAAAXNSR0IArs4c6QAAAAlwSFlzAAAOxAAADsMB2mqY3AAAN1hJREFUeF7tnX9sVUX6/+8FG0iXWjZZwYAREltA0gR0QdmSCKKJ2xKFPzYETQzfP7DFxA03UZKNqdndSHb/AJI2a7K0+IfGjRL0D8Btq8naj2yWygKrmG0q5dakGEv8sYnUsk1Jt+33OWfOmTv31zlzzpkz55x73yeNltuZZ555PXOfO/eZZ2bS8/PzKTwgAAIgAAJaCCzQ0goaAQEQAAEQMAjA52IcgAAIgIA+AvC5+lijJRAAARCAz8UYAAEQAAF9BOBz9bFGSyAAAiAAn4sxAAIgAAL6CMDn6mONlkAABEAAPhdjAARAAAT0EYDP1ccaLYEACIAAfC7GAAiAAAjoIwCfq481WgIBEAAB+FyMARAAARDQRwA+Vx9r5S2Ndm1N5z3t/WIb/e3p9NauUeXNOgqMpFG9XURrIBCEAHxuEHoR180OD6ba+uhkOPb0tfW0pnNud/TaUKp5z84GrUpG0qjWHqIxEAhGII2zHIMBjLA2TXMbT+3Jnj/I3SpNMluHOsVXIlQPTYMACJQggHlucoeFMc1tWiPOY1t2t6UGh7NGl4y4gx1YoN/pV+Nbv/kYL+fCEsXxCKGYCadEdQYtT2Jho+LfSSJvRl5aci0DzUHAgQD/ZopfEkYg29ksRhYM7Y2XrNf62lLNnVk76GAMAPZPswg9LCZBpXIyhNqsnCXAKGRXF2oIdfvaWFGhUVOYrYEoWV5awuwBdUFAigDmuUn9RB7tPTXYvL4xT30zwLu7xZibUjDXngMbv5NnFWIQ5HC7jVLiM9q1L5PqzFqvG9ItAcXV2ezXkDp0zVyia+k2hQuN9h/JDOaabNi5p9kqKy8tqYaB3iDgTEDKM6NQ/AjkTVFz81k2fxVmqXm/l5jZWnNRe/7LR4u9OCeKYoL5sh2bsvKZcsHUWFjdy82O5aXFjzg0AgEVBDDPTeiHspkgkDfN7W9v7WnuPGTOX8VQb17Y16jGZsIFxQpyIOb5RDi/et7kuqWbufeew2ZGWnF82Wbbf7rHUlZeWkLtArVBwIUAfG5Ch0i+gzOWxFp7+Jd50bMWedmcpxb/1Lje/vZfEHIo6aSpPXtRjCuS31Aq1XPayhZmHwZv2tGHYpdfSlpCzQK1QcCVgIrJMmRoJ2B/rbfty9fL/EUW+AIcHy9CyCEnW4gs5GIRJUvytTpDIBdQNrJQJE07UDQIApoIID/X9VMJBUAABEBAGQHEFpShhCAQAAEQcCUAn+uKCAVAAARAQBkB+FxlKCEIBEAABFwJwOe6Iqr2Al9//XW1I0D/QUAdAfhcdSwrVNJ7771XoT1Dt0AgAgLwuRFAR5MgAAJVSwA+t2pNj46DAAhEQAA+NwLoiWvy5s2bidMZCoNAPAnA58bTLjHS6tatWzHSBqqAQMIJwOcm3IDhq/+///0v/EbQAghUCwH43GqxdJB+Tk9PB6mOuiAAApwAfC4GgxMB5m0Rz8UoAQFVBOBzVZGsTDnM28LnVqZ10asoCMDnRkE9OW2OjY0tXbqU/psclaEpCMSaAM5y9GCeN954o9q8zx133LF3796//vWv1TbVpU+a/fv3L1myxMP4QFEQkCAAnysBySxC3vaDDz44cOCAbAWUSzIBsvXixYu3b9+e5E5A9zgSQGxB1ip01Mu6detkS6NcwgmQrXG4T8JtGFP14XNlDfOf//znZz/7mWxplEs4AYotVFs4JeEWS4z68LmypqJ3IKJ7srCSXw4+N/k2jGkP4HNlDUM+l96HsqVRLvkEsAEv+TaMYw/gc+NoFegEAiBQqQTgc2Ute/fdd1PilGxplEs+gdWrVye/E+hB7AggVyx2JoFCIAACFUwA89wKNi66BgIgEDsC8LnhmWS2a2AifWYiPTA9ajRS8E/ndl0LOwt3rR5er+Ul20rmEAmU8l6Ul1mupN3W5RkJWa70KgC+BAYUCYcAfK7BdXRk0nCOxs9k16Qq0nPDTNTkXFaVyJwcZ+GhNi3VGVek/ZdvZThqG1HJF6Xa01oo7vC1wkBjHgnA5xpzq97xOZvb3Kkbsx4Zliu+YH2d+ae6BY2KJApinIUX/fXGlOpPFOcuuSKdOT1uSlhZO7+rfn5XbYvxj5IvqmcXWGLM4QfuHwSESQA+l+ahM6eMCVdN20qD9ODV2/1qiC88uIO8Sf38jsUNagSKUpyFh9q0RGekkTbXlRiBJV+UaFVbkXjD14YBDfkiAJ+bGr0xM2hMuGq6V9SYDGdO32AsecBxSvDChcE+4Uu0EZ3YOsKnya5hQTGmUVBXMObk9FYr7iEvPK/p/ssT6UssjjmXYSHmy7fzY835/ZUKejoNt/JIjVomsakeU8Dg1VumPjMlX7TbECK/hfEf60/tN1JGN42/2saypvbGi/TX4qe84cKGPx0qfF9+AJX0EYDPtb4Ft5HDXVHTZpLvucE81MKdKxmf2Wu5yCObFKeaV9Y0mE658SqPS1hORHC7DoaUqzt5u3HgtvGRYHsoOeGuA2iB1bXJ20e4P7Imp6nA00wHpKUVc5rtGh85QuTXEGB8chS40Z5LE60sWMEecrjWx4xp0EsFEqKFb4+rUOC7mh4FIiZQ9T73xm1zJYdF6Gp2m+GF1PgMm9g2rKhpNt/kPMhrzeBSC/asWGiZzopIGkFJ5rIHx2fMRAWJR6auHfG0hHsPfbRsqp/fzKbwCzpZuGNTjd01/gFjz/fFrkn0oEQRR6QG1bV1nFXzuiWkz/m1C0u+aHzVuMQ+cmzNbcg9l8QvH6YWudDwbNdV9qlZ02cEi+v7mFkLnoqE789kqKWRQLX73H42pa2r2Wmud7UUhBfqavaYr9tu1F4assqbcb1NzJ0Z73DLZUvZT65u3aKsJb/m0LqiSbdUQ2UK1S3uyPuAKeiaf9EuSD0J5lPvdbUH2YJkqhyHmj5uiFytRebSXKpl05JOqzoTUrHwPdFF4UgIVLnPtRbKzUCB+ZQLL0zO9BrTYStJKFc+xcOIRtww7+uthD3tEKSnunaikoR85yJ5HzDcT3EUPuW7IvUid3KOxVWa6uxvFTRNtn7P57CyhrlX4ylVq7jVSoTvhS3KRkSgun3ujZm8lRxzBYa9khqfZom6eeEFqzwPLBhhQa9+1jZ0kLqKBovwAVMiZuKvEQmk/gQrrVWh8JUygrCQCFS1z7W+BZdGa8dwhfBCV34gImUFLlNtm80gabm4YUn53utmJ9linZ0cqmBE5OLXR5hwO8biW7YUUnnpdQvMeHpqaDKXND1q/V6eQ6laeW1WKHx5rigZIYFq9rn2t2BzGUf4KVgKy60yZ8yVcTGwkGe5yenD4tK5J6uWq0t5CyxzixcI5BYL4xI8vNBjat62LmAqsSRSaTT8A4+vHE5O72OJIg4c6hY2mS3wVOu87W3FjVcIfGmqKBgpger1uaMj02YYQchAsCxhz/6sGC4PL7A/C+X5d/NL7FyFXFKXu03l646bW8hs4T7dYkFzPAN3xSJhcalm9wp3xR1KyCOVbmbhwXVW0nQrS1K2OCzo3Ozw8cDX2WZYrcL4TyXCl0aKghETqFqf67RMb8/+CsMLRdOrmm47dck0Y02flZIlY1SpukYqlSCTghjdPt1iTbcgR8iH5TnIxq6Q3DKUTA8Ky3hBKi9/BW0Otr55WJUol2NXnZ3GUFoQZZ5lrTQPowBxE/9JlpIxXKLgywNFyYgJ4PzciA0QffP29oEADj36TiRVA8BPquX8612181z/yCqspr3qpXBprsIIhdgdwA8RblxFw+fG1TKa9LKP8gq0NKdJ14prBvArzqQSHYLPlYBUwUXsdNqyyRgV3PfIuwb4kZsgCgUQz42COtoEARCoVgKY51ar5dFvEACBKAjA50ZBHW2CAAhUKwH43GgsP9W19Vw6fW5r11Q07VdCq2BYCVasvj7A51afzdFjEACB6AjA50bHHi2DAAhUHwH43OqzOXoMAiAQHQH43OjYo2UQAIHqIwCfW302R49BAASiIwCfGx17tAwCIFB9BOBzNdn8+/b0uXT79+Vbcy2gSdEYN+OKyLVAjDsH1aqFAHyuFkuPdo0ZB6T3DJdxu+Qshs0CY12y17Rr0TtOjYBhnKwBXXwTgM/1jc5LxYaDm7OdtUaNEm7Xdrip2s7s5oPWBcRepFdHWTCsDjtXfC/hc3WZuIzLgMP1YAAw9AALRWNKAOeK6TXMaNelxkzxfl/McD2YAQw9wELR2BHAPFevSXIztVy7cLjebACG3nihdLwIwOdqt0e+y4DD9WMAMPRDDXViQQCxhYjMYH5BTmHRLAh+MAxCD3UjIgCfGxF4NAsCIFCVBBBbqEqzo9MgAAIREYDPjQg8mgUBEKhKAvC5VWl2dBoEQCAiAvC5EYFHsyAAAlVJAD43LLN//vnnJ0+eDEs65HonQOYgo3ivhxogoJIA8hZU0uSyvvvuu/3799M/33rrrfr6+lDagFAvBCYmJp599lmq8frrry9btsxLVZQFAZUEMM9VSZPL+tOf/vT++++//PLLcLih8PUulAxB5iCjkGm810YNEFBGAD5XGUouaGBg4PDhw0ePHt2yZYt66ZDolwCZg4xCpiED+ZWBeiAQlABiC0EJFtSnqMLy5cvpxZs3b2KSqxhuYHEswkCz3W+//RYRhsA4IcAPAcxz/VBzqEMBXPrrRx99BIermKwKcWSUTCZDkpiZ8ICAfgKY56pkTsviGzduPHDgwJ///GeVciFLKYHnn3/++PHjn3zyCYI/SrlCmBQBzHOlMEkWeuWVV6gk+VzJ8igWCQFmoD/84Q+RtI5Gq5wAfK6yAUBRQnro/bxhwwZlQiEoBAJkIDITs1cI4iESBJwIILagZnzQ4szSpUtJ1vXr1++99141QiElNAJfffXVqlWrSDyWOkNjDMGlCWCeq2Zk9Pf3kyBKRYLDVQM0ZClkJjIWNcIMhwcEtBHAPFcBaj7JvXLlCgILCoBqEcEWPDHV1QIbjeQIYJ6rYDSwuRIiuQpQahTBorqY6mpEjqYMApjnBh0HmOQGJRhdfUx1o2NfvS1jnhvU9myS++STTyKqEBSl9vpkMjIcprrawVd1g5jnBjI/n+SePXuWvXvxJIsApYs99dRTiOomy2qJ1hbz3EDm+9e//sXqP/LII4EEoXJEBLjhuCkjUgTNVgsB+NxAlu7s7KT6lHWE0xUCcYyuMhmup6eH2memxAMCYROAz/VPmFZg2Eamxx9/3L8U1IyawEMPPUQqkClxi0TUpqiK9uFz/Zv5vffeo8pYPfNPMB41+UoaMygeEAiVANbQfOLl5+S+8847e/fu9SkF1eJBgK5Ke/rpp0kXnKsbD4NUshaY5/q07j//+U9W88EHH/QpAtViQ4AbkZs1NqpBkUojAJ/r06InTpxggYU1a9b4FIFqsSFARmSpfsyseEAgPALwuX7YXrt2ja2ePfPMM37qo078CDBTklnJuPHTDhpVDgH4XD+2/PTTTxFY8AMuxnV4eIEbN8bKQrUEE4DP9WO8t99+G4EFP+BiXIeHF9hiGh4QCIkAfK5nsDwtF5t9PbOLdwVuUCTqxttQydYOPtez/S5evMjqsFx6PBVDgBuUm7hiuoaOxIcA8nM924KORGELaLjWxTO7eFfgJxaRmvPz8/FWFtollQDmud4sxzMWOjo6cMaCN3axL00GJbMyNZG9EHtzJVVB+FxvluOL2ggseAOXkNLcrMheSIjFkqcmfK43m507d45VWLt2rbeaKJ0EAtysLDUFDwgoJ4B4rgek/IwFqoN4nwdwiSqaTqeZvjh7IVF2S4yymOd6MNXQ0BArzaN+HiqjaEIIcONycydEcaiZDALwuR7s9Nlnn7HSCOZ6oJa0oty43NxJ6wH0jTUB+FwP5nnppZdY6XvvvddDNRRNFAFuXG7uRKkPZWUI9LdTCImerV2jMsWVloHPlcUpJg+tXr1athrKJY2AaFxkjCXNepL6tnTPz/e1pVKDmUbmfK2n3bjEO+QHPlcW8MjICCtKO0SRmStLLYHlyLh8EzA3egL7AZVdCBh+V3iync2pVE9r+LNf+FzZocmnPNu2bZOtg3LJJMBNjHluMg3oQ+vR3lODPqr5qIJcMVloPIXoo48+2rFjh2w1lEsggYGBgccee4wpjqTABBrQVeXRrq2NmWIf29Y3393iWjlgAcxzpQB+9dVXvNw999wjVQeFEktANLFo+sR2CIoXEMgOGw6XXGz+E77DpVYxz5UajuLEB0fbSCFLciHxsBt8rYmbJU+fPk3vQa4VrXnecccd9F+vkyHKXWjtSTV3Zs8fbNDZR8xzpWh/+eWXrBwW0KR4JbyQuIzGTZ/wPlWO+tvzH+rY9PT0Bx988Nprr4m+2LXDxhpatjNlZC7oSFfg+sDnuprGKMBPPFG7gEbzKbrlm0eKpVRBoTIE1MLkhsZhN3EbcUuXLqVZLX/IAz/++OP79+9vamq6cuWKN20bDp43HO8QpStoS9WFz5Wy0fHjx1k5hbf8Urzi2WefxU0wUgZwK6QcJjc0N72bCvh7UgmYjrevyZjw6nC88LnuA0VcRbnrrrvcK7iVoAyk559/nlbG2dnneIIQCAmmaGgsowUxUCzr2vvQzK0QZmTBzNbV4njhc92HxOhobn/gihUr3CuUL0Enkx07dowODMTsKQhGVjdUmKKhxQEQXG39EkZHJtNnJuhn68gsa734FWetvJbX30ePLdr7IYSNEIb3pTU1egZP9Ya5Ixg+191Y9N7mhYKctECh2+XLl9MJvBR1os9UT/F+dy2rrETYMMWthuIAqAzM2ck51pFB+xfnfnktr4bSjSn2UZE+MxXWllwzrJB7TBcc9gOf6054fHzcvZBEiWXLlp01nw0bNlBxbCCWYFa2SNgwReuoGgBB+qu2bmOd9cZvtn9xlu+1fGBtZ9rJ1V6aCSzHowDLBYebPIb8XHer8LyCo0ePvvjii+4VpEtwydjsJM2sbEHlMCkKxI8WczPQ6PWtjd/tyW7Wm+kpCY0iA41XjYlt87ol59culKwVWTGa3hZ625q+XbWu+8M+/vhj0pnSGKQ0F3eiadl+xrXCPNfFQJSBxEvceeedUuZEoYojIA6Dos4ZDndsMDWV2Xc9zDhgxUF17FDdouyuWjr5K5xntGtfJtWZNfLEmpvbUod1pujC53rwuffdd184IwBS40jggQce4GqV97mWw6WSd/WdX+W2oclejDIDlLl4pRCyFF8cmC7hw/NqTaRLlem/zMKgeetmIuKya2L5wh3W3Mp2xK/OOfVW1M7vqp/fsdiNZZAxQ3t/m/fstFpYf+i8kaKra2MEfK6L5W7cuMFL1NbWBrEz6iaXgDgMhF6IDnd+veu331zN2cMDBfHKmdYzU+3kK8Wv1ZO3G/NdmOFMC753U5m8JabZroGJVmEBYvDqLRZYcHuMigXCqW57bviXFFDUkUJ9UhI6u6kWyt+b1ghOveFgR1vP6bBW6vL1h891sefU1BQvETBRLJSRA6GhEWhoyL0pxWFgN+jb4ZKAucHJVGqlOaHLfYOe6SFfWfDi5O0jttejqSVzphSWNSvWz2+uMZWZab1sLTeNjkxlSDI9xndzo0zfSilAxRUFxRwkGB2x9VnSWcdKzpz2orOUfooLNa5vHrpmfIloWNM0OJxVLN1ZHHyuC25ciqV1PMa1saJhwB1u8+qspxmu3UPyrZuYx6w5tM5+G5Z6cWiSJdXO9o6z6WpNB18HW7HIcnPjM+YcjZdZ0LnZ+m7esknG7ZaoSA1176rvdstHF9blFu5caXXEi86RmDznai3vO3ptqHl9oxZd4HM9YA6SnOuhGRSNBwEHc3/fbiyamc/gWGP6XLr8T5kooZik1VBn5RKUfNGGMTfMJrA0q7Vjtekzt6xZrVWIl1m4xppyGn/gmV7luZau6NUOvCN22q+Mzl4bUVOeNkWwgxsbDr6551RjujHT1KEp6QQ+18WEuIhQzRhPuJToh8Hk7JArQ16mboG3KZvvis4qyejs2qnQC1hJuVrOzjU6A58ra9IDBw7IFkW5SiFQzuh3dWdXWzuWjNjCtvnyP8rfynag1grpssAuS1+tW9jEyE/O+QxR+q7obHEHnSMaKpSem7t5UsfJNrl+wuc62VzMEBJXVCIaJ2hWNwHR6PnpYg2rzjO3a8QWhjUseOf86UyvFWRwoDF7LVeGx2rLl+fCU2LFwLS96Ry4OQ8C+o9kBnO3RHQM6zxDFz5X1ud6MCiKViKBohRd7nZT37dqcLs1u60MhLnMJTFv19gmayfSlijTf7kg5lvSNiWFG9ljbrlizpaW0TmqsSIsmVFwt6+t53CXnh0t8LmyJld4cq5skygXNQE3o2t1uy2b7H1ZRg4s3/UwZZ6FZT3FZYz0sroFrke3lBIu46xdLCSjc6EI4Wgbu2t82VDVYTctu9sGM0eEryeUvqArZww+12nQiIf4/eQnP4naA6B93QREo5c50VF0u1tD3vtrJG9leWKZBWNB54564RQFKsPzZI0SbZuNPV1WnNeJnyG8IJmX8sBcc8XcTCKjs5sM9X/vPz3U3NzTyi/lMW5a15UrZlwljaccAbp/kJubflcOigtXLrkKBYYBU3oAZMeaUxeN/ft4IiPwf+Yj13zxoY10F6Vc1cClMM+V/QzFxl9ZUtVYjma78/E8VKwareHeZ+HcXMv9DhpX8/BbI9wl+C8Bn+vETrzzVfnG388//5y3jdtf/A9hs2ZIMMW8BVwAHNBGMa0uHlveRweZUcQh3HvR4HOdRsKPP/6odqCQb6WHLkykaw42btzIhb/wwgv0yoULF1gBtY1WqjTNMJUPhkq1SxL6lbsPLS83176yZz7UU8sDRycqWQAdUs4H0PXr14N3VXI4Bm+oGiRogElG563QYKgGqsnto5d4rtnLwqCukbBrzHRDDu1iniv5zlVTTHJAq2ms0qUAZqVbOOT+FVyGZh7AQDljIbeKvb9hA4Z8EACB5BAw/HCogQWct+A8GOiOXl4Ah4ol542jTFPR6OJgUNYABEVFIBfRDXfFrLh/iC042fz999+Pakig3bgRwGCIm0UC6NPf3tqD8xYCAERVEAABEPBGAOcteOOF0iAAAiDgm0DR8Qo4b8E3S1QEARAAATcCdDtE3k2/Os9bSFPCjZt+1ft32pDCOw9Q1TkOMAaSYvePP/6YVN2+fbt3hekE88bhDuu2Hu/VvdXAGpo3XigNAiBQcQSMDDHlF3qUowSfW3HjBx0CARBwIVBm768WbvC5WjCjERAAgRgRMA9WMPf+8vPE0uZhuoYzDvl6NPjcGA0EqAICIKCPQER7f7GG5mRirJ/oewPEtSWMgbhaplCvAGtoWruIea5W3GgMBEAgBgQQz42BEaACCIBA1RDgB+XOm/esa7ohguENZZ5Lh0nb3cj9n87k5hb97rvvigvgrO6qGfHoKAjEhQBfTWvr05QtForPpdOYRkZGRKiffPLJ3r17+SvLli0TCzz55JN0ODQO7orLMIQeIFBdBMxtaYe7RrX0OhSfS5qvWbPmnXfe4V3YsmVLQXeoAHuFHO7rr79enQ53YmKCpv/iKo0Wo1dUI/T16MSJE0899RT75nTs2DHxbrSK6io6EyqBweFsqPK5cMnD9n0UE+81Ka5OM1+mA014fQjXU0U0gfIW6R5v+rxhTSgXXiUC+ec6/UI8e3p6GM+Ojg5VBEIdA6qUhBwi4OVuHuMKnsIn5Ct5uI3CfbcfOHCAdezbb78Vh8XNmzeZuyHPG+fhEtL7jT5mOBn4XN8D4OzZs+x7kji66JOeDS1VbjekMeC716hYjoAXnxslxXB9LntXFPtWej/QizQ3ibLrEm0rf7+RdxDvteTyJXRBkTwC/FtU8d2gNOFlYOmX4NSUj4HgKkFCSQJJ8blhxXPZSH3kkUfYL729vXzsUgTz8OHD5HbFVbXCeX4l/ps6vnz5crri5cqVKzRoaLJfib3U1KcPP/yQTWaLVwJ27NjBlHj33Xc1aRP7ZkZHJtNnJuhn68gsU7b4FedOeC0fdyR0lBjPnTI2/ep7wvW59fX1bEpLTpbWi+gXWt94+umn6dvfq6++qq+X8WiJsjVo4k/Phg0bSCOCEw+9kqcFjaW2NiMi9+ijj5bUngV2jx8/TlmJyeueFo2zk3OsnUH7F+dmvZYP3ImZdvNDgv/wT4vAko1PnK59mVRn1jxzobktdZgdtqDnCdfnUh927tzJevLFF1/QG+CVV15hiQp6uherVmj+xRfNYqVY4pQZGxtjOjc0NJRU/r777mOvDw0Naezd6PWt6UuaMo6CdquxznrvN9u/OEv0Wj6Ifv2XydVOWeuhtqDBq7fSA9OK0rmyw4PNe3Zag2f9ofPZvBPMg+juXjd0n3v//fczLSi88Nvf/pYu8qMZLs343FUrVWJgYKB4M4X8K1TdX7uoFSsC9PnN9Fm8eHFJxbgv/vLLL3VpTg63cWwwNZXZd12RYwhV84a1dfO76unn/NqFMg15LS8j06lM3aKsqd78riWddXbBydtHbgQVbNdvWiN8Xjcc7GjrOa1nqhu6zxXDC/Rdj3+zVkUOcqqQwPj4OOu164f3jz/+qIWP5XCprbv6zq8qPfnWokhFNNKyqX5+x2Kb4sKDmxfRoYvsGZq04tHBOkrXnw1dMz4aG9Y0acvLtVQO3edSOzy8QLHdgF+u6et5kEVbvroSzGCoHTGB0VGXmSRfWHMtqaInosOdX9/iItJejJoyZlU3poSQpflKwYslv03n1Zoo+Y3b/Hpu/ZSMhJZdE8sX7rDmVrYjfnVWYQtJGTlXa3nf0WtDwkXAklL8FdPhc//9738z5WglzZ+WqAUCIgH6wiQJRL6kpMCiYt4crlB99vDARPrSjPDKTOuZqXbyleKLk7cb812Y4UzzaqVSVOaM7a8NcbNdAxOt1jcB498UCW28aq2YOXbTqFggnOq2u3ydL+pIoT4pCZ1l8TfVSUVCXMXRMQvseAXa9rvnVGO6MdPUcVDP95PQfS6FUNkSM3uwL9N1NKBAcghwh9u8Ous+w83v19zgZCq1stYMWdba75CZHvKVBS8KQUyaWjJn2rxuCYvGzm+uMcXOtF623PfoyFSGJNNjh0T7VkoRLa4oKOYgweiIrQ+Pvc6ctj21jM7O+vWP3B60StTsXiHVF9dCRqqYlapgHV2u6YSbkM4VEz3sY489RnuueAb7xYsXXXGgAAgkgsD37caimfkMjjWmz6XL/5TMRCLfuol5zJpD6+zZT6kX7SDmbO84m67WdPCFrxWLrCWm8RkzNMHLLOjcbIVEKTwq4XZLVKSGunfVd7u5OXK49kLcwp0rrY540dnR2jem+Jy9ed0it8iN5MDpP0KpYodMYVaibsgX8gh6hTjPZZlh1Nbvf/97iq+x3a7inFcSD4qBgG8CAdcPfLcrU1FM0mqwvzKXfNGWNjfMJrA0q80lrt6yZrVWIV5m4Rq+3J9K8Uyv8oqVrijTEbEM74id9iujc/lGJqe38kDKylrJFAs5nVnegpGo29Q3P9/XlNmnKcsvRJ/LMsNozxVbXG5tbWUsLly4IAcFpUAgKIFt27YFFVG+/l3d2dXWgroRW9g2X/5HyRfXyVn3ZGNepm5Bo6ee+67o3IqMzmUlzLQP2FEFipNY3wnKlr7jjjuk93bSypmZGjbae4rNd1t2t+nKXwjL59IMl5Yv6EQFtueKnocffpj9Iu4D9jQqqDDyc70Sq8jybHOjw8M2PYb/NKw6z9yuEVsY1pPeSa3lcldZBiv7qTW+KtctbGLdnpzzeTKh74rOuB10Ll2RFvT4toiavlzqWNlm6MBYytf+3e9+98Ybb/zlL3+h69H4c/Xq1fxq5oG5renc0hnlLYQ/XFgLofjckicq0GyXhRf4PmBdfUQ7lUZg6dKlrEvl7hbhPveBBx4IufPc7aa+bw3Z7eb86UyvFWRw6NzstVwZHqstX54LT4kVA8PzpnOuuf7LPGZS08c+TtwemufSXph169bRNkXKEaSHfuFPYW370l/2DYTmu4Ntu2VacdPC/e/qfS4/UeHXv/51Qfs8vMD3EbkrmF8C+bleiVVkee5Jf/jhh5Id5K/fc8894RPQ5nZrdlsZCHOZS+IuWONoAjuRtkQZwX85wCgp3Mgec8sVcwYso3OhBJ7qQJPCzh1SDpdE0GSWJrYbN278zW9+Q7Pd/fv3/z/7+eUvf+mspeGBlcR/JEabYp9L8w7qM7WbyWSK9wjxTPU333xTQjcUAYHSBLgnLffhzee//DqSkFGKbndreHt/WzbZWWVGDizf9ZB3NEFxGWPdv24B38pVDkUp4QULdH4oyuicL3fmSC6beC5DKcN5h91MdpWZ43/zzTdsnltuR7gf7UOoo9LnUqLCCy+8wJQsuePrpz/9KfsrhXpx42QI1qwWkeRJWUICHYxZss99fX30Or82QgsXy+3Wdr4Z5t5fI3kryxPLrI7RZFA8NoHKCGcUULLQZmMrrRXndWJhCC/IKqM8MNdcMTe8Mjq7yaikvwfZSivWpdO4xbycgoshWEnxPaDqGH9V+peUIxo6jIa4/DCEV7ZMfjA5O4xYfOgVBrb4OHMfTMIeAz5UQhWcWZ6iGS6dxk2ZYXyA0j8LPpnolkAxOZdW0uiVSvr08toXcUseZv1e6dEXKbYkSxky4iG5PCucTlOqzotNvZJEec0E0vSJobnJBDUn3sirBBTzrbSiSq6Bzm7nKOgrwjPPPLN69eoVK4xNP3AWMoOEGFIOODtRgb5C0Zm5nColKaq6hUT5GJDpGsr4IEALaFRr+/btEnX729Ot7HxeunnyvKaDFiy94HOdDKT8/SZ5rboS/y4x8iqhCG2xoYxvdnwSfXTR88QTTyj80FI+BioBeiz74MXnmh2gXb+NGfskB/p3W998d4qc8VC4bhg+V6vPjeVYhVIYA5UwBjz73FKdNvzwqT2hTn3hc/F+q4T3W3h9wDw3PLZqJSvxuWpVKilNZa6YBnXRBAiAAAgEJkDxXOvRd56YrTR8bmDzQQAIgEDCCNCR5dbTMdxou19NV//C5yZssEBdEAABhQRM70s3rtMKmqbNv/C5Cs0HUSAAAkkkYB4zdjj55+cmkT10BgEQqFICST8/t0rNhm6DAAgkgEBuDc0K5lKirq57f5Er5jRAkCeUgLdPyCpiDIQMWJl45IopQwlBIAACIFAxBLCGVjGmREdAAAQSQAA+NwFGgoogAAIVQwA+t2JMiY6AAAgkgAB8bgKMBBVBAAQqhgB8bsWYEh0BARCQJUDnh9lbftNpvWcuwOc6GUm8bUjWmChXoQQwGCrIsP1HMoN0XK545oKm0xboKuMK4qi+K9u2beNClV+fQwJPnDhBFxSxz9tjx46Jt/Wo7wwkeicgGl0cDN4loUbcCAhbIOjMhb62Huz9jZuJ1Opz8uTJVatW0QVxdCUPXadIV8u89NJLdE093e6ltiFIAwEQKCLQsrtt8FTvaO71xvXN2PtbwQOFLuuky9DouyrdjkzXdtF1is899xxdUkuv0B0zcLsVbHp0LSYEWg51pjL7cqfaZIe17f1FbEH3GKCvq+zC49dee23ZsmW8ebrCK5PJ0D/J7Q4MDOhWC+2BQBUR6G83bkIbzPCzc1t72jo0XUUJn+s00O68807l4/DDDz8kmR0dHcX3JNKElzX37rvvKm8XAgMSCGMwBFQJ1f0SyJ1Zbhydaz49rZpW0eBznaxG13fzP9+4ccOvgXP1JiYmKIZL/3700UdLSqPALr1O94fTteHBm4OEgAREo4uDIaBYVI8PgYaD5+30BZxZHh+zmJpMTU0F12hsbIwJaWhoKCmNv7GHhoaCNwcJAQkoMXpAHZRUHx2ZTJ+ZoJ+tI7NMYPErzg15La9E7YoUgnmuVrN+8cUXrL3FixeXbJj74i+//FKrZmisyghkJ+dYjwftX5wBeC0fFOfk9FbzQ4L/8E+LoJKjrg+f62QBcTb63//+N7ixxsfHmRBx9ayk2B9//DF4c5AQkIBo9HJfTQI2EVX1xjrrvd9s/+KsidfyQfrVf3kiPXB7MF/E4NVb6cszQcTGpC58rqwhrl27Jlu0fLnRUSElsFQxvrDmWjK4MpDgSsC70Uevb01f0nSxlqv6jvOJtXXzu+rp5/zahTKCGjyWl5HpVKZuUdZUj36y62w3NT7dNRlUcOT14XOdTFBfX6/WQrQ4JilQvqSkQBQLSEBiMJDDbRwbTE1l9l13+WwNqEuFV2/ZVD+/YzFf8WhYu9hYdzaeuWE1Pte+m0fvSQusD/C5sj4XE88Kf6OX6p5odDefazlcEnNX3/lVpZdIjTbsxaipfvrHjSkhZGm+UvDiwHQJ751Xi76GlyhjfD23g6ElI6Fl18TyhTusuZXtiF+dJQbYgvV1EqXci9A+NLOQkKFr7sDXkS4Gn+tuH1YCE09ZUhVUTtroosOdX98ig2D28MBE+pIYoJxpPTPVTr5SfHHydmO+CzOcaV6tVIrKnLH9tdHybNfARKu1cGA6lqu3Gq9aK2aOmhkVC4RT3XaXJMmijhTqk5LQ2UEv0mrKyKCkZ+Xig2p8bv/pnubOrJ0kZuToNre1UaJuTys7/STM+S98rsvb4+jRozJvIOVlcIqVcqRBBDoOAx8Ol3SZG6SvyStrzZBlrf3deaaHfGXBi5O3j9hej6aWzJk2r1vCYp3zm2vMfs202utLoyNTGfYF3A6J9q2U6npxRUExBwlGR2x9lnRaPnHmtBedi6XzaXj6zC3WHaOJTayzqh/K0c3uGRraw53w/PkQ96TB53qwn/KjxRzaxilWHgwTTlE5c3OH27w6KznDtdUl32o5kZpDfJmo1ItDkyypdrZ3nE1Xazr4wteKRZabG58xQxO8zILOzVZIlMKjEm63REVqqHtXffcKF77kDe2FuIU7V1ouxYvOUvajGbe6dDHjjJvMESuWYzWPM26k7BB+oQceeEBhI7Tl11kabVRT2BxEqSJQbhh8324smpnP4Fhj+ly6/E9RqFBM0mqos5IHSr5o94KvIFEUgodrrWlgUZmFa4Sv4TzTqzwQLjyvoleAvCN22q+MziUa4WkSYt6CQrfb0k2nN7by+O1o7ymccePV1mGVr62t5aKDb/9dunQpk1ZuDsV9rlpfHxadipYrLqCJwyCyTk/Ouu9N5GXqFjR6UtR3RedWZHR205P8L08XGxyfUZQSYhy5kF1/mMVvGzOpzjdDjCeIXURswcXgK1bkvlkF3wnKPekPP/xQsmH++j333OM2FPF3fQTEYSC2eld3drV1RooRW9g2X/5H4W5+IXfViuqaQWFj6a5uYRPTb3Iu6w+P74rOzTnoLKEnnz7771epVoTDFsKM4OY3DZ/rYnAxQyj4flzuSfkm4ILm+fx3zZo1EkMRRUIk8Nlnn3Hp5RPFGladZ27XiC0M50cIFSuX86czve5pqrPXcmV4rLa8Rlx4SqwYuAfedC7b3KgV0aYPFY/z9zIicR9aYNOGJkB8swXfj0uelCUknDt3rqTKfX199Do7XQxPfAg4Judyt5v6vjVUt1uz28pAmMtcEnNyZ9pzh9eUKNN/uSDmW5JrSeFG9phbrpizlWR0LpAgdsf80+T0PjvXrXllTfnUZ/nxgvvQ5FlFUZLnCdH1OcHbZweTU+Jn8e1n9ApLCH3iiSeCN1S1Evh9rgEJcHNL5Atqcrstm+ysMiMHli+j2emrZoeLyxjpZXULrBBIeSilhMs4axfMMjoXizBOV+Ad5GcvrKyV3KksYXrchyYBKaoiK1fK5TfK6UcHkx84cIDK0h084iG59Du7lefs2bPFx5nLyUYpZQTEBBK5ASC63a2h7f01krdy5w9Y3V3QuUM8NoHK8DxZo0TbZmMrrRXndSJkCC/IKqM8MNdcMTfoMjqLMsqWV5efG+V9aCl7Kwb+X5YA3RHJRwTdWhacFF2DxtwuPRRGIPnvvPMO+yf9Elx+lUvgxgrCgQzN5ZCBpEVlx5pTF3M7nKTroWBgAv9nPrJizL1nOUP1tYn/khXiq1yaarl9TFX732ldi+7oZRQ++eSTLVu2KCFy4cKF3t5euv2MpFGQlx4KKWCGG5wtxRaYkCBjm6zzi1/8gskh/wu7BLdL2BI+/vhjamL79u0SDdEZN60FayZtffMKU0scdIDPlTBQKsXfxvTFH7typZAlvBDdzcyuCg3ouxOOIUnqe/G5uX5RAoNxHaXxaPK6yBWTGlU8FOD9QFUp+SgUNwLc0Nz0cdMQ+gQhYOSKmTsDcR9aEIwh1n3wwQeZ9HI5XiG2DdFREOCG5qaPQgu0GRIByhVLdR4yz3+zMnXDPEksvxOY50oZlV8NSV85cSSCFLIkFyITk6FZD3Ddb5It6aB70xoj0Xe0a1+mqW9+vq8ps0/T9R7wuVIjSrwLi7IOpOqgUGIJiCausGvQEmsTtYo3rm/uOU2hBTrbhs13jeSxYZ+7pT2qBp8rBUxctv7666+l6qBQYgmIJkbGQmLN6KB4w8E3O4fofPLGTFOHebTN6DX384MUgYDPlQXJNyOJ2/BlK6NcoghwE0vsQEtUx6AsJ2CvnbH8MOMsx7bdUvd7BGYInyuLkB86g2U0WWSJLcdNjJOGEmtDb4obHlhPdi7uoJS3zNq1a1lhLKPJQ0tiSXEBjRs9iR2BzuUJ4N7fJIwOccozNjaWBJWhox8ConExz/VDUG+db775hkz2j3/84+rVq6tXr5ZrnA4sNzb/RnLvL/ahydnILHXs2DF21hR2o3mglrSifAcaBXNffPHFpKlf+fq+8cYb4ufi3XffvXjxYjqZuqmpiX731n9zF1qqM0snlgsb0ujCS/Mlb7KkS/s6paFKK/HDbuhasypFUAXd5nfWeTnapgq4VFoXzXkuf2jfr64H81zpT6dUio5bXL58OasQ5PwUD02iqHYC/GwNytJdtmyZ9vbRoAYC1hk31gkL7F+ajltIIW/Bg4HpHYiDFzzwSmBRfswCnWQEh5tAA0qqbFxASY+VqkD/orMczU0SGh74XG+Qt23bxiqMjIx4q4nSSSDAzfrMM88kQV/o6JeAeCEaHXZjLKod8nZTst+W4XO9keMnnly8eNFbTZROAgFuVhxtkwRz+dbROGaBls7otvXO5ua2FN243t6va5c34rmezUbHqrIDUG7evOl4NaFnyagQLQHKzF26dCnTAfH6aG0RcusUwT283spW2Jd68/zO3q2Nwx16tkVgnuvZuPzMcmTpemYX7wrcoLh3Od6GUqIdO1fMehoOdrQhnquEaxhCHnroISYW4YUw8EYokxuUmzhCZdB0mAToXLGha6PUQsOaJl3nidn9wTzXs2U3bNjAprr8iFXPIlAhlgS4QcnEsVQQSqkikHO1lvelc8WEy9dVNVNSDuK5fvCePHny6aefppq0zI3toX4Ixq8OZYmx0xXo6uW9e/fGT0FoFBIBawOarvRc5Of6siNf1P700099CUCl2BHgpkTGQuxsE65C1qmOuo4Vg8/1ZU6a27Lwwttvv+1LACrFjgAzJZkVX1xiZ5swFGIniwm3oNELeu5EQzzXpz2fe+45FtLFTcA+CcapGhmRBXOZWfFUOoH+dtrsS89gptFwvcbDXtDwwOf6hPzwww+zmggv+CQYp2rciNyscdIOuoRBgI4OE061ob2/tgc2r2AP8YHP9QmXNuOzA6gQXvBJME7VmBHJoDhjIU5mCU8XylbITxFr6Savy9xw2IFd+Fz/dv3Vr35Flek76eeff+5fCmpGTYDMxwILzKB4qoCAzrt4CnHC5/ofYDxR929/+5t/KagZNQFmPlo9Q1pu1KaIsn065Sa8Y8rFjiE/N5CZBwYGHnvsMRKBsxcCcYyuMj9jgU4o37FjR3SKoOVqIYB5biBL//znP2f1//73vwcShMoREeCG46aMSBE0Wy0E4HMDWZrOFaNtSyTixIkTgQShckQEmOHIiDgiLiILVF2ziC0ENTn/cnrlyhUEBIPS1FufVs82btyI0JBe6tXeGua5QUcAn+oeP348qCzU10uAmQyTXL3Uq701zHMVjABMdRVA1C4Ck1ztyNGgQQDzXAXjgE91kTSmgKYuEcxYmOTq4o12LAKY56oZCnyqe/369XvvvVeNUEgJjcBXX321atUqRHJDAwzBZQlgnqtmcNBU9+zZsyTrj3/8oxqJkBImAWYmMhnSFcLEDNklCGCeq3JYsOspkcCgkmkIslgklzaesY9JPCCgkwDmuSppv/rqqyQOCQwqmYYgixno5ZdfDkE2RIKACwH4XJVDhPJzjx49Sm9p2hOsUi5kqSNApiEDkZm2bNmiTiokgYAsAcQWZElJlvvuu++WL19OhXECgyQxncVoqfPZZ5+l+M+3336LYxt1kkdbnADmuYoHA72T6bQUEvr6668rFg1xgQmQUcjhkoHgcAOzhACfBOBzfYJzqEbHU9Hp1y+99NKFCxfUS4dEvwTIHGQUMg3OD/OLEPUUEEBsQQHEYhEUYdi/fz+9/tZbbyEbKRTEHoWyqAL7/oFJrkd4KK6SAHyuSpqiLEpI+uKLL/bu3RtWA5DrkcDJkyfvv/9+nEPkERuKKyYAn6sYKMSBAAiAgAMBxHMxPEAABEBAHwH4XH2s0RIIgAAIwOdiDIAACICAPgLwufpYoyUQAAEQgM/FGAABEAABfQTgc/WxRksgAAIgAJ+LMQACIAAC+gjA5+pjjZZAAARAAD4XYwAEQAAE9BGAz9XHGi2BAAiAAHwuxgAIgAAI6CMAn6uPNVoCARAAAfhcjAEQAAEQ0EcAPlcfa7QEAiAAAvC5GAMgAAIgoI8AfK4+1mgJBEAABOBzMQZAAARAQB8B+Fx9rNESCIAACMDnYgyAAAiAgD4C8Ln6WKMlEAABEIDPxRgAARAAAX0E4HP1sUZLIAACIACfizEAAiAAAvoIwOfqY42WQAAEQOD/A0cni9fwCQuXAAAAAElFTkSuQmCC\"></p>\n<p>In this basket the 1<sup>st</sup> medicine is fully accessible (i.e. it is both available and affordable), the 2<sup>nd</sup> medicine is partially accessible (i.e. it is available but not affordable), while the 3<sup>rd</sup> medicine is inaccessible (i.e. it is not available and thus it is not possible to collect information on prices).</p>\n<p>In this example, the first medicine is accessible and the third medicine is not. However, the second medicine is partially deprived indicating that specific policies applied in the country may be effective for availability of the medicine but not for its affordability. Applying the union identification approach by S. Alkire and G. Robles that treats elements (medicines) in the matrix with partial deprivation as fully deprived, the second medicine is considered not accessible as well (<em>Figure 3</em>). </p>\n<p><strong>Figure 4.2.</strong> Achievement matrix of access to medicine (two dimensions &amp; deprivation of dimensions)</p>\n<p><img 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\"></p>\n<p>At the end of this step, the variable &#x201C;access&#x201D; to medicines is generated, combining the 2 dimensions of availability and affordability. This variable remains binary in nature with 1 &#x2013; medicine is accessible (both available and affordable) and 0 &#x2013; medicine is not accessible (not available or available but not affordable).</p>", "indicator_sort_order"=>"03-0b-03", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.c.1", "slug"=>"3-c-1", "name"=>"Densidad y distribución del personal sanitario", "url"=>"/site/es/3-c-1/", "sort"=>"03cc01", "goal_number"=>"3", "target_number"=>"3.c", "global"=>{"name"=>"Densidad y distribución del personal sanitario"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Profesión", "value"=>"Médico"}, {"field"=>"Profesión", "value"=>"Profesionales en enfermería y obstetricia"}, {"field"=>"Profesión", "value"=>"Dentista"}, {"field"=>"Profesión", "value"=>"Farmaceútico"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Densidad y distribución del personal sanitario", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Densidad y distribución del personal sanitario", "indicator_number"=>"3.c.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Anual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176781&idp=1254735573175", "url_text"=>"Estadística de profesionales sanitarios colegiados", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Densidad y distribución del personal sanitario", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.c- Aumentar considerablemente la financiación de la salud y la contratación, el perfeccionamiento, la capacitación y la retención del personal sanitario en los países en desarrollo, especialmente en los países menos adelantados y los pequeños Estados insulares en desarrollo", "definicion"=>"Número de personas colegiadas por cada 10.000 habitantes según ocupación sanitaria", "formula"=>"\n$$TPSC_{profesión\\, sanitaria}^{t} = \\frac{PSC_{profesión\\, sanitaria}^{t}}{P^{t+1}} \\cdot 10.000$$\n\ndonde:\n\n$PSC_{profesión\\, sanitaria}^{t} =$ número de personas colegiadas en la especialidad sanitaria a 31 de diciembre del año $t$\n\n$P^{t+1} =$ población total a 1 de enero del año $t+1$\n", "desagregacion"=>"Profesión sanitaria: médico, profesionales en enfermería y obstetricia, dentista, farmacéutico.\n\nSexo\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Para alcanzar cualquier objetivo de salud de la población, es fundamental contar con un personal sanitario\nde tamaño y una combinación de competencias adecuados, así como con los docentes y formadores necesarios. \nEsto incluye el logro de la cobertura sanitaria universal y las metas relacionadas con la salud de los \nObjetivos de Desarrollo Sostenible de las Naciones Unidas.\n\nSin embargo, los países de todo el mundo se ven afectados por problemas de personal sanitario de \nnaturaleza multifacética, como dificultades en la educación y la formación, el despliegue, el rendimiento \ny la retención de su personal sanitario. Una asignación subóptima de trabajadores sanitarios es uno de\n los principales problemas que influye directamente en la disponibilidad, la accesibilidad, la calidad\n y el rendimiento de los servicios nacionales de salud. En el peor de los casos, esto puede dejar a las\n poblaciones con un acceso inadecuado a los servicios de salud que necesitan. Está claro que los esfuerzos \npor alcanzar los ODS y la cobertura sanitaria universal se ven frustrados por estos problemas de personal sanitario.\n\nPor ello, la Organización Mundial de la Salud (OMS) y sus socios desarrollaron la Estrategia mundial sobre \nrecursos humanos para la salud: fuerza laboral 2030 (GSHRH), que establece la agenda \nde políticas para garantizar una fuerza laboral de salud que sea adecuada para alcanzar \nlas metas de la cobertura sanitaria universal y los ODS (OMS 2016c).\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.c.1&seriesCode=SH_MED_DEN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=DENT\">Densidad de trabajadores de la salud, por tipo de ocupación (por cada 10.000 habitantes) SH_MED_DEN</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0c-01.pdf\">Metadatos 3-c-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Densidad y distribución del personal sanitario", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.c- Aumentar considerablemente la financiación de la salud y la contratación, el perfeccionamiento, la capacitación y la retención del personal sanitario en los países en desarrollo, especialmente en los países menos adelantados y los pequeños Estados insulares en desarrollo", "definicion"=>"Number of registered health workers per 10,000 inhabitants by healthcare occupation", "formula"=>"\n$$TPSC_{health\\, worker}^{t} = \\frac{PSC_{health\\, worker}^{t}}{P^{t+1}} \\cdot 10.000$$\n\nwhere:\n\n$PSC_{health\\, worker}^{t} =$ number of health workers registered in the healthcare speciality as of 31 December in year $t$\n\n$P^{t+1} =$  total population as of 1 January of year $t+1$\n", "desagregacion"=>"Healthcare profession: medical doctors, nursing and midwifery professionals, dentists, pharmacists.\n\nSex\n\nProvince\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"A health workforce of adequate size and skill mix, as well as required teachers and trainers, \nare critical to the attainment of any population health goal. This includes the achievement \nof universal health coverage and the health-related targets of the United Nations Sustainable \nDevelopment Goals.\n\nYet countries globally are affected by health workforce challenges of a multifaceted nature, \nsuch as difficulties in the education and training, deployment, performance and retention of \ntheir health workforces. A suboptimal allocation of health workers is one of the main challenges \nthat directly influences the availability, accessibility, quality and performance of national \nhealth services. In the worst case, this may leave populations with inadequate access to the \nhealth services they need. It is clear that efforts to achieve the SDGs and UHC are thwarted by \nthese health workforce challenges.\n\nTherefore, the World Health Organization (WHO) and its partners developed the Global Strategy \non Human Resources for Health: Workforce 2030 (GSHRH), which sets out the policy agenda to ensure \na health workforce that is fit for purpose to attain the targets of UHC and the SDGs (WHO 2016c).\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.c.1&seriesCode=SH_MED_DEN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=DENT\">Health worker density, by type of occupation (per 10,000 population) SH_MED_DEN</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0c-01.pdf\">Metadata 3-c-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Densidad y distribución del personal sanitario", "objetivo_global"=>"3- Garantizar una vida sana y promover el bienestar de todos a todas las edades", "meta_global"=>"3.c- Aumentar considerablemente la financiación de la salud y la contratación, el perfeccionamiento, la capacitación y la retención del personal sanitario en los países en desarrollo, especialmente en los países menos adelantados y los pequeños Estados insulares en desarrollo", "definicion"=>"Elkargokide kopurua 10.000 biztanleko, osasun-okupazioaren arabera", "formula"=>"\n$$TPSC_{osasun-okupazioa}^{t} = \\frac{PSC_{osasun-okupazioa}^{t}}{P^{t+1}} \\cdot 10.000$$\n\nnon:\n\n$PSC_{osasun-okupazioa}^{t} =$ osasun-espezialitateko elkargokide-kopurua, $t$ urteko abenduaren 31n\n\n\n$P^{t+1} =$ biztanleria $t+1$ urteko urtarrilaren 1ean\n", "desagregacion"=>"Osasun-lanbidea: medikua; erizaintzako eta obstetriziako profesionalak; dentista; farmazialaria\n\nSexua\n\nLurralde historikoa\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Biztanleriaren osasunarekin lotutako edozein helburu lortzeko, funtsezkoa da tamaina eta gaitasun-konbinazio egokiko \nosasun-langileak eta beharrezkoak diren irakasle eta prestatzaileak edukitzea. Horretarako, osasun-estaldura unibertsala \neta Nazio Batuen Garapen Jasangarriko Helburuen osasunarekin lotutako xedeak bete behar dira. \n\nHala ere, mundu osoko herrialdeek osasun-langileen arloan askotariko arazoak dituzte, besteak beste zailtasunak \nosasun-langileen hezkuntzan eta prestakuntzan, hedapenean, errendimenduan eta iraunkortasunean. Osasun-langileen \nesleipen suboptimoa da osasun zerbitzu nazionalen erabilgarritasunean, irisgarritasunean, kalitatean eta errendimenduan \nzuzenean eragiten duen arazo nagusietako bat. Kasurik txarrenean, biztanleei eragozpenak sor diezazkieke behar dituzten \nosasun-zerbitzuak eskura ditzaten. Argi dago GJHak eta osasun-estaldura unibertsala lortzeko ahaleginetan eragozpen direla \nosasun-langileen arazo hauek. \n\nHorregatik, Osasunaren Mundu Erakundeak (OMEk) eta beren bazkideek osasunerako giza baliabideei buruzko mundu-mailako \nestrategia garatu zuten: lan-indarra 2030. Estrategia horrek osasun-estaldura unibertsalaren xedeak eta GJHak lortzeko \negokia den osasuneko lan-indarra bermatzeko politiken agenda ezartzen du (OME 2016c). \n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.c.1&seriesCode=SH_MED_DEN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=DENT\">Osasun-langileen dentsitatea, okupazio-motaren arabera (10.000 biztanleko) SH_MED_DEN</a> UNSTATS", "comparabilidad"=>"Eaen eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0c-01.pdf\">Metadatuak 3-c-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.c: Substantially increase health financing and the recruitment, development, training and retention of the health workforce in developing countries, especially in least developed countries and small island developing States</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.c.1: Health worker density and distribution </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_MED_DEN - Health worker density, by type of occupation (per 10,000 population) [3.c.1]</p>\n<p>SH_MED_HWRKDIS - Health worker distribution, by sex and type of occupation [3.c.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Health Workforce Department, World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong><u>Health worker densities by occupation</u></strong></p>\n<p><strong>Definition:</strong></p>\n<p><strong>Density of medical doctors: </strong>The density of medical doctors is defined as the number of medical doctors, including generalists and specialist medical practitioners per 10,000 population in the given national and/or subnational area. The International Standard Classification of Occupations (ISCO) unit group codes included in this category are 221, 2211 and 2212 of ISCO-08.</p>\n<p><strong>Density of nursing and midwifery personnel</strong>: The density of nursing and midwifery personnel is defined as the number of nursing and midwifery personnel per 10,000 population in the given national and/or subnational area. The ISCO-08 codes included in this category are 2221, 2222, 3221 and 3222.</p>\n<p><strong>Density of dentists</strong>: The density of dentists is defined as the number of dentists per 10,000 population in the given national and/or subnational area. The ISCO-08 codes included in this category are 2261.</p>\n<p><strong>Density of pharmacists:</strong> The density of pharmacists is defined as the number of pharmacists per 10,000 population in the given national and/or subnational area. The ISCO-08 codes included in this category are 2262.</p>\n<p><strong><u>Health worker distribution by sex</u></strong></p>\n<p><strong>Percentage of male medical doctors</strong>:<strong> </strong>Male doctors as percentage of all medical doctors at national level. The ISCO-08 codes included in this category are 221, 2211 and 2212.</p>\n<p><strong>Percentage of female medical doctors</strong>: Female doctors as percentage of all medical doctors at national level. The ISCO-08 codes included in this category are 221, 2211 and 2212.</p>\n<p><strong>Percentage of male nursing personnel:</strong> Male nursing personnel as percentage of all nursing personnel at national level. The ISCO-08 codes included in this category are 2221 and 3221.</p>\n<p><strong>Percentage of female nursing personnel:</strong> Female nursing personnel as percentage of all nursing personnel at national level. The ISCO-08 codes included in this category are 2221 and 3221.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Health worker densities by occupation: Per 10,000 population</p>\n<p>Health worker distribution by sex and type of occupation: Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>International Standard Classification of Occupations (ISCO-08)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>In response to the Sixty-ninth World Health Assembly (WHA69.19), an online National Health Workforce Accounts (NHWA) data platform was developed to facilitate national reporting. In addition to the reporting, the platform also serves as an analytical tool at the national/regional and global levels. Since Its launch in November 2017, Member States are called to use the NHWA data platform to report health workforce data. Complementing the national reporting through the NHWA data platform, additional sources such as the National Census, Labour Force Surveys and key administrative national and regional sources are also employed. Most of the data from administrative sources are derived from published national health sector reviews and/or official country reports to WHO offices.</p>", "COLL_METHOD__GLOBAL"=>"<p>Countries are encouraged to adopt a progressive NHWA implementation approach building on multi-stakeholder engagement at national and sub-national levels. National focal points share the data with WHO through the online NHWA data platform. The platform hosted in WHO, is built to facilitate data reporting on the indicators listed in the NHWA Handbook and data sharing across all the 3 levels of WHO. </p>", "FREQ_COLL__GLOBAL"=>"<p>Ongoing process</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data is released yearly.</p>", "DATA_SOURCE__GLOBAL"=>"<p>NHWA focal point at national level</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Global Strategy for Human Resources for Health: 2030 agenda and the progressive implementation of NHWA adopted in Sixty-ninth World Health Assembly (WHA69.19). WHA69.19 urges Member States to share health workforce data to WHO, to increase the evidence base on health workforce statistics globally.</p>", "RATIONALE__GLOBAL"=>"<p>For detailed metadata and definitions, refer to the National Health Workforce Accounts (NHWA) Handbook (<a href=\"https://www.who.int/publications/i/item/9789241513111\">https://www.who.int/publications/i/item/9789241513111</a>)</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data on health workers tend to be more complete for the public health sector and may underestimate the active workforce in the private, military, nongovernmental organization and faith-based health sectors. In many cases, information maintained at the national regulatory bodies and professional councils is not updated.</p>\n<p>As data is not always published annually for each country, the latest available data has been used. Due to the differences in data sources, considerable variability remains across countries in the coverage, periodicity, quality and completeness of the original data. Densities are calculated using the latest national population estimates from the United Nations Population Division&apos;s World Population Prospects database and may vary from densities produced by the country.</p>", "DATA_COMP__GLOBAL"=>"<p><strong><u>Health worker densities by occupation</u></strong></p>\n<p>The figures for number of medical doctors (including generalist and specialist medical practitioners) depending on the nature of the original data source may include practising medical doctors only or all registered medical doctors.</p>\n<p>The figures for number of nursing and midwifery include nursing personnel and midwifery personnel, whenever available. In many countries, nurses trained with midwifery skills are counted and reported as nurses. This makes the distinction between nursing personnel and midwifery personnel difficult to draw.</p>\n<p>The figures for number of dentists include dentists in the given national and/or subnational area. Depending on the nature of the original data source may include practising (active) only or all registered in the health occupation. The ISCO -08 codes included here are 2261.</p>\n<p>The figures for number of pharmacists include in the given national and/or subnational area. Depending on the nature of the original data source may include practising (active) only or all registered in the health occupation. The ISCO-08 codes that relate to this occupation is 2262.</p>\n<p>In general, the denominator data for workforce density (i.e. national population estimates) are obtained from the United Nations Population Division&apos;s World Population Prospects database. In cases where the official health workforce report provides density indicators instead of counts, estimates of the stock were then calculated using the latest population estimates from the United Nations Population Division&apos;s World population prospects database.</p>\n<p><strong><u>Health worker distribution by sex and type of occupation</u></strong></p>\n<p>The number of male medical doctors as reported by the country is expressed as a percentage of total male and female medical doctors reported by the country.</p>\n<p>The number of female medical doctors as reported by the country is expressed as a percentage of total male and female medical doctors reported by the country.</p>\n<p>The number of male nursing personnel as reported by the country is expressed as a percentage of total male and female nursing personnel reported by the country.</p>\n<p>The number of female nursing personnel as reported by the country is expressed as a percentage of total male and female nursing personnel reported by the country.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The data recorded in the NHWA data platform is validated by country focal points. Data quality checks and country consultation are employed.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>&#x2022;<strong> At country level</strong></p>\n<p>Data for the countries with missing values, if any in the last 5 years are estimated with neighbouring comparable countries.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>The global average density was estimated as the population weighted average of the national densities.</p>\n<p>For the regional average density, data for the countries with missing values, if any in the last 5 years were first estimated with neighbouring comparable countries. Then the regional average was also computed as a weighted average by pooling these estimated values plus the available national densities.</p>\n<p>The population for estimating densities at regional and global level are based on the latest available estimates from the UN Population Division.</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries are requested to refer to the National Health Workforce Accounts (NHWA) Handbook (<a href=\"https://www.who.int/publications/i/item/9789241513111\">https://www.who.int/publications/i/item/9789241513111</a>), for guidance on indicators and methodology.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>All national health occupations data is mapped to the International Standard Classification of Occupations (ISCO-08) to enable cross country comparability.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data is collected through a standardised online data entry form based on DHIS2 application. Data validations and quality checks are in-built to minimise data entry errors</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>We perform internal validation for outliers and completeness and raise queries to countries directly to the national focal points and /or through the WHO country and regional offices, for clarification.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data available for all 194 WHO Member States</p>\n<p><strong>Time series:</strong></p>\n<p>From year 2000. </p>\n<p>Global Health Workforce Statistics in Global Health Observatory data repository: <a href=\"https://apps.who.int/gho/data/node.main.HWFGRP?lang=en\">https://apps.who.int/gho/data/node.main.HWFGRP?lang=en</a> </p>\n<p>NHWA data portal: <a href=\"https://apps.who.int/nhwaportal/\">https://apps.who.int/nhwaportal/</a> </p>\n<p><strong>Disaggregation:</strong></p>\n<p>National level data</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Population estimates utilised by countries and/or regional offices may differ from those of the UN Population Division</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://www.who.int/activities/improving-health-workforce-data-and-evidence\">https://www.who.int/activities/improving-health-workforce-data-and-evidence</a></p>\n<p><strong>References:</strong></p>\n<ul>\n  <li>Sixty-ninth World Health Assembly Agenda Item 16.1. Global strategy on human resources for health: workforce 2030 (2016), available from (<a href=\"http://apps.who.int/gb/ebwha/pdf_files/WHA69/A69_R19-en.pdf\" target=\"_blank\"><u>http://apps.who.int/gb/ebwha/pdf_files/WHA69/A69_R19-en.pdf</u></a>)</li>\n  <li>WHO (2014). Global strategy on human resources for health: Workforce 2030 (<a href=\"https://www.who.int/publications/i/item/9789241511131\">https://www.who.int/publications/i/item/9789241511131</a>)</li>\n  <li>&quot;WHO Global Health Workforce Statistics.&quot; World Health Organization, n.d. Web. Feb. 2018. (<a href=\"https://apps.who.int/gho/data/node.main.HWFGRP?lang=en\">https://apps.who.int/gho/data/node.main.HWFGRP?lang=en</a>)</li>\n  <li>&quot;WHO Global Health Workforce Statistics.&quot; World Health Organization, n.d. Web. Feb. 2018. (<a href=\"http://apps.who.int/gho/data/node.main.A1444?lang=en&amp;showonly=HWF\" target=\"_blank\"><u>http://apps.who.int/gho/data/node.main.A1444?lang=en&amp;showonly=HWF</u></a>)</li>\n  <li>WHO, National Health Workforce Accounts: A Handbook, n.d. Wed. Feb. 2018. (<a href=\"https://www.who.int/publications/i/item/9789241513111\">https://www.who.int/publications/i/item/9789241513111</a>)</li>\n  <li>WHO 13<sup>th</sup> Global Programme of Work (https://www.who.int/about/what-we-do/gpw-thirteen-consultation/en/)</li>\n  <li>WHO NHWA data portal: <a href=\"https://apps.who.int/nhwaportal/\">https://apps.who.int/nhwaportal/</a> </li>\n</ul>", "indicator_sort_order"=>"03-0c-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.d.1", "slug"=>"3-d-1", "name"=>"Capacidad prevista en el Reglamento Sanitario Internacional (RSI) y preparación para emergencias de salud", "url"=>"/site/es/3-d-1/", "sort"=>"03dd01", "goal_number"=>"3", "target_number"=>"3.d", "global"=>{"name"=>"Capacidad prevista en el Reglamento Sanitario Internacional (RSI) y preparación para emergencias de salud"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Capacidad prevista en el Reglamento Sanitario Internacional (RSI) y preparación para emergencias de salud", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Capacidad prevista en el Reglamento Sanitario Internacional (RSI) y preparación para emergencias de salud", "indicator_number"=>"3.d.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nLos indicadores utilizados representan la capacidad esencial de salud \npública que los Estados deben tener en todo su territorio de conformidad \ncon los requisitos de los artículos 5 y 12 y del Anexo 1A del RSI (2005). \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.d.1&seriesCode=SH_IHR_CAPS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=_T\">Capacidad del Reglamento Sanitario Internacional (RSI), por tipo de capacidad del RSI (%) SH_IHR_CAPS</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0d-01.pdf\">Metadatos 3-d-1.pdf</a> (solo en inglés)\"", "informacion_interes"=>"", "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2025-03-12", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe indicators used represent the essential public health capacity that States \nParties must have in place throughout their territories under Articles 5 and 12 \nand Annex 1A of the IHR (2005) requirements. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.d.1&seriesCode=SH_IHR_CAPS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=_T\">International Health Regulations (IHR) capacity, by type of IHR capacity (%) SH_IHR_CAPS</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0d-01.pdf\">Metadata 3-d-1.pdf</a>\"", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nErabilitako adierazleek erakusten dute estatuek eduki behar duten osasun publikoko funtsezko gaitasuna beren \nlurralde osoan, Nazioarteko Osasun Araudiaren 5. eta 12. artikuluetako eta 1A eranskineko betekizunen arabera (2005). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.d.1&seriesCode=SH_IHR_CAPS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=_T\">Nazioarteko Osasun Araudiaren (NOA) gaitasuna, ISSren gaitasun motaren arabera (%) SH_IHR_CAPS</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0d-01.pdf\">Metadatuak 3-d-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.d: Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.d.1: International Health Regulations (IHR) capacity and health emergency preparedness</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_IHR_CAPS - International Health Regulations (IHR) capacity, by type of IHR capacity [3.d.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-05-24", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Country Capacity Assessment and Planning Group (CAP) </p>\n<p>Department of Health Security Preparedness (HSP)</p>\n<p>WHO Health Emergency Programme</p>", "STAT_CONC_DEF__GLOBAL"=>"<p>The revised International Health Regulations (IHR) were adopted in 2005 and entered into force in 2007. Under the IHR, States Parties are obliged to develop and maintain minimum core capacities for surveillance and response, including at points of entry, to detect, assess, notify, and respond to any potential public health events of international concern.</p>\n<p>Article 54 of the IHR states, &quot;States Parties and the Director-General shall report to the Health Assembly on the implementation of these Regulations as decided by the Health Assembly.&quot;</p>\n<p>The IHR States Parties Self-assessment Annual Reporting tool captures the level of self-assessed national capacities. They are essential public health capacities that States Parties are required to put in place throughout their territories according to Articles 5 and 12 and Annex 1A of the IHR (2005) requirements. </p>\n<p>Based on the lessons learned from the COVID-19 pandemic, WHO published the revised second edition of the IHR State Parties Self-assessment Annual Reporting Tool in 2021 with new indicators related to gender equality in health emergencies, advocacy for IHR implementation, and community engagement, to name a few. The revisions are intended to improve the assessment of the IHR core capacities and the preparedness of States Parties for health emergencies. The indicator SDG 3.d.1 reflects the capacities State Parties of the International Health Regulations (2005) (IHR) had agreed and committed to developing.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percentage</p>\n<p> </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>We use the WHO Official list of countries that are States Parties of the International Health Regulations (IHR2005), distributed according to the six WHO administrative regions (<a href=\"http://www.who.int\">www.who.int</a> ). </p>\n<p>The second edition SPAR tool has been expanded from 13 to 15 capacities. The 15 core capacities are (1) Policy, legal and normative instruments to implement IHR; (2) IHR Coordination and National Focal Point Functions; (3) Financing; (4) Laboratory; (5) Surveillance; (6) Human resources; (7) Health emergency management (8) Health Service Provision; (9) Infection Prevention and Control; (10) Risk communication and community engagement; (11) Points of entry and border health; (12) Zoonotic diseases; (13) Food safety; (14) Chemical events; (15) Radiation emergencies. </p>\n<p>The 13 core capacities of the first edition of the IHR States Parties Self-assessment Annual Reporting Tool are (1) Legislation and financing; (2) IHR Coordination and National Focal Point Functions; (3) Zoonotic events and the Human-Animal Health Interface; (4) Food safety; (5) Laboratory; (6) Surveillance; (7) Human resources; (8) National Health Emergency Framework; (9) Health Service Provision; (10) Risk communication; (11) Points of entry; (12) Chemical events; (13) Radiation emergencies. </p>\n<p>Both SPAR questionnaires (1st and 2nd editions) use a five-level scoring with indicators based on five cumulative levels to measure the implementation status for each capacity. For each indicator, the reporting State Party is asked to select which of the five levels best describes the State Party&apos;s current status. To move to the next level, all capacities described in previous levels should be in place for each indicator.</p>\n<p>For the years 2010 to 2017, Member States used the IHR monitoring questionnaire. The questionnaire is divided into thirteen sections, one for each of the eight core capacities, PoE and four hazards. Individual questions are grouped by components and indicators in the questionnaires. States Parties can provide additional information on the questions in the comment boxes. Responses to the questions include marking one appropriate value (Yes, No, or Not Known) or the appropriate percentages. For statistical purposes, the &quot;Not Known&quot; value is computed as a &quot;No&quot; value. The IHR monitoring questionnaire includes the following: IHR01. National legislation, policy and financing; IHR02. Coordination and National Focal Point communications; IHR03. Surveillance; IHR04. Response; IHR05. Preparedness; IHR06. Risk communication; IHR07. Human resources; IHR08. Laboratory; IHR09. Points of entry; IHR10. Zoonotic events; IHR11. Food safety; IHR12. Chemical events; IHR13. Radio nuclear emergencies.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The data is collected annually from State Parties since 2010 and registered and available on the e-SPAR platform (<a href=\"https://extranet.who.int/e-spar\">https://extranet.who.int/e-spar</a>). There are 196 States Parties that are signatory to the International Health Regulations and are mandated to reporting annually to the WHO to report to the World Health Assembly. The number of reports received has increased annually. By 2021, WHO received SPAR data from 184 (out of 196) Member States, reflecting 94% of submissions. In 2022, SPAR submission reached 95% and in 2023, SPAR submission rate reached 99%,the highest number for a SPAR reporting cycle since 2010. </p>\n<p> </p>", "COLL_METHOD__GLOBAL"=>"<p>The data is collected using an online questionnaire (<a href=\"https://extranet.who.int/e-spar\">https://extranet.who.int/e-spar</a>). An interactive questionnaire in PDF and MS Excel forms for Points of Entry are available in case of limitations in internet connectivity. The multisectoral approach remains critical to completing the IHR States Parties Self-assessment Annual Report. </p>", "FREQ_COLL__GLOBAL"=>"<p>The IHR States Parties Self-assessment Annual Reporting questionnaire is sent out in August every year and must be submitted by February 28th of the following year.,</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Results of the IHR States Parties Self-Assessment Annual Report are readily available in the e-SPAR platform <a href=\"https://extranet.who.int/e-spar\">https://extranet.who.int/e-spar</a> after the submission deadline and disseminated to other WHO homepages on WHO websites, including the Strategic Partnership for Health Security and Emergency Preparedness (SPH) Portal (<a href=\"https://extranet.who.int/sph/\">https://extranet.who.int/sph/</a>), the Global Health Observatory (<a href=\"https://www.who.int/data/gho\">https://www.who.int/data/gho</a> ), WHO GPW13 triple billion targets dashboard (<a href=\"https://portal.who.int/triplebillions/\">https://portal.who.int/triplebillions/</a> ).</p>\n<p> </p>", "DATA_SOURCE__GLOBAL"=>"<p>All data is collected from 196 States Parties and disseminated by WHO.</p>", "COMPILING_ORG__GLOBAL"=>"<p>All data is compiled and disseminated by WHO.</p>", "INST_MANDATE__GLOBAL"=>"<p>The IHR States Parties Self-assessment Annual Reporting (SPAR) Tool is the only mandatory tool that assesses countries&#x2019; progress in implementing the IHR (Article 54.1). SPAR tool provides an interpretation of the national capacities required under the IHR for self-assessment and monitoring purposes, specifically those outlined in Annex 1. It is the primary tool for ensuring mutual accountability between States Parties and the WHO Secretariat.</p>\n<p>In 2008, the World Health Assembly, through the adoption of Resolution WHA61(2), and later in 2018 with the Resolution WHA71(15), decided that &quot;that States Parties and the Director-General shall continue to report annually to the Health Assembly on the implementation of the International Health Regulations (2005), using the self-assessment annual reporting tool&quot;. In December 2021, and under Resolution WHA75, an updated SPAR tool second edition was published.</p>", "RATIONALE__GLOBAL"=>"<p>The indicators used represent the essential public health capacity that States Parties must have in place throughout their territories under Articles 5 and 12 and Annex 1A of the IHR (2005) requirements. Further detailed information and guidance on how to use the State Parties Self-Assessment Annual Reporting Tool &#x2013; SPAR indicators, can be found in a guidance document at: <a href=\"https://extranet.who.int/e-spar\">https://extranet.who.int/e-spar</a></p>", "REC_USE_LIM__GLOBAL"=>"<p>1) it is based on a self-assessment and reporting by the State Party</p>\n<p>2) There are three datasets based on the different tools to collect data for SPAR. For the period 2010 to 2017, the questionnaire, known as the IHR monitoring questionnaire, is divided into thirteen sections, one for each of the eight core capacities, PoE and four hazards and information on the status of implementation for each capacity. The IHR monitoring questionnaire ( 2010 to 2017) was replaced by the IHR State Parties Self-Assessment Tool &#x2013; SPAR, published in July 2018 also known as SPAR 1st edition. The States Parties used the SPAR first edition during the 2018 &#x2013; 2020 SPAR reporting cycle. The current questionnaire replaced the SPAR 1st edition and was used by the States Parties from 2021 up to present (2024). Under each capacity, the indicators were either retained, replaced or added. Historical trends and data analysis of scores for similar capacity titles should be taken with caution. </p>", "DATA_COMP__GLOBAL"=>"<p>All data are from the questionnaires submitted by States Parties annually. </p>\n<p>For each of the 15 capacities, one to five indicators are used to measure implementation status. For each indicator, the reporting State Party is asked to select which of the five levels best describes the State Party&apos;s current status. To move to the next level, all capacities described in previous levels should be in place for each indicator. The score of each indicator level is classified as a percentage of performance along the &quot;1 to 5&quot; scale. e.g. for a country selecting level 3 for indicator 2.1, the indicator level is expressed as: 3/5*100=60%</p>\n<p>CAPACITY LEVEL</p>\n<p>The level of capacity is expressed as the average of all indicators. e.g. for a country selecting level 3 for indicator 2.1 and level 4 for indicator 2.2. The indicator level for 2.1 is expressed as 3/5*100=60%, the indicator level for 2.2 will be expressed as 4/5*100=80% and the capacity level for 2 will be expressed as (60+80)/2=70%</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The e-SPAR electronic platform has mechanisms and checks to monitor reports received and proceed with quality checks. The eSPAR is also accessible to WHO staff working with the Member States on SPAR (all levels). When the national authority fills in the questionnaire, electronic checks (pop-up alerts) are automatically available to avoid potential mistakes and missing critical information on the report before final submission. </p>\n<p>Seminars are promoted, tutorials are available (under revision) and consultation with national authorities can be made in coordination with all levels of WHO. More details with references, short videos and links in several languages at: <a href=\"https://extranet.who.int/e-spar/\">https://extranet.who.int/e-spar/</a> </p>", "ADJUSTMENT__GLOBAL"=>"<p>Based on the result of the SPAR Consultation Meeting in July 3-5, 2023, there were two capacities where adjustments were made: Points of Entry and Border Health and the Health Services Provision.</p>\n<p> </p>", "IMPUTATION__GLOBAL"=>"<p>Usually, no methodology is employed to replace missing reports. Eventually, on an ad-hoc basis, the last report received can be used just for a specific request for data analysis.</p>\n<p> </p>", "REG_AGG__GLOBAL"=>"<p>The regional aggregation is based on the list of WHO State Parties on each administrative region as the denominator.</p>", "DOC_METHOD__GLOBAL"=>"<p>There are specific tutorials and guidance for national authorities to use the e-SPAR platform and to report using the State Parties Self-Assessment and Reporting Tool &#x2013; SPAR, accessible from the e-SPAR public page at: <a href=\"https://extranet.who.int/e-spar/\">https://extranet.who.int/e-spar/</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>WHO have specific teams working in a collaborative manner to manage the quality of the statistical products and process, such as the Division of Data Analytics and Delivery for Impact (more details at <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a> )</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Please see details from the statistical WHO Programmes at <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Please see details from the statistical WHO Programmes at <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a> </p>", "COVERAGE__GLOBAL"=>"<p>Since 2010, when the IHR Annual Reporting was implemented, all 196 State Parties have reported at least once. All reports and regional breakdowns are available, including for download of excel spreadsheet with all countries capacities reported since 2010 at: <a href=\"https://extranet.who.int/e-spar/\">https://extranet.who.int/e-spar/</a> , at Health Security and Emergency Preparedness (SPH) Portal (<a href=\"https://extranet.who.int/sph/\">https://extranet.who.int/sph/</a> ) and</p>\n<p>the Global Health Observatory (<a href=\"https://www.who.int/data/gho\">https://www.who.int/data/gho</a> ).</p>", "COMPARABILITY__GLOBAL"=>"<p>The IHR States Parties Self-assessment Annual Reporting has specific indicators based on IHR requirements for core capacities needed to detect, assess, notify, report and respond, including at points of entry, to public health risks and acute events of domestic and international concern. </p>\n<p>External voluntary evaluation of similar capacities can be done by the same country, such as using the Joint external evaluation (JEE) tool, supported by several countries, to complement the self-assessment. More details are available at the Health Security and Emergency Preparedness (SPH) Portal (<a href=\"https://extranet.who.int/sph/\">https://extranet.who.int/sph/</a>)</p>", "OTHER_DOC__GLOBAL"=>"<h1> </h1>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>International health regulations (&#x200E;2005)&#x200E;: state party self-assessment annual reporting tool, 2nd ed</strong></p>\n      </td>\n      <td colspan=\"7\">\n        <p><strong>English</strong></p>\n      </td>\n      <td>\n        <p><a href=\"https://www.who.int/publications/i/item/9789240040120\">https://www.who.int/publications/i/item/9789240040120</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>&#x41C;&#x435;&#x436;&#x434;&#x443;&#x43D;&#x430;&#x440;&#x43E;&#x434;&#x43D;&#x44B;&#x435; &#x43C;&#x435;&#x434;&#x438;&#x43A;&#x43E;-&#x441;&#x430;&#x43D;&#x438;&#x442;&#x430;&#x440;&#x43D;&#x44B;&#x435; &#x43F;&#x440;&#x430;&#x432;&#x438;&#x43B;&#x430; (&#x200E;2005 &#x433;.)&#x200E;: &#x418;&#x43D;&#x441;&#x442;&#x440;&#x443;&#x43C;&#x435;&#x43D;&#x442; &#x435;&#x436;&#x435;&#x433;&#x43E;&#x434;&#x43D;&#x43E;&#x439; &#x43E;&#x442;&#x447;&#x435;&#x442;&#x43D;&#x43E;&#x441;&#x442;&#x438; &#x433;&#x43E;&#x441;&#x443;&#x434;&#x430;&#x440;&#x441;&#x442;&#x432;-&#x443;&#x447;&#x430;&#x441;&#x442;&#x43D;&#x438;&#x43A;&#x43E;&#x432; &#x43D;&#x430; &#x43E;&#x441;&#x43D;&#x43E;&#x432;&#x435; &#x441;&#x430;&#x43C;&#x43E;&#x43E;&#x446;&#x435;&#x43D;&#x43A;&#x438;, 2-&#x435; &#x438;&#x437;&#x434;&#x430;&#x43D;&#x438;&#x435;</p>\n      </td>\n      <td colspan=\"5\">\n        <p>Russian</p>\n      </td>\n      <td colspan=\"2\">\n        <p><a href=\"https://www.who.int/ru/publications/i/item/9789240040120\">https://www.who.int/ru/publications/i/item/9789240040120</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>R&#xE8;glement sanitaire international (&#x200E;2005)&#x200E; : outil d&#x2019;auto&#xE9;valuation pour l&#x2019;&#xE9;tablissement de rapports annuels par les &#xE9;tats parties, 2e ed</p>\n      </td>\n      <td colspan=\"7\">\n        <p>French</p>\n      </td>\n      <td>\n        <p><a href=\"https://www.who.int/fr/publications/i/item/9789240040120\">https://www.who.int/fr/publications/i/item/9789240040120</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Regulamento Sanit&#xE1;rio Internacional (&#x200E;2005)&#x200E;: ferramenta de auto-avalia&#xE7;&#xE3;o e relat&#xF3;rio anual dos Estados Partes, segunda edi&#xE7;&#xE3;o</p>\n      </td>\n      <td colspan=\"7\">\n        <p>Portuguese</p>\n      </td>\n      <td>\n        <p><a href=\"https://www.who.int/pt/publications/i/item/9789240040120\">https://www.who.int/pt/publications/i/item/9789240040120</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&#x627;&#x644;&#x644;&#x648;&#x627;&#x626;&#x62D; &#x627;&#x644;&#x635;&#x62D;&#x64A;&#x629; &#x627;&#x644;&#x62F;&#x648;&#x644;&#x64A;&#x629; (2005): &#x623;&#x62F;&#x627;&#x629; &#x625;&#x639;&#x62F;&#x627;&#x62F; &#x627;&#x644;&#x62A;&#x642;&#x627;&#x631;&#x64A;&#x631; &#x627;&#x644;&#x633;&#x646;&#x648;&#x64A;&#x629; &#x644;&#x644;&#x62A;&#x642;&#x64A;&#x64A;&#x645; &#x627;&#x644;&#x630;&#x627;&#x62A;&#x64A; &#x644;&#x644;&#x62F;&#x648;&#x644;&#x629; &#x627;&#x644;&#x637;&#x631;&#x641; &#x60C; &#x627;&#x644;&#x625;&#x635;&#x62F;&#x627;&#x631; &#x627;&#x644;&#x62B;&#x627;&#x646;&#x64A;</p>\n      </td>\n      <td colspan=\"7\">\n        <p>Arabic</p>\n      </td>\n      <td>\n        <p><a href=\"https://www.who.int/ar/publications/i/item/9789240040120\">https://www.who.int/ar/publications/i/item/9789240040120</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"3\">\n        <p>&#x56FD;&#x9645;&#x536B;&#x751F;&#x6761;&#x4F8B;&#xFF08;2005)&#x200E;: &#x7F14;&#x7EA6;&#x56FD;&#x81EA;&#x8BC4;&#x5E74;&#x5EA6;&#x62A5;&#x544A;&#x5DE5;&#x5177;, &#x7B2C;&#x4E8C;&#x7248;</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Chinese</p>\n      </td>\n      <td colspan=\"4\">\n        <p><a href=\"https://www.who.int/zh/publications/i/item/9789240040120\">https://www.who.int/zh/publications/i/item/9789240040120</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"4\">\n        <p>Reglamento sanitario internacional (&#x200E;2005)&#x200E;: instrumento de autoevaluaci&#xF3;n para la presentaci&#xF3;n anual de informes de los estados partes, 2a ed</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Spanish</p>\n      </td>\n      <td colspan=\"3\">\n        <p><a href=\"https://www.who.int/es/publications/i/item/9789240040120\">https://www.who.int/es/publications/i/item/9789240040120</a></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>International health regulations (&#x200E;2005)&#x200E;: state party self-assessment annual reporting tool second edition: C11. Points of entry (&#x200E;PoE)&#x200E; and border health</strong></p>\n      </td>\n      <td>\n        <p>English</p>\n      </td>\n      <td rowspan=\"4\">\n        <p><a href=\"https://www.who.int/publications/i/item/WHO-WPE-HSP-CCI-CAP-2021.1\">https://www.who.int/publications/i/item/WHO-WPE-HSP-CCI-CAP-2021.1</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&#x627;&#x644;&#x644;&#x648;&#x627;&#x626;&#x62D; &#x627;&#x644;&#x635;&#x62D;&#x64A;&#x629; &#x627;&#x644;&#x62F;&#x648;&#x644;&#x64A;&#x629; (2005): &#x623;&#x62F;&#x627;&#x629; &#x625;&#x639;&#x62F;&#x627;&#x62F; &#x627;&#x644;&#x62A;&#x642;&#x627;&#x631;&#x64A;&#x631; &#x627;&#x644;&#x633;&#x646;&#x648;&#x64A;&#x629; &#x644;&#x644;&#x62A;&#x642;&#x64A;&#x64A;&#x645; &#x627;&#x644;&#x630;&#x627;&#x62A;&#x64A; &#x644;&#x644;&#x62F;&#x648;&#x644;&#x629; &#x627;&#x644;&#x637;&#x631;&#x641; &#x60C; &#x627;&#x644;&#x625;&#x635;&#x62F;&#x627;&#x631; &#x627;&#x644;&#x62B;&#x627;&#x646;&#x64A;: C11. &#x646;&#x642;&#x627;&#x637; &#x627;&#x644;&#x62F;&#x62E;&#x648;&#x644; (PoE) &#x648;&#x635;&#x62D;&#x629; &#x627;&#x644;&#x62D;&#x62F;&#x648;&#x62F;</p>\n      </td>\n      <td>\n        <p>Arabic</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&#x300A;&#x56FD;&#x9645;&#x536B;&#x751F;&#x6761;&#x4F8B;&#xFF08;2005&#xFF09;&#x300B;: &#x7F14;&#x7EA6;&#x56FD;&#x81EA;&#x8BC4;&#x5E74;&#x5EA6;&#x62A5;&#x544A;&#x5DE5;&#x5177;, &#x7B2C;&#x4E8C;&#x7248;&#xFF1A;C11&#x3002; &#x5165;&#x5883;&#x70B9; (&#x200E;PoE)&#x200E; &#x548C;&#x8FB9;&#x5883;&#x536B;&#x751F;</p>\n      </td>\n      <td>\n        <p>Chinese</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&#x41C;&#x435;&#x436;&#x434;&#x443;&#x43D;&#x430;&#x440;&#x43E;&#x434;&#x43D;&#x44B;&#x435; &#x43C;&#x435;&#x434;&#x438;&#x43A;&#x43E;-&#x441;&#x430;&#x43D;&#x438;&#x442;&#x430;&#x440;&#x43D;&#x44B;&#x435; &#x43F;&#x440;&#x430;&#x432;&#x438;&#x43B;&#x430; (2005 &#x433;.). &#x418;&#x43D;&#x441;&#x442;&#x440;&#x443;&#x43C;&#x435;&#x43D;&#x442; &#x435;&#x436;&#x435;&#x433;&#x43E;&#x434;&#x43D;&#x43E;&#x439; &#x43E;&#x442;&#x447;&#x435;&#x442;&#x43D;&#x43E;&#x441;&#x442;&#x438; &#x433;&#x43E;&#x441;&#x443;&#x434;&#x430;&#x440;&#x441;&#x442;&#x432;-&#x443;&#x447;&#x430;&#x441;&#x442;&#x43D;&#x438;&#x43A;&#x43E;&#x432; &#x43D;&#x430; &#x43E;&#x441;&#x43D;&#x43E;&#x432;&#x435; &#x441;&#x430;&#x43C;&#x43E;&#x43E;&#x446;&#x435;&#x43D;&#x43A;&#x438;, &#x432;&#x442;&#x43E;&#x440;&#x43E;&#x435; &#x438;&#x437;&#x434;&#x430;&#x43D;&#x438;&#x435;: C11. &#x422;&#x43E;&#x447;&#x43A;&#x438; &#x432;&#x44A;&#x435;&#x437;&#x434;&#x430; (&#x200E;PoE)&#x200E; &#x438; &#x441;&#x43E;&#x441;&#x442;&#x43E;&#x44F;&#x43D;&#x438;&#x435; &#x433;&#x440;&#x430;&#x43D;&#x438;&#x446;&#x44B;</p>\n      </td>\n      <td>\n        <p>Russian</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>R&#xE8;glement sanitaire international (2005) : outil d&#x2019;auto&#xE9;valuation pour l&#x2019;&#xE9;tablissement de rapports annuels par les &#xE9;tats parties,deuxi&#xE8;me &#xE9;dition: C11. Points d&apos;entr&#xE9;e (&#x200E;PoE)&#x200E; et sant&#xE9; aux fronti&#xE8;res</p>\n      </td>\n      <td>\n        <p>French</p>\n      </td>\n      <td>\n        <p><a href=\"https://who.int/fr/publications/i/item/WHO-WPE-HSP-CCI-CAP-2021.1\">https://who.int/fr/publications/i/item/WHO-WPE-HSP-CCI-CAP-2021.1</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Regulamento Sanit&#xE1;rio Internacional (2005): Ferramenta de auto-avalia&#xE7;&#xE3;o e relat&#xF3;rio anual dos Estados Partes, segunda edi&#xE7;&#xE3;o: C11. Pontos de entrada (&#x200E;PoE)&#x200E; e sa&#xFA;de da fronteira</p>\n      </td>\n      <td>\n        <p>Portuguese</p>\n      </td>\n      <td>\n        <p><a href=\"https://who.int/pt/publications/i/item/WHO-WPE-HSP-CCI-CAP-2021.1\">https://who.int/pt/publications/i/item/WHO-WPE-HSP-CCI-CAP-2021.1</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Reglamento sanitario internacional (2005): instrumento de autoevaluaci&#xF3;n para la presentaci&#xF3;n anual de informes de los estados partes, 2&#xAA; edici&#xF3;n: C11. Puntos de entrada (&#x200E;PoE)&#x200E; y sanidad fronteriza</p>\n      </td>\n      <td>\n        <p>Spanish</p>\n      </td>\n      <td>\n        <p><a href=\"https://who.int/es/publications/i/item/WHO-WPE-HSP-CCI-CAP-2021.1\">https://who.int/es/publications/i/item/WHO-WPE-HSP-CCI-CAP-2021.1</a></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>International Health Regulations (&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;2005)&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;&#x200E;: guidance document for the State Party self-assessment annual reporting tool</p>\n      </td>\n      <td>\n        <p>English</p>\n      </td>\n      <td>\n        <p><a href=\"https://www.who.int/publications/i/item/WHO-WHE-CPI-2018.17\">https://www.who.int/publications/i/item/WHO-WHE-CPI-2018.17</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>R&#xE8;glement sanitaire international (&#x200E;&#x200E;&#x200E;&#x200E;2005)&#x200E;&#x200E;&#x200E;&#x200E; : document d&#x2019;orientation sur l&#x2019;outil d&#x2019;auto&#xE9;valuation pour l&#x2019;&#xE9;tablissement de rapports annuels par les &#xC9;tats Parties</p>\n      </td>\n      <td>\n        <p>French</p>\n      </td>\n      <td>\n        <p><a href=\"https://www.who.int/fr/publications/i/item/WHO-WHE-CPI-2018.17\">https://www.who.int/fr/publications/i/item/WHO-WHE-CPI-2018.17</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Reglamento Sanitario Internacional (&#x200E;&#x200E;2005)&#x200E;&#x200E;: documento de orientaci&#xF3;n sobre el instrumento de autoevaluaci&#xF3;n para la presentaci&#xF3;n anual de informes de los Estados Partes</p>\n      </td>\n      <td>\n        <p>Spanish</p>\n      </td>\n      <td>\n        <p><a href=\"https://www.who.int/es/publications/i/item/WHO-WHE-CPI-2018.17\">https://www.who.int/es/publications/i/item/WHO-WHE-CPI-2018.17</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&#x627;&#x644;&#x644;&#x648;&#x627;&#x626;&#x62D; &#x627;&#x644;&#x635;&#x62D;&#x64A;&#x629; &#x627;&#x644;&#x62F;&#x648;&#x644;&#x64A;&#x629; (2005): &#x648;&#x62B;&#x64A;&#x642;&#x629; &#x62A;&#x648;&#x62C;&#x64A;&#x647;&#x64A;&#x629; &#x628;&#x634;&#x623;&#x646; &#x623;&#x62F;&#x627;&#x629; &#x627;&#x625;&#x644;&#x628;&#x627;&#x644;&#x63A; &#x627;&#x644;&#x633;&#x646;&#x648;&#x64A; &#x644;&#x644;&#x62F;&#x648;&#x644; &#x627;&#x623;&#x644;&#x637;&#x631;&#x627;&#x641; &#x628;&#x627;&#x644;&#x62A;&#x642;&#x64A;&#x64A;&#x645;</p>\n      </td>\n      <td>\n        <p>Arabic</p>\n      </td>\n      <td>\n        <p><a href=\"https://www.who.int/ar/publications/i/item/WHO-WHE-CPI-2018.17\">https://www.who.int/ar/publications/i/item/WHO-WHE-CPI-2018.17</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&#x41C;&#x435;&#x436;&#x434;&#x443;&#x43D;&#x430;&#x440;&#x43E;&#x434;&#x43D;&#x44B;&#x435; &#x43C;&#x435;&#x434;&#x438;&#x43A;&#x43E;-&#x441;&#x430;&#x43D;&#x438;&#x442;&#x430;&#x440;&#x43D;&#x44B;&#x435; &#x43F;&#x440;&#x430;&#x432;&#x438;&#x43B;&#x430; (&#x200E;&#x200E;&#x200E;2005 &#x433;.)&#x200E;&#x200E;&#x200E;: &#x440;&#x443;&#x43A;&#x43E;&#x432;&#x43E;&#x434;&#x441;&#x442;&#x432;&#x43E; &#x43F;&#x43E; &#x438;&#x43D;&#x441;&#x442;&#x440;&#x443;&#x43C;&#x435;&#x43D;&#x442;&#x443; &#x435;&#x436;&#x435;&#x433;&#x43E;&#x434;&#x43D;&#x43E;&#x439; &#x43E;&#x442;&#x447;&#x435;&#x442;&#x43D;&#x43E;&#x441;&#x442;&#x438; &#x433;&#x43E;&#x441;&#x443;&#x434;&#x430;&#x440;&#x441;&#x442;&#x432;-&#x443;&#x447;&#x430;&#x441;&#x442;&#x43D;&#x438;&#x43A;&#x43E;&#x432; &#x43D;&#x430; &#x43E;&#x441;&#x43D;&#x43E;&#x432;&#x435; &#x441;&#x430;&#x43C;&#x43E;&#x43E;&#x446;&#x435;&#x43D;&#x43A;&#x438;</p>\n      </td>\n      <td>\n        <p>Russian</p>\n      </td>\n      <td>\n        <p><a href=\"https://www.who.int/ru/publications/i/item/WHO-WHE-CPI-2018.17\">https://www.who.int/ru/publications/i/item/WHO-WHE-CPI-2018.17</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&#x56FD;&#x9645;&#x536B;&#x751F;&#x6761;&#x4F8B;&#xFF08;2005): &#x7F14;&#x7EA6;&#x56FD;&#x81EA;&#x8BC4;&#x5E74;&#x5EA6;&#x62A5;&#x544A; &#x5DE5;&#x5177;&#x6307;&#x5BFC;&#x6587;&#x4EF6;</p>\n      </td>\n      <td>\n        <p>Chinese</p>\n      </td>\n      <td>\n        <p><a href=\"https://www.who.int/zh/publications/i/item/WHO-WHE-CPI-2018.17\">https://www.who.int/zh/publications/i/item/WHO-WHE-CPI-2018.17</a></p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "indicator_sort_order"=>"03-0d-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"3.d.2", "slug"=>"3-d-2", "name"=>"Porcentaje de infecciones del torrente sanguíneo debidas a determinados organismos resistentes a los antimicrobianos ", "url"=>"/site/es/3-d-2/", "sort"=>"03dd02", "goal_number"=>"3", "target_number"=>"3.d", "global"=>{"name"=>"Porcentaje de infecciones del torrente sanguíneo debidas a determinados organismos resistentes a los antimicrobianos "}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Porcentaje de infecciones del torrente sanguíneo debidas a determinados organismos resistentes a los antimicrobianos ", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Porcentaje de infecciones del torrente sanguíneo debidas a determinados organismos resistentes a los antimicrobianos ", "indicator_number"=>"3.d.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nLa resistencia a los antimicrobianos (RAM) es una amenaza mundial para la salud, \nlos medios de vida, la seguridad alimentaria y el logro de muchos de los Objetivos \nde Desarrollo Sostenible. Los antibióticos, antivirales, agentes antiparasitarios \ny antifúngicos son cada vez más ineficaces debido a la resistencia desarrollada \npor su uso excesivo o inadecuado, con graves consecuencias para la salud humana y \nanimal (terrestre y acuática) y la salud de las plantas, e impactos negativos en \nla producción de alimentos, el medio ambiente y la economía mundial. \n\nEn particular, la resistencia a los antimicrobianos afectará negativamente \nel logro de muchas de las metas enumeradas en el Objetivo 3 debido a la \nreducción de las opciones de tratamiento para las infecciones por \npatógenos resistentes; afectará a las metas del Objetivo 2 al afectar \nla productividad agrícola, incluida la producción de animales destinados \nal consumo; y afectará a las metas del Objetivo 1, ya que el aumento de \nla resistencia a los antimicrobianos dará lugar a grandes disminuciones \ndel crecimiento económico, aumentará la desigualdad económica y llevará \na otros 24 millones de personas a la pobreza extrema para 2030. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.d.2&seriesCode=SH_BLD_MRSA&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Porcentaje de infección del torrente sanguíneo debida a Staphylococcus aureus resistente a la meticilina (SARM) entre pacientes que buscan atención y cuya muestra de sangre se toma y analiza (%) SH_BLD_MRSA</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.d.2&seriesCode=SH_BLD_ECOLI&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Porcentaje de infección del torrente sanguíneo debido a Escherichia coli resistente a cefalosporinas de tercera generación (p. ej., ESBL-E. coli) entre pacientes que buscan atención y cuya muestra de sangre se toma y analiza (%) SH_BLD_ECOLI</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0d-02.pdf\">Metadatos 3-d-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2025-03-12", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nAntimicrobial resistance (AMR) is a global threat to health, livelihoods, \nfood security and the achievement of many of the Sustainable Development \nGoals. Antibiotics, antivirals, antiparasitic agents and antifungals are \nincreasingly ineffective owing to resistance developed through their excessive \nor inappropriate use, with serious consequences for human and animal health \n(terrestrial and aquatic), and plant health, and negative impacts on food production, \nthe environment and the global economy. \n\nIn particular, antimicrobial resistance will negatively impact the achievement \nof many of the targets listed under Goal 3 due to reduced treatment options for \ninfections by resistant pathogens; will impact targets under Goal 2 by impacting \nthe agricultural productivity, including food animal production; and will impact \ntargets in Goal 1 as increased antimicrobial resistance will result in large declines \nin economic growth, increase economic inequality and drive an additional 24 million \npeople into extreme poverty by 2030. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.d.2&seriesCode=SH_BLD_MRSA&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Percentage of bloodstream infection due to methicillin-resistant Staphylococcus aureus (MRSA) among patients seeking care and whose blood sample is taken and tested (%) SH_BLD_MRSA</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.d.2&seriesCode=SH_BLD_ECOLI&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Percentage of bloodstream infection due to Escherichia coli resistant to 3rd-generation cephalosporin (e.g., ESBL- E. coli) among patients seeking care and whose blood sample is taken and tested (%) SH_BLD_ECOLI</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0d-02.pdf\">Metadata 3-d-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nAntimikrobianoekiko erresistentzia (AME) munduko mehatxu bat da osasunerako, bizitzeko baliabideetarako, \nelikadura-segurtasunerako eta Garapen Jasangarriko Helburu askoren lorpenerako. Antibiotikoek, antibiralek, \nparasitoen aurkako eragileek eta antifungikoek gero eta eraginkortasun gutxiago dute, gehiegi edo desegoki \nerabiltzeagatik erresistentzia garatzen delako. Horrez gain, ondorio larriak eragiten dituzte gizakien eta \nanimalien osasunean (lehorrekoa eta uretakoa) eta landareen osasunean, eta inpaktu negatiboak elikagaien \nekoizpenean, ingurumenean eta munduko ekonomian. \n\nBereziki, antimikrobianoekiko erresistentziak eragin negatiboa izango du 3. helburuan zerrendatutako jomuga \nasko lortzeko orduan, patogeno erresistenteek eragindako infekzioetarako tratamendu-aukerak murriztu egingo \nbaitira; 2. helburuko jomugei eragingo die, nekazaritza-produktibitatean ondorioak izanik, kontsumorako \nanimalien ekoizpenean barne; eta 1. helburuko xedeei eragingo die, antimikrobianoekiko erresistentzia handitzeak \nhazkunde ekonomikoaren murrizketa handiak ekarriko baititu, desberdintasun ekonomikoa areagotuko baitu eta \nbeste 24 milioi pertsona muturreko pobreziara eramango baititu 2030erako. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.d.2&seriesCode=SH_BLD_MRSA&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Metizilinarekiko erresistentea den Staphylococcus aureus-ek eragindako odol-uholdearen infekzioaren ehunekoa, arreta bilatzen duten eta odol-lagina hartu eta aztertzen duten pazienteen artean (%) SH_BLD_MRSA</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=3.d.2&seriesCode=SH_BLD_ECOLI&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Hirugarren belaunaldiko (adibidez, ESBL-E. coli) zefalosporinekiko erresistentea den Escherichia coli-ren ondoriozko odol-uholdearen infekzio-ehunekoa, arreta bilatzen duten eta odol-lagina hartu eta aztertzen zaien pazienteen artean (%) SH_BLD_ECOLI</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-03-0d-02.pdf\">Metadatuak 3-d-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 3: Ensure healthy lives and promote well-being for all at all ages</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 3.d: Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 3.d.2: Percentage of bloodstream infections due to selected antimicrobial-resistant organisms</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_BLD_MRSA - Percentage of bloodstream infection due to methicillin-resistant Staphylococcus aureus (MRSA) among patients seeking care and whose blood sample is taken and tested [3.d.2]</p>\n<p>SH_BLD_ECOLI - Percentage of bloodstream infection due to Escherichia coli resistant to 3rd-generation cephalosporin (e.g., ESBL- E. coli) among patients seeking care and whose blood sample is taken and tested [3.d.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 3.d.1 International Health Regulations (IHR) capacity and health emergency preparedness</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p>Percentage of bloodstream infection due to methicillin-resistant <em>Staphylococcus aureus</em> (MRSA) and <em>Escherichia coli</em> resistant to 3rd-generation cephalosporin (e.g., ESBL- <em>E. coli</em>) among patients seeking care and whose blood sample is taken and tested. </p>\n<p> </p>\n<ul>\n  <li>Presumptive methicillin-resistant <em>S. aureus</em> (MRSA) isolates as defined by oxacillin minimum inhibitory concentration (MIC) and cefoxitin disc diffusion tests according to current internationally recognized clinical breakpoints (e.g., EUCAST or CLSI)<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> </li>\n  <li><em>E. coli</em> resistant to third generation cephalosporins: <em>E. coli</em> isolates that are resistant as defined by current internationally recognized clinical breakpoints for third generation cephalosporins (e.g., EUCAST or CLSI)<sup>1</sup>, specifically ceftriaxone or cefotaxime or ceftazidime. </li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> EUCAST guidelines for detection of resistance mechanisms and specific resistances of clinical and/or epidemiological importance. Version 2.0. 2017. Both for species identification and antimicrobial susceptibility testing (AST)</p><p>Clinical and Laboratory Standards Institute (CLSI). Performance Standards for Antimicrobial Susceptibility Testing. 32<sup>nd</sup> ed. CLSI supplement M100 (ISBN 978-1-68440-134-5 [Print]; ISBN 978-1-68440-135-2 [Electronic]). Clinical and Laboratory Standards Institute, USA, 2022. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Preferred sources:</strong> National antimicrobial resistance (AMR) data collected through the national AMR surveillance system and reported to Global Antimicrobial Resistance and Use Surveillance System (GLASS).</p>\n<p>GLASS provides a standardised approach to the collection, analysis, and sharing of AMR data by countries, and seeks to document the status of existing or newly developed national AMR surveillance systems. Furthermore, GLASS promotes a shift from surveillance approaches based solely on laboratory data to a system that includes epidemiological, clinical, and population-level data. GLASS also collaborates with regional and national AMR surveillance networks to produce timely and comprehensive data. Collaboration with the UN Food and Agriculture Organization (FAO), the United Nations Environment Programme (UNEP), and the World Organisation for Animal Health (WOAH) &#x2013; which together with WHO form the Quadripartite Collaboration &#x2013; is ongoing to improve a comprehensive understanding of AMR across sectors and to promote the One Health Approach to AMR control.</p>\n<p>GLASS also collects information on the status of national AMR surveillance systems through a short questionnaire completed by AMR national focal points (NFPs) in each country. The questionnaire covers three main areas: 1) overall coordination; 2) surveillance system; and 3) quality control. Each area consists of a set of indicators developed to measure development and strengthening of national AMR surveillance. </p>\n<p> </p>\n<p><strong>Other possible data sources:</strong> Published and non-published data from national centres and research/academic institutions and from other regional surveillance networks. </p>", "FREQ_COLL__GLOBAL"=>"<p>Yearly</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>15 February to 1 March 2024</p>", "DATA_SOURCE__GLOBAL"=>"<p>Ministries of Health </p>\n<p> </p>", "COMPILING_ORG__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "INST_MANDATE__GLOBAL"=>"<p>The World Health Organization (WHO) is the Custodian Agency for reporting on SDG indicator 3.d.2</p>", "RATIONALE__GLOBAL"=>"<p>Antimicrobial resistance (AMR) is a global threat to health, livelihoods, food security and the achievement of many of the Sustainable Development Goals. Antibiotics, antivirals, antiparasitic agents and antifungals are increasingly ineffective owing to resistance developed through their excessive or inappropriate use, with serious consequences for human and animal health (terrestrial and aquatic), and plant health, and negative impacts on food production, the environment and the global economy<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>.</p>\n<p>In particular, antimicrobial resistance (AMR) will negatively impact the achievement of many of the targets listed under Goal 3 due to reduced treatment options for infections by resistant pathogens; will impact targets under Goal 2 by impacting agricultural productivity, including food animal production; and will impact targets in Goal 1 as increased AMR will result in large declines in economic growth, increase economic inequality and drive an additional 24 million people into extreme poverty by 2030<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup>.</p>\n<p>Given the above context, there is an urgent need to build country capacity, especially in developing countries, to address this growing national and global multisectoral risk. The current indicator (3.d.1) for target 3.d has a focus on strengthening 13 core capacities &#x2013; essential public health capacity that State Parties are required to have in place throughout their territories pursuant to IHR (2005) requirements by the year 2012. While a few of these 13 core capacities<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup> can be considered &#x201C;AMR-sensitive&#x201D;, they do not specifically monitor or address the significant risks associated with AMR. So, with the adoption of the Global Action Plan on AMR in 2015 by the World Health Assembly, the adoption of a Political Declaration on AMR at the high-level meeting of the UN General Assembly in 2016, and the report in 2019 of the Ad-hoc Inter-Agency Coordination Group established by the UN Secretary-General, an urgent need has been identified for an additional indicator on AMR to be considered for inclusion within the global SDG indicator framework. </p>\n<p>This indicator, based on establishing a functional national AMR surveillance system, is considered a basic building block for AMR monitoring and response in countries. Surveillance is the cornerstone to assessing the spread of AMR, providing early warning, and informing and monitoring the impact of local, national, and global risk reduction and management strategies. The global antimicrobial surveillance system (GLASS<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup>) managed by WHO recommends the establishment of three core components to set up a well-functioning national AMR surveillance system: 1) a National Coordinating Centre (NCC); 2) a National Reference Laboratory (NRL); and 3) Sentinel surveillance sites where both diagnostic and epidemiological data are collected. </p>\n<p>This indicator, therefore will help catalyse the establishment of national AMR surveillance systems to ensure the collection of data at the national level and can also be used for tracking progress of country capacity for early warning of outbreaks of resistant infections. The indicator aims to address critical elements of the SDG target 3.d through a strategic approach derived from the evidence gathered through this indicator, as well as allows to &#x2018;strengthen the capacity of all countries, in particular developing countries&#x2019;, &#x2018;reduction&#x2019; and &#x2018;management of national&#x2019; and &#x2018;global health risks&#x2019;, as part of the SDG global monitoring framework. The surveillance and diagnostics data thus generated will also help countries give early warning for public health preparedness, and for appropriate response measures. </p>\n<p><strong>Rationale for selecting the types of AMR organisms</strong>:</p>\n<p>(i) <em>E. coli</em> and <em>S. aureus</em> are among the most common human fast-growing bacteria causing acute human infections; </p>\n<p>(ii) <em>E. coli</em> is highly prevalent in both humans, animals and environment, being an ideal indicator for monitoring AMR across the sectors in line with the One Health approach. It recognizes that the health of humans, animals and ecosystems are interconnected and therefore requires a coordinated, collaborative, multidisciplinary and cross-sectoral approach to address potential or existing risks that originate at the animal-human-ecosystems interface;</p>\n<p>(iii) both MRSA and <em>E. coli</em> resistant to 3rd-generation cephalosporin are largely disseminated and found in high frequency in human infections observed in hospital settings all over the world and increasingly very frequent in the community. Infections with these types of AMR lead to increase in use of the last resort drugs (e.g., vancomycin for MRSA infections, and carbapenems for <em>E. coli</em> resistant to 3rd-generation cephalosporin) against which new types of AMR are emerging.</p>\n<p>Effective control of these two types of AMR will ultimately help preserve the capacity to treat infections with available antimicrobials while new prevention and treatment solutions can be developed. WHO has well defined global infection prevention and control standards and strategies.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> Antimicrobial Resistance Collaborators. (2022). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet; 399(10325): P629-655. DOI: https://doi.org/10.1016/S0140-6736(21)02724-0 <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> World Bank Group, Drug-resistant Infections: A Threat to Our Economic Future &#x2013; Final Report (Washington, D.C., March 2017). <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> (1) Legislation and financing; (2) IHR Coordination and National Focal Point Functions; (3)Zoonotic events and the Human-Animal Health Interface; (4) Food safety; (5) Laboratory;; (6) Surveillance; (7) Human resources; (8) National Health Emergency Framework; (9) Health Service Provision; (10) Risk communication; (11) Points of entry; (12) Chemical events; (13) Radiation emergencies <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> https://www.who.int/initiatives/glass <a href=\"#footnote-ref-6\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>AMR is an emerging global threat and risk to public health worldwide. In its early implementation phase of the global antimicrobial resistance surveillance system (GLASS), WHO recognizes various constraints in obtaining unbiased, representative AMR data: number and distribution of surveillance sites and representativeness of surveillance data, sampling bias, poor diagnostic capacity, measurements errors, issues with data management. It is imperative that countries should have a functioning national system to support AMR surveillance and report to GLASS. More detailed GLASS methodology and limitations of data currently submitted by countries can be found in the GLASS manual<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup>. AMR surveillance, country preparedness and response are now high priority for WHO and its Member States. In the coming years, WHO aims to provide intensified technical assistance. Experience gained and lessons learnt from further implementation of the national AMR surveillance systems will increase effectiveness, address limitations, and make the data more robust. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> World Health Organization. (&#x200E;2023)&#x200E;. GLASS manual for antimicrobial resistance surveillance in common bacteria causing human infection. World Health Organization. <a href=\"https://iris.who.int/handle/10665/372741\">https://iris.who.int/handle/10665/372741</a> <a href=\"#footnote-ref-7\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>The WHO Global AMR Surveillance System (GLASS) supports countries to implement an AMR standardized surveillance system. Cases of AMR infection are found among patients from whom routine clinical samples have been collected for blood culture at surveillance sites (health care facility) according to local clinical practices, and antimicrobial susceptibility tests (AST) are performed for the isolated blood pathogens as per international standards<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup>. The microbiological results (bacteria identification and AST) are de-duplicated and combined with the patient data and related to population data from the surveillance sites. GLASS does collect information on the origin of the infection, either community origin (less than 2 calendar days in hospital) or hospital origin (patients hospitalized for more than 2 calendar days). Data are collated and validated at national level and reported to GLASS where epidemiological statistics and metrics are generated. GLASS has published guidelines on the setup of national AMR surveillance systems<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup> and the GLASS methodology implementation manual<sup>6</sup> is available to countries. </p>\n<p>Although national representativeness of generated AMR rates is not a strict requirement, GLASS encourages countries to derive representative national data.</p>\n<p> </p>\n<p><strong>Formulation of the proposed new indicator: </strong>Percentage of patients with <strong> </strong>bloodstream infections due to selected antimicrobial resistant organisms.</p>\n<p><strong> </strong> </p>\n<p>This is derived from the following and multiplied by 100<sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup><strong>:</strong> </p>\n<p><strong>Numerator:</strong> Number of patients with growth of methicillin-resistant <em>S. aureus </em>or<em> E. coli</em> resistant to third generation cephalosporins in tested blood samples </p>\n<p><strong>Denominator:</strong> Total number of patients with growth of <em>S. aureus</em> or <em>E. coli </em>in tested blood samples</p>\n<p>Stratification:</p>\n<p>The data are stratified by gender, and age group. Data are aggregated at the country level. Data are analysed and reported according to whether specimen is within 2 calendar days of admission (community origin) or after 2 calendar days of admission (hospital origin).</p>\n<p> </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> EUCAST guidelines for detection of resistance mechanisms and specific resistances of clinical and/or epidemiological importance. Version 2.0. 2017. Both for species identification and antimicrobial susceptibility testing (AST)</p><p>Clinical and Laboratory Standards Institute (CLSI). Performance Standards for Antimicrobial Susceptibility Testing. 32nd ed. CLSI supplement M100 (ISBN 978-1-68440-134-5 [Print]; ISBN 978-1-68440-135-2 [Electronic]). Clinical and Laboratory Standards Institute, USA, 2022. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> National antimicrobial resistance surveillance systems and participation in the Global Antimicrobial Resistance Surveillance System (GLASS):<strong> </strong>A guide to planning, implementation, and monitoring and evaluation (2016). <a href=\"https://www.who.int/glass/resources/publications/national-surveillance-guide/en/\">https://www.who.int/glass/resources/publications/national-surveillance-guide/en/</a> <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> Both for species identification and antimicrobial susceptibility testing (AST) <a href=\"#footnote-ref-10\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>Data uploaded to the GLASS IT platform undergo automated consistency checks and then reviewed with Member States by dedicated WHO team every time new data are generated</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Countries with no data are reported as blank. </p>\n<ul>\n  <li><strong>At regional level</strong></li>\n</ul>", "REG_AGG__GLOBAL"=>"<p>In addition to the country estimates, the resistance proportions are calculated at the regional and global levels</p>", "DOC_METHOD__GLOBAL"=>"<p>In addition to the GLASS manual<sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup>, a number of supporting documents and tools are available at the GLASS website<sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup>. Training is provided via regular global webinars or face-to-face regional and national training courses. GLASS national focal points are supported by a dedicated GLASS helpdesk.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> World Health Organization. (&#x200E;2023)&#x200E;. GLASS manual for antimicrobial resistance surveillance in common bacteria causing human infection. World Health Organization. <a href=\"https://iris.who.int/handle/10665/372741\">https://iris.who.int/handle/10665/372741</a> <a href=\"#footnote-ref-11\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> https://www.who.int/initiatives/glass/resource-centre <a href=\"#footnote-ref-12\">&#x2191;</a></p></div></div>", "QUALITY_MGMNT__GLOBAL"=>"<p>For information on data quality management, assurance, and assessment processes at WHO, please refer to: <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a> </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>For information on data quality management, assurance, and assessment processes at WHO, please refer to: <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a></p>\n<p>WHO is faciloitating provision of external quality assurance for the national reference laboratories supporting national AMR surveillance</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>For information on data quality management, assurance, and assessment processes at WHO, please refer to: https://www.who.int/data/ddi</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are available by country, gender, and age group, as well as whether infection is of community or hospital origin. </p>", "COMPARABILITY__GLOBAL"=>"<p>The metrics used for 3.d.2 are commonly used by AMR surveillance systems all over the world and could be considered an international standard</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> https://www.who.int/initiatives/glass</p>", "indicator_sort_order"=>"03-0d-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.1.1", "slug"=>"4-1-1", "name"=>"Proporción de niños y adolescentes que, a) en los grados 2 o 3, b) al final de la educación primaria y c) al final de la educación secundaria inferior, han alcanzado al menos un nivel mínimo de competencia en i) lectura y ii) matemáticas, desglosada por sexo", "url"=>"/site/es/4-1-1/", "sort"=>"040101", "goal_number"=>"4", "target_number"=>"4.1", "global"=>{"name"=>"Proporción de niños y adolescentes que, a) en los grados 2 o 3, b) al final de la educación primaria y c) al final de la educación secundaria inferior, han alcanzado al menos un nivel mínimo de competencia en i) lectura y ii) matemáticas, desglosada por sexo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Tipo de competencia", "value"=>"Comprensión lectora"}, {"field"=>"Tipo de competencia", "value"=>"Matemáticas"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de personas de 15 años que han alcanzado al menos el nivel 2 de competencia en lectura y matemáticas", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de niños y adolescentes que, a) en los grados 2 o 3, b) al final de la educación primaria y c) al final de la educación secundaria inferior, han alcanzado al menos un nivel mínimo de competencia en i) lectura y ii) matemáticas, desglosada por sexo", "indicator_number"=>"4.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Organización para la Cooperación y Desarrollo Económico (OCDE)", "periodicity"=>"Trienal", "url"=>"https://www.oecd.org/en/about/programmes/pisa.html", "url_text"=>"Programa para la Evaluación Internacional de los Estudiantes (PISA)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/OCDE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de personas de 15 años que han alcanzado al menos el nivel 2 de competencia en lectura y matemáticas", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.1- De aquí a 2030, asegurar que todas las niñas y todos los niños terminen la enseñanza primaria y secundaria, que ha de ser gratuita, equitativa y de calidad y producir resultados de aprendizaje pertinentes y efectivos", "definicion"=>"Proporción de personas de 15 años que han alcanzado al menos el nivel 2 de competencia en lectura y  en matemáticas, desglosada por sexo ", "formula"=>"\n$$PPN2_{competencia}^{t} = \\frac{PN2_{competencia}^{t}}{P_{15}^{t}}$$\n\ndonde:\n\n$PN2_{competencia}^{t} =$ número de personas de 15 años que han alcanzado o superado el \nnivel mínimo de competencia del nivel de educación 2 en la materia o competencia en el año $t$\n\n$P_{15}^{t} =$ población de 15 años en el año $t$\n", "desagregacion"=>"Tipo de competencia (materia): Comprensión lectora; matemáticas\n\nSexo \n", "periodicidad"=>"Trienal", "observaciones"=>"\nEl Programa para la evaluación internacional de los estudiantes utiliza un sistema de puntuaciones, donde no hay una puntuación máxima ni mínima. Los resultados se escalan de manera que respondan a una distribución normal con una media de 500 puntos y una desviación típica de 100 puntos. \n\nPara ayudar a interpretar esas puntuaciones, se establecen categorías de competencia en lectura:\n\n- Inferior al nivel 1c: puntuación inferior a 189,33\n- Nivel 1c: puntuación entre 189,33 y 262,03\n- Nivel 1b: puntuación entre 262,04 y 334,74\n- Nivel 1a: puntuación entre 334,75 y 407,46\n- Nivel 2: puntuación entre 407,47 y 480,17\n- Nivel 3: puntuación entre 480,18 y 552,88\n- Nivel 4: puntuación entre 552,89 y 625,60\n- Nivel 5: puntuación entre 625,61 y 698,31\n- Nivel 6: puntuación superior a 698,31\n\nUna persona alcanza al menos el nivel 2 de competencia en lectura cuando, para un texto de extensión media, puede identificar la idea principal, entender las relaciones entre sus partes, interpretar el significado de una parte limitada del texto a través de inferencias básicas, localizar uno o más fragmentos de información atendiendo a múltiples criterios parcialmente implícitos, reflexionar sobre el propósito general o específico del texto, comparar afirmaciones y evaluar los razonamientos en que estas se apoyan estableciendo conexiones entre el texto y el conocimiento externo.\n\nSe establecen estas categorías de competencia en matemáticas:\n\n- Inferior al nivel 1: puntuación inferior a 357,77\n- Nivel 1: puntuación entre 357,77 y 420,06\n- Nivel 2: puntuación entre 420,07 y 482,37\n- Nivel 3: puntuación entre 482,38 y 544,67\n- Nivel 4: puntuación entre 544,68 y 606,98\n- Nivel 5: puntuación entre 606,99 y 669,29\n- Nivel 6: puntuación superior a 669,29\n\nUna persona alcanza al menos el nivel 2 de competencia en matemáticas cuando sabe interpretar y reconocer situaciones en contextos que solo requieren una inferencia directa, extraer información pertinente de una sola fuente y hacer uso de un único modo de representación, utilizar algoritmos, fórmulas, procedimientos o convenciones elementales para resolver problemas relacionados con números enteros y efectuar razonamientos directos e interpretaciones literales de los resultados.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador pretende medir el porcentaje de niños y jóvenes que han alcanzado los\nresultados mínimos de aprendizaje en lectura y matemáticas durante o al final de las etapas \npertinentes de la educación. Cuanto mayor sea la cifra, mayor será la proporción de niños\n y/o jóvenes que alcanzan al menos la competencia mínima en el dominio respectivo (lectura o matemáticas).\n\nLos resultados de aprendizaje de las evaluaciones transnacionales de aprendizaje son directamente \ncomparables para todos los países que participaron en las mismas evaluaciones transnacionales de aprendizaje.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.1.1&seriesCode=SE_TOT_PRFL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20LOWSEC%20%7C%20SKILL_READ\"> Proporción de niños y jóvenes que alcanzan un nivel mínimo de competencia en lectura en la educación secundaria inferior (%) SE_TOT_PRFL</a> UNSTATS \n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.1.1&seriesCode=SE_TOT_PRFL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20LOWSEC%20%7C%20SKILL_MATH\"> Proporción de niños y jóvenes que alcanzan un nivel mínimo de competencia en matemáticas en la educación secundaria inferior (%) SE_TOT_PRFL</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-01-01.pdf\">Metadatos 4-1-1.pdf </a>(solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de personas de 15 años que han alcanzado al menos el nivel 2 de competencia en lectura y matemáticas", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.1- De aquí a 2030, asegurar que todas las niñas y todos los niños terminen la enseñanza primaria y secundaria, que ha de ser gratuita, equitativa y de calidad y producir resultados de aprendizaje pertinentes y efectivos", "definicion"=>"Proportion of 15-year-olds who have achieved at least level 2 proficiency  in i) reading and ii) mathematics, by sex ", "formula"=>"\n$$PPN2_{proficiency}^{t} = \\frac{PN2_{proficiency}^{t}}{P_{15}^{t}}$$\n\nwhere:\n\n$PN2_{proficiency}^{t} =$ 15-year-old population that has reached at least level 2 \nproficiency in the subject in year $t$\n\n$P_{15}^{t} =$ population aged 15 in year $t$\n", "desagregacion"=>"Type of proficiency (subject): Reading comprehension, mathematics\n\nSex \n", "periodicidad"=>"Trienal", "observaciones"=>"\nThe Programme for International Student Assessment uses a scoring system with \nno minimum or maximum score. The results are scaled to fit a normal distribution \nwith a mean of 500 and a standard deviation of 100. To aid in the interpretation \nof these scores, reading proficiency categories are established: \n\n- Below Level 1c: score below 189.33\n- Level 1c: score between 189.33 and 262.03\n- Level 1b: score between 262.04 and 334.74\n- Level 1a: score between 334.75 and 407.46\n- Level 2: score between 407.47 and 480.17\n- Level 3: score between 480.18 and 552.88\n- Level 4: score between 552.89 and 625.60\n- Level 5: score between 625.61 and 698.31\n- Level 6: score above 698.31\n\n\nA person reaches at least Level 2 reading proficiency when, for a text of average length, \nhe or she can identify the main idea, understand the relationships between its parts, \ninterpret the meaning of a limited part of the text through basic inferences, locate one \nor more pieces of information according to multiple partially implicit criteria, reflect \non the general or specific purpose of the text, compare statements and evaluate the reasoning \non which they are based by establishing connections between the text and external knowledge.\n\nThe following proficiency categories are established in mathematics:\n\n- Below Level 1: score below 357.77 \n- Level 1: score between 357.77 and 420.06\n- Level 2: score between 420.07 and 482.37\n- Level 3: score between 482.38 and 544.67\n- Level 4: score between 544.68 and 606.98\n- Level 5: score between 606.99 and 669.29\n- Level 6: score above 669.29 \n\nAn individual achieves at least Level 2 proficiency in mathematics when he or she can interpret \nand recognize situations in contexts that only require direct inference, extract relevant information \nfrom a single source and make use of a single mode of representation, use elementary algorithms, \nformulas, procedures or conventions to solve problems related to integers and carry out direct reasoning \nand literal interpretations of the results.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The indicator aims to measure the percentage of children and young people who have achieved the \nminimum learning outcomes in reading and mathematics during or at the end of the relevant stages of \neducation. The higher the figure, the higher the proportion of children and/or young people reaching \nat least minimum proficiency in the respective domain (reading or mathematic). \n\nLearning outcomes from cross-national learning assessment are directly comparable for all countries \nwhich participated in the same cross-national learning assessments.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.1.1&seriesCode=SE_TOT_PRFL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20LOWSEC%20%7C%20SKILL_READ\"> Proportion of children and young people achieving a minimum proficiency level in reading (%) SE_TOT_PRFL</a> UNSTATS \n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.1.1&seriesCode=SE_TOT_PRFL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20LOWSEC%20%7C%20SKILL_MATH\"> Series: Proportion of children and young people achieving a minimum proficiency level in mathematics (%) SE_TOT_PRFL</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-01-01.pdf\">Metadata 4-1-1.pdf </a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Proporción de personas de 15 años que han alcanzado al menos el nivel 2 de competencia en lectura y matemáticas", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.1- De aquí a 2030, asegurar que todas las niñas y todos los niños terminen la enseñanza primaria y secundaria, que ha de ser gratuita, equitativa y de calidad y producir resultados de aprendizaje pertinentes y efectivos", "definicion"=>"Irakurtzeko eta matematikako konpetentzietan gutxienez 2. maila lortu duten 15  urteko pertsonen proportzioa, sexuaren arabera bereizita ", "formula"=>"\n$$PPN2_{konpetentzia}^{t} = \\frac{PN2_{konpetentzia}^{t}}{P_{15}^{t}}$$ \n\nnon: \n\n$PN2_{konpetentzia}^{t} =$ konpetentzia bakoitzean 2. mailako gutxieneko maila lortu edo gainditu \nduten 15 urteko pertsonen kopurua $t$ urtean \n\n$P_{15}^{t} =$ 15 urteko biztanleak $t$ urtean \n", "desagregacion"=>"Konpetentzia: Irakurketa; matematika \n\nSexua \n", "periodicidad"=>"Trienal", "observaciones"=>"\nIkasleen nazioarteko ebaluaziorako programak puntuazio-sistema bat erabiltzen du, eta ez dago gehieneko eta \ngutxieneko puntuaziorik. Emaitzak eskalatu egiten dira, batez beste 500 puntuko banaketa normal bati eta 100 puntuko \ndesbiderapen tipiko bati erantzuteko moduan. \n\nPuntuazio horiek interpretatzen laguntzeko, ondoko kategoriak ezartzen dira irakurketa konpetentziarako: \n\n-1c maila baino txikiagoa: 189,33 puntu baino gutxiago \n-1c maila: 189,33 eta 262,03 arteko puntuazioa \n-1b maila: 262,04 eta 334,74 arteko puntuazioa \n-1. maila: 334,75 eta 407,46 arteko puntuazioa \n-2. maila: 407,47 eta 480,17 arteko puntuazioa \n-3. maila: 480,18 eta 552,88 arteko puntuazioa \n-4. maila: 552,89 eta 625,60 arteko puntuazioa \n-5. maila: 625,61 eta 698,31 arteko puntuazioa \n-6. maila: 698,31tik gorako puntuazioa  \n\nPertsona batek, gutxienez, irakurtzeko gaitasunaren 2. maila lortzen du, baldin eta, luzera ertaineko testu \nbaten ideia nagusia identifika badezake, bere atalen arteko harremanak uler baditzake, testuaren zati mugatu \nbaten esanahia oinarrizko inferentzien bidez interpreta badezake, informazio zati bat edo gehiago koka baditzake \npartzialki inplizituak diren hainbat irizpide kontuan hartuta, testuaren helburu orokorrari edo espezifikoari \nburuz gogoeta egin badezake, baieztapenak alderatu baditzake, eta baieztapen horien oinarrian dauden arrazoibideak \nebaluatu baditzake, testuaren eta kanpoko ezagutzaren arteko loturak ezarriz.\n\nMatematikako konpetentziarako kategoria hauek ezartzen dira: \n\n-1. maila baino txikiagoa: 357,77 puntu baino gutxiago \n-1. maila: 357,77 eta 420,06 arteko puntuazioa \n-2. maila: 420,07 eta 482,37 arteko puntuazioa \n-3. maila: 482,38 eta 544,67 arteko puntuazioa \n-4. maila: 544,68 eta 606,98 arteko puntuazioa \n-5. maila: 606,99 eta 669,29 arteko puntuazioa \n-6. maila: 669,29tik gorako puntuazioa \n\nPertsona batek matematikako konpetentziako 2. maila lortzen du, gutxienez, zuzeneko inferentzia baino eskatzen \nez duten testuinguruetako egoerak interpretatzen eta ezagutzen dakienean, iturri bakar batetik informazio \negokia ateratzen eta irudikatzeko modu bakarra erabiltzen dakienean, algoritmoak, formulak, prozedurak \nedo oinarrizko konbentzioak erabiltzen dituenean zenbaki osoekin lotutako problemak ebazteko, eta zuzeneko \narrazoibideak eta emaitzen hitzez hitzeko interpretazioak egiten baditu.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Adierazlearen helburua da hezkuntzako etapa egokietan edo horien amaieran irakurketaren eta matematikaren \nikaskuntzako gutxieneko emaitzak lortu dituzten haurren eta gazteen ehunekoa neurtzea. Zenbat eta handiagoa \nizan kopurua, orduan eta handiagoa izango da kasuan kasuko menderatzean (irakurketan edo matematikan) gutxienez \ngutxieneko gaitasuna lortzen duten haurren edo gazteen proportzioa. \n\nNazioz gaindiko ikaskuntza-ebaluazioen ikaskuntzaren emaitzak zuzenean konpara daitezke ikaskuntza-ebaluazio \ntransnazional berberetan parte hartu zuten herrialde guztietan. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.1.1&seriesCode=SE_TOT_PRFL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20LOWSEC%20%7C%20SKILL_READ\"> Beheko bigarren hezkuntzan irakurketako gutxieneko gaitasun-maila lortzen duten haur eta gazteen proportzioa (%) SE_TOT_PRFL</a> UNSTATS \n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.1.1&seriesCode=SE_TOT_PRFL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20LOWSEC%20%7C%20SKILL_MATH\"> Beheko bigarren hezkuntzan matematikan gutxieneko gaitasun-maila lortzen duten haur eta gazteen proportzioa (%) SE_TOT_PRFL</a> UNSTATS\n", "comparabilidad"=>"Eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-01-01.pdf\">Metadatuak 4-1-1.pdf </a>(ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomes</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.1.1: Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sex</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Not applicable</p>", "META_LAST_UPDATE__GLOBAL"=>"2022-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>The parity indices for this indicator are reported in SDG indicator 4.5.1.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Institute of Statistics (UIS)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Institute of Statistics (UIS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Percentage of children and young people achieving at least a minimum proficiency level in (i) reading and (ii) mathematics during primary education (Grade 2 or 3), at the end of primary education, and at the end of lower secondary education. The minimum proficiency level will be measured relative to new common reading and mathematics scales currently in development.</p>\n<p><strong>Concepts:</strong></p>\n<p><em>Minimum proficiency level (MPL)</em> is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. In September 2018, an agreement was reached on a verbal definition of the global minimum proficiency level of reference for each of the areas and domains of Indicator 4.1.1 as described in the <a href=\"http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/02/MPLs_revised_doc_20190204.docx\"><u>Minimum Proficiency Levels (MPLs): Outcomes of the consensus building meeting</u></a>.</p>\n<p><em>Minimum proficiency levels defined by each learning assessment</em></p>\n<p>To ensure comparability across learning assessments, a verbal definition of MPL for each domain and levels between cross-national assessments (CNAs) was established by conducting an analysis of the performance level descriptors (PLDs)<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup> of cross-national, regional, and community-led tests in reading and mathematics. The analysis was led and completed by the UIS and a consensus among experts on the proposed methodology was deemed adequate and pragmatic.</p>\n<p>The global MPL definitions for the domains of reading and mathematics are presented in Table 1.</p>\n<p> Table 1. Minimum proficiency levels defined by each learning assessment</p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Reading</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Educational Level</strong></p>\n      </td>\n      <td>\n        <p><strong>Descriptor</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Grade 2 </strong></p>\n      </td>\n      <td>\n        <p>They read and comprehend most of written words, particularly familiar ones, and extract explicit information from sentences.<strong> </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Grade 3</strong></p>\n      </td>\n      <td>\n        <p>Students read aloud written words accurately and fluently. They understand the overall meaning of sentences and short texts. Students identify the texts&#x2019; topic.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Grades 4 &amp; 6</strong></p>\n      </td>\n      <td>\n        <p>Students interpret and give some explanations about the main and secondary ideas in different types of texts. They establish connections between main ideas on a text and their personal experiences as well as general knowledge. <strong> </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Grades 8 &amp; 9</strong></p>\n      </td>\n      <td>\n        <p>Students establish connections between main ideas on different text types and the author&#x2019;s intentions. They reflect and draw conclusions based on the text.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Mathematics</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Educational Level</strong></p>\n      </td>\n      <td>\n        <p><strong>Descriptor</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Grades 2-3</strong></p>\n      </td>\n      <td>\n        <p>Students demonstrate skills in number sense and computation, shape recognition and spatial orientation.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Grades 4-6</strong></p>\n      </td>\n      <td>\n        <p>Students demonstrate skills in number sense and computation, basic measurement, reading, interpreting, and constructing graphs, spatial orientation, and number patterns. </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Grades 8 &amp; 9</strong></p>\n      </td>\n      <td>\n        <p>Students demonstrate skills in computation, application problems, matching tables and graphs, and making use of algebraic representations. </p>\n      </td>\n    </tr>\n  </tbody>\n</table><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> PLD: Performance level descriptors are descriptions of the performance levels to express the knowledge and skills required to achieve each performance level, by domain. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>This indicator expresses a <em>Minimum proficiency level (MPL) </em>that is the benchmark of basic knowledge in a domain (mathematics, reading, etc.) measured through learning assessments. In September 2018, an agreement was reached on a verbal definition of the global minimum proficiency level of reference for each of the areas and domains of Indicator 4.1.1 as described in the <a href=\"http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/02/MPLs_revised_doc_20190204.docx\"><u>Minimum Proficiency Levels (MPLs): Outcomes of the consensus building meeting</u></a><u>.</u></p>", "SOURCE_TYPE__GLOBAL"=>"<p>Type of data sources: In school and population-based learning assessments.</p>\n<p><strong>Table 2. How reporting is structured?</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td rowspan=\"2\">\n        <p> </p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>In-school based</strong></p>\n      </td>\n      <td rowspan=\"2\">\n        <p><strong>Household Based Surveys</strong></p>\n      </td>\n      <td rowspan=\"2\">\n        <p><strong>Grade</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Cross-national</strong></p>\n      </td>\n      <td>\n        <p><strong>National </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"4\">\n        <p><strong>Grade 2 or 3</strong></p>\n      </td>\n      <td>\n        <p>LLECE</p>\n      </td>\n      <td rowspan=\"4\">\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>MICS6</p>\n      </td>\n      <td rowspan=\"4\">\n        <p>2/3 plus one year when primary lasts more than 4 years according to ISCED level of the country, except for TIMSS/PIRLS grade 4, which are mapped to the end of primary when primary lasts six or less years.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>PASEC</p>\n      </td>\n      <td>\n        <p>EGRA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>TIMSS </p>\n      </td>\n      <td>\n        <p>EGMA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>PIRLS </p>\n      </td>\n      <td>\n        <p>PAL network </p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"7\">\n        <p><strong>End of primary</strong></p>\n      </td>\n      <td>\n        <p>LLECE</p>\n      </td>\n      <td rowspan=\"7\">\n        <p>Yes</p>\n      </td>\n      <td rowspan=\"7\">\n        <p>PAL network</p>\n      </td>\n      <td rowspan=\"7\">\n        <p>plus or minus one year of last year of primary according to ISCED level of the country except for TIMSS/PIRLS grade 4, which are mapped to the end of primary when primary lasts six or less years. </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>PASEC</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>TIMSS </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>PIRLS </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>PILNA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>SEAMEO</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>SACMEQ</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"3\">\n        <p><strong>End of lower secondary</strong></p>\n      </td>\n      <td>\n        <p>PISA</p>\n      </td>\n      <td rowspan=\"3\">\n        <p>Yes</p>\n      </td>\n      <td rowspan=\"3\">\n        <p><a href=\"https://www.younglives.org.uk/\"><u>Young Lives</u></a></p>\n      </td>\n      <td rowspan=\"3\">\n        <p>plus two or minus one of last year of lower secondary according to ISCED level of the country</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>PISA-D</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>TIMSS </p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Definition of minimum level until 2018 release</strong></p>\n      </td>\n      <td colspan=\"3\">\n        <p>Those defined by each assessment by point of measurement and domain.</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Definition of minimum level from 2019 </strong></p>\n      </td>\n      <td colspan=\"3\">\n        <p>According to alignment as adopted by Global Alliance to Monitoring Learning (<a href=\"http://gaml.uis.unesco.org/\"><u>GAML</u></a>) and Technical Cooperation Group (<a href=\"http://tcg.uis.unesco.org/\"><u>TCG</u></a>)</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Grade for end of primary and end of lower secondary</strong></p>\n      </td>\n      <td colspan=\"3\">\n        <p>As defined by the ISCED levels in each country</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Validation </strong></p>\n      </td>\n      <td colspan=\"3\">\n        <p>Sent from UIS for countries&#x2019; approval</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "COLL_METHOD__GLOBAL"=>"<p>The UIS compiles information from data source providers at international level and from countries at the national level.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data collection is rolling during the year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Biannual UIS data release (March and September)</p>", "DATA_SOURCE__GLOBAL"=>"<p><strong>School-Based assessments</strong></p>\n<ul>\n  <li><u>International Large-Scale Assessments</u> are reported to the UIS by cross-national organisations (LLECE, PASEC, TIMSS, and PIRLS). Typically, Cross-National Large-Scale Assessment, either regional or international, define various performance levels, and report as well the mean and standard deviation. They choose as well one level as the cut-off point that defines what children/youth are below or above level.</li>\n  <li><u>Regional assessments</u>: PASEC, SACMEQ, ERCE, PILNA, SEAMEO.</li>\n  <li><u>National Large-Scale Assessments</u> either sample- or census-based. Countries should report the proportion of students by level of competency for each domain indicating as well the minimum proficiency level, when it is defined by the national assessment. EGRA and EGMA as reported by USAID or individual countries. </li>\n</ul>\n<p><strong>Household-Based surveys</strong></p>\n<ul>\n  <li>MICS6: reported to the UIS by UNICEF</li>\n  <li>Pal Network: reported to the UIS by Pal Network</li>\n</ul>", "COMPILING_ORG__GLOBAL"=>"<p>UNESCO Institute of Statistics (UIS)</p>", "INST_MANDATE__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The Institute produces internationally comparable data and methodologies in the fields of education, science, culture and communication for countries at all stages of development.</p>\n<p>The <a href=\"https://www.moe.gov.bn/DocumentDownloads/Education%202030/Education2030.pdf\"><u>Education 2030 Framework for Action &#xA7;100</u></a> has clearly stated that: &#x201C;<em>In recognition of the importance of harmonization of monitoring and reporting, the UIS will remain the official source of cross-nationally comparable data on education. It will continue to produce international monitoring indicators based on its annual education survey and on other data sources that guarantee international comparability for more than 200 countries and territories. In addition to collecting data, the UIS will work with partners to develop new indicators, statistical approaches and monitoring tools to better assess progress across the targets related to UNESCO&#x2019;s mandate, working in coordination with the SDG-Education 2030 SC&#x201D;.</em></p>", "RATIONALE__GLOBAL"=>"<p>The indicator aims to measure the percentage of children and young people who have achieved the minimum learning outcomes in reading and mathematics during or at the end of the relevant stages of education.</p>\n<p>The higher the figure, the higher the proportion of children and/or young people reaching at least minimum proficiency in the respective domain (reading or mathematic) with the limitations indicated under the &#x201C;Comments and limitations&#x201D; section.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Learning outcomes from cross-national learning assessment are directly comparable for all countries which participated in the same cross-national learning assessments. However, these outcomes are not comparable across different cross-national learning assessments or with national learning assessments. A level of comparability of learning outcomes across assessments could be achieved by using different methodologies, each with varying standard errors. The UIS has implemented a mechanism of comparability through a consensus on the definition of the skills and contents. The comparability of learning outcomes over time has additional complications, which require, ideally, to design and implement a set of comparable items as anchors in advance. Methodological developments are underway to address comparability of assessments outcomes over time.</p>", "DATA_COMP__GLOBAL"=>"<p>The number of children and/or young people at the relevant stage of education <strong>n</strong> in year <strong>t</strong> achieving or exceeding the pre-defined proficiency level in subject s expressed as a percentage of the number of children and/or young people at stage of education <strong>n</strong>, in year <strong>t</strong>, in any proficiency level in subject <strong>s</strong>.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>M</mi>\n        <mi>P</mi>\n        <mi>L</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n        <mo>,</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mo>,</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>M</mi>\n        <mi>P</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mo>,</mo>\n        <mi>n</mi>\n        <mo>,</mo>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>P</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n            <mo>,</mo>\n            <mi>&amp;nbsp;</mi>\n            <mi>n</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where: </p>\n<p><em>MP<sub>t,n,s</sub></em> = the number of children and young people at stage of education n, in year t, who have achieved or exceeded the minimum proficiency level in subject s.</p>\n<p><em>P<sub>t,n</sub></em> = the total number of children and young people at stage of education n, in year t.</p>\n<p><em>n</em> = the stage of education that was assessed.</p>\n<p><em>s</em> = the subject that was assessed (reading or mathematics).</p>\n<p><strong>Harmonize various data sources</strong></p>\n<p>To address the challenges posed by the limited capacity of some countries to implement cross-national, regional, and national assessments, actions have been taken by the UIS and its partners. The strategies are used according to its level of precision and following a <a href=\"http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/05/GAML6-WD-2-Protocol-for-reporting-4.1.1_v1.pdf\"><u>reporting protocol</u></a> that includes the national assessments under specific circumstances. </p>\n<p><strong>Completion status </strong></p>\n<p>Combining completion rates with learning outcomes improves our understanding of progress towards Target 4.1. Almost all information regarding learning is school-based and does not take into account the completion of the level. The inclusion of completion in the global list offers to report according to completion status. The greatest differences between the SDG 4.1.1 on learning before completion and the disaggregation by completion are found in regions or countries with lower completion and enrolment rates because the adjusted (or children completing and learning) indicator is based on a quality-adjusted completion rate. This also explains why the largest differences occur at the lower-secondary level. Globally, 47% of lower-secondary students achieve minimum proficiency in reading according to the original SDG 4.1.1 Indicator, but the value for the adjusted indicator would fall to 34% of adolescents completing lower secondary and achieving minimum proficiency in mathematics. References <a href=\"http://tcg.uis.unesco.org/wp-content/uploads/sites/4/2020/10/WG-GAML-3-Children-Completing-and-Learning.pdf\">here</a>.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The quality control is granted by the setting of a Review Panel to discuss any problem/disagreement on implementation. The Review Panel is constituted by regionally representative experts on learning.</p>", "ADJUSTMENT__GLOBAL"=>"<p>As currently measured, most learning assessments have different methodologies for establishing a Minimum Proficiency level (MPL). The UIS and GAM establish standardization guidelines to guide the choice of the minimum thresholds based on the frameworks of each assessment program. The most critical decision is to choose in each assessment a level for international reporting that is consistent with the international definition of MPL. In the case of some assessment program, it means choosing a different level than the one the assessment program had been using for reporting results.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values are not imputed.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Missing values are not imputed.</p>", "REG_AGG__GLOBAL"=>"<p>Population weighted averages.</p>", "DOC_METHOD__GLOBAL"=>"<p>The UIS has elaborated guidance for the countries regarding the contents, the procedures and the reporting in the Global Alliance to monitor learning <a href=\"http://gaml.uis.unesco.org/learning-poverty/\">microsite</a>. </p>\n<p>In terms of selection of data sources, the <a href=\"http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/05/GAML6-WD-2-Protocol-for-reporting-4.1.1_v1.pdf\">Protocol for Reporting on SDG Global Indicator 4.1.1</a> is guiding the countries about the selection of the assessment program. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The UIS maintains a global database on learning assessments in basic education. For transparency purposes, the inclusion</p>\n<p> of a data point in the database is completed by following a protocol and is reviewed by UIS technical focal points to ensure consistency and overall data quality, based on objective criteria to ensure that only the most recent and reliable information are included in the database.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Information produced by the cross-national and national assessment programs are described in their manuals. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The criteria to ensure the quality and standardization of the data are: the data sources must include adequate documentation; data values should be representative at the national population level and should otherwise be included in a footnote; data values are based on a sufficiently large sample; the learning assessment framework covers the minimum set of content in the global content framework and the proficiency levels are aligned with the minimum proficiency level (MPL) as defined in the global proficiency framework; and the data are plausible and based on trends and consistency with previously published or reported estimates for the indicator.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability: </strong></p>\n<p>Data available at the national level.</p>\n<p><strong>Time series: </strong></p>\n<p>Data available since 2000.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Indicator is published disaggregated by sex and completion status (Global Indicator 4.1.2). Other disaggregation such as location, socio-economic status, immigrant status, ethnicity and language of the test at home are based on data produced by international organizations administering cross learning assessment detailed in the <a href=\"http://tcg.uis.unesco.org/wp-content/uploads/sites/4/2020/09/metadata-4.1.1.pdf\"><u>expanded metadata document</u></a> and validated by countries. Parity indexes are estimated in the reporting of Indicator 4.5.1.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not yet applicable. Data are reported at the national level only.</p>", "OTHER_DOC__GLOBAL"=>"<p>Minimum Proficiency Levels</p>\n<p><a href=\"http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/07/MPLs_revised_doc_20190506_v2.pdf\"><u>http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/07/MPLs_revised_doc_20190506_v2.pdf</u></a></p>\n<p>Costs and Benefits of Different Approaches to Measuring the Learning Proficiency of Students (SDG Indicator 4.1.1)</p>\n<p><a href=\"http://uis.unesco.org/sites/default/files/documents/ip53-costs-benefits-approaches-measuring-proficiency-2019-en.pdf\"><u>http://uis.unesco.org/sites/default/files/documents/ip53-costs-benefits-approaches-measuring-proficiency-2019-en.pdf</u></a></p>\n<p>Protocol for Reporting on SDG Global Indicator 4.1.1</p>\n<p><a href=\"http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/05/GAML6-WD-2-Protocol-for-reporting-4.1.1_v1.pdf\"><u>http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/05/GAML6-WD-2-Protocol-for-reporting-4.1.1_v1.pdf</u></a></p>\n<p>Global Proficiency Framework for Reading and Mathematics - Grade 2 to 6</p>\n<p><a href=\"http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/05/Global-Proficiency-Framework-18Oct2019_KD.pdf\"><u>http://gaml.uis.unesco.org/wp-content/uploads/sites/2/2019/05/Global-Proficiency-Framework-18Oct2019_KD.pdf</u></a></p>", "indicator_sort_order"=>"04-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.1.2", "slug"=>"4-1-2", "name"=>"Tasa de finalización (educación primaria, educación secundaria inferior y educación secundaria superior)", "url"=>"/site/es/4-1-2/", "sort"=>"040102", "goal_number"=>"4", "target_number"=>"4.1", "global"=>{"name"=>"Tasa de finalización (educación primaria, educación secundaria inferior y educación secundaria superior)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de finalización (educación secundaria inferior y educación secundaria superior)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de finalización (educación primaria, educación secundaria inferior y educación secundaria superior)", "indicator_number"=>"4.1.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_57/opt_1/ti_encuesta-de-poblacion-en-relacion-con-la-actividad/temas.html", "url_text"=>"Encuesta de población en relación con la actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Tasa de finalización (educación secundaria inferior y educación secundaria superior)", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.1- De aquí a 2030, asegurar que todas las niñas y todos los niños terminen la enseñanza primaria y secundaria, que ha de ser gratuita, equitativa y de calidad y producir resultados de aprendizaje pertinentes y efectivos", "definicion"=>"Proporción de personas entre 18 y 20 años que han completado al menos la primera etapa de enseñanza  secundaria (CINE 2) respecto al total de personas entre 18 y 20 años, y proporción de personas entre  20 y 22 años que han completado al menos la segunda etapa de enseñanza secundaria (CINE 3) respecto  al total de personas entre 20 y 22 años.", "formula"=>"\n$$PCINE_n = \\frac{P_{n,Edad_a+3t5}}{P_{Edad_a+3t5}}$$\n\ndonde:\n\n$P_{n,Edad_a+3t5} =$ población con edades entre $3$ y $5$ años por encima de la edad oficial de ingreso $a$ en el último grado del nivel $n$ de educación que ha completado dicho nivel\n\n$P_{Edad_a+3t5} =$ población con edades entre $3$ y $5$ años por encima de la edad oficial de ingreso $a$ en el último grado del nivel $n$ de educación\n\n$n =$ Nivel CINE 2 (primera etapa de la educación secundaria) y CINE 3 (segunda etapa de la educación secundaria)\n", "desagregacion"=>"Nivel educativo: primera etapa educación secundaria; segunda etapa educación secundaria\n\nEdad\n", "periodicidad"=>"Anual", "observaciones"=>"\nCINE: Clasificación Internacional Normalizada de la Educación (UNESCO - 2011). \nRepresenta una clasificación estadística de referencia que permite ordenar los \nprogramas educativos y sus certificaciones correspondientes por niveles de educación\ny campos de estudio.\n\nLas definiciones y los conceptos básicos de la CINE están concebidos\npara ser válidos a escala internacional e incluir toda la gama de sistemas educativos. \nSu elaboración es el resultado de un acuerdo internacional adoptado formalmente por \nla Conferencia General de los Estados Miembros de la UNESCO.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador se menciona explícitamente en el texto de la meta 4.1: “garantizar que todos los niños \ny niñas terminen […] la enseñanza primaria y secundaria”. Una tasa de finalización del 100% o cercana \na ella indica que todos o la mayoría de los niños y adolescentes han completado un nivel de educación \ncuando tienen entre 3 y 5 años más que la edad oficial de ingreso al último grado de ese nivel de\n educación. Una tasa de finalización baja indica un ingreso bajo o tardío a un nivel de educación\n determinado, un alto índice de abandono, un alto índice de repetición, finalización tardía o \nuna combinación de estos factores.\n\nLa tasa de finalización puede utilizarse como indicador independiente o en combinación con el indicador \n4.1.1 de los ODS (proporción de niños y jóvenes (a) en segundo o tercer grado; (b) al final de la educación \nprimaria; y (c) al final de la educación secundaria inferior que alcanzan al menos un nivel mínimo de \ncompetencia en (i) lectura y (ii) matemáticas. La combinación de la tasa de finalización con el \nindicador 4.1.1 proporciona información sobre el porcentaje de niños o jóvenes de una cohorte que alcanzan \nun nivel mínimo de competencia, y no solo sobre el porcentaje de niños en la escuela que alcanzan la\n competencia mínima.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.1.2&seriesCode=SE_TOT_CPLR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA%20%7C%20BOTHSEX%20%7C%20UPPSEC%20%7C%20_T\">Tasa de finalización en el nivel educativo secundaria superior (%) SE_TOT_CPLR</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-01-02.pdf\">Metadatos 4-1-2.pdf </a>(solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Tasa de finalización (educación secundaria inferior y educación secundaria superior)", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.1- De aquí a 2030, asegurar que todas las niñas y todos los niños terminen la enseñanza primaria y secundaria, que ha de ser gratuita, equitativa y de calidad y producir resultados de aprendizaje pertinentes y efectivos", "definicion"=>"\nProportion of people aged 18-20 who have completed at least the lower secondary  education (ISCED 2) out of the total number of people aged 18-20, and proportion  of people aged 20-22 who have completed at least the upper secondary education  (ISCED 3) out of the total number of people aged 20-22.", "formula"=>"\n$$PCINE_n = \\frac{P_{n,age_a+3t5}}{P_{age_a+3t5}}$$\n\nwhere:\n\n$P_{n,age_a+3t5} =$ population aged between $3$ y $5$ years above the $a$ official age of entry in the last grade of education level $n$ who has completed that level\n\n$P_{age_a+3t5} =$ population aged between $3$ y $5$ years above the $a$ official age of entry in the last grade of education level $n$\n\n$n =$ ISCED level 2 (lower secondary education) and ISCED level 3 (upper secondary education)\n", "desagregacion"=>"Educational level: first and second stage of secondary education \n\nAge\n", "periodicidad"=>"Anual", "observaciones"=>"\nISCED: International Standard Classification of Education (UNESCO - 2011).\nIt represents a statistical reference classification that allows the organization of educational \nprograms and their corresponding qualifications by educational level and field of study. \n\nThe definitions and basic concepts of ISCED are designed to be internationally valid and to encompass \nthe full range of educational systems. Its development is the result of an international agreement \nformally adopted by the General Conference of UNESCO Member States.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The indicator is explicitly referenced in the text of target 4.1: ‘ensure that all girls and boys complete […] \nprimary and secondary education’. A completion rate at or near 100% indicates that all or most children \nand adolescents have completed a level of education by the time they are 3 to 5 years older than the \nofficial age of entry into the last grade of that level of education. A low completion rate indicates low or \ndelayed entry into a given level of education, high drop-out, high repetition, late completion, or a \ncombination of these factors. \n\nLa tasa de finalización puede utilizarse como indicador independiente o en combinación con el indicador \n4.1.1 de los ODS (proporción de niños y jóvenes (a) en segundo o tercer grado; (b) al final de la educación \nprimaria; y (c) al final de la educación secundaria inferior que alcanzan al menos un nivel mínimo de \ncompetencia en (i) lectura y (ii) matemáticas. La combinación de la tasa de finalización con el \nindicador 4.1.1 proporciona información sobre el porcentaje de niños o jóvenes de una cohorte que alcanzan \nun nivel mínimo de competencia, y no solo sobre el porcentaje de niños en la escuela que alcanzan la\ncompetencia mínima.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.1.2&seriesCode=SE_TOT_CPLR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA%20%7C%20BOTHSEX%20%7C%20UPPSEC%20%7C%20_T\">Completion rate at upper secondary education level (%) SE_TOT_CPLR</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-01-02.pdf\">Metadata 4-1-2.pdf </a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Tasa de finalización (educación secundaria inferior y educación secundaria superior)", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.1- De aquí a 2030, asegurar que todas las niñas y todos los niños terminen la enseñanza primaria y secundaria, que ha de ser gratuita, equitativa y de calidad y producir resultados de aprendizaje pertinentes y efectivos", "definicion"=>"Gutxienez bigarren hezkuntzako lehen zikloa (Hezkuntzaren Nazioarteko Sailkapen Normalizatua 2)  amaitu duten 18 eta 20 urte bitarteko pertsonen proportzioa, 18 eta 20 urte bitarteko pertsona  guztiekiko, eta gutxienez bigarren hezkuntzako bigarren zikloa (HNSN 3) amaitu  duten 20 eta 22 urte bitarteko pertsonen proportzioa, 20 eta 22 urte bitarteko pertsona guztiekiko. ", "formula"=>"\n$$PCINE_n = \\frac{P_{n,Adina_a+3t5}}{P_{Adina_a+3t5}}$$\n\nnon:\n\n$P_{n,Adina_a+3t5} =$ $n$ zikloko azken urtean sartzeko adin ofiziala baino $3$ - $5$ urte gehiago izanik, maila hori gainditu duen biztanleria\n\n$P_{Adina_a+3t5} =$ $n$ zikloko azken urtean sartzeko adin ofiziala baino $3$ - $5$ urte gehiagoko biztanleria\n\n$n =$ HNSN 2 (Bigarren Hezkuntzako lehen zikloa) eta HNSN 3 (Bigarren Hezkuntzako bigarren zikloa)\n", "desagregacion"=>"Hezkuntza-maila: Bigarren Hezkuntzako lehen zikoa; Bigarren Hezkuntzako bigarren zikloa\n\nAdina\n", "periodicidad"=>"Anual", "observaciones"=>"\nHNSN: Hezkuntzaren Nazioarteko Sailkapen Normalizatua (UNESCO - 2011). \nErreferentziako sailkapen estatistiko bat da, hezkuntza-programak eta dagozkien ziurtagiriak \nhezkuntza-mailen eta ikasketa-eremuen arabera sailkatzeko aukera ematen duena.\n\nHNSNren definizioak eta oinarrizko kontzeptuak nazioartean baliozkoak izateko eta hezkuntza-sistema guztiak \nsartzeko pentsatuta daude. UNESCOko Estatu Kideen Konferentzia Orokorrak formalki onartutako nazioarteko \nakordio baten emaitza da.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Adierazlea berariaz aipatzen da 4.1 xedearen testuan: “bermatzea haur guztiek […] lehen eta bigarren hezkuntza \namaitzen dituztela”. % 100 edo horren bueltako amaiera-tasak adierazten du haur eta nerabe guztiek edo gehienek \namaitu dutela hezkuntza-maila bat, hezkuntza-maila horren azken gradura sartzeko adin ofiziala baino 3-5 urte \ngehiago dituztenean. Amaiera-tasa baxuak adierazten du hezkuntza-maila jakin batera gutxi edo berandu sartu direla, \nabandonu-indizea handia dela, errepikapen-indizea handia dela, berandu amaitu dela edo faktore horien konbinazioaren \nbat dagoela. \n\nAmaiera-tasa adierazle independente gisa erabil daiteke, edo GJHen 4.1.1 adierazlearekin konbinatuta (haur eta gazteen \nproportzioa (a) bigarren edo hirugarren graduan; (b) lehen hezkuntzaren amaieran; eta (c) beheko bigarren hezkuntzaren \namaieran, gutxienez (i) irakurketan eta (ii) matematikan gutxieneko gaitasun-maila lortuz). Amaiera-tasa 4.1.1 \nadierazlearekin konbinatzeak informazioa ematen du gutxieneko gaitasun-maila lortzen duten kohorte bateko haurren edo \ngazteen ehunekoari buruz, eta ez soilik gutxieneko gaitasuna lortzen duten eskolako haurren ehunekoari buruz. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.1.2&seriesCode=SE_TOT_CPLR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA%20%7C%20BOTHSEX%20%7C%20UPPSEC%20%7C%20_T\">Goi-mailako bigarren hezkuntza amaitze-tasa (%) SE_TOT_CPLR</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-01-02.pdf\">Metadatuak 4-1-2.pdf </a>(ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomes</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.1.2: Completion rate (primary education, lower secondary education, upper secondary education)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Not applicable </p>", "META_LAST_UPDATE__GLOBAL"=>"2022-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>4.1.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Percentage of a cohort of children or young people aged 3-5 years above the intended age for the last grade of each level of education who have completed that grade.</p>\n<p><strong>Concepts:</strong></p>\n<p>The intended age for the last grade of each level of education is the age at which pupils would enter the grade if they had started school at the official primary entrance age, had studied full-time and had progressed without repeating or skipping a grade. </p>\n<p>For example, if the official age of entry into primary education is 6 years, and if primary education has 6 grades, the intended age for the last grade of primary education is 11 years. In this case, 14-16 years (11 + 3 = 14 and 11 + 5 = 16) would be the reference age group for calculation of the primary completion rate.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The International Standard Classification of Education (ISCED) is used to define primary, lower secondary and upper secondary education. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The data can be obtained from population censuses and household surveys that collect information on the highest level of education and/or grade completed by children and young people in a household. Typical questions in a survey to collect data on educational attainment are:</p>\n<p>- What is the highest level of education [name of household member] has attended?</p>\n<p>- What is the highest grade of education [name of household member] has completed at that level?</p>\n<p>Sources include publicly available data from Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), European Union Statistics on Income and Living Condition (EU-SILC), the Integrated Public Use Microdata Series (IPUMS), and national household surveys and censuses.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data from all publicly available household surveys and censuses with the required information are compiled and used to calculate the completion rate. For international comparability, national data are mapped to the ISCED before indicator calculation.</p>\n<p>Indicator values intended for dissemination and addition to the global SDG Indicators Database are submitted by the UNESCO Institute for Statistics to National Statistical Offices (NSOs), Ministries of Education or other relevant agencies in individual countries for their review and feedback.</p>", "FREQ_COLL__GLOBAL"=>"<p>Household survey and census datasets are publicly available from the sources described above and do not follow any particular release calendar.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Household survey and census datasets are publicly available from the sources described above and do not follow any particular release calendar.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Household survey and census datasets are publicly available from the sources described above and national statistical agencies.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "INST_MANDATE__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The Institute produces internationally comparable data and methodologies in the fields of education, science, culture and communication for countries at all stages of development.</p>\n<p>The <a href=\"https://www.moe.gov.bn/DocumentDownloads/Education%202030/Education2030.pdf\"><u>Education 2030 Framework for Action &#xA7;100</u></a> has clearly stated that: &#x201C;<em>In recognition of the importance of harmonization of monitoring and reporting, the UIS will remain the official source of cross-nationally comparable data on education. It will continue to produce international monitoring indicators based on its annual education survey and on other data sources that guarantee international comparability for more than 200 countries and territories. In addition to collecting data, the UIS will work with partners to develop new indicators, statistical approaches and monitoring tools to better assess progress across the targets related to UNESCO&#x2019;s mandate, working in coordination with the SDG-Education 2030 SC&#x201D;.</em></p>", "RATIONALE__GLOBAL"=>"<p>The indicator is explicitly referenced in the text of target 4.1: &#x2018;ensure that all girls and boys complete [&#x2026;] primary and secondary education&#x2019;. A completion rate at or near 100% indicates that all or most children and adolescents have completed a level of education by the time they are 3 to 5 years older than the official age of entry into the last grade of that level of education. A low completion rate indicates low or delayed entry into a given level of education, high drop-out, high repetition, late completion, or a combination of these factors.</p>\n<p>The completion rate can be used either as a self-standing indicator or in combination with SDG indicator 4.1.1 (proportion of children and young people (a) in Grade 2 or 3; (b) at the end of primary education; and (c) at the end of lower secondary education achieving at least a minimum proficiency level in (i) reading and (ii) mathematics). Combining the completion rate with indicator 4.1.1 provides information on the percentage of children or young people in a cohort who achieve a minimum level of proficiency, and not only on the percentage of children in school who achieve minimum proficiency.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Three common issues affect the indicator. First, the age group 3-5 years above the official age of entry into the last grade for a given level of education was selected for the calculation of the completion rate to allow for some delayed entry or repetition. In countries where entry can occur very late or where repetition is common, some children or adolescents in the age group examined may still attend school and the eventual rate of completion may therefore be underestimated. Second, as the indicator is calculated from household survey data, it is subject to time lag in the availability of data. Third, when multiple surveys are available, they may provide conflicting information due to the possible presence of sampling and non-sampling errors in survey data. </p>\n<p>Responding to a request by the Technical Cooperation Group (TCG) on the Indicators for SDG 4 - Education 2030, a refinement of the methodology to model completion rate estimates has been developed (Barakat et al. 2021), following an approach similar to that used for the estimation of child mortality rates. The model ensures that these common challenges with household survey data, such as timeliness and sampling or non-sampling errors are addressed to provide annual, up-to-date (through short-term projections) and more robust data, including for children and youth who complete each level later than 3-5 years above the official age of entry into the last grade. </p>", "DATA_COMP__GLOBAL"=>"<p>The number of persons in the relevant age group who have completed the last grade of a given level of education is divided by the total population (in the survey sample) of the same age group.</p>\n<p>Formula:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n        <mi>R</mi>\n      </mrow>\n      <mrow>\n        <mi>n</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>P</mi>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>C</mi>\n              </mrow>\n              <mrow>\n                <mi>n</mi>\n              </mrow>\n            </msub>\n            <mo>,</mo>\n            <msub>\n              <mrow>\n                <mi>A</mi>\n                <mi>G</mi>\n              </mrow>\n              <mrow>\n                <mi>a</mi>\n                <mo>+</mo>\n                <mn>3</mn>\n                <mi>t</mi>\n                <mn>5</mn>\n              </mrow>\n            </msub>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>P</mi>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>A</mi>\n                <mi>G</mi>\n              </mrow>\n              <mrow>\n                <mi>a</mi>\n                <mo>+</mo>\n                <mn>3</mn>\n                <mi>t</mi>\n                <mn>5</mn>\n              </mrow>\n            </msub>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n        <mi>R</mi>\n      </mrow>\n      <mrow>\n        <mi>n</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n  </math> completion rate for level n of education</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mi>n</mi>\n          </mrow>\n        </msub>\n        <mo>,</mo>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n          </mrow>\n          <mrow>\n            <mi>a</mi>\n            <mo>+</mo>\n            <mn>3</mn>\n            <mi>t</mi>\n            <mn>5</mn>\n          </mrow>\n        </msub>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n  </math> population aged 3 to 5 years above the official entrance age <em>a</em> into the last grade of level <em>n</em> of education who completed level <em>n</em></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n          </mrow>\n          <mrow>\n            <mi>a</mi>\n            <mo>+</mo>\n            <mn>3</mn>\n            <mi>t</mi>\n            <mn>5</mn>\n          </mrow>\n        </msub>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n  </math> population aged 3 to 5 years above the official entrance age <em>a</em> into the last grade of level <em>n</em> of education</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>n</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n  </math>ISCED level 1 (primary education), 2 (lower secondary education), or 3 (upper secondary education)</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The UNESCO Institute for Statistics shares all indicator values and notes on methodology with National Statistical Offices (NSO), Ministries of Education, or other relevant agencies in individual countries for their review, feedback and validation before the publication of the data.</p>\n<p> </p>\n<p>In a different validation and capacity building exercise, the completion rate model estimates will be consulted with countries. This annual consultation process will give each country&#x2019;s education ministry and NSO the opportunity to review and provide feedback on all data inputs, the estimation methodology and the draft estimates.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Description of any adjustments with respect to use of standard classifications and harmonization of breakdowns for age group and other dimensions, or adjustments made for compliance with specific international or national definitions. To take into account countries where the eventual rate of completion is underestimated because entry occurs very late or repetition is common, estimated completion rates are also available for cohorts of children or young people aged up to 8 years above the intended age for the last grade of each level of education.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>The completion rate can be calculated from older cohorts who are outside of the age bracket specified in the definition of the indicator to obtain estimates for different years. Gaps in national time series can be imputed using the aforementioned model to estimate the completion rate.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>See above.</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional estimates of the primary, lower secondary and upper secondary completion rate are derived by using the national population in the respective age groups as weights for aggregation of national values.</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries can calculate the completion rate using the methodology described in this document. ISCED mappings that help countries report their data in an internationally comparable framework are available on the website of the UNESCO Institute for Statistics (<a href=\"http://uis.unesco.org/en/isced-mappings\">http://uis.unesco.org/en/isced-mappings</a>).</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The global database with completion rates is maintained by the UIS and the Global Education Monitoring Report. The UIS sets standards, develops questionnaires and quality control protocols for country data reporting, and maintains the global database on the structure of national education systems.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The process for quality assurance includes review of survey documentation, calculation of measures of reliability, examination of consistency of indicator values derived from different sources and, if necessary, consultation with data providers.</p>\n<p>Before its annual data release and addition to the global SDG Indicators Database, the UNESCO Institute for Statistics submits all indicator values and notes on methodology to National Statistical Offices, Ministries of Education or other relevant agencies in individual countries for their review and feedback.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Accurate data on the structure of the national education system and on educational attainment by single year of age are needed for calculating this indicator. Criteria for quality assessment include: data sources must include proper documentation; data values must be representative at the national population level and, if not, should be footnoted; data values are based on a sufficiently large sample; data are plausible and based on trends and consistency with previously published/reported values for the indicator.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The primary completion rate is currently available for 150 Member States, representing 77% of all Member States. The lower secondary completion rate is available for 155 Member States, representing 80% of all Member States. Coverage for the upper secondary completion rate is similar, with data for 155 Member States, representing 80% of all Member States. The countries with completion rates are home to more than 90% of the global population.</p>\n<p><strong>Time series:</strong></p>\n<p>The completion rate is available for the years since 2000. National time series of raw data are incomplete due to the infrequent implementation of household surveys and censuses but <a id=\"OLE_LINK512\"></a><a id=\"OLE_LINK513\"></a>time series without gaps are available through the aforementioned reconstructed model-based estimates of the completion rate.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator is disaggregated by sex, location, wealth and other dimensions specified in global indicator 4.5.1 (parity index). Model-based estimates are disaggregated by sex.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>None.</p>", "OTHER_DOC__GLOBAL"=>"<p>Barakat, B., Dharamshi, A., Alkema, L., &amp; Antoninis, M. (2021). Adjusted Bayesian Completion Rates (ABC) Estimation. <em>SocArXiv</em>. https://doi.org/https://doi.org/10.31235/osf.io/at368</p>\n<p>UNESCO Institute for Statistics (UIS). 2019. UIS.Stat online database.</p>\n<ul>\n  <li>Primary Completion rate, Lower secondary completion rate, and Upper secondary completion rate: <a href=\"http://data.uis.unesco.org/?lang=en&amp;SubSessionId=c3260ae3-3921-4be9-bae6-e0138dbff642&amp;themetreeid=-200\">http://data.uis.unesco.org/?lang=en&amp;SubSessionId=c3260ae3-3921-4be9-bae6-e0138dbff642&amp;themetreeid=-200</a></li>\n</ul>\n<p>UNESCO Institute for Statistics (UIS) and Global Education Monitoring Report. 2019. World Inequality Database on Education (WIDE).</p>\n<ul>\n  <li>Primary completion rate: <a href=\"https://www.education-inequalities.org/indicators/comp_prim_v2\">https://www.education-inequalities.org/indicators/comp_prim_v2</a></li>\n  <li>Lower secondary completion rate: <a href=\"https://www.education-inequalities.org/indicators/comp_lowsec_v2\">https://www.education-inequalities.org/indicators/comp_lowsec_v2</a></li>\n  <li>Upper secondary completion rate: <a href=\"https://www.education-inequalities.org/indicators/comp_upsec_v2\">https://www.education-inequalities.org/indicators/comp_upsec_v2</a> </li>\n</ul>", "indicator_sort_order"=>"04-01-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.1.E1", "slug"=>"4-1-E1", "name"=>"Tasa de abandono escolar prematuro de la población de 18-24 años", "url"=>"/site/es/4-1-E1/", "sort"=>"0401E1", "goal_number"=>"4", "target_number"=>"4.1", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[{"series"=>"", "unit"=>"", "label_content"=>"Objetivo europeo", "value"=>9}], "graph_title"=>"Tasa de abandono escolar prematuro de la población de 18-24 años", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Tasa de abandono escolar prematuro de la población de 18-24 años", "indicator_number"=>"4.1.E1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Meta:</b> Menos de 9% en el año 2030", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_57/opt_1/ti_encuesta-de-poblacion-en-relacion-con-la-actividad/temas.html", "url_text"=>"Encuesta de población en relación con la actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Tasa de abandono escolar prematuro de la población de 18-24 años", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.1- De aquí a 2030, asegurar que todas las niñas y todos los niños terminen la enseñanza primaria y secundaria, que ha de ser gratuita, equitativa y de calidad y producir resultados de aprendizaje pertinentes y efectivos", "definicion"=>"Porcentaje de población que ha completado como máximo la primera etapa de la educación  secundaria y no sigue ningún estudio o formación", "formula"=>"\n$$PP_{abandono}^{t}  = \\frac{P_{abandono\\, 18-24}^{t}}{P_{18-24}^{t}}$$\n\ndonde:\n\n$P_{abandono\\, 18-24}^{t} =$ población de 18 a 24 años que ha completado como máximo la primera etapa de la educación secundaria y no ha seguido ningún estudio o formación en el año $t$\n\n$P_{18-24}^{t} =$ población de 18 a 24 años en el año $t$\n", "desagregacion"=>"\nSexo", "periodicidad"=>"Anual", "observaciones"=>"", "justificacion_global"=>"", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Tasa de abandono escolar prematuro de la población de 18-24 años", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.1- De aquí a 2030, asegurar que todas las niñas y todos los niños terminen la enseñanza primaria y secundaria, que ha de ser gratuita, equitativa y de calidad y producir resultados de aprendizaje pertinentes y efectivos", "definicion"=>"Percentage of the population that has completed at most the first stage of secondary  education and is not pursuing any further study or training", "formula"=>"\n$$PP_{dropout}^{t}  = \\frac{P_{dropout,18-24}^{t}}{P_{18-24}^{t}}$$\n\nwhere:\n\n$P_{dropout,18-24}^{t} =$ population aged 18 to 24 who have completed at most the first stage of secondary education and have not continued any further study or training in the year $t$\n\n$P_{18-24}^{t} =$ population aged 18 to 24 in the year $t$\n", "desagregacion"=>"\nSex", "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>nil, "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>nil, "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Tasa de abandono escolar prematuro de la población de 18-24 años", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.1- De aquí a 2030, asegurar que todas las niñas y todos los niños terminen la enseñanza primaria y secundaria, que ha de ser gratuita, equitativa y de calidad y producir resultados de aprendizaje pertinentes y efectivos", "definicion"=>"Gehienez bigarren hezkuntzako lehen zikloa amaitu eta ikasketarik edo prestakuntzarik  egiten ari ez den biztanleen ehunekoa", "formula"=>"\n$$PP_{eskola\\, uztea}^{t}  = \\frac{P_{eskola\\, uztea\\, 18-24}^{t}}{P_{18-24}^{t}}$$\n\nnon:\n\n$P_{eskola\\, uztea\\, 18-24}^{t} =$ gehienez bigarren hezkuntzako lehen etapa amaitu eta beste ikasketa edo prestakuntzarik egin ez duten 18 eta 24 urte bitarteko biztanleak $t$ urtean\n\n$P_{18-24}^{t} =$ 18-24 urteko biztanleak $t$ urtean\n", "desagregacion"=>"\nSexua", "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>nil, "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>nil, "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "indicator_sort_order"=>"04-01-E1", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.2.1", "slug"=>"4-2-1", "name"=>"Proporción de niños de 24 a 59 meses cuyo desarrollo es adecuado en cuanto a la salud, el aprendizaje y el bienestar psicosocial, desglosada por sexo ", "url"=>"/site/es/4-2-1/", "sort"=>"040201", "goal_number"=>"4", "target_number"=>"4.2", "global"=>{"name"=>"Proporción de niños de 24 a 59 meses cuyo desarrollo es adecuado en cuanto a la salud, el aprendizaje y el bienestar psicosocial, desglosada por sexo "}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de niños de 24 a 59 meses cuyo desarrollo es adecuado en cuanto a la salud, el aprendizaje y el bienestar psicosocial, desglosada por sexo ", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de niños de 24 a 59 meses cuyo desarrollo es adecuado en cuanto a la salud, el aprendizaje y el bienestar psicosocial, desglosada por sexo ", "indicator_number"=>"4.2.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El desarrollo de la primera infancia (DPI) sienta las bases para un desarrollo \nintegral a lo largo de la vida. Invertir en DPI es una de las inversiones más \ncruciales y rentables que un país puede realizar para mejorar la salud, la educación \ny la productividad de los adultos, con el fin de fortalecer el capital humano y \npromover el desarrollo sostenible. \n\nEl DPI implica equidad desde el principio y constituye un buen indicador \ndel desarrollo nacional. Los esfuerzos para mejorar el DPI pueden generar \nmejoras humanas, sociales y económicas tanto para las personas como para las sociedades.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.2.1&seriesCode=SE_DEV_ONTRK&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=M36T59%20%7C%20BOTHSEX\">Proporción de niños de 36 a 59 meses que están encaminados en su desarrollo en al menos tres de los siguientes dominios; alfabetización y aritmética, desarrollo físico, desarrollo socioemocional y aprendizaje (% de niños de 36 a 59 meses) SE_DEV_ONTRK</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-02-01.pdf\">Metadatos 4-2-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Early childhood development (ECD) sets the stage for life-long thriving. \nInvesting in ECD is one of the most critical and cost-effective investments \na country can make to improve adult health, education and productivity in \norder to build human capital and promote sustainable development. \n\nECD is equity from the start and provides a good indication of national development. \nEfforts to improve ECD can bring about human, social and economic improvements for \nboth individuals and societies.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.2.1&seriesCode=SE_DEV_ONTRK&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=M36T59%20%7C%20BOTHSEX\">Proportion of children aged 36−59 months who are developmentally on track in at least three of the following domains: literacy-numeracy, physical development, social-emotional development, and learning (% of children aged 36-59 months) SE_DEV_ONTRK</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-02-01.pdf\">Metadata 4-2-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Lehen haurtzaroaren garapenak (LHG) bizitzan zehar garapen integrala izateko oinarriak ezartzen ditu. LHGan inbertitzea \nherrialde batek helduen osasuna, hezkuntza eta produktibitatea hobetzeko egin dezakeen inbertsiorik erabakigarrienetako \neta errentagarrienetako bat da, giza kapitala indartu eta garapen iraunkorra sustatze aldera. \n\nLHGak ekitatea dakar hasieratik, eta garapen nazionalaren adierazle ona da. LHGa hobetzeko ahaleginek hobekuntza humanoak, \nsozialak eta ekonomikoak eragin ditzakete, bai pertsonentzat, bai gizarteentzat. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.2.1&seriesCode=SE_DEV_ONTRK&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=M36T59%20%7C%20BOTHSEX\">Honako eremu hauetako hiru gutxienez garatzera bideratutako 36 eta 59 hilabete bitarteko haurren proportzioa: alfabetatzea eta aritmetika, garapen fisikoa, garapen sozioemozionala eta ikaskuntza (36 eta 59 hilabete bitarteko haurren %) SE_DEV_ONTRK</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-02-01.pdf\">Metadatuak 4-2-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.2: By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary education</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.2.1: Proportion of children aged 24&#x2013;59 months who are developmentally on track in health, learning and psychosocial well-being, by sex</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SE_DEV_ONTRK - Percentage of children who are developmentally on track in at least three of the following domains: literacy-numeracy, physical development, social-emotional development, and learning [4.2.1]</p>\n<p>SE_DEV_ONTRKWB - Percentage of children who are developmentally on track in health, learning and psychosocial well-being [4.2.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 4.2.2: Participation rate in organized learning (one year before the official primary entry age), by sex</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The proportion of children aged 24 to 59 months who are developmentally on track in health, learning and psychosocial well-being.</p>\n<p><strong>Concepts:</strong></p>\n<p>The domains included in the indicator for SDG indicator 4.2.1 include the following concepts:</p>\n<ul>\n  <li><strong>Health</strong>: gross motor development, fine motor development and self-care.</li>\n  <li><strong>Learning</strong>: expressive language, literacy, numeracy, pre-writing, and executive functioning.</li>\n  <li><strong>Psychosocial well-being: </strong>emotional skills, social skills, internalizing behavior, and externalizing behavior.</li>\n</ul>\n<p>The recommended measure for SDG 4.2.1 is the Early Childhood Development Index 2030 (ECDI2030) which is a 20-item instrument to measure developmental outcomes among children aged 24 to 59 months in population-based surveys. The indicator derived from the ECDI2030 is the proportion of children aged 24 to 59 months who have achieved the minimum number of milestones expected for their age group, defined as follows:</p>\n<ul>\n  <li>Children age 24 to 29 months are classified as developmentally on-track if they have achieved at least 7 milestones;</li>\n  <li>Children age 30 to 35 months are classified as developmentally on-track if they have achieved at least 9 milestones;</li>\n  <li>Children age 36 to 41 months are classified as developmentally on-track if they have achieved at least 11 milestones;</li>\n  <li>Children age 42 to 47 months are classified as developmentally on-track if they have achieved at least 13 milestones;</li>\n  <li>Children age 48 to 59 months are classified as developmentally on-track if they have achieved at least 15 milestones. </li>\n</ul>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>In 2015, UNICEF initiated a process of methodological development that involved extensive consultations with experts, partner agencies and national statistical authorities. Over the following five years, a sequence of carefully planned technical steps were executed, incorporating both qualitative and quantitative methods to identify the best items to measure indicator 4.2.1. This process led to the development of the ECDI2030.</p>\n<p>The ECDI2030 addresses the need for nationally representative and internationally comparable data on early childhood development, collected in a standardized way. It captures the achievement of key developmental milestones by children between the ages of 24 and 59 months. Mothers or primary caregivers are asked 20 questions about the way their children behave in certain everyday situations, and the skills and knowledge they have acquired.</p>\n<p>The ECDI2030 can be integrated into existing national data collection efforts, including international household survey programmes such as UNICEF-supported Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). </p>\n<p>The ECDI2030 is meant to replace the Early Childhood Development Index (or ECDI) which collects data on the proxy indicator for SDG 4.2.1 that has been in use since 2015. The former ECDI and the new ECDI2030 target different age groups and measure slightly different development domains. Therefore, the indicators generated by both instruments may not be fully comparable and caution is needed when interpreting estimates produced by the two measures.</p>", "COLL_METHOD__GLOBAL"=>"<ul>\n  <li>\n    <ol>\n      <li>UNICEF undertakes a wide consultative process of compiling and assessing data from national sources for the purposes of updating its global databases on the situation of children. Up until 2017, the mechanism UNICEF used to collaborate with national authorities on ensuring data quality and international comparability on key indicators of relevance to children was known as Country Data Reporting on the Indicators for the Goals (CRING).</li>\n    </ol>\n  </li>\n</ul>\n<p>As of 2018, UNICEF launched a new country consultation process with national authorities on selected child-related global SDG indicators for which it is custodian or co-custodian to meet emerging standards and guidelines on data flows for global reporting of SDG indicators, which place strong emphasis on technical rigour, country ownership and use of official data and statistics. The consultation process solicits feedback directly from National Statistical Offices (NSOs), as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why. </p>", "FREQ_COLL__GLOBAL"=>"<p>UNICEF will undertake an annual country consultation likely between December and January every year to allow for review and processing of the feedback received in order to meet global SDG reporting deadlines.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Updated data on 4.2.1 as measured by the ECDI2030 will be available in the SDG reporting period every February/March.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (in most cases)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "INST_MANDATE__GLOBAL"=>"<p>UNICEF is responsible for global monitoring and reporting on the wellbeing of children. It provides technical and financial assistance to Member States to support their efforts to collect quality data on early childhood development (ECD), including through the UNICEF-supported MICS household survey programme. UNICEF also compiles ECD statistics with the goal of making internationally comparable datasets publicly available, and it analyses ECD statistics which are included in relevant data-driven publications, including in its flagship publication, <em>The State of the World&#x2019;s Children.</em></p>", "RATIONALE__GLOBAL"=>"<p>Early childhood development (ECD) sets the stage for life-long thriving. Investing in ECD is one of the most critical and cost-effective investments a country can make to improve adult health, education and productivity in order to build human capital and promote sustainable development. ECD is equity from the start and provides a good indication of national development. Efforts to improve ECD can bring about human, social and economic improvements for both individuals and societies.</p>", "REC_USE_LIM__GLOBAL"=>"<p>SDG 4.2.1 was initially classified as Tier 3 and was upgraded to Tier 2 in 2019; additionally, changes to the indicator were made during the 2020 comprehensive review. In light of this and given that the ECDI2030 was officially released in March 2020, it will take some time for country uptake and implementation of the new measure and for data to become available from a sufficiently large enough number of countries. Therefore, in the meantime, some countries will continue to rely on use of a proxy indicator (children aged 36-59 months who are developmentally on-track in at least three of the following four domains: literacy-numeracy, physical, social-emotional and learning) to report on 4.2.1. This proxy indicator has been used for global SDG reporting since 2015 but is not fully aligned with the definition and age group covered by the SDG indicator formulation. When the proxy indicator is used for SDG reporting on 4.2.1 for a country, it will be disseminated under the series code SE_DEV_ONTRK in the global SDG database.</p>", "DATA_COMP__GLOBAL"=>"<p>The number of children aged 24 to 59 months who are developmentally on track in health, learning and psychosocial well-being divided by the total number of children aged 24 to 59 months in the population multiplied by 100.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>A wide consultative process is undertaken to compile, assess and validate data from national sources. </p>\n<p>The consultation process solicits feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. The results of this country consultation are reviewed by UNICEF as the custodian agency. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why. </p>", "ADJUSTMENT__GLOBAL"=>"<p>The estimates compiled and presented at global level come directly from nationally produced data and are not adjusted or recalculated.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>When data for a country are entirely missing, UNICEF does not publish any country-level estimate.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>The regional average is applied to those countries within the region with missing values for the purposes of calculating regional aggregates only but are not published as country-level estimates. Regional aggregates are only published when at least 50 percent of the regional population for the relevant age group are covered by the available data.</p>", "REG_AGG__GLOBAL"=>"<p>The global aggregate is a weighted average of all countries with available data. Global aggregates are published regardless of population coverage, but the number of countries and the proportion of the relevant population group represented by the available data are clearly indicated.</p>\n<p>Regional aggregates are weighted averages of all the countries within the region.</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries gather prevalence data on children&#x2019;s developmental status through household surveys such as UNICEF-supported MICS or Demographic and Health Surveys.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The process behind the production of reliable statistics on ECD is well established within UNICEF. The quality and process leading to the production of the SDG indicator 4.2.1 is ensured by working closely with the statistical offices and other relevant stakeholders through a consultative process.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UNICEF maintains the global database on ECD that is used for SDG and other official reporting. Before the inclusion of any data point in the database, it is reviewed by technical focal points at UNICEF headquarters to check for consistency and overall data quality. This review is based on a set of objective criteria to ensure that only the most recent and reliable information are included in the databases. These criteria include the following: data sources must include proper documentation; data values must be representative at the national population level; data are collected using an appropriate methodology (e.g., sampling); data values are based on a sufficiently large sample; data conform to the standard indicator definition including age group and concepts, to the extent possible; data are plausible based on trends and consistency with previously published/reported estimates for the indicator. </p>\n<p>As of 2018, UNICEF undertakes an annual consultation with government authorities on 10 of the child-related SDG indicators in its role of sole or joint custodian, and in line with its global monitoring mandate and normative commitments to advancing the 2030 Agenda for children. This includes indicator 4.2.1. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data on the indicator collected through implementation of the ECDI2030 are currently available for around 15 countries. Comparable data collected by the ECDI are currently available for 78 countries. Countries with data on the proxy indicator collected with the ECDI will continue to be used for global SDG reporting until new data using the ECDI2030 are available. </p>\n<p><strong>Time series:</strong></p>\n<p>Not available</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Sex</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The estimates compiled and presented at global level come directly from nationally produced data and are not adjusted or recalculated.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>data.unicef.org</p>\n<p><strong>References:</strong></p>\n<p><a href=\"http://data.unicef.org/ecd/development-status.html\">http://data.unicef.org/ecd/development-status.html</a> </p>", "indicator_sort_order"=>"04-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.2.2", "slug"=>"4-2-2", "name"=>"Tasa de participación en el aprendizaje organizado (un año antes de la edad oficial de ingreso en la educación primaria), desglosada por sexo", "url"=>"/site/es/4-2-2/", "sort"=>"040202", "goal_number"=>"4", "target_number"=>"4.2", "global"=>{"name"=>"Tasa de participación en el aprendizaje organizado (un año antes de la edad oficial de ingreso en la educación primaria), desglosada por sexo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de participación en el aprendizaje organizado (un año antes de la edad oficial de ingreso en la educación primaria), desglosada por sexo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de participación en el aprendizaje organizado (un año antes de la edad oficial de ingreso en la educación primaria), desglosada por sexo", "indicator_number"=>"4.2.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_68/opt_1/ti_estadistica-de-la-actividad-escolar/temas.html", "url_text"=>"Estadística de la Actividad Escolar", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tasa neta de escolarización a los 5 años", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.2- De aquí a 2030, asegurar que todas las niñas y todos los niños tengan acceso a servicios de atención y desarrollo en la primera infancia y educación preescolar de calidad, a fin de que estén preparados para la enseñanza primaria", "definicion"=>"Proporción de niños y niñas de 5 años matriculados en educación infantil, primaria y  especial sobre el total de niños y niñas de 5 años", "formula"=>"\n$$TE_{5}^{t} = \\frac{AM_{5}^{t}}{P_{5}^{t}} \\cdot 100$$\n\ndonde:\n\n$AM_{5}^{t} =$ alumnado de 5 años matriculado en educación infantil, primaria y especial en el curso \nescolar $t-1$\n\n$P_{5}^{t} =$ población de 5 años a 1 de enero del año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa tasa de participación en el aprendizaje organizado (un año antes de la edad oficial de \ningreso a la educación primaria), por sexo, se define como el porcentaje de niños en el \nrango de edad determinado que participan en uno o más programas de aprendizaje organizado, \nincluidos los programas que ofrecen una combinación de educación y cuidado. Se incluye tanto \nla participación en la primera infancia como en la educación primaria. El rango de edad variará \nsegún el país en función de la edad oficial de ingreso a la educación primaria.\n\nEl indicador mide la exposición de los niños a actividades de aprendizaje organizadas durante \nel año anterior al inicio de la escuela primaria. Un valor alto del indicador muestra un alto \ngrado de participación en el aprendizaje organizado inmediatamente antes de la edad oficial de \ningreso a la educación primaria.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.2.2&seriesCode=SE_PRE_PARTN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Tasa de participación en el aprendizaje organizado (un año antes de la edad oficial de ingreso a la primaria)(%) SE_PRE_PARTN</a> UNSTATS<br> ", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-02-02.pdf\">Metadatos 4-2-2.pdf </a>(solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Tasa neta de escolarización a los 5 años", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.2- De aquí a 2030, asegurar que todas las niñas y todos los niños tengan acceso a servicios de atención y desarrollo en la primera infancia y educación preescolar de calidad, a fin de que estén preparados para la enseñanza primaria", "definicion"=>"Proportion of 5-year-old boys and girls enrolled in early childhood, primary  and special education over the total of 5-year-old boys and girls", "formula"=>"\n$$TE_{5}^{t} = \\frac{AM_{5}^{t}}{P_{5}^{t}} \\cdot 100$$\n\nwhere:\n\n$AM_{5}^{t} =$ 5-year-old students enrolled in early childhood, primary and special \neducation in the school year $t-1$\n\n$P_{5}^{t} =$ 5-year-old population as of January 1 of year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe participation rate in organized learning (one year before the official \nprimary entry age), by sex is defined as the percentage of children in the \ngiven age range who participate in one or more organized learning programme, \nincluding programmes which offer a combination of education and care. \nParticipation in early childhood and in primary education are both included. \nThe age range will vary by country depending on the official age of entry to \nprimary education.\n\nThe indicator measures children’s exposure to organized learning activities \nin the year prior to the start of primary school. A high value of the indicator \nshows a high degree of participation in organized learning immediately before \nthe official entrance age to primary education.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.2.2&seriesCode=SE_PRE_PARTN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Participation rate in organized learning (one year before the official primary entry age) (%) SE_PRE_PARTN</a> UNSTATS<br> ", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-02-02.pdf\">Metadata 4-2-2.pdf </a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Tasa neta de escolarización a los 5 años", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.2- De aquí a 2030, asegurar que todas las niñas y todos los niños tengan acceso a servicios de atención y desarrollo en la primera infancia y educación preescolar de calidad, a fin de que estén preparados para la enseñanza primaria", "definicion"=>"Haur Hezkuntzan, Lehen Hezkuntzan eta Hezkuntza Berezian matrikulatutako  5 urteko haurren proportzioa, 5 urteko haur guztiekiko", "formula"=>"\n$$TE_{5}^{t} = \\frac{AM_{5}^{t}}{P_{5}^{t}} \\cdot 100$$\n\nnon:\n\n$AM_{5}^{t} =$ Haur Hezkuntzan, Lehen Hezkuntzan eta Hezkuntza Berezian matrikulatutako 5 urteko ikasleak $t-1/t$ ikasturtean \n\n$P_{5}^{t} =$ urteko biztanleria $t$ urteko urtarrilaren 1ean \n", "desagregacion"=>"Sexua\n\nLurralde historiakoa\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAntolatutako ikaskuntzako partaidetza-tasa (lehen hezkuntzan sartzeko adin ofiziala baino urtebete lehenago), \nsexuaren arabera, honela definitzen da: adin-tarte jakin batean antolatutako ikaskuntza-programa batean edo \ngehiagotan parte hartzen duten haurren ehunekoa, hezkuntza eta zaintza uztartzen dituzten programak barne. \nLehen haurtzaroko eta lehen hezkuntzako parte-hartzea sartzen da. Adin-tartea aldatu egiten da herrialdearen \narabera, lehen hezkuntzan sartzeko adin ofiziala zein den. \n\nAdierazleak haurrek lehen hezkuntza hasi aurreko urtean antolatutako ikaskuntza jarduerekiko duten esposizioa \nneurtzen du. Adierazlearen balio altuak lehen hezkuntzan sartzeko adin ofiziala baino lehentxeago antolatutako \nikaskuntzan partaidetza maila handia erakusten du. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.2.2&seriesCode=SE_PRE_PARTN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Antolatutako ikaskuntzako parte hartze tasa (Lehen Hezkuntzan sartzeko adin ofiziala baino urtebete lehenago) (%) SE_PRE_PARTN</a> UNSTATS<br> ", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-02-02.pdf\">Metadatuak 4-2-2.pdf </a>(ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.2: By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary education</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.2.2: Participation rate in organized learning (one year before the official primary entry age), by sex</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SE_PRE_PARTN - Participation rate in organized learning (one year before the official primary entry age) [4.2.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.4, 4.5</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The participation rate in organized learning (one year before the official primary entry age), by sex is defined as the percentage of children in the given age range who participate in one or more organized learning programme, including programmes which offer a combination of education and care. Participation in early childhood and in primary education are both included. The age range will vary by country depending on the official age of entry to primary education.</p>\n<p><strong>Concepts:</strong></p>\n<p>An organized learning programme is one which consists of a coherent set or sequence of educational activities designed with the intention of achieving pre-determined learning outcomes or the accomplishment of a specific set of educational tasks. Early childhood and primary education programmes are examples of organized learning programmes. </p>\n<p>Early childhood and primary education are defined in the 2011 revision of the International Standard Classification of Education (ISCED 2011). Early childhood education is typically designed with a holistic approach to support children&#x2019;s early cognitive, physical, social and emotional development and to introduce young children to organized instruction outside the family context. Primary education offers learning and educational activities designed to provide students with fundamental skills in reading, writing and mathematics and establish a solid foundation for learning and understanding core areas of knowledge and personal development. It focuses on learning at a basic level of complexity with little, if any, specialisation. </p>\n<p>The official primary entry age is the age at which children are obliged to start primary education according to national legislation or policies. Where more than one age is specified, for example, in different parts of a country, the most common official entry age (i.e. the age at which most children in the country are expected to start primary) is used for the calculation of this indicator at the global level.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The International Standard Classification of Education (ISCED) is used to define early childhood and primary education. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>Administrative data from schools and other centres of organized learning or from household surveys on enrolment by single year of age in early learning programmes; population censuses and surveys for population estimates by single year of age (if using administrative data on enrolment); administrative data from ministries of education on the official entrance age to primary education.</p>", "COLL_METHOD__GLOBAL"=>"<p>The UNESCO Institute for Statistics produces time series based on enrolment data reported by Ministries of Education or National Statistical Offices and population estimates produced by the UN Population Division. The enrolment data are gathered through the annual Survey of Formal Education. Countries are asked to report data according to the levels of education defined in the International Standard Classification of Education (ISCED) to ensure international comparability of resulting indicators.</p>\n<p>The data received are validated using electronic error detection systems that check for arithmetic errors and inconsistencies and trend analysis for implausible results. Queries are taken up with the country representatives reporting the data so that corrections can be made (of errors) or explanations given (of implausible but correct results). During this process, countries are also encouraged to provide estimates for missing or incomplete data items.</p>\n<p>In addition, countries also have an opportunity to see and comment on the main indicators the UIS produces in an annual &#x201C;country review&#x201D; of indicators. </p>", "FREQ_COLL__GLOBAL"=>"<p>Annual UIS survey (usually launched in the 4<sup>th</sup> quarter) and UOE survey (usually launched in June).</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Biannual UIS data release (March and September).</p>", "DATA_SOURCE__GLOBAL"=>"<p>Ministries of Education and/or National Statistical Offices.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "INST_MANDATE__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The Institute produces internationally comparable data and methodologies in the fields of education, science, culture and communication for countries at all stages of development.</p>\n<p>The <a href=\"https://www.moe.gov.bn/DocumentDownloads/Education%202030/Education2030.pdf\"><u>Education 2030 Framework for Action &#xA7;100</u></a> has clearly stated that: &#x201C;<em>In recognition of the importance of harmonization of monitoring and reporting, the UIS will remain the official source of cross-nationally comparable data on education. It will continue to produce international monitoring indicators based on its annual education survey and on other data sources that guarantee international comparability for more than 200 countries and territories. In addition to collecting data, the UIS will work with partners to develop new indicators, statistical approaches and monitoring tools to better assess progress across the targets related to UNESCO&#x2019;s mandate, working in coordination with the SDG-Education 2030 SC&#x201D;.</em></p>", "RATIONALE__GLOBAL"=>"<p>The indicator measures children&#x2019;s exposure to organized learning activities in the year prior to the start of primary school. A high value of the indicator shows a high degree of participation in organized learning immediately before the official entrance age to primary education.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Participation in learning programmes in the early years is not full time for many children, meaning that exposure to learning environments outside of the home will vary in intensity. The indicator measures the percentage of children who are exposed to organized learning but not the intensity of the programme, which limits the ability to draw conclusions on the extent to which this target is being achieved. More work is needed to ensure that the definition of learning programmes is consistent across various surveys and defined in a manner that is easily understood by survey respondents, ideally with complementary information collected on the amount of time children spend in learning programmes.</p>", "DATA_COMP__GLOBAL"=>"<p>The number of children in the relevant age group who participate in an organized learning programme is expressed as a percentage of the total population in the same age range. The indicator can be calculated both from administrative data and from household surveys. If the former, the number of enrolments in organized learning programmes are reported by schools and the population in the age group one year below the official primary entry age is derived from population estimates. For the calculation of this indicator at the global level, population estimates from the UN Population Division are used. If derived from household surveys, both enrolments and population are collected at the same time.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>R</mi>\n        <mi>O</mi>\n        <mi>L</mi>\n      </mrow>\n      <mrow>\n        <mn>0</mn>\n        <mi>t</mi>\n        <mn>1</mn>\n        <mo>,</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>A</mi>\n        <mi>G</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>a</mi>\n            <mo>-</mo>\n            <mn>1</mn>\n          </mrow>\n        </mfenced>\n        <mo>=</mo>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n    </msub>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>E</mi>\n          </mrow>\n          <mrow>\n            <mn>0</mn>\n            <mi>t</mi>\n            <mn>1</mn>\n            <mo>,</mo>\n            <mi>&amp;nbsp;</mi>\n            <mi>A</mi>\n            <mi>G</mi>\n            <mo>(</mo>\n            <mi>a</mi>\n            <mo>-</mo>\n            <mn>1</mn>\n            <mo>)</mo>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>S</mi>\n            <mi>A</mi>\n            <mi>P</mi>\n          </mrow>\n          <mrow>\n            <mi>A</mi>\n            <mi>G</mi>\n            <mo>(</mo>\n            <mi>a</mi>\n            <mo>-</mo>\n            <mn>1</mn>\n            <mo>)</mo>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where:</p>\n<p><em>PROL<sub>0t1,AG(a-1)</sub></em> = participation rate in organized learning one year before the official entry age <em>a</em> to primary education</p>\n<p><em>E<sub>0t1,AG(a-1)</sub></em> = enrolment in early childhood or primary education (ISCED levels 0 and 1) aged one year below the official entry age <em>a</em> to primary education</p>\n<p><em>SAP<sub>AG(a-1)</sub></em> = school-age population aged one year below the official entry age <em>a</em> to primary education</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The UNESCO Institute for Statistics shares all indicator values and notes on methodology with National Statistical Offices, Ministries of Education, or other relevant agencies in individual countries for their review, feedback and validation before the publication of the data. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Data should be reported according to the levels of education defined in the International Standard Classification of Education (ISCED) to ensure international comparability of resulting indicators. </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>The UIS estimates certain key items of data that may be missing or incomplete in order to have publishable estimates at the country level. Where this is not possible the UIS imputes missing values for use only for calculating regional and global aggregates.</p>\n<p>For the purposes of calculating participation rates by age, the UIS may make one or more of the following:</p>\n<p>&#x2022; An adjustment to account for over- or under-reporting, for example:</p>\n<p>o To include enrolments in a type of education &#x2013; such as private education or special education &#x2013; not reported by the country; and/or </p>\n<p>o To include enrolments in a part of the country not reported by the country.</p>\n<p>&#x2022; An estimate of the number of enrolments in the given age group if the age distribution was not reported by the country.</p>\n<p>&#x2022; A redistribution of enrolments of unknown age (across known ages).</p>\n<p>&#x2022; An estimate of the population in the official age group for small countries (if neither the UN Population Division (UNPD) nor the country itself can provide estimates of their own).</p>\n<p>In all cases estimates are based on evidence from the country itself (e.g. information from the data provider on the size of the missing component, via correspondence, publications or data on the Ministry&#x2019;s or National Statistical Office&#x2019;s (NSO&#x2019;s) Webpage, or via surveys conducted by other organizations) or on data from the country for a previous year. These figures may be published: (i) as observed data if the missing items are found in a national source; (ii) as national estimates if the country is persuaded to produce estimates and submit them in place of missing data; or (iii) as UIS estimates, if the estimates are made by the UIS.</p>\n<p>The age distribution of enrolments is most commonly estimated from the age distribution reported in a previous year. If the country has never reported the age distribution of enrolments, the age distribution reported in another survey, if available, is used (such as Multiple Indicator Cluster Surveys (MICS) or Demographic Health Surveys (DHS)).</p>\n<p>Enrolments of unknown age are redistributed across known ages if they constitute more than 5% of the total enrolments in that level of education. No estimation is made if they are 5% or less.</p>\n<p>Population estimates by age for countries with small population &#x2013; produced only where there are no other suitable estimates available either from UNPD or from the country itself &#x2013; are made only for countries which have reported education data to the UIS and for which population estimates from a reliable source are available in some years.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Regional and global aggregates are derived from both publishable and imputed national data. Publishable data are the data submitted to the UIS by Member States or the result of an explicit estimation made by the Institute based on pre-determined standards. In both cases, these data are sent to Member States for review before they are considered publishable by the UIS. </p>\n<p>When data are not available for all countries, the UIS imputes national data for the sole purpose of calculating regional averages. These imputed data are neither published nor otherwise disseminated. </p>\n<p>Where data are available for a country for both an earlier and a more recent year than the missing year, a simple linear interpolation is made. Where data are only available for an earlier year, the most recent value is used as an estimate. Similarly, where data are only available for a more recent year, the last value is used as an estimate.</p>\n<p>Where the relevant data are not available at all for a country, estimates may be based on another variable which is clearly linked to the item being estimated. For example, enrolments by age may be based on total enrolments.</p>\n<p>Where no data are available for the country in any year that can inform the estimate, the unweighted average for the region in which the country lies is used.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates are calculated as weighted averages using the denominator of the indicator as the weight. As described previously, where publishable data are not available for a given country or year, values are imputed for the purpose of calculating the regional and global aggregates.</p>", "DOC_METHOD__GLOBAL"=>"<p>The UIS has elaborated guidance for the countries on the methodology that should be used to calculate this indicator. ISCED mappings that help countries report their data in an internationally comparable framework are available on the website of the UNESCO Institute for Statistics (<a href=\"http://uis.unesco.org/en/isced-mappings\">http://uis.unesco.org/en/isced-mappings</a>).</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The UIS maintains the global database used to produce this indicator. For transparency purposes, the inclusion of a data point in the database is completed by following a protocol and is reviewed by UIS technical focal points to ensure consistency and overall data quality, based on objective criteria to ensure that only the most recent and reliable information are included in the database. The international reporting of enrolment data should be based on the 2011 International Standard Classification of Education maintained by the UIS. Population data are produced and maintained by the United Nations Population Division (UNPD).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The process for quality assurance includes review of survey documentation to make sure that the definition of organized learning programmes is consistent across various surveys, review of the indicator values across time, calculation of measures of reliability, examination of consistency of indicator values derived from different sources and, if necessary, consultation with data providers.</p>\n<p>Before its annual data release and the addition of any indicators to the global SDG Indicators Database, the UNESCO Institute for Statistics submits all indicator values and notes on methodology to National Statistical Offices, Ministries of Education or other relevant agencies in individual countries for their review and feedback.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The indicator should be based on enrolment by single year of age in early learning programmes in all types of education institutions, including public, private and all other institutions that provide organized educational programmes. Criteria for quality assessment include: data sources must include proper documentation; data values must be representative at the national population level and, if not, should be footnoted; data are plausible and based on trends and consistency with previously published/reported values for the indicator.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>167 countries with at least one data point in the period 2010-2019.</p>\n<p><strong>Time series:</strong></p>\n<p>1998-2019 in UIS database; 2000-2019 in SDG global database. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>By age and sex from administrative sources, and by age, sex, location and income from household surveys.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Nationally-published figures may differ from the international ones because of differences between national education systems and the International Standard Classification of Education (ISCED); or differences in coverage (i.e. the extent to which different types of education &#x2013; e.g. private or special education &#x2013; are included in one rather than the other) and/or between national and the United Nations Population Division (UNPD) population estimates.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.uis.unesco.org\">http://www.uis.unesco.org</a> </p>\n<p><strong>References:</strong></p>\n<p>The Survey of Formal Education Instruction Manual <a href=\"http://uis.unesco.org/sites/default/files/documents/instruction-manual-survey-formal-education-2017-en.pdf\">http://uis.unesco.org/sites/default/files/documents/instruction-manual-survey-formal-education-2017-en.pdf</a></p>\n<p>UIS Questionnaire on Students and Teachers (ISCED 0-4)</p>\n<p><a href=\"http://uis.unesco.org/en/uis-questionnaires\">http://uis.unesco.org/en/uis-questionnaires</a></p>\n<p> </p>", "indicator_sort_order"=>"04-02-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.3.1", "slug"=>"4-3-1", "name"=>"Tasa de participación de jóvenes y adultos en la educación y formación académica y no académica en los últimos 12 meses, desglosada por sexo", "url"=>"/site/es/4-3-1/", "sort"=>"040301", "goal_number"=>"4", "target_number"=>"4.3", "global"=>{"name"=>"Tasa de participación de jóvenes y adultos en la educación y formación académica y no académica en los últimos 12 meses, desglosada por sexo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de participación de jóvenes y adultos en la educación y formación académica y no académica, desglosada por sexo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de participación de jóvenes y adultos en la educación y formación académica y no académica en los últimos 12 meses, desglosada por sexo", "indicator_number"=>"4.3.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Quinquenal", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176759&menu=ultiDatos&idp=1254735573113", "url_text"=>"Encuesta sobre la participación de la población adulta en las actividades de aprendizaje", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_57/opt_1/ti_encuesta-de-poblacion-en-relacion-con-la-actividad/temas.html", "url_text"=>"Encuesta de población en relación con la actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Tasa de participación de jóvenes y adultos en la educación y formación académica y no académica, desglosada por sexo", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.3- De aquí a 2030, asegurar el acceso igualitario de todos los hombres y las mujeres a una formación técnica, profesional y superior de calidad, incluida la enseñanza universitaria", "definicion"=>"Proporción de personas que han realizado estudios o formación (reglada o no reglada) en las últimas cuatro semanas o 12 meses, desglosada por sexo y grupos de edad", "formula"=>"\n$$PPE_{grupo\\, edad}^{t} = \\frac{PE_{grupo\\, edad}^{t}}{P_{grupo\\, edad}^{t}} \\cdot 100$$\n\ndonde:\n\n$PE_{grupo\\, edad}^{t} =$ población de un grupo de edad que ha realizado estudios o formación (reglada o no reglada) en el año $t$\n\n$P_{grupo\\, edad}^{t} =$ población de un grupo de edad en el año $t$\n", "desagregacion"=>"Edad: 15-24; 15-64; 25-64; 18-64\n\nSexo\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"Los datos que provienen de la Encuesta de Población en Relación con la Actividad son datos anuales por grupo de edad, y\nse pregunta por las actividades realizadas en las últimas 4 semanas. \n\nLos datos que provienen de la Encuesta sobre la participación de la población adulta en las actividades de aprendizaje se refieren al\ngrupo de edad 18-64 y se pregunta por las actividades realizadas en los últimos 12 meses.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Naciones Unidas define el indicador como el porcentaje de jóvenes y adultos en un rango \nde edad determinado (15-24 años, 25-54 años, 55-64 años, 15-64 años) que participaron en educación y capacitación formal \no no formal en los 12 meses anteriores.\n\nEl indicador mide el nivel de participación de jóvenes y adultos en la educación y la formación de todo tipo. \nUn valor alto indica que una gran proporción de la población del grupo de edad pertinente participa en la educación \ny la formación formales y no formales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible no cumple con los metadatos de Naciones Unidas pero aporta información similar.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-03-01.pdf\">Metadatos 4-3-1.pdf </a>(solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Tasa de participación de jóvenes y adultos en la educación y formación académica y no académica, desglosada por sexo", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.3- De aquí a 2030, asegurar el acceso igualitario de todos los hombres y las mujeres a una formación técnica, profesional y superior de calidad, incluida la enseñanza universitaria", "definicion"=>"Proportion of people who engaged in educational activities (formal or informal) in  the last four weeks or 12 months, by age groups", "formula"=>"\n$$PPE_{age\\, group}^{t} = \\frac{PE_{age\\, group}^{t}}{P_{age\\, group}^{t}} \\cdot 100$$\n\nwhere:\n\n$PE_{age\\, group}^{t} =$ population of an age group that has engaged in educational \nactivities (formal or informal) in the year $t$\n\n$P_{age\\, group}^{t} =$ population of an age group in the year $t$\n", "desagregacion"=>"Age: 15-24, 15-64, 25-64, 18-64\n\nSex\n\nProvince\n", "periodicidad"=>"Anual", "observaciones"=>"The data from the Population Activity Survey are annual data by age group, about activities \nperformed in the past 4 weeks. \n\nThe data from the Survey on Adult Participation in Learning Activities refer to the 18-64 \nage group and asks about activities performed in the past 12 months.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The United Nations define the indicator as the percentage of youth and adults in a given age \nrange (15-24 years, 25-54 years, 55-64 years, 15-64 years) participating in formal or non-formal \neducation and training in the previous 12 months.\n\nThe indicator measures the level of participation of youth and adults in education and training of all types. \nA high value indicates a large share of the population in the relevant age group is participating in formal \nand nonformal education and training\n\nSource: United Nations Statistics Division\n", "dato_global"=>"", "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-03-01.pdf\">Metadata 4-3-1.pdf </a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Tasa de participación de jóvenes y adultos en la educación y formación académica y no académica, desglosada por sexo", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.3- De aquí a 2030, asegurar el acceso igualitario de todos los hombres y las mujeres a una formación técnica, profesional y superior de calidad, incluida la enseñanza universitaria", "definicion"=>"Azken 4 asteetan edo 12 hilabeteetan hezkuntza-jarduerak (arautuak edo arautu gabeak) egin dituztenen proportzioa,  sexuaren eta adin taldearen arabera", "formula"=>"\n$$PPE_{adin\\, taldea}^{t} = \\frac{PE_{adin\\, taldea}^{t}}{P_{adin\\, taldea}^{t}} \\cdot 100$$\n\nnon:\n\n$PE_{adin\\, taldea}^{t} =$  ikasketak edo prestakuntza (arautua edo arautu gabea) egin duen adin-talde bateko biztanleria $t$ urtean\n\n$P_{adin\\, taldea}^{t} =$ adin-talde bateko biztanleria $t$ urtean\n", "desagregacion"=>"Adina: 15-24; 15-64; 25-64; 18-64 \n\nSexua\n\nLurralde historikoa\n", "periodicidad"=>"Anual", "observaciones"=>"Biztanleria Jardueraren Arabera Sailkatzeko Inkestatik datozen datuak urtekoak dira, adin-taldearen arabera, \neta azken 4 asteetan egindako jarduerei buruz galdetzen da.\n\nBiztanleria helduak ikaskuntza-jardueretan duen parte-hartzeari buruzko inkestatik datozen datuak 18-64 \nadin-taldekoak dira, eta azken 12 hilabeteetan egindako jarduerei buruz galdetzen da.  \n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Nazio Batuen adierazleak honela definitzen du adierazlea: aurreko 12 hilabeteetan hezkuntzan eta trebakuntza \nformalean edo ez-formalean parte hartu zuten adin-tarte jakin bateko (15-24 urte, 25-54 urte, 55-64 urte, 15-64 \nurte) gazteen eta helduen ehunekoa. \n\nAdierazleak gazteek eta helduek hezkuntzan eta era guztietako prestakuntzan duten partaidetza-maila neurtzen \ndu. Balio handi batek adierazten du dagokion adin-taldeko biztanleriaren proportzio handi batek hezkuntza eta \nprestakuntza formal eta ez-formaletan parte hartzen duela. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen metadatuak betetzen, baina antzeko informazioa ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-03-01.pdf\">Metadatuak 4-3-1.pdf </a>(ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including university</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.3.1: Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sex</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SE_ADT_EDUCTRN - Participation rate in formal and non-formal education and training [4.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.4, 4.4, 4.5, 5.b, 8.5, 9.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The percentage of youth and adults in a given age range (15-24 years, 25-54 years, 55-64 years, 15-64 years) participating in formal or non-formal education and training in the previous 12 months.</p>\n<p><strong>Concepts:</strong></p>\n<p>Formal education and training is defined as education provided by the system of schools, colleges, universities and other formal educational institutions that normally constitutes a continuous &#x2018;ladder&#x2019; of full-time education for children and young people, generally beginning at the age of 5 to 7 and continuing to up to 20 or 25 years old. In some countries, the upper parts of this &#x2018;ladder&#x2019; are organized programmes of joint part-time employment and part-time participation in the regular school and university system.</p>\n<p>Non-formal education and training is defined as any organized and sustained learning activities that do not correspond exactly to the above definition of formal education. Non-formal education may therefore take place both within and outside educational institutions and cater to people of all ages. Depending on national contexts, it may cover educational programmes to impart adult literacy, life-skills, work-skills, and general culture.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The SDG 4.3.1 indicator is calculated by the UIS based on the household-based survey data compiled by the Department of Statistics of the International Labour Organisation (ILO), which maintains a global database on national Labour Force Surveys or other relevant household surveys that cover labour market.</p>\n<p> </p>", "COLL_METHOD__GLOBAL"=>"<p>Data are collected from the respective organizations responsible for each survey. </p>", "FREQ_COLL__GLOBAL"=>"<p>Various depending on survey and country.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Various depending on survey and country.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Ministries of Education and /or National Statistical Offices (NSOs).</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "INST_MANDATE__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The Institute produces internationally comparable data and methodologies in the fields of education, science, culture and communication for countries at all stages of development.</p>\n<p>The <a href=\"https://www.moe.gov.bn/DocumentDownloads/Education%202030/Education2030.pdf\"><u>Education 2030 Framework for Action &#xA7;100</u></a> has clearly stated that: &#x201C;<em>In recognition of the importance of harmonization of monitoring and reporting, the UIS will remain the official source of cross-nationally comparable data on education. It will continue to produce international monitoring indicators based on its annual education survey and on other data sources that guarantee international comparability for more than 200 countries and territories. In addition to collecting data, the UIS will work with partners to develop new indicators, statistical approaches and monitoring tools to better assess progress across the targets related to UNESCO&#x2019;s mandate, working in coordination with the SDG-Education 2030 SC&#x201D;.</em></p>", "RATIONALE__GLOBAL"=>"<p>To show the level of participation of youth and adults in education and training of all types. A high value indicates a large share of the population in the relevant age group is participating in formal and non-formal education and training.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Formal and non-formal education and training can be offered in a variety of settings including schools and universities, workplace environments and others and can have a variety of durations. Administrative data often capture only provision in formal settings such as schools and universities. Participation rates do not capture the intensity or quality of the provision nor the outcomes of the education and training on offer.</p>", "DATA_COMP__GLOBAL"=>"<p>The number of people in selected age groups participating in formal or non-formal education or training is expressed as a percentage of the population of the same age.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>R</mi>\n      </mrow>\n      <mrow>\n        <mi>A</mi>\n        <mi>G</mi>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>E</mi>\n          </mrow>\n          <mrow>\n            <mi>A</mi>\n            <mi>G</mi>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>P</mi>\n          </mrow>\n          <mrow>\n            <mi>A</mi>\n            <mi>G</mi>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where:</p>\n<p><em>PR<sub>AGi</sub></em> = participation rate of the population in age group <em>i</em> in formal and non-formal education and training</p>\n<p><em>E<sub>AGi</sub></em> = enrolment of the population in age group <em>i</em> in formal and non-formal education and training</p>\n<p><em>P<sub>AGi</sub></em> = population in age group <em>i</em></p>\n<p><em>i</em> = 15-24, 25-54 years, 55-64 years, 15-64 years</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The UNESCO Institute for Statistics shares all indicator values and notes on methodology with National Statistical Offices, Ministries of Education, or other relevant agencies in individual countries for their review, feedback and validation before the publication of the data. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level:</strong></p>\n<p>None by data compiler.</p>\n<p><strong>&#x2022; At regional and global levels:</strong></p>\n<p>None by data compiler.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates are not currently available for this indicator.</p>", "DOC_METHOD__GLOBAL"=>"<p>The UIS has elaborated guidance for the countries on the methodology that should be used to calculate this indicator. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The UIS maintains the global database used to produce this indicator. For transparency purposes, the inclusion of a data point in the database is completed by following a protocol and is reviewed by UIS technical focal points to ensure consistency and overall data quality, based on objective criteria to ensure that only the most recent and reliable information are included in the database. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The process for quality assurance includes review of survey documentation, review of the indicator values across time, calculation of measures of reliability, examination of consistency of indicator values derived from different sources and, if necessary, consultation with data providers.</p>\n<p>Before its annual data release and the addition of any indicators to the global SDG Indicators Database, the UNESCO Institute for Statistics submits all indicator values and notes on methodology to National Statistical Offices, Ministries of Education or other relevant agencies in individual countries for their review and feedback.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Accurate data on participation in formal and non-formal education and training by age or specific age-groups and by sex, and the corresponding population data from all types of educational institutions (public and private), formal and non-formal, are essential for calculating this indicator. Criteria for quality assessment include: data sources must include proper documentation; data values must be representative at the national population level and, if not, should be footnoted; data are plausible and based on trends and consistency with previously published/reported values for the indicator.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>154 countries with at least one data point for the period 1976-2022.</p>\n<p><strong>Time series:</strong></p>\n<p>1976-2022 in UIS database; 2000-2022 in SDG global database.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>By age and sex.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>None</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"file:///\\\\uissv3\\teams\\LO\\Learning%20Outcomes\\Metadata\\IAEG-SDG-reviewed%20metadata_121_13022022\\Revised%2013022022\\uis.unesco.org\">uis.unesco.org</a></p>\n<p><strong>References:</strong></p>\n<p>Department of Statistics of the International Labour Organisation (ILO) (global database on national Labour Force Surveys and other relevant household surveys that cover labour market):</p>\n<p><a href=\"https://ilostat.ilo.org/\">https://ilostat.ilo.org/</a> </p>\n<p>European Adult Education Survey (AES): <a href=\"http://www.eui.eu/Research/Library/ResearchGuides/Economics/Statistics/DataPortal/AES.aspx\">http://www.eui.eu/Research/Library/ResearchGuides/Economics/Statistics/DataPortal/AES.aspx</a> </p>\n<p>European Continuing Vocational Training Survey: </p>\n<p><a href=\"https://ec.europa.eu/eurostat/web/microdata/continuing-vocational-training-survey\">https://ec.europa.eu/eurostat/web/microdata/continuing-vocational-training-survey</a></p>\n<p>European Labour Force Survey: <a href=\"http://ec.europa.eu/eurostat/cache/metadata/en/trng_lfs_4w0_esms.htm\">http://ec.europa.eu/eurostat/cache/metadata/en/trng_lfs_4w0_esms.htm</a> </p>", "indicator_sort_order"=>"04-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.4.1", "slug"=>"4-4-1", "name"=>"Proporción de jóvenes y adultos con competencias en tecnología de la información y las comunicaciones (TIC), desglosada por tipo de competencia", "url"=>"/site/es/4-4-1/", "sort"=>"040401", "goal_number"=>"4", "target_number"=>"4.4", "global"=>{"name"=>"Proporción de jóvenes y adultos con competencias en tecnología de la información y las comunicaciones (TIC), desglosada por tipo de competencia"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Habilidad informática", "value"=>"Alguna habilidad informática"}, {"field"=>"Edad", "value"=>"16-74 años"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_series_breaks"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Proporción de jóvenes y adultos con competencias en tecnología de la información y las comunicaciones (TIC), desglosada por tipo de competencia", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción de jóvenes y adultos con competencias en tecnología de la información y las comunicaciones (TIC), desglosada por tipo de competencia", "indicator_number"=>"4.4.1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_15/opt_1/ti_encuesta-sobre-la-sociedad-de-la-informacion-familias/temas.html", "url_text"=>"Encuesta sobre la sociedad de la información. Familias", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de jóvenes y adultos con competencias en tecnología de la información y las comunicaciones (TIC), desglosada por tipo de competencia", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.4- De aquí a 2030, aumentar considerablemente el número de jóvenes y adultos que tienen las competencias necesarias, en particular técnicas y profesionales, para acceder al empleo, el trabajo decente y el emprendimiento", "definicion"=>"Proporción de personas, por grupo de edad, que han utilizado alguna de las siguientes habilidades informáticas: \ndescargar o instalar software o apps; cambiar la configuración del software, la app o del dispositivo; copiar \no mover ficheros entre carpetas, dispositivos o en la nube; crear presentaciones que integren texto, \nimágenes o tablas; programar en un lenguaje de programación y usar funciones básicas de una hoja de cálculo\n", "formula"=>"\n$$PPHAB_{grupo\\, edad}^{t} = \\frac{PHAB_{grupo\\, edad}^{t}}{P_{grupo\\, edad}^{t}} \\cdot 100$$\n\ndonde:\n\n$PHAB_{grupo\\, edad}^{t} =$ población de un grupo de edad que en los últimos 12 meses ha utilizado alguna habilidad informática en el año $t$\n\n$P_{grupo\\, edad}^{t} =$ población de un grupo de edad en el año $t$\n", "desagregacion"=>"Habilidades informáticas: descargar o instalar software o apps; cambiar la configuración del \nsoftware, la app o del dispositivo; copiar o mover ficheros entre carpetas, dispositivos o en la \nnube; crear presentaciones que integren texto, imágenes o tablas; programar en un lenguaje \nde programación y usar funciones básicas de una hoja de cálculo\n\nEdad: 16-24; 25-74\n\nSexo\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Las competencias en materia de TIC determinan el uso eficaz de las tecnologías de la información y la \ncomunicación, por lo que este indicador puede ayudar a establecer el vínculo entre el uso de las TIC y su impacto. \nLa falta de dichas competencias sigue siendo una de las principales barreras que impiden a las personas aprovechar \nal máximo el potencial de las tecnologías de la información y la comunicación.\n\nEstos datos pueden utilizarse para fundamentar políticas específicas destinadas a mejorar las competencias \nen TIC y, de este modo, contribuir a una sociedad de la información inclusiva. Este es también un indicador \nfundamental de la Lista básica de indicadores de la Alianza para la medición de las TIC para el \ndesarrollo, que ha sido aprobada por la Comisión de Estadística de las Naciones Unidas (en 2020).\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-04-01.pdf\">Metadatos 4-4-1.pdf </a>(solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de jóvenes y adultos con competencias en tecnología de la información y las comunicaciones (TIC), desglosada por tipo de competencia", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.4- De aquí a 2030, aumentar considerablemente el número de jóvenes y adultos que tienen las competencias necesarias, en particular técnicas y profesionales, para acceder al empleo, el trabajo decente y el emprendimiento", "definicion"=>"Proportion of people, by age group, who have used any of the following computer skills: downloading or \ninstalling software or apps; changing software, app, or device settings; copying or moving files between \nfolders, devices, or in the cloud; creating presentations that integrate text, images, or tables; \nprogramming in a programming language and using basic spreadsheet functions\n", "formula"=>"\n$$PPHAB_{age\\, group}^{t} = \\frac{PHAB_{age\\, group}^{t}}{P_{age\\, group}^{t}} \\cdot 100$$\n\nwhere:\n\n$PHAB_{age\\, group}^{t} =$ population of an age group that in the last 12 months has used a computer skill in the year $t$\n\n$P_{age\\, group}^{t} =$ population of an age group in the year $t$\n", "desagregacion"=>"Computer skills: downloading or installing software or apps; changing software, app, or device settings; \ncopying or moving files between folders, devices, or in the cloud; creating presentations that integrate \ntext, images, or tables; programming in a programming language and using basic spreadsheet functions\n\nAge: 16-24, 25-74\n\nSex\n\nProvince\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"ICT skills determine the effective use of information and communication technology, so this indicator \nmay therefore assist in making the link between ICT usage and impact. The lack of such skills continues to \nbe one of the key barriers keeping people from fully benefitting from the potential of information and \ncommunication technologies. \n\nThese data may be used to inform targeted policies to improve ICT skills, and thus contribute to an \ninclusive information society. This is also a core indicator of the Partnership on Measuring ICT for \nDevelopment's Core List of Indicators, which has been endorsed by the UN Statistical Commission (in 2020). \n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-04-01.pdf\">Metadata 4-4-1.pdf </a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de jóvenes y adultos con competencias en tecnología de la información y las comunicaciones (TIC), desglosada por tipo de competencia", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.4- De aquí a 2030, aumentar considerablemente el número de jóvenes y adultos que tienen las competencias necesarias, en particular técnicas y profesionales, para acceder al empleo, el trabajo decente y el emprendimiento", "definicion"=>"Honako trebetasun informatiko hauetakoren bat erabili duten pertsonen proportzioa, adin-taldearen arabera: \nsoftwarea edo app-ak deskargatzea edo instalatzea; softwarearen, app-aren edo gailuaren konfigurazioa aldatzea; \nfitxategiak karpeten, gailuen edo hodeiaren artean kopiatzea edo mugitzea; testua, irudiak edo taulak \nintegratzen dituzten aurkezpenak sortzea; programazio-lengoaia batean programatzea; eta kalkulu-orri baten \noinarrizko funtzioak erabiltzea.\n", "formula"=>"\n$$PPHAB_{adin\\, taldea}^{t} = \\frac{PHAB_{adin\\, taldea}^{t}}{P_{adin\\, taldea}^{t}} \\cdot 100$$\n\nnon:\n\n$PHAB_{adin\\, taldea}^{t} =$ azken 12 hilabeteetan trebetasun informatikoren bat erabili duen adin-talde bateko biztanleria \n$t$ urtean \n\n$P_{adin\\, taldea}^{t} =$ Adin-talde bateko biztanleria $t$ urtean\n", "desagregacion"=>"Trebetasun informatikoak: softwarea edo app-ak deskargatzea edo instalatzea; softwarearen, app-aren edo \ngailuaren konfigurazioa aldatzea; fitxategiak karpeten, gailuen edo hodeiaren artean kopiatzea edo mugitzea; \ntestua, irudiak edo taulak integratzen dituzten aurkezpenak sortzea; programazio-lengoaia batean programatzea; \nkalkulu-orri baten oinarrizko funtzioak erabiltzea\n\nAdina: 16-24; 25-74\n\nSexua\n\nLurralde historikoa\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"IKTen arloko gaitasunek informazioaren eta komunikazioaren teknologien erabilera eraginkorra zehazten dute; \nberaz, adierazle horrek IKTen erabileraren eta haren eraginaren arteko lotura ezartzen lagun dezake. Gaitasun \nhorien falta da oraindik ere pertsonek informazioaren eta komunikazioaren teknologien potentziala ahalik eta \ngehien aprobetxatzea eragozten duen oztopo nagusietako bat. \n\nDatu horiek IKTen gaitasunak hobetzeko politika espezifikoak oinarritzeko erabil daitezke, eta, horrela, \ninformazioaren gizarte inklusiboa sustatzeko. Garapenerako IKTak neurtzeko Aliantzaren adierazleen oinarrizko \nzerrendan ere funtsezko adierazlea da. Zerrenda hori Nazio Batuen Estatistika Batzordeak onartu zuen 2020an. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-04-01.pdf\">Metadatuak 4-4-1.pdf </a>(ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.4: By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurship</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.4.1: Proportion of youth and adults with information and communications technology (ICT) skills, by type of skill</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SE_ADT_ACTS - Proportion of youth and adults with information and communications technology (ICT) skills [4.4.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>4.5.1, 9.c.1, 17.6.1, 17.8.1 </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The proportion of youth and adults with Information and Communications Technology (ICT) skills, by type of skill defined as the percentage of individuals that have undertaken certain ICT-related activities in the last 3 months. From 2023, the percentage of individuals that have basic or above-basic ICT skills, by skill area can also be calculated. From 2024, the percentage of individuals with basic or above-basic <strong>overall</strong> ICT skills can also be calculated. The indicator is expressed as a percentage. <br></p>\n<p><strong>Concepts:</strong></p>\n<p>The indicator on the &#x201C;proportion of individuals with ICT skills, by type of skills&#x201D; refers to individuals that have undertaken certain activities using ICTs in the last three months. Most individuals will have carried out more than one activity and therefore multiple responses are possible. </p>\n<p>The skills categories are:</p>\n<p><u>Information and data literacy</u></p>\n<ul>\n  <li>Verifying the truthfulness of information found online</li>\n  <li>Finding information about goods or services*</li>\n  <li>Accessing news or books in a digital format (e.g. reading online news, watching news videos online, reading e-books on an e-reading device)*</li>\n  <li>Finding health information* </li>\n</ul>\n<p><u>Communication and collaboration</u></p>\n<ul>\n  <li>Sending content (e.g. document, picture, video through attached files, embedded content, hyperlinks) in messages (e.g. e-mail, messaging service, MMS)</li>\n  <li>Making calls (Telephoning over the Internet/VoIP, using Skype, Whatsapp, Viber, iTalk, etc.; includes video calls via webcam)*</li>\n  <li>Participating on social networking platforms (e.g. creating user profiles, reading or posting messages and other contributions to Facebook, X, Instagram, Snapchat, TikTok)*</li>\n  <li>Taking part in consultations via the Internet to define civic or social issues (e.g. urban planning, signing a petition, voting)*</li>\n</ul>\n<p><u>Digital content creation</u></p>\n<ul>\n  <li>Duplicating or moving data, information and content in digital environments (e.g. within a document, between devices, on the cloud)</li>\n  <li>Using spreadsheet software (e.g. using basic arithmetic formulae functions, macros)</li>\n  <li>Creating content combining different digital media (including text, images, sound, video or charts)</li>\n  <li>Programming or coding in digital environments</li>\n  <li>Editing text documents, spreadsheets or presentations using digital tools (e.g. Google Docs, Sharepoint, Apple iCloud, etc)*</li>\n</ul>\n<p>Problem solving</p>\n<ul>\n  <li>Connecting new devices (e.g. camera, printer, wireless speakers, wireless headphones)</li>\n  <li>Installing software or apps</li>\n  <li>Using Internet or mobile banking (includes electronic transactions with a bank for payment, transfers, etc. such as M-Pesa, or for looking up account information)*</li>\n  <li>Doing an online course or accessing online learning material (e.g. video tutorials, webinars, learning apps)*</li>\n  <li>Purchasing or ordering goods or services (via the Internet whether or not payment was made online; includes purchasing of products such as music, travel and accommodation via the Internet)*</li>\n</ul>\n<p><u>Safety</u></p>\n<ul>\n  <li>Taking security measures to protect devices and online accounts (e.g. changing passwords, avoiding unsecure links or downloads, setting up two-factor authentication)</li>\n  <li>Taking measures to protect privacy on your device, account or app (e.g. to limit the sharing of personal data and information, restrict access to social network profiles or geolocation, prevent targeted marketing)</li>\n</ul>\n<p>* These questions should be asked to Internet users about the activities in which they have partaken in using the Internet. However, some countries with lower Internet use penetration may wish to adjust their surveys by not implementing filters on Internet use and including reference to locally available services that do not require an Internet connection. For example, countries where mobile banking is often done through SMS without an Internet connection or where widely used integrated voice recognition (IVR) services to find health information are available may wish to consider such adjustments.</p>\n<p>ICT skills are measured irrespective of the device used (until 2019 data on ICT skills referred to computer-related skills only). From 2023, skills have been organized by areas and additional activities have been added to provide more balance to the assessment of ICT skills. The wording and organization of these indicators was subsequently revised in 2024 to increase their robustness, relevance, and clarity. To further increase clarity for respondents, countries are encouraged to adapt examples to reference the most popular local or national services.</p>\n<p><em>Aggregate measure of ICT skills</em></p>\n<p>From 2023, additional indicators to provide an overall view of an individual&#x2019;s level of ICT skills have been added. Countries should first assess each individual&#x2019;s skill level by the above listed skill areas.</p>\n<ul>\n  <li>Individuals are assessed on the number of activities within a skill area they report having done in the last three months using the following categories:</li>\n</ul>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>None</p>\n      </td>\n      <td>\n        <p>Basic</p>\n      </td>\n      <td>\n        <p>Above basic</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0 activities</p>\n      </td>\n      <td>\n        <p>1 activity</p>\n      </td>\n      <td>\n        <p>More than 1 activity</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<ul>\n  <li>Skill levels are not assessed in skill areas where fewer than two components of the skill area are collected.</li>\n  <li>Indicators are weighted equally within each skill area.</li>\n</ul>\n<p>Countries that have sufficient data to assess skill levels for each of the five skill areas should also assess the <strong>overall</strong> skill level of individuals. Countries not collecting sufficient data for all five skill areas cannot assess overall skill levels for international comparisons.</p>\n<ul>\n  <li>Overall skill levels for individuals should be assessed based on their skill level in the five skill areas as shown below:</li>\n</ul>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Category</strong></p>\n      </td>\n      <td>\n        <p><strong>Definition</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Above basic skills</p>\n      </td>\n      <td>\n        <p>Above basic skills in all five areas</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Basic skills</p>\n      </td>\n      <td>\n        <p>At least basic skills in all five areas &#x2013; can be basic or above basic, but not all five at above basic</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4 of 5</p>\n      </td>\n      <td>\n        <p><em>Basic </em>or <em>above basic </em>in any four areas and no skills in one area (at least basic in four of five areas).</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3 of 5</p>\n      </td>\n      <td>\n        <p><em>Basic </em>or <em>above basic </em>in any three areas and no skills in two areas (at least basic in three of five areas).</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2 of 5</p>\n      </td>\n      <td>\n        <p><em>Basic </em>or <em>above basic </em>in any two areas and no skills in three areas (at least basic in two of five areas).</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0-1 of 5</p>\n      </td>\n      <td>\n        <p>No skills in four or five areas (at least basic in one or fewer of five areas).</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Activities are classified according to agreement at meetings of the ITU Expert Group on ICT Household Indicators (EGH).</p>\n<p>Furthermore, for countries that collect this data through an official survey, and if data allow breakdown and disaggregation, the indicator can be broken down by region (urban/rural), by sex, by age group, by educational level (ISCED), by labour force status (ILO), and by occupation (ISCO). International Telecommunication Union (ITU) collects data for all of these breakdowns from countries.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Countries can collect data on this indicator through national household surveys. Data for different countries are compiled by the International Telecommunication Union (ITU).</p>", "COLL_METHOD__GLOBAL"=>"<p>Data for different countries are compiled and provided by the International Telecommunication Union (ITU).</p>", "FREQ_COLL__GLOBAL"=>"<p>Various. Each survey has its own data collection cycle. The International Telecommunication Union (ITU) collects data twice a year from Member States, in Q1 and in Q3.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The International Telecommunication Union (ITU) releases data twice per year on ICT skills.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Bodies responsible for conducting household surveys (including National Statistical Offices and Government Ministries) in which information on the use of ICT skills is collected. Data is compiled by the International Telecommunication Union (ITU).</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "INST_MANDATE__GLOBAL"=>"<p>As the United Nations (UN) specialized agency for ICTs, the International Telecommunication Union (ITU) is an official source for global ICT statistics, collecting ICT data from its Member States. </p>", "RATIONALE__GLOBAL"=>"<p>ICT skills determine the effective use of information and communication technologies, so this indicator may therefore assist in making the link between ICT usage and impact. The lack of such skills continues to be one of the key barriers keeping people from fully benefitting from the potential of information and communication technologies. These data may be used to inform targeted policies to improve ICT skills, and thus contribute to an inclusive information society.</p>\n<p>This is also a core indicator of the Partnership on Measuring ICT for Development&apos;s Core List of Indicators, which has been endorsed by the UN Statistical Commission (in 2022).</p>", "REC_USE_LIM__GLOBAL"=>"<p>This indicator is based on an internationally-agreed definition and methodology, which has been developed under the coordination of International Telecommunications Union (ITU), through its Expert Group on ICT Household Indicators and following an extensive consultation process with countries. It was also endorsed by the UN Statistical Commission in 2014<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup>, 2020, and 2022.</p>\n<p>The indicator is based on the responses provided by interviewees regarding certain activities that they have carried out in a reference period of time. However, it is not a direct assessment of skills nor do we know if those activities were undertaken effectively. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> As one of the Core List of Indicators of the Partnership on Measuring ICT for Development. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>This indicator is calculated as the proportion of in-scope individuals who have carried out each activity in the past 3 months, regardless of where that activity took place. </p>\n<p>[<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mo>-</mo>\n        <mi>s</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>b</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>y</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>k</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>/</mo>\n    <mo>(</mo>\n    <mi>n</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>b</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mo>-</mo>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>v</mi>\n    <mi>i</mi>\n    <mi>d</mi>\n    <mi>u</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>s</mi>\n    <mo>)</mo>\n    <mo>]</mo>\n    <mi>*</mi>\n    <mn>100</mn>\n  </math></p>\n<p>For aggregate measures by skill area the indicator is calculated as the proportion of in-scope individuals who have basic or above-basic ICT skill levels in each skill area. This is based on the activities that in-scope individuals have carried out within each skill area in the past 3 months, regardless of where that activity took place. </p>\n<p>Proportion of individuals with basic ICT skills = <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mo>-</mo>\n        <mi>s</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>r</mi>\n        <mi>y</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>g</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>k</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>/</mo>\n    <mo>(</mo>\n    <mi>n</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>b</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mo>-</mo>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>v</mi>\n    <mi>i</mi>\n    <mi>d</mi>\n    <mi>u</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>s</mi>\n    <mo>)</mo>\n    <mo>]</mo>\n    <mi>*</mi>\n    <mn>100</mn>\n  </math></p>\n<p>Proportion of individuals with above-basic ICT skills = <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mo>-</mo>\n        <mi>s</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>r</mi>\n        <mi>y</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>g</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>k</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>/</mo>\n    <mo>(</mo>\n    <mi>n</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>b</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mo>-</mo>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>v</mi>\n    <mi>i</mi>\n    <mi>d</mi>\n    <mi>u</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>s</mi>\n    <mo>)</mo>\n    <mo>]</mo>\n    <mi>*</mi>\n    <mn>100</mn>\n  </math></p>\n<p>For an overall measure of ICT skills the indicator is calculated as the proportion of in-scope individuals who have basic or above-basic ICT skill levels in all skill areas. This is based on the assessed skill levels of in-scope individuals for each skill area as calculated above. </p>\n<p>Proportion of individuals with basic overall ICT skills = <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mo>-</mo>\n        <mi>s</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>b</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>k</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>i</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>k</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>/</mo>\n    <mo>(</mo>\n    <mi>n</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>b</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mo>-</mo>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>v</mi>\n    <mi>i</mi>\n    <mi>d</mi>\n    <mi>u</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>s</mi>\n    <mo>)</mo>\n    <mo>]</mo>\n    <mi>*</mi>\n    <mn>100</mn>\n  </math></p>\n<p>Proportion of individuals with above-basic overall ICT skills = <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mo>-</mo>\n        <mi>s</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>b</mi>\n        <mi>o</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>b</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>k</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>i</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>k</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>/</mo>\n    <mo>(</mo>\n    <mi>n</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>b</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mo>-</mo>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>v</mi>\n    <mi>i</mi>\n    <mi>d</mi>\n    <mi>u</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>s</mi>\n    <mo>)</mo>\n    <mo>]</mo>\n    <mi>*</mi>\n    <mn>100</mn>\n  </math></p>\n<p>Figures supplied are expressed as a proportion of the in-scope population.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are submitted by Member States to the International Telecommunication Union (ITU). ITU checks and validates the data, in consultation with the Member States. </p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made to the data submitted by countries.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>None by data compiler.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>None by data compiler.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates are not currently available for this indicator.</p>", "DOC_METHOD__GLOBAL"=>"<p>International Telecommunication Union (ITU) Manual for Measuring Information and Communications Technology (ICT) Access and Use by Households and Individuals 2020:</p>\n<p><a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx\">https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx</a> </p>\n<p>Report of the ITU Expert Group on ICT Household Indicators subgroup on measuring ICT skills using household surveys, 2024:</p>\n<p><a href=\"https://www.itu.int/itu-d/meetings/egh2024/wp-content/uploads/sites/28/2024/09/EGH2024_ICTSkillsReport.pdf\">https://www.itu.int/itu-d/meetings/egh2024/wp-content/uploads/sites/28/2024/09/EGH2024_ICTSkillsReport.pdf</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data are checked and validated by the ICT Data and Analytics (IDA) Division of the International Telecommunication Union (ITU). Countries are contacted to clarify and correct their submissions.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The guidelines of the Manual for Measuring ICT Access and Use by Households and Individuals 2020 are followed.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The guidelines of the Manual for Measuring ICT Access and Use by Households and Individuals 2020 are followed.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Overall, the indicator is available for more than 90 countries from at least one survey.</p>\n<p><strong>Time series:</strong></p>\n<p>2005 onwards</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Since data for the indicator on the proportion of individuals with ICT skills, by type of skills are collected through a survey, classificatory variables for individuals can provide further information on the differences in ICT skills among men/women, children/adults (age groups), employed/unemployed, etc., according to national requirements These data may be used to inform targeted policies to improve ICT skills, and thus contribute to the development of an inclusive information society.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>None</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>International Telecommunication Union:</p>\n<p><a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx\">https://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx</a> </p>\n<p><strong>References:</strong></p>\n<p>ITU Manual for Measuring ICT Access and Use by Households and Individuals 2020:</p>\n<p><a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx\">https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx</a> </p>\n<p>Report of the ITU Expert Group on ICT Household indicators subgroup on measuring ICT skills using household surveys 2023:</p>\n<p><a href=\"https://www.itu.int/itu-d/meetings/statistics/wp-content/uploads/sites/8/2023/09/Report-of-the-EGH-subgroup-on-ICT-Skills.pdf\">https://www.itu.int/itu-d/meetings/statistics/wp-content/uploads/sites/8/2023/09/Report-of-the-EGH-subgroup-on-ICT-Skills.pdf</a></p>\n<p>Report of the ITU Expert Group on ICT Household Indicators subgroup on measuring ICT skills using household surveys, 2024:</p>\n<p><a href=\"https://www.itu.int/itu-d/meetings/egh2024/wp-content/uploads/sites/28/2024/09/EGH2024_ICTSkillsReport.pdf\">https://www.itu.int/itu-d/meetings/egh2024/wp-content/uploads/sites/28/2024/09/EGH2024_ICTSkillsReport.pdf</a> </p>", "indicator_sort_order"=>"04-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.5.1", "slug"=>"4-5-1", "name"=>"Índices de paridad (entre mujeres y hombres, zonas rurales y urbanas, quintiles de riqueza superior e inferior y grupos como los discapacitados, los pueblos indígenas y los afectados por los conflictos, a medida que se disponga de datos) para todos los indicadores de educación de esta lista que puedan desglosarse", "url"=>"/site/es/4-5-1/", "sort"=>"040501", "goal_number"=>"4", "target_number"=>"4.5", "global"=>{"name"=>"Índices de paridad (entre mujeres y hombres, zonas rurales y urbanas, quintiles de riqueza superior e inferior y grupos como los discapacitados, los pueblos indígenas y los afectados por los conflictos, a medida que se disponga de datos) para todos los indicadores de educación de esta lista que puedan desglosarse"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Índices de paridad (entre mujeres y hombres, quintiles de riqueza superior e inferior, municipios de más y menos de 10.000 habitantes y personas con y sin limitaciones por problemas de salud) en la realización de actividades educativas (formales o no formales) en los últimos 12 meses o 4 semanas", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Índices de paridad (entre mujeres y hombres, zonas rurales y urbanas, quintiles de riqueza superior e inferior y grupos como los discapacitados, los pueblos indígenas y los afectados por los conflictos, a medida que se disponga de datos) para todos los indicadores de educación de esta lista que puedan desglosarse", "indicator_number"=>"4.5.1", "national_geographical_coverage"=>"", "page_content"=>"Se mide el siguiente indicador de educación: realización de actividades educativas (formales o no formales). <b>Dirección deseada:</b> Índice=1", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_57/opt_1/ti_encuesta-de-poblacion-en-relacion-con-la-actividad/temas.html", "url_text"=>"Encuesta de población en relación con la actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Quinquenal", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176759&menu=ultiDatos&idp=1254735573113", "url_text"=>"Encuesta sobre la participación de la población adulta en las actividades de aprendizaje", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Índices de paridad (entre mujeres y hombres, quintiles de riqueza superior e inferior, municipios de más y menos de 10.000 habitantes y personas con y sin limitaciones por problemas de salud) en la realización de actividades educativas (formales o no formales) en los últimos 12 meses o 4 semanas", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.5- De aquí a 2030, eliminar las disparidades de género en la educación y asegurar el acceso igualitario a todos los niveles de la enseñanza y la formación profesional para las personas vulnerables, incluidas las personas con discapacidad, los pueblos indígenas y los niños en situaciones de vulnerabilidad", "definicion"=>"Relación entre las proporciones entre los siguientes grupos que han realizado actividades \neducativas (formales o no formales) en los últimos 12 meses o 4 semanas:\n\n - Mujeres y hombres entre 18 y 64 años (en los últimos 12 meses)\n - Personas entre 18 y 64 años pertenecientes a hogares que se encuentran en el nivel \n   de ingresos inferior y superior (en los últimos 12 meses) \n - Personas entre 18 y 64 años con y sin limitaciones por problemas de salud \n   (graves o no graves)(en los últimos 12 meses)\n - Personas entre 15 y 64 años residentes en municipios de menos de 10.000 habitantes y más de 10.000 \n   habitantes (en las últimas 4 semanas)\n", "formula"=>"\n$$IPD = \\frac{[Ind_i]_d}{[Ind_i]_a}$$\n\ndonde:\n\n$IPD$ = el Índice de Paridad de la Dimensión (Sexo, Riqueza, Ubicación, etc.)\n\n$Ind_i$ = el Indicador de Educación: realización de actividades educativas (formales o no formales) en \nlos últimos  12 meses o 4 semanas\n\n$d$ = el grupo desfavorecido (mujeres, personas más pobres, personas que viven en municipios pequeños, \npersonas con limitaciones por problemas de salud)\n\n$a$ = el grupo favorecido (hombres, personas más ricas, personas que viven en municipios grandes, \npersonas sin limitaciones por problemas de salud)\n", "desagregacion"=>"Territorio histórico", "periodicidad"=>"Anual", "observaciones"=>"\nEl valor de referencia de este indicador es 1, que se tiene cuando la paridad es absoluta.  El grado de disparidad es mayor cuanto más se aleja de 1 el valor del indicador, reflejando  una situación desfavorable o favorable del grupo poblacional del numerador respecto al  grupo poblacional del denominador según tome un valor inferior o superior a 1 respectivamente. ", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Medir el nivel general de disparidad entre dos subpoblaciones de interés con respecto a un  indicador determinado. Cuanto más alejado de 1 se encuentre el índice de paridad, mayor será la  disparidad entre los dos grupos de interés. \nFuente: División de Estadísticas de las Naciones Unidas", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.5.1&seriesCode=SE_GPI_PTNPRE&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Índice de paridad de género ajustado para la tasa de participación en el aprendizaje organizado (un año antes de la edad oficial de ingreso a la primaria), (ratio) SE_GPI_PTNPRE</a> UNSTATS<br>", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-05-01.pdf\"> Metadatos 4-5-1.pdf </a>(solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Índices de paridad (entre mujeres y hombres, quintiles de riqueza superior e inferior, municipios de más y menos de 10.000 habitantes y personas con y sin limitaciones por problemas de salud) en la realización de actividades educativas (formales o no formales) en los últimos 12 meses o 4 semanas", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.5- De aquí a 2030, eliminar las disparidades de género en la educación y asegurar el acceso igualitario a todos los niveles de la enseñanza y la formación profesional para las personas vulnerables, incluidas las personas con discapacidad, los pueblos indígenas y los niños en situaciones de vulnerabilidad", "definicion"=>"\nRelationship between the proportions among the following groups who have participated in educational activities\n(formal or non-formal) in the last 12 months or 4 weeks:\n\n- Women and men between 18 and 64 years of age (in the last 12 months)\n- People between 18 and 64 years of age belonging to households in the lowest and highest income brackets \n  (in the last 12 months)\n- People between 18 and 64 years of age with and without limitations due to health problems (serious or non-serious) \n  (in the last 12 months)\n- People between 15 and 64 years of age residing in municipalities with fewer than 10,000 inhabitants and more \n  than 10,000 inhabitants (in the last 4 weeks)\n", "formula"=>"\n$$IPD = \\frac{[Ind_i]_d}{[Ind_i]_a}$$\n\nwhere:\n\n$IPD$ = Parity Index in dimension (Sex, Wealth, Location, etc.)\n\n$Ind_i$ = Education Index: participation in educational activities (formal or informal) \nin last 12 months or 4 weeks\n\n$d$ = Disadvantaged group (women, poorer people, people living in small towns, people \nwith limitations due to health problems)\n\n$a$ = Favored group (men, wealthier people, people living in large municipalities,\npeople without health limitations)\n", "desagregacion"=>"Province", "periodicidad"=>"Anual", "observaciones"=>"\nThe reference value for this indicator is 1, which indicates absolute parity.  The degree of disparity is greater the further the indicator value is from 1, reflecting  an unfavorable or favorable situation for the numerator population group relative to the  denominator population group, depending on whether the value is lower or higher than 1,  respectively. ", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nTo measure the general level of disparity between two sub-populations of interest  with regard to a given indicator. The further from 1 the parity index lies, the greater  the disparity between the two groups of interest. \nSource: United Nations Statistics Division ", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.5.1&seriesCode=SE_GPI_PTNPRE&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Adjusted gender parity index for participation rate in organized learning (one year before the official primary entry age), (ratio) SE_GPI_PTNPRE</a> UNSTATS<br>", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-05-01.pdf\"> Metadata 4-5-1.pdf </a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Índices de paridad (entre mujeres y hombres, quintiles de riqueza superior e inferior, municipios de más y menos de 10.000 habitantes y personas con y sin limitaciones por problemas de salud) en la realización de actividades educativas (formales o no formales) en los últimos 12 meses o 4 semanas", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.5- De aquí a 2030, eliminar las disparidades de género en la educación y asegurar el acceso igualitario a todos los niveles de la enseñanza y la formación profesional para las personas vulnerables, incluidas las personas con discapacidad, los pueblos indígenas y los niños en situaciones de vulnerabilidad", "definicion"=>"Azken 12 hilabeteetan edo 4 asteetan hezkuntza-jarduerak (formalak edo ez-formalak) egin dituzten \ntalde hauen arteko proportzioa:\n\n - 18 eta 64 urte bitarteko emakumeak eta gizonak (azken 12 hilabeteetan)\n - 18 eta 64 urte bitarteko pertsonak, diru-sarrera gutxiko etxeeetan eta diru-sarrera altuko etxeetan bizi direnak (azken 12 hilabeteetan)\n - 18 eta 64 urte bitarteko pertsonak, osasun-arazoengatik muga larriak edo ez larriak dituztenak, eta muga horiek ez dituztenak (azken 12 hilabeteetan)\n - 15 eta 64 urte bitarteko pertsonak, 10.000 biztanle baino gutxiagoko eta 10.000 biztanle baino gehiagoko udalerrietan bizi direnak (azken 4 asteetan)\n", "formula"=>"\n$$IPD = \\frac{[Ind_i]_d}{[Ind_i]_a}$$\n\nnon:\n\n$IPD$ = Dimentsioaren Parekotasun Indizea (Sexua, Aberastasuna, Kokapena, etab.)\n\n$Ind_i$ = Hezkuntza Indizea: hezkuntza-jarduerak (formalak edo ez-formalak) egitea azken 12 hilabete edo 4 asteetan.\n\n$d$ = talde baztertua (emakumeak, pertsona pobreagoak, udalerri txikietan bizi diren pertsonak, osasun-arazoengatiko mugak dituzten pertsonak)\n\n$a$ = talde mesedetua (gizonak, pertsona aberatsagoak, udalerri handietan bizi diren pertsonak, osasun-arazoengatik mugarik ez duten pertsonak)\n", "desagregacion"=>"Lurralde historikoa", "periodicidad"=>"Anual", "observaciones"=>"\nAdierazle honen erreferentzia-balioa 1 da, parekotasun absolutua adierazten duena. Adierazlearen balioa  1etik zenbat eta urrunago egon, orduan eta handiagoa da desberdintasun-maila; balioa 1 baino txikiagoa denean,  zenbakitzaileko populazio taldeak izendatzaileko populazio-taldearekiko egoera baztertua adierazten du, eta  1 baino altuagoa denean, egoera mesedetua. ", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Adierazle jakin batekiko intereseko bi azpipopulazioen arteko desberdintasun-maila orokorra neurtzen du.  Parekotasun-indizea 1etik zenbat eta urrunago egon, orduan eta handiagoa izango da bi interes-taldeen arteko  desberdintasuna. \n\nIturria: Nazio Batuen Estatistika Sekzioa ", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.5.1&seriesCode=SE_GPI_PTNPRE&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Genero-parekotasunaren indize doitua ikaskuntza antolatuko parte hartzeko tasarako (Lehen Hezkuntzan sartzeko adin ofiziala baino urtebete lehenago), (ratio) SE_GPI_PTNPRE</a> UNSTATS<br>", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-05-01.pdf\"> Metadatuak 4-5-1.pdf </a>(ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situations</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregated</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SE_AGP_CPRA - Adjusted gender parity index for completion rate, by sex, location, wealth quintile and education level [4.5.1]</p>\n<p>SE_ALP_CPLR - Adjusted location parity index for completion rate, by sex, location, wealth quintile and education level [4.5.1]</p>\n<p>SE_AWP_CPRA - Adjusted wealth parity index for completion rate, by sex, location, wealth quintile and education level [4.5.1]</p>\n<p>SE_GPI_ICTS - Gender parity index for youth/adults with information and communications technology (ICT) skills [4.5.1]</p>\n<p>SE_GPI_PART - Adjusted gender parity index for participation rate in formal and non-formal education and training (ratio) [4.5.1]</p>\n<p>SE_GPI_PTNPRE - Adjusted gender parity index for participation rate in organized learning (one year before the official primary entry age), (ratio) [4.5.1]</p>\n<p>SE_GPI_TCAQ - Adjusted gender parity index for the proportion of teachers with the minimum required qualifications, by education level (ratio) [4.5.1]</p>\n<p>SE_IMP_FPOF - Adjusted immigration status parity index for achieving at least a fixed level of proficiency in functional skills, by numeracy/literacy skills (ratio) [4.5.1]</p>\n<p>SE_LGP_ACHI - Adjusted language test parity index for achieving a minimum proficiency level in reading and mathematics (ratio) [4.5.1]</p>\n<p>SE_NAP_ACHI - Adjusted immigration status parity index for achieving a minimum proficiency level in reading and mathematics [4.5.1]</p>\n<p>SE_TOT_GPI - Adjusted gender parity index for achieving a minimum proficiency level in reading and mathematics (ratio) [4.5.1]</p>\n<p>SE_TOT_GPI_FS - Adjusted gender parity index for achieving at least a fixed level of proficiency in functional skills, by numeracy/literacy skills (ratio) [4.5.1]</p>\n<p>SE_TOT_RUPI - Adjusted rural to urban parity index for achieving a minimum proficiency level in reading and mathematics (ratio) [4.5.1]</p>\n<p>SE_TOT_SESPI - Adjusted low to high socio-economic parity index for achieving a minimum proficiency level in reading and mathematics (ratio) [4.5.1]</p>\n<p>SE_TOT_SESPI_FS - Adjusted low to high socio-economic parity status index for achieving at least a fixed level of proficiency in functional skills, by numeracy/literacy skills (ratio) [4.5.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>All equity targets and targets associated with the underlying indicators.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Parity indices require data for the specific groups of interest. They represent the ratio of the indicator value for one group to that of the other. Typically, the likely more disadvantaged group is placed in the numerator. A value of exactly 1 indicates parity between the two groups.</p>\n<p><strong>Concepts:</strong></p>\n<p>See metadata for relevant underlying indicator.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Ratio. This indicator is expressed as the ratio of the value of the indicator for the likely more disadvantaged group to that of the likely more advantaged group. </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The sources are the same as for the underlying indicators for this goal.</p>", "COLL_METHOD__GLOBAL"=>"<p>The same as the underlying indicator.</p>", "FREQ_COLL__GLOBAL"=>"<p>Depends on underlying indicator.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Depends on underlying indicator.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The same as the underlying indicator.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "INST_MANDATE__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The Institute produces internationally comparable data and methodologies in the fields of education, science, culture and communication for countries at all stages of development.</p>\n<p>The <a href=\"https://www.moe.gov.bn/DocumentDownloads/Education%202030/Education2030.pdf\"><u>Education 2030 Framework for Action &#xA7;100</u></a> has clearly stated that: &#x201C;<em>In recognition of the importance of harmonization of monitoring and reporting, the UIS will remain the official source of cross-nationally comparable data on education. It will continue to produce international monitoring indicators based on its annual education survey and on other data sources that guarantee international comparability for more than 200 countries and territories. In addition to collecting data, the UIS will work with partners to develop new indicators, statistical approaches and monitoring tools to better assess progress across the targets related to UNESCO&#x2019;s mandate, working in coordination with the SDG-Education 2030 SC&#x201D;.</em></p>", "RATIONALE__GLOBAL"=>"<p>To measure the general level of disparity between two sub-populations of interest with regard to a given indicator. The further from 1 the parity index lies, the greater the disparity between the two groups of interest.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator is not symmetrical about 1 but a simple transformation can make it so (by inverting ratios that exceed 1 and subtracting them from 2). This will make interpretation easier.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator value of the likely more disadvantaged group is divided by the indicator value of the other sub-population of interest. </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>P</mi>\n    <mi>I</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mfenced open=\"[\" close=\"]\" separators=\"|\">\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>l</mi>\n                    <mi>n</mi>\n                    <mi>d</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>i</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfenced>\n          </mrow>\n          <mrow>\n            <mi>d</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mfenced open=\"[\" close=\"]\" separators=\"|\">\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>l</mi>\n                    <mi>n</mi>\n                    <mi>d</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>i</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfenced>\n          </mrow>\n          <mrow>\n            <mi>a</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where:</p>\n<p><em>DPI </em>= the Dimension (Gender, Wealth, Location, etc.) Parity Index</p>\n<p><em>Ind<sub>i</sub> </em>= the Education 2030 Indicator <em>i</em> for which an equity measure is needed.</p>\n<p><em>d</em> = the likely disadvantaged group (e.g. female, poorest, etc.)</p>\n<p><em>a </em>= the likely advantaged group (e.g. male, richest, etc.)</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The UNESCO Institute for Statistics shares all indicator values and notes on methodology with National Statistical Offices, Ministries of Education, or other relevant agencies in individual countries for their review, feedback and validation before the publication of the data. </p>", "ADJUSTMENT__GLOBAL"=>"<p>The same as the underlying indicator.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>The same as the underlying indicator.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>The same as the underlying indicator.</p>", "REG_AGG__GLOBAL"=>"<p>The same as the underlying indicator.</p>", "DOC_METHOD__GLOBAL"=>"<p>The UIS has elaborated guidance for the countries on the methodology that should be used to calculate this indicator. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management for this indicator is the same as quality management of the underlying indicators. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance for this indicator is the same as quality assurance of the underlying indicators.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment for this indicator is the same as quality assessment of the underlying indicators.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Depends on underlying indicator.</p>\n<p><strong>Time series:</strong></p>\n<p>Depends on underlying indicator.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>None because the parity indices directly compare two sub-populations of interest.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The same as the underlying indicator.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.uis.unesco.org\">http://www.uis.unesco.org</a></p>\n<p><strong>References:</strong></p>\n<p>See references for each underlying indicator.</p>", "indicator_sort_order"=>"04-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.6.1", "slug"=>"4-6-1", "name"=>"Proporción de la población en un grupo de edad determinado que ha alcanzado al menos un nivel fijo de competencia funcional en a) alfabetización y b) aritmética, desglosada por sexo", "url"=>"/site/es/4-6-1/", "sort"=>"040601", "goal_number"=>"4", "target_number"=>"4.6", "global"=>{"name"=>"Proporción de la población en un grupo de edad determinado que ha alcanzado al menos un nivel fijo de competencia funcional en a) alfabetización y b) aritmética, desglosada por sexo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Tipo de competencia", "value"=>"Matemáticas"}, {"field"=>"Tipo de competencia", "value"=>"Comprensión lectora"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de personas entre 16 y 65 años que han alcanzado al menos el nivel 2 de competencia en la escala de comprensión lectora y en matemáticas", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población en un grupo de edad determinado que ha alcanzado al menos un nivel fijo de competencia funcional en a) alfabetización y b) aritmética, desglosada por sexo", "indicator_number"=>"4.6.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Organización para la Cooperación y Desarrollo Económico (OCDE)", "periodicity"=>"Decenal", "url"=>"https://www.oecd.org/en/about/programmes/piaac.html", "url_text"=>"Programa de Evaluación de Competencias de Adultos (PIAAC)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/OCDE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de personas entre 16 y 65 años que han alcanzado al menos el nivel 2 de competencia en la escala de comprensión lectora y en matemáticas", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.6- De aquí a 2030, asegurar que todos los jóvenes y una proporción considerable de los adultos, tanto hombres como mujeres, estén alfabetizados y tengan nociones elementales de aritmética", "definicion"=>"Proporción de personas entre 16 y 65 años que han alcanzado al menos el nivel 2 de competencia  en la escala de comprensión lectora y en matemáticas", "formula"=>"\n$$PPN2_{c}^{t} = \\frac{PN2_{16-65\\,c}^{t}}{P_{16-65}^{t}}$$\n\ndonde:\n\n$PN2_{16-65\\,c}^{t} =$ población entre 16 y 65 años que ha alcanzado al menos el nivel 2 de competencia \nen la escala del tipo de competencia $c$ en el año $t$\n\n$P_{16-65}^{t} =$ población entre 16 y 65 años años en el año $t$\n", "desagregacion"=>"Tipo de competencia: comprensión lectora, matemáticas\n\nSexo\n", "periodicidad"=>"Decenal", "observaciones"=>"\nEl Programa de evaluación internacional de competencias de adultos utiliza una escala de 500 puntos \npara evaluar la comprensión lectora y la competencia en matemáticas. Para ayudar a \ninterpretar esas puntuaciones, la escala se divide en seis categorías de  competencia:\n\n - Inferior al nivel 1: puntuación inferior a 176\n - Nivel 1: puntuación entre 176 y 225\n - Nivel 2: puntuación entre 226 y 275\n - Nivel 3: puntuación entre 276 y 325\n - Nivel 4: puntuación entre 326 y 375\n - Nivel 5: puntuación entre 376 y 500\n\nUna persona alcanza al menos el nivel 2 de competencia en la escala de comprensión lectora cuando \nhabitualmente es capaz de entender textos sobre temas que no conoce, aunque tenga dificultades ante \ntareas como comparar y contrastar diferentes textos o buscar textos digitales sobre un tema concreto \nque no conoce.\n\nUna persona alcanza al menos el nivel 2 de competencia en la escala de matemáticas cuando \nhabitualmente es capaz de responder a preguntas que requieren cálculos intermedios, entiende \nla información matemática de una tabla y no tiene dificultades para interpretar gráficas simples, \naunque tenga dificultades ante tareas como manejar proporciones o interpretar gráficas complejas.\n\nEl dato de 2012 corresponde al estudio que se llevó a cabo entre el tercer cuatrimestre \nde 2011 y el primer cuatrimestre de 2012.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador es una medida directa de los niveles de habilidades de los jóvenes y los adultos  en dos áreas: alfabetización y aritmética.\nEl nivel fijo de competencia (FLP) es el parámetro de referencia de los conocimientos básicos  en un dominio (alfabetización o aritmética) medido a través de evaluaciones de aprendizaje. Actualmente,  el FLP para los informes globales es el descriptor de nivel 2 del Programa para la Evaluación Internacional  de Competencias de Adultos (PIAAC).\nLos conceptos de alfabetización funcional y aritmética funcional se basan en las  definiciones de la UNESCO, que ubren un continuo de niveles de competencia en lugar de una dicotomía.  Una persona es alfabetizada funcionalmente si puede participar en todas aquellas actividades en las que la  alfabetización es necesaria para el funcionamiento eficaz de su grupo y comunidad y también le permite  seguir utilizando la lectura, la escritura y el cálculo para su propio desarrollo y el de la comunidad. \nFuente: División de Estadísticas de las Naciones Unidas", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-06-01.pdf\">Metadatos 4-6-1.pdf </a>(solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de personas entre 16 y 65 años que han alcanzado al menos el nivel 2 de competencia en la escala de comprensión lectora y en matemáticas", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.6- De aquí a 2030, asegurar que todos los jóvenes y una proporción considerable de los adultos, tanto hombres como mujeres, estén alfabetizados y tengan nociones elementales de aritmética", "definicion"=>"Proportion of people aged 16 to 65 who have reached at least level 2  proficiency on the reading comprehension scale and the mathematics scale", "formula"=>"\n$$PPN2_{c}^{t} = \\frac{PN2_{16-65\\,c}^{t}}{P_{16-65}^{t}}$$\n\nwhere:\n\n$PN2_{16-65\\,c}^{t} =$ Population aged 16 to 65 who have achieved at least level 2 \nof proficiency on $c$ type of competence in the year $t$\n\n$P_{16-65}^{t} =$ population aged 16 to 65 in year $t$\n", "desagregacion"=>"Type of competence: reading comprehension, mathematics\n\nSex\n", "periodicidad"=>"Decenal", "observaciones"=>"\nThe Programme for the International Assessment of Adult Competencies uses a 500-point scale to assess \nreading comprehension and mathematics proficiency. To help interpret these scores, the scale is divided \ninto six proficiency categories:\n\n- Below Level 1: score below 176 \n- Level 1: score between 176 and 225 \n- Level 2: score between 226 and 275 \n- Level 3: score between 276 and 325 \n- Level 4: score between 326 and 375 \n- Level 5: score between 376 and 500 \n\nA person reaches at least Level 2 proficiency on the reading comprehension scale when they can usually \nunderstand texts on unfamiliar topics, although they may struggle with tasks such as comparing and \ncontrasting different texts or searching for digital texts on a specific topic. \n\nA person reaches at least Level 2 proficiency on the mathematics scale when they are routinely able \nto answer questions requiring intermediate calculations, understand the mathematical information in a \ntable, and have no difficulty interpreting simple graphs, although they may have difficulty with tasks \nsuch as handling proportions or interpreting complex graphs. \n\nThe 2012 data corresponds to the study conducted between the third quarter of 2011 and the first quarter \nof 2012.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The indicator is a direct measure of the skill levels of youth and adults in the two  areas: literacy and numeracy. \nThe fixed level of proficiency (FLP) is the benchmark of basic knowledge in a domain  (literacy or numeracy) measured through learning assessments. Currently, the FLP for  global reporting is the Programme for the International Assessment of Adult Competencies  (PIAAC) level 2 descriptor. \nThe concepts of functional literacy and functional numeracy are based on the UNESCO  definitions, which cover a continuum of proficiency levels rather than a dichotomy. A  person is functionally literate if he/she can engage in all those activities in which  literacy is required for the effective functioning of his/her group and community and  also which enables them to continue to use reading, writing and calculation for his/her  own and the community’s development. \nSource: United Nations Statistics Division", "dato_global"=>"", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-06-01.pdf\">Metadata 4-6-1.pdf </a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Proporción de personas entre 16 y 65 años que han alcanzado al menos el nivel 2 de competencia en la escala de comprensión lectora y en matemáticas", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.6- De aquí a 2030, asegurar que todos los jóvenes y una proporción considerable de los adultos, tanto hombres como mujeres, estén alfabetizados y tengan nociones elementales de aritmética", "definicion"=>"Irakurketa ulermenaren eskalan eta matematikan gutxienez 2. mailako gaitasuna lortu duten 16-65 urteko pertsonen proportzioa  ", "formula"=>"\n$$PPN2_{c}^{t} = \\frac{PN2_{16-65\\,c}^{t}}{P_{16-65}^{t}}$$\n\nnon:\n\n$PN2_{16-65\\,c}^{t} =$ $c$ konpetentziaren eskalan gutxienez 2. mailako gaitasuna lortu duten 16-65 urteko biztanleak $t$ urtean\n\n$P_{16-65}^{t} =$ 16-65 urteko biztanleria $t$ urtean\n", "desagregacion"=>"Konpetentzia: irakurketa; matematika\n\nSexua\n", "periodicidad"=>"Decenal", "observaciones"=>"\nHelduen gaitasunak nazioartean ebaluatzeko programak 500 puntuko eskala erabiltzen du irakurmen eta matematika \ngaitasunak ebaluatzeko. Puntuazio horiek interpretatzen laguntzeko, eskala sei kategoriatan banatzen da:\n\n - 1. maila baino txikiagoa: 176 puntu baino gutxiago \n - 1. maila: 176 eta 225 arteko puntuazioa\n - 2. maila: 226 eta 275 arteko puntuazioa\n - 3. maila: 276 eta 325 arteko puntuazioa\n - 4. maila: 326 eta 375 arteko puntuazioa\n - 5. maila: 376 eta 500 arteko puntuazioa\n\nPertsona batek, gutxienez, 2. mailako gaitasuna lortzen du irakurmen-eskalan, normalean gai denean ezagutzen \nez dituen gaiei buruzko testuak ulertzeko, nahiz eta zailtasunak izan hainbat testu alderatu eta kontrastatzeko \nedo ezagutzen ez duen gai jakin bati buruzko testu digitalak bilatzeko.\n\nPertsona batek, gutxienez, 2. mailako gaitasuna lortzen du matematika eskalan, normalean gai denean bitarteko kalkuluak behar \ndituzten galderei erantzuteko, taula bateko informazio matematikoa ulertzen duenean eta grafiko \nsinpleak interpretatzeko zailtasunik ez duenean, nahiz eta zailtasunak izan proportzioak maneiatzeko edo grafiko \nkonplexuak interpretatzeko.\n\n2012ko datua 2011ko hirugarren lauhilekoaren eta 2012ko lehen lauhilekoaren artean egindako azterketari dagokio.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Adierazlea gazteen eta helduen trebetasun-mailen zuzeneko neurria da, bi arlotan: alfabetizazioa eta aritmetika. \nGaitasun-maila finkoa (FLP) domeinu bateko (alfabetizazioa edo aritmetika) oinarrizko ezagutzen erreferentzia-parametroa  da, ikaskuntza-ebaluazioen bidez neurtuta. Gaur egun, txosten orokorretarako FLP Helduen Konpetentzien Nazioarteko  Ebaluaziorako Programaren (PIAAC) 2. mailako deskribatzailea da. \nAlfabetatze funtzionalaren eta aritmetika funtzionalaren kontzeptuak UNESCOren definizioetan oinarritzen dira. Definizio  horiek, dikotomia baten ordez, gaitasun-maila jarraitua adierazten dute.  Pertsona bat funtzionalki alfabetatzen da,  baldin eta bere taldearen eta komunitatearen funtzionamendu eraginkorrerako alfabetatzea beharrezkoa den jarduera  guztietan parte har badezake, eta horrek irakurketa, idazketa eta kalkulua bere garapenerako eta komunitatearenerako  erabiltzen jarraitzeko aukera ematen badio. \n\nIturria: Nazio Batuen Estatistika Sekzioa", "dato_global"=>"", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-06-01.pdf\">Metadatuak 4-6-1.pdf </a>(ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.6: By 2030, ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracy</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.6.1: Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sex</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Not applicable. </p>", "META_LAST_UPDATE__GLOBAL"=>"2022-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.2, 1.5, 2.1, 2.2, 2.3, 3.1, 3.3, 3.4, 3.7, 4.5, 5.3, 5.4, 5.5, 5.6, 8.5, 8.6, 8.b, 10.2, 12.8, 13.3, 13.b</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The proportion of youth (aged 15-24 years) and of adults (aged 15 years and above) who have achieved or exceeded a fixed level of proficiency in (a) literacy and (b) numeracy. </p>\n<p><strong>Concepts:</strong></p>\n<p>The <strong>fixed level of proficiency (FLP)</strong> is the benchmark of basic knowledge in a domain (literacy or numeracy) measured through learning assessments. Currently, the FLP for global reporting is the Programme for the International Assessment of Adult Competencies (PIAAC) level 2 descriptor.</p>\n<p>The concepts of functional literacy and functional numeracy are based on the UNESCO definitions, which cover a continuum of proficiency levels rather than a dichotomy. A person is functionally literate if he/she can engage in all those activities in which literacy is required for the effective functioning of his/her group and community and also which enables them to continue to use reading, writing and calculation for his/her own and the community&#x2019;s development.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><strong>Level 2 descriptor of PIAAC</strong></p>\n<p><strong>Literacy:</strong></p>\n<p>At this level, the medium of texts may be digital or printed, and texts may comprise continuous, non-continuous, or mixed types. Tasks at this level require respondents to make matches between the text and information, and may require paraphrasing or low-level inferences. Some competing pieces of information may be present. Some tasks require the respondent to:</p>\n<ul>\n  <li>cycle through or integrate two or more pieces of information based on criteria;</li>\n  <li>compare and contrast or reason about information requested in the question; or</li>\n  <li>navigate within digital texts to access and identify information from various parts of a document.</li>\n</ul>\n<p><strong>Numeracy:</strong></p>\n<p>Tasks at this level require the respondent to identify and act on mathematical information and ideas embedded in a range of common contexts where the mathematical content is fairly explicit or visual with relatively few distractors. Tasks tend to require the application of two or more steps or processes involving calculation with whole numbers and common decimals, percentages and fractions; simple measurement and spatial representation; estimation; and interpretation of relatively simple data and statistics in texts, tables and graphs.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>This indicator is collected via skills&apos; assessment surveys of the adult population (e.g., PIAAC, STEP, LAMP<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>, RAMAA) and national adult literacy surveys.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Literacy Assessment and Monitoring Programme.</p><p>Note: the full forms of PIAAC and STEP can be found in the rest of the document. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p>Data are collected from the respective organizations responsible for each assessment. </p>", "FREQ_COLL__GLOBAL"=>"<p>Various depending on survey and country.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Biannual UIS data release (March and September)</p>", "DATA_SOURCE__GLOBAL"=>"<p>This indicator is collected via skills national or international assessment surveys of youth and adult populations. The Organization for Economic Co-operation and Development&#x2019;s (OECD) Survey of Adult Skills in its Programme for the International Assessment of Adult Competencies (PIAAC) and the World Bank&#x2019;s Skills Towards Employment and Productivity (STEP) measurement programme, both based on the PIAAC framework and scale, and bodies responsible for conducting national learning assessments (including Ministries of Education, National Statistical Offices and other data providers) are sources of data of this indicator</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "INST_MANDATE__GLOBAL"=>"<p>&#x201D;</p>\n<p>The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The Institute produces internationally comparable data and methodologies in the fields of education, science, culture and communication for countries at all stages of development.</p>\n<p>The <a href=\"https://www.moe.gov.bn/DocumentDownloads/Education%202030/Education2030.pdf\">Education 2030 Framework for Action &#xA7;100</a> has clearly states that: &#x201C;<em>Inrecognition of the importance of harmonization of monitoring and reporting, the UIS will remain the official source of cross-nationally comparable data on education. It will continue to produce international monitoring indicators based on its annual education survey and on other data sources that guarantee international comparability for more than 200 countries and territories. In addition to collecting data, the UIS will work with partners to develop new indicators, statistical approaches and monitoring tools to better assess progress across the targets related to UNESCO&#x2019;s mandate, working in coordination with the SDG-Education 2030 SC&#x201D;.</em></p>", "RATIONALE__GLOBAL"=>"<p>The indicator is a direct measure of the skill levels of youth and adults in the two areas: literacy and numeracy.</p>\n<p> </p>", "REC_USE_LIM__GLOBAL"=>"<p>Functional literacy and numeracy are related to context, thus survey programs need further development in order to frame questions in a way that are meaningful to different economic and social-settings and could be more efficient to reflect population level of skills.</p>", "DATA_COMP__GLOBAL"=>"<p>Proportion of youth and adults who have achieved at least a fixed level of proficiency as defined for large-scale (sample representative) adult literacy and numeracy assessments:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>F</mi>\n        <mi>L</mi>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n        <mo>,</mo>\n        <mi>a</mi>\n        <mo>,</mo>\n        <mi>d</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>F</mi>\n            <mi>L</mi>\n            <mi>P</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n            <mo>,</mo>\n            <mi>a</mi>\n            <mo>,</mo>\n            <mi>d</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>P</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n            <mo>,</mo>\n            <mi>a</mi>\n            <mo>,</mo>\n            <mi>d</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where:</p>\n<p><strong><em>PFLP<sub>t,a,d</sub></em></strong> = the proportion of people in a skills survey in age group <strong><em>a</em></strong>, in year <strong><em>t</em></strong>, who have achieved or exceeded the fixed level of proficiency in domain <strong><em>d</em></strong>.</p>\n<p><strong><em>FLP<sub>t,a,d</sub></em></strong> = the number of people in a skills survey in age group <strong><em>a</em></strong>, in year <strong><em>t</em></strong>, who have achieved or exceeded the fixed level of proficiency in domain <strong><em>d</em></strong>.</p>\n<p><strong><em>P<sub>t,a,d</sub></em></strong> = the total number of people in age group <strong><em>a</em></strong>, in year <strong><em>t</em></strong>, who participated in the skills survey of domain <strong><em>d</em>.</strong></p>\n<p><strong><em>a</em></strong> = 16-65 years (youth and adults).</p>\n<p><strong><em>d</em></strong> = the domain which was assessed (literacy or numeracy).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>In each data update period, surveys of recent publications of results of national and international assessments are carried out. Then, consultations are made with national references and UIS technical focal points to verify the availability and validity of the data.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>None by data compiler.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>None by data compiler. </p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates are not currently available for this indicator.</p>", "DOC_METHOD__GLOBAL"=>"<p>The UIS has elaborated guidance for the countries regarding the contents, the procedures and the reporting in the Global Alliance to monitor learning <a href=\"https://gaml.uis.unesco.org/\">microsite</a>. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The UIS maintains a global database on learning assessments. The inclusion of a data point in the database to show transparency is completed by following a protocol and is reviewed by UIS technical focal points to ensure consistency and overall data quality, based on objective criteria to ensure that only the most recent and reliable information are included in the database.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>OECD is the data compiler for PIAAC and the World Bank Group is the compiler for STEP, both used the PIAAC framework and skills level descriptors.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The criteria to ensure the quality and standardization of the data are: the data sources must include adequate documentation; data values should be representative at the national population level and should otherwise be included in a footnote; data values are based on a sufficiently large sample; and the data are plausible and based on trends and consistency with previously published or reported estimates for the indicator.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>45 countries with at least one data point for the period 2010-2017.</p>\n<p><strong>Time series:</strong></p>\n<p>2006 onwards. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>By age-group, sex, location, income and type of skill. Disability status is not currently available in most national and cross-national learning assessments.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>None.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://uis.unesco.org/\">http://uis.unesco.org/</a> </p>\n<p><strong>References:</strong></p>\n<p>Programme for the International Assessment of Adult Competencies (PIAAC): </p>\n<p><a href=\"https://www.oecd.org/skills/piaac/\">https://www.oecd.org/skills/piaac/</a></p>\n<p>STEP Skills Measurement Programme: </p>\n<p><a href=\"https://microdata.worldbank.org/index.php/catalog/step/about\">https://microdata.worldbank.org/index.php/catalog/step/about</a> </p>\n<p>Action Research: Measuring Literacy Programme Participants&#x2019; Learning Outcomes (RAMAA): </p>\n<p><a href=\"https://uil.unesco.org/literacy/learning-outcomes-ramaa/action-research-measuring-literacy-programme-participants-learning\">https://uil.unesco.org/literacy/learning-outcomes-ramaa/action-research-measuring-literacy-programme-participants-learning</a> </p>", "indicator_sort_order"=>"04-06-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.7.1", "slug"=>"4-7-1", "name"=>"Grado en que i) la educación para la ciudadanía mundial y ii) la educación para el desarrollo sostenible se incorporan en a) las políticas nacionales de educación, b) los planes de estudio, c) la formación de docentes y d) la evaluación de los estudiantes", "url"=>"/site/es/4-7-1/", "sort"=>"040701", "goal_number"=>"4", "target_number"=>"4.7", "global"=>{"name"=>"Grado en que i) la educación para la ciudadanía mundial y ii) la educación para el desarrollo sostenible se incorporan en a) las políticas nacionales de educación, b) los planes de estudio, c) la formación de docentes y d) la evaluación de los estudiantes"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Grado en que i) la educación para la ciudadanía mundial y ii) la educación para el desarrollo sostenible se incorporan en a) las políticas nacionales de educación, b) los planes de estudio, c) la formación de docentes y d) la evaluación de los estudiantes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Grado en que i) la educación para la ciudadanía mundial y ii) la educación para el desarrollo sostenible se incorporan en a) las políticas nacionales de educación, b) los planes de estudio, c) la formación de docentes y d) la evaluación de los estudiantes", "indicator_number"=>"4.7.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Para alcanzar las metas 4.7, 12.8 y 13.3 de los ODS, es necesario que los gobiernos \ngaranticen la plena integración de la Educación para el Desarrollo Sostenible (EDS), \nla Educación para la Ciudadanía Mundial (ECM) y sus subtemas en todos los aspectos \nde sus sistemas educativos. Los estudiantes no alcanzarán los resultados de \naprendizaje deseados si la EDS y la ECM no se han identificado como \nprioridades en las políticas o leyes educativas, si los currículos no incluyen \nespecíficamente los temas y subtemas de la EDS y la ECM, y si el profesorado \nno está capacitado para impartir estos temas de forma integral. \n\nEste indicador busca brindar una evaluación simple de si existe la \ninfraestructura básica que permita a los países impartir EDS y ECM de \ncalidad a sus estudiantes, para garantizar que sus poblaciones cuenten \ncon información adecuada sobre desarrollo sostenible y estilos de \nvida en armonía con la naturaleza. Unas políticas educativas, \ncurrículos, formación docente y evaluación del alumnado adecuados \nson aspectos clave del compromiso y el esfuerzo nacionales para \nimplementar la ECM y la EDS de manera eficaz y proporcionar un \nentorno de aprendizaje propicio. \n\nCada componente del indicador se evalúa en una escala de cero a uno. \nCuanto más cercano a uno sea el valor, mejor se integrarán la EDS y \nla ECM en ese componente. Al presentar los resultados por separado \npara cada componente, los gobiernos podrán identificar en qué \náreas podrían ser necesarios mayores esfuerzos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-07-01.pdf\">Metadatos 4-7-1.pdf</a> (solo en inglés)", "dato_global"=>"", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"In order to achieve SDG targets 4.7, 12.8 and 13.3, it is necessary for \ngovernments to ensure that ESD and GCED and their sub-themes are fully \nintegrated in all aspects of their education systems. Students will not \nachieve the desired learning outcomes if Education for Sustainable Development \n(ESD) and Global Citizenship Education (GCED) have not been identified as \npriorities in education policies or laws, if curricula do not specifically \ninclude the themes and sub-themes of ESD and GCED, and if teachers are not \ntrained to teach these topics across the curriculum. \n\nThis indicator aims to give a simple assessment of whether the basic \ninfrastructure exists that would allow countries to deliver quality ESD and \nGCED to learners, to ensure their populations have adequate information on \nsustainable development and lifestyles in harmony with nature. Appropriate \neducation policies, curricula, teacher education, and student assessment are \nkey aspects of national commitment and effort to implement GCED and ESD effectively \nand to provide a conducive learning environment. \n\nEach component of the indicator is assessed on a scale of zero to one. The closer \nto one the value, the better mainstreamed are ESD and GCED in that component. \nBy presenting results separately for each component, governments will be able to \nidentify in which areas more efforts may be needed. \n\nSource: United Nations Statistics Division\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-07-01.pdf\">Metadata 4-7-1.pdf</a>", "dato_global"=>nil, "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"GJHen 4.7, 12.8 eta 13.3 xedeak lortzeko, beharrezkoa da gobernuek bermatzea Garapen Jasangarrirako Hezkuntza \n(GJHe), Munduko Herritartasunerako Hezkuntza (MHE) eta haren azpigaiak erabat integratuko direla hezkuntza-sistemen \nalderdi guztietan. Ikasleek ez dituzte lortu asmo dituzten ikaskuntza-emaitzak lortuko, baldin eta GJHe eta MHE ez \nbadira lehentasuntzat hartu hezkuntza-politiketan edo -legeetan, curriculumetan berariaz sartzen ez badira GJHeko \neta MHEko gaiak eta azpigaiak, eta irakasleak gai horiek modu integralean emateko gaituta ez badaude. \n\nAdierazle honen helburua da ebaluazio sinple bat egitea, jakiteko ea badagoen herrialdeek ikasleei kalitatezko GJHe \neta MHEak eman ahal izateko oinarrizko azpiegiturarik, haien biztanleek garapen iraunkorrari eta naturarekin harmonian \ndauden bizimoduei buruzko informazio egokia izan dezaten bermatzeko. Hezkuntza-politika, curriculum, irakaskuntza-prestakuntza \neta ikasleen ebaluazio egokiak funtsezko alderdiak dira MHE eta GJHe modu eraginkorrean ezartzeko eta ikaskuntza-ingurune \negokia eskaintzeko konpromiso eta ahalegin nazionalean. \n\nAdierazlearen osagai bakoitza hutsetik baterako eskalan ebaluatzen da. Batetik zenbat eta hurbilago egon balio hori, orduan \neta hobeto integratuko dira osagai horretan GJHe eta MHE. Emaitza bakoitza bere aldetik aurkeztean, gobernuek identifikatu \nahal izango dute zein arlotan izan daitezkeen beharrezkoak ahalegin handiagoak. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-07-01.pdf\">Metadatuak 4-7-1.pdf</a> (ingelesez bakarrik)", "dato_global"=>nil, "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture&#x2019;s contribution to sustainable development</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.7.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessment</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SE_GCEDESD_CUR - Extent to which global citizenship education and education for sustainable development are mainstreamed in curricula [4.7.1,12.8.1,13.3.1]</p>\n<p>SE_GCEDESD_NEP - Extent to which global citizenship education and education for sustainable development are mainstreamed in national education policies [4.7.1,12.8.1,13.3.1]</p>\n<p>SE_GCEDESD_SAS - Extent to which global citizenship education and education for sustainable development are mainstreamed in student assessment [4.7.1,12.8.1,13.3.1]</p>\n<p>SE_GCEDESD_TED - Extent to which global citizenship education and education for sustainable development are mainstreamed in teacher education [4.7.1,12.8.1,13.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>12.8.1 and 13.3.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Education Sector, Division for Peace and Sustainable Development, Section of Education for Sustainable Development (UNESCO-ED/PSD/ESD)</p>\n<p>UNESCO Institute for Statistics (UNESCO-UIS)</p>\n<p>Global Education Monitoring Report</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Education Sector, Division for Peace and Sustainable Development, Section of Education for Sustainable Development (UNESCO-ED/PSD/ESD), UNESCO Institute for Statistics (UNESCO-UIS), and Global Education Monitoring Report.</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Indicator 4.7.1/12.8.1/13.3.1 measures the extent to which countries mainstream Global Citizenship Education (GCED) and Education for Sustainable Development (ESD) in their education systems. This is an indicator of characteristics of different aspects of education systems: education policies, curricula, teacher education and student assessment as reported by government officials, ideally following consultation with other government ministries, national human rights institutes, the education sector and civil society organizations. It measures government intentions and not necessarily what is implemented in practice in schools and classrooms.</p>\n<p>For each of the four components of the indicator (policies, curricula, teacher education, and student assessment), a number of criteria are measured, which are then combined to give a single score between zero and one for each component. (See methodology section for full details).</p>\n<p>The indicator and its methodology have been reviewed and endorsed by UNESCO&#x2019;s <a href=\"https://tcg.uis.unesco.org/\">Education Data and Statistics Commission (EDSC)</a> (former TCG), which is responsible for the development and maintenance of the thematic indicator framework for the follow-up and review of SDG 4. The EDSC also has an interest in education-related indicators in other SDGs, including global indicators 12.8.1 and 13.3.1. The EDSC is composed of 28 regionally representative experts from UNESCO Member States (nominated by the respective geographic groups of UNESCO) as per the revised <a href=\"https://ces.uis.unesco.org/wp-content/uploads/sites/23/2024/01/EDS-2.1.-TCG-TOR-_Final-WEB.pdf\">Terms of Reference </a> (November 2023), as well as international and regional partners and civil society. The <a href=\"http://uis.unesco.org/\">UNESCO Institute for Statistics</a> acts as the Secretariat.</p>\n<p><strong>Concepts:</strong></p>\n<p>Global Citizenship Education (GCED) and Education for Sustainable Development (ESD) nurture respect for all, build a sense of belonging to a common humanity, foster responsibility for a shared planet, and help learners become responsible and active global citizens and proactive contributors to a more peaceful, tolerant, inclusive, secure and sustainable world. They aim to empower learners of all ages to address and resolve local and global challenges and to take informed decisions and actions for environmental integrity, economic viability and a just society for present and future generations, while respecting cultural diversity.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>For Survey Index (between 0.000 and 1.000).</p>\n<p>For Greening Curriculum Index (between 0 and 100).</p>\n<p>For reporting on harmonised scale, 0-100 range values will be used.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>For the time period 2017-2020, responses to the quadrennial reporting by UNESCO Member States on the implementation of the 1974 <a href=\"http://portal.unesco.org/en/ev.php-URL_ID=13088&amp;URL_DO=DO_TOPIC&amp;URL_SECTION=201.html\"><em>Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms</em></a>. The last round of reporting took place in 2020-21. The results were published in the Global SDG Indicator Database in July and September 2021. In November 2023, the 1974 Recommendation was superseded by the 2023 Recommendation on Education for Peace, Human Rights and Sustainable Development. The first reporting on the new Recommendation will take place in 2026-2027 covering the period 2024-2026. It will be one data source for the global indicator. In 2024 a short, one-off survey is being considered by UNESCO to collect data for the global indicator covering the time period 2021-2023. (See methodology section for details of questions asked).</p>\n<p><strong>Greening</strong></p>\n<p>To measure the extent to which green content is integrated in the official intended curriculum of primary and (lower) secondary education, two types of documents were analysed to create a country&#x2019;s Greening Curriculum Indicator (GCI) score: 1) <strong>national curriculum frameworks</strong> and 2) <strong>subject curricula documents</strong> from science and social science subjects taught in grades 3, 6, and 9. The terms curriculum or syllabus here should be distinguished from related terms such as textbook, lesson plan, and teaching guidelines. A database of over 1,700 curriculum documents has been compiled for the 2025 data release.</p>\n<h3><em>National curriculum frameworks (NCFs)</em></h3>\n<p>NCFs<strong> </strong>are defined as national-level policy documents that overview a country&#x2019;s educational goals and priorities and set forth key parameters of the country&#x2019;s official intended curriculum. NCFs are written and approved by the relevant ministry of education or another officially designated body. A comprehensive NCF: 1) delineates the aims of the curriculum at various stages of schooling; 2) explains the educational philosophy underlying the curriculum and approaches to teaching, learning, and assessment that align with that philosophy; 3) describes curricular structures; 4) assigns names to subject/learning areas; 5) allocates time to each subject (or group of subjects) in each grade level (or set of grades); 6) provides guidelines to curriculum developers, teacher trainers, and textbook writers; 7) prescribes curricular standards and mechanisms for inspection and monitoring; and 8) refers to learning assessments to be conducted (UNESCO-IBE, 2017a; UNESCO-IBE, 2017b). </p>\n<p>To be considered an NCF for the purposes of the GCI, the document has to:</p>\n<ul>\n  <li>Be written by the ministry of education or other official designated body.</li>\n  <li>Cover primary, lower secondary, or upper secondary levels of formal education (categories 1, 2, and 3 according to the International Standard Classification of Education or ISCED).<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup></li>\n  <li>Have a title or opening matter that describes the document as a National Curriculum Framework.</li>\n  <li>Include content that aligns with the sections outlined in the document definitions above.</li>\n</ul>\n<p>In cases where an NCF matching the above criteria was not identified, other documents containing similar content to an NCF were considered for inclusion. For example:</p>\n<ul>\n  <li>The introductory or front matter of document(s) specifying the content of subject curricula similarly to an NCF.</li>\n  <li>Laws or regulations passed by legislative or executive bodies that specify curricular structures and contents of a national education system along the lines of an NCF.</li>\n  <li>Official websites of national governments or subnational political units that present in a similar manner to an NCF.</li>\n</ul>\n<h3><em>Subject curricula</em></h3>\n<p>Subject curricula or subject syllabi are defined as subject- and grade-specific documents that include most or all of the following information: 1) a general rationale for the teaching of the subject; 2) the intended aims and learning outcomes; 3) clearly defined content areas (topics and themes) to be included in the teaching of each subject; and 4) ideally, a weekly, monthly, or yearly timetable allocating instructional time to each topic/subject, pedagogical considerations, and possibly assessment guidelines. The name given to such documents varies by language &#x2013; for example, &#x201C;programme&#x201D; (French), &#x201C;Lehrplan&#x201D; (German), &#x201C;programma&#x201D; (Italian), &#x201C;plan de estudios&#x201D; (Spanish) and &#x201C;almanhaj&#x201D; (Arabic) &#x2013; and may have slightly different connotations. There are no international guidelines for subject curricula, partly because they reflect national traditions in the development and implementation of the official curriculum, the extent of teacher and school autonomy, and patterns of pre-service and in-service teacher training.</p>\n<p>Subject curricula were included for subjects in two broad knowledge domains: science and social science. Curricula for up to four subjects in each knowledge domain (thus up to eight subjects in total) were included at each grade level (3, 6, and 9) in each country or sub-national jurisdiction. Table 1 lists the typical subjects found in each knowledge domain internationally. Many countries organize instruction in a single general science and/or social science subject (more common in grades 3 and 6), rather than numerous specialized subjects (more common in grade 9). Some countries teach interdisciplinary subjects such as environmental education (EE) or education for sustainable development (ESD) or special hybrid subjects that combine science and social science content. Such interdisciplinary or hybrid subjects were included among the up to eight subjects per grade level. </p>\n<p>Table 1: List of typical science, social science and EE/ESD subjects included in GCI calculations</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Science Subjects</strong></p>\n      </td>\n      <td>\n        <p><strong>Social Science Subjects</strong></p>\n      </td>\n      <td>\n        <p><strong> EE/ESD subjects</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ul>\n          <li>General Science</li>\n          <li>Applied Science / Technology</li>\n          <li>Earth Science</li>\n          <li>Life Science</li>\n          <li>Physical Science</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>General Social Science</li>\n          <li>Geography</li>\n          <li>History</li>\n          <li>Civics/Citizenship</li>\n          <li>Economics</li>\n          <li>Religious, Moral, and Philosophy</li>\n          <li>Cultural and Art Studies</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Environmental Education</li>\n          <li>Environmental Education / Education for Sustainable Development</li>\n          <li>Environmental and Outdoor Education </li>\n          <li>Sustainability</li>\n        </ul>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><em>Note</em>. The number of science subjects never exceeded four subjects in any country, so all science subjects were collected for the countries included in the sample. Any curricula related to EE or ESD were also collected. In total, 17 countries had EE/ESD specific curricula.</p>\n<p><em>Green keywords</em></p>\n<p>A set of 13 green keywords were defined in relation to four themes: environment, sustainability, climate change, and biodiversity (see Table 2).</p>\n<p>Table 2: List of green keywords used in the analysis</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Themes </strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>Keywords</strong></p>\n      </td>\n      <td>\n        <p><strong>Total number of keywords</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Environment and </p>\n        <p>sustainability</p>\n      </td>\n      <td>\n        <ul>\n          <li>environmental*</li>\n          <li>sustainability</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>greening</li>\n          <li>&#x201C;sustainable development&#x201D;</li>\n        </ul>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Climate change</p>\n      </td>\n      <td>\n        <ul>\n          <li>&#x201C;climate change&#x201D;</li>\n          <li>&#x201C;global warming&#x201D;</li>\n          <li>&#x201C;greenhouse gas*&quot;</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>&quot;climate justice&quot;</li>\n          <li>&#x201C;renewable energy&#x201D;</li>\n        </ul>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Biodiversity</p>\n      </td>\n      <td>\n        <ul>\n          <li>biodiversity</li>\n          <li>ecosystem*</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>extinction*</li>\n          <li>invasive species</li>\n        </ul>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Total</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>13</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>All keywords were translated into 40 languages and then validated by language proficient experts. The keyword searches are carried out using a bespoke Python application.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> See <a href=\"https://uis.unesco.org/en/topic/international-standard-classification-education-isced\">https://uis.unesco.org/en/topic/international-standard-classification-education-isced</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Responses are submitted by national governments, typically by officials in Ministries of Education. Respondents are asked to consult widely across other government ministries, with national human rights institutes, the education sector and civil society organizations in compiling their responses. Respondents are also asked to submit supporting evidence in the form of documents or links (e.g. to education policies or laws, curricula, etc.), which will be made publicly available in UNESCO&#x2019;s <a href=\"https://en.unesco.org/themes/right-to-education/database\">Observatory on the Right to Education</a>. </p>\n<p><strong>Greening</strong></p>\n<p><em>National curriculum frameworks</em></p>\n<p>NCF documents are identified by searching ministry of education websites, as well as databases such as UNESCO IIEP Planipolis, UNESCO International Institute for Educational Planning (IIEP), Siteal, UNESCO Regional Comparative and Explanatory Study (ERCE), Eurydice, Organization for Economic Cooperation and Development (OECD) Policy Outlook, and the Educational Media Research (Edumeres), as well as consulting country experts. </p>\n<p><em>Subject curricula</em></p>\n<p>Subject curricula are included for subjects in two broad knowledge domains: science and social science. Curricula for up to four subjects in each knowledge domain (thus up to eight subjects in total) are included at each grade level (3, 6, and 9) in each country or sub-national jurisdiction. Table 1 above lists the typical subjects found in each knowledge domain internationally. Many countries organize instruction in a single general science and/or social science subject (more common in grades 3 and 6), rather than numerous specialized subjects (more common in grade 9). Some countries teach interdisciplinary subjects on environmental education (EE) or education for sustainable development (ESD) or special hybrid subjects that combine science and social science content. Such interdisciplinary or hybrid subjects are included among the up to eight subjects per grade level. </p>\n<p>Subject curricula documents are identified through a range of sources, including through manually reviewing ministry of education websites and searching archives of recent curriculum studies. National Commissions for UNESCO also provided subject curricula following a request by the UNESCO International Bureau of Education and UNESCO headquarters. In cases where these methods do not yield the relevant subject curricula, additional documents are collected through consultation with country education experts. </p>", "FREQ_COLL__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>2020-21 round (covering 2017-2020) completed in April 2020. Next round foreseen in 2024 (covering 2021-2023). Data for the period 2024-2026 are expected to be collected in 2026-2027, as the first reporting on the 2023 Recommendation on Education for Peace, Human Rights and Sustainable Development.</p>\n<p><strong>Greening</strong></p>\n<p>Data collection is expected to be annual. The collection of 2025 data was carried out between 2023 and 2024.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Q2 and Q3 of 2021 (from 2020-21 reporting round). 2025 (for 2024 reporting round).</p>\n<p><strong>Greening</strong></p>\n<p>2025 (for 2023-24 data).</p>", "DATA_SOURCE__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Requests for reports are submitted to Ministers Responsible for Relations with UNESCO who are typically Education Ministers. Reports are usually completed by government officials in Ministries of Education. Countries are requested to consult widely before submitting their reports. To assist with this, requests for reports are also copied to NGOs in official partnership with UNESCO and the Office of the High Commissioner for Human Rights (OHCHR). </p>\n<p><strong>Greening</strong></p>\n<p>Data were provided by the UNESCO ESD Section and the Monitoring and Evaluating Climate Communication and Education (MECCE) Project.</p>", "COMPILING_ORG__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>UNESCO&#x2019;s Sections for Education for Sustainable Development and Global Citizenship and Peace Education.</p>\n<p><strong>Greening</strong></p>\n<p>Global Education Monitoring Report and the Monitoring and Evaluating Climate Communication and Education (MECCE) Project.</p>", "INST_MANDATE__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>In 1974, UNESCO Member States adopted the <em>Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms</em>, which encapsulates many of the aims of SDG targets 4.7, 12.8 and 13.3. Every four years, countries report on the implementation of the Recommendation. This well-established formal mechanism is the data source for indicator 4.7.1/12.8.1/13.3.1. The seventh quadrennial reporting round took place in 2020-2021.</p>\n<p><strong>Greening</strong></p>\n<p>During the UN Transforming Education Summit (November 16-19, 2022), which sought to mobilize solutions to accelerate national and international efforts to achieve ADG 4, participants agreed to seven global initiatives, one of which is &#x201C;Greening Education: to get every learner climate ready.&#x201D; UNESCO established the Greening Education Partnership in 2022, which prioritized the greening of schools, curricula, teacher training and system capacities and communities. In December 2022 the SDG 4 High-level Steering Committee met in Paris and decided to &#x201C;add indicators for&#x2026;greening education and requested that its Data and Monitoring Technical Committee&#x2026;develop a methodology for these indicators&#x2026;&#x201D; The Steering committee also mandated UIS and the GEM Report to develop benchmark indicators on greening education.</p>", "RATIONALE__GLOBAL"=>"<p>In order to achieve SDG targets 4.7, 12.8 and 13.3, it is necessary for governments to ensure that ESD and GCED and their sub-themes are fully integrated in all aspects of their education systems. Students will not achieve the desired learning outcomes if Education for Sustainable Development (ESD) and Global Citizenship Education (GCED) have not been identified as priorities in education policies or laws, if curricula do not specifically include the themes and sub-themes of ESD and GCED, and if teachers are not trained to teach these topics across the curriculum. </p>\n<p>This indicator aims to give a simple assessment of whether the basic infrastructure exists that would allow countries to deliver quality ESD and GCED to learners, to ensure their populations have adequate information on sustainable development and lifestyles in harmony with nature. Appropriate education policies, curricula, teacher education, and student assessment are key aspects of national commitment and effort to implement GCED and ESD effectively and to provide a conducive learning environment.</p>\n<p><strong>Survey</strong></p>\n<p>Each component of the indicator is assessed on a scale of zero to one. The closer to one the value, the better mainstreamed are ESD and GCED in that component. By presenting results separately for each component, governments will be able to identify in which areas more efforts may be needed.</p>\n<p><strong>Greening</strong></p>\n<p>Greening related to environment, sustainability, climate change and biodiversity (SDG indicator 13.3.1) is captured under the component &#x201C;Curricula&#x201D; of the indicator. The measurement of greening follows a specific computation method and is presented separately. The goal is to assess the extent to which green content (related to environment, sustainability, climate and biodiversity) is prioritized and integrated into national curriculum policy frameworks and science and social science subject curricula (syllabi) in grades 3, 6, and 9.</p>", "REC_USE_LIM__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>The indicator is based on self-reporting by government officials. However, countries are asked to provide supporting evidence in the form of documents or links (e.g. education policies or laws, curricula, etc.) to back up their responses. In addition, UNESCO compares responses with available information from alternative sources and, if appropriate, raises queries with national respondents. At the end of the reporting cycle, country responses and the supporting documents will be made publicly available.</p>\n<p><strong>Greening</strong></p>\n<p>The greening indicator analyses the content of official policy and curriculum documents for themes related to sustainability, environment, climate change and biodiversity to determine the extent to which relevant green content is prioritized. As it is based on counts of keywords, it does not capture how these keywords are used.</p>", "DATA_COMP__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Information collected with the questionnaire for monitoring the implementation by UNESCO Member States of the 1974 <em>Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms</em> and from 2026, the 2023 <em>Recommendation on Education for Peace, Human Rights and Sustainable Development </em>is used for the construction of the global indicator. For each of the four components of the indicator (policies, curricula, teacher education, and student assessment), a number of criteria are measured, which are then combined to give a single score between zero and one for each component. Only information for primary and secondary education are used for calculation of indicator 4.7.1/12.8.1/13.3.1.</p>\n<ol>\n  <li><u>Laws and policies</u></li>\n</ol>\n<p>The following questions are used to calculate the policies component of the indicator:</p>\n<p><em>A2: Please indicate which global citizenship education (GCED) and education for sustainable development) ESD themes are covered in national or sub-national <u>laws, legislation or legal frameworks</u> on education. </em></p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) and two levels of government (national and sub-national) = 16 responses.</p>\n<p>Response categories are no = 0, yes = 1, unknown, which is treated as zero, and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank the question score is not calculated. </p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = simple mean of the 0 and 1 scores, <u>excluding not applicables </u>(i.e., if eight of the 16 responses are &#x2018;not applicable&#x2019;, the sum of the 0 and 1 scores is divided by 8 to get the mean and not by 16).</p>\n<p><em>A4. Please indicate which GCED and ESD themes are covered in national or sub-national <u>education policies, frameworks or strategic objectives</u>.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) = 8 responses. </p>\n<p>Response categories are no = 0, yes = 1, and unknown (treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>A5. Please indicate whether national or sub-national <u>education policies, frameworks or strategic objectives</u> on education provide a mandate to integrate GCED and ESD. </em></p>\n<p>There are two levels of government (national, sub-national) and five areas of integration (curricula, learning objectives, textbooks, teacher education, and student assessment) = 10 responses. </p>\n<p>Response categories are no = 0, yes = 1, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding not applicables</u> are unknown or blank, the question score is not calculated. </p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = simple mean of the 0 and 1 scores, <u>excluding not applicables </u>(i.e., if five of the 10 responses are &#x2018;not applicable&#x2019;, the sum of the 0 and 1 scores is divided by 5 to get the mean and not by 10).</p>\n<p><em>E1a. Based on your responses to questions in the previous section (laws and policies) please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed</em><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup><em> in education laws and policies in your country.</em> </p>\n<p>There are two levels of government (national, sub-national) = 2 responses. </p>\n<p>Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding not applicables</u> are unknown or blank, the question score is not calculated. </p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = half the simple mean of the 0, 1 and 2 scores, <u>excluding not applicables </u>(i.e., if one of the two responses is &#x2018;not applicable&#x2019;, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1 as do the scores for the other three questions in this section.</p>\n<p>Policy component score = simple mean of the scores for questions A2, A4, A5 and E1a. Where a question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.</p>\n<ol>\n  <li><u>Curricula</u></li>\n</ol>\n<p>The following questions are used to calculate the curricula component of the indicator:</p>\n<p><em>B2: Please indicate which global citizenship education (GCED) and education for sustainable development (ESD) themes are taught as part of the curriculum.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) = 8 responses.</p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated. </p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>B3. Please indicate in which subjects or fields of study GCED and ESD are taught in primary and secondary education.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) and twelve subjects in which they may be taught (arts; civics, civil or citizenship education; ethics/moral studies; geography; health, physical education and sports; history; languages; mathematics; religious education; science; social studies and integrated studies) = 96 responses. </p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank the question score is not calculated.</p>\n<p>Note that responses to &#x2018;other subjects, please specify&#x2019; in the question are ignored. If appropriate, during quality assurance answers in this category may be recoded to one of the other 12 subjects.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>B4. Please indicate the approaches used to teach GCED and ESD in primary and secondary education.</em> </p>\n<p>There are four teaching approaches (GCED/ESD as separate subjects, cross-curricular, integrated, whole school) = 4 responses</p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>E1b. Based on your responses to questions in the previous section (curricula) please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed</em><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup><em> in curricula in your country.</em> </p>\n<p>There are two levels of government (national, sub-national) = 2 responses.</p>\n<p>Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding &#x2018;not applicables&#x2019;</u> are unknown or blank, the question score is not calculated.</p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = half the simple mean of the 0, 1 and 2 scores, <u>excluding &#x2018;not applicables&#x2019; </u>(i.e., if one of the two responses is &#x2018;not applicable&#x2019;, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1, as do the scores for the other three questions in this section.</p>\n<p>Curricula component score = simple mean of the scores for questions B2, B3, B4 and E1b. Where a question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.</p>\n<ol>\n  <li><u>Teacher education</u></li>\n</ol>\n<p>The following questions are used to calculate the teacher education component of the indicator:</p>\n<p><em>C2: Please indicate whether teachers, trainers and educators are trained to teach global citizenship education (GCED) and education for sustainable development (ESD) during initial or pre-service training and/or through continuing professional development.</em> </p>\n<p>There are two types of training (initial/pre-service and continuing professional development) and two types of teachers (of selected subjects in which ESD/GCED are typically taught, and of other subjects) = 4 responses. </p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>C3. Please indicate on which GCED and ESD themes pre-service or in-service training is available for teachers, trainers and educators.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) = 8 responses.</p>\n<p>Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>C4. Please indicate whether teachers, trainers and educators are trained to teach the following dimensions of learning in GCED and ESD.</em> </p>\n<p>There are four learning dimensions (knowledge, skills, values, and attitudes/behaviours) = 4 responses. </p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>C5. Please indicate whether teachers, trainers and educators are trained to use the following approaches to teach GCED and ESD in primary and secondary education</em>. </p>\n<p>There are four teaching approaches (GCED/ESD as separate subjects, cross-curricular, integrated, whole school) = 4 responses. </p>\n<p>Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>E1c. Based on your responses to questions in the previous section (teacher education), please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed</em><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup><em> in teacher education in your country.</em> </p>\n<p>There are two levels of government (national, sub-national) = 2 responses. </p>\n<p>Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable (which is ignored). Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding &#x2018;not applicables&#x2019;</u> are unknown or blank, the question score is not calculated.</p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = half the simple mean of the 0, 1 and 2 scores, <u>excluding &#x2018;not applicables&#x2019; </u>(i.e., if one of the two responses is &#x2018;not applicable&#x2019;, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1, as do the scores for the other three questions in this section.</p>\n<p>Teacher education component score = simple mean of the scores for questions C2, C3, C4, C5 and E1c. Where component question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.</p>\n<ol>\n  <li><u>Student assessment</u></li>\n</ol>\n<p>The following questions are used to calculate the student assessment component of the indicator:</p>\n<p><em>D2: Please indicate whether the global citizenship education (GCED) and education for sustainable development (ESD) themes below are generally included in <u>student assessments or examinations</u>.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) = 8 responses. </p>\n<p>Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>D3. Please indicate which of the dimensions of learning in GCED and ESD below are generally included in <u>student assessments or examinations</u>.</em> </p>\n<p>There are four learning dimensions (knowledge, skills, values, and attitudes/behaviours) = 4 responses.</p>\n<p>Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated. </p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>E1d. Based on your responses to questions in the previous section (student assessment), please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed</em><sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup><em> in student assessment in your country.</em> </p>\n<p>There are two levels of government (national, sub-national) = 2 responses. </p>\n<p>Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding &#x2018;not applicables&#x2019;</u> are unknown or blank, the question score is not calculated. </p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = half the simple mean of the 0, 1 and 2 scores, <u>excluding &#x2018;not applicables&#x2019; </u>(i.e., if one of the two responses is &#x2018;not applicable&#x2019;, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1, as do the scores for the other three questions in this section.</p>\n<p>Student assessment component score = simple mean of the scores for questions D2, D3 and E1d. Where component question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.</p>\n<p>The component scores all lie between zero and one and are presented as a dashboard of four scores. They are not combined to create a single overall score for the indicator. The higher the score, the more GCED and ESD are mainstreamed in the given component. In this way, users can make a simple assessment in which component area more efforts may be needed. </p>\n<p><strong>Greening</strong></p>\n<ol>\n  <li><u>Curricula</u></li>\n</ol>\n<h3><em>Document preparation</em></h3>\n<p>All collected documents are added to a single database in a standardized fashion. Documents are downloaded if found online and converted to PDF if in another format. In many cases, subject curricula are part of a larger document, in which case, relevant subject- and grade-specific material are extracted into separate documents. Documents in the database are named using the following protocol:</p>\n<p><em> &#x201C;country_state/province_documenttype_region_year_language_grade_knowledgedomain&#x201D;</em></p>\n<p>Information about each document is stored in a database (one row per document), including document title, year of publication, subject, author, source, and language. </p>\n<p>For documents in languages for which there are fewer than three documents in that language (Burmese, Norwegian, Swedish, and Urdu), the documents are machine translated into English using Google Translate.</p>\n<h2><em>Keyword selection and analysis</em></h2>\n<p>The GCI measures the inclusion of green content in four document types (NCF, grade 3 subject curricula, grade 6 subject curricula, and grade 9 subject curricula). It counts the presence of 13 keywords corresponding to three themes of Environment/Sustainability, Climate Change, and Biodiversity. The selected keywords: 1) best represent the theme, 2) can be translated into all relevant languages, and 3) are sufficiently prevalent in the analysed documents to provide data for measuring components of the GCI (see Table 2 above). Additional sources such as recent UNESCO studies of greening education and the Greening Education Partnership curriculum guidance were also used to identify relevant green keywords.</p>\n<p>Each keyword includes its plural and singular as well as the many forms the word may take depending on the language.<sup><sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></sup> Some languages and/or countries employ distinctive language/culture-specific keywords to capture a theme. Thus, each theme includes space for a culture- or language-specific keyword to be added, if appropriate.<sup><sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup></sup> The keywords and their translations into 40 languages are reviewed and validated by native speaking experts who are familiar with greening education concepts.</p>\n<p>A Python-based application is used to bulk process text files and identify keywords in documents in all the required languages. To be read by the Python application, all the text documents are converted to UTF-8 text format and stored in a local folder. The Python application also requires a two-column spreadsheet with columns for &quot;File Name&quot; and &quot;Language&quot; and a second spreadsheet with columns for &#x201C;Keyword&#x201D; and the keyword&#x2019;s &#x201C;Language.&#x201D; These files and the folder location are then loaded into the Python application. The application uses the language file to determine which column from the keyword file to utilize in searching for keywords for each text file. The application then counts relevant keywords in every document (NCF and subject curricula) in the specified language. After completing the keyword search processing, the application outputs a spreadsheet file that contains a row for each curriculum document and columns for every keyword.<sup><sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup></sup> This output file becomes the raw data used in the calculation of the GCI. </p>\n<h2><em>Calculation of the greening curriculum indicator</em></h2>\n<p>After the prevalence of each keyword in each document is determined, keyword counts are compiled into an output spreadsheet which is then used to calculate a country&#x2019;s GCI score. The following specific steps are taken to calculate a country&#x2019;s GCI score:</p>\n<h3><em>Phase 1) Development of standardized keyword counts</em></h3>\n<p>The analysis of the green content of each country&#x2019;s NCFs and subject curricula is done at the country level.</p>\n<ul>\n  <li>For the NCF and each grade level (3, 6, and 9), the frequency of keywords belonging to the themes of Environment/Sustainability, Climate Change, and Biodiversity is calculated by summing up the counts of the keywords.</li>\n  <li>To account for varying document lengths, the number of keywords is standardized for each theme by dividing the keywords counts in that country&#x2019;s theme with the total number of words in the country&#x2019;s documents. </li>\n  <li>This standardized number is then multiplied by 1 million to transform the result into a number that is more easily interpreted (i.e., not a very small decimal). The result is a keyword count per million words for each theme at each grade level and NCF for each country. The standardization calculation is as follows:<ul>\n      <li>1,000,000*(Keywords in that theme for a country) / (Total words in documents for that country) </li>\n    </ul>\n  </li>\n</ul>\n<h3><em>Phase 2) Transformation of standardized keyword counts into an ordinal scale</em></h3>\n<p>The distribution of these standardized numbers presents a statistical challenge since it is both zero bounded<sup><sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup></sup> and has a long tail.<sup><sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup></sup> </p>\n<ul>\n  <li>To create a more normal distribution, the standardized numbers are transformed into an ordinal scale ranging from 0 to 10 in the following way: <ul>\n      <li>If there are no keywords, the score is 0, otherwise it ranges from 1 to 10 using a &#xBD; life logarithmic transformation.<sup><sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup></sup> </li>\n      <li>For the Environment/Sustainability &apos;core&apos; theme, the maximum score of 10 is achieved with 10,000 standardized keywords. The following formulas are used:<ul>\n          <li>&gt;10,000 standardized keywords are assigned a score of 10,</li>\n          <li>&lt;=20 standardized keywords are assigned a score of 1,</li>\n          <li>0 standardized keywords are assigned a score of 0,</li>\n          <li>Otherwise, 10-log.5(#/10,000)</li>\n          <li>Result multiplied by 10</li>\n        </ul>\n      </li>\n      <li>For the Climate Change and Biodiversity themes, the maximum score of 10 is achieved with 5,000 standardized keywords, given that these keywords are used less commonly. The following formulas are used:<ul>\n          <li>&gt;5,000 standardized keywords are assigned a score of 10, </li>\n          <li>&lt;=10 standardized keywords are assigned a score of 1, </li>\n          <li>0 standardized keywords are assigned a score of 0, </li>\n          <li>Otherwise, 10-log.5(#/5,000)</li>\n          <li>Result multiplied by 10</li>\n        </ul>\n      </li>\n    </ul>\n  </li>\n</ul>\n<h3><em>Phase 3) Calculating GCIs for federated countries</em></h3>\n<p>To calculate the GCI for federated countries (e.g., Australia, Canada, Switzerland, United Kingdom), all of the above mentioned steps are carried out for <u>each</u> sub-national jurisdiction, which results in a number of (sub-national) GCIs. The sub-national GCI scores for the country are then averaged into a national GCI score. The data for all federated countries are then added to the dataset produced in Phase 1.</p>\n<h3><em>Phase 4) Final calculation of the GCI</em></h3>\n<p>At this point, each country has either three or four document-specific scores (ranging from 0 to 10) for each of the three themes (i.e., 9 or 12 total scores, since countries are included if they have at least 3 of the 4 main document types (NCF, grade 3 subject curricula, grade 6 subject curricula, and grade 9 subject curricula).</p>\n<ul>\n  <li>Within each of the Environment/Sustainability, Climate Change, and Biodiversity themes, the three grade level scores and the NCF score are averaged together (i.e., each contributes &#xBC; of the total score per theme in a country). For countries with only three document types, the same procedure is done but each document score contributes &#x2153; of the total theme-focused score.</li>\n  <li>A single overall GCI score is now calculated based on a weighted mean, with the Environment/Sustainability core theme weighted 50% and the Climate Change and Biodiversity themes each weighted at 25%.</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities), and/or education professionals (e.g. teachers and lecturers), as appropriate. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities), and/or education professionals (e.g. teachers and lecturers), as appropriate. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities) and/or education professionals (e.g. teachers and lecturers), as appropriate. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities) and/or education professionals (e.g. teachers and lecturers) as appropriate. <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> Different forms of the word are included only due to genders, definite articles, etc. but not when they change the meaning or part of speech. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> For example, in China the phrase &#x2018;ecological civilization&#x2019; is now being used much more frequently than &#x2018;sustainable development&#x2019; or &#x2018;environmental.&#x2019; In Japan, the term &#x2018;sustainable societies&#x2019; is becoming more prevalent than the term &#x2018;sustainable development.&#x2019; At this point in time, no culture- or language-specific keywords are included in the GCI. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> To determine the accuracy of the counts generated by the Python program, a validation exercise was carried out in October 2024 by sampling 30 documents in English, Spanish, Arabic and French, the four most prevalent languages. A three-way comparison of results from NVivo (the software used for all related UNESCO consultancies), Python, and manual counts identified several minor issues (e.g., keywords split across lines or the lack of a definite article in the Arabic keyword list), which were immediately corrected in the Python program and the keyword list. Since then, the Python program has been reviewed by several experts and undergone further refinements to ensure its counting accuracy is comparable to NVivo and manual counting. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> While there are many documents lacking any keywords related to Environment/Sustainability, Climate Change and Biodiversity, there are no documents with a negative number of keywords. Such a situation represents a zero-bounded distribution and creates a lopsided and non-normal distribution. <a href=\"#footnote-ref-10\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> While more than half the document types have less than 120 standardized keywords in a theme, they range to over 9,000 (75+ times as much as the median). Log transformations are conceptually useful when dealing with such data. For example, going from 0 to 50 standardized keywords is more significant than going from 1000 to 1050 standardized keywords. <a href=\"#footnote-ref-11\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> This means that for each time the standardized counts are halved, the score goes down by 1. So, for example, if 10,000 standardized references is a score of 10, 5,000 is a score of 9, 2,500 is a score of 8, and so on. <a href=\"#footnote-ref-12\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Responses are reviewed by UNESCO for consistency and credibility and, if necessary, queries are raised with national respondents. Where feasible, reference is made to national documents and links supplied by respondents and to available alternative sources of information. </p>\n<p>Any proposed changes in response values in the questionnaire as a result of quality assurance procedures are communicated and verified with countries by UNESCO. Final results are shared before publication by UNESCO with the national data providers and with national SDG indicator focal points where they exist.</p>", "ADJUSTMENT__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>The only adjustments made are where question response categories are not valid and responses between different questions are inconsistent. In those circumstances, proposed changes are communicated to and verified with countries.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p><strong>At country level: </strong>A small number of missing values &#x2013; unknown responses and/or blanks &#x2013; are treated as zeros in the calculation of the question scores. Where they represent more than 50% of the responses to a single question, the component score is not calculated. In such cases, the component score is reported as not available when results are disseminated.</p>\n<p><strong>At regional level: </strong>Regional values are not calculated.</p>\n<p><strong>Greening</strong></p>\n<p>As previously noted, the GCI aligns with commitments made by parties to the UN Framework Convention on Climate Change (UN, 1992), by UN Member States in the 2030 Agenda for Sustainable Development (UN, 2015), and by attendees to the UN Transforming Education Summit (UN, 2022; 2023). As such, the focus of document compilation is all 193 UN Member States as well as 3 additional entities (i.e., Cook Islands, Niue, and Palestine), which are parties to the UNFCCC. Among these 196 possible countries, inclusion in the GCI is dependent on whether a sufficiently complete set of documents for that country has been compiled. A sufficient set of documents means having at least three of the following four types of documents that meet the previously outlined criteria:</p>\n<ul>\n  <li>Grade 3 subject curricula</li>\n  <li>Grade 6 subject curricula</li>\n  <li>Grade 9 subject curricula</li>\n  <li>National Curriculum Framework (NCF)</li>\n</ul>\n<p>A special notation (i.e., &quot;Qualifier of Data-Partial Data&quot;) is placed in the database to indicate cases where the GCI was calculated based on three of the four document types. When missing document types are obtained, a revised GCI score based on a complete set of document types is calculated for the bi-annual data releases.</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates are not calculated.</p>", "DOC_METHOD__GLOBAL"=>"<p><strong>Survey</strong></p>\n<ul>\n  <li>Countries wishing to calculate this indicator for themselves should follow the steps described in section 4.c. Method of computation above.</li>\n  <li>The questionnaires for the monitoring of the implementation of UNESCO Recommendations are approved by the Member States of the Executive Board of UNESCO. The questionnaire contains guidelines for completion and a glossary of key terms. In addition, UNESCO provides direct support to Member States in completing the questionnaire and responds to queries in a timely manner.</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>None related to the processing of qualitative data collected principally for non-statistical purposes.</p>", "QUALITY_ASSURE__GLOBAL"=>"<ul>\n  <li>UNESCO reviews country responses for consistency and credibility and, if necessary, raises queries with national respondents. To assist with this, countries are asked to provide, in addition to completed questionnaires, supporting evidence of their responses in the form of documents or links (e.g. to education policies, laws, curricula, etc.). These will be made publicly available during 2022 along with completed questionnaires. UNESCO also takes into account alternative sources of information, where available. These may include national responses to similar intergovernmental consultation processes, such as the Council of Europe&#x2019;s consultations on the <a href=\"https://www.coe.int/en/web/edc/2016-report-analysis\">Charter on Education for Democratic Citizenship and Human Rights Education</a>, the UN Economic Commission for Europe&#x2019;s consultations on the <a href=\"http://www.unece.org/env/esd/implementation.html\">Strategy for Education for Sustainable Development</a>, or other information on education for sustainable development (ESD) and global citizenship education (GCED) in countries&#x2019; national education systems.</li>\n  <li>Any proposed changes to response values in the questionnaire as a result of quality assurance procedures are communicated to and verified with countries by UNESCO. Final results are shared before publication by UNESCO with the national data providers and SDG indicator focal points.</li>\n  <li>Regarding greening, keywords and their translations were reviewed by native speakers who were also familiar with greening concepts. Documents were reviewed against a set of criteria before being included for analysis.</li>\n</ul>\n<p>Before data release and addition to the global SDG indicators database, the indicator&#x2019;s values and notes on methodology are submitted to National Statistical Offices, Ministries of Education or other relevant agencies in individual countries for their review and feedback.</p>\n<p> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>None related to the processing of qualitative data collected principally for non-statistical purposes.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p><strong>Data availability: </strong>During the last consultation on the implementation of the 1974 Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms carried out in 2020-2021, 75 countries provided reports: Central and Southern Asia (4), Eastern and South-Eastern Asia (7), Europe and Northern America (32), Latin America and the Caribbean (10), Northern Africa and Western Asia (14), Oceania (2), and sub-Saharan Africa (6).</p>\n<p><strong>Time series: </strong>The first data are available for the time period 2017-2020 (as a single time point). Data for the period 2021-2023 (from UNESCO one-off survey conducted in 2024) are expected in 2025. Data for the period 2024-2026 from the first reporting on the 2023 Recommendation on Education for Peace, Human Rights and Sustainable Development will be collected in 2026-2027.</p>\n<p><strong>Disaggregation: </strong>None</p>\n<p><strong>Greening</strong></p>\n<p>Data currently available refer to 2023-2024.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies: </strong>There should be no difference as the indicator values are calculated from the responses submitted by countries. If any changes are proposed to responses as a result of quality assurance procedures, these are communicated to and verified with countries.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<ul>\n  <li><a href=\"http://uis.unesco.org/\"><u>http://uis.unesco.org/</u></a>; <a href=\"https://databrowser.uis.unesco.org/\"><u>https://databrowser.uis.unesco.org/</u></a></li>\n  <li><a href=\"https://www.unesco.org/en/sustainable-development/education\"><u>https://www.unesco.org/en/sustainable-development/education</u></a></li>\n  <li>https://www.unesco.org/gem-report/en </li>\n  <li>https://tcg.uis.unesco.org/wp-content/uploads/sites/4/2025/02/EDSC.11.3.4.GCI-Methods.pdf</li>\n</ul>\n<p><strong>References: </strong></p>\n<p><a href=\"http://portal.unesco.org/en/ev.php-URL_ID=13088&amp;URL_DO=DO_TOPIC&amp;URL_SECTION=201.html\"><u>Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms</u></a>.</p>\n<p>Recommendation on Education for Peace, Human Rights and Sustainable Development.</p>", "indicator_sort_order"=>"04-07-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.a.1", "slug"=>"4-a-1", "name"=>"Proporción de escuelas que ofrecen servicios básicos, desglosada por tipo de servicio", "url"=>"/site/es/4-a-1/", "sort"=>"04aa01", "goal_number"=>"4", "target_number"=>"4.a", "global"=>{"name"=>"Proporción de escuelas que ofrecen servicios básicos, desglosada por tipo de servicio"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Servicios básicos", "value"=>"Electricidad"}, {"field"=>"Servicios básicos", "value"=>"Conexión a internet"}, {"field"=>"Servicios básicos", "value"=>"Agua potable"}, {"field"=>"Servicios básicos", "value"=>"Baños separados por sexo"}, {"field"=>"Servicios básicos", "value"=>"Lavado de manos"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de escuelas que ofrecen servicios básicos, desglosada por tipo de servicio", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de escuelas que ofrecen servicios básicos, desglosada por tipo de servicio", "indicator_number"=>"4.a.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Educación, Formación Profesional y Deportes", "periodicity"=>"Anual", "url"=>"https://www.educacionfpydeportes.gob.es/servicios-al-ciudadano/estadisticas/no-universitaria/centros/sice.html", "url_text"=>"Estadística sobre la sociedad de la información y la comunicación en los centros educativos no universitarios", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de escuelas que ofrecen servicios básicos, desglosada por tipo de servicio", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.a- Construir y adecuar instalaciones educativas que tengan en cuenta las necesidades de los niños y las personas con discapacidad y las diferencias de género, y que ofrezcan entornos de aprendizaje seguros, no violentos, inclusivos y eficaces para todos", "definicion"=>"Proporción de centros educativos no universitarios con acceso a servicios básicos (electricidad, conexión a internet,  instalaciones de saneamiento básicas separadas por sexo, acceso a suministro básico de agua potable,  instalaciones básicas para el lavado de manos).", "formula"=>"\n$$PCENU_{servicio\\, básico}^{t} = \\frac{CENU_{servicio\\, básico}^{t}}{CENU^{t}} \\cdot 100$$\n\ndonde:\n\n$CENU_{servicio\\, básico}^{t} =$ centros educativos no universitarios con acceso al servicio básico (electricidad, ordenador con internet, baño, agua potable, instalaciones para el lavado de manos) en el curso escolar $t-1/t$\n\n$CENU^{t} =$ centros educativos no universitarios en el curso escolar $t-1/t$\n", "desagregacion"=>"Servicios básicos: electricidad, conexión a internet, instalaciones de saneamiento básicas separadas  por sexo, acceso a suministro básico de agua potable, instalaciones básicas para el lavado de manos", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador mide el acceso en las escuelas a los servicios básicos y las instalaciones clave necesarios \npara garantizar un entorno de aprendizaje seguro y eficaz para todos los estudiantes. Un valor alto \nindica que las escuelas tienen un buen acceso a los servicios e instalaciones pertinentes. \nLo ideal sería que cada escuela tuviera acceso a todos estos servicios e instalaciones.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.a.1&seriesCode=SE_ACS_INTNT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=UPPSEC\">Proporción de escuelas con acceso a Internet con fines pedagógicos, en secundaria superior(%) SE_ACS_INTNT</a> UNSTATS<br>", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-0a-01.pdf\">Metadatos 4-a-1.pdf </a>(solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de escuelas que ofrecen servicios básicos, desglosada por tipo de servicio", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.a- Construir y adecuar instalaciones educativas que tengan en cuenta las necesidades de los niños y las personas con discapacidad y las diferencias de género, y que ofrezcan entornos de aprendizaje seguros, no violentos, inclusivos y eficaces para todos", "definicion"=>"Proportion of non-university educational institutions with access to basic services (electricity, internet access, basic sanitation facilities separated by sex, access to basic drinking water, basic handwashing facilities).", "formula"=>"\n$$PCENU_{basic\\, service}^{t} = \\frac{CENU_{basic\\, service}^{t}}{CENU^{t}} \\cdot 100$$\n\nwhere:\n\n$CENU_{basic\\, service}^{t} =$ non-university educational institutions with access to basic services (electricity, internet access, basic sanitation facilities separated by sex, access to basic drinking water, basic handwashing facilities) in school year $t-1/t$\n\n$CENU^{t} =$ non-university educational institutions in school year $t-1/t$\n", "desagregacion"=>"Basic services: electricity, internet access, basic sanitation facilities separated by sex,  access to basic drinking water, basic handwashing facilities", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The indicator measures access in schools to key basic services and facilities necessary to ensure a safe \nand effective learning environment for all students. A high value indicates that schools have good access \nto the relevant services and facilities. Ideally, each school should have access to all these services \nand facilities.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.a.1&seriesCode=SE_ACS_INTNT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=UPPSEC\">Proportion of schools with access to the internet for pedagogical purposes in upper secondary (%) SE_ACS_INTNT</a> UNSTATS<br>", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-0a-01.pdf\">Metadata 4-a-1.pdf </a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Proporción de escuelas que ofrecen servicios básicos, desglosada por tipo de servicio", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.a- Construir y adecuar instalaciones educativas que tengan en cuenta las necesidades de los niños y las personas con discapacidad y las diferencias de género, y que ofrezcan entornos de aprendizaje seguros, no violentos, inclusivos y eficaces para todos", "definicion"=>"Oinarrizko zerbitzuak (elektrizitatea, Interneteko konexioa, sexuaren arabera bereizitako oinarrizko  saneamendu-instalazioak, edateko uraren oinarrizko hornidurarako sarbidea, eskuak garbitzeko oinarrizko  instalazioak) dituzten unibertsitatez kanpoko ikastetxeen proportzioa.", "formula"=>"\n$$PCENU_{oinarrizko\\, zerbitzua}^{t} = \\frac{CENU_{oinarrizko\\, zerbitzua}^{t}}{CENU^{t}} \\cdot 100$$\n\nnon:\n\n$CENU_{oinarrizko\\, zerbitzua}^{t} =$ oinarrizko zerbitzuak dituzten unibertsitatez kanpoko ikastetxeak (elektrizitatea, ordenagailua internetekin, bainugela, edateko ura, eskuak garbitzeko instalazioak) $t-1/t$ ikasturtean \n\n$CENU^{t} =$ unibertsitatez kanpoko ikastetxeak $t-1/t$ ikasturtean \n", "desagregacion"=>"Oinarrizko zerbitzuak: elektrizitatea; interneterako konexioa; sexuaren arabera bereizitako oinarrizko saneamendu-instalazioak; edateko uraren oinarrizko  hornidurarako sarbidea; eskuak garbitzeko oinarrizko instalazioak", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Ikasle guztientzako ikaskuntza-ingurune segurua eta eraginkorra bermatzeko beharrezkoak diren oinarrizko \nzerbitzuetarako eta funtsezko instalazioetarako sarbidea neurtzen du adierazleak. Balio altuak adierazten \ndu eskolek sarbide ona dutela zerbitzu eta instalazio egokietara. Egokiena litzateke eskola bakoitzak zerbitzu \neta instalazio horiek guztiak erabiltzeko aukera izatea. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.a.1&seriesCode=SE_ACS_INTNT&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=UPPSEC\">Helburu pedagogikoetarako Interneterako sarbidea duten Bigarren Hezkuntzako goi zikloko ikastetxeen proportzioa (%) SE_ACS_INTNT</a> UNSTATS<br>", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-0a-01.pdf\">Metadatuak 4-a-1.pdf </a>(ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.a: Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.a.1: Proportion of schools offering basic services, by type of service</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SE_ACC_HNDWSH - Proportion of schools with basic handwashing facilities, by education level [4.a.1]</p>\n<p>SE_ACS_CMPTR - Proportion of schools with access to computers for pedagogical purposes, by education level [4.a.1]</p>\n<p>SE_ACS_ELECT - Proportion of schools with access to electricity, by education level [4.a.1]</p>\n<p>SE_ACS_H2O - Proportion of schools with access to basic drinking water, by education level [4.a.1]</p>\n<p>SE_ACS_INTNT - Proportion of schools with access to the internet for pedagogical purposes, by education level [4.a.1]</p>\n<p>SE_ACS_SANIT - Proportion of schools with access to single-sex basic sanitation, by education level [4.a.1]</p>\n<p>SE_INF_DSBL - Proportion of schools with access to adapted infrastructure and materials for students with disabilities [4.a.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>6.1, 6.2, 7.1, 9.c, 17.8</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>The percentage of schools by level of education (primary, lower secondary and upper secondary education) with access to the given facility or service.</p>\n<p><strong>Concepts:</strong></p>\n<p>Electricity: Regularly and readily available sources of power (e.g. grid/mains connection, wind, water, solar and fuel-powered generator, etc.) that enable the adequate and sustainable use of ICT infrastructure for educational purposes.</p>\n<p>Internet for pedagogical purposes: Internet that is available for enhancing teaching and learning and is accessible by pupils. Internet is defined as a worldwide interconnected computer network, which provides pupils access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files, irrespective of the device used (i.e. not assumed to be only via a computer) and thus can also be accessed by mobile telephone, tablet, personal digital assistant, games machine, digital TV etc.). Access can be via a fixed narrowband, fixed broadband, or via mobile network.</p>\n<p>Computers for pedagogical use: Use of computers to support course delivery or independent teaching and learning needs. This may include activities using computers or the Internet to meet information needs for research purposes; develop presentations; perform hands-on exercises and experiments; share information; and participate in online discussion forums for educational purposes. A computer is a programmable electronic device that can store, retrieve and process data, as well as share information in a highly-structured manner. It performs high-speed mathematical or logical operations according to a set of instructions or algorithms. Computers include the following types:</p>\n<p>- A desktop computer usually remains fixed in one place; normally the user is placed in front of it, behind the keyboard;</p>\n<p>- A laptop computer is small enough to carry and usually enables the same tasks as a desktop computer; it includes notebooks and netbooks but does not include tablets and similar handheld devices; and</p>\n<p>- A tablet (or similar handheld computer) is a computer that is integrated into a flat touch screen, operated by touching the screen rather than using a physical keyboard.</p>\n<p>Adapted infrastructure is defined as any built environment related to education facilities that are accessible to all users, including those with different types of disability, to be able to gain access to use and exit from them. Accessibility includes ease of independent approach, entry, evacuation and/or use of a building and its services and facilities (such as water and sanitation), by all of the building&apos;s potential users with an assurance of individual health, safety and welfare during the course of those activities. </p>\n<p>Adapted materials include learning materials and assistive products that enable students and teachers with disabilities/functioning limitations to access learning and to participate fully in the school environment. </p>\n<p>Accessible learning materials include textbooks, instructional materials, assessments and other materials that are available and provided in appropriate formats such as audio, braille, sign language and simplified formats that can be used by students and teachers with disabilities/functioning limitations. </p>\n<p>Basic drinking water is defined as a functional drinking water source (MDG &#x2018;improved&#x2019; categories) on or near the premises and water points accessible to all users during school hours. </p>\n<p>Basic sanitation facilities are defined as functional sanitation facilities (MDG &#x2018;improved&#x2019; categories) separated for males and females on or near the premises. </p>\n<p>Basic handwashing facilities are defined as functional handwashing facilities, with soap and water available to all girls and boys.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The International Standard Classification of Education (ISCED) is used to define primary, lower secondary and upper secondary education. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>(1) Administrative data from schools and other providers of education or training</p>\n<p>(2) Cross-national learning assessments</p>", "COLL_METHOD__GLOBAL"=>"<p><strong>For administrative sources:</strong></p>\n<p>The UNESCO Institute for Statistics (UIS) produces time series based on data reported by Ministries of Education or National Statistical Offices (NSOs). The data are gathered through the annual Survey of Formal Education (on access to electricity, drinking water, sanitation and handwashing facilities) and through the Survey on ICTs in Education (on access to electricity, Internet and computers). Data on adapted infrastructure are not collected currently. Countries are asked to report data according to the levels of education defined in the International Standard Classification of Education (ISCED) to ensure international comparability of resulting indicators.</p>\n<p>The data received are validated using electronic error detection systems that check for arithmetic errors and inconsistencies and trend analysis for implausible results. Queries are taken up with the country representatives reporting the data so that corrections can be made (of errors) or explanations given (of implausible but correct results). During this process, countries are also encouraged to provide estimates for missing or incomplete data items.</p>\n<p>In addition, countries also have an opportunity to see and comment on the main indicators the UIS produces in an annual &#x201C;country review&#x201D; of indicators.</p>\n<p><strong>For cross-national learning assessments:</strong></p>\n<p>Data is acquired from the administrators of cross-national assessment; typically, these are available for download publicly. UIS analyses this data to provide estimates of the indicator. When there is more than one data point available for a given level of schooling, an average is used as the indicator. Annex Table 2 presents the questionnaire used to collect data in the cross-national assessments included. </p>", "FREQ_COLL__GLOBAL"=>"<p>For administrative sources: Annual UIS survey (usually launched in the 4<sup>th</sup> quarter) and UOE survey (usually launched in June).</p>\n<p>For cross-national assessments: as data is released publicly.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Biannual UIS data release (March and September).</p>", "DATA_SOURCE__GLOBAL"=>"<p>For administrative sources: Ministries of Education and/or National Statistical Offices.</p>\n<p>For cross-national learning assessments: International student assessment programme administrators.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "INST_MANDATE__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The Institute produces internationally comparable data and methodologies in the fields of education, science, culture and communication for countries at all stages of development.</p>\n<p>The <a href=\"https://www.moe.gov.bn/DocumentDownloads/Education%202030/Education2030.pdf\"><u>Education 2030 Framework for Action &#xA7;100</u></a> has clearly stated that: &#x201C;<em>In recognition of the importance of harmonization of monitoring and reporting, the UIS will remain the official source of cross-nationally comparable data on education. It will continue to produce international monitoring indicators based on its annual education survey and on other data sources that guarantee international comparability for more than 200 countries and territories. In addition to collecting data, the UIS will work with partners to develop new indicators, statistical approaches and monitoring tools to better assess progress across the targets related to UNESCO&#x2019;s mandate, working in coordination with the SDG-Education 2030 SC&#x201D;.</em></p>", "RATIONALE__GLOBAL"=>"<p>The indicator measures access in schools to key basic services and facilities necessary to ensure a safe and effective learning environment for all students.</p>\n<p>A high value indicates that schools have good access to the relevant services and facilities. Ideally, each school should have access to all these services and facilities.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator measures the existence in schools of the given service or facility but not its quality or operational state.</p>", "DATA_COMP__GLOBAL"=>"<p>The number of schools in a given level of education with access to the relevant facilities is expressed as a percentage of all schools at that level of education.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>S</mi>\n      </mrow>\n      <mrow>\n        <mi>n</mi>\n        <mo>,</mo>\n        <mi>f</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>S</mi>\n          </mrow>\n          <mrow>\n            <mi>n</mi>\n            <mo>,</mo>\n            <mi>f</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>S</mi>\n          </mrow>\n          <mrow>\n            <mi>n</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>where:</p>\n<p><em>PS<sub>n,f</sub></em> = percentage of schools at level <em>n</em> of education with access to facility <em>f</em></p>\n<p><em>S<sub>n,f</sub></em> = schools at level <em>n</em> of education with access to facility <em>f</em></p>\n<p><em>S<sub>n</sub></em> = total number of schools at level <em>n</em> of education</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The UIS shares all indicator values and notes on methodology with NSOs, Ministries of Education, or other relevant agencies in individual countries for their review, feedback and validation before the publication of the data. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Data should be reported according to the levels of education defined in the International Standard Classification of Education (ISCED) to ensure international comparability of resulting indicators.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>The UIS estimates certain key items of data that may be missing or incomplete in order to have publishable estimates at the country level. Where this is not possible, the UIS imputes missing values for use only for calculating regional and global aggregates. </p>\n<p>In all cases, estimates are based on evidence from the country itself (e.g., information from the data provider on the size of the missing component, via correspondence, publications or data on the Ministry&#x2019;s or National Statistical Office&#x2019;s Webpage, or via surveys conducted by other organizations) or on data from the country for a previous year.</p>\n<p>Where data are available for a country for both an earlier and a more recent year than the missing year, a simple linear interpolation is made. Where data are only available for an earlier year, the most recent value is used as an estimate. Similarly, where data are only available for a more recent year, the last value is used as an estimate.</p>\n<p>Where the relevant data are not available at all for a country, estimates may be based on another variable which is clearly linked to the item being estimated. For example, schools with access to basic services or facilities may be estimated from the total number of schools.</p>\n<p>Where no data are available for the country in any year that can inform the estimate, the unweighted average for the region in which the country lies is used.</p>\n<p>Currently no estimates are made for this indicator for the purpose of having publishable country-level data.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Regional and global aggregates are derived from both publishable and imputed national data. Publishable data are the data submitted to the UIS by Member States or the result of an explicit estimation made by the Institute based on pre-determined standards. In both cases, these data are sent to Member States for review before they are considered publishable by the UIS. </p>\n<p>When data are not available for all countries, the UIS imputes national data for the sole purpose of calculating regional averages. These imputed data are not published nor otherwise disseminated. </p>\n<p>The regional and global aggregates are then calculated as weighted averages using the denominator of the indicator as the weight.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates are calculated as weighted averages using the denominator of the indicator as the weight. As described previously, where publishable data are not available for a given country or year, values are imputed for the purpose of calculating the regional and global aggregates.</p>", "DOC_METHOD__GLOBAL"=>"<p>The UIS has elaborated guidance for the countries on the methodology that should be used to calculate this indicator. ISCED mappings that help countries report their data in an internationally comparable framework are available on the website of the UNESCO Institute for Statistics (<a href=\"http://uis.unesco.org/en/isced-mappings\">http://uis.unesco.org/en/isced-mappings</a>).</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The UIS maintains the global database used to produce this indicator. For transparency purposes, the inclusion of a data point in the database is completed by following a protocol and is reviewed by UIS technical focal points to ensure consistency and overall data quality, based on objective criteria to ensure that only the most recent and reliable information are included in the database. Quality assurance of information produced by the cross-national assessment programs are described in their manuals.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The process for quality assurance includes review of survey documentation, review of the indicator values across time, calculation of measures of reliability, examination of consistency of indicator values derived from different sources and, if necessary, consultation with data providers</p>\n<p>Before its annual data release and the addition of any indicators to the global SDG Indicators Database, the UNESCO Institute for Statistics submits all indicator values and notes on methodology to National Statistical Offices, Ministries of Education or other relevant agencies in individual countries for their review and feedback.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The indicator should be calculated based on data from accurate and comprehensive enumeration of schools or training institutions by level of education with and without access to the given facilities, whether these schools or training institutions are from public or private sector. Criteria for quality assessment include: data sources must include proper documentation; data values must be representative at the national population level and, if not, should be footnoted; data are plausible and based on trends and consistency with previously published/reported values for the indicator.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p><em>For administrative data sources:</em></p>\n<p>140 countries for electricity, 113 countries for computers, 106 countries for Internet, 109 countries for water, 103 countries for sanitation, 105 countries for hand-washing facilities and 50 countries for adapted infrastructure that have at least one data point in the period 2010-2019.</p>\n<p><em>For student assessment sources:</em></p>\n<p>Annex Table 1 presents indicator availability by suggested cross-national learning assessment included in the data as well as number of countries which participate in the assessment programme. </p>\n<p><strong>Time series:</strong></p>\n<p>2000-2019 </p>\n<p><strong>Disaggregation:</strong></p>\n<p>By level of education</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Nationally-published figures may differ from the international ones because of differences between national education systems and the International Standard Classification of Education (ISCED); or differences in coverage (i.e. the extent to which different types of education &#x2013; e.g. private or special education &#x2013; are included in one rather than the other).</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://uis.unesco.org/\">http://uis.unesco.org/</a> </p>\n<p><strong>References:</strong></p>\n<p>The proportion of schools with access to electricity, the Internet for pedagogical purposes and computers for pedagogical purposes: see Guide to Measuring Information and Communication Technologies (ICT) in Education, UIS Technical Paper No. 2.</p>\n<p>WASH Monitoring Indicators: <a href=\"https://www.unicef.org/wash\">https://www.unicef.org/wash</a> </p>\n<p>UIS Questionnaires on Statistics of Information and Communication Technologies (ICT) in Education and the Regional Module for Africa: <a href=\"http://uis.unesco.org/en/uis-questionnaires\">http://uis.unesco.org/en/uis-questionnaires</a> </p>\n<p><strong>Annex: methods used to estimate indicator values using cross-national assessments</strong></p>\n<p>Cross national assessments are sample-based and, as such, provide estimates of the proportion of schools with the given facility. Estimation methods followed those suggested by the respective organization providing the cross-national assessment data. All surveys utilized a two-stage sampling procedure, randomly selecting schools and within those classes or students. School-level (first stage) data was used to estimate the percentages of schools with the given facilities. Data was weighted by school sampling weights. The population which the sample of schools represented are presented in Annex Table 1.</p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"10\">\n        <p>Annex Table 1. Data on school environment indicators collected by suggested cross-national learning assessment</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td></td>\n      <td>\n        <p> </p>\n      </td>\n      <td colspan=\"7\">\n        <p>Data collected on the following</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Assessment</p>\n      </td>\n      <td>\n        <p>Number of participants (includes sub-national entities in some cases; data may not be available for all countries for a given indicator)</p>\n      </td>\n      <td>\n        <p>Target population</p>\n      </td>\n      <td>\n        <p>electricity</p>\n      </td>\n      <td>\n        <p>internet for pedagogical purposes</p>\n      </td>\n      <td>\n        <p>computers for pedagogical purposes</p>\n      </td>\n      <td>\n        <p>adapted infrastructure for students with disabilities</p>\n      </td>\n      <td>\n        <p>basic drinking water</p>\n      </td>\n      <td>\n        <p>single-sex basic sanitation facilities</p>\n      </td>\n      <td>\n        <p>basic hand-washing facilities </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>PISA 2018</p>\n      </td>\n      <td>\n        <p>80</p>\n      </td>\n      <td>\n        <p>secondary schools with 15 year-old students</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>TIMSS 2015</p>\n      </td>\n      <td>\n        <p>54 4<sup>th</sup> grade; 46 8<sup>th</sup> grade</p>\n      </td>\n      <td>\n        <p>schools with 8th grade; schools with 4th grade</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>X </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>PASEC 2014</p>\n      </td>\n      <td>\n        <p>10 both grades</p>\n      </td>\n      <td>\n        <p>schools with 2nd grade; schools with 6th grade</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>LLECE (TERCE) 2013</p>\n      </td>\n      <td>\n        <p>16 both grades</p>\n      </td>\n      <td>\n        <p>schools with 3rd grade; schools with 6th grade</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"4\">\n        <p>Annex Table 2. School questionnaire items related to SDG 4.a.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Survey</p>\n      </td>\n      <td>\n        <p>Population</p>\n      </td>\n      <td>\n        <p>Questionnaire item</p>\n      </td>\n      <td>\n        <p>SDG 4.a.1 sub-indicator</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>LLECE 2013</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>schools with 3rd grade students; schools with 6th grade students</p>\n      </td>\n      <td>\n        <p>&#xBF;Con cu&#xE1;les de estos servicios cuenta la escuela?<br>Luz el&#xE9;ctrica. S&#xED; / No<br>Agua potable. S&#xED; / No</p>\n      </td>\n      <td>\n        <p>Electricity and basic drinking water</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&#xBF;Cu&#xE1;ntos computadores hay en la escuela para uso de los estudiantes?<br>Con conexi&#xF3;n a Internet: No hay / Entre 1 y 10 / Entre 11 y 20 / Entre 21 y 30 / M&#xE1;s de 30<br>Sin conexi&#xF3;n a Internet: No hay / Entre 1 y 10 / Entre 11 y 20 / Entre 21 y 30 / M&#xE1;s de 30</p>\n      </td>\n      <td>\n        <p>Internet for pedagogical purposes; computers for pedagogical purposes</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>PASEC 2014</p>\n      </td>\n      <td>\n        <p>schools with 2nd grade; schools with 6th grade</p>\n      </td>\n      <td>\n        <p>65.Is there in the school...?<br>Electricity: yes/no<br>Piped-in water: yes/no<br>Another source of drinking water (well, borehole&#x2026;): yes/no</p>\n      </td>\n      <td>\n        <p>Electricity; drinking water</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>PISA 2018</p>\n      </td>\n      <td>\n        <p>secondary schools with 15 year-old students</p>\n      </td>\n      <td>\n        <p>The goal of the following set of questions is to gather information about the student-computer ratio for students in the &lt;national modal grade for 15-year-olds&gt; at your school.<br><br>(Please enter a number for each response. Enter &#x201C;0&#x201D; (zero) if there<br>are none.)<br><br>At your school, what is the total number of students in the &lt;national modal grade for 15-year-olds&gt;?<br>Approximately, how many computers are available for these students for educational purposes?<br>Approximately, how many of these computers are connected to the Internet/World Wide Web?</p>\n      </td>\n      <td>\n        <p>Internet for pedagogical purposes; computers for pedagogical purposes</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>TIMSS 2015 4<sup>th</sup> &amp; 8<sup>th</sup> grade</p>\n      </td>\n      <td>\n        <p>Math and science teachers&#x2019; classes of 4<sup>th</sup> grade &amp; 8<sup>th</sup> grade students (can be aggregated to school level)</p>\n      </td>\n      <td>\n        <p>Do the students in this class have computers (including tablets) available to use during their mathematics lessons? Yes / No</p>\n        <p>Do the students in this class have computers (including tablets) available to use during their science lessons? Yes / No</p>\n      </td>\n      <td>\n        <p>Computers for pedagogic use</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "indicator_sort_order"=>"04-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.b.1", "slug"=>"4-b-1", "name"=>"Volumen de la asistencia oficial para el desarrollo destinada a becas, desglosado por sector y tipo de estudio", "url"=>"/site/es/4-b-1/", "sort"=>"04bb01", "goal_number"=>"4", "target_number"=>"4.b", "global"=>{"name"=>"Volumen de la asistencia oficial para el desarrollo destinada a becas, desglosado por sector y tipo de estudio"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Volumen de la asistencia oficial para el desarrollo destinada a becas, desglosado por sector y tipo de estudio", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Volumen de la asistencia oficial para el desarrollo destinada a becas, desglosado por sector y tipo de estudio", "indicator_number"=>"4.b.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Los flujos totales de ayuda oficial al desarrollo (AOD) a los países en \ndesarrollo cuantifican el esfuerzo público que los donantes proporcionan a los \npaíses en desarrollo para becas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-0b-01.pdf\">Metadatos 4-b-1.pdf</a> (solo en inglés)", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.b.1&seriesCode=DC_TOF_SCHIPSL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Flujos oficiales totales para becas, por países receptores (millones de dólares estadounidenses constantes de 2022) DC_TOF_SCHIPSL</a> UNSTATS", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-12", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Total ODA flows to developing countries quantify the public effort that donors provide to developing\ncountries for scholarships.\n\nSource: United Nations Statistics Division\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-0b-01.pdf\">Metadata 4-b-1.pdf</a>", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.b.1&seriesCode=DC_TOF_SCHIPSL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Total official flows for scholarships, by recipient countries (millions of constant 2022 United States dollars) DC_TOF_SCHIPSL</a> UNSTATS", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Garapen-bidean dauden herrialdeei ematen zaien garapenerako laguntza ofizialaren (GLO) guztizko fluxuek zenbatesten \ndute emaileek garapen-bidean dauden herrialdeei ematen dieten ahalegin publikoa beketarako. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-0b-01.pdf\">Metadatuak 4-b-1.pdf</a> (ingelesez bakarrik)", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.b.1&seriesCode=DC_TOF_SCHIPSL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Beken guztizko fluxu ofizialak, herrialde hartzaileen arabera (2022ko Estatu Batuetako dolar konstante milioiak) DC_TOF_SCHIPSL</a> UNSTATS", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.b: By 2020, substantially expand globally the number of scholarships available to developing countries, in particular least developed countries, small island developing States and African countries, for enrolment in higher education, including vocational training and information and communications technology, technical, engineering and scientific programmes, in developed countries and other developing countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.b.1: Volume of official development assistance flows for scholarships by sector and type of study</p>", "META_LAST_UPDATE__GLOBAL"=>"2017-07-09", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Other ODA indicators.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Gross disbursements of total ODA from all donors for scholarships.</p>\n<p><strong>Concepts:</strong></p>\n<p>ODA: The DAC defines ODA as &#x201C;those flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are </p>\n<p>i) provided by official agencies, including state and local governments, or by their executive agencies; and </p>\n<p>ii) each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and</p>\n<p>is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent). (See http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm)</p>\n<p>Scholarships: Financial aid awards for individual students and contributions to trainees. The beneficiary students and trainees are nationals of developing countries. Financial aid awards include bilateral</p>\n<p>grants to students registered for systematic instruction in private or public institutions of higher education to follow full-time studies or training courses in the donor country. Estimated tuition costs</p>\n<p>of students attending schools financed by the donor but not receiving individual grants are not included here, but under item imputed student costs (CRS sector code 1520). Training costs relate to contributions</p>\n<p>for trainees from developing countries receiving mainly non-academic, practical or vocational training in the donor country.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The OECD/DAC has been collecting data on official and private resource flows from 1960 at an aggregate level and 1973 at an activity level through the Creditor Reporting System (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements). </p>\n<p>Data for scholarships are only available since 2010 when the new typology of aid was introduced in DAC statistics.</p>\n<p>The data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm). </p>\n<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.</p>", "COLL_METHOD__GLOBAL"=>"<p>A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are published on an annual basis in December for flows in the previous year.</p>\n<p>Detailed 2015 flows was published in December 2016.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.</p>", "COMPILING_ORG__GLOBAL"=>"<p>OECD</p>", "RATIONALE__GLOBAL"=>"<p>Total ODA flows to developing countries quantify the public effort that donors provide to developing countries for scholarships.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data in the Creditor Reporting System are available from 1973. However, the data coverage is considered complete from 1995 for commitments at an activity level and 2002 for disbursements. </p>\n<p>Data for scholarships are only available since 2010 when the new typology of aid was introduced in DAC statistics.</p>", "DATA_COMP__GLOBAL"=>"<p>The sum of ODA flows from all donors to developing countries for scholarships.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Due to high quality of reporting, no estimates are produced for missing data.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable.</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional figures are based on the sum of ODA flows for scholarships.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>On a recipient basis for all developing countries eligible for ODA.</p>\n<p><strong>Time series:</strong></p>\n<p>Data are available from 2010.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator can be disaggregated by donor, recipient country, type of finance, etc.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.oecd.org/dac/stats\">www.oecd.org/dac/stats</a></p>\n<p><strong>References:</strong></p>\n<p>See all links here: <a href=\"http://www.oecd.org/dac/stats/methodology.htm\">http://www.oecd.org/dac/stats/methodology.htm</a></p>", "indicator_sort_order"=>"04-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"4.c.1", "slug"=>"4-c-1", "name"=>"Proporción de docentes con las calificaciones mínimas requeridas, desglosada por nivel educativo", "url"=>"/site/es/4-c-1/", "sort"=>"04cc01", "goal_number"=>"4", "target_number"=>"4.c", "global"=>{"name"=>"Proporción de docentes con las calificaciones mínimas requeridas, desglosada por nivel educativo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Nivel educativo", "value"=>"Preescolar"}, {"field"=>"Nivel educativo", "value"=>"Primaria"}, {"field"=>"Nivel educativo", "value"=>"Secundaria inferior"}, {"field"=>"Nivel educativo", "value"=>"Secundaria superior"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de docentes con las calificaciones mínimas requeridas, desglosada por nivel educativo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de docentes con las calificaciones mínimas requeridas, desglosada por nivel educativo", "indicator_number"=>"4.c.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de docentes con las calificaciones mínimas requeridas, desglosada por nivel educativo", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.c- De aquí a 2030, aumentar considerablemente la oferta de docentes calificados, incluso mediante la cooperación internacional para la formación de docentes en los países en desarrollo, especialmente los países menos adelantados y los pequeños Estados insulares en desarrollo", "definicion"=>"Proporción del profesorado de los distintos niveles educativos que ha recibido al menos  la mínima formación docente, organizada previa al empleo o en el empleo, exigida  para impartir esa enseñanza sobre el total del profesorado de ese nivel educativo.", "formula"=>"\n$$PPRFD_{nivel\\, educativo}^{t} = \\frac{PRFD_{nivel\\, educativo}^{t}}{PR^{t}} \\cdot 100$$\n\ndonde:\n\n$PRFD_{nivel\\, educativo}^{t} =$ profesorado del nivel educativo con formación docente en el curso escolar $t-1/t$\n\n$CENU^{t} =$ total del profesorado del nivel educativo en el curso escolar  $t-1/t$\n", "desagregacion"=>"Nivel educativo: preescolar; primaria; secundaria inferior; secundaria superior ", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Los docentes desempeñan un papel fundamental para garantizar la calidad de la educación impartida. \nLo ideal sería que todos los docentes recibieran una formación pedagógica adecuada, apropiada y pertinente \npara enseñar en el nivel de educación elegido y que estuvieran académicamente bien cualificados en la(s) materia(s) \nque se espera que enseñen.\n\nEste indicador mide la proporción de la fuerza laboral docente que está bien formada \npedagógicamente. Un valor alto indica que los estudiantes reciben clases de docentes que están \nbien formados pedagógicamente para enseñar.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.c.1&seriesCode=SE_TRA_GRDL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20UPPSEC\">Proporción de docentes con las cualificaciones mínimas requeridas, en secundaria superior (%) SE_TRA_GRDL</a> UNSTATS<br>", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-0c-01.pdf\">Metadatos 4-c-1.pdf </a>(solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-12", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de docentes con las calificaciones mínimas requeridas, desglosada por nivel educativo", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.c- De aquí a 2030, aumentar considerablemente la oferta de docentes calificados, incluso mediante la cooperación internacional para la formación de docentes en los países en desarrollo, especialmente los países menos adelantados y los pequeños Estados insulares en desarrollo", "definicion"=>"Proportion of teachers at different educational levels who have received  at least the minimum teacher training, organized prior to employment or on-the-job,  required to teach at that level, out of the total number of teachers at that  educational level", "formula"=>"\n$$PPRFD_{educational\\, level}^{t} = \\frac{PRFD_{educational\\, level}^{t}}{PR^{t}} \\cdot 100$$\n\nwhere:\n\n$PRFD_{educational\\, level}^{t} =$ teachers of the educational level with teacher training in the school year $t-1/t$\n\n$CENU^{t} =$ total number of teachers at the educational level in the school year $t-1/t$\n", "desagregacion"=>"Educational level: Preschool; primary; lower secondary; upper secondary ", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Teachers play a key role in ensuring the quality of education provided. Ideally all \nteachers should receive adequate, appropriate and relevant pedagogical training to \nteach at the chosen level of education and be academically well-qualified in the subject(s) \nthey are expected to teach. \n\nThis indicator measures the share of the teaching work force which is pedagogically \nwell-trained. A high value indicates that students are being taught by teachers who \nare pedagogically well-trained to teach. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.c.1&seriesCode=SE_TRA_GRDL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20UPPSEC\">Proportion of teachers with the minimum required qualifications, in upper secondary (%) SE_TRA_GRDL</a> UNSTATS<br>", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-0c-01.pdf\">Metadata 4-c-1.pdf </a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de docentes con las calificaciones mínimas requeridas, desglosada por nivel educativo", "objetivo_global"=>"4- Garantizar una educación inclusiva y equitativa de calidad y promover oportunidades de aprendizaje permanente para todos", "meta_global"=>"4.c- De aquí a 2030, aumentar considerablemente la oferta de docentes calificados, incluso mediante la cooperación internacional para la formación de docentes en los países en desarrollo, especialmente los países menos adelantados y los pequeños Estados insulares en desarrollo", "definicion"=>"Irakasten duten mailarako eskatzen den gutxieneko irakaskuntzarako prestakuntza  jaso duten hezkuntza-maila desberdinetako irakasleen proportzioa, hezkuntza-maila  horretako irakasle guztiekiko. Irakaskuntzarako prestakuntza hori lanean hasi aurretik  ala lanean ari den bitartean izan daiteke.", "formula"=>"\n$$PPRFD_{hezkuntza\\, maila}^{t} = \\frac{PRFD_{hezkuntza\\, maila}^{t}}{PR^{t}} \\cdot 100$$\n\nnon:\n\n$PRFD_{hezkuntza\\, maila}^{t} =$ irakaskuntza prestakuntza jaso duten hezkuntza-maila jakin bateko irakasleak $t-1/t$ ikasturtean\n\n$CENU^{t} =$ hezkuntza-maila jakin bateko irakasle guztiak $t-1/t$ ikasturtean\n", "desagregacion"=>"Hezkuntza-maila: Haur Hezkuntza; Lehen Hezkuntza; Bigarren Hezkuntzako lehen zikloa; Bigarren Hezkuntzako bigarren zikloa  ", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Irakasleek funtsezko zeregina dute emandako hezkuntzaren kalitatea bermatzeko. Egokiena litzateke irakasle guztiek \nprestakuntza pedagogiko egokia, zuzena eta bidezkoa jasotzea aukeratutako hezkuntza-mailan irakasteko, eta irakastea \nespero den irakasgai(et)an akademikoki ondo kualifikatuta egotea. \n\nAdierazle honek pedagogikoki ondo prestatuta dagoen irakaskuntzako lan-indarraren proportzioa neurtzen du. Balio handia \nizateak adierazten du irakasteko pedagogikoki ondo prestatuta dauden eskolak jasotzen dituztela ikasleek. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=4.c.1&seriesCode=SE_TRA_GRDL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20UPPSEC\">Eskatutako gutxieneko kualifikazioak dituzten irakasleen proportzioa, Bigarren Hezkuntzako bigarren zikloan (%) SE_TRA_GRDL</a> UNSTATS<br>", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-04-0c-01.pdf\">Metadatuak 4-c-1.pdf </a>(ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.c: By 2030, substantially increase the supply of qualified teachers, including through international cooperation for teacher training in developing countries, especially least developed countries and small island developing States</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.c.1: Proportion of teachers with the minimum required qualifications, by education level</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SE_TRA_GRDL - Proportion of teachers with the minimum required qualifications, by education level and sex [4.c.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-08-02", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>4.7.1, 12.8.1, 13.3.1.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Institute for Statistics (UNESCO-UIS)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Institute for Statistics (UNESCO-UIS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have received at least the minimum organized pedagogical teacher training pre-service and in-service required for teaching at the relevant level in a given country.</p>\n<p><strong>Concepts:</strong></p>\n<p>A teacher is trained if they have received at least the minimum organized pedagogical teacher training pre-service and in-service required for teaching at the relevant level in a given country.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The International Standard Classification of Education (ISCED) is used as reference to define and classify educational programmes across countries in a comparative manner.</p>\n<p>The minimum organized pedagogical teacher training pre-service and in-service required for teaching at the relevant level is defined according to national standards. </p>\n<p>The UNESCO Institute for Statistics (UIS) is developing an International Standard Classification of Teacher Training Programmes (ISCED-T) to support the production of internationally comparable data on teacher training programmes, and to improve the availability and quality of teacher statistics, especially in reference to national programmes for pre-service teacher education. ISCED-T will also aid explore the development of an international standard for &#x201C;trained&#x201D; and &#x201C;qualified&#x201D; teachers that could be used alongside the national standards currently used for the monitoring of this target. A draft proposal of ISCED-T is submitted to the 41st Session of the UNESCO General Conference for consideration and adoption in November 2021.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Administrative data from schools and other organized learning centres.</p>", "COLL_METHOD__GLOBAL"=>"<p>The data are collected from: a) official country respondents through the <a href=\"https://uis.unesco.org/uis-questionnaires\">UIS Formal Education Survey</a> and the <a href=\"https://tcg.uis.unesco.org/wp-content/uploads/sites/4/2022/11/WG_EMIS_2_Dynamic-Templates.pdf\">UIS Dynamic Templates</a>; and b) official national documents publicly available through web scraping.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are collected all year round.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Biannual UNESCO Institute for Statistics (UIS) data release (February/March and September).</p>", "DATA_SOURCE__GLOBAL"=>"<p>Ministries of Education and/or National Statistical Offices.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS).</p>", "INST_MANDATE__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the United Nations depository for global statistics in the fields of education, science, technology and innovation, culture and communication. The UIS is the official source of internationally comparable data used to monitor progress towards the Sustainable Development Goal on education (SDG4) and key targets related to science, culture and communication, and gender equality. The Institute also produces standards and methodologies to support the monitoring of these goal and targets.</p>\n<p>Moreover, as part of UIS mandate attribution, Education 2030 Framework for Action stressed that &#x201C;[&#x2026;] <em>Countries should seek to improve the quality, levels of disaggregation and timeliness of reporting to the UNESCO Institute for Statistics [&#x2026;]</em>&#x201D; (<a href=\"http://uis.unesco.org/sites/default/files/documents/education-2030-incheon-framework-for-action-implementation-of-sdg4-2016-en_2.pdf\">http://uis.unesco.org/sites/default/files/documents/education-2030-incheon-framework-for-action-implementation-of-sdg4-2016-en_2.pdf</a>, &amp;18). The Education 2030 Framework for Action also stated that: &#x201C;<em>In recognition of the importance of harmonization of monitoring and reporting, the UIS will remain the official source of cross-nationally comparable data on education. It will continue to produce international monitoring indicators based on its annual education survey and on other data sources that guarantee international comparability for more than 200 countries and territories. In addition to collecting data, the UIS will work with partners to develop new indicators, statistical approaches and monitoring tools to better assess progress across the targets related to UNESCO&#x2019;s mandate, working in coordination with the SDG-Education 2030 SC</em>&#x201D; (<a href=\"http://uis.unesco.org/sites/default/files/documents/education-2030-incheon-framework-for-action-implementation-of-sdg4-2016-en_2.pdf\">http://uis.unesco.org/sites/default/files/documents/education-2030-incheon-framework-for-action-implementation-of-sdg4-2016-en_2.pdf</a>, &amp;100).</p>", "RATIONALE__GLOBAL"=>"<p>Teachers play a key role in ensuring the quality of education provided. Ideally all teachers should receive adequate, appropriate and relevant pedagogical training to teach at the chosen level of education and be academically well-qualified in the subject(s) they are expected to teach. This indicator measures the share of the teaching work force which is pedagogically well-trained. </p>\n<p>A high value indicates that students are being taught by teachers who are pedagogically well-trained to teach.</p>", "REC_USE_LIM__GLOBAL"=>"<p>It is important to note that national minimum training requirements can vary widely from one country to the next. This variability between countries lessens the usefulness of global tracking because the indicator would only show the percent reaching national standards, not whether teachers in different countries have similar levels of training. Further work would be required if a common standard for teacher training is to be applied across countries.</p>", "DATA_COMP__GLOBAL"=>"<p>The number of teachers in a given level of education who are trained is expressed as a percentage of all teachers in that level of education.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>P</mi>\n    <mi>T</mi>\n    <mi>T</mi>\n    <mi>n</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">n</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">n</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where:</p>\n<p>PTT<sub>n</sub> = percentage of trained teachers at level n of education</p>\n<p>TT<sub>n</sub> = trained teachers at level n of education</p>\n<p>T<sub>n</sub> = total teachers at level n of education</p>\n<p>n = 02 (pre-primary), 1 (primary), 2 (lower secondary), 3 (upper secondary) and 23 (secondary)</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Teachers&#x2019; data used to produce this indicator are gathered through the annual Survey of Formal Education. The data received are validated using electronic error detection systems that check for arithmetic errors and inconsistencies and trend analysis for implausible results. Queries are taken up with the relevant agencies in individual countries or country representatives reporting the data so that corrections can be made (of errors) or explanations given (of implausible but correct results). During this process countries are also encouraged to provide estimates for missing or incomplete data items.</p>\n<p>In addition, countries have an opportunity to review, comment on, and validate the main indicators the UNESCO Institute of Statistics (UIS) produces in an annual &#x201C;country review&#x201D; of indicators before the publication of the data by the UIS. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Data should be reported according to the levels of education defined in the International Standard Classification of Education (ISCED) to ensure international comparability of resulting indicators. </p>", "IMPUTATION__GLOBAL"=>"<h5><strong>At country level</strong></h5>\n<p>The UNESCO Institute of Statistics (UIS) estimates certain key items of data that may be missing or incomplete in order to have publishable estimates at the country level. Where this is not possible the UIS imputes missing values for use only for calculating regional and global aggregates.</p>\n<p>For the purposes of calculating the percentage of trained teachers, the UIS may make one or more of the following:</p>\n<p>&#x2022; An adjustment to account for over- or under-reporting, for example:</p>\n<p>o To include teachers in a type of education &#x2013; such as private education or special education &#x2013; not reported by the country; and/or </p>\n<p>o To include teachers in a part of the country not reported by the country.</p>\n<p>&#x2022; An estimate of the number of trained teachers in each level of education if the country only reported data for combined levels (e.g., total secondary rather than lower and upper secondary separately).</p>\n<p>In all cases estimates are based on evidence from the country itself (e.g., information from the data provider on the size of the missing component, via correspondence, publications or data on the Ministry&#x2019;s or National Statistical Office&#x2019;s Webpage, or via surveys conducted by other organizations) or on data from the country for a previous year. These figures may be published: (i) as observed data if the missing items are found in a national source; (ii) as national estimates if the country is persuaded to produce estimates and submit them in place of missing data; or (iii) as UIS estimates, if the estimates are made by the UIS.</p>\n<h5><strong>At regional and global levels</strong></h5>\n<p>Regional and global aggregates are derived from both publishable and imputed national data. Publishable data are the data submitted to the UIS by Member States or the result of an explicit estimation made by the Institute based on pre-determined standards. In both cases, these data are sent to Member States for review before they are considered publishable by the UIS. </p>\n<p>When data are not available for all countries, the UIS imputes national data for the sole purpose of calculating regional averages. These imputed data are not published nor otherwise disseminated. </p>\n<p>Where data are available for a country for both an earlier and a more recent year than the missing year, a simple linear interpolation is made. Where data are only available for an earlier year, the most recent value is used as an estimate. Similarly, where data are only available for a more recent year, the last value is used as an estimate.</p>\n<p>Where the relevant data are not available at all for a country, estimates may be based on another variable which is clearly linked to the item being estimated. For example, trained teachers may be based on total teachers.</p>\n<p>Where no data are available for the country in any year that can inform the estimate, the unweighted average for the region in which the country lies is used.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates are calculated as weighted averages using the denominator of the indicator as the weight. As described previously, where publishable data are not available for a given country or year, values are imputed for the purpose of calculating the regional and global aggregates.</p>", "DOC_METHOD__GLOBAL"=>"<p>The UNESCO Institute of Statistics (UIS) has elaborated guidance for the countries on the methodology that should be used to calculate this indicator. ISCED mappings that help countries report their data in an internationally comparable framework are available on the website of the UNESCO Institute for Statistics (<a href=\"http://uis.unesco.org/en/isced-mappings\">http://uis.unesco.org/en/isced-mappings</a>). </p>\n<p>Administrative teachers&#x2019; data from schools and other organized learning centres are gathered through the national annual schools census. The collected data are usually stored in the national Education Management Information System (EMIS) according to procedures in place in each country.</p>\n<p>To assist countries to make a more informed choice in relation to EMIS, by developing standards about what an EMIS must be able to do in order to supply accurate and valid information to education sector policymakers, school managers, and international organisations as part of international data reporting, the UIS in collaboration with the Global Partnership for Education, has developed EMIS user&#x2019;s and buyer&#x2019;s guides ( <a href=\"http://emis.uis.unesco.org/buyers-and-users-guide/\">http://emis.uis.unesco.org/buyers-and-users-guide/</a>).</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The UNESCO Institute of Statistics (UIS) maintains the global database used to produce this indicator. For transparency purposes, the inclusion of a data point in the database is completed by following a protocol and is reviewed by UIS technical focal points to ensure consistency and overall data quality, based on objective criteria to ensure that only the most recent and reliable information are included in the database. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The indicator should be based on available data on trained teachers for the given level of education, from all types of educational institutions in the country (public and private). The process for quality assurance includes review of survey documentation, review of the indicator values across time, calculation of measures of reliability, examination of consistency of indicator values derived from different sources and, if necessary, consultation with data providers.</p>\n<p>Before its annual data release and the addition of any indicators to the global SDG Indicators Database, the UNESCO Institute for Statistics (UIS) submits all indicator values and notes on methodology to National Statistical Offices, Ministries of Education or other relevant agencies in individual countries for their review and feedback. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Accurate data on the number of teachers at each level of education who have the minimum required qualifications and the total number of teachers at each level in a given academic year are essential for calculating this indicator. Criteria for quality assessment include: data sources must include proper documentation; data values must be representative at the national population level and, if not, should be footnoted; data are plausible and based on trends and consistency with previously published/reported values for the indicator. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are available in 150+ countries (measured in terms of number of countries having at least 1</p>\n<p>data point since 2015)..</p>\n<p><strong>Disaggregation:</strong></p>\n<p>By sex and level of education. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Nationally-published figures may differ from the international ones because of differences between national education systems and the International Standard Classification of Education (ISCED); or differences in coverage (i.e. the extent to which different types of education &#x2013; e.g. private or special education &#x2013; are included in one rather than the other).</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.uis.unesco.org\">http://www.uis.unesco.org</a> </p>\n<p><strong>References:</strong></p>\n<p>EMIS user&#x2019;s and buyer&#x2019;s guides: </p>\n<p><a href=\"http://emis.uis.unesco.org/buyers-and-users-guide/\">http://emis.uis.unesco.org/buyers-and-users-guide/</a></p>\n<p>The Survey of Formal Education Instruction Manual <a href=\"http://uis.unesco.org/sites/default/files/documents/instruction-manual-survey-formal-education-2017-en.pdf\">http://uis.unesco.org/sites/default/files/documents/instruction-manual-survey-formal-education-2017-en.pdf</a></p>\n<p>The International Standard Classification of Education (ISCED): <a href=\"http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf\">http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf</a></p>\n<p>UIS Questionnaire on Students and Teachers (ISCED 0-4) </p>\n<p><a href=\"http://uis.unesco.org/en/uis-questionnaires\">http://uis.unesco.org/en/uis-questionnaires</a></p>", "indicator_sort_order"=>"04-0c-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"5.1.1", "slug"=>"5-1-1", "name"=>"Determinar si existen o no marcos jurídicos para promover, hacer cumplir y supervisar la igualdad y la no discriminación por razón de sexo", "url"=>"/site/es/5-1-1/", "sort"=>"050101", "goal_number"=>"5", "target_number"=>"5.1", "global"=>{"name"=>"Determinar si existen o no marcos jurídicos para promover, hacer cumplir y supervisar la igualdad y la no discriminación por razón de sexo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Existencia de marcos jurídicos para promover, reforzar y supervisar la igualdad de género", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Determinar si existen o no marcos jurídicos para promover, hacer cumplir y supervisar la igualdad y la no discriminación por razón de sexo", "indicator_number"=>"5.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de la Presidencia, Justicia y Relaciones con las Cortes", "periodicity"=>"Anual", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Existencia de marcos jurídicos para promover, reforzar y supervisar la igualdad de género", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.1- Poner fin a todas las formas de discriminación contra todas las mujeres y las niñas en todo el mundo", "definicion"=>"Proporción de respuestas afirmativas sobre el total de preguntas recogidas para medir la  existencia de marcos jurídicos para promover, reforzar y supervisar la igualdad de género  en las cuatro áreas trazadas en la Convención sobre la eliminación de todas las formas de  discriminación contra la mujer (CEDAW) y la Plataforma de acción de Beijing", "formula"=>"\n$$MJML^{t} = \\frac{NSI_{Área}^{t}}{NSI_{Área}^{t} + NNO_{Área}^{t}} \\cdot 100$$\n\ndonde:\n\n$NSI_{Área}^{t} =$ número de respuestas 'SÍ' a las cuestiones del área correspondiente \ndel cuestionario específico diseñado para el indicador 5.1.1 por Naciones Unidas en el año $t$\n\n$NNO_{Área}^{t} =$ número de respuestas 'NO' a las cuestiones del área correspondiente \ndel cuestionario específico diseñado para el indicador 5.1.1 por Naciones Unidas en el año $t$\n", "desagregacion"=>"Áreas: Marco legal global y vida pública, Violencia contra la mujer, Empleo y \nsubsidios económicos, Matrimonio y familia\n", "observaciones"=>"\nNaciones Unidas estableció un cuestionario de 42 preguntas del tipo “SÍ/NO” para medir \nlos esfuerzos de los gobiernos a la hora de establecer marcos jurídicos para \npromover, reforzar y supervisar la igualdad de género. Este cuestionario se \ndivide en cuatro áreas trazadas en la Convención sobre la eliminación de todas \nlas formas de discriminación contra la mujer (CEDAW) y la Plataforma de acción de Beijing:\n\n 1. Marco legal global y vida pública (preguntas 1-12)\n 2. Violencia contra la mujer (preguntas 13-21)\n 3. Empleo y subsidios económicos (preguntas 22-31)\n 4. Matrimonio y familia (preguntas 32-42)\n", "periodicidad"=>"Bienal", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa igualdad y la no discriminación por motivos de sexo son principios fundamentales en \nel marco jurídico y normativo internacional, incluida la Convención sobre la eliminación de \ntodas las formas de discriminación contra la mujer (CEDAW), que cuenta con 189 Estados \npartes, y la Plataforma de Acción de Beijing. Este marco establece los compromisos de \nlos Estados para eliminar la discriminación contra la mujer y promover la igualdad \nde género, incluyendo el ámbito de los marcos jurídicos.\n\nEn la Plataforma de Acción de Beijing, los Estados se comprometieron a revocar \ntodas las leyes restantes que discriminan por motivos de sexo. El examen y evaluación \nquinquenal de la Plataforma de Acción de Beijing (Beijing +5) estableció el año 2005 como \nfecha límite para la derogación de las leyes que discriminan a la mujer. Esta fecha \nlímite ha llegado y pasado. Si bien se ha avanzado en la reforma de las leyes para \npromover la igualdad de género, la discriminación contra la mujer en la ley continúa \nen muchos países. Incluso donde se han realizado reformas legales, persisten \nlagunas en la aplicación.\n\nEliminar las leyes discriminatorias y establecer marcos jurídicos que promuevan \nla igualdad de género son requisitos previos para poner fin a la discriminación contra \nla mujer y lograr la igualdad de género (Objetivo 5, Meta 5.1). El indicador 5.1.1 será \ncrucial para acelerar el progreso en la implementación del ODS 5 y todos los \ndemás compromisos relacionados con el género en la Agenda 2030 para el Desarrollo Sostenible.\n\nEl indicador 5.1.1 mide los esfuerzos del Gobierno para establecer marcos jurídicos que \npromuevan, apliquen y vigilen la igualdad de género.\n\nLa evaluación es realizada por homólogos nacionales, incluidas las Oficinas Nacionales \nde Estadística y/o los Mecanismos Nacionales de la Mujer (MNM), y profesionales \ndel derecho/investigadores sobre igualdad de género, utilizando un cuestionario que consta \nde 42 preguntas de respuesta sí/no en cuatro áreas del derecho:\n\n (i) marcos jurídicos generales y vida pública<br>  \n (ii) violencia contra la mujer<br>  \n (iii) empleo y beneficios económicos<br>  \n (iv) matrimonio y familia<br>  \n\nLas áreas del derecho y las preguntas se extraen del marco jurídico y de políticas \ninternacionales sobre igualdad de género, en particular la Convención sobre la \nEliminación de Todas las Formas de Discriminación contra la Mujer (CEDAW), que \ncuenta con 189 Estados partes, y la Plataforma de Acción de Beijing. \n\nLas principales fuentes de información pertinentes para el indicador 5.1.1 son la \nlegislación y las políticas y planes de acción.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.1.1&seriesCode=SG_LGL_GENEQLFP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Marcos jurídicos que promueven, hacen cumplir y supervisan la igualdad de género (porcentaje de logro, 0 - 100) -- Área 1: marcos jurídicos generales y vida pública SG_LGL_GENEQLFP</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.1.1&seriesCode=SG_LGL_GENEQVAW&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Marcos jurídicos que promueven, hacen cumplir y monitorean la igualdad de género (porcentaje de logro, 0 - 100) -- Área 2: violencia contra las mujeres SG_LGL_GENEQVAW</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.1.1&seriesCode=SG_LGL_GENEQEMP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Marcos jurídicos que promueven, hacen cumplir y monitorean la igualdad de género (porcentaje de logro, 0 - 100) -- Área 3: empleo y beneficios económicos SG_LGL_GENEQEMP</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.1.1&seriesCode=SG_LGL_GENEQMAR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Marcos jurídicos que promueven, hacen cumplir y monitorean la igualdad de género (porcentaje de logro, 0 - 100) -- Área 4: matrimonio y familia SG_LGL_GENEQMAR</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-01-01.pdf\">Metadatos 5-1-1.pdf (solo en inglés)</a>", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Existencia de marcos jurídicos para promover, reforzar y supervisar la igualdad de género", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.1- Poner fin a todas las formas de discriminación contra todas las mujeres y las niñas en todo el mundo", "definicion"=>"Proportion of affirmative responses out of the total number of questions  collected to measure the existence of legal frameworks to promote, strengthen,  and monitor gender equality in the four areas outlined in the Convention on  the Elimination of All Forms of Discrimination against Women (CEDAW) and the  Beijing Platform for Action", "formula"=>"\n$$MJML^{t} = \\frac{NSI_{area}^{t}}{NSI_{area}^{t} + NNO_{area}^{t}} \\cdot 100$$\n\nwhere:\n\n$NSI_{area}^{t} =$ Number of \"YES\" responses to the questions in the corresponding \narea of the specific questionnaire designed for indicator 5.1.1 by the United Nations \nin the year $t$\n\n$NNO_{area}^{t} =$ Number of \"NO\" responses to the questions in the corresponding \narea of the specific questionnaire designed for indicator 5.1.1 by the United Nations \nin the year $t$\n", "desagregacion"=>"Areas: Global legal framework and public life, Violence against women, Employment and \neconomic benefits, Marriage and family \n", "observaciones"=>"\nThe United Nations established a 42-question \"YES/NO\" questionnaire to measure governments' \nefforts to establish legal frameworks to promote, strengthen, and monitor gender equality. \nThis questionnaire is divided into four areas outlined in the Convention on the Elimination \nof All Forms of Discrimination against Women (CEDAW) and the Beijing Platform for Action: \n\n1. Global Legal Framework and Public Life (questions 1-12)\n2. Violence against Women (questions 13-21)\n3. Employment and Income Support (questions 22-31)\n4. Marriage and Family (questions 32-42)\n", "periodicidad"=>"Bienal", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEquality and non-discrimination based on sex are core principles under \nthe international legal and policy framework, including the Convention \non the Elimination of All Forms of Discrimination against Women (CEDAW), \nwhich has 189 States parties, and the Beijing Platform for Action. This \nframework sets out the commitments of States to eliminate discrimination \nagainst women and promote gender equality, including in the area of legal \nframeworks. \n\nIn the Beijing Platform for Action, States pledged to revoke any remaining \nlaws that discriminate based on sex. The five-year review and appraisal of \nthe Beijing Platform for Action (Beijing +5) established 2005 as the target \ndate for the repeal of laws that discriminate against women. This deadline \nhas come and gone. While there has been progress in reforming laws to promote \ngender equality, discrimination against women in the law continues in many \ncountries. Even where legal reforms have taken place, gaps in implementation \npersist. \n\nRemoving discriminatory laws and putting in place legal frameworks that advance \ngender equality are prerequisites to ending discrimination against women and \nachieving gender equality (Goal 5, Target 5.1). Indicator 5.1.1 will be crucial \nin accelerating progress on the implementation of SDG 5 and all other gender-related \ncommitments in the 2030 Agenda for Sustainable Development. \n\nIndicator 5.1.1 measures Government efforts to put in place legal frameworks that \npromote, enforce and monitor gender equality. \n\nThe assessment is carried out by national counterparts, including National Statistical \nOffices (NSOs) and/or National Women’s Machinery (NWMs), and legal practitioners/\nresearchers on gender equality, using a questionnaire comprising 42 yes/no questions \nunder four areas of law: \n\n (i) overarching legal frameworks and public life<br>  \n (ii) violence against women<br>  \n (iii) employment and economic benefits<br>  \n (iv) marriage and family<br>  \n\nThe areas of law and questions are drawn from the international legal and policy \nframework on gender equality, in particular the Convention on the Elimination of All \nForms of Discrimination against Women (CEDAW), which has 189 States parties, and the \nBeijing Platform for Action. \n\nThe primary sources of information relevant for indicator 5.1.1 are legislation and \npolicy/action plans. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.1.1&seriesCode=SG_LGL_GENEQLFP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 1: overarching legal frameworks and public life SG_LGL_GENEQLFP</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.1.1&seriesCode=SG_LGL_GENEQVAW&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 2: violence against women SG_LGL_GENEQVAW</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.1.1&seriesCode=SG_LGL_GENEQEMP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 3: employment and economic benefits SG_LGL_GENEQEMP</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.1.1&seriesCode=SG_LGL_GENEQMAR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 4: marriage and family SG_LGL_GENEQMAR</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-01-01.pdf\">Metadata 5-1-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Existencia de marcos jurídicos para promover, reforzar y supervisar la igualdad de género", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.1- Poner fin a todas las formas de discriminación contra todas las mujeres y las niñas en todo el mundo", "definicion"=>"Emakumeen aurkako diskriminazio mota guztiak ezabatzeari buruzko Konbentzioan eta Beijingeko  Ekintza Plataforman ezarritako lau esparruetan, genero-berdintasuna sustatzeko, indartzeko eta  ikuskatzeko marko juridikorik badagoen neurtzeko ezarri ziren galderei emandako baiezko  erantzunen proportzioa, galdera guztiekiko", "formula"=>"\n$$MJML^{t} = \\frac{NSI_{esparrua}^{t}}{NSI_{esparrua}^{t} + NNO_{esparrua}^{t}} \\cdot 100$$\n\nnon:\n\n$NSI_{esparrua}^{t} =$ Nazio Batuek 5.1.1 adierazlerako diseinatutako galdetegi espezifikoan \nesparruari dagozkion galderetako 'BAI' erantzunen kopurua, $t$ urtean\n\n$NNO_{Esparru}^{t} =$ Nazio Batuek 5.1.1 adierazlerako diseinatutako galdetegi espezifikoan \nesparruari dagozkion galderetako 'EZ' erantzunen kopurua, $t$ urtean\n", "desagregacion"=>"Esparruak: Lege-marko orokorra eta bizitza publikoa; emakumearen aurkako indarkeria; enplegua eta \ndirulaguntza ekonomikoak; ezkontza eta familia\n", "observaciones"=>"\nNazio Batuek \"BAI/EZ\" motakoak 42 galderako galdetegi bat ezarri zuten, gobernuek genero-berdintasuna \nsustatzeko, indartzeko eta ikuskatzeko marko juridikoak ezartzeko egiten dituzten ahaleginak \nneurtzeko. Galdetegi hori Emakumearen aurkako diskriminazio mota guztiak ezabatzeari buruzko Konbentzioan \neta Beijingo Ekintza Plataforman ezarritako lau arlotan zatitzen da:\n\n 1. Lege-marko orokorra eta bizitza publikoa (1-12 galderak)\n 2. Emakumeen aurkako indarkeria (13-21 galderak)\n 3. Enplegua eta dirulaguntza ekonomikoak (22-31 galderak)\n 4. Ezkontza eta familia (32-42 galderak)\n", "periodicidad"=>"Bienal", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nBerdintasuna eta sexuagatiko bereizkeriarik eza funtsezko printzipioak dira nazioarteko esparru juridiko eta arautzailean, \nEmakumearen aurkako diskriminazio-mota guztiak ezabatzeko Konbentzioa (CEDAW, 189 estatu partaide dituena) eta Beijingkko \nEkintza Plataforma barne. Esparru horrek emakumeen aurkako diskriminazioa ezabatzeko eta genero-berdintasuna sustatzeko \nestatuen konpromisoak ezartzen ditu, esparru juridikoen eremua barne. \n\nBeijingko Ekintza Plataforman sexuagatik diskriminatzen duten gainerako lege guztiak baliogabetzeko konpromisoa hartu zuten \nestatuek. Beijingko Ekintza Plataformaren (Beijing +5) bost urteko azterketak eta ebaluazioak 2005. urtea ezarri zuen emakumea \ndiskriminatzen duten legeak indargabetzeko epemuga gisa. Mugaegun hori iritsi eta igaro da. Genero-berdintasuna sustatzeko \nlegeen erreforman aurrera egin bada ere, emakumeen aurkako lege-mailako diskriminazioak herrialde askotan jarraitzen du. \nLege-erreformak egin diren lekuetan ere, hutsuneak daude aplikazioan. \n\nLege diskriminatzaileak ezabatzea eta genero-berdintasuna sustatzen duten esparru juridikoak ezartzea aldez aurreko baldintzak \ndira emakumearen aurkako diskriminazioa amaitzeko eta genero-berdintasuna lortzeko (5. helburua, 5.1 xedea). 5.1.1 adierazlea \nfuntsezkoa izango da 5. GJH eta Garapen Jasangarrirako 2030eko Agendan generoarekin zerikusia duten gainerako konpromiso \nguztiak ezartzeko aurrerapena bizkortzeko. \n\nGobernuak genero-berdintasuna sustatu, aplikatu eta zainduko duten esparru juridikoak ezartzeko egiten dituen ahaleginak \nneurtzen ditu 5.1.1 adierazleak. \n\nEbaluazioa homologo nazionalek egiten dute, Estatistikako Bulego Nazionalak edota Emakumearen Mekanismo Nazionalak (EMN) \neta genero-berdintasunaren arloko zuzenbideko profesionalak/ikertzaileak barne, zuzenbideko lau arlotan bai/ez erantzuteko \n42 galdera dituen galdetegi bat erabiliz: \n\n\n (i) esparru juridiko orokorra eta bizitza publikoa<br>  \n (ii) emakumearen aurkako indarkeria<br>  \n (iii) enplegua eta onura ekonomikoak<br>  \n (iv) ezkontza eta familia<br>  \n\nZuzenbidearen arloak eta galderak genero-berdintasunari buruzko esparru juridikotik eta nazioarteko politiketatik ateratzen \ndira, bereziki Emakumearen aurkako diskriminazio-mota guztiak ezabatzeko Konbentziotik (CEDAW, 189 estatu partaide dituena) \neta Beijingko Ekintza Plataformatik. \n\n5.1.1 adierazlerako egokiak diren informazio-iturri nagusiak legeria eta ekintza-politikak eta -planak dira. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.1.1&seriesCode=SG_LGL_GENEQLFP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Genero-berdintasuna sustatu, indartu eta ikuskatzeko marko juridikoak (lorpenaren ehunekoa, 0 - 100) -- 1. arloa: marko juridiko orokorra eta bizitza publikoa SG_LGL_GENEQLFP</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.1.1&seriesCode=SG_LGL_GENEQVAW&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Genero-berdintasuna sustatu, indartu eta ikuskatzeko marko juridikoak (lorpenaren ehunekoa, 0 - 100) -- 2. arloa: emakumeen aurkako indarkeria SG_LGL_GENEQVAW</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.1.1&seriesCode=SG_LGL_GENEQEMP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Genero-berdintasuna sustatu, indartu eta ikuskatzeko marko juridikoak (lorpen-ehunekoa, 0 - 100) -- 3. arloa: enplegua eta dirulaguntza ekonomikoak SG_LGL_GENEQEMP</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.1.1&seriesCode=SG_LGL_GENEQMAR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Genero-berdintasuna sustatu, indartu eta ikuskatzeko marko juridikoak (lorpenaren ehunekoa, 0 - 100) -- 4. arloa: ezkontza eta familia SG_LGL_GENEQMAR</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-01-01.pdf\">Metadatuak 5-1-1.pdf</a> (ingelesez bakarrik) ", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.1: End all forms of discrimination against all women and girls everywhere</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.1.1: Whether or not legal frameworks are in place to promote, enforce and monitor equality and non&#x2011;discrimination on the basis of sex</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_LGL_GENEQLFP - Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 1: overarching legal frameworks and public life. [5.1.1]</p>\n<p>SG_LGL_GENEQVAW - Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 2: violence against women. [5.1.1]</p>\n<p>SG_LGL_GENEQEMP - Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 3: employment and economic benefits. [5.1.1]</p>\n<p>SG_LGL_GENEQMAR - Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 4: marriage and family. [5.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>There are other legal SDGs indicators: </p>\n<p>&#x2022; Indicator 5.a.2, &#x2018;Proportion of countries where the legal framework (including customary law) guarantees women&#x2019;s equal rights to land ownership and/or control&#x2019;; and </p>\n<p>&#x2022; Indicator 5.6.2, &#x2018;Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education&#x2019;. </p>\n<p>To avoid duplication, indicator 5.1.1 does not cover areas of law that are addressed under indicators 5.a.2 and 5.6.2. Indicator 5.1.1 complements these other indicators.</p>\n<p>Legal frameworks that advance gender equality generally relate to all of Goal 5 as well as other Goals since gender equality is central to the achievement of all SDGs. See UN Women and UN Statistics Division&#x2019;s annual <a href=\"https://unstats.un.org/sdgs/gender-snapshot/2021/\"><em>Progress on the Sustainable Development Goals: The Gender Snapshot</em></a> which each year uses latest available data to demonstrate how gender equality, including progress on Target 5.1, is fundamental to achievement of all 17 Goals. </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UN Women, World Bank Group, OECD Development Centre</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UN Women, World Bank Group, OECD Development Centre</p>", "STAT_CONC_DEF__GLOBAL"=>"<h2>Definitions:</h2>\n<p>Indicator 5.1.1 measures Government efforts to put in place legal frameworks that promote, enforce and monitor gender equality. </p>\n<p>The indicator is based on an assessment of legal frameworks that promote, enforce and monitor gender equality. The assessment is carried out by national counterparts, including National Statistical Offices (NSOs) and/or National Women&#x2019;s Machinery (NWMs), and legal practitioners/researchers on gender equality, using a questionnaire comprising 42 yes/no questions under four areas of law: (i) overarching legal frameworks and public life; (ii) violence against women; (iii) employment and economic benefits; and (iv) marriage and family<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>. The areas of law and questions are drawn from the international legal and policy framework on gender equality, in particular the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), which has 189 States parties, and the Beijing Platform for Action. As such, no new internationally agreed standard on equality and non-discrimination on the basis of sex was needed. The primary sources of information relevant for indicator 5.1.1 are legislation and policy/action plans.</p>\n<p>The 42 questions in the questionnaire are:</p>\n<h2>Area 1: Overarching legal frameworks and public life</h2>\n<h2>Promote</h2>\n<ol>\n  <li>If customary law is a valid source of law under the constitution, is it invalid if it violates constitutional provisions on equality or non-discrimination? </li>\n  <li>If personal law is a valid source of law under the constitution, is it invalid if it violates constitutional provisions on equality or non-discrimination? </li>\n  <li>Is there a discrimination law that prohibits both direct and indirect discrimination against women? </li>\n  <li>Do women and men enjoy equal rights and access to hold public and political office (legislature, executive, judiciary)? </li>\n  <li>Are there quotas for women (reserved seats) in, or quotas for women in candidate lists for, national parliament?</li>\n  <li>Do women and men have equal rights to confer citizenship to their spouses and their children?</li>\n</ol>\n<h2>Enforce and monitor </h2>\n<ol>\n  <li>Does the law establish a specialized independent body tasked with receiving complaints of discrimination based on gender (e.g., national human rights institution, women&#x2019;s commission, ombudsperson)? </li>\n  <li>Is legal aid mandated in criminal matters? </li>\n  <li>Is legal aid mandated in civil/family matters?</li>\n  <li>Does a woman&#x2019;s testimony carry the same evidentiary weight in court as a man&#x2019;s? </li>\n  <li>Are there laws that explicitly require the production and/or dissemination of gender statistics?</li>\n  <li>Are there sanctions for noncompliance with mandated candidate list quotas, or incentives for political parties to field women candidates in national parliamentary elections? </li>\n</ol>\n<h2>Area 2: Violence against women</h2>\n<h2>Promote</h2>\n<ol>\n  <li>Is there legislation specifically addressing domestic violence?</li>\n  <li>Have provisions exempting perpetrators from facing charges for rape if the perpetrator marries the victim after the crime been removed, or never existed in legislation? </li>\n  <li>Have provisions reducing penalties in cases of so-called honor crimes been removed, or never existed in legislation?</li>\n  <li>Are laws on rape based on lack of consent, without requiring proof of physical force or penetration? </li>\n  <li>Does legislation explicitly criminalize marital rape or does legislation entitle a woman to file a complaint about rape against her husband or partner?</li>\n  <li>Is there legislation that specifically addresses sexual harassment? </li>\n</ol>\n<h2>Enforce and monitor</h2>\n<ol>\n  <li>Are there budgetary commitments provided for by government entities for the implementation of legislation addressing violence against women by creating an obligation on the government to provide a budget or allocation of funding for the implementation of relevant programs or activities? </li>\n  <li>Are there budgetary commitments provided by government entities for the implementation of legislation addressing violence against women by allocating a specific budget, funding, and/or incentives to support non-governmental organizations for activities to address violence against women? </li>\n  <li>Is there a national action plan or policy to address violence against women that is overseen by a national mechanism with the mandate to monitor and review implementation? </li>\n</ol>\n<h2>Area 3: Employment and economic benefits</h2>\n<h2>Promote</h2>\n<ol>\n  <li>Does the law mandate non-discrimination based on gender in employment?</li>\n  <li>Does the law mandate equal remuneration for work of equal value? </li>\n  <li>Can women work in jobs deemed hazardous, arduous, or morally inappropriate in the same way as men?</li>\n  <li>Are women able to work in the same industries as men?</li>\n  <li>Are women able to perform the same tasks as men?</li>\n  <li>Does the law allow women to work the same night hours as men? </li>\n  <li>Does the law provide for maternity or parental leave available to mothers in accordance with the ILO standards? </li>\n  <li>Does the law provide for paid paternity or parental leave available to fathers or partners?</li>\n</ol>\n<h2>Enforce and monitor</h2>\n<ol>\n  <li>Is there a public entity that can receive complaints on gender discrimination in employment? </li>\n  <li>Is childcare publicly provided or subsidized?</li>\n</ol>\n<h2>Area 4: Marriage and family</h2>\n<h2>Promote</h2>\n<ol>\n  <li>Is the minimum age of marriage at least 18, with no legal exceptions, for both women and men?</li>\n  <li>Do women and men have equal rights to enter marriage (i.e., consent) and initiate divorce? </li>\n  <li>Do women and men have equal rights to be the legal guardian of their children during and after marriage? </li>\n  <li>Do women and men have equal rights to be recognized as head of household or head of the family?</li>\n  <li>Do women and men have equal rights to choose where to live? </li>\n  <li>Do women and men have equal rights to choose a profession?</li>\n  <li>Do women and men have equal rights to obtain an identity card?</li>\n  <li>Do women and men have equal rights to apply for passports? </li>\n  <li>Do women and men have equal rights to own, access, and control marital property including upon divorce?</li>\n</ol>\n<h2>Enforce and monitor </h2>\n<ol>\n  <li>Is marriage under the legal age void or voidable?</li>\n  <li>Are there dedicated and specialized family courts? </li>\n</ol>\n<h2>Concepts: </h2>\n<p>Article 1 of CEDAW provides a comprehensive definition of discrimination against women covering direct and indirect discrimination and article 2 sets out general obligations for States, in particular on required legal frameworks, to eliminate discrimination against women. Article 1 of CEDAW states: &#x201C;&#x2026; the term &quot;discrimination against women&quot; shall mean any distinction, exclusion or restriction made on the basis of sex which has the effect or purpose of impairing or nullifying the recognition, enjoyment or exercise by women, irrespective of their marital status, on a basis of equality of men and women, of human rights and fundamental freedoms in the political, economic, social, cultural, civil or any other field&#x201D;. Article 2 of CEDAW states: States Parties condemn discrimination against women in all its forms, agree to pursue by all appropriate means and without delay a policy of eliminating discrimination against women and, to this end, undertake: (a) To embody the principle of the equality of men and women in their national constitutions or other appropriate legislation if not yet incorporated therein and to ensure, through law and other appropriate means, the practical realization of this principle; (b) To adopt appropriate legislative and other measures, including sanctions where appropriate, prohibiting all discrimination against women; (c) To establish legal protection of the rights of women on an equal basis with men and to ensure through competent national tribunals and other public institutions the effective protection of women against any act of discrimination; (d) To refrain from engaging in any act or practice of discrimination against women and to ensure that public authorities and institutions shall act in conformity with this obligation; (e) To take all appropriate measures to eliminate discrimination against women by any person, organization or enterprise; (f) To take all appropriate measures, including legislation, to modify or abolish existing laws, regulations, customs and practices which constitute discrimination against women; (g) To repeal all national penal provisions which constitute discrimination against women&#x201D;. </p>\n<p>The term &#x201C;legal frameworks&#x201D; is defined broadly to encompass laws, mechanisms, and policies/plans to &#x2018;promote, enforce and monitor&#x2019; gender equality. </p>\n<p>Legal frameworks that &#x201C;promote&#x201D; are those that establish women&#x2019;s equal rights with men and enshrine non-discrimination based on sex. Legal frameworks that &#x201C;enforce and monitor&#x2019; are directed to the realization of equality and non-discrimination and implementation of laws, such as policies/plans, the establishment of enforcement and monitoring mechanisms, and allocation of financial resources.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> The areas of law were agreed at the expert workshop, held on 14 and 15 June 2016, to discuss the methodological development of SDG indicator 5.1.1. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) of legal frameworks that promote, enforce, and monitor gender equality</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The data for the indicator are derived from an assessment of legal frameworks using primary sources/official government documents, in particular laws, policies and action plans. The assessment is carried out by national counterparts, including National Statistical Offices (NSOs) and/or National Women&#x2019;s Machinery (NWMs), and legal practitioners/researchers on gender equality, using a questionnaire comprising 42 yes/no questions under four areas of law: (i) overarching legal frameworks and public life; (ii) violence against women; (iii) employment and economic benefits; and (iv) marriage and family. The areas of law and questions are drawn from the international legal and policy framework on gender equality, in particular the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), which has 189 States parties, and the Beijing Platform for Action. </p>", "COLL_METHOD__GLOBAL"=>"<p>Countries are asked to designate a focal point to undertake the coordination at the country level necessary for the collection and validation of the data. Most designated focal points are within the NWMs, a number are within the NSOs, and some are within both the NWMs and the NSOs. After verification,<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> the data with relevant laws, policies, and other sources included are sent to the designated focal points/country counterparts to review and validate. Final answers are arrived at after the process of validation with country counterparts.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> Verification includes information (eg national legal sources) compiled under World Bank Group and OECD Development Centre procedures by legal practitioners/researchers on gender equality. The World Bank Group&#x2019;s Women, Business and the Law and the OECD Development Centre&#x2019;s Social Institutions and Gender Index are two well-known global databases on national legal frameworks that promote gender equality which have been collecting data in this area for 10 and 9 years respectively. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "FREQ_COLL__GLOBAL"=>"<p>Data will be compiled every two years starting in 2018.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>First quarter, every two years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National counterparts, including National Statistical Offices and National Women&#x2019;s Machinery.</p>", "COMPILING_ORG__GLOBAL"=>"<p>The World Bank Group, the OECD Development Centre, UN Women</p>", "INST_MANDATE__GLOBAL"=>"<p>The World Bank works closely with international agencies, regional development banks, donors, and other partners to develop frameworks, guidance, and standards of good practice for statistics, build consensus and define internationally agreed indicators, establish data exchange processes and methods, and help countries improve statistical capacity. Since 2009, the World Bank Group&#x2019;s Women, Business and the Law project has contributed to the study of gender equality and informed discussions on improving women&apos;s economic opportunities and empowerment through a unique dataset that measures the legal differences in access to economic opportunities between men and women in 190 economies. </p>\n<p>The OECD Development Centre&#x2019;s core mission is to provide a platform for evidence-based policy dialogue between OECD and non-OECD countries to design better policies, by identifying policy solutions to improve lives in developing countries. Through its Gender Programme, particularly since the creation of the Social Institutions and Gender Index (SIGI) in 2009, the OECD Development Centre has played an instrumental role in highlighting the data gaps and fostering policy dialogue and mutual learning on the social institutions that discriminate against women and girls across their life cycle. It is also building the capacity of member states in data collection through the SIGI Country Studies, and advocates for more, better, and comparable data through its SIGI Global and Regional Reports and policy dialogue events.</p>\n<p>UN Women is committed through its work at the global, regional, and county level to support Member States in filling critical gaps in generating and using data, statistics, evidence, and analysis on gender equality in crucial areas. As part of its mandate, the organization supports Member States in setting norms. It conducts research, and compiles and provides evidence, including good practices and lessons learned, to inform intergovernmental debates and decisions. It also assists in implementing norms and standards through its country programs. In addition, it leads and <a href=\"https://www.unwomen.org/en/how-we-work/un-system-coordination\">coordinates</a> the UN system&#x2019;s work in support of gender equality and the empowerment of women.</p>", "RATIONALE__GLOBAL"=>"<p>Equality and non-discrimination based on sex are core principles under the international legal and policy framework, including the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), which has 189 States parties, and the Beijing Platform for Action. This framework sets out the commitments of States to eliminate discrimination against women and promote gender equality, including in the area of legal frameworks. </p>\n<p>In the Beijing Platform for Action, States pledged to revoke any remaining laws that discriminate based on sex. The five-year review and appraisal of the Beijing Platform for Action (Beijing +5) established 2005 as the target date for the repeal of laws that discriminate against women. This deadline has come and gone. While there has been progress in reforming laws to promote gender equality, discrimination against women in the law continues in many countries. Even where legal reforms have taken place, gaps in implementation persist.</p>\n<p>Removing discriminatory laws and putting in place legal frameworks that advance gender equality are prerequisites to ending discrimination against women and achieving gender equality (Goal 5, Target 5.1). Indicator 5.1.1 will be crucial in accelerating progress on the implementation of SDG 5 and all other gender-related commitments in the 2030 Agenda for Sustainable Development.</p>", "REC_USE_LIM__GLOBAL"=>"<p>To avoid duplication, the indicator does not cover areas of law that are addressed under indicator 5.a.2, &#x2018;Proportion of countries where the legal framework (including customary law) guarantees women&#x2019;s equal rights to land ownership and/or control&#x2019;, and indicator 5.6.2, &#x2018;Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education&#x2019;. Indicator 5.1.1 complements these indicators.</p>", "DATA_COMP__GLOBAL"=>"<h2>Scoring:</h2>\n<p>The indicator is based on an assessment of legal frameworks that promote, enforce, and monitor gender equality using a questionnaire comprising 42 Yes/No questions under four areas of law drawn from the international legal and policy framework on gender equality, in particular, CEDAW and the Beijing Platform for Action. </p>\n<p>The answers to the questions are coded with <u>simple &#x201C;Yes/No&#x201D;</u> answers with &#x201C;1&#x201D; for &#x201C;Yes&#x201D; and &#x201C;0&#x201D; for &#x201C;No&#x201D;. For questions 1 and 2 only, they may be scored &#x201C;N/A&#x201D; in which case they are not included as part of the overall score calculation for the area.<sup><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></sup> </p>\n<p>The scoring methodology is the unweighted average of the questions under each area of law calculated by:<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>q</mi>\n          </mrow>\n          <mrow>\n            <mn>1</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <mo>&#x22EF;</mo>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>q</mi>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>m</mi>\n              </mrow>\n              <mrow>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>m</mi>\n          </mrow>\n          <mrow>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math> .</p>\n<p>Where <em>A<sub>i</sub> </em>refers the area of law <em>i</em>; <em>m<sub>i</sub> </em>refers to the total number of questions under the area of law <em>i;</em><sup><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></sup> <em>q<sub>1</sub>+...+q<sub>mi</sub></em> refers to the sum of the coded questions under the area of law and where <em>q<sub>i</sub>=&#x201D;1&#x201D;</em> if the answer is &#x201C;Yes&#x201D; and <em>q<sub>i</sub>=&#x201D;0&#x201D;</em> if the answer is &#x201C;No&#x201D;. </p>\n<p>Results of the four areas are reported as percentages as a dashboard: <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced open=\"&#x2329;\" close=\"&#x232A;\" separators=\"|\">\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n          </mrow>\n          <mrow>\n            <mn>1</mn>\n            <mo>,</mo>\n          </mrow>\n        </msub>\n        <mo>,</mo>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n          </mrow>\n          <mrow>\n            <mn>2</mn>\n          </mrow>\n        </msub>\n        <mo>,</mo>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n          </mrow>\n          <mrow>\n            <mn>3</mn>\n          </mrow>\n        </msub>\n        <mo>,</mo>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n          </mrow>\n          <mrow>\n            <mn>4</mn>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfenced>\n  </math>. The score for each area (a number between 0 and 100) therefore represents the percentage of achievement of that country in that area, with 100 being best practice met on all questions in the area. </p>\n<p>The choice of presenting all four area scores without further aggregation is the result of adopting the posture that high values in one area in a given country need not compensate in any way the country having low values in some other area, and that a comprehensive examination of the value of those four numbers for each country is potentially more informative than trying to summarize all four numbers into a single index.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> For questions 1 and 2, the methodology does not attribute a score (positive or negative) to the existence of customary or personal law but does score whether they are subject to constitutional principles of equality or non-discrimination. Therefore, in countries where customary or personal law does not apply, these questions are scored as &#x201C;N/A&#x201D; and are not included as part of the overall score calculation for the area &#x2018;overarching legal frameworks and public life&#x2019;. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> If a question is coded as &#x201C;N/A&#x201D;, it will not be counted in the total number of questions in an area of the law. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>Countries are asked to designate a focal point to undertake the coordination at the country level necessary for the collection and validation of the data. Most designated focal points are within the NWMs, a number are within the NSOs and some are within both the NWMs and the NSOs. </p>\n<p>After verification, the data with relevant laws, polices and other sources included, are sent to the designated focal points/country counterparts to review and validate. Final answers are arrived at after the process of validation with country counterparts.</p>", "ADJUSTMENT__GLOBAL"=>"<p> Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level: </strong></p>\n<p>Not imputed</p>\n<p><strong>&#x2022; At regional and global levels: </strong></p>\n<p>Not imputed</p>", "REG_AGG__GLOBAL"=>"<p>The regional and global aggregate calculations will be the unweighted average of the scores of each country in that region (or globally), per area of law.</p>", "DOC_METHOD__GLOBAL"=>"<p>&#x2022; Methodology used by countries for the compilation of the data at the national level: The questionnaires provided to countries include guidance, definitions and instructions. </p>\n<p>&#x2022; International recommendations and guidelines: The areas of law and questions are drawn from the international legal and policy framework on gender equality, in particular the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), which has 189 States parties, (http://www.ohchr.org/EN/HRBodies/CEDAW/Pages/CEDAWIndex.aspx), and the Beijing Platform for Action (http://www.unwomen.org/en/how-we-work/intergovernmental-support/world-conferences-on-women). The attached Methodological Note sets out the international standards supporting the areas of law and questions and also attaches the background paper for the expert workshop which provides a useful summary of the international legal and policy framework on equality and non-discrimination on the basis of sex and the relevance for SDG indicator 5.1.1.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>See section 4.d. on validation.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The assessment of laws is initially carried out by national counterparts, and legal practitioners and researchers on gender equality. The data is checked and verified by the World Bank Group, OECD Development Centre, and UN Women. The data is then sent to the designated focal points/country counterparts to review and validate. Please refer to section 3 above on Data source type and data collection method for more details. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See section 4.d. on validation. In addition, coding guidelines are used to set criteria that are applied equally to all countries for the purposes of ensuring comparability across countries.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Pilot data collection and validation was carried out for 14 countries.</p>\n<p><strong>Time series:</strong></p>\n<p>First release of data was in 2019. </p>\n<p><strong>Disaggregation: </strong></p>\n<p>The indicator captures and is disaggregated into four areas of law: (i) overarching legal frameworks and public life; (ii) violence against women; (iii) employment and economic benefits; and (iv) marriage and family. Data in the global database corresponds to these disaggregations.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There should be no discrepancies. Data is collected through validated surveys.</p>", "OTHER_DOC__GLOBAL"=>"<p>World Bank Group: <a href=\"http://wbl.worldbank.org/\">http://wbl.worldbank.org/</a> </p>\n<p>OECD Development Centre: <a href=\"http://oe.cd/sigi\">http://oe.cd/sigi</a></p>\n<p>UN Women: <a href=\"https://data.unwomen.org/data-portal/sdg\">https://data.unwomen.org/data-portal/sdg</a> </p>\n<h1>UN Women and UN Statistics Division annual SDG and gender monitoring report: <a href=\"https://www.unwomen.org/en/what-we-do/2030-agenda-for-sustainable-development\"><em>Progress on the Sustainable Development Goals: The Gender Snapshot</em></a><em> </em></h1>", "indicator_sort_order"=>"05-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"5.2.1", "slug"=>"5-2-1", "name"=>"Proporción de mujeres y niñas a partir de 15 años de edad que han sufrido violencia física, sexual o psicológica a manos de su actual o anterior pareja en los últimos 12 meses, desglosada por forma de violencia y edad", "url"=>"/site/es/5-2-1/", "sort"=>"050201", "goal_number"=>"5", "target_number"=>"5.2", "global"=>{"name"=>"Proporción de mujeres y niñas a partir de 15 años de edad que han sufrido violencia física, sexual o psicológica a manos de su actual o anterior pareja en los últimos 12 meses, desglosada por forma de violencia y edad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de victimizaciones contra las mujeres a manos de su actual o anterior pareja ", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de mujeres y niñas a partir de 15 años de edad que han sufrido violencia física, sexual o psicológica a manos de su actual o anterior pareja en los últimos 12 meses, desglosada por forma de violencia y edad", "indicator_number"=>"5.2.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Seguridad", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_292/opt_2/tipo_10/ti_violencia-contra-las-mujeres/temas.html", "url_text"=>"Estadística de violencia contra las mujeres", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tasa de victimizaciones contra las mujeres a manos de su actual o anterior pareja ", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.2- Eliminar todas las formas de violencia contra todas las mujeres y las niñas en los ámbitos público y privado, incluidas la trata y la explotación sexual y otros tipos de explotación", "definicion"=>"Número de casos registrados de violencia contra las mujeres (victimizaciones) a manos de su actual  o anterior pareja por cada 10.000 mujeres", "formula"=>"$$TVPAAP^{t} = \\frac{VAAP^{t}}{P_{mujeres}^{t}} \\cdot 10000$$\n\ndonde:\n\n$VAAP^{t} =$ número de casos registrados de violencia contra las mujeres a manos de su actual o anterior pareja en el año $t$\n\n$P_{mujeres}^{t} =$ mujeres a 1 de julio del año $t$\n", "desagregacion"=>"Territorio histórico/Comarca/Municipio", "periodicidad"=>"Anual", "justificacion_global"=>"La violencia de pareja es una de las formas más comunes de violencia que enfrentan \nlas mujeres en todo el mundo. Dadas las normas sociales predominantes que sancionan \nel dominio masculino sobre las mujeres, la violencia masculina hacia sus \nparejas íntimas femeninas a menudo se percibe como un elemento ordinario/normal \nde las relaciones en el contexto del matrimonio u otras uniones/relaciones.\n\nLa violencia contra las mujeres es una manifestación extrema de la desigualdad y \nla discriminación de género.\n\nSe requieren datos de prevalencia para medir la magnitud del problema; comprender las diversas \nformas de violencia y sus consecuencias; identificar grupos de alto riesgo y explorar \nlas barreras para buscar ayuda para garantizar que se brinden las respuestas apropiadas. \nEstos datos son el punto de partida para informar leyes, políticas y desarrollar \nrespuestas efectivas y programas de prevención. También permiten a los \npaíses monitorear los cambios a lo largo del tiempo y orientar de manera óptima \nlos recursos para maximizar la efectividad de las intervenciones \n(especialmente en entornos con recursos limitados).\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.2.1&seriesCode=VC_VAW_MARR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-49%20%7C%20FEMALE\">Proporción de mujeres y niñas que alguna vez tuvieron pareja y que fueron víctimas de violencia física o sexual por parte de su pareja actual o anterior en los últimos 12 meses, por edad (%) VC_VAW_MARR</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas, pero aporta información complementaria.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-02-01.pdf\">Metadatos 5-2-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Tasa de victimizaciones contra las mujeres a manos de su actual o anterior pareja ", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.2- Eliminar todas las formas de violencia contra todas las mujeres y las niñas en los ámbitos público y privado, incluidas la trata y la explotación sexual y otros tipos de explotación", "definicion"=>"Number of cases (victimizations) of violence against women at the hands of their current or  former partner over total number of women", "formula"=>"$$TVPAAP^{t} = \\frac{VAAP^{t}}{P_{women}^{t}} \\cdot 10000$$\n\nwhere:\n\n$VAAP^{t} =$ Number of registered cases of violence against women at the hands of their current or former partner in the year $t$\n\n$P_{women}^{t} =$ women on July 1 of year $t$\n", "desagregacion"=>"Province/County/Municipality", "periodicidad"=>"Anual", "justificacion_global"=>"Intimate partner violence is one of the most common forms of violence that \nwomen face globally. Given prevailing social norms that sanction male dominance \nover women, male violence towards their female intimate partners is often perceived \nas an ordinary/normal element of relationships in the context of marriage or other \nunions/relationships. \n\nViolence against women is an extreme manifestation of gender inequality and \ndiscrimination. \n\nPrevalence data are required to measure the magnitude of the problem; understand \nthe various forms of violence and their consequences; identify groups at high risk \nand explore the barriers to seeking help to ensure that the appropriate responses \nare being provided. These data are the starting point for informing laws, policies \nand developing effective responses and prevention programs. They also allow countries \nto monitor change over time and optimally target resources to maximize the effectiveness \nof interventions (especially in resource-constrained settings). \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.2.1&seriesCode=VC_VAW_MARR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-49%20%7C%20FEMALE\">Proportion of ever-partnered women and girls subjected to physical and/or sexual violence by a current or former intimate partner in the previous 12 months, by age (%) VC_VAW_MARR</a> UNSTATS", "comparabilidad"=>"The available indicator does not comply with the metadata of the United Nations indicator,  but provides complementary information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-02-01.pdf\">Metadata 5-2-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Tasa de victimizaciones contra las mujeres a manos de su actual o anterior pareja ", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.2- Eliminar todas las formas de violencia contra todas las mujeres y las niñas en los ámbitos público y privado, incluidas la trata y la explotación sexual y otros tipos de explotación", "definicion"=>"Emakumeen aurkako indarkeria-kasu erregistratuen kopurua (biktimizazioak), uneko edo aurreko  bikotekidearen eskutik, 10.000 emakumeko", "formula"=>"$$TVPAAP^{t} = \\frac{VAAP^{t}}{P_{emakumeak}^{t}} \\cdot 10000$$\n\nnon:\n\n$VAAP^{t} =$ emakumeen aurkako indarkeria-kasu erregistratuen kopurua, uneko edo aurreko \nbikotekidearen eskutik $t$ urtean\n\n$P_{emakumeak}^{t} =$ emakumeak $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Lurralde historikoa/Eskualdea/Udalerria", "periodicidad"=>"Anual", "justificacion_global"=>"Bikote-indarkeria da emakumeek mundu osoan jasaten duten indarkeria-modu ohikoenetako bat. Emakumeen gaineko nagusitasun \nmaskulinoa zigortzen duten arau sozial nagusiak direla eta, beren bikotekide intimo femeninoen aurkako indarkeria askotan \nelementu arrunt/normaltzat hartzen da ezkontzaren edo beste lotura/harreman batzuen testuinguruan. \n\nEmakumeen aurkako indarkeria desberdintasunaren eta genero-diskriminazioaren muturreko adierazpena da. \n\nNagusitasun-datuak behar dira arazoaren tamaina neurtzeko; indarkeria-motak eta horien ondorioak ulertzeko; arrisku handiko \ntaldeak identifikatzeko eta oztopoak aztertzeko, erantzun egokiak emango direla bermatzeko laguntza bilatze aldera. Datu \nhoriek abiapuntua dira legeei eta politikei buruzko informazioa emateko eta erantzun eraginkorrak eta prebentzio-programak \ngaratzeko. Era berean, herrialdeei aukera ematen diete aldaketak denboran zehar monitorizatzeko eta baliabideak ahalik eta \nondoen bideratzeko, esku-hartzeen eraginkortasuna areagotze aldera (bereziki, baliabide mugatuak dituzten inguruneetan). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.2.1&seriesCode=VC_VAW_MARR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-49%20%7C%20FEMALE\">Noizbait bikotekidea izan duten eta azken 12 hilabeteetan bikotekidearen edo bikotekide ohiaren indarkeria fisiko edo sexualaren biktima izan diren emakumeen eta neskatoen proportzioa, adinaren arabera (%) VC_VAW_MARR</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina informazio  osagarria ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-02-01.pdf\">Metadatuak 5-2-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.2: Eliminate all forms of violence against all women and girls in the public and private spheres, including trafficking and sexual and other types of exploitation</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.2.1: Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Proportion of ever-partnered women and girls subjected to physical and/or sexual violence by a current or former intimate partner in the previous 12 months, by age (%) VC_VAW_MARR</p>", "META_LAST_UPDATE__GLOBAL"=>"2022-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>5.2.2: Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence</p>\n<p>5.6.1: Proportion of women aged 15-49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care. ( as includes a component on saying no to sex.)</p>\n<p>11.7.2: Proportion of persons victim of physical or sexual harassment, by sex, age, disability status and place of occurrence, in the previous 12 months</p>\n<p>16.1.3: Proportion of population subjected to physical, psychological, or sexual violence in the previous 12 months</p>\n<p>16.2.3: Proportion of young women and men aged 18-29 years who experienced sexual violence by age 18</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>\n<p>United Nations Children&apos;s Fund (UNICEF)</p>\n<p>United Nations Entity for Gender Equality and the Empowerment of Women (UN Women)</p>\n<p>United Nations Office on Drugs and Crime (UNODC)</p>\n<p>United Nations Population Fund (UNFPA)</p>\n<p>United Nations Statistics Division (UNSD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>\n<p>United Nations Children&apos;s Fund (UNICEF)</p>\n<p>United Nations Entity for Gender Equality and the Empowerment of Women (UN Women)</p>\n<p>United Nations Office on Drugs and Crime (UNODC)</p>\n<p>United Nations Population Fund (UNFPA)</p>\n<p>United Nations Statistics Division (UNSD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator measures the percentage of ever-partnered women and girls aged 15 years and older who have been subjected to physical, sexual, or psychological violence by a current or former intimate partner, in the previous 12 months. The definition of violence against women and girls (VAWG) and the forms of violence specified under this indicator are presented in the next section (Concepts).</p>\n<p><u>NOTE</u>: References to &#x201C;violence against women&#x201D; (VAW) throughout also include adolescent girls (15-19 years old).</p>\n<p> </p>\n<p><strong>Concepts:</strong></p>\n<p>According to the UN Declaration on the Elimination of Violence against Women (1993), violence against women is &#x201C;Any act of gender-based violence that results in, or is likely to result in, physical, sexual, or psychological harm or suffering to women, including threats of such acts, coercion or arbitrary deprivation of liberty, whether occurring in public or in private life. Violence against women shall be understood to encompass, but not be limited to, the following: Physical, sexual and psychological violence occurring in the family [&#x2026;]&#x201D;. See here for the full definition: <a href=\"https://undocs.org/en/A/RES/48/104\">https://undocs.org/en/A/RES/48/104</a> </p>\n<p>Intimate partner violence (IPV) against women includes any abuse perpetrated by a current or former partner within the context of marriage, cohabitation, or any other formal or informal union.</p>\n<p>The <u>different forms of violence</u> included in the indicator are defined as follows: </p>\n<p>1. Physical violence consists of acts aimed at physically hurting the victim and include, but are not limited to acts like pushing, grabbing, twisting the arm, pulling hair, slapping, kicking, biting, hitting with a fist or object, trying to strangle or suffocate, burning or scalding on purpose, or threatening or attacking with some sort of weapon, gun or knife. </p>\n<p>2. Sexual violence is defined as any sort of harmful or unwanted sexual behaviour that is imposed on someone, whether by use of physical force, intimidation, or coercion. It includes acts of abusive sexual contact, forced sexual acts, attempted or completed sexual acts (intercourse) without consent (rape or attempted rape), non-contact acts such as being forced to watch or participate in pornography, etc. In intimate partner relationships, sexual violence is commonly operationally defined in surveys as being physically forced to have sexual intercourse, having sexual intercourse out of fear for what the partner might do or through coercion, and/or being forced to do something sexual that the woman considers humiliating or degrading.</p>\n<p>3. Psychological violence consists of any act that induces fear or emotional distress. It includes a range of behaviours that encompass acts of emotional abuse such as being frequently humiliated in public, intimidated or having things you care for destroyed, etc. These often coexist with acts of physical and sexual violence by intimate partners. In addition, surveys often measure controlling behaviours (e.g., being kept from seeing family or friends, or from seeking health care without permission). These are also considered acts of psychological abuse, although usually measured separately. . </p>\n<p>For a more detailed definition of physical, sexual, and psychological violence against women, see <em>Guidelines for Producing Statistics on Violence against Women- Statistical Surveys</em> (UN, 2014), and the <em>International Classification of Crime for Statistical Purposes</em> ICCS (UNODC, 2015), and <em>Violence against Women Prevalence Estimates, 2018. Global, regional, and national prevalence estimates for intimate partner violence against women and global and regional prevalence estimates for non-partner sexual violence against women</em> (WHO, 2021). </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The &#x2018;gold standard&#x2019; and operational definitions applied to the generation of the 2018 global, regional and national estimates for intimate partner violence against women (WHO, 2021) reference the UN Guidelines for Producing Statistics on Violence against Women(UN, 2014) and the UNODC International Classification of Crime for Statistical Purposes ICCS (UNODC, 2015. These international standards on measurement and reporting include </p>\n<ol>\n  <li>standardized definitions of physical, sexual, and psychological IPV against women;</li>\n  <li> measurement of these forms of violence using acts-based questions</li>\n  <li>Appropriate sample size </li>\n  <li>disaggregation by age groups</li>\n  <li>application of the appropriate denominator/target population (ever-partnered women)</li>\n  <li>reporting by type of perpetrator</li>\n  <li>comprehensive interviewer training to administer violence against women questions, and following internationally agreed ethical and safety guidelines, including on privacy, confidentiality and support service information. </li>\n</ol>\n<p>Survey measurement, should be guided by these international standards and documentation should report on all of the above to allow for an overall assessment of data quality.</p>\n<p>However, to date, individual studies and surveys use different measures, methodologies and reporting standards. This makes it challenging to compare the prevalence across studies and requires the use of adjusted estimates for international comparability (see section 4b).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The SDG 5.2.1 Indicator Database comprises data from population-based household surveys representative at the national and/or sub-national level and implementing a methodology that uses act-based questions. All sources date from 2000 to 2018.</p>\n<p>A significant proportion of data from low- and middle-income countries are obtained from the Domestic Violence Module of the Demographic and Health Surveys (DHS). Some data come from dedicated surveys on violence against women in countries that have implemented, for example, WHO&#x2019;s violence against women survey methodology or other methodologies consistent with international guidelines and best practices. In the case of higher-income countries, data were obtained from Crime Victimisation Surveys (CVS) or dedicated surveys.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are collated by the WHO on behalf of the Inter-Agency Working Group on Violence against Women.</p>\n<p>Data come from publicly available survey data or data provided by National Statistics Offices (NSOs) or other relevant national entities through the consultation process with countries. For efficiency, some data are collated using existing data-compiling online platforms (e.g., DHS StatCompiler and the EU-wide Survey on Violence Against Women (Fundamental Rights Agency) Data Explorer)). </p>", "FREQ_COLL__GLOBAL"=>"<p>Countries are encouraged to conduct surveys at regular intervals. The recommended interval, depending on available resources, is three (3) to five (5) years which will allow countries to effectively measure progress. The prevalence database will be updated on an annual basis.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data on SDG indicator 5.2.1 were collected, compiled, and sent back to countries alongside the country estimates for their review. It is expected that the modeled estimates will be updated every 2 years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are provided by nationally or sub-nationally representative surveys on violence against women conducted by National Statistical Offices (in most cases),line ministries/other national institutions or other entities. </p>", "COMPILING_ORG__GLOBAL"=>"<p>Data are compiled and reviewed by the Interagency Working Group on Violence against Women Data (WHO, UN Women, UNICEF, UNSD, UNFPA, UNODC).</p>", "INST_MANDATE__GLOBAL"=>"<p>WHO is the directing and coordinating authority on international health within the United Nations System. It supports countries as they coordinate the efforts of multiple sectors of the government and partners to attain their health objectives and support their national health policies and strategies, including through developing norms and standards, and strengthening data collection, reporting, and use. The organization produces estimates and statistics for a wide range of diseases and health conditions including in its annual world health statistics report. It has led work on the measurement of violence against women since 1998, developed and tested new instruments for measuring VAW cross-culturally, as well as ethical and safety standards for research on VAW. </p>\n<p>In 2016, WHO Member States endorsed the <em>Global plan of action on strengthening the health system role in addressing violence, in particular against women and girls, and against children</em>(WHA Resolution 69.5) Improving the collection and use of data was one of its four strategic directions and included: a) Developing and disseminating harmonized indicators and measurement tools to support Member States in collecting standardized information on VAWG; b) Supporting Member States to implement population-based surveys on VAW; c) Building capacity in the collection, analysis and use of data; d) Regularly updating estimates of the prevalence of VAW.</p>\n<p><strong>UNICEF</strong> is responsible for global monitoring and reporting on the wellbeing of children. It provides technical and financial assistance to the Member States to support their efforts to collect quality data on violence against children, including through the UNICEF-supported Multiple Indicator Cluster Surveys (MICS) household survey program. UNICEF develops standards, tools, and guidelines for data collection. Furthermore, it compiles statistics on violence to make internationally comparable datasets publicly available, and it analyses such data which are included in relevant publications, including in its flagship publication, <em>The State of the World&#x2019;s Children</em>.</p>\n<p><strong>UN Women</strong> is committed through the conjunction of its triple mandate of normative support, UN coordination, and operational activities to work at the global, regional, and country-level to support the Member States in filling critical gaps in generating and using data, statistics, evidence, and analysis on gender equality in crucial areas. The organization supports the Member States in setting norms that include global standards. It conducts research, and compiles and provides evidence, including good practices and lessons learned, to inform intergovernmental debates and decisions; that help design specific policies and development plans at the regional, national, and local levels as part of its operational activities. It also assists in implementing norms and standards through its country programmes. In addition, UN Women leads and <a href=\"https://www.unwomen.org/en/how-we-work/un-system-coordination\"><u>coordinates</u></a> the UN system&#x2019;s work in support of gender equality and the empowerment of women.</p>\n<p><strong>The Statistics Division of the Department of Economic and Social Affairs (UNSD</strong>) helps the Member States to build sound national statistical systems, which includes solid institutional infrastructures, systematic data collection activities, the compilation of aggregate macroeconomic, social, and environment statistics according to global standards and norms, and a multichannel data dissemination system. In the area of methodological work, the Division develops international statistical standards and methods essential for the compilation of reliable and comparable statistics and methodological guidelines for the collection, processing, analysis, and dissemination of data. the Division has unparalleled recognition around gender statistics. Over the past 4 decades, it has supported countries in their efforts to produce and use high quality and timely gender data for better evidence-based policymaking; developed and promoted standards and methodological guidelines addressing emerging issues of gender concern; produced the World&#x2019;s Women report every 5 years, and compiled gender statistics and facilitated access to data. (<a href=\"https://unstats.un.org/unsd/demographic-social/gender/\">https://unstats.un.org/unsd/demographic-social/gender/</a>).</p>\n<p><strong>UNODC</strong> &#x2013; as custodian of the UN standards and norms in crime prevention and criminal justice, UNODC assists the Member States in reforming their criminal justice systems to be effective, fair, and humane for the entire population, including women and girls. UNODC develops technical tools to assist Member States in implementing the UN standards and norms and supports the Member States through the provision of technical assistance in crime prevention and criminal justice reform. It does so through several Global programs and the UNODC field office network.</p>\n<p><strong>UNFPA</strong> - is the United Nations sexual and reproductive health agency. Our mission is to deliver a world where every pregnancy is wanted, every childbirth is safe and every young person&apos;s potential is fulfilled. The agency collects and facilitates the gathering of the most accurate population data available to empower countries to make informed decisions on crucial development issues and humanitarian response. Its Population Data strategy addresses long-standing shortfalls in population data and related human capacity. The strategy seeks to expand the scope and quality of modern census and registry data, increase the use of geo-referenced population data to accelerate progress towards the SDGs, and advance the objectives of its mandate. The agency provides census technical support to more than 125 countries, through strong partnerships with governments, UN country teams, the US Census Bureau, and the population and data sectors worldwide. Census data provide the denominators for computation of many of the Sustainable Development Goals (SDGs) and a basis for weights when calculating regional and global aggregates of various indicators, including SDGs. </p>", "RATIONALE__GLOBAL"=>"<p>Intimate partner violence is one of the most common forms of violence that women face globally. Given prevailing social norms that sanction male dominance over women, male violence towards their female intimate partners is often perceived as an ordinary/normal element of relationships in the context of marriage or other unions/relationships. Violence against women is an extreme manifestation of gender inequality and discrimination.</p>\n<p>Prevalence data are required to measure the magnitude of the problem; understand the various forms of violence and their consequences; identify groups at high risk and explore the barriers to seeking help to ensure that the appropriate responses are being provided. These data are the starting point for informing laws, policies and developing effective responses and prevention programs. They also allow countries to monitor change over time and optimally target resources to maximize the effectiveness of interventions (especially in resource-constrained settings).</p>", "REC_USE_LIM__GLOBAL"=>"<p><u>Comparability:</u></p>\n<p>The availability of comparable data remains a challenge in this area as many data collection efforts have relied on different survey methodologies, or used different definitions of partner or spousal violence and recall periods (e.g., different definitions of &#x201C;lifetime&#x201D;). Many survey measures and/or reports lack disaggregation by different forms of intimate partner violence (physical, sexual, psychological). There are often differences in survey question formulations and/or denominators e.g. all women [various age ranges], or only ever-married/partnered or currently married/partnered women). There is also heterogeneity in age groups sampled and reported on. The quality of interviewer training also likely varies, although this is difficult to quantify. Willingness to discuss experiences of violence and understanding of relevant concepts may also differ according to how the survey is implemented and the social/cultural context, and this can affect reported prevalence levels.</p>\n<p>Given the wide variations in methodologies, measurement, and quality across studies from different countries, statistically adjusted estimates are currently needed to ensure comparability across countries and regions. However, generating estimates is an interim solution and individual countries need to collect robust, internationally comparable, high-quality data that reflect the relevant socio-economic, political and cultural risk, and protective factors associated with the prevalence of violence against women (VAW) to inform appropriate policy responses and programmatic decision-making. As more countries adopt international recommendations and guidelines, including the key elements described in this document, the need for adjustments for estimates for global monitoring will be greatly reduced.</p>\n<p><u>Regularity of data production:</u></p>\n<p>Since 2000, only about 78 countries have conducted more than one survey on VAW. Obtaining data on VAW is a costly and time-consuming exercise, whether they are obtained through stand-alone dedicated surveys or modules in other surveys. Demographic and Health Surveys (DHS), the main source of data for low- and lower-middle Income Countries (LMICs), are conducted every 5 years or so and dedicated surveys, if repeated, are conducted usually with less periodicity than this. Monitoring this indicator with certain periodicity may be a challenge if sustained capacities are not built and financial resources are not available for regular surveys. At the same time prevalence is unlikely to change from year to year so, depending on resources, every 3-5 years is recommended.</p>\n<p><u>Feasibility:</u></p>\n<p>This indicator calls for global reporting on three types of intimate partner violence (IPV): physical, sexual, and psychological. While there is global consensus on how physical and sexual IPV are generally defined and measured, psychological partner violence&#x2014;is conceptualized differently across cultures and in different contexts. This indicator therefore currently reports on physical and/or sexual intimate partner violence only. Efforts are underway by custodian agencies to develop a global standard for measuring and reporting psychological IPV. This will enable reporting on the three stipulated types of partner violence in the future. </p>\n<p>Similarly, this indicator calls for global reporting of violence ever-partnered women aged 15 years and above have been subjected to. Most data come from DHS, which typically sample only women aged 15-49, and there is a lack of consistency in the age range of sample populations across other country surveys. For those surveys that interview a sample of women from a different age group, the prevalence for the 15-49 age group is often published or can be calculated from available data. The global indicator therefore currently reports on both violence ever-partnered women 15-49 years of age and 15 years and older have been subjected to. Given the existing limited data availability on violence against women aged 50 years and older, efforts are underway by the custodian agencies to improve the measurement and encourage increased availability of data for women of this age group. This will enable a better estimation of the extent of this problem and understanding the experiences of partner violence for women over 50. </p>", "DATA_COMP__GLOBAL"=>"<p>This indicator calls for breakdown by form of violence and by age group. Countries are encouraged to compute prevalence data for each form of violence as detailed below to assist comparability at the regional and global levels: </p>\n<p><u>1. Physical intimate partner violence: </u></p>\n<p>Number of ever-partnered women (aged 15 years and above) subjected to any act of physical violence by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women and girls (aged 15 years and above) in the population multiplied by 100 .</p>\n<p><u>2. Sexual intimate partner violence: </u></p>\n<p>Number of ever-partnered women (aged 15 years and above) subjected to any act of sexual violence by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women (aged 15 years and above) in the population multiplied by 100. </p>\n<p><u>3. Psychological intimate partner violence: </u></p>\n<p>Number of ever-partnered women (aged 15 years and above) subjected to psychological violence by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women (aged 15 years and above) multiplied by 100. </p>\n<p><u>4. Any form of physical and/or sexual intimate partner violence: </u></p>\n<p>Number of ever-partnered women (aged 15 years and above) who experience physical and/or sexual violence by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women (aged 15 years and above) multiplied by 100.</p>\n<p><u>5. Any form of physical, sexual and/or psychological intimate partner violence:</u></p>\n<p>Number of ever-partnered women (aged 15 years and above) subjected to any act of physical, sexual and/or psychological violence by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women (aged 15 years and above) multiplied by 100.</p>\n<p><u>NOTE</u>: To assist comparability at the regional and global level, and due to more comparable data available, countries are encouraged to <em>additionally</em> compute the above figures for ever-partnered <em>women aged 15 to 49</em>. Regional and global reporting on this indicator currently only includes data computed by countries for #4 above (i.e., <em>any form of physical and/or sexual partner violence)</em>, and for both the 15-49 and the 15 years and older age groups). For further details, see Feasibility section above.<u> </u></p>", "DATA_VALIDATION__GLOBAL"=>"<p>A country consultation on the intimate partner violence (IPV) estimates was conducted in early 2020. All countries received their country profile which included their data sources, estimate, and a technical note explaining the methodology (available in six official languages). The consultations ensured: i) countries had the opportunity to review their nationally modeled IPV estimate and the data sources (surveys/studies) used in the production of these estimates, ii) the identification of any additional surveys/studies that met the inclusion criteria (i.e published between 2000-2018, used acts-based measures of IPV, nationally or subnationally representative) but which may not have been previously identified; and iii) familiarize countries with the statistical modeling approach used to derive the global, regional, and national estimates. </p>", "ADJUSTMENT__GLOBAL"=>"<p>There have been significant improvements in the measurement, availability, and quality of population-based survey data on intimate partner violence (IPV) globally. However, substantial heterogeneity remains in how national surveys and studies measure the different forms of intimate partner violence against women (VAW). For international comparability, data are statistically adjusted to ensure harmonization concerning: definitions (e.g. severity); age groupings (5 year age groups and aggregate 15 to 49 or 15 years and above), type of IPV (physical IPV only or sexual IPV only), the perpetrator of partner violence (spouse only or spouse/partner; current or most recent spouse/partner only or any current or previous spouse/partner), sample profile (ever-married/partnered women or currently-married/partnered women or all women) and geographical scope (national or sub-national, rural, urban). </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>When data for a country are entirely missing, no country-level estimate is published. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Imputations are made in cases where country data are not available for the purposes of regional and global figures. The number of countries included in the average and with data available is clearly indicated by SDG region. </p>", "REG_AGG__GLOBAL"=>"<p>Global aggregates are weighted averages of all the countries that make up the world. Regional aggregates are weighted averages of all the countries within the region. Weights used are the population of women aged 15 to 49 from the most recent 2019 revision of the World Population Prospects. Where data are not available for all countries in any given region, regional aggregates may still be calculated. The number of countries included in the average is indicated.</p>\n<p>It should be noted that regional and global figures should be interpreted with caution, as they do not necessarily represent with accuracy the region or world, especially for regions where population coverage is below 50 percent. </p>\n<p>Custodian agencies, in consultation with the Member States, have produced-to-date global, regional, and country estimates, enhancing the quality and accuracy of 5.2.1 reporting and addressing the comparability challenges outlined above. These new regional and global estimates (2018) are included in this round and form a baseline for the monitoring of progress. These are also available for World Bank, Global Burden of Disease and individual agency regions. </p>", "DOC_METHOD__GLOBAL"=>"<p>Countries gather data on intimate partner violence (IPV) through (1) specialized national prevalence surveys dedicated to measuring violence against women (VAW), (2) VAW modules that are added to international/national household surveys, such as the DHS; and (3) crime victimization surveys.</p>\n<p>Although administrative data from health, police, courts, justice, and social services, among other services used by survivors of violence, can provide information on VAW, these do not provide prevalence data, but rather incidence data or service use (i.e., number of cases received in/who seek services). Many women who are subjected to abuse do not report or seek help for the violence and those who do, tend to be the most serious cases. Therefore, administrative data are not recommended as a data source for this indicator. </p>\n<p>For more information on recommended practices in the production of VAW statistics, see <em>UN Guidelines for Producing Statistics on Violence against Women- Statistical Surveys</em> (UN, 2014). The WHO with other co-custodians are finalizing a &#x201C;Quality checklist for surveys on IPV against women&#x201D; as a tool for strengthening country capacity in collecting and reporting high-quality data on violence against women.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The identification of surveys and entering of data in the database was independently checked by 2 or 3 people and consistency checks were carried out by 2 analysts. The estimates followed the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) and were reviewed by WHO&#x2019;s Data Department and reviewed by the other co-custodians.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The Interagency Working Group on Violence against Women Data, which comprises all custodian agencies for this SDG indicator, thoroughly reviews all country data, including its primary source when deemed necessary, to assess quality and comparability based on exclusion/inclusion criteria agreed upon a priori. These criteria refer to, inter-alia, survey population coverage, operational definitions, methodology, and period. All data points have been discussed and a consensual decision made for every data point included/excluded from the current SDG Indicators Database. </p>\n<p>In 2020, a country consultation and validation process of data compiled by custodian agencies for this indicator was undertaken, including with identified SDG indicators focal points and focal points in other relevant ministries. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The e estimates on physical and/or sexual intimate partner violence against women were based on the <em>Global Database on the Prevalence of Violence against Women</em> (housed at WHO). Studies included within this database were identified through a comprehensive systematic review of published global prevalence data, metadata repositories of national statistics offices and through the country consultation process as explained below. </p>\n<p>Informed by international guidelines on the survey measurement of violence against women, including the <em>Guidelines for Producing Statistics on Violence against Women- Statistical Surveys</em> (UN, 2014) only population-based studies, representative at national or subnational level that used the &#x2018;gold standard&#x2019; act-specific measures of IPV were eligible for inclusion. This criterion aimed to minimise the under-estimation of the prevalence of IPV that is associated with the use of broader non-acts-based measures. Data extractions were conducted by two data analysts independently and underwent additional quality control and rigorous consistency checks by a third reviewer.</p>\n<p>The United Nations Inter-Agency Working Group on Violence Against Women Estimation and Data (VAW-IAWGED) guided the process of developing the estimates and reviewed the Technical Note for the country consultation and published estimates&#x2019; report. The independent external Technical Advisory Group (TAG) to the VAW-IAWGED provided expert advice and input throughout the process of developing the methodology and the estimates.</p>\n<p>In addition to the above, and in line with WHO&#x2019;s quality standards for data production and publication a formal country consultation process was conducted with 194 Member States and one territory (occupied Palestinian territory). The purpose this consultation process was to (i) to ensure that countries had the opportunity to review their national modelled intimate partner violence estimates and the data sources (surveys/studies) used in the production of these estimates; (ii) to ensure the inclusion of any additional surveys/studies that met these inclusion criteria but not been previously identified; and (iii) to familiarize countries with the statistical modelling approach used to derive the global, regional and national estimates.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Since 2000, 161 countries have conducted violence against women (VAW) national or subnational prevalence surveys or have included a module on VAW in a DHS or other national household surveys. However, not all these data are comparable and in many cases, they are not collected on a regular basis.</p>\n<p><strong>Time series:</strong></p>\n<p>Some countries (~77) have data on physical and/or sexual intimate partner violence for two or more time points. Global time series with comparable data are not yet available. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>In addition to form of violence and age, income/wealth, education, ethnicity (including indigenous status), disability status, marital/partnership status, relationship with the perpetrator (i.e., current/former partner), geographic location, migration status, and frequency of violence are suggested as desired variables for disaggregation for this indicator. Though disaggregated data by these variables is not yet feasible to report on at regional and global levels, countries are encouraged to report these levels of disaggregation in their national reports; and&#x2014;whenever possible&#x2014;include these data for the age group 15 to 49.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>All available survey data sources that are representative at the national and subnational level, are used to generate the prevalence estimates. The data are from published survey reports and/or data and datasets provided by countries. In cases where only data disaggregated by violence type were presented in the report, microdata was used to calculate the aggregate measure of physical and/or sexual intimate partner violence (IPV). As there is variability in the measurement across surveys and countries, relevant covariate adjustments were made to enhance comparability. These include adjustments for case definitions (e.g. severity), type of violence (i.e. physical IPV only or sexual IPV only), population surveyed (e.g. currently married women only or all women), reference partners (e.g. current/most recent partners), and geographical strata (rural or urban), aggregate measure of physical and/or sexual IPV where only one of the two forms were available.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://srhr.org/vaw-data\">https://srhr.org/vaw-data</a> </p>\n<p>http://evaw-global-database.unwomen.org/en</p>\n<p>data.unicef.org </p>\n<p><a href=\"http://unstats.un.org/unsd/gender/default.html\">http://unstats.un.org/unsd/gender/default.html</a> </p>\n<p><strong>References:</strong></p>\n<p>1. World Health Organization, 2021. Violence against Women Prevalence Estimates, 2018. Global, regional, and national prevalence estimates for intimate partner violence against women and global and regional prevalence estimates for non-partner sexual violence against women. Available at: https://www.who.int/publications/i/item/9789240022256</p>\n<p>2. United Nations, 2014. Guidelines for Producing Statistics on Violence against Women- Statistical Surveys. Available at: https://unstats.un.org/unsd/gender/docs/guidelines_statistics_vaw.pdf</p>\n<p>3. United Nations, 2015. The World&#x2019;s Women 2015, Trends and Statistics. Available at: https://unstats.un.org/unsd/gender/downloads/worldswomen2015_report.pdf</p>\n<p>4. World Health Organization, Department of Reproductive Health and Research, London School of Hygiene and Tropical Medicine, South African Medical Research Council, 2013. Global and regional estimates of violence against women: prevalence and health effects of intimate partner violence and non-partner sexual violence. Available at: https://www.who.int/publications/i/item/9789241564625</p>\n<p>5. UN Women. 2016. Global Database on Violence against Women. Available at: <a href=\"http://evaw-global-database.unwomen.org/en\">http://evaw-global-database.unwomen.org/en</a> </p>\n<p>6. UNICEF Data portal: <a href=\"http://data.unicef.org/child-protection/violence.html\">http://data.unicef.org/child-protection/violence.html</a> </p>\n<p>7. UNSD Portal on the minimum set of gender indicators: <a href=\"https://genderstats.un.org/#/home\">https://genderstats.un.org/#/home</a> </p>\n<p>8. UNSD dedicated portal for data and metadata on violence against women: <a href=\"http://unstats.un.org/unsd/gender/vaw/\">http://unstats.un.org/unsd/gender/vaw/</a> </p>\n<p>9.UNODC, 2015. International Classification of Crime for Statistical Purposes. Available at: https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html</p>", "indicator_sort_order"=>"05-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"5.2.2", "slug"=>"5-2-2", "name"=>"Proporción de mujeres y niñas a partir de 15 años de edad que han sufrido violencia sexual a manos de personas que no eran su pareja en los últimos12 meses, desglosada por edad y lugar del hecho", "url"=>"/site/es/5-2-2/", "sort"=>"050202", "goal_number"=>"5", "target_number"=>"5.2", "global"=>{"name"=>"Proporción de mujeres y niñas a partir de 15 años de edad que han sufrido violencia sexual a manos de personas que no eran su pareja en los últimos12 meses, desglosada por edad y lugar del hecho"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de victimizaciones contra las mujeres por violencia sexual fuera del ámbito familiar", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de mujeres y niñas a partir de 15 años de edad que han sufrido violencia sexual a manos de personas que no eran su pareja en los últimos12 meses, desglosada por edad y lugar del hecho", "indicator_number"=>"5.2.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Seguridad", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_292/opt_2/tipo_10/ti_violencia-contra-las-mujeres/temas.html", "url_text"=>"Estadística de violencia contra las mujeres", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tasa de victimizaciones contra las mujeres por violencia sexual fuera del ámbito familiar", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.2- Eliminar todas las formas de violencia contra todas las mujeres y las niñas en los ámbitos público y privado, incluidas la trata y la explotación sexual y otros tipos de explotación", "definicion"=>"Número de casos registrados de violencia sexual contra las mujeres (victimizaciones) cometidos por autores de fuera del ámbito familiar, por cada 10.000 mujeres", "formula"=>"$$TVIS^{t} = \\frac{VS^{t}}{P_{mujeres}^{t}} \\cdot 10000$$\n\ndonde:\n\n$VS^{t} =$ número de casos registrados de violencia sexual contra las mujeres cometidos por autores de fuera del ámbito familiar en el año $t$\n\n$P_{mujeres}^{t} =$ mujeres a 1 de julio del año $t$\n", "desagregacion"=>"\nTerritorio histórico/Comarca/Municipio\n", "periodicidad"=>"Anual", "justificacion_global"=>"La violencia contra las mujeres y las niñas es una de las formas más \nextendidas de violación de los derechos humanos en el mundo. Hay datos que demuestran \nque, a nivel mundial, aproximadamente el 7% de las mujeres han sido agredidas \nsexualmente por alguien que no es su pareja en algún momento de sus vidas \n(OMS et al., 2013).\n\nDisponer de datos sobre este indicador ayudará a comprender el alcance y la naturaleza \nde esta forma de violencia y a elaborar políticas y programas adecuados.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas, pero aporta información complementaria.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-02-02.pdf\">Metadatos 5-2-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Tasa de victimizaciones contra las mujeres por violencia sexual fuera del ámbito familiar", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.2- Eliminar todas las formas de violencia contra todas las mujeres y las niñas en los ámbitos público y privado, incluidas la trata y la explotación sexual y otros tipos de explotación", "definicion"=>"Number of registered cases of sexual violence against women (victimizations) committed by perpetrators outside the family environment, per 10,000 women", "formula"=>"$$TVIS^{t} = \\frac{VS^{t}}{P_{women}^{t}} \\cdot 10000$$\n\nwhere:\n\n$VS^{t} =$ Number of registered cases of sexual violence against women (victimizations) committed by perpetrators outside the family environment in year $t$\n\n$P_{women}^{t} =$ women on July 1 of year $t$\n", "desagregacion"=>"\nProvince/County/Municipality\n", "periodicidad"=>"Anual", "justificacion_global"=>"Violence against women and girls is one of the most pervasive forms of human rights \nviolations in the world. Evidence has shown that globally, an estimated 7% of women \nhave been sexually assaulted by someone other than a partner at some point in their \nlives (WHO et al., 2013). \n\nHaving data on this indicator will help understand the extent and nature of this \nform of violence and develop appropriate policies and programmes. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"", "comparabilidad"=>"The available indicator does not comply with the metadata of the United Nations indicator,  but provides complementary information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-02-02.pdf\">Metadata 5-2-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Tasa de victimizaciones contra las mujeres por violencia sexual fuera del ámbito familiar", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.2- Eliminar todas las formas de violencia contra todas las mujeres y las niñas en los ámbitos público y privado, incluidas la trata y la explotación sexual y otros tipos de explotación", "definicion"=>"Emakumeen aurkako sexu-indarkeria kasu erregistratuen kopurua (biktimizazioak), familia-eremutik kanpoko egileen eskutik, 10.000 emakumeko", "formula"=>"$$TVIS^{t} = \\frac{VS^{t}}{P_{emakumeak}^{t}} \\cdot 10000$$\n\nnon:\n\n$VS^{t} =$ emakumeen aurkako sexu-indarkeria kasu erregistratuen kopurua, familia-eremutik kanpoko egileen eskutik $t$ urtean\n\n$P_{mujeres}^{t} =$ emakumeak $t$ urteko uztailaren 1ean\n", "desagregacion"=>"\nLurralde historikoa/Eskualdea/Udalerria\n", "periodicidad"=>"Anual", "justificacion_global"=>"Emakumeen eta nesken aurkako indarkeria da munduan giza eskubideak urratzeko modurik hedatuenetako bat. Datu batzuen \narabera, munduan, emakumeen % 7k, gutxi gorabehera, sexu-erasoak jasan dituzte beren bizitzako uneren batean bikotekidea \nez den norbaiten eskutik (OME et al., 2013). \n\nAdierazle horri buruzko datuak izateak lagunduko du indarkeria-mota horren irismena eta izaera ulertzen eta politika eta \nprograma egokiak egiten. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina informazio  osagarria ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-02-02.pdf\">Metadatuak 5-2-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.2: Eliminate all forms of violence against all women and girls in the public and private spheres, including trafficking and sexual and other types of exploitation</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.2.2: Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence</p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2017-07-09</p>", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>5.2.1: Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age</p>\n<p>11.7.2: Proportion of persons victim of physical or sexual harassment, by sex, age, disability status and pace of occurrence, in the previous 12 months</p>\n<p>16.1.3: Proportion of population subjected to physical, psychological or sexual violence in the previous 12 months</p>\n<p>16.2.3: Proportion of young women and men aged 18-29 years who experienced sexual violence by age 18</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Entity for Gender Equality and the Empowerment of Women (UN Women)</p>\n<p>United Nations Children&apos;s Fund (UNICEF)</p>\n<p>United Nations Statistics Division (UNSD)</p>\n<p>World Health Organization (WHO)</p>\n<p>United Nations Population Fund (UNFPA)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Entity for Gender Equality and the Empowerment of Women (UN Women)</p>\n<p>United Nations Children&apos;s Fund (UNICEF)</p>\n<p>United Nations Statistics Division (UNSD)</p>\n<p>World Health Organization (WHO)</p>\n<p>United Nations Population Fund (UNFPA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator measures the percentage of women and girls aged 15 years and older who have experienced sexual violence by persons other than an intimate partner, in the previous 12 months. </p>\n<p>Definition of sexual violence against women and girls is presented in the next section (Concepts).</p>\n<p><strong>Concepts:</strong></p>\n<p>According to the UN Declaration on the Elimination of Violence against Women (1993), Violence against Women is &#x201C;Any act of gender-based violence that results in, or is likely to result in, physical, sexual or psychological harm or suffering to women, including threats of such acts, coercion or arbitrary deprivation of liberty, whether occurring in public or in private life. Violence against women shall be understood to encompass, but not be limited to, the following: [&#x2026;], Physical, sexual and psychological violence occurring within the general community, including rape, sexual abuse, sexual harassment and intimidation at work, in educational institutions and elsewhere, trafficking in women and forced prostitution [&#x2026;]&#x201D;. See here for full definition: http://www.un.org/documents/ga/res/48/a48r104.htm</p>\n<p>Sexual violence is defined as any sort of harmful or unwanted sexual behaviour that is imposed on someone. It includes acts of abusive sexual contact, forced engagement in sexual acts, attempted or completed sexual acts without consent, incest, sexual harassment, etc. However, in most surveys that collect data on sexual violence against women and girls by non-partners the information collected is limited to forcing someone into sexual intercourse when she does not want to, as well as attempting to force someone to perform a sexual act against her will or attempting to force her into sexual intercourse. </p>\n<p>For a more detailed definition of sexual violence against women see <em>Guidelines for Producing Statistics on Violence against Women- Statistical Surveys</em> (UN, 2014).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The main sources of intimate partner violence prevalence data are (1) specialized national surveys dedicated to measuring violence against women and (2) international household surveys that include a module on experiences of violence by women, such as the DHS.</p>\n<p>Although administrative data from health, police, courts, justice and social services, among other services used by survivors of violence, can provide information on violence against women and girls, these do not produce prevalence data, but rather incidence data or number of cases received in/reported to these services. We know that many abused women do not report violence and those who do, tend to be only the most serious cases. Therefore, administrative data should not be used as a data source for this indicator. </p>\n<p>For more information on recommended practices in production of violence against women statistics see: <em>UN Guidelines for Producing Statistics on Violence against Women- Statistical Surveys</em> (UN, 2014).</p>", "COLL_METHOD__GLOBAL"=>"<p>An Inter-Agency Group on Violence against Women Data and its Technical Advisory Group is currently being established (jointly by WHO, UN Women, UNICEF, UNSD and UNFPA) to establish a mechanism for compiling harmonized country level data on this indicator.</p>", "FREQ_COLL__GLOBAL"=>"<p>NA</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>NA</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (in most cases) or line ministries/other government agencies that have conducted national surveys on violence against women and girls.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UN Women, UNICEF, UNSD, WHO, UNFPA</p>", "RATIONALE__GLOBAL"=>"<p>Violence against women and girls is one of the most pervasive forms of human rights violations in the world. Evidence has shown that globally, an estimated 7% of women have been sexually assaulted by someone other than a partner at some point in their lives (WHO et al., 2013). Having data on this indicator will help understand the extent and nature of this form of violence and develop appropriate policies and programmes.</p>", "REC_USE_LIM__GLOBAL"=>"<p><u>Comparability:</u></p>\n<p>The availability of comparable data remains a challenge in this area as many data collection efforts have relied on different survey methodologies and used different definitions of sexual violence and different survey question formulation. Diverse age groups are also often utilized. Willingness to discuss experiences of violence and understanding of relevant concepts may also differ according to the cultural context and this can affect reported prevalence levels.</p>\n<p>Efforts and investment will be required to develop an internationally-agreed standard and definition of sexual violence by non-partners that will enable comparison across countries.</p>\n<p><u>Regularity of data production:</u></p>\n<p>Since 1995, only some 40 countries have conducted more than one survey on violence against women and girls. Obtaining data on violence against women and girls is a costly and time-consuming exercise, no matter if they are obtained through stand-alone dedicated surveys or through modules inserted in other surveys. Not all VAW surveys, however, collect information on non-intimate partner violence. Monitoring this indicator with certain periodicity may be a challenge if sustained capacities are not built and financial resources are not available.</p>", "DATA_COMP__GLOBAL"=>"<p>This indicator calls for disaggregation by age group and place of occurrence. No standard definitions and methods have been globally agreed yet to collect data on the place where the violence occurs, therefore this is not presented at this point in the computation method below. </p>\n<p>Number of women and girls aged 15 years and above who experience sexual violence by persons other than an intimate partner in the previous 12 months divided by the number of women and girls aged 15 years and above in the population multiplied by 100.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>When data for a country are entirely missing, no country-level estimate is published. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>No imputations are made in cases where country data are not available. Where regional and global figures are presented, clear notes on data limitations are provided. The number of countries included in the average is clearly indicated.</p>", "REG_AGG__GLOBAL"=>"<p>Global aggregates are weighted averages of all the sub-regions that make up the world. Regional aggregates are weighted averages of all the countries within the region. Where data are not available for all countries in a given region, regional aggregates may still be calculated if the minimum threshold for population coverage is met. The number of countries included in the average is clearly indicated.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>About 100 countries have conducted violence against women national prevalence surveys or have included a module on violence against women in a national household survey on other topic, although not all include data on non-partner sexual violence. Moreover, not all these data are comparable and in many cases they are not collected on a regular basis.</p>\n<p>Comparable data are available for a sub-sample of women and girls aged 15-49 for 37 low- and middle-income countries.</p>\n<p><strong>Time series:</strong></p>\n<p>Time series are available for some countries. Global time series with comparable data not yet available. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>In addition to age and place of occurrence, income/wealth, education, ethnicity (including indigenous status), disability status, geographic location, relationship with the perpetrator (including sex of perpetrator) and frequency and type of sexual violence (as proxy to severity) are suggested as desired variables for disaggregation for this indicator.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Only figures published by countries are used.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>http://evaw-global-database.unwomen.org/en</p>\n<p>data.unicef.org</p>\n<p>http://unstats.un.org/unsd/gender/default.html</p>\n<p><strong>References:</strong></p>\n<p>1. United Nations, 2014. Guidelines for Producing Statistics on Violence against Women- Statistical Surveys. </p>\n<p>2. United Nations, 2015. The World&#x2019;s Women 2015, Trends and Statistics. </p>\n<p>3. World Health Organization, Department of Reproductive Health and Research, London School of Hygiene and Tropical Medicine, South African Medical Research Council, 2013. Global and regional estimates of violence against women: prevalence and health effects of intimate partner violence and non-partner sexual violence. </p>\n<p>4. UN Women. 2016. Global Database on Violence against Women. Available at: <a href=\"http://evaw-global-database.unwomen.org/en\">http://evaw-global-database.unwomen.org/en</a></p>\n<p>5. UNICEF Data portal: <a href=\"http://data.unicef.org/child-protection/violence.html\">http://data.unicef.org/child-protection/violence.html</a></p>\n<p>6. UNSD Portal on the minimum set of gender indicators: <a href=\"http://genderstats.un.org/beta/index.html#/home\">http://genderstats.un.org/beta/index.html#/home</a></p>\n<p>7. UNSD dedicated portal for data and metadata on violence against women: http://unstats.un.org/unsd/gender/vaw/</p>", "indicator_sort_order"=>"05-02-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"5.3.1", "slug"=>"5-3-1", "name"=>"Proporción de mujeres de entre 20 y 24 años que estaban casadas o mantenían una unión estable antes de cumplir los 15 años y antes de cumplir los 18 años", "url"=>"/site/es/5-3-1/", "sort"=>"050301", "goal_number"=>"5", "target_number"=>"5.3", "global"=>{"name"=>"Proporción de mujeres de entre 20 y 24 años que estaban casadas o mantenían una unión estable antes de cumplir los 15 años y antes de cumplir los 18 años"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[{"unit"=>"Porcentaje", "minimum"=>0, "maximum"=>5}], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de mujeres de 16 y 17 años que han contraído matrimonio con un hombre", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de mujeres de entre 20 y 24 años que estaban casadas o mantenían una unión estable antes de cumplir los 15 años y antes de cumplir los 18 años", "indicator_number"=>"5.3.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_50/opt_1/tipo_1/ti_estadistica-de-matrimonios/temas.html", "url_text"=>"Estadística de matrimonios", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de mujeres de 16 y 17 años que han contraído matrimonio con un hombre", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.3- Eliminar todas las prácticas nocivas, como el matrimonio infantil, precoz y forzado y la mutilación genital femenina", "definicion"=>"Matrimonios de mujeres de 16 y 17 años con un hombre por cada 100 mujeres de 16 y 17 años", "formula"=>"\n$$PMAT_{mujeres\\, 16-17}^{t} = \\frac{MAT_{mujeres\\, 16-17}^{t}}{P_{mujeres\\, 16-17}^{t}} \\cdot 100$$\n\ndonde:\n\n$MAT_{mujeres\\, 16-17}^{t} =$ matrimonios de mujeres de 16 y 17 años con un hombre en el año $t$\n\n$P_{mujeres\\, 16-17}^{t} =$ mujeres de 16 y 17 años a 1 de julio del año $t$\n", "desagregacion"=>"Territorio histórico", "periodicidad"=>"Anual", "justificacion_global"=>"El matrimonio antes de los 18 años es una violación fundamental de los derechos humanos. \nEl matrimonio infantil a menudo compromete el desarrollo de la niña al provocar un embarazo \nprecoz y aislamiento social, interrumpir su educación, limitar sus oportunidades de \ndesarrollo profesional y vocacional y exponerla a un mayor riesgo de violencia de pareja.\n\nEn muchas culturas, se espera que las niñas que llegan a la pubertad asuman roles \nde género asociados con la feminidad, como entrar en una unión y convertirse en madres. \nLa práctica del matrimonio precoz/infantil es una manifestación directa de la \ndesigualdad de género.\n\nLa cuestión del matrimonio infantil se aborda en varios convenios y acuerdos \ninternacionales. Aunque el matrimonio no se menciona directamente en la Convención \nsobre los Derechos del Niño, el matrimonio infantil está vinculado a otros derechos, \ncomo el derecho a la libertad de expresión, el derecho a la protección contra \ntodas las formas de abuso y el derecho a ser protegida de prácticas \ntradicionales nocivas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.3.1&seriesCode=SP_DYN_MRBF18&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=20-24%20%7C%20FEMALE\">Proporción de mujeres de 20 a 24 años que estaban casadas o en unión antes de los 18 años (%) SP_DYN_MRBF18</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-03-01.pdf\">Metadatos 5-3-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de mujeres de 16 y 17 años que han contraído matrimonio con un hombre", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.3- Eliminar todas las prácticas nocivas, como el matrimonio infantil, precoz y forzado y la mutilación genital femenina", "definicion"=>"Marriages of women aged 16–17 years with a man for every 100 women aged 16–17 years", "formula"=>"\n$$PMAT_{women\\, 16-17}^{t} = \\frac{MAT_{women\\, 16-17}^{t}}{P_{women\\, 16-17}^{t}} \\cdot 100$$\n\nwhere:\n\n$MAT_{women\\, 16-17}^{t} =$ Marriages of women aged 16–17 years with a man in year $t$\n\n$P_{women\\, 16-17}^{t} =$ women aged 16–17 years as of 1 July of year $t$\n", "desagregacion"=>"Province", "periodicidad"=>"Anual", "justificacion_global"=>"Marriage before the age of 18 is a fundamental violation of human rights. \nChild marriage often compromises a girl’s development by resulting in early \npregnancy and social isolation, interrupting her schooling, limiting her \nopportunities for career and vocational advancement and placing her at increased \nrisk of intimate partner violence. \n\nIn many cultures, girls reaching puberty are expected to assume gender roles \nassociated with womanhood. These include entering a union and becoming a mother. \nThe practice of early/child marriage is a direct manifestation of gender inequality. \n\nThe issue of child marriage is addressed in a number of international conventions \nand agreements. Although marriage is not mentioned directly in the Convention on the \nRights of the Child, child marriage is linked to other rights – such as the right to \nfreedom of expression, the right to protection from all forms of abuse, and the right \nto be protected from harmful traditional practices.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.3.1&seriesCode=SP_DYN_MRBF18&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=20-24%20%7C%20FEMALE\">Proportion of women aged 20-24 years who were married or in a union before age 18 (%) SP_DYN_MRBF18</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-03-01.pdf\">Metadata 5-3-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de mujeres de 16 y 17 años que han contraído matrimonio con un hombre", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.3- Eliminar todas las prácticas nocivas, como el matrimonio infantil, precoz y forzado y la mutilación genital femenina", "definicion"=>"16-17 urteko emakumeen ezkontzak gizonekin, 16-17 urteko 100 emakumeko ", "formula"=>"\n$$PMAT_{emakumeak\\, 16-17}^{t} = \\frac{MAT_{emakumeak\\, 16-17}^{t}}{P_{emakumeak\\, 16-17}^{t}} \\cdot 100$$\n\nnon:\n\n$MAT_{emakumeak\\, 16-17}^{t} =$ 16-17 urteko emakumeen ezkontzak gizonekin $t$ urtean\n\n$P_{emakumeak\\, 16-17}^{t} =$ 16-17 urteko emakumeak $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Lurralde historikoa", "periodicidad"=>"Anual", "justificacion_global"=>"18 urte bete aurretik ezkontzea giza eskubideen funtsezko urraketa da. Haurren ezkontzak askotan neskaren garapena arriskuan \njartzen du, haurdunaldi goiztiarra eta isolamendu soziala eragiten dituelako, bere hezkuntza eteten duelako, garapen \nprofesionalerako eta bokazionalerako aukerak mugatzen dituelako eta bikotearen indarkeria-arrisku handiagoa eragiten \nduelako. \n\nKultura askotan, pubertarora iristen diren neskek feminitatearekin lotutako genero-rolak bereganatzea espero da, hala nola \nharreman batean sartzea eta ama bihurtzea. Ezkontza goiztiarra/haurren arteko ezkontza genero-desberdintasunaren adierazpen \nzuzena da. \n\nHaurren ezkontzaren gaia nazioarteko hainbat hitzarmen eta akordiotan lantzen da. Nahiz eta ezkontza ez den zuzenean aipatzen \nHaurren Eskubideei buruzko Hitzarmenean, haurren ezkontza beste eskubide batzuekin lotuta dago, hala nola \nadierazpen-askatasunerako eskubidearekin, abusu-mota guztien aurka babesteko eskubidearekin eta praktika tradizional \nkaltegarrietatik babestua izateko eskubidearekin. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.3.1&seriesCode=SP_DYN_MRBF18&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=20-24%20%7C%20FEMALE\">18 urte bete aurretik ezkonduta zeuden edo bikotekidea zuten 20-24 urteko emakumeen proportzioa (%) SP_DYN_MRBF18</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-03-01.pdf\">Metadatuak 5-3-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.3: Eliminate all harmful practices, such as child, early and forced marriage and female genital mutilation</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.3.1: Proportion of women aged 20&#x2013;24 years who were married or in a union before age 15 and before age 18</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SP_DYN_MRBF15 - Proportion of women aged 20-24 years who were married or in a union before age 15 [5.3.1]</p>\n<p>SP_DYN_MRBF18 - Proportion of women aged 20-24 years who were married or in a union before age 18 [5.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of women aged 20-24 years who were married or in a union before age 15 and before age 18.</p>\n<p><strong>Concepts:</strong></p>\n<p>Both formal (i.e., marriages) and informal unions are covered under this indicator. Informal unions are generally defined as those in which a couple lives together for some time, intends to have a lasting relationship, but for which there has been no formal civil or religious ceremony (i.e., cohabitation).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The indicator captures all formal and informal cohabiting unions. For comparability, age 18 is used as a standard across countries as the common age of majority, though the threshold age between childhood and adulthood varies across countries, as does the legal age at marriage.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Household surveys such as UNICEF-supported Multiple Indicator Cluster Survey (MICS) and Demographic and Health Surveys (DHS) have been collecting data on this indicator in low- and middle-income countries since around the late 1980s. In some countries, such data are also collected through national censuses, other national household surveys, or administrative data.</p>", "COLL_METHOD__GLOBAL"=>"<ul>\n  <li>\n    <ol>\n      <li>UNICEF undertakes a wide consultative process of compiling and assessing data from national sources for the purposes of updating its global databases on the situation of children. Up until 2017, the mechanism it used to collaborate with national authorities on ensuring data quality and international comparability on key indicators of relevance to children was known as Country Data Reporting on the Indicators for the Goals (CRING).</li>\n      <li>As of 2018, UNICEF launched a new country consultation process with national authorities on selected child-related global SDG indicators it is custodian or co-custodian for, to meet emerging standards and guidelines on data flows for global reporting of SDG indicators, which place strong emphasis on technical rigour, country ownership and use of official data and statistics. The consultation process solicited feedback directly from National Statistical Offices (NSOs), as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why. </li>\n    </ol>\n  </li>\n</ul>", "FREQ_COLL__GLOBAL"=>"<p>UNICEF will undertake an annual country consultation likely between December and January every year to allow for review and processing of the feedback received in order to meet global SDG reporting deadlines.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Annually in March.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (in most cases)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "INST_MANDATE__GLOBAL"=>"<p>UNICEF is responsible for global monitoring and reporting on the wellbeing of children. It provides technical and financial assistance to Member States to support their efforts to collect quality data on child marriage, including through the UNICEF-supported MICS household survey programme. UNICEF also compiles child marriage statistics with the goal of making internationally comparable datasets publicly available, and it analyses child marriage statistics which are included in relevant data-driven publications, including in its flagship publication, <em>The State of the World&#x2019;s Children.</em></p>", "RATIONALE__GLOBAL"=>"<p>Marriage before the age of 18 is a fundamental violation of human rights. Child marriage often compromises a girl&#x2019;s development by resulting in early pregnancy and social isolation, interrupting her schooling, limiting her opportunities for career and vocational advancement and placing her at increased risk of intimate partner violence. In many cultures, girls reaching puberty are expected to assume gender roles associated with womanhood. These include entering a union and becoming a mother. The practice of early/child marriage is a direct manifestation of gender inequality. </p>\n<p>The issue of child marriage is addressed in a number of international conventions and agreements. Although marriage is not mentioned directly in the Convention on the Rights of the Child, child marriage is linked to other rights &#x2013; such as the right to freedom of expression, the right to protection from all forms of abuse, and the right to be protected from harmful traditional practices.</p>", "REC_USE_LIM__GLOBAL"=>"<p>There are existing tools and mechanisms for data collection that countries have implemented to monitor the situation with regards to this indicator. The modules used to collect information on marital status among women and men of reproductive age (15-49 years) in the DHS and MICS have been fully harmonized.</p>\n<p>The measure of child marriage is retrospective in nature by design, capturing age at first marriage among a population that has completed the risk period (i.e., adult women). While it is also possible to measure the current marital status of girls under age 18, such measures would provide an underestimate of the level of child marriage, as girls who are not currently married may still do so before they turn 18. For more details on interpretation and common pitfalls for this indicator, see: <a href=\"https://data.unicef.org/wp-content/uploads/2020/06/A-Generation-to-Protect-publication-English_2020.pdf\"><em>A Generation to Protect: Monitoring violence exploitation and abuse of children within the SDG framework</em></a><em> </em>(UNICEF 2020).</p>", "DATA_COMP__GLOBAL"=>"<p>Number of women aged 20-24 who were first married or in union before age 15 (or before age 18) divided by the total number of women aged 20-24 in the population multiplied by 100.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>A wide consultative process is undertaken to compile, assess and validate data from national sources. </p>\n<p>The consultation process solicited feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. The results of this country consultation are reviewed by UNICEF as the custodian agency. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>When data for a country are entirely missing, UNICEF does not publish any country-level estimate.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>The regional average is applied to those countries within the region with missing values for the purposes of calculating regional aggregates only but are not published as country-level estimates. Regional aggregates are only published when at least 50 percent of the regional population for the relevant age group are covered by the available data.</p>", "REG_AGG__GLOBAL"=>"<p>Global aggregates are weighted averages of all the sub-regions that make up the world. Regional aggregates are weighted averages of all the countries within the region.</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries gather data on child marriage through household surveys such as UNICEF-supported MICS or Demographic and Health Surveys. In some countries, such data are also collected through other national household surveys. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The process behind the production of reliable statistics on child marriage is well established within UNICEF. The quality and process leading to the production of the SDG indicator 5.3.1 is ensured by working closely with the statistical offices and other relevant stakeholders through a consultative process.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UNICEF maintains the global database on child marriage that is used for SDG and other official reporting. Before the inclusion of any data point in the database, it is reviewed by technical focal points at UNICEF headquarters to check for consistency and overall data quality. This review is based on a set of objective criteria to ensure that only the most recent and reliable information are included in the databases. These criteria include the following: data sources must include proper documentation; data values must be representative at the national population level; data are collected using an appropriate methodology (e.g., sampling); data values are based on a sufficiently large sample; data conform to the standard indicator definition including age group and concepts, to the extent possible; data are plausible based on trends and consistency with previously published/reported estimates for the indicator.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Comparable data on this indicator are currently available for 126 countries.</p>\n<p><strong>Time series:</strong></p>\n<p>At the country level, the latest available data for indicator 5.3.1 are published. At the regional and global levels, time series estimates are published for 5-year intervals beginning from 2000.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>None </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The estimates compiled and presented at global level come directly from nationally produced data and are not adjusted or recalculated.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://data.unicef.org/topic/child-protection/child-marriage/\">https://data.unicef.org/topic/child-protection/child-marriage/</a> </p>\n<p><strong>References:</strong></p>\n<p><a href=\"https://data.unicef.org/topic/child-protection/child-marriage/\">https://data.unicef.org/topic/child-protection/child-marriage/</a> </p>\n<p><a href=\"https://data.unicef.org/resources/a-generation-to-protect/\">https://data.unicef.org/resources/a-generation-to-protect/</a> </p>", "indicator_sort_order"=>"05-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"5.3.2", "slug"=>"5-3-2", "name"=>"Proporción de niñas y mujeres de entre 15 y 49 años que han sufrido mutilación genital femenina, desglosada por edad", "url"=>"/site/es/5-3-2/", "sort"=>"050302", "goal_number"=>"5", "target_number"=>"5.3", "global"=>{"name"=>"Proporción de niñas y mujeres de entre 15 y 49 años que han sufrido mutilación genital femenina, desglosada por edad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de niñas y mujeres de entre 15 y 49 años que han sufrido mutilación genital femenina, desglosada por edad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de niñas y mujeres de entre 15 y 49 años que han sufrido mutilación genital femenina, desglosada por edad", "indicator_number"=>"5.3.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La mutilación genital femenina (MGF) constituye una violación de los derechos \nhumanos de las niñas y las mujeres. Existe una amplia bibliografía que \ndocumenta las consecuencias negativas para la salud de la MGF, tanto \na corto como a largo plazo. Esta práctica es una manifestación directa \nde la desigualdad de género. Diversos tratados y convenios internacionales \ncondenan la MGF. \n\nDado que se considera una práctica tradicional perjudicial para la salud \ninfantil y, en la mayoría de los casos, se practica en menores de edad, \nviola la Convención sobre los Derechos del Niño. La legislación nacional \nvigente en muchos países también incluye prohibiciones explícitas contra la MGF.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.3.2&seriesCode=SH_STA_FGMS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-19%20%7C%20FEMALE\">Proporción de niñas y mujeres de 15 a 49 años que han sufrido mutilación genital femenina, por edad (%) SH_STA_FGMS</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-03-02.pdf\">Metadatos 5-3-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2025-03-12", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Female genital mutilation (FGM) is a violation of girls’ and women’s human rights. \nThere is a large body of literature documenting the adverse health consequences of \nFGM over both the short and long term. The practice of FGM is a direct manifestation \nof gender inequality. FGM is condemned by a number of international treaties and conventions. \n\nSince FGM is regarded as a traditional practice prejudicial to the health of children \nand is, in most cases, performed on minors, it violates the Convention on the Rights \nof the Child. Existing national legislation in many countries also include explicit bans \nagainst FGM.\n\nSource: United Nations Statistics Division\n", "comparabilidad"=>nil, "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.3.2&seriesCode=SH_STA_FGMS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-19%20%7C%20FEMALE\">Proportion of girls and women aged 15-49 years who have undergone female genital mutilation, by age (%) SH_STA_FGMS</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-03-02.pdf\">Metadata 5-3-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Emakumeen mutilazio genitala (EMG) nesken eta emakumeen giza eskubideen urraketa da. Bibliografia zabalak EMGk \nosasunerako dituen ondorio negatiboak dokumentatzen ditu, epe laburrera zein luzera. Praktika hau genero-desberdintasunaren \nadierazpen zuzena da. Nazioarteko hainbat itun eta hitzarmenek EMG gaitzesten dute. \n\nHaurren osasunerako kaltegarria den praktika tradizionaltzat jotzen denez eta gehienetan adingabeekin praktikatzen denez, \nHaurren Eskubideei buruzko Hitzarmena urratzen du. Herrialde askotan indarrean dagoen legeria nazionalak ere EMGren aurkako \ndebeku esplizituak jasotzen ditu. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "comparabilidad"=>nil, "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.3.2&seriesCode=SH_STA_FGMS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-19%20%7C%20FEMALE\">Emakumeen genitalen mutilazioa jasan duten 15-49 urteko nesken eta emakumeen proportzioa, adinaren arabera (%) SH_STA_FGMS</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-03-02.pdf\">Metadatuak 5-3-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.3: Eliminate all harmful practices, such as child, early and forced marriage and female genital mutilation</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.3.2 Proportion of girls and women aged 15&#x2013;49 years who have undergone female genital mutilation, by age</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_STA_FGMS - Proportion of girls and women aged 15-49 years who have undergone female genital mutilation, by age (%) [5.3.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>The prevalence of female genital mutilation can be interpreted alongside other indicators about women&#x2019;s well-being, including those on women&#x2019;s health under Goal 3, those on the status of women under Goal 5, and those around violence against women under Goal 16. </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of girls and women aged 15-49 years who have undergone female genital mutilation.</p>\n<p>This indicator can be measured among smaller age groups, with the experience of younger women representing FGM/C that has occurred more recently and the experience of older women representing levels of the practice in the past. At the regional and global level, this indicator is currently being reported as the proportion of adolescent girls aged 15-19 years who have undergone female genital mutilation.</p>\n<p><strong>Concepts:</strong></p>\n<p>Female genital mutilation (FGM) refers to &#x201C;all procedures involving partial or total removal of the female external genitalia or other injury to the female genital organs for non-medical reasons&quot; (World Health Organization, Eliminating Female Genital Mutilation: An interagency statement, WHO, UNFPA, UNICEF, UNIFEM, OHCHR, UNHCR, UNECA, UNESCO, UNDP, UNAIDS, WHO, Geneva, 2008, p.4)</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Proportion</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The indicator captures all experiences of FGM, regardless of type. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>Household surveys such as UNICEF-supported MICS and DHS have been collecting data on this indicator in low- and middle-income countries since the late 1980s. In some countries, such data are also collected through other national household surveys.</p>", "COLL_METHOD__GLOBAL"=>"<ul>\n  <li>\n    <ol>\n      <li>UNICEF undertakes a wide consultative process of compiling and assessing data from national sources for the purposes of updating its global databases on the situation of children. Up until 2017, the mechanism UNICEF used to collaborate with national authorities on ensuring data quality and international comparability on key indicators of relevance to children was known as Country Data Reporting on the Indicators for the Goals (CRING).</li>\n    </ol>\n  </li>\n</ul>\n<p>As of 2018, UNICEF launched a new country consultation process with national authorities on selected child-related global SDG indicators it is custodian or co-custodian to meet emerging standards and guidelines on data flows for global reporting of SDG indicators, which place strong emphasis on technical rigour, country ownership and use of official data and statistics. The consultation process solicited feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why. </p>", "FREQ_COLL__GLOBAL"=>"<p>UNICEF will undertake an annual country consultation likely between December and January every year to allow for review and processing of the feedback received in order to meet global SDG reporting deadlines.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>March 2021</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (in most cases)</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNICEF</p>", "INST_MANDATE__GLOBAL"=>"<p>UNICEF is responsible for global monitoring and reporting on the wellbeing of children. It provides technical and financial assistance to Member States to support their efforts to collect quality data on FGM, including through the UNICEF-supported MICS household survey programme. UNICEF also compiles FGM statistics with the goal of making internationally comparable datasets publicly available, and it analyses FGM statistics which are included in relevant data-driven publications, including in its flagship publication, <em>The State of the World&#x2019;s Children.</em></p>", "RATIONALE__GLOBAL"=>"<p>FGM is a violation of girls&#x2019; and women&#x2019;s human rights. There is a large body of literature documenting the adverse health consequences of FGM over both the short and long term. The practice of FGM is a direct manifestation of gender inequality </p>\n<p>FGM is condemned by a number of international treaties and conventions. Since FGM is regarded as a traditional practice prejudicial to the health of children and is, in most cases, performed on minors, it violates the Convention on the Rights of the Child. Existing national legislation in many countries also include explicit bans against FGM.</p>", "REC_USE_LIM__GLOBAL"=>"<p>There are existing tools and mechanisms for data collection that countries have implemented to monitor the situation with regards to this indicator. The modules used to collect information on the circumcision status of girls aged 0-14 and girls and women aged 15-49 in the DHS and MICS have been fully harmonized.</p>\n<p>Data on FGM inform policymakers of critically important variables in an effort to better understand the practice and develop policies for its abandonment. That said, these data must be analysed in light of the extremely delicate and often sensitive nature of the topic. Self-reported data on FGM need to be treated with caution for several reasons. Women may be unwilling to disclose having undergone the procedure because of the sensitivity of the issue or the illegal status of the practice in their country. In addition, women may be unaware that they have been cut or of the extent of the cutting, particularly if FGM was performed at an early age.</p>\n<p>Data users should also keep in mind the retrospective nature of these data, which results in this indicator not being sensitive to recent change. For more details on interpretation and common pitfalls for this indicator, see: <a href=\"https://data.unicef.org/wp-content/uploads/2020/06/A-Generation-to-Protect-publication-English_2020.pdf\"><em>A Generation to Protect: Monitoring violence exploitation and abuse of children within the SDG framework</em></a><em> </em>(UNICEF 2020). </p>", "DATA_COMP__GLOBAL"=>"<p>Number of girls and women aged 15-49 who have undergone FGM divided by the total number of girls and women aged 15-49 in the population multiplied by 100</p>", "DATA_VALIDATION__GLOBAL"=>"<p>A wide consultative process is undertaken to compile, assess and validate data from national sources. </p>\n<p>The consultation process solicited feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. The results of this country consultation are reviewed by UNICEF as the custodian agency. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why. </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>When data for a country are entirely missing, UNICEF does not publish any country-level estimate</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Regional aggregates are only published when at least 50 per cent of the regional population for the relevant age group are covered by the available data.</p>", "REG_AGG__GLOBAL"=>"<p>Global aggregates are not presented for this indicator as data are only collected in a subset of countries where the practice is sufficiently widespread to warrant national-level data collection. Regional aggregates are weighted averages of countries with available data within the region. </p>", "DOC_METHOD__GLOBAL"=>"<p>Countries gather data on FGM through household surveys such as UNICEF-supported MICS or Demographic and Health Surveys. In some countries, such data are also collected through other national household surveys.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The process behind the production of reliable statistics on FGM is well established within UNICEF. The quality and process leading to the production of the SDG indicator 5.3.2 is ensured by working closely with the statistical offices and other relevant stakeholders through a consultative process.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UNICEF maintains the global database on FGM/C that is used for SDG and other official reporting. Before the inclusion of any data point in the database, it is reviewed by technical focal points at UNICEF headquarters to check for consistency and overall data quality. This review is based on a set of objective criteria to ensure that only the most recent and reliable information are included in the databases. These criteria include the following: data sources must include proper documentation; data values must be representative at the national population level; data are collected using an appropriate methodology (e.g., sampling); data values are based on a sufficiently large sample; data conform to the standard indicator definition including age group and concepts, to the extent possible; data are plausible based on trends and consistency with previously published/reported estimates for the indicator.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Nationally representative prevalence data are currently available for 30 low- and middle-income countries</p>\n<p><strong>Time series: </strong></p>\n<p>At the country level, the latest available data for indicator 5.3.2 are published. At the regional level, time series estimates for indicator 5.3.2 (as measured among adolescent girls aged 15-19 years) are published for 5-year intervals beginning from 2000.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Age (15-49 years at the national level, 15-19 years at the regional level)</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The estimates compiled and presented at global level come directly from nationally produced data and are not adjusted or recalculated.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>data.unicef.org</p>\n<p><strong>References:</strong></p>\n<p><a href=\"https://data.unicef.org/topic/child-protection/female-genital-mutilation/\">https://data.unicef.org/topic/child-protection/female-genital-mutilation/</a>https://data.unicef.org/resources/a-generation-to-protect/</p>", "indicator_sort_order"=>"05-03-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"5.4.1", "slug"=>"5-4-1", "name"=>"Proporción de tiempo dedicado al trabajo doméstico y asistencial no remunerado, desglosada por sexo, edad y ubicación", "url"=>"/site/es/5-4-1/", "sort"=>"050401", "goal_number"=>"5", "target_number"=>"5.4", "global"=>{"name"=>"Proporción de tiempo dedicado al trabajo doméstico y asistencial no remunerado, desglosada por sexo, edad y ubicación"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Sexo", "value"=>"Mujer"}, {"field"=>"Sexo", "value"=>"Hombre"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tiempo dedicado al hogar y familia en un día promedio", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de tiempo dedicado al trabajo doméstico y asistencial no remunerado, desglosada por sexo, edad y ubicación", "indicator_number"=>"5.4.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Para poder calcular el progreso hacia el objetivo de reparto equitativo de las funciones parentales y las responsabilidades familiares, se valora la relación entre la proporción de tiempo de las mujeres y el de los hombres, con un objetivo deseado de 1", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Quinquenal", "url"=>"https://www.eustat.eus/estadisticas/tema_77/opt_1/ti_encuesta-de-presupuestos-de-tiempo/temas.html", "url_text"=>"Encuesta de presupuestos de tiempo", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tiempo dedicado al hogar y familia en un día promedio", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.4- Reconocer y valorar los cuidados y el trabajo doméstico no remunerados mediante servicios públicos, infraestructuras y políticas de protección social, y promoviendo la responsabilidad compartida en el hogar y la familia, según proceda en cada país", "definicion"=>"Promedio diario de minutos dedicados a actividades destinadas al hogar y la familia por habitante", "formula"=>"\n$$THFP^{t} = \\frac{THF^{t}}{PHF^{t}}$$\n\ndonde:\n\n$THF^{t} =$ minutos dedicados por la población al hogar y la familia en un día promedio en el año $t$\n\n$PHF^{t} =$ población que destina tiempo al hogar y la familia en un día promedio en el año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "periodicidad"=>"Quinquenal", "justificacion_global"=>"El objetivo del indicador es medir la cantidad de tiempo que las mujeres y los hombres \ndedican a realizar tareas no remuneradas, para garantizar que todo trabajo, ya sea remunerado \no no, sea valorado.\n\nAdemás, también proporciona una evaluación de la igualdad de género, al destacar las \ndiscrepancias entre el tiempo que las mujeres y los hombres dedican a tareas no remuneradas, \ncomo cocinar, limpiar o cuidar a los niños.\n\nEste indicador mide la cantidad promedio de tiempo como proporción en un día, de modo \nque si para un país determinado se informa que las mujeres de 15 años o más dedican el \n10% de su día a tareas domésticas no remuneradas mientras que los \nhombres del mismo grupo de edad dedican el 1%, indica que las mujeres dedican\n 2,4 horas (2 horas y 24 minutos), mientras que los hombres dedican 14,4 minutos a ello \nal día, en promedio. Como se explica más adelante en 4.c, este promedio \ndiario se obtiene a partir de un promedio tomado durante el período de referencia \npara la recopilación de datos y, por lo tanto, no significa que las mujeres y los \nhombres dediquen estas cantidades de tiempo dadas todos los días.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas, pero aporta información similar.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-04-01.pdf\">Metadatos 5-4-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Tiempo dedicado al hogar y familia en un día promedio", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.4- Reconocer y valorar los cuidados y el trabajo doméstico no remunerados mediante servicios públicos, infraestructuras y políticas de protección social, y promoviendo la responsabilidad compartida en el hogar y la familia, según proceda en cada país", "definicion"=>"Average number of minutes dedicated each day to activities related to home and family per inhabitant", "formula"=>"\n$$THFP^{t} = \\frac{THF^{t}}{PHF^{t}}$$\n\nwhere:\n\n$THF^{t} =$ minutes dedicated by the population to home and family on an average day in year $t$\n\n$PHF^{t} =$  population that dedicates time to home and family on an average day in year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "periodicidad"=>"Quinquenal", "justificacion_global"=>"The purpose of the indicator is to measure the amount of time women and men spend doing unpaid \nwork, to ensure that all work, whether paid or unpaid, is valued. \n\nIn addition, it also provides an assessment of gender equality, by highlighting discrepancies \nbetween how much time women and men spend on unpaid work, like cooking, cleaning, or taking \ncare of children. \n\nThis indicator measures the average amount of time as a proportion in a day, so that if for a given \ncountry it is reported that women aged 15+ spend 10% of their day on unpaid domestic chores while men \nin the same age group spend 1%, it indicates that women spend 2.4 hours (2 hours and 24 minutes), \nwhile men spend 14.4 minutes on it a day, on average. As explained further in 4.c, this daily average is \nobtained from an average taken over the reference period for the data collection, and thus not mean \nthat women and men spend these given amounts of time every single day. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"", "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-04-01.pdf\">Metadata 5-4-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Tiempo dedicado al hogar y familia en un día promedio", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.4- Reconocer y valorar los cuidados y el trabajo doméstico no remunerados mediante servicios públicos, infraestructuras y políticas de protección social, y promoviendo la responsabilidad compartida en el hogar y la familia, según proceda en cada país", "definicion"=>"Batez besteko egun batean etxearekin eta familiarekin lotutako jardueretan emandako minutuak, biztanleko", "formula"=>"\n$$THFP^{t} = \\frac{THF^{t}}{PHF^{t}}$$\n\nnon:\n\n$THF^{t} =$ biztanleriak batez besteko egun batean etxearekin eta familiarekin lotutako jardueretan emandako minutuak $t$ urtean \n\n$PHF^{t} =$ batez besteko egun batean etxeari eta familiari denbora ematen dion biztanleria $t$ urtean \n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "periodicidad"=>"Quinquenal", "justificacion_global"=>"Adierazlearen helburua da emakumeek eta gizonek ordaindu gabeko lanak egiten ematen duten denbora neurtzea, lan oro, \nordaindua izan ala ez, balioesten dela bermatzeko. \n\nGainera, genero-berdintasunaren ebaluazioa ere egiten du, emakumeek eta gizonek ordaindu gabeko zereginetan (janaria \nprestatzen, garbitzen edo haurrak zaintzen) ematen duten denboraren arteko aldeak nabarmentzen baititu. \n\nAdierazle horrek egun bateko batez besteko denbora-kopurua neurtzen du proportzio gisa. Hala, adierazten bada herrialde \njakin batean 15 urteko edo gehiagoko emakumeek beren egunaren % 10 ematen dutela ordaindu gabeko etxeko lanetan eta \nadin-talde bereko gizonek % 1, emakumeek 2,4 ordu ematen dituzte (2 ordu eta 24 minutu) eta gizonek, aldiz, 14,4 minutu \negunean, batez beste. Aurrerago 4.c atalean azaltzen den bezala, eguneko batezbesteko hori datuak biltzeko erreferentziazko \naldian hartutako batezbesteko batetik ateratzen da, eta, beraz, ez du esan nahi emakumeek eta gizonek egunero ematen \ndituztenik denbora-kopuru horiek. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen adierazlearen metadatuak betetzen, baina antzeko informazioa ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-04-01.pdf\">Metadatuak 5-4-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.4: Recognize and value unpaid care and domestic work through the provision of public services, infrastructure and social protection policies and the promotion of shared responsibility within the household and the family as nationally appropriate</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.4.1: Proportion of time spent on unpaid domestic and care work, by sex, age and location</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_DOM_TSPD - Proportion of time spent on unpaid domestic chores and care work [5.4.1]</p>\n<p>SL_DOM_TSPDCW - Proportion of time spent on unpaid care work [5.4.1]</p>\n<p>SL_DOM_TSPDDC - Proportion of time spent on unpaid domestic chores [5.4.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Time-use information collected and analyzed around the world has shown that there is a very close link between economic poverty (SDG 1) and time poverty; most of health care is provided by households (SDG 3) and these activities are socially allocated to women in general; the provision of early childhood education services (SDG 4) not only prepares children for primary education but also frees up time for their caregivers; the sexual division of labour is a structural challenge of gender inequalities (SDG 5, 8 and 10); and the lack of services such as drinkable water, electricity or transport infrastructure increases unpaid work time and disproportionately affects women (SDG 6, 7, 9,11). Please see the section &#x201C;Time-use data crucial for monitoring the 2030 Agenda for Sustainable Development: going beyond SDG 5&#x201D; of the <em>Policy relevance: Making the case for time-use data collections in support of SDGs monitoring paper submitted to the SC</em> of 2020<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-PolicyRelevance-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-PolicyRelevance-E.pdf</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UN Statistics Division (UNSD) and UN WOMEN</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UN Statistics Division (UNSD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator is defined as the proportion of time spent in a day on unpaid domestic and care work by men and women. Unpaid domestic and care work refers to activities related to the provision of services for own final use by household members, or by family members living in other households. These activities are listed in the International Classification of Activities for Time-Use Statistics 2016 (ICATUS 2016)<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> under the major divisions &#x201C;3. Unpaid domestic services for household and family members&#x201D; and &#x201C;4. Unpaid caregiving services for household and family members&#x201D;. </p>\n<p><strong>Concepts:</strong></p>\n<p><em>Unpaid domestic work </em>refers to activities including food and meals management preparation, cleaning and maintaining of own dwelling and surroundings, , do-it-yourself decoration, maintenance and repair of personal and household goods, care and maintenance of textiles and footwear, household management, pet care, shopping for own household and family members and travel related to previous listed unpaid domestic services.</p>\n<p>Unpaid care work refers to activities related to childcare and instruction,, care of the sick, elderly, or disabled household and family members, and travel related to these unpaid caregiving services.. </p>\n<p>Concepts and definitions for this indicator are based on the following international standards:</p>\n<ul>\n  <li>International Classification of Activities for Time Use Statistics 2016 (ICATUS 2016)</li>\n  <li>System of National Accounts 2008 (SNA 2008)</li>\n  <li>The Resolution concerning statistics of work, employment, and labour underutilization, adopted by the International Conference of Labour Statisticians (ICLS) at its 19th Session in 2013</li>\n</ul>\n<p>As much as possible, statistics compiled by UNSD are based on the International Classification of Activities for Time Use Statistics 2016 (ICATUS 2016), which classifies activities undertaken by persons during the survey period. ICATUS 2016 was adopted by the United Nations Statistical Commission for use as an international statistical classification at its 48th session, 7-10 March 2017.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> https://unstats.un.org/unsd/gender/timeuse/23012019%20ICATUS.pdf <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) (proportion of time in a day)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The data for SDG 5.4.1 is as much as possible, in line with relevant international standards, including</p>\n<p>&#x25AA; The International Classification of Activities for Time Use Statistics 2016 (ICATUS 2016)</p>\n<p>&#x25AA; System of National Accounts 2008 (SNA 2008)</p>\n<p>&#x25AA; Resolution concerning statistics of work, employment, and labour underutilization, adopted by the International Conference of Labour Statisticians (ICLS) at its 19th Session in 2013</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Most data on time use are collected through dedicated time use surveys or from time-use modules integrated into multi-purpose household surveys, conducted at the national level.</p>\n<p>Data on time-use can be collected through a 24-hour diary (light diary) or a stylized questionnaire. With diaries, respondents are asked to report on what activity they were performing when they started the day, what activity followed and the time that activity began and ended (in most of the cases based on fixed intervals), and so forth through the 24 hours of the day. Stylized time-use questions ask respondents to recall the amount of time they allocated to a certain activity over a specified period, such as a day or a week.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are collected by national statistical offices, the official counterparts at the country level. Data are compiled and validated by UNSD. If there are inconsistencies or issues with the data, UNSD consults the focal point in the national statistical office. The data for SDG 5.4.1 is, as much as possible, in line with relevant international standards, or properly footnoted. International standards include:</p>\n<ul>\n  <li>The International Classification of Activities for Time Use Statistics 2016 (ICATUS 2016)</li>\n  <li>System of National Accounts 2008 (SNA 2008)</li>\n  <li>Resolution concerning statistics of work, employment, and labour underutilization, adopted by the International Conference of Labour Statisticians (ICLS) at its 19th Session in 2013 </li>\n  <li>Guide to Producing Statistics on Time-Use: Measuring Paid and Unpaid Work</li>\n</ul>", "FREQ_COLL__GLOBAL"=>"<p>Once national time-use data become available, they are added to the UNSD database.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released regularly as soon as they are updated</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Statistics Division</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Statistics Division is committed to the advancement of the global statistical system. UNSD compiles and disseminates global statistical information, develops standards and norms for statistical activities, and supports countries&apos; efforts to strengthen their national statistical systems. UNSD facilitates the coordination of international statistical activities and supports the functioning of the United Nations Statistical Commission as the apex entity of the global statistical system. The Social and Gender Statistics Section of UNSD works on migration statistics, gender statistics, and time use statistics.</p>\n<p>The Global Gender Statistics Programme is mandated by the United Nations Statistical Commission, implemented by the United Nations Statistics Division (UNSD, and coordinated by the Inter-Agency and Expert Group on Gender Statistics (IAEG-GS).</p>\n<p>The Programme encompasses:</p>\n<ul>\n  <li>improving coherence among existing initiatives on gender statistics through international coordination</li>\n  <li>developing and promoting methodological guidelines in existing domains as well as in emerging areas of gender concern</li>\n  <li>strengthening national statistical and technical capacity for the production, dissemination, and use of gender-relevant data</li>\n  <li>facilitating access to gender-relevant data and metadata through a gender data portal<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup>.</li>\n</ul>\n<p>UNSD serves as the Secretariat of the Inter-Agency and Expert Group on Gender Statistics (IAEG-GS), the coordinating, and guiding body of the Global Gender Statistics Programme. The IAEG-GS was first convened in 2006, meets annually and functions through advisory groups. Presently, the main advisory group&apos;s work concentrates on examining emerging and unaddressed key gender issues and related data gaps with the aim to develop proposals on how to fill these gaps.</p>\n<p>In addition, UNSD serves as Secretariat of the United Nations Expert Group on Innovative and Effective Ways to Collect Time-Use Statistics (EG-TUS), which initiated its work in June 2018 with the overall objective of taking stock and reviewing country practices in time-use surveys and providing technical guidance and recommendations to improve the collection and use of time use data, in line with international standards and in support of SDGs implementation. In particular, the Group was established to develop methodological guidelines on how to operationalize <a href=\"https://unstats.un.org/unsd/gender/timeuse/23012019%20ICATUS.pdf\" target=\"_blank\">ICATUS 2016</a> and produce time-use statistics using the latest technologies, as requested by the <a href=\"https://unstats.un.org/unsd/statcom/\" target=\"_blank\">United Nations Statistical Commission</a> at its <a href=\"https://unstats.un.org/unsd/statcom/48th-session/\" target=\"_blank\">forty-eighth session</a> in 2017 in its decision <a href=\"https://unstats.un.org/unsd/statcom/decisions-ref/?code=48/109\" target=\"_blank\">48/109</a>. The 51st Session of the Statistical Commission in 2020 (decision <a href=\"https://unstats.un.org/unsd/statcom/decisions-ref/?code=51/115\" target=\"_blank\">51/115</a>) endorsed the work of UNSD and the EG-TUS, approved the <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-ToR-E.pdf\" target=\"_blank\">terms of reference of the Expert Group</a>, and congratulated the group on the progress made in developing a <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Placemat-E.pdf\" target=\"_blank\">conceptual framework to modernize time-use surveys</a>. The 53rd Statistical Commission in 2022(decision <a href=\"https://unstats.un.org/unsd/statcom/decisions-ref/?code=53/111\" target=\"_blank\">53/111b</a>) endorsed the work of UNSD and the EG-TUS. This included the <a href=\"https://unstats.un.org/unsd/statcom/53rd-session/documents/BG-3h-Quality_UN_EG_TUS2021_FINAL_SENT_rev-E.pdf\" target=\"_blank\">minimum harmonized instrument for time-use data collection, quality considerations for time-use surveys</a>, and <a href=\"https://unstats.un.org/unsd/statcom/53rd-session/documents/BG-3h-Modernization_UN_EG_TUS2021_FINAL_SENT_rev-E.pdf\">options to modernize time-use data production</a>. These three documents are the core components of the upcoming revision of the <a href=\"https://unstats.un.org/unsd/publication/seriesf/seriesf_93e.pdf\" target=\"_blank\">United Nations guidelines for producing time-use statistics</a>.</p>\n<p>For more information and resources on the work of UNSD and the EG-TUS, please visit <a href=\"https://unstats.un.org/unsd/demographic-social/time-use/#eg\">UNSD &#x2014; Demographic and Social Statistics</a>.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> https://gender-data-hub-2-undesa.hub.arcgis.com/ <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "RATIONALE__GLOBAL"=>"<p>The purpose of the indicator is to measure the amount of time women and men spend doing unpaid work, to ensure that all work, whether paid or unpaid, is valued. In addition, it also provides an assessment of gender equality, by highlighting discrepancies between how much time women and men spend on unpaid work, like cooking, cleaning, or taking care of children.</p>\n<p>This indicator measures the average amount of time as a proportion in a day, so that if for a given country it is reported that women aged 15+ spend 10% of their day on unpaid domestic chores while men in the same age group spend 1%, it indicates that women spend 2.4 hours (2 hours and 24 minutes), while men spend 14.4 minutes on it a day, on average. As explained further in 4.c, this daily average is obtained from an average taken over the reference period for the data collection, and thus not mean that women and men spend these given amounts of time every single day. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Time use statistics have been used: (1) to provide a measure of the quality of life or general well-being of individuals and households; (2) to offer a more comprehensive measurement of all forms of work, including unpaid household service work; (3) to produce data relevant for monitoring gender equality and the empowerment of women and girls and are essential inputs for the policy and political dialogue on gender equality.</p>\n<p>International comparability of time-use statistics is limited by several factors, including: </p>\n<ol>\n  <li>Diary versus stylized time-use survey. Data on time-use can be collected through a 24-hour diary (light diary) or a stylized questionnaire. With diaries, respondents are asked to report on what activity they were performing when they started the day, what activity followed, the time that activity began and ended, and so forth through the 24 hours of the day. Stylized time-use questions ask respondents to recall the amount of time they allocated to a certain activity over a specified period, such as a day or week. Data obtained from these two different data collection methods are usually not comparable, and even data collected with different stylized questions might not be comparable given that the level of detail asked about activities performed might differ from one instrument to another, thus impacting the total time spent on a given activity. </li>\n  <li>Time-use activity classification. Regional and national classifications of time-use activities may differ from the The International Classification of Activities for Time Use Statistics 2016 (ICATUS 2016), resulting in data that are not comparable across countries. </li>\n  <li>Time-use data presented refer to the &#x201C;main activity&#x201D; only. Any &#x201C;secondary activity&#x201D; performed simultaneously with the main activity is not reflected in the average times shown. For instance, a woman may be cooking and looking after a child simultaneously. For countries reporting cooking as the main activity, time spent caring for children is not accounted for and reflected in the statistics. This may affect the international comparability of data on time spent caring for children; it may also underestimate the time women spend on this activity. </li>\n  <li>Different target age populations used by countries and age groups used also make time use data difficult to compare across countries.</li>\n</ol>", "DATA_COMP__GLOBAL"=>"<p>Data presented for this indicator are expressed as a proportion of time in a day. In the case when the reference period is one week, weekly data is averaged over seven days of the week to obtain the daily average time. </p>\n<p>Proportion of time spent on unpaid domestic and care work is calculated by dividing the daily average number of hours spent on unpaid domestic and care work by 24 hours. </p>\n<p>Proportion of time spent on unpaid domestic and care work (<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>5</mn>\n    <mo>.</mo>\n    <mn>4</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n  </math>) is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>5</mn>\n    <mo>.</mo>\n    <mn>4</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>D</mi>\n        <mi>a</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>d</mi>\n        <mi>o</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>k</mi>\n        <mo>+</mo>\n        <mi>D</mi>\n        <mi>a</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>k</mi>\n      </mrow>\n      <mrow>\n        <mn>24</mn>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>where,</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>a</mi>\n    <mi>i</mi>\n    <mi>l</mi>\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>n</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>b</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>h</mi>\n    <mi>o</mi>\n    <mi>u</mi>\n    <mi>r</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>s</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>t</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>l</mi>\n    <mi>e</mi>\n    <mi>v</mi>\n    <mi>a</mi>\n    <mi>n</mi>\n    <mi>t</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mi>c</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>v</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>T</mi>\n                <mi>o</mi>\n                <mi>t</mi>\n                <mi>a</mi>\n                <mi>l</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>n</mi>\n                <mi>u</mi>\n                <mi>m</mi>\n                <mi>b</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>h</mi>\n                <mi>o</mi>\n                <mi>u</mi>\n                <mi>r</mi>\n                <mi>s</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>s</mi>\n                <mi>p</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>b</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>p</mi>\n                <mi>u</mi>\n                <mi>l</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>l</mi>\n                <mi>e</mi>\n                <mi>v</mi>\n                <mi>a</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>a</mi>\n                <mi>c</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>v</mi>\n                <mi>i</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>e</mi>\n                <mi>s</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>T</mi>\n                <mi>o</mi>\n                <mi>t</mi>\n                <mi>a</mi>\n                <mi>l</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>p</mi>\n                <mi>u</mi>\n                <mi>l</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mo>(</mo>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>g</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n                <mi>d</mi>\n                <mi>l</mi>\n                <mi>e</mi>\n                <mi>s</mi>\n                <mi>s</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>w</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>p</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>c</mi>\n                <mi>i</mi>\n                <mi>p</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>a</mi>\n                <mi>c</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>v</mi>\n                <mi>i</mi>\n                <mi>t</mi>\n                <mi>y</mi>\n                <mo>)</mo>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>If data on time spent are weekly, data are averaged over seven days of the week to obtain daily time spent.</p>\n<p>Average number of hours spent on unpaid domestic and care work derives from time-use statistics that are collected through stand-alone time-use surveys or a time-use module in multi-purpose household surveys. Data on time-use may be summarized and presented as either (1) average time spent for participants (in each activity) only or (2) average time spent for all populations of a certain age (total relevant population). In the former type of average, the total time spent by the individuals who performed the activity is divided by the number of persons who performed it (participants). In the latter type of averages, the total time is divided by the total relevant population (or a sub-group thereof), regardless of whether people performed the activity or not. </p>\n<p><em>SDG indicator 5.4.1 is calculated based on the average number of hours spent on unpaid domestic and unpaid care work for the total relevant population. This type of measure can be used to compare groups and assess changes over time. Differences among groups or over time may be due to a difference (or change) in the proportion of those participating in the specific activity or a difference (or change) in the amount of time spent by participants, or both. </em></p>", "DATA_VALIDATION__GLOBAL"=>"<p>Most of the data are provided and validated by national statistical offices. In some cases, data have been obtained from publicly available national databases and publications. The United Nations Statistics Division (UNSD) communicates with countries if there are inconsistencies or possible errors in the data.</p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments concerning international standards are made.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>UNSD does not produce estimates for missing values</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>No imputation is done. Aggregates are computed based on available data only.</p>", "REG_AGG__GLOBAL"=>"<p>The number of countries conducting such surveys is insufficient to allow the computation of annual regional aggregates for SDG reporting. Furthermore, limited comparability across national data hampers the computation of regional aggregates. Nevertheless, UNSD regularly produces regional estimates to monitor and report on global trends. This is done by using the latest available data from each country in the region. In the case of insufficient data from a region, regional aggregates are not reported for the region. The SDG regions of &#x201C;Australia and New Zealand&#x201D; and &#x201C;Europe and North America&#x201D; are combined to produce a single aggregate for &#x201C;Developed region.&#x201D; In addition, the ratio of time spent by women and men are computed separately for each country and then averaged over the countries in the region to ensure comparability. </p>", "DOC_METHOD__GLOBAL"=>"<p>International Classification of Activities for Time Use Statistics 2016: https://unstats.un.org/unsd/gender/timeuse/23012019%20ICATUS.pdf</p>\n<p>Guide to Producing Statistics on Time-Use: Measuring Paid und Unpaid Work: <a href=\"https://unstats.un.org/unsd/publication/SeriesF/SeriesF_93E.pdf\">https://unstats.un.org/unsd/publication/SeriesF/SeriesF_93E.pdf</a></p>\n<p>System of National Accounts 2008 (SNA 2008): <a href=\"https://unstats.un.org/unsd/nationalaccount/sna2008.asp\">https://unstats.un.org/unsd/nationalaccount/sna2008.asp</a></p>\n<p>The Resolution concerning statistics of work, employment and labour underutilization:</p>\n<p><a href=\"http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm\">http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Details on quality management are available in the data quality for time use statistics paper, presented to the Statistical Commission in 2020:</p>\n<p><a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Defining_Quality-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Defining_Quality-E.pdf</a></p>\n<p>This technical report was updated and further developed by the Expert Group on Innovative and Effective Ways to Collect Time-Use Statistics (EG-TUS)and was presented at the 53<sup>rd</sup> session of the Statistical Commission in March 2022. The updated report is available at: <a href=\"https://unstats.un.org/unsd/statcom/53rd-session/documents/BG-3h-Quality_UN_EG_TUS2021_FINAL_SENT_rev-E.pdf\">BG-3h-Quality_UN_EG_TUS2021_FINAL_SENT_rev-E.pdf</a> </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The United Nations Statistics Division (UNSD) has been reviewing in detail the survey methodology followed to collect time use data and the classification of activities used by countries, to assess the level of comparability across countries and over time in each country.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>UNSD reviews and assesses the quality of the data received from countries and reverts to the data providers for clarifications if needed. The data received are compared to previous years to ensure consistency over time. In addition, the indicator calculations are verified, and data are checked for anomalies.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>92 countries with data between 2000 and 2022</p>\n<p>By Year:</p>\n<p>From 2000 &#x2013; 2004: 41 countries</p>\n<p>From 2005 &#x2013; 2009: 38 countries</p>\n<p>From 2010 - 2019: 66 countries</p>\n<p>From 2020: 4 countries</p>\n<p><strong>Time series:</strong></p>\n<p>From 2000 to 2022</p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator should be disaggregated by the following dimensions: sex, age, and location.</p>\n<p>The categories for disaggregation, by dimension, are as follows: </p>\n<p>Sex: female/male.</p>\n<p>Age: the recommended age groups are 15+, 15-24, 25-44, 45-54, 55-64 and 65+</p>\n<p>Location: urban/rural (following national definitions given the lack of international definition).</p>\n<p>These categories have been recommended by the Inter-Agency and Expert Group on Gender Statistics (IAEG-GS) during its 11th meeting in Rome, Italy on 30-31 October 2017.</p>\n<p>Available data are currently disaggregated by sex, age, and location</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://unstats.un.org/unsd/gender/default.html\">http://unstats.un.org/unsd/gender/default.html</a> </p>\n<p><strong>References:</strong></p>\n<ul>\n  <li>Guide to Producing Statistics on Time-Use: Measuring Paid and Unpaid Work (<a href=\"https://unstats.un.org/unsd/publication/SeriesF/SeriesF_93E.pdf\">https://unstats.un.org/unsd/publication/SeriesF/SeriesF_93E.pdf</a>)</li>\n  <li>International Classification of Activities for Time Use Statistics 2016 (https://unstats.un.org/unsd/gender/timeuse/23012019%20ICATUS.pdf</li>\n  <li>Minimum Set of Gender Indicators (<a href=\"http://genderstats.un.org\">http://genderstats.un.org</a>) </li>\n  <li>Modernization of the production of time-use statistics: A placemat linking priority components of the conceptual framework: </li>\n</ul>\n<p><a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Placemat-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Placemat-E.pdf</a></p>\n<ul>\n  <li>Policy relevance: Making the case for time-use data collections in support of SDGs monitoring:</li>\n</ul>\n<p><a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-PolicyRelevance-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-PolicyRelevance-E.pdf</a></p>\n<ul>\n  <li>Time use Concepts and Definitions:</li>\n</ul>\n<p><a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Concepts_and_definitions-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Concepts_and_definitions-E.pdf</a></p>\n<ul>\n  <li>Minimum Harmonized Instrument for the collection of time-use data:</li>\n</ul>\n<p><a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-MinimumHarmonizedInstrument-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-MinimumHarmonizedInstrument-E.pdf</a> </p>\n<p>https://unstats.un.org/unsd/statcom/53rd-session/documents/BG-3h-TimeUseStats-rev2-E.pdf</p>\n<ul>\n  <li>Towards defining quality for data and statistics on time use:</li>\n</ul>\n<p><a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Defining_Quality-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3m-Defining_Quality-E.pdf</a></p>\n<ul>\n  <li>Quality considerations for Time-use Surveys</li>\n</ul>\n<p>https://unstats.un.org/unsd/statcom/53rd-session/documents/BG-3h-Quality_UN_EG_TUS2021_FINAL_SENT_rev-E.pdf</p>\n<ul>\n  <li>Modernization of the Production of Time-use Statistics</li>\n</ul>\n<p><a href=\"https://unstats.un.org/unsd/statcom/53rd-session/documents/BG-3h-Modernization_UN_EG_TUS2021_FINAL_SENT_rev-E.pdf\">BG-3h-Modernization_UN_EG_TUS2021_FINAL_SENT_rev-E.pdf</a></p>", "indicator_sort_order"=>"05-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"5.5.1", "slug"=>"5-5-1", "name"=>"Proporción de escaños ocupados por mujeres en a) los parlamentos nacionales y b) los gobiernos locales", "url"=>"/site/es/5-5-1/", "sort"=>"050501", "goal_number"=>"5", "target_number"=>"5.5", "global"=>{"name"=>"Proporción de escaños ocupados por mujeres en a) los parlamentos nacionales y b) los gobiernos locales"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Institución", "value"=>"Electas en parlamento autonómico"}, {"field"=>"Institución", "value"=>"Departamentos en el gobierno autonómico"}, {"field"=>"Institución", "value"=>"Electas en Juntas Generales"}, {"field"=>"Institución", "value"=>"Departamentos en gobiernos forales"}, {"field"=>"Institución", "value"=>"Alcaldías"}, {"field"=>"Institución", "value"=>"Concejalías"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de escaños ocupados por mujeres en a) el parlamento autonómico y b) los gobiernos autonómicos, forales y locales", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de escaños ocupados por mujeres en a) los parlamentos nacionales y b) los gobiernos locales", "indicator_number"=>"5.5.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Bienal", "url"=>"https://www.eustat.eus/estadisticas/tema_515/opt_0/ti_indice-de-igualdad-de-genero/temas.html", "url_text"=>"Índice de Igualdad de Género", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de escaños ocupados por mujeres en a) el parlamento autonómico y b) los gobiernos autonómicos, forales y locales", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.5- Asegurar la participación plena y efectiva de las mujeres y la igualdad de oportunidades de liderazgo a todos los niveles decisorios en la vida política, económica y pública", "definicion"=>"Proporción de escaños ocupados por mujeres en el parlamento autonómico, consejeras en los departamentos  del gobierno autonómico, electas en Juntas Generales, diputadas en los departamentos de los  gobiernos forales, y alcaldías y concejalías en gobiernos locales.", "formula"=>"\n$$PINST_{mujeres}^{t} = \\frac{INST_{mujeres}^{t}}{INST_{mujeres}^{t}+INST_{hombres}^{t}} \\cdot 100$$\n\ndonde:\n\n$INST_{mujeres}^{t} =$ puestos ocupados por mujeres en la institución correspondiente en el año $t$\n\n$INST_{hombres}^{t} =$ puestos ocupados por hombres en la institución correspondiente en el año $t$\n", "desagregacion"=>"Puestos en instituciones: electas en el parlamento autonómico; consejeras en los departamentos \ndel gobierno autonómico; electas en Juntas Generales; diputadas en los departamentos de los \ngobiernos forales; alcaldías y concejalías en gobiernos locales.\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "justificacion_global"=>"\nEl indicador mide el grado en que las mujeres tienen acceso igualitario a la toma de \ndecisiones parlamentarias. La participación de las mujeres en los parlamentos es un \naspecto clave de las oportunidades de las mujeres en la vida política y \npública y está vinculada al empoderamiento de las mujeres. Un número igual de \nmujeres y hombres en las cámaras bajas daría un valor del indicador del 50 por ciento.\n\nUna mayor presencia de mujeres en el parlamento permite destacar nuevas preocupaciones \nen las agendas políticas y poner en práctica nuevas prioridades mediante la adopción \ny aplicación de políticas y leyes. La inclusión de las perspectivas e intereses de las \nmujeres es un requisito previo para la democracia y la igualdad de género y contribuye \na la buena gobernanza. Un parlamento representativo también permite que las \ndiferentes experiencias de hombres y mujeres afecten el futuro social, político \ny económico de las sociedades.\n\nAunque la comunidad internacional ha apoyado y promovido la participación de las mujeres \nen las estructuras de toma de decisiones políticas durante varias décadas, la mejora \nen el acceso de las mujeres al parlamento ha sido lenta. Esto ha llevado a la \nintroducción de políticas especiales y medidas legales para aumentar la proporción \nde mujeres en los escaños parlamentarios en varios países. Los países que han adoptado \nmedidas especiales generalmente tienen una mayor representación de mujeres en el parlamento \nque los países que no las han adoptado.\n\nEl indicador 5.5.1(b) complementa el indicador 5.5.1(a) sobre las mujeres en los \nparlamentos nacionales y da cuenta de la representación de las mujeres entre los millones \nde miembros de los gobiernos locales que influyen (o tienen el potencial de influir) en \nla vida de las comunidades locales en todo el mundo. El indicador abarca todos \nlos niveles de gobierno local, de conformidad con los marcos jurídicos nacionales \nque definen el gobierno local.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.5.1&seriesCode=SG_GEN_PARL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=FEMALE\">Proporción de escaños ocupados por mujeres en los parlamentos nacionales (% del número total de escaños) SG_GEN_PARL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.5.1&seriesCode=SG_GEN_LOCGELS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=FEMALE\">Proporción de escaños electos ocupados por mujeres en órganos deliberativos de los gobiernos locales (%) SG_GEN_LOCGELS</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-05-01a.pdf\">Metadatos 5-5-1 (1).pdf</a> (solo en inglés)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-05-01b.pdf\">Metadatos 5-5-1 (2).pdf</a> (solo en inglés)\n", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de escaños ocupados por mujeres en a) el parlamento autonómico y b) los gobiernos autonómicos, forales y locales", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.5- Asegurar la participación plena y efectiva de las mujeres y la igualdad de oportunidades de liderazgo a todos los niveles decisorios en la vida política, económica y pública", "definicion"=>"Proportion of seats held by women in the regional parliament, councilors in regional government  departments, elected to provincial parliaments, deputies in provincial government departments, and mayoral  and councilor positions in local governments.", "formula"=>"\n$$PINST_{women}^{t} = \\frac{INST_{women}^{t}}{INST_{women}^{t}+INST_{men}^{t}} \\cdot 100$$\n\nwhere:\n\n$INST_{women}^{t} =$ positions held by women in the corresponding institution in the year $t$\n\n$INST_{men}^{t} =$ positions held by men in the corresponding institution in the year $t$\n", "desagregacion"=>"Positions in institutions: elected to the regional parliament; councilors in the departments of the regional government; \nelected to the provincial parliaments; deputies in the departments of the provincial governments; and mayors and \ncouncilors in local governments.\n\nProvince\n", "periodicidad"=>"Anual", "justificacion_global"=>"\nThe indicator measures the degree to which women have equal access to parliamentary decision-making. \nWomen’s participation in parliaments is a key aspect of women’s opportunities in political and public \nlife and is linked to women’s empowerment. Equal numbers of women and men in lower chambers would \ngive an indicator value of 50 percent. \n\nA stronger presence of women in parliament allows new concerns to be highlighted on political agendas, \nand new priorities to be put into practice through the adoption and implementation of policies and laws. \nThe inclusion of the perspectives and interests of women is a prerequisite for democracy and gender \nequality and contributes to good governance. A representative parliament also allows the different \nexperiences of men and women to affect the social, political, and economic future of societies. \n\nAlthough the international community has supported and promoted women’s participation in political \ndecision-making structures for several decades, improvement in women’s access to parliament has been \nslow. This has led to the introduction of special policies and legal measures to increase women’s \nshares of parliamentary seats in several countries. Those countries that have adopted special measures \ngenerally have greater representation of women in parliament than countries without special measures. \n\nIndicator 5.5.1(b) complements Indicator 5.5.1(a) on women in national parliaments, and accounts for the \nrepresentation of women among the millions of members of local governments that influence (or have the \npotential to influence) the lives of local communities around the world. All tiers of local government are \ncovered by the indicator, consistent with national legal frameworks defining local government. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.5.1&seriesCode=SG_GEN_PARL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=FEMALE\">Proportion of seats held by women in national parliaments (% of total number of seats) SG_GEN_PARL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.5.1&seriesCode=SG_GEN_LOCGELS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=FEMALE\">Proportion of elected seats held by women in deliberative bodies of local governments (%) SG_GEN_LOCGELS</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-05-01a.pdf\">Metadata 5-5-1 (1).pdf</a>\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-05-01b.pdf\">Metadata 5-5-1 (2).pdf</a>\n", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de escaños ocupados por mujeres en a) el parlamento autonómico y b) los gobiernos autonómicos, forales y locales", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.5- Asegurar la participación plena y efectiva de las mujeres y la igualdad de oportunidades de liderazgo a todos los niveles decisorios en la vida política, económica y pública", "definicion"=>"Emakumeen proportzioa parlamentu autonomikoko eserlekuetan, gobernu autonomikoko sailburuen artean,  Batzar Nagusietako hautetsien artean, foru gobernuetako sailetako diputatuen artean, eta tokiko gobernuetako  alkateen eta zinegotzien artean. ", "formula"=>"\n$$PINST_{emakumeak}^{t} = \\frac{INST_{emakumeak}^{t}}{INST_{emakumeak}^{t}+INST_{gizonak}^{t}} \\cdot 100$$ \n\nnon: \n\n$INST_{emakumeak}^{t} =$ dagokion erakundean emakumeek betetako postuak $t$ urtean \n\n$INST_{gizonak}^{t} =$ dagokion erakundean gizonek betetako postuak $t$ urtean \n", "desagregacion"=>"Erakundeetako postuak: parlamentu autonomikoko hautetsiak; gobernu autonomikoko sailburuak; \nBatzar Nagusietako hautetsiak; foru gobernuetako sailetako diputatuak; tokiko \ngobernuetako alkate eta zinegotziak.\n\nLurralde historikoa\n", "periodicidad"=>"Anual", "justificacion_global"=>"\nAdierazleak neurtzen du zenbateraino diren berdinak emakumeak, parlamentuan erabakiak hartzeko orduan. Emakumeek \nparlamentuan parte hartzea funtsezkoa da emakumeek bizitza politikoan eta publikoan dituzten aukeretan, eta emakumeen \nahalduntzeari lotuta dago. Behe-ganberetan emakumeen eta gizonen kopuru berdina balitz, adierazlearen ehunekoa \n50ekoa litzateke. \n\nParlamentuan emakume gehiago egoteak aukera ematen du agenda politikoetan kezka berriak nabarmentzeko eta lehentasun \nberriak praktikan jartzeko, politikak eta legeak onartuz eta aplikatuz. Emakumeen ikuspegiak eta interesak kontuan \nhartzea aldez aurreko baldintza da demokraziarako eta genero-berdintasunerako, eta gobernantza ona lortzen laguntzen \ndu. Parlamentu ordezkagarri batek aukera ematen du, halaber, gizonen eta emakumeen esperientziek eragina izan dezaten \ngizarteen etorkizun sozial, politiko eta ekonomikoan. \n\nNahiz eta nazioarteko komunitateak hainbat hamarkadaz sustatu eta babestu duen emakumeen parte-hartzea erabaki politikoak \nhartzeko egituretan, emakumeak parlamentura sartzeko orduan egondako hobekuntza motela izan da. Horren ondorioz, politika \nbereziak eta lege-neurriak sartu dira hainbat herrialdetan emakumeek parlamentuko eserlekuetan duten proportzioa handitzeko. \nNeurri bereziak hartu dituzten herrialdeek, oro har, emakumeen ordezkaritza handiagoa dute parlamentuan, neurri bereziak \nhartu ez dituzten herrialdeek baino. \n\n5.5.1 (b) adierazleak estatuetako parlamentuetako emakumeei buruzko 5.5.1 (a) adierazlea osatzen du. Halaber, mundu osoan \ntokiko komunitateen bizitzan eragiten duten (edo eragiteko ahalmena duten) tokiko gobernuetako milioika kideen artean \nemakumeek duten ordezkaritzaren berri ematen du. Adierazleak tokiko gobernu-maila guztiak hartzen ditu, tokiko gobernua \ndefinitzen duten esparru juridiko nazionalekin bat etorriz. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.5.1&seriesCode=SG_GEN_PARL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=FEMALE\">Estatuetako parlamentuetan emakumeek okupatutako eserlekuen proportzioa (eserleku guztien %) SG_GEN_PARL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.5.1&seriesCode=SG_GEN_LOCGELS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=FEMALE\">Tokiko gobernuetako eztabaida-organoetan emakumeek okupatutako eserleku hautetsien proportzioa (%) SG_GEN_LOCGELS</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-05-01a.pdf\">Metadatuak 5-5-1 (1).pdf</a> (ingelesez bakarrik)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-05-01b.pdf\">Metadatuak 5-5-1 (2).pdf</a> (ingelesez bakarrik)\n", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.5: Ensure women&#x2019;s full and effective participation and equal opportunities for leadership at all levels of decision-making in political, economic and public life</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.5.1: Proportion of seats held by women in (a) national parliaments and (b) local governments</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_GEN_PARL - Proportion of seats held by women in national parliaments (% of total number of seats) [5.5.1]</p>\n<p>SG_GEN_PARLN - Number of seats held by women in national parliaments [5.5.1]</p>\n<p>SG_GEN_PARLNT - Current number of seats in national parliaments [5.5.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 16.7.1: Proportions of positions (by age group, sex, persons with disabilities and population groups) in public institutions (national and local), including (a) the legislatures; (b) the public service; and (c) the judiciary, compared to national distributions.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Inter-Parliamentary Union (IPU)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Inter-Parliamentary Union (IPU)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The proportion of seats held by women in national parliaments, currently as of 1 January of reporting year, is currently measured as the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats.</p>\n<p>National parliaments can be bicameral or unicameral. This indicator covers the single chamber in unicameral parliaments and the lower chamber in bicameral parliaments. It does not cover the upper chamber of bicameral parliaments. Seats are usually won by members in general parliamentary elections. Seats may also be filled by nomination, appointment, indirect election, rotation of members, and by-election.</p>\n<p>Seats refer to the number of parliamentary mandates or the number of members of parliament.</p>\n<p><strong>Concepts:</strong></p>\n<p>Seats refer to the number of parliamentary mandates, also known as the number of members of parliament. Seats are usually won by members in general parliamentary elections. Seats may also be filled by nomination, appointment, indirect election, rotation of members, and by-election.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number:</p>\n<p>Number of seats held by women in national parliaments (number)</p>\n<p>Current number of seats in national parliaments (number)</p>\n<p>Percent (%): </p>\n<p>Proportion of seats held by women in national parliaments (% of total number of seats)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The data used are official statistics received from national parliaments.</p>", "COLL_METHOD__GLOBAL"=>"<p>The data are provided by national parliaments and updated after an election or parliamentary renewal. National parliaments also transmit their data to the Inter-Parliamentary Union (IPU) at least once a year and when the numbers change significantly. IPU member parliaments provide information on changes and updates to the IPU secretariat. After each general election or renewal, a questionnaire is dispatched to parliaments to solicit the latest available data. If no response is provided, other methods are used to obtain the information, such as from the electoral management body, parliamentary websites, or Internet searches. Additional information gathered from other sources is regularly crosschecked with parliament. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are updated on a monthly basis, up to the last day of the month.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are updated on a monthly basis, up to the last day of the month.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National parliaments</p>", "COMPILING_ORG__GLOBAL"=>"<p>Inter-Parliamentary Union (IPU)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Inter-Parliamentary Union (IPU) is the global organization of parliaments. It was founded in 1889 as the first multilateral political organization in the world, encouraging cooperation and dialogue between all nations. Today, the Inter-Parliamentary Union (IPU) comprises 179 national Member Parliaments and 13 regional parliamentary bodies. It promotes democracy and helps parliaments become stronger, younger, gender-balanced, and more diverse. </p>\n<p>The IPU recognizes gender equality as a key component of democracy. It works to achieve equal participation of men and women in politics and supports parliaments in advancing gender equality. This includes the collection and dissemination of quantitative and qualitative data on women in politics. In particular, the IPU has tracked the percentage of women in national parliaments since 1945 and is the authority for this data. See historical and comparative data on women in parliament at <a href=\"https://data.ipu.org/historical-women\">https://data.ipu.org/historical-women</a>. </p>", "RATIONALE__GLOBAL"=>"<p>The indicator measures the degree to which women have equal access to parliamentary decision-making. Women&#x2019;s participation in parliaments is a key aspect of women&#x2019;s opportunities in political and public life and is linked to women&#x2019;s empowerment. Equal numbers of women and men in lower chambers would give an indicator value of 50 percent.</p>\n<p>A stronger presence of women in parliament allows new concerns to be highlighted on political agendas, and new priorities to be put into practice through the adoption and implementation of policies and laws. The inclusion of the perspectives and interests of women is a prerequisite for democracy and gender equality and contributes to good governance. A representative parliament also allows the different experiences of men and women to affect the social, political, and economic future of societies.</p>\n<p>Changes in the indicator have been tracked over time. Although the international community has supported and promoted women&#x2019;s participation in political decision-making structures for several decades, improvement in women&#x2019;s access to parliament has been slow. This has led to the introduction of special policies and legal measures to increase women&#x2019;s shares of parliamentary seats in several countries. Those countries that have adopted special measures generally have greater representation of women in parliament than countries without special measures.</p>", "REC_USE_LIM__GLOBAL"=>"<p>- The number of countries covered varies with suspensions or dissolutions of parliaments. As of 1 February 2016, 193 countries are included.</p>\n<p>- There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events that are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are.</p>\n<p>- The data excludes the numbers and percentages of women in the upper chambers of parliament. The information is available on the Inter-Parliamentary Union (IPU) website at <a href=\"https://data.ipu.org/women-ranking\">https://data.ipu.org/women-ranking</a>.</p>\n<p>- Parliaments vary considerably in their internal workings and procedures, however, generally legislate, oversee government and represent the electorate. In terms of measuring women&#x2019;s contribution to political decision-making, this indicator may not be sufficient because some women may face obstacles in fully and efficiently carrying out their parliamentary mandate.</p>", "DATA_COMP__GLOBAL"=>"<p>The proportion of seats held by women in the national parliament is derived by dividing the total number of seats occupied by women by the total number of seats in parliament.</p>\n<p>There is no weighting or normalising of statistics.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Inter-Parliamentary Union (IPU) member parliaments provide information on changes and updates to the IPU secretariat via IPU Groups within each parliamentary chamber or via the Parline Correspondent&#x2019;s Network. </p>\n<p>Parline Correspondents are staff members of national parliaments who act as the IPU focal point for IPU&#x2019;s Parline database within each chamber or parliament. Their main role is to make sure that all the data in Parline for their parliament is up&#x2011;to&#x2011;date and correct, including for this indicator. If no response is provided to questionnaires, other methods are used to obtain the information, such as from the electoral management body, parliamentary websites, or Internet searches. Additional information gathered from other sources is regularly crosschecked with parliaments. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>No adjustments are made for missing values.</p>\n<p><strong>&#x2022; At country level</strong></p>\n<p><strong>&#x2022; At regional and global levels</strong></p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregations are a simple sum of country and chamber level data. A weighting structure is not applied.</p>", "DOC_METHOD__GLOBAL"=>"<p>Guidance is not required to provide information for this indicator (i.e. current number of members and the total number of women members in a given single or lower chamber of a national parliament).</p>\n<p>A &#x201C;Checklist for Parline Correspondents&#x201D; is provided to remind parliaments to inform the Inter-Parliamentary Union (IPU) of changes to the number of seats or the total number of women in a parliamentary chamber, every time there is a change.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data for this indicator is input and housed within the Parline database (data.ipu.org).</p>\n<p>The Inter-Parliamentary Union (IPU) has dedicated staff for data collection and management, a Network of Parline Correspondents to provide data updates and a constant exchange with parliaments via IPU groups housed within member parliaments.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>There is no significant statistical processing required for this indicator aside from checking coherence over time. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The Inter-Parliamentary Union&#x2019;s (IPU) data is housed within the Parline database which automatically generates calculations on the number and percentage of women to ensure accuracy. Exports from the database are utilised for SDG reporting.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are available for 193 countries. Information is available in all countries where a national legislature exists and therefore does not include parliaments that have been dissolved or suspended for an indefinite period.</p>\n<p><strong>Time series:</strong></p>\n<p>According to the Inter-Parliamentary Union (IPU) website the data extraction has changed over time as follows;</p>\n<p>2020 &#x2013; Present As at 1 January</p>\n<p>2013 &#x2013; 2019 As at 1 February</p>\n<p>1999 As at 5 February</p>\n<p>2002 As at 4 February</p>\n<p>2003, 2005 &#x2013; 2007, 2009 - 2012 As at 31 January</p>\n<p>2001, 2004 As at 30 January</p>\n<p>2008 As at 29 January</p>\n<p>1998, 2000 As at 25 January</p>\n<p>1997 As at 1 January</p>\n<p>Prior to 1997 Unknown</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator can be disaggregated for analysis by geographical region and sub-region, legislature type (single or lower, parliamentary or presidential), the method of filling seats (directly elected, indirectly elected, appointed), and the use of special measures.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Data are not adjusted for international comparability. Though, for international comparisons, generally only the single or lower house is considered in calculating the indicator.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://data.ipu.org/women-ranking\">https://data.ipu.org/women-ranking</a></p>\n<p><a href=\"http://www.ipu.org/wmn-e/classif-arc.htm\">http://www.ipu.org/wmn-e/classif-arc.htm</a></p>\n<p><strong>References:</strong></p>\n<p>Inter-parliamentary Union (2008). Equality in Politics: A Survey of Women and Men in Parliaments. Geneva. Available from <a href=\"http://www.ipu.org/english/surveys.htm#equality08\">http://www.ipu.org/english/surveys.htm#equality08</a></p>\n<p>Inter-parliamentary Union (2010). Is Parliament Open to Women? Available from <a href=\"http://www.ipu.org/PDF/publications/wmn09-e.pdf\">http://www.ipu.org/PDF/publications/wmn09-e.pdf</a></p>\n<p>Inter-parliamentary Union (2011). Gender-Sensitive Parliaments. A Global Review of Good Practice. Available from <a href=\"http://www.ipu.org/pdf/publications/gsp11-e.pdf\">http://www.ipu.org/pdf/publications/gsp11-e.pdf</a></p>\n<p>Inter-parliamentary Union (2020). Women in parliament: 1995&#x2013;2020 - 25 years in review. Available from <a href=\"https://www.ipu.org/resources/publications/reports/2020-03/women-in-parliament-1995-2020-25-years-in-review\">https://www.ipu.org/resources/publications/reports/2020-03/women-in-parliament-1995-2020-25-years-in-review</a></p>\n<p>Inter-parliamentary Union and UN Women (2021). Women in Politics: 2021. Available from <a href=\"https://www.ipu.org/women-in-politics-2021\">https://www.ipu.org/women-in-politics-2021</a></p>", "indicator_sort_order"=>"05-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"5.5.2", "slug"=>"5-5-2", "name"=>"Proporción de mujeres en cargos directivos", "url"=>"/site/es/5-5-2/", "sort"=>"050502", "goal_number"=>"5", "target_number"=>"5.5", "global"=>{"name"=>"Proporción de mujeres en cargos directivos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Ocupacion", "value"=>"Cargo directivo"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de mujeres en cargos directivos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de mujeres en cargos directivos", "indicator_number"=>"5.5.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_57/opt_1/ti_encuesta-de-poblacion-en-relacion-con-la-actividad/temas.html", "url_text"=>"Encuesta de población en relación con la actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de mujeres en cargos directivos", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.5- Asegurar la participación plena y efectiva de las mujeres y la igualdad de oportunidades de liderazgo a todos los niveles decisorios en la vida política, económica y pública", "definicion"=>"Proporción de mujeres ocupadas en cargos directivos, en la alta dirección, y mujeres empresarias respecto al total de personas ocupadas en esos cargos o actividades económicas.", "formula"=>"\n$$PDIR_{mujeres}^{t} = \\frac{DIR_{mujeres}^{t}}{DIR_{mujeres}^{t}+DIR_{hombres}^{t}} \\cdot 100$$\n\ndonde:\n\n$DIR_{mujeres}^{t} =$ mujeres en la ocupación correspondiente en el año $t$\n\n$DIR_{hombres}^{t} =$ hombres en la ocupación correspondiente en en el año $t$\n", "desagregacion"=>"Ocupación: Alta dirección; cargo directivo; empresaria.\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"Se entiende por cargos directivos todas las ocupaciones incluidas en el grupo 1 de la CNO-2011, \nmientras que se entiende por alta dirección aquellas ocupaciones pertenecientes a los \nsubgrupos 11, 12 y 13 de la CNO-2011.\n\nEl grupo 1 de la CNO-2011 comprende las ocupaciones de dirección y gerencia, cuyas tareas \nprincipales son planificar, dirigir y coordinar la actividad general de las empresas, \ngobiernos u otras organizaciones, así como formular y revisar la estrategia de las empresas \ny normativas de los gobiernos. Este grupo se subdivide en cinco subgrupos principales:\n\n 11 Miembros del poder ejecutivo y de los cuerpos legislativos; directivos de la Administración Pública y organizaciones de interés social; directores ejecutivos\n 12 Directores de departamentos administrativos y comerciales\n 13 Directores de producción y operaciones\n 14 Directores y gerentes de empresas de alojamiento, restauración y comercio\n 15 Directores y gerentes de otras empresas de servicios no clasificados bajo otros epígrafes\n\nSe entiende por mujeres empresarias las que ejercen una actividad económica por cuenta propia.\n", "justificacion_global"=>"\nEl indicador proporciona información sobre la proporción de mujeres empleadas \nen puestos de toma de decisiones y gestión en el gobierno, grandes empresas e \ninstituciones, lo que proporciona una idea del poder de las mujeres en la toma de decisiones \ny en la economía (especialmente en comparación con el poder de los hombres en esas áreas).\n\nSe recomienda utilizar dos medidas diferentes de forma conjunta para este \nindicador: la proporción de mujeres en la gestión (total) y la proporción de mujeres en la \ngestión superior e intermedia (excluyendo así la gestión de nivel inferior). El cálculo conjunto \nde estas dos medidas proporciona información sobre si las mujeres están más representadas en \nla gestión de bajo nivel que en la gestión superior e intermedia, lo que \nindica que existe un techo para el acceso de las mujeres a puestos directivos de \nnivel superior. En estos casos, calcular solo la proporción de mujeres en la gestión (total) \nsería engañoso, ya que sugeriría que las mujeres ocupen puestos con más poder de \ndecisión y responsabilidades de las que realmente ocupan.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.5.2&seriesCode=IC_GEN_MGTL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20FEMALE\">Proporción de mujeres en puestos directivos - 13.ª CIET (%) IC_GEN_MGTL</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-05-02.pdf\">Metadatos 5-5-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de mujeres en cargos directivos", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.5- Asegurar la participación plena y efectiva de las mujeres y la igualdad de oportunidades de liderazgo a todos los niveles decisorios en la vida política, económica y pública", "definicion"=>"Proportion of women employed in managerial positions, senior management, and entrepeneurs in relation to the total number of people employed in these positions or economic activities.", "formula"=>"\n$$PDIR_{women}^{t} = \\frac{DIR_{women}^{t}}{DIR_{women}^{t}+DIR_{men}^{t}} \\cdot 100$$\n\nwhere:\n\n$DIR_{women}^{t} =$ women in the corresponding occupation in the year $t$\n\n$DIR_{men}^{t} =$ men in the corresponding occupation in the year $t$\n", "desagregacion"=>"Occupation: Senior management; managerial position; entrepeneurs.\n\nProvince\n", "periodicidad"=>"Anual", "observaciones"=>"Management positions are defined as all occupations included in Group 1 of the CNO-2011, while senior \nmanagement occupations are defined as those belonging to subgroups 11, 12, and 13 of the CNO-2011. \n\nGroup 1 of the CNO-2011 includes administration and management jobs, whose main tasks are to plan, \ndirect and coordinate the general activity of companies, governments or other organisations, and to \nformulate and review company strategies and government legislation. This group is subdivided into \nfive main subgroups: \n\n11 Members of the executive power and of the legislative bodies; directors of the Public Administration \nand social interest organisations\n12 Directors of administrative and commercial departments\n13 Directors of production and operations\n14 Directors and managers of accommodation, catering and trading companies\n15 Directors and managers of other companies offering services not classified in other sections\n\nWomen entrepreneurs are those who are self-employed. \n", "justificacion_global"=>"\nThe indicator provides information on the proportion of women who are employed in decision-making \nand management roles in government, large enterprises and institutions, thus providing some insight into \nwomen’s power in decision making and in the economy (especially compared to men's power in those \nareas).\n\nIt is recommended to use two different measures jointly for this indicator: the share of females \nin (total) management and the share of females in senior and middle management (thus excluding junior \nmanagement). The joint calculation of these two measures provides information on whether women are \nmore represented in junior management than in senior and middle management, thus pointing to an \neventual ceiling for women to access higher-level management positions. In these cases, calculating only \nthe share of women in (total) management would be misleading, in that it would suggest that women \nhold positions with more decision-making power and responsibilities than they actually do. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.5.2&seriesCode=IC_GEN_MGTL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20FEMALE\">Proportion of women in managerial positions - 13th ICLS (%) IC_GEN_MGTL</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-05-02.pdf\">Metadata 5-5-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de mujeres en cargos directivos", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.5- Asegurar la participación plena y efectiva de las mujeres y la igualdad de oportunidades de liderazgo a todos los niveles decisorios en la vida política, económica y pública", "definicion"=>"Zuzendaritza-karguetan diharduten, goi-zuzendaritzan diharduten, eta enpresaburu diren emakumeen proportzioak,  kargu edo jarduera ekonomiko horietan diharduten pertsona guztiekiko  ", "formula"=>"\n$$PDIR_{emakumeak}^{t} = \\frac{DIR_{emakumeak}^{t}}{DIR_{emakumeak}^{t}+DIR_{gizonak}^{t}} \\cdot 100$$\n\nnon:\n\n$DIR_{emakumeak}^{t} =$ emakumeak dagokion jardueran $t$ urtean\n\n$DIR_{gizonak}^{t} =$ gizonak dagokion jardueran $t$ urtean\n", "desagregacion"=>"Jarduera: goi-zuzendaritza; zuzendaritza-kargua; enpresaburua.\n\nLurralde historiakoa\n", "periodicidad"=>"Anual", "observaciones"=>"Zuzendaritza-kargutzat hartzen dira LSN-2011ko 1. taldeko okupazio guztiak; goi-zuzendaritzatzat, \nberriz, LSN-2011ko 11., 12. eta 13. azpitaldeetako okupazioak hartzen dira.\n\nLSN-2011ko 1. taldeak zuzendaritza- eta gerentzia-lanak hartzen ditu barne, eta horien zeregin \nnagusiak dira enpresen, gobernuen edo beste erakunde batzuen jarduera orokorra planifikatzea, \nzuzentzea eta koordinatzea, bai eta enpresen estrategia eta gobernuen araudiak formulatzea eta \nberrikustea ere. Talde hori bost azpitalde nagusitan banatzen da:\n\n 11 Botere exekutiboko eta kidego legegileetako kideak; Administrazio Publikoko eta interes \nsozialeko erakundeetako zuzendariak; zuzendari exekutiboak \n 12 Administrazio- eta merkataritza-sailetako zuzendariak\n 13 Produkzioko eta eragiketetako zuzendariak\n 14 Ostatu-, jatetxe- eta merkataritza-enpresetako zuzendariak eta kudeatzaileak\n 15 Beste epigrafe batzuetan sailkatu gabeko beste zerbitzu-enpresa batzuetako zuzendariak eta kudeatzaileak\n\nEmakume enpresaritzat hartzen dira norberaren konturako jarduera ekonomikoa egiten dutenak.  \n", "justificacion_global"=>"\nGobernuan, enpresa handietan eta erakundeetan erabakiak hartzeko eta kudeatzeko postuetan lan egiten duten emakumeen \nproportzioari buruzko informazioa ematen du adierazleak, eta horrek emakumeek erabakiak hartzean eta ekonomian duten \nboterearen berri ematen du (batez ere gizonek arlo horietan duten boterearekin alderatuta). \n\nAdierazle honetarako bi neurri desberdin batera erabiltzea gomendatzen da: emakumeen proportzioa kudeaketan (guztira) \neta emakumeen proportzioa goi- eta erdi-mailako kudeaketan (maila baxuagoko kudeaketa alde batera utzita). Bi neurri \nhoriek batera kalkulatzeak informazioa ematen du jakiteko emakumeek ordezkaritza handiagoa duten behe-mailako kudeaketan \ngoi-mailako eta erdi-mailako kudeaketan baino, eta horrek adierazten du muga bat dagoela emakumeak goi-mailako \nzuzendaritza-postuetan sartzeko. Kasu horietan, kudeaketan (guztira) emakumeen proportzioa bakarrik kalkulatzea \nengainagarria izango litzateke; izan ere, emakumeek benetan betetzen dituztenak baino erabaki-ahalmen eta erantzukizun \ngehiagoko postuak betetzen dituztela iradokiko luke. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.5.2&seriesCode=IC_GEN_MGTL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20FEMALE\">Zuzendaritza-postuetan dauden emakumeen proportzioa - 13. CIET (%) IC_GEN_MGTL</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-05-02.pdf\">Metadatuak 5-5-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.5: Ensure women&#x2019;s full and effective participation and equal opportunities for leadership at all levels of decision-making in political, economic and public life</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.5.2: Proportion of women in managerial positions</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>IC_GEN_MGTL - Proportion of women in managerial positions - 13th ICLS (%) [5.5.2]</p>\n<p>IC_GEN_MGTL_19ICLS - Proportion of women in managerial positions - 19th ICLS (%) [5.5.2]</p>\n<p>IC_GEN_MGTN - Proportion of women in senior and middle management positions - 13th ICLS (%) [5.5.2]</p>\n<p>IC_GEN_MGTN_19ICLS - Proportion of women in senior and middle management positions - 19th ICLS (%) [5.5.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>8.5.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator refers to the proportion of females in the total number of persons employed in managerial positions. It is recommended to use two different measures jointly for this indicator: the share of females in (total) management and the share of females in senior and middle management (thus excluding junior management). The joint calculation of these two measures provides information on whether women are more represented in junior management than in senior and middle management, thus pointing to an eventual ceiling for women to access higher-level management positions. In these cases, calculating only the share of women in (total) management would be misleading, in that it would suggest that women hold positions with more decision-making power and responsibilities than they actually do.</p>\n<p><strong>Concepts:</strong></p>\n<p>- Employment comprises all persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. The difference between the 13<sup>th</sup> and 19<sup>th</sup> ICLS series for a given country is the operational criteria used to define employment, with two series based on the statistical standards from the 13th International Conference of Labour Statisticians (ICLS) and the other two series based on 19th ICLS standards. In the 19th ICLS series, employment is defined more narrowly as work done for pay or profit, while activities not done mainly in exchange for remuneration (i.e., own-use production work, volunteer work and unpaid trainee work) are recognized as other forms of work.</p>\n<p>- Employment in management is determined according to the categories of the latest version of the International Standard Classification of Occupations (ISCO-08), which organizes jobs into a clearly defined set of groups based on the tasks and duties undertaken in the job. For the purpose of this indicator, it is preferable to refer separately to senior and middle management only, and to total management (including junior management). The share of women tends to be higher in junior management than in senior and middle management, so limiting the indicator to a measure including junior management may introduce bias. Senior and middle management correspond to sub-major groups 11, 12 and 13 in ISCO-08 and sub-major groups 11 and 12 in ISCO-88. If statistics are not available disaggregated at the sub-major group level (two-digit level of ISCO), then major group 1 of ISCO-88 and ISCO-08 can be used as a proxy and the indicator would then refer only to total management (including junior management).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Employment in management is determined according to the categories of the latest version of the International Standard Classification of Occupations (ISCO-08) as described above.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The recommended source for this indicator is a labour force survey or, if not available, other similar types of household surveys, including a module on employment. In the absence of any labour-related household survey, establishment surveys or administrative records may be used to gather information on the female share of employment by the required ISCO groups. In cases where establishment surveys or administrative records are used, the coverage is likely to be limited to formal enterprises or enterprises of a certain size. Information on the enterprises covered should be provided with the figures. When comparing figures across years, any changes in the versions of ISCO that are used should be taken into account.</p>", "COLL_METHOD__GLOBAL"=>"<p>The ILO Department of Statistics processes national household survey microdata in line with internationally agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians (ICLS). For data that could not be obtained through this processing or directly from government websites, the ILO sends out an annual ILOSTAT questionnaire to all relevant agencies within each country (national statistical office (NSO), labour ministry, etc.) requesting the latest annual data and any revisions on numerous labour market topics and indicators, including many SDG indicators.</p>", "FREQ_COLL__GLOBAL"=>"<p>Continuous</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Continuous for country-level data and annually for global and regional estimates (November or December).</p>", "DATA_SOURCE__GLOBAL"=>"<p>National statistical offices</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "INST_MANDATE__GLOBAL"=>"<p>The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally-comparable datasets, and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.</p>", "RATIONALE__GLOBAL"=>"<p>The indicator provides information on the proportion of women who are employed in decision-making and management roles in government, large enterprises and institutions, thus providing some insight into women&#x2019;s power in decision making and in the economy (especially compared to men&apos;s power in those areas).</p>", "REC_USE_LIM__GLOBAL"=>"<p>This indicator&apos;s main limitation is that it does not reflect differences in the levels of responsibility of women in these high- and middle-level positions or the characteristics of the enterprises and organizations in which they are employed. Its quality is also heavily dependent on the reliability of the employment statistics by occupation at the ISCO two-digit level.</p>", "DATA_COMP__GLOBAL"=>"<ul>\n  <li>Using ISCO-08:</li>\n</ul>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">w</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">g</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>08</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>1</mn>\n        <mo>-</mo>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>08</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>14</mn>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>08</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>1</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mo>-</mo>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>08</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>14</mn>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Which can be also expressed as: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">w</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">g</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>08</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>11</mn>\n        <mo>+</mo>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mo>+</mo>\n        <mn>13</mn>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>08</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>11</mn>\n        <mo>+</mo>\n        <mn>12</mn>\n        <mo>+</mo>\n        <mn>13</mn>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>And</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">w</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">g</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>08</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>1</mn>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>08</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>1</mn>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<ul>\n  <li>Using ISCO-88:</li>\n</ul>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">w</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">g</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mo>:</mo>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>88</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>1</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mo>&#x2013;</mo>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>88</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>13</mn>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>88</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>1</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mo>-</mo>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>88</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>13</mn>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Which can also be expressed as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">w</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">g</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mo>:</mo>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>88</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>11</mn>\n        <mo>+</mo>\n        <mn>12</mn>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>88</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>11</mn>\n        <mo>+</mo>\n        <mn>12</mn>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>And</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">w</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">g</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mo>:</mo>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>88</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>1</mn>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">O</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>88</mn>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>1</mn>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The ILO engages in annual consultations with Member States through the ILOSTAT questionnaire and related Statistics Reporting System (StaRS). National data providers receive a link to the portal where they can review all national SDG data available on ILOSTAT.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Through the ILO Harmonized Microdata initiative, the ILO strives to produce internationally comparable labour statistics based on the indicator concepts and definitions adopted by the International Conference of Labour Statisticians.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Multivariate regression and cross-validation techniques are used to impute missing values at the country level. The additional variables used for the imputation include a range of indicators, including labour market and economic data. However, the imputed missing country values are only used to calculate the global and regional estimates; they are not used for international reporting on the SDG indicators by the ILO. </p>\n<p>For further information, refer to the ILO modelled estimates methodological overview: <a href=\"https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\">https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/</a></p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Regional and global figures are aggregates of the country-level figures including the imputed values.</p>", "REG_AGG__GLOBAL"=>"<p>The aggregates are derived from the ILO modelled estimates that are used to produce global and regional estimates of, amongst others, employment by occupation and gender, with employment based on the 13<sup>th</sup> ICLS standards. These models use multivariate regression and cross-validation techniques to impute missing values at the country level, which are then aggregated to produce regional and global estimates. The regional and global proportions of women in managerial positions are obtained by first adding up, across countries, the numerator and denominator of the formula that defines the proportion of women in managerial positions - outlined above. Once both magnitudes are produced at the desired level of aggregation, the ratio between the two is used to compute the rate for each regional grouping and the global level. Notice that this direct aggregation method can be used due to the imputation of missing observations. For further information, refer to the ILO modelled estimates methodological overview: https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/</p>", "DOC_METHOD__GLOBAL"=>"<p>In order to calculate this indicator, data on employment by sex and occupation is needed, using at least the 2-digit level of the ISCO. This data are collected at the national level mainly through labour force surveys (or other types of household surveys with an employment module). For the methodology of each national household survey, one must refer to the most comprehensive survey report or to the methodological publications of the national statistical office in question.</p>\n<ul>\n  <li>ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators ( <a href=\"https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm</a>)</li>\n  <li>ILO Manual &#x2013; Decent Work Indicators, Concepts and Definitions &#x2013; Chapter 8, Equal opportunity and treatment in employment <a href=\"http://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm\">http://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm</a> (second version, page 146)</li>\n  <li>Resolution concerning statistics of work, employment and labour underutilization <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf</a></li>\n  <li>International Standard Classification of Occupations 2008 (ISCO-08) https://www.ilo.org/publications/international-standard-classification-occupations-2008-isco-08-structure</li>\n  <li>ILOSTAT portal (<a href=\"https://ilostat.ilo.org/\">https://ilostat.ilo.org/</a>)</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>The processes of compilation, production, and publication of data, including its quality control, are carried out following the methodological framework and standards established by the ILO Department of Statistics, in compliance with the information technology and management standards of the ILO.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination in the ILOSTAT database.</p>\n<p> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The final assessment of the quality of information is carried out by the Data Production and Analysis Unit of the ILO Department of Statistics. In cases of doubt about the quality of specific data, these values are reviewed with the participation of the national agencies responsible for producing the data if appropriate. If the issues cannot be clarified, the respective information is not published.</p>", "COVERAGE__GLOBAL"=>"<p>Data may differ from those published on ILOSTAT due to different update schedules and stricter criteria applied for inclusion in the SDG database.</p>\n<p><strong>Data availability: </strong></p>\n<p>Data on proportion of women in managerial positions is available for 196 countries and territories in the 13<sup>th</sup> ICLS series and 106 countries and territories in the 19<sup>th</sup> ICLS series.</p>\n<p>Data on women in senior and middle management positions is available for 154 countries and territories in the 13<sup>th</sup> ICLS series and 85 countries and territories in the 19<sup>th</sup> ICLS series.</p>\n<p><strong>Time series:</strong></p>\n<p>Data for this indicator is available as of 2000 in the SDG Indicators Global Database, but time series going back several decades are available in ILOSTAT.</p>\n<p>Global and regional data on proportion of women in managerial positions are available from 2000 to 2023.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator requires no disaggregation per se, although employment statistics by both sex and occupation are needed to calculate it. If statistics are available and the sample size permits, it may be of interest to cross-tabulate this indicator by economic activity (ISIC) or disaggregate further to observe the share of women across more detailed occupational groups.</p>", "COMPARABILITY__GLOBAL"=>"<p>Through the ILO Harmonized Microdata initiative, the ILO strives to produce internationally comparable labour statistics based on the indicator concepts and definitions adopted by the ICLS.</p>\n<p>Work statistics for countries not using the same set of statistical standards are not comparable. As such, each series is based on a single set of standards (i.e., 13<sup>th</sup> or 19<sup>th</sup> ICLS) and contains only data comparable within and across countries, allowing data users to continue making meaningful time series analysis and international comparisons. Users should not compare data across series.</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>ILOSTAT portal: <a href=\"https://ilostat.ilo.org/resources/methods/description-employment-by-occupation/\">https://ilostat.ilo.org</a> </li>\n  <li>Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators <a href=\"https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm</a></li>\n  <li>Decent work indicators - ILO Manual: <a href=\"http://www.ilo.org/wcmsp5/groups/public/---dgreports/---integration/documents/publication/wcms_229374.pdf\">http://www.ilo.org/wcmsp5/groups/public/---dgreports/---integration/documents/publication/wcms_229374.pdf</a> </li>\n  <li>ISCO-08: https://www.ilo.org/publications/international-standard-classification-occupations-2008-isco-08-structure</li>\n  <li>Resolution concerning statistics of work, employment and labour underutilization, adopted by the 19<sup>th</sup> ICLS: <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf</a></li>\n  <li>Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the 13<sup>th</sup> International Conference of Labour Statisticians (October 1982), available at <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf</a></li>\n  <li>Quick guide to understanding the impact of the new statistical standards on ILOSTAT databases, available at <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf</a> </li>\n</ul>", "indicator_sort_order"=>"05-05-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"5.6.1", "slug"=>"5-6-1", "name"=>"Proporción de mujeres de entre 15 y 49 años que toman sus propias decisiones informadas sobre las relaciones sexuales, el uso de anticonceptivos y la atención de la salud reproductiva", "url"=>"/site/es/5-6-1/", "sort"=>"050601", "goal_number"=>"5", "target_number"=>"5.6", "global"=>{"name"=>"Proporción de mujeres de entre 15 y 49 años que toman sus propias decisiones informadas sobre las relaciones sexuales, el uso de anticonceptivos y la atención de la salud reproductiva"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de mujeres de entre 15 y 49 años que toman sus propias decisiones informadas sobre las relaciones sexuales, el uso de anticonceptivos y la atención de la salud reproductiva", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de mujeres de entre 15 y 49 años que toman sus propias decisiones informadas sobre las relaciones sexuales, el uso de anticonceptivos y la atención de la salud reproductiva", "indicator_number"=>"5.6.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La autonomía de las mujeres y niñas en la toma de decisiones sobre servicios de salud \nsexual y reproductiva, uso de anticonceptivos y relaciones sexuales consensuadas es \nclave para su empoderamiento y el pleno ejercicio de sus derechos reproductivos. \n\nLas mujeres que toman sus propias decisiones sobre la búsqueda de atención médica se \nconsideran empoderadas para ejercer sus derechos reproductivos. En cuanto a la \ntoma de decisiones sobre el uso de anticonceptivos, se obtiene una comprensión más \nclara del empoderamiento de las mujeres al considerar el indicador desde la perspectiva \nde las decisiones tomadas \"principalmente por la pareja\", en contraposición a una decisión \ntomada \"solo por la mujer\" o \"junto con la pareja\". \n\nDependiendo del tipo de método anticonceptivo utilizado, una decisión tomada \n\"solo por la mujer\" o \"junto con la pareja\" no siempre implica que la mujer \nesté empoderada o tenga capacidad de negociación. Por el contrario, es seguro \nasumir que una mujer que no participa, en absoluto, en la toma de decisiones \nanticonceptivas está desempoderada en lo que respecta a las decisiones sexuales y \nreproductivas. La capacidad de una mujer de decir no a su marido/pareja si no quiere \ntener relaciones sexuales está bien alineada con el concepto de autonomía sexual y \nempoderamiento de las mujeres.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-06-01.pdf\">Metadatos 5-6-1.pdf</a> (solo en inglés)", "dato_global"=>"", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-12", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Women’s and girls’ autonomy in decision-making about sexual and reproductive health services, \ncontraceptive use, and consensual sexual relations is key to their empowerment and the complete \nexercise of their reproductive rights. \n\nWomen who make their own decision regarding seeking healthcare for themselves are considered \nempowered to exercise their reproductive rights. Regarding decision-making on the use of contraception, \na clearer understanding of women empowerment is obtained by looking at the indicator from the perspective \nof decisions being made “mainly by the partner”, as opposed to a decision being made “by the woman alone” \nor “by the woman jointly with the partner”. \n\nDepending on the type of contraceptive method being used, a decision by the woman “alone” or “jointly \nwith the partner” does not always entail that the woman is empowered or has bargaining skills. \nConversely, it is safe to assume that a woman that does not participate, at all, in making contraceptive \nchoices is disempowered as far as sexual and reproductive decisions are concerned. A woman’s ability to \nsay no to her husband/partner if she does not want to have sexual intercourse is well aligned with the \nconcept of sexual autonomy and women’s empowerment. \n\nSource: United Nations Statistics Division\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-06-01.pdf\">Metadata 5-6-1.pdf</a>", "dato_global"=>nil, "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Emakumeek eta neskek sexu- eta ugalketa-osasuneko zerbitzuei, antisorgailuen erabilerari eta adostutako \nsexu-harremanei buruzko erabakiak hartzean autonomia izatea funtsezkoa da emakumeak ahalduntzeko eta beren \nugalketa-eskubideak bete-betean baliatzeko. \n\nArreta medikoa bilatzeari buruzko erabakiak hartzen dituzten emakumeak ahaldundutzat jotzen dira ugalketa-eskubideak \nbaliatzeko. Antisorgailuen erabilerari buruzko erabakiak hartzeari dagokionez, argiago ulertzen da emakumeen \nahalduntzea, adierazlea \"batez ere bikoteak\" hartutako erabakien ikuspegitik kontuan hartuta, \"emakumeak bakarrik\" \nedo \"bikotekidearekin batera\" hartutako erabakiaren aldean. \n\nErabilitako antisorgailu motaren arabera, \"emakumeak bakarrik\" edo \"bikotekidearekin batera\" hartutako erabaki batek \nez du beti esan nahi emakumea ahaldunduta dagoenik edo negoziatzeko gaitasuna duenik. Aitzitik, segurua da antisorgailuak \nhartzen inola ere parte hartzen ez duen emakume batek ahalmen gutxiago duela sexu- eta ugalketa-erabakietan. Emakume \nbatek sexu-harremanik izan nahi ez badu, senarrari/bikotekideari ezetz esateko duen gaitasuna bat dator emakumeen \nautonomia sexualaren eta ahalduntzearen kontzeptuarekin. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-06-01.pdf\">Metadatuak 5-6-1.pdf</a> (ingelesez bakarrik)", "dato_global"=>nil, "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferences</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.6.1: Proportion of women aged 15&#x2013;49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_FPL_INFM - Proportion of women who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care (% of women aged 15-49 years) [5.6.1]</p>\n<p>SH_FPL_INFMCU - Proportion of women who make their own informed decisions regarding contraceptive use (% of women aged 15-49 years) [5.6.1]</p>\n<p>SH_FPL_INFMRH - Proportion of women who make their own informed decisions regarding reproductive health care (% of women aged 15-49 years) [5.6.1]</p>\n<p>SH_FPL_INFMSR - Proportion of women who make their own informed decisions regarding sexual relations (% of women aged 15-49 years) [5.6.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 3.1.1: Maternal mortality ratio</p>\n<p>Indicator 3.1.2: Proportion of births attended by skilled health personnel</p>\n<p>Indicator 3.7.1: Proportion of women of reproductive age (aged 15&#x2013;49 years) who have their need for family planning satisfied with modern methods</p>\n<p>Indicator 3.7.2: Adolescent birth rate (aged 10&#x2013;14 years; aged 15&#x2013;19 years) per 1,000 women in that age group</p>\n<p>Indicator 5.2.1: Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age</p>\n<p>Indicator 5.6.2: Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Population Fund (UNFPA)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Population Fund (UNFPA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of women aged 15-49 years (married or in union) who make their own decision on all three selected areas i.e. decide on their own health care; decide on use of contraception; and can say no to sexual intercourse with their husband or partner if they do not want. Only women who provide a &#x201C;yes&#x201D; answer to all three components are considered as women who make their own decisions regarding sexual and reproductive health. A union involves a man and a woman regularly cohabiting in a marriage-like relationship.</p>\n<p>Women&#x2019;s autonomy in decision-making and exercise of their reproductive rights is assessed from responses to the following three questions:</p>\n<p>1. Who usually makes decisions about health care for yourself?</p>\n<p>&#x2013; RESPONDENT </p>\n<p>&#x2013; HUSBAND/PARTNER</p>\n<p>&#x2013; RESPONDENT AND HUSBAND/PARTNER JOINTLY</p>\n<p>&#x2013; SOMEONE ELSE</p>\n<p>&#x2013; OTHER, SPECIFY </p>\n<p>2. Who usually makes the decision on whether or not you should use contraception? </p>\n<p>&#x2013; RESPONDENT </p>\n<p>&#x2013; HUSBAND/PARTNER </p>\n<p>&#x2013; RESPONDENT AND HUSBAND/PARTNER JOINTLY</p>\n<p>&#x2013; SOMEONE ELSE</p>\n<p>&#x2013; OTHER, SPECIFY</p>\n<p>3. Can you say no to your husband/partner if you do not want to have sexual intercourse?</p>\n<p>&#x2013; YES </p>\n<p>&#x2013; NO </p>\n<p>&#x2013; DEPENDS/NOT SURE</p>\n<p>A woman is considered to have autonomy in reproductive health decision making and to be empowered to exercise their reproductive rights if they (1) decide on health care for themselves, either alone or jointly with their husbands or partners, (2) decide on use or non-use of contraception, either alone or jointly with their husbands or partners; and (3) can say no to sex with their husband/partner if they do not want to.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Adopted by 179 governments, the 1994 International Conference on Population and Development (ICPD) Programme of Action marked a fundamental shift in global thinking on population and development issues. It moved away from a focus on reaching specific demographic targets to a focus on the needs, aspirations and rights of individual women and men. The Programme of Action asserted that everyone counts, that the true focus of development policy must be the improvement of individual lives and the measure of progress should be the extent to which we address inequalities. For more information on ICPD Programme of Action, please visit </p>\n<p><a href=\"https://www.unfpa.org/sites/default/files/pub-pdf/programme_of_action_Web%20ENGLISH.pdf\">https://www.unfpa.org/sites/default/files/pub-pdf/programme_of_action_Web%20ENGLISH.pdf</a>. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data are mainly derived from nationally representative Demographic and Health Surveys (DHS). Data sources increasingly include Multiple Indicator Cluster Surveys (MICS) and Generations and Gender Surveys (GGS), and other country-specific household surveys.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data is collected in line with the methodology used for the relevant national survey. Data for SDG indicator 5.6.1 may be collected through existing country-specific surveys. For existing national household surveys, it must be ascertained that the sampling design does not systematically exclude subgroups of the population that are important to SDG 5.6.1, specifically, women of reproductive age (15-49) that are currently married or in a union. Surveys that cover only certain population subgroups, such as women who speak the dominant language or women from the main ethnic group, may exclude the experiences of many women. Data on the ethnicity and religion of the survey participants should be collected whenever available. The survey should have a large sample size (usually between 5,000 and 30,000 households), be nationally representative, and be representative, at least, at one administrative level below the national level.</p>\n<p>Surveys on unrelated topics may not be good candidates for the incorporation of the SDG 5.6.1 questions. The sensitivity of the topics addressed in health surveys those examining women&#x2019;s health, makes them a feasible instrument for incorporating questions on women&#x2019;s experience of decision making in health care, use of contraceptives, and sexual relations for themselves.</p>\n<p>To generate data for SDG 5.6.1, all three questions must be included in the survey. The three questions in the Definition section provide generic questions that can be used in country-specific surveys. The first and the second questions should include distinct categories for women making decisions themselves, and women making decisions jointly with their husband/partner. </p>", "FREQ_COLL__GLOBAL"=>"<p>As per DHS, MICS, GGS and country-specific survey cycles</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Annual</p>", "DATA_SOURCE__GLOBAL"=>"<p>Agencies responsible for household surveys at national level.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Population Fund (UNFPA)</p>", "INST_MANDATE__GLOBAL"=>"<p>The mandate of UNFPA, as established by the United Nations Economic and Social Council (ECOSOC) in 1973 and reaffirmed in 1993, is (1) to build the knowledge and the capacity to respond to needs in population and family planning; (2) to promote awareness in both developed and developing countries of population problems and possible strategies to deal with these problems; (3) to assist their population problems in the forms and means best suited to the individual countries&apos; needs; (4) to assume a leading role in the United Nations system in promoting population programmes, and to coordinate projects supported by the Fund.</p>\n<p> </p>\n<p>At the International Conference on Population and Development (ICPD), held in Cairo in 1994, these broad ideas were elaborated to emphasize the gender and human rights dimensions of population. UNFPA was given the lead in helping countries carry out the Programme of Action adopted by 179 governments at the Cairo Conference. In 2010, the United Nations General Assembly extended the ICPD beyond 2014, which was the original end date for the 20-year Programme of Action.</p>", "RATIONALE__GLOBAL"=>"<p>Women&#x2019;s and girls&#x2019; autonomy in decision-making about sexual and reproductive health services, contraceptive use, and consensual sexual relations is key to their empowerment and the complete exercise of their reproductive rights. </p>\n<p>Women who make their own decision regarding seeking healthcare for themselves are considered empowered to exercise their reproductive rights.</p>\n<p>Regarding decision-making on the use of contraception, a clearer understanding of women empowerment is obtained by looking at the indicator from the perspective of decisions being made &#x201C;mainly by the partner&#x201D;, as opposed to a decision being made &#x201C;by the woman alone&#x201D; or &#x201C;by the woman jointly with the partner&#x201D;. Depending on the type of contraceptive method being used, a decision by the woman &#x201C;alone&#x201D; or &#x201C;jointly with the partner&#x201D; does not always entail that the woman is empowered or has bargaining skills. Conversely, it is safe to assume that a woman that does not participate, at all, in making contraceptive choices is disempowered as far as sexual and reproductive decisions are concerned. </p>\n<p>A woman&#x2019;s ability to say no to her husband/partner if she does not want to have sexual intercourse is well aligned with the concept of sexual autonomy and women&#x2019;s empowerment.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Until recently, the indicator captured results for married and in-union women and adolescent girls of reproductive age (15&#x2013;49 years old) <u>who are using any type of contraception</u>. In the phase of the national Demographic and Health Survey (DHS&#x2013;7) and later rounds, as well as in other data collection instruments including the MICS and GGS, the questionnaire is extended to respondents whether they are using contraception or not. The measure does not cover women and girls that are not married or in a union, as they do not usually make &#x201C;joint decisions&#x201D; on their health care with their partners.</p>\n<p>As of early 2022, a total of 64 countries, the majority in sub-Saharan Africa, have at least one survey with data on all three questions necessary for calculating Indicator 5.6.1. 28 countries have at least 2 data points between 2006 and 2020. Broader data sources are needed, and efforts to increase data coverage are underway. </p>\n<p>In many national contexts, household surveys, which are the main data source for this indicator, exclude the homeless and are likely to under-enumerate linguistic or religious minority groups.</p>", "DATA_COMP__GLOBAL"=>"<p>Numerator: Number of married or in union women and girls aged 15-49 years old:</p>\n<p>&#x2013; for whom decision on health care for themselves is not usually made by the husband/partner or someone else; and</p>\n<p>&#x2013; for whom the decision on contraception is not mainly made by the husband/partner; and </p>\n<p>&#x2013; who can say no to sex.</p>\n<p>Only women who satisfy all three empowerment criteria are included in the numerator. </p>\n<p>Denominator: Total number of women and girls aged 15-49 years old, who are married or in union. </p>\n<p>Proportion = (Numerator/Denominator) * 100</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Annual country consultation on new and existing data that were calculated from survey microdata sets was conducted in the first three year of the SDG reporting. Countries are encouraged to publish indicator data in the survey reports. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>No attempt from UNFPA to provide and publish estimates for individual countries or areas when country or area data are not available. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Regional aggregates are based on countries where data are available within the region. They should not be treated as country-level estimates for countries with missing values within the region.</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional aggregates are computed as weighted averages of country-level data. The weighting is based on the estimated population of married women aged 15-49 who are using any type of contraception in the reporting year. The estimates of the number of women married/ in union and contraceptive prevalence rate are obtained from UN Population Division.</p>", "DOC_METHOD__GLOBAL"=>"<p>For more information, please refer to <a href=\"https://www.unfpa.org/sdg-5-6\">https://www.unfpa.org/sdg-5-6</a>. Further guidelines on collecting data for SDG 5.6.1 in national household surveys is available upon request. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>UNFPA has released technical guidance on core questions for collecting data for SDG indicator 5.6.1, and provides technical support through UNFPA regional and country offices to strengthen national monitoring of women&apos;s decision making on sexual and reproductive health.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UNFPA maintains the global database on SDG 5.6.1. Before including any national data in the global database, UNFPA technical focal points thoroughly assess the survey methodology used to collect SDG 5.6.1 data to determine the level of comparability across countries and over time in a specific country.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Currently, 64 countries have at least one survey with data on all the questions above which are necessary for calculating Indicator 5.6.1. The 64 countries with data are distributed as follows: </p>\n<ul>\n  <li>Central Asia and Southern Asia (7)</li>\n  <li>Eastern Asia and South-eastern Asia (5) </li>\n  <li>Northern America and Europe (5)</li>\n  <li>Western Asia and Northern Africa (3)</li>\n  <li>Latin America and the Caribbean (7) </li>\n  <li>Sub-Saharan Africa (36)</li>\n  <li>Oceania (1)</li>\n</ul>\n<p>Several countries have only one or two of the three questions needed to calculate Indicator 5.6.1. UNFPA engages with major international and regional survey programs, as well as national and international organizations and agencies to incorporate the questions in relevant household surveys to cover all countries on a global scale. </p>\n<p><strong>Time series:</strong></p>\n<p>Currently data comes from household surveys which have three to five- year cycles.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Based on available household survey data, disaggregation is possible by age, geographic location, place of residence, education, and wealth quintile.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<p><a href=\"https://www.unfpa.org/sdg-5-6\">https://www.unfpa.org/sdg-5-6</a></p>\n<p> </p>\n<p><strong>References:</strong></p>\n<p>International Conference on Population and Development Programme of Action</p>\n<p><a href=\"https://www.unfpa.org/sites/default/files/pub-pdf/programme_of_action_Web%20ENGLISH.pdf\">https://www.unfpa.org/sites/default/files/pub-pdf/programme_of_action_Web%20ENGLISH.pdf</a>. </p>", "indicator_sort_order"=>"05-06-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"5.6.2", "slug"=>"5-6-2", "name"=>"Número de países con leyes y reglamentos que garantizan a los hombres y las mujeres a partir de los 15 años de edad un acceso pleno e igualitario a los servicios de salud sexual y reproductiva y a la información y educación al respecto", "url"=>"/site/es/5-6-2/", "sort"=>"050602", "goal_number"=>"5", "target_number"=>"5.6", "global"=>{"name"=>"Número de países con leyes y reglamentos que garantizan a los hombres y las mujeres a partir de los 15 años de edad un acceso pleno e igualitario a los servicios de salud sexual y reproductiva y a la información y educación al respecto"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países con leyes y reglamentos que garantizan a los hombres y las mujeres a partir de los 15 años de edad un acceso pleno e igualitario a los servicios de salud sexual y reproductiva y a la información y educación al respecto", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países con leyes y reglamentos que garantizan a los hombres y las mujeres a partir de los 15 años de edad un acceso pleno e igualitario a los servicios de salud sexual y reproductiva y a la información y educación al respecto", "indicator_number"=>"5.6.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El indicador 5.6.2 busca proporcionar la primera evaluación global integral \nde los marcos jurídicos y regulatorios, en consonancia con el Programa de \nAcción (PdA) de la Conferencia Internacional sobre la Población y el Desarrollo \n(CIPD) de 1994, la Plataforma de Acción de Beijing y las normas internacionales \nde derechos humanos. El indicador mide el entorno jurídico y regulatorio en \ncuatro secciones temáticas, definidas como los parámetros clave de la atención, \nla información y la educación en salud sexual y reproductiva, según estos \ndocumentos de consenso internacional y las normas de derechos humanos: \n - Atención materna  \n - Servicios de anticoncepción \n - Educación sexual \n - VIH y VPH\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-06-02.pdf\">Metadatos 5-6-2.pdf</a> (solo en inglés)", "dato_global"=>"", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nIndicator 5.6.2 seeks to provide the first comprehensive global assessment of legal and regulatory \nframeworks in line with the 1994 International Conference on Population and Development (ICPD) \nProgramme of Action (PoA), the Beijing Platform for Action, and international human rights standards. \nThe indicator measures the legal and regulatory environment across four thematic sections, defined as \nthe key parameters of sexual and reproductive health care, information and education according to these \ninternational consensus documents and human rights standards: \n\n- Maternity care\n- Contraception services\n- Sexuality education\n- HIV and HPV\n\nSource: United Nations Statistics Division\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-06-02.pdf\">Metadata 5-6-2.pdf</a>", "dato_global"=>nil, "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"5.6.2 adierazlearen helburua da esparru juridiko eta arautzaileen lehen ebaluazio global integrala ematea, 1994ko \nBiztanleria eta Garapenari buruzko Nazioarteko Konferentziaren (BGNK) Ekintza Programarekin (EP), Beijingko Ekintza \nPlataformarekin eta giza eskubideen nazioarteko arauekin bat etorriz. \n\nAdierazleak ingurune juridikoa eta arautzailea neurtzen du lau atal tematikotan, sexu- eta ugalketa-osasuneko \narretaren, informazioaren eta hezkuntzaren funtsezko parametro gisa definituta, nazioarteko adostasun-dokumentu \nhauen eta giza eskubideen arauen arabera: \n - Amaren arreta \n - Antisorgailu-zerbitzuak \n - Sexu-heziketa \n - GIB eta GPB \n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-06-02.pdf\">Metadatuak 5-6-2.pdf</a> (ingelesez bakarrik)", "dato_global"=>nil, "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferences</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.6.2: Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_LGR_ACSRHE - Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education [5.6.2]</p>\n<p>SH_LGR_ACSRHEC1 - (S.1.C.1) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 1: Maternity Care [5.6.2]</p>\n<p>SH_LGR_ACSRHEC10 - (S.4.C.10) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 10: HIV Counselling and Test Services [5.6.2]</p>\n<p>SH_LGR_ACSRHEC11 - (S.4.C.11) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 11: HIV Treatment and Care Services [5.6.2]</p>\n<p>SH_LGR_ACSRHEC12 - (S.4.C.12) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 12: HIV Confidentiality [5.6.2]</p>\n<p>SH_LGR_ACSRHEC13 - (S.4.C.13) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 13: HPV Vaccine [5.6.2]</p>\n<p>SH_LGR_ACSRHEC2 - (S.1.C.2) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 2: Life Saving Commodities [5.6.2]</p>\n<p>SH_LGR_ACSRHEC3 - (S.1.C.3) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 3: Abortion [5.6.2]</p>\n<p>SH_LGR_ACSRHEC4 - (S.1.C.4) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 4: Post-Abortion Care [5.6.2]</p>\n<p>SH_LGR_ACSRHEC5 - (S.2.C.5) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 5: Contraceptive Services [5.6.2]</p>\n<p>SH_LGR_ACSRHEC6 - (S.2.C.6) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 6: Contraceptive Consent [5.6.2]</p>\n<p>SH_LGR_ACSRHEC7 - (S.2.C.7) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 7: Emergency Contraception [5.6.2]</p>\n<p>SH_LGR_ACSRHEC8 - (S.3.C.8) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 8: Sexuality Education Curriculum Laws [5.6.2]</p>\n<p>SH_LGR_ACSRHEC9 - (S.3.C.9) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 9: Sexuality Education Curriculum Topi [5.6.2]</p>\n<p>SH_LGR_ACSRHES1 - (S.1) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 1: Maternity Care [5.6.2]</p>\n<p>SH_LGR_ACSRHES2 - (S.2) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 2: Contraceptive and Family Planning [5.6.2]</p>\n<p>SH_LGR_ACSRHES3 - (S.3) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 3: Sexuality Education [5.6.2]</p>\n<p>SH_LGR_ACSRHES4 - (S.4) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 4: HIV and HPV [5.6.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 3.1.1: Maternal mortality ratio</p>\n<p>Indicator 3.1.2: Proportion of births attended by skilled health personnel</p>\n<p>Indicator 3.3.1: Number of new HIV infections per 1,000 uninfected population, by sex, age and key populations</p>\n<p>Indicator 3.7.1: Proportion of women of reproductive age (aged 15&#x2013;49 years) who have their need for family planning satisfied with modern methods</p>\n<p>Indicator 3.7.2: Adolescent birth rate (aged 10&#x2013;14 years; aged 15&#x2013;19 years) per 1,000 women in that age group</p>\n<p>Indicator 5.2.1: Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age</p>\n<p>Indicator 5.6.1: Proportion of women aged 15-49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Population Fund (UNFPA)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Population Fund (UNFPA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Sustainable Development Goal (SDG) Indicator 5.6.2 seeks to measure the extent to which countries have national laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information, and education. </p>\n<p>The indicator is a percentage (%) scale of 0 to 100 (national laws and regulations exist to guarantee full and equal access), indicating a country&#x2019;s status and progress in the existence of such National laws and regulations. Indicator 5.6.2 measures only the existence of laws and regulations; it does not measure their implementation. </p>\n<p><strong>Concepts:</strong></p>\n<p>Laws: laws and statutes are official rules of conduct or action prescribed, or formally recognized as binding, or enforced by a controlling authority that governs the behavior of actors (including people, corporations, associations, government agencies). They are adopted or ratified by the legislative branch of government and may be formally recognized in the Constitution or interpreted by courts. Laws governing sexual and reproductive health are not necessarily contained in one law. </p>\n<p>Regulations: are executive, ministerial, or other administrative orders or decrees. At the municipal level, regulations are sometimes called ordinances. Regulations and ordinances issued by governmental entities have the force of law, although circumscribed by the level of the issuing authority. Under this methodology, only regulations with the national-level application are considered.</p>\n<p> </p>\n<p>Restrictions: many laws and regulations contain restrictions in the scope of their applicability. Such restrictions, which include, though are not limited to, those by age, sex, marital status, and requirement for third party authorization, represent barriers to full and equal access to sexual and reproductive health care, information, and education. </p>\n<p>Plural legal systems: are defined as legal systems in which multiple sources of law co-exist. Such legal systems have typically developed over a period because of colonial inheritance, religion, and other socio-cultural factors. Examples of sources of law that might co-exist under a plural legal system include English common law, French civil or other law, statutory law, and customary and religious law. The co-existence of multiple sources of law can create fundamental contradictions in the legal system, which result in barriers to full and equal access to sexual and reproductive health care, information, and education. </p>\n<p>&#x201C;Guarantee&#x201D; (access): for this methodology, &#x201C;guarantee&#x201D; is understood as a law or regulation that assures a particular outcome or condition. The methodology recognizes that laws can only guarantee &#x201C;in principle&#x201D;; for the outcomes to be fully realized in practice, additional steps, including policy and budgetary measures will need to be in place.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Adopted by 179 governments, the 1994 International Conference on Population and Development (ICPD) Programme of Action (PoA) marked a fundamental shift in global thinking on population and development issues. It shifted from an emphasis on meeting particular demographic targets to a focus on individual women&apos;s and men&apos;s needs, aspirations, and rights.. The PoA asserted that everyone counts, that the true focus of development policy must be the improvement of individual lives and the measure of progress should be the extent to which we address inequalities. For more information on ICPD PoA, please visit <a href=\"https://www.unfpa.org/sites/default/files/pub-pdf/programme_of_action_Web%20ENGLISH.pdf\">https://www.unfpa.org/sites/default/files/pub-pdf/programme_of_action_Web%20ENGLISH.pdf</a> </p>", "SOURCE_TYPE__GLOBAL"=>"<p>Indicator 5.6.2 is calculated based on official government responses collected through the United Nations Inquiry among Governments on Population and Development. The Inquiry has been conducted since 1963. All questions required for indicator 5.6.2 are integrated into Module II on fertility, family planning, and reproductive health of the Inquiry.</p>", "COLL_METHOD__GLOBAL"=>"<p>The Inquiry is sent to the Permanent Missions by UN Population Division (DESA). UNFPA then follow-up with UNFPA Country Offices to facilitate the data submissions from national governments. </p>", "FREQ_COLL__GLOBAL"=>"<p>Baseline data was collected in 2019 through the 12<sup>th</sup> Inquiry and a second round was collected in 2021-2022 through the 13<sup>th</sup> inquiry. Further data collection will be scheduled every 4 years.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Every 4 years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data will be provided by relevant government ministries, departments and agencies.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Population Fund (UNFPA), in collaboration with UN Population Division. </p>", "INST_MANDATE__GLOBAL"=>"<p>The mandate of UNFPA, as established by the United Nations Economic and Social Council (ECOSOC) in 1973 and reaffirmed in 1993, is (1) to build the knowledge and the capacity to respond to needs in population and family planning; (2) to promote awareness in both developed and developing countries of population problems and possible strategies to deal with these problems; (3) to assist their population problems in the forms and means best suited to the individual countries&apos; needs; (4) to assume a leading role in the United Nations system in promoting population programmes, and to coordinate projects supported by the Fund.</p>\n<p> </p>\n<p>At the International Conference on Population and Development (ICPD), held in Cairo in 1994, these broad ideas were elaborated to emphasize the gender and human rights dimensions of population. UNFPA was given the lead in helping countries carry out the Programme of Action (PoA) adopted by 179 governments at the Cairo Conference. In 2010, the United Nations General Assembly extended the ICPD beyond 2014, which was the original end date for the 20-year PoA.</p>", "RATIONALE__GLOBAL"=>"<p>Indicator 5.6.2 seeks to provide the first comprehensive global assessment of legal and regulatory frameworks in line with the 1994 International Conference on Population and Development (ICPD) Programme of Action (PoA)<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>, the Beijing Platform for Action<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>, and international human rights standards<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup>. The indicator measures the legal and regulatory environment across four thematic sections, defined as the key parameters of sexual and reproductive health care, information and education according to these international consensus documents and human rights standards:</p>\n<ul>\n  <li>Maternity care</li>\n  <li>Contraception services </li>\n  <li>Sexuality education</li>\n  <li>HIV and HPV</li>\n</ul>\n<p>Each of the four thematic areas (sections) is represented by individual components, reflecting topics that are: i) critical from a substantive perspective, ii) span a broad spectrum of sexual and reproductive health care, information, and education, and iii) the subject of national legal and regulatory frameworks. In total, Indicator 5.6.2 measures 13 components, categorized as follows:</p>\n<p><strong>SECTION I: MATERNITY CARE</strong></p>\n<p>Component 1. Maternity care</p>\n<p>Component 2. Life-saving commodities</p>\n<p>Component 3. Abortion</p>\n<p>Component 4. Post-abortion care</p>\n<p><strong>SECTION II: CONTRACEPTION SERVICES</strong></p>\n<p>Component 5. Contraception</p>\n<p>Component 6. Consent for contraceptive services</p>\n<p>Component 7. Emergency contraception</p>\n<p><strong>SECTION III: SEXUALITY EDUCATION</strong></p>\n<p>Component 8. CSE law</p>\n<p>Component 9. CSE curriculum</p>\n<p><strong>SECTION IV: HIV and HPV</strong></p>\n<p>Component 10. HIV testing and counselling</p>\n<p>Component 11. HIV treatment and care</p>\n<p>Component 12. Confidentiality of health status for men and women living with HIV</p>\n<p>Component 13. HPV vaccine</p>\n<p>For each of the 13 components, information is collected on the existence of i) specific legal <em>enablers</em> (positive laws, and regulations) and ii) specific legal <em>barriers</em><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup>. Such barriers encompass <em>restrictions</em> to positive laws, and regulations (e.g. by age, sex, marital status and requirement for third party authorization), as well as <em>plural legal systems that contradict</em> co-existing positive laws and regulations. For each component, the specific enablers and barriers on which data are collected are defined as the principle enablers and barriers for that component. Even where positive laws are in place, legal barriers can undermine <em>full and equal</em> access to sexual and reproductive health care, information, and education; the methodology is designed to capture this.</p>\n<p>The percentage value reflects a country&#x2019;s status and progress in the existence of national laws and regulations that guarantee full and equal access to sexual and reproductive health care, information, and education. By reflecting the &#x201C;extent to which&#x201D; countries guarantee full and equal access to sexual and reproductive health care, information, and education, this indicator allows cross-country comparison and within-country progress over time to be captured. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> United Nations (1994) International Conference on Population and Development: Programme of Action. Cairo, Egypt. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> United Nations (1995) Fourth World Conference on Women: Programme of Action. Beijing, China. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> CEDAW General Recommendation no. 24. Accessed online 24 May 2018: <a href=\"http://www.refworld.org/docid/453882a73.html\">http://www.refworld.org/docid/453882a73.html</a>; CEDAW General Comment no. 35 (2017). Accessed online 23<sup> </sup>May 2018: <a href=\"http://tbinternet.ohchr.org/Treaties/CEDAW/Shared%20Documents/1_Global/CEDAW_C_GC_35_8267_E.pdf\">http://tbinternet.ohchr.org/Treaties/CEDAW/Shared%20Documents/1_Global/CEDAW_C_GC_35_8267_E.pdf</a>; CESCR General Comment no. 14. Accessed online 23 May 2018: <a href=\"http://www.refworld.org/pdfid/4538838d0.pdf\">http://www.refworld.org/pdfid/4538838d0.pdf</a>; CESCR General Comment no. 20. Accessed 24 May 2018: <a href=\"http://www.refworld.org/docid/4a60961f2.html\">http://www.refworld.org/docid/4a60961f2.html</a>; <a href=\"https://www.escr-net.org/resources/general-comment-no-22-2016-right-sexual-and-reproductive-health#_ftn44\" target=\"_blank\">C</a>ESCR General Comment no. 22. Accessed online 23<sup> </sup>May 2018: <a href=\"https://www.escr-net.org/resources/general-comment-no-22-2016-right-sexual-and-reproductive-health\">https://www.escr-net.org/resources/general-comment-no-22-2016-right-sexual-and-reproductive-health</a>; CRC General Comment No. 15. Accessed 24 May 2018: <a href=\"http://www.refworld.org/docid/51ef9e134.html\">http://www.refworld.org/docid/51ef9e134.html</a>; CRPD Articles 23 and 25. Accessed online 24 May 2018: <a href=\"https://www.un.org/development/desa/disabilities/convention-on-the-rights-of-persons-with-disabilities/convention-on-the-rights-of-persons-with-disabilities-2.html\">https://www.un.org/development/desa/disabilities/convention-on-the-rights-of-persons-with-disabilities/convention-on-the-rights-of-persons-with-disabilities-2.html</a>. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> Legal barriers are not deemed applicable for the two operational components: C2: life-saving commodities and C9: CSE curriculum. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>Indicator 5.6.2 measures exclusively the existence of laws and regulations and their barriers. It does not measure the implementation of such laws/regulations. In addition, the 13 components are intended to be indicative of sexual and reproductive health care, information, and education, instead of a complete or exhaustive list of the care, information, and education. These components were selected because they were identified as key parameters according to international consensus documents and human rights standards.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator measures specific legal enablers and barriers for 13 components across four sections. The calculation of the indicator requires data for all 13 components.</p>\n<p>The 13 components are placed on the same scale, with 0% being the lowest value and 100% being the most optimal value. Each component is calculated independently and weighted equally. Each <u>component</u> is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>i</mi>\n    <mo>=</mo>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mi>e</mi>\n            <mi>i</mi>\n          </mrow>\n          <mrow>\n            <mi>E</mi>\n            <mi>i</mi>\n          </mrow>\n        </mfrac>\n        <mo>-</mo>\n        <mfrac>\n          <mrow>\n            <mi>b</mi>\n            <mi>i</mi>\n          </mrow>\n          <mrow>\n            <mi>B</mi>\n            <mi>i</mi>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfenced>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>where;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>i</mi>\n  </math>: Data for component i</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>E</mi>\n    <mi>i</mi>\n  </math>: Total number of enablers in component i</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>e</mi>\n    <mi>i</mi>\n  </math>: Number of enablers that exist in component i</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>B</mi>\n    <mi>i</mi>\n  </math>: Total number of barriers in component i</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>b</mi>\n    <mi>i</mi>\n  </math>: Number of barriers that exist in component i</p>\n<p>As legal barriers are not deemed applicable for C2: life-saving commodities and C9: CSE curriculum, they are calculated as:</p>\n<p> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>i</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>e</mi>\n        <mi>i</mi>\n      </mrow>\n      <mrow>\n        <mi>E</mi>\n        <mi>i</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>where;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>i</mi>\n  </math>: Data for component i</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>E</mi>\n    <mi>i</mi>\n  </math>: Total number of enablers in component i</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>e</mi>\n    <mi>i</mi>\n  </math>: Number of enablers that exist in component i</p>\n<p>In addition, as C3: Abortion collects information on four types of legal ground (to save a woman&#x2019;s life, to preserve a woman&#x2019;s health, in cases of rape, and in cases of fetal impairment), and that the legal barriers apply to each type, it is calculated as: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>i</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>e</mi>\n        <mi>i</mi>\n      </mrow>\n      <mrow>\n        <mi>E</mi>\n        <mi>i</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mn>1</mn>\n    <mo>-</mo>\n    <mfrac>\n      <mrow>\n        <mi>b</mi>\n        <mi>i</mi>\n      </mrow>\n      <mrow>\n        <mi>B</mi>\n        <mi>i</mi>\n      </mrow>\n    </mfrac>\n    <mo>)</mo>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>where;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>i</mi>\n  </math>: Data for component i</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>E</mi>\n    <mi>i</mi>\n  </math>: Total number of enablers in component i</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>e</mi>\n    <mi>i</mi>\n  </math>: Number of enablers that exist in component i</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>B</mi>\n    <mi>i</mi>\n  </math>: Total number of barriers in component i</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>b</mi>\n    <mi>i</mi>\n  </math>: Number of barriers that exist in component i</p>\n<p>Value for Indicator 5.6.2 is calculated as <em>the arithmetic mean of the 13-component data</em>. Similarly, the value for each section is calculated as the arithmetic mean of its constituent component data. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>Country consultation is conducted for every round of data collection. Indicator data and methodology are shared back with National governments together with the original submissions. Indicator 5.6.2 relies on official responses provided by National governments. UNFPA may follow up with national governments and request further information if the responses differ from country-specific information on legal and regulatory developments on issues about respective mandates of key stakeholders including UN Country teams and UN agencies. UNFPA also encourages each country to establish a national validation committee to review and validate all input from the Inquiry.</p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made at the global level. </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level:</strong></p>\n<p>No imputation will be made for a country with missing data. </p>\n<p> </p>\n<p><strong>&#x2022; At regional and global levels:</strong></p>\n<p>No imputation will be made at regional and global levels. </p>", "REG_AGG__GLOBAL"=>"<p>Global and regional aggregates are computed as unweighted averages of country-specific data for constituent countries. </p>", "DOC_METHOD__GLOBAL"=>"<p>Indicator 5.6.2 is calculated based on official government responses collected through the United Nations Inquiry among Governments on Population and Development. The Inquiry, mandated by the General Assembly in its resolution 1838 (XVII) of 18 December 1962, has been conducted by the Secretary-General since 1963. All questions required for indicator 5.6.2 are integrated into Module II on fertility, family planning and reproductive health of the Inquiry.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Indicator 5.6.2 relies on official responses provided by national governments. UNFPA performs quality checks and follows-up with national governments, requesting further information if the responses differ from country-specific information on legal and regulatory developments on issues about respective mandates of key stakeholders including UN Country teams and UN agencies, or if the responses are incomplete or differ from the government&#x2019;s responses to a previous Inquiry. UNFPA also encourages each country to establish a national validation committee to review and validate all input from the Inquiry.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>153 countries have complete or partial data for indicator 5.6.2, covering 89 percent of the world&#x2019;s population. A total of 115 countries have complete data, allowing calculation of data for indicator 5.6.2. </p>\n<p><strong>Time series:</strong></p>\n<p>Not applicable</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data will be disaggregated by section and component. This will enable countries to identify the areas of sexual and reproductive health care, information and education in which progress is required. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable, as indicator 5.6.2 relies on official data provided by National governments, and no estimation is produced at the international level.</p>", "OTHER_DOC__GLOBAL"=>"<p><a href=\"https://www.unfpa.org/sdg-5-6\">https://www.unfpa.org/sdg-5-6</a> </p>\n<p><strong>References:</strong></p>\n<p>United Nations (1994) International Conference on Population and Development: Programme of Action. Cairo, Egypt.</p>\n<p>United Nations (1995) Fourth World Conference on Women: Programme of Action. Beijing, China.</p>\n<p>CEDAW General Recommendation no. 24. Accessed online 24 May 2018: <a href=\"http://www.refworld.org/docid/453882a73.html\">http://www.refworld.org/docid/453882a73.html</a>; CEDAW General Comment no. 35 (2017). Accessed online 23<sup> </sup>May 2018: <a href=\"http://tbinternet.ohchr.org/Treaties/CEDAW/Shared%20Documents/1_Global/CEDAW_C_GC_35_8267_E.pdf\">http://tbinternet.ohchr.org/Treaties/CEDAW/Shared%20Documents/1_Global/CEDAW_C_GC_35_8267_E.pdf</a>; CESCR General Comment no. 14. Accessed online 23 May 2018: <a href=\"http://www.refworld.org/pdfid/4538838d0.pdf\">http://www.refworld.org/pdfid/4538838d0.pdf</a>; CESCR General Comment no. 20. Accessed 24 May 2018: <a href=\"http://www.refworld.org/docid/4a60961f2.html\">http://www.refworld.org/docid/4a60961f2.html</a>; <a href=\"https://www.escr-net.org/resources/general-comment-no-22-2016-right-sexual-and-reproductive-health#_ftn44\" target=\"_blank\">C</a>ESCR General Comment no. 22. Accessed online 23<sup> </sup>May 2018: <a href=\"https://www.escr-net.org/resources/general-comment-no-22-2016-right-sexual-and-reproductive-health\">https://www.escr-net.org/resources/general-comment-no-22-2016-right-sexual-and-reproductive-health</a>; CRC General Comment No. 15. Accessed 24 May 2018: <a href=\"http://www.refworld.org/docid/51ef9e134.html\">http://www.refworld.org/docid/51ef9e134.html</a>; CRPD Articles 23 and 25. Accessed online 24 May 2018: <a href=\"https://www.un.org/development/desa/disabilities/convention-on-the-rights-of-persons-with-disabilities/convention-on-the-rights-of-persons-with-disabilities-2.html\">https://www.un.org/development/desa/disabilities/convention-on-the-rights-of-persons-with-disabilities/convention-on-the-rights-of-persons-with-disabilities-2.html</a>. </p>", "indicator_sort_order"=>"05-06-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"5.a.1", "slug"=>"5-a-1", "name"=>"a) Proporción del total de la población agrícola con derechos de propiedad o derechos seguros sobre tierras agrícolas, desglosada por sexo; y b) proporción de mujeres entre los propietarios o los titulares de derechos sobre tierras agrícolas, desglosada por tipo de tenencia", "url"=>"/site/es/5-a-1/", "sort"=>"05aa01", "goal_number"=>"5", "target_number"=>"5.a", "global"=>{"name"=>"a) Proporción del total de la población agrícola con derechos de propiedad o derechos seguros sobre tierras agrícolas, desglosada por sexo; y b) proporción de mujeres entre los propietarios o los titulares de derechos sobre tierras agrícolas, desglosada por tipo de tenencia"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"a) Proporción del total de la población agrícola con derechos de propiedad o derechos seguros sobre tierras agrícolas, desglosada por sexo; y b) proporción de mujeres entre los propietarios o los titulares de derechos sobre tierras agrícolas, desglosada por tipo de tenencia", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"a) Proporción del total de la población agrícola con derechos de propiedad o derechos seguros sobre tierras agrícolas, desglosada por sexo; y b) proporción de mujeres entre los propietarios o los titulares de derechos sobre tierras agrícolas, desglosada por tipo de tenencia", "indicator_number"=>"5.a.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Irregular / Aperiódica", "url"=>"https://www.eustat.eus/estadisticas/tema_92/opt_1/ti_encuesta-sobre-la-estructura-de-las-explotaciones-agrarias/temas.html", "url_text"=>"Encuesta sobre la estructura de las explotaciones agrícolas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Decenal", "url"=>"https://www.eustat.eus/estadisticas/tema_260/opt_1/ti_censo-agrario/temas.html", "url_text"=>"Censo agrario", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"a) Proporción del total de la población agrícola con derechos de propiedad o derechos seguros sobre tierras agrícolas, desglosada por sexo; y b) proporción de mujeres entre los propietarios o los titulares de derechos sobre tierras agrícolas, desglosada por tipo de tenencia", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.a- Emprender reformas que otorguen a las mujeres igualdad de derechos a los recursos económicos, así como acceso a la propiedad y al control de la tierra y otros tipos de bienes, los servicios financieros, la herencia y los recursos naturales, de conformidad con las leyes nacionales", "definicion"=>"Proporción de titulares de explotaciones agrícolas respecto al total de personas trabajadoras  en la agricultura y proporción de mujeres titulares de explotaciones agrícolas respecto al  total de titulares de explotaciones agrícolas desglosada por tipo de tenencia.", "formula"=>"\n<b>Proporción de titulares de explotaciones agrícolas respecto al total de personas trabajadoras \nen la agricultura</b>\n\n$$PPOA_{titulares}^{t} = \\frac{TEA^{t}}{POA^{t}} \\cdot 100$$\n\ndonde:\n\n$TEA^{t} =$ personas titulares de explotaciones agrícolas en el año $t$\n\n$POA^{t} =$ personas ocupadas en la agricultura en el año $t$\n\n<br>\n\n<b>Proporción de mujeres titulares de explotaciones agrícolas respecto al \ntotal de titulares de explotaciones agrícolas desglosada por tipo de tenencia</b>\n\n$$PTEA_{mujeres}^{t} = \\frac{TEA_{mujeres}^{t}}{TEA^{t}} \\cdot 100$$\n\ndonde:\n\n$TEA_{mujeres}^{t} =$ mujeres titulares de explotaciones agrícolas en el año $t$ por \ntipo de tenencia\n\n$TEA^{t} =$ personas titulares de explotaciones agrícolas en el año $t$ por tipo de \ntenencia\n", "desagregacion"=>"Tipo de tenencia: Propiedad; arrendamiento\n\nSexo\n\nTerritorio histórico\n", "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>"\nEl subindicador 5.a.1 (a) es una medida de prevalencia. Mide la prevalencia de personas \nen la población agrícola con propiedad o derechos seguros sobre tierras agrícolas, \ndesglosada por sexo.\n\nEl subindicador 5.a.1 (b) se centra en la paridad de género y mide en qué medida \nlas mujeres se encuentran en desventaja en la propiedad o en los derechos seguros \nsobre las tierras agrícolas.\n", "justificacion_global"=>"\nEl indicador 5.a.1 tiene como objetivo monitorear el equilibrio de género en \nla propiedad/derechos seguros sobre tierras agrícolas. El subindicador (a) y \nel subindicador (b) se basan en los mismos datos y monitorean la propiedad/derechos \ndesde dos ángulos diferentes.\n\nMientras que el subindicador (a) utiliza la población agrícola total masculina/femenina \ncomo población de referencia, y nos dice cuántos hombres/mujeres poseen tierras, \nel subindicador (b) se centra en la población agrícola con propiedad de la tierra/derechos \nseguros, y nos dice cuántas de ellas son mujeres.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-0a-01.pdf\">Metadatos 5-a-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"a) Proporción del total de la población agrícola con derechos de propiedad o derechos seguros sobre tierras agrícolas, desglosada por sexo; y b) proporción de mujeres entre los propietarios o los titulares de derechos sobre tierras agrícolas, desglosada por tipo de tenencia", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.a- Emprender reformas que otorguen a las mujeres igualdad de derechos a los recursos económicos, así como acceso a la propiedad y al control de la tierra y otros tipos de bienes, los servicios financieros, la herencia y los recursos naturales, de conformidad con las leyes nacionales", "definicion"=>"\nProportion of heads of agricultural holdings, to the total number of agricultural workers and  proportion of female heads of agricultural holdings to the total number of heads of agricultural  holdings, by type of tenure. ", "formula"=>"\n<b>Proportion of heads of agricultural holdings, to the total number of agricultural workers</b>\n\n$$PPOA_{head}^{t} = \\frac{TEA^{t}}{POA^{t}} \\cdot 100$$\n\nwhere:\n\n$TEA^{t} =$ heads of agricultural holdings in year $t$\n\n$POA^{t} =$ people employed in agriculture in year $t$\n\n<br>\n\n<b>proportion of female heads of agricultural holdings to the total number of heads of agricultural \nholdings, by type of tenure</b>\n\n$$PTEA_{women}^{t} = \\frac{TEA_{women}^{t}}{TEA^{t}} \\cdot 100$$\n\nwhere:\n\n$TEA_{women}^{t} =$ female heads of agricultural holdings in year $t$ by type of tenure\n\n$TEA^{t} =$ heads of agricultural holdings in year $t$ by type of tenure\n", "desagregacion"=>"Type of tenure: Ownership; lease\n\nSex\n\nProvince\n", "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>"\nSub-indicator 5.a.1 (a) is a prevalence measure. It measures the prevalence of people \nin the agricultural population with ownership or secure rights over agricultural land, disaggregated by sex. \n\nSub-indicator 5.a.1 (b) focuses on gender parity, measuring the extent to which women are disadvantaged \nin ownership or secure rights over agricultural land. \n", "justificacion_global"=>"\nIndicator 5.a.1 aims to monitor the gender balance on ownership/secure rights over agricultural land. \nSubindicator (a) and sub-indicator (b) are based on the same data and they monitor ownership/rights from \ntwo different angles. \n\nWhile sub-indicator (a) uses the total male/female agricultural population as reference population, \nand it tells us how many male/female own land, sub-indicator (b) focuses on the agricultural population \nwith land ownership/secure rights, and it tells us how many of them are women. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-0a-01.pdf\">Metadata 5-a-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"a) Proporción del total de la población agrícola con derechos de propiedad o derechos seguros sobre tierras agrícolas, desglosada por sexo; y b) proporción de mujeres entre los propietarios o los titulares de derechos sobre tierras agrícolas, desglosada por tipo de tenencia", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.a- Emprender reformas que otorguen a las mujeres igualdad de derechos a los recursos económicos, así como acceso a la propiedad y al control de la tierra y otros tipos de bienes, los servicios financieros, la herencia y los recursos naturales, de conformidad con las leyes nacionales", "definicion"=>"Nekazaritza-ustiategietako titularren proportzioa, nekazaritzako langile guztiekiko, eta nekazaritza-ustiategietako  titular guztiekiko emakumeen proportzioa, edukitza-motaren arabera.", "formula"=>"\n<b>Nekazaritza-ustiategietako titularren proportzioa nekazaritzako langile guztiekiko</b>\n\n$$PPOA_{titularrak}^{t} = \\frac{TEA^{t}}{POA^{t}} \\cdot 100$$\n\nnon:\n\n$TEA^{t} =$ nekazaritza-ustiategietako titularrak $t$ urtean\n\n$POA^{t} =$ nekazaritzan diharduten pertsonak $t$ urtean\n\n<br>\n\n<b>Nekazaritza-ustiategien titular diren emakumeen proportzioa, nekazaritza-ustiategien \ntitular guztiekiko, edukitza-motaren arabera</b>\n\n$$PTEA_{emakumeak}^{t} = \\frac{TEA_{emakumeak}^{t}}{TEA^{t}} \\cdot 100$$\n\nnon:\n\n$TEA_{emakumeak}^{t} =$ nekazaritza-ustiategietako emakume titularrak $t$ urtean edukitza-motaren arabera\n\n$TEA^{t} =$ nekazaritza-ustiategietako pertsona titularrak $t$ urtean edukitza-motaren arabera\n", "desagregacion"=>"Edukitza-mota: jabetza; errentamendua\n\nSexua\n\nLurralde historikoa\n", "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>"\n5.a.1 (a) azpiadierazlea prebalentzia-neurri bat da. Nekazaritzako lurren gaineko jabetza edo \neskubide seguruak dituzten pertsonen prebalentzia neurtzen du, sexuaren arabera banakatuta.\n\n5.a.1 (b) azpiadierazleak genero-parekotasuna du ardatz, eta neurtzen du emakumeak jabetzan edo \nnekazaritza-lurren gaineko eskubide seguruetan zein neurritan dauden desabantailan.\n", "justificacion_global"=>"\n5.a.1 adierazlearen helburua nekazaritza-lurren gaineko eskubide seguruetan/jabetzan dagoen genero-oreka \nmonitorizatzea da. Horretarako, (a) azpiadierazlea eta (b) azpiadierazlea datu berberetan oinarritzen dira, \neta jabetza/eskubideak bi angelutatik kontrolatzen dituzte. \n\nZehazki, (a) azpiadierazleak nekazaritzako guztizko biztanleria maskulinoa/femeninoa erabiltzen du erreferentziako \npopulazio gisa, eta zenbat gizon/emakumek dituzten lurrak esaten digu; (b) azpiadierazleak, berriz, lurraren \njabetza/eskubide seguruak dituzten biztanleak aipatzen ditu, eta horietako zenbat diren emakumeak esaten digu. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-0a-01.pdf\">Metadatuak 5-a-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.a: Undertake reforms to give women equal rights to economic resources, as well as access to ownership and control over land and other forms of property, financial services, inheritance and natural resources, in accordance with national laws</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.a.1: (a) Proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and (b) share of women among owners or rights-bearers of agricultural land, by type of tenure</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SP_GNP_WNOWNS - Share of women among owners or rights-bearers of agricultural land, by type of tenure [5.a.1]</p>\n<p>SP_LGL_LNDAGSEC - Proportion of total agricultural population with ownership or secure rights over agricultural land [5.a.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>SDG indicator 1.4.2.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator consists of two sub-indicators. </p>\n<p><u>Sub-indicator 5.a.1 (a</u>):</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>No. of people in agricultural population with ownership or secure rights over agricultural land</p>\n      </td>\n      <td rowspan=\"2\">\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mo>&#xD7;</mo>\n          </math> 100, by sex</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Total agricultural population</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Sub-indicator 5.a.1 (a) is a prevalence measure. It measures the prevalence of people in the agricultural population with ownership or secure rights over agricultural land, disaggregated by sex. </p>\n<p><strong><em>Land ownership</em></strong> is a legally recognised right to acquire, to use and to transfer land. &#x201C;Secure rights&#x201D; in the context of indicator 5.a.1 is defined as secure tenure rights, i.e., rights to use, manage and control land, fisheries and forests, in the sense of the Voluntary Guidelines on the Responsible Governance of Tenure of Land, Fisheries and Forests in the Context of National Food Security<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>&#xFFFC;. <em>Operationally</em>, for the purposes of measurement of this indicator, secure tenure rights comprise both land ownership and two key alienation rights: the right to sell and the right to bequeath agricultural land.</p>\n<p><u>Sub-indicator 5.a.1 (b): </u></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Number of women in the agricultural population with ownership or secure rights over agricultural land</p>\n      </td>\n      <td rowspan=\"2\">\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mo>&#xD7;</mo>\n          </math> 100, by type of tenure</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Total in the agricultural population with ownership or secure rights over agricultural land</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Sub-indicator 5.a.1 (b) focuses on gender parity, measuring the extent to which women are disadvantaged in ownership or secure rights over agricultural land.</p>\n<p>Broad types of tenure identified by the IAEG-SDG are freehold, customary and leasehold.</p>\n<h2>Concepts and terms:</h2>\n<p>The basic concepts and terms essential to collecting data needed to compute SDG indicator 5.a.1 are the following:</p>\n<ol>\n  <li>Agricultural land</li>\n  <li>Agricultural household</li>\n  <li>Agricultural population</li>\n  <li>Ownership or secure rights over agricultural land</li>\n</ol>\n<p><u>(1) Agricultural land</u></p>\n<p>Land is considered &#x2018;agricultural land&#x2019; according to its use. The classes and definitions of land use are based on the classification of land use for the agricultural census recommended by the World Programme for the Census of Agriculture 2020<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>.</p>\n<p>As shown in Figure 1 below, agricultural land is a subset of the total land of a country. In particular, <strong>agricultural land</strong> includes:</p>\n<ul>\n  <li>LU1- land under temporary crops<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></li>\n  <li>LU2- land under temporary meadows and pastures<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></li>\n  <li>LU3- land temporarily fallow<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup></li>\n  <li>LU4- land under permanent crops<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></li>\n  <li>LU5- land under permanent meadows and pastures<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup></li>\n</ul>\n<p><img src=\"data:image/png;base64,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\"></p>\n<p><strong>Figure 1. Classification of land use (WCA 2020)</strong></p>\n<p>Since indicator 5.a.1 focuses on agricultural land, it <em>excludes</em> all the forms of land that are not considered &#x2018;agricultural&#x2019;, namely:</p>\n<ul>\n  <li>LU6- land under farm buildings and farmyards</li>\n  <li>LU7- forest and other wooded land</li>\n  <li>LU8- area used for aquaculture (including inland and coastal waters if part of the holding)</li>\n  <li>LU9- other area not elsewhere classified</li>\n</ul>\n<p><em>The land use class of agricultural land is with respect to a specific reference period</em>; thus, the reference period should be specified when collecting data on land use. As further discussed below, the reference period should cover a 12-month period. In agricultural censuses and surveys, this is generally the preceding 12 months.</p>\n<p><u>(2) Agricultural household</u></p>\n<p>Ownership or secure rights over agricultural land are specifically relevant to individuals whose livelihood relies on agriculture. These individuals are identified by way of whether their household<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup> can be classified as an <strong><em><u>agricultura</u>l household</em></strong> which for purposes of calculating indicator 5.a.1 is characterized by the following:</p>\n<ul>\n  <li>Criterion 1: A member or members of the household operated land for agricultural purposes <em>or </em>raised livestock over the past 12 months regardless of the final purpose of production</li>\n</ul>\n<p><u>and</u></p>\n<ul>\n  <li>Criterion 2: At least one member of the household operated land for agricultural purposes or raised livestock <em>as an own-account worker</em>.</li>\n</ul>\n<p>The definition considers that since agricultural land includes <em>both</em> crop land (LU1-LU4) and meadows and pastures (LU5), ownership or secure rights over agricultural land are relevant for households operating land and/or raising or tending livestock. Engagement in forestry, logging, fishing and aquaculture activities is <em>not included</em> because the focus of the indicator is on agricultural land.</p>\n<p>Households who own or have secure rights over agricultural land <em>but did not farm the land nor used the land in raising/tending livestock during the reference period are excluded</em>, because the indicator focuses on households whose livelihood is linked to practicing agriculture.</p>\n<p>The long reference period-- previous 12 months-- allows to capture agricultural households even when data collection occurs during the off-season or when households are not engaged in agricultural activity at the time of the survey. That is, since agricultural work is highly irregular and strongly affected by seasonality, a short reference period would exclude such households.</p>\n<p>The specification &#x201C;regardless of the final purpose of production&#x201D; ensures the inclusion of households that produce only for own consumption. It addresses a common problem where agriculture practiced only or mainly for own consumption, without any market orientation (so, with no or little income) is not perceived as an economic activity by respondents<em>.</em></p>\n<p>The second criterion for a household to be classified as an agricultural household for purposes of computing the sub-indicators 5.a.1(a) and 5.a.1(b) is that at least one household member farms or raises livestock as an own-account worker. Thus, information on the <em>status in employment</em> and, for those employed, the <em>industry</em> in which they are employed, and their <em>occupation</em> need to be collected for each member of the household.</p>\n<p><u>(3) Agricultural population</u></p>\n<p>The reference population for indicator 5.a.1 is the population of <strong><em>adult individuals living in agricultural households </em></strong><em>(as defined above). </em>For purposes of international comparability, the recommended definition of &#x201C;<strong>adult</strong>&#x201D; is a person who is 18 years old or older. However, countries could use their own definitions of adult but allow for the calculation of statistics based on the 18-years old definition.</p>\n<p>Once a household is classified as an &#x2018;agricultural household&#x2019;, <em>all</em> the adult household members are considered as part of the reference population (to be referred to simply as the &#x201C;agricultural population&#x201D; in this document).</p>\n<p>The adoption of a household perspective is particularly important from the gender perspective, because in many agricultural households, women often consider themselves as not being involved in agriculture, even though they provide substantive support to the household&#x2019;s agricultural activities. In addition, the individual&#x2019;s livelihood cannot be completely detached from the livelihood of the other household members; and in particular, for households operating agricultural land or raising livestock, land is an important asset for all the individuals and protects them in case the household dissolves.</p>\n<p>When the data is collected in agricultural surveys or censuses, usually the statistical unit is the <em>agricultural</em> <em>holding</em> or <em>farm</em>. The WCA 2020 classifies holdings into two types: (i) holdings in the household sector; i.e., those operated by household members and (ii) holdings in the non-household sector, such as corporations. For a given household sector holding, there may be one or more producers and the agricultural population is defined as the adult members of the households of the producers. It is important to note, that someone employed in the agricultural holding is not a producer. Holdings in the non-household sector are not relevant for the estimation of indicator 5.a.1.</p>\n<p><u>(4) Ownership of agricultural land and secure rights over agricultural land</u></p>\n<p>It is challenging to operationalize the definition of ownership of and secure rights to agricultural land for purposes of data collection. In addition, differences in legal systems and how legal systems protect rights to agricultural land across countries poses challenges in providing comparable statistics across countries. The discussion below: </p>\n<p><strong><em>Land ownership</em></strong> is a legally recognised right to acquire, to use and to transfer land. For purposes of specifying the data that needs to be collected, it is useful to recognize three broad typologies of land ownership systems:</p>\n<ul>\n  <li><strong>Private property systems</strong>, where land ownership is predominantly a right akin to a freehold tenure. </li>\n  <li><strong>Systems where land is owned by the State</strong>, where &#x201C;land ownership&#x201D; in the sense of private property systems does not exist but refers to <em>possession of the rights</em> most akin to ownership in a private property system. In this context, it is more appropriate to speak of <strong><em>tenure rights</em></strong> that capture an individual&#x2019;s capacity to control and take decisions over the land-- for instance, long-term leases, occupancy, tenancy or use rights granted by the State that are transferable and are granted to users for several decades (e.g., 99 years). </li>\n  <li><strong>Communal land tenure system, </strong>where land is primarily held under a tribal, communal, or traditional form of tenure. Such arrangements usually involve land being held on a tribal, village, kindred or clan basis, with land ownership being communal in character but with certain individual rights being held by virtue of membership in the social unit.</li>\n</ul>\n<p>In many countries, a combination of systems of ownership as well as secure tenure rights to land may exist. A common combination would be where the private property system prevails, but with pockets of state-owned and/or communal land. For some countries, the system may primarily be that of state-owned land or communal land.</p>\n<p>Considering the above, as well as the need for comparability of estimates across countries, to determine whether an individual is said to have ownership or secure rights to agricultural land three conditions (proxies) are considered:</p>\n<p><em>Formal documentation:</em></p>\n<p><u>Proxy 1</u>-<em> </em>Presence of legally recognised documents in the name of the individual</p>\n<p><em>Alienation rights:</em></p>\n<p><u>Proxy 2</u>- Right to sell</p>\n<p><u>Proxy 3</u>- Right to bequeath </p>\n<p>These proxies are further described below.</p>\n<p><strong><em><u>Formal documentation</u></em></strong></p>\n<p>Proxy 1 refers to the existence of any document that an individual can use to claim property rights <em>before the law</em> over an asset by virtue of the individual&#x2019;s name being listed as owner/co-owner or holder/co-holder on the document. </p>\n<p>It is not possible to provide an exhaustive list of documents that could be considered as formal proof of ownership (for private property systems) or secure tenure rights (for state-owned or communal land systems) across countries. Examples of common relevant legal documents are provided in the discussion below. It is recommended that the list of documents be customised in accordance with land ownership laws of the country. It is further recommended that: </p>\n<p><u>Private property systems</u></p>\n<p>For private property systems, the following are typically considered as <strong>formal written proof of ownership:</strong></p>\n<ul>\n  <li><strong>Title deed</strong>: &#x201C;<em>a written or printed instrument that effects a legal disposition</em>&#x201D;<em><sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup></em></li>\n  <li><strong>Certificate of occupancy or land certificate</strong>: &#x201C;<em>A land certificate is a certified copy of an entry in a land title system and provides proof of the ownership and of encumbrances on the land at that time</em>&#x201D;<em><sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup></em></li>\n  <li><strong>Purchase agreement</strong>: <em>a contract between a seller and a buyer to dispose of land</em></li>\n  <li><strong>Registered certificate of hereditary acquisition</strong></li>\n  <li><strong>Certificate issued for adverse possession or prescription</strong>: <em>is a certificate indicating that the adverse possessor acquires the land after a prescribed statutory period</em>. </li>\n</ul>\n<p><u>It is to be noted that agricultural land possessed or used under a rental contract or leasehold is outside the coverage of indicator 5.a.1. Ownership of land confers on the holder a series of crucial benefits leading to economic empowerment - from being able to use it as collateral to having a higher propensity to invest in one&#x2019;s own asset &#x2013; these benefits are drastically reduced or even absent in the case of rentals or leases.</u></p>\n<p><u>Customary/communal land tenure</u></p>\n<p><u>For land covered by customary tenure laws, the types of tenure and associated rights vary considerably. Thus, it is recommended that the list of relevant documents be prepared according to each country&#x2019;s customary laws. </u>An example of a relevant document is:</p>\n<ul>\n  <li><strong>Certificate of customary tenure</strong>: <em>an official state document indicating the owner or holder of the land because customary law has recognized that particular person as the rightful owner. It can be used as proof of legal right over the land. </em>These certificates include, among others, certificates of customary ownership and customary use.</li>\n</ul>\n<p><u>Systems where land is owned by the state</u></p>\n<p>Similarly, for state-owned land, associated formal documents of ownership-like possession should be specified according to the country&#x2019;s land laws. An example of a relevant document is:</p>\n<ul>\n  <li><strong>Registered certificate of perpetual / long term lease</strong>: &#x201C;<em>a contractual agreement between the state and a tenant for the tenancy of land. A lease or </em>tenancy<em> agreement is the contractual document used to create a leasehold interest or tenancy</em>&#x201D;<em> <sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup></em></li>\n</ul>\n<p>Note that findings from the Evidence and Data for Gender Equality (EDGE) project<sup><a href=\"#footnote-13\" id=\"footnote-ref-13\">[12]</a></sup> clearly show that using legally recognized documents alone to establish ownership is not sufficient to analyse the complexity of rights related to land, especially in developing countries and from the gender perspective. The main factor limiting the universal applicability of legally recognized documents to define ownership is the diverse penetration of such legally binding documents.</p>\n<p><strong><em><u>Alienation rights</u></em></strong></p>\n<p>In the absence of formal written documentation <strong>alienation rights over land</strong>, which can be present even in contexts where tenure rights are not formally documented, can serve as a proxy for ownership or secure rights. <strong>Alienation</strong> is defined as the ability to transfer a given asset during lifetime (Proxy 2- right to sell) or after death (Proxy 3- right to bequeath). </p>\n<p>The &#x201C;right to sell&#x201D; refers to the ability of an individual to permanently transfer the asset in question in return for cash or in-kind benefits. </p>\n<p>The &#x201C;right to bequeath&#x201D; refers to the ability of an individual to pass on the asset in question to other person(s) after their death, by written will, oral will (if recognized by the country) or when the deceased left no will, through intestate succession. </p>\n<p>The right to sell and the right to bequeath are considered as objective facts that carry legal force as opposed to a simple self-reported declaration of tenure rights over land.</p>\n<p>For purposes of data collection for 5.a.1, countries should clearly indicate whether these two alienation rights are relevant to the concept of land ownership in their legal contexts. This is particularly important in relation to land use under systems where land is owned by the state and customary/communal land.</p>\n<p>It is recommended that data on all three proxies be collected for purposes of compiling indicator 5.a.1. The decision to rely on the three proxies is based on the results of seven field tests conducted by the EDGE project. The tests demonstrated: </p>\n<ul>\n  <li><em>The lower reliability of data on reported ownership/possession</em>. Data on ownership/possession are often collected through a question on whether the individual owns any agricultural land. The data collected captures the self-perception of the respondent&#x2019;s ownership or possession status of the land, irrespective of whether the respondent has formal documentation. The study showed that such data was often neither supported by any kind of documentation nor by the possession of any alienation right.</li>\n  <li>The need to consider as &#x2018;owners&#x2019; or &#x2018;holders of tenure rights&#x2019; only the individuals who are linked to the agricultural land by an objective right over it, including both formal legal possession and alienation rights.</li>\n  <li>The need to combine different proxies, as no single proxy is universally applicable in defining land ownership or secure tenure rights. </li>\n</ul>\n<p><em><u>A Note on &#x201C;(Self) Reported Ownership/Possession of Agricultural Land&#x201D;</u></em></p>\n<p>As mentioned above, <em>reported ownership or possession</em> is relatively less reliable than documented ownership. However, in a situation where a country has scarce data on formal documentation along with missing information on alienation rights, reported ownership could still be a temporarily useful alternative for comparing ownership between men and women. However, estimates computed based mainly on reported ownership weakens the international comparability of estimates across countries. Therefore, it is highly recommended that the survey questionnaire be modified in a manner that both documented ownership and alienation rights are included, as defined above, in order to calculate the indicator using the correct methodology. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Refer to <a href=\"https://www.fao.org/tenure/voluntary-guidelines/en/\">https://www.fao.org/tenure/voluntary-guidelines/en/</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> FAO. 2015. <a href=\"https://www.fao.org/3/i4913e/i4913e.pdf\">World Programme for the Census of Agriculture 2020- Volume 1: Programme, concepts and definitions. FAO Statistical Development Series 15</a>, paras 8.2.13 &#x2013; 8.2.28. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> Defined as: &#x201C;all land used for crops with a less than one-year growing cycle&#x201D; (WCA 2020). Temporary crops comprise all the crops that need to be sown or planted after each harvest for new production (e.g., cereals). The full list of crops classified as &#x2018;temporary&#x2019; is provided in the WCA 2020 (page 165, <a href=\"http://www.fao.org/3/a-i4913e.pdf\">http://www.fao.org/3/a-i4913e.pdf</a>) <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> Defined as: &#x201C;land that has been cultivated for less than five years with herbaceous or forage crops for mowing or pasture&#x201D;. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> When arable land is kept at rest for at least one agricultural year because of crop rotation or other reasons, such as the impossibility to plant new crops, this is defined as temporarily fallow. This category does not include the land that it is not cultivated at the time of the survey but will be sowed and planted before the end of the agricultural year. <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> Area that is cultivated with long term crops that do not need to be replanted every year, such as fruits and nuts, some types of stimulant crops, etc. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> Land cultivated with herbaceous forage crops or is left as wild prairie or grazing land for more than five years. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> Household is defined according to the United Nations Principles and Recommendations for Population and Housing Censuses, Revision 3 @ https://unstats.un.org/unsd/publication/seriesM/Series_M67rev3en.pdf <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> Source: <em>&#x201C;Multilingual thesaurus on land tenure&#x201D;</em>, FAO 2003 <a href=\"#footnote-ref-10\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> Source: <em>&#x201C;Multilingual thesaurus on land tenure&#x201D;</em>, FAO 2003 <a href=\"#footnote-ref-11\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> Source: <em>&#x201C;Multilingual thesaurus on land tenure&#x201D;</em>, FAO 2003 <a href=\"#footnote-ref-12\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-13\">12</sup><p> Source: <em>&#x201C;UN Methodological Guidelines on the Production of Statistics on Asset Ownership from a Gender Perspective&#x201D;</em> Draft Guidelines submitted at the UN Statistical Commission in March 2017 <a href=\"#footnote-ref-13\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>5.a.1 (a): percent (%)</p>\n<p>5.a.1 (b): percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Classification of land use - World Census of Agriculture 2020 (WCA 2020).</p>", "SOURCE_TYPE__GLOBAL"=>"<p><u>Recommended data sources </u></p>\n<p>Indicator 5.a.1 focuses on adult individuals living in agricultural households, as defined above. Thus, the data required to estimate the indicator, can be collected through agricultural surveys/ censuses or national household-based surveys having a suitable coverage of agricultural households. </p>\n<p><strong>Agricultural Survey</strong>: Agricultural surveys are a recommended data source for two main reasons:</p>\n<ol>\n  <li>The unit of analysis is the agricultural holding, and, in most countries, the relationship between the household-sector agricultural holding and the agricultural households is known. Therefore, agricultural surveys capture well the reference population of indicator 5.a.1 </li>\n  <li>Agricultural surveys can easily accommodate questions on ownership or secure rights to agricultural land since they frequently collect data regarding tenure of agricultural land of the holding as well as data on agricultural producers households.</li>\n</ol>\n<p><strong>General household survey (GHS)</strong><sup><sup><a href=\"#footnote-14\" id=\"footnote-ref-14\">[13]</a></sup></sup><strong>:</strong> Nationally representative general household surveys are a recommended data source for indicator 5.a.1 for the following reasons:</p>\n<ol>\n  <li>Nationally representative general household surveys are the most common data source available in both developed and developing countries.</li>\n  <li>Countries that have an integrated household survey system can integrate the data requirements for 5.a.1 as part of the core survey or as a module in one of the rounds of the survey.</li>\n  <li>Nationally representative general household surveys generate social, demographic, health and economic statistics (depending on their particular focus). When data requirements for 5.a.1 are integrated in the survey, it allows for exploring associations between the individual status on indicator 5.a.1 and other individual or household characteristics, such as education, health, income level, etc.</li>\n</ol>\n<p>However, if a GHS is used to collect data to generate estimates for indicator 5.a.1, it is necessary to have a representative sample of agricultural households in the full sample. In countries where a low proportion of households is engaged in agricultural production, oversampling may be needed, especially in urban and peri-urban areas and procedures for doing so need to be part of the survey design.</p>\n<p>Also, some household surveys may have limitations in relation to the population coverage as defined by the age classes typically used in these surveys-- for example, having upper bound age cut-offs.</p>\n<p><strong>Agricultural Census: </strong>In the absence of agricultural or household-based surveys, agricultural censuses can be used for collecting data on SDG 5.a.1. However, the Census presents some disadvantages: </p>\n<ol>\n  <li>The Census is usually conducted every 10 years; therefore, it cannot provide data to closely monitor the progress on indicator 5.a.1. </li>\n  <li>It is much more expensive to add the questions for 5.a.1 in agricultural censuses than in surveys as the number of holdings to be enumerated is much larger. </li>\n  <li>With the need for a much larger number of interviewers in a census, the quality of interviewers selected may be adversely affected.</li>\n</ol><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-14\">13</sup><p> Examples of GHS that could be used to generate the indicator 5.a.1 are: Household Budget Surveys (HBS), Living Standard Measurement Surveys (LSMS), Living Conditions Surveys, Labour Force Surveys (LFS), Multipurpose Household Surveys, Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). <a href=\"#footnote-ref-14\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p>The data should be collected through surveys that collect information on an individual&#x2019;s land ownership and tenure rights. </p>\n<p>In collecting data for indicator 5.a.1 through an agricultural survey, agriculture census or general household survey, two decisions need to be made:</p>\n<p>i) Determine the number of adult members of an agricultural household (eligible respondents) on whom information is to be collected, and </p>\n<p>ii) Determine who should report this information</p>\n<p>Possible options are shown in Table 1 below:</p>\n<p><strong>Table 1. Options and respondent approaches for data collection</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Number of eligible respondents</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Who should report</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Self-Respondent</p>\n      </td>\n      <td>\n        <p>Proxy-Respondent</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>All</p>\n      </td>\n      <td>\n        <p>Option 1</p>\n      </td>\n      <td>\n        <p>Option 3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Randomly selected number, n</p>\n      </td>\n      <td>\n        <p>Option 2</p>\n      </td>\n      <td>\n        <p>Option 4</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>When collecting data on asset ownership from a gender perspective, the self-respondent approach where the concerned individuals themselves are interviewed is recommended over the <em>proxy respondent approach</em>, where the most knowledgeable household member is interviewed to collect information on all the household members<sup><a href=\"#footnote-15\" id=\"footnote-ref-15\">[14]</a></sup>. Thus, among the possible options, Option 1 and Option 2 are recommended:</p>\n<ul>\n  <li><strong>Option 1</strong>: <em>Self-respondent approach applied to all members. </em>Each adult member of the household is interviewed on their ownership / secure rights over agricultural land. </li>\n  <li><strong>Option 2</strong>: <em>Self-respondent approach applied to a random sample of adult members of the household. </em>Randomly selected adult household members are interviewed on their ownership / secure rights over agricultural land. </li>\n</ul>\n<p>In practice, due to budget constraints and interview time limitations, interviewing only n = 1 eligible respondent per household or a proxy respondent are the most viable options. Furthermore, in agricultural surveys and censuses, only the producers respond to the whole questionnaire, so using a self-respondent approach is not viable. However, if a country wants to study intra-household dynamics or to increase the accuracy of the 5.a.1 estimates, it may decide to collect information about two or more and even all adult household members. </p>\n<p><u>Minimum Set of Data</u></p>\n<p>The minimum set of data needed to calculate the indicator is summarized in Table 2 below:</p>\n<p><strong>Table 2. Minimum set of data for indicator 5.a.1</strong></p>\n<table>\n  <thead>\n    <tr>\n      <th>\n        <p><strong>Data Item</strong></p>\n      </th>\n      <th>\n        <p><strong>Purpose</strong></p>\n      </th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>\n        <p>Whether or not the household has operated <strong>land</strong> for agricultural purposes and/or raised <strong>livestock</strong> over the past 12 months regardless of final purpose of production</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>To identify agricultural households</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Whether operating land or raising livestock was done only as wage labour</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Sex</strong> of agricultural household members</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>To identify adult agricultural population, by sex</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Age</strong> of agricultural household members</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong><em>For adult agricultural population, data on ownership or secure rights to agricultural land based on the three proxies</em></strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Whether or not the <strong>individual owns or holds</strong> <strong>secure</strong> <strong>rights</strong> to any <strong>agricultural land</strong></p>\n      </td>\n      <td>\n        <p>Filter question on whether owns/has secure rights to agricultural land.</p>\n        <p>Also provides data on (<em>self-</em>) <em>reported ownership</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Proxy 1: Whether or not any of the land owned or held by the individual has a <strong>legally recognized document </strong>that allows protecting his/her ownership/secure rights over the land </p>\n      </td>\n      <td>\n        <p>To determine ownership/secure rights based on legally recognized document</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>If yes to Proxy 1: Whether or not the individual is<strong> listed as an owner or holder</strong> on any of the legally recognized documents, either alone or jointly with someone else</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>(Proxy 2) Whether or not the individual has the <strong>right to sell</strong> any of the agricultural land, either alone or jointly with someone else</p>\n      </td>\n      <td>\n        <p>To determine ownership/secure rights based on <em>possession of alienation rights</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>(Proxy 3) Whether or not the individual has the <strong>right to bequeath</strong> any of the agricultural land, either alone or jointly with someone else</p>\n      </td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n<p><u>Question formulation to collect minimum data items required for indicator 5.a.1</u></p>\n<p><em><u>Questions to Identify agricultural households and adult Individuals in the agricultural population </u></em></p>\n<p>As mentioned above, the reference population (denominator) for indicator 5.a.1 <em>are the adult individuals living in agricultural households</em>. The first step to identify the agricultural population is to identify agricultural households.</p>\n<p>The module presented in Table 3 suggests how to identify agricultural households from among households covered by the data collection vehicle (survey/census) for purposes of indicator 5.a.1. The questions aim to capture the household&#x2019;s involvement in agriculture over the preceding 12 months and screen out households where all members are involved in agricultural activity only as wage workers. The respondent to the questions in the module should be the <em>most knowledgeable member of the household.</em></p>\n<p>Table 3. Module for identifying agricultural households</p>\n<table>\n  <thead>\n    <tr>\n      <th colspan=\"2\">\n        <p><strong>Question</strong></p>\n      </th>\n      <th>\n        <p><strong>Function</strong></p>\n      </th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td colspan=\"3\">\n        <p><em>Check Criterion 1 defining an agricultural household</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Q1</strong></p>\n      </td>\n      <td>\n        <p>Did anyone in this <strong>household</strong> operate any <strong>land</strong><sup>(1)</sup> for agricultural purposes in the past 12 months<sup> (2)</sup>? </p>\n        <p>1. Yes</p>\n        <p>2. No</p>\n      </td>\n      <td>\n        <p><em>Screening (farming) (Response = 1)</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Q2</strong></p>\n      </td>\n      <td>\n        <p>Did anyone in this <strong>household raise or tend </strong>any livestock (e.g., cattle, goats, etc.) in the last 12 months?</p>\n        <p>1. Yes</p>\n        <p>2. No (If Q1 = 2 and Q2 = 2, questions end. Else, go to Q3.)</p>\n      </td>\n      <td>\n        <p><em>Screening (livestock) (Response = 1)</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"3\">\n        <p><em>Check Criterion 2 defining an agricultural household</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Q3</strong></p>\n      </td>\n      <td>\n        <p>Identify all people in the household roster who operated land for agricultural purposes and/or raise or tend livestock in the last 12 months (i.e., Q1=1 and/or Q2=1).</p>\n      </td>\n      <td>\n        <p><em>List members of agricultural households engaged in farming or raising livestock </em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Q4</strong></p>\n      </td>\n      <td>\n        <p>For each individual in the household who operated land for agricultural purposes and/or raise or tend livestock in the last 12 months, was this performed&#x2026;</p>\n        <p><em>(tick all that apply)</em></p>\n        <ol>\n          <li>For use / consumption of the household?</li>\n          <li>For profit / trade?</li>\n          <li>As wage work for others?</li>\n        </ol>\n      </td>\n      <td>\n        <p><em>Filter out households where agricultural activities were done <u>only as wage labor</u> (Response = 3)</em></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><sup>(1) </sup>Including orchards and kitchen gardens</p>\n<p><sup>(2)</sup> Alternative phrasings:</p>\n<ul>\n  <li>Did anyone in this household cultivate/use any land for agricultural purposes in the last 12 months?</li>\n  <li>Did anyone in this household operate any land to produce crops in the last 12 months?</li>\n  <li>Did anyone in this household cultivate/use any land to produce crops in the last 12 months?</li>\n</ul>\n<p><em><u>Specific application to agricultural surveys or censuses</u></em></p>\n<p>When we collect data using an agricultural survey or an agricultural census, the agricultural population will be all the adult members of the household of <strong>the agricultural holder</strong>. As per the World Programme for the Census of Agriculture 2020 Volume 1, the agricultural holder is defined as &#x201C;the civil person, group of civil persons or juridical person who makes the major decisions regarding resource use and exercises management control over the agricultural holding operation. The agricultural holder has technical and economic responsibility for the holding and may uptake all responsibilities directly, or delegate responsibilities related to the day-to-day work management to a hire manager.&#x201D; </p>\n<p>As the indicator refers to individuals, only household sector holdings&#x2014;i.e., holdings for which the agricultural holder is a civil person (i.e., one person) or group of civil persons-- should be considered. When the agricultural holder is a (single) civil person, the adult members of the household of the single holder are part of the agricultural population. When the holder is a <em><u>group</u> of civil persons</em>, adult members of households of each of the persons in the group belong to the agricultural population.</p>\n<p><em><u>Questions to identify owners of, or holders of secure rights to agricultural land from among the agricultural population</u></em></p>\n<p>Data on ownership of, or secure rights to agricultural land of members of the agricultural population for purposes of estimating indicator 5.a.1 refers to individual members of agricultural households (as defined above) whose <em><u>age</u></em> is 18 years old or over.</p>\n<p>An example of a module that can be utilized for collecting the data using the self-respondent approach is presented in Table 4. </p>\n<p><u>Table 4. Example of minimum set of questions for collecting data on ownership of or secure rights to agricultural land at the person/individual<s> </s>level</u></p>\n<table>\n  <thead>\n    <tr>\n      <th>\n        <p><strong>Questions</strong></p>\n      </th>\n      <th>\n        <p><strong>Function</strong></p>\n      </th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Q1. Do you own or hold secure rights<sup><a href=\"#footnote-16\" id=\"footnote-ref-16\">[15]</a></sup> to any agricultural land, either alone or jointly with someone else?</strong></p>\n        <p>1 - Yes</p>\n        <p>2 &#x2013; No (end of the module)</p>\n      </td>\n      <td>\n        <p><em>This question refers to whether the respondent, not the respondent&#x2019;s household, holds any agricultural land. It measures reported possession, which captures the respondent&#x2019;s self-perception of his/her possession status, irrespective of whether the respondent has a formal or legal documentation of ownership.</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Q2. Is there a formal document for <u>any</u> of the agricultural land you own or hold secure rights to that is issued by or registered at the Land Registry/Cadastral Agency, such as a title deed, certificate of ownership, or certificate of hereditary acquisition? </strong></p>\n        <p>1 - Yes</p>\n        <p>2 &#x2013; No &gt;&gt; Q4</p>\n      </td>\n      <td>\n        <p><em>This question identifies whether there is a legally recognized document for any of the agricultural land the respondent reports having. </em></p>\n        <p><em>Documented ownership/secure rights refer to the existence of any document an individual can use to claim ownership or secure rights in law over the land.</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Q3a. What type of documents are there for the agricultural land you own? </strong></p>\n        <p><strong>LIST UP TO 3. </strong></p>\n        <p><strong>CODES FOR DOCUMENT TYPE:</strong></p>\n        <p><strong>TITLE DEED.........................1</strong></p>\n        <p><strong>CERTIFICATE OF CUSTOMARY</strong></p>\n        <p><strong>OWNERSHIP..........................2</strong></p>\n        <p><strong>CERTIFICATE OF OCCUPANCY....3</strong></p>\n        <p><strong>CERTIFICATE OF HEREDITARY ACQUISITION LISTED IN REGISTRY............................4</strong></p>\n        <p><strong>SURVEY PLAN......................5</strong></p>\n        <p><strong>OTHER (SPECIFY).................6</strong></p>\n      </td>\n      <td>\n        <p><em>The list of options presented here is indicative. It is of utmost importance that the list includes all the legal documents recognized/ enforceable by law according to the national land tenure system. </em><strong><em>Refer to discussion in Section 2.a on formal documentation. </em></strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Q3b. Is your name listed on any of the documents as owner? </strong></p>\n        <p>1 &#x2013; Yes</p>\n        <p>2 &#x2013; No</p>\n        <p>98 - Don&#x2019;t know</p>\n        <p>99 - Refusal</p>\n      </td>\n      <td>\n        <p><em>Because individual names can be listed as witnesses on a document, it is important to ask if the respondent is listed &#x201C;as an owner&#x201D; or &#x201C;holder&#x201D; on the document. <u>The respondent does not need to show the document to the enumerator.</u></em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Q4. Do you have the right to <em>sell </em>any of the agricultural land held (alternatively &#x2018;land possessed, used or occupied&#x2019;), either alone or jointly with someone else?</strong></p>\n        <p>1 - Yes</p>\n        <p>2 &#x2013; No &gt;&gt; Q5</p>\n        <p>98 - Don&#x2019;t know</p>\n        <p>99 - Refuses to respond</p>\n      </td>\n      <td>\n        <p><em><u>Alienation rights- Proxy 2</u></em></p>\n        <p><em>This question obtains information on whether the respondent believes that he/she has the right to sell any of the agricultural land s/he reports possessing. When a respondent has the right to sell the land, it means that he or she has the right to permanently transfer the land to another person or entity for cash or in-kind benefits.</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Q5. Do you have the right to <em>bequeath </em>any of the agricultural land held (alternatively &#x2018;land possessed, used or occupied&#x2019;), alone or jointly with someone else?</strong></p>\n        <p>1 - Yes</p>\n        <p>2 - No </p>\n        <p>98 - Don&#x2019;t know</p>\n        <p>99 - Refuses to respond</p>\n      </td>\n      <td>\n        <p><em><u>Alienation rights- Proxy 3</u></em></p>\n        <p><em>This question obtains information on whether the respondent believes that he/she has the right to bequeath any of the agricultural land he/she reports possessing.</em></p>\n        <p><em>When a respondent has the right to bequeath the land, it means that he/she has the right to give the land by oral or written will to another person upon his/her death his/her death.</em></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><em><u>In agricultural surveys or censuses </u></em></p>\n<p>In agricultural surveys and censuses, usually there is a question about land tenure<sup><a href=\"#footnote-17\" id=\"footnote-ref-17\">[16]</a></sup> of land used for agricultural activities. The data to calculate SDG indicator 5.a.1 can be collected by adding a few questions to the land tenure question as shown in Table 5 in the example below which uses a proxy-respondent approach:</p>\n<p><em>Example</em></p>\n<p>Usual question on land tenure in agricultural surveys/census: </p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Q1. Of the total Agricultural Area Utilized (AAU) <em>of the </em>agricultural holding, how much is:</p>\n      </td>\n      <td>\n        <p>AREA</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>a. <strong>Owned with </strong>written documentation (such as title deeds, wills, purchase agreements)</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>b. <strong>Owned without </strong>written documentation</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>c. <strong>Rented-</strong>in, leased or sharecropped <strong>with </strong>written agreement</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>d. <strong>Rented</strong>-in, leased or sharecropped <strong>without </strong>written agreement</p>\n      </td>\n      <td colspan=\"2\"></td>\n    </tr>\n    <tr>\n      <td>\n        <p>e. State or communal land used <strong>with </strong>written agreement (certified use rights)</p>\n      </td>\n      <td colspan=\"2\"></td>\n    </tr>\n    <tr>\n      <td>\n        <p>f. State or communal land used <strong>without </strong>written agreement (uncertified use rights)</p>\n      </td>\n      <td colspan=\"2\"></td>\n    </tr>\n    <tr>\n      <td>\n        <p>g. Occupied/squatted without any permission</p>\n      </td>\n      <td colspan=\"2\"></td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Control Total land (total of options a to g)</strong></p>\n      </td>\n      <td colspan=\"2\"></td>\n    </tr>\n  </tbody>\n</table>\n<p>Add the following questions to obtain the data needed for 5.a.1:</p>\n<p><strong>Table 5. Q2. If Q1 = a, b, e or f, please fill the table below.</strong></p>\n<table>\n  <thead>\n    <tr>\n      <th colspan=\"4\">\n        <p>a- List all the household members of the agricultural holder/s (producers) of the holding </p>\n      </th>\n      <th>\n        <p>b- Sex of the person</p>\n      </th>\n      <th>\n        <p>c- If Q1= a or e, Is this person&#xB4;s name listed as owner in the written documentation? </p>\n      </th>\n      <th colspan=\"2\">\n        <p>d- If Q1= a, b, e or f: does this person have the right to sell any of the agricultural land owned, either alone or jointly with someone else?</p>\n      </th>\n      <th>\n        <p>e- If Q1= a, b, e or f: does this person have the right to bequeath any of the agricultural land owned, either alone or jointly with someone else?</p>\n      </th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td colspan=\"4\">\n        <p>Agricultural holder/producer</p>\n      </td>\n      <td>\n        <p>F/M</p>\n      </td>\n      <td>\n        <p>Y/N</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Y/N</p>\n      </td>\n      <td>\n        <p>Y/N</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Member 1</p>\n      </td>\n      <td>\n        <p>F/M</p>\n      </td>\n      <td>\n        <p>Y/N</p>\n      </td>\n      <td>\n        <p>Y/N</p>\n      </td>\n      <td colspan=\"4\">\n        <p>Y/N</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Member 2</p>\n      </td>\n      <td>\n        <p>F/M</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Y/N</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Y/N</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Y/N</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>...</p>\n      </td>\n      <td></td>\n      <td colspan=\"2\"></td>\n      <td colspan=\"2\"></td>\n      <td colspan=\"2\"></td>\n    </tr>\n  </tbody>\n</table><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-15\">14</sup><p> Findings from the EDGE pilot studies reveal that data from proxy respondents yield different estimates than self-reported data, with variations by asset, by type of ownership and by the sex of the owner. In particular, it was found that proxy-reported data decrease both women&#x2019;s and men&#x2019;s reported ownership of agricultural land. Such underestimation is greater for men (-15 percentage points) than for women (-10 percentage points) and is less pronounced when we consider documented ownership (-7 percentage points for men and -2 percentage points for women). <a href=\"#footnote-ref-15\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-16\">15</sup><p> Alternatively &#x2018;do you have, use or occupy&#x2019; &#x2026; <a href=\"#footnote-ref-16\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-17\">16</sup><p> Refer to the WCA 2020 Volumes @ <a href=\"https://www.fao.org/world-census-agriculture/wcarounds/wca2020/en/\">https://www.fao.org/world-census-agriculture/wcarounds/wca2020/en/</a> or Handbook on the Agricultural Integrated Survey @ https://www.fao.org/in-action/agrisurvey/resources/resource-detail/en/c/1198081/ <a href=\"#footnote-ref-17\">&#x2191;</a></p></div></div>", "FREQ_COLL__GLOBAL"=>"<p>The data collection calendar depends on the frequency of surveys required to compute the indicators. FAO is engaging with countries to include the questions needed to measure the indicator into their existing national surveys, i.e., household-based surveys, agricultural surveys and censuses through capacity development activities at national/ regional levels and provision of technical assistance needed to compute the indicator. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The data release depends highly on the frequency of surveys required to compute the indicators. </p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices. If agricultural surveys or censuses are used, the responsible organization may be the Ministry of Agriculture or, more generally, the organization responsible for agricultural surveys or censuses in the country.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agricultural Organization (FAO)</p>", "INST_MANDATE__GLOBAL"=>"<p>Article I of the FAO constitution requires that the Organization collect, analyses, interpret and disseminate information relating to nutrition, food and agriculture <a href=\"http://www.fao.org/3/K8024E/K8024E.pdf\">http://www.fao.org/3/K8024E/K8024E.pdf</a>.</p>", "RATIONALE__GLOBAL"=>"<p>Indicator 5.a.1 aims to monitor the gender balance on ownership/secure rights over agricultural land. Sub-indicator (a) and sub-indicator (b) are based on the same data and they monitor ownership/rights from two different angles. While sub-indicator (a) uses the total male/female agricultural population as reference population, and it tells us how many male/female own land, sub-indicator (b) focuses on the agricultural population with land ownership/secure rights, and it tells us how many of them are women.</p>\n<p>Therefore, it is sufficient to have: </p>\n<ol>\n  <li>The number in the agricultural population with ownership or secure rights over agricultural land (by sex), and</li>\n  <li>The total agricultural population</li>\n</ol>\n<p>An illustration of how to compute the sub-indicators is presented here, using the data in Table 6. </p>\n<p>Table 6. Data for Illustrative example</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Variable</p>\n      </td>\n      <td>\n        <p>Women</p>\n      </td>\n      <td>\n        <p>Men</p>\n      </td>\n      <td>\n        <p>Total</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Number in agricultural population with ownership/secure rights over agricultural land</p>\n      </td>\n      <td>\n        <p>10</p>\n      </td>\n      <td>\n        <p>100</p>\n      </td>\n      <td>\n        <p>110</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Number in agricultural population</p>\n      </td>\n      <td>\n        <p>100</p>\n      </td>\n      <td>\n        <p>200</p>\n      </td>\n      <td>\n        <p>300</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"2\">\n        <p>Sub-indicator 5.a.1 (a): Percentage of the agricultural population with ownership or secure rights over agricultural land, by sex</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAWgAAADZCAYAAAANBKqxAAAAAXNSR0IArs4c6QAAAHhlWElmTU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUAAAABAAAARgEoAAMAAAABAAIAAIdpAAQAAAABAAAATgAAAAAAAABIAAAAAQAAAEgAAAABAAOgAQADAAAAAQABAACgAgAEAAAAAQAAAWigAwAEAAAAAQAAANkAAAAAjin4YwAAAAlwSFlzAAALEwAACxMBAJqcGAAANkNJREFUeAHtnQu8VFX1x5cKGKCCIiAoAj5RgXhopqigRg8w0bJMURTJDEgNiTT6a/hCKQOjDCFJyhckGpoiIoJIBKYi4StKDEQU5ekbIbz/+a7c42GYM3dm7sxl7tzf/nxmzsw5Z++zz3fvvc7a65yz1k5Lly6tMCUREAEREIGSI1CHGh1yyCElVzFVSAREQARqM4F//etftnNtBqBzFwEREIFSJiABXcqto7qJgAjUagIS0LW6+XXyIiACpUxAArqUW0d1EwERqNUEJKBrdfPr5EVABEqZgAR0KbeO6iYCIlCrCUhA1+rm18mLgAiUMgEJ6FJuHdVNBESgVhOoNQK6oqLCPvnkE2OplJnAO++8Y7///e9t1KhRtnDhwsw757l106ZNNmfOHONh/HzT2rVr7Ve/+pU99thj+RaRU7477rjDfvvb3+aUp5g7V+X8C8G/KueWzfFLjXdVzjffvLVCQP/1r3+1vn372jnnnOPLQYMG2T333GP//e9/8+VW0HzPPvusPf300wUtsyqF/eEPf7BZs2bZihUrqiRAM9XhxRdftN/97nf2pz/9KdNuGbdt2LDBnnrqKXvppZd8P+pLW7/33nsZ8+W7kWNRfqmk1PPPVK9UNoXgn+l4lW1LPf7777/vbJcvX57MWmq8kxWrxh+1QkCjEZIOPPBA+9KXvmRbtmyxv/zlL/6pRtaxh7r11ltLSjMLg2T06NHWr1+/2HpXZcOhhx5q3/zmN+0b3/hGVYrZJi8XFTTcZcuWbbNef8wvuFE2xeCfC+fU43MBoX6PPvpoLsWU/b7ui6Psz/LTE+zYsaN961vfstatW9vEiRPtn//8p29ZtWqVa3IrV660vfbay7761a/akUceaW+++aZreV27drUFCxbYnnvuaUOHDrUnnnjC5s6dawj+ww47zM477zyrV6+eawBM21m///7723e+8x1r1qyZzZ492+bPn+/lzpgxw9AWTjjhBOvdu7fdcsst9tFHH7np5YYbbrBu3br5hWTatGn2n//8x7Zu3WoHHXSQa/+NGjWyDz/80KZMmeJ1r1Onju22226200472ZAhQ/xc7r33XnvhhRds5513tk6dOrkAZL9o4gJF+c8//7yXR13h0qJFCxs3bpwxdSaNGTPGTj/9dGvXrl0yO6zi6kZ977vvPlu9erXtscceVrduXT+X448/fjuOzGjQfKnb4Ycf7uWn48qgZabzox/9yPe5/fbbjTr8+Mc/TtaJHzB+7rnnfB18/v73v9v3vvc9u/nmm2Pzow3TLrT1zJkz7etf/7rVr1/fTS9vv/227brrrnbsscdanz59tjlW6p/QT4477jhbsmSJvfbaa9a2bVs7//zzbffdd3eFII53ZXkz1T+1HvRR+l9q3dOxOfXUU7fhn6lPhDrSZ9F8X331VTvggAP8/Bo2bJisxvr16+03v/mNjy/GBGaMX/7ylz5umLWS+E/fPOuss5LHb9y4sd11112+/R//+IcxDr773e/6f74eeOABbyf6OvkOPvjg5LZy/7HtyC33s/30/D7++GP/RYMjLEeMGOH26WOOOcZNDWiOI0eO9IGFEOeDIKETM6AnTJjgQhHBiaD+yle+4sIcYYKwa9WqlU+9Gag///nP7Y033rCXX37ZP1wA6Mh0SATfu+++68IZ2zjr6dTYVBno7du398HGMTn+RRdd5KaZxx9/3BAGaIoIRY5HQhNnWnj00Ud7PoQC6dvf/rYvwxf7MZhbtmxpDDDszJwj5019sNWTqA/nHE1xdWNAYbMm7xe+8AW/WG3evNkHMoxTOWKGgAmCnBTHlX1Ce7EfNmu0rVTzFBc5PiSOx3mQMuWPtgsXWEwGmFwQGGh4tAHCvk2bNvb5z3/ey0v3FT0/zoe2hC8X1x/+8IfeLnG8K8ubqf6pdcE0la7u6dik8s/UJ6J15AJGor1QWM4+++xkNTh3+uO///1vV05ocwQ6CcH6wQcfGOY8LsjR46MAcQwSbU2/C32QdaFNXn/9db8AcN+htqRaJaDpHGjJQdNCyCGc6DinnHKKC9omTZoYWuiiRYusQ4cO3g/oeNdee62x7frrr/d1l156qQsiBjla8h//+Edfj9bUtGlT10IRoGgfIfXs2dP69+/vN+CCjfcnP/mJXXjhhS5wfvGLX/iuaOB0fDRQtFG0djonCe0FoTpw4ECbN2+eD/5evXoZwhDhjNDF1k6Hp+xnnnlmGwFN2QgLBjIXIQQTS7RuPpdffrn94Ac/8EES6uMH/vQLjTpd3RhUCMUTTzzRtR+EHZzRPmFOinJMNUOg5ZFSufrKLL6YjcCKi9eAAQN89pBFNt8FDZrjMhPp3r277b333t4ejzzyiF8QYZ9JQIfjwP6aa67x/kR59KHKeDMrIqXLm+sN7euuuy5t3dOxQYiGlG0d99lnHz8/FA+OFUxhoRyUCMYMfQ7hHO4NsJ3ZWriAdu7cOWTx5X777efKB2OLizuKSDT99Kc/dY+bl1xyia1Zs8b5RjX36L7l9rtWCWimxly5ca+KGYOOEqZWDz30kPEJKXQm/mPGQOiS3nrrLV+GaTkDi8S0ksQAjSY04pBCnubNm/uqqGYY9mGJiWHSpEkujMMgRRsjMbVES2GA0FkRKtjWQ724YFx88cW+L1/R4/M/7IeZB+FMokyEM0K1shRXN7QphA2aFYIa7ZOB97nPfS5ZZJRjcuWnP0K9AqPANXW/YvzHrLTLLrt40ZhG6AcbN25MHipVW09uSPnBTKZBgwb+4WIEh3BecbyDgE6XN9oHUw6V9m++dc+2jpwDs87Qf1EKUhNmNQQ0Ahntn30pn/+hLzLuUIqyTaFPoCDRLhxXAjpbejVoP+xu2FqjKTQ09kdsbCGhRaEppKYwxWPw0VnpLGgODEzSlVdemZy2M+jROtBYowmhSgpLfiOAEcasu+2223wa//3vf9/zj0iYYIKgRgBj16Rs7JwXXHCBC8KgqWNiiQpo6hhN4XzReENat26d/wzCIqxPt4yrG1oYg27fffd1TalHjx4G72xTHFfslZhZ+MAgm4EdFai55IfhnXfe6drs4MGDXQvEpp5tok+QuE9APekX2fJOlxcm2dY/27pH2YTzyraOYf9MSwQ0iZkpF3xmUIsXL/YLNsdGYHPxjWrw0fLS1S9sj46XsK7cl7VKg07XmOHqzA0jOg+DKhFlxjBHpEvsjybOzRA6I2YGbtCxHoHODQ0EPZo65ghMEZUlBggDGuFHmUFzQiChkZJC52QajyaC9ssHmyyaKmaWcFEhDxoZU3M0OWzkIXGO7IfZgRulCHAGE3UILMK+6ZZxdUPIM7gwrXAMfmPiOOmkk9IVs926OK7UFeHPDUuW4QbmdgUkVgRBgwZMW/CESC75g4YHa9oDE0UuCY0ROzwXbS643JSujHe4sKbLSz2yrX9ldU9lE8x3nF+2dcyGBfdY0LS5T0CiXbEnP/jgg/4fE1K6FOrHzAtzIWYZJasdDvvD9DUsow2PuSPcwEBAcrOEKRqaS9g/LMnHo2FM1bG/cRMOOzGdkvXc7KCDIby5YROmjpRFCstQXviPAGUwcgceoXraaad5uePHj3chR/lsR2CjkSP8GJCYOqjDjTfe6HXFdsxgY92vf/1rHxSpN/m4ALEfAh17LRcUyudmVtC2qV+oo1c88hVXN0wcXDA4HkIa4YaZ5s9//nOyrGiZ4XdYxnE944wz/GkKtDDOmRunJHiEvGGJqYLZBRctpvukTPkD/7BEsGAD5QLM0xOwIoXtHCf89g0pX3Dk2Ahb+J577rleRmW8KSZdXtZnqn84b5aV1T2VTTRvZX0iui91CgzCetZFEzZ9Ek/BML6iNuewLeQNS2Z+jCsUG550wvzHtnAsygv7RtexvqwTMQlrQ0o0eMbTTGg8FQntrCKhCVYkrvjJfRPaUAXbUlOiI/n+qesTQqQioeVWsD0kyks9fur/hMbmxw55Eppqsi4JgVzBJ3FRqEhcTCoSNyx9N/IkbghWJB5p2qbOCU2zImETr6DumRL78UlNHCshaFNXJ/+nq9vkyZO9bonH1Xy/J5980v8nHiP0/+k4pluXjit1oW1ItEW0bqllsC313OPyp2sXjpGwc1YkTA78dIahP8RxSQhlP9eEUK+g/UNdvYDIVzre2eSNq3+oX7R/xtWdfVPZpLJjn3R1ZH3qvpQVPS77RBP9O7qd/flEU2qZbKP+Yeyk8k5t+2hZ5fgb2VxrTBzhhljc1ZarMjchUhMacrqEthk0zuh2tAa02GhC20s9fur/cHMp5OPmWrjBFjQH6ofJgrvjPOOb6PA+leYJFI4REvtkk+L2C8eLKyNd3bjRSOI5ZTR4bubAjheDSOk4pluXjisaXmgb2imqQaWWwb7hhq4fOPEVlz9du5AnaouPll8ZF/LS/nzSpTjeYd+4vHH1J1+0fvyPqzvbUtmk5mWfuDqm7ktZmVJq/063f2qZlBetfyrv1LbPdPxy2bYTUlpBY2tOcya0C3+WlBsw2O14FjsIxx19Fth9ee41oT35lJ1H06IDbkfXr1jHp00wT2FmyOZxvGg9qpI3Wo5+lx8BzGUS0OXXrjojERCBMiCAgP7f3asyOBmdggiIgAiUGwEJ6HJrUZ2PCIhA2RCQgC6bptSJiIAIlBsBCegCtSgve/Ds7PLE89Gpied3uYmETUmpcgLZ8uKllep02F95zXf8Hrxpime+xONyeVcmU1/Ou9CYjJnasFj14O1QvDYmHuOLqVXprJaALlBbvPLKK/5yRHg5JVpsqnPy6Db93p5Atrx4kgUHUVGnPNHSUp3UR7eV628eceQFoaoEgEjty8UMKJGpDVPrUag240U03gBOPIddqCKLVk7mhxmLdtjaVXBwTs6bUkqVEygULzwG8rYkvqODj4jKj16z98AJGP5QunTpUrAT4e1atE2ecVeqXgIlL6DRBBhoeG7jdVhefPjiF7/oPipYj6vNo446ynD0jfbAu/49evRwUwPv9LMvD8njn4LOm+owH9w8txvn6J7Xtnl9GN8MXNFx4cmgx88EnZZj45g/JLzCcQwc5gSn/Lz6HHVOj2vNv/3tb+7v4+GHH/bjxzmG52UUziudc33K4byiDudPivi+SLwB575009UV3yOYXZgK85ptCC7AeTyRJiBBJsf5+OEgfFVqYINMxwi88EWRmjfVmT+vtKcLBBBeIealGNqJ/Xgm/PyEy1c0a86bFHXgjzZINB3yEKAAh1Spz2ozC6I/oIHz8givl+PXA4+F+C/hQht8bPMqO6/3Uw79Ad/F2QZ+CAxY4lCL9uAYHDPaH2hn/I7Tp3ilnhdseDmJMZHa/rzMwVjABwbjhTxTp07159PZxvjhAkjMSfoN44c+MHbsWH8t+8wzz4xWK21ACRhnyp9uzHAOcOK5b14kgucRRxyxzbEy/UHbDaxTywh9KC6gAG2NLKA9eecDJjUllbSARjjjJAevXoBFyPHhP50Yp0YIT4Qk9ir+J175dAEdfNLS0bHL4R+BD50WT27BYT6uOtEQ4hzd49c2eH7jzScEJY5fGLQMcMqMJoR39BgM5sQrqr5feEuL+oRzwbcGAw0hwhuIXFCiKZMj9eh58eYW08VoQtCmqyvnGhdcAEGRLiAB58mFLCTs6XR4hBIXoFSH/JmOEX0TMF1eBiDHgxfs4gIBhLrQ7sEbHgIruD4Njp04Bt7iEETcJ0Ag0y8wpdC2UQHNhfiqq65yQYKQQ2DCkPz4wcYL2/LEfQb8YvOm2/Tp05PuL3GaxUUxm8APoe5hGedsH+UCHzG8kIR/Dy469Hn6ebr255yjfTb4liEP54lCQJ9jH8ojIbDgne5tP86bY/GBFfcH4Jgpf/T4jBnszDiwQiBzTPyk0A74msk24T88roxoH0rtB7QZfmk4P8YlciLaj7M9/o7ar6QFNJooiY6PQOQqTGdFM8MpOx0KQcegYLCR0HLpcOE/WhaO10npHObTYRAmuEDM5Oger3Q4nLn//vu9LIQAjoNYRlPqMRjMIeJJdD9+4yQp4UfDO97dd9/tbwhGBTQDIZNz/VAemlBwOB/WsQwXltS6hijYaJqpwQVgTMrHcT4CNQQ2IAgAKd0xmIKnpmjeqDN/zgEhkSkQAAIHP9y8yRgcyQ8fPnw7B/44okLgc3HHKRKDNzUh4LlAMBMhSgxtgLMj+hyBFeiHaILMlHjlHYEOfwY+v7MN/JB6XOqN5zoueNFAAfRtEm5y0XzhiQaLpzzqQYq2P/0oJLihqCC0uJlKfWHE+WWb0gWUCGOrsjLCmOEcEJIEiUDA06Zc6EL/rKwcthPmqrIy0vUDtGeEMxenm266yc+dAAAI9ZqQSlpA41qThPZAwv8xCU2RzkbIKAYKg4OrNA1EHrQMBDWCMfhwIB8aESn4yuBKGm7qoY1E/SjTkaKJuHoklmim3GRgMOMWEW94IaU7RtiWugyx/tDiSakDJ9SNV4iDbwMGZ6pz/ajD+egx4uqKVkhKF1wgHDOcRy6O85ktBD8YmY4RrWP4Hc0b1rEM2jCsETgIpdRAAPDBh0do17ibP1wYEGYILbRkBB4X/zCz4Xjh/EOfY2DThzgfBjXhxKhDEHzkYR0XYhJaHp+QghbP/7hzZFucs/1QD0wraICk1Jh8ce0fzoVxw3ghYc7KVsB6hip8hTFDezBDwCNhVHvlYplt4sJVWRnp+kHwdR4YwIH2lIDOlnyG/fA1gTDmgzkhXHHDlBTtGGGFeYCEHZWpEzY3Gj/YKFMPERwLsQx+aCtzdB/KQAgQuw+BkfDg5vbMdD5uo8cIeVOXaGckLi4kpqHRFOoWzpttocPBIAzAaJ7o77i6ZgouEKaICEOEHoOLmQpmCezhfJjuoi1mSpmOkSlf6jYYcax8AwFQHoObRJtcdtllPuvCxoxpBJMU5oqQQr2DuYjz5cKJOYP2QMAjLHGniiYOG27IYaYiZRv4IRyPJTbUuEABuD4l0ZbUHxtxtr6SQ1tGL/xcMIITotCGaJmVJcYT7U4dcs3PTJhZKuOEGSb24FwfOc23jHBhCuOGvhDOu7JzLoXtJa1BM51kWsozi2gpYWqOzZmEAL7jjjt8cCBMWE9HDoIrmzvZmDiYWjL1Q+jGOboPjYXNkQZGk0XzCoM3bM9liR2cG0rcuCIFrTWUwblQtzjn+pV18ri6chzON11wAbalC0iQreP4UPdMxwj7ZLNkYDGo0HjgwW9mSNGboXHlhAscGi3ni3DFnox5g5kBAjpqD6cc+hw3JLHVso2pOEKN/sd/LqLYUrkXQeLGKIIwtB2mEOqJ4Kb8uMAPnvnTrzBbQ/jRt6KBAsIskn3Q/LhYM2WPOtyPlhX9zYySGQj9h+jxCCtMgpgtSGi03KCt7JE8OFKvEFAizGSzzR9mEZwfQSxoi1xTvmXQFoxTGCBHmAkFhSjXOuyI/Uv6OWju9CJkMVcgzNBqcOwe7LR0wGDPZGDRAYLWjIYZpodhEIZl0AD4z+9Mju7ZHvLRQJgaEGyEj2fwENaJThD2CcvUY5A3rOM3CXMAGhwaDAIgTAn/t/V/7iEzOXsPxwrLkC8s4+qaKbhAIRznc/xMxwj1YxmYhGXqOgQM54Emi5DOFAggcAhlpTqpR+PFPMEsiycmuPF88sknR6vjZg9MVhyPmRkXAx7ROz9h+w2JckMKvykrl8APIT9LpuZxgQLo4yQENBcr7hEQoIEbtOF8w5L9wm+WXCQGDRrktl+UD/oa9eRi++Uvf9lnmXBA2COEo3mjZaUGlKgsP/xDWZTD/QPyYBpEe2Y2HMoPbRWWvuHTr1AGy1zKCPkoEwbcS6DtMUsiQ8LFFHlR8ilxlU/MXEo7JTqnO2FPTLO2qyjrEnatbRzW8z+6b+ImRaUO8yk4MZ3eztl7QmPbztF44mruTvmpV0iVHSNhKkjWKaH1u4P3hO18Gwfloax0S+rGJ5rSHTO6nd/p6hr2of6Ji0zSQXpYzzIxM9jO8XxCaCXXwZf/IUXPL6xjmekYYb90ecO6XAMBUKdo2/M/oTUlgxewLTHD2o5lqEtYsl9C0/L6h3XRJeVGzz9sC/kSwnSbPhnOJ+yXbpm4UG8XKCBhI69I3MxM1jcRad37TmLW5eXT16MpXZ9gHeeSuMBFd/U2Duui3NKVkdCgtwkoQUH0kXT5040ZuCTMQMkACPAIKY5Naj1yKSN6PhyHOsGARDnR4/vKEvxCNsvd6A64hGJvxPzAkxJozkrxBMKjluyBNs1sA62IO/Fog+WeeKaYZ+Z5XA1TCvcGuF8yYsSI5I2/cmdQW88PE6YE9A5ofcBjR+RZ3PDUww6oRo05JDZLnjzgCQCeZKgtgQBooIS252YZGHBhwuTB+fNbqbwJSECXd/vq7ERABGowAQR0Sd8krMFsVXUREAERqDIBCegqI1QBIiACIlAcAhLQxeGqUkVABESgygQkoKuMUAWIgAiIQHEISEAXh6tKFQEREIEqE5CArjJCFSACIiACxSEgAV0cripVBERABKpMQAK6yghVgAiIgAgUh4AEdHG4qlQREAERqDIBCegqI1QBIiACIlAcAhlf6Ce4JgEocfdIZGRCSeGsHIc1uPXEzSZuDAm/g39egrjiI6BHjx7Fqa1KFQEREIFaRCBWQBOphHfBie0WElENCM+Ef1icfuPjlZBBCReIHs4HB95Dhw4Nu2spAiIgAiJQBQKxJg68h+HkGgfbxPwjLU/EXcMxPo6wEdREI5k2bZo7BJ80aZL179/fneZXoT7KKgIiIAIi8CmBWA2aEDMIYCJGjB071tq0aePhhkIUAoQ04X4IJ0PkaUIITZgwweOWDRs2zP3XxlEmMgQfJREQARGoTQQIW8Yn2xQroAmRjgZNSCkKxBcvJg3i1RG3jyW+abFHE0aGsDkI82XLlnnMrxDWJl1Fcq1kujK0TgREQATKnUCsgEbYjho1yp2FE2wSDZoAmGjTxEUjjhra8/jx490OjZDGzEE8MAS5kgiIgAiIQNUIZIyokogJ5sEz0aRDSoTu8nU82UGEZZ7sQFCT+I/pI5hBQh4tRUAEREAEciPAQxqxGjRFoQ1HhTPrEL4IZxKP1AXhHP77Bn2JgAiIgAhUmUDsUxxVLlkFiIAIiIAIVImABHSV8CmzCIiACBSPgAR08diqZBEQARGoEgEJ6CrhU2YREAERKB4BCejisVXJIiACIlAlAhLQVcKnzCIgAiJQPAIZH7Mr3mFVsggUj8DatWvtyiuvdLcDHAVnXrz5+utf/9rfiD333HP9bdibb77ZGjVqZCNGjLAVK1bYyJEjbdy4cRndFBSv1ipZBLYnIAG9PROtqeEEeHlq5cqV7omR5/YbNGhgo0ePdncEuDBAMPfq1cuOPfZYe/TRR909wQ033GCXXHKJhHMNb/tyq75MHOXWojofJ7B582b3wog2jZB+5ZVX7Pjjj7djjjnG/cfgY2bevHn+JuzkyZPtuOOOs06dOomeCJQUAWnQJdUcqkwhCDRt2tQuvfRSd+I1ZswYN2PgooA3Y3FFQMLXzKGHHmrPP/+8zZw509566y2bO3euDRgwwLp3716IaqgMEagyAWnQVUaoAkqNAJ4XifBz6qmnuqsCTB577rmnvfrqq/7ZfffdjU/Dhg0N7Xnw4MHu/vbMM8+0RYsWldrpqD61mIA06Frc+OV66kQDuvrqq/0m4RFHHOEaMUL6lltucS0agUy69tprbeDAgdauXTsX5FOnTrXzzz/ft+lLBEqBQEZvdqVQQdVBBPIhsGnTJtu6datrySE/dmns0XXr1vVVq1evdp/m/MEEwvZU52Ahr5YiUN0EKvVmV90V0vFEoFAE8GGemoIXxrCegBMhIbglnAMNLUuFgGzQpdISqocIiIAIpBCItUEvXbrU7rrrLr/rffDBB9vZZ5/tjyRNnDjRH1Pq3LmzNW/e3ObPn28dO3b0mzKzZs1yH9E9evRIOYz+ioAIiIAI5EogVkC/++67dvTRR1vv3r2TZc6YMcOjeX/lK1+xn/zkJx7aasiQIf6m1mGHHWaLFy+2oUOHJvfXDxEQAREQgfwJZDRxPPPMM/anP/3J38riEMuXL7f27du7Vn3ggQd61O9p06YZb2cRj7B///4Kd5V/WyinCIiACGxDIFaDJko3kbnffvttu/HGG92PATEHuZlC4oH/rl27esirBQsWWMuWLW3ChAl+N3zYsGHJO+XbHO3TP+vWrfPnTtNt0zoRqIzA3nd+p7Jdir597TmTi34MHaD8CDRp0sT4ZJtiBTR3wffbbz8XvAhlhDPRunE606pVK19yF3zjxo3+xhYCnbezli1bZrxei3CPS7lWMq4cra+dBNaXwGkfcsghJVALVaHcCcQKaEwXPIf33nvv2cknn+yBYnv27Gljx4612bNnW+vWrV17Hj9+vGGH3rBhg5s5eJ0WQa4kAiIgAiJQNQIZX1ThYX8id/MJiQf6t2zZ4gIbrZo3tEJkb/5j+ghmkJBHSxEoJIH1V3UpZHF5lbXXNXolPC9wypQ1gUpfVEn3sD/CNzzwj+AOwpmjRgV51rXQjiIgAiIgAmkJZHyKI20OrRQBERABEagWAhLQ1YJZBxEBERCB3AlIQOfOTDlEQAREoFoISEBXC2YdRAREQARyJyABnTsz5RABERCBaiEgAV0tmHUQERABEcidgAR07syUQwREQASqhYAEdLVg1kFEQAREIHcCEtC5M1MOERABEagWAhLQ1YJZBxEBERCB3AlIQOfOTDlEQAREoFoISEBXC2YdRAREQARyJyABnTsz5RABERCBaiEgAV0tmHUQERABEcidwGeOnnPPqxwiIAIikBWBKVOm2MMPP+yuigcNGmRdunSxWbNm2eTJk23z5s123XXX2f777+9lPfHEE74+FHzaaadZmzZtPOzexx9/bOeee65Hdbr55putUaNGNmLECFuxYoWNHDnSxo0blzHcXiizpix3ufjii0fExcjCOf+YMWPsnXfesYMOOsid89966602c+ZMj7TC+nvvvdfXt23b1oG/9tprDrOmAFA9ax6Bj+aM3+GVrn/iRTu8DjWpAq+//rqdfvrpRvzSlStXWrt27Wzo0KF26aWXWp8+faxp06ZJP/P169f3cHsHHHCAPfDAA9ajRw+77bbb7IgjjvB8t99+u/ueRyY999xzvv7666+3hCyzfffdtyZhyVhXYrdmNHHMnTvXhTM7kmbMmGFE8x4+fLg9+eSTNmfOHBs8eLAtXLjQ3nzzTVu8eLF1794940G1UQREoPYROPzww+2pp56y1atX29FHH21LliwxAoLMmzfPHnzwQY95Gqg0b97cjjvuOJc9e+65p8uUV155xY4//ng75phjPA4qgpi8RHRCC2f/Tp06hSLKZhkroD/44AMXwKecckryZJcvX27t27f3sFYI6mbNmhmxCxs3buzxCPv3769wV0la+iECIhAIYJpgxk1IvGeffdY++ugj/33UUUfZ3//+d7v//vvDrr4kfN7UqVPt1FNPNWbyfIh3Sn4SAaovu+wyO+uss1zoL1q0yM477zxDqSynFGuDBk6vXr38XLdu3eqAgBbiDQKqa9euHvKKaUvLli1twoQJvt+wYcMy2oHQyINWXk4wdS7VQ6AUQhITL04pewJvvPGGfelLX7K3337bnn76aSMqOvIEudGgQQNbs2aNz9Dr1q3r9mVm5QSixqyBYrj77rv7TJ19+TBjX79+vd155502YMAAN4H07t3blcoWLVpkX7Fq3hNzcpxJOV1VYgU0VyymEFz1iOzN1IJo3atWrXKALPfZZx+fbgCyQ4cOflVbtmyZrV271jJByrWS6SqudbWXwPoSOHUEjFL2BG666SYXtOS46KKL7Gtf+5rfy8IOjTzo16+f3XLLLS5jTj75ZBs9erSddNJJhoZNOv/88/0GIFo0ZlX4Y3Pmg3kDMwfyiv3KqW0yRvUGDEb4l156yfr27evG/bFjxxp2Ia58TC9+8Ytf2JAhQ/xqN2nSJJ+GVKZBU66SCORLQFG98yW3Y/Oh7DVs2DAZXBolEOUvBJ5mpo4AZpYe/R1qzdMebEPLJmHPRkkkURbbd911V/9fDl/M0ioV0JzoJ5984uD4DYgtW7b4HVemKBjpA2D+Y/oIZhD2VxKBQhOQgC40UZVXigQQ0LEmjmiFuaqFhPCtV6+e/61Tp05SOLOC/0oiIAIiIAKFIfCZ5C1MeSpFBERABESgQAQkoAsEUsWIgAiIQKEJSEAXmqjKEwEREIECEZDRuEAgVYwIiMC2BHoOnb/tih3w77FfdtsBRy3cIaVBF46lShIBERCBghKQgC4oThUmAiIgAoUjIAFdOJYqSQREQAQKSkACuqA4VZgIiIAIFI6ABHThWKokERABESgoAQnoguJUYSIgAiJQOAIS0IVjqZJEQAREoKAEJKALilOFiYAIiEDhCEhAF46lShIBERCBghKQgC4oThUmAiIgAoUjIAFdOJYqSQREQAQKSiDWFwehq6ZMmeLBHQkcSyRenPNPnDjRw1x17tzZiL47f/5869ixo4enmTVrlvuE7tGjR0ErqcJEQAREoDYSiNWgCdJI1FwidU+fPt3ZzJgxw4jmPXz4cHvyySc9QCPxwQjwSBDHxYsXe4j02ghS5ywCIiAChSYQK6CbNWtmS5YsceHcpUsXPy7Rddu3b+9hrRDU7DNt2jRr3LixEY8QYa5wV4VuIpUnAiJQWwnEmjiIPYhJo379+h40tk+fPh4mPQhgYg927drVQ14tWLDAg8hOmDDBYxZWFjR23bp1xkdJBPIhsHc+mQqch3hxSqVPoNTaiQjmfLJNsQKaqLrYkk844QS78MILPVDs3nvvbatWrbJWrVr5koi6Gzdu9IjeHTp0sG7duhm267Vr11qLFi1i65BrJWML0oZaSWB9CZz1IYccUgK1KPUqrNnhFazp7RQroB955BE3cWzatMlOPPFED3Xes2dPGzt2rM2ePdtat27t2vP48eNtyJAhLqQxcxBgFkGuJAIiIAIiUDUCOy1durQi7iqzefNmtynXrVs3eRRMH1u2bPHI3v/973/dDLLHHnv4dv5j+ghmkGQm/RCBAhJYf9X/7okUsMici9rrmkU556ltGRRRpWotjnkmVoOm6Hr16m13BIRvWF+nTh3XosNO/FcSAREQAREoDIHYpzgKU7xKEQEREAERyJeABHS+5JRPBERABIpMQAK6yIBVvAiIgAjkS0ACOl9yyicCIiACRSYgAV1kwCpeBERABPIlIAGdLznlEwEREIEiE5CALjJgFS8CIiAC+RKQgM6XnPKJgAiIQJEJSEAXGbCKFwEREIF8CUhA50tO+URABESgyAQkoIsMWMWLgAiIQL4EJKDzJad8IiACIlBkAhLQRQas4kVABEQgXwIS0PmSUz4REAERKDIBCegiA1bxIiACIpAvgVgHzoSuuvvuu42IKsQj/MIXvuDO+SdOnOhhrjp37mzNmze3+fPnW8eOHe2kk06yWbNmGT6hCZWlJAIiIAIiUDUCsRp0gwYN7LLLLrPzzjvP7rvvPj/KjBkzjGjew4cPtyeffNLmzJljgwcPtoULF9qbb75pixcvtu7du1etRsotAiIgAiLgBGIFNEFfGzZsaB988EEyCu3y5cutffv2HtYKQd2sWTObNm2aNW7c2IhH2L9/f4W7UscSAREQgQIRiDVxUP77779vU6ZMsYEDB/rhiDkY4g0Se7Br164e8mrBggXWsmVLmzBhghGzcNiwYR5kNq6O69atMz5KIpAPgVIISUy8OKXSJ1Bq7dSkSZOkwpsNvVgBje159OjR9q1vfcsjeFMY0bpXrVplrVq18uU+++zj9ugNGzZYhw4drFu3bobteu3atYYGHpdyrWRcOVpfOwmsL4HTjgu0XAJVK6EqrNnhdanp7RRr4nj44Ydt5cqVNn36dLvuuuv8BmHPnj3dHj1y5EgX2kTzRsPu16+ftWnTxu3S2KIR5EoiIAIiIAJVI7DT0qVLK3K5ymDC2LJli0f2xuTx4YcfJiN78x/TRzCDVK1qyi0C6Qmsv6pL+g3VuHavaxZV49Fq5qF6Dp2/wyv+2C+77fA65FsBzDOxJo64QhG+9erV8808UocWHRL/lURABERABApDINbEUZjiVYoIiIAIiEC+BCSg8yWnfCIgAiJQZAIS0EUGrOJFQAREIF8CEtD5klM+ERABESgyAQnoIgNW8SIgAiKQLwEJ6HzJKZ8IiIAIFJmABHQOgLdu3Wr33HOPXXDBBfbOO+94zkceecR9kFxyySW2YsUKe/XVV43fV155pbE//7/73e/6s+M5HEq7ioAIiIBJQOfQCWbPnm1PPfWU/ec//zFeykFI8zp837593bHU73//e3v88cft2GOPtddff91fe7/22mvd41/dunVzOJJ2FQEREAGTgM6lE/CqOy5YQ0JQoyXjCxvHUa+99prtu+++Nm/ePH/DcvLkyXbcccdZp06dQhYtRUAERCBrAnr1L2tU2++4efNmX7nzzjv7K+68Bt+rVy879NBD7fnnn7eZM2faW2+9ZXPnzrUBAwbIV/b2CLVGBEQgAwGZODLAqWwT3vwQyvjJxta83377eRb8aKM9E8wAt6pnnnmmLVok3w2V8dR2ERCBbQlIQG/LI+O/xx57zCPMsNMZZ5zh5g3Ce3ETkAgz2KI/+eQTw+6MD+127drZrrvualOnTrUuXXa8g5+MJ6eNIiACJUcgZ292JXcGJVAhPPrVr18/6cVv9erVhnZNQsPGFIKgVioMAXmzKwzHYpcib3ZVI5yXN7uqHbI8cxO/MZqCcGYd3v8knKN09FsERCBbAjJxZEtK+4mACIhANRPI+BTHX//6V+PZ36uuusqrxVR+4sSJHuaqc+fO1rx5c5s/f7517NjRHzWbNWuW4RMau6ySCIiACIhA1QjEatA848sTCGvWfBZXbMaMGUY07+HDh/tNsTlz5viTCgsXLjRCXS1evFiPklWtPZRbBERABJIEYgV027ZtrU+fPv58b9ibx8nat2/v6xDUzZo1s2nTplnjxo1t0qRJ/sqzwl0FWlqKgAiIQNUIZDRxpBbN681BABN7kLfnCHm1YMECa9mypU2YMMGfWhg2bJhlerUZzZyPkgjkQ6AUQhJzh12p9AmUWjs1adLE+GSbchLQROtetWqVtWrVypc8rbBx40bbsGGDdejQwbp16+b+J9auXWstWrSIrUOulYwtqIAbhtz/1QKWln9RY74xI//MtSTn+hI4z1wCLZdAdXdQFT4zj+6gClhNb6dYEwfOfkaNGuXCl+X7779v+KK47777bOTIkda6dWvXnqdMmWL9+vWzNm3auF0aWzSCXEkEREAERKBqBGI1aF5bvvzyy7cpfbfddrOf//zn7jqTyN6YPHCtyavNfK644gq3TwczyDaZ9UcEREAERCAnArECOq4UhC/CmcQjddigQ+K/kgiIgAiIQGEIxJo4ClO8ShEBERABEciXgAR0vuSUTwREQASKTEACusiAVbwIiIAI5EtAAjpfcsonAiIgAkUmIAFdZMAqXgREQATyJSABnS855RMBERCBIhOQgC4yYBUvAiIgAvkSkIDOl5zyiYAIiECRCUhAFxmwihcBERCBfAlIQOdLTvlEQAREoMgEJKCLDFjFi4AIiEC+BCSg8yWnfCIgAiJQZAIS0EUGrOJFQAREIF8CEtD5klM+ERABESgyAQnoIgNW8SIgAiKQLwEJ6HzJKZ8IiIAIFJlATgJ64cKFdu2113pUlTVr1nhElXHjxtmvfvUr++CDD2zTpk1244032tatW4tcbRUvAiIgAuVPIOsQKB9//LHde++9dv3119uiRYvsgQcesBNPPNH23HNP23333Y3ouc8884ydeuqpHvaq/NHpDEVABESguASyFtCrV6+2fffd1z73uc/ZoYceao899pjttdde9u9//9vXffLJJx6X8PDDD6+0xuvWrTM+pZQGth9bEtUptTDxJQEltRLnTE5dU+3/1yYUEqXMBG65qGnmHapha6mNpyZNmhifbFPWApoAsTvv/D+LCMuKigrXngcNGmQbN240onu3bNnSbrrpJjvyyCOtR48esXXItZKxBWmDCIiACJQxgaxt0AjVN954w1G8/vrrts8++/hv1v/lL3+xc88917d/73vfsxdeeKGMkenUREAERKB6CGStQTdu3NhNG9wk5GbgwIEDvYazZ8+2gw8+2Fq1auWq++23324dO3asntrrKCIgAiJQxgR2Wrp0acUhhxyS9Slu3rzZ6tWrl9wf80ajRo1sp5128nVbtmyxunXrJrfrhwiIgAiIQO4EsJ9nrUGH4qPCmXVo1tEk4Rylod8iIAIikD+BrG3Q+R9COUVABERABPIhIAGdDzXlEQEREIFqICABXQ2QdQgREAERyIeABHQ+1JRHBESg5Ai8+uqrxkMM5ZR2ufjii0fk8mZLOZ0853LnnXf6K+y8st6iRQsbP368vfvuu9a2bVs/1Y8++siuuuoqO+igg/yGKL5GeAuyXbt2xiOGTzzxhHXu3LncsNSY83nttdfs5ptvdvcDRx99tK1cudL9wfCiVHixasOGDXbDDTfYkiVLjH144gi/MrQ1KbR1jTnpGlZRHssdPXq0zZo1y4444gh3BfGzn/3MH9vdY489/GwYR7fddpuPKcbV3/72NzvhhBO2O9PnnnvO23W33XbbbtvgwYOtS5cu/obzdhtr4ArkTM5PcdTA84ytMm9HPv744/b973/f5s2bZ++9957hBOrCCy9M5qlfv759+OGH9uKLL7rPEQb5+vXrrU+fPu57pE2bNsZr7osXL3bB3qFDB38e/KWXXvL9ly1b5s+Jv/POO4ag+OIXv+gC4tlnnzX8m/DWJVd+OirLAw880F+pT1ZAPzISYCAzKGm/FStWGM67zjvvPKtT57OuTbu+8sorhjBPPFbqF1d8yfDiFZ8333zT/vnPf1rr1q3tgAMO8H6wfPlyFyQ8RopQ32WXXTLWQxvjCYTXrffff3/7xz/+4W1Bm+23337JTIybpk2b+sX25JNPdqWHcYdA5oLatWtXf2OZ9yw+//nPu8+f999/3xhfKJidOnVKllVOPz7rxeV0VlmeC4MYR08IaZ7lvu+++4wre9C8QjHt27f3gc0jhfgf4U1KtGz8kJxyyik2ceJEe/nll/3tyrvuusvGjBnjZTHI6XSTJk2yZs2a2dq1a72TrVq1yoUCgx7BwoWCfXk78+2337Zbb71VAiHAr2QJszlz5hgzHd5o5YIX5w+GQQ5vLrpvvfVW8oWq//u//7Pjjz/e/vjHP9oVV1zh3hhHjRrlL1+xHxfW3r17V1ITbY4jsPfee/uYQSPE0yV9PKoEkQ8hy4cxieBmxkpboLjgKRPtG0ds/OZii2+gyZMnuxZ+9913J1+ci6tDTV1f623QI0eOtG9/+9vegRDE/L/yyitdKw6NyrQMLQAtq3v37m7qQKhj7+ItyqeeesrOOuss+9GPfuTruKqTevXqZbz6zn4jRoxw4cGAf/rpp10rQDMI2gX7Xnrppd4B0fiUsiOAKWPAgAH29a9/3S+AzHRoB9okNR1zzDG+nsF+1FFHJV+44oKMNsYz/Fx0SazjrVn2Qxgo5U8AHz2MqbPPPtvNTCg5w4YNszvuuCO2UJQgFJkhQ4ZY//79fexx4UXR6dmzp6Fxc9FE4PNuBjPfcky1XkBjy2KaTOMjMBnoaExRIYmARmNesGCBT48PO+wwmz59uv9mUPNhKowZA224YcOG3lfoOGGqzW80ZpxMsT9l9uvXzwU3O0f3ZR+l7AmgET/00EN2zjnneNsxq0FQpyZcENDGXFy7deuW3Ex70O7Dhw9P2j3Zj7bjo/ZIosr7B/d4pk6d6n0eU94FF1zg5o64AmkTUjAN7rrrrt4WtAuzJe41/OY3v3HTB7Pgcm2jWm3ioANgO8YuefXVV7ugfvDBB93cQaOHhPkD+xm2SjRmpmoI6+Bz5PTTT3dtAPMGWhrTMzpS+IRy0Mr4nHbaaXbPPffY3LlzfToX9mNJCsuQT8t4Atj/Gah9+/Z1+z0XOi6eZ5xxRjJT4ImwxZ6MP3NmS5g7aA/ampuIlDV06FBfx3pSaJtkYfqRFwF8yXN/BRMU91/+8Ic/uBacWljgjdZ97LHH+kWTdWjftAnjj3HGRRmT44QJE1wpCvlCu6WWW1P/5+yLo6aeaFy9mdoycPFzTUr1NRLycYXmEzoAmjL5QuI/n1AOgz10GqZhaM/RdeyLJoBwiK7ndzhGKFvLzASY3mLrJ9FG+INBUEdT4Bptxyh3+gF5Qr5om1GO2iRKM/ff3FhH0Qk3W7lBjlacmkI7hfU8AcI4i4412opZKvsyXimHscb/UH7IX5OXmD9rvYCuyQ2ououACJQvAQR0rbdBl2/z6sxEQARqOgEJ6Jregqq/CIhA2RKQgC7bptWJiYAI1HQCEtA1vQVVfxEQgbIlIAFdtk2rExMBEajpBCSga3oLqv4iIAJlS8Af5A2vG5ftWerEREAERKAGEvh/n7imRncz/BkAAAAASUVORK5CYII=\"></p>\n      </td>\n      <td>\n        <p>The sub-indicator 5.a.1 (a) measures the percentage of individuals with ownership or secure rights over agricultural land among the total agricultural population, by sex. In this example, overall, 37 percent (110/300*100) of the agricultural population has ownership or secure rights over agricultural land. When the indicator is disaggregated by sex, gender disparities become visible: 50 per cent of the adult men living in agricultural households ((100/200)*100) own or hold secure rights over agricultural land compared to 10 per cent of adult women (10/100*100).</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>To construct 5.a.1 (b) we divide the number of women in the agricultural population who own or hold secure rights to agricultural land by the total number of the agricultural population who own or hold secure rights to agricultural land. In the example above the indicator value is 9 percent ((10/110)*100).</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "REC_USE_LIM__GLOBAL"=>"<p>One recommendation is for countries to take into consideration the impact of the expected sample size on the precision of the estimates. One way of attaining a large enough sample size is to consider collecting information on all eligible respondents through a proxy respondent, as this can be relatively easily done using the household rosters in the surveys. However, it is important to keep in mind that when a proxy respondent provides the information for the member of the household, it is likely that some bias or response errors are introduced </p>\n<p>It is critical that the list of legally binding documents of ownership proposed to be included in questions relating to proxy 1 in this document are customized to consider only documents that are enforceable before the law and that guarantee individual&#x2019;s rights in the national context. </p>", "DATA_COMP__GLOBAL"=>"<p><strong><u>How the indicator is calculated:</u></strong></p>\n<p>The indicator 5.a.1 considers as owners or holders of secure rights to agricultural land all the individuals in the reference population who:</p>\n<ul>\n  <li>Are listed as &#x2018;owners&#x2019; or &#x2018;holders&#x2019; on a written legal document that testifies security of rights over agricultural land</li>\n</ul>\n<p>OR</p>\n<ul>\n  <li>Have the right to sell agricultural land</li>\n</ul>\n<p>OR</p>\n<ul>\n  <li>Have the right to bequeath agricultural land</li>\n</ul>\n<p>The presence of one of the three proxies is sufficient to define a person as &#x2018;owner&#x2019; or &#x2018;holder&#x2019; of secure tenure rights over agricultural land. The advantage of this approach is its applicability to different countries. Indeed, based on the analysis of the seven EDGE pilot countries, these proxies provide the most robust measure of ownership/tenure rights that is comparable across countries. In fact, individuals may still have the right to sell or bequeath an asset in the absence of legally recognized document, therefore the indicator combines documented ownership / tenure rights with the right to sell or bequeath to render it comparable across countries.</p>\n<p><strong><u>Operationalization of indicator 5.a.1 expressed through mathematical formulas</u></strong></p>\n<p><u>Sub-indicator 5.a.1 (a) </u></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Total agricultural population with:</p>\n        <p>Legally recognized document of ownership of agricultural land OR the right to sell it OR the right to bequeath it</p>\n      </td>\n      <td rowspan=\"2\">\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mo>&#xD7;</mo>\n          </math> 100, by sex</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Total agricultural population</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><u>Sub-indicator 5.a.1 (b)</u></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Number of women in the agricultural population with:</p>\n        <p>Legally recognized document of ownership of agricultural land OR the right to sell it OR the right to bequeath it</p>\n      </td>\n      <td rowspan=\"2\">\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mo>&#xD7;</mo>\n          </math> 100, by type of tenure</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Number of people in the agricultural population with:</p>\n        <p>Legally recognized document on agricultural land OR the right to sell it OR the right to bequeath it</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Use of Sampling Weights</strong></p>\n<p>When the data source is a sample survey, the appropriate survey sampling weights&#x2014;base weights, non-response adjustments and poststratification adjustments&#x2014;should be used in estimating the sub-indicators. Further, if subsampling of eligible respondents to the 5.a.1 questions is done in a census or survey, the weights need to account for this.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>FAO is responsible to check the syntaxes used in the computation of the indicator as well as the questions. </p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustment with respect to use of standard classification and harmonization of breakdown for age groups and other dimension is performed. </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>No imputation of data is made at country level.</p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p>No imputation of data is made at the regional and global level. </p>", "REG_AGG__GLOBAL"=>"<p>Weighted regional aggregates will be generated only if a sufficient number of countries in the region report on the indicator. This will be the case if (1) at least 50 percent of countries have a value or (2) if enough countries have a value as to cover 50 percent of the population in the region. </p>", "DOC_METHOD__GLOBAL"=>"<p>Countries can rely on the background paper describing the methodology and other relevant documents available at <a href=\"http://www.fao.org/sustainable-development-goals/indicators/5a1/en/?ADMCMD_view=1%20as\">http://www.fao.org/sustainable-development-goals/indicators/5a1/en/?ADMCMD_view=1 as</a> well as the e-learning available at <a href=\"https://elearning.fao.org/course/view.php?id=363\">https://elearning.fao.org/course/view.php?id=363</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Logical and arithmetic control of reporting data is carried out.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>FAO is collaborating with the countries to design/complete/improve the survey questionnaires and contributing to develop and check the syntaxes used to compute the indicator. The microdata of surveys utilized in the computation of indicators are collected by the national institutions, hence their quality rests with the data producers. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessments are performed on the final estimation of the indicator when it is updated and compared with previous results. Some countries have data that needs to be assessed further, either check on the raw data and/or the processing of data.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data availability is currently limited (though growing) around the world, and most of the available data points derive from suitable surveys in countries in Africa and Asia. The limited data availability does not yet allow for producing regional and global aggregates.</p>\n<h2>Disaggregation:</h2>\n<p>We can distinguish between levels of disaggregation which are &#x2018;mandatory&#x2019; for the global monitoring and levels of disaggregation which are recommended especially for the country level analysis, as they provide insights for policy making. </p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>&#x2018;Mandatory&#x2019; levels of disaggregation</strong></p>\n      </td>\n      <td>\n        <p><strong>&#x2018;Recommended&#x2019; levels of disaggregation </strong></p>\n        <p>(not exhaustive list)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ul>\n          <li>[for sub-indicator (a)] sex of the individuals </li>\n          <li>[for sub-indicator (b)] type of tenure</li>\n        </ul>\n      </td>\n      <td>\n        <p>[for both sub-indicators]</p>\n        <ul>\n          <li>Income level</li>\n          <li>age group</li>\n          <li>ethnic group</li>\n          <li>geographic location (urban/rural)</li>\n          <li>type of legally recognized document</li>\n        </ul>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>If the country collects data by type of tenure, the disaggregation is required by type of tenure. However, if the country does not do this, the disaggregation by type of tenure would not be possible as the information will be collected at an aggregated level.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There is currently no known source of difference.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>1- URL: </strong><a href=\"http://www.fao.org/sustainable-development-goals/indicators/5.a.1/en/\">http://www.fao.org/sustainable-development-goals/indicators/5.a.1/en/</a></p>\n<p>2- AGRIS handbook on the integrated agricultural surveys, <a href=\"https://www.fao.org/in-action/agrisurvey/resources/resource-detail/en/c/1198081/\">https://www.fao.org/in-action/agrisurvey/resources/resource-detail/en/c/1198081/</a></p>\n<p>3- Measuring Individuals&#x2019; Rights to Land. An Integrated Approach to Data Collection for SDG Indicators 1.4.2 and 5.a.1. <a href=\"https://www.fao.org/publications/card/en/c/CA4885EN/\">https://www.fao.org/publications/card/en/c/CA4885EN/</a></p>\n<p>4- World Programme for the Census of Agriculture 2020 Volume 1. <a href=\"https://www.fao.org/3/i4913e/i4913e.pdf\">https://www.fao.org/3/i4913e/i4913e.pdf</a> </p>", "indicator_sort_order"=>"05-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"5.a.2", "slug"=>"5-a-2", "name"=>"Proporción de países cuyo ordenamiento jurídico (incluido el derecho consuetudinario) garantiza la igualdad de derechos de la mujer a la propiedad o el control de las tierras", "url"=>"/site/es/5-a-2/", "sort"=>"05aa02", "goal_number"=>"5", "target_number"=>"5.a", "global"=>{"name"=>"Proporción de países cuyo ordenamiento jurídico (incluido el derecho consuetudinario) garantiza la igualdad de derechos de la mujer a la propiedad o el control de las tierras"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Grado en que el marco jurídico garantiza la igualdad de derechos de la mujer a la propiedad o el control de tierras", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de países cuyo ordenamiento jurídico (incluido el derecho consuetudinario) garantiza la igualdad de derechos de la mujer a la propiedad o el control de las tierras", "indicator_number"=>"5.a.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de la Presidencia, Justicia y Relaciones con las Cortes", "periodicity"=>"Anual", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Grado en que el marco jurídico garantiza la igualdad de derechos de la mujer a la propiedad o el control de tierras", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.a- Emprender reformas que otorguen a las mujeres igualdad de derechos a los recursos económicos, así como acceso a la propiedad y al control de la tierra y otros tipos de bienes, los servicios financieros, la herencia y los recursos naturales, de conformidad con las leyes nacionales", "definicion"=>"Grado en que el marco jurídico (incluido el derecho consuetudinario) garantiza la  igualdad de derechos de la mujer a la propiedad o el control de tierras medido a  partir de la evaluación de seis proxies.", "formula"=>"\n\n$$MJDMPT^{t} = \\begin{cases} 1 & \\text{Si ninguno de los proxies está presente en la legislación en el año 𝑡} \\\\ 2 & \\text{Si uno de los proxies está presente en la legislación en el año 𝑡} \\\\ 3 & \\text{Si dos de los proxies están presentes en la legislación en el año 𝑡} \\\\ 4 & \\text{Si tres de los proxies están presentes en la legislación en el año 𝑡} \\\\ 5 & \\text{Si cuatro de los proxies están presentes en la legislación en el año 𝑡} \\\\ 6 & \\text{Si cinco o seis de los proxies están presentes en la legislación en el año 𝑡} \\end{cases} $$\n", "periodicidad"=>"Anual", "desagregacion"=>"", "observaciones"=>"\nEl indicador mide el nivel en el que el marco normativo garantiza la igualdad de derechos de\nla mujer a la propiedad y el control de tierras, contrastando el marco legal frente\na seis proxies aceptados internacionalmente, en concreto en la Convención sobre la eliminación de todas las \nformas de discriminación contra la mujer (CEDAW) y las Directrices voluntarias sobre la \ngobernanza responsable de la tenencia de la tierra (VGGT):  \n -Proxy A: Registro conjunto de tierras obligatorio o incentivado\n -Proxy B: Obligación del consentimiento conyugal para transacciones de tierras  \n -Proxy C: Igualdad de derechos de herencia  \n -Proxy D: Asignación de recursos financieros para incrementar la tenencia y control de tierras por parte de la mujer  \n -Proxy E: En sistemas legales que reconocen el derecho consuetudinario, existencia de protección explícita sobre los derechos de la mujer a la propiedad  \n -Proxy F: Cuotas obligatorias en la gestión y administración de tierras  \n", "justificacion_global"=>"\nEl indicador 5.a.2 mide en qué medida el marco jurídico de los países \n(incluido el derecho consuetudinario) garantiza la igualdad de derechos de las mujeres \nen materia de propiedad y/o control de la tierra.\n\nEl enfoque en la tierra del indicador 5.a.2 refleja el reconocimiento \nde que la tierra es un recurso económico clave inextricablemente vinculado al acceso, \nuso y control de otros recursos económicos y productivos. Es un insumo clave para la \nproducción agrícola; puede facilitar el acceso a servicios financieros y de extensión \no para unirse a organizaciones de productores. Además, puede generar ingresos \ndirectamente si se alquila o se vende.\n\nTambién reconoce que la propiedad y/o el control de la tierra por parte de las \nmujeres es fundamental para la reducción de la pobreza, la seguridad alimentaria, la inclusión \ny los objetivos generales de desarrollo sostenible. Por último, la igualdad de género \nen la propiedad y el control de la tierra es un derecho humano. Por ejemplo, \nel Pacto Internacional de Derechos Civiles y Políticos (PIDCP) garantiza la igualdad \nentre mujeres y hombres y prohíbe la discriminación por motivos de sexo en su artículo 2.\n\nEl artículo 26 del PIDCP consagra la igualdad ante la ley y puede aplicarse para \ndefender el derecho de las mujeres a la no discriminación y la igualdad, incluidos \nlos derechos económicos y sociales. Además, la Convención sobre la eliminación de todas \nlas formas de discriminación contra la mujer (CEDAW) destaca que la discriminación \ncontra la mujer “viola los principios de igualdad de derechos y respeto a la dignidad humana”.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.a.2&seriesCode=SG_LGL_LNDFEMOD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Grado en que el marco jurídico (incluido el derecho consuetudinario) garantiza la igualdad de derechos de las mujeres a la propiedad y/o control de la tierra (1=Sin evidencia a 6=Niveles más altos de garantías) SG_LGL_LNDFEMOD</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-0A-02.pdf\">Metadatos 5-a-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Grado en que el marco jurídico garantiza la igualdad de derechos de la mujer a la propiedad o el control de tierras", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.a- Emprender reformas que otorguen a las mujeres igualdad de derechos a los recursos económicos, así como acceso a la propiedad y al control de la tierra y otros tipos de bienes, los servicios financieros, la herencia y los recursos naturales, de conformidad con las leyes nacionales", "definicion"=>"Extent to which the legal framework (including customary law) guarantees women's equal  rights to own or control land, measured by assessing six proxies. ", "formula"=>"\n\n$$MJDMPT^{t} = \\begin{cases} 1 & \\text{If none of the proxies are present in the legislation in the year 𝑡} \\\\ 2 & \\text{If one of the proxies is present in the legislation in the year 𝑡} \\\\ 3 & \\text{If two of the proxies are present in the legislation in the year 𝑡} \\\\ 4 & \\text{If three of the proxies are present in the legislation in the year 𝑡} \\\\ 5 & \\text{If four of the proxies are present in the legislation in the year 𝑡} \\\\ 6 & \\text{If five or six of the proxies are present in the legislation in the year 𝑡} \\end{cases} $$\n", "periodicidad"=>"Anual", "desagregacion"=>"", "observaciones"=>"\nThe indicator “measures” the level to which a country’s legal framework supports women’s land rights, by \ntesting that framework against six proxies drawn from international law and internationally accepted good \npractices, in particular the Convention on the Elimination of Discrimination Against Women (CEDAW) \nand the Voluntary Guidelines for the Responsible Governance of the Tenure of Land Fisheries and Forestry (VGGT):  \n- Proxy A: Joint registration of land is compulsory or encouraged through economic incentives \n- Proxy B: Compulsory spousal consent for land transactions \n- Proxy C: Women’s and girls’ equal inheritance rights \n- Proxy D: Allocation of financial resources to increase women’s ownership and control over land \n- Proxy E: In legal systems that recognize customary land tenure, the existence of explicit protection of the \nland rights of women\n- Proxy F: Mandatory quotas for women’s participation in land management and administration institutions\n", "justificacion_global"=>"\nIndicator 5.a.2 measures the extent to which countries’ legal framework (including customary law) \nguarantees women’s equal rights to land ownership and/or control. \n\nThe focus on land of Indicator 5.a.2 reflects the recognition that land is a key economic resource \ninextricably linked to access to, use of and control over other economic and productive resources. \nIt is a key input for agricultural production; it can facilitate access to financial and extension \nservices or to join producer organisations. Moreover, it can generate income directly if rented or sold. \n\nIt also acknowledges that women’s ownership of and/or control of land is critical for poverty reduction, \nfood security, inclusiveness and overall sustainable development objectives. Finally, gender equality \nin land ownership and control is a human right. For example, the International Covenant on Civil and \nPolitical Rights (ICCPR) guarantees equality between women and men, and prohibits discrimination based \non sex in Article 2. \n\nArticle 26 of the ICCPR enshrines equality before the law and can be applied to defend women’s right to \nnon-discrimination and equality, including economic and social rights. Further, the Convention on the \nElimination of Discrimination Against Women (CEDAW), emphasizes that discrimination against women \n“violates the principles of equality of rights and respect for human dignity\". \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.a.2&seriesCode=SG_LGL_LNDFEMOD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Degree to which the legal framework (including customary law) guarantees women’s equal rights to land ownership and/or control (1=No evidence to 6=Highest levels of guarantees) SG_LGL_LNDFEMOD</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-0A-02.pdf\">Metadata 5-a-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Grado en que el marco jurídico garantiza la igualdad de derechos de la mujer a la propiedad o el control de tierras", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.a- Emprender reformas que otorguen a las mujeres igualdad de derechos a los recursos económicos, así como acceso a la propiedad y al control de la tierra y otros tipos de bienes, los servicios financieros, la herencia y los recursos naturales, de conformidad con las leyes nacionales", "definicion"=>"Lege-esparruak (ohiturazko zuzenbidea barne) zenbateraino bermatzen duen lurren jabetza edo kontrolerako  emakumeen eskubide berdintasuna, sei proxyren ebaluazioaren bidez.", "formula"=>"\n\n$$MJDMPT^{t} = \\begin{cases} 1 & \\text{Proxyetako bat ere ez dago legerian 𝑡 urtean} \\\\ 2 & \\text{Proxyetako bat dago legerian 𝑡 urtean} \\\\ 3 & \\text{Proxyetako bi daude legerian 𝑡 urtean} \\\\ 4 & \\text{Proxyetako hiru daude legerian 𝑡 urtean} \\\\ 5 & \\text{Proxyetako lau daude legerian 𝑡 urtean} \\\\ 6 & \\text{Proxyetako bost edo sei daude legerian 𝑡 urtean} \\end{cases} $$\n", "periodicidad"=>"Anual", "desagregacion"=>"", "observaciones"=>"\nAdierazleak neurtzen du lege-esparruak zenbateraino bermatzen duen lurren jabetza edo kontrolerako \nemakumeen eskubide berdintasuna, lege-esparrua nazioartean onartutako sei proxyrekiko alderatuz; proxy horiek\nEmakumearen aurkako diskriminazio-modu guztiak ezabatzeari buruzko Konbentzioan eta Lurra edukitzeko \ngobernantza arduratsuari buruzko borondatezko jarraibideetan onartutakoak dira:\n - A proxya: Lurren nahitaezko edo sustatutako erregistro bateratua\n - B proxya: Lurren transakzioetarako ezkontidearen adostasunaren betebeharra\n - C proxya: Herentzia-eskubideen berdintasuna\n - D proxya: Emakumeen lurren jabetza eta kontrola areagotzeko finantza-baliabideen esleipena\n - E proxya: Ohiturazko eskubidea aitortzen duten legezko sistemetan, emakumeen jabetzarako eskubideen babes esplizitua egotea\n - F proxya: Nahitaezko kuotak lurren kudeaketan eta administrazioan  \n", "justificacion_global"=>"\n5.a.2 adierazleak neurtzen du herrialdeen esparru juridikoak (ohiturazko zuzenbidea barne) zenbateraino bermatzen \nduen emakumeen eskubide-berdintasuna lurraren jabetzaren eta kontrolaren arloan. \n\n5.a.2 adierazlearen lurreko ikuspegiak erakusten du lurra funtsezko baliabide ekonomikoa dela, eta beste baliabide \nekonomiko eta produktibo batzuk eskuratu, erabili eta kontrolatzeari lotuta dagoela. Nekazaritza-ekoizpenerako \nfuntsezko intsumoa da; finantza- eta hedapen-zerbitzuetarako sarbidea erraztu dezake, edo ekoizle-erakundeekin \nbat egiteko lagungarri izan. Gainera, zuzenean sor ditzake diru-sarrerak, alokatzen edo saltzen bada. \n\nOnartzen du, halaber, emakumeek lurraren jabetza eta kontrola izatea funtsezkoa dela pobreziaren murrizketa, \nelikadura-segurtasuna, inklusioa eta garapen iraunkorraren helburu orokorrak lortzeko. Azkenik, lurraren jabetzako \neta kontroleko genero-berdintasuna giza eskubide bat da. Adibidez, Eskubide Zibil eta Politikoen Nazioarteko Itunak \n(EZPNI) emakumeen eta gizonen arteko berdintasuna bermatzen du, eta 2. artikuluan sexuagatiko diskriminazioa debekatzen du. \n\nEZPNIren 26. artikuluak legearen aurreko berdintasuna berresten du, eta emakumeek diskriminaziorik ez izateko eta \nberdintasuna izateko duten eskubidea defendatzeko aplika daiteke, eskubide ekonomiko eta sozialak barne. Gainera, \nEmakumearen aurkako diskriminazio-mota guztiak ezabatzeko Konbentzioak (CEDAW) nabarmentzen duenez, emakumearen \naurkako diskriminazioak \"eskubide-berdintasunaren eta giza duintasunarekiko errespetuaren printzipioak urratzen ditu\". \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.a.2&seriesCode=SG_LGL_LNDFEMOD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Lege-esparruak (ohiturazko zuzenbidea barne) zenbateraino bermatzen duen lurren jabetza edo kontrolerako emakumeen eskubide berdintasuna (1 = ebidentziarik gabe 6 = berme-maila altuenak) SG_LGL_LNDFEMOD</a> UNSTATS ", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-0A-02.pdf\">Metadatuak 5-a-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.a: Undertake reforms to give women equal rights to economic resources, as well as access to ownership and control over land and other forms of property, financial services, inheritance and natural resources, in accordance with national laws</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.a.2: Proportion of countries where the legal framework (including customary law) guarantees women&#x2019;s equal rights to land ownership and/or control</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_LGL_LNDFEMOD - Degree to which the legal framework (including customary law) guarantees women&#x2019;s equal rights to land ownership and/or control (1=No evidence to 6=Highest levels of guarantees) [5.a.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Goal 1, specifically indicator 1.4.2, and Goal 5, specifically 5.a.1 and 5.1.1.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Indicator 5.a.2 assesses the extent to which the national legal frameworks (including customary law) guarantee women&#x2019;s equal rights to land ownership and/or control. </p>\n<p>The indicator &#x201C;measures&#x201D; the level to which a country&#x2019;s legal framework supports women&#x2019;s land rights, by testing that framework against six proxies drawn from international law and internationally accepted good practices , in particular the Convention on the Elimination of Discrimination Against Women (CEDAW) ratified by 189 countries, and the Voluntary Guidelines for the Responsible Governance of the Tenure of Land Fisheries and Forestry (VGGT) endorsed unanimously by Committee of Food Security (CFS) members in 2012.</p>\n<p>The six proxies through which indicator 5.a.2 is monitored are the following:</p>\n<p>Proxy A: Joint registration of land is compulsory or encouraged through economic incentives</p>\n<p>Proxy B: Compulsory spousal consent for land transactions</p>\n<p>Proxy C: Women&#x2019;s and girls&#x2019; equal inheritance rights</p>\n<p>Proxy D: Allocation of financial resources to increase women&#x2019;s ownership and control over land</p>\n<p>Proxy E: In legal systems that recognize customary land tenure, the existence of explicit protection of the land rights of women</p>\n<p>Proxy F: Mandatory quotas for women&#x2019;s participation in land management and administration institutions</p>\n<p><strong>Concepts:</strong></p>\n<p>The indicator tracks progress on legal reforms that guarantee women&#x2019;s land rights (including customary law) in terms of ownership and/or control. </p>\n<p>The customary dimension of the indicator is very important because in many contexts in which customary law prevails, women&#x2019;s land rights tend to be denied or insecure. However, the enormous diversity of customs and social norms that govern customary land among and within countries and their unwritten nature, create a significant challenge for assessing whether the proxies are present in these systems. Therefore, the customary dimension will only be considered in the case it has been incorporated the legal system. </p>\n<p>Finally, the indicator refers to ownership and/or control of land which are two critical but different dimensions of women&#x2019;s land rights. Land ownership refers to the legally recognized right to acquire, use and transfer land property, while control over land is associated with the ability to make decisions over land.</p>\n<p>Key definitions are the following:</p>\n<p><em>Land</em></p>\n<p>Land is defined as all immovable property &#x2013; for instance the house, the land upon which a house is built and land which is used for other purposes, such as agricultural production. It also encompasses any other structures built on land to meet permanent purposes. Legal frameworks commonly use the terms &#x2018;immovable property&#x2019; or &#x2018;real property&#x2019; when referring to land.</p>\n<p><em>Land ownership</em></p>\n<p>Land ownership is a legally recognized right to acquire, use and transfer land. In private property systems, this is a right akin to freehold tenure. In systems where land is owned by the state, the term &#x201C;land ownership&#x201D; refers to possession of the rights most akin to ownership in a private property system &#x2013; for instance, long-term leases, occupancy, tenancy or use rights granted by the state that are transferrable and are granted to users for several decades (for instance 99 years).</p>\n<p><em>Control over land</em></p>\n<p>Control over land is the ability to make decisions over land. It may include rights to make decisions about how the land should be used, including what crops should be planted, and to benefit financially from the sale of crops. </p>\n<p><em>Customary land tenure</em></p>\n<p>Customary land tenure is defined as the bodies of rules and institutions governing the way land and natural resources are held, managed, used and transacted within customary legal systems.</p>\n<p><em>Customary legal systems</em></p>\n<p>Customary legal systems are systems that exist at the local or community level, that have not been set up by the state, and that derive their legitimacy from the values and traditions of the indigenous or local group. Customary legal systems may or may not be recognized by national law. </p>\n<p><em>Legal and policy framework</em></p>\n<p>The legal and policy framework comprises a set of publicly available legal and policy instruments governing land and family matters in force when conducting the assessment, including the Constitution, primary - and secondary legislation and policies. It includes customary legal systems where they have been recognized by statutory law.</p>\n<p> </p>\n<p><em>Personal laws </em></p>\n<p>Personal law is defined as a set of codified rules and norms applying to a group of people sharing a common religious faith about personal matters. These laws usually cover family relations, marriage, and inheritance. The term can be used interchangeably with &#x2018;religious laws&#x2019;.</p>\n<p><em>Primary legislation </em></p>\n<p>Primary legislation refers to (i) acts or statutes that have been formally adopted at the national level following the official parliamentary procedure for the passage of laws (in parliamentary systems); (ii) other acts at the national level with the force of law, such as decree-laws and legislative decrees and otherwise (in parliamentary systems); (iii) other legal instruments that have been formally endorsed by a law-making body, for instance presidential and royal orders or presidential and royal decrees (in non-parliamentary systems or systems where law-making power lies in an additional institution to the parliament). In all cases, primary legislation must have the force of law, be binding. For this assessment primary legislation includes the Constitution. </p>\n<p><em>Secondary legislation</em></p>\n<p>Secondary legislation includes subsidiary, delegated or subordinate legal instruments that have the force of law, are binding, and shall not be in contradiction with primary legislation. They are usually passed by the executive, such as national regulations, rules, by-laws, determinations, directions, circulars, orders, and implementing decrees.</p>\n<p><em>Joint registration</em></p>\n<p>Joint registration is where the names of both spouses or both partners in an unmarried couple, are entered into the land registry as the owners or principal users of the land being registered. Joint registration signifies a form of shared tenure over the land &#x2013; usually either a joint tenancy/occupancy or a tenancy in common). In legal systems which include a framework for land titling, joint registration is commonly referred to as joint titling. </p>\n<p><em>Unmarried couples</em></p>\n<p>Unmarried couples are defined as couples who live together (cohabit) in an intimate relationship, but who are not married following the marriage law of the country. It refers to couples who were married under custom or religious laws, where such marriages are not recognized or do not comply with the requirements of the formal law. It may also refer to relationships that are recognized by the state but that are not considered a marriage &#x2013; for instance a civil partnership and a de facto relationship that is registered with the state. The term &#x2018;unmarried couples&#x2019; is often used interchangeably with &#x2018;de facto unions&#x2019;, &#x2018;consensual unions&#x2019; or &#x2018;irregular unions&#x2019;. The members of an unmarried couple are referred to as &#x2018;partners&#x2019;.</p>\n<p><em>Land transactions</em></p>\n<p>Land transactions for the methodology are major land transactions, specifically the sale and encumbrance (mortgage) of land. </p>\n<p><em>Inheritance</em></p>\n<p>Inheritance is defined as property passing at the owner&apos;s death to the heir or those entitled to succeed.</p>\n<p><em>Deceased&#x2019;s estate</em></p>\n<p>The deceased&#x2019;s estate encompasses the legal rights, interests and entitlements, to property of any kind (not only land) which the deceased spouse or partner enjoyed at the time of death, less any liabilities. Depending on the legal system, marital property may be excluded fully from the calculation of deceased&#x2019;s estate, or the deceased&#x2019;s 50% share in the marital property will be included. </p>\n<p><em>Equal inheritance rights for sons and daughters</em></p>\n<p>Equal inheritance rights for sons and daughters require the law on intestate inheritance to either be gender-neutral or provide for both an equal rank and equal shares in the inheritance for brothers and sisters (or daughters and sons).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>The proportion of countries where the legal framework (including customary law) guarantees women&#x2019;s equal rights to land ownership and/or control is the unit for measuring progress at the global and/or regional level. </p>\n<p>At the national level, it &#x2018;measures&#x2019; the extent to which the legal and policy framework protects women&#x2019;s land rights against the 6 proxies defined for monitoring SDG indicator 5.a.2. According to the number of proxies identified countries are classified in a band system ranging from 1=No evidence to 6=Highest levels of guarantees.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The 6 proxies are drawn from international law and internationally accepted good practices, in particular the Convention on the Elimination of Discrimination Against Women (CEDAW) ratified by 189 countries, and the Voluntary Guidelines for the Responsible Governance of the Tenure of Land Fisheries and Forestry (VGGT) endorsed unanimously by Committee of Food Security (CFS) members in 2012.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Sources of data for measuring Indicator 5.a.2 are the official versions of national policies, primary law and secondary legislation which must be publicly available. More specifically, the relevant laws include the following: land, family, marriage, inheritance, land registration, gender equality laws, constitution, agrarian reform. Relevant policies include land, agriculture and gender policies.</p>", "COLL_METHOD__GLOBAL"=>"<p>For the official reporting ONLY the proxies localized in <strong>the primary and/or secondary law</strong> will be reported because of their binding nature. The only exception to this rule is Proxy D where also national land/agrarian reform or titling programs are considered for the purpose of the assessment. However, for the meaningfulness of the assessment, relevant policies are considered for the analysis, but recorded only in the additional information section, because they represent the foundations of the law setting out the principles that indicate the direction towards which the country aims to move and very often suggest reforms that need to be adopted in the legal framework. In this sense, if the proxies are present in these types of instruments they constitute an important step towards a more gender sensitive legal framework.</p>\n<p>The data are extracted directly from the laws in force when the assessment is carried out. Data collection/provision entails the assessment of the relevant laws to determine if the six proxies are present or not in the legal framework. For proxies D and F, in case that no provisions are identified in the legal and policy framework, they can be considered equally present if <strong>official national statistics</strong> showing that at least 40 percent of <strong>those who own </strong>or <strong>have secure rights to</strong> land are <strong>women</strong>. This is because these proxies are associated with special temporary measures for ensuring equal women&#x2019;s and men&#x2019;s land ownership and/or control. </p>\n<p>Data will be compiled in an electronic questionnaire organized as follows:</p>\n<p><strong>Section 1: General Instructions </strong></p>\n<p>&#x2022;. Respondent Information</p>\n<p>&#x2022; Instructions for filling the questionnaire</p>\n<p><strong>Section 2: Legal Assessment</strong></p>\n<p>&#x2022;. Checklist of policy and legal instruments relevant for the assessment to guide the expert in the identification of the proxies in the policy and legal framework of the country analyzed.</p>\n<p>&#x2022;. Form 1 &#x201C;Policy and legal instruments, including provisions for Proxy (x)&#x201D;. This form is composed of a set of questions to be answered (Yes or No) to determine if the proxy is present. The details of the instruments containing the Proxy are to be provided in this form. </p>\n<p>&#x2022;. Form 2 &#x201C;Results of Assessment &#x2013; Proxy (x)&#x201D;. This form summarizes the results of the assessment for each proxy.</p>\n<p><strong>Section 3: Summary of the Assessment (Country Results)</strong></p>\n<p>To complete the indicator 5.a.2 assessment, national legal experts must examine the national legal and policy framework and complete the electronic questionnaire following the methodological guidelines. This involves three steps that must be repeated for each proxy. </p>\n<p>1. Collect all the relevant policy and legal documents, using the checklist contained in the questionnaire as a guide.</p>\n<p>2. Using the detailed methodological guidelines, determine whether the proxy exists in the legal and policy framework and in which instruments. </p>\n<p>3. Complete the questionnaire for each proxy, citing the instrument and the relevant provisions where the proxy was located in Form 1, and any relevant information or exception directly associated with the proxy in the additional information box (Form 2) such as policies and/or adopted bills. Include a hyperlink to the text of the legal and policy instrument. </p>\n<p>After these three steps have been undertaken for all six proxies the responsible national institution will identify the level of protection to women&#x2019;s land rights present in the legal framework according to the number of proxies located. </p>\n<p>The filled questionnaire will be communicated to FAO for the quality control and global reporting to the UN SDGs Secretariat.</p>", "FREQ_COLL__GLOBAL"=>"<p>As policy and law reforms usually take a long time, countries should report on this indicator only <strong>every four years</strong>. However, if countries that have already submitted their report experience legal reforms that change their scores, those countries should send to FAO an updated questionnaire with the revised assessment for quality control and re-classification in the band system.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>All countries are able to start reporting on the first year, as the source of data (the laws and policies in force when the assessment takes place) are publicly available in all of them and &#x201C;measuring&#x201D; the indicator is done by conducting a legal analysis. Moreover, the assessment can be conducted by a legal expert in a very short timeframe (about 15 days).</p>", "DATA_SOURCE__GLOBAL"=>"<p>Governments should nominate a national entity responsible for the process of monitoring and reporting on indicator 5.a.2. The designation of the responsible institution should be guided by nature of the information required in particular relevant provisions from land and family laws. In view of this, the most adequate national institutions that could be designated for having this responsibility are land related institutions (i.e. Ministries of Land or the national institution governing land matters), and/or the national gender institution (i.e. Gender Equality Commissions, Women&#xB4;s Affairs or Gender Ministries).</p>", "COMPILING_ORG__GLOBAL"=>"<p>FAO is responsible for compilation and reporting on this indicator at the global level. After checking and validating the results, the responsible national entity submits the questionnaire to FAO. Upon receipt of the questionnaire, FAO will undertake a quality check, and revert to the responsible national institution in case clarifications or revisions are needed. FAO will then compute the indicator based on the information supplied by countries and communicate the results to the UN SDGs Secretariat.</p>", "INST_MANDATE__GLOBAL"=>"<p>Article I of the FAO constitution requires that the Organization collects, analyses, interprets and disseminates information relating to nutrition, food and agriculture. </p>\n<p>http://www.fao.org/3/K8024E/K8024E.pdf</p>", "RATIONALE__GLOBAL"=>"<p>Indicator 5.a.2 measures the extent to which countries&#x2019; legal framework (including customary law) guarantees women&#x2019;s equal rights to land ownership and/or control.</p>\n<p>The focus on land of Indicator 5.a.2 reflects the recognition that land is a key economic resource inextricably linked to access to, use of and control over other economic and productive resources. It is a key input for agricultural production; it can facilitate access to financial and extension services or to join producer organisations. Moreover, it can generate income directly if rented or sold. It also acknowledges that women&#x2019;s ownership of and/or control of land is critical for poverty reduction, food security, inclusiveness and overall sustainable development objectives. Finally, gender equality in land ownership and control is a human right. For example, the International Covenant on Civil and Political Rights (ICCPR) guarantees equality between women and men, and prohibits discrimination based on sex in Article 2. Article 26 of the ICCPR enshrines equality before the law and can be applied to defend women&#x2019;s right to non-discrimination and equality, including economic and social rights. Further, the Convention on the Elimination of Discrimination Against Women (CEDAW), emphasizes that discrimination against women &#x201C;violates the principles of equality of rights and respect for human dignity&quot;.</p>\n<p>The following paragraphs describe the scope and rationale of the proxies, as well as their specific content. </p>\n<p>For guidance on the meaning of the terms used in the proxies please refer to the terminology in section 2.a &#x201C;Definitions and concepts&#x201D; of this document. For detailed information on the conditions determining whether the proxy exists in the legal framework please refer to the methodological guidelines &#x201C;Realizing women&#x2019;s rights to land in the law. A Guide for reporting on SDG Indicator 5.a.2&#x201D;.</p>\n<p><strong>Proxy A:</strong> Is the joint registration of land compulsory or encouraged through economic incentives?</p>\n<p>Without the inclusion of their names on the land title, deed or certificate, women&#x2019;s property rights remain insecure, especially in the context of land registration programs and property acquired by the spouses during the marriage. This is particularly the case for married women who separate, divorce, are abandoned, or become widows.</p>\n<p>The proxy therefore assesses whether the legal and policy framework includes provisions requiring joint registration of land or encouraging joint registration through economic incentives for both married and unmarried couples. For the proxy to be present it is sufficient that joint registration is provided at least for married couples.</p>\n<p><strong>Proxy B:</strong> Does the legal and policy framework require spousal consent for land transactions?</p>\n<p>Major land transactions, such as the sale, mortgage or lease of family land or the family home, can directly affect women&#x2019;s land rights if they do not participate in the decisions. Therefore, spousal or partner consent requirements for such transaction strengthen women&#x2019;s control rights over land by protecting them against unilateral actions taken by their husband or, in the case of unmarried couples, partner. Provisions that support equality in marriage relations and that provide for joint administration of matrimonial property including land, directly contribute to gender equality in the control over land. </p>\n<p>The proxy examines whether national laws provide for mandatory spouse or partner consent for land transactions. As with proxy A, the assessment covers both married and unmarried couples. Yet, for proxy B to be present it is sufficient that spousal consent is provided at least for married couples.</p>\n<p><strong>Proxy C:</strong> Does the legal and policy framework support women&#x2019;s and girls&#x2019; equal inheritance rights?</p>\n<p>Inheritance is one of the main channels through which women acquire property and secure independent land rights. However, the persistence of discriminatory cultural and legal norms often denies women&#x2019;s and girls&#x2019; equal inheritance rights and hinders women&#x2019;s opportunity to acquire property on an equal footing to men. Personal laws and customary laws, in particular, often deny women&#x2019;s right to inherit or to inherit in equal shares. However, many post-colonial governments have incorporated these rules in the formal legal architecture. In some cases, daughters may only be entitled to inherit in the absence of a traceable male relative. </p>\n<p>Proxy C examines the extent to which national laws on intestate inheritance establish equal inheritance rights for surviving children and the surviving spouse(s) regardless of sex.</p>\n<p>This proxy aims to identify if the legal and policy framework of a country provides that: </p>\n<p>1. Sons and daughters have equal inheritance rights and equal shares; and</p>\n<p>2. Male and female surviving spouse and/or partner are entitled to an equal right of the deceased spouse&#x2019;s estate and/or to a lifetime user right to the family home.</p>\n<p>The law must prescribe both equal inheritance rights for sons and daughters and for the surviving spouse and/or partner for Proxy to be present.</p>\n<p><strong>Proxy D:</strong> Does the legal and policy framework provide for the allocation of financial resources to increase women&#x2019;s ownership and control over land?</p>\n<p>Legal reforms to support gender equality in land ownership and/or control and access to other productive resources have not always translated into practice. The poor implementation of land and agriculture related policies and laws geared towards enhancing gender equality, is partially due to the lack or insufficiency of financial resources. </p>\n<p>For this reason, this proxy identifies any legal provision that commits the government to allocate financial resources to increase women&#x2019;s ownership and control over land or access to productive resources, including land. Such provisions are widely regarded as innovative measures to support women&#x2019;s land rights and have been consistently endorsed by the CEDAW Committee in its deliberations and comments on state parties&#x2019; reports under the treaty. For this proxy to be present, the fund must be anchored into a national law that explicitly mentions the purpose of improving women&#x2019;s land rights.</p>\n<p>Since Proxy D amounts to a &#x201C;special measure&#x201D;, as per Art. 4 of CEDAW, countries that do not include this measure in their legal framework, may provide official statistical data that show, nationally, at least 40 percent of those who own or have secure rights to land <strong>are women</strong> to satisfy the proxy.</p>\n<p><strong>Proxy E</strong>: In legal systems that recognize customary land tenure, does the legal and policy framework explicitly protect the land rights of women?</p>\n<p>Many countries have incorporated customary land tenure rights into the formal legal system, in effect &#x2018;formalizing&#x2019; them. The legal recognition of customary land tenure however may reinforce discriminatory practices where there is no explicit protection for women&#x2019;s customary land rights. Further, the use of gender-neutral provisions in the context of formalization of customary land tenure has in practice been associated with a lack of protection of women&#x2019;s rights. To avoid such outcomes explicit provisions protecting the land rights of women should accompany legal provisions recognizing customary land rights. </p>\n<p>Proxy E assesses whether the Constitution and/or any land related law that recognizes customary land tenure, explicitly protect s women&#x2019;s land rights. </p>\n<p>It is important to note that for those countries where customary law has not been incorporated into the legal framework, Proxy E is not applicable and will not be assessed in the computation. As noted above, the customary dimension of this indicator will only be considered when it has been legally recognized. </p>\n<p><strong>Proxy F:</strong> Does the legal and policy framework mandate women&#x2019;s participation in land management and administration institutions?</p>\n<p>Land related institutions are responsible for governing the land tenure systems and are in charge of land administration and management. Women are often excluded from participating in the day-to-day processes of land governance at all levels, and therefore have limited capacity to influence decision-making. A lack of women&#x2019;s representation in land governance tends to lead to biased outcomes in land recording and registration processes and the hindering of women&#x2019;s land claims, for instance by overlooking women&#x2019;s rights on common lands. </p>\n<p>Proxy F aims to identify provisions within the legal framework requiring mandatory participation of women (quotas) in land related management and administration institutions.</p>\n<p>Since Proxy F amounts to a &#x201C;special measure&#x201D;, as per Art. 4 of CEDAW, countries that do not include this measure in their legal and policy framework, yet provide official statistical data that show, nationally, at least 40 percent of those who own or have secure rights to land are women, will equally satisfy the proxy.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Customary law </p>\n<p>Since customary law is not a homogenous system of law, assessing whether it establishes equal rights to land ownership and/or control for women and men is very challenging. Therefore, the methodology determines that customary law will only be considered to the extent that it has been recognized in the legal framework. However, this also means that reporting data does not cover the legal systems where customary law has not been formalized but continues to govern family and land matters, possibly constituting a major factor of discrimination against women. Further, given that customary law does not exist in all countries, it is not universally applicable. The methodology has addressed this issue by creating a dual system of computation of the results, which is explained below in section 4. </p>\n<p>Geographical scope. </p>\n<p>The data collected for the SDG indicator 5.a.2 is collected at the national level to ensure that it adequately represents the national legal system. This means that the 5.a.2 assessment to determine the existence of the proxies should focus on legal and policy instruments that have nationwide authority. In countries where law-making power for land or gender matters does not reside with the central authority (or is shared between the national government and a sub-national government), the assessment may require analysing laws at state, provincial or county level. However, any research at sub-national level can only be undertaken <em>after </em>mapping and analysing the relevant provisions in the overarching legal framework at constitutional and federal level for a focused and efficient data collection process. </p>\n<p>In case the assessment requires data collection and data analysis at the sub-national level, a sample of the states, provinces or counties will be established, including the most populous states up until reaching 50 percent of the total country&#x2019;s population. Since the results must have nationwide authority, the proxy should be located in the laws of each state, province or county that is part of the sample. If it is not the case, the proxy is not present.</p>", "DATA_COMP__GLOBAL"=>"<p>The qualitative and legal nature of this indicator required the development of a nuanced and articulated methodology that could be feasible, universally relevant and meaningful. </p>\n<p>The computation of results under Indicator 5.a.2 involves two steps: (1) classification of the country according to the number of proxies located <strong>in primary or primary and secondary legislation</strong> and (2) consolidation of all country results for global reporting.</p>\n<h2>Step 1: Classification categories of country </h2>\n<p>The country will be classified according to the total number of proxies found in <u>primary legislation</u> or <u>primary and secondary legislation</u>. Given that <strong>not all countries</strong> recognize customary land tenure or customary law (related to proxy E), a dual approach for computing national results has been developed: </p>\n<ul>\n  <li>For countries where customary land tenure is <strong>NOT</strong> recognized in the legal framework (either via statute or the constitution), regardless of whether it exists <em>de facto </em>or not, Proxy E is marked <strong>non-applicable</strong> and the country will be assessed out of the five remaining proxies. </li>\n  <li>For countries where customary land tenure is recognized in the legal framework, the country will be assessed against all six proxies,</li>\n</ul>\n<p>The table below describes the dual approach for computing results and the classification bands. As is shown below, in countries where customary law is applicable (Proxy E) the presence of five or six proxies are included in the same band (band 6 - very high levels of guarantees). This is due to the necessity of making universal the calculation of the component of customary law, which is not universal and not always formalized in the legal system.</p>\n<p>Table 1: Classification Band System</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Result of assessment </strong></p>\n        <p><strong>Where Proxy E is <u>applicable</u></strong></p>\n      </td>\n      <td>\n        <p><strong>Result of assessment </strong></p>\n        <p><strong>Where Proxy E is <u>not</u> applicable</strong></p>\n      </td>\n      <td>\n        <p><strong>Classification </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><u>None of the six proxies</u> are present in the primary or primary and secondary legislation</p>\n      </td>\n      <td>\n        <p><u>None of the five proxies</u> are present in the primary or primary and secondary legislation</p>\n      </td>\n      <td>\n        <p><u>Band 1:</u> No evidence of guarantees of gender equality in the land ownership and/or control in the legal framework.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><u>One</u> of the proxies present in primary or primary and secondary legislation</p>\n      </td>\n      <td>\n        <p><u>One of the proxies</u> present in primary or primary and secondary legislation</p>\n      </td>\n      <td>\n        <p><u>Band 2:</u> Very low levels of guarantees of gender equality in land ownership and/or control in the legal framework.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><u>Two</u><strong><u> </u></strong><u>the proxies</u> present in primary or primary and secondary legislation</p>\n      </td>\n      <td>\n        <p><u>Two of the proxies</u> present in primary and secondary legislation</p>\n      </td>\n      <td>\n        <p><u>Band 3:</u> Low levels of guarantees of gender equality in land ownership and/or control in the legal framework.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><u>Three of the proxies</u> are present in primary legislation or primary and secondary legislation</p>\n      </td>\n      <td>\n        <p><u>Three of the proxies</u> are present in primary legislation or primary and secondary legislation</p>\n      </td>\n      <td>\n        <p><u>Band 4:</u> Medium levels of guarantees of gender equality in land ownership and/or control in the legal framework.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><u>Four of the proxies </u>are present in primary legislation or primary and secondary legislation</p>\n      </td>\n      <td>\n        <p><u>Four of the proxies</u> are present in primary legislation or primary and secondary legislation</p>\n      </td>\n      <td>\n        <p><u>Band 5:</u> High levels of guarantees of gender equality in land ownership and/or control in the legal framework.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><u>Five or six proxies</u> are present in primary legislation or primary and secondary legislation</p>\n      </td>\n      <td>\n        <p><u>All five proxies</u> are present in primary legislation or primary and secondary legislation</p>\n      </td>\n      <td>\n        <p><u>Band 6:</u> Very high levels of guarantees of gender equality in land ownership and/or control in the legal framework.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Under the methodology all proxies have an equal weight. This implies that no dimension is more important than another in terms of supporting gender equality in land ownership and/or control.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>As with all the SDG targets and indicators, the monitoring and reporting process for target 5.2 a is global in scope and country-led. </p>\n<p>FAO provides technical support to the designated focal point(s) and national legal expert to carry out the assessment and fill the questionnaire. To facilitate the process FAO also shares with them relevant materials, including the methodological guidelines &#x201C;Realizing women&#x2019;s rights to land in the law&#x201D; (https://www.fao.org/3/i8785en/I8785EN.pdf), the questionnaire and the e-learning platform (<a href=\"https://elearning.fao.org/course/view.php?id=364\">https://elearning.fao.org/course/view.php?id=364</a>). The key materials currently exist in English, French, Spanish, and Arabic. </p>\n<p>When the assessment has been finalized, the responsible institution submits the questionnaire to FAO for quality control to ensure that the assessment fulfil the criteria and thresholds established in the methodology. The reviewed questionnaire is sent back to the country for validation and official submission. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not Applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Although all UN countries are expected to report, this might not be the case. Different countries may report at different times and a non-negligible share of countries may choose not to report on the indicator at all during the reporting period creating missing values.</p>\n<p>The missing values will be treated in the following way:</p>\n<p>a) For countries, which have reported in only 1 period, FAO does not have information on whether they are making progress on the indicator. However, FAO can alleviate the problem with missing values. First, FAO can assume that there was no progress on the indicator over the reporting periods and keep the same results until a reviewed questionnaire is submitted. </p>\n<p>b) <strong>Not imputed</strong>. The only way to include countries that will never report is to cluster them in a category of missing information. This is because no assumption can be done regarding the status of each country&#x2019;s laws. However, it is important to keep track of the countries which do not report rather than limit the analysis to the reporting countries</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p><strong>Not imputed</strong>. The regional and global aggregates will be based solely on those countries for which data are available, but at no point will countries with missing data be treated as if they were the same as those for which data are available. The global or regional aggregates would be valid for the reporting countries but not necessarily for the region as whole or at the global level as a whole. Missing values for individual countries or areas cannot be imputed or estimated to derive regional or global aggregates of the indicator because no assumption can be done regarding the status of each country&#x2019;s laws.</p>", "REG_AGG__GLOBAL"=>"<p>The band classification system used at the country level illustrated in table 1 also applies for regional and global aggregates for this indicator. Once 50% of the countries of a particular region has officially reported, the mean/average score for an SDG region will be calculated without weighting national scores. The region will be classified into a particular band reflecting the extent to which the relevant national laws recognise and protect women&#x2019;s rights to land. The same applies to the global aggregation which will be calculated based on the unweighted regional average/mean score, once 50% of the regions have been classified in a particular band. </p>", "DOC_METHOD__GLOBAL"=>"<p> The Methodology used by countries for the compilation of the data at the national level (<a href=\"https://www.fao.org/3/i8785en/I8785EN.pdf\">https://www.fao.org/3/i8785en/I8785EN.pdf</a>) and the </p>\n<p> questionnaires provided to countries include guidance, definitions and instructions.</p>\n<ul>\n  <li>Technical support provided by FAO to the representatives and legal experts from the designated responsible institutions</li>\n  <li>E-learning available in the FAO learning academy <a href=\"https://elearning.fao.org/course/view.php?id=364\">https://elearning.fao.org/course/view.php?id=364</a></li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>This is a qualitative, legal indicator. Upon submission of the reporting questionnaire by the focal point in the responsible institution, FAO performs a quality assessment based on the methodology. This ensures that the reporting is carried out consistently across all reporting countries. During this quality review, FAO may provide methodological clarifications to ensure conformity with the methodological guidelines.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The assessment of laws is initially carried out by national counterparts, and legal practitioners in the relevant areas of law (land, land registration, land programmes, matrimonial property, inheritance, quota&#x2019;s ensuring women&#x2019;s participation in land administration and management bodies, for all types of land -including agrarian, customary, housing-). The data is checked and verified by the FAO. The data is then sent to the designated focal points/country counterparts to review and validate. Please refer to section 3 above on Data source type and data collection method for more details. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See section 4.d. on validation. The methodological guidelines are used to set criteria that are applied equally to all countries for the purposes of ensuring comparability across countries and regions.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Not applicable</p>\n<p><strong>Time series:</strong></p>\n<p>Not applicable</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Not applicable</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable.</p>", "OTHER_DOC__GLOBAL"=>"<p><a href=\"https://www.fao.org/3/i8785en/I8785EN.pdf\">https://www.fao.org/3/i8785en/I8785EN.pdf</a></p>\n<p><a href=\"http://www.fao.org/nr/tenure/voluntary-guidelines/en/\">http://www.fao.org/nr/tenure/voluntary-guidelines/en/</a> </p>\n<p><a href=\"http://www.un.org/womenwatch/daw/cedaw/\">http://www.un.org/womenwatch/daw/cedaw/</a> </p>\n<p><a href=\"http://www.fao.org/gender-landrights-database/en/\">http://www.fao.org/gender-landrights-database/en/</a> </p>", "indicator_sort_order"=>"05-0a-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"5.b.1", "slug"=>"5-b-1", "name"=>"Proporción de personas que poseen un teléfono móvil, desglosada por sexo", "url"=>"/site/es/5-b-1/", "sort"=>"05bb01", "goal_number"=>"5", "target_number"=>"5.b", "global"=>{"name"=>"Proporción de personas que poseen un teléfono móvil, desglosada por sexo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Sexo", "value"=>"Hombre"}, {"field"=>"Sexo", "value"=>"Mujer"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de personas entre 16 y 74 años que han usado el móvil en los últimos tres meses", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de personas que poseen un teléfono móvil, desglosada por sexo", "indicator_number"=>"5.b.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_15/opt_1/ti_encuesta-sobre-la-sociedad-de-la-informacion-familias/temas.html", "url_text"=>"Encuesta sobre la sociedad de la información. Familias", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de personas entre 16 y 74 años que han usado el móvil en los últimos tres meses", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.b- Mejorar el uso de la tecnología instrumental, en particular la tecnología de la información y las comunicaciones, para promover el empoderamiento de las mujeres", "definicion"=>"Proporción de personas entre 16 y 74 años que utilizan el teléfono móvil", "formula"=>"\n$$PPMOV_{16-74}^{t} = \\frac{PMOV_{16-74}^{t}}{P_{16-74}^{t}} \\cdot 100$$\n\ndonde:\n\n$PMOV_{16-74}^{t} =$ población entre 16 y 74 años que en los últimos tres meses ha utilizado el móvil en el año $t$\n\n$P_{16-74}^{t} =$ población entre 16 y 74 años en el año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nLas redes de telefonía móvil se han extendido rápidamente en la última década y el\n número de abonados a teléfonos móviles es casi igual al número de personas que viven en la \nTierra. Sin embargo, no todas las personas utilizan o poseen un teléfono móvil. La posesión de \nun teléfono móvil, en particular, es importante para el seguimiento de la igualdad \nde género, ya que el teléfono móvil es un dispositivo personal que, si se posee y no solo \nse comparte, proporciona a las mujeres un cierto grado de independencia y autonomía, \nincluso para fines profesionales. Varios estudios han destacado el vínculo entre la posesión \nde un teléfono móvil y el empoderamiento y el crecimiento de la productividad.\n\nLos datos existentes sobre la proporción de mujeres que poseen un teléfono móvil \nsugieren que menos mujeres que hombres poseen un teléfono móvil. Este indicador destaca la \nimportancia de la posesión de un teléfono móvil para el seguimiento y mejora de la igualdad de género, \ny el seguimiento ayudará a diseñar políticas específicas para superar la brecha de género.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.b.1&seriesCode=IT_MOB_OWN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Proporción de personas que poseen un teléfono móvil, por sexo (%) IT_MOB_OWN</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-0B-01.pdf\">Metadatos 5-b-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-25", "en"=>{"indicador_disponible"=>"Proporción de personas entre 16 y 74 años que han usado el móvil en los últimos tres meses", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.b- Mejorar el uso de la tecnología instrumental, en particular la tecnología de la información y las comunicaciones, para promover el empoderamiento de las mujeres", "definicion"=>"Proportion of people aged between 16 and 74 who use a mobile phone", "formula"=>"\n$$PPMOV_{16-74}^{t} = \\frac{PMOV_{16-74}^{t}}{P_{16-74}^{t}} \\cdot 100$$\n\nwhere:\n\n$PMOV_{16-74}^{t} =$ Proportion of people aged between 16 and 74 who have used a mobile phone in the last three months in year $t$\n\n$P_{16-74}^{t} =$ population aged between 16 and 74 in year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nMobile phone networks have spread rapidly over the last decade and the number \nof mobile-cellular subscriptions is quasi equal to the number of people living \non earth. However, not every person uses or owns a mobile-cellular telephone. \nMobile phone ownership, in particular, is important to track gender equality \nsince the mobile phone is a personal device that, if owned and not just shared, \nprovides women with a degree of independence and autonomy, including for professional \npurposes. Several studies have highlighted the link between mobile phone ownership and \nempowerment, and productivity growth. \n\nExisting data on the proportion of women owning a mobile phone suggest that fewer \nwomen than men own a mobile phone. This indicator highlights the importance of mobile \nphone ownership to track and improve gender equality, and monitoring will help design \ntargeted policies to overcome the gender divide. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.b.1&seriesCode=IT_MOB_OWN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Proportion of individuals who own a mobile telephone, by sex (%) IT_MOB_OWN</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-0B-01.pdf\">Metadata 5-b-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de personas entre 16 y 74 años que han usado el móvil en los últimos tres meses", "objetivo_global"=>"5- Lograr la igualdad de género y empoderar a todas las mujeres y las niñas", "meta_global"=>"5.b- Mejorar el uso de la tecnología instrumental, en particular la tecnología de la información y las comunicaciones, para promover el empoderamiento de las mujeres", "definicion"=>"Telefono mugikorra erabiltzen duten 16 eta 74 urte bitarteko pertsonen proportzioa", "formula"=>"\n$$PPMOV_{16-74}^{t} = \\frac{PMOV_{16-74}^{t}}{P_{16-74}^{t}} \\cdot 100$$\n\nnon:\n\n$PMOV_{16-74}^{t} =$ azken hiru hilabeteetan telefono mugikorra erabili duen 16-74 urteko biztanleria $t$ urtean\n\n$P_{16-74}^{t} =$ 16-74 urteko biztanleria $t$ urtean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nTelefonia mugikorreko sareak azkar hedatu dira azken hamarkadan, eta telefono mugikorretarako abonatuen kopurua \nLurrean bizi direnen kopuruaren ia berdina da. Hala ere, pertsona guztiek ez dute telefono mugikorrik edo ez dute \nhura erabiltzen. Telefono mugikor bat edukitzea bereziki garrantzitsua da genero-berdintasunaren jarraipena egiteko; \nizan ere, telefono mugikorra gailu pertsonala da, eta, norberak berea edukiz gero (partekatua izan gabe), nolabaiteko \nindependentzia eta autonomia ematen die emakumeei, baita helburu profesionaletarako ere. Hainbat ikerketak telefono \nmugikorra edukitzearen eta ahalduntzearen eta produktibitatearen hazkundearen arteko lotura nabarmendu dute. \n\nTelefono mugikorra duten emakumeen proportzioari buruzko datuen arabera, gizon baino emakume gutxiagok dute telefono \nmugikorra. Adierazle honek genero-berdintasunaren jarraipena egin eta egoera hobetzeko telefono mugikorra izateak \nduen garrantzia nabarmentzen du. Beraz, jarraipena egiteak genero-arrakala gainditzeko politika espezifikoak diseinatzen \nlagunduko du. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.b.1&seriesCode=IT_MOB_OWN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Telefono mugikorra duten pertsonen proportzioa, sexuaren arabera (%) IT_MOB_OWN</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-0B-01.pdf\">Metadatuak 5-b-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.b: Enhance the use of enabling technology, in particular information and communications technology, to promote the empowerment of women</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.b.1: Proportion of individuals who own a mobile telephone, by sex</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>IT_MOB_OWN - Proportion of individuals who own a mobile telephone [5.b.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>4.4.1, 9.c.1, 17.6.1, 17.8.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The proportion of individuals who own a mobile telephone, by sex is defined as the &#x2018;proportion of individuals who own a mobile telephone, by sex&#x2019;.</p>\n<p><strong>Concepts:</strong></p>\n<p>An individual owns a mobile cellular phone if he/she has a mobile cellular phone device with at least one active SIM card for personal use. Mobile cellular phones supplied by employers that can be used for personal reasons (to make personal calls, access the Internet, etc.) are included. Individuals who have only active SIM card(s) and not a mobile phone device are excluded. Individuals who have a mobile phone for personal use that is not registered under his/her name are also included. An active SIM card is a SIM card that has been used in the last three months.</p>\n<p>A mobile (cellular) telephone refers to a portable telephone subscribing to a public mobile telephone service using cellular technology, which provides access to the Public Switched Telephone Network (PSTN). This includes analogue and digital cellular systems and technologies such as IMT-2000 (3G) and IMT-Advanced. Users of both postpaid subscriptions and prepaid accounts are included.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) (of individuals)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>For countries that collect this data through an official survey, and if data allow breakdown and disaggregation, the indicator can be broken down by region (urban/rural), sex, age group, educational level (International Standard Classification of Education (ISCED) ), by labour force status (International Labour Organization (ILO)), and by occupation (International Standard Classification of Occupation (ISCO)). The International Telecommunication Union (ITU)) collects data for all these breakdowns from countries.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>This indicator is a newly developed International Telecommunication Union (ITU) indicator that was approved by the World Telecommunication/ICT Indicators Symposium (WTIS) in 2014. The indicator&#x2019;s definition and methodology were developed under the coordination of ITU, through its Expert Groups, and following an extensive consultation process with countries. Data for the proportion of individuals owning a mobile phone were first collected in 2015 through an annual questionnaire that ITU sends to National Statistical Offices (NSO). In this questionnaire, through which ITU already collects several ICT indicators, ITU collects absolute values. The percentages are calculated a-posteriori. The survey methodology is verified to ensure that it meets adequate statistical standards. The data are verified to ensure consistency with previous years&#x2019; data and other relevant country-level indicators (ICT and economic).</p>\n<p>Data are usually not adjusted, but discrepancies in the definition, age scope of individuals, reference period, or the break in comparability between years are noted in a data note. For this reason, data are not always strictly comparable.</p>", "COLL_METHOD__GLOBAL"=>"<p>The International Telecommunication Union (ITU) collects data on this indicator through an annual questionnaire that it sends to the heads of the National Statistical Offices (NSO). In this questionnaire, through which ITU already collects several ICT indicators, ITU collects absolute values. The percentages are calculated a-posteriori. The survey methodology is verified to ensure that it meets adequate statistical standards. The data are verified to ensure consistency with previous years&#x2019; data and other relevant country-level indicators (ICT and economic). </p>", "FREQ_COLL__GLOBAL"=>"<p>The data are collected using the ITU Short and Long ICT Household questionnaires. . Each survey has its own data collection cycle. The International Telecommunication Union (ITU) collects data twice a year from Member States, in Q1 using the short questionnaire and in Q3 using the long questionnaire.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released twice a year, In July and December, in the <a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/wtid.aspx\">Wor&#x200B;ld Telecommun&#x200B;ic&#x200B;ation/ICT Indicators Database&#x200B;</a> (WTID) and in the ITU DataHub&#x200B;, see https://datahub.itu.int/.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (NSOs).</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "INST_MANDATE__GLOBAL"=>"<p>As the UN specialized agency for information and communication technologies (ICTs), the International Telecommunication Union (ITU) is the official source for global ICT statistics, collecting ICT data from its Member States. </p>", "RATIONALE__GLOBAL"=>"<p>Mobile phone networks have spread rapidly over the last decade and the number of mobile-cellular subscriptions is quasi equal to the number of people living on earth. However, not every person uses or owns a mobile-cellular telephone. Mobile phone ownership, in particular, is important to track gender equality since the mobile phone is a personal device that, if owned and not just shared, provides women with a degree of independence and autonomy, including for professional purposes. Several studies have highlighted the link between mobile phone ownership and empowerment, and productivity growth.</p>\n<p>Existing data on the proportion of women owning a mobile phone suggest that fewer women than men own a mobile phone. This indicator highlights the importance of mobile phone ownership to track and improve gender equality, and monitoring will help design targeted policies to overcome the gender divide. The collection of this indicator was proposed by the Task Group on Gender of the Partnership on Measuring ICT for Development.</p>", "REC_USE_LIM__GLOBAL"=>"<p>While the data on the &#x2018;proportion of individuals who own a mobile telephone&#x2019; currently only exist for very few countries, ITU is encouraging all countries to collect data on this indicator through national household surveys and the indicator is expected to be added to the Partnership on Measuring ICT for Development&#x2019;s Core List of Indicators. The number of countries with official data for this indicator is expected to increase in the near future.</p>", "DATA_COMP__GLOBAL"=>"<p>Countries can collect data on this indicator through national household surveys. This indicator is calculated by dividing the total number of in-scope individuals who own a mobile phone by the total number of in-scope individuals.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>&amp;nbsp;</mi>\n    <mo>[</mo>\n    <mo>(</mo>\n    <mi>n</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>b</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mo>-</mo>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>v</mi>\n    <mi>i</mi>\n    <mi>d</mi>\n    <mi>u</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>w</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>g</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>m</mi>\n    <mi>o</mi>\n    <mi>b</mi>\n    <mi>i</mi>\n    <mi>l</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>h</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>e</mi>\n    <mo>)</mo>\n    <mi>&amp;nbsp;</mi>\n    <mo>/</mo>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>n</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>b</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mo>-</mo>\n    <mi>s</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>v</mi>\n    <mi>i</mi>\n    <mi>d</mi>\n    <mi>u</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>s</mi>\n    <mo>)</mo>\n    <mo>]</mo>\n    <mi>*</mi>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are submitted by Member States to The International Telecommunication Union (ITU). ITU checks and validates the data, in consultation with the Member States. </p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made to the data submitted by countries.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values are not estimated. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>In the absence of official household surveys, International Telecommunication Union (ITU) estimates the percentage of individuals owning mobile phones (owners of mobile phones as a percentage of total population) using various techniques, such as hot-deck imputation, regression models and time series forecast, using data such as Internet use, income, education and other ICT indicators.</p>", "REG_AGG__GLOBAL"=>"<p>Country-level data on the percentage of individuals owning mobile phones (owners of mobile phones as a percentage of total population) are first estimated using various techniques, such as hot-deck imputation, regression models and time series forecast. Hot-deck imputation uses data from countries with &#x201C;similar&#x201D; characteristics, such as GNI per capita and geographic location. In cases when it is not possible to find an adequate imputation based on similar cases, regression models are applied.</p>\n<p>Once the country-level percentages are available for all countries, the number of mobile phone owners are calculated by multiplying the percentages to the population of the country. The regional and world total mobile phone owners were calculated by summing the country-level data. The aggregate percentages were calculated by dividing the regional totals by the population of respective groups.</p>", "DOC_METHOD__GLOBAL"=>"<p>ITU Manual for Measuring ICT Access and Use by Households and Individuals 2020:</p>\n<p><a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx\">https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data are checked and validated by the ICT Data and Analytics (IDA) Division of the International Telecommunication Union (ITU). Countries are contacted to clarify and correct their submissions.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The guidelines of the Manual for Measuring ICT Access and Use by Households and Individuals 2020 are followed.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The guidelines of the Manual for Measuring ICT Access and Use by Households and Individuals 2020 are followed.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Overall, the indicator is available for more than 80 countries at least from one survey.</p>\n<p><strong>Time series:</strong></p>\n<p>2015 onwards</p>\n<p><strong>Disaggregation:</strong></p>\n<p>For countries that collect this indicator through a national household survey, and if data allow breakdown and disaggregation, the indicator can be broken down not only by sex but also by region (urban/rural), age group, educational level, labour force status, and occupation. Estimates of regional aggregates by sex are also calculated.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>None. The International Telecommunication Union (ITU) uses the data provided by countries, including the in-scope population that is used to calculate the percentages.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx\">http://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx</a> </p>\n<p><strong>References:</strong></p>\n<p>ITU Manual for Measuring ICT Access and Use by Households and Individuals 2020:</p>\n<p><a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx\">https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx</a> </p>", "indicator_sort_order"=>"05-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"5.c.1", "slug"=>"5-c-1", "name"=>"Proporción de países con sistemas para el seguimiento de la igualdad de género y el empoderamiento de las mujeres y la asignación de fondos públicos para ese fin", "url"=>"/site/es/5-c-1/", "sort"=>"05cc01", "goal_number"=>"5", "target_number"=>"5.c", "global"=>{"name"=>"Proporción de países con sistemas para el seguimiento de la igualdad de género y el empoderamiento de las mujeres y la asignación de fondos públicos para ese fin"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de países con sistemas para el seguimiento de la igualdad de género y el empoderamiento de las mujeres y la asignación de fondos públicos para ese fin", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de países con sistemas para el seguimiento de la igualdad de género y el empoderamiento de las mujeres y la asignación de fondos públicos para ese fin", "indicator_number"=>"5.c.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Una financiación adecuada y eficaz es esencial para alcanzar el ODS 5 y \nlas metas relacionadas con el género en todo el marco de los ODS. Al dar seguimiento \ny hacer públicas las asignaciones para la igualdad de género, los gobiernos promueven \nuna mayor transparencia, lo que puede respaldar una rendición de cuentas más sólida.\n\nEl indicador alienta a los gobiernos a implementar un sistema para dar seguimiento \ny hacer públicas las asignaciones de recursos que luego puedan fundamentar la \nrevisión de políticas, una mejor formulación de políticas y una gestión financiera \npública más eficaz. \n\nEl principio de financiación adecuada para la igualdad de \ngénero se basa en la Declaración y Plataforma de Acción de Beijing (párrafos 345 y 346), \nadoptadas en 1995. Sin embargo, el informe del Secretario General sobre la revisión y \nevaluación de veinte años de la Plataforma de Acción concluyó que la inversión insuficiente \nen igualdad de género y empoderamiento de las mujeres ha contribuido a un progreso \nlento y desigual en las 12 áreas críticas de preocupación. La financiación inadecuada \nobstaculiza la implementación de leyes y políticas con perspectiva de género. \n\nLos datos muestran que las brechas de financiación a veces alcanzan el 90%, con déficits \ncríticos en los sectores de infraestructura, productivo y económico.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.c.1&seriesCode=SG_GEN_EQPWN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de países con sistemas para hacer seguimiento y hacer públicas las asignaciones para la igualdad de género y el empoderamiento de las mujeres (%) SG_GEN_EQPWN</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-0c-01.pdf\">Metadatos 5-c-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-12", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Adequate and effective financing is essential to achieve SDG 5 and gender-related \ntargets across the SDG framework. By tracking and making public gender equality \nallocations, governments promote greater transparency which can support stronger \naccountability.\n\nThe indicator encourages governments to put in place a system to track and make public \nresource allocations which can then inform policy review, better policy formulation, \nand more effective public financial management. \n\nThe principle of adequate financing for gender equality is rooted in the Beijing \nDeclaration and Platform of Action (para 345 and 346) adopted in 1995. However, \nthe Secretary General’s report on the twentyyear review and appraisal of the Platform \nfor Action found that underinvestment in gender equality and women’s empowerment has \ncontributed to slow and uneven progress in all 12 critical areas of concern. \nInadequate financing hinders the implementation of gender-responsive laws and policies. \n\nData shows that financing gaps are sometimes as high as 90% with critical shortfalls \nin infrastructure, productive and economic sectors. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.c.1&seriesCode=SG_GEN_EQPWN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of countries with systems to track and make public allocations for gender equality and women's empowerment (%) SG_GEN_EQPWN</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-0c-01.pdf\">Metadata 5-c-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Finantzaketa egokia eta eraginkorra funtsezkoa da 5. GJHa eta GJHen esparru osoan generoarekin lotutako xedeak \nlortzeko. Genero-berdintasunerako esleipenen jarraipena egitean eta argitara ematean, gobernuek gardentasun \nhandiagoa sustatzen dute, eta horrek kontuak ematean sendotasun handiagoa eragin dezake. \n\nAdierazleak sistema bat ezartzera bultzatzen ditu gobernuak, gero politikak berrikusteko, politikak hobeto \nformulatzeko eta finantza-kudeaketa publiko eraginkorragoa egiteko oinarri izan daitezkeen baliabideen \nesleipenen jarraipena egiteko eta argitara emateko. \n\nGenero-berdintasunerako finantzaketa egokiaren printzipioa 1995ean onartutako Beijingko Adierazpen eta Ekintza \nPlataforman (345. eta 346. paragrafoak) oinarritzen da. Hala ere, Ekintza Plataformaren hogei urteko berrikuspenari \neta ebaluazioari buruzko idazkari nagusiaren txostenak ondorioztatu zuen genero-berdintasunean eta emakumeen \nahalduntzean egindako inbertsio eskasak aurrerapen motela eta desorekatua ekarri duela 12 kezka-arlo kritikoetan. \nFinantzaketa desegokiak oztopatu egiten du genero-ikuspegia duten legeak eta politikak ezartzea. \n\nDatuek erakusten dute finantzaketa-arrakalak batzuetan % 90era iristen direla, eta defizit kritikoak dituztela \nazpiegitura, produkzio eta ekonomiaren sektoreetan. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=5.c.1&seriesCode=SG_GEN_EQPWN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Genero-berdintasunerako eta emakumeen ahalduntzerako diru-esleipenen jarraipena egiteko eta horiek publiko egiteko sistemak dituzten herrialdeen proportzioa (%) SG_GEN_EQPWN</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-05-0c-01.pdf\">Metadatuak 5-c-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 5: Achieve gender equality and empower all women and girls</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 5.c: Adopt and strengthen sound policies and enforceable legislation for the promotion of gender equality and the empowerment of all women and girls at all levels</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 5.c.1: Proportion of countries with systems to track and make public allocations for gender equality and women&#x2019;s empowerment</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Applies to all series</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Entity for Gender Equality and the Empowerment of Women (UN Women) in collaboration with Organisation for Economic Co-operation and Development (OECD) and United Nations Development Programme (UNDP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Entity for Gender Equality and the Empowerment of Women (UN Women) in collaboration with Organisation for Economic Co-operation and Development (OECD) and United Nations Development Programme (UNDP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Sustainable Development Goal (SDG) Indicator 5.c.1 seeks to measure government efforts to track budget allocations for gender equality throughout the public finance management cycle and to make these publicly available. This is an indicator of the characteristics of the fiscal system. It is not an indicator of the quantity or quality of finance allocated for gender equality and women&#x2019;s empowerment (GEWE). The indicator measures three criteria. The first focuses on the intent of a government to address GEWE by identifying if it has programs/policies and resource allocations for GEWE. The second assesses if a government has planning and budget tools to track resources for GEWE throughout the public financial management cycle. The third focuses on transparency by identifying if a government has provisions to make allocations for GEWE publicly available.</p>\n<p>The indicator aims to encourage national governments to develop appropriate budget tracking and monitoring systems and commit to making information about allocations for gender equality readily available to the public. The system should be led by the Ministry of Finance in collaboration with the sectoral ministries and National Women&#x2019;s Machineries and overseen by an appropriate body such as Parliament or Public Auditors.</p>\n<h2>Concepts: </h2>\n<p>To determine if a country has a system to track and make public allocations for gender equality and women&#x2019;s empowerment, the following questionnaire is sent to its Ministry of Finance, or agency in charge of the government budget:</p>\n<p>Criterion 1. Which of the following aspects of public expenditure are reflected in your government programs and its resource allocations? (In the last completed fiscal year)</p>\n<p>Question 1.1. Are there policies and/or programs of the government designed to address well-identified gender equality goals, including those where gender equality is not the primary objective (such as public services, social protection, and infrastructure) but incorporates action to close gender gaps? (Yes=1/No=0)</p>\n<p>Question 1.2. Do these policies and/or programs have adequate resources allocated within the budget, sufficient to meet both their general objectives and their gender equality goals? (Yes=1/No=0)</p>\n<p>Question 1.3. Are there procedures in place to ensure that these resources are executed according to the budget? (Yes=1/No=0)</p>\n<p>Criterion 2. To what extent does your Public Financial Management system promote gender-related or gender-responsive goals? (In the last completed fiscal year)</p>\n<p>Question 2.1. Does the Ministry of Finance/budget office issue call circulars, or other such directives, that provide specific guidance on gender-responsive budget allocations? (Yes=1/No=0)</p>\n<p>Question 2.2. Are key policies and programs, proposed for inclusion in the budget, subject to an ex-ante gender impact assessment? (Yes=1/No=0)</p>\n<p>Question 2.3. Are sex-disaggregated statistics and data used across key policies and programs in a way which can inform budget-related policy decisions? (Yes=1/No=0)</p>\n<p>Question 2.4. Does the government provide, in the context of the budget, a clear statement of gender-related objectives (i.e. gender budget statement or gender responsive budget legislation)? (Yes=1/No=0)</p>\n<p>Question 2.5. Are budgetary allocations subject to &#x201C;tagging&#x201D; including by functional classifiers, to identify their linkage to gender-equality objectives? (Yes=1/No=0)</p>\n<p>Question 2.6. Are key policies and programs subject to ex-post gender impact assessment? (Yes=1/No=0)</p>\n<p>Question 2.7. Is the budget as a whole subject to independent audit to assess the extent to which it promotes gender-responsive policies? (Yes=1/No=0)</p>\n<p>Criterion 3. Are allocations for gender equality and women&#x2019;s empowerment made public? (In the last completed fiscal year)</p>\n<p>Question 3.1. Is the data on gender equality allocations published? (Yes=1/No=0)</p>\n<p>Question 3.2. If published, has this data been published in an accessible manner on the Ministry of Finance (or office responsible for budget) website and/or related official bulletins or public notices? (Yes=1/No=0)</p>\n<p>Question 3.3. If so, has the data on gender equality allocations been published in a timely manner? (Yes=1/No=0)</p>\n<h2>Concept Definitions:</h2>\n<h2><u>For Criterion 1:</u></h2>\n<ul>\n  <li>&#x201C;<strong>Programs or policies of the government, that are designed to address well-identified gender equality goals</strong>&#x201D; can be defined as:<ul>\n      <li>Programs or policies that specifically target only women and/or girls. For example, a government program that provides scholarships for girls only, or a prenatal care program, or a National Action Plan on Gender Equality; or</li>\n      <li>Programs or policies that target both women or girls and men or boys and have gender equality as the primary objective. For example, a national public information campaign against gender violence, or on-the-job training programs on gender equality; or</li>\n      <li>Programs or policies where gender equality is not the primary objective, but the program includes action to close gender gaps. These programs could include the provision of infrastructure, public services, and social protection. For example, an infrastructure program that has a provision for using women&#x2019;s labour, or a public transportation program that takes into consideration the mobility needs of women in its design. </li>\n    </ul>\n  </li>\n  <li>&#x201C;<strong>Programs or policies have</strong> <strong>adequate resources allocated within the budget</strong>, sufficient to meet both their general objectives and their gender equality goals&#x201D; can be defined as:<ul>\n      <li>The programs or policies that are designed to address well-identified gender equality goals are allocated sufficient resources to cover the costs of meeting those goals from funding that is included in the budget rather than from off-budget sources.</li>\n    </ul>\n  </li>\n  <li>&#x201C;<strong>Procedures in place to ensure that these resources are executed according to the budget</strong>&#x201D; can be defined as:<ul>\n      <li>There are procedures established in laws or regulations so that resources for programs or policies that are designed to address well-identified gender equality goals are executed as specified in the budget or if there are deviations in the exercise from the budgeted allocations, government agencies must justify to a supervising entity (e.g. ministries of finance, parliaments, audit bodies, or other relevant authorities) the reason for not executing resources according to budget.</li>\n    </ul>\n  </li>\n</ul>\n<h2><u>For Criterion 2:</u></h2>\n<ul>\n  <li>&#x201C;<strong>Call circulars</strong>&#x201D; can be defined as:<ul>\n      <li>Call circulars are the official notices that are issued by the Ministry of Finance or Budget Office in a country towards the beginning of each annual budget cycle. The circular instructs government agencies how they must submit their bids or demands for budget allocations for the coming year (in some countries the notice may have another name, such as budget guidelines or Treasury guidelines). It may inform each agency what its budget &#x201C;ceiling&#x201D; for the next fiscal year.<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></li>\n    </ul>\n  </li>\n  <li>&#x201C;<strong>Key programs and policies</strong>&#x201D; can be defined as:<ul>\n      <li>Programs or policies of the government, that are designed to address well-identified gender equality goals (as identified in Criterion 1).</li>\n    </ul>\n  </li>\n  <li>&#x201C;<strong>Ex-ante gender impact assessment</strong>&#x201D; can be defined as:<ul>\n      <li>Assessing individual resource allocations, in advance of their inclusion in the budget, specifically for their impact on gender equality.<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> For example, before its inclusion in the budget, there is an estimate of how a conditional cash transfer program will impact school attendance of girls.</li>\n    </ul>\n  </li>\n  <li>&#x201C;<strong>Sex-disaggregated statistics and data are available in a systematic manner across all key programs and policies</strong>&#x201D; can be defined as:<ul>\n      <li>There is routine availability of gender-specific data sets and statistics that would greatly facilitate the evidential basis for the identification of gender equality gaps, design of policy interventions, and the evaluation of impacts.<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></li>\n    </ul>\n  </li>\n  <li>&#x201C;<strong>Gender budget statements</strong>&#x201D; can be defined as:<ul>\n      <li>A document that, either as part of the budget documentation or separately, provides a clear statement of gender-related goals. It is a document produced by a government agency, usually, the Ministry of Finance or Budget Office, to show what its programs and budgets are doing in respect of gender. It is generally prepared after government agencies have completed the process of drawing up the budget and allocating resources to different programs in response to the annual call circular.<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></li>\n    </ul>\n  </li>\n  <li>&#x201C;<strong>Functional classifiers</strong>&#x201D; can be defined as<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup>:<ul>\n      <li>Categorization of expenditure according to the purposes and objectives for which they are intended. A functional classifier on gender would identify expenditure that goes to programs or activities that address gender issues.</li>\n    </ul>\n  </li>\n  <li>&#x201C;Ex-post gender impact assessment&#x201D; can be defined as:<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup>\n    <ul>\n      <li>Assessing individual resource allocations, after their implementation, specifically for their impact on gender equality. For example, once the resources are spent and the program executed, how did a conditional cash transfer program affect the school attendance rate of girls when compared to boys&#x2019; attendance rate?</li>\n    </ul>\n  </li>\n  <li>&#x201C;The budget as a whole is subject to independent audit, to assess the extent to which it promotes gender-responsive policies&#x201D; can be defined as:<ul>\n      <li>Independent, objective analysis, conducted by a competent authority different from the central budget authority, of the extent to which gender equality is effectively promoted and/or attained through the policies set out in the annual budget.<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup></li>\n    </ul>\n  </li>\n</ul>\n<h2><u>For Criterion 3:</u></h2>\n<ul>\n  <li>&#x201C;Published in an accessible manner&#x201D; can be defined as:<ul>\n      <li>Allocations for gender equality and women&#x2019;s empowerment are published on the Ministry of Finance (or office responsible for budget) website and/or related official bulletins or public notices in a way that is clearly signalled and/or made available in hard copies that are distributed to parliamentarians and NGOs.</li>\n    </ul>\n  </li>\n  <li>&#x201C;Published in a timely manner&#x201D; can be defined as:<ul>\n      <li>Allocations for gender equality and women&#x2019;s empowerment and/or its exercise are published in the same quarter as when approved/exercised.</li>\n    </ul>\n  </li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Ibid. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> &#x201C;Gender Budgeting in OECD Countries,&#x201D; OECD, 2016. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> Ibid. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> &#x201C;Budget Call Circulars and Gender Budget Statements in the Asia Pacific: A Review,&#x201D; UN Women, 2015. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> &#x201C;Budget Classification,&#x201D; IMF, 2009. <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> Ibid. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> Ibid. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) (Proportion of countries that have a system in place to track budget allocations to gender equality out of the total number of reporting countries)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p> Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>An electronic questionnaire composed of thirteen binary questions with accompanying monitoring guidance will be used to collect data on this indicator.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data collection is undertaken as part of the country-level monitoring of effective development cooperation where the Global Partnership monitoring framework provides a useful platform and mechanism. The Global Partnership monitoring is led by national coordinators appointed by their respective government to coordinate data collection and validation across relevant government ministries, departments, and agencies. Where countries are not reporting through the Global Partnership, efforts are made to expand country coverage by reaching out to national coordinators/focal points directly or through custodian/co-custodian country offices. </p>\n<p>For this indicator, the national coordinator/focal point will liaise with the Ministry of Finance, the Ministry of Women, and other relevant ministries to complete the questionnaire. UN Women&#x2019;s country office focal points will be available for support. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data collected every 3 years</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>First quarter, every 3 years</p>", "DATA_SOURCE__GLOBAL"=>"<p>Response to questionnaire completed by the Ministries of Finance&#x2014;as part of national statistical systems&#x2014;or Budget Office in coordination with National Statistical Offices and relevant sectoral ministries and national women&#x2019;s machineries.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UN Women, with UNDP and the OECD.</p>", "INST_MANDATE__GLOBAL"=>"<p>UN Women is committed through its work at the global, regional, and county level to support the Member States in filling critical gaps in generating and using data, statistics, evidence, and analysis on gender equality. As part of its triple mandate, UN Women supports the Member States in setting norms. UN Women also assists in implementing norms and standards through its country programmes. In addition, UN Women leads and coordinates the UN system&#x2019;s work in support of gender equality and the empowerment of women.</p>", "RATIONALE__GLOBAL"=>"<p>Adequate and effective financing is essential to achieve SDG 5 and gender-related targets across the SDG framework. By tracking and making public gender equality allocations, governments promote greater transparency which can support stronger accountability. The indicator encourages governments to put in place a system to track and make public resource allocations which can then inform policy review, better policy formulation, and more effective public financial management. </p>\n<p>The principle of adequate financing for gender equality is rooted in the Beijing Declaration and Platform of Action (para 345 and 346) adopted in 1995. However, the Secretary General&#x2019;s report on the twenty-year review and appraisal of the Platform for Action found that underinvestment in gender equality and women&#x2019;s empowerment has contributed to slow and uneven progress in all 12 critical areas of concern. Inadequate financing hinders the implementation of gender-responsive laws and policies. Data shows that financing gaps are sometimes as high as 90% with critical shortfalls in infrastructure, productive and economic sectors.</p>\n<p>The 2030 Agenda for Sustainable Development Agenda commits to a &#x201C;significant increase in investments to close the gender gap.&#x201D; Ensuring requisite resources for gender equality is central to implementing and achieving SDG 5 and all gender targets across the framework. Tracking these allocations and making the data publicly available are important steps to assessing progress towards meeting these goals. This has been reaffirmed at the Third International Conference on Financing for Development, where member states adopted the Addis Ababa Action Agenda which commits to tracking gender equality allocations and increasing transparency on public spending.<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup> Furthermore, the Commission on the Status of Women at its 60th session called upon states to support and institutionalize gender-responsive budgeting and tracking across all sectors of public expenditure to address gaps in resourcing for gender equality and the empowerment of women and girls.</p>\n<p>Indicator 5.c.1 measure the proportion of governments with systems to track and make public resource allocations for gender equality. It builds on Indicator 8 of the Global Partnership for Effective Development Co-operation (GPEDC) that has been piloted, tested, and rolled out in 81 countries. Indicator 8 allowed, for the first time, the systematic collection of data on government efforts to track resource allocations for gender equality across countries. Indicator 5.c.1 is defined in almost identical terms to Indicator 8 of the Global Partnership for Effective Development Co-operation (GPEDC). In addition, Indicator 5.c.1 is the only indicator in the SDG monitoring framework that links national budgeting systems with the implementation of legislation and policies for gender equality and women&#x2019;s empowerment.</p>\n<p>The refined methodology for Indicator 5.c.1 is an improvement over the original methodology for Indicator 8. The increased specificity of the criteria provides a greater level of detail and therefore, captures the variability in countries&#x2019; gender equality policies and public financial management systems. The application of a tiered scoring approach with specific thresholds increases the indicator&#x2019;s rigor and gives incentive to countries to improve these systems over time. </p>\n<p></p>\n<p>Further, it is envisaged that the Organisation for Economic Co-operation and Development (OECD) Survey of Budget Practices and Procedures, conducted regularly among OECD countries, will be modified, and updated to align closely with Indicator 5.c.1. This will allow greater global coverage by strengthening the indicator&#x2019;s relevance to ministries of finance in OECD countries.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> Addis Ababa Action Agenda paragraphs 30 and 53. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator does not measure allocation of resources but the existence of mechanisms to track resource allocations and that make that information available publicly. However, there is an optional question in the questionnaire (not scored) that requests countries to report the percentage of the government budget allocated for gender equality programs. </p>\n<p>Another limitation is that the indicator, which is process oriented, does not provide data on the adequacy or quality of resource allocations.</p>", "DATA_COMP__GLOBAL"=>"<p>Data is collected via a questionnaire comprising 13 binary (Yes/No) questions to assess whether a country has a system in place to track and make public allocations for gender equality and women&#x2019;s empowerment.</p>\n<p><strong>Scoring:</strong></p>\n<p>Each criterion is weighted equally. A country would need to satisfy the threshold of &#x201C;yes&#x201D; responses per criterion. A country will be considered to satisfy each criterion as follows: </p>\n<table>\n  <tbody>\n    <tr>\n      <td></td>\n      <td>\n        <p><strong>Requirements per criterion</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A country will satisfy Criterion 1</p>\n      </td>\n      <td>\n        <p>if it answers &#x201C;Yes&#x201D; to 2 out of 3 questions in Criterion 1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A country will satisfy Criterion 2</p>\n      </td>\n      <td>\n        <p>if it answers &#x201C;Yes&#x201D; to 4 out of 7 questions in Criterion 2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A country will satisfy Criterion 3</p>\n      </td>\n      <td>\n        <p>if it answers &#x201C;Yes&#x201D; to 2 out of 3 questions in Criterion 3</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Countries then will be classified as &#x2018;fully meets requirements&#x2019;, &#x2018;approaches requirements&#x2019;, and &#x2018;does not meet requirements&#x2019; per the following matrices (There are 8 possible combinations of criteria being satisfied, Cases A-G below):</p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"4\">\n        <p><strong>Fully meets requirements</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong>Criterion 1</strong></p>\n      </td>\n      <td>\n        <p><strong>Criterion 2</strong></p>\n      </td>\n      <td>\n        <p><strong>Criterion 3</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Case A</strong></p>\n      </td>\n      <td>\n        <p><strong>&#xFC;</strong></p>\n      </td>\n      <td>\n        <p><strong>&#xFC;</strong></p>\n      </td>\n      <td>\n        <p><strong>&#xFC;</strong></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Note</strong>: &#x201C;Checked&#x201D; boxes represent satisfied criteria; </p>\n<p>&#x201C;unchecked&#x201D; boxes represent unsatisfied criteria.</p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"4\">\n        <p><strong>Approaches requirements</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong>Criterion 1</strong></p>\n      </td>\n      <td>\n        <p><strong>Criterion 2</strong></p>\n      </td>\n      <td>\n        <p><strong>Criterion 3</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Case B</strong></p>\n      </td>\n      <td>\n        <p><strong>&#xFC;</strong></p>\n      </td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Case C</strong></p>\n      </td>\n      <td></td>\n      <td>\n        <p><strong>&#xFC;</strong></p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Case D</strong></p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p><strong>&#xFC;</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Case E</strong></p>\n      </td>\n      <td>\n        <p><strong>&#xFC;</strong></p>\n      </td>\n      <td>\n        <p><strong>&#xFC;</strong></p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Case F</strong></p>\n      </td>\n      <td>\n        <p><strong>&#xFC;</strong></p>\n      </td>\n      <td></td>\n      <td>\n        <p><strong>&#xFC;</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Case G</strong></p>\n      </td>\n      <td></td>\n      <td>\n        <p><strong>&#xFC;</strong></p>\n      </td>\n      <td>\n        <p><strong>&#xFC;</strong></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Note</strong>: &#x201C;Checked&#x201D; boxes represent satisfied criteria; </p>\n<p>&#x201C;unchecked&#x201D; boxes represent unsatisfied criteria.</p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"4\">\n        <p><strong>Does not meet requirements</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong>Criterion 1</strong></p>\n      </td>\n      <td>\n        <p><strong>Criterion 2</strong></p>\n      </td>\n      <td>\n        <p><strong>Criterion 3</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Case H</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Note</strong>: &#x201C;Checked&#x201D; boxes represent satisfied criteria; </p>\n<p>&#x201C;unchecked&#x201D; boxes represent unsatisfied criteria.</p>\n<p>Because the three criteria are equally important, a country would need to satisfy the three to fully meet requirements.</p>\n<p>The method of computation is as follows: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mn>5</mn>\n    <mo>.</mo>\n    <mi>c</mi>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>N</mi>\n            <mi>u</mi>\n            <mi>m</mi>\n            <mi>b</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mi>r</mi>\n            <mi>i</mi>\n            <mi>e</mi>\n            <mi>s</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>t</mi>\n            <mi>h</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold-italic\">f</mi>\n            <mi mathvariant=\"bold-italic\">u</mi>\n            <mi mathvariant=\"bold-italic\">l</mi>\n            <mi mathvariant=\"bold-italic\">l</mi>\n            <mi mathvariant=\"bold-italic\">y</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>m</mi>\n            <mi>e</mi>\n            <mi>e</mi>\n            <mi>t</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>q</mi>\n            <mi>u</mi>\n            <mi>i</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>m</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mi>s</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n          </mrow>\n        </mfenced>\n        <mo>&#xD7;</mo>\n        <mn>100</mn>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<h2>Unit:</h2>\n<p>Percent (%)</p>\n<h2>Disaggregation: </h2>\n<ol>\n  <li>In addition to reporting Indicator 5.c.1 as described above; the following two country classification global proportions will also be reported:</li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>N</mi>\n            <mi>u</mi>\n            <mi>m</mi>\n            <mi>b</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mi>r</mi>\n            <mi>i</mi>\n            <mi>e</mi>\n            <mi>s</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>t</mi>\n            <mi>h</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold-italic\">d</mi>\n            <mi mathvariant=\"bold-italic\">o</mi>\n            <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold-italic\">n</mi>\n            <mi mathvariant=\"bold-italic\">o</mi>\n            <mi mathvariant=\"bold-italic\">t</mi>\n            <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold-italic\">m</mi>\n            <mi mathvariant=\"bold-italic\">e</mi>\n            <mi mathvariant=\"bold-italic\">e</mi>\n            <mi mathvariant=\"bold-italic\">t</mi>\n            <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>q</mi>\n            <mi>u</mi>\n            <mi>i</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>m</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mi>s</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n          </mrow>\n        </mfenced>\n        <mo>&#xD7;</mo>\n        <mn>100</mn>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>N</mi>\n            <mi>u</mi>\n            <mi>m</mi>\n            <mi>b</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mi>r</mi>\n            <mi>i</mi>\n            <mi>e</mi>\n            <mi>s</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>t</mi>\n            <mi>h</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold-italic\">a</mi>\n            <mi mathvariant=\"bold-italic\">p</mi>\n            <mi mathvariant=\"bold-italic\">p</mi>\n            <mi mathvariant=\"bold-italic\">r</mi>\n            <mi mathvariant=\"bold-italic\">o</mi>\n            <mi mathvariant=\"bold-italic\">a</mi>\n            <mi mathvariant=\"bold-italic\">c</mi>\n            <mi mathvariant=\"bold-italic\">h</mi>\n            <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n            <mi>a</mi>\n            <mi>p</mi>\n            <mi>p</mi>\n            <mi>r</mi>\n            <mi>o</mi>\n            <mi>a</mi>\n            <mi>c</mi>\n            <mi>h</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>q</mi>\n            <mi>u</mi>\n            <mi>i</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>m</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mi>s</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n          </mrow>\n        </mfenced>\n        <mo>&#xD7;</mo>\n        <mn>100</mn>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<ol>\n  <li>Additional disaggregation by region as follows:</li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>N</mi>\n            <mi>u</mi>\n            <mi>m</mi>\n            <mi>b</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mi>r</mi>\n            <mi>i</mi>\n            <mi>e</mi>\n            <mi>s</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold-italic\">r</mi>\n            <mi mathvariant=\"bold-italic\">e</mi>\n            <mi mathvariant=\"bold-italic\">g</mi>\n            <mi mathvariant=\"bold-italic\">i</mi>\n            <mi mathvariant=\"bold-italic\">o</mi>\n            <mi mathvariant=\"bold-italic\">n</mi>\n            <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold-italic\">x</mi>\n            <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n            <mi>w</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>h</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mi>r</mi>\n            <mi>y</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold-italic\">c</mi>\n            <mi mathvariant=\"bold-italic\">l</mi>\n            <mi mathvariant=\"bold-italic\">a</mi>\n            <mi mathvariant=\"bold-italic\">s</mi>\n            <mi mathvariant=\"bold-italic\">s</mi>\n            <mi mathvariant=\"bold-italic\">i</mi>\n            <mi mathvariant=\"bold-italic\">f</mi>\n            <mi mathvariant=\"bold-italic\">i</mi>\n            <mi mathvariant=\"bold-italic\">c</mi>\n            <mi mathvariant=\"bold-italic\">a</mi>\n            <mi mathvariant=\"bold-italic\">t</mi>\n            <mi mathvariant=\"bold-italic\">i</mi>\n            <mi mathvariant=\"bold-italic\">o</mi>\n            <mi mathvariant=\"bold-italic\">n</mi>\n            <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold\">y</mi>\n            <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n          </mrow>\n        </mfenced>\n        <mo>&#xD7;</mo>\n        <mn>100</mn>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">r</mi>\n        <mi mathvariant=\"bold-italic\">e</mi>\n        <mi mathvariant=\"bold-italic\">g</mi>\n        <mi mathvariant=\"bold-italic\">i</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">n</mi>\n        <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">x</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where <strong><em>x</em></strong> refers to the region of analysis and <strong><em>y</em></strong> refers to the country classification based on the questionnaire.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Guidance and instructions for reporting on the indicator recommend coordination between the Ministry of Finance, national women&#x2019;s machineries and/or national statistical institution. The validation process is led by country governments, in-line with existing standards and mechanisms. UN Women, as lead custodian, supports validation through review of questionnaire submissions and direct follow-up with government focal points. Further, qualitative data is requested to support the validation of &#x2018;yes&#x2019; responses by a country. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not Imputed</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not Imputed</p>", "REG_AGG__GLOBAL"=>"<p>Global aggregates are weighted averages of all the sub-regions that make up the world. Regional aggregates are weighted averages of all the countries within the region.</p>\n<p>Country-level data are updated on a periodic basis. Where data are not updated, the last reported year may be used for the global and/or regional aggregates. </p>", "DOC_METHOD__GLOBAL"=>"<p>Methodology used by countries for the compilation of the data at the national level: questionnaire with monitoring guidance that includes definitions and instructions.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p> See 4.d on validation</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>See 4.d on validation</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p> See 4.d on validation</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>As identified in the pilot exercise for Indicator 5.c.1, the information that is collected through administering the questionnaire is readily available by Ministries of Finance and/or Budget Offices.</p>\n<p><strong>Time series:</strong></p>\n<p>First release of data was 2019</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Not applicable</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Since data is reported by countries via a validated questionnaire, there should be no discrepancies.</p>", "OTHER_DOC__GLOBAL"=>"<p>The Sustainable Development Goals Report 2022 (<a href=\"https://unstats.un.org/sdgs/report/2022/\">Glossy Report</a>; <a href=\"https://unstats.un.org/sdgs/report/2022/extended-report/\">Extended Report</a>; <a href=\"https://unstats.un.org/sdgs/gender-snapshot/2022/\">Gender Snapshot 2022</a>)</p>\n<p>Organisation for Economic Co-operation and Development (OECD) and UN Women (2021). Gender responsive COVID-19 recovery. https://www.oecd-ilibrary.org/development/gender-responsive-covid-19-recovery_edb0172d-en</p>\n<p>Global Partnership for Effective Development Corporation: <a href=\"https://effectivecooperation.org/4thMonitoringRound\">https://effectivecooperation.org/4thMonitoringRound</a> </p>\n<p>Technical materials on how to incorporate gender equality into public finance management systems: <a href=\"http://gender-financing.unwomen.org/en\">http://gender-financing.unwomen.org/en</a></p>\n<p>IMF research on gender responsive budgeting and tracking systems: <a href=\"https://www.imf.org/external/np/res/dfidimf/topic7.htm\">https://www.imf.org/external/np/res/dfidimf/topic7.htm</a> </p>\n<p><a href=\"https://www.imf.org/external/pubs/ft/wp/2016/wp16149.pdf\">https://www.imf.org/external/pubs/ft/wp/2016/wp16149.pdf</a></p>\n<p>Gender budgeting and tracking in OECD countries:</p>\n<p><a href=\"https://www.imf.org/external/pubs/ft/wp/2016/wp16149.pdf\">https://www.imf.org/external/pubs/ft/wp/2016/wp16149.pdf</a></p>\n<p><a href=\"https://www.oecd.org/gender/Gender-Budgeting-in-OECD-countries.pdf\">https://www.oecd.org/gender/Gender-Budgeting-in-OECD-countries.pdf</a></p>", "indicator_sort_order"=>"05-0c-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"6.1.1", "slug"=>"6-1-1", "name"=>"Proporción de la población que utiliza servicios de suministro de agua potable gestionados sin riesgos", "url"=>"/site/es/6-1-1/", "sort"=>"060101", "goal_number"=>"6", "target_number"=>"6.1", "global"=>{"name"=>"Proporción de la población que utiliza servicios de suministro de agua potable gestionados sin riesgos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"related indicators", "embedded_feature_title"=>"", "embedded_feature_url"=>"https://eustat-des.github.io/site/embeded/6-1-1", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población que utiliza servicios de suministro de agua potable gestionados sin riesgos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población que utiliza servicios de suministro de agua potable gestionados sin riesgos", "indicator_number"=>"6.1.1", "national_geographical_coverage"=>"", "page_content"=>"La calidad del agua distribuida, los niveles máximos de contaminantes y la frecuencia mínima de análisis han sido establecidas en el ámbito de la Unión Europea (Directiva UE 2020/2184), y han sido transpuestas a la legislación española (Real Decreto 3/2023). Los sistemas de abastecimiento como la calidad del agua están estrechamente vigilados por los titulares de los abastecimientos a través de las unidades de control y vigilancia (UCV), y por el Departamento de Salud.", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Gobierno Vasco", "periodicity"=>"Anual", "url"=>"https://beta.euskadi.eus/informacion/el-abastecimiento-de-agua-en-la-capv/web01-a3aguas/es/", "url_text"=>"Sistema de Información de las Aguas de Consumo (EKUIS)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de la población que utiliza servicios de suministro de agua potable gestionados sin riesgos", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.1- De aquí a 2030, lograr el acceso universal y equitativo al agua potable a un precio asequible para todos", "definicion"=>"La proporción de la población que utiliza servicios de agua potable gestionados \nde forma segura se define como la proporción de la población que utiliza una \nfuente de agua potable mejorada a la que se puede acceder en el lugar, \nestá disponible cuando se la necesita y está libre de contaminación fecal y \nquímica. \n\nLas fuentes de agua potable mejoradas incluyen: suministro \npor tuberías, pozos perforados y entubados, pozos excavados protegidos, \nmanantiales protegidos, agua de lluvia, quioscos de agua y agua envasada y \nentregada a domicilio.\n", "formula"=>"\n$$PPC^{t} = \\frac{PC^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$PC^{t} =$ población que utiliza servicios de agua potable gestionados \nde forma segura en el año $t$\n\n$P{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nEl acceso al agua potable es esencial para la buena salud, el bienestar y \nla productividad y es ampliamente reconocido como un derecho humano. El \nagua potable puede estar contaminada con heces humanas o animales que \ncontienen patógenos o con contaminantes químicos y físicos, lo que \nproduce efectos nocivos para la salud. \n\nSi bien mejorar la calidad del agua es fundamental para prevenir \nla transmisión de muchas enfermedades (como la diarrea, que exacerba la \nmalnutrición y sigue siendo una de las principales causas mundiales de \nmuerte infantil), mejorar la accesibilidad y disponibilidad del agua potable \nes igualmente importante para la salud y el bienestar, en particular para \nlas mujeres y las niñas, que a menudo son las principales responsables de \nrecoger agua potable de fuentes distantes.\n\nEl indicador 6.1.1 de los ODS está diseñado para abordar la gestión segura \nde los servicios de agua potable, incluidas las dimensiones de \naccesibilidad, disponibilidad y calidad.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.1.1&seriesCode=SH_H2O_SAFE&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">Proporción de la población que utiliza servicios de agua potable gestionados de forma segura(%) SH_H2O_SAFE</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-01-01.pdf\">Metadatos 6-1-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-27", "en"=>{"indicador_disponible"=>"Proporción de la población que utiliza servicios de suministro de agua potable gestionados sin riesgos", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.1- De aquí a 2030, lograr el acceso universal y equitativo al agua potable a un precio asequible para todos", "definicion"=>"The proportion of the population using safely managed drinking \nwater services is defined as the proportion of population using \nan improved drinking water source which is accessible on premises, \navailable when needed and free from faecal and priority chemical \ncontamination. \n\n\"Improved\" drinking water sources include: piped supplies, boreholes \nand tubewells, protected dug wells, protected springs, rainwater, water \nkiosks, and packaged and delivered water. \n", "formula"=>"\n$$PPC^{t} = \\frac{PC^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$PC^{t} =$ population using safely managed drinking water services in the year $t$\n\n$P{t} =$ population on July 1 of year $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nAccess to safe drinking water is essential for good health, welfare \nand productivity and is widely recognized as a human right. Drinking \nwater may be contaminated with human or animal faeces containing pathogens \nor with chemical and physical contaminants, leading to harmful effects on \nhealth. \n\nWhile improving water quality is critical to prevent the transmission of \nmany diseases (such as diarrhoea which exacerbates malnutrition and remains \na leading global cause of child deaths), improving the accessibility and \navailability of drinking water is equally important for health and welfare, \nparticularly for women and girls who often bear the primary responsibility \nfor collecting drinking water from distant sources. \n\nSDG indicator 6.1.1 is designed to address safe management of drinking water \nservices, including dimensions of accessibility, availability and quality. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.1.1&seriesCode=SH_H2O_SAFE&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">Proportion of population using safely managed drinking water services (%) SH_H2O_SAFE</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-01-01.pdf\">Metadata 6-1-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de la población que utiliza servicios de suministro de agua potable gestionados sin riesgos", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.1- De aquí a 2030, lograr el acceso universal y equitativo al agua potable a un precio asequible para todos", "definicion"=>"Modu seguruan kudeatutako edateko uraren zerbitzuak erabiltzen dituzten biztanleen proportzioa \nhonela definitzen da: edateko uraren iturri hobetua, irisgarria, behar denean eskuragarri dagoena \neta kutsadura fekal eta kimikorik ez duena erabiltzen duten biztanleen proportzioa \n\nHonako hauek dira edateko uraren iturri hobetuak: hodien bidezko hornidura, zulatu eta \nhodiratutako putzuak, zulatutako putzu babestuak, iturburu babestuak, euri-ura, ur-kioskoak eta \nontziratutako eta etxera eramandako ura.\n", "formula"=>"\n$$PPC^{t} = \\frac{PC^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$PC^{t} =$ modu seguruan kudeatutako edateko uraren zerbitzuak erabiltzen dituzten biztanleak $t$ urtean \n\n$P{t} =$ biztanleria $t$ urteko uztailaren 1ean \n", "desagregacion"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nEdateko ura eskuratzea funtsezkoa da osasun onerako, ongizaterako eta produktibitaterako, eta giza eskubidetzat \nhartzen da. Edateko ura patogenoak dituzten gizakien edo animalien gorozkiekin edo kutsatzaile kimiko eta fisikoekin \nkutsatuta egon daiteke, eta horrek osasunerako ondorio kaltegarriak eragiten ditu. \n\nNahiz eta uraren kalitatea hobetzea funtsezkoa den gaixotasun askoren transmisioa prebenitzeko (beherakoa, esaterako, \nmalnutrizioa areagotzen duena eta haurren heriotzaren munduko kausa nagusietako bat izaten jarraitzen duena), edateko \nuraren eskuragarritasuna eta irisgarritasuna hobetzea ere garrantzitsua da osasunerako eta ongizaterako, bereziki \nemakumeentzat eta neskentzat, horiek baitira sarritan urruneko iturrietatik edateko ura biltzearen erantzule nagusiak. \n\nGJHen 6.1.1 adierazlea edateko uraren zerbitzuen kudeaketa seguruari ekiteko diseinatuta dago, irisgarritasunaren, \nerabilgarritasunaren eta kalitatearen dimentsioak barne. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.1.1&seriesCode=SH_H2O_SAFE&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">Modu seguruan kudeatutako edateko uraren zerbitzuak erabiltzen dituzten biztanleen proportzioa (%) SH_H2O_SAFE</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-01-01.pdf\">Metadatuak 6-1-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 6: Ensure availability and sustainable management of water and sanitation for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 6.1: By 2030, achieve universal and equitable access to safe and affordable drinking water for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 6.1.1: Proportion of population using safely managed drinking water services</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_H2O_SAFE - Proportion of population using safely managed drinking water services [6.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>All targets under Goal 6, as well as targets 1.2, 1.4, 2.2, 3.2, 3.8, 3.9, 4a, 5.4 and 11.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>\n<p>United Nations Children&apos;s Fund (UNICEF)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>\n<p>United Nations Children&apos;s Fund (UNICEF)</p>\n<p>WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The proportion of the population using safely managed drinking water services is defined as the proportion of population using an improved drinking water source which is accessible on premises, available when needed and free from faecal and priority chemical contamination. &#x2018;Improved&#x2019; drinking water sources include: piped supplies, boreholes and tubewells, protected dug wells, protected springs, rainwater, water kiosks, and packaged and delivered water. </p>\n<p><strong>Concepts:</strong></p>\n<p>The term &#x2018;drinking water source&#x2019; refers to the point where people collect water for drinking and not the origin of the water supplied. For example, water collected from a distribution network that draws water from a surface water reservoir would be classified as piped water, while water collected directly from a lake or river would be classified as surface water.</p>\n<p>&#x2018;Improved&#x2019; drinking water sources include the following: piped water, boreholes or tubewells, protected dug wells, protected springs, rainwater, water kiosks, and packaged or delivered water. </p>\n<p>&#x2018;Unimproved&#x2019; drinking water sources include: unprotected dug wells, unprotected springs, and surface water (rivers, reservoirs, lakes, ponds, streams, canals, and irrigation channels), all of which are by nature of their design and construction unlikely to deliver safe water.</p>\n<p>A water source is &#x2018;accessible on premises&#x2019; if the point of collection is within the dwelling, compound, yard or plot, or water is delivered to the household.</p>\n<p>Drinking water is &#x2018;available when needed&#x2019; if households report having &#x2018;sufficient&#x2019; water, or water is available &#x2018;most of the time&#x2019; (i.e. at least 12 hours per day or 4 days per week).</p>\n<p>&#x2018;Free from faecal and priority chemical contamination&#x2019; requires that drinking water meets international standards for microbiological and chemical water quality specified in the WHO Guidelines for Drinking Water Quality. For the purposes of global monitoring the priority indicator of microbiological contamination is <em>E. coli</em> (or thermotolerant coliforms), and the priority chemical contaminants are arsenic and fluoride.</p>\n<p>For detailed guidance on water quality, please refer to the most recent version of the WHO Guidelines for drinking water quality:</p>\n<p><a href=\"https://www.who.int/teams/environment-climate-change-and-health/water-sanitation-and-health/water-safety-and-quality/drinking-water-quality-guidelines\">https://www.who.int/teams/environment-climate-change-and-health/water-sanitation-and-health/water-safety-and-quality/drinking-water-quality-guidelines</a> </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) &#x2013; Proportion of population</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) has established international standards for classification of drinking water facilities and service levels to benchmark and compare progress across countries (see <a href=\"https://washdata.org/\">https://washdata.org/</a>).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data sources included in the WHO/UNICEF Joint Monitoring Programme (JMP) database are: </p>\n<ul>\n  <li>Censuses, which in principle collect basic data from all people living within a country and are led by national statistical offices. </li>\n  <li>Household surveys, which collect data from a subset of households. These may target national, rural, or urban populations, or more limited project or sub-national areas. An appropriate sample design is necessary for survey results to be representative, and surveys are often led by or reviewed and approved by national statistical organizations. </li>\n  <li>Administrative data, which may consist of information collected by government or non-government entities involved in the delivery or oversight of services. Examples include water and sanitation inventories and databases, and reports of regulators. </li>\n  <li>Other datasets may be available such as compilations by international or regional initiatives (e.g. Eurostat), studies conducted by research institutes, or technical advice received during country consultations.</li>\n</ul>\n<p>Access to water, sanitation and hygiene are considered core socio-economic and health indicators, as well as key determinants of child survival, maternal, and children&#x2019;s health, family wellbeing, and economic productivity. Drinking water, sanitation and hygiene facilities are also used in constructing wealth quintiles used by many integrated household surveys to analyse inequalities between rich and poor. Access to drinking water, sanitation and hygiene are therefore core indicators for many household surveys and censuses. In high-income countries where household surveys or censuses do not collect detailed information on the types of facilities used by households, the JMP relies on administrative records.</p>\n<p>Data on availability and quality of drinking water are currently available from both household surveys and from government departments responsible for drinking water supply and regulators. In many low- and middle-income countries, existing water quality data from regulatory authorities is limited, especially for rural areas and populations using non-piped supplies. To complement regulatory data, an increasing number of low- and middle-income countries are collecting nationally representative data on drinking water quality through multi-topic household surveys. Beginning in 2012, a water quality module was developed standardized by the JMP in collaboration with UNICEF&#x2019;s Multiple Indicator Cluster Survey (MICS) programme. Integration of water quality testing has become a feasible option due to the increased availability of affordable and accurate testing procedures and their adaptation for use by household survey experts. The growing interest in ensuring the implementation of water quality testing in these surveys can, to a large extent, be attributed to the incorporation of drinking water quality in the SDG global indicator for &#x2018;safely managed drinking water services&#x2019;. Data gaps will be reduced even more as regulation becomes more widespread in low- and middle-income countries.</p>\n<p>Some datasets available to the JMP are not representative of national, rural or urban populations, or may be representative of only a subset of these populations (e.g. the population using piped water supplies). The JMP enters datasets into the global database when they represent at least 20% of the national, urban or rural populations. However, datasets representing less than 80% of the relevant population, or which are considered unreliable or inconsistent with other datasets covering similar populations, are not used in the production of estimates (see section 2.6, Data Acceptance in JMP Methodology: 2017 update and SDG baselines).</p>\n<p>The population data used by the JMP, including the proportion of the population living in urban and rural areas, are those routinely updated by the UN Population Division (World Population Prospects: <a href=\"https://population.un.org/wpp/\">https://population.un.org/wpp/</a>; World Urbanization Projects: <a href=\"https://population.un.org/wup\">https://population.un.org/wup</a>)).</p>", "COLL_METHOD__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme (JMP) conducts regular data searches by systematically visiting the websites of national statistical offices, and key sector institutions such as ministries of water and sanitation, regulators of drinking water and sanitation services, etc. Other regional and global databases are also reviewed for new datasets. UNICEF and WHO regional and country offices provide support to identify newly available household surveys, censuses and administrative datasets.</p>\n<p>Before publishing, all JMP estimates undergo rigorous country consultations facilitated by WHO and UNICEF country offices. Often these consultations give rise to in-country visits or virtual meetings about data on drinking water, sanitation and hygiene services and the monitoring systems that collect these data. </p>", "FREQ_COLL__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme (JMP) begins its biennial data collection cycle in October of even years and publishes estimates during the following year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The SDG Progress Report and relevant data are published every two years since the publication of the baseline report in 2017, usually between March and July of odd years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National statistics offices; ministries of water, health, and environment; regulators of drinking water service providers.</p>", "COMPILING_ORG__GLOBAL"=>"<p>WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP)</p>", "INST_MANDATE__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme (JMP) was established in 1990 to monitor global progress on drinking water, sanitation and hygiene (see <a href=\"https://washdata.org/\">https://washdata.org/</a>).</p>", "RATIONALE__GLOBAL"=>"<p>Access to safe drinking water is essential for good health, welfare and productivity and is widely recognized as a human right. Drinking water may be contaminated with human or animal faeces containing pathogens or with chemical and physical contaminants, leading to harmful effects on health. While improving water quality is critical to prevent the transmission of many diseases (such as diarrhoea which exacerbates malnutrition and remains a leading global cause of child deaths), improving the accessibility and availability of drinking water is equally important for health and welfare, particularly for women and girls who often bear the primary responsibility for collecting drinking water from distant sources. </p>\n<p>The WHO/UNICEF Joint Monitoring Programme (JMP) uses a simple improved/unimproved facility type classification that has been refined over time. &#x2018;Improved&#x2019; water sources are those that have the potential to deliver safe water by nature of their design and construction, and this metric was used beginning in 2000 to track progress towards MDG target 7c. International consultations since 2011 have established consensus on the need to build on and address the shortcomings of this indicator; specifically, to address normative criteria of the human rights to water and sanitation (UN General Assembly Resolution A/RES/64/292) and concluded that global monitoring should go beyond the basic level of access. As a result, the SDG indicator 6.1.1 is designed to address safe management of drinking water services, including dimensions of accessibility, availability and quality.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data are widely available on the type and location of drinking water sources used by households. Data on availability and safety of drinking water are increasingly available through a combination of household surveys and administrative sources including regulators, but definitions have yet to be standardized. The WHO/UNICEF Joint Monitoring Programme (JMP) has been collaborating with international survey programmes (such as the UNICEF Multiple Indicator Cluster Survey programme) and national survey programmes to develop standardized questions that address the SDG criteria for service levels, as well as a module for testing water quality in household surveys. The JMP gives high importance to extending these collaborations to reduce data gaps, ensure consistency and to progressively improve the quality and comparability of data used for national, regional and global estimates.</p>", "DATA_COMP__GLOBAL"=>"<p>The production of estimates follows a consistent series of steps, which are explained in this and following sections: </p>\n<p>1. Identification of appropriate national datasets </p>\n<p>2. Extraction of data from national datasets into harmonized tables of data inputs </p>\n<p>3. Use of the data inputs to model country estimates </p>\n<p>4. Consultation with countries to review the estimates </p>\n<p>5. Aggregation of country estimates to create regional and global estimates</p>\n<p>The WHO/UNICEF Joint Monitoring Programme (JMP) compiles national data on drinking water from a wide range of different data sources. Household surveys and censuses provide information on types of drinking water sources, and also indicate if sources are accessible on premises. These data sources often have information on the availability of water and increasingly on the quality of water at the household level, through direct testing of drinking water for faecal or chemical contamination. These data are combined with data on availability and compliance with drinking water quality standards (faecal and chemical) from administrative reporting or regulatory bodies. </p>\n<p>The JMP uses original microdata to produce its own tabulations by using populations weights (or household weights multiplied by de jure household size), where possible. However, in many cases microdata are not readily accessible so relevant data are transcribed from reports available in various formats (PDFs, Word files, Excel spreadsheets, etc.) if data are tabulated for the proportion of the population, or household/dwelling. National data from each country, area, or territory are recorded in the JMP country files, with water, sanitation, and hygiene data recorded on separate sheets. Country files can be downloaded from the JMP website: <a href=\"https://washdata.org/data/downloads\"></a>https://washdata.org/data/downloads</p>\n<p>The JMP calculates the proportion of population using improved water sources by fitting a linear regression line to all available data inputs within the reference period, starting from the year 2000. To calculate the proportion of the population using safely managed drinking water services, three ratios must be calculated: the proportion of the population using improved water supplies which are accessible on premises, have water available when needed, and are free from contamination. Those ratios are then multiplied with the proportion of the population using improved water sources, respectively. Safely managed drinking water services is taken as the minimum of these three indicators for any given year. National estimates are generated as weighted averages of the separate estimates for urban and rural areas, using population data from the most recent report of the United Nations Population Division.</p>\n<p>For more details on JMP rules and methods, please refer to recent JMP progress reports and &#x201C;JMP Methodology: 2017 update and SDG baselines&#x201D;: <a href=\"https://washdata.org/reports/jmp-2017-methodology\">https://washdata.org/reports/jmp-2017-methodology</a> </p>", "DATA_VALIDATION__GLOBAL"=>"<p>Every two years the WHO/UNICEF Joint Monitoring Programme (JMP) updates its global databases to incorporate the latest available national data for the global SDG indicators. National authorities are consulted on the estimates generated from national data sources through a country consultation process facilitated by WHO and UNICEF country offices. The country consultation aims to engage national statistical offices and other relevant national stakeholders to review the draft estimates and provide technical feedback to the JMP team.</p>\n<p>The purpose of the consultation is not to compare JMP and national estimates of drinking water, sanitation and hygiene (WASH) coverage but rather to review the completeness or correctness of the datasets in the JMP country file and to verify the interpretation of national data in the JMP estimates. The JMP provides detailed guidance to facilitate country consultation on the estimates contained in JMP country files. The consultation focusses on three main questions:</p>\n<ol>\n  <li>Is the country file missing any relevant national sources of data that would allow for better estimates?</li>\n  <li>Are the data sources listed considered reliable and suitable for use as official national statistics?</li>\n  <li>Is the JMP interpretation and classification of the data extracted from national sources accurate and appropriate?</li>\n</ol>\n<p>The JMP estimates are circulated for a 2 month period of consultation with national authorities starting in the fourth quarter of the year prior to publication (see <a href=\"https://washdata.org/how-we-work/jmp-country-consultation\">https://washdata.org/how-we-work/jmp-country-consultation</a>).</p>", "ADJUSTMENT__GLOBAL"=>"<p>See 4.c. Method of computation.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>The WHO/UNICEF Joint Monitoring Programme (JMP) method uses a simple regression model to generate time series estimates for all years including for years without data points. The JMP then shares all its estimates using its country consultation mechanism to get consensus from countries before publishing its estimates.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Regional and global estimates for individual elements of safely managed services are calculated provided (non-imputed) data are available for at least 30% of the population using improved drinking water sources within the region. In order to produce estimates for regional or global levels, imputed estimates are produced for countries lacking data. Imputed country estimates are not published and only used for aggregation. </p>\n<p>In the 2021 and earlier updates, regional population-weighted averages of M49 subregions were used to impute missing values (For the lists of M49 regions and sub-regions see <a href=\"https://unstats.un.org/unsd/methodology/m49/overview/\">https://unstats.un.org/unsd/methodology/m49/overview/</a>). Since the 2023 update, an iterative approach has been applied to all variables: </p>\n<ol>\n  <li>If any estimates were available within an M49 subregion, the subregion average was used for imputation. </li>\n  <li>If estimates were available at the regional but not subregion level, the M49 regional average was used. </li>\n  <li>If no estimates were available for any country or territory in the M49 region, the global average was used for imputation.</li>\n</ol>", "REG_AGG__GLOBAL"=>"<p>For safely managed drinking water services, the proportions of the regional population using improved drinking water sources that are accessible on premises, available when needed and free from contamination are calculated as weighted averages amongst populations using improved drinking water sources. The resulting ratios are multiplied by the proportion of the population using improved drinking water sources in each region. Following the approach taken for countries, the proportion of the population using safely managed drinking water services is then calculated at regional and global levels by taking a minimum of the three elements, or of two elements if either accessibility or availability is missing. These proportions are calculated separately for urban and rural areas and, where possible, a weighted average is made of rural and urban populations to produce total estimates for the region or world.</p>\n<p>Regional aggregates are generated for various regions, including SDG regional groupings, Landlocked Developing Countries (LLDCs), Least Developed Countries (LDCs), Small Island Developing States (SIDs), OECD fragile contexts, and World Bank income groupings (see <a href=\"https://washdata.org/data/country/REG/household/download\">https://washdata.org/data/country/REG/household/download</a>). In addition, WHO/UNICEF Joint Monitoring Programme (JMP) produces regional snapshots to provide detailed analysis within regions (See JMP 2023 Regional snapshots for WASH in households: <a href=\"https://washdata.org/how-we-work/country-and-regional-engagement/regional-analysis-2023-household-update\">https://washdata.org/how-we-work/country-and-regional-engagement/regional-analysis-2023-household-update</a>).</p>\n<p>For more details on JMP rules and methods: JMP Methodology: 2017 update and SDG baselines:</p>\n<p><a href=\"https://washdata.org/reports/jmp-2017-methodology\">https://washdata.org/reports/jmp-2017-methodology</a></p>\n<p>JMP 2023 WASH in Households Report &#x2013; Annex 1: </p>\n<p>Progress on household drinking water, sanitation and hygiene 2000-2022: special focus on gender</p>\n<p><a href=\"https://washdata.org/reports/jmp-2023-wash-households\">https://washdata.org/reports/jmp-2023-wash-households</a></p>", "DOC_METHOD__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme (JMP) has published guidance on core questions and indicators for monitoring drinking water, sanitation and hygiene (WASH) in households, schools and health care facilities (see <a href=\"https://washdata.org/monitoring/methods/core-questions\">https://washdata.org/monitoring/methods/core-questions</a>) and provides technical support through WHO and UNICEF regional and country offices to strengthen national monitoring of SDG indicators relating to drinking water, sanitation and hygiene. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme (JMP) has been instrumental in developing global norms to benchmark progress on drinking water, sanitation and hygiene, and has produced regular updates on country, regional, and global trends. The JMP regularly convenes expert task forces to provide technical advice on specific issues and methodological challenges related to drinking water, sanitation and hygiene (WASH) monitoring. WHO and UNICEF have also established a Strategic Advisory Group (SAG) to provide independent advice on the continued development of the JMP as a trusted custodian of global WASH data (see <a href=\"https://washdata.org/how-we-work/about-jmp\">https://washdata.org/how-we-work/about-jmp</a>). </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>National statistical offices are primarily responsible for assuring the quality of national data sources. A key objective of WHO/UNICEF Joint Monitoring Programme (JMP) country consultations is to establish whether data sources are considered reliable and suitable for use as official national statistics. The JMP has established criteria for acceptance of national data sources based on representativeness, quality and comparability. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See quality assurance. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>As of July 2023, national estimates could be produced for 142 countries, areas and territories, , and covering 51% of the global population. Estimates were available for rural areas in countries representing 64% of the global rural population, and for urban areas in countries representing 63% of the global urban population.</p>\n<p><strong>Time series:</strong></p>\n<p>Time series data are available for the basic level of drinking water service since 2000. These serve as the foundation for the safely managed drinking water service indicator. Some elements of safe management (e.g. water quality) were not collected during the MDG period (from 2000 to 2015) and for some countries and regions trend analysis is not possible for all years from 2000 to 2022.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregation by geographic location (urban/rural, sub-national regions, etc.) and by socioeconomic characteristics (wealth, education, ethnicity, etc) is possible in a growing number of countries. Drinking water services can also be disaggregated by service level (i.e. no services/surface water, unimproved, limited, basic, and safely managed services). Disaggregated data are more widely available for basic and lower levels of service than for safely managed services.</p>\n<p>Disaggregation by individual characteristics (e.g. age, sex, disability, etc.) may also be made where data permit. Many of the datasets used for producing estimates are household surveys and censuses which collect information on drinking water at the household level. Such data cannot be disaggregated to provide information on intra-household variability (e.g. differential use of services by gender, age, or disability). The WHO/UNICEF Joint Monitoring Programme (JMP) seeks to highlight individual datasets which do allow assessment of intra-household variability, but these are not numerous enough to integrate into the main indicators estimated in JMP reports.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The WHO/UNICEF Joint Monitoring Programme (JMP) estimates are based on national sources of data approved as official statistics. Differences between global and national figures arise due to differences in indicator definitions and methods used in calculating national coverage estimates. In some cases, national estimates are based on the most recent data point rather than from regression on all data points as done by the JMP. In order to generate national estimates, the JMP uses data that are representative of urban and rural populations and UN population estimates and projections (UN DESA World Population Prospects: <a href=\"https://population.un.org/wpp/\">https://population.un.org/wpp/</a>; World Urbanization Projects: <a href=\"https://population.un.org/wup\">https://population.un.org/wup</a>) which may differ from national population estimates. </p>", "OTHER_DOC__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) Website: <a href=\"https://www.washdata.org/\">https://www.washdata.org/</a></p>\n<p>JMP Data: <a href=\"https://washdata.org/data\">https://washdata.org/data</a></p>\n<p>JMP Reports: <a href=\"https://washdata.org/reports\">https://washdata.org/reports</a></p>\n<p>JMP Methods: <a href=\"https://washdata.org/monitoring/methods\">https://washdata.org/monitoring/methods</a></p>\n<p>JMP Country and inequalities files: <a href=\"https://washdata.org/data/downloads#\">https://washdata.org/data/downloads#</a></p>\n<p>JMP Regional snapshots: <a href=\"https://washdata.org/how-we-work/country-and-regional-engagement\">https://washdata.org/how-we-work/country-and-regional-engagement</a></p>\n<p>JMP Country consultations: <a href=\"https://washdata.org/how-we-work/jmp-country-consultation\">https://washdata.org/how-we-work/jmp-country-consultation</a></p>\n<p>JMP Methodology: 2017 update and SDG baselines:</p>\n<p><a href=\"https://washdata.org/reports/jmp-2017-methodology\">https://washdata.org/reports/jmp-2017-methodology</a></p>\n<p>JMP Core questions on water, sanitation and hygiene for household surveys</p>\n<p>Available in English (EN), Spanish (ES), French (FR), Russian (RU) and Arabic (AR):</p>\n<p><a href=\"https://washdata.org/reports/jmp-2018-core-questions-household-surveys\">https://washdata.org/reports/jmp-2018-core-questions-household-surveys</a></p>\n<p>A comprehensive set of tools to guide survey teams through every step of the MICS process &#x2013; including </p>\n<p>survey questions, snapshots and manuals for WASH:</p>\n<p><a href=\"https://mics.unicef.org/tools\">https://mics.unicef.org/tools</a></p>\n<p>JMP 2023 WASH in Households Report: Progress on household drinking water, sanitation and hygiene 2000-2022: special focus on gender</p>\n<p>Available in English (EN), Spanish (ES), French (FR), Russian (RU) and Arabic (AR):</p>\n<p><a href=\"https://washdata.org/reports/jmp-2023-wash-households\">https://washdata.org/reports/jmp-2023-wash-households</a></p>\n<p>JMP Integrating water quality testing into household surveys</p>\n<p>Available in English (EN), Spanish (ES), and French (FR):</p>\n<p><a href=\"https://washdata.org/reports/jmp-2020-water-quality-testing-household-surveys\">https://washdata.org/reports/jmp-2020-water-quality-testing-household-surveys</a></p>\n<p>WHO Guidelines for Drinking Water Quality, 4th edition, incorporating the 1st and 2nd addenda:</p>\n<p><a href=\"https://www.who.int/publications/i/item/9789240045064\">https://www.who.int/publications/i/item/9789240045064</a></p>\n<p>WHO. Ending the neglect to attain the Sustainable Development Goals: a global strategy on water, </p>\n<p>sanitation and hygiene to combat neglected tropical diseases, 2021-2030. 2021:</p>\n<p><a href=\"https://www.who.int/teams/environment-climate-change-and-health/water-sanitation-and-health/burden-of-disease/wash-and-neglected-tropical-diseases\">https://www.who.int/teams/environment-climate-change-and-health/water-sanitation-and-health/burden-of-disease/wash-and-neglected-tropical-diseases</a></p>\n<p>UN General Assembly Resolution A/RES/64/292 for the right to water and sanitation</p>\n<p>Available in English (EN), Spanish (ES), French (FR), Russian (RU) and Arabic (AR):</p>\n<p><a href=\"https://undocs.org/Home/Mobile?FinalSymbol=A%2FRES%2F64%2F292&amp;Language=E&amp;DeviceType=Desktop&amp;LangRequested=False\">https://undocs.org/Home/Mobile?FinalSymbol=A%2FRES%2F64%2F292&amp;Language=E&amp;DeviceType=Desktop&amp;LangRequested=False</a></p>\n<p>UN General Assembly Resolution A/RES/70/169 for the human rights to safe drinking water and sanitation </p>\n<p>Available in English (EN), Spanish (ES), French (FR), Russian (RU) and Arabic (AR):</p>\n<p><a href=\"https://undocs.org/Home/Mobile?FinalSymbol=A%2FRES%2F70%2F169&amp;Language=E&amp;DeviceType=Desktop&amp;LangRequested=False\">https://undocs.org/Home/Mobile?FinalSymbol=A%2FRES%2F70%2F169&amp;Language=E&amp;DeviceType=Desktop&amp;LangRequested=False</a></p>\n<p>The Human Right to Water and Sanitation Milestones:</p>\n<p><a href=\"https://www.un.org/waterforlifedecade/pdf/human_right_to_water_and_sanitation_milestones.pdf\">https://www.un.org/waterforlifedecade/pdf/human_right_to_water_and_sanitation_milestones.pdf</a></p>\n<p>For queries: <a href=\"mailto:info@washdata.org\">info@washdata.org</a></p>", "indicator_sort_order"=>"06-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"embed", "label"=>"related indicators"}]}, {"number"=>"6.2.1", "slug"=>"6-2-1", "name"=>"Proporción de la población que utiliza: a) servicios de saneamiento gestionados sin riesgos y b) instalaciones para el lavado de manos con agua y jabón", "url"=>"/site/es/6-2-1/", "sort"=>"060201", "goal_number"=>"6", "target_number"=>"6.2", "global"=>{"name"=>"Proporción de la población que utiliza: a) servicios de saneamiento gestionados sin riesgos y b) instalaciones para el lavado de manos con agua y jabón"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[{"field"=>"Servicios básicos", "value"=>"BSRVH2O"}, {"field"=>"Servicios básicos", "value"=>"BSRVSAN"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de viviendas principales con acceso a servicios básicos (abastecimiento de agua corriente y baño)", "graph_type"=>"line", "indicator_name"=>"Proporción de la población que utiliza: a) servicios de saneamiento gestionados sin riesgos y b) instalaciones para el lavado de manos con agua y jabón", "indicator_number"=>"6.2.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Aumento", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_25/opt_1/ti_censos-de-poblacion-y-viviendas/temas.html", "url_text"=>"Censos de población y viviendas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Proporción de viviendas principales con acceso a servicios básicos (abastecimiento de agua corriente y baño)", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.2- De aquí a 2030, lograr el acceso a servicios de saneamiento e higiene adecuados y equitativos para todos y poner fin a la defecación al aire libre, prestando especial atención a las necesidades de las mujeres y las niñas y las personas en situaciones de vulnerabilidad", "definicion"=>"Series disponibles: \n\n <b> - Proporción de viviendas con agua corriente</b>: Proporción de viviendas principales \n con abastecimiento de agua corriente \n\n <b> - Proporción de viviendas con baño</b>: Proporción de viviendas principales con baño ", "formula"=>"<b>Proporción de viviendas con agua corriente</b>\n\n$$PVP_{agua\\, corriente}^{t} = \\frac{VP_{agua\\, corriente}^{t}}{VP^{t}} \\cdot 100$$\n\ndonde:\n\n$VP_{agua\\, corriente}^{t} =$ viviendas principales con abastecimiento de agua corriente en el año $t$\n\n$VP^{t} =$ viviendas principales en el año $t$\n\n<br>\n\n<b>Proporción de viviendas con baño</b>\n\n$$PVP_{baño}^{t} = \\frac{VP_{baño}^{t}}{VP^{t}} \\cdot 100$$\n\ndonde:\n\n$VP_{baño}^{t} =$ viviendas principales con baño en el año $t$\n\n$VP^{t} =$ viviendas principales en el año $t$\n", "desagregacion"=>"Territorio histórico/Comarca/Municipio\n", "observaciones"=>"Vivienda principal es aquella vivienda que es utilizada toda o la mayor parte del año como  residencia habitual por una o más personas", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl acceso a servicios de saneamiento e higiene seguros es esencial para la buena salud, el bienestar y la productividad y se reconoce \nampliamente como un derecho humano. La gestión insegura de los excrementos humanos y las prácticas deficientes de saneamiento están \nestrechamente asociadas con las enfermedades diarreicas, que exacerban la malnutrición y siguen siendo un importante problema de salud \npública y una de las principales causas mundiales de muerte infantil, así como con las infecciones parasitarias, como los helmintos \ntransmitidos por el suelo (gusanos) y una serie de otras enfermedades tropicales desatendidas. Si bien el acceso a instalaciones sanitarias \nhigiénicas es esencial para reducir la transmisión de patógenos, es igualmente importante garantizar la gestión, el tratamiento y la \neliminación seguros de los excrementos producidos. \n\nEl uso compartido de las instalaciones de saneamiento también es una consideración importante, dados los impactos negativos en la \ndignidad, la privacidad y la seguridad personal. La falta de acceso a instalaciones de saneamiento e higiene adecuadas es una causa \nimportante de riesgos y ansiedad, especialmente para las mujeres y las niñas. Por todas estas razones, el acceso a servicios \nde saneamiento e higiene que prevengan enfermedades, proporcionen privacidad y garanticen la dignidad se ha reconocido como un \nderecho humano básico. \n\nLa meta 6.2 de los ODS relacionada con el saneamiento y la higiene tiene por objeto lograr este derecho \nmediante el acceso universal a servicios gestionados de manera segura.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.2.1&seriesCode=SH_SAN_HNDWSH&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">Proporción de la población con instalaciones básicas para el lavado de manos en sus instalaciones, por zona urbana/rural (%) SH_SAN_HNDWSH</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.2.1&seriesCode=SH_SAN_SAFE&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">\nProporción de la población que utiliza servicios de saneamiento gestionados de forma segura, por zonas urbanas o rurales (%) SH_SAN_SAFE</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible no cumple los metadatos de Naciones Unidas, pero aporta información similar", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01a.pdf\">Metadatos 6-2-1(a).pdf</a> (solo en inglés)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01b.pdf\">Metadatos 6-2-1(b).pdf</a> (solo en inglés)\n", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de viviendas principales con acceso a servicios básicos (abastecimiento de agua corriente y baño)", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"6.2- De aquí a 2030, lograr el acceso a servicios de saneamiento e higiene adecuados y equitativos para todos y poner fin a la defecación al aire libre, prestando especial atención a las necesidades de las mujeres y las niñas y las personas en situaciones de vulnerabilidad", "definicion"=>"Available series:  \n\n - Proportion of homes with running water: Proportion of main family dwellings with \n running water supply\n\n - Proportion of homes with a bathroom: Proportion of main family dwellings with a bathroom ", "formula"=>"<b>Proportion of homes with running water</b>\n\n$$PVP_{running\\, water}^{t} = \\frac{VP_{running\\, water}^{t}}{VP^{t}} \\cdot 100$$\n\nwhere:\n\n$VP_{running\\, water}^{t} =$ main family dwellings with running water supply in year $t$\n\n$VP^{t} =$ main family dwellings in year $t$\n\n<br>\n\n<b>Proportion of homes with a bathroom</b>\n\n$$PVP_{bathroom}^{t} = \\frac{VP_{bathroom}^{t}}{VP^{t}} \\cdot 100$$\n\nwhere:\n\n$VP_{bathroom}^{t} =$ main family dwellings with a bathroom in year $t$\n\n$VP^{t} =$ main family dwellings in year $t$\n", "desagregacion"=>"Province/County/Municipality\n", "observaciones"=>"A main family dwelling is a home that is used all or most of the year  as a habitual residence by one or more persons.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAccess to safe sanitation and hygiene services is essential \nfor good health, welfare and productivity and is  widely recognized \nas a human right. Unsafe management of human excreta and poor \nsanitation practices are closely associated with diarrhoeal diseases, \nwhich exacerbate malnutrition and remain a major public health concern \nand a leading global cause of child deaths, as well as parasitic infections \nsuch as soil  transmitted helminths (worms) and a range of other neglected \ntropical diseases. While access to a hygienic toilet facility is essential \nfor reducing the transmission of pathogens, it is equally important to ensure \nsafe management, treatment and disposal of the excreta produced. \n\nSharing of sanitation facilities is also an important consideration given \nthe negative impacts on dignity, privacy and personal safety. Lack of access \nto suitable sanitation and hygiene facilities is a major cause of risks and \nanxiety, especially for women and girls. For all these reasons, access to \nsanitation and hygiene services that prevent disease, provide privacy and ensure \ndignity has been recognized as a basic human right. \n\nThe SDG target 6.2 relating to sanitation and hygiene aim to achieve this right \nthrough universal access to safely managed services. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.2.1&seriesCode=SH_SAN_HNDWSH&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">Proportion of population with basic handwashing facilities on premises, by urban/rural (%) SH_SAN_HNDWSH</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.2.1&seriesCode=SH_SAN_SAFE&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">\nProportion of population using safely managed sanitation services, by urban/rural (%) SH_SAN_SAFE</a> UNSTATS\n", "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information", "indicador_meta_enlace"=>"\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01a.pdf\">Metadata 6-2-1(a).pdf</a> \n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01b.pdf\">Metadata 6-2-1(b).pdf</a>\n", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de viviendas principales con acceso a servicios básicos (abastecimiento de agua corriente y baño)", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.2- De aquí a 2030, lograr el acceso a servicios de saneamiento e higiene adecuados y equitativos para todos y poner fin a la defecación al aire libre, prestando especial atención a las necesidades de las mujeres y las niñas y las personas en situaciones de vulnerabilidad", "definicion"=>"Eskuragarri dauden serieak: \n\n <b> - Txorrotako ura duten etxebizitzen proportzioa</b>: Txorrotako uraren hornidura duten duten etxebizitza \nnagusien proportzioa \n\n <b> - Bainugela duten etxebizitzen proportzioa</b>: Bainugela duten etxebizitza nagusien proportzioa  ", "formula"=>"<b>Txorrotako ura duten etxebizitzen proportzioa</b>\n\n$$PVP_{txorrotako\\, ura}^{t} = \\frac{VP_{txorrotako\\, ura}^{t}}{VP^{t}} \\cdot 100$$\n\nnon:\n\n$VP_{txorrotako\\, ura}^{t} =$ txorrotako uraren hornidura duten etxebizitza nagusiak $t$ urtean\n\n$VP^{t} =$ etxebizitza nagusiak $t$ urtean\n\n<br>\n\n<b>Bainugela duten etxebizitzen proportzioa</b>\n\n$$PVP_{bainugela}^{t} = \\frac{VP_{bainugela}^{t}}{VP^{t}} \\cdot 100$$\n\nnon:\n\n$VP_{bainugela}^{t} =$ bainugela duten etxebizitza nagusiak $t$ urtean\n\n$VP^{t} =$ etxebizitza nagusiak $t$ urtean\n", "desagregacion"=>"Lurralde historikoa/Eskualdea/Udalerria \n", "observaciones"=>"Etxebizitza nagusia da urte osoan edo urtearen zatirik handienean pertsona batek edo gehiagok ohiko  bizileku gisa erabiltzen duten etxebizitza.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nSaneamendu- eta higiene-zerbitzu seguruak izatea funtsezkoa da osasun onerako, ongizaterako eta produktibitaterako, \neta giza eskubidetzat hartzen da. Giza gorotzen kudeaketa ez-seguruak eta saneamendu-jardunbide eskasak oso lotuta \ndaude beherako-gaixotasunekin, malnutrizioa areagotzen dutelarik –oraindik ere osasun publikoko arazo garrantzitsua \neta haurren heriotzaren munduko kausa nagusietako bat izaten jarraitzen dute–, bai eta parasito-infekzioekin ere, \nhala nola lurzorutik transmititutako helmintoekin (harrak) eta artatu gabeko beste gaixotasun tropikal batzuekin. \nInstalazio sanitario higienikoetarako sarbidea izatea patogenoen transmisioa murrizteko funtsezkoa bada ere, sortutako \ngorotzen kudeaketa, tratamendua eta deuseztapen segurua bermatzea ere garrantzitsua da. \n\nSaneamendu-instalazioen erabilera partekatua ere garrantzitsua da, duintasunean, pribatutasunean eta segurtasun \npertsonalean eragin negatiboak baititu. Saneamendu- eta higiene-instalazio egokietarako sarbiderik eza arrisku eta \nantsietaterako kausa garrantzitsua da, batez ere emakume eta neskentzat. Arrazoi horiengatik guztiengatik, gaixotasunak \nprebenitu, pribatutasuna eman eta duintasuna bermatzen duten saneamendu- eta higiene-zerbitzuetarako sarbidea oinarrizko \ngiza eskubidetzat hartu da. \n\nSaneamenduarekin eta higienearekin lotutako GJHen 6.2 xedearen helburua eskubide hori modu seguruan kudeatutako \nzerbitzuetarako sarbide unibertsalaren bidez lortzea da. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.2.1&seriesCode=SH_SAN_HNDWSH&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">Etxean eskuak garbitzeko oinarrizko instalazioak dituzten biztanleen proportzioa, landa-eremua/hiri-eremua izatearen arabera (%) SH_SAN_HNDWSH</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.2.1&seriesCode=SH_SAN_SAFE&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">\nModu seguruan kudeatutako saneamendu-zerbitzuak erabiltzen dituzten biztanleen proportzioa, landa-eremua/hiri-eremua izatearen arabera (%) SH_SAN_SAFE</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen metadatuak betetzen, baina antzeko informazioa ematen du", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01a.pdf\">Metadatuak 6-2-1(a).pdf</a> (ingelesez bakarrik)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-02-01b.pdf\">Metadatuak 6-2-1(b).pdf</a> (ingelesez bakarrik)\n", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 6: Ensure availability and sustainable management of water and sanitation for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 6.2: By 2030, achieve access to adequate and equitable sanitation and hygiene for all and end open defecation, paying special attention to the needs of women and girls and those in vulnerable situations</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 6.2.1: Proportion of population using (a) safely managed sanitation services and (b) a hand-washing facility with soap and water</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SH_SAN_HNDWSH - Proportion of population with basic handwashing facilities on premises [6.2.1] </p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>All targets under Goal 6, as well as targets 1.2, 1.4, 2.2, 3.2, 3.8, 3.9, 4a, 5.4 and 11.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>\n<p>United Nations Children&apos;s Fund (UNICEF)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>\n<p>United Nations Children&apos;s Fund (UNICEF)</p>\n<p>WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The proportion of the population with basic hygiene services is defined as the proportion of population with a handwashing facility with soap and water available at home. Handwashing facilities may be located within the dwelling, yard or plot. They may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.</p>\n<p><strong>Concepts:</strong></p>\n<p>Household handwashing facilities may be located in the dwelling, yard or plot. A handwashing facility is a device to contain, transport or regulate the flow of water to facilitate handwashing. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents. In some cultures, ash, soil, sand or other materials are used as handwashing agents, but these are less effective than soap and are therefore counted as limited handwashing facilities.</p>\n<p>In 2008, the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) supported a review of indicators of handwashing practice, and determined that the most practical approach leading to reliable measurement of handwashing in national household surveys was observation of the place where household members wash their hands and noting the presence of water and soap (or local alternative) at that location. This provides a measure of whether households have the necessary tools for handwashing and is a proxy for their behaviour. Observation by survey enumerators represents a more reliable, valid and efficient indicator for measuring handwashing behaviour than asking individuals to report their own behaviour. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) &#x2013; Proportion of population</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) has established international standards for classification of handwashing facilities and service levels to benchmark and compare progress across countries (see <a href=\"https://washdata.org/\">https://washdata.org/</a>).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data sources included in the WHO/UNICEF Joint Monitoring Programme (JMP) database are: </p>\n<ul>\n  <li>Censuses, which in principle collect basic data from all people living within a country and led by national statistical offices. </li>\n  <li>Household surveys, which collect data from a subset of households. These may target national, rural, or urban populations, or more limited project or sub-national areas. An appropriate sample design is necessary for survey results to be representative, and surveys are often led by or reviewed and approved by national statistical organizations. </li>\n  <li>Other datasets may be available such as compilations by international or regional initiatives (e.g. Eurostat), studies conducted by research institutes, or technical advice received during country consultations.</li>\n</ul>\n<p>Access to water, sanitation and hygiene are considered core socio-economic and health indicators, and key determinants of child survival, maternal, and children&#x2019;s health, family wellbeing, and economic productivity. Drinking water, sanitation and hygiene facilities are also used in constructing wealth quintiles used by many integrated household surveys to analyse inequalities between rich and poor. Access to drinking water, sanitation and hygiene are therefore core indicators for many household surveys and censuses.</p>\n<p>The JMP uses data on the observation of handwashing facilities with water and soap, typically available in Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS), as well as other household surveys. Any available surveys recording observation of handwashing facilities are included in the JMP database and JMP regression rules are applied to estimate the proportion of the population with a handwashing facility, as well as the proportion with a handwashing facility with water and soap.</p>\n<p>Household surveys increasingly include a section on hygiene practices where the surveyor visits the handwashing facility and observes if water and soap are present. Observation of handwashing materials by surveyors represents a more reliable proxy for handwashing behaviour than asking individuals whether they wash their hands. The small number of cases where households refuse to give enumerators permission to observe their facilities are excluded from JMP estimates. </p>\n<p>Direct observation of handwashing facilities has been included as a standard module in MICS and DHS since 2009. Following the standardization of hygiene questions in international surveys, data on handwashing facilities are available for a growing number of low- and middle-income countries. This type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal. </p>\n<p>Some datasets reviewed by the JMP are not representative of national, rural or urban populations, or may be representative of only a subset of these populations. The JMP enters datasets into the global database when they represent at least 20% of the national, urban or rural populations. However, datasets representing less than 80% of the relevant population, or which are considered unreliable or inconsistent with other datasets covering similar populations, are not used in the production of estimates (see section 2.6, Data Acceptance in JMP Methodology: 2017 update and SDG baselines). </p>\n<p>The population data used by the JMP, including the proportion of the population living in urban and rural areas, are those routinely updated by the UN Population Division (World Population Prospects: <a href=\"https://population.un.org/wpp/\">https://population.un.org/wpp/</a>; World Urbanization Projects: <a href=\"https://population.un.org/wup\">https://population.un.org/wup</a>).</p>", "COLL_METHOD__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme (JMP) team conducts regular data searches by systematically visiting the websites of national statistical offices, and key sector institutions such as ministries of water and sanitation, regulators of drinking water and sanitation services, etc. Other regional and global databases are also reviewed for new datasets. UNICEF and WHO regional and country offices provides support to identify newly available household surveys, censuses and administrative datasets.</p>\n<p>Before publishing, all JMP estimates undergo rigorous country consultations facilitated by WHO and UNICEF country offices. Often these consultations give rise to in-country visits, and meetings about data on drinking water, sanitation and hygiene services and the monitoring systems that collect these data. </p>", "FREQ_COLL__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme (JMP) begins its biennial data collection cycle in October of even years and publishes estimates during the following year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The SDG Progress Report and relevant data are published every two years since the publication of the baseline report in 2017, usually between March and July of odd years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National statistics offices; ministries of water, health, and environment; regulators of drinking water service providers.</p>", "COMPILING_ORG__GLOBAL"=>"<p>WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP)</p>", "INST_MANDATE__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme (JMP) was established in 1990 to monitor global progress on drinking water, sanitation and hygiene (see <a href=\"https://washdata.org/\">https://washdata.org/</a>).</p>", "RATIONALE__GLOBAL"=>"<p>Access to safe drinking water, sanitation and hygiene services, areessential for good health, welfare and productivity and are widely recognised as human rights. Improved hygiene is one of the most important measures to prevent the spread of infectious diseases including diarrhoeal diseases and acute respiratory infections which remain leading global causes of disease. Most infectious diseases are caused by bacteria or viruses which are transmitted either through the air, via surfaces or food, or via human faeces. Because people frequently touch their face, food, and surfaces, handwashing reduces the spread of these bacteria and viruses and is widely regarded as a top priority for improving global health outcomes. </p>\n<p> </p>\n<p>Monitoring handwashing behaviour is difficult, but household surveys increasingly include a module that involves direct observation of facilities and presence of water and soap that has been shown to be a reasonable proxy for actual handwashing practices. International consultations among drinking water, sanitation and hygiene (WASH) sector professionals identified the presence of a handwashing facility with soap and water available within the dwelling, yard or plot as a priority indicator for national and global monitoring of hygiene under SDG 6.2. The SDG indicator 6.2.1.b is therefore designed to address both access to facilities and the availability of soap and water for handwashing at the household level.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The presence of a handwashing facility with soap and water available does not guarantee that household members consistently wash hands at key times. But direct observation of handwashing is challenging, and people tend to behave differently when being observed. The presence of a handwashing facility with soap and water available has been shown to be a reasonable proxy for handwashing. Enumerators ask households to show them where members of the household most often wash their hands and record the type of facility and whether soap and water are present at the time of the survey.</p>\n<p>Since 2016 household surveys have refined the questions asked about handwashing facilities to include separate response categories for different types of handwashing facilities, including both fixed devices like sinks and taps, and mobile devices like jugs and portable basins. These surveys have shown that mobile devices are widely used in low-income countries. Older surveys that don&#x2019;t include responses for mobile devices may therefore underestimate the population with access to handwashing facilities.</p>\n<p>Households surveys in high-income countries rarely include questions about handwashing facilities, and as such, have very low data coverage. Some countries have data on the proportion of households with piped water supplies, hot water, showers or bathrooms but further work is required to determine how many of these also have basic hygiene services.</p>", "DATA_COMP__GLOBAL"=>"<p>The production of estimates follows a consistent series of steps, which are explained in this and following sections: </p>\n<p>1. Identification of appropriate national datasets </p>\n<p>2. Extraction of data from national datasets into harmonized tables of data inputs </p>\n<p>3. Use of the data inputs to model country estimates </p>\n<p>4. Consultation with countries to review the estimates </p>\n<p>5. Aggregation of country estimates to create regional and global estimates</p>\n<p>Household surveys and censuses provide data on the presence of handwashing facilities and soap and water in the home. The WHO/UNICEF Joint Monitoring Programme (JMP) uses data from household surveys in which the enumerator observes the handwashing facility and confirms the presence or absence of soap and water at the facility. Datasets that include availability of soap in the household (i.e. not at the handwashing facility), or self-reported availability of handwashing facilities, soap and water may be included in the JMP database and country files, but in most cases are not used for making estimates. </p>\n<p>In some parts of the world, households sometimes do not give permission for survey enumerators to enter the premises and observe handwashing facilities. These households are excluded from calculations of the proportion of households having handwashing facilities.</p>\n<p>The JMP uses original microdata to produce its own tabulations and estimates by using populations weights (or household weights multiplied by de jure household size), where possible. However, in many cases microdata are not readily accessible so relevant data are transcribed from reports available in various formats (PDFs, Word files, Excel spreadsheets, etc.) if data are tabulated for the proportion of the population, or household/dwelling. National data from each country, area, or territory are recorded in the JMP country files, with water, sanitation, and hygiene data recorded on separate sheets. Country files can be downloaded from the JMP website: <a href=\"https://washdata.org/data/downloads\">https://washdata.org/data/downloads</a>.</p>\n<p>The JMP estimates the proportion of population with a basic handwashing facility with soap and water on premises by fitting a regression model to all available and validated data points within the reference period, starting from year 2000. </p>\n<p> </p>\n<p>For more details on JMP rules and methods on how data on the type of sanitation facility used and the disposal and treatment of excreta are combined to compute the safely managed sanitation services indicator, please refer to recent JMP progress reports and &#x201C;JMP Methodology: 2017 update and SDG baselines&#x201D;: <a href=\"https://washdata.org/reports/jmp-2017-methodology\">https://washdata.org/reports/jmp-2017-methodology</a></p>", "DATA_VALIDATION__GLOBAL"=>"<p>Every two years the WHO/UNICEF Joint Monitoring Programme (JMP) updates its global databases to incorporate the latest available national data for the global SDG indicators. National authorities are consulted on the estimates generated from national data sources through a country consultation process facilitated by WHO and UNICEF country offices. The country consultation aims to engage national statistical offices and other relevant national stakeholders to review the draft estimates and provide technical feedback to the JMP team.</p>\n<p>The purpose of the consultation is not to compare JMP and national estimates of drinking water, sanitation and hygiene (WASH) coverage but rather to review the completeness or correctness of the datasets in the JMP country file and to verify the interpretation of national data in the JMP estimates. The JMP provides detailed guidance to facilitate country consultation on the estimates contained in JMP country files. The consultation focusses on three main questions:</p>\n<ol>\n  <li>Is the country file missing any relevant national sources of data that would allow for better estimates?</li>\n  <li>Are the data sources listed considered reliable and suitable for use as official national statistics?</li>\n  <li>Is the JMP interpretation and classification of the data extracted from national sources accurate and appropriate?</li>\n</ol>\n<p>The JMP estimates are circulated for a 2 month period of consultation with national authorities starting in the fourth quarter of the year prior to publication (<a href=\"https://washdata.org/how-we-work/jmp-country-consultation\">https://washdata.org/how-we-work/jmp-country-consultation</a>).</p>", "ADJUSTMENT__GLOBAL"=>"<p>See 4.c Method of computation</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>The WHO/UNICEF Joint Monitoring Programme (JMP) method uses a simple regression model to generate time series estimates for all years including for years without data points. The JMP then shares all its estimates using its country consultation mechanism to get consensus from countries before publishing its estimates.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Regional and global estimates for basic hygiene services are calculated if there are (non-imputed) data available for at least 50% of the relevant population within the region. In order to produce estimates for regional or global levels, imputed estimates are produced for countries lacking data. Imputed country estimates are not published and only used for aggregation. </p>\n<p>In the 2021 and earlier updates, regional population-weighted averages of M49 regions were used to impute missing values (For the lists of M49 regions and sub-regions see: <a href=\"https://unstats.un.org/unsd/methodology/m49/overview/\">https://unstats.un.org/unsd/methodology/m49/overview/</a>). In the 2023 update, an iterative approach was applied to all variables: </p>\n<ol>\n  <li>If any estimates were available within an M49 subregion, the subregion average was used for imputation. </li>\n  <li>If estimates were available at the regional but not subregion level, the M49 regional average was used. </li>\n  <li>If no estimates were available for any country or territory in the M49 region, the global average was used for imputation.</li>\n</ol>", "REG_AGG__GLOBAL"=>"<p>Regional estimates for basic hygiene services are calculated by summing up the actual or imputed estimates for each country, area or territory in the region, provided data are available for at least half (50%) of the relevant population within the region. Global estimates are made by directly aggregating country (and imputed country) estimates, not by aggregating regional estimates.</p>\n<p>These estimates are calculated separately for urban and rural areas and, where possible, a weighted average is made of rural and urban populations to produce total estimates for the region or world.</p>\n<p>Regional aggregates are generated for various regions, including SDG regional groupings, Landlocked Developing Countries (LLDCs), Least Developed Countries (LDCs), Small Island Developing States (SIDs), OECD fragile contexts, and World Bank income groupings (see: <a href=\"https://washdata.org/data/country/REG/household/download\">https://washdata.org/data/country/REG/household/download</a>). In addition, WHO/UNICEF Joint Monitoring Programme (JMP) produces regional snapshots to provide detailed analysis within regions (See JMP 2023 Regional snapshots: <a href=\"https://washdata.org/how-we-work/country-and-regional-engagement/regional-analysis-2023-household-update\">https://washdata.org/how-we-work/country-and-regional-engagement/regional-analysis-2023-household-update</a>).</p>\n<p>For more details on JMP rules and methods: JMP Methodology: 2017 update and SDG baselines:</p>\n<p><a href=\"https://washdata.org/reports/jmp-2017-methodology\">https://washdata.org/reports/jmp-2017-methodology</a></p>\n<p>JMP 2023 WASH in Households Report &#x2013; Annex 1: </p>\n<p>Progress on household drinking water, sanitation and hygiene 2000-2022: special focus on gender</p>\n<p><a href=\"https://washdata.org/reports/jmp-2023-wash-households\">https://washdata.org/reports/jmp-2023-wash-households</a></p>", "DOC_METHOD__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme (JMP) has published guidance on core questions and indicators for monitoring drinking water, sanitation and hygiene (WASH) in households, schools and health care facilities (see <a href=\"https://washdata.org/monitoring/methods/core-questions\">https://washdata.org/monitoring/methods/core-questions</a>) and provides technical support through WHO and UNICEF regional and country offices to strengthen national monitoring of SDG indicators relating to drinking water, sanitation and hygiene</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme (JMP) has been instrumental in developing global norms to benchmark progress on drinking water, sanitation and hygiene, and has produced regular updates on country, regional, and global trends. The JMP regularly convenes expert task forces to provide technical advice on specific issues and methodological challenges related to drinking water, sanitation and hygiene (WASH) monitoring. WHO and UNICEF have also established a Strategic Advisory Group (SAG) to provide independent advice on the continued development of the JMP as a trusted custodian of global WASH data (see <a href=\"https://washdata.org/how-we-work/about-jmp\">https://washdata.org/how-we-work/about-jmp</a>). </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>National statistical offices are primarily responsible for assuring the quality of national data sources. A key objective of the WHO/UNICEF Joint Monitoring Programme (JMP) country consultations is to establish whether data sources are considered reliable and suitable for use as official national statistics. The JMP has established criteria for acceptance of national data sources based on representativeness, quality and comparability. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See 4.j Quality assurance.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>As of July 2023, national estimates could be produced for 84 countries, areas and territories, and covering 69% of the global population. Estimates were available for rural areas in countries representing 82% of the global rural population, and for urban areas in countries representing 59% of the global urban population.</p>\n<p><strong>Time series:</strong></p>\n<p>Data on drinking water and sanitation services have been routinely collected for many years, but collecting data on handwashing has only recently become standardized: both the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS) added handwashing questions to their standard questionnaires in 2009. Accordingly, while time series data are available for drinking water and sanitation services since 2000, time series for hygiene are only available since 2015.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregation by geographic location (urban/rural, sub-national regions, etc) and by socioeconomic characteristics (wealth, education, ethnicity, etc) is possible in a growing number of countries. Hygiene facilities are disaggregated by service level (i.e. no facility, limited, and basic facility). </p>\n<p>Disaggregation by individual characteristics (age, sex, disability, etc) may also be made where data permit. Many of the datasets used for producing estimates are household surveys and censuses which collect information on handwashing facilities at the household level. Such data cannot be disaggregated to provide information on intra-household variability, e.g. differential use of services by gender, age, or disability. The WHO/UNICEF Joint Monitoring Programme (JMP) seeks to highlight individual datasets which do allow assessment of intra-household variability, but these are not numerous enough to integrate into the main indicators estimated in JMP reports.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The WHO/UNICEF Joint Monitoring Programme (JMP) estimates are based on national sources of data approved as official statistics. Differences between global and national figures arise due to differences in indicator definitions and methods used in calculating national coverage estimates. In some cases, national estimates are based on the most recent data point rather than from regression on all data points as done by the JMP. In some cases, national estimates draw on administrative sector data rather than the nationally representative surveys and censuses used by the JMP. In order to generate national estimates, JMP uses data that are representative of urban and rural populations and UN population estimates and projections (UN DESA World Population Prospects: <a href=\"https://population.un.org/wpp/\">https://population.un.org/wpp/</a>) which may differ from national population estimates.</p>", "OTHER_DOC__GLOBAL"=>"<p>The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) Website: <a href=\"https://www.washdata.org/\">https://www.washdata.org/</a> </p>\n<p>JMP Data: <a href=\"https://washdata.org/data\">https://washdata.org/data</a></p>\n<p>JMP Reports: <a href=\"https://washdata.org/reports\">https://washdata.org/reports</a></p>\n<p>JMP Methods: <a href=\"https://washdata.org/monitoring/methods\">https://washdata.org/monitoring/methods</a></p>\n<p>JMP Country and inequalities files: <a href=\"https://washdata.org/data/downloads#\">https://washdata.org/data/downloads#</a></p>\n<p>JMP Regional snapshots: <a href=\"https://washdata.org/how-we-work/country-and-regional-engagement\">https://washdata.org/how-we-work/country-and-regional-engagement</a></p>\n<p>JMP Country consultations: <a href=\"https://washdata.org/how-we-work/jmp-country-consultation\">https://washdata.org/how-we-work/jmp-country-consultation</a></p>\n<p>JMP Methodology: 2017 update and SDG baselines</p>\n<p><a href=\"https://washdata.org/reports/jmp-2017-methodology\">https://washdata.org/reports/jmp-2017-methodology</a></p>\n<p>JMP Core questions on water, sanitation and hygiene for household surveys:</p>\n<p>Available in English (EN), Spanish (ES), French (FR), Russian (RU), and Arabic (AR):</p>\n<p><a href=\"https://washdata.org/reports/jmp-2018-core-questions-household-surveys\">https://washdata.org/reports/jmp-2018-core-questions-household-surveys</a></p>\n<p>JMP 2023 WASH in Households Report: Progress on household drinking water, sanitation and hygiene 2000-2022: special focus on gender</p>\n<p>Available in English (EN), Spanish (ES), French (FR), Russian (RU) and Arabic (AR):</p>\n<p><a href=\"https://washdata.org/reports/jmp-2023-wash-households\">https://washdata.org/reports/jmp-2023-wash-households</a></p>\n<p>JMP 2017 WASH in Households Report: Progress on household drinking water, sanitation and hygiene 2000-2017: Special focus on inequalities</p>\n<p>Available in English (EN), Spanish (ES), French (FR), Russian (RU) and Arabic (AR):</p>\n<p><a href=\"https://washdata.org/reports/progress-household-drinking-water-sanitation-and-hygiene-2000-2017-special-focus\">https://washdata.org/reports/progress-household-drinking-water-sanitation-and-hygiene-2000-2017-special-focus</a></p>\n<p>WHO and UNICEF Hand Hygiene for All Global Initiative: </p>\n<p><a href=\"https://www.who.int/initiatives/hand-hygiene-for-all-global-initiative\">https://www.who.int/initiatives/hand-hygiene-for-all-global-initiative</a></p>\n<p>WHO Guidelines on sanitation and health. Geneva: World Health Organization; 2018. Licence: CC BY-NC-SA 3.0 IGO. Available in English (EN), Spanish (ES), French (FR), Russian (RU) and Arabic (AR)::</p>\n<p><a href=\"https://www.who.int/publications/i/item/9789241514705\">https://www.who.int/publications/i/item/9789241514705</a></p>\n<p>WHO. Ending the neglect to attain the Sustainable Development Goals: a global strategy on water, </p>\n<p>sanitation and hygiene to combat neglected tropical diseases, 2021-2030. 2021:</p>\n<p><a href=\"https://www.who.int/teams/environment-climate-change-and-health/water-sanitation-and-health/burden-of-disease/wash-and-neglected-tropical-diseases\">https://www.who.int/teams/environment-climate-change-and-health/water-sanitation-and-health/burden-of-disease/wash-and-neglected-tropical-diseases</a></p>\n<p>Ram, P. 2013. Practical Guidance for Measuring Handwashing Behavior: 2013 Update. Global Scaling Up Handwashing. Washington DC: World Bank Press.</p>\n<p><a href=\"https://www.scribd.com/document/469101426/WSP-Practical-Guidance-Measuring-Handwashing-Behavior-2013-Update-pdf\">https://www.scribd.com/document/469101426/WSP-Practical-Guidance-Measuring-Handwashing-Behavior-2013-Update-pdf</a></p>\n<p>UN General Assembly Resolution A/RES/64/292 for the right to water and sanitation:</p>\n<p>Available in English (EN), Spanish (ES), French (FR), Russian (RU) and Arabic (AR):</p>\n<p>https://undocs.org/Home/Mobile?FinalSymbol=A%2FRES%2F64%2F292&amp;Language=E&amp;DeviceType=Desktop&amp;LangRequested=False</p>\n<p>The Human Right to Water and Sanitation Milestones:</p>\n<p><a href=\"https://www.un.org/waterforlifedecade/pdf/human_right_to_water_and_sanitation_milestones.pdf\">https://www.un.org/waterforlifedecade/pdf/human_right_to_water_and_sanitation_milestones.pdf</a></p>\n<p>For queries: <a href=\"mailto:info@washdata.org\">info@washdata.org</a></p>", "indicator_sort_order"=>"06-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"6.3.1", "slug"=>"6-3-1", "name"=>"Proporción de los flujos de aguas residuales domésticas e industriales tratados de manera adecuada", "url"=>"/site/es/6-3-1/", "sort"=>"060301", "goal_number"=>"6", "target_number"=>"6.3", "global"=>{"name"=>"Proporción de los flujos de aguas residuales domésticas e industriales tratados de manera adecuada"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de los flujos de aguas residuales conectadas a un sistema de colectores, y tratados de manera adecuada", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de los flujos de aguas residuales domésticas e industriales tratados de manera adecuada", "indicator_number"=>"6.3.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio para la Transición Ecológica y el Reto Demográfico", "periodicity"=>"Anual", "url"=>"https://www.miteco.gob.es/es/agua/temas/saneamiento-depuracion.html", "url_text"=>"Tratamiento de las aguas residuales urbanas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de los flujos de aguas residuales conectadas a un sistema de colectores, y tratados de manera adecuada", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.3- De aquí a 2030, mejorar la calidad del agua reduciendo la contaminación, eliminando el vertimiento y minimizando la emisión de productos químicos y materiales peligrosos, reduciendo a la mitad el porcentaje de aguas residuales sin tratar y aumentando considerablemente el reciclado y la reutilización sin riesgos a nivel mundial", "definicion"=>"\nEl indicador está constituido por 2 series: \n\n <b> - Proporción de flujos de aguas residuales domésticas tratadas de manera adecuada</b>: Proporción de la carga contaminante \n    correspondiente a las aglomeraciones urbanas de más de 2.000 habitantes equivalentes que depuran sus aguas \n    residuales reduciendo su nivel de contaminación hasta un límte aceptable para las aguas receptoras \n\n <b> - Proporción de flujos de aguas residuales domésticas conectadas a un sistema de colectores</b>: Proporción de la carga contaminante \n    correspondiente a las aglomeraciones urbanas de más de 2.000 habitantes equivalentes conectadas a un sistema \n    de conductos que recoja y conduzca sus aguas residuales hasta una instalación de tratamiento o a un punto de vertido final \n", "formula"=>"\n<b>Proporción de flujos de aguas residuales domésticas tratadas de manera adecuada</b>\n\n  $$PCC_{\\text{depuración}}^{t} =\n  \\left( \\frac{CC_{\\text{depuración}}^{t}}{CC^{t}} \\cdot 100 \\right)$$\n\ndonde:\n\n$CC_{\\text{depuración}}^{t} =$ carga contaminante correspondiente a las aglomeraciones urbanas de más de \n2.000 habitantes equivalentes que depuran adecuadamente sus aguas residuales en el año $t$\n\n$CC^{t} =$ carga contaminante correspondiente a las aglomeraciones urbanas de más de 2.000 habitantes equivalentes en el año $t$\n\n<br>\n\n<b>Proporción de flujos de aguas residuales domésticas conectadas a un sistema de colectores</b>\n\n  $$PCC_{\\text{colectores}}^{t} =\n  \\left( \\frac{CC_{\\text{colectores}}^{t}}{CC^{t}} \\cdot 100 \\right)$$\n\ndonde:\n\n$CC_{\\text{colectores}}^{t} =$ carga contaminante correspondiente a las aglomeraciones urbanas de más de 2.000 \nhabitantes equivalentes conectadas a un sistema de colectores de aguas residuales en el año $t$\n\n$CC^{t} =$ carga contaminante correspondiente a las aglomeraciones urbanas de más de 2.000 habitantes equivalentes en el año $t$\n", "desagregacion"=>"", "observaciones"=>"\nLa carga contaminante de una aglomeración urbana se mide en habitantes equivalentes, definiéndose un \nhabitante equivalente como la carga orgánica biodegradable con una demanda bioquímica de oxígeno de \ncinco días de 60 gramos de oxígeno por día. Un habitante equivalente viene a representar la carga \norgánica biodegradable generada por una persona en un día en una vivienda normal. \n\nSe debe tener en cuenta que la carga contaminante de una aglomeración urbana no solo incluye \nla contaminación debida a las excreciones humanas, sino también la contaminación procedente \nde aquellos locales utilizados para efectuar una actividad comercial o industrial, siempre \nque sea de naturaleza orgánica biodegradable. Así pues, el número de habitantes equivalentes de \nuna aglomeración urbana siempre es superior a su número de habitantes reales.\n\nEste indicador solo tiene en cuenta a las aglomeraciones urbanas de más de 2.000 habitantes equivalentes, \nen consonancia con los artículos 3 y 4 de la Directiva 91/271/CEE sobre el tratamiento de las aguas residuales urbanas.\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLos datos sobre aguas residuales son cruciales para promover estrategias de uso sostenible y \nseguro de las aguas residuales o su reutilización en beneficio de la salud de la población \nmundial y el medio ambiente global, pero también para responder a las crecientes demandas \nde agua, el aumento de las cargas de contaminación del agua y los impactos del cambio \nclimático en los recursos hídricos. \n\nEl Objetivo de Desarrollo Sostenible 6 (ODS 6) trata de garantizar la disponibilidad \ny sostenibilidad del agua y el saneamiento para todos para 2030. La meta 6.3 de los \nODS se propone mejorar la calidad del agua ambiental, que es esencial para proteger \ntanto los ecosistemas como la salud humana, eliminando, minimizando y reduciendo s\nignificativamente las diferentes corrientes de contaminación en los cuerpos de agua. \n\nEl propósito de monitorear el progreso utilizando el indicador 6.3.1 de los ODS es proporcionar \nla información necesaria y oportuna a los tomadores de decisiones y las partes interesadas \npara que tomen decisiones informadas para acelerar el progreso hacia la reducción de la \ncontaminación del agua, minimizando la liberación de productos químicos peligrosos y \naumentando el tratamiento y la reutilización de las aguas residuales. \n\nLa redacción de la meta cubre el reciclaje de aguas residuales y la reutilización segura con \nimplicancias en la eficiencia del uso del agua, aunque no se aborda completamente en el indicador \ny la metodología globales. El indicador 6.3.1 de los ODS rastrea la proporción de flujos de \naguas residuales de hogares, servicios y actividades económicas industriales que son tratados \nde manera segura en la fuente o mediante plantas centralizadas de tratamiento de aguas residuales \nantes de ser descargados al medio ambiente, del volumen total de aguas residuales generadas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas, pero aporta información similar.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-03-01.pdf\">Metadatos 6-3-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de los flujos de aguas residuales conectadas a un sistema de colectores, y tratados de manera adecuada", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.3- De aquí a 2030, mejorar la calidad del agua reduciendo la contaminación, eliminando el vertimiento y minimizando la emisión de productos químicos y materiales peligrosos, reduciendo a la mitad el porcentaje de aguas residuales sin tratar y aumentando considerablemente el reciclado y la reutilización sin riesgos a nivel mundial", "definicion"=>"\nThe indicator is made up of 2 series:\n\n  - Proportion of safely treated domestic wastewater flows: Proportion of the pollution \n load corresponding to urban agglomerations with more than 2,000 population equivalents \n that treat their wastewater, reducing their pollution level to an acceptable limit for \n the receiving waters\n\n  - Proportion of domestic wastewater flows connected to a collection system: Proportion \n of the pollution load corresponding to urban agglomerations with more than 2,000 population \n equivalents connected to a conduit system that collects and conveys their wastewater to a \n treatment facility or final discharge point\n", "formula"=>"\n<b>Proportion of safely treated domestic wastewater flows</b>\n\n  $$PCC_{\\text{purification}}^{t} =\n  \\left( \\frac{CC_{\\text{purification}}^{t}}{CC^{t}} \\cdot 100 \\right)$$\n\nwhere:\n\n$CC_{\\text{purification}}^{t} =$ pollution load corresponding to urban agglomerations \nwith more than 2,000 population equivalents that adequately treat their wastewater in year $t$\n\n$CC^{t} =$ pollution load corresponding to urban agglomerations with more than 2,000 \n population equivalents in year $t$\n\n<br>\n\n<b>Proportion of domestic wastewater flows connected to a collection system</b>\n\n  $$PCC_{\\text{collectors}}^{t} =\n  \\left( \\frac{CC_{\\text{collectors}}^{t}}{CC^{t}} \\cdot 100 \\right)$$\n\nwhere:\n\n$CC_{\\text{collectors}}^{t} =$ pollution load corresponding to urban agglomerations \nwith more than 2,000 population equivalents connected to a conduit system that collects \nand conveys their wastewater to a treatment facility or final discharge point $t$ \n\n$CC^{t} =$ pollution load corresponding to urban agglomerations with more than 2,000 \npopulation equivalents in year $t$\n", "desagregacion"=>nil, "observaciones"=>"\nThe pollution load of an urban agglomeration is measured in equivalent inhabitants, \nwith an equivalent inhabitant defined as the biodegradable organic load with a five-day \nbiochemical oxygen demand of 60 grams of oxygen per day. An equivalent inhabitant \nrepresents the biodegradable organic load generated by one person in one day in a typical home. \n\nIt should be noted that the pollution load of an urban agglomeration not only includes \npollution due to human excrement, but also pollution from premises used for commercial  \nor industrial activities, provided that it is biodegradable organic. Therefore, the number \nof equivalent inhabitants of an urban agglomeration is always greater than its actual number \nof inhabitants.\n\nThis indicator only takes into account urban agglomerations with more than 2,000 population \nequivalents, in accordance with Articles 3 and 4 of Directive 91/271/EEC on urban wastewater \ntreatment.\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nWastewater data are crucial to promote strategies for sustainable and safe wastewater \nuse or reuse to the benefit of the world’s population health and the global environment, \nbut also to respond to growing water demands, increasing water pollution loads, and climate \nchange impacts on water resources. \n\nSustainable Development Goal 6 (SDG 6) is about ensuring the availability and sustainability \nof water and sanitation for all by 2030. SDG Target 6.3 sets out to improve ambient water \nquality, which is essential to protecting both ecosystem and human health, by eliminating, \nminimizing and significantly reducing different streams of pollution into water bodies. \n\nThe purpose of monitoring progress using SDG indicator 6.3.1 is to provide necessary and timely \ninformation to decision makers and stakeholders to make informed decisions to accelerate progress \ntowards reducing water pollution, minimizing release of hazardous chemicals and increasing wastewater \ntreatment and reuse. \n\nThe target wording covers wastewater recycling and safe reuse with implication on water use efficiency, \nalthough it is not fully addressed by the global indicator and methodology. SDG indicator 6.3.1 tracks \nthe proportion of wastewater flows from households, services and industrial economic activities that \nare safely treated at the source or through centralized wastewater treatment plants before being discharged \ninto the environment, out of the total volume of wastewater generated. \n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-03-01.pdf\">Metadata 6-3-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Proporción de los flujos de aguas residuales conectadas a un sistema de colectores, y tratados de manera adecuada", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.3- De aquí a 2030, mejorar la calidad del agua reduciendo la contaminación, eliminando el vertimiento y minimizando la emisión de productos químicos y materiales peligrosos, reduciendo a la mitad el porcentaje de aguas residuales sin tratar y aumentando considerablemente el reciclado y la reutilización sin riesgos a nivel mundial", "definicion"=>"\nAdierazleak 2 serie ditu:\n\n  <b> - Modu egokian tratatutako etxeko hondakin-uren fluxuen proportzioa</b>: 2.000 biztanle baliokidetik gorako \n    hiri-aglomerazioei dagokien karga kutsatzailearen proportzioa, hondakin-urak araztu eta kutsadura-maila \n    ur hartzaileentzat onargarria den mugaraino murrizten duena.\n\n  <b> - Kolektore-sistema bati konektatutako etxeko hondakin-uren fluxuen proportzioa</b>: 2.000 biztanle baliokidetik gorako \n    hiri-aglomerazioei dagokien karga kutsatzailearen proportzioa, hondakin-urak bildu eta tratamendu-instalazio \n    batera edo azken isurketa-puntu batera eramango dituen hodi-sistema batera konektatuta.\n", "formula"=>"\n<b>Modu egokian tratatutako etxeko hondakin-uren fluxuen proportzioa</b>\n\n  $$PCC_{\\text{ur-arazketa}}^{t} = \\left( \\frac{CC_{\\text{ur-arazketa}}^{t}}{CC^{t}} \\cdot 100 \\right)$$\n\nnon: \n\n$CC_{\\text{ur-arazketa}}^{t} =$ 2.000 biztanle baliokidetik gorako hiri-aglomerazioei dagokien karga \nkutsatzailea, hondakin-urak behar bezala arazten dituena $t$ urtean\n\n$CC^{t} =$ 2.000 biztanle baliokidetik gorako hiri-aglomerazioei dagokien karga kutsatzailea $t$ urtean\n\n<br>\n\n<b>Kolektore-sistema batera konektatutako etxeko hondakin-uren fluxuen proportzioa</b>\n\n  $$PCC_{\\text{kolektoreak}}^{t} =\n  \\left( \\frac{CC_{\\text{kolektoreak}}^{t}}{CC^{t}} \\cdot 100 \\right)$$\n\nnon:\n\n$CC_{\\text{kolektoreak}}^{t} =$ 2.000 biztanle baliokidetik gorako hiri-aglomerazioei dagokien karga kutsatzailea, \nhondakin-uren kolektore-sistema bati konektatuta $t$ urtean\n\n$CC^{t} =$ 2.000 biztanle baliokidetik gorako hiri-aglomerazioei dagokien karga kutsatzailea $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>"\nHiri-aglomerazio baten karga kutsatzailea biztanle baliokideetan neurtzen da, eta biztanle baliokide bat \nhonela definitzen da: karga organiko biodegradagarria, eguneko 60 gramo oxigenoko bost eguneko oxigeno-eskari \nbiokimikoarekin. Biztanle baliokide bat pertsona batek etxebizitza arrunt batean egun batean sortzen duen \nkarga organiko biodegradagarria da.\n\nKontuan izan behar da hiri-aglomerazio baten karga kutsatzaileak ez duela soilik giza iraizteek eragindako \nkutsadura hartzen, baita merkataritza- edo industria-jarduera bat egiteko erabiltzen diren lokalek eragindako \nkutsadura ere, betiere izaera organiko biodegradagarria badu. Beraz, hiri-aglomerazio bateko biztanle \nbaliokideen kopurua beti da benetako biztanle-kopurua baino handiagoa.\n\nAdierazle honek 2.000 biztanletik gorako hiri-aglomerazioak bakarrik hartzen ditu kontuan, hiriko hondakin-uren \ntratamenduari buruzko 91/271/EEE Zuzentarauaren 3. eta 4. artikuluekin bat etorriz.\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nHondakin-urei buruzko datuak funtsezkoak dira hondakin-urak modu jasangarrian eta seguruan erabiltzeko \nestrategiak sustatzeko edo horiek munduko biztanleriaren eta ingurumen globalaren onerako berrerabiltzeko, \nbaina baita ur-eskaera gero eta handiagoei, uraren kutsadura-kargen igoerari eta klima-aldaketak baliabide \nhidrikoetan dituen inpaktuei erantzuteko ere. \n\nGarapen Jasangarriko 6. Helburuak (6. GJH) 2030erako uraren erabilgarritasuna eta jasangarritasuna eta \nguztiontzako saneamendua bermatu nahi ditu. GJHen 6.3 xedeak ingurumen-uraren kalitatea hobetzea du helburu, \nfuntsezkoa baita ekosistemak eta giza osasuna babesteko, ur-gorputzetako kutsadura-korronteak ezabatuz, \nminimizatuz eta murriztuz. \n\nGJHen 6.3.1 adierazlea erabiliz aurrerabidea monitorizatzearen helburua da beharrezkoa eta egokia den \ninformazioa ematea erabaki-hartzaileei eta alderdi interesdunei, erabaki informatuak har ditzaten uraren \nkutsadura murrizteko bidean aurrerapena bizkortzeko, produktu kimiko arriskutsuen askapena minimizatuz eta \nhondakin-uren tratamendua eta berrerabilera areagotuz. \n\nHelburuaren idazketak hondakin-uren birziklapena eta berrerabilpen segurua estaltzen ditu, uraren erabileraren \neraginkortasunean eraginez, adierazle eta metodologia globaletan erabat jorratzen ez den arren. GJHen 6.3.1 \nadierazleak iturrian edo hondakin-urak tratatzeko instalazio zentralizatuen bidez modu seguruan tratatzen diren \netxeetako, zerbitzuetako eta jarduera ekonomiko industrialetako hondakin-uren fluxuen proportzioa arakatzen du, \ningurumenera deskargatu aurretik, sortutako hondakin-uren guztizko bolumenean. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina antzeko informazioa ematen du. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-03-01.pdf\">Metadatuak 6-3-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 6: Ensure availability and sustainable management of water and sanitation for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 6.3: By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globally</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 6.3.1: Proportion of domestic and industrial wastewater flows safely treated</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_WWT_WWDS - Proportion of safely treated domestic wastewater flows [6.3.1]</p>\n<p>EN_WWT_GEN - Total wastewater generated (million m3/year) [6.3.1]</p>\n<p>EN_WWT_TREAT - Total wastewater treated (million m3/year) [6.3.1]</p>\n<p>EN_WWT_TREATR - Proportion of wastewater treated [6.3.1]</p>\n<p>EN_WWT_TREAT_SF - Total wastewater safely treated (million m3/year) [6.3.1]</p>\n<p>EN_WWT_TREATR_SF - Proportion of wastewater safely treated, by activity and location (%) [6.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>The domestic portion of wastewater treated is closely linked to indicator 6.2.1a on the &#x201C;proportion of population using safely managed sanitation services&#x201D;, and draws upon some of the same data sources. </p>\n<p>The indicator is also directly linked to indicator 6.3.2 on the &#x201C;proportion of bodies of water with good ambient water quality&#x201D;, because unsafe wastewater treatment leads to degradation in quality of the receiving waters. It directly informs progress towards target 6.3 and is strongly linked to target 6.6 on water-related ecosystems, as well as target 14.1 on marine pollution (coastal eutrophication).</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>\n<p>World Health Organization (WHO)</p>\n<p>United Nations Statistics Division (UNSD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>\n<p>World Health Organization (WHO)</p>\n<p>United Nations Statistics Division (UNSD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>This indicator measures the volumes of wastewater which are generated through different activities, and the volumes of wastewater which are safely treated before discharge into the environment. Both of these indicators are measured in units of million m<sup>3</sup>/year, although some data sources may use other units that require conversion. The ratio of the volume safely treated to the volume generated is taken as the &#x2018;proportion of wastewater flow safely treated&#x2019;. </p>\n<p>Domestic wastewater:<strong> </strong>Water discharged from residential settlements and service industries which originates predominantly from human metabolism and from household activities. Domestic wastewater includes both blackwater (wastewater that comes from toilets and contains faecal matter and urine) and greywater (wastewater that does not come from toilets and typically derived from baths, showers, sinks, laundry machines, or other non-excreta related functions and facilities). </p>\n<p>Industrial (process) wastewater: Water discharged after being used in, or produced by, industrial production processes and which is of no further immediate value to these processes. Where process water recycling systems have been installed, process wastewater is the final discharge from these circuits. To meet quality standards for eventual discharge into public sewers, this process wastewater is understood to be subjected to ex-process in-plant treatment. Cooling water is not considered here. Sanitary wastewater and surface runoff from industries are also excluded here.</p>\n<p>Total wastewater generated is the total volume of wastewater generated by economic activities (agriculture, forestry and fishing; mining and quarrying; manufacturing; electricity, gas, steam and air conditioning supply; and other economic activities) and households. Cooling water is excluded. </p>\n<p>Urban wastewater: Domestic wastewater or the mixture of domestic wastewater with industrial wastewater and/or runoff rainwater. </p>\n<p>Wastewater: Wastewater is water which is of no further immediate value to the purpose for which it was used because of its quality, quantity, or time of occurrence. Cooling water is not considered here.</p>\n<p>Wastewater discharge: The amount of water (in m<sup>3</sup>) or substance (in kilograms of Biochemical Oxygen Demand per day or comparable) added/leached to a water body (fresh or non-fresh) from a point source.</p>\n<p>Wastewater treatment: Process to render wastewater fit to meet applicable environmental standards or other quality norms for recycling or reuse. &#x2018;Safely treated wastewater&#x2019; is defined as wastewater that has been treated and discharged in compliance with relevant standards, or has been treated by processes commensurate with secondary or higher treatment.</p>\n<p>Urban wastewater treatment: All treatment of wastewater in Urban Wastewater Treatment Plants (UWWTPs). UWWTPs are usually operated by public authorities or by private companies working by order of public authorities. It includes wastewater delivered to treatment plants by trucks. UWWTPs are classified under ISIC 37 (Sewerage). </p>\n<p>Independent treatment: Facilities for preliminary treatment, treatment, infiltration or discharge of domestic wastewater from dwellings generally between 1 and 50 population equivalents, not connected to an urban wastewater collecting system. Examples of such systems are septic tanks. Excluded are systems with storage tanks from which the wastewater is transported periodically by trucks to an urban wastewater treatment plant. </p>\n<p>Other wastewater treatment: Treatment of wastewater in any non-public treatment plant, e.g., Industrial Wastewater Treatment Plants (IWWTPs). Excluded from &quot;other wastewater treatment&quot; is the treatment in septic tanks. IWWTPs may also be classified under ISIC 37 (Sewerage) or under the main activity class of the industrial establishment they belong to.</p>\n<p>Non-treated wastewater: Wastewater which hasn&#x2019;t undergone any form of treatment. </p>\n<p>Primary wastewater treatment: Treatment of wastewater by a physical and/or chemical process involving settlement of suspended solids, or other process in which the Biochemical Oxygen Demand (BOD<sub>5</sub>) of the incoming wastewater is reduced by at least 20% before discharge and the total suspended solids of the incoming wastewater are reduced by at least 50%. To avoid double counting, water subjected to more than one type of treatment should be reported under the highest level of treatment only.</p>\n<p>Secondary wastewater treatment: Post-primary treatment of wastewater by a process generally involving biological treatment with a secondary settlement or other process, resulting in a Biochemical oxygen demand (BOD&#xAD;<sub>5</sub>) removal of at least 70% and a Chemical Oxygen Demand (COD) removal of at least 75%. Natural biological treatment processes are also considered under secondary treatment if the constituents of the effluents from this type of treatment are similar to the conventional secondary treatment. To avoid double counting, water subjected to more than one type of treatment should be reported under the highest level of treatment only.</p>\n<p>Tertiary wastewater treatment: Treatment (additional to secondary treatment) of nitrogen and/or phosphorous and/or any other pollutant affecting the quality or a specific use of water: microbiological pollution, colour etc. The different possible treatment efficiencies (&apos;organic pollution removal&apos; of at least 95% for BOD<sub>5</sub>, 85% for COD, &apos;nitrogen removal&apos; of at least 70%, &apos;phosphorous removal&apos; of at least 80% and &apos;microbiological removal&apos;) cannot be added and are exclusive. To avoid double counting, water subjected to more than one type of treatment should be reported under the highest level of treatment only</p>\n<p><strong>Concepts:</strong></p>\n<p>SDG indicator 6.3.1 tracks the proportion of wastewater flows from domestic (households and services) and industrial economic activities that are safely treated at the source or through wastewater treatment plants before being discharged into the environment, out of the total volume of wastewater generated. </p>\n<p>Total wastewater generation and treatment can be quantified at the national level, and wastewater can also be disaggregated into different types of flows, based on the International Standard Industrial Classification (ISIC) categories. Domestic wastewater generated by private households, as well as wastewater generated by economic activities covered by ISIC categories, may or may not be pre-treated on premises before discharge to either the sewer for further treatment or directly to the environment, as shown in Figure 1.</p>\n<p><img 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\" alt=\"A diagram of a system AI-generated content may be incorrect.\"></p>\n<p>Figure 1: Schematic Representation of wastewater sources, collecting systems and treatment</p>\n<p>(UN Habitat and WHO, 2024).</p>\n<p>The main sources of wastewater include wastewater from households, services and industries, i.e. point sources of one or more pollutant(s) that can be geographically located and represented as a point on a map. Diffused pollution from non-point sources such as runoff from urban and agricultural land can contribute quite significantly to wastewater flows (Figure 1), and therefore its progressive inclusion in the global monitoring framework will be important. Presently, it cannot be monitored at source and its impact on ambient water quality will be monitored under indicator 6.3.2 &#x201C;Proportion of bodies of water with good ambient water quality&#x201D;. </p>\n<p>Differentiating between the different wastewater streams is important as policy decisions need to be guided by the polluter pays principle. However, wastewater conveyed by combined sewers usually combines both hazardous and non-hazardous substances discharged from different sources, but also runoff and urban stormwater, which cannot be separately tracked and monitored. As a consequence, although the flow of wastewater generated can be disaggregated by sources (household, services industrial), the treated wastewater statistics are most commonly disaggregated by type (e.g. urban and industrial) and/or level of treatment (e.g. secondary) rather than by sources.</p>\n<p>Total wastewater flows can be classified into three main categories (see &#x2018;disaggregation section&#x2019; for details:</p>\n<ul>\n  <li>Industrial (ISIC divisions 05-43)</li>\n  <li>Services (ISIC divisions 45-96)</li>\n  <li>Households</li>\n</ul>\n<p>Wastewater treatment can be classified into three main categories (see &#x2018;disaggregation section&#x2019; for details:</p>\n<ul>\n  <li>Primary</li>\n  <li>Secondary</li>\n  <li>Tertiary</li>\n</ul>\n<p>Where possible, treatment will additionally be classified into either on-premises or off-premises treatment. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) for proportions safely treated or </p>\n<p>Million m<sup>3</sup>/year for volumes of wastewater flows generated, treated, or safely treated</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Wastewater flows will be classified into industrial, services, and householddomestic flows, with reference to the International Standard Industrial Classification of All Economic Activities Revision 4 (ISIC). To the extent possible, the proportion of each of these waste streams that is safely treated before discharge to the environment will be calculated.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>A clear specification of the terminology and methodology for wastewater statistics is essential to contribute to harmonising international data collection practices and SDG 6.3.1 reporting. The objective of indicator 6.3.1 is to cover households and the entire economy, and to build on the existing international methodology for global monitoring wastewater generation and treatment. This approach reduces the monitoring burden that SDG reporting can impose on countries, and provides well-defined and internationally comparable variables for global data analysis and use by policymakers and urban/land planners.</p>\n<p>Data are extracted from a number of pre-existing sources: </p>\n<ul>\n  <li>Indicator tables from the United Nations Statistical Division (UNSD) / United Nations Environmental Programme (UNEP) data collection on environment statistics</li>\n</ul>\n<p><a href=\"https://unstats.un.org/unsd/envstats/qindicators\">https://unstats.un.org/unsd/envstats/qindicators</a> (refer to &#x201C;Inland Water Resources&#x201D;)</p>\n<ul>\n  <li>Country files from the UNSD/UNEP data collection on environment statistics (<a href=\"https://unstats.un.org/unsd/envstats/country_files\">https://unstats.un.org/unsd/envstats/country_files</a>) </li>\n  <li>Website of Eurostat water statistics (<a href=\"https://ec.europa.eu/eurostat/web/environment/information-data/water\">https://ec.europa.eu/eurostat/web/environment/information-data/water</a>)</li>\n  <li>Website of Organisation for Economic Co-operation and Development (OECD) water statistics (<a href=\"https://stats.oecd.org/index.aspx?DataSetCode=water_treat\">https://stats.oecd.org/index.aspx?DataSetCode=water_treat#</a>).</li>\n  <li>Country files from the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) data collection on safely managed sanitation services, and the reports referenced therein<br>(<a href=\"https://washdata.org/\">https://washdata.org/</a>)</li>\n</ul>", "COLL_METHOD__GLOBAL"=>"<p>Total flows of wastewater generated and treated are reported by countries to UNSD and OECD/Eurostat databases. Eurostat deals with Member States of the European Union (EU) and the European Free Trade Association (EFTA) as well as the respective candidate countries. OECD works with all its Member States not contacted by Eurostat. UNSD sends the UNSD/UNEP Questionnaire to the rest of the world (approx. 165 countries). However, the response rate for the UNSD/UNEP questionnaire is around 50% and data completeness and quality remain a challenge, especially for developing countries. While efforts will continue to collect data from National Statistical Offices and Ministries of Environment at the national level, it is also critical to improve the availability and accessibility of wastewater statistics and increase training for collection of data and capacity development at the national and sub-national levels.</p>\n<p>The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) collects and compiles national data related to the use of sanitation services including wastewater treatment, for calculation of SDG indicator 6.2.1a &#x201C;proportion of the population using safely managed sanitation services.&#x201D; National data sources are collected from National Statistical Offices, ministries responsible for service delivery, and regulatory authorities, as well as other regional and global initiatives (e.g. the European Protocol on Water and Health). The database is updated every two years following a country consultation process facilitated by WHO and UNICEF regional offices. </p>\n<p>These databases rely on a comparable harmonized terminology for water statistics. Wastewater data are nonetheless still relatively sparse on a global scale. The United Nations Human Settlements Programme (UN-Habitat) and World Health Organization (WHO) will disseminate information about these data collection processes, and will liaise with their technical focal points in regions and countries, to work with them to produce estimates which could then feed into the official statistical system via the NSOs. It is expected that over time, a better reporting of the wastewater data collected can be made to populate the SDG Indicator 6.3.1. </p>", "FREQ_COLL__GLOBAL"=>"<p>UNSD/UNEP data collection is conducted every two years while OECD/Eurostat data collection is conducted annually. WHO continuously compiles newly published data, and holds a country consultation every two years before data finalization and release. UN-Habitat updates its database every three years.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>UN-Habitat: Every three years</p>\n<p>WHO: Every two years (odd years, aligned with SDG indicator 6.2.1).</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (NSOs) are the primary responsible authorities for providing data to be used for global statistics. NSOs may draw on data collected or compiled by relevant national or other authorities, such as ministries, municipalities, or regulatory authorities.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat), World Health Organization (WHO), and the United Nations Statistics Division (UNSD) are co-custodians for this indicator at the global level.</p>\n<p>UNSD leads on collecting, compiling, and processing of data submitted by National Statistical Offices through the UNSD/UNEP Questionnaire on Environment Statistics for the non-OECD/Eurostat member states. </p>\n<p>UN-Habitat leads on compilation and processing of data from UNSD and OECD/Eurostat databases. UN-Habitat also leads on collection of additional data on total and industrial wastewater generation and treatment directly from countries. </p>\n<p>WHO leads on collection, compilation and processing of additional data on domestic wastewater generation and treatment.</p>", "INST_MANDATE__GLOBAL"=>"<p>UN-Habitat, WHO and UNSD have been identified by IAEG-SDGs as custodian agencies for indicator 6.3.1. UN-Habitat is responsible for monitoring total and industrial wastewater, while WHO is responsible for monitoring domestic wastewater.</p>", "RATIONALE__GLOBAL"=>"<p>Wastewater data are crucial to promote strategies for sustainable and safe wastewater use or reuse to the benefit of the world&#x2019;s population health and the global environment, but also to respond to growing water demands, increasing water pollution loads, and climate change impacts on water resources.</p>\n<p>Sustainable Development Goal 6 (SDG 6) is about ensuring the availability and sustainability of water and sanitation for all by 2030. SDG Target 6.3 sets out to improve ambient water quality, which is essential to protecting both ecosystem and human health, by eliminating, minimizing and significantly reducing different streams of pollution into water bodies.</p>\n<p>The purpose of monitoring progress using SDG indicator 6.3.1 is to provide necessary and timely information to decision makers and stakeholders to make informed decisions to accelerate progress towards reducing water pollution, minimizing release of hazardous chemicals and increasing wastewater treatment and reuse. </p>", "REC_USE_LIM__GLOBAL"=>"<p>There is a relative lack of knowledge about the volumes of wastewater generated and treated, because wastewater statistics are in an early stage of development in many countries and not regularly produced or reported. Monitoring is relatively complex and costly, and data are not systematically aggregated to the national level and/or accessible, especially industrial wastewater data which are in general poorly monitored and seldom aggregated at national level. To some extent, this may be explained by the fact that a large proportion of the industrial water requirements are covered by the use of private systems using non-public/drinking water supply (groundwater, rivers and wells) which are not systematically included in the national statistics.</p>\n<p>At present, WHO monitoring of domestic wastewater (flows generated by private households and services) for SDG purposes is restricted to flows from households only and flows generated by services are not accounted for in the estimates. This is reflected in the terminology &#x2013; whereby &#x2018;household&#x2019; flows, rather than &#x2018;domestic&#x2019;, are referred to when making specific reference to the estimates.</p>\n<p>Diffused pollution from non-point sources such as runoff from urban and agricultural land can contribute significantly to wastewater flows, and therefore its progressive inclusion in the global monitoring framework will be important. Presently, it cannot be monitored at source and its impact on ambient water quality will be monitored indirectly under indicator 6.3.2 on the proportion of bodies of water with good ambient water quality.</p>\n<p>Different types of wastewater have different degrees of contamination and pose different levels of threat to the environment and public health. Some data exist on the pollutant loading in terms of 5-day Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) (kg O<sub>2</sub>/day), but these are not as widely available as data on volumes and will not be used at present for indicator 6.3.1. It is anticipated that future data drives will include more information on pollutant loadings that could be eventually featured in SDG 6.3.1 reporting. </p>\n<p>Whether wastewater is classified as safely treated or not depends on the wastewater treatment plant&#x2019;s compliance rate to the effluent standards (i.e. performance). Many wastewater plants produce effluent which does not meet quality standards, due to improper design or loading. Effluent standards rely on both national and local requirements, as well as on specific water uses and potential reuse options, so that this approach may not provide strictly comparable variables between countries. For the purposes of global monitoring, in the absence of data on compliance, technology-based proxies will be used, in which compliance is assumed if the treatment plant provides at least secondary treatment.</p>", "DATA_COMP__GLOBAL"=>"<p>For total and industrial wastewater, the amount of wastewater generated is calculated by summing all of the wastewater generated by different economic activities and households. Wastewater flows are expressed in units of million m<sup>3</sup>/year, although some data sources may use other units that require conversion. </p>\n<p>The amount of wastewater safely treated is calculated by summing all of the wastewater flows which receive treatment considered equivalent to secondary treatment or better. This wastewater flow is expressed in units of million m<sup>3</sup>/year, although some data sources may use other units that require conversion.</p>\n<p>The proportion of wastewater flows which are safely treated is calculated as a ratio of the amount of wastewater safely treated to the amount of wastewater generated.</p>\n<p>For household wastewater, the proportion of wastewater safely treated is a function of the total volume generated and proportions of flow collected in sewers and septic tanks that are respectively delivered to treatment and safely treated and discharged. Associated formulas and methodological details can be referred to in a <a href=\"https://www.unwater.org/publications/domestic-wastewater-treatment-methodology-2024\">Methodological Note</a> (WHO, 2024).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>See section 4.j Quality assurance</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Outside of the UNSD and OECD/Eurostat databases, data on wastewater generation and treatment are not widely available, and what data do exist may not align with international definitions and classifications (e.g. ISIC codes). </p>\n<p>For statistics on total wastewater generated and treated, missing values are not imputed. No estimated or modelled data are produced.</p>\n<p>Some countries do not separately report the volume of wastewater generated by households. In the absence of reported data on household wastewater generation, an estimate of the wastewater generated at the household level is made. It is estimated that 80% of the water supply which enters private households will subsequently exit the household as wastewater. Therefore, if data are available on per capita water consumption, these are used to estimate household wastewater generation. If data on per capita water consumption are not available, data from household surveys and censuses are used to indicate the proportion of the population which has water supplies available on premises (e.g. municipal piped water, private boreholes with overhead tanks) and the proportion of the population which collects water from off-premises sources (e.g. communal standposts, community boreholes). In the absence of other data on household water consumption, it is estimated that households with on-premises water supply consume approximately 120 litres per capita per day, and therefore generate 96 litres of wastewater per capita per day; those with off-premises water supply are assumed to consume approximately 20 litres per capita per day, and therefore generate 16 litres of wastewater per capita per day. </p>\n<p>Missing values needed for the calculation of the proportion of household wastewater which receives appropriate treatment will be handled in a similar way to the calculation of &#x2018;safely managed sanitation services&#x2019; for SDG indicator 6.2.1. Household wastewater collected in sewers will be assumed to reach UWWTPs, unless national data are available about direct discharges to the environment. If data are available on the proportion of wastewater flows received by UWWTPs which receive secondary treatment or better, this proportion can be assumed to apply equally to the flows generated by households, industries, and services which discharge into public sewers. Household wastewater which enters on-site storage and treatment systems such as septic tanks will be assumed to be safely treated if national data on compliance of on-site wastewater treatment systems to relevant standards are available. In the absence of such data, standard assumptions will be used to characterise flows contained in on-site storage receiving safe emptying and treatment.</p>\n<p> </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>See &#x2018;4.g Regional aggregations&#x2019;.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates have only been produced for household wastewater to date, by combining volumes of wastewater generated and treated from countries with data. For the purpose of calculating regional aggregate statistics, values for countries without national estimates are imputed on the basis of regional averages (e.g. using M49 sub-regions). These imputed data are never published separately as national statistics. </p>\n<p>Regional and global aggregate statistics are only produced when the data available without imputation represent at least 50% of the regional or global total. Ideally this coverage threshold would be based on wastewater volumes, but data on the volumes of wastewater generated are not available for all countries. Accordingly, as an interim measure, data coverage thresholds and weighting of national statistics will be done on the basis of national population, drawing on the latest statistics available from the UN World Population Prospects.</p>", "DOC_METHOD__GLOBAL"=>"<p>Guidance for household wastewater can be referred to in a <a href=\"https://www.unwater.org/publications/domestic-wastewater-treatment-methodology-2024\">Methodological Note</a> (WHO, 2024).</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data submitted to UNSD or OECD/Eurostat come directly from national statistical offices and/or line ministries. Data treatment and validation is done jointly by Eurostat and the OECD for their member states according to an agreed process and timeline. For those data submitted to UNSD a review is undertaken by the Environment Statistics Section for consistency. UNSD carries out extensive data validation procedures that include built-in automated procedures, manual checks and cross-references to national sources of data. Communication is carried out with countries for clarification and validation of data. UNSD and OECD/Eurostat do not make any estimation or imputation for missing values. UN-Habitat and WHO use the resulting data without modification, except to generate totals when sub-elements but not totals are reported by countries. In case of any observed discrepancies or anomalies the national authorities are consulted for clarification.</p>\n<p>Estimates of household wastewater treatment are calculated based on national data and draft estimates are shared with countries for a consultation process similar to, and coordinated with, the consultation process used by WHO and UNICEF for indicators 6.1.1 and 6.2.1. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>As reported in the <a href=\"https://www.unwater.org/publications/progress-wastewater-treatment-2024-update\">2024 indicator report</a> (UN Habitat and WHO, 2024), across the 107 countries reporting some wastewater statistics for 2022 (representing 73 percent of the world&#x2019;s population), the proportion of total wastewater receiving some level of treatment could only be calculated for 73 countries (representing 42 percent of the global population); whereas the proportion of total wastewater &#x201C;safely&#x201D; treated, i.e. at least secondary treatment could only be calculated for 42 countries (representing 12 percent of the population). These data were insufficient to establish regional aggregates on the proportion of total wastewater treated and safely treated. Reporting on industrial wastewater treatment remains limited, with only 49 countries reporting some statistics on flows generated and only 27 countries reporting some statistics on flows treated. As a consequence, the proportion of industrial wastewater receiving some level of treatment could only be calculated for 22 countries representing 8% of the global population.</p>\n<p>In 2022 estimates of data on &#x2018;proportion of safely treated household wastewater flows&#x2019; were available for 129 countries representing 89% of the global population, as well as for 11 areas and territories which are not United Nations Member States. These are available through the UN SDG Database (EN_WWT_WWDS), and <a href=\"https://www.who.int/teams/environment-climate-change-and-health/water-sanitation-and-health/monitoring-and-evidence/wash-monitoring/2023-country-files-for-sdg-6.3.1\">detailed country files are available from WHO</a>. Aggregates were also produced for the world and for various regional groupings, including the SDG regions. </p>\n<p>The <a href=\"https://unstats.un.org/unsd/envstats/questionnaire\">UNSD/UNEP Questionnaire on Environment Statistics</a> has collected data on wastewater generation and treatment for since 2004. The Questionnaire is regularly sent to more than 160 countries, covering both national and city levels. However, the response rate for the UNSD/UNEP questionnaire is around 50% and data completeness and quality remain a challenge, especially for developing countries. A similar <a href=\"https://ec.europa.eu/eurostat/documents/1798247/6664269/Data+Collection+Manual+for+the+OECD_Eurostat+Joint+Questionnaire+on+Inland+Waters+%28version+3.0%2C+2014%29.pdf/f5f60d49-e88c-4e3c-bc23-c1ec26a01b2a\">Joint Questionnaire for Inland Waters</a> is regularly sent out by Eurostat and OECD to their respective member countries. </p>\n<p>For those variables relevant to this indicator which are collected through the above questionnaires, data for up to 64 countries are available in some years (wastewater treated in urban wastewater treatment plants), though for other relevant variables, for a given year, data for 30 countries or less may be available. More details on the availability of data reported through these questionnaires is available in the <a href=\"https://www.unwater.org/publications/progress-wastewater-treatment-2024-update\">2024 indicator report</a> (UN-Habitat and WHO, 2024, Annex 3).</p>\n<p><strong>Time series:</strong></p>\n<p>Some indicators have time series available for multiple years, while others currently only have most recent year availability.</p>\n<p><strong>Disaggregation:</strong></p>\n<p><strong>Wastewater generation </strong></p>\n<p>Wastewater can be generated through a variety of economic activities as well as through private households. The following categories of wastewater flows can be distinguished:</p>\n<ul>\n  <li>Agricultural (ISIC 01-03) covers crop and animal production, hunting and related service activities; forestry and logging; and fishing and aquaculture. Wastewater generated from these activities for the most part enters the environment as non-point pollution and will not be monitored as part of indicator 6.3.1.</li>\n  <li>Mining and quarrying (ISIC 05-09) include the extraction of minerals occurring naturally as solids (coal and ores), liquids (petroleum) or gases (natural gas). Extraction can be achieved by different methods such as underground or surface mining, well operation, seabed mining, etc.</li>\n  <li>Manufacturing (ISIC 10-33) includes the physical or chemical transformation of materials, substances, or components into new products. The materials, substances, or components transformed are raw materials that are products of agriculture, forestry, fishing, mining or quarrying as well as products of other manufacturing activities. Substantial alteration, renovation or reconstruction of goods is generally considered to be manufacturing.</li>\n  <li>Electricity (ISIC 35) includes electric power generation, transmission and distribution, as well as the manufacture and distribution of gas, and steam and air conditioning supply. Water used for cooling in power generation is explicitly excluded from calculations of wastewater flows. </li>\n  <li>Water supply, sewerage, waste management and remediation activities (ISIC 36&#x2013;39)</li>\n  <li>Construction (ISIC 41-43) includes general construction and specialized construction activities for buildings and civil engineering works. It includes new work, repair, additions and alterations, the erection of prefabricated buildings or structures on the site and also construction of a temporary nature.</li>\n  <li>Services (ISIC 45-96) include a wide range of economic activities where water is mainly used for sanitary purposes, washing, cleaning, cooking, etc.</li>\n  <li>Wastewater can also be generated by private households, originating predominantly from the human metabolism and from household activities. A portion of the water which is brought into private households for domestic purposes (e.g. cooking, drinking, bathing, washing,) exits the household as wastewater. Domestic wastewater flows are not directly covered by ISIC codes, unless the household generates water in the course of an economic activity. Note that wastewater generated by residents of communal institutions may be covered under ISIC divisions, e.g. 85 (education) or 87 (residential care activities). </li>\n</ul>\n<p><strong>Wastewater treatment</strong></p>\n<p>OECD/Eurostat databases disaggregate the flow of treated wastewater by type (e.g. urban and industrial discharges), whereas the UNSD database reports the flow of wastewater treated in other treatment plants and in urban wastewater treatment plants (see definitions in Section 2.a above) by level of treatment (primary, secondary and tertiary). The variables and terms used for indicator 6.3.1 are listed below. </p>\n<p><img src=\"data:image/png;base64,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alt=\"A screenshot of a computer AI-generated content may be incorrect.\"></p>\n<p>Figure 2: Disaggregated variables used for the generation (left) and treatment (right) of wastewater used to report on SDG Indicator 6.3.1.</p>\n<p>For all of the treatment categories, some but not all countries have data available on the compliance of treatment to relevant effluent standards or targets. When available, such data are not routinely reported to UNSD or OECD/Eurostat, but may be available in other national data sources (e.g. statistical or wastewater analysis reports). Where available, data on the proportion of flows that meet relevant criteria will be used for indicator 6.3.1. In the absence of such data, treatment nominally classified as secondary or better (or equivalent) will be used as a proxy for safe treatment.</p>\n<p>Where it is possible to quantify both generation and treatment by source (industrial, services, or households), the proportion of wastewater treated will also be calculated separately by source.</p>", "COMPARABILITY__GLOBAL"=>"<p>See &#x2018;3.b Data collection method&#x2019;.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>References:</strong></p>\n<ul>\n  <li>OECD/Eurostat, 2018. Data Collection Manual for the OECD/Eurostat Joint Questionnaire on Inland Waters and Eurostat regional water questionnaire. <a href=\"https://ec.europa.eu/eurostat/documents/1798247/6664269/Data+Collection+Manual+for+the+OECD_Eurostat+Joint+Questionnaire+on+Inland+Waters+%28version+3.0%2C+2014%29.pdf/f5f60d49-e88c-4e3c-bc23-c1ec26a01b2a\">https://ec.europa.eu/eurostat/documents/1798247/6664269/Data+Collection+Manual+for+the+OECD_Eurostat+Joint+Questionnaire+on+Inland+Waters+%28version+3.0%2C+2014%29.pdf/f5f60d49-e88c-4e3c-bc23-c1ec26a01b2a</a></li>\n  <li>UN Habitat and WHO, 2021. Progress on wastewater treatment &#x2013; Global status and acceleration needs for SDG indicator 6.3.1. United Nations Human Settlements Programme (UN-Habitat) and World Health Organization (WHO), Geneva. <a href=\"https://www.unwater.org/publications/progress-wastewater-treatment-2021-update\">https://www.unwater.org/publications/progress-wastewater-treatment-2021-update</a></li>\n  <li>UN Habitat and WHO, 2024. Progress on the proportion of domestic and industrial wastewater flows safely treated &#x2013; Mid-term status of SDG Indicator 6.3.1 and acceleration needs, with a special focus on climate change, wastewater reuse and health. United Nations Human Settlements Programme (UN-Habitat) and World Health Organization (WHO), Geneva. <a href=\"https://www.unwater.org/publications/progress-wastewater-treatment-2024-update\">https://www.unwater.org/publications/progress-wastewater-treatment-2024-update</a></li>\n  <li>UNSD. Manual on the Basic Set of Environment Statistics <a href=\"https://unstats.un.org/unsd/envstats/fdes/manual_bses.cshtml\">https://unstats.un.org/unsd/envstats/fdes/manual_bses.cshtml</a> (wastewater statistics - forthcoming)</li>\n  <li>UNSD, 2018. International Standard Industrial Classification of All Economic Activities, Revision 4. <a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf</a> </li>\n  <li>UNSD Indicator Tables (inland water resources) (<a href=\"https://unstats.un.org/unsd/envstats/qindicators\">https://unstats.un.org/unsd/envstats/qindicators</a>)</li>\n  <li>UNSD/UNEP Questionnaire 2018 on Environment Statistics. <a href=\"https://unstats.un.org/unsd/envstats/questionnaire\">https://unstats.un.org/unsd/envstats/questionnaire</a>WHO and UN Habitat, 2018. Progress on Safe Treatment and Use of Wastewater 2018: Piloting the monitoring methodology and initial findings for SDG indicator 6.3.1. <br><a href=\"https://www.unwater.org/publications/progress-on-wastewater-treatment-631/\">https://www.unwater.org/publications/progress-on-wastewater-treatment-631/</a></li>\n  <li>WHO, 2024. Safely Treated Domestic Wastewater: 2024 methodology. <a href=\"https://www.unwater.org/publications/domestic-wastewater-treatment-methodology-2024\">https://www.unwater.org/publications/domestic-wastewater-treatment-methodology-2024</a></li>\n</ul>", "indicator_sort_order"=>"06-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"6.3.2", "slug"=>"6-3-2", "name"=>"Proporción de masas de agua de buena calidad", "url"=>"/site/es/6-3-2/", "sort"=>"060302", "goal_number"=>"6", "target_number"=>"6.3", "global"=>{"name"=>"Proporción de masas de agua de buena calidad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de masas de agua de buena calidad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de masas de agua de buena calidad", "indicator_number"=>"6.3.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Bienal", "url"=>"https://www.euskadi.eus/informacion/estadistica-de-calidad-de-las-masas-de-aguas-090214/web01-s2ing/es/", "url_text"=>"Estadística de la calidad de las masas de agua", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de masas de agua de buena calidad", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.3- De aquí a 2030, mejorar la calidad del agua reduciendo la contaminación, eliminando el vertimiento y minimizando la emisión de productos químicos y materiales peligrosos, reduciendo a la mitad el porcentaje de aguas residuales sin tratar y aumentando considerablemente el reciclado y la reutilización sin riesgos a nivel mundial", "definicion"=>"\nEl indicador se define como la proporción de cuerpos de agua en un territorio que \ntienen una buena calidad ambiental del agua. La calidad ambiental del agua \nse refiere al agua natural, no tratada, en ríos, lagos y aguas subterráneas \ny representa una combinación de influencias naturales junto con los impactos \nde todas las actividades antropogénicas.\n\nSe presentan 3 series diferentes para distintas masas de agua: \n\n  - ríos\n  - aguas subterráneas\n  - aguas superficiales (incluye aguas costeras, aguas de transición, embalses, lagos y humedales y ríos)\n", "formula"=>"\n$$PMABC^{t} = \\frac{MBC_{agua}^{t}}{M_{agua}^{t}} \\cdot 100$$\n\ndonde:\n\n$MBC_{agua}^{t} =$ masas de agua de buena calidad en el año $t$\n\n$M_{agua}^{t} =$ masas de agua evaluadas en el año $t$\n", "desagregacion"=>"", "observaciones"=>"\nLa evaluación de las aguas superficiales (estado ecológico) se realiza según la Directiva 2000/60/CE del Parlamento Europeo y del Consejo.\n\nLa evaluación de aguas subterráneas se realiza según la Guía para la evaluación del estado de las aguas superficiales y subterráneas, \naprobada por Instrucción del 14 de octubre de 2020 del secretario de Estado de Medio Ambiente (SEMA)\n\nEl censo de masas de agua se corresponde con las masas que cuentan con presencia relevante dentro de la \nComunidad Autónoma del País Vasco de acuerdo con el Real Decreto 35/2023, de 24 de enero, por el que se \naprueba la revisión de los planes hidrológicos de las demarcaciones hidrográficas del Cantábrico Occidental, \nGuadalquivir, Ceuta, Melilla, Segura y Júcar, y de la parte española de las demarcaciones hidrográficas del Cantábrico \nOriental, Miño-Sil, Duero, Tajo, Guadiana y Ebro.\n\nPara el periodo indicado, las evaluaciones se corresponden con revisiones de estado mediante criterios \nhomogéneos atendiendo al Real Decreto 35/2023 y a los avances técnicos habidos.\n", "periodicidad"=>"Anual", "justificacion_global"=>"\nLa buena calidad del agua ambiental es esencial para proteger los \necosistemas acuáticos y los servicios que prestan, entre ellos: \nla preservación de la biodiversidad; la protección de la salud humana \ndurante el uso recreativo y mediante el suministro de agua potable; \nel apoyo a la nutrición humana mediante el suministro de peces y agua \npara riego; la facilitación de una variedad de actividades económicas; \ny el fortalecimiento de la resiliencia de las personas frente a los \ndesastres relacionados con el agua. Por lo tanto, la buena calidad del agua \nambiental está estrechamente vinculada al logro de muchos otros \nObjetivos de Desarrollo Sostenible.\n\nLa meta 6.3 tiene por objeto mejorar la calidad del agua y el \nindicador 6.3.2 proporciona un mecanismo para determinar si las medidas de \ngestión de la calidad del agua están contribuyendo a la mejora de la calidad \ndel agua a lo largo del tiempo y en qué medida. El indicador también \nestá directamente vinculado con el indicador 6.3.1 sobre el tratamiento \nde aguas residuales porque el tratamiento inadecuado de las aguas \nresiduales conduce a la degradación de la calidad de las aguas que \nreciben los efluentes de aguas residuales. \n\nInforma directamente sobre el progreso hacia la meta 6.3 y \nestá fuertemente vinculada a la meta 6.6 sobre ecosistemas relacionados \ncon el agua, así como a la meta 14.1 sobre contaminación \nmarina (eutrofización costera).\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EN_H2O_OPAMBQ&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de cuerpos de agua abiertos con buena calidad de agua ambiental (%) EN_H2O_OPAMBQ</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EN_H2O_RVAMBQ&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de cuerpos de agua fluviales con buena calidad ambiental del agua (%) EN_H2O_RVAMBQ</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EN_H2O_GRAMBQ&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de masas de agua subterránea con buena calidad de agua ambiental (%) EN_H2O_GRAMBQ</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EN_H2O_WBAMBQ&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de masas de agua con buena calidad ambiental del agua (%) EN_H2O_WBAMBQ</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas, pero aporta información similar.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-03-02.pdf\">Metadatos 6-3-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-27", "en"=>{"indicador_disponible"=>"Proporción de masas de agua de buena calidad", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.3- De aquí a 2030, mejorar la calidad del agua reduciendo la contaminación, eliminando el vertimiento y minimizando la emisión de productos químicos y materiales peligrosos, reduciendo a la mitad el porcentaje de aguas residuales sin tratar y aumentando considerablemente el reciclado y la reutilización sin riesgos a nivel mundial", "definicion"=>"\nThe indicator is defined as the proportion of water bodies in a territory that \nhave good environmental water quality. Environmental water quality refers to natural, \nuntreated water in rivers, lakes, and groundwater and represents a combination of \nnatural influences along with the impacts of all anthropogenic activities. \n\n3 series are presented for different water bodies: \n\n  - rivers\n  - groundwater\n  - surface waters (includes coastal waters, transitional waters, reservoirs, lakes and wetlands, and rivers)\n", "formula"=>"\n$$PMABC^{t} = \\frac{MBC_{water}^{t}}{M_{water}^{t}} \\cdot 100$$\n\nwhere:\n\n$MBC_{water}^{t} =$ good quality water bodies in year $t$\n\n$M_{water}^{t} =$ total number of water bodies evaluated in year $t$\n", "desagregacion"=>nil, "observaciones"=>"\nThe assessment of surface waters (ecological status) is carried out according to Directive 2000/60/EC \nof the European Parliament and of the Council. \n\nGroundwater assessment is carried out according to the Guide for the Assessment of the Status of \nSurface and Groundwater, approved by Instruction of October 14, 2020, from the Secretary of State \nfor the Environment (SEMA). \n\nThe water body census corresponds to the water bodies that have a significant presence within the \nAutonomous Community of the Basque Country in accordance with Royal Decree 35/2023, of January 24, \nwhich approves the revision of the hydrological plans of the Western Cantabrian, Guadalquivir, Ceuta, \nMelilla, Segura, and Júcar river basins, and of the Spanish part of the Eastern Cantabrian, Miño-Sil, \nDuero, Tajo, Guadiana, and Ebro river basins. \n\nFor the indicated period, the evaluations correspond to status reviews using uniform criteria in \naccordance with Royal Decree 35/2023 and the technical advances that have occurred. \n", "periodicidad"=>"Anual", "justificacion_global"=>"\nGood ambient water quality is essential for protecting aquatic ecosystems and the services \nthey provide, including: the preservation of biodiversity; the protection of human health \nduring recreational use and through the provision of drinking water; the support of human \nnutrition through the provision of fish and water for irrigation; the enabling of a variety \nof economic activities; and the strengthening of the resilience of people against water-related \ndisasters. Good ambient water quality is therefore closely linked to the achievement of many \nother Sustainable Development Goals. \n\nTarget 6.3 aims at improving water quality and indicator 6.3.2 provides a mechanism for determining \nwhether, and to which extent, water quality management measures are contributing to the improvement \nof water quality over time. The indicator is also directly linked to indicator 6.3.1 on wastewater \ntreatment because inadequate wastewater treatment leads to degradation in quality of the waters \nreceiving the wastewater effluents. \n\nIt directly informs progress towards target 6.3 and is strongly linked to target 6.6 on water-related \necosystems, as well as target 14.1 on marine pollution (coastal eutrophication). \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EN_H2O_OPAMBQ&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of open water bodies with good ambient water quality (%) EN_H2O_OPAMBQ</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EN_H2O_RVAMBQ&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of river water bodies with good ambient water quality (%) EN_H2O_RVAMBQ</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EN_H2O_GRAMBQ&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of groundwater bodies with good ambient water quality (%) EN_H2O_GRAMBQ</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EN_H2O_WBAMBQ&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of bodies of water with good ambient water quality (%) EN_H2O_WBAMBQ</a> UNSTATS\n", "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-03-02.pdf\">Metadata 6-3-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de masas de agua de buena calidad", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.3- De aquí a 2030, mejorar la calidad del agua reduciendo la contaminación, eliminando el vertimiento y minimizando la emisión de productos químicos y materiales peligrosos, reduciendo a la mitad el porcentaje de aguas residuales sin tratar y aumentando considerablemente el reciclado y la reutilización sin riesgos a nivel mundial", "definicion"=>"\nAdierazlea honela definitzen da: uraren ingurumen-kalitate ona duen lurralde bateko ur-gorputzen \nproportzioa. Uraren ingurumen-kalitatea ibaietan, aintziretan eta lurpeko uretan tratatu gabeko ur \nnaturalari dagokio, eta eragin naturalen eta jarduera antropogeniko guztien inpaktuen arteko \nkonbinazioa adierazten du.\n\nOndoko ur-masetarako 3 serie desberdin aurkezten dira: \n\n  - ibaiak\n  - lurpeko urak\n  - azaleko urak (kostaldeko urak, trantsizioko urak, urtegiak, lakuak eta hezeguneak eta ibaiak barne)\n", "formula"=>"\n$$PMABC^{t} = \\frac{MBC_{ura}^{t}}{M_{ura}^{t}} \\cdot 100$$\n\nnon:\n\n$MBC_{ura}^{t} =$ kalitate oneko ur-masak $t$ urtean\n\n$M_{ura}^{t} =$ ebaluatutako ur-masak $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>"\nGainazaleko uren ebaluazioa (egoera ekologikoa) Europako Parlamentuaren eta Kontseiluaren 2000/60/EE \nZuzentarauaren arabera egiten da.\n\nLurpeko uren ebaluazioa Lurrazaleko eta lurpeko uren egoera ebaluatzeko gidaren arabera egiten da. Gida \nhori Ingurumeneko Estatu Idazkariaren (SEMA) 2020ko urriaren 14ko Instrukzioaren bidez onartu zen.\n\nUr-masen zentsua bat dator urtarrilaren 24ko 35/2023 Errege Dekretuaren arabera Euskal Autonomia Erkidegoan \npresentzia nabarmena duten masekin. Errege Dekretu horrek onartzen du Kantauri Mendebaldeko, Guadalquivirreko, Ceutako, Melillako, Segurako eta \nJucarreko demarkazio hidrografikoen eta Kantauri Ekialdeko, Miño-Sileko, Dueroko, Tajoko, Guadianako eta \nEbroko demarkazio hidrografikoen Espainiako zatiaren plan hidrologikoen berrikuspena.\n\nAdierazitako aldirako, ebaluazioak irizpide homogeneoen bidezko egoera-berrikuspenei dagozkie, 35/2023 Errege \nDekretuari eta izandako aurrerapen teknikoei jarraiki.\n", "periodicidad"=>"Anual", "justificacion_global"=>"\nIngurumen-uraren kalitate ona funtsezkoa da uretako ekosistemak eta horiek ematen dituzten zerbitzuak babesteko, \nbesteak beste: biodibertsitatea babestea; giza osasuna babestea aisialdian eta edateko uraren hornikuntzaren bidez; \ngiza nutrizioari laguntzea arrainak eta ureztatzeko ura hornituz; askotariko jarduera ekonomikoak erraztea; eta \npertsonen erresilientzia indartzea urarekin lotutako hondamendien aurrean. Beraz, ingurumen-uraren kalitate onak \nlotura estua du garapen jasangarriko beste helburu asko lortzearekin. \n\n6.3 xedearen helburua uraren kalitatea hobetzea da, eta 6.3.2 adierazleak mekanismo bat eskaintzen du uraren \nkalitatea kudeatzeko neurriek uraren kalitatea denboran zehar zein neurritan hobetzen laguntzen duten zehazteko. \nAdierazlea zuzenean lotuta dago hondakin-uren tratamenduari buruzko 6.3.1 adierazlearekin, hondakin-uren tratamendu \ndesegokiak hondakin-uren efluenteek jasotzen dituzten uren kalitatea degradatzera eramaten duelako. \n\n6.3 xederantz egindako aurrerapenaren berri ematen du zuzenean, eta lotura handia du 6.6 xedearekin (urarekin \nlotutako ekosistemak) eta 14.1 xedearekin (itsasoko kutsadura) (kostaldeko eutrofizazioa). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EN_H2O_OPAMBQ&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Ingurumen-uraren kalitate ona duten aire-zabaleko ur-gorputzen proportzioa (%) EN_H2O_OPAMBQ</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EN_H2O_RVAMBQ&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Uraren ingurumen-kalitate ona duten ibai-uren gorputzen proportzioa (%) EN_H2O_RVAMBQ</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EN_H2O_GRAMBQ&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Ingurumen-uraren kalitate ona duten lurpeko ur-masen proportzioa (%) EN_H2O_GRAMBQ</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EN_H2O_WBAMBQ&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Uraren ingurumen-kalitate ona duten ur-masen proportzioa (%) EN_H2O_WBAMBQ</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina antzeko informazioa ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-03-02.pdf\">Metadatuak 6-3-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 6: Ensure availability and sustainable management of water and sanitation for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 6.3: By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globally</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 6.3.2: Proportion of bodies of water with good ambient water quality</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_H2O_GRAMBQ - Proportion of groundwater bodies with good ambient water quality [6.3.2]</p>\n<p>EN_H2O_OPAMBQ - Proportion of open water bodies with good ambient water quality [6.3.2]</p>\n<p>EN_H2O_RVAMBQ - Proportion of river water bodies with good ambient water quality [6.3.2]</p>\n<p>EN_H2O_WBAMBQ - Proportion of bodies of water with good ambient water quality [6.3.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>6.3.1, 6.6.1, 14.1.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator is defined as the proportion of water bodies in the country that have good ambient water quality. Ambient water quality refers to natural, untreated water in rivers, lakes and groundwaters and represents a combination of natural influences together with the impacts of all anthropogenic activities. The indicator relies on water quality data derived from in situ measurements and the analysis of samples collected from surface and groundwaters. Water quality is assessed by means of core physical and chemical parameters that reflect natural water quality related to climatological and geological factors, together with major impacts on water quality. The continuous monitoring of all surface and groundwaters is economically unfeasible and not required to sufficiently characterize the status of ambient water quality in a country. Therefore, countries select river, lake and groundwater bodies that are representative and significant for the assessment and management of water quality to monitor and report on indicator 6.3.2. The quality status of individual water bodies is classified based on the compliance of the available water quality monitoring data for the core parameters with target values defined by the country. The indicator is computed as the proportion of the number of water bodies classified as having good quality (i.e. with at least 80 % compliance) to the total number of assessed water bodies, expressed as a percentage. </p>\n<p><strong>Concepts:</strong></p>\n<p>The concepts and definitions used in the methodology have been based on existing international frameworks and glossaries (WMO, 2012) unless where indicated otherwise below.</p>\n<p><strong>Aquifer: </strong>Geological formation capable of storing, transmitting and yielding exploitable quantities of water. </p>\n<p><strong>Classification of water quality: </strong>If at least 80% of the monitoring values for prescribed parameters in a water body comply with their respective target values, the water body is classified as having a &#x201C;good&#x201D; water quality status<strong>. </strong>Each water body is classified as being of &#x201C;good&#x201D; or &#x201C;not good&#x201D; status.<em> </em></p>\n<p><strong>Groundwater</strong>: Subsurface water occupying the saturated zone. </p>\n<p><strong>Groundwater body</strong>: A distinct volume of groundwater within an aquifer or aquifers (EU, 2000). Groundwater bodies that cross river basin district (RBD) boundaries should be divided at the boundary with each separate portion of the groundwater body being reported separately along with its respective RBD. </p>\n<p><strong>Lake:</strong> Inland body of standing surface water of significant extent. </p>\n<p><strong>Non-point-source pollution:</strong> Pollution of water bodies from dispersed sources such as fertilizers, chemicals and pesticides used in agricultural activities.</p>\n<p><strong>Parameter: </strong>Water quality variable or characteristic of water quality, also called a determinand.</p>\n<p><strong>Point source pollution</strong>: Pollution with a precisely located origin.</p>\n<p><strong>Pollution (of water)</strong>: Introduction into water of any undesirable substance which renders the water unfit for its intended use.</p>\n<p><strong>Pollutant</strong>: Substance which disrupts and interferes with the equilibrium of a water system and impairs the suitability of using the water for a desired purpose. </p>\n<p><strong>Reservoir: </strong>Body of water, either natural or man-made, used for storage, regulation and control of water resources.</p>\n<p><strong>River: </strong>Large stream which serves as the natural drainage for a basin.</p>\n<p><strong>River basin: </strong>Geographical area having a common outlet for its surface runoff.</p>\n<p><strong>River basin district: </strong>Area of land, made up of one or more neighbouring river basins together with their associated groundwaters (EU, 2000).</p>\n<p><strong>River water body</strong>: A coherent section of a river that is discrete (does not overlap with another water body) and is significant rather than arbitrarily designated. </p>\n<p><strong>Stream: </strong>Flowing body of water in a natural surface channel.</p>\n<p><strong>Surface water: </strong>Water which flows over, or lies on, the ground surface. </p>\n<p>Note: Indicator 6.3.2 does not include the monitoring of water quality in wetlands under monitoring level 1.</p>\n<p><strong>Target value</strong>: A value (or range) for any given water quality parameter that indicates the threshold for a designated water quality, such as good water quality rather than acceptable water quality.</p>\n<p><strong>Toxic substance</strong>: Chemical substance which can disturb the physiological functions of humans, animals and plants.</p>\n<p><strong>Transboundary waters: </strong>Surface or ground waters which mark, cross or are located on boundaries between two or more States; wherever transboundary waters flow directly into the sea, these transboundary waters end at a straight line across their respective mouths between points on the low-water line of the banks (UNECE, 1992).</p>\n<p><strong>Water quality index: </strong>The measured water quality results for all parameters combined into a numeric value for each monitoring location. These scores are then aggregated over the time of the assessment period. The index score can range between zero (worst) to 100 (best).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%): The<strong> proportion</strong> of the number of bodies of water with good water quality compared to the total number of assessed water bodies expressed as a percent. </p>\n<p>To classify whether a water body is of &#x201C;good ambient water quality&#x201D; or not, a threshold is applied where 80 percent or more of monitoring values meet their target values. The number of water bodies that are classified as either good ambient water quality or not can be reported at the Reporting Basin District, and then at the national level to generate the national indicator score.</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>Classification of inland water bodies (UNEP uses this classification, but does not analyze water quality for all categories, but only for lakes and rivers.): <a href=\"https://unstats.un.org/unsd/classifications/Family/Detail/2002\">https://unstats.un.org/unsd/classifications/Family/Detail/2002</a> </li>\n  <li>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>The recommended sources of data are water quality monitoring data derived from in situ measurements and the analysis of samples collected from surface and groundwaters in national or sub-national ambient water quality monitoring programmes implemented by governmental authorities. Additional water quality monitoring data from research or citizen-science monitoring programmes can be used to supplement the available authoritative monitoring data, provided they are authorised by the national reporting agency.</p>\n<p>The number of monitoring locations required to determine the quality status of a water body depends on the type and size of the water body, but a minimum of one monitoring location per water body is required. The minimum data requirements for calculating this indicator are measurements for all of the recommended or alternative core parameters appropriate to the type of water body as defined in the methodology. </p>\n<p>Measurements should be taken routinely, at prescribed intervals, or the same time of year each year, from the same locations. Even if new monitoring stations are introduced, data should continue to be collected from the original locations. This ensures that results are comparable between reports, thereby enabling trends to be established over time. The monitoring data needed for the indicator computation may be collected by different monitoring programmes involving different agencies and organizations. It is therefore important to establish and maintain centralized data repositories at the national level that collate the data from the various stakeholders, ensuring compatibility in reporting units between all agencies submitting data. Data should be compiled for each core parameter at each sampling location in order to calculate the indicator.</p>", "COLL_METHOD__GLOBAL"=>"<p>The data is collected by UNEP and its Global Environment Monitoring System for Water (GEMS/Water) through electronic reporting in the global water quality information system <a href=\"https://gemstat.org/data/data-portal/\">GEMStat</a>. At the national level, data reports are provided by the GEMS/Water National Focal Points or any other official counterpart appointed by the respective government. GEMS/Water offers consultation and support in selecting and compiling the required monitoring data, defining suitable river basin districts and delineating water bodies, as well as computing the indicator, upon request through its helpdesk. Data reported by the countries are checked for consistency with respect to the monitoring parameters, target values and spatial units and compared with monitoring data available in GEMStat, if applicable. </p>", "FREQ_COLL__GLOBAL"=>"<ol>\n  <li>First reporting cycle: 2017</li>\n  <li>Second reporting cycle: 2020</li>\n  <li>Third reporting cycle: 2023</li>\n  <li>Fourth reporting cycle: 2026</li>\n  <li>Fifth reporting cycle: 2029</li>\n</ol>", "REL_CAL_POLICY__GLOBAL"=>"<ol>\n  <li>First reporting cycle: 2018</li>\n  <li>Second reporting cycle: 2021</li>\n  <li>Third reporting cycle: 2024</li>\n  <li>Fourth reporting cycle: 2027</li>\n  <li>Fifth reporting cycle: 2030</li>\n</ol>", "DATA_SOURCE__GLOBAL"=>"<ol>\n  <li>GEMS/Water National Focal Points in relevant Ministries, Water Authorities, National Statistical Offices etc. or their nominated representative.</li>\n</ol>", "COMPILING_ORG__GLOBAL"=>"<ol>\n  <li>United Nations Environment Programme (UNEP)</li>\n  <li>UNEP GEMS/Water Data Centre, International Centre for Water Resources and Global Change (ICWRGC), German Federal Institute of Hydrology (BfG)</li>\n</ol>", "INST_MANDATE__GLOBAL"=>"<p>Identification of UNEP as custodian agency for SDG indicator 6.3.2 by Inter-agency and Expert Group on SDG Indicators. GEMS/Water is the mechanism within UNEP supporting countries on all aspects around ambient freshwater quality.</p>", "RATIONALE__GLOBAL"=>"<p>Good ambient water quality is essential for protecting aquatic ecosystems and the services they provide, including: the preservation of biodiversity; the protection of human health during recreational use and through the provision of drinking water; the support of human nutrition through the provision of fish and water for irrigation; the enabling of a variety of economic activities; and the strengthening of the resilience of people against water-related disasters. Good ambient water quality is therefore closely linked to the achievement of many other Sustainable Development Goals.</p>\n<p>Target 6.3 aims at improving water quality and indicator 6.3.2 provides a mechanism for determining whether, and to which extent, water quality management measures are contributing to the improvement of water quality over time. The indicator is also directly linked to indicator 6.3.1 on wastewater treatment because inadequate wastewater treatment leads to degradation in quality of the waters receiving the wastewater effluents. It directly informs progress towards target 6.3 and is strongly linked to target 6.6 on water-related ecosystems, as well as target 14.1 on marine pollution (coastal eutrophication).</p>\n<p>The methodology recognises that countries have different capacity levels to monitor water quality, with many developed countries operating extensive and complex programmes that collect and report data to existing reporting frameworks beyond the scope of this methodology. For these countries it is recognised that this methodology will not contribute to improving their water quality; however it must be sufficiently flexible to capture data from existing monitoring frameworks without burdening countries with additional reporting obligations. Conversely, many of the least developed countries currently do not monitor water quality or operate very limited monitoring programmes. The methodology must therefore allow these countries to contribute to the global indicator, according to their national capacity and available resources. </p>\n<p>The development of the methodology builds on best practice for water quality monitoring promoted by the UNEP GEMS/Water programme since 1978 together with testing by several pilot countries during the Integrated Monitoring Initiative Proof of Concept phase of 2016, and external review by experts and international organizations. This led to revision of the original methodology, which was then further tested through the 2017 global data drive. The feedback received has contributed to the present refined methodology.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The monitoring and reporting of SDG Indicator 6.3.2 requires considerable national financial and human capacities to regularly measure water quality parameters at sufficient spatial and temporal resolutions, and to consistently collect, quality-assure and process the monitoring data to compute the indicator. Substantial investments in monitoring and data management infrastructures, as well as targeted capacity development in water quality monitoring programme design and operation, will be required in many countries to enhance national capacities to regularly and consistently report on the indicator. </p>\n<p>Recognizing the differences in monitoring and data processing capacities among countries, the indicator methodology offers a progressive monitoring approach allowing countries to start with reporting based on their existing capacity and progressively enhance the data coverage and indicator significance with increasing capacity.</p>", "DATA_COMP__GLOBAL"=>"<p><strong>Computation Method:</strong></p>\n<p>The indicator is computed by first classifying all assessed water bodies based on the compliance of the monitoring data collected for selected parameters at monitoring locations within the water body with parameter-specific target values: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n      </mrow>\n      <mrow>\n        <mi>w</mi>\n        <mi>q</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>n</mi>\n          </mrow>\n          <mrow>\n            <mi>c</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>n</mi>\n          </mrow>\n          <mrow>\n            <mi>m</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Where</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n      </mrow>\n      <mrow>\n        <mi>w</mi>\n        <mi>q</mi>\n      </mrow>\n    </msub>\n  </math> is the percentage compliance [%];</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>n</mi>\n      </mrow>\n      <mrow>\n        <mi>c</mi>\n      </mrow>\n    </msub>\n  </math> is the number of monitoring values in compliance with the target values;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>n</mi>\n      </mrow>\n      <mrow>\n        <mi>m</mi>\n      </mrow>\n    </msub>\n  </math> is the total number of monitoring values.</p>\n<p>A threshold value of 80% compliance is defined to classify water bodies as &#x201C;good&#x201D; quality. Thus, a body of water is classified as having a good quality status if at least 80% of all monitoring data from all monitoring stations within the water body comply with the respective targets.</p>\n<p>In a second step, the classification results are used to compute the indicator as the proportion of the number of water bodies classified as having a good quality status to the total number of classified water bodies expressed in percentage:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>W</mi>\n    <mi>B</mi>\n    <mi>G</mi>\n    <mi>Q</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>n</mi>\n          </mrow>\n          <mrow>\n            <mi>g</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>n</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Where</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>W</mi>\n    <mi>B</mi>\n    <mi>G</mi>\n    <mi>Q</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n  </math> is the percentage of water bodies classified as having a good quality status;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>n</mi>\n      </mrow>\n      <mrow>\n        <mi>g</mi>\n      </mrow>\n    </msub>\n  </math> is the number of classified water bodies classified as having a good quality status;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>n</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> is the total number of monitored and classified water bodies.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The UNEP SDG6 Helpdesk assists countries in ensuring the quality of their submission during its preparation. </p>\n<p>Following the initial submission, the Helpdesk undertakes several checks on the data and calrifies any irregularities with the country technical focal point until both sides agree to finalize the report.</p>\n<p>The data is then submitted to the UNEP SDG focal point, who collates all indicators data for which UNEP is the Custodian Agency, where a further quality check is undertaken, prior to submission to the SDG Global Database.</p>", "ADJUSTMENT__GLOBAL"=>"<p>In case national definitions such as water quality target values change, countries can retroactively adjust previous submissions.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values are not imputed.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Missing values are not imputed.</p>", "REG_AGG__GLOBAL"=>"<p>The data will be aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see:</p>\n<p><a href=\"https://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">https://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a>. </p>", "DOC_METHOD__GLOBAL"=>"<p>6.3.2 Online Support Platform with official methodology, technical materials, case studies and presentations to guide the reporting process available under: <a href=\"https://communities.unep.org/display/sdg632\">https://communities.unep.org/display/sdg632</a></p>\n<p>SDG 6.3.2 Helpdesk reachable via: <a href=\"mailto:sdg632@un.org\">sdg632@un.org</a> (Q&amp;A, arranging of individual support calls, indicator calculation services etc.).</p>\n<p>Various capacity development activities around the indicator: online webinars, country visits, workshops.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The GEMS/Water Data Centre is hosted by the Federal Institute of Hydrology, a government entity of the Federal Republic of Germany and complies with the government&#x2019;s quality management, assurance, and assessment procedures.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>See 4.i </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See 4.i.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>An initial baseline data collection has been conducted in 2017 with 48 country data submissions as of February 2018.</p>\n<p><strong>Time series:</strong></p>\n<p>Second reporting cycle 2020: 89 submissions as of February 2021. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Depending on the level of detail provided by countries in their submissions, the indicator can be disaggregated by water body type (river, lake, groundwater) and river basin district. This disaggregated data can support informed decision-making at the national and sub-national levels to monitor and improve water quality management measures.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable as no internationally estimated data is used to impute. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong><a href=\"http://www.sdg6monitoring.org/indicators/target-63/indicators632/\"><strong>http://www.sdg6monitoring.org/indicators/target-63/indicators632/</strong></a><strong> </strong></p>\n<p><strong>References:</strong></p>\n<p>EU (European Parliament, Council of the European Union), 2000. Water Framework Directive (WFD) 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy, Official Journal L327, 1&#x2013;72. Available at: <a href=\"http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32000L0060\"><u>http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32000L0060</u></a></p>\n<p>UNECE, 1992. Convention on the Protection and Use of Transboundary Watercourses and International Lakes. Available at: <a href=\"http://www.unece.org/fileadmin/DAM/env/water/pdf/watercon.pdf\" target=\"_blank\">http://www.unece.org/fileadmin/DAM/env/water/pdf/watercon.pdf</a></p>\n<p>WMO, 2012. <em>International Glossary of Hydrology. </em>No. 385 World Meteorological Organization and United Nations Educational, Scientific and Cultural Organization. Available at: <a href=\"http://library.wmo.int/pmb_ged/wmo_385-2012.pdf\"><u>http://library.wmo.int/pmb_ged/wmo_385-2012.pdf</u></a><em> </em></p>", "indicator_sort_order"=>"06-03-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"6.4.1", "slug"=>"6-4-1", "name"=>"Cambio en el uso eficiente de los recursos hídricos con el paso del tiempo", "url"=>"/site/es/6-4-1/", "sort"=>"060401", "goal_number"=>"6", "target_number"=>"6.4", "global"=>{"name"=>"Cambio en el uso eficiente de los recursos hídricos con el paso del tiempo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Cambio en el uso eficiente de los recursos hídricos con el paso del tiempo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Cambio en el uso eficiente de los recursos hídricos con el paso del tiempo", "indicator_number"=>"6.4.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La razón de ser de este indicador consiste en proporcionar información sobre la eficiencia \ndel uso económico y social de los recursos hídricos, es decir, el valor añadido generado \npor el uso del agua en los principales sectores de la economía y las pérdidas en la red de distribución.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.4.1&seriesCode=ER_H2O_WUEYST&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=TOTAL\">Eficiencia en el uso del agua (dólares estadounidenses por metro cúbico) ER_H2O_WUEYST</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-04-01.pdf\">Metadatos 6-4-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-27", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The rationale behind this indicator consists in providing information \non the efficiency of the economic and social usage of water resources, \ni.e., value added generated by the use of water in the main sectors \nof the economy, and distribution network losses. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.4.1&seriesCode=ER_H2O_WUEYST&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=TOTAL\">Water Use Efficiency (United States dollars per cubic meter) ER_H2O_WUEYST</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-04-01.pdf\">Metadata 6-4-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Adierazle honen helburua baliabide hidrikoen erabilera ekonomiko eta sozialaren eraginkortasunari buruzko \ninformazioa ematea da, hau da, uraren erabilerak ekonomiaren sektore nagusietan sortzen duen balio erantsia \neta banaketa-sareko galerak neurtzea. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.4.1&seriesCode=ER_H2O_WUEYST&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=TOTAL\">Uraren erabileraren efizientzia (dolar estatubatuarrak metro kubiko bakoitzeko) ER_H2O_WUEYST</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-04-01.pdf\">Metadatuak 6-4-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 6: Ensure availability and sustainable management of water and sanitation for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 6.4: By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 6.4.1: Change in water-use efficiency over time</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_H2O_WUEYST - Water Use Efficiency (United States dollars per cubic meter) [6.4.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>This indicator needs to be combined with the water stress indicator 6.4.2 to provide adequate follow-up of the target 6.4.</p>\n<p>Other indicators, specifically those for Targets 1.1, 1.2, 2.1, 2.2, 5.4, 5.a, 6.1, 6.2, 6.3, 6.5 will complement the information provided by this indicator.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Change in water use efficiency over time (CWUE): The change in the ratio of the value added to the volume of water use, over time.</p>\n<p>Water Use Efficiency (WUE) is defined as the value added of a given major sector<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup> divided by the volume of water used. Following the United Nations International Standard Industrial Classification of All Economic Activities ISIC 4 coding , sectors are defined as:</p>\n<ol>\n  <li>agriculture; forestry; fishing (ISIC A), hereinafter &#x201C;agriculture&#x201D;;</li>\n  <li>mining and quarrying; manufacturing; electricity, gas, steam and air conditioning supply; constructions (ISIC B, C, D and F), hereinafter &#x201C;MIMEC&#x201D;;</li>\n  <li>all the service sectors (ISIC E and ISIC G-T), hereinafter &#x201C;services&#x201D;.</li>\n</ol>\n<p><strong>Concepts:</strong></p>\n<ul>\n  <li>Water use: water that is received by an industry or households from another industry or is directly abstracted. [SEEA-Water (ST/ESA/STAT/SER.F/100), par. 2.21]</li>\n  <li>Water abstraction: water removed from the environment by the economy. [SEEA-Water (ST/ESA/STAT/SER.F/100), par. 2.9]</li>\n  <li>Water use for irrigation (km&#xB3;/year) <ul>\n      <li>Annual quantity of water used for irrigation purposes. It includes water from renewable freshwater resources, as well as water from over-abstraction of renewable groundwater or abstraction of fossil groundwater, direct use of agricultural drainage water, (treated) wastewater, and desalinated water. [AQUASTAT Glossary]</li>\n    </ul>\n  </li>\n  <li>Water use for livestock (watering and cleaning) (km&#xB3;/year) <ul>\n      <li>Annual quantity of water used for livestock purposes. It includes water from renewable freshwater resources, as well as water from over-abstraction of renewable groundwater or abstraction of fossil groundwater, direct use of agricultural drainage water, (treated) wastewater, and desalinated water. It includes livestock watering, sanitation, cleaning of stables, etc. If connected to the public water supply network, water used for livestock is included in the services water use. [AQUASTAT Glossary]</li>\n    </ul>\n  </li>\n  <li>Water use for aquaculture (km&#xB3;/year) <ul>\n      <li>Annual quantity of water used for aquaculture. It includes water from renewable freshwater resources, as well as water from over-abstraction of renewable groundwater or abstraction of fossil groundwater, direct use of agricultural drainage water, (treated) wastewater, and desalinated water. Aquaculture is the farming of aquatic organisms in inland and coastal areas, involving intervention in the rearing process to enhance production and the individual or corporate ownership of the stock being cultivated. [AQUASTAT Glossary]</li>\n    </ul>\n  </li>\n  <li>Water use for the MIMEC sectors (km&#xB3;/year)<ul>\n      <li>Annual quantity of water used for the MIMEC sector. It includes water from renewable freshwater resources, as well as over-abstraction of renewable groundwater or abstraction of fossil groundwater and use of desalinated water or direct use of (treated) wastewater. This sector refers to self-supplied industries not connected to the public distribution network. [AQUASTAT Glossary. To be noted that in AQUASTAT, the sectors included in the MIMEC group are referred to as &#x201C;industry&#x201D;]<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></li>\n    </ul>\n  </li>\n  <li>Water use for the services sectors (km&#xB3;/year)<ul>\n      <li>Annual quantity of water used primarily for the direct use by the population. It includes water from renewable freshwater resources, as well as over-abstraction of renewable groundwater or abstraction of fossil groundwater and the use of desalinated water or direct use of treated wastewater. It is usually computed as the total water used by the public distribution network. It can include that part of the industries, which is connected to the municipal network. [AQUASTAT Glossary. To be noted that in AQUASTAT, the sectors included in &#x201C;services&#x201D; are referred to as &#x201C;municipal&#x201D;]</li>\n    </ul>\n  </li>\n  <li>Value added (gross)<ul>\n      <li>Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4. [WB Databank, metadata glossary, modified]</li>\n    </ul>\n  </li>\n  <li>Arable land<ul>\n      <li>Arable land is the land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for &#x201C;Arable land&#x201D; are not meant to indicate the amount of land that is potentially cultivable. [FAOSTAT]</li>\n    </ul>\n  </li>\n  <li>Permanent crops<ul>\n      <li>Permanent crops are the land cultivated with long-term crops which do not have to be replanted for several years (such as cocoa and coffee); land under trees and shrubs producing flowers, such as roses and jasmine; and nurseries (except those for forest trees, which should be classified under &quot;forest&quot;). Permanent meadows and pastures are excluded from land under permanent crops. [FAOSTAT]</li>\n    </ul>\n  </li>\n  <li>Proportion of irrigated land on the total cultivated land<ul>\n      <li>Total harvested irrigated crop area, expressed in percentage. Area under double irrigated cropping (same area cultivated and irrigated twice a year) is counted twice.</li>\n    </ul>\n  </li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> In order to maintain consistency with the terminology used in SEEA-Water, the terms water use and water abstraction are utilized in this text. In particular, &#x201C;water abstraction&#x201D; must be considered synonym of &#x201C;water withdrawal, as expressed in both AQUASTAT and the statement of the SDG target 6.4. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> In AQUASTAT, as well as in the World Bank databank and in other national and international datasets, the MIMEC sector is referred to as &#x201C;Industry&#x201D;. Also, SEEA-Water uses the term &#x201C;industrial use&#x201D; of water. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>The unit of the indicator is expressed in Value/Volume, commonly USD/m<sup>3</sup>.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>System of Environmental-Economic Accounting for Water (SEEA-water)</p>\n<p>SEEA-water is used to define the concept of &#x201C;water use&#x201D; in the context of this indicator, and to describe the water flows among users.</p>\n<p>International Standard Industrial Classification of All Economic Activities, revision 4</p>\n<p>ISIC-4 is used as the standard for the definition of the economic sectors.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data needed for the calculation of the indicator are administrative data collected at country level by the relevant institutions, either technical (for water and irrigation) or economic (for value added). Official counterparts at country level are the national statistics offices and/or the line Ministry for water resources and irrigation. More specifically, FAO requests countries to nominate a National Correspondent to act as the focal point for the data collection and communication. Data are mainly published within national statistical yearbooks, national water resources and irrigation master plans, and other reports (such as those from projects, international surveys or results and publications from national and international research centres).</p>", "COLL_METHOD__GLOBAL"=>"<p>Data collection is done through FAO&apos;s global information system on water and agriculture (AQUASTAT) </p>\n<p>and the AQUASTAT questionnaire on water and agriculture. The data collection process relies on a network of National Correspondents, officially nominated by their respective countries, in charge of the provision of official national data to AQUASTAT. As of August 2020, 150 countries have nominated national correspondents, as well as alternate correspondents from different agencies. Countries submit data through the annual AQUASTAT questionnaire on water and agriculture, which contains- among others - the information required for the calculation of SDG indicator 6.4.1. Regarding the economic indicators Gross Value Added (GVA), FAO uses UNSD database and aggregates it following the revision 4 ISIC-4 is used as the standard for the definition of the economic sectors.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are collected every year through the AQUASTAT network of National Correspondents. FAO has dispatched the questionnaires to the National Correspondents in July 2021.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released every year, usually in February following the UNSD collection schedule. </p>", "DATA_SOURCE__GLOBAL"=>"<p>Data come from governmental sources. Data providers are different depending on the country. In many cases data collection at country level is coordinated by the National Statistics Office (NSO). Data not generated by a country is displayed with an appropriate qualifier.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Calculation rules are predefined and use data referring to the same year to generate aggregate values. </p>", "INST_MANDATE__GLOBAL"=>"<p>FAO has a mandate to &#x201C;collect, analyse, interpret and disseminate information relating to nutrition, food and agriculture&#x201D;. (FAO Constitution, Article 1)</p>", "RATIONALE__GLOBAL"=>"<p>The rationale behind this indicator consists in providing information on the efficiency of the economic and social usage of water resources, i.e., value added generated by the use of water in the main sectors of the economy, and distribution network losses.</p>\n<p>The distribution efficiency of water systems is implicit within the calculations and could be made explicit if needed and where data are available.</p>\n<p>This indicator addresses specifically the target component &#x201C;substantially increase water-use efficiency across all sectors&#x201D;, by measuring the output per unit of water from productive uses of water as well as losses in municipal water use. It does not aim at giving an exhaustive picture of the water utilization in a country. Other indicators, specifically those for Targets 1.1, 1.2, 2.1, 2.2, 5.4, 5.a, 6.1, 6.2, 6.3, 6.5 will complement the information provided by this indicator. In particular, the indicator needs to be combined with the water stress indicator 6.4.2 to provide adequate follow-up of the target 6.4.</p>\n<p>Together, the three sectoral efficiencies provide a measure of overall water efficiency in a country. The indicator provides incentives to improve water use efficiency through all sectors, highlighting those sectors where water use efficiency is lagging behind.</p>\n<p>The interpretation of the indicator would be enhanced by the utilization of supplementary indicators to be used at country level. Particularly important in this sense would be the indicator on efficiency of water for energy and the indicator on the efficiency of the municipality distribution networks.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The corrective coefficient, C<sub>r</sub>, for the agricultural sector is needed in order to focus the indicator on the irrigated production. This is done for two main reasons:</p>\n<ul>\n  <li>To ensure that only runoff water and groundwater (so-called blue water) are considered in computing the indicator;</li>\n  <li>To eliminate a potential bias of the indicators, which otherwise would tend to decrease if rainfed cropland is converted to irrigated.</li>\n</ul>", "DATA_COMP__GLOBAL"=>"<p><strong>Computation Method:</strong></p>\n<p>Water use efficiency is computed as the sum of the three sectors listed above, weighted according to the proportion of water used by each sector over the total use. In formula:</p>\n<p><img src=\"data:image/png;base64,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\"></p>\n<p>Where:</p>\n<p>WUE = Water use efficiency</p>\n<p>A<sub>we</sub> = Irrigated agriculture water use efficiency [USD/m<sup>3</sup>]</p>\n<p>M<sub>we</sub> = MIMEC water use efficiency [USD/m<sup>3</sup>]</p>\n<p>S<sub>we</sub> = Services water use efficiency [USD/m<sup>3</sup>]</p>\n<p>P<sub>A</sub> = Proportion of water used by the agricultural sector over the total use</p>\n<p>P<sub>M</sub> = Proportion of water used by the MIMEC sector over the total use</p>\n<p>P<sub>S</sub> = Proportion of water used by the service sector over the total use</p>\n<p>The computing of each sector is described below.</p>\n<p><em>Water use efficiency in irrigated agriculture</em> is calculated as the agricultural value added per agricultural water use, expressed in USD/m<sup>3</sup>. </p>\n<p>In formula:</p>\n<p><img src=\"data:image/png;base64,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\"></p>\n<p>Where:</p>\n<p>A<sub>we</sub> = Irrigated agriculture water use efficiency [USD/m<sup>3</sup>]</p>\n<p>GVA<sub>a </sub>= Gross value added by agriculture (excluding river and marine fisheries and forestry) [USD]</p>\n<p>C<sub>r</sub> = Proportion of agricultural GVA produced by rainfed agriculture </p>\n<p>V<sub>a</sub> = Volume of water used by the agricultural sector (including irrigation, livestock and aquaculture) [m<sup>3</sup>]</p>\n<p>The volume of water used by the agricultural sectors (V) is collected at country level through national records and reported in questionnaires, in units of m<sup>3</sup>/year (see example in AQUASTAT <a href=\"http://www.fao.org/nr/water/aquastat/sets/aq-5yr-quest_eng.xls\"><u>http://www.fao.org/nr/water/aquastat/sets/aq-5yr-quest_eng.xls</u></a>). Agricultural value added in national currency is obtained from national statistics, converted to USD and deflated to the baseline year.</p>\n<p>C<sub>r</sub> can be calculated from the proportion of irrigated land on the total Arable land and Permanent crops (hereinafter &#x201C;cultivated land&#x201D;, as follows:</p>\n<p><img src=\"data:image/png;base64,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\"></p>\n<p>Where:</p>\n<p>A<sub>i</sub> = proportion of irrigated land on the total cultivated land, in decimals</p>\n<p>0.563 = generic default ratio between rainfed and irrigated yields</p>\n<p>More detailed estimations are however possible and encouraged at country level.</p>\n<p><em>Water efficiency of the MIMEC sectors (including power production): </em>MIMEC value added per unit of water used for the MIMEC sector, expressed in USD/m<sup>3</sup>.</p>\n<p>In formula:</p>\n<p><img src=\"data:image/png;base64,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\"></p>\n<p>Where:</p>\n<p>M<sub>we</sub> = Industrial water use efficiency [USD/m<sup>3</sup>]</p>\n<p>GVA<sub>m</sub> = Gross value added by MIMEC (including energy) [USD]</p>\n<p>V<sub>m</sub> = Volume of water used by MIMEC (including energy) [m<sup>3</sup>]</p>\n<p>MIMEC water use (V<sub>m</sub>) is collected at country level through national records and reported in questionnaires, in units of m<sup>3</sup>/year (see example in AQUASTAT <a href=\"http://www.fao.org/nr/water/aquastat/sets/aq-5yr-quest_eng.xls\">http://www.fao.org/nr/water/aquastat/sets/aq-5yr-quest_eng.xls</a>). MIMEC value added is obtained from national statistics, deflated to the baseline year.</p>\n<p><em>Services water supply efficiency </em>is calculated as the service sector value added (ISIC 36-39 and ISIC 45-98) divided by water used for distribution by the water collection, treatment and supply industry (ISIC 36), expressed in USD/m<sup>3</sup>.</p>\n<p>In formula:</p>\n<p><img src=\"data:image/png;base64,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\"></p>\n<p>Where:</p>\n<p>S<sub>we</sub> = <em>Services water use efficiency [USD/m<sup>3</sup>]</em></p>\n<p>GVA<sub>s</sub> = Gross value added by services [USD]</p>\n<p>V<sub>s</sub> = Volume of water used by the service sector [m<sup>3</sup>]</p>\n<p>Data on volumes of used and distributed water are collected at country level from the municipal supply utilities records and reported in questionnaires, in units of km<sup>3</sup>/year or million m<sup>3</sup>/year (see example in AQUASTAT <a href=\"http://www.fao.org/nr/water/aquastat/sets/aq-5yr-quest_eng.xls\"><u>http://www.fao.org/nr/water/aquastat/sets/aq-5yr-quest_eng.xls</u></a>). Services value added is obtained from national statistics, deflated to the baseline year.</p>\n<p>Change in water use efficiency (CWUE) is computed as the ratio of water use efficiency (WUE) in time t minus water use efficiency in time t-1, divided by water use efficiency in time t-1 and multiplied by 100:</p>\n<p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkMAAABCCAIAAADbmEVAAAAAAXNSR0IArs4c6QAAC+9JREFUeF7tnTusDd8ex+//dtSURBQ4JxqUHtF4F4IoiCAhXgkiJIioxCshiEIICUEliEI4VF6NBI3Eo1A4HRIdpfu595esuzLnYZ/9nj2fKXb27Fl75rc+v8n6zvqt31rzz58/f/7lJgEJSEACEigtgX+X1nINl4AEJCABCfyXgErmfSABCUhAAuUmoJKV239aLwEJSEACKpn3gAQkIAEJlJuASlZu/2m9BCQgAQmoZN4DEpCABCRQbgIqWbn9p/USkIAEJKCSeQ9IQAISkEC5Cahk5faf1ktAAhKQgErmPSABCUhAAuUmoJKV239aLwEJSEACKpn3gAQkIAEJlJuASlZu/2m9BCQgAQmoZN4DEpCABCRQbgIqWbn9p/USkIAEJKCSeQ9IQAISkEC5CfzjmzbL7UCtb5jA58+fjx8//uPHj8WLF+/du5fzXbly5dOnT2fPnuX70aNH3759y5eDBw8uWLBg//79Hz9+ZPfhw4d87ty58+vXrzNmzIjCsZtbtGXLltWrVzdi45jMW7FiBdcKe75//75582Z2V65cuW3bthaZ10jV/K8EmkXAPlmzSHqeshKYNm3a3LlzJ0yYsH79euqAADx9+jRVZs+ePXxft24dMsaXRYsWhapFAUSCz0OHDuW7iFxskydPnjlzZoNcxmQeYjx+/PiwZ+LEiexiQ8hYsra55jVYO/8ugaYQUMmagtGTlJ4ASkbTTzUGBgbo06T6fPjwgd0NGzakXygZqsb269cvjsYfY8v/e+nSJXSoKWhqN2/27NnJHsybNWtWbkCLzGtKHT2JBOomoJLVjc4/9iaBd+/eTZo0KdXtwYMH0fGKjagjvZy0Ozg4OH369Pxo7N67d48QZSsAjW4ekc/c+Ih8ttO8VlTZc0rgrwRUsr8iskDvE0jihALlnZjnz5/nPTBAoA25dKEcubBxFOVjsOratWu5hDRIcEzm9ff3x+UIk2JPX19funqLzGuwdv5dAo0TUMkaZ+gZSk+AsaWow6tXr5YsWZLqgyytWbMmrx7SVZCKOXPmpAIcPXfuHANRyFguIcMCotOG5g27FcrXaB66S0mii/H3N2/eIIF55HNM5pXeqVagSgRUsip527qOSgAloL+Vmn52UYJ8oItfOEEuFWn4it9DSKI8qYO5hAx7WRIxUvJF4cuw5f9qXqGDWAiEjtU8bxYJlIiASlYiZ2lqawmQsrhq1aq8QxbZjPmWxwwZssp3C4NSmzZtij+SRk8qf+Om12JeHvlk8kC+24h5VIHpByHkbhLoQgIqWRc6RZM6QICGnh5V6kjRv8n7Z8kgfo/pZY8fP3758mUee6QPlLItiBzSXaMYhY8cOcInUcRGlKBG84iOMjzGdS9cuMBnPpWtbvOQsffv35MG2QGveEkJ1EZAJauNk6V6nQAtdS5L6FCahpWqTvI9M7ToYCFLDKHt3r07xR7RKkQu0j3iaKRpEIpkoyTxw5S7XwfLWsyjQ4nZzIbGACZ6xyzv2BoxjzqiiGmsrg7j/YsEWk3ANT5aTdjzV50AYcYzZ878ddisU5hy89IKJsmYWMqEjUPMRmhEjDtVQa9bBQL2yarg5c7UkTDXyZMno49Cc0lsjS12Cc2l9jHNu1q7dm0c5VAqGUcpH4ciQJd2U+JfGpTqTFVHvioQojdDfn+32YY9BfNIVBkp94ROYd0BRnqErONVqD7xT9yH0/OJd9iDZMYNk26SLuSmSd1GQCXrNo/0iD00Sbt27aIy169fJzE9GkGe6EmRINq2dOnSkKtYwzA2lijk89ixY1GS6ByFI8RHeXaJ7EWfIHbpIkSzS+yuiZO3mu4AAn0HDhyYMmVK08/clBPWYh7SwkDdxYsXkZmxXhQJ54GmsBwlPyJv3BuHDx8mEptE6+rVqwg/PmVVMObkMUQ31stZvpoEVLJq+r3ltaZJQmxop4iqMdDC9xSYSmkRJOOljHYMorFDnPJfWA4xGTrKwksIGxdqeZXqugDVv3Pnzo0bN/J61XWmlvypRvNSRy0WSq59Q6JwHI8ahb/gerSKq8c4IokqFODphySaSB+N5cFev35d+7UsWWUCKlmVvd+quvMoTZOUL/I0tAWkDE/fKWBFK0ZzlpLgY32K1I9hl65D0sI4yi5fhoatWlUrzzsygZH6ajxk5EtWphPgvrRwCU82cRuwxCWfSfIpUHc8U19VjYBKVjWPt6O+PEoXFnlKV03t1927d/PVNO7fv4/ypbQIGrWhy1Wkk0STR8iLPL2RcurSSNvQRTQayYZvB75SXYNRrhjogmrd0+byIHNee2YOlAqGxnaMgErWMfQ9fGEaoJEGrsaNGxdxJPpY6embXWYZ55OfaNoKS9/muxydN28eIS/erpIve5gjpcc20goaJuA18d5jIPP37994hIBhvAGnjm2kxxFT/+uAWc2/mIVfTb+3ttbEmphWPHQ+FldNiWooU7y4MtahQJAiDSS2whkIITKskqSOo6zzO2zYqo6KRbakW+0EUmp+cijPLgxq8jgSCTuFje7a6dOn83/BPN5cGmeI95oWio1yF9VuqiUrQkAlq4ij21rNvypZeiMzJYlDMhxSaAHz2UuMqO3bt48EyDQNmdRtUjyiS0cWHN27oaoZzeKw1U5taFuh9PTF8NcoySBDlYwke5QvvEZMkr4XDg1HJ+/gZV5w2uAbt3uaupX7PwGji94NzSeAOPF4Hssm3bp1qzB8QhgqT0okNyRexFzY4sXNnOTy5cvEEvPVNFC+kDHaPnK4h41kGl1svl9HPuPoOY1DEze4AWJhrVh8JO4HXIwrw+8x/W7+/PntrIXXKi8B+2Tl9V33Wk4LderUqRjGR9VI5UhP1oSSaMLISg/ro082NIeeBo6Zs3S2Iu9j69atKRmEyUmIX155piV11Qoa1BF9xcIwLKbWkYmOuFKv0HWabFr/VDI6IlT5yZMnHI1AHCnsTOHKawqrhK573Z9ZRn1v376d7gRcGWtowYR5GvgR/+Ydr1SemlKyO6culIJ85Yz84yYBCTSbwPLly2/evBlnpU/J7rNnz2L3/PnzGzdu/PbtG9/55BAF4lDsPnr0KJmzY8eOdJTfT5w40WxLPZ8EeoGA0cXKPbtY4fYQSEmVBFoL75VmNZO8E7lw4cIw6efPn3zmmS/Re4uj3TwBvD1IvYoERiKgknlvSKCFBBjvYRQwrdUUU9nSNABe65xezsnvX758ycf8nADeQsd46t4ioJL1lj+tTXcQSJ0w8hfy7DvGz7Zv355sLLz9svCW51omgHdHdbVCAh0moJJ12AFevicJxJReUjaY95YqSIeMRIaUhMnvSFf+Wud4vWcqX8sE8J6kZ6UkMFYCKtlYiVleArUSyFeS5D/0z/KXefILWpV6bxFLnDp1ajp7essz0cj8tZm1Xt5yEqgMAZWsMq62ou0lgCzRA0uZHeR9sFRu3iELc9KY2YsXLwo9Ns7Q398fxRhvS8ujMA+BbPXRa0P/r2vf2dZeP3i1ShBQySrhZivZfgIMiS1btixdN72vpGAJM8f5BWWifP72AH4ZdgJ4TKdjUtoorwpDxvgvs/HaX2uvKIGOEFDJOoLdi/Y+AWb15j0w5v8Onb5NOj4CxiKEzIlmnYs8N2RgYABGsZA/azghS319ffxCfJKAJFOnR1lWg35eIZW/93Fbw2oTcI2Pavvf2peNAGHGwcHBNGxWWP44lg6JOnEoX7S3bBXVXgmMgYBKNgZYFpVAxwnQeyO/keAhaxKOskYX+SO8vK3b1vHqOD0N6FUCRhd71bPWqzcJ8II3FmOkWzaKjDFOhoxRfz5Tnkhv4rBWEvgfAftk3ggSkIAEJFBuAvbJyu0/rZeABCQgAZXMe0ACEpCABMpNQCUrt/+0XgISkIAEVDLvAQlIQAISKDcBlazc/tN6CUhAAhJQybwHJCABCUig3ARUsnL7T+slIAEJSEAl8x6QgAQkIIFyE1DJyu0/rZeABCQgAZXMe0ACEpCABMpNQCUrt/+0XgISkIAEVDLvAQlIQAISKDcBlazc/tN6CUhAAhJQybwHJCABCUig3ARUsnL7T+slIAEJSOA/nJhEDxtIirEAAAAASUVORK5CYII=\"></p>\n<p>It must be noted that computing the indicator in an aggregated manner, i.e. total GDP over total water use, would lead to an overestimation of the indicator. That is due to the fact that, for the agricultural sector, only the value produced under irrigation has to be counted in calculating the indicator. Hence, the sum of the value added of the various sectors used in these formulas is not equivalent to the total GDP of the country.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data validation is done in a number of steps. </p>\n<ul>\n  <li>The AQUASTAT questionnaire embeds automatic validation rules to allow National Correspondents to identify any data consistency errors while compiling the data. </li>\n  <li>Once the questionnaire is submitted, FAO thoroughly reviews the information reported, using the following tools:<ul>\n      <li>Manual cross-variable check. This includes cross-comparison with similar countries as well as historic data for the countries. </li>\n      <li>Time-series coherency by running an R-script to compare reported data with those corresponding to previous years</li>\n    </ul>\n  </li>\n</ul>\n<p>Verification of the metadata, in particular the source of the proposed data. The critical analysis of the compiled data gives preference to national sources and expert knowledge.</p>\n<ul>\n  <li>After this verification, exchanges between the National Correspondents and FAO takes place to correct and confirm the collected data. </li>\n  <li>The last validation step is an automated validation routine included in the Statistical Working System (SWS), which uses almost 200 validation rules. </li>\n</ul>", "ADJUSTMENT__GLOBAL"=>"<p>Since national level data is frequently tailored to be useful at national level and not for international comparisons, data may be manipulated in order to maximize international comparability. Adjusted data is displayed with an appropriate qualifier. Data is rounded according to a specific methodology <a href=\"http://www.fao.org/aquastat/en/databases/maindatabase/metadata/\">http://www.fao.org/aquastat/en/databases/maindatabase/metadata/</a></p>\n<p>Additionally, the Statistical Working System (SWS) has the correspondence among different international codes (FAOSTAT, UNSDM49, ISO2, ISO3) for geographic areas and is used to convert area codes in the external sources to UNSDM49 codes which is the standard used in the SWS.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>If scattered data (over time) are available, linear interpolation method takes place if there are at least two non-missing values in the time series. If not, the only possible way to impute it is through the carry-forward. Imputed data is displayed with an appropriate qualifier. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>If country data are missing, the value of the indicator will be considered in the average of the others in the same region. Imputed data is displayed with an appropriate qualifier. </p>", "REG_AGG__GLOBAL"=>"<p>The aggregation for global and regional estimations is done by summing up the values of the various parameters constituting the elements of the formula, i.e. value added by sector and water use by sector. The aggregated indicator is then calculated by applying the formula with those aggregated data, as if it were a single country.</p>\n<p>An Excel sheet with the calculations exists, and can be shared with the IAEG if required.</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>A set of tools is available to countries for the compilation of the indicator. Among them, a step-by-step methodological guide, an interpretation paper, and an e-learning course. All the tools are available on the FAO web pages, at: <a href=\"http://www.fao.org/sustainable-development-goals/indicators/641/en/\">http://www.fao.org/sustainable-development-goals/indicators/641/en/</a></li>\n  <li>During 2020 and 2021, FAO has organized four virtual trainings for Asia, Latin-America and the Caribbean and Africa on SDG 6.4. </li>\n  <li>FAO&#x2019;s AQUASTAT team provides continued guidance to the countries through the National Correspondents during the data collection time to ensure data is duly and timely compiled.</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<ul>\n  <li>The annual AQUASTAT questionnaire, used for collecting information on SDG indicator 6.4.1 has been endorsed by FAO&#x2019;s Office of the Chief Statistician (OCS).</li>\n  <li>During the SDG reporting process, the OCS provides overall guidance, including metadata reporting, based on the Metadata Dissemination Standard approved by the FAO IDWG-Statistics Technical Task Force.</li>\n  <li>After revision and validation, SDG indicators are submitted to the OCS which also ensures the quality of the data and results.</li>\n</ul>", "QUALITY_ASSURE__GLOBAL"=>"<p>FAO is responsible for the quality of the internal statistical processes used to compile the published datasets. The FAO Statistics Quality Assurance Framework (SQAF), available at: <a href=\"http://www.fao.org/docrep/019/i3664e/i3664e.pdf\">http://www.fao.org/docrep/019/i3664e/i3664e.pdf</a> provides the necessary principles, guidelines, and tools to carry out quality assessments. FAO is performing an internal bi-annual survey (FAO Quality Assessment and Planning Survey) designed to gather information on all of FAO&#x2019;s statistical activities, notably to assess the extent to which quality standards are being implemented with a view to increasing compliance with the quality dimensions of SQAF, documenting best practices and prepare quality improvement plans, where necessary. Domain-specific quality assurance activities are carried out systematically (e.g. quality reviews, self-assessments, compliance monitoring).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Overall evaluation of data quality is based on standard quality criteria and follows FAO&#x2019;s SQAF. It also includes:</p>\n<ul>\n  <li>A qualitative and quantitative manual cross-variable check after data is received. This consists of the verification that all the numbers are consistent based on the internal validation rules embedded in the questionnaire. Any issues identified are flagged and listed to be followed-up with the countries. </li>\n  <li>Time-series coherency check done by running an R-script to compare reported data with those corresponding to previous years. Based on this, a scattered diagram is also made by variable and country to allow for a visual verification of historical data. The critical analysis of the compiled data gives preference to national sources and expert knowledge, unless these greatly diverge from historic data or in the case of drastic changes in methodologies used by countries. </li>\n  <li>Verification of the metadata, in particular the source of the proposed data. When data sources are not provided, the questionnaire is added as the data source of a given value. </li>\n</ul>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The data needed for the indicator are collected through AQUASTAT and other databases (FAOSTAT, UNSD) for 168 countries worldwide</p>\n<p><strong>Time series:</strong></p>\n<p>1961-2019 (Discontinuous depending on the country. Data are interpolated to create timelines). </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator covers all the economic sectors according to the ISIC classification, providing the means for more detailed analysis of the water use efficiency for national planning and decision-making. </p>\n<p>Although the subdivision into three major aggregated economic sectors is sufficient for the purpose of compiling the indicator, wherever possible it is advisable to further disaggregate the indicator, according to the following criteria:</p>\n<ul>\n  <li>Economically, a more refined subdivision of the economic sector can be done using ISIC Rev.4 by the following groups:<ul>\n      <li>Agriculture, Forestry and Fisheries (ISIC A); </li>\n      <li>Mining and Quarrying (ISIC B); </li>\n      <li>Manufacturing (ISIC C);</li>\n      <li>Electricity, Gas, Steam and Air Conditioning Supply (ISIC D); </li>\n      <li>Water Supply, Sewerage, Waste Management and Remediation Activities (ISIC E), by</li>\n      <li>Water Collection, Treatment and Supply (ISIC 36) <ul>\n          <li>Sewerage (ISIC 37)</li>\n          <li>Construction (ISIC F)</li>\n        </ul>\n      </li>\n      <li>Other industries (sum of remaining industries)</li>\n    </ul>\n  </li>\n  <li>Geographically, computing the indicator by river basin, watershed or administrative units within a country.</li>\n</ul>\n<p>These levels of disaggregation, or a combination of those, will give further insight on the dynamics of water use efficiency, providing information for remedial policies and actions.</p>\n<p>Data are vertically interpolated in the presence of missing values to allow for a time series analysis.</p>", "COMPARABILITY__GLOBAL"=>"<p>Geographical: Regional differences, especially in relation to irrigated agriculture and different climatic conditions (including variability) are to be considered in the interpretation of this indicator, especially in countries with substantial amounts of available water resources. Also for this reason, coupling this indicator with water stress (6.4.2) is important for the interpretation of the data.</p>\n<p>Over-time: time series are comparable across time. </p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>AQUASTAT main page: <a href=\"http://www.fao.org/aquastat/en/\">http://www.fao.org/aquastat/en/</a> </li>\n  <li>AQUASTAT glossary: <a href=\"http://www.fao.org/aquastat/en/databases/glossary/\">http://www.fao.org/aquastat/en/databases/glossary/</a> </li>\n  <li>AQUASTAT Main country database: <a href=\"http://www.fao.org/aquastat/statistics/query/index.html\">http://www.fao.org/aquastat/statistics/query/index.html</a> </li>\n  <li>AQUASTAT Water use: <a href=\"http://www.fao.org/aquastat/en/overview/methodology/water-use/\">http://www.fao.org/aquastat/en/overview/methodology/water-use/</a> </li>\n  <li>AQUASTAT Water resources: <a href=\"http://www.fao.org/aquastat/en/overview/methodology/water-resources/\">http://www.fao.org/aquastat/en/overview/methodology/water-resources/</a> </li>\n  <li>AQUASTAT publications dealing with concepts, methodologies, definitions, terminologies, metadata, etc.: <a href=\"http://www.fao.org/aquastat/en/resources/\">http://www.fao.org/aquastat/en/resources/</a> </li>\n  <li>AQUASTAT methodology - quality Control: <a href=\"https://www.fao.org/aquastat/en/overview/methodology#main\">https://www.fao.org/aquastat/en/overview/methodology#main</a></li>\n  <li>AQUASTAT metadata <a href=\"http://www.fao.org/aquastat/en/databases/maindatabase/metadata/\">http://www.fao.org/aquastat/en/databases/maindatabase/metadata/</a> </li>\n  <li>AQUASTAT Statistical working system (SWS). Migration of the Statistical Processes into the SWS. <a href=\"https://sws-methodology.github.io/faoswsAquastat/index.html#welcome\">https://sws-methodology.github.io/faoswsAquastat/index.html#welcome</a> </li>\n  <li>FAOSTAT production database: <a href=\"http://faostat3.fao.org/download/Q/*/E\">http://faostat3.fao.org/download/Q/*/E</a> </li>\n  <li>UNSD/UNEP Questionnaire on Environment Statistics &#x2013; Water Section</li>\n</ul>\n<p><a href=\"http://unstats.un.org/unsd/environment/questionnaire.htm\">http://unstats.un.org/unsd/environment/questionnaire.htm</a></p>\n<p><a href=\"http://unstats.un.org/unsd/environment/qindicators.htm\">http://unstats.un.org/unsd/environment/qindicators.htm</a><u> </u></p>\n<ul>\n  <li>Framework for the Development of Environment Statistics (FDES 2013) (Chapter 3): <a href=\"http://unstats.un.org/unsd/environment/FDES/FDES-2015-supporting-tools/FDES.pdf\">http://unstats.un.org/unsd/environment/FDES/FDES-2015-supporting-tools/FDES.pdf</a></li>\n  <li>International Recommendations for Water Statistics (IRWS) (2012): <a href=\"https://unstats.un.org/unsd/publication/seriesM/seriesm_91e.pdf\">https://unstats.un.org/unsd/publication/seriesM/seriesm_91e.pdf</a></li>\n  <li>OECD/Eurostat Questionnaire on Environment Statistics &#x2013; Water Section: <a href=\"http://ec.europa.eu/eurostat/web/environment/water\">http://ec.europa.eu/eurostat/web/environment/water</a></li>\n  <li>OECD National Accounts data files: <a href=\"http://www.oecd-ilibrary.org/economics/data/oecd-national-accounts-statistics_na-data-en\">http://www.oecd-ilibrary.org/economics/data/oecd-national-accounts-statistics_na-data-en</a></li>\n  <li>SEEA-Water: <a href=\"https://seea.un.org/sites/seea.un.org/files/seeawaterwebversion_final_en.pdf\">https://seea.un.org/sites/seea.un.org/files/seeawaterwebversion_final_en.pdf</a></li>\n  <li>SEEA Central Framework: <a href=\"https://seea.un.org/sites/seea.un.org/files/seea_cf_final_en.pdf\">https://seea.un.org/sites/seea.un.org/files/seea_cf_final_en.pdf</a> </li>\n  <li>UNSD National Accounts Main Aggregates Database: <a href=\"http://unstats.un.org/unsd/snaama/selbasicFast.asp\">http://unstats.un.org/unsd/snaama/selbasicFast.asp</a></li>\n  <li>World Bank Databank (World Economic Indicators) <a href=\"http://databank.worldbank.org/data/home.aspx\">http://databank.worldbank.org/data/home.aspx</a></li>\n  <li>ISIC rev. 4: </li>\n</ul>\n<p> <a href=\"https://unstats.un.org/unsd/classifications/Econ/Download/In%20Text/CPCprov_english.pdf\">https://unstats.un.org/unsd/classifications/Econ/Download/In%20Text/CPCprov_english.pdf</a></p>\n<ul>\n  <li>FAO e-learning course SDG Indicator 6.4.1 - Change in water-use efficiency over time: <a href=\"https://elearning.fao.org/course/view.php?id=475\">https://elearning.fao.org/course/view.php?id=475</a> </li>\n</ul>", "indicator_sort_order"=>"06-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"6.4.2", "slug"=>"6-4-2", "name"=>"Nivel de estrés hídrico: extracción de agua dulce en proporción a los recursos de agua dulce disponibles", "url"=>"/site/es/6-4-2/", "sort"=>"060402", "goal_number"=>"6", "target_number"=>"6.4", "global"=>{"name"=>"Nivel de estrés hídrico: extracción de agua dulce en proporción a los recursos de agua dulce disponibles"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Consumo de agua (volumen de agua extraída)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Nivel de estrés hídrico: extracción de agua dulce en proporción a los recursos de agua dulce disponibles", "indicator_number"=>"6.4.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> No evaluable", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/estadistica-de-consumo-del-agua-090215/web01-s2ing/es/", "url_text"=>"Estadística de consumo del agua", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Consumo de agua (volumen de agua extraída)", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.4- De aquí a 2030, aumentar considerablemente el uso eficiente de los recursos hídricos en todos los sectores y asegurar la sostenibilidad de la extracción y el abastecimiento de agua dulce para hacer frente a la escasez de agua y reducir considerablemente el número de personas que sufren falta de agua", "definicion"=>"Volumen de agua suministrado a los consumidores finales por medio de sistemas de abastecimiento urbano", "formula"=>"$$VOL_{agua}^{t}$$\n", "desagregacion"=>"\nTipo de uso: doméstico, industrial, comercial, público, agropecuario, otros usos\n\nTerritorio histórico\n", "observaciones"=>"\nEl uso público incluye consumo propio de las entidades suministradoras y suministros gratuitos\n\nEl uso agropecuario no incluye el consumo de las Comunidades de Regantes, y usos agropecurios en general de la CAE\n", "periodicidad"=>"Anual", "justificacion_global"=>"\nEl propósito de este indicador es mostrar el grado de explotación de los recursos hídricos \npara satisfacer la demanda nacional. Mide la presión de un país sobre sus recursos hídricos \ny, por lo tanto, el desafío que supone para la sostenibilidad de su uso. Monitorea el \nprogreso en materia de extracción y suministro de agua dulce para abordar la escasez \nde agua, es decir, el componente ambiental de la meta 6.4. \n\nEl indicador muestra en qué medida se utilizan ya los recursos hídricos y señala la \nimportancia de políticas eficaces de gestión de la oferta y la demanda. Indica la \nprobabilidad de un aumento de la competencia y los conflictos entre los diferentes \nusos y usuarios del agua en una situación de creciente escasez. Un mayor estrés hídrico, \nreflejado en un aumento del valor del indicador, tiene efectos potencialmente \nnegativos en la sostenibilidad de los recursos naturales y en el desarrollo económico. \nPor otro lado, valores bajos del indicador indican que el agua no representa un desafío \nparticular para el desarrollo económico y la sostenibilidad.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.4.2&seriesCode=ER_H2O_STRESS&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=TOTAL\">Nivel de estrés hídrico: extracción de agua dulce como proporción de los recursos de agua dulce disponibles (%) ER_H2O_STRESS</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible no cumple los metadatos de Naciones Unidas, mide la  extracción de recursos hídricos pero no el estrés hídrico.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-04-02.pdf\">Metadatos 6-4-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Consumo de agua (volumen de agua extraída)", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.4- De aquí a 2030, aumentar considerablemente el uso eficiente de los recursos hídricos en todos los sectores y asegurar la sostenibilidad de la extracción y el abastecimiento de agua dulce para hacer frente a la escasez de agua y reducir considerablemente el número de personas que sufren falta de agua", "definicion"=>"Volume of water supplied to final consumers through urban supply systems", "formula"=>"$$VOL_{water}^{t}$$\n", "desagregacion"=>"Type of use: domestic, industrial, commercial, public, agricultural, other uses\n  \nProvince\n", "observaciones"=>"\nPublic use includes the supplier entities' own consumption and free supplies. \n\nAgricultural use does not include consumption by Irrigation Communities and agricultural \nuses in general in the Autonomous Community of the Basque Country. \n", "periodicidad"=>"Anual", "justificacion_global"=>"\nThe purpose of this indicator is to show the degree to which water resources \nare being exploited to meet the country's water demand. It measures a country's \npressure on its water resources and therefore the challenge on the sustainability \nof its water use. It tracks progress regarding “withdrawals and supply of \nfreshwater to address water scarcity”, i.e. the environmental component of target 6.4. \n\nThe indicator shows to what extent water resources are already used, and signals the \nimportance of effective supply and demand management policies. It indicates the \nlikelihood of increasing competition and conflict between different water uses and users \nin a situation of increasing water scarcity. Increased water stress, shown by an increase \nin the value of the indicator, has potentially negative effects on the sustainability \nof the natural resources and on economic development. On the other hand, low values of \nthe indicator indicate that water does not represent a particular challenge for economic \ndevelopment and sustainability.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.4.2&seriesCode=ER_H2O_STRESS&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=TOTAL\">Level of water stress: freshwater withdrawal as a proportion of available freshwater resources (%) ER_H2O_STRESS</a> UNSTATS\n", "comparabilidad"=>"The available indicator does not comply with the United Nations metadata requirements.  The available indicator measures water resource extraction but not water stress. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-04-02.pdf\">Metadata 6-4-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Consumo de agua (volumen de agua extraída)", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.4- De aquí a 2030, aumentar considerablemente el uso eficiente de los recursos hídricos en todos los sectores y asegurar la sostenibilidad de la extracción y el abastecimiento de agua dulce para hacer frente a la escasez de agua y reducir considerablemente el número de personas que sufren falta de agua", "definicion"=>"Azken kontsumitzaileei hiri-hornidurako sistemen bidez hornitutako ur-bolumena", "formula"=>"$$VOL_{ura}^{t}$$\n", "desagregacion"=>"\nErabilera mota: etxekoa; industriala; merkataritzakoa; publikoa; nekazaritza eta abeltzaintzakoa; beste erabilera batzuk\n\nLurralde historikoa\n", "observaciones"=>"\nErabilera publikoak barne hartzen ditu erakunde hornitzaileen kontsumo propioa eta doako hornidurak.\n\nNekazaritza eta abeltzaintzako erabilerak ez du barne hartzen Ureztatzaileen Komunitateen kontsumoa, \nezta EAEko nekazaritza eta abeltzaintzako erabilera orokorrak ere.\n", "periodicidad"=>"Anual", "justificacion_global"=>"\nAdierazle honen helburua da erakustea zein den ur-baliabideen ustiapen-maila eskari nazionalari \nerantzuteko. Herrialde batek bere ur-baliabideen gainean duen presioa neurtzen du, eta, beraz, bere \nerabileraren jasangarritasunerako duen erronka. Ur geza erauzteko eta hornitzeko orduan egondako \naurrerapena ikuskatzen du, ur-eskasiari aurre egiteko, hau da, 6.4 xedeko ingurumen-osagaia jorratzeko. \n\nAdierazleak ur-baliabideak zein neurritan erabiltzen diren erakusten du, eta eskaintza eta eskaria \nkudeatzeko politika eraginkorren garrantzia adierazten du. Lehia areagotzeko probabilitatea adierazten \ndu, bai eta uraren erabileren eta erabiltzaileen arteko gatazkak ere, gero eta urriagoa den egoera batean. \nEstres hidriko handiagoak, adierazlearen balioa handitzean islatzen denak, ondorio negatiboak izan ditzake \nbaliabide naturalen jasangarritasunean eta garapen ekonomikoan. Bestalde, adierazlearen balio baxuek \nadierazten dute ura ez dela erronka berezia garapen ekonomikorako eta jasangarritasunerako. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.4.2&seriesCode=ER_H2O_STRESS&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=TOTAL\">Estres hidrikoaren maila: ur gezaren erauzketa, eskuragarri dagoen ur gezaren proportzio gisa (%) ER_H2O_STRESS</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen metadatuak betetzen, baliabide hidrikoen  erauzketa neurtzen du, baina ez estres hidrikoa.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-04-02.pdf\">Metadatuak 6-4-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 6: Ensure availability and sustainable management of water and sanitation for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 6.4: By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 6.4.2: Level of water stress: freshwater withdrawal as a proportion of available freshwater resources</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_H2O_STRESS - Level of water stress: freshwater withdrawal as a proportion of available freshwater resources [6.4.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>6.4.1: Change in water-use efficiency over time</p>\n<p>6.1.1: Proportion of population using safely managed drinking water services</p>\n<p>6.3.1: Proportion of wastewater safely treated</p>\n<p>6.6.1: Change in the extent of water-related ecosystems over time</p>\n<p>6.5.1: Degree of integrated water resources management implementation (0-100)</p>\n<p>2.4.1: Proportion of agricultural area under productive and sustainable agriculture</p>\n<p>15.3.1: Proportion of land that is degraded over total land area</p>\n<p>1.5.1: Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]</p>\n<p>11.5.1: Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The level of water stress: freshwater withdrawal as a proportion of available freshwater resources is the ratio between total freshwater withdrawn by all major sectors and total renewable freshwater resources, after taking into account environmental flow requirements. Main sectors, as defined by ISIC standards, include agriculture; forestry and fishing; manufacturing; electricity industry; and services. This indicator is also known as water withdrawal intensity.</p>\n<p><strong>Concepts:</strong></p>\n<p>This indicator provides an estimate of pressure by all sectors on the country&#x2019;s renewable freshwater resources. A low level of water stress indicates a situation where the combined withdrawal by all sectors is marginal in relation to the resources, and has therefore little potential impact on the sustainability of the resources or on the potential competition between users. A high level of water stress indicates a situation where the combined withdrawal by all sectors represents a substantial share of the total renewable freshwater resources, with potentially larger impacts on the sustainability of the resources and potential situations of conflicts and competition between users. </p>\n<p>Total renewable freshwater resources (TRWR) are expressed as the sum of internal and external renewable water resources. The terms &#x201C;water resources&#x201D; and &#x201C;water withdrawal&#x201D; are understood here as freshwater resources and freshwater withdrawal. </p>\n<p>Internal renewable water resources are defined as the long-term average annual flow of rivers and recharge of groundwater for a given country generated from endogenous precipitation. </p>\n<p>External renewable water resources refer to the flows of water entering the country, taking into consideration the quantity of flows reserved to upstream and downstream countries through agreements or treaties. </p>\n<p>Total freshwater withdrawal (TFWW) is the volume of freshwater extracted from its source (rivers, lakes, aquifers) for agriculture, industries and services<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>. It is estimated at the country level for the following three main sectors: agriculture, services (including domestic water withdrawal) and industries (including cooling of thermoelectric plants). Freshwater withdrawal includes fossil groundwater. It does not include non-conventional water, i.e. direct use of treated wastewater, direct use of agricultural drainage water and desalinated water. </p>\n<p>Environmental flow requirements (EFR) are defined as the quantity and timing of freshwater flows and levels necessary to sustain aquatic ecosystems, which, in turn, support human cultures, economies, sustainable livelihoods, and wellbeing. Water quality and also the resulting ecosystem services are excluded from this formulation which is confined to water volumes. This does not imply that quality and the support to societies which are dependent on environmental flows are not important and should not be taken care of.<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> Methods of computation of EFR are extremely variable and range from global estimates to comprehensive assessments for river reaches. For the purpose of the SDG indicator, water volumes can be expressed in the same units as the TFWW, and then as percentages of the available water resources.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> In AQUASTAT, Services water withdrawal is reported as Municipal water withdrawal. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> They are indeed taken into account by other targets and indicators, such as 6.3.2, 6.5.1 and 6.6.1. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>The System of Environmental-Economic Accounting for Water: SEEA-Water for water resources and withdrawals (Available at https://seea.un.org/content/seea-water</li>\n  <li>The World Census of Agriculture 2020: WCA (Volume 1), for irrigation definitions (Available at: http://www.fao.org/world-census-agriculture).</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>Data for this indicator are usually collected by national ministries and institutions having water-related issues in their mandate, such as national statistic offices, ministries of water resources, agriculture or environment. Official counterparts at country level are the national statistics office and/or the line ministry for water resources and irrigation. More specifically, FAO requests countries to nominate a National Correspondent to act as the focal point for the data collection and communication. Data are mainly published within national statistical yearbooks, national water resources and irrigation master plans and other reports (such as those from projects, international surveys or results and publications from national and international research centres).</p>", "COLL_METHOD__GLOBAL"=>"<p>Data collection is done through FAO&#x2019;s Global Information System on Water and Agriculture (AQUASTAT) and AQUASTAT questionnaire on water and agriculture. The data collection process relies on a network of National Correspondents, officially nominated by their respective countries, in charge of the provision of official national data to AQUASTAT. As at August 2020, 150 countries have nominated national correspondents as well as alternate correspondents from different agencies. Countries submit data through the annual AQUASTAT questionnaire on water and agriculture, which contains, among others, the information required for the calculation of SDG indicator 6.4.2.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are collected every year through the AQUASTAT network of National Correspondents. FAO has dispatched the questionnaires to the National Correspondents in July 2022.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data for the indicator are released every year, usually in February following the UNSD collection schedule.</p>\n<p> </p>", "DATA_SOURCE__GLOBAL"=>"<p>Data come from governmental sources. The institutions responsible for data collection at national level vary according to countries. However, in general data for this indicator are provided by the Ministry of Agriculture, Ministry of Water, Ministry of Environment and other line Ministries. In many cases, data collection at country level is coordinated by the National Statistics office (NSO).</p>", "COMPILING_ORG__GLOBAL"=>"<p>Calculation rules are predefined and use data referring to the same year to general aggregate values.</p>", "INST_MANDATE__GLOBAL"=>"<p>FAO has, as part of its mandate, the function of &#x201C;collect, analyse, interpret and disseminate information relating to nutrition, food and agriculture&#x201D;. (FAO Constitution, Article 1)</p>", "RATIONALE__GLOBAL"=>"<p>The purpose of this indicator is to show the degree to which water resources are being exploited to meet the country&apos;s water demand. It measures a country&apos;s pressure on its water resources and therefore the challenge on the sustainability of its water use. It tracks progress regarding &#x201C;withdrawals and supply of freshwater to address water scarcity&#x201D;, i.e. the environmental component of target 6.4.</p>\n<p>The indicator shows to what extent water resources are already used, and signals the importance of effective supply and demand management policies. It indicates the likelihood of increasing competition and conflict between different water uses and users in a situation of increasing water scarcity. Increased water stress, shown by an increase in the value of the indicator, has potentially negative effects on the sustainability of the natural resources and on economic development. On the other hand, low values of the indicator indicate that water does not represent a particular challenge for economic development and sustainability.</p>\n<p>However, extremely low values may indicate the inability of a country to use properly its water resources for the benefit of the population. In such cases, a moderate and controlled increase in the value of the indicator can be a sign of positive development.</p>\n<p>This indicator provides an estimate of pressure by all sectors on the country&#x2019;s renewable freshwater resources. A low level of water stress indicates a situation where the combined withdrawal by all sectors is marginal in relation to the resources, and has therefore little potential impact on the sustainability of the resources or on the potential competition between users. A high level of water stress indicates a situation where the combined withdrawal by all sectors represents a substantial share of the total renewable freshwater resources, with potentially larger impacts on the sustainability of the resources and potential situations of conflicts and competition between users.</p>\n<p>The indicator is computed based on three components:</p>\n<ul>\n  <li>Total renewable freshwater resources (TRWR)</li>\n  <li>Total freshwater withdrawal (TFWW)</li>\n  <li>Environmental flow requirements (EFR)</li>\n</ul>", "REC_USE_LIM__GLOBAL"=>"<p>Freshwater withdrawal as a percentage of renewable freshwater resources is a good indicator of pressure on limited water resources, one of the most important natural resources. However, it only partially addresses the issues related to sustainable water management. </p>\n<p>Supplementary indicators that capture the multiple dimensions of water management would combine data on water demand management, behavioural changes with regard to water use and the availability of appropriate infrastructure, and measure progress in increasing the efficiency and sustainability of water use, in particular in relation to population and economic growth. They would also recognize the different climatic environments that affect water use in countries, especially in agriculture, which is the main user of water. Sustainability assessment is also linked to the critical thresholds fixed for this indicator. Although there is no universal consensus on such thresholds, a proposal is presented below.</p>\n<p>Trends in freshwater withdrawal show relatively slow patterns of change. Usually, three-five years are a minimum frequency to be able to detect significant changes, as it is unlikely that the indicator would show meaningful variations from one year to the other. </p>\n<p>Estimation of water withdrawal by sector may represent a limitation to the computation of the indicator. Few countries publish water withdrawal data on a regular basis by sector. </p>\n<p>There is no universally agreed method for the computation of incoming freshwater flows originating outside of a country&apos;s borders. Nor is there any standard method to account for return flows, the part of the water withdrawn from its source and which flows back to the river system after use. In countries where return flow represents a substantial part of water withdrawal, the indicator tends to underestimate available water and therefore overestimate the level of water stress. </p>\n<p>Other limitations that affect the interpretation of the water stress indicator include: </p>\n<ul>\n  <li>difficulty to obtain accurate, complete and up-to-date data; </li>\n  <li>potentially large variation of sub-national data; </li>\n  <li>lack of account of historical (e.g., due to climate change and population growth) and seasonal variations in water resources;</li>\n  <li>lack of consideration to the distribution among water uses; </li>\n  <li>lack of consideration of water quality and its suitability for use; and</li>\n  <li>the indicator can be higher than 100 percent when water withdrawal non-renewable freshwater (fossil groundwater), when annual groundwater withdrawal is higher than annual replenishment (over-abstraction) or when freshwater withdrawal includes part or all the water set aside for environmental flow requirements. </li>\n</ul>\n<p>Some of these issues can be solved through disaggregation of the indicator at the level of hydrological units and by distinguishing between different use sectors. However, due to the complexity of water flows, both within a country and between countries, care should be taken not to double-count.</p>", "DATA_COMP__GLOBAL"=>"<p><strong>Computation Method:</strong></p>\n<p>Method of computation: The indicator is computed as the total freshwater withdrawn (TFWW) divided by the difference between the total renewable freshwater resources (TRWR) and the environmental flow requirements (EFR), multiplied by 100. All variables are expressed in km<sup>3</sup>/year (10<sup>9</sup> m<sup>3</sup>/year).</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>S</mi>\n    <mi>t</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>%</mi>\n      </mrow>\n    </mfenced>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>T</mi>\n        <mi>F</mi>\n        <mi>W</mi>\n        <mi>W</mi>\n      </mrow>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>T</mi>\n            <mi>R</mi>\n            <mi>W</mi>\n            <mi>R</mi>\n            <mo>-</mo>\n            <mi>E</mi>\n            <mi>F</mi>\n            <mi>R</mi>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Following the experience of the initial five years of application of the indicator, and consistent with the approach taken during the MDG program, the threshold of 25% has been identified as the upper limit for a full and unconditional safety of water stress as assessed by the indicator 6.4.2.</p>\n<p>That means on one hand, that values below 25% can be considered safe in any instance (no stress); on the other, that values above 25% should be regarded as potentially and increasingly problematic, and should be qualified and/or reduced.</p>\n<p>Above 25% of water stress, four classes have been identified to signal different levels of stress severity:</p>\n<ul>\n  <li>NO STRESS &lt;25% </li>\n  <li>LOW 25% - 50% </li>\n  <li>MEDIUM 50% - 75% </li>\n  <li>HIGH 75-100%</li>\n  <li>CRITICAL &gt;100% </li>\n</ul>", "DATA_VALIDATION__GLOBAL"=>"<p>Data validation is done in a few steps. </p>\n<ul>\n  <li>the AQUASTAT questionnaire embeds automatic validation rules to allow National Correspondents to identify any data consistency errors while compiling the data.</li>\n  <li>Once the questionnaire is submitted, FAO thoroughly reviews the information reported, using the following tools:<ul>\n      <li>Manual cross-variable check. This includes cross-comparison with similar countries as well as historic data for the countries. </li>\n      <li>Time-series coherency by running an R-script to compare reported data with those corresponding to previous years</li>\n      <li>Verification of the metadata, especially the source of the proposed data. The critical analysis of the compiled data gives preference to national sources and expert knowledge.</li>\n    </ul>\n  </li>\n  <li>After this verification, exchanges between the National Correspondents and FAO take place to correct and confirm the collected data. </li>\n  <li>The last validation step is an automated validation routine included in the Statistical Working System (SWS), which uses almost 200 validation rules.</li>\n</ul>", "ADJUSTMENT__GLOBAL"=>"<p>Since national level data is frequently tailored to be useful at national level and not for international comparisons, data may be manipulated in order to maximize international comparability. Adjusted data is displayed with an appropriate qualifier. Data is rounded according to a specific methodology<u> </u><a href=\"http://www.fao.org/aquastat/en/databases/maindatabase/metadata/\">http://www.fao.org/aquastat/en/databases/maindatabase/metadata/</a></p>\n<p>Additionally, the Statistical Working System (SWS) has the correspondence among different international codes (FAOSTAT, UNSDM49, ISO2, ISO3) for geographic areas and is used to convert area codes in the external sources to UNSDM49 codes which is the standard used in the SWS.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Three types of imputation are made at country level to fill in missing years in the timeseries:</p>\n<ul>\n  <li>Linear imputation: between two available data-points.</li>\n  <li>Carry forward: after the last available data-points and up to 10 years.</li>\n  <li>Vertical imputation: in case of available total freshwater withdrawal but missing disaggregation by sources, and if existing disaggregation existed for previous years, the respective ratio by sources is applied to the available total.</li>\n</ul>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Thanks to the imputation methods at country level, data will be available for the whole time series (unless the latest official value was obtained more than 10 years ago). Imputed data is displayed with an appropriate qualifier.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global estimates will be done by summing up the national figures on renewable freshwater resources and total freshwater withdrawal, considering only the internal renewable water resources of each country to avoid double counting, and the external renewable freshwater resources of the region, if any. In case of regional aggregation without physical continuity (such as income groupings or Least Developed Countries group, etc.), total renewable water resources are summed up. The EFR at regional level is estimated as the average of the countries&#x2019; EFRs, in percentage, and applied to the regional water resources.</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>A set of tools is available to countries for the compilation of the indicator. Among them, a step-by-step methodological guide, an interpretation paper, and an e-learning course. All the tools are available on the FAO web pages, at: http://www.fao.org/sustainable-development-goals/indicators/642/en/. </li>\n  <li>During 2020,2021 and 2022, FAO has organized regional virtual trainings for Asia, Latin-America and the Caribbean and Africa on SDG 6.4. and contributed to global workshops on SDG 6. </li>\n  <li>FAO&#x2019;s AQUASTAT team provides continued guidance to the countries thought the National Correspondents during the data collection time to ensure data is duly and timely compiled.</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<ul>\n  <li>The AQUASTAT questionnaire on water and agriculture, used for collecting information on SDG indicator 6.4.2, was endorsed by FAO&#x2019;s Office of the Chief Statistician (OCS).</li>\n  <li>During the reporting process, the OCS provides overall guidance, including on metadata reporting, based on the Metadata Dissemination Standard approved by the FAO IDWG-Statistics Technical Task Force.</li>\n  <li>Data on Environmental flow requirements is reupdated only when detailed methodology and metadata are provided and when consistency in the values is ensured.</li>\n  <li>After revision and validation, data are submitted to the OCS who also ensures the quality of the data and results.</li>\n</ul>", "QUALITY_ASSURE__GLOBAL"=>"<p>FAO is responsible for the quality of the internal statistical processes used to compile the published datasets. The FAO Statistics Quality Assurance Framework (SQAF), available at: http://www.fao.org/docrep/019/i3664e/i3664e.pdf, provides the necessary principles, guidelines and tools to carry out quality assessments. FAO is performing an internal bi-annual survey (FAO Quality Assessment and Planning Survey) designed to gather information on all of FAO&#x2019;s statistical activities, notably to assess the extent to which quality standards are being implemented with a view to increasing compliance with the quality dimensions of SQAF, documenting best practices and prepare quality improvement plans, where necessary. Domain-specific quality assurance activities are carried out systematically (e.g. quality reviews, self-assessments, compliance monitoring).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Overall evaluation of data quality is based on standard quality criteria and follows FAO&#x2019;s SQAF. It also includes:</p>\n<ul>\n  <li>A qualitative and quantitative manual cross-variable check after data is received. This consists of the verification that all the numbers are consistent based on the internal validation rules embedded in the questionnaire. Any issues identified are flagged and listed to be followed-up with the countries. </li>\n  <li>Time-series coherency check done by running an R-script to compare reported data with those corresponding to previous years. Based on this, a scattered diagram is also made by variable and country to allow for a visual verification of historical data. The critical analysis of the compiled data gives preference to national sources and expert knowledge, unless these greatly diverge from historic data or in the case of drastic changes in methodologies used by countries with significant influence on the results. </li>\n  <li>Verification of the metadata, especially the source of the proposed data. When data sources were not provided, the questionnaire was added as the data source of a given value. For the </li>\n</ul>", "COVERAGE__GLOBAL"=>"<p>Data needed for the indicator are collected through AQUASTAT for 168 countries worldwide. </p>\n<p><strong>Time series:</strong></p>\n<p>1961-2019 (Discontinuous, depending on country. Data are interpolated to create timelines.) </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Sectoral disaggregated data are provided to show the respective contribution of the different sectors to the water stress level, and therefore the relative importance of actions needed to contain water demand in the different sectors (agriculture, services and industry). The contribution of the different sectors to the water stress level is calculated as the proportion of sectoral withdrawals over total freshwater withdrawals, after taking into account the EFR. sectors are defined following the United Nations International Standard Industrial Classification of All Economic Activities ISIC 4 coding, </p>\n<ol>\n  <li>agriculture; forestry; fishing (ISIC A), hereinafter &#x201C;agriculture&#x201D;;</li>\n  <li>mining and quarrying; manufacturing; electricity, gas, steam and air conditioning supply; constructions (ISIC B, C, D and F), hereinafter &#x201C;MIMEC&#x201D;;</li>\n  <li>all the service sectors (ISIC E and ISIC G-T), hereinafter &#x201C;services&#x201D;.</li>\n</ol>\n<p>At national level, water resources and withdrawals are estimated or measured at the level of appropriate hydrological units (river basins, aquifers). It is therefore possible to obtain a geographical distribution of water stress by hydrological unit, thus allowing for more targeted response in terms of water demand management.</p>", "COMPARABILITY__GLOBAL"=>"<p>Geographical: For national estimates incoming freshwater is counted as being part of the country&#x2019;s available freshwater resources, while global estimates can only be done by adding up the internal renewable freshwater resources (water generated within the country) of all countries in order to avoid double counting. Moreover, external freshwater resources are computed according to treaties, if present, which may lead to different values with respect to the actual freshwater resources assessed through hydrology.</p>\n<p>Over-time: time series are comparable across time. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.fao.org/aquastat/en/\">http://www.fao.org/aquastat/en/</a> </p>\n<p><strong>References:</strong></p>\n<p>Food and Agricultural Organization of the United Nations (FAO). AQUASTAT, FAO&apos;s Global Water Information System. Rome. Website <a href=\"http://www.fao.org/aquastat/en/\">http://www.fao.org/aquastat/en/</a>. </p>\n<p>The following resources of specific interest to this indicator are available on these sites: </p>\n<p>AQUASTAT glossary (<a href=\"http://www.fao.org/aquastat/en/databases/glossary/\">http://www.fao.org/aquastat/en/databases/glossary/</a>). </p>\n<p>AQUASTAT Main country database (<a href=\"http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en\">http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en</a> ) </p>\n<p>AQUASTAT Water use (<a href=\"http://www.fao.org/aquastat/en/overview/methodology/water-use/\">http://www.fao.org/aquastat/en/overview/methodology/water-use/</a>). </p>\n<p>AQUASTAT Water resources (<a href=\"http://www.fao.org/aquastat/en/overview/methodology/water-resources/\">http://www.fao.org/aquastat/en/overview/methodology/water-resources/</a>). </p>\n<p>AQUASTAT publications dealing with concepts, methodologies, definitions, terminologies, metadata, etc. (<a href=\"http://www.fao.org/aquastat/en/resources/publications/reports/\">http://www.fao.org/aquastat/en/resources/publications/reports/</a>)</p>\n<p>IWMI &#x2013; Global environmental flows assessment <br><a href=\"http://eflows.iwmi.org/\">http://eflows.iwmi.org/</a> </p>\n<p>IWMI - Global Environmental Flow Information for the Sustainable Development Goals<br><a href=\"http://www.iwmi.cgiar.org/Publications/IWMI_Research_Reports/PDF/pub168/rr168.pdf\">http://www.iwmi.cgiar.org/Publications/IWMI_Research_Reports/PDF/pub168/rr168.pdf</a> </p>\n<p>UNSD/UNEP Questionnaire on Environment Statistics &#x2013; Water Section</p>\n<p><a href=\"http://unstats.un.org/unsd/environment/qindicators.htm\">http://unstats.un.org/unsd/environment/qindicators.htm</a> </p>\n<p>Framework for the Development of Environment Statistics (FDES 2013) (Chapter 3) <a href=\"http://unstats.un.org/unsd/environment/FDES/FDES-2015-supporting-tools/FDES.pdf\">http://unstats.un.org/unsd/environment/FDES/FDES-2015-supporting-tools/FDES.pdf</a> </p>\n<p>OECD/Eurostat Questionnaire on Environment Statistics &#x2013; Water Section </p>\n<p>https://ec.europa.eu/eurostat/documents/1798247/6664269/Data-Collection-Manual-for-OECD_Eurostat-Questionnaire-on-Inland-Waters.pdf/f5f60d49-e88c-4e3c-bc23-c1ec26a01b2a?t=1611245054001</p>\n<p>Several documents exist that can be used to support countries in the computation of this indicator. Among them:</p>\n<p><strong>Understanding AQUASTAT - FAO&apos;s global water information system<br></strong>This information note covers a twenty-year history of the collection and analysis of water-related data and its dissemination as an international public good, freely available to all. The process of collecting and checking the data has resulted in the establishment of a unique network of collaborators who provide data, use data from other countries for comparative purposes, and exchange views and experiences on how best to measure and account for water-related use. Users range from international private companies to non-governmental organizations, and virtually all significant reports related to water depend on the data provided by AQUASTAT.<br><a href=\"http://www.fao.org/3/a-bc817e.pdf\">http://www.fao.org/3/a-bc817e.pdf</a></p>\n<p><strong>Incorporating environmental flows into &#x201C;water stress&#x201D; indicator 6.4.2 - Guidelines for a minimum standard method for global reporting.</strong></p>\n<p>These guidelines are intended to assist countries to participate in the assessment of SDG 6.4.2 on water stress by contributing data and information on environmental flows (EF). They provide a minimum standard method, principally based on the Global Environmental Flows Information System (GEFIS), which is accessible via <a href=\"http://eflows.iwmi.org\">http://eflows.iwmi.org</a>.</p>\n<p><a href=\"https://www.unwater.org/app/uploads/2019/01/SDG6_EF_LOW2.pdf\">https://www.unwater.org/app/uploads/2019/01/SDG6_EF_LOW2.pdf</a></p>\n<p><strong>Renewable Water Resources Assessment - 2015 AQUASTAT methodology review<br></strong><a href=\"http://www.fao.org/3/a-bc818e.pdf\">http://www.fao.org/3/a-bc818e.pdf</a></p>\n<p><strong>Global database on municipal wastewater production, collection, treatment, discharge and direct use in agriculture <br></strong>This paper describes the rationale and method to setup and feed the AQUASTAT database on municipal wastewater production, collection, treatment, discharge or direct use in agriculture. The best available sources of information have been reviewed, including peer-reviewed papers, proceedings of workshops, conferences and expert meetings, global or regional databases, as well as country briefs, national reports and direct communications by country government officials and experts.<br><a href=\"http://www.fao.org/3/a-bc823e.pdf\">http://www.fao.org/3/a-bc823e.pdf</a></p>\n<p><strong>Cooling water for energy generation and its impact on national-level water statistics <br></strong>This technical note, describing the issue of cooling water for energy generation and its impact on national-level water statistics, has two purposes: 1) to act as a general informational resource and 2) to encourage governmental agencies responsible for water usage to gather and report information disaggregated by sub-sector (keeping thermoelectric withdrawals separate from industrial and hydroelectric withdrawals), and to determine the point at which lower water withdrawal designs are more favourable, even if the required capital cost is higher.<br><a href=\"http://www.fao.org/3/a-bc822e.pdf\">http://www.fao.org/3/a-bc822e.pdf</a></p>\n<p><strong>Municipal and industrial water withdrawal modelling for the years 2000 and 2005 using statistical methods <br></strong>This document describes the efforts to generate models that estimate the municipal and industrial water withdrawals for the years 2000 and 2005. <br><a href=\"http://www.fao.org/3/a-bc821e.pdf\">http://www.fao.org/3/a-bc821e.pdf</a></p>\n<p><strong>Disambiguation of water statistics <br></strong>The nomenclature surrounding water information is often confusing and gives rise to different interpretations and thus confusion. When discussing the way in which renewable water resources are utilized, the terms water use, usage, withdrawal, consumption, abstraction, extraction, utilization, supply and demand are often used without clearly stating what is meant. <br><a href=\"http://www.fao.org/3/a-bc816e.pdf\">http://www.fao.org/3/a-bc816e.pdf</a></p>\n<p><strong>FAO-AQUASTAT questionnaire on water and agriculture</strong><br>These annual Guidelines and questionnaires have been prepared specifically designed to collect the SDG 6.4. related water variables, and therefore to update the core variables in AQUASTAT database.<br><a href=\"http://www.fao.org/aquastat/en/overview/methodology/\">http://www.fao.org/aquastat/en/overview/methodology/</a></p>\n<p> </p>\n<p><strong>International Recommendations for Water Statistics<br></strong>The International Recommendations for Water Statistics (IRWS) were developed to help strengthen national information systems for water in support of design and evaluation of Integrated Water Resources Management (IWRM) policies.<br><a href=\"https://unstats.un.org/unsd/EconStatKB/KnowledgebaseArticle10209.aspx\">https://unstats.un.org/unsd/EconStatKB/KnowledgebaseArticle10209.aspx</a> </p>\n<p><strong>UNSD/UNEP Questionnaire on Environment Statistics &#x2013; Water Section<br></strong><a href=\"http://unstats.un.org/unsd/environment/questionnaire.htm\">http://unstats.un.org/unsd/environment/questionnaire.htm</a><u><br></u><a href=\"http://unstats.un.org/unsd/environment/qindicators.htm\">http://unstats.un.org/unsd/environment/qindicators.htm</a></p>\n<p><strong>UNSD &#x2018;National Accounts Main Aggregates Database&#x2019;<br></strong><a href=\"http://unstats.un.org/unsd/snaama/selbasicFast.asp\">http://unstats.un.org/unsd/snaama/selbasicFast.asp</a></p>\n<p><strong>FAO e-learning course on </strong>SDG Indicator 6.4.2 - Level of water stress: https://elearning.fao.org/course/view.php?id=365 </p>", "indicator_sort_order"=>"06-04-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"6.5.1", "slug"=>"6-5-1", "name"=>"Grado de gestión integrada de los recursos hídricos", "url"=>"/site/es/6-5-1/", "sort"=>"060501", "goal_number"=>"6", "target_number"=>"6.5", "global"=>{"name"=>"Grado de gestión integrada de los recursos hídricos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Grado de gestión integrada de los recursos hídricos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Grado de gestión integrada de los recursos hídricos", "indicator_number"=>"6.5.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El indicador proporciona una medición directa del progreso de la primera parte de la \nMeta 6.5: “…implementar la gestión integrada de los recursos hídricos a todos los niveles…”.\n\nLa puntuación del indicador proporciona una forma sencilla y comprensible de medir el progreso \nhacia la meta: “0” se interpreta como falta de implementación de la gestión integrada de los recursos \nhídricos (GIRH) y “100” como una implementación completa de la GIRH.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.5.1&seriesCode=ER_H2O_IWRMD&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=_T\">Grado de implementación de la gestión integrada de los recursos hídricos (%) ER_H2O_IWRMD</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-05-01.pdf\">Metadatos 6-5-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-27", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The indicator provides a direct progress measurement of the first part of Target 6.5 “…implement \nintegrated water resources management at all levels…”. \n\nThe indicator score provides an easy and understandable way of measuring progress towards \nthe target, with ‘0’ interpreted as no implementation of IWRM, and ‘100’ interpreted as IWRM \nbeing fully implemented. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.5.1&seriesCode=ER_H2O_IWRMD&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=_T\">Degree of integrated water resources management implementation (%) ER_H2O_IWRMD</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-05-01.pdf\">Metadata 6-5-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Adierazleak 6.5 xedearen lehen zatiaren aurrerapena zuzenean neurtzen du: \"... baliabide hidrikoen kudeaketa \nintegratua maila guztietan ezartzea...\". \n\nAdierazlearen puntuazioak xederako aurrerapena neurtzeko modu erraz eta ulergarria eskaintzen du: “0\" bada, \nbaliabide hidrikoen kudeaketa integratua (BHKI) ezarri ez dela ulertzen da, eta \"100” bada, berriz, BHKI \nosorik ezarri dela. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.5.1&seriesCode=ER_H2O_IWRMD&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=_T\">Ur-baliabideen kudeaketa integratuaren ezarpen-maila (%) ER_H2O_IWRMD</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-05-01.pdf\">Metadatuak 6-5-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 6: Ensure availability and sustainable management of water and sanitation for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 6.5: By 2030, implement integrated water resources management at all levels, including through transboundary cooperation as appropriate</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 6.5.1: Degree of integrated water resources management</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_H2O_IWRMD - Degree of integrated water resources management implementation (%) [6.5.1]</p>\n<p>ER_H2O_IWRMD_EE - Degree of integrated water resources management implementation, enabling environment (%) [6.5.1]</p>\n<p>ER_H2O_IWRMD_FI - Degree of integrated water resources management implementation, financing (%) [6.5.1]</p>\n<p>ER_H2O_IWRMD_IP - Degree of integrated water resources management implementation, institutions and participation (%) [6.5.1]</p>\n<p>ER_H2O_IWRMD_MI - Degree of integrated water resources management implementation, management instruments (%) [6.5.1]</p>\n<p>ER_H2O_IWRMP - Proportion of countries by IWRM implementation category [6.5.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.4.1, 1.4.2, 2.3.1, 2.3.2, 4.7.1, 5.5.1, 5.5.2, 6.6.1, 6.a.1, 6.b.1, 7.1.1, 7.1.2, 8.5.1, 8.5.2, 10.2.1, 11.3.1, 11.3.2, 13.2.1, 13.2.2, 15.9.1, 16.3.1, 16.3.2, 16.5.1, 16.5.2, 16.7.1, 16.7.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), implemented by the UNEP-DHI Centre on Water and Environment</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Indicator 6.5.1 is &#x2018;degree of integrated water resources management implementation&#x2019;. It measures the stages of development and implementation of Integrated Water Resources Management (IWRM), on a scale of 0 to 100, in six categories (see Rationale section). The indicator score is calculated from a country survey with 33 questions, with each question scored on the same scale of 0-100. </p>\n<p>The definition of IWRM is based on an internationally agreed definition, and is universally applicable. IWRM was officially established in 1992 and is defined as &#x201C;a process which promotes the coordinated development and management of water, land and related resources in order to maximise economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems.&#x201D; (GWP 2010). </p>\n<p>The method builds on official UN IWRM status reporting, from 2008 and 2012, of the Johannesburg Plan of Implementation from the UN World Summit for Sustainable Development (1992).</p>\n<p><strong>Concepts:</strong></p>\n<p>The concept of IWRM is measured in 4 main sections, each representing key dimension of IWRM: </p>\n<ol>\n  <li>Enabling environment: this includes the policies, laws, plans and strategies which create the &#x2018;enabling environment&#x2019; for IWRM. </li>\n  <li>Institutions and participation: includes the range and roles of political, social, economic and administrative institutions that help to support the implementation of IWRM. </li>\n  <li>Management Instruments: The tools and activities that enable decision-makers and users to make rational and informed choices between alternative actions. </li>\n  <li>Financing: Budgeting and financing made available and used for water resources development and management from various sources.</li>\n</ol>\n<p>The indicator is based on a national survey structured around these four main sections. Each section is split into two parts: questions concerning the &#x2018;National level&#x2019; and &#x2018;Other levels&#x2019; respectively. &#x2018;Other levels&#x2019; includes sub-national (including provinces/states for federated countries), basin level, and the transboundary level as appropriate. These two parts address the wording of Target 6.5 &#x2018;implement [IWRM] at all levels &#x2026;&#x2019;.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) </p>", "CLASS_SYSTEM__GLOBAL"=>"<h3>Classification of inland water bodies: <a href=\"https://unstats.un.org/unsd/classifications/Family/Detail/2002\">https://unstats.un.org/unsd/classifications/Family/Detail/2002</a> </h3>\n<ul>\n  <li>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>Monitoring progress on meeting SDG 6.5 is owned by and is the responsibility of the national government. The government assigns a ministry with the primary responsibility for overseeing this survey, which then takes on the responsibility of coordinating the national IWRM monitoring and reporting process. As water management issues cut across a wide number of sectors, often overseen by different ministries and other administrative bodies at national or other levels, the process should be inclusive. Major stakeholders should be involved in order to contribute to well informed and objective answers to the survey. </p>\n<p>The ministry is invited to nominate a national &#x201C;IWRM focal point&#x201D;, who may or may not be a government official. The UN provides support where needed and possible. The following steps are suggested as guidance only, as it is up to countries to decide which process or processes would best serve their needs. It should also be noted that the following steps represent a &#x2018;ladder&#x2019; approach, in that completing all the steps will generally lead to a more robust indicator. However, it may not be possible or necessary for all countries to complete all steps. </p>\n<ol>\n  <li>The responsible ministry or IWRM focal point contacts other relevant ministries/agencies to compile responses to the questionnaire. Each possible response option has a score which is used to calculate the overall indicator score.</li>\n  <li>The completed draft survey is reviewed by government stakeholders. These stakeholders could include those involved in water-relevant sectors, such as agriculture, energy, water supply and environment, as well as water management at different administrative levels. This process may be electronic (e.g. via email) and/or through workshops. </li>\n  <li>The revised draft survey is validated at a multi-stakeholder workshop. Apart from government representatives these stakeholders could include water user associations, private sector, interest groups concerned with e.g. environment, agriculture, poverty, and academia. The suggested process is through a workshop but alternative means of consultation e.g. email or online call for public submissions could be considered. Note that steps 2 and 3 could be combined if desired. </li>\n  <li>The responsible ministry or IWRM focal point discusses with relevant officials and consolidates the input into a final version. This version is the basis for calculating the degree of IWRM implementation (0-100) for global reporting. </li>\n  <li>The responsible ministry submits the final indicator score to the national statistics office responsible for compiling all national SDG target data. </li>\n</ol>\n<p>Based on the national survey, UN-Water periodically prepares synthesis reports for regional and global levels to provide overall progress on meeting SDG target 6.5. </p>\n<p>Temporal Coverage: A reporting cycle of three years is recommended.</p>", "COLL_METHOD__GLOBAL"=>"<p>Official counterparts at the country level oversee the validation and consultation process. </p>\n<p>The survey has been designed so that the indicator is comparable between countries and time periods. No adjustments are foreseen. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data is collected approximately every 3-4 years. The baseline dataset was collected in 2017, the second data collection round in 2020, and the third data collection round in 2023. Subsequent data collection rounds are expected in 2026-27, and 2030. Each data collection round spans approximately 9-12 months. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data is released approximately 3 months after the close of each data collection round. </p>", "DATA_SOURCE__GLOBAL"=>"<p>The information required to complete the survey is expected to be held by government officials responsible for water resources management in the country, supported by official documentation. E.g. Ministry of Water in coordination with Ministry of Environment, Ministry of Finance, Ministry of Planning, Ministry of Lands and Agriculture, Ministry of Industry and Mining etc. See also &#x2018;data sources&#x2019; section above. As a minimum, a small group of officials may be able to complete the survey. However, these government officials may belong to various government authorities, and coordination is required to determine and validate the responses to each question. Increased government and non-government stakeholder participation in validating the question scores will lead to a more robust indicator score and facilitate tracking progress over time.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), implemented by the UNEP-DHI Centre on Water and Environment, and UN-Water partners, under the <a href=\"https://www.sdg6monitoring.org/about/integrated-monitoring-initiative/\" target=\"_blank\">UN-Water Integrated Monitoring Initiative for SDG 6 (IMI-SDG6)</a>.</p>", "INST_MANDATE__GLOBAL"=>"<p>UNEP is the designated Custodian Agency for the indicator. Support on the collection, processing, and dissemination of statistics for this indicator is provided by the UNEP-DHI Centre on Water and Environment, the Global Water Partnership (GWP), and Cap-Net. </p>", "RATIONALE__GLOBAL"=>"<p>The indicator provides a direct progress measurement of the first part of Target 6.5 &#x201C;&#x2026;implement integrated water resources management at all levels &#x2026;&#x201D;. The indicator score provides an easy and understandable way of measuring progress towards the target, with &#x2018;0&#x2019; interpreted as no implementation of IWRM, and &#x2018;100&#x2019; interpreted as IWRM being fully implemented.</p>\n<p> </p>\n<p>To further aid interpretation and comparison, the indicator results can be categorized as follows: </p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Degree of implementation</p>\n      </td>\n      <td>\n        <p>Score range (%)</p>\n      </td>\n      <td>\n        <p>General interpretation for overall IWRM score</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Very high</p>\n      </td>\n      <td>\n        <p>91 - 100</p>\n      </td>\n      <td>\n        <p>Vast majority of IWRM elements are fully implemented, with objectives consistently achieved, and plans and programmes periodically assessed and revised.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>High</p>\n      </td>\n      <td>\n        <p>71 - 90</p>\n      </td>\n      <td>\n        <p>IWRM objectives of plans and programmes are generally met, and geographic coverage and stakeholder engagement is generally good.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Medium-high</p>\n      </td>\n      <td>\n        <p>51 - 70</p>\n      </td>\n      <td>\n        <p>Capacity to implement elements of IWRM is generally adequate, and elements are generally being implemented under long-term programmes.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Medium-low</p>\n      </td>\n      <td>\n        <p>31 - 50</p>\n      </td>\n      <td>\n        <p>Elements of IWRM are generally institutionalized, and implementation is underway.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Low</p>\n      </td>\n      <td>\n        <p>11 - 30</p>\n      </td>\n      <td>\n        <p>Implementation of elements of IWRM has generally begun, but with limited uptake across the country, and potentially low engagement of stakeholder groups.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Very low</p>\n      </td>\n      <td>\n        <p>0 - 10</p>\n      </td>\n      <td>\n        <p>Development of elements of IWRM has generally not begun, or has stalled.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>The concept of the survey is that it provides sufficient information to be of real value to the countries in determining their progress towards the target, and through this, various aspects of IWRM. A balance has been sought between providing sufficient information to cover the core principles of IWRM, and thus providing a robust indicator value, and not overburdening countries with unnecessary reporting requirements. </p>\n<p>Countries are encouraged to provide additional information on each question, which may help to qualify their choice of score, and/or put that score into their national context. </p>\n<p>Indicator 6.5.1 is supported by indicator 6.5.2 &#x201C;Proportion of transboundary basin area with an operational arrangement for water cooperation&#x201D;, which directly addresses the portion of Target 6.5 &#x201C;&#x2026;, including through transboundary cooperation as appropriate.&#x201D;.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The challenge of subjectivity in responses associated with this type of survey is being addressed in a number of ways: </p>\n<ol>\n  <li>Draft responses are reviewed by a number of governmental and non-governmental stakeholders in an open, inclusive and transparent process. </li>\n  <li>Countries are encouraged to provide further information to qualify their responses and/or set them in the national context. </li>\n  <li>Guidelines are provided for each of the four main sections, each question, and each of the six thresholds for every single question, to ensure responses are as objective as possible, and are comparable both between countries, and between reporting periods. </li>\n</ol>\n<p>To achieve robust indicator results requires a country process involving a wide range of stakeholders which requires a certain amount of time and resources. The advantage of this is that it puts in place a process that addresses the integrated and indivisible nature of the SDG targets, as well as stressing the importance of &#x201C;leaving no one behind&#x201D;.</p>", "DATA_COMP__GLOBAL"=>"<ol>\n  <li>The survey contains 33 questions divided into the four main sections described above. </li>\n  <li>Each question is given a score between 0 and 100, in increments of 10, guided by threshold descriptions for the following 6 categories:<ul>\n      <li>Very low (0)</li>\n      <li>Low (20)</li>\n      <li>Medium-low (40)</li>\n      <li>Medium-high (60)</li>\n      <li>High (80)</li>\n      <li>Very high (100)</li>\n    </ul>\n  </li>\n</ol>\n<p>Where question is not applicable, n/a can be selected as a reply, providing adequate explanation. <br><br>Note that more question-specific guidance is provided for each threshold for each question, to ensure objective and comparable results. </p>\n<ol>\n  <li>The un-weighted average of the question scores within each of the four sections is calculated to give a score of 0 &#x2013; 100 for each section, rounded to the nearest whole number. Questions with response n/a are omitted from calculation.</li>\n  <li>The section scores (rounded to the nearest whole number), are averaged (un-weighted), and rounded to the nearest whole number, to give the indicator score, expressed as a number between 0 and 100.</li>\n</ol>", "DATA_VALIDATION__GLOBAL"=>"<p>There is a dedicated SDG 6.5.1 Help Desk for ensuring the quality of the statistical results. Firstly, the data goes through any national quality assurance and approval processes, before being submitted to the Help Desk. The Help Desk then undertakes the Quality Assurance procedure described in section 4.j. All issues are discussed between the Help Desk and the Focal Point(s). Only when all issues are resolved, are the data finalised and entered into the Help Desk Database. The data is then submitted to the UNEP SDG focal point, who collates all indicator data for which UNEP is the Custodian Agency, where a further quality check is undertaken, prior to submission to the SDGs Indicator Database. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Once the validation process described above is complete, no further adjustments are made. </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>The indicator and survey have been designed for all countries to be able to submit an indicator value, and the number of country responses under the SDG process is in excess of 95%. Estimates for countries not responding to the survey are therefore not made.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>As the number of country responses is in excess of 95%, this coverage of data is deemed to be representative of regional and global aggregates. Estimates for countries not responding to the survey are therefore not made.</p>", "REG_AGG__GLOBAL"=>"<p>Following the Agenda 2030 principle of &#x201C;leaving no one behind&#x201D;, regional and global values are based on simple, un-weighted averages of country scores. The country scores are presented as a whole number, and regional and global averages are also presented as a whole number. Global averages are based on country values, not regional averages.</p>\n<p> </p>\n<p>Regional values may be assembled by regional bodies responsible for water resources in the region, such as the African Ministerial Council on Water (AMCOW), the European Environment Agency (EEA), and the United Nations Economic and Social Commission for West Asia (ESCWA).</p>", "DOC_METHOD__GLOBAL"=>"<ol>\n  <li>National focal points selected by each country. </li>\n  <li>National focal points are responsible for coordinating a national process to engage governmental and non-governmental stakeholders, as appropriate in the context of each country, to develop draft responses and finalise responses. This may be via email, workshops, and online notices. </li>\n  <li>The following guidance materials are available for national focal points in 7 languages (English, Spanish, French, Arabic, Russian, Chinese, Portuguese), at <a href=\"https://iwrmdataportal.unepdhi.org/data-collection\">https://iwrmdataportal.unepdhi.org/data-collection</a> the Survey (MS Word); a Monitoring Guide (MS Word, Videos, PowerPoint (PPTX or PDF) versions available); 1-page survey overview; calculation template, and a Gender Checklist. More detailed guidance on running the consultation processes is available in the &#x201C;Stakeholder Consultation Manual&#x201D;. In addition, focal points may access the following country-level materials at https://iwrmdataportal.unepdhi.org/country-reportsthe 2017,2020 and 2023 surveys and 2-page results summaries (for 191 countries); and 2017, 2020 and 2023 workshop reports (for 78 countries) from <a href=\"https://www.gwp.org/en/sdg6support/sdgmap/\">https://www.gwp.org/en/sdg6support/sdgmap/</a>. </li>\n  <li>In addition, an &#x201C;SDG 6.5.1 Facilitator&#x2019;s Training Course&#x201D; is available online via <a href=\"https://www.gwp.org/en/sdg6support/consultations/where-we-are/stage-1-activities/\">https://www.gwp.org/en/sdg6support/consultations/where-we-are/stage-1-activities/</a> , through the SDG 6 IWRM Support Programme. </li>\n  <li>Further technical and financial assistance to engage a facilitator is available through the SDG 6 IWRM Support Programme (Stage 1). Across 2017 and 2023, 78 countries have received this support, see <a href=\"https://www.gwp.org/en/sdg6support/consultations/where-we-are/stage-1-activities/\">https://www.gwp.org/en/sdg6support/consultations/where-we-are/stage-1-activities/</a>. </li>\n</ol>\n<p>Extensive explanations are provided in the monitoring guide and in the survey itself. The survey contains: an overall introduction and explanation; a glossary; an introduction and glossary in each of the four sections; threshold descriptions for six thresholds for each question; and a number of footnotes to explain aspects of questions or threshold descriptions. All materials can be downloaded from <a href=\"http://iwrmdataportal.unepdhi.org\">http://iwrmdataportal.unepdhi.org</a>. In addition, a dedicated Help Desk is available to provide assistance at all times. The Help Desk is accessible via email <a href=\"mailto:iwrmsdg651@un.org\">iwrmsdg651@un.org</a>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>UNEP-DHI Centre, which manages the statistical reporting processes for UNEP on indicator 6.5.1, operates through a Business Management System that fulfils the requirements of ISO 9001 (quality management), which covers relevant areas such as consulting and capacity development and training courses. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The following quality assurance guidelines are available to all individuals involved in quality assurance for 6.5.1. </p>\n<p>Process: </p>\n<ol>\n  <li>Nominate person responsible for the quality assurance (QA) for a country response once it is submitted for the first time. </li>\n  <li>Acknowledge receipt and inform the country of the QA process. </li>\n  <li>Update the QA spreadsheet, indicating date of receipt and who submitted. </li>\n  <li>Upload draft survey (MS Word) to the Sharepoint folder. </li>\n  <li>Undertake ALL checks described below. </li>\n  <li>If there are any discrepancies, revert to UNEP-DHI colleagues. </li>\n  <li>Once action is agreed, respond to the countries. </li>\n  <li>Complete all checks on each subsequent version of the survey until all quality issues are resolved and survey is marked &#x2018;final&#x2019;. </li>\n</ol>\n<p>Checks: </p>\n<ol>\n  <li><strong>Focal point:</strong> Confirm the person submitting is the formal national focal point. If not, any reply should also add the national focal point in Cc. </li>\n  <li><strong>Cover sheet:</strong> check if cover sheet is correctly filled out. Cross-check if the person submitting is the formal national focal point. If not, any reply should include the national focal point in Cc.</li>\n  <li><strong>Question scores and calculations:</strong> In the spreadsheet &#x2018;Quality_Assurance_651_2023.xlsx&#x2019; on Sharepoint, fill in the given responses in sheet &#x201C;QA 2023 scores-status&#x201D;. Make the following checks to scores:<ol>\n      <li>All questions answered. The official guidance is that all questions should be answered (either with a score or n/a). </li>\n      <li>Scores are in range from 0-100, in increments of 10. If they only give &#x2018;even&#x2019; scores (e.g. 0, 20, 40 etc), then they may not have understood that they can also give &#x2018;odd&#x2019; scores (10, 30, 50 etc), if they feel their situation lies between two threshold descriptions.</li>\n      <li>Any differences between &#x2018;given&#x2019; and &#x2018;calculated&#x2019; section scores and overall score are given in columns C &#x2013; G. If the difference is greater than +/- 0.5, the cells are automatically highlighted in red using conditional formatting. Ensure to complete the date of last submission in column B, otherwise the differences will not be calculated. </li>\n      <li>Compare with previous scores. The QA &#x2018;Comparison&#x2019; workshsheet automatically calculates differences. Note any negative changes (orange), or increases of more than 20 (yellow). If there are any significant/unexpected differences, the country should have given some explanation in the free text fields. </li>\n      <li>In the &#x2018;given&#x2019; calculations (section 5 of the survey instrument), check that section averages and overall score are rounded to the nearest whole number. Rounding mistakes might occur.</li>\n      <li>Note: in the calculations, 0 scores are included, and N/A scores should be omitted. N/A scores should always have explanation (unless obvious &#x2013; e.g. transboundary questions for island states).</li>\n      <li>Check if the final score is calculated as average of rounded section averages.</li>\n      <li>In the free text responses in columns (BE-BF) in the main &#x201C;QA 2023-score status&#x201D; tab, for assigning Low/ Medium/ High categories the following criteria should be followed: <u>Low:</u> Less than three quarters of questions have responses and/or responses are poor quality. <u>Medium:</u> At least three quarters of the questions have responses, and/or responses are varying quality. Each question and the points make sense and are useful. <u>High:</u> All questions have responses and most responses are high quality. NB: Quality responses mean ones that are useful/informative/detailed and can contribute to stakeholder understanding/discussions and planning.</li>\n    </ol>\n  </li>\n  <li><strong>Free text fields: </strong>Using the &#x2018;text&#x2019; tabs: </li>\n  <li>Check that the free text make sense in the context of the score (and vice versa) (particularly in the case of (n/a or 100 responses).</li>\n  <li>Check that n/a (not applicable) is used appropriately. i.e. only if the question is not applicable to the country. In some cases, a score of zero should be given, and in others, perhaps they need more help to figure out how to answer the question. </li>\n  <li>Guidance for assigning Low/ Medium/ High categories: Low: Blank or not useful. Medium: Some text and details. High: Useful amount of text and detail than can contribute to stakeholder understanding/consensus and planning.</li>\n</ol>\n<p><strong>ANNEXES</strong>. </p>\n<ol>\n  <li><strong>Annex B: Key priorities and targets for IWRM implementation: </strong>\n    <ol>\n      <li>Check if completed. Low, moderate, or high level of information?</li>\n    </ol>\n  </li>\n  <li><strong>Annex C: 6.5.1 Country process reporting form:</strong> Level of info in the free text field, the table, and in the &#x2018;additional info&#x2019; field completed. </li>\n  <li>Guidance for assigning Low/ Medium/ High categories: <u>Low</u>: Blank to few words. <u>Medium</u>: Minimum info to be useful to understand transparency. <u>High</u>: More detailed description that gives good idea of robustness and transparency of the process. </li>\n</ol>\n<p>All data is provided by each country and is therefore fully owned by the countries. Each country undertakes stakeholder consultation, to a level that is appropriate given resources and capacity available to them, to ensure that the data has adequate acceptance and ownership within the country. Guidance on consultation processes are provided in the monitoring guide and through the introductory PowerPoint and video for focal points (all materials available at <a href=\"http://iwrmdataportal.unepdhi.org\">http://iwrmdataportal.unepdhi.org</a>).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The quality management procedures in place are deemed sufficient to ensure the data submitted to the SDGs Indicator Database is of acceptable quality. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Total number of countries: 190 (98% of UN Member States) (UNEP 2024) </p>\n<p>The following covers the region (UNSD regional groupings): followed by the number of countries with data / total countries in region (as of 2023); followed by the percentage of countries with data.</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Regional grouping</strong></p>\n      </td>\n      <td>\n        <p><strong>Number of countries with data / total countries in region (as of 2023)</strong></p>\n      </td>\n      <td>\n        <p><strong>Percentage of countries with data</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Australia and New Zealand</p>\n      </td>\n      <td>\n        <p>2/2</p>\n      </td>\n      <td>\n        <p>100%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Central and Southern Asia</p>\n      </td>\n      <td>\n        <p>14/14</p>\n      </td>\n      <td>\n        <p>100%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Eastern and South-Eastern Asia</p>\n      </td>\n      <td>\n        <p>16/16</p>\n      </td>\n      <td>\n        <p>100%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Europe and Northern America</p>\n      </td>\n      <td>\n        <p>45/45</p>\n      </td>\n      <td>\n        <p>100%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Latin America and the Caribbean</p>\n      </td>\n      <td>\n        <p>32/33</p>\n      </td>\n      <td>\n        <p>97%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Northern Africa and Western Asia</p>\n      </td>\n      <td>\n        <p>23/23</p>\n      </td>\n      <td>\n        <p>100%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Oceania (excluding Australia and New Zealand)</p>\n      </td>\n      <td>\n        <p>10/12</p>\n      </td>\n      <td>\n        <p>83%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Sub-Saharan Africa</p>\n      </td>\n      <td>\n        <p>48/48</p>\n      </td>\n      <td>\n        <p>100%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>World</p>\n      </td>\n      <td>\n        <p>190/193</p>\n      </td>\n      <td>\n        <p>98%</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Time series:</strong></p>\n<p>Pre-SDGs: 2008, 2012 (UN-Water 2008, 2012). </p>\n<p>SDG period: 2017, 2020, 2023. </p>\n<p>All on IWRM Portal (<a href=\"http://iwrmdataportal.unepdhi.org\">http://iwrmdataportal.unepdhi.org</a>)</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The strength of the indicator lies in the potential for disaggregating the country score into the four main dimensions of IWRM, and further to the questions in the survey. This provides countries with a quick assessment of which aspects of IWRM are progressing well, and which aspects require increased efforts to reach the target. </p>\n<p>The nature of the target, indicator and survey does not lend itself to disaggregation by sex, age group, income etc. However, social equality is an integral part of IWRM, and there are questions which directly address issues such as gender (2.2d), vulnerable groups (2.2c), geographic coverage and broad stakeholder participation in water resources development and management. These questions provide an indication of the national and sub-national situation regarding social equality.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Indicator is calculated by countries according to the internationally agreed methodology, and there are no deviations from international standards. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URLs: </strong><a href=\"http://iwrmdataportal.unepdhi.org\">http://iwrmdataportal.unepdhi.org</a> . This contains the latest survey, monitoring guide, and all supporting documentation. </p>\n<p><a href=\"https://www.gwp.org/en/sdg6support/\">https://www.gwp.org/en/sdg6support/</a> : SDG 6 IWRM Support Programme. </p>\n<p><strong>References:</strong></p>\n<p>- UNEP (2024) <a href=\"https://unepdhi.org/wp-content/uploads/sites/2/2024/08/SDG_651_2024_Progress_Report_FINAL_20Aug_WEB.pdf\">Progress on implementation of Integrated Water Resources Management. Mid-term status of SDG indicator 6.5.1 and acceleration needs, with a special focus on climate change.</a></p>\n<p>- UNEP (2021). <a href=\"https://www.unepdhi.org/progress-on-integrated-water-resources-management-global-indicator-6-5-1-updates-and-acceleration-needs/\">Progress on Integrated Water Resources Management. Tracking SDG 6 series: global indicator 6.5.1 updates and acceleration needs</a>.</p>\n<p>- UNEP 2018: <a href=\"https://unepdhi.org/publications-iwrm/\">Progress on integrated water resources management. Global baseline for SDG 6 Indicator 6.5.1: degree of IWRM implementation.</a> </p>\n<p>- GWP and UNEP-DHI (2021). <a href=\"https://www.unepdhi.org/progress-on-integrated-water-resources-management-iwrm-in-the-asia-pacific-region-2021/\">Progress on Integrated Water Resources Management (IWRM) in the</a></p>\n<p><a href=\"https://www.unepdhi.org/progress-on-integrated-water-resources-management-iwrm-in-the-asia-pacific-region-2021/\">Asia-Pacific Region 2021: Learning exchange on monitoring and implementation towards SDG</a></p>\n<p><a href=\"https://www.unepdhi.org/progress-on-integrated-water-resources-management-iwrm-in-the-asia-pacific-region-2021/\">6.5.1</a></p>\n<p>- GWP Centroam&#xE9;rica, mayo de 2021: <a href=\"https://www.unepdhi.org/wp-content/uploads/sites/2/2021/09/SDG6.5.1-status-Central-America-2020-full-ESP.pdf\">Estado de la implementaci&#xF3;n de la Gesti&#xF3;n Integrada de los Recursos H&#xED;dricos en Centroam&#xE9;rica y Republica Dominicana al 2020</a>.</p>\n<p>- AMCOW 2018: Status Report on the Implementation of Water Resources Management in Africa: a regional report for SDG indicator 6.5.1 on IWRM implementation. <a href=\"http://iwrmdataportal.unepdhi.org/IWRMDataJsonService/Service1.svc/DownloadOnAboutPage/Status_Report/English\">AMCOW 2018: Status Report on the Implementation of Water Resources Management in Africa: a regional report for SDG indicator 6.5.1 on IWRM implementation.</a></p>\n<p>- ESCWA (2021): <a href=\"https://iwrmdataportal.unepdhi.org/publications/regional-reports\">Status Report on the Implementation of Integrated Water Resources Management in the Arab Region</a></p>\n<p>- United Nations Economic and Social Commission for West Asia (2019). <a href=\"https://iwrmdataportal.unepdhi.org/publications/regional-reports\">Status Report on the Implementation of Integrated Water Resources Management in the Arab Region: Progress on SDG indicator 6.5.1.</a> </p>\n<p>- UN-Water initiative on integrated monitoring of SDG 6. <a href=\"http://sdg6data.org\">http://sdg6data.org</a></p>\n<p>- UN-Water, 2016: Water and Sanitation Interlinkages across the 2030 Agenda for Sustainable Development. Geneva. <a href=\"https://www.unwater.org/app/uploads/2016/08/Water-and-Sanitation-Interlinkages.pdf\">https://www.unwater.org/app/uploads/2016/08/Water-and-Sanitation-Interlinkages.pdf</a> </p>", "indicator_sort_order"=>"06-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"6.5.2", "slug"=>"6-5-2", "name"=>"Proporción de la superficie de cuencas transfronterizas sujetas a arreglos operacionales para la cooperación en materia de aguas", "url"=>"/site/es/6-5-2/", "sort"=>"060502", "goal_number"=>"6", "target_number"=>"6.5", "global"=>{"name"=>"Proporción de la superficie de cuencas transfronterizas sujetas a arreglos operacionales para la cooperación en materia de aguas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la superficie de cuencas transfronterizas sujetas a arreglos operacionales para la cooperación en materia de aguas", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la superficie de cuencas transfronterizas sujetas a arreglos operacionales para la cooperación en materia de aguas", "indicator_number"=>"6.5.2", "national_geographical_coverage"=>"", "page_content"=>"La gestión integrada de ríos y lagos se organiza en comisiones internacionales para cuencas hidrográficas enteras en el caso de aguas transfronterizas. Las actividades de las comisiones se basan en acuerdos internacionales. Parte de la C.A. de Euskadi se ubica en la cuenca hidrográfica internacional de Garonne – Cantabrico -Ebro. En febrero del año 2006 se firmó en Toulouse el acuerdo de cooperación para coordinar de la mejor manera posible las medidas tomadas en las cuencas hidrográficas situadas a ambos lados de la frontera, en aplicación de la Directiva Marco del Agua (DMA), así como establecer una cooperación administrativa regular", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de la superficie de cuencas transfronterizas sujetas a arreglos operacionales para la cooperación en materia de aguas", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.5- De aquí a 2030, implementar la gestión integrada de los recursos hídricos a todos los niveles, incluso mediante la cooperación transfronteriza, según proceda", "definicion"=>"Proporción del área de cuencas transfronterizas cubierta por un acuerdo operativo de cooperación en materia de agua", "formula"=>"\n$$PSTCOOP^{t} = \\frac{STCOOP^{t}}{ST^{t}} \\cdot 100$$\n\ndonde:\n\n$STCOOP^{t} =$ área de cuencas transfronterizas cubierta por un acuerdo operativo de cooperación en materia de agua en el año $t$ \n\n$ST^{t} =$ área de cuencas transfronterizas en el año $t$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nLa mayoría de los recursos hídricos del mundo son compartidos: en 2021 se identificaron 468 acuíferos \ntransfronterizos y 310 cuencas fluviales y lacustres transfronterizas cubren casi la mitad de la superficie \nterrestre y representan aproximadamente el 60 % del agua dulce mundial. Aproximadamente el 40 % de la \npoblación mundial vive en cuencas fluviales y lacustres compartidas por dos o más países, y más del 90 % \nvive en países que comparten cuencas. \n\nEl desarrollo de los recursos hídricos tiene impactos en las cuencas transfronterizas, potencialmente \nen los países que las comparten, y el uso de las aguas superficiales o subterráneas puede afectar a \nestos otros recursos, que a menudo están interrelacionados. El uso intensivo del agua, la regulación del \ncaudal o la contaminación corren el riesgo de comprometer las aspiraciones de desarrollo de los países que \ncomparten cuencas transfronterizas, por lo que se requiere cooperación transfronteriza. \nSin embargo, en muchos casos, la cooperación no ha avanzado.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.5.2&seriesCode=EG_TBA_H2CO&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de cuencas transfronterizas (cuencas fluviales, lacustres y acuíferos) con un acuerdo operativo de cooperación hídrica (%) EG_TBA_H2CO</a> UNSTATS\n", "comparabilidad"=>"El indicador cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-05-02.pdf\">Metadatos 6-5-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-27", "en"=>{"indicador_disponible"=>"Proporción de la superficie de cuencas transfronterizas sujetas a arreglos operacionales para la cooperación en materia de aguas", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.5- De aquí a 2030, implementar la gestión integrada de los recursos hídricos a todos los niveles, incluso mediante la cooperación transfronteriza, según proceda", "definicion"=>"Proportion of transboundary basin area with an operational arrangement for water  cooperation", "formula"=>"\n$$PSTCOOP^{t} = \\frac{STCOOP^{t}}{ST^{t}} \\cdot 100$$\n\nwhere:\n\n$STCOOP^{t} =$ transboundary basin area covered by an operational arrangement for water in the year $t$ \n\n$ST^{t} =$ transboundary basin area in the year $t$\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Anual", "justificacion_global"=>"\nThe majority of the world’s water resources are shared: 468 transboundary aquifers \nhave been identified in 2021 and 310 transboundary lake and river basins cover nearly \none half of the Earth’s land surface and account for an estimated 60% of global freshwater. \nApproximately 40% of the world’s population lives in river and lake basins shared by two \nor more countries and over 90% lives in countries that share basins. \n\nDevelopment of water resources has impacts across transboundary basins, potentially on \ncountries sharing transboundary basins, and use of surface water or groundwater may affect \nthe other resource, which are often interlinked. Intensive water use, flow regulation or \npollution risks going as far as compromising the development aspirations of countries \nsharing transboundary basins and therefore transboundary cooperation is required. However, \ncooperation is in many cases not advanced. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.5.2&seriesCode=EG_TBA_H2CO&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of transboundary basins (river and lake basins and aquifers) with an operational arrangement for water cooperation (%) EG_TBA_H2CO</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-05-02.pdf\">Metadata 6-5-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de la superficie de cuencas transfronterizas sujetas a arreglos operacionales para la cooperación en materia de aguas", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.5- De aquí a 2030, implementar la gestión integrada de los recursos hídricos a todos los niveles, incluso mediante la cooperación transfronteriza, según proceda", "definicion"=>"Uraren arloko lankidetzarako akordio operatibo batek estalitako mugaz gaindiko arroen eremuaren proportzioa", "formula"=>"\n$$PSTCOOP^{t} = \\frac{STCOOP^{t}}{ST^{t}} \\cdot 100$$\n\nnon:\n\n$STCOOP^{t} =$ mugaz gaindiko arroen eremua, uraren arloko lankidetza-akordio operatibo batek estalia $t$ urtean \n\n$ST^{t} =$ mugaz gaindiko arroen eremua $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Anual", "justificacion_global"=>"\nMunduko ur-baliabide gehienak partekatuak dira: 2021ean mugaz gaindiko 468 akuifero identifikatu ziren, \neta mugaz gaindiko 310 ibai-arro eta aintzirek lurrazalaren ia erdia hartzen dute, eta munduko ur gezaren \n% 60 inguru dira. Munduko biztanleriaren % 40, gutxi gorabehera, bi herrialdek edo gehiagok partekatzen \ndituzten ibai-arro eta aintziretan bizi da, eta % 90 baino gehiago arroak partekatzen dituzten herrialdeetan \nbizi da. \n\nBaliabide hidrikoen garapenak inpaktuak ditu mugaz haraindiko arroetan, horiek partekatzen dituzten \nherrialdeetan potentzialki, eta lurrazaleko edo lurpeko uren erabilerak eragina izan dezake beste baliabide \nhorietan, askotan elkarri lotuta baitaude. Uraren erabilera intentsiboak, emariaren erregulazioak edo \nkutsadurak arriskuan jar ditzakete mugaz gaindiko arroak partekatzen dituzten herrialdeen garapen-asmoak, \neta, beraz, mugaz gaindiko lankidetza behar da. Hala ere, kasu askotan, lankidetzak ez du aurrera egin. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.5.2&seriesCode=EG_TBA_H2CO&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Mugaz gaindiko arroen proportzioa (ibai-arroak, aintzira-arroak eta akuiferoak), uraren arloko lankidetza akordio operatiboa dutenak (%) EG_TBA_H2CO</a> UNSTATS\n", "comparabilidad"=>"Adierazleak Nazio Batuen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-05-02.pdf\">Metadatuak 6-5-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 6: Ensure availability and sustainable management of water and sanitation for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 6.5: By 2030, implement integrated water resources management at all levels, including through transboundary cooperation as appropriate</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 6.5.2: Proportion of transboundary basin area with an operational arrangement for water cooperation</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EG_TBA_H2CO - Proportion of transboundary basins (river and lake basins and aquifers) with an operational arrangement for water cooperation [6.5.2]</p>\n<p>EG_TBA_H2COAQ - Proportion of transboundary aquifers with an operational arrangement for water cooperation [6.5.2]</p>\n<p>EG_TBA_H2CORL - Proportion of transboundary river and lake basins with an operational arrangement for water cooperation [6.5.2] </p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>SDG indicator 6.5.2 provides a complement to SDG indicator 6.5.1 which measures the advancement of Integrated Water Resources Management (IWRM) at all levels. </p>\n<p>In addition, as the only indicator in the 2030 Agenda for Sustainable Development explicitly referring to transboundary cooperation, indicator 6.5.2 can play a catalytic role across multiple SDGs and targets including:</p>\n<p>SDG 1 &#x2013; No Poverty </p>\n<p>- Indicator 1.1.1 Proportion of population below the international poverty line, by sex, age, employment status and geographical location (urban/rural)</p>\n<p>- Indicator 1.4.1 Proportion of population living in households with access to basic services </p>\n<p>SDG 2 &#x2013; Zero Hunger</p>\n<p>- Indicator 2.4.1 Proportion of agricultural area under productive and sustainable agriculture</p>\n<p>SDG 3 &#x2013; Good Health and Well-being</p>\n<p>- Indicator 3.9.2 Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)</p>\n<p>SDG 4 &#x2013; Quality Education</p>\n<p>- Indicator 4.7.1 Extent to which (i) global citizenship education and (ii) education for sustainable development, including gender equality and human rights, are mainstreamed at all levels in: (a) national education policies, (b) curricula, (c) teacher education and (d) student assessment</p>\n<p>SDG 5 &#x2013; Gender Equality </p>\n<p>- Indicator 5.5.2 Proportion of women in managerial positions</p>\n<p>SDG 7 &#x2013; Affordable and Clean Energy </p>\n<p>- Indicator 7.1.2 Proportion of population with primary reliance on clean fuels and technology </p>\n<p>- Indicator 7.2.1 Renewable energy share in the total final energy consumption </p>\n<p>SDG 11 &#x2013; Sustainable Cities and Communities</p>\n<p>- Indicator 11.5.2 Direct disaster economic loss in relation to global GDP, including disaster damage to critical infrastructure and disruption of basic services </p>\n<p>SDG 13 &#x2013; Climate Action</p>\n<p>- Indicator 13.3.2 Number of countries that have communicated the strengthening of institutional, systemic and individual capacity-building to implement adaptation, mitigation and technology transfer, and development actions</p>\n<p>SDG 14 &#x2013; Life below Water</p>\n<p>- Indicator 14.1.1 Index of coastal eutrophication and floating plastic debris density </p>\n<p>- Indicator 14.2.1 Proportion of national exclusive zones managed using ecosystem-based approaches </p>\n<p>SDG 15 &#x2013; Life on Land </p>\n<p>- Indicator 15.1.2 Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type</p>\n<p>- Indicator 15.2.1 Progress towards sustainable forest management </p>\n<p>SDG 16 &#x2013; Peace, Justice and Strong Institutions </p>\n<p>- Indicator 16.1.2 Conflict-related deaths per 100,000 population, by sex, age and cause </p>\n<p>- Indicator 16.7.2 Proportion of population who believe decision-making is inclusive and responsive, by sex, age, disability and population group </p>\n<p>SDG 17 &#x2013; Partnerships for the Goals </p>\n<p>- Indicator 17.9.1 Dollar value of financial and technical assistance (including through North-South, South-South and triangular cooperation) committed to developing countries</p>\n<p>- Indicator 17.14.1 Number of countries with mechanisms in place to enhance policy coherence of sustainable development</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Intergovernmental Hydrological Programme of United Nations Educational, Scientific and Cultural Organization (UNESCO-IHP)</p>\n<p>United Nations Economic Commission for Europe (UNECE)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Intergovernmental Hydrological Programme of United Nations Educational, Scientific and Cultural Organization (UNESCO-IHP)</p>\n<p>United Nations Economic Commission for Europe (UNECE)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator monitors the &#x201C;transboundary basin&#x201D; area within a country covered by an &#x201C;operational&#x201D; &#x201C;arrangement for water cooperation&#x201D;. </p>\n<p>A &#x201C;transboundary basin&#x201D; refers to a river or lake basin, or an aquifer system that marks, crosses or is located on boundaries between two or more states. A basin comprises the entire catchment area of a surface water body (river or lake), or for groundwater, the area of the aquifer, i.e. the entire permeable water-bearing geological formation. For the purpose of calculating the value of SDG indicator 6.5.2 the transboundary basin area is the extent of the catchment area (river or lake basin); or the extent of the aquifer. </p>\n<p>&#x201C;Arrangement for water cooperation&#x201D; refers to a bilateral or multilateral treaty, convention, agreement or other formal arrangement, such as memorandum of understanding between countries sharing transboundary basins that provides a framework for cooperation on transboundary water management. Agreements or other kinds of formal arrangements may be interstate, intergovernmental, interministerial, interagency or between regional authorities. </p>\n<p>&#x201C;Operational&#x201D; means that an agreement for cooperation between the countries sharing transboundary basins meets all the following four criteria: </p>\n<p>- There is a joint body or mechanism (e.g. a river basin organization) for transboundary cooperation;</p>\n<p>- There are regular, i.e., at least annual, formal communications between riparian countries in form of meetings (either at the political and/or technical level);</p>\n<p>- There is a joint or coordinated water management plan(s), or joint objectives have been set;</p>\n<p>- There is a regular, i.e., at least annual, exchange of data and information.</p>\n<p><strong>Concepts:</strong></p>\n<p>The monitoring has as its basis the spatial coverage of transboundary basins shared by each country, and focuses on monitoring whether these are covered by cooperation arrangements that are &#x201C;operational&#x201D;. The criteria to be met for the cooperation on a specific basin to be considered &#x201C;operational&#x201D; seek to capture whether the arrangement(s) provides the basic elements needed to allow that arrangement to implement cooperation in water management.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Basin area, in km<sup>2</sup>, covered by operational arrangements.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>At the country level, ministries and agencies responsible for surface water and groundwater resources (depends on the country but commonly the ministry of the environment, water, natural resources, energy or agriculture; institutes of water resources, hydrology or geology, or geological surveys) typically have the spatial information about the location and extent of the surface water basin boundaries and aquifer delineations (as Geographical Information Systems (GIS) shapefiles). Information on existing arrangements and their operationality is also commonly available from the same institutions.</p>\n<p>Some countries already report to regional organizations on the advancement of transboundary water cooperation, and similar arrangements could be strengthened and facilitated. </p>\n<p>In the absence of available information at the national level, global datasets on transboundary basins as well as databases of agreements and organizations for transboundary cooperation are available, which could be used in the absence of more detailed information, in the short term in particular.</p>\n<p>-Delineations of transboundary basins</p>\n<p>In global databases, the delineations are available through the Global Environment Facility&#x2019;s Transboundary Waters Assessment Programme (GEF TWAP) (see <a href=\"http://www.geftwap.org/\">http://www.geftwap.org/</a>). GEF TWAP covered 286 main transboundary rivers, 206 transboundary lakes and reservoirs and 199 transboundary aquifers. GEF TWAP groundwater component was prepared by UNESCO-IHP International Groundwater Resources Assessment Centre (IGRAC). </p>\n<p>In 2021, the Oregon State University identified 310 international river basins (<a href=\"https://transboundarywaters.science.oregonstate.edu/sites/transboundarywaters.science.oregonstate.edu/files/Database/Data/register/McCracken_Wolf_2019.pdf\">https://transboundarywaters.science.oregonstate.edu/sites/transboundarywaters.science.oregonstate.edu/files/Database/Data/register/McCracken_Wolf_2019.pdf</a>) and IGRAC produced the Transboundary Aquifers of the World Map featuring 468 shared aquifers discovered so far (see <a href=\"https://ggis.un-igrac.org/\">https://ggis.un-igrac.org/</a>). Relevant information has also been compiled for transboundary aquifers by the UNESCO Internationally Shared Aquifers Resources Management Programme (ISARM) (see <a href=\"http://www.isarm.org/\">http://www.isarm.org/</a>)</p>\n<p> </p>\n<p>-Cooperation arrangements</p>\n<p>Existing agreements or other arrangements for transboundary water cooperation are available from the International Freshwater Treaties Database (IFTD), maintained by Oregon State University (OSU) (see <a href=\"https://transboundarywaters.science.oregonstate.edu/content/international-freshwater-treaties-database\">https://transboundarywaters.science.oregonstate.edu/content/international-freshwater-treaties-database</a>). This was updated to include freshwater-related arrangements up to 2008. A current update is ongoing to bring the database to 2019. The treaty database includes in total 686 international freshwater treaties. </p>\n<p>-Organizations for transboundary water cooperation </p>\n<p>OSU&#x2019;s International River Basin Organization (RBO) Database provides detailed information about over 120 international river basin organizations, including bilateral commissions, around the world (see <a href=\"https://transboundarywaters.science.oregonstate.edu/content/international-river-basin-organization-rbo-database\">https://transboundarywaters.science.oregonstate.edu/content/international-river-basin-organization-rbo-database</a>). </p>\n<p>Regional assessments describing and inventorying agreements have been undertaken, contributing to the baseline globally. For example, the assessment by UNECE of transboundary water cooperation in the pan-European region; the inventory of Shared Water Resources in Western Asia by the United Nations Economic and Social Commission for Western Asia (UNESCWA); and regional inventories of transboundary aquifers under the UNESCO-IHP ISARM: ISARM Americas, ISARM Africa, ISARM South-East Europe and ISARM Asia. </p>", "COLL_METHOD__GLOBAL"=>"<p>Data on transboundary basins and their operational arrangements has not been traditionally included within the National Statistical Systems but the information needed to calculate the indicator is simple, does not require advanced monitoring capacities and is normally available to all countries.</p>\n<p>Spatial information (&#x201C;transboundary basin area&#x201D;) is normally available in ministries in charge of water resources. Regarding the operationality of arrangement, the data needed for calculating the indicator can be directly obtained from information from administrative records (Member States have records of cooperation arrangements). </p>\n<p>The limitations in terms of comparability of the results between countries are the same as the ones described in Section 4.b. However, a clear definition and consideration of the criteria as developed in the detailed methodology is available to countries to ensure a common reference for the countries.</p>\n<p>Moreover, the elements of the indicator are based on the main principles of customary international water law, which are also contained in the two UN conventions &#x2013; 1997 Convention on the Law of the Non-navigational Uses of International Watercourses (Watercourses Convention) and the 1992 Convention on the Protection and Use of Transboundary Watercourses and International Lakes (Water Convention) &#x2013; as well as the draft Articles on The Law of Transboundary Aquifers (2008; UN General Assembly resolutions 63/124, 66/104, 68/118, 71/150, 74/193 and 77/112).</p>\n<p>The mechanism of reporting under the Water Convention also allows for sub-components of the indicator to be reported by countries, which will ensure both more confidence on the final indicator value (validation) and increased comparability. </p>", "FREQ_COLL__GLOBAL"=>"<p>First reporting exercise, in 2017; and then at three yearly intervals. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Early 2018; and then at three yearly intervals</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are not so far included in the National Statistical Systems but the information needed to calculate the indicator is simple, does not require advanced monitoring capacities and is normally available to all countries at the ministries or agencies responsible for water resources. Spatial information (&#x201C;transboundary basin area&#x201D;) is normally available in ministries in charge of water resources. The value of this component is relatively fixed although the precision may vary (especially on aquifers), and may require only limited update on the basis of improved knowledge. Regarding operationality of arrangement the data needed for calculating the indicator can be directly obtained from information from administrative records (Member States have records of cooperation arrangements).</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNECE and UNESCO-IHP gather the information needed from the 153 countries sharing transboundary basins for the calculation of the indicator, especially on the transboundary basins (rivers, lakes and aquifers) shared by countries, the applicable cooperative arrangements, and their operationality. </p>\n<p>Since 2017, the Water Convention&#x2019;s regular reporting on transboundary water cooperation, commits its Parties to collect information relevant to SDG indicator 6.5.2, as part of an established mandatory mechanism for Parties every 3 years. The reporting covers transboundary rivers, lakes and groundwaters. More than 130 countries participate in the Water Convention&#x2019;s activities, as non-Parties are also invited. UNECE acts as Secretariat for the Water Convention. </p>\n<p>Some countries also report to regional organizations (e.g. the European Union or the Southern African Development Community) on the advancement of transboundary water cooperation, and similar arrangements could be strengthened and facilitated.</p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>The majority of the world&#x2019;s water resources are shared: 468 transboundary aquifers have been identified in 2021 and 310 transboundary lake and river basins cover nearly one half of the Earth&#x2019;s land surface and account for an estimated 60% of global freshwater. Approximately 40% of the world&#x2019;s population lives in river and lake basins shared by two or more countries and over 90% lives in countries that share basins. Development of water resources has impacts across transboundary basins, potentially on countries sharing transboundary basins, and use of surface water or groundwater may affect the other resource, which are often interlinked. Intensive water use, flow regulation or pollution risks going as far as compromising the development aspirations of countries sharing transboundary basins and therefore transboundary cooperation is required. However, cooperation is in many cases not advanced. </p>\n<p>Specific agreements or other arrangements concluded between countries sharing transboundary basins are a key precondition to ensure long-term, sustainable cooperation. International customary water law (as reflected in the 1992 Water Convention, the 1997 Watercourses Convention, and the 2008 draft Articles on the Law of Transboundary Aquifers), as well as existing experience and good practices, all point to minimum requirements for operational cooperation. These minimum requirements are captured by the four criteria for operationality.</p>\n<p>This is the basis for the explicit call for transboundary water cooperation in the wording of target 6.5 and the importance of monitoring this indicator to complement indicator 6.5.1 which measures the advancement of Integrated Water Resources Management (IWRM).</p>\n<p>Progress by a particular country towards the cooperation aspect of target 6.5, reflected by the value of indicator 6.5.2, can be achieved either by establishing new operational cooperation arrangements, or making existing arrangements operational by developing and regularizing activities, or expanding the coverage of cooperation arrangements with the ultimate objective to cover all surface waters and groundwaters.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The spatial information on transboundary surface water basins&#x2019; boundaries and the extent of the catchment areas are commonly available and essentially static; consequently, once determined, no updating need is expected.</p>\n<p>The information on the areal extent of transboundary aquifers may evolve over time as such information is generally more coarse but likely to improve because of the evolving knowledge on aquifers. Technical studies and exchange of information will improve the delineation and might also lead to the identification of additional transboundary aquifers.</p>\n<p>In situations where more than two riparian countries share a basin, but only some of them have operational cooperation arrangements, the indicator value may mask the gap that a riparian country does not have cooperation arrangements with all its upstream and downstream neighbours. Such complementary information can be obtained by aggregating data at the level of the basins but not from the reporting at the national level. </p>\n<p>The legal basis for cooperation develops slowly: conclusion of new agreements on transboundary waters is commonly a long process that takes many years. </p>\n<p>The operationality of cooperation is more dynamic as it evolves with the expansion of cooperation. The operationality can be expected to evolve over shorter time frames, and in a year or two, progress could potentially be observed.</p>", "DATA_COMP__GLOBAL"=>"<p>Step 1 Identify the transboundary surface waters and aquifers in the territory of the country</p>\n<p>While the identification of transboundary surface water is relatively straightforward, the identification of transboundary aquifers often requires more considered investigations.</p>\n<p>If there are no transboundary surface waters or groundwaters, reporting is not applicable.</p>\n<p>Step 2 Calculate the surface area of each transboundary basin and the total sum</p>\n<p>Commonly at least the basins of the rivers and lakes have been delineated through topographic maps and the basin area is known or easily measurable. </p>\n<p>The total transboundary surface area in the country is the sum of the surface areas in the country of each of the transboundary basins and aquifers (expressed in km2). Transboundary areas for different types of systems (e.g. river and lakes basin and aquifers) or multiple aquifers may overlap. The area of transboundary aquifers, even if located within a transboundary river or lake basin, should be added to be able to track progress of cooperation on transboundary aquifers. </p>\n<p>The calculations can most easily be carried with Geographical Information Systems (GIS). Once generated, with appropriate tools for spatial analysis, the shapes of the surface river and lake basins and the aquifers can be used to report both disaggregated (for the surface water basin or aquifer) and aggregated (agreement exists on either one).</p>\n<p>Step 3 Review existing arrangements for transboundary water cooperation and verify which transboundary waters are covered</p>\n<p>Some operational arrangements for transboundary water cooperation in place cover both surface waters and groundwaters (and their associated river and lakes basins and aquifers). In such cases, it should be clear that the geographical extent of both is used to calculate the indicator value. In other cases, the area of application may be limited to a border section of the river basin or sub-basin and in such cases only the corresponding area should be considered as potentially having an operational arrangement for calculating the indicator value. At the end of this step, it should be known which transboundary basins are covered by arrangements for transboundary water cooperation (and their respective areas). </p>\n<p>Step 4 Check which of the existing arrangements for transboundary water cooperation are operational</p>\n<p>The following check-list allows countries to determine whether the cooperation arrangement on a particular basin or in relation to a particular country is operational:</p>\n<p>- does a joint body or mechanism for transboundary water cooperation exist? </p>\n<p>- is there at least annual (on average) formal communication in form of meetings, either at the political and/or technical level?</p>\n<p>- has a joint or coordinated water management plan(s), or joint objectives been adopted? </p>\n<p>- is there at least annual (on average) exchange of information and data?</p>\n<p>If any of the conditions are not met, the arrangement for transboundary water cooperation cannot be considered operational. This information is currently available in countries and can also be withdrawn from global, regional or basin databases.</p>\n<p>Step 5 Calculate the indicator value</p>\n<p>Calculate the indicator value, by adding up the total surface area in the country of the transboundary surface waters and aquifers that are covered by an operational cooperation arrangement and dividing it by the total summed up area in the country of all transboundary basins (including aquifers). The sum should then be multiplied by 100 to obtain a percentage.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Countries are requested to submit data on their transboundary basins covered by operational arrangements through the uses of a reporting template or questionnaire. The templates are submitted to the co-custodian agencies, UNECE and UNESCO for review. Countries are encouraged to submit drafts of their templates to the custodian agencies for feedback prior to the final submission. Once submitted, the custodian agencies review the national templates to assess firstly, whether sufficient and accurate information is provided in order to calculate the national SDG indicator value, and secondly, whether an official endorsement of the template is provided (in the form of a signature).</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>In the case of spatial data: For the basin delineations, Digital Elevation Model information can be used to delineate surface water basin boundaries. For aquifers, geological maps can provide a basis for approximating aquifer extent. In the case of groundwater, uncertainty about transboundary nature remains unless investigations of hydraulic properties have been made. In the absence of administrative records, gaps about the cooperation arrangements are difficult to fill, although such arrangements tend to be widely available. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>The indicator does not apply to countries without a terrestrial border, so notably island states will not report a value on this indicator.</p>\n<p>International databases and inventories (as described in section 3.a) are available for reference in the absence of information reported by countries. Missing surface water basin extent can be extracted from Digital Elevation Models available globally. Global geological maps and maps of hydrogeology/groundwater potential also exist which could be used to approximate aquifer extent (surface area).</p>\n<p>Concerning arrangements, consistency of information reported by countries sharing the same transboundary basins can be used to fill gaps in information about arrangements and their operationality.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global estimates are obtained by undertaking the average of individual country values at regional and global level.</p>\n<p>However, baseline assessment from global databases can be performed at any desired geographical scale: sub-national, national, regional, basin scale, global, etc. However, data gaps can limit this possibility starting from regional level.</p>", "DOC_METHOD__GLOBAL"=>"<p>Through UN-Water, UNECE and UNESCO have developed a step-by-step methodology that countries can use to compile data at the national level on SDG indicator 6.5.2. The methodology, which was revised in January 2020 prior to the second reporting exercise, is available in English, French, Russian and Spanish through the UN-Water website - <a href=\"https://www.unwater.org/publications/step-step-methodology-monitoring-transboundary-cooperation-6-5-2/\">https://www.unwater.org/publications/step-step-methodology-monitoring-transboundary-cooperation-6-5-2/</a>. </p>\n<p>In addition, UNECE, through an expert group made up of both parties and non-parties to the Water Convention, developed a <em>Guide to reporting under the Water Convention and as a contribution to SDG indicator 6.5.2 (</em>see<em> </em><a href=\"https://unece.org/environment-policy/publications/guide-reporting-under-water-convention-and-contribution-sdg\">https://unece.org/environment-policy/publications/guide-reporting-under-water-convention-and-contribution-sdg</a>) in January 2020. The guide, which is available in Arabic, English, French, Russian and Spanish, supports countries in the completion of the reporting template by explaining key terminology and providing examples of how particular questions might be addressed. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Before starting the SDG reporting process, data were not included in the National Statistical Systems but the information needed to calculate the indicator is simple, does not require advanced monitoring capacities and is normally available to all countries at the ministries or agencies responsible for water resources.</p>\n<p>Data is available for the 153 countries having territorial borders in a number of existing databases. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data would be most reliably collected at the national level. Basin level data can also be disaggregated to country level (for national reporting) and aggregated to regional and global level.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>As the computation of the indicator is based on the spatial information (&#x201C;transboundary basin area&#x201D;) and operationality of arrangements as the two basic components, differences can arise in the computation of each of these components individually.</p>\n<p>Regarding both components, countries have the most up-to-date information, which can be supplemented by the data from various international projects and inventories, which contribute also to establishing a baseline globally.</p>\n<p>The difference on the value of transboundary basin area can arise from a different delineation of the transboundary water bodies, especially aquifers, or even the consideration of their transboundary nature as their identification and delineation can be based on different hydrogeological studies and can be updated, which is not necessarily reflected in international databases.</p>\n<p>The difference in the consideration of the operationality of the arrangements may arise from not identifying the same arrangements or considering differently the four criteria that serve as the basis for the operationality classification: </p>\n<p>- existence of a joint body or mechanism for transboundary cooperation </p>\n<p>- regularity of formal communication in form of meetings </p>\n<p>- existence of joint or coordinated water management plan(s), or of joint objectives </p>\n<p>- regularity of the exchange of information and data </p>\n<p>A different interpretation in the object of application (only surface water or both surface water and groundwater) may constitute another reason.</p>\n<p>Collection of country input through validation mechanisms, has improved and will continue to improve, the consistency and accuracy of the information across the countries as the monitoring progresses.</p>", "OTHER_DOC__GLOBAL"=>"<p>UNECE: <a href=\"https://unece.org/environmental-policy/water/transboundary_water_cooperation_reporting\">https://unece.org/environmental-policy/water/transboundary_water_cooperation_reporting</a></p>\n<p>UNESCO: https://www.unesco.org/en/ihp/sdg6-5-2 ;</p>\n<p>UN-WATER SDG6 monitoring: <a href=\"http://www.sdg6monitoring.org/indicator-652\">www.sdg6monitoring.org/indicator-652</a> </p>\n<p>UN-WATER SDG6 data portal: <a href=\"http://www.sdg6data.org/indicator/6.5.2\">www.sdg6data.org/indicator/6.5.2</a></p>\n<p>Decision VII/2 establishing the reporting mechanism under the Water Convention: <a href=\"https://unece.org/DAM/env/documents/2015/WAT/11Nov_17-19_MOP7_Budapest/ece.mp.wat.49.add.2.eng.pdf\">https://unece.org/DAM/env/documents/2015/WAT/11Nov_17-19_MOP7_Budapest/ece.mp.wat.49.add.2.eng.pdf</a></p>\n<p><strong>Additional documentation:</strong></p>\n<p>Global Environment Facility&#x2019;s Transboundary Waters Assessment Programme: <a href=\"http://www.geftwap.org/\">http://www.geftwap.org/</a> </p>\n<p>Internationally Shared Aquifer Resources Management Programme (UNESCO&#x2019;s International Hydrological Programme): <a href=\"http://www.isarm.org/\">http://www.isarm.org/</a> </p>\n<p>Treaties on transboundary waters, Oregon State University: <a href=\"https://transboundarywaters.science.oregonstate.edu/content/international-freshwater-treaties-database\">https://transboundarywaters.science.oregonstate.edu/content/international-freshwater-treaties-database</a></p>\n<p>International River Basin Organisations database, Oregon State University: <a href=\"https://transboundarywaters.science.oregonstate.edu/content/international-river-basin-organization-rbo-database\">https://transboundarywaters.science.oregonstate.edu/content/international-river-basin-organization-rbo-database</a></p>\n<p><strong><em>Regional examples: </em></strong></p>\n<p>Assessment of transboundary water cooperation in the pan-European region: <a href=\"https://unece.org/environment-policy/publications/second-assessment-transboundary-rivers-lakes-and-groundwaters\">https://unece.org/environment-policy/publications/second-assessment-transboundary-rivers-lakes-and-groundwaters</a></p>\n<p>Inventory of Shared Water Resources in Western Asia: <a href=\"https://www.unescwa.org/publications/inventory-shared-water-resources-western-asia\">https://www.unescwa.org/publications/inventory-shared-water-resources-western-asia</a> </p>", "indicator_sort_order"=>"06-05-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"6.6.1", "slug"=>"6-6-1", "name"=>"Cambio en la extensión de los ecosistemas relacionados con el agua con el paso del tiempo", "url"=>"/site/es/6-6-1/", "sort"=>"060601", "goal_number"=>"6", "target_number"=>"6.6", "global"=>{"name"=>"Cambio en la extensión de los ecosistemas relacionados con el agua con el paso del tiempo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Cambio en la extensión de los ecosistemas relacionados con el agua con el paso del tiempo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Cambio en la extensión de los ecosistemas relacionados con el agua con el paso del tiempo", "indicator_number"=>"6.6.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La Meta 6.6 busca proteger y restaurar los ecosistemas relacionados con el agua, incluidas las \nmontañas, los bosques, los humedales, los ríos, los acuíferos y los lagos, a través del indicador \n6.6.1 de los ODS, que busca comprender cómo y por qué la extensión de estos ecosistemas cambia con \nel tiempo. Todos los componentes del indicador 6.6.1 de los ODS son importantes para crear una \nvisión integral que permita tomar decisiones informadas para la protección y restauración de los \necosistemas relacionados con el agua.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-06-01a.pdf\">Metadatos 6-6-1 (1).pdf</a> (solo en inglés)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-06-01b.pdf\">Metadatos 6-6-1 (2).pdf</a> (solo en inglés)\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-27", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Target 6.6 aims to “protect and restore water-related ecosystems, including mountains, \nforests, wetlands, rivers, aquifers and lakes” through SDG indicator 6.6.1 which aims to \nunderstand how and why these ecosystems are changing in extent over time. All of the \ndifferent components of SDG indicator 6.6.1 are important to form a comprehensive picture \nthat enables informed decisions towards the protection and restoration of water-related \necosystems.\n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-06-01a.pdf\">Metadata 6-6-1 (1).pdf</a>\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-06-01b.pdf\">Metadata 6-6-1 (2).pdf</a>\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"6.6 xedearen helburua urarekin lotutako ekosistemak babestea eta lehengoratzea da, baita mendiak, basoak, \nhezeguneak, ibaiak, akuiferoak eta aintzirak ere, GJHen 6.6.1 adierazlearen bidez. Adierazle horrek \nekosistema horien hedadura denborarekin nola eta zergatik aldatzen den ulertu nahi du. GJHen 6.6.1 adierazlearen \nosagai guztiak garrantzitsuak dira urarekin lotutako ekosistemak babesteko eta lehengoratzeko erabaki \ninformatuak hartzeko aukera emango duen ikuspegi integral bat sortzeko. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-06-01a.pdf\">Metadatuak 6-6-1 (1).pdf</a> (ingelesez bakarrik)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-06-01b.pdf\">Metadatuak 6-6-1 (2).pdf</a> (ingelesez bakarrik)\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 6: Ensure availability and sustainable management of water and sanitation for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 6.6: By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers and lakes</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 6.6.1: Change in the extent of water-related ecosystems over time</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_WBE_HMWTL - Extent of human made wetlands (square kilometres) [6.6.1]</p>\n<p>EN_WBE_INWTL - Extent of inland wetlands (square kilometres) [6.6.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>15.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Secretariat of the Ramsar Convention on Wetlands</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Secretariat of the Ramsar Convention on Wetlands</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<ul>\n  <li>&#x201C;extent of wetlands&#x201D; </li>\n</ul>\n<p>This term can be defined as the surface area of wetlands. It is measured in km2 or hectares. It is expected that the surface reported by countries in 2018 correspond to that of 2017; if not, the reference year should be indicated. </p>\n<ul>\n  <li>&#x201C;change in the extent of wetlands&#x201D; </li>\n</ul>\n<p>This term refers to the percentage change in area of wetlands from a baseline reference. For reporting such change, the previous extent, if known, and the period over which the change has taken place should be specified. </p>\n<p><strong>Concepts:</strong></p>\n<p>In order to provide a precise definition of the indicator, it is crucial to provide a definition of </p>\n<p><strong>&#x201C;Water related ecosystems</strong>&#x201D;. For this purpose, the definition of the Ramsar Convention on Wetlands is used.</p>\n<ul>\n  <li><strong>the Ramsar definition of &#x201C;wetlands&#x201D;</strong></li>\n</ul>\n<p>The Ramsar definition is very broad, reflecting the purpose and global coverage of the Convention:</p>\n<p>In accordance with Article 1.1 of the Convention, <br><em>&#x201C;Wetlands are areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six metres&#x201D;.</em></p>\n<p>In addition, in accordance with Article 2.1, Ramsar Sites <br><em>&#x201C;may incorporate riparian and coastal zones adjacent to the wetlands, and islands or bodies of marine water deeper than six metres at low tide lying within the wetlands&#x201D;.</em></p>\n<p><strong>- the Ramsar system of classifying wetland types</strong></p>\n<p>Many national definitions and classifications of &#x201C;wetlands&#x201D; are in use. They have been developed in response to different national needs and take into account the main biophysical features (generally vegetation, landform and water regime, and sometimes also water chemistry such as salinity) and the variety and size of wetlands in the locality or region being considered.</p>\n<p>The Ramsar Classification System for Wetland Types, adopted at COP4 in 1990, and amended at COP6 in 1996 (Resolution VI.5) and at COP7 in 1999 (Resolution VII.11) has value as a basic internationally applicable habitat description for sites designated for the Ramsar List of Wetlands of International Importance.</p>\n<p>The System (see Annex 1) describes the types of wetland covered by each of the wetland type codes. Note that the wetland types are grouped in three major categories: marine/coastal, inland, and human-made wetlands. Within a single Ramsar Site or other wetland, there may be wetland types from two or more of these categories, particularly if the wetland is large. </p>\n<p>For the purpose of the Target and Indicator, and based on the National Reports Parties report on the use of the three major categories. Countries also use Ramsar definition that has been internationally agreed under the Convention. The minimum information that should be provided is the total area of wetlands for each of these three categories with an emphasis on inland wetlands or freshwater ecosystems for purpose of indicator 6.6.1 (see table below, the explanations of each wetland type code is in Annex 1). </p>\n<p><strong>Table 1: Tabulations of Wetland Type characteristics, Inland Wetlands:</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td rowspan=\"16\">\n        <p>Fresh water</p>\n      </td>\n      <td rowspan=\"4\">\n        <p>Flowing water</p>\n      </td>\n      <td rowspan=\"3\">\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>Rivers, streams, creeks </p>\n      </td>\n      <td>\n        <p>M</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Deltas</p>\n      </td>\n      <td>\n        <p>L</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> Springs, oases</p>\n      </td>\n      <td>\n        <p>Y</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Seasonal/intermittent</p>\n      </td>\n      <td>\n        <p>Rivers, streams, creeks</p>\n      </td>\n      <td>\n        <p>N</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"4\">\n        <p>Lakes and pools</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>&gt; 8 ha</p>\n      </td>\n      <td>\n        <p>O</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&lt; 8 ha</p>\n      </td>\n      <td>\n        <p>Tp</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Seasonal/intermittent</p>\n      </td>\n      <td>\n        <p>&gt; 8 ha</p>\n      </td>\n      <td>\n        <p>P</p>\n      </td>\n    </tr>\n    <tr>\n      <td></td>\n      <td>\n        <p>&lt; 8 ha</p>\n      </td>\n      <td>\n        <p>Ts</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"4\">\n        <p>Marshes on inorganic soils</p>\n      </td>\n      <td>\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>Herb-dominated</p>\n      </td>\n      <td>\n        <p>Tp</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Permanent/ Seasonal/intermittent</p>\n      </td>\n      <td>\n        <p>Shrub-dominated</p>\n      </td>\n      <td>\n        <p>W</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Tree-dominated</p>\n      </td>\n      <td>\n        <p>Xf</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Seasonal/intermittent</p>\n      </td>\n      <td>\n        <p>Herb-dominated</p>\n      </td>\n      <td>\n        <p>Ts</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Marshes on peat soils</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>Non-forested</p>\n      </td>\n      <td>\n        <p>U</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Forested</p>\n      </td>\n      <td>\n        <p>Xp</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Marshes on inorganic or peat soils</p>\n      </td>\n      <td colspan=\"2\">\n        <p>High altitude (alpine)</p>\n      </td>\n      <td>\n        <p>Va</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Tundra</p>\n      </td>\n      <td>\n        <p>Vt</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"4\">\n        <p>Saline, brackish or alkaline water</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Lakes</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>Q</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Seasonal/intermittent</p>\n      </td>\n      <td>\n        <p>R</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Marshes &amp; pools</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>Sp</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Seasonal/intermittent</p>\n      </td>\n      <td>\n        <p>Ss</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Fresh, saline, brackish or alkaline water</p>\n      </td>\n      <td colspan=\"3\">\n        <p>Geothermal</p>\n      </td>\n      <td>\n        <p>Zg</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"3\">\n        <p>Subterranean</p>\n      </td>\n      <td>\n        <p>Zk(b)</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "UNIT_MEASURE__GLOBAL"=>"<p>The extent of wetlands is measured in km<sup>2</sup></p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The international standard classification being used is the Ramsar Classification System for Wetland Types, adopted at COP4 in 1990, and amended at COP6 in 1996 (Resolution VI.5) and at COP7 in 1999 (Resolution VII.11) which is a basic internationally applicable habitat description for sites designated for the Ramsar List of Wetlands of International Importance and other wetlands. See item 7 Annex 1 for the full classification.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The Ramsar Convention on Wetlands Secretariat has been collecting and analysing data on country implementation since 2000 including information about wetland inventories. This is done at intervals of 3 years, that is the cycle of Country reporting under the Convention </p>\n<p>The 1999 review of the state of wetland inventory worldwide (<em>Global review of wetland resources and priorities for wetland inventory </em>- GRoWI), which was undertaken for the Ramsar Convention, identified not only the major gaps in the extent to which wetland inventory had been undertaken, but also found that for the inventories which had been made, it was frequently very hard to trace their existence, to identify their purpose, scope and coverage, and/or to access the information contained in them.</p>\n<p>Another source of information is the update of the Wetland Extent Trends (WET) Index that was commissioned by the Ramsar Convention Secretariat to WCMC. The Wet Index is an updatable indicator of wetland area trends where there are still gaps of information. However, it is not applicable at national level and has not been used, as data are not available at national level. This will be fixed with national reports. </p>\n<p>In the format for National Report for COP13 the Contracting Parties agreed the inclusion of an indicator on the extent of wetlands and change in the extent (indicator 6.6.1). For COP13, 44% of Contracting Parties have completed national wetlands inventories and 16% of Parties reported that their wetland inventories are in progress. Therefore, all data are provided to the Ramsar Secretariat by countries in the form of a country report following a standard format, which includes the original data and reference sources and descriptions of how these have been used to estimate the extent of wetlands. </p>", "COLL_METHOD__GLOBAL"=>"<p>All data are provided by Ramsar Administrative Authorities to the Ramsar Secretariat in the form of country reports of implementation of the Convention based on a standard format that it is been approved by the Standing Committee. The format includes indicators to estimate wetland extent with reference sources.</p>\n<p>As indicated in the Quality Assurance section, for remaining countries where no information is provided, a report is prepared by the Ramsar Secretariat using existing information and a literature search. All country reports (including those prepared by the Ramsar Secretariat) are sent to the respective Administrative Authority for validation before finalization.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data collection process for indicator 6.6.1 has started in 2018 and data collection will take place also in 2019. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Updated data with time series and including year 2020 will be released late 2020. </p>", "DATA_SOURCE__GLOBAL"=>"<p>Ramsar Administrative Authorities prepare and submit to the Ramsar Secretariat their National Reports on implementation for each Conference of the Parties. Countries with dependent territories prepare more than one report. For the remaining countries where no information is provided, a report is prepared by the Ramsar Secretariat using existing information and a literature search that is validated by the concerned countries. </p>", "COMPILING_ORG__GLOBAL"=>"<p>Secretariat of the Ramsar Convention on Wetlands: The Secretariat expects to work with UNEP as co-custodian of this indicator and other<strong> </strong>UN agencies and partners.<strong> </strong></p>", "INST_MANDATE__GLOBAL"=>"<p>At the 52nd meeting of the Standing Committee (SC52) in 2016, Contracting Parties of the Convention on Wetlands approved the inclusion of an indicator on wetland extent in the National Report to COP13. Subsequently, the UN General Assembly in July 2017 adopted the global indicator framework (A/RES/71/313) that included Indicator 6.6.1 on change in the extent of water-related ecosystems over time. Given that Contracting Parties were reporting on extent as part of the National Reports, the Interagency Expert Group on SDGs in 2017 appointed the Convention on Wetlands as co-custodian of Indicator 6.6.1 using data coming from National Reports, which used wetland inventories as a main source.</p>\n<p>As noted in Resolution XIII.7, enhancing the Convention&#x2019;s visibility and synergies with other multilateral environmental agreements and other international institutions, the Convention on Wetlands is co-custodian with UNEP of SDG Indicator 6.6.1. The Convention contributes to monitoring progress with data from National Reports on extent of wetlands, based on the Convention&#x2019;s definitions and requirements for reporting. </p>\n<p>Paragraph 40 of Resolution XIII.7 &#x201C;requests the Secretariat to continue working with Contracting Parties on the completion of national wetland inventories and wetland extent to report on SDG Indicator 6.6.1&#x201D;.</p>\n<p>The Standing Committee at its 54th and 57th meetings, through Decisions SC54-26 and <br>SC57-47, approved the allocation of funds to support Contracting Parties in the completion of wetland inventories and report on wetland extent under Indicator 6.6.1. </p>", "RATIONALE__GLOBAL"=>"<p>The Ramsar Convention on Wetlands is the Intergovernmental treaty that provides the framework for the Conservation and wise use of wetlands and their resources. The Convention was adopted in 1971 and came into force in 1975. Since then, 170 Countries representing almost 90% on UN member states, from all the world&#xB4;s geographic regions have acceded to become Contracting Parties under the Convention.</p>\n<p>At its 52nd meeting, in 2016, the Standing Committee of the Ramsar Convention agreed that Parties would include in their national reports for the 13th meeting of the Conference of the Parties, which have been submitted in January 2018, data on the &#x201C;extent&#x201D; of wetlands. This requirement provides an intergovernmental mechanism to obtain verified data that clearly contribute to Indicator 6.6.1 on wetland extent, but also to collect information for Target 15.1 which consider other types of ecosystems. </p>\n<p>The indicator provides a measure of the relative extent of inland wetlands in a country. It follows the rationale of the forest indicator (Indicator 15.1.1). The availability of accurate data on a country&apos;s wetland extent based on the country&#xB4;s wetland inventory is crucial for decision making regarding policies, restoration of critical wetlands or designation under national or international management or protected area categories. </p>\n<p>Changes in the wetland extent reflect wetland loss and degradation for land use changes or for other uses and may help identify unsustainable practices from different sectors.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The 1999 review of the state of wetland inventory worldwide (Global review of wetland resources and priorities for wetland inventory - GRoWI), which was undertaken for the Ramsar Convention, identified not only the major gaps in the extent to which wetland inventory had been undertaken, but also found that for the inventories which had been made, it was frequently very hard to trace their existence, to identify their purpose, scope and coverage, and/or to access the information contained in them. </p>\n<p>In the light of these findings and to help address this lack of access by those who need to use wetland inventory for a wide range of Convention implementation purposes, the Convention&#x2019;s Scientific &amp; Technical Review Panel (STRP) developed a standard model for wetland inventory metadata (i.e., data about the characteristics of a wetland inventory, rather than the inventory data itself) in order to facilitate those who have inventories in making the existence and availability of these more publicly accessible.</p>\n<p>In 2002, several limitations were identified (Ramsar COP8) in the use of EO for routinely deriving wetland information. These included the cost of the technology, the technical capacity needed to use the data, the unsuitability of the data available for some basic applications (in terms of spatial resolution), the lack of clear, robust and efficient user-oriented methods and guidelines for using the technology, and a lack of solid track record of successful case studies that could form a basis for operational activities. </p>\n<p>Historical optical data is available from Landsat and Spot missions; however, persistent cloud cover in certain regions renders much of these data unusable. Distinguishing between permanent and temporary surface water and wetlands can therefore be difficult considering the available historical data. It is further noted that for complex environments with different wetland types, in situ data or local knowledge is critical to support the analysis of the EO data, and is sometimes the only way to obtain information on certain wetland types.</p>\n<p>Another limitation is that some countries are in the process of updating or completing their national wetlands inventories. In others, there are still gaps or difficulty to access the available information.</p>\n<p>Despite the above limitations, the use of the measure of extent of wetlands will respond to the indicator and will allow having a practical mechanism in the short term to track the status of water related ecosystems with robust data and foster action for the conservation of these important ecosystems. </p>", "DATA_COMP__GLOBAL"=>"<p>Wetland area (Km<sup>2</sup> or ha, reference year)/Change in the extent of wetlands (water-related ecosystems over time) a baseline reference and year.</p>\n<p> </p>\n<p>Based upon the national wetland inventory (complete or partial), countries provide a baseline figure in square kilometres for the extent of wetlands (according to the Ramsar definition) for the year 2017. The minimum information that should be provided is the total area of wetlands for each of the three major categories; &#x201C;marine/coastal&#x201D;, &#x201C;inland&#x201D; and &#x201C;human-made.</p>\n<p>If the information is available, countries indicate the % change in the extent of wetlands over the last three years. If the period of data covers more than three years, countries provide the available information, and indicate the period of the change. For reporting such change, the previous extent, if known, and the period over which the change has taken place should be specified. </p>\n<p>This indicator can be aggregated to global or regional level by adding all country values globally or in a specific region. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The Convention contributes to monitoring progress of Indicator 6.6.1 with data from National Reports on extent of wetlands, based on the Convention&#x2019;s definitions and requirements for reporting. State Parties to the Convention report to the Secretariat every three years that is the cycle of the Convention. The data submitted by the State Parties on their National Reports on Indicator 6.6.1 are review by the Secretariat and Focal Points of the State Parties are contacted in case clarifications are necessary. Once the clarifications are made, the data are submitted to the SDGs Indicators Database. </p>", "ADJUSTMENT__GLOBAL"=>"<p>As indicated in item 2.c, the international standard classification being used is the Ramsar Classification System for Wetland Types, adopted at the Fourth Meeting of the Conference of the Parties to the Convention on Wetlands (COP4) in 1990.</p>\n<p>When reporting on the SDGs data, we use the regional aggregates according to the &#x201C;SDG regional groupings for compliance with SDG processes. </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>For countries where no information on wetland inventories was provided to the Ramsar Convention on Wetlands Secretariat as part of their National Reports to COP13 (16% of countries) a report is in preparation by the Ramsar Secretariat using existing information from previous assessments and literature search. The reports are shared with the concerned countries in order to comment and make any adjustment complementation to the data. </p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>As indicated above </p>", "REG_AGG__GLOBAL"=>"<p>Since information is available for all countries, regional and global estimates are produced by summation. </p>", "DOC_METHOD__GLOBAL"=>"<p>Countries under the Ramsar Convention provide all data in the form of a country report following a standard format approved by the Standing Committee, which includes the original data and reference of wetland inventories as the main source of information. </p>\n<p>Detailed methodology and guidance on how to provide the data on extent for indicator 6.6.1 in their National Reports and to use Ramsar definition and classification is found in the document &#x201C;Guidance on information on national wetland extent, is provided in Target 8 National Wetlands Inventory of the Ramsar National Report for COP13 and COP14 &#x201D;. </p>\n<p>The Ramsar Convention on Wetlands has taken many steps to ensure the wise use and conservation of wetlands globally. This has included the development and promotion of guidance and best practice tools for the inventory, assessment and monitoring of change in wetlands with emphasis in recent years on the application of an increasing number of satellite-based remote sensing approaches (Davidson &amp; Finlayson 2007; Mackay et al. 2009; Ramsar Secretariat 2010a). This has become necessary as there is an increasing demand for information that can be readily used by wetland managers to help stem the ongoing loss and degradation of wetlands. </p>\n<p>The utility of different remote sensing datasets for wetland inventory, monitoring and assessment is well established, through the provision of site based (Land Use Land Cover (LULC)) maps characterising an ecosystem, to the analysis of time series data (remote sensing datasets collected consistently over a particular time period) to determine changes.</p>\n<p>The availability and accessibility of EO datasets suitable for addressing the information needs of the Ramsar Convention and wetland practitioners has increased dramatically in the recent past; increasing capabilities in terms of spatial, temporal and spectral resolution of the data have enabled more efficient and reliable monitoring of the environment over time at global, regional and local scales.</p>\n<p>The Scientific and Technical Review Panel of the Convention has produced a Ramsar Technical Report on &#x201C;Best practice guidelines for the use of Earth Observation for wetland inventory, assessment and monitoring: An information source for wetland managers provided by the Ramsar Convention for Wetlands&#x201D;. The Ramsar Convention and EO based approaches build on those previously undertaken on the use of EO technologies for implementation of the Convention (Ramsar, 2002; Davidson &amp; Finlayson, 2007; Mackay et al., 2009) and are placed within the conceptualisation of wetland inventory, assessment and monitoring that were incorporated into the IF-WIAM (Ramsar Secretariat, 2010b). </p>\n<p>The purpose of the report is to provide an overview of the application of EO technologies to inform wetland managers and practitioners, and stakeholders, including those from related sectors, such as protected area managers and wetland education centre staff (Ramsar Convention, 2015) about &#x201C;best practice&#x201D; use of EO technologies, taking into account requirements and recommendations from the Convention. </p>\n<p>EO provides an effective means for periodic mapping and monitoring over regional to global scales. It should, however, not be expected that global datasets, can achieve the same high level of accuracy everywhere as a local scale map derived through ground surveys and the use of finer resolution (aerial, drones) geospatial data.</p>\n<p>Although mapping of land cover and land uses are one of the most common uses of EO data, there are still challenges in assessing the current status and changes in wetlands over time. Monitoring historical trends and changing patterns of wetlands is complicated by the lack of medium to high-resolution data in particular prior to 2000. </p>\n<p>Despite the ever-expanding data archives, improving quality and increasing suitability of EO data for wetland inventory, monitoring and assessment, it is important to note that &#x201C;ground-truthing&#x201D; or field-based assessments and validation are still a vital component of any work involving EO data, whose occasional omission may still lead to problematic results. </p>\n<p>Ramsar partners such as Jaxa and ESA have conducted pilot projects that provide geospatial information to provide changes to Ramsar, national wetland practitioners, decision makers, and NGOs. </p>\n<p>Wetland inventory provides the basis for guiding the development of appropriate assessment and monitoring, and is used to collect information to describe the ecological character of wetlands including that used to support the listing of Ramsar sites, as recorded in the Ramsar Information Sheet (Ramsar Secretariat, 2012), assessment considers the pressures and associated risks of adverse change in ecological character; and monitoring, which can include both survey and surveillance, provides information on the extent of any change that occurs as a consequence of management actions. </p>\n<p>Under the Convention, multiple guidelines have been developed to support countries to complete national wetland inventories (NWIs) including the use of metadata (Some of these guidelines are mentioned below). More recently in 2020, the Secretariat prepared a toolkit on wetlands inventory to assist Contracting Parties to implement or update a NWI. The aim of the toolkit is to provide practical guidance and examples of how to implement an NWI, including a step-by-step process and resources to support each recommendation. Good practices and examples on the areas of carrying out and updating NWIs, inventory methods, data collection, Earth observation and use of wetland inventories in decision-making are provided. Examples that illustrate how to solve the challenges faced by Contracting Parties are also included. The toolkit includes an introduction linking NWIs to SDG targets and expounding on the importance of an NWI for decision-making, including suggestions for building the case for supporting and protecting wetlands. </p>\n<p>The Secretariat is using the toolkit as a central resource for the development of training materials, webinars and other training opportunities for Contracting Parties.</p>\n<p><strong>Ramsar Guidelines</strong></p>\n<p>A new toolkit for National Wetlands Inventories</p>\n<p><a href=\"https://www.ramsar.org/sites/default/files/documents/library/nwi_toolkit_2020_e.pdf\">https://www.ramsar.org/sites/default/files/documents/library/nwi_toolkit_2020_e.pdf</a></p>\n<p> </p>\n<p>Handbook 15 Wetland Inventory. Ramsar Secretariat 2010a.</p>\n<p><a href=\"https://www.ramsar.org/sites/default/files/documents/pdf/lib/hbk4-15.pdf\">https://www.ramsar.org/sites/default/files/documents/pdf/lib/hbk4-15.pdf</a></p>\n<p>Ramsar Handbooks: Handbook 13 Inventory, assessment and monitoring. Ramsar Secretariat 2010b <a href=\"https://www.ramsar.org/sites/default/files/documents/pdf/lib/hbk4-13.pdf\">https://www.ramsar.org/sites/default/files/documents/pdf/lib/hbk4-13.pdf</a></p>\n<p>Ramsar Technical Report 2 Low-cost GIS software and data for wetland inventory, assessment &amp; monitoring. </p>\n<p><a href=\"https://www.ramsar.org/sites/default/files/documents/pdf/lib/lib_rtr02.pdf\">https://www.ramsar.org/sites/default/files/documents/pdf/lib/lib_rtr02.pdf</a></p>\n<p><a href=\"http://www.ramsar.org/sites/default/files/documents/pdf/lib/lib_rtr04.pdf\">Ramsar Technical Report 4: A Framework for a wetland inventory metadatabase.</a></p>\n<p><a href=\"https://www.ramsar.org/sites/default/files/documents/pdf/lib/lib_rtr04.pdf\">https://www.ramsar.org/sites/default/files/documents/pdf/lib/lib_rtr04.pdf</a></p>\n<p>Ramsar (2002). The Ramsar Convention on Wetlands, The 8th Meeting of the Conference of the Parties to the Convention on Wetlands, Valencia, Spain, 18-26 November 2002, COP8 DOC. 35, The use of Earth Observation technology to support the implementation of the Ramsar Convention, <a href=\"http://www.ramsar.org/sites/default/files/documents/pdf/cop8/cop8_doc_35_e.pdf\">http://www.ramsar.org/sites/default/files/documents/pdf/cop8/cop8_doc_35_e.pdf</a></p>\n<p>Resolution VIII.6 A Ramsar Framework for Wetland Inventory <a href=\"http://www.ramsar.org/document/resolution-viii6-a-ramsar-framework-for-wetland-inventory\">http://www.ramsar.org/document/resolution-viii6-a-ramsar-framework-for-wetland-inventory</a></p>\n<p>Resolution VI.12 National Wetland Inventories and candidate sites for listing <a href=\"http://www.ramsar.org/sites/default/files/documents/pdf/res/key_res_vi.12e.pdf\">http://www.ramsar.org/sites/default/files/documents/pdf/res/key_res_vi.12e.pdf</a></p>\n<p>Resolution VII.20 Priorities for wetland inventory <a href=\"http://www.ramsar.org/sites/default/files/documents/library/key_res_vii.20e.pdf\">http://www.ramsar.org/sites/default/files/documents/library/key_res_vii.20e.pdf</a></p>\n<p>Resolution IX.1 Additional scientific and technical guidance for implementing the Ramsar wise use concept Annex E. An Integrated Framework for wetland inventory assessment and monitoring <a href=\"http://www.ramsar.org/sites/default/files/documents/pdf/res/key_res_ix_01_annexe_e.pdf\">http://www.ramsar.org/sites/default/files/documents/pdf/res/key_res_ix_01_annexe_e.pdf</a></p>\n<p>Resolution X.15 Describing the ecological character of wetlands and data needs and formats for core inventory: harmonized scientific and technical guidance <a href=\"http://www.ramsar.org/sites/default/files/documents/pdf/res/key_res_x_15_e.pdf\">http://www.ramsar.org/sites/default/files/documents/pdf/res/key_res_x_15_e.pdf</a></p>\n<p><a href=\"https://www.ramsar.org/document/ramsar-technical-report-10-the-use-of-earth-observation-for-wetland-inventory-assessment\">Ramsar Technical Report 10: The use of Earth Observation for wetland inventory, assessment and monitoring | Ramsar</a></p>\n<p>The Ramsar Convention on Wetlands. (2011). The 11th Meeting of the Conference of the Parties to the Convention on Wetlands, Bucharest, Romania, 6-13 July, 2012. Resolution XI.8, Annex 2: Strategic Framework and guidelines for the future development of the List of Wetlands of International Importance of the Convention on Wetlands (Ramsar, Iran, 1971) &#x2013; 2012 revision. </p>\n<p><a href=\"https://www.ramsar.org/sites/default/files/documents/library/cop11-res08-e-anx2_revcop13.pdf\">https://www.ramsar.org/sites/default/files/documents/library/cop11-res08-e-anx2_revcop13.pdf</a></p>\n<p>Davidson, N.C. &amp; Finlayson, C.M. (2007). Earth Observation for wetland inventory, assessment and monitoring. Aquatic Conservation: Marine and Freshwater Ecosystems, 17, 219-228.</p>\n<p><a href=\"https://booksc.org/book/538915/be9741\">Earth Observation for wetland inventory, assessment and monitoring | N.C. Davidson; C.M. Finlayson | download (booksc.org)</a></p>\n<p>MacKay, H., Finlayson, C.M., Fern&#xE1;ndez-Prieto, D., Davidson, N., Pritchard, D. &amp; Rebelo, L.-M. (2009). The role of Earth Observation (EO) technologies in supporting implementation of the Ramsar Convention on Wetlands. Journal of Environmental Monitoring 90(7), 2234-2242.</p>\n<p><a href=\"https://booksc.org/book/3680727/08a074\">The role of Earth Observation (EO) technologies in supporting implementation of the Ramsar Convention on Wetlands | H. MacKay; C.M. Finlayson; D. Fern&#xE1;ndez-Prieto; N. Davidson; D. Pritchard; L.-M. Rebelo | download (booksc.org)</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>At the 52nd meeting of the Standing Committee (SC52) in 2016, Contracting Parties of the Convention on Wetlands approved the inclusion of an indicator on wetland extent in the National Report to COP13. The Secretariat provides guidance and training to Contracting Parties for the submission of National Reports to COP13/COP14 and developed a toolkit and training on wetlands inventories to enable them to provide data that could be used for SDG Indicator 6.6.1 reporting. The Secretariat also works with Parties to complete and refine information on extent that has been submitted to the Secretariat and to identify information that is available in existing inventories referred in National Reports, that has not been used to report on wetland extent. Through this mechanism, national validated data using accepted international definitions of wetlands are provided to measure the extent of water-related ecosystems under SDG 6. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Once received, the country reports undergo a rigorous review process to ensure correct use of definitions and methodology as well as internal consistency. A comparison is made with past information and other existing data sources. Regular contacts between national correspondents and Ramsar Staff by e-mail and webinars/regional/sub-regional review workshops form part of this review process in order to support country capacities in particular for monitoring purposes. </p>\n<p>Missing reports prepared by the Ramsar Secretariat for Indictor 6.6.1 are sent to the respective Ramsar Administrative Authority for validation before finalization and publishing of data. The data are then aggregated at sub-regional, regional and global levels by the Ramsar Secretariat team. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Refinement of data includes reporting on wetland type using the two main categories in the Ramsar classification: inland and human-made wetlands. Through this mechanism, national validated data using accepted international definitions of wetlands under the Convention are provided to measure the extent of water-related ecosystems under SDG 6. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are available for all countries (143) that submitted National Reports for COP13 as well as for previous COPs as indicated below. The data collected include information on wetland inventories and extent. For the missing country data (16%) as indicated in the &#x201C;Quality assurance section&#x201D;, the Secretariat will prepare in 2018 reports with the available source of information for Indictor 6.6.1 that will be sent to the respective Ramsar Administrative Authorities for validation. The gaps of information will be addressed during 2018 and 2019 to fully report in late 2020. </p>\n<p><strong>Time series:</strong></p>\n<p>The Secretariat holds National Report information from COP8 (2002), COP9 (2005), COP10 (2008), COP11 (2012), COP12 (2015) and COP13 (2018) National Reports, in databases which permit an analysis of trends in implementation over time, from the 2002-2005 triennium to 2012-2015 that includes specific indicators such as wetland inventories. However, for wetland extent, the data collection has started in 2018. Contracting Parties report in two main categories in the Ramsar classification: inland and human-made wetlands.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>No further disaggregation of this indicator </p>", "COMPARABILITY__GLOBAL"=>"<p>The national figures are reported by the countries themselves following standardized format for the National Reports for the COPs that included definitions and reporting years, thus eliminating any discrepancies between global and national figures. The reporting format ensures that countries provide the full reference for original data sources as well as national definitions and terminology. </p>", "OTHER_DOC__GLOBAL"=>"<p>References and links are provided in the section of methods and guidance available to countries for the compilation of the data at the national level.</p>\n<p><strong>Annex 1 Ramsar Wetland Classification</strong></p>\n<p>The codes are based upon the Ramsar Classification System for Wetland Types, as approved by the Conference of the Contracting Parties in Recommendation 4.7 and amended by Resolutions VI.5 and VII.11. </p>\n<p>To assist in identification of the correct Wetland Types, the Secretariat has provided below tabulations of some of the characteristics of each Wetland Type, for Marine/Coastal Wetlands and Inland Wetlands. </p>\n<p><strong>Marine/Coastal Wetlands</strong></p>\n<p>A -- <strong>Permanent shallow marine waters</strong> in most cases less than six metres deep at low tide; includes sea bays and straits.</p>\n<p>B -- <strong>Marine subtidal aquatic beds</strong>; includes kelp beds, sea-grass beds, tropical marine meadows.</p>\n<p>C -- <strong>Coral reefs</strong>.</p>\n<p>D -- <strong>Rocky marine shores</strong>; includes rocky offshore islands, sea cliffs.</p>\n<p>E -- <strong>Sand, shingle or pebble shores</strong>; includes sand bars, spits and sandy islets; includes dune systems and humid dune slacks.</p>\n<p>F -- <strong>Estuarine waters</strong>; permanent water of estuaries and estuarine systems of deltas.</p>\n<p>G -- <strong>Intertidal mud, sand or salt flats</strong>.</p>\n<p>H -- <strong>Intertidal marshes</strong>; includes salt marshes, salt meadows, saltings, raised salt marshes; includes tidal brackish and freshwater marshes.</p>\n<p>I -- <strong>Intertidal forested wetlands</strong>; includes mangrove swamps, nipah swamps and tidal freshwater swamp forests. </p>\n<p>J -- <strong>Coastal brackish/saline lagoons</strong>; brackish to saline lagoons with at least one relatively narrow connection to the sea.</p>\n<p>K -- <strong>Coastal freshwater lagoons</strong>; includes freshwater delta lagoons.</p>\n<p>Zk(a) &#x2013; <strong>Karst and other subterranean hydrological systems</strong>, marine/coastal</p>\n<p><strong>Table 2: Tabulations of Wetland Type characteristics, Marine / Coastal Wetlands:</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td rowspan=\"5\">\n        <p>Saline water</p>\n      </td>\n      <td rowspan=\"3\">\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>&lt; 6 m deep</p>\n      </td>\n      <td>\n        <p>A</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Underwater vegetation</p>\n      </td>\n      <td>\n        <p>B</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Coral reefs</p>\n      </td>\n      <td>\n        <p>C</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Shores</p>\n      </td>\n      <td>\n        <p>Rocky</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Sand, shingle or pebble</p>\n      </td>\n      <td>\n        <p>E</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"5\">\n        <p>Saline or brackish water</p>\n      </td>\n      <td rowspan=\"3\">\n        <p>Intertidal</p>\n      </td>\n      <td>\n        <p>Flats (mud, sand or salt)</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Marshes</p>\n      </td>\n      <td>\n        <p>H</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Forested</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Lagoons</p>\n      </td>\n      <td>\n        <p>J</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Estuarine waters</p>\n      </td>\n      <td>\n        <p>F</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Saline, brackish or fresh water</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Subterranean</p>\n      </td>\n      <td>\n        <p>Zk(a)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Fresh water</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Lagoons</p>\n      </td>\n      <td>\n        <p>K</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Inland Wetlands</strong></p>\n<p>L -- <strong>Permanent inland deltas</strong>.</p>\n<p>M -- <strong>Permanent rivers/streams/creeks</strong>; includes waterfalls.</p>\n<p>N -- <strong>Seasonal/intermittent/irregular rivers/streams/creeks</strong>.</p>\n<p>O -- <strong>Permanent freshwater lakes</strong> (over 8 ha); includes large oxbow lakes.</p>\n<p>P -- <strong>Seasonal/intermittent freshwater lakes</strong> (over 8 ha); includes floodplain lakes.</p>\n<p>Q -- <strong>Permanent saline/brackish/alkaline lakes</strong>.</p>\n<p>R -- <strong>Seasonal/intermittent saline/brackish/alkaline lakes and flats</strong>.</p>\n<p>Sp -- <strong>Permanent saline/brackish/alkaline marshes/pools</strong>.</p>\n<p>Ss -- <strong>Seasonal/intermittent saline/brackish/alkaline marshes/pools</strong>. </p>\n<p>Tp -- <strong>Permanent freshwater marshes/pools</strong>; ponds (below 8 ha), marshes and swamps on inorganic soils; with emergent vegetation water-logged for at least most of the growing season.</p>\n<p>Ts -- <strong>Seasonal/intermittent freshwater marshes/pools on inorganic soils</strong>; includes sloughs, potholes, seasonally flooded meadows, sedge marshes.</p>\n<p>U -- <strong>Non-forested peatlands</strong>; includes shrub or open bogs, swamps, fens.</p>\n<p>Va -- <strong>Alpine wetlands</strong>; includes alpine meadows, temporary waters from snowmelt.</p>\n<p>Vt -- <strong>Tundra wetlands</strong>; includes tundra pools, temporary waters from snowmelt.</p>\n<p>W -- <strong>Shrub-dominated wetlands</strong>; includes shrub swamps, shrub-dominated freshwater marshes, shrub carr, alder thicket on inorganic soils.</p>\n<p>Xf -- <strong>Freshwater, tree-dominated wetlands</strong>; includes freshwater swamp forests, seasonally flooded forests, wooded swamps on inorganic soils.</p>\n<p>Xp -- <strong>Forested peatlands</strong>; peatswamp forests.</p>\n<p>Y -- <strong>Freshwater springs; oases</strong>. </p>\n<p>Zg -- <strong>Geothermal wetlands.</strong></p>\n<p>Zk(b) &#x2013; <strong>Karst and other subterranean hydrological systems</strong>, inland.</p>\n<p><u>Note</u>: &#x201C;<strong>floodplain</strong>&#x201D; is a broad term used to refer to one or more wetland types, which may include examples from the R, Ss, Ts, W, Xf, Xp, or other wetland types. Some examples of floodplain wetlands are seasonally inundated grassland (including natural wet meadows), shrublands, woodlands and forests. Floodplain wetlands are not listed as a specific wetland type herein.</p>\n<p><strong>Table 3: Tabulations of Wetland Type characteristics, Inland Wetlands:</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td rowspan=\"16\">\n        <p>Fresh water</p>\n      </td>\n      <td rowspan=\"4\">\n        <p>Flowing water</p>\n      </td>\n      <td rowspan=\"3\">\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>Rivers, streams, creeks </p>\n      </td>\n      <td>\n        <p>M</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Deltas</p>\n      </td>\n      <td>\n        <p>L</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Springs, oases</p>\n      </td>\n      <td>\n        <p>Y</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Seasonal/intermittent</p>\n      </td>\n      <td>\n        <p>Rivers, streams, creeks</p>\n      </td>\n      <td>\n        <p>N</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"4\">\n        <p>Lakes and pools</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>&gt; 8 ha</p>\n      </td>\n      <td>\n        <p>O</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&lt; 8 ha</p>\n      </td>\n      <td>\n        <p>Tp</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Seasonal/intermittent</p>\n      </td>\n      <td>\n        <p>&gt; 8 ha</p>\n      </td>\n      <td>\n        <p>P</p>\n      </td>\n    </tr>\n    <tr>\n      <td></td>\n      <td>\n        <p>&lt; 8 ha</p>\n      </td>\n      <td>\n        <p>Ts</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"4\">\n        <p>Marshes on inorganic soils</p>\n      </td>\n      <td>\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>Herb-dominated</p>\n      </td>\n      <td>\n        <p>Tp</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Permanent/ Seasonal/intermittent</p>\n      </td>\n      <td>\n        <p>Shrub-dominated</p>\n      </td>\n      <td>\n        <p>W</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Tree-dominated</p>\n      </td>\n      <td>\n        <p>Xf</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Seasonal/intermittent</p>\n      </td>\n      <td>\n        <p>Herb-dominated</p>\n      </td>\n      <td>\n        <p>Ts</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Marshes on peat soils</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>Non-forested</p>\n      </td>\n      <td>\n        <p>U</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Forested</p>\n      </td>\n      <td>\n        <p>Xp</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Marshes on inorganic or peat soils</p>\n      </td>\n      <td colspan=\"2\">\n        <p>High altitude (alpine)</p>\n      </td>\n      <td>\n        <p>Va</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Tundra</p>\n      </td>\n      <td>\n        <p>Vt</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"4\">\n        <p>Saline, brackish or alkaline water</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Lakes</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>Q</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Seasonal/intermittent</p>\n      </td>\n      <td>\n        <p>R</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Marshes &amp; pools</p>\n      </td>\n      <td colspan=\"2\">\n        <p>Permanent</p>\n      </td>\n      <td>\n        <p>Sp</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Seasonal/intermittent</p>\n      </td>\n      <td>\n        <p>Ss</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Fresh, saline, brackish or alkaline water</p>\n      </td>\n      <td colspan=\"3\">\n        <p>Geothermal</p>\n      </td>\n      <td>\n        <p>Zg</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"3\">\n        <p>Subterranean</p>\n      </td>\n      <td>\n        <p>Zk(b)</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Human-made wetlands</strong></p>\n<p>1 -- <strong>Aquaculture</strong> (e.g. fish/shrimp) <strong>ponds.</strong></p>\n<p>2 -- <strong>Ponds</strong>; includes farm ponds, stock ponds, small tanks (generally below 8 ha).</p>\n<p>3 -- <strong>Irrigated land</strong>; includes irrigation channels and rice fields.</p>\n<p>4 -- <strong>Seasonally flooded agricultural land</strong> (including intensively managed or grazed wet meadow or pasture).</p>\n<p>5 -- <strong>Salt exploitation sites</strong>; salt pans, salines, etc.</p>\n<p>6 -- <strong>Water storage areas</strong>; reservoirs/barrages/dams/impoundments (generally over 8 ha).</p>\n<p>7 -- <strong>Excavations</strong>; gravel/brick/clay pits; borrow pits, mining pools.</p>\n<p>8 -- <strong>Wastewater treatment areas</strong>; sewage farms, settling ponds, oxidation basins, etc.</p>\n<p>9 --<strong>Canals and drainage channels, ditches.</strong></p>\n<p>Zk(c) &#x2013; <strong>Karst and other subterranean hydrological systems</strong>, human-made</p>", "indicator_sort_order"=>"06-06-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"6.a.1", "slug"=>"6-a-1", "name"=>"Volumen de la asistencia oficial para el desarrollo destinada al agua y el saneamiento que forma parte de un plan de gastos coordinados por el gobierno", "url"=>"/site/es/6-a-1/", "sort"=>"06aa01", "goal_number"=>"6", "target_number"=>"6.a", "global"=>{"name"=>"Volumen de la asistencia oficial para el desarrollo destinada al agua y el saneamiento que forma parte de un plan de gastos coordinados por el gobierno"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Volumen de la asistencia oficial para el desarrollo destinada al agua y el saneamiento que forma parte de un plan de gastos coordinados por el gobierno", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Volumen de la asistencia oficial para el desarrollo destinada al agua y el saneamiento que forma parte de un plan de gastos coordinados por el gobierno", "indicator_number"=>"6.a.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La cantidad de Ayuda Oficial al Desarrollo (AOD) relacionada con el agua y el saneamiento \nes una medida cuantificable que representa la cooperación internacional y el apoyo al \ndesarrollo de capacidades en términos financieros. Es fundamental evaluar la AOD en \nproporción a la cantidad incluida en el presupuesto gubernamental para comprender mejor \nsi los donantes están alineados con los gobiernos nacionales, a la vez que se destacan \nlos desembolsos totales de AOD para agua y saneamiento a los países en desarrollo a \nlo largo del tiempo.\n\nUn valor bajo de este indicador (cercano al 0%) sugeriría que los donantes internacionales \nestán invirtiendo en actividades y programas relacionados con el agua y el saneamiento en el \npaís, fuera del ámbito de competencia del gobierno nacional. Un valor alto (cercano al 100%) \nindicaría que los donantes están alineados con el gobierno nacional y las políticas y planes \nnacionales de agua y saneamiento.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.a.1&seriesCode=DC_TOF_WASHL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Asistencia oficial total para el desarrollo (desembolso bruto) para el suministro de agua y el saneamiento, por países receptores (millones de dólares de los Estados Unidos constantes de 2022) DC_TOF_WASHL</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-0A-01.pdf\">Metadatos 6-a-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-27", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The amount of water and sanitation-related Official Development Assistance \n(ODA) is a quantifiable measurement as a proxy for “international cooperation \nand capacity development support” in financial terms. It is essential to be able \nto assess ODA in proportion with how much of it is included in the government \nbudget to gain a better understanding of whether donors are aligned with national \ngovernments while highlighting total water and sanitation ODA disbursements to \ndeveloping countries over time. \n\nA low value of this indicator (near 0%) would suggest that international donors \nare investing in water and sanitation related activities and programmes in the \ncountry outside the purview of the national government. A high value (near 100%) \nwould indicate that donors are aligned with national government and national policies \nand plans for water and sanitation. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.a.1&seriesCode=DC_TOF_WASHL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Total official development assistance (gross disbursement) for water supply and sanitation, by recipient countries (millions of constant 2022 United States dollars) DC_TOF_WASHL</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-0A-01.pdf\">Metadata 6-a-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Urarekin eta saneamenduarekin lotutako Garapenerako Laguntza Ofizialaren (GLO) kopurua kuantifikatzeko \nmoduko neurria da, eta nazioarteko lankidetza eta gaitasunen garapenerako laguntza adierazten du \nfinantza-arloan. Funtsezkoa da GLOa gobernuaren aurrekontuan sartutako kopuruaren proportzioan ebaluatzea, \nemaileak gobernu nazionalekin lerrokatuta dauden hobeto ulertzeko. Aldi berean, garapen-bidean dauden \nherrialdeentzako GLOen guztizko ordainketak nabarmentzen dira denboran zehar. \n\nAdierazle horren balio txikiak (% 0 ingurukoa) iradokitzen du nazioarteko emaileak urarekin eta \nsaneamenduarekin lotutako jardueretan eta programetan inbertitzen ari direla herrialdean, gobernu \nnazionalaren eskumen-eremutik kanpo. Balio altuak (% 100 inguru) emaileak gobernu nazionalarekin \neta urari eta saneamenduari buruzko politika eta plan nazionalekin lerrokatuta daudela adieraziko luke. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.a.1&seriesCode=DC_TOF_WASHL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Garapenerako laguntza ofizial osoa (ordainketa gordina) ur-hornidurarako eta saneamendurako, herrialde hartzaileen arabera (2022ko Estatu Batuetako dolar konstante milioiak) DC_TOF_WASHL</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-0A-01.pdf\">Metadatuak 6-a-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 6: Ensure availability and sustainable management of water and sanitation for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 6.a: By 2030, expand international cooperation and capacity-building support to developing countries in water- and sanitation-related activities and programmes, including water harvesting, desalination, water efficiency, wastewater treatment, recycling and reuse technologies</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 6.a.1: Amount of water- and sanitation-related official development assistance that is part of a government-coordinated spending plan</p>", "META_LAST_UPDATE__GLOBAL"=>"2017-07-11", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>6.5:</p>\n<p>Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]</p>\n<p>7.a:</p>\n<p>Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]</p>\n<p>13.b:</p>\n<p>Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]</p>\n<p>15.9:</p>\n<p>Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]</p>\n<p>Comments:</p>\n<p>6.5 (implement integrated water resources management at all levels, including transboundary cooperation as appropriate) 7.a (enhance international cooperation to facilitate access to clean energy research and technology) 13.b (mechanisms for raising capacity for climate change-related planning and management, focusing on women, youth and local and marginalized communities) 15.9 (integrate ecosystem and biodiversity values into national and local planning, development processes, poverty reduction strategies and accounts).</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>\n<p>United Nations Environment Programme (UNEP)</p>\n<p>Organisation for Economic Co-operation and Development (OECD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>\n<p>United Nations Environment Programme (UNEP)</p>\n<p>Organisation for Economic Co-operation and Development (OECD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Amount of water- and sanitation-related official development assistance that is part of a government-coordinated spending plan is defined as the proportion of total water and sanitation-related Official Development Assistance (ODA) disbursements that are included in the government budget.</p>\n<p><strong>Concepts:</strong></p>\n<p>&#x201C;International cooperation and capacity-building support&#x201D; implies aid (most of it quantifiable) in the form of grants or loans by external support agencies. The amount of water and sanitation-related Official Development Assistance (ODA) can be used as a proxy for this, captured by OECD Creditor Reporting System (CRS). ODA is defined as flows of official financing administered with the promotion of the economic development and welfare of developing countries as the main objective, and which are concessional in character with a grant element of at least 25 per cent (using a fixed 10 per cent rate of discount). By convention, ODA flows comprise contributions of donor government agencies, at all levels, to developing countries (&#x201C;bilateral ODA&#x201D;) and to multilateral institutions. ODA receipts, from a recipient perspective, comprise disbursements by bilateral donors and multilateral institutions. Lending by export credit agencies&#x2014;with the pure purpose of export promotion&#x2014;is excluded (see http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm).</p>\n<p>&#x201C;Developing countries&#x201D; refer to countries, which are eligible to receive official development assistance (see http://www.oecd.org/dac/stats/daclist.htm). This limits the scope of reporting to those countries receiving water and sanitation ODA, and the number of such countries is expected to decrease going forward.</p>\n<p>Water and sanitation-related activities and programmes include those for water supply, sanitation and hygiene (WASH) (targets 6.1, 6.2), wastewater and water quality (6.3), water efficiency (6.4), water resource management (6.5), and water-related ecosystems (6.6). As per target 6.a wording, it includes activities and programmes for water harvesting, desalination, water efficiency, wastewater treatment, recycling and reuse technologies. </p>\n<p>A government coordinated spending plan is defined as a financing plan/budget for the water and sanitation sector, clearly assessing the available sources of finance and strategies for financing future needs.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) provides information on governance, monitoring, human resources, and financing in the water, sanitation, and hygiene (WASH) sector. The UN-Water GLAAS survey is currently conducted on a biennial basis, led by WHO, and collected data from 94 countries (predominantly low and lower-middle income countries) in the most recent cycle in 2013-2014. The scope of the question on external funding has been expanded beyond WASH for the 2016-17 GLAAS cycle to include wastewater and water quality, water efficiency, water resource management, and the status of water-related ecosystems. GLAAS has completed three full cycles (2009-2010, 2011-2012, and 2013-2014), as well as a pilot conducted in 2008. </p>\n<p>National governments participating in the GLAAS survey fill out the questionnaire, preferably supported by a multi-stakeholder review. Although one ministry leads the process, it is often the case that many different ministries and departments must be involved in the process in order to obtain the data required to complete the questionnaire. A GLAAS national focal person supports the lead ministry to coordinate data collection, to compile the national response to the questionnaire, and to lead on the process of data validation.</p>\n<p>The OECD Development Assistance Committee (DAC) has been collecting data on aid flows since 1973 through the OECD Creditor Reporting System based on a standard methodology and agreed definitions from member countries and other aid providers. The data are generally obtained on an activity level, and include numerous parameters to allow disaggregation by provider and recipient country, by type of finance, and by type of resources provided. Data are available for essentially all high-income countries as bilateral donors, and for an increasing number of middle-income aid providers, as well as multi-lateral lending institutions. Methodology on ODA data collection by OECD can be found here: http://www.oecd.org/dac/stats/methodology.htm</p>\n<p>The data will be complemented by Integrated Water Resources Management (IWRM) reporting in SDG target 6.5 (for wastewater and water quality, water efficiency, water resource management, and the status of water-related ecosystems) (UNEP 2016). The analysis of IWRM has been done in the past by UN-Water in 2008 (led by UN-DESA) and in 2012 (led by UNEP, UNDP, GWP and SIWI) as requested by the UN Commission for Sustainable Development (UN-Water 2008, 2012).</p>", "COLL_METHOD__GLOBAL"=>"<p>National governments participating in the UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) survey fill out the questionnaire, preferably supported by a multi-stakeholder review. Although one ministry leads the process (e.g. Ministry of Water, Ministry of Environment, etc. depending on country), it is often the case that many different ministries and departments must be involved in the process in order to obtain the data required to complete the questionnaire. A GLAAS national focal person supports the lead ministry to coordinate data collection, to compile the national response to the questionnaire, and to lead on the process of data validation. For each GLAAS submission, information on the country processes is collected (number of ministries involved, whether a national meeting was held to support the filling of the questionnaire, stakeholder validation, use of documentation, etc.). Once received, the country submission undergoes a thorough data validation process, which is often an iterative process requiring communication and feedback with regional and country counterparts.</p>\n<p>Countries are also requested to provide consent to publish individual, validated data responses as supplied to GLAAS. Thus through the data collection, validation and consultation processes, the results are expected to be comparable and no further adjustments are foreseen.</p>", "FREQ_COLL__GLOBAL"=>"<p>The current round of GLAAS has been launched and data for 2015 ODA disbursements channelled through national government budgets will be available by end-2016. OECD data on ODA disbursements for 2015 will be made available through CRS in December 2016. (From NA to NA)</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Q1 2017</p>", "DATA_SOURCE__GLOBAL"=>"<p>Ministries with responsibilities related to finance, water supply and sanitation, agriculture, water resources development and management, environment, and foreign affairs</p>", "COMPILING_ORG__GLOBAL"=>"<p>WHO and OECD, with support from UNEP</p>", "RATIONALE__GLOBAL"=>"<p>The amount of water and sanitation-related Official Development Assistance (ODA) is a quantifiable measurement as a proxy for &#x201C;international cooperation and capacity development support&#x201D; in financial terms. It is essential to be able to assess ODA in proportion with how much of it is included in the government budget to gain a better understanding of whether donors are aligned with national governments while highlighting total water and sanitation ODA disbursements to developing countries over time. </p>\n<p>A low value of this indicator (near 0%) would suggest that international donors are investing in water and sanitation related activities and programmes in the country outside the purview of the national government. A high value (near 100%) would indicate that donors are aligned with national government and national policies and plans for water and sanitation.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data on water and sanitation-related ODA included in the government budget will be available by end-2016 with the current cycle of UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) data. Until then, total water and sanitation-related ODA (denominator) will be reported. Total water and sanitation-related ODA will continue to be reported as an additional indicator going forward.</p>\n<p>In addition, the proportion of ODA channelled through the government treasury will be reported as an additional indicator. ODA channelled through treasury indicates a high level of cooperation and alignment between donors and national government in which the donors channel funds through the national budget process. </p>\n<p>The OECD Creditor Reporting System (CRS) currently disaggregates ODA for the water and sanitation among several categories including: sector policy and administration, water resources protection, large and basic water and sanitation systems, river basin infrastructure, waste management, agricultural water resources, and education and training. While these categories do not align directly with the target areas of SDG 6 individually, which limits the disaggregation of ODA among the SDG target areas, the combined ODA from these categories does align with a majority of the reported ODA to the water sector.</p>\n<p>As the numerator and denominator come from different sources, there is the possibility of different underlying assumptions regarding what should be included/excluded in the ODA figures. This could lead to situations in which the proportion of ODA included in government budget is greater than 1 (100%) if total ODA reported to OECD is lower than ODA reported to be included the budget. To guard against this possibility, the OECD will supply GLAAS with the reported ODA figures, broken down to the project level, so that respondents can match these with their on-budget project data. </p>\n<p>ODA represents only one aspect of international cooperation. To capture other dimensions, additional supporting indicators are available, including indicators for the Collaborative Behaviours identified by the Sanitation and Water for All (SWA) partnership. Each behaviour has one or two key indicators for governments and for development partners. If the behaviours are jointly adapted by governments and development partners, long-term sector performance and sustainability would improve. For additional information on the Collaborative Behaviours see: http://sanitationandwaterforall.org/about/the-four-swa-collaborative-behaviours/</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is computed as the proportion of total water and sanitation-related ODA that is included in the government budget, i.e. the amount of water and sanitation-related ODA in the government budget divided by the total amount of water and sanitation-related ODA.</p>\n<p>The numerator on water and sanitation-related ODA in the government budget will be obtained from the UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) survey for the 2016-2017 cycle. The question on external funding collects data on the amount of donor funds that were included in government budget. Data for 2015 ODA disbursements through GLAAS will be available by end-2016. The scope of the question on external funding has been expanded beyond WASH for the 2016-17 cycle to address all targets under SDG 6, including wastewater and water quality, water efficiency, water resource management, and water-related ecosystems.</p>\n<p>The denominator on total water and sanitation-related ODA disbursements will be obtained through OECD Creditor Reporting System (CRS) (purpose codes 14000-series for the water sector and purpose code 31140 for agricultural water resources). Data on ODA disbursements for 2015 will be made available through CRS in December 2016.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Due to the highly country- and context-specific nature of ODA disbursements and whether they are aligned with national government plans, no estimates are produced for countries that are missing data.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>If no data is provided for the amount of ODA included in the budget, then the country is excluded from the regional and/or global analysis.</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional aggregates for ODA are derived based on summation of recipient country ODA disbursement for the water sector (purpose codes 14000- series) and agricultural water resources (purpose code 31140) from the OECD Creditor Reporting System. </p>\n<p>Global and regional proportions of ODA disbursements as part of a government budget are derived for countries based on a summation of ODA for the water sector that is included in the budget divided by a summation of total ODA for water sector. The calculation of global and regional aggregates would only be performed for those countries reporting the amount of ODA for the water sector that is included in the budget. If no data is provided for the amount of ODA in the budget, then the country is excluded from the regional and/or global analysis.</p>", "DOC_METHOD__GLOBAL"=>"<p>Questionnaires for providers of development cooperation are available at the following link: http://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/ The data included in the indicator are ODA flows from all donors to developing countries eligible for ODA for the water sector (water and sanitation (purpose codes 14000- series), agricultural water resources (purpose code 31140), flood prevention/control (purpose code 41050), and hydroelectric power plants (purpose code 23220)).</p>\n<p>The OECD Development Assistance Committee (DAC) has been collecting data on aid flows since 1973 through the OECD Creditor Reporting System based on a standard methodology and agreed definitions from member countries and other aid providers. The data are generally obtained on an activity level, and include numerous parameters to allow disaggregation by provider and recipient country, by type of finance, and by type of resources provided. Data are available for essentially all high-income countries as bilateral donors, and for an increasing number of middle-income aid providers, as well as multi-lateral lending institutions. Methodology on ODA data collection by OECD can be found here: <a href=\"http://www.oecd.org/dac/stats/methodology.htm\">http://www.oecd.org/dac/stats/methodology.htm</a>. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data are collected using a converged reporting system whereby bilateral and multilateral providers of development co-operation use a single file format (Creditor Reporting System &#x2013; CRS) to report at item level on all flows of resources to developing countries. Item-level reporting is validated against key aggregates also reported by donors and then serves as the basis for producing various other aggregate statistics. For further details, see: <a href=\"http://www.oecd.org/dac/stats/methodology.htm\">http://www.oecd.org/dac/stats/methodology.htm</a> </p>\n<p>A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Asia and Pacific: Most countries (at least 80% of the countries covering 90% of the population from the region)</p>\n<p>Africa: Most countries (at least 80% of the countries covering 90% of the population from the region)</p>\n<p>Latin America and the Caribbean: Most countries (at least 80% of the countries covering 90% of the population from the region)</p>\n<p>Europe, North America, Australia, New Zealand and Japan: Some countries</p>\n<p>Please note that these reflect availability of data on total water and sanitation ODA. Data on proportion included in government budget will be available through the current cycle of GLAAS (cf. 7.1, 10.1, and 10.2).</p>\n<p><strong>Time series:</strong></p>\n<p>Time series of parameters under the indicator are available for 2008, 2010, 2012, and 2014.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Subsector disaggregation (basic vs. large systems)</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There may be differences in how much development aid is reported by a recipient country and the amount of ODA disbursed to that country as reported by the OECD-CRS. While OECD captures a significant amount of the aid flows (as reported by external donors) to the water and sanitation sector, countries may receive development aid for water and sanitation from national and international donors that do not report to the OECD-CRS data system. Other differences may occur if recipient countries define development aid more or less rigorously than OECD&#x2019;s definition of ODA, or use different timeframes (e.g. fiscal year instead of calendar year) to report aid flows. In order to ensure data is as consistent as possible, the OECD will supply the reported ODA figures broken down to the project level, so that respondents can match these with their on-budget project data.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>http://www.who.int/water_sanitation_health/glaas/en/</p>\n<p>http://www.unep.org/</p>\n<p>http://www.oecd.org/dac/stats/data.htm</p>\n<p><strong>References:</strong></p>\n<p>- UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water. http://www.who.int/water_sanitation_health/glaas/en/</p>\n<p>- UN-Water 2008: Status Report on IWRM for CSD-16, http://www.unwater.org/publications/publications-detail/en/c/206480/UNEP-DHI </p>\n<p>- UN-Water 2012: Status Reports on IWRM. http://www.unwater.org/publications/status-report-on-integrated-water-resources-management/en/ </p>\n<p>- Data from the 2012 Survey on the Application of Integrated Approaches to Water Resources Management. http://www.unepdhi.org/rioplus20 </p>\n<p>- UNEP 2016. Degree of implementation of integrated water resources management. Draft survey to support SDG indicator 6.5.1 http://www.unepdhi.org/whatwedo/gemi .</p>\n<p>Organisation for Economic Co-operation and Development Creditor Reporting System</p>\n<p>http://www.oecd.org/dac/stats/data.htm</p>", "indicator_sort_order"=>"06-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"6.b.1", "slug"=>"6-b-1", "name"=>"Proporción de dependencias administrativas locales que han establecido políticas y procedimientos operacionales para la participación de las comunidades locales en la gestión del agua y el saneamiento", "url"=>"/site/es/6-b-1/", "sort"=>"06bb01", "goal_number"=>"6", "target_number"=>"6.b", "global"=>{"name"=>"Proporción de dependencias administrativas locales que han establecido políticas y procedimientos operacionales para la participación de las comunidades locales en la gestión del agua y el saneamiento"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de dependencias administrativas locales que han establecido políticas y procedimientos operacionales para la participación de las comunidades locales en la gestión del agua y el saneamiento", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de dependencias administrativas locales que han establecido políticas y procedimientos operacionales para la participación de las comunidades locales en la gestión del agua y el saneamiento", "indicator_number"=>"6.b.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de dependencias administrativas locales que han establecido políticas y procedimientos operacionales para la participación de las comunidades locales en la gestión del agua y el saneamiento", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.b- Apoyar y fortalecer la participación de las comunidades locales en la mejora de la gestión del agua y el saneamiento", "definicion"=>"\nProporción de dependencias administrativas locales (ayuntamientos) que \nhan establecido políticas y procedimientos operacionales para la participación \nde las comunidades locales en la gestión y el saneamiento de aguas de consumo \ny en la gestión de aguas de baño\n", "formula"=>"\n$$PDAL_{gestión\\, de\\, agua}^{t} = \\frac{DAL_{gestión\\, de\\, agua}^{t}}{DAL^{t}} \\cdot 100$$\n\ndonde:\n\n$DAL_{gestión\\, de\\, agua}^{t} =$ dependencias administrativas locales (ayuntamientos) que han establecido políticas y procedimientos operacionales para la gestión del agua en el año $t$\n\n$DAL^{t} =$ dependencias administrativas locales (ayuntamientos) en el año $t$\n", "desagregacion"=>"Tipo de gestión del agua: gestión y saneamiento de aguas de consumo; gestión de aguas de baño", "periodicidad"=>"Anual", "observaciones"=>"\nEl indicador es del 100% ya que los ayuntamientos son responsables de la calidad  del agua de consumo humano, pudiendo gestionar su suministro directamente o a  través de un gestor (Real Decreto 140/2003, de 7 de febrero, por el que se  establecen los criterios sanitarios de la calidad del agua de consumo humano),  y son asimismo responsables de la gestión de las aguas de baño  (Real Decreto 1341/2007, de 11 de octubre, sobre la gestión de la calidad  de las aguas de baño)", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nDefinir los procedimientos en las políticas o leyes para la participación \nde las comunidades locales es vital para garantizar que se satisfagan \nlas necesidades de toda la comunidad, incluidas las de los más vulnerables, \ny también fomenta la apropiación de los planes, lo que a su vez contribuye \na su sostenibilidad.\n\nUn valor bajo de este indicador sugeriría que la participación de las \ncomunidades locales en la gestión del agua y el saneamiento es baja, \nmientras que un valor alto indicaría altos niveles de participación, \nlo que indica una mayor apropiación y una mayor probabilidad de prestación \ny gestión sostenibles de los servicios de agua y saneamiento.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.b.1&seriesCode=ER_WAT_PARTIC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de países con un alto nivel de usuarios/comunidades que participan en programas de planificación y gestión de recursos hídricos (%) ER_WAT_PARTIC</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-0B-01.pdf\">Metadatos 6-b-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de dependencias administrativas locales que han establecido políticas y procedimientos operacionales para la participación de las comunidades locales en la gestión del agua y el saneamiento", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.b- Apoyar y fortalecer la participación de las comunidades locales en la mejora de la gestión del agua y el saneamiento", "definicion"=>"\nProportion of local administrative units (municipalities) with established and operational policies \nand procedures for participation of local communities in drinking water and sanitation management \nand in bathing water management\n \n", "formula"=>"\n$$PDAL_{water\\, management}^{t} = \\frac{DAL_{water\\, management}^{t}}{DAL^{t}} \\cdot 100$$\n\nwhere:\n\n$DAL_{water\\, management}^{t} =$ local administrative units (municipalities) with established and operational policies \nand procedures for participation of local communities in water management in year $t$\n\n$DAL^{t} =$ local administrative units (municipalities) in year $t$\n", "desagregacion"=>"Type of water management: drinking water and sanitation management; bathing water management ", "periodicidad"=>"Anual", "observaciones"=>"\nThe indicator is 100% since local governments are responsible for the quality of drinking water,  and can manage its supply directly or through a manager (Royal Decree 140/2003, of February 7,  establishing the health criteria for the quality of drinking water). They are also responsible for  the management of bathing water (Royal Decree 1341/2007, of October 11, on the management of bathing  water quality). ", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nDefining the procedures in policy or law for the participation of local communities is vital \nto ensure the needs of all the community are met, including the most vulnerable and also encourages \nownership of schemes which in turn contributes to their sustainability. \n\nA low value of this indicator would suggest that participation of local communities in water and \nsanitation management is low, whereas a high value would indicate high levels of participation, \nindicating greater ownership and a higher likelihood of sustainable delivery and management of \nwater and sanitation services. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.b.1&seriesCode=ER_WAT_PARTIC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of countries with high level of users/communities participating in planning programs in water resources planning and management (%) ER_WAT_PARTIC</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-0B-01.pdf\">Metadata 6-b-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de dependencias administrativas locales que han establecido políticas y procedimientos operacionales para la participación de las comunidades locales en la gestión del agua y el saneamiento", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.b- Apoyar y fortalecer la participación de las comunidades locales en la mejora de la gestión del agua y el saneamiento", "definicion"=>"\nTokiko administrazioen (udalen) proportzioa, kontsumorako uren kudeaketa eta saneamenduan \neta bainatzeko uren kudeaketan tokiko komunitateek parte har dezaten politikak eta prozedura \noperatiboak finkatu dituztenena\n", "formula"=>"\n$$PDAL_{uraren\\, kudeaketa}^{t} = \\frac{DAL_{uraren\\, kudeaketa}^{t}}{DAL^{t}} \\cdot 100$$\n\nnon:\n\n$DAL_{uraren\\, kudeaketa}^{t} =$ tokiko administrazioak (udalak), uraren kudeaketan tokiko komunitateek \nparte har dezaten politikak eta prozedura operatiboak finkatu dituztenak, $t$ urtean\n\n$DAL^{t} =$ tokiko administrazioak (udalak) $t$ urtean\n", "desagregacion"=>"Uraren kudeaketa mota: Kontsumorako uren kudeaketa eta saneamendua; bainatzeko uren kudeaketa", "periodicidad"=>"Anual", "observaciones"=>"\nAdierazlearen emaitza % 100 da, udalak baitira giza kontsumorako uraren kalitatearen erantzule,  eta zuzenean edo kudeatzaile baten bidez kudeatu dezakete uraren hornidura (140/2003 Errege Dekretua,  otsailaren 7koa, giza kontsumorako uraren kalitatearen osasun-irizpideak ezartzen dituena). Halaber,  udalak dira bainatzeko uren kudeaketaren arduradunak (1341/2007 Errege Dekretua, urriaren 11koa,  bainatzeko uren kalitatearen kudeaketari buruzkoa).", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nTokiko komunitateek parte hartzeko politiketan edo legeetan prozedurak zehaztea ezinbestekoa da \nkomunitate osoaren beharrak asetzen direla bermatzeko, baita ahulenenak ere; era berean, planak \nbereganatzea ere sustatzen du, eta horrek, aldi berean, haien jasangarritasunari laguntzen dio. \n\nAdierazle horren balio baxuak iradokitzen du tokiko erkidegoek uraren kudeaketan eta saneamenduan \nduten parte-hartzea txikia dela; balio altuak, berriz, partaidetza-maila altuak adieraziko lituzke, \nuraren eta saneamenduaren zerbitzuak gehiago bereganatzen direla eta horiek modu jasangarrian emateko \neta kudeatzeko aukera handiagoa dagoela iradokiz. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.b.1&seriesCode=ER_WAT_PARTIC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Ur-baliabideak planifikatzeko eta kudeatzeko programetan parte hartzen duten erabiltzaile/komunitate maila handia duten herrialdeen proportzioa (%) ER_WAT_PARTIC</a> UNSTATS\n", "comparabilidad"=>"EAEn erabilgarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-06-0B-01.pdf\">Metadatuak 6-b-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 6: Ensure availability and sustainable management of water and sanitation for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 6.b: Support and strengthen the participation of local communities in improving water and sanitation management</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 6.b.1: Proportion of local administrative units with established and operational policies and procedures for participation of local communities in water and sanitation management</p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2017-07-11</p>", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>6.5:</p>\n<p>Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]</p>\n<p>15.9:</p>\n<p>Number of deaths, missing persons and persons affected by disaster per 100,000 people [a]</p>\n<p>Comments:</p>\n<p>6.5 (implement integrated water resources management at all levels, including transboundary cooperation as appropriate) 15.9 (integrate ecosystem and biodiversity values into national and local planning, development processes, poverty reduction strategies and accounts)</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>\n<p>United Nations Environment Programme (UNEP)</p>\n<p>Organisation for Economic Co-operation and Development (OECD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>\n<p>United Nations Environment Programme (UNEP)</p>\n<p>Organisation for Economic Co-operation and Development (OECD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator assesses the percentage of local administrative units (as defined by the national government) that have an established and operational mechanism by which individuals and communities can meaningfully contribute to decisions and directions about water and sanitation management.</p>\n<p>The indicator Proportion of local administrative units with established and operational policies and procedures for participation of local communities in water and sanitation management is currently being measured by the Proportion of countries with clearly defined procedures in law or policy for participation by service users/communities in planning program in water and sanitation management, and hygiene promotion and the Proportion of countries with high level of users/communities participating in planning programs in water and sanitation management, and hygiene promotion.</p>\n<p><strong>Concepts:</strong></p>\n<p>Stakeholder participation is essential to ensure the sustainability of water and sanitation management options over time, e.g. the choice of appropriate solutions for a given social and economic context, and the full understanding of the impacts of a certain development decision. Defining the procedures in policy or law for the participation of local communities is vital to ensure needs of all the community is met, including the most vulnerable and also encourages ownership of schemes which in turn contributes to their sustainability.</p>\n<p>Local administrative units refers to non-overlapping sub-districts, municipalities, communes, or other local community-level units covering both urban and rural areas to be defined by the government. </p>\n<p>Policies and procedures for participation of local communities in water and sanitation management would define a formal mechanism to ensure participation of users in planning water and sanitation activities. </p>\n<p>A policy or procedure is considered to be established if the mechanism for participation of local communities is defined in law or has been formally approved and published. It is considered to be operational if the policy or procedure is being implemented, with appropriate funding in place and with means for verifying that participation took place.</p>\n<p>&#x2018;Water and sanitation&#x2019; includes all areas of management related to each of the targets under SDG 6, namely: water supply (6.1), sanitation and hygiene (6.2), wastewater treatment and ambient water quality (6.3), efficiency and sustainable use (6.4), integrated water resources management (6.5) and water-related ecosystems (6.6).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) provides information on governance, monitoring, human resources, and financing in the water, sanitation, and hygiene (WASH) sector. The UN-Water GLAAS survey is currently conducted on a biennial basis, led by WHO, and collected data from 94 countries (predominantly low and lower-middle income countries) in the most recent cycle in 2013-2014. The scope of the question on community and user participation has been expanded beyond WASH for the 2016-17 GLAAS cycle to address all targets in SDG 6, including water quality, water rights/allocation, water resource management, and the status of water-related ecosystems. GLAAS has completed three full cycles (2009-2010, 2011-2012, and 2013-2014), as well as a pilot conducted in 2008. </p>\n<p>National governments participating in the GLAAS survey fill out the questionnaire, preferably supported by a multi-stakeholder review. Although one ministry leads the process, it is often the case that many different ministries and departments must be involved in the process in order to obtain the data required to complete the questionnaire. A GLAAS national focal person supports the lead ministry to coordinate data collection, to compile the national response to the questionnaire, and to lead on the process of data validation.</p>\n<p>The data will be complemented by Integrated Water Resources Management (IWRM) reporting in SDG target 6.5 (for wastewater and water quality, water efficiency, water resource management, and the status of water-related ecosystems) (UNEP 2016). A key component of IWRM is community participation and management of water resources at the local level. The analysis of IWRM has been done in the past by UN-Water in 2008 (led by UN-DESA) and in 2012 (led by UNEP, UNDP, GWP and SIWI) as requested by the UN Commission for Sustainable Development (UN-Water 2008, 2012).</p>\n<p>The OECD Water Governance Initiative (WGI), a technical platform gathering 100+ members from the public, private and non-for-profit sectors, is currently developing a set of Water Governance Indicators, within the implementation strategy of the OECD Principles on Water Governance (OECD 2015a). The Water Governance Indicators are expected to be able to provide additional information on local participation on the basis of an indicators system proposed in OECD (2015b) for measuring &#x201C;stakeholder engagement for inclusive water governance&#x201D;. An indicator providing metrics on local participation will be developed and tested by 2017. Data will be made available through interactive platforms and databases in a format to foster policy dialogue and peer learning by 2018. A dedicated publication on &#x201C;Water Governance at a Glance&#x201D; will be launched at the 8th World Water Forum in Brasilia (2018).</p>", "COLL_METHOD__GLOBAL"=>"<p>National governments participating in the GLAAS survey fill out the questionnaire, preferably supported by a multi-stakeholder review. Although one ministry leads the process (e.g. Ministry of Water, Ministry of Environment, etc. depending on country), it is often the case that many different ministries and departments must be involved in the process in order to obtain the data required to complete the questionnaire. A GLAAS national focal person supports the lead ministry to coordinate data collection, to compile the national response to the questionnaire, and to lead on the process of data validation. For each GLAAS submission, information on the country processes are collected (number of ministries involved, whether a national meeting was held to support the filling of the questionnaire, stakeholder validation, use of documentation, etc.) Once received, the country submission undergoes a thorough data validation process, which is often an iterative process requiring communication and feedback with regional and country counterparts.</p>\n<p>Countries are also requested to provide consent to publish individual, validated data responses as supplied to GLAAS. Thus through the data collection, validation and consultation processes, the results are expected to be comparable and no further adjustments are foreseen.</p>", "FREQ_COLL__GLOBAL"=>"<p>The current round of UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) has been launched and data will be available by end-2016. (From NA to NA)</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Q1 2017 </p>", "DATA_SOURCE__GLOBAL"=>"<p>Ministries with responsibilities related to water supply and sanitation, agriculture, water resources development and management, and environment</p>", "COMPILING_ORG__GLOBAL"=>"<p>Name:</p>\n<p>WHO, OECD and UNEP</p>\n<p>Description:</p>\n<p>WHO, with support from OECD and UNEP</p>", "RATIONALE__GLOBAL"=>"<p>Defining the procedures in policy or law for the participation of local communities is vital to ensure the needs of all the community are met, including the most vulnerable and also encourages ownership of schemes which in turn contributes to their sustainability.</p>\n<p>A low value of this indicator would suggest that participation of local communities in water and sanitation management is low, whereas a high value would indicate high levels of participation, indicating greater ownership and a higher likelihood of sustainable delivery and management of water and sanitation services.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data on local administrative units with established and operational policies and procedures for local participation is being collected through the current cycle of GLAAS, and will be available by end-2016. Until then, the presence of policies and procedures as reported at the national level for different subsectors will be reported.</p>\n<p>Additional data, including data measuring local participation from the OECD Water Governance Indicators and administrative data, will be progressively included in the calculation of the indicator as they become available.</p>", "DATA_COMP__GLOBAL"=>"<p>The UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) questionnaire provides information on whether there are &#x201C;clearly defined procedures in laws or policies for participation by service users (e.g. households) and communities in planning programs&#x201D;. For countries that have data available from the local administrative unit level, they are asked to provide data on the number of local administrative units for which policies and procedures for local participation (i) exist, and (ii) are operational, as well as (iii) the number of local administrative units assessed, and (iv) the total number of units in the country. The indicator is computed as (ii) the number of local admin units with operation policies and procedures for local participation divided by (iv) the total number of local administrative units in the country.</p>\n<p>Both numerator and denominator will be obtained through the GLAAS survey for the 2016-2017 cycle.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Due to the highly country- and context-specific nature of the indicator, no estimates are produced for countries that are missing data.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Operational mechanism by which individuals and communities can meaningfully contribute to water and sanitation management then the country will be excluded from the regional and global estimates for this indicator. </p>\n<p>Global and regional estimates for a related indicator on the presence and use of participation policies and procedures at the national level for different water subsectors are also derived to support the target indicator. Similarly, countries with missing values are excluded from global and regional analysis for this indicator.</p>", "REG_AGG__GLOBAL"=>"<p>For global and regional aggregates, the percentage of local administrative units that have a defined and operational mechanism by which individuals and communities can meaningfully contribute to decisions and directions about water and sanitation management will be averaged among countries, with each country&#x2019;s percent value weighted based on total country population for the data year, as a proportion of the global population.</p>", "DOC_METHOD__GLOBAL"=>"<p>National governments participating in GLAAS fill out the country survey, preferably supported by a multi-stakeholder review. Although one ministry leads the process, it is often the case that many different ministries and departments must be involved in the process in order to obtain the data required to complete the questionnaire. A GLAAS national focal person supports the lead ministry to coordinate data collection, to compile the national response to the questionnaire, and to lead on the process of data validation. GLAAS survey documents for the current cycle can be found at the following link: <a href=\"http://www.who.int/water_sanitation_health/monitoring/investments/glaas-2017-survey/en/\">http://www.who.int/water_sanitation_health/monitoring/investments/glaas-2017-survey/en/</a> </p>\n<p>The UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS) provides information on governance, monitoring, human resources, and financing in the water, sanitation, and hygiene (WASH) sector. The UN-Water GLAAS survey is currently conducted on a biennial basis, led by WHO and has completed three full cycles (2009/2010, 2011/2012, and 2013/2014), as well as a pilot conducted in 2008. GLAAS survey documents for the current cycle of data collection (2016/2017) can be found at the following link: <a href=\"http://www.who.int/water_sanitation_health/monitoring/investments/glaas-2017-survey/en/\">http://www.who.int/water_sanitation_health/monitoring/investments/glaas-2017-survey/en/</a></p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Once received, the country submission undergoes a thorough data validation process, which is often an iterative process requiring communication and feedback with regional and country counterparts. Quality of the submission is also assessed through an analysis of data collected on country processes (number of ministries involved, whether a national meeting was held to support the filling of the questionnaire, stakeholder validation, use of documentation, etc.) as well as supporting documentation provided. In addition, an external validation with key informants is conducted, in which WASH experts who have not participated in the GLAAS process respond to selected questions from the survey for a specific country within their area of expertise, and agreement with country responses is evaluated.</p>\n<p>Data submitted through GLAAS are endorsed by the national government prior to submission. A form (<a href=\"http://www.who.int/entity/water_sanitation_health/monitoring/investments/glaas-consent-form-2016.doc?ua=1\">http://www.who.int/entity/water_sanitation_health/monitoring/investments/glaas-consent-form-2016.doc?ua=1</a>) providing consent to WHO for the release and publication of the country data is signed and submitted along with the filled survey.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Asia and Pacific: Most countries (at least 50% of the countries covering 60% of the population from the region)</p>\n<p>Africa: Some countries (approximately 50% of the countries covering 50% of the population from the region)</p>\n<p>Latin America and the Caribbean: Most countries (at least 60% of the countries covering 80% of the population from the region)</p>\n<p>Europe, North America, Australia, New Zealand and Japan: Most countries (at least 60% of the countries covering 60% of the population from the region)</p>\n<p>Please note that these reflect data on presence of policies and procedures for local participation at the national level. Data at the local administrative unit level is being collected through the current cycle of </p>\n<p>GLAAS and through administrative data that will be progressively included in the calculation of the indicator (cf. 7.1, 10.1, and 10.2).</p>\n<p><strong>Time series:</strong></p>\n<p>Time series of parameters under the indicator are available for 2008, 2010, 2012, and 2014. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>This indicator will be generated by countries, thus no differences in global and national figures are expected.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>http://www.who.int/water_sanitation_health/glaas/en/</p>\n<p>http://www.unep.org/</p>\n<p>http://www.oecd.org/env/watergovernanceprogramme.htm</p>\n<p><strong>References:</strong></p>\n<p>UN-Water Global Analysis and Assessment of Sanitation and Drinking-Water. http://www.who.int/water_sanitation_health/glaas/en/</p>\n<p>OECD (2015a), OECD Principles on Water Governance, available at: https://www.oecd.org/gov/regional-policy/OECD-Principles-on-Water-Governance-brochure.pdf</p>\n<p>OECD (2015b), Stakeholder Engagement for Inclusive Water Governance, OECD Studies on Water, OECD Publishing, Paris., http://dx.doi.org/10.1787/9789264231122-en</p>\n<p>UN-Water 2008 : Status Report on IWRM for CSD-16, http://www.unwater.org/publications/publications-detail/en/c/206480/UNEP-DHI </p>\n<p>UN-Water 2012: Status Reports on IWRM. http://www.unwater.org/publications/status-report-on-integrated-water-resources-management/en/ </p>\n<p>Data from the 2012 Survey on the Application of Integrated Approaches to Water Resources Management. http://www.unepdhi.org/rioplus20 </p>\n<p>UNEP 2016. Degree of implementation of integrated water resources management. Draft survey to support SDG indicator 6.5.1 http://www.unepdhi.org/whatwedo/gemi </p>\n<p>OECD 2015. Stakeholder Engagement for Inclusive Water Governance. http://www.oecd-ilibrary.org/governance/stakeholder-engagement-for-inclusive-water-governance_9789264231122-en</p>", "indicator_sort_order"=>"06-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"7.1.1", "slug"=>"7-1-1", "name"=>"Proporción de la población que tiene acceso a la electricidad", "url"=>"/site/es/7-1-1/", "sort"=>"070101", "goal_number"=>"7", "target_number"=>"7.1", "global"=>{"name"=>"Proporción de la población que tiene acceso a la electricidad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población que tiene acceso a la electricidad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población que tiene acceso a la electricidad", "indicator_number"=>"7.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Bienestar, Juventud y Reto Demográfico", "periodicity"=>"Bienal", "url"=>"https://www.euskadi.eus/encuesta-de-pobreza-y-desigualdades-sociales-epds/web01-s2enple/es/", "url_text"=>"Encuesta de pobreza y desigualdades sociales (EPDS)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de la población que tiene acceso a la electricidad", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.1- De aquí a 2030, garantizar el acceso universal a servicios energéticos asequibles, fiables y modernos", "definicion"=>"\nPorcentaje de población con acceso a fuentes consistentes de electricidad\n", "formula"=>"\n$$PA_{electricidad}^{t} = \\frac{PA_{electricidad}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$PA_{electricidad}^{t} =$ población con acceso a electricidad en el año $t$\n\n$P^{t} =$ población en el año $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"", "justificacion_global"=>"\nEl acceso a la electricidad aborda cuestiones fundamentales en todas \nlas dimensiones del desarrollo sostenible. La meta tiene una amplia gama \nde impactos sociales y económicos, entre ellos, la facilitación \ndel desarrollo de actividades generadoras de ingresos y el alivio de la \ncarga de las tareas domésticas.\n\nEn el marco de la meta mundial de acceso igualitario a la energía, el \nODS 7.1.1 se centra específicamente en el acceso a la electricidad disponible \npara la población mundial. Para obtener una imagen clara, solo \nse consideran que hay acceso si la fuente principal de iluminación es \nel proveedor local de electricidad, los sistemas solares, las minirredes y \nlos sistemas autónomos. \n\nLas fuentes como generadores, velas, baterías, etc., no se consideran \ndebido a su limitada capacidad de trabajo y a que generalmente se \nmantienen como fuentes de respaldo para la iluminación.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.1.1&seriesCode=EG_ACS_ELEC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">Proporción de población con acceso a electricidad, por zonas urbanas/rurales (%) EG_ACS_ELEC</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-01-01.pdf\">Metadatos 7-1-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de la población que tiene acceso a la electricidad", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.1- De aquí a 2030, garantizar el acceso universal a servicios energéticos asequibles, fiables y modernos", "definicion"=>"\nPercentage of population with access to consistent sources of electricity\n", "formula"=>"\n$$PA_{electricity}^{t} = \\frac{PA_{electricity}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$PA_{electricity}^{t} =$ population with access to electricity in year $t$\n\n$P^{t} =$ population in year $t$\n", "desagregacion"=>nil, "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nAccess to electricity addresses major critical issues in all the \ndimensions of sustainable development. The target has a wide range \nof social and economic impacts, including facilitating development \nof income generating activities and lightening the burden of household \ntasks.\n\nUnder the global target of equal access to energy, SDG7.1.1 focuses \nspecifically on electricity access available to the global population. \nIn order to gain a clear picture, access rates are only considered if the \nprimary source of lighting is the local electricity provider, solar \nsystems, mini-grids and stand-alone systems. \n\nSources such as generators, candles, batteries, etc., are not considered \ndue to their limited working capacities and since they are usually kept \nas backup sources for lighting. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EG_ACS_ELEC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">Proportion of population with access to electricity, by urban/rural (%) EG_ACS_ELEC</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-01-01.pdf\">Metadata 7-1-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Proporción de la población que tiene acceso a la electricidad", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.1- De aquí a 2030, garantizar el acceso universal a servicios energéticos asequibles, fiables y modernos", "definicion"=>"\nElektrizitate-iturri sendoetarako sarbidea duten biztanleen ehunekoa\n", "formula"=>"\n$$PA_{elektrizitatea}^{t} = \\frac{PA_{elektrizitatea}^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$PA_{elektrizitatea}^{t} =$ elektrizitaterako sarbidea duen biztanleria $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urtean\n", "desagregacion"=>nil, "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nElektrizitaterako sarbideak funtsezko gaiak jorratzen ditu garapen jasangarriaren dimentsio guztietan. \nXedeak inpaktu sozial eta ekonomiko ugari ditu, besteak beste diru-sarrerak sortzen dituzten jardueren \ngarapena erraztea eta etxeko lanen zama arintzea. \n\nEnergia berdintasunez eskuratzeko mundu-mailako xedearen esparruan, 7.1.1 GJHak munduko biztanleentzat \neskuragarri dagoen elektrizitaterako sarbidea jorratzen du espezifikoki. Irudi argi bat lortzeko, \nsarbidea dagoela jotzen da, soilik baldin eta argiztapen-iturri nagusia tokiko elektrizitate-hornitzailea, \neguzki-sistemak, minisareak eta sistema autonomoak badira. \n\nIturriak, hala nola sorgailuak, kandelak, bateriak eta abar, ez dira kontuan hartzen lanerako gaitasun \nmugatua dutelako eta, oro har, argiztapenerako babes-iturri gisa mantentzen direlako. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EG_ACS_ELEC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">Elektrizitaterako sarbidea duten biztanleen proportzioa, hirigune/landa-eremuaren arabera (%) EG_ACS_ELEC</a> UNSTATS\n", "comparabilidad"=>"EAEn erabilgarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-01-01.pdf\">Metadatuak 7-1-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 7.1: By 2030, ensure universal access to affordable, reliable and modern energy services</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 7.1.1: Proportion of population with access to electricity</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EG_ACS_ELEC - Proportion of population with access to electricity [7.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank Group</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank Group</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of population with access to electricity is the percentage of population with access to electricity.</p>\n<p>SDG7 ensures access to affordable, reliable, sustainable and modern energy for all. Specifically, Indicator 7.1.1 refers to the proportion of population with access to electricity. This is expressed in percentage figures and is disaggregated by total, urban and rural access rates per country, as well as by UN regional and global classifications. </p>\n<p><strong>Concepts:</strong></p>\n<p>Electricity access in this scenario refers to the proportion of population in the considered area (country, region, and global context) that has access to consistent sources of electricity. </p>\n<p>The World Bank&#x2019;s Global Electrification Database compiles nationally representative household survey data as well as census data since 1990. It also incorporates data from the Socio-Economic Database for Latin America and the Caribbean, the Middle East and North Africa Poverty Database, and the Europe and Central Asia Poverty Database, all of which are based on similar surveys. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>Regional and global classifications refer to the list of standard country or area codes for statistical use (M49) provided by the United Nations Statistics Division </li>\n  <li>Country classification by income group is based on the World Bank Country and Lending Groups. </li>\n  <li>Country population data are extracted from the World Development Indicators. </li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>Data for access to electricity are collected from household surveys and censuses, tapping into a wide number of different household survey types including: Multi-tier Framework (MTF), Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, including those by various government agencies (for example, ministries of energy and utilities).</p>\n<p>The World Bank is the agency that has taken responsibility for compiling a meta-database of statistics on electricity access harvested from the full global body of household surveys. The World Bank Electrification Database covers more than 219 countries for the period from 1990 and is updated regularly.</p>\n<p>For more information on compiling access to energy data see Global Tracking Framework report (2013) (Chapter 2, Annex 2, page 127-129).</p>\n<p>Reports produced by international agencies such as the UN, World Bank, USAID, National Statistics Offices, as well as country censuses are used to collect data. Though some of the reports might not directly focus on energy access, they tend to include questions regarding access to electricity. Also, for the sake of consistency in methodology across countries, government and utility data are not considered. </p>", "COLL_METHOD__GLOBAL"=>"<p>If data sources have any information on electricity access, it is collected and analysed in line with the previous trends and future projections of each country. Data validation is conducted by checking that the figures are reflective of the ground level scenario as well as are in line with country populations, income levels and electrification programs. </p>", "FREQ_COLL__GLOBAL"=>"<p>The database collected from household surveys and censuses is updated annually for the second half of the year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The annual release of new data for SDG7.1.1 is usually in early June.</p>", "DATA_SOURCE__GLOBAL"=>"<p>It varies according to the country and its context. Data are collected from national statistics agencies as well as international agencies such as the UN and World Bank.</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Bank Group</p>", "INST_MANDATE__GLOBAL"=>"<p>Along with the SDG 7 custodian agencies, including the International Energy Agency (IEA), the International Renewable Energy Agency (IRENA), the United Nations Statistics Division (UNSD), and the World Health Organization (WHO), the World Bank is designated by the UN Statistical Commission to collect, process, and disseminate data with regional, and global aggregates, in relation to the progress in achieving the SDG 7 goal. During the process of updating and disseminating the electrification database, as a consultation organization, the World Bank is responsible for acting in consultation with internal stakeholders, national statistics agencies, and the UN regional commissions. </p>", "RATIONALE__GLOBAL"=>"<p>Access to electricity addresses major critical issues in all the dimensions of sustainable development. The target has a wide range of social and economic impacts, including facilitating development of income generating activities and lightening the burden of household tasks.</p>\n<p>Under the global target of equal access to energy, SDG7.1.1 focuses specifically on electricity access available to the global population. In order to gain a clear picture, access rates are only considered if the primary source of lighting is the local electricity provider, solar systems, mini-grids and stand-alone systems. Sources such as generators, candles, batteries, etc., are not considered due to their limited working capacities and since they are usually kept as backup sources for lighting.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The World Bank aims to estimate demand side access rates to better understand the access levels experienced by the population. This is different from the supply side access rates usually provided by governments, ministries, etc. The data are primarily compiled from national household surveys and censuses. But since these are carried out infrequently, it is difficult to understand the ground level trends for short term periods. Collecting data for rural areas as well as last-mile connectivity problems also cause errors in data collection that could skew results. </p>\n<p>While the existing global household survey evidence base provides a good starting point for tracking household energy access, it also presents several limitations that will need to be addressed over time. In many parts of the world, the presence of an electricity connection in the household does not necessarily guarantee that the energy supplied is adequate in quality and reliability or affordable in cost and it would be desirable to have fuller information about these critical attributes of the service, which have been highlighted in SDG7.</p>\n<p>Substantial progress has already been made toward developing and piloting a new methodology known as the Multi-Tier Framework for Measuring Energy Access (World Bank) which is able to capture these broader dimensions of service quality and would make it possible to go beyond a simple yes/no measure of energy access to a more refined approach that recognizes different levels of energy access, and also takes into account the affordability and reliability of energy access explicitly referenced in the language of SDG7. The methodology for the Multi-Tier Framework for Measuring Energy Access has already been published based on a broad consultative exercise and represents a consensus view across numerous international agencies working in the field. Discussions are also progressing with the World Bank&#x2019;s Household Survey Technical Working Group regarding the mainstreaming of this methodology into the standardized household questionnaire design that will be applied every three years in all low-income countries between 2015 and 2030 as part of the broader SDG monitoring exercise.</p>\n<p>The adoption of this methodology will allow &#x2013; over time &#x2013; the more refined measurement of energy access, making it possible to report more disaggregated information regarding the type of electricity supply (grid or off-grid), the capacity of electricity supply provided (in Watts), the duration of service (daily hours and evening hours), the reliability of service (in terms of number and length of unplanned service interruptions), the quality of service (in terms of voltage fluctuations), as well as affordability and legality of service.</p>\n<p>Another advantage of this approach is that they can be applied not only to measuring energy access at the household level, but also its availability to support enterprises and deliver critical community services, such as health and education.</p>\n<p>Methodological challenges associated with the measurement of energy access are more fully described in the Global Tracking Framework (2013) (Chapter 2, Section 1, page 75-82), and in the ESMAP (2015) Report &#x201C;Beyond Connections: Energy Access Redefined&#x201D; both of which are referenced below.</p>", "DATA_COMP__GLOBAL"=>"<p>To estimate values, <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701999/pdf/ehp.1205987.pdf\">a multilevel nonparametric modelling approach</a>&#x2014;developed by the World Health Organization to estimate clean fuel usage&#x2014;was adapted to predict electricity access and used to fill in the missing data points for the period from 1990 onwards. Where data is available, access estimates are weighted by population. Multilevel nonparametric modelling considers the hierarchical structure of data (country and regional levels), using the regional classification of the United Nations. </p>\n<p>The model is applied for all countries with at least one data point. To use as much real data as possible, results based on real survey data are reported in their original form for all years available. The statistical model is used to fill in data only for years where they are missing and to conduct global and regional analyses. In the absence of survey data for a given year, information from regional trends was borrowed. The difference between real data points and estimated values is clearly identified in the database. </p>\n<p>Countries classified as &#x201C;High Income&#x201D; based on the World Bank Country and Lending Groups are assumed to reach universal access from the first year the country joined the category. </p>\n<p>In the present report, to avoid having electrification trends from 1990 to 2010 overshadow electrification efforts since 2010, the model was run twice: </p>\n<ul>\n  <li>With survey data and assumptions from 1990 to the latest year for model estimates from 1990 to the latest year</li>\n  <li>With survey data and assumptions from 2010 to the latest year for model estimates from 2010 to the latest year </li>\n</ul>\n<p>Given the low frequency and the regional distribution of some surveys, several countries have gaps in available data. To develop the historical evolution and starting point of electrification rates, a simple modelling approach was adopted to fill in the missing data points. This modelling approach allowed the estimation of electrification rates for 219 countries over the time periods. The SE4ALL Global Tracking Framework Report (2013) referenced below provides more details on the suggested methodology for tracking access to energy (Chapter 2, Section 1, page 82-87).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>After completing data compilation, the World Bank initially contacted each energy team for highly strategic countries or some countries with data discrepancy issues. Following the initial round, the World Bank coordinates with internal stakeholders and the UN regional commissions to validate the accuracy of the data. In this process, the World Bank is in charge of responding to any inquiries and comments. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Given the low frequency and regional distribution of some surveys, many countries have gaps in data availability. A simple modelling approach was adopted to fill in the missing data points, in order to develop the historical evolution and starting point of the electrification rates. The estimation is conducted using a model with region, country, and time variables. The model keeps the original observation if data is available. The statistical model is used to fill in data only for years where they are missing and to help conduct global and regional analyses. In the absence of survey data for a given year, information from regional trends was borrowed. The estimated values are clearly identified (&#x201C;Estimate&#x201D;) in the database. In the meantime, if a country value indicates a high discrepancy compared with either IEA data or data from the past publication, the country is considered as an outlier and not affected by the regional trends. As a result, such countries only have their country effects in model estimates. </p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Values for regional and global levels are calculated by incorporating all survey data along with model-estimated values substituting missing values. Regional and global classifications are based on the UN M49 series for statistical use.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global data are population-weighted by summing up all available values across countries listed in the UN regional classification. </p>", "DOC_METHOD__GLOBAL"=>"<p>Countries generally use internationally accepted methods of conducting censuses and national surveys. There is some level of disparity between countries and regional methodologies, but the efforts to harmonize data is improving. The Multi-Tier Framework (MTF) by the World Bank is one such method being used to increase accuracy of data collection.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>A non-parametric model is consistently used to obtain a complete set of annual trends of electricity access rates by fulfilling the data gaps with model estimates. The model draws from solid fuel use modelling used in Bonjour et al (2013). The model closely follows empirical data without being influenced by large fluctuations in survey estimates. In general, regional trends are borrowed for the absence of survey data. However, some countries, which have significant discrepancies with IEA data, are considered as an outlier, not reflecting the regional trends, but just relying on their country effects.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>A multi-level review process in collaboration with industry experts, national statistical offices, country and regional experts as well as partnering international agencies and UN bodies is conducted before finalizing the data.</p>\n<p>Before finalizing electricity access data, the World Bank team contacts the relevant national statistical offices and the UN regional commissions asking for reviews and suggestions for the prepared figures. The database also goes through multiple rounds of vetting process internally through departments. The relevant links are provided below under References.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Good quality data of electricity access should be generally aligned with the trends from the past data at country level. Also, the World Bank&#x2019;s data results would not have high discrepancies about more than 5 percentage points with IEA data, although the World Bank (based on standardized household surveys and censuses) and IEA (based on government-reported values) maintain separate database of global electricity access rates. Meanwhile, given the consultation with internal stakeholders and the UN regional commissions, data points of some countries are adjusted to reflect their certain circumstances, such as national conflict. Therefore, for these countries, the access rate is not linearly increased.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data have been collected from 1990 through the latest year on an annual basis. </p>\n<p><strong>Time series:</strong></p>\n<p>Data for countries have been compiled from 1990 to the latest year, but there are gaps in accurate data availability.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Electricity access rates are disaggregated by geographic location into total, urban and rural rates. Countries that are classified as &#x201C;High Income&#x201D; are assumed to reach universal access from the first year it was added to the category. Disaggregation of access to electricity by rural or urban place of residence is available at country, regional and global levels.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The World Bank database compiles electricity usage data, while many international agencies and national ministries report electricity production data. This is the main cause for data discrepancies. The quality and accuracy of population data can also lead to differences in assessing electrification.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://databank.worldbank.org/source/world-development-indicators\">https://databank.worldbank.org/source/world-development-indicators</a></p>\n<p><a href=\"https://trackingsdg7.esmap.org/\">https://trackingsdg7.esmap.org/</a></p>\n<p><strong>References: </strong></p>\n<ul>\n  <li>Bonjour, S., Adair-Rohani, H., Wolf, J., Bruce, N. G., Mehta, S., Pr&#xFC;ss-Ust&#xFC;n, A., Lahiff, M., Rehfuess, E. A., Mishra, V. &amp; Smith, K. R. (2013). Solid fuel use for household cooking: country and regional estimates for 1980&#x2013;2010. <em>Environmental health perspectives</em>, <em>121</em>(7), 784-790.</li>\n  <li>Global Tracking Framework Report (2013). <a href=\"http://trackingenergy4all.worldbank.org\">http://trackingenergy4all.worldbank.org</a></li>\n  <li>Global Tracking Framework Report (2015). <a href=\"http://trackingenergy4all.worldbank.org/\">http://trackingenergy4all.worldbank.org/</a></li>\n  <li>International Energy Agency&#x2019;s World Energy Outlook. <a href=\"https://www.iea.org/topics/world-energy-outlook\">https://www.iea.org/topics/world-energy-outlook</a> </li>\n  <li>Multi-Tier Framework for Measuring Energy Access. <a href=\"https://www.esmap.org/node/55526\">https://www.esmap.org/node/55526</a></li>\n  <li>UNSD Standard country or area codes for statistical use (M49). <a href=\"https://unstats.un.org/unsd/methodology/m49/\">https://unstats.un.org/unsd/methodology/m49/</a> </li>\n  <li>World Bank Country and Lending Groups. <a href=\"https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups\">https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups</a> </li>\n  <li>World Development Indicators. <a href=\"https://databank.worldbank.org/source/world-development-indicators\">https://databank.worldbank.org/source/world-development-indicators</a> </li>\n</ul>", "indicator_sort_order"=>"07-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"7.1.2", "slug"=>"7-1-2", "name"=>"Proporción de la población cuya fuente primaria de energía son los combustibles y tecnologías limpios", "url"=>"/site/es/7-1-2/", "sort"=>"070102", "goal_number"=>"7", "target_number"=>"7.1", "global"=>{"name"=>"Proporción de la población cuya fuente primaria de energía son los combustibles y tecnologías limpios"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población cuya fuente primaria de energía son los combustibles y tecnologías limpios", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población cuya fuente primaria de energía son los combustibles y tecnologías limpios", "indicator_number"=>"7.1.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Organización Mundial de la Salud", "periodicity"=>"Anual", "url"=>"https://www.who.int/data/gho/data/themes/air-pollution/household-air-pollution", "url_text"=>"Organización Mundial de la Salud", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/OMS.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de la población cuya fuente primaria de energía son los combustibles y tecnologías limpios", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.1- De aquí a 2030, garantizar el acceso universal a servicios energéticos asequibles, fiables y modernos", "definicion"=>"\nNúmero de personas que utilizan combustibles y tecnologías limpios para cocinar, \ncalentar e iluminar, dividido por la población total que hace uso de cocina, \ncalefacción o iluminación, expresado en porcentaje. \n\n“Limpio” se define por los objetivos de tasa de emisiones y recomendaciones \nespecíficas sobre combustibles incluidos en las Directrices de la OMS para la calidad del \naire en interiores. Se consideran combustibles no limpios el carbón sin procesar y el \nqueroseno.\n", "formula"=>"\n$$PPUC_{combustibles\\, limpios}^{t} = \\frac{PUC_{combustibles\\, limpios}^{t}}{PUC^{t}} \\cdot 100$$\n\ndonde:\n\n$PUC_{combustibles\\, limpios}^{t} =$ población que utiliza combustibles y tecnologías \nlimpios para cocinar, calentar e iluminar en el año $t$\n\n$P^{t} =$ población que hace uso de cocina, calefacción o iluminación en el año $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"", "justificacion_global"=>"\nCocinar, iluminar y calentar representan una gran parte del consumo de energía \nen los hogares de los países de ingresos bajos y medianos. Para cocinar y \ncalentar, los hogares suelen depender de combustibles sólidos (como madera, \ncarbón vegetal, biomasa) o queroseno combinados con tecnologías ineficientes \n(por ejemplo, fuegos abiertos, estufas, calentadores de ambiente o lámparas). \n\nEs bien sabido que la dependencia de esa energía ineficiente para cocinar, \ncalentar e iluminar está asociada con altos niveles de contaminación del aire \nen los hogares (interiores). \n\nSe estima que el uso de combustibles ineficientes \npara cocinar por sí solo causa más de 4 millones de muertes al año, principalmente \nentre mujeres y niños. Esto es más que la tuberculosis, el virus de inmunodeficiencia \nhumana y la malaria juntos. Estos impactos adversos para la salud pueden evitarse \nadoptando combustibles y tecnologías limpios para todos los principales \nfines energéticos del hogar o, en algunas circunstancias, adoptando \ncocinas de combustión avanzada (es decir, aquellas que alcanzan \nlos objetivos de tasas de emisión previstos en las directrices de la OMS) y \nadoptando protocolos estrictos para su uso seguro. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.1.2&seriesCode=EG_EGY_CLEAN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">Proporción de la población que depende principalmente de combustibles y tecnologías limpios (%) EG_EGY_CLEAN</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-01-02.pdf\">Metadatos 7-1-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-28", "en"=>{"indicador_disponible"=>"Proporción de la población cuya fuente primaria de energía son los combustibles y tecnologías limpios", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.1- De aquí a 2030, garantizar el acceso universal a servicios energéticos asequibles, fiables y modernos", "definicion"=>"\nNumber of people using clean fuels and technologies for cooking, heating, \nand lighting, divided by the total population using cooking, heating, or \nlighting, expressed as a percentage. \n\n“Clean” is defined by the emission rate targets and specific fuel recommendations \nincluded in the normative guidance WHO guidelines for indoor air quality. Unprocessed \ncoal and kerosene are considered unclean fuels.\n", "formula"=>"\n$$PPUC_{clean\\, fuels}^{t} = \\frac{PUC_{clean\\, fuels}^{t}}{PUC^{t}} \\cdot 100$$\n\nwhere:\n\n$PUC_{clean\\, fuels}^{t} =$ people using clean fuels and technologies for cooking, heating, \nand lighting in year $t$\n\n$P^{t} =$ population that uses cooking, heating or lighting in year $t$\n", "desagregacion"=>nil, "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nCooking, lighting and heating represent a large share of household \nenergy use across the low- and middle-income countries. For cooking \nand heating, households typically rely on solid fuels (such as wood, \ncharcoal, biomass) or kerosene paired with inefficient technologies \n(e.g. open fires, stoves, space heaters or lamps). \n\nIt is well known that reliance on such inefficient energy for cooking, \nheating and lighting is associated with high levels of household (indoor) \nair pollution. \n\nThe use of inefficient fuels for cooking alone is estimated to cause over \n4 million deaths annually, mainly among women and children. This is \nmore than Tuberculosis, Human Immuno-deficiency Virus and malaria combined. \nThese adverse health impacts can be avoided by adopting clean fuels and \ntechnologies for all main household energy end-or in some circumstances by \nadopting advanced combustion cook stoves (i.e. those which achieve the \nemission rates targets provided by the WHO guidelines) and adopting strict \nprotocols for their safe use. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.1.2&seriesCode=EG_EGY_CLEAN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">Proportion of population with primary reliance on clean fuels and technology (%) EG_EGY_CLEAN</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-01-02.pdf\">Metadata 7-1-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Proporción de la población cuya fuente primaria de energía son los combustibles y tecnologías limpios", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.1- De aquí a 2030, garantizar el acceso universal a servicios energéticos asequibles, fiables y modernos", "definicion"=>"\nJanaria prestatzeko, berotzeko eta argiztatzeko erregai eta teknologia \ngarbiak erabiltzen dituzten pertsonen kopurua, zati sukaldea, berogailua \nedo argiztapena erabiltzen duen biztanleria osoa, ehunekotan adierazita. \n\n\"Garbia\" definitzen da OMEk barnealdeetako airearen kalitateari buruz emandako \njarraibideetan jasotzen diren isuri-tasen eta erregaiei buruzko gomendio \nespezifikoetako helburuen arabera. Erregai ez-garbitzat hartzen dira prozesatu \ngabeko ikatza eta kerosenoa. \n", "formula"=>"\n$$PPUC_{erregai\\, garbiak}^{t} = \\frac{PUC_{erregai\\, garbiak}^{t}}{PUC^{t}} \\cdot 100$$\n\nnon:\n\n$PUC_{erregai\\, garbiak}^{t} =$ Janaria prestatzeko, berotzeko eta argiztatzeko erregai eta teknologia \ngarbiak erabiltzen dituzten pertsonak $t$ urtean\n\n$P^{t} =$ sukaldea, berogailua edo argiztapena erabiltzen duen biztanleria $t$ urtean\n", "desagregacion"=>nil, "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nJanaria prestatzea, argiztatzea eta berotzea diru-sarrera txikiko eta ertaineko herrialdeetako etxeetako \nenergia-kontsumoaren zati handi bat dira. Janaria prestatzeko eta berotzeko, etxeak erregai solidoen \n(hala nola egurra, egur-ikatza, biomasa) edo kerosenoaren menpe egoten dira, teknologia ez-eraginkorrekin \nkonbinatuta (adibidez, su irekiak, berogailuak, giro-berogailuak edo lanparak). \n\nJakina da janaria prestatzeko, berotzeko eta argiztatzeko energia ez-eraginkor horrekiko mendekotasuna \netxeetan (barnealdean) airearen kutsadura-maila handiekin lotuta dagoela. \n\nJanaria prestatzeko erregai ez-eraginkorrak erabiltzeak urtean 4 milioi heriotza baino gehiago eragiten \ndituela kalkulatzen da, batez ere emakumeen eta haurren artean. Tuberkulosia, giza immunoeskasiaren \nbirusa eta malaria batera baino gehiago da hori. Osasunerako kaltegarriak diren inpaktu horiek saihestu \negin daitezke erregai eta teknologia garbiak hartuz etxeko helburu energetiko nagusi guztietarako, edo, \negoera batzuetan, errekuntza aurreratuko sukaldeak hartuz (hau da, OMEren jarraibideetan aurreikusitako \nemisio-tasen helburuak lortzen dituztenak) eta erabilera segururako protokolo zorrotzak ezarriz. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.1.2&seriesCode=EG_EGY_CLEAN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAREA\">Batez ere erregai eta teknologia garbien mende dauden biztanleen proportzioa (%) EG_EGY_CLEAN</a> UNSTATS\n", "comparabilidad"=>"EAEn erabilgarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-01-02.pdf\">Metadatuak 7-1-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 7.1: By 2030, ensure universal access to affordable, reliable and modern energy services</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 7.1.2: Proportion of population with primary reliance on clean fuels and technology</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EG_EGY_CLEAN - Proportion of population with primary reliance on clean fuels and technology [7.1.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>3.9.1: Mortality rate attributed to household and ambient air pollution</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of population with primary reliance on clean fuels and technology is calculated as the number of people using clean fuels and technologies for cooking, heating and lighting divided by total population reporting that any cooking, heating or lighting, expressed as percentage. &#x201C;Clean&#x201D; is defined by the emission rate targets and specific fuel recommendations (i.e. against unprocessed coal and kerosene) included in the normative guidance WHO guidelines for indoor air quality: household fuel combustion.</p>\n<p><strong>Concepts:</strong></p>\n<p>Current global data collection focuses on the primary fuel used for cooking, categorized as solid or non-solid fuels, where solid fuels are considered polluting and non-modern, while non-solid fuels are considered clean. This single measure captures a good part of the lack of access to clean cooking fuels but fails to collect data on type of device or technology used for cooking, and fails to capture other polluting forms of energy use in the home such as those used for lighting and heating.</p>\n<p>New evidence-based normative guidance from the WHO (i.e. WHO Guidelines for indoor air quality guidelines: household fuel combustion), highlights the importance of addressing both fuel and the technology for adequately protecting public health. These guidelines provide technical recommendations in the form of emissions targets for as to what fuels and technology (stove, lamp, and so on) combinations in the home are clean. These guidelines also recommend against the use of unprocessed coal and discourage the use of kerosene (a non-solid but highly polluting fuel) in the home. They also recommend that all major household energy end uses (e.g. cooking, space heating, lighting) use efficient fuels and technology combinations to ensure health benefits.</p>\n<p>For this reason, the technical recommendations in the WHO guidelines, access to modern cooking solution in the home will be defined as &#x201C;access to clean fuels and technologies&#x201D; rather than &#x201C;access to non-solid fuels.&#x201D; This shift will help ensure that health and other &#x201C;nexus&#x201D; benefits are better counted, and thus realized.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Regional and global classifications refer to the list of standard country or area codes for statistical use (M49) provided by the United Nations Statistics Division</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Primary household fuels and technologies, particularly for cooking, is routinely collected at the national levels in most countries using censuses and surveys. Household surveys used include: United States Agency for International Development (USAID)-supported Demographic and Health Surveys (DHS); United Nations Children&#x2019;s Fund (UNICEF)-supported Multiple Indicator Cluster Surveys (MICS); WHO-supported World Health Surveys (WHS); and other reliable and nationally representative country surveys.</p>\n<p>The World Health Organization is the agency that has taken responsibility for compiling a database of statistics on access to clean and polluting fuels and technologies harvested from the full global body of household surveys for cooking, heating and lighting. Currently, the WHO Database covers cooking energy for 171 countries and one territory for the period 1960-2023 and is updated regularly and publicly available. For lighting, the WHO database includes data for 125 countries for the period 1963-2023. For heating, the WHO database includes data for 80 countries for the period 1977-2023.</p>\n<p>Presently WHO is working with national surveying agencies, country statistical offices and other stakeholders (e.g. researchers) to enhance multipurpose household survey instruments to gather data on the fuels and technologies used for heating and lighting.</p>\n<p>In 2021, as a result of a survey enhancement process, data collection for the cooking database included main cooking fuel, exhaust systems (chimney or fan), cooking technology and cooking location. Lighting data collection focused on main lighting fuel. Data collection for the heating database included main heating fuel as well as heating technology.</p>", "COLL_METHOD__GLOBAL"=>"<p>Surveys collected are nationally representative and contain data at household or population level.</p>\n<p>Typical cooking survey questions include: &#x201C;Major fuel used for cooking&#x201D;, &#x201C;What is the main source of cooking fuel in your household?&#x201D;, &#x201C;What type of fuel does your household mainly use for cooking?&#x201D;, &#x201C;Which is the main source of energy for cooking?&#x201D;, &#x201C;In your household, what type of cookstove is mainly used for cooking?&#x201D;.</p>\n<p>Typical heating survey questions include: &#x201C;Main fuel used for heating&#x201D;, &#x201C;What type of fuel and energy source is used in the heater?&#x201D;, &#x201C;What does your household mainly use for space heating when needed?&#x201D;</p>\n<p>Typical lighting survey questions include: &#x201C;Main fuel use for lighting&#x201D;, &#x201C;At night, what does your household mainly use to light the household?&#x201D;.</p>", "FREQ_COLL__GLOBAL"=>"<p>The next round of data collection is planned for the second half of 2025.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The annual release of new data for SDG7.1.2 is usually in April (global and regional estimates) and June (country estimates).</p>\n<p> </p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices or any national providers of household surveys and censuses.</p>", "COMPILING_ORG__GLOBAL"=>"<p>WHO, Environment, Climate Change and Health Department (ECH).</p>", "INST_MANDATE__GLOBAL"=>"<p>Along with the SDG 7 custodian agencies, including the International Energy Agency (IEA), the International Renewable Energy Agency (IRENA), the United Nations Statistics Division (UNSD), and the World Bank (WB), the World Health Organisation is designated by the UN Statistical Commission to collect, process, and disseminate data with regional, and global aggregates, in relation to the progress in achieving the SDG 7 goals. During the process of updating and disseminating the clean cooking estimates, the WHO is responsible for acting in consultation with SDG 7 custodian agencies, national statistics agencies, and the UN regional commissions.</p>", "RATIONALE__GLOBAL"=>"<p>Cooking, lighting and heating represent a large share of household energy use across the low- and middle-income countries. For cooking and heating, households typically rely on solid fuels (such as wood, charcoal, biomass) or kerosene paired with inefficient technologies (e.g. open fires, stoves, space heaters or lamps). It is well known that reliance on such inefficient energy for cooking, heating and lighting is associated with high levels of household (indoor) air pollution. The use of inefficient fuels for cooking alone is estimated to cause over 4 million deaths annually, mainly among women and children. This is more than Tuberculosis, Human Immuno-deficiency Virus and malaria combined. These adverse health impacts can be avoided by adopting clean fuels and technologies for all main household energy end-or in some circumstances by adopting advanced combustion cook stoves (i.e. those which achieve the emission rates targets provided by the WHO guidelines) and adopting strict protocols for their safe use. Given the importance of clean and safe household energy use as a human development issue, universal access to energy among the technical practitioner community is currently taken to mean access to both electricity and clean fuels and technologies for cooking, heating and lighting. For this reason, clean cooking forms part of the universal access objective under the UN Secretary General&#x2019;s Sustainable Energy for All initiative.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator uses the type of primary fuels and technologies used for cooking, heating, and lighting as a practical surrogate for estimating human exposure to household (indoor) air pollution and its related disease burden, as it is not currently possible to obtain nationally representative samples of indoor concentrations of criteria pollutants, such as fine particulate matter and carbon monoxide. However, epidemiological studies provide a science-based evidence for establishing those estimates using these surrogates.</p>\n<p>The indicator is based on the main type of fuel and technology used for cooking as cooking occupies the largest share of overall household energy needs. However, many households use more than one type of fuel and stove for cooking and, depending on climatic and geographical conditions, heating with polluting fuels can also be a contributor to household (indoor) air pollution levels. In addition, lighting with kerosene, a very polluting and hazardous fuel is also often used, and in some countries is the main fuel used for cooking.</p>\n<p>While the existing global household survey evidence base provides a good starting point for tracking household energy access for cooking fuel, it also presents limitations that will need to be addressed over time. Currently there is a limited amount of available data capturing the type of fuel and devices used in the home for heating and lighting. Accordingly, WHO in cooperation with World Bank, and the Global Alliance for Clean Cook stoves, led a survey enhancement process with representatives from country statistical offices and national household surveying agencies (e.g. Demographic and Health Survey, Multiple Indicator Cluster Survey, Living Standards Measurement Survey) to better gather efficiently and harmoniously information on the fuels and technologies for cooking, heating and lighting. The efforts concluded in the creation of 6 new questions that will replace and slightly expand the current set of questions commonly used on national multipurpose surveys to assess household energy.</p>\n<p>Substantial progress has already been made toward developing and piloting a new methodology known as the Multi-Tier Framework for Measuring Energy Access (World Bank) which is able to capture the affordability and reliability of energy access explicitly referenced in the language of SDG7 and harnesses the normative guidance in the WHO guidelines to benchmark tiers of energy access. The methodology for the Multi-Tier Framework for Measuring Energy Access has already been published based on a broad consultative exercise and represents a consensus view across numerous international agencies working in the field. The newest estimates are based on data extracted from these surveys. </p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is modelled with household survey data compiled by WHO. The information on cooking fuel use and cooking practices comes from more than 1600 nationally representative surveys and censuses. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), and other nationally developed and implemented surveys.</p>\n<p>Estimates of primary cooking energy for the total, urban and rural population for a given country and year are obtained together using a single multivariate hierarchical model. Using household survey data as inputs, the model jointly estimates primary reliance on 6 specific fuel types: </p>\n<ol>\n  <li>unprocessed biomass (e.g. wood), </li>\n  <li>charcoal, </li>\n  <li>coal, </li>\n  <li>kerosene, </li>\n  <li>gaseous fuels (e.g. LPG), and </li>\n  <li>electricity; and a final category including other clean fuels (e.g. alcohol). </li>\n</ol>\n<p>Estimates of the proportion of the population with primary reliance on clean fuels and technology (SDG indicator 7.1.2) are then derived by aggregating the estimates for primary reliance on clean fuel types from the model. Details on the model are published in Stoner et al. (2020).</p>\n<p>Only survey data with less than 15% of the population reporting &#x201C;missing&#x201D; and &#x201C;no cooking&#x201D; and &#x201C;other fuels&#x201D; were included in the analysis. Surveys were also discarded if the sum of all mutually exclusive categories reported was not within 98-102%. Fuel use values were uniformly scaled (divided) by the sum of all mutually exclusive categories excluding &#x201C;missing&#x201D;, &#x201C;no cooking&#x201D; and &#x201C;other fuels&#x201D;.</p>\n<p>Countries classified as high-income according to the World Bank country classification in the 2023 fiscal year were assumed to have fully transitioned to clean household energy and therefore are reported as 100% access to clean technologies.</p>\n<p>No estimates were reported for low- and middle-income countries without data (Bulgaria, Lebanon and Libya). Modelled specific fuel estimates were derived for 128 low- and middle-income countries and 3 countries with no World Bank income classification (Cook Islands, Niue and Venezuela). Estimates of overall clean fuel use were reported for 195 countries.</p>\n<p>Estimates of clean cooking access are updated on an annual basis for the whole time series (e.g. 1990-2023). This means that there may be changes in previous annual estimates due to the inclusion of new data points influencing the overall trend for a given country.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Countries are consulted annually on the national data collected for the 7.1.2 SDG indicator.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values for individual fuels within a survey are automatically imputed by the model (Stoner et al., 2020). For surveys where fuel use is only reported for the whole population (i.e. with no urban or rural disaggregation), the urban and rural values are automatically imputed by the model (Stoner et al., 2020).</p>\n<p>No estimates are reported for low- and middle-income countries with no data (Bulgaria, Lebanon, Libya). All central estimates are reported alongside measures of uncertainty. Where countries have very limited survey data (e.g. only one survey suitable for modelling within 1990-2023), the measures of uncertainty are naturally wider for 2023 and preceding years. For countries with very wide uncertainty intervals, point estimates should be treated with some caution. High income countries are assumed to have transitioned to clean fuels and technologies, and are reported as 100% of their population using clean fuels and technologies.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Low- and middle-income countries with no data were excluded from regional and global aggregations, and values of 100% clean fuel and technology use were used for high income countries for regional and global calculations.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global estimates are population-weighted; within a region, the country values are multiplied by the corresponding country populations to obtain weighted fuel values. These values are then summed and divided by the sum of the population of the countries included.</p>\n<p>Low- and middle-income countries with no data were excluded from regional and global aggregations, and values of 100% clean fuel and technology use were used for high income countries for regional and global calculations.</p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The input data for single multivariate hierarchical model used to estimate the access to clean cooking presents some challenges that are related to inconsistencies in both the quality and the quantity of information that is available from the surveys:</p>\n<ol>\n  <li>inconsistency in survey design and collection, together with missing values, which can lead to highly unstable time series for some individual fuels in some countries,</li>\n  <li>for surveys where the number of respondents is not available, only the proportions using each fuel are given and the original counts (the number of respondents using each fuel) are non-recoverable.</li>\n  <li>information on trends in the use of specific fuels is required for both urban and rural areas but, in many cases, surveys provide data for only the overall population (Stoner et al., 2020).</li>\n</ol>\n<p>Therefore, several adjustments are included in the model in order to tackle the observed challenges coming from the source data (for more on this see Stoner et al., 2020).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Before finalizing clean cooking estimates, the WHO team contacts the UN regional commissions asking for reviews and suggestions for the prepared figures. The data also goes through multiple rounds of internal consultations with SDG 7 custodian agencies.</p>\n<p> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Following the consultations with internal stakeholders and the UN regional commissions, data estimates of some countries may undergo additional revisions. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>For cooking fuels, coverage of 171 countries is available through the WHO Global Household Energy Database.</p>\n<p>For lighting fuels, the WHO database includes data for 125 countries.</p>\n<p>For heating fuels, the WHO database includes data for 80 countries.</p>\n<p><strong>Time series:</strong></p>\n<p>From 1960 to 2023</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregated estimates for different end-uses (i.e. cooking, heating and lighting; with expected improvements in household surveys, this will be possible for heating and lighting for all countries.</p>\n<p>Disaggregation of access to clean fuel and technologies for cooking by rural or urban place of residence is possible for all countries with survey data.</p>\n<p>Gender disaggregation by main user (i.e. cook) of cooking energy will be available with expected improvements in household surveys.</p>\n<p>Gender disaggregation of head of household for cooking, lighting and heating is available.</p>\n<p>Energy is a service provided at the household, rather than individual level.</p>\n<p>Nonetheless, it is used differentially by men and women and has different impacts on their health and well-being. What will be possible, in principle, is to report energy access disaggregated by the main user of cooking energy.</p>\n<p>In addition, WHO&apos;s Household energy database includes country data from thirty countries on the time spent by children collecting fuelwood and water disaggregated by sex. With the improvements in data collection via the below mentioned survey harmonization process, data will be available on reporting time spent exclusively on fuel collection rather than in combination with water collection.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There may be discrepancies between internationally reported and nationally reported figures. The reasons are the following:</p>\n<ul>\n  <li>Modelled estimates versus survey data point.</li>\n  <li>Use of different definitions of polluting (or previously solid) fuels (wood only or wood and any other biomass, e.g. dung residues; kerosene included or not as polluting fuels).</li>\n  <li>Use of different total population estimate.</li>\n  <li>Estimates are expressed as percentage of population using polluting (or solid) fuels (as per SDG indicator) as compared to percentage of household using polluting (or solid) fuels (as assessed by surveys such as DHS or MICS).</li>\n</ul>\n<p>Changes in modelling methodology:</p>\n<p>Prior to 2018, estimates of the proportion of the population primarily relying on solid fuels were obtained from a multilevel model with region and nonparametric functions of time as the only covariates (Bonjour et al., 2013). For tracking SDG7 in 2018 and 2019 this model was used to estimate polluting and clean fuel use, though this time it was implemented in the Bayesian framework for increased robustness and more reliable quantification of uncertainty. For 2020, the model has been expanded to allow estimates for individual fuels, and extra flexibility has been added to the functions of time to better capture nonlinear trends in some countries (Stoner et al. 2020). These refinements have been introduced alongside an ever-expanding collection of data, which underwent a major quality-control effort. Due to the increased data availability, borrowing of information across regions is no longer essential, hence time is now the only covariate. </p>\n<p>On both occasions where the model changed, the WHO conducted a thorough sensitivity analysis, including full country-by-country comparisons of estimates between the existing model and the candidate model. In most cases, estimates of the proportion using clean fuels exhibited little change, see annex below. Where larger discrepancies were identified, they were carefully investigated to determine the likely cause. Many of these were in fact the result of the new model better capturing nonlinear trends.</p>\n<p>The same model is used for the latest revision, with updated data inputs as described in previous sections.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://www.who.int/data/gho/data/themes/air-pollution/household-air-pollution\">https://www.who.int/data/gho/data/themes/air-pollution/household-air-pollution</a> </p>\n<p><strong>References:</strong></p>\n<p>Global Tracking Framework report (2013)</p>\n<p><a href=\"http://trackingenergy4all.worldbank.org/\">http://trackingenergy4all.worldbank.org/</a> </p>\n<p>Global Tracking Framework Report (2015)</p>\n<p><a href=\"http://trackingenergy4all.worldbank.org/\">http://trackingenergy4all.worldbank.org/</a> </p>\n<p>Global Tracking Framework database (2015)</p>\n<p><a href=\"http://data.worldbank.org/data-catalog/sustainable-energy-for-all\">http://data.worldbank.org/data-catalog/sustainable-energy-for-all</a> </p>\n<p>Multi-Tier Framework for Measuring Energy Access,</p>\n<p>https://www.esmap.org/mtf_multi-tier_framework_for_energy_access </p>\n<p>WHO Guidelines for indoor air quality: Household Fuel Combustion, WHO (2014) </p>\n<p><a href=\"https://www.who.int/publications/i/item/9789241548885\">https://www.who.int/publications/i/item/9789241548885</a></p>\n<p>WHO Clean Household Energy Solutions Toolkit (CHEST) (2022)</p>\n<p><a href=\"https://www.who.int/tools/clean-household-energy-solutions-toolkit\">https://www.who.int/tools/clean-household-energy-solutions-toolkit</a> </p>\n<p>WHO CHEST &#x2013; Module 7 Defining clean fuels and technologies</p>\n<p>https://www.who.int/tools/clean-household-energy-solutions-toolkit/module-7-defining-clean</p>\n<p>Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G., &amp; Adair-Rohani H. (2020) Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. <em>Journal of the Royal Statistical Society: Series C (Applied Statistics) </em>69(4), 815-839. DOI: <a href=\"https://doi.org/10.1111/rssc.12428\">10.1111/rssc.12428</a></p>\n<p>Bonjour S, Adair-Rohani H, Wolf J, Bruce NG, Mehta S, Pr&#xFC;ss-Ust&#xFC;n A, Lahiff M, Rehfuess EA, Mishra V, and Smith KR (2013). <a href=\"https://ehp.niehs.nih.gov/doi/abs/10.1289/ehp.1205987\">Solid Fuel Use for Household Cooking: Country and Regional Estimates for 1980&#x2013;2010</a>. <em>Environmental Health Perspectives</em>, <a href=\"https://doi.org/10.1289/ehp.1205987\">https://doi.org/10.1289/ehp.1205987</a></p>\n<p>Population using solid fuels metadata, WHO</p>\n<p><a href=\"https://www.who.int/data/gho/indicator-metadata-registry/imr-details/318\">https://www.who.int/data/gho/indicator-metadata-registry/imr-details/318</a> </p>\n<p>Annex</p>\n<p>A comparison plot is provided to illustrate the differences between existing model and the candidate model. Estimated values for each of the WHO regions are plotted, showing consistency between the existing model and the candidate model.</p>", "indicator_sort_order"=>"07-01-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"7.2.1", "slug"=>"7-2-1", "name"=>"Proporción de energía renovable en el consumo final total de energía", "url"=>"/site/es/7-2-1/", "sort"=>"070201", "goal_number"=>"7", "target_number"=>"7.2", "global"=>{"name"=>"Proporción de energía renovable en el consumo final total de energía"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de energía renovable en el consumo final total de energía", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de energía renovable en el consumo final total de energía", "indicator_number"=>"7.2.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://eve.eus/Conoce-la-Energia/La-energia-en-Euskadi/Publicaciones?lang=es-es", "url_text"=>"Datos energéticos de la C. A. de Euskadi", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de energía renovable en el consumo final total de energía", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.2- De aquí a 2030, aumentar considerablemente la proporción de energía renovable en el conjunto de fuentes energéticas", "definicion"=>"\nProporción de energías renovables en el consumo final bruto de energía\n", "formula"=>"\n$$PER^{t} = \\frac{ER^{t}}{CFE^{t}} \\cdot 100$$ \n\ndonde: \n\n$ER^{t} =$ consumo de energía procedente de fuentes renovables en el año $t$ \n\n$CFE^{t} =$ consumo final bruto de energía en el año $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl objetivo “De aquí a 2030, aumentar sustancialmente la proporción de \nenergía renovable en la matriz energética mundial” afecta a las tres dimensiones \ndel desarrollo sostenible. \n\nLas tecnologías de energía renovable representan un elemento importante en \nlas estrategias para hacer más ecológicas las economías en todo el mundo \ny para abordar el crítico problema mundial del cambio climático. \n\nExisten varias definiciones de energía renovable; lo que tienen en común es destacar \ncomo renovables todas las formas de energía cuyo consumo no agote \nsu disponibilidad en el futuro. Entre ellas se incluyen la solar, la eólica, \nla oceánica, la hidroeléctrica, las fuentes geotérmicas y la bioenergía \n(en el caso de la bioenergía, que puede agotarse, las fuentes de bioenergía \npueden reemplazarse en un marco de corto a mediano plazo). \n\nEs importante destacar que este indicador se centra en la cantidad de \nenergía renovable \nrealmente consumida en lugar de en la capacidad de producción de energía \nrenovable, que no siempre puede utilizarse en su totalidad. Al centrarse \nen el consumo por parte del usuario final, evita las distorsiones causadas \npor el hecho de que las fuentes de energía convencionales están sujetas a \npérdidas de energía significativas a lo largo de la cadena de producción.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.2.1&seriesCode=EG_FEC_RNEW&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Participación de las energías renovables en el consumo final total de energía (%) EG_FEC_RNEW</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-02-01.pdf\">Metadatos 7-2-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de energía renovable en el consumo final total de energía", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.2- De aquí a 2030, aumentar considerablemente la proporción de energía renovable en el conjunto de fuentes energéticas", "definicion"=>"\nRenewable energy share in gross final energy consumption", "formula"=>"\n$$PER^{t} = \\frac{ER^{t}}{CFE^{t}} \\cdot 100$$ \n\nwhere:\n\n$ER^{t} =$ consumption of energy from renewable sources in the year $t$ \n\n$CFE^{t} =$ renewable energy share in gross final energy consumption in year $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe target “By 2030, increase substantially the share of renewable \nenergy in the global energy mix” impacts all three dimensions of \nsustainable development. \n\nRenewable energy technologies represent a major element in strategies \nfor greening economies everywhere in the world and for tackling the critical \nglobal problem of climate change. \n\nA number of definitions of renewable energy exist; what they have in \ncommon is highlighting as renewable all forms of energy that their \nconsumption does not deplete their availability in the future. These \ninclude solar, wind, ocean, hydropower, geothermal sources, and bioenergy \n(in the case of bioenergy, which can be depleted, sources of bioenergy can \nbe replaced withina short to medium-term frame). \n\nImportantly, this indicator focuses on the amount of renewable energy actually \nconsumed rather than the capacity for renewable energy production, which cannot \nalways be fully utilized. By focusing on consumption by the end user, it avoids \nthe distortions caused by the fact that conventional energy sources are subject \nto significant energy losses along the production chain. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.2.1&seriesCode=EG_FEC_RNEW&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Renewable energy share in the total final energy consumption (%) EG_FEC_RNEW</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-02-01.pdf\">Metadata 7-2-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de energía renovable en el consumo final total de energía", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.2- De aquí a 2030, aumentar considerablemente la proporción de energía renovable en el conjunto de fuentes energéticas", "definicion"=>"\nEnergia berriztagarrien proportzioa energiaren azken kontsumo gordinean", "formula"=>"\n$$PER^{t} = \\frac{ER^{t}}{CFE^{t}} \\cdot 100$$ \n\nnon: \n\n$ER^{t} =$ iturri berriztagarrietatik sortutako energiaren kontsumoa $t$ urtean\n\n$CFE^{t} =$ energiaren azken kontsumo gordina $t$ urtean \n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\n\"Hemendik 2030era, munduko energia-matrizean energia berriztagarriaren proportzioa nabarmen handitzea\" \nhelburuak garapen jasangarriaren hiru dimentsioei eragiten die. \n\nEnergia berriztagarrien teknologiak elementu garrantzitsuak dira mundu osoko ekonomiak ekologikoagoak \negiteko eta klima-aldaketaren munduko arazo kritikoari aurre egiteko estrategietan. \n\nEnergia berriztagarriari buruzko hainbat definizio daude; komunean dutena zera da, berriztagarri gisa \nnabarmentzen dituztela kontsumituz gero etorkizunean agortuko ez diren energia-mota guztiak. Horien \nartean daude eguzki-energia, eolikoa, ozeanikoa, hidroelektrikoa, iturri geotermikoak eta bioenergia \n(bioenergiaren kasuan –zeina agortu egin baitaiteke–, bioenergia-iturriak epe laburretik ertainerako \ntartean ordezkatu daitezke). \n\nGarrantzitsua da nabarmentzea adierazle hau benetan kontsumitutako energia berriztagarriaren kopuruan \nzentratzen dela, eta ez energia berriztagarria ekoizteko gaitasunean, zeina ezin baita beti osorik \nerabili. Azken erabiltzailearen kontsumoan zentratzen denez, saihestu egiten dira energia-iturri \nkonbentzionalak eragiten dituen distortsioak ekoizpen-katean energia-galera nabarmenen pean egoteagatik. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=6.3.2&seriesCode=EG_FEC_RNEW&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Energia berriztagarrien parte-hartzea energiaren azken kontsumo osoan (%) EG_FEC_RNEW</a> UNSTATS\n", "comparabilidad"=>"EAEn erabilgarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-02-01.pdf\">Metadatuak 7-2-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 7.2: By 2030, increase substantially the share of renewable energy in the global energy mix</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 7.2.1: Renewable energy share in the total final energy consumption</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EG_FEC_RNEW - Renewable energy share in the total final energy consumption [7.2.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 7.3.1: Energy intensity measured in terms of primary energy and GDP</p>\n<p>Indicator 9.4.1: CO<sub>2</sub> emission per unit of value added</p>\n<p>Indicator 13.2.2: Total greenhouse gas emissions per year</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Energy Agency (IEA) </p>\n<p>United Nations Statistics Division (UNSD) </p>\n<p>International Renewable Energy Agency (IRENA)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Energy Agency (IEA) </p>\n<p>United Nations Statistics Division (UNSD) </p>\n<p>International Renewable Energy Agency (IRENA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The renewable energy share in total final consumption is the percentage of final consumption of energy that is derived from renewable resources.</p>\n<p><strong>Concepts:</strong></p>\n<p>Renewable energy consumption includes consumption of energy derived from: hydro, wind, solar, solid biofuels, liquid biofuels, biogas, geothermal, marine and renewable waste. Total final energy consumption is calculated from balances as total final consumption minus non-energy use. </p>\n<p>Comments regarding specific renewable energy sources: </p>\n<ul>\n  <li>Solar energy includes solar PV and solar thermal. </li>\n  <li>Liquid biofuels include biogasoline, biodiesels and other liquid biofuels. </li>\n  <li>Solid biofuels include fuelwood, animal waste, vegetable waste, black liquor, bagasse and charcoal. </li>\n  <li>Renewable waste energy covers energy from renewable municipal waste.</li>\n</ul>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The &#x201C;International Recommendations for Energy Statistics&#x201D; (IRES), adopted by the UN Statistical Commission, is the globally recognized standard used to develop the energy statistics underlying the calculation of the indicator. </p>\n<p>This standard is available at: <a href=\"https://unstats.un.org/unsd/energystats/methodology/ires\">unstats.un.org/unsd/energystats/methodology/ires</a>.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data on renewable energy consumption are available through national energy balances compiled based on data collected by the International Energy Agency (for around 150 countries) and the United Nations Statistics Division (UNSD) for all countries. The energy balances make it possible to trace all the different sources and uses of energy at the national level. </p>\n<p>Some technical assistance may be needed to improve these statistics, particularly in the case of renewable energy sources. Specialized industry surveys (e.g. on bioenergy use) or household surveys (in combination with the measurement of other indicators) would be feasible approaches to filling in data gaps (e.g. for use of firewood, off-grid solar energy).</p>", "COLL_METHOD__GLOBAL"=>"<p>The IEA collects energy data at the national level according to harmonised international definitions and questionnaires, as described in the UN International Recommendations for Energy Statistics (<a href=\"https://unstats.un.org/unsd/energystats/methodology/ires/\">unstats.un.org/unsd/energystats/methodology/ires/</a>).</p>\n<p>UNSD also collects energy statistics from countries according to the same harmonised methodology.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are collected on an annual basis.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The IEA World Energy Balances are published in February, April and July with progressively broader geographical coverage (publishing full information for two calendar years prior and selected information for one year prior). The UN Energy Statistics Database is made available towards the end of the calendar year with full geographical coverage (publishing information for two calendar years prior).</p>", "DATA_SOURCE__GLOBAL"=>"<p>National administrations, as described in documentation on sources for IEA and UNSD:</p>\n<p><a href=\"http://wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf\">http://wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf</a></p>\n<p><a href=\"https://unstats.un.org/unsd/energystats/data\">unstats.un.org/unsd/energystats/data</a></p>", "COMPILING_ORG__GLOBAL"=>"<p>The International Energy Agency (IEA) and the United Nations Statistics Division (UNSD) </p>\n<p>The IEA and UNSD are the primary compilers of energy statistics across countries and develop internationally comparable energy balances based on internationally agreed methodologies. Aggregates are based on analysis merging of IEA and UNSD data.</p>", "INST_MANDATE__GLOBAL"=>"<p>IEA as one of the custodian agencies responsible for monitoring progress towards the SDG 7.2 target, leverage on their national data efforts and add value by promoting coherent standards, definitions and methodologies for both raw data and the derived indicators with the ultimate goal of producing internationally comparable datasets. </p>\n<p>The UNSD mission in the area of energy statistics is to strengthen national statistical systems in order to assist countries to produce high quality energy statistics and balances. The mission is realized through four workstreams: Data collection (since 1950); Development of methodological guidelines and standards in energy statistics (e.g., IRES, ESCM); Capacity building (to disseminate such methodology and to assist countries to strengthen their energy statistical systems); and International cooperation and coordination. UNSD was selected as one of the custodians of indicator 7.2.1 because it collects for all countries the underlying data necessary to calculate the indicator. </p>", "RATIONALE__GLOBAL"=>"<p>The target &#x201C;By 2030, increase substantially the share of renewable energy in the global energy mix&#x201D; impacts all three dimensions of sustainable development. Renewable energy technologies represent a major element in strategies for greening economies everywhere in the world and for tackling the critical global problem of climate change. A number of definitions of renewable energy exist; what they have in common is highlighting as renewable all forms of energy that their consumption does not deplete their availability in the future. These include solar, wind, ocean, hydropower, geothermal sources, and bioenergy (in the case of bioenergy, which can be depleted, sources of bioenergy can be replaced within a short to medium-term frame). Importantly, this indicator focuses on the amount of renewable energy actually consumed rather than the capacity for renewable energy production, which cannot always be fully utilized. By focusing on consumption by the end user, it avoids the distortions caused by the fact that conventional energy sources are subject to significant energy losses along the production chain.</p>", "REC_USE_LIM__GLOBAL"=>"<ul>\n  <li>A limitation with existing renewable energy statistics is that they are not able to distinguish whether renewable energy is being sustainably produced. For example, a substantial share of today&#x2019;s renewable energy consumption comes from the use of wood and charcoal by households in the developing world, which sometimes may be associated with unsustainable forestry practices. There are efforts underway to improve the ability to measure the sustainability of bio-energy, although this remains a significant challenge. </li>\n  <li>Off-grid renewables data are limited and not sufficiently captured in national and international energy statistics. </li>\n  <li>The method of allocation of renewable energy consumption from electricity and heat output assumes that the share of transmission and distribution losses are the same among all technologies. However, this is not always true; for example when renewables are usually located in more remote areas and may incur larger losses. </li>\n  <li>Likewise, imports and exports of electricity and heat are assumed to follow the renewable share of electricity and heat generation, respectively. This is a simplification that in many cases will not affect the indicator too much, but that might do so in some cases, for example, when a country only generates electricity from fossil fuels but imports a great share of the electricity it uses from a neighboring country&#x2019;s hydroelectric power plant. </li>\n  <li>Methodological challenges associated with defining and measuring renewable energy are more fully described in the Global Tracking Framework (IEA and World Bank, 2013) Chapter 4, Section 1, pages 194-200. Data for traditional use of solid biofuels are generally scarce globally, and developing capacity in tracking such energy use, including developing national-level surveys, is essential for sound global energy tracking.</li>\n</ul>", "DATA_COMP__GLOBAL"=>"<p>This indicator is based on the development of comprehensive energy statistics across supply and demand for all energy sources &#x2013; statistics used to produce the energy balance. Internationally agreed methodologies for energy statistics are described in the &#x201C;International Recommendations for Energy Statistics&#x201D; (IRES), adopted by the UN Statistical Commission, available at: <a href=\"https://unstats.un.org/unsd/energystats/methodology/ires\">unstats.un.org/unsd/energystats/methodology/ires</a>.</p>\n<p>Once an energy balance is developed, the indicator can be calculated by dividing final energy consumption from all renewable sources by total final energy consumption. Renewable energy consumption is derived as the sum of direct final consumption of renewable sources plus the components of electricity and heat consumption estimated to be derived from renewable sources based on generation shares. The indicator is calculated based on the following formula:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>T</mi>\n        <mi>F</mi>\n        <mi>E</mi>\n        <mi>C</mi>\n      </mrow>\n      <mrow>\n        <mi>R</mi>\n        <mi>E</mi>\n        <mi>S</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>T</mi>\n            <mi>F</mi>\n            <mi>E</mi>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mi>R</mi>\n            <mi>E</mi>\n            <mi>S</mi>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <mfenced separators=\"|\">\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>T</mi>\n                <mi>F</mi>\n                <mi>E</mi>\n                <mi>C</mi>\n              </mrow>\n              <mrow>\n                <mi>E</mi>\n                <mi>L</mi>\n                <mi>E</mi>\n              </mrow>\n            </msub>\n            <mo>&#xD7;</mo>\n            <mfrac>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>E</mi>\n                    <mi>L</mi>\n                    <mi>E</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>R</mi>\n                    <mi>E</mi>\n                    <mi>S</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>E</mi>\n                    <mi>L</mi>\n                    <mi>E</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>T</mi>\n                    <mi>O</mi>\n                    <mi>T</mi>\n                    <mi>A</mi>\n                    <mi>L</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </mfenced>\n        <mo>+</mo>\n        <mfenced separators=\"|\">\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>T</mi>\n                <mi>F</mi>\n                <mi>E</mi>\n                <mi>C</mi>\n              </mrow>\n              <mrow>\n                <mi>H</mi>\n                <mi>E</mi>\n                <mi>A</mi>\n                <mi>T</mi>\n              </mrow>\n            </msub>\n            <mo>&#xD7;</mo>\n            <mfrac>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>H</mi>\n                    <mi>E</mi>\n                    <mi>A</mi>\n                    <mi>T</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>R</mi>\n                    <mi>E</mi>\n                    <mi>S</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>H</mi>\n                    <mi>E</mi>\n                    <mi>A</mi>\n                    <mi>T</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>T</mi>\n                    <mi>O</mi>\n                    <mi>T</mi>\n                    <mi>A</mi>\n                    <mi>L</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>T</mi>\n            <mi>F</mi>\n            <mi>E</mi>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mi>T</mi>\n            <mi>O</mi>\n            <mi>T</mi>\n            <mi>A</mi>\n            <mi>L</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>T</mi>\n    <mi>F</mi>\n    <mi>E</mi>\n    <mi>C</mi>\n  </math>: Total final energy consumption is the sum of final energy consumption in the transport, industry and other sectors (also equivalent to the total final consumption minus the non-energy use).</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>E</mi>\n    <mi>L</mi>\n    <mi>E</mi>\n  </math>: Gross electricity production </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>H</mi>\n    <mi>E</mi>\n    <mi>A</mi>\n    <mi>T</mi>\n  </math>: Gross heat production</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>R</mi>\n    <mi>E</mi>\n    <mi>S</mi>\n  </math>: Renewable energy sources which include hydropower, wind, solar photovoltaic, solar thermal, geothermal, tide/wave/ocean, renewable municipal waste, solid biofuels, liquid biofuels, and biogases.</p>\n<p>The denominator is the total final energy consumption of all energy products, while the numerator includes the direct consumption of renewable energy sources plus the final consumption of gross electricity and heat that is estimated to have come from renewable sources. This estimation allocates the amount of electricity and heat consumption to renewable sources based on the share of renewables in gross production in order to perform the calculation at the final energy level. For instance, if total final consumption is 150 TJ for biogas energy, while total final consumption of electricity is 400 TJ and heat 100 TJ, and the share of biogas is 10 percent in electricity output and 5 percent in heat output, the total reported number for biogas consumption will be 195 TJ (150 TJ+400TJ*10%+100TJ*5%).</p>\n<p>The Global Tracking Framework Report (IEA and World Bank, 2013) provides more details on the suggested methodology for defining and measuring renewable energy (Chapter 4, Section 1, page 201-202). </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The IEA has several internal procedures in place for energy data validation. This includes energy balance checks, time series analysis and reconciling differences in statistical classifications and definitions. UNSD also has a number of internal validation procedures to ensure internal data consistency, for instance through energy balance checks, and trend consistency, e.g. by way of time series analysis. </p>", "ADJUSTMENT__GLOBAL"=>"<p>The country specific commodity balances underlying the IEA energy data are based on national energy data of heterogeneous nature converted and adapted to fit the IEA format and methodology. Considerable effort has been made to ensure that the data adhere to the IEA definitions based on the guidelines provided by IRES. Nevertheless, energy statistics at the national level are often collected using criteria and definitions which differ, sometimes considerably, from those of international organisations. This is especially true for non-OECD countries, which are submitting data to the IEA on a voluntary basis. The IEA has identified most of these differences and, where possible, adjusted the data to meet international definitions. For details on recognized country specific anomalies and the corresponding adjustments, please refer to country specific notes included in the IEA World energy balances documentation file available at: <a href=\"http://wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf\">wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf</a> </p>\n<p>Likewise, UNSD also needs to adjust certain data to fit the international methodology set by IRES, thus ensuring data comparability across countries. Data from all countries are submitted voluntarily to UNSD, sometimes via non-standard formats or through sharing of national publications. The identification of such deviations from the standard is an ongoing task, and UNSD has started publishing some of this information in a supplement to the Energy Statistics Database named &#x201C;Notes on sources&#x201D;, available at: <a href=\"https://unstats.un.org/unsd/energystats/pubs/yearbook/\">unstats.un.org/unsd/energystats/pubs/yearbook/</a>, with the goal of increasing transparency and providing more and more information with time.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>The IEA has attempted to provide all the elements of energy balances down to the level of final consumption, for over 150 countries. Providing all the elements of supply, as well as all inputs and outputs of the main transformation activities and final consumption has often required estimations. Estimations have been generally made after consultation with national statistical offices, energy companies, utilities and national energy experts.</p>\n<p>Likewise, UNSD attempts to provide full energy balances for the 225 countries and areas it covers, including the 75 or so it covers for SDG reporting. This may require searching for national official publications, data from other international organizations and expert estimation based on reputable sources and other publicly available information. Generally speaking, data on the supply side is more widely available than transformation activities and final consumption.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>In addition to estimates at a country level, adjustments addressing differences in definitions alongside estimations for informal and/or confidential trade, production or consumption of energy products are sometimes required to complete major aggregates, when key statistics are missing. Such estimations and adjustments implemented by IEA have been generally made after consultation with national statistical offices, energy companies, utilities and national energy experts.</p>", "REG_AGG__GLOBAL"=>"<p>Aggregates are calculated, whether by region or global, using final energy consumption as weights.</p>", "DOC_METHOD__GLOBAL"=>"<p>The IEA data corresponding to OECD countries are derived based on information provided in</p>\n<p>the five fuel-specific annual IEA/Eurostat joint questionnaires completed by the national administrations. These questionnaires are available online at: <a href=\"https://www.iea.org/about/data-and-statistics/questionnaires\">iea.org/about/data-and-statistics/questionnaires</a></p>\n<p>The IEA commodity balances for all other countries are based on national energy data of heterogeneous nature, converted and adapted to fit the IEA format and methodology based on IRES recommendations.</p>\n<p>In addition to IRES, UNSD has published the <em>Energy Statistics Compilers Manual</em> (ESCM - <a href=\"https://unstats.un.org/unsd/energystats/methodology/escm/\">unstats.un.org/unsd/energystats/methodology/escm/</a>) as a practical companion to assist countries in the compilation of data according to the international methodology. UNSD sends countries its own questionnaire (<a href=\"https://unstats.un.org/unsd/energystats/questionnaire\">unstats.un.org/unsd/energystats/questionnaire/),</a> except to the countries which are mandated to submit the IEA/Eurostat joint questionnaires. In the latter case, UNSD obtains data from the IEA.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The IEA, in co-operation with the Statistical Office of the European Communities (Eurostat), has published an Energy Statistics Manual. This Manual helps the energy statisticians have a better grasp of definitions, units and methodologies. Moreover, IEA has established a quality management framework based on the internationally recognized guidelines recommended by IRES to ensure quality of statistical products.</p>\n<p> </p>\n<p>ESCM contains a full chapter on the Generic Statistical Business Process Model applied to energy statistics, helping countries manage energy data quality. Inside UNSD, processes are established to ensure the quality of its products, and such processes are reviewed periodically.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The IEA follows the guidelines recommended by the IRES to ensure relevance, accuracy and reliability, timeliness and punctuality, accessibility and clarity as well as coherence and comparability of the data.</p>\n<p>UNSD coordinated input from international organizations and countries to publish IRES and its practical companion, the ESCM. Each of both contains a chapter on quality assurance and metadata to help guide all countries ensure good energy data quality. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The IEA has an extensive data quality validation process through exchange with national data providers worldwide. It also convenes its Energy Statistics Development Group meeting to discuss energy statistics developments with its Members, and cooperates with partners worldwide to ensure coherence of data and methods.</p>\n<p>UNSD assesses many quality aspects of the data by means of internal checks, exchanges with national data providers, and comparison with alternative sources. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Between the various existing data sources, primarily the IEA World Energy Balances and the UN Energy Statistics Database, annual total and renewable energy consumption for every country and area can be collected. The Tracking SDG7: The Energy Progress Report (formerly <em>Sustainable Energy for All Global Tracking Framework</em>) is reporting this indicator at a global level between 1990 and 2030. </p>\n<p><strong>Time series:</strong></p>\n<p>2000 &#x2013; present</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregation of the data on consumption of renewable energy, e.g. by resource and end-use sector, could provide insights into other dimensions of the goal, such as affordability and reliability. For solar energy, it may also be of interest to disaggregate between on-grid and off-grid capacity.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The IEA World energy balances and the UN Energy Statistics Database, which provide the underlying data for calculating this indicator, are global databases obtained following harmonised definitions and comparable methodologies across countries. However, they do not represent an official source for national submissions of the indicator 7.2.1 on renewable energy. Due to possible deviations from IRES in national methodologies, national indicators may differ from the internationally comparable ones.</p>\n<p>Difference may arise due to different sources of official energy data, dissimilarities in the underlying methodologies, adjustments and estimations.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<p><a href=\"https://iea.org\">iea.org</a>; <a href=\"https://unstats.un.org/unsd/energystats/\">unstats.un.org/unsd/energystats</a></p>\n<p><strong>References: </strong></p>\n<p>IEA Energy Balances and Statistics </p>\n<p><a href=\"https://iea.org/data-and-statistics\">iea.org/data-and-statistics</a></p>\n<p>UN Energy Statistics Database <a id=\"OLE_LINK2\"></a><a id=\"OLE_LINK1\"></a></p>\n<p> <a href=\"https://unstats.un.org/unsd/energystats/data/\">unstats.un.org/unsd/energystats/data</a> (description) and <a href=\"http://data.un.org/Explorer.aspx?d=EDATA\">data.un.org/Explorer.aspx?d=EDATA</a> (data). Downloadable though API (<a href=\"https://data.un.org/ws\">https://data.un.org/ws</a>). Browse contents on <a href=\"https://data.un.org/SdmxBrowser/start\">https://data.un.org/SdmxBrowser/start</a>.</p>\n<p>IEA SDG 7 webpage: <a href=\"https://iea.org/reports/sdg7-data-and-projections\">iea.org/reports/sdg7-data-and-projections</a></p>\n<p>United Nations. 2018. &#x201C;International Recommendations for Energy Statistics&#x201D;. . <a href=\"https://unstats.un.org/unsd/energystats/methodology/ires\">unstats.un.org/unsd/energystats/methodology/ires</a></p>\n<p>International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistics Division (UNSD), the World Bank, World Health Organization (WHO). 2019. &#x201C;Tracking SDG7: The Energy Progress Report 2019&#x201D;. <a href=\"https://trackingsdg7.esmap.org/\">trackingsdg7.esmap.org/</a> </p>\n<p>International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistics Division (UNSD), the World Bank, World Health Organization (WHO). 2018. &#x201C;Tracking SDG7: The Energy Progress Report 2018&#x201D;. <a href=\"https://trackingsdg7.esmap.org/\">trackingsdg7.esmap.org/</a></p>\n<p>International Energy Agency (IEA) and the World Bank. 2017. &#x201C;Global Tracking Framework 2017&#x2014;Progress toward Sustainable Energy&#x201D;. World Bank, Washington, DC. License: Creative Commons Attribution CC BY 3.0 IGO. <a href=\"https://www.seforall.org/sites/default/files/eegp17-01_gtf_full_report_final_for_web_posting_0402.pdf\">seforall.org/sites/default/files/eegp17-01_gtf_full_report_final_for_web_posting_0402.pdf</a></p>\n<p>International Energy Agency (IEA) and the World Bank. 2015. &#x201C;Global Tracking Framework 2015&#x2014;Progress Toward Sustainable Energy&#x201D;, World Bank, Washington, DC. Doi: 10.1596/978-1-4648 -0690-2 License: Creative Commons Attribution CC BY 3.0 IGO. <a href=\"http://seforall.org/sites/default/files/GTF-2105-Full-Report.pdf\">seforall.org/sites/default/files/GTF-2105-Full-Report.pdf</a></p>\n<p>International Energy Agency (IEA) and the World Bank. 2013. &#x201C;Global Tracking Framework 2013&#x201D;. <a href=\"https://trackingsdg7.esmap.org/data/files/download-documents/gtf-2013-full-report.pdf\">trackingsdg7.esmap.org/data/files/download-documents/gtf-2013-full-report.pdf</a></p>\n<p>IRENA Renewable Energy Database </p>\n<p><a href=\"https://www.irena.org/statistics\">https://www.irena.org/statistics</a>.</p>\n<p>United Nations. 2022. &#x201C;Energy Statistics Compilers Manual&#x201D; </p>\n<p><a href=\"https://unstats.un.org/unsd/energystats/methodology/escm/\">unstats.un.org/unsd/energystats/methodology/escm/</a></p>", "indicator_sort_order"=>"07-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"7.3.1", "slug"=>"7-3-1", "name"=>"Intensidad energética medida en función de la energía primaria y el PIB", "url"=>"/site/es/7-3-1/", "sort"=>"070301", "goal_number"=>"7", "target_number"=>"7.3", "global"=>{"name"=>"Intensidad energética medida en función de la energía primaria y el PIB"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Intensidad energética medida en función de la energía primaria y el PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Intensidad energética medida en función de la energía primaria y el PIB", "indicator_number"=>"7.3.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://eve.eus/Conoce-la-Energia/La-energia-en-Euskadi/Publicaciones?lang=es-es", "url_text"=>"Datos energéticos de la C. A. de Euskadi", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Intensidad energética medida en función de la energía primaria y el PIB", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.3- De aquí a 2030, duplicar la tasa mundial de mejora de la eficiencia energética", "definicion"=>"\nNivel de intensidad energética de la energía primaria (megajulios por PIB en paridad de poder adquisitivo constante de 2017) \n", "formula"=>"\n$$IE^{t} = \\frac{CE^{t}}{PIB_{2017PPA}^{t}}$$\n\ndonde:\n\n$CE^{t} =$ consumo interior bruto de energía en el año $t$\n\n$PIB_{2017PPA}^{t} =$ producto interior bruto en paridad de poder adquisitivo en precios constantes de USD de 2017 en el año $t$ \n", "desagregacion"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nLa intensidad energética es un indicador de cuánta energía \nse utiliza para producir una unidad de producción económica. Es \nun indicador inverso de la eficiencia con la que una economía es \ncapaz de utilizar la energía para producir una producción económica.\n\nUn índice más bajo indica que se utiliza menos energía para producir \nuna unidad de producción, por lo que las tendencias decrecientes indican progreso.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.3.1&seriesCode=EG_EGY_PRIM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Nivel de intensidad energética de la energía primaria (megajulios por PIB en paridad de poder adquisitivo constante de 2017) EG_EGY_PRIM</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-03-01.pdf\">Metadatos 7-3-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Intensidad energética medida en función de la energía primaria y el PIB", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.3- De aquí a 2030, duplicar la tasa mundial de mejora de la eficiencia energética", "definicion"=>"\nEnergy intensity level of primary energy (megajoules per GDP in constant 2017 purchasing power parity) \n", "formula"=>"\n$$IE^{t} = \\frac{CE^{t}}{PIB_{2017PPA}^{t}}$$\n\nwhere:\n\n$CE^{t} =$ gross domestic energy consumption in the year $t$\n\n$PIB_{2017PPA}^{t} =$ gross domestic product at purchasing power parity in constant 2017 USD prices in the year $t$ \n", "desagregacion"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nEnergy intensity is an indication of how much energy is used to produce \none unit of economic output. It is an inverse proxy of the efficiency with \nwhich an economy is able to use energy to produce economic output. \n\nA lower ratio indicates that less energy is used to produce one unit of output, \nso decreasing trends indicate progress. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.3.1&seriesCode=EG_EGY_PRIM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Energy intensity level of primary energy (megajoules per constant 2021 purchasing power parity GDP) EG_EGY_PRIM</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-03-01.pdf\">Metadata 7-3-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Intensidad energética medida en función de la energía primaria y el PIB", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.3- De aquí a 2030, duplicar la tasa mundial de mejora de la eficiencia energética", "definicion"=>"\nEnergia primarioaren intentsitate energetikoaren maila \n(BPGaren araberako megajulioak 2017ko erosteko ahalmen konstantearen \nparekotasunean)\n", "formula"=>"\n$$IE^{t} = \\frac{CE^{t}}{PIB_{2017PPA}^{t}}$$\n\nnon:\n\n$CE^{t} =$ energiaren barne-kontsumo gordina $t$ urtean\n\n$PIB_{2017PPA}^{t} =$ barne-produktu gordina erosteko ahalmenaren parekotasunean, \n2017ko USDren prezio konstanteetan $t$ urtean \n", "desagregacion"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nIntentsitate energetikoa ekoizpen ekonomikoko unitate bat ekoizteko zenbat energia erabiltzen \nden adierazten duen adierazlea da. Ekonomia batek energia ekoizpen ekonomiko bat ekoizteko \nerabiltzen duen eraginkortasunaren alderantzizko adierazlea da. \n\nIndize baxuagoak adierazten du energia gutxiago erabiltzen dela ekoizpen-unitate bat ekoizteko; \nberaz, beheranzko joerek aurrerapena adierazten dute. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.3.1&seriesCode=EG_EGY_PRIM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Energia primarioaren intentsitate energetikoaren maila (BPGaren araberako megajulioak 2017ko erosteko ahalmen konstantearen parekotasunean) EG_EGY_PRIM</a> UNSTATS", "comparabilidad"=>"EAEn erabilgarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-03-01.pdf\">Metadatuak 7-3-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 7.3: By 2030, double the global rate of improvement in energy efficiency</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 7.3.1: Energy intensity measured in terms of primary energy and GDP</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EG_EGY_PRIM - Energy intensity level of primary energy [7.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 7.2.1: Renewable energy share in the total final energy consumption</p>\n<p>Indicator 9.4.1: CO<sub>2</sub> emission per unit of value added</p>\n<p>Indicator 13.2.2: Total greenhouse gas emissions per year</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Energy Agency (IEA) </p>\n<p>United Nations Statistics Division (UNSD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Energy Agency (IEA) </p>\n<p>United Nations Statistics Division (UNSD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition: </strong></p>\n<p>Energy intensity is defined as the energy supplied to the economy per unit value of economic output.</p>\n<p><strong>Concepts: </strong></p>\n<p>Total energy supply, as defined by the International Recommendations for Energy Statistics (IRES), is made up of production plus net imports minus international marine and aviation bunkers plus-stock changes. Gross Domestic Product (GDP) is the measure of economic output. For international comparison purposes, GDP is measured in constant terms at purchasing power parity.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Energy intensity is expressed in megajoules per unit of purchasing power parity GDP in constant 2021 USD figures. </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The &#x201C;International Recommendations for Energy Statistics&#x201D; (IRES), adopted by the UN Statistical Commission, is the globally recognized standard used to develop the energy statistics underlying the calculation of the indicator. </p>\n<p>This standard is available at: <a href=\"https://unstats.un.org/unsd/energystats/methodology/ires\">unstats.un.org/unsd/energystats/methodology/ires</a>.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Total energy supply is typically calculated in the making of energy balances. Energy balances are compiled based on data collected for around 150 economies from the International Energy Agency (IEA) and for all countries in the world from the United Nations Statistics Division (UNSD). GDP data are primarily sourced from the International Monetary Fund (IMF) &#x2013; World Economic Outlook database, complemented with data from the World Bank &#x2013; World Development Indicators and the CEPII &#x2013; CHELEM database.</p>", "COLL_METHOD__GLOBAL"=>"<p>The IEA collects energy data at the national level according to harmonised international definitions and questionnaires, as described in the UN International Recommendations for Energy Statistics available at: <a href=\"https://unstats.un.org/unsd/energystats/methodology/ires\">unstats.un.org/unsd/energystats/methodology/ires</a>. UNSD also collects energy statistics from countries according to the same harmonised methodology.</p>\n<p>The most recent GDP estimates published in the International Monetary Fund World Economic Outlook, after re-referencing data to reference year 2021, have been used when calculating this indicator. Additionally, missing years for countries with at least one data point for GDP reported by IMF have been estimated using the World Bank World Development indicators database and the CEPII - Comptes Harmonis&#xE9;s sur les Echanges et L&#x2019;Economie Mondiale (CHELEM) database. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are collected on an annual basis.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The IEA World Energy Balances are published in February, April and July with progressively broader geographical coverage (publishing full information for two calendar years prior and selected information for one year prior). The UN Energy Balances are made available towards the end of the calendar year with full geographical coverage (publishing information for two calendar years prior).</p>", "DATA_SOURCE__GLOBAL"=>"<p>National administrations, as described in documentation on sources for IEA and UNSD:</p>\n<p><a href=\"http://wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf\">wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf</a></p>\n<p><a href=\"https://unstats.un.org/unsd/energystats/data/\">unstats.un.org/unsd/energystats/data/</a></p>", "COMPILING_ORG__GLOBAL"=>"<p>The International Energy Agency (IEA) and the United Nations Statistics Division (UNSD). </p>\n<p>The IEA and UNSD are the primary compilers of energy statistics from across countries and develop internationally comparable energy balances based on internationally agreed methodologies. Aggregates are based on a merging between IEA and UNSD data.</p>", "INST_MANDATE__GLOBAL"=>"<p>IEA as one of the custodian agencies responsible for monitoring progress towards the SDG 7.3 target, leverage on their national data efforts and add value by promoting coherent standards, definitions and methodologies for both raw data and the derived indicators with the ultimate goal of producing internationally comparable datasets. </p>\n<p>The UNSD mission in the area of energy statistics is to strengthen national statistical systems in order to assist them in producing high quality energy statistics and balances. The mission is realized through four workstreams: Data collection (since 1950); Development of methodological guidelines and standards in energy statistics (e.g., IRES, ESCM); Capacity building (to disseminate such methodology and to assist countries to strengthen their energy statistical systems); and International cooperation and coordination. UNSD was selected as one of the custodians of indicator 7.3.1 because it collects for all countries the underlying data necessary to calculate the denominator of this indicator.</p>", "RATIONALE__GLOBAL"=>"<p>Energy intensity is an indication of how much energy is used to produce one unit of economic output. It is an inverse proxy of the efficiency with which an economy is able to use energy to produce economic output. A lower ratio indicates that less energy is used to produce one unit of output, so decreasing trends indicate progress.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Energy intensity is only an imperfect proxy for energy efficiency. It can be affected by a number of factors, such as climate, structure of the economy, nature of economic activities etc. that are not necessarily linked to pure efficiency. For better assessment of energy efficiency progress, more disaggregated data are needed, such as those at the sectoral and end-use level.</p>", "DATA_COMP__GLOBAL"=>"<p>This indicator is based on the development of comprehensive energy statistics across supply and demand for all energy sources &#x2013; statistics used to produce the energy balance. Internationally agreed methodologies for energy statistics are described in the &#x201C;International Recommendations for Energy Statistics&#x201D; (IRES), adopted by the UN Statistical Commission, available at: <a href=\"https://unstats.un.org/unsd/energystats/methodology/ires/\">unstats.un.org/unsd/energystats/methodology/ires/</a>.</p>\n<p>Once the energy balance is developed, the indicator can be obtained by dividing total energy supply over GDP.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The IEA has several internal procedures in place for energy data validation. This includes energy balance checks, time series analysis and reconciling differences in statistical classifications and definitions.</p>\n<p>UNSD also has a number of internal validation procedures to ensure internal data consistency, for instance through energy balance checks, and trend consistency, e.g. by way of time series analysis. </p>", "ADJUSTMENT__GLOBAL"=>"<p>The country specific commodity balances underlying the IEA energy data are based on national energy data of heterogeneous nature converted and adapted to fit the IEA format and methodology. Considerable effort has been made to ensure that the data adhere to the IEA definitions based on the guidelines provided by IRES. Nevertheless, energy statistics at the national level are often collected using criteria and definitions which differ, sometimes considerably, from those of international organisations. This is especially true for non-OECD countries, which are submitting data to the IEA on a voluntary basis. The IEA has identified most of these differences and, where possible, adjusted the data to meet international definitions. For details on recognized country specific anomalies and the corresponding adjustments, please refer to country specific notes included in the IEA World energy balances documentation file available at: <a href=\"http://wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf\">wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf</a></p>\n<p>Likewise, UNSD also needs to adjust certain data to fit the international methodology set by IRES, thus ensuring data comparability across countries. Data from all countries are submitted voluntarily to UNSD, sometimes via non-standard formats or through sharing of national publications. The identification of such deviations from the standard is an ongoing task, and UNSD has started publishing some of this information in a supplement to the Energy Statistics Database named &#x201C;Notes on sources&#x201D;, available at: <a href=\"https://unstats.un.org/unsd/energystats/pubs/yearbook/\">unstats.un.org/unsd/energystats/pubs/yearbook/</a>, with the goal of increasing transparency and providing more and more information with time.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>The IEA has attempted to provide all the elements of energy balances, for over 150 countries. Providing all the elements of energy supply, has often required estimations. Estimations have been generally made after consultation with national statistical offices, energy companies, utilities and national energy experts.</p>\n<p>Likewise, UNSD attempts to provide full energy balances for the 227 countries and areas it covers, including the 84 it covers for SDG reporting. This may require searching for national official publications, data from other international organizations and expert estimations based on reputable sources and other publicly available information. Generally speaking, data on the supply side is more widely available than transformation activities and final consumption.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>In addition to estimates at a country level, adjustments addressing differences in definitions alongside estimations for informal and/or confidential trade, production or stock changes of energy products are sometimes required to complete major aggregates, when key statistics are missing. Such estimations and adjustments implemented by IEA have been generally made after consultation with national statistical offices, energy companies, utilities and national energy experts.</p>", "REG_AGG__GLOBAL"=>"<p>Aggregates are calculated, whether by region or globally, by summing both total energy supply and gross domestic products over the group of relevant countries.</p>", "DOC_METHOD__GLOBAL"=>"<p>The IEA data corresponding to OECD countries are derived based on information provided in the five fuel specific annual OECD questionnaires completed by the national administrations. These questionnaires are available online at: <a href=\"https://www.iea.org/about/data-and-statistics/questionnaires\">https://www.iea.org/about/data-and-statistics/questionnaires</a> </p>\n<p>The IEA commodity balances for all other countries are based on national energy data of heterogeneous nature, converted and adapted to fit the IEA format and methodology based on IRES recommendations.</p>\n<p>In addition to IRES, UNSD has published the <em>Energy Statistics Compilers Manual</em> (ESCM - <a href=\"https://unstats.un.org/unsd/energystats/methodology/escm/\">unstats.un.org/unsd/energystats/methodology/escm/</a>) as a practical companion to assist countries in the compilation of data according to the international methodology. UNSD sends countries its own questionnaire (<a href=\"https://unstats.un.org/unsd/energystats/questionnaire/\">unstats.un.org/unsd/energystats/questionnaire/</a>), except to the countries which are mandated to submit the IEA/Eurostat joint questionnaires. In the latter case, UNSD obtains data from the IEA.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The IEA, in co-operation with the Statistical Office of the European Communities (Eurostat), has published an Energy Statistics Manual. This Manual helps the energy statisticians have a better grasp of definitions, units and methodologies. Moreover, IEA has established a quality management framework based on the internationally recognized guidelines recommended by IRES to ensure quality of statistical products. </p>\n<p>ESCM contains a full chapter on the Generic Statistical Business Process Model applied to energy statistics, helping countries manage energy data quality. Inside UNSD, processes are established to ensure the quality of its products, and such processes are reviewed periodically.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The IEA follows the guidelines recommended by the IRES to ensure relevance, accuracy and reliability, timeliness and punctuality, accessibility and clarity as well as coherence and comparability of the data.</p>\n<p>UNSD coordinated input from international organizations and countries to publish IRES and its practical companion, the ESCM. Each of both contains a chapter on quality assurance and metadata to help guide all countries ensure good energy data quality. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The IEA has an extensive data quality validation process through exchange with national data providers worldwide. It also convenes its Energy Statistics Development Group meeting to discuss energy statistics developments with its Members, and cooperates with partners worldwide to ensure coherence of data and methods.</p>\n<p>UNSD assesses many quality aspects of the data by means of internal checks, exchanges with national data providers, and comparison with alternative sources. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>IEA and UN Energy Balances combined provide total energy supply data for all countries on an annual basis. GDP data are available for most countries on an annual basis. </p>\n<p><strong>Time series: </strong></p>\n<p>2000 &#x2013; present </p>\n<p><strong>Disaggregation: </strong></p>\n<p>Disaggregation of energy intensity, e.g. by final consumption sectors or end-uses, could provide further insights into progress towards energy efficiency. At present it is only feasible to calculate such sector disaggregation for the following sectors &#x2013; industry, residential, transport, agriculture, households &#x2013; as reported in the Tracking SDG7: The Energy Progress Report (formerly Sustainable Energy for All Global Tracking Framework). It would be desirable, over time, to develop more refined sectoral level energy intensity indicators that make it possible to look at energy intensity by industry (e.g. cement, steel) or by type of vehicle (e.g. cars, trucks), for example. Doing so will not be possible without further statistical data collection, also including collaboration with relevant institutions and energy consumers. Full methodological explanations are provided in the IEA <em>Energy Efficiency Indicators: Fundamentals on Statistics </em>manual available at: <a href=\"https://www.iea.org/reports/energy-efficiency-indicators-fundamentals-on-statistics\">iea.org/reports/energy-efficiency-indicators-fundamentals-on-statistics</a></p>\n<p>Decomposition analysis of energy intensity trends seeks to filter out factors that affect energy demand, such as economy wide scale and structural shifts, from more narrowly defined energy intensity shifts. This analysis is also reported in the <em>Tracking SDG7: The Energy Progress Report</em> or in the <em>IEA Energy Efficiency Indicators Highlights</em> available at: <a href=\"https://www.iea.org/data-and-statistics/data-product/energy-efficiency-indicators-highlights\">https://www.iea.org/data-and-statistics/data-product/energy-efficiency-indicators-highlights</a> </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The IEA World energy balances and the UN Energy Statistics Database, which provide the underlying data for calculating this indicator, are global databases obtained following harmonised definitions and comparable methodologies across countries. However, they do not represent an official source for national submissions of the indicator 7.3.1 on energy efficiency. Due to possible deviations from IRES in national methodologies, national indicators may differ from the internationally comparable ones.</p>\n<p>Difference may arise due to different sources of official energy data, dissimilarities in the underlying methodologies, adjustments and estimations.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<p><a href=\"https://www.iea.org/\">www.iea.org/</a>; <a href=\"https://unstats.un.org/unsd/energystats\">unstats.un.org/unsd/energystats</a></p>\n<p><strong>References: </strong></p>\n<p>IEA Energy Balances and Statistics </p>\n<p><a href=\"https://www.iea.org/data-and-statistics/\">https://www.iea.org/data-and-statistics/</a> </p>\n<p>UN Energy Statistics Database </p>\n<p><a href=\"https://unstats.un.org/unsd/energystats/data/\"> unstats.un.org/unsd/energystats/data</a> (description) and <a href=\"http://data.un.org/Explorer.aspx?d=EDATA\">data.un.org/Explorer.aspx?d=EDATA</a> </p>\n<p>Downloadable though API (<a href=\"https://data.un.org/ws\">https://data.un.org/ws</a>). Browse contents on <a href=\"https://data.un.org/SdmxBrowser/start\">https://data.un.org/SdmxBrowser/start</a>.</p>\n<p>IEA SDG7 webpage: <a href=\"https://www.iea.org/reports/sdg7-data-and-projections\">iea.org/reports/sdg7-data-and-projections</a></p>\n<p>IEA Energy Efficiency Indicators Highlights</p>\n<p><a href=\"https://www.iea.org/reports/energy-efficiency-indicators-fundamentals-on-statistics\">iea.org/reports/energy-efficiency-indicators</a></p>\n<p>IEA <em>Energy Efficiency Indicators Overview</em></p>\n<p><a href=\"https://www.iea.org/reports/energy-efficiency-indicators-overview\">https://www.iea.org/reports/energy-efficiency-indicators-overview</a></p>\n<p>United Nations (2018). &#x201C;International Recommendations for Energy Statistics (IRES)&#x201D;.<a href=\"https://unstats.un.org/unsd/energystats/methodology/ires\">unstats.un.org/unsd/energystats/methodology/ires</a></p>\n<p>International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistics Division (UNSD), the World Bank, World Health Organization (WHO) (2019). &#x201C;Tracking SDG7: The Energy Progress Report 2019&#x201D;. <a href=\"https://trackingsdg7.esmap.org/\">trackingsdg7.esmap.org/</a> </p>\n<p>International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistics Division (UNSD), the World Bank, World Health Organization (WHO) (2018). &#x201C;Tracking SDG7: The Energy Progress Report 2018&#x201D;. <a href=\"https://trackingsdg7.esmap.org/\">trackingsdg7.esmap.org/</a></p>\n<p>International Energy Agency (IEA) and the World Bank (2017). &#x201C;Global Tracking Framework 2017&#x2014;Progress toward Sustainable Energy&#x201D;. World Bank, Washington, DC. License: Creative Commons Attribution CC BY 3.0 IGO. <a href=\"https://www.seforall.org/sites/default/files/eegp17-01_gtf_full_report_final_for_web_posting_0402.pdf\">seforall.org/sites/default/files/eegp17-01_gtf_full_report_final_for_web_posting_0402.pdf</a></p>\n<p>International Energy Agency (IEA) and the World Bank (2015). &#x201C;Global Tracking Framework 2015&#x2014;Progress Toward Sustainable Energy&#x201D;, World Bank, Washington, DC. Doi: 10.1596/978-1-4648 -0690-2 License: Creative Commons Attribution CC BY 3.0 IGO. <a href=\"http://seforall.org/sites/default/files/GTF-2105-Full-Report.pdf\">seforall.org/sites/default/files/GTF-2105-Full-Report.pdf</a></p>\n<p>International Energy Agency (IEA) and the World Bank (2013). &#x201C;Global Tracking Framework 2013&#x201D;.</p>\n<p><a href=\"https://trackingsdg7.esmap.org/data/files/download-documents/gtf-2013-full-report.pdf\">trackingsdg7.esmap.org/data/files/download-documents/gtf-2013-full-report.pdf</a></p>\n<p>United Nations (2022). &#x201C;Energy Statistics Compilers Manual (ESCM)&#x201D; </p>\n<p><a href=\"https://unstats.un.org/unsd/energystats/methodology/escm/\">https://unstats.un.org/unsd/energystats/methodology/escm/</a> </p>", "indicator_sort_order"=>"07-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"7.a.1", "slug"=>"7-a-1", "name"=>"Corrientes financieras internacionales hacia los países en desarrollo para apoyar la investigación y el desarrollo de energías limpias y la producción de energía renovable, incluidos los sistemas híbridos", "url"=>"/site/es/7-a-1/", "sort"=>"07aa01", "goal_number"=>"7", "target_number"=>"7.a", "global"=>{"name"=>"Corrientes financieras internacionales hacia los países en desarrollo para apoyar la investigación y el desarrollo de energías limpias y la producción de energía renovable, incluidos los sistemas híbridos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Corrientes financieras internacionales hacia los países en desarrollo para apoyar la investigación y el desarrollo de energías limpias y la producción de energía renovable, incluidos los sistemas híbridos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Corrientes financieras internacionales hacia los países en desarrollo para apoyar la investigación y el desarrollo de energías limpias y la producción de energía renovable, incluidos los sistemas híbridos", "indicator_number"=>"7.a.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La Asistencia Oficial para el Desarrollo (AOD) y otros flujos oficiales (FO) totales para \nlos países en desarrollo cuantifican el esfuerzo financiero público (excluidos los créditos \na la exportación) que los donantes proporcionan a los países en desarrollo para energías \nrenovables. Los flujos adicionales (de la base de datos de IRENA) capturan los flujos hacia países no \nreceptores de AOD en regiones en desarrollo, los flujos de países e instituciones que \nactualmente no informan al CAD y otros tipos de flujos, como los créditos a la exportación.\n\nEl acceso a la energía es una importante limitación para el desarrollo en muchos \npaíses en desarrollo y, si bien parte de una base relativamente baja, se prevé \nque la demanda de energía crezca muy rápidamente en muchos de estos países en el \nfuturo. Esto representa una oportunidad para que los países en desarrollo utilicen \ntecnologías limpias y renovables para satisfacer sus futuras necesidades energéticas \nsi pueden acceder a las tecnologías y la experiencia adecuadas.\n\nEste indicador proporciona una medida adecuada del apoyo internacional brindado a los \npaíses en desarrollo para acceder a estas tecnologías.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.a.1&seriesCode=EG_IFF_RANDN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">Flujos financieros internacionales hacia países en desarrollo en apoyo de la investigación y el desarrollo de energías limpias y la producción de energías renovables, incluso en sistemas híbridos (millones de dólares estadounidenses constantes de 2022) EG_IFF_RANDN</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-0a-01.pdf\">Metadatos 7-a-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Total Official Development Assistance (ODA) and Other Official flows (OOF) \nto developing countries quantify the public financial effort (excluding export \ncredits) that donors provide to developing countries for renewable energies. \nThe additional flows (from the IRENA database) capture the flows to non-ODA \nRecipients in developing regions, flows from countries and institutions not \ncurrently reporting to the DAC and certain other types of flows, such as export \ncredits. \n\nEnergy access is a major development constraint in many developing countries and, \nwhile starting from a relatively low base, energy demand is expected to grow very \nrapidly in many of these countries in the future. This presents an opportunity for \ndeveloping countries to utilize clean and renewable technologies to meet their future \nenergy needs if they can gain access to the appropriate technologies and expertise. \n\nThis indicator provides a suitable measure of the international support given to \ndeveloping countries to access these technologies. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.a.1&seriesCode=EG_IFF_RANDN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">International financial flows to developing countries in support of clean energy research and development and renewable energy production, including in hybrid systems (millions of constant 2022 USD) EG_IFF_RANDN</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-0a-01.pdf\">Metadata 7-a-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Garapen-bidean dauden herrialdeetara egiten diren Garapenerako Laguntza Ofizialaren (GLO) fluxuek eta \nbeste fluxu ofizial batzuek (FO) emaileek garapen-bidean dauden herrialdeei energia berriztagarrietarako \nematen dieten ahalegin publikoa zenbatesten dute (esportazioetarako kredituak kanpo). Fluxu gehigarriek \n(IRENAren datu-basekoak) garapen bidean dauden eskualdeetan GLO jasotzen ez duten herrialdeetarako fluxuak, \ngaur egun Garapenerako Laguntza Batzordeari informaziorik ematen ez dioten herrialde eta erakundeen fluxuak \neta beste fluxu mota batzuk harrapatzen dituzte, hala nola esportaziorako kredituak. \n\nGarapen-bidean dauden herrialde askoren garapenerako muga garrantzitsua da energia eskuratzea, eta, oinarri \nnahiko baxua badu ere, etorkizunean herrialde horietako askotan energia-eskaria oso azkar haztea aurreikusten \nda. Hori aukera bat da garapen-bidean dauden herrialdeek teknologia garbiak eta berriztagarriak erabil ditzaten \netorkizuneko energia beharrak asetzeko, baldin eta teknologia eta esperientzia egokiak eskura baditzakete. \n\nAdierazle honek garapen-bidean dauden herrialdeei teknologia horiek eskuratzeko ematen zaien nazioarteko \nlaguntzaren neurri egokia ematen du. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.a.1&seriesCode=EG_IFF_RANDN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">Nazioarteko finantza-fluxuak garapen bidean dauden herrialdeetara, energia garbien ikerketa eta garapenari eta energia berriztagarrien ekoizpenari laguntzeko, baita sistema hibridoetan ere (2022ko Estatu Batuetako dolar konstante milioiak) EG_IFF_RANDN</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-0a-01.pdf\">Metadatuak 7-a-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 7.a: By 2030, enhance international cooperation to facilitate access to clean energy research and technology, including renewable energy, energy efficiency and advanced and cleaner fossil-fuel technology, and promote investment in energy infrastructure and clean energy technology</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 7.a.1: International financial flows to developing countries in support of clean energy research and development and renewable energy production, including in hybrid systems</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Not applicable</p>", "META_LAST_UPDATE__GLOBAL"=>"2022-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD) and International Renewable Energy Agency (IRENA)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD) and International Renewable Energy Agency (IRENA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The flows are covered through two complementary sources.</p>\n<p>OECD: The flows covered by the OECD are defined as all official loans, grants and equity investments received by countries on the DAC List of ODA Recipients from foreign governments and multilateral agencies, for the purpose of clean energy research and development and renewable energy production, including in hybrid systems extracted from the OECD/DAC Creditor Reporting System (CRS) with the following sector codes:</p>\n<p>&#x2022; 23210 Energy generation, renewable sources &#x2013; multiple technologies - Renewable energy generation programmes that cannot be attributed to one single technology (codes 23220 through 23280 below). Fuelwood/charcoal production should be included under forestry 31261. </p>\n<p>&#x2022; 23220 Hydro-electric power plants - Including energy generating river barges. </p>\n<p>&#x2022; 23230 Solar energy for centralised grids </p>\n<p>&#x2022; 23231 Solar energy for isolated grids and standalone systems</p>\n<p>&#x2022; 23232 Solar energy &#x2013; thermal applications</p>\n<p>&#x2022; 23240 Wind energy - Wind energy for water lifting and electric power generation. </p>\n<p>&#x2022; 23250 Marine energy - Including ocean thermal energy conversion, tidal and wave power. </p>\n<p>&#x2022; 23260 Geothermal energy - Use of geothermal energy for generating electric power or directly as heat for agriculture, etc. </p>\n<p>&#x2022; 23270- Biofuel-fired power plants Use of solids and liquids produced from biomass for direct power generation. Also includes biogases from anaerobic fermentation (e.g. landfill gas, sewage sludge gas, fermentation of energy crops and manure) and thermal processes (also known as syngas); waste fired power plants making use of biodegradable municipal waste (household waste and waste from companies and public services that resembles household waste, collected at installations specifically designed for their disposal with recovery of combustible liquids, gases or heat). See code 23360 for non-renewable waste-fired power plants.</p>\n<p>&#x2022; 23410 Hybrid energy electric power plants</p>\n<p>&#x2022; 23631 Electric power transmission and distribution (isolated mini-grids) </p>\n<p>Research and development of energy efficiency technologies and measures is captured under CRS sector code 23182 on Energy research. The above flows also include technical assistance provided to support production, research and development as defined above.</p>\n<p>IRENA: The flows covered by IRENA are defined as all additional loans, grants and equity investments received by developing countries (defined as countries in developing regions, as listed in the UN M49 composition of regions) from all foreign governments, multilateral agencies and additional development finance institutions (including export credits, where available) for the purpose of clean energy research and development and renewable energy production, including in hybrid systems. These additional flows cover the same technologies and other activities (research and development, technical assistance, etc.) as listed above and exclude all flows extracted from the OECD/DAC CRS.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Million United States Dollars (USD) at constant prices for a base year. The base year for the constant prices and exchange rates is updated every year and it usually has a two-year lag behind the publication cycle. (e.g., the 2020 cycle would report 2018 constant prices)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The definition and classification of renewable technologies complies with the UN Standard International Energy Product Classification (SIEC). Definitions of other concepts are given above.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The OECD/DAC has been collecting data on official and private resource flows from 1960 at an aggregate level and 1973 at an activity level through the Creditor Reporting System (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements). Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc).</p>\n<p>IRENA&#x2019;s data on financial flows from public sources in support of renewable energy are available in IRENA&#x2019;s Public Renewable Energy Investment Database. IRENA collects these data from a wide range of publicly available sources, including the databases and annual reports of all of the main development finance institutions and 20 other bilateral and multilateral agencies investing in renewable energy. The database is updated annually and (at end-2021) covers public renewable energy investment flowing to 41 developed countries and 109 developing countries, for the period 2000-2020. As new publicly-funded financial institutions start investing in renewable energy, the IRENA database will expand to include these new investors over time.</p>", "COLL_METHOD__GLOBAL"=>"<p>See above</p>", "FREQ_COLL__GLOBAL"=>"<p>Data for a year is collected during the following year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>OECD DAC data is updated four times a year, with complete and detailed data published at year-end (covering the previous year). IRENA investment data is available at year-end (covering the previous year).</p>", "DATA_SOURCE__GLOBAL"=>"<p>See above</p>", "COMPILING_ORG__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD) and International Renewable Energy Agency (IRENA).</p>", "INST_MANDATE__GLOBAL"=>"<p>The OECD DAC Mandate states that the overarching objective of the DAC for the period 2018-2022 is to promote development co-operation and other relevant policies to contribute to implementation of the 2030 Agenda for Sustainable Development, including sustained, inclusive and sustainable economic growth, poverty eradication, improvement of living standards in developing countries, and to a future in which no country will depend on aid.</p>\n<p>In order to achieve this overarching objective, the Committee shall:</p>\n<ol>\n  <li>monitor, assess, report, and promote the provision of resources that support sustainable development by collecting and analysing data and information on ODA and other official and private flows, in a transparent way.</li>\n</ol>\n<p>With a mandate from countries around the world, IRENA encourages governments to adopt enabling policies for renewable energy investments, provides practical tools and policy advice to accelerate renewable energy deployment, and facilitates knowledge sharing and technology transfer to provide clean, sustainable energy for the world&#x2019;s growing population. Collecting official statistics (including international public finance flows) is in line with these aims. </p>", "RATIONALE__GLOBAL"=>"<p>Total Official Development Assistance (ODA) and Other Official flows (OOF) to developing countries quantify the public financial effort (excluding export credits) that donors provide to developing countries for renewable energies. The additional flows (from the IRENA database) capture the flows to non-ODA Recipients in developing regions, flows from countries and institutions not currently reporting to the DAC and certain other types of flows, such as export credits. </p>\n<p>Energy access is a major development constraint in many developing countries and, while starting from a relatively low base, energy demand is expected to grow very rapidly in many of these countries in the future. This presents an opportunity for developing countries to utilize clean and renewable technologies to meet their future energy needs if they can gain access to the appropriate technologies and expertise. This indicator provides a suitable measure of the international support given to developing countries to access these technologies.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data in the Creditor Reporting System are available from 1973. However, the data coverage is considered complete since 1995 for commitments at an activity level and 2002 for disbursements. At present, flows to clean energy research and development are only partially covered by the database and a few other areas (e.g. off-grid electricity supply, investments in improved cookstove projects) may be covered only partially. </p>\n<p>The IRENA database currently only covers financial institutions that have invested a total of USD 400 million or more in renewable energy. The process of continuous improvement of the database includes verifying the data against data produced by the multilateral development banks for climate finance reporting and by comparing the data with other independent reporting by international development finance agencies.</p>", "DATA_COMP__GLOBAL"=>"<p>The OECD flows are calculated by taking the total official flows (ODA and OOF) from DAC member countries, multilateral organisations and other providers of development assistance to the sectors listed above. The IRENA (additional) flows are calculated by taking the total public investment flows from IRENA&#x2019;s Public Renewable Energy Investment Database and excluding: domestic financial flows; international flows to countries outside developing regions; international flows to multilateral recipients not elsewhere specified; and flows reported by OECD (as described above). The flows are commitments measured in current United States Dollars (USD).</p>\n<p>Flows are tracked by individual commitment or activity level. When there are duplicate commitments between the OECD and IRENA databases, these are excluded from the IRENA database.</p>\n<p>The flows are converted to constant USD at a base year that normally has a two-year lag behind the publication year. The computation uses the DAC deflator methodology explained by the OECD on their <a href=\"https://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/informationnoteonthedacdeflators.htm\">web site</a>. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>For OECD, see: <a href=\"http://www.oecd.org/dac/stats/methodology.htm\">http://www.oecd.org/dac/stats/methodology.htm</a></p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not applicable - there is no imputation of missing values.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable - there is no imputation of missing values to obtain regional or global totals. </p>", "REG_AGG__GLOBAL"=>"<p>Regional and global totals are calculated by summing all available data from countries.</p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>IRENA validates this indicator for regional, technological, donor, and time aggregations. Any values that are not properly categorised are reviewed at the project level, and manually categorised under the appropriate technology, country, year or instrument type. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>OECD/DAC data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm). IRENA data are compiled from national sources following the United Nations Fundamental Principles of Official Statistics: <a href=\"https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx\">https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx</a>.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>This indicator is considered in good order when all the international financial flows of the database are correctly allocated to a country, year, technology, instrument type, and any other category respective to the requirements for the Global SDG database, and as required by the UNSD. Furthermore, the flows are in good order when properly deflated to account for inflation and exchange rate changes.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The CRS contains flows to all DAC recipient countries. Global and regional figures are based on the sum of ODA and OOF flows to the renewable energy projects.</p>\n<p>IRENA currently includes data about renewable energy projects in 41 developed countries and 109 developing countries (150 countries overall).</p>\n<p><strong>Time series:</strong></p>\n<p>OECD: annual data from 1960 onwards (see above). </p>\n<p>IRENA: annual data from 2000 onwards.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data in the CRS contain markers which reflect whether a policy objective is attained through the activity. Measuring gender equality is included in the CRS. Data from the CRS are reported at the project level and can be disaggregated by type of flow (ODA or OOF), by donor, recipient country, type of finance, type of aid (project, agriculture sub-sector, etc.).</p>\n<p>Data in IRENA are stored by country (source and recipient) at the project-level, allowing disaggregation of the data in several dimensions. For example, financial flows can be divided by technologies (i.e. bioenergy, geothermal energy, hydropower, marine energy, solar energy, and wind energy) and sub-technologies (e.g. onshore and offshore wind), by geography (both at the country and regional level), by financial instrument and by type of recipient.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Neither OECD nor IRENA make estimates of these figures. The data all come from national sources reported to OECD or, in the case of IRENA, from officially published statistics.</p>", "OTHER_DOC__GLOBAL"=>"<p>CRS: See all links here: <a href=\"http://www.oecd.org/dac/stats/methodology.htm\">http://www.oecd.org/dac/stats/methodology.htm</a> </p>\n<p>IRENA Renewable Energy Finance Flows: <a href=\"http://resourceirena.irena.org/gateway/dashboard/?topic=6&amp;subTopic=8\">http://resourceirena.irena.org/gateway/dashboard/?topic=6&amp;subTopic=8</a> </p>", "indicator_sort_order"=>"07-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"7.b.1", "slug"=>"7-b-1", "name"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)", "url"=>"/site/es/7-b-1/", "sort"=>"07bb01", "goal_number"=>"7", "target_number"=>"7.b", "global"=>{"name"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)", "indicator_number"=>"7.b.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://eve.eus/Conoce-la-Energia/La-energia-en-Euskadi/Publicaciones?lang=es-es", "url_text"=>"Datos energéticos de la C. A. de Euskadi", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.b- De aquí a 2030, ampliar la infraestructura y mejorar la tecnología para prestar servicios energéticos modernos y sostenibles para todos en los países en desarrollo, en particular los países menos adelantados, los pequeños Estados insulares en desarrollo y los países en desarrollo sin litoral, en consonancia con sus respectivos programas de apoyo", "definicion"=>"\nCapacidad instalada de las instalaciones que generan electricidad \na partir de fuentes de energía renovables dividida por la población\n", "formula"=>"\n$$CINSTPC_{renovable}^{t} = \\frac{CINST_{renovable}^{t}}{P^{t}} \\cdot 100$$ \n\ndonde:\n\n$CINST_{renovable}^{t} =$ capacidad instalada de generación de energía eléctrica procedente de fuentes renovables en el año $t$ \n\n$P^{t} =$ población total en el año  $t$\n", "desagregacion"=>"\nTipo de fuente de energía renovable: hidraúlica, fotovoltaica, eólica, solar térmica\n", "periodicidad"=>"Anual", "observaciones"=>"", "justificacion_global"=>"\nLa infraestructura y las tecnologías necesarias para suministrar \nservicios energéticos modernos y sostenibles abarcan una\namplia gama de equipos y dispositivos que se utilizan en numerosos \nsectores económicos. No existe un mecanismo fácilmente disponible \npara recopilar, agregar y medir la contribución de este grupo dispar de \nproductos a la prestación de servicios energéticos modernos y sostenibles.\n\nSin embargo, una parte importante de la cadena de suministro de energía \nque puede medirse fácilmente es la infraestructura utilizada para \nproducir electricidad. \n\nLas energías renovables se consideran una forma sostenible de suministro \nde energía, ya que su uso actual no suele agotar su disponibilidad para \nsu uso en el futuro. El enfoque de este indicador en la electricidad \nrefleja el énfasis del objetivo en las fuentes modernas de energía y \nes particularmente relevante para los países en desarrollo donde la \ndemanda de electricidad suele ser alta y su disponibilidad es limitada. \n\nAdemás, el enfoque en las energías renovables refleja el hecho de que las \ntecnologías utilizadas para producir electricidad renovable son \ngeneralmente modernas y más sostenibles que las no renovables, \nen particular en los subsectores de mayor crecimiento de la generación \nde electricidad a partir de energía eólica y solar.\n\nLa división de la capacidad de electricidad renovable por población \n(para producir una medida de vatios per cápita) propone escalar los datos \nde capacidad para tener en cuenta la gran variación de necesidades entre \npaíses. Utiliza la población en lugar del PIB para escalar los datos, \nporque este es el indicador más básico de la demanda de servicios \nenergéticos modernos y sostenibles en un país.\n\nEste indicador también debería complementar los indicadores \n7.1.1 y 7.2.1. Con respecto al acceso a la electricidad, proporcionará \ninformación adicional sobre la proporción de personas con acceso a la \nelectricidad al mostrar cuánta infraestructura está disponible \npara brindar ese acceso (en términos de la cantidad de capacidad por persona). \nEl enfoque en la capacidad renovable también agregará valor al indicador \nde energías renovables existente (7.2.1) al mostrar cuánto contribuye \nla energía renovable a la necesidad de un mejor acceso a la electricidad.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.b.1&seriesCode=EG_EGY_RNEW&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">Capacidad instalada de generación de electricidad renovable (vatios per cápita) EG_EGY_RNEW</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-0b-01.pdf\">Metadatos 7-b-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-28", "en"=>{"indicador_disponible"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.b- De aquí a 2030, ampliar la infraestructura y mejorar la tecnología para prestar servicios energéticos modernos y sostenibles para todos en los países en desarrollo, en particular los países menos adelantados, los pequeños Estados insulares en desarrollo y los países en desarrollo sin litoral, en consonancia con sus respectivos programas de apoyo", "definicion"=>"\nInstalled renewable electricity-generating capacity divided by population\n", "formula"=>"\n$$CINSTPC_{renewable}^{t} = \\frac{CINST_{renewable}^{t}}{P^{t}} \\cdot 100$$ \n\nwhere:\n\n$CINST_{renewable}^{t} =$ installed renewable electricity-generating capacity in year $t$ \n\n$P^{t} =$ total population in year $t$\n", "desagregacion"=>"\nType of renewable energy source: hydraulic, photovoltaic, wind, solar thermal\n", "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nThe infrastructure and technologies required to supply modern \nand sustainable energy services cover a wide range of equipment \nand devices that are used across numerous economic sectors. There \nis no readily available mechanism to collect, aggregate and measure \nthe contribution of this disparate group of products to the delivery \nof modern and sustainable energy services. \n\nHowever, one major part of the energy supply chain that can be readily \nmeasured is the infrastructure used to produce electricity. \n\nRenewables are considered a sustainable form of energy supply, as their \ncurrent use does not usually deplete their availability to be used in \nthe future. The focus of this indicator on electricity reflects the \nemphasis of the target on modern sources of energy and is particularly \nrelevant for developing countries where the demand for electricity is often \nhigh and its availability is constrained. \n\nFurthermore, the focus on renewables reflects the fact that the technologies \nused to produce renewable electricity are generally modern and more \nsustainable than non-renewables, particularly in the fastest growing sub-sectors \nof electricity generation from wind and solar energy. \n\nThe division of renewable electricity capacity by population (to produce a \nmeasure of Watts per capita) is proposing to scale the capacity data to account \nfor the large variation in needs between countries. It uses population rather \nthan GDP to scale the data, because this is the most basic indicator of the \ndemand for modern and sustainable energy services in a country. \n\nThis indicator should also complement indicators 7.1.1 and 7.2.1. With respect \nto electricity access, it will provide additional information to the proportion \nof people with electricity access by showing how much infrastructure is available \nto deliver that access (in terms of the amount of capacity per person). The focus \non renewable capacity will also add value to the existing renewables indicator \n(7.2.1) by showing how much renewable energy is contributing to the need for \nimproved electricity access. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.b.1&seriesCode=EG_EGY_RNEW&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">Installed renewable electricity-generating capacity (watts per capita) EG_EGY_RNEW</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-0b-01.pdf\">Metadata 7-b-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)", "objetivo_global"=>"7- Garantizar el acceso a una energía asequible, fiable, sostenible y moderna para todos", "meta_global"=>"7.b- De aquí a 2030, ampliar la infraestructura y mejorar la tecnología para prestar servicios energéticos modernos y sostenibles para todos en los países en desarrollo, en particular los países menos adelantados, los pequeños Estados insulares en desarrollo y los países en desarrollo sin litoral, en consonancia con sus respectivos programas de apoyo", "definicion"=>"\nEnergia-iturri berriztagarrietatik elektrizitatea sortzen \nduten instalazioen ahalmen instalatua, biztanleriaren arabera zatitua  \n", "formula"=>"\n$$CINSTPC_{berriztagarria}^{t} = \\frac{CINST_{berriztagarria}^{t}}{P^{t}} \\cdot 100$$ \n\nnon:\n\n$CINST_{berriztagarria}^{t} =$ energia-iturri berriztagarrietatik elektrizitatea sortzeko ahalmen instalatua $t$ urtean \n\n$P^{t} =$ biztanleria $t$ urtean\n", "desagregacion"=>"\nEnergia berriztagarrien iturri mota: hidraulikoa; fotovoltaikoa; eolikoa; eguzki-energia termikoa\n", "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nEnergia-zerbitzu moderno eta jasangarriak hornitzeko beharrezkoak diren azpiegitura eta teknologiek sektore \nekonomiko askotan erabiltzen diren ekipo eta gailu ugari hartzen dituzte. Ez dago erraz eskura daitekeen \nmekanismorik produktu-talde desberdin horrek energia-zerbitzu moderno eta jasangarriak emateko egiten duen \nekarpena biltzeko, gehitzeko eta neurtzeko. \n\nHala ere, energia-horniduraren katearen zati handi bat (erraz neur daitekeena) elektrizitatea ekoizteko \nerabiltzen den azpiegitura da.\n\nEnergia berriztagarriak energia hornitzeko modu jasangarritzat jotzen dira, gaur egun duten erabilerak ez \nbaitu etorkizunean erabiltzeko aukera agortzen. Adierazle horrek elektrizitatean duen ikuspegiak helburuak \nenergia-iturri modernoetan duen enfasia islatzen du, eta bereziki garrantzitsua da garapen-bidean dauden \nherrialdeentzat, non elektrizitate-eskaria handia izaten den eta erabilgarritasuna mugatua. \n\nGainera, energia berriztagarrien ikuspegiak erakusten du elektrizitate berriztagarria ekoizteko erabiltzen \ndiren teknologiak modernoak eta jasangarriagoak direla berriztagarriak ez direnak baino, bereziki energia \neolikotik eta eguzki-energiatik elektrizitatea sortzeko hazkunde handiena duten azpisektoreetan. \n\nElektrizitate berriztagarriaren ahalmena biztanleriaren arabera banatzeak (biztanle bakoitzeko watt-en \nneurri bat sortzeko) gaitasun-datuak eskalatzea proposatzen du, herrialdeen arteko premien artean dagoen \naldaketa handia kontuan hartzeko. BPGaren ordez biztanleria erabiltzen du datuak eskalatzeko, herrialde \nbatean energia-zerbitzu moderno eta jasangarrien eskariaren adierazlerik oinarrizkoena delako. \n\nAdierazle horrek 7.1.1 eta 7.2.1 adierazleak ere osatu beharko lituzke. Elektrizitaterako sarbideari dagokionez, \ninformazio gehigarria emango du elektrizitaterako sarbidea duten pertsonen proportzioari buruz; izan ere, \nerakutsiko du zenbat azpiegitura dagoen erabilgarri sarbide hori emateko (pertsona bakoitzeko edukiera-kantitateari \ndagokionez). Ahalmen berriztagarriaren ikuspegiak ere balioa gehituko dio energia berriztagarrien adierazleari \n(7.2.1), energia berriztagarriak elektrizitatea hobeto eskuratzeko zer ekarpen egiten duen erakusten baitu. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.b.1&seriesCode=EG_EGY_RNEW&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">Elektrizitate berriztagarria sortzeko ahalmen instalatua (watt per capita) EG_EGY_RNEW</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-07-0b-01.pdf\">Metadatuak 7-b-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 7.b: By 2030, expand infrastructure and upgrade technology for supplying modern and sustainable energy services for all in developing countries, in particular least developed countries, small island developing States and landlocked developing countries, in accordance with their respective programmes of support</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 7.b.1: Installed renewable energy-generating capacity in developing and developed countries (in watts per capita)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EG_EGY_RNEW - Installed renewable electricity-generating capacity (watts per capita) [7.b.1, 12.a.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>This indicator is also used as indicator 12.a.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Renewable Energy Agency (IRENA)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Renewable Energy Agency (IRENA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator is defined as the installed capacity of power plants that generate electricity from renewable energy sources divided by the total population of a country. Capacity is defined as the net maximum electrical capacity installed at the year-end and renewable energy sources are as defined in the IRENA Statute (see concepts below).</p>\n<p><strong>Concepts:</strong></p>\n<p>Electricity capacity is defined in the International Recommendations for Energy Statistics or IRES (UN, 2018) as the maximum active power that can be supplied continuously (i.e., throughout a prolonged period in a day with the whole plant running) at the point of outlet (i.e., after taking the power supplies for the station auxiliaries and allowing for the losses in those transformers considered integral to the station). This assumes no restriction of interconnection to the network. It does not include overload capacity that can only be sustained for a short period of time (e.g., internal combustion engines momentarily running above their rated capacity).</p>\n<p>The IRENA Statute defines renewable energy to include energy from the following sources: hydropower; marine energy (ocean, tidal and wave energy); wind energy; solar energy (photovoltaic and thermal energy); bioenergy; and geothermal energy.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Watts per capita</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Electricity capacity classifications follow the International Recommendations for Energy Statistics or IRES</p>", "SOURCE_TYPE__GLOBAL"=>"<p>IRENA&#x2019;s electricity capacity database contains information about the electricity generating capacity installed at the year-end, measured in megawatt (MW). The dataset covers all countries and areas from the year 2000 onwards. The dataset also records whether the capacity is on-grid or off-grid and is split into 36 different renewable energy types that can be aggregated into the six main sources of renewable energy.</p>\n<p><strong>Population data:</strong></p>\n<p>For the population part of this indicator, IRENA uses population data from the United Nations World Population Prospects. The population data reflects the residents in a country or area regardless of legal status or citizenship. The values are midyear estimates.</p>\n<p>The United Nations Department of Economic and Social Affairs published information about its methodology on the link below:</p>\n<p><a href=\"https://population.un.org/wpp/Methodology/\">https://population.un.org/wpp/Methodology/</a></p>", "COLL_METHOD__GLOBAL"=>"<p>The capacity data are collected as part of IRENA&#x2019;s annual questionnaire cycle. Questionnaires are sent to countries at the start of a year asking for renewable energy data for two years previously (i.e. at the start of 2019, questionnaires ask for data for the year 2017). The data are then validated and checked with countries and published in the IRENA Renewable Energy Statistics Yearbook at the end of June. To minimise reporting burden, the questionnaires for some countries are pre-filled with data collected by other agencies (e.g. Eurostat) and are sent to countries for them to complete any additional details requested by IRENA.</p>\n<p>At the same time as this, preliminary estimates of capacity for the previous year are also collected from official sources where available (e.g. national statistics, data from electricity grid operators) and from other unofficial sources (mostly industry associations for the different renewable energy sectors). These are published at the end of March.</p>", "FREQ_COLL__GLOBAL"=>"<p>Capacity data are recorded as a year-end figure. The data are collected in the first six months of every year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Estimates of generating capacity for a year are published at the end of March in the following year. Final figures for the previous year are published at the end of June.</p>", "DATA_SOURCE__GLOBAL"=>"<p><strong>Renewable energy generating capacity:</strong></p>\n<p>National Statistical Offices and National Energy Agencies of Ministries (the authority to collect this data varies between countries). Data for preliminary estimates may also be collected from industry associations, national utility companies or grid operators.</p>\n<p><strong>Population:</strong></p>\n<p>United Nations Population Division- World Population Prospects.</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Renewable Energy Agency (IRENA).</p>", "INST_MANDATE__GLOBAL"=>"<p>With a mandate from countries around the world, IRENA encourages governments to adopt enabling policies for renewable energy investments, provides practical tools and policy advice to accelerate renewable energy deployment, and facilitates knowledge sharing and technology transfer to provide clean, sustainable energy for the world&#x2019;s growing population. Renewable energy capacity statistics are in line with these aims.</p>", "RATIONALE__GLOBAL"=>"<p>The infrastructure and technologies required to supply modern and sustainable energy services cover a wide range of equipment and devices that are used across numerous economic sectors. There is no readily available mechanism to collect, aggregate and measure the contribution of this disparate group of products to the delivery of modern and sustainable energy services. However, one major part of the energy supply chain that can be readily measured is the infrastructure used to produce electricity.</p>\n<p>Renewables are considered a sustainable form of energy supply, as their current use does not usually deplete their availability to be used in the future. The focus of this indicator on electricity reflects the emphasis of the target on modern sources of energy and is particularly relevant for developing countries where the demand for electricity is often high and its availability is constrained. Furthermore, the focus on renewables reflects the fact that the technologies used to produce renewable electricity are generally modern and more sustainable than non-renewables, particularly in the fastest growing sub-sectors of electricity generation from wind and solar energy. </p>\n<p>The division of renewable electricity capacity by population (to produce a measure of Watts per capita) is proposing to scale the capacity data to account for the large variation in needs between countries. It uses population rather than GDP to scale the data, because this is the most basic indicator of the demand for modern and sustainable energy services in a country.</p>\n<p>This indicator should also complement indicators 7.1.1 and 7.2.1. With respect to electricity access, it will provide additional information to the proportion of people with electricity access by showing how much infrastructure is available to deliver that access (in terms of the amount of capacity per person). The focus on renewable capacity will also add value to the existing renewables indicator (7.2.1) by showing how much renewable energy is contributing to the need for improved electricity access.</p>", "REC_USE_LIM__GLOBAL"=>"<p>At present, electricity only accounts for about one-quarter of total energy use in the World and an even lower share of energy use in most developing countries. The focus of this indicator on electricity capacity does not capture any trends in the modernisation of technologies used to produce heat or provide energy for transport.</p>\n<p>However, with the growing trend towards electrification of energy end-uses, the focus here on electricity may become less of a weakness in the future and may also serve as a general indicator of the progress towards greater electrification in developing counties. That, in itself, should be seen as a shift towards the use of more modern technology to deliver sustainable energy services.</p>\n<p>Furthermore, as reflected in many national policies, plans and targets, increasing the production of electricity and, in particular, renewable electricity, is seen by many countries as a first priority in their transition to the delivery of more modern and sustainable energy services. Thus, this indicator is a useful first-step towards measuring overall progress on this target that reflects country priorities and can be used until other additional or better indicators can be developed.</p>", "DATA_COMP__GLOBAL"=>"<p>For each country and year, the renewable electricity generating capacity at the end of the year is divided by the total population of the country as of mid-year (July 1st).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>All countries are invited to provide their capacity data or at least review the data that IRENA has compiled (from other official and unofficial sources) through an annual process of data collection using the IRENA Renewable Energy Questionnaire. This process is reinforced through IRENA&#x2019;s renewable energy statistics training workshops, which are held twice a year in different (rotating) regions. To date, over 200 energy statisticians have participated in these workshops, many of whom provide renewable energy data to IRENA. In addition, IRENA&#x2019;s statistics are presented each year to member countries at one of IRENA&#x2019;s three governing body meetings, where discrepancies or other data issues can be discussed with country representatives.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>At country level:</strong></p>\n<p>At the country level, electricity capacity data are sometimes missing for two reasons:</p>\n<ol>\n  <li>Delays in responding to IRENA questionnaires or publication of official data. In such cases, estimates are made so that global and regional totals can be calculated. The most basic treatment is to repeat the value of capacity from the previous year. However, IRENA also checks unofficial data sources and collects data about investment projects (see Indicator 7.a.1). These other sources can be used to identify if any new power plants have been commissioned in a year and are used where available to update the capacity value at the end of a year. Any such estimates are eventually replaced by official or questionnaire data when that becomes available.</li>\n  <li>Off-grid capacity data are frequently missing from national energy statistics or is presented in non-standard units (e.g. numbers of mini-hydro plants in a country rather than their capacity in MW). Where official data are not available, off-grid capacity figures are collected by IRENA from a wide variety of other official and unofficial sources in countries (e.g. development agencies, government departments, NGOs, project developers and industry associations) and this information is added to the capacity database to give a more complete picture of developments in the renewable energy sector in a country. These data are peer reviewed each year through an extensive network of national correspondents (the REN21 Network) and is checked with IRENA country focal points when they attend IRENA meetings and training workshops.</li>\n</ol>\n<p>When capacity data are missing, mostly in non-state territories, these are excluded from the dataset. </p>\n<p><strong>At regional and global levels:</strong></p>\n<p>See above. Regional and global totals are only estimated to the extent that figures for some countries may be estimated in each year. (See also data availability below). </p>", "REG_AGG__GLOBAL"=>"<p>Regional and global averages are calculated by summing the renewable generating capacity for a region or the World and dividing that by the corresponding figure for the total population. </p>\n<p>This calculation excludes the population of those countries and/or territories that have missing capacity data. As such, the regional and global population values used in the calculation might differ from those reported in the UN World Population Prospects.</p>\n<p>Furthermore, the indicator is also aggregated by development regions: developed and developing regions as per the historical distinction of May 2022 from the United Nations Statistics Division.</p>\n<p><strong>Developed</strong></p>\n<p>Aland Islands, Albania, Andorra, Australia, Austria, Belarus, Belgium, Bermuda, Bosnia and Herzegovina, Bulgaria, Canada, Christmas Island, Cocos (Keeling) Islands, Croatia, Cyprus, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Greenland, Guernsey, Heard Island and McDonald Islands, Holy See, Hungary, Iceland, Ireland, Isle of Man, Israel, Italy, Japan, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Monaco, Montenegro, Netherlands, New Zealand, Norfolk Island, North Macedonia, Norway, Poland, Portugal, Republic of Korea, Republic of Moldova, Romania, Russian Federation, Saint Pierre and Miquelon, San Marino, Sark, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Mayen Islands, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland, United States of America</p>\n<p><strong>Developing</strong></p>\n<p>Afghanistan, Algeria, American Samoa, Angola, Anguilla, Antigua and Barbuda, Argentina, Armenia, Aruba, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belize, Benin, Bhutan, Bolivia (Plurinational State of), Bonaire, Sint Eustatius and Saba, Botswana, Bouvet Island, Brazil, British Indian Ocean Territory, British Virgin Islands, Brunei Darussalam, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Cayman Islands, Central African Republic, Chad, Chile, China, China, Hong Kong Special Administrative Region, China, Macao Special Administrative Region, Colombia, Comoros, Congo, Cook Islands, Costa Rica, C&#xF4;te d&#x2019;Ivoire, Cuba, Cura&#xE7;ao, Democratic People&apos;s Republic of Korea, Democratic Republic of the Congo, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Falkland Islands (Malvinas), Fiji, French Guiana, French Polynesia, French Southern Territories, Gabon, Gambia, Georgia, Ghana, Grenada, Guadeloupe, Guam, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, India, Indonesia, Iran (Islamic Republic of), Iraq, Jamaica, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Kyrgyzstan, Lao People&apos;s Democratic Republic, Lebanon, Lesotho, Liberia, Libya, Madagascar, Malawi, Malaysia, Maldives, Mali, Marshall Islands, Martinique, Mauritania, Mauritius, Mayotte, Mexico, Micronesia (Federated States of), Mongolia, Montserrat, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, New Caledonia, Nicaragua, Niger, Nigeria, Niue, Northern Mariana Islands, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Pitcairn, Puerto Rico, Qatar, R&#xE9;union, Rwanda, Saint Barth&#xE9;lemy, Saint Helena, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French Part), Saint Vincent and the Grenadines, Samoa, Sao Tome and Principe, Saudi Arabia, Senegal, Seychelles, Sierra Leone, Singapore, Sint Maarten (Dutch part), Solomon Islands, Somalia, South Africa, South Georgia and the South Sandwich Islands, South Sudan, Sri Lanka, State of Palestine, Sudan, Suriname, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Togo, Tokelau, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Turks and Caicos Islands, Tuvalu, Uganda, United Arab Emirates, United Republic of Tanzania, United States Minor Outlying Islands, United States Virgin Islands, Uruguay, Uzbekistan, Vanuatu, Venezuela (Bolivarian Republic of), Viet Nam, Wallis and Futuna Islands, Western Sahara, Yemen, Zambia, Zimbabwe</p>", "DOC_METHOD__GLOBAL"=>"<p>Guidance for the collection of electricity capacity data is provided by the International Recommendations for Energy Statistics. IRENA also produces methodological guidance for countries, specifically about how to measure renewable energy and collect renewable energy data. This is supported by a comprehensive programme of regional renewable energy statistics training workshops and ongoing communications with countries as part of the annual questionnaire cycle. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data for renewable energy capacity is validated by technology, year and country during the IRENA statistics cycle. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>IRENA data are compiled from national sources following the United Nations Fundamental Principles of Official Statistics: <a href=\"https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx\">https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx</a>.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The quality of the data are verified by automated validation routines for aggregates. Furthermore, official questionnaires guarantee the validity for each data point, where applicable. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The total number of capacity records in the database (all developing countries/areas, all years since 2000, all technologies) is 11,000. In terms of numbers of records, 3,120 (28%) are estimates and 740 (7%) are from unofficial sources. The remaining records (65%) are all from returned questionnaires or official data sources. </p>\n<p>However, in terms of the amount of capacity covered in the database, the shares of data from estimated and unofficial sources is only 5% and 1% respectively. The large difference between these measures is due to the inclusion of off-grid capacity figures in the database. The amount of off-grid generating capacity in a country is frequently estimated by IRENA, but the amounts of off-grid capacity recorded in each case is often relatively small.</p>\n<p><strong>Time series:</strong></p>\n<p>Renewable generating capacity data are available from 2000 onwards. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>IRENA&#x2019;s renewable capacity data are available for every country and area in the world from the year 2000 onwards. These figures can also be disaggregated by technology (solar, hydro, wind, etc.) and by on-grid and off-grid capacity.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The main source of discrepancies between different sources of electricity capacity data are likely to be due to the under-reporting or non-reporting of off-grid capacity data (see above) or slight variations in the definition of installed capacity. IRENA uses the IRES definition of capacity agreed by the Oslo Group on Energy Statistics, while some countries and institutions may use slightly different definitions of capacity to reflect local circumstances (e.g. the reporting of derated rather than maximum net installed capacity or the reporting of built rather than commissioned capacity at year-end).</p>", "OTHER_DOC__GLOBAL"=>"<p>UN, 2018. International Recommendations for Energy Statistics (IRES). New York City: United Nations. Retrieved from <a href=\"https://unstats.un.org/unsd/energystats/methodology/documents/IRES-web.pdf\">https://unstats.un.org/unsd/energystats/methodology/documents/IRES-web.pdf</a></p>\n<p>IRENA Statistical Yearbooks: <a href=\"https://www.irena.org/Statistics\">https://www.irena.org/Statistics</a></p>", "indicator_sort_order"=>"07-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"8.1.1", "slug"=>"8-1-1", "name"=>"Tasa de crecimiento anual del PIB real per cápita", "url"=>"/site/es/8-1-1/", "sort"=>"080101", "goal_number"=>"8", "target_number"=>"8.1", "global"=>{"name"=>"Tasa de crecimiento anual del PIB real per cápita"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"Los datos del último año son provisionales", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de crecimiento anual del PIB real per cápita", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de crecimiento anual del PIB real per cápita", "indicator_number"=>"8.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Cambio porcentual positivo en la tasa de crecimiento del PIB real anual per cápita", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_10/opt_1/ti_cuentas-economicas/temas.html", "url_text"=>"Cuentas económicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Tasa de crecimiento anual del PIB real per cápita", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.1- Mantener el crecimiento económico per cápita de conformidad con las circunstancias nacionales y, en particular, un crecimiento del producto interno bruto de al menos el 7% anual en los países menos adelantados", "definicion"=>"Tasa de crecimiento anual del PIB real por persona", "formula"=>"\n$$TCPIBPC^{t} = \\frac{{\\frac{PIB_{2022}^{t}}{P^t}-\\frac{PIB_{2022}^{t-1}}{P^{t-1}}}}{\\frac{PIB_{2022}^{t-1}}{P^{t-1}}} \\cdot 100$$\n\ndonde:\n\n$PIB_{2022}^{t} =$ producto interior bruto en volumen encadenado con referencia 2022 en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Territorio histórico", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl Producto Interno Bruto (PIB) real per cápita es un indicador del \nnivel de vida promedio de los residentes de un país o área.\n\nUn cambio porcentual positivo en el PIB real anual per cápita puede \ninterpretarse como un aumento en el nivel de vida promedio de los \nresidentes de un país o área.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.1.1&seriesCode=NY_GDP_PCAP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Tasa de crecimiento anual del PIB real per cápita (%) NY_GDP_PCAP</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-01-01.pdf\">Metadatos 8-1-1.pdf</a> (solo en inglés)", "informacion_interes"=>"\n<a href=\"https://www.eustat.eus/estadisticas/tema_489/opt_0/ti_pib-municipal/temas.html\">PIB Municipal</a> Eustat", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-28", "en"=>{"indicador_disponible"=>"Tasa de crecimiento anual del PIB real per cápita", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.1- Mantener el crecimiento económico per cápita de conformidad con las circunstancias nacionales y, en particular, un crecimiento del producto interno bruto de al menos el 7% anual en los países menos adelantados", "definicion"=>"Annual growth rate of real GDP per capita", "formula"=>"\n$$TCPIBPC^{t} = \\frac{{\\frac{PIB_{2022}^{t}}{P^t}-\\frac{PIB_{2022}^{t-1}}{P^{t-1}}}}{\\frac{PIB_{2022}^{t-1}}{P^{t-1}}} \\cdot 100$$\n\nwhere:\n\n$PIB_{2022}^{t} =$ gross domestic product in chain volumes with reference to 2022 in year $t$\n\n$P^{t} =$ population as of 1 July of year $t$\n", "desagregacion"=>"Province", "periodicidad"=>"Anual", "observaciones"=>nil, "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nReal Gross Domestic Product (GDP) per capita is a proxy for the average \nstandard of living of residents in a country or area. \n\nA positive percentage change in annual real GDP per capita can be interpreted \nas an increase in the average standard of living of the residents in a country \nor area. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.1.1&seriesCode=NY_GDP_PCAP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Annual growth rate of real GDP per capita (%) NY_GDP_PCAP</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-01-01.pdf\">Metadata 8-1-1.pdf</a>", "informacion_interes"=>"\n<a href=\"https://https://en.eustat.eus/estadisticas/tema_489/opt_0/temas.html\">Municipal GDP</a> Eustat", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Tasa de crecimiento anual del PIB real per cápita", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.1- Mantener el crecimiento económico per cápita de conformidad con las circunstancias nacionales y, en particular, un crecimiento del producto interno bruto de al menos el 7% anual en los países menos adelantados", "definicion"=>"BPG errealaren urteko hazkunde-tasa pertsonako", "formula"=>"\n$$TCPIBPC^{t} = \\frac{{\\frac{PIB_{2022}^{t}}{P^t}-\\frac{PIB_{2022}^{t-1}}{P^{t-1}}}}{\\frac{PIB_{2022}^{t-1}}{P^{t-1}}} \\cdot 100$$\n\nnon:\n\n$PIB_{2022}^{t} =$ barne-produktu gordina, 2022 erreferentziarekin kateatutako bolumenean $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Lurralde historikoa", "periodicidad"=>"Anual", "observaciones"=>nil, "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nBiztanleko Barne Produktu Gordin (BPG) erreala herrialde edo eremu bateko biztanleen batez besteko \nbizi-mailaren adierazle bat da. \n\nBiztanleko urteko BPG errealaren ehuneko-aldaketa positiboa herrialde edo eremu bateko biztanleen \nbatez besteko bizi-mailaren igoera gisa interpreta daiteke. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.1.1&seriesCode=NY_GDP_PCAP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Per capita BPG errealaren urteko hazkunde-tasa (%) NY_GDP_PCAP</a> UNSTATS", "comparabilidad"=>"Eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-01-01.pdf\">Metadatuak 8-1-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>"\n<a href=\"https://eu.eustat.eus/estadisticas/tema_489/opt_0/temas.html\">Udal BPG</a> Eustat", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.1: Sustain per capita economic growth in accordance with national circumstances and, in particular, at least 7 per cent gross domestic product growth per annum in the least developed countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.1.1: Annual growth rate of real GDP per capita</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>NY_GDP_PCAP - Annual growth rate of real GDP per capita [8.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Any economic statistics related SDG indicator</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Statistics Division (UNSD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Statistics Division (UNSD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p>Annual growth rate of real Gross Domestic Product (GDP) per capita is calculated as the percentage</p>\n<p>change in the real GDP per capita between two consecutive years. Real GDP per capita is calculated by</p>\n<p>dividing GDP at constant prices by the population of a country or area. The data for real GDP are</p>\n<p>measured in constant US dollars to facilitate the calculation of country growth rates and aggregation of</p>\n<p>the country data.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Annual growth rate of real GDP per capita: Percent (%)</p>\n<p>GDP: US dollars</p>\n<p>Population: Number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Different versions of The System of National Accounts (1968, 1993 and 2008 SNA)</p>\n<p>International Standard Industrial Classification (ISIC 3) of all Economic Activities</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The underlying annual GDP estimates in domestic currency are collected from countries or areas annually through a national accounts questionnaire (NAQ), while the underlying population estimates are obtained from the UN Population Division on <a href=\"https://population.un.org/wpp/Download/Standard/Population/\" target=\"_blank\"><u>https://population.un.org/wpp/Download/Standard/Population/</u></a></p>", "COLL_METHOD__GLOBAL"=>"<p>Each year, the national accounts section of the UNSD sends a pre-filled NAQ to countries or areas to collect the latest data on official annual national accounts in domestic currency. In order to lighten the reporting burden of countries to different international and regional organizations, the UNSD receives data from the Organisation for Economic Co-operation and Development (OECD), the United Nations Economic Commission for Europe (ECE) and the Caribbean Community (CARICOM) on behalf of their constituents. </p>", "FREQ_COLL__GLOBAL"=>"<p>The exercise to collect official annual national accounts estimates from countries or areas using the national accounts questionnaire starts in February of each year for the data available up to the end of the previous year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>December of each year</p>", "DATA_SOURCE__GLOBAL"=>"<p>National statistics offices, central banks or national agencies responsible for compiling official national accounts estimates for a country or area.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Statistics Division (UNSD)</p>", "INST_MANDATE__GLOBAL"=>"<p>The National Accounts Section of the United Nations Statistics Division:</p>\n<p> </p>\n<p>Contributes to the international coordinated development and updating of the System of National Accounts (SNA); and undertakes methodological research on issues on the research agenda of the SNA in collaboration with the Intersecretariat Working Group on National Accounts (ISWGNA).</p>\n<p> </p>\n<p>Supports the implementation programme of the SNA by developing and updating supporting normative standards, training material and compilation guidance for the implementation of national accounts and supporting economic statistics and maintaining a knowledge base on economic statistics.</p>\n<p> </p>\n<p>Delivers a statistical capacity building programme for the implementation of the 2008 SNA and supporting statistics through a series of regional and interregional workshops and seminars in collaboration with the regional commissions and regional agencies and through a limited number of individual country technical assistance missions.</p>\n<p>Collects and disseminates annual national accounts statistics from countries and provides substantive service to the Committee on Contributions of the Fifth Committee of the United Nations on technical aspects of the elements of scale methodology for assessing the contributions to the United Nations by Member States.</p>\n<p> </p>\n<p>Publishes the outputs of the Section in various publications of UNSD.</p>", "RATIONALE__GLOBAL"=>"<p>Real Gross Domestic Product (GDP) per capita is a proxy for the average standard of living of residents in</p>\n<p>a country or area.</p>\n<p>A positive percentage change in annual real GDP per capita can be interpreted as an increase in the average standard of living of the residents in a country or area.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Although countries or areas calculate GDP using the common principles and recommendations in the United Nations System of National Accounts (SNA), there are still problems in international comparability of GDP estimates. These include:</p>\n<ol>\n  <li>Different versions of the SNA (for example, 1968, 1993 or 2008) countries or areas use in calculating their GDP estimates.</li>\n  <li>Different degree of coverage of informal and non-observed economic activities in the GDP estimates.</li>\n</ol>\n<p>Further, as a necessary condition to being a key economic performance indicator of sustainable development, one of the often-cited limitations of GDP is that it does not account for the social and environmental costs of production. It is designed as a measure of the level of overall well-being. For example, growth in real GDP per capita reveals nothing concerning energy and material interactions with the environment.</p>", "DATA_COMP__GLOBAL"=>"<p>The annual growth rate of real Gross Domestic Product (GDP) per capita is calculated as follows:</p>\n<ol>\n  <li>Convert annual real GDP in domestic currency at 2015 prices for a country or area to US dollars at 2015 prices using the 2015 exchange rates.</li>\n  <li>Divide the result by the population of the country or area to obtain annual real GDP per capita in constant US dollars at 2015 prices.</li>\n  <li>Calculate the annual growth rate of real GDP per capita in year t+1 using the following formula: <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n      <mfrac>\n        <mrow>\n          <msub>\n            <mrow>\n              <mi>G</mi>\n            </mrow>\n            <mrow>\n              <mi>t</mi>\n              <mo>+</mo>\n              <mn>1</mn>\n            </mrow>\n          </msub>\n          <mo>-</mo>\n          <msub>\n            <mrow>\n              <mi>G</mi>\n            </mrow>\n            <mrow>\n              <mi>t</mi>\n            </mrow>\n          </msub>\n        </mrow>\n        <mrow>\n          <msub>\n            <mrow>\n              <mi>G</mi>\n            </mrow>\n            <mrow>\n              <mi>t</mi>\n            </mrow>\n          </msub>\n        </mrow>\n      </mfrac>\n      <mo>&#xD7;</mo>\n      <mn>100</mn>\n    </math>, where G<sub>t+1</sub> is the real GDP per capita in 2015 US dollars in year t+1 and G<sub>t</sub> is the real GDP per capita in 2015 US dollars in year t.</li>\n</ol>", "DATA_VALIDATION__GLOBAL"=>"<p>The official national accounts data in domestic currency are validated to check for errors. The validation procedure involves ensuring that aggregates are equal to the sum of their components and that data series which are provided in multiple tables are represented consistently.</p>", "ADJUSTMENT__GLOBAL"=>"<p>The current and constant price GDP series are converted into US dollars by applying the corresponding market exchange rates as reported by the International Monetary Fund (IMF). When these conversion rates are not available, other IMF rates are used (official rates or principal rates). For countries whose exchange rates are not reported by the IMF, the annual average of United Nations operational rates of exchange (UNOPs) is applied. The UNOPs are conversion rates that are applied in official transactions of the United Nations with these countries. These exchange rates are based on official, commercial and/or tourist rates of exchange. </p>\n<p>In cases where a country experiences considerable distortion in the conversion rates, the UNSD uses price-adjusted rates of exchange (PARE) as an alternative to the exchange rates reported by the IMF or UN operational rates of exchange. The conversion based on PARE corrects the distorting effects of uneven price changes that are not well reflected in the other conversion rates. Consequently, unrealistic levels in GDP and other national accounts aggregates expressed in US Dollars may have been adjusted for certain time periods to improve the economic analysis at national, regional and local levels. </p>\n<p>The constant-price GDP series for each country is then divided by its population to obtain its real GDP per capita. </p>\n<p> </p>\n<p>More information on the methodology to estimate the data is available on <a href=\"https://unstats.un.org/unsd/snaama/assets/pdf/methodology.pdf\">https://unstats.un.org/unsd/snaama/assets/pdf/methodology.pdf</a> </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>When a full set of official annual GDP data is not available, estimation procedures are employed to obtain estimates for the entire time series. When full data are not available, a hierarchy of other data sources is used to gather information on the national accounts of a country or area. The data gathered are then either used directly or estimation procedures are applied to obtain the annual GDP data.</p>\n<p>If official data are not available, the selection of data sources is based on following hierarchy:</p>\n<ul>\n  <li>\n    <ol>\n      <li>Official publications and websites of national statistical offices, central banks or relevant government ministries;</li>\n      <li>Official statistics disseminated by Eurostat, European Central Bank and the Organization for Economic Cooperation and Development (OECD) for their members;</li>\n      <li>Information provided by Permanent Missions to the United Nations;</li>\n      <li>Economic surveys and estimates prepared by United Nations&#x2019; Regional Economic Commissions (i.e. UNECE, ECLAC, ESCAP, UNECA and ESCWA);</li>\n      <li>Publications of international organizations with a strong focus on statistical data collection (including regional development banks). The most common sources used for their respective countries are listed below: Asia: Asian Development Bank, ASEAN, Arab Monetary Fund, Secretariat of the Pacific Community (SPC) Africa: African Development Bank, Afristat, Banque des &#xC9;tats de l&#x2019;Afrique Centrale (BEAC), Union &#xC9;conomique Mon&#xE9;taire Ouest Africain (UEMOA) Americas: CARICOM, Caribbean Development Bank, Eastern Caribbean Central Bank (ECCB) Other: OECD for non-member countries Statistical Committee of the Commonwealth of Independent States.</li>\n      <li>Estimates and indicators from other international organizations. The most common sources used are: the International Monetary Fund (IMF) and the World Bank;</li>\n      <li>Publications or websites of specialized groups, the most common sources used are: the Gulf Cooperation Council, the Asia-Pacific Economic Cooperation (APEC), the Committee of Central Bank Governors in SADC; the Islamic Development Bank, and the Statistical Training Centre for Islamic Countries;</li>\n      <li>Economic data from commercial providers and other sources, the most common sources used are: the Economic Intelligence Unit and the United States Central Intelligence Agency;</li>\n      <li>Information from neighbouring countries where no alternative source is available (Switzerland for Liechtenstein; France for Monaco; Italy for San Marino; Spain for Andorra; and some Pacific Islands for other Pacific Islands);</li>\n    </ol>\n  </li>\n</ul>\n<p>The estimation methods involved in preparing the GDP estimates using sources other than official data include trend extrapolation, using appropriate indices for inflating or deflating relevant data series, and share distribution of GDP. A hierarchical assessment is followed to determine which method should be used. Effort is made to keep data estimation methods consistent from year to year.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>After the missing real GDP country or area data are imputed using the methods as described above, they are summed up to derive the respective regional or global aggregates and then divided by the corresponding population data to obtain the regional or global real GDP per capita. After that, annual growth rates in regional or global real GDP per capita are calculated using the formula described above.</p>", "REG_AGG__GLOBAL"=>"<p>For each year, the real GDP and population estimates for each country or area are summed up to derive the regional and global aggregates. The regional and global aggregates are then divided by the corresponding population to derive the regional and global real GDP per capita estimates. These estimates are then used to calculate the annual growth rates in regional and global real GDP per capita using the formula described above.</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>GDP: National Accounts Statistics: Main Aggregates and Detailed Tables, 2023 See <a href=\"https://unstats.un.org/unsd/nationalaccount/pubsDB.asp?pType=3\" target=\"_blank\">https://unstats.un.org/unsd/nationalaccount/pubsDB.asp?pType=3</a> </li>\n</ul>\n<p> </p>\n<ul>\n  <li>Population: United Nations Demographic Yearbook </li>\n</ul>\n<p>See <a href=\"https://unstats.un.org/unsd/demographic-social/products/dyb/dybsets/2023.pdf\">https://unstats.un.org/unsd/demographic-social/products/dyb/dybsets/2023.pdf</a> </p>\n<p> </p>\n<ul>\n  <li>GDP: 2008 SNA </li>\n</ul>\n<p>See <a href=\"https://unstats.un.org/unsd/nationalaccount/docs/SNA2008.pdf\" target=\"_blank\">https://unstats.un.org/unsd/nationalaccount/docs/SNA2008.pdf</a> </p>\n<p> </p>\n<ul>\n  <li>Population: Principles and Recommendations for Population and Housing Censuses See <a href=\"https://unstats.un.org/unsd/publication/seriesM/Series_M67rev3en.pdf\" target=\"_blank\">https://unstats.un.org/unsd/publication/seriesM/Series_M67rev3en.pdf</a> </li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>All official data received by the United Nations Statistics Division are checked for errors prior to incorporation in the United Nations official data database. The checking involves ensuring that aggregate indicators are equal to the sum of their components and that indicators which are provided in multiple tables are represented consistently. Footnotes are added to the data when necessary.</p>\n<p>Similarly, the estimated data are checked for consistency by ensuring that aggregate indicators are equal to the sum of their components and that indicators which are represented in multiple tables are represented consistently. The estimates derived for each year are compared to previous years to ensure that estimates are prepared consistently from year to year. Additionally, the growth rate from year to year is analyzed to identify anomalies in the data.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data are validated in accordance with the international statistical standards. Discrepancies are resolved through written communication with countries.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The estimates derived for each year are compared to previous years to ensure that estimates are prepared consistently from year to year. Additionally, the growth rate from year to year is analysed to identify anomalies in the data. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>National statistics offices, central banks or national agencies responsible for compiling official national accounts estimates for a country or area.</p>\n<p><strong>Time series:</strong></p>\n<p>Annual data from 1970 to 2023 are available.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>It is possible to disaggregate the country data by region, if countries can make available the underlying</p>\n<p>regional data which are consistent with the national accounts data to perform the disaggregation.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies: </strong></p>\n<p>The differences with country data include the following: </p>\n<p>Official country data are typically available in domestic currency only. The data estimates for this indicator are in US dollars. </p>\n<p>Countries or areas may not have a full set of official GDP data. The GDP data estimated by UNSD include imputations using various estimation procedures as described above to obtain estimates for the entire time series. </p>\n<p>Official country data are often reported as multiple sets of time series versions, with each version representing a unique methodology used to compile the national accounts data (for example, a difference between two time series versions could reflect a change in currency, a switch from 1968 SNA to 1993 SNA, a change in the office responsible for compiling national accounts, etc.). These time series versions may not be comparable, especially when a country has shifted from the 1968 SNA to 1993 SNA or 2008 SNA.</p>\n<p>When a single version of a time series does not exist for the entire period (1970 to t-1), backcasting procedures are used to link the most recently reported time series version with the previous series. Note that if there is a change of fiscal year between two official data time series, the older series are converted to the fiscal year type of the most recent time series prior to backcasting.</p>\n<p>Backcasting procedures are also used when constant price time series versions include multiple base years or when constant price time series versions are reported as constant prices of the previous year (CPPY). CPPY data are backcasted by using the officially reported current price data and the officially reported constant price data. The data are backcasted into a single series with a fixed base year. </p>\n<p>The population estimates from the United Nations Population Division may be different from country-produce estimates as the former include analysis carried out to take into account deficiencies such as incompleteness of coverage, lack of timeliness and errors in the reporting or coding of the basic information and to establish past population trends by resolving the inconsistencies affecting the basic data.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://unstats.un.org/unsd/snaama/Index\">https://unstats.un.org/unsd/snaama/Index</a> </p>\n<p><strong>References:</strong></p>\n<p><a href=\"http://unstats.un.org/unsd/nationalaccount/sna.asp\">http://unstats.un.org/unsd/nationalaccount/sna.asp</a> <a href=\"http://unstats.un.org/unsd/nationalaccount/data.asp\">http://unstats.un.org/unsd/nationalaccount/data.asp</a> </p>\n<p><a href=\"https://unstats.un.org/unsd/snaama/Index\">https://unstats.un.org/unsd/snaama/Index</a> </p>\n<p><a href=\"http://data.un.org/Explorer.aspx?d=SNAAMA\">http://data.un.org/Explorer.aspx?d=SNAAMA</a> </p>\n<p><a href=\"https://population.un.org/wpp/\">https://population.un.org/wpp/</a> </p>", "indicator_sort_order"=>"08-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.2.1", "slug"=>"8-2-1", "name"=>"Tasa de crecimiento anual del PIB real por persona empleada", "url"=>"/site/es/8-2-1/", "sort"=>"080201", "goal_number"=>"8", "target_number"=>"8.2", "global"=>{"name"=>"Tasa de crecimiento anual del PIB real por persona empleada"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"Los datos del último año son provisionales", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de crecimiento anual del PIB real por persona empleada", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de crecimiento anual del PIB real por persona empleada", "indicator_number"=>"8.2.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Cambio porcentual positivo en la tasa de crecimiento del PIB real por persona empleada", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_10/opt_1/ti_cuentas-economicas/temas.html", "url_text"=>"Cuentas económicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tasa de crecimiento anual del PIB real por persona empleada", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.2- Lograr niveles más elevados de productividad económica mediante la diversificación, la modernización tecnológica y la innovación, entre otras cosas centrándose en los sectores con gran valor añadido y un uso intensivo de la mano de obra", "definicion"=>"Tasa de crecimiento anual del PIB real por persona empleada", "formula"=>"\n$$TCPIBEP^{t} = \\frac{{\\frac{PIB_{2022}^{t}}{PE^t}-\\frac{PIB_{2022}^{t-1}}{PE^{t-1}}}}{\\frac{PIB_{2022}^{t-1}}{PE^{t-1}}} \\cdot 100$$\n\ndonde:\n\n$PIB_{2022}^{t} =$ producto interior bruto en volumen encadenado con referencia 2022 en el año $t$\n\n$PE^{t} =$ personas empleadas en el año $t$\n", "desagregacion"=>"Territorio histórico", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl PIB real por persona empleada es una medida de la productividad laboral, \npor lo que este indicador representa una medida del crecimiento de la productividad \nlaboral, brindando así información sobre la evolución, la eficiencia y la calidad \ndel capital humano en el proceso de producción.\n\nEl crecimiento económico de un país puede atribuirse a muchos factores, entre \nellos el aumento del empleo y el trabajo más eficaz de quienes están empleados. \nEste indicador arroja luz sobre este último efecto, por lo que es una medida clave \ndel desempeño económico. Las estimaciones de la productividad laboral \n(y del crecimiento) pueden respaldar la formulación de políticas del mercado \nlaboral y monitorear sus efectos. También pueden contribuir a la comprensión \nde cómo el desempeño del mercado laboral afecta los niveles de vida.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.2.1&seriesCode=SL_EMP_PCAP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B\">Tasa de crecimiento anual del PIB real por persona ocupada (%) SL_EMP_PCAP</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-02-01.pdf\">Metadatos 8-2-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-28", "en"=>{"indicador_disponible"=>"Tasa de crecimiento anual del PIB real por persona empleada", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.2- Lograr niveles más elevados de productividad económica mediante la diversificación, la modernización tecnológica y la innovación, entre otras cosas centrándose en los sectores con gran valor añadido y un uso intensivo de la mano de obra", "definicion"=>"Annual growth rate of real GDP per employed person", "formula"=>"\n$$TCPIBEP^{t} = \\frac{{\\frac{PIB_{2022}^{t}}{PE^t}-\\frac{PIB_{2022}^{t-1}}{PE^{t-1}}}}{\\frac{PIB_{2022}^{t-1}}{PE^{t-1}}} \\cdot 100$$\n\nwhere:\n\n$PIB_{2022}^{t} =$ gross domestic product in chain volumes with reference to 2022 in year $t$\n\n$PE^{t} =$ employed people in year $t$\n", "desagregacion"=>"Province", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nReal GDP per employed person being a measure of labour productivity, \nthis indicator represents a measure of labour productivity growth, \nthus providing information on the evolution, efficiency and quality \nof human capital in the production process. \n\nEconomic growth in a country can be ascribed to many factors, including \nincreased employment and more effective work by those who are employed. \nThis indicator casts light on the latter effect, therefore being a key \nmeasure of economic performance. Labour productivity (and growth) estimates \ncan support the formulation of labour market policies and monitor their effects. \nThey can also contribute to the understanding of how labour market performance \naffects living standards. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.2.1&seriesCode=SL_EMP_PCAP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B\">Annual growth rate of real GDP per employed person (%) SL_EMP_PCAP</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-02-01.pdf\">Metadata 8-2-1.pdf</a>", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Tasa de crecimiento anual del PIB real por persona empleada", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.2- Lograr niveles más elevados de productividad económica mediante la diversificación, la modernización tecnológica y la innovación, entre otras cosas centrándose en los sectores con gran valor añadido y un uso intensivo de la mano de obra", "definicion"=>"BPG errealaren urteko hazkunde-tasa, enplegatuko", "formula"=>"\n$$TCPIBEP^{t} = \\frac{{\\frac{PIB_{2022}^{t}}{PE^t}-\\frac{PIB_{2022}^{t-1}}{PE^{t-1}}}}{\\frac{PIB_{2022}^{t-1}}{PE^{t-1}}} \\cdot 100$$\n\nnon:\n\n$PIB_{2022}^{t} =$ barne-produktu gordina, kateatutako bolumenean, 2022 erreferentziarekin $t$ urtean \n\n$PE^{t} =$ enplegatuak $t$ urtean \n", "desagregacion"=>"Lurralde historikoa", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLangile bakoitzeko BPG erreala lan-produktibitatearen neurri bat da, eta, beraz, adierazle hori \nlan-produktibitatearen hazkundearen neurri bat da, eta, horrela, ekoizpen-prozesuan giza kapitalaren \nbilakaerari, eraginkortasunari eta kalitateari buruzko informazioa ematen du. \n\nHerrialde bateko hazkunde ekonomikoa faktore askori egotz dakioke; besteak beste, enpleguaren hazkundeari \neta enplegatuen lan eraginkorragoari. Adierazle horrek azken efektu horren berri ematen du, eta, beraz, \njarduera ekonomikoaren funtsezko neurria da. Lan-produktibitatearen (eta hazkundearen) zenbatespenak \nlagungarriak izan daitezke lan-merkatuaren politikak egiteko eta horien ondorioak ikuskatzeko. Lan-merkatuaren \njardunak bizi-mailei nola eragiten dien ulertzen ere lagun dezakete. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.2.1&seriesCode=SL_EMP_PCAP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B\">BPG errealaren urteko hazkunde-tasa, enplegatuko (%) SL_EMP_PCAP</a> UNSTATS", "comparabilidad"=>"Eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-02-01.pdf\">Metadatuak 8-2-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.2: Achieve higher levels of economic productivity through diversification, technological upgrading and innovation, including through a focus on high value added and labour-intensive sectors</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.2.1: Annual growth rate of real GDP per employed person</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_EMP_PCAP - Annual growth rate of real GDP per employed person [8.2.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.1.1, 8.3.1, 8.5.2, 10.4.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Labour Organization (ILO)</p>\n<p> </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>The annual growth rate of real Gross Domestic Product (GDP) per employed person conveys the annual percentage change in real GDP per employed person.</p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>GDP: It is the main measure of national output, representing the total value of all final goods and services within the System of National Accounts (SNA) production boundary produced in a particular economy (that is, the dollar value of all goods and services within the SNA production boundary produced within a country&#x2019;s borders in a given year). According to the SNA, &#x201C;GDP is the sum of gross value added of all resident producer units plus that part (possibly the total) of taxes on products, less subsidies on products, that is not included in the valuation of output &#x2026; GDP is also equal to the sum of the final uses of goods and services (all uses except intermediate consumption) measured at purchasers&#x2019; prices, less the value of imports of goods and services GDP is also equal to the sum of primary incomes distributed by resident producer units.&#x201D; </p>\n<p> </p>\n<p>Real GDP: Real GDP refers to GDP calculated at constant prices, that is, the volume level of GDP, excluding the effect of inflation and favouring comparisons of quantities beyond price changes. Constant price estimates of GDP are calculated by expressing values in terms of a base period. In theory, the price and quantity components of a value are identified and the price in the base period is substituted for that in the current period. </p>\n<p> </p>\n<p>Employment: All persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Data are based on the SNA. </p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Description:</strong> </p>\n<p>Output measures used in the numerator of this indicator Gross Domestic Product (GDP) are best obtained from the production side of national accounts and represent, as much as possible, GDP at market prices for the aggregate economy (adjusted for inflation, in constant prices). </p>\n<p> </p>\n<p>Employment data used in the denominator are preferably derived from labour force or other household surveys with an employment module. In the absence of a household survey, establishment surveys, administrative records or official estimates based on reliable sources can be used as well as population censuses. It is however important to note that employment data from establishment surveys will capture the number of jobs and not the number of persons employed as preferred for the denominator. Also, establishment surveys cover, in many cases, the formal sector and employers and employees only, not accounting for the whole economy. </p>\n<p> </p>\n<p>When calculating this indicator, it is important to ensure that the coverage of the employment data is consistent with that of the national accounts. </p>", "COLL_METHOD__GLOBAL"=>"<p>For the purposes of international reporting on the SDG indicators, the ILO uses country-level estimates of GDP in constant 2015 US$ from the World Bank&#x2019;s World Development Indicators database and country-level estimates on employment from household surveys or derived from the ILO modelled estimates to calculate levels and growth rates of labour productivity at the country, regional and global levels. </p>", "FREQ_COLL__GLOBAL"=>"<p>Continuous</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>ILO estimates of labour productivity are part of the ILO modelled estimates series, analysed in the ILO&apos;s World Employment and Social Outlook reports. The ILO estimates are released once per year alongside these reports. </p>", "DATA_SOURCE__GLOBAL"=>"<p>Input GDP and employment data are provided by national statistical offices, and in some cases labour ministries or other related agencies. </p>", "COMPILING_ORG__GLOBAL"=>"<p>International Labour Organization (ILO) </p>", "INST_MANDATE__GLOBAL"=>"<p>The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.</p>", "RATIONALE__GLOBAL"=>"<p>Real GDP per employed person being a measure of labour productivity, this indicator represents a measure of labour productivity growth, thus providing information on the evolution, efficiency and quality of human capital in the production process. </p>\n<p> </p>\n<p>Economic growth in a country can be ascribed to many factors, including increased employment and more effective work by those who are employed. This indicator casts light on the latter effect, therefore being a key measure of economic performance. Labour productivity (and growth) estimates can support the formulation of labour market policies and monitor their effects. They can also contribute to the understanding of how labour market performance affects living standards. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Output measures are obtained from national accounts and represent, as much as possible, GDP at market prices for the aggregate economy. However, despite common principles that are mostly based on the United Nations SNA, there are still significant problems in international consistency of national accounts estimates, based on factors such as differences in the treatment of output in services sectors, differences in methods used to correct output measures for price changes (in particular, the use of different weighting systems to obtain deflators) and differences in the degree of coverage of informal economic activities. </p>\n<p> </p>\n<p>Data on employment used in the denominator of this indicator refer, as much as possible, to the average number of persons with one or more paid jobs during the year. Employment data are based on the statistical standards from the 13th International Conference of Labour Statisticians (ICLS).</p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">R</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">D</mi>\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>The numerator and denominator of the equation above should refer to the same reference period, for example, the same calendar year.</p>\n<p>If we call the real GDP per employed person &#x201C;LabProd&#x201D;, then the annual growth rate of real GDP per employed person is calculated as follows:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">A</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">g</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">w</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">D</mi>\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">L</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mo>)</mo>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mo>&#x2013;</mo>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi mathvariant=\"normal\">L</mi>\n            <mi mathvariant=\"normal\">a</mi>\n            <mi mathvariant=\"normal\">b</mi>\n            <mi mathvariant=\"normal\">P</mi>\n            <mi mathvariant=\"normal\">r</mi>\n            <mi mathvariant=\"normal\">o</mi>\n            <mi mathvariant=\"normal\">d</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">y</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">a</mi>\n            <mi mathvariant=\"normal\">r</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mo>-</mo>\n            <mn>1</mn>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">L</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mo>-</mo>\n        <mn>1</mn>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The ILO engages in annual consultations with Member States through the ILOSTAT questionnaire and related Statistics Reporting System (StaRS). National data providers receive a link to the portal where they can review all national SDG data available on ILOSTAT.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Through the ILO Harmonized Microdata initiative, the ILO strives to produce internationally comparable labour statistics based on the indicator concepts and definitions adopted by the International Conference of Labour Statisticians.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>Multivariate regression techniques are used to impute missing employment values at the country level. </p>\n<p>For further information on the estimates, please refer to the ILO modelled estimates methodological overview, available at <a href=\"https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/\">https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/</a></p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p> Regional and global figures are aggregates of the country-level figures including the imputed values.</p>", "REG_AGG__GLOBAL"=>"<p>To address the problem of missing data, the ILO designed several econometric models which are used to produce estimates of labour market indicators in the countries and years for which real data are not available. The employment data derived from the ILO modelled estimates are used to produce estimates on labour productivity. These models use multivariate regression techniques to impute missing values at the country level, which are then aggregated to produce regional and global estimates. For further information, refer to the ILO modelled estimates methodological overview, available at <a href=\"https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/\">https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/</a> </p>", "DOC_METHOD__GLOBAL"=>"<p>See section 7 </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The processes of compilation, production, and publication of data, including its quality control, are carried out following the methodological framework and standards established by the ILO Department of Statistics, in compliance with the information technology and management standards of the ILO.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination on ILOSTAT. These checks consist of data and metadata revision of all the relevant inputs applying protocols to ensure that international comparability and time-series consistency are maintained. For the resulting modelled estimates, both statistical and judgmental assessments of the output data are carried out.</p>\n<p> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The final assessment of the quality of information is carried out by the Data Production and Analysis Unit of the ILO Department of Statistics. If any issues encountered cannot be clarified, the respective information is not published. </p>", "COVERAGE__GLOBAL"=>"<p>Data may differ from those published on ILOSTAT due to different update schedules and stricter criteria applied for inclusion in the SDG database.</p>\n<p><strong>Data Availability: </strong></p>\n<p>Data for this indicator is available for 188 countries and territories.</p>\n<p><strong>Time series:</strong></p>\n<p>Data for this indicator is available as of 2000 in the SDG Indicators Global Database, but time series going back to 1991 and including estimates up to 2024 are available in ILOSTAT.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>No disaggregation required for this indicator.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The main limitations of the use of labour productivity as a global indicator arise from problems in the international comparability of data, more specifically from methodological differences across countries. Even though national output measures, in particular GDP estimates, are derived mainly from national accounts which should be based on internationally agreed principles consolidated in the United Nations SNA, there are still significant obstacles to the international consistency of national accounts estimates. These range from differences in the treatment of the output of service sectors to adjustments for price changes and variations in the coverage of informal activities and the underground economy.</p>\n<p>Employment or labour input figures also suffer from comparability issues, especially in terms of differences in age coverage, the definition of employment, geographical and institutional coverage, the treatment of special groups and the coverage of informal employment.</p>\n<p>In cases where the contribution to GDP of forms of work other than employment are expected to be significant, such as in the case of own-use production of goods (subsistence agriculture and fishing) or volunteer work, the exclusion of participation and time-spent in these productive activities can be an important source of bias in the resulting indicators.</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (https://www.ilo.org/publications/decent-work-and-sustainable-development-goals-guidebook-sdg-labour-market) </li>\n  <li>Estimates and projections of labour market indicators (<a href=\"http://www.ilo.org/empelm/projects/WCMS_114246/lang--en/index.htm\" target=\"_blank\"><u>http://www.ilo.org/empelm/projects/WCMS_114246/lang--en/index.htm</u></a>) </li>\n  <li>ILO Manual &#x2013; Decent Work Indicators, Concepts and Definitions &#x2013; Chapter 1, Economic and social context for decent work <a href=\"http://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm\" target=\"_blank\"><u>http://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm</u></a> (second version, page 2149) </li>\n  <li>Resolution concerning statistics of work, employment and labour underutilization <a href=\"http://www.ilo.ch/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm\" target=\"_blank\"><u>http://www.ilo.ch/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm</u></a> </li>\n  <li>System of National Accounts 2008 <a href=\"http://unstats.un.org/unsd/nationalaccount/sna2008.asp\" target=\"_blank\"><u>http://unstats.un.org/unsd/nationalaccount/sna2008.asp</u></a> </li>\n  <li>Trends Econometric Models: A Review of Methodology <a href=\"http://www.ilo.org/empelm/pubs/WCMS_120382/lang--en/index.htm\" target=\"_blank\"><u>http://www.ilo.org/empelm/pubs/WCMS_120382/lang--en/index.htm</u></a> </li>\n  <li>ILOSTAT portal (<a href=\"https://ilostat.ilo.org/\" target=\"_blank\"><u>https://ilostat.ilo.org/</u></a>) </li>\n  <li>ILOSTAT portal &#x2013; Topics - Labour productivity (<a href=\"https://ilostat.ilo.org/topics/labour-productivity/\" target=\"_blank\"><u>https://ilostat.ilo.org/topics/labour-productivity/</u></a>). </li>\n  <li>Quick guide to understanding the impact of the new statistical standards on ILOSTAT databases, available at <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf</a> </li>\n</ul>", "indicator_sort_order"=>"08-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.3.1", "slug"=>"8-3-1", "name"=>"Proporción de empleo informal con respecto al empleo total, desglosada por sector y sexo", "url"=>"/site/es/8-3-1/", "sort"=>"080301", "goal_number"=>"8", "target_number"=>"8.3", "global"=>{"name"=>"Proporción de empleo informal con respecto al empleo total, desglosada por sector y sexo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de empleo informal con respecto al empleo total, desglosada por sector y sexo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de empleo informal con respecto al empleo total, desglosada por sector y sexo", "indicator_number"=>"8.3.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"En contextos donde la cobertura de la protección social es limitada, las prestaciones \nde la seguridad social (como el seguro de desempleo) son insuficientes o incluso \ninexistentes, o donde los salarios y las pensiones son bajos, las personas pueden \nverse obligadas a aceptar empleos informales para asegurar su sustento. \n\nEn estas situaciones, indicadores como la tasa de desempleo ofrecerían una imagen \nmuy incompleta de la situación del mercado laboral, pasando por alto importantes \ndéficits en la calidad del empleo. \n\nLas estadísticas sobre la informalidad son \nclave para evaluar la calidad del empleo en una economía y son relevantes \ntanto para los países en desarrollo como para los desarrollados.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.3.1&seriesCode=SL_ISV_IFEM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20BOTHSEX%20%7C%20TOTAL\">Proporción de empleo informal, por sector y sexo - 13ª CIET (%) SL_ISV_IFEM</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-03-01.pdf\">Metadatos 8-3-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-28", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"In contexts where social protection coverage is limited, social security \nbenefits (such as unemployment insurance) are insufficient or even inexistent, \nand/or where wages and pensions are low, individuals may have to take up \ninformal employment to ensure their livelihood. \n\nIn these situations, indicators such as the unemployment rate would provide \na very incomplete picture of the labour market situation, overlooking major \ndeficits in the quality of employment. \n\nStatistics on informality are key to assessing the quality of employment in \nan economy and are relevant to developing and developed countries alike. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.3.1&seriesCode=SL_ISV_IFEM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20BOTHSEX%20%7C%20TOTAL\">Proportion of informal employment, by sector and sex - 13th ICLS (%) SL_ISV_IFEM</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-03-01.pdf\">Metadata 8-3-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Gizarte-babesaren estaldura mugatua den testuinguruetan, Gizarte Segurantzaren prestazioak \n(langabezia-asegurua, adibidez) ez dira nahikoak edo, are, ez dira existitzen, edo soldatak \neta pentsioak txikiak dira. Testuinguru horietan, pertsonek beren burua behartuta ikus dezakete \nenplegu informalak onartzera, beren mantenua bermatzeko. \n\nEgoera horietan, langabezia-tasa bezalako adierazleek lan-merkatuaren egoeraren oso irudi \nosatugabea eskainiko lukete, ez bailituzkete kontuan hartuko enpleguaren kalitatean dauden \ngabezia handiak. \n\nInformaltasunari buruzko estatistikak funtsezkoak dira ekonomia batean enpleguaren kalitatea \nebaluatzeko, eta garrantzitsuak dira garapen-bidean dauden herrialdeentzat zein herrialde garatuentzat. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.3.1&seriesCode=SL_ISV_IFEM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20BOTHSEX%20%7C%20TOTAL\">Enplegu informalaren proportzioa, sektorearen eta sexuaren arabera - 13. CIET (%) SL_ISV_IFEM</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-03-01.pdf\">Metadatuak 8-3-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.3: Promote development-oriented policies that support productive activities, decent job creation, entrepreneurship, creativity and innovation, and encourage the formalization and growth of micro-, small- and medium-sized enterprises, including through access to financial services</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.3.1: Proportion of informal employment in total employment, by sector and sex</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_ISV_IFEM - Proportion of informal employment, by sector and sex - 13th ICLS [8.3.1]</p>\n<p>SL_ISV_IFEM_19ICLS - Proportion of informal employment, by sector and sex - 19th ICLS [8.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.1.1, 1.3.1, 8.5.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator presents the share of employment which is classified as informal employment in the total economy, and separately in agriculture and in non-agriculture.</p>\n<p><strong>Concepts:</strong></p>\n<p>Employment comprises all persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. The difference between the two series for a given country is the operational criteria used to define employment, with one series based on the statistical standards from the 13<sup>th</sup> International Conference of Labour Statisticians (ICLS) and the other series based on 19<sup>th</sup> ICLS standards. In the 19<sup>th</sup> ICLS series, employment is defined more narrowly as work done for pay or profit, while activities not done mainly in exchange for remuneration (i.e., own-use production work, volunteer work and unpaid trainee work) are recognized as other forms of work.</p>\n<p>Informal employment comprises persons who in their main or secondary jobs were in one of the following categories:</p>\n<p>- Own-account workers, employers and members of producers&#x2019; cooperatives employed in their own informal sector enterprises (the characteristics of the enterprise determine the informal nature of their jobs)</p>\n<p>- Own-account workers engaged in the production of goods exclusively for own final use by their household (e.g., subsistence farming), if covered in employment (i.e. 13<sup>th</sup> ICLS series)</p>\n<p>- Contributing family workers if they work in formal or informal sector enterprises (they usually do not have explicit, written contracts of employment, and are not subject to labour legislation, social security regulations, collective agreements, etc., which determines the informal nature of their jobs)</p>\n<p>- Employees holding informal jobs, whether employed by formal sector enterprises, informal sector enterprises, or as paid domestic workers by households (employees are considered to have informal jobs if their employment relationship is, in law or in practice, not subject to national labour legislation, income taxation, social protection or entitlement to certain employment benefits)</p>\n<p>To classify persons into formal or informal employment for this indicator, only the characteristics of the main job are considered, as the required information to assess (in)formality of the second job is usually unavailable. </p>\n<p>An enterprise belongs to the informal sector if it fulfils the three following conditions:</p>\n<p>- It is an unincorporated enterprise (it is not constituted as a legal entity separate from its owners, and it is owned and controlled by one or more members of one or more households, and it is not a quasi-corporation: it does not have a complete set of accounts, including balance sheets)</p>\n<p>- It is a market enterprise (it sells at least some of the goods or services it produces);</p>\n<p>- The enterprise is not registered or the employees of the enterprise are not registered </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The breakdown by sector is based on the International Standard Industrial Classification of All Economic Activities (ISIC). Agriculture corresponds to ISIC Rev. 4 section A, Rev. 3 sections A and B, and Rev.2 section 1 and non-agriculture corresponds to Rev. 4 sections B-U, Rev. 3 sections C-Q, and Rev. 2 sections 2-9. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The preferred source of data for this indicator is a labour force survey, with sufficient questions to determine the informal nature of jobs and whether the establishment where the person works in belongs to the formal or the informal sector.</p>", "COLL_METHOD__GLOBAL"=>"<p>The ILO Department of Statistics processes national household survey micro datasets in line with internationally agreed indicator concepts and definitions set forth by the ICLS.</p>", "FREQ_COLL__GLOBAL"=>"<p>Continuous</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Continuous for country-level data and annually for global and regional estimates (November or December).</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "INST_MANDATE__GLOBAL"=>"<p>The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.</p>", "RATIONALE__GLOBAL"=>"<p>In contexts where social protection coverage is limited, social security benefits (such as unemployment insurance) are insufficient or even inexistent, and/or where wages and pensions are low, individuals may have to take up informal employment to ensure their livelihood. In these situations, indicators such as the unemployment rate would provide a very incomplete picture of the labour market situation, overlooking major deficits in the quality of employment. Statistics on informality are key to assessing the quality of employment in an economy and are relevant to developing and developed countries alike.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The considerable heterogeneity of definitions and operational criteria used by countries to measure informal employment greatly hinders the international comparability of statistics on informality. </p>\n<p>To counter this challenge, for the purpose of SDG global reporting and monitoring, the series is solely based on harmonized data produced by the ILO using the same operational process for all countries. Although some differences in criteria and definitions remain across countries, the process is designed to produce data that are as internationally comparable as possible given the underlying data sources. </p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">g</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">g</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The ILO engages in annual consultations with Member States through the ILOSTAT questionnaire and related Statistics Reporting System (StaRS). National data providers receive a link to the portal where they can review all national SDG data available on ILOSTAT.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Through the ILO Harmonized Microdata initiative, the ILO strives to produce internationally comparable labour statistics based on the indicator concepts and definitions adopted by the ICLS.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>See below</p>", "REG_AGG__GLOBAL"=>"<p>The ILO produces global and regional estimates of informal employment by sex based on available national estimates reflecting the 13<sup>th</sup> ICLS standards. Global and regional estimates do not include the breakdown by sector (agriculture, non-agriculture). Input data for informality is available for at least one year of the series in about three-quarters of the countries in the target sample. Benchmark employment data are derived from the ILO modelled estimates series. </p>\n<p>Missing observations are imputed using a series of models that establish statistical relationships between the observed incidence of informal employment and explanatory variables. The explanatory variables used include economic and demographic variables, such as GDP per capita and urbanisation. Panel data regression and cross-validation techniques are used to establish the statistical relationships necessary for the imputation. The global and regional proportions of informal employment are obtained by first adding up, across countries, the numerator and denominator of the formula that defines the proportion of workers in informal employment outlined above.</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Resolution concerning statistics on the informal economy adopted by the 21<sup>st</sup> ICLS (October 2023), available at <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_901516.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_901516.pdf</a> </li>\n  <li>ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (https://www.ilo.org/publications/decent-work-and-sustainable-development-goals-guidebook-sdg-labour-market)</li>\n  <li>Resolution concerning statistics of employment in the informal sector, adopted by the Fifteenth International Conference of Labour Statisticians (January 1993), available at https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@dgreports/@stat/documents/normativeinstrument/wcms_087484.pdf</li>\n  <li>Guidelines concerning a statistical definition of informal employment, adopted by the Seventeenth International Conference of Labour Statisticians (November-December 2003) available at https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@dgreports/@stat/documents/normativeinstrument/wcms_087622.pdf</li>\n  <li>ILO manual Measuring informality: A statistical manual on the informal sector and informal employment available at <a href=\"http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_222979.pdf\"><u>http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_222979.pdf</u></a></li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>The processes of compilation, production, and publication of data, including its quality control, are carried out following the methodological framework and standards established by the ILO Department of Statistics, in compliance with the information technology and management standards of the ILO.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination on ILOSTAT.</p>\n<p> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The final assessment of the quality of information is carried out by the Data Production and Analysis Unit of the ILO Department of Statistics. In cases of doubt about the quality of specific data, these values are reviewed with the participation of the national agencies responsible for producing the data if appropriate. If the issues cannot be clarified, the respective information is not published. </p>", "COVERAGE__GLOBAL"=>"<p>Data may differ from those published on ILOSTAT due to different update schedules and stricter criteria applied for inclusion in the SDG database.</p>\n<p><strong>Data availability: </strong></p>\n<p>Data for this indicator is available for 144 countries and territories in the 13<sup>th</sup> ICLS series and 88 countries and territories in the 19<sup>th</sup> ICLS series.</p>\n<p><strong>Time series: </strong></p>\n<p>The submission covers global and regional data for 2004 to 2022 and country data from 2000 to 2023.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data on this indicator is requested disaggregated by sector and sex.</p>\n<p>Here, sector refers to the breakdown by agriculture/non-agriculture. Where necessary and possible, the disaggregation by sector could go into a more detailed breakdown by economic activity. For global and regional monitoring, no breakdowns of agriculture and non-agriculture are used.</p>\n<p>To produce this indicator, employment statistics disaggregated by sex, formal/informal employment, and economic activity (agriculture/non-agriculture) are needed.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Although some international standards do exist for the compilation of informal employment statistics, the relevant concepts and definitions have been left relatively flexible to accommodate national contexts and needs. This means that, in practice, the operational criteria used by countries to compile data at the national level vary significantly from country to country, hindering the international comparability of statistics. The comparability of informal employment statistics is also highly sensitive to differences in the geographical areas covered, the economic activities covered and the treatment of special groups of workers.</p>\n<p>Work statistics for countries not using the same set of statistical standards are not comparable. As such, each series is based on a single set of standards (i.e., 13<sup>th</sup> or 19<sup>th</sup> ICLS) and contains only data comparable within and across countries, allowing data users to continue making meaningful time series analysis and international comparisons. Users should not compare data across series. </p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>ILOSTAT portal: <a href=\"https://ilostat.ilo.org\">https://ilostat.ilo.org</a> </li>\n  <li>Resolution concerning statistics on the informal economy adopted by the 21<sup>st</sup> ICLS (October 2023), available at <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_901516.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_901516.pdf</a> </li>\n  <li>ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (<a href=\"https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm</a>)</li>\n  <li>Resolution concerning statistics of employment in the informal sector, adopted by the Fifteenth International Conference of Labour Statisticians (January 1993), available at https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@dgreports/@stat/documents/normativeinstrument/wcms_087484.pdf</li>\n  <li>Guidelines concerning a statistical definition of informal employment, adopted by the Seventeenth International Conference of Labour Statisticians (November-December 2003) available at https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@dgreports/@stat/documents/normativeinstrument/wcms_087622.pdf</li>\n  <li>ILO manual Measuring informality: A statistical manual on the informal sector and informal employment, available at https://www.ilo.org/publications/measuring-informality-statistical-manual-informal-sector-and-informal </li>\n  <li>Resolution concerning statistics of work, employment and labour underutilization adopted by the 19th ICLS (October 2013) and amended by the 21<sup>st</sup> ICLS (October 2023), available at <a href=\"https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm</a></li>\n  <li>Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the 13<sup>th</sup> ICLS (October 1982), available at https://www.ilo.org/resource/resolution-concerning-statistics-economically-active-population-employment</li>\n  <li>Quick guide to understanding the impact of the new statistical standards on ILOSTAT databases, available at <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf</a> </li>\n  <li>International Standard Industrial Classification of All Economic Activities <a href=\"https://unstats.un.org/unsd/classifications/Econ/isic\">https://unstats.un.org/unsd/classifications/Econ/isic</a> </li>\n</ul>", "indicator_sort_order"=>"08-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.4.1", "slug"=>"8-4-1", "name"=>"Huella material en términos absolutos, huella material per cápita y huella material por PIB", "url"=>"/site/es/8-4-1/", "sort"=>"080401", "goal_number"=>"8", "target_number"=>"8.4", "global"=>{"name"=>"Huella material en términos absolutos, huella material per cápita y huella material por PIB"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Huella material en términos absolutos, huella material per cápita y huella material por PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Huella material en términos absolutos, huella material per cápita y huella material por PIB", "indicator_number"=>"8.4.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La huella material del consumo informa la cantidad de materias primas necesarias \npara satisfacer la demanda final de un país y puede interpretarse como un indicador \ndel nivel de vida material/nivel de capitalización de una economía. La huella material \nper cápita describe el uso promedio de materiales para la demanda final.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-04-01.pdf\">Metadatos 8-4-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-28", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Material footprint of consumption reports the amount of primary materials \nrequired to serve final demand of a country and can be interpreted as an \nindicator of the material standard of living/level of capitalization of an \neconomy. Per-capita MF describes the average material use for final demand. \n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-04-01.pdf\">Metadata 8-4-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Kontsumoaren aztarna materialak herrialde baten azken eskariari erantzuteko behar den lehengai-kopuruaren \nberri ematen du, eta bizi-maila materialaren/ekonomia baten kapitalizazio-mailaren adierazle gisa interpreta \ndaiteke. Biztanleko aztarna materialak azken eskarirako materialen batez besteko erabilera deskribatzen du. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-04-01.pdf\">Metadatuak 8-4-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.4: Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation, in accordance with the 10-Year Framework of Programmes on Sustainable Consumption and Production, with developed countries taking the lead</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.4.1: Material Footprint, material footprint per capita, and material footprint per GDP</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_MAT_FTPRPC - Material footprint per capita [8.4.1, 12.2.1]</p>\n<p>EN_MAT_FTPRPG - Material footprint per unit of GDP [8.4.1, 12.2.1]</p>\n<p>EN_MAT_FTPRTN - Material footprint [8.4.1, 12.2.1] </p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>12.2.1, 8.4.2, 12.2.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Material Footprint (MF) is the attribution of global material extraction to domestic final demand of a country. The total material footprint is the sum of the material footprint for biomass, fossil fuels, metal ores and non-metallic minerals. </p>\n<p><strong>Concepts:</strong></p>\n<p>Domestic Material Consumption (DMC) and MF need to be looked at in combination, as they cover the two aspects of the economy, production and consumption. The DMC reports the actual amount of material in an economy, MF the virtual amount required across the whole supply chain to service final demand. A country can, for instance, have a very high DMC because it has a large primary production sector for export or a very low DMC because it has outsourced most of the material intensive industrial process to other countries. The material footprint corrects for both phenomena. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Tonnes;</p>\n<p>Kilograms per constant United States dollar;</p>\n<p>Tonnes per capita.</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>Material categories accordance to the global EW-MFA guide &#x201C;UNEP (2023). The use of natural resources in the economy: A Global Manual on Economy Wide Material Flow Accounting&#x201D; (<a href=\"https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y\">https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y</a>);</li>\n  <li><u>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)</u></li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>The global estimation for MF is based on data available from different national and international datasets in the domain of material flow accounts, agriculture, forestry, fisheries, mining and energy statistics. International statistical sources for MF include the International Energy Agency, the United Nations Statistical Division, the United States Geological Survey, the Food and Agriculture Organization and COMTRADE databases. </p>", "COLL_METHOD__GLOBAL"=>"<p>For global estimation, the International Resource Panel (IRP) Global Material Flows and Resource Productivity working group compiles the data from national and international databases.</p>\n<p>At the same time, country-provided indicators are collected through the QUESTIONNAIRE ON ECONOMY WIDE MATERIAL FLOW ACCOUNTS for the SDG indicators 8.4.1/12.2.1 and 8.4.2/12.2.2.</p>", "FREQ_COLL__GLOBAL"=>"<p> First data collection in 2022 and every 2 to 3 years after.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>First data release in 2017, the second in 2021 (fully estimated data). Then, in 2022 and every 2 to 3 years after (both globally estimated and country data).</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), Organization for Economic Co-operation and Development (OECD) and EUROSTAT</p>", "INST_MANDATE__GLOBAL"=>"<p>UNEP was mandated as a Custodian Agency for indicator 8.4.1 / 12.2.1 by the Inter-agency and Expert Group on SDG Indicators. UNEP IRP is the mechanism within UNEP supporting all work aspect in relation to Material Flow Accounting.</p>", "RATIONALE__GLOBAL"=>"<p>Material footprint of consumption reports the amount of primary materials required to serve final demand of a country and can be interpreted as an indicator of the material standard of living/level of capitalization of an economy. Per-capita MF describes the average material use for final demand.</p>", "REC_USE_LIM__GLOBAL"=>"<p>A footprint calculation uses the global Multi-Regional Input Output<strong> </strong>(MRIO) analysis, which compiles information from many countries national statistics to create a global multi-regional input-output table. This process requires a high level of computing capacity by supercomputers. Therefore, a limited number of countries can do the analysis on its own.</p>", "DATA_COMP__GLOBAL"=>"<p>Material footprint by type of raw material (tonnes) is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>M</mi>\n    <mi>F</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi>D</mi>\n        <mi>E</mi>\n        <mo>+</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>R</mi>\n        <mi>M</mi>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mi>I</mi>\n        <mi>M</mi>\n      </mrow>\n    </msub>\n    <mo>-</mo>\n    <mi>&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi>R</mi>\n        <mi>M</mi>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mi>E</mi>\n        <mi>X</mi>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n    </msub>\n  </math></p>\n<p>Where: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>M</mi>\n    <mi>F</mi>\n  </math> &#x2013; material footprint;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>E</mi>\n  </math><em> </em>&#x2013; domestic extraction of materials;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>R</mi>\n        <mi>M</mi>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mi>I</mi>\n        <mi>M</mi>\n      </mrow>\n    </msub>\n  </math> &#x2013; raw material equivalent of imports;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>R</mi>\n        <mi>M</mi>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mi>E</mi>\n        <mi>X</mi>\n      </mrow>\n    </msub>\n  </math> &#x2013; raw material equivalents of exports.</p>\n<p> </p>\n<p>For the attribution of the primary material needs of final demand a global, multi-regional input-output (MRIO) framework is employed. The attribution method based on I-O analytical tools is described in detail in Wiedmann et al. 2015. It is based on the Eora MRIO framework developed by the University of Sydney, Australia (Lenzen et al. 2013) which is an internationally well-established and the most detailed and reliable MRIO framework available to date. </p>\n<p>Material footprint per capita, by type of raw material (tonnes), is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>M</mi>\n    <mi>F</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>p</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>M</mi>\n        <mi>F</mi>\n      </mrow>\n      <mrow>\n        <mi>A</mi>\n        <mi>n</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Material footprint per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollar), is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>M</mi>\n    <mi>F</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>G</mi>\n    <mi>D</mi>\n    <mi>P</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>M</mi>\n        <mi>F</mi>\n      </mrow>\n      <mrow>\n        <mi>G</mi>\n        <mi>D</mi>\n        <mi>P</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>2015</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>U</mi>\n        <mi>n</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>S</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>D</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP) sends a prefilled questionnaire with estimated data to the National Statistical Office (NSO) Focal Points (FP) with a request to validate globally estimated data for this indicator and replace the data if needed/possible. The FPs coordinate data validation with stakeholders within their countries and report back the data to UNEP. For countries with no national data collected for this indicator, UNEP asks to agree on publishing and releasing the estimated data on UNEP&#x2019;s World Environment Situation Room and UNSD SDG Global database.</p>", "ADJUSTMENT__GLOBAL"=>"<p>UNEP replaces globally estimated data by national data if requested by the country. </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level:</strong></p>\n<p>A zero is imputed when no positive real value was officially recorded, in the base data sets used, for any of the underlying components which make up this aggregated total. Thus &#x201C;0.0&#x201D; can represent either NA, or a genuine 0.0, or (crucially) a combination of both, which is a common situation. This allows for values to be easily aggregated further; however, it should be thus noted that due to imputing missing values as &#x201C;0.0&#x201D;, the aggregations may represent a lower value than the actual situation. </p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels:</strong></p>\n<p>Similarly, missing values are imputed as zero in the regional and global aggregations. However, in the case where no data is available at all for a particular country, then the per capita and per GDP estimates are weighted averages of the available data. </p>", "REG_AGG__GLOBAL"=>"<p>The data are aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see: <a href=\"http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a></p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>United Nations Environment Programme (UNEP) jointly with the International Resource Panel (IRP), United Nations Statistics Division (UNSD), the Statistical Office of the European Union (Eurostat) and the Organisation for Economic Co-operation and Development (OECD) have developed a global manual on Economy-Wide Material Flow Accounting (EW-MFA) which brings in the European guidelines but provides a modular approach for countries looking to develop EW-MFA for the first time and it addresses specific issues related to resource extractive based economies. UNEP (2023). The use of natural resources in the economy - A Global Manual on Economy Wide Material Flow Accounting: <a href=\"https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y\">https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y</a></li>\n  <li>EUROSTAT (2018). The EU Economy-wide material flow accounts handbook 2018: <a href=\"https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006\">https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006</a> </li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), using the Global Manual on Economy-Wide Material Flow Accounting (UNEP, 2023).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), using the Global Manual on Economy Wide Material Flow Accounting (UNEP, 2023).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), in consultation with countries (nominated Focal Points) after receiving their feedback on the globally estimated indicators.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The data covers about 160 countries (either globally estimated or country data).</p>\n<p><strong>Time series:</strong></p>\n<p>The data set presented in the SDG database covers a time period of 24 years (2000-2023). </p>\n<p>The International Resource Panel (IRP) publishes estimated data series for 1970-2024 on its website. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The Material Footprint indicator is disaggregated into four main material categories (biomass, fossil fuels, metal ores and non-metallic minerals). </p>", "COMPARABILITY__GLOBAL"=>"<p>Material Footprint is calculated coherent with international standards, recommendations, and classifications such as the System of National Accounts 2008, the System of Environmental-Economic Accounting &#x2013; Central Framework 2012, the Balance of Payments and International Investment Position, the International Standard Industrial Classification of All Economic Activities (ISIC), the Central Product Classification (CPC) and the Framework for the Development of Environment Statistics.</p>\n<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>UNEP (2023), The use of National Resources in the Economy: a Global Manual on Economy Wide Material Flow Accounting. https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y</p>\n<p><strong>References:</strong></p>\n<p>EUROSTAT (2013). Economy-Wide Material Flow Accounts. Compilation guide 2013: https://ec.europa.eu/eurostat/documents/1798247/6191533/2013-EW-MFA-Guide-10Sep2013.pdf/54087dfb-1fb0-40f2-b1e4-64ed22ae3f4c</p>\n<p>EUROSTAT (2018). The EU Economy-wide material flow accounts handbook 2018: <a href=\"https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006\">https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006</a></p>\n<p>Wiedmann, T., H. Schandl, M. Lenzen, D. Moran, S. Suh, J. West, K. Kanemoto, (2013) The Material Footprint of Nations, Proc. Nat. Acad. Sci. Online before print.</p>\n<p>Lenzen, M., Moran, D., Kanemoto, K., Geschke, A. (2013) Building Eora: A global Multi-regional Input-Output Database at High Country and Sector Resolution, Economic Systems Research, 25:1, 20-49. </p>", "indicator_sort_order"=>"08-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.4.2", "slug"=>"8-4-2", "name"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB", "url"=>"/site/es/8-4-2/", "sort"=>"080402", "goal_number"=>"8", "target_number"=>"8.4", "global"=>{"name"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB", "indicator_number"=>"8.4.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso en la serie Consumo doméstico de materiales por PIB", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/estadistica-de-flujo-de-materiales-090217/web01-s2ing/es/", "url_text"=>"Estadística de Flujo de Materiales", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.4- Mejorar progresivamente, de aquí a 2030, la producción y el consumo eficientes de los recursos mundiales y procurar desvincular el crecimiento económico de la degradación del medio ambiente, conforme al Marco Decenal de Programas sobre Modalidades de Consumo y Producción Sostenibles, empezando por los países desarrollados", "definicion"=>"El consumo doméstico de materiales (CDM), o consumo interno de materiales, es un indicador \nestándar de contabilidad de flujo de materiales (MFA) e informa el consumo aparente \nde materiales en la economía de la C.A. de Euskadi.\n\nEl CDM mide la cantidad total de material (biomasa, combustibles fósiles, minerales \nmetálicos y minerales no metálicos) utilizado directamente en una economía y basado \nen cuentas de flujos directos de materiales, es decir, material extraido, \nimportaciones y exportaciones físicas.\n\nLos datos se presentan en términos absolutos, por habitante y por unidad de PIB.\n", "formula"=>"<b>Consumo doméstico de materiales</b>\n\n$$CDM^t = Extr^t + Imp^t - Exp^t$$\n\ndonde:\n\n$Extr^t =$ Extracción doméstica en el año $t$\n\n$Imp^t =$ Importaciones en el año $t$\n\n$Exp^t =$ Exportaciones en el año $t$\n\n<br>\n\n<b>Consumo doméstico de materiales per cápita</b>\n\n$$CMDPC^{t} = \\frac{{CMD^{t}}}{P^{t}}$$\n\ndonde:\n\n$CMD^{t} =$ consumo doméstico de materiales en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n\n<br>\n\n<b>Consumo doméstico de materiales per cápita por unidad de PIB</b>\n\n$$CMDPIB^{t} = \\frac{{CMD^{t}}}{PIB_{2022}^{t}}$$\n\ndonde:\n\n$CMD^{t} =$ consumo doméstico de materiales en el año $t$\n\n$PIB_{2022}^{t} =$ Producto interior bruto en volumen encadenado con referencia 2022 \nen millones de euros en el año $t$ \n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"", "justificacion_global"=>"\nEl consumo doméstico de materiales (CDM) informa sobre la cantidad de materiales que se \nutilizan en una economía. Es un indicador territorial (del lado de la producción). \n\nEl CDM \ntambién presenta la cantidad de materiales que se deben manipular dentro de una economía, \nque se agregan a las existencias de materiales de los edificios y la infraestructura \nde transporte o se utilizan para impulsar la economía como producción de materiales. \nDescribe la dimensión física de los procesos e interacciones económicas. También se \npuede interpretar como equivalente de residuos a largo plazo. \n\nEl CDM per cápita \ndescribe el nivel promedio de uso de materiales en una economía (un indicador \nde presión ambiental) y también se lo conoce como perfil metabólico.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-04-02.pdf\">Metadatos 8-4-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.4- Mejorar progresivamente, de aquí a 2030, la producción y el consumo eficientes de los recursos mundiales y procurar desvincular el crecimiento económico de la degradación del medio ambiente, conforme al Marco Decenal de Programas sobre Modalidades de Consumo y Producción Sostenibles, empezando por los países desarrollados", "definicion"=>"Domestic material consumption (DMC), or internal material consumption, \nis a standard material flow accounting (MFA) indicator and reports the \napparent consumption of materials in the Basque Country economy. \n\nDMC measures the total amount of material (biomass, fossil fuels, metallic \nminerals, and non-metallic minerals) directly used in an economy and is based \non direct material flow accounts, i.e., domestic material extraction, and physical imports\nand exports. \n\nData are presented in absolute terms, per capita, and per unit of GDP. \n", "formula"=>"<b>Domestic material consumption</b>\n\n$$CDM^t = Extr^t + Imp^t - Exp^t$$\n\nwhere:\n\n$Extr^t =$ domestic extraction of materials in year $t$\n\n$Imp^t =$ imports in year $t$\n\n$Exp^t =$ exports in year $t$\n\n<br>\n\n<b>Domestic material consumption per capita</b>\n\n$$CMDPC^{t} = \\frac{{CMD^{t}}}{P^{t}}$$\n\nwhere:\n\n$CMD^{t} =$ domestic material consumption in year $t$\n\n$P^{t} =$ population as of 1 July of year $t$\n\n<br>\n\n<b>Domestic material consumption per unit of GDP</b>\n\n$$CMDPIB^{t} = \\frac{{CMD^{t}}}{PIB_{2022}^{t}}$$\n\nwhere:\n\n$CMD^{t} =$ domestic material consumption in year $t$\n\n$PIB_{2022}^{t} =$ Gross domestic product in chained volumes with reference to 2022\nin millions of euros for the year $t$ \n", "desagregacion"=>nil, "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nDomestic Material Consumption (DMC) reports the amount of materials that are used \nin a national economy. It is a territorial (production side) indicator. \n\nDMC also presents the amount of material that needs to be handled within an economy, \nwhich is either added to material stocks of buildings and transport infrastructure \nor used to fuel the economy as material throughput. \n\nPer-capita DMC describes the average level of material use in an economy – an \nenvironmental pressure indicator – and is also referred to as metabolic profile. \n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-04-02.pdf\">Metadata 8-4-2.pdf</a>", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.4- Mejorar progresivamente, de aquí a 2030, la producción y el consumo eficientes de los recursos mundiales y procurar desvincular el crecimiento económico de la degradación del medio ambiente, conforme al Marco Decenal de Programas sobre Modalidades de Consumo y Producción Sostenibles, empezando por los países desarrollados", "definicion"=>"Materialaren bertako kontsumoa (MBK), material-fluxuaren kontabilitate-adierazle estandarra \nda, eta Euskal AEko ekonomian materialen itxurazko kontsumoaren berri ematen du.\n\nMBKak ekonomia batean zuzenean erabilitako eta material-fluxu zuzenen kontuetan oinarritutako \nmaterial kantitate osoa neurtzen du (biomasa, erregai fosilak, mineral metalikoak eta mineral \nez-metalikoak), hau da, erauzitako materiala, inportazioak eta esportazio fisikoak.\n\nDatuak balio absolututan aurkezten dira, biztanleko eta BPGren unitateko.  \n", "formula"=>"<b>Materialaren bertako kontsumoa</b>\n\n$$CDM^t = Extr^t + Imp^t - Exp^t$$\n\nnon:\n\n$Extr^t =$ bertako erauzketa $t$ urtean\n\n$Imp^t =$ inportazioak $t$ urtean\n\n$Exp^t =$ esportazioak $t$ urtean\n\n<br>\n\n<b>Materialaren bertako kontsumoa Per capita </b>\n\n$$CMDPC^{t} = \\frac{{CMD^{t}}}{P^{t}}$$\n\nnon:\n\n$CMD^{t} =$ materialaren bertako kontsumoa $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n\n<br>\n\n<b>Materialaren bertako kontsumoa per capita BPG unitateko</b>\n\n$$CMDPIB^{t} = \\frac{{CMD^{t}}}{PIB_{2022}^{t}}$$\n\nnon:\n\n$CMD^{t} =$ materialaren bertako kontsumoa $t$ urtean\n\n$PIB_{2022}^{t} =$ Barne-produktu gordina, kateatutako bolumenean, 2022ko erreferentziarekin, milioi eurotan $t$ urtean \n", "desagregacion"=>nil, "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nMaterialen etxeko kontsumoak (MEK) ekonomia batean erabiltzen den material-kopuruari buruzko \ninformazioa ematen du. Lurralde-adierazle bat da (ekoizpenaren aldetik). \n\nEkonomia baten barruan manipulatu behar diren, eraikinen material-izakinei eta garraio azpiegiturari \ngehitzen zaizkien, edo ekonomia materialen ekoizpen gisa bultzatzeko erabiltzen diren materialen \nkopurua ere adierazten du MEK neurtzeak. Prozesu eta elkarreragin ekonomikoen dimentsio fisikoa \ndeskribatzen du. Epe luzeko hondakinen baliokidetzat ere interpreta daiteke. \n\nBiztanleko MEK adierazleak ekonomia bateko materialen batez besteko erabilera-maila deskribatzen du \n(ingurumen-presioaren adierazle bat), eta profil metaboliko ere esaten zaio. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-04-02.pdf\">Metadatuak 8-4-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.4: Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation, in accordance with the 10-Year Framework of Programmes on Sustainable Consumption and Production, with developed countries taking the lead</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.4.2: Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDP</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_MAT_DOMCMPC - Domestic material consumption per capita, by type of raw material [8.4.2,12.2.2]</p>\n<p>EN_MAT_DOMCMPG - Domestic material consumption per unit of GDP [8.4.2,12.2.2]</p>\n<p>EN_MAT_DOMCMPT - Domestic material consumption [8.4.2,12.2.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>8.4.1, 12.2.1, 12.2.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Domestic Material Consumption (DMC) is a standard material flow accounting (MFA) indicator and reports the apparent consumption of materials in a national economy. </p>\n<p>DMC measures the total amount of material (biomass, fossil fuels, metal ores and non-metallic minerals) directly used in an economy and based on accounts of direct material flows, i.e., domestic material extraction and physical imports and exports.</p>\n<p><strong>Concepts:</strong></p>\n<p>DMC and Material Footprint (MF) need to be looked at in combination, as they cover the two aspects of the economy, production and consumption. The DMC reports the actual amount of material in an economy, MF the virtual amount required across the whole supply chain to service final demand. A country can, for instance, have a very high DMC because it has a large primary production sector for export or a very low DMC because it has outsourced most of the material intensive industrial process to other countries. The material footprint corrects for both phenomena. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Tonnes;</p>\n<p>Kilograms per constant United States dollar;</p>\n<p>Tonnes per capita.</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>Material categories accordance to the global EW-MFA guide &#x201C;UNEP (2023). The use of natural resources in the economy: A Global Manual on Economy Wide Material Flow Accounting&#x201D; (<a href=\"https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y\">https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y</a>);</li>\n  <li>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>The global estimation of DMC is based on data available from different national and international datasets in the domain of agriculture, forestry, fisheries, mining and energy statistics. International statistical sources for DMC include the International Energy Agency, the United Nations Statistical Division, the United States Geological Survey, the Food and Agriculture Organisation and COMTRADE databases. </p>", "COLL_METHOD__GLOBAL"=>"<p>For global estimation, the International Resource Panel (IRP) Global Material Flows and Resource Productivity working group compiles the data from national and international databases.</p>\n<p>At the same time, country-provided indicators are collected through the QUESTIONNAIRE ON ECONOMY WIDE MATERIAL FLOW ACCOUNTS for the SDG indicators 8.4.1/12.2.1 and 8.4.2/12.2.2.</p>", "FREQ_COLL__GLOBAL"=>"<p> First data collection in 2022 and every 2 to 3 years after.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p> First data release in 2017, the second in 2021 (fully estimated data). Then, in 2022 and every 2 to 3 years after (both globally estimated and country data).</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices </p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), Organization for Economic Co-operation and Development (OECD) and EUROSTAT</p>", "INST_MANDATE__GLOBAL"=>"<p>UNEP was mandated as Custodian Agency for indicator 8.4.2 / 12.2.2 by the Inter-agency and Expert Group on SDG Indicators. UNEP IRP is the mechanism within UNEP supporting all work aspect in relation to Material Flow Accounting.</p>", "RATIONALE__GLOBAL"=>"<p>Domestic Material Consumption (DMC) reports the amount of materials that are used in a national economy. It is a territorial (production side) indicator. DMC also presents the amount of material that needs to be handled within an economy, which is either added to material stocks of buildings and transport infrastructure or used to fuel the economy as material throughput. It describes the physical dimension of economic processes and interactions. It can also be interpreted as long-term waste equivalent. Per-capita DMC describes the average level of material use in an economy &#x2013; an environmental pressure indicator &#x2013; and is also referred to as metabolic profile. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Domestic Material Consumption cannot be disaggregated to economic sectors which limits its potential to become a satellite account to the System of National Accounts (SNA). </p>", "DATA_COMP__GLOBAL"=>"<p>Domestic Material Consumption (DMC) is a standard material flow accounting (MFA) indicator. MFAs below to environmental-economic accounts and apply the accounting concepts, structures, rules and principles of the System of Environmental-Economic Accounting 2012 - Central Framework. It should be used in conjunction with reading the global EW-MFA guide The use of natural resources in the economy: A Global Manual on Economy Wide Material Flow Accounting (https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y).</p>\n<p>Domestic Material Consumption (DMC), by type of raw material (tonnes) is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>M</mi>\n    <mi>C</mi>\n    <mo>=</mo>\n    <mi>D</mi>\n    <mi>E</mi>\n    <mo>+</mo>\n    <mi>I</mi>\n    <mi>M</mi>\n    <mo>-</mo>\n    <mi>E</mi>\n    <mi>X</mi>\n    <mo>,</mo>\n  </math></p>\n<p>Where:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>M</mi>\n    <mi>C</mi>\n  </math> &#x2013; domestic material consumption;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>E</mi>\n  </math> &#x2013; domestic extraction of materials; </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>M</mi>\n  </math> &#x2013; direct imports;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>E</mi>\n    <mi>X</mi>\n  </math> &#x2013; direct exports.</p>\n<p>DMC measure the amount of materials that are used in economic processes. It does not include materials that are mobilized for the process of domestic extraction but do not enter the economic process. </p>\n<p>Domestic material consumption per capita, by type of raw material (tonnes), is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>M</mi>\n    <mi>C</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>p</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>D</mi>\n        <mi>M</mi>\n        <mi>C</mi>\n      </mrow>\n      <mrow>\n        <mi>A</mi>\n        <mi>n</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollars), is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>M</mi>\n    <mi>C</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>G</mi>\n    <mi>D</mi>\n    <mi>P</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>D</mi>\n        <mi>M</mi>\n        <mi>C</mi>\n      </mrow>\n      <mrow>\n        <mi>G</mi>\n        <mi>D</mi>\n        <mi>P</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>2015</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>U</mi>\n        <mi>n</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>S</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>D</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP) sends a prefilled questionnaire with estimated data to the National Statistical Office (NSO) Focal Points (FP) with a request to validate globally estimated data for this indicator and replace the data if needed/possible. The FPs coordinate data validation with stakeholders within their countries and report back the data to UNEP. For countries with no national data collected for this indicator, UNEP asks to agree on publishing and releasing the estimated data on UNEP&#x2019;s World Environment Situation Room and UNSD SDG Global database.</p>", "ADJUSTMENT__GLOBAL"=>"<p>UNEP replaces globally estimated data by national data if requested by the country.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level:</strong></p>\n<p>A zero is imputed when no positive real value was officially recorded, in the base data sets used, for any of the underlying components which make up this aggregated total. Thus &#x201C;0.0&#x201D; can represent either NA, or a genuine 0.0, or (crucially) a combination of both, which is a common situation. This allows for values to be easily aggregated further; however, it should be thus noted that due to imputing missing values as &#x201C;0.0&#x201D;, the aggregations may represent a lower value than the actual situation. </p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels:</strong></p>\n<p>Similarly, missing values are imputed as zero in the regional and global aggregations. However, in the case where no data is available at all for a particular country, the per capita and per GDP estimates are weighted averages of the available data. </p>", "REG_AGG__GLOBAL"=>"<p>The data are aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see: <a href=\"http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a></p>", "DOC_METHOD__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), jointly with the International Resource Panel (IRP) and United Nations Statistics Division (UNSD), the Statistical Office of the European Union (Eurostat) and the Organisation for Economic Co-operation and Development (OECD) have developed a global manual on Economy-Wide Material Flow Accounting (EW-MFA) which brings in the European guidelines, but provides a modular approach for countries looking to develop EW-MFA for the first time and it addresses specific issues related to resource extractive based economies. </p>\n<ul>\n  <li>UNEP (2023). The use of natural resources in the economy - A Global Manual on Economy Wide Material Flow Accounting: https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y</li>\n  <li>EUROSTAT (2018). The EU Economy-wide material flow accounts handbook 2018:https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), using the Global Manual on Economy-Wide Material Flow Accounting (UNEP, 2023).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), using the Global Manual on Economy Wide Material Flow Accounting (UNEP, 2023).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), in consultation with countries (nominated Focal Points) after receiving their feedback on the globally estimated indicators.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The data covers 193 countries (either globally estimated or country data).</p>\n<p><strong>Time series:</strong></p>\n<p>The data set presented in the SDG database covers a time period of 24 years (2000-2023). </p>\n<p>The International Resource Panel (IRP) publishes estimated data series for 1970-2024 on its website.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The Domestic Material Consumption (DMC) indicator is disaggregated by main material categories (biomass, fossil fuels, metal ores and non-metallic minerals). </p>", "COMPARABILITY__GLOBAL"=>"<p>Domestic Material Consumption is calculated coherent with international standards, recommendations, and classifications such as the System of National Accounts 2008, the System of Environmental-Economic Accounting &#x2013; Central Framework 2012, the Balance of Payments and International Investment Position, the International Standard Industrial Classification of All Economic Activities (ISIC), the Central Product Classification (CPC) and the Framework for the Development of Environment Statistics.</p>\n<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>UNEP (2023), The use of National Resources in the Economy: a Global Manual on Economy Wide Material Flow Accounting. https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y</p>\n<p><strong>References:</strong></p>\n<p>EUROSTAT (2013). Economy-Wide Material Flow Accounts. Compilation Guide 2013: https://ec.europa.eu/eurostat/documents/1798247/6191533/2013-EW-MFA-Guide-10Sep2013.pdf/54087dfb-1fb0-40f2-b1e4-64ed22ae3f4c</p>\n<p>EUROSTAT (2018). The EU Economy-wide material flow accounts handbook 2018: <a href=\"https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006\">https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006</a></p>\n<p>Wiedmann, T., H. Schandl, M. Lenzen, D. Moran, S. Suh, J. West, K. Kanemoto, (2013) The Material Footprint of Nations, Proc. Nat. Acad. Sci. Online before print.</p>\n<p>Lenzen, M., Moran, D., Kanemoto, K., Geschke, A. (2013) Building Eora: A global Multi-regional Input-Output Database at High Country and Sector Resolution, Economic Systems Research, 25:1, 20-49. </p>", "indicator_sort_order"=>"08-04-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.5.1", "slug"=>"8-5-1", "name"=>"Ingreso medio por hora de las personas empleadas, desglosado por sexo, edad, ocupación y personas con discapacidad", "url"=>"/site/es/8-5-1/", "sort"=>"080501", "goal_number"=>"8", "target_number"=>"8.5", "global"=>{"name"=>"Ingreso medio por hora de las personas empleadas, desglosado por sexo, edad, ocupación y personas con discapacidad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Ingreso medio por hora de las personas empleadas, desglosado por sexo, edad, ocupación y personas con discapacidad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Ingreso medio por hora de las personas empleadas, desglosado por sexo, edad, ocupación y personas con discapacidad", "indicator_number"=>"8.5.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Cuatrienal", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736177025&menu=ultiDatos&idp=1254735976596", "url_text"=>"Encuesta de estructura salarial", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}, {"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Anual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736177025&menu=ultiDatos&idp=1254735976596", "url_text"=>"Encuesta anual de estructura salarial", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}, {"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Anual", "url"=>"https://www.ine.es/dynt3/inebase/es/index.htm?padre=9494", "url_text"=>"Estadística del Salario de las Personas con Discapacidad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Ingreso medio por hora de personas empleadas, desglosado por sexo y personas con o sin discapacidad", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.5- De aquí a 2030, lograr el empleo pleno y productivo y el trabajo decente para todas las mujeres y los hombres, incluidos los jóvenes y las personas con discapacidad, así como la igualdad de remuneración por trabajo de igual valor", "definicion"=>"Ganancia bruta por hora normal de trabajo del personal asalariado\n", "formula"=>"<b>Ingreso medio por hora de las personas empleadas:</b>\n\n$$GH^{t} = \\frac{G^{t}}{H^{t}} $$\n\ndonde:\n\n$G^{t} =$ ganancia bruta del personal asalariado en el año $t$\n\n$H^{t} =$ horas normales de trabajo del personal asalariado en el año $t$\n\n<br>\n\n<b>Brecha salarial de género:</b>\n\n$$BSG^{t} = \\frac{GPH_{hombre}^{t} - GPH_{mujer}^{t}}{GPH_{hombre}^{t}} \\cdot 100$$\n\ndonde:\n\n$GPH_{hombre}^{t} =$ ganancia promedio por hora de los hombres en el año $t$\n\n$GPH_{mujer}^{t} =$ ganancia promedio por hora de las mujeres en el año $t$\n", "desagregacion"=>"Sexo\n\nPersonas sin discapacidad, y con discapacidad (personal asalariado con un \ngrado de discapacidad superior o igual al 33%)\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLos ingresos son un aspecto clave de la calidad del empleo y de las condiciones de \nvida. La información sobre los ingresos por hora desglosada por diversas clasificaciones \n(sexo, edad, ocupación, situación de discapacidad) proporciona cierta \nindicación de hasta qué punto se respeta o se logra la igualdad salarial.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-05-01.pdf\">Metadatos 8-5-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"Ingreso medio por hora de personas empleadas, desglosado por sexo y personas con o sin discapacidad", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.5- De aquí a 2030, lograr el empleo pleno y productivo y el trabajo decente para todas las mujeres y los hombres, incluidos los jóvenes y las personas con discapacidad, así como la igualdad de remuneración por trabajo de igual valor", "definicion"=>"Gross earnings per normal hour of work of salaried employees\n", "formula"=>"<b>Average hourly income of employed persons:</b>\n\n$$GH^{t} = \\frac{G^{t}}{H^{t}} $$\n\nwhere:\n\n$G^{t} =$ gross earnings of salaried personnel in year $t$\n\n$H^{t} =$ normal working hours of salaried personnel in year $t$\n\n<br>\n\n<b>Gender pay gap:</b>\n\n$$BSG^{t} = \\frac{GPH_{man}^{t} - GPH_{woman}^{t}}{GPH_{man}^{t}} \\cdot 100$$\n\nwhere:\n\n$GPH_{man}^{t} =$ average hourly earnings of men in year $t$\n\n$GPH_{woman}^{t} =$ average hourly earnings of women in year $t$\n", "desagregacion"=>"Sex\n\nPeople without disabilities and people with disabilities (salaried employees \nwith a disability level of 33% or greater) \n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEarnings are a key aspect of quality of employment and living conditions. Information \non hourly earnings disaggregated by various classifications (sex, age, occupation, \ndisability status) provide some indication of the extent to which pay equality is respected \nor achieved.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-05-01.pdf\">Metadata 8-5-1.pdf</a>", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Ingreso medio por hora de personas empleadas, desglosado por sexo y personas con o sin discapacidad", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.5- De aquí a 2030, lograr el empleo pleno y productivo y el trabajo decente para todas las mujeres y los hombres, incluidos los jóvenes y las personas con discapacidad, así como la igualdad de remuneración por trabajo de igual valor", "definicion"=>"Soldatapeko langileen irabazi gordina lanordu arrunt bakoitzeko\n", "formula"=>"<b>Enplegatuen batez besteko diru-sarrerak, lanorduko</b>\n\n$$GH^{t} = \\frac{G^{t}}{H^{t}} $$\n\nnon:\n\n$G^{t} =$ soldatapeko langileen irabazi gordina $t$ urtean\n\n$H^{t} =$ soldatapeko langileen lanordu arruntak $t$ urtean\n\n<br>\n\n<b>Generoko soldata-arrakala</b>\n\n$$BSG^{t} = \\frac{GPH_{gizona}^{t} - GPH_{emakumea}^{t}}{GPH_{gizona}^{t}} \\cdot 100$$\n\nnon:\n\n$GPH_{gizona}^{t} =$ gizonen orduko batez besteko irabazia $t$ urtean\n\n$GPH_{emakumea}^{t} =$ emakumeen orduko batez besteko irabazia $t$ urtean\n", "desagregacion"=>"Sexua\n\nDesgaitasunik ez duten eta desgaitasuna duten pertsonak (soldatapeko langileak, % 33ko \nedo hortik gorako desgaitasun-maila dutenak)\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nDiru-sarrerak enpleguaren kalitatearen eta bizi-baldintzen funtsezko alderdia dira. Orduko diru-sarrerei \nburuzko informazioak, hainbat sailkapenen arabera banakatuta (sexua, adina, okupazioa, desgaitasun-egoera), \nsoldata-berdintasuna zenbateraino errespetatzen edo lortzen den adierazten du. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-05-01.pdf\">Metadatuak 8-5-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.5: By 2030, achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal value</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.5.1: Average hourly earnings of employees, by sex, age, occupation and persons with disabilities</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_EMP_EARN - Average hourly earnings [8.5.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.1.1, 5.5.2, 8.2.1, 10.4.1 </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Labour Organization (ILO) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>This indicator provides information on the mean hourly earnings from paid employment of employees by sex, occupation, age, and disability status. </p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>Earnings refer to the gross remuneration in cash or in kind paid to employees, as a rule at regular intervals, for time worked or work done together with remuneration for time not worked, such as annual vacation, other type of paid leave or holidays. Earnings exclude employers&#x2019; contributions in respect of their employees paid to social security and pension schemes and also the benefits received by employees under these schemes. Earnings also exclude severance and termination pay. </p>\n<p> </p>\n<p>For international comparability purposes, statistics of earnings used relate to employees&#x2019; gross remuneration, i.e. the total before any deductions are made by the employer in respect of taxes, contributions of employees to social security and pension schemes, life insurance premiums, union dues and other obligations of employees. As stated in the indicator title, data on earnings should be presented on the basis of the arithmetic average of the hourly earnings of all employees. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Current local currency </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The breakdown by occupation is based on the latest version of the International Standard Classification of Occupation (ISCO).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>There are a variety of possible sources of data on employees&#x2019; earnings. </p>\n<p>Establishment surveys are usually the most reliable source, given the high accuracy of earnings figures derived from them (the information typically comes from the payroll, so is precise). However, the scope of these statistics is limited to the coverage of the establishment survey in question (usually excluding small establishments, agricultural establishments and/or informal sector establishments).</p>\n<p>Household surveys (and especially labour force surveys) can provide earnings statistics covering all economic activities, and all establishment types and sizes, but the quality of the data is highly dependent on the accuracy of respondents&#x2019; answers.</p>\n<p>Data on earnings could also be derived from a variety of administrative records.</p>", "COLL_METHOD__GLOBAL"=>"<p>The ILO Department of Statistics processes national household survey micro datasets in line with internationally agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians. For data that could not be obtained through this processing or directly from government websites, the ILO sends out an annual ILOSTAT questionnaire to all relevant agencies within each country (national statistical office, labour ministry, etc.) requesting the latest annual data and any revisions on numerous labour market topics and indicators, including many SDG indicators.</p>", "FREQ_COLL__GLOBAL"=>"<p>Continuous </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Continuous</p>", "DATA_SOURCE__GLOBAL"=>"<p>At the national level, the agency responsible for producing data on earnings is usually the national statistical office. </p>", "COMPILING_ORG__GLOBAL"=>"<p>International Labour Organization (ILO) </p>", "INST_MANDATE__GLOBAL"=>"<p>The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.</p>", "RATIONALE__GLOBAL"=>"<p>Earnings are a key aspect of quality of employment and living conditions. Information on hourly earnings disaggregated by various classifications (sex, age, occupation, disability status) provide some indication of the extent to which pay equality is respected or achieved. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The variety of possible sources for statistics on earnings greatly hinders international comparability, as each type of source has its own coverage, scope, and characteristics. It would not be fully accurate to compare, for example, hourly earnings from a labour force survey for one country with hourly earnings from an establishment survey for another. The use of non-standard definitions and the heterogeneity of operational criteria applied further hamper cross-country comparisons. </p>", "DATA_COMP__GLOBAL"=>"<p><strong>Computation Method:</strong> </p>\n<p>The method of calculation used to obtain the average hourly earnings of employees depends on the source of data used and the type of information it provides. For instance, where there is information available on each worker&#x2019;s hourly earnings and hours worked, the average is a weighted average calculated by summing up the product of each worker&#x2019;s hourly earnings times the hours worked and dividing it by the total number of hours worked by all workers. In other words:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>A</mi>\n    <mi>v</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>a</mi>\n    <mi>g</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>h</mi>\n    <mi>o</mi>\n    <mi>u</mi>\n    <mi>r</mi>\n    <mi>l</mi>\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>e</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>g</mi>\n    <mi>s</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <mo stretchy=\"false\">&#x2211;</mo>\n          <mrow>\n            <mo>(</mo>\n            <mi>h</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>r</mi>\n            <mi>l</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>a</mi>\n            <mi>r</mi>\n            <mi>n</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>g</mi>\n            <mi>s</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>a</mi>\n            <mi>c</mi>\n            <mi>h</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>m</mi>\n            <mi>p</mi>\n            <mi>l</mi>\n            <mi>o</mi>\n            <mi>y</mi>\n            <mi>e</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>x</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>h</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>r</mi>\n            <mi>s</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>w</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>k</mi>\n            <mi>e</mi>\n            <mi>d</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>b</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>a</mi>\n            <mi>c</mi>\n            <mi>h</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>m</mi>\n            <mi>p</mi>\n            <mi>l</mi>\n            <mi>o</mi>\n            <mi>y</mi>\n            <mi>e</mi>\n            <mi>e</mi>\n            <mo>)</mo>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>k</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>b</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>m</mi>\n        <mi>p</mi>\n        <mi>l</mi>\n        <mi>o</mi>\n        <mi>y</mi>\n        <mi>e</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Statistics on average hourly earnings by sex can be used to calculate the gender pay gap, as follows: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>G</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>a</mi>\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>g</mi>\n    <mi>a</mi>\n    <mi>p</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>A</mi>\n            <mi>v</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>a</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>h</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>r</mi>\n            <mi>l</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>a</mi>\n            <mi>r</mi>\n            <mi>n</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>g</mi>\n            <msub>\n              <mrow>\n                <mi>s</mi>\n              </mrow>\n              <mrow>\n                <mi>m</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfenced>\n        <mi>&amp;nbsp;</mi>\n        <mo>-</mo>\n        <mi>&amp;nbsp;</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>A</mi>\n            <mi>v</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>a</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>h</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>r</mi>\n            <mi>l</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>a</mi>\n            <mi>r</mi>\n            <mi>n</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>g</mi>\n            <msub>\n              <mrow>\n                <mi>s</mi>\n              </mrow>\n              <mrow>\n                <mi>w</mi>\n                <mi>o</mi>\n                <mi>m</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>A</mi>\n            <mi>v</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>a</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>h</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>r</mi>\n            <mi>l</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>a</mi>\n            <mi>r</mi>\n            <mi>n</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>g</mi>\n            <msub>\n              <mrow>\n                <mi>s</mi>\n              </mrow>\n              <mrow>\n                <mi>m</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math> </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The ILO engages in annual consultations with Member States through the ILOSTAT questionnaire and related Statistics Reporting System (StaRS). National data providers receive a link to the portal where they can review all national SDG data available on ILOSTAT.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Through the ILO Harmonized Microdata initiative, the ILO strives to produce internationally comparable labour statistics based on the indicator concepts and definitions adopted by the International Conference of Labour Statisticians.</p>", "IMPUTATION__GLOBAL"=>"<p>Not applicable </p>", "REG_AGG__GLOBAL"=>"<p>Not applicable </p>", "DOC_METHOD__GLOBAL"=>"<p>- Resolution concerning the measurement of employment-related income, adopted by the Sixteenth International Conference of Labour Statisticians (January 1998), available at <a href=\"http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087490/lang--en/index.htm\" target=\"_blank\"><u>http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087490/lang--en/index.htm</u></a> </p>\n<p>- Resolution concerning the International Classification of Status in Employment (ICSE), adopted by the Fifteenth International Conference of Labour Statisticians (January 1993), available at <a href=\"http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087562/lang--en/index.htm\" target=\"_blank\"><u>http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087562/lang--en/index.htm</u></a> </p>\n<p>- Resolution concerning an integrated system of wages statistics, adopted by the Twelfth International Conference of Labour Statisticians (January 1973), available at <a href=\"http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087496/lang--en/index.htm\" target=\"_blank\"><u>http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087496/lang--en/index.htm</u></a> </p>\n<p>- ILO manual: An integrated system of wages statistics, available at <a href=\"http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/presentation/wcms_315657.pdf\" target=\"_blank\"><u>http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/presentation/wcms_315657.pdf</u></a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The processes of compilation, production, and publication of data, including its quality control, are carried out following the methodological framework and standards established by the ILO Department of Statistics, in compliance with the information technology and management standards of the ILO.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination in the ILOSTAT database.</p>\n<p> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The final assessment of the quality of information is carried out by the Data Production and Analysis Unit of the ILO Department of Statistics. In cases of doubt about the quality of specific data, these values are reviewed with the participation of the national agencies responsible for producing the data if appropriate. If the issues cannot be clarified, the respective information is not published.</p>", "COVERAGE__GLOBAL"=>"<p>Data may differ from those published on ILOSTAT due to different update schedules and stricter criteria applied for inclusion in the SDG database.</p>\n<p><strong>Data availability:</strong></p>\n<p>Data for this indicator is available for 132 countries and territories. </p>\n<p>Time series: The submission covers data from 2000 to 2024.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator should be disaggregated by sex, occupation, age, and disability status.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Earnings statistics present a number of complications in terms of their international comparability, most of which arise from the variety of possible sources of data. The various sources available -- establishment surveys, household surveys and administrative records -- differ in their methods, objectives and scope, which influences the results obtained. The coverage of the source may vary in terms of the geographical areas covered, the workers covered (for example, part-time workers or informal workers may be excluded) and the establishments covered (for example, establishments below a certain size or of a certain sector may be excluded). In cases where the earnings of workers excluded from the coverage of the source are significantly different than those of workers included, the statistics would not be representative of the country as a whole and would not be strictly comparable to those of countries using a more comprehensive source. </p>\n<p>When using household surveys as a source of earnings statistics, there are a number of issues related to the accuracy of the earnings information reported by the respondents. They may over declare or under declare their earnings for various reasons, or they may report gross or net wages while including or excluding bonuses and benefits, without distinction. This naturally affects the reliability of the results.</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators, available at <a href=\"https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm</a> </li>\n  <li>Resolution concerning the measurement of employment-related income, adopted by the Sixteenth International Conference of Labour Statisticians (January 1998), available at <a href=\"http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087490/lang--en/index.htm\" target=\"_blank\"><u>http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087490/lang--en/index.htm</u></a></li>\n  <li>Resolution concerning the International Classification of Status in Employment (ICSE), adopted by the Fifteenth International Conference of Labour Statisticians (January 1993), available at <a href=\"http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087562/lang--en/index.htm\" target=\"_blank\"><u>http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087562/lang--en/index.htm</u></a></li>\n  <li>Resolution concerning an integrated system of wages statistics, adopted by the Twelfth International Conference of Labour Statisticians (January 1973), available at <a href=\"http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087496/lang--en/index.htm\" target=\"_blank\"><u>http://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087496/lang--en/index.htm</u></a></li>\n  <li>ILO manual: An integrated system of wages statistics, available <u>at https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@dgreports/@stat/documents/presentation/wcms_315657.pdf</u></li>\n  <li>ILOSTAT portal : <a href=\"https://ilostat.ilo.org\">https://ilostat.ilo.org</a> </li>\n  <li>ILOSTAT&#x2019;s Wages and Working Time (COND) database description, at https://ilostat.ilo.org/methods/concepts-and-definitions/description-wages-and-working-time-statistics/</li>\n  <li>International Standard Classification of Occupations (ISCO), at https://ilostat.ilo.org/methods/concepts-and-definitions/classification-occupation/ </li>\n</ul>\n<p> </p>", "indicator_sort_order"=>"08-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.5.2", "slug"=>"8-5-2", "name"=>"Tasa de desempleo, desglosada por sexo, edad y personas con discapacidad", "url"=>"/site/es/8-5-2/", "sort"=>"080502", "goal_number"=>"8", "target_number"=>"8.5", "global"=>{"name"=>"Tasa de desempleo, desglosada por sexo, edad y personas con discapacidad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasa de desempleo, desglosada por sexo, edad y personas con discapacidad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de desempleo, desglosada por sexo, edad y personas con discapacidad", "indicator_number"=>"8.5.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_57/opt_1/ti_encuesta-de-poblacion-en-relacion-con-la-actividad/temas.html", "url_text"=>"Encuesta de población en relación con la actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_276/opt_1/ti_censo-de-poblacion-y-viviendas-actividad/temas.html", "url_text"=>"Censo de población y viviendas. Actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Anual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736055502&menu=resultados&idp=1254735976595", "url_text"=>"Estadística del Empleo de las Personas con Discapacidad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Tasa de desempleo, desglosada por sexo, edad y personas con discapacidad", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.5- De aquí a 2030, lograr el empleo pleno y productivo y el trabajo decente para todas las mujeres y los hombres, incluidos los jóvenes y las personas con discapacidad, así como la igualdad de remuneración por trabajo de igual valor", "definicion"=>"Proporción de personas desempleadas respecto a las personas económicamente activas\n", "formula"=>"\n$$TD^{t} = \\frac{D^{t}}{A^{t}} \\cdot 100$$\n\ndonde:\n\n$D^{t} =$ personas desempleadas en el año $t$\n\n$A^{t} =$ personas económicamente activas en el año $t$\n", "desagregacion"=>"Sexo\n\nEdad\n\nPersonas sin discapacidad, y con discapacidad (personas con un \ngrado de discapacidad superior o igual al 33%)\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa tasa de desempleo es una medida útil de la subutilización de la oferta laboral. \nRefleja la incapacidad de una economía para generar empleo para aquellas personas \nque quieren trabajar pero no lo hacen, a pesar de que están disponibles para \ntrabajar y buscan trabajo activamente. Por lo tanto, se considera \nun indicador de la eficiencia y eficacia de una economía para absorber su fuerza \nlaboral y del desempeño del mercado laboral. \n\nLas series temporales de corto plazo de la tasa de desempleo pueden \nutilizarse para  señalar cambios en el ciclo económico; los movimientos \nascendentes del indicador a menudo coinciden con períodos recesivos o, en algunos casos, \ncon el comienzo de un período expansivo, ya que personas que anteriormente no \nestaban en el mercado laboral comienzan a probar las condiciones mediante una \nbúsqueda activa de empleo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.5.2&seriesCode=SL_TLF_UEM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-24%20%7C%20BOTHSEX\">Tasa de desempleo, por sexo y edad - 13ª CIET (%) SL_TLF_UEM</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-05-02.pdf\">Metadatos 8-5-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-28", "en"=>{"indicador_disponible"=>"Tasa de desempleo, desglosada por sexo, edad y personas con discapacidad", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.5- De aquí a 2030, lograr el empleo pleno y productivo y el trabajo decente para todas las mujeres y los hombres, incluidos los jóvenes y las personas con discapacidad, así como la igualdad de remuneración por trabajo de igual valor", "definicion"=>"Proportion of unemployed people compared to economically active people\n", "formula"=>"\n$$TD^{t} = \\frac{D^{t}}{A^{t}} \\cdot 100$$\n\nwhere:\n\n$D^{t} =$ unemployed people in year $t$\n\n$A^{t} =$ economically active people in year $t$\n", "desagregacion"=>"Sex\n\nAge\n\nPeople without disabilities, and people with disabilities (people with a disability \nlevel of 33% or greater)\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe unemployment rate is a useful measure of the underutilization of the \nlabour supply. It reflects the inability of an economy to generate employment \nfor those persons who want to work but are not doing so, even though they are \navailable for employment and actively seeking work. It is thus seen as an \nindicator of the efficiency and effectiveness of an economy to absorb its labour \nforce and of the performance of the labour market. \n\nShort-term time series of the unemployment rate can be used to signal changes in \nthe business cycle; upward movements in the indicator often coincide with recessionary \nperiods or in some cases with the beginning of an expansionary period as persons \npreviously not in the labour market begin to test conditions through an active job \nsearch. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.5.2&seriesCode=SL_TLF_UEM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-24%20%7C%20BOTHSEX\">Unemployment rate, by sex and age - 13th ICLS (%) SL_TLF_UEM</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-05-02.pdf\">Metadata 8-5-2.pdf</a>", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Tasa de desempleo, desglosada por sexo, edad y personas con discapacidad", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.5- De aquí a 2030, lograr el empleo pleno y productivo y el trabajo decente para todas las mujeres y los hombres, incluidos los jóvenes y las personas con discapacidad, así como la igualdad de remuneración por trabajo de igual valor", "definicion"=>"Langabeen proportzioa ekonomikoki aktiboak diren pertsonekiko\n", "formula"=>"\n$$TD^{t} = \\frac{D^{t}}{A^{t}} \\cdot 100$$\n\nnon:\n\n$D^{t} =$ langabeak $t$ urtean\n\n$A^{t} =$ ekonomikoki aktiboak diren pertsonak $t$ urtean\n", "desagregacion"=>"Sexua\n\nAdina\n\nDesgaitasunik ez duten eta desgaitasuna duten pertsonak ( % 33ko \nedo hortik gorako desgaitasun-maila duten pertsonak)\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLangabezia-tasa lan-eskaintzaren azpi-erabileraren neurri erabilgarria da. Tasa horrek adierazten \ndu ekonomia batek duen ezintasuna lan egin nahi baina lan egiten ez duten pertsonentzako enplegua \nsortzeko, nahiz eta pertsona horiek lan egiteko prest egon eta lana aktiboki bilatu. Beraz, ekonomia \nbatek bere lan-indarra xurgatzeko eta lan-merkatuan jarduteko duen eraginkortasunaren eta efizientziaren \nadierazletzat hartzen da. \n\nLangabezia-tasaren epe laburreko denbora-serieak ziklo ekonomikoan aldaketak adierazteko erabil \ndaitezke; adierazlearen goranzko mugimenduak, askotan, aldi atzerakorrekin bat datoz, edo, kasu \nbatzuetan, hedapen-aldi baten hasierarekin; izan ere, lehen lan-merkatuan ez zeuden pertsonak \nenplegu-bilaketa aktibo baten bidez hasten dira baldintzak probatzen. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.5.2&seriesCode=SL_TLF_UEM&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-24%20%7C%20BOTHSEX\">Langabezia-tasa, sexuaren eta adinaren arabera - 13. CIET (%) SL_TLF_UEM</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-05-02.pdf\">Metadatuak 8-5-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.5: By 2030, achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal value</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.5.2: Unemployment rate, by sex, age and persons with disabilities </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_TLF_UEM - Unemployment rate, by sex and age - 13th ICLS (%) [8.5.2]</p>\n<p>SL_TLF_UEM_19ICLS - Unemployment rate, by sex and age - 19th ICLS (%) [8.5.2]</p>\n<p>SL_TLF_UEMDIS - Unemployment rate, by sex and disability - 13th ICLS (%) [8.5.2]</p>\n<p>SL_TLF_UEMDIS_19ICLS - Unemployment rate, by sex and disability - 19th ICLS (%) [8.5.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.1.1, 8.2.1, 8.3.1, 8.6.1, 10.4.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Labour Organization (ILO) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Labour Organization (ILO)</p>\n<p> </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The unemployment rate conveys the percentage of persons in the labour force who are unemployed. </p>\n<p><strong>Concepts:</strong></p>\n<p>Unemployed persons are defined as all those of working age (usually aged 15 and above) who were not in employment, carried out activities to seek employment during a specified recent period and were currently available to take up employment given a job opportunity, where: (a) &#x201C;not in employment&#x201D; is assessed with respect to the short reference period for the measurement of employment; (b) to &#x201C;seek employment&#x201D; refers to any activity when carried out, during a specified recent period comprising the last four weeks or one month, for the purpose of finding a job or setting up a business or agricultural undertaking; (c) the point when the enterprise starts to exist should be used to distinguish between search activities aimed at setting up a business and the work activity itself, as evidenced by the enterprise&#x2019;s registration to operate or by when financial resources become available, the necessary infrastructure or materials are in place or the first client or order is received, depending on the context; (d) &#x201C;currently available&#x201D; serves as a test of readiness to start a job in the present, assessed with respect to a short reference period comprising that used to measure employment (depending on national circumstances, the reference period may be extended to include a short subsequent period not exceeding two weeks in total, so as to ensure adequate coverage of unemployment situations among different population groups). </p>\n<p> </p>\n<p>Persons in employment are defined as all those of working age (usually aged 15 and above) who, during a short reference period such as one week or one day, were engaged in any activity to produce goods or provide services for pay or profit. The difference between the 13th and 19th ICLS series for a given country is the operational criteria used to define employment, with two series based on the statistical standards from the 13th International Conference of Labour Statisticians (ICLS) and the other two series based on 19th ICLS standards. In the 19th ICLS series, employment is defined more narrowly as work done for pay or profit, while activities not done mainly in exchange for remuneration (i.e., own-use production work, volunteer work and unpaid trainee work) are recognized as other forms of work.</p>\n<p> </p>\n<p>The labour force corresponds to the sum of persons in employment and in unemployment. </p>\n<p>For more information on the definitions of employment and unemployment refer to the Resolution concerning statistics of work, employment and labour underutilization Adopted by the 19<sup>th</sup> International Conference of Labour Statisticians.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Disability status is based on the WHO International Classification of Functioning, Disability and Health (ICF), according to which disability covers impairments (problems in body function or structure such as a significant deviation or loss), activity limitations (difficulties in executing activities) and participation restrictions (problems in involvement in life situations). For measurement purposes, the ICF defines a person with disability as a person who is limited in the kind or amount of activities that he or she can do because of ongoing difficulties due to a long-term physical condition, mental condition or health problem.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The preferred official national data source for this indicator is a household-based labour force survey. </p>\n<p>In the absence of a labour force survey, a population census and/or other type of household surveys with an appropriate employment module may also be used to obtain the required data. It is important to note that unemployment data derived from employment office records or unemployment registers would not refer to unemployment (as defined for the purposes of this indicator, using the three-criteria of being without a job, seeking employment and available for employment) but to registered unemployment, and thus, it would not be comparable with indicator 8.5.2.</p>", "COLL_METHOD__GLOBAL"=>"<p>The ILO Department of Statistics processes national household survey microdata sets in line with internationally agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians. For data that could not be obtained through this processing or directly from government websites, the ILO sends out an annual ILOSTAT questionnaire to all relevant agencies within each country (national statistical office, labour ministry, etc.) requesting the latest annual data and any revisions on numerous labour market topics and indicators, including many SDG indicators. </p>", "FREQ_COLL__GLOBAL"=>"<p>Continuous</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Continuous for country-level data and annually for global and regional estimates (November or December).</p>", "DATA_SOURCE__GLOBAL"=>"<p>Mainly national statistical offices, and in some cases labour ministries or other related agencies, at the country-level. In some cases, regional or international statistical offices can also act as data providers. </p>", "COMPILING_ORG__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "INST_MANDATE__GLOBAL"=>"<p>The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.</p>", "RATIONALE__GLOBAL"=>"<p>The unemployment rate is a useful measure of the underutilization of the labour supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labour force and of the performance of the labour market. Short-term time series of the unemployment rate can be used to signal changes in the business cycle; upward movements in the indicator often coincide with recessionary periods or in some cases with the beginning of an expansionary period as persons previously not in the labour market begin to test conditions through an active job search. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Even though in most developed countries the unemployment rate is useful as an indicator of labour market performance, and specifically, as a key measure of labour underutilization, in many developing countries, the significance and meaning of the unemployment rate could be questioned. In the absence of unemployment insurance systems or social safety nets, persons of working age must avoid unemployment, resorting to engaging in some form of economic activity, however insignificant or inadequate. Thus, in this context, other measures should supplement the unemployment rate to comprehensively assess labour underutilization. </p>", "DATA_COMP__GLOBAL"=>"<p>The computation is identical for both series:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">U</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">e</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The ILO engages in annual consultations with Member States through the ILOSTAT questionnaire and related Statistics Reporting System (StaRS). National data providers receive a link to the portal where they can review all national SDG data available on ILOSTAT.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Through the ILO Harmonized Microdata initiative, the ILO strives to produce internationally comparable labour statistics based on the indicator concepts and definitions adopted by the International Conference of Labour Statisticians.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>Multivariate regression techniques are used to impute missing values at the country level. However, the imputed missing country values are only used to calculate the global and regional estimates; they are not used for international reporting on the SDG indicators by the ILO. For further information, refer to the ILO modelled estimates methodological overview, available at https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/ .</p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p>Regional and global figures are aggregates of the country-level figures including the imputed values.</p>", "REG_AGG__GLOBAL"=>"<p>To address the problem of missing data, the ILO designed several econometric models which are used to produce estimates of labour market indicators based on the 13<sup>th</sup> ICLS standards in the countries and years for which real data are not available. The unemployment estimates derived from the ILO modelled estimates are used to produce global and regional estimates on unemployment rates. These models use multivariate regression techniques to impute missing values at the country level, which are then aggregated to produce regional and global estimates. For further information, refer to the ILO modelled estimates methodological overview, available at <a href=\"https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\">https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/</a> </p>", "DOC_METHOD__GLOBAL"=>"<p>To calculate this indicator (according to the ILO definitions of unemployment and unemployment rate), data is needed on both the labour force and the unemployed, by sex and age (and eventually disability status). This data is collected at the national level mainly through labour force surveys (or other types of household surveys with an employment module). For the methodology of each national household survey, one must refer to the most comprehensive survey report or to the methodological publications of the national statistical office in question. </p>\n<p>For further information, see section 7 with references and documentation. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The processes of compilation, production, and publication of data, including its quality control, are carried out following the methodological framework and standards established by the ILO Department of Statistics, in compliance with the information technology and management standards of the ILO.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination on ILOSTAT.</p>\n<p> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The final assessment of the quality of information is carried out by the Data Production and Analysis Unit of the ILO Department of Statistics. In cases of doubt about the quality of specific data, these values are reviewed with the participation of the national agencies responsible for producing the data if appropriate. If the issues cannot be clarified, the respective information is not published. </p>", "COVERAGE__GLOBAL"=>"<p>Data may differ from those published on ILOSTAT due to different update schedules and stricter criteria applied for inclusion in the SDG database.</p>\n<p><strong>Data availability:</strong></p>\n<p>Data disaggregated by sex and age for this indicator is available for 218 countries and territories in the 13th ICLS series and 124 countries and territories in the 19th ICLS series. </p>\n<p>The indicator is widely available based on real observations provided by countries and derived from national labour force surveys, other types of household surveys or population census. </p>\n<p>However, the disaggregation by disability is not as widely available and this submission only includes 127 countries and territories in the 13th ICLS series and 80 countries and territories in the 19th ICLS series. </p>\n<p><strong>Time series:</strong><br>Data for disaggregation by sex and age for this indicator is available as of 2000 for countries in the SDG Indicators Global Database, but time series going back further are available in ILOSTAT. Global and regional aggregates disaggregated by sex and age are available through 2024.</p>\n<p>Data for disaggregation by disability status is available for the period from 2003 to 2024 at the country level. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator should be disaggregated by sex, age group and disability status, preferably simultaneously. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Differences in the questionnaires used in the household surveys as the basic measurement tool may entail differences in specific definitions of employment and unemployment, differences in the treatment of specific groups or differences in the operational criteria used to determine the individual&#x2019;s labour force status. </p>\n<p>Work statistics for countries not using the same set of statistical standards are not comparable. As such, each series is based on a single set of standards (i.e., 13<sup>th</sup> or 19<sup>th</sup> ICLS) and contains only data comparable within and across countries, allowing data users to continue making meaningful time series analysis and international comparisons. Users should not compare data across series.</p>\n<p>The unemployment rate is dependent on the geographical coverage of the survey since urban and rural areas tend to have significant differences in the incidence of unemployment. It is important to note that unemployment indicators do not convey any information on the characteristics of the unemployed (their education level, ethnic origin, socio-economic background, work experience, duration of unemployment, etc.), which is crucial to cast light on labour market failures.</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators, available at <a href=\"https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm</a> </li>\n  <li>ILOSTAT portal : <a href=\"https://ilostat.ilo.org/\" target=\"_blank\"><u>https://ilostat.ilo.org/</u></a> </li>\n  <li>ILOSTAT Labour Force Statistics (LFS) database description : https://ilostat.ilo.org/methods/concepts-and-definitions/description-labour-force-statistics/</li>\n  <li>Decent Work Indicators Manual: <u>http://www.ilo.org/wcmsp5/groups/public/---dgreports/--- </u></li>\n</ul>\n<p><u>stat/documents/publication/wcms_223121.pdf </u></p>\n<ul>\n  <li>Resolution concerning statistics of work, employment and labour underutilization, adopted by the 19<sup>th</sup> ICLS (October 2013) and amended by the 21<sup>st</sup> ICLS (October 2023): <a href=\"https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm</a> </li>\n  <li>Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the ICLS in 1982: https://www.ilo.org/resource/resolution-concerning-statistics-economically-active-population-employment</li>\n  <li>Quick guide to understanding the impact of the new statistical standards on ILOSTAT databases (<a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf</a>)</li>\n  <li>Trends Econometric Models: A Review of Methodology: <a href=\"http://www.ilo.org/wcmsp5/groups/public/---ed_emp/---emp_elm/---trends/documents/publication/wcms_120382.pdf\" target=\"_blank\"><u>http://www.ilo.org/wcmsp5/groups/public/---ed_emp/---emp_elm/---trends/documents/publication/wcms_120382.pdf</u></a> </li>\n</ul>", "indicator_sort_order"=>"08-05-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"8.5.E1", "slug"=>"8-5-E1", "name"=>"Tasa de ocupación de la población (Indicador UE sdg_08_30)", "url"=>"/site/es/8-5-E1/", "sort"=>"0805E1", "goal_number"=>"8", "target_number"=>"8.5", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[{"series"=>"Tasa de ocupación", "field"=>"Edad", "value"=>"20-64 años"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[{"series"=>"Tasa de ocupación", "unit"=>"", "label_content"=>"Objetivo UE", "value"=>78.0}, {"series"=>"Brecha de género en la ocupación", "unit"=>"", "label_content"=>"Objetivo UE", "value"=>5.8}], "graph_title"=>"Tasa de ocupación de la población (Indicador UE sdg_08_30)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa de ocupación de la población (Indicador UE sdg_08_30)", "indicator_number"=>"8.5.E1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> En la serie Tasa de ocupación, el objetivo europeo es alcanzar un 78% para el año 2030. Por su parte, en la serie Brecha de género, la meta es reducir la diferencia a un máximo de 5,8 puntos porcentuales en ese mismo año.", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_57/opt_1/ti_encuesta-de-poblacion-en-relacion-con-la-actividad/temas.html", "url_text"=>"Encuesta de población en relación con la actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_276/opt_1/ti_censo-de-poblacion-y-viviendas-actividad/temas.html", "url_text"=>"Censo de población y viviendas. Actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tasa de ocupación de la población (Indicador UE sdg_08_30)", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.5- De aquí a 2030, lograr el empleo pleno y productivo y el trabajo decente para todas las mujeres y los hombres, incluidos los jóvenes y las personas con discapacidad, así como la igualdad de remuneración por trabajo de igual valor", "definicion"=>"Proporción de personas entre 20 y 64 años que están ocupadas o empleadas, según tramos de edad, y brecha de género en el empleo.\n\nSe considera persona empleada a quien, durante una semana de referencia, trabajó al menos una hora \npor remuneración o beneficio, o bien no trabajó, pero tuvo un empleo del que estuvo ausente temporalmente.\n", "formula"=>"<b>Tasa de ocupación</b>\n\n$$TA^{t} = \\frac{PO^{t}}{P^{t}} $$\n\ndonde:\n\n$PO^{t} =$ población ocupada en el año $t$\n\n$P^{t} =$ población en el año $t$\n\n<br>\n\n<b>Brecha género en el empleo</b>\n\n$$BGE^{t} ={TA_{hombre}^{t} - TA_{mujer}^{t}}$$\n\ndonde:\n\n$TA_{hombre}^{t} =$ tasa de ocupación de los hombres en el año $t$\n\n$TA_{mujer}^{t} =$ tasa de ocupación de las mujeres en el año $t$\n", "desagregacion"=>"Sexo\n", "periodicidad"=>"Anual", "observaciones"=>"", "justificacion_global"=>"\nEl empleo remunerado es fundamental para garantizar un nivel de vida adecuado y \nproporciona la base necesaria para que las personas alcancen sus metas y aspiraciones \npersonales. Además, el empleo contribuye al rendimiento económico, la calidad de \nvida y la inclusión social, lo que lo convierte en una piedra angular del \ndesarrollo socioeconómico y el bienestar.\n\nEl pilar europeo de derechos sociales, proclamado conjuntamente por la Comisión Europea, \nel Parlamento Europeo y el Consejo Europeo en 2017, establece 20 principios y derechos \nclave esenciales para unos mercados laborales y unos sistemas de protección social \njustos y que funcionen correctamente. El <a href=\"https://op.europa.eu/webpub/empl/european-pillar-of-social-rights/es/\">\nPlan de Acción del Pilar Europeo de Derechos Sociales</a>, adoptado en 2021, convierte los \nPrincipios en acciones concretas en beneficio de los ciudadanos. También propone un ambicioso objetivo \nde empleo del 78 % de la población de 20 a 64 años que la UE debe alcanzar de aquí a 2030. Para respaldarlo, \nel Plan de Acción propone reducir a la mitad la brecha de género en materia de \nempleo en el año 2030 en comparación con  2019 y reducir al 9 % la tasa de jóvenes \nde entre 15 y 29 años que ni trabajan ni siguen estudios ni formación (ninis) \nal 9 % en el año 2030.\n\nFuente: Eurostat\n", "dato_global"=>"\n<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_08_30/default/table?lang=en&category=sdg.sdg_08\"> Tasa de empleo por sexo (sdg_08_30)</a> Eurostat", "comparabilidad"=>"El indicador disponible es comparable con el indicador europeo \"Tasa de empleo por sexo (sdg_08_30)\"", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_08_30_esmsip2.htm\"> Metadatos Eurostat sdg_08_30 </a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Tasa de ocupación de la población (Indicador UE sdg_08_30)", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.5- De aquí a 2030, lograr el empleo pleno y productivo y el trabajo decente para todas las mujeres y los hombres, incluidos los jóvenes y las personas con discapacidad, así como la igualdad de remuneración por trabajo de igual valor", "definicion"=>"Proportion of people aged 20-64 who are employed, by age group, and gender employment gap.\n\nAn employed person is someone who, during a reference week, worked at least one hour for \npay or benefit, or who did not work but had a job from which they were temporarily absent. \n", "formula"=>"<b>Employment rate</b>\n\n$$TA^{t} = \\frac{PO^{t}}{P^{t}} $$\n\nwhere:\n\n$PO^{t} =$ employed population in year $t$\n\n$P^{t} =$ population in year $t$\n\n<br>\n\n<b>Gender gap in employment:</b>\n\n$$BGE^{t} ={TA_{men}^{t} - TA_{women}^{t}}$$\n\nwhere:\n\n$TA_{men}^{t} =$ employment rate of men in year $t$\n\n$TA_{women}^{t} =$ employment rate of women in year $t$\n", "desagregacion"=>"Sex\n", "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nPaid employment is crucial for ensuring sufficient living standards and \nit provides the necessary foundation for people to achieve their personal \ngoals and aspirations. Moreover, employment contributes to economic performance, \nquality of life and social inclusion, making it a cornerstone of socioeconomic \ndevelopment and well-being. \n\nThe European Pillar of Social Rights, jointly proclaimed by the European Commission, \nthe European Parliament and the European Council in 2017, sets out 20 key principles \nand rights essential for fair and well-functioning labour markets and social protection \nsystems. The <a href=\"https://op.europa.eu/webpub/empl/european-pillar-of-social-rights/es/\">\nEuropean Pillar of Social Rights Action Plan</a>, adopted in 2021, turns the \nPrinciples into concrete actions to benefit citizens. It also proposes an ambitious \nemployment target of 78 % of the population aged 20 to 64 for the EU to reach by 2030. \nIn support of this, the Action Plan proposes to halve the gender employment gap by 2030 \ncompared with 2019 and to decrease the rate of young people neither in employment nor in \neducation or training (NEET) aged 15 to 29 to 9 % by 2030.\n\nSource: Eurostat\n", "dato_global"=>"\n<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_08_30/default/table?lang=en&category=sdg.sdg_08\"> Employment rate by sex (sdg_08_30)</a> Eurostat", "comparabilidad"=>"The available indicator is comparable with the European indicator Employment rate by sex (sdg_08_30)\"", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_08_30_esmsip2.htm\"> Metadata Eurostat sdg_08_30 </a>", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Tasa de ocupación de la población (Indicador UE sdg_08_30)", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.5- De aquí a 2030, lograr el empleo pleno y productivo y el trabajo decente para todas las mujeres y los hombres, incluidos los jóvenes y las personas con discapacidad, así como la igualdad de remuneración por trabajo de igual valor", "definicion"=>"20 eta 64 urte bitarteko pertsona okupatuen edo enplegatuen proportzioa, adin-tarteen arabera, eta genero-arrakala enpleguan.\n\nEnplegatutzat jotzen da erreferentziako astean ondoko bi egoeretako batean egon zena: gutxienez ordubetez lan egin zuena ordainsari edo \nonura baten truke, edo lanpostu bat izanda ere lanik egin ez zuena, aldi baterako lanpostutik kanpo zegoelako.\n", "formula"=>"<b>Okupazio-tasa</b>\n\n$$TA^{t} = \\frac{PO^{t}}{P^{t}} $$\n\nnon:\n\n$PO^{t} =$ lanean ari diren biztanleak $t$ urtean\n\n$P^{t} =$ biztanleak $t$ urtean\n\n<br>\n\n<b>Genero-arrakala enpleguan</b>\n\n$$BGE^{t} ={TA_{gizona}^{t} - TA_{emakumea}^{t}}$$\n\nnon:\n\n$TA_{gizona}^{t} =$ gizonen okupazio-tasa $t$ urtean\n\n$TA_{emakumea}^{t} =$ emakumeen okupazio-tasa $t$ urtean\n", "desagregacion"=>"Sexua\n", "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nOrdaindutako enplegua funtsezkoa da bizi-maila egokia bermatzeko, eta pertsonek beren xede eta helburu \npertsonalak lortzeko behar duten oinarria ematen du. Gainera, enpleguak etekin ekonomikoari, bizi-kalitateari \neta gizarte-inklusiori laguntzen die, eta horrek garapen sozioekonomikoaren eta ongizatearen giltzarri bihurtzen \ndu. \n\nEskubide sozialen zutabe europarrak –Europako Batzordeak, Europako Parlamentuak eta Kontseilu Europarrak 2017an \nbatera aldarrikatuak–, 20 printzipio eta oinarrizko eskubide ezartzen ditu bidezkoak diren eta behar bezala \nfuntzionatzen duten babes sozialeko sistemetarako eta lan-merkatuetarako. \n<a href=\"https://op.europa.eu/webpub/empl/european-pillar-of-social-rights/es/\">Giza Eskubideen Europako Zutabeko \nEkintza Planak, 2021ean ezarriak, herritarren onerako ekintza zehatz bihurtzen ditu printzipioak. Halaber, \nEBk hemendik 2030era bitartean lortu behar duen helburu handizalea proposatzen du: 20-64 urteko biztanleriaren \n% 78k enplegua izatea. Hori babesteko, Ekintza Planak proposatzen du 2030ean enpleguaren arloko genero-arrakala \nerdira murriztea 2019arekin alderatuta, eta % 9ra jaistea 2030ean lanean edo ikasten ez dauden 15 eta 29 urte \narteko gazteen tasa. \n\n\nIturria: Eurostat \n", "dato_global"=>"\n<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_08_30/default/table?lang=en&category=sdg.sdg_08\"> Enplegu-tasa sexuaren arabera (sdg_08_30)</a> Eurostat", "comparabilidad"=>"EAEn eskuragarri dagoen adierazlea \"Enplegu-tasa sexuaren arabera (sdg_08_30)\" Europako adierazlearekin aldera daiteke", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_08_30_esmsip2.htm\"> Eurostat metadatuak sdg_08_30 </a> (ingelesez bakarrik)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "indicator_sort_order"=>"08-05-E1", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"8.6.1", "slug"=>"8-6-1", "name"=>"Proporción de jóvenes (entre 15 y 24 años) que no cursan estudios, no están empleados ni reciben capacitación", "url"=>"/site/es/8-6-1/", "sort"=>"080601", "goal_number"=>"8", "target_number"=>"8.6", "global"=>{"name"=>"Proporción de jóvenes (entre 15 y 24 años) que no cursan estudios, no están empleados ni reciben capacitación"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[{"series"=>"", "unit"=>"", "label_content"=>"Objetivo UE", "value"=>9}], "graph_title"=>"Proporción de jóvenes (entre 15 y 24 años) que no cursan estudios, no están empleados ni reciben capacitación", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de jóvenes (entre 15 y 24 años) que no cursan estudios, no están empleados ni reciben capacitación", "indicator_number"=>"8.6.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> El objetivo establecido a nivel europeo es que, para el año 2030, el valor del indicador correspondiente a los jóvenes de entre 15 y 29 años no supere el 9%", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_57/opt_1/ti_encuesta-de-poblacion-en-relacion-con-la-actividad/temas.html", "url_text"=>"Encuesta de población en relación con la actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de jóvenes (entre 15 y 24 años) que no cursan estudios, no están empleados ni reciben capacitación", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.6- De aquí a 2020, reducir considerablemente la proporción de jóvenes que no están empleados y no cursan estudios ni reciben capacitación", "definicion"=>"Proporción de jóvenes entre 15 y 24 años que no han tenido empleo ni han realizado \nestudios o formación (reglada o no reglada) en las últimas cuatro semanas respecto \nal total de jóvenes entre 15 y 24 años\n", "formula"=>"\n$$PPNINI_{15-24}^{t} = \\frac{PNINI_{15-24}^{t}}{P_{15-24}^{t}} \\cdot 100$$\n\ndonde:\n\n$PNINI_{15-24}^{t} =$ población entre 15 y 24 años que no ha tenido empleo ni ha realizado estudios o formación (reglada o no reglada) en las últimas cuatro semanas en el año $t$\n\n$P_{15-24}^{t} =$ población entre 15 y 24 años en el año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa proporción de jóvenes que no tienen empleo, educación ni formación \n(tasa de jóvenes que no estudian ni reciben formación) proporciona una medida de \nlos jóvenes que están fuera del sistema educativo, que no reciben formación ni empleo \ny, por lo tanto, sirve como una medida más amplia de los jóvenes que pueden \ningresar al mercado laboral que el desempleo juvenil. \n\nIncluye a los jóvenes que no se animan a trabajar, así como a aquellos que \nestán fuera de la fuerza laboral debido a una discapacidad o por su participación en \nlas tareas domésticas, entre otras razones. \n\nLa tasa de jóvenes que no estudian ni reciben formación también es una mejor medida \ndel universo actual de jóvenes que pueden ingresar al mercado laboral en comparación \ncon la tasa de inactividad juvenil, ya que esta última incluye a aquellos jóvenes \nque están fuera de la fuerza laboral y están recibiendo educación, y por lo tanto \nestán mejorando sus habilidades y calificaciones.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.6.1&seriesCode=SL_TLF_NEET&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-24%20%7C%20BOTHSEX\">Proporción de jóvenes que no estudian, ni trabajan ni reciben formación, por sexo y edad - 13.ª CIET (%) SL_TLF_NEET</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-06-01.pdf\">Metadatos 8-6-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de jóvenes (entre 15 y 24 años) que no cursan estudios, no están empleados ni reciben capacitación", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.6- De aquí a 2020, reducir considerablemente la proporción de jóvenes que no están empleados y no cursan estudios ni reciben capacitación", "definicion"=>"Proportion of young people aged between 15 and 24 who do not have a job or are have undergone studies \nor training (regulated or unregulated) in the last four weeks with respect to the total number of young \npeople aged between 15 and 24 \n", "formula"=>"\n$$PPNINI_{15-24}^{t} = \\frac{PNINI_{15-24}^{t}}{P_{15-24}^{t}} \\cdot 100$$\n\nwhere:\n\n$PNINI_{15-24}^{t} =$ people aged between 15 and 24 who do not have a job or are have undergone studies or training (regulated or unregulated)\nin the last four weeks in year $t$\n\n$P_{15-24}^{t} =$ people aged between 15 and 24 in year $t$\n", "desagregacion"=>"Sex\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe share of youth not in employment, education or training (youth NEET rate) provides a measure of \nyouth who are outside the educational system, not in training and not in employment, and thus serves as \na broader measure of potential youth labour market entrants than youth unemployment. \n\nIt includes discouraged worker youth as well as those who are outside the labour force due to disability or \nengagement in household chores, among other reasons. \n\nYouth NEET is also a better measure of the current universe of potential youth labour market entrants \nas compared with the youth inactivity rate, as the latter includes those youth who are outside the labour \nforce and are in education, and thus are furthering their skills and qualifications. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.6.1&seriesCode=SL_TLF_NEET&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-24%20%7C%20BOTHSEX\">Proportion of youth not in education, employment or training, by sex and age - 13th ICLS (%) SL_TLF_NEET</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-06-01.pdf\">Metadata 8-6-1.pdf</a>", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de jóvenes (entre 15 y 24 años) que no cursan estudios, no están empleados ni reciben capacitación", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.6- De aquí a 2020, reducir considerablemente la proporción de jóvenes que no están empleados y no cursan estudios ni reciben capacitación", "definicion"=>"Azken lau asteetan lanik izan ez duten eta araututako zein arautu gabeko ikasketa edo prestakuntzarik \negin ez duten 15-24 urteko gazteen proportzioa, 15-24 urteko gazte guztiekiko\n", "formula"=>"\n$$PPNINI_{15-24}^{t} = \\frac{PNINI_{15-24}^{t}}{P_{15-24}^{t}} \\cdot 100$$\n\nnon:\n\n$PNINI_{15-24}^{t} =$ azken lau asteetan lanik izan ez duen eta araututako zein arautu gabeko ikasketarik \nedo prestakuntzarik egin ez duen 15-24 urteko biztanleria $t$ urtean\n\n$P_{15-24}^{t} =$ 15-24 urteko biztanleak $t$ urtean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLanik, hezkuntzarik edo prestakuntzarik ez duten gazteen proportzioak (ikasten ez duten eta prestakuntzarik \njasotzen ez duten gazteen tasa) neurri bat ematen du hezkuntza-sistematik kanpo dauden gazteen eta \nprestakuntzarik eta enplegurik jasotzen ez dutenen inguruan, eta, beraz, lan-merkatuan sar daitezkeen gazteen \ngaineko neurri zabalagoa da gazteen langabezia baino. \n\nHorren barruan sartzen dira, besteak beste, lan egitera animatzen ez diren gazteak, baita ezintasunen bat \ndutelako edo etxeko lanetan parte hartzen dutelako (besteak beste) lan indarretik kanpo daudenak ere. \n\nIkasten ez duten eta prestakuntzarik jasotzen ez duten gazteen tasa ere neurri hobea da lan-merkatuan sar \ndaitezkeen gazteen egungo unibertsoa ulertzeko, gazteen jarduerarik ezaren tasarekin alderatuta; izan ere, \nazken horren barruan sartzen dira lan-indarretik kanpo dauden eta hezkuntza jasotzen ari diren gazteak, eta, \nberaz, beren trebetasunak eta kalifikazioak hobetzen ari direnak. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.6.1&seriesCode=SL_TLF_NEET&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15-24%20%7C%20BOTHSEX\">Ikasi, lan egin edo prestakuntzarik jasotzen ez duten gazteen proportzioa, sexuaren eta adinaren arabera - 13.ª CIET (%) SL_TLF_NEET</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-06-01.pdf\">Metadatuak 8-6-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.6: By 2020, substantially reduce the proportion of youth not in employment, education or training</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.6.1: Proportion of youth (aged 15&#x2013;24 years) not in education, employment or training</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_TLF_NEET - Proportion of youth not in education, employment or training, by sex and age - 13th ICLS (%) [8.6.1]</p>\n<p>SL_TLF_NEET_19ICLS - Proportion of youth not in education, employment or training, by sex and age - 19th ICLS (%) [8.6.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>8.5.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition: </strong></p>\n<p>This indicator conveys the proportion of youth (aged 15-24 years) not in education, employment or training (also known as &quot;the youth NEET rate&quot;). </p>\n<p><strong>Concepts: </strong></p>\n<p>For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive). According to the International Standard Classification of Education (ISCED), education is defined as organized and sustained communication designed to bring about learning. Formal education is defined in ISCED as education that is institutionalized, intentional, and planned through public organizations and recognized private bodies and, in their totality, make up the formal education system of a country. </p>\n<p>Non-formal education, like formal education is defined in ISCED as education that is institutionalized, intentional and planned by an education provider but is considered an addition, alternative and/or a complement to formal education. It may be short in duration and/or low in intensity and it is typically provided in the form of short courses, workshops, or seminars. Informal learning is defined in ISCED as forms of learning that are intentional or deliberate, but not institutionalized. It is thus less organized and less structured than either formal or non-formal education. Informal learning may include learning activities that occur in the family, in the workplace, in the local community, and in daily life, on a self-directed, family-directed or socially directed basis. For the purposes of this indicator, persons will be considered in education if they are in formal or non-formal education, as described above, but excluding informal learning. </p>\n<p>Employment is defined as all persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. The difference between the two series for a given country is the operational criteria used to define employment, with one series based on the statistical standards from the 13th International Conference of Labour Statisticians (ICLS) and the other series based on 19th ICLS standards. In the 19th ICLS series, employment is defined more narrowly as work done for pay or profit, while activities not done mainly in exchange for remuneration (i.e., own-use production work, volunteer work and unpaid trainee work) are recognized as other forms of work. </p>\n<p>For the purpose of this indicator, persons are considered to be in training if they are in a non-academic learning activity through which they acquire specific skills intended for vocational or technical jobs. </p>\n<p>Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation, or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Youth are defined as persons aged 15 to 24 (inclusive) for the purpose of this indicator.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The preferred official national data source for this indicator is a household-based labour force survey. </p>\n<p>In the absence of a labour force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data. </p>", "COLL_METHOD__GLOBAL"=>"<p>The ILO Department of Statistics processes national household survey microdata sets in line with internationally agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians. For data that could not be obtained through this processing or directly from government websites, the ILO sends out an annual ILOSTAT questionnaire to all relevant agencies within each country (national statistical office, labour ministry, etc.) requesting the latest annual data and any revisions on numerous labour market topics and indicators, including many SDG indicators. </p>", "FREQ_COLL__GLOBAL"=>"<p>Continuous</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Continuous for country-level data and annual for global and regional estimates </p>", "DATA_SOURCE__GLOBAL"=>"<p>Mainly national statistical offices, and in some cases labour ministries or other related agencies, at the country-level. In some cases, regional or international statistical offices can also act as data providers. </p>", "COMPILING_ORG__GLOBAL"=>"<p>International Labour Organization (ILO) </p>", "INST_MANDATE__GLOBAL"=>"<p>The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.</p>", "RATIONALE__GLOBAL"=>"<p>The share of youth not in employment, education or training (youth NEET rate) provides a measure of youth who are outside the educational system, not in training and not in employment, and thus serves as a broader measure of potential youth labour market entrants than youth unemployment. It includes discouraged worker youth as well as those who are outside the labour force due to disability or engagement in household chores, among other reasons. Youth NEET is also a better measure of the current universe of potential youth labour market entrants as compared with the youth inactivity rate, as the latter includes those youth who are outside the labour force and are in education, and thus are furthering their skills and qualifications. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The calculation of this indicator requires having reliable information on both the labour market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents&apos; answers. </p>\n<p>To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labour force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analysing the NEET rate. </p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">Y</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">N</mi>\n    <mi mathvariant=\"normal\">E</mi>\n    <mi mathvariant=\"normal\">E</mi>\n    <mi mathvariant=\"normal\">T</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>&amp;nbsp;</mi>\n        <mi>Y</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>&#x2013;</mo>\n        <mo>(</mo>\n        <mi>Y</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>m</mi>\n        <mi>p</mi>\n        <mi>l</mi>\n        <mi>o</mi>\n        <mi>y</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mo>+</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>Y</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>m</mi>\n        <mi>p</mi>\n        <mi>l</mi>\n        <mi>o</mi>\n        <mi>y</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>b</mi>\n        <mi>u</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>g</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>Y</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>It is important to note here that youth simultaneously in employment and education or training should not be double counted when subtracted from the total number of youth. The formula can also be expressed as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">Y</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">N</mi>\n    <mi mathvariant=\"normal\">E</mi>\n    <mi mathvariant=\"normal\">E</mi>\n    <mi mathvariant=\"normal\">T</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mi>U</mi>\n                    <mi>n</mi>\n                    <mi>e</mi>\n                    <mi>m</mi>\n                    <mi>p</mi>\n                    <mi>l</mi>\n                    <mi>o</mi>\n                    <mi>y</mi>\n                    <mi>e</mi>\n                    <mi>d</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>y</mi>\n                    <mi>o</mi>\n                    <mi>u</mi>\n                    <mi>t</mi>\n                    <mi>h</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mo>+</mo>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>Y</mi>\n                    <mi>o</mi>\n                    <mi>u</mi>\n                    <mi>t</mi>\n                    <mi>h</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>o</mi>\n                    <mi>u</mi>\n                    <mi>t</mi>\n                    <mi>s</mi>\n                    <mi>i</mi>\n                    <mi>d</mi>\n                    <mi>e</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>t</mi>\n                    <mi>h</mi>\n                    <mi>e</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>l</mi>\n                    <mi>a</mi>\n                    <mi>b</mi>\n                    <mi>o</mi>\n                    <mi>u</mi>\n                    <mi>r</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>f</mi>\n                    <mi>o</mi>\n                    <mi>r</mi>\n                    <mi>c</mi>\n                    <mi>e</mi>\n                  </mrow>\n                </mfenced>\n                <mo>&#x2013;</mo>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mo>(</mo>\n                <mi>U</mi>\n                <mi>n</mi>\n                <mi>e</mi>\n                <mi>m</mi>\n                <mi>p</mi>\n                <mi>l</mi>\n                <mi>o</mi>\n                <mi>y</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>y</mi>\n                <mi>o</mi>\n                <mi>u</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mi>u</mi>\n                <mi>c</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>r</mi>\n                <mi>a</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>g</mi>\n                <mo>+</mo>\n                <mi>Y</mi>\n                <mi>o</mi>\n                <mi>u</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>u</mi>\n                <mi>t</mi>\n                <mi>s</mi>\n                <mi>i</mi>\n                <mi>d</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>l</mi>\n                <mi>a</mi>\n                <mi>b</mi>\n                <mi>o</mi>\n                <mi>u</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>f</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>c</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mi>u</mi>\n                <mi>c</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>r</mi>\n                <mi>a</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>g</mi>\n                <mo>)</mo>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n      <mrow>\n        <mi>Y</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The ILO engages in annual consultations with Member States through the ILOSTAT questionnaire and related Statistics Reporting System (StaRS). National data providers receive a link to the portal where they can review all national SDG data available on ILOSTAT.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Through the ILO Harmonized Microdata initiative, the ILO strives to produce internationally comparable labour statistics based on the indicator concepts and definitions adopted by the International Conference of Labour Statisticians.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>Multivariate regression and cross-validation techniques are used to impute missing values at the country level. The additional variables used for the imputation include a range of indicators, including labour market and school enrolment data. However, the imputed missing country values are only used to calculate the global and regional estimates; they are not used for international reporting on the SDG indicators by the ILO. </p>\n<p> </p>\n<p>For further information, refer to the ILO modelled estimates methodological overview, available at <a href=\"https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\">https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/</a> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p>Regional and global figures are aggregates of the country-level figures including the imputed values.</p>", "REG_AGG__GLOBAL"=>"<p>The NEET aggregates are derived from the ILO modelled estimates that are used to produce global and regional estimates of, amongst others, rates of youth not in employment, with employment based on the 13<sup>th</sup> ICLS standards. These models use multivariate regression and cross-validation techniques to impute missing values at the country level, which are then aggregated to produce regional and global estimates. The regional and global NEET rates are obtained by first adding up, across countries, the numerator and denominator of the formula that defines NEET rates as outlined above. Once both magnitudes are produced at the desired level of aggregation, the ratio between the two is used to produce the NEET rate for each regional grouping and the global level. Notice that this direct aggregation method can be used due to the imputation of missing observations. For further, refer to the ILO modelled estimates methodological overview, available at https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/. </p>", "DOC_METHOD__GLOBAL"=>"<p>To calculate this indicator, reliable data are needed on both the labour market situation and the participation in the educational system of the youth. These data are collected at the national level mainly through labour force surveys (or other types of household surveys with an employment module). For the methodology of each national household survey, one must refer to the most comprehensive survey report or to the methodological publications of the national statistical office in question. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The processes of compilation, production, and publication of data, including its quality control, are carried out following the methodological framework and standards established by the ILO Department of Statistics, in compliance with the information technology and management standards of the ILO.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination on ILOSTAT. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The final assessment of the quality of information is carried out by the Data Production and Analysis Unit of the ILO Department of Statistics. In cases of doubt about the quality of specific data, these values are reviewed with the participation of the national agencies responsible for producing the data if appropriate. If the issues cannot be clarified, the respective information is not published.</p>", "COVERAGE__GLOBAL"=>"<p>Data may differ from those published on ILOSTAT due to different update schedules and stricter criteria applied for inclusion in the SDG database.</p>\n<p><strong>Data availability: </strong></p>\n<p>Data for this indicator is available for 185 countries and territories in the 13<sup>th</sup> ICLS series and 109 countries and territories in the 19<sup>th</sup> ICLS series. </p>\n<p><strong>Time series:</strong></p>\n<p>Country data for this indicator is available as of 2000 in the SDG Indicators Global Database, but longer time series are available in ILOSTAT. Global and regional data in this submission covers a period of 2005 to 2024.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>No disaggregation is specifically required for this indicator, although having it disaggregated by sex is desirable, as is disaggregation by detailed age groups within the youth age band. </p>\n<p> </p>", "COMPARABILITY__GLOBAL"=>"<p>A number of factors can limit the comparability of statistics on the youth NEET rate between countries or over time. When differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.).</p>\n<p>Work statistics for countries not using the same set of statistical standards are not comparable. As such, each series is based on a single set of standards (i.e., 13th or 19th ICLS) and contains only data comparable within and across countries, allowing data users to continue making meaningful time series analysis and international comparisons. Users should not compare data across series.</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (<a href=\"https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm</a>) </li>\n  <li>Decent Work Indicators Manual: <a href=\"http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_223121.pdf\" target=\"_blank\"><u>http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_223121.pdf</u></a> </li>\n  <li>Resolution concerning statistics of work, employment and labour underutilization, adopted by the 19<sup>th</sup> ICLS (October 2013) and amended by the 21<sup>st</sup> ICLS (October 2023): <a href=\"https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm</a> </li>\n  <li>Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the ICLS in 1982: https://www.ilo.org/resource/resolution-concerning-statistics-economically-active-population-employment </li>\n  <li>Quick guide to understanding the impact of the new statistical standards on ILOSTAT databases: <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf</a> </li>\n  <li>International Standard Classification of Education (ISCED) developed by UNESCO: <a href=\"http://uis.unesco.org/en/topic/international-standard-classification-education-isced\">http://uis.unesco.org/en/topic/international-standard-classification-education-isced</a> </li>\n  <li>What does NEETs mean and why is the concept so easily misinterpreted? (ILO, W4Y, Technical brief n&#xB0;1): <a href=\"http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/documents/publication/wcms_343153.pdf\" target=\"_blank\"><u>http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/documents/publication/wcms_343153.pdf</u></a> </li>\n  <li>ILOSTAT portal : <a href=\"https://ilostat.ilo.org/\" target=\"_blank\"><u>https://ilostat.ilo.org/</u></a> </li>\n  <li>ILO Manual &#x2013; Decent Work Indicators, Concepts and Definitions &#x2013; Chapter 1, Employment opportunities <a href=\"http://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm\" target=\"_blank\"><u>http://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm</u></a> (second version, page 38). </li>\n</ul>", "indicator_sort_order"=>"08-06-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.7.1", "slug"=>"8-7-1", "name"=>"Proporción y número de niños de entre 5 y 17 años que realizan trabajo infantil, desglosados por sexo y edad", "url"=>"/site/es/8-7-1/", "sort"=>"080701", "goal_number"=>"8", "target_number"=>"8.7", "global"=>{"name"=>"Proporción y número de niños de entre 5 y 17 años que realizan trabajo infantil, desglosados por sexo y edad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción y número de niños de entre 5 y 17 años que realizan trabajo infantil, desglosados por sexo y edad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción y número de niños de entre 5 y 17 años que realizan trabajo infantil, desglosados por sexo y edad", "indicator_number"=>"8.7.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Demasiados niños en el mundo siguen atrapados en el trabajo infantil, lo que compromete \nsu futuro individual y el nuestro. Según las últimas estimaciones mundiales de la OIT, \nalrededor de 152 millones de niños en todo el mundo (64 millones de niñas y 88 millones de niños) \nson trabajadores infantiles, lo que representa casi el 10 % de la población infantil. \n\nEstas crudas cifras subrayan la necesidad de acelerar los avances contra el trabajo infantil \nde cara a la fecha límite de 2025 para erradicar el trabajo infantil en todas sus formas, \ny la consiguiente necesidad de estadísticas sobre trabajo infantil para supervisar y \norientar los esfuerzos en este sentido.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.7.1&seriesCode=SL_TLF_CHLDEA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=5-17%20%7C%20BOTHSEX\">Proporción de niños que participan en actividades económicas, por sexo y edad (%) SL_TLF_CHLDEA</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-07-01.pdf\">Metadatos 8-7-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-28", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Far too many children in the world remain trapped in child labour, \ncompromising their individual future and our collective futures. \nAccording to the latest ILO global estimates, about 152 million children \nworldwide – 64 million girls and 88 million boys - are child labourers, \naccounting for almost 10 percent of the child population. \n\nThese stark figures underscore the need for accelerated progress against child \nlabour in the lead up to the 2025 target date for ending child labour in all its \nforms, and the accompanying need for child labour statistics to monitor and guide \nefforts in this regard. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.7.1&seriesCode=SL_TLF_CHLDEA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=5-17%20%7C%20BOTHSEX\">Proportion of children engaged in economic activity, by sex and age (%) SL_TLF_CHLDEA</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-07-01.pdf\">Metadata 8-7-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Munduan oraindik ere haur gehiegi daude lanean, eta horrek arriskuan jartzen du haien eta gure etorkizuna. \nLANEren munduko azken kalkuluen arabera, mundu osoko 152 milioi haur inguru (64 milioi neska eta 88 milioi mutil) \nhaur-langileak dira, hau da, haurren ia % 10. \n\nZifra gordin horiek agerian uzten dute beharrezkoa dela haurren lanaren aurkako aurrerapenak bizkortzea \n2025eko azken datari begira, haurren lana bere forma guztietan desagerrarazteko. Ondorioz, haurren lanari \nburuzko estatistikak behar dira, ildo horretan egiten diren ahaleginak gainbegiratzeko eta bideratzeko. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.7.1&seriesCode=SL_TLF_CHLDEA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=5-17%20%7C%20BOTHSEX\">Jarduera ekonomikoetan parte hartzen duten haurren proportzioa, sexuaren eta adinaren arabera (%) SL_TLF_CHLDEA</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-07-01.pdf\">Metadatuak 8-7-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.7: Take immediate and effective measures to eradicate forced labour, end modern slavery and human trafficking and secure the prohibition and elimination of the worst forms of child labour, including recruitment and use of child soldiers, and by 2025 end child labour in all its forms</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.7.1: Proportion and number of children aged 5&#x2013;17 years engaged in child labour, by sex and age</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_TLF_CHLDEA - Proportion of children engaged in economic activity [8.7.1]</p>\n<p>SL_TLF_CHLDEC - Proportion of children engaged in economic activity and household chores [8.7.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Many other SDG indicators have links and are relevant to child labour.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>\n<p>International Labour Organization (ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>\n<p>International Labour Organization (ILO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The number of children engaged in child labour corresponds to the number of children reported to be in child labour during the reference period (usually the week prior to the survey). The proportion of children in child labour is calculated as the number of children in child labour divided by the total number of children in the population. For the purposes of this indicator, children include all persons aged 5 to 17. </p>\n<p> </p>\n<p><strong>Concepts:</strong></p>\n<p>Three principal international legal instruments &#x2013; ILO Convention No. 138 (Minimum Age) (C138), United Nations Convention on the Rights of the Child (CRC), ILO Convention No. 182 (Worst Forms) (C182) together set the legal boundaries for child labour, and provide the legal basis for national and international actions against it. In accordance with these instruments, child labour is work that children should <em>not </em>be doing because (a) they are too young or (b) is likely to harm their health, safety or morals, due to its nature or the conditions in which it is carried out.</p>\n<p> </p>\n<p>The resolutions adopted by the International Conference of Labour Statisticians (ICLS), the world&#x2019;s acknowledged standard-setting body in the area of labour statistics, provide the basis for translating the legal standards governing the concept of child labour into statistical terms for the purpose of child labour measurement. </p>\n<p> </p>\n<p>In accordance with the ICLS resolutions<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>, child labour can be measured on the basis of the production boundary set by the United Nations System of National Accounts (UN SNA) or on the basis of the general production boundary. The former limits the frame of reference to economic activity, while the latter extends it to include both economic activity <em>and</em> unpaid household services, that is, the production of domestic and personal services by a household member for consumption within their own household, commonly called &#x201C;household chores&#x201D;. </p>\n<p> </p>\n<p>Following from this, two indicators are used for measuring child labour for the purpose of SDG reporting, the first based on the production boundary set by the UN SNA and the second based on the general production boundary.</p>\n<p> </p>\n<p>Indicator 1: Proportion and number of children aged 5-17 years engaged in economic activities at or above age-specific hourly thresholds (<strong>SNA production boundary basis</strong>)</p>\n<p><em>Child labour for the 5 to 11 age range</em>: children working for 1 hour or more per week in economic activity; </p>\n<p><em>Child labour for the 12 to 14 age range</em>: children working for 14 hours or more per week in economic activity;</p>\n<p><em>Child labour for the 15 to 17 age range</em>: children working for 43 hours or more per week in economic activity.</p>\n<p> </p>\n<p>Indicator 2: Proportion and number of children aged 5-17 years engaged in economic activities and household chores at or above age-specific hourly thresholds (<strong>general production boundary basis</strong>): </p>\n<p><em>Child labour for the 5 to 11 age range</em>: children working for 1 hour or more per week in economic activity and/or involved in unpaid household services for 21 hours or more per week; </p>\n<p><em>Child labour for the 12 to 14 age range</em>: children working for 14 hours or more per week in economic activity and/or involved in unpaid household services for 21 hours or more per week; </p>\n<p><em>Child labour for the 15 to 17 age range</em>: children working for 43 hours or more per week in economic activity.<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> </p>\n<p> </p>\n<p>The concept of child labour also includes the worst forms of child labour other than hazardous (18<sup>th</sup> ICLS paragraphs 33 to 34) as well as hazardous work (18<sup>th</sup> ICLS paragraphs 21 to 32). The worst forms of child labour include all forms of slavery or similar practices such as trafficking and the recruitment and use of child soldiers, the use or procurement of children for prostitution or other illicit activities, and other work that is likely to harm children&#x2019;s health, safety or well-being. </p>\n<p> </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> 20th International Conference of Labour Statisticians. Resolution to amend the 18th ICLS Resolution concerning statistics of child labour. ILO. Geneva, October 2019. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> No hourly threshold is set for household chores for ages 15-17. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The definition of child labour is in line with the standard set by the latest 20<sup>th</sup> International Conference of Labour Statisticians. Resolution to amend the 18th ICLS Resolution concerning statistics of child labour. ILO. Geneva, October 2019</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Household surveys such as National Labour Force Surveys, National Multipurpose Household Surveys, UNICEF-supported Multiple Indicator Cluster Surveys (MICS), Demographic and Health Surveys (DHS), ILO-supported Statistical Information and Monitoring Programme on Child Labour (SIMPOC), and World Bank Living Standard Measurement surveys (LSMS) are among the most important instruments for generating information on child labour in developing countries. Estimates of child labour generated by these survey instruments are increasingly relied on by countries to monitor progress towards national and global child labour elimination targets. Many countries also produce national labour estimates and reports that often include data on child labour and/or employment among children. </p>", "COLL_METHOD__GLOBAL"=>"<p>UNICEF undertakes a wide consultative process of compiling and assessing data from national sources for the purposes of updating its global databases on the situation of children. Up until 2017, the mechanism UNICEF used to collaborate with national authorities on ensuring data quality and international comparability on key indicators of relevance to children was known as Country Data Reporting on the Indicators for the Goals (CRING). </p>\n<p>As of 2018, UNICEF launched a new country consultation process with national authorities on selected child-related global SDG indicators it is custodian or co-custodian to, to meet emerging standards and guidelines on data flows for global reporting of SDG indicators, which place strong emphasis on technical rigour, country ownership and use of official data and statistics. The consultation process solicited feedback directly from National Statistical Offices (NSOs), as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. The results of this country consultation are reviewed and discussed with ILO. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why. </p>", "FREQ_COLL__GLOBAL"=>"<p>UNICEF will undertake an annual country consultation likely between December and January every year to allow for review and processing of the feedback received in order to meet global SDG reporting deadlines. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Updated data on 8.7.1 will be available in the SDG reporting period every February/March.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (for the most part) and line ministries/other government agencies and International agencies that have conducted labour force surveys or other household surveys through which data on child labour were collected. </p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF) and International Labour Organization (ILO) </p>", "INST_MANDATE__GLOBAL"=>"<p>The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets, and provides technical assistance and training to ILO member States to support their efforts to produce high quality labour market data, including child labour data.</p>\n<p>UNICEF provides technical and financial assistance to Member States to support their efforts to collect high quality data on child labour, including through UNICEF-supported MICS household survey programme. UNICEF also compiles child labour statistics with the goal of making internationally comparable datasets publicly available, and it analyzes child labour statistics, which are included in relevant data-driven publications, including in its flagship publication, <em>The State of the World&#x2019;s Children</em>. </p>", "RATIONALE__GLOBAL"=>"<p>Far too many children in the world remain trapped in child labour, compromising their individual future and our collective futures. According to the latest ILO global estimates, about 152 million children worldwide &#x2013; 64 million girls and 88 million boys - are child labourers, accounting for almost 10 percent of the child population. These stark figures underscore the need for accelerated progress against child labour in the lead up to the 2025 target date for ending child labour in all its forms, and the accompanying need for child labour statistics to monitor and guide efforts in this regard. Reliable, comprehensive and timely data on the nature and extent of child labour provide a basis for determining priorities for national global action against child labour. Statistical information on child labour, and more broadly on all working children, also provide a basis for increasing public awareness of the situation of working children and for the development of appropriate regulatory frameworks and policies. </p>", "REC_USE_LIM__GLOBAL"=>"<p>While the concept of child labour includes working in activities that are hazardous in nature, to ensure comparability of estimates over time and to minimize data quality issues, work beyond age-specific hourly thresholds are used as a proxy for hazardous work for the purpose of reporting on SDG indicator 8.7.1. Further methodological work is needed to validate questions specifically aimed at identifying children in hazardous working conditions. </p>\n<p> </p>\n<p>Similarly, while the worst forms of child labour other than hazardous also form part of the concept of child labour more broadly, data on the worst forms of child labour are not currently captured in regular household surveys given difficulties with accurately and reliably measuring it. Therefore, this element of child labour is not captured by the indicators used for reporting on SDG 8.7.1. </p>\n<p> </p>\n<p>In addition, &#x2018;own use production of goods&#x2019;, including activities such as fetching water and collecting firewood, falls within the production boundary set by the United Nations System of National Accounts (SNA). However, for the purpose of SDG reporting of indicator 8.7.1, and with the goal of facilitating international comparability, fetching water and collecting firewood have been classified as unpaid household services (i.e., household chores), a form of production that lies outside the SNA production boundary. </p>\n<p> </p>\n<p>More broadly, child labour estimates based on the statistical standards set out in the ICLS resolution represent useful benchmarks for international comparative purposes but are <u>not</u> necessarily consistent with estimates based on national child labour legislation. ILO Convention No. 138 contains a number of flexibility clauses left to the discretion of the competent national authority in consultation (where relevant) with workers&#x2019; and employers&#x2019; organizations (e.g., minimum ages, scope of application).<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup> This means that there is no single legal definition of child labour across countries, and thus, no single statistical measure of child labour consistent with national legislation across countries. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> Principal areas of flexibility in the Convention include: (a)<em>minimum ages</em>: Members whose economy and educational facilities are insufficiently developed may specify a lower general minimum age of 14 years (Art. 2.4) and a lower age range for light work of 12 to 14 years (Art 7.4); and (b) <em>scope of application</em>: Members may exclude from the application of the Convention limited (non-hazardous) categories of employment or work in respect of which special and substantial problems of application arise (Art. 4.1). Members whose economy and administrative facilities are insufficiently developed may also initially limit the scope of application of the Convention (Art. 5.1) beyond a core group of economic activities or undertakings (Art. 5.3). <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>Children aged 5-17: Number of children aged 5-17 reported in child labour during the week prior to the survey divided by the total number of children aged 5-17 in the population, multiplied by 100. </p>\n<p> </p>\n<p>Children aged 5-14: Number of children aged 5-14 reported in child labour during the week prior to the survey divided by the total number of children aged 5-14 in the population, multiplied by 100. </p>\n<p> </p>\n<p>Children aged 15-17: Number of children aged 15-17 reported child labour during the week prior to the survey divided by the total number of children aged 15-17 in the population, multiplied by 100. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>A wide consultative process is undertaken to compile, assess and validate data from national sources. </p>\n<p>The consultation process solicited feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the compilation of the indicators, including the data sources used, and the application of internationally agreed definitions, classification and methodologies to the data from that source. The results of this country consultation are reviewed and discussed between the co-custodian agencies, UNICEF and ILO. Once reviewed, feedback is made available to countries on whether or not specific data points are accepted, and if not, the reasons why. </p>", "ADJUSTMENT__GLOBAL"=>"<p>While the concept of child labour includes working in activities that are hazardous in nature, to ensure</p>\n<p>comparability of estimates over time and to minimize data quality issues, work beyond age-specific hourly thresholds are used as a proxy for hazardous work for the purpose of reporting on SDG indicator 8.7.1. Similarly, while the worst forms of child labour other than hazardous also form part of the concept of child labour more broadly, data on the worst forms of child labour are not currently captured in regular household surveys given difficulties with accurately and reliably measuring it. Therefore, this element of child labour is not captured by the indicators used for reporting on SDG 8.7.1. In addition, &#x2018;own use production of goods&#x2019;, including activities such as fetching water and collecting firewood,</p>\n<p>falls within the production boundary set by the United Nations System of National Accounts (SNA). However, for the purpose of SDG reporting of indicator 8.7.1, and with the goal of facilitating international comparability, fetching water and collecting firewood have been classified as unpaid household services (i.e., household chores), a form of production that lies outside the SNA production boundary.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>Country data are not published when data for a country are entirely missing. </p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p>For details on the methodology for treatment of missing data in the calculation of regional and global aggregates see, <a href=\"https://data.unicef.org/resources/child-labour-2020-global-estimates-trends-and-the-road-forward/\">Child Labour: Global estimates 2020, trends and the road forward</a></p>", "REG_AGG__GLOBAL"=>"<p>For details on the methodology for calculation of regional aggregates, see <a href=\"https://data.unicef.org/resources/child-labour-2020-global-estimates-trends-and-the-road-forward/\">Child Labour: Global estimates 2020, trends and the road forward</a> </p>", "DOC_METHOD__GLOBAL"=>"<p>See Section 3.a. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The process behind the production of reliable statistics on child labour is well established within UNICEF and the ILO. The quality and process leading to the production of the SDG indicator 8.7.1 is ensured by working closely with the statistical offices and other relevant stakeholders through a consultative process. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UNICEF and ILO maintain the global database on child labour that is used for official SDG reporting. Before the inclusion of any data point in the database, it is reviewed by technical focal points at UNICEF and ILO to check for consistency and overall data quality. This review is based on a set of objective criteria to ensure that only the most recent and reliable information are included in the databases. These criteria include the following: data sources must include proper documentation; data values must be representative at the national population level; data are collected using an appropriate methodology (e.g., sampling); data values are based on a sufficiently large sample; data conform to the standard indicator definition including age group and concepts, to the extent possible; data are plausible, based on trends and consistency with previously published/reported estimates for the indicator. </p>\n<p> </p>\n<p>As of 2018, UNICEF undertakes an annual consultation with government authorities on 10 of the child-related SDG indicators in its role of sole or joint custodian, and in line with its global monitoring mandate and normative commitments to advancing the 2030 Agenda for children. This includes indicator 8.7.1. More details on the process for the country consultation are outlined below. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Nationally representative and comparable data are currently available for around 100 low-and middle-income countries. </p>\n<p><strong>Time series:</strong></p>\n<p>Not available. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Sex. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong> </p>\n<p>The country estimates compiled and presented in the global SDG database have been re-analyzed by UNICEF and ILO in accordance with the definitions and criteria detailed above (see &#x2018;Concepts&#x2019;). This means that the country data values included in the global SDG database will differ from those published and presented in national survey reports. </p>", "OTHER_DOC__GLOBAL"=>"<p>UNICEF statistics on child labour: https://data.unicef.org/topic/child-protection/child-labour/ </p>\n<p>ILO statistics on child labour: <a href=\"http://www.ilo.org/ipec/ChildlabourstatisticsSIMPOC/Questionnairessurveysandreports/lang--en/index.htm\">http://www.ilo.org/ipec/ChildlabourstatisticsSIMPOC/Questionnairessurveysandreports/lang--en/index.htm</a> </p>\n<p>Child Labour: Global estimates 2020, trends and the road forward: <a href=\"https://data.unicef.org/resources/child-labour-2020-global-estimates-trends-and-the-road-forward/\">https://data.unicef.org/resources/child-labour-2020-global-estimates-trends-and-the-road-forward/</a></p>", "indicator_sort_order"=>"08-07-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.8.1", "slug"=>"8-8-1", "name"=>"Lesiones ocupacionales mortales y no mortales por cada 100.000 trabajadores, desglosadas por sexo y estatus migratorio", "url"=>"/site/es/8-8-1/", "sort"=>"080801", "goal_number"=>"8", "target_number"=>"8.8", "global"=>{"name"=>"Lesiones ocupacionales mortales y no mortales por cada 100.000 trabajadores, desglosadas por sexo y estatus migratorio"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Lesiones ocupacionales mortales y no mortales por cada 100.000 trabajadores, desglosadas por sexo y estatus migratorio", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Lesiones ocupacionales mortales y no mortales por cada 100.000 trabajadores, desglosadas por sexo y estatus migratorio", "indicator_number"=>"8.8.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Índice de incidencia de accidentes con baja en jornada/in itinere, según gravedad", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.8- Proteger los derechos laborales y promover un entorno de trabajo seguro y sin riesgos para todos los trabajadores, incluidos los trabajadores migrantes, en particular las mujeres migrantes y las personas con empleos precarios", "definicion"=>"Índice de incidencia de accidentes con baja en jornada/in itinere, según gravedad", "formula"=>"\n$$IACC_{gravedad}^{t} = \\frac{ACC_{gravedad}^{t}}{PE_{OIT}^{t}} \\cdot 100.000$$\n\ndonde:\n\n$ACC_{gravedad}^{t} =$ número de accidentes con baja en jornada/in itinere, según gravedad en el año $t$\n\n$PE_{OIT}^{t} =$ personas empleadas en el año $t$\n", "desagregacion"=>"\nGravedad: mortal; no mortal\n\nSexo\n\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEste indicador proporciona información valiosa que podría utilizarse para formular \npolíticas y programas de prevención de lesiones, enfermedades y muertes laborales. \nTambién podría utilizarse para supervisar la implementación de estos programas y \nseñalar áreas específicas de mayor riesgo, como una ocupación, industria o ubicación \nen particular. Si bien el objetivo principal de este indicador es proporcionar \ninformación con fines de prevención, puede utilizarse para otros fines, como \nidentificar las ocupaciones y actividades económicas con mayor riesgo de lesiones \nlaborales; detectar cambios en los patrones y la ocurrencia de lesiones laborales, \na fin de supervisar las mejoras en seguridad y revelar nuevas áreas de riesgo; \ninformar a empleadores, organizaciones de empleadores, trabajadores y organizaciones \nde trabajadores sobre los riesgos asociados con su trabajo y lugares de trabajo, \npara que puedan participar activamente en su propia seguridad; evaluar la \neficacia de las medidas preventivas; estimar las consecuencias de las lesiones \nlaborales, especialmente en términos de días perdidos o costos; y proporcionar \nuna base para la formulación de políticas destinadas a alentar a los empleadores, \nlas organizaciones de empleadores, los trabajadores y las organizaciones de \ntrabajadores a introducir medidas de prevención de accidentes.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-08-01.pdf\">Metadatos 8-8-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Índice de incidencia de accidentes con baja en jornada/in itinere, según gravedad", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.8- Proteger los derechos laborales y promover un entorno de trabajo seguro y sin riesgos para todos los trabajadores, incluidos los trabajadores migrantes, en particular las mujeres migrantes y las personas con empleos precarios", "definicion"=>"Rate of incidence of accidents with sick leave during the working day/in transit, by severity", "formula"=>"\n$$IACC_{severity}^{t} = \\frac{ACC_{severity}^{t}}{PE_{OIT}^{t}} \\cdot 100.000$$\n\nwhere:\n\n$ACC_{severity}^{t} =$ number of accidents with sick leave during the working day/in transit, by severity in year $t$\n\n$PE_{OIT}^{t} =$ employed people in year $t$\n", "desagregacion"=>"\nSeverity: Fatal; non-fatal\n\nSex\n\nProvince\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThis indicator provides valuable information that could be used to formulate policies \nand programmes for the prevention of occupational injuries, diseases and deaths. \nIt could also be used to monitor the implementation of these programmes and to signal \nparticular areas of increasing risk such as a particular occupation, industry or location. \nAlthough the principal objective of this indicator is to provide information for prevention \npurposes, it may be used for a number of other purposes, such as to identify the occupations \nand economic activities with the highest risk of occupational injuries; to detect changes \nin the pattern and occurrence of occupational injuries, so as to monitor improvements in safety \nand reveal any new areas of risk; to inform employers, employers’ organizations, workers and \nworkers’ organizations of the risks associated with their work and workplaces, so that they can \ntake an active part in their own safety; to evaluate the effectiveness of preventive measures; \nto estimate the consequences of occupational injuries, particularly in terms of days lost or costs; \nand to provide a basis for policymaking aimed at encouraging employers, employers’ organizations, \nworkers and workers’ organizations to introduce accident prevention measures. \n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-08-01.pdf\">Metadata 8-8-1.pdf</a>", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Índice de incidencia de accidentes con baja en jornada/in itinere, según gravedad", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.8- Proteger los derechos laborales y promover un entorno de trabajo seguro y sin riesgos para todos los trabajadores, incluidos los trabajadores migrantes, en particular las mujeres migrantes y las personas con empleos precarios", "definicion"=>"Lanaldian/in itinere baja eragin duten istripuen intzidentzia-tasa, larritasunaren arabera", "formula"=>"\n$$IACC_{larritasuna}^{t} = \\frac{ACC_{larritasuna}^{t}}{PE_{OIT}^{t}} \\cdot 100.000$$\n\nnon:\n\n$ACC_{larritasuna}^{t} =$ lanaldian/in itinere baja eragin duten istripuen kopurua, larritasunaren arabera $t$ urtean\n\n$PE_{OIT}^{t} =$ enplegatuak $t$ urtean\n", "desagregacion"=>"\nLarritasuna: heriotza eragindakoa; heriotzarik gabea\n\nSexua\n\nLurralde historikoa\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAdierazle honek lesioak, gaixotasunak eta laneko heriotzak prebenitzeko politikak eta programak formulatzeko \nerabil daitekeen informazio baliotsua ematen du. Programa horien ezarpena gainbegiratzeko eta arrisku handiagoko \neremu espezifikoak adierazteko ere erabil liteke, hala nola okupazio, industria edo kokapen jakin bat. Adierazle \nhonen helburu nagusia prebentzio-helburuetarako informazioa ematea bada ere, beste helburu batzuetarako ere \nerabil daiteke, hala nola, laneko lesioen arrisku handiagoa duten okupazioak eta jarduera ekonomikoak \nidentifikatzeko; laneko lesioen patroietan eta gertaeretan aldaketak hautemateko, segurtasun-hobekuntzak \ngainbegiratzeko eta arrisku-eremu berriak ezagutarazteko; enplegatzaileei, enplegatzaileen erakundeei, langileei \neta langileen erakundeei beren lanarekin eta lantokiekin lotutako arriskuei buruzko informazioa emateko, hartara \nberen segurtasunean aktiboki parte hartu ahal izan dezaten; prebentzio-neurrien eraginkortasuna ebaluatzeko; laneko \nlesioen ondorioak balioesteko, bereziki galdutako egun edo kostuei dagokienez; eta enplegatzaileak, enplegatzaileen \nerakundeak, langileak eta langileen erakundeak istripuak prebenitzeko neurriak sartzera animatzeko politikak \nformulatzeko oinarri bat emateko. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-08-01.pdf\">Metadatuak 8-8-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.8: Protect labour rights and promote safe and secure working environments for all workers, including migrant workers, in particular women migrants, and those in precarious employment</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.8.1: Fatal and non-fatal occupational injuries per 100,000 workers, by sex and migrant status</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_EMP_FTLINJUR - Fatal occupational injuries among employees (rate) [8.8.1]</p>\n<p>SL_EMP_INJUR - Non-fatal occupational injuries among employees (rate) [8.8.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.3.1, 8.8.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Labour Organisation (ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Labour Organisation (ILO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>This indicator provides information on the number of fatal and non-fatal occupational injuries per 100,000 workers in the reference group during the reference period. It is a measure of the personal likelihood or risk of having a fatal or a non-fatal occupational injury for each worker in the reference group. </p>\n<p> </p>\n<p>The number of occupational injuries expressed per a given number of workers in the reference group is also known as the incidence rate of occupational injuries. </p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>Definitions of the main concepts presented below are derived from the Resolution concerning statistics of occupational injuries (resulting from occupational accidents), adopted by the 16th International Conference of Labour Statisticians (ICLS) in 1998 </p>\n<p>(<a href=\"https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087528/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087528/lang--en/index.htm</a>). </p>\n<p> </p>\n<p>Occupational accident: an unexpected and unplanned occurrence, including acts of violence, arising out of or in connection with work which results in one or more workers incurring a personal injury, disease or death. Occupational accidents are to be considered travel, transport or road traffic accidents in which workers are injured and which arise out of or in the course of work; that is, while engaged in an economic activity, or at work, or carrying out the business of the employer. </p>\n<p> </p>\n<p>Occupational injury: any personal injury, disease or death resulting from an occupational accident. An occupational injury is different from an occupational disease, which comes as a result of an exposure over a period of time to risk factors linked to the work activity. Diseases are included only in cases where the disease arose as a direct result of an accident. An occupational injury can be fatal or non-fatal (and non-fatal injuries could entail the loss of workdays). </p>\n<p> </p>\n<p>Fatal occupational injury: an occupational injury leading to death within one year of the day of the occupational accident. </p>\n<p> </p>\n<p>Case of occupational injury: the case of one worker incurring one or more occupational injuries as a result of one occupational accident. </p>\n<p> </p>\n<p>Workers in the reference group: workers in the reference group refer to the average number of workers in the particular group under consideration and who are covered by the source of the statistics on occupational injuries (for example, those of a specific sex or in a specific economic activity, occupation, region, age group, or any combination of these, or those covered by a particular insurance scheme, accident notification systems, or household or establishment survey). </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Ratio of cases per 100,000 workers</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Migrant status is determined according to country of birth (native-born or foreign-born) or country of citizenship (citizen or non-citizen). </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The recommended data sources are different types of administrative records, such as records of national systems for the notification of occupational injuries (labour inspection records and annual reports; insurance and compensation records, death registers), supplemented by household surveys (especially in order to cover informal sector enterprises and the self-employed) and/or establishment surveys. </p>\n<p>The metadata should clearly specify (i) whether the statistics relate to cases of occupational injury which have been reported (to an accident notification system or to an accident compensation scheme), compensated (by an accident insurance scheme) or identified in some other way (for example through a survey of households or establishments) and (ii) whether cases of occupational disease and cases of injury due to commuting accidents are excluded from the statistics, as recommended. </p>", "COLL_METHOD__GLOBAL"=>"<p>The ILO Department of Statistics processes national household survey microdata sets in line with internationally agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians. For data that could not be obtained through this processing or directly from government websites, the ILO sends out an annual ILOSTAT questionnaire to all relevant agencies within each country (national statistical office, labour ministry, etc.) requesting the latest annual data and any revisions on numerous labour market topics and indicators, including many SDG indicators. </p>", "FREQ_COLL__GLOBAL"=>"<p>Continuous</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Continuous</p>", "DATA_SOURCE__GLOBAL"=>"<p>Labour ministries, labour inspection, national insurance, and/or national statistical offices </p>", "COMPILING_ORG__GLOBAL"=>"<p>International Labour Organisation (ILO) </p>", "INST_MANDATE__GLOBAL"=>"<p>The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data. </p>", "RATIONALE__GLOBAL"=>"<p>This indicator provides valuable information that could be used to formulate policies and programmes for the prevention of occupational injuries, diseases and deaths. It could also be used to monitor the implementation of these programmes and to signal particular areas of increasing risk such as a particular occupation, industry or location. Although the principal objective of this indicator is to provide information for prevention purposes, it may be used for a number of other purposes, such as to identify the occupations and economic activities with the highest risk of occupational injuries; to detect changes in the pattern and occurrence of occupational injuries, so as to monitor improvements in safety and reveal any new areas of risk; to inform employers, employers&#x2019; organizations, workers and workers&#x2019; organizations of the risks associated with their work and workplaces, so that they can take an active part in their own safety; to evaluate the effectiveness of preventive measures; to estimate the consequences of occupational injuries, particularly in terms of days lost or costs; and to provide a basis for policymaking aimed at encouraging employers, employers&#x2019; organizations, workers and workers&#x2019; organizations to introduce accident prevention measures. </p>", "REC_USE_LIM__GLOBAL"=>"<p>There may be problems of underreporting of occupational injuries, and proper systems should be put in place to ensure the best reporting and data quality. Underreporting is thought to be present in countries at all levels of development but may be particularly problematic in some developing countries. Data users should be aware of this issue when analysing the data. Double counting of cases of occupational injury may also happen in cases where data from several registries (records kept by different agencies, for example) are consolidated to have more comprehensive statistics. </p>\n<p> </p>\n<p>Because data quality issues may be present, it may be more relevant to analyse indicator trends rather than levels. When measured over a period of time, the data can reveal progress or deterioration in occupational safety and health, and thus point to the effectiveness of prevention measures. This indicator is volatile and strong annual fluctuations may occur due to unexpected but significant accidents or national calamities. The underlying trend should therefore be analysed. </p>", "DATA_COMP__GLOBAL"=>"<p>The incidence rates of fatal and non-fatal occupational injuries will be calculated separately, since statistics on fatal injuries tend to come from a different source than those on non-fatal injuries, which would make their sum into total occupational accidents inaccurate.</p>\n<p>The fatal occupational injury incidence rate is expressed per 100,000 workers in the reference group, and thus, is calculated as follows:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">F</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">j</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">N</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">w</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">j</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">k</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n  </math></p>\n<p>Similarly, the non-fatal occupational injury incidence rate is calculated as follows:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">N</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">j</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">N</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">w</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">j</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">W</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">k</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n  </math></p>\n<p>In calculating the average number of workers, the number of part-time workers should be converted to full-time equivalents. For the calculation of rates, the numerator and the denominator should have the same coverage. For example, if self-employed persons are not covered by the source of statistics on fatal occupational injuries, they should also be taken out of the denominator. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The ILO engages in annual consultations with Member States through the ILOSTAT questionnaire and related Statistics Reporting System (StaRS). National data providers receive a link to the portal where they can review all national SDG data available on ILOSTAT.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<p>This indicator could come from a variety of sources at the national level, including various kinds of administrative records (insurance records, labour inspection records, etc.), household surveys and establishment surveys. </p>\n<ul>\n  <li>ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (<a href=\"https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm</a>) </li>\n  <li>ILO Manual &#x2013; Decent Work Indicators, Concepts and Definitions &#x2013; Chapter 8, Safe work environment <a href=\"https://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm\">https://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm</a> (second version, page 156) </li>\n  <li>Resolution concerning statistics of occupational injuries (resulting from occupational accidents) <a href=\"https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087528/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087528/lang--en/index.htm</a> </li>\n  <li>Global database on occupational safety and health legislation &#x2013; LEGOSH https://wwwex.ilo.org/dyn/legosh_en/f?p=14100:1000:0::NO::: </li>\n  <li>Occupational injuries statistics from household surveys and establishment surveys <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_173153.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_173153.pdf</a> </li>\n  <li>ILOSTAT (<a href=\"https://ilostat.ilo.org/\" target=\"_blank\"><u>https://ilostat.ilo.org</u></a>) </li>\n  <li>ILOSTAT Metadata &#x2013; Indicator descriptions(https://ilostat.ilo.org/methods/concepts-and-definitions/description-occupational-safety-and-health-statistics/) </li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>The processes of compilation, production, and publication of data, including its quality control, are carried out following the methodological framework and standards established by the ILO Department of Statistics, in compliance with the information technology and management standards of the ILO.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination in the ILOSTAT database.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The final assessment of the quality of information is carried out by the Data Production and Analysis Unit of the ILO Department of Statistics. In cases of doubt about the quality of specific data, these values are reviewed with the participation of the national agencies responsible for producing the data if appropriate. If the issues cannot be clarified, the respective information is not published.</p>", "COVERAGE__GLOBAL"=>"<p>Data may differ from those published on ILOSTAT due to different update schedules and stricter criteria applied for inclusion in the SDG database.</p>\n<p><strong>Data availability:</strong></p>\n<p>Data on fatal injuries per 100,000 workers is available for 99 countries and territories. Data on non-fatal injuries per 100,000 workers is available for 97 countries and territories.</p>\n<p><strong>Time series:</strong></p>\n<p>The submission covers data from 2000 to 2023.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator should be disaggregated by both sex and migrant status. </p>\n<p>Wherever possible, it would also be useful to have information disaggregated by economic activity and occupation.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The variety of possible sources of data on occupational injuries hinders the comparability of data across countries since each type of source provides information on different specific concepts. Data derived from administrative records are not strictly comparable since they include numerous types of records that follow different rules and are maintained by different agencies. Two main sources of data are records of notifications by employers to the competent authority and insurance records of the authority compensating the victims. These two would clearly yield different results, since it is possible that not all injuries that were compensated to workers were reported by the employer and vice versa. It is also possible that these records have a different geographical coverage or that they cover different economic activities.</p>\n<p>When statistics come from an establishment survey, the results would be closer to those from records of notifications made by employers since it is also the employer who provides the establishment survey information. However, establishment surveys tend not to cover the informal sector, establishments of a very small size and sometimes the agricultural sector.</p>\n<p>When statistics come from a household survey, their reliability depends heavily on the accuracy of the respondents, who may be subjective in the information given.</p>\n<p> </p>", "OTHER_DOC__GLOBAL"=>"<p> </p>\n<ul>\n  <li>ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (<a href=\"https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm</a>) </li>\n  <li>ILOSTAT portal: <a href=\"https://ilostat.ilo.org\">https://ilostat.ilo.org</a> </li>\n  <li>ILOSTAT Safety and Health Statistics (OSH) database description: https://ilostat.ilo.org/methods/concepts-and-definitions/description-occupational-safety-and-health-statistics/ </li>\n  <li>Decent Work Indicators Manual: <a href=\"https://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm\">https://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm</a> </li>\n  <li> Resolution concerning statistics of occupational injuries (resulting from occupational accidents) adopted by the 16th ICLS in 1998: <a href=\"https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087528/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_087528/lang--en/index.htm</a> </li>\n</ul>", "indicator_sort_order"=>"08-08-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.8.2", "slug"=>"8-8-2", "name"=>"Nivel de cumplimiento nacional de los derechos laborales (libertad de asociación y negociación colectiva) con arreglo a las fuentes textuales de la Organización Internacional del Trabajo (OIT) y la legislación interna, desglosado por sexo y estatus migratorio", "url"=>"/site/es/8-8-2/", "sort"=>"080802", "goal_number"=>"8", "target_number"=>"8.8", "global"=>{"name"=>"Nivel de cumplimiento nacional de los derechos laborales (libertad de asociación y negociación colectiva) con arreglo a las fuentes textuales de la Organización Internacional del Trabajo (OIT) y la legislación interna, desglosado por sexo y estatus migratorio"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Nivel de cumplimiento nacional de los derechos laborales (libertad de asociación y negociación colectiva) con arreglo a las fuentes textuales de la Organización Internacional del Trabajo (OIT) y la legislación interna, desglosado por sexo y estatus migratorio", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Nivel de cumplimiento nacional de los derechos laborales (libertad de asociación y negociación colectiva) con arreglo a las fuentes textuales de la Organización Internacional del Trabajo (OIT) y la legislación interna, desglosado por sexo y estatus migratorio", "indicator_number"=>"8.8.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El indicador mide el nivel de cumplimiento nacional de los derechos fundamentales \nen el trabajo (libertad sindical y negociación colectiva, FACB) para todos \nlos Estados miembros de la OIT, basándose en la codificación de seis \nfuentes textuales de los órganos de control de la OIT y también en la \nlegislación nacional, frente a una lista de criterios de evaluación, y \nposteriormente convirtiendo la codificación en indicadores.\n\n\nEl indicador tiene un rango de 0 a 10, siendo 0 la mejor puntuación posible \n(que indica niveles más altos de cumplimiento de los derechos de la FACB) \ny 10 la peor (que indica niveles más bajos de cumplimiento de los derechos de \nla FACB).\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.8.2&seriesCode=SL_LBR_NTLCPL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Nivel de cumplimiento nacional de los derechos laborales (libertad de asociación y negociación colectiva) basado en fuentes textuales de la Organización Internacional del Trabajo (OIT) y la legislación nacional SL_LBR_NTLCPL</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-08-02.pdf\">Metadatos 8-8-2.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The indicator measures the level of national compliance with fundamental rights at work (freedom of \nassociation and collective bargaining, FACB) for all ILO member states based on the coding of six ILO \nsupervisory body textual sources and also on national legislation against a list of evaluation criteria and \nthen converting the coding into indicators. \n\nThe indicator has a range from 0 to 10, with 0 being the best possible score (indicating higher levels of \ncompliance with FACB rights) and 10 the worst (indicating lower levels of compliance with FACB rights). \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.8.2&seriesCode=SL_LBR_NTLCPL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Level of national compliance with labour rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislation SL_LBR_NTLCPL</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-08-02.pdf\">Metadata 8-8-2.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Adierazleak LANEko kide diren estatu guztiek lanean dituzten oinarrizko eskubideen (askatasun sindikala \neta negoziazio kolektiboa, FACB) betetze-maila neurtzen du, LANEko kontrol-organoen sei testu-iturriren \nkodifikazioan eta legeria nazionalean oinarrituta, ebaluazio-irizpideen zerrenda baten aldean, eta, ondoren, \nkodetze horiek adierazle bihurtzen ditu. \n\nAdierazleak 0tik 10era bitarteko tartea du, eta 0 da puntuaziorik onena (FACBren eskubideen betetze-maila \naltuenak adierazten ditu) eta 10 txarrena (FACBren eskubideen betetze-maila baxuenak adierazten ditu). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.8.2&seriesCode=SL_LBR_NTLCPL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Lan-eskubideen betetze-maila (elkartzeko askatasuna eta negoziazio kolektiboa), Lanaren Nazioarteko Erakundearen (LNE) testu-iturrietan eta legeria nazionalean oinarrituta SL_LBR_NTLCPL</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-08-02.pdf\">Metadatuak 8-8-2.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.8: Protect labour rights and promote safe and secure working environments for all workers, including migrant workers, in particular women migrants, and those in precarious employment</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.8.2: Level of national compliance with labour rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislation, by sex and migrant status</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_LBR_NTLCPL - Level of national compliance with labour rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislation [8.8.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>This indicator links with 8.8.1 &amp; 8.b.1; 16.2.2; 16.10.1; 16.a.1; 16.b.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator measures the level of national compliance with fundamental rights at work (freedom of association and collective bargaining, FACB) for all ILO member states based on six international ILO supervisory body textual sources and also on national legislation. It is based on the coding of textual sources against a list of evaluation criteria and then converting the coding into indicators.</p>\n<p><strong>Concepts:</strong></p>\n<p><em>Freedom of association and collective bargaining rights and their supervision</em></p>\n<p>The principles of freedom of association and collective bargaining (FACB) are and have long been at the core of the ILO&#x2019;s normative foundations. These foundations have been established in the ILO&#x2019;s Constitution (1919), the ILO Declaration of Philadelphia (1944), in two key ILO Conventions (namely the <em>Freedom of Association and Protection of the Right to Organise Convention, 1948 (No. 87)</em> and the <em>Right to Organise and Collective Bargaining Convention, 1949 (No. 98)</em>) and the ILO Declaration on Fundamental Principles and Rights at Work (1998). They are also rights proclaimed in the Universal Declaration of Human Rights (1948) and other international and regional human rights instruments. With the adoption of the 1998 ILO Declaration, the promotion and realization of these fundamental principles and rights also became a constitutional obligation of all ILO member States.</p>\n<p>FACB rights are considered as &#x2018;enabling rights&#x2019;, the realisation of which is necessary to promote and realise other rights at work. They provide an essential foundation for social dialogue, effective labour market governance and realization of decent work. They are vital in enabling employers and workers to associate and efficiently negotiate work relations, to ensure that both employers and workers have an equal voice in negotiations, and that the outcome is fair and equitable. As such they play a crucial role in the elaboration of economic and social policies that take on board the interests and needs of all actors in the economy. FACB rights are also salient because they are indispensable pillars of democracy as well as the process of democratization.</p>\n<p>FACB rights, together with other international labour standards, are backed by the ILO&#x2019;s unique supervisory system. The ILO regularly examines the application of standards in member States and highlights areas where those standards are violated and where they could be better applied. The ILO&#x2019;s supervisory system includes two kinds of supervisory mechanisms: the regular system of supervision and the special procedures. The prior entails the examination of periodic reports submitted by member States on the measures taken to implement the provisions of ILO Conventions ratified by them. The special procedures, that is, representations, complaints and the special procedure for complaints regarding freedom of association through the Freedom of Association Committee, allow for the examination of violations on the basis of a submission of a representation or a complaint.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>The unit of measurement is the number of coded evaluation criteria (see Tables 1-2 of <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/meetingdocument/wcms_648636.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/meetingdocument/wcms_648636.pdf</a>).</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The method makes use of six ILO textual sources: </p>\n<ol>\n  <li><em>Reports of the Committee of Experts on the Application of Conventions and Recommendations;</em></li>\n  <li><em>Reports of the Conference Committee on the Application of Standards;</em></li>\n  <li><em>Country Baselines Under the ILO Declaration Annual Review;</em></li>\n  <li><em>Representations under Article 24 of the ILO Constitution;</em></li>\n  <li><em>Complaints under Article 26 of the ILO Constitution;</em> and</li>\n  <li><em>Report on the Committee on Freedom of Association.</em></li>\n</ol>\n<p>For non-ratifying countries, the method also codes relevant national legislation with the goal to offset information asymmetries between ratifying and non-ratifying countries as regards FACB rights in law. Ratifying countries are defined as those that have ratified both Conventions 87 and 98, in which case its national legislation is not coded. Non-ratifying countries, on the other hand, fall into two categories, those that have ratified neither 87 nor 98 and those that have ratified only one of these Conventions. If a country has ratified only 87, its national legislation is coded for violations pertaining to 98, as violations under 87 fall under the remit of the ILO&#x2019;s Committee of Experts as well as Committee on the Application of Standards. Similarly, if a country has ratified only 98, its national legislation is coded for violations pertaining to 87. Note that for federal states, only federal-level legislation is coded.</p>\n<p>The coding of national legislation is carried out in close collaboration with the International Labour Office to ensure that it is done in a manner consistent with the ILO&#x2019;s supervisory system. In addition, countries may also make available information on national legislation when reporting on this indicator through Voluntary National Reports or national reporting platforms or any other national reports.</p>", "COLL_METHOD__GLOBAL"=>"<p>Given that the statistical foundation of the indicator are the ILO textual sources (see above) and that those sources are themselves based on information provided by the Governments, workers&#x2019; and employers&#x2019; organizations, the data collection is carried out by the ILO.</p>\n<p>The data collection is based on the coding of the relevant textual sources (see above) against a list of evaluation criteria and then converting the coding into indicators.</p>", "FREQ_COLL__GLOBAL"=>"<p>Not applicable</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released in February of each year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Given that the statistical foundation of the indicator are the ILO textual sources (see below) and that those sources are themselves based on information provided by the Governments, workers&#x2019; and employers&#x2019; organizations, the data is provided by the ILO.</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "INST_MANDATE__GLOBAL"=>"<p>In 2018, the 20th International Conference of Labour Statisticians (ICLS) adopted a &#x2018;Resolution concerning the methodology of the SDG indicator 8.8.2 on labour rights&#x2019;. Point (b) of the resolution recommends that the International Labour Office communicate on behalf of the ICLS, the confirmation that the ILO should be the custodian agency for indicator 8.8.2, given that ILO textual sources are its statistical foundation.<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> See at: <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/meetingdocument/wcms_648636.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/meetingdocument/wcms_648636.pdf</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "RATIONALE__GLOBAL"=>"<p>The indicator measures the level of national compliance with fundamental rights at work (freedom of association and collective bargaining, FACB) for all ILO member states based on the coding of six ILO supervisory body textual sources and also on national legislation against a list of evaluation criteria and then converting the coding into indicators.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Based on the consultation with the ILO&#x2019;s tripartite constituents (i.e., representatives of government, employers&#x2019;, and workers&#x2019; organizations), it was decided to prominently present the following chapeau text in the reporting of SDG indicator 8.8.2:</p>\n<p>&#x201C;SDG indicator 8.8.2 seeks to measure the level of national compliance with fundamental labour rights (freedom of association and collective bargaining). It is based on six International Labour Organization (ILO) supervisory body textual sources and also on national legislation. National law is not enacted for the purpose of generating a statistical indicator of compliance with fundamental rights, nor were any of the ILO textual sources created for this purpose. Indicator 8.8.2 is compiled from these sources and its use does not constitute a waiver of the respective ILO Constituents&#x2019; divergent points of view on the sources&#x2019; conclusions.&#x201D;<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> </p>\n<p>To highlight the difference between ratifying and non-ratifying countries, the following additional clarification is provided:</p>\n<p>&#x201C;SDG indicator 8.8.2 is not intended as a tool to compare compliance among ILO member States. It should specifically be noted that reporting obligations of an ILO member State to the ILO&#x2019;s supervisory system and thus ILO textual sources are different for ratifying and non&#x2010;ratifying ILO member States.&#x201D;<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></p>\n<p>Based on the decisions adopted by the tripartite technical committee set up to further address refinements to the methodology<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup>, for countries where the score should be treated with care due to the possibility of insufficient information in the textual sources, the following note will be added:</p>\n<p>&#x201C;The score should be treated with care due to the possibility of insufficient information in the textual sources, based on comparison with an externally produced indicator (see Metadata, point 4.f.).&#x201D;<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> Idem. P. 17 <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> Idem. P. 18 <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> Idem. PP. 1-2 of &#x201C;Amendment: Refinements to the methodology for SDG indicator 8.8.2: Level of national compliance with labour rights (freedom of association and collective bargaining) based on ILO textual sources and national legislation, by sex and migrant status&#x201D; <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> Idem. P. 1 of &#x201C;Amendment: Refinements to the methodology for SDG indicator 8.8.2: Level of national compliance with labour rights (freedom of association and collective bargaining) based on ILO textual sources and national legislation, by sex and migrant status&#x201D; <a href=\"#footnote-ref-6\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>The method is based on the coding of textual sources (see above) against a list of evaluation criteria and then converting the coding into indicators. For the list of evaluation criteria, see Table 1 and 2 (pp. 6-12.) at: <a href=\"https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/meetingdocument/wcms_648636.pdf\">https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/meetingdocument/wcms_648636.pdf</a> </p>\n<h1>The indicator has a range from 0 to 10, with 0 being the best possible score (indicating higher levels of compliance with FACB rights) and 10 the worst (indicating lower levels of compliance with FACB rights). For the purpose of computation, in the first step, the coding of textual sources is transformed into a binary coding, with 1 assigned to observed non-compliance and 0 to no observed non-compliance (unweighted raw scores). The binary coding is then multiplied by the weights as derived from the Delphi method (weighted raw scores). The final scores are the weighted raw scores normalized in a range from 0 to 10.</h1>\n<h1>Using the Delphi Method to Construct Evaluation Criteria Weights</h1>\n<p>The weights were constructed with the use of the Delphi method. The application of the Delphi method involved two rounds of surveys conducted via email of internationally-recognized experts in labour law having knowledge of the ILO&#x2019;s supervisory system and particular knowledge of FACB rights as defined by the ILO. Regional representation was another consideration. Experts remained anonymous with respect to each other throughout the process. </p>\n<h1>Applying the weights, normalization and default scores</h1>\n<p>The raw coding uses the letters &#x201C;a&#x201D; through &#x201C;g&#x201D; (with each letter corresponding to one of the seven textual sources) to represent coded violations of FACB rights for each evaluation criteria, yielding a column of 180 cells for any given country and year. In order to apply the weights, any cell containing one or more letters is assigned a value of 1 and any blank cell for which there are no coded violations is assigned a value of 0, creating a binary coding column. The number of letters in a cell does not affect the construction of the binary coding column, in order to avoid double-counting given that the textual sources commonly reference each other. The cells of the column of weights are then multiplied by corresponding cells of the binary coding column and summing across the cells of the resultant column yields a weighted non-normalized score for any given country and year. </p>\n<p>To normalize the indicators over time, 95 is assigned as the maximum weighted non-normalized score for the indicator. This roughly equals to the maximum weighted non-normalized score of one-half of the countries having the most coded violations of FACB rights of workers and their organizations for the years 2000, 2005, 2009 and 2012. The highest weighted non-normalized score for several countries hovered around 80. On this basis, the non-normalized score for any given country and year is normalized to range in value from 0 to 10, the best and worst possible scores respectively. In the future, if any country should receive a non-normalized score of greater than 95, this will be capped at 95, yielding a normalized score of 10.<sup><sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></sup></p>\n<p>In addition, the method applies the notion that general prohibitions in law imply general prohibitions in practice (though not vice versa). In terms of coding, this means that &#x2013; both for workers and employers -the direct coding of &#x201C;General prohibition of the right to establish and join organizations&#x201D; in law automatically triggers the coding of &#x201C;General prohibition of the development of independent organizations&#x201D; in practice; the direct coding of &#x201C;General prohibition of the right to collective bargaining&#x201D; in law automatically triggers the coding of the &#x201C;General prohibition of collective bargaining&#x201D; in practice ; and, finally, for workers, the direct coding of &#x201C;General prohibition of the right to strike&#x201D; in law automatically triggers the coding of the &#x201C;General prohibition of strikes&#x201D; in practice . </p>\n<p>Based on the decisions adopted by the tripartite technical committee set up to further address refinements to the methodology, in addition to the above normalization rules, a &#x201C;load&#x201D; of 3.5 will be added to the normalized score of the country in cases of all-encompassing violations of FACB rights, that is, for &#x201C;General prohibition of the right to establish and join organizations&#x201D; in law, &#x201C;General prohibition of the development of independent organizations&#x201D; in practice, &#x201C;General prohibition of the right to collective bargaining&#x201D; in law, and &#x201C;General prohibition of collective bargaining&#x201D; in practice. </p>\n<p><strong>Table 1. Hypothetical Example of Coding and Indicator Construction (for a Single Country and Year)</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Evaluation Criteria</strong></p>\n      </td>\n      <td>\n        <p><strong>Textual coding</strong></p>\n      </td>\n      <td>\n        <p><strong>Binary coding</strong></p>\n      </td>\n      <td>\n        <p><strong>Weights</strong></p>\n      </td>\n      <td>\n        <p><strong>Binary coding x Weights</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong>Ia. Fundamental civil liberties in law</strong></p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>Infringements of trade unionists&apos; basic freedoms </p>\n      </td>\n      <td>\n        <p>a</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,93</p>\n      </td>\n      <td>\n        <p>1,93</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong>Ib. Fundamental civil liberties in practice</strong></p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>6</p>\n      </td>\n      <td>\n        <p>Killing or disappearance of trade unionists in relation to their trade union activities</p>\n      </td>\n      <td>\n        <p>af</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>2,00</p>\n      </td>\n      <td>\n        <p>2,00</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>9</p>\n      </td>\n      <td>\n        <p>Other violent actions against trade unionists in relation to their trade union activities</p>\n      </td>\n      <td>\n        <p>af</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,82</p>\n      </td>\n      <td>\n        <p>1,82</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>12</p>\n      </td>\n      <td>\n        <p>Arrest, detention, imprisonment, charging and fining of trade unionists in relation to their trade union activities</p>\n      </td>\n      <td>\n        <p>af</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,95</p>\n      </td>\n      <td>\n        <p>1,95</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong>IIa. Right of workers to establish and join organizations in law</strong></p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>24</p>\n      </td>\n      <td>\n        <p>Exclusion of workers from the right to establish and join organizations</p>\n      </td>\n      <td>\n        <p>a</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,86</p>\n      </td>\n      <td>\n        <p>1,86</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>Lack of adequate legal guarantees against anti-union discriminatory measures</p>\n      </td>\n      <td>\n        <p>a</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,75</p>\n      </td>\n      <td>\n        <p>1,75</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>33</p>\n      </td>\n      <td>\n        <p>Infringements of the right to establish and join federations/confederations/international organizations</p>\n      </td>\n      <td>\n        <p>abf</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,73</p>\n      </td>\n      <td>\n        <p>1,73</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong>IIb. Right of workers to establish and join organizations in practice</strong></p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>37</p>\n      </td>\n      <td>\n        <p>Previous authorization requirements</p>\n      </td>\n      <td>\n        <p>af</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,70</p>\n      </td>\n      <td>\n        <p>1,70</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>42</p>\n      </td>\n      <td>\n        <p>Committed against trade union officials re violation no. 41</p>\n      </td>\n      <td>\n        <p>f</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,89</p>\n      </td>\n      <td>\n        <p>1,89</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>43</p>\n      </td>\n      <td>\n        <p>Lack of guarantee of due process and/or justice re violation no. 41</p>\n      </td>\n      <td>\n        <p>f</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,80</p>\n      </td>\n      <td>\n        <p>1,80</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong>IIIa. Other union activities in law</strong></p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>49</p>\n      </td>\n      <td>\n        <p>Infringements of the right to freely elect representatives</p>\n      </td>\n      <td>\n        <p>a</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,80</p>\n      </td>\n      <td>\n        <p>1,80</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>50</p>\n      </td>\n      <td>\n        <p>Infringements of the right to freely organize and control financial administration</p>\n      </td>\n      <td>\n        <p>ab</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,59</p>\n      </td>\n      <td>\n        <p>1,59</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>52</p>\n      </td>\n      <td>\n        <p>Prohibition of all political activities</p>\n      </td>\n      <td>\n        <p>ab</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,73</p>\n      </td>\n      <td>\n        <p>1,73</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong>IVa. Right to collective bargaining in law</strong></p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>66</p>\n      </td>\n      <td>\n        <p>Acts of interference in collective bargaining</p>\n      </td>\n      <td>\n        <p>a</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,66</p>\n      </td>\n      <td>\n        <p>1,66</p>\n      </td>\n    </tr>\n    <tr>\n      <td></td>\n      <td>\n        <p><strong>IVb. Right to collective bargaining in practice</strong></p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>72</p>\n      </td>\n      <td>\n        <p>Exclusion of workers from the right to collective bargaining</p>\n      </td>\n      <td>\n        <p>a</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>1,84</p>\n      </td>\n      <td>\n        <p>1,84</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong>Sum (non-normalized score)</strong></p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>15</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>27,05</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong>Normalized score (0 = best, 10 = worst)<sup>1</sup></strong></p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong>2,85</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"6\">\n        <p><sup>1</sup> The formula used is: (x*10/95), where x = the weighted non-normalized score for a given country and year and is capped at 95. </p>\n      </td>\n    </tr>\n  </tbody>\n</table><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p><sup> </sup>The formula is thus: (x*10/95), where x = the weighted non-normalized score for a given country and year and is capped at 95. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>The indicator is based on three key premises: (i) definitional validity &#x2013; the extent to which the evaluation criteria and their corresponding definitions accurately reflect the phenomena they are meant to measure; (ii) transparency &#x2013; how readily a coded violation can be traced back to any given textual source; and (iii) inter-coder reliability &#x2013; the extent to which different evaluators working independently are able to consistently arrive at the same results.</p>\n<p><em>Definitional validity</em>. As these are meant to be indicators of <em>international</em> FACB rights, the evaluation criteria and their corresponding definitions are directly based on the ILO Constitution, ILO Conventions No. 87 and 98 and the related body of comments of the ILO supervisory bodies.<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup> Given that the ILO supervisory system is also guided by these definitions, this facilitates the coding itself given the heavy reliance on ILO textual sources produced by the supervisory system. </p>\n<p><em>Transparency</em>. A key rationale for the large number of evaluation criteria is to eliminate catchall evaluation criteria for violations of FACB rights not elsewhere coded, that is, violations for which there are no explicit evaluation criteria. This level of detail also facilitates the transparency of the method, in that very specific violations can be readily traced back to individual textual sources. This is made possible by the coding itself, in which violations are coded with the letters &#x201C;a&#x201D; through &#x201C;g,&#x201D; with each letter standing for one of the seven textual sources coded (see Table 1.). </p>\n<p><em>Inter-coder reliability</em>. The method is based on clear and comprehensive coding rules as well as definitions for each of the evaluation criteria with the aim of making the indicators reproducible. Inter-coder reliability was assessed in the process of training teams of lawyers (sequentially and independently of each other) to do the coding and in double-checking their coding, which resulted in a number of clarifications and refinements to the coding rules and definitions. This process led to the conclusion that the inter-coder reliability of the method depends first and foremost on the coders being sufficiently well-trained and in particular being sufficiently well-versed in the coding rules and definitions to be able to apply them consistently. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> The related body of comments of the ILO supervisory bodies are: <em>Digest of Decisions and Principles of the Freedom of Association Committee of the Governing Body of the ILO</em> (ILO, 2006); <em>Freedom of Association and Collective Bargaining: General Survey of the Reports on the Freedom of Association and the Right to Organise Convention (No. 87), 1948, and the Right to Organise and Collective Bargaining Convention (No. 98)</em> (ILO, 1994); <em>General Survey on the Fundamental Conventions Concerning Rights at Work in Light of the ILO Declaration on Social Justice for a Fair Globalization, 2008</em> (ILO, 2012). <a href=\"#footnote-ref-8\">&#x2191;</a></p></div></div>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>There is no treatment of missing values at country level. The indicator will be reported for countries where, based on comparison with an externally produced indicator, the score should be treated with care due to the possibility of insufficient information in the textual sources. For these countries, based on the decisions adopted by the tripartite technical committee set up to further address refinements to the methodology, the indicator will be reported with the following note: &#x201C;The score should be treated with care due to the possibility of insufficient information in the textual sources, based on comparison with an externally produced indicator (see Metadata, point 4.f.).&#x201D;</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>For the computation of the regional aggregates, treatment of missing values (i.e. scores that are recommended to be dropped) is based on the following rules: (1). If scores are missing for all years, the country is dropped from the sample; (2). If scores are available for a single year, the available score is used for all other years; (3). If scores are available for multiple but not all years, the missing value is computed as the average of available scores.</p>", "REG_AGG__GLOBAL"=>"<p>The regional and global aggregates are weighted averages (with weights derived from ILO labour force estimates). </p>\n<p>A country&#x2019;s weight is the share of its labour force in the global labour force for a given time period, where the labour force is derived from the latest edition of the ILO modelled estimates (for further information on the estimates, please refer to the ILO modelled estimates methodological description, available at <a href=\"https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\">https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/</a>). </p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The processes of compilation, production, and publication of data, including its quality control, are carried out following the methodological framework and standards established by the ILO Department of Statistics, in compliance with the information technology and management standards of the ILO.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The data is available for all ILO member states. This submission covers country, regional and global data from 2015 to 2023.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The disaggregation by sex and migrant status is not currently available. </p>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p>International Conference of Labour Statisticians (2018) 20<sup>th</sup> Session, <a href=\"http://www.ilo.org/20thicls\">www.ilo.org/20thicls</a></p>", "indicator_sort_order"=>"08-08-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.9.1", "slug"=>"8-9-1", "name"=>"PIB generado directamente por el turismo en proporción al PIB total y a la tasa de crecimiento", "url"=>"/site/es/8-9-1/", "sort"=>"080901", "goal_number"=>"8", "target_number"=>"8.9", "global"=>{"name"=>"PIB generado directamente por el turismo en proporción al PIB total y a la tasa de crecimiento"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"PIB generado directamente por el turismo en proporción al PIB total y a la tasa de crecimiento", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"PIB generado directamente por el turismo en proporción al PIB total y a la tasa de crecimiento", "indicator_number"=>"8.9.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> No evaluable", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_220/opt_0/ti_cuenta-satelite-del-turismo/temas.html", "url_text"=>"Cuenta satélite de turismo", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"PIB generado directamente por el turismo en proporción al PIB total y tasa de crecimiento", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.9- De aquí a 2030, elaborar y poner en práctica políticas encaminadas a promover un turismo sostenible que cree puestos de trabajo y promueva la cultura y los productos locales", "definicion"=>"PIB generado por el turismo en proporción al PIB a precios corrientes, y tasa de crecimiento anual del PIB generado por el turismo.\n", "formula"=>"\n<b>PIB generado por el turismo en proporción al PIB</b>\n\n$$PPIB_{turístico}^{t} = \\frac{PIB_{turístico}^{t}}{PIB^{t}} \\cdot 100$$\n\ndonde:\n\n$PIB_{turístico}^{t} =$ producto interior bruto generado por el turismo en precios \ncorrientes en el año $t$\n\n$PIB^{t} =$ producto interior bruto en precios corrientes en el año $t$\n\n<br>\n\n<b>Tasa de crecimiento anual del PIB generado por el turismo</b>\n\n$$TCPIB^{t}_{turístico} = \\left( \\frac{PIB^{t}_{turístico} - PIB^{t-1}_{turístico}}{PIB^{t-1}_{turístico}} \\right) \\cdot 100$$\n\ndonde:\n\n$PIB^{t-1}_{turístico} =$ producto interior bruto en precios corrientes en el año $t-1$\n", "desagregacion"=>"Territorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nLa meta 8.9 tiene varias dimensiones y el indicador 8.9.1 responde a la intención central \nde la meta, que llama a “promover el turismo sostenible”. Si bien el turismo \nsostenible es multidimensional en sí mismo (con aspectos económicos, sociales y ambientales), \nla contribución económica del turismo captada por este indicador, y sus aumentos o \ndisminuciones (relativos), indican el grado en que se está promoviendo con éxito \nel turismo. \n\nLo ideal sería que este indicador se complementara con indicadores adicionales sobre \nlos aspectos sociales (por ejemplo, empleo, etc.) y ambientales (uso de energía, emisiones de \nGEI, etc.) del turismo que se puedan desglosar para proporcionar un panorama más completo \nde la promoción del turismo sostenible y, por lo tanto, el seguimiento de esta meta.\n\nEste indicador es útil para las políticas sobre turismo a nivel internacional, nacional \ny de regiones subnacionales, ya que proporciona una medida de la contribución económica \ndel turismo que se puede comparar a lo largo del tiempo, entre países, con el PIB total \ny con las contribuciones al PIB de otras actividades económicas.\n\nEl PIB directo del turismo incluye las contribuciones de todas las formas de turismo \n(turismo receptor, turismo interno y turismo emisor), de acuerdo con las \nRecomendaciones internacionales para las estadísticas de turismo 2008 (RIET 2008). \nEl indicador ha resultado especialmente útil para concienciar sobre la importancia \neconómica del turismo y defender una gestión más proactiva y sostenible de un sector \nque a menudo se pasa por alto en las agendas políticas a todos los niveles.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.9.1&seriesCode=ST_GDP_ZS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">PIB directo del turismo como proporción del PIB total (%) ST_GDP_ZS</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-09-01.pdf\">Metadatos 8-9-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"PIB generado directamente por el turismo en proporción al PIB total y tasa de crecimiento", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.9- De aquí a 2030, elaborar y poner en práctica políticas encaminadas a promover un turismo sostenible que cree puestos de trabajo y promueva la cultura y los productos locales", "definicion"=>"GDP generated by tourism as a proportion of total GDP at current prices, and annual \ngrowth rate of GDP generated by tourism.\n", "formula"=>"\n<b>GDP generated by tourism as a proportion of total GDP</b>\n\n$$PPIB_{tourism}^{t} = \\frac{PIB_{tourism}^{t}}{PIB^{t}} \\cdot 100$$\n\nwhere:\n\n$PIB_{tourism}^{t} =$ gross domestic product generated by tourism at current prices in year $t$\n\n$PIB^{t} =$ gross domestic product at current prices in year $t$\n\n<br>\n\n<b>Annual growth rate of GDP generated by tourism</b>\n\n$$TCPIB^{t}_{tourism} = \\left( \\frac{PIB^{t}_{tourism} - PIB^{t-1}_{tourism}}{PIB^{t-1}_{tourism}} \\right) \\cdot 100$$\n\nwhere:\n\n$PIB^{t-1}_{tourism} =$ gross domestic product at current prices in year $t-1$\n", "desagregacion"=>"Province\n", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nTarget 8.9 has several dimensions and indicator 8.9.1 caters to the core intention of the target which calls \nto “promote sustainable tourism”. While sustainable tourism is multidimensional in itself (with economic, \nsocial and environmental aspects), the economic contribution of tourism captured by this indicator, and \n(relative) increases or decreases in it, indicates the degree to which tourism is being successfully \npromoted. \n\nIdeally, this indicator needs to be complemented with additional indicators on the social (e.g. \nemployment, etc.) and environmental (energy use, GHG emissions, etc.) aspects of tourism that can be \ndisaggregated to provide a more complete picture of the promotion of sustainable tourism and thus the \nmonitoring of this target. \n\nThis indicator is useful for policy on tourism at international, national level and the level of sub-national \nregions as it provides a measure of the economic contribution of tourism which can be compared over \ntime, across countries, to total GDP and to the GDP contributions of other economic activities. \n\nTourism Direct GDP includes the contributions from all forms of tourism—inbound tourism, domestic tourism and \noutbound tourism—in line with the International Recommendations for Tourism Statistics 2008 (IRTS \n2008). The indicator has been found especially useful in raising awareness of the economic importance of \ntourism and making the case for a more proactive, sustainable management of a sector that is often \noverlooked in policy agendas at all levels. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.9.1&seriesCode=ST_GDP_ZS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Tourism direct GDP as a proportion of total GDP (%) ST_GDP_ZS</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-09-01.pdf\">Metadata 8-9-1.pdf</a>", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"PIB generado directamente por el turismo en proporción al PIB total y tasa de crecimiento", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.9- De aquí a 2030, elaborar y poner en práctica políticas encaminadas a promover un turismo sostenible que cree puestos de trabajo y promueva la cultura y los productos locales", "definicion"=>"Turismoak sortutako BPGa, uneko prezioetako BPGarekiko proportzioan, eta turismoak sortutako BPGaren urteko hazkunde-tasa.\n", "formula"=>"\n<b>Turismoak sortutako BPGa, BPGarekiko proportzioan</b>\n\n$$PPIB_{turismoa}^{t} = \\frac{PIB_{turismoa}^{t}}{PIB^{t}} \\cdot 100$$\n\nnon:\n\n$PIB_{turismoa}^{t} =$ turismoak sortutako barne-produktu gordina, uneko prezioetan $t$ urtean\n\n$PIB^{t} =$ barne-produktu gordina, uneko prezioetan $t$ urtean\n\n<br>\n\n<b>Turismoak sortutako BPGren urteko hazkunde-tasa</b>\n\n$$TCPIB^{t}_{turismoa} = \\left( \\frac{PIB^{t}_{turismoa} - PIB^{t-1}_{turismoa}}{PIB^{t-1}_{turismoa}} \\right) \\cdot 100$$\n\nnon:\n\n$PIB^{t-1}_{turismoa} =$ barne-produktu gordina, uneko prezioetan $t-1$ urtean\n", "desagregacion"=>"Lurralde historikoa\n", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\n8.9 xedeak hainbat dimentsio ditu, eta 8.9.1 adierazleak helburuaren asmo nagusiari erantzuten dio, \n\"turismo jasangarria sustatzeko\" deia egiten baitu. Turismo jasangarria berez dimentsio anitzekoa bada \nere (alderdi ekonomikoak, sozialak eta ingurumenekoak kontuan hartuta), adierazle honek bildutako turismoaren \nekarpen ekonomikoak eta haren gehikuntzek edo murrizketek (erlatiboak) turismo arrakastatsua zein mailatan \nsustatzen ari den adierazten dute. \n\nEgokiena litzateke adierazle hori turismoaren alderdi sozialei (adibidez, enpleguari etab.) eta ingurumenekoei \n(energiaren erabilera, berotegi-efektuko gasen emisioak, etab.) buruzko adierazle gehigarriekin osatzea, eta \nadierazle horiek turismo jasangarriaren sustapenaren ikuspegi osatuagoa emateko eta, beraz, helburu horren \njarraipena egiteko banakatu ahal izatea. \n\nAdierazle hori erabilgarria da turismoari buruzko politiketarako nazioartean, nazioan eta nazioz azpiko \neskualdeetan; izan ere, turismoaren ekarpen ekonomikoaren berri ematen du, denboran zehar, herrialdeen artean, \nBPG osoarekin eta beste jarduera ekonomiko batzuen BPGari egindako ekarpenekin alderatzeko aukera emanez. \n\nTurismoaren BPG zuzenak turismo-mota guztien ekarpenak biltzen ditu (turismo hartzailea, barne-turismoa eta \nturismo igorlea), 2008ko turismo-estatistiketarako nazioarteko gomendioen arabera. Adierazlea bereziki \nbaliagarria izan da turismoaren garrantzi ekonomikoari buruz kontzientziatzeko eta sektore baten kudeaketa \nproaktiboagoa eta iraunkorragoa defendatzeko, askotan maila guztietako agenda politikoetan kontuan hartzen \nez dena. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.9.1&seriesCode=ST_GDP_ZS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Turismoaren zuzeneko BPGa, guztizko BPGaren proportzio gisa (%) ST_GDP_ZS</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-09-01.pdf\">Metadatuak 8-9-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.9: By 2030, devise and implement policies to promote sustainable tourism that creates jobs and promotes local culture and products</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.9.1: Tourism direct GDP as a proportion of total GDP and in growth rate </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ST_GDP_ZS - Tourism direct GDP as a proportion of total GDP [8.9.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Target 14.7: By 2030, increase the economic benefits to small island developing States and least developed countries from the sustainable use of marine resources, including through sustainable management of fisheries, aquaculture and tourism. </p>\n<p>Target 12.b: Develop and implement tools to monitor sustainable development impacts for sustainable tourism that creates jobs and promotes local culture and products</p>\n<p>Indicator 12.b.1 Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism sustainability </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Tourism Organization (UNWTO) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Tourism Organization (UNWTO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>Tourism Direct GDP (TDGDP) is defined as the sum of the part of gross value added (at basic prices) generated by all industries in response to internal tourism consumption plus the amount of net taxes on products and imports included within the value of this expenditure at purchasers&#x2019; prices. The indicator relies on the Tourism Satellite Account: Recommended Methodological Framework 2008, an international standard adopted by the UN Statistical Commission and led by UNWTO, Organisation for Economic Co-operation and Development (OECD) and EUROSTAT. </p>\n<p> </p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>Tourism direct gross value added (TDGVA) is the part of gross value added generated by tourism industries and other industries of the economy that directly serve visitors in response to internal tourism consumption. </p>\n<p> </p>\n<p>Gross Domestic Product (GDP): It is the main measure of national output, representing the total value of all final goods and services within the System of National Accounts (SNA) production boundary produced in a particular economy (that is, the dollar value of all goods and services within the SNA production boundary produced within a country&#x2019;s borders in a given year). According to the SNA, &#x201C;GDP is the sum of gross value added of all resident producer units plus that part (possibly the total) of taxes on products, less subsidies on products, that is not included in the valuation of output. GDP is also equal to the sum of the final uses of goods and services (all uses except intermediate consumption) measured at purchasers&#x2019; prices, less the value of imports of goods and services. GDP is also equal to the sum of primary incomes distributed by resident producer units.&#x201D; </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The methodology for the calculation of Tourism Direct GDP is in line with the <a href=\"https://unstats.un.org/unsd/publication/seriesf/seriesf_80rev1e.pdf\">Tourism Satellite Account: Recommended Methodological Framework (TSA:RMF 2008)</a> and the International Recommendations for Tourism Statistics 2008 (IRTS 2008) which defines the tourism characteristic industries (i.e. tourism industries) and provides a list of tourism industries for international comparability purposes based on the International Standard Industrial Classification of All Economic Activities (ISIC Rev. 4)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The indicator is sourced from countries&#x2019; Tourism Satellite Account (TSA), which is a satellite account to the National Accounts. </p>", "COLL_METHOD__GLOBAL"=>"<p>UNWTO sends a pre-filled excel questionnaire (including data from official publications and official websites) to countries to collect the latest data on TDGDP. To lighten the reporting burden on countries, UNWTO cooperates with the Organisation for Economic Co-operation and Development (OECD) which provides to UNWTO the data collected from its member and partner countries<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>. UNWTO then integrates the data received from OECD with the data it collects directly from non-OECD countries. This exercise is being carried out on a yearly basis since 2019. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> OECD list of member countries is available at: https://www.oecd.org/about/members-and-partners/ <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "FREQ_COLL__GLOBAL"=>"<p>The questionnaire is sent out to countries in September and data collection is closed in February of the following year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The data is released twice a year in the <a href=\"https://www.unwto.org/tourism-statistics/economic-contribution-SDG\">UNWTO&#x2019;s Tourism Statistics Database</a>, the first update is done in November and the second in January </p>", "DATA_SOURCE__GLOBAL"=>"<p>Only official country entities, usually National Statistics Offices and/or National Tourism Administrations. </p>", "COMPILING_ORG__GLOBAL"=>"<p>World Tourism Organization (UNWTO) </p>", "INST_MANDATE__GLOBAL"=>"<p>As per the article 13 of the agreement between the United Nations and the World Tourism Organization: &#x201C;the United Nations recognizes the World Tourism Organization as the appropriate organization to collect, to analyse, to publish, to standardize and to improve the statistics of tourism, and to promote the integration of these statistics within the sphere of the United Nations system.&#x201D; The World Tourism Organization is the custodian agency for SDG indicator 8.9.1.</p>", "RATIONALE__GLOBAL"=>"<p>Target 8.9 has several dimensions and indicator 8.9.1 caters to the core intention of the target which calls to<em> &#x201C;promote sustainable tourism</em>&#x201D;. While sustainable tourism is multidimensional in itself (with economic, social and environmental aspects), the economic contribution of tourism captured by this indicator, and (relative) increases or decreases in it, indicates the degree to which tourism is being successfully promoted. Ideally, this indicator needs to be complemented with additional indicators on the social (e.g. employment, etc.) and environmental (energy use, GHG emissions, etc.) aspects of tourism that can be disaggregated to provide a more complete picture of the promotion of sustainable tourism and thus the monitoring of this target. </p>\n<p>This indicator is useful for policy on tourism at international, national level and the level of sub-national regions as it provides a measure of the economic contribution of tourism which can be compared over time, across countries, to total GDP and to the GDP contributions of other economic activities. Tourism Direct GDP includes the contributions from all forms of tourism&#x2014;inbound tourism, domestic tourism and outbound tourism&#x2014;in line with the International Recommendations for Tourism Statistics 2008 (IRTS 2008). The indicator has been found especially useful in raising awareness of the economic importance of tourism and making the case for a more proactive, sustainable management of a sector that is often overlooked in policy agendas at all levels. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Given that a significant number of countries already have or are working to implement Tourism Satellite Accounts (TSA), data on the suggested indicators could become available in more countries in the near future. </p>\n<p>The data demands for implementing TSA (detailed input-output or supply and use tables, among others); means that it is often not possible or cost effective to realize frequent updating of the TSA. Therefore, some countries produce estimates of TSA aggregates, in between reference years and/or nowcast estimates, to have more current data and to produce a time series. </p>\n<p>In the absence of important shocks to the economy and to tourism, historically TDGDP/GDP tended to not show very large variations from one year to the next, however the effects of the Covid-19 pandemic on tourism are quite evident through this indicator in many countries. Considering also that variations may stem from the numerator and/or denominator, it is often useful from an analytical perspective to consider the indicator in different forms and adaptations: absolute value, % change in constant price, and TDGDP per visitor or per employed person. </p>\n<p>Related economic aggregates on tourism like Tourism Direct Gross Value Added and the Gross Value Added of the Tourism Industries (in aggregate form and disaggregated by tourism industry) are also important and may be used as approximations to indicator 8.9.1 for analytical purposes. </p>", "DATA_COMP__GLOBAL"=>"<p>Tourism direct GDP as a proportion of total GDP (in%):</p>\n<p><em><br></em><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced separators=\"|\">\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mi>T</mi>\n            <mi>D</mi>\n            <mi>G</mi>\n            <mi>D</mi>\n            <mi>P</mi>\n          </mrow>\n          <mrow>\n            <mi>G</mi>\n            <mi>D</mi>\n            <mi>P</mi>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfenced>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math>Tourism direct GDP in growth rate =</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced open=\"[\" close=\"]\" separators=\"|\">\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mfrac>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>T</mi>\n                    <mi>D</mi>\n                    <mi>G</mi>\n                    <mi>D</mi>\n                    <mi>P</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>t</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>T</mi>\n                    <mi>D</mi>\n                    <mi>G</mi>\n                    <mi>D</mi>\n                    <mi>P</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>t</mi>\n                    <mo>-</mo>\n                    <mn>1</mn>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </mfenced>\n        <mo>-</mo>\n        <mn>1</mn>\n      </mrow>\n    </mfenced>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>Every year historical data is requested. If there are differences in the newly reported data for the country with respect to the data available previously, countries are consulted. Similarly, if other inconsistencies are found, there is ongoing follow up with countries. </p>\n<p>UNWTO is also custodian for indicator 12.b.1 and the data collected there serves as a valuable validation step for the data provided for indicator 8.9.1. For example, since Table 6 of TSA is necessary for the compilation of TDGDP, the data reported by countries are cross validated with the availability of this table ( data reported for SDG indicator 12.b.1).</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>When a country does not measure the Tourism direct GDP but measures the Tourism Direct Gross Value Added (TDGVA), the indicator Tourism Direct Gross Value Added as a proportion of total Gross Value Added (in %) is used as a proxy. When it is the case, a footnote is included in the data.</p>\n<p>Tourism Direct Gross Value added (TDGVA) as a proportion of total Gross Value Added (GVA), in %:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced separators=\"|\">\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mi>T</mi>\n            <mi>D</mi>\n            <mi>G</mi>\n            <mi>V</mi>\n            <mi>A</mi>\n          </mrow>\n          <mrow>\n            <mi>G</mi>\n            <mi>V</mi>\n            <mi>A</mi>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfenced>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "REG_AGG__GLOBAL"=>"<p>Aggregates are computed for the SDG regions and at the global level. </p>\n<p>For every year, estimates for countries with missing data are computed as follows:</p>\n<ul>\n  <li><em>For countries without any reported data</em><br>A multivariate linear regression model is used with as explanatory variables the number of hotel rooms in the country and inbound tourism expenditure (computed from Balance of Payments data provided by the International Monetary Fund (IMF)), both available via UNWTO&#xB4;s statistical database.</li>\n  <li><em>For countries with reported data for years other than the year of reference</em><br>A simple linear model based on inbound tourism expenditure (computed from the IMF Balance of Payments data) as explanatory variable is used to estimate the nominal percentage change in TDGDP. These values are used to retro- and extrapolate the values reported by the country, using these as benchmark. </li>\n</ul>\n<p>For reference years between years with reported data, a linear trend between reported years is used. </p>\n<ul>\n  <li><em>Special cases<br></em>Some data reported by countries that do not correspond to Tourism Direct GDP or GVA and are therefore not published, may still be used in the calculation of aggregates. </li>\n</ul>\n<p>For each year, countries without reported data for which the methodology yields negative estimates or for which no data to feed the linear models are available are discarded. Regional (and global) aggregates are then obtained by computing weighted averages of TDGDP, using total GDP as the weight, for countries within the region of interest for which data or estimates are available. </p>\n<p>GDP coverage for each aggregate is obtained by calculating the percentage of total regional GDP that is represented by countries for which data is reported or for which an estimate is available. If this coverage is relatively low (below 60 percent), estimates are published with a cautionary footnote. </p>", "DOC_METHOD__GLOBAL"=>"<p>The methodology is described in the <a href=\"https://unstats.un.org/unsd/tourism/methodology.asp\" target=\"_blank\"><u>Tourism Satellite Account: Recommended Methodological Framework 2008</u></a>. </p>\n<p> </p>\n<p> For the purposes of SDG reporting, UNWTO suggests an experimental approach that might be used by countries with limited data to compile estimates of TDGDP using the conceptual framing of the TSA and the most commonly available data but not requiring the full compilation of TSA. In this regard, the approach is intended to provide a starting point for countries with limited data that can then move towards the compilation of TSA and the more complete measurement of TDGDP. For more information, see <a href=\"https://webunwto.s3-eu-west-1.amazonaws.com/imported_images/49465/mst_research_paper_measuring_tdgdp.pdf\" target=\"_blank\"><u>Proposals for estimating Tourism Direct GDP with limited data</u></a>. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Recommendations on quality management for the underlying tourism data needed to compile a TSA are available in <a href=\"https://unstats.un.org/unsd/publication/Seriesm/SeriesM_83rev1e.pdf\">the International Recommendations for Tourism Statistics 2008</a> (IRTS 2008), the UN ratified methodological framework for measuring tourism.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Any discrepancies are resolved through written communication with countries. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The data should comply with the recommendations on concepts, definitions and classifications provided in the international standards: the <a href=\"http://unstats.un.org/unsd/tradeserv/tourism/manual.html\">Tourism Satellite Account: Recommended Methodological Framework 2008</a>.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>As of March 2024, more than 70 countries have data available for this indicator in 2020. The number of countries with a TSA exercise is monitored by SDG indicator 12.b.1. According to data reported by countries for the SDG indicator 12.b.1, 99 countries have conducted a TSA exercise in the period between 2017 and 2022. </p>\n<p><strong>Time series:</strong></p>\n<p>Annual data from 2008 onwards are available. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>TDGDP is derived from the productive activities that cater directly to tourism and so it could be possible to disaggregate by tourism industries (e.g. accommodation for visitors, the different kinds of passenger transportation, etc.). </p>\n<p>Sub-national disaggregation/estimates of Tourism Direct GDP are possible and there are a number of countries&#x2019; subnational regions that have information on this. However, there is no consensus on a methodology for doing this in a standardized way, compromising international comparability. In any case, it seems that collection of data would be warranted only for those regions where tourism is considered a significant (economic) activity and/or a policy priority. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Discrepancies might arise from different degrees of adherence to Tourism Satellite Account: Recommended Methodological Framework 2008 and different TSA reference years. </p>", "OTHER_DOC__GLOBAL"=>"<p><a href=\"https://unstats.un.org/unsd/publication/Seriesm/SeriesM_83rev1e.pdf\" target=\"_blank\"><em><u>International Recommendations for Tourism Statistics 2008 (IRTS 2008)</u></em></a> </p>\n<p><a href=\"https://unstats.un.org/unsd/publication/Seriesf/SeriesF_80rev1e.pdf\" target=\"_blank\"><em><u>Tourism Satellite Account: Recommended Methodological Framework 2008 (TSA: RMF 2008)</u></em></a> </p>\n<p> </p>", "indicator_sort_order"=>"08-09-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.9.2", "slug"=>"8-9-2", "name"=>"Employed persons in the tourism industries", "url"=>"/site/es/8-9-2/", "sort"=>"080902", "goal_number"=>"8", "target_number"=>"8.9", "global"=>{}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.9: By 2030, devise and implement policies to promote sustainable tourism that creates jobs and promotes local culture and products</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.9.2: Employed persons in the tourism industries</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ST_EMP_TRSMN - Employed persons in the tourism industries (number) [8.9.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Target 8.5: By 2030, achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal value</p>\n<p>Target 8.9: By 2030, devise and implement policies to promote sustainable tourism that creates jobs and promotes local culture and products. </p>\n<p>Indicator 8.9.1: Tourism direct GDP as a proportion of total GDP and in growth rate. </p>\n<p>Target 12.b: Develop and implement tools to monitor sustainable development impacts for sustainable tourism that creates jobs and promotes local culture and products</p>\n<p>Indicator 12.b.1 Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism sustainability.</p>\n<p>Target 14.7: By 2030, increase the economic benefits to small island developing States and least developed countries from the sustainable use of marine resources, including through sustainable management of fisheries, aquaculture and tourism</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Tourism Organization (UN Tourism) with the collaboration of the International Labour Organization (ILO).</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UN Tourism</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Number of employed persons in the tourism industries</p>\n<p><strong>Concepts:</strong></p>\n<p>The number of &#x201C;employed persons in the tourism industries&#x201D; is an indicator identified in the &#x201C;Statistical Framework for Measuring the Sustainability of Tourism&#x201D; endorsed by the UN Statistical Commission at its fifty-fifth session held from 27 February to 1 March 2024.</p>\n<p>The employed persons comprise &#x201C;all persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit&#x201D; (ILO, 2023). The indicator shows the number of persons employed in tourism industries in any of their jobs. The tourism industries comprise all establishments for which the principal activity is a tourism characteristic activity. This is an activity that typically produces tourism characteristic products, as defined in the International Recommendations for Tourism Statistics 2008 (IRTS 2008) (United Nations and World Tourism Organization, 2010). The internationally comparable tourism industries are grouped in ten main categories (IRTS 2008, para 5.29), as shown in table 1.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of people</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>&#x201C;Employed persons in the tourism industries&#x201D; has been identified in the Statistical Framework for Measuring the Sustainability of Tourism (SF-MST), endorsed by the UN Statistical Commission at its fifty-fifth session held from 27 February to 1 March 2024 (United Nations Statistical Commission, 2024). SF-MST builds on and is coherent with other well-established statistical frameworks and international classifications such as the IRTS 2008, the Tourism Satellite Account: Recommended Methodological Framework 2008 (United Nations et al., 2010), ILO statistical manuals, and the International Standard Industrial Classification of All Economic Activities (ISIC), revision 4 (United Nations, 2008).</p>\n<p>The SF-MST adopts the tourism characteristic activities (i.e. tourism industries) defined in IRTS 2008, which provides a list of tourism industries for international comparability purposes based on ISIC (rev. 4), as follows:</p>\n<p><strong>Table 1. List of internationally comparable tourism characteristic activities (tourism industries) <br>and grouping by main categories according to ISIC Rev. 4</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Tourism Industries</strong></p>\n      </td>\n      <td>\n        <p><strong>ISIC Rev. 4</strong></p>\n      </td>\n      <td>\n        <p><strong>Description</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1. Accommodation for visitors</p>\n      </td>\n      <td>\n        <p>5510</p>\n        <p>5520</p>\n        <p>5590</p>\n        <p>6810</p>\n        <p>6820</p>\n      </td>\n      <td>\n        <p>Short term accommodation activities</p>\n        <p>Camping grounds, recreational vehicle parks and trailer parks</p>\n        <p>Other accommodation</p>\n        <p>Real estate activities with own or leased property*</p>\n        <p>Real estate activities on a fee or contract basis*</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2. Food and beverage serving activities</p>\n      </td>\n      <td>\n        <p>5610</p>\n        <p>5629</p>\n        <p>5630</p>\n      </td>\n      <td>\n        <p>Restaurants and mobile food service activities</p>\n        <p>Other food service activities</p>\n        <p>Beverage serving activities</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3. Railway passenger transport</p>\n      </td>\n      <td>\n        <p>4911</p>\n      </td>\n      <td>\n        <p>Passenger rail transport, interurban</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4. Road passenger transport</p>\n      </td>\n      <td>\n        <p>4922</p>\n      </td>\n      <td>\n        <p>Other passenger land transport</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5. Water passenger transport</p>\n      </td>\n      <td>\n        <p>5011</p>\n        <p>5021</p>\n      </td>\n      <td>\n        <p>Sea and coastal passenger water transport</p>\n        <p>Inland passenger water transport</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>6. Air passenger transport</p>\n      </td>\n      <td>\n        <p>5110</p>\n      </td>\n      <td>\n        <p>Passenger air transport</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>7. Transport equipment rental</p>\n      </td>\n      <td>\n        <p>7710</p>\n      </td>\n      <td>\n        <p>Renting and leasing of motor vehicles</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>8. Travel agencies and other reservation service activities</p>\n      </td>\n      <td>\n        <p>7911</p>\n        <p>7912</p>\n        <p>7990</p>\n      </td>\n      <td>\n        <p>Travel agency activities</p>\n        <p>Tour operator activities</p>\n        <p>Other reservation service and related activities</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>9. Cultural activities</p>\n      </td>\n      <td>\n        <p>9000</p>\n        <p>9102</p>\n        <p>9103</p>\n      </td>\n      <td>\n        <p>Creative, arts and entertainment activities</p>\n        <p>Museums activities and operation of historical sites and buildings</p>\n        <p>Botanical and zoological gardens and nature reserves activities</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>10. Sports and Recreational activities</p>\n      </td>\n      <td>\n        <p>7721</p>\n        <p>9200</p>\n        <p>9311</p>\n        <p>9319</p>\n        <p>9321</p>\n        <p>9329</p>\n      </td>\n      <td>\n        <p>Renting and leasing of recreational and sports goods</p>\n        <p>Gambling and betting activities</p>\n        <p>Operation of sports facilities</p>\n        <p>Other sports activities</p>\n        <p>Activities of amusement parks and theme parks</p>\n        <p>Other amusement and recreation activities n.e.c.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><em>* Part related to second homes and timeshare properties</em></p>\n<p>Source: International Recommendations for Tourism Statistics 2008, Annex 3 (United Nations and World Tourism Organization, 2010).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The indicator is sourced from countries&#x2019; household-based labour force surveys. In the absence of a labour force survey, a population census and/or another type of household survey or tourism survey with an appropriate employment module may also be used to obtain the required data. Where no household survey exists, establishment surveys or certain administrative records may be used to derive the required data. Proper account should be taken of the limitations of these sources in terms of their coverage, which may exclude, for instance, some types of establishments, establishments of certain sizes, some economic activities, or some geographical areas.</p>", "COLL_METHOD__GLOBAL"=>"<p>UN Tourism manages an international database based on official statistical data reported through a rolling questionnaire distributed to official data providers in countries. UN Tourism pre-fills the questionnaire, to the extent possible, with data publicly available in countries&apos; official sources (publications and websites) and, in collaboration with ILO, with data from ILOSTAT obtained via its Labour Force Survey (LFS) Database, available at: <a href=\"https://ilostat.ilo.org/data/\">https://ilostat.ilo.org/data/</a>.</p>\n<p>The ILO Department of Statistics processes national household survey micro datasets in line with internationally agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians. For data that cannot be obtained through this processing or directly from government websites, ILO sends out an annual ILOSTAT questionnaire to all relevant agencies within each country (e.g., National Statistical Office, Labour Ministry, etc.) requesting the latest annual data and any revisions.</p>\n<p>The primary ILO dataset used to fill data gaps in this indicator on &#x201C;employed persons in the tourism industries&#x201D; is the dataset &#x201C;Tourism sector employment by economic activity&#x201D; dataset, comprising the number of employed persons in the following ISIC rev.4 classes (International Labour Organization, no date):</p>\n<ul>\n  <li><u>Accommodation for visitors</u>: 5510 Short term accommodation activities, 5520 Camping grounds, recreational vehicle parks and trailer parks, 5590 Other accommodation.</li>\n  <li><u>Food and beverage serving activities</u>: 5610 Restaurants and mobile food service activities, 5629 Other food service activities, 5630 Beverage serving activities.</li>\n  <li><u>Passenger transportation</u>: 4911 Passenger rail transport, interurban, 4921 Urban and suburban passenger land transport, 4922 Other passenger land transport, 5011 Sea and coastal passenger water transport, 5021 Inland passenger water transport, 5110 Passenger air transport, 5221 Service activities incidental to land transportation, 5222 Service activities incidental to water transportation, 5223 Service activities incidental to air transportation, 7710 Renting and leasing of motor vehicles.</li>\n  <li><u>Recreation and entertainment</u>: 7721 Renting and leasing of recreational and sports goods, 9000 Creative, arts and entertainment activities, 9102 Museums activities and operation of historical sites and buildings, 9103 Botanical and zoological gardens and nature reserves activities, 9200 Gambling and betting activities, 9311 Operation of sports facilities, 9319 Other sports activities, 9321 Activities of amusement parks and theme parks, 9329 Other amusement and recreation activities.</li>\n  <li><u>Travel services</u>: 7911 Travel agency activities, 7912 Tour operator activities, 7990 Other reservation service and related activities.</li>\n</ul>\n<p>Should country-specific data not be available in the ILO &quot;Tourism sector employment by economic activity&quot; dataset, UN Tourism adjusts the country data obtained from the ILO &quot;Employment by economic activity - ISIC level 2&#x201D; dataset, using the following ISIC groups: </p>\n<ul>\n  <li>49 Land transport and transport via pipelines</li>\n  <li>50 Water transport</li>\n  <li>51 Air transport</li>\n  <li>55 Accommodation</li>\n  <li>56 Food &amp; Beverages service activities</li>\n  <li>77 Rental and leasing activities</li>\n  <li>79 Travel agency, tour operator, reservation services and related activities</li>\n  <li>90 Creative, arts and entertainment activities</li>\n  <li>91 Libraries, archives, museums and other cultural activities</li>\n  <li>92 Gambling &amp; betting activities</li>\n  <li>93 Sports activities, amusement and recreation activities</li>\n</ul>\n<p>All data sourced from ILO and any adjustments to the country data obtained through ILO datasets are included in the pre-filled questionnaires sent by UN Tourism to countries, for their verification, confirmation and updating as necessary.</p>", "FREQ_COLL__GLOBAL"=>"<p>UN Tourism dispatches its pre-filled statistical questionnaire on SDG-related data in September/October, to allow the most recent information to be included and ready for submission to the Global SDG Indicators Database in January/February, in line with the UN Statistics Division calendar. Since the 2024-2025 reporting cycle onwards, UN Tourism includes ILO data in its pre-filled questionnaires. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>This indicator will be released every year as part of an update in the UN Tourism Statistics Database (<a href=\"https://www.unwto.org/tourism-statistics/tourism-statistics-database\">https://www.unwto.org/tourism-statistics/tourism-statistics-database</a>), in March at the latest, in line with UNSD reporting calendar.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data providers are official country entities, mainly National Statistics Offices and, in some cases, ministries in charge of tourism or related official national entities. The data available in ILOSTAT, and incorporated in the UN Tourism questionnaire, are provided by National Statistical Offices and, in some cases, Ministries of Labour or other related agencies operating at the country-level.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UN Tourism and ILO.</p>", "INST_MANDATE__GLOBAL"=>"<p>As per Article 13.3 of the agreement between the United Nations and the World Tourism Organization (UN Tourism), whereby UN Tourism became a specialized agency, &#x201C;the United Nations recognizes the World Tourism Organization as the appropriate organization to collect, to analyse, to publish, to standardize and to improve the statistics of tourism, and to promote the integration of these statistics within the sphere of the United Nations system&#x201D; (United Nations, 2003). The UN Tourism Department of Statistics, Standards and Data is charged with upholding this mandated and its work focuses on the development of international standards for measuring tourism, on capacity development, and on compiling and disseminating tourism statistics from all countries in the world.</p>\n<p>UN Tourism is the custodian agency for this SDG indicator 8.9.2.</p>\n<p>The data collection process is supported by the partnership between UN Tourism and the International Labour Organization (ILO), which is the UN focal point for labour statistics. ILO sets international standards for labour statistics through the International Conference of Labour Statisticians, and compiles and produces labour statistics, with the goal of disseminating internationally comparable datasets. It also provides technical assistance and training to ILO Member States, to support their efforts to produce high-quality labour market data.</p>", "RATIONALE__GLOBAL"=>"<p>This indicator on &#x201C;employed persons in the tourism industries&#x201D; covers a crucial aspect currently not monitored of SDG target 8.9 (&#x201C;by 2030, devise and implement policies to promote sustainable tourism that creates jobs and promotes local culture and products&#x201D;). The proposed indicator directly addresses the target&#x2019;s aim of promoting tourism that creates jobs, and aligns well with overall intent of Goal 8 on economic growth and decent work.</p>\n<p>Also, &#x201C;Employed persons in the tourism industries&#x201D; addresses a critical policy issue that has become increasingly prominent since the COVID-19 pandemic. The pandemic increased the realization that tourism &#x2014;and especially its income (indicator 8.9.1) and employment (this indicator, 8.9.2) components &#x2014; sustains livelihoods, wellbeing and conservation efforts worldwide. It was acknowledged that tourism is be especially critical for sustainable development in remote, rural and natural places, and it offers employment opportunities for diverse groups of people. </p>\n<p>In addition, this indicator responds to requests made by the United Nations Statistical Commission (UNSC) over the years. In 2017 the Statistical Commission &#x201C;<em>supported the development of the statistical framework for measuring sustainable tourism</em> [...] <em>which will include the development of indicators for sustainable tourism</em> [...]&#x201D; (United Nations Statistical Commission, 2017), and in 2022 it &#x201C;<em>requested the Group </em>[IAEG-SDGs]<em> to work in close coordination with the custodian agency</em> [UN Tourism] <em>on a proposal for indicators on sustainable tourism to better monitor target 8.9 </em>[&#x2026;]&#x201D; (United Nations Statistical Commission, 2017). Therefore, this indicator builds on the work of the IAEG-SDG Task Team on sustainable tourism, and draws on three decades of tourism statistics expertise.</p>\n<p>Finally, the indicator reflects extensive research and development through intergovernmental processes and in collaboration with ILO, to define an indicator that optimizes the criteria of: (i) agreed methodology and conceptual rigour (it is based on the UN-endorsed Statistical Framework for Measuring the Sustainability of Tourism), (ii) high policy relevance, responding to issues with respect to the related Target and Goal that are currently unmet in the indicator framework, and (iii) data availability in countries (with existing reporting mechanisms by ILO and UN Tourism), thus avoiding additional reporting burden for countries. Consequently, this indicator classifies as a Tier I indicator. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The following points aim to describe some limitations to be considered:</p>\n<p>The characteristics of the data source may impact international comparability, especially when the coverage of the country source is not complete (notably in terms of the tourism industries included and the level of ISIC disaggregation available), as some countries do not measure employment in all the ten internationally-comparable tourism industries. </p>\n<p>In the absence of labour force surveys, some countries may use an establishment survey to derive information for this indicator. However, such surveys usually have a minimum establishment size cut&#x2011;off point, and small units are not officially registered and are therefore not included. Consequently, employment data in the tourism industries may be underestimated.</p>\n<p>An important issue in tourism relates to the seasonality, stemming from variations in visitor flows over the year and therefore in the related demand for services and labour inputs (in particular, in the industries pertaining to accommodation for visitors and to food and beverage serving activities). Fluctuations over the year are smoothed over in yearly figures.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is the sum of employed persons in the tourism industries listed in the section above: &#x201C;2.c. Classifications&#x201D;.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>UN Tourism maintains close communication with the reporting countries. Countries are invited to report their official data and are requested to verify, confirm, and update any pre-filled data. In case of any inconsistencies in the reported data, follow-ups are conducted with the respective countries.</p>\n<p>ILO holds annual consultations with its Member States through the ILOSTAT questionnaire and the related Statistics Reporting System (StaRS). National data providers receive a link to the portal where they can review the data available on ILOSTAT.</p>", "ADJUSTMENT__GLOBAL"=>"<p>When the data are only available in the ILO &#x201C;Employment by economic activity &#x2013; ISIC level 2&#x201D; dataset, these are adjusted using ISIC level 4 shares from countries with similar characteristics. Such adjusted data values are indicated with a note, as &#x201C;country adjusted data&#x201D;. In the UN Tourism pre-filled questionnaires, countries are invited to verify and confirm their agreement with these values, and the note is adjusted accordingly.</p>", "IMPUTATION__GLOBAL"=>"<p>Country level employment data, if available for categories broader than those in scope, may be adjusted to estimate the part that is relevant for this indicator, as indicated in section 4.e above: &#x201C;adjustments&#x201D;. Those country adjusted data are identified with the note for Nature &#x201C;CA&#x201D;. </p>\n<p>Gaps in the time series of country data may also be imputed with linear interpolation or carry forward/backward, for the calculation of regional and global aggregates. Multiple regression and cross-validation techniques may also be used. These imputed country values are used only as inputs to calculate the global and regional estimates and are not published as country data.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates are obtained by a simple sum of the employed persons in the tourism industries. Each individual record may correspond to: (i) official statistics reported by countries to UN Tourism and to ILO, (ii) data adjusted by UN Tourism based on ILO available data at ISIC level 2, and (iii) imputed missing values calculated by UN Tourism specifically for regional aggregate purposes. </p>", "DOC_METHOD__GLOBAL"=>"<p>The Statistical Framework for Measuring the Sustainability of Tourism (United Nations Statistical Commission, 2024) is the internationally agreed reference framework for measuring the economic, social and environmental aspects of tourism.</p>\n<p>The International Recommendations for Tourism Statistics 2008 (United Nations and World Tourism Organization, 2010) provide a comprehensive methodological framework for the collection and compilation of tourism statistics in all countries.</p>\n<p>The Guidelines on the implementation of the IRTS 2008 are provided in the Compilation Guide (United Nations, 2016).</p>\n<p>The Resolution concerning statistics of work, employment and labour underutilization (including amendments) (International Labour Organization, 2023) provides the definition of employment.</p>\n<p>The International Standard Industrial Classification of All Economic Activities 2008 (United Nations, 2008) provide a description of the activities identified as corresponding to each tourism industry.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>UN Tourism follows the recommendations on quality management available in the International Recommendations for Tourism Statistics 2008 (United Nations and World Tourism Organization, 2010) and the Statistical Framework for Measuring the sustainability of Tourism (United Nations Statistical Commission, 2024), as based on international good practice. </p>\n<p>The processes of compilation, production, and publication of ILO data, including its quality control, are carried out following the methodological framework and standards established by the ILO Department of Statistics, in compliance with the information technology and management standards of ILO.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination. Any discrepancies are addressed through written communication with countries.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The data should comply with the recommendations on concepts, definitions, and classifications provided in the Statistical Framework for Measuring the Sustainability of Tourism (United Nations Statistical Commission, 2024) and the International Recommendations for Tourism Statistics 2008 (United Nations and World Tourism Organization, 2010).</p>\n<p>The final assessment of the quality of the information is carried out by UN Tourism&apos;s Statistics, Standards and Data Department. In cases of doubt about the quality of specific data, values are reviewed by engaging with the official national agencies responsible for producing the data. If the issue cannot be entirely solved, the data may be either published with a note or removed.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong> Data are currently included for 89 countries. These comprise countries in all regions. UN Tourism is working with ILO and with the countries to further harmonise the available data to expand this coverage.</p>\n<p><strong>Time series:</strong> Data for this indicator are available from 2008.</p>\n<p><strong>Disaggregation:</strong> The current edition of this indicator provides the total number of employed persons in tourism industries, by country and year. Notwithstanding, this indicator can potentially be disaggregated by tourism industry, sex, status in employment, age, education level, etc., depending on data availability in countries. UN Tourism is working with ILO to expand the availability of data disaggregated by tourism industry, sex and status in employment, to include those dimension in this indicator.</p>", "COMPARABILITY__GLOBAL"=>"<p>Discrepancies between categories may arise due to a) the national data sources used and their coverage (geographical, tourism industries covered, types of establishments covered, etc.), b) the ISIC revision used by a country, c) the inclusion or not of informal employment and e) the working-age population definition.</p>", "OTHER_DOC__GLOBAL"=>"<p>International Labour Organization (no date) Worker and sector profiles (PROFILES database). Available at: <a href=\"https://ilostat.ilo.org/methods/concepts-and-definitions/description-worker-and-sector-profiles/#elementor-toc__heading-anchor-13\">https://ilostat.ilo.org/methods/concepts-and-definitions/description-worker-and-sector-profiles/#elementor-toc__heading-anchor-13</a>.</p>\n<p>International Labour Organization (2018) Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators. Department of Statistics. Available at: <a href=\"https://www.ilo.org/publications/decent-work-and-sustainable-development-goals-guidebook-sdg-labour-market\">https://www.ilo.org/publications/decent-work-and-sustainable-development-goals-guidebook-sdg-labour-market</a>. </p>\n<p>International Labour Organization (2023) Resolution concerning statistics of work, employment and labour underutilization (including amendments). Adopted by the 21st International Conference of Labour Statisticians (October 2023). Available at: <a href=\"https://www.ilo.org/resource/resolution-concerning-statistics-work-employment-and-labour\">https://www.ilo.org/resource/resolution-concerning-statistics-work-employment-and-labour</a>.</p>\n<p>United Nations (2003) Agreement between the United Nations and the World Tourism Organization. Available at: <a href=\"https://digitallibrary.un.org/record/505135\">https://digitallibrary.un.org/record/505135</a>. </p>\n<p>United Nations (2008) International Standard Industrial Classification of All Activities (ISIC). Revision 4. Available at: <a href=\"https://unstats.un.org/unsd/classifications/Econ/Download/In%20Text/ISIC_Rev_4_publication_English.pdf\">https://unstats.un.org/unsd/classifications/Econ/Download/<br>In%20Text/ISIC_Rev_4_publication_English.pdf</a>. </p>\n<p>United Nations and World Tourism Organization (2010), International Recommendations for Tourism Statistics 2008. Available at: <a href=\"https://www.unwto.org/tourism-statistics/on-basic-tourism-statistics-irts-2008\">https://www.unwto.org/tourism-statistics/on-basic-tourism-statistics-irts-2008</a>.</p>\n<p>United Nations, World Tourism Organization, Commission of the European Communities, and Organization for Economic Co-operation and Development (2010) Tourism Satellite Account: Recommended Methodological Framework 2008. Available at: <a href=\"https://unstats.un.org/unsd/publication/Seriesf/SeriesF_80rev1e.pdf\">https://unstats.un.org/unsd/publication/Seriesf/SeriesF_80rev1e.pdf</a></p>\n<p>United Nations (2016), International Recommendations for Tourism Statistics 2008 Compilation Guide. Available at: <a href=\"https://unstats.un.org/unsd/tourism/publications/E-IRTS-Comp-Guide%202008%20For%20Web.pdf\">https://unstats.un.org/unsd/tourism/publications/E-IRTS-Comp-Guide%202008%20For%20Web.pdf</a>. </p>\n<p>United Nations Statistical Commission (2017) UNSC Decision 48/115, chapter: I, section: C. Available at: <a href=\"https://unstats.un.org/unsd/statcom/decisions-ref/?code=48/115\">https://unstats.un.org/unsd/statcom/decisions-ref/?code=48/115</a>. </p>\n<p>United Nations Statistical Commission (2022) UNSC Decision 53/101, chapter: I, section: C. Available at: <a href=\"https://unstats.un.org/unsd/statcom/decisions-ref/?code=53/101\">https://unstats.un.org/unsd/statcom/decisions-ref/?code=53/101</a>.</p>\n<p>United Nations Statistical Commission (2024) Statistical Framework for Measuring the Sustainability of Tourism (unedited version). Available at: <a href=\"https://unstats.un.org/UNSDWebsite/statcom/session_55/documents/BG-4a-SF-MST-E.pdf\">https://unstats.un.org/UNSDWebsite/statcom/session_55/documents/BG-4a-SF-MST-E.pdf</a></p>", "indicator_number"=>"8.9.2", "reporting_status"=>"notstarted", "data_non_statistical"=>true, "graph_type"=>"line", "indicator_sort_order"=>"08-09-02", "indicator_name"=>"Employed persons in the tourism industries", "graph_title"=>"Employed persons in the tourism industries", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.10.1", "slug"=>"8-10-1", "name"=>"a) Número de sucursales de bancos comerciales por cada 100.000 adultos y b) número de cajeros automáticos por cada 100.000 adultos", "url"=>"/site/es/8-10-1/", "sort"=>"081001", "goal_number"=>"8", "target_number"=>"8.10", "global"=>{"name"=>"a) Número de sucursales de bancos comerciales por cada 100.000 adultos y b) número de cajeros automáticos por cada 100.000 adultos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de sucursales de bancos comerciales por cada 100.000 habitantes de 15 y más años", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"a) Número de sucursales de bancos comerciales por cada 100.000 adultos y b) número de cajeros automáticos por cada 100.000 adultos", "indicator_number"=>"8.10.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> No evaluable", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Banco de España", "periodicity"=>"Anual", "url"=>"https://clientebancario.bde.es/pcb/es/menu-horizontal/productosservici/relacionados/entidades/guia-textual/tiposentidadesso/Establecimientos-financieros-de-credito.html", "url_text"=>"Entidades de crédito y establecimientos financieros de crédito", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/BE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Número de sucursales de bancos comerciales por cada 100.000 habitantes de 15 y más años", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.10- Fortalecer la capacidad de las instituciones financieras nacionales para fomentar y ampliar el acceso a los servicios bancarios, financieros y de seguros para todos", "definicion"=>"Número de oficinas de entidades de crédito y establecimientos financieros de crédito respecto a la población de 15 y más años\n", "formula"=>"\n$$TOECEFC^{t} = \\frac{OECEFC^{t}}{P_{15+}^{t}} \\cdot 100.000$$\n\ndonde:\n\n$OECEFC^{t} =$  oficinas de entidades de crédito y establecimientos financieros de crédito en el año $t$\n\n$P_{15+}^{t} =$ población de 15 y más años a 1 de julio del año $t$\n", "desagregacion"=>"Territorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl acceso a los servicios financieros formales y su uso son esenciales. \nServicios como los ahorros, los seguros, los pagos, los créditos y las remesas permiten \na las personas gestionar sus vidas, planificar y pagar gastos, hacer crecer sus \nnegocios y mejorar su bienestar general. Como los bancos siguen siendo una de las \ninstituciones clave para el acceso a los servicios financieros formales, \ndisponer de una sucursal bancaria accesible es un importante punto inicial \nde acceso a los servicios financieros y, por lo tanto, de uso de los mismos. \n\nLas sucursales bancarias se complementan con otros puntos de acceso importantes, \ncomo los cajeros automáticos de todas las instituciones financieras formales, \nque pueden extender los servicios financieros a lugares remotos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.10.1&seriesCode=FB_CBK_BRCH&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B\">Número de sucursales de bancos comerciales por cada 100.000 adultos FB_CBK_BRCH</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas pero aporta información similar.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-10-01.pdf\">Metadatos 8-10-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"Número de sucursales de bancos comerciales por cada 100.000 habitantes de 15 y más años", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.10- Fortalecer la capacidad de las instituciones financieras nacionales para fomentar y ampliar el acceso a los servicios bancarios, financieros y de seguros para todos", "definicion"=>"Number of branches of credit institutions and financial credit establishments with respect to the population aged 15 and over\n", "formula"=>"\n$$TOECEFC^{t} = \\frac{OECEFC^{t}}{P_{15+}^{t}} \\cdot 100.000$$\n\nwhere:\n\n$OECEFC^{t} =$  branches of credit institutions and financial credit establishments in year $t$\n\n$P_{15+}^{t} =$ population aged 15 and over as of July 1 of year $t$\n", "desagregacion"=>"Province\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAccess to and use of formal financial services is essential. Services such as savings, insurance, payments, \ncredit and remittances allow people to manage their lives, plan and pay expenses, grow their \nbusinesses and improve their overall welfare. As banks remain one of the key institutions for access to \nformal financial services, having an accessible bank branch is an important initial point of access to \nfinancial services and therefore use of them. \n\nBank branches are complemented by other important points of access such as automated teller machines of \nall formal financial institutions, which can extend financial services to remote locations. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.10.1&seriesCode=FB_CBK_BRCH&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B\">Number of commercial bank branches per 100,000 adults FB_CBK_BRCH</a> UNSTATS", "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-10-01.pdf\">Metadata 8-10-1.pdf</a>", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Número de sucursales de bancos comerciales por cada 100.000 habitantes de 15 y más años", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.10- Fortalecer la capacidad de las instituciones financieras nacionales para fomentar y ampliar el acceso a los servicios bancarios, financieros y de seguros para todos", "definicion"=>"Kreditu-erakundeen eta kredituko finantza-establezimenduen bulegoen kopurua, 15 urte eta gehiagoko biztanleriarekiko\n", "formula"=>"\n$$TOECEFC^{t} = \\frac{OECEFC^{t}}{P_{15+}^{t}} \\cdot 100.000$$\n\nnon:\n\n$OECEFC^{t} =$  kreditu-erakundeen eta kredituko finantza-establezimenduen bulegoak $t$ urtean\n\n$P_{15+}^{t} =$ 15 urte eta gehiagoko biztanleak $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Lurralde historiako\n", "periodicidad"=>"Anual", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nFinantza-zerbitzu formalak eskuratzea eta erabiltzea funtsezkoa da. Aurrezkiek, aseguruek, ordainketek, \nkredituek eta diru-bidalketek aukera ematen diete pertsonei beren bizitzak kudeatzeko, gastuak planifikatzeko \neta ordaintzeko, negozioak hazteko eta ongizate orokorra hobetzeko. Bankuek finantza-zerbitzu formalak \neskuratzeko funtsezko erakundeetako bat izaten jarraitzen dutenez, banku-sukurtsal irisgarri bat izatea \nfinantza-zerbitzuak eskuratzeko hasierako puntu garrantzitsua da, eta, beraz, baita zerbitzu horiek \nerabiltzeko ere. \n\nBanku-sukurtsalak beste sarbide-puntu garrantzitsu batzuekin osatzen dira, hala nola finantza-erakunde \nformal guztietako kutxazain automatikoekin, finantza-zerbitzuak urruneko lekuetara zabal baititzakete. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.10.1&seriesCode=FB_CBK_BRCH&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B\">Banku komertzialen sukurtsalen kopurua 100.000 heldu bakoitzeko FB_CBK_BRCH</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina antzeko informazioa ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-10-01.pdf\">Metadatuak 8-10-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.10: Strengthen the capacity of domestic financial institutions to encourage and expand access to banking, insurance and financial services for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.10.1: (a) Number of commercial bank branches per 100,000 adults and (b) Number of automated teller machines (ATMs) per 100,000 adults </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Applies to all series </p>", "META_LAST_UPDATE__GLOBAL"=>"2023-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Monetary Fund (STAFI - Financial Access Survey Team) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Monetary Fund (STAFI - Financial Access Survey Team) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>The number of commercial bank branches per 100,000 adults </p>\n<p>The number of automated teller machines (ATMs) per 100,000 adults </p>\n<p>Adult population<strong> </strong>refers to the total population in the reporting jurisdiction of individuals 15 years old and above</p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>The number of commercial bank branches refers to the total number of commercial bank branches in the country reported annually by the central bank or the main financial regulator of the country to the Financial Access Survey (FAS). To make the indicator meaningful for cross-country comparison, the number of commercial bank branches is scaled per 100,000 adults. </p>\n<p>The number of automated teller machines (ATMs), refers to the number of ATMs in the country for all types of financial institutions such as: commercial banks, non-deposit taking microfinance institutions, deposit taking micro finance institutions, credit unions and credit cooperatives, and other deposit takers. This information is reported annually by the central bank or the main financial regulator of the country to the FAS. To make the indicator meaningful for cross-country comparison, the number of ATMs is scaled per 100,000 adults. </p>\n<p> </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Per 100,000 adults</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The indicators in the Financial Access Survey (FAS) database are collected on an annual basis since 2009, covering the period since 2004. Information is collected from central banks or other main financial regulators for 189 jurisdictions. </p>\n<p> </p>\n<p>All data and metadata are available free of charge to the public on the IMF&#x2019;s <a href=\"http://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C\" target=\"_blank\"><u>FAS website</u></a>, along with other key documents. </p>", "COLL_METHOD__GLOBAL"=>"<p>Every year, the Financial Access Survey (FAS) Team reaches out to FAS respondents to initiate the annual survey process. Data are compiled by countries and sent to the IMF through the Integrated Collection System (ICS) or National Summary Data Page (NSDP), which allows for a secure submission of country information. </p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>The data collection round is launched around end-March every year; collection occurs on an annual basis. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The Financial Access Survey (FAS) data are publicly disseminated on a rolling basis as soon as submissions are reviewed and validated by the FAS Team, with complete dissemination at end-September each year. Submissions that have passed through the validation process are made available on the FAS website on the following Monday after successful validation. </p>", "DATA_SOURCE__GLOBAL"=>"<p>Central banks or other financial regulators. </p>", "COMPILING_ORG__GLOBAL"=>"<p>International Monetary Fund. </p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>Access to and use of formal financial services is essential. Services such as savings, insurance, payments, credit and remittances allow people to manage their lives, plan and pay expenses, grow their businesses and improve their overall welfare. As banks remain one of the key institutions for access to formal financial services, having an accessible bank branch is an important initial point of access to financial services and therefore use of them. Bank branches are complemented by other important points of access such as automated teller machines of all formal financial institutions, which can extend financial services to remote locations. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Since 2009, the Financial Access Survey (FAS) collects information from administrative sources on an annual basis. The central bank or the main financial regulator reports yearly information including the two indicators that are part of the SDGs. Since its launch, 189 economies have contributed to the FAS, which now contains more than 100 series on financial inclusion covering the period since 2004. </p>", "DATA_COMP__GLOBAL"=>"<p>The indicators are calculated based on data collected directly from the central bank or the main financial regulator in the country. The formula to obtain these indicators are:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">T</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">b</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">b</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">k</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">b</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">N</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">k</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <msub>\n          <mrow>\n            <mi mathvariant=\"normal\">b</mi>\n            <mi mathvariant=\"normal\">r</mi>\n            <mi mathvariant=\"normal\">a</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mi mathvariant=\"normal\">c</mi>\n            <mi mathvariant=\"normal\">h</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">s</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">t</mi>\n          </mrow>\n        </msub>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mi mathvariant=\"normal\">A</mi>\n            <mi mathvariant=\"normal\">d</mi>\n            <mi mathvariant=\"normal\">u</mi>\n            <mi mathvariant=\"normal\">l</mi>\n            <mi mathvariant=\"normal\">t</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">p</mi>\n                <mi mathvariant=\"normal\">o</mi>\n                <mi mathvariant=\"normal\">p</mi>\n                <mi mathvariant=\"normal\">u</mi>\n                <mi mathvariant=\"normal\">l</mi>\n                <mi mathvariant=\"normal\">a</mi>\n                <mi mathvariant=\"normal\">t</mi>\n                <mi mathvariant=\"normal\">i</mi>\n                <mi mathvariant=\"normal\">o</mi>\n                <mi mathvariant=\"normal\">n</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">i</mi>\n                <mi mathvariant=\"normal\">t</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <mn>100</mn>\n            <mo>,</mo>\n            <mn>000</mn>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">T</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">b</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi mathvariant=\"normal\">A</mi>\n    <mi mathvariant=\"normal\">T</mi>\n    <mi mathvariant=\"normal\">M</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mo>)</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">N</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <msub>\n          <mrow>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mi mathvariant=\"normal\">A</mi>\n                <mi mathvariant=\"normal\">T</mi>\n                <mi mathvariant=\"normal\">M</mi>\n                <mi mathvariant=\"normal\">s</mi>\n              </mrow>\n            </mfenced>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">t</mi>\n          </mrow>\n        </msub>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mi>&amp;nbsp;</mi>\n        <mfrac>\n          <mrow>\n            <mi mathvariant=\"normal\">A</mi>\n            <mi mathvariant=\"normal\">d</mi>\n            <mi mathvariant=\"normal\">u</mi>\n            <mi mathvariant=\"normal\">l</mi>\n            <mi mathvariant=\"normal\">t</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">p</mi>\n                <mi mathvariant=\"normal\">o</mi>\n                <mi mathvariant=\"normal\">p</mi>\n                <mi mathvariant=\"normal\">u</mi>\n                <mi mathvariant=\"normal\">l</mi>\n                <mi mathvariant=\"normal\">a</mi>\n                <mi mathvariant=\"normal\">t</mi>\n                <mi mathvariant=\"normal\">i</mi>\n                <mi mathvariant=\"normal\">o</mi>\n                <mi mathvariant=\"normal\">n</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">i</mi>\n                <mi mathvariant=\"normal\">t</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <mn>100</mn>\n            <mo>,</mo>\n            <mn>000</mn>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where &#x201C;i&#x201D; indicates the country and &#x201C;t&#x201D; indicates the year. The source of information for the number of commercial bank branches and the number of ATMs is the Financial Access Survey (FAS), while the source of information for the adult population is the World Development Indicators or the World Factbook.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The Financial Access Survey (FAS) questionnaire has built-in consistency checks to help data reporters spot inconsistencies in data reporting. Once the data is reported to the FAS, it undergoes a round of automated validation checks. If any inconsistency is detected, the FAS Team engages with the country authorities for clarifications or adjustments to the data provided. In case a country needs to add additional relevant information pertinent to the data reported, they can do so through the metadata portal in Integrated Collection System (ICS). </p>\n<p>Every year, submissions are disseminated on the FAS website (<a href=\"https://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C\">data.imf.org/fas</a>) on a rolling basis as soon as they are reviewed and validated. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Data are taken from the World Bank&apos;s World Development Indicators database and the World Factbook. In cases where data for the most recent period are not available, data for the previous period is used.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>Missing values are registered as empty. &#x201C;n/a&#x201D; are used when the country indicates that those services or institutions do not exist in the country, or alternatively, do not fall under the supervisory scope of a regulatory agency. </p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p>&#x201C;n/a&#x201D; are used when the country indicates that those services or institutions do not exist in the country, or alternatively, do not fall under the supervisory scope of a regulatory agency. Trend extrapolation is used for countries that have not reported the latest data. </p>", "REG_AGG__GLOBAL"=>"<p>Country level: information provided by the authorities, recalculated as number of access points per 100,000 adults. For regional values, the Financial Access Survey (FAS) aggregates information of all countries and uses country&#x2019;s adult population as weights. </p>", "DOC_METHOD__GLOBAL"=>"<p>Information collected by the Financial Access Survey (FAS) relies on the &#x201C;<em>FAS Guidelines and Manual</em>&#x201D;, which is published every year in English, Spanish and French. To foster the use of a common methodology, the definitions of financial institutional units and instruments covered in the FAS are primarily based on the IMF&#x2019;s <em>Monetary and Financial Statistics Manual and Compilation Guide</em> (<a href=\"https://www.imf.org/-/media/Files/Data/Guides/mfsmcg-final.ashx\" target=\"_blank\"><u>https://www.imf.org/-/media/Files/Data/Guides/mfsmcg-final.ashx</u></a>). The FAS also publishes a <em>Glossary</em> for FAS indicators. </p>\n<p> </p>\n<p>All these documents can be found in <a href=\"http://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C&amp;sId=1460040555909\" target=\"_blank\"><u>FAS website - documents</u></a>. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The Financial Access Survey (FAS) questionnaire has built-in consistency checks to help data reporters spot inconsistencies in data reporting. Once the data is reported to the FAS, it undergoes a round of automated validation checks and careful review by the FAS team. The analytical work on the reported data also aids spotting and correcting inconsistencies in the data, if any.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The Financial Access Survey (FAS) data are collected through the Integrated Collection System (ICS) or the National Summary Data Page (NSDP), which allows for a secure submission of country information. Data submitted by countries are received internally in a system that facilitates the validation process conducted by the FAS Team. </p>\n<p> </p>\n<p>Each submission is carefully reviewed, and when necessary, the FAS Team engages with the country authorities for clarifications or adjustments to the data provided. In case a country needs to add additional relevant information pertinent to the data reported, they can do so through the metadata portal in ICS. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The Financial Access Survey (FAS) is a supply-side database with data reported from central banks and other financial regulators sourced from administrative data. Supply-side data tend to be more accurate than demand-side surveys. Furthermore, any deviations from the FAS methodology or fluctuations are reported by the country in the metadata, which is available on the FAS data portal. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Covering 189 economies, the Financial Access Survey (FAS) provides a unique set of high-quality global supply side data. It contains 121 times series and 70 indicators that are expressed as ratios to GDP, land area, or adult population to facilitate cross-country comparisons. </p>\n<p><strong>Time series:</strong></p>\n<p>Since 2004; on an annual basis. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data are provided at country level, by year. Aggregates are compiled by region in accordance with UN suggested regional aggregations. </p>", "COMPARABILITY__GLOBAL"=>"<p>The Financial Access Survey (FAS) is a supply-side database based on administrative data from central banks or other main financial regulators. The data collection is centralized at the regulatory agency, which sources data from financial institutions and financial services providers for series for which data are available. The regulatory agency reports aggregates for the total economy to the FAS. The FAS provides country-level metadata that explain the institutional coverage of each reporting economy. Data from the FAS may differ from household-based surveys because of possible difference in coverage, scope, or concept definitions. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p>http://data.imf.org/fas </p>", "indicator_sort_order"=>"08-10-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.10.2", "slug"=>"8-10-2", "name"=>"Proporción de adultos (a partir de 15 años de edad) que tienen una cuenta en un banco u otra institución financiera o un proveedor de servicios de dinero móvil", "url"=>"/site/es/8-10-2/", "sort"=>"081002", "goal_number"=>"8", "target_number"=>"8.10", "global"=>{"name"=>"Proporción de adultos (a partir de 15 años de edad) que tienen una cuenta en un banco u otra institución financiera o un proveedor de servicios de dinero móvil"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de personas entre 18 y 79 años que tienen una cuenta corriente", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de adultos (a partir de 15 años de edad) que tienen una cuenta en un banco u otra institución financiera o un proveedor de servicios de dinero móvil", "indicator_number"=>"8.10.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Banco de España", "periodicity"=>"Anual", "url"=>"https://www.bde.es/wbe/es/publicaciones/analisis-economico-investigacion/encuesta-de-competencias-financieras/", "url_text"=>"Encuesta de competencias financieras", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/BE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de personas entre 18 y 79 años que tienen una cuenta corriente", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.10- Fortalecer la capacidad de las instituciones financieras nacionales para fomentar y ampliar el acceso a los servicios bancarios, financieros y de seguros para todos", "definicion"=>"Proporción de personas entre 18 y 79 años que tienen personal o conjuntamente una cuenta corriente, \nlibreta u otro depósito que pueda ser utilizado para realizar pagos mediante tarjetas o cheques \nrespecto al total de personas entre 18 y 79 años\n", "formula"=>"\n$$PPCC_{18-79}^{t} = \\frac{PCC_{18-79}^{t}}{P_{18-79}^{t}} \\cdot 100$$\n\ndonde:\n\n$PCC_{18-79}^{t} =$ población entre 18 y 79 años que tiene personal o conjuntamente una cuenta corriente, libreta u otro depósito que pueda ser utilizado para realizar pagos mediante tarjetas o cheques en el año  $t$\n\n$P_{18-79}^{t} =$ población entre 18 y 79 años en el año $t$\n", "desagregacion"=>"Sexo\n", "periodicidad"=>"Quinquenal", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa inclusión financiera significa que los hogares tienen acceso y pueden utilizar \neficazmente servicios financieros apropiados que se prestan de manera responsable y \nsostenible en un entorno bien regulado. Los estudios muestran que cuando las personas \nparticipan en el sistema financiero, están en mejores condiciones de iniciar y \nexpandir negocios, invertir en educación, gestionar el riesgo y absorber los shocks \nfinancieros. \n\nLa medición es clave para comprender la inclusión financiera e identificar oportunidades \npara eliminar las barreras que pueden impedir que las personas utilicen los servicios \nfinancieros.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.10.2&seriesCode=FB_BNK_ACCSS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20ALLAREA%20%7C%20BOTHSEX%20%7C%20_T%20%7C%20_T\">Proporción de adultos (de 15 años o más) con una cuenta en una institución financiera o proveedor de servicios de dinero móvil (% de adultos de 15 años o más) FB_BNK_ACCSS</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas pero aporta información similar.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-10-02.pdf\">Metadatos 8-10-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de personas entre 18 y 79 años que tienen una cuenta corriente", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.10- Fortalecer la capacidad de las instituciones financieras nacionales para fomentar y ampliar el acceso a los servicios bancarios, financieros y de seguros para todos", "definicion"=>"Proportion of people aged between18-79 years who personally or jointly have a checking account, \npassbook or other deposit that can be used to make payments by cards or checks compared to the \ntotal of people aged between 18-79 years\n", "formula"=>"\n$$PPCC_{18-79}^{t} = \\frac{PCC_{18-79}^{t}}{P_{18-79}^{t}} \\cdot 100$$\n\nwhere:\n\n$PCC_{18-79}^{t} =$ people aged between18-79 years who personally or jointly have a checking account, \npassbook or other deposit that can be used to make payments by cards or checks in year  $t$\n\n$P_{18-79}^{t} =$ population aged between 18-79 years in year $t$\n", "desagregacion"=>"Sex\n", "periodicidad"=>"Quinquenal", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nFinancial inclusion means that households have access to and can effectively use appropriate financial \nservices that are provided responsibly and sustainably in a well-regulated environment. Studies show that \nwhen people participate in the financial system, they are better able to start and expand businesses, \ninvest in education, manage risk and absorb financial shocks.  \n\nMeasurement is key to understanding financial inclusion and identifying opportunities to remove barriers \nthat may be preventing people from using financial services. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.10.2&seriesCode=FB_BNK_ACCSS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20ALLAREA%20%7C%20BOTHSEX%20%7C%20_T%20%7C%20_T\">Proportion of adults (15 years and older) with an account at a financial institution or mobile-money-service provider, by sex (% of adults aged 15 years and older) FB_BNK_ACCSS</a> UNSTATS", "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-10-02.pdf\">Metadata 8-10-2.pdf</a>", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de personas entre 18 y 79 años que tienen una cuenta corriente", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.10- Fortalecer la capacidad de las instituciones financieras nacionales para fomentar y ampliar el acceso a los servicios bancarios, financieros y de seguros para todos", "definicion"=>"18 eta 79 urte bitarteko pertsonen artean, txartel edo txekeen bidez ordainketak \negiteko erabil daitekeen kontu korronte, libreta edo beste gordailuren bat pertsonalki \nedo beste norbaitekin batera daukaten pertsonen proportzioa \n", "formula"=>"\n$$PPCC_{18-79}^{t} = \\frac{PCC_{18-79}^{t}}{P_{18-79}^{t}} \\cdot 100$$\n\nnon:\n\n$PCC_{18-79}^{t} =$ pertsonalki edo beste norbaitekin batera, kontu korronte bat, libreta bat edo \ntxartel edo txekeen bidez ordainketak egiteko erabil daitekeen beste gordailuren bat duten \n18-79 urteko biztanleak $t$ urtean \n\n$P_{18-79}^{t} =$ 18-79 urteko biztanleak $t$ urtean \n", "desagregacion"=>"Sexo\n", "periodicidad"=>"Quinquenal", "observaciones"=>"", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nFinantza-inklusioak esan nahi du familiek finantza-zerbitzu egokiak eskura dituztela eta eraginkortasunez \nerabil ditzaketela, modu arduratsuan eta jasangarrian, ondo araututako ingurunean. Ikerketek erakusten \ndute pertsonek finantza-sisteman parte hartzen dutenean, baldintza hobeetan daudela negozioak hasteko \neta hedatzeko, hezkuntzan inbertitzeko, arriskua kudeatzeko eta finantza-shockak xurgatzeko. \n\nNeurketa funtsezkoa da finantza-inklusioa ulertzeko eta pertsonek finantza-zerbitzuak erabiltzea eragotz \ndezaketen oztopoak ezabatzeko aukerak identifikatzeko. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.10.2&seriesCode=FB_BNK_ACCSS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=15%2B%20%7C%20ALLAREA%20%7C%20BOTHSEX%20%7C%20_T%20%7C%20_T\">Finantza-erakunde batean edo diru mugikorreko zerbitzuen hornitzaile batean kontu bat duten helduen (15 urte edo gehiagokoen) proportzioa (15 urte edo gehiagoko helduen %) FB_BNK_ACCSS</a> UNSTATS", "comparabilidad"=>"EAEn ekuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina antzeko informazioa ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-10-02.pdf\">Metadatuak 8-10-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.10: Strengthen the capacity of domestic financial institutions to encourage and expand access to banking, insurance and financial services for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.10.2: Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service provider</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>FB_BNK_ACCSS - Proportion of adults (15 years and older) with an account at a financial institution or mobile-money-service provider [8.10.2]</p>\n<p>FB_BNK_ACCSS_ILF - Proportion of adults (15 years and older) active in labor force with an account at a financial institution or mobile-money-service provider [8.10.2]</p>\n<p>FB_BNK_ACCSS_OLF - Proportion of adults (15 years and older) out of labor force with an account at a financial institution or mobile-money-service provider [8.10.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p>The percentage of adults (ages 15+) who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or personally using a mobile money service in the past 12 months. </p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>Financial institution accounts (excluding mobile money) denote the percentage of respondents who report having an account (by themselves or together with someone else) at a bank, credit union, microfinance institution, or post office that falls under prudential regulation by a government body.</p>\n<p>Data on adults with a financial institution account include respondents who reported having an account at a bank or at another type of financial institution, such as a credit union, a microfinance institution, a cooperative, or the post office (if applicable). The data also include an additional 3 percent of respondents in 2021 who reported receiving wages, government transfers, a public sector pension, or payments for agricultural products into a financial institution account in the past year; paying utility bills or school fees from a financial institution account in the past year; or receiving wages, government transfers, or agricultural payments into a card in the past year. The definition does not include non-bank financial institutions such as pension funds, retirement accounts, insurance companies, or equity holdings such as stocks. </p>\n<p>Mobile money accounts denote the percentage of respondents who report personally using a mobile money service to make payments, buy things, or to send or receive money in the past year. Data on adults with a mobile money account include respondents who reported personally using services included in the GSM Association&#x2019;s Mobile Money for the Unbanked (GSMA MMU) database to pay bills or to send or receive money in the past year. The data also include an additional 2 percent of respondents in 2021 who reported receiving wages, government transfers, a public sector pension, or payments for agricultural products through a mobile phone in the past year. Unlike the definition of account at a financial institution, the definition of mobile money account does not include the payment of utility bills or school fees through a mobile phone. The reason is that the phrasing of the possible answers leaves it open as to whether those payments were made using a mobile money account or an over-the-counter service.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Data is also aggregated using the World Bank classifications for World, low- and middle-income economies, income groups (low-income, lower-middle-income, and upper-middle-income economies), and developing regions.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The indicators in the Global Findex Database 2021 are drawn from survey data covering almost 128,000 people in 123 economies&#x2014;representing 91 percent of the world&#x2019;s population. The survey was carried out over the 2021 calendar year, and now marks the fourth round of Global Findex data since 2011. Typically, the survey captures data from more than 140 economies, but surveying was postponed in a handful of countries in 2021 due to COVID-19. These countries were surveyed in 2022, and data on these additional 17 countries will be available in 2023.</p>\n<p>The surveying is undertaken by Gallup, Inc. as part of its Gallup World Poll, , which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above.</p>\n<p> </p>\n<p>Full report, including methodology and interview procedures, data preparation, margin of error and notes by country are all available under Methodology Table A.1 here:</p>\n<p><a href=\"https://www.worldbank.org/en/publication/globalfindex/Report\" target=\"_blank\"><u>https://www.worldbank.org/en/publication/globalfindex/Report</u></a></p>", "COLL_METHOD__GLOBAL"=>"<p>In most developing economies, Global Findex data have traditionally been collected through face-to-face</p>\n<p>interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, during 2021, due to ongoing COVID-19&#x2013;related mobility restrictions, face-to-face interviewing was not possible in some of these economies.</p>\n<p>Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. </p>\n<p>All samples are probability-based and nationally representative of the resident adult population. </p>", "FREQ_COLL__GLOBAL"=>"<p>Four rounds of data collection are completed, for years: 2011, 2014, 2017, and 2022. </p>\n<p>Surveying for 2020 was delayed to 2021 due to COVID-19 restrictions.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are collected every three years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Collected by Gallup, Inc. through the Gallup World Poll and compiled by the World Bank</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Bank </p>", "INST_MANDATE__GLOBAL"=>"<p>Development Research Group (DECRG) is the World Bank&apos;s principal research department under the Development Economics Vice Presidency. Under the guidance of the World Bank&#x2019;s Chief Economist, DECRG provides high quality economic information, data, analysis, research, and training to the Bank Group and its partners. DECRG&apos;s research programs generate and disseminate knowledge about development policies essential for the achievement of the World Bank&apos;s ultimate mandate of poverty reduction and shared prosperity. Within DECRG, the &#x201C;Findex team&#x201D; manages the extensive Global Findex Database that provides in-depth data on how people, especially women and the poor, save, borrow, make payments and manage risk.</p>\n<p>The Global Findex Database is collected in partnership with Gallup, Inc., as a part of its Gallup World Poll. For more information on their mandate and methodology , please see here: <a href=\"https://www.gallup.com/178667/gallup-world-poll-work.aspx\">https://www.gallup.com/178667/gallup-world-poll-work.aspx</a>. </p>", "RATIONALE__GLOBAL"=>"<p>Financial inclusion means that households have access to and can effectively use appropriate financial services that are provided responsibly and sustainably in a well-regulated environment. Studies show that when people participate in the financial system, they are better able to start and expand businesses, invest in education, manage risk and absorb financial shocks. Measurement is key to understanding financial inclusion and identifying opportunities to remove barriers that may be preventing people from using financial services. The Global Findex Database 2021 is the first global, comparable database of demand-side financial inclusion indicators, capturing insights into how adults around the world save, borrow, make payments and manage risk.</p>", "REC_USE_LIM__GLOBAL"=>"<p> Global Findex Database&#x2014;only measures the &#x2018;perception&#x2019; people have about their account ownership and usage by providing individual-level survey data on the demographic characteristics of users of financial services. This demand-side data collects information on the percent of adults who think of themselves as &#x2018;banked&#x2019; and having access to an account. The database complements but does replace existing supply-side data and other household surveys.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is based on data collected through individual level surveys in each country with representative samples. Data weighting is used to ensure a nationally representative sample for each economy. Final weights consist of the base sampling weight, which corrects for unequal probability of selection based on household size, and the poststratification weight, which corrects for sampling and nonresponse error. Poststratification weights use economy-level population statistics on gender and age and, where reliable data are available, education or socioeconomic status. Regional population weights are used to calculate regional aggregates. </p>\n<p>Full report, including methodology and interview procedures, data preparation, margin of error and notes by country are all available under Methodology Table A.1 here: <a href=\"https://www.worldbank.org/en/publication/globalfindex/Report\"></a><a href=\"https://www.worldbank.org/en/publication/globalfindex/Report\">https://www.worldbank.org/en/publication/globalfindex/Report</a></p>", "DATA_VALIDATION__GLOBAL"=>"<p>There is a thorough review process of the Global Findex surveys to ensure its quality and integrity. Gallup, Inc, the survey vendor for the Global Findex database, follows the highest standards for sampling and collecting the data and thoroughly vets the data before sharing it with the Global Findex team. Once the data is received by the Findex team, the team vets the data by comparing headline indicators from the database against data from previous rounds of the database.</p>", "ADJUSTMENT__GLOBAL"=>"<p><a href=\"https://www.gallup.com/178667/gallup-world-poll-work.aspxNot\">Not</a> applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p> Data for &#x201C;don&#x2019;t know&#x201D; and &#x201C;refused to answer&#x201D; form 0-1% of the total responses and are counted as &#x201C;no&#x201D; instead of missing. Any estimates based on fewer than 10% of survey sample observations are considered economically insignificant and therefore suppressed.</p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p><strong> </strong>Not applicable </p>", "REG_AGG__GLOBAL"=>"<p>Regional estimates are calculated by aggregating individual level surveys for each country in the developing regions. Appropriate regional population-based weights are applied. Information on developing regional classification(s) is taken from the World Bank and the population data is taken from the World Development Indicators (WDI). </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The Findex team oversees the quality management of the Global Findex Database.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>For quality assurance, the Findex team has additional processes in place after it receives the data from Gallup Inc. The Findex team compares the data against (the limited number of) demand-side survey data from external sources for countries whenever available such as from the Financial Inclusion Insights Survey, FinScope, or from Central Banks. The team also consults with key practitioners both within the World Bank and at other key organizations to confirm the credibility of headline numbers on account ownership. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>All the source code for the headline indicator is reviewed by an independent and external statistics department within the World Bank. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability: </strong></p>\n<p>There are 162 countries with regional and World aggregates that have at least 1 data point after 2011 for this indicator. </p>\n<p><strong>Time series:</strong></p>\n<p>Triennial (2011,2014, 2017 and 2021)</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregation are available by: Income (Adults in the poorest 40 percent of households vs. Adults in the richest 60 percent of households); Participation in labour force (In labor force vs. Out of labor force); Age (Ages 15-25 vs. Age 25+); Education level (Primary and below vs. Primary and above); Urbanicity of residence (Rural vs. Urban ); Gender (Female vs. Male)</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Global Findex Database is an individual level survey, measuring an individual&#x2019;s perception of ownership of accounts. It assumes financial inclusion is an individual-level concept which may create two potential discrepancies. First, the data may deviate from supply side data which counts the number of accounts and may overstate the level of financial inclusion in a country if a significant number of adults have an account they do not use and did not formally close. Second, not all demand-side data is the same and that the data from different demand-side surveys cannot necessarily be compared if the survey method is different. In particular, surveys of individuals cannot be compared directly with surveys of household heads, since the use of financial services can differ considerably between different household members.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p>https://www.worldbank.org/en/publication/globalfindex </p>\n<p><strong>References:</strong> </p>\n<p>Demirg&#xFC;&#xE7;-Kunt, Asli, Leora Klapper, Dorothe Singer, and Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank. </p>", "indicator_sort_order"=>"08-10-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.a.1", "slug"=>"8-a-1", "name"=>"Compromisos y desembolsos en relación con la iniciativa Ayuda para el Comercio", "url"=>"/site/es/8-a-1/", "sort"=>"08aa01", "goal_number"=>"8", "target_number"=>"8.a", "global"=>{"name"=>"Compromisos y desembolsos en relación con la iniciativa Ayuda para el Comercio"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Compromisos y desembolsos en relación con la iniciativa Ayuda para el Comercio", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Compromisos y desembolsos en relación con la iniciativa Ayuda para el Comercio", "indicator_number"=>"8.a.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nLa Ayuda Oficial al Desarrollo (AOD) para la ayuda al comercio en los países en \ndesarrollo cuantifica el esfuerzo público que los donantes proporcionan a los \npaíses en desarrollo para la ayuda al comercio.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.a.1&seriesCode=DC_TOF_TRDDBML&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Flujos oficiales totales (desembolsos) de Ayuda para el Comercio, por países receptores (millones de dólares de los Estados Unidos constantes de 2022) DC_TOF_TRDDBML</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-0a-01.pdf\">Metadatos 8-a-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nODA for aid for trade to developing countries quantify the public effort that donors provide to developing \ncountries for aid for trade.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.a.1&seriesCode=DC_TOF_TRDDBML&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Total official flows (disbursement) for Aid for Trade, by recipient countries (millions of constant 2022 United States dollars) DC_TOF_TRDDBML</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-0a-01.pdf\">Metadata 8-a-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nGarapen-bidean dauden herrialdeetan merkataritzari laguntzeko Garapenerako Laguntza Ofizialak (GLO) \nzenbatesten du emaileek garapen-bidean dauden herrialdeei merkataritzan laguntzeko ematen dieten \nahalegin publikoa. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=8.a.1&seriesCode=DC_TOF_TRDDBML&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Merkataritzarako Laguntzaren guztizko fluxu ofizialak (ordainketak), herrialde hartzaileen arabera (2022ko Estatu Batuetako dolar konstante milioiak) DC_TOF_TRDDBML</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-0a-01.pdf\">Metadatuak 8-a-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.a: Increase Aid for Trade support for developing countries, in particular least developed countries, including through the Enhanced Integrated Framework for Trade-related Technical Assistance to Least Developed Countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.a.1: Aid for Trade commitments and disbursements </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>DC_TOF_TRDCMDL - Total official flows (commitments) for Aid for Trade, by donor countries [8.a.1]</p>\n<p>DC_TOF_TRDCML - Total official flows (commitments) for Aid for Trade, by recipient countries [8.a.1]</p>\n<p>DC_TOF_TRDDBMDL - Total official flows (disbursement) for Aid for Trade, by donor countries [8.a.1]</p>\n<p>DC_TOF_TRDDBML - Total official flows (disbursement) for Aid for Trade, by recipient countries [8.a.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-01-31", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong> </p>\n<p> This indicator is defined as gross disbursements and commitments of total Official Development Assistance (ODA) from all donors for aid for trade.</p>\n<p> </p>\n<p><strong>Concepts:</strong> </p>\n<p>The Development Assistance Committee (DAC) defines Official Development Assistance (ODA) as &#x201C;those flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are i) provided by official agencies, including state and local governments, or by their executive agencies; and ii) concessional (i.e. grants and soft loans) and administered with the promotion of the economic development and welfare of developing countries as the main objective.</p>\n<p>For more details see here: <a href=\"https://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/officialdevelopmentassistancedefinitionandcoverage.htm\">https://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/officialdevelopmentassistancedefinitionandcoverage.htm</a></p>\n<p> </p>\n<p><em>All donors </em>refer to DAC donors, other bilateral providers of development cooperation and multilateral organizations. <em> </em></p>\n<p><em>Aid for Trade </em>is captured in the OECD&#x2019;s Creditor Reporting System (CRS) under four categories:</p>\n<ol>\n  <li>Economic infrastructure: Amounts relating to trade-related infrastructure are provided in the CRS by data under the sector code heading &#x201C;Economic Infrastructure and Services&#x201D; and cover the sectors transport and storage, communications and energy generation and supply.</li>\n  <li>Trade policy and regulations: In the CRS, five sector codes (in the 331xx series) are used to capture trade policy and regulations activities: trade policy and administrative management; trade facilitation; regional trade agreements; multilateral trade negotiations; and trade education/training.</li>\n  <li>Trade-related adjustment: this sector code (33150) was introduced in the CRS as a separate data item in 2007 to track flows corresponding to trade-related adjustment. This category identifies contributions to developing country budgets to assist the implementation of trade reforms and adjustments to trade policy measures by other countries and alleviate shortfalls in balance-of-payments due to changes in the world trading environment.</li>\n  <li>Building Productive capacity (BPC), including trade development: The CRS captures full data on all activities in the productive and services sectors, such as agriculture; industry; mineral resources and mining; business; and banking. </li>\n</ol>\n<p>See for reference: <a href=\"http://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/dacandcrscodelists.htm\"><u>http://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/dacandcrscodelists.htm</u></a></p>\n<p> </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Millions of constant United States dollars</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The OECD/DAC has been collecting data on official and private resource flows from 1960 at an aggregate level and 1973 at an activity level through the Creditor Reporting System (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements). </p>\n<p> </p>\n<p>The data are reported by donors according to the same standards and methodologies (see here: <a href=\"http://www.oecd.org/dac/stats/methodology.htm\">http://www.oecd.org/dac/stats/methodology.htm</a>). </p>\n<p> </p>\n<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc. )</p>\n<p>The OECD/DAC Secretariat prepares and submits an annual questionnaire (at an aggregate level and at an activity level) to national statistical reporters, and is responsible for collecting, validating and publishing these data. </p>", "COLL_METHOD__GLOBAL"=>"<p>A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are published on an annual basis in December for flows in the previous year. For example, detailed 2022 flows were published in December 2023. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>December of each year. </p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc. </p>", "COMPILING_ORG__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD) </p>", "RATIONALE__GLOBAL"=>"<p>ODA for aid for trade to developing countries quantify the public effort that donors provide to developing countries for aid for trade.</p>\n<p> </p>", "REC_USE_LIM__GLOBAL"=>"<p>Data in the Creditor Reporting System are available from 1973. However, the data coverage at an activity level is considered complete from 1995 for commitments and 2002 for disbursements. </p>\n<p> </p>", "DATA_COMP__GLOBAL"=>"<p>The sum of Total Official Development Assistance (ODA) from all donors to developing countries for aid for trade. </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p> </p>\n<p>Due to high quality of reporting, no estimates are produced for missing data. </p>\n<p> </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p> </p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional figures are based on the sum of ODA flows for aid for trade activities. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>On a donor basis for all DAC countries and many non-DAC providers (bilateral and multilateral) that report to the DAC. </p>\n<p>On a recipient basis for all developing countries eligible for ODA. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator can be disaggregated by donor, recipient country, type of finance, type of aid, trade policy and regulations and trade related adjustment sub-sectors, etc. </p>\n<p> </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong> </p>\n<p><a href=\"http://www.oecd.org/dac/stats\">www.oecd.org/dac/stats</a> </p>\n<p> </p>\n<p><strong>References:</strong> </p>\n<p>See all links here: <a href=\"http://www.oecd.org/dac/stats/methodology.htm\" target=\"_blank\"><u>http://www.oecd.org/dac/stats/methodology.htm</u></a> </p>", "indicator_sort_order"=>"08-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"8.b.1", "slug"=>"8-b-1", "name"=>"Existencia de una estrategia nacional organizada y en marcha para el empleo de los jóvenes, como estrategia independiente o como parte de una estrategia nacional de empleo", "url"=>"/site/es/8-b-1/", "sort"=>"08bb01", "goal_number"=>"8", "target_number"=>"8.b", "global"=>{"name"=>"Existencia de una estrategia nacional organizada y en marcha para el empleo de los jóvenes, como estrategia independiente o como parte de una estrategia nacional de empleo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Gasto de las administraciones públicas autonómicas en fomento del empleo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Existencia de una estrategia nacional organizada y en marcha para el empleo de los jóvenes, como estrategia independiente o como parte de una estrategia nacional de empleo", "indicator_number"=>"8.b.1", "national_geographical_coverage"=>"", "page_content"=>"La C.A. de Euskadi cuenta con una Estrategia Vasca 2030 de Emancipación Juvenil y un \n<a href=\"https://www.euskadi.eus/planes-proyectos-juventud/web01-a2gazter/es/\">V Plan Joven 2022-2026</a>, \ncuyo objetivo es facilitar la transición de las personas jóvenes hacia la vida adulta, promoviendo su autonomía y su incorporación al ámbito laboral, especialmente de aquellas alejadas actualmente del mercado de trabajo.\n\n\nLa <a href=\"https://www.euskadi.eus/planes-informes-planificacion-innovacion-empleo/web01-s2enple/es/\">Estrategia Vasca de Empleo 2030 los Planes Estratégicos de Empleo</a> \ntambién incluyen líneas de actuación específicas para fomentar el empleo juvenil, mediante programas de formación, incentivos a la contratación y apoyo al emprendimiento joven.\n", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Hacienda", "periodicity"=>"Anual", "url"=>"https://www.hacienda.gob.es/es-ES/Areas%20Tematicas/Financiacion%20Autonomica/Paginas/DatosPresupuestarios.aspx", "url_text"=>"Liquidación de los presupuestos de las comunidades autónomas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Gasto de las administraciones públicas autonómicas en fomento del empleo", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.b- De aquí a 2020, desarrollar y poner en marcha una estrategia mundial para el empleo de los jóvenes y aplicar el Pacto Mundial para el Empleo de la Organización Internacional del Trabajo", "definicion"=>"Proporción del gasto de la administración autonómica en fomento del empleo (política de gasto 24 de la clasificación funcional empleada en las liquidaciones presupuestarias de las comunidades autónomas) \n", "formula"=>"\n$$G_{fomento\\, empleo}^{t} = \\frac{G_{fomento\\, empleo}^{t}}{G^{t}} \\cdot 100$$\n\ndonde:\n\n$G_{fomento\\, empleo}^{t} =$ gasto en fomento del empleo en la liquidación de presupuestos consolidados de la comunidad autónoma (política de gasto 24 de la clasificación funcional) en el año $t$\n\n$G^{t} =$ gasto total en la liquidación de presupuestos consolidados de la comunidad autónoma en el año $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"\nLos datos consolidados de las liquidaciones presupuestarias de las comunidades autónomas  se toman depurados de IFL (intermediación financiera local) y PAC (política agrícola común)", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl objetivo del indicador 8.b.1 de los ODS es proporcionar una indicación del \nprogreso de los países en la resolución de las cuestiones relacionadas con el \nempleo juvenil. \n\nEn este sentido, se supone que haber adoptado oficialmente lo que puede \nreconocerse como una estrategia estructurada para el empleo juvenil \nsignificaría que un país prestaría mayor atención a los desafíos del mercado \nlaboral juvenil, en comparación con los países que no cuentan con una estrategia. \n\nDe hecho, el desarrollo de una estrategia de este tipo suele implicar una amplia \nparticipación y consulta/coordinación entre las diferentes partes interesadas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas pero aporta información complementaria.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-0b-01.pdf\">Metadatos 8-b-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-12", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Gasto de las administraciones públicas autonómicas en fomento del empleo", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.b- De aquí a 2020, desarrollar y poner en marcha una estrategia mundial para el empleo de los jóvenes y aplicar el Pacto Mundial para el Empleo de la Organización Internacional del Trabajo", "definicion"=>"Expenditure of the autonomous public administrations on employment promotion (expenditure policy \n24 of the functional classification used in the budgetary settlements of the autonomous communities) \nand total expenditure \n", "formula"=>"\n$$G_{employment\\, promotion}^{t} = \\frac{G_{employment\\, promotion}^{t}}{G^{t}} \\cdot 100$$\n\nwhere:\n\n$G_{employment\\, promotion}^{t} =$ expenditure on employment promotion in the settlement of consolidated budgets of the autonomous community (expenditure policy 24 of the functional classification) in year $t$\n\n$G^{t} =$ total expenditure in the settlement of consolidated budgets of the autonomous community in year $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"\nConsolidated data of the budgetary settlements of the autonomous communities are taken from IFL  (Local Financial Intermediation) and PAC (Common Agricultural Policy) ", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe purpose of SDG indicator 8.b.1 is to provide an indication of the progress of countries in addressing \nyouth employment issues. \n\nIn this respect, it is assumed that having officially adopted what can be recognised as a structured \nstrategy for youth employment would mean larger attention given by a country to youth labour market \nchallenges, compared to countries with no strategy. \n\nIn fact, the development of such a strategy usually entails broad participation of and consultation/\ncoordination among different stakeholders. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"", "comparabilidad"=>"The available indicator does not comply with the metadata of the United Nations indicator, but provides complementary information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-0b-01.pdf\">Metadata 8-b-1.pdf</a>", "informacion_interes"=>"", "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Gasto de las administraciones públicas autonómicas en fomento del empleo", "objetivo_global"=>"8- Promover el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo y el trabajo decente para todos", "meta_global"=>"8.b- De aquí a 2020, desarrollar y poner en marcha una estrategia mundial para el empleo de los jóvenes y aplicar el Pacto Mundial para el Empleo de la Organización Internacional del Trabajo", "definicion"=>"Administrazio autonomikoak enplegua sustatzeko egiten duen gastuaren proportzioa (autonomia erkidegoetako \naurrekontu-likidazioetan erabilitako sailkapen funtzionalaren 24. gastu-politika)\n", "formula"=>"\n$$G_{enplegu\\, sustapena}^{t} = \\frac{G_{enplegu\\, sustapena}^{t}}{G^{t}} \\cdot 100$$\n\nnon:\n\n$G_{enplegu\\, sustapena}^{t} =$ autonomia erkidegoko aurrekontu bateratuen likidazioan enplegua sustatzeko gastua (sailkapen funtzionalaren 24. gastu-politika) $t$ urtean\n\n$G^{t} =$ autonomia erkidegoko aurrekontu bateratuen likidazioko guztizko gastua $t$ urtean\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"\nAutonomia erkidegoetako aurrekontu-likidazioen datu bateratuak araztuta hartzen dira tokiko finantza-bitartekaritzatik  eta NPEtik (nekazaritza-politika erkidea)", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nGJHen 8.b.1 adierazlearen helburua da herrialdeen aurrerapenaren azalpen bat ematea gazteen enpleguarekin \nlotutako gaien ebazpenean. \n\nSuposatzen da gazteen enplegurako estrategia egituratu gisa aitortu daitekeen hori ofizialki ezarrita, \nherrialde batek arreta gehiago jarriko diela gazteen lan-merkatuaren erronkei, estrategiarik ez duten \nherrialdeekin alderatuta. \n\nEra horretarako estrategia bat garatzeak, gainera, sarritan alde interesdunen arteko partaidetza eta \nkontsulta/koordinazio handia inplikatzen du. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina informazio osagarria ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-08-0b-01.pdf\">Metadatuak 8-b-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.b: By 2020, develop and operationalize a global strategy for youth employment and implement the Global Jobs Pact of the International Labour Organization</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.b.1: Existence of a developed and operationalized national strategy for youth employment, as a distinct strategy or as part of a national employment strategy </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_CPA_YEMP - Existence of a developed and operationalized national strategy for youth employment, as a distinct strategy or as part of a national employment strategy [8.b.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>8.5.2; 8.6.1; 8.7.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p>The proposed methodology draws on:</p>\n<ol>\n  <li>Global policy instruments, notably:</li>\n</ol>\n<ul>\n  <li>Resolution on <em>The youth employment crisis: A call for action<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></em>, adopted at the 101<sup>st</sup> session of the International Labour Conference (ILC) in June 2012. In calling for vigorous, collective action to address an aggravated youth employment crisis, this resolution advocates for a multi-pronged approach with policy measures that are context-specific and integrated, entailing strategies which bring together in a coherent manner a variety of instruments to increase the demand, enhance the supply and improve matching in youth labour markets.</li>\n  <li><em>Recovering from the crisis: A Global Jobs Pact<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></em> adopted by the ILC at its June 2009 session. Based on the ILO&#x2019;s Decent Work Agenda, the Global Jobs Pact presents an integrated portfolio of policies that puts employment and social protection at the centre of crisis response, recognising the critical role of participation and social dialogue.</li>\n</ul>\n<ol>\n  <li>ILO databases:</li>\n</ol>\n<ul>\n  <li>International monitoring of youth employment policies was carried out over the period 2010-2012 by the Youth Employment Network (YEN) &#x2013; a partnership between the ILO, United Nations and World Bank &#x2013; utilising a questionnaire sent to national authorities. This evolved into YouthPOL<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup>, an inventory of youth employment policies and programmes maintained by the ILO (65 countries covered to date). </li>\n  <li>The ILO also maintains EmPol, a dataset of broader national employment policies (143 countries covered).</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Available online at: https://www.ilo.org/ilc/ILCSessions/101stSession/texts-adopted/WCMS_185950/lang--en/index.htm <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> https://www.ilo.org/ilc/ILCSessions/98thSession/texts/WCMS_115076/lang--en/index.htm <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> <a href=\"http://www.ilo.org/dyn/youthpol/en/f?p=30850:1001:0::NO\">http://www.ilo.org/dyn/youthpol/en/f?p=30850:1001:0::NO</a><u>:::</u> <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Categorical variable with values possible values of 0, 1, 2 or 3.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<ol>\n  <li><em>Global survey for data collection</em>: Requesting responsible national entities to provide relevant information and support documents; a survey questionnaire is developed and administered by the ILO with biennial frequency to assess progress.This will be complemented by regular information and updates from ILO country offices on development, adoption and implementation of youth employment policies in countries covered by these offices, every year.</li>\n</ol>\n<p> </p>\n<ol>\n  <li><em>Data compilation</em>: by the ILO; disseminated through ILOSTAT, a new repository dedicated to Indicator 8.b.1 and the active use of YouthPOL, EmPol and other databases (e.g. NATLEX &#x2013; the ILO database of national labour, social security and related human rights legislation), as appropriate. </li>\n</ol>\n<p> </p>\n<ol>\n  <li><em>Data validation</em>: Regular quality checks are conducted on all data, in particular when: (i) an already available document has not been directly provided by the government itself; (ii) it is unclear if the strategy and related action plan have been officially adopted; or (iii) there are doubts regarding the implementation of the strategy. </li>\n</ol>\n<p> </p>", "COLL_METHOD__GLOBAL"=>"<p>See section 3.a.</p>", "FREQ_COLL__GLOBAL"=>"<ul>\n  <li>Proposed methodology to the ICLS: October 2018 </li>\n  <li>Refinement of survey questionnaire and technical guidelines: October &#x2013; November 2018 </li>\n  <li>Final testing: November 2018 - February 2019 </li>\n  <li>Regular administration of the survey: started early 2019 </li>\n</ul>\n<p> </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Annual</p>", "DATA_SOURCE__GLOBAL"=>"<p>National entities (ministries or other government agencies) responsible for development, employment and youth policies. The ILO maintains a roster of national actors involved in the monitoring process. </p>", "COMPILING_ORG__GLOBAL"=>"<p>ILO</p>", "INST_MANDATE__GLOBAL"=>"<p>The Department of Statistics (STATISTICS) works to provide relevant, timely and reliable labour statistics, to develop international standards for better measurement of labour issues and enhanced international comparability, and to help member States develop and improve their labour statistics. </p>\n<p>The Employment Policy Department (EMPLOYMENT) is responsible for promoting full and productive employment by developing integrated employment, development and skills policies (ILO, 2012) that are inclusive, gender sensitive and sustainable. The department is mandated to coordinate ILO efforts to promote decent job opportunities for young women and men; over the years, it has supported the formulation, implementation and review of national youth employment strategies and action plans in different countries and regions (ILO, 2008; ILO, 2015). This type of targeted action and related achievements have been included in the ILO programming framework and performance system. </p>\n<p>The ILO supports its constituents and other development stakeholders through knowledge and capacity building as well as through policy advocacy and advice. The list of references at the end of this note offers examples of recent major ILO contributions to knowledge building on youth employment and youth employment policy (ILO, 2017).</p>", "RATIONALE__GLOBAL"=>"<p>The purpose of SDG indicator 8.b.1 is to provide an indication of the progress of countries in addressing youth employment issues. In this respect, it is assumed that having officially adopted what can be recognised as a structured strategy for youth employment would mean larger attention given by a country to youth labour market challenges, compared to countries with no strategy. In fact, the development of such a strategy usually entails broad participation of and consultation/coordination among different stakeholders.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Governments may have <em>de facto</em> national strategies for youth employment, but lack an officially adopted <em>de jure</em> document. For SDG 8.b.1 monitoring purposes only what emerges from <em>de jure</em> documents is considered.</p>", "DATA_COMP__GLOBAL"=>"<p>The information and documents provided by national authorities will be analysed by the ILO to classify countries according to this grid: </p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Value</strong></p>\n      </td>\n      <td>\n        <p><strong>Description</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Missing value</p>\n      </td>\n      <td>\n        <p>No information available to assess the existence of a national strategy for youth employment.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>The country has not developed any national strategy for youth employment or taken steps to develop or adopt one.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>The country is in the process of developing a national strategy for youth employment. </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>The country has developed and adopted a national strategy for youth employment</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>The country has operationalised a national strategy for youth employment. </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>In all cases, the grid refers to a national strategy for youth employment as a distinct strategy or as part of a national employment strategy.</p>\n<p>Missing values (i.e. no response/unknown) are noted as such. They are omitted from the final global and regional breakdown: proportions are only calculated on the basis of received responses. However, the global and regional response rates will be indicated.</p>\n<p>The possible development of metadata notes complementing the grid is being considered. Among other aspects, these notes may refer to the measures and provisions in place, and would also consider the involvement of national constituents in the development and operationalization of the strategies. </p>\n<p>The ILO may also envisage to conduct a more detailed analysis of selected country documents for purposes which go beyond the scope of SDG monitoring, in order to gather insights on institutional and operational matters in national efforts for youth employment.</p>\n<p>The following steps are followed in developing the indicator methodology: </p>\n<ol>\n  <li>Examination of relevant policy instruments, including the above-mentioned <em>Call for action </em>and <em>Global Jobs Pact.</em> Adopted by ILO tripartite constituents, these documents provide a sound framework for defining SDG indicator 8.b.1.</li>\n  <li>Review of ILO databases on employment and youth employment policies (EmPOL and YouthPOL), maintained by the Employment Policy Department. </li>\n  <li>A methodology for defining, measuring and validating this indicator (the present document).</li>\n  <li>A survey instrument (questionnaire) to collect national-level information on youth employment policies from national entities. The information is used to determine if countries have developed and operationalized a national strategy for youth employment as a stand-alone strategy or as part of a national employment or sectoral strategy, in line with the above-mentioned ILC resolutions. </li>\n  <li>Technical guidelines for data providers and compilers, along with the above-mentioned questionnaire and detailed notes.</li>\n</ol>\n<p>Consultations with pertinent ministries and social partners&#x2019; representatives are held throughout the process.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>For countries that have not responded in the current survey round, the last country responses to the ILO survey are reported. The underlying assumption is that policy changes are unlikely to occur each year and therefore recent responses to the ILO survey remain valid.</p>", "REG_AGG__GLOBAL"=>"<p>None</p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The methodology is mainly based on a methodology used for the ILO youth employment policies database (YouthPOL) that covers 170 countries in 5 regions, including: Colombia, Mexico, Jordan, Australia, Cambodia, China, Republic of Korea, Philippines, Germany, Kazakhstan, Russian Federation, Italy, Spain, and Ukraine. The data can be accessed <a href=\"https://www.ilo.org/dyn/youthpol/en/f?p=30850:1001:0::NO:::\" target=\"_blank\"><u>in this link.</u></a> The methodology is based on a simplified version of the questionnaires used in this database.</p>\n<p> </p>\n<p><strong>Time series:</strong> This submission covers data from 2019 to 2020. </p>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p>International Conference of Labour Statisticians, 20<sup>th</sup>. Session. Resolution III <a href=\"http://www.ilo.org/20thicls\" target=\"_blank\"><u>www.ilo.org/20thicls</u></a></p>\n<p>International Labour Office (ILO). 2008. <em>Guide for the preparation of National action Plans on Youth Employment. </em>(Geneva, ILO) </p>\n<p>_. 2012. <em>Guide for the formulation of national employment policies. </em>(Geneva).</p>\n<p>_. 2015. <em>Comparative Analysis of Policies for Youth Employment in Asia and the Pacific. </em>(Geneva).</p>\n<p>_. 2017. Global employment trends for youth 2017: paths to a better working future (Geneva) </p>\n<p>O&#x2019;higgins, N. 2017. Rising to the youth employment challenge: new evidence on key policy issues (Geneva, ILO).</p>", "indicator_sort_order"=>"08-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"9.1.1", "slug"=>"9-1-1", "name"=>"Proporción de la población rural que vive a menos de 2 km de una carretera transitable todo el año", "url"=>"/site/es/9-1-1/", "sort"=>"090101", "goal_number"=>"9", "target_number"=>"9.1", "global"=>{"name"=>"Proporción de la población rural que vive a menos de 2 km de una carretera transitable todo el año"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Territorio histórico", "value"=>"Araba/Álava"}, {"field"=>"Territorio histórico", "value"=>"Gipuzkoa"}, {"field"=>"Territorio histórico", "value"=>"Bizkaia"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población rural que vive a menos de 2 km de una carretera transitable todo el año", "graph_titles"=>[], "graph_type"=>"bar", "indicator_name"=>"Proporción de la población rural que vive a menos de 2 km de una carretera transitable todo el año", "indicator_number"=>"9.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Geoeuskadi", "url"=>"https://www.geo.euskadi.eus/", "url_text"=>"Geoeuskadi", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de la población rural que vive a menos de 2 km de una carretera transitable todo el año", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.1- Desarrollar infraestructuras fiables, sostenibles, resilientes y de calidad, incluidas infraestructuras regionales y transfronterizas, para apoyar el desarrollo económico y el bienestar humano, haciendo especial hincapié en el acceso asequible y equitativo para todos", "definicion"=>"El Índice de Acceso Rural (IAR) mide la proporción de la población rural que vive a una distancia máxima  de 2 km de una carretera transitable durante todo el año.", "formula"=>"\n$$IAR^{t} = \\frac{PR_{carretera\\, transitable}^{t}}{PR^{t}} \\cdot 100$$\n\ndonde:\n\n$PR_{carretera\\, transitable}^{t} =$ población rural que vive a una distancia máxima de \n2 km de una carretera transitable durante todo el año en el año $t$\n\n$PR^{t} =$ población rural en el año $t$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nEl Índice de Acceso Rural (IAR), desarrollado originalmente por el Banco Mundial \nen 2006, es uno de los indicadores de desarrollo global más importantes en el \nsector del transporte, y proporciona un indicador sólido, claramente comprensible \ny conceptualmente coherente en todos los países. Mide la proporción de personas que viven \nen zonas rurales que tienen acceso a una carretera transitable durante todo \nel año a una distancia aproximadamente caminable de 2 kilómetros (km). \n\nEntre otros factores, la conectividad del transporte es una parte esencial del entorno \npropicio para un crecimiento inclusivo y sostenido. En los países en desarrollo, \nen particular en África, la gran mayoría de la producción agrícola sigue siendo de \npequeños agricultores con un acceso limitado a los mercados locales, regionales o mundiales. \n\nLas empresas manufactureras aisladas y otras empresas locales (excepto las relacionadas \ncon la minería) a menudo quedan rezagadas en el mercado mundial. La conectividad \nlimitada del transporte también es una limitación crítica para el acceso a los servicios \nsociales y administrativos, especialmente en las zonas rurales donde vive la mayoría \nde los pobres.\n\nEl acceso rural es clave para liberar el potencial económico desaprovechado y erradicar \nla pobreza en muchos países en desarrollo. A corto plazo, los costos de transporte y el tiempo \nde viaje pueden reducirse mediante la mejora de las condiciones de las carreteras. A largo \nplazo, la productividad agrícola aumentará y las empresas serán más rentables con la \ncreación de más puestos de trabajo, lo que en última instancia ayudará a aliviar la pobreza.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-01-01.pdf\">Metadatos 9-1-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de la población rural que vive a menos de 2 km de una carretera transitable todo el año", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.1- Desarrollar infraestructuras fiables, sostenibles, resilientes y de calidad, incluidas infraestructuras regionales y transfronterizas, para apoyar el desarrollo económico y el bienestar humano, haciendo especial hincapié en el acceso asequible y equitativo para todos", "definicion"=>"The indicator (commonly known as the Rural Access Index or RAI) measures the share of a country’s rural  population that lives within 2 km of an all-season road.", "formula"=>"\n$$IAR^{t} = \\frac{PR_{all\\, season\\, road}^{t}}{PR^{t}} \\cdot 100$$\n\nwhere:\n\n$PR_{all\\, season\\, road}^{t} =$ rural population that lives within 2 km of an all-season road in year $t$\n\n$PR^{t} =$ rural population in year $t$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nThe Rural Access Index (RAI), originally developed by the World Bank in 2006, is among the most \nimportant global development indicators in the transport sector, providing a strong, clearly \nunderstandable and conceptually consistent indicator across countries. It measures the proportion of \npeople living in rural areas who have access to an all-season road within a walking distance of \napproximately 2 kilometres (km). \n\nAmong other factors, transport connectivity is an essential part of the enabling environment for inclusive \nand sustained growth. In developing countries, particularly in Africa, the vast majority of agricultural \nproduction remains smallholder farming with limited access to local, regional, or global markets. \n\nIsolated manufacturing and other local businesses (except for those related to mining) often lag behind \nin the   global market. Limited transport connectivity is also a critical constraint to accessing social \nand administrative services, especially in rural areas where the majority of the poor live. \n\nRural access is key to unleashing untapped economic potential and eradicating poverty in many \ndeveloping countries. In the short term, transport costs and travel time can be reduced by improved road \nconditions. Over the longer term, agricultural productivity will be increased, and firms will become more \nprofitable with the creation of more jobs, eventually helping to alleviate poverty. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-01-01.pdf\">Metadata 9-1-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de la población rural que vive a menos de 2 km de una carretera transitable todo el año", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.1- Desarrollar infraestructuras fiables, sostenibles, resilientes y de calidad, incluidas infraestructuras regionales y transfronterizas, para apoyar el desarrollo económico y el bienestar humano, haciendo especial hincapié en el acceso asequible y equitativo para todos", "definicion"=>"\nAdierazleak, normalean Landa Sarbidearen Indizea bezala ezagutzen denak, urte osoan zehar ibiltzeko moduko errepide batetik gehienez 2 km-ra bizi den landa-eremuko biztanleriaren proportzioa neurtzen du. ", "formula"=>"\n$$IAR^{t} = \\frac{PR_{ibiltzeko\\, moduko\\, errepidea}^{t}}{PR^{t}} \\cdot 100$$\n\nnon:\n\n$PR_{ibiltzeko\\, moduko\\, errepidea}^{t} =$ urte osoan zehar ibiltzeko moduko errepide batetik gehienez 2 km-ra bizi den landa-eremuko biztanleria $t$ urtean\n\n$PR^{t} =$ landa-eremuko biztanleria $t$ urtean\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"\nLanda Sarbidearen Indizea (LSI), 2006an Munduko Bankuak garatua, garraioaren sektorean garapen orokorraren \nadierazle garrantzitsuenetakoa da, eta herrialde guztietan kontzeptualki koherentea, ulerterraza eta sendoa \nden adierazle bat ematen du. Landaguneetan bizi diren eta urte osoan zehar oinez 2 kilometroko distantzian \nibiltzeko moduko errepide baterako sarbidea duten pertsonen proportzioa da. \n\nBeste faktore batzuen artean, garraioaren konektibitatea funtsezko alderdia da hazkunde inklusibo eta \njarraitua sustatzen duen inguru batean. Garapen-bidean dauden herrialdeetan, bereziki Afrikan, \nnekazaritza-ekoizpenaren zati handiena oraindik ere tokiko, eskualdeko eta munduko merkatuetara sarbide \nmugatua duten nekazari txikiei dagokie. \n\nManufaktura-enpresa isolatuak eta tokiko beste enpresa batzuk (meatzaritzarekin lotutakoak salbu) sarritan \natzeratuta geratzen dira mundu-mailako merkatuan.  Garraioaren konektibitate mugatua ere muga kritikoa da \ngizarte- eta administrazio-zerbitzuak eskuratzeko, batez ere landaguneetan, bertan bizi direlarik pobre gehienak. \n\nLandagunea funtsezkoa da ondo aprobetxatu gabeko ahalmen ekonomikoa askatzeko eta garapen-bidean dauden \nherrialde askotan pobrezia ezabatzeko. Epe laburrera, garraio-kostuak eta bidaia-denborak murriztu egin \ndaitezke, errepideetan baldintzak hobetuta. Epe luzera, nekazaritzako ekoizpena areagotu egingo da, eta \nenpresak errentagarriagoak izango dira lanpostu gehiago sortuta. Horrek, azken batean, pobrezia arintzen \nlagunduko du. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"", "comparabilidad"=>"EAEko adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-01-01.pdf\">Metadatuak 9-1-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure, including regional and transborder infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 9.1.1: Proportion of the rural population who live within 2 km of an all-season road</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SP_ROD_R2KM - Proportion of the rural population who live within 2 km of an all-season road [9.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank Group</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>The indicator (commonly known as the Rural Access Index or RAI) measures the share of a country&#x2019;s rural population that lives within 2 km of an all-season road.</p>\n<p><strong>Concepts:</strong></p>\n<p>The indicator is measured by combining three sets of geospatial data: where people live, the spatial distribution of the road network, and road passability. The use of spatial data has various advantages. It can help ensure consistency across countries. The level of spatial resolution is broadly the same regardless of the size of the country or subnational boundaries. Any given norm of connectivity (for example, 2 km distance from a road) is uniquely and unambiguously applied for all countries. </p>\n<p>Population Distribution - Quality population distribution data are essential for correct measurement of rural access. In some countries, census data is available in a geospatially detailed, reliable format. For other countries, population distribution datasets have been developed by the international research community, interpreting subnational census data through various modelling techniques. For the RAI, <a href=\"https://www.worldpop.org/\">WorldPop</a> data has been found to provide a reliable estimate. That estimate can also be refined through engagement between the national statistics offices and WorldPop to reconcile data at the level of enumeration areas.</p>\n<p>Rural-Urban Definition &#x2013; Related to population distribution data, an important challenge facing the index is the need for a consistent and reliable urban and rural definition to exclude urban areas from the calculation. The inclusion of urban areas would create a substantial upward bias in the RAI, because most urban residents have access to a road, no matter how it is defined. Ideally, spatial data determining urban-rural boundaries are needed at the same level of resolution as the population data. As such data may rely on different sources and definitions across different countries. Globally produced urban extents may be used, such as the <a href=\"https://sedac.ciesin.columbia.edu/data/collection/grump-v1\">Global Urban Rural Mapping Project</a> v1 Urban Extent Polygons.</p>\n<p>Road Network Data &#x2013; Data on road location or alignment can come from a number of sources. Ideally government data are used because they are consistent with the official road network for which road agencies are responsible. The government data are also relatively easily merged with other operational databases in the road sector. In countries where the road location data may not be detailed enough or entirely missing or where there is a large unclassified network, alternative data sources may be used, such as the open source <a href=\"http://www.openstreetmap.org\">OpenStreetMap</a>. </p>\n<p>Road Condition Data &#x2013; The principle of the &#x201C;all-season&#x201D; road network remains central to the original concept of measuring the RAI. An &#x201C;all-season road&#x201D; is defined as a road that is motorable all year round by the prevailing means of rural transport (often a pick-up or a truck which does not have four-wheel-drive). Predictable interruptions of short duration during inclement weather (e.g. heavy rainfall) are accepted, particularly on low volume roads. A road that it is likely to be impassable to the prevailing means of rural transport for a total of 7 days or more per year is not regarded as all-season. Note that some roads agencies use the term &#x201C;all-weather&#x201D; to describe their roads, however &#x201C;all-weather&#x201D; typically means &#x201C;paved&#x201D; and should not be confused with &#x201C;all-season&#x201D; which can include unpaved roads too.</p>\n<p>It is important to determine whether access to facilities and services is available all year round, and hence the possibility of the road throughout the year is an essential factor in this aspect of contributing to economic growth and poverty reduction. Information on the road conditions is typically maintained by national road agencies as part of their operational responsibilities. </p>\n<p>The traditional road inventory survey can collect data on road condition, including the International Roughness Index (IRI), at a high level of information quality, to determine whether a road is &#x201C;all-season&#x201D;. For the purpose of the RAI, for instance, the road condition threshold can be set at an IRI of less than 6 meters/km for paved roads, and an IRI of less than 13 meters/km for unpaved roads. When IRI is not available, other types of condition assessment may be used if comparable. There are smartphone-based software using GPS and accelerometer that can map roads, estimate road roughness, and identify which rural roads are all-seasonal. The road condition thresholds should be calibrated to the local conditions, i.e. checks should be made to determine that paved roads in poor condition are largely not all-season, and that unpaved roads in fair or poor condition are largely not all-season. The parameters can be adjusted accordingly to the local conditions, based on a systematic and documented study.</p>\n<p>In the event that accurate road condition data is not available, then, accessibility factors may provide an alternative means to road condition for identifying &#x201C;all-season&#x201D; roads. Such factors may not require ground measurements of road condition but are those which determine the likelihood of a road being all-season, or the risk of a road being inaccessible, given all other available geospatial data and information related to the road sector. Ideally, the used data change over time but can be time-invariant. In such a case, the estimated indicator will be fixed and not change over time. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data on population distribution are typically sourced from WorldPop or national census results, depending on the reliability and spatial granularity of country systems. Road location and quality data are provided by the national road agencies responsible for their upkeep. Accessibility factors are defined by national roads agencies in collaboration with national statistics offices and other agencies as appropriate.</p>", "COLL_METHOD__GLOBAL"=>"<p>A partnership between NSOs, national road agencies, and the World Bank as custodian agency is necessary to effectively generate RAI results. In some countries, World Bank transport staff work closely with national agencies, with data generation and calculation of the RAI built into a broader engagement. In other countries, NSOs and road agencies provide RAI results directly to the World Bank as custodian. </p>", "FREQ_COLL__GLOBAL"=>"<p>Source collection is ongoing by the Transport Global Practice of the World Bank Group in coordination with NSOs and national road agencies. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The World Bank Group is committed to releasing available RAI updates on a yearly basis.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The World Bank Group typically receives data from national road agencies and NSOs directly. As the underlying calculation relies primarily on road agency data, such agencies are generally the primary counterpart for RAI data. </p>", "COMPILING_ORG__GLOBAL"=>"<p>Within the World Bank Group, the Transport Global Practice is providing assistance to calculate the indicator, as needed, and in charge of the validation of RAI data and results. The Global Practice archives the datasets obtained from NSOs and road agencies and then harmonizes them, applying common methodologies. Where NSOs and road agencies calculate the RAI using their own data and methodologies, the Transport Global Practice is responsible for reviewing the underlying data and assumptions and validating the results for inclusion in the global SDG dataset. The objective is to ensure that the data generated, curated, and disseminated by the World Bank Group are up to date, meet high-quality standards, and are well documented and consistent across dissemination channels. World Bank Group country staff works in close collaboration with national statistical authorities on the data collection and dissemination process. </p>", "INST_MANDATE__GLOBAL"=>"<p>The Rural Access Index was one of several Transport Headline Indicators endorsed by the World Bank Transport Sector Board in 2003. The index has been adopted for the Results Measurement System (RMS)2 of the 14th round of the International Development Association (IDA-14) which was launched in July, 2005. The Index was developed in response to the consensus led by borrowers that it identifies an important priority for poverty reduction strategies in view of the established links between physical isolation and poverty. The Rural Access Index provides a consistent basis for estimating the proportion of the rural population which has adequate access to the transport system. It can help to inform policies and strategies which ensure that the rewards of development are distributed more equitably to the rural population.</p>", "RATIONALE__GLOBAL"=>"<p>Among other factors, transport connectivity is an essential part of the enabling environment for inclusive and sustained growth. In developing countries, particularly in Africa, the vast majority of agricultural production remains smallholder farming with limited access to local, regional, or global markets. Isolated manufacturing and other local businesses (except for those related to mining) often lag behind in the global market. Limited transport connectivity is also a critical constraint to accessing social and administrative services, especially in rural areas where the majority of the poor live. </p>\n<p>Rural access is key to unleashing untapped economic potential and eradicating poverty in many developing countries. In the short term, transport costs and travel time can be reduced by improved road conditions. Over the longer term, agricultural productivity will be increased, and firms will become more profitable with the creation of more jobs, eventually helping to alleviate poverty. </p>\n<p>To make good investments, quality data are required. Since resources are limited, it is essential to understand where the most critical unmet needs exist, and monitor efforts made over time. In the transport sector, there are few global indicators. The quality of roads is often unknown and a matter of concern in developing countries. In Africa, the Road Management Initiative, started by the Africa Transport Policy Program in the late 1990s, developed a road sector database, which includes road network condition data such as the share of roads in good or bad condition. But this database is largely outdated and insufficient. </p>\n<p>The Rural Access Index (RAI), originally developed by the World Bank in 2006, is among the most important global development indicators in the transport sector, providing a strong, clearly understandable and conceptually consistent indicator across countries. It measures the proportion of people living in rural areas who have access to an all-season road within a walking distance of approximately 2 kilometres (km). Although the underlying methodology has been updated to leverage additional sources of data, the RAI remains the most widely accepted metric for tracking access to transport in rural areas. </p>\n<p>The RAI has four primary benefits: sustainability due to its reliance on already existing data, consistency in methodology across countries and time, simplicity in understanding, and operational relevance for the government agencies responsible for generating and aggregating the underlying data.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator relies substantially on data collected by road agencies and national statistics offices for their operational work. As such, its update is dependent on the frequency of update of the road condition surveys and national census. When these data sets are not from the same year, the basic principle to be followed is that a more stable data set should be used with more flexibility. For instance, a national rural roads program could dramatically improve the quality of roads in a certain locality in a relatively short term, while population data are fairly stable over five years. In such a case, the road quality data would be considered as an anchor, with the closest or adjusted population data applied.</p>\n<p>The indicator depends heavily on the quality and extent of the underlying spatial data. The extent of the road network data, and how well it reflects the reality on the ground, can be a particular issue. Verification against open source data and satellite data where possible is recommended. More data are always better. Efforts should also be made to collect detailed road data, including on tertiary or feeder roads, which may not be covered in the existing spatial road network data regardless of whether government or open data sources are used. If condition data is not available, then use of accessibility factors can be considered.</p>\n<p>The 2 km norm of access may not be as applicable in all areas. In extremely mountainous countries, there has been significant research into walking times and preparation of accessibility maps that take into account mountainous terrain, locations of rivers and footbridges. However, for global consistency purposes and comparability across countries, the 2 km distance threshold has been maintained (equivalent to a 20-30 minute walk in most regions).</p>\n<p>While the RAI provides an objective benchmark for assessing access to transport in rural areas, &#x201C;universal&#x201D; road access of 100% should not be set as a target. First mile or last mile connectivity is not intended to imply all-season road access. Connectivity can be a system of engineered trails and footbridges as in Nepal, or designated river navigation channels and jetties as in Bangladesh, or a system of solar lit beacons and marked desert trails in Sudan. There are many more such examples: most rural settlements in the Amazon, Orinoco, Congo and Upper Nile River basins, have no or limited hinterland road access. The outer islands of the archipelagos of Indonesia and Philippines and South Pacific Islands rely heavily on coastal shipping. Similarly, vast regions of Siberia, the Russian steppes and Mongolia depend on rail. The deltas of Mekong, the Ganges-Brahmaputra, Indus rely on water transport. It is simply not possible, nor desirable, to address last mile connectivity by all-season rural roads in many situations. In addition, in South Asia and growingly in Africa, motorcycles and autorickshaws are the mainstay of personal mobility and account for a growing share of rural commerce. &#x201C;All-season&#x201D; for motorcycles and autorickshaws is not the same as &#x201C;all-season&#x201D; for 4-wheeled vehicles. And in the not too distant future, self-driving all-terrain vehicles, or drones, could provide an important transport service. As a global benchmark, however, the RAI should be considered as a starting point to begin discussions of all season access.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated by overlying three basic geospatial datasets: population distribution, road location, and road passability. The RAI is calculated as the rural population within a 2 km buffer of a good road divided by the total rural population of the country. </p>\n<p>First, the spatial distribution of the rural population needs to be determined. This involves obtaining the population dataset for the country, either from country sources or global datasets such as WorldPop. </p>\n<p>Next, the road network should be merged with road condition assessments, either in terms of IRI if available, or visual assessment. Those roads with a quality not meeting the threshold of the RAI (not providing &#x201C;all-season&#x201D; access) should be excluded. In general, the RAI adopts a road condition threshold is generally set at an IRI of less than 6 meters/km for paved roads and an IRI of less than 13 meters/km for unpaved roads. If IRI is unavailable, alternative assessments of road condition may be used, if comparable. If road condition data is not available, then accessibility factors can be defined to identify those roads at highest risk of impassability. A 2 km buffer should be generated around the road network meeting the condition threshold or highest risk. Urban areas should be removed from both the road data and the population data. </p>\n<p>Finally, the rural population living within the 2 km buffer should be calculated. The final RAI is determined by dividing this portion of the rural population with the total rural population.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>By spatially combining (i) global population distribution data, (ii) geo-referenced road alignment data, and (iii) road condition data, the RAI is virtually computed by spatial software. The methodology ensures accuracy, consistency, and sustainability. The estimated RAIs are found to be broadly consistent with basic demographic indicators and with available household-level data.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>No gap filling is done to report national numbers.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>This is a country specific indicator and no aggregation is currently planned.</p>", "REG_AGG__GLOBAL"=>"<p>This is a country specific indicator and no aggregation is currently planned. As additional country level data becomes available aggregation may be possible at a supranational level.</p>", "DOC_METHOD__GLOBAL"=>"<p>The World Bank, as custodian agency, with support from the UK Department for International Development (DFID) and the Research for Community Access Partnership (ReCAP), has developed and published a full methodological document for the RAI, including detailed descriptions of various data sources, variations on the standard methodology, and a step-by-step guide. In addition, a GIS tool has been developed to calculate the RAI from provided data sets. These resources and others are being collected into an online portal for the Rural Access Index. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The quality of the estimates is managed through the Transport Global Practice. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Within the World Bank Group, the Transport Global Practice is in charge of the collection and validation of RAI data and results. The Global Practice archives the datasets obtained from NSOs and road agencies and then harmonizes them, applying common methodologies. The objective is to ensure that the data generated, curated, and disseminated by the World Bank Group are up to date, meet high-quality standards, and are well documented and consistent across dissemination channels. World Bank Group country staff works in close collaboration with national statistical authorities on the data collection and dissemination process.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Where NSOs and road agencies calculate the RAI using their own data and methodologies, the Transport Global Practice is responsible for reviewing the underlying data and assumptions and validating the results for inclusion in the global SDG dataset. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>As of 2019, data is readily available for more than 30 countries, with consultations ongoing for a number more. While data is available for some Asian and Latin American countries as well, Africa accounts for the largest share of the available information. Consultations are underway to engage with additional countries.</p>\n<p><strong>Time series:</strong></p>\n<p>Due to the long update cycle of national road condition surveys, the RAI is not expected to be updated on an annual basis, but instead aligned with national systems. Whenever the national road agencies update their road condition data, the indicator can be updated. Current data spans the period from 2009-2019, with 1-2 data points per country.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Due to its nature as a geospatially derived indicator, the RAI can be calculated at subnational levels down to the level of granularity of the underlying datasets. While the World Bank will only report country level results for SDG monitoring, subnational results can be calculated for country use.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Relying heavily on national data, differences in national systems undoubtedly are reflected in the top level indicator (including road quality classification, national census methodologies, etc.). Use of globally derived datasets such as WorldPop may result in somewhat different results from national data if the NSO has not engaged with WorldPop. However, an assessment of sample countries indicates that these discrepancies are likely limited in their impact of the overall result.</p>", "OTHER_DOC__GLOBAL"=>"<p>The guiding methodology for the RAI can be found at:</p>\n<p>World Bank. 2016. Measuring rural access: using new technologies (English). Washington, D.C.: World Bank Group. <a href=\"http://documents.worldbank.org/curated/en/367391472117815229/Measuring-rural-access-using-new-technologies\">http://documents.worldbank.org/curated/en/367391472117815229/Measuring-rural-access-using-new-technologies</a> </p>\n<p>More information on the RAI, including Supplemental Guidelines on the use of accessibility factors prepared in collaboration with ReCAP, correlations with poverty and other development indicators, and the latest data sets can be accessed on the World Bank Group&#x2019;s RAI data catalogue entry: <a href=\"https://datacatalog.worldbank.org/dataset/rural-access-index-rai\">https://datacatalog.worldbank.org/dataset/rural-access-index-rai</a></p>\n<p>The Sustainable Mobility for All initiative provides input and leverages the RAI in its global tracking framework. More information here: <a href=\"http://sum4all.org/\">http://sum4all.org/</a> </p>", "indicator_sort_order"=>"09-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"9.1.2", "slug"=>"9-1-2", "name"=>"Volumen de transporte de pasajeros y carga, desglosado por medio de transporte", "url"=>"/site/es/9-1-2/", "sort"=>"090102", "goal_number"=>"9", "target_number"=>"9.1", "global"=>{"name"=>"Volumen de transporte de pasajeros y carga, desglosado por medio de transporte"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Volumen de transporte de pasajeros y carga, desglosado por medio de transporte", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Volumen de transporte de pasajeros y carga, desglosado por medio de transporte", "indicator_number"=>"9.1.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nDesarrollar infraestructuras de calidad, fiables, sostenibles y resilientes, \nincluidas las infraestructuras regionales y transfronterizas, para apoyar el \ndesarrollo económico y el bienestar humano, con especial atención al acceso asequible \ny equitativo para todos. \n\nEl desarrollo de las infraestructuras transfronterizas se refleja mejor \nen los volúmenes de pasajeros y mercancías transportados por los Estados \nmiembros y las regiones. Un crecimiento de los volúmenes de pasajeros y mercancías \nmuestra un sólido desarrollo de las infraestructuras en los Estados y las \nregiones, junto con el consiguiente beneficio socioeconómico. \n\nEl transporte aéreo es especialmente importante no solo por los beneficios \neconómicos y laborales, sino también porque es uno de los únicos modos de \ntransporte en los que se puede confiar durante las emergencias y los brotes \nde enfermedades para hacer llegar rápidamente alimentos, medicamentos, personal \nmédico, vacunas y otros suministros a las personas afectadas en las zonas afectadas. \n\nAdemás, el seguimiento de cómo cambia con el tiempo la proporción de los volúmenes \nde mercancías no destinados a la carretera y la proporción de los volúmenes de \npasajeros correspondientes al transporte público permite obtener información \nsobre la sostenibilidad general del sistema de transporte mundial.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.1.2&seriesCode=IS_RDP_FRGVOL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=AIR\">Volumen de carga, por modo de transporte (toneladas-kilómetro) IS_RDP_FRGVOL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.1.2&seriesCode=IS_RDP_PFVOL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=AIR\">Volumen de pasajeros (pasajeros-kilómetros), por modo de transporte IS_RDP_PFVOL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.1.2&seriesCode=IS_RDP_PORFVOL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=SEA\">Tráfico portuario de contenedores, transporte marítimo (unidades equivalentes a veinte pies - TEUs) IS_RDP_PORFVOL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.1.2&seriesCode=IS_RDP_LULFRG&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=SEA\">Carga cargada y descargada, transporte marítimo (toneladas métricas) IS_RDP_LULFRG</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-01-02.pdf\">Metadatos 9-1-2.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nDevelop quality, reliable, sustainable and resilient infrastructure, including regional and trans-border \ninfrastructure, to support economic development and human well-being, with a focus on affordable and \nequitable access for all. \n\nTrans-border infrastructure development is best captured by passenger and freight volumes moved by Member \nStates and Regions. A growth in passenger and freight volumes shows a robust infrastructure development \nhappening in States and Regions along with the resultant socioeconomic benefit. \n\nAir Transport is particularly important not only for the economic and job benefits but also because \nit is one of the only mode of transport that can be relied on during emergencies and disease \noutbreaks to reach food, medicines, medical personnel, vaccines and other supplies speedily to the \naffected persons in the affected areas. \n\nIn addition, tracking how the non-road share of freight volumes, and the public transport share of \npassenger volumes, changes over time allows insights into the overall sustainability of the global \ntransport system. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.1.2&seriesCode=IS_RDP_FRGVOL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=AIR\">Freight volume, by mode of transport (tonne kilometres) IS_RDP_FRGVOL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.1.2&seriesCode=IS_RDP_PFVOL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=AIR\">Passenger volume (passenger kilometres), by mode of transport IS_RDP_PFVOL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.1.2&seriesCode=IS_RDP_PORFVOL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=SEA\">Container port traffic, maritime transport (twenty-foot equivalent units - TEUs) IS_RDP_PORFVOL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.1.2&seriesCode=IS_RDP_LULFRG&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=SEA\">Freight loaded and unloaded, maritime transport (metric tons) IS_RDP_LULFRG</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-01-02.pdf\">Metadata 9-1-2.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nKalitatezkoak, fidagarriak, jasangarriak eta erresilienteak diren azpiegiturak garatzea, eskualdeko eta \nmugaz gaindiko azpiegiturak barne, garapen ekonomikoa eta giza ongizatea babesteko, bereziki erreparatuz \nguztien eskura dagoen eta ekitatiboa den sarbideari. \n\nMugaz gaindiko azpiegituren garapena hobeto jasotzen da estatu-kideek eta eskualdeek garraiatutako \nbidaiari eta salgaien bolumenetan. Bidaiari eta salgaien bolumenen igoerak estatuetan eta eskualdeetan \nazpiegituren garapena sendoa izan dela adierazten du, horrek dakarren onura sozioekonomikoarekin. \n\nAireko garraioa bereziki garrantzitsua da ez soilik ekonomian eta lanean dituen onurengatik, baizik \neta baita ere larrialdietan eta gaixotasunen agerraldietan konfiantzako garraiobide bakarra delako \nelikagaiak, sendagaiak, medikuak, txertoak eta bestelako hornidurak helarazteko ukitutako guneetako pertsonei. \n\nGainera, errepidera bideratzen ez diren salgaien bolumenen proportzioa eta garraio publikoko bidaiarien \nbolumenen proportzioa denboraren poderioz nola aldatzen den ulertzeko jarraipena eginda, informazioa lor \ndaiteke mundu-mailako garraio-sistemaren jasangarritasun orokorrari buruz. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.1.2&seriesCode=IS_RDP_FRGVOL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=AIR\">Karga-bolumena, garraio-moduaren arabera (tonak-kilometroko) IS_RDP_FRGVOL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.1.2&seriesCode=IS_RDP_PFVOL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=AIR\">Bidaiarien bolumena (bidaiariak-kilometroak), garraio-moduaren arabera IS_RDP_PFVOL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.1.2&seriesCode=IS_RDP_PORFVOL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=SEA\">Edukiontzien portuko trafikoa, itsas garraioa (hogei oineko unitate baliokideak - TEUak) IS_RDP_PORFVOL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.1.2&seriesCode=IS_RDP_LULFRG&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=SEA\">Zama kargatua eta deskargatua, itsas garraioa (tona metrikoak) IS_RDP_LULFRG</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-01-02.pdf\">Metadatuak 9-1-2.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure, including regional and trans-border infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 9.1.2: Passenger and freight volumes, by mode of transport</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>IS_RDP_FRGVOL - Freight volume (tonne kilometres) [9.1.2]</p>\n<p>IS_RDP_LULFRG - Freight loaded and unloaded (metric tons) [9.1.2]</p>\n<p>IS_RDP_PFVOL - Passenger volume (passenger kilometres) [9.1.2]</p>\n<p>IS_RDP_PORFVOL - Container port traffic (twenty-foot equivalent units - TEUs) [9.1.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-05-24", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Civil Aviation Organization (ICAO); International Transport Forum (ITF); United Nations Conference on Trade and Development (UNCTAD).</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Civil Aviation Organization (ICAO); International Transport Forum (ITF); United Nations Conference on Trade and Development (UNCTAD).</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Passenger volumes are measured in passenger-kilometres while freight volumes are measured in tonne-kilometres, and broken down by mode of transport. For the purposes of monitoring this indicator, passenger-km data are split between aviation, road (broken down between passenger cars, buses and motorcycles) and rail, and tonne-km are split between aviation, road, rail and inland waterways. Maritime freight is measured in metric tons and container port traffic is measured in twenty-foot equivalent unit (TEU).</p>\n<p><strong>Concepts:</strong></p>\n<p>Aviation:</p>\n<p>The International Civil Aviation Organization (ICAO) through its Statistics Division has established standard methodologies and definitions to collect and report traffic (passenger and freight volume) data related to air transport. These standards and methodologies have been adopted by the 193 Member States of ICAO and also by the Industry stakeholders i.e. air carriers and airports. The data of ICAO is used by States and also the World Bank for its development indicators. ICAO uses Air Transport Reporting Forms A, AS, B and C to arrive at the passenger and freight volumes for air transport. The aviation data reported under indicator 9.1.2 is for scheduled traffic.</p>\n<p>Precise definition of all different concepts and metadata related to Air Transport Reporting Forms A, AS, B and C to arrive at the passenger and freight volumes for air transport, as approved by the ICAO Statistics Division and Member States can be found at the ICAO website given below -</p>\n<p><a href=\"http://www.icao.int/sustainability/pages/eap-sta-excel.aspx/\">http://www.icao.int/sustainability/pages/eap-sta-excel.aspx/</a>.</p>\n<p><em>Martime</em></p>\n<p><strong>Definitions:</strong></p>\n<p>International maritime freight is an indicator reflecting (1) the sum of international freight volumes loaded (exports) and unloaded (imports) at ports worldwide and measured in metric tonnes, and (2) container port traffic at world ports measured in twenty-foot equivalent unit (TEU).</p>\n<p>Data is collected by the UNCTAD secretariat from various sources, including industry, government and specialised maritime transport data providers and consultancies. Volumes are expressed in metric tonnes and twenty-foot equivalent unit (TEU).</p>\n<p>As data on international maritime freight volumes are not widely available, only the data in tonnes (rather than tonne-km) and at the regional level are reported.</p>\n<p>Data at country level are available for container port traffic measured in twenty-foot equivalent unit (TEU).</p>\n<p><strong>Concepts:</strong></p>\n<p>The UNCTAD secretariat collects and compiles the data from various websites and reports, including, by port and industry associations and authorities, national statistics offices, UN Monthly Bulletin of Statistics, governments, specialised agencies such as the International Energy Agency (IEA), the US Energy Information Administration (EIA), the Organization of the Petroleum Exporting Countries (OPEC), and British Petroleum (BP). Data is also collected from reports issued by maritime specialised sources such as Drewry Maritime Research (DMR), Clarksons Research Services (CRS), Dynamar, and Lloyd&#x2019;s List Intelligence (LLI).</p>\n<p>Road, Rail, Inland waterways</p>\n<p>For definitions of all relevant terms, the UNECE/ITF/Eurostat Glossary for Transport Statistics can be consulted. The 5<sup>th</sup> edition of this publication is available at https://unece.org/DAM/trans/main/wp6/pdfdocs/Glossary_for_Transport_Statistics_EN.pdf</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Aviaiton: Revenue Passenger-Kilometres (RPK) and Freight Tonne-Kilometres (FTK)</p>\n<p>Martime: Metric tonnes and twenty-foot equivalent unit (TEU).</p>\n<p>Road, Rail: </p>\n<p>Passenger-Kilometres (Pkm) and Tonne-Kilometres (Tkm)</p>\n<p><em>Inland Waterways: </em>Tonne-Kilometres (Tkm)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><em>Maritime: </em></p>\n<p>Regional and sub-regional level data based on UNSD classification.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Aviation</p>\n<p>ICAO Air Transport Reporting Forms approved by the Statistics Division of ICAO and its Member States has been used to define standards, methodologies and to collect aviation data since the 1950&apos;s. ICAO definitions and metadata is also used by the Aviation Industry as the basis of collecting data and conducting analysis.</p>\n<p><em>Maritime:</em></p>\n<p>The UNCTAD secretariat collects and compiles the data from various websites and reports, including, by port and industry associations and authorities, national statistics offices, UN Monthly Bulletin of Statistics, governments, specialised agencies such as the International Energy Agency (IEA), the US Energy Information Administration (EIA), the Organization of the Petroleum Exporting Countries (OPEC), and, British Petroleum (BP). Data is also collected from reports issued by maritime specialised sources such as Drewry Maritime Research (DMR), Clarksons Research Services (CRS), Dynamar, and Lloyd&#x2019;s List Intelligence (LLI).</p>\n<p><em>Road, Rail, Inland waterways:</em> </p>\n<p>The ITF runs transport models that are used to provide transport information for all regions.</p>", "COLL_METHOD__GLOBAL"=>"<p><em>Aviation: </em></p>\n<p>Official aviation statistics are reported on a regular basis by Member States to ICAO through Air Transport Reporting Forms.</p>\n<p><em>Maritime:</em></p>\n<p>Data are not based on a systematic reporting by countries and relies mainly on secondary sources that may vary over time. Official reporting by countries is very limited. Data for freight loaded and unloaded in metric tons areonly available at regional or sub-regional level.</p>\n<p>The UNCTAD secretariat is currently collaborating with a specialized data provider and UN-DESA to elaborate a standard methodology that is based on UN Comtrade data to generate annual data on maritime freight flows, at country level and for all UN member countries.</p>\n<p>Note: Data on international maritime freight excludes transhipments and domestic maritime freight volumes.</p>\n<p>Road, Rail, Inland waterways:</p>\n<p>Data come from the ITF Global Models.</p>\n<p>ITF (forthcoming), ITF Transport Outlook 2023, OECD Publishing, Paris </p>", "FREQ_COLL__GLOBAL"=>"<p>Aviation:</p>\n<p>Every year by the fall data for the previous year is available to ICAO Member States at a country level. </p>\n<p>Road/Rail/Inland waterways:</p>\n<p>There is no compilation of data submitted from the countries. Data comes from the ITF Global Models which are updated every two years. In the last iteration of the ITF Global Models, data are available for 2015, 2019, 2020 and 2022. 2021 data are an interpolation of 2020 and 2022 data.</p>\n<p>ITF (forthcoming), ITF Transport Outlook 2023, OECD Publishing, Paris</p>", "REL_CAL_POLICY__GLOBAL"=>"<p><em>Aviation:</em></p>\n<p>Data are collected on a regular basis and a high level of coverage is expected to be available by the fall following the reference year.</p>\n<p>Maritime:</p>\n<p>Data are collected for the reference year on-ongoing process. Data are published annually on-line on UNCTADstat and in the annual Review of Maritime Transport in September of each year.</p>\n<p>Road, Rail, Inland waterways:</p>\n<p>Data come from the ITF Global Models which are updated every two years.</p>\n<p>ITF (forthcoming), ITF Transport Outlook 2023, OECD Publishing, Paris </p>", "DATA_SOURCE__GLOBAL"=>"<p>Name:</p>\n<p>ICAO, ITF, UNCTAD</p>\n<p><em>Aviation :</em></p>\n<p>International Civil Aviation organisation (ICAO).</p>\n<p><em>Maritime:</em></p>\n<p>Name: United Nations Conference on Trade and Development (UNCTAD)</p>\n<p>Description: Data collected by UNCTAD from various sources, including government, industry and specialized maritime data sources and providers.</p>\n<p>Road, Rail, Inland waterway:</p>\n<p>Data are from ITF Global Model estimation.</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Civil Aviation organisation (ICAO)</p>\n<p>International Transport Forum (ITF)</p>\n<p>UN Trade and Development </p>", "INST_MANDATE__GLOBAL"=>"<p>ICAO:</p>\n<p>ICAO is funded and directed by 193 national governments to support their diplomacy and cooperation in air transport as signatory states to the Chicago Convention (1944). Its core function is to maintain an administrative and expert bureaucracy (the ICAO Secretariat) supporting these diplomatic interactions, and to research new air transport policy and standardization innovations as directed and endorsed by governments through the ICAO Assembly, or by the ICAO Council which the assembly elects.</p>\n<p>https://www.icao.int/about-icao/Pages/default.aspx</p>\n<p>UNCTAD:</p>\n<p>Established in 1964, the United Nations Conference on Trade and Development (UNCTAD), rebranded as UN Trade and Development since April 2024, published its annual Review of Maritime Transport for the first time in 1968. The publication is part of UNCTAD&apos;s research and analytical work in the field of maritime transport aimed at helping developing countries maximize their trade and investment opportunities and increase their participation in the world economy. It has been regularly reconfirmed in the quadrennial Ministerial Conferences, most recently by UNCTAD XIII in Doha (2012) and UNCTAD XIV in Nairobi (2016). The mandates emanating from these conferences have emphasized sustainable and resilient transport as priority action areas and established &#x201C;Sustainable and Climate Resilient Maritime Transport&#x201D; as an important thematic area n UNCTAD&#x2019;s work programme and the Review of Maritime Transport. </p>\n<p>ITF:</p>\n<p>The International Transport Forum (ITF) was created by Ministerial Declaration in Dublin in 2006 on the legal basis of the European Conference of Ministers of Transport (ECMT), itself established as an international organisation by treaty (Protocol) signed in Brussels on 17 October 1953. The objectives of the ITF are to serve as a global platform for discussion and prenegotiation of transport policy issues across all modes. Unique in its global and modal scope, the ITF works to foster a deeper understanding of the role of transport in economic growth, environmental sustainability and social inclusion. It aspires to raise the public profile of transport policy.</p>", "RATIONALE__GLOBAL"=>"<p>Develop quality, reliable, sustainable and resilient infrastructure, including regional and trans-border infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all. Trans-border infrastructure development is best captured by passenger and freight volumes moved by Member States and Regions. A growth in passenger and freight volumes shows a robust infrastructure development happening in States and Regions along with the resultant socio-economic benefit. Air Transport is particularly important not only for the economic and job benefits but also because it is one of the only mode of transport that can be relied on during emergencies and disease outbreaks to reach food, medicines, medical personnel, vaccines and other supplies speedily to the affected persons in the affected areas. In addition, tracking how the non-road share of freight volumes, and the public transport share of passenger volumes, changes over time allows insights into the overall sustainability of the global transport system.</p>\n<p><em>Aviation:</em></p>\n<p>Informed decision-making is the foundation upon which successful businesses are built. In a fast-growing industry like aviation, planners and investors require the most comprehensive, up-to-date, and reliable data. ICAO&#x2019;s aviation data/statistics programme is to provide accurate, reliable and consistent aviation data so that States, international organizations, aviation industry, tourism and other stakeholders can make better projections. The UN recognized ICAO as the central agency responsible for the collection, analysis, publication, standardization, improvement and dissemination of statistics pertaining to civil aviation.</p>\n<p><em>Maritime:</em></p>\n<p>The volume of international maritime freight and container port traffic movements provide an overall indication of the importance of port infrastructure for trade and development and may be relied upon to infer the quality and adequacy of seaports and their hinterland connections. Maritime transport is the dominant mode of international freight transport when flows are measured in volume terms. Behind the global and regional headline estimates, individual contributions vary by region and type of cargo, reflecting, among other factors, differences in countries&#x2019; economic structures, composition of trade, urbanization, levels of development, extent of integration into global trading networks, degree of participation in global supply chains, and the quality of transport infrastructure.</p>\n<p>World container port traffic reflects the importance of containerized trade and countries&#x2019; participation in global liner shipping networks and globalized manufacturing production processes.</p>\n<p>Road, Rail, Inland waterways:</p>\n<p>The International Transport Forum has developed a set of modelling tools to build its own forward-looking scenarios of transport activity. Covering all modes of transport, freight and passenger, the tools are unified under a single framework.</p>\n<p><img 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\"></p>\n<p>For passenger volumes, the following models are used to generate the data: the urban passenger transport model and the non-urban passenger transport model.</p>\n<p>The urban passenger transport model is a strategic tool to test the impacts of policies and technology trends on urban travel demand, related CO2 emissions and accessibility indicators.</p>\n<p>The non-urban passenger transport model is a strategic tool that tests the impacts of multiple policies and trends on the non-urban passenger sector. </p>\n<p>For freight volumes, the non-urban freight transport model is used to generate the data. The non-urban freight transport model assesses and provides scenario forecasts for freight flows around the globe. It is a network model that assigns freight flows of all major transport modes to specific routes, modes, and network links.</p>\n<p>The ITF Modelling Framework is available at <a href=\"https://www.itf-oecd.org/itf-modelling-framework-1\">The ITF Modelling Framework</a>.</p>", "REC_USE_LIM__GLOBAL"=>"<p><em>Aviation:</em></p>\n<p>Coverage for aviation is for all ICAO 193 Member States.</p>\n<p><em>Maritime: </em></p>\n<p>Freight loaded and unloaded, maritime transport: Coverage only at regional and sub-regional level.</p>\n<p>Container port traffic, maritime transport: Coverage at regional, sub-regional and member state level. Totals may conceal the fact that some minor ports are not included. The data includes container port throughput for ports with time series that are complete since 2010 or that can be made complete by repeating an observation for a maximum of three years after the original observation. This means that on country level time series are comparable over time but that the coverage of ports may differ between countries.</p>\n<p>Road, Rail, Inland waterways:</p>\n<p>Coverage at regional and sub-regional level.</p>", "DATA_COMP__GLOBAL"=>"<p>Aviation</p>\n<p>The aviation passenger and freight volumes are reported for the air carriers through ICAO Air Transport Reporting Forms and grouped by Member States of ICAO.</p>\n<p>Road/Rail/Inland waterways</p>\n<p><u>Urban passenger transport model</u></p>\n<p>The model is designed as a systems dynamic model (stock and flow model) to evaluate the development of urban mobility in all cities over 50 000 inhabitants around the world. It combines data from various sources that form one of the most extensive databases on global city mobility to account for fifteen transport modes. These range from the conventional private car and public transport to new alternative modes such as shared mobility.</p>\n<p><u>Non-urban passenger transport model</u></p>\n<p>The model provides scenario forecasts for non-urban transport activity and its related CO2 emissions up to 2050. The model estimates activity between urban areas (intercity travel) and passenger activity happening locally in non-urban areas (intra-regional travel). The latter includes travel in peri-urban and rural areas. The model is developed to assess the impact of transport, economic and environmental policy measures (air liberalisation, carbon pricing, etc.), as well as the impact of technological developments and breakthroughs (electric aviation, autonomous vehicles, etc.).</p>\n<p><u>Non-urban freight transport model</u></p>\n<p>The most recent version of the ITF freight model integrates the (previously distinct) surface and international freight models. International and domestic freight flows are calibrated on data on national freight transport activity (in tonnes-kilometres, tkm) as reported by ITF member countries. Reported data is also used to validate the route assignment of freight flows. Trade projections in value terms stem from the OECD trade model and converted into cargo weight (tonnes). These weight movements are then assigned to an intermodal freight network that develops over time in line with scenario settings. These define infrastructure availability, available services and related costs.</p>\n<p>The model uses 2015 as its baseline year and provides estimation values for 2015, 2019, 2020, 2022, and 2025, then with computations done in five-year intervals. Therefore, the data for 2021 is derived through interpolation of the simulated values for 2020 and 2022.</p>\n<p>The ITF Modelling Framework is available at <a href=\"https://www.itf-oecd.org/itf-modelling-framework-1\">The ITF Modelling Framework</a>.</p>\n<p>Maritime:</p>\n<p>The indicator is calculated through a sum of international maritime freight volumes and container port traffic as collected by UNCTAD secretariat from websites and reports by various industry, government and specialised maritime transport data providers and consultancies. Data on international maritime freight excludes transhipments and domestic maritime freight volumes.</p>\n<p>Cargo flows originating in or destined to landlocked countries are attributed to the ports of neighbouring coastal transit countries. The mode of transport &#x201C;maritime&#x201D; is assigned to an international trade transaction when the goods arrived at the country&#x2019;s external border (the seaport) transported by ship. </p>\n<p>Data on container port traffic include full and empty containers as well as transhipment traffic. </p>\n<p>Data is collected and compiled from various websites and reports, including, by port and industry associations and authorities, national statistics offices, UN Monthly Bulletin of Statistics, governments, specialised agencies such as the International Energy Agency (IEA), the US Energy Information Administration (EIA), the Organization of the Petroleum Exporting Countries (OPEC), and British Petroleum (BP). Data is also collected from reports issued by maritime specialised sources such as Drewry Maritime Research (DMR), Clarksons Research Services (CRS), Dynamar, and Lloyd&#x2019;s List Intelligence (LLI).</p>", "DATA_VALIDATION__GLOBAL"=>"<p><em>Aviation:</em></p>\n<p>ICAO Statistics Programme has put in place a series of robust data quality control functions to automate all the necessary calculations and producing a report for each reporting form. These quality control processes were divided into two main activities: verification and validation.</p>\n<p><em>Maritime:</em></p>\n<p>UNCTAD secretariat monitors, collects, and compiles the data at the country level as well as at regional/sub-regional level. It continuously updates the data as new data and information becomes available. Some commercial providers of maritime statistics publish global data that is derived, for example, from shipping contracts, and UNCTAD compares its own data with those published by commercial providers. </p>\n<p>Road/Rail/Inland waterways:</p>\n<p>There is no compilation of data submitted from the countries. Data comes from the ITF Global Models.</p>\n<p>ITF (forthcoming), ITF Transport Outlook 2023, OECD Publishing, Paris</p>", "ADJUSTMENT__GLOBAL"=>"<p>Road, rail, inland waterways:</p>\n<p>In order to provide a worldwide regional coverage, data from the ITF transport models are used (see point 4.f).</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level </strong></p>\n<p><u>Aviation data are broadly complete.</u></p>\n<p><u>For inland transport statistics:</u> In case of missing data for a country for which at least one data point is available since 2000, we calculate estimates based on the expected growth rate for the country. The growth rates are computed from other socio-economic variables, such as Gross Domestic Product (GDP), population or urbanization.</p>\n<p><u>For road, rail, and inland waterways:</u></p>\n<p>Not applicable</p>\n<p><em>Maritime:</em></p>\n<p>International maritime freight: In case of missing data for a country or a sub-region for which a data point is available since 2006, UNCTAD makes an estimate based on the expected growth rate of the volume of merchandise trade. If not available, use is made of the latest year for which data was available.</p>\n<p>Container port traffic: The data includes container port throughput for ports with time series that are complete since 2010 or that can be made complete by repeating an observation for a maximum of three years after the original observation. Repeated port level observations are treated as estimates and country level figures that are made up of more than 40 per cent estimates are not published but included in the group level totals. UNCTAD continuously seeks to improve coverage in updates to this table. Ports included are noted in the economy level notes for data on https://unctadstat.unctad.org/datacentre/dataviewer/US.ContPortThroughput.</p>", "REG_AGG__GLOBAL"=>"<p>Aggregation by region based on UN classification of country groupings, including by geography and development status.</p>\n<p>Road/Rail/Inland waterways: The model estimations are at a country level but the analysis is only possible at the regional groupings using simple summation from country level.</p>", "DOC_METHOD__GLOBAL"=>"<p>Aviation:</p>\n<p>States refer to the ICAO Reference Manual on the Statistics Programme (Doc 9060) to compile and file traffic reports at a national level.</p>\n<p>Road/Rail/Inland waterways</p>\n<p>ITF only provides model results to be public at the regional level.</p>\n<p><em>Maritime:</em></p>\n<p>Countries do not systematically collect or report data on international maritime freight and container port traffic. UNCTAD relies on data published by industry and information published by specialized sources. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p><em>Aviation:</em></p>\n<p>ICAO applies the recommendations of the Committee for the Coordination of Statistical Activities (CCSA), including the Principles Governing International Statistical Activities.</p>\n<p><em>Maritime: </em></p>\n<p>UNCTAD statistics are compiled and disseminated in accordance with the Principles Governing International Statistical Activities published by the Committee for the Coordination of Statistical Activities (https://unstats.un.org/unsd/ccsa/principles_stat_activities/) and in line with the UNCTAD Statistics Quality Assurance Framework (https://unctad.org/publication/statistics-quality-assurance-framework).</p>\n<p>Road/Rail/Inland waterways</p>\n<p>This is not a statistical product resulting of data collection. Data are generated from a modelling exercise. </p>\n<p>ITF (forthcoming), ITF Transport Outlook 2023, OECD Publishing, Paris</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Aviation:</p>\n<p>ICAO applies the United Nations Statistics Division (UNSD) fundamental principles and good practices of official statistics, and particularly the generic national quality assurance framework (NQAF). The complete version of the guidelines of NQAF is available at: http://unstats.un.org/unsd/dnss/qualityNQAF/nqaf.aspx.</p>\n<p>Maritime:</p>\n<p>UNCTAD conducts annual checks of collected data by updating the data with latest data available and comparing the data for internal consistency, against previous years, or similar data published or produced by other sources, including commercial sources specialized maritime transport data providers and research entities. Correspondence is undertaken with countries when necessary to collect, compare or confirm relevant data.</p>\n<p>Road/Rail/Inland waterways:</p>\n<p>Not Applicable </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Aviation</p>\n<p>Data already provided for all 193 Member States that have air transport activities</p>\n<p>Road/Rail/Inland waterways</p>\n<p>2015,2019,2020,2021</p>\n<p><strong>Time series:</strong></p>\n<p>Aviation</p>\n<p>From 1970&apos;s</p>\n<p>Road/Rail/Inland waterways</p>\n<p>2015,2019,2020,2021</p>\n<p><strong>Disaggregation: </strong></p>\n<p>Aviation</p>\n<p>The indicator can be dis-aggregated by -Country, Country pair, City Pair, Region, Segment (International and domestic)</p>\n<p>Road/Rail/Inland waterways</p>\n<p>The indicator can be disaggregated by mode of transport.</p>\n<p><em>Maritime:</em></p>\n<p><strong>Data availability:</strong> International maritime freight data at regional and sub-regional level annually from 2006.</p>\n<p>Container port traffic data annually from 2010.</p>\n<p><strong>Disaggregation:</strong> International maritime freight: global, regional and subregional levels. </p>\n<p>Container port traffic: global, regional and country levels</p>", "COMPARABILITY__GLOBAL"=>"<p><em>Maritime:</em></p>\n<p><strong>Sources of discrepancies:</strong></p>\n<p>Data based on varied and mixed sources. This entails differences in computational systems and methods which may result in discrepancies. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.icao.int\">www.icao.int</a></p>\n<p><a href=\"https://www.itf-oecd.org/itf-modelling-framework-1\">https://www.itf-oecd.org/itf-modelling-framework-1</a></p>\n<p><a href=\"https://w3.unece.org/PXWeb/en\">https://w3.unece.org/PXWeb/en</a></p>\n<p><a href=\"https://unctadstat.unctad.org/EN/\">https://unctadstat.unctad.org/EN/</a></p>\n<p>UNCTAD. Review of Maritime Transport Series: <a href=\"https://unctad.org/topic/transport-and-trade-logistics/review-of-maritime-transport\">https://unctad.org/topic/transport-and-trade-logistics/review-of-maritime-transport</a></p>\n<p>UNCTADstat:</p>\n<p>Seaborne Trade: <a href=\"https://unctadstat.unctad.org/datacentre/dataviewer/US.SeaborneTrade\">https://unctadstat.unctad.org/datacentre/dataviewer/US.SeaborneTrade</a></p>\n<p>Container Port Throughput: https://unctadstat.unctad.org/datacentre/dataviewer/US.ContPortThroughput <br></p>", "indicator_sort_order"=>"09-01-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"9.2.1", "slug"=>"9-2-1", "name"=>"Valor añadido del sector manufacturo en proporción al PIB y per cápita", "url"=>"/site/es/9-2-1/", "sort"=>"090201", "goal_number"=>"9", "target_number"=>"9.2", "global"=>{"name"=>"Valor añadido del sector manufacturo en proporción al PIB y per cápita"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Valor añadido del sector manufacturo en proporción al PIB y per cápita", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Valor añadido del sector manufacturo en proporción al PIB y per cápita", "indicator_number"=>"9.2.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Mantenimiento o ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_10/opt_1/ti_cuentas-economicas/temas.html", "url_text"=>"Cuentas económicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Valor añadido del sector manufacturo en proporción al PIB y per cápita", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.2- Promover una industrialización inclusiva y sostenible y, de aquí a 2030, aumentar significativamente la contribución de la industria al empleo y al producto interno bruto, de acuerdo con las circunstancias nacionales, y duplicar esa contribución en los países menos adelantados", "definicion"=>"Valor añadido bruto del sector manufacturero en proporción al PIB real y per cápita", "formula"=>"\n<b>Valor añadido bruto del sector manufacturero en proporción al PIB real</b>\n\n$$PPIBVAB_{manufacturero,2022}^{t} = \\frac{VAB_{manufacturero,2022}^{t}}{PIB_{2022}^{t}} \\cdot 100$$\n\ndonde:\n\n$VAB_{manufacturero,2022}^{t} =$ valor añadido bruto del sector manufacturero en volumen encadenado con referencia 2022 en el año $t$\n\n$PIB^{t} =$ producto interior bruto en volumen encadenado con referencia 2022 en el año $t$\n\n<br>\n\n<b>Valor añadido bruto del sector manufacturero per cápita</b>\n\n$$PPIBPC_{manufacturero,2022}^{t} = \\frac{VAB_{manufacturero,2022}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$VAB_{manufacturero,2022}^{t} =$ valor añadido bruto del sector manufacturero en volumen encadenado con referencia 2022 en el año $t$\n\n$P^{t} =$ población a 1 de julio en el año $t$\n", "desagregacion"=>"\nTerritorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl Valor añadido del sector manufacturero (VAM) es un indicador ampliamente reconocido y \nutilizado por investigadores y responsables de políticas para evaluar el nivel \nde industrialización de un país. La proporción del VAM en el PIB refleja el \npapel de la industria manufacturera en la economía y el desarrollo nacional de un \npaís en general. \n\nEl VAM per cápita es el indicador básico del nivel de industrialización de un país \najustado al tamaño de la economía. Uno de los usos estadísticos del VAM per cápita \nes la clasificación de grupos de países según la etapa de desarrollo industrial.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.2.1&seriesCode=NV_IND_MANF&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\"> Valor añadido manufacturero (dólares estadounidenses constantes de 2015) como proporción del PIB (%) NV_IND_MANF</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.2.1&seriesCode=NV_IND_MANFPC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\"> Valor añadido manufacturero per cápita (dólares estadounidenses constantes de 2015) NV_IND_MANFPC</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas, pero  aporta información similar. Los datos se presentan en euros, no en dólares estadounidenses", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-02-01.pdf\">Metadatos 9-2-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"Valor añadido del sector manufacturo en proporción al PIB y per cápita", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.2- Promover una industrialización inclusiva y sostenible y, de aquí a 2030, aumentar significativamente la contribución de la industria al empleo y al producto interno bruto, de acuerdo con las circunstancias nacionales, y duplicar esa contribución en los países menos adelantados", "definicion"=>"Gross value added of the manufacturing sector as a proportion of real GDP and per capita", "formula"=>"\n<b>Gross value added of the manufacturing sector as a proportion of real GDP</b>\n\n$$PPIBVAB_{manufacturing,2022}^{t} = \\frac{VAB_{manufacturing,2022}^{t}}{PIB_{2022}^{t}} \\cdot 100$$\n\nwhere:\n\n$VAB_{manufacturing,2022}^{t} =$ Gross added value of the manufacturing sector in chained volume \nwith reference to 2022 in year $t$\n\n$PIB^{t} =$ Gross domestic product in chained volume with reference to 2022 in year $t$\n\n<br>\n\n<b>Gross added value of the manufacturing sector per capita</b>\n\n$$PPIBPC_{manufacturing,2022}^{t} = \\frac{VAB_{manufacturing,2022}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$VAB_{manufacturing,2022}^{t} =$ Gross added value of the manufacturing sector in chained volume \nwith reference to 2022 in year $t$\n\n$P^{t} =$ population as of 1 July of year $t$\n", "desagregacion"=>"\nProvince\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nManufacturing value added (MVA) is a well-recognized and widely used indicator by researchers \nand policy makers to assess the level of industrialization of a country. The share of MVA in GDP \nreflects the role of manufacturing in the economy and a country’s national development in general. \n\nMVA per capita is the basic indicator of a country’s level of industrialization adjusted for the \nsize of the economy. One of the statistical uses of MVA per capita is classifying country groups \naccording to the stage of industrial development. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.2.1&seriesCode=NV_IND_MANF&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\"> Manufacturing value added (constant 2015 United States dollars) as a proportion of GDP (%) NV_IND_MANF</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.2.1&seriesCode=NV_IND_MANFPC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\"> Manufacturing value added per capita (constant 2015 United States dollars) NV_IND_MANFPC</a> UNSTATS\n", "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information. Data are presented in euros, not US dollars.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-02-01.pdf\">Metadata 9-2-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Valor añadido del sector manufacturo en proporción al PIB y per cápita", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.2- Promover una industrialización inclusiva y sostenible y, de aquí a 2030, aumentar significativamente la contribución de la industria al empleo y al producto interno bruto, de acuerdo con las circunstancias nacionales, y duplicar esa contribución en los países menos adelantados", "definicion"=>"Manufaktura-sektorearen balio erantsi gordina, BPG errealarekiko proportzioan eta per capita", "formula"=>"\n<b>Manufaktura-sektorearen balio erantsi gordina BPG errealarekiko proportzioan</b>\n\n$$PPIBVAB_{manufaktura,2022}^{t} = \\frac{VAB_{manufaktura,2022}^{t}}{PIB_{2022}^{t}} \\cdot 100$$ \n\nnon: \n\n$VAB_{manufaktura,2022}^{t} =$ manufaktura-sektorearen balio erantsi gordina 2022ko erreferentziarekin kateatutako bolumenean $t$ urtean \n\n$PIB^{t} =$ barne-produktu gordina 2022ko erreferentziarekin kateatutako bolumenean $t$ urtean \n\n<br>\n\n<b>Manufaktura-sektorearen balio erantsi gordina per capita</b> \n\n$$PPIBPC_{manufaktura,2022}^{t} = \\frac{VAB_{manufaktura,2022}^{t}}{P^{t}} \\cdot 100$$ \n\nnon: \n\n$VAB_{manufaktura,2022}^{t} =$ manufaktura-sektorearen balio erantsi gordina 2022ko erreferentziarekin kateatutako bolumenean $t$ urtean \n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean \n", "desagregacion"=>"\nLurralde historikoa\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nManufaktura-sektorearen balio erantsia (MBE) politiken arduradun eta ikertzaileek herrialde baten \nindustrializazio-maila ebaluatzeko erabiltzen duten (eta aski aitortua dagoen) adierazlea da. MBEk \nBPGan duen proportzioak manufaktura-industriak oro har herrialde baten ekonomia eta garapen nazionalean \nduen zeregina islatzen du. \n\nBiztanleko MBE herrialde baten industrializazio-mailaren oinarrizko adierazlea da, ekonomiaren \ntamainara egokitua. Biztanleko MBEren erabilera estatistikoetako bat herrialdeetako taldeen \nsailkapena da, garapen industrialeko etaparen arabera. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.2.1&seriesCode=NV_IND_MANF&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\"> Manufaktura-balio erantsia (2015eko dolar estatubatuar konstanteak), BPGren proportzio gisa (%) NV_IND_MANF</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.2.1&seriesCode=NV_IND_MANFPC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\"> Manufaktura-balio erantsia per capita (2015eko dolar estatubatuar konstanteak) NV_IND_MANFPC</a> UNSTATS\n", "comparabilidad"=>"EAEko adierazleak ez ditu Nazio Batuen adierazlearen metadatuak betetzen, baina  antzeko informazioa ematen du. Datuak eurotan aurkezten dira, ez dolar estatubatuarretan. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-02-01.pdf\">Metadatuak 9-2-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 9.2: Promote inclusive and sustainable industrialization and, by 2030, significantly raise industry&#x2019;s share of employment and gross domestic product, in line with national circumstances, and double its share in least developed countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 9.2.1: Manufacturing value added as a proportion of GDP and per capita</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>NV_IND_MANF - Manufacturing value added as a proportion of GDP [9.2.1]</p>\n<p>NV_IND_MANF_CD - Manufacturing value added (current United States dollars) as a proportion of GDP [9.2.1]</p>\n<p>NV_IND_MANFPC - Manufacturing value added per capita [9.2.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.1.1: Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)</p>\n<p>8.1.1: Annual growth rate of real GDP per capita</p>\n<p>10.2.1: Proportion of people living below 50 per cent of median income, by sex, age and persons with disabilities</p>\n<p>10.4.1: Labour share of GDP</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Manufacturing value added (MVA) as a proportion of gross domestic product (GDP) is a ratio between MVA and GDP, both reported in constant 2015 USD.</p>\n<p>MVA per capita is calculated by dividing MVA in constant 2015 USD by population of a country or area.</p>\n<p><strong>Concepts:</strong></p>\n<p>The gross value added measures the contribution to the economy of each individual producer, industry or sector in a country. The gross value added generated by any unit engaged in production activity can be calculated as the residual of the units&#x2019; total output less intermediate consumption, goods and services used up in the process of producing the output, or as the sum of the factor incomes generated by the production process (System of National Accounts 2008). Manufacturing refers to industries belonging to the section C defined by International Standard Industrial Classification of All Economic Activities (ISIC) Revision 4, or D defined by ISIC Revision 3.</p>\n<p>GDP represents the sum of gross value added from all institutional units resident in the economy. For the purpose on comparability over time and across countries MVA and GDP are estimated in terms of constant prices in USD. The current series are given at constant prices of 2015.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>MVA as a proportion of GDP: Percent (%) </p>\n<p>MVA per capita: constant 2015 USD</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><a href=\"https://unstats.un.org/unsd/nationalaccount/docs/SNA2008.pdf\">System of National Accounts 2008</a></p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 4</a></p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev3_1e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3</a></p>", "SOURCE_TYPE__GLOBAL"=>"<p>UNIDO maintains the MVA database. Figures for updates are obtained from national account estimates produced by UN Statistics Division (UNSD) and from official publications.</p>", "COLL_METHOD__GLOBAL"=>"<p>The MVA and GDP country data are collected through a national accounts questionnaire (NAQ) sent by UNSD. More information on the methodology is available on</p>\n<p><a href=\"https://unstats.un.org/unsd/snaama/methodology.pdf\">https://unstats.un.org/unsd/snaama/methodology.pdf</a></p>\n<p>Missing or inconsistent values are verified with national sources and World Development Indicators (WDI). The preference is given to the data from national sources.</p>\n<p>Population data are obtained from UN DESA Population Division. More information on the methodology is available on</p>\n<p><a href=\"https://population.un.org/wpp/Publications/Files/WPP2019_Methodology.pdf\">https://population.un.org/wpp/Publications/Files/WPP2019_Methodology.pdf</a></p>", "FREQ_COLL__GLOBAL"=>"<p>Data collection is carried out by receiving data electronically throughout the year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>UNIDO MVA database is updated between March and April every year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>United Nations Statistics Division (UNSD) and official publications</p>\n<p>UNSD from National Statistical Offices (NSOs)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>", "INST_MANDATE__GLOBAL"=>"<p>UNIDO, as the specialized UN agency on industrial development, has the international mandate for collecting, producing and disseminating internationally comparable industrial statistics. UNIDO&#x2019;s mandate covers (i) the maintenance and updating of international industrial statistics databases; (ii) methodological and analytical products based on statistical research and experience of maintaining internationally comparable statistics; (iii) contributions to the development and implementation of international statistical standards and methodology; and (iv) technical cooperation services to countries in the field of industrial statistics. With the repositioning of UNIDO as the focal agency for inclusive and sustainable industrial development (ISID), its statistical mandate was expanded to cover all dimensions of industrial development, including its inclusiveness and environmental sustainability.</p>", "RATIONALE__GLOBAL"=>"<p>MVA is a well-recognized and widely used indicator by researchers and policy makers to assess the level of industrialization of a country. The share of MVA in GDP reflects the role of manufacturing in the economy and a country&#x2019;s national development in general. MVA per capita is the basic indicator of a country&#x2019;s level of industrialization adjusted for the size of the economy. One of the statistical uses of MVA per capita is classifying country groups according to the stage of industrial development.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Differences may appear due to different versions of System of National Accounts (SNA) or ISIC revisions used by countries.</p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>M</mi>\n    <mi>V</mi>\n    <mi>A</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>r</mi>\n    <mi>o</mi>\n    <mi>p</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>G</mi>\n    <mi>D</mi>\n    <mi>P</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>M</mi>\n        <mi>V</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>G</mi>\n        <mi>D</mi>\n        <mi>P</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>M</mi>\n    <mi>V</mi>\n    <mi>A</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>p</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>M</mi>\n        <mi>V</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>UNIDO engages with countries in regular consultations during the data collection process to ensure the data quality and international comparability.</p>", "ADJUSTMENT__GLOBAL"=>"<p>UNSD collects national accounts data through a regular consultation with countries and areas by sending the UN NAQ to obtain important information about differences in concept, scope, coverage and classification used. The final estimates are provided to facilitate international comparability. More detailed information on estimation methods is available here:</p>\n<p><a href=\"https://unstats.un.org/unsd/snaama/assets/pdf/methodology.pdf\">https://unstats.un.org/unsd/snaama/assets/pdf/methodology.pdf</a></p>\n<p>The MVA data are nowcasted by UNIDO to enhance a timely analysis of manufacturing trends.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p><a href=\"https://unstats.un.org/unsd/snaama/assets/pdf/methodology.pdf\">Methodology for the National Accounts Main Aggregates Database</a></p>\n<p>Because of a time-gap of at least one year between the latest year, UNIDO applies nowcasting methods to fill in the missing data up to the current year (Boudt et al., 2009).</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>No imputation used.</p>", "REG_AGG__GLOBAL"=>"<p>Regional, global aggregation of direct summation of country values within the country groups.</p>", "DOC_METHOD__GLOBAL"=>"<p>International Recommendations for Industrial Statistics (IRIS) 2008</p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesM/seriesm_90e.pdf\">https://unstats.un.org/unsd/publication/seriesM/seriesm_90e.pdf</a></p>\n<p>System of National Accounts 2008 </p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesf/SeriesF_2Rev5e.pdf\">https://unstats.un.org/unsd/publication/seriesf/SeriesF_2Rev5e.pdf</a></p>\n<p>International Standard Industrial Classification of All Economic Activities (ISIC) </p>\n<p><a href=\"https://unstats.un.org/unsd/classifications/Econ/isic\">https://unstats.un.org/unsd/classifications/Econ/isic</a></p>\n<p> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The National Accounts Section of the UNSD supports the implementation programme of the SNA by developing and updating supporting normative standards, training material and compilation guidance for the implementation of national accounts and supporting economic statistics and maintaining a knowledge base on economic statistics. Moreover, UNSD provides substantive service to the Committee on Contributions of the Fifth Committee of the United Nations on technical aspects of the elements of scale methodology for assessing the contributions to the United Nations by Member States. UNIDO collects and disseminates National Accounts statistics in consultation with UNSD.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p><a href=\"https://unstats.un.org/unsd/unsystem/Documents/QAF-UNIDO.pdf\">The UNIDO Quality Assurance Framework</a> is followed to ensure that the statistical activities of UNIDO are relevant and the data compiled and disseminated are accurate, complete within the defined scope and coverage, timely, comparable in terms of internationally recommended methods and classification standards and internally coherent to variables included in the datasets. While these generally accepted, broad dimensions of quality of statistical data may be defined in each NSO&apos;s own quality assurance framework. UNIDO makes maximum effort that data produced from the statistical operation undertaken with the UNIDO technical cooperation are accurate, internationally comparable and coherent.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The National Accounts Section of the UNSD and UNIDO employ a wide range of data quality techniques and consultations with national providers to assure quality principles supported by the Fundamental Principles of Official Statistics. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>For more than 200 economies</p>\n<p><strong>Time series:</strong></p>\n<p>Data for this indicator are available as of 2000 in the UN Global SDG Database, but longer time series are available in the UNIDO MVA database.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>No disaggregation available.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies: </strong>Minor differences may arise due to 1) exchange rates for conversion to USD 2) different base years used for constant price data 3) methods for recent period estimation and 4) different versions of SNA and ISIC revisions used by countries.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.unido.org/statistics\">www.unido.org/statistics</a></p>\n<p><a href=\"https://unstats.un.org/unsd/snaama/methodology.pdf\">https://unstats.un.org/unsd/snaama/methodology.pdf</a><br>https://population.un.org/wpp/Publications/Files/WPP2019_Methodology.pdf</p>\n<p><strong>References:</strong></p>\n<p>Boudt, Todorov, Upadhyaya (2009): Nowcasting manufacturing value added for cross-country comparison; Statistical Journal of IAOS</p>\n<p>International Recommendations for Industrial Statistics 2008. <a href=\"https://unstats.un.org/unsd/industry/Docs/IRIS_2008_En.pdf\">https://unstats.un.org/unsd/industry/Docs/IRIS_2008_En.pdf</a></p>\n<p>International Yearbook of Industrial Statistics; UNIDO, <a href=\"https://www.unido.org/resources-publications-flagship-publications/international-yearbook-industrial-statistics\">https://www.unido.org/resources-publications-flagship-publications/international-yearbook-industrial-statistics</a></p>\n<p>International Standard Industrial Classification of All Economic Activities 2008. <a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf</a></p>\n<p>System of National Accounts 2008. <a href=\"https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf\">https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf</a></p>\n<p>UNIDO (2009), UNIDO Data Quality: A quality assurance framework for UNIDO statistical activities <a href=\"https://open.unido.org/api/documents/4814740/download/UNIDO-Publication-2009-4814740\">https://open.unido.org/api/documents/4814740/download/UNIDO-Publication-2009-4814740</a> </p>", "indicator_sort_order"=>"09-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"9.2.2", "slug"=>"9-2-2", "name"=>"Empleo del sector manufacturero en proporción al empleo total", "url"=>"/site/es/9-2-2/", "sort"=>"090202", "goal_number"=>"9", "target_number"=>"9.2", "global"=>{"name"=>"Empleo del sector manufacturero en proporción al empleo total"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Empleo del sector manufacturero en proporción al empleo total", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Empleo del sector manufacturero en proporción al empleo total", "indicator_number"=>"9.2.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_57/opt_1/ti_encuesta-de-poblacion-en-relacion-con-la-actividad/temas.html", "url_text"=>"Encuesta de población en relación con la actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Empleo del sector manufacturero en proporción al empleo total", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.2- Promover una industrialización inclusiva y sostenible y, de aquí a 2030, aumentar significativamente la contribución de la industria al empleo y al producto interno bruto, de acuerdo con las circunstancias nacionales, y duplicar esa contribución en los países menos adelantados", "definicion"=>"Proporción de personas ocupadas en el sector manufacturero respecto al total de personas ocupadas", "formula"=>"\n$$PPO_{manufacturero}^{t} = \\frac{PO_{manufacturero}^{t}}{PO^{t}} \\cdot 100$$\n\ndonde:\n\n$PO_{manufacturero}^{t} =$ población ocupada en el sector manufacturero (sección C de la CNAE-2009) en el año $t$\n\n$PO^{t} =$ población ocupada en el año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEste indicador refleja la contribución de la industria manufacturera al empleo total. \nMide la capacidad del sector manufacturero para absorber el excedente de mano de obra \nde la agricultura y otros sectores tradicionales. Sin embargo, en los países \ndesarrollados se espera una tendencia opuesta, donde el énfasis se ha desplazado \nhacia la reducción de la mano de obra en la industria manufacturera como parte \nde las medidas de reducción de costos, para promover industrias más intensivas en \ncapital.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.2.2&seriesCode=SL_TLF_MANF&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20ISIC4_C\"> Empleo manufacturero como proporción del empleo total - 13.ª CIET (%) SL_TLF_MANF</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-02-02.pdf\">Metadatos 9-2-2.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Empleo del sector manufacturero en proporción al empleo total", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.2- Promover una industrialización inclusiva y sostenible y, de aquí a 2030, aumentar significativamente la contribución de la industria al empleo y al producto interno bruto, de acuerdo con las circunstancias nacionales, y duplicar esa contribución en los países menos adelantados", "definicion"=>"Proportion of people employed in the manufacturing sector with respect to the total number of employed people", "formula"=>"\n$$PPO_{manufacturing}^{t} = \\frac{PO_{manufacturing}^{t}}{PO^{t}} \\cdot 100$$\n\nwhere:\n\n$PO_{manufacturing}^{t} = population employed in the manufacturing sector (section C of the National \nClassification of Economic Activities (CNAE-2009)) in year $t$\n\n$PO^{t} =$ employed population in year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThis indicator conveys the contribution of manufacturing in total employment. It measures the \nability of the manufacturing sector to absorb surplus labour from agricultural and other traditional \nsectors. However, in developed countries an opposite trend is expected where emphasis has shifted \nto reduction in labor in manufacturing as part of cost-cutting measures, to promote more capital-intensive \nindustries. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.2.2&seriesCode=SL_TLF_MANF&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\"> Empleo manufacturero como proporción del empleo total - 13.ª CIET (%) SL_TLF_MANF</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-02-02.pdf\">Metadata 9-2-2.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Empleo del sector manufacturero en proporción al empleo total", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.2- Promover una industrialización inclusiva y sostenible y, de aquí a 2030, aumentar significativamente la contribución de la industria al empleo y al producto interno bruto, de acuerdo con las circunstancias nacionales, y duplicar esa contribución en los países menos adelantados", "definicion"=>"Manufaktura-sektorean okupatutako pertsonen proportzioa, okupatutako pertsona guztiekiko", "formula"=>"\n$$PPO_{manufaktura}^{t} = \\frac{PO_{manufaktura}^{t}}{PO^{t}} \\cdot 100$$\n\nnon:\n\n$PO_{manufaktura}^{t} =$ Manufaktura-sektorean okupatutako biztanleria (EJSN-2009ko C sekzioa) $t$ urtean\n\n$PO^{t} =$ biztanleria okupatua $t$ urtean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAdierazle horrek manufaktura-industriak enplegu osoari egiten dion ekarpena islatzen du. Manufaktura-industriak \nnekazaritzako eta beste sektore tradizional batzuetako eskulanaren soberakina xurgatzeko duen gaitasuna \nneurtzen du. Hala ere, garatutako herrialdeetan kontrako joera espero da. Horietan, manufaktura-industrian \neskulana murrizteko ahaleginetan dabiltza, kostuak murrizteko, eta, hala, kapitalean industria intentsiboagoak \nsustatzeko helburuz. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.2.2&seriesCode=SL_TLF_MANF&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX%20%7C%20ISIC4_C\"> Manufaktura-enplegua guztizko enpleguaren proportzio gisa - 13. CIET (%) SL_TLF_MANF</a> UNSTATS\n", "comparabilidad"=>"EAEko adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-02-02.pdf\">Metadatuak 9-2-2.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 9.2: Promote inclusive and sustainable industrialization and, by 2030, significantly raise industry&#x2019;s share of employment and gross domestic product, in line with national circumstances, and double its share in least developed countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 9.2.2: Manufacturing employment as a proportion of total employment</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_TLF_MANF - Manufacturing employment as a proportion of total employment - 13th ICLS (%) [9.2.2]</p>\n<p>SL_TLF_MANF_19ICLS - Manufacturing employment as a proportion of total employment - 19th ICLS(%) [9.2.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>8.2.1 Annual growth rate of real GDP per employed person</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>\n<p>(with the collaboration of the International Labour Organization &#x2013; ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>\n<p>(with the collaboration of the International Labour Organization &#x2013; ILO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>This indicator presents the share of manufacturing employment in total employment.</p>\n<p><strong>Concepts:</strong></p>\n<p>Employment comprises all persons of working age who during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. The difference between the two series for a given country is the operational criteria used to define employment, with one series based on the statistical standards from the 13th International Conference of Labour Statisticians (ICLS) and the other series based on 19th ICLS standards. In the 19th ICLS series, employment is defined more narrowly as work done for pay or profit, while activities not done mainly in exchange for remuneration (i.e., own-use production work, volunteer work and unpaid trainee work) are recognized as other forms of work. </p>\n<p>No distinction is made between persons employed full time and those working less than full time.</p>\n<p>The manufacturing sector is defined according to the International Standard Industrial Classification of all Economic Activities (ISIC) revision 4 (2008, the latest) or revision 3 (1990). It refers to industries belonging to sector C in revision 4 or sector D in revision 3.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 4</a></p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev3_1e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3</a></p>", "SOURCE_TYPE__GLOBAL"=>"<p>The preferred official national data source for this indicator is a household-based labour force survey. </p>\n<p>In the absence of a labour force survey, a population census and/or other type of household survey with an appropriate employment module may also be used to obtain the required data. </p>\n<p>Where no household survey exists, establishment surveys or some types of administrative records may be used to derive the required data, keeping into account the limitations of these sources in their coverage. Specifically, these sources may exclude some types of establishments, establishments of certain sizes, some economic activities or some geographical areas.</p>", "COLL_METHOD__GLOBAL"=>"<p>The ILO Department of Statistics processes national household survey micro datasets in line with internationally agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians. For data that could not be obtained through this processing or directly from government websites, the ILO sends out an annual ILOSTAT questionnaire to all relevant agencies within each country (national statistical office, labour ministry, etc.) requesting the latest annual data and any revisions on numerous labour market topics and indicators, including many SDG indicators.</p>\n<p>UNIDO employment data are collected using General Industrial Statistics Questionnaire which is filled by NSOs and submitted to UNIDO annually. </p>", "FREQ_COLL__GLOBAL"=>"<p>Continuous</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Continuous</p>", "DATA_SOURCE__GLOBAL"=>"<p>Mainly national statistical offices, and in some cases labour ministries or other related agencies, at the country-level. In other cases, regional or international statistical offices can also act as data providers.</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "INST_MANDATE__GLOBAL"=>"<p>The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.</p>\n<p>UNIDO, as the specialized UN agency on industrial development, has the international mandate for collecting, producing and disseminating internationally comparable industrial statistics. UNIDO&#x2019;s mandate covers (i) the maintenance and updating of international industrial statistics databases; (ii) methodological and analytical products based on statistical research and experience of maintaining internationally comparable statistics; (iii) contributions to the development and implementation of international statistical standards and methodology; and (iv) technical cooperation services to countries in the field of industrial statistics. With the repositioning of UNIDO as the focal agency for inclusive and sustainable industrial development (ISID), its statistical mandate was expanded to cover all dimensions of industrial development, including its inclusiveness and environmental sustainability.</p>", "RATIONALE__GLOBAL"=>"<p>This indicator conveys the contribution of manufacturing in total employment. It measures the ability of the manufacturing sector to absorb surplus labour from agricultural and other traditional sectors. However, in developed countries an opposite trend is expected where emphasis has shifted to reduction in labor in manufacturing as part of cost-cutting measures, to promote more capital-intensive industries. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The characteristics of the data source impact the international comparability of the data, especially in cases where the coverage of the source is less than comprehensive (either in terms of country territory or economic activities). In the absence of a labour force survey (the preferred source of data for this indicator), some countries may use an establishment survey to derive this indicator, but these usually have a minimum establishment size cut-off point and small units which are not officially registered (whether in manufacturing or not) would thus not be included in the survey. Consequently, employment data may be underestimated. Discrepancies can also be caused by differences in the definition of employment or the working&#x2013;age population. </p>", "DATA_COMP__GLOBAL"=>"<h2>Computation Method:</h2>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The ILO engages in annual consultations with Member States through the ILOSTAT questionnaire and related Statistics Reporting System (StaRS). National data providers receive a link to the portal where they can review all national SDG data available on ILOSTAT.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Through the ILO Harmonized Microdata initiative, the ILO strives to produce internationally comparable labour statistics based on the indicator concepts and definitions adopted by the International Conference of Labour Statisticians.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>Multivariate regression and cross-validation techniques are used to impute missing values at the country level. The additional variables used for the imputation include a range of indicators, including labour market and economic data. However, the imputed missing country values are only used to calculate the global and regional estimates; they are not used for international reporting on the SDG indicators by the ILO. For further information on the estimates, please refer to the ILO modelled estimates methodological overview, available at <a href=\"https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\">https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/</a>.</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>The aggregates are derived from the ILO modelled estimates that are used to produce global and regional estimates of, amongst others, employment by economic activity, with employment based on the 13th ICLS standards. These models use multivariate regression and cross-validation techniques to impute missing values at the country level, which are then aggregated to produce regional and global estimates. The regional and global shares of employment in manufacturing are obtained by first adding up, across countries, the numerator and denominator of the formula that defines the manufacturing employment as a proportion of total employment - outlined above. Once both magnitudes are produced at the desired level of aggregation, the ratio between the two is used to compute the share for each regional grouping and the global level. Notice that this direct aggregation method can be used due to the imputation of missing observations. For further information on the estimates, please refer to the ILO modelled estimates methodological overview, available at <a href=\"https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\">https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/</a>.</p>", "REG_AGG__GLOBAL"=>"<p>The global and regional aggregates are calculated after direct summation of country values within country groups.</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators, (<a href=\"https://www.ilo.org/stat/Publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/stat/Publications/WCMS_647109/lang--en/index.htm</a>).</li>\n  <li>Decent Work Indicators Manual: <a href=\"http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_223121.pdf\">http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_223121.pdf</a> </li>\n  <li>Resolution concerning statistics of work, employment and labour underutilization, adopted by the 19<sup>th</sup> ICLS in 2013: <a href=\"https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm</a></li>\n  <li>Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the ICLS in 1982: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf</li>\n  <li>International Standard Industrial Classification of All Economic Activities 2008. (<a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf</a>).</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>The processes of compilation, production, and publication of data, including its quality control, are carried out following the methodological framework and standards established by the ILO Department of Statistics, in compliance with the information technology and management standards of the ILO.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination in the ILOSTAT database. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The final assessment of the quality of information is carried out by the Data Production and Analysis Unit of the ILO Department of Statistics. In cases of doubt about the quality of specific data, these values are reviewed with the participation of the national agencies responsible for producing the data. If the issues cannot be clarified, the respective information is not published. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data is available for 207 countries and territories in the 13<sup>th</sup> ICLS series and 120 countries and territories in the 19<sup>th</sup> ICLS series.</p>\n<p><strong>Time series:</strong></p>\n<p>Data for this indicator is available from 2000 in the UN Global SDG Database, but longer time series are available in ILOSTAT.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator can be disaggregated by sex, occupation, age, region and others.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Work statistics for countries not using the same set of statistical standards are not comparable. As such, each series is based on a single set of standards (i.e., 13<sup>th</sup> or 19<sup>th</sup> ICLS) and contains only data comparable within and across countries, allowing data users to continue making meaningful time series analysis and international comparisons. Users should not compare data across series.</p>\n<p>Other differences may arise due to: a) discrepancies in data sources; b) ISIC Revision used by a country; c) informal employment; d) coverage of data source (geographical coverage, economic activities covered, types of establishments covered, etc.); e) working-age population definition.</p>", "OTHER_DOC__GLOBAL"=>"<h2>URL:</h2>\n<p><a href=\"https://ilostat.ilo.org/\">https://ilostat.ilo.org/</a></p>\n<p><a href=\"https://ilostat.ilo.org/resources/concepts-and-definitions/%20\">https://ilostat.ilo.org/resources/concepts-and-definitions/ </a></p>\n<p><a href=\"http://www.unido.org/statistics\">www.unido.org/statistics</a></p>\n<p><a href=\"https://stat.unido.org/\">https://stat.unido.org/</a></p>\n<h2>References:</h2>\n<ul>\n  <li>ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators, (<a href=\"https://www.ilo.org/stat/Publications/WCMS_647109/lang--en/index.htm\">https://www.ilo.org/stat/Publications/WCMS_647109/lang--en/index.htm</a>).</li>\n  <li>Decent Work Indicators Manual: <a href=\"http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_223121.pdf\">http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_223121.pdf</a> </li>\n  <li>Resolution concerning statistics of work, employment and labour underutilization, adopted by the 19<sup>th</sup> ICLS in 2013: <a href=\"https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm\">https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm</a></li>\n  <li>Resolution concerning statistics of the economically active population, employment, unemployment and underemployment, adopted by the ICLS in 1982: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/normativeinstrument/wcms_230304.pdf</li>\n  <li>Quick guide to understanding the impact of the new statistical standards on ILOSTAT databases (https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/wcms_854830.pdf)</li>\n  <li>International Standard Industrial Classification of All Economic Activities 2008. (<a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf</a>).</li>\n</ul>", "indicator_sort_order"=>"09-02-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"9.3.1", "slug"=>"9-3-1", "name"=>"Proporción del valor añadido total del sector industrial correspondiente a las pequeñas industrias", "url"=>"/site/es/9-3-1/", "sort"=>"090301", "goal_number"=>"9", "target_number"=>"9.3", "global"=>{"name"=>"Proporción del valor añadido total del sector industrial correspondiente a las pequeñas industrias"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción del valor añadido total del sector industrial correspondiente a las pequeñas industrias", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción del valor añadido total del sector industrial correspondiente a las pequeñas industrias", "indicator_number"=>"9.3.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_112/opt_1/ti_encuesta-industrial/temas.html", "url_text"=>"Encuesta industrial", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción del valor añadido total del sector industrial correspondiente a las pequeñas industrias", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.3- Aumentar el acceso de las pequeñas industrias y otras empresas, particularmente en los países en desarrollo, a los servicios financieros, incluidos créditos asequibles, y su integración en las cadenas de valor y los mercados", "definicion"=>"Proporción del valor añadido bruto correspondiente a las empresas del sector manufacturero que tienen menos de 20 trabajadores respecto al valor añadido bruto total del sector manufacturero", "formula"=>"\n$$PVAB_{pequeñas\\, empresas\\, manufactura}^{t} = \\frac{VAB_{pequeñas\\, empresas\\, manufactura}^{t}}{VAB_{manufactura}^{t}} \\cdot 100$$\n\ndonde:\n\n$VAB_{pequeñas\\, empresas\\, manufactura}^{t} =$ valor añadido bruto correspondiente a las empresas del sector manufacturero que tienen menos de 20 trabajadores en el año $t$\n\n$VAB_{manufactura}^{t} =$ valor añadido bruto total del sector manufacturero en el año $t$\n", "desagregacion"=>"Territorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLas empresas industriales se clasifican en pequeñas en comparación con las grandes \no medianas por su naturaleza distintiva de organización económica, capacidad \nde producción, escala de inversión y otras características económicas.\n\nLas “industrias de pequeña escala” pueden funcionar con una pequeña cantidad de \ncapital, mano de obra relativamente no calificada y utilizando \nmateriales locales. \n\nA pesar de su pequeña contribución a la producción industrial \ntotal, se reconoce que su papel en la creación de empleo, especialmente en los \npaíses en desarrollo, es significativo allí donde el alcance de absorción de la \nfuerza laboral excedente de sectores tradicionales como la agricultura o la pesca \nes muy alto. Las “industrias de pequeña escala” son capaces de \nsatisfacer la demanda interna de bienes de consumo básicos como alimentos, ropa, muebles, etc.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-03-01.pdf\">Metadatos 9-3-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción del valor añadido total del sector industrial correspondiente a las pequeñas industrias", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.3- Aumentar el acceso de las pequeñas industrias y otras empresas, particularmente en los países en desarrollo, a los servicios financieros, incluidos créditos asequibles, y su integración en las cadenas de valor y los mercados", "definicion"=>"Proportion of gross value added of manufacturing industries with fewer than 20 workers  in total manufacturing gross value added", "formula"=>"\n$$PVAB_{small\\, manufacturing\\, industries}^{t} = \\frac{VAB_{small\\, manufacturing\\, industries}^{t}}{VAB_{manufacturing}^{t}} \\cdot 100$$\n\nwhere:\n\n$VAB_{small\\, manufacturing\\, industries}^{t} =$ gross value added of manufacturing industries with fewer than 20 workers in year $t$\n\n$VAB_{manufacturing}^{t} =$ total gross value added of manufacturing sector in year $t$\n", "desagregacion"=>"Province\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nIndustrial enterprises are classified as small for their distinct nature of economic organization, \nproduction capability, scale of investment and other economic characteristics. \n\n“Small-scale industries” can be run with a small amount of capital, relatively unskilled labor \nand using local materials. \n\nDespite their relatively small contribution to total industrial output, their role in job creation, \nespecially in developing countries is recognized to be significant where the scope of absorbing \nsurplus labor force from traditional sectors such as agriculture or fishery is very high. “Small-scale \nindustries” are capable of meeting domestic demand of basic consumer goods such as food, clothes, \nfurniture, etc. \n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-03-01.pdf\">Metadata 9-3-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción del valor añadido total del sector industrial correspondiente a las pequeñas industrias", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.3- Aumentar el acceso de las pequeñas industrias y otras empresas, particularmente en los países en desarrollo, a los servicios financieros, incluidos créditos asequibles, y su integración en las cadenas de valor y los mercados", "definicion"=>"20 langile baino gutxiago dituzten manufaktura-sektoreko enpresei dagokien  balio erantsi gordinaren proportzioa manufaktura-sektoreko balio erantsi gordin  osoarekiko ", "formula"=>"\n$$PVAB_{manufaktura\\, enpresa\\, txikiak}^{t} = \\frac{VAB_{manufaktura\\, enpresa\\, txikiak}^{t}}{VAB_{manufaktura}^{t}} \\cdot 100$$\n\nnon:\n\n$VAB_{manufaktura\\, enpresa\\, txikiak}^{t} =$ 20 langile baino gutxiago dituzten manufaktura-sektoreko \nenpresei dagokien balio erantsi gordina $t$ urtean\n\n$VAB_{manufaktura}^{t} =$ manufaktura-sektoreko balio erantsi gordina $t$ urtean\n", "desagregacion"=>"Lurralde historikoa\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nIndustria-enpresa txikitzat jotzen dira, handi edo ertainekin alderatuta, antolaketa ekonomikoaren izaera \nbereizgarriaren, ekoizpen-gaitasunaren, inbertsio-eskalaren eta beste ezaugarri ekonomiko batzuen ondorioz. \n\n“Eskala txikiko industriek” kapital-kopuru txikiarekin eta ez-kalifikatutako eskulanarekin funtziona \ndezakete, tokiko materialak erabilita. \n\nIndustria-ekoizpen osoan egiten duten ekarpena txikia izanik ere, enplegua sortzeko orduan, batez ere \ngarapen-bidean dauden herrialdeetan, zeregin garrantzitsua dute nekazaritza eta arrantza bezalako sektore \ntradizionaletako soberako lan-indarraren xurgapen-irismena oso altua den lekuetan.  “Eskala txikiko industriak” \ngai dira oinarrizko kontsumo-ondasunen barne-eskaria asetzeko, besteak beste elikagai, arropa edo altzariena. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>nil, "comparabilidad"=>"EAEko adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-03-01.pdf\">Metadatuak 9-3-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 9.3: Increase the access of small-scale industrial and other enterprises, in particular in developing countries, to financial services, including affordable credit, and their integration into value chains and markets</p>", "SDG_INDICATOR__GLOBAL"=>"<p>9.3.1 Proportion of small-scale industries in total industry value added, based on (a) international classification and (b) national classifications</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>NV_IND_SSIS - Proportion of small-scale manufacturing industries in total manufacturing value added based on international classification [9.3.1]<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></p>\n<p>NV_IND_SSIS_NC - Proportion of small-scale manufacturing industries in total manufacturing value added based on national classification [9.3.1]<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> In March 2023, the series description was updated from &#x201C;Proportion of small-scale industries in total industry value added&#x201D; to &#x201C;Proportion of small-scale manufacturing industries in total manufacturing value added&#x201D; for clarity; content in the series is the same. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> New series added in 2025 as part of the revisions in the 2025 Comprehensive Review (<a href=\"https://unstats.un.org/UNSDWebsite/statcom/session_56/documents/2025-6-SDG-IAEG-E.pdf\">2025-6-SDG-IAEG-E.pdf</a>) <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>9.3.2: Proportion of small-scale industries with a loan or line of credit</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Small-scale industrial enterprises, in the SDG framework also called &#x201C;<strong>small-scale industries</strong>&#x201D;, defined here for the purpose of statistical data collection and compilation, refer to statistical units, generally enterprises, engaged in production of goods for market below a designated size class.</p>\n<ul>\n  <li>Proportion of &#x201C;small-scale industries&#x201D; in total industry value added based on international classification is an indicator calculated as the share of manufacturing value added of small-scale manufacturing enterprises in the total manufacturing value added according to <em>UNIDO&#x2019;s international definition of &#x201C;small-scale industries&#x201D;</em> (less than 20 people employed on average during the reference period).</li>\n  <li>Proportion of &#x201C;small-scale industries&#x201D; in total industry value added based on national classification is an indicator calculated as the share of manufacturing value added of small-scale manufacturing enterprises in the total manufacturing value added according to the <em>national definition of &#x201C;small-scale industries&#x201D;.</em></li>\n</ul>\n<p><strong>Concepts:</strong></p>\n<p>International recommendations for industrial statistics 2008 (IRIS 2008) (United Nations, 2011) define an <strong>enterprise</strong> as the smallest legal unit that constitutes an organizational unit producing goods or services. The enterprise is the basic statistical unit at which all information relating to its production activities and transactions, including financial and balance-sheet accounts, are maintained. It is also used for institutional sector classification in the 2008 System of National Accounts.</p>\n<p>An <strong>establishment</strong> is defined as an enterprise or part of an enterprise that is situated in a single location and in which only a single productive activity is carried out or in which the principal productive activity accounts for most of the value added. An establishment can be defined ideally as an economic unit that engages, under single ownership or control, that is, under a single legal entity, in one, or predominantly one, kind of economic activity at a single physical location. Mines, factories and workshops are examples. This ideal concept of an establishment is applicable to many of the situations encountered in industrial inquiries, particularly in manufacturing.</p>\n<p>Although the definition of an establishment allows for the possibility that there may be one or more secondary activities carried out in it, their magnitude should be small compared with that of the principal activity. If a secondary activity within an establishment is as important, or nearly as important, as the principal activity, then the unit is more like a local unit. It should be subdivided so that the secondary activity is treated as taking place within an establishment separate from the establishment in which the principal activity takes place.</p>\n<p>In the case of most <strong>small-sized businesses</strong>, the enterprise and the establishment will be identical. Some enterprises are large and complex with different kinds of economic activities undertaken at different locations. Such enterprises should be broken down into one or more establishments, provided that smaller and more homogeneous production units can be identified for which production data may be meaningfully compiled. </p>\n<p>As introduced in IRIS 2008 (United Nations, 2011), an <strong>economic activity</strong> is understood as referring to a process, that is, the combination of actions carried out by a certain entity that uses labor, capital, goods and services to produce specific products (goods and services). In general, industrial statistics reflect the characteristics and economic activities of units engaged in a class of industrial activities that are defined in terms of the International Standard Industrial Classification of All Economic Activities, Revision 4 (ISIC Rev.4) (United Nations, 2008) or International Standard Industrial Classification of All Economic Activities, Revision 3.1 (ISIC Rev. 3) (United Nations, 2002).</p>\n<p><strong>Total numbers of persons employed</strong> is defined as the total number of persons who work in or for the statistical unit, whether full-time or part-time, including:</p>\n<ul>\n  <li>Working proprietors</li>\n  <li>Active business partners</li>\n  <li>Unpaid family workers</li>\n  <li>Paid employees (for more details see United Nations, 2011).</li>\n</ul>\n<p>The size of a statistical unit based on employment should be defined primarily in terms of the average number of persons employed in that unit during the reference period. If the average number of persons employed is not available, the total number of persons employed in a single period may be used as the size criterion. The size classification should consist of the following classes of the average number of persons employed: 1-9, 10-19, 20-49, 50-249, 250 and more. This should be considered a minimum division of the overall range; more detailed classifications, where required, should be developed within this framework.</p>\n<p><strong>Value added</strong> cannot be directly observed from the accounting records of the units. It is derived as the difference between gross output or census output and intermediate consumption or census input (United Nations, 2011). The value added at basic prices is calculated as the difference between the gross output at basic prices and the intermediate consumption at purchasers&#x2019; prices. The valuation of value added closely corresponds to the valuation of gross output. If the output is valued at basic prices, then the valuation of value added is also at basic prices (the valuation of intermediate consumption is always at purchasers&#x2019; prices).</p>\n<p>All above mentioned terms are introduced to be in line with IRIS 2008 (United Nations, 2011).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 4</a></p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev3_1e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3</a></p>", "SOURCE_TYPE__GLOBAL"=>"<p>National statistical offices (NSOs)</p>", "COLL_METHOD__GLOBAL"=>"<p>Countries were contacted to provide information on data availability for monitoring small-scale manufacturing enterprises. The data come mostly from annual industrial surveys, where value added is disaggregated by size classes given in terms of number of employees, assets, turnover, etc. and from surveys focusing particularly on small enterprises, or small and medium enterprises in general. Data based in the international definition and according to national classification systems are collected simultaneously, with countries encouraged to provide both whenever available.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are collected annually from NSOs, OECD and EUROSTAT.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>UNIDO SDG 9 database is updated between March and April every year including the 9.3.1 indicator.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are collected primary from national sources, from official publications and official websites, and from OECD (Structural and Demographic Business Statistics) and EUROSTAT (Structural Business Statistics database).</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>", "INST_MANDATE__GLOBAL"=>"<p>UNIDO, as the specialized UN agency on industrial development, has the international mandate for collecting, producing and disseminating internationally comparable industrial statistics. UNIDO&#x2019;s mandate covers (i) the maintenance and updating of international industrial statistics databases; (ii) methodological and analytical products based on statistical research and experience of maintaining internationally comparable statistics; (iii) contributions to the development and implementation of international statistical standards and methodology; and (iv) technical cooperation services to countries in the field of industrial statistics. With the repositioning of UNIDO as the focal agency for inclusive and sustainable industrial development (ISID), its statistical mandate was expanded to cover all dimensions of industrial development, including its inclusiveness and environmental sustainability.</p>", "RATIONALE__GLOBAL"=>"<p>Industrial enterprises are classified as small for their distinct nature of economic organization, production capability, scale of investment and other economic characteristics. &#x201C;Small-scale industries&#x201D; can be run with a small amount of capital, relatively unskilled labor and using local materials. Despite their relatively small contribution to total industrial output, their role in job creation, especially in developing countries is recognized to be significant where the scope of absorbing surplus labor force from traditional sectors such as agriculture or fishery is very high. &#x201C;Small-scale industries&#x201D; are capable of meeting domestic demand of basic consumer goods such as food, clothes, furniture, etc. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The main limitation for building an international indicator based on existing national data is varying size classes by country, indicating that data are obtained from different target populations. Data are frequently not comparable among countries due to differences in size classes. Size classes may be based on the same variable (e.g. number of persons employed), but with different, non-comparable thresholds, or they may be determined according to different variables (e.g. turnover, capital formation, etc.) The definition of size class in many countries is tied to the legal and policy framework in the country. It has implications on registration procedure, taxation and different policies aimed at promoting &#x201C;small-scale industries&#x201D;. </p>\n<p>Therefore, it is necessary for countries to agree on a common size class for compilation purposes and international comparability. In this context, UNIDO proposes that all countries should aim to compile value-added data by a size class of &#x201C;small-scale industries&#x201D; as with 1-19 employed (based on the size classes recommended in the IRIS 2008 (United Nations, 2011) and the current practice in the World Bank Enterprise Surveys (World Bank, 2023)). However, slight deviations are permitted (e.g. if number of employees range from 0-19, 1-20 (opposed to 1-19)). Nonetheless, due to the aforementioned limitations, it is challenging for a considerable number of countries to adopt an international definition of small-scale industries. UNIDO reports two series for SDG indicator 9.3.1 to address varying classification systems: 9.3.1.a relies on UNIDO&apos;s international classification system to ensure comparability across countries, while 9.3.1.b is based on national classification systems to enhance data coverage. When national and international definitions align, or when only data for the international definition is available, this information is also included in 9.3.1.b for completeness. The term &quot;small-scale industries&quot; includes all enterprises classified as &quot;small&quot; as well as those that fall into smaller categories (e.g. very small, micro), as defined by national standards. If data is provided for national class sizes without specifying their magnitude (e.g., micro, small, etc.), the smallest class is used as the default definition for &quot;small-scale industries.&quot;</p>", "DATA_COMP__GLOBAL"=>"<p>The proportion of &#x201C;small-scale industries&#x201D; in total value added is an indicator calculated as a share of value added for small-scale manufacturing enterprises in total manufacturing value added:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi>M</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>f</mi>\n        <mi>a</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>g</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>&quot;</mo>\n        <mi>s</mi>\n        <mi>m</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mo>-</mo>\n        <mi>s</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mo>&quot;</mo>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>f</mi>\n        <mi>a</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>g</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Manufacturing sector<em> </em>is defined according to the International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3 (1990) or Revision 4 (2008). It refers to industries belonging to sector D in revision 3 or sector C in Revision 4. The indicator is calculated based on respective national definitions of small-scale industries and, if available, also based on UNIDO&#x2019;s international definition of small-scale industries (less than 20 employees).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>UNIDO engages with countries in regular consultations during the data collection process to ensure the data quality and international comparability. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Data are collected through the UNIDO Small Industrial Enterprises Questionnaire to receive information on differences in concept, scope, coverage and classification used. The final data are adjusted to follow ISIC and facilitate international comparability.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>No treatment of missing values is applied at country level.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>No treatment of missing values is applied at regional and global levels.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates are currently not provided due to a limited geographical coverage and regional representativeness. The 2025 edition of 9.3.1.a data covers 71 economies, while 9.3.1.b covers 78 economies, mostly classified as high- and middle-income economies.</p>", "DOC_METHOD__GLOBAL"=>"<p>International Recommendations for Industrial Statistics (IRIS) 2008</p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesM/seriesm_90e.pdf\">https://unstats.un.org/unsd/publication/seriesM/seriesm_90e.pdf</a></p>\n<p>International Standard Industrial Classification of All Economic Activities (ISIC) </p>\n<p><a href=\"https://unstats.un.org/unsd/classifications/Econ/isic\">https://unstats.un.org/unsd/classifications/Econ/isic</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>UNIDO published a handbook for statisticians involved in the regular industrial statistics programmes of NSOs or line ministries (<a href=\"https://www.unido.org/sites/default/files/2012-07/Industrial%20Statistics%20-%20Guidelines%20and%20Methdology_0.pdf\">Industrial Statistics - Guidelines and Methodology</a>). It describes the statistical methods related to the major stages of industrial statistics operation. Moreover, UNIDO has established a quality management framework based on the internationally recognized guidelines recommended by IRIS to ensure quality of statistical products.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p><a href=\"https://unstats.un.org/unsd/unsystem/Documents/QAF-UNIDO.pdf\">The UNIDO Quality Assurance Framework</a> is followed to ensure that the statistical activities of UNIDO are relevant and the data compiled and disseminated are accurate, complete within the defined scope and coverage, timely, comparable in terms of internationally recommended methods and classification standards and internally coherent to variables included in the datasets. While these generally accepted, broad dimensions of quality of statistical data may be defined in each NSO&apos;s own quality assurance framework. UNIDO makes maximum effort that data produced from the statistical operation undertaken with the UNIDO technical cooperation are accurate, internationally comparable and coherent.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>UNIDO employs a wide range of data quality techniques and consultations with national providers to assure quality principles supported by the Fundamental Principles of Official Statistics. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data for 9.3.1.a was collected for 71 economies, while data for 9.3.1.b was gathered for 78 economies. Data availability varies, ranging from sporadic to regular across different countries.</p>\n<p><strong>Time series:</strong></p>\n<p>Data are provided on an irregular basis. Data available from annual industrial surveys show yearly frequency, while surveys on small and medium enterprises are conducted either irregularly or at fixed intervals (for instance once in five years).</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data disaggregated by manufacturing sub-sectors are occasionally available.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Difference in ISIC combinations may cause discrepancy between national and international figures.</p>", "OTHER_DOC__GLOBAL"=>"<h2>URL:</h2>\n<p><a href=\"http://www.unido.org/statistics\">www.unido.org/statistics</a></p>\n<p><a href=\"https://stat.unido.org/\">https://stat.unido.org/</a></p>\n<h2>References:</h2>\n<p>United Nations (2002). International Standard Industrial Classification of All Economic Activities (ISIC Revision 4). New York: United Nations.</p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf</a></p>\n<p>United Nations (2008). International Standard Industrial Classification of All Economic Activities (ISIC Revision 3.1). New York: United Nations.</p>\n<p> <a href=\"https://unstats.un.org/unsd/publication/SeriesM/seriesm_4rev3_1e.pdf\">https://unstats.un.org/unsd/publication/SeriesM/seriesm_4rev3_1e.pdf</a></p>\n<p>United Nations (2011). International Recommendations for Industrial Statistics 2008 (IRIS 2008), New York: United Nations. <a href=\"http://dx.doi.org/10.18356/677c08dd-en\">http://dx.doi.org/10.18356/677c08dd-en</a></p>\n<p>OECD (2019). Structural and Demographic Business Statistics (SDBS). Paris: OECD.</p>\n<p> <a href=\"http://www.oecd.org/std/business-stats/structuralanddemographicbusinessstatisticssdbsoecd.htm\">http://www.oecd.org/std/business-stats/structuralanddemographicbusinessstatisticssdbsoecd.htm</a></p>\n<p>UNIDO (2009). UNIDO Data Quality: A quality assurance framework for UNIDO statistical activities <a href=\"https://open.unido.org/api/documents/4814740/download/UNIDO-Publication-2009-4814740\">https://open.unido.org/api/documents/4814740/download/UNIDO-Publication-2009-4814740</a></p>\n<p>UNIDO (2010). Industrial Statistics - Guidelines and Methodology <a href=\"https://www.unido.org/sites/default/files/2012-07/Industrial%20Statistics%20-%20Guidelines%20and%20Methdology_0.pdf\">https://www.unido.org/sites/default/files/2012-07/Industrial%20Statistics%20-%20Guidelines%20and%20Methdology_0.pdf</a></p>\n<p>World Bank (2024). Enterprise Surveys. Washington, D.C.: World Bank https://www.enterprisesurveys.org </p>", "indicator_sort_order"=>"09-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"9.3.2", "slug"=>"9-3-2", "name"=>"Proporción de las pequeñas industrias que han obtenido un préstamo o una línea de crédito", "url"=>"/site/es/9-3-2/", "sort"=>"090302", "goal_number"=>"9", "target_number"=>"9.3", "global"=>{"name"=>"Proporción de las pequeñas industrias que han obtenido un préstamo o una línea de crédito"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de las pequeñas industrias que han obtenido un préstamo o una línea de crédito", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de las pequeñas industrias que han obtenido un préstamo o una línea de crédito", "indicator_number"=>"9.3.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nLas empresas industriales se clasifican como pequeñas, en comparación \ncon las grandes o medianas, por su naturaleza distintiva de organización \neconómica, capacidad de producción, escala de inversión y otras características \neconómicas. \n\nLas “industrias de pequeña escala” pueden funcionar con una pequeña \ncantidad de capital, mano de obra relativamente no calificada y utilizando \nmateriales locales. A pesar de su pequeña contribución a la producción industrial \ntotal, se reconoce que su papel en la creación de empleo, especialmente en los \npaíses en desarrollo, es significativo, en los que el alcance de absorber \nla fuerza laboral excedente de los sectores tradicionales, como la agricultura \no la pesca, es muy alto. \n\nLas “industrias de pequeña escala” son capaces de satisfacer la demanda \ninterna de bienes de consumo básicos, como alimentos, ropa, muebles, etc. \nPor lo tanto, las “industrias de pequeña escala” desempeñan un papel importante \nen la economía. Sin embargo, tienen un acceso bastante limitado a los servicios \nfinancieros, especialmente en los países en desarrollo. \n\nPara mejorar la \ncapacitación de los trabajadores y la tecnología para la producción, las empresas \nindustriales de pequeña escala necesitan apoyo financiero en forma de préstamos \npreferenciales, crédito, etc. Este indicador muestra hasta qué punto las instituciones \nfinancieras están prestando servicios a las “industrias de pequeña escala”. Junto con el \nindicador ODS 9.3.1, este indicador refleja el mensaje principal de la meta 9.3 que busca \naumentar el acceso de las “pequeñas industrias” a los servicios financieros.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.3.2&seriesCode=FC_ACC_SSID&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\">Proporción de pequeñas industrias con un préstamo o línea de crédito (%) FC_ACC_SSID</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-03-02.pdf\">Metadatos 9-3-2.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nIndustrial enterprises are classified to small compared to large or medium for their distinct \nnature of economic organization, production capability, scale of investment and other economic \ncharacteristics. \n\n“Small-scale industries” can be run with a small amount of capital, relatively unskilled labor \nand using local materials. Despite their small contribution to total industrial output, their \nrole in job creation, especially in developing countries is recognized to be significant where \nthe scope of absorbing surplus labor force from traditional sectors such as agriculture or fishery \nis very high. \n\n“Small-scale industries” are capable of meeting domestic demand of basic consumer goods such as food, \nclothes, furniture, etc. Thus “small-scale industries” play an important role in the economy. \nHowever, it has quite limited access to financial services, especially in developing countries. \n\nIn order to improve the skill of workers and technology for production, small-scale industrial \nenterprises require financial support in the form of preferential loan, credit etc. This indicator \nshows how widely financial institutions are serving the “smallscale industries”. Together with the \nindicator SDG 9.3.1, this indicator reflects the main message of the target 9.3 which seeks to \nincrease the access of “small-scale industries” to financial services. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.3.2&seriesCode=FC_ACC_SSID&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\">Proportion of small-scale industries with a loan or line of credit (%) FC_ACC_SSID</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-03-02.pdf\">Metadata 9-3-2.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nIndustria-enpresa txikitzat jotzen dira, handi edo ertainekin alderatuta, antolaketa ekonomikoaren \nizaera bereizgarriaren, ekoizpen-gaitasunaren, inbertsio-eskalaren eta beste ezaugarri ekonomiko \nbatzuen ondorioz. \n\n“Eskala txikiko industriek” kapital-kopuru txikiarekin eta ez-kalifikatutako eskulanarekin funtziona \ndezakete, tokiko materialak erabilita. Industria-ekoizpen osoan egiten duten ekarpena txikia izanik \nere, enplegua sortzeko orduan, batez ere garapen-bidean dauden herrialdeetan, zeregin garrantzitsua \ndute nekazaritza eta arrantza bezalako sektore tradizionaletako soberako lan-indarraren xurgapen-irismena \noso altua den lekuetan. \n\n“Eskala txikiko industriak” gai dira oinarrizko kontsumo-ondasunen barne-eskaria asetzeko, besteak beste \nelikagai, arropa edo altzariena. Ondorioz, “eskala txikiko industriek” zeregin garrantzitsua dute ekonomian. \nHala ere, finantza-zerbitzuetara sarbide nahiko mugatua dute, bereziki garapen-bidean dauden herrialdeetan. \n\nLangileen trebakuntza eta ekoizpenerako teknologiak hobetzeko, eskala txikiko industria-enpresek finantza-babesa \nbehar dute, besteak beste lehentasunezko maileguak, kredituak eta abar. Adierazle horrek zehazten du zein puntura \narte dauden finantza-erakundeak zerbitzuak ematen “eskala txikiko industriei”. GJHen 9.3.1 adierazlearekin batera, \nadierazle honek 9.3 xedearen mezu nagusia islatzen du, “industria txikiek” finantza-zerbitzuak eskuratzeko dituzten \naukerak areagotuz.  \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.3.2&seriesCode=FC_ACC_SSID&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\">Mailegu edo kreditu-lerro bat duten industria txikien proportzioa (%) FC_ACC_SSID</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-03-02.pdf\">Metadatuak 9-3-2.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 9.3: Increase the access of small-scale industrial and other enterprises, in particular in developing countries, to financial services, including affordable credit, and their integration into value chains and markets</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 9.3.2: Proportion of small-scale industries with a loan or line of credit</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>FC_ACC_SSID - Proportion of small-scale industries with a loan or line of credit [9.3.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>9.3.1: Proportion of small-scale industries in total industry value added</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>\n<p>World Bank</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>\n<p>World Bank</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions</strong><sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup><strong>:</strong></p>\n<p>Small-scale industrial enterprises, in the SDG framework also called &#x201C;small-scale industries&#x201D;, defined here for the purpose of statistical data collection and compilation refer to statistical units, generally enterprises, engaged in production of goods and services for market below a designated size class.</p>\n<p>This indicator shows the number of &#x201C;small-scale industries&#x201D; with an active line of credit or a loan from a financial institution in the reference year in percentage to the total number of such enterprises.</p>\n<p><strong>Concepts:</strong></p>\n<p>International recommendations for industrial statistics 2008 (IRIS 2008) (United Nations, 2011) define an <strong>enterprise</strong> as the smallest legal unit that constitutes an organizational unit producing goods or services. The enterprise is the basic statistical unit at which all information relating to its production activities and transactions, including financial and balance-sheet accounts, are maintained. It is also used for institutional sector classification in the 2008 System of National Accounts.</p>\n<p>An <strong>establishment</strong> is defined as an enterprise or part of an enterprise that is situated in a single location and in which only a single productive activity is carried out or in which the principal productive activity accounts for most of the value added. An establishment can be defined ideally as an economic unit that engages, under single ownership or control, that is, under a single legal entity, in one, or predominantly one, kind of economic activity at a single physical location. Mines, factories and workshops are examples. This ideal concept of an establishment is applicable to many of the situations encountered in industrial inquiries, particularly in manufacturing.</p>\n<p>Although the definition of an establishment allows for the possibility that there may be one or more secondary activities carried out in it, their magnitude should be small compared with that of the principal activity. If a secondary activity within an establishment is as important, or nearly as important, as the principal activity, then the unit is more like a local unit. It should be subdivided so that the secondary activity is treated as taking place within an establishment separate from the establishment in which the principal activity takes place.</p>\n<p>In the case of most <strong>small-sized businesses</strong>, the enterprise and the establishment will be identical. Some enterprises are large and complex with different kinds of economic activities undertaken at different locations. Such enterprises should be broken down into one or more establishments, provided that smaller and more homogeneous production units can be identified for which production data may be meaningfully compiled. </p>\n<p>As introduced in IRIS 2008 (United Nations, 2011), an <strong>economic activity</strong> is understood as referring to a process, that is , the combination of actions carried out by a certain entity that uses labor, capital, goods and services to produce specific products (goods and services). In general, industrial statistics reflect the characteristics and economic activities of units engaged in a class of industrial activities that are defined in terms of the International Standard Industrial Classification of All Economic Activities, Revision 4 (ISIC Rev.4) (United Nations, 2008) or International Standard Industrial Classification of All Economic Activities, Revision 3.1 (ISIC Rev. 3) (United Nations, 2002).</p>\n<p><strong>Total numbers of persons employed</strong> is defined as the total number of persons who work in or for the statistical unit, whether full-time or part-time, including:</p>\n<ul>\n  <li>Working proprietors</li>\n  <li>Active business partners</li>\n  <li>Unpaid family workers</li>\n  <li>Paid employees (for more details see United Nations, 2011).</li>\n</ul>\n<p>The size of a statistical unit based on employment should be defined primarily in terms of the average number of persons employed in that unit during the reference period. If the average number of persons employed is not available, the total number of persons employed in a single period may be used as the size criterion. The size classification should consist of the following classes of the average number of persons employed: 1-9, 10-19, 20-49, 50-249, 250 and more. This should be considered a minimum division of the overall range; more detailed classifications, where required, should be developed within this framework.</p>\n<p>A <strong>loan</strong> is a financial instrument that is created when a creditor lends funds directly to a debtor and receives a non-negotiable document as evidence of the asset. This category includes overdrafts, mortgage loans, loans to finance trade credit and advances, repurchase agreements, financial assets and liabilities created by financial leases, and claims on or liabilities to the International Monetary Fund (IMF) in the form of loans. Trade credit and advances and similar accounts payable/receivable are not loans. Loans that have become marketable in secondary markets should be reclassified under debt securities. However, if only traded occasionally, the loan is not reclassified under debt securities (IMF, 2011).</p>\n<p><strong>Lines of credit</strong> and loan commitments provide a guarantee that undrawn funds will be available in the future, but no financial liability/asset exists until such funds are provided. Undrawn lines of credit and undisbursed loan commitments are contingent liabilities of the issuing institutions&#x2014; generally, banks (IMF, 2011). A loan or line of credit refers to regulated financial institutions only.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Some of the text on concepts and definition may be identical to Metadata submitted for Indicators 9.3.1. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 4</a></p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev3_1e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3</a></p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data were collected from the World Bank Enterprise Surveys as a pilot study on this indicator, however the preferable source of data are national statistical offices.</p>\n<p> </p>", "COLL_METHOD__GLOBAL"=>"<p>One of the main sources of data for this indicator currently available is the Enterprise Survey conducted by the World Bank (www.enterprisesurveys.org), which covers the formal sector and contains data for small and medium enterprises only (with 5 or more employees). In some countries, additional surveys, including Informal Surveys of unregistered enterprises and/or Micro Surveys for registered firms with less than five employees, are conducted and available at country level. </p>\n<p>The Enterprise Survey is based on a representative sample of enterprises run by the private sector. The surveys cover a broad range of business environment topics including access to finance, corruption, infrastructure, crime, competition, and performance measures. Since 2002, the World Bank has collected these data from face-to-face interviews with top managers and business owners in over 174,000 companies in 151 economies.</p>\n<p>The surveys have been conducted since 2002 by different units within the World Bank. Since 2005-06, most data collection efforts have been centralized within the Enterprise Analysis Unit. Data from 2006 onward is comparable across countries. The raw individual country datasets, aggregated datasets (across countries and years), panel datasets, and all relevant survey documentation are publicly available on the Enterprise Surveys web site.</p>\n<p>The indicator uses a simple weighted percentage formula, where the weights are the sampling weights. The strata for Enterprise Surveys are firm size, business sector, and geographic region within a country. Enterprise Surveys provide indicators covering manufacturing and services activities. Proportion of &#x201C;small-scale industries&#x201D; with a loan or line of credit for manufacturing only can be extracted from the micro data.</p>\n<p>Enterprises are classified as small, medium or large based on the number of employees as follows: </p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <h2>Size of enterprise</h2>\n      </td>\n      <td>\n        <h2>Number of employees</h2>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Small</p>\n      </td>\n      <td>\n        <p>5 to 19 </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Medium</p>\n      </td>\n      <td>\n        <p>20 to 99 </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Large</p>\n      </td>\n      <td>\n        <p>more than 99</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>The survey also defines an enterprise with female ownership as an enterprise having at least one female owner, and female-managed is measured by whether the top manager is a woman.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are collected through the World Bank Enterprise Surveys conducted in countries.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The data are regularly updated on the World Bank Enterprise Surveys website. The Enterprise Surveys are implemented every year in around 20 countries. Data frequency for each country is around 4 years.</p>\n<p>The UNIDO SDG-9 database is updated between March and April every year including the 9.3.2 indicator.</p>", "DATA_SOURCE__GLOBAL"=>"<p>World Bank Enterprise Surveys</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>\n<p>World Bank Enterprise Surveys</p>", "INST_MANDATE__GLOBAL"=>"<p>UNIDO, as the specialized UN agency on industrial development, has the international mandate for collecting, producing and disseminating internationally comparable industrial statistics. UNIDO&#x2019;s mandate covers (i) the maintenance and updating of international industrial statistics databases; (ii) methodological and analytical products based on statistical research and experience of maintaining internationally comparable statistics; (iii) contributions to the development and implementation of international statistical standards and methodology; and (iv) technical cooperation services to countries in the field of industrial statistics. With the repositioning of UNIDO as the focal agency for inclusive and sustainable industrial development (ISID), its statistical mandate was expanded to cover all dimensions of industrial development, including its inclusiveness and environmental sustainability.</p>", "RATIONALE__GLOBAL"=>"<p>Industrial enterprises are classified to small compared to large or medium for their distinct nature of economic organization, production capability, scale of investment and other economic characteristics. &#x201C;Small-scale industries&#x201D; can be run with a small amount of capital, relatively unskilled labor and using local materials. Despite their small contribution to total industrial output, their role in job creation, especially in developing countries is recognized to be significant where the scope of absorbing surplus labor force from traditional sectors such as agriculture or fishery is very high. &#x201C;Small-scale industries&#x201D; are capable of meeting domestic demand of basic consumer goods such as food, clothes, furniture, etc. </p>\n<p>Thus &#x201C;small-scale industries&#x201D; play an important role in the economy. However, it has quite limited access to financial services, especially in developing countries. In order to improve the skill of workers and technology for production, small-scale industrial enterprises require financial support in the form of preferential loan, credit etc. This indicator shows how widely financial institutions are serving the &#x201C;small-scale industries&#x201D;. Together with the indicator SDG 9.3.1, this indicator reflects the main message of the target 9.3 which seeks to increase the access of &#x201C;small-scale industries&#x201D; to financial services.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The main limitation of existing national data is varying size classes by country indicating that data are obtained from different target populations. Data of one country are not comparable to another. </p>\n<p>The definition of size class in many countries is tied up with the legal and policy framework of the country. It has implications on registration procedure, taxation and different waivers aimed to promote &#x201C;small-scale industries&#x201D;. Therefore, countries may agree on a common size class for compilation purposes. In this context, UNIDO proposes that all countries compile the data by a size class of &#x201C;small-scale industries&#x201D; as with less than 20 persons employed. From such data, an internationally comparable data on the share of &#x201C;small-scale industries&#x201D; in total could be derived.</p>", "DATA_COMP__GLOBAL"=>"<p>The proportion of &#x201C;small-scale industries&#x201D; with a loan or line of credit is calculated as the number of &#x201C;small-scale industries&#x201D; with an active line of credit or a loan from a financial institution in the reference year in percentage to the total number of such enterprises:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>&quot;</mo>\n        <mi>s</mi>\n        <mi>m</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mo>-</mo>\n        <mi>s</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mo>&quot;</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>o</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>&quot;</mo>\n        <mi>s</mi>\n        <mi>m</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mo>-</mo>\n        <mi>s</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mo>&quot;</mo>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>The indicator is calculated as a share of small-scale manufacturing enterprises with a loan or line of credit in the total number of small-scale manufacturing enterprises. Calculation of the indicator can be extended for other economic activities.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>This indicator is computed using data collected from the World Bank&#x2019;s Enterprise Surveys. A detailed manual and guide on the Enterprise Surveys implementation is found here (<a href=\"https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf\">https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf</a>). Section 4.4 &#x201C;Data Collection Cycle&#x201D; of this document describes the processes in place used to validate or check the survey data which is collected to ensure quality.</p>", "ADJUSTMENT__GLOBAL"=>"<p>For any given survey, during the quality checks outlined in the Enterprise Surveys manual and guide (section 4.4), if inconsistencies or mistakes are found in the data, the World Bank transmits this feedback to the fieldwork team that is conducting the survey in the first place. The fieldwork team should make sure that any data mistakes are corrected (or if the data is indeed correct, provide the justification to the World Bank) when submitting the final survey dataset.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>No treatment of missing values is applied at country level.</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>No treatment of missing values is applied at regional and global levels.</p>", "REG_AGG__GLOBAL"=>"<p>The Enterprise Surveys are implemented every year in around 20 countries. Data frequency is limited for each country around 4 years. Regional and global averages are thus computed by taking a simple average of country-level point estimates. For each economy, only the latest available year of survey data is used in this computation. Only surveys adhering to the <a href=\"https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/ES_QuestionnaireManual_2019.pdf\">Enterprise Surveys Global Methodology</a> are used to compute these regional and global aggregates.</p>", "DOC_METHOD__GLOBAL"=>"<p>International Recommendations for Industrial Statistics. (2008).</p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesM/seriesm_90e.pdf\">https://unstats.un.org/unsd/publication/seriesM/seriesm_90e.pdf</a></p>\n<p>International Standard Industrial Classification of All Economic Activities (ISIC). </p>\n<p><a href=\"https://unstats.un.org/unsd/classifications/Econ/isic\">https://unstats.un.org/unsd/classifications/Econ/isic</a></p>\n<p>International Monetary Fund. (2011). Public Sector Debt Statistics: Guide for Compilers and Users. Washington, DC: International Monetary Fund. </p>\n<p><a href=\"https://www.elibrary.imf.org/view/IMF069/11874-9781616351564/11874-9781616351564/front.xml?language=en&amp;redirect=true\">https://www.elibrary.imf.org/view/IMF069/11874-9781616351564/11874-9781616351564/front.xml?language=en&amp;redirect=true</a> </p>\n<p>World Bank Enterprise Surveys methodology.</p>\n<p><a href=\"https://www.enterprisesurveys.org/en/methodology\">https://www.enterprisesurveys.org/en/methodology</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>A detailed manual and guide on the Enterprise Surveys implementation is found here (<a href=\"https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf\">https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf</a>). This manual provides a comprehensive overview of the quality management of the Enterprise Surveys.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The process of quality assurance includes the review of survey questionnaires/documentations/metadata, examination of reliability of data, and making sure they comply with international standards (e.g. workforce concepts in the survey questions correspond to ILO standards), and examining the consistency and coherence within the data set as well as with the time series of data and the resulting indicators.</p>\n<p>The UNIDO quality assurance framework is followed to check data quality and consistency before data dissemination.</p>\n<p>UNIDO (2009). UNIDO Data Quality: A quality assurance framework for UNIDO statistical activities <a href=\"https://open.unido.org/api/documents/4814740/download/UNIDO-Publication-2009-4814740\">https://open.unido.org/api/documents/4814740/download/UNIDO-Publication-2009-4814740</a> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>For any given survey, quality checks outlined in the Enterprise Surveys manual and guide (section 4.4), are implemented during data collection (survey fieldwork), and the World Bank transmits the resulting feedback to the fieldwork team that is conducting the survey in the first place.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data for around 150 economies were collected.</p>\n<p><strong>Time series:</strong></p>\n<p>Surveys are implemented every year in around 20 countries. Data frequency for each country is around 4 years.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>No disaggregation available.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Discrepancies might arise due to the natural evolution of questionnaire design and survey methodology over time.</p>", "OTHER_DOC__GLOBAL"=>"<h2>URL:</h2>\n<p><a href=\"https://www.enterprisesurveys.org/\">https://www.enterprisesurveys.org/</a> </p>\n<p><a href=\"http://www.unido.org/statistics\">www.unido.org/statistics</a></p>\n<p><a href=\"https://stat.unido.org/\">https://stat.unido.org/</a></p>\n<h2>References:</h2>\n<p>International Monetary Fund. (2011). Public Sector Debt Statistics: Guide for Compilers and Users. Washington, DC: International Monetary Fund. </p>\n<p><a href=\"https://www.elibrary.imf.org/view/IMF069/11874-9781616351564/11874-9781616351564/front.xml?language=en&amp;redirect=true\">https://www.elibrary.imf.org/view/IMF069/11874-9781616351564/11874-9781616351564/front.xml?language=en&amp;redirect=true</a></p>\n<p> </p>\n<p>United Nations. (2002). International Standard Industrial Classification of All Economic Activities (ISIC Revision 4). New York: United Nations.</p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf</a></p>\n<p>United Nations. (2008). International Standard Industrial Classification of All Economic Activities (ISIC Revision 3.1). New York: United Nations.</p>\n<p> <a href=\"https://unstats.un.org/unsd/publication/SeriesM/seriesm_4rev3_1e.pdf\">https://unstats.un.org/unsd/publication/SeriesM/seriesm_4rev3_1e.pdf</a></p>\n<p>United Nations. (2011). International Recommendations for Industrial Statistics 2008 (IRIS 2008), New York: United Nations. <a href=\"http://dx.doi.org/10.18356/677c08dd-en\">http://dx.doi.org/10.18356/677c08dd-en</a></p>\n<p>World Bank Enterprise Surveys. 2020. Methodology. <a href=\"http://www.enterprisesurveys.org/methodology\">http://www.enterprisesurveys.org/methodology</a></p>", "indicator_sort_order"=>"09-03-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"9.4.1", "slug"=>"9-4-1", "name"=>"Emisiones de CO2 por unidad de valor añadido", "url"=>"/site/es/9-4-1/", "sort"=>"090401", "goal_number"=>"9", "target_number"=>"9.4", "global"=>{"name"=>"Emisiones de CO2 por unidad de valor añadido"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"related indicators", "embedded_feature_title"=>"", "embedded_feature_url"=>"https://eustat-des.github.io/site/embeded/9-4-1", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Emisiones de CO2 por unidad de valor añadido", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Emisiones de CO2 por unidad de valor añadido", "indicator_number"=>"9.4.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/inventario-de-gases-de-efecto-invernadero-090205/web01-a2ingair/es/", "url_text"=>"Inventario de gases de efecto invernadero", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Emisiones de CO2 por unidad de valor añadido", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.4- De aquí a 2030, modernizar la infraestructura y reconvertir las industrias para que sean sostenibles, utilizando los recursos con mayor eficacia y promoviendo la adopción de tecnologías y procesos industriales limpios y ambientalmente racionales, y logrando que todos los países tomen medidas de acuerdo con sus capacidades respectivas", "definicion"=>"Emisiones de dióxido de carbono (CO2) por unidad de PIB real", "formula"=>"<b>Emisiones de CO2 por unidad de valor añadido</b>\n\n$$PPIBECO2^{t} = \\frac{ECO2^{t}}{PIB_{2022}^{t}} $$\n\ndonde:\n\n$ECO2^{t} =$ emisiones de CO2 en el año $t$\n\n$PIB_{2022}^{t} =$ producto interior bruto en volumen encadenado con referencia 2022 en el año $t$\n\n <br>\n \n <b>Emisiones de CO2 por unidad de valor añadido del sector manufacturero</b>\n\n$$PVABECO2_{manufacturero}^{t} = \\frac{ECO2_{manufacturero}^{t}}{VAB_{manufacturero\\, 2022}^{t}} $$\n\ndonde:\n\n$ECO2_{manufacturero}^{t} =$ emisiones de CO2 del sector manufacturero en el año $t$\n\n$VAB_{manufacturero\\, 2022}^{t} =$ valor añadido bruto del sector manufacturero en volumen encadenado con referencia 2022 en el año $t$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl indicador de emisiones de CO2 por unidad de valor añadido representa la \ncantidad de emisiones procedentes de la combustión de combustibles producidas \npor una actividad económica, por unidad de producción económica. \n\nCuando se calcula para el conjunto de la economía, combina los efectos de la \nintensidad media de carbono de la combinación energética (vinculada a las \nproporciones de los diversos combustibles fósiles en el total); de la estructura \nde una economía (vinculada al peso relativo de los sectores más o menos intensivos \nen energía); de la eficiencia media en el uso de la energía. \n\nCuando se calcula para el sector manufacturero (emisiones de CO2 procedentes de \nla combustión de combustibles por unidad de valor añadido de la industria \nmanufacturera), mide la intensidad de carbono de la producción económica \nmanufacturera, y sus tendencias son resultado de los cambios en la \nintensidad media de carbono de la combinación energética utilizada, \nla estructura del sector manufacturero, la eficiencia energética de las tecnologías \nde producción en cada subsector y el valor económico de los diversos productos. \n\nEn general, las industrias manufactureras están mejorando la intensidad de sus \nemisiones a medida que los países avanzan hacia niveles más altos de industrialización, \npero cabe señalar que las intensidades de las emisiones también pueden \nreducirse mediante cambios estructurales y la diversificación de productos en \nla industria manufacturera.\n\nLas emisiones de CO2 representan alrededor del 80% de todas las emisiones de GEI de \nlos procesos de fabricación.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.4.1&seriesCode=EN_ATM_CO2GDP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Emisiones de dióxido de carbono por unidad de PIB en Paridad de Poder Adquisitivo (kilogramos de CO2 por dólar estadounidense constante de 2021) EN_ATM_CO2GDP</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.4.1&seriesCode=EN_ATM_CO2MVA&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\"> Emisiones de dióxido de carbono de las industrias manufactureras por unidad de valor añadido manufacturero (kilogramos de CO2 por dólar estadounidense constante de 2015) EN_ATM_CO2MVA</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-04-01.pdf\">Metadatos 9-4-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Emisiones de CO2 por unidad de valor añadido", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.4- De aquí a 2030, modernizar la infraestructura y reconvertir las industrias para que sean sostenibles, utilizando los recursos con mayor eficacia y promoviendo la adopción de tecnologías y procesos industriales limpios y ambientalmente racionales, y logrando que todos los países tomen medidas de acuerdo con sus capacidades respectivas", "definicion"=>"Carbon dioxide (CO2) emissions per unit of real GDP", "formula"=>"<b>CO2 emissions per unit of value added</b>\n\n$$PPIBECO2^{t} = \\frac{ECO2^{t}}{PIB_{2022}^{t}} $$\n\nwhere:\n\n$ECO2^{t} =$ CO2 emissions in year $t$\n\n$PIB_{2022}^{t} =$ gross domestic product in chain volumes with reference to 2022 in year $t$\n\n <br>\n \n <b>CO2 emissions per unit of added value in the manufacturing sector</b>\n\n$$PVABECO2_{manufacturing}^{t} = \\frac{ECO2_{manufacturing}^{t}}{VAB_{manufacturing\\, 2022}^{t}} $$\n\nwhere:\n\n$ECO2_{manufacturing}^{t} =$ CO2 emissions from the manufacturing sector in year $t$\n\n$VAB_{manufacturing\\, 2022}^{t} =$ Gross added value of the manufacturing sector in chained volume with reference to 2022 in year $t$\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe indicator CO2 emissions per unit of value added represents the amount of emissions \nfrom fuel combustion produced by an economic activity, per unit of economic output. \n\nWhen computed for the whole economy, it combines effects of the average carbon intensity \nof the energy mix (linked to the shares of the various fossil fuels in the total); of the \nstructure of an economy (linked to the relative weight of more or less energy-intensive sectors); \nof the average efficiency in the use of energy. \n\nWhen computed for the manufacturing sector (CO2 emissions from fuel combustion per unit of \nmanufacturing value added), it measures the carbon intensity of the manufacturing economic \noutput, and its trends result from changes in the average carbon intensity of the energy mix \nused, the structure of the manufacturing sector, the energy efficiency of production technologies \nin each sub-sector and the economic value of the various output. \n\nManufacturing industries are generally improving their emission intensity as countries move to \nhigher levels of industrialization, but it should be noted that emission intensities can also be \nreduced through structural changes and product diversification in manufacturing. \n\nCO2 emission accounts for around 80% of all GHG emission from the manufacturing processes. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.4.1&seriesCode=EN_ATM_CO2GDP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Carbon dioxide emissions per unit of GDP PPP (kilogrammes of CO2 per constant 2021 United States dollars) EN_ATM_CO2GDP</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.4.1&seriesCode=EN_ATM_CO2MVA&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\"> Carbon dioxide emissions from manufacturing industries per unit of manufacturing value added (kilogrammes of CO2 per constant 2015 United States dollars) EN_ATM_CO2MVA</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-04-01.pdf\">Metadata 9-4-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Emisiones de CO2 por unidad de valor añadido", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.4- De aquí a 2030, modernizar la infraestructura y reconvertir las industrias para que sean sostenibles, utilizando los recursos con mayor eficacia y promoviendo la adopción de tecnologías y procesos industriales limpios y ambientalmente racionales, y logrando que todos los países tomen medidas de acuerdo con sus capacidades respectivas", "definicion"=>"Isuritako karbono dioxidoa (CO2), barne-produktu gordin errealeko unitate bakoitzeko", "formula"=>"<b>CO2 isurketak balio erantsiko unitate bakoitzeko</b>\n\n$$PPIBECO2^{t} = \\frac{ECO2^{t}}{PIB_{2022}^{t}} $$\n\nnon:\n\n$ECO2^{t} =$ isuritako CO2a $t$ urtean\n\n$PIB_{2022}^{t} =$ barne-produktu gordina, 2022 erreferentziarekin kateatutako bolumenean $t$ urtean\n\n <br>\n \n <b>CO2 isurketak manufaktura-sektoreko balio erantsiaren unitateko</b>\n\n$$PVABECO2_{manufaktura}^{t} = \\frac{ECO2_{manufaktura}^{t}}{VAB_{manufaktura\\, 2022}^{t}} $$\n\nnon:\n\n$ECO2_{manufaktura}^{t} =$ manufaktura-sektorearen CO2 isuriak $t$ urtean\n\n$VAB_{manufaktura\\, 2022}^{t} =$ manufaktura sektorearen barne-produktu gordina, \n2022 erreferentziarekin kateatutako bolumenean $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nBalio erantsiko unitateen araberako CO2 emisioen adierazleak adierazten du jarduera ekonomiko batek \nsortutako erregaien errekuntzatik sortutako emisioen kopurua, ekoizpen ekonomikoko unitateko. \n\nEkonomia osorako kalkulatzen denean, honako hauen efektuak uztartzen ditu: konbinazio energetikoko \nbatez besteko karbono-intentsitatea (erregai fosil desberdinen proportzioei lotuta); ekonomia baten \negitura (energian intentsiboak –gehiago edo gutxiago– diren sektoreetako pisu erlatiboari lotuta); \nbatez besteko eraginkortasuna energiaren erabileran. \n\nManufaktura-sektorerako kalkulatzen denean (erregaien errekuntzatik eratorritako CO2 emisioak, \nmanufaktura-industriako balio erantsiko unitatearen arabera), manufaktura-ekoizpen ekonomikoaren \nkarbono-intentsitatea neurtzen da, eta haren joerak honako hauetan egondako aldaketen emaitza dira: \nerabilitako konbinazio energetikoaren karbonoaren batez besteko intentsitatea, manufaktura-sektorearen \negitura, ekoizpen-teknologien eraginkortasun energetikoa azpisektore bakoitzean, eta produktu \ndesberdinen balio ekonomikoa. \n\nOrokorrean, manufaktura-industriak beren emisioen intentsitatea hobetzen ari dira, herrialdeek \nindustrializazio-maila altuagoetara jotzen duten neurrian, baina aipatzekoa da emisioen intentsitateak \nmurriztu ere egin daitezkeela manufaktura-industriako egiturazko aldaketen eta produktuak dibertsifikatzearen \nondorioz. \n\nCO2 emisioak fabrikazio-prozesuetako berotegi-efektuko gasen emisio guztien % 80 inguru dira. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.4.1&seriesCode=EN_ATM_CO2GDP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Karbono dioxidoaren isuriak, Eros Ahalmenaren Parekotasunean, BPGaren unitateko (CO2 kilogramoak 2021eko AEBetako dolar konstante bakoitzeko) EN_ATM_CO2GDP</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.4.1&seriesCode=EN_ATM_CO2MVA&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC4_C\"> Manufaktura-industrien karbono dioxidoaren isurketak manufakturaren balio erantsiko unitate bakoitzeko (CO2 kilogramoak 2015eko AEBetako dolar konstante bakoitzeko) EN_ATM_CO2MVA</a> UNSTATS\n", "comparabilidad"=>"EAEko adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-04-01.pdf\">Metadatuak 9-4-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 9.4: By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies and industrial processes, with all countries taking action in accordance with their respective capabilities</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 9.4.1: CO<sub>2</sub> emission per unit of value added</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_ATM_CO2 - Carbon dioxide emissions from fuel combustion [9.4.1] </p>\n<p>EN_ATM_CO2GDP - Carbon dioxide emissions per unit of GDP PPP (kilogrammes of CO<sub>2</sub> per constant 2021 United States dollars)[9.4.1] </p>\n<p>EN_ATM_CO2MVA - Carbon dioxide emissions from manufacturing industries per unit of manufacturing value added (kilogrammes of CO<sub>2</sub> per constant 2015 United States dollars)[9.4.1] </p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 7.2.1: Renewable energy share in the total final energy consumption</p>\n<p>Indicator 7.3.1: Energy intensity measured in terms of primary energy and GDP</p>\n<p>Indicator 13.2.2 : Total greenhouse gas emissions per year</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Energy Agency (IEA)</p>\n<p>United Nations Industrial Development Organization (UNIDO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Energy Agency (IEA)</p>\n<p>United Nations Industrial Development Organization (UNIDO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Carbon dioxide (here after, CO<sub>2</sub>) emissions per unit of value added is an indicator computed as ratio between CO<sub>2</sub> emissions from fuel combustion and the value added of associated economic activities. The indicator can be computed for the whole economy (total CO<sub>2</sub> emissions/GDP) or for specific sectors, notably the manufacturing sector (CO<sub>2</sub> emissions from manufacturing industries per manufacturing value added (MVA)). </p>\n<p><strong>Concepts:</strong></p>\n<p>Total CO<sub>2</sub> emissions for an economy are estimated based on energy consumption data for all sectors.</p>\n<p>CO<sub>2</sub> emissions from manufacturing are based on energy data collected across the following subsectors (energy used for transport by industry is not included here but reported under transport):</p>\n<ul>\n  <li>Iron and steel industry [ISIC Group 241 and Class 2431];</li>\n  <li>Chemical and petrochemical industry [ISIC Divisions 20 and 21] excluding petrochemical feedstocks;</li>\n  <li>Non-ferrous metals basic industries [ISIC Group 242 and Class 2432];</li>\n  <li>Non-metallic minerals such as glass, ceramic, cement, etc. [ISIC Division 23];</li>\n  <li>Transport equipment [ISIC Divisions 29 and 30];</li>\n  <li>Machinery comprises fabricated metal products, machinery and equipment other than transport equipment [ISIC Divisions 25 to 28];</li>\n  <li>Food and tobacco [ISIC Divisions 10 to 12];</li>\n  <li>Paper, pulp and printing [ISIC Divisions 17 and 18];</li>\n  <li>Wood and wood products (other than pulp and paper) [ISIC Division 16];</li>\n  <li>Textile and leather [ISIC Divisions 13 to 15];</li>\n  <li>Non-specified (any manufacturing industry not included above) [ISIC Divisions 22, 31 and 32].</li>\n</ul>\n<p>Energy data are collected at a country level, based on internationally agreed standards (UN International Recommendations on Energy Statistics (IRES)). CO<sub>2</sub> emissions need to be estimated based on energy data and on internationally agreed methodologies (2006 IPCC Guidelines for National GHG Inventories). </p>\n<p>The IEA collects national energy data, according to internationally agreed energy statistics definitions and estimates CO<sub>2</sub> emissions based on the 2006 IPCC Guidelines for National GHG Inventories&#x2019; Tier 1 methodology, producing internationally comparable CO<sub>2</sub> emissions data for over 150 countries and regions.</p>\n<p>The gross value added measures the contribution to the economy of each individual producer, industry or sector in a country. The gross value added generated by any unit engaged in production activity can be calculated as the residual of the units&#x2019; total output less intermediate consumption, goods and services used up in the process of producing the output, or as the sum of the factor incomes generated by the production process (System of National Accounts 2008). Manufacturing refers to industries belonging to the sector C defined by International Standard Industrial Classification of All Economic Activities (ISIC) Revision 4, or D defined by ISIC Revision 3.</p>\n<p>GDP represents the sum of gross value added from all institutional units resident in the economy. For the purpose on comparability over time and across countries, GDP based on purchasing power parity (PPP) is used to calculate the total CO<sub>2</sub> emissions intensity of the economy. MVA is estimated in terms of constant prices in USD. The current series are given at constant prices of 2015.</p>", "UNIT_MEASURE__GLOBAL"=>"<ul>\n  <li>CO<sub>2</sub> emissions from fuel combustion: millions of tonnes </li>\n  <li>CO<sub>2</sub> emissions per unit of GDP PPP: kilogrammes of CO<sub>2</sub> per constant 2021 USD PPP </li>\n  <li>CO<sub>2</sub> emissions from manufacturing industries per unit of MVA: kilogrammes of CO<sub>2</sub> per constant 2015 USD </li>\n</ul>", "CLASS_SYSTEM__GLOBAL"=>"<p><a href=\"https://unstats.un.org/unsd/energy/ires/\">UN International Recommendations for Energy Statistics</a> (IRES)</p>\n<p><a href=\"http://www.ipcc-nggip.iges.or.jp/public/2006gl/\">2006 IPCC Guidelines for National Greenhouse Gas Inventories</a></p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 4</a></p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev3_1e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3</a></p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data on total CO<sub>2</sub> emissions from fuel combustion, also disaggregated by sector, are taken from the International Energy Agency (IEA) Greenhouse Gas Emissions from Energy database available at: (<a href=\"https://www.iea.org/data-and-statistics/data-product/greenhouse-gas-emissions-from-energy\">https://www.iea.org/data-and-statistics/data-product/greenhouse-gas-emissions-from-energy</a>).</p>\n<p>The IEA produces the indicator on total CO<sub>2</sub> emissions/GDP, based on secondary sources for GDP (International Monetary Fund. World Economic Outlook (IMF WEO), World Bank Development indicators and CEPII &#x2013; CHELEM database).</p>\n<p>UNIDO maintains the MVA database. Figures for updates are obtained from national account estimates produced by UN Statistics Division (UNSD) and from national publications.</p>", "COLL_METHOD__GLOBAL"=>"<p>The IEA collects energy data at the national level according to harmonised international definitions and questionnaires, as described in the UN International Recommendations for Energy Statistics available at: (<a href=\"https://unstats.un.org/unsd/energy/ires/\">unstats.un.org/unsd/energy/ires/</a>).</p>\n<p>The estimates of CO<sub>2</sub> emissions from fuel combustion are calculated by the IEA based on the IEA energy data and the default methods and emission factors from the 2006 IPCC Guidelines for National GHG Inventories available at: (<a href=\"http://www.ipcc-nggip.iges.or.jp/public/2006gl/\">ipcc-nggip.iges.or.jp/public/2006gl/</a>). More information on methodologies from the IEA is available at: <a href=\"https://www.iea.org/data-and-statistics/data-product/greenhouse-gas-emissions-from-energy\">https://www.iea.org/data-and-statistics/data-product/greenhouse-gas-emissions-from-energy</a></p>\n<p>The most recent GDP estimates published by the International Monetary Fund and the World Bank with reference year of 2021 have been used when calculating CO<sub>2</sub> emissions per unit of GDP indicator. Additionally, missing years for countries with at least one data point for GDP reported by IMF or WB have been estimated using CEPII - CHELEM growth rates.</p>\n<p>For the calculation of the CO<sub>2</sub> emissions from manufacturing industries per unit of MVA indicator, the MVA and GDP country data are collected through a national account questionnaire (NAQ) sent by UNSD. More information on the methodology is available at:</p>\n<p><a href=\"https://unstats.un.org/unsd/snaama/methodology.pdf\">unstats.un.org/unsd/snaama/methodology.pdf</a>.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data collection is carried out by receiving data electronically throughout the year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The IEA Greenhouse Gas Emissions from Energy statistics are published in April and August with progressively broader geographical coverage (publishing full information for two calendar years prior and selected information for one year prior).</p>\n<p>UNIDO MVA database is updated between March and April every year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>International Energy Agency (IEA), United Nations Statistics Division (UNSD), United Nations Industrial Development Organization (UNIDO)</p>\n<p><strong>Description:</strong></p>\n<p>National Statistical Offices (NSOs) and national energy data collecting agencies provide the data to UNSD and IEA.</p>", "COMPILING_ORG__GLOBAL"=>"<p><strong>Name:</strong></p>\n<p>United Nations Industrial Development Organization (UNIDO), International Energy Agency (IEA)</p>\n<p><strong>Description:</strong></p>\n<p>IEA provides data on total CO<sub>2</sub> emissions, CO<sub>2</sub> emissions/GDP PPP and manufacturing CO<sub>2</sub> emissions.</p>\n<p>UNIDO compiles the data using its source for MVA data and IEA for data on CO<sub>2</sub> emissions.</p>", "INST_MANDATE__GLOBAL"=>"<p>IEA as one of the custodian agencies responsible for monitoring progress towards the SDG 7.3 target, leverage on their national data efforts and add value by promoting coherent standards, definitions and methodologies for both raw data and the derived indicators with the ultimate goal of producing international comparable datasets. </p>\n<p>UNIDO, as the specialized UN agency on industrial development, has the international mandate for collecting, producing and disseminating internationally comparable industrial statistics. UNIDO&#x2019;s mandate covers (i) the maintenance and updating of international industrial statistics databases; (ii) methodological and analytical products based on statistical research and experience of maintaining internationally comparable statistics; (iii) contributions to the development and implementation of international statistical standards and methodology; and (iv) technical cooperation services to countries in the field of industrial statistics. With the repositioning of UNIDO as the focal agency for inclusive and sustainable industrial development (ISID), its statistical mandate was expanded to cover all dimensions of industrial development, including its inclusiveness and environmental sustainability.</p>", "RATIONALE__GLOBAL"=>"<p>The indicator CO<sub>2</sub> emissions per unit of value added represents the amount of emissions from fuel combustion produced by an economic activity, per unit of economic output. When computed for the whole economy, it combines effects of the average carbon intensity of the energy mix (linked to the shares of the various fossil fuels in the total); of the structure of an economy (linked to the relative weight of more or less energy-intensive sectors); of the average efficiency in the use of energy. When computed for the manufacturing sector (CO<sub>2</sub> emissions from fuel combustion per unit of manufacturing value added), it measures the carbon intensity of the manufacturing economic output, and its trends result from changes in the average carbon intensity of the energy mix used, the structure of the manufacturing sector, the energy efficiency of production technologies in each sub-sector and the economic value of the various output. Manufacturing industries are generally improving their emission intensity as countries move to higher levels of industrialization, but it should be noted that emission intensities can also be reduced through structural changes and product diversification in manufacturing. </p>\n<p>CO<sub>2</sub> emission accounts for around 80% of all GHG emission from the manufacturing processes. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Estimation of CO<sub>2</sub> emission data is not systematized in many countries, although is performed internationally based on harmonised energy data collected at national level. Energy data collection is generally well established, although in some cases national methodologies may differ from internationally agreed methodologies. National data sources include statistical offices, energy ministries, environment agencies, among others. Energy consumption data and value added data are coming from different data sources which may raise some consistency issues.</p>", "DATA_COMP__GLOBAL"=>"<p>CO<sub>2</sub> emissions from fuel combustion are estimated based on energy consumption and on the 2006 IPCC Guidelines on National GHG Inventories.</p>\n<p>The total intensity of the economy is defined as the ratio of total CO<sub>2</sub> emissions from fuel combustion and per unit of GDP. For international comparison purposes, GDP is measured in constant terms at purchasing power parity and the indicator is expressed in kilogrammes of CO<sub>2 </sub>per constant 2021 USD PPP for the current series.</p>\n<p>The sectoral intensity is defined as CO<sub>2</sub> emission from manufacturing (in physical measurement unit such as tonnes) divided by manufacturing value added (MVA) in constant 2015 USD.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n        <mi>O</mi>\n      </mrow>\n      <mrow>\n        <mn>2</mn>\n      </mrow>\n    </msub>\n    <mi>&amp;nbsp;</mi>\n    <mi>e</mi>\n    <mi>m</mi>\n    <mi>i</mi>\n    <mi>s</mi>\n    <mi>s</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>u</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>v</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>u</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mi>d</mi>\n    <mi>d</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>C</mi>\n        <mi>O</mi>\n        <mn>2</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>m</mi>\n        <mi>i</mi>\n        <mi>s</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>f</mi>\n        <mi>a</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>g</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>k</mi>\n        <mi>g</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>M</mi>\n        <mi>V</mi>\n        <mi>A</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>U</mi>\n        <mi>S</mi>\n        <mi>D</mi>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The IEA has several internal procedures in place for energy data validation. This includes energy balance checks, time series analysis and reconciling differences in statistical classifications and definitions.</p>\n<p>UNIDO engages with countries in regular consultations during the data collection process to ensure data quality and international comparability.</p>", "ADJUSTMENT__GLOBAL"=>"<p>The country specific commodity balances underlying the IEA CO<sub>2</sub> emissions estimates are based on national energy data of heterogeneous nature converted and adapted to fit the IEA format and methodology. Considerable effort has been made to ensure that the data adhere to the IEA definitions based on the guidelines provided by IRES. Nevertheless, energy statistics at the national level are often collected using criteria and definitions which differ, sometimes considerably, from those of international organisations. This is especially true for non-OECD countries, which are submitting data to the IEA on a voluntary basis. The IEA has identified most of these differences and, where possible, adjusted the data to meet international definitions. For details on recognized country specific anomalies and the corresponding adjustments, please refer to country specific notes included in the IEA World energy balances documentation file available at: <a href=\"http://wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf\">wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf</a></p>\n<p>UNIDO compiles the MVA data based on the UNSD National Accounts Main Aggregates Database (NAMAD) and national publications. UNSD collects national accounts data through a regular consultation with countries and areas by sending the UN NAQ to obtain important information about differences in concept, scope, coverage and classification used. The final estimates are provided to facilitate international comparability. More detailed information on estimation methods is available here: <a href=\"https://unstats.un.org/unsd/snaama/assets/pdf/methodology.pdf\">https://unstats.un.org/unsd/snaama/assets/pdf/methodology.pdf</a></p>\n<p>The MVA data are nowcasted by UNIDO to enhance a timely analysis of manufacturing trends.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Providing all the elements of energy balance, underlying the IEA CO<sub>2 </sub>emissions estimations has often required estimations. Estimations have been generally made after consultation with national statistical offices, energy companies, utilities and national energy experts.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>In the compilation of the IEA energy balances which are the underlying for estimating the CO<sub>2</sub> emissions and in addition to estimates at a country level, adjustments addressing differences in definitions alongside estimations for informal and/or confidential trade, production or stock changes of energy products are sometimes required to complete major aggregates, when key statistics are missing. Such estimations and adjustments implemented by IEA have been generally made after consultation with national statistical offices, energy companies, utilities and national energy experts.</p>\n<p>No imputation is provided if values are missing for the entire country or the region. It can only be projected from the data reported for previous years.</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates are calculated by summing both the numerator and denominator over the group of relevant countries.</p>", "DOC_METHOD__GLOBAL"=>"<p>It is important that energy data collection and emissions calculations are consistent with international standards. CO<sub>2</sub> emissions need to be estimated based on energy data and on internationally agreed methodologies. Energy data are collected at a country level, based on internationally agreed standards (UN International Recommendations on Energy Statistics (IRES)). The IEA collects the energy data from countries, according to internationally agreed energy statistics definitions and estimates CO<sub>2</sub> emissions based on the 2006 IPCC Guidelines for National GHG Inventories&#x2019; producing internationally comparable CO<sub>2</sub> emissions data for over 150 countries and regions.</p>\n<p>The IEA collects energy data through standardised fuels-specific questionnaires shared with OECD Member countries and more selected economies. These questionnaires are available at: </p>\n<p><a href=\"https://www.iea.org/about/data-and-statistics/questionnaires\">iea.org/areas-of-work/data-and-statistics/questionnaires</a>.</p>\n<p>The IEA energy balances for all other countries are based on national energy data of heterogeneous nature, converted and adapted to fit the IEA format and methodology based on IRES recommendations.</p>\n<p>More detail on methods and sources is available at: <a href=\"http://wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf\">wds.iea.org/wds/pdf/WORLDBAL_Documentation.pdf</a>.</p>\n<p>For the underlying energy data, the reference is the UN International Recommendations on Energy Statistics, available at: <a href=\"https://unstats.un.org/unsd/energy/ires/\">unstats.un.org/unsd/energy/ires/</a>. </p>\n<p>To estimate CO<sub>2</sub> emissions, the internationally agreed reference is the 2006 IPCC Guidelines on National GHG Inventories available at: <a href=\"https://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html\">ipcc-nggip.iges.or.jp/public/2006gl/</a>. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The IEA, in co-operation with the Statistical Office of the European Communities (Eurostat), has published an Energy Statistics Manual. This Manual helps the energy statisticians have a better grasp of definitions, units and methodologies. Moreover, IEA has established a quality management framework based on the internationally recognized guidelines recommended by IRES to ensure quality of statistical products. </p>\n<p>The National Accounts Section of the UNSD supports the implementation programme of the SNA by developing and updating supporting normative standards, training material and compilation guidance for the implementation of national accounts and supporting economic statistics and maintaining a knowledge base on economic statistics. Moreover, UNSD provides substantive service to the Committee on Contributions of the Fifth Committee of the United Nations on technical aspects of the elements of scale methodology for assessing the contributions to the United Nations by Member States. UNIDO collects and disseminates National Accounts statistics in consultation with UNSD.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The IEA has extensive data quality checks on the energy data submissions (around 30 statisticians working on it), and iterates with countries on data issues and how to address them. </p>\n<p>The IEA also works in cooperation with the IPCC and the UNFCCC to ensure the highest consistency between international methodologies and those adopted at the IEA; the IEA validates energy data submitted to the UNFCCC by countries within their inventories. The IEA convenes international workshops among partner Agencies working on energy data to ensure that consistency between energy data at global level is enhanced continuously, and methodologies are harmonised.</p>\n<p><a href=\"https://unstats.un.org/unsd/unsystem/Documents/QAF-UNIDO.pdf\">The UNIDO Quality Assurance Framework</a> is followed to ensure that the statistical activities of UNIDO are relevant and the data compiled and disseminated are accurate, complete within the defined scope and coverage, timely, comparable in terms of internationally recommended methods and classification standards and internally coherent to variables included in the datasets. While these generally accepted, broad dimensions of quality of statistical data may be defined in each NSO&apos;s own quality assurance framework. UNIDO makes maximum effort that data produced from the statistical operation undertaken with the UNIDO technical cooperation are accurate, internationally comparable and coherent.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The IEA has an extensive data quality validation process through exchange with national data providers worldwide. It also convenes its Energy Statistics Development Group meeting to discuss energy statistics developments with its Members, and cooperates with partners worldwide to ensure coherence of data and methods.</p>\n<p>The National Accounts Section of the UNSD and UNIDO employ a wide range of data quality techniques and consultations with national providers to assure quality principles supported by the Fundamental Principles of Official Statistics. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are available for more than 140 countries.</p>\n<p><strong>Time series:</strong></p>\n<p>Data for this indicator are available as of 2000 in the UN Global SDG Database, but longer time series are available in the IEA database (IEA Greenhouse Gas Emissions from Energy) and the UNIDO MVA database.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data can be presented for national totals, for the manufacturing sector, and by industrial subsector.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The IEA Greenhouse Gas Emissions from Energy, used for calculating these indicators, is a global database obtained following harmonised definitions and comparable methodologies across countries. However, it does not represent an official source for national GHG inventories submissions by the countries.</p>\n<p>Difference may arise due to different sources of official energy data, dissimilarities in the underlying methodologies, adjustments and estimations. More information on these sources of differences is available in the IEA database documentation file available at:</p>\n<p><a href=\"https://www.iea.org/data-and-statistics/data-product/greenhouse-gas-emissions-from-energy\">https://iea.blob.core.windows.net/assets/d755e4d6-9572-4549-9421-7d2bc377cd2f/WORLD_GHG_Documentation.pdf</a></p>\n<p>Additionally, difference may arise if the country has not submitted energy consumption data adequately disaggregated by sector or by energy sources and/or due to conversion of value data into USD.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://www.iea.org/data-and-statistics/\">iea.org/statistics </a></p>\n<p><a href=\"https://www.iea.org/data-and-statistics/data-product/greenhouse-gas-emissions-from-energy\">https://iea.blob.core.windows.net/assets/d755e4d6-9572-4549-9421-7d2bc377cd2f/WORLD_GHG_Documentation.pdf</a></p>\n<p><a href=\"http://www.unido.org/statistics\">unido.org/statistics</a></p>\n<p><a href=\"https://unstats.un.org/unsd/snaama/methodology.pdf\">unstats.un.org/unsd/snaama/methodology.pdf</a></p>\n<p><strong>References:</strong></p>\n<p>Boudt, K., Todorov, V., &amp; Upadhyaya, S. (2009). Nowcasting manufacturing value added for cross-country comparison. <em>Statistical Journal of the IAOS</em>, <em>26</em>(1, 2), 15-20.</p>\n<p>International Yearbook of Industrial Statistics; UNIDO: </p>\n<p><a href=\"https://www.unido.org/resources-publications-flagship-publications/international-yearbook-industrial-statistics\">unido.org/resources-publications-flagship-publications/international-yearbook-industrial-statistics</a></p>\n<p>IEA, Greenhouse Gas Emissions from Energy:</p>\n<p><a href=\"https://www.iea.org/data-and-statistics/data-product/greenhouse-gas-emissions-from-energy\">https://www.iea.org/data-and-statistics/data-product/greenhouse-gas-emissions-from-energy</a></p>\n<p>System of National Accounts 2008:</p>\n<p><a href=\"https://unstats.un.org/unsd/nationalaccount/sna2008.asp\">unstats.un.org/unsd/nationalaccount/sna2008.asp</a></p>\n<p>The International Monetary Fund. World Economic Outlook (IMF WEO):</p>\n<p><a href=\"https://www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases#sort=%40imfdate%20descending\">World Economic Outlook Databases (imf.org)</a></p>\n<p>The World Bank Development Indicators:</p>\n<p><a href=\"https://databank.worldbank.org/source/world-development-indicators\">databank.worldbank.org/source/world-development-indicators</a></p>\n<p>CEPII &#x2013; CHELEM database:</p>\n<p><a href=\"http://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=17\">http://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=17</a></p>\n<p>International Standard Industrial Classification of All Economic Activities 2008:</p>\n<p><a href=\"https://unstats.un.org/unsd/iiss/International-Standard-Industrial-Classification-of-all-Economic-Activities-ISIC.ashx\">unstats.un.org/unsd/iiss/International-Standard-Industrial-Classification-of-all-Economic-Activities-ISIC.ashx</a></p>", "indicator_sort_order"=>"09-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"embed", "label"=>"related indicators"}]}, {"number"=>"9.4.E1", "slug"=>"9-4-E1", "name"=>"Intensidad de las emisiones atmosféricas de la industria (Indicador UE sdg_09_70)", "url"=>"/site/es/9-4-E1/", "sort"=>"0904E1", "goal_number"=>"9", "target_number"=>"9.4", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Partículas en suspensión", "value"=>"PM 2,5"}, {"field"=>"Partículas en suspensión", "value"=>"PM 10"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Intensidad de las emisiones atmosféricas de la industria (Indicador UE sdg_09_70)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Intensidad de las emisiones atmosféricas de la industria (Indicador UE sdg_09_70)", "indicator_number"=>"9.4.E1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/inventario-de-gases-de-efecto-invernadero-090205/web01-a2ingair/es/", "url_text"=>"Inventario de gases de efecto invernadero", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Intensidad de las emisiones atmosféricas de la industria (Indicador UE sdg_09_70)", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.4- De aquí a 2030, modernizar la infraestructura y reconvertir las industrias para que sean sostenibles, utilizando los recursos con mayor eficacia y promoviendo la adopción de tecnologías y procesos industriales limpios y ambientalmente racionales, y logrando que todos los países tomen medidas de acuerdo con sus capacidades respectivas", "definicion"=>"Emisiones de PM10 y PM2,5 por unidad de valor añadido del sector manufacturero", "formula"=>"\n$$PVABEPM_{manufacturero}^{t} = \\frac{EPM_{manufacturero}^{t}}{VAB_{manufacturero\\, 2022}^{t}} $$\n\ndonde:\n\n$EPM_{manufacturero}^{t} =$ emisiones de partículas en suspensión del sector manufacturero en el año $t$\n\n$VAB_{manufacturero\\, 2022}^{t} =$ valor añadido bruto del sector manufacturero en volumen encadenado con referencia 2022 en el año  $t$\n", "desagregacion"=>"\nPartículas en suspensión: partículas de tamaño inferior a 10 micras (PM10); partículas de tamaño inferior a 2,5 micras (PM25)", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl indicador forma parte del conjunto de indicadores de los Objetivos de Desarrollo \nSostenible (ODS) de la UE. Se utiliza para supervisar el progreso hacia el ODS 9 sobre \nindustria, innovación e infraestructura, que está integrado en las prioridades de la \nComisión Europea en el marco del Pacto Verde Europeo, una economía al servicio de las \npersonas y un modo de vida europeo. \n\nEl ODS 9 reconoce la importancia del progreso tecnológico y la innovación para \nencontrar soluciones duraderas a los desafíos sociales, económicos y medioambientales, \ncomo la creación de nuevos puestos de trabajo y la promoción de la eficiencia energética \ny de los recursos. Para fomentar la innovación y el espíritu emprendedor, el ODS 9 \ntambién busca aumentar el acceso a los servicios financieros para las pequeñas empresas \ny reducir la brecha digital aumentando el acceso a las tecnologías de la información y la comunicación.\n\nEste indicador mide la intensidad de las emisiones de partículas en suspensión (PM10 y PM2,5) \ndel sector manufacturero (sector C de la NACE Rev. 2). Las emisiones atmosféricas se definen como \nflujos de materiales gaseosos y particulados emitidos a la atmósfera. \n\nLas partículas finas y gruesas \n(PM10) tienen un diámetro inferior a 10 micrómetros y pueden llegar a las zonas más profundas de \nlos pulmones, donde pueden causar inflamación y agravar el estado de salud de las personas que \npadecen enfermedades cardíacas y pulmonares. \n\nLas partículas finas (PM2,5) tienen un \ndiámetro inferior a 2,5 micrómetros y, por tanto, son un subconjunto de las partículas PM10. \nSus efectos negativos para la salud son más graves que los de las PM10 porque pueden llegar a \nmayores niveles en los pulmones y pueden ser más tóxicas. \n\nLa intensidad de las emisiones \nse calcula dividiendo las emisiones de PM del sector por su valor añadido bruto (VAB), que \nse define como la producción (a precios básicos) menos el consumo intermedio (a precios de comprador).\n\nFuente: Eurostat\n", "dato_global"=>"\n<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_09_70/default/table?lang=en&category=sdg.sdg_09\">Intensidad de las emisiones atmosféricas de la industria (sdg_09_70)</a> Eurostat\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de la Unión Europea.", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_09_70_esmsip2.htm\">Metadatos sdg_09_70.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Intensidad de las emisiones atmosféricas de la industria (Indicador UE sdg_09_70)", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.4- De aquí a 2030, modernizar la infraestructura y reconvertir las industrias para que sean sostenibles, utilizando los recursos con mayor eficacia y promoviendo la adopción de tecnologías y procesos industriales limpios y ambientalmente racionales, y logrando que todos los países tomen medidas de acuerdo con sus capacidades respectivas", "definicion"=>"PM10 and PM2.5 emissions per unit of added value in the manufacturing sector", "formula"=>"\n$$PVABEPM_{manufacturing}^{t} = \\frac{EPM_{manufacturing}^{t}}{VAB_{manufacturing\\, 2022}^{t}} $$\n\nwhere:\n\n$EPM_{manufacturing}^{t} =$ emissions of suspended particles from the manufacturing sector in year $t$\n\n$VAB_{manufacturing\\, 2022}^{t} =$ Gross added value of the manufacturing sector in chained volume with \nreference to 2022 in year $t$\n", "desagregacion"=>"\nSuspended particles: particles smaller than 10 microns (PM10); particles smaller than 2.5 microns (PM25)", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe indicator is part of the EU Sustainable Development Goals (SDG) indicator set. \nIt is used to monitor progress towards  SDG 9 on industry, innovation and infrastructure; \nwhich is embedded in the European Commission’s Priorities under the European Green Deal, \nEconomy that works for people and European way of life. \n\nSDG 9 recognises the importance of technological progress and innovation for finding lasting \nsolutions to social, economic and environmental challenges such as the creation of new jobs \nand promotion of resource and energy efficiency. To foster innovation and entrepreneurship, \nSDG 9 also seeks to increase access to financial services for small-scale enterprises and to \nbridge the digital divide by increasing access to information and communication technologies. \n\nThis indicator measures the emissions intensity of particulate matter (PM10 and PM2.5) from the \nmanufacturing sector (NACE Rev. 2 sector ‘C’). Air emissions are defined as flows of gaseous and \nparticulate materials emitted into the atmosphere. \n\nFine and coarse particulates (PM10) are less than 10 micrometres in diameter and can be carried \ndeep into the lungs, where they can cause inflammation and exacerbate the condition of people \nsuffering from heart and lung diseases. \n\nFine particulates (PM2.5) are less than 2.5 micrometres in diameter and are therefore a subset \nof the PM10 particles. Their negative health impacts are more serious than PM10 because they can \nbe drawn further into the lungs and may be more toxic. \n\nEmission intensity is calculated by dividing the sector’s PM emissions by its gross value added \n(GVA), which is defined as output (at basic prices) minus intermediate consumption (at purchaser \nprices). \n\nFuente: Eurostat\n", "dato_global"=>"\n<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_09_70/default/table?lang=en&category=sdg.sdg_09\">Air emission intensity from industry (sdg_09_70)</a> Eurostat\n", "comparabilidad"=>"The available indicator complies with European Union metadata.", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_09_70_esmsip2.htm\">Metadata sdg_09_70.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Intensidad de las emisiones atmosféricas de la industria (Indicador UE sdg_09_70)", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.4- De aquí a 2030, modernizar la infraestructura y reconvertir las industrias para que sean sostenibles, utilizando los recursos con mayor eficacia y promoviendo la adopción de tecnologías y procesos industriales limpios y ambientalmente racionales, y logrando que todos los países tomen medidas de acuerdo con sus capacidades respectivas", "definicion"=>"\nPM10 eta PM2,5 isuriak manufaktura-sektoreko balio erantsiko unitate bakoitzeko  ", "formula"=>"\n$$PVABEPM_{manufaktura}^{t} = \\frac{EPM_{manufaktura}^{t}}{VAB_{manufaktura\\, 2022}^{t}} $$\n\nnon:\n\n$EPM_{manufaktura}^{t} =$ manufaktura-sektoreko partikula esekien isuriak $t$ urtean\n\n$VAB_{manufaktura\\, 2022}^{t} =$ manufaktura-sektorearen balio erantsi gordina, 2022 erreferentziarekin kateatutako bolumenean $t$ urtean\n", "desagregacion"=>"\nPartikula esekiak: 10 mikra baino gutxiagoko partikulak (PM10);  2,5 mikra baino gutxiagoko partikulak (PM25)", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAdierazlea EBko Garapen Jasangarriko Helburuen (GJH) adierazleen parte da. Industria, berrikuntza \neta azpiegiturari buruzko 9. GJHrako aurrerapena ikuskatzeko erabiltzen da, Europako Itun Berdearen \nesparruan Europar Batasunaren lehentasunetan txertatuta, pertsonen zerbitzura dagoen ekonomia eta \nbizimodu-europarra sustatze aldera. \n\n9. GJHk aitortzen du aurrerapen teknologikoa eta berrikuntza garrantzitsuak direla gizarte, ekonomia \neta ingurumeneko erronken aurrean irtenbide iraunkorrak aurkitzeko, besteak beste lanpostu berrien \nsorrera eta eraginkortasun energetikoaren eta baliabideen sustapena. Berrikuntza eta espiritu ekintzailea \nindartzeko, 9. GJHk finantza-zerbitzuetarako sarbidea areagotu nahi die enpresa txikiei, eta arrakala \ndigitala murriztu, informazioaren eta komunikazioaren teknologietarako sarbidea areagotuz. \n\nAdierazle honek manufaktura-sektorean (C sektorea, NACE, 2. berrikuspena) eskegita dauden partikulen \nemisioen (PM10 eta PM2,5) intentsitatea neurtzen du. Emisio atmosferikoak atmosferara isuritako material \ngaseoso eta partikulatuen fluxuak dira. \n\nPartikula fin eta lodiek (PM10) 10 mikrometrotik beherako diametroa dute, eta biriketako alderik sakonenetara \nirits daitezke, bertan hanturak eraginez eta bihotz eta biriketako gaixotasunak dituzten pertsonen osasun-egoera \nlarriagotuz. \n\nPartikula finek (PM2,5) 2,5 mikrometrotik beherako diametroa dute, eta, beraz, PM10 partikulen azpimultzo \nbat dira. Osasunerako ondorio kaltegarriak PM10enak baino okerragoak dira, maila handiagoak eragin \nditzaketelako biriketan eta toxikoagoak izan daitezkeelako. \n\nEmisioen intentsitatea kalkulatzeko eragiketa hau egin behar da: sektoreko PM emisioak zati beren balio erantsi gordina (BEGd), zeina ekoizpena (oinarrizko prezioetan) ken bitarteko kontsumoa (eroslearen prezioetan) kalkulatuta ateratzen baita.\n\n\nIturria: Eurostat\n", "dato_global"=>"\n<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_09_70/default/table?lang=en&category=sdg.sdg_09\">Industriaren isuri atmosferikoen intentsitatea (sdg_09_70)</a> Eurostat\n", "comparabilidad"=>"EAEko adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_09_70_esmsip2.htm\">Metadatuak sdg_09_70.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "indicator_sort_order"=>"09-04-E1", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"9.5.1", "slug"=>"9-5-1", "name"=>"Gastos en investigación y desarrollo en proporción al PIB", "url"=>"/site/es/9-5-1/", "sort"=>"090501", "goal_number"=>"9", "target_number"=>"9.5", "global"=>{"name"=>"Gastos en investigación y desarrollo en proporción al PIB"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Gastos en investigación y desarrollo en proporción al PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Gastos en investigación y desarrollo en proporción al PIB", "indicator_number"=>"9.5.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_179/opt_0/ti_estadistica-sobre-actividades-de-investigacion-cientifica-y-desarrollo-tecnologico-id/temas.html", "url_text"=>"Estadística sobre actividades de investigación científica y desarrollo tecnológico-I+D", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Gastos en investigación y desarrollo en proporción al PIB", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.5- Aumentar la investigación científica y mejorar la capacidad tecnológica de los sectores industriales de todos los países, en particular los países en desarrollo, entre otras cosas fomentando la innovación y aumentando considerablemente, de aquí a 2030, el número de personas que trabajan en investigación y desarrollo por millón de habitantes y los gastos de los sectores público y privado en investigación y desarrollo", "definicion"=>"Proporción que representa el gasto en investigación y desarrollo respecto al PIB", "formula"=>"\n$$PPIBG_{I+D}^{t} = \\frac{G_{I+D}^{t}}{PIB^{t}}  \\cdot 100$$\n\ndonde:\n\n$G_{I+D}^{t} =$ gasto en investigación y desarrollo en el año $t$\n\n$PIB^{t} =$ producto interior bruto a precios corrientes en el año $t$\n", "desagregacion"=>"Territorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl indicador es una medida directa del gasto en investigación y desarrollo \nexperimental (I+D) al que se refiere la meta.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.5.1&seriesCode=GB_XPD_RSDV&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Gasto en investigación y desarrollo como proporción del PIB (%) GB_XPD_RSDV</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-05-01.pdf\">Metadatos 9-5-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"Gastos en investigación y desarrollo en proporción al PIB", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.5- Aumentar la investigación científica y mejorar la capacidad tecnológica de los sectores industriales de todos los países, en particular los países en desarrollo, entre otras cosas fomentando la innovación y aumentando considerablemente, de aquí a 2030, el número de personas que trabajan en investigación y desarrollo por millón de habitantes y los gastos de los sectores público y privado en investigación y desarrollo", "definicion"=>"Proportion of expenditure on research and development with respect to GDP", "formula"=>"\n$$PPIBG_{I+D}^{t} = \\frac{G_{I+D}^{t}}{PIB^{t}}  \\cdot 100$$\n\nwhere:\n\n$G_{I+D}^{t} =$ expenditure on research and development in year $t$\n\n$PIB^{t} =$ gross domestic product at current prices in year $t$\n", "desagregacion"=>"Province\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe indicator is a direct measure of research and experimental development (R&D) spending \nreferred to in the target. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.5.1&seriesCode=GB_XPD_RSDV&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Research and development expenditure as a proportion of GDP (%) GB_XPD_RSDV</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-05-01.pdf\">Metadata 9-5-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Gastos en investigación y desarrollo en proporción al PIB", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.5- Aumentar la investigación científica y mejorar la capacidad tecnológica de los sectores industriales de todos los países, en particular los países en desarrollo, entre otras cosas fomentando la innovación y aumentando considerablemente, de aquí a 2030, el número de personas que trabajan en investigación y desarrollo por millón de habitantes y los gastos de los sectores público y privado en investigación y desarrollo", "definicion"=>"Ikerketa eta garapeneko gastuak BPGarekiko duen proportzioa", "formula"=>"\n$$PPIBG_{I+D}^{t} = \\frac{G_{I+D}^{t}}{PIB^{t}}  \\cdot 100$$\n\nnon:\n\n$G_{I+D}^{t} =$ ikerketa eta garapeneko gastua $t$ urtean\n\n$PIB^{t} =$ barne-produktu gordina uneko prezioetan $t$ urtean\n", "desagregacion"=>"Lurralde historikoa\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nXedeak aipatzen duen garapen esperimentaleko (I+G) eta ikerketako gastuaren neurri zuzena da adierazlea. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.5.1&seriesCode=GB_XPD_RSDV&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Ikerketa eta garapeneko gastua, BPGren proportzio gisa (%) GB_XPD_RSDV</a> UNSTATS\n", "comparabilidad"=>"EAEko adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-05-01.pdf\">Metadatuak 9-5-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries, in particular developing countries, including, by 2030, encouraging innovation and substantially increasing the number of research and development workers per 1 million people and public and private research and development spending</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 9.5.1: Research and development expenditure as a proportion of GDP</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>GB_XPD_RSDV - Research and development expenditure as a proportion of GDP [9.5.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Linkages with goals and targets: 2a, 3b, 12a, 14a, 17.6, 17.7</p>\n<p>Linkages with indicators: 3.b.2 Total net official development assistance to medical research and basic health sectors; 14.a.1 Proportion of total research budget allocated to research in the field of marine technology</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Research and experimental development (R&amp;D) expenditure as a proportion of Gross Domestic Product (GDP) is the amount of Research and experimental development (R&amp;D) expenditure divided by the total output of the economy.</p>\n<p><strong>Concepts:</strong></p>\n<p>The Organisation for Economic Co-operation and Development (OECD) Frascati Manual (OECD, 2015) provides the relevant definitions for research and experimental development (R&amp;D), gross domestic expenditure on research and experimental development (R&amp;D) and researchers. Although an Organisation for Economic Co-operation and Development (OECD) manual, the application is global. During the 6th revision of the Frascati Manual, developing country issues were mainstreamed in the core of the Manual. The 7th edition was released in October 2015.</p>\n<p>The following definitions, taken from the 2015 edition of the Frascati Manual are relevant for computing the indicator. </p>\n<p>Research and experimental development (R&amp;D) comprise creative and systematic work undertaken in order to increase the stock of knowledge &#x2013; including knowledge of humankind, culture and society &#x2013; and to devise new applications of available knowledge.</p>\n<p>Expenditures on intramural research and experimental development (R&amp;D) represent the amount of money spent on research and experimental development (R&amp;D) that is performed within a reporting unit.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%): proportion of GDP</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The main methodological guide, which provides international standard guidelines for measuring research and experimental development (R&amp;D) is the Organisation for Economic Co-operation and Development (OECD) Frascati Manual (Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development: <a href=\"http://www.oecdilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en\">http://www.oecdilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en</a>).</p>\n<p>In addition to the above, the following international classifications are used to facilitate the research and experimental development (R&amp;D) data compilation process and the presentation of research and experimental development (R&amp;D) statistics by various disaggregation:</p>\n<p>International Standard Industrial Classification of All Economic Activities</p>\n<p>(ISIC), Rev. 4, United Nations (2008): https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf.</p>\n<p>International Standard Classification of Education (ISCED) 2011, UNESCO-UIS (2012): <a href=\"http://www.uis.unesco.org/Education/Documents/isced-2011-en.pdf\">www.uis.unesco.org/Education/Documents/isced-2011-en.pdf</a>.</p>\n<p>International Standard Classification of Occupations (ISCO), International Labour Organization (2012): <a href=\"http://www.ilo.org/public/english/bureau/stat/isco/isco08/index.htm\">www.ilo.org/public/english/bureau/stat/isco/isco08/index.htm</a>.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data are collected through national research and experimental development (R&amp;D) surveys, either by the national statistical office or a line ministry (such as the Ministry for Science and Technology).</p>", "COLL_METHOD__GLOBAL"=>"<p>The UNESCO Institute of Statistics (UIS) sends out a questionnaire every year to collect research and experimental development (R&amp;D) data from all countries (around 125 countries), which are not covered by the data collections of the other partner organizations such as the Organisation for Economic Co-operation and Development (OECD), Eurostat (Statistical Office of the European Union) and the Network on Science and Technology Indicators &#x2013; Ibero-American and Inter-American (RICYT). In agreement with these three organisations, their data (which were collected from their member states/associated member states &#x2013; around 65 countries-) are directly obtained from the respective databases (in the case of the Organisation for Economic Co-operation and Development - OECD and Statistical Office of the European Union - Eurostat) or received from the partner (in the case of the Network on Science and Technology Indicators &#x2013; Ibero-American and Inter-American - RICYT). There is also collaboration in Africa with the African Science, Technology and Innovation (STI) Indicators Initiative (ASTII) of the African Union Development Agency-NEPAD (AUDA-NEPAD). </p>\n<p>For the countries to which the UNESCO Institute for Statistics (UIS) sends a questionnaire, the process is the following:</p>\n<p>i. A questionnaire is sent to focal points in countries, generally within the Ministry of Science and Technology or the national statistical office.</p>\n<p>ii. The UNESCO Institute for Statistics (UIS) processes the questionnaires, communicating with the countries in case of questions, calculates indicators and releases the data and indicators on its website. </p>\n<p>iii. Countries are requested to complete the questionnaire using the standard international classifications, therefore adjustments are generally not needed.</p>\n<p>The other agencies have similar procedures.</p>", "FREQ_COLL__GLOBAL"=>"<p>The UNESCO Institute of Statistics (UIS) sends out the questionnaire in June every year. The Organisation for Economic Co-operation and Development (OECD) and the Statistical Office of the European Union (Eurostat) collect data twice per year. The Network on Science and Technology Indicators &#x2013; Ibero-American and Inter-American (RICYT) collects data once per year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>March and October every year</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are collected through national research and experimental development (R&amp;D) surveys, either by the national statistical office or a line ministry (such as the Ministry for Science and Technology).</p>", "COMPILING_ORG__GLOBAL"=>"<p>The UNESCO Institute of Statistics (UIS), Organisation for Economic Co-operation and Development (OECD), Eurostat (Statistical Office of the European Union) and the Network on Science and Technology Indicators &#x2013; Ibero-American and Inter-American (RICYT), African Science, Technology and Innovation (STI) Indicators Initiative (ASTII) of the African Union Development Agency-NEPAD (AUDA-NEPAD).</p>", "INST_MANDATE__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The Institute produces internationally comparable data and methodologies in the fields of education, science, culture and communication for countries at all stages of development.</p>", "RATIONALE__GLOBAL"=>"<p>The indicator is a direct measure of research and experimental development (R&amp;D) spending referred to in the target.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Research and experimental development (R&amp;D) data need to be collected through surveys, which are expensive, and are not done on a regular basis in many developing countries. Furthermore, (developing) countries do not always cover all sectors of performance. In particular the business sector is not always covered.</p>", "DATA_COMP__GLOBAL"=>"<p>Computation of the indicator Research and experimental development (R&amp;D) expenditure as a proportion of Gross Domestic Product (GDP) is self-explanatory, using readily available GDP data as denominator.</p>\n<p>Research and experimental development (R&amp;D) expenditure as a proportion of GDP <em>(R&amp;D<sub>Intensity</sub>) </em>is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>R</mi>\n        <mo>&amp;amp;</mo>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mi>I</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>y</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">x</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">R</mi>\n        <mo>&amp;amp;</mo>\n        <mi mathvariant=\"normal\">D</mi>\n      </mrow>\n      <mrow>\n        <mi>G</mi>\n        <mi>D</mi>\n        <mi>P</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>For each questionnaire received from countries where the UNESCO Institute for Statistics (UIS) sends questionnaire to, the UNESCO Institute for Statistics (UIS) executes a series of quality checks and sends back a data processing report identifying problematic issues/inconsistent data to countries for their feedback on corrections as well as validation of indicators.</p>", "ADJUSTMENT__GLOBAL"=>"<p>To inform of any discrepancies between standard classifications and national practices, appropriate footnotes are accompanied with data/indicators to adequately document the results and provide explanations.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing data are not estimated by the UNESCO Institute for Statistics (UIS).</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Imputations are based on interpolations or extrapolations of data for other reference years. In case no data are available at all, the unweighted regional average is used as an estimate.</p>", "REG_AGG__GLOBAL"=>"<p>Data are converted using purchasing power parities. Missing data are imputed using the methodology described above. Research and experimental development (R&amp;D) expenditure data are then added up by region and divided by GDP in Purchasing Power Parities (PPPs) for that region. Similar for the global total.</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries are responsible themselves for the collection of research and experimental development (R&amp;D) data at the national level, compile national totals and submit them to international organisations. All countries follow the guidelines of the Frascati Manual: <a href=\"http://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en\">http://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en</a>.</p>\n<p>All countries follow the international guidelines of the Organisation for Economic Co-operation and Development (OECD) Frascati Manual: <a href=\"http://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en\">http://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en</a>. Countries starting to measure research and experimental development (R&amp;D) can use the UNESCO Institute for Statistics (UIS) Technical Paper 11 for assistance, which can be downloaded here: <a href=\"http://uis.unesco.org/sites/default/files/documents/guide-to-conducting-an-rd-survey-for-countries-starting-to-measure-research-and-experimental-development-2014-en.pdf\">http://uis.unesco.org/sites/default/files/documents/guide-to-conducting-an-rd-survey-for-countries-starting-to-measure-research-and-experimental-development-2014-en.pdf</a>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The UNESCO Institute of Statistics (UIS) maintains a set of data processing guidelines/standards as well as data processing tools to facilitate processing of data and ensure the quality of data. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The process of quality assurance includes review of survey documentations/metadata, examination of reliability of data, making sure they comply with international standards (including the Organisation for Economic Co-operation and Development - OECD Frascati Manual), and examining the consistency and coherence within the data set as well as with the time series of data and the resulting indicators. During the data processing stage, for each questionnaire received from countries where UNESCO Institute for Statistics (UIS) sends questionnaires to, the above quality aspects are looked into and a data report is produced identifying problematic issues/inconsistent data for each respective country. The UNESCO Institute for Statistics (UIS) sends such data reports, including the calculated indicators for target 9.5, providing the countries with the opportunity to review the data/indicators and submit any clarifications or modifications/additions before releasing data at the UNESCO Institute for Statistics (UIS) Data Centre and submitting the data to UN Statistics Division for inclusion in the global SDG Indicators Database.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The data should comply with the concepts/definitions and guidelines provided in the international standards (i.e. the Organisation for Economic Co-operation and Development - OECD Frascati Manual) and should cover all sectors of performance, representing all institutions, which are engaged in research and experimental development (R&amp;D) activities in the country. Criteria for quality assessment include: data sources must include proper documentation; data values must be nationally representative, if not, should be footnoted; data are plausible and based on trends and consistency with previously published/reported values.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data available for 150 countries for research and experimental development (R&amp;D) expenditure as % of GDP</p>\n<p><strong>Time series:</strong></p>\n<p>Data available in the UNESCO Institute for Statistics (UIS) database since reference year 1996, but historical data available back to 1981</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Research and experimental development (R&amp;D) expenditure can be broken down by sector of performance, source of funds, field of science, type of research and type of cost.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There are no differences in the underlying data. Difference may occur due to the use of difference data for the denominator used to calculate indicators.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.uis.unesco.org\">www.uis.unesco.org</a> </p>\n<p><strong>References:</strong></p>\n<p>OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris. DOI:</p>\n<p><a href=\"http://dx.doi.org/10.1787/9789264239012-en\">http://dx.doi.org/10.1787/9789264239012-en</a></p>\n<p>UNESCO Institute for Statistics (UIS) Data centre:</p>\n<p><a href=\"http://data.uis.unesco.org/index.aspx?queryid=3684\">http://data.uis.unesco.org/index.aspx?queryid=3684</a> </p>", "indicator_sort_order"=>"09-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"9.5.2", "slug"=>"9-5-2", "name"=>"Número de investigadores (en equivalente a tiempo completo) por cada millón de habitantes", "url"=>"/site/es/9-5-2/", "sort"=>"090502", "goal_number"=>"9", "target_number"=>"9.5", "global"=>{"name"=>"Número de investigadores (en equivalente a tiempo completo) por cada millón de habitantes"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de investigadores (en equivalente a tiempo completo) por cada millón de habitantes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de investigadores (en equivalente a tiempo completo) por cada millón de habitantes", "indicator_number"=>"9.5.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_179/opt_0/ti_estadistica-sobre-actividades-de-investigacion-cientifica-y-desarrollo-tecnologico-id/temas.html", "url_text"=>"Estadística sobre actividades de investigación científica y desarrollo tecnológico-I+D", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Número de investigadores (en equivalente a tiempo completo) por cada millón de habitantes", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.5- Aumentar la investigación científica y mejorar la capacidad tecnológica de los sectores industriales de todos los países, en particular los países en desarrollo, entre otras cosas fomentando la innovación y aumentando considerablemente, de aquí a 2030, el número de personas que trabajan en investigación y desarrollo por millón de habitantes y los gastos de los sectores público y privado en investigación y desarrollo", "definicion"=>"Número de personas investigadoras en equivalencia a jornada completa por cada millón de habitantes", "formula"=>"\n$$TINV^{t} = \\frac{INV^{t}}{P^{t}} \\cdot 1.000.000$$\n\ndonde:\n\n$INV^{t} =$ personas investigadoras en equivalencia a jornada completa en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl indicador es una medida directa del número de trabajadores de investigación y desarrollo experimental (I+D) por cada millón de personas a las que se refiere la meta.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.5.2&seriesCode=GB_POP_SCIERD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Investigadores (en equivalente a tiempo completo) por millón de habitantes (por cada 1.000.000 de habitantes) GB_POP_SCIERD</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-05-02.pdf\">Metadatos 9-5-2.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"Número de investigadores (en equivalente a tiempo completo) por cada millón de habitantes", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.5- Aumentar la investigación científica y mejorar la capacidad tecnológica de los sectores industriales de todos los países, en particular los países en desarrollo, entre otras cosas fomentando la innovación y aumentando considerablemente, de aquí a 2030, el número de personas que trabajan en investigación y desarrollo por millón de habitantes y los gastos de los sectores público y privado en investigación y desarrollo", "definicion"=>"Number of researchers in full-time equivalent jobs per million inhabitants", "formula"=>"\n$$TINV^{t} = \\frac{INV^{t}}{P^{t}} \\cdot 1.000.000$$\n\nwhere:\n\n$INV^{t} =$ researchers in full-time equivalent jobs in year $t$\n\n$P^{t} =$ population as of 1 July of year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe indicator is a direct measure of the number of research and experimental development (R&D) \nworkers per 1 million people referred to in the target.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.5.2&seriesCode=GB_POP_SCIERD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Researchers (in full-time equivalent) per million inhabitants (per 1,000,000 population) GB_POP_SCIERD</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-05-02.pdf\">Metadata 9-5-2.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Número de investigadores (en equivalente a tiempo completo) por cada millón de habitantes", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.5- Aumentar la investigación científica y mejorar la capacidad tecnológica de los sectores industriales de todos los países, en particular los países en desarrollo, entre otras cosas fomentando la innovación y aumentando considerablemente, de aquí a 2030, el número de personas que trabajan en investigación y desarrollo por millón de habitantes y los gastos de los sectores público y privado en investigación y desarrollo", "definicion"=>"Ikertzaileen kopurua, lanaldi osoko baliokidetzan, milioi bat biztanleko", "formula"=>"\n$$TINV^{t} = \\frac{INV^{t}}{P^{t}} \\cdot 1.000.000$$\n\nnon:\n\n$INV^{t} =$ ikertzaileen kopurua lanaldi osoko baliokidetzan $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nXedeak aipatzen duen garapen esperimentaleko eta ikerketako langile-kopuruaren neurri zuzena adierazten \ndu milioi pertsonako. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.5.2&seriesCode=GB_POP_SCIERD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Ikertzaileak (lanaldi osoaren baliokidetzan) milioi biztanleko (1.000.000 biztanleko) GB_POP_SCIERD</a> UNSTATS\n", "comparabilidad"=>"EAEko adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-05-02.pdf\">Metadatuak 9-5-2.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries, in particular developing countries, including, by 2030, encouraging innovation and substantially increasing the number of research and development workers per 1 million people and public and private research and development spending</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 9.5.2: Researchers (in full-time equivalent) per million inhabitants</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>GB_POP_SCIERD - Researchers (in full-time equivalent) per million inhabitants [9.5.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Linkages with goals and targets: 9.b, 12.a, 17.6, 17.7, 17.8</p>\n<p>Linkages with indicators: Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>The researchers (in full-time equivalent - FTE) per million inhabitants is a direct measure of the number of research and experimental development (R&amp;D) workers per 1 million people.</p>\n<p><strong>Concepts:</strong></p>\n<p>The Organisation for Economic Co-operation and Development (OECD) Frascati Manual (OECD, 2015) provides the relevant definitions for research and experimental development (R&amp;D), gross domestic expenditure on research and experimental development (R&amp;D) and researchers. Although an Organisation for Economic Co-operation and Development (OECD) manual, the application is global. During the 6th revision of the Frascati Manual, developing country issues were mainstreamed in the core of the Manual. The 7th edition was released in October 2015.</p>\n<p>The following definitions, taken from the 2015 edition of the Frascati Manual are relevant for computing the indicator. </p>\n<p>Research and experimental development (R&amp;D) comprise creative and systematic work undertaken in order to increase the stock of knowledge &#x2013; including knowledge of humankind, culture and society &#x2013; and to devise new applications of available knowledge.</p>\n<p>Researchers are professionals engaged in the conception or creation of new knowledge. They conduct research and improve or develop concepts, theories, models, techniques instrumentation, software or operational methods.</p>\n<p>The Full-time equivalent (FTE) of research and experimental development (R&amp;D) personnel is defined as the ratio of working hours actually spent on research and experimental development (R&amp;D) during a specific reference period (usually a calendar year) divided by the total number of hours conventionally worked in the same period by an individual or by a group.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Per million population</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The main methodological guide, which provides international standard guidelines for measuring research and experimental development (R&amp;D) is the Organisation for Economic Co-operation and Development (OECD) Frascati Manual (Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development: <a href=\"http://www.oecdilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en\">http://www.oecdilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en</a>).</p>\n<p>In addition to the above, the following international classifications are used to facilitate the research and experimental development (R&amp;D) data compilation process and the presentation of research and experimental development (R&amp;D) statistics by various disaggregation: </p>\n<p>International Standard Industrial Classification of All Economic Activities</p>\n<p>(ISIC), Rev. 4, United Nations (2008): https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf.</p>\n<p>International Standard Classification of Education (ISCED) 2011, UNESCO-UIS (2012): <a href=\"http://www.uis.unesco.org/Education/Documents/isced-2011-en.pdf\">www.uis.unesco.org/Education/Documents/isced-2011-en.pdf</a>.</p>\n<p>International Standard Classification of Occupations (ISCO), International Labour Organization (2012): <a href=\"http://www.ilo.org/public/english/bureau/stat/isco/isco08/index.htm\">www.ilo.org/public/english/bureau/stat/isco/isco08/index.htm</a>.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data are collected through national research and experimental development (R&amp;D) surveys, either by the national statistical office or a line ministry (such as the Ministry for Science and Technology).</p>", "COLL_METHOD__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) sends out a questionnaire every year to collect research and experimental development (R&amp;D) data from all countries (around 125 countries), which are not covered by the data collections of the other partner organizations such as the Organisation for Economic Co-operation and Development (OECD), Eurostat (Statistical Office of the European Union) and the Network on Science and Technology Indicators &#x2013; Ibero-American and Inter-American (RICYT). In agreement with these three organisations, their data (which were collected from their member states/associated member states &#x2013; around 65 countries-) are directly obtained from the respective databases (in the case of the Organisation for Economic Co-operation and Development - OECD and Statistical Office of the European Union - Eurostat) or received from the partner (in the case of the Network on Science and Technology Indicators &#x2013; Ibero-American and Inter-American - RICYT). There is also collaboration in Africa with the African Science, Technology and Innovation (STI) Indicators Initiative (ASTII) of the African Union Development Agency-NEPAD (AUDA-NEPAD). </p>\n<p>For the countries to which the UNESCO Institute for Statistics (UIS) sends a questionnaire, the process is the following:</p>\n<ol>\n  <li>A questionnaire is sent to focal points in countries, generally within the Ministry of Science and Technology or the national statistical office.</li>\n  <li>The UNESCO Institute for Statistics (UIS) processes the questionnaires, communicating with the countries in case of questions, calculates indicators and releases the data and indicators on its website. </li>\n  <li>Countries are requested to complete the questionnaire using the standard international classifications, therefore adjustments are generally not needed.</li>\n</ol>\n<p>The other agencies have similar procedures.</p>", "FREQ_COLL__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) sends out the questionnaire in June every year. The Organisation for Economic Co-operation and Development (OECD) and the Statistical Office of the European Union (Eurostat) collect data twice per year. The Network on Science and Technology Indicators &#x2013; Ibero-American and Inter-American (RICYT) collects data once per year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>March and October every year</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are collected through national research and experimental development (R&amp;D) surveys, either by the national statistical office or a line ministry (such as the Ministry for Science and Technology).</p>", "COMPILING_ORG__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS), Organisation for Economic Co-operation and Development (OECD), Eurostat (Statistical Office of the European Union) and the Network on Science and Technology Indicators &#x2013; Ibero-American and Inter-American (RICYT), African Science, Technology and Innovation (STI) Indicators Initiative (ASTII) of the African Union Development Agency-NEPAD (AUDA-NEPAD).</p>", "INST_MANDATE__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The Institute produces internationally comparable data and methodologies in the fields of education, science, culture and communication for countries at all stages of development.</p>", "RATIONALE__GLOBAL"=>"<p>The indicator is a direct measure of the number of research and experimental development (R&amp;D) workers per 1 million people referred to in the target.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Research and experimental development (R&amp;D) data need to be collected through surveys, which are expensive, and are not done on a regular basis in many developing countries. Furthermore, (developing) countries do not always cover all sectors of performance. In particular the business sector is not always covered.</p>", "DATA_COMP__GLOBAL"=>"<p>Computation of the indicator Researchers (in full-time equivalent) per million inhabitants uses available population data as denominator.</p>\n<p>The number researchers (in full-time equivalent - FTE) per million inhabitants <em>(RES<sub>Density</sub>)</em> is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>R</mi>\n        <mi>E</mi>\n        <mi>S</mi>\n      </mrow>\n      <mrow>\n        <mi>D</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>y</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>T</mi>\n            <mi>o</mi>\n            <mi>t</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>a</mi>\n            <mi>r</mi>\n            <mi>c</mi>\n            <mi>h</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>s</mi>\n            <mi>&amp;nbsp;</mi>\n            <mo>(</mo>\n            <mi>F</mi>\n            <mi>T</mi>\n            <mi>E</mi>\n            <mo>)</mo>\n          </mrow>\n          <mrow></mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>y</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>1</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n  </math></p>\n<p>where<em> &#x2018;Total researchers (FTE)&#x2019; </em> is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>T</mi>\n    <mi>o</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>e</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>c</mi>\n    <mi>h</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>F</mi>\n        <mi>T</mi>\n        <mi>E</mi>\n      </mrow>\n    </mfenced>\n    <mo>=</mo>\n    <mi>N</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>b</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>f</mi>\n    <mi>u</mi>\n    <mi>l</mi>\n    <mi>l</mi>\n    <mo>-</mo>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>m</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>e</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>c</mi>\n    <mi>h</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>s</mi>\n    <mo>+</mo>\n    <mo>[</mo>\n    <mfrac>\n      <mrow>\n        <mi>&amp;nbsp;</mi>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>k</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>g</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>R</mi>\n        <mo>&amp;amp;</mo>\n        <mi>D</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>b</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>t</mi>\n        <mo>-</mo>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>c</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>&amp;nbsp;</mi>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>t</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>k</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>g</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mo>-</mo>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>c</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n      </mrow>\n    </mfrac>\n    <mo>]</mo>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>For each questionnaire received from countries where the UNESCO Institute for Statistics (UIS) sends questionnaire to, the UNESCO Institute for Statistics (UIS) executes a series of quality checks and sends back a data processing report identifying problematic issues/inconsistent data to countries for their feedback, corrections as well as validation of indicators.</p>", "ADJUSTMENT__GLOBAL"=>"<p>To inform of any discrepancies between standard classifications and national practices, appropriate footnotes are accompanied with data/indicators to adequately document the results and provide explanations.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p> Missing data are not estimated by the UNESCO Institute for Statistics (UIS).</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Imputations are based on interpolations or extrapolations of data for other reference years. Second option is to make an estimate for full-time equivalent (FTE) based on available headcount data. In case no data are available at all, the unweighted regional average is used as an estimate.</p>", "REG_AGG__GLOBAL"=>"<p>Missing data are imputed using the methodology described above. The data for researchers in full-time equivalent (FTE) are then added up by region and divided by the population data for that region. Similar for the global total.</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries are responsible themselves for the collection of research and experimental development (R&amp;D) data at the national level, compile national totals and submit them to international organisations. All countries follow the guidelines of the Frascati Manual: <a href=\"http://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en\">http://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en</a>.</p>\n<p>All countries follow the international guidelines of the Organisation for Economic Co-operation and Development (OECD) Frascati Manual: <a href=\"http://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en\">http://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en</a>. Countries starting to measure research and experimental development (R&amp;D) can use the UNESCO Institute for Statistics (UIS) Technical Paper 11 for assistance, which can be downloaded here: <a href=\"http://uis.unesco.org/sites/default/files/documents/guide-to-conducting-an-rd-survey-for-countries-starting-to-measure-research-and-experimental-development-2014-en.pdf\">http://uis.unesco.org/sites/default/files/documents/guide-to-conducting-an-rd-survey-for-countries-starting-to-measure-research-and-experimental-development-2014-en.pdf</a>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) maintains a set of data processing guidelines/standards as well as data processing tools to facilitate processing of data and ensure the quality of data.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The process of quality assurance includes review of survey documentations/metadata, examination of reliability of data, make sure they comply with international standards (including the Organisation for Economic Co-operation and Development - OECD Frascati Manual), and examine the consistency and coherence within the data set as well as with the time series of data and the resulting indicators. During the data processing stage, for each questionnaire received from countries where the UNESCO Institute for Statistics (UIS) sends questionnaire to, the above quality aspects are looked into and a data report is produced identifying problematic issues/inconsistent data for each respective country. The UNESCO Institute for Statistics (UIS) sends such data reports, including the calculated indicators for target 9.5, providing the countries with the opportunity to review the data/indicators and submit any clarifications or modifications/additions before releasing data at the UNESCO Institute for Statistics (UIS) Data Centre and submitting the data to UN Statistics Division for inclusion in the global SDG Indicators Database.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The data should comply with the concepts/definitions and guidelines provided in the international standards (i.e. the Organisation for Economic Co-operation and Development - OECD Frascati Manual) and should cover all sectors of performance, representing all institutions, which are engaged in research and experimental development (R&amp;D) activities in the country. Criteria for quality assessment include: data sources must include proper documentation; data values must be nationally representative, if not, should be footnoted; data are plausible and based on trends and consistency with previously published/reported values.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data available for over 140 countries for Researchers (in full-time equivalent - FTE) per million inhabitants</p>\n<p><strong>Time series:</strong></p>\n<p>Data available in the UNESCO Institute for Statistics (UIS) database since reference year 1996, but historical data available back to 1981 </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Researchers can be broken down by sector of employment, field of science, sex and age.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There are no differences in the underlying data. Difference may occur due to the use of difference data for the denominator used to calculate indicators.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.uis.unesco.org\">www.uis.unesco.org</a> </p>\n<p><strong>References:</strong></p>\n<p>OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris. DOI:</p>\n<p><a href=\"http://dx.doi.org/10.1787/9789264239012-en\">http://dx.doi.org/10.1787/9789264239012-en</a>. </p>\n<p>UNESCO Institute for Statistics (UIS) Data centre: </p>\n<p><a href=\"http://data.uis.unesco.org/index.aspx?queryid=3685\">http://data.uis.unesco.org/index.aspx?queryid=3685</a> </p>", "indicator_sort_order"=>"09-05-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"9.a.1", "slug"=>"9-a-1", "name"=>"Total de apoyo internacional oficial (asistencia oficial para el desarrollo más otras corrientes oficiales de recursos) destinado a la infraestructura", "url"=>"/site/es/9-a-1/", "sort"=>"09aa01", "goal_number"=>"9", "target_number"=>"9.a", "global"=>{"name"=>"Total de apoyo internacional oficial (asistencia oficial para el desarrollo más otras corrientes oficiales de recursos) destinado a la infraestructura"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Total de apoyo internacional oficial (asistencia oficial para el desarrollo más otras corrientes oficiales de recursos) destinado a la infraestructura", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Total de apoyo internacional oficial (asistencia oficial para el desarrollo más otras corrientes oficiales de recursos) destinado a la infraestructura", "indicator_number"=>"9.a.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nLos flujos totales de Ayuda Oficial para el Desarrollo (AOD) más otras corrientes oficiales de recursos \na los países en desarrollo cuantifican el esfuerzo \npúblico (excluidos los créditos a la exportación) que los donantes proporcionan \na los países en desarrollo para infraestructura.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.a.1&seriesCode=DC_TOF_INFRAL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Flujos oficiales totales para infraestructura, por países receptores (millones de dólares estadounidenses constantes de 2022) DC_TOF_INFRAL</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-0a-01.pdf\">Metadatos 9-a-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nTotal ODA (Official Development Assistance) and OOF (Other Official \nFlows) to developing countries quantify the public effort (excluding \nexport credits) that donors provide to developing countries for infrastructure.\n\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.a.1&seriesCode=DC_TOF_INFRAL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Total official flows for infrastructure, by recipient countries (millions of constant 2022 United States dollars) DC_TOF_INFRAL</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-0a-01.pdf\">Metadata 9-a-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nGarapen-bidean dauden herrialdeetara egiten diren Garapenerako Laguntza Ofizialaren eta bulegotik \nkanpoko beste funts batzuen fluxuek zenbatesten dute emaileek garapen-bidean dauden herrialdeei \nazpiegituretarako ematen dieten ahalegin publikoa (esportazioetarako kredituak kanpo). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.a.1&seriesCode=DC_TOF_INFRAL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Azpiegituretarako guztizko fluxu ofizialak, herrialde hartzaileen arabera (2022ko Estatu Batuetako dolar konstante milioiak) DC_TOF_INFRAL</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-0a-01.pdf\">Metadatuak 9-a-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 9.a: Facilitate sustainable and resilient infrastructure development in developing countries through enhanced financial, technological and technical support to African countries, least developed countries, landlocked developing countries and small island developing States</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 9.a.1: Total official international support (official development assistance plus other official flows) to infrastructure</p>", "META_LAST_UPDATE__GLOBAL"=>"2017-07-09", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Gross disbursements of total ODA and other official flows from all donors in support of infrastructure.</p>\n<p><strong>Concepts:</strong></p>\n<p>ODA: The DAC defines ODA as &#x201C;those flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are </p>\n<ol>\n  <li>provided by official agencies, including state and local governments, or by their executive agencies; and </li>\n  <li>each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and</li>\n</ol>\n<p>is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent). </p>\n<p>(See <a href=\"http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm\">http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm</a>)</p>\n<p>Other official flows (OOF): Other official flows (excluding officially supported export credits) are defined as transactions by the official sector which do not meet the conditions for eligibility as ODA, either because they are not primarily aimed at development, or because they are not sufficiently concessional.</p>\n<p>(See <a href=\"http://www.oecd.org/dac/stats/documentupload/DCDDAC(2016)3FINAL.pdf\">http://www.oecd.org/dac/stats/documentupload/DCDDAC(2016)3FINAL.pdf</a>, Para 24).</p>\n<p>Support to infrastructure includes all CRS sector codes in the 200 series (see here: <a href=\"http://www.oecd.org/dac/stats/purposecodessectorclassification.htm\">http://www.oecd.org/dac/stats/purposecodessectorclassification.htm</a>)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The OECD/DAC has been collecting data on official and private resource flows from 1960 at an aggregate level and 1973 at an activity level through the Creditor Reporting System (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements). </p>\n<p>The data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm). </p>\n<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.</p>", "COLL_METHOD__GLOBAL"=>"<p>A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are published on an annual basis in December for flows in the previous year.</p>\n<p>Detailed 2015 flows was published in December 2016.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.</p>", "COMPILING_ORG__GLOBAL"=>"<p>OECD</p>", "RATIONALE__GLOBAL"=>"<p>Total ODA and OOF flows to developing countries quantify the public effort (excluding export credits) that donors provide to developing countries for infrastructure.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data in the Creditor Reporting System are available from 1973. However, the data coverage is considered complete since 1995 for commitments at an activity level and 2002 for disbursements.</p>", "DATA_COMP__GLOBAL"=>"<p>The sum of ODA and OOF flows from all donors to developing countries for infrastructure.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Due to high quality of reporting, no estimates are produced for missing data.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable.</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional figures are based on the sum of ODA and OOF flows to the agriculture sector.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>On a recipient basis for all developing countries eligible for ODA.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator can be disaggregated by type of flow (ODA or OOF), by donor, recipient country, type of finance, type of aid, sub-sector, etc.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>www.oecd.org/dac/stats</p>\n<p><strong>References:</strong></p>\n<p>See all links here: http://www.oecd.org/dac/stats/methodology.htm</p>", "indicator_sort_order"=>"09-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"9.b.1", "slug"=>"9-b-1", "name"=>"Proporción del valor añadido por la industria de tecnología mediana y alta en el valor añadido total", "url"=>"/site/es/9-b-1/", "sort"=>"09bb01", "goal_number"=>"9", "target_number"=>"9.b", "global"=>{"name"=>"Proporción del valor añadido por la industria de tecnología mediana y alta en el valor añadido total"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción del valor añadido por la industria de tecnología mediana y alta en el valor añadido total", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción del valor añadido por la industria de tecnología mediana y alta en el valor añadido total", "indicator_number"=>"9.b.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_652/opt_1/ti_estadistica-del-sector-de-alta-tecnologia/temas.html", "url_text"=>"Estadística del sector de alta tecnología", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción del valor añadido por la industria de tecnología mediana y alta en el valor añadido total", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.b- Apoyar el desarrollo de tecnologías, la investigación y la innovación nacionales en los países en desarrollo, incluso garantizando un entorno normativo propicio a la diversificación industrial y la adición de valor a los productos básicos, entre otras cosas", "definicion"=>"Proporción del valor añadido bruto del sector manufacturero correspondiente a las actividades de media-alta y alta tecnología", "formula"=>"\n$$PVAB_{manufacturero\\,media-alta\\, y\\, alta}^{t} = \\frac{VAB_{manufacturero\\,media-alta\\, y\\, alta}^{t}}{VAB_{manufacturero}^{t}} \\cdot 100$$\ndonde:\n\n$VAB_{manufacturero\\,media-alta\\, y\\, alta}^{t}$ = valor añadido bruto correspondiente a las ctividades manufactureras de media-alta y alta tecnología en el año $t$\n\n$VAB_{manufacturero}^{t}$ = valor añadido bruto total del sector manufacturero en el año $t$\n", "desagregacion"=>"", "observaciones"=>"\nSe consideran actividades manufactureras de media-alta y alta tecnología las actividades \ncon CNAE-2009:\n\nMedia-alta tecnología\n- 20: Industria química\n- 25.4: Fabricación de armas y municiones\n- 27 a 29: Fabricación de material y equipo eléctrico; Fabricación de maquinaria \ny equipo n.c.o.p;   Fabricación de vehículos de motor, remolques y semirremolques\n- 30 excepto 30.1 y 30.3: Fabricación de otro material de transporte excepto \nconstrucción naval, construcción aeronáutica y espacial y su maquinaria\n- 32.5: Fabricación de instrumentos y suministros médicos y odontológicos\n\nAlta tecnología\n- 21: Fabricación de productos farmaceúticos\n- 26: Fabricación de productos informáticos, electrónicos y ópticos\n- 30.3: Construcción aeronáutica y espacial y su maquinaria\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl desarrollo industrial generalmente implica una transición estructural desde \nactividades basadas en recursos y de baja tecnología a actividades manufactureras de MAT. \n\nUna estructura de producción moderna y altamente compleja ofrece mejores oportunidades \npara el desarrollo de habilidades y la innovación tecnológica. Las actividades \nde MHT generalmente corresponden a las industrias con mayor valor agregado y \nproductividad laboral. El aumento de la participación de los sectores de MAT \ntambién refleja el impacto de la innovación.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.b.1&seriesCode=NV_IND_TECH&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC3_D\">Proporción del valor añadido de las manufacturas de tecnología media y alta en el valor añadido total (%) NV_IND_TECH</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-0B-01.pdf\">Metadatos 9-b-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción del valor añadido por la industria de tecnología mediana y alta en el valor añadido total", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.b- Apoyar el desarrollo de tecnologías, la investigación y la innovación nacionales en los países en desarrollo, incluso garantizando un entorno normativo propicio a la diversificación industrial y la adición de valor a los productos básicos, entre otras cosas", "definicion"=>"Proportion of gross value added of the manufacturing sector corresponding to medium-high and high technology activities", "formula"=>"\n$$PVAB_{manufacturing\\, medium-high\\, and\\, high}^{t} = \\frac{VAB_{manufacturing\\, medium-high\\, and\\, high}^{t}}{VAB_{manufacturing}^{t}} \\cdot 100$$\n\nwhere:\n\n$VAB_{manufacturing\\, medium-high\\, and\\, high}^{t} $ = gross value added of the manufacturing sector corresponding to medium-high and high technology activities in year $t$\n\n$VAB_{manufacturing}^{t} $ = gross value added of the manufacturing sector in year $t$\n", "desagregacion"=>nil, "observaciones"=>"\nMedium-high and high technology manufacturing activities are considered activities with CNAE-2009:\n\nMedium-high technology \n- 20: Chemical industry \n- 25.4: Manufacture of weapons and ammunition \n- 27 to 29: Manufacture of electrical material and equipment; Manufacture of machinery and equipment n.c.o.p; \n  Manufacture of vehicles motor, trailers and semi-trailers \n- 30 except 30.1 and 30.3: Manufacture of other transport material except shipbuilding, aeronautical construction \n  and spacecraft and its machinery \n- 32.5: Manufacture of medical and dental instruments and supplies\n\nHigh technology \n- 21: Manufacture of pharmaceuticals\n- 26: Manufacture of computer, electronic and optical products\n- 30.3: Aeronautical and space construction and its machinery\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nIndustrial development generally entails a structural transition from resource-based and low technology \nactivities to MHT manufacturing activities. \n\nA modern, highly complex production structure offers better opportunities for skills development \nand technological innovation. MHT activities generally correspond to the industries with higher \nvalue addition and labour productivity. Increasing the share of MHT sectors also reflects the \nimpact of innovation. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.b.1&seriesCode=NV_IND_TECH&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC3_D\">Proportion of medium and high-tech manufacturing value added in total value added (%) NV_IND_TECH</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-0B-01.pdf\">Metadata 9-b-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción del valor añadido por la industria de tecnología mediana y alta en el valor añadido total", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.b- Apoyar el desarrollo de tecnologías, la investigación y la innovación nacionales en los países en desarrollo, incluso garantizando un entorno normativo propicio a la diversificación industrial y la adición de valor a los productos básicos, entre otras cosas", "definicion"=>"Teknologia ertain-altuko eta altuko jarduerei dagokien manufaktura-sektorearen balio erantsi gordinaren proportzioa", "formula"=>"\n$$PVAB_{manufaktura\\, ertain-altua\\, eta\\, altua}^{t} = \\frac{VAB_{manufaktura\\, ertain-altua\\, eta\\, altua}^{t}}{VAB_{manufaktura}^{t}} \\cdot 100$$\n\nnon:\n\n$VAB_{manufaktura\\, ertain-altua\\, eta\\, altua}^{t}$ = teknologia ertain-altuko eta altuko jarduerei dagokien manufaktura-sektorearen balio erantsia $t$ urtean\n\n$VAB_{manufaktura}^{t}$ = manufaktura-sektorearen balio erantsi gordin osoa $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>"\nTeknologia ertain-altuko eta altuko manufaktura-jardueratzat hartzen dira JESN-2009ko ondoko jarduerak: \n\nTeknologia ertain-altua\n- 20: Industria kimikoa \n- 25.4: Armen eta munizioen fabrikazioa \n- 27tik 29ra: Material eta ekipo elektrikoen fabrikazioa; Makineria eta ekipoen fabrikazioa b.i.s.g.; ibilgailu motordunen, atoien eta erdi-atoien fabrikazioa \n- 30 (30.1 eta 30.3 izan ezik): Beste garraio-material batzuen fabrikazioa (ontzigintza, eraikuntza aeronautikoa eta espaziala eta hartarako makineria izan ezik) \n- 32.5: Tresna eta hornidura mediko eta odontologikoen fabrikazioa \n\nTeknologia altua \n- 21: Produktu farmazeutikoen fabrikazioa \n- 26: Produktu informatiko, elektroniko eta optikoen fabrikazioa \n- 30.3: Eraikuntza aeronautikoa eta espaziala eta bere makineria \n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nGarapen industrialak, oro har, egiturazko trantsizioa dakar: baliabideetan eta teknologia baxuetan \noinarritutako teknologietatik MATeko manufaktura-industrietara. \n\nEkoizpen modernoko egitura oso konplexuak aukera hobeak eskaintzen ditu gaitasunak eta berrikuntza \nteknologia garatzeko. MHTren jarduerak, oro har, balio erantsi eta lan-produktibitate handiagoko \nindustriei dagozkie. MATeko sektoreen partaidetzaren igoerak, halaber, berrikuntzaren eragina islatzen du. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.b.1&seriesCode=NV_IND_TECH&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ISIC3_D\">Teknologia ertaineko eta altuko manufakturen balio erantsiaren proportzioa guztizko balio erantsian (%) NV_IND_TECH</a> UNSTATS\n", "comparabilidad"=>"EAEko adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-0B-01.pdf\">Metadatuak 9-b-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 9.b: Support domestic technology development, research and innovation in developing countries, including by ensuring a conducive policy environment for, inter alia, industrial diversification and value addition to commodities</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 9.b.1: Proportion of medium and high-tech industry value added in total value added</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>NV_IND_TECH - Proportion of medium and high-tech manufacturing value added in total value added [9.b.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p> </p>\n<p>9.c.1: Proportion of population covered by a mobile network, by technology</p>\n<p><strong>9.5.1:</strong> Research and development expenditure as a proportion of GDP</p>\n<p>9.2.1: Manufacturing value added as a proportion of GDP and per capita</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>The proportion of medium-high and high-tech industry (MHT hereafter) value added in total value added of manufacturing (MVA hereafter) is a ratio value between the value added of MHT industry and MVA.</p>\n<p><strong>Concepts:</strong></p>\n<p>The value added of an industry (industry value added) is a survey concept that refers to the given industry&#x2019;s net output derived from the difference of gross output and intermediate consumption. Manufacturing sector<em> </em>is defined according to the International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3 (1990) or Revision 4 (2008). It refers to industries belonging to Section D in ISIC Revision 3 or Section C in ISIC Revision 4.</p>\n<p>Technology classification is based on research and development (R&amp;D) expenditure relative to value added (R&amp;D intensity hereafter). Data for R&amp;D intensity are presented in a report published by the OECD (OECD, 2003; Galindo-Rueda and Verger, 2016, for ISIC Revision 3 and 4 respectively), which also proposes a taxonomy for industry groups with different ranges of R&amp;D expenditure relative to their gross value added. MHT industries have traditionally been defined exclusively to manufacturing industries. However, there have been recent efforts (Galindo-Rueda and Verger, 2016) to extend the definition to non-manufacturing industries as well. Nevertheless, medium-high and high technology sectors are primarily represented by manufacturing industries.</p>\n<p>The following table includes the classification of MHT industries by ISIC Rev. 3 and ISIC Rev. 4.</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>ISIC Rev.4</p>\n      </td>\n      <td>\n        <p>Description</p>\n      </td>\n      <td>\n        <p>ISIC Rev.3 </p>\n      </td>\n      <td>\n        <p>Description</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>20</p>\n      </td>\n      <td>\n        <p>Manufacture of chemicals and chemical products</p>\n      </td>\n      <td>\n        <p>24</p>\n      </td>\n      <td>\n        <p>Manufacture of chemicals and chemical products</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>21</p>\n      </td>\n      <td>\n        <p>Manufacture of basic pharmaceutical products and pharmaceutical preparations</p>\n      </td>\n      <td>\n        <p>29</p>\n      </td>\n      <td>\n        <p>Manufacture of machinery and equipment n.e.c.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>252</p>\n      </td>\n      <td>\n        <p>Manufacture of weapons and ammunition</p>\n      </td>\n      <td>\n        <p>30</p>\n      </td>\n      <td>\n        <p>Manufacture of office, accounting and computing machinery</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>26</p>\n      </td>\n      <td>\n        <p>Manufacture of computer, electronic and optical products</p>\n      </td>\n      <td>\n        <p>31</p>\n      </td>\n      <td>\n        <p>Manufacture of electrical machinery and apparatus n.e.c.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>27</p>\n      </td>\n      <td>\n        <p>Manufacture of electrical equipment</p>\n      </td>\n      <td>\n        <p>32</p>\n      </td>\n      <td>\n        <p>Manufacture of radio, television and communication equipment and apparatus</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>28</p>\n      </td>\n      <td>\n        <p>Manufacture of machinery and equipment n.e.c.</p>\n      </td>\n      <td>\n        <p>33</p>\n      </td>\n      <td>\n        <p>Manufacture of medical, precision and optical instruments, watches and clocks</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>29</p>\n      </td>\n      <td>\n        <p>Manufacture of motor vehicles, trailers and semi-trailers</p>\n      </td>\n      <td>\n        <p>34</p>\n      </td>\n      <td>\n        <p>Manufacture of motor vehicles, trailers and semi-trailers</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>30<sup>*</sup></p>\n      </td>\n      <td>\n        <p>Manufacture of other transport equipment </p>\n      </td>\n      <td>\n        <p>35<sup>**</sup></p>\n      </td>\n      <td>\n        <p>Manufacture of other transport equipment</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>325</p>\n      </td>\n      <td>\n        <p>Manufacture of medical and dental instruments and supplies</p>\n      </td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>* </strong>Excluding 301 (Building of ships and boats)</p>\n<p><strong>** </strong>Excluding 351 (Building and repairing of ships and boats)</p>\n<p>MVA is the value added of manufacturing industry, which is Section C of ISIC Rev.4 or Section D of ISIC Rev.3. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 4</a></p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev3_1e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 3</a></p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data can be found in UNIDO INDSTAT Database by ISIC Revision 3 and ISIC Revision 4.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are collected using General Industrial Statistics Questionnaire, which is filled by National Statistical Offices (NSOs) and submitted to UNIDO annually. Data for Eurostat countries are obtained directly from Eurostat. Additional data are also collected from official publications and official websites.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are collected annually from NSOs and Eurostat.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>UNIDO INDSTAT database is updated between March and May every year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National statistical offices (NSOs) in non-Eurostat countries, and Eurostat countries by Eurostat.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Industrial Development Organization (UNIDO)</p>", "INST_MANDATE__GLOBAL"=>"<p>UNIDO, as the specialized UN agency on industrial development, has the international mandate for collecting, producing and disseminating internationally comparable industrial statistics. UNIDO&#x2019;s mandate covers (i) the maintenance and updating of international industrial statistics databases; (ii) methodological and analytical products based on statistical research and experience of maintaining internationally comparable statistics; (iii) contributions to the development and implementation of international statistical standards and methodology; and (iv) technical cooperation services to countries in the field of industrial statistics. With the repositioning of UNIDO as the focal agency for inclusive and sustainable industrial development (ISID), its statistical mandate was expanded to cover all dimensions of industrial development, including its inclusiveness and environmental sustainability.</p>", "RATIONALE__GLOBAL"=>"<p>Industrial development generally entails a structural transition from resource-based and low technology activities to MHT manufacturing activities. A modern, highly complex production structure offers better opportunities for skills development and technological innovation. MHT activities generally correspond to the industries with higher value addition and labour productivity. Increasing the share of MHT sectors also reflects the impact of innovation.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Value added by economic activity should be reported at least at 3-digit ISIC for compiling MHT values. However, if the 3-digit data is not available, the indicator is calculated exclusively using 2-digit data. In addition, the indicator is reported in the ISIC revision provided by the countries, and this may affect comparability between countries reporting data according to different ISIC revisions.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated as the share of the sum of the value added from MHT economic activities to MVA using current US dollars</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">M</mi>\n        <mi mathvariant=\"normal\">H</mi>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">M</mi>\n        <mi mathvariant=\"normal\">V</mi>\n        <mi mathvariant=\"normal\">A</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>UNIDO engages with countries in regular consultations during the data collection process to ensure the data quality and international comparability. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Data are collected through the UNIDO General Industrial Statistics Questionnaire to receive information on differences in concept, scope, coverage and classification used. The final data are adjusted to follow ISIC and facilitate international comparability.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level:</strong></p>\n<p> </p>\n<p>If the values are only available sporadically, then the missing values are imputed by linear interpolation, carrying the last observation forward and carrying the first observation backward. If more than five consecutive values are missing, then only the first five missing values are imputed to accommodate the changing dynamics of time series. In the case of complete non-availability of value added data, output is used as a proxy to compute the indicator, if available. However, the imputed missing country values are only used to calculate the global and regional estimates and are not used for international reporting.</p>\n<ul>\n  <li><strong>At regional and global levels:</strong></li>\n</ul>\n<p>Imputation is applied at the country level to facilitate the computation of the regional aggregates. </p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates are calculated as a weighted average of countries&#x2019; MHT shares in a group. Weights are taken based on the MVA share in a group (sourced from UNSD&#x2019;s National Accounts Database). </p>\n<p>The aggregates are computed for a specified year, if one of the following conditions are met:</p>\n<p>1) If at least 50% of all countries are available.</p>\n<p>2) If the MVA share of at least 50% of all available countries falls within the top 60% of all MVA shares within the regarded group and at least 25% of the countries in the group are available.</p>\n<p>Furthermore, an aggregate is not computed, if a country accounting for at least 80% of the total MVA is unavailable.</p>\n<p>Aggregates are reported using ISIC Rev. 3, as long as at least one member continues to report data under ISIC Rev.3. The transition to reporting aggregates in ISIC Rev.4 only occurs, when all members within the group report data on ISIC Rev.4.</p>", "DOC_METHOD__GLOBAL"=>"<p>International Recommendations for Industrial Statistics (IRIS) (2008)</p>\n<p><a href=\"https://unstats.un.org/unsd/publication/seriesM/seriesm_90e.pdf\">https://unstats.un.org/unsd/publication/seriesM/seriesm_90e.pdf</a></p>\n<p>International Standard Industrial Classification of All Economic Activities (ISIC) </p>\n<p><a href=\"https://unstats.un.org/unsd/classifications/Econ/isic\">https://unstats.un.org/unsd/classifications/Econ/isic</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data are checked and validated by the Statistics Division of UNIDO. Countries are contacted to clarify and correct their submissions.</p>\n<p>UNIDO published a handbook for statisticians involved in the regular industrial statistics programmes of NSOs or line ministries (<a href=\"https://www.unido.org/sites/default/files/2012-07/Industrial%20Statistics%20-%20Guidelines%20and%20Methdology_0.pdf\">Industrial Statistics - Guidelines and Methodology</a>). It describes the statistical methods related to the major stages of industrial statistics operation. Moreover, UNIDO has established a quality management framework based on the internationally recognized guidelines recommended by IRIS to ensure quality of statistical products.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p><a href=\"https://unstats.un.org/unsd/unsystem/Documents/QAF-UNIDO.pdf\">The UNIDO Quality Assurance Framework</a> is followed to ensure that the statistical activities of UNIDO are relevant and the data compiled and disseminated are accurate, complete within the defined scope and coverage, timely, comparable in terms of internationally recommended methods and classification standards and internally coherent to variables included in the datasets. While these generally accepted, broad dimensions of quality of statistical data may be defined in each NSO&apos;s own quality assurance framework. UNIDO makes maximum effort that data produced from the statistical operation undertaken with the UNIDO technical cooperation are accurate, internationally comparable and coherent.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>UNIDO employs a wide range of data quality techniques and consultations with national providers to assure quality principles supported by the Fundamental Principles of Official Statistics. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>In the year 2021, the indicator is available for 75 economies. 93% of these countries reported their data in ISIC Rev. 4 and 7% in ISIC Rev. 3.</p>\n<p><strong>Time series:</strong></p>\n<p>Data for this indicator are available from 2000 in the UN Global SDG Database, but longer time series are available in the UNIDO&#x2019;s databases.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>No disaggregation available.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Conversion to USD, data reported only for industry combinations or differences between national classifications and ISIC may cause discrepancy between national and international figures.</p>", "OTHER_DOC__GLOBAL"=>"<h2>URL:</h2>\n<p><a href=\"http://www.unido.org/statistics\">www.unido.org/statistics</a></p>\n<p><a href=\"https://stat.unido.org/\">https://stat.unido.org/</a></p>\n<h2>References:</h2>\n<p>Competitive Industrial Performance (CIP) Report (2018). <a href=\"https://www.unido.org/sites/default/files/files/2019-05/CIP_Report_2019.pdf\">https://www.unido.org/sites/default/files/files/2019-05/CIP_Report_2019.pdf</a></p>\n<p>International Standard Industrial Classification of All Economic Activities (2008). <a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf</a></p>\n<p>Galindo-Rueda, F. and F. Verger (2016). OECD Taxonomy of Economic Activities Based on R&amp;D Intensity, OECD Science, Technology and Industry Working Papers, 2016/04, OECD Publishing, Paris. Available at:</p>\n<p><a href=\"http://dx.doi.org/10.1787/5jlv73sqqp8r-en\">http://dx.doi.org/10.1787/5jlv73sqqp8r-en</a></p>\n<p>OECD (2003). Science, Technology and Industry Scoreboard 2003. Available at <a href=\"https://doi.org/10.1787/sti_scoreboard-2003-en\">https://doi.org/10.1787/sti_scoreboard-2003-en</a></p>\n<p>UNIDO (2009). UNIDO Data Quality: A quality assurance framework for UNIDO statistical activities. <a href=\"https://open.unido.org/api/documents/4814740/download/UNIDO-Publication-2009-4814740\">https://open.unido.org/api/documents/4814740/download/UNIDO-Publication-2009-4814740</a></p>\n<p>UNIDO (2010). Industrial Statistics - Guidelines and Methodology. <a href=\"https://www.unido.org/sites/default/files/2012-07/Industrial%20Statistics%20-%20Guidelines%20and%20Methdology_0.pdf\">https://www.unido.org/sites/default/files/2012-07/Industrial%20Statistics%20-%20Guidelines%20and%20Methdology_0.pdf</a></p>\n<p>UNIDO (2013). The Industrial Competitiveness of Nations 2013. <a href=\"https://www.unido.org/sites/default/files/2013-07/Competitive_Industrial_Performance_Report_UNIDO_2012_2013_0.PDF\">https://www.unido.org/sites/default/files/2013-07/Competitive_Industrial_Performance_Report_UNIDO_2012_2013_0.PDF</a></p>", "indicator_sort_order"=>"09-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"9.c.1", "slug"=>"9-c-1", "name"=>"Proporción de la población con cobertura de red móvil, desglosada por tecnología", "url"=>"/site/es/9-c-1/", "sort"=>"09cc01", "goal_number"=>"9", "target_number"=>"9.c", "global"=>{"name"=>"Proporción de la población con cobertura de red móvil, desglosada por tecnología"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población con cobertura de red móvil, desglosada por tecnología", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población con cobertura de red móvil, desglosada por tecnología", "indicator_number"=>"9.c.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio para la Transformación Digital y de la Función Pública", "periodicity"=>"Anual", "url"=>"https://avancedigital.mineco.gob.es/banda-ancha/cobertura/consulta/Paginas/consulta-cobertura-banda-ancha.aspx", "url_text"=>"Cobertura de banda ancha en España", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de la población con cobertura de red móvil, desglosada por tecnología", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.c- Aumentar significativamente el acceso a la tecnología de la información y las comunicaciones y esforzarse por proporcionar acceso universal y asequible a Internet en los países menos adelantados de aquí a 2020", "definicion"=>"Proporción de hogares cubiertos con redes móviles de tercera generación (3G) basadas \nen el estándar UMTS (Universal Mobile Telecommunications System) y equipadas con HSPA \n(High Speed Packet Access), también conocidas como 3,5G, con redes móviles LTE \n(Long Term Evolution), también conocidas como 4G (evolución de las redes 3,5G \nde comunicaciones móviles), o con redes móviles 5G\n", "formula"=>"\n$$PHRM^{t} = \\frac{HRM_{tecnología}^{t}}{HRM^{t}} \\cdot 100$$\n\ndonde:\n\n$HRM_{tecnología}^{t} =$ hogares cubiertos con redes móviles en el año $t$\n\n$HRM^{t} =$ hogares en el año $t$\n", "desagregacion"=>"Tecnología de las redes móviles: 3,5G; 4G; 5G", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEl porcentaje de la población cubierta por una red móvil \npuede considerarse un indicador mínimo de acceso a las Tecnologías de la \nInformación y la Comunicación (TIC), ya que brinda a las personas la posibilidad \nde suscribirse y utilizar servicios móviles para comunicarse. \n\nDurante la última década, las redes móviles se han expandido \nrápidamente y han ayudado a superar barreras de infraestructura muy básicas \nque existían cuando las redes de telefonía fija, a menudo limitadas a áreas \nurbanas y densamente pobladas, eran la infraestructura de telecomunicaciones dominante.\n\nMientras que las redes móviles 2G (de banda estrecha) ofrecen servicios \nlimitados (y principalmente basados en voz), las redes de mayor velocidad (3G y LTE) \nbrindan un acceso cada vez más rápido, confiable y de alta calidad a Internet \ny su creciente cantidad de información, contenido, servicios y aplicaciones. \n\nLas redes móviles son, por lo tanto, esenciales para superar las barreras de \ninfraestructura, ayudando a las personas a unirse a la sociedad de la información y \nbeneficiarse del potencial de las TIC, en particular en los países menos desarrollados.\n\nEl indicador destaca la importancia de las redes móviles para la prestación de servicios \nde comunicación básicos y avanzados y ayudará a diseñar políticas específicas para \nsuperar las barreras de infraestructura restantes y abordar la brecha digital. \nMuchos gobiernos hacen un seguimiento de este indicador y han establecido objetivos \nespecíficos en términos de la cobertura de la población móvil (por tecnología) \nque los operadores deben lograr.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.c.1&seriesCode=IT_MOB_4GNTWK&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de la población cubierta por al menos una red móvil 4G (%) IT_MOB_4GNTWK</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-0C-01.pdf\">Metadatos 9-c-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de la población con cobertura de red móvil, desglosada por tecnología", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.c- Aumentar significativamente el acceso a la tecnología de la información y las comunicaciones y esforzarse por proporcionar acceso universal y asequible a Internet en los países menos adelantados de aquí a 2020", "definicion"=>"\nProportion of households covered by third generation (3G) mobile networks based on the UMTS \n(Universal Mobile Telecommunications System) standard and equipped with HSPA (High Speed \nPacket Access), also known as 3.5G, by LTE (Long Term Evolution) mobile networks, also known \nas 4G, or by 5G mobile networks\n", "formula"=>"\n$$PHRM^{t} = \\frac{HRM_{technology}^{t}}{HRM^{t}} \\cdot 100$$\n\nwhere:\n\n$HRM_{technology}^{t} =$ households covered by mobile networks in the year $t$\n\n$HRM^{t} =$ households in year $t$\n", "desagregacion"=>"Mobile network technology: 3.5G, 4G, 5G", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe percentage of the population covered by a mobile cellular network can be \nconsidered as a minimum indicator for Information and Communication Technology \n(ICT) access since it provides people with the possibility to subscribe to and \nuse mobile-cellular services to communicate. \n\nOver the last decade, mobilecellular networks have expanded rapidly and helped \novercome very basic infrastructure barriers that existed when fixed-telephone \nnetworks – often limited to urban and highly populated areas - were the dominant \ntelecommunication infrastructure. \n\nWhile 2G (narrowband) mobile-cellular networks offer limited (and mainly voice-based) \nservices, higherspeed networks (3G and LTE and above) provide increasingly high-speed, \nreliable and high-quality access to the Internet and its increasing amount of information, \ncontent, services, and applications. \n\nMobile networks are therefore essential to overcoming infrastructure barriers, helping \npeople join the information society and benefit from the potential of ICTs, in particular \nin least developed countries. \n\nThe indicator highlights the importance of mobile networks in providing basic, as well \nas advanced communication services and will help design targeted policies to overcome \nremaining infrastructure barriers, and address the digital divide. Many governments \ntrack this indicator and have set specific targets in terms of the mobile population \ncoverage (by technology) that operators must achieve. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.c.1&seriesCode=IT_MOB_4GNTWK&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of population covered by at least a 4G mobile network (%) IT_MOB_4GNTWK</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-0C-01.pdf\">Metadata 9-c-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de la población con cobertura de red móvil, desglosada por tecnología", "objetivo_global"=>"9- Construir infraestructuras resilientes, promover la industrialización inclusiva y sostenible y fomentar la innovación", "meta_global"=>"9.c- Aumentar significativamente el acceso a la tecnología de la información y las comunicaciones y esforzarse por proporcionar acceso universal y asequible a Internet en los países menos adelantados de aquí a 2020", "definicion"=>"Ondoko sare mugikorrekin estalitako etxeen proportzioa: UMTS (Universal Mobile Telecommunications System) \nestandarrean oinarritutako eta HSPA (High Speed Packet Access) duten hirugarren belaunaldiko (3G) sare \nmugikorrak (3,5G bezala ere ezagutzen direnak), LTE (Long Term Evolution) sare mugikorrak (4G bezala ere \nezagutzen direnak), eta 5G sare mugikorrak\n", "formula"=>"\n$$PHRM^{t} = \\frac{HRM_{teknologia}^{t}}{HRM^{t}} \\cdot 100$$\n\nnon:\n\n$HRM_{teknologia}^{t} =$ sare mugikorrekin estalitako etxeak $t$ urtean\n\n$HRM^{t} =$ etxeak $t$ urtean\n", "desagregacion"=>"Sare mugikorraren teknologia: 3,5G; 4G; 5G", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nSare mugikor batek estalitako biztanleria-ehunekoa informazioaren eta komunikazioaren teknologien (IKT) \nsarbidearen gutxieneko adierazle gisa uler daiteke, pertsonei aukera ematen baitie komunikazio-zerbitzu \nmugikorretara harpidetu eta horiek erabiltzeko. \n\nAzken hamarkadan, sare mugikorrak azkar hedatu dira, eta lagungarriak izan dira telefonia finkoko \nsareak –sarritan populazio handiko hiriguneetara mugatuta– telekomunikazio azpiegitura nagusia zirenean \nsortzen ziren oso oinarrizko azpiegituren oztopoak gainditzeko. \n\n2G sare mugikorrek (banda estua) zerbitzu mugatuak eskaintzen dituzte (eta, batez ere, ahotsean oinarrituak); \naldiz, abiadura handiagoko sareek (3G eta LTE) Interneterako sarbide askoz azkarragoa, fidagarriagoa eta \nkalitate handiagokoa ematen dute, eta informazio, eduki, zerbitzu eta aplikazioen kopurua geroz eta handiagoa da. \n\nSare mugikorrak, beraz, funtsezkoak dira azpiegitura-oztopoak gainditzeko, pertsonei lagundu egiten baitiete \ninformazioaren gizartera batzen eta IKTen ahalmenaz baliatzen, bereziki hain garatuta ez dauden herrialdeetan. \n\nAdierazle honek agerian jartzen du zein garrantzitsuak diren sare mugikorrak oinarrizko eta aurreratutako \nkomunikazio-zerbitzuak emateko. Halaber, lagungarria da gainerako azpiegitura-oztopoak gainditzeko eta arrakala \ndigitala jorratzeko politika zehatzak diseinatzeko. Gobernu askok adierazle honen jarraipena egiten dute, eta \nhelburu zehatzak ezarri dituzte operadoreek lortu beharreko populazio mugikorren estalduran (teknologia bidez). \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=9.c.1&seriesCode=IT_MOB_4GNTWK&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Gutxienez 4G sare mugikor batek estaltzen duen biztanleriaren proportzioa (%) IT_MOB_4GNTWK</a> UNSTATS\n", "comparabilidad"=>"EAEko adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-09-0C-01.pdf\">Metadatuak 9-c-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 9: Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 9.c: Significantly increase access to information and communications technology and strive to provide universal and affordable access to the Internet in least developed countries by 2020</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 9.c.1: Proportion of population covered by a mobile network, by technology</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>IT_MOB_2GNTWK - Proportion of population covered by at least a 2G mobile network [9.c.1]</p>\n<p>IT_MOB_3GNTWK - Proportion of population covered by at least a 3G mobile network [9.c.1]</p>\n<p>IT_MOB_4GNTWK - Proportion of population covered by at least a 4G mobile network [9.c.1]</p>\n<p>IT_MOB_5GNTWK - Proportion of population covered by at least a 5G mobile network [9.c.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>4.4.1, 4.5.1, 17.6.1, 17.8.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Proportion of population covered by a mobile network, broken down by technology, refers to the percentage of inhabitants living within range of a mobile-cellular signal, irrespective of whether or not they are mobile phone subscribers or users. This is calculated by dividing the number of inhabitants within range of a mobile-cellular signal by the total population and multiplying by 100.</p>\n<p><strong>Concepts:</strong></p>\n<p>The indicator is based on where the population lives, and not where they work or go to school, etc. When there are multiple operators offering the service, the maximum population number covered should be reported. Coverage should refer to at least 5G, at least LTE and above (4G), at least 3G and any technology (2G) mobile-cellular technologies and include:</p>\n<p>- at least 2G mobile population coverage: refers to the percentage of inhabitants that are within range of at least a 2G mobile-cellular signal, irrespective of whether or not they are subscribers. This includes mobile-cellular technologies such as GPRS, CDMA2000 1x and most EDGE implementations. The indicator refers to the theoretical ability of subscribers to use non-broadband speed mobile data services, rather than the number of active users of such services. </p>\n<p>- at least 3G population coverage: refers to the percentage of inhabitants that are within range of at least a 3G mobile-cellular signal, irrespective of whether or not they are subscribers. This is calculated by dividing the number of inhabitants that are covered by at least a 3G mobile-cellular signal by the total population and multiplying by 100. It excludes people covered only by GPRS, EDGE or CDMA 1xRTT.</p>\n<p>- at least LTE population coverage: Refers to the percentage of inhabitants that live within range of LTE/LTE-Advanced, mobile WiMAX/WirelessMAN or other more advanced mobile-cellular networks, irrespective of whether or not they are subscribers. This is calculated by dividing the number of inhabitants that are covered by the previously mentioned mobile-cellular technologies by the total population and multiplying by 100. It excludes people covered only by HSPA, UMTS, EV-DO and previous 3G technologies, and also excludes fixed WiMAX coverage.</p>\n<p>- at least 5G population coverage: refers to the percentage of inhabitants that are within range of at least a 5G mobile cellular signal, irrespective of whether or not they are subscribers. This is calculated by dividing the number of inhabitants that are covered by a 5G mobile-cellular signal by the total population and multiplying by 100.</p>\n<p>As technologies evolve and as more and more countries will deploy and commercialize more advanced mobile-broadband networks, the indicator will include further breakdowns.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Technologies as defined in the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>This indicator is based on an internationally agreed definition and methodology, which have been developed under the coordination of the International Telecommunication Union (ITU), through its Expert Groups and following an extensive consultation process with countries. It is also a core indicator of the Partnership on Measuring ICT for Development&apos;s Core List of Indicators, which has been endorsed by the UN Statistical Commission in 2022. </p>\n<p>ITU collects data for this indicator through an annual questionnaire from national regulatory authorities or Information and Communication Technology Ministries, who collect the data from mobile network operators (MNOs).</p>", "COLL_METHOD__GLOBAL"=>"<p>The International Telecommunication Union (ITU) collects data for this indicator through a questionnaire from national regulatory authorities or Information and Communication Technology Ministries, who collect the data from mobile network operators (MNOs).</p>", "FREQ_COLL__GLOBAL"=>"<p>The International Telecommunication Union (ITU) collects data twice a year from Member States, in 1<sup>st</sup> quarter and in 3<sup>rd</sup> quarter. The calendar is available in the following link: <a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/datacollection/default.aspx\">ITU data collection</a>.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released twice a year, In July and December, in the ITU DataHub, see <a href=\"https://datahub.itu.int/\">https://datahub.itu.int/</a>. </p>", "DATA_SOURCE__GLOBAL"=>"<p>Telecommunication/ Information and Communication Technology (ICT) regulatory authority, or Ministry of ICTs.</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "INST_MANDATE__GLOBAL"=>"<p>As the UN specialized agency for Information and Communication Technology (ICTs), the International Telecommunication Union (ITU) is the official source for global ICT statistics, collecting ICT data from its Member States, see resolution 131 of the ITU Plenipotentiary Conference, https://www.itu.int/pub/S-CONF-ACTF-2022 . </p>", "RATIONALE__GLOBAL"=>"<p>The percentage of the population covered by a mobile cellular network can be considered as a minimum indicator for Information and Communication Technology (ICT) access since it provides people with the possibility to subscribe to and use mobile-cellular services to communicate. Over the last decade, mobile-cellular networks have expanded rapidly and helped overcome very basic infrastructure barriers that existed when fixed-telephone networks &#x2013; often limited to urban and highly populated areas - were the dominant telecommunication infrastructure.</p>\n<p>While 2G (narrowband) mobile-cellular networks offer limited (and mainly voice-based) services, higher-speed networks (3G and LTE and above) provide increasingly high-speed, reliable and high-quality access to the Internet and its increasing amount of information, content, services, and applications. In addition, 5G technology offers opportunities for growth, innovation, and efficiency. To name a few examples: healthcare innovation, industrial transformation, smart infrastructure, agricultural efficiency, automotive and transformation, among others. Moreover, it has a direct impact in people development as it requires competencies as well as enhance educational experiences through high-quality online learning, virtual classrooms, and immersive educational content, making education more accessible and engaging. Mobile networks are therefore essential to overcoming infrastructure barriers, helping people join the information society and benefit from the potential of ICTs, in particular in least developed countries.</p>\n<p>The indicator highlights the importance of mobile networks in providing basic, as well as advanced communication services and will help design targeted policies to overcome remaining infrastructure barriers, and address the digital divide. Many governments track this indicator and have set specific targets in terms of the mobile population coverage (by technology) that operators must achieve.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Some countries have difficulty calculating overall mobile-cellular population coverage. In some cases, data refer only to the operator with the largest coverage, and this may understate the true coverage.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator percentage of the population covered by a mobile network, broken down by technology, refers to the percentage of inhabitants living within range of a mobile-cellular signal, irrespective of whether or not they are mobile phone subscribers or users. This is calculated by dividing the number of inhabitants within range of a mobile-cellular signal by the total population and multiplying by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>(</mo>\n    <mi>N</mi>\n    <mi>r</mi>\n    <mo>.</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>h</mi>\n    <mi>a</mi>\n    <mi>b</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mi>n</mi>\n    <mi>t</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>v</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>b</mi>\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mi>n</mi>\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>m</mi>\n    <mi>o</mi>\n    <mi>b</mi>\n    <mi>i</mi>\n    <mi>l</mi>\n    <mi>e</mi>\n    <mo>-</mo>\n    <mi>c</mi>\n    <mi>e</mi>\n    <mi>l</mi>\n    <mi>l</mi>\n    <mi>u</mi>\n    <mi>l</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>s</mi>\n    <mi>i</mi>\n    <mi>g</mi>\n    <mi>n</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mo>)</mo>\n    <mo>/</mo>\n    <mo>(</mo>\n    <mi>T</mi>\n    <mi>o</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>o</mi>\n    <mi>p</mi>\n    <mi>u</mi>\n    <mi>l</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mo>)</mo>\n    <mo>&#xD7;</mo>\n    <mi>&amp;nbsp;</mi>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are submitted by Member States to the International Telecommunication Union (ITU). ITU checks and validates the data, in consultation with the Member States. </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values are estimated using data published by mobile cellular operators that have the largest market share.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Missing values are estimated using data published by mobile cellular operators that have the largest market share.</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional estimates are produced using weighted country-level data. First, the missing country-level data are estimated using data of the dominant mobile operator. Once all the country-level percentages are available, the number of people covered by the mobile signal is calculated by multiplying the percentage of population covered by the signal to the population of the country. The regional and world total population covered by a signal were calculated by summing the country-level data. The aggregate percentages were calculated by dividing the regional totals by the population of respective groups.</p>", "DOC_METHOD__GLOBAL"=>"<p>ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020: <a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/handbook.aspx\">https://www.itu.int/en/ITU-D/Statistics/Pages/publications/handbook.aspx</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data are checked and validated by the ICT Data and Analytics (IDA) Division of the International Telecommunication Union (ITU). Countries are contacted to clarify and correct their submissions.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The guidelines of the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020 are followed.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The guidelines of the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020 are followed.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data for this indicator exist for more than 160 economies.</p>\n<p><strong>Time series:</strong></p>\n<p>1997 onwards for 2G</p>\n<p>2007 onwards for 3G</p>\n<p>2012 onwards for LTE</p>\n<p>2019 onwards for 5G</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Based on the data for the percentage of the population covered by a mobile network, broken down by technology, and on rural population figures, countries can produce estimates on rural and urban population coverage. International Telecommunication Union (ITU) produces global estimates for the rural population coverage, by technology.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>None. International Telecommunication Union (ITU) uses the data provided by countries, including the in-scope population that is used to calculate the percentages.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx\">http://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx</a> </p>\n<p><strong>References:</strong></p>\n<p>ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020: <a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/handbook.aspx\">https://www.itu.int/en/ITU-D/Statistics/Pages/publications/handbook.aspx</a> </p>", "indicator_sort_order"=>"09-0c-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.1.1", "slug"=>"10-1-1", "name"=>"Tasas de crecimiento per cápita de los gastos o ingresos de los hogares del 40% más pobre de la población y la población total", "url"=>"/site/es/10-1-1/", "sort"=>"100101", "goal_number"=>"10", "target_number"=>"10.1", "global"=>{"name"=>"Tasas de crecimiento per cápita de los gastos o ingresos de los hogares del 40% más pobre de la población y la población total"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Grupo de ingresos", "value"=>"El 40 % más pobre"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tasas de crecimiento per cápita de los gastos o ingresos de los hogares del 40% más pobre de la población y la población total", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasas de crecimiento per cápita de los gastos o ingresos de los hogares del 40% más pobre de la población y la población total", "indicator_number"=>"10.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Mantener el crecimiento de los ingresos del 40% más pobre de la población a una tasa superior a la media", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Bienestar, Juventud y Reto Demográfico", "periodicity"=>"Bienal", "url"=>"https://www.euskadi.eus/encuesta-de-pobreza-y-desigualdades-sociales-epds/web01-s2enple/es/", "url_text"=>"Encuesta de pobreza y desigualdades sociales (EPDS)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_237/opt_1/ti_encuesta-de-gasto-familiar/temas.html", "url_text"=>"Encuesta de gasto familiar", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Tasas de crecimiento per cápita de los gastos o ingresos de los hogares del 40% más pobre de la población y la población total", "objetivo_global"=>"10- Reducir la desigualdad en los países y entre ellos", "meta_global"=>"10.1- De aquí a 2030, lograr progresivamente y mantener el crecimiento de los ingresos del 40% más pobre de la población a una tasa superior a la media nacional", "definicion"=>"Tasa de crecimiento promedio anualizada en un período de cinco años de los ingresos y gastos por persona de los hogares del 40% más pobre de la población de la comunidad autónoma (40% de las personas con menores ingresos por unidad de consumo (escala OCDE modificada)) y del total de la población.", "formula"=>"\nEl crecimiento anualizado del ingreso real medio per cápita o del consumo se calcula \nestimando primero el ingreso real medio per cápita o el consumo del 40% inferior de la \ndistribución del bienestar en los años T0 y T1 y luego calculando la tasa de crecimiento \npromedio anual entre esos años utilizando una fórmula de crecimiento compuesto:\n\nCrecimiento del ingreso o consumo = $$\\left( \\frac{Media\\, en\\, T_1}{Media\\, en\\, T_0} \\right)^{\\frac{1}{T_1 - T_0}} - 1$$\n\n\nEl crecimiento del ingreso real per cápita medio o del consumo de la población total \nse calcula de la misma manera utilizando datos de la población total.\n", "desagregacion"=>"Grupo de ingresos: 40% más pobre, total\n\nTerritorio histórico\n", "observaciones"=>"\nEl número de unidades de consumo de un hogar se calcula utilizando la escala OCDE modificada,  que asigna un peso de 1 a la primera persona de 14 o más años, un peso de 0,5 al resto de  personas de 14 o más años y un peso de 0,3 a las personas de menos de 14 años.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLas mejoras en la prosperidad compartida requieren tanto una economía en crecimiento como \nuna consideración de la equidad. La prosperidad compartida reconoce explícitamente que si \nbien el crecimiento es necesario para mejorar el bienestar económico \nen una sociedad, el progreso se mide por la forma en que esas ganancias se comparten \ncon sus miembros más pobres. \n\nAdemás, en una sociedad inclusiva no es suficiente elevar a todos por encima de un nivel \nde vida mínimo absoluto, debe garantizarse que el crecimiento económico aumente la \nprosperidad entre los pobres a lo largo del tiempo.\n\nLa decisión de medir la prosperidad compartida en función del ingreso o el consumo \nno se tomó para ignorar las muchas otras dimensiones del bienestar. Está motivada por la \nnecesidad de un indicador que sea fácil de entender, comunicar y medir, aunque existen \ndesafíos de medición. De hecho, la prosperidad compartida comprende muchas dimensiones \ndel bienestar de los menos favorecidos, y al analizar la prosperidad compartida en \nel contexto de un país, es importante considerar una amplia gama de indicadores de bienestar.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-01-01.pdf\">Metadatos 10-1-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"Tasas de crecimiento per cápita de los gastos o ingresos de los hogares del 40% más pobre de la población y la población total", "objetivo_global"=>"10- Reducir la desigualdad en los países y entre ellos", "meta_global"=>"10.1- De aquí a 2030, lograr progresivamente y mantener el crecimiento de los ingresos del 40% más pobre de la población a una tasa superior a la media nacional", "definicion"=>"Average annualized growth rate over a five-year period of income and expenditure per  capita of households in the poorest 40% of the population of the autonomous community  (40% of people with the lowest income per consumption unit (modified OECD scale)) and  of the total population.", "formula"=>"\nThe annualized growth in average real per capita income or consumption is calculated by \nfirst estimating the average real per capita income or consumption of the bottom 40% of \nthe welfare distribution in years T0 and T1 and then calculating the average annual growth \nrate between those years using a compound growth formula: \n\nCrecimiento del ingreso o consumo = $$\\left( \\frac{Average\\, T_1}{Average\\, T_0} \\right)^{\\frac{1}{T_1 - T_0}} - 1$$\n\n\nThe growth in average real per capita income or consumption of the total population is \ncalculated in the same way using data for the total population. \n", "desagregacion"=>"Income group: bottom 40%; total\n\nProvince\n", "observaciones"=>"\nThe number of units of consumption of a household is calculated using the modified OECD scale,  which assigns a weighting of 1 to the first person aged 14 or over, a weighting of 0.5 to other  persons aged 14 or over and a weighting of 0.3 to persons under the age of 14. ", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nImprovements in shared prosperity require both a growing economy and a consideration \nof equity. Shared prosperity explicitly recognizes that while growth is necessary for \nimproving economic welfare in a society, progress is measured by how those gains are \nshared with its poorest members.  \n\nMoreover, in an inclusive society it is not sufficient to raise everyone above an \nabsolute minimum standard of living; it must ensure that economic growth increases \nprosperity among the poor over time. \n\nThe decision to measure shared prosperity based on income or consumption was not taken \nto ignore the many other dimensions of welfare. It is motivated by the need for an \nindicator that is easy to understand, communicate, and measure – though measurement \nchallenges exist. Indeed, shared prosperity comprises many dimensions of well-being \nof the less well-off, and when analyzing shared prosperity in the context of a country, \nit is important to consider a wide range of indicators of welfare. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-01-01.pdf\">Metadata 10-1-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Tasas de crecimiento per cápita de los gastos o ingresos de los hogares del 40% más pobre de la población y la población total", "objetivo_global"=>"10- Reducir la desigualdad en los países y entre ellos", "meta_global"=>"10.1- De aquí a 2030, lograr progresivamente y mantener el crecimiento de los ingresos del 40% más pobre de la población a una tasa superior a la media nacional", "definicion"=>"\nBost urteko aldian, EAEko biztanleriaren % 40 pobreeneko etxeko pertsona bakoitzeko diru-sarrera eta gastuen  urteko batez besteko hazkunde-tasa (diru-sarrera txikienak dituzten pertsonen % 40, kontsumo-unitate bakoitzeko  (ELGA eskala aldatua)), eta biztanleria osoarena", "formula"=>"\nBatez besteko per capita diru-sarrera errealaren edo kontsumoaren urteko hazkundea kalkulatzeko, lehenik eta behin, \nongizate-banaketaren % 40 baxuenaren T0 eta T1 urteetako batez besteko per capita diru-sarrera erreala edo kontsumoa \nkalkulatzen da, eta, ondoren, urte horien arteko urteko batez besteko hazkunde-tasa kalkulatzen da, hazkunde \nkonposatuaren formula bat erabiliz:\n\nDiru-sarreren edo kontsumoaren hazkundea = $$\\left( \\frac{Batezbestekoa\\, T_1}{Batezbestekoa\\, T_0} \\right)^{\\frac{1}{T_1 - T_0}} - 1$$\n\n \nBiztanleria osoaren batez besteko per capita diru-sarrera errealaren edo kontsumoaren hazkundea modu berean kalkulatzen \nda, biztanleria osoaren datuak erabiliz.\n", "desagregacion"=>"Diru-sarreren taldea: %40 pobreena; guztizkoa\n\nLurralde historikoa\n", "observaciones"=>"\nEtxeko kontsumo-unitateen kopurua kalkulatzeko, ELGA eskala aldatua erabiltzen da.  Eskala horrek 1 pisua esleitzen dio 14 urte edo gehiagoko lehen pertsonari, 0,5 pisua 14 urte edo  gehiagoko gainerako pertsonei eta 0,3 pisua 14 urtetik beherako pertsonei.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nOparotasun partekatuan hobekuntzak egoteko beharrezkoa da ekonomia hazten egotea eta ekitatea kontuan \nhartzea. Oparotasun partekatuak berariaz aitortzen du, hazkundea gizarte baten ongizate ekonomikoa \nhobetzeko beharrezkoa den arren, irabazi horiek kiderik pobreenekin partekatzeko moduaren arabera \nneurtzen dela aurrerapena. \n\nGainera, gizarte inklusibo batean ez da aski guztiak gutxieneko bizi-maila absolutuaren gainetik \nigotzea; bermatu beharra dago hazkunde ekonomikoak pobreen artean oparotasuna areagotzen duela denboran \nzehar. \n\nOparotasun partekatua diru-sarreren edo kontsumoaren arabera neurtzeko erabakia ez zen hartu ongizatearen \nbeste dimentsio ugariak ahanzturan uzteko. Ulertzeko, komunikatzeko eta neurtzeko erraza den adierazle \nbat behar delako ezarri zen, nahiz eta neurketan erronkak dauden. Berez, oparotasun partekatuaren barruan \nkaltetuen dauden pertsonen ongizatearen dimentsio ugari sartzen dira eta, herrialde baten testuinguruan \noparotasun partekatua aztertzean, garrantzitsua da ongizatearen adierazle ugariak hartzea kontuan. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-01-01.pdf\">Metadatuak 10-1-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.1: By 2030, progressively achieve and sustain income growth of the bottom 40 per cent of the population at a rate higher than the national average</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total population</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SI_HEI_TOTL - Growth rates of household expenditure or income per capita [10.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.1.1, 1.2.1, 10.2.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank (WB)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank (WB)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The growth rate in the welfare aggregate of bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the income distribution in a country from household surveys over a roughly 5-year period.</p>\n<p>The national average growth rate in the welfare aggregate is computed as the annualized average growth rate in per capita real consumption or income of the total population in a country from household surveys over a roughly 5-year period.</p>\n<p><strong>Concepts:</strong></p>\n<p>Promoting shared prosperity is defined as fostering income growth of the bottom 40 percent of the welfare distribution in every country and is measured by calculating the annualized growth of mean per capita real income or consumption of the bottom 40 percent. The choice of the bottom 40 percent as the target population is one of practical compromise. The bottom 40 percent differs across countries depending on the welfare distribution, and it can change over time within a country. Because boosting shared prosperity is a country-specific goal, there is no numerical target defined globally.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The Global Database of Shared Prosperity is prepared by the Global Poverty Working Group, which comprises poverty measurement specialists of different departments of the World Bank Group. The database&#x2019;s primary source of data is the World Bank Group&#x2019;s Poverty and Inequality Platform (PIP), an interactive computational tool that allows users to replicate the World Bank Group&#x2019;s official poverty estimates measured at international poverty lines ($2.15, $3.65 or $6.85 per day per capita). The datasets included in PIP are provided and reviewed by the members of the Global Poverty Working Group. The choice of consumption or income to measure shared prosperity for a country is consistent with the welfare aggregate used to estimate extreme poverty rates in PIP, unless there are strong arguments for using a different welfare aggregate. The practice adopted by the World Bank Group for estimating global and regional poverty rates is, in principle, to use per capita consumption expenditure as the welfare measure wherever available and to use income as the welfare measure for countries for which consumption data are unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank Group for recent survey years. In these cases, if data on income are available, income is used for estimating shared prosperity.</p>", "COLL_METHOD__GLOBAL"=>"<p>To generate measures of shared prosperity that are reasonably comparable across countries, the World Bank Group has a standardized approach for choosing time periods, data sources, and other relevant parameters. The Global Database of Shared Prosperity is the result of these efforts. Its purpose is to allow for cross-country comparison and benchmarking, but users should consider alternative choices for surveys and time periods when cross-country comparison is not the primary consideration.</p>", "FREQ_COLL__GLOBAL"=>"<p>Source collection is ongoing by the Global Poverty Working Group of the World Bank; same data used for estimating poverty (indicator 1.1.1).</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The World Bank Group is committed to updating the shared prosperity indicators twice every year. Given that new household surveys are not available for every year for most countries, updated estimates will be reported for only a subset of countries. Updated estimates are released at the World Bank&#x2019;s Spring and Annual Meetings in April and October every year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The World Bank typically receives data from National Statistical Offices (NSOs) directly. In other cases it uses NSO data received indirectly. Please see the section on data sources for further details.</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Bank (WB)</p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>Improvements in shared prosperity require both a growing economy and a consideration of equity. Shared prosperity explicitly recognizes that while growth is necessary for improving economic welfare in a society, progress is measured by how those gains are shared with its poorest members. Moreover, in an inclusive society it is not sufficient to raise everyone above an absolute minimum standard of living; it must ensure that economic growth increases prosperity among the poor over time.</p>\n<p>The decision to measure shared prosperity based on income or consumption was not taken to ignore the many other dimensions of welfare. It is motivated by the need for an indicator that is easy to understand, communicate, and measure &#x2013; though measurement challenges exist. Indeed, shared prosperity comprises many dimensions of well-being of the less well-off, and when analyzing shared prosperity in the context of a country, it is important to consider a wide range of indicators of welfare.</p>", "REC_USE_LIM__GLOBAL"=>"<p><strong>Comments and limitations:</strong></p>\n<p>There are mainly two limitations of shared prosperity indicators: data availability and data quality. </p>\n<p><strong>Data availability</strong></p>\n<p>Lack of household survey data is even more problematic for monitoring shared prosperity than for monitoring poverty. To monitor shared prosperity, two surveys of a country have to be conducted within five years or so during a chosen period. They have to be reasonably comparable to each other in terms of both the survey design and the construction of the welfare aggregates. Thus, not every survey that can generate poverty estimates can generate shared prosperity estimates.</p>\n<p>The second consideration is the coverage of countries, with data that are as recent as possible. Since shared prosperity must be estimated and used at the country level, there are good reasons for obtaining a wide coverage of countries, regardless of the size of their population. Moreover, for policy purposes it is important to have indicators for the most recent period possible for each country. The selection of survey years and countries needs to be made consistently and transparently, achieving a balance between matching the time period as closely as possible across all countries, including the most recent data, and ensuring the widest possible coverage of countries, across regions and income levels. In practice, this means that time periods will not match perfectly across countries. This is a compromise: while it introduces a degree of incomparability, it also creates a database that includes a larger set of countries than would be otherwise possible.</p>\n<p><strong>Data quality</strong></p>\n<p>Like for poverty rates, estimates of annualized growth of mean per capita real income or consumption are based on income or consumption data collected in household surveys. The same quality issues applying to poverty rates apply here. Specifically, measuring household living standards has its own complications. Surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is difficult to measure accurately, and consumption comes closer to the notion of living standards. Moreover, income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption for about two-thirds of countries.</p>\n<p>Similar surveys may not be strictly comparable because of differences in timing, sampling frames, or the quality and training of enumerators. Comparisons of countries at different levels of development also pose problems because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure, but in practice are often not. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.</p>\n<p>The statistics reported here are based on consumption data or, when unavailable, on income data. Analysis of some 20 countries for which both consumption and income data were available from the same surveys found income to yield a higher mean than consumption but also higher inequality. When poverty measures based on consumption and income were compared, the two effects roughly cancelled each other out: there was no significant statistical difference.</p>\n<p>Invariably some sampled households do not participate in surveys because they refuse to do so or because nobody is at home during the interview visit. This is referred to as &#x201C;unit nonresponse&#x201D; and is distinct from &#x201C;item nonresponse,&#x201D; which occurs when some of the sampled respondents participate but refuse to answer certain questions, such as those pertaining to income or consumption. To the extent that survey nonresponse is random, there is no concern regarding biases in survey-based inferences; the sample will still be representative of the population. However, households with different incomes may not be equally likely to respond. Richer households may be less likely to participate because of the high opportunity cost of their time or because of privacy concerns. It is conceivable that the poorest can likewise be underrepresented; some are homeless or nomadic and hard to reach in standard household survey designs, and some may be physically or socially isolated and thus less likely to be interviewed. This can bias both poverty and inequality measurement if not corrected for.</p>", "DATA_COMP__GLOBAL"=>"<p>Growth rates are calculated as annualized average growth rates over a roughly five-year period. Since many countries do not conduct surveys on a precise five-year schedule, the following rules guide selection of the survey years used to calculate the growth rates in the 2023 update: the final year of the growth period (T1) is the most recent year of a survey but no earlier than 2018, and the initial year (T0) is as close to T1-5 as possible, within a two-year band. Thus the gap between initial and final survey years ranges from three to seven years. If two surveys are equidistant from T1-5, other things being equal, the more recent survey year is selected as T0. The comparability of welfare aggregates (income or consumption) for the years chosen for T0 and T1 is assessed for every country. If incomparability across the two surveys is a concern, the selection criteria are re-applied to select the next best survey year.</p>\n<p>A roughly five-year period is used because shorter periods may be vulnerable to short-term volatility not strongly related to long term progress. Windows longer than five years, on the other hand, would limit the number of datapoints available due to lack of comparable data within countries over longer periods of time.</p>\n<p>Once two surveys are selected for a country, consumer price indices are used to express the income or consumption of the two surveys in the same year&#x2019;s prices. Then, the annualized growth of mean per capita real income or consumption is computed by first estimating the mean per capita real income or consumption of the bottom 40% of the welfare distribution in years T0 and T1 and then computing the annual average growth rate between those years using a compound growth formula:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">w</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mo>=</mo>\n    <msup>\n      <mrow>\n        <mo>(</mo>\n        <mfrac>\n          <mrow>\n            <mi mathvariant=\"normal\">M</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">a</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">T</mi>\n              </mrow>\n              <mrow>\n                <mn>1</mn>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">M</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">a</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">T</mi>\n              </mrow>\n              <mrow>\n                <mn>0</mn>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfrac>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mo>(</mo>\n        <mfrac bevelled=\"true\">\n          <mrow>\n            <mn>1</mn>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mo>(</mo>\n                <mi mathvariant=\"normal\">T</mi>\n              </mrow>\n              <mrow>\n                <mn>1</mn>\n              </mrow>\n            </msub>\n            <mo>-</mo>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">T</mi>\n              </mrow>\n              <mrow>\n                <mn>0</mn>\n              </mrow>\n            </msub>\n            <mo>)</mo>\n          </mrow>\n        </mfrac>\n        <mo>)</mo>\n      </mrow>\n    </msup>\n    <mo>-</mo>\n    <mn>1</mn>\n  </math></p>\n<p>Growth of mean per capita real income or consumption of the total population is computed in the same way using data for the total population.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The raw data are obtained by poverty economists through their contacts in the NSOs, and checked for quality before being submitted for further analysis. The raw data can be unit-record survey data, or grouped data, depending on the agreements with the country governments. In most cases, the welfare aggregate, the essential element for poverty estimation, is generated by the country governments. Sometimes, the World Bank constructs the welfare aggregate or adjusts the aggregate provided by the country.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Shared prosperity indicators are country-specific because the welfare distribution is country-specific. There&#x2019;s no aggregation.</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries may refer to the report &#x201C;On the Construction of a</p>\n<p> Consumption Aggregate for Inequality and Poverty Analysis&#x201D;. The report is available here:</p>\n<p><a href=\"https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099225003092220001/p1694340e80f9a00a09b20042de5a9cd47e\">https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099225003092220001/p1694340e80f9a00a09b20042de5a9cd47e</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The quality of the estimates is managed through the World Bank&#x2019;s Global Poverty Working Group. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The poverty estimates released by the World Bank are quality checked by members of the Global Poverty Working Group.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Assessments of the quality behind povety estimates are often available in World Bank Poverty Assessments and in Global Poverty Moniotring Technical Notes.</p>", "COVERAGE__GLOBAL"=>"<p>In the latest version of the database, around 80 countries had a shared prosperity estimate.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>If there are country produced shared prosperity indicators like these, the main sources of differences could be different welfare aggregates and years of surveys used in the calculation.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>[1] <a href=\"https://pip.worldbank.org\">https://pip.worldbank.org</a></p>\n<p><strong>References:</strong></p>\n<p>The Global Database of Shared Prosperity, World Bank, <a href=\"http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity\">http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity</a>. World Development Indicators, World Bank.</p>", "indicator_sort_order"=>"10-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.2.1", "slug"=>"10-2-1", "name"=>"Proporción de personas que viven por debajo del 50% de la mediana de los ingresos, desglosada por sexo, edad y personas con discapacidad", "url"=>"/site/es/10-2-1/", "sort"=>"100201", "goal_number"=>"10", "target_number"=>"10.2", "global"=>{"name"=>"Proporción de personas que viven por debajo del 50% de la mediana de los ingresos, desglosada por sexo, edad y personas con discapacidad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"related indicators", "embedded_feature_title"=>"", "embedded_feature_url"=>"https://eustat-des.github.io/site/embeded/10-2-1", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de personas que viven por debajo del 50% de la mediana de los ingresos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de personas que viven por debajo del 50% de la mediana de los ingresos, desglosada por sexo, edad y personas con discapacidad", "indicator_number"=>"10.2.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Bienestar, Juventud y Reto Demográfico", "periodicity"=>"Bienal", "url"=>"https://www.euskadi.eus/encuesta-de-pobreza-y-desigualdades-sociales-epds/web01-s2enple/es/", "url_text"=>"Encuesta de pobreza y desigualdades sociales (EPDS)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de personas que viven por debajo del 50% de la mediana de los ingresos", "objetivo_global"=>"10- Reducir la desigualdad en los países y entre ellos", "meta_global"=>"10.2- De aquí a 2030, potenciar y promover la inclusión social, económica y política de todas las personas, independientemente de su edad, sexo, discapacidad, raza, etnia, origen, religión o situación económica u otra condición", "definicion"=>"Proporción de personas que viven con unos ingresos por unidad de consumo por debajo del 50% de la mediana de los ingresos por unidad de consumo (escala OCDE modificada)", "formula"=>"\n$$PP50ME^{t} = \\frac{P50ME^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$P50ME^{t} =$ población que vive con unos ingresos por unidad de consumo por debajo del 50% de la mediana de los ingresos por unidad de consumo (escala OCDE modificada) en el año $t$\n\n$P^{t} =$ población total en el año $t$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAbordar la inclusión social y la desigualdad es importante en la agenda de desarrollo global \nasí como en la agenda de desarrollo nacional de muchos países. La proporción de la población \nque vive por debajo del 50% del ingreso nacional medio es una medida útil para monitorear \nel nivel y las tendencias de la inclusión social, la pobreza relativa y la desigualdad \ndentro de un país.\n\nLa proporción de personas que viven por debajo del 50% de la mediana es un indicador \nde la pobreza relativa y la desigualdad de la distribución del ingreso dentro de un país. \n\nEste indicador y otras medidas relativas similares se utilizan comúnmente para medir la pobreza \nen los países ricos (incluidos los indicadores de pobreza de la Organización para la Cooperación y \nel Desarrollo Económicos (OCDE) y los indicadores de riesgo de pobreza o exclusión social de \nEurostat) y se utilizan cada vez más también como una medida complementaria de la desigualdad \ny la pobreza en los países de ingresos bajos y medios.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-02-01.pdf\">Metadatos 10-2-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"Proporción de personas que viven por debajo del 50% de la mediana de los ingresos", "objetivo_global"=>"10- Reducir la desigualdad en los países y entre ellos", "meta_global"=>"10.2- De aquí a 2030, potenciar y promover la inclusión social, económica y política de todas las personas, independientemente de su edad, sexo, discapacidad, raza, etnia, origen, religión o situación económica u otra condición", "definicion"=>"Proportion of people living with an income per unit of consumption under 50% of the  national median income per unit of consumption (modified OECD scale) ", "formula"=>"\n$$PP50ME^{t} = \\frac{P50ME^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$P50ME^{t} =$ population living with an income per unit of consumption under 50% \nof the national median income per unit of consumption (modified OECD scale) in year $t$\n\n$P^{t} =$ total population in year $t$\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nAddressing social inclusion and inequality is important on the global development \nagenda as well as on the national development agenda of many countries. The share \nof the population living below 50% of median national income is a measure that is \nuseful for monitoring the level and trends in social inclusion, relative poverty \nand inequality within a country. \n\nThe share of people living below 50% of the median is an indicator of relative \npoverty and inequality of the income distribution within a country. \n\nThis indicator and similar relative measures are commonly used for poverty measurement \nin rich countries (including Organization for Economic Cooperation and Development’s \n(OECD) poverty indicators and Eurostat’s indicators of risk of poverty or social exclusion) \nand are increasingly also used as a complementary measure of inequality and poverty in low- \nand middle- income countries. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-02-01.pdf\">Metadata 10-2-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de personas que viven por debajo del 50% de la mediana de los ingresos", "objetivo_global"=>"10- Reducir la desigualdad en los países y entre ellos", "meta_global"=>"10.2- De aquí a 2030, potenciar y promover la inclusión social, económica y política de todas las personas, independientemente de su edad, sexo, discapacidad, raza, etnia, origen, religión o situación económica u otra condición", "definicion"=>"Kontsumo-unitate bakoitzeko diru-sarreren mediana nazionalaren % 50etik beherako diru-sarrerekin bizi diren  pertsonen proportzioa (ELGAren eskala aldatua) ", "formula"=>"\n$$PP50ME^{t} = \\frac{P50ME^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$P50ME^{t} =$ Kontsumo-unitate bakoitzeko diru-sarreren mediana nazionalaren % 50etik beherako diru-sarrerekin bizi den \nbiztanleria (ELGAren eskala aldatua) $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nGizarte-inklusioa eta desberdinkeria jorratzea garrantzitsua da bai garapen globalaren agendan bai \nherrialde askoren garapen nazionalaren agendan. Batez besteko diru-sarrera nazionalaren % 50aren azpitik \nbizi diren biztanleen proportzioa neurri erabilgarria da herrialde baten barruan gizarte-inklusioaren, \npobrezia erlatiboaren eta desberdinkeriaren maila eta joerak kontrolatzeko. \n\nMedianaren % 50aren azpitik bizi diren pertsonen proportzioa herrialde baten barruan diru-sarreraren \nbanaketaren desberdinkeriaren eta pobrezia erlatiboaren adierazlea da. \n\nAdierazle hau eta beste neurri erlatibo antzeko batzuk erabili ohi dira herrialde aberatsetako pobrezia \nneurtzeko (Ekonomia Lankidetza eta Garapenerako Antolakundeko -ELGA- pobrezia-adierazleak eta Eurostateko \npobreziako edo gizarte-bazterketako arrisku-adierazleak barne), eta geroz eta gehiago erabiltzen dira \ndiru-sarrera baxuak edo ertainak dituzten herrialdeetan desberdinkeriaren eta pobreziaren neurri osagarri gisa. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-02-01.pdf\">Metadatuak 10-2-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.2.1: Proportion of people living below 50 per cent of median income, by sex, age and persons with disabilities</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SI_POV_50MI - Proportion of people living below 50 percent of median income [10.2.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 1.1.1: Proportion of population below the international poverty line, by sex, age, employment status and geographical location (urban/rural)</p>\n<p>Indicator 1.2.1: Proportion of population living below the national poverty line, by sex and age</p>\n<p>Indicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total population.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank (WB)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank (WB)</p>", "STAT_CONC_DEF__GLOBAL"=>"<h2>Definition:</h2>\n<p>The proportion of people living below 50 percent of median income (or consumption) is the share (%) of a country&#x2019;s population living on less than half of the consumption/income level of the median of the national income/consumption distribution. </p>\n<h2>Concepts:</h2>\n<p>The indicator is measured using per capita welfare measure of consumption or income. The indicator is calculated by estimating the share of the population in a country living on less than 50% of median of the national distribution of income or consumption, as estimated from survey data. </p>\n<p>Consumption distributions typically capture household expenditure on a set of items over a given period of time. These usually include purchased, own-produced, exchanged, and gifted food and non-food items (for example clothing, housing&#x2014;including imputed rent&#x2014;and the use value of durable consumer goods). Income distributions capture the value of monetary inflow a household receives or earns over a given period of time. Household surveys usually provide information on labor income (salaries, own-business, and self-employment income), as well as non-labor income coming from pensions, subsidies, transfers, property income, scholarships, etc. Income distributions used here aim to measure disposable income defined as the sum of labor and non-labor income (including transfers) less taxes and contributions. The exact definition and operationalization of income aggregates varies across different data sources. Per capita income or consumption is estimated using total household income or consumption divided by the total household size. </p>\n<p>The estimation relies on the same harmonized welfare vectors (distributions) that are used for 10.1.1 and 1.1.1. Using the same data and closely related methodologies ensures internal consistency across these closely related indicators. The data is available through the Poverty and Inequality Platform (PIP), the World Bank&#x2019;s online tool for reporting global poverty and inequality numbers. For details on concepts and standards, refer to documentation available on the PIP website.</p>\n<p>The methodology entails measuring the share of people living below 50% of national median. A threshold set at 50% of the median of the income or consumption is used to derive a headcount rate, similar to how monetary poverty is typically measured. The national median is readily available from the distributional data in PIP. The measurement follows a two-step process of first estimating half of the national median income (or consumption) and then the share of people living below this relative threshold.</p>\n<p>The indicator uses the same data on household income and consumption that is used for monitoring SDG indicators 1.1.1 and 10.1.1, which have been classified as Tier 1 indicators. The methodology and data are similar to that used in measuring international poverty, which has been tested and vetted over many years, including for the purpose of monitoring Millennium Development Goals (MDG) 1. It is also closely related to a large literature of relative poverty measurement.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data of income or consumption comes from nationally representative household surveys or assessments of income or consumption distributions, typically carried out and overseen by National Statistical Offices (NSOs). After some quality control and harmonization the data is available through PIP, the World Bank online tool for global poverty and inequality measurement.</p>", "COLL_METHOD__GLOBAL"=>"<p>NSOs typically lead survey efforts for data collection at the country level. Within the World Bank, the Global Poverty Working Group (GPWG) oversees the collection, validation of income and consumption survey data used in estimation. GPWG archives the datasets obtained from NSOs and harmonizes them, applying common methodologies to ensure comparability, before estimation.</p>", "FREQ_COLL__GLOBAL"=>"<p>Source collection is ongoing by the Global Poverty Working Group of the World Bank. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The World Bank Group is committed to updating the poverty and inequality data every year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The World Bank typically receives data from National Statistical Offices (NSOs) directly. In other cases it uses NSO data received indirectly. For example, it receives data from Eurostat and from LIS (Luxemburg Income Study), who provide the World Bank NSO data they have received / harmonized. The Universidad Nacional de La Plata, Argentina and the World Bank jointly maintain the SEDLAC (Socio-Economic Database for Latin American and Caribbean) database that includes harmonized statistics on poverty and other distributional and social variables from 24 Latin American and Caribbean countries, based on microdata from household surveys conducted by NSOs.</p>\n<p>Data is obtained through country specific programs, including technical assistance programs and joint analytical and capacity building activities. The World Bank has relationships with NSOs on work programs involving statistical systems and data analysis. Poverty economists from the World Bank typically engage with NSOs broadly on poverty measurement and analysis as part of technical assistance activities.</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Bank (WB)</p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>Addressing social inclusion and inequality is important on the global development agenda as well as on the national development agenda of many countries. The share of the population living below 50% of median national income is a measure that is useful for monitoring the level and trends in social inclusion, relative poverty and inequality within a country.</p>\n<p>The share of people living below 50% of the median is an indicator of relative poverty and inequality of the income distribution within a country. This indicator and similar relative measures are commonly used for poverty measurement in rich countries (including Organization for Economic Cooperation and Development&#x2019;s (OECD) poverty indicators and Eurostat&#x2019;s indicators of risk of poverty or social exclusion) and are increasingly also used as a complementary measure of inequality and poverty in low- and middle- income countries.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Like for poverty rates (SDG 1.1.1) and growth in household incomes across the distribution (SDG 10.1.1), estimates are based on income or consumption data collected in household surveys, led by NSOs. Many of the same data quality issues applying to those indicators apply here, some of which are summarized below:</p>\n<p>Data is collected with great heterogeneity and ex-post harmonization will always face limitations. Similar surveys may not be strictly comparable because of differences in timing, sampling frames, or the quality and training of enumerators. Comparisons of countries at different levels of development also pose problems because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure, but in practice are often not. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.</p>\n<p>Estimating the share of people living below 50% of the national median is less sensitive to comparability limitations than estimates of international poverty. The relative nature of the threshold (a function of the distribution median) means that it is not sensitive price differences across time and countries. Appropriately adjusting for price differences is a major challenger in absolute poverty measurement.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is measured using the national distribution per capita measure of consumption or income, as derived from surveys. The indicator is calculated by estimating the share (in percent) of the population living on less than 50% of median of the national distribution of income or consumption. The median is estimate from the same distribution as the indicator is estimated from, thus the 50% of median threshold will vary over time.</p>\n<p>Per capita income or consumption is estimated using total household income or consumption divided by the total household size.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Within the WB, the GPWG is in charge of the collection and validation of income and consumption survey data used in estimation. GPWG archives the datasets obtained from NSOs and then harmonizes them, applying common methodologies.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>No gap filling is done to report national numbers.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>This is a country specific indicator and no aggregation is currently planned. Aggregation could be carried out in the same as for SDG 1.1.1, with alignment of estimates to reference years. This requires assumption of distribution neutral growth between survey estimates and reference years.</p>", "REG_AGG__GLOBAL"=>"<p>This is a country specific indicator and no aggregation is currently planned. Aggregation could be carried out in the same as for SDG 1.1.1, with alignment of estimates to reference years.</p>", "DOC_METHOD__GLOBAL"=>"<p>Guidance is the same as for collection of income and consumption of poverty data, for which the World Bank has published several hand books and manuals. A useful reference is also the &#x201C;Report of the World Bank on poverty statistics&#x201D; submitted to the Forty-ninth session of the UN Statistical Commission.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Within the WB, the GPWG is in charge of the collection, validation of income and consumption survey data used in estimation. GPWG archives the datasets obtained from NSOs and then harmonizes them, applying common methodologies.</p>\n<p>Members of GPWG generate and update the estimates for the proportion of population below the international poverty line using raw data typically provided by country governments. WB country staff works in close collaboration with national statistical authorities on the data collection and dissemination process.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The objective of the GPWG is to ensure that poverty and inequality data generated, curated, and disseminated by the World Bank are up to date, meet high-quality standards, and are well documented and consistent across dissemination channels.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Assessments of the quality behind povety estimates are often available in World Bank Poverty Assessments and in Global Poverty Moniotring Technical Notes.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>As of 2023, data is readily available on more than 160 countries, and the methodology is building on well-established practice used in international poverty measurement tested over many years. Estimates for the particular indicator have now been tested and validated and data are ready to be reported for all countries for which we report data for 1.1.1.</p>\n<p><strong>Time series:</strong></p>\n<p>The database coveres decades of information and is updated up to twice a year.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The World Bank is working to improve the methodology and disaggregation of poverty and inequality measures by subgroups. Until methodological issues are resolved, disaggregation below the country level will not be addressed.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The harmonization of welfare vectors to per capita standards can lead to differences with nationally estimated welfare vectors which may use other adjustments of the welfare vector.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL References:</strong></p>\n<p>[1]: <a href=\"http://pip.worldbank.org/\">http://pip.worldbank.org/</a> (PIP, World Bank: The World Bank&#x2019;s online tool for analysis of income and consumption data)</p>\n<p>[2]: <a href=\"https://unstats.un.org/unsd/statcom/49th-session/documents/2018-23-Poverty-E.pdf\">https://unstats.un.org/unsd/statcom/49th-session/documents/2018-23-Poverty-E.pdf</a> (UN. 2018. Report of the World Bank on poverty statistics. Statistical Commission Statistical Commission, Forty-ninth session)</p>\n<p><strong>References:</strong></p>\n<ul>\n  <li>A Measured Approach to Ending Poverty and Boosting Shared Prosperity: Concepts, Data, and the Twin Goals. (<a href=\"https://www.worldbank.org/en/research/publication/a-measured-approach-to-ending-poverty-and-boosting-shared-prosperity\">https://www.worldbank.org/en/research/publication/a-measured-approach-to-ending-poverty-and-boosting-shared-prosperity</a></li>\n</ul>\n<p>Ferreira, Francisco H. G.; Chen, Shaohua; Dabalen, Andrew L.; Dikhanov, Yuri M.; Hamadeh, Nada; Jolliffe, Dean Mitchell; Narayan, Ambar; Prydz, Espen Beer; Revenga, Ana L.; Sangraula, Prem; Serajuddin, Umar; Yoshida, Nobuo. 2015. <a href=\"http://documents.worldbank.org/curated/en/360021468187787070/A-global-count-of-the-extreme-poor-in-2012-data-issues-methodology-and-initial-results\"><em>A global count of the extreme poor in 2012 : data issues, methodology and initial results</em></a>. Policy Research working paper; no. WPS 7432. Washington, D.C. : World Bank Group.</p>", "indicator_sort_order"=>"10-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"embed", "label"=>"related indicators"}]}, {"number"=>"10.3.1", "slug"=>"10-3-1", "name"=>"Proporción de la población que declara haberse sentido personalmente discriminada o acosada en los últimos 12 meses por motivos de discriminación prohibidos por el derecho internacional de los derechos humanos", "url"=>"/site/es/10-3-1/", "sort"=>"100301", "goal_number"=>"10", "target_number"=>"10.3", "global"=>{"name"=>"Proporción de la población que declara haberse sentido personalmente discriminada o acosada en los últimos 12 meses por motivos de discriminación prohibidos por el derecho internacional de los derechos humanos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población que declara haberse sentido personalmente discriminada o acosada en los últimos 12 meses por motivos de discriminación prohibidos por el derecho internacional de los derechos humanos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población que declara haberse sentido personalmente discriminada o acosada en los últimos 12 meses por motivos de discriminación prohibidos por el derecho internacional de los derechos humanos", "indicator_number"=>"10.3.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nEl compromiso de no dejar a nadie atrás y eliminar la discriminación es un \nelemento central de la Agenda 2030 para el Desarrollo Sostenible. La \neliminación de la discriminación también está consagrada en la \nDeclaración Universal de Derechos Humanos y en los principales tratados \ninternacionales de derechos humanos. \n\nEl propósito de este indicador es medir la prevalencia de la discriminación \na partir de la experiencia personal relatada por las personas. Se \nconsidera un indicador de resultados que ayuda a medir la eficacia \nde las leyes, políticas y prácticas no discriminatorias para los grupos \nde población afectados.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-03-01.pdf\">Metadatos 10-3-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe pledge to leave no-one behind and eliminate discrimination is at the \ncentre of the 2030 Agenda for Sustainable Development. The elimination of \ndiscrimination is also enshrined in the Universal Declaration of Human Rights \nand the core international human rights treaties. \n\nThe purpose of this indicator is to measure a prevalence of discrimination \nbased on the personal experience reported by individuals. It is considered \nan outcome indicator helping to measure the effectiveness of non-discriminatory \nlaws, policy and practices for the concerned population groups. \n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-03-01.pdf\">Metadata 10-3-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nInor atzean ez uzteko eta diskriminazioa ezabatzeko konpromisoa Garapen Jasangarrirako 2030eko Agendaren \nelementu nagusia da. Diskriminazioa ezabatu beharra jasotzen dute, era berean, Giza Eskubideen Adierazpen \nUnibertsalak eta giza eskubideen nazioarteko itun nagusiek. \n\nAdierazle honen asmoa da diskriminazioaren nagusitasuna neurtzea, pertsonek kontatutako esperientzia \npertsonalean oinarrituta. Era berean, ukitutako biztanleria-taldeentzat diskriminatzaileak ez diren lege, \npolitika eta praktiken eraginkortasuna neurtzen laguntzen duen emaitza-adierazlea da. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-03-01.pdf\">Metadatuak 10-3-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.3: Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regard</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.3.1: Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights law</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_VOV_GDSD - Proportion of population reporting having felt discriminated against [10.3.1, 16.b.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>5.1.1 Whether or not legal frameworks are in place to promote, enforce and monitor equality and non-discrimination on the basis of sex</p>\n<p>16.1.3 Proportion of population subjected to physical, psychological or sexual violence in the previous 12 months</p>\n<p>16.a.1 Existence of independent national human rights institutions in compliance with the Paris Principles</p>\n<p>16.6.2 Proportion of population satisfied with their last experience of public services</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Office of the United Nations High Commissioner for Human Rights (OHCHR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Office of the United Nations High Commissioner for Human Rights (OHCHR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator is defined as the proportion of the population (adults) who self-report that they personally experienced discrimination or harassment during the last 12 months based on ground(s) prohibited by international human rights law (IHRL). IHRL refers to the body of international legal instruments aiming to promote and protect human rights, including the Universal Declaration of Human Rights (UDHR) and subsequent international human rights treaties adopted by the United Nations (UN).</p>\n<p><strong>Concepts:</strong></p>\n<p>Discrimination is any distinction, exclusion, restriction or preference or other differential treatment that is directly or indirectly based on prohibited grounds of discrimination, and which has the intention or effect of nullifying or impairing the recognition, enjoyment or exercise, on an equal footing, of human rights and fundamental freedoms in the political, economic, social, cultural or any other field of public life.<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> Harassment is a form of discrimination when it is also based on prohibited grounds of discrimination. Harassment may take the form of words, gestures or actions, which tend to annoy, alarm, abuse, demean, intimidate, belittle, humiliate or embarrass another or which create an intimidating, hostile or offensive environment. While generally involving a pattern of behaviours, harassment can take the form of a single incident.<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></p>\n<p>IHRL provides lists of the prohibited grounds of discrimination. The inclusion of &#x201C;other status&#x201D; in these lists indicate that they are not exhaustive and that other grounds may be recognized by international human rights mechanisms. A review of the international human rights normative framework helps identify a list of grounds that includes race, colour, sex, language, religion, political or other opinion, national origin, social origin, property, birth status, disability, age, nationality, marital and family status, sexual orientation, gender identity, health status, place of residence, economic and social situation, pregnancy, indigenous status, afro-descent and other status.<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup> In practice, it will be difficult to include all potentially relevant grounds of discrimination in household survey questions. For this reason, it is recommended that data collectors identify contextually relevant and feasible lists of grounds, drawing on the illustrative list and formulation of prohibited grounds of discrimination outlined in the methodology section below, and add an &#x201C;other&#x201D; category to reflect other grounds that may not have been listed explicitly.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> See, for instance, Art. 1 of the International Convention on the Elimination of All Forms of Racial Discrimination (ICERD); Art. 1 of the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW); Art. 2 of the Convention on the Rights of Persons with Disabilities (CRPD); General Comment 18 of the Human Rights Committee (paragraphs 6 and 7) and General Comment 20 of the Committee on Economic, Social and Cultural Rights (paragraph 7). <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> See, for instance, General Comment 20 of the Committee on Economic, Social and Cultural Rights, and United Nations Secretary-General&#x2019;s bulletin (ST/SGB/2008/5) on Prohibition of discrimination, harassment, including sexual harassment, and abuse of authority. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> More information on the grounds of discrimination prohibited by IHRL is available at: <a href=\"http://www.ohchr.org/Documents/Issues/HRIndicators/HumanRightsStandards.pdf\">http://www.ohchr.org/Documents/Issues/HRIndicators/HumanRightsStandards.pdf</a>. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>International Classification of Crime for Statistical Purposes </p>", "SOURCE_TYPE__GLOBAL"=>"<p>Household surveys, such as Multiple Indicator Cluster Surveys (MICS), victimisation surveys and other social surveys, are the main data sources for this indicator.</p>", "COLL_METHOD__GLOBAL"=>"<p>See Guidance Note for Implementation of Survey Module on SDG Indicator 16.b.1 &amp; 10.3.1</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are requested annually in October.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Yearly</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (NSOs). If the data are not collected by the NSO but another source, they will be sent to the NSO for consultation prior to their publication in global SDG databases.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Office of the United Nations High Commissioner for Human Rights (OHCHR)</p>", "INST_MANDATE__GLOBAL"=>"<p>As the international custodian of this indicator and with a global mandate to promote and protect human rights, OHCHR has the responsibility and mandated authority to collect, process, and disseminate statistics for this indicator.</p>", "RATIONALE__GLOBAL"=>"<p>The pledge to leave no-one behind and eliminate discrimination is at the centre of the 2030 Agenda for Sustainable Development. The elimination of discrimination is also enshrined in the Universal Declaration of Human Rights and the core international human rights treaties. The purpose of this indicator is to measure a prevalence of discrimination based on the personal experience reported by individuals. It is considered an outcome indicator (see <a href=\"https://www.ohchr.org/EN/Issues/Indicators/Pages/documents.aspx\">HR/PUB/12/5</a>) helping to measure the effectiveness of non-discriminatory laws, policy and practices for the concerned population groups.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator measures an overall population prevalence of discrimination and harassment in the total adult population at the national level. The indicator will not necessarily inform on the prevalence of discrimination within specific population groups. This will depend on sample frames. For example, if disability is included within the selected grounds, the resulting data for discrimination on the ground of disability will represent only the proportion of the total population who feel that they had personally experienced discrimination against on the ground of disability. Unless the sample design provides adequate coverage of people with disability to allow disaggregation on this characteristic, the data cannot be understood as an indication of the prevalence of discrimination (on the ground of disability) within the population of people with a disability.</p>\n<p>The indicator is not measuring a general perception of respondents on the overall prevalence of discrimination in a country. It is based on personal experience self-reported by individual respondents. The indicator does not provide a legal determination of any alleged or proven cases of discrimination. The indicator will also not capture the cases of discrimination or harassment the respondents are not personally aware off or willing to disclose to data collectors. The indicator should be a starting point for further efforts to understand patterns of discrimination and harassment (e.g. location/context of incidents, relationship of the respondent to the person or entity responsible for discrimination or harassment, and frequency and severity of incidents). More survey questions will be needed for examining policy and legislative impact and responses.</p>\n<p>OHCHR advises that data collectors engage in participatory processes to identify contextually relevant grounds and formulations. The process should be guided by the principles outlined in OHCHR&#x2019;s <a href=\"https://www.ohchr.org/en/documents/tools-and-resources/human-rights-based-approach-data-leaving-no-one-behind-2030-agenda\">Human Rights-Based Approaches to Data</a> (HRBAD), which stems from internationally agreed human rights and statistics standards. National Institutions with mandates related to human rights or non-discrimination and equality are ideal partners for these activities. Data collectors are also strongly encouraged to work with civil society organisations that are the representatives of or have better access to groups more are risk of being discriminated or left behind.</p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mtable>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>P</mi>\n            <mi>r</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>g</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>x</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>i</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>c</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>a</mi>\n            <mi>n</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>f</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>m</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>d</mi>\n            <mi>i</mi>\n            <mi>s</mi>\n            <mi>c</mi>\n            <mi>r</mi>\n            <mi>i</mi>\n            <mi>m</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>h</mi>\n            <mi>a</mi>\n            <mi>r</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>s</mi>\n            <mi>m</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n    </mtable>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>N</mi>\n                <mi>u</mi>\n                <mi>m</mi>\n                <mi>b</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>s</mi>\n                <mi>u</mi>\n                <mi>r</mi>\n                <mi>v</mi>\n                <mi>e</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>s</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>d</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>s</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>w</mi>\n                <mi>h</mi>\n                <mi>o</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>t</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>e</mi>\n                <mi>x</mi>\n                <mi>p</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>c</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>a</mi>\n                <mi>n</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>f</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>m</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>d</mi>\n                <mi>i</mi>\n                <mi>s</mi>\n                <mi>c</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>m</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>h</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n                <mi>a</mi>\n                <mi>s</mi>\n                <mi>s</mi>\n                <mi>m</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>&amp;nbsp;</mi>\n                <mi>d</mi>\n                <mi>u</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>g</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>l</mi>\n                <mi>a</mi>\n                <mi>s</mi>\n                <mi>t</mi>\n                <mi>&amp;nbsp;</mi>\n                <mn>12</mn>\n                <mi>&amp;nbsp;</mi>\n                <mi>m</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>s</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#x2219;</mo>\n    <mn>100</mn>\n    <mi>%</mi>\n  </math></p>\n<p>To minimize the effect of <em>forward telescoping<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></em>, the module asks two questions: a first question about the respondent&#x2019;s experience over the last 5 years, and a second question about the last 12 months:</p>\n<ul>\n  <li>Question 1: In [COUNTRY], do you feel that you personally experienced any form of discrimination or harassment during the last 5 years, namely since [YEAR OF INTERVIEW MINUS 5] (or since you have been in the country), on the following grounds?</li>\n  <li>Question 2: In [COUNTRY], do you feel that you personally experienced any form of discrimination or harassment during the past 12 months, namely since [MONTH OF INTERVIEW] [YEAR OF INTERVIEW MINUS 1], on any of these grounds?</li>\n</ul>\n<p>The proposed survey module recommends that interviewer reads or the data collection mechanism provides a short definition of discrimination/harassment to the respondent before asking the questions. Providing respondents with a basic introduction to these notions helps improve their comprehension and recall of incidents. Following consultations with experts and complementary cognitive testing, the following introductory text is recommended:</p>\n<p><em>Discrimination happens when you are treated less favourably compared to others or harassed because of the way you look, where you come from, what you believe or for other reasons. You may be refused equal access to work, housing, healthcare, education, marriage or family life, the police or justice system, shops, restaurants, or any other services or opportunities. You may also encounter comments, gestures or other behaviours that make you feel offended, threatened or insulted, or have to stay away from places or activities to avoid such behaviours.</em></p>\n<p>The proposed survey module also recommends that a list of grounds is provided to respondents to facilitate comprehension and recall of incidents. As a starting point, OHCHR recommends the use of the following list of grounds prohibited by international human rights law and adding an &#x201C;any other ground&#x201D; category to capture grounds that are not explicitly listed. The module recommends that the following illustrative list is reviewed and contextualised at national level through a participatory process (see HRBAD and accompanying guidance) to reflect specific population groups and data collection/disaggregation needs:</p>\n<p>1. SEX: such as being a woman or a man</p>\n<p>2. AGE: such as being perceived to be too young or too old</p>\n<p>3. DISABILITY OR HEALTH STATUS: such as having difficulty in seeing, hearing, walking or moving, concentrating or communicating, having a disease or other health conditions and no reasonable accommodation provided for it</p>\n<p>4. ETHNICITY, COLOUR OR LANGUAGE: such as skin colour or physical appearance, ethnic origin or way of dressing, culture, traditions, native language, indigenous status, or being of African descent</p>\n<p>5. MIGRATION STATUS: such as nationality or national origin, country of birth, refugees, asylum seekers, migrant status, undocumented migrants or stateless persons</p>\n<p>6. SOCIO-ECONOMIC STATUS: such as wealth or education level, being perceived to be from a lower or different social or economic group or class, land or home ownership or not</p>\n<p>7. GEOGRAPHIC LOCATION OR PLACE OF RESIDENCE: such as living in urban or rural areas, formal or informal settlements</p>\n<p>8. RELIGION: such as having or not a religion or religious beliefs</p>\n<p>9. MARITAL AND FAMILY STATUS: such as being single, married, divorced, widowed, pregnant, with or without children, orphan or born from unmarried parents</p>\n<p>10. SEXUAL ORIENTATION OR GENDER IDENTITY: such as being attracted to person of the same sex, self-identifying differently from sex assigned at birth or as being either sexually, bodily and/or gender diverse</p>\n<p>11. POLITICAL OPINION: such as expressing political views, defending the rights of others, being a member or not of a political party or trade union</p>\n<p>12. OTHER GROUNDS</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> Pattern of reporting events as having occurred more recently that they actually did. This is a phenomenon commonly observed in crime victimization surveys. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>If the data are not collected by the NSO but another source, they will be sent to the NSO for consultation prior to their publication in global SDG databases.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Estimates will not be produced for missing values.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Estimates will not be produced for missing values.</p>", "REG_AGG__GLOBAL"=>"<p>Not available</p>", "DOC_METHOD__GLOBAL"=>"<p>See Guidance Note for Implementation of Survey Module on SDG Indicator 16.b.1 &amp; 10.3.1</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>See Guidance Note for Implementation of Survey Module on SDG Indicator 16.b.1 &amp; 10.3.1</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>See Guidance Note for Implementation of Survey Module on SDG Indicator 16.b.1 &amp; 10.3.1</p>\n<p>OHCHR consults NSOs focal points for the SDG indicator framework (list maintained by the UNSD) on the availability of national data for the SDGs indicators database.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See Guidance Note for Implementation of Survey Module on SDG Indicator 16.b.1 &amp; 10.3.1</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>As of 2024, at least one data point available for more than 40% countriess</p>\n<p><strong>Time series:</strong></p>\n<p>Data available since 2015</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregation for this indicator is in line with SDG target 17.18 (income, gender/sex, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts). </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>OHCHR compiles data from national sources only, possibly regional sources, if available/appropriate. Therefore, there should not be discrepancies.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong><a href=\"http://www.ohchr.org\">www.ohchr.org</a><strong> </strong></p>\n<p><strong>References: </strong></p>\n<p>https://www.ohchr.org/en/instruments-and-mechanisms/human-rights-indicators/sdg-indicators-under-ohchrs-custodianship </p>\n<p> </p>\n<p>https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16b1_10_3_1_Guidance_Note_.pdf </p>\n<p>https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/GuidanceNoteonApproachtoData.pdf </p>", "indicator_sort_order"=>"10-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.4.1", "slug"=>"10-4-1", "name"=>"Proporción del PIB generada por el trabajo", "url"=>"/site/es/10-4-1/", "sort"=>"100401", "goal_number"=>"10", "target_number"=>"10.4", "global"=>{"name"=>"Proporción del PIB generada por el trabajo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción del PIB generada por el trabajo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción del PIB generada por el trabajo", "indicator_number"=>"10.4.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> No evaluable", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_10/opt_1/ti_cuentas-economicas/temas.html", "url_text"=>"Cuentas económicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción del PIB generada por el trabajo", "objetivo_global"=>"10- Reducir la desigualdad en los países y entre ellos", "meta_global"=>"10.4- Adoptar políticas, especialmente fiscales, salariales y de protección social, y lograr progresivamente una mayor igualdad", "definicion"=>"Remuneración de personas asalariadas en proporción al PIB a precios corrientes", "formula"=>"\n$$PPIBRA^{t} = \\frac{RA^{t}}{PIB^{t}} \\cdot 100$$\n\ndonde:\n\n$RA^{t} =$ remuneración de personas asalariadas en el año $t$\n\n$PIB^{t} =$ producto interior bruto a precios corrientes en el año $t$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa participación del trabajo en el PIB busca informar sobre la proporción relativa del PIB \nque corresponde a los trabajadores en comparación con la que corresponde al capital en \nun período de referencia determinado. \n\nPara interpretar este indicador de manera efectiva, es importante considerarlo junto \ncon las tendencias de crecimiento económico. La proporción de la remuneración del trabajo \nen la producción nacional puede resaltar hasta qué punto el crecimiento económico se traduce \nen mayores ingresos para los empleados a lo largo del tiempo (y/o mayores ganancias para \nlos trabajadores por cuenta propia). En períodos de recesión económica, la participación \ndel ingreso laboral proporciona una indicación de hasta qué punto la caída de la \nproducción reduce el ingreso laboral en relación con las ganancias. \n\nSi el ingreso laboral cae a un ritmo mayor que las ganancias, se espera que la \nparticipación del ingreso laboral caiga. Por el contrario, si hay una disminución más \npronunciada de las ganancias que de los ingresos laborales, la participación aumentará. \n\nPara cualquier nivel dado de PIB y ganancias, la participación del \ningreso laboral puede caer como resultado de la caída de los salarios, la caída de los ingresos \nde los trabajadores por cuenta propia, cambios en la composición del empleo por ingresos o \nuna combinación de estos. \n\nEl aumento de la producción y del PIB a menudo conduce a mejores niveles de vida de los individuos \nen la economía, pero esto dependerá de la distribución del ingreso real y de las políticas \npúblicas, entre otros factores. \n\nFuente: División de Estadísticas de las Naciones Unidas \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.4.1&seriesCode=SL_EMP_GTOTL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAGE\">Participación del trabajo en el PIB (%) SL_EMP_GTOTL</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-04-01.pdf\">Metadatos 10-4-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"Proporción del PIB generada por el trabajo", "objetivo_global"=>"10- Reducir la desigualdad en los países y entre ellos", "meta_global"=>"10.4- Adoptar políticas, especialmente fiscales, salariales y de protección social, y lograr progresivamente una mayor igualdad", "definicion"=>"Salaried employees' compensation as a proportion of GDP at current prices", "formula"=>"\n$$PPIBRA^{t} = \\frac{RA^{t}}{PIB^{t}} \\cdot 100$$\n\nwhere:\n\n$RA^{t} =$ salaried employees' compensation in year $t$\n\n$PIB^{t} =$ gross domestic product at current prices in year $t$\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLabour share of GDP seeks to inform about the relative share of GDP which \naccrues to workers as compared to the share which accrues to capital in a \ngiven reference period. \n\nIn order to interpret this indicator effectively, it is important to consider \nit together with economic growth trends. The share of labour compensation in \nnational output can highlight the extent to which economic growth translates \ninto higher incomes for employees over time (and/or higher earnings for the \nself-employed). In periods of economic recession, the labour income share provides \nan indication of the extent to which falling output reduces labour income relative \nto profits. \n\nIf labour income falls at a greater rate than profits, the labour income share will \nbe expected to fall. By contrast, if there is a sharper decline in profits than in \nlabour income, the share will rise. \n\nFor any given level of GDP and profits, the labour income share can fall as a result \nof falling wages, falling earnings of the self-employed, changes in the composition \nof employment by income or a combination of these. \n\nIncreased production and GDP often lead to improved living standards of individuals \nin the economy, but this will depend on the distribution of real income and public \npolicy among other factors. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.4.1&seriesCode=SL_EMP_GTOTL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAGE\">Labour share of GDP (%) SL_EMP_GTOTL</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-04-01.pdf\">Metadata 10-4-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción del PIB generada por el trabajo", "objetivo_global"=>"10- Reducir la desigualdad en los países y entre ellos", "meta_global"=>"10.4- Adoptar políticas, especialmente fiscales, salariales y de protección social, y lograr progresivamente una mayor igualdad", "definicion"=>"Soldatapeko pertsonen ordainsaria, BPGrekiko proportzioan, uneko prezioetan", "formula"=>"\n$$PPIBRA^{t} = \\frac{RA^{t}}{PIB^{t}} \\cdot 100$$\n\nnon:\n\n$RA^{t} =$ soldatapeko pertsonen ordainsaria $t$ urtean\n\n$PIB^{t} =$ barne produktu gordina uneko prezioetan $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLanak BPGan duen partaidetzak informazioa eman asmo du langileei dagokien BPGaren proportzio erlatiboari \nburuz, kapitalari dagokionaren aldean, erreferentzia-aldi jakin batean. \n\nAdierazle hori eraginkortasunez interpretatzeko, garrantzitsua da hazkunde ekonomikoaren joerekin batera \naztertzea. Lanaren ordainsariak produkzio nazionalean duen proportzioak agerian jar dezake zein neurritaraino \nesan nahi duen hazkunde ekonomikoak diru-sarrera gehiago daudela enplegatuentzat epe luzean (edo irabazi \nhandiagoak norbere konturako langileentzat). Atzerapen ekonomikoko garaietan, laneko sarreraren partaidetzak \nadierazten du zein neurritan murrizten dituen ekoizpenaren erorketak laneko diru-sarrerak, irabaziei lotuta. \n\nLaneko diru-sarrerak irabaziak baino erritmo handiagoan erortzen badira, laneko sarreraren partaidetza \nere erortzea espero da. Bestalde, irabaziak laneko diru-sarrerak baino gehiago murrizten badira, partaidetza \nareagotu egingo da. \n\nBPGaren eta irabazien edozein mailarako, laneko diru-sarreren partaidetza erori egin daiteke soldaten \nerorketaren, norbere kontuko langileen diru-sarreren erorketaren, enplegua osatzeko orduan diru-sarreren \narabera egondako aldaketen edo horien arteko konbinazio baten ondorioz. \n\nSarritan, ekoizpena eta BPGa areagotzeak norbanakoen bizi-maila hobeak ekartzen ditu ekonomian, baina hori \nbenetako diru-sarreren banaketaren eta politika publikoen araberakoa izango da, besteak beste. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.4.1&seriesCode=SL_EMP_GTOTL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAGE\">Lanaren partaidetza BPGean (%) SL_EMP_GTOTL</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-04-01.pdf\">Metadatuak 10-4-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.4: Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equality</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.4.1: Labour share of GDP</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_EMP_GTOTL - Labour share of GDP [10.4.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>8.2.1, 8.5.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Labour share of Gross Domestic Product (GDP) is the total compensation of employees and the labour income of the self-employed given as a percent of GDP, which is a measure of total output. It provides information about the relative share of output which accrues to workers as compared with the share that accrues to capital in the production process for a given reference period.</p>\n<p><strong>Concepts:</strong></p>\n<p>Compensation of employees is the total in-cash or in-kind remuneration payable to the employee by the enterprise for the work performed by the employee during the accounting period. Compensation of employees includes: (i) wages and salaries (in cash or in kind) and (ii) social insurance contributions payable by employers. This concept views compensation of employees as a cost to employer, thus compensation equals zero for unpaid work undertaken voluntarily. Moreover, it does not include taxes payable by employers on the wage and salary bill, such as payroll tax.</p>\n<p>The indicator should be produced using data that cover all economic activities, all employees, and the self-employed. Thus, in addition to the compensation of employees, the indicator should also include the labour income of the self-employed.</p>\n<p>GDP represents the market value of all final goods and services produced during a specific time period (for the purposes of this indicator, one year) in a country&apos;s territory.</p>\n<p>Persons in employment are defined as all those persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. For the sake of clarity, the term &#x201C;workers&#x201D; is used as shorthand for &#x201C;persons in employment&#x201D;.</p>\n<p>Persons in employment include employees and self-employed.</p>\n<p>Employees are all those workers who hold the type of job defined as paid employment jobs, that is, jobs where the incumbents hold explicit or implicit employment contracts giving them a basic remuneration not directly dependent on the revenue of the unit for which they work. </p>\n<p>The self-employed are workers in jobs where the remuneration is directly dependent upon the profits (or the potential for profits) derived from the goods and services produced (where own consumption is considered to be part of profits). The incumbents make the operational decisions affecting the enterprise, or delegate such decisions while retaining responsibility for the welfare of the enterprise. (In this context &#x201C;enterprise&quot; includes one-person operations.) </p>\n<p>The labour income of a self-employed worker is the implicit element of the remuneration for work done by themselves, as opposed to the element of remuneration generated by the ownership of assets.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The recommended primary data sources for this indicator are the national accounts estimates of GDP and compensation of employees. The periodicity of this indicator will hence depend on the national accounts data produced in the given country. For self-employed workers, an imputation model is necessary to account for their labour income, in combination with national accounts data.</p>\n<p>The source of the data should be presented when providing estimates of the indicator, as well as the System of National Accounts revision (preferably the SNA 2008).</p>", "COLL_METHOD__GLOBAL"=>"<p>The data on compensation of employees and GDP are collected from the repository National Accounts Official Country Data. The Economic Statistics Branch of the United Nations Statistics Division (UNSD) maintains and updates the National Accounts Official Country Data database.</p>\n<p>The necessary data to model and impute the labour of the self-employed are national household survey microdata sets in line with internationally agreed indicator concepts and definitions. The ILO Department of Statistics processes national household survey microdatasets in line with internationally-agreed indicator concepts and definitions set forth by the International Conference of Labour Statisticians (ICLS).</p>", "FREQ_COLL__GLOBAL"=>"<p>Annual for compensation of employees and GDP data and continuous for household survey microdata sets.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The target frequency of data release is approximately biennial.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National statistical offices (NSOs) are the primary providers of both the required national accounts data and household survey microdata sets.</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Labour Organization (ILO)</p>", "INST_MANDATE__GLOBAL"=>"<p>The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians. It also compiles and produces labour statistics with the goal of disseminating internationally comparable datasets, and provides technical assistance and training to ILO Member States to support their efforts to produce high quality labour market data.</p>", "RATIONALE__GLOBAL"=>"<p>Labour share of GDP seeks to inform about the relative share of GDP which accrues to workers as compared to the share which accrues to capital in a given reference period. </p>\n<p>In order to interpret this indicator effectively, it is important to consider it together with economic growth trends. The share of labour compensation in national output can highlight the extent to which economic growth translates into higher incomes for employees over time (and/or higher earnings for the self-employed). In periods of economic recession, the labour income share provides an indication of the extent to which falling output reduces labour income relative to profits. If labour income falls at a greater rate than profits, the labour income share will be expected to fall. By contrast, if there is a sharper decline in profits than in labour income, the share will rise. For any given level of GDP and profits, the labour income share can fall as a result of falling wages, falling earnings of the self-employed, changes in the composition of employment by income or a combination of these.</p>\n<p>Increased production and GDP often lead to improved living standards of individuals in the economy, but this will depend on the distribution of real income and public policy among other factors. </p>", "REC_USE_LIM__GLOBAL"=>"<p>GDP may exclude or underreport activities that are difficult to measure, such as transactions in the informal sector or in illegal markets, etc., thus understating the GDP. Moreover, GDP does not account for the social and environmental costs of production, and is therefore not a good measure of the level of over-all wellbeing.</p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>L</mi>\n    <mi>a</mi>\n    <mi>b</mi>\n    <mi>o</mi>\n    <mi>u</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>s</mi>\n    <mi>h</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>G</mi>\n    <mi>r</mi>\n    <mi>o</mi>\n    <mi>s</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>D</mi>\n    <mi>o</mi>\n    <mi>m</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>P</mi>\n    <mi>r</mi>\n    <mi>o</mi>\n    <mi>d</mi>\n    <mi>u</mi>\n    <mi>c</mi>\n    <mi>t</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>T</mi>\n            <mi>o</mi>\n            <mi>t</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>o</mi>\n            <mi>m</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>s</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>m</mi>\n            <mi>p</mi>\n            <mi>l</mi>\n            <mi>o</mi>\n            <mi>y</mi>\n            <mi>e</mi>\n            <mi>e</mi>\n            <mi>s</mi>\n          </mrow>\n        </mfenced>\n        <mi>&amp;nbsp;</mi>\n        <mo>+</mo>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>L</mi>\n        <mi>a</mi>\n        <mi>b</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>l</mi>\n        <mi>f</mi>\n        <mo>-</mo>\n        <mi>e</mi>\n        <mi>m</mi>\n        <mi>p</mi>\n        <mi>l</mi>\n        <mi>o</mi>\n        <mi>y</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>G</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>s</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>D</mi>\n        <mi>o</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>P</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mi>u</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The ILO engages in annual consultations with Member States through the ILOSTAT questionnaire and related Statistics Reporting System (StaRS). National data providers receive a link to the portal where they can review all national SDG data available on ILOSTAT.</p>", "ADJUSTMENT__GLOBAL"=>"<p>To ensure that the labour share data are internationally comparable, an adjustment for the labour income earned by the self-employed is necessary. Self-employment constitutes a large share of the global workforce. Moreover, the share of the self-employed in the total workforce tends to be higher in countries with lower national income. As a consequence, using only national accounts data on compensation of employees &#x2013; computing the unadjusted labour share &#x2013; reduces international comparability.</p>\n<p>Using the ILO Harmonized Microdata collection, the labour income of the self-employed relative to the labour income of employees is imputed. The imputation is based on observable characteristics of workers, such as economic sector, occupation, education and age. For a description of the procedure please refer to sections 2.1-2.4 of: <a href=\"https://www.ilo.org/ilostat-files/Documents/Labour%20income%20share%20and%20distribution.pdf\">The Global Labour Income Share and Distribution</a>.</p>\n<p>The labour income of the self-employed at the national level is computed on the basis of this estimate, and is added to the numerator of the expression in 4.c.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Multivariate regression and cross-validation techniques are used to impute missing values at the country level. The additional variables used for the imputation include a range of indicators, including labour market and economic data. For further information, refer to the ILO modelled estimates methodological overview, available at <a href=\"https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\">https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/</a>.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable (see 4.g below)</p>", "REG_AGG__GLOBAL"=>"<p>The aggregates are derived from the country level data (including country level imputed observations). The regional and global labour shares are obtained by first adding up, across countries, the numerator and denominator of the formula that define the labour share - outlined above. Once both magnitudes are produced at the desired level of aggregation, the ratio between the two is used to compute the share for each regional grouping and the global level. Notice that this direct aggregation method can be used due to the imputation of missing observations at the country level. For further information, refer to the ILO modelled estimates methodological overview, available at https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/</p>", "DOC_METHOD__GLOBAL"=>"<p>In order to compute this indicator, two key variables are required. </p>\n<p>First, the national accounts estimates of GDP and compensation of employees. Comprehensive documentation on the System of National Accounts can be found here: <a href=\"https://unstats.un.org/unsd/nationalaccount/sna.asp\">https://unstats.un.org/unsd/nationalaccount/sna.asp</a></p>\n<p>Second, the necessary data to model and impute the labour income of the self-employed are national household survey microdata sets. For the methodology of each national household survey, one must refer to the most comprehensive survey report or to the methodological publications of the national statistical office in question. For detailed guidance on the estimation of the labour income of the self-employed please refer to sections 2.1-2.4 of: <a href=\"https://www.ilo.org/ilostat-files/Documents/Labour%20income%20share%20and%20distribution.pdf\">The Global Labour Income Share and Distribution</a>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The quality management system of the ILOSTAT database concerning modelled estimates is based on a combination of automated checks and peer review. These procedures guarantee that the standards of international comparability and time-series consistency are met. </p>\n<p> </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination in the ILOSTAT database. These checks consist of data and metadata revision of all the relevant inputs applying protocols to ensure that international comparability and time-series consistency are maintained. In many cases, input data are obtained through ILO processing of microdata sets of national household surveys. Data are also reported by national statistical offices or other relevant national agencies to the ILO Department of Statistics through its annual questionnaire on labour statistics. Data from international organizations official repositories are used as well. All these inputs are subject to the review procedure. For the resulting modelled estimates, both statistical and judgmental assessments of the output data are carried out.</p>\n<p> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The adjustment procedure to take into account the labour income of the self-employed enhances the international comparability of the indicator. For a detailed discussion on the bias reduction assessment of the estimation procedure, please refer to section 3.1 of: <a href=\"https://www.ilo.org/ilostat-files/Documents/Labour%20income%20share%20and%20distribution.pdf\">The Global Labour Income Share and Distribution</a>. </p>", "COVERAGE__GLOBAL"=>"<p>Data may differ from those published on ILOSTAT due to different update schedules and stricter criteria applied for inclusion in the SDG database.</p>\n<p><strong>Data availability:</strong></p>\n<p>Data for this indicator is available for 94 countries and territories.</p>\n<p><strong>Time series:</strong></p>\n<p>Data for this indicator is available for the period from 2004 to 2024.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>No disaggregation is required for this indicator.</p>", "COMPARABILITY__GLOBAL"=>"<p>The data on compensation of employees and GDP used for the indicator is estimated at the country level, hence no substantial discrepancies should arise. In contrast, the adjustment to reflect the labour income of the self-employed can be a source of sizeable differences between national and international estimates.</p>\n<p> </p>\n<p>The indicator is estimated using a model to impute the labour income of the self-employed on the basis of household survey microdata sets. This is done to provide a comprehensive estimate of labour income and to enhance the international comparability of the estimates. Country level estimates might rely on different models for imputing the labour income of the self-employed or not include the self-employed labour income at all. </p>\n<p>For a detailed description of the different procedures to produce the labour share and their performance please refer to sections 2.1-2.4, and section 3.1 of: <a href=\"https://www.ilo.org/ilostat-files/Documents/Labour%20income%20share%20and%20distribution.pdf\">The Global Labour Income Share and Distribution</a>.</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>ILO Guidebook - Decent Work and the Sustainable Development Goals: A Guidebook on SDG Labour Market Indicators (https://www.ilo.org/global/statistics-and-databases/publications/WCMS_647109/lang--en/index.htm )</li>\n  <li>ILOSTAT portal: <a href=\"https://ilostat.ilo.org/\">https://ilostat.ilo.org/</a></li>\n  <li>Labour income and inequality topic page on ILOSTAT: <a href=\"https://ilostat.ilo.org/topics/labour-income/\">https://ilostat.ilo.org/topics/labour-income/</a> </li>\n  <li>System of National Accounts: <a href=\"http://unstats.un.org/unsd/nationalaccount/sna.asp\">http://unstats.un.org/unsd/nationalaccount/sna.asp</a> </li>\n  <li>Decent Work Indicators Manual: <a href=\"https://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm\">https://www.ilo.org/integration/resources/pubs/WCMS_229374/lang--en/index.htm</a> </li>\n  <li><a href=\"https://www.ilo.org/ilostat-files/Documents/Labour%20income%20share%20and%20distribution.pdf\">The Global Labour Income Share and Distribution</a></li>\n</ul>", "indicator_sort_order"=>"10-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.4.2", "slug"=>"10-4-2", "name"=>"Efecto redistributivo de la política fiscal", "url"=>"/site/es/10-4-2/", "sort"=>"100402", "goal_number"=>"10", "target_number"=>"10.4", "global"=>{"name"=>"Efecto redistributivo de la política fiscal"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Índice de Gini del ingreso disponible equivalente (postfiscal)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Efecto redistributivo de la política fiscal", "indicator_number"=>"10.4.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Bienestar, Juventud y Reto Demográfico", "periodicity"=>"Bienal", "url"=>"https://www.euskadi.eus/encuesta-de-pobreza-y-desigualdades-sociales-epds/web01-s2enple/es/", "url_text"=>"Encuesta de pobreza y desigualdades sociales (EPDS)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Índice de Gini del ingreso disponible equivalente (postfiscal)", "objetivo_global"=>"10- Reducir la desigualdad en los países y entre ellos", "meta_global"=>"10.4- Adoptar políticas, especialmente fiscales, salariales y de protección social, y lograr progresivamente una mayor igualdad", "definicion"=>"\nÍndice de Gini de la renta disponible equivalente per cápita después de transferencias sociales (postfiscal). \n\nEl Índice de Gini mide la dispersión estadística en la distribución del ingreso en una población. \nSe utiliza para comparar la desigualdad en la distribución de los ingresos en distintos\nterritorios. Su valor está entre 0 y 100, de manera que cuanto más próximo sea a 100, mayor será la \nconcentración de los ingresos y cuanto más próximo a 0, más equitativa es la distribución de la riqueza.\n", "formula"=>"\n$$I_{gini}^{t}= \\frac{\\sum_{i=1}^{n-1} (P_i^{t} - Y_i^{t})}{\\sum_{i=1}^{n-1} P_i^{t}}$$\n\ndonde:\n\n$P_i^{t} =$ proporción acumulada de personas, ordenadas por ingreso ascendente, en el año $t$\n\n$Y_i^{t} =$ proporción acumulada de ingresos correspondientes a esas personas en el año $t$\n\n$n =$ número total de personas\n", "desagregacion"=>"", "observaciones"=>"\nLa renta equivalente es un valor derivado de los ingresos totales de un hogar y del número y la edad de las personas que viven de estos ingresos.", "periodicidad"=>"Bienal", "justificacion_global"=>"\nEl indicador de Impacto Redistributivo de la Política Fiscal demuestra en un marco \ncontable la cantidad total en la que la actual desigualdad de ingresos se reduce o \naumenta mediante la ejecución de la política fiscal (incluidos los impuestos directos \ne indirectos; las contribuciones a la seguridad social y a las pensiones de vejez; \nlas transferencias directas en efectivo o cuasiefectivo; y los subsidios). \n\nPor ejemplo, si el Impacto Redistributivo de la Política Fiscal es positivo, \neso indica que el efecto neto de la política fiscal es reducir el índice de Gini respecto \nde lo que sería de otra manera sin la política fiscal (en un sentido contable, no como un \ncontrafactual económico). \n\nEl indicador permite a los responsables de las políticas y a las \ncomunidades más amplias de partes interesadas y promotoras hacer un seguimiento sistemático del \nprogreso a nivel de país en la contribución de la política fiscal a sociedades más equitativas.\n\nFuente: División de Estadísticas de las Naciones Unidas \n", "dato_global"=>"", "comparabilidad"=>"El indicador cumple parcialmente con los metadatos del indicador de Naciones Unidas. Se presenta la serie correspondiente al índice de Gini postfiscal, pero no el índice de Gini prefiscal.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-04-02.pdf\">Metadatos 10-4-2.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"Índice de Gini del ingreso disponible equivalente (postfiscal)", "objetivo_global"=>"10- Reducir la desigualdad en los países y entre ellos", "meta_global"=>"10.4- Adoptar políticas, especialmente fiscales, salariales y de protección social, y lograr progresivamente una mayor igualdad", "definicion"=>"\nThe Gini index measures the statistical dispersion in income distribution \nwithin a population. It is used to compare inequality in income distribution \nacross different territories. \n\nIts value is between 0 and 100, so the closer it is to 100, the greater the \nconcentration of income, and the closer it is to 0, the more equitable the \ndistribution of wealth. \n", "formula"=>"\n$$I^{t}_{GINI} = \\frac{A^t}{(A + B)^t}$$\n\nwhere:\n\n$A^{t} =$ Area over the Lorenz curve in the year $t$\n\n$B^{t} =$ Area under the Lorenz curve in the year $t$\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Bienal", "justificacion_global"=>"\nThe Redistributive Impact of Fiscal Policy indicator demonstrates in an \naccounting framework the total amount by which current income inequality \nis reduced or increased by the current execution of fiscal policy (including \ndirect and indirect taxes; social insurance and old-age pension contributions; \ndirect cash or near-cash transfers; and subsidies). \n\nFor example, if the Redistributive Impact of Fiscal Policy is positive, that \nindicates that the net effect of Fiscal Policy is to reduce the Gini index from \nwhat it otherwise would be without Fiscal Policy (in an accounting sense, not \nas an economic counterfactual). \n\nThe indicator allows policy makers and the broader stakeholder and advocacy \ncommunities to systematically track progress at the country level in the \ncontribution of fiscal policy to more equitable societies. \n", "dato_global"=>nil, "comparabilidad"=>"The indicator partially complies with the UN indicator metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-04-02.pdf\">Metadata 10-4-2.pdf</a>", "national_data_updated_date"=>"2024-10-29", "national_metadata_updated_date"=>"2024-10-29"}, "eu"=>{"indicador_disponible"=>"Índice de Gini del ingreso disponible equivalente (postfiscal)", "objetivo_global"=>"10- Reducir la desigualdad en los países y entre ellos", "meta_global"=>"10.4- Adoptar políticas, especialmente fiscales, salariales y de protección social, y lograr progresivamente una mayor igualdad", "definicion"=>"Errenta erabilgarri baliokidearen Gini-indizea per capita transferentzia sozialen ondoren (postfiskala)\n\nGini Indizeak biztanleria baten diru-sarreren banaketan dagoen sakabanatze estatistikoa neurtzen du. \nDiru-sarreren banaketan dagoen desberdintasuna lurraldeka alderatzeko erabiltzen da. Balioa \n0tik 100era bitartekoa da; zenbat eta hurbilago izan 100etik, orduan eta handiagoa izango da \ndiru-sarreren kontzentrazioa, eta 0tik zenbat eta hurbilago egon, orduan eta ekitatiboagoa izango da \naberastasunaren banaketa.\n", "formula"=>"\n$$I_{gini}^{t}= \\frac{\\sum_{i=1}^{n-1} (P_i^{t} - Y_i^{t})}{\\sum_{i=1}^{n-1} P_i^{t}}$$\n\nnon:\n\n$P_i^{t} =$ pertsonen proportzio metatua, goranzko diru-sarreraren arabera ordenatuta $t$ urtean\n\n$Y_i^{t} =$ pertsona horiei dagozkien diru-sarreren proportzio metatua $t$ urtean\n\n$n =$ guztizko pertsona kopurua\n", "desagregacion"=>nil, "observaciones"=>"\nErrenta baliokidea etxe bateko guztizko diru-sarreretatik eta diru-sarrera horietatik bizi diren pertsonen kopuru eta adinetik eratorritako balio bat da.", "periodicidad"=>"Bienal", "justificacion_global"=>"\nZerga Politikaren Birbanaketa Eraginaren adierazleak, kontabilitateko esparruan, agerian jartzen du \ndiru-sarreren egungo desberdinkeria zein neurritan murrizten edo igotzen den guztira, zerga-politikaren \negungo egikaritzaren bidez (kontuan hartuta zuzeneko eta zeharkako gastuak; gizarte-segurantzarako eta \nzahartzaroko pentsioetarako ekarpenak; dirutan edo ia dirutan egindako transferentzia zuzenak; eta laguntzak). \n\nAdibidez, Zerga Politikaren Birbanaketa Eragina positiboa bada, zerga-politikaren eragin garbia hauxe dela \nesan nahi du: Gini indizea zerga-politikarik gabe izango litzatekeen horren aldean murriztea (kontabilitateari \nbegira, ez suposizio ekonomiko gisa). \n\nAdierazle honek aukera ematen die politiken arduradunei eta alde interesdunen eta sustatzaileen komunitate \nhandiagoei herrialde-mailako aurrerapenaren jarraipen sistematikoa egiteko, zerga-politikak gizarte \nekitatiboagoak lortzeko orduan egin duten ekarpena neurtuz. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>nil, "comparabilidad"=>"\n\nAdierazleak partzialki betetzen ditu Nazio Batuen adierazlearen metadatuak. Zergen ondoko Gini  indizeari dagokion seriea aurkezten da, baina ez zergen aurreko Gini indizeari dagokiona. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-04-02.pdf\">Metadatuak 10-4-2.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.4: Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equality</p>", "SDG_INDICATOR__GLOBAL"=>"<p>10.4.2 Redistributive impact of fiscal policy on the Gini index</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SI_DST_FISP - Redistributive impact of fiscal policy, Gini index (%) [10.4.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>The Impact of Fiscal Policy on Poverty (see Lustig, 2022, chapter 6).</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Institutional information: The World Bank is the official custodian for this indicator. This metadata documentation was developed and agreed upon by the three institutional data providers, Commitment to Equity (CEQ) Institute, OECD and The World Bank (WB).</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>The World Bank </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>The <a href=\"https://unstats.un.org/sdgs/dataportal/database\">Redistributive Impact of Fiscal Policy</a> indicator is defined as the Gini Index of pre-fiscal per capita (or equivalized) income less the Gini Index of post-fiscal per capita (or equivalized) income. These terms are elaborated below and can be calculated with some different variations. The positive sign means that inequality after taxes and transfers is lower than market income inequality; the larger the number, the higher the redistributive effect.</p>\n<p>Concepts:</p>\n<p><u>-Gini Index</u>: a commonly used measure of inequality capturing the statistical dispersion in the distribution of income over a population (Gini, 1936). A Gini Index of zero expresses perfect equality: that is, every individual in the population has the same income. A Gini Index of 100 expresses maximum inequality: that is, all income accrues to a single individual, and all other individuals have zero income.<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></p>\n<p><strong>Household income: this can be calculated: (i) in <u>per capita</u> terms (household income divided by the number of household members); or (ii) in <u>equivalized</u> terms (household income divided by the square root of the number of household members).</strong><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup><strong> If a different definition is used, it should be noted in the reporting document. In particular, it should be specified if the indicator is consumption rather than income.</strong></p>\n<p><u>-Pre-fiscal income:</u> the cumulative income accruing to an individual (or a household) from market and private sources only. The <u>Redistributive Impact of Fiscal Policy</u> indicator can be estimated with reference to two different pre-fiscal income concepts depending on assumptions regarding the treatment of public, contributory old-age pensions (please also see the figure below and Lustig (2022) chapter 1, Section 2.2, pp. 23-33): </p>\n<p>I) Pre-fiscal income 1 - under the &#x201C;pensions as deferred income&#x201D; scenario: When incomes from public contributory old-age pension-system are counted as deferred market income and old-age pension-system contributions are counted as savings from current income (that is, the old-age pension system is treated as the equivalent of a mandatory savings program), <u>pre-fiscal income</u> is defined as an individual&#x2019;s earned and unearned incomes from market and other private sources: wages, interest and dividend income; imputed income from owner-occupied housing and from consumption of own production;<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup> remittances; private transfers; old-age pension income from the public contributory pension system; and, <em>less</em> any contributions to the public old-age contributory pension system. In sum, in this case <u>pre-fiscal income is defined as follows: market income </u><strong><u>plus</u></strong><u> private transfers, contributory old-age pensions, imputed rent for owner&apos;s occupied housing and consumption of own production, and employers&apos; contributions to all social insurance benefits, </u><strong><u>less</u></strong><u> employees&apos; and employers&apos; contributions to social insurance for old-age pensions.</u><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup><u> </u>In this case, the pre-fiscal income concept is called <em>Market income plus pensions. </em></p>\n<p>II) Pre-fiscal income 2 - under the &#x201C;pensions as government transfer&#x201D; scenario: <em> </em>When incomes from current pension system are counted as a government transfer and old age pension system contributions are counted as a tax on current income, <u>pre-fiscal income</u> is defined as: wages, interest and dividend income; imputed income from owner-occupied housing and from consumption of own production; remittances; and private transfers only. In sum, in this case <u>pre-fiscal income is defined as follows: market income plus private transfers, imputed rent for owner&apos;s occupied housing, consumption of own production, and employers&apos; contributions to all social insurance benefits.<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup> </u>In this case, the pre-fiscal income concept is called <em>Market income.</em></p>\n<p>When pensions are treated as pure government transfers, the redistributive effect of pensions may be exaggerated as retirees with zero or near zero pre-fiscal incomes will receive pension income that is &#x2013; at least in part &#x2013; income deferred when the individual was working. It is important to note that deferral of own income from one&#x2019;s working years to one&#x2019;s retired self is possible regardless of whether the pension system is actuarially fair and in both defined-contribution and defined-benefit pension plans. Treating the public contributory pension system income as pure deferred income, however, does not allow us to capture any portion of the redistributive effect of pensions which may in effect exist. Therefore, we view the pensions as government transfer and pensions as deferred income scenarios as imperfect upper and lower bound estimates (respectively) of the true redistributive effect of contributory pensions. Rather than generating estimates of the redistributive effect of fiscal policy under specific assumptions about public contributory pension system income, the OECD instead reports estimates of the redistributive effect for the population under 65 years of age (while treating contributions to the public contributory pension system as a tax). This is most comparable to the &#x201C;pensions as deferred income&#x201D; scenario, although not exactly the same. </p>\n<p><u>-Post-fiscal income:</u> The <u>Redistributive Impact of Fiscal Policy</u> indicator can be estimated with reference to two different post-fiscal income concepts, Disposable Income and Consumable Income. The most comprehensive concept is that of Consumable Income, which incorporates not only the impact of direct taxes and transfers but also of indirect taxes and price subsidies. </p>\n<p>Disposable and Consumable Income under the &#x201C;pensions as deferred income&#x201D; are equal in value to Disposable and Consumable Income under &#x201C;pensions as government transfer&#x201D; scenarios. However, they are derived from pre-fiscal income I and pre-fiscal income II differently; please see the figure below, from Lustig (2022): </p>\n<ol>\n  <li>Post-fiscal incomes under the &#x201C;pensions as deferred income&#x201D; scenario: </li>\n</ol>\n<p><u>Post-fiscal Income A - Disposable Income:</u> pre-fiscal income as defined under I) above <strong>less</strong> direct taxes paid and social insurance contributions made to the public fiscal authority for all contributory benefits excluding old-age pensions <strong>plus</strong> direct cash transfers, benefits from contributory systems <em>excluding</em> old-age pensions, and the monetary value of benefits (measured at what governments spend) received by households in the form of near-cash transfers (e.g., food stamps, school breakfasts, school uniforms). </p>\n<p><u>Post-fiscal Income B - Consumable Income</u>: disposable income as defined immediately above less indirect taxes plus indirect price subsidies. </p>\n<ol>\n  <li>Post-fiscal incomes under the &#x201C;pensions as government transfer&#x201D; scenario: </li>\n</ol>\n<p><u>Post-fiscal Income A - Disposable Income</u>: pre-fiscal income as defined under II) above <strong>less</strong> direct taxes paid and social insurance contributions made to the public fiscal authority <strong>plus</strong> all benefits from contributory systems (e.g., old-age pensions, unemployment benefits, etc.), direct cash transfers and the monetary value of benefits (measured at what governments spend) received by households in the form of near-cash transfers (e.g., food stamps, school breakfasts, school uniforms). </p>\n<p><u>Post-fiscal Income B - Consumable Income</u>: disposable income as defined immediately above less indirect taxes plus indirect price subsidies. </p>\n<p><strong>CEQ Income Concepts </strong></p>\n<p><strong><img src=\"data:image/png;base64,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\" alt=\"A diagram of a company Description automatically generated\"></strong></p>\n<p>Source: Lustig (2022), Figure 6-1, p. 242. </p>\n<p>Note: The concept of &quot;final income&quot; has been deleted from the original version of the figure because it is not referred to in this document. Table 6-5 describes how to compute prefiscal incomes in both scenarios in detail (Lustig, 2022, pp. 45-46).</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> The Gini Coefficient is the same indicator but measured between 0 and 1 as a proportion rather than a percentage. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> Other equivalence scales exist but this is the one used by OECD countries in generating this SDG indicator. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> Some of the income items mentioned may not be part of the income definition used by various NSOs and IGOS, with imputed rents or consumption of own production being a case in point. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> Contributory old-age pensions are considered deferred income (or consumption) obtained from the contributions during working years, a form of forced savings. Other contributory benefits such as unemployment benefits, disability benefits, etc., are treated as pure government transfers. Employer&apos;s contributions to all social insurance program are added because the assumptions underlying typical fiscal incidence analysis is that the workers bear the burden of the employer&apos;s contributions in full in the form of lower wages (lower market income) and, thus, they need to be added to obtain the correct pre-fiscal income and subsequently subtracted to obtain disposable income. For details see Lustig (2022), Chapter 1 and 6. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> Employer&apos;s contributions to all social insurance program are added because the assumptions underlying typical fiscal incidence analysis is that the workers bear the burden of the employer&apos;s contributions in full in the form of lower wages (lower market income) and, thus, they need to be added to obtain the correct pre-fiscal income and subsequently subtracted to obtain disposable income. For details see Lustig (2022), Chapter 1 and 6. <a href=\"#footnote-ref-6\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p><u>- Gini Index points: </u>The <u>Redistributive Impact of Fiscal Policy</u> indicator is the difference between pre-fiscal Gini Index and the post-fiscal Gini Index. Thus, if a simple difference is applied the measure is the change in Gini Index points. Two indicators are reported: (i) the change in the Gini index, measured as the difference between the Gini of postfiscal disposable and that of prefiscal income; and (ii) the change in the Gini index, measured as the difference between the Gini of postfiscal consumable and that of prefiscal income.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The Redistributive Impact of Fiscal Policy indicator is <em>constructed</em> from a range of data sources using a standardized methodology as outlined in Lustig, 2022. Its construction requires a nationally representative micro-data set (a Household Budget Survey, for example, or an Income and Expenditure Survey) and fiscal or budgetary or administrative data on revenue collections, social expenditures, and expenditures on consumption subsidies. Country-specific data sources are provided in the corresponding footnotes. For OECD countries, the indicators follow the methodology in <em>OECD (2013), Framework for Statistics on the Distribution of Household Income, Consumption and Wealth.</em></p>", "COLL_METHOD__GLOBAL"=>"<p>Nationally representative micro-data sets are often collected and hosted by the national statistics agency. However, access to such data sets is frequently given to a different part of the administration (the Ministry of Finance, for example, or the Ministry of Development and Planning). Fiscal or budgetary or administrative data is occasionally available in unabridged summaries with enough detail at the program or policy level for the estimation of the indicator. More often, however, budgetary and administrative data is kept by the agency executing the program (so, for example, the Ministry of Education will keep data on its own fiscal-year expenditures). These datasets are then used to construct the <u>Redistributive Impact of Fiscal Policy</u> indicator.</p>", "FREQ_COLL__GLOBAL"=>"<p>Source data collection follows the update cycle for country-specific micro-data sets as well as the audit cycle for fiscal year revenues and expenditures. The final constructed SDG indicator relies upon the calendar of the source data collection as well as availability of analytical capacity by the data compilers (see below).</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>A biannual update to the SDG database will be made by the custodians, but it is expected that most countries will have updated indicators only every five years or so, given the underlying source data collection calendars. The WBG would be the custodian of any international agreement committing individual countries to an update schedule. Existing CEQ Assessments are listed here: <a href=\"http://commitmentoequity.org/publications-ceqworkingpapers/\">commitmentoequity.org/publications-ceqworkingpapers/</a> </p>", "DATA_SOURCE__GLOBAL"=>"<p>Ultimately the data providers are national-level statistical agencies for the micro-data sets and national-level fiscal agencies and bodies for budgetary and administrative data. </p>\n<p>For non-OECD countries, the prefiscal and postfiscal Gini coefficients are produced by the WBG and/or the Commitment to Equity Institute. Where a country produces its own 10.4.2 indicator, it will take precedence, subject to meeting the reporting requirements below. Most OECD countries also calculate their own pre- and post-fiscal Gini indices. That is, they directly calculate the 10.4.2 indicator. These are collated by the OECD and sent to the World Bank as custodian. Data for Malta are produced by EUROMOD based on calculations performed by the Joint Research Centre, European Commission.</p>", "COMPILING_ORG__GLOBAL"=>"<p>There will be three main data compilers: the World Bank, the Commitment to Equity (CEQ) Institute and the OECD. Data compilers will be responsible for compiling the necessary information and documentation in ways that are compliant with the posting requirements described as follows:</p>\n<ul>\n  <li>The WBG will compile information all CEQ Assessments conducted by WBG teams and by (non-OECD) national participants working independently. The focus of this exercise will be on assessments conducted in or after 2015.</li>\n  <li>The CEQ Institute will compile information on all assessments conducted by the Institute. The Institute&#x2019;s submissions to the WB will include information on pre-fiscal and post-fiscal Gini Indices, information needed to complete the necessary metadata (when available) and do-files needed for replication (when available).</li>\n  <li>The OECD will compile information on all fiscal assessments conducted by OECD national participants. The OECD&#x2019;s submissions to the WBG will include information on pre-fiscal and post-fiscal Gini Indices.</li>\n</ul>\n<p>The three data compilers will meet periodically to review the reporting and submission process, exchange information on (new) methodological changes, and coordinate on further methodological innovations regarding the estimation of the prefiscal and postfiscal Gini coefficients as needed. </p>", "INST_MANDATE__GLOBAL"=>"<p>The WBG has the mandate to measure and disseminate international poverty numbers and inequality. The World Bank is the formal custodian for the SDG 10.4.2 fiscal redistribution. </p>", "RATIONALE__GLOBAL"=>"<p>Developed by the <a href=\"http://commitmentoequity.org/\">Commitment to Equity Institute</a> (CEQ)<a href=\"http://commitmentoequity.org/\"> Institute</a> at Tulane University, the <u>Redistributive Impact of Fiscal Policy</u> indicator demonstrates in an accounting framework the total amount by which current income inequality is reduced or increased by the current execution of fiscal policy (including direct and indirect taxes; social insurance and old-age pension contributions; direct cash or near-cash transfers; and subsidies). For example, if the Redistributive Impact of Fiscal Policy is positive, that indicates that the net effect of Fiscal Policy is to reduce the Gini index from what it otherwise would be without Fiscal Policy (in an accounting sense, not as an economic counterfactual). The indicator allows policy makers and the broader stakeholder and advocacy communities to systematically track progress at the country level in the contribution of fiscal policy to more equitable societies. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Reporting on assumptions: The choice of whether to report the <u>Redistributive Impact of Fiscal Policy</u> indicator under the pensions as deferred income or pensions as transfers scenario will be left to the country authority or international agency in charge of submitting this indicator, <em>but the choice must be clearly indicated in the reporting document.</em> For countries for which the data exist, pre-fiscal and post-fiscal inequality should be calculated for both pension scenarios, and the default included in the SDGs database is pension as deferred income. If only data treating pensions as transfers are available, it is recommended to report them only for the working age population (under 65 years of age). Some authorities may also choose to use equivalized income instead of per capita income as the welfare indicator. This too should be clearly indicated in the reporting document. Last, some authorities may report these data based on a micro-data set using expenditure (or consumption) as the relevant welfare concept. In addition, where possible, specify if welfare concept includes imputed rent for owner&apos;s occupied housing, consumption of own production, and, in the case of consumption-based surveys, expenditures on durables. Once these decisions are taken, they should be maintained in subsequent years in order to ensure comparability, except that all countries are encouraged to provide data with pension as deferred income. The data reported in the UN Global Database try, to the extent possible, to distinguish between the different concepts used for different countries.</p>\n<p>Feasibility: The Redistributive Impact of Fiscal Policy indicator can be estimated for any country with a micro-data set detailing incomes or expenditures (or both) at the household or individual level and with a set of fiscal, administrative, or budgetary records detailing public expenditures at the program level and revenue collections at the revenue-collection instrument level.</p>\n<p>Suitability/Relevance: The Redistributive Impact of Fiscal Policy indicator provides a direct estimate of the current impact of fiscal policy on redistribution (of incomes). It therefore provides a direct estimate of progress on SDG Target 10.4: &#x201C;<strong>Adopt policies, especially fiscal</strong>, <strong>wage</strong> <strong>and social protection policies</strong>, <strong>and progressively achieve greater equality</strong>.&#x201D; </p>\n<p>Limitations: The Redistributive Impact of Fiscal Policy indicator does not address wage policy. It does not include the benefits of public provision of in-kind benefits, such as health, education, sanitation and housing services, which may have both present-day and longer-term impacts on present-day and future inequality. And, if the post-fiscal income is &quot;disposable income,&quot; the indicator does not capture the impact of indirect taxes and indirect price subsidies on inequality. The indicator does not take into account behavioral responses and general equilibrium effects.</p>", "DATA_COMP__GLOBAL"=>"<p><u>Pre-fiscal income</u> can be derived from a nationally representative micro-data set (an Income and Expenditure Survey, for example). <u>Post-fiscal income</u> is estimated via the allocation of the tax burdens and the expenditure-based benefits that stem from fiscal policy (direct and indirect taxes, social contributions, direct cash and near-cash transfers, subsidies, <em>et cetera</em>). Procedures for constructing pre-fiscal and post-fiscal income concepts and estimating their distribution<em> </em>from an underlying microdata set are detailed comprehensively in Lustig (2022) (Chapters 1, 6, and 7). </p>\n<p>The Gini Index is calculated rescaling the Gini Coefficient by a factor of 100. The Gini Coefficient is calculated according to standard formulas for a (generalized) Gini Coefficient. See, for example, Duclos and Araar (2006):</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">I</mi>\n    <mi mathvariant=\"normal\">N</mi>\n    <mi mathvariant=\"normal\">I</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">I</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">x</mi>\n    <mo>=</mo>\n    <mn>100</mn>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">x</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">I</mi>\n    <mi mathvariant=\"normal\">N</mi>\n    <mi mathvariant=\"normal\">I</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi mathvariant=\"normal\">&#x3A7;</mi>\n        <mo>;</mo>\n        <mi mathvariant=\"normal\">&#x3C5;</mi>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">I</mi>\n    <mi mathvariant=\"normal\">N</mi>\n    <mi mathvariant=\"normal\">I</mi>\n    <mo>(</mo>\n    <mi mathvariant=\"normal\">X</mi>\n    <mo>;</mo>\n    <mi mathvariant=\"normal\">&#x3C5;</mi>\n    <mo>)</mo>\n    <mo>=</mo>\n    <mo>-</mo>\n    <mi mathvariant=\"normal\">&#x3C5;</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">C</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">v</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mi mathvariant=\"normal\">X</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">&#x3BC;</mi>\n            <mo>(</mo>\n            <mi mathvariant=\"normal\">X</mi>\n            <mo>)</mo>\n          </mrow>\n        </mfrac>\n        <mo>,</mo>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <msup>\n          <mrow>\n            <mo>(</mo>\n            <mn>1</mn>\n            <mo>-</mo>\n            <mi mathvariant=\"normal\">F</mi>\n            <mo>(</mo>\n            <mi mathvariant=\"normal\">X</mi>\n            <mo>)</mo>\n            <mo>)</mo>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">&#x3C5;</mi>\n            <mo>-</mo>\n            <mn>1</mn>\n          </mrow>\n        </msup>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p>where X is a random variable of interest with mean &#x3BC;(X), F(X) is its cumulative distribution function, &#x3C5; is a parameter tuning the degree of &#x2018;aversion to inequality&#x2019;. The standard Gini corresponds to &#x3C5; = 2. Cov is a Covariance estimate.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The validation process would require consultation with line ministries and agencies responsible for executing programmatic expenditures or revenue collections.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>When a nationally representative micro-data set or country-level fiscal, budgetary, and administrative data are not available, the indicator cannot be generated. An exception is Argentina where the household survey is representative only of the urban population. Budget and administrative data exists for every fiscal system but is not always public. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Currently no regional or global aggregates exist for this indicator.</p>", "REG_AGG__GLOBAL"=>"<p>Currently no regional or global aggregates exist for this indicator. </p>", "DOC_METHOD__GLOBAL"=>"<p>A complete description of the methodology, recommendations, and guidelines behind the generation of the Redistributive Impact of Fiscal Policy indicator can be found in Chapters 1, 6, 7, 8 and Part IV in <a href=\"https://commitmentoequity.org/publications-ceq-handbook\">Lustig (2022).</a></p>\n<p>This indicator can be calculated based on the current state of household surveys micro-data and budget administrative data.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The World Bank as custodian will coordinate with data compilers on the quality of their respective country indicators. The Poverty Global Department at the WBG verifies the SDG 10.4.2 indicators produced by WBG.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>In its role as custodian agency of the proposed indicator for SDG 10.4.2, the World Bank is responsible for quality control of and quality assurance over all data submitted to the SDG Indicators Database, as well as the underlying analysis and documentation.</p>\n<p>In practice and taking advantage of the proposed partnership between the WBG and the <a href=\"http://www.ceqinstitute.org/\">Commitment to Equity Institute</a> (<a href=\"http://commitmentoequity.org/\">CEQ) Institute</a> at Tulane University regarding the monitoring of the proposed indicator, the Institute will be responsible for quality control of and quality assurance over the Redistributive Impact of Fiscal Policy indicators submitted by the Institute. Similarly, the OECD will be responsible for quality control of and quality assurance over the Redistributive Impact of Fiscal Policy indicators submitted by OECD member nations.</p>\n<p>For any data reporting outside of the CEQ Institute and OECD, the World Bank will review accompanying technical documentation to confirm that the methodology employed is consistent with that described in Lustig (2022). Where questions arise, the World Bank will engage with the reporting institution to verify the analysis.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p><strong>Reporting requirements:</strong></p>\n<p>The World Bank requests the following information from Commitment to Equity (CEQ) Assessments for submission to the SDG Indicator Database:</p>\n<ul>\n  <li>Information on both pre-fiscal and post-fiscal Gini </li>\n  <li>Metadata </li>\n  <li>Technical report on methodology </li>\n  <li>Master Workbook or equivalent </li>\n</ul>\n<p>While reporting requirements specify that the post-fiscal Gini should be reported for either Consumable or Disposable Income, countries and international agencies are encouraged to report both whenever possible. When this is not feasible in the short term, they should work towards reporting both indicators over time. Unlike most data from the CEQ Institute and the World Bank, OECD submissions do not report consumable income, and therefore do not account for indirect taxes and price subsidies. For any country-year with both OECD and CEQ estimates, the OECD data take precedence. CEQ results for all available countries are accessible at <a href=\"https://commitmentoequity.org/datacenter/\">https://commitmentoequity.org/datacenter/</a>.</p>\n<p>WBG submissions to the SDG Indicator Database indicate whether information has been prepared by the WBG, the CEQ Institute, or another agency (e.g. OECD for OECD countries).</p>\n<p>Required metadata should specify:</p>\n<ul>\n  <li>Welfare aggregate: consumption or income</li>\n  <li>Welfare aggregate: per capita or equivalized</li>\n  <li>Income/consumption includes imputed rent for owner&apos;s occupied housing: YES/NO</li>\n  <li>Income/consumption includes consumption of own production: YES/NO</li>\n  <li>Consumption includes expenditures on durable goods: YES/NO</li>\n  <li>Treatment of pensions: pensions as deferred incomes or government transfers</li>\n  <li>Population coverage: all or working age</li>\n  <li>Indirect effects of indirect taxes and subsidies included: YES/NO</li>\n  <li>Level of government: general or consolidated; federal or federal plus subnational</li>\n  <li>Alternative prefiscal income Gini using (PDI/PGT, whichever is not one of the main indicators), where available</li>\n  <li>Date of household survey</li>\n  <li>Date of submission</li>\n  <li>Link to official report and technical documentation</li>\n  <li>Reporting institution and contact person</li>\n</ul>", "COVERAGE__GLOBAL"=>"<p>As of March 2025, <a href=\"https://unstats.un.org/sdgs/dataportal/database\">the Redistributive Impact of Fiscal Policy indicator</a> covers 105 countries. Of these, 67 countries have data from the Commitment to Equity Institute (CEQ) or the World Bank (WB) for at least one year. All included data points are from 2013 onward, except for the Dominican Republic, where the only available estimate is from 2007. The Joint Research Centre of the European Commission separately submits data for Malta. </p>\n<p>Data on Pre-fiscal and Disposable Income is available for 37 of the 38 OECD member countries. OECD data is available annually through the OECD Income Distribution Database, with the exception of countries where income surveys are conducted every two or three years. For countries with both CEQ and OECD estimates, only OECD data is reported. The regional distribution of country coverage is as follows: </p>\n<ul>\n  <li>East Asia and the Pacific: 14</li>\n  <li>Europe and Central Asia: 42</li>\n  <li>Latin America and the Caribbean: 18</li>\n  <li>Middle East and North Africa: 8</li>\n  <li>North America: 2</li>\n  <li>South Asia: 2</li>\n  <li>Sub-Saharan Africa: 19</li>\n</ul>\n<p><strong>Time series:</strong></p>\n<p>The Redistributive Impact of Fiscal Policy indicator is currently <em>for the most part</em> available for single country/year pairs only. The main limitation to producing more frequent time series is the availability of more frequent household surveys. However, that is also a limitation faced by other SDG indicators. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The Redistributive Impact of Fiscal Policy indicator can be shown separately for as many different subgroups as are represented in the survey or micro-data from which it is drawn: income subgroups; by gender, age group, ethnic grouping; geographic location; disability status, household size; household dependency ratios, and so on. These are frequently reported in the main CEQ studies which the SDG indicators are drawn from but not reported within the SG database itself.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p>Duclos, Jean Yves, and Abdelkrim Araar. 2006. <a href=\"https://idrc-crdi.ca/en/book/poverty-and-equity-measurement-policy-and-estimation-dad\">Poverty and Equity: Measurement, Policy, and Estimation with DAD</a>. Springer US.</p>\n<p>Gini, Corrado. (1936). &quot;On the Measure of Concentration with Special Reference to Income and Statistics&quot;, Colorado College Publication, General Series No. 208, 73&#x2013;79. </p>\n<p>Lustig, Nora (ed). 2022. <a href=\"https://tulane.app.box.com/s/l72r8kez5b1r38fibghgyb439i6849pm\">CEQ Handbook: Estimating the Impact of Fiscal Policy on Inequality and Poverty</a>, Second Edition, CEQ Institute at Tulane University and Brookings Institution Press. www.<a href=\"http://commitmentoequity.org/publications-ceq-handbook\">commitmentoequity.org/publications-ceq-handbook</a> (open source; available online free of charge).</p>", "indicator_sort_order"=>"10-04-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.5.1", "slug"=>"10-5-1", "name"=>"Indicadores de solidez financiera", "url"=>"/site/es/10-5-1/", "sort"=>"100501", "goal_number"=>"10", "target_number"=>"10.5", "global"=>{"name"=>"Indicadores de solidez financiera"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Indicadores de solidez financiera", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Indicadores de solidez financiera", "indicator_number"=>"10.5.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nSe incluyen siete Indicadores de Solidez Financiera (ISF) como indicadores de los \nODS para el punto 10.5.1, expresados en porcentaje.\n (1) Capital de Nivel 1 Regulatorio sobre activos\n (2) Capital de Nivel 1 Regulatorio sobre activos ponderados por riesgo\n (3) Préstamos morosos netos de provisiones sobre capital\n (4) Préstamos morosos sobre préstamos brutos totales\n (5) Rentabilidad de los activos\n (6) Activos líquidos sobre pasivos a corto plazo\n (7) Posición neta abierta en divisas sobre capital\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-05-01.pdf\">Metadatos 10-5-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nSeven Financial Soundness Indicators (FSIs) are included as SDG indicators \nfor point 10.5.1, expressed as a percentage  \n (1) Regulatory Tier 1 capital to assets \n (2) Regulatory Tier 1 capital to risk-weighted assets \n (3) Nonperforming loans net of provisions to capital \n (4) Nonperforming loans to total gross loans \n (5) Return on assets \n (6) Liquid assets to short-term liabilities \n (7) Net open position in foreign exchange to capita \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-05-01.pdf\">Metadata 10-5-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\n10.5.1 punturako Finantza Sendotasun Adierazleak (SFA) –zehazki, zazpi– eta GJHen adierazleak sartzen dira, \nehunekotan adieraziak.\n\n (1) 1. mailako kapitala, aktiboak arautzen dituena\n (2) 1. mailako kapitala, arriskuen arabera haztatutako aktiboak arautzen dituena \n (3) Kapitalaren gaineko horniduren mailegu berankor garbiak\n (4) Mailegu gordin guztien gaineko mailegu berankorrak\n (5) Aktiboen errentagarritasuna \n (6) Epe laburreko pasiboen gaineko aktibo likidoak\n (7) Kapitalaren gaineko posizio garbi irekia dibisatan\n\nIturria: Nazio Batuen Estatistika Sekzioa\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-05-01.pdf\">Metadatuak 10-5-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.5: Improve the regulation and monitoring of global financial markets and institutions and strengthen the implementation of such regulations</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.5.1: Financial Soundness Indicators</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>FI_FSI_FSKNL - Non-performing loans net of provisions to capital (%) [10.5.1]</p>\n<p>FI_FSI_FSERA - Return on assets (%) [10.5.1]</p>\n<p>FI_FSI_FSKA - Regulatory capital to assets (%) [10.5.1]</p>\n<p>FI_FSI_FSKNL - Non-performing loans net of provisions to capital (%) [10.5.1]</p>\n<p>FI_FSI_FSKRTC - Regulatory Tier 1 capital to risk-weighted assets (%) [10.5.1]</p>\n<p>FI_FSI_FSLS - Liquid assets to short term liabilities (%) [10.5.1]</p>\n<p>FI_FSI_FSSNO - Net open position in foreign exchange to capital (%) [10.5.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Linkages with any other Goals and Targets: Recommendation II.2 of G-20 Data Gap Initiative &#x2013; 2 concerned Financial Soundness Indicators (FSIs). The G-20 economies were asked to report the seven FSIs required from Special Data Dissemination Standards Plus (SDDS Plus) adherent economies on a quarterly frequency, with a timeliness of one quarter. These are the same FSIs as covered by this SDG Indicator 10.5.1 except that the SDG indicator includes the FSI net open position in foreign exchange to capital instead of the residential real estate prices. The G-20 economies were also asked to voluntarily initiate regular collection of Concentration and Distribution Measures, depending on the results of their cost-benefit analysis and national priorities. The implementation of the two FSI-related recommendations was concluded at end-2021.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Monetary Fund (IMF)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Monetary Fund (IMF)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Seven Financial Soundness Indicators (FSIs) are included as SDG indicators for 10.5.1 and expressed as percent.</p>\n<p>(1) Regulatory Tier 1 capital to assets</p>\n<p>(2) Regulatory Tier 1 capital to risk-weighted assets</p>\n<p>(3) Nonperforming loans net of provisions to capital</p>\n<p>(4) Nonperforming loans to total gross loans</p>\n<p>(5) Return on assets</p>\n<p>(6) Liquid assets to short-term liabilities</p>\n<p>(7) Net open position in foreign exchange to capital</p>\n<p>(1)<strong> </strong><em>Regulatory Tier 1 capital to assets:</em> This is the ratio of the core capital (Tier 1) to total (balance sheet) assets. For jurisdictions that have implemented the Basel III leverage ratio, this indicator would be calculated using regulatory Tier 1 capital as the numerator and the exposure measure as the denominator, which comprises balance sheet assets, derivatives exposures, securities financing transaction exposures, and off-balance-sheet items.</p>\n<p>(2)<strong> </strong><em>Regulatory Tier 1 capital to risk-weighted assets:</em> It is calculated using regulatory Tier 1 capital as the numerator and risk-weighted assets as the denominator. The data for this FSI are compiled in accordance with the implemented Basel Accord (i.e., Basel I, Basel II, or Basel III).</p>\n<p>(3)<strong> </strong><em>Nonperforming loans net of provisions to capital:</em> This FSI is calculated by taking the value of nonperforming loans (NPLs) less the value of specific provisions for NPLs as the numerator and total regulatory capital as the denominator.</p>\n<p>(4)<strong> </strong><em>Nonperforming loans to total gross loans</em>: This FSI is calculated by using the value of NPLs as the numerator and the total value of the loan portfolio (including NPLs, and before the deduction of specific provisions for NPLs) as the denominator.</p>\n<p>(5)<strong> </strong><em>Return on assets</em>: This FSI is calculated by dividing annualized net income before taxes by the average value of total assets (financial and nonfinancial) over the same period.</p>\n<p>(6)<strong> </strong><em>Liquid assets to short-term liabilities</em>: This FSI is calculated by using liquid assets as the numerator and short-term liabilities as the denominator. The components of liquid assets are defined in the IMF&#x2019;s <em>2019 FSIs Compilation Guide</em> (<em>2019 FSIs Guide</em>).</p>\n<p>(7)<strong> </strong><em>Net open position in foreign exchange to capital</em>: The net open position in foreign exchange should be calculated based on the guidance in the <em>2019 FSIs Guide</em>. Capital should be total regulatory capital as net open position in foreign exchange is a supervisory concept.</p>\n<p><strong>Concepts:</strong></p>\n<p>(1)<strong> </strong><em>Regulatory Tier 1 capital to assets</em>: Regulatory Tier 1 capital is calculated based on Basel I, II, or III depending on countries&#x2019; supervisory practices. Denominator is total balance sheet (non-risk weighted) assets. For jurisdictions that have implemented the Basel III leverage ratio, the denominator also includes off-balance-sheet items.</p>\n<p>(2)<strong> </strong><em>Regulatory Tier 1 capital to risk- weighted assets</em>: Regulatory Tier 1 capital is calculated based on Basel I, II, or III depending on countries&#x2019; supervisory practices. Denominator is risk-weighted assets also calculated based on Basel standards.</p>\n<p>(3)<strong> </strong><em>Nonperforming loans net of provisions to capital:</em> A loan is classified as nonperforming (NPL) when payment of principal or interest is past due by 90 days or more, or when evidence exists that a full or partial amount of a loan is not going to be recovered even in the absence of a 90-day past due payment. Only specific provisions for NPLs are used in this calculation and they refer to charges against the value of specific NPLs. Data exclude accrued interest on NPLs. Capital is measured as total regulatory capital calculated based on Basel I, II, or III depending on countries&#x2019; supervisory practices.</p>\n<p>(4)<strong> </strong><em>Nonperforming loans to total gross loans</em>: A loan is classified as NPL when payment of principal or interest is past due by 90 days or more, or when evidence exists that a full or partial amount of a loan is not going to be recovered even in the absence of a 90-day past due payment. The denominator is the total value of the loan portfolio (including NPLs, and before the deduction of specific provisions for NPLs).</p>\n<p>(5)<strong> </strong><em>Return on assets:</em> The numerator is annualized net income before taxes. The denominator is the average value of total assets (financial and nonfinancial) over the same period.</p>\n<p>(6)<strong> </strong><em>Liquid assets to short-term liabilities:</em> Liquid assets include currency and deposits and other financial assets available on demand or within three months as well as securities traded in liquid markets that can be converted into cash with minimal change in value. The denominator is short-term elements of debt liabilities plus net market value of financial derivatives position. The latter is calculated as financial derivatives liability position minus financial derivative asset position. Short-term refers to three months and should be defined on a remaining maturity basis. If remaining maturity is not available, original maturity can be used as an alternative.</p>\n<p>(7)<strong> </strong><em>Net open position in foreign exchange to capital:</em> Net open position should be calculated in accordance with the guidance in the <em>2019 FSIs Guide</em>. The denominator is total regulatory capital as defined above.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>\n<p>Data in the sectoral financial statements and other memorandum series used to calculate FSIs are in national currency.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Classification of financial positions by type of financial instruments and by counterpart sector, and definition of financial corporations subsectors are provided in the 2019 FSIs Guide: <a href=\"http://data.imf.org/FSI\">http://data.imf.org/FSI</a>.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The common source data are data reported by banks for supervisory purposes. They include balance sheet, income statement, and memorandum series (such as Tier 1 capital, Tier 2 capital, risk-weighted assets).</p>", "COLL_METHOD__GLOBAL"=>"<p>The national central banks or supervisory agencies collect these data for supervisory purposes, and these data are used for FSIs compilation.</p>", "FREQ_COLL__GLOBAL"=>"<p>There are no predetermined deadlines. Countries report new FSIs as soon as they are ready.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are disseminated on the IMF website as soon as they are ready.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The national central banks or bank supervisory agencies.</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Monetary Fund (IMF)</p>", "INST_MANDATE__GLOBAL"=>"<p>The banks&#x2019; supervisory authorities/agencies in countries or jurisdictions collect source data from the banks &#x2013; whose complete reporting is usually mandated by law.</p>", "RATIONALE__GLOBAL"=>"<p>(1)<strong> Regulatory Tier 1 capital to assets</strong>: It is a measure of leverage indicating the extent to which assets are funded by other than own funds.</p>\n<p>(2)<strong> Regulatory Tier 1 capital to risk-weighted assets</strong>: It measures the capital adequacy of deposit takers based on the core capital concept of the Basel Committee on Banking Supervision (BCBS). Capital adequacy and availability ultimately determine the degree of robustness of financial institutions to withstand shocks to their balance sheets.</p>\n<p>(3)<strong> Nonperforming loans net of provisions to capital</strong>: This FSI is a capital adequacy ratio and is an important indicator of the capacity of bank capital to withstand losses from NPLs that are not covered by specific provisions for NPLs.</p>\n<p>(4)<strong> Nonperforming loans to total gross loans</strong>: This FSI is often used as a proxy for asset quality and is intended to identify problems with asset quality in the loan portfolio.</p>\n<p>(5)<strong> Return on assets</strong>: It is an indicator of bank profitability and is intended to measure deposit takers&#x2019; efficiency in using their assets.</p>\n<p>(6)<strong> Liquid assets to short-term liabilities</strong>: It is a liquidity ratio and is intended to capture the liquidity mismatch of assets and liabilities and provides an indication of the extent to which deposit takers can meet the short-term withdrawal of funds without facing liquidity problems.</p>\n<p>(7)<strong> Net open position in foreign exchange to capital</strong>: This FSI is an indicator of sensitivity to market risk, which is intended to gauge deposit takers&#x2019; exposure to exchange rate risk compared with capital. It measures the mismatch of foreign currency asset and liability positions to assess the vulnerability to exchange rate movements.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data for most countries are reported on a monthly or quarterly basis; a few countries report some FSI data on a semi-annual or annual basis and with a lag of more than a quarter. As of August 2024, there were more than 150 FSI reporters. Some countries&#x2019; compilation practices deviate from the <em>2019 FSIs Guide</em> methodology in certain areas and are documented in the FSIs metadata also posted on the IMF&#x2019;s FSIs website. Reporting countries provide all or most core FSIs and some encouraged FSIs that can be used to support the interpretation of these seven SDG indicators. FSIs data and metadata reported by countries are available at <a href=\"http://data.imf.org/FSI\">http://data.imf.org/FSI</a>.</p>", "DATA_COMP__GLOBAL"=>"<p>(1) Regulatory Tier 1 capital to assets</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mtable>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>R</mi>\n            <mi>e</mi>\n            <mi>g</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>T</mi>\n            <mi>i</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>&amp;nbsp;</mi>\n            <mn>1</mn>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>c</mi>\n            <mi>a</mi>\n            <mi>p</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>t</mi>\n            <mi>o</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>t</mi>\n            <mi>s</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n    </mtable>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi mathvariant=\"normal\">T</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">r</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mn>1</mn>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi mathvariant=\"normal\">n</mi>\n            <mi mathvariant=\"normal\">o</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mo>-</mo>\n            <mi mathvariant=\"normal\">r</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">s</mi>\n            <mi mathvariant=\"normal\">k</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">w</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">g</mi>\n            <mi mathvariant=\"normal\">h</mi>\n            <mi mathvariant=\"normal\">t</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">d</mi>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mfrac>\n    <mo>&#x2219;</mo>\n    <mn>100</mn>\n    <mi>%</mi>\n  </math></p>\n<p>(2)<strong> </strong>Regulatory Tier 1 capital to risk-weighted assets</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mtable>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>R</mi>\n            <mi>e</mi>\n            <mi>g</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>T</mi>\n            <mi>i</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>&amp;nbsp;</mi>\n            <mn>1</mn>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>c</mi>\n            <mi>a</mi>\n            <mi>p</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>t</mi>\n            <mi>o</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>r</mi>\n            <mi>i</mi>\n            <mi>s</mi>\n            <mi>k</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>w</mi>\n            <mi>e</mi>\n            <mi>i</mi>\n            <mi>g</mi>\n            <mi>h</mi>\n            <mi>t</mi>\n            <mi>e</mi>\n            <mi>d</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>t</mi>\n            <mi>s</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n    </mtable>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">C</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi mathvariant=\"normal\">T</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">r</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mn>1</mn>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">R</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">k</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">w</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#x2219;</mo>\n    <mn>100</mn>\n    <mi>%</mi>\n  </math></p>\n<p>(3)<strong> </strong>Nonperforming loans net of provisions to capital</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">N</mi>\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">L</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">v</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">V</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">N</mi>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">L</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mo>-</mo>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">V</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">N</mi>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">L</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#x2219;</mo>\n    <mn>100</mn>\n    <mi>%</mi>\n  </math></p>\n<p>(4)<strong> </strong>Nonperforming loans to total gross loans</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">N</mi>\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">L</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">g</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">V</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">N</mi>\n        <mi mathvariant=\"normal\">P</mi>\n        <mi mathvariant=\"normal\">L</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#x2219;</mo>\n    <mn>100</mn>\n    <mi>%</mi>\n  </math></p>\n<p>(5)<strong> </strong>Return on assets</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">R</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">A</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">z</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">x</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">A</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#x2219;</mo>\n    <mn>100</mn>\n    <mi>%</mi>\n  </math></p>\n<p>(6)<strong> </strong>Liquid assets to short-term liabilities</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mtable>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>L</mi>\n            <mi>i</mi>\n            <mi>q</mi>\n            <mi>u</mi>\n            <mi>i</mi>\n            <mi>d</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>t</mi>\n            <mi>s</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>t</mi>\n            <mi>o</mi>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>s</mi>\n            <mi>h</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>t</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>t</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>m</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>l</mi>\n            <mi>i</mi>\n            <mi>a</mi>\n            <mi>b</mi>\n            <mi>i</mi>\n            <mi>l</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>e</mi>\n            <mi>s</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n    </mtable>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">L</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">q</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#x2219;</mo>\n    <mn>100</mn>\n    <mi>%</mi>\n  </math></p>\n<p>(7)<strong> </strong>Net open position in foreign exchange to capital</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mtable>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>N</mi>\n            <mi>e</mi>\n            <mi>t</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>s</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>f</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>i</mi>\n            <mi>g</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>e</mi>\n            <mi>x</mi>\n            <mi>c</mi>\n            <mi>h</mi>\n            <mi>a</mi>\n            <mi>n</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>t</mi>\n            <mi>o</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>a</mi>\n            <mi>p</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n    </mtable>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">N</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">x</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">e</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#x2219;</mo>\n    <mn>100</mn>\n    <mi>%</mi>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>Country authorities validate data that they collect from the supervised deposit-taking (or banking) institutions. The authorities use these data to compile FSIs, following the methodology in the IMF 2019 FSI Guide.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>The FSIs are not compiled at regional or global levels.</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<p>The <em>2019 FSIs Guide</em> is available at <a href=\"http://data.imf.org/FSI\">http://data.imf.org/FSI</a>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Country authorities are responsible for the quality of FSIs and underlying data.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>National banking sector supervisors check and validate supervisory and prudential data reported by individual banks for supervisory purposes, which is then used to compile aggregated FSI data for the country that has reported to the IMF. IMF staff review the data and engage the national compilers in case there are issues to be resolved. After the data is successfully reviewed, it is published on the IMF FSI website.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is done as part of the validation and consistency checks implemented in the IMF data processing system. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>As of August 2024, there were more than 150 FSI reporters. All reporters provide all or most core FSIs and additional FSIs that are useful for the interpretation of these seven SDG indicators.</p>\n<p><strong>Time series:</strong></p>\n<p>Data for most countries are reported on a monthly or quarterly basis (about 20 percent and 80 percent of total number of reporting countries, respectively); a few countries report data on a semi-annual basis and with a lag of more than a quarter. Data are available as far back as 2005 for some countries.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The FSIs disseminated by the IMF are weighted averages for the sector as a whole (e.g., deposit takers, other financial corporations, nonfinancial corporations). Data for parent banks, their branches, and relevant subsidiaries are consolidated; if this consolidation is not possible or not applicable, an explanation is provided in the metadata. There are no disaggregated breakdowns of the FSIs reported to the IMF.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Data calculated by other sources could be different from the FSIs disseminated by the IMF due to the use of different compilation methodology and/or institutional coverage. The FSIs disseminated by the IMF are compiled based on the <em>2019</em> <em>FSIs Guide</em>, which provides the guidance on the concepts and definitions, and sources and techniques for the compilation of cross-country comparable data to support national and international surveillance of financial systems. To facilitate identification of possible discrepancies across countries, reporters provide metadata to the IMF that detail departures from recommendations in the <em>2019</em> <em>FSIs Guide</em>.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong>[1] <a href=\"http://data.imf.org/FSI\">http://data.imf.org/FSI</a></p>\n<p>[2] <a href=\"https://www.imf.org/en/Data/Statistics/FSI-guide\">https://www.imf.org/en/Data/Statistics/FSI-guide</a> (2019 FSI Guide)</p>\n<p><strong>References: </strong></p>", "indicator_sort_order"=>"10-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.6.1", "slug"=>"10-6-1", "name"=>"Proporción de miembros y derechos de voto de los países en desarrollo en organizaciones internacionales", "url"=>"/site/es/10-6-1/", "sort"=>"100601", "goal_number"=>"10", "target_number"=>"10.6", "global"=>{"name"=>"Proporción de miembros y derechos de voto de los países en desarrollo en organizaciones internacionales"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de miembros y derechos de voto de los países en desarrollo en organizaciones internacionales", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de miembros y derechos de voto de los países en desarrollo en organizaciones internacionales", "indicator_number"=>"10.6.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notapplicable", "reporting_status"=>"notapplicable", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nLa ONU se basa en el principio de igualdad soberana de todos sus Estados Miembros \n(Artículo 2 de la Carta de la ONU). Este indicador busca medir el grado de \nrepresentación equitativa de los Estados en las organizaciones internacionales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-06-01.pdf\">Metadatos 10-6-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe UN is based on a principle of sovereign equality of all its Member States \n(Article 2, UN Charter). Thisindicator aims to measure the degree to which States \nenjoy equal representation in international organizations. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-06-01.pdf\">Metadata 10-6-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nNBE estatu-kide guztien berdintasun subiranoaren printzipioan oinarritzen da (NBEren Gutuneko 2. artikulua). \nAdierazle honek estatuen ordezkaritza ekitatiboko maila neurtzen du nazioarteko erakundeetan. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-06-01.pdf\">Metadatuak 10-6-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.6: Ensure enhanced representation and voice for developing countries in decision-making in global international economic and financial institutions in order to deliver more effective, credible, accountable and legitimate institutions</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.6.1: Proportion of members and voting rights of developing countries in international organizations</p>", "META_LAST_UPDATE__GLOBAL"=>"2022-07-07", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Financing for Sustainable Development Office (FSDO), United Nations Department of Economic and Social Affairs (UN-DESA)</p>\n<p></p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Financing for Sustainable Development Office (FSDO), United Nations Department of Economic and Social Affairs (UN-DESA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<h3>Definition:</h3>\n<p>The indicator <em>Proportion of members and voting rights of developing countries in international organizations </em>has two separate components: the developing country proportion of voting rights and the developing country proportion of membership in international organisations. In some institutions, these two components are identical.</p>\n<p>The indicator is calculated independently for eleven different international institutions: The United Nations General Assembly, the United Nations Security Council, the United Nations Economic and Social Council, the International Monetary Fund, the International Bank for Reconstruction and Development, the International Finance Corporation, the African Development Bank, the Asian Development Bank, the Inter-American Development Bank, the World Trade Organisation, and the Financial Stability Board.</p>\n<h3>Concepts:</h3>\n<p>There is no established convention for the designation of &quot;developed&quot; and &quot;developing&quot; countries or areas in the United Nations system. The aggregation across all institutions is currently done according to the &#x201C;historical&#x201D; classification of &#x201C;Developed regions&#x201D; and &#x201C;Developing regions&#x201D; as of December 2021 in the United Nations M49 statistical standard. The removal of this classification from the M49 standard at the end of 2021 makes it more urgent to reach agreement on how to define these terms for the purposes of SDG monitoring. The designations &quot;developed&quot; and developing&quot; are intended for statistical convenience and do not necessarily express a judgement about the stage reached by a particular country or area in the development process.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percentage</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Classification of countries as least developed countries (LDCs), landlocked developing countries (LLDCs), and small island developing States (SIDS) according to the United Nations M49 standard. The classification of developing countries and developed countries is based on the &#x201C;historical&#x201D; classification of &#x201C;Developed regions&#x201D; and &#x201C;Developing regions&#x201D; as of December 2021 in the United Nations M49 statistical standard). </p>", "SOURCE_TYPE__GLOBAL"=>"<h3>Description:</h3>\n<p>Annual reports, as presented on the website of the institution in question, are used as sources of data. Sources of information by institution:</p>\n<p><u>United Nations General Assembly (UNGA):</u> website of the General Assembly (http://www.un.org/en/member-states/index.html)</p>\n<p><u>United Nations Security Council (UNSC):</u> Report of the Security Council for the respective year (https://www.un.org/securitycouncil/content/sc_annual_reports)</p>\n<p><u>United Nations Economic and Social Council (ECOSOC):</u> Report of the Economic and Social Council for the respective year (https://www.un.org/ecosoc/en/documents/reports-general-assembly)</p>\n<p><u>International Monetary Fund (IMF):</u> Annual Report for the respective year (https://www.imf.org/en/Publications/AREB)</p>\n<p><u>International Bank for Reconstruction and Development (IBRD):</u> 2000: The World Bank Annual Report 2000: Financial Statement and Appendixes to the Annual Report; from 2005: International Bank for Reconstruction and Development Management&#x2019;s Discussion &amp; Analysis and Financial Statements for the respective year (https://www.worldbank.org/en/about/annual-report/world-bank-group-downloads)</p>\n<p><u>International Finance Corporation (IFC):</u> IFC Annual Report (volume 2) for the respective year (<a href=\"https://openknowledge.worldbank.org/handle/10986/2128\">https://openknowledge.worldbank.org/handle/10986/2128</a>) </p>\n<p><u>African Development Bank (AFDB):</u> African Development Bank Group Annual Report for the respective year (https://www.afdb.org/en/documents-publications/annual-report)</p>\n<p><u>Asian Development Bank (ADB):</u> 2000-2017: Annual Report for the respective year; from 2018: Financial Report for the respective year (https://www.adb.org/documents/series/adb-annual-reports)</p>\n<p><u>Inter-American Development Bank (IADB):</u> Inter-American Development Bank Annual Report for the respective year (https://www.iadb.org/en/about-us/annual-reports) </p>\n<p><u>World Trade Organisation (WTO):</u> WTO Annual Report for the respective year (https://www.wto.org/english/res_e/reser_e/annual_report_e.htm)</p>\n<p><u>Financial Stability Board (FSB):</u> 2010, 2015: charter of the Financial Stability Board; 2016-2018: Financial Stability Board Financial Report for the respective year; from 2019: Financial Stability Board Financial Statements for the respective year (<a href=\"https://www.fsb.org/publications/\">https://www.fsb.org/publications/</a>)</p>\n<h3>List:</h3>\n<p>Website of the General Assembly; Report of the Security Council for the respective year; Report of the Economic and Social Council for the respective year; IMF Annual Report for the respective year; IBRD Management&#x2019;s Discussion &amp; Analysis and Financial Statements for the respective year; IFC Annual Report (volume 2) for the respective year; AFDB Annual Report for the respective year; AFDB Group Annual Report for the respective year; ADB Financial Report for the respective year; IADB Annual Report for the respective year; WTO Annual Report for the respective year; FSB Financial Statements for the respective year</p>", "COLL_METHOD__GLOBAL"=>"<p>Desk review, annually, pulling data from the above-mentioned sources.</p>", "FREQ_COLL__GLOBAL"=>"<p>Annually in March</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>United Nations General Assembly: continuous</p>\n<p>United Nations Security Council: annually in September</p>\n<p>United Nations Economic and Social Council: annually in August </p>\n<p>International Monetary Fund: annually in October</p>\n<p>International Bank for Reconstruction and Development: annually in September </p>\n<p>International Finance Corporation: annually in September</p>\n<p>African Development Bank: annually in June</p>\n<p>Asian Development Bank: annually in April</p>\n<p>Inter-American Development Bank: annually in March</p>\n<p>World Trade Organisation: annually in May </p>\n<p>Financial Stability Board: annually in August</p>\n<p>Next release: UNGA continuous; UNSC September 2022; ECOSOC August 2022; IMF October 2022; IBRD September 2022; IFC September 2022; AFDB June 2022; ADB April 2022; IADB March 2022; WTO May 2022; FSB August 2022.</p>", "DATA_SOURCE__GLOBAL"=>"<h3>Name:</h3>\n<p>UNGA, UNSC, ECOSOC, IMF, IBRD, IFC, AfDB, ADB, IADB, WTO, FSB</p>\n<h3>Description:</h3>\n<p>The United Nations General Assembly, the United Nations Security Council, the United Nations Economic and Social Council, the International Monetary Fund, the International Bank for Reconstruction and Development, the International Finance Corporation, the African Development Bank, the Asian Development Bank, the Inter-American Development Bank, the World Trade Organisation, and the Financial Stability Board</p>", "COMPILING_ORG__GLOBAL"=>"<h3>Name:</h3>\n<p>FSDO/UN-DESA</p>\n<h3>Description:</h3>\n<p>The data is compiled and the proportions calculated by the Financing for Sustainable Development Office, United Nations Department of Economic and Social Affairs.</p>", "INST_MANDATE__GLOBAL"=>"<p>At its second meeting in October 2015, the Inter-agency and Expert Group on SDG Indicators (IAEG-SDG) agreed to a draft indicator and to UN-DESA being designated as the compiling entity. The Statistical Commission, at its 47<sup>th</sup> session in March 2016, approved the report of the IAEG-SDG containing the proposed set of indicators. </p>", "RATIONALE__GLOBAL"=>"<p>The UN is based on a principle of sovereign equality of all its Member States (Article 2, UN Charter). This indicator aims to measure the degree to which States enjoy equal representation in international organizations.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Cross institutional comparisons need to pay attention to the different membership of the institutions. Voting rights and membership in their institutions are agreed by the Member States themselves. As a structural indicator, there will be only small changes over time to reflect agreement on new States joining as Members, suspension of voting rights, membership withdrawal and negotiated voting rights changes. The indicator is not intended for use at country-level or for cross-country comparisons.</p>", "DATA_COMP__GLOBAL"=>"<p>The computation uses each institutions&#x2019; own published membership and voting rights data from their respective annual reports. The ratio of voting rights is computed as the number of voting rights allocated to developing countries (as classified by the &#x201C;historical&#x201D; classification of &#x201C;Developed regions&#x201D; and &#x201C;Developing regions&#x201D; as of December 2021 in the United Nations M49 statistical standard), divided by the total number of voting rights. The ratio of membership is calculated by taking the number of developing country members (using the same classification), divided by the total number of members. Both ratios are expressed as percentages.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Not applicable</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Countries which are not a member of the specific international organisation/body will not have a figure for the related sub-indicator. These are intentionally left blank.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>", "REG_AGG__GLOBAL"=>"<p>Aggregations are additive, with no weighting.</p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Internal review undertaken by data compiler, FSDO/UN-DESA</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Available for all countries.</p>\n<p><strong>Time series:</strong></p>\n<p>2000, 2005, 2010, 2015, and annually thereafter</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data is calculated and presented separately for each international organization.</p>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<h3>URL:</h3>\n<p><a href=\"https://www.un.org/development/desa/en/\"><u>https://www.un.org/development/desa/en/</u></a></p>\n<h3>Data Sources:</h3>\n<p><u>United Nations General Assembly (UNGA):</u> http://www.un.org/en/member-states/index.html</p>\n<p><u>United Nations Security Council (UNSC):</u> https://www.un.org/securitycouncil/content/sc_annual_reports</p>\n<p><u>United Nations Economic and Social Council (ECOSOC):</u> https://www.un.org/ecosoc/en/documents/reports-general-assembly</p>\n<p><u>International Monetary Fund (IMF):</u> https://www.imf.org/en/Publications/AREB</p>\n<p><u>International Bank for Reconstruction and Development (IBRD):</u> https://www.worldbank.org/en/about/annual-report/world-bank-group-downloads</p>\n<p><u>International Finance Corporation (IFC):</u> <a href=\"https://openknowledge.worldbank.org/handle/10986/2128\">https://openknowledge.worldbank.org/handle/10986/2128</a> </p>\n<p><u>African Development Bank (AFDB):</u> https://www.afdb.org/en/documents-publications/annual-report</p>\n<p><u>Asian Development Bank (ADB):</u> https://www.adb.org/documents/series/adb-annual-reports</p>\n<p><u>Inter-American Development Bank (IADB):</u> https://www.iadb.org/en/about-us/annual-reports </p>\n<p><u>World Trade Organisation (WTO):</u> https://www.wto.org/english/res_e/reser_e/annual_report_e.htm</p>\n<p><u>Financial Stability Board (FSB):</u> https://www.fsb.org/publications/</p>", "indicator_sort_order"=>"10-06-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.7.1", "slug"=>"10-7-1", "name"=>"Costo de la contratación sufragado por el empleado en proporción a los ingresos mensuales percibidos en el país de destino", "url"=>"/site/es/10-7-1/", "sort"=>"100701", "goal_number"=>"10", "target_number"=>"10.7", "global"=>{"name"=>"Costo de la contratación sufragado por el empleado en proporción a los ingresos mensuales percibidos en el país de destino"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Costo de la contratación sufragado por el empleado en proporción a los ingresos mensuales percibidos en el país de destino", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Costo de la contratación sufragado por el empleado en proporción a los ingresos mensuales percibidos en el país de destino", "indicator_number"=>"10.7.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nLos elevados costos económicos y sociales que soportan los migrantes se reconocen \ncada vez más como graves impedimentos para alcanzar resultados de desarrollo \nsostenible derivados de la migración internacional. Un papel fundamental de las \npolíticas migratorias es reducir los costos financieros de contratación que \nincurren los trabajadores migrantes que buscan empleo en el extranjero. \n\nLos costos de contratación que pagan los trabajadores migrantes a las agencias \nde contratación, además de las comisiones que pagan los empleadores, suponen \nuna importante carga para los ingresos y las remesas de los migrantes pobres.\n Desvían el dinero que envían sus familias a agencias de contratación y \nprestamistas ilícitos. Casi 10 millones de personas utilizan los canales \nregulares para migrar en busca de empleo cada año. Un gran número de \nellas paga comisiones ilegales a las agencias de contratación.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-01.pdf\">Metadatos 10-7-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe high economic and social costs incurred by migrants are increasingly \nrecognized as serious impediments to realizing sustainable development \noutcomes from international migration. A critical role of migration policies \nis reducing the financial costs of recruitment incurred by migrant workers \nseeking jobs abroad. \n\nRecruitment costs paid by migrant workers to recruitment agents, on top of \nthe fees paid by the employers, are a major drain on poor migrants’ incomes \nand remittances. They divert the money sent by migrants from the family to \nillicit recruitment agents and money lenders. Almost 10 million people use \nregular channels to migrate in search of employment every year. A large number \nof them pay illegal recruitment fees to the recruitment agents. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-01.pdf\">Metadata 10-7-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nMigratzaileek jasaten dituzten kostu ekonomiko eta sozial altuak geroz eta sarriago jotzen dira oztopo \nlarritzat nazioarteko migrazioaren ziozko garapen jasangarriko emaitzak lortzeko orduan. Migrazio-politiken \noinarrizko zeregina da, beraz, atzerrian lana bilatzen duten langile migratzaileek dituzten kontratazio-kostuak \nmurriztea. \n\nLangile migratzaileek kontratazio-agentziei ordaintzen dizkieten kontratazio-kostuak eta enplegatzaileek \nordaintzen dituzten komisioak karga handia dira migratzaile pobreen diru sarreretarako eta diru-bidalketetarako. \nBeren familiek bidaltzen dieten dirua kontratazio-agentziei eta legez kanpoko mailegu-emaileei ematen diete. \nUrtero, ia 10 milioi pertsonak erabiltzen dituzte kanal erregularrak enplegua bilatzera migratzeko. Horietako \naskok legez kanpoko komisioak ordaintzen dizkiete kontratazio-agentziei. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-01.pdf\">Metadatuak 10-7-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.7: Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policies</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.7.1: Recruitment cost borne by employee as a proportion of monthly income earned in country of destination</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SL_EMP_RCOST_MO - Migrant recruitment costs (number of months of earnings) [10.7.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Applies to all series</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Labour Organization (ILO) and World Bank (WB)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Labour Organization (ILO) and World Bank (WB)</p>", "STAT_CONC_DEF__GLOBAL"=>"<h2>Definitions:</h2>\n<p>SDG indicator 10.7.1 is defined as: &#x201C;Recruitment cost borne by employee [migrants] as a proportion of monthly income earned in country of destination&#x201D;, i.e. a ratio between a cost measure and an income measure. The statistics used for the numerators and denominators for indicator 10.7.1 should be based on costs and earnings observed for the same individual international migrant worker.</p>\n<h2>Concepts:</h2>\n<p><u>Target population:</u> International migrant workers who, in a recent past period, changed their country of usual residence in order to work as wage or salary earners in another country, whether they were engaged through formal or through &#x2018;informal&#x2019; recruitment processes. From a country of destination perspective, the target population are immigrant workers, whereas from a country of origin perspective, the target population may refer to emigrant workers and/or to returned international migrant workers. </p>\n<p>Excluded are migrant workers who moved to a foreign country for self-employment purposes, short-term migrant workers who (are/were) employed in a foreign country for such short-periods that they (do/did) not change their usual residence (often taken as residence in a country for at least 12 months). Also excluded are persons who migrated to a destination country with intentions other than employment such as for leisure, tourism, family union, education and the like, even if they end up working in the foreign country at a later date, as they are not likely to incur recruitment costs since their primary motive for the move was not work related. However, employed persons who moved to a destination country with employment intentions but without work visas are covered. </p>\n<p><u>Reference period:</u> The statistics/estimates on costs and earnings used to calculate 10.7.1 should refer to the first job obtained in the last country of destination, within a recent past period (e.g. 3 years prior to the date of measurement).</p>\n<p><u>Costs:</u> Recruitment costs refer to any fees or costs incurred in the recruitment process in order for workers to secure employment or placement, regardless of the manner, timing or location of their imposition or collection. These are equal to the total amount that migrant workers and/or their families paid to find, qualify for, and secure a concrete job offer from a foreign employer and to reach the place of employment for the first job abroad. Recommended costs items are indicated in Paragraphs 22 to 24 of the draft Guidelines on statistics for SDG indicator 10.7.1.</p>\n<p><u>Earnings:</u> The measure of earnings for the calculation of recruitment costs should<u> </u>be the monthly earnings in the first job held in the last destination country within the established recent past period. Monthly earnings should cover the gross income received or accrued for the first full month of employment within the reference period, including bonuses and other earnings (e.g. for over-time work). Adjustments should be made for any deductions for destination country taxes and social security contributions, as well as for any deductions in wages made to recover any recruitment costs initially paid by the employer.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of months of earnings</p>\n<p>The recruitment cost indicator is a ratio between a cost measure and an income measure. It may be viewed as a duration expressed in number of months of earnings; i.e. the duration in terms of months of earnings that it takes for an international migrant employee to recover the cost of his or her recruitment. </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Statistics on SDG indicator 10.7.1 should be collected primarily by using existing data collection systems, particularly household-based surveys. This will ensure coherence with existing national sources, methodologies and sampling frames, including types of interviews, field organization, etc. It will also contribute to the long-term sustainability of data collection on this topic. </p>\n<p>A large-scale national household survey strategy has two advantages: a) a survey of this type may already have been well established in the country of origin as well as in host countries; and b) this type of survey may already collect some of the relevant information from the members of the household (even from absent members in the country of origin).</p>\n<p>The most appropriate surveys to include measurement of SDG 10.7.1 include household-based surveys designed to capture the target population, such as a dedicated migration survey, if these exist in the country, as well as national large-scale household surveys covering closely related topics, particularly employment and/or earnings (such as a labour force survey, household income and expenditure survey, or multi-purpose surveys that include questions on employment and migration). Data collected through household surveys could be complemented with establishment surveys for destination countries, and administrative records. In cases where such data are not available, as a last recourse, shorter traveller surveys of migrant workers at ports of departure/entry may be considered. </p>", "COLL_METHOD__GLOBAL"=>"<p>To produce SDG indicator 10.7.1, information on costs and earnings should be collected at person level for the target population (e.g. immigrant workers, emigrant workers and/or international return migrant workers). For this reason, the recommended data collection method is through a survey that collects information on the recruitment costs incurred and monthly earnings of international migrant workers. This may be a household or person survey. The selected household or person survey should use a sampling strategy and data collection instrument (questionnaire) designed to gather representative statistics for the concerned country and/or corridors, if major bilateral migration corridors are targeted.</p>\n<p> </p>\n<p>In a country of origin the sampling strategy may have to be modified to over- sample in regions/villages from which migrant workers are most frequently recruited, to obtain a large enough number of target group respondents for sufficiently precise estimates. Different strategies could be used to design an adequate sampling frame including use of area sampling and stratification methods that support efficient sampling of areas with higher concentration of migrants, , </p>\n<p>In a destination country, the sampling frame for the household survey may, in addition, need to be supplemented with a frame covering collective households (workers&#x2019; residence, dormitories) likely to serve as dwellings for international migrant workers. </p>\n<p>Additionally, questions on the costs and earnings of migrant workers need to be added to the existing standard questionnaire in both origin and destination countries, such as by adding a migration module or including survey questions on recruitment costs and monthly earnings in an existing migration module. Model recruitment costs modules and operational guidance, aligned with the <em>draft Guidelines for the collection of statistics for SDG indicator 10.7.1</em>, are available in the <a href=\"https://www.ilo.org/wcmsp5/groups/public/---ed_protect/---protrav/---migrant/documents/publication/wcms_745663.pdf\"><em>Operational Manual on Recruitment Costs -SDG 10.7.1</em></a> (December 2019) prepared by the ILO and World Bank as co-custodian agencies.</p>", "FREQ_COLL__GLOBAL"=>"<p>SDG 10.7.1 is a tier II indicator since October 2019. National Statistical Offices are at different stages of piloting the methodology and data collection strategy at nationa level. It is recommended that National Statistical Offices in countries with important inflows or outflows of international migrant workers and/or international return migrant workers collect statistics on SDG 10.7.1 every few years, so as to monitor trends and inform policy formulation and planning. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>SDG 10.7.1 is a tier II indicator since October 2019. National Statistical Offices are at different stages of piloting the methodology and data collection strategy at nationa level. It is recommended that National Statistical Offices release official estimates for SDG 10.7.1 in a timely manner, once the methodology and data collection strategy have been established at national level.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The statistics collected for this indicator should be recognized at the national level as official statistics by the proper authorities in the country producing them, e.g. the National Statistical Office (NSO), the Ministry of Labour (MoL), or other official agency within the system for national official statistics. The NSO, MoL or other official agency should be the counterpart for the collection of statistics on SDG 10.7.1.</p>", "COMPILING_ORG__GLOBAL"=>"<p>ILO and the World Bank.</p>", "INST_MANDATE__GLOBAL"=>"<p>The ILO is the UN focal point for labour statistics. It sets international standards for labour statistics through the International Conference of Labour Statisticians (ICLS). It also compiles and produces labour statistics with the goal of disseminating internationally-comparable datasets, and provides technical assistance and training to ILO member States to support their efforts to produce high quality labour market data.</p>", "RATIONALE__GLOBAL"=>"<p>The high economic and social costs incurred by migrants are increasingly recognized as serious impediments to realizing sustainable development outcomes from international migration. A critical role of migration policies is reducing the financial costs of recruitment incurred by migrant workers seeking jobs abroad. Recruitment costs paid by migrant workers to recruitment agents, on top of the fees paid by the employers, are a major drain on poor migrants&#x2019; incomes and remittances. They divert the money sent by migrants from the family to illicit recruitment agents and money lenders. Almost 10 million people use regular channels to migrate in search of employment every year. A large number of them pay illegal recruitment fees to the recruitment agents.</p>\n<p>High costs that migrants pay for their jobs, including recruitment fees, significantly increase risk of forced labour, debt bondage, and human trafficking, especially for low-skilled workers. Too often, migrant workers are subject to abusive practices in the workplace and pay high fees that can deplete their savings and make them more vulnerable during the recruitment and placement processes. The international community, such as through the Addis Ababa Action Agenda (4A) of the Third UN International Conference on Financing for Development affirmed the imperative to lower the cost of recruitment for migrant workers.</p>\n<p>Policymakers should endeavour to eliminate illegal recruitment fees, and this would require effective regulation and monitoring of recruitment agencies and combating unscrupulous recruiters implemented in constructive collaboration between the sending and the receiving countries. Improving migrants&#x2019; access to information can help improve the effectiveness of migration&#x2013;related policies and regulations. The recent ILO General principles and operational guidelines for fair recruitment emphasizes as one of key principles that &#x201C;No recruitment fees or related costs should be charged to, or otherwise borne by, workers or jobseekers&#x201D; (<a href=\"http://www.ilo.org/global/topics/fair-recruitment/WCMS_536755/lang--en/index.htm\">http://www.ilo.org/global/topics/fair-recruitment/WCMS_536755/lang--en/index.htm</a> ).The indicator is meant to show the levels of costs that are still incurred by migrant workers in order to secure a job abroad, relative to the income they earn from working abroad. The recruitment costs indicator can be expressed as a multiple of the number of monthly earnings for the reporting of the indicator in order to illustrate the financial burden on the worker.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The proposed Guidelines have recommended using one month of earnings as the denominator, and to express the indicator as the proportion of monthly earnings paid by the migrant worker to obtain the job abroad. The Guidelines recognize as most relevant for calculation of recruitment costs the earnings of the first job held in the last or most recent country of destination. However monthly earnings of migrant workers may vary considerably for each month worked, particularly if migrant workers often change their job during their first 12 months abroad. Accordingly, the Guidelines recommend using the actual income received for the first month of employment, including bonuses and other earnings (e.g. for over-time work).</p>\n<p>Recall may be an issue if the first job abroad was undertaken many years ago. The Guidelines suggests that when developing the data collection system, the focus should be on international migrant workers or international return migrant workers who started their first job in their last or most recent country of destination within a recent past period (e.g. in the last 3 years prior to the date of measurement).</p>", "DATA_COMP__GLOBAL"=>"<p>RCI = Proportion of recruitment costs in the monthly employment earnings, is a ratio</p>\n<p>Calculation: </p>\n<p> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>R</mi>\n    <mi>C</mi>\n    <mi>I</mi>\n    <mo>=</mo>\n    <mi>f</mi>\n    <mo>(</mo>\n    <mi>C</mi>\n    <mi>k</mi>\n    <mo>/</mo>\n    <mi>E</mi>\n    <mi>k</mi>\n    <mo>)</mo>\n  </math></p>\n<p>Where</p>\n<p> f may take on various functions&#x2019; forms, such as: mean, median and 4th quintile</p>\n<p> C<sub>k </sub>= is the recruitment costs paid by individual migrant worker k;</p>\n<p> E<sub>k</sub> = is the monthly earnings of the same migrant worker k.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>SDG 10.7.1 was reclassified as a tier II indicator on October 2019. National Statistical Offices are at different stages of piloting the methodology and data collection strategy at nationa level. The ILO, as co-custodian agency, provides ongoing technical support to countries with the planning, conduct, analysis and quality assessment of the resulting data. Only data on SDG 10.7.1 that has been officially published by the relevant national authority is reported to the SDGs Indicators Database.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>The indicator is expected to be produced every 3 to 5 years, subject to a country&#x2019;s administration of household-based surveys. In years when a household survey is not conducted, the indicator will not be reported. Imputation of missing values at this level is not feasible given the complex interplay of various agents and factors that directly and indirectly influence the indicator.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>As recruitments costs are country-specific, there is no aggregation at the regional or global</p>\n<p>level.</p>", "REG_AGG__GLOBAL"=>"<p>No regional aggregates will be produced for this indicator.</p>", "DOC_METHOD__GLOBAL"=>"<p>ILO and the WB, as co-custodians of SDG Indicator 10.7.1, issued in October 2019, a set of draft <em>Guidelines for the collection of statistics for SDG indicator</em> <em>10.7.1</em>. The draft Guidelines were validated through a consultative process with National Statistical Offices and as a result of this process the Inter-Agency and Expert Group on Sustainable Development Goals (IAEG-SDG) moved SDG indicator 10.7.1 from Tier 3 to Tier 2, in October 2019.</p>\n<p>The validated Guidelines and an accompanying Operational Manual are available at:</p>\n<p>Statistics for SDG indicator 10.7.1 Draft Guidelines for their Collection:</p>\n<p><a href=\"https://www.ilo.org/global/topics/labour-migration/publications/WCMS_670175/lang--en/index.htm\">https://www.ilo.org/global/topics/labour-migration/publications/WCMS_670175/lang--en/index.htm</a></p>\n<p>Operational Manual on Recruitment Costs -SDG 10.7.1:</p>\n<p><a href=\"https://www.ilo.org/global/topics/labour-migration/WCMS_745663/lang--en/index.htm\">https://www.ilo.org/global/topics/labour-migration/WCMS_745663/lang--en/index.htm</a></p>\n<p>Detailed practical guidance on how to measure, compute and analyze data on SDG indicator 10.7.1 is available in an ILO self-paced e-course, accessible for free at: </p>\n<p>https://www.itcilo.org/courses/measuring-migrant-recruitment-costs-sdg-indicator-1071</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>ILO provides ongoing support to National Statistical Offices with planning and conducting household surveys covering measurement of migrant recruitment costs, as well as with the analysis of the results and report drafting. Results are assessed in terms of achieved sample size, standard errors associated with the main results, issues with disaggregation by essential characteristics and potential coverage issues. Results for selected pilot survey implementations are available in the ILO website at: <a href=\"https://www.ilo.org/global/topics/fair-recruitment/WCMS_726736/lang--en/index.htm\">https://www.ilo.org/global/topics/fair-recruitment/WCMS_726736/lang--en/index.htm</a></p>", "COVERAGE__GLOBAL"=>"<p>Following the reclassification of SDG indicator 10.7.1 from tier III to tier II on October 2019, a number of countries have conducted activities to pilot the methodology and data collection strategy at national level.. Results for selected national pilot survey implementations are available in the ILO website at: <a href=\"https://www.ilo.org/global/topics/fair-recruitment/WCMS_726736/lang--en/index.htm\">https://www.ilo.org/global/topics/fair-recruitment/WCMS_726736/lang--en/index.htm</a></p>\n<p>To complement official SDG 10.7.1 data, the ILO and the Global Knowledge Partnership on Migration and Development (KNOMAD), which is hosted at the World Bank, have supported several rounds of small scale Migration and Recruitment Costs Surveys for research and advocacy purposes. These surveys cover selected bilateral corridors.</p>\n<p>The datasets and documentation for these surveys can be found at: <a href=\"https://www.knomad.org/data/recruitment-costs\">https://www.knomad.org/data/recruitment-costs</a>. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Desired disaggregation includes: sex, age group, education groups, and major destination countries (as recruitment costs have been documented to vary considerably by migration corridors).</p>\n<p>Additional statistics may be presented by:</p>\n<ul>\n  <li>type of migration process (documented, undocumented migrant workers)</li>\n  <li>occupation (ISCO-08): to assess skills levels such as high-skill and low-skill groups</li>\n  <li>major occupational groups: to assess which skills groups have the highest recruitment costs</li>\n  <li>major industry (ISIC Rev.4): to assess main sectors where migrant workers are engaged and to assess recruitment costs in industries of key policy concern (e.g. agriculture, construction, retail, and domestic work)</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable for this indicator.</p>", "OTHER_DOC__GLOBAL"=>"<p>ILO_ITC e-course on Measuring migrant recruitment costs for SDG indicator 10.7.1, available at:</p>\n<p>https://www.itcilo.org/courses/measuring-migrant-recruitment-costs-sdg-indicator-1071</p>\n<p>ILO-KNOMAD. 2019. Statistics for SDG indicator 10.7.1 Draft Guidelines for their Collection, available at:</p>\n<p><a href=\"https://www.ilo.org/global/topics/labour-migration/publications/WCMS_670175/lang--en/index.htm\">https://www.ilo.org/global/topics/labour-migration/publications/WCMS_670175/lang--en/index.htm</a></p>\n<p>ILO-KNOMAD. 2019. Operational Manual on SDG 10.7.1 recruitment costs, available at:</p>\n<p><a href=\"https://www.ilo.org/wcmsp5/groups/public/---ed_protect/---protrav/---migrant/documents/publication/wcms_745663.pdf\">https://www.ilo.org/wcmsp5/groups/public/---ed_protect/---protrav/---migrant/documents/publication/wcms_745663.pdf</a></p>\n<p>KNOMAD. 2016. &#x201C;KNOMAD-ILO Migration Costs Surveys 2015 Dataset: User&#x2019;s Guide&#x201D;</p>\n<p>KNOMAD. 2016. &#x201C;KNOMAD-ILO Migration Costs Surveys 2016 Dataset: User&#x2019;s Guide&#x201D;</p>\n<p><a href=\"https://www.knomad.org/data/recruitment-costs\">https://www.knomad.org/data/recruitment-costs</a></p>", "indicator_sort_order"=>"10-07-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.7.2", "slug"=>"10-7-2", "name"=>"Proporción de países que han aplicado políticas migratorias bien gestionadas que facilitan la migración y la movilidad ordenadas, seguras, regulares y responsables de las personas", "url"=>"/site/es/10-7-2/", "sort"=>"100702", "goal_number"=>"10", "target_number"=>"10.7", "global"=>{"name"=>"Proporción de países que han aplicado políticas migratorias bien gestionadas que facilitan la migración y la movilidad ordenadas, seguras, regulares y responsables de las personas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de países que han aplicado políticas migratorias bien gestionadas que facilitan la migración y la movilidad ordenadas, seguras, regulares y responsables de las personas", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de países que han aplicado políticas migratorias bien gestionadas que facilitan la migración y la movilidad ordenadas, seguras, regulares y responsables de las personas", "indicator_number"=>"10.7.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nEl objetivo principal de la metodología propuesta es formular un \nindicador claro y sencillo basado en una fuente de datos existente \nque pueda generar información significativa, práctica y oportuna \nsobre las tendencias y brechas clave en relación con las políticas \nmigratorias para facilitar la migración y la movilidad ordenada, \nsegura, regular y responsable de las personas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.7.2&seriesCode=SG_CPA_MIGRP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLDOMAINS\">Proporción de países con políticas migratorias para facilitar la migración y la movilidad ordenada, segura, regular y responsable de las personas, por ámbito de política (%) SG_CPA_MIGRP</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-02.pdf\">Metadatos 10-7-2.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe main goal of the proposed methodology is to formulate a clear \nand simple indicator based on an existing data source which can \nproduce meaningful, actionable and timely information on key trends \nand gaps in relation to migration policies to facilitate orderly, \nsafe, regular and responsible migration and mobility of people. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.7.2&seriesCode=SG_CPA_MIGRP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLDOMAINS\">Proportion of countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (%) SG_CPA_MIGRP</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-02.pdf\">Metadata 10-7-2.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nProposatutako metodologiaren helburu nagusia da adierazle argi eta sinple bat zehaztea lehendik baden \ndatu-iturri batean oinarrituta, informazio esanguratsua, praktikoa eta egokia sortu ahal izateko \nmigrazio-politikekin lotutako joera eta arrakala gakoei buruz, pertsonen migrazio eta mugikortasun \nantolatu, seguru, erregular eta arduratsua errazte aldera. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.7.2&seriesCode=SG_CPA_MIGRP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLDOMAINS\">Migrazio eta pertsonen mugikortasun ordenatu, seguru, erregular eta arduratsua errazteko migrazio-politikak dituzten herrialdeen proportzioa, politika-eremuaren arabera (%) SG_CPA_MIGRP</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-02.pdf\">Metadatuak 10-7-2.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.7: Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policies</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.7.2: Proportion of countries with migration policies that facilitate orderly, safe, regular and responsible migration and mobility of people</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Proportion of countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (%)</p>\n<p>Countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (1 = Requires further progress; 2 = Partially meets; 3 = Meets; 4 = Fully meets)</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 10.7.2 is complementary to several related SDGs indicators. These include, but are not limited to: </p>\n<p>Indicator 8.8.1 &#x201C;Frequency rates of fatal and non-fatal occupational injuries, by sex and migrant status&#x201D;;</p>\n<p>Indicator 8.8.2. &#x201C;Level of national compliance of labour rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislation, by sex and migrant status&#x201D;;</p>\n<p>Indicator 10.7.1. &#x201C;Recruitment cost borne by employee as a proportion of yearly income earned in country of destination&#x201D;;</p>\n<p>Indicator 10.7.3. &#x201C;Number of people who died or disappeared in the process of migration towards an international destination&#x201D;;</p>\n<p>Indicator 10.7.4. &#x201C;Proportion of the population who are refugees, by country of origin&#x201D;;</p>\n<p>Indicator 10.c.1 &#x201C;Remittance costs as a proportion of the amount remitted&#x201D;.</p>\n<p>Indicator 10.7.2 is also complementary to other national migration monitoring frameworks, including IOM&#x2019;s MGI, which entered its third phase in 2018. The MGI operates as a policy-benchmarking framework and offers insights into policy levers that countries could use to further develop their migration governance. It contains nearly 90 questions related to countries&#x2019; national migration policies, which fall under the same six domains as indicator 10.7.2.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Organization for Migration (IOM) and United Nations Department of Economic and Social Affairs (DESA) as custodian agencies</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Organization for Migration (IOM) and United Nations Department of Economic and Social Affairs (DESA) as custodian agencies</p>\n<p>Organisation for Economic Co-operation and Development (OECD) as partner agency</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>SDG Indicator 10.7.2 aims to describe the state of national migration policies and how such policies change over time. The information collected seeks to identify both progress made and gaps, thus contributing to the evidence base for actionable recommendations for the implementation of SDG target 10.7. The indicator also serves for the future thematic reviews at the High-level Political Forum on Sustainable Development (HLPF). </p>\n<p>The conceptual framework for indicator 10.7.2 is IOM&#xB4;s Migration Governance Framework (MiGOF), which was welcomed by 157 countries (IOM Council Resolution C/106/RES/1310). The MiGOF has three principles and three objectives (figure 1). </p>\n<h2>Figure 1. Principles and objectives of the Migration Governance Framework</h2><img src=\"data:image/png;base64,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\"><img 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\">\n<p>The three principles propose the necessary conditions for migration to be well-managed by creating a more effective environment for maximized results for migration to be beneficial to all. These represent the means through which a State can ensure that the systemic requirements for good migration governance are in place. </p>\n<p>The three objectives are specific and do not require any further conventions, laws or practices than the ones that are already existing. Taken together, these objectives ensure that migration is governed in an integrated and holistic way, responding to the need to consider mobile categories of people and address their needs for assistance in the event of an emergency, building resilience of individuals and communities, as well as ensuring opportunities for the economic and social health of the State.</p>\n<p>In line with the MiGOF, the proposed methodology for SDG indicator 10.7.2 is comprised of six policy domains, with one proxy measure for each domain (table 1). </p>\n<p><strong>Table 1. Domains and proxy measures for SDG indicator 10.7.2</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td></td>\n      <td>\n        <p><strong>Domain</strong></p>\n      </td>\n      <td>\n        <p><strong>Proxy measure</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.</p>\n      </td>\n      <td>\n        <p>Migrant rights</p>\n      </td>\n      <td>\n        <p>Degree to which migrants have equity in access to services, including health care, education, decent work, social security and welfare benefits</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.</p>\n      </td>\n      <td>\n        <p>Whole-of-government/ Evidence-based policies</p>\n      </td>\n      <td>\n        <p>Dedicated institutions, legal frameworks and policies or strategies to govern migration</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3.</p>\n      </td>\n      <td>\n        <p>Cooperation and partnerships</p>\n      </td>\n      <td>\n        <p>Government measures to foster cooperation and encourage stakeholder inclusion and participation in migration policy</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.</p>\n      </td>\n      <td>\n        <p>Socioeconomic well-being</p>\n      </td>\n      <td>\n        <p>Government measures to maximize the positive development impact of migration and the socioeconomic well-being of migrants</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.</p>\n      </td>\n      <td>\n        <p>Mobility dimensions of crises</p>\n      </td>\n      <td>\n        <p>Government measures to deliver comprehensive responses to refugees and other forcibly displaced persons</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>6.</p>\n      </td>\n      <td>\n        <p>Safe, orderly and regular migration </p>\n      </td>\n      <td>\n        <p>Government measures to address regular or irregular immigration</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>For each of the domains and corresponding proxy measures, one question was specified, each one of them informed by five sub-categories or responses (table 2), to capture key aspects of the range of migration policies at the national level, while allowing the indicator to detect relevant variations across countries and over time.</p>\n<p><strong>Table 2. Questions and sub-categories for SDG indicator 10.7.2</strong></p>\n<table>\n  <thead>\n    <tr>\n      <th></th>\n      <th>\n        <p><strong>Question</strong></p>\n      </th>\n      <th>\n        <p><strong>Sub-categories</strong></p>\n      </th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Domain 1:</strong></p>\n      </td>\n      <td>\n        <p>Does the Government provide non-nationals equal access to the following services, welfare benefits and rights?</p>\n      </td>\n      <td>\n        <p>a. Essential and/or emergency health care</p>\n        <p>b. Public education</p>\n        <p>c. Equal pay for equal work </p>\n        <p>d. Social protection</p>\n        <p>e. Access to justice</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Domain 2:</strong></p>\n      </td>\n      <td>\n        <p>Does the Government have any of the following institutions, policies or strategies to govern immigration or emigration?</p>\n      </td>\n      <td>\n        <p>a. A dedicated Government agency to implement national migration policy</p>\n        <p>b. A national policy or strategy for regular migration pathways, including labour migration</p>\n        <p>c. A national policy or strategy to promote the inclusion or integration of immigrants</p>\n        <p>d. Formal mechanisms to ensure that the migration policy is gender responsive</p>\n        <p>e. A mechanism to ensure that migration policy is informed by data, appropriately disaggregated</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Domain 3:</strong></p>\n      </td>\n      <td>\n        <p>Does the Government take any of the following measures to foster cooperation among countries and encourage stakeholder inclusion and participation in migration policy?</p>\n      </td>\n      <td>\n        <p>a. An interministerial coordination mechanism on migration</p>\n        <p>b. Bilateral agreements on migration, including labour migration </p>\n        <p>c. Regional agreements promoting mobility</p>\n        <p>d. Agreements for cooperation with other countries on return and readmission</p>\n        <p>e. Formal mechanisms to engage civil society and the private sector in the formulation and implementation of migration policy</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Domain 4:</strong></p>\n      </td>\n      <td>\n        <p>Does the Government take any of the following measures to maximize the positive development impact of migration and the socioeconomic well-being of migrants?</p>\n      </td>\n      <td>\n        <p>a. Align, through periodic assessments, labour migration policies with actual and projected labour market needs</p>\n        <p>b. Facilitate the portability of social security benefits</p>\n        <p>c. Facilitate the recognition of skills and qualifications acquired abroad</p>\n        <p>d. Facilitate or promote the flow of remittances</p>\n        <p>e. Promote fair and ethical recruitment of migrant workers</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Domain 5:</strong></p>\n      </td>\n      <td>\n        <p>Does the Government take any of the following measures to respond to refugees and other persons forcibly displaced across international borders?</p>\n      </td>\n      <td>\n        <p>a. System for receiving, processing and identifying those forced to flee across international borders</p>\n        <p>b. Contingency planning for displaced populations in terms of basic needs such as food, sanitation, education and medical care</p>\n        <p>c. Specific measures to provide assistance to citizens residing abroad in countries in crisis or post-crisis situations </p>\n        <p>d. A national disaster risk reduction strategy with specific provisions for addressing the displacement impacts of disasters</p>\n        <p>e. Grant permission for temporary stay or temporary protection for those forcibly displaced across international borders and those unable to return</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Domain 6:</strong></p>\n      </td>\n      <td>\n        <p>Does the Government address regular or irregular immigration through any of the following measures?</p>\n      </td>\n      <td>\n        <p>a. System to monitor visa overstays</p>\n        <p>b. Pre-arrival authorization controls</p>\n        <p>c. Provisions for unaccompanied minors or separated children</p>\n        <p>d. Migration information and awareness-raising campaigns</p>\n        <p>e. Formal strategies to address trafficking in persons and migrant smuggling</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Concepts:</strong></p>\n<p>SDG target 10.7 is broad in scope and many, but not all, of the terms are well defined. The IOM Glossary on Migration<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> provides a definition of key concepts such as orderly and regular migration, but not others such as safe and responsible migration. According to the Glossary, orderly migration refers to &#x201C;the movement of a person from his/her usual place of residence, in keeping with the laws and regulations governing exit of the country of origin and travel, transit and entry into the host country&#x201D;. Regular is defined as &#x201C;migration that occurs through recognized, legal channels&#x201D;. </p>\n<p>While the concept of &#x201C;well-managed migration policies&#x201D; is not explicitly defined, according to the IOM Glossary, it is included in references to migration management, migration governance and facilitated migration. Migration management refers to the planned approach to the development of policy, and legislative and administrative responses to key migration issues. Migration governance is defined as a system of institutions, legal frameworks, mechanisms and practices aimed at regulating migration and protecting migrants. Facilitated migration refers to fostering or encouraging regular migration, for example through streamlined visa application process.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> IOM (2019). <em>Glossary on Migration</em>. Available at: <a href=\"https://publications.iom.int/system/files/pdf/iml_34_glossary.pdf\">https://publications.iom.int/system/files/pdf/iml_34_glossary.pdf</a>. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) (refers to the proportion of countries with values between specific ranges for regional and global aggregates (see also 4.c. Method of computation)). </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The source of data is the UN Inquiry among Governments on Population and Development, which has been used to survey global population policies since 1963, including policies on international migration. The Inquiry is mandated by the General Assembly in its resolution 1838 (XVII) of 18 December 1962. The Inquiry consists mostly of multiple-choice questions. </p>\n<p>Two successive rounds of the Inquiry have been used to collect data on indicator 10.7.2: the Twelfth Inquiry, conducted between September 2018 and October 2019, and the Thirteenth Inquiry, conducted between November 2020 and October of 2021. The Twelfth Inquiry is divided into three thematic modules: Module I on population ageing and urbanization; Module II on fertility, family planning and reproductive health; and Module III on international migration. Module III of the Twelfth Inquiry has been updated to include core questions for all the six migration policy domains mentioned above. The Thirteenth Inquiry is divided into two thematic modules: Module I on reproductive health; and Module II on international migration.</p>", "COLL_METHOD__GLOBAL"=>"<p>The Inquiry is conducted on behalf of the Secretary-General and is sent to all Permanent Missions in New York: 193 Member States, 2 observer States, and 2 non-member States. As per past practice, the Permanent Missions redirect the three thematic modules of the Inquiry to the relevant line ministries or government departments who are tasked with answering the questions. The Inquiry modules can be completed either through an online questionnaire or a fillable questionnaire in PDF. Countries responses are transmitted back to UN DESA for basic consistency checking. The data are then compiled/integrated into the World Population Policies database. The results of the Inquiry are disseminated though the database, updated every two years. </p>\n<p>As part of the collaboration on SDG indicator 10.7.2, IOM assisted in garnering country responses to the international migration module of the Inquiry by following up through its respective country or regional counterparts. OECD, as partner agency for this indicator, supported these efforts for its member countries. The collaboration increased response rates from countries and improved the quality of the data. </p>\n<p>The data were collected biennially between 2019 and 2021, to ensure that there is sufficient information to monitor progress in the achievement of the target. In the future, the periodicity of the Inquiry will be modified to quadrennial. This will also allow for gathering benchmark data once within each HLPF 4-year cycle.</p>\n<p>No adjustments to standard classifications are envisioned. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data will be collected and compiled every four years starting in 2024.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Fourth quarter every four years</p>", "DATA_SOURCE__GLOBAL"=>"<p>Governments of 193 Member States, 2 observer States, and 2 non-member States</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Department of Economic and Social Affairs (UN DESA), International Organization for Migration (IOM) and Organisation for Economic Co-operation and Development (OECD)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Inquiry is conducted on behalf of the Secretary-General. Permanent Missions in New York facilitate the transmission of the Inquiry to the relevant line ministries or government departments. National Statistical Offices are also included in the correspondence from the Permanent Missions. </p>", "RATIONALE__GLOBAL"=>"<p>The main goal of the proposed methodology is to formulate a clear and simple indicator based on an existing data source which can produce meaningful, actionable and timely information on key trends and gaps in relation to migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people (figure 2). The proposed indicator can be used as a synthetic measure for monitoring of SDG target 10.7 and is complementary to other national migration monitoring frameworks, including IOM&#x2019;s Migration Governance Indicators (MGI)<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>.</p>\n<p><strong>Figure 2. Scope and limitations of the proposed indicator</strong></p>\n<p><strong>SDG indicator 10.7.2</strong></p>\n<ul>\n  <li>\n    <ul>\n      <li>Document the existence and range of migration policies at the country level</li>\n      <li>Monitor progress across comparable policy domains</li>\n      <li>Document policy gaps, allowing to identify countries in need of capacity building</li>\n      <li>Reflect the different realities of countries of origin, transit and destination</li>\n    </ul>\n  </li>\n</ul>\n<p><strong>DOES:</strong></p>\n<ul>\n  <li>\n    <ul>\n      <li>Serve as a national monitoring framework for migration policies</li>\n      <li>Provide an exhaustive picture of migration policies</li>\n      <li>Address the implementation of migration policies</li>\n      <li>Assess the impact or effectiveness of migration policies</li>\n    </ul>\n  </li>\n</ul>\n<p><strong>DOES NOT:</strong></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> For additional information on the MGI see: <a href=\"https://gmdac.iom.int/migration-governance-indicators\">https://gmdac.iom.int/migration-governance-indicators</a>. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>Developing a synthetic, robust indicator with the breadth and scope of target 10.7 as formulated in the 2030 Agenda for Sustainable Development is challenging. As co-custodians of indicator 10.7.2, UN DESA and IOM recognize that the indicator is neither expected nor designed to be comprehensive (figure 2); hence the importance of other, complementary tools such as IOM&#x2019;s Migration Governance Indicators (MGI) Project.<sup>1</sup></p>", "DATA_COMP__GLOBAL"=>"<p>The indicator includes a total of 30 sub-categories, under 6 questions/domains. All sub-categories, except for those under domain 1, have dichotomous &#x201C;Yes/No&#x201D; answers, coded &#x201C;1&#x201D; for &#x201C;Yes&#x201D; and &#x201C;0&#x201D; for &#x201C;No&#x201D;. For the sub-categories under domain 1, there are three possible answers: &#x201C;Yes, regardless of immigration status&#x201D;, coded &#x201C;1&#x201D;; &#x201C;Yes, only for those with legal immigration status&#x201D;, coded &#x201C;0.5&#x201D;; and &#x201C;No&#x201D; coded &#x201C;0&#x201D;.</p>\n<p>For each domain, the computational methodology is the unweighted average of the values across sub-categories :</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <msubsup>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>j</mi>\n            </mrow>\n            <mrow>\n              <mi>n</mi>\n            </mrow>\n          </msubsup>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>s</mi>\n              </mrow>\n              <mrow>\n                <mi>j</mi>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Where <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n  </math><em> </em>refers to the value for domain <em>i</em>;<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mrow>\n      <msubsup>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi>j</mi>\n        </mrow>\n        <mrow>\n          <mi>n</mi>\n        </mrow>\n      </msubsup>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>s</mi>\n          </mrow>\n          <mrow>\n            <mi>j</mi>\n            <mi>i</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mrow>\n  </math><em> </em>refers to the sum of the values across sub-categories (indexed by <em>j</em>)<em> </em>under domain <em>i</em>; and <em>n</em> refers to the total number of sub-categories in a domain (n=5). Results are reported as percentages. For each domain, values of <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n      </mrow>\n    </msub>\n  </math><em> </em>range from a minimum of 0 to a maximum of 100 per cent.</p>\n<p>The overall summary indicator 10.7.2 for a country is obtained by computing the unweighted average of the values of the 30 sub-categories under the six domains, with values ranging between 0 and 100 per cent. </p>\n<p>For ease of interpretation and to summarize results, the resulting country-level averages (for the overall indicator and by domain) are then categorized as follows: values of less than 40 are coded as &#x201C;Requires further progress&#x201D;; values of 40 to less than 80 are coded as &#x201C;Partially meets&#x201D;, values of 80 to less than 100 are coded as &#x201C;Meets&#x201D;; and values of 100 are coded as &#x201C;Fully meets&#x201D;. </p>\n<p>Data on country-level averages for the overall indicator and by domain used to compute indicator 10.7.2 are accessible through the SDG database, at the country level in the series 3230 (SG_CPA_MIGRS).</p>\n<p>The unit of measure of the country-level averages for the overall indicator and by domain is categorical/score (1 = Requires further progress; 2 = Partially meets; 3 = Meets; 4 = Fully meets).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The ownership of the data on indicator 10.7.2 rests with the Governments of the 193 Member States, 2 observer States, and 2 non-member States. They are, individually, responsible for validating the quality of the data they provide through the Inquiry. </p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>To ensure comparability of the indicator across countries and over time, missing values are assigned a value of &#x201C;0&#x201D;.</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>Not imputed.</p>", "REG_AGG__GLOBAL"=>"<p>The regional and global aggregates are calculated and reported as the proportion of countries in that region (or globally) that &#x201C;Require further progress&#x201D;, &#x201C;Partially meet&#x201D; and &#x201C;Meet or fully meet&#x201D; target 10.7 as conceptualized and measured by indicator 10.7.2, among those that responded to the Inquiry module on international migration. The regional and global aggregates can be presented for both the overall indicator and by domain.</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>The Inquiry questionnaire includes guidance, definitions and instructions. UN DESA, IOM and OECD are available to respond to country queries and provide further clarifications. In addition, IOM and OECD have identified focal points/country offices available to assist with the implementation of the Inquiry at the country level. To facilitate responses and to accommodate requests for material in different languages, the survey tool was translated into the six official languages of the UN (Arabic, Chinese, English, French, Russian and Spanish).</li>\n  <li>No new international recommendations and guidelines are proposed. As noted in the previous paragraphs, the methodology is based on an IOM Council resolution regarding the Migration Governance Framework, and an existing data collection mechanism, the Inquiry, mandated by the UN General Assembly. </li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>The Governments of the 193 Member States, 2 observer States, and 2 non-member States are responsible for the management of the quality of the data related to indicator 10.7.2.</p>", "QUALITY_ASSURE__GLOBAL"=>"<ul>\n  <li>Answers to the Inquiry are provided and validated directly by responding government entities. UN DESA, with support from IOM and OECD as needed, carried out basic consistency checking. Any inconsistencies are flagged to national counterparts for resolution.</li>\n</ul>\n<p> </p>\n<ul>\n  <li>Since the indicator is informed directly by country responses to the Inquiry, no additional consultation process with countries on the national data submitted to the SDGs Indicators Database is envisaged. </li>\n</ul>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data are checked for internal consistency. In cases where there are concerns about the validity of national responses to the Inquiry, data providers at the country level are contacted and clarification is sought. If deemed necessary, the responding government entity is asked to submit revised data. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Thirty countries were invited to take part in a pilot of the proposed methodology for indicator 10.7.2; six from each of the UN regional commissions. Ten countries responded to the pilot: Cote d&apos;Ivoire; Democratic Republic of the Congo; Finland; France; Lesotho; Lithuania; Mexico; Morocco; Sweden and Yemen. Results of the pilot are presented in the addendum &#x201C;Methodology development narrative&#x201D;.</p>\n<p>As of 31 October 2021, 138 Governments had provided data on SDG indicator 10.7.2 through the international migration module of the Inquiry; equivalent to 70 per cent of all countries globally. Of these, 49 countries responded to the Twelfth Inquiry only, 27 to the Thirteenth Inquiry only and 62 to both the Twelfth Inquiry and the Thirteenth Inquiry. </p>\n<p>Coverage of the indicator by SDG region is uneven. In terms of country coverage, three regions (Europe and Northern America, Northern Africa and Western Asia and sub-Saharan Africa) had data available for 75 per cent or more of countries. Although the coverage was lower for other regions, all regions had data for at least 50 per cent of countries. </p>\n<p><strong>Table 3. Coverage of responses to module on international migration of the Inquiry</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>SDG region</p>\n      </td>\n      <td>\n        <p>Number of countries that provided data</p>\n      </td>\n      <td>\n        <p>Country coverage</p>\n      </td>\n      <td>\n        <p>Population coverage</p>\n      </td>\n      <td>\n        <p>Total number of countries by region</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Sub-Saharan Africa</p>\n      </td>\n      <td>\n        <p>37</p>\n      </td>\n      <td>\n        <p>77%</p>\n      </td>\n      <td>\n        <p>79%</p>\n      </td>\n      <td>\n        <p>48</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Northern Africa and Western Asia</p>\n      </td>\n      <td>\n        <p>18</p>\n      </td>\n      <td>\n        <p>75%</p>\n      </td>\n      <td>\n        <p>71%</p>\n      </td>\n      <td>\n        <p>24</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Central and Southern Asia</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n      <td>\n        <p>57%</p>\n      </td>\n      <td>\n        <p>82%</p>\n      </td>\n      <td>\n        <p>14</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Eastern and South-Eastern Asia</p>\n      </td>\n      <td>\n        <p>10</p>\n      </td>\n      <td>\n        <p>63%</p>\n      </td>\n      <td>\n        <p>93%</p>\n      </td>\n      <td>\n        <p>16</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Latin America and the Caribbean</p>\n      </td>\n      <td>\n        <p>17</p>\n      </td>\n      <td>\n        <p>52%</p>\n      </td>\n      <td>\n        <p>87%</p>\n      </td>\n      <td>\n        <p>33</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Oceania</p>\n      </td>\n      <td>\n        <p>9</p>\n      </td>\n      <td>\n        <p>56%</p>\n      </td>\n      <td>\n        <p>97%</p>\n      </td>\n      <td>\n        <p>16</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Europe and Northern America</p>\n      </td>\n      <td>\n        <p>39</p>\n      </td>\n      <td>\n        <p>85%</p>\n      </td>\n      <td>\n        <p>69%</p>\n      </td>\n      <td>\n        <p>46</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>World</p>\n      </td>\n      <td>\n        <p>138</p>\n      </td>\n      <td>\n        <p>70%</p>\n      </td>\n      <td>\n        <p>83%</p>\n      </td>\n      <td>\n        <p>197</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Note: Based on the two rounds of the Inquiry combined. Where Governments replied to both rounds of the Inquiry, data from the Thirteenth Inquiry were used.</p>\n<p><strong>Time series:</strong></p>\n<p>The time series for this indicator refers to the period 2018-2019 and 2020-2021.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Six policy domains: (i) migrant rights; (ii) whole-of-government/evidence-based policies; (iii) cooperation and partnerships; (iv) socioeconomic well-being; (v) mobility dimensions of crises; and (vi) safe, orderly and regular migration.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>No discrepancies are envisaged, since data are collected through the UN Inquiry among Governments on Population and Development (the &#x201C;Inquiry&#x201D;), directly from Governments.</p>", "OTHER_DOC__GLOBAL"=>"<h2>URL: </h2>\n<p><u>UN DESA </u>: <a href=\"https://www.un.org/development/desa/pd/data/sdg-indicator-1072-migration-policies\">https://www.un.org/development/desa/pd/data/sdg-indicator-1072-migration-policies</a></p>\n<p><u>IOM</u> : <a href=\"https://www.iom.int/\">https://www.iom.int/</a> </p>\n<p><u>OECD </u>: <a href=\"http://www.oecd.org/migration/\">http://www.oecd.org/migration/</a></p>\n<h2>References:</h2>\n<ul>\n  <li>Migration Governance Framework (MiGOF): <a href=\"https://www.iom.int/sites/default/files/about-iom/migof_brochure_a4_en.pdf\">https://www.iom.int/sites/default/files/about-iom/migof_brochure_a4_en.pdf</a> </li>\n  <li>UN Inquiry among Governments on Population and Development: <a href=\"https://www.un.org/development/desa/pd/themes/population-policies/inquiry\">https://www.un.org/development/desa/pd/themes/population-policies/inquiry</a> </li>\n</ul>", "indicator_sort_order"=>"10-07-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.7.3", "slug"=>"10-7-3", "name"=>"Número de personas muertas o desaparecidas en el proceso de migración hacia un destino internacional", "url"=>"/site/es/10-7-3/", "sort"=>"100703", "goal_number"=>"10", "target_number"=>"10.7", "global"=>{"name"=>"Número de personas muertas o desaparecidas en el proceso de migración hacia un destino internacional"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de personas muertas o desaparecidas en el proceso de migración hacia un destino internacional", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de personas muertas o desaparecidas en el proceso de migración hacia un destino internacional", "indicator_number"=>"10.7.3", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nLos datos del Proyecto Migrantes Desaparecidos (MMP) dan testimonio de la \nactual crisis mundial de muertes durante la migración y constituyen la única \nbase de datos global sobre este tema. Se espera que, al contabilizar estas muertes, \ncasi todas vinculadas a la migración irregular, los responsables políticos, \nel mundo académico y el público en general estén mejor informados sobre los \nriesgos asociados a la migración insegura. \n\nSi bien los datos por sí solos podrían no generar cambios, pueden \nproporcionar la evidencia necesaria para impulsar la acción. Sin \nembargo, es probable que los datos actualmente disponibles estén \nmuy por debajo del número real de vidas perdidas durante la migración.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.7.3&seriesCode=SM_DTH_MIGR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Total de muertes y desapariciones registradas durante la migración (número) SM_DTH_MIGR</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-03.pdf\">Metadatos 10-7-3.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nMissing Migrants Project (MMP) data bears witness to the ongoing \nglobal crisis of deaths during migration and is the only global \ndatabase on this topic. It is hoped that by counting and accounting \nfor these deaths, almost all of which are linked to irregular migration, \npolicymakers, academics, and the general public will be better informed \nabout the risks linked to unsafe migration. \n\nWhile data by itself might not bring about change, it can provide the \nnecessary evidence to prompt action. However, it is likely that the data \ncurrently available is a vast undercount of the true number of lives lost \nduring migration. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.7.3&seriesCode=SM_DTH_MIGR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Total deaths and disappearances recorded during migration (number) SM_DTH_MIGR</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-03.pdf\">Metadata 10-7-3.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nDesagertutako Migratzaileen Proiektuko datuek agerian jartzen dute migrazioan zehar egoten diren heriotzek \nmundu-mailako krisia direla. Gai honi buruzko datu-base global bakarra da. Heriotza horiek zenbatzean –ia \nguztiak migrazio irregularrarekin lotuta–, erantzule politikoek, mundu akademikoak eta, oro har, jendeak \nmigrazio ez-seguruari lotutako arriskuen informazio gehiago izatea espero da. \n\nNahiz eta datu horiek beren kabuz ezin duten aldaketarik eragin, ekintza sustatzeko ebidentzia nahikoa eman \ndezakete. Hala ere, ziurrenik gaur egun eskuragarri dauden datuak migrazioan zehar benetan galdu diren \npertsonak baino gutxiago izango dira. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.7.3&seriesCode=SM_DTH_MIGR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Migrazioan erregistratutako heriotzak eta desagertzeak, guztira (kopurua) SM_DTH_MIGR</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-03.pdf\">Metadatuak 10-7-3.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.7: Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policies</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.7.3: Number of people who died or disappeared in the process of migration towards an international destination</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SM_DTH_MIGR - Total deaths and disappearances recorded during migration [10.7.3]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-05-24", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 10.7.3 is complementary to indicator 10.7.2 &#x201C;Number of countries with migration policies that facilitate orderly, safe, regular and responsible migration and mobility of people.&#x201D;</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Organization for Migration (IOM)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Organization for Migration (IOM)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Data on SDG 10.7.3 are currently based on the International Organization for Migration (IOM)&#x2019;s Missing Migrants Project (MMP) dataset, which since 2014 has documented incidents in which migrants (regardless of legal status) have died or are presumed to have died in the process of migration towards an international destination. This selection of data is based on the currently available sources and can provide some insight into the risks of migration routes.</p>\n<p>The MMP aims to provide information on the risks linked to unsafe and irregular international migration movements along key routes and corridors, and thus its definition of a migrant death excludes cases of migrants who die in countries where they have established residence. Deaths in refugee housing, immigration detention centres or camps are similarly excluded unless the death can clearly be linked to a hazard of the journey, e.g. a sickness contracted en route. MMP data also exclude deaths that occur after return to a migrant&#x2019;s homeland or third country, as well as deaths more loosely connected with migrants&#x2019; precarious or irregular status, such as those resulting from labour exploitation, occupational illness or accidents Ior resulting from lack of access to health care. Disappearances of migrants en route in which there is no presumption of death (i.e. excluding shipwrecks and potential drownings) are also excluded.</p>\n<p>Data on SDG 10.7.3 are organized by the country in which the incidents occurred. If the incident took place at an international border or in international waters the country of departure is used.</p>\n<p><strong>Concepts:</strong></p>\n<p><em>(based on the IOM Glossary on Migration, 2019)</em></p>\n<p>Migrant: An umbrella term, not defined under international law, reflecting the common lay understanding of a person who moves away from his or her place of usual residence, whether within a country or across an international border, temporarily or permanently, and for a variety of reasons. The term includes a number of well-defined legal categories of people, such as migrant workers; persons whose particular types of movements are legally-defined, such as smuggled migrants; as well as those whose status or means of movement are not specifically defined under international law, such as international students.</p>\n<p>Irregular migration: Movement of persons that takes place outside the laws, regulations, or international agreements governing the entry into or exit from the State of origin, transit or destination.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of people who have died during international migration</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable. No national or international standards used barring United Nations Statistics Division (UNSD) geographical standards.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>See <a href=\"#_Table_1:_Missing\">Table 1</a> for details on data sources used in the MMP database. For each incident recorded, the specific source of information is listed in the &#x2018;Information Source&#x2019; variable, along with a link to the report if relevant, in the downloadable dataset available from <a href=\"https://missingmigrants.iom.int/downloads\">mmp.iom.int/downloads</a>.</p>\n<h5>Table 1: Missing Migrants Project data sources and their strengths and weaknesses</h5>\n<table>\n  <thead>\n    <tr>\n      <th>\n        <p><strong>Data source</strong></p>\n      </th>\n      <th>\n        <p><strong>Data format</strong></p>\n      </th>\n      <th>\n        <p><strong>Strengths</strong></p>\n      </th>\n      <th>\n        <p><strong>Weaknesses</strong></p>\n      </th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>\n        <p>Government: Data on repatriations of human remains</p>\n      </td>\n      <td>\n        <p>Database (bodies repatriated)</p>\n      </td>\n      <td>\n        <ul>\n          <li>Credible information, covers many cases (not just individual incidents)</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Available for very few countries</li>\n          <li>Often aggregated figures (typically annual)</li>\n          <li>Can be outdated</li>\n          <li>Includes only information on the recovered bodies and not on missing persons</li>\n          <li>Little contextual information available, difficult to differentiate between deaths during migration journeys vs. deaths in other circumstances</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Government: Press releases, official statements</p>\n      </td>\n      <td>\n        <p>Incident reports</p>\n      </td>\n      <td>\n        <ul>\n          <li>Reliable information about individual events</li>\n          <li>Available for isolated events from government agencies (typically police, coast guard, border enforcement actors)</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Often only includes basic information about an incident</li>\n          <li>Usually includes only information on bodies recovered and not missing persons</li>\n          <li>Not centralized/systematically reported to IOM</li>\n          <li>Not published regularly</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Government: Records of border deaths from border enforcement authorities</p>\n      </td>\n      <td>\n        <p>Database (human remains)</p>\n      </td>\n      <td>\n        <ul>\n          <li>Reliable information from government actors encountering human remains</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Disaggregation by incident/death often not available</li>\n          <li>Incomplete coverage can reflect only cases in which border enforcement authorities encounter</li>\n          <li>Does not include deaths in which human remains are not recovered (missing persons)</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Forensic data (i.e. from medical examiners/coroners)</p>\n      </td>\n      <td>\n        <p>Database (human remains) or summary figures</p>\n      </td>\n      <td>\n        <ul>\n          <li>Reliable and detailed information about individual incidents/deaths</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Fragmentation of national systems of human remains means coverage of border regions is incomplete</li>\n          <li>Data disaggregated by migrant deaths are rarely available</li>\n          <li>Does not include deaths in which human remains are not recovered (missing persons)</li>\n          <li>Data are not systematically reported; extremely labour-intensive to request information and parse records; consequently often outdated</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Search and rescue reports from coast guards/police/border patrol/non-governmental organizations (NGOs)</p>\n      </td>\n      <td>\n        <p>Incident reports</p>\n      </td>\n      <td>\n        <ul>\n          <li>Credible information for individual cases</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Completeness of coverage is unknown</li>\n          <li>Often includes only information on bodies recovered and not missing persons</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Testimonies of shipwreck survivors</p>\n      </td>\n      <td>\n        <p>Incident reports</p>\n      </td>\n      <td>\n        <ul>\n          <li>Indicative data where little other information exists</li>\n          <li>Useful to estimate number of missing persons at sea</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Impossible to verify reports of people who went missing at sea if their bodies are not recovered</li>\n          <li>Survivors may provide different information</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Testimonies of families of missing migrants</p>\n      </td>\n      <td>\n        <p>Incident reports</p>\n      </td>\n      <td>\n        <ul>\n          <li>Indicative data where little other information exists</li>\n          <li>Often only source of information on missing persons, especially in cases of shipwrecks in which no remains are ever recovered</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Impossible to verify reports, if no search and rescue is conducted or remains are not recovered and identified</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Testimonies of migrants: Survey programmes</p>\n      </td>\n      <td>\n        <p>Summary figures, Incident-based database often available on request</p>\n      </td>\n      <td>\n        <ul>\n          <li>Indicative data where no other data sources exist, interviewees may speak more honestly with interviewers who speak their native language and/or are also migrants</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Impossible to verify reports for veracity or double-counting, sample size is generally small and unrepresentative</li>\n          <li>Breaks between funding for survey programmes and changes in methodology can inhibit comparison or end data availability entirely</li>\n          <li>Dates of deaths are often imprecise or unavailable</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>NGO reports on deaths during migration</p>\n      </td>\n      <td>\n        <p>Summary figures, incident-based database often available upon request</p>\n      </td>\n      <td>\n        <ul>\n          <li>(Can) provide credible information from local contexts, sometimes with specialized knowledge from NGO staff. Though usually these are summary figures released annually, NGOs are sometimes willing to provide underlying data if asked</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Cover only regional or localized areas</li>\n          <li>Often release data annually as summary figure, which are difficult to check for veracity and double counting</li>\n          <li>Definition of &#x201C;migrant death&#x201D; may vary</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Media: Traditional media reporting</p>\n      </td>\n      <td>\n        <p>Incident reports</p>\n      </td>\n      <td>\n        <ul>\n          <li>Provides current information on events that may not be reported otherwise</li>\n          <li>Contextual information may be included that is unavailable in other data sources</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Quality varies significantly, and information can be limited or inaccurate</li>\n          <li>Generally no follow-up reporting (e.g. the aftermath of a car crash)</li>\n          <li>&#x201C;Big&#x201D; news/mass casualty events are more likely to receive pickup &#x2013; i.e. smaller incidents not part of a &#x201C;crisis&#x201D; may not be reported</li>\n          <li>Requires frequent data mining/searching of sources</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Media: Social media</p>\n      </td>\n      <td>\n        <p>Incident reports</p>\n      </td>\n      <td>\n        <ul>\n          <li>(Can) provide the most current information about incidents, can foster connections between data sources (e.g. IOM with local NGOs), information about cases not reported in news (e.g. European Asylum Support Office weekly social media monitoring reports)</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Little information is provided that can be incomplete or inaccurate</li>\n          <li>It can be difficult/unfeasible to follow-up to get more information and/or verify</li>\n          <li>False information can travel quickly</li>\n          <li>Requires frequent data mining/searching of sources</li>\n        </ul>\n      </td>\n    </tr>\n  </tbody>\n</table>", "COLL_METHOD__GLOBAL"=>"<p>Data are collected by IOM staff based at IOM&#x2019;s Global Migration Data Analysis Centre and in its Regional Offices on a daily basis. Disaggregated, incident-based data is uploaded to a public dataset twice weekly at <a href=\"https://missingmigrants.iom.int\">https://missingmigrants.iom.int</a>. This consists of</p>\n<p>(1) receiving information from the key stakeholders/data sources listed in <a href=\"#_Table_1:_Missing\">Table 1</a>;</p>\n<p>(2) monitoring online news and social media for relevant reports; and</p>\n<p>(3) verifying incidents as discussed in the &#x2018;quality assurance&#x2019; section below.</p>", "FREQ_COLL__GLOBAL"=>"<p>On-going (updated twice weekly to <a href=\"https://missingmigrants.iom.int/downloads\">public dataset</a>).</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Disaggregated, incident-based data collected by the Missing Migrants Project is updated on a daily basis and is uploaded to <a href=\"https://missingmigrants.iom.int/downloads\">missingmigrants.iom.int</a> twice weekly, typically on Tuesdays and Fridays. The aggregated SDG 10.7.3 dataset is updated annually in March with provisional data for the prior year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>No country currently collects/reports comprehensive data on deaths during migration at a national level on their territory/area of effective control. As such, MMP and therefore the 10.7.3 dataset are constructed by IOM staff at the Global Migration Data Analysis Centre (GMDAC) and rely on other data providers &#x2013; including local authorities, NGOs, surveys with survivors, and other sources &#x2013; which are outlined in <a href=\"#_Table_1:_Missing\">Table 1</a>.</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Organization for Migration (IOM)</p>", "INST_MANDATE__GLOBAL"=>"<p>IOM began documenting deaths during migration in 2014 under the Missing Migrants Project. SDG indicator 10.7.3 was adopted in March 2020 as one measure of &#x2018;safe&#x2019; migration called for in Target 10.7.</p>", "RATIONALE__GLOBAL"=>"<p>Missing Migrants Project (MMP) data bears witness to the ongoing global crisis of deaths during migration and is the only global database on this topic. It is hoped that by counting and accounting for these deaths, almost all of which are linked to irregular migration, policymakers, academics, and the general public will be better informed about the risks linked to unsafe migration. While data by itself might not bring about change, it can provide the necessary evidence to prompt action. However, it is likely that the data currently available is a vast undercount of the true number of lives lost during migration.</p>\n<p>There are few official sources of data on deaths during migration, and none at a national level. Thus, MMP data are best understood as a minimum estimate of the true number of deaths during migration worldwide. Data are collected from a variety of sources outlined in <a href=\"#_Table_1:_Missing\">Table 1</a>. In the disaggregated public database available from the <a href=\"https://missingmigrants.iom.int\">MMP website</a>, there are several variables which indicate the information source and quality of each incident involving death(s) during migration.</p>\n<p>An important consideration for SDG 10.7.3 data is that these information sources change over time. These changes are linked to the varying capacity of the MMP team to gather these data, but also to narratives of migration &#x2018;crises&#x2019; that shape public attention and therefore data availability from media and non-governmental sources. This politicization of irregular migration profoundly affect access to relevant information and thus data coverage, quality and comparability. With this in mind, SDG 10.7.3 data are best understood as indicative of the global nature of migrant fatalities and should be used to identify trends over time with the adequate caveats.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data on deaths during migration are fragmented, incomplete and scattered among many different sources. The Missing Migrants Project (MMP) database provides a global overview of data on migrant fatalities, but it is primarily dependent on secondary sources of information. The reliability and completeness of data vary greatly from region to region, from country to country and over time. In addition to undercounting the absolute number of deaths which occur during migration, MMP data also lack identifying information in many cases (incl. age, gender, country of origin).</p>\n<p><a href=\"#_Table_1:_Missing\">Table 1</a> illustrates the wide variety of sources used in the MMP database and gives some insight into the various advantages and disadvantages of each.</p>", "DATA_COMP__GLOBAL"=>"<p>Missing Migrants Project (MMP) is an incident-based database, meaning that each entry in the database represents a single occurrence in which an individual or group of individuals die during migration or at international borders in one particular place and time.<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> This approach is used instead of a body/human remains-based database due to the fact that many migrant bodies are never recovered, particularly in overseas routes such as the Mediterranean Sea, or remote terrains such as the Sahara Desert. Indicator 10.7.3 does not include statistical estimates of the true number of lives lost given the extreme variance in completeness (coverage and quality) of data but MMP is currently developing an estimation methodology.</p>\n<p>The MMP database provides a global overview of data on migrant fatalities, but it is primarily dependent on secondary sources of information. Information is gathered from diverse sources such as official records &#x2013; including from coast guards and medical examiners &#x2013; and other sources such as media reports, non-governmental organizations (NGOs), and surveys and interviews of migrants. When a record is added to the MMP database, often it is a result of bringing together several different data sources. For example, a death may be reported first by the media, and subsequently there may be a government statement confirming what happened, and then migrant families and community members may offer information on the likely identity of the person who died. The reliability and completeness of data vary greatly from region to region, from country to country and over time. <a href=\"#_Table_1:_Missing\">Table 1</a> gives an overview of the data sources used and their strengths and limitations. The MMP dataset cites the data source for each entry in its fully disaggregated incident-based database, available for download from <a href=\"https://missingmigrants.iom.int/downloads\">missingmigrants.iom.int/downloads</a>.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> In some cases, official statistics are not disaggregated by incident, in which case the entry will be marked as a &#x201C;cumulative total&#x201D; in the disaggregated dataset on the <a href=\"https://missingmigrants.iom.int\">MMP website</a>. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>In order for an incident involving a migrant death to be recorded in the dataset, there must be reasonable grounds to believe that it occurred. In practice, this means that whenever possible each incident is based on multiple independent sources of information. Whenever possible, and especially for incidents reported in the media, Missing Migrants Project (MMP) verifies each incident through consultation with local IOM staff and/or other relevant stakeholders. In mass casualty events where large numbers of people die and no remains are recovered (i.e. in shipwrecks) MMP data reflect the lowest estimated number of dead and missing persons. Several variables in the disaggregated dataset available from the <a href=\"https://missingmigrants.iom.int\">MMP website</a> (Information source, Source Quality) reflect the level to which each incident could be validated.</p>", "ADJUSTMENT__GLOBAL"=>"<p>As the Missing Migrants Project (MMP) database is incident-based and includes only verified deaths, no adjustments are made for Indicator 10.7.3.</p>", "IMPUTATION__GLOBAL"=>"<p>As Missing Migrants Project (MMP) data is incident-based and reflects only deaths during migration, which can be verified, data are highly incomplete. Missing values at the country and regional level are left blank for reporting MMP data for SDG 10.7.3, this does not indicate that no deaths during migration occurred but rather that none were recorded.</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates represent the sum of the number of migrant deaths recorded in that region, per the UNSD geoscheme, according to the country where each incident took place. The location (region, route, etc.) categorizations used in the Missing Migrants Project (MMP) database are described <a href=\"https://missingmigrants.iom.int/regional-classification\">here</a>.</p>\n<p>World aggregates differ from the sum of regional totals because incidents in international bodies of water are not classified within any region but are included in the world total, providing a more accurate representation of deaths and disappearances worldwide.</p>", "DOC_METHOD__GLOBAL"=>"<p>IOM guidance for data collection on 10.7.3 is available in <a href=\"https://missingmigrants.iom.int/sites/g/files/tmzbdl601/files/publication/file/MMP%2520data%2520collection%2520guidelines_EN.pdf\">English</a>, <a href=\"https://missingmigrants.iom.int/sites/g/files/tmzbdl601/files/publication/file/MMP_data_collection_guidelines-ESP.pdf\">Spanish</a>, and <a href=\"https://missingmigrants.iom.int/sites/g/files/tmzbdl601/files/publication/file/MMP%2520Data%2520collection%2520guidelines_FR.pdf\">French</a>. on the Missing Migrants Project (MMP) <a href=\"https://missingmigrants.iom.int/\">website</a>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Missing Migrants Project (MMP) data are managed by a team of experts based at IOM&#x2019;s Global Migration Data Analysis Centre. Data cleaning is undertaken at least once annually. Incidents recorded in the MMP database are generally quite timely; however, given the dearth of official information on deaths during migration the database as a whole is both highly incomplete and individual records often have low accuracy, especially in terms of the identities of those who die during migration.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>As the data contained in the Missing Migrants Project (MMP) dataset comes from a wide variety of sources, all data are verified by a team at IOM&#x2019;s Global Migration Data Analysis Centre to ensure that</p>\n<ul>\n  <li>the incident reported meets MMP&#x2019;s definition of a death during migration</li>\n  <li>the information contained in the report is accurate and complete</li>\n  <li>all new incidents reported are checked against existing records to reduce the likelihood of double counting.</li>\n</ul>\n<p>The latter process usually consists of searching for separate reports on the same incident which contain similar information, including contacting the relevant authorities for confirmation where possible. The &#x2018;Source quality&#x2019; variable indicates the reliability of the information reported (see <a href=\"#_Table_2:_Variables\">Table 2</a> for details).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data on deaths during migration remains highly incomplete to the point that statistical assessment is nearly impossible. For this reason, the fully disaggregated Missing Migrants Project (MMP) database includes a &#x2018;source quality&#x2019; indicator that indicates the type of information source for each incident involving a migrant death recorded. Little information is typically known about the overall population of irregular migrants in many countries, let alone of those on the move irregularly or the risks to life that they face on their journeys.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The Missing Migrants Project (MMP) is a global project, and as such collects data in all regions of the world. However, as mentioned throughout this document, MMP data is only as robust as the data sources available, meaning that for remote geographies less data tends to be available. Generally, MMP&#x2019;s coverage is strongest in the Mediterranean and the US-Mexico border, whereas for the rest of the world data coverage is believed to be poor. However, coverage should not be equated with data quality, as for example in the case of the Mediterranean Sea, many remains are lost and consequently the data on the identities (age, gender, country of origin, name) of the decedents is highly incomplete.</p>\n<p><strong>Time series:</strong></p>\n<p>2014-present (ongoing data collection)</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data on SDG 10.7.3 is aggregated by country and year per the SDG reporting standards. However, far more disaggregated data are available in the public database available on the <a href=\"https://missingmigrants.iom.int/\">MMP website</a>, with exceptions following UN and IOM data protection principles. <a href=\"#_Table_2:_Variables\">Table 2</a>, below, presents the list of variables that constitute the MMP database. While ideally all incidents recorded would include entries for each of these variables &#x2013; as these inform both the situation in which a death occurred and the profiles of those who died &#x2013; the lack of official data on deaths during migration, as described above, mean that this is not always possible. The minimum information necessary to record an incident in the MMP database is the date of the incident, the number of dead and/or the number of missing, and the location of death. If the information for other variables is unavailable, the cell is left blank or &#x201C;unknown&#x201D; is recorded, as indicated in the table below.</p>\n<h5>Table 2: Variables recorded in IOM&#x2019;s Missing Migrants Project database</h5>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Variable Name</p>\n      </td>\n      <td>\n        <p>Description</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Incident ID</p>\n      </td>\n      <td>\n        <p>An automatically generated number used to identify each unique entry in the dataset.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Region of incident</p>\n      </td>\n      <td>\n        <p>The region in which an incident took place. For more about regional classifications used in the dataset, <a href=\"https://missingmigrants.iom.int/regional-classification\">click here</a>.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Reported date</p>\n      </td>\n      <td>\n        <p>Estimated date of death. In cases where the exact date of death is not known, this variable indicates the date in which the body or bodies were found. In cases where data are drawn from surviving migrants, witnesses or other interviews, this variable is entered as the date of the death as reported by the interviewee. At a minimum, the month and the year of death is recorded. In some cases, official statistics are not disaggregated by the incident, meaning that data is reported as a total number of deaths occurring during a certain time period. In such cases the entry is marked as a &#x201C;cumulative total,&#x201D; and the latest date of the range is recorded, with the full dates recorded in the comments.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Reported year</p>\n      </td>\n      <td>\n        <p>The year in which the incident occurred.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Reported month</p>\n      </td>\n      <td>\n        <p>The month in which the incident occurred.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Number dead</p>\n      </td>\n      <td>\n        <p>The total number of people confirmed dead in one incident, i.e. the number of bodies recovered. If migrants are missing and presumed dead, such as in cases of shipwrecks, it is left blank.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Number missing</p>\n      </td>\n      <td>\n        <p>The total number of those who are missing and are thus assumed to be dead. This variable is generally recorded in incidents involving shipwrecks. The number of missing is calculated by subtracting the number of bodies recovered from a shipwreck and the number of survivors from the total number of migrants reported to have been on the boat. Disappearances in remote areas on land are also recorded when the person is presumed to be dead. This number may be reported by surviving migrants or witnesses. If no missing persons are reported, it is left blank.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Total dead and missing</p>\n      </td>\n      <td>\n        <p>The sum of the &#x2018;number dead&#x2019; and &#x2018;number missing&#x2019; variables.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Number of survivors</p>\n      </td>\n      <td>\n        <p>The number of migrants that survived the incident, if known. The age, gender, and country of origin of survivors are recorded in the &#x2018;Comments&#x2019; variable if known. If unknown, it is left blank.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Number of females</p>\n      </td>\n      <td>\n        <p>Indicates the number of females found dead or missing. If unknown, it is left blank. This gender identification is based on a third-party interpretation of the victim&apos;s gender from information available in official documents, autopsy reports, witness testimonies, and/or media reports.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Number of males</p>\n      </td>\n      <td>\n        <p>Indicates the number of males found dead or missing. If unknown, it is left blank. This gender identification is based on a third-party interpretation of the victim&apos;s gender from information available in official documents, autopsy reports, witness testimonies, and/or media reports.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Number of children</p>\n      </td>\n      <td>\n        <p>Indicates the number of individuals under the age of 18 found dead or missing. If unknown, it is left blank.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Age</p>\n      </td>\n      <td>\n        <p>The age of the decedent(s). Occasionally, an estimated age range is recorded. If unknown, it is left blank.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Name</p>\n      </td>\n      <td>\n        <p>The name of the decedent(s). If unknown, it is left blank. Not available in the public dataset.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Country of origin</p>\n      </td>\n      <td>\n        <p>Presumed country of birth or of nationality of the decedent. If unknown, the entry will be marked &#x201C;unknown&#x201D;. Not available in the public dataset.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Region of origin</p>\n      </td>\n      <td>\n        <p>Region of origin of the decedent(s). In some incidents, region of origin may be marked as &#x201C;Presumed&#x201D; or &#x201C;(P)&#x201D; if migrants travelling through that location are known to hail from a certain region. If unknown, the entry will be marked &#x201C;unknown&#x201D;.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Cause of death</p>\n      </td>\n      <td>\n        <p>The determination of conditions resulting in the migrant&apos;s death i.e. the circumstances of the event that produced the fatal injury. If unknown, the reason why is included where possible. For example, &#x201C;Unknown &#x2013; skeletal remains only&#x201D;, is used in cases in which only the skeleton of the decedent was found. Not available in the public dataset. </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Cause of death category</p>\n      </td>\n      <td>\n        <p>All causes of death recorded in the previous variable are categorized under one of the following categories: Accidental death; Drowning; Harsh environmental conditions / lack of adequate shelter, food, water; Mixed or unknown; Sickness / lack of access to adequate healthcare; Vehicle accident / death linked to hazardous transport; or Violence.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Location description</p>\n      </td>\n      <td>\n        <p>Place where the death(s) occurred or where the body or bodies were found. Nearby towns or cities or borders are included where possible. When incidents are reported in an unspecified location, this will be noted.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Location coordinates</p>\n      </td>\n      <td>\n        <p>Place where the death(s) occurred or where the body or bodies were found. In many regions, most notably the Mediterranean, geographic coordinates are estimated as precise locations are not often known. The location description should always be checked against the location coordinates.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Migration route</p>\n      </td>\n      <td>\n        <p>Name of the migrantion route on which incident occurred, if known. If unknown, it is left blank.</p>\n        <p>The routes currently used for this categorization are: Afghanistan to Iran; Belarus-EU border; Caribbean to Central America; Caribbean to US; Central Mediterranean; Darien; Dominican Republic to Puerto Rico; DRC to Uganda; Eastern Mediterranean; Eastern Route to/from EHOA; English Channel to the UK; Haiti to Dominican Republic; Horn of Africa Route; Iran to T&#xFC;rkiye; Italy to France; Northern Route from EHOA; Route to Southern Africa; Sahara Desert crossing; Sea crossings to Mayotte; Syria to T&#xFC;rkiye; T&#xFC;rkiye-Europe land route; Ukraine to Europe; US-Mexico border crossing; Venezuela to Caribbean; Western Africa / Atlantic route to the Canary Islands; Western Balkans; and Western Mediterranean.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>UNSD geographical grouping</p>\n      </td>\n      <td>\n        <p>Geographical region in which the incident took place, as designated by the UNSD geoscheme.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Information source</p>\n      </td>\n      <td>\n        <p>Name of source of information for each incident. Multiple sources may be listed.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Link</p>\n      </td>\n      <td>\n        <p>Links to original reports of migrant deaths/disappearances if available. Multiple links may be listed.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Source quality</p>\n      </td>\n      <td>\n        <p>Incidents are ranked on a scale from 1-5 based on the source(s) of information available. Incidents ranked as level 1 are based on information from only one media source. Incidents ranked as level 2 are based on information from uncorroborated eyewitness accounts or data from survey respondents. Incidents ranked as level 3 are based on information from multiple media reports, while level 4 incidents are based on information from at least one NGO, IGO, or another humanitarian actor with direct knowledge of the incident. Incidents ranked at level 5 are based on information from official sources such as coroners, medical examiners, or government officials OR from multiple humanitarian actors.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Comments</p>\n      </td>\n      <td>\n        <p>Brief description narrating additional facts about the death. If no extra information is available, this is left blank. Not available in the public dataset.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>As the Missing Migrants Project (MMP) dataset does rely on multiple types of data sources, there may be discrepancies about specific cases with government reports. The full incident-based dataset, including all sources, can be downloaded for comparison and verification at <a href=\"https://missingmigrants.iom.int/downloads\">missingmigrants.iom.int/downloads</a>.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>[1]: <a href=\"https://missingmigrants.iom.int/\"><strong>missingmigrants.iom.int</strong></a></p>\n<p><strong>References:</strong></p>\n<ul>\n  <li>Garcia Borja, A. and J. Black (2021) <a href=\"https://www.fmreview.org/issue66/garciaborja-black\">Measuring migrant deaths and disappearances</a>. In <em>Forced Migration Review </em>66: 58-60.</li>\n  <li>Singleton, A., F. Laczko and J. Black (2017) <a href=\"https://publications.iom.int/system/files/pdf/migration_policy_practice_journal_30.pdf\">Measuring unsafe migration: the challenge of collecting accurate data on migrant fatalities</a>. In <em>Migration Policy Practice</em> VII: 4-9.</li>\n  <li>See full list of Missing Migrants Project publications at <a href=\"https://missingmigrants.iom.int/publications\">mmp.iom.int/publications</a>.</li>\n</ul>\n<p>IOM guidance for countries on 10.7.3 will be published in 2022.</p>", "indicator_sort_order"=>"10-07-03", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.7.4", "slug"=>"10-7-4", "name"=>"Proporción de la población integrada por refugiados, desglosada por país de origen", "url"=>"/site/es/10-7-4/", "sort"=>"100704", "goal_number"=>"10", "target_number"=>"10.7", "global"=>{"name"=>"Proporción de la población integrada por refugiados, desglosada por país de origen"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población integrada por refugiados, desglosada por país de origen", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población integrada por refugiados, desglosada por país de origen", "indicator_number"=>"10.7.4", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Número de personas refugiadas por cada 100.000 habitantes", "definicion"=>"\nNúmero de personas que tienen reconocida la condición de refugiada o una condición  de protección internacional similar por cada 100.000 habitantes", "formula"=>"\n$$TR^{t} = \\frac{R^{t}}{P^{t+1}} \\cdot 100.000$$\n\ndonde:\n\n$PR^{t} =$ personas con documento de identidad de refugiada, documento de identidad de protección \nsubsidiaria o autorización de residencia temporal por circunstancias excepcionales en vigor a 31 de diciembre del año $t$\n\n$P^{t+1} =$ población a 1 de enero del año $t+1$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "observaciones"=>"Se debe tener en cuenta que la desagregación por comunidades autónomas del número de personas que tienen reconocida la condición de refugiada o una condición de protección internacional similar en España no se realiza por el lugar de residencia, sino por el lugar donde se realizó el trámite administrativo que dio lugar a esa condición.", "justificacion_global"=>"\nEl desplazamiento forzado como resultado de conflictos, violencia \ny otras causas socava el desarrollo sostenible y puede aumentar \nel riesgo de inestabilidad regional, especialmente cuando los refugiados \nson acogidos en países vecinos, lo que genera posibles tensiones con \nlas poblaciones locales. \n\nLa Resolución de la Asamblea General de las Naciones Unidas (A/Res/70/1),\n que adoptó la Agenda 2030 para el Desarrollo Sostenible, en su párrafo 23, \nreconoce la pertinencia de la Agenda para satisfacer las necesidades de los \nrefugiados, los desplazados internos y los migrantes, dado que se \nencuentran entre los grupos más vulnerables. También declara explícitamente \nque los Estados Miembros se comprometen a adoptar nuevas medidas y \nacciones eficaces para fortalecer el apoyo y atender las necesidades \nespeciales de las personas que viven en zonas afectadas por emergencias \nhumanitarias complejas. \n\nAdemás, la meta 10.7 reconoce por primera vez la contribución de \nla migración al desarrollo sostenible al proponerse facilitar \nla migración y la movilidad ordenadas, seguras y responsables de las \npersonas, incluso mediante la aplicación de políticas migratorias \nplanificadas y bien gestionadas. \n\nEste indicador registra el número de personas desplazadas a través \nde fronteras nacionales como resultado de persecución, conflicto, \nviolencia, violaciones de derechos humanos o eventos que alteran \ngravemente el orden público. Mide el recuento total de la población \nrefugiada por país o territorio de origen como proporción de la \npoblación total.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.7.4&seriesCode=SM_POP_REFG_OR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Número de refugiados por cada 100.000 habitantes, por país de origen (por cada 100.000 habitantes) SM_POP_REFG_OR</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-04.pdf\">Metadatos 10-7-4.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"Número de personas refugiadas por cada 100.000 habitantes", "definicion"=>"\nNumber of people who have recognized refugee status or a similar  international protection status per 100,000 inhabitants ", "formula"=>"\n$$TR^{t} = \\frac{R^{t}}{P^{t+1}} \\cdot 100.000$$\n\nwhere:\n\n$PR^{t} =$ people with a refugee identity document, subsidiary protection identity document or temporary residence authorization\ndue to exceptional circumstances in force on December 31 of the year $t$\n\n$P^{t+1} =$  population on January 1 of year $t+1$\n", "desagregacion"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "observaciones"=>"It should be noted that the breakdown by autonomous communities of the number of people who have been granted refugee status or a similar international protection status in Spain is not based on place of residence, but rather on the location where the administrative procedure that gave rise to that status was carried out.", "justificacion_global"=>"\nForced displacement as a result of conflict, violence, and other causes \nundermine sustainable development, and can increase the risk of regional \ninstability, especially when refugees are hosted in neighbouring countries, \nresulting in possible tensions with local populations. \n\nThe United Nations General Assembly Resolution (A/Res/70/1) that adopted \nthe 2030 Agenda for Sustainable Development at paragraph 23 recognizes \nthe relevance of the Agenda to meet the needs of refugees, internally displaced \npersons and migrants on the basis that they are among the most vulnerable. It \nalso explicitly states that Member States resolve to take further effective \nmeasures and actions, to strengthen support and meet the special needs of \npeople living in areas affected by complex humanitarian emergencies. \n\n In addition, target 10.7 recognizes for the first time the contribution \nof migration to sustainable development by aiming to facilitate orderly, \nsafe, and responsible migration and mobility of people, including through \nimplementation of planned and well-managed migration policies. \n\nThis indicator tracks the number of people displaced across national borders \nas a result of persecution, conflict, violence, human rights violations, or \nevents seriously disturbing public order. It measures the total count of refugee \npopulation by country or territory of origin as a proportion of the total population. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.7.4&seriesCode=SM_POP_REFG_OR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Number of refugees per 100,000 population, by country of origin (per 100,000 population) SM_POP_REFG_OR</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-04.pdf\">Metadata 10-7-4.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Número de personas refugiadas por cada 100.000 habitantes", "definicion"=>"\nErrefuxiatu-izaera edo antzeko nazioarteko babes-baldintza aitortuta duten pertsonen  kopurua 100.000 biztanleko", "formula"=>"\n$$TR^{t} = \\frac{R^{t}}{P^{t+1}} \\cdot 100.000$$\n\nnon:\n\n$PR^{t} =$  $t$ urteko abenduaren 31n indarrean dagoen errefuxiatuaren nortasun-agiria, \nbabes subsidiarioko nortasun-agiria edo salbuespenezko egoeren ondoriozko aldibaterako \nbizileku-baimena duten pertsonak  \n\n$P^{t+1} =$ biztanleria $t+1$ urteko urtarrilaren 1ean\n", "desagregacion"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "observaciones"=>"Kontuan izan behar da Espainian errefuxiatu-izaera edo antzeko nazioarteko babes-baldintza aitortuta duten pertsonen kopurua autonomiaerkidegoen arabera bereiztea ez dela bizilekuaren arabera egiten, baizik eta izaera hori eragin zuen administrazio-izapidea egin zen lekuaren arabera.", "justificacion_global"=>"\nGatazka, indarkeria edo bestelako arrazoien ondorioz behartutako lekualdaketek garapen jasangarria \nkaltetzen dute, eta eskualdearen ezegonkortasun-arriskua handitu dezakete, batez ere errefuxiatuak \nondoko herrialdeetan hartzen direnean. Horrek tentsioak sortzen ditu tokiko biztanleekin. \n\nNazio Batuen Batzar Nagusiaren ebazpenak (A/Res/70/1), Garapen Jasangarrirako 2030eko Agenda ezarri \nzuenak, 23. paragrafoan dio Agenda egokia dela errefuxiatuen, barneko lekualdatuen eta migratzaileen \nbeharrizanak asetzeko, horiek guztiak talderik zaurgarrienen artean daudelako. Halaber, berariaz \nadierazten du estatu-kideek hitz ematen dutela neurri eta ekintza eraginkorrak hartuko dituztela \nlarrialdi humanitario konplexuek eragindako eremuetan bizi diren pertsonen beharrizan bereziak artatu \neta babesa indartzeko. \n\nGainera, 10.7 xedeak lehen aldiz aitortzen du migrazioak garapen jasangarriari egiten dion ekarpena. \nIzan ere, pertsonen migrazio eta mugikortasun antolatu, seguru eta arduratsua sustatzen ditu, eta ondo \nkudeatutako eta planifikatutako migrazio-politikak aplikatzen ditu. \n\nAdierazle horrek muga nazionalen bidez lekualdatutako pertsonen kopurua erregistratzen du, lekualdatzeko \narrazoiak honakoak direnean: jazarpena, gatazka, indarkeria, giza eskubideen urraketak edo ordena publikoa \nlarriki asaldatzen duten ekintzak. Jatorrizko herrialde edo lurralde bakoitzeko errefuxiatuen zenbateko \nosoa neurtzen du, biztanleria osoari lotuta. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.7.4&seriesCode=SM_POP_REFG_OR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Errefuxiatu kopurua 100.000 biztanleko, jatorrizko herrialdeka (100.000 biztanleko) SM_POP_REFG_OR</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-07-04.pdf\">Metadatuak 10-7-4.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.7: Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policies</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.7.4: Proportion of the population who are refugees, by country of origin</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SM_POP_REFG_OR - Number of refugees per 100,000 population, by country of origin [10.7.4]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Goals 1 (&#x201C;No Poverty&#x201D;), 2 (&#x201C;Zero Hunger&#x201D;), 3 (&#x201C;Good Health and Wellbeing&#x201D;), 4 (&#x201C;Quality Education&#x201D;), 5 (&#x201C;Gender Equality&#x201D;), 8 (&#x201C;Decent Work and Economic Growth&#x201D;), and 16 (&#x201C;Peace, Justice and Strong Institutions&#x201D;).</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations High Commissioner for Refugees (UNHCR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations High Commissioner for Refugees (UNHCR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator is defined as the total count of population who have been recognized as refugees as a proportion of the total population of their country of origin, expressed per 100,000 population. </p>\n<p>Refugees refers to persons recognized by the Government and/or UNHCR, those in a refugee-like situation and other persons in need of international protection. </p>\n<p>Population refers to total resident population in a given country in a given year.</p>\n<p><strong>Concepts:</strong></p>\n<p>Refugees recognized by the Government and/or UNHCR include: </p>\n<p>(a) persons recognized as refugees by Governments having ratified the 1951 <em>United Nations Convention Relating to the Status of Refugees</em>, and/or its <em>1967 Protocol; </em></p>\n<p>(b) persons recognized as refugees under the <em>1969 Organization of African Unity (OAU) Convention Governing the Specific Aspects of Refugee Problems in Africa; </em></p>\n<p>(c) those recognized in accordance with the principles enshrined in the <em>Cartagena Declaration; </em></p>\n<p>(d) persons recognized by UNHCR as refugees in accordance with its Statute (otherwise referred to as &#x201C;mandate&#x201D; refugees); </p>\n<p>(e) those who have been granted a complementary form of protection (i.e. non-Convention); </p>\n<p>(f) persons who have been granted temporary protection on a group basis; </p>\n<p>Persons in a <em>refugee-like situation</em> refer to those outside their territory of origin who face protection risks similar to those of refugees, but who, for practical or other reasons, have not been formally recognized or issued documentation to that effect. </p>\n<p><em>Other persons in need of international protection </em>are<em> </em>defined as<em> </em>people who are outside their country or territory of origin, typically because they have been forcibly displaced across international borders, who have not been reported under other categories (asylum-seekers, refugees, people in refugee-like situations) but who likely need international protection, including protection against forced return, as well as access to basic services on a temporary or longer-term basis.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of refugees per 100,000 population in country of origin</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Two main sources exist at country level: a) administrative asylum systems; b) direct refugee registration databases. In cases where UNHCR performs refugee registration directly, operations provide data which is available with a highest degree of disaggregation. In cases where refugees go through a Refugee Status Determination (RSD) administrative procedure, data is collected by Governments in the biannual Population Statistics Review exercise facilitated by focal points in UNHCR country offices. </p>\n<p>Population data are derived from annual estimates produced by the UN Population Division (2022 Revision of World Population Prospects, Total Population, both sexes). Estimates until 2020 and medium fertility variant projection for years thereafter. </p>", "COLL_METHOD__GLOBAL"=>"<p>At the international level, data on refugee populations are routinely collected by UNHCR through the biannual Population Statistic Review (PSR) data collection. Focal points in each UNHCR operation submit data to the Statistics and Demographics Section in the Global Data Service that performs consistency checks. In most cases these focal points obtain data either from the UNHCR registration database (in countries where UNHCR performs registration directly), or from national institutions responsible for data production in the area of asylum and refugee matters (National Statistical Offices, Ministry of Interior, Ministry of Justice, Administrative Tribunals). When a country does not report refugee figures to UNHCR, estimations based on positive decisions on asylum applications from previous years are used. Once consolidated, data are shared to countries to check their accuracy. Data for Sustainable Development Goals (SDG) monitoring will also be sent to countries for consultation before publication. </p>", "FREQ_COLL__GLOBAL"=>"<p>Twice a year: by March (data for year-end) and September (data for mid-year).<strong><em> </em></strong></p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Twice a year: by December (data for mid-year) and by June (data for year-end)</p>", "DATA_SOURCE__GLOBAL"=>"<p>Refugee data are sent to UNHCR Country Offices by member states, usually through national institutions responsible for data production in the area of refugee and asylum (National Statistical Offices, Ministry of Interior, Ministry of Justice, and Administrative Tribunals). Data obtained by UNHCR registration systems is provided directly by UNHCR country operations.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations High Commissioner for Refugees (UNHCR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The collection and use of refugee data are mandated by the 1951 Refugee Convention and by the Statute of the Office of the High Commissioner for Refugees. The confidentiality of refugee data and related information is highly respected by UNHCR and our partners and the processing and protection of personal data are anchored in UNHCR&#x2019;s Data Protection Policy.</p>", "RATIONALE__GLOBAL"=>"<p>Forced displacement as a result of conflict, violence, and other causes undermine sustainable development, and can increase the risk of regional instability, especially when refugees are hosted in neighbouring countries, resulting in possible tensions with local populations. The United Nations General Assembly Resolution (A/Res/70/1) that adopted the 2030 Agenda for Sustainable Development at paragraph 23 recognizes the relevance of the Agenda to meet the needs of refugees, internally displaced persons and migrants on the basis that they are among the most vulnerable. It also explicitly states that Member States resolve to take further effective measures and actions, to &#x201C;<em>strengthen support and meet the special needs of people living in areas affected by complex humanitarian emergencies</em>&#x201D;. In addition, target 10.7 recognizes for the first time the contribution of migration to sustainable development by aiming to &#x201C;facilitate orderly, safe, and responsible migration and mobility of people, including through implementation of planned and well-managed migration policies&#x201D;.</p>\n<p> </p>\n<p>This indicator tracks the number of people displaced across national borders as a result of persecution, conflict, violence, human rights violations, or events seriously disturbing public order. It measures the total count of refugee population by country or territory of origin as a proportion of the total population. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The estimates of the refugee population by country of origin are collected on a bi-annual basis by UNHCR during its annual and mid-year statistical reviews. Data is therefore already available and does not impose an additional burden on national statistical systems.</p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced open=\"[\" close=\"]\" separators=\"|\">\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mi>N</mi>\n            <mi>u</mi>\n            <mi>m</mi>\n            <mi>b</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>f</mi>\n            <mi>u</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n            <mi>e</mi>\n            <mi>s</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>b</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mi>r</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>i</mi>\n            <mi>g</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>y</mi>\n            <mi>e</mi>\n            <mi>a</mi>\n            <mi>r</mi>\n            <mo>-</mo>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>d</mi>\n          </mrow>\n          <mrow>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mi>Y</mi>\n                <mi>e</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n                <mo>-</mo>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>d</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>p</mi>\n                <mi>u</mi>\n                <mi>l</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>c</mi>\n                <mi>o</mi>\n                <mi>u</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>r</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>g</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mo>+</mo>\n                <mi>n</mi>\n                <mi>u</mi>\n                <mi>m</mi>\n                <mi>b</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>f</mi>\n                <mi>u</mi>\n                <mi>g</mi>\n                <mi>e</mi>\n                <mi>e</mi>\n                <mi>s</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>b</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>c</mi>\n                <mi>o</mi>\n                <mi>u</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>r</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>g</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>y</mi>\n                <mi>e</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n                <mo>-</mo>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>d</mi>\n              </mrow>\n            </mfenced>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfenced>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n  </math></p>\n<p>The indicator is presented as the number of refugees per 100,000 population in country of origin. </p>\n<p><em>*For years where the number of refugee refers to the end-year figure (as of 31. December), the total population estimate as of 01. January the next year is applied. For years where the number of refugees refers to the mid-year figures (as of 30. June), the total population estimate as of 01. July is applied. </em></p>", "DATA_VALIDATION__GLOBAL"=>"<p>At the international level, data on refugee populations are routinely collected by UNHCR through the biannual Population Statistic Review (PSR) data collection. Focal points in each UNHCR operation submit data to the Statistics and Demographics Section in the Global Data Service that performs consistency checks. In most cases, these focal points obtain data either from the UNHCR registration database (in countries where UNHCR performs registration directly), or from national institutions responsible for data production in the area of asylum and refugee matters (National Statistical Offices, Ministry of Interior, Ministry of Justice, Administrative Tribunals). When a country does not report refugee figures to UNHCR, estimations based on positive decisions on asylum applications from previous years are used. Once consolidated, data are shared to countries to check their accuracy. Data for SDG monitoring will also be sent to countries for consultation before publication.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>UNHCR produces estimates for countries where national data are not available from neither administrative systems nor from refugee registration.</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p> The regional average is applied to those countries within the region with missing values for the purposes of calculating regional aggregates only, but are not published as country-level estimates.</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional estimates are calculated as weighted averages of national data, with weights provided by the national resident population of the country of origin augmented by the number of refugees pertaining to that country.</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>UNHCR Annual Statistical Report methodological guidance note.</li>\n  <li>The Expert Group on Refugee and IDP Statistics, in which UNHCR belongs to the steering committee, has released the <em>International Recommendations on Refugee Statistics (IRRS)</em>, which were adopted by the United Nations Statistical Commission during its 2018 session and is a strong reference for refugee statistics reporting methodologies. UNHCR supports NSOs to build capacity to report on forced displacement in countries that currently lack disaggregated data on refugees. </li>\n  <li>Expert Group on Refugee and IDP Statistics (EGRIS):</li>\n</ul>\n<p><a href=\"https://egrisstats.org/\">https://egrisstats.org/</a></p>\n<ul>\n  <li>International Recommendations on Refugee Statistics (IRRS):</li>\n</ul>\n<p><a href=\"https://egrisstats.org/recommendations/international-recommendations-on-refugee-statistics-irrs/\">https://egrisstats.org/recommendations/international-recommendations-on-refugee-statistics-irrs/</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>UNHCR follows its Statistical Quality Assurance Framework when producing official statistics, including this SDG indicator.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>A number of validation rules are included in the global database, so that that data containing errors will not be accepted. All data submitted by countries are additionally verified for consistency by the UNHCR Statistics and Demographics Section. This includes checks with previous years&#x2019; data, and among data reported by different countries. When inconsistencies exist, for instance when refugee returns reported by a country differ from the arrivals reported by another, the difference is taken back to the countries until the difference is resolved. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Assessing the quality of UNHCR&#x2019;s population statistics is a core component of the Statistical Quality Assurance Framework noted above.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>National data on refugee populations are available for 192 countries (at least one data point between 1951-2020). Time series data on refugees suitable for monitoring are available for 192 countries. Approximately 83 percent of the refugee population have data which can be disaggregated by sex and 76 percent which can be disaggregated by age. The age and sex disaggregation for the remainder of the population is estimated with statistical methods. </p>\n<p>National population estimates and projections are available in the World Population Prospects prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, and presented in a series of Excel files displaying key demographic indicators for each UN development group, World Bank income group, geographic region, Sustainable Development Goals (SDGs) region, subregion and country or area.</p>\n<p><strong>Time series:</strong></p>\n<p>1951-present</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Recommended disaggregation for this indicator are:</p>\n<p>- sex </p>\n<p> - age (esp. % of children) </p>\n<p>- geographical location (urban/rural)</p>\n<p>- place of residence (in camps/out of camps)</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>UNHCR makes all efforts to obtain data reported directly by member states to include in its statistical reports. The gradual implementation of IRRS (see below) by countries should improve quality and consistency of national and international data.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<p><a href=\"http://www.unhcr.org\">www.unhcr.org</a></p>\n<p><strong>References: </strong></p>\n<p>UNHCR Refugee Population Statistics Database (<a href=\"https://www.unhcr.org/refugee-statistics/\">https://www.unhcr.org/refugee-statistics/</a> )</p>\n<p>UNHCR, Global Trends report (<a href=\"https://www.unhcr.org/globaltrends.html\">https://www.unhcr.org/globaltrends.html</a>)</p>\n<p>UNHCR, Mid-Year Trends report (<a href=\"https://www.unhcr.org/mid-year-trends.html\">https://www.unhcr.org/mid-year-trends.html</a>)</p>\n<p>UNHCR Statistical Yearbook (<a href=\"https://www.unhcr.org/statistical-yearbooks.html\">https://www.unhcr.org/statistical-yearbooks.html</a>)</p>\n<p>UN Population Division, World Population Prospects (<a href=\"https://population.un.org/wpp/\">https://population.un.org/wpp/</a>)</p>", "indicator_sort_order"=>"10-07-04", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.a.1", "slug"=>"10-a-1", "name"=>"Proporción de líneas arancelarias que se aplican a las importaciones de los países menos adelantados y los países en desarrollo con arancel cero", "url"=>"/site/es/10-a-1/", "sort"=>"10aa01", "goal_number"=>"10", "target_number"=>"10.a", "global"=>{"name"=>"Proporción de líneas arancelarias que se aplican a las importaciones de los países menos adelantados y los países en desarrollo con arancel cero"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de líneas arancelarias que se aplican a las importaciones de los países menos adelantados y los países en desarrollo con arancel cero", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de líneas arancelarias que se aplican a las importaciones de los países menos adelantados y los países en desarrollo con arancel cero", "indicator_number"=>"10.a.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "texto_oceca"=>"", "observaciones"=>"", "justificacion_global"=>"\nEl cálculo de este indicador permitirá observar cuántos productos de los países \nen desarrollo y los países menos adelantados (PMA) tendrán libre acceso a los \nmercados de los países desarrollados. En comparación con los aranceles aplicados \na otros países, este indicador permitirá evaluar en qué medida se ha otorgado \nun trato especial y diferenciado en materia de aranceles de importación. \n\nLa evolución de este indicador indicará el progreso en la eliminación gradual de los \naranceles sobre los bienes procedentes de países en desarrollo y PMA.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.a.1&seriesCode=TM_TRF_ZERO&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=AGR\">Proporción de líneas arancelarias aplicadas a las importaciones con arancel cero (%) TM_TRF_ZERO</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-0a-01.pdf\">Metadatos 10-a-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"Número de personas refugiadas por cada 100.000 habitantes", "definicion"=>"\nNumber of people who have been recognized as refugees or have a similar international  protection status per 100,000 inhabitants", "formula"=>"\n$$TR^{t} = \\frac{R^{t}}{P^{t+1}} \\cdot 100.000$$\n\nwhere:\n\n$PR^{t} =$ People with a refugee identity document, subsidiary protection identity document or temporary residence permit for exceptional circumstances in force as of December 31 of year $t$\n\n${P^{t+1} =$ population as of January 1 of year $t+1$\n", "desagregacion"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "observaciones"=>"It should be noted that the breakdown by autonomous communities of the number of people who have been granted refugee status or a similar international protection status in Spain is not carried out by place of residence, but by the place where the administrative procedure that gave rise to that status was carried out.", "justificacion_global"=>"\nThe calculation of this indicator will allow observing on how many products Developing countries and \nLDCs will have free access to Developed countries markets. When compared to the tariff rates applied to \nother countries, this indicator will allow assessing to which extent special and differential treatment has \nbeen accorded in terms of import tariffs. \n\nThe evolution of this indicator will indicate progress on the \nphasing out of tariff rates on goods coming from Developing and LDCs. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.7.4&seriesCode=SM_POP_REFG_OR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Number of refugees per 100,000 inhabitants, by country of origin (per 100,000 inhabitants) SM_POP_REFG_OR</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-0a-01.pdf\">Metadata 10-a-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "texto_oceca"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nAdierazle hau kalkulatuta antzeman ahalko da garapen-bidean dauden herrialdeetako eta Aurrerapen \nGutxien duten Herrialdeetako (AGH) zenbat produktuk izango duten sarbide askea garatutako herrialdeetako \nmerkatuetara. Beste herrialde batzuei aplikatutako muga zergekin alderatuta, adierazle honi esker ebaluatu \nahal izango da zein neurritan eman den tratu berezi eta bereizia inportazioko muga-zergetan. \n\nAdierazle honen bilakaerak adieraziko du zein aurrerapen egon den garapen-bidean dauden herrialdeetatik \neta AGHtik datozen ondasunen gaineko muga-zergak apurka-apurka ezabatzeko prozesuan. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.a.1&seriesCode=TM_TRF_ZERO&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=AGR\">Inportazioei zero muga-zerga aplikatutako muga-zergen lerroen proportzioa (%) TM_TRF_ZERO</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-0a-01.pdf\">Metadatuak 10-a-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.a: Implement the principle of special and differential treatment for developing countries, in particular least developed countries, in accordance with World Trade Organization agreements</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.a.1: Proportion of tariff lines applied to imports from least developed countries and developing countries with zero-tariff</p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2016-07-19</p>", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Linkages with indicator 17.12 on the implementation of duty-free and quota-free market access</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Trade Centre (ITC)</p>\n<p>United Nations Conference on Trade and Development (UNCTAD)</p>\n<p>The World Trade Organization (WTO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Trade Centre (ITC)</p>\n<p>United Nations Conference on Trade and Development (UNCTAD)</p>\n<p>The World Trade Organization (WTO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of total number of tariff lines (in per cent) applied to products imported from least developed countries and developing countries corresponding to a 0% tariff rate in HS chapter 01-97.</p>\n<p><strong>Concepts:</strong></p>\n<p>Tariff line or National Tariff lines (NTL): National Tariff Line codes refer to the classification codes, applied to merchandise goods by individual countries, that are longer than the HS six digit level. Countries are free to introduce national distinctions for tariffs and many other purposes. The national tariff line codes are based on the HS system but are longer than six digits. For example, the six digit HS code 010120 refers to Asses, mules and hinnies, live, whereas the US National Tariff line code 010120.10 refers to live purebred breeding asses, 010120.20 refers to live asses other than purebred breeding asses and 010120.30 refers to mules and hinnies imported for immediate slaughter.</p>\n<p>Tariffs: Tariffs are customs duties on merchandise imports, levied either on an ad valorem basis (percentage of value) or on a specific basis (e.g. $7 per 100 kg). Tariffs can be used to create a price advantage for similar locally-produced goods and for raising government revenues. Trade remedy measures and taxes are not considered to be tariffs.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The main information used to calculate indicators 10.a.1 is import tariff data. Information on import tariffs might be retrieved by contacting directly National statistical offices, permanent country missions to the UN, regional organizations or focal points within the customs, ministries in charge of customs revenues (Ministry of economy/finance and related revenue authorities) or, alternatively, the Ministry of trade. Tariff data for the calculation of this indicator are retrieved from ITC (MAcMap) - http://www.macmap.org/ - WTO (IDB) - http://tao.wto.org - and UNCTAD (TRAINS) databases. Import tariff data included in the ITC (MAcMap) database are collected by contacting directly focal points in line national agencies or regional organizations (in the case of custom unions or regional economic communities). When available, data are downloaded from national or regional official websites. In some cases, data are purchased from private companies. Import tariff data included in the WTO (IDB) database are sourced from official notifications of WTO members. Import tariff included in the UNCTAD (TRAINS) database are collected from official sources, including official country or regional organizations websites.</p>", "FREQ_COLL__GLOBAL"=>"<p>Continuously updated all year round</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Indicatively the indicators calculations can be ready by March every year. However, the date of release will depend on the period envisaged for the launching of the SDG monitoring report. </p>", "DATA_SOURCE__GLOBAL"=>"<p>NA</p>", "COMPILING_ORG__GLOBAL"=>"<p>Name<strong>:</strong></p>\n<p>ITC, WTO and UNCTAD</p>\n<p>Description<strong>:</strong></p>\n<p>ITC, WTO and UNCTAD will jointly report on this indicator</p>", "RATIONALE__GLOBAL"=>"<p>The calculation of this indicator will allow observing on how many products Developing countries and LDCs will have free access to Developed countries markets. When compared to the tariff rates applied to other countries, this indicator will allow assessing to which extent special and differential treatment has been accorded in terms of import tariffs. The evolution of this indicator will indicate progress on the phasing out of tariff rates on goods coming from Developing and LDCs.</p>", "REC_USE_LIM__GLOBAL"=>"<p>&quot;The following caveats should be taken in consideration while reviewing this indicator:</p>\n<p>Accurate estimates on special and differential treatment for developing countries do not exist, thus the calculations are limited to tariffs only. These are only part of the trade limitation factors, especially when looking at exports of developing or least developed countries under non-reciprocal preferential treatment that set criteria for eligibility.</p>\n<p>A full coverage of preferential schemes of developed countries are used for the computation, but preferential treatment may not be fully used by developing countries&apos; exporters for different reasons such as the inability of certain exporters to meet eligibility criteria (i.e., complying with rules of origin). As there is no accurate statistical information on the extent of the actual utilisation of each of these preferences, it is assumed that they are fully utilised.</p>\n<p>Duty free treatment is an indicator of market access, but is not always synonymous with preferential treatment for beneficiary countries, because a number of MFN tariffs are already at, or close to, zero, especially for fuels and minerals. International agreements on IT products also offer duty-free treatment for components and equipment used for production purpose&quot;</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated as the average share of national tariff lines that are free of duty</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values are calculated using the most recent year available.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Missing values are calculated using the most recent year available.</p>", "REG_AGG__GLOBAL"=>"<p>Share of duty-free tariff lines in the total number of tariff lines by country or country groups. At the tariff line level, the minimum rate between the MFN and others imports regime is always take into account in our calculation</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Asia and Pacific: 42</p>\n<p>Africa: 49</p>\n<p>Latin America and the Caribbean: 34</p>\n<p>Europe, North America, Australia, New Zealand and Japan: 48</p>\n<p><strong>Time series:</strong></p>\n<p>Yearly data from 2005 to latest year </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregation is available by product sector (e.g. Agriculture, Textile, Environmental goods), geographical regions and country income level (e.g. Developed, Developing, LDCs)</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable. The same national data are used at the global level.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>http://www.intracen.org / www.wto.org / http://unctad.org/en/Pages/Home.aspx</p>\n<p><strong>References:</strong></p>\n<p>No available references.</p>", "indicator_sort_order"=>"10-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.b.1", "slug"=>"10-b-1", "name"=>"Corrientes totales de recursos para el desarrollo, desglosadas por país receptor y país donante y por tipo de corriente (por ejemplo, asistencia oficial para el desarrollo, inversión extranjera directa y otras corrientes)", "url"=>"/site/es/10-b-1/", "sort"=>"10bb01", "goal_number"=>"10", "target_number"=>"10.b", "global"=>{"name"=>"Corrientes totales de recursos para el desarrollo, desglosadas por país receptor y país donante y por tipo de corriente (por ejemplo, asistencia oficial para el desarrollo, inversión extranjera directa y otras corrientes)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Corrientes totales de recursos para el desarrollo, desglosadas por país receptor y país donante y por tipo de corriente (por ejemplo, asistencia oficial para el desarrollo, inversión extranjera directa y otras corrientes)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Corrientes totales de recursos para el desarrollo, desglosadas por país receptor y país donante y por tipo de corriente (por ejemplo, asistencia oficial para el desarrollo, inversión extranjera directa y otras corrientes)", "indicator_number"=>"10.b.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "texto_oceca"=>"", "observaciones"=>"", "justificacion_global"=>"\nLos flujos totales de recursos hacia los países en desarrollo cuantifican \nlos gastos generales que los donantes realizan a dichos países.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.b.1&seriesCode=DC_TRF_TFDV&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Flujos totales de recursos para el desarrollo, por países receptores y donantes (millones de dólares corrientes de los Estados Unidos) DC_TRF_TFDV</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-0b-01.pdf\">Metadatos 10-b-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "texto_oceca"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nTotal resource flows to developing countries quantify the overall \nexpenditures that donors incur in these countries. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.b.1&seriesCode=DC_TRF_TFDV&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Total resource flows for development, by recipient and donor countries (millions of current United States dollars) DC_TRF_TFDV</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-0b-01.pdf\">Metadata 10-b-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "texto_oceca"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nGarapen-bidean dauden herrialdeetarako baliabideen fluxuek emaileek herrialde horietara egiten dituzten \ngastu orokorrak zenbatesten dituzte. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.b.1&seriesCode=DC_TRF_TFDV&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Garapenerako baliabideen guztizko fluxuak, herrialde hartzaile eta emaileen arabera (Estatu Batuetako uneko dolar milioiak) DC_TRF_TFDV</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-0b-01.pdf\">Metadatuak 10-b-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.b: Encourage official development assistance and financial flows, including foreign direct investment, to States where the need is greatest, in particular least developed countries, African countries, small island developing States and landlocked developing countries, in accordance with their national plans and programmes</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.b.1: Total resource flows for development, by recipient and donor countries and type of flow (e.g. official development assistance, foreign direct investment and other flows)</p>", "META_LAST_UPDATE__GLOBAL"=>"2016-07-19", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Total resource flows for development, by recipient and donor countries and type of flow comprises of Official Development Assistance (ODA), other official flows (OOF) and private flows.</p>\n<p><strong>Concepts:</strong></p>\n<p>Official and private flows, both concessional and non-concessional to developing countries. For official flows the major distinction is between official development assistance (ODA) and other official flows</p>\n<p>OOF, while private flows are broken down into flows at market terms and charitable grants. Flows include contributions to multilateral development agencies, which are themselves official bodies.</p>\n<p>See http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The OECD Development Assistance Committee (DAC) has been collecting data on official and private resource flows from 1960 at an aggregate level.</p>\n<p>The data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm).</p>\n<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.</p>", "COLL_METHOD__GLOBAL"=>"<p>A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are published on an annual basis in December for flows in the previous year. Detailed 2015 flows will be published in December 2016.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>December 2016</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.</p>", "COMPILING_ORG__GLOBAL"=>"<p>OECD</p>", "RATIONALE__GLOBAL"=>"<p>Total resource flows to developing countries quantify the overall expenditures that donors provide to developing countries.</p>", "DATA_COMP__GLOBAL"=>"<p>The sum of official and private flows from all donors to developing countries.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>None - no estimates are made for missing values</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional figures are based on the sum of total resource flows to developing countries.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>On a donor basis for all DAC countries and many non-DAC providers (bilateral and multilateral) that report to the DAC.</p>\n<p>On a recipient basis for all developing countries eligible for ODA.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator can be disaggregated by type of flow (ODA, OOF, private), by donor, recipient country, type of finance, type of aid etc.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Development Assistance Committee (/DAC) statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>www.oecd.org/dac/stats</p>\n<p><strong>References:</strong></p>\n<p>See all links here: http://www.oecd.org/dac/stats/methodology.htm</p>", "indicator_sort_order"=>"10-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"10.c.1", "slug"=>"10-c-1", "name"=>"Costo de las remesas en proporción a las sumas remitidas", "url"=>"/site/es/10-c-1/", "sort"=>"10cc01", "goal_number"=>"10", "target_number"=>"10.c", "global"=>{"name"=>"Costo de las remesas en proporción a las sumas remitidas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Costo de las remesas en proporción a las sumas remitidas", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Costo de las remesas en proporción a las sumas remitidas", "indicator_number"=>"10.c.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "texto_oceca"=>"", "observaciones"=>"", "justificacion_global"=>"\nEl Banco Mundial ha recopilado datos para este indicador a través de la base \nde datos Precios de Remesas a Nivel Mundial (RPW) desde 2008 con el fin de \nmonitorear el objetivo del G8/G20 de reducir los precios de las remesas. También \nconocido como el \"Objetivo 5x5\", este objetivo fue adoptado por el \nG8 en 2009 y se refiere a la reducción del costo total promedio mundial de \nlas remesas de los migrantes en 5 puntos porcentuales en 5 años. \n\nPara lograr \neste objetivo, los gobiernos de los países emisores y receptores deberían \nconsiderar la implementación de reformas basadas en los Principios Generales \npara los Servicios de Remesas Internacionales del Banco Mundial/Comité de \nSistemas de Pago y Liquidación (enero de 2007). Este marco, acordado \ninternacionalmente, ha demostrado ser eficaz \npara ayudar a reducir el costo de las remesas y orientar las acciones \npara mejorar la eficiencia de las remesas internacionales. \n\nLa base de datos RPW del Banco Mundial es la única base de datos global que monitorea \nlos precios de las remesas en todas las regiones del mundo. El RPW fue \nlanzado por el Banco Mundial en septiembre de 2008 y es una herramienta \nclave para monitorear la evolución de los costos para los remitentes y \nlos beneficiarios del envío y recepción de dinero en los principales \ncorredores nacionales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.c.1&seriesCode=SI_RMT_COST&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Costos promedio de envío de $200 a un país receptor como proporción del monto remitido (%) SI_RMT_COST</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-0c-01.pdf\">Metadatos 10-c-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-03-31", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "texto_oceca"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nData for this indicator have been collected by the World Bank \nthrough the Remittance Prices Worldwide (RPW) database since 2008 \nfor the purpose of monitoring the G8/G20 target on reducing remittance \nprices. Also known as the “5x5 Objective”, this goal was adopted by \nthe G8 in 2009, and it refers to the reduction of the global average \ntotal cost of migrant remittances by 5 percentage points in 5 years. \n\nTo achieve this objective, the governments in both sending and receiving \ncountries should consider implementing reforms based upon the General \nPrinciples for International Remittances Services by the World Bank/Committee \non Payment and Settlement Systems (January 2007). This internationally agreed \nframework has proven effective in helping reduce the cost of remittances and \nguiding actions to enhance the efficiency of international remittances. \n\nThe World Bank’s RPW database is the only global database that monitors \nremittance prices across all regions of the world. RPW was launched by the \nWorld Bank in September 2008, and is a key tool in monitoring the evolution \nof costs to the remitters and the beneficiaries from sending and receiving \nmoney in major country corridors. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.c.1&seriesCode=SI_RMT_COST&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Average remittance costs of sending $200 to a receiving country as a proportion of the amount remitted (%) SI_RMT_COST</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-0c-01.pdf\">Metadata 10-c-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "texto_oceca"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nMunduko Bankuak adierazle honetarako datuak bildu ditu, Mundu Mailako Bidalketen Prezioak izeneko \ndatu-basearen bidez (RPW), 2008tik, diru-bidalketen prezioak murrizteko G8/G20 helburua ikuskatze \naldera. “5x5 helburua” izenez ere ezagutzen den hau G8ak hartu zuen 2009an, eta migratzaileen \ndiru-bidalketen munduko batezbesteko kostu osoa 5 urtetan ehuneko 5 puntu jaistea du helburu. \n\nHelburu hori lortzeko, herrialde igorle eta hartzaileetako gobernuek kontuan hartu beharko lukete \nMunduko Bankuko edo Ordainketa eta Likidazio Sistemen Batzordeko nazioarteko bidalketa-zerbitzuetarako \nprintzipio orokorretan oinarritutako erreformak ezartzeko aukera (2007ko urtarrila). Nazioartean \nadostutako esparru hau eraginkorra da bidalketen kostua murrizten lagundu eta nazioarteko bidalketen \neraginkortasuna hobetuko duten ekintzak orientatzeko. \n\nMunduko Bankuaren RPW datu-basea da munduko eskualde guztietan bidalketen prezioak ikuskatzen dituen \ndatu-base orokor bakarra. Munduko Bankuak 2008ko irailean abiarazi zuen RPW, eta tresna gakoa da \nartekari nazional nagusietan dirua bidali eta jasotzeko eragiketen onuradun eta bidaltzaile direnentzat \nkostuen bilakaera ikuskatzeko. \n\n\nIturria: Nazio Batuen Estatistika Sekzioa \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=10.c.1&seriesCode=SI_RMT_COST&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">200 $-ko bidalketaren batez besteko kostuak herrialde hartzaile batera, igorritako zenbatekoaren proportzio gisa (%) SI_RMT_COST</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-10-0c-01.pdf\">Metadatuak 10-c-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.c: By 2030, reduce to less than 3 per cent the transaction costs of migrant remittances and eliminate remittance corridors with costs higher than 5 per cent</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.c.1: Remittance costs as a proportion of the amount remitted</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SI_RMT_COST - Average remittance costs of sending $200 to a receiving country as a proportion of the amount remitted [10.c.1]</p>\n<p>SI_RMT_COST_BC - Average remittance costs of sending $200 in a corridor as a proportion of the amount remitted [10.c.1]</p>\n<p>SI_RMT_COST_SC - SmaRT average remittance costs of sending $200 in a corridor as a proportion of the amount remitted [10.c.1]</p>\n<p>SI_RMT_COST_SND - Average remittance costs of sending $200 for a sending country as a proportion of the amount remitted [10.c.1] </p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank</p>", "STAT_CONC_DEF__GLOBAL"=>"<h2>Definition:</h2>\n<p>The target includes two components. The first component is that transaction costs for migrant remittances should be 3% or less by 2030. This transaction cost should be intended as <strong>&#x201C;</strong><strong>Global average total cost of sending $200 (or equivalent in local sending currency) and expressed as % of amount sent&#x201D;.</strong> This indicator is readily available and published on a quarterly basis by the World Bank in the Remittance Prices Worldwide (RPW) database, which covers 365 country corridors, from 48 sending to 105 receiving countries. The second component is to eliminate corridor where cost is 5% or higher. This should be intended in the sense that it should be possible for remittance senders to send money to the beneficiary for an average cost of 5% or less of the amount sent. For this purpose, it should suffice that in each corridor there are at least 3 services, meeting a defined set of service requirements (including service quality, reach, etc.), for which the average is 5% or less.</p>\n<p><strong>Concepts:</strong></p>\n<p><strong>International remittance transfer:</strong> A cross-border person-to-person payment of relatively low value. The transfers are typically recurrent payments by migrant workers (who send money to their families in their home country every month). In the report, the term &#x201C;remittance transfer&#x201D; is used for simplicity (i.e. it is assumed the transfer is international).</p>\n<p><strong>Remittance service:</strong> A service that enables end users to send and/or receive remittance transfers.</p>\n<p><strong>Remittance service provider (RSP):</strong> An entity, operating as a business, that provides a remittance service for a price to end users, either directly or through agents. These include both banks and money transfer operators, as defined below.</p>\n<p><strong>Money transfer operator (MTO):</strong> A non-deposit taking payment service provider where the service involves payment per transfer (or possibly payment for a set or series of transfers) by the sender to the payment service provider (for example, by cash or bank transfer) &#x2013; i.e. as opposed to a situation where the payment service provider debits an account held by the sender at the payment service provider. MTOs may include both traditional players focusing on delivering funds in cash and innovative players which may adopt a variety of different business models for the delivery of the transactions.</p>\n<p><strong>Price: </strong>The total cost to the end users of sending a remittance transfer (including the fees charged to the sender and the margin by which the exchange rate charged to the end users is above the current interbank exchange rate).</p>\n<p><strong>Transparent service:</strong> A remittance service for which the sending cost can be split into its two components: transfer fee and foreign exchange margin. If a provider does not disclose the foreign exchange rate applicable to the transaction, then the service is considered not transparent.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%); cost expressed as % of amount sent</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data sources are the remittance service providers (RSPs) themselves.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are collected quarterly through a mystery shopping exercise, which takes one week. Every year, in each corridor, a market analysis is conducted to compile a sample of RSPs covering at least 80% of the market.</p>", "FREQ_COLL__GLOBAL"=>"<p>Quarterly</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>March, June, September, December</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are collected by mystery shopping from remittance service providers.</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Bank</p>", "INST_MANDATE__GLOBAL"=>"<p>Mandate by the G20 (2011 Cannes Summit Leaders&#x2019; Declaration) to the World Bank to collect data via RPW to monitor the progress to the G20 5x5 remittances target. </p>", "RATIONALE__GLOBAL"=>"<p>Data for this indicator have been collected by the World Bank through the Remittance Prices Worldwide (RPW) database since 2008 for the purpose of monitoring the G8/G20 target on reducing remittance prices. Also known as the &#x201C;5x5 Objective&#x201D;, this goal was adopted by the G8 in 2009, and it refers to the reduction of the global average total cost of migrant remittances by 5 percentage points in 5 years. To achieve this objective, the governments in both sending and receiving countries should consider implementing reforms based upon the General Principles for International Remittances Services by the World Bank/Committee on Payment and Settlement Systems (January 2007). This internationally agreed framework has proven effective in helping reduce the cost of remittances and guiding actions to enhance the efficiency of international remittances. The World Bank&#x2019;s RPW database is the only global database that monitors remittance prices across all regions of the world. RPW was launched by the World Bank in September 2008, and is a key tool in monitoring the evolution of costs to the remitters and the beneficiaries from sending and receiving money in major country corridors.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Not applicable</p>", "DATA_COMP__GLOBAL"=>"<p>Data is collected through a mystery shopping exercise of remittance service providers (RSPs). A sample of RSPs including at least 80% of the market share in each corridor are included in the mystery shopping exercise. The average cost is calculated as the simple average of total costs (including both fee and exchange rate margin) quoted by each RSP operating in a corridor.</p>\n<p>In 2016, the Smart Remitter Target (SmarRT) was introduced to monitor remittance transactions at a more granular level. It aims to reflect the cost that a savvy consumer with access to sufficiently complete information would pay to transfer remittances in each corridor. SmaRT is calculated as the simple average of the three cheapest services for sending the equivalent of $200 in each corridor and is expressed in terms of the percentage of the total amount sent. In addition to transparency, services must meet additional criteria to be included in SmaRT, including transaction speed (5 days or less) and accessibility determined by geographic proximity of branches for services that require physical presence or access to any technology or device necessary to use the service, such as a bank account, mobile phone or the internet. The SmaRT methodology was developed in collaboration with the Global Remittances Working Group, a working group which was formed by the World Bank at the request of the G8/G20 to monitor the progress towards the 5x5 Objective.</p>\n<p>For additional information on the methodology of SmaRT, please see: <a href=\"https://remittanceprices.worldbank.org/sites/default/files/smart_methodology.pdf\">https://remittanceprices.worldbank.org/sites/default/files/smart_methodology.pdf</a></p>\n<p>Target 10.c.1 includes two components, which require two separate calculations:</p>\n<ol>\n  <li>Global average of remittance costs to be reduced to less than 3 percent: this is calculated as the simple average of the total cost for all transparent services included in the RPW database.</li>\n  <li>Enabling remittance senders in all corridors to send money to their receivers at a cost of 5 percent or less: this is calculated as the average cost of the three cheapest (and transparent) services in each corridor which meet a defined set of minimum requirements, as described in the World Bank SmaRT methodology. The target is that the SmaRT average for all corridors should be 5 percent or lower.</li>\n</ol>\n<p>RPW database includes several indicators. Four of these indicators are used to monitor (2) above:</p>\n<ol>\n  <li>Average cost of sending $200 (%) for a sending country: simple average of the cost of all transparent services from a sending country (regardless of the destination country). Sample size is 48.</li>\n  <li>Average cost of sending $200 (%) to a receiving country: simple average of the cost of all transparent services to a receiving country (regardless of the origin country). Sample size is 105.</li>\n  <li>Average cost of sending $200 (%) in a corridor: simple average of the cost of all transparent services from a sending country to a specific destination country. Sample size is 367.</li>\n  <li>SmaRT average cost of sending $200 (%) in a corridor: SmaRT average cost of all SmaRT qualifying services from a sending country to a specific destination country. Sample size changes by the quarter, depending on the availability of the SmaRT qualifying services in corridors satisfying the specific prerequisites identified in the SmaRT methodology.</li>\n</ol>", "DATA_VALIDATION__GLOBAL"=>"<p>Not applicable</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>A sample of corridors is collected for each sending and receiving country. It is assumed that the cost of other corridors from/to each country fall in similar cost range.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Regional aggregates are computed by calculating simple averages of the cost of individual transparent services remitting to the recipient countries in the region for which there is data. Countries with no data are not included, however, as a representative sample is built, it is assumed that missing data fall in the same cost range as collected data.</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates are computed by calculating simple averages of individual transparent services remitting to the recipient countries in the region for which there is data.</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Minimum requirements for national and regional databases are provided on the Remittance Prices Worldwide (RPW) website at: <a href=\"https://remittanceprices.worldbank.org/national-and-regional-databases-certified-by-the-world-bank\">https://remittanceprices.worldbank.org/national-and-regional-databases-certified-by-the-world-bank</a>. For consistent methodology, the following minimum requirements were established:<ol>\n      <li>Double price points data gathering</li>\n      <li>Collection of fees for the sender</li>\n      <li>Collection of the exchange rate applied</li>\n      <li>Provision of total amount of the identified costs</li>\n      <li>Speed of the transaction</li>\n      <li>Type of service provided</li>\n      <li>Minimum of 60% of market coverage per corridor</li>\n      <li>Independence of the researchers</li>\n      <li>Validation through mystery shopping exercises</li>\n      <li>No advertisement policy</li>\n      <li>No subscription policy and clear funding process</li>\n      <li>Linkage with other WB-approved databases</li>\n    </ol>\n  </li>\n</ul>\n<p>More information is available in the policy paper on <em>Remittance Price Comparison Databases: Minimum Requirements and Overall Policy Strategy &#x2013; Guide and Special-Purpose Note</em>, available at: <a href=\"https://remittanceprices.worldbank.org/sites/default/files/StandardsNationalDatabases.pdf\">https://remittanceprices.worldbank.org/sites/default/files/StandardsNationalDatabases.pdf</a></p>\n<ul>\n  <li>Web site for RPW database and related resources: <a href=\"http://remittanceprices.worldbank.org\">http://remittanceprices.worldbank.org</a></li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data are collected by researchers through mystery shopping, and subsequently compiled, cleaned, and reviewed. The World Bank uses vendor services for data collection and compilation. The data is then reviewed in detail by the World Bank Remittance Prices Worldwide (RPW) team, who also undertakes the analysis.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not Applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The data are available for 367 corridors, which include 48 sending countries and 105 receiving countries. The data are collected quarterly.</p>\n<p><strong>Time series:</strong></p>\n<p>The data are available since 2008 (all data available online; data available online in Excel format starting from Q1 2011).</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Remittance Prices Worldwide (RPW) tracks the cost of remittances by the type of remittance service providers: commercial banks, money transfer operators, post offices, mobile money providers (more provider types may be added as market evolves). In addition, disaggregation is also possible by the instrument used to fund the transaction: including but not limited to cash, bank account, debit/credit card, mobile money, etc. and by the instrument used to disburse the funds: including but not limited to cash, bank account, mobile wallet, etc.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There are no country-produced alternatives for this data, except for countries that have established a remittance price database in line with World Bank minimum requirements. It has been observed that data are broadly in line and no significant discrepancies exist.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>[1]: <a href=\"https://remittanceprices.worldbank.org/\">https://remittanceprices.worldbank.org/</a></p>\n<p><strong>References:</strong></p>\n<p>Please see various resources on <a href=\"https://remittanceprices.worldbank.org/resources\">https://remittanceprices.worldbank.org/resources</a> <strong> </strong></p>", "indicator_sort_order"=>"10-0c-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.1.1", "slug"=>"11-1-1", "name"=>"Proporción de la población urbana que vive en barrios marginales, asentamientos informales o viviendas inadecuadas", "url"=>"/site/es/11-1-1/", "sort"=>"110101", "goal_number"=>"11", "target_number"=>"11.1", "global"=>{"name"=>"Proporción de la población urbana que vive en barrios marginales, asentamientos informales o viviendas inadecuadas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de personas que viven en hogares con determinadas deficiencias en la vivienda", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población urbana que vive en barrios marginales, asentamientos informales o viviendas inadecuadas", "indicator_number"=>"11.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Bienestar, Juventud y Reto Demográfico", "periodicity"=>"Bienal", "url"=>"https://www.euskadi.eus/encuesta-de-pobreza-y-desigualdades-sociales-epds/web01-s2enple/es/", "url_text"=>"Encuesta de pobreza y desigualdades sociales (EPDS)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de personas que viven en hogares con determinadas deficiencias en la vivienda", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.1- De aquí a 2030, asegurar el acceso de todas las personas a viviendas y servicios básicos adecuados, seguros y asequibles y mejorar los barrios marginales", "definicion"=>"Proporción de personas que en sus viviendas tienen determinadas deficiencias:\n\n - problemas de humedad (techo con goteras; paredes, suelos, techos o cimientos con humedad, o podredumbre en marcos de ventanas, puertas o suelo)\n - vivienda sin retrete o bañera/ducha\n - superficie inferior al número de miembros por 20 metros cuadrados\n - problemas de exposición a ruidos y contaminación \n - vivienda oscura, sin luz suficiente, o escasez de luz natural", "formula"=>"\n$$PPDV^{t} = \\frac{PDV^{t}}{P^{t}} \\cdot 100$$\n\ndonde: \n\n$PDV^{t} =$ población que experimenta una determinada deficiencia en la condiciones de su vivienda en el año $t$ \n\n$P^{t} =$ población total en el año $t$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Bienal", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa naturaleza del sector de la vivienda, con sus instituciones, leyes y reglamentos, \nafecta a todos los aspectos de la economía de un país y tiene interrelación con \nprácticamente todos los sectores del desarrollo social. \n\nLas personas que viven en viviendas adecuadas tienen mejor salud, mayores posibilidades \nde mejorar su capital humano y aprovechar las oportunidades disponibles en los contextos \nurbanos. Al mismo tiempo, un sector de la vivienda que funciona bien actúa como un \n\"multiplicador del desarrollo\" que beneficia a las industrias complementarias, \ncontribuyendo al desarrollo económico, la generación de empleo, la prestación de \nservicios y la reducción general de la pobreza.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.1.1&seriesCode=EN_LND_SLUM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=URBAN\"> Proporción de la población urbana que vive en barrios marginales (%) EN_LND_SLUM</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas, pero aporta información complementaria.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-01-01.pdf\">Metadatos 11-1-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de personas que viven en hogares con determinadas deficiencias en la vivienda", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.1- De aquí a 2030, asegurar el acceso de todas las personas a viviendas y servicios básicos adecuados, seguros y asequibles y mejorar los barrios marginales", "definicion"=>"Proportion of people who have certain deficiencies in their homes:\n\n - Moisture problems (leaking roof; damp walls, floors, ceilings, or foundations; or rot in window frames, doors, or floors)\n - housing without a toilet or bathtub/shower\n - surface area less than the number of members by 20 square meters\n - problems of exposure to noise and pollution \n - dark home, without enough light, or lack of natural light", "formula"=>"\n$$PPDV^{t} = \\frac{PDV^{t}}{P^{t}} \\cdot 100$$\n\nwhere: \n\n$PDV^{t} =$ population that experiences a certain deficiency in their housing conditions in year $t$ \n\n$P^{t} =$ total popùlation in year $t$\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Bienal", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe nature of the housing sector with its institutions, laws and regulations, \nis one that touches every single aspect of the economy of a country and has \ninterface with practically every social development sector. \n\nPeople living in adequate homes have better health, higher chances to improve \ntheir human capital and seize the opportunities available in urban contexts. \nAt the same time, a housing sector that performs well acts as a ‘development \nmultiplier’ benefiting complementary industries, contributing to economic \ndevelopment, employment generation, service provision and overall poverty reduction. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.1.1&seriesCode=EN_LND_SLUM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=URBAN\"> Proportion of urban population living in slums (%) EN_LND_SLUM</a> UNSTATS", "comparabilidad"=>"The available indicator does not comply with the metadata of the United Nations indicator, but provides complementary information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-01-01.pdf\">Metadata 11-1-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de personas que viven en hogares con determinadas deficiencias en la vivienda", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.1- De aquí a 2030, asegurar el acceso de todas las personas a viviendas y servicios básicos adecuados, seguros y asequibles y mejorar los barrios marginales", "definicion"=>"Proporción de personas que en sus viviendas tienen determinadas deficiencias:\n\n - problemas de humedad (techo con goteras; paredes, suelos, techos o cimientos con humedad, o podredumbre en marcos de ventanas, puertas o suelo)\n - vivienda sin retrete o bañera/ducha\n - superficie inferior al número de miembros por 20 metros cuadrados\n - problemas de exposición a ruidos y contaminación \n - vivienda oscura, sin luz suficiente, o escasez de luz natural", "formula"=>"\n$$PPDV^{t} = \\frac{PDV^{t}}{P^{t}} \\cdot 100$$\n\nnon: \n\n$PDV^{t} =$ etxebizitzaren baldintzetan gabezia jakin bat duen biztanleria $t$ urtean \n\n$P^{t} =$ biztanleria $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Bienal", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa naturaleza del sector de la vivienda, con sus instituciones, leyes y reglamentos, \nafecta a todos los aspectos de la economía de un país y tiene interrelación con \nprácticamente todos los sectores del desarrollo social. \n\nLas personas que viven en viviendas adecuadas tienen mejor salud, mayores posibilidades \nde mejorar su capital humano y aprovechar las oportunidades disponibles en los contextos \nurbanos. Al mismo tiempo, un sector de la vivienda que funciona bien actúa como un \n\"multiplicador del desarrollo\" que beneficia a las industrias complementarias, \ncontribuyendo al desarrollo económico, la generación de empleo, la prestación de \nservicios y la reducción general de la pobreza.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.1.1&seriesCode=EN_LND_SLUM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=URBAN\"> Auzo marjinaletan bizi den hiri-biztanleriaren proportzioa (%) EN_LND_SLUM</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina informazio osagarria ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-01-01.pdf\">Metadatuak 11-1-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 11.1: By 2030, ensure access for all to adequate, safe and affordable housing and basic services and upgrade slums</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 11.1.1: Proportion of urban population living in slums, informal settlements or inadequate housing</p>", "META_LAST_UPDATE__GLOBAL"=>"2021-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Direct relation </p>\n<p>1.1.1 Poverty rate; 1.1.2 Poverty rate, national; 6.1.1 Access to Improved Water; 6.2.1 Access to Improved Sanitation; 7.1.1 Access to Electricity; 8.3.1 Informal Employment; 8.5.2 Unemployment Rate </p>\n<p>8.6.1 Youth Unemployment; 10.2.1 Population below Median Income; 10.1.1 Grow rates of the poorest 40%; 11.2.1 Public Transit Stop Coverage; 11.5.1 Population Affected by Hazardous Events; 11.6.1 Solid Waste Collection; 11.7.1 Accessibility to Open Public Area; 11.7.2 Public Space Safety for Women; 16.1.1 Homicide rate; 16.1.3 Population subjected to Violence. </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p>The nature of the housing sector with its institutions, laws and regulations, is one that touches every single aspect of the economy of a country and has interface with practically every social development sector. People living in adequate homes have better health, higher chances to improve their human capital and seize the opportunities available in urban contexts. At the same time, a housing sector that performs well acts as a &#x2018;development multiplier&#x2019; benefiting complementary industries, contributing to economic development, employment generation, service provision and overall poverty reduction. </p>\n<p>Broadly, for every job in the house-building sector, an additional 1.5 to 2 jobs are generally created in the construction materials and other input industries. The contributions of housing to urban prosperity are also evident. The UN-Habitat City Prosperity Initiative reveals indicates that inadequate housing has negative effects on several other dimensions of urban prosperity. Urban contexts with housing conditions below average experience poorer equity and inclusion, reduced urban safety and livelihood opportunities, and have neglected connectivity and provision of public space.</p>\n<p>Inadequate housing thus remains a global urban sustainability challenge, but also development opportunity. At the same time, the thematic area of &#x2018;adequate housing&#x2019; and especially the term &#x2018;slums&#x2019; - are often highly politicized. More nuanced definitions of these terms would enable and support a more robust and measured debate, greater engagement by all key stakeholders and the development of specific recommendations for application within each context and place. </p>\n<p>There are a number of interrelated terms that must be grappled with when considering an indicator for the SDG Target 11.1. They include inadequate housing and housing affordability, informal settlements and slums. </p>\n<p><em>Housing affordability</em></p>\n<p>One of the most daunting challenges of urbanization globally has been the provision of adequate housing that people can afford. Findings from the UN Global Sample of Cities<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup> show that people across all types of urban centres are not able to afford home ownership or even the cost of rental housing. In low-income countries for example, households need to save the equivalent of nearly eight times their annual household income in order to be able to afford the price of a standard house in their town or city. If they rent, households have to commit more than 25 per cent of their monthly income to rent payments.<sup><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></sup> </p>\n<p>The affordability issue is affecting the developing and developed worlds alike. In Latin America, high house price-to-income ratio and inaccessible housing finance compel households to resort to informal solutions without the benefits of planning and safety regulations. In many parts of Sub-Saharan Africa, less than 10 per cent of households are able to afford a mortgage for even the cheapest newly built house. In fact, African households face 55 per cent higher housing costs relative to their per capita GDP than in other regions.<sup><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></sup> In many European countries, families, especially the youth, are severely cost burdened and have much less to spend on other necessities such as food, health, transport and clothing. In extreme circumstances, households are forced to leave their accommodation because of the inability to pay. The current migration crisis has worsened housing conditions in the region, a trend that seems set to continue in the next few years.</p>\n<p><em>Inadequate housing, informal settlements and slums</em></p>\n<p>Today, an estimated 1.6 billion people live in inadequate housing globally, of which 1 billion live in slums and informal settlements<sup><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></sup>. This means that about one in four people in cities live in conditions that harm their health, safety, prosperity and opportunities. Lack of access to basic services is a common constraint in informal settlements and slums: worldwide 2.4 billion people live without improved sanitation and 2 billion are affected by water stress. In spite of a decrease from 39 to 30 per cent of urban population living in slums between 2000 and 2014, absolute numbers continue to grow: currently, one quarter of the world&#x2019;s urban population is estimated to live in slums, 881 million urban residents as opposed to 792 million in 2000. Young women- and children-headed households are often the most vulnerable to inadequate housing conditions. Homelessness is also a growing challenge and it is estimated that more than 100 million people worldwide are homeless.<sup><sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup></sup></p>\n<p>Slums represent one of the most extreme forms of deprivation and exclusion and remain a critical factor for the persistence of poverty and exclusion in the world &#x2013; indeed a challenge for sustainable and inclusive urbanization. Research shows that other forms of urban poverty in the form of informal settlements increasingly become a worldwide phenomenon found also in the developed world.</p>\n<p>At the same time, not all people who live in inadequate housing live in slums but are nonetheless living in very substandard conditions in the urban contexts in which they are situated. The nature of these unsatisfactory living conditions must be captured and better represented in the global, country and city-level data to ensure a more robust picture of inadequate housing is documented. In light of this, the following definitions are proposed.</p>\n<p><strong>Definition and concept: </strong></p>\n<p>As per the 2030 Agenda, it is necessary to identify and quantify the proportion of the population that live in<strong> slums, informal settlements</strong> and those living in <strong>inadequate housing</strong> in order to inform the development of the appropriate policies and programmes for ensuring access for all to adequate housing and the upgrading of slums. </p>\n<p><strong>a. Slums </strong>&#x2013; An expert group meeting was convened in 2002 by UN-Habitat, the United Nations Statistics Division and the Cities Alliance to agree on an operational definition for slums to be used for measuring the indicator of MDG 7 Target 7.D. The agreed definition classified a <em>&#x2018;slum household&#x2019;</em> as one in which the inhabitants suffer <u>one or more</u> of the following <em>&#x2018;household deprivations&#x2019;</em>:</p>\n<ol>\n  <li>Lack of access to improved water source,</li>\n  <li>Lack of access to improved sanitation facilities, </li>\n  <li>Lack of sufficient living area, </li>\n  <li>Lack of housing durability and,</li>\n  <li>Lack of security of tenure. </li>\n</ol>\n<p>By extension, the term <em>&#x2018;slum dweller&#x2019;</em> refers to a person living in a household that lacks any of the above attributes.<sup><sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></sup></p>\n<p>These five components &#x2013;all derived from the adequate housing&#x2019;s definition have been used ever since for reporting and tracking of the MDGs, as the primary or secondary data measured to determine the number of slum dwellers living in developing countries. They were also the basis to establish the successful achievement of MDG Target 7.D. For each component, the experts agreed with the following sub-definitions:<sup><sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup></sup></p>\n<p>1) <u>Access to improved water</u> &#x2013; A household is considered to have access to improved drinking water if the household members use a facility that is protected from outside contamination, in particular from faecal matters&#x2019; contamination. Improved drinking water sources include: piped water into dwelling, plot or yard; public tap/stand pipe serving no more than 5 households; protected spring; rainwater collection; bottled water (if secondary source is also improved); bore hole/tube well; and, protected dug well.</p>\n<p>2) <u>Access to improved sanitation</u> &#x2013; A household is considered to have access to improved sanitation if household members have access to a facility with an excreta disposal system that hygienically separates human waste from human contact. Improved facilities include: flush/pour-flush toilets or latrines connected to a sewer, septic tank or pit; ventilated improved pit latrine; pit latrine with a slab or platform, which covers the pit entirely; and, composting toilets/latrines.</p>\n<p>3) <u>Sufficient living area</u> /overcrowding&#x2013; A dwelling unit provides sufficient living area for the household members if not more than three people share the same habitable room.<sup><sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup></sup> Additional indicators of overcrowding have been proposed: area-level indicators such as average in-house living area per person or the number of households per area. Additionally, housing-unit level indicators such as the number of persons per bed or the number of children under five per room may also be viable. However, the number of persons per room has been shown to correlate with adverse health risks and is more commonly collected through household survey.<sup><sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup></sup>. UN-Habitat believes that the definition as it stands does not reflect the practical experience of overcrowding and as noted below, is proposing an alternative. </p>\n<p><img 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1- Example of Overcrowding</p>\n<p>4) <u>Structural quality/durability of dwellings</u> &#x2013; A house is considered as &#x2018;durable&#x2019; if it is built on a non-hazardous location and has a permanent and adequate structure able to protect its inhabitants from the extremes of climatic conditions such as rain, heat, cold, and humidity. The following criteria are used to determine the structural quality/durability of dwellings: permanency of structure (permanent building material for the walls, roof and floor; compliance with building codes; the dwelling is not in a dilapidated state; the dwelling is not in need of major repair); and location of house (hazardous location; the dwelling is not located on or near toxic waste; the dwelling is not located in a flood plain; the dwelling is not located on a steep slope; the dwelling is not located in a dangerous right of way: rail, highway, airport, power lines).</p>\n<p>5) <u>Security of tenure</u> &#x2013; Secure tenure is the right of all individuals and groups to effective protection by the State against forced evictions. Security of tenure is understood as a set of relationships with respect to housing and land, established through statutory or customary law or informal or hybrid arrangements, that enables one to live in one&#x2019;s home with security, peace and dignity (A/HRC/25/54). Regardless of the type of tenure, all persons with security of tenure have a legal status against arbitrary unlawful eviction, harassment and other threats. People have secure tenure when: there is evidence of documentation that can be used as proof of secure tenure status; and, there is either de facto or perceived protection from forced evictions. Important progress has been made to integrate the measurement of this component into the computation of the people living in slums.</p>\n<p><em>Informal Settlements </em></p>\n<p>b. Informal Settlements<strong> </strong>&#x2013; Informal settlements are usually seen as synonymous of slums, with a particular focus on the formal status of land, structure and services. They are defined by three main criteria, according to Habitat III Issue Paper #22<sup><sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup></sup>, which are already covered in the definition of slums. These are: </p>\n<ol>\n  <li><u>Inhabitants have no security of tenure</u> vis-&#xE0;-vis the land or dwellings they inhabit, with modalities ranging from squatting to informal rental housing, </li>\n  <li>The <u>neighbourhoods usually lack, or are cut off from, formal basic services</u> and city infrastructure, and </li>\n  <li>The <u>housing may not comply with current planning and building regulations</u>, is often situated in geographically and environmentally hazardous areas, and may lack a municipal permit. </li>\n</ol>\n<p>Informal settlements can be occupied by all income levels of urban residents, affluent and poor. </p>\n<p><em>Inadequate Housing</em></p>\n<p>c. Inadequate Housing &#x2013; Article 25 of the Universal Declaration of Human Rights includes housing as one of the components of the right to adequate standards of living for all.<sup><sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup></sup> The United Nations Committee on Economic, Social and Cultural Rights&#x2019; general comments No.4 (1991) on the right to adequate housing and No.7 (1997) on forced evictions have underlined that the right to adequate housing should be seen as the right to live somewhere in security, peace and dignity. For housing to be adequate, it must provide more than four walls and a roof, and at a minimum, meet the following criteria: </p>\n<ol>\n  <li><u>Legal security of tenure</u>, which guarantees legal protection against forced evictions, harassment and other threats; </li>\n  <li><u>Availability of services, materials, facilities and infrastructure</u>, including safe drinking water, adequate sanitation, energy for cooking, heating, lighting, food storage or refuse disposal; </li>\n  <li><u>Affordability</u>, as housing is not adequate if its cost threatens or compromises the occupants&#x2019; enjoyment of other human rights; </li>\n  <li><u>Habitability</u>, as housing is not adequate if it does not guarantee physical safety or provide adequate space, as well as protection against the cold, damp, heat, rain, wind, other threats to health and structural hazards; </li>\n  <li><u>Accessibility</u>, as housing is not adequate if the specific needs of disadvantaged and marginalized groups are not taken into account (such as the poor, people facing discrimination; persons with disabilities, victims of natural disasters); </li>\n  <li><u>Location</u>, as housing is not adequate if it is cut off from employment opportunities, health-care services, schools, childcare centres and other social facilities, or if located in dangerous or polluted sites or in immediate proximity to pollution sources; and</li>\n  <li><u>Cultural adequacy</u>, as housing is not adequate if it does not respect and take into account the expression of cultural identity and ways of life.</li>\n</ol>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"4\">\n        <p><strong>Table 1. Criteria defining slums, informal settlements and inadequate housing</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td></td>\n      <td>\n        <p>Slums </p>\n      </td>\n      <td>\n        <p>Informal Settlements</p>\n      </td>\n      <td>\n        <p>Inadequate Housing</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>access to water</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>access to sanitation</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>sufficient living area, overcrowding</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>structural quality, durability and location</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>security of tenure</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>affordability</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>accessibility</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>cultural adequacy</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n  </tbody>\n</table><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> UN-Habitat (2016). Fundaments of Urbanization. Evidence Base for Policy Making. Nairobi: UN-Habitat <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> Ibid <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> World Bank, 2017. Africa&#x2019;s Cities: Opening Doors to the World. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> UN-Habitat (2016). World Cities Report. UN-Habitat (2005). Financing Shelter. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> UN-HABITAT (2005). Financing Urban Shelter: Global Report on Human Settlements 2005. Nairobi: UN-Habitat <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> UN-Habitat (2003), Slums of the World: The face of urban poverty in the new millennium; &lt;mirror.unhabitat.org/pmss/getElectronicVersion.aspx?nr=1124&amp;alt=1&gt; <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> United Nations (2007), Indicators of Sustainable Development: Guidelines and Methodologies. Third Edition, United Nations, New York; &lt; https://sustainabledevelopment.un.org/index.php?page=view&amp;type=400&amp;nr=107&amp;&gt;; UN-Habitat (2003), Slums of the World: The face of urban poverty in the new millennium. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> The original EGM&#x2019;s advice considered a range of less than three to four people per habitable room. When this indicator got operationalized during the MDG 7 Target 7.D&#x2019;s tracking, overcrowding was fixed at a maximum of three people per habitable room (&#x2018;minimum of four square meters,&#x2019; &lt;http://mdgs.un.org/unsd/mdg/Metadata.aspx&gt;). <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> UN-Habitat (1998), Crowding and Health in Low Income Settlements of Guinea Bissau, SIEP Occasional Series No.1. <a href=\"#footnote-ref-10\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> United Nations (2015), Conference on Housing and Sustainable Urban Development &#x2013; Habitat III, Issue Paper No. 22 on Informal Settlements; UN-Habitat (2015), Slum Almanac 2015-2016. <a href=\"#footnote-ref-11\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> Article 25 (1) &#x201C;Everyone has the right to a standard of living adequate for the health and well-being of himself and of his family, including food, clothing, housing and medical care and necessary social services, and the right to security in the event of unemployment, sickness, disability, widowhood, old age or other lack of livelihood in circumstances beyond his control.&#x201D; <a href=\"#footnote-ref-12\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Proportion (percentage)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data for the slum/informal settlements components of the indicator can be computed from Census and national household surveys, including DHS and MICS. Data for the inadequate housing component can be computed through income and household surveys that capture housing expenditures.</p>\n<p>As per all the agreed Agenda 2030&#x2019;s goals and targets, to measure the achievement of this indicator will require the mobilisation of means required to efficiently monitor them, calling for revitalised partnerships with the participation of all countries, all stakeholders and all communities concerned.</p>\n<p>For primary reporting, national data providers (especially the Statistical agencies) will play an important role generating the primary data through census and surveys. Regional and global estimates will be derived from national figures with appropriate disaggregation. Specialized tools will be developed and agreed upon with local and international stakeholders. Quality assurance on the use of the tools, analysis and reporting will be deployed regionally and globally, to ensure that standards are uniform and that definitions are universally applied.</p>", "COLL_METHOD__GLOBAL"=>"<p>The computation of this indicator is mainly based on analysis of existing data sources including population and housing censuses and household surveys that contain information on all five components of slum: improved water, improved sanitation, durable housing, sufficient living area and secure tenure. Nationally representative household surveys, which typically collect information on water, sanitation and housing conditions, include Urban Inequities Surveys (UIS), Multiple Indicator Cluster Surveys (MICS), Demographic Health Surveys (DHS), World Health Surveys (WHS), Living Standards and Measurement Surveys (LSMS), Core Welfare Indicator Questionnaires (CWIQ), and other relevant surveys. National-level household surveys are generally conducted every 3-5 years in most developing countries, while censuses are generally conducted every 10 years. At the Global level, data will be assembled and compiled for international use and comparison by UN-Habitat and other partners. </p>", "FREQ_COLL__GLOBAL"=>"<p>All major surveys and census data collection process will continue to incorporate the aspects/components necessary for reporting on this indicator. The monitoring of this indicator will be repeated at regular intervals of 3-5 years, allowing for 3 five-year reporting points until the year 2030. </p>\n<p>UN-Habitat has developed simple reporting template to collect data from countries (<a href=\"https://data.unhabitat.org/pages/guidance\">https://data.unhabitat.org/pages/guidance</a>). The template, which is sent to countries on an annual basis is expected to be used until 2030, but slight changes may be effected as data on more aspects becomes available. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>While continuous follow-up is done with countries and compilation of data sources occur on an annual basis, changes in trends within individual countries are likely to happen in spans of about 3-5 years, so a three-year window will be applied for comprehensive review of all data, with updates made based on availability of new data.</p>", "DATA_SOURCE__GLOBAL"=>"<p>This indicator has largely been successfully due to the collaborations between several organizations and institutions including UN- Habitat, UNEP, Cities Alliance, Slum dwellers International, and World Bank. There are several other experts who have also contributed to the development of the concepts, rationale and definitions, and metadata and will also support measurement, reporting and policy dialogue at the country level, based on the indicators.</p>\n<p> </p>\n<p>National Statistical Offices will play an important role in the monitoring and reporting process through census and surveys. Final Compilation &amp; reporting at the global level will be lead and guided by UN-Habitat with support from selected partners. </p>", "COMPILING_ORG__GLOBAL"=>"<p>UN-Habitat</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Human Settlements Programme (UN-Habitat) is the specialized agency for sustainable urbanization and human settlements in the United Nations. The mandate derives from the priorities established in relevant General Assembly resolutions and decisions, including General Assembly resolution 3327 (XXIX), by which the General Assembly established the United Nations Habitat and Human Settlements Foundation, and resolution 32/162 by which the Assembly established the United Nations Center for Human Settlements (Habitat). In 2001, by its Resolution 56/206, the General Assembly transformed the Habitat into the secretariat of the United Nations Human Settlements Programme (UN-Habitat), with a mandate to coordinate human settlements activities within the United Nations System. As such, UN-Habitat has been designated the overall coordinator of SDG 11 and specifically as a custodian agency for 9 of the 14 indicators under SDG 11 including indicator 11.1.1. UN-Habitat also supports the monitoring and reporting of 4 urban specific indicators in other goals.</p>", "RATIONALE__GLOBAL"=>"<p>As seen in Table 1, most of the criteria for defining slums, informal settlements and inadequate housing overlap. The three criteria of informal settlements are essentially captured in the definition of slums, which sustains the combination of both (slums/informal settlements). Both aspects of slums and informal settlements are therefore combined into one component of the indicator, providing some continuity with what was captured under MDG 7. At a later stage, a composite index will be developed that will incorporate all measures (combining slum/informal settlements and inadequate housing) and provide one estimate. </p>\n<p>The second component of the indicator is on inadequate housing. From the seven criteria of adequate housing, the three that are not covered by slums / informal settlements are <em>affordability</em>, <em>accessibility</em> and <em>cultural adequacy</em>. However, affordability is the most relevant and easier to measure. </p>\n<p>In this regard, <em>housing affordability</em> is not only a key housing adequacy criterion, but is a suitable means of measuring inadequate housing in a more encompassing manner, as it remains a global challenge across different countries and income levels, with strong negative impact on urban inequality. </p>\n<p>The underlying principle is that household financial costs associated with housing should not threaten or compromise the attainment and satisfaction of other basic needs such as, food, education, access to health care, transport, etc. Based on the existing method and data of UN-Habitat&#x2019;s Urban Indicators Program (1996-2006), unaffordability is currently measured as the net monthly expenditure on housing cost that exceeds 30% of the total monthly income of the household.</p>\n<p>Table 2 details the proposed definition of Slum/Informal Settlements and Inadequate Housing as well as the respective measurements. </p>\n<p><strong>Table 2 &#x2013; Definition and measurement criteria for slums, informal settlements and inadequate housing</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Slums / Informal Settlements</strong></p>\n      </td>\n      <td>\n        <p><strong>DEFINITION:</strong></p>\n        <p>As adopted in the MDG, slum households are households whose members suffer one or more of the following &#x2018;household deprivations&#x2019;: 1) Lack of access to improved water source, 2) Lack of access to improved sanitation facilities, 3) Lack of sufficient living area, 4) Lack of housing durability and, 5) Lack of security of tenure.</p>\n      </td>\n      <td>\n        <p><strong>MEASUREMENT<sup><sup><a href=\"#footnote-13\" id=\"footnote-ref-13\">[12]</a></sup></sup>:</strong></p>\n        <p><em>Security of Tenure:</em></p>\n        <ul>\n          <li>Proportion of households with formal title deeds to both land and residence.</li>\n          <li>Proportion of households with formal title deeds to either one of land or residence.</li>\n          <li>Proportion of households with agreements or any document as a proof of a tenure arrangement.</li>\n        </ul>\n        <p><em>Access to improved water sources:</em></p>\n        <ul>\n          <li>Proportion of households whose members have access to improved drinking water sources (i.e. piped in water into dwelling, plot or yard; public tap/stand pipe service; protected spring; rain water collection; bottled water if secondary source is also improved; bore hole/tube well; and protected dug well). </li>\n        </ul>\n        <p><em>Access to improved sanitation facilities:</em></p>\n        <ul>\n          <li>Proportion of households whose members have access to improved sanitation facilities (i.e. pour-flush toilets or latrines connected to a sewer, septic tank or pit; ventilated improved pit latrine; pit latrine with a slab or platform that covers the pit entirely; composting toilets/latrines).</li>\n        </ul>\n        <p><em>Structural quality of Housing and location:</em> </p>\n        <ul>\n          <li>Proportion of households residing on or near a hazardous site. The following locations should be considered:<ul>\n              <li>housing in geologically hazardous zones (landslide/earthquake and flood areas);</li>\n              <li>housing on or under garbage mountains;</li>\n              <li>housing around high-industrial pollution areas;</li>\n              <li>housing around other unprotected high-risk zones (e.g. railroads, airports, energy transmission lines).</li>\n            </ul>\n          </li>\n        </ul>\n        <p><em>Structural quality of the housing and permanency of the structure:</em></p>\n        <ul>\n          <li>Proportion of households living in temporary and/or dilapidated structures. The following factors should be considered when placing a housing unit in these categories: <ul>\n              <li>quality of construction (e.g. materials used for wall, floor and roof);</li>\n              <li>compliance with local building codes, standards and bylaws.</li>\n            </ul>\n          </li>\n        </ul>\n        <p><em>Sufficient living area: </em></p>\n        <ul>\n          <li>Proportion of households in which not more than three people share the same habitable room.</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Inadequate housing </strong></p>\n      </td>\n      <td>\n        <p><strong>DEFINITION: </strong></p>\n        <p>Proposed to complement the slums/informal settlements measuring affordability of housing at the global level. A housing is considered inadequate if it is not affordable to the household, i.e. the net monthly expenditure on its cost exceeds 30% of the total monthly income of the household.</p>\n      </td>\n      <td>\n        <p><strong>MEASUREMENT: </strong></p>\n        <p><em>Inadequate housing: </em></p>\n        <ul>\n          <li>Proportion of households with net monthly expenditure on housing exceeding 30% of the total monthly income of the household<sup><sup><a href=\"#footnote-14\" id=\"footnote-ref-14\">[13]</a></sup></sup>.</li>\n        </ul>\n      </td>\n    </tr>\n  </tbody>\n</table><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-13\">12</sup><p> Measurements based on those in the (2003) UN-Habitat Challenge of Slums, p.12. <a href=\"#footnote-ref-13\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-14\">13</sup><p> To note, housing affordability can also be measured using house price-to-income ratio (HPIR) and the house rent-to-income ratio (HRIR). Housing is considered affordable when the house-price-to-annual household income ratio (HPIR) is 3.0 or less and the rent-to-monthly household income ratio (RIR) is 25% or less. <a href=\"#footnote-ref-14\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>As with all indicators, there are some potential challenges and limitations. Some of these are outlined below.</p>\n<ul>\n  <li>Difficulties to agree universally on some definitions and characteristics when referring to deteriorated housing conditions, often due to political or economic considerations.</li>\n  <li>Lack of appropriate tools at national and city levels to measure all components required by Indicator 11.1.1, sometimes resulting in the underestimation of deteriorated housing units.</li>\n  <li>The complicated relation between security of tenure with land and property makes it a difficult, but vital, aspect to include in the different surveys, and thus, to measure and monitor. </li>\n  <li>Indicator 11.1.1 does not capture homelessness.</li>\n  <li>Many countries still have limited capacities for data collection, management and analysis, their update and monitoring. These are key to ensure national and global data consistency.</li>\n</ul>", "DATA_COMP__GLOBAL"=>"<p>The indicator considers two components to be computed as follows:</p>\n<ol>\n  <li>Percentage of people living in Slum/Informal Settlements households (SISH): </li>\n</ol>\n<p> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mn>100</mn>\n    <mfenced open=\"[\" close=\"]\" separators=\"|\">\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mi>N</mi>\n            <mi>u</mi>\n            <mi>m</mi>\n            <mi>b</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>l</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>l</mi>\n            <mi>i</mi>\n            <mi>v</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>g</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>S</mi>\n            <mi>I</mi>\n            <mi>S</mi>\n            <mi>H</mi>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n          <mrow>\n            <mi>U</mi>\n            <mi>r</mi>\n            <mi>b</mi>\n            <mi>a</mi>\n            <mi>n</mi>\n            <mo>/</mo>\n            <mi>C</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfenced>\n  </math></p>\n<ol>\n  <li>Percentage of people living in Inadequate housing households (IHH):</li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mn>100</mn>\n    <mfenced open=\"[\" close=\"]\" separators=\"|\">\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mi>N</mi>\n            <mi>u</mi>\n            <mi>m</mi>\n            <mi>b</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>l</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>l</mi>\n            <mi>i</mi>\n            <mi>v</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>g</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>I</mi>\n            <mi>H</mi>\n            <mi>H</mi>\n          </mrow>\n          <mrow>\n            <mi>U</mi>\n            <mi>r</mi>\n            <mi>b</mi>\n            <mi>a</mi>\n            <mi>n</mi>\n            <mo>/</mo>\n            <mi>C</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p>The unit of measurements for all these indicators will be %. Currently, the data for this indicator is already being reported in nearly all developing countries on what refers to slums and informal settlements, and in some countries for what refers to expenditure on housing (for inadequate housing). The SDG indicator 11.1.1 will therefore contribute to report on a broader spectrum of inadequate housing conditions affecting households in all countries.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>As part of the validation process, UN-Habitat developed a template to compile data generated by countries through the National Statistics Offices as well as other government agencies responsible for official statistics (<a href=\"https://data.unhabitat.org/pages/guidance\">https://data.unhabitat.org/pages/guidance</a> ). Data compiled is then checked against several criteria including the data sources used, the application of internationally agreed definitions, classification and methodologies to the data from that source, etc. Once reviewed, appropriate feedback is then provided to individual countries for further discussion. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Any adjustment to the data is jointly agreed after consultations with the relevant national agencies that share the data points for reporting.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>All countries are expected to fully report on this indicator more consistently with few challenges where missing values will be reported at the national/global level. At the national level, it is possible that missing values will be recorded perhaps representing gaps of non-measurements among populations whose status of slum-hood or informality or inadequate housing is not recorded, unknown or where data is unavailable. Because the values will be aggregated at the national levels, missing values will be less observed at these levels, but are likely to affect the estimates. At the survey and data collection level, survey procedures for managing missing values will be applied based on the unit of analysis/ primary sampling units.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Global estimates will be adjusted with modelling based on trends to cater for missing information or data.</p>\n<p><em>Regional and global estimates for global monitoring</em></p>\n<p>Regional and global estimates will be derived from national figures with an appropriate disaggregation level. Specialized tools will be developed and agreed upon with local and international stakeholders. Systems of quality assurance on the use of the tools, analysis and reporting will be deployed regionally, and global to ensure that standards are uniform and that definitions are universally applied.</p>\n<p>We expect that investments in improved data collection and monitoring at country level will produce incentives for governments to improve reporting and performance and also greater readiness to engage with multiple stakeholders in data collection and analysis and in achieving better understanding of the strengths and weaknesses of existing slum definitions and their applications. </p>\n<p><em>Sources of differences between global and national figures:</em></p>\n<p>As national agencies are responsible for data collection, no differences between country produced data and international estimated data on the indicator are expected to arise if standard methodologies and procedures are followed at all stages of the reporting process. Missing data and other local variables and frequency of data collection usually affects the figures reported at the global and national level. For this indicator, national data will be used to derive global figures. In instances where global values differ from national figures, efforts will be made for harmonization.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global estimates will be derived from national figures using weighed averages. Weighting is done using urban population sizes from the World Urbanization Prospects. Global monitoring will be led by UN-Habitat with the support of other partners and regional commissions.</p>", "DOC_METHOD__GLOBAL"=>"<p>UN-Habitat has developed a step-by-step data compilation and computation methodological document, which is applicable at national level. The document is available here: <a href=\"https://unhabitat.org/sites/default/files/2020/06/indicator_11.1.1_training_module_adequate_housing_and_slum_upgrading.pdf\">https://unhabitat.org/sites/default/files/2020/06/indicator_11.1.1_training_module_adequate_housing_and_slum_upgrading.pdf</a>. The agency also provides on-the-task training to countries on a need basis, as well as continuous technical support throughout the data compilation process to ensure alignment of national processes with the globally adopted methodology. </p>\n<p>In addition, UN-Habitat has developed audio-visual content for indicator 11.1.1 that is available through its E-Learning Portal, offering more interactive learning for data producers at different levels. The content includes self-paced e-learning courses which present descriptive and practical step-by-step guidance on how to compute each indicator. These courses are aimed at strengthening national capacities in collecting, analyzing, and monitoring the urban SDG indicators. They are also designed to be attractive to different groups, from data producers to people just interested in understanding the indicators and their interpretation. This was intended to broaden the pool of experts on urban monitoring and increase the uptake and use of the tools within countries.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>To ensure consistency in data production across countries, UN-Habitat has developed detailed step-by-step tutorials on the computation of indicator 11.1.1, which further explain the steps presented in this metadata. The detailed tutorials, which will be continuously updated are available at <a href=\"https://unhabitat.org/knowledge/data-and-analytics\">https://unhabitat.org/knowledge/data-and-analytics</a>, <a href=\"https://www.urbanagendaplatform.org/learning\">https://www.urbanagendaplatform.org/learning</a>, and <a href=\"https://data.unhabitat.org/\">https://data.unhabitat.org/</a>. Within its Data and Analytics Section which is responsible for the indicator data compilation, UN-Habitat has a team of data experts who check all submitted data and provide direct support to countries in the indicator computation. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UN-Habitat maintains the global urban indicators database that is used for monitoring of the urban metrics drawn from SDGs, NUA, flagship reports (e.g. World Cities Report) and other official reporting. In general, for all new data, a thorough review is done to check for consistency and overall data quality by technical staff in the Data and Analytics unit before publication in the urban indicators database. This ensures that only the most accurate and reliable information are included in the database. Key elements considered in the review include: proper documentation of data sources; representativeness of data at national level, use of appropriate methodology for data collection and analysis (e.g. appropriate sampling process, values based on valid sample sizes), use of appropriate concepts and definitions, consistency of data trends with previously published/reported estimates for the indicator.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Once data is received from member states, UN-Habitat uses a checklist specific to each indicator to assess a) whether the data production process followed the metadata provisions, and b) confirm the accuracy of the data sources used for the indicator computation. Both components are captured in the reporting template shared with National Statistical Offices, which helps to assess whether computation was done using the proposed indicator inputs or proxies. The reporting template also requests for information that helps understand whether national data for the indicator was produced from the appropriate data sources. In addition, the received data is also checked for other qualities such as data disaggregation, reporting period and consistency with other previously reported trends, which ensures reliable regional estimates. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data on slums is available for all developing countries, as it has been reported yearly by UN-Habitat in the MDGs&#x2019; reports. Recently, UN-Habitat has disaggregated information on this indicator at city level, increasing its suitability for SDG 11. The people living in slums&#x2019; indicator is currently measured in more than 320 cities across the world as part of UN-Habitat City Prosperity Initiative. UN-Habitat and World Bank computed this indicator for many years (1996-2006) as part of the Urban Indicators Programme. Data on inadequate housing, measured through housing affordability, is available for all OECED countries as well as in UN Global Sample of Cities covering 200 cities. </p>\n<p>Data on inadequate housing, measured through housing affordability, is available in many countries. UN-Habitat and World Bank computed this indicator for many years (1996-2006) as part of the Urban Indicators Programme. Recently, the Global Housing Indicators Working Group, a collaborative effort of Cities Alliance, Habitat for Humanity International, the Inter-American Development Bank, UN-Habitat proposed the collection of data on this indicator worldwide. </p>\n<p><strong>Time series: </strong></p>\n<p>Available data cover the period 1990-2018. Because the efforts and capacity of collecting and analysing survey data are different for each country, the length of the time series for each country varies greatly. </p>\n<p><strong>Disaggregation:</strong></p>\n<p><em>Potential Disaggregation:</em></p>\n<ul>\n  <li>Disaggregation by location (intra-urban)</li>\n  <li>Disaggregation by income group</li>\n  <li>Disaggregation by sex, race, ethnicity, religion, migration status (head of household)</li>\n  <li>Disaggregation by age (household members)</li>\n  <li>Disaggregation by disability status (household members)</li>\n</ul>\n<p><em>Quantifiable Derivatives:</em></p>\n<ul>\n  <li>Proportion of households with durable housing</li>\n  <li>Proportion of households with improved water</li>\n  <li>Proportion of households with improved sanitation</li>\n  <li>Proportion of households with sufficient living space</li>\n  <li>Proportion of households with security of tenure</li>\n  <li>Proportion of households with one (1) housing deprivation</li>\n  <li>Proportion of households with multiple (2 or more) housing deprivations</li>\n  <li>Proportion of households with approved municipal permit</li>\n  <li>Proportion of households with (in) adequate housing (affordability)</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p>As national agencies are responsible for data collection, no differences between country produced data and international estimated data on the indicator are expected to arise if standard methodologies and procedures are followed at all stages of the reporting process. Where such discrepancies exist, these will be resolved through planned technical meetings and capacity development workshops.</p>\n<p>Missing data and other local variables and frequency of data collection usually affects the figures reported at the global and national level. For this indicator, national data will be used to derive global figures. In instances where global values differ from national figures, efforts will be made for harmonization. There are many instances where lack of new data will be replaced with modelled data for the global figures. These figures will be acceptable for reporting at the national and global levels with the relevant notes attached to such figures. This is likely to be the case for countries where they have long intervals of collection of new data, or where countries face unstable situations such post-disaster or post-war years.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Bibliographic References:</strong> </p>\n<p>&#x2022; United Nations (2007). Indicators of Sustainable Development: Guidelines and Methodologies. Third Edition, United Nations, New York </p>\n<p>&#x2022; A/HRC/25/54 (2013), Report of the Special Rapporteur on adequate housing as a component of the right to an adequate standard of living, and on the right to non-discrimination in this context </p>\n<p>&#x2022; UN-Habitat (2002) Urban Indicators Guidelines. Nairobi </p>\n<p>&#x2022; UN-Habitat, Global Urban Indicators Database 2012 a. Nairobi </p>\n<p>&#x2022; UN-Habitat (2002), Expert Group Meeting on Urban Indicators, Nairobi, Kenya, November 2002 </p>\n<p>&#x2022; UN-Habitat (2003a), Slums of the World: The face of urban poverty in the new millennium </p>\n<p>&#x2022; UN-Habitat (2003b), Improving the Lives of 100 Million Slum Dwellers &#x2013; Guide to Monitoring Target 11 </p>\n<p>&#x2022; UN-Habitat (1998), Crowding and Health in Low Income Settlements of Guinea Bissau, SIEP Occasional Series No.1 </p>\n<p>&#x2022; Global report on Human settlement on Slums (2002) </p>\n<p>&#x2022; Turkstra, J. and Raithelhuber, M. (2004). Urban slum Monitoring. ESRI User Conference paper 1667 </p>\n<p>&#x2022; Urban Indicators Programme, World Bank and UN-Habitat, Guidelines </p>\n<p>&#x2022; Habitat for Humanity, Global Housing Indicators </p>\n<p>&#x2022; Habitat for Humanity, Housing Indicators for the Sustainable Development Goals, 2015 </p>\n<p>&#x2022; McKinsey Global Institute (2014), A Blueprint for Addressing the Global Affordable Housing Challenge </p>\n<p>&#x2022; United Nations (2015), Conference on Housing and Sustainable Urban Development &#x2013; Habitat III, Issue Paper No. 22 on Informal Settlements </p>\n<p>&#x2022; UN-Habitat, UN-AIDS (2015a) Ending the Urban Aids Epidemic. Nairobi </p>\n<p>&#x2022; UN-Habitat (2015b). Slum Almanac 2015-2016 </p>\n<p>&#x2022; UN-Habitat (2016). World Cities Report 2016 </p>\n<p> </p>\n<p><strong>URL References:</strong> </p>\n<p>[1]: http://www.un.org/esa/sustdev/natlinfo/indicators/methodology_sheets.pdf, </p>\n<p>[2]: http://unhabitat.org/urban-indicators-guidelines/ </p>\n<p>[3]: http://mdgs.un.org/unsd/mdg/Metadata.aspx?IndicatorId=0&amp;SeriesId=710, </p>\n<p>[4]: http://unhabitat.org/urban-initiatives/initiatives-programmes/participatory-slum-upgrading/ </p>\n<p>[5]: http://unhabitat.org/slum-almanac-2015-2016/ </p>\n<p>[6]: http://wcr.unhabitat.org/ </p>\n<p>[7]: http://www.unhabitat.org/programmes/guo/documents/EGM final report 4 Dec 02.pdf </p>", "indicator_sort_order"=>"11-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.2.1", "slug"=>"11-2-1", "name"=>"Proporción de la población que tiene fácil acceso al transporte público, desglosada por sexo, edad y personas con discapacidad", "url"=>"/site/es/11-2-1/", "sort"=>"110201", "goal_number"=>"11", "target_number"=>"11.2", "global"=>{"name"=>"Proporción de la población que tiene fácil acceso al transporte público, desglosada por sexo, edad y personas con discapacidad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población que tiene fácil acceso al transporte público, desglosada por sexo, edad y personas con discapacidad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población que tiene fácil acceso al transporte público, desglosada por sexo, edad y personas con discapacidad", "indicator_number"=>"11.2.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "texto_oceca"=>"", "observaciones"=>"", "justificacion_global"=>"\nEste indicador busca monitorear con éxito el uso y el acceso al sistema de transporte público \ny la transición hacia la reducción de la dependencia del transporte privado, mejorando el \nacceso a zonas con una alta proporción de grupos con dificultades de transporte, como personas \nmayores, personas con discapacidad física y personas de bajos ingresos, o zonas con tipos de \nvivienda específicos, como edificios de alta ocupación o viviendas públicas, y reduciendo \nla necesidad de movilidad mediante la disminución del número de viajes y las distancias recorridas. \n\nEl paradigma de la movilidad urbana basada en la accesibilidad también requiere sistemas de \ntransporte público de alta capacidad y bien integrados en un sistema multimodal con puntos \nde acceso al transporte público ubicados a distancias cómodas para caminar o andar en \nbicicleta desde los hogares y lugares de trabajo para todos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.2.1&seriesCode=SP_TRN_PUBL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=NOCITI\">Proporción de la población que tiene acceso conveniente al transporte público (%) SP_TRN_PUBL</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-02-01.pdf\">Metadatos 11-2-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "texto_oceca"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThis indicator aims to successfully monitor the use of and access to the \npublic transportation system and the move towards easing the reliance on \nthe private means of transportation, improving the access to areas with a \nhigh proportion of transport disadvantaged groups such as elderly citizens, \nphysically challenged individuals, and low-income earners or areas with \nspecific dwelling types such as high occupancy buildings or public housing \nand reducing the need for mobility by decreasing the number of trips and the \ndistances travelled. \n\nThe accessibility based urban mobility paradigm also critically needs good, \nhigh-capacity public transport systems that are well integrated in a multimodal \narrangement with public transport access points located within comfortable walking \nor cycling distances from homes and jobs for all. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.2.1&seriesCode=SP_TRN_PUBL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=NOCITI\">Proportion of population that has convenient access to public transport (%) SP_TRN_PUBL</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-02-01.pdf\">Metadata 11-2-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "texto_oceca"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nEste indicador busca monitorear con éxito el uso y el acceso al sistema de transporte público \ny la transición hacia la reducción de la dependencia del transporte privado, mejorando el \nacceso a zonas con una alta proporción de grupos con dificultades de transporte, como personas \nmayores, personas con discapacidad física y personas de bajos ingresos, o zonas con tipos de \nvivienda específicos, como edificios de alta ocupación o viviendas públicas, y reduciendo \nla necesidad de movilidad mediante la disminución del número de viajes y las distancias recorridas. \n\nEl paradigma de la movilidad urbana basada en la accesibilidad también requiere sistemas de \ntransporte público de alta capacidad y bien integrados en un sistema multimodal con puntos \nde acceso al transporte público ubicados a distancias cómodas para caminar o andar en \nbicicleta desde los hogares y lugares de trabajo para todos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.2.1&seriesCode=SP_TRN_PUBL&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=NOCITI\">Garraio publikorako sarbide egokia duen biztanleriaren proportzioa (%) SP_TRN_PUBL</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-02-01.pdf\">Metadatuak 11-2-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 11.2: By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older persons</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 11.2.1: Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SP_TRN_PUBL - Proportion of population that has convenient access to public transport [11.2.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.3.1, 11.7.1, 9.1.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p>This indicator will be monitored by the proportion of the population that has convenient access to public transport. Because most public transport users walk from their trip origins to public transport stops and from public transport stops to their trip destination, local spatial availability and accessibility is sometimes evaluated in terms of pedestrian (walk) access, as opposed to park and ride or transfers.</p>\n<p>Hence, the access to public transport is considered convenient when an officially recognized stop is accessible within a walking distance along the street network of 500 m from a reference point such as a home, school, work place, market, etc. to a low-capacity public transport system (e.g. bus, Bus Rapid Transit) and/or 1 km to a high-capacity system (e.g. rail, metro, ferry). Additional criteria for defining public transport that is convenient include:</p>\n<ol>\n  <li>Public transport accessible to all special-needs customers, including those who are physically, visually, and/or hearing-impaired, as well as those with temporary disabilities, the elderly, children and other people in vulnerable situations.</li>\n  <li>Public transport with frequent service during peak travel times.</li>\n  <li>Stops present a safe and comfortable station environment.</li>\n</ol>\n<p>The following definitions are required to define and measure convenient access to public transport.</p>\n<p><strong>City or urban area</strong>: Since 2016 UN-Habitat and partners organized global consultations and discussions to narrow down the set of meaningful definitions that would be helpful for the global monitoring and reporting process. Following consultations with 86 member states, the United Nations Statistical Commission (UNSC), in its 51<sup>st</sup> Session (March 2020) endorsed the Degree of Urbanisation (DEGURBA) as a workable method to delineate cities, urban and rural areas for international statistical comparisons.<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup> This definition combines population size and population density thresholds to classify the entire territory of a country along the urban-rural continuum, and captures the full extent of a city, including the dense neighbourhoods beyond the boundary of the central municipality. DEGURBA is applied in a two-step process: First, 1 km<sup>2</sup> grid cells are classified based on population density, contiguity and population size. Subsequently, local units are classified as urban or rural based on the type of grid cells in which the majority of their population resides. For the computation of indicator 11.2.1, countries are encouraged to adopt the DEGURBA to define the analysis area (city or urban area).</p>\n<p><strong>Public transport</strong> is defined as a shared passenger transport service that is available to the public and is provided for the public good. It includes cars, buses, trolleys, trams, trains, subways, and ferries that are shared by strangers without prior arrangement. It may also include informal modes of transport (e.g. community transport, paratransit, unregulated public transport) &#x2013; but it is noted that these are often lacking in designated routes or stops.</p>\n<p>For a city to understand the nature of its transport system and in turn make the necessary planning and investment decisions, it is recommended to do an inventory of its public transport modes including major characteristics. For cities where a mix of formal and informal transport systems exist, it is also recommended to disaggregate the indicator findings by the share of population with access to each type of transport system, which is critical for decision-making processes. Recent data has shown that many cities in developing regions may lack a formal public transport system, but residents still enjoy a high level of access to public transport driven by a comprehensive informal public transport network (e.g. community transport, paratransit, unregulated public transport etc) which does not necessarily have designated stops. A mapping of the transport routes where these informal public transport networks can stop is thus recommended, and countries are encouraged to document each type of transport mode.</p>\n<p><strong>Street Network</strong> is defined as a system of interconnected lines that represent a system of streets or roads for a given area. A street network provides the foundation for network analysis that will help to measure the pedestrian access/walking distance of 500 m and/or 1 km to a public transport stop; or the network along which random informal transport modes can be accessed. Some cities have detailed data on their street network, type, street design (e.g. availability of a safe walking path) or topological structure of the network. However, if such data is not available, it is proposed to use OpenStreetMap as a baseline and fill missing gaps through digitizing of missing lines from satellite imagery (e.g. Google Earth). The major assumption in the use of these data sources is that all streets are walkable and on the same elevation level.</p>\n<p><strong>Service Area, </strong>in the context of indicator 11.2.1 is defined as the area served by public transport within 500 m walking distance to a low capacity-system and/or 1 km to a high-capacity system based on the street network.</p>\n<p><strong>Low-capacity public transport system, </strong>in the context of indicator 11.2.1 includes systems such as buses, trams, and Bus Rapid Transit (BRT), which largely run along the street network (including on dedicated lanes or tracks that follow the street network). These low-capacity public transport carriers are smaller in size and require less space for stopping-dropping-picking passengers (compared to high-capacity carriers such as metros), meaning their stops can be provided within shorter distances to each other and along majority of the city streets. In countries where informal public transport systems are common, most of the services/carriers will fall under this category of public transport system.</p>\n<p><strong>High-capacity public transport system,</strong> in the context of indicator 11.2.1 includes systems such as trains, metros and ferries. The carriers in this category of public transport system are large and require significantly large terminus infrastructure (e.g. metro stations) which makes it impossible to provide their stopping-dropping-picking stations (stops) within short distances. Majority of the carriers in this category also operate along dedicated infrastructure (e.g. metro-lines, waterways) and reach higher speeds than low-capacity carriers. Several surveys have indicated that passengers are more likely to walk longer distances to access high-capacity than they would walk to access low-capacity public transport systems.</p>\n<p><strong>Built up area </strong>within the context of indicator 11.2.1 is defined as all areas occupied by buildings.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> A recommendation on the method to delineate cities, urban and rural areas for international statistical comparisons. <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf</a>. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The indicator depends on international classifications on boundaries of countries and regions and city boundaries. Guidance on the city definitions is provided based on a harmonized global city definition, see: <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf</a>.</p>", "SOURCE_TYPE__GLOBAL"=>"<ul>\n  <li><strong>Location of public transport stops:</strong> Typically available from city administration or transport service providers, General Transit Feed Specification (GTFS) feeds, OpenStreetMap, Google (if not available at all, for instance in cities with informal public transport services, innovative technologies/apps and stakeholder consultations could assist the cities to map out the routes and stops).</li>\n  <li><strong>Street Network:</strong> Ideally available from city administration but could also come from OpenStreetMap, the Global Roads Open Access Data Set (gROADS) and other open-source streets data providers.</li>\n  <li><strong>Population data:</strong> Available from censuses or other demographic surveys at individual dwelling units or enumeration zones, which can be further disaggregated to uniform grids through population modelling approaches.</li>\n  <li><strong>Number of residents per dwelling unit:</strong> Available from census/household surveys.</li>\n  <li><strong>Demographic data for disaggregation:</strong> Typically available from household surveys that collect information both on household/individual characteristics and travel patterns. Must also provide information on the location of the respondent. These surveys could also be used to collect information about the perceived quality of the service, such as time to reach a station considering obstacles, typical wait times, safety, etc. Note that such household surveys are often not easily available and rarely updated on a frequent (e.g. every 2-3 years) basis.</li>\n</ul>", "COLL_METHOD__GLOBAL"=>"<p>Data collection is supposed to be done at the local city/urban level, with national aggregates made from all cities in the country, or from a sample of representative cities (selected using the National Sample of Cities Approach developed by UN-Habitat). At the Global level, data will be assembled and compiled for international consumption and comparison by UN-Habitat and other partners. UN-Habitat and partners will explore several capacity building options to ensure that uniform standards for generation, reporting and analysing data for this indicator are applied by all countries and regions.</p>", "FREQ_COLL__GLOBAL"=>"<p>The monitoring of the indicator can be repeated at an annual interval, allowing several reporting points until the year 2030. Monitoring at annual intervals will help in determining whether the proportion of the population with convenient public transport is increasing significantly over time, as well as monitor what is the share of the global urban population living in cities where the convenient access to public transport is below the acceptable minimum. Indicator 11.2.1 has the potential to measure improvement within short term intervals. Moreover, the disaggregated monitoring for this indicator will provide increasing attention on the access to transport especially among the vulnerable populations such as women, children, persons with disabilities and older persons. It will also help to track a modal shift towards more sustainable modes of transport including public transport integrated with walking and cycling.</p>\n<p>UN-Habitat has developed a simple reporting template appended to this metadata to collect city level data. The template, which is sent to countries on an annual basis is expected to be used until 2030, but slight changes may be effected as data on more aspects becomes available. The template is appended to this metadata and can also be accessed <a href=\"https://data.unhabitat.org/datasets/template-for-compilation-of-sdg-indicator-11-2-1\">here</a>.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data for indicator 11.2.1 will be released on an annual basis, to cater for an anticipated increase in the number of cities/urban areas and countries reporting on the indicator. Changes in trends within individual cities and/or countries are likely to happen in spans of about 3-5 years, so a three-year window will be applied for comprehensive review of all data, with updates made based on availability of new data.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National focal points as designated by respective governments underpins the governance framework for monitoring the transport target. Such focal points could be the ministries themselves, National Statistical Offices (NSOs), academic or research institutions, Civil Society Organisations (CSOs), transport operators or a combination of these working under an agreement facilitated by the national government. UN-Habitat will be working with its partner organizations to support countries in the data collection efforts, by providing capacity building and quality assurance support. UN-Habitat and partners will also ensure the exchange of knowledge and experience between participating countries. Specific agreements will be drawn up with respective countries and cities for collaboration in the monitoring &#x2013; as well as with partner organizations involved in transport data collection including the International Association of Public Transport (UITP), the Institute for Transport and Development Policy (ITDP), the World Bank, the International Transport Forum (ITF), the Partnership on Sustainable, Low Carbon Transport (SLoCaT), the Wuppertal Institute of Climate, Energy and Environment, the German Aerospace Center (DLR) and others. Comprehensive reporting will be undertaken on a biennial basis. Reports will be published in the public domain with data available in the UN-Habitat global databases.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Human Settlements Programme (UN-Habitat is the specialized agency for sustainable urbanization and human settlements in the United Nations. The mandate derives from the priorities established in relevant General Assembly resolutions and decisions, including General Assembly resolution 3327 (XXIX), by which the General Assembly established the United Nations Habitat and Human Settlements Foundation, and resolution 32/162 by which the Assembly established the United Nations Center for Human Settlements (Habitat). In 2001, by its Resolution 56/206, the General Assembly transformed the Habitat into the secretariat of the UN-Habitat, with a mandate to coordinate human settlements activities within the United Nations System. As such, UN-Habitat has been designated the overall coordinator of SDG 11 and specifically as a custodian agency for 9 of the 15 indicators under SDG 11 including indicator 11.2.1. UN-Habitat also supports the monitoring and reporting of 4 urban specific indicators in other goals.</p>", "RATIONALE__GLOBAL"=>"<p>This indicator aims to successfully monitor the use of and access to the public transportation system and the move towards easing the reliance on the private means of transportation, improving the access to areas with a high proportion of transport disadvantaged groups such as elderly citizens, physically challenged individuals, and low-income earners or areas with specific dwelling types such as high occupancy buildings or public housing and reducing the need for mobility by decreasing the number of trips and the distances travelled. The accessibility based urban mobility paradigm also critically needs good, high-capacity public transport systems that are well integrated in a multimodal arrangement with public transport access points located within comfortable walking or cycling distances from homes and jobs for all.</p>\n<p>The ability of residents including persons with disabilities and businesses to access markets, employment opportunities, and service centers such as schools and hospitals is critical to urban economic development. The transport system provides access to resources and employment opportunity. Moreover, accessibility allows planners to measure the effects of changes in transport and land use systems. The accessibility of jobs, services and markets also allows policymakers, citizens and businesses to discuss the state of the transport system in a comprehensible way. The transportation system is a critical enabler of economic activities and social inclusion. The access to transport SDG indicator addresses a significant gap that was never addressed by the Millennium Development Goals (MDGs), i.e. directly addressing transport as a critical enabler of economic activities and social inclusion. Already, the &#x201C;externalities&#x201D; associated with transport in terms of greenhouse gas emissions, traffic congestion and road traffic accidents have been increasing. Emissions from transport are now (2022) responsible for 23% of global greenhouse gas emissions<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> and are increasing faster than any other source; outdoor air pollution alone, a major source of which is transport, is responsible for 4.2 million<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup> premature deaths annually, road traffic accidents kill more than 1.3 million<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup> people every year and severe traffic congestion is choking cities and impacting gross domestic product (GDP). Achieving SDG 11 requires a fundamental shift in the thinking on transport with the focus on the goal of transport rather than on its means. With accessibility to services, goods and opportunities for all as the ultimate goal, priority is given to making cities more compact and walkable through better planning and the integration of land-use planning with transport planning. The means of transport are also important but the SDG&#x2019;s imperative to make the city more inclusive means that cities will have to move away from car-based travel to public transport and active modes of transport such as walking and cycling with good inter-modal connectivity.</p>\n<p>The rising traffic congestion levels and the resulting negative air quality in many metropolitan areas have elevated the need for a successful public transportation system to ease the reliance on the private means of transportation. Cities that choose to invest in effective public transportation options stand out to gain in the long run. Cities that have convenient access to public transport, including access by persons with disabilities are more preferred as these are more likely to offer lower transportation costs while improving on the environment, congestion and travel times within the city. At the same time, improving the access to areas with a high proportion of transport disadvantaged groups such as elderly citizens, physically challenged individuals, and low-income earners or areas with specific dwelling types such as high occupancy buildings or public housing also helps increase the efficiency and the sustainability of the public transport system. Public transport is a very important equalizer of income, consumption and spatial inequalities. This indicator is empirically proven that public transport makes cities more inclusive, safe and sustainable. Effective and low-cost transportation is critical for reducing urban poverty and inequalities and enhancing economic development because it provides access to jobs, health care, education services and other public goods.</p>\n<p>Clean public transport is a very efficient mean for the reduction of CO<sub>2</sub> emissions and therefore it contributes to reduction of climate change impacts and lower levels of energy consumption. Most importantly public transport needs to be easily accessible to the elderly and disabled citizens.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> IEA, Global energy-related CO2 emissions by sector, IEA, Paris <a href=\"https://www.iea.org/data-and-statistics/charts/global-energy-related-co2-emissions-by-sector\">https://www.iea.org/data-and-statistics/charts/global-energy-related-co2-emissions-by-sector</a>, IEA. Licence: CC BY 4.0. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> <a href=\"https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health\">https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health</a>. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> <a href=\"https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries\">https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries</a>. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>Experts in the transport sector, during different Expert Group Meetings held in 2016, 2017 and 2019 established that measuring accessibility to public transport using the distance to stop metric (spatial access of 500 m or 1 km walking distance to a public transport stop) provides a good measurement of the indicator. They however also pointed out that this distance computation is not enough to properly measure &#x201C;convenient access&#x201D; to public transport. At a minimum, they recommended that additional features of quality be taken into account, as described in the recommended secondary indicators section. Eventually, a complete shift to a measure of access of destinations and opportunities would be ideal, if data systems can be developed to support this, and applied in a consistent manner in cities around the world. </p>", "DATA_COMP__GLOBAL"=>"<p>The method to estimate the proportion of the population that has convenient access to public transport is based on <strong>five</strong> steps (core indicator):</p>\n<p>a) Delimitation of the urban area/city which will act as the spatial analysis scope,</p>\n<p>b) Inventory of the public transport stops in the city or the service area,</p>\n<p>c) Network analysis based on street network to measure walkable distance of 500 m and/or 1 km to nearest transport stop (&#x201C;service area&#x201D;),</p>\n<p>d) Estimation of population living within the walkable distance to public transport, and</p>\n<p>e) Estimation of the proportion of the population with convenient access out of the total population of the city.</p>\n<p><strong>a. Delimitation of the urban area/city which will act as the spatial analysis scope:</strong> Following consultations with 86 member states, the United Nations Statistical Commission in its 51<sup>st</sup> session (March 2020) endorsed the Degree of Urbanisation (DEGURBA) as a workable method to delineate cities, urban and rural areas for international statistical comparisons. Countries are thus encouraged to adopt this approach for delimitation of the urban area/city within which indicator 11.2.1 is measured, which will help them produce data that is comparable across urban areas within their territories, as well as with urban areas and cities in other countries. More details on DEGURBA and its application are available here: <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf</a>.</p>\n<p><strong>b. Inventory of public transport stops:</strong> Data and information on types of public transport available in each urban area/city, as well as the location of public transport stops can be obtained from city administration or transport service providers. In many cases, however, this information is lacking, incomplete, outdated, or difficult to access (especially where strong inter-agency collaboration is lacking). In these cases, alternative sources which have proven to be useful include open data sources (e.g. OpenStreetMap, Google and the General Transit Feed Specification - GTFS feeds), volunteered geospatial data, community transport /paratransit mapping, community-based maps, and point mapping using global positioning systems (GPS) or from high to very high resolution satellite imagery (e.g. Google Earth). When information is available, characteristics of the quality, universal accessibility for people with disabilities, safety, and frequency of the service can be &#x2018;assigned&#x2018; to the public transport stops&#x2019; inventory for detailed analysis and further disaggregation according to the statistical capacities of countries and cities.</p>\n<p><strong>c. Network analysis based on street network to measure walkable distance of 500 m and/or 1 km to nearest transport stop (&#x201C;service area&#x201D;):</strong> To calculate the walking distance to each stop, data on a well-defined street network (by city authorities or from open sources such as OpenStreetMap) is required. Network analyst tools (Geographic Information System, GIS) can be used to identify service areas around any location on a network. A network service area is a region that encompasses all accessible areas via the streets network within a specified impedance/distance. The distance in each direction (and in turn the shape of the surface area) varies depending on, among other things, existence of streets, presence of barriers along each route (e.g. lack of footbridges and turns) or availability of pedestrian walkways along each street section. In the absence of detailed information on barriers and walkability along each street network, the major assumption in creating the service areas is that all streets are walkable. Since the analysis is done at the city and national level, local knowledge can be used to exclude streets which are not walkable. The recommendation is to run the service area analysis for each public transport stop per applicable walking distance thresholds (500 m or 1 km), and then merge all individual service areas to create a merged service area polygon.</p>\n<p>In urban areas where informal services are the main mode of public transport, the use of street networks along which the carriers stop should be used in place of the designated stops. Cities and countries are encouraged to provide notes on their type of public transport system (whether formal, informal or a mix).</p>\n<p><strong>d.</strong> <strong>Estimation of population within the walkable distance to public transport:</strong> The combined service area of 500 m walking distance to the low-capacity stops and/or 1 km to the high-capacity stops generated in (c) above is overlaid in GIS with high resolution demographic data. The best source of population data for the analysis is individual dwelling or block level total population which is collected by National Statistical Offices through censuses and other surveys. Where this level of population data is not available, or where data is released at large population units, countries are encouraged to create population grids, which can help disaggregate the data from large and different sized census/population data release units to smaller uniform sized grids. For more details on the available methods for creation of population grids, explore the links provided under the references section on &#x201C;Some population gridding approaches&#x201D;. A generic description of the different sources of population data for the indicator computation is also provided in the detailed indicator 11.2.1 training module (see link in references section). Once the appropriate source of population data is acquired, the total population with convenient access to public transport in the city will be equal to the population encompassed within the combined service area for all public transport modes.</p>\n<p><strong>e. Estimation of the proportion of the population with convenient access to public transport out of the total population of the city or urban area. </strong>Estimate the proportion of population with access to public transport within 500 m and/or 1 km walking distance out of the total population of the city or urban area.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>&amp;nbsp;</mi>\n    <mtable>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>S</mi>\n            <mi>h</mi>\n            <mi>a</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>w</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>h</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>v</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>i</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>&amp;nbsp;</mi>\n            <mi>a</mi>\n            <mi>c</mi>\n            <mi>c</mi>\n            <mi>e</mi>\n            <mi>s</mi>\n            <mi>s</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>t</mi>\n            <mi>o</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>P</mi>\n            <mi>u</mi>\n            <mi>b</mi>\n            <mi>l</mi>\n            <mi>i</mi>\n            <mi>c</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>t</mi>\n            <mi>r</mi>\n            <mi>a</mi>\n            <mi>n</mi>\n            <mi>s</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>t</mi>\n            <mi>&amp;nbsp;</mi>\n            <mo>(</mo>\n            <mi>%</mi>\n            <mo>)</mo>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n    </mtable>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>T</mi>\n                <mi>o</mi>\n                <mi>t</mi>\n                <mi>a</mi>\n                <mi>l</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>p</mi>\n                <mi>u</mi>\n                <mi>l</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>l</mi>\n                <mi>i</mi>\n                <mi>v</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>g</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>w</mi>\n                <mi>i</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>m</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>g</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>s</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>v</mi>\n                <mi>i</mi>\n                <mi>c</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>a</mi>\n                <mi>s</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>&amp;nbsp;</mi>\n                <mi>f</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>l</mi>\n                <mi>o</mi>\n                <mi>w</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>a</mi>\n                <mi>n</mi>\n                <mi>d</mi>\n                <mi>&amp;nbsp;</mi>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mi>o</mi>\n                    <mi>r</mi>\n                  </mrow>\n                </mfenced>\n                <mi>&amp;nbsp;</mi>\n                <mi>h</mi>\n                <mi>i</mi>\n                <mi>g</mi>\n                <mi>h</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>c</mi>\n                <mi>a</mi>\n                <mi>p</mi>\n                <mi>a</mi>\n                <mi>c</mi>\n                <mi>i</mi>\n                <mi>t</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>p</mi>\n                <mi>u</mi>\n                <mi>b</mi>\n                <mi>l</mi>\n                <mi>i</mi>\n                <mi>c</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>r</mi>\n                <mi>a</mi>\n                <mi>n</mi>\n                <mi>s</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>t</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>s</mi>\n                <mi>t</mi>\n                <mi>o</mi>\n                <mi>p</mi>\n                <mi>s</mi>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n      <mrow>\n        <mi>C</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Countries and cities are encouraged to disaggregate the data on access to public transport by the capacity of the carriers &#x2013; that is between low-capacity and high-capacity systems. Where applicable, countries and cities are also encouraged to disaggregate the data by type of carrier &#x2013; whether formal or informal . The disaggregation is directly relevant in understanding the entire public transport system and also identifying the weaknesses and opportunities in the system which are relevant in making policy and investment decisions.</p>\n<p><strong>Recommended secondary indicators </strong></p>\n<p>While the core indicator provides a good measurement that will help cities and urban areas identify their public transport situation, it does not cover the entire spectrum of information required to comprehensively analyse &#x201C;convenient access&#x201D; to public transport and to in turn inform policy and investments. Here, we recommend some secondary indicators which can be used to measure &#x201C;convenient access&#x201D; to public transport, and which may provide a useful complement to the core indicator of spatial distance to stops. Several are identified here, but there may be others. It should however be noted that these secondary indicators may require more data inputs and sometimes field-based surveys, and that their collection may vary significantly across jurisdictions making comparisons difficult. Despite this, these indicators provide critical information that can help cities and urban areas improve their public transport systems and ensure the needs of all urban dwellers are catered for. The suggested secondary indicators include:</p>\n<ul>\n  <li><strong>Transit system performance:</strong> The methodology described above for monitoring the core indicator covers public transport service solely based on spatial access to stops and does not address the performance of the system, such as frequency of service, capacity, comfort, etc. We note that performance aspects of public transport are important because a service within walking distance is not necessarily considered as accessible if waiting times are long, frequency of service is low or if conditions are unsafe/insecure. The system cannot also be considered as accessible and reliable when passengers spend many hours from their trip origin to destination. These are not included in the core indicator, but countries are encouraged to collect and report this information as a secondary indicator. Transport stakeholders participating in Expert Group Meeting held in Berlin on 19 -20 October 2017 recommended the use of 20 minutes average waiting time during peak hours (from 5 am to 9 pm) to assess the frequency of the service. This data can be acquired from public transport timetables for some cities, from public transport service providers or through surveys. This measurement may however be limited in cities where informal public transport modes are prevalent since they often do not operate according to fixed schedules. </li>\n  <li><strong>Affordability:</strong> This can be used to further explain the indicator since access is only convenient for those who can afford the transport services. Affordability is often measured as the percentage of household income spent on transport of the poorest quintile of the population. Data can be obtained from surveys. The recommended indicator for affordability is that the poorest quintile should not spend more than 5% of their net household income on transport. </li>\n  <li><strong>Safety/security:</strong> This parameter may be difficult to measure but could be quantitatively captured in part from accident and crime statistics near stations and on the transit systems themselves. For example, safety of the public transport can be measured by the share or number of crimes within the public transport system to the total crimes in the city. In addition, it is recommended to include a question on the perception of safety of public transport in the national crime surveys, or in transport user surveys. </li>\n  <li><strong>Comfort and access to information:</strong> An additional feature of &#x201C;convenient access&#x201D; may be the presence of information systems such as real-time electronic schedule displays or other user information systems (e.g. apps), while comfort may also relate to features on the system and typical crowding or load factor levels. </li>\n  <li><strong>Modal shift to sustainable transport:</strong> It is important to continuously monitor the modal share (percentage of travellers using a particular type of transportation incl. private cars, taxis, non-motorised transport, Public transport, etc.), as well as passenger-km travelled on electric vehicles as percentage of total passenger-km travelled in the urban area from city mobility surveys. This parameter is important to understand the city&#x2019;s overall mobility mix, monitor the modal shift towards more sustainable transport over time, and provide actionable recommendations to move towards low carbon, shared, high-capacity mobility systems in the future. The data on this secondary indicator is largely available for many cities. UN-Habitat thus requests for such information in the country reporting template every year to understand the transitions in the modal share.</li>\n</ul>\n<p><br> </p>\n<p>Other measurement considerations which can be considered in the indicator measurement, and which can further improve understanding of prevailing public transport trends in cities include:</p>\n<ul>\n  <li><strong>Alternative metrics of &#x201C;spatial access&#x201D;:</strong> In some cities, alternative modes to reach a public transport stop exist -&#x2013; such as safe cycling lanes, bike share systems or other forms of micro-mobility. In these contexts, experts in the transport sector have suggested that a cycling distance of 2 km can be included in the creation of service areas to each public transport stop. </li>\n  <li><strong>Obstacles to reaching stations:</strong> Distance to stations may be adjusted by considering factors that create obstacles and make accessing the station difficult, at least for some travelers. An obvious example is the presence of walkways along the street network and the need to take stairs or steep ramps to reach a station, making it difficult for elderly or people with disabilities. Alternative routes would need to be identified, or stations indicated as not providing convenient access for some population groups. To identify the prevailing limitations, field observations will be required, which should capture, among other information, availability of safe walkways along the street network and existence of ramps or elevators (&#x201C;universal access&#x201D;), and special seating areas for the elderly and disabled. </li>\n</ul>\n<p><strong>Achieving a higher level of &#x201C;convenient access&#x201D; &#x2013; Access to opportunities</strong></p>\n<p>Beyond the secondary indicators for measuring convenient access to public transport lies another approach that understands <em>Transportation</em> as a <em>means</em>, <em>not an</em> <em>end. </em>This is based on the purpose of &apos;transportation&apos; to gain access to destinations, activities, services and goods. Ultimately, people do not wish to access transit stations, they wish to access destinations, and even access non-physical objectives such as &#x201C;opportunities&#x201D;. </p>\n<p>Operationally, access to &#x201C;opportunities&#x201D; means the ability of individuals to reach desired final destinations in a reasonable amount of time, for a reasonable cost, with adequate safety/security/ comfort, etc. For example, this may be measured as a maximum one-hour travel time between any origins and destinations (O-Ds) within a city, or at least those O-D combinations used (or desired to be used) by individuals. </p>\n<p>While measuring &#x201C;access to opportunities&#x201D; has more analytical and policy value to measuring &#x201C;access to transit stations&#x201D;, it is more difficult and data intensive, so it is not proposed as the core indicator. Though, as data systems improve and cities become more able to collect the needed data, it may eventually make sense to shift to this as a core indicator. We note here that there are three basic types of data needed to construct this indicator:</p>\n<ul>\n  <li>Data on the residential locations of individuals,</li>\n  <li>Data on the desired destinations of individuals (such as job, shopping, school, hospital locations),</li>\n  <li>Data on the available travel options and travel times linking the origins to the destinations.</li>\n</ul>\n<p>In fact, the first and third of these are very similar to what is needed to construct the core indicator, since residential locations and transit data are needed. The main additional data requirement is on the destinations, and there may be some additional complexities in putting the three types of data together. Efforts are ongoing to try to operationalize this approach and help cities beginning to collect the needed data.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>As part of the validation process, UN-Habitat developed a template to compile data generated by countries through the National Statistics Offices as well as other government agencies responsible for official statistics (see: <a href=\"https://data.unhabitat.org/datasets/template-for-compilation-of-sdg-indicator-11-2-1\">https://data.unhabitat.org/datasets/template-for-compilation-of-sdg-indicator-11-2-1</a>). Data compiled is then checked against several criteria including the data sources used, the application of internationally agreed definitions, classification and methodologies to the data from that source, etc. Once reviewed, appropriate feedback is then provided to individual countries for further discussion.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Any adjustments to the data is jointly agreed after consultations with the relevant national agencies that share the data points for reporting.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>This indicator is measured at city level, and aggregations from available cities made to represent national averages. UN-Habitat has proposed use of the <a href=\"https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf\">National Sample of Cities approach</a> to identify cities/urban areas for which data can be calculated in a manner that is nationally representative. Noting gaps in the availability of public transport data in many countries, particularly on smaller cities/urban areas which may impact negatively on the production of national aggregates, countries are requested to report on the individual city values without creating national aggregates. The data reporting template provided by UN-Habitat requests for both city and national values, allowing countries to report incrementally on the available data points. </p>\n<p>&#x2022;<strong> At regional and global levels</strong></p>\n<p>This indicator is measured at city level, and population weighted aggregates from available cities are undertaken to represent national, regional and global averages. Currently, there is adequate representative data on the indicator to undertake population weighted regional and global averages. The continued production of data on the indicator is also enhancing the accuracy of regional and global estimates and has eliminated the risk of missing data at this level. </p>", "REG_AGG__GLOBAL"=>"<p>Data at the global/regional levels will be estimated from national figures derived from an aggregation of performance for all cities/urban areas or a sample of nationally representative cities (selected using the national sample of cities approach developed by UN-Habitat). Regional estimates will incorporate national representations using a weighting by population sizes. Global monitoring will be led by UN-Habitat with the support of other partners and regional commissions.</p>", "DOC_METHOD__GLOBAL"=>"<p>Data for indicator 11.2.1 is to be collected at the city/urban level and aggregates made to the national level. For countries which have adequate capacity (personnel, systems, resources) and baseline data, the indicator can be computed for all cities/urban areas then averages used to report on national performances. For countries which do not have the capacity to collect data and undertake computations for all their cities/urban areas, UN-Habitat has proposed the use of the National Sample of Cities Approach, which allows them to select a representative sample from where weighted national aggregates can be undertaken.</p>\n<p>The guidance on implementation of the National Sample of Cities Approach is available here: <a href=\"https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf\">https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf</a>.</p>\n<p>UN-Habitat will continuously undertake capacity building on the sampling approach, and directly support countries to develop a national sample of cities where needed.</p>\n<p>UN-Habitat has developed a step-by-step data compilation and computation methodological document, which is applicable at the city and national level. The document is available here: <a href=\"https://data.unhabitat.org/pages/guidance\">https://data.unhabitat.org/pages/guidance</a> . The agency also provides on-the-task training to countries on a need basis, as well as continuous technical support throughout the data compilation process to ensure alignment of national processes with the globally adopted methodology.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>To ensure consistency in data production across countries, UN-Habitat has developed detailed step-by-step tutorials on the computation of indicator 11.2.1, which further explain the steps presented in this metadata. The detailed tutorials, which will be continuously updated are available at <a href=\"https://unhabitat.org/knowledge/data-and-analytics\">https://unhabitat.org/knowledge/data-and-analytics</a>, <a href=\"https://www.urbanagendaplatform.org/learning\">https://www.urbanagendaplatform.org/learning</a>, and <a href=\"https://data.unhabitat.org/\">https://data.unhabitat.org/</a>.</p>\n<p>Within its Data and Analytics Unit which is responsible for the indicator data compilation, UN-Habitat has a team of spatial data experts who check all submitted data and provide direct support to countries in the indicator computation.</p>\n<p>As part of its global custodianship of indicator 11.2.1, UN-Habitat has also established partnerships with major institutions and organizations involved in production of baseline data relevant for the indicator computation. The main aim of this is to create a common understanding on the approach for the indicator computation, and to encourage continuous production of high-quality global data that responds to the indicator computation needs. Examples of some ongoing initiatives with partners to manage quality of products and processes include, among others providing support to apply the Degree of Urbanisation at the local level for the indicator computation (in partnership with the European Commission), development of an Earth Observation Toolkit for SDG 11 (in partnership with EO4SDG and GEO), and continuous feedback to global products produced by partners such as ITDP, UITP, the German Aerospace Center (DLR) and the European Commission, among others. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UN-Habitat maintains the global urban indicators database that is used for monitoring of the urban metrics drawn from SDGs, New Urban Agenda (NUA), flagship reports (e.g. World Cities Report) and other official reporting. In general, for all new data, a thorough review is done to check for consistency and overall data quality by technical staff in the Data and Analytics unit before publication in the urban indicators database. This ensures that only the most accurate and reliable information are included in the database. Key elements considered in the review include: proper documentation of data sources; representativeness of data at national level, use of appropriate methodology for data collection and analysis (e.g. appropriate sampling process, values based on valid sample sizes), use of appropriate concepts and definitions, consistency of data trends with previously published/reported estimates for the indicator.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Once data is received from member states, UN-Habitat uses a checklist specific to each indicator to a) assess whether the data production process followed the metadata provisions, and b) confirm the accuracy of the data sources used for the indicator computation. Both components are captured in the reporting template shared with National Statistical Offices, which helps to assess whether computation was done using the proposed indicator inputs or proxies. The reporting template also requests for information that helps understand whether national data for the indicator was produced from a representative sample of the country&#x2019;s urban systems, or if estimates were done for only select cities/urban areas where data is easily available.</p>\n<p>In addition, the received data is also checked for other qualities such as data disaggregation, reporting period and consistency with other previously reported trends, which ensures reliable regional estimates. For indicator 11.2.1, one extra assessment that is done is to check the completeness of open-source data (such as OpenStreetMap and General Transit Feeds Specification, GTFS) for the specific country/city, where such is used for the indicator estimation.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>In 2024, data on indicator 11.2.1 is available for more than 2,000 cities from 190 countries. Some countries have also produced national averages based on the city level data. UN-Habitat has calculated populated weighted regional aggregates based on the M49 categories, as well as the UN regional commissions. UN-Habitat and partners are continuously supporting national statistical systems to increase data availability on the indicator, including disaggregation by gender and persons with disability. </p>\n<p><strong>Time series:</strong></p>\n<p>Annual based on data availability. Regional and global aggregates to be produced for 2020, 2025 and 2030. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The core indicator of access to public transport stations, and the proposed secondary indicators can in principle be disaggregated by various characteristics of groups within the population, to track whether all such groups have good access. Information can be disaggregated as shown below, including potential disadvantages such as disability, and by various other characteristics. Obtaining such data typically requires major efforts (mainly surveys) and often changes in mainstream mechanisms of data collection.</p>\n<p>Typical types of disaggregation include:</p>\n<ul>\n  <li>Disaggregation by location (intra-urban)</li>\n  <li>Disaggregation by income group</li>\n  <li>Disaggregation by sex (female-headed household)</li>\n  <li>Disaggregation by age group</li>\n  <li>Disaggregation by type of public transport system (low-capacity vs. high-capacity systems)</li>\n  <li>Disaggregation by formality of public transport carrier (formal vs. informal transport modes/ services)</li>\n  <li>Disaggregation by mode to reach public transport<strong> </strong>(walking vs. cycling)</li>\n</ul>\n<p>Quantifiable Derivatives:</p>\n<ul>\n  <li>Proportion of urban area that is served by convenient public transport systems</li>\n  <li>Proportion of population/urban area that has convenient access to public transport stop with universal accessibility for people with disabilities</li>\n  <li>Proportion of population/urban area that has frequent access to public transport during peak hours</li>\n  <li>Proportion of population/urban area that has frequent access to public transport during off-peak hours</li>\n  <li>Proportion of population with<strong> </strong>access to low-capacity systems (e.g. bus) and high-capacity systems (e.g. metros), access by <strong>walking vs. biking</strong>, etc.</li>\n  <li>Proportion of population with access to <strong>formal vs. informal</strong> transport modes/services</li>\n  <li>Share of population using different transport modes (modal share)</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>For this indicator, national data built up from a <a href=\"https://unhabitat.org/national-sample-of-cities/\">&#x201C;national sample of cities approach&#x201D;,</a> complemented with internationally available spatial data sources will be used to derive final estimates for reporting at national and global figures. As national agencies are responsible for data collection, no differences between country produced data and international estimated data on the indicator are expected to arise. Where such discrepancies exist, these will be resolved through planned technical meetings and capacity development workshops.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>[1] <a href=\"http://unhabitat.org/knowledge/data-and-analytics\">http://unhabitat.org/knowledge/data-and-analytics</a></p>\n<p>https://data.unhabitat.org/</p>\n<p><strong>References:</strong></p>\n<ul>\n  <li>Alain Bertaud, Cities as Labor Markets, February 2014, <a href=\"http://marroninstitute.nyu.edu/uploads/content/Cities_as_Labor_Markets.pdf\">http://marroninstitute.nyu.edu/uploads/content/Cities_as_Labor_Markets.pdf</a> (Accessed May 29, 2016)</li>\n  <li>Tracking the SDG Targets: An Issue Based Alliance for Transport</li>\n  <li><a href=\"http://unhabitat.org/planning-and-design-for-sustainable-urban-mobility-global-report-on-human-settlements-2013/\">http://unhabitat.org/planning-and-design-for-sustainable-urban-mobility-global-report-on-human-settlements-2013/</a></li>\n  <li>https://unhabitat.org/topic/mobility-and-transport<a href=\"http://www.digitalmatatus.com/\">http://www.digitalmatatus.com/</a></li>\n  <li>https://slocat.net/gsr-collection/</li>\n  <li><a href=\"https://www.jtlu.org/index.php/jtlu/article/view/683/665\">https://www.jtlu.org/index.php/jtlu/article/view/683</a></li>\n  <li><a href=\"http://data.london.gov.uk/dataset/public-transport-accessibility-levels/resource/86bbffe1-8af1-49ba-ac9b-b3eacaf68137/proxy\">http://data.london.gov.uk/dataset/public-transport-accessibility-levels/resource/86bbffe1-8af1-49ba-ac9b-b3eacaf68137/proxy</a></li>\n  <li>Presentations by transport stakeholders participating in Expert Group Meeting on 19/20 October 2017 in Berlin: <a href=\"https://www.dropbox.com/sh/ktfyvi34s3v4wzi/AADm4z0fvSJ17Se89zyU6lswa?dl=0\">https://www.dropbox.com/sh/ktfyvi34s3v4wzi/AADm4z0fvSJ17Se89zyU6lswa?dl=0</a></li>\n  <li>National Sample of Cities: https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf</li>\n  <li>Access to Opportunities (World Bank): <a href=\"http://www.worldbank.org/en/topic/transport/brief/connections-note-25\">http://www.worldbank.org/en/topic/transport/brief/connections-note-25</a></li>\n  <li>Global Mobility Report 2017 (SUM4All): <a href=\"https://openknowledge.worldbank.org/bitstream/handle/10986/28542/120500.pdf?sequence=4\">https://openknowledge.worldbank.org/bitstream/handle/10986/28542/120500.pdf?sequence=4</a></li>\n  <li>Coverage Areas for Public Transport: <a href=\"https://www.witpress.com/Secure/elibrary/papers/UT08/UT08017FU1.pdf\">https://www.witpress.com/Secure/elibrary/papers/UT08/UT08017FU1.pdf</a> </li>\n  <li>Detailed Indicator 11.2.1 training module: https://data.unhabitat.org/pages/guidance</li>\n  <li>Some population gridding approaches: <a href=\"https://sedac.ciesin.columbia.edu/data/collection/usgrid/methods\">https://sedac.ciesin.columbia.edu/data/collection/usgrid/methods</a>; <a href=\"https://www.ciesin.columbia.edu/data/hrsl/\">https://www.ciesin.columbia.edu/data/hrsl/</a>; <a href=\"https://ec.europa.eu/eurostat/statistics-explained/index.php/Population_grids\">https://ec.europa.eu/eurostat/statistics-explained/index.php/Population_grids</a>; <a href=\"https://www.worldpop.org/methods\">https://www.worldpop.org/methods</a></li>\n  <li>Sustainable Mobility for All. 2017. <em>Global Mobility Report 2017: Tracking Sector Performance.</em> Washington DC, License: Creative Commons Attribution CC BY 3.0</li>\n  <li>Poelman, H., L. Dijkstra, 2015. <em>Regional Working Paper 2015: Measuring access to public transport in European cities</em>, WP01/2015. Accessed at <a href=\"https://ec.europa.eu/regional_policy/sources/docgener/work/2015_01_publ_transp.pdf\">https://ec.europa.eu/regional_policy/sources/docgener/work/2015_01_publ_transp.pdf</a>.</li>\n  <li>Fulton, L, 2017. <em>Summary of recommendations provided by key stakeholders towards a refined Monitoring Methodology of SDG 11.2.</em> Urban Pathways Conference, 19-20 October 2017, Berlin.</li>\n</ul>", "indicator_sort_order"=>"11-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.3.1", "slug"=>"11-3-1", "name"=>"Relación entre la tasa de consumo de tierras y la tasa de crecimiento de la población", "url"=>"/site/es/11-3-1/", "sort"=>"110301", "goal_number"=>"11", "target_number"=>"11.3", "global"=>{"name"=>"Relación entre la tasa de consumo de tierras y la tasa de crecimiento de la población"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Relación entre la tasa de consumo de tierras y la tasa de crecimiento de la población", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Relación entre la tasa de consumo de tierras y la tasa de crecimiento de la población", "indicator_number"=>"11.3.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "texto_oceca"=>"", "observaciones"=>"", "justificacion_global"=>"\nA nivel mundial, la cobertura del suelo actual se ve alterada principalmente por el \nuso humano directo: agricultura y ganadería, aprovechamiento y gestión forestal, y \nconstrucción y desarrollo urbano y suburbano. Una característica distintiva de \nmuchas ciudades del mundo es su expansión hacia el exterior, mucho más allá de los \nlímites administrativos formales, impulsada en gran medida por el uso del automóvil, \nla deficiente planificación urbana y regional y la especulación inmobiliaria. \n\nUna gran proporción de ciudades, tanto de países desarrollados como en desarrollo, \npresentan patrones de expansión suburbana de alto consumo, que a menudo se \nextienden a zonas periféricas aún más lejanas. Un estudio global sobre 120 ciudades \nmuestra que la cobertura del suelo urbano ha crecido, en promedio, más del triple que \nla población urbana; en algunos casos, estudios similares a nivel nacional mostraron \nuna diferencia de tres a cinco veces. \n\nPara monitorear eficazmente el crecimiento del consumo de suelo, no solo es \nnecesario contar con información sobre la cobertura del uso del suelo existente, \nsino también con la capacidad de monitorear la dinámica del uso del suelo resultante \ntanto de las demandas cambiantes del aumento de la población como de las fuerzas \nnaturales que influyen en la configuración del paisaje.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-03-01.pdf\">Metadatos 11-3-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "texto_oceca"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nGlobally, land cover today is altered principally by direct human use: \nby agriculture and livestock raising, forest harvesting and management \nand urban and suburban construction and development. A defining feature \nof many of the world’s cities is an outward expansion far beyond formal \nadministrative boundaries, largely propelled by the use of the automobile, \npoor urban and regional planning and land speculation. \n\nA large proportion of cities both from developed and developing countries \nhave high consuming suburban expansion patterns, which often extend to even \nfurther peripheries. A global study on 120 cities shows that urban land cover \nhas, on average, grown more than three times as much as the urban population; \nin some cases similar studies at national level showed a difference that was \nthree to five times fold. \n\nIn order to effectively monitor land consumption growth, it is not only necessary \nto have the information on existing land use cover but also the capability to \nmonitor the dynamics of land use resulting out of both changing demands of increasing \npopulation and forces of nature acting to shape the landscape. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-03-01.pdf\">Metadat 11-3-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "texto_oceca"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nA nivel mundial, la cobertura del suelo actual se ve alterada principalmente por el \nuso humano directo: agricultura y ganadería, aprovechamiento y gestión forestal, y \nconstrucción y desarrollo urbano y suburbano. Una característica distintiva de \nmuchas ciudades del mundo es su expansión hacia el exterior, mucho más allá de los \nlímites administrativos formales, impulsada en gran medida por el uso del automóvil, \nla deficiente planificación urbana y regional y la especulación inmobiliaria. \n\nUna gran proporción de ciudades, tanto de países desarrollados como en desarrollo, \npresentan patrones de expansión suburbana de alto consumo, que a menudo se \nextienden a zonas periféricas aún más lejanas. Un estudio global sobre 120 ciudades \nmuestra que la cobertura del suelo urbano ha crecido, en promedio, más del triple que \nla población urbana; en algunos casos, estudios similares a nivel nacional mostraron \nuna diferencia de tres a cinco veces. \n\nPara monitorear eficazmente el crecimiento del consumo de suelo, no solo es \nnecesario contar con información sobre la cobertura del uso del suelo existente, \nsino también con la capacidad de monitorear la dinámica del uso del suelo resultante \ntanto de las demandas cambiantes del aumento de la población como de las fuerzas \nnaturales que influyen en la configuración del paisaje.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-03-01.pdf\">Metadatuak 11-3-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 11.3: By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated and sustainable human settlement planning and management in all countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 11.3.1: Ratio of land consumption rate to population growth rate</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_LND_CNSPOP - Ratio of land consumption rate to population growth rate [11.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.2.1: Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities</p>\n<p>11.6.2: Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted)</p>\n<p>11.7.1: Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities</p>\n<p>11.a.1: Proportion of population living in cities that implement urban and regional development plans integrating population projections and resource needs, by size of city</p>\n<p>15.1.2: Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type</p>\n<p>11.7.2: Proportion of persons victim of physical or sexual harassment, by sex, age, disability status and place of occurrence, in the previous 12 months</p>\n<p>11.b.1: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015-2030 [a]</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>The indicator is defined as the ratio of land consumption rate to population growth rate.</p>\n<p>This indicator requires defining the two components of population growth and land consumption rate. Computing the population growth rate is more straightforward and more readily available, while land consumption rate is slightly challenging, and requires the use of new techniques. In estimating the land consumption rate, one needs to define what constitutes &#x201C;consumption&#x201D; of land since this may cover aspects of &#x201C;consumed&#x201D; or &#x201C;preserved&#x201D; or available for &#x201C;development&#x201D; for cases such as land occupied by wetlands. Secondly, there is not one unequivocal measure of whether land that is being developed is truly &#x201C;newly-developed&#x201D; (or vacant) land, or if it is at least partially &#x201C;redeveloped&#x201D;. As a result, the percentage of current total urban land that was newly developed (consumed) will be used as a measure of the land consumption rate. The fully developed area is also sometimes referred to as built up area.</p>\n<p><strong>Concepts:</strong></p>\n<p><strong>City or urban area</strong>: Since 2016, UN-Habitat and partners organized global consultations and discussions to narrow down the set of meaningful definitions that would be helpful for the global monitoring and reporting process. Following consultations with 86 member states, the United Nations Statistical Commission, in its 51st Session (March 2020) endorsed the Degree of Urbanisation (DEGURBA) as a workable method to delineate cities, urban and rural areas for international statistical comparisons.<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> </sup>This definition combines population size and population density thresholds to classify the entire territory of a country along the urban-rural continuum, and captures the full extent of a city, including the dense neighbourhoods beyond the boundary of the central municipality. DEGURBA is applied in a two-step process: First, 1 km<sup>2</sup> grid cells are classified based on population density, contiguity and population size. Subsequently, local units are classified as urban or rural based on the type of grid cells in which majority of their population resides. For the computation of indicator 11.3.1, countries are encouraged to adopt the degree of urbanisation to define the analysis area (city or urban area).</p>\n<p><strong>Population growth rate (PGR)</strong> is the change of a population in a defined area (country, city, etc) during a period, usually one year, expressed as a percentage of the population at the start of that period. It reflects the number of births and deaths during a period and the number of people migrating to and from the focus area. In SDG 11.3.1, this is computed at the area defined as urban/city. </p>\n<p><strong>Land consumption</strong> within the context of indicator 11.3.1 is defined as the uptake of land by urbanized land uses, which often involves conversion of land from non-urban to urban functions. </p>\n<p><strong>Land consumption rate (LCR)</strong> is the rate at which urbanized land or land occupied by a city/urban area changes during a period of time (usually one year), expressed as a percentage of the land occupied by the city/urban area at the start of that time. </p>\n<p><strong>Built up area </strong>within the context of indicator 11.3.1 is defined as all areas occupied by buildings. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> A recommendation on the method to delineate cities, urban and rural areas for international statistical comparisons. <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Land consumption rate: Percent (%)</p>\n<p>Population growth rate: Percent (%) </p>\n<p>Ratio of land consumption rate to population growth rate: Ratio</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The indicator depends on international classifications on boundaries of countries and regions and city boundaries. Guidance on the city definitions is provided based on a harmonized global city definition, see: <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf</a></p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Sources and collection process:</strong></p>\n<p>Population data required for this indicator is available from National Statistical Offices, UNDESA as well as through newly emerging multi-temporal gridded population datasets for the world. Historical built-up area data can also be generated for most countries and cities using mid-to-high resolution satellite imagery from the Landsat and Sentinel missions. Higher resolution data is available for several countries which have a rich repository of earth observation missions or partnerships with commercial providers of high to very high-resolution imagery. Other sources of data for this indicator include urban planning authorities and multi-temporal analytical databases on built-up area at the global level produced by organizations working in the earth observation field. </p>\n<p>The production of data for this indicator requires some level of understanding of geospatial analysis techniques at the country level. Several tools have been developed to help with the indicator computation, including systems that allow for on-the-cloud analysis, but users still require some good level of understanding of the process and geospatial analysis to efficiently utilize these tools. Equally, access to internet is needed either to download the free satellite imagery or undertake analysis using existing cloud-based architecture. </p>\n<p>National level capacity building initiatives will aim to balance the knowledge and understanding of the analysis, compilation and reporting of this indicator. Global reporting will rely on the estimates that come from the national statistical agencies, who should work collaboratively with mapping agencies and city data producers. With uniform standards in computation at the national level, few errors of omission or bias will be observed at the global/regional level. A rigorous analysis routine will be used to re-assess the quality and accuracy of the data at the regional and global levels. This will involve cross-comparisons with expected ranges of the values reported for cities.</p>\n<p>UN-Habitat has developed a simple reporting template that allows countries to input data on the intermediate products (built-up area and population) then get the computed values for each analysis city and period. The template, which will be send to countries every year to report any new data is appended to this metadata and can also be accessed <a href=\"https://data.unhabitat.org/datasets/template-for-compilation-of-sdg-indicator-11-3-1\">HERE</a>.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data for this indicator combines earth observation, geospatial analysis and use of population data from censuses and surveys. Input data for computation of the land consumption rate is extracted from multi-temporal satellite imagery through remote sensing and geospatial analysis processes. The quality of data for this component is greatly reliant on the resolution of the input satellite imagery, but the freely available Landsat and Sentinel Imagery provide good quality data that can consistently be used to compute the indicator. The methods of extracting data from these imageries vary from standalone commercial and open-source software (e.g. Erdas Imagine, QGIS, Saga GIS, ENVI, etc) to cloud-based processing systems such as Google Earth Engine.</p>\n<p>Computation of the population growth rate component of the indicator relies on data from statistical sources such as censuses, which should be disaggregated to the smallest units possible. Use of population modelling approaches (such as to produce gridded population datasets) is encouraged where high resolution data from the National Statistical Offices is not available. The approaches for disaggregating population to grids vary, but the most common ones include disaggregating populations to built-up areas. Some examples of common approaches are provided in the references section. </p>\n<p>To implement the Degree of Urbanisation approach to city/urban area definition, which is proposed for this indicator computation, the European Commission Joint Research Centre (EC-JRC) have developed a standalone application which uses either locally or globally produced input data on population and built up layers. The tool is available <a href=\"https://human-settlement.emergency.copernicus.eu/tools.php\">HERE</a>, while the description of how to implement the approach is available <a href=\"https://human-settlement.emergency.copernicus.eu/degurbaDocumentation.php\">HERE</a>. </p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>The monitoring of the indicator can be repeated at regular intervals of 5 years, allowing for three reporting points until the year 2030. Since this indicator considers historical growth trends of urban areas, analysis can cover periods as far back as data allows. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Updates will be undertaken every year, which would allow for annual updates in reporting at the global level.</p>", "DATA_SOURCE__GLOBAL"=>"<p>UN-Habitat and other partners such as the Global Human Settlement Layer (GHSL) team, the German Aerospace Center (DLR), partners in the Group on Earth Observations (GEO) and ESRI among others will support various components for reporting on this indicator. The global responsibility of building the capacity of national governments and statistical agencies to report on this indicator will be led by UN-Habitat. National governments/national statistical agencies will have the primary responsibility of reporting on this indicator at national level with the support of UN-Habitat to ensure uniform standards in analysis and reporting.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UN-Habitat with the support of other selected partners will lead the compilation of data for this indicator.</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Human Settlements Programme (UN-Habitat) is the specialized agency for sustainable urbanization and human settlements in the United Nations. The mandate derives from the priorities established in relevant General Assembly resolutions and decisions, including General Assembly resolution 3327 (XXIX), by which the General Assembly established the United Nations Habitat and Human Settlements Foundation, and resolution 32/162 by which the Assembly established the United Nations Center for Human Settlements (Habitat). In 2001, by its Resolution 56/206, the General Assembly transformed the Habitat into the secretariat of the United Nations Human Settlements Programme (UN-Habitat), with a mandate to coordinate human settlements activities within the United Nations System. As such, UN-Habitat has been designated the overall coordinator of SDG 11 and specifically as a custodian agency for 9 of the 15 indicators under SDG 11 including indicator 11.3.1. UN-Habitat also supports the monitoring and reporting of 4 urban specific indicators in other goals.</p>", "RATIONALE__GLOBAL"=>"<p>Globally, land cover today is altered principally by direct human activity: by agriculture and livestock raising, forest harvesting and management and urban and suburban construction and development. A defining feature of many of the world&#x2019;s cities is an outward expansion far beyond formal administrative boundaries, largely propelled by the use of the automobile, poor urban and regional planning and land speculation. A large proportion of cities both from developed and developing countries have high consuming suburban expansion patterns, which often extend to even further peripheries. A global study on 120 cities shows that urban land cover has, on average, grown more than three times as much as the urban population [1]; in some cases, similar studies at national level showed a difference that was three to five times fold [3]. In order to effectively monitor land consumption growth, it is not only necessary to have the information on existing land use cover but also the capability to monitor the dynamics of land use resulting out of both changing demands of increasing population and forces of nature acting to shape the landscape.</p>\n<p>Cities require an orderly urban expansion that makes the land use more efficient. They need to plan for future internal population growth and city growth resulting from migrations. They also need to accommodate new and thriving urban functions such as transportation routes, etc., as they expand. However, frequently the physical growth of urban areas is disproportionate in relation to population growth, and these results in land use that is less efficient in many forms. This type of growth turns out to violate every premise of sustainability that an urban area could be judged by including impacting on the environment and causing other negative social and economic consequences such as increasing spatial inequalities and lessening of economies of agglomeration.</p>\n<p>This indicator is connected to many other indicators of the SDGs. It ensures that the SDGs integrate the wider dimensions of space, population and land adequately, providing the framework for the implementation of other goals such as poverty, health, education, energy, inequalities and climate change. The indicator has a multipurpose measurement as it is not only related to the type/form of the urbanization pattern. It is also used to capture various dimensions of land use efficiency: economic (proximity of factors of production); environmental (lower per capita rates of resource use and GHG emissions); social (reduced travel distance and cost expended). Finally, this indicator integrates an important spatial component and is fully in line with the recommendations made by the Data Revolution initiative.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The major limitation for this indicator lies in its interpretation. In each human settlement structure, there are many factors at play, that make it more difficult to generalize the implication of a single land consumption rate to population growth rate (LCRPGR) value to sustainable urbanization. For example, while a value less than 1 could be a good indicator of urban compactness and its associated benefits, intra-city analysis may reveal high levels of congestion and poor living environments, which is against the principles of sustainable development. On the other hand, a value of one may not mean an optimal balance between spatial growth of urban areas and their populations, since it would imply new developments with every unit increase in population. To help explain the values of the indicator, two secondary indicators have been proposed, which use the same inputs as the core indicator: built up area per capita and total change in built up area. </p>\n<p>Another limitation in the indicator is where zero or negative growth get reported, such as where population over the analysis period decreases or a natural disaster results in loss of the built-up area mass. Without looking at the land consumption and population growth rates separately, it is difficult to correctly interpret the indicator and its meaning. To address this, it is recommended to understand the individual rates, and also use the proposed secondary indicators to explain the trends. </p>\n<p>Aggregating the indicator values for more than one city may also make the interpretation ambiguous. For example, an average value for a country with two cities might be between 0 and 1 if both cities are record values within this range, or if one has a value above 1 and the other a value below 0. The use of the national sample of cities approach, which produces a representative sample for each country will help resolve this challenge. </p>\n<p>In some cases, it is difficult to measure the urban expansion by conurbations of two or more urban areas that are in close proximity; to whom to attribute the urban growth and how to include it as one metric usually becomes a challenge. At the same time, data would not always coincide to administrative levels, boundaries and built-up areas. To resolve this, the use of a harmonized approach to defining urban areas and cities has been identified as helping to resolve this challenge.</p>\n<p>In the absence of the GIS layers, this indicator may not be computed as defined. As a result, more alternative measures for land that is developed or consumed per year can be adequately used. Alternatively, one can monitor the efficient use of urban land by measuring how well we are achieving the densities in residential zones that any city plans, or international guidance call for. Comparing achieved to planned densities is very useful at the city level. However, planned densities vary greatly from country to country, and at times from city to city. At the sub-regional or city levels, it is more appropriate to compare average densities achieved currently to those achieved in the recent past. While building more densely does use land more efficiently, high density neighborhoods, especially in and around urban centers, have a number of other advantages. They support more frequent public transportation, and more local stores and shops; they encourage pedestrian activity to and from local establishments; and they create lively (and sometimes safer) street life.</p>", "DATA_COMP__GLOBAL"=>"<p>The method to compute ratio of land consumption rate to population growth rate follows five broad steps:</p>\n<ol>\n  <li>Deciding on the analysis period/years</li>\n  <li>Delimitation of the urban area or city which will act as the geographical scope for the analysis</li>\n  <li>Spatial analysis and computation of the land consumption rate</li>\n  <li>Spatial analysis and computation of the population growth rate</li>\n  <li>Computation of the ratio of land consumption rate to population growth rate</li>\n  <li>Computation of recommended secondary indicators</li>\n  <li><strong>Deciding on the analysis period/years</strong></li>\n</ol>\n<p>This step involves selecting the period during which the measurement of the indicator will be undertaken. Since this indicator considers historical growth of urban areas, analysis can be done annually or in 5-year or 10-year cycles. Cycles of 5 or 10 years are recommended, especially where use of mid-to-high resolution satellite imagery is used to extract data on built up areas, which is used to compute the land consumption rate component of the indicator. UN-Habitat and partners have been creating a repository of some data on this indicator using 1990 as the baseline year. Countries can however compute the indicator as far as back as satellite imagery is available (1975 for Landsat free imagery) and can maintain the current/most recent year as the final reporting year. </p>\n<ol>\n  <li><strong>Delimitation of the urban area or city which will act as the spatial analysis scope</strong></li>\n</ol>\n<p>Urban areas and cities grow in different ways, the most common of which include infill (new developments within existing urban areas resulting in densification), extension (new developments at the edge of existing urban areas), leapfrogging (new urban threshold developments which are not attached to the urban area but which are functionally linked) and inclusion (engulfing of outlying urban clusters or leapfrog developments into the urban area, often forming urban conurbations). Key to note also is that growth of urban areas is not always positive. Sometimes, negative growth can be recorded, such as where disasters (e.gs floods, earthquakes) result in collapse of buildings and/or reduction in the built-up area mass. </p>\n<p>Understanding the spatial growth of urban areas requires two important pre-requisites: a) delimitation of an appropriate spatial analysis scope which captures the entire urban fabric (as opposed to just the administratively defined boundaries), and b) use of a growth tracking measurement that helps understand when both positive and negative growth happen. For the former, a harmonized urban area/city definition approach which allows for consistent analysis is recommended, while the use of built up areas is recommended for the latter since it allows for measurement of both positive and negative urban growth. </p>\n<p>Following consultations with 86 member states, the United Nations Statistical Commission in its 51<sup>st</sup> Session (March 2020) endorsed the Degree of Urbanisation (DEGURBA) as a workable method to delineate cities, urban and rural areas for international statistical comparisons. Countries are thus encouraged to adopt this approach, which will help them produce data that is comparable across urban areas within their territories, as well as with urban areas and cities in other countries. More details on DEGURBA are available here: <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf</a></p>\n<ol>\n  <li><strong>Spatial analysis and computation of the land consumption rate</strong></li>\n</ol>\n<p>Using the urban boundaries defined in step (b), spatial analysis is undertaken to determine the land consumption rate. To implement this, the three steps below are followed:</p>\n<ol>\n  <li>From satellite imagery, extract data on built up areas for each analysis year </li>\n  <li>Calculate the total area covered by the built-up areas for each of the analysis years </li>\n  <li>Compute the (annual) land consumption rate using the formula:</li>\n</ol>\n<p></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"bold-italic\">L</mi>\n    <mi mathvariant=\"bold-italic\">C</mi>\n    <mi mathvariant=\"bold-italic\">R</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>V</mi>\n          </mrow>\n          <mrow>\n            <mi>p</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n        <mo>-</mo>\n        <msub>\n          <mrow>\n            <mi>V</mi>\n          </mrow>\n          <mrow>\n            <mi>p</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>V</mi>\n          </mrow>\n          <mrow>\n            <mi>p</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mfrac>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where: <em>V<sub>present</sub></em> is total built-up area in current year</p>\n<p><em>V<sub>past</sub></em> is total built-up area in past year</p>\n<p><em>t</em> is the number of years between <em>V<sub>present</sub></em> and <em>V<sub>past</sub></em> (or length in years of the period considered)</p>\n<ol>\n  <li><strong>Spatial analysis and computation of the population growth rate</strong></li>\n</ol>\n<p>Using the urban boundaries defined in step (b), calculate the total population within the urban area in each of the analysis years where the land consumption rate is computed. Population data collected by National Statistical Offices through censuses and other surveys should be used for this analysis. Where this type of population data is not available, or where data is released at large population units which exceed the defined urban area, countries are encouraged to create population grids, which can help disaggregate the data from large and different sized census/population data release units to smaller uniform sized grids. </p>\n<p>The (annual) population growth rate is calculated using the total population within the urban area for the analysis period using the formula below:</p>\n<p><strong>Population Growth rate i.e. </strong><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">P</mi>\n    <mi mathvariant=\"bold\">G</mi>\n    <mi mathvariant=\"bold\">R</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"bold\">L</mi>\n        <mi mathvariant=\"bold\">N</mi>\n        <mfenced separators=\"|\">\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"bold\">P</mi>\n                <mi mathvariant=\"bold\">o</mi>\n                <mi mathvariant=\"bold\">p</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"bold\">t</mi>\n                <mo>+</mo>\n                <mi mathvariant=\"bold\">n</mi>\n              </mrow>\n            </msub>\n            <mo>/</mo>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"bold\">P</mi>\n                <mi mathvariant=\"bold\">o</mi>\n                <mi mathvariant=\"bold\">p</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"bold\">t</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <mo>(</mo>\n        <mi mathvariant=\"bold\">y</mi>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where </p>\n<p>LN is the natural logarithm value</p>\n<p>Pop<sub>t </sub>is the total population within the urban area/city in the past/initial year</p>\n<p>Pop<sub>t+n</sub> is the total population within the urban area/city in the current/final year</p>\n<p><em>y</em> is the number of years between the two measurement periods</p>\n<ol>\n  <li><strong>Computation of the ratio of land consumption rate to population growth rate</strong></li>\n</ol>\n<p>The ratio of land consumption rate to population growth rate (LCRPGR) is calculated using the formula: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"bold\">L</mi>\n    <mi mathvariant=\"bold\">C</mi>\n    <mi mathvariant=\"bold\">R</mi>\n    <mi mathvariant=\"bold\">P</mi>\n    <mi mathvariant=\"bold\">G</mi>\n    <mi mathvariant=\"bold\">R</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mfrac>\n              <mrow>\n                <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n                <mi mathvariant=\"bold\">L</mi>\n                <mi mathvariant=\"bold\">a</mi>\n                <mi mathvariant=\"bold\">n</mi>\n                <mi mathvariant=\"bold\">d</mi>\n                <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n                <mi mathvariant=\"bold\">C</mi>\n                <mi mathvariant=\"bold\">o</mi>\n                <mi mathvariant=\"bold\">n</mi>\n                <mi mathvariant=\"bold\">s</mi>\n                <mi mathvariant=\"bold\">u</mi>\n                <mi mathvariant=\"bold\">m</mi>\n                <mi mathvariant=\"bold\">p</mi>\n                <mi mathvariant=\"bold\">t</mi>\n                <mi mathvariant=\"bold\">i</mi>\n                <mi mathvariant=\"bold\">o</mi>\n                <mi mathvariant=\"bold\">n</mi>\n                <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n                <mi mathvariant=\"bold\">r</mi>\n                <mi mathvariant=\"bold\">a</mi>\n                <mi mathvariant=\"bold\">t</mi>\n                <mi mathvariant=\"bold\">e</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n                <mi mathvariant=\"bold\">P</mi>\n                <mi mathvariant=\"bold\">o</mi>\n                <mi mathvariant=\"bold\">p</mi>\n                <mi mathvariant=\"bold\">u</mi>\n                <mi mathvariant=\"bold\">l</mi>\n                <mi mathvariant=\"bold\">a</mi>\n                <mi mathvariant=\"bold\">t</mi>\n                <mi mathvariant=\"bold\">i</mi>\n                <mi mathvariant=\"bold\">o</mi>\n                <mi mathvariant=\"bold\">n</mi>\n                <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n                <mi mathvariant=\"bold\">g</mi>\n                <mi mathvariant=\"bold\">r</mi>\n                <mi mathvariant=\"bold\">o</mi>\n                <mi mathvariant=\"bold\">w</mi>\n                <mi mathvariant=\"bold\">t</mi>\n                <mi mathvariant=\"bold\">h</mi>\n                <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n                <mi mathvariant=\"bold\">r</mi>\n                <mi mathvariant=\"bold\">a</mi>\n                <mi mathvariant=\"bold\">t</mi>\n                <mi mathvariant=\"bold\">e</mi>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p>The overall formula can be summarized as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"bold\">L</mi>\n    <mi mathvariant=\"bold\">C</mi>\n    <mi mathvariant=\"bold\">R</mi>\n    <mi mathvariant=\"bold\">P</mi>\n    <mi mathvariant=\"bold\">G</mi>\n    <mi mathvariant=\"bold\">R</mi>\n    <mo>=</mo>\n    <mfrac bevelled=\"true\">\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mfrac>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>V</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>p</mi>\n                    <mi>r</mi>\n                    <mi>e</mi>\n                    <mi>s</mi>\n                    <mi>e</mi>\n                    <mi>n</mi>\n                    <mi>t</mi>\n                  </mrow>\n                </msub>\n                <mo>-</mo>\n                <msub>\n                  <mrow>\n                    <mi>V</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>p</mi>\n                    <mi>a</mi>\n                    <mi>s</mi>\n                    <mi>t</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>V</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>p</mi>\n                    <mi>a</mi>\n                    <mi>s</mi>\n                    <mi>t</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n            <mo>&#xD7;</mo>\n            <mfrac>\n              <mrow>\n                <mn>1</mn>\n              </mrow>\n              <mrow>\n                <mi>t</mi>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mfrac>\n              <mrow>\n                <mi mathvariant=\"bold\">L</mi>\n                <mi mathvariant=\"bold\">N</mi>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mfrac>\n                      <mrow>\n                        <msub>\n                          <mrow>\n                            <mi mathvariant=\"bold\">P</mi>\n                            <mi mathvariant=\"bold\">o</mi>\n                            <mi mathvariant=\"bold\">p</mi>\n                          </mrow>\n                          <mrow>\n                            <mi mathvariant=\"bold\">t</mi>\n                            <mo>+</mo>\n                            <mi mathvariant=\"bold\">n</mi>\n                          </mrow>\n                        </msub>\n                      </mrow>\n                      <mrow>\n                        <msub>\n                          <mrow>\n                            <mi mathvariant=\"bold\">P</mi>\n                            <mi mathvariant=\"bold\">o</mi>\n                            <mi mathvariant=\"bold\">p</mi>\n                          </mrow>\n                          <mrow>\n                            <mi mathvariant=\"bold\">t</mi>\n                          </mrow>\n                        </msub>\n                      </mrow>\n                    </mfrac>\n                  </mrow>\n                </mfenced>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"bold\">y</mi>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mfrac>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n  </math></p>\n<p>The analysis years for both the land consumption rate and the population growth rate should be the same. </p>\n<ol>\n  <li><strong>Computation of recommended secondary indicators</strong></li>\n</ol>\n<p>There are two important secondary indicators which help interpret the value of the main indicator - LGRPGR, thus helping in better understanding the nature of urban growth in each urban area. Both indicators use the same input data as the LCRPGR and will thus not require additional work by countries. These are: </p>\n<ol>\n  <li><strong>Built-up area per capita</strong> &#x2013; which is a measure of the average amount of built-up area available to each person in an urban area during each analysis year. This indicator can help identify when urban areas become too dense and/or when they become too sparsely populated. It is computed by dividing the total built-up area by the total urban population within the urban area/city at a given year, using the formula below:</li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"bold\">B</mi>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">l</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mo>-</mo>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">r</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">r</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mo>(</mo>\n    <msup>\n      <mrow>\n        <mi mathvariant=\"bold-italic\">m</mi>\n      </mrow>\n      <mrow>\n        <mn>2</mn>\n      </mrow>\n    </msup>\n    <mo>/</mo>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">r</mi>\n    <mi mathvariant=\"bold\">s</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">n</mi>\n    <mo>)</mo>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mfrac>\n              <mrow>\n                <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n                <msub>\n                  <mrow>\n                    <mi mathvariant=\"bold\">U</mi>\n                    <mi mathvariant=\"bold\">r</mi>\n                    <mi mathvariant=\"bold\">B</mi>\n                    <mi mathvariant=\"bold\">U</mi>\n                  </mrow>\n                  <mrow>\n                    <mi mathvariant=\"bold\">t</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n                <msub>\n                  <mrow>\n                    <mi mathvariant=\"bold\">P</mi>\n                    <mi mathvariant=\"bold\">o</mi>\n                    <mi mathvariant=\"bold\">p</mi>\n                  </mrow>\n                  <mrow>\n                    <mi mathvariant=\"bold\">t</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p>Where</p>\n<p>UrBU<sub>t </sub>is the total built-up area/city in the urban area in time t (in square meters) </p>\n<p>Pop<sub>t</sub> is the population in the urban area in time t</p>\n<ol>\n  <li><strong>Total change in built up area</strong> &#x2013; which is a measure of the total increase in built up areas within the urban area over time. When applied to a small part of an urban area, such as the core city (or old part of the urban area), this indicator can be used to understand densification trends in urban areas. It is measured using the same inputs as the land consumption rate for the different analysis years, based on the below formula: </li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"bold\">T</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">l</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">h</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">n</mi>\n    <mi mathvariant=\"bold\">g</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">n</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">b</mi>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">l</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">r</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi mathvariant=\"bold\">%</mi>\n    <mo>)</mo>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"bold\">U</mi>\n                <mi mathvariant=\"bold\">r</mi>\n                <mi mathvariant=\"bold\">B</mi>\n                <mi mathvariant=\"bold\">U</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"bold\">t</mi>\n                <mo>+</mo>\n                <mi mathvariant=\"bold\">n</mi>\n              </mrow>\n            </msub>\n            <mo>-</mo>\n            <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"bold\">U</mi>\n                <mi mathvariant=\"bold\">r</mi>\n                <mi mathvariant=\"bold\">B</mi>\n                <mi mathvariant=\"bold\">U</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"bold\">t</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi mathvariant=\"bold\">U</mi>\n            <mi mathvariant=\"bold\">r</mi>\n            <mi mathvariant=\"bold\">B</mi>\n            <mi mathvariant=\"bold\">U</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"bold\">t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where</p>\n<p>UrBU<sub>t +n </sub>is the total built-up area in the urban area/city in time the current/final year </p>\n<p>UrBU<sub>t </sub>is the total built-up area in the urban area/city in time the past/initial year </p>\n<p>Detailed steps for computation of the core indicator and the secondary indicators are available in the detailed training module for indicator 11.3.1: https://data.unhabitat.org/pages/guidance </p>", "DATA_VALIDATION__GLOBAL"=>"<p>As part of the validation process, UN-Habitat developed a template to compile data generated by countries through the National Statistics Offices as well as other government agencies responsible for official statistics (see: <a href=\"https://data.unhabitat.org/datasets/template-for-compilation-of-sdg-indicator-11-3-1\">https://data.unhabitat.org/datasets/template-for-compilation-of-sdg-indicator-11-3-1</a>). Data compiled is then checked against several criteria including the data sources used, the application of internationally agreed definitions, classification and methodologies to the data from that source, etc. Once reviewed, appropriate feedback is then provided to individual countries for further discussion. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Any adjustments to the data is jointly agreed after consultations with the relevant national agencies that share the data points for reporting.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>All countries are expected to fully report on this indicator more consistently, owing to the high availability of freely and openly available input data. As a result, only limited cases of missing values are anticipated, which can emanate from situations where population growth figures are unavailable or where land consumption rates are inestimable due to lack or poor quality of multi-temporal coverage of satellite imagery. Because the values will be aggregated at the national levels from a national sample of cities, missing values will be less observed at national and global levels. UN-Habitat is continuously working to enhance the capacities of national statistical systems to produce data on the indicator, further contributing to data availability. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>All countries are expected to fully report on this indicator more consistently starting in 2020 with few challenges where missing values will be reported due to missing base map files. Only limited cases of missing values are anticipated, which can emanate from situations where population growth figures are unavailable or where land consumption rates are inestimable due to lack or poor quality of multi-temporal coverage of satellite imagery. Because the values will be aggregated at the national levels from a national sample of cities, missing values will be less observed at national and global levels.</p>", "REG_AGG__GLOBAL"=>"<p>Data at the global/regional levels will be estimated from national figures derived from national sample of cities. Regional estimates will incorporate national representations using a weighting by population sizes. Global monitoring will be led by UN-Habitat with the support of other partners and regional commissions.</p>", "DOC_METHOD__GLOBAL"=>"<p>Data for indicator 11.3.1 is to be collected at the city/urban level and aggregates made to the national level. For countries which have adequate capacity (personnel, systems, resources) and baseline data, the indicator can be computed for all cities/urban areas, then averages used to report on national performances. For countries which do not have the capacity to collect data and undertake computations for all their cities/urban areas, UN-Habitat has proposed the use of the National Sample of Cities Approach, which allows them to select a representative sample from where weighted national aggregates can be undertaken. </p>\n<p>The guidance on implementation of the National Sample of Cities Approach is available here: <a href=\"https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf\">https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf</a></p>\n<p>UN-Habitat will continuously undertake capacity building on the sampling approach, and directly support countries to develop a national sample of cities where needed. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>To ensure consistency in data production across countries, UN-Habitat has developed detailed step-by-step tutorials on the computation of indicator 11.3.1, which further explain the steps presented in this metadata. The detailed tutorials, which will be continuously updated are available at <a href=\"https://unhabitat.org/knowledge/data-and-analytics\">https://unhabitat.org/knowledge/data-and-analytics</a>, <a href=\"https://www.urbanagendaplatform.org/learning\">https://www.urbanagendaplatform.org/learning</a>, and <a href=\"https://data.unhabitat.org/\">https://data.unhabitat.org/</a>. </p>\n<p>Within its Data and Analytics Unit which is responsible for the indicator data compilation, UN-Habitat has a team of spatial data experts who check all submitted data and provide direct support to countries in the indicator computation. </p>\n<p>As part of its global custodianship of indicator 11.3.1, UN-Habitat has also established partnerships with major institutions and organizations involved in production of baseline data relevant for the indicator computation. The main aim of this is to create a common understanding on the approach for the indicator computation, and to encourage continuous production of high-quality global data that responds to the indicator computation needs. Examples of some ongoing initiatives with partners to manage quality of products and processes include, among others providing support to apply the Degree of Urbanisation at the local level for the indicator computation (in partnership with the European Commission), development of an Earth Observation Toolkit for SDG 11 (in partnership with EO4SDG and GEO), and continuous feedback to global products produced by partners such as the German Aerospace Center (DLR) and the European Commission Joint Research Centre (EC-JRC) among others. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UN-Habitat maintains the global urban indicators database that is used for monitoring of the urban metrics drawn from SDGs, NUA, flagship reports (e.g. World Cities Report) and other official reporting. In general, for all new data, a thorough review is done to check for consistency and overall data quality by technical staff in the Data and Analytics unit before publication in the urban indicators database. This ensures that only the most accurate and reliable information are included in the database. Key elements considered in the review include: proper documentation of data sources; representativeness of data at national level, use of appropriate methodology for data collection and analysis (e.g. appropriate sampling process, values based on valid sample sizes), use of appropriate concepts and definitions, consistency of data trends with previously published/reported estimates for the indicator.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Once data is received from member states, UN-Habitat uses a checklist specific to each indicator to assess a) whether the data production process followed the metadata provisions, and b) confirm the accuracy of the data sources used for the indicator computation. Both components are captured in the reporting template shared with National Statistical Offices, which helps to assess whether computation was done using the proposed indicator inputs or proxies. The reporting template also requests for information that helps understand whether national data for the indicator was produced from a representative sample of the country&#x2019;s urban systems, or if estimates were done for only select cities/urban areas where data is easily available. </p>\n<p>In addition, the received data is also checked for other qualities such as data disaggregation, reporting period and consistency with other previously reported trends, which ensures reliable regional estimates. For indicator 11.3.1, one extra assessment is done is to compare reported urbanization values (at the city/urban level) against visual interpretation of growth trends from multi-temporal high resolution Google Earth Imagery and population projections from the World Urbanization Prospects. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>In 2024, data on indicator 11.3.1 is available for more than 2,000 cities from 185 countries. Some countries have also produced national averages based on the city level data. UN-Habitat has calculated regional aggregates based on the M49 categories, as well as the UN regional commissions. This indicator is one of the few for which a lot of development of relevant global products has been implemented by partners since 2015, some of the key ones including Global Human Settlement Layer (GHSL), the World Settlement Footprint (WSF), the Gridded Population of the World (GPW), WorldPop dataset, the High Resolution Settlement Layer (HRSL) among others. UN-Habitat and partners are continuously supporting national statistical systems to increase data availability on the indicator, including calculation of the proposed secondary indicators. </p>\n<p><strong>Time series:</strong></p>\n<p>Available time series runs at the city and national level for countries with data. Specific time series vary per country depending on the availability of their population data. UN-Habitat aims to produce aggregated regional averages for 5 &#x2013; 10 year intervals starting from 2000. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Potential Disaggregation:</p>\n<ul>\n  <li>Disaggregation by location (operational urban area vs administratively defined urban area, urban wide vs intra-urban growth trends)</li>\n  <li>Disaggregation by type of growth<strong> </strong>(infill, expansion, leapfrogging)</li>\n  <li>Disaggregation by city type (large vs medium sized vs small)</li>\n  <li>Disaggregation by type of land use consumed by the urbanization process </li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Significant variations between global and national figures are anticipated where globally produced built-up layers are used to compute the indicator. This is largely due to the uniqueness of some local contexts and variations in image reflectance and land cover types, which make it difficult to accurately capture built up areas consistently. While the national figures will be used for reporting &#x2013; resulting in less differences being observed, some countries may opt to use the globally available products, which may create some variations as locally generated data becomes available. UN-Habitat will be responsible for checking all figures to ensure that no inconsistencies are reported. </p>\n<p>The second likely source of differences between figures is the approach used to define urban areas and cities for the purpose of the indicator computation. To resolve this, the use of the degree of urbanization approach to definition of urban and rural areas and production of comparable data is recommended. This approach was endorsed by the UN Statistical Commission in March 2020, and its incremental adoption by countries is likely to reduce any differences in the figures reported in future. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL references:</strong></p>\n<ul>\n  <li>http://unhabitat.org/knowledge/data-and-analytics </li>\n  <li>http://www.lincolninst.edu/pubs/1880_Making-Room-for-a-Planet-of-Cities-urban-expansion</li>\n  <li>http://www.lincolninst.edu/subcenters/atlas-urban-expansion/</li>\n  <li>http://unhabitat.org/books/construction-of-more-equitable-cities/</li>\n  <li>http://unhabitat.org/books/state-of-the-worlds-cities-20102011-cities-for-all-bridging-the-urban-divide/)</li>\n  <li>http://dx.doi.org/10.1787/reg_glance-2013-7-en</li>\n  <li><a href=\"https://newclimateeconomy.net/content/better-growth-better-climate-new-climate-economy-report\">https://newclimateeconomy.net/content/better-growth-better-climate-new-climate-economy-report</a> http://2015.newclimateeconomy.report/wp-content/uploads/2014/08/NCE2015_workingpaper_cities_final_web.pdf</li>\n  <li>https://smartgrowthamerica.org/wp-content/uploads/2016/08/measuring-sprawl-2014.pdf</li>\n  <li><a href=\"https://www.mckinsey.com/featured-insights/urbanization/tackling-the-worlds-affordable-housing-challenge\">https://www.mckinsey.com/featured-insights/urbanization/tackling-the-worlds-affordable-housing-challenge</a> http://indicators.report/indicators/i-68/ (Accessed on May 30, 2016)</li>\n</ul>\n<p><strong>References:</strong></p>\n<p>Blais, P. (2011). Perverse cities: hidden subsidies, wonky policy, and urban sprawl. UBC Press.</p>\n<p>Ewing, R., Pendall, R, and Chen, D. (2002). Measuring Sprawl and its Impact. Smart Growth America. [6]</p>\n<p>Glaeser and Abha Joshi-Ghani. (2015). &#x201C;Rethinking Cities,&#x201D; in The Urban Imperative: towards Competitive Cities, Oxford University Press.</p>\n<p>Global Commission on the Economy and Climate (2014). Better Growth, Better Climate: The New Climate Economy Report. Washington DC: Global Commission on the Economy and Climate. [7]</p>\n<p>Global Commission on the Economy of Cities and Climate (2015). Accelerating Low Carbon Growth in the World&#x2019;s Cities [8]</p>\n<p>Lincoln Institute (n.d) Atlas of Urban Expansion [2]</p>\n<p>Lincoln institute (2011) Making Room for a Planet of Cities [1]</p>\n<p>OECD (2013). &#x201C;Urbanisation and urban forms&#x201D;, in OECD Regions at a Glance 2013, OECD Publishing. [6]</p>\n<p>Robert Burchell et al., Costs of Sprawl Revisited: The Evidence of Sprawl&#x2019;s Negative and Positive Impacts, Transit Cooperative Research Program, Transportation Research Board, Washington, D.C., 1998</p>\n<p>Sedesol (2012) La expansi&#xF3;n de las ciudades 1980-2010. [3]</p>\n<p>UN-Habitat (2012). State of the World&#x2019;s Cities Report: Bridging the Urban Divide, 2012. Nairobi [5]</p>\n<p>UN-Habitat, CAF. (2014). Construction of More Equitable Cities. Nairobi [4] </p>\n<p>Smart Growth America, Measuring Sprawl 2014 [9]</p>\n<p>Woetzel, J., Ram, S., Mischke, J., Garemo, N., and Sankhe, S. (2014). A blueprint for addressing the global affordable housing challenge. McKinsey Global Institute. [10]</p>\n<p>Dijkstra, L., &amp; H. Poelman (2014). A harmonized definition of cities and rural areas: the new degree of urbanisation. Directorate General for Regional and Urban Policy, Regional working paper 2014; </p>\n<p>Florczyk, A.J., Melchiorri, M., Corbane, C., Schiavina, M., Maffenini, M., Pesaresi, M., Politis, P., Sabo, S., Freire, S., Ehrlich, D., Kemper, T., Tommasi, P., Airaghi, D. and L. Zanchetta, Description of the GHS Urban Centre Database 2015, Public Release 2019, Version 1.0, Publications Office of the European Union, Luxembourg, 2019, ISBN 978-92-79- 99753-2, doi:10.2760/037310, JRC115586.; </p>\n<p><a href=\"http://atlasofurbanexpansion.org/data\">http://atlasofurbanexpansion.org/data</a> </p>", "indicator_sort_order"=>"11-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.3.2", "slug"=>"11-3-2", "name"=>"Proporción de ciudades que cuentan con una estructura de participación directa de la sociedad civil en la planificación y la gestión urbanas y funcionan con regularidad y democráticamente", "url"=>"/site/es/11-3-2/", "sort"=>"110302", "goal_number"=>"11", "target_number"=>"11.3", "global"=>{"name"=>"Proporción de ciudades que cuentan con una estructura de participación directa de la sociedad civil en la planificación y la gestión urbanas y funcionan con regularidad y democráticamente"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de ciudades que cuentan con una estructura de participación directa de la sociedad civil en la planificación y la gestión urbanas y funcionan con regularidad y democráticamente", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de ciudades que cuentan con una estructura de participación directa de la sociedad civil en la planificación y la gestión urbanas y funcionan con regularidad y democráticamente", "indicator_number"=>"11.3.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de ciudades que cuentan con una estructura de participación directa de la sociedad civil en la planificación y la gestión urbanas y funcionan con regularidad y democráticamente", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.3- De aquí a 2030, aumentar la urbanización inclusiva y sostenible y la capacidad para la planificación y la gestión participativas, integradas y sostenibles de los asentamientos humanos en todos los países", "definicion"=>"Proporción de ciudades que cuentan con una estructura de participación directa de la sociedad civil en la planificación y la gestión urbana que funcione con regularidad y democráticamente", "formula"=>"\n$$PCPD^{t} = \\frac{CPD^{t}}{C^{t}} \\cdot 100$$\n\ndonde: \n\n$CPD^{t} =$ ciudades con una estructura de participación directa de la sociedad civil en la planificación y gestión urbana en el año $t$ \n\n$C^{t} =$ ciudades en el año $t$\n", "desagregacion"=>"", "observaciones"=>"El indicador es del 100% dada la legislación estatal del suelo, de aplicación general a todas las ciudades", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEste indicador mide el progreso y la voluntad de los representantes electos, \nlos administradores urbanos y los planificadores para integrar la participación de \nlos residentes y la sociedad civil en la planificación y la gestión urbanas \na distintos niveles. Las autoridades locales y los gobiernos, junto con la comunidad internacional, \nreconocen cada vez más el valor de la participación de la sociedad civil y los \nresidentes en el fortalecimiento de los procesos de desarrollo urbano.\n\nEste enfoque centrado en las personas es clave para orientar los procesos de \ndesarrollo urbano hacia la propiedad local y la implementación de proyectos comunitarios a \nnivel de toda la ciudad o local.\n\nLa sociedad civil y la participación pública fomentan una relación positiva \nentre el gobierno y el público mediante la comunicación eficaz y la solución de los \nconflictos de manera cooperativa. En muchos casos, cuando las decisiones de planificación \nurbana se toman sin consulta, no se logran los resultados deseados y hay \nun impacto negativo en la sociedad, debido a la asignación y el uso ineficientes de \nlos recursos. Garantizar que se tengan en cuenta amplias variedades de opiniones ayuda \na los encargados de la toma de decisiones a comprender las interrelaciones y la naturaleza de \nlos problemas y las posibles soluciones que enfrentan los diferentes entornos urbanos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas, pero aporta información similar.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-03-02.pdf\">Metadatos 11-3-2.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Proporción de ciudades que cuentan con una estructura de participación directa de la sociedad civil en la planificación y la gestión urbanas y funcionan con regularidad y democráticamente", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.3- De aquí a 2030, aumentar la urbanización inclusiva y sostenible y la capacidad para la planificación y la gestión participativas, integradas y sostenibles de los asentamientos humanos en todos los países", "definicion"=>"Proportion of cities that have a structure for direct participation of civil society in  urban planning and management that functions regularly and democratically ", "formula"=>"\n$$PCPD^{t} = \\frac{CPD^{t}}{C^{t}} \\cdot 100$$\n\nwhere: \n\n$CPD^{t} =$ cities that have a structure for direct participation of civil society in urban planning and management in year $t$ \n\n$C^{t} =$ number of cities in year $t$\n", "desagregacion"=>nil, "observaciones"=>"The indicator is 100% given the Land Law, generally applicable to all cities", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThis indicator measures the progress and willingness of elected officials, \nurban managers and planners to integrate resident and civil society participation \nin urban planning and management at various levels. Local authorities and governments, \nalong with the international community, are increasingly recognizing the value of \ncivil society and residents’ participation in strengthening the urban development \nprocesses. \n\nThis people-centered approach is key in guiding urban development processes for \nlocal ownership, and the implementation of community projects at citywide or local levels. \n\nCivil society and public participation fosters a positive relationship between \ngovernment and the public by communicating effectively and solving the conflicts in a \ncooperative manner. In many cases when urban planning decisions are made without \nconsultation, the desired results are not achieved and there is a negative impact \non society, due to inefficient allocation and use of resources. Ensuring that wide \nvarieties of opinions are considered assists the decision makers with understanding \nthe interlinkages and nature of problems and potential solutions facing different \nurban settings. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-03-02.pdf\">Metadata 11-3-2.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de ciudades que cuentan con una estructura de participación directa de la sociedad civil en la planificación y la gestión urbanas y funcionan con regularidad y democráticamente", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.3- De aquí a 2030, aumentar la urbanización inclusiva y sostenible y la capacidad para la planificación y la gestión participativas, integradas y sostenibles de los asentamientos humanos en todos los países", "definicion"=>"Proporción de ciudades que cuentan con una estructura de participación directa de la sociedad civil en la planificación y la gestión urbana que funcione con regularidad y democráticamente", "formula"=>"\n$$PCPD^{t} = \\frac{CPD^{t}}{C^{t}} \\cdot 100$$\n\nnon: \n\n$CPD^{t} =$  gizarte zibilak hiri-plangintzan eta -kudeaketan zuzenean parte hartzeko egitura duten hiriak $t$ urtean \n\n$C^{t} =$ hiriak $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>"El indicador es del 100% dada la legislación estatal del suelo, de aplicación general a todas las ciudades", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEste indicador mide el progreso y la voluntad de los representantes electos, \nlos administradores urbanos y los planificadores para integrar la participación de \nlos residentes y la sociedad civil en la planificación y la gestión urbanas \na distintos niveles. Las autoridades locales y los gobiernos, junto con la comunidad internacional, \nreconocen cada vez más el valor de la participación de la sociedad civil y los \nresidentes en el fortalecimiento de los procesos de desarrollo urbano.\n\nEste enfoque centrado en las personas es clave para orientar los procesos de \ndesarrollo urbano hacia la propiedad local y la implementación de proyectos comunitarios a \nnivel de toda la ciudad o local.\n\nLa sociedad civil y la participación pública fomentan una relación positiva \nentre el gobierno y el público mediante la comunicación eficaz y la solución de los \nconflictos de manera cooperativa. En muchos casos, cuando las decisiones de planificación \nurbana se toman sin consulta, no se logran los resultados deseados y hay \nun impacto negativo en la sociedad, debido a la asignación y el uso ineficientes de \nlos recursos. Garantizar que se tengan en cuenta amplias variedades de opiniones ayuda \na los encargados de la toma de decisiones a comprender las interrelaciones y la naturaleza de \nlos problemas y las posibles soluciones que enfrentan los diferentes entornos urbanos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen adierazlearen metadatuak betetzen, baina antzeko informazioa eskaintzen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-03-02.pdf\">Metadatuak 11-3-2.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 11.3: By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated and sustainable human settlement planning and management in all countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 11.3.2: Proportion of cities with a direct participation structure of civil society in urban planning and management that operates regularly and democratically</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_URB_CSPART - Proportion of cities with a direct participation structure of civil society in urban planning and management that operate regularly and democratically [11.3.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Civil Society Organizations (CSOs) make a difference in international development. They provide development services and humanitarian relief, innovate in service delivery, build local capacity and advocate with and for the poor. Acting alone, however, their impact is limited in scope, scale and sustainability. CSOs need to engage in government policy processes more effectively. The development of sustainable human settlements calls for the active engagement of all key stakeholders with particular attention to project/programme beneficiaries and vulnerable groups. Therefore local and national governments should strive to:</p>\n<p>a) facilitate and protect people&#x2019;s participation and civic engagement through independent CSOs that can be from diverse backgrounds &#x2013; local, national, and international;</p>\n<p>b) promote civic and human rights education and training programmes to make urban residents aware of their rights and the changing roles of diverse women, men, and young women and men in urban settings;</p>\n<p>c) remove the barriers that block participation of socially marginalized groups and promote non-discrimination and the full and equal participation of women, young men and women and marginalized groups. To monitor this indicator fully, it is important to define cities as unique entities and define what constitutes direct participation structures of civil society. Urban planning and management are more clear concepts that UN-Habitat has worked on developing for the last few decades and these are well articulated in the urban agenda documents. Experts who have worked on the methodological developments of this indicator have therefore put forth the below definitions to help guide the work on this indicator.</p>\n<p><strong>Concepts:</strong></p>\n<p><strong>City or urban area</strong>: Since 2016 UN-Habitat and partners organized global consultations and discussions to narrow down the set of meaningful definitions that would be helpful for the global monitoring and reporting process. Following consultations with 86 member states, the United Nations Statistical Commission (UNSC), in its 51<sup>st</sup> Session (March 2020) endorsed the Degree of Urbanisation (DEGURBA) as a workable method to delineate cities, urban and rural areas for international statistical comparisons.<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup> This definition combines population size and population density thresholds to classify the entire territory of a country along the urban-rural continuum, and captures the full extent of a city, including the dense neighbourhoods beyond the boundary of the central municipality. DEGURBA is applied in a two-step process: First, 1 km<sup>2</sup> grid cells are classified based on population density, contiguity and population size. Subsequently, local units are classified as urban or rural based on the type of grid cells in which the majority of their population resides.</p>\n<p><strong>Other concepts:</strong></p>\n<p><strong>Democratic participation:</strong> Structures allow and encourage participation of civil society representing a cross-section of society that allows for equal representation of all members of the community with equal rights for participation and voting.</p>\n<p><strong>Direct participation:</strong> Structures allow and encourage civil society accessing and actively engaging in decision-making, without intermediaries, at every stage of the urban planning and management process.</p>\n<p><strong>Regular participation:</strong> Structures allow and encourage civil society participation in urban planning and management processes at every stage, and at least every six months.</p>\n<p><strong>Marginalized groups:</strong> Groups of people that are not traditionally given equal voice in governance processes. These include, but are not limited to, women, young men and women, low-income communities, ethnic minorities, religious minorities, people with disabilities, the elderly, sexual and gender identity minorities and migrants.</p>\n<p><strong>Structures:</strong> Any formal structure that allows for participation of civil society. This can include, but is not limited to national or local legislation, policy, town council meetings, websites, elections, suggestion boxes, appeals processes, notice period for planning proposals, etc.</p>\n<p><strong>Civil Society:</strong> The combination of non-governmental organizations, community groups, community-based organizations, regional representative groups, unions, research institutes, think tanks, professional bodies, non-profit sports and cultural groups, and any other groups that represent the interests and wills of the members and wider community.</p>\n<p><strong>Urban Management:</strong> The officials, including elected officials and public servants, that are responsible for city-management, across all sectors, such as roads, water, sanitation, energy, public space, land title, etc.</p>\n<p><strong>Urban Budget decision making:</strong> The process by which money is allocated to various sectors of urban management, including roads, water, sanitation, energy, public space, land title, etc.</p>\n<p><strong>Urban Planning, including design and agreements</strong>: The technical and political process that concerns the development and use of land, how the natural environment is used etc. Design includes over-arching and specific design of public space, as well as zoning and land use definitions. Agreements refer to specific contract/arrangements made with various groups in regard to their land, e.g. indigenous groups, protected natural environments, etc.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> A recommendation on the method to delineate cities, urban and rural areas for international statistical comparisons. <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf</a>. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Option 1:</strong> Evaluators will examine structures at the city level, with data aggregated from city levels for national averages through local national statistical systems constituted and chaired by the National Statistical Offices (NSOs).</p>\n<p><strong>Option 2:</strong> For countries, where civil society engagement is covered within the law as a requirement and legally enforced, evaluators can provide a direct national level assessment of the practice and coverage for the cities as one estimated percentage.</p>", "COLL_METHOD__GLOBAL"=>"<p><strong>Option 1:</strong> To measure the level of direct participation structures of civil society in urban planning and management at the city level, a scorecard approach will be used to evaluate the available structures for civil society participation in urban planning and management, as evaluated by five local experts including those from academia, Urban Planning Experts, City Leaders and officials from local government authorities.</p>\n<p>As part of the monitoring and reporting on SDG 11, UN-Habitat developed an online questionnaire (<a href=\"https://ee.humanitarianresponse.info/x/sh3jEDMr\">https://ee.humanitarianresponse.info/x/sh3jEDMr</a>) that NSOs can administer to stakeholders on public participation in urban planning and management to evaluate public participation in urban planning programs in their cities.</p>\n<p>To note, the selection of cities in which the evaluation will be conducted may be determined using the National Sample of Cities approach (<a href=\"https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf\">https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf</a>). The approach will help draw a sample of cities using sound statistical and scientific methodologies based on several relevant city-specific criteria/characteristics that capture the specific contexts of countries, ensuring that the sample is representative of a given country&#x2019;s territory, geography, size, history, etc.</p>\n<p><strong>Option 2:</strong> To measure the level of direct participation structures of civil society in urban planning and management at the city level and aggregate national level performances, evaluators will first confirm that there is an established legal requirement that civil society must be involved in urban planning and management of cities or municipalities, if it is yes, then evaluators will assess whether this is being practiced in all cities and all municipalities in the country, if it is yes, national level coverage can be considered as 100%, otherwise if it is partial coverage then the true average coverage has to be estimated.</p>", "FREQ_COLL__GLOBAL"=>"<p>The monitoring of the indicator will be repeated at regular intervals of four years, allowing for four reporting points until the year 2030.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data for indicator 11.3.2 will be released on an annual basis, to cater for an anticipated increase in the number of cities/urban areas and countries reporting on the indicator. Changes in trends within individual cities and/or countries are likely to happen in spans of about 3-5 years, so a three-year window will be applied for comprehensive review of all data, with updates made based on availability of new data.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National statistical organisations</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>\n<p>UN-Habitat and other partners are supporting various components (systems, tools development and capacity strengthening, etc.) for reporting on this indicator.</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Human Settlements Programme (UN-Habitat) is the specialized agency for sustainable urbanization and human settlements in the United Nations. The mandate derives from the priorities established in relevant United Nations General Assembly (UNGA) resolutions and decisions, including General Assembly resolution 3327 (XXIX), by which UNGA established the United Nations Habitat and Human Settlements Foundation, and resolution 32/162 by which the Assembly established the United Nations Center for Human Settlements (Habitat). In 2001, by its Resolution 56/206, UNGA transformed the Habitat into the secretariat of the UN-Habitat, with a mandate to coordinate human settlements activities within the United Nations System. As such, UN-Habitat has been designated the overall coordinator of SDG 11 and specifically as a custodian agency for 9 of the 14 indicators under SDG 11 including indicator 11.3.2. UN-Habitat also supports the monitoring and reporting of 4 urban specific indicators in other goals.</p>", "RATIONALE__GLOBAL"=>"<p>This indicator measures the progress and willingness of elected officials, urban managers and planners to integrate resident and civil society participation in urban planning and management at various levels. Local authorities and governments, along with the international community, are increasingly recognizing the value of civil society and residents&#x2019; participation in strengthening the urban development processes. This people-centered approach is key in guiding urban development processes for local ownership, and the implementation of community projects at citywide or local levels.</p>\n<p>Civil society and public participation fosters a positive relationship between government and the public by communicating effectively and solving the conflicts in a cooperative manner. In many cases when urban planning decisions are made without consultation, the desired results are not achieved and there is a negative impact on society, due to inefficient allocation and use of resources. Ensuring that wide varieties of opinions are considered assists the decision makers with understanding the interlinkages and nature of problems and potential solutions facing different urban settings.</p>\n<p>Urban development is a reflection of ideology and national institutions. Public participation means a broader consensus is built and this greatly enhances political interaction between citizens and government, and enhances the legitimacy of the planning process and the plan itself. A plan is more effective if a broad coalition supports the proposal and works together to deliver it.</p>\n<p>Civil society and public participation in urban management and governance also shows respect to participants&#x2019; opinion, needs, aspirations and assets. It can boost their enthusiasm for citizenship and politics, and strengthens their influence in urban planning and public life. When conflicting claims and views are considered, there is a much higher possibility that public trust and buy-in increases in the outcome. This has broader implications for building an active, inclusive and equitable society and more inclusive and sustainable urban environments.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator measures the availability of structures for civil society participation in urban planning and management, which is a reflection of structures for citizen voices/participation. The fact that informed evaluators conduct the evaluation can introduce biases. These biases and discrepancies have been examined in the pilot phases and so far the experience is that the marginal differences are not as large as expected. Overall, the evaluators&#x2019; assessments sometimes do not reflect a full analysis of the effectiveness or accessibility of these structures in its totality, but gives a local idea of how these evaluators view the inclusiveness and openness on these structures to accommodate the participation of citizens and civil society. Changes in data will be examined for intra-city differences and within country differences over time to understand more sources for variations and internal consistencies.</p>\n<p>Within the civic society landscape, there are many types of players including civil societies led by individuals, community groups, advocates, corporations and foundations</p>\n<p>Finally, civic society engagement in urban planning and management involves overlapping pathways, and goals as well as a mix of planned and unpredicted elements. Advancing toward a measurement frame is intended to help sort out theories and pathways &#x2013; not to set hard boundary lines, but rather to help both urban managers and communities better understand what they are trying to achieve, and how they are getting there.</p>\n<p>We also recognize that there are some countries where the legal instruments that govern cities and municipalities require that civil society are involved in the day-to-day urban planning and management of cities/municipalities. Hence, such countries can report directly the national level engagement of civil society as 100%, if in practice all municipalities apply the legal requirements for civil society engagement in urban planning and management.</p>", "DATA_COMP__GLOBAL"=>"<p>To measure existence of direct participation structures of civil society in urban planning and management at the city level, we recommend two options:</p>\n<ol>\n  <li>For countries where there is no legal requirement for civil society engagement and the practice is also not known at the city or municipality levels OR for countries where there is a legal requirement for civil society engagement in urban planning and management but the practice is not known across the system of cities.</li>\n  <li>For countries where there is a legal requirement for civil society engagement in urban planning and management and the practice is also known across the system of cities and municipalities.</li>\n</ol>\n<p><strong>Option 1:</strong> a scorecard approach will be used to evaluate the available structures for civil society participation in urban planning and management, as evaluated by five local experts from government, academia, civil society and international organizations. The identifications and selection of these five local evaluators/experts will be guided by local urban observatories teams that are available in many cities. In the pilot exercises, these urban observatories as local custodians of urban data at the city level are able to coordinate the assessments and check for consistencies and relevant local references that guide the decisions and scores of the evaluators.</p>\n<p>A questionnaire with a 4-point Likert Scale (strongly disagree, disagree, agree, and strongly agree) will be used to measure and test the existence of structures for civil society participation in urban governance and management. As experts, we agreed that these structures are examined through four core elements and these were assessed in the completed pilot exercises as follows:</p>\n<ol>\n  <li>Are there structures for civil society participation in urban planning, including design and agreements that are direct, regular and democratic?</li>\n  <li>Are there structures for civil society participation in local urban budget decision-making, that are direct, regular and democratic?</li>\n  <li>Are there structures for civil society evaluation and feedback on the performance of urban management that are direct, regular and democratic?</li>\n  <li>Do these structures promote the participation of women, young men and women, and/or other marginalized groups?</li>\n</ol>\n<p>The evaluators score each of the questions on the Likert Scale, as below:</p>\n<p>1 - Strongly disagree, 2 - Disagree, 3 - Agree, 4 - Strongly agree</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Questions</strong></p>\n      </td>\n      <td>\n        <p><strong>Strongly Disagree</strong></p>\n        <p><strong>(1)</strong></p>\n      </td>\n      <td>\n        <p><strong>Disagree</strong></p>\n        <p><strong>(2)</strong></p>\n      </td>\n      <td>\n        <p><strong>Agree</strong></p>\n        <p><strong>(3)</strong></p>\n      </td>\n      <td>\n        <p><strong>Strongly Agree</strong></p>\n        <p><strong>(4)</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Are there structures for civil society participation in urban planning, including design and agreements that are direct, regular and democratic?</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Are there structures for civil society participation in urban budget decision making that are direct, regular and democratic?</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Are there structures for civil society evaluation and feedback on the performance of urban management, which are direct, regular and democratic?</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Do the structures promote the participation of women, young men and women, and/or other marginalized groups?</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n<p>The Likert Scale use the following guidance for grading:</p>\n<p><strong>Strongly Disagree:</strong> There are no structures in place or available structures do not allow civil society participation that is direct, regular or democratic.</p>\n<p><strong>Disagree:</strong> Structures exist that allow civil society participation, but they are only partially direct, regular and democratic; or they are only one of direct, regular or democratic.</p>\n<p><strong>Agree:</strong> Structures exist that allow and encourage civil society participation that is direct and/or regular and/or democratic, but not all three.</p>\n<p><strong>Strongly Agree</strong>: Structures exist that allow and encourage civil society participation that is fully direct, regular and democratic.</p>\n<p>Once each of the four categories is evaluated as shown in the table above by a single evaluator, the total average score of the single evaluator is computed. The various scores of the evaluators are then <strong>averaged</strong> to compute the final score for every city.</p>\n<p>To determine the proportion of cities with a direct participation structure of civil society in urban planning and management that operates regularly and democratically, a midpoint on the Likert Scale of 2.5 will be used. The value of the indicator is the proportion of cities with an overall score that is greater than the mid-point.</p>\n<p>As a result, if we have <strong>N</strong> cities selected for the evaluation in a given country, and <strong>n</strong> is the number of cities with scores that are higher than the mid-point, the value of the indicator will be calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"bold\">V</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">l</mi>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">I</mi>\n    <mi mathvariant=\"bold\">n</mi>\n    <mi mathvariant=\"bold\">d</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">r</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"bold\">n</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"bold\">N</mi>\n      </mrow>\n    </mfrac>\n  </math> (to be expressed in percentage)</p>\n<p>To note, the number of cities in which the evaluation will be conducted may be determined using the National Sample of Cities approach. The approach will help draw a sample of cities using sound statistical and scientific methodologies based on several relevant city-specific criteria/characteristics that capture the specific contexts of countries, ensuring that the sample is representative of a given country&#x2019;s territory, geography, size, history, etc.</p>\n<p><strong>Option 2:</strong> a scorecard approach <strong><u>will not be used</u></strong> to evaluate the available structures for civil society participation in urban planning and management, instead a national level assessment will be provided based on a confirmation of the existence of the legal requirement for civil society participation in urban planning and management, followed by a confirmation that this is indeed practice as per the legal requirement. Hence, if <strong>N</strong> is the number of cities in the country that are covered by the legal instruments of civil society participation in urban planning and management, and <strong>n </strong>is the number of<strong> </strong>cities/municipalities<strong> where in practice </strong>civil society participation is happening in the urban planning and management, then</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"bold\">V</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">l</mi>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">I</mi>\n    <mi mathvariant=\"bold\">n</mi>\n    <mi mathvariant=\"bold\">d</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">r</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"bold\">n</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"bold\">N</mi>\n      </mrow>\n    </mfrac>\n  </math> (to be expressed in percentage)</p>", "DATA_VALIDATION__GLOBAL"=>"<p>As part of the validation process, UN-Habitat developed a template to compile data generated by countries through the National Statistics Offices as well as other government agencies responsible for official statistics (<a href=\"https://data.unhabitat.org/pages/guidance\">https://data.unhabitat.org/pages/guidance</a>). Data compiled is then checked against several criteria including the data sources used, the application of internationally agreed definitions, classification and methodologies to the data from that source, etc. Once reviewed, appropriate feedback is then provided to individual countries for further discussion.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Any adjustment to the data is jointly agreed after consultations with the relevant national agencies that share the data points for reporting.</p>", "IMPUTATION__GLOBAL"=>"<p>All countries are expected to fully report on this city-based indicator more consistently after 2-4 years post 2015.</p>", "REG_AGG__GLOBAL"=>"<p>Data at the global/regional levels will be estimated from national figures derived from a weighted aggregation of performance for all cities/urban areas or a sample of nationally representative cities (selected using the national sample of cities approach developed by UN-Habitat). Weighting for regional and global averages is done using urban population sizes from the World Urbanization Prospects. Global monitoring will be led by UN-Habitat with the support of other partners and regional commissions.</p>", "DOC_METHOD__GLOBAL"=>"<p><strong>Option 1:</strong> UN-Habitat has developed a step-by-step data compilation and computation methodological document, which is available here: <a href=\"https://unhabitat.org/sites/default/files/2021/08/indicator_11.3.2_training_module_civic_participation.pdf\">https://unhabitat.org/sites/default/files/2021/08/indicator_11.3.2_training_module_civic_participation.pdf</a>. In addition, UN-Habitat has developed audio-visual content for indicator 11.3.2 that is available through its E-Learning Portal, offering more interactive learning for data producers at different levels. The content includes self-paced E-Learning courses, which present descriptive and practical step-by-step guidance on how to compute each indicator. These courses are aimed at strengthening national capacities in collecting, analysing, and monitoring the urban SDG indicators. They are also designed to be attractive to different groups, from data producers to people just interested in understanding the indicators and their interpretation. This was intended to broaden the pool of experts on urban monitoring and increase the uptake and use of the tools within countries. The guidance on implementation of the National Sample of Cities Approach is available here: <a href=\"https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf\">https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf</a>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>To ensure consistency in data production across countries, UN-Habitat has developed detailed step-by-step tutorials on the computation of indicator 11.3.2, which further explain the steps presented in this metadata. The detailed tutorials, which will be continuously updated are available at <a href=\"https://unhabitat.org/knowledge/data-and-analytics\">https://unhabitat.org/knowledge/data-and-analytics</a>, <a href=\"https://www.urbanagendaplatform.org/learning\">https://www.urbanagendaplatform.org/learning</a>, and <a href=\"https://data.unhabitat.org/\">https://data.unhabitat.org/</a>. Within its Data and Analytics Section, which is responsible for the indicator data compilation, UN-Habitat has a team of data experts who check all submitted data and provide direct support to countries in the indicator computation.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UN-Habitat maintains the global urban indicators database that is used for monitoring of the urban metrics drawn from SDGs, New Urban Agenda (NUA), flagship reports (e.g. World Cities Report) and other official reporting. In general, for all new data, a thorough review is done to check for consistency and overall data quality by technical staff in the Data and Analytics unit before publication in the urban indicators database. This ensures that only the most accurate and reliable information are included in the database. Key elements considered in the review include: proper documentation of data sources; representativeness of data at national level, use of appropriate methodology for data collection and analysis (e.g. appropriate sampling process, values based on valid sample sizes), use of appropriate concepts and definitions, consistency of data trends with previously published/reported estimates for the indicator.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Once data is received from member states, UN-Habitat uses a checklist specific to each indicator to a) assess whether the data production process followed the metadata provisions, and b) confirm the accuracy of the data sources used for the indicator computation. Both components are captured in the reporting template shared with National Statistical Offices, which helps to assess whether computation was done using the proposed indicator inputs or proxies. The reporting template also requests for information that helps understand whether national data for the indicator was produced from a representative sample of the country&#x2019;s urban systems, or if estimates were done for only select cities/urban areas where data is easily available.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data is available in selected countries/cities on some components:</p>\n<ul>\n  <li><strong>For Africa regions:</strong> Egypt (Cairo), Mauritania (Tevragh-zeina), Mozambique (Matola), Senegal (Dakar), Morocco (Casablanca), Tanzania, Namibia, Malawi.</li>\n  <li><strong>In the European region:</strong> Spain (Barcelona), UK (Stanford city council), France (plaine commune), Belgium (Brussels), Berlin (Germany), Nanterre (France), Ireland, Iceland.</li>\n  <li><strong>In Latin America:</strong> data is available for selected cities in Brazil, Colombia.</li>\n  <li><strong>Other countries</strong> in the pipeline to provide data for cities include South Africa (several cities), Sweden, UK (selected cities) and Kenya (five selected counties).</li>\n</ul>\n<p><strong>Time series:</strong></p>\n<p>Available data cover the period starting 2018. Because the effort and capacity of collecting and analysing this kind of data are different for each country, the length of the time series for each country will vary greatly.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Potential Disaggregation:</p>\n<ul>\n  <li>Disaggregation by city characteristics</li>\n  <li>Disaggregation by regularity of participation</li>\n  <li>Disaggregation by nature and typology of existing structures</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>For this indicator, national data built up from a <a href=\"https://unhabitat.org/national-sample-of-cities/\">&#x201C;national sample of cities approach&#x201D;,</a> will be used to derive final estimates for reporting at national and global figures. As national agencies are responsible for data collection, no differences between country produced data and international estimated data on the indicator are expected to arise. Where such discrepancies exist, these will be resolved through planned technical meetings and capacity development workshops.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>References:</strong></p>\n<ul>\n  <li>UN-Habitat. <em>Planning Sustainable Cities: Global Report on Human Settlements 2009.</em> Pages 93-109.</li>\n  <li>Ziari Keramat Allah, Nikpay Vahid, Hosseini Ali. <em>Measuring The Level Of Public Participation In Urban Management Based On The Urban Good Governing Pattern: A Case Study Of Yasouj.</em> Housing and Rural Environment Spring 2013, Volume 32, Number 141; Pages 69-86.</li>\n</ul>", "indicator_sort_order"=>"11-03-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.4.1", "slug"=>"11-4-1", "name"=>"Total de gastos per cápita destinados a la preservación, protección y conservación de todo el patrimonio cultural y natural, desglosado por fuente de financiación (pública y privada), tipo de patrimonio (cultural y natural) y nivel de gobierno (nacional, regional y local/municipal)", "url"=>"/site/es/11-4-1/", "sort"=>"110401", "goal_number"=>"11", "target_number"=>"11.4", "global"=>{"name"=>"Total de gastos per cápita destinados a la preservación, protección y conservación de todo el patrimonio cultural y natural, desglosado por fuente de financiación (pública y privada), tipo de patrimonio (cultural y natural) y nivel de gobierno (nacional, regional y local/municipal)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Gasto per cápita de las administraciones públicas autonómicas y locales en la preservación, protección y conservación del patrimonio cultural", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Total de gastos per cápita destinados a la preservación, protección y conservación de todo el patrimonio cultural y natural, desglosado por fuente de financiación (pública y privada), tipo de patrimonio (cultural y natural) y nivel de gobierno (nacional, regional y local/municipal)", "indicator_number"=>"11.4.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Cultura", "periodicity"=>"Bienal", "url"=>"https://www.cultura.gob.es/servicios-al-ciudadano/estadisticas/cultura/mc/culturabase/gasto-publico/resultados-gasto-publico.html", "url_text"=>"Estadística de Financiación y Gasto Público en Cultura", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Gasto per cápita de las administraciones públicas autonómicas y locales en la preservación, protección y conservación del patrimonio cultural", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.4- Redoblar los esfuerzos para proteger y salvaguardar el patrimonio cultural y natural del mundo", "definicion"=>"Gasto liquidado por persona de las administraciones públicas autonómicas y locales en  la preservación, protección y conservación del patrimonio cultural (patrimonio histórico  y artístico, archivos y bibliotecas)", "formula"=>"\n$$GLPCP^{t} = \\frac{GLPC_{CCAA}^{t}+GLPC_{EELL}^{t}}{P^{t}}$$\n\ndonde: \n\n$GLPC_{CCAA}^{t}$ = gasto liquidado por la comunidad autónoma en la preservación, protección y conservación del patrimonio cultural (patrimonio histórico y artístico, archivos y bibliotecas) en el año $t$\n\n$GLPC_{EELL}^{t}$ = gasto liquidado por las entidades locales de la comunidad autónoma en la preservación, protección y conservación del patrimonio cultural (patrimonio histórico y artístico, archivos y bibliotecas) en el año $t$\n\n$P^{t}$ = población a 1 de julio del año $t$\n", "desagregacion"=>"Administración pública: administración autonómica, administración local", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEste indicador mide el gasto per cápita (público y privado) en la preservación, \nprotección y conservación del patrimonio cultural y/o natural a lo largo del tiempo. \nPara monitorear la evolución a lo largo del tiempo de los esfuerzos \nnacionales para la protección y salvaguarda del patrimonio cultural y/o natural.\n\nEste indicador ilustra cómo los esfuerzos/acciones financieras realizadas por \nlas autoridades públicas, tanto a nivel local, nacional como internacional, solas o en \nasociación con organizaciones de la sociedad civil (OSC) y el sector privado, para proteger \ny salvaguardar el patrimonio cultural y natural del mundo tienen un impacto directo en hacer \nque las ciudades y los asentamientos humanos sean más sostenibles. \n\nEsto significa que los recursos y activos culturales se salvaguardan para seguir \natrayendo/atraer personas (habitantes, trabajadores, turistas, etc.) e inversiones \nfinancieras, para en última instancia aumentar el monto total del gasto. \n\nEste indicador es un indicador indirecto para medir el objetivo. Este indicador permitiría \nsaber si los países están reforzando o no sus esfuerzos para salvaguardar su patrimonio cultural \ny natural. Ayudará a identificar áreas que requieren más atención a efectos de políticas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas. Solo se incluye el patrimonio cultural, y no el patrimonio natural.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-04-01.pdf\">Metadatos 11-4-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"Gasto per cápita de las administraciones públicas autonómicas y locales en la preservación, protección y conservación del patrimonio cultural", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.4- Redoblar los esfuerzos para proteger y salvaguardar el patrimonio cultural y natural del mundo", "definicion"=>"Liquidated expenditure per person of the regional and local public administrations  in the preservation, protection and conservation of cultural heritage (historical  and artistic heritage, archives and libraries) ", "formula"=>"\n$$GLPCP^{t} = \\frac{GLPC_{CCAA}^{t}+GLPC_{EELL}^{t}}{P^{t}}$$\n\nwhere: \n\n$GLPC_{CCAA}^{t}$ = expenditure of the autonomous community in the preservation, \nprotection and conservation of cultural heritage (historical and artistic heritage, \narchives and libraries) in year $t$ \n\n$GLPC_{EELL}^{t}$ = expenditure settled by the local entities of the autonomous \ncommunity in the preservation, protection and conservation of cultural heritage \n(historical and artistic heritage, archives and libraries) in year $t$ \n\n$P^{t}$ = population on July 1 of year $t$ \n", "desagregacion"=>"Public administration: regional administration, local administration", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThis indicator measures the per capita expenditure (public and private) \nin the preservation, protection and conservation of cultural and/or natural \nheritage over time. To monitor change over time of national efforts for the \nprotection and safeguard of cultural and/or natural heritage. \n\nThis indicator illustrates how financial efforts/actions made by public \nauthorities, both at the local, national and international levels, alone or \nin partnership with civil society organizations (CSO) and the private sector, \nto protect and safeguard the world’s cultural and natural heritage has a direct \nimpact in making cities and human settlements more sustainable. \n\nThis means that cultural resources and assets are safeguarded to keep attracting/to \nattract people (inhabitants, workers, tourists, etc.) and financial investments, to \nultimately enhance the total amount of expenditure. \n\nThis indicator is a proxy to measure the target. This indicator would allow insight \ninto whether or not countries are strengthening their efforts into safeguarding \ntheir cultural and natural heritage. It will help to identify areas that require \nmore attention for policy purposes. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>"The available indicator does not comply with the UN indicator metadata. Only cultural heritage  is included, not natural heritage. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-04-01.pdf\">Metadata 11-4-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Gasto per cápita de las administraciones públicas autonómicas y locales en la preservación, protección y conservación del patrimonio cultural", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.4- Redoblar los esfuerzos para proteger y salvaguardar el patrimonio cultural y natural del mundo", "definicion"=>"\nGasto liquidado por persona de las administraciones públicas autonómicas y locales en  la preservación, protección y conservación del patrimonio cultural (patrimonio histórico  y artístico, archivos y bibliotecas)", "formula"=>"\n$$GLPCP^{t} = \\frac{GLPC_{CCAA}^{t}+GLPC_{EELL}^{t}}{P^{t}}$$\n\nnon: \n\n$GLPC_{CCAA}^{t}$ = Autonomia-erkidegoko herri-administrazioek kultura-ondarea (historia- eta arte-ondarea, artxiboak eta liburutegiak) zaintzeko, babesteko eta kontserbatzeko likidatutako gastua $t$ urtean\n\n$GLPC_{EELL}^{t}$ = Toki-entitateetako herri-administrazioek kultura-ondarea (historia- eta arte-ondarea, artxiboak eta liburutegiak) zaintzeko, babesteko eta kontserbatzeko likidatutako gastua $t$ urtean\n\n$P^{t}$ = biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Herri-administrazioa: Autonomia-erkidegoa; toki-entitatea", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nEste indicador mide el gasto per cápita (público y privado) en la preservación, \nprotección y conservación del patrimonio cultural y/o natural a lo largo del tiempo. \nPara monitorear la evolución a lo largo del tiempo de los esfuerzos \nnacionales para la protección y salvaguarda del patrimonio cultural y/o natural.\n\nEste indicador ilustra cómo los esfuerzos/acciones financieras realizadas por \nlas autoridades públicas, tanto a nivel local, nacional como internacional, solas o en \nasociación con organizaciones de la sociedad civil (OSC) y el sector privado, para proteger \ny salvaguardar el patrimonio cultural y natural del mundo tienen un impacto directo en hacer \nque las ciudades y los asentamientos humanos sean más sostenibles. \n\nEsto significa que los recursos y activos culturales se salvaguardan para seguir \natrayendo/atraer personas (habitantes, trabajadores, turistas, etc.) e inversiones \nfinancieras, para en última instancia aumentar el monto total del gasto. \n\nEste indicador es un indicador indirecto para medir el objetivo. Este indicador permitiría \nsaber si los países están reforzando o no sus esfuerzos para salvaguardar su patrimonio cultural \ny natural. Ayudará a identificar áreas que requieren más atención a efectos de políticas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak.  Kultura-ondarea bakarrik hartzen da kontuan, eta ez natura-ondarea.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-04-01.pdf\">Metadatuak 11-4-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 11.4: Strengthen efforts to protect and safeguard the world&#x2019;s cultural and natural heritage</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 11.4.1: Total per capita expenditure on the preservation, protection and conservation of all cultural and natural heritage, by source of funding (public, private), type of heritage (cultural, natural) and level of government (national, regional, and local/municipal)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>GB_XPD_CUL_PBPV - Total expenditure per capita spent on cultural heritage, public and private [11.4.1]</p>\n<p>GB_XPD_CULNAT_PB - Total expenditure per capita spent on cultural and natural heritage, public [11.4.1]</p>\n<p>GB_XPD_CULNAT_PBPV - Total expenditure per capita spent on cultural and natural heritage, public and private [11.4.1]</p>\n<p>GB_XPD_CULNAT_PV - Total expenditure per capita spent on cultural and natural heritage, private [11.4.1]</p>\n<p>GB_XPD_NAT_PBPV - Total expenditure per capita spent on natural heritage, public and private [11.4.1]</p>\n<p>GB_XPD_CUL_PB - Total expenditure per capita spent on cultural heritage, public [11.4.1]</p>\n<p>GB_XPD_CUL_PV - Total expenditure per capita spent on cultural heritage, private [11.4.1]</p>\n<p>GB_XPD_NAT_PB - Total expenditure per capita spent on natural heritage, public [11.4.1]</p>\n<p>GB_XPD_NAT_PV - Total expenditure per capita spent on natural heritage, private [11.4.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>8.3.1, 11.3.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Institute for Statistics (UIS)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition</strong><br>Total funding from government (central, regional, local), private sources (household, corporate &amp; sponsorship and international sources) in the preservation, protection and conservation of cultural and/or natural heritage for a given year per capita. The results should be express in Purchasing Power Parities (PPP) in constant $. </p>\n<p>Purchasing Power Parities (PPPs) are the rates of currency conversion that try to equalise the purchasing power of different currencies, by eliminating the differences in price levels between countries. The basket of goods and services priced is a sample of all those that are part of final expenditures: final consumption of households and government, fixed capital formation, and net exports. This indicator is measured in terms of national currency per USD dollar. (OECD)</p>\n<p><strong>Concepts</strong></p>\n<p><strong>Cultural heritage:</strong> includes artefacts, monuments, a group of buildings and sites, museums that have a diversity of values including symbolic, historic, artistic, aesthetic, ethnological or anthropological, scientific and social significance. It includes tangible heritage (movable, immobile and underwater), intangible heritage (ICH) embedded into cultural, and natural heritage artefacts, sites or monuments. The definition excludes ICH related to other cultural domains such as festivals, celebration etc. It covers industrial heritage and cave paintings. Mixed heritage that refer to sites containing elements of both natural and cultural significance are including in cultural heritage.</p>\n<p><strong>Natural heritage</strong>: refers to natural features, geological and physiographical formations and delineated areas that constitute the habitat of threatened species of animals and plants and natural sites of value from the point of view of science, conservation or natural beauty. It includes private and publically protected natural areas, zoos, aquaria and botanical gardens, natural habitat, marine ecosystems, sanctuaries and reservoirs.</p>\n<p><strong>Conservation</strong> <strong>of cultural heritage</strong> refers to the measures taken to extend the life of cultural heritage while strengthening transmission of its significant heritage messages and values. In the domain of cultural property, the aim of conservation is to maintain the physical and cultural characteristics of the object to ensure that its value is not diminished and that it will outlive our limited time span.</p>\n<p><strong>Conservation of natural heritage</strong> refers to the protection, care, management and maintenance of ecosystems, habitats, wildlife species and populations, within or outside of their natural environments, in order to safeguard the natural conditions for their long-term permanence.</p>\n<p>The aim of<strong> Preservation </strong>is to obviate damage liable to be caused by environmental or accidental factors, which pose a threat in the immediate surroundings of the object to be conserved. Accordingly, preventive methods and measures are not usually applied directly but are designed to control the microclimatic conditions of the environment with the aim of eradicating harmful agents or elements, which may have a temporary or permanent influence on the deterioration of the object.</p>\n<p><strong>Protection</strong>: is the act or process of applying measures designed to affect the physical condition of a property by defending or guarding it from deterioration, loss or attack, or to cover or shield the property from danger or injury. In the case of buildings and structures, such treatment is generally of a temporary nature and anticipates future historic preservation treatment; in the case of archaeological sites, the protective measure may be temporary or permanent.</p>\n<p><strong>Public expenditure</strong> refers to spending on heritage incurred by public funds. Public funds are state, regional and local government bodies (Adapted from OECD glossary). Expenditure that is not directly related to cultural and natural heritage is, in principle, not included. Public expenditure in the preservation, protection and conservation of national cultural and/or natural heritage covers direct expenditure (including subsides), transfers and indirect public expenditures including tax incentives.</p>\n<p><strong>Direct public expenditure</strong> includes subsidies, grants and awards. Direct expenditure comprises generally spent on personnel, goods and services, capital investment and other heritage activities. </p>\n<p><strong>A Transfer </strong>is a transaction in which one institutional unit provides a good, service, or asset to another unit without receiving from the latter any good, service, or asset in return as a direct counterpart (IMF, 2014). </p>\n<p><strong>Net Intergovernmental transfers </strong>are net transfers of funds designated for heritage activities from one level of government to another.</p>\n<p><strong>Indirect public expenditures</strong> include tax incentives&#x2013; reduction of taxable income that arises due to several of heritage expenses incurred by a taxpayer<strong>.</strong></p>\n<p><strong>National/Federal level </strong>consists of the institutional units of central government and non-market institutions controlled by central government. Central government expends their authority over the entire territory of country. It is responsible for providing heritage services for the benefit of the community as a whole, but also it may make transfers to other institutional units, as well levels of government.</p>\n<p><strong>Regional/State/Provincial level </strong>is a subdivision of government, which shares political, fiscal and economic power with central government. In federal government, regional level is represented by state government. In unitary states, regional government is known as a provincial government. This level of government consists of institutional units, which have some of the functions of government at a level below of that of central government and above the local level. A regional government usually has the fiscal authority to raise taxes within its territory and has the ability to spend at least some of its income according to its own policies, and appoint or elect its own officers.</p>\n<p>If a regional unit is fully dependent on funds from the central government and a central government determines those funds, expenditures on regional level should be treated as a part of central government for statistical purposes.</p>\n<p><strong>Local/municipal level </strong>is a public administration that exists at the lowest administration level within government state such as municipality of district. Local level refers to local government units, which consist of local government institutional units and nonmarket institutions controlled by local level. A local government often has the fiscal authority to raise taxes within its territory and should have the ability to spend at least some of its income according to its own policies, and appoint or elect its own officers.</p>\n<p><strong>Total Public expenditure on heritage </strong>is consolidated expenditure on heritage made by national/federal, regional/States/Provincial and local governments. </p>\n<p><strong>Private heritage expenditure </strong>refers to privately funded preservation, protection and conservation of national cultural and/or natural heritage and includes, but is not limited to: donations in kind, private non-profit sector and sponsorship. Private funding includes donations by individual and legal entities, donations by bilateral and multilateral funds such as Official Development Aid (ODA), income from admissions/selling services and goods to individual and legal entities and corporate sponsorship.</p>\n<p><strong>Donation</strong> refers to cash and gifts-in-kind given by a physical or legal entity. Donations can be in the form of cash and in kind donations. Cash donations refer to the gift in money, payment checks or other monetary equivalents. Gifts-in-kind donations refer to donations in goods, services or other things such as supplies. Donations can be conditional or unconditional. Conditional donations are limited by the conditions imposed by the donor. Unconditional donations refer to the gift, which has no concrete purpose, given to organization/institution in order to help them in realization of their mission.</p>\n<p><strong>Donations by individuals</strong> refer to cash and in kind donation given by individual or physical person. </p>\n<p><strong>Donations by legal entity </strong>(corporation, enterprises) refer to any cash or in kind contributions given as a gift by legal entity &#x2013; corporation, enterprises etc. This kind of donation is also known as a corporate philanthropy charitable giving to any organization/institution. </p>\n<p><strong>Corporate sponsorships</strong> refer to financial or in kind contribution by business sector in exchange for benefits in the form of advertising, reputation, promotion etc. Corporate sponsorships represent some kind of marketing in which corporation pays to programme/project/event in exchange for some marketing benefits. </p>\n<p><strong>Income from admissions/membership fees/ selling services and goods </strong>refers to amount of money received by entree sales to households / membership fees or selling services and goods to households or legal entities. </p>\n<p><strong>Official Development </strong>Aid refers to the flows of official financing administered with the promotion of the economic development and welfare of developing countries as the main objective, and which are concessional in character with a grant element of at least 25 percent (using a fixed 10 percent rate of discount). By convention, ODA flows comprise contributions of donor government agencies, at all levels, to developing countries (&#x201C;bilateral ODA&#x201D;) and to multilateral institutions. ODA receipts comprise disbursements by bilateral donors and multilateral institutions. Lending by export credit agencies&#x2014;with the pure purpose of export promotion is excluded. (OECD).</p>\n<p><strong>Donations by bilateral and multilateral</strong> sources refer to any cash and in kind contribution given to another organization as a gift by bilateral party (foreign states) or multilateral party (international body, organization, etc.). It can be in the form of development assistance or official development assistance or private international/foreign donation. Private bilateral/multilateral donation is financial aid given by private foundation from one foreign country or private foundations from several foreign countries. </p>\n<p><strong>Total heritage expenditure </strong>refers to private and public spending on conservation, protection and preservation of heritage. Total expenditure comprises public and private expenditure for natural and cultural heritage. Using the International Standard Industrial Classification of all Economic Activities Revision 4 (ISIC Rev. 4) classification, total heritage expenditure covers expenditures (public and private) for library and archives activities, museum activities and operation of historical sites and buildings as well resources invested in botanical and zoological gardens and nature reserve activities.<strong> </strong></p>", "UNIT_MEASURE__GLOBAL"=>"<p>PPP, constant 2017 United States dollars</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Classification of the Functions of Government (COFOG) defined according to the breakdown proposed in the International Monetary Fund (IMF) Manual Government Finance Statistics (GFS), available at:</p>\n<p><a href=\"http://www.imf.org/external/Pubs/FT/GFS/Manual/2014/gfsfinal.pdf\">http://www.imf.org/external/Pubs/FT/GFS/Manual/2014/gfsfinal.pdf</a>.</p>\n<p>2009 UNESCO Framework for cultural statistics </p>\n<p><a href=\"http://uis.unesco.org/sites/default/files/documents/unesco-framework-for-cultural-statistics-2009-en_0.pdf\">http://uis.unesco.org/sites/default/files/documents/unesco-framework-for-cultural-statistics-2009-en_0.pdf</a></p>\n<p>International Standard Industrial Classification of all Economic Activities Revision 4 (ISIC Rev. 4). <a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf</a> </p>", "SOURCE_TYPE__GLOBAL"=>"<p><u>For public expenditure:</u></p>\n<p>At national level, ministries of finance, and/or ministries of culture, environment financial management systems are the source of government expenditure on culture. Data on expenditure by lower levels of government can be centralized or collected directly from local authorities.</p>\n<p>Household expenditure on culture is collected through general consumption expenditure surveys or dedicated cultural participation and consumption surveys.</p>\n<p><u>For private expenditure:</u></p>\n<p>Data on other private sources of funding for heritage such (e.g. corporate sponsorship and philanthropy; private donations) are rarely collected systematically and would often require additional surveys proceeded by significant analytical, preparatory and advocacy work.</p>\n<p>International sources may be available through governmental financial systems when they are recorded on-budget, and off-budget international funding may sometimes be available through governmental aid management systems, although rarely with the disaggregation needed (ex. For heritage only). Data sources for international funding, such as the Official Development Aid data from the OECD-DAC database may be used as a complement, but often present problems of compatibility with other sources, such as government records. </p>\n<p>The UIS produces the indicator based on the population estimates produced by the UN Population Division.</p>", "COLL_METHOD__GLOBAL"=>"<p>The first global data collection cycle was launched in June 2020 and will thereafter occur on an annual basis.</p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p> Yearly data collection: launch in Q3 of each year</p>", "REL_CAL_POLICY__GLOBAL"=>"<p> Annual data release (March). </p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices: Focal point</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNESCO Institute for Statistics</p>", "INST_MANDATE__GLOBAL"=>"<p>The UNESCO Institute for Statistics (UIS) is the statistical branch of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The Institute produces internationally comparable data and methodologies in the fields of education, science, culture and communication for countries at all stages of development.</p>", "RATIONALE__GLOBAL"=>"<p>This indicator measures the per capita expenditure (public and private) in the preservation, protection and conservation of cultural and/or natural heritage over time. To monitor change over time of national efforts for the protection and safeguard of cultural and/or natural heritage.</p>\n<p>This indicator illustrates how financial efforts/actions made by public authorities, both at the local, national and international levels, alone or in partnership with civil society organizations (CSO) and the private sector, to protect and safeguard the world&#x2019;s cultural and natural heritage has a direct impact in making cities and human settlements more sustainable. This means that cultural resources and assets are safeguarded to keep attracting/to attract people (inhabitants, workers, tourists, etc.) and financial investments, to ultimately enhance the total amount of expenditure. This indicator is a proxy to measure the target. </p>\n<p>This indicator would allow insight into whether or not countries are strengthening their efforts into safeguarding their cultural and natural heritage. It will help to identify areas that require more attention for policy purposes.</p>\n<p>Expressing the indicator in PPP$ allows for comparison between countries and using constant values when looking at time-series is necessary to evaluate how real (eliminating the effects of inflation) resources evolve over time.</p>", "REC_USE_LIM__GLOBAL"=>"<p>1) In general, the availability of public expenditure data for heritage varies between countries.</p>\n<p>2) In general, the availability of private expenditure data for heritage is significantly lower so that it will take several years, capacity building, and financial investment in order to increase coverage to an acceptable level. </p>\n<p>This indicator comprises public and private monetary investments in heritage. It does not measure non-monetary factors such as national regulations or national/local policies for the preservation, protection and conservation of national cultural and/or natural heritage including World Heritage. These policies could take the form of fiscal incentives such as tax benefits for donations or sponsorships.</p>\n<p>International definitions and concepts that will support the harmonization of the data and indicators for cultural and natural heritage will be defined according to the 2009 UNESCO Framework for cultural statistics.</p>\n<p>The use of existing international classifications such as the Classification of the Function of the Government (COFOG) could be used. </p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated by dividing total public funding in heritage (i.e. including transfers paid but excluding transfers received) from government (central, regional, local) and the total of private funding from households, other private sources such as donations, sponsorships or international sources in a given year by the number of inhabitants and by the PPP$ conversion factor.</p>\n<p>HCExp per capita <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfenced separators=\"|\">\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mrow>\n              <mo stretchy=\"false\">&#x2211;</mo>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>E</mi>\n                    <mi>x</mi>\n                    <mi>p</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>p</mi>\n                    <mi>u</mi>\n                  </mrow>\n                </msub>\n                <mo>+</mo>\n                <msub>\n                  <mrow>\n                    <mi>E</mi>\n                    <mi>x</mi>\n                    <mi>p</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>p</mi>\n                    <mi>r</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mrow>\n          </mrow>\n          <mrow>\n            <mi>P</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfenced>\n    <mo>/</mo>\n    <mi>P</mi>\n    <mi>P</mi>\n    <mi>P</mi>\n    <mi>f</mi>\n  </math> </p>\n<p>Where:</p>\n<p>HCExp per capita = Expenditure per inhabitant in heritage in constant PPP$</p>\n<p>HC Exp = Expenditure on Preservation, Protection and Conservation of all cultural and/or natural heritage</p>\n<p><em>Exp<sub>pu</sub></em>= Sum of public expenditure by all levels of government on the preservation, protection and conservation of cultural and/or natural heritage</p>\n<p><em>Exp<sub>pr</sub></em> = Sum of all types of private expenditure on the preservation, protection and conservation of cultural and/or natural heritage</p>\n<p>PPPf: Purchase Power Parity = PPP Constant $ conversion factor </p>", "DATA_VALIDATION__GLOBAL"=>"<p>For each questionnaire received from countries, UIS executes a series of quality checks and send back a data processing report identifying problematic issues/inconsistent data to countries for their feedback on corrections.</p>", "ADJUSTMENT__GLOBAL"=>"<p>To inform of any discrepancy between standard classification and national practice, adequate metadata and footnote are created to adequately document the results.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Missing data will not be estimated by the UIS.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Missing data will not be estimated by the UIS.</p>", "REG_AGG__GLOBAL"=>"<p>To be determined. </p>", "DOC_METHOD__GLOBAL"=>"<p><strong>Methods and guidance available to countries for the compilation of the data at the national level</strong></p>\n<p>Total public expenditure on heritage is calculated in either of two ways:</p>\n<ul>\n  <li>With sector data from financial reports from heritage institutions, business registers, structural business statistics or survey heritage institutions. Heritage is defined by ISIC Rev. 4 codes (or equivalent at national/regional level) as presented in Table 1 below.</li>\n</ul>\n<p>Table 1: Cultural and Natural Heritage Activities by ISIC Rev. 4</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Type of Heritage</p>\n      </td>\n      <td>\n        <p>ISIC Rev. 4 codes</p>\n      </td>\n      <td>\n        <p>Type of activities</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>Cultural Heritage</p>\n      </td>\n      <td>\n        <p>9101</p>\n      </td>\n      <td>\n        <p>Library and Archives activities </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>9102</p>\n      </td>\n      <td>\n        <p>Museums activities and operation of historical sites and buildings</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Natural Heritage</p>\n      </td>\n      <td>\n        <p>9103</p>\n      </td>\n      <td>\n        <p>Botanical and zoological gardens and nature reserves activities </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<ul>\n  <li>Alternatively, by using government expenditure data by function from the Ministry of Finance or equivalent, database of government finance statistics. Heritage expenditure is calculated from government expenditure by function using the Classification of the Functions of Government (COFOG).<ul>\n      <li>The methodology to measure public heritage expenditure can be estimated based on four-digit codes of the COFOG classification </li>\n      <li>The majority of cultural and natural heritage expenditure is estimated from the Cultural Services (IS) code 7082. Heritage expenditure refers to:</li>\n    </ul>\n  </li>\n</ul>\n<p> </p>\n<ul>\n  <li>\n    <ul>\n      <li>\n        <ul>\n          <li>The provision of cultural heritage services; administration of cultural heritage affairs; supervision and regulation of cultural heritage facilities;</li>\n          <li>The operation or support of facilities for cultural pursuits (libraries, museums, monuments, historic houses and sites, zoological and botanical gardens, aquaria, arboreta, etc.); production <br><br>Natural heritage also includes the Protection of biodiversity and landscape (CS) code 7054 defined as:</li>\n          <li>The administration, supervision, inspection, operation or support of activities relating to the protection of biodiversity and landscape;</li>\n          <li>Grants, loans or subsidies to support activities relating to the protection of biodiversity and landscape.</li>\n        </ul>\n      </li>\n    </ul>\n  </li>\n  <li>International recommendations<ul>\n      <li>COFOG classification defined according to the breakdown proposed in the International Monetary Fund (IMF) Manual Government Finance Statistics (GFS), available at:</li>\n    </ul>\n  </li>\n</ul>\n<p> <a href=\"http://www.imf.org/external/Pubs/FT/GFS/Manual/2014/gfsfinal.pdf\">http://www.imf.org/external/Pubs/FT/GFS/Manual/2014/gfsfinal.pdf</a>. </p>\n<ul>\n  <li>\n    <ul>\n      <li>2009 UNESCO Framework for cultural statistics </li>\n    </ul>\n  </li>\n</ul>\n<p> <a href=\"http://uis.unesco.org/sites/default/files/documents/unesco-framework-for-cultural-statistics-2009-en_0.pdf\"><u>http://uis.unesco.org/sites/default/files/documents/unesco-framework-for-cultural-statistics-2009-en_0.pdf</u></a></p>\n<p>Available in eight languages (Arabic, Chinese, English, French, Mongolian, Russian, Spanish and Vietnamese)</p>\n<ul>\n  <li>\n    <ul>\n      <li>International Standard Industrial Classification of all Economic Activities Revision 4 (ISIC Rev. 4). <a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\"><u>https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf</u></a> </li>\n      <li>Recommendation concerning the International Standardization of Statistics on the Public Financing of Cultural Activities, UNESCO 1980</li>\n    </ul>\n  </li>\n</ul>\n<p><a href=\"http://portal.unesco.org/en/ev.php-URL_ID=13140&amp;URL_DO=DO_TOPIC&amp;URL_SECTION=201.html#targetText=1.,in%20education%20and%20science%20statistics).\"><u>http://portal.unesco.org/en/ev.php-URL_ID=13140&amp;URL_DO=DO_TOPIC&amp;URL_SECTION=201.html#targetText=1.,in%20education%20and%20science%20statistics).</u></a></p>\n<ul>\n  <li>\n    <ul>\n      <li>What is Official Development Aid?, OECD , April 2019</li>\n    </ul>\n  </li>\n</ul>\n<p><strong> </strong><a href=\"http://www.oecd.org/dac/stats/What-is-ODA.pdf\"><u>http://www.oecd.org/dac/stats/What-is-ODA.pdf</u></a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The UIS maintains a set of data processing guidelines/standards as well as data processing tools to facilitate processing of data and ensure the quality of data.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>All data collected will be reviewed by UIS for accuracy and quality.</p>\n<p>The process for quality assurance includes review of survey documentation, making sure compliance with international standards (for example the 2009 UNESCO FCS, COFOG, ISIC), calculation of measures of reliability, and examining the consistency and coherence within the data set as well as with the time series of data and the resulting indicators examination of consistency of indicator values derived from different sources and, if necessary, consultation with data providers.</p>\n<p>Before its annual data release and addition to the global SDG Indicators Database, the UNESCO Institute for Statistics submits all indicator values and notes on methodology to SDG focal points, National Statistical Offices, Ministries of culture or other relevant agencies in individual countries for their review and validation.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The data should comply with the definitions and guidelines provided international and comprehensive coverage of public and private expenditure on cultural and natural heritage </p>\n<p>Criteria for quality assessment include: data sources must include proper documentation; data values shall be nationally representative and, if not, should be footnoted; data are plausible and based on trends and consistency with previously published/reported values for the indicator.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>For the first data collection on SDG 11.4.1 in 2020, 50 countries representing 24% of all countries worldwide completed the UIS questionnaire. Due to lack of available data, less than 30 were able to calculate the indicator fully or partially.</p>\n<p>The availability of private expenditure on heritage is limited.</p>\n<p>If further disaggregation is not available at national level, the identification of cultural and natural heritage using the COFOG classification in public Finance statistics is not always straightforward. This explains why some countries were not able to report the relevant data to calculate SDG 11.4.1.</p>\n<p><strong>Time series:</strong></p>\n<p>Annual data collection as of 2020.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregated by source of funding (public, private) <br>Disaggregated by type of heritage (cultural, natural)<br>Disaggregated by type of level of government (national, regional and local/municipal)</p>\n<p>Quantifiable derivatives (1). Comparison of the relative expenditures in heritage with GDP per capita of countries, which will provide a complementary measure of a nation&#x2019;s capacities and levels of development.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There are no differences in the underlying data. Difference may occur due to the use of difference data for population data used to calculate indicators.</p>", "OTHER_DOC__GLOBAL"=>"<p><a href=\"http://uis.unesco.org/en/topic/sustainable-development-goal-11-4\"><u>http://uis.unesco.org/en/topic/sustainable-development-goal-11-4</u></a></p>\n<p><strong>References:</strong></p>\n<ul>\n  <li>2009 UNESCO Framework for cultural statistics:<br><a href=\"http://uis.unesco.org/sites/default/files/documents/unesco-framework-for-cultural-statistics-2009-en_0.pdf\"><u>http://uis.unesco.org/sites/default/files/documents/unesco-framework-for-cultural-statistics-2009-en_0.pdf</u></a></li>\n  <li>Public (government) expenditure on culture, <em>Guide to Eurostat culture statistics, Eurostat 2018</em></li>\n</ul>\n<p><a href=\"https://ec.europa.eu/eurostat/documents/3859598/9433072/KS-GQ-18-011-EN-N.pdf/72981708-edb7-4007-a298-8b5d9d5a61b5\"><u>https://ec.europa.eu/eurostat/documents/3859598/9433072/KS-GQ-18-011-EN-N.pdf/72981708-edb7-4007-a298-8b5d9d5a61b5</u></a></p>\n<ul>\n  <li>Manual on sources and methods for the compilation of COFOG statistics, Eurostat, 2011.</li>\n</ul>\n<p><a href=\"https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-RA-11-013\">https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-RA-11-013</a> </p>\n<ul>\n  <li> Government expenditure on recreation, culture and religion, Eurostat, 2019</li>\n</ul>\n<p><a href=\"https://ec.europa.eu/eurostat/statistics-explained/index.php/Government_expenditure_on_recreation,_culture_and_religion\"><u>https://ec.europa.eu/eurostat/statistics-explained/index.php/Government_expenditure_on_recreation,_culture_and_religion</u></a></p>\n<ul>\n  <li>Statistics Sweden: Public and private expenditure on culture</li>\n</ul>\n<p><a href=\"https://www.scb.se/en/finding-statistics/statistics-by-subject-area/culture-and-leisure/cultural-expenditure/public-and-private-expenditure-on-culture/\"><u>https://www.scb.se/en/finding-statistics/statistics-by-subject-area/culture-and-leisure/cultural-expenditure/public-and-private-expenditure-on-culture/</u></a></p>\n<ul>\n  <li>D&#xE9;partement des &#xE9;tudes, de la prospective et des statistiques &#xAB; Local and regional authority cultural expenditure in 2010, Culture et chiffres, 2014-3 France. <a href=\"http://www.culturecommunication.gouv.fr/Etudes-et-statistiques\">http://www.culturecommunication.gouv.fr/Etudes-et-statistiques</a></li>\n  <li>Erasmus University Rotterdam, Boekmanstichting, Public and private financing of the arts and culture: their interrelations and measurement, ROUNDTABLE October 5-6, 2007, Amsterdam, the Netherlands</li>\n  <li>European Parliament, Financing the Arts and Culture in the EU, 2006<br><a href=\"http://www.culturalpolicies.net/web/files/134/en/Financing_the_Arts_and_Culture_in_the_EU.pdf\"><u>http://www.culturalpolicies.net/web/files/134/en/Financing_the_Arts_and_Culture_in_the_EU.pdf</u></a><u><br></u></li>\n  <li>Canada: Government expenditures on culture, by function and level of government, 2009/2010 <a href=\"http://www.statcan.gc.ca/pub/87f0001x/2012001/t012-eng.htm\"><u>http://www.statcan.gc.ca/pub/87f0001x/2012001/t012-eng.htm</u></a><u><br></u></li>\n  <li>Canada: Federal government capital grants, contributions and transfers for culture, by function and province or territory, 2009/2010 <br><a href=\"http://www.statcan.gc.ca/pub/87f0001x/2012001/t004-eng.htm\"><u>http://www.statcan.gc.ca/pub/87f0001x/2012001/t004-eng.htm</u></a><u><br></u></li>\n  <li>Council of Europe, Ericarts. Monitoring Public Cultural Expenditure in Selected European Countries 2000-2013. (8) <br><a href=\"http://www.culturalpolicies.net/web/statistics-funding.php?aid=232&amp;cid=80\"><u>http://www.culturalpolicies.net/web/statistics-funding.php?aid=232&amp;cid=80</u></a><u><br></u></li>\n  <li>Germany: Public expenditure on culture (Protection and preservation of historical monuments)<br><a href=\"https://www.destatis.de/EN/FactsFigures/SocietyState/EducationResearchCulture/EducationalCulturalFinance/Tables/ExpenditurePublicBugetsArtsCulture.html\"><u>https://www.destatis.de/EN/FactsFigures/SocietyState/EducationResearchCulture/EducationalCulturalFinance/Tables/ExpenditurePublicBugetsArtsCulture.html</u></a></li>\n</ul>", "indicator_sort_order"=>"11-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.5.1", "slug"=>"11-5-1", "name"=>"Número de personas muertas, desaparecidas y afectadas directamente atribuido a desastres por cada 100.000 personas", "url"=>"/site/es/11-5-1/", "sort"=>"110501", "goal_number"=>"11", "target_number"=>"11.5", "global"=>{"name"=>"Número de personas muertas, desaparecidas y afectadas directamente atribuido a desastres por cada 100.000 personas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de personas muertas directamente atribuido a desastres por cada 100.000 habitantes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de personas muertas, desaparecidas y afectadas directamente atribuido a desastres por cada 100.000 personas", "indicator_number"=>"11.5.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_28/opt_0/tipo_1/ti_defunciones/temas.html", "url_text"=>"Estadística de defunciones", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Número de personas muertas directamente atribuido a desastres por cada 100.000 habitantes", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.5- De aquí a 2030, reducir significativamente el número de muertes causadas por los desastres, incluidos los relacionados con el agua, y de personas afectadas por ellos, y reducir considerablemente las pérdidas económicas directas provocadas por los desastres en comparación con el producto interno bruto mundial, haciendo especial hincapié en la protección de los pobres y las personas en situaciones de vulnerabilidad", "definicion"=>"Defunciones atribuidas a desastres naturales por cada 100.000 habitantes", "formula"=>"\n$$TM_{desastres}^{t} = \\frac{D_{desastres}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$D_{desastres}^{t} =$ defunciones atribuidas a desastres naturales (códigos X30-X39 de la CIE-10) en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico/Comarca/Municipio\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue \nadoptado por los Estados miembros de las Naciones Unidas en marzo de 2015 \ncomo una política global de reducción del riesgo de desastres.\n\nEntre sus metas se encuentran:\n\nMeta A: Reducir sustancialmente la mortalidad global por desastres para \n2030, con el objetivo de reducir el promedio de mortalidad global por cada 100.000 habitantes entre \n2020-2030 en comparación con 2005-2015\n\nMeta B: Reducir sustancialmente el número de personas afectadas\na nivel mundial para 2030 , con el objetivo de reducir la cifra promedio \nmundial por cada 100.000 habitantes \nentre 2020 y 2030 en comparación con el período 2005-2015.\n\nLa consecución de sus metas contribuirá al desarrollo sostenible y fortalecerá la \nresiliencia económica, social, sanitaria y ambiental.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos de Naciones Unidas", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-05-01.pdf\">Metadatos 11-5-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Número de personas muertas directamente atribuido a desastres por cada 100.000 habitantes", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.5- De aquí a 2030, reducir significativamente el número de muertes causadas por los desastres, incluidos los relacionados con el agua, y de personas afectadas por ellos, y reducir considerablemente las pérdidas económicas directas provocadas por los desastres en comparación con el producto interno bruto mundial, haciendo especial hincapié en la protección de los pobres y las personas en situaciones de vulnerabilidad", "definicion"=>"Deaths attributed to natural disasters per 100.000 inhabitants ", "formula"=>"\n$$TM_{disasters}^{t} = \\frac{D_{disasters}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$D_{disasters}^{t} =$ deaths attributed to natural disasters (codes X30-X39 of the ICD-10) in year $t$\n\n$P^{t} =$ population as of 1 July of year $t$\n", "desagregacion"=>"Sex\n\nProvince/County/Municipality\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted \nby UN Member States in March 2015 as a global policy of disaster risk \nreduction. \n\nIts targets include: \n\nTarget A: Substantially reduce global disaster mortality by 2030, aiming \nto lower average per 100,000 global mortality between 2020-2030 compared \nwith 2005-2015. \n\nTarget B: Substantially reduce the number of affected people globally by \n2030, aiming to lower the average global figure per 100,000 between 2020-2030 \ncompared with 2005-2015. \n\nThe achievement of its targets will contribute to sustainable development \nand strengthen economic, social, health, and environmental resilience. \n\nSource: United Nations Statistics Division \n", "comparabilidad"=>"The available indicator partially complies with the United Nations metadata", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-05-01.pdf\">Metadata 11-5-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Número de personas muertas directamente atribuido a desastres por cada 100.000 habitantes", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.5- De aquí a 2030, reducir significativamente el número de muertes causadas por los desastres, incluidos los relacionados con el agua, y de personas afectadas por ellos, y reducir considerablemente las pérdidas económicas directas provocadas por los desastres en comparación con el producto interno bruto mundial, haciendo especial hincapié en la protección de los pobres y las personas en situaciones de vulnerabilidad", "definicion"=>"Defunciones atribuidas a desastres naturales por cada 100.000 habitantes", "formula"=>"\n$$TM_{hondamendiak}^{t} = \\frac{D_{hondamendiak}^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$D_{hondamendiak}^{t} =$ hondamendi naturalei egotzitako heriotzak (GNS-10eko X30-X39 kodeak) $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa/Eskualdea/Udalerria\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue \nadoptado por los Estados miembros de las Naciones Unidas en marzo de 2015 \ncomo una política global de reducción del riesgo de desastres.\n\nEntre sus metas se encuentran:\n\nMeta A: Reducir sustancialmente la mortalidad global por desastres para \n2030, con el objetivo de reducir el promedio de mortalidad global por cada 100.000 habitantes entre \n2020-2030 en comparación con 2005-2015\n\nMeta B: Reducir sustancialmente el número de personas afectadas\na nivel mundial para 2030 , con el objetivo de reducir la cifra promedio \nmundial por cada 100.000 habitantes \nentre 2020 y 2030 en comparación con el período 2005-2015.\n\nLa consecución de sus metas contribuirá al desarrollo sostenible y fortalecerá la \nresiliencia económica, social, sanitaria y ambiental.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"Eskuragarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen metadatuak", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-05-01.pdf\">Metadatuak 11-5-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_DSR_AFFCT - Number of people affected by disaster [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_DAFF - Number of directly affected persons attributed to disasters per 100,000 population [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_IJILN - Number of injured or ill people attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_MISS - Number of missing persons due to disaster [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_MMHN - Number of deaths and missing persons attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_MORT - Number of deaths due to disaster [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_MTMP - Number of deaths and missing persons attributed to disasters per 100,000 population [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_PDAN - Number of people whose damaged dwellings were attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_PDLN - Number of people whose livelihoods were disrupted or destroyed, attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_PDYN - Number of people whose destroyed dwellings were attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.5.1, 13.1.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator measures the number of people who died, went missing or were directly affected by disasters per 100,000 population. </p>\n<p><strong>Concepts:</strong></p>\n<p><strong>Disasters</strong>: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Death</strong>: The number of people who died during the disaster, or directly after, as a direct result of the hazardous event.</p>\n<p><strong>Missing persons</strong>: The number of people whose whereabouts is unknown since the hazardous event. It includes people who are presumed dead, for whom there is no physical evidence such as a body, and for which an official/legal report has been filed with competent authorities.</p>\n<p><strong>Disaster-affected persons</strong>: People who are affected, either directly or indirectly, by a hazardous event. Directly affected are those who have suffered injury, illness or other health effects. Indirectly affected are people who have suffered consequences, other than or in addition to direct effects (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Injured or ill persons</strong>: People suffering from a new or exacerbated physical or psychological harm, trauma or an illness as a result of a disaster.</p>\n<p><strong>Livelihood</strong>: The capacities, productive assets (both living and material) and activities required for securing a means of living, on a sustainable basis, with dignity. </p>\n<p><strong>People whose damaged or destroyed dwellings were attributed to disasters</strong>: The estimated number of inhabitants previously living in the dwellings (houses, or housing units) damaged or destroyed. These inhabitants are considered affected by the fact that their dwellings were damaged (asset property damage), and because in many cases they would be included in those evacuated, displaced, or relocated. The categories of <em>evacuated, displaced, or relocated</em> should not be included in the indicators.</p>\n<p><strong>Houses damaged</strong>: Houses (housing units) with minor damage, not structural or architectural, and which may continue to be habitable, although they may require repair and/or cleaning.</p>\n<p><strong>Houses destroyed</strong>: Houses (housing units) levelled, buried, collapsed, washed away or damaged to the extent that they are no longer habitable, or must be rebuilt.</p>\n<p><strong>Notes</strong>: </p>\n<p>1) The data on number of deaths and number of missing/presumed dead are mutually exclusive, so no-one should be double counted.</p>\n<p>2) It&#x2019;s important to remember that disasters are not natural, they result from human choices.</p>\n<p>Detailed definitions, concepts, including composition and calculation for each of the data series, can be found in the SFM Technical Guidance (see below the Reference section)</p>", "UNIT_MEASURE__GLOBAL"=>"<p>For VC_DSR_MTMP - Number of deaths and missing persons attributed to disasters per 100,000 population; and VC_DSR_DAFF - Number of directly affected persons attributed to disasters per 100,000 population: ratio</p>\n<p>For other data series: Number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Data sources and collection method:</strong></p>\n<p>Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are reported by national Sendai Framework focal points in the Sendai Framework Monitor (SFM) and national disaster loss database: DesInventar-Sendai. Data are consisted of official, national reporting exclusively. Number of deaths attributed to disasters, number of missing persons attributed to disasters, number of injured or ill people attributed to disasters, number of people whose damaged dwellings were attributed to disasters, number of people whose destroyed dwellings were attributed to disasters, and number of people whose livelihoods were disrupted or destroyed, attributed to disasters are reported in SFM and DesInventar-Sendai. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are reported in Sendai Framework Monitor (SFM) on an ongoing basis, and snapshotted once every year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released once a year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OIEWG) report, endorsed by the United Nations General Assembly in Resolution A/RES/71/276, requested the UNDRR to undertake technical work and provide technical guidance to develop minimum standards and metadata, the methodologies, and the global monitoring and measurements of the SFM global indicators.</p>\n<p>This indicator is recommended by the OIEWG for the measurement of global Targets A and B of the Sendai Framework, which were endorsed by the UN General Assembly in its Resolution A/RES/71/276, <em>Report of the open-ended intergovernmental</em> <em>expert working group on indicators and terminology relating to disaster risk</em>.</p>", "RATIONALE__GLOBAL"=>"<p>The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. Among the global targets, &#x201C;Target A: Substantially reduce global disaster mortality by 2030, aiming to lower average per 100,000 global mortality between 2020-2030 compared with 2005-2015&#x201D; and &#x201C;Target B: Substantially reduce the number of affected people globally by 2030, aiming to lower the average global figure per 100,000 between 2020-2030 compared with 2005-2015&#x201D; will contribute to sustainable development and strengthen economic, social, health and environmental resilience. The economic, environmental and social perspectives would include poverty eradication, urban resilience, and climate change adaptation.</p>\n<p>The open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OIEWG) established by the General Assembly (resolution 69/284) has developed a set of indicators to measure global progress in the implementation of the Sendai Framework, which was endorsed by the United Nations General Assembly (UNGA) (OIEWG report A/71/644). The relevant global indicators for the Sendai Framework will be used to report for this indicator. </p>\n<p>Disaster loss, mortality and affected populations are greatly influenced by large-scale catastrophic events, as well as a high number of small-scale hazardous events. UNDRR recommends Member States to report the data by event in DesInventar-Sendai, and per the minimum reporting requirement of the Sendai Framework Monitor (SFM) using the Technical Guidance (see Reference and Documentation section), so complementary analysis can be done on the regional and global scale.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The Sendai Framework Monitoring (SFM) System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States report through the system since 2018. The data for SDG indicators are compiled and reported by UNDRR.</p>\n<p>Proxy, alternative and additional indicators:</p>\n<p>In most cases international data sources only record events that surpass some threshold of impact and use secondary data sources which usually have non uniform or even inconsistent methodologies, producing heterogeneous datasets</p>", "DATA_COMP__GLOBAL"=>"<p>Related indicators as of December 2017</p>\n<p>For death and missing perons:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mo>(</mo>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n          </mrow>\n          <mrow>\n            <mn>2</mn>\n            <mi>a</mi>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n          </mrow>\n          <mrow>\n            <mn>3</mn>\n            <mi>a</mi>\n          </mrow>\n        </msub>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n  </math></p>\n<p>Where:</p>\n<p>A<sub>1</sub>: Number of deaths and missing persions attributed to disasters per 100 000; corresponding to Sendai Framework Indicator A-1.</p>\n<p>A<sub>2a</sub>: Number of deaths attributed to disasters; </p>\n<p>A<sub>3a</sub>: Number of missing persons attributed to disasters; and </p>\n<p>Population: Represented population.</p>\n<p>* Detailed methodologies can be found in the Technical Guidance (see below the Reference section)</p>\n<p>For number of disaster-affected person:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>B</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mo>(</mo>\n        <msub>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n          <mrow>\n            <mn>2</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n          <mrow>\n            <mn>3</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n          <mrow>\n            <mn>4</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n          <mrow>\n            <mn>5</mn>\n          </mrow>\n        </msub>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n  </math></p>\n<p>Where:</p>\n<p>B<sub>1</sub>: Number of directly affected people attributed to disasters, per 100,000 population; corresponding to Sendai Framework Indicator B-1.</p>\n<p>B<sub>2</sub>: Number of injured or ill people attributed to disasters; corresponding to Sendai Framework Indicator B-2.</p>\n<p>B<sub>3</sub>: Number of people whose damaged dwellings were attributed to disasters; corresponding to Sendai Framework Indicator B-3.</p>\n<p>B<sub>4</sub>: Number of people whose destroyed dwellings were attributed to disasters; corresponding to Sendai Framework Indicator B-4.</p>\n<p>B<sub>5</sub>: Number of people whose livelihoods were disrupted or destroyed, attributed to disasters; corresponding to Sendai Framework Indicator B-5.</p>\n<p>Population: Represented population.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are validation by UNDRR and national focal points.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction</li>\n  <li>ADPC Disaster and Climate Resilience e-Learning: An orientation to using the online Sendai Framework Monitor, https://courses.adpc.net/courses/course-v1:UNISDR+SFM001+2019Y1/about</li>\n</ul>\n<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>2005-2023</p>\n<p><strong>Time series:</strong></p>\n<p>Annual</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Number of deaths attributed to disasters; </p>\n<p>Number of missing persons attributed to disasters; and </p>\n<p>Number of directly affected people attributed to disasters. </p>\n<p> [Optional Disaggregation]:</p>\n<p>Hazard types</p>\n<p>Geography (Administrative Unit)</p>\n<p>Sex</p>\n<p>Age (3 categories)</p>\n<p>Disability</p>\n<p>Income</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Official SDG Metadata URL: </strong><a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-01.pdf\">https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-01.pdf</a></p>\n<p><strong>Internationally agreed methodology and guideline URL: </strong></p>\n<p><strong>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017)</strong></p>\n<p><a href=\"https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf\">https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf</a></p>\n<p>Sendai Framework Monitor</p>\n<p><a href=\"https://sendaimonitor.undrr.org/\">https://sendaimonitor.undrr.org/</a></p>\n<p><br>DesInventar-Sendai</p>\n<p><a href=\"https://www.desinventar.net/\">https://www.desinventar.net/</a></p>\n<p><strong>Other references:</strong></p>\n<p><strong>Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG</strong>). Endorsed by UNGA on 2nd February 2017. Available at: <a href=\"https://www.preventionweb.net/publications/view/51748\">https://www.preventionweb.net/publications/view/51748</a></p>", "indicator_sort_order"=>"11-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"11.5.2", "slug"=>"11-5-2", "name"=>"Pérdidas económicas directas atribuidas a los desastres en relación con el producto interno bruto (PIB) mundial", "url"=>"/site/es/11-5-2/", "sort"=>"110502", "goal_number"=>"11", "target_number"=>"11.5", "global"=>{"name"=>"Pérdidas económicas directas atribuidas a los desastres en relación con el producto interno bruto (PIB) mundial"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Indemnizaciones por daños personales, pérdidas pecuniarias y daños de bienes pagadas y/o provisionadas en proporción al PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Pérdidas económicas directas atribuidas a los desastres en relación con el producto interno bruto (PIB) mundial", "indicator_number"=>"11.5.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Consorcio de Compensación de Seguros", "periodicity"=>"Anual", "url"=>"https://www.consorseguros.es/noticias/-/asset_publisher/ya2OdYGgbjgX/content/publicacion-de-la-estadistica-de-riesgos-extraordinarios-1971-2023-", "url_text"=>"Estadística de riesgos extraordinarios", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_10/opt_1/ti_cuentas-economicas/temas.html", "url_text"=>"Cuentas económicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Mensual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176802&menu=ultiDatos&idp=1254735976607", "url_text"=>"Índice de precios de consumo", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Indemnizaciones por daños personales, pérdidas pecuniarias y daños de bienes pagadas y/o provisionadas en proporción al PIB", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.5- De aquí a 2030, reducir significativamente el número de muertes causadas por los desastres, incluidos los relacionados con el agua, y de personas afectadas por ellos, y reducir considerablemente las pérdidas económicas directas provocadas por los desastres en comparación con el producto interno bruto mundial, haciendo especial hincapié en la protección de los pobres y las personas en situaciones de vulnerabilidad", "definicion"=>"Proporción de indemnizaciones por daños personales, pérdidas pecuniarias  y daños de bienes pagadas y/o provisionadas (pendientes de liquidación o pago)  en relación al PIB a precios corrientes", "formula"=>"$$INDEMPIB^{t} = \\frac{(IDP^{t}+IPP^{t}+IDB^{t})/IUIPC_{refT}^{t}}{PIB^{t}} \\cdot 100 \\quad \\mathrm t=1,\\dots,\\mathrm T$$\n\n$$IUIPC_{refT}^{T} = 1$$\n\n$$IUIPC_{refT}^{t-1} = IUIPC_{refT}^{t} \\cdot \\frac{(100+TIPC_{dic}^{t})}{100} \\quad \\mathrm t=\\mathrm T,\\mathrm T-1,\\dots,\\mathrm 2$$\n\ndonde:\n\n$IDP^{t}$ = indemnizaciones por daños personales pagadas y/o provisionadas en el año $t$ a precios del año $T$\n\n$IPP^{t}$ = indemnizaciones por pérdidas pecuniarias pagadas y/o provisionadas en el año $t$ a precios del año $T$\n\n$IDB^{t}$ = indemnizaciones por daños de bienes pagadas y/o provisionadas en el año $t$ a precios del año $T$\n\n$TIPC_{dic}^{t}$ = tasa de variación anual del IPC nacional en el mes de diciembre del año $t$ \n\n$PIB^{t}$ = producto interior bruto en términos corrientes en el año $t$\n", "desagregacion"=>"Territorio histórico", "observaciones"=>"Las indemnizaciones se asignan al lugar y año de ocurrencia del siniestro,  no incluyendo los siniestros ocurridos en el extranjero", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue \nadoptado por los Estados miembros de las Naciones Unidas en marzo de 2015 \ncomo una política global de reducción del riesgo de desastres.\n\nEntre sus metas se encuentra la Meta C: Reducir las pérdidas económicas directas \npor desastres en relación con el producto interno bruto (PIB) mundial para 2030.\n\nLa consecución de sus metas contribuirá al desarrollo sostenible y fortalecerá la \nresiliencia económica, social, sanitaria y ambiental.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible no cumple con los metadatos de Naciones Unidas,  pero aporta información similar", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-02.pdf\">Metadatos 1-5-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"Indemnizaciones por daños personales, pérdidas pecuniarias y daños de bienes pagadas y/o provisionadas en proporción al PIB", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.5- De aquí a 2030, reducir significativamente el número de muertes causadas por los desastres, incluidos los relacionados con el agua, y de personas afectadas por ellos, y reducir considerablemente las pérdidas económicas directas provocadas por los desastres en comparación con el producto interno bruto mundial, haciendo especial hincapié en la protección de los pobres y las personas en situaciones de vulnerabilidad", "definicion"=>"Proportion of compensation for personal injury, pecuniary loss and property  damage paid and/or provisioned (pending settlement or payment) in relation to  GDP at current prices", "formula"=>"$$INDEMPIB^{t} = \\frac{(IDP^{t}+IPP^{t}+IDB^{t})/IUIPC_{refT}^{t}}{PIB^{t}} \\cdot 100 \\quad \\mathrm t=1,\\dots,\\mathrm T$$\n\n$$IUIPC_{refT}^{T} = 1$$\n\n$$IUIPC_{refT}^{t-1} = IUIPC_{refT}^{t} \\cdot \\frac{(100+TIPC_{dec}^{t})}{100} \\quad \\mathrm t=\\mathrm T,\\mathrm T-1,\\dots,\\mathrm 2$$\n\nwhere:\n\n$IDP^{t}$ = compensation for personal injuries paid and/or provisioned in year $t$ at prices of year $T$\n\n$IPP^{t}$ = compensation for pecuniary losses paid and/or provisioned in year $t$ at prices of year $T$\n\n$IDB^{t}$ = compensation for property damage paid and/or provisioned in year $t$ at prices of year $T$\n\n$TIPC_{dec}^{t}$ = annual variation rate of the national CPI in December of year $t$ \n\n$PIB^{t}$ = gross domestic product at current prices in year $t$\n", "desagregacion"=>"Province", "observaciones"=>"Compensation is assigned to the place and year of occurrence of the accident,  accidents occurred abroad not included.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by \nUN Member States in March 2015 as a global policy of disaster risk reduction. \n\nIts targets include Target C: Reduce direct disaster economic loss in relation \nto global GDP by 2030.\n\nThe achievement of its targets will contribute to sustainable development and \nstrengthen economic, social, health and environmental resilience. \n\nSource: United Nations Statistics Division \n", "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-02.pdf\">Metadata 1-5-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Indemnizaciones por daños personales, pérdidas pecuniarias y daños de bienes pagadas y/o provisionadas en proporción al PIB", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.5- De aquí a 2030, reducir significativamente el número de muertes causadas por los desastres, incluidos los relacionados con el agua, y de personas afectadas por ellos, y reducir considerablemente las pérdidas económicas directas provocadas por los desastres en comparación con el producto interno bruto mundial, haciendo especial hincapié en la protección de los pobres y las personas en situaciones de vulnerabilidad", "definicion"=>"Proporción de indemnizaciones por daños personales, pérdidas pecuniarias  y daños de bienes pagadas y/o provisionadas (pendientes de liquidación o pago)  en relación al PIB a precios corrientes", "formula"=>"$$INDEMPIB^{t} = \\frac{(IDP^{t}+IPP^{t}+IDB^{t})/IUIPC_{refT}^{t}}{PIB^{t}} \\cdot 100 \\quad \\mathrm t=1,\\dots,\\mathrm T$$\n\n$$IUIPC_{refT}^{T} = 1$$\n\n$$IUIPC_{refT}^{t-1} = IUIPC_{refT}^{t} \\cdot \\frac{(100+TIPC_{dic}^{t})}{100} \\quad \\mathrm t=\\mathrm T,\\mathrm T-1,\\dots,\\mathrm 2$$\n\nnon:\n\n$IDP^{t}$ = kalte pertsonalengatik ordaindutako edota hornitutako kalte-ordainak $T$ urteko prezioetan, $t$ urtean\n\n$IPP^{t}$ = diru galerengatik ordaindutako edota hornitutako kalte-ordainak $T$ urteko prezioetan, $t$ urtean\n\n$IDB^{t}$ =  ondasunetan izandako kalteengatik ordaindutako edota hornitutako kalte-ordainak $T$ urteko prezioetan, $t$ urtean\n\n$TIPC_{dic}^{t}$ = KPI nazionalaren urteko aldakuntza-tasa $t$ urteko abenduan \n\n$PIB^{t}$ = barne-produktu gordina, uneko prezioetan $t$ urtean\n", "desagregacion"=>"Lurralde historikoa", "observaciones"=>"Las indemnizaciones se asignan al lugar y año de ocurrencia del siniestro,  no incluyendo los siniestros ocurridos en el extranjero", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue \nadoptado por los Estados miembros de las Naciones Unidas en marzo de 2015 \ncomo una política global de reducción del riesgo de desastres.\n\nEntre sus metas se encuentra la Meta C: Reducir las pérdidas económicas directas \npor desastres en relación con el producto interno bruto (PIB) mundial para 2030.\n\nLa consecución de sus metas contribuirá al desarrollo sostenible y fortalecerá la \nresiliencia económica, social, sanitaria y ambiental.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen adierazlearen metadatuak betetzen, baina antzeko informazioa eskaintzen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-02.pdf\">Metadatuak 1-5-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_DSR_GDPLS - Direct economic loss attributed to disasters [1.5.2,11.5.2]</p>\n<p>VC_DSR_LSGP - Direct economic loss attributed to disasters relative to GDP [1.5.2, 11.5.2]</p>\n<p>VC_DSR_AGLH - Direct agriculture loss attributed to disasters [1.5.2, 11.5.2]</p>\n<p>VC_DSR_HOLH - Direct economic loss in the housing sector attributed to disasters, by hazard type [1.5.2, 11.5.2]</p>\n<p>VC_DSR_CILN - Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters [1.5.2, 11.5.2]</p>\n<p>VC_DSR_CHLN - Direct economic loss to cultural heritage damaged or destroyed attributed to disasters [1.5.2, 11.5.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.5.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator measures the ratio of direct economic loss attributed to disasters in relation to gross domestic product (GDP).</p>\n<p><strong>Concepts:</strong></p>\n<p>Disasters: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Economic Loss:</strong> Total economic impact that consists of direct economic loss and indirect economic loss.</p>\n<p><strong>Direct economic loss:</strong> The monetary value of total or partial destruction of physical assets existing in the affected area. Direct economic loss is nearly equivalent to physical damage.</p>\n<p><strong>Indirect economic loss:</strong> A decline in economic value added as a consequence of direct economic loss and/or human and environmental impacts.</p>\n<p><em><u>Annotations:</u></em></p>\n<p><em>Examples of physical assets that are the basis for calculating direct economic loss include homes, schools, hospitals, commercial and governmental buildings, transport, energy, telecommunications infrastructures and other infrastructure; business assets and industrial plants; production such as crops, livestock and production infrastructure. They may also encompass environmental assets and cultural heritage. Direct economic losses usually happen during the event or within the first few hours after the event and are often assessed soon after the event to estimate recovery cost and claim insurance payments. These are tangible and relatively easy to measure.</em></p>\n<p>Detailed definitions, concepts, including composition and calculation for each of the data series, can be found in the SFM Technical Guidance (see below the Reference section)</p>", "UNIT_MEASURE__GLOBAL"=>"<p>For VC_DSR_LSGP - Direct economic loss attributed to disasters relative to GDP (%): per cent (%).</p>\n<p>For other data series: current United States Dollar.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are reported by national Sendai Framework focal points in the Sendai Framework Monitor (SFM) and national disaster loss database: DesInventar-Sendai. Data are consisted of official, national reporting exclusively. Direct agricultural loss attributed to disasters, direct economic loss to all other damaged or destroyed productive assets attributed to disasters, direct economic loss in the housing sector attributed to disasters, direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters, and direct economic loss to cultural heritage damaged or destroyed attributed to disasters are reported in SFM and DesInventar-Sendai.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are reported in Sendai Framework Monitor (SFM) on an ongoing basis, and snapshotted once every year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released once a year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OIEWG) report, endorsed by the United Nations General Assembly in Resolution A/RES/71/276, requested the UNDRR to undertake technical work and provide technical guidance to develop minimum standards and metadata, the methodologies, and the global monitoring and measurements of the SFM global indicators.</p>\n<p>This indicator is recommended by the OIEWG for the measurement of global Target C of the Sendai Framework, which were endorsed by the UN General Assembly in its Resolution A/RES/71/276, <em>Report of the open-ended intergovernmental</em> <em>expert working group on indicators and terminology relating to disaster risk</em>.</p>", "RATIONALE__GLOBAL"=>"<p>The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. Among the global targets, &#x201C;Target C: Reduce direct disaster economic loss in relation to global GDP by 2030&#x201D; will contribute to sustainable development and strengthen economic, social, health and environmental resilience. The economic, environmental and social perspectives would include poverty eradication, urban resilience, and climate change adaptation.</p>\n<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology relating to disaster risk reduction (OIEWG) established by the United Nations General Assembly (UNGA) (resolution 69/284) has developed a set of indicators to measure global progress in the implementation of the Sendai Framework, which was endorsed by the UNGA (OIEWG <a href=\"http://www.preventionweb.net/publications/view/51748\">report A/71/644</a>). The relevant global indicators for the Sendai Framework will be used to report for this indicator.</p>\n<p>Direct economic losses are significantly influenced by both large-scale catastrophic events. In the meantime, a high number of small-scale hazardous events also impose heavy impacts on economies especially in vulnerable environments. UNDRR recommends Member States to report the data by event in DesInventar-Sendai, and per the minimum reporting requirement of the Sendai Framework Monitor (SFM) using the Technical Guidance (see Reference and Documentation section), so complementary analysis can be done on the regional and global scale.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The Sendai Framework Monitoring (SFM) System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States report through the system since March 2018. The data for SDG indicators are compiled and reported by UNDRR.</p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mn>2</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mn>3</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mn>4</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mn>5</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n          </mrow>\n          <mrow>\n            <mn>6</mn>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mi>G</mi>\n        <mi>D</mi>\n        <mi>P</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where:</p>\n<p>C<sub>1</sub>: Direct economic loss attributed to disasters in relation to gross domestic product; corresponding to Sendai Framework Indicator C-1.</p>\n<p>C<sub>2</sub>:Direct agricultural loss attributed to disasters; corresponding to Sendai Framework Indicator C-2. Agriculture is understood to include the crops, livestock, fisheries, apiculture, aquaculture and forest sectors as well as associated facilities and infrastructure.</p>\n<p>C<sub>3</sub>: Direct economic loss to all other damaged or destroyed productive assets attributed to disasters; corresponding to Sendai Framework Indicator C-3. Productive assets would be disaggregated by economic sector, including services, according to standard international classifications. Member States would report against those economic sectors relevant to their economies.</p>\n<p>C<sub>4</sub>: Direct economic loss in the housing sector attributed to disasters; corresponding to Sendai Framework Indicator C-4. Data would be disaggregated according to damaged and destroyed dwellings.</p>\n<p>C<sub>5</sub>: Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters; corresponding to Sendai Framework Indicator C-5. The decision regarding those elements of critical infrastructure to be included in the calculation will be left to the Member States. Protective infrastructure and green infrastructure should be included where relevant.</p>\n<p>C<sub>6</sub>: Direct economic loss to cultural heritage damaged or destroyed attributed to disasters; corresponding to Sendai Framework Indicator C-6.</p>\n<p>GDP: national gross domestic product, current United States Dollar.</p>\n<p>* Detailed methodologies can be found in the Technical Guidance (see below the Reference section)</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are validation by UNDRR and national focal points.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction</li>\n  <li>ADPC Disaster and Climate Resilience e-Learning: An orientation to using the online Sendai Framework Monitor, https://courses.adpc.net/courses/course-v1:UNISDR+SFM001+2019Y1/about</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>2005-2023</p>\n<p><strong>Time series:</strong></p>\n<p>Annual</p>\n<p><strong>Disaggregation:</strong></p>\n<ul>\n  <li>Direct agricultural loss attributed to disasters.</li>\n  <li>Direct economic loss to all other damaged or destroyed productive assets attributed to disasters.</li>\n  <li>Direct economic loss in the housing sector attributed to disasters.</li>\n  <li>Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters.</li>\n  <li>Direct economic loss to cultural heritage damaged or destroyed attributed to disasters.</li>\n</ul>\n<p><strong>Desirable Disaggregation:</strong></p>\n<p>For direct agricultural loss attributed to disasters:</p>\n<ul>\n  <li>By loss of aquaculture production area affected</li>\n  <li>By loss of crops damaged or destroyed attributed to disasters</li>\n  <li>By loss of fisheries production area affected</li>\n  <li>By loss of forests affected/destroyed by disasters</li>\n  <li>By loss of livestock attributed to disasters</li>\n  <li>By loss of agricultural assets area affected</li>\n  <li>By loss of agricultural stock affected</li>\n  <li>By hazard types</li>\n  <li>By geography region (administrative unit)</li>\n</ul>\n<p>For direct economic loss to all other damaged or destroyed productive assets attributed to disasters:</p>\n<ul>\n  <li>By productive asset types</li>\n  <li>By hazard types</li>\n  <li>By geography region (administrative unit)</li>\n</ul>\n<p>For direct economic loss in the housing sector attributed to disasters:</p>\n<ul>\n  <li>By housing sectors</li>\n  <li>By hazard types</li>\n  <li>By geography region (administrative unit)</li>\n</ul>\n<p>For direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters:</p>\n<ul>\n  <li>By loss of health facilities</li>\n  <li>By loss of education facilities</li>\n  <li>By loss of other facilities</li>\n  <li>By hazard types</li>\n  <li>By geography region (administrative unit)</li>\n</ul>\n<p>For direct economic loss to cultural heritage damaged or destroyed attributed to disasters:</p>\n<ul>\n  <li>By number of buildings, monuments and fixed infrastructures of cultural heritage assets</li>\n  <li>By number of mobile cultural heritage assets (such as artworks) damaged</li>\n  <li>By number of mobile cultural heritage assets destroyed</li>\n  <li>By cost of rehabilitation or reconstruction</li>\n  <li>By cost of rehabilitation</li>\n  <li>By acquisition cost, if available</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Internationally agreed methodology and guideline URL:</strong></p>\n<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017)</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf\">https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf</a></p>\n<p>Sendai Framework Monitor</p>\n<p><a href=\"https://sendaimonitor.undrr.org/\">https://sendaimonitor.undrr.org/</a></p>\n<p><br>DesInventar-Sendai</p>\n<p><a href=\"https://www.desinventar.net/\">https://www.desinventar.net/</a></p>\n<p><strong>Other references:</strong></p>\n<ul>\n  <li>Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG). Endorsed by UNGA on 2<sup>nd</sup> February 2017. Available at:</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/publications/view/51748\">https://www.preventionweb.net/publications/view/51748</a></p>\n<p><strong>Country examples:</strong></p>\n<ul>\n  <li><strong>Proxy, alternative and additional indicators:</strong> In most cases international data sources only record events that surpass some threshold of impact and use secondary data sources which usually have non uniform or even inconsistent methodologies, producing heterogeneous datasets.</li>\n</ul>", "indicator_sort_order"=>"11-05-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.5.3", "slug"=>"11-5-3", "name"=>"a) Daños en la infraestructura crítica y b) número de interrupciones de los servicios básicos, atribuidos a desastres", "url"=>"/site/es/11-5-3/", "sort"=>"110503", "goal_number"=>"11", "target_number"=>"11.5", "global"=>{"name"=>"a) Daños en la infraestructura crítica y b) número de interrupciones de los servicios básicos, atribuidos a desastres"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"a) Daños en la infraestructura crítica y b) número de interrupciones de los servicios básicos, atribuidos a desastres", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"a) Daños en la infraestructura crítica y b) número de interrupciones de los servicios básicos, atribuidos a desastres", "indicator_number"=>"11.5.3", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "sources"=>"", "standalone"=>false, "tags"=>[], "x_axis_label"=>"", "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "texto_oceca"=>"", "observaciones"=>"", "justificacion_global"=>"\nLa pérdida de infraestructuras y las interrupciones de los servicios básicos \nrelacionadas con los desastres están significativamente influidas tanto por \neventos catastróficos de gran escala como por un elevado número de eventos \npeligrosos de pequeña escala.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-05-03.pdf\">Metadatos 11-5-3.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "texto_oceca"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe disaster related infrastructure loss and basic service disruptions \nare significantly influenced by both large-scale catastrophic events, \nas well as a high number of small-scale hazardous events. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-05-03.pdf\">Metadata 11-5-3.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "texto_oceca"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nLa pérdida de infraestructuras y las interrupciones de los servicios básicos \nrelacionadas con los desastres están significativamente influidas tanto por \neventos catastróficos de gran escala como por un elevado número de eventos \npeligrosos de pequeña escala.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-05-03.pdf\">Metadatuak 11-5-3.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 11.5: By 2030, significantly reduce the number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 11.5.3: (a) Damage to critical infrastructure and (b) number of disruptions to basic services, attributed to disasters</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_DSR_CDAN - Number of damaged critical infrastructure attributed to disasters [11.5.3]</p>\n<p>VC_DSR_HFDN - Number of destroyed or damaged health facilities attributed to disasters [11.5.3]</p>\n<p>VC_DSR_EFDN - Number of destroyed or damaged educational facilities attributed to disasters [11.5.3]</p>\n<p>VC_DSR_CDYN - Number of other destroyed or damaged critical infrastructure units and facilities attributed to disasters [11.5.3]</p>\n<p>VC_DSR_BSDN - Number of disruptions to basic services attributed to disasters [11.5.3]</p>\n<p>VC_DSR_ESDN - Number of disruptions to educational services attributed to disasters [11.5.3]</p>\n<p>VC_DSR_HSDN - Number of disruptions to health services attributed to disasters [11.5.3]</p>\n<p>VC_DSR_OBDN - Number of disruptions to other basic services attributed to disasters [11.5.3]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.5.1, 1.5.2, 11.5.1, 11.5.2, 13.1.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator measures the number of damaged or destroyed critical infrastructure attributed to disasters, including health and education facilities, as well as the number of disruptions to basic services.</p>\n<p><strong>Concepts:</strong></p>\n<p><strong>Disasters</strong>: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Critical infrastructure</strong>: The physical structures, facilities, networks and other assets which provide services that are essential to the social and economic functioning of a community or society.</p>\n<p><strong>Basic services</strong>: Services that are needed for all of society to function effectively or appropriately. </p>\n<p>Examples of basic services include water supply, sanitation, health care, and education. They also include services provided by critical infrastructure such as electricity, telecommunications, transport, and waste management that are needed for all of society to function. For this indicator, disruption, interruption or lower quality of basic services is proposed to be measured for the following public services: </p>\n<ul>\n  <li>Educational facilities: play schools, kindergartens, primary, secondary or middle schools, technical-vocational schools, colleges, universities, training centres, adult education, military schools and prison schools</li>\n  <li>Healthcare facilities: health centres, clinics, local, regional and tertiary hospitals, outpatient centres, health laboratories and in general facilities used by primary health providers</li>\n  <li>Power/energy system: generation facilities, transmission and distribution system and dispatch centres and other works</li>\n  <li>Sewerage system: sanitation and sanitary sewage systems and collection and treatment of solid waste.</li>\n  <li>Solid waste management: collection and treatment of solid waste.</li>\n  <li>Transport system: road networks, railways (including stations), airports and ports</li>\n  <li>Water supply: drinking water supply system (water outlets, water treatment plants, aqueducts and canals which carry drinking water, storage tanks.)</li>\n  <li>Information and Communication Technology (ICT) system: plants and telephone networks (telecommunication network), radio and television stations, post offices and public information offices, internet services, radio telephones and mobile phones.</li>\n  <li>Emergency Response: disaster management office, fire management service, police, army and emergency operation centres.</li>\n</ul>\n<p>An open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction established by the General Assembly (resolution 69/284) is developing a set of indicators to measure global progress in the implementation of the Sendai Framework. These indicators will eventually reflect the agreements on the Sendai Framework indicators.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are reported by national Sendai Framework focal points in the Sendai Framework Monitor (SFM) and national disaster loss database: DesInventar-Sendai.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are reported in Sendai Framework Monitor (SFM) on an ongoing basis, and snapshotted once every year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released once a year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OIEWG) report, endorsed by the United Nations General Assembly in Resolution A/RES/71/276, requested the UNDRR to undertake technical work and provide technical guidance to develop minimum standards and metadata, the methodologies, and the global monitoring and measurements of the SFM global indicators.</p>\n<p>This indicator is recommended by the OIEWG for the measurement of global Target D of the Sendai Framework, which were endorsed by the UN General Assembly in its Resolution A/RES/71/276, <em>Report of the open-ended intergovernmental</em> <em>expert working group on indicators and terminology relating to disaster risk</em>.</p>", "RATIONALE__GLOBAL"=>"<p>The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. </p>\n<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology relating to disaster risk reduction (OIEWG) established by the United Nations General Assembly (UNGA) (resolution 69/284) has developed a set of indicators to measure global progress in the implementation of the Sendai Framework, which was endorsed by the UNGA (OIEWG <a href=\"http://www.preventionweb.net/publications/view/51748\">report A/71/644</a>). The relevant global indicators for the Sendai Framework will be used to report for this indicator.</p>\n<p>The disaster related infrastructure loss and basic service disruptions are significantly influenced by both large-scale catastrophic events, as well as a high number of small-scale hazardous events. UNDRR recommends Member States to report the data by event in DesInventar-Sendai, and per the minimum reporting requirement of the Sendai Framework Monitor (SFM) using the Technical Guidance (see Reference and Documentation section), so complementary analysis can be done on the regional and global scale.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The Sendai Framework Monitoring (SFM) System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States report through the system since March 2018. The data for SDG indicators are compiled and reported by UNDRR.</p>\n<p>Not every country has a national disaster loss database that is consistent with these guidelines (although current coverage exceeds 113 countries). It is expected that all countries will build/adjust national disaster loss databases according to the recommendations and guidelines by the OEIWG.</p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <msub>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mn>2</mn>\n      </mrow>\n    </msub>\n    <mo>+</mo>\n    <msub>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mn>3</mn>\n      </mrow>\n    </msub>\n    <mo>+</mo>\n    <msub>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mn>4</mn>\n      </mrow>\n    </msub>\n  </math></p>\n<p>Where:</p>\n<p>D<sub>1</sub>: Number of damaged to critical infrastructure attributed to disasters; corresponding to Sendai Framework Indicator D-1.</p>\n<p>D<sub>2</sub>: Number of destroyed or damaged health facilities attributed to disasters; corresponding to Sendai Framework Indicator D-2.</p>\n<p>D<sub>3</sub>: Number of destroyed or damaged educational facilities attributed to disasters; corresponding to Sendai Framework Indicator D-3.</p>\n<p>D<sub>4</sub>: Number of other destroyed or damaged critical infrastructure units and facilities attributed to disasters; corresponding to Sendai Framework Indicator D-4. The decision regarding those elements of critical infrastructure to be included in the calculation will be left to the Member States. Protective infrastructure and green infrastructure should be included where relevant.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mn>5</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <msub>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mn>6</mn>\n      </mrow>\n    </msub>\n    <mo>+</mo>\n    <msub>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mn>7</mn>\n      </mrow>\n    </msub>\n    <mo>+</mo>\n    <msub>\n      <mrow>\n        <mi>D</mi>\n      </mrow>\n      <mrow>\n        <mn>8</mn>\n      </mrow>\n    </msub>\n  </math></p>\n<p>Where:</p>\n<p>D<sub>5</sub>: Number of disruptions to basic services attributed to disasters; corresponding to Sendai Framework Indicator D-5.</p>\n<p>D<sub>6</sub>: Number of disruptions to educational services attributed to disasters; corresponding to Sendai Framework Indicator D-6.</p>\n<p>D<sub>7</sub>: Number of disruptions to health services attributed to disasters; corresponding to Sendai Framework Indicator D-7.</p>\n<p>D<sub>8</sub>: Number of disruptions to other basic services attributed to disasters; corresponding to Sendai Framework Indicator D-8. The decision regarding those elements of basic services to be included in the calculation will be left to the Member States.</p>\n<p>The full methodologies can be obtained at the Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are validation by UNDRR and national focal points.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Not applicable</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction</li>\n  <li>ADPC Disaster and Climate Resilience e-Learning: An orientation to using the online Sendai Framework Monitor, https://courses.adpc.net/courses/course-v1:UNISDR+SFM001+2019Y1/about</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p><strong>Data availability:</strong></p>\n<p>2005-2023</p>\n<p><strong>Time series:</strong></p>\n<p>Annual</p>\n<p><strong>Disaggregation:</strong></p>\n<ul>\n  <li>By hazard types</li>\n  <li>By geography (administrative unit)</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Internationally agreed methodology and guideline URL:</strong></p>\n<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017)</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf\">https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf</a></p>\n<p>Sendai Framework Monitor</p>\n<p><a href=\"https://sendaimonitor.undrr.org/\">https://sendaimonitor.undrr.org/</a></p>\n<p><br>DesInventar-Sendai</p>\n<p><a href=\"https://www.desinventar.net/\">https://www.desinventar.net/</a></p>\n<p><strong>References:</strong></p>\n<ul>\n  <li>\n    <ul>\n      <li>The OEIWG was given the responsibility by the UNGA for the development of a set of indicators to measure global progress in the implementation of the Sendai Framework, against the seven global targets. The work of the OEIWG shall be completed by December 2016 and its report submitted to the General Assembly for consideration. The IAEG-SDGs and the UN Statistical Commission formally recognizes the role of the OEIWG, and has deferred the responsibility for the further refinement and development of the methodology for disaster-related SDGs indicators to this working group.</li>\n    </ul>\n  </li>\n</ul>\n<p><a href=\"http://www.preventionweb.net/drr-framework/open-ended-working-group/\">http://www.preventionweb.net/drr-framework/open-ended-working-group/</a></p>\n<ul>\n  <li>\n    <ul>\n      <li>The latest version of documents are located at:</li>\n    </ul>\n  </li>\n</ul>\n<p><a href=\"http://www.preventionweb.net/drr-framework/open-ended-working-group/sessional-intersessional-documents\">http://www.preventionweb.net/drr-framework/open-ended-working-group/sessional-intersessional-documents</a></p>", "indicator_sort_order"=>"11-05-03", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.6.1", "slug"=>"11-6-1", "name"=>"Proporción de residuos sólidos municipales recogidos y administrados en instalaciones controladas con respecto al total de residuos municipales generados, desglosada por ciudad", "url"=>"/site/es/11-6-1/", "sort"=>"110601", "goal_number"=>"11", "target_number"=>"11.6", "global"=>{"name"=>"Proporción de residuos sólidos municipales recogidos y administrados en instalaciones controladas con respecto al total de residuos municipales generados, desglosada por ciudad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Residuos urbanos recogidos per cápita y proporción de residuos tratados, desglosados por tipo de tratamiento", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de residuos sólidos municipales recogidos y administrados en instalaciones controladas con respecto al total de residuos municipales generados, desglosada por ciudad", "indicator_number"=>"11.6.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/estadistica-de-residuos-solidos-urbanos-090218/web01-a2inghon/es/", "url_text"=>"Estadística de Residuos Urbanos", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Residuos urbanos recogidos per cápita y proporción de residuos tratados, desglosados por tipo de tratamiento", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.6- De aquí a 2030, reducir el impacto ambiental negativo per cápita de las ciudades, incluso prestando especial atención a la calidad del aire y la gestión de los desechos municipales y de otro tipo", "definicion"=>"Residuos urbanos recogidos per cápita y proporción de residuos urbanos tratados (incinerados,  vertidos y reciclados) en relación al total de residuos urbanos generados. Se  consideran residuos urbanos los procedentes de los hogares y del sector servicios  (comercios, oficinas e instituciones públicas o privadas), cuya gestión es asumida por  las entidades locales", "formula"=>"\n<b> Residuos urbanos recogidos per cápita</b> \n\n$$RURP^{t} = \\frac{RUR^{t}}{P^{t}} $$\n\ndonde:\n\n$RUR^{t} =$ cantidad de residuos urbanos recogidos en el año $t$ \n\n$P^{t} =$ población a 1 de julio del año $t$\n\n<br>\n\n<b> Proporción de residuos urbanos recogidos y gestionados en instalaciones controladas</b>\n\n$$PRUR^{t} = \\frac{RUG^{t}}{RUR^{t}} \\cdot 100$$\n\ndonde:\n\n$RUG^{t} =$ cantidad de residuos urbanos generados en el año $t$ \n\n$RUR^{t} =$ cantidad de residuos urbanos recogidos y gestionados en instalaciones controladas en el año $t$ \n\n\n<br>\n\n<b> Proporción de residuos urbanos tratados, por operación de tratamiento</b> \n\n$$PRU_{tratamiento}^{t} = \\frac{RU_{tratamiento}^{t}}{RU^{t}} \\cdot 100$$ <br> \n\ndonde:  \n\n$RU_{tratamiento}^{t} =$ cantidad de residuos urbanos tratados (según tipo de tratamiento) en el año $t$\n\n$RUR^{t} =$ cantidad total de residuos urbanos recogidos y gestionados en instalaciones controladas en el año $t$\n", "desagregacion"=>"Operación de tratamiento de residuos: reciclaje, incineración con recuperación de energía,\ndepósito en vertedero\n\nTerritorio histórico\n", "observaciones"=>"\nSe entiende por residuos urbanos los residuos domésticos y comerciales, procedentes  de hogares y del sector servicios (comercio, oficinas e instituciones) gestionados por  las entidades locales, no incluyéndose los residuos comerciales gestionados por canales  privados distintos al municipal, ni residuos procedentes de la industria.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Los hogares y empresas urbanas generan cantidades sustanciales de residuos que deben recolectarse regularmente, \nreciclarse o tratarse y eliminarse adecuadamente para mantener condiciones de vida saludables \ne higiénicas. \n\nMuchas ciudades enfrentan cada vez más desafíos en la gestión de residuos\ndebido a la rápida urbanización, la falta de capacidad técnica y financiera o la baja prioridad \npolítica. Además, cuanto mayor es el nivel de ingresos de una ciudad, mayor es la cantidad de \nresiduos sólidos que se producen. \n\nPor lo tanto, el crecimiento económico que experimentarán los países en desarrollo y emergentes \nplanteará mayores desafíos a los gobiernos locales en la gestión de residuos en las próximas décadas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.6.1&seriesCode=EN_REF_WASCOL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=NOCITI\"> Cobertura de recolección de residuos sólidos municipales, por ciudades (%) EN_REF_WASCOL</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-06-01.pdf\">Metadatos 11-6-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Residuos urbanos recogidos per cápita y proporción de residuos tratados, desglosados por tipo de tratamiento", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.6- De aquí a 2030, reducir el impacto ambiental negativo per cápita de las ciudades, incluso prestando especial atención a la calidad del aire y la gestión de los desechos municipales y de otro tipo", "definicion"=>"Urban waste collected per capita and proportion of urban waste treated  (incinerated, landfilled, and recycled) in relation to total urban waste  generated. Urban waste is considered to be waste from households and the  service sector (businesses, offices, and public or private institutions),  the management of which is assumed by local authorities.", "formula"=>"\n<b> Urban waste collected per capita</b> \n\n$$RURP^{t} = \\frac{RUR^{t}}{P^{t}} $$\n\nwhere:\n\n$RUR^{t} =$ amount of urban waste collected in year $t$ \n\n$P^{t} =$ population as of 1 July of year $t$\n\n<br>\n\n<b> Proportion of urban waste collected and managed in controlled facilities</b>\n\n$$PRUR^{t} = \\frac{RUG^{t}}{RUR^{t}} \\cdot 100$$\n\nwhere:\n\n$RUG^{t} =$ amount of urban waste generated in year $t$ \n\n$RUR^{t} =$ amount of urban waste collected and managed in controlled facilities in year $t$ \n\n\n<br>\n\n<b> Proportion of urban waste treated, per treatment operation</b> \n\n$$PRU_{treatment}^{t} = \\frac{RU_{treatment}^{t}}{RU^{t}} \\cdot 100$$ <br> \n\n$RU_{treatment}^{t} =$ amount of urban waste treated (by type of treatment) in year $t$\n\n$RUR^{t} =$ amount of urban waste collected and managed in controlled facilities in year $t$\n", "desagregacion"=>"Waste treatment operations: recycling; incineration with energy recovery; landfill disposal\n\nProvince\n", "observaciones"=>"\nUrban waste is understood to mean domestic and commercial waste from homes and  the service sector (commerce, offices, and institutions) managed by local authorities.  It does not include commercial waste managed by private channels other than municipal  waste or waste from industry. ", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Urban households and businesses produce substantial amounts of solid waste that \nmust be collected regularly, recycled or treated and disposed properly in order \nto maintain healthy and sanitary living conditions. \n\nMany cities are increasingly facing solid waste management challenges due to rapid \nurbanization, lack of technical and financial capacity or low policy priority. In \naddition, the higher the income level of a city, the greater the amount of the solid \nwaste produced. \n\nTherefore, the economic growth to be experienced in the developing and emerging \ncountries will pose greater challenges in solid waste management to local governments \nin the next decades. \n\nSource: United Nations Statistics Division \n", "comparabilidad"=>"The available indicator complies with United Nations metadata ", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.6.1&seriesCode=EN_REF_WASCOL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=NOCITI\"> Municipal Solid Waste collection coverage, by cities (%) EN_REF_WASCOL</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-06-01.pdf\">Metadata 11-6-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Residuos urbanos recogidos per cápita y proporción de residuos tratados, desglosados por tipo de tratamiento", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.6- De aquí a 2030, reducir el impacto ambiental negativo per cápita de las ciudades, incluso prestando especial atención a la calidad del aire y la gestión de los desechos municipales y de otro tipo", "definicion"=>"Residuos urbanos recogidos per cápita y proporción de residuos urbanos tratados (incinerados,  vertidos y reciclados) en relación al total de residuos urbanos generados. Se  consideran residuos urbanos los procedentes de los hogares y del sector servicios  (comercios, oficinas e instituciones públicas o privadas), cuya gestión es asumida por  las entidades locales", "formula"=>"\n<b> Biztanle bakoitzeko bildutako hiri-hondakinak</b> \n\n$$RURP^{t} = \\frac{RUR^{t}}{P^{t}} $$\n\nnon:\n\n$RUR^{t} =$ bildutako hiri-hondakinen kantitatea $t$ urtean \n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n\n<br>\n\n<b> Instalazio kontrolatuetan bildu eta kudeatutako hiri-hondakinen proportzioa</b>\n\n$$PRUR^{t} = \\frac{RUG^{t}}{RUR^{t}} \\cdot 100$$\n\nnon:\n\n$RUG^{t} =$ sortutako hiri-hondakinen kantitatea $t$ urtean  \n\n$RUR^{t} =$ instalazio kontrolatuetan bildu eta kudeatutako hiri-hondakinen kantitatea $t$ urtean \n\n\n<br>\n\n<b> Tratatutako hiri-hondakinen proportzioa, tratamendu-eragiketaren arabera</b> \n\n$$PRU_{tratamendua}^{t} = \\frac{RU_{tratamendua}^{t}}{RU^{t}} \\cdot 100$$ <br> \n\nnon:  \n\n$RU_{tratamendua}^{t} =$ Tratatutako hiri-hondakinen kantitatea (tratamendu-eragiketaren arabera) $t$ urtean\n\n$RUR^{t} =$ instalazio kontrolatuetan bildu eta kudeatutako hiri-hondakinen kantitatea $t$ urtean\n", "desagregacion"=>"Hondakinak tratatzeko eragiketa: birziklatzea; erraustea eta energia berreskuratzea; zabortegian uztea \n\nLurralde historikoa\n", "observaciones"=>"\nSe entiende por residuos urbanos los residuos domésticos y comerciales, procedentes  de hogares y del sector servicios (comercio, oficinas e instituciones) gestionados por  las entidades locales, no incluyéndose los residuos comerciales gestionados por canales  privados distintos al municipal, ni residuos procedentes de la industria.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Los hogares y empresas urbanas generan cantidades sustanciales de residuos que deben recolectarse regularmente, \nreciclarse o tratarse y eliminarse adecuadamente para mantener condiciones de vida saludables \ne higiénicas. \n\nMuchas ciudades enfrentan cada vez más desafíos en la gestión de residuos\ndebido a la rápida urbanización, la falta de capacidad técnica y financiera o la baja prioridad \npolítica. Además, cuanto mayor es el nivel de ingresos de una ciudad, mayor es la cantidad de \nresiduos sólidos que se producen. \n\nPor lo tanto, el crecimiento económico que experimentarán los países en desarrollo y emergentes \nplanteará mayores desafíos a los gobiernos locales en la gestión de residuos en las próximas décadas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.6.1&seriesCode=EN_REF_WASCOL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=NOCITI\"> Udal-hondakin solidoen bilketaren estaldura, hirika (%) EN_REF_WASCOL</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-06-01.pdf\">Metadatuak 11-6-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 11.6.1: Proportion of municipal solid waste collected and managed in controlled facilities out of total municipal waste generated, by cities</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_REF_COLDIS - Proportion of population served by municipal waste collection [11.6.1]</p>\n<p>EN_REF_WASCOL - Municipal Solid Waste collection coverage, by cities (%) [11.6.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>1.4.1, 6.3.1, 12.3.1.b, 12.5.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>\n<p>United Nations Statistics Division (UNSD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>\n<p>United Nations Statistics Division (UNSD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>SDG 11.6 targets an improved environmental performance of cities and SDG indicator 11.6.1 measures the progress of the performance of a city&#x2019;s municipal solid waste management. It quantifies the parameters listed below, which are essential for planning and implementing sustainable Municipal Solid Waste (MSW). In most cases, these variables are generally compatible with those collected through the United Nations Statistics Division (UNSD)/United Nations Environment Programme (UNEP) Questionnaire on Environment Statistics (waste section).</p>\n<ol>\n  <li>Total MSW generated in the city (tonnes/day)</li>\n  <li>Total MSW collected in the city (tonnes/day)</li>\n  <li>Proportion of population with access to basic MSW collection services in the city (%)</li>\n  <li>Total MSW managed in controlled facilities in the city (tonnes/day)</li>\n  <li>MSW composition</li>\n</ol>\n<p>It is important to realize that part (b) total MSW collected in the city and (c) proportion of population with access to basic MSW collection services in the city are two different concepts. While part (b) refers to amounts of waste reaching waste management facilities, part (c) considers the population who receive waste collection services. In some cities it is common to dump waste &#x2018;collected&#x2019; from households into the surrounding areas instead of transporting it to a disposal or recovery facility. In this case the household has waste collection services, but the collected waste is polluting the environment. Therefore, it is possible that a city has a high proportion of population with access to basic waste collection services, but the amount of MSW collected and transported to waste management facilities is low.</p>\n<p>Although part (c) is covered by SDG 1 (&#x201C;End poverty in all its forms everywhere&#x201D;), under target 1.4 and SDG indicator 1.4.1 which focuses on universal access to basic services, with a particular emphasis on poor and vulnerable groups, this document provides guidelines, quality ladders and household questionnaires to measure the proportion of the population with access to &#x2018;basic&#x2019; MSW collection services. The household questionnaire can be integrated into the national census or global household survey mechanism such as Demographic and Health Survey or UNICEF&#x2019;s Multiple Indicator Cluster Surveys. Due to the lack of standardized concepts and definitions that differentiate these two concepts, many cities report the proportion of collected MSW in their own terms. Therefore, this metadata distinguishes clearly between part (b) and (c) and offers introduction to the approaches to monitor and report on part (c).</p>\n<p><strong>Concepts:</strong></p>\n<p><em>Municipal Solid Waste (MSW)</em><strong>:</strong> MSW includes waste generated from: households, commerce and trade, small businesses, office buildings and institutions (schools, hospitals, government buildings). It also includes bulky waste (e.g. white goods, old furniture, mattresses) and waste from selected municipal services, e.g. waste from park and garden maintenance, waste from street cleaning services (street sweepings, the content of litter containers, market cleansing waste), if managed as waste. The definition excludes waste from municipal sewage network and treatment, municipal construction and demolition waste.</p>\n<p><em>Generation</em><strong>:</strong> Total MSW generated is the sum of the amount of municipal waste collected plus the estimated amount of municipal waste from areas not served by a municipal waste collection service.</p>\n<p><em>Collection</em><strong>:</strong> Total MSW collected refers to the amount of municipal waste collected by or on behalf of municipalities, as well as municipal waste collected by the private sector. It includes mixed waste, and fractions collected separately for recovery operations (through door-to-door collection and/or through voluntary deposits).</p>\n<p><img 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\" alt=\"A diagram of a recycling process AI-generated content may be incorrect.\"></p>\n<p>Figure 1: What MSW collected means for SDG indicator 11.6.1</p>\n<p>The proportion of the population with access to basic MSW collection services is the proportion of the population who receive waste collection services that are either basic, improved or full, defined by the service ladder of MSW collection service. It considers aspects of frequency, regularity and proximity of the collection points (</p>\n<p>Table<strong> 1</strong>). This aspect is measured under the SDG indicator 11.6.1 assessment but it is reported through a different indicator, SDG 1.4.1 on access to basic services.</p>\n<p><strong>Table 1: Ladder of MSW collection service that household receives</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>SERVICE LEVEL</strong></p>\n      </td>\n      <td>\n        <p><strong>DEFINITION</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Full</p>\n      </td>\n      <td>\n        <ul>\n          <li>Receiving door-to-door MSW collection service with basic frequency and regularity and MSW is collected in three or more separate fractions; or</li>\n          <li>Having a designated collection point within 200m distance served with basic frequency and regularity and without major littering and MSW is collected in three or more separate fractions</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Improved</p>\n      </td>\n      <td>\n        <ul>\n          <li>Receiving door-to-door MSW collection service with basic frequency and regularity and MSW is collected in a minimum of two, separate fractions (e.g. wet and dry fractions)</li>\n          <li>Having a designated collection point within 200m distance served with basic frequency and regularity and without major littering and MSW is collected in a minimum of two, separate fractions (e.g. wet and dry fractions)</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Basic</p>\n      </td>\n      <td>\n        <ul>\n          <li>Receiving door-to-door MSW collection service with basic frequency and regularity or</li>\n          <li>Having designated collection point within 200m distance served with basic frequency and regularity</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Limited</p>\n      </td>\n      <td>\n        <ul>\n          <li>Receiving door-to-door MSW collection service without basic frequency and regularity;</li>\n          <li>Having a designated collection point within 200m distance but not served with basic frequency and regularity; or</li>\n          <li>Having designated collection point in further than 200 m distance</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>No</p>\n      </td>\n      <td>\n        <ul>\n          <li>Receiving no waste collection service</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Note: Basic frequency and regularity: served at least once a week for one year</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p></p>\n<p><em>Recovery</em><strong>:</strong> Recovery means any operation the principal result of which is waste serving a useful purpose by replacing other materials which would otherwise have been used to fulfil a particular function, or waste being prepared to fulfil that function, in the plant or in the wider economy.</p>\n<p>Recovery facilities include any facility with recovery activities defined above including recycling, composting, incineration with energy recovery, materials recovery facilities (MRF), mechanical biological treatment (MBT), etc.</p>\n<p><strong>Materials recovery facilities (MRF)</strong> or materials reclamation facility, materials recycling facility, multi re-use facility is a specialized recovery facility that receives, separates and prepares recyclable materials for marketing to further processors or end-user manufacturers.</p>\n<p><strong>Mechanical biological treatment (MBT)</strong> facilities are a type of recovery facility that combines an MRF with a form of biological treatment such as composting or anaerobic digestion.</p>\n<p><strong>Incineration</strong> is the controlled combustion of waste with or without energy recovery.</p>\n<p><strong>Incineration with energy recovery</strong> is the controlled combustion of waste with energy recovery.</p>\n<p><strong>Recycling</strong> is defined under the UNSD/UNEP Questionnaire and further for the purpose of these indicators as &#x201C;Any reprocessing of waste material in a production process that diverts it from the waste stream, except reuse as fuel. Both reprocessing as the same type of product, and for different purposes should be included. Recycling within industrial plants i.e., at the place of generation should be excluded&#x201D;. For the purpose of consistency with the Basel Convention reporting and correspondence with EUROSTAT reporting system, Recovery operations R2 to R12 listed in Basel Convention Annex IV, are to be considered as &#x201C;Recycling&#x201D; under the UNSD reporting for hazardous waste.</p>\n<p><strong>Recycling value chain </strong>usually involves several steps of the private recycling industry, which purchase, process and trade materials from the point a recyclable material is extracted from the waste stream until it will be reprocessed into products, materials or substances that have market value. In many low and low-to-middle income countries, this involves informal waste pickers, many middlemen, traders, apex traders and end-of-chain recyclers.</p>\n<p><strong>Apex traders</strong> collect recyclable materials from different sources and suppliers (in different cities across municipal or even national boundaries) and supply them to different end-of-chain recyclers (sometimes after pre-processing such as sorting, washing and bailing).</p>\n<p><strong>End-of-chain recyclers</strong> purchase recyclable material from suppliers such as apex traders and reprocess it into products, materials, or substances that have market value.</p>\n<p><img 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\" alt=\"A collection of pictograms of different types of garbage AI-generated content may be incorrect.\"></p>\n<p>Figure 2: Complexity in the recovery chain (plastic example)</p>\n<p></p>\n<p><em>Disposal</em><strong>:</strong> Disposal means any operation whose main purpose is not the recovery of materials or energy even if the operation has as a secondary consequence the reclamation of substances or energy.</p>\n<p><strong>Disposal facilities </strong>refer to sites, which are regularly used by the public authorities and private collectors, regardless of their level of control and legality, to dispose of waste. Such sites may or may not have an official recognition, a permit or a license. Disposal sites may be managed in either a controlled or uncontrolled manner. The definition excludes other unrecognized places where waste is deposited occasionally in small amounts which public authorities may organise clean ups to remove the waste from these sites.</p>\n<p><strong>Landfill</strong> is the deposit of waste into or onto land. It includes specially engineered landfill sites and temporary storage of over one year on permanent sites. The definition covers both landfills at internal sites, i.e. where a generator of waste is carrying out its own waste disposal at the place of generation, and at external sites.</p>\n<p>Control level of MSW recovery and disposal facilities<strong>:</strong> MSW managed in controlled facilities refers to MSW collected and transported to recovery and disposal facilities with basic, improved or full control according to the ladder of waste management facilities&#x2019; control level (<strong>Table 2: Ladder of waste management facilities&#x2019; control level.</strong>). The ladder can be used as a checklist for assessing the level of control of a particular recovery or disposal facility. The facility has the level of control, where it checks the most boxes. Note that the emphasis is on operational control rather than engineering/design. A facility that is constructed to a high standard, but not operated in compliance with Level 3 (or above) standard is not regarded as a controlled facility.</p>\n<p><strong>Table 2: Ladder of waste management facilities&#x2019; control level.</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>CONTROL LEVEL</strong></p>\n      </td>\n      <td>\n        <p><strong>Landfill site</strong></p>\n      </td>\n      <td>\n        <p><strong>Incineration with energy recovery</strong></p>\n      </td>\n      <td>\n        <p><strong>Other recovery facilities</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Full Control</p>\n      </td>\n      <td>\n        <ul>\n          <li>Waste daily covered</li>\n          <li>Waste compacted</li>\n          <li>Site fenced and full 24-hour control of access</li>\n          <li>Properly sited, designed and functional sanitary landfill</li>\n          <li>Leachate containment and treatment (naturally consolidated clay on the site or constructed liner)</li>\n          <li>Landfill gas collection and flaring and/or utilization</li>\n          <li>Site staffed</li>\n          <li>Post closure plan</li>\n          <li>Weighing and recording conducted</li>\n          <li>Protection of workers&#x2019; health and safety</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Built to and operating in compliance with current national laws and standards including stringent stack and greenhouse gas emission criteria</li>\n          <li>Emission controls are conducted compliant to environmental standards and results of tests are accessible and transparent to citizens/users</li>\n          <li>Fly ash managed as a hazardous waste using the best appropriate technology</li>\n          <li>Weighing and recording conducted</li>\n          <li>A strong and robust environmental regulator inspects and monitors emissions</li>\n          <li>Protection of workers&#x2019; health and safety </li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Built to and operating in compliance with current national laws and standards</li>\n          <li>Pollution control compliant to environmental standards</li>\n          <li>Protection of workers&#x2019; health and safety</li>\n          <li>The nutrient value of biologically treated materials utilized for separate organic waste (e.g. in agriculture/horticulture)</li>\n          <li>Materials are extracted, processed according to market specifications, and sold to recycling markets</li>\n          <li>Weighing and recording of incoming loads conducted</li>\n          <li>All outgoing loads registered by weight and type of destination</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Improved Control</p>\n      </td>\n      <td>\n        <ul>\n          <li>Waste periodically covered</li>\n          <li>Waste compacted</li>\n          <li>Site fenced and control of access</li>\n          <li>Leachate containment and treatment</li>\n          <li>Landfill gas collection (depending on landfill technology)</li>\n          <li>Site staffed</li>\n          <li>Weighing and recording conducted</li>\n          <li>Provisions made for workers&#x2019; health and safety</li>\n        </ul>\n      </td>\n      <td>\n        <p>Not applicable</p>\n      </td>\n      <td>\n        <ul>\n          <li>Engineered facilities with effective process control</li>\n          <li>Pollution control compliant to environmental standards</li>\n          <li>Protection of workers&#x2019; health and safety</li>\n          <li>Evidence of materials extracted being delivered into recycling or recovery markets</li>\n          <li>Weighing and recording of incoming and outgoing loads conducted</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Basic Control</p>\n      </td>\n      <td>\n        <ul>\n          <li>Some use of cover</li>\n          <li>Waste compacted</li>\n          <li>Sufficient equipment for compaction</li>\n          <li>Site fenced and control of access</li>\n          <li>No fire/smoke existence</li>\n          <li>Site staffed</li>\n          <li>Weighing and recording conducted</li>\n          <li>The slope of the landfill is stable, landslides not possible</li>\n          <li>Provisions made for workers&#x2019; health and safety</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Emission controls to capture particulates</li>\n          <li>Trained staff follow set operating procedures</li>\n          <li>Equipment maintained</li>\n          <li>Ash management carried out</li>\n          <li>Weighing and recording conducted</li>\n          <li>Provisions made for workers&#x2019; health and safety</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Registered facilities with marked boundaries</li>\n          <li>Some environmental pollution control</li>\n          <li>Provisions made for workers&#x2019; health and safety</li>\n          <li>Weighing and recording of incoming and outgoing loads conducted</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Limited Control</p>\n      </td>\n      <td>\n        <ul>\n          <li>No cover</li>\n          <li>Some compaction</li>\n          <li>Some equipment for compaction</li>\n          <li>Some level of access control/fencing</li>\n          <li>No leachate control</li>\n          <li>Some fire/smoke existence</li>\n          <li>Site staffed</li>\n          <li>Weighing and recording conducted</li>\n          <li>The slope of the landfill is unstable with high possibility of a landslide</li>\n        </ul>\n      </td>\n      <td>\n        <p>Not applicable</p>\n      </td>\n      <td>\n        <ul>\n          <li>Unregistered facilities with distinguishable boundaries</li>\n          <li>No environmental pollution control</li>\n          <li>No provisions made for workers&#x2019; health and safety</li>\n          <li>Weighing and recording conducted</li>\n        </ul>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>No Control</p>\n      </td>\n      <td>\n        <ul>\n          <li>No cover</li>\n          <li>No compaction</li>\n          <li>No/limited equipment</li>\n          <li>No fencing </li>\n          <li>No leachate control</li>\n          <li>Fire/smoke existence</li>\n          <li>No staff</li>\n          <li>The slope of the landfill is unstable with high possibility of a landslide</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Uncontrolled burning</li>\n          <li>No air/water pollution control</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Unregistered locations with no distinguishable boundaries</li>\n          <li>No provisions made for workers&#x2019; health and safety</li>\n          <li>No environmental pollution control</li>\n        </ul>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><em>Formality of </em><strong>Municipal Solid Waste Management (</strong><em>MSWM</em><strong>):</strong> The formality of MSWM activities is an important aspect to take into consideration when conducting the SDG indicator 11.6.1 assessment. MSWM activities are carried out by formal and informal economic units, both public and private, and by generators for the purpose of prevention, collection, transportation, treatment and disposal of waste.</p>\n<p><strong>Formal waste management</strong> relates to waste management activities undertaken by units working within the context of the formal governmental or non-state actors regulating and operating waste management; that is, organisations or individuals registered as economic units with government authorities and assumed to generally abide by local laws and regulations related to wastes and their management.</p>\n<p><strong>Informal waste management, recycling and recovery </strong>refers to waste management and recovery activities undertaken by individuals, economic units, or enterprises which are not sponsored, financed, recognised, supported, organised or acknowledged by the formal solid waste authorities, or which operate in violation of or in competition with formal authorities (Scheinberg et al., 2010). Informal units are assumed to abide by local waste-related laws and regulations when it is in their interests to do so.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Countries and cities/municipalities that have the data already are recommended to answer the UNSD/UNEP Questionnaire on Environment Statistics to provide the data related to SDG 11.6.1. For countries and municipalities/cities that do not have the data, it is recommended to apply UN-Habitat&#x2019;s<em> </em><strong><em>Waste Wise Cities Tool &#x2013; Step by Step Guide to Assess a City&#x2019;s MSMW Performance</em></strong> <strong><em>through SDG indicator 11.6.1 Monitoring</em></strong>.</p>", "COLL_METHOD__GLOBAL"=>"<p>It is recommended to establish a system where local or municipal governments collect SDG 11.6.1 data utilizing Waste Wise Cities Tool, then the data aggregated by the ministries and agencies in charge of environmental protection. These collected data should be reported to UNSD/UNEP Questionnaire on Environment Statistics every two years from National Statistical Offices (NSOs) of countries. Currently the response rate for the UNSD/UNEP Questionnaire is around 50% and data completeness and quality remain a challenge, especially for developing countries.</p>\n<p>Countries may report their data to UNSD via the UNSD/UNEP Questionnaire on Environment Statistics (waste section) following application of the methods specified in this metadata template. UNSD engages in an extensive data validation process including automated checks, and liaisons with the country&#x2019;s NSO or Ministry of Environment.</p>", "FREQ_COLL__GLOBAL"=>"<p>The data for this indicator is updated biennially depending on the data source stated above.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data for Indicator 11.6.1 is released annually, and the monitoring of the indicator can be repeated at annual intervals, to cater for an anticipated increase in the number of cities/urban areas and countries reporting on the indicator.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Ministry of environment or equivalent agency to it, responsible for environmental protection and National statistical offices (NSOs). For the UNSD/UNEP Questionnaire on Environment Statistics (waste section), countries typically specify one of the above two institutions as the preferred focal point.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>\n<p>United Nations Statistics Division (UNSD)</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Human Settlements Programme (UN-Habitat) is the specialized agency for sustainable urbanization and human settlements in the United Nations. The mandate derives from the priorities established in relevant General Assembly resolutions and decisions, including General Assembly resolution 3327 (XXIX), by which the General Assembly established the United Nations Habitat and Human Settlements Foundation, and resolution 32/162 by which the Assembly established the United Nations Center for Human Settlements (Habitat). In 2001, by its Resolution 56/206, the General Assembly transformed the Habitat into the secretariat of the UN-Habitat, with a mandate to coordinate human settlements activities within the United Nations System. As such, UN-Habitat has been designated the overall coordinator of SDG 11 and specifically as a custodian agency for 9 of the 14 indicators under SDG 11 including indicator 11.6.1. UN-Habitat also supports the monitoring and reporting of four urban specific indicators in other goals.</p>", "RATIONALE__GLOBAL"=>"<p>Urban households and businesses produce substantial amounts of solid waste that must be collected regularly, recycled or treated and disposed properly in order to maintain healthy and sanitary living conditions. Many cities are increasingly facing solid waste management challenges due to rapid urbanization, lack of technical and financial capacity or low policy priority. In addition, the higher the income level of a city, the greater the amount of the solid waste produced. Therefore, the economic growth to be experienced in the developing and emerging countries will pose greater challenges in solid waste management to local governments in the next decades.</p>\n<p>Adverse environmental impact of uncollected waste in a city is significant. Uncollected solid waste can end up in drains leading to blocked drainages and cause unsanitary conditions that have a direct health impact on residents. Open burning of uncollected waste produces pollutants that are highly damaging locally and globally. Vectors such as mosquitos usually breed in blocked drainages and blocked drainage contributes to the cause of flooding. In 2015, the Global Waste Management Outlook estimated that at least 2 billion people do not have access to regular waste collection. This is particularly worse in informal settlements and the UN-Habitat&#x2019;s report Solid Waste Management in World Cities published in 2010 estimated only 5% of waste in squatter areas is regularly collected.</p>\n<p>The global scale of urbanization and economic growth are creating a potential &#x201C;time-bomb&#x201D; regarding the waste we generate in the world. If not addressed now, the significant negative impact on human health and the environment will be felt by nations at all levels of development. An estimated 2.24 billion tonnes of municipal solid waste (MSW) were generated in 2020, and this number is expected to grow to 3.88 billion tonnes by 2050 under a business-as-usual scenario (World Bank). </p>\n<p>There is a need for SDG indicator 11.6.1 monitoring as it provides critical information for cities and countries to establish better waste and resource management strategies. Reliable data and information on MSW generation and management is limited globally, especially in low- and middle-income country settings where waste data is often produced based on international estimates, without having been validated in the local context.</p>\n<p>Many developing and transitional country cities still have an active informal sector and micro-enterprise recycling, reuse and repair; often achieve recycling and recovery rates comparable to those in developed countries, resulting in savings to the waste management budget of the cities. There is a major opportunity for the city to build on these existing recycling systems, reducing some unsustainable practices and enhancing them to protect and develop people&#x2019;s livelihoods, and to reduce still further the costs to the city of managing the residual wastes. The formal and informal sectors need to work together, for the benefit of both. Promoting this indicator also can help formalization of the informal sector in the process of increasing the portion of &#x201C;solid waste with adequate discharge&#x201D;.</p>\n<p>A global data collection and publication system through the UNSD/UNEP Questionnaire on Environment Statistics has collected data on MSW collection and treatment for about 20 years. The Questionnaire has been sent to more than 160 countries, covering both national and city levels. However, the response rate for the UNSD/UNEP questionnaire is around 50% and data completeness and quality remain a challenge, especially for developing countries. While efforts will continue to collect data from NSOs and Ministries of Environment at the national level, it is also critical to improve the availability and accessibility of waste statistics and increase training for collection of data and capacity development at the national and sub-national levels.</p>\n<p>This paucity of evidence-based data hinders the development of waste management strategies and constrains investment decision-making in infrastructure and service expansion, leading to many countries having insufficient or absent MSW management services. Poor MSW collection and management trigger severe threats to public health and pollute air and water. Furthermore, uncollected and mismanaged waste is the main source of marine plastic pollution.</p>\n<p>The indicator 11.6.1 will also promote Integrated Solid Waste Management (ISWM). An integrated solid waste management system is strongly connected to three dimensions: urban environmental health, the environment and resource management. Moreover, a regular solid waste management strategy is a clear indicator of the effectiveness of a municipal administration. Good waste governance that is inclusive, financially sustainable and based on sound institutions is one of the key challenges of the 21st century, and one of the key responsibilities of a city government.</p>\n<p>SDG indicator 11.6.1 quantifies parameters that will help cities and countries to better manage resources, mitigate and prevent environmental pollution, create business, employment and livelihood opportunities, and shift towards a circular economy. The methodology to monitor SDG indicator 11.6.1 provides guidelines for ladders for MSW collection services and control level of waste management facilities and aims to bring standardization around MSW data points.</p>\n<p>The indicator 11.6.1 has strong linkages to other SDG indicators such as 6.3.1 (proportion of wastewater safely treated), 12.3.1 (food waste), 12.4.2 (Hazardous waste generated per capita and proportion of hazardous waste treated and by type of treatment) and 12.5.1 (National recycling rate).</p>\n<p>UN-Habitat has also developed an additional document <strong><em>Waste Wise Cities Tool &#x2013; Step by Step Guide to Assess a City&#x2019;s MSWM Performance through SDG indicator 11.6.1 Monitoring</em></strong> which provides detailed methodology for data collection if not available.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Collection of data for the indicator has been demonstrated by pilot data collection using UN-Habitat&#x2019;s Waste Wise Cities Tool in Mombasa (see flow diagram) and many other cities, but continuous training and capacity development for tool application at city level will be required to strengthen the global waste statistics and improve its data quality. In general, developed countries have good Municipal solid waste data collection systems. Some of the best available data for middle- and low-income countries is available from UNSD, though it is relatively sporadic<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup>. In countries and cities where data availability is particularly challenging, household surveys and other complimentary surveys are being conducted for the estimation of municipal waste generation per capita. Also, the collection of the data, such as the amount of waste managed in controlled facilities, remains a challenge for many national and local governments.</p>\n<p><strong><img 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\" alt=\"A diagram of a waste recycling process AI-generated content may be incorrect.\"></strong></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> UNSD, UNSD Environmental Indicators. Refer specifically to: &#x201C;Municipal waste collection at city level in selected cities (latest year)&#x201D;; &#x201C;Municipal waste treatment at city level in selected cities (latest year)&#x201D;; and &#x201C;Total population served by Municipal Waste Collection&#x201D;. Available at: <a href=\"https://unstats.un.org/unsd/envstats/qindicators\">https://unstats.un.org/unsd/envstats/qindicators</a>. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>The numerator of this indicator is &#x201C;total Municipal Solid Waste (MSW) collected and managed in controlled facilities (tonnes/day)&#x201D; and the denominator is &#x201C;total municipal solid waste generated by the city (tonnes/day)&#x201D;.</p>\n<p>SDG indicator 11.6.1 is calculated as follows:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>S</mi>\n    <mi>D</mi>\n    <mi>G</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>11</mn>\n    <mo>.</mo>\n    <mn>6</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>M</mi>\n        <mi>S</mi>\n        <mi>W</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>a</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>a</mi>\n        <mi>c</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>t</mi>\n        <mo>/</mo>\n        <mi>d</mi>\n        <mi>a</mi>\n        <mi>y</mi>\n        <mo>)</mo>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>M</mi>\n        <mi>S</mi>\n        <mi>W</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>t</mi>\n        <mo>/</mo>\n        <mi>d</mi>\n        <mi>a</mi>\n        <mi>y</mi>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>x</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>100</mn>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi>%</mi>\n    <mo>)</mo>\n  </math></p>\n<p>The calculation of SDG indicator 11.6.1. provides two important sub-categories with varying policy implications:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>S</mi>\n    <mi>D</mi>\n    <mi>G</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>11</mn>\n    <mo>.</mo>\n    <mn>6</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mo>.</mo>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>g</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>M</mi>\n        <mi>S</mi>\n        <mi>W</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>t</mi>\n        <mo>/</mo>\n        <mi>d</mi>\n        <mi>a</mi>\n        <mi>y</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>M</mi>\n        <mi>S</mi>\n        <mi>W</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>t</mi>\n        <mo>/</mo>\n        <mi>d</mi>\n        <mi>a</mi>\n        <mi>y</mi>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>x</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>100</mn>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi>%</mi>\n    <mo>)</mo>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>S</mi>\n    <mi>D</mi>\n    <mi>G</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>11</mn>\n    <mo>.</mo>\n    <mn>6</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mo>.</mo>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>g</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>b</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>M</mi>\n        <mi>S</mi>\n        <mi>W</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>a</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>a</mi>\n        <mi>c</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>t</mi>\n        <mo>/</mo>\n        <mi>d</mi>\n        <mi>a</mi>\n        <mi>y</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>M</mi>\n        <mi>S</mi>\n        <mi>W</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>t</mi>\n        <mo>/</mo>\n        <mi>d</mi>\n        <mi>a</mi>\n        <mi>y</mi>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>x</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>100</mn>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi>%</mi>\n    <mo>)</mo>\n  </math></p>\n<p><strong><em>Applying the national sample of cities/municipalities approach</em></strong></p>\n<p>When countries collect all data from the municipalities on solid waste, the above methodologies will provide you with the national averages/estimates. However, in cases where countries only have samples of data available from the selected municipalities, data producers are encouraged to use the national sample of cities/municipalities approach which has been developed by UN-Habitat<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>. </p>\n<p>Figure 3 summarizes the elements measured by SDG indicator 11.6.1. The MSW generated by the city is either collected or uncollected, and the collected MSW is delivered to recovery or disposal facilities. Recovery facilities generate residues that are sent to disposal facilities. In many cities, recyclables are also recovered from disposal facilities and brought back into the recycling value chain. Recovery or disposal facilities can be categorized as either &#x2018;controlled&#x2019; or &#x2018;uncontrolled&#x2019; depending on the operational measures put in place to minimize the environmental, health and safety impacts from the facilities. When both recovery and disposal occur within the same facility, it is necessary to evaluate the control level of the recovery and disposal operations independently of each other.</p>\n<p><img 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\"></p>\n<p>Figure 3: Concept figure of SDG indicator 11.6.1</p>\n<p><strong>Data points</strong></p>\n<p>The data points required to calculate SDG indicator 11.6.1 include:</p>\n<ol>\n  <li>Total MSW generated by the city</li>\n  <li>Total MSW collected</li>\n  <li>Total MSW managed in controlled facilities</li>\n</ol>\n<p>These data also help cities to identify the proportion of MSW that remains uncollected.</p>\n<ol>\n  <li>Total MSW generated by the city</li>\n</ol>\n<p>For cities that do not have reliable data on MSW generation, it can be estimated through the multiplication of the total population and per capita MSW generation from the household. Detailed methodology for this is provided in Steps 1, 2 and 3 in <strong><em>Waste Wise Cities Tool &#x2013; Step by Step Guide to Assess a City&#x2019;s MSMW Performance</em></strong> <strong><em>through SDG indicator 11.6.1 Monitoring</em></strong> (UN-Habitat, 2020).</p>\n<p><img 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yoNnXDZIo1V/8sLPBTklGsE8pzGY9aFKlEICFsKHBkCF7pXCiJLs+4EQY9egM3uCjlRekFP8/K0f2EocSJlyt3kkVAkc2UeV9pAFHeWugN61+sCccGenBssV2Lz5LqW9PDrSP1nvN69bOeO3H156+F85B/aStwSqnT9nT102bwYkMDKUCi4Cc5zRZFZrQpkqmWSVCoxkqsFF7kPBGguPaFp32G0s2JZse1gnnRisQhVS3dSpU9esWZObm3vC4Jw8h0w95jrwG2ZzPDNjMLENVf59BryFknORYA8/IU10R1xtYtSCBUF1M4XKVHfeBoenfFnm1AAn/6NIaagM/d7AzJ8Wn3nlKTc+fPGw8/puWZW+ct7Gc64ZCntOeYl99cIt+3fmJCZbUZWxbRXnlfLDAJfP3gDwmK0mMmeiNsvYlvny3Z/e9PDF9758Q8cerQaf3avP0DTyVWo06t1bDqz/c2teZlFSkwQQiy/j/vQcdlj0jSvnb3LZ3SnNE4P+gFKlaNKiUaPURI/Ts3zuxpk//dkmrRl0znFWE09l7s5du2hrVkaeNSHOEKdHgDtahhckQo+63KhMsOgT685C+ALBjZl2pVrndqHkiMLpT6czrFuxOHt/hl5vyMvJPOOCy4lWnT35JzDszIuu8no8JrMlJ3PfkrnTsvbusSYmWayJCFv28rK96TsSkxuvXDx3767tyU1SqYe3IypdqjzbrLLH7UYh7ve4rIZaCAI5llS3ceNGUn8hAJ1gOCdDHWdJhjZx4kQ0tHXnC3CC9iQolS2RSHl+Apvo/rlyGO0MGkX5UuFzXWeuDdmLNEaly1WFoZovCFhIULlKo0Ku4jPC1p5tmU/f+O70Hxb98f3CZ256F5hJaBS3esHmV+79/JX7Pv/uzd83Ltuhh8ZWSA/qJTPWIoGdd8PwsqJyANJe5kxpnhSfZBn/8Yy3Hv160dQ1Y0f/+sJtHzhtToK+J3w6+9X7xo55YOy07xY+c/O74979A4nN7w8+csWYtQu3FOeX/fHNAlxUUKv++sksrUGzYPLKl+/5jN8/vvvHk9e/nZ9ZrIVu4igXRjtkxI25i32B2rQVHdY7osW1JgsMq1EEFaArzprwr4eenTzui60b1hiMhxDUGBe3cObk/7vs1F+//vjzt/7774uHr1uxKKGRdc+OLU/eefVTd17zxVsvvfL4XU/ccRVeoCp1TNgJ2HJdDte+YsiXasHj96hQBwAAA4BBHaEBjPpegcyONykD/O23307UMUZ90qpZoWOHZCg9wU10/5wa9hkSnhF7YN9azXmL9mM55Xtz3XtAnaqFCBJTOdyURFab9fOfwy7oi7Lxk+d/6jG44wfTn/lw+jPJzRK/HjNJoyXPsjpzT26/07px/5RzejtsIUc7hbR3RzYyGYJgRcQe0hUk5t0Hdfzv1/e9PmHUmF8fzdieuW7JNvSZJGkHR69/8IK3Jj1++5NX/PzBtL3bs0AvcrfTjQ49Wt09+lpA9KE3bnn07duQGpu3a/L0J3eO+eXR96Y+hWfNoqmrkSOPMVtIitDZb8ldEe0ZrWZ9Nrc/xxa026LPy4OUNvLiazp06fn5m/8FsGS9JTregtys0Y/8+8Krbx37+5Ivpy7v2LXnK4/d5XV78FvJzdzXvHX7z39f9Pa3U1cvmb9iwSyUnOQ6IbkXPxSI4vbocjn0Zkt63pGV59WczfAeOyrUkfoLGMB9o167XB57EuTwA+IFoZMNb7oaSkU+AwFnkITOqMvIeHmyXYzY7ZUcayR/LXy3D5tsf9C/PnsBiCUTGh/jYnvU63VTvp7/n0tefvPhr069aMA1956HjhH44anv3pzy4wfTSSG2Z+t+twvloM+aFHfp7WfALSDA6a9Fxs0WifCwaGhh/xvexe/1T/l6HjIZkiK2NMQ1t8PTd3iXQSN7IUcOOa+3Mc6Qvnk/1js5ibxwv1QJg5y4o1ICmV0HtDdZjdN/XDxt3GJG5CTAoyr0dtrcu0vXl7mL6sI7uDPXgcETpWLUO4NqGsvc3U+MXjB90tpl6/XQDECwqFdsWrPc43Fdees9HrdLq9dfddu9Wfv2ZO3PZG4RBG+6Z1QwqGjdIS21Rev83Cy48T59/fl3/juKn42rlqARjVY/WUtbWXmhS1kMv3HNXkeGuvXr1xN/hl/+iae6PGx6+S6B6PAQNagxY/XiObYo4pWSgxwQJ+WFN79VJdk31frg95VsxUEDLKmyJ8CVx+Mdcm6f/7x288vjHrr3f9eDMUCF1+MtKSjbvyt718a9jVs0uvre8xCwUA6irkR0Q8lZ2buyaeuUgqxiELHirEwxXFF++3z287d/sGr+5sw9eWCe7FSJbwqIhR0OFRzhBSQJrpyNk/5wXxQTusiA3qhbNGX1E9e++ee0NXTG4/birlnloHxen86k3pKzvMqSsS5QaPe6FQZbeenxe10esavELQw98/xeA4eNfetFXH3k6EhIErQ6g06nxzPT5/HgtIJnitcjMrOErGikP+UvckIyMc0lRQUFeTkFudmhbJxVT2/4k+b3+/QGw54aD7M7whgInpsxY0Z+fv4JLM9VLIxYZo+nqKho9uzZ4a9WQ8lwZyDgkhxbRFT1yXyBLu5ttSvYBYL+bXmr2OXCXIeAL9C8XeMegzslplhQIeLob0kw6Yw6vEue/fzuR9+5bdS7t593/XCfzy/nmD1Mx4WTSI9BnQpzSxZPXR2fFCcIB6zGovwyPDC/eX3SBTeeNmb8o/e9fENcohkEEuKaWpWfXYzMl5gSX5BdVFxQJsISKnkw4dsJ+Gn1WgIY2Iy/evW3nqd0fmPCKHrSpGUyaBfOuJAgsx3ppa6CcArHrsyefAdbK3gSoyZCqxG467GXVi6as2XdShwsOSc0bdGmvKRof0Z6YjKZKbVb1q/C+79xs+YAnDhDVNK4+L0+s8Xy8IvvPP/OV8+/+3Wfwac5nfbodhUtqzOoLbKHtWrRavoIULdo0SIoCxyOKITuR6uXMa1HVmNCpE7sYEwbOhkrB+esaslVo+90nZtnNmKLWkB+7V37SrcH9fj6hasxczqE/IcZDFlNZFTx+RMbW8+8fPBHz/743ZuTF/6+6uPnfpz63QIELAoI/eHfL9Cx55BO+HC+PeqbMQ+OnfTl3Hce+/bZm991u7xNWzZas3DLjHGL33rk65y94jzNcVNv0m9fu+fV+z+f8ePiNx/+unPvNp16taEwfQgJi4FGjeODknBLmf3rUq/Xn9o6Zcf6jFm/LKHaravTkerCkZAQIvUm7fa81bW3DtB4eL1Kgx1vlGhfyG6IZfj0U7HTbu8zePiwsy7M2r8HKcrtDnbtPaDXgKHP3XfjxO+///SNNz98+cnr73w4PtGMVyRSoIC6kFUPNxmv14uUDRrZykr5QfILZ24jGg0LStgDjjkRPXWchQ8PNmDTJ5MILHTUG/URHmdfY/e4fCwltLxv374nz6hjN58Haw54pNIFUtAj1X9qmOOdK6ETLJEMHSVFLbhZo2VcuX+mF//XMKQ6seEppPys4rS+bVNbJwNy8thRUfYY1BHz25Lp6zYs3QEK9hqahuNJaWE5xrZ+p3YThf5uje1/WjcAMn3Tvs0rduGfctkdZ6X1aduuW4v1S7at+3N7l75t23dt2apjaosOTYltSGmW1LJD0xk//dk2rfmdL1xtijNAhIkKtNuADklNEonSS05NXDpjXVmxfcAZ3bsO6LBzQ8aK2Ruat23S45ROTVsmU0/VQe4KCUmx3FPSwtKxtih+tuc6JLVBBBhE+1IqVWgdCRoeeOpZrANSY1r3voX5OX0Gn4qXCvvbKWecl599YNbvv2Ts3HrjXY9edeu9bpcPs5yjvGzwiHPJjsMzRCN07zuoRZv2PgAvllco2EVl1Sv1mmhqR4/R5cOZDX7//XcMVydeIF2Vq8ZBA8ryf/3rXx07dqyycEOBsGbAuU1SrBYx4w0XM5Bolvw9JGMIEmr2yi7bsyp/OpJWuK50ChGgjRKS7ehQxFdQUqqV5C5B5SjrEoEN6sQDE/Uj/imHj4nyKiXGOb5ZVIUTIK0jpeGKgggGghJLTg2gL04oz97ynlarHv3dgyg58W0R5iQ3TOgK/CppjsJC8jPqaDdkWPJi9qNCr9un0Ycq8YqdM8zgNDSxTdVpPZsOq9lFEK2Vu/xrDjhQF8qmx+heTC+JwYgBl3FU/JNV1GrhSsDeJuL0tTocUjDLYXujJPIfK4NOC00mSZnldwMbnjDaeT01cOK3xifoA/auqTUUdfo3RCVXFno8DFcnvDfKEV8yIspR3kb3/TuZawvad55EAeNVrjSnWEd6laViUWBvyTacPsLFObFNSqBICFcqdSckEhFLAA4RpgbIAW/IcxQ7sqmM8oGAvdzpcnh4BCWn0y5UYXAOoJkE/Jw2l+AfkA1ywSBqSYcdLiGhDqW8vNVSMwXkz7QInsnmRhG34PDwp1AlfAgX53iW1g+U7gQfYzHVx64zt8xjjouLBc7RrgiFhOnOfVCZHIqM9LgcdmZf/isAhq6SowM6SbutDHseN0Wm0L9wLjTh8NAfnPxYz4/T4SiweXibYt2QXP/foA6cI4eI0NWefBc2A8a+fft2WIpOvtHHYMS+EsmfJznCNQ7FoAd1rEp8UAMFQbLG1Ozl9jlzbBkirXMk1zFwMeT08Dc/lGOD6D/dVircWOQHEfWufeCCa+87D5AL1Xyoo4fVXPFgCBwP9kGUjySMBXz1KZ0FDhE4UZMX7ql5ZZwPYuqK/Ld1kWfpb0E+fy1e5YFXnuTQ50hm8zhmEFnTYLYW2mrozHEI6oinJjcKIt3J4Hh5xAWSzXUNMXbH8fZWetS1WxFvaLDSHZoRxBerQXLtjs70hl1LdtlujUHEooX9RE0XRM7APtcmrbnsjRnrS6jy9Jq9xTUd11/s9EkaA1gX6wHWr/pzymOK/Ycm4xDU7dixo7i4mEiDGtDS1s3FAOOJrtuwYUNDjN1xL1BQcu0RGS9jd6HTIkoZ1Zb8I38+nstikPSa2Ma544oJj10w+naaY4x7X+mOmJ7T2S6OdjgW9yutCaY79KjyKoWeOvg32fwmjG1/3ZELVP7n8SzsP5/FEzWrbLfHX6Ook1/mxSoWZowBeEwiZnKWiLCEGsm9YI6zYtgLpy3K6PR6kznunzI3CaOJbQinEnlRXA5HqdPv9NSEDvMQ1K1duxaZ5qTFOXnqUaMD9uRPie5X66SrzVsiBUpjG2OgUUtajQAnki4aNJIu9Jl0+0fUvhBifPT892I7Viqn/Lp8y4Z9op7YXS6vQiqTUO3W1IWHXaEjG5Vd7Bok4g1MgpTusCZCBAUiYC60ayvxqMSqh4GNlNDEBmAiElj31+kkFHIgQR5UUYkILY/ZFo8vjFIbKHLkxG5aDqsZ7WWRAxtZuBIM8d0lhQXfffwGkea4/cS0n/Iq/PzlB+nbNhNjXmVbuK6sXb7495++0mr/tug8O3fqr8vmz6RAlZX8td96jWZLiSOWZ+K/unLw3UKOgbAUqDtptZfyhMgWO5LFhLlUDcWOPAPeXMmsjZX2ki+mSf/pF3PPuOjVsy567dJr3r7wijfPuvC1kRe/9ufS7ZL5H/yfCgUeDWVFkAcdRewTW7LygWd/mjh9nWSMjD40shcAHWacXvLU3A5b5MhWakVUXGT9DLs0CDf3t2XP3fLe9B8WV2ZeBc+mfrMAv0pSaBL0Dch9+epvj131xqNXjHntgS/QphI2MOb+sQAAGIc6MWtv3nO3vr9tzW7cLGmc0IKfP5xO0mc5eXTUL9BXrdXk2Q9EveajVWj3+CExxDoVZougSEFO5rsvPlpSVIi3ZJhPVa9YSHAMfDLm2c1rVyCTVVmJwWj4c87Ubz98DQ/PyqIRz/78xft//PotBaqsRC4QCudTFjtjeBSr6MlBqMMdA1sdvjdhdvFELcbKETsPPx9B9CfqGGtgXEFP5vGqE4/RS+Q2n79zh6bnjuh6/rm9Zi7YUlBsP/+iPuee3q1JslWC+dOsl2A1g/MaDOPLlGL58KsFN973hdTEelBFifQglwF4hPQgNHzWOIMekS5mksShAXlr7tXC+QKH8/AVSpG9GyI1pXLD0u2bV+4CloryysgfRg1EAuRnFf380XTYf3Zu3Eeyygmfzpr2w6Jr7jvvjmevRFwrzCtNSLYsn7MhKyOXkAN6uHVVOpUsm72BZ9l5PU7v7F+WCAY7bawEGvQ3hfaa80xBTWeKswTCjjE4qMCMsxJUARGg2RJPqIC8jvxWqdToG7kJLw/hdLKLjsFkRq8oe+ugiiRls1yYBykpavgrcTMzjPqRO5SpkG3MFqsGucwocb8y4LElGk2iMAUEy13I8QW5jabl/iCdy7UhEHI3pMCM4D1C1C1xeCN6JILaKxU9CHUYqIg0iJFIx1t1xAyzIuCm0trzT5w/K+7wofJX9LDCDOGwAtUb/z+fknWYpACNVoUnXT1Bn8KTJ9Icx+5yeYaf2vWRJy594IlL27dJGTm8ywOPXvHQoxcRniy5PHOmr/vm8zkr/twe0mrq2G3nL9m+YeuBmZNWbt+8X9JriRyizNiPZ83+Y62AxpoKYhXzgS7RDTdbLCen0rTn2faTWDJ26yD760F3YLLqyUhJLhLaQjKbN3FFSrPEtl1aiO+pz79qweYBI7pffOuI/iO6P/zGLU1bNGrXrRWpwggtB9uQ7bav28NTO9bvcZQ70XzuT8/GLXPgmT1i501DqrMiZ67DG/2sJUecbXR0SE6RLoRKIzgfJn7/1U9j38nLPkDqyJCdjOB676Rxn3/xzkvzp/+G+p3DAoi4dN70bRvWYg7kn/t271wwfSICE8CTm33gu4/HfP3+y+nbNoFVIiBRpZ77x4Qv3xm9cMYkPsuhZSJyUW9YNGvudx+N2bp+lUC7IEcZNc0tmvU76TQnjRtL8pTDkAygRb+6cObv3344ZsuGzSIOPUIHTiRdb0BVHnt6LwF1drs9IyMjRpnAgCiEcUAUGKuQduUQEBG9qNUKrb1CgUzJb1hS4dKFToHPBsMhKTh0kDlYWH5duENJbkb9xEpXmYpdu3ZF+l42lD84A/5SKWiPsU8KHDMeqcQhldo5R7mEu4edOyQjHnnVW/c+8cOXv64465q373noa1Sdc2dvXLJ6d0GR7en/jp+5YGvAqPvXw9+MenH81Pmbb77/y5vu+yKIYFfJISK26ygIUZ01Y65zeG2lzsJwMqQcz5A5GsY3sgw5p88cUna5vOycpJpcMHnFGZcPQr1JKB6SX/eBHRdNXTXl2wXEEiBphx6J69yn7drFW/m6lRaU7dq0/7I7RkLWemB3rsGs27wyPaGRpWXHpoT3HU/fjvEsgX0aHaqzvBjVX7lafyBYToKzCHkMcEvhTPbyqP+b/tsPv3376X3Xnk3yZZPZVFZS9OD15/3w6du7t2187cl7n77rOmIEAaT3Rz8+5ZevKcAF8/gbzzyAkJebtX/UbZdtWr1s15aNYBXQxdb63//cBnQV5me/++KoN559AEANgaIBneTYN//755w/7rl65Ko/5yLLIVi/+uQ98Njt2bFp3Cdv3XXFiAN7dx8y54GOBsNHrzzz9N3XbVi18Pn7b960ZpnRKKTJ8C/eEGRBguvDf6R6JQXUZWVlgS6xCGwEz+Li4p555pmXX365efPmSEtyL8mwnJyc/NJLLz311FNGo1HOyMU/n332WUpedtllFHj88cdHjBgBDFMeILzhhhueeOIJOeYR1GzUqNELL7xAnZSs3siP9pSMu3v37uV3dGs+WWrzlUo4KSAt1fBl0L748sSde/KXzX5m3uxn/vjuvg+/mj/zt6X/9/Clt159SlqHpisWvnDfv8+QCsvuu23E6rnPTZj83B/f3z9+yuqM7dnS0Yk9ozwIWb9aI54pTm+ZpEJzErEwEemQPS7f6ZcMhOVn69o9EPqsWbTZVuoYel5fnFCoCnfHq+85d+DInv+765OnbngHrSZpozHRwdqza9M+e5mDBznZj7zyFGjNIXeFA2/9n9s6922LJ0uVfEORdrWivNDy6TQ2T3G1awj/QY8v6PZhMY0MttmICPTuPWj4R+Nnfjh+blFB3uLZU+OTdF+993Jhfu7YyUte//Ln98bNWDpv2owJ4wxGNdrHkJJT6OCR3gwGI0wGa5YuyNq/+5VPx7/7w7i7H3uJ+zN++wFSVijr3vrig3e/n/bH+G83rlpKYZwhoSZ/4+vfP580v0PXnhO+/dSSoFk4Y/KUn75646vJr3/181d/CFKIz954QYdlIDR4BMgdm9eP++ytp1//7KNfpjz39lcIh6QeCH9mQiWFvZbkARE+FXFxAXVs6zEKGxfHt/h4Em5xnXPOORVQB7KeeeaZbdq0adq0KexKCQkJDz74IH6PgCIZODnoUYDCw4YN4zOVAIf9+vXr0qUL5ekq8JaWlta4ceO8vLxYuCfxUgKxZEeLeDobHmAGfOUSJpYa0L5Xnm2lImhzTZ276Z5bTrO2SJKKbaec3q1/r9aIcSIoQVzC6Ygsf0qNus9pXVav3v3lOxN+nbpGo1HZ2ZGPM1Ah/HVnWlQKMUWxv+yeMtw6KhPrxKhNj9uD6rhtWos545eiS5vx458wtSanJshxcj4vtDuaUe/c/sKX9xLJ/sR1b0EsLkS9QR1hTAXndm3cl9Q4niSW7bu3Wr9ke3F+6d4dWX1P7Rrwx/y0VO4qidGcVK7WJSRIffiGOvlZRDrsYRdf9y+30xufkNisVdui/FwO9vAVnHPZdYnJ8YX5pZ279+zZf8j6lX8CPuIVr/waKxRup7vPoOFJKU1vu3DwpB8nYJnT69VwGiDDffUu4uLD4BmbKXkvoR0ndh/iVku8kMnap3UvLS7gVV2xcDZ5orv3HVCYV2JNiBt50VXbNq524OCFgVCScMDctnEtRAmk0CzIs6f16JbWq1810nsKAl5vzA9kAurwwoiR9lJANonvHI6ZM2eCVUhyIp+d35+YmDhgwACSbSI5AV2pqalmsxkaHbgFSM01fvx4EG7p0qWtWrWiJLAHUlIVwl/Xrl0pT509e/bcvHlz7HKY0UmUujXwNTisCVxhGdfxXJs2baqVnh8aiL+MlFA1PXUKRdDrB7QswsXRLzoQDFrMKMOxign+LZHDmN8KZUCpePCeL9Bhrtl6IDe/DMtCRYBXDfWZTQUdb+wvm7ukZiBc2BfUqjOvGLx6weYVczbCZjfyqlMqzGxML7SfqCLBv9d+eYSczhPHzsF6n9qmcZOWjdYt3rpz496u/dsBfr2HdYZ/bt2irawd5AZw5sV0kjAi2r01sRDEjYVO5NVBbpfTwdvLthlyBwmSzgxDHerK0AvOm4ShTgTDCYwD6YTJjXccEBKOrISrp7Zq+/6PM4Gr0Q//6+VRdyqUCJdkEMVpxaw3IvYZ/vPCW4Cl0yHCzMgQRjvULFA5yG9qcNMW1Yp31i+ERapnGH9FRJJlzSHsfxoykfopH+pCxBcxlUi91ZmdSJpSIiGhwIxp5LhOp1u9ejXgMXjwYHALeOMDi8dN7G1Idfv372eLv+eee5DVKCab5bZs2cL70bZtWwSsHj16sH0DfuAlowMX27dvTyBgjPQbsg6TXkUyk9Epy5x88MEHH3/88UfVvT777LOvvvoqOr2pXi3s47E/jx/eNYK3TLpmTeMXLd8lGXRSstVb6li7aX+/nq3YYtRqpaD6NBnIubxjy4F3Pp/z/Qf/eu/jx1596jJ22INpGKs32Go8hXHaH65U9884AZIth9lmuae4OhtPmLX/vRjhHING9sCX8u1HvybvSVrvtsgiFBGpF93e/KxC7HawIpgthjirEZ89dmkkzt5D0xZMXrVvRzbGPLvN2aVve2TQXz+d1apTs+RmiXDuVKsv4T6Euc7mLoXJL8wHyuzeDduL120t3Lzbtjcr3BWkcrvHh/NGFLwK8EkxSC3adMCQhitJcpOEovz8LetWpfXsi1IRP8ns/RkmM+CHsLU6hH+E2dhbtW/32tj3Rn80bsrPX2XuzWrVrhOwdPt/Hnv8lRcffenFW+97MCm5CZqywychpAdpn9Zt+8Y1xUVFKU0SEOSWL5zdtlNXmkAtTnnAr1nLNsia+3fvbNLcUlpSujd9WzVCI2DIc5IVNcabhnDBwPcyUj1ymC+HXAzEIg8LQHXGGWfwmWvkyJFIb0SwIb3xT/IsY6ID3t5+++37778f8x4YBjk4V/fu3amhc+fO+ImsWLECnSc4hxSI6RXx5bAYxoh6dezCnADgM4pihWFWxQtqtVoZIOrZalxNmjTBtSdGnrThDSEgOEhjbx862BlCssudTlxUOBOqlU88cN6PE1c+fNenn38+5/TLXm/VPPGqKwZKHlevri1Xb9z32tM/zvx9NeSfCVbju2Pnjvt28nX3jC0udYisHApFWTlKc29NiEHkt2WKglWbNMrtntVby/dmllXMfEGRc+02e16hI5y1cHpJXR9b8VqIAk5yCrvZqBo1Texzahe4xU+/dCBBArzJ5LTkPnvLq/d9PvquT75/63dsdXigXH7HSGCGP/U9rVvG9kycidp2bYEyEyEPv81NK3b2GtoZUSEK2HDMaRJW/4CbHKHhTKZALIe3zKUrdWrsHlNuYbhP8SAuhpHKLCGnBD/x47LXHpVAI47UBcTAv7NpzfJHb7vm8zdfuf+6c1q07XDuZTfYygMjLrhi4czJLz066qk7b5k3bSKeliZT3PQJPzxy65VfvP35r9983LFrL0IUzrnseo4at1142udvffTmcy88fMvVhQW5OG1iF5QJDWgNJSTkPk5H8JxLrydjy4M3nD/2rVcevOHSfek7bnvwaWAReRFiBLcz0HvQqWk9+j5559UfvPy/J+68uihfuPlEag0Q3vWBYKzzPqvuvvvuZcuWgXaxyJOCiIYdDoQD2AhRv/rqqwE8PEouvvhihBaUk4h3c+bMAVcgPZ83bx7mMf7Uu3dvElEChCgwkfPo3hVXXIG2Mz09/aKLLlq3bl27du0AgwkTJiARxqLbcp24gA4cOJAmwvwmRKUYKlwmikblUIrqXfT/1FNPjUp/Iq4EN3r7Jknlj1X8eOUOhZQ1Bfll2OS6dWkm2dyde7Tq37X5tDmb1qxK79ur9di3byH0CA70Dh2aBv2BBUt3tG4SP/TsnoO7tvh95oYdm/dff/mgzu2bDO7XLj7BlJtVPLB3mw7tGksxFiaAZCmglIydj81dV2rzrN5SFFAYcvLtRp0izqwtLnVzR1IZ9meVWMwaEzlijnntKlzvlQS1acSLGPYDQmjz+Jq3awJWgV6Y3BDs8L0MSZNBr8ffvltLhLxmbZvk7C2AuC4+2Xr7k5d37d9eoKM/IKhBXd4BZ/bs0q89QRGkViECAU6fkVcMltl5wu5IdQqiWcUc2Capm04VVsizw+XL2F8c0ij6TXqpaXK47DPZpaTIU4cfPy4PRjivqjV9Bp0K8w77AEa1tJ79Upq2TW3RutfAofiDYCfr0f+UR//3HiY9p93ZuVvvpJQmq5fMa9Mh7cZ7RiUkJVMeLWX6lg3rVixJbtr8/mfHWKwJBqPp1LMuysnat3HVkrLS4j6nnNq11wDWi0wu3fsOpgZBVeFxYxrs2LW30WgeftaFxDmsX7EsISll1MsfdEjr7nLC9epq3LQ50qRapT5lxLk5B/alb9t6/lU3nXbOxdTQukMXmdw8zCtkQVA0trDmMeSuU6xZs+bLL7/EvyMWogB6UeSw0aNHI7RBm/Dhhx/SHEpLhI/HHnvs9NNPR2n55JNPwgSLbCfcjktLccWkPN6YixcvHjp0KAW++OKLa665Bo+VwsLCN954Y9WqVTinYLf75JNPkPDCnM1Ii/FugaajRo1CtIr02eMpD/DPmDEDubZ6DrGhQ5m4mK7j6Ub1nw04pfzxkkbwgFa/kvCfRJjDOIdOj8z98mES/0bhzx4QmcC4KXcDdGFz4mjNvu90HyzzF/OyKEYNcQYJNwpse5EeSsPvrVwSicenUiRfISmPuldyyF24MkujT8zPyzIYTD6vq3PbhN0HyiSlntO3NT7JaS89Y3Az/dEdR9HLzdr1vUsqj5TTIKLRiJgfcBiFvx3naiKXNTqDxmkTMMYskjOFfROuH8qE+OrE1AK9B7skCO0UBjP+GoHQ46EIIoMoidMmLi2xOMVWHh2GEvp5Vsfr4/XJ4Yy6sNS9fH0BpN6oCpMsgV6dG4XzFGXW7Cv3KiJjHg+FV6kNJhPSVYiiSMRxgx/IW3wmuA3zWEhFqYQbCRQMnfpUpKAM2dKCMM4Rq4AgiNFOR0x3gPyiSsBM9hkRvpp6gzDKhb4xFKMqwsD5OwHd3KF+EXMVIjQ/WDjE+MNfkffETb2BHRtKIPE6Y6AiE1iQ75yfOqmK00BEa0dbDLZrE22iKYZyhRL8iKn2Un4bxDR5vUhm559//mmnnTZt2rSKuaB1oAuPFWxySLIy4mIqYwJRWvIBke7AgQMY8/grKbuGDx+OVAdkxpRUjwWjOaA3zLe5odhfM4BBW9i0a+jiu2pz/Q2f+KfNKdCr1CFi+0LmeoF8ZQ6p3Cmi8dgV2FjtLglWHVHYJf7KTT6Ai7HGudCOLiz+xwwo5pib1i7BYSvmDO5yOSSFak+Wx+dn9ykn5wU0ZF07JOi0wgvuaBdQx6Ycax1ghQIz5P0HQZrPXuYM6dxEv1BgygyrYBv3CQ/n9yHoFX5CQe7IOCfvEuAiFHcyBWusXyEgAfvTsYnrbA5vdr57f7aNH8RrhiYELK/HZvfKN7PyXbnH1iez2jh6RPiVkBWY9vKyQwpMe3mIVVVMC1hCNDd/tZWVgGqhmwo2LHGnXNyXNZChXddDGTCJ8vLjXAhtoZtllORHfklCVHayApNVcDod9r8VFm2VokE9eDOkwJQ/A7T8iXZpkY7JYBzR2tE8tsyY2+pwYqwc3B1RF8MpzLABLQAMuxqqSH7LSSa5CVYRRYD2slOnTq+88griHZFzCFLz589HiYf+EAsf3iioMWFdoJOo9XBFkd01uRM7Q508Ll4yOhDOGKNYhtcudMY5rivWG9wxxitcxLBCxdhEVMWEixQeVYFtzdFy/aOzhFEr8RE9loaHb02TRsaenawqJT41aLN9bF1sW7wWGrWqQ0tD62aWY28o/qDfG/Dg/RHFl/MEq4rJQQz1HXMhcgocBwp1Ow/4+MnMP3h6YEMvswfkm/sL1Ft3HWuXYAEwRcTaaFqvl4bZwZklYntmhGNWnXfeeXJcXaRQHE5D1Mm2i0BGjk2awP0STSnmOjSWcp4UXD+wToFn5JyU/SmmT5/+66+/8qAstFEAvMF0ByICdcAkfjT8k6diEVFXMSgh0ZtMuMMAtOGMNFplCPwgSRuyLDIuI63GxSQzMyiHo9WlyOrxOxT2zSL5SJVgE1m9J1BpjrzkZ9F3llRVpNY1i0h8f7lLhTe2PJ9osRonKjq3TaxyOtjBdxasCYqMCw1od+TZQvbQ6rSpxnZmXfzR5rO03IMwZysvEfTcf7kpyiIXd/hhfkky2jL1qClCkB33F6FLEKlPqly1k7aA3miIU/vj9LFKecrEKrCfLV++PHbBBrwKbNzY59h/5Rg78ExO+oXqkn0ZwY7PfJD1qCAchWU1phCl3W5ATo5JkH00wADKVE4bFov3g26jU73kkkvwqYlF/UerEyEbGvTjOXbQc4vFgo63Jrt9qC1SpRSMlzilhO0Tf/BZmdtMCFuRKnuqGiieKegqw/SulJOExdKVQ+Ac/haNLpfUCcfuOmGCq7cW2x3CTUAuiYRn0Kt7dLQ2SqjCkwIytunbv/aTjy/G0WlVzX7d/Tuet1qtenCzi1NMIlfnEa9d+0qLHZby8hKRB8TnQa1HMXYhbGBkPOYjXo7O8tzTBjY7Wg0iEDujzK/UIQvWpbmQv2x14mK7w1LY0uxrYo0hr4ji9ddfR50YI6muTkxktToBYCQlJV1wwQXnnntutSo4WR/y2wTUqQMR5MAUcbDiNOcn/AgYwK8E4xn2m8gU/keacFzsfH5oDc4YlpbWrfmRPWUANrJCY9vjErwWblKOWyE9iJ1UioMGOpSUKyXVsbIF2p3e5RvyA5IekQKVNqmb3G7BPSJcD4K+fl2TEo65L6C9nLnjW6+SUIAIfOHY/fAK0RmJShbzQZoZjG2ihuNfi/C+EPIhT9bA8xliIDw8ZYNfeBVEUCp0ng6e3vaaJGOToz1WUOzML+aoIWxUeGDmF7lRRnLgQJJLSTSgc8NNX6sKtG9lPQbUrcYtRanHlBVB5yIsyoyBveAvriuylU6ugPuYe/FhkX1SuM/7g4Ek5NKiwFDHWPDwlD1TKE32ZpKqYImjvKyT43Ex9kp1Rti1qovjMoNnS4dEZXJcLN1SELNkD5+qe3SSlWCZo55g88SfQgUCnToy5w6DdufO7OEXvNLv7NH9z3rp0ivfXLN8p6CdO/5Dp1Lp8wWefnXiqg17ITQ4wuQrFTj77dpyQNgJ+AZYjc+8NuncG94VKcQq0YRGedX4simZomM6lQSCKzfmafTxtvJi1CGoMSxG+kjmdOKfylUaw5J1OW6Swhz9YgAqheYg53d4Awi5U2pxLJj81bxX7v70xX9/9O2bkzMz8tTH9H8Jr+5wS0HxI0hZQVmVEnn0pf/7GK6fykx44VYURrkQyzntHEtphvSc1tbcs3OjHp2SwDMWQjgf6A1JCfrunZK436Vt3DFwLgTYQk0Q0UKE0ffDi9Alcjq/99KoNUvn47Jb8WeCDTasWkJa5w0r/6QMkujyhbOeu++mh266aNynb4Exa5cvJPWz7MxJ3mfOVe+PfmzX1o3kcaYSXDHnT59ETB4JVqrRqzAfEX5jZP+PYaCB6Ah7QYMG+agr0mBMDvNlPVSMHVz8RHJy0mq27MhetGLXvbedNurxSxCqhl/6+q6tmYJenHoQ8hLNUoIpRDUX8jchwSY3EQS5yY+cb5MfbkLsKX/mJpJZqBeJ8SadXIYth8iEBGozi9owYlmNm7ZnD71kjIjsiTfjlnn7tUNfe/rykM9cyBUb0ruK1kOHZFEzYQlEMqAXpXU+RCr/iR0WnDvWN5txt2thtZeVYJwzGM1tmxsH9mzMbgsOoDHz+7wdWlk1xwxCUnF0V4o09uGvILEBBIDDngq9nCXBnNoqec3CLRColhXbEfVk+IFGlci5g9WSzhMUDrGQcxM6nsptHSoss0aIwsgMgIVSEJSHGMh5hM/iwVARk8Uw/uMZEz4TrSNyYWUkAXSIb0/8VZTXwSmjlTsTuqPgWfk3HYs0XEp4jYoDQbj2oQqrZ8g9O4KDmIBHsnXF8gKZZk7+6buPXh/9yB2CuDwUDcxcI5y9/vQD33/8xpwp4+OTDDD7PHHHVZD1tO3UZdK4L4A0CpDBmTQrxAwAbGTIhI5nxm/jyBnGGvG+ffHWi2uXLZRluxiNgJoRRmOdn0+JGUwWVGM0jPpbbYVrTP0dQi30XKGROCZHxIlDCJFSYbUYrrqo35XXDZvy7X18p36bskbAjFG7YPbGF5/+8e3XJu/fnSeASqvetSN78dxN5Lp95b/j3xkzGZ5PgXBazdwZ65cu3Cq0kVr13t15E8b9Sdj4oe8PXyd/4I9Jq154atyrL43fszMboNq/K+fbX5YWFNre+WzO5PFL2Ty1GnZjpcjIzBCM+sXzt7z0zE9vvjIpY1eOgFKtOiM9Z860tSWFtnfHTH7/zSnAwJHlxWNMvTjkayQm6ugX716LpuYenSwkDUmJD6a1E34orVLj2rc0cL5uk6rt2Dq+KkFBoVXpFGEflcEJ6H7efOQriEDfm/rUA6/deMfzV73608P3/u96kTvN5weT+L1jfQa5mIEWGeFKi8odNic9Sd+yPz+7WCYNF8o00kz7Azs37M3Ylgky8UNh5op4A5w5YBtHLanVa0FWcqPA46rXa4HG0kLbhqXb4Hrljq3UTjTeHc9d2W1AB+IT6IPeqM/ak7d1TXppYTmozMoSopefVQzI5ewvyNiayWuDABz+Tia+4AqylYUrrxDsiMAkfowmPof/5RKjD3shwq+2cklGzQGIxCV+r5ecKfSQv0KOAyOP1+tu16krfjRqlfTr1x9ClfDaF5899MKYT39bSJKwLr36o7Fct2KxTi9c4jesWMKDcPE4bXat1pCTuZefMy+8Uo6yqF7fqn4qhKLEvsb0UuIVEtMAtZj2PvzKK5O+hvmU0FSQWq7himwG2MdN1XltRbiSX6TVUysBGxE0ptU++cxPl9/+0aaM/PFT1qDeXLlku5RkXboq/cp/f3LxzR8s33zgo68XDLt4TEFeGbj14ltT3vpstoA9k37eku23/edryKyF8U++jIaPv17w2EsTskvscxZtO+WCV/fvzi0uti9dvZuz/eSZGxYt2SFZDJ98t+jep34EOAG2F14cf8ktH27YnTtp+rp+5/xv8bzNtL5y/d5Lb/voops/mLsq/dUPZpx3/bsuYvUi+priWozv5TEVmHKXU1PMfTqZundMqliB9i2tvTro27eKD2dNjBpko3BP4qguURVCMnDXi9fGJZiJdSP7CRIV/AN6pGK9Nn3z/ieufevr1ya++fBX74z6FquHJdE85ev5bz/6zRsPffneE989fOmrM39ZirIRiAIOn7r+7S9envD2Y9+88Z+vPG6fNdE886c/efaVez579f6xm5bv/PS/P495YCwVPnb1m5O+nBcXbxr37tTdWzJ3bznwzM3vQWVH6w9f9tqymetJocIphPIv3P7h2NG/jrrqjV8/mWmON4GI/7v7k4+f++n1B7948f8+fuXez9wurzrsjBtC7lQbdCp9OJNJGR2YrXBrVF5V0BVnjMCqFNMkIBWdRwfYtEWb4Wdf9PuPX3IoCbGTS5O+/3zYyAsbp7aUtXeNU1tsWbdy3fIt4gthMuPrRwJMkqqsWDRHCpIw07FxzdKzL70WroO9u7ab4nRb1q8k4q1bn0FE1IU5S9UoJqKrlJIuxqcBAXWRCv7yYDBz4gwpX/hYxhDzjzR5ETVHYbKREY0X0VO1AnVwGpDuGbfY47mI1qjGCxe1R9TWSA1dTDXH5G27cjas3HXrfV/w+YorBm+av/GVd6f99sVdP/3y0OKFz/ft2erh535G62g0aAuL7Y/fe85vkx5bPeuZA9nF3/ywSFJpjQYOplpZgUnwWRxCWOXL6bz83N4bF77w8dgHZ859jsTuv/yyrMewHm8+f6XJqJ3+/X1jxtyI0hKxwARY6jWwg77wxpRxH97+8/iHFyx8fvjADg/Rut9v0GthS3jo/86cOPnJub88tGRV+soV6RLthn+R6V8ZLn2lNe7ww1aCNdyt2aSLD/+Fh4QTnEtItrRo14RI8C2r0//4bsGMnxaDNOyYOIYAZn2Gpb0z5UnYCTat3Dn1mwXoVpGrUHJ2HdDhrYmPn37JgG/GTBQh5MHg+098D/Pqu1OffP3XUemb900cO9toMSDnrftzW7dBHd/4dVSbzs069Wz93Ni7kSAv+/fInz6YBm7d8tilnfu06dKv3ejvHhh4Rg+nzVVaQNy0F0Fz6ncLZv785+Pv//uVnx6+/sELvh4zEU47YHXv9qysjNxnPr37yQ/vWL1gy7KZ68i3EuZSsL/Gaa3hqxYTrbqhfVOG92sysLu1U5uwThtyTwxaYSeKnQJQbgWZi2wqF1x9y9YNq7dvWhsXb96xeR0foObBoYkCoNWtDzwF5851Z/Z698UnS4oKUVmC96ecfs7mNcvITpCdubcgJ+vq2x8gw8q6lYvhIV+5aG77zj1SmjZDZAxzVqtRDGs0pwGhTYnlJRSYkUp1csxAhw4dbrrppkcfffT2228na6UcCRDLrh6qW2ZnDbM5kUvU73/kkUdgyKPb4feQVzPWUer/7AwRhzAqEE1IOtBqXIQt8iz0COEPM/olVZbIbHWSFCKNc1/xr0+uu/kDoGv2z/9JTWsxddaGTu2bDBueJqGiVChuvnLwTnSYxWUk+2/dotEF5/WR8ktNTeOHD+qApEXEfyhhx6HRHG5ZCASbdmy6dl3Gq0999cRDX5eWix2Zp0jxzP87SMOPcCZzoFCJToNc2LZlo5EjutEK+HrzVYMz9hcE8ktJJdU8NeGS83pLpWWpKdbUJvE5FEDhGf6FqMVpIPaXRZcY0TdS+NDrNWga0TdCxLNs1nrYdhC8PC7P/p3ZOXvzm7ZKWTh5Vfqm/YnJFqQuRoA2skP3VlfceZbepIeLlRpsZc7M9Bw0w83bNl48Zc32dRkkxqQw8dqAVmrrlGvvPz+xsTWpacJ5N5zKXPMnvqF4e5SX2KF1RQVKH0j6rNOL4CJ6goUPQIXW/KyrhvQ4pSPfyrOvGdquawswWCSL16hvfeJy8kRD+tOhRys0ouG/e+hgzdoIECv0ehy6wl9AgwblcE1wIpLkpHuf/i1at5s24TtLvHLyT1+269wtrWdXPCpDixWA0+fjX+fffM9jP3/5/r8vGZq+fTNve78hp5PuZP+eXelbN6LM7Nl/QMduPVcumu20S5tWLx102lmsQZibbfhzUrkklkU9St5IvkPVaEhJukWgLvyRUJIYOBI3k6YSJh3yRBP6/Z///AcgqaDt5ggDFFX26ZCTF8v3D2tLvlk55aNcmJuy0M0/8YSsqJDPpNCkReTRCq7XisoPcySRK6cSOTIvognii8RTET1y/IXltTieXCkVYYvH35lq1gDUhRnE9lcDLDWy1NTv7l068+lZk0YNGNJJJFd3e0mMKooISCKfQsjbBZkt5AQvnEpk92glBimKiaB1oa8TOZIUeix2lS7xJTLq33/nj0tv+7CEDPpNE3BukP8u+xeIr9nfzmmwOP2l/Ay1jg1PnMpDxGDiK0BwQpDQQQGXkZ3veJ6IQ6Yo9pdREwddQJg6G0Supi2TC3NKyovtsIddeMvpyF43/OdC6CWZHtLcezy+lXM3zh6/9Pev5yU0jh9+QT+KycEJfMBbkpkUBjwc020uvq+rF26ZNX7plK/noWk8/eIBGOdCbibQZAfFg4HgZy/+8sLtH+DtCcsdC8DaoXYThEoitbEoLM8QFbI9YOeD6xU+IGCPrsY3stjKHAGIdgUZngBRHhGGukhWgweN2po4c7CP+8l0E6IzjenFqV5vVJ57xY3z//gtfWsWHOIIeYKQ+C+/HrfTaY6z3PPki9/PWotDyocvP8mEte6Q1rhZy7XLF61fuaRDWg+jWTFw+Fm7tm7auGoNkl+/oSMiTVQd6RhZ4hrQ8SrR7EWEAeAc+f6vvfZaqAmefvpp9Gz/+9//+EAqLyxbcsA4lAUkGWG/prD8ssqwRKQaab1Edp2QOM+rRmFoyilMHyoXBp9IjEnfuIloRUB0s2bNZGGOry5MrUOGDAHwiJUWui+/n2JUTj38tSIcnsohxOFBGSwjdaek/9QZ6bIdf/mQf9dxXcffh+OqAZEFISkSQUd4RyoVLZslxuHuSBQXQXXeQO+uLfDMzMzIlxpboaCbOG1th9bJUmIcwJZbUEp8gpSSGChxLF21u18PmHsVcXGGvQcKBVmdQbtk5S543SpURgICPd4xH8y466bTXn77oQcevxgeH08oTlynU7N7sktK8ZVyl/h9vbo237E7Nz09V0qJx843Ydra1s2TlI2sRC8cNjmRHUbpCck2NTWxw5q0FuINkIrCWU1SNfQ4pRMzNvGLOXALYCfjKbRb8vcrzmpiim4adfHo7x985rO7Xvz6/nOvHw47gTiH/KXOkY+wvLiY1sC/6x44/38/yIXvu+jWEXLqS+HcAFOdUYuC9OcPp9/3vxte+/mR2564nDnkT6H1Ej/kgA6lhxYV8hsvGAS+vTuydQadKU5PEyGpsQn4GEo5cBAUKRm+khDhFYC06mviC46XqFDQRXjUDmfV/lkG5h28SDjhj370TvSTw0de6LQffGOZH4xiuFa6nN6uvdv1H3p6fk4WpwuT2YxgN3/axM1rl/caOMztkiBrpYYfx76N6rJNhy6AYvU6E85TLBlqN5M2rLc0nAqPVkbJbh5+hi1eMjAJnIOmgJTNAJjMSEBeZpJSgg1g2PXXXw/y/fvf/4bNANJwxD7whuSWt956K2wGzz777MMPP4y0BGgBP0iH3KEweNmrVy/4CrCokQYTpShSI1VBygrRwf/93/9x8/HHH6c5qBK4z7Lde++98B6EHFV9VE4KzTvuuIN60KyCfFxnn332mDFj5AfpakRhFbJchch7PJN7kj6LyMIPnh1hXvKxvVyIcSJNiXwEdbjPPa/3aYM7EQnw5GPf3XzVm3iOvPrclUAiJha0jpfe/tGjj3w5/OLXsMndcM0pSIHXXTZw3p/b7771g9uve2fcxJW4tctZ9WCkc5H3WaMZ0r/dR98seGX02NuueXvT9qyQwsTfoW0TXmCI614bPYGmye0OAZ7k8IwY2eOcEd1Ov+KNJ0Z9e9u1b/00adWrz12BByZOKFQoehgS7/jsgQ8hfOULtAZKc1BVE1CnUxsTDCmHxQAcbU2QtFq0b3LbE5f9/tW85299b+nM9SgwkeG4j38KCsN23VpggVu7aCsmvd8+n7120RYQS8R3yyHqodzN4BnazlYdm6X1affBUz+smrcJbeekL+YgDqKZpDAKT4FeIr2vEHGyMvKo59s3JpGjWPjgqZXJqYlbV6UvnbFOxPNp1CGKAw4iyrOvHjJv4nJYW3dt3vfB0+NA2dMvHcBv5DwBdaHjBj0RjEXhHT3oQNCrZH7CfEmPpxjnhTiDKrahaWT48bhJVCY4lVq3A7oWz/r99PMuTUpJRuGPigQ48bhcz957/TcfvLZl3dLP3/xg7tRfL7zmFrF/ev1DR14A+w/EQD36DyGtdMt2HVOaNPtj/Le9Bg03Wy0yEWuMLr7QpEG0GGIu8qqApSVLloTJVyfzzwF1kydPRozjMxIeROFITgAJJAmoMSGce/HFF3/++Wfob2SeOaQ9rHpsKG+99RYUPLfccgvsdFATkHPrqquuApzGjRsnVwuFDcUojFwIXw/poZH5yJ/57bff0kkQDoltwYIFOMJgHaQ2KO5IsAlbAlyvzz//PAx2CHakOAGGW7RoAZHN+PHjP/30U6TDQYMGkSp669atYZrfwFQGNWLEiEgNmcf5QpDbkzSYzJh8RK3eRR9qja9O7HqKoLdQobcJ+SycC5OMQpGcFDdiSCf0hAehDm2YTn31Bf3YQLdvyUxKjPvwlev6DOqAOnv1yl1bduS89cJVfy7e1qldk8/fuLFxiySpzNkprXmXdinrNx/o1jF19BOXNGsSP6BXGxHIpVQOGdCucaO4s4d1cjg827ccuOCsnped1wdDYJvUhDiroX/3lul78hrFm4ac0kntD7Zvk9y3e0vew6su6MMmtW3zgXir8YP/XTdwaGcC75TBYJMUy9AB7fkTB1GdVnPq4I50Plw2WjOxPakKfdtwJub4yzg8ZWWBPMSXcKpil0zr265152abV+wCV5ZMXwuYXXXPOdjAEIt7ntKZSINZvyzBSJazP59/Nm2VvHvLflhYB57ZA/4We5kDYWvQyJ5IdT0Gd8SREkcSCoNn3Qd1ata2ccb2LFBu0Fk9wSSscchtv389f/PKXZ17t0Vo6z2sC66bjVs0QuCb/cvSVh1T4brDUROC8pTmSZDe4YQyY9zieb8tZ+8mFqJ1p2alRbbdm/edck6fuHgjp5qdGzJgzuvYozVuNVWOl6HFq5u0S+pRZcmoFEBpXeYVNDpRqe2flajV2l1bNxAYN3jEOfwVBru96dtvve8JkyWeowVel+DfwOGnF+Tlzpjww7Rff9i5dcPVt99/xS33kEMFFLQmJm5ctaxz9z4XXHUzHihkXSGNGTR1193xYHLjZhiBYtRtqiXITyN52zQyhn9crF5nFCRQ/u9//4s3RDjPI4phmXv//feBmdmzZ2OrQyBjO4aaAGz76quvXnvtNf75448/gigoGOF9xagGjr777rtIaWQgYwehDGkeSUjGHWS47777jsJg2wMPPIDYR97njz/+GC9EYA+hStZqIskhlt11113UQFXg61NPPfXQQw9BUw4UUQ+j+O2336gHhKNLCIWnnHIKPK6IfXLGEyokSfT3339PEucqRwrMkGOzX79+9L/KwtEtMHPmTPCbTkaqbpW7IUMjk8acRLdjEdUWdOxUBJcJ0pxwDFkVUeFIS/6DJ3TRHM+CfBWOlOzXKMGsxm8+n/Psa79nbBgjWfToOcVNmZqH4zzB3SLDZFAYCxEryyH7loRm0kEZrwS7G6x1XGw8lJe56+QodUx33KQDuF8icMD4w0WU+mGt01U0e2RhLgnVjDYSP/jwE2xSp/Cb7yd4WWvkyrPtX5I5CVNomBQHTIbBbPB7/bZSB98CkxUvEQ2sOiGPFS2nhrJiGxY1Qrx5zRDRDlMYipwzIWEX/SeHjGMUFuYZoxYLHBuCOd7osrtChlfxIKY4rH0AW+UwbfSNBqPOAVO8001YAj1xOgRJ298uViQ8kY6nsCC21vfskjKwRtYB2mD/qoxywuuw3sSyxYNTgCMJohgCk/CcPDRNItWkyCRfXkbgHYzkeF3K+wwl0SzLqynfoQb0bkiiBOpFoLSIfGyWeBI62Ds3qXpbjrzuvz2hZFdFhxmmuY65QKJiJ8VaxmeEs+eeew7UASllT05qQ5hDsANmBgwYAL0qIMR9OQIRYQU0Qv4TKROUomlSKlPywgsvxPaG3IaIhjgFoPIUYMMjoCn8PnfeeSfF6Ce4Rbtybyt8N0BBJDBSM1OmT58+stiHmAjEyo1SXnZOCfOSnwKqwywfxWIIoAxHaNWrdckvKyeDKHapOlVpUySbhxUK61kZdYjF/ksTdfAphgL8lNgP/shpKsEaLDcWgw97Ho/w1woXGL7mZU5xE8QC2IArGWiBJRSYchJnuTasZRXcdRTgn0U28Sw9oRX+JDti/rN1blIVFXIJT5agqE2w4oW3xQKiNndQU3Nkv0JBh7vGX+lFqlwO4Yxa7kQKJJYOgxmqLXBOjBUlM8pCpwe8MVtNyFVyXspQQo2/xq4Q+VAOFkZX+c/CwvfkoElG2OkdbtCOBCgEFcge/6IVDxx7EsZCWY0tGwvFsgeC4J9SrTRZsET4UWzKU145NLuicJXDFIY6p6eRMbXKktEqYNQq8cMkI0m0KvxnPaEN4+A3DpxjrgWshqZJJFsLJfyEo44vRRzemSoVSVUqztNyeUpWvkP+G8HGEOa7Xa2ByYa6hEiCFKvVTuj7yjuH/hCKONAlHKMu/vr3338/chWmMlzbgS5Q5J133lm3bh3SGFpH1JiIFDiMyF4kIBYOJsAVpjv0hzTxwgsvII0h23EHhSS2usqFAT8MbG+++SYV0j8MdXiX8Bv04j6O+LSFNhJ8RQoEYoExKkebStMYBWWfF9YVKyAICkZi/wOGkUSBwB9++CEcqY6e02f0pQh21Z7Zk/3BgkmSvkzARnQvpcJW7iostrVqXhMOBdHtu5ARXXFS8iURSB/H3YMle6eUBDPxVzzumk6cCohkUPp053a+RU3ampq6duY5SwOGspLimmqwHrQj0NTvOaVdvDYSL7bqDUwgOcY2WYQKpwqgBWgEPwCYc845p2PHjqeddlrr1q3l4wDKt2HDhmE8Q8/ZvXt3yPAoz59AxAoc5Q4X/6QwoIUISGEcWHgKTJILV5z+5JLoOamqS5cusgcmTDdIXYMHD0bm469z5849K3RRD74tcBEwHIyC2PmwBdI3VJqIaJXjGY49UlkN2Lx583AmpKHMkWfA0FboEqN++YNmi6FV+1oQuKMwFDSiTEv4WrYoNCm1jO+EB38sj+bR6GXN1oEytnl8h5rEOcaXEqdBdxhm7EfNzkettUY26iSTpgZwjhGq8OZAEwhdKugVjlSHghHFIHYvFJU4oeC4gZMkMcszZsxAu4gRjt+AzfDhw9ElIrThDAKAgVIrVqwoLS2libS0NMKct23blpWVhawGhuFD0bdvX6Qx6qH+bt26AVTgGa8FYhy6TRoC7aCQJaoab0/kPPqM+wmWOXxB8VuhWuqhUdxVGAg5RyB0ReikJyBxenq6HJfNs1WqaoXBgDSDbdrQaMN7Wf0vgcokFW+U1KEAuChestrwHx7/UWwhVlWhvXT5JesQSRlDLdY/O2/UWnblblBogzhSHs/Q8Bkh1TI1YKsLZ6OIqK3DKqz8z8PbEqrl8NTFR+mBSCTtD/RoPJy4w4g6eZyFdRplTomTrD4VFK/HWWHNPB5SU/8VmhfVJqmY7KgtrUqTLjxLx/G1LhSYYMyrr74qE4WH+RrJ8d24jQBjYBs2NiBEJhYHh7gPMvEBUJTlOZCpQqiS8UO23lGGkrL7CRrUisLyX3mQ+whwaDhBPp7ic0WAOUKbHLFASYpRBuUk+lWgTs5dyU3u0EN0qtyRTYZVThdlsPMBkKhAqyzcUOBYM1A0XdIXCMtZw8UM4JDiTJSSzqv5yVh1YHZ+YDfpSKrdNN4im1ftIndzl37t8Y0Mx8UxzLY4txBLAP8btr0KxZKcWhq7HXsFn4lP93lDginMhnpBZRAZCd/fu4LDi9YXd1anG0KOTDV6ZRS6Cn360uI6rcMU0CZv1yE9HzkwuYF7S5hqv/AnlLC/oM89qE1crFOCyV0SqIMghR4yohSRABsQgtAGyAFXKAxllnCmCa8KEAh04X6F3lIGJLlJMFIOcZMLy1EKQFTlwjImye4hfAbnZCcU2buEB2kOaATMQmcOkcmTzlCPDI1yQzIMI0rygarCVGBSktqYkPDXrKHkkWfA2FF8W2p6P6nDq2HsUCuda5WQhuNi+KmfD+skNALE2I3+v49Xz9+8Y/0eHEmiOArMZnu2Z77/5Pf4fBKlHvJzUX77+qTlszfggeJ0uD58+gcYEgA8GoUzds74ZRD9HE8fiI5omdC55nGO/qeYNfYywYYaxQmMelVsgCRVIY0FNRvNlonff3b/dWezeUY97Ar6vEZmstXGwMxxpEk52EzPnj3Z3yOaNVlWk3kjD5MFRW66v9+vXEZ2Laxo69iFBRqHapPtZxUP8lluoqKeis5UHoV8U64kTIEVEEUWJHwiotloKHyEGdC1khyCZ6BhckQMg00j6WvHMxZXw3h1YxCregthjNPPn7SCuLd3pz51xuWD8V2UYwlw0RSB2OSx4zMemyZdRbg63zXASZbJaFcEjMP0p9dQ5jB3UB7JO1BIuB7xBrILJUEHi6auJvYcfSmiHn/K3psv1wyl0cblOwhIRxAM8+t82JARHz3lgTYJXao3Fcf5lFGnSjQqjWbzYWnojlRtkI2LiAAIC/VGEwFzYmZCBKp85iahb/JT3OeOmGr+z2QWOVn+EirkP/2zMJsiJfmTmFKtjgL8oJUTQVZGU0Fe1mP/ury0uDDOmkDOlFZtOg469Wyh9Q1ZIkR5cxz0QDJgC1/xUAYWCvAs1Yo9OQy3Dx63l5e3SKw5Zf5ByMF3g8i2Bs4aJEgUofjIRAr8x/kdODEfh6fG1E2EqTVcMMGaukhhs4BGd8LY8jo16vtX5q3I6uaIuH3tnoKcEh6DxICQg5LCcrIqI4Qt+n1VYW4JsQdECyyZsY6forxSwIzm0HCmb9qHQE88+LJZ69xOtyXRBA0QSViIpdPqDqUPlpES7gIZ5+QLdERuO6glioOiT+CcjG1gJ1bDMPbSIw/TYNC2ju+i18Q8iutos9wyUe90OACbYyyD0GbpDQ6Hfd4fE+b+8ev2DWs2rl5GKmfgjXAFuOVmTf7pQMYucCU01d5tG9YAgcSJL5r1e3lpiRbFoJhVaGwNG1cvnTX5R8LJAScK456+beMazD7w2EG4SgFIWWf//vP8aRNKiguBKh5fu2wRVa1fsXjLuhVup6Nzr37nXHZdCGgDRKYX5GTOnvzTkjl/ELcAthHGYCst3bVlAx9WLJq9dP4MtHRqKMurWCGyoZoTDQpTDRLcC7cUJh0oRhWJ7wZ+ItU7LkX2BaqrpYE6oh3wcCG+ra72sV71S50QLNqqMIRi2k7aC+Wbkxybp4bIx2vnitMn7i3YqSRSjijAsC+hO9Govn97yqZlO4AxcGvQyF5kZ/7i5V9XztkItsH4Q/quF277AMZUSAwmfDa7cfMkSAZy9he+9H8fc5MUX4t+X71lVTqJpH/5cPqaRVvnTljWb0R3S7wJ35CQlKAmxwrqygtuPo0kzigwCc6b/sNiUlz2HpYG9euMHxcPGtmDHCh8N8FR0q9gdBxx2cBqeNkgFAY8iv4tztaoak6YOGyycU4pRX7Vx5HE62jrAKRl7d/72O2XwxK+Zsn8sW+/uHvH5mFnXWCxWl/8z+1Tf/mmMDfr24/GADy9Bw3Nzc5ECFu3fCGJvhbPnDJ9wvdDR54fn5CEBvLVJ+757fvPivJzf/j4DTwp+p4yvKggDxbyFQtnTR73ha2sBPR69fG7Qc0l82agqKSJ4sKC9158jCzPGTu37t6+5exLrp3845fvvPDI5TfdBbIumDGJx/em71gybxrlu/Tq165zxzVLF//3wVvXr1y8eNaUP375Zv2KJSQkA2iPYdtTqTQgdOcmel14CVrDfmGPVfCQIhEdJia3qFRaTythbbDq4ZNC9EU9HUKd67ZCrTB3Jf9ynetYTXYIKcRAepRattCkpfQLX40vT4/wWfP67x19XYcerQef1evN3x6DLkfOttzntK4fzXy23+ndXr3v8+Ydmr458fE3Jow67eIB7z35XVFeGRrOvMwivy/4yriHnv/iHjJnkk7zmc/vJrkz4uDcX5fJtjf5whBBSPjr//nyyeveeubGd5696d3sffla0kWFJDmw9sf3po266vXnbnmP3ytIpwk9fbUswIY4fXNLxxp2vPzni9Yq0UCGsIpw738WQOn489h3tVrdZ5Pmw7nTrGXb86+6Ka1Hx3GfvrNp7fIvpy7/Zvqke5585b3Rj+dmZSKZ5WVn4p733rjpn05cWJif+8fP3yalGIColYvnjJ285JtpEx968e2PXnkqc+8encFIuq+iwrwvpy4b9fIHTVu0fvHD7z+bNOfHeeuRdiZ881mvAX2feO0TizX+2be+fOmjcWgmORyx4ghq+TmZox/+10XX3T729z/pQ8s2HV95/G7ERI1Ol31gb2rLtmN/XzTmy0nLFsxYvWS+3nBUuRl5z2SJs+qCcfpoGn2r/EIfgjoc9yEQwBkyHDfFKuutjwUYOFpcwvWqDEioj6OrtT4b0qRS5aEMW7XWj1pqGAsZdHumrrXU/KFmm1s76HxWTD+R9kTkNiLbhkopKxLJkwKDK3yq5DTJO1B0ID3nkttGAFcoSC+46VSErV2b9oJPWMXgsTOaDchnjZomnHPdsJbtm8QnxbXr1hIBsXIfBPuPSkmGaPJq9hjcmYSZqChlmQ8LH39t2bFpr6Fp8KH3HpqWmGKthjxHTaCvu8zXOaV/pMOPenmIHyxqr9liOSrlkBg1BBHxeoOwjcGhw2yQT2bVn/PiE5NnThz33uj3MvftRixD/cjqkEYSzlUkueSmTTt16519IANVL4WtiUlzp45/96X3UGA67LYDGekicY5a8++HnmveuhWODz36DW7SrOW0X8dP+PYzhlleViJ8XFE/QtUJ5YfwNBSnjZAhULNh9VIyf191673gtNFsuuq2e/el78g+kMMiQfd6y32PByVV+7RuEJ3nHNiLdH60edNoNS67vW1yTYtVh6COIRH9jQd/1J1Ko/6uxKhCEA5DXf/+tf9liNEAa6daElJYBgvd/dHf/trpWA20inmJUZsHSopa05hVjJIveO/U09xOn8wqEP4lR/6EfJgPwg+Yx+ZLPICdLKMkF44zoBfln2y7AlFESlKRT4pcmgiF5PESmBUQLHQ48omca39/E0TWCJ3m4ltPhx7v2gfOv/aB88Azryf0FM/5A8PO63PTIxdfc995/O7YszUZ/MNw6zh8fIiJackDITYKf+CxK9mhscntICbqyMcOJgQyHUxfd19xyd1XjmjavOWI8y8npReYR9bKELf4OuDkhrseRSzjJliE+U0EgHnF9HIRKQDRgdNhpzA/BzJ2X3/nwy3adPC4iEhWA2Zut4/Wl86fecelp0795WvIygnSUqsEtrGuCM30gZRdh14eSSJzJpgKBOKr4vN4if4GC71uVyjQEUYkFyuNcBniuDhWTBecQSkmqYZFOgbyN0fPzp07kxAL/8OTEO34EiLSgXMEXcTuFT9Ja9a1lHypUnyt+QLUzrSHMk0H3Y1CGVLqxJVobNLa2g1kOs7eVCBfYrKVzxnbM62N4qyJcTDv4KLSol1TQE4AVSXfhIrPR/RX4K+kuCR7GfQI9jIYZ/6W3gX1JmpP7vMbvIzYmQCNWZxB7TK3b9TrOAcercdJTNbCqpJ9IA+7ZB/UOVN+OfeyG86+9Lqrbr//o1/mJjdpxlnBmpCIzPTyJ+/97+Ox/Dz28mvIbQJdKk21PM94QfKnRslNRn/03sufUPjzJ159PSmliccrTglgGK1wLvlg9GMdu/T8aurvr372YZuOaa4QWTk4BxUTeGi2wMJxsHegHrBaUlSQtT8jsZHFmqDZun41utPGqYLT5rC1PsYC4cnisZe1bRSZt39Upv1vUMfgEezIqnwS6jAZO16X5PaMyrQ2VHL4DFgHSeU+KWSAOVkuTBF2r8I6uE6Nt1vjwX67UgQehJ3Ehv1OENHJ+Z1D3pVOu3Cp8Hl8TVsnD7+w36cv/Dzx8zlTvp3/zqhvTrtkIDHmbpfgsQshltgsKc9ToacPVSVPixD+fH5wrjL3giCiC0EaGzfYWZGfRUgPLpF1OiJTnUqjpLbezU4lZq/urEWLREPAWUbm5cPkCtnvNK1nvwXTJ07/7Yc/xn8z5un7tqxfqVQp4NzBS+Wpux+cN23Gtx99PObpx6GgQ7nssJWHxGVxuZx22OmAv8tuvHPL+lWP33H33Kkzfvj0s1cefxSJEJEuVFgOa1Y2ad4KNp/ZU+a8+sTzq5csCCXZkBqlNPW63T9/8e608b+4XS4KO+02PnTvO7hLz/7P33/T1F9+/vzNdz4e8+z1//dQXIJByJo2kVhfNqDSCiLmEdFOpKNWa9o20hM/WfMLcdADs6Jh/A9JykzYdfiZU2q+01FvkReFPGfkJyOZWdQrb6hQzADZsIJaSZUTIl89CaaELRzhSdlL0reqU6NVKdVx2vj95TsEtWx4Oduw0uXuL2zetjFmNgSC0iI7GrP+p3Vjd0O12Wd4F4/Hu+D3VVtX7z71gn43PnKRyPPu9uVnF/Ud3hW6H8rkHihM69sWfjvAD/NeauuUDt1bChpVEfCqYHN22twDzuiOLz2qUnZJmoMMFsgE2PKzirHhYe0D8NgrC/NKqBMSuzAzQtAEFOrNdV1qjJouzOUOvSCq7BI32//fVIUhlJg16Sfc8c+74qaWbTogP/385ftnXHBFhy69OnTttXTuNHwd9+3e0T6te/d+p6A0zMncP3D4SOF1GQyi2ERR2bFr/8ZNm3XtPXDZ/BmLZk3BnbIt4tuAoSglseT1H3pGYqPGPp8HtvFd2zbOmfxrYnLjAcPPbMaT3foSlpDSrMW8qRMK83NOPefi8tJijgjUj1Pl4NPPydybPmvij3iE3nT3o1feeo/H5bPbym1lpUPOOE9kywoGs/Zl9Oh3SvPW7f/JcmdNiNf7a8FKd/BQ9U9dJVAH8xwJJ0+SDJDMAFY6uMsffPDBBtrxML+o1SxWPFcy5UqF5dV8vL48BpY3MgftjRQJZ9XNLq/PWpjt30aIW5jdwzgnvDE90AlKeDbgRSLTvcppvXCn9LgRFATbHCIXxjm2Dj5jP4KyFUwleBxfEtmSVFHVQckMG65aSZ2gY8VexLPYBREEqYcAOx4MkMAzJAlQEiwIM2SC7sHaqvfGn9buyjol0lVM+75CZ57XQDRbxdgxgPHPiwe2wQfy5ntuwDz6wyc/vPTwv8bNWd+oSSp6RSYduY0PmNywnyGdYXXDMier4vDbpCphwCM80YjJAFpBB3Xi3kLINmuE1hGdJwY5lhLDG38gYg/DG4+DmvyJB4krCFndkOA9SGKAHKY4qhWBfVodrYt10epwdQE7eRuE70yoAB3Q6fWoNA9jueNPZotV4XX2aWki+j/Mty66xYSK4J81AnXLly+X03FFt706WBvHQ+j3Lr/8cmgW6mD3TqguBT1S4STJ4JJK4YQ7oUZ2aDB8nywGyaWVEi+WlBG7O9bMpCCPzd8z3qkqLi9xhJM8OSRp/LVViJxah+8bsqdJZTFRVj8ePFD//XMII/+27VQuzF9le5Us/f/jT+JemM4EYLBa0p7a8kqzLr5mJrYarWzKtLvVporEmIwXGHt/9GMLZ0zu2nsAykCi60KM4Xe7HOJoIisqZXNpldP7V2Em/KCryD+nmjuh9AKiskN1hlI8i3/+be3/al2s9SHfk8PXWj4EVboASOrv3kRrMdRogEHlPhz5pYFAALLvjIwMoP6IWtdqrGjdfIQBIsnBNwT93kkixdbuQgQ9uQr7DEnhlxywG9duX2LQOl9wYY9USsazJV2dZhqyuUvm7fkpqPGHmFFPvJUQ8h/h7b0bjWwRXzupR8N8vbz+4Jr9doVWZCqRF0KkQtTg97EqY9d2hLBO3Xq1at8ZEaqqFCRhNljTxRALzXGWxlon5smabrtSe0c9H0EC98cff5A/5QQW7Diz8FaRFO2OO+5o1apu2VRq8Z2IedOOXZJnsYRqBq3XCbbHwkhHriPVQMlEzHhdv3Jt+5dl/h5UkN73IKl3Xe9x2P3DvkjmyDaG3mmNB4T9UK0VtLn9G7NdkkqLf4eMdviMoAlk7+XsJFz43e5a69zxNcxALPFWi+Ts0Lg2cU4cII42EFhsIJbD/z588+/xzUktPI1Il5qaOnLkyAacq9HZN7aX1P0k0kJq6pBHXBRmgBztRq2k7F0vcI7xNja36NP4TDRRJKWMwvDrTBVoU4kuaKZNqxc4x7SZdarOyVoi2siTIqsQ0Te6CIuzlQN+9RfnGIg1wWoIOGod544FdYg7l112GUggM+DUmdc4ah0BwgmrIJQQx8uoVdpQUZgzYO4W9KdJccaYMJWH2YfoFgPnCBz0tZfMPaJbcUxraxHfsUfycFxFBOHACfEtRyrCRTNJ2bJn6vCYTl10K08wadolCB0bwXYnzH4bn5ig8tg6N6lleU5eqWMdq8kGeckll4B2h1kyo7vGtVIb8hxRdM2aNQPOT0hDRa3MakSNKiz9JE87siZI9X2TFV6JSsmKK0oryVL/PJvaJfXsENcfs1aITCCiNaxzhZHn4hJM1kBqv2Z11Pf1GFOWYtG1jRfOIHhO1ne0Y1MN4Zy9a1MTjBV14UU5PK7usD4Rbcad/Px8Iu1OGEjgNYILqnnz5tdddx2/68IynKR90DWX3H5JVSBOXH95k9ezqQAb0P7ptUF/J0U9xDl5thuZUiHTy3fuQ7arSC5VzxZCpAhRkpYzRd1uQPOz6+lmRbosgzpY4sStX4Rm19NRoBG0xMcbgs4uTQy1Ei1+xFe3amPJmWeeiRc+st2JYbQD53iByG2NPNfAv1r725m5t6QaIAlmO2LM65tMQYcxzgka5V6KuPqdOrVtYve+jc9WBEGLCBKp1P77I/dAJClWkyS6hb5b39Qz6kqvqtWP5Dhtp0ZKKeC1xCdUq4JafUhIEZo4i9UsOdMa66EgrNXe/K3xqqGO4hdddBEUP0h49R3t5GAUcA787tevX91ZhpO6J8bOkn64yKWSUN+yj8abCKGWdEMkc/cTYAWbW9sPbn6hNmhAB1i/5An40PH06xg3sHvjISfAQiSYtF0bq5U+pzUxoR5FQAlDo8lEmES80tmpsaGuvULhBmPabLbvvvtu8+bNeXl59TT8oALnhg4devHFF58AX4kTagjeYsmxRDKUSiUOEYdQxy8kOatRcpkkwymSNrmOdzai7jk85evzFpQEs5zlJK701/FoEPZTs9WocOt6Nzm9cVzLiEZaxwv7/IFdBe5yv94NiburHsQ+kgTa67a3S9KkxNVFfspwoY7XAoLyH3/8ccOGDTk5OfUO7WS9JZa5ESNGnHPOOXX8LT85uxcM+BSOVVJwh3AFDCUUrqOXUYcfSjDQWmEaJEFRdCJeW/NXbC9codapHOV1dyHIH6bVaxOVzXo0PtWorW8qgfBemwMl7r3Ffq3eUF5WWkc1/CRW1OlgsFN7nR2StUbiSuvkFQHU0X+32/3LL7+sWbMmMzMzUkbjWhw+/pZgM3pLQujOOKN+q/JrcRprqGlXuuReKxk8UplTgrSsDmn78bRUhZJ+ESfeWzLU6Rwcx79YOeUZm/OXeNQ20jGTjvL4K4xiDWw+Josh6FF2SOjbIal3FGuug1WVOny7izw+tdHtdJJqsk71EL0xljmPy9HUomyVoCN5d53qXuXORAZ1PEkqz99++23lypWgHaa7uq9KppMGgwEevgsuuGDIkBNBlV9nX6aodSzgkhzrJPc2yagWgBdeAv6otX7EinCYFiDnlTQdJSOuNLVAuBXbAR6pdrL27ixas6t4rUqndJQ76wK3F7oZg0lHMuimxvZpjQbG1eHkllFcL3JUZpd59xV7tUaTrbwcEtQoVl7tqgxGM1lpzCpfy3hVzVOtRtrtiKFObmD+/PkzZszIzs52OqG1raMSK0pLvpxEB5IM5dJLLyVaPNLZaShfmzPgzZPsqyVlnnBxtLlqDfAEjbhetB5IChp6K7SptTkntdF2iatgW8GKHHu6Vqd12F0Q8dRGL0SiYXwskRuMioROif1TLXWF8LbGZsPp8e8r8eWV+wwmE4lUagvwxEIYoCDXKv2u5hZlY0tdtMz9c1GqCXVUBNfPhAkTkO0KCwvroDJTTlSNMNelSxcC4Rs1alRjb2RDQ1GcgaBnn+TcpAjkifA1AK8m91kgFpBDdxdsFDR0U+haR3Fc9a6qAkfmzqJ1WWXpCFWoNGGPq7EhsL3oTZDXSAYpvn1irxaWjjDL1Fjrda2hMpcvu8yfV+6FWM5pt0N9UGM9FAthMCHJKX2uVIs6JU6tFpE29eOqPtQxPkLLp0yZAubhqIIZr46Id7IwZ7FYyPs1fPhw4grgDKwfq9HQy6PNgPuA5Noi+bIkk0by+CR4qGOn1USM02slnUayuyVNalDXWaFrSAV+cGHy7ZnpRetz7Rlaowg2dzu9sdNqclQNOZ5oECWTjc3aJfZMNberay7stfWVLXf5M0s8hc6AVm+CfI6EmbGLBAstBKR4BoyFxJGmWjTJZkLm6q5Z7oiLclxQR4286KtXr545cyZoB/Jxp3atd6w3NISEALZt2xZPy3bt2tXWu9jQbvRnwF8seTIkV4YULBGx22Ae3Aj+KNGak9wLeIPgzOkJSlYhw/GjSoz+KOp/jTZPcVb57gOlO4tdeUazHqcVqFnFVhuNHAAIDcSD8wOU6iRzalzbZpb2iYY6zYhUW0vq8gYK7f58m7fE4YX7FJUmIoff541KXjFEZ1TWnDUEj6vPnWRUJcdprHowrp6BnLw6xwt1ci2QuEL6s2LFCvKHcdUK4PFNI90XkhzGOTnDC5hXW69gQ7sxnAFIJn25kmdv0JGpCJRI5lC+EjAPlRp0kdBeh5N1ha+rSsG5TDhVohrlKbzqVfGSIVUgnLqxSODScFUxA8FCR3ZmeXpe+YEyd5HWwN6oBJ/QbXIChn88nA2XdVBwOuZRtUA4HrGXOa2GpEamZs0s7ZKNzVXKE4p1IUbvFEJevs1T6gzYXD6NHkOaxuf1+nywwHMFKnhZj9U6zpRiAflPBZMqns+QKhg0SqtRk2hQJZhYn3qJcBVDjg7UydWlp6cvXrx406ZNZWVlNQZ4sroSkINvCJLVwYMHA3LJySdUVG+Mvh4nQrX+EsmbF3RlSr5Chd8hwXKOUUfkjz7IYS3GKIsaFd9TPrC/Ehxt90hKOHeMkjpJ0qdK6pSgKr6enlhrfSnZZosc2RDglbry7O5yb8CtNbDfqkNq5tACVBb4Dq6FmGycXBw2t1at06uNCYYUEC7J2NSiS1Iq6o0RqNYnv3IHkPNs7kCR3WPzBB1uny8QRCyD+k4cOw5+Jw4TvYWMFiIiD9htZRpOFkp8jTVxepXVoDJpgb76jXAxgTq50j179ixatGjjxo04ZwJ4Hg+sj+K8EPUXQjYSEEhgtVrNZjMg179//5SUlKg31FBhfZgBPCTdkt8u+cukgE3y2aWgVwr6JOIWFPJWS35HnUKhkfhRmySlWVLFSSqzpNAiVtSHAdabPnr8boe33OEpFb+95V6/xxfwYtcLIjuLpVBoVXo1PNsqnU5tNGksRq3FpLXoVCK5V70ZZH3oqM8fdPsDTk/A7Qu6kLYDQe4AfkFJGRK4AwAbQMZvfvQahV6jQoxDzVHv7HDhrEY0pbrK7QF469evJ7UKEl55eTl5xWSrKbB3PAdnObkX9eBpggwHEQ8JUHr16tW1a9cGH8tw1ruhTMMMNMxAwwychDMQK6iTp5JcYjt27ADzdu3ahZCHhAfmYTit8NqqDHuVP1do+Sur+9FSwhNrMpkwwgFskKR369atdevWdcTz8yR8exqG3DADDTPQMAP1YgZiC3UVUwDC7d+/f+/evSBfVlaWF30G1mufD/Djt7CcBgJ8kIFNWEdDFx+Q3gA2+TNiXMuWLXGqbNOmTZMmTXB/rRdT3NDJhhlomIGGGWiYgdqdgRqCusqDRNQD+Qg8LygoID6BD9xxuVzcrFBOopnECIcAh+0Np0p+x8fH80+kutqdr4bWG2agYQYaZqBhBurdDPw/8nMaPsT7HxAAAAAASUVORK5CYII=\"></p>\n<p>Equation 1: Total MSW generated</p>\n<ol>\n  <li>Total MSW collected</li>\n</ol>\n<p>When measuring total MSW collected, there is a risk of double counting, concerning the residue or rejects from recovery facilities and the amount of waste recovered from disposal facilities going to recovery. Therefore, these amounts need to be deducted from the sum of waste received by both recovery and disposal facilities. It is assumed residue of recovery facilities is going to disposal facilities or other recovery facilities. Steps 4 and 5 in <strong><em>Waste Wise Cities Tool &#x2013; Step by Step Guide to Assess a City&#x2019;s MSMW Performance</em></strong> <strong><em>through SDG indicator 11.6.1 Monitoring</em></strong> provide detailed methodology on how to collect this data if not available.</p>\n<p><img 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\"></p>\n<p>Equation 2: Total MSW<sup><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></sup> collected</p>\n<ol>\n  <li>Total MSW managed in controlled facilities</li>\n</ol>\n<p>MSW managed in controlled facilities is MSW collected and transported to recovery and disposal facilities with basic control or above according to the <a href=\"#_Control_level_of\"><u>control ladder</u></a>. Steps 4 and 5 in <strong><em>Waste Wise Cities Tool &#x2013; Step by Step Guide to Assess a City&#x2019;s MSMW Performance</em></strong> <strong><em>through SDG indicator 11.6.1 Monitoring</em></strong> provide detailed methodology on how to collect this data if not available.</p>\n<p><img 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\"></p>\n<p>Equation 3: Total MSW managed in controlled facilities</p>\n<p><strong>Additional data points</strong></p>\n<p>The SDG indicator 11.6.1 assessment provides information for the calculation of three more very relevant MSW management data points. Although they are not necessary for the calculation of the SDG indicator, these figures are of interest for city authorities:</p>\n<ol>\n  <li>Per capita MSW generation rate</li>\n  <li>MSW composition</li>\n  <li>Uncollected waste</li>\n  <li>Per capita MSW generation rate</li>\n</ol>\n<p>A very relevant parameter that can be derived from the previous formula is the &#x201C;total per capita MSW generation rate&#x201D;. Steps 2 and 3 in <strong><em>Waste Wise Cities Tool &#x2013; Step by Step Guide to Assess a City&#x2019;s MSMW Performance</em></strong> <strong><em>through SDG indicator 11.6.1 Monitoring</em></strong> explain how to calculate this through waste sampling from households, if no reliable or updated data is available. Particularly for cities where a large amount of MSW remains uncollected, it is recommended to sample the waste from households, as provided by the Waste Wise Cities Tool.</p>\n<ol>\n  <li>MSW composition</li>\n</ol>\n<p>The SDG indicator 11.6.1 assessment determines the waste composition at the point of generation (i.e. households) and at the point of disposal. Understanding MSW composition at the beginning and end of the MSW service chain is a useful exercise for several reasons; Understanding composition helps identifying how the existing recovery/recycling sector is functioning, it enables further recovery facilities to be identified and planned, and overall assists triangulation (i.e. test validity and reliability) of data collected.</p>\n<p>Note that MSW also includes waste from non-household sources. In Step 3 of <strong><em>Waste Wise Cities Tool &#x2013; Step by Step Guide to Assess a City&#x2019;s MSMW Performance</em></strong> <strong><em>through SDG indicator 11.6.1 Monitoring</em></strong>, the quantities of MSW generated from commercial and institutional sources, as well as from public spaces, is estimated. However, specific composition analysis on MSW from non-household sources is beyond the scope of this tool as it is complex and resource intensive.</p>\n<ol>\n  <li>Total uncollected waste</li>\n</ol>\n<p>Total uncollected MSW can be calculated by subtracting the total MSW regularly collected from the total MSW generated.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> Note that MSW collected for recovery includes mixed MSW, commingled recyclables or recoverable fractions extracted from MSW. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>As part of the validation process, UN-Habitat developed a template to compile data generated by countries through the NSOs as well as other government agencies responsible for official statistics (<a href=\"https://data.unhabitat.org/pages/guidance\">https://data.unhabitat.org/pages/guidance</a>). Data compiled is then checked against several criteria including the data sources used, the application of internationally agreed definitions, classification and methodologies to the data from that source, etc. Once reviewed, appropriate feedback is then provided to individual countries for further discussion.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Any adjustment to the data is jointly agreed after consultations with the relevant national agencies that share the data points for reporting.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Missing values may arise at the reporting of the city level estimates. At the national level, estimates will be derived by relevant national entities from the nationally representative sample of cities, in which case there will be very few missing entries.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Regarding promoting data quality assurance through the collection of data via the UNSD/UNEP Questionnaire on Environment Statistics, UNSD carries out extensive data validation procedures that include built-in automated procedures, manual checks and cross-references to national sources of data. Communication is carried out with countries for clarification and validation of data. UNSD does not make any estimation or imputation for missing values so the number of data points provided are actual country data. Only data that are considered accurate or those confirmed by countries during the validation process are included in UNSD&#x2019;s environment statistics database and disseminated on UNSD&#x2019;s website.</p>", "REG_AGG__GLOBAL"=>"<p>Data at the global/regional levels will be estimated from national figures derived from a weighted aggregation of performance for all cities/urban areas or a sample of nationally representative cities (selected using the national sample of cities approach developed by UN-Habitat). Weighting for regional and global averages is done using urban population sizes from the World Urbanization Prospects. Global monitoring will be led by UN-Habitat with the support of other partners and regional commissions.</p>", "DOC_METHOD__GLOBAL"=>"<p>It is recommended to establish a system where SDG 11.6.1 data is collected at the municipal level using Waste Wise Cities Tool, consolidated at prefecture or province level then further consolidated at national level. This process can be led by Ministry of Environment or any other national agency with environmental control and protection mandate.</p>\n<p>UN-Habitat&#x2019;s<em> </em><strong><em>Waste Wise Cities Tool &#x2013; Step by Step Guide to Assess a City&#x2019;s MSMW Performance</em></strong> <strong><em>through SDG indicator 11.6.1 Monitoring</em></strong> provides the step-by-step guide for cities to collect relevant parameters necessary to estimate SDG 11.6.1. This also can be utilized as an assessment tool for the environmental performance of city&#x2019;s solid waste management. The ministries and agencies responsible for environmental protection and waste management is recommended to actively promote and disseminate this tool to collect the fact-based waste data for the policy formulation and infrastructure development for sustainable waste management. The guidance on implementation of the National Sample of Cities Approach is available here: <a href=\"https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf\">https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf</a>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>To ensure consistency in data production across countries, UN-Habitat has developed detailed step-by-step tutorials on the computation of indicator 11.6.1, which further explain the steps presented in this metadata. The detailed tutorials, which will be continuously updated are available at <a href=\"https://unhabitat.org/knowledge/data-and-analytics\">https://unhabitat.org/knowledge/data-and-analytics</a>, <a href=\"https://www.urbanagendaplatform.org/learning\">https://www.urbanagendaplatform.org/learning</a>, and <a href=\"https://data.unhabitat.org/\">https://data.unhabitat.org/</a>.</p>\n<p>Within its Data and Analytics Section which is responsible for the indicator data compilation, UN-Habitat has a team of data experts who check all submitted data and provide direct support to countries in the indicator computation in collaboration with the Agency&#x2019;s waste management experts. As part of its global custodianship of indicator 11.6.1, UN-Habitat has also worked closely with relevant UN agencies such as UNSD and UNEP, as well as prominent waste management experts and environmental statisticians from all over the world. This helped create a common understanding on the approach for the indicator computation, and to encourage continuous production of high-quality global data that responds to the indicator computation needs.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>As custodian agencies, we provide national and local level support to data collection and share global tools for data collection with municipalities so that the data is correctly captured. Municipalities are advised to share their data with one national entity for national level compilation before the data is sent to the custodian agencies for consolidation in the global tables.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Once data is received from member states, UN-Habitat uses a checklist specific to each indicator to a) assess whether the data production process followed the metadata provisions, and b) confirm the accuracy of the data sources used for the indicator computation. Both components are captured in the reporting template shared with NSOs, which helps to assess whether computation was done using the proposed indicator inputs or proxies. The reporting template also requests for information that helps understand whether national data for the indicator was produced from a representative sample of the country&#x2019;s urban systems, or if estimates were done for only select cities/urban areas where data is easily available. In addition, the received data is also checked for other qualities such as data disaggregation, reporting period and consistency with other previously reported trends, which ensures reliable regional estimates.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>MSW data is available through What a Waste 2.0 by World Bank (World Bank, 2018), the UNSD/UNEP Questionnaire on Environment Statistics and UN-Habitat CPI. These have key MSW data such as MSW generation, MSW generation rate, MSW collection rate, etc., but the aspect of &#x201C;controlled management&#x201D; is missing.</p>\n<p>The UNSD/UNEP Questionnaire on Environment Statistics has collected data on municipal waste collection and treatment for about 20 years. The Questionnaire has been sent to more than 160 countries, covering both national and city levels. However, the response rate for the UNSD/UNEP questionnaire is hovering around 50% and data completeness and quality remain a challenge, especially for developing countries.</p>\n<p>For those variables relevant to this indicator which are collected via the UNSD/UNEP Questionnaire, data for up to 120 cities are available in some years (municipal waste collected), though for other relevant variables, for a given year, data for 30 to 60 cities may be available. In the case of the variable, municipal waste generated (which was only collected for the first time in 2018), data are available for 20 cities. More details on the availability of data obtained from the UNSD/UNEP Questionnaire can be found in the <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/2020-33-EnvironmentStats-E.pdf\"><u>Report of the Secretary-General on Environment Statistics</u></a><sup><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></sup> (Part C) and the Background Report<sup><sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup></sup> (Part 1) submitted to the fifty-first session of the Statistical Commission (New York, 3-6 March 2020). Data received via the UNSD/UNEP Questionnaire have been published on the UNSD website in the form of indicator tables (UNSD Indicator Tables (waste)) (<a href=\"https://unstats.un.org/unsd/envstats/qindicators\"><u>https://unstats.un.org/unsd/envstats/qindicators</u></a>) as well as in country files (<a href=\"https://unstats.un.org/unsd/envstats/country_files\"><u>https://unstats.un.org/unsd/envstats/country_files</u></a>).</p>\n<p>In parallel with the effort to establish a global data reporting outlet establishment according to the SDG indicator 11.6.1, training and capacity development on data production and data quality improvement both for at national and local government level is essential to accelerate the progress towards the achievement of this SDG. UN-Habitat is providing capacity development and trainings through both offline and online for cities and municipalities and NSIs for them to apply the Waste Wise Cities Tool. </p>\n<p><strong>Time series:</strong></p>\n<p>The indicator can be updated annually or biennially depending on the data source stated above. Data is sporadically available on an annual basis in the UNSD Indicator Tables (waste) (<a href=\"https://unstats.un.org/unsd/envstats/qindicators\"><u>https://unstats.un.org/unsd/envstats/qindicators</u></a><u>).</u></p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data for this indicator can be disaggregated at various levels in accordance with the country&#x2019;s policy information needs. For instance:</p>\n<ul>\n  <li>Disaggregation by location (intra-urban)</li>\n  <li>Disaggregation by source of waste generation e.g. residential, industrial, office, or MSW material received by recovery facilities</li>\n  <li>Disaggregation by type of final treatment and disposal</li>\n  <li>MSW generation rate of different income level (high, middle, low)</li>\n  <li>MSW generation rate in different cities</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/2020-33-EnvironmentStats-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/2020-33-EnvironmentStats-E.pdf</a> <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-item-4e-EnvironmentStats-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-item-4e-EnvironmentStats-E.pdf</a> <a href=\"#footnote-ref-6\">&#x2191;</a></p></div></div>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Data on formal Municipal solid waste collection and management may be available from municipal bodies and/or private contractors. Informal collection data may be available from NGOs and community organizations. It is important that all data sources are used for reporting, otherwise discrepancies in forms and guides used are likely to introduce inconsistencies in reported figures. Discrepancies are also likely to arise where geographical jurisdictions are not well marked out for service providers and facilities that manage collected waste.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>[1]: Waste Wise Cities, UN-Habitat: <a href=\"https://unhabitat.org/waste-wise-cities\"><u>https://unhabitat.org/waste-wise-cities</u></a></p>\n<p><u>[2] : National Sample of Cities Approach, UN-Habitat : https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf</u></p>\n<p><strong>References:</strong></p>\n<ol>\n  <li>Jambeck et al (2015). <em>Plastic waste inputs from land into the ocean.</em> Science 13 Feb 2015: Vol. 347, Issue 6223, pp. 768-771.</li>\n</ol>\n<ul>\n  <li>GIZ, University of Leeds, Eawag-Sandec, Wasteaware (2020). <em>User Manual: Waste Flow Diagram (WFD): A rapid assessment tool for mapping waste flows and quantifying plastic leakage.</em> Version 1.0. Principal Investigator: Velis C.A. Research team: Cottom J., Zabaleta I., Zurbruegg C., Stretz J. and Blume S. Eschborn, Germany. Obtain from: <a href=\"http://plasticpollution.leeds.ac.uk\"><u>http://plasticpollution.leeds.ac.uk</u></a><u>.</u></li>\n</ul>\n<ol>\n  <li>UN Environment (2015). <em>Global Waste management Outlook.</em></li>\n  <li>Wilson et al. (2015). <em>&#x201C;Wasteaware&#x201D; benchmark indicators for integrated sustainable Waste management in cities.</em> Waste Management 35, 329-342.</li>\n  <li>Wilson et al (2014). <em>User Manual for Wasteaware ISWM Benchmark Indicators</em> Supporting Information to: Wilson et al., 2014 &#x2013; doi: 10.1016/j.wasman.2014.10.006.</li>\n  <li>World Bank (2018). <em>What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050.</em></li>\n  <li>UN-Habitat (2010). <em>Solid Waste Management in World Cities.</em></li>\n  <li>Framework for the Development of Environment Statistics (FDES) (<a href=\"https://unstats.un.org/unsd/environment/FDES/FDES-2015-supporting-tools/FDES.pdf\"><u>https://unstats.un.org/unsd/environment/FDES/FDES-2015-supporting-tools/FDES.pdf</u></a>).</li>\n  <li>Manual on the Basic Set of Environment Statistics (<a href=\"https://unstats.un.org/unsd/envstats/fdes/manual_bses.cshtml\"><u>https://unstats.un.org/unsd/envstats/fdes/manual_bses.cshtml</u></a>): Generation and Management of Waste (<a href=\"https://unstats.un.org/unsd/environment/FDES/MS_3.3.1_3.3.2_Waste.pdf\"><u>https://unstats.un.org/unsd/environment/FDES/MS_3.3.1_3.3.2_Waste.pdf</u></a>).</li>\n  <li>UNSD/UNEP Questionnaire on Environment Statistics (waste section) (<a href=\"https://unstats.un.org/unsd/envstats/questionnaire\"><u>https://unstats.un.org/unsd/envstats/questionnaire</u></a>).</li>\n</ol>\n<p>UNSD Indicator Tables (waste) (<a href=\"https://unstats.un.org/unsd/envstats/qindicators\"><u>https://unstats.un.org/unsd/envstats/qindicators</u></a>).</p>", "indicator_sort_order"=>"11-06-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.6.2", "slug"=>"11-6-2", "name"=>"Niveles medios anuales de partículas finas en suspensión (por ejemplo, PM2.5 y PM10) en las ciudades (ponderados según la población)", "url"=>"/site/es/11-6-2/", "sort"=>"110602", "goal_number"=>"11", "target_number"=>"11.6", "global"=>{"name"=>"Niveles medios anuales de partículas finas en suspensión (por ejemplo, PM2.5 y PM10) en las ciudades (ponderados según la población)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Partículas en suspensión", "value"=>"PM 2,5"}, {"field"=>"Partículas en suspensión", "value"=>"PM 10"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Niveles medios anuales de partículas finas en suspensión ($PM_{2,5}$ y $PM_{10}$) en las ciudades (ponderados según la población)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Niveles medios anuales de partículas finas en suspensión (por ejemplo, PM2.5 y PM10) en las ciudades (ponderados según la población)", "indicator_number"=>"11.6.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/web01-s2ekono/es/contenidos/informacion/estatistika_ing_090203/es_def/index.shtml", "url_text"=>"Estadística de calidad del aire", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Niveles medios anuales de partículas finas en suspensión ($PM_{2,5}$ y $PM_{10}$) en las ciudades (ponderados según la población)", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.6- De aquí a 2030, reducir el impacto ambiental negativo per cápita de las ciudades, incluso prestando especial atención a la calidad del aire y la gestión de los desechos municipales y de otro tipo", "definicion"=>"Concentración media de partículas en suspensión de tamaño inferior a 10 micras ($PM_{10}$) o 2,5 micras ($PM_{2,5}$) en los municipios de más de 50.000 habitantes ponderada según la población", "formula"=>"\n$$CMPM^{t} = \\frac{\\sum_{m \\in M^{t}}CMPM_{m}^{t}}{\\sum_{m \\in M^{t}}P_{m}^{t}} $$ \n\ndonde: \n\n$M^{t} $ conjunto de municipios de más de 50.000 habitantes donde existen estaciones de vigilancia que participan en la evaluación de la calidad del aire en el año $t$\n\n$CMPM_{m}^{t} $ concentración media de partículas en suspensión en el municipio $m$ en el año $t$\n\n$P_{m}^{t}$ población del municipio $m$ en el año $t$\n", "desagregacion"=>"Tamaño de las partículas en suspensión: $PM_{10}$ y $PM_{2,5}$\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La contaminación del aire está compuesta por numerosos contaminantes, entre ellos, \npartículas en suspensión. Estas partículas pueden penetrar profundamente en las vías \nrespiratorias y, por lo tanto, constituyen un riesgo para la salud al \naumentar la mortalidad por infecciones y enfermedades respiratorias, cáncer de \npulmón y determinadas enfermedades cardiovasculares.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.6.2&seriesCode=EN_ATM_PM25&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=CITY\"> Niveles medios anuales de partículas finas (ponderados por población), en ciudades (microgramos por metro cúbico de PM2,5) EN_ATM_PM25</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-06-02.pdf\">Metadatos 11-6-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"Niveles medios anuales de partículas finas en suspensión ($PM_{2,5}$ y $PM_{10}$) en las ciudades (ponderados según la población)", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.6- De aquí a 2030, reducir el impacto ambiental negativo per cápita de las ciudades, incluso prestando especial atención a la calidad del aire y la gestión de los desechos municipales y de otro tipo", "definicion"=>"Average concentration of suspended particles smaller than 10 microns ($PM_{10}$) or  2.5 microns ($PM_{2,5}$) in municipalities with more than 50,000 inhabitants weighted  according to population ", "formula"=>"\n$$CMPM^{t} = \\frac{\\sum_{m \\in M^{t}}CMPM_{m}^{t}}{\\sum_{m \\in M^{t}}P_{m}^{t}} $$ \n\nwhere: \n\n$M^{t} $ = set of municipalities with more than 50,000 inhabitants where there are monitoring stations that participate in the evaluation of air quality in year $t$\n\n$CMPM_{m}^{t} $ = average concentration of suspended particles in the municipality $m$ in year $t$\n\n$P_{m}^{t}$ = population of the municipality $m$ in year $t$\n", "desagregacion"=>"Size of suspended particles: $PM_{10}$ y $PM_{2,5}$\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Air pollution consists of many pollutants, among other particulate matter. \nThese particles are able to penetrate deeply into the respiratory tract and \ntherefore constitute a risk for health by increasing mortality from respiratory \ninfections and diseases, lung cancer, and selected cardiovascular diseases. \n\nSource: United Nations Statistics Division \n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.6.2&seriesCode=EN_ATM_PM25&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=CITY\"> Annual mean levels of fine particulate matter (population-weighted), by location (PM2.5 micrograms per cubic meter) EN_ATM_PM25</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-06-02.pdf\">Metadata 11-6-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Niveles medios anuales de partículas finas en suspensión ($PM_{2,5}$ y $PM_{10}$) en las ciudades (ponderados según la población)", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.6- De aquí a 2030, reducir el impacto ambiental negativo per cápita de las ciudades, incluso prestando especial atención a la calidad del aire y la gestión de los desechos municipales y de otro tipo", "definicion"=>"Concentración media de partículas en suspensión de tamaño inferior a 10 micras ($PM_{10}$) o 2,5 micras ($PM_{2,5}$) en los municipios de más de 50.000 habitantes ponderada según la población", "formula"=>"\n$$CMPM^{t} = \\frac{\\sum_{m \\in M^{t}}CMPM_{m}^{t}}{\\sum_{m \\in M^{t}}P_{m}^{t}} $$ \n\nnon: \n\n$M^{t} =$ 50.000 biztanletik gorako udalerriak, airearen kalitatearen ebaluazioan parte hartzen duten zaintza-estazioak dituztenak $t$ urtean\n\n$CMPM_{m}^{t} =$ partikula esekien batez besteko kontzentrazioa $m$ udalerrian $t$ urtean\n\n$P_{m}^{t} =$ biztanleria $m$ udalerrian $t$ urtean\n", "desagregacion"=>"Partikula esekien tamaina: $PM_{10}$ y $PM_{2,5}$\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La contaminación del aire está compuesta por numerosos contaminantes, entre ellos, \npartículas en suspensión. Estas partículas pueden penetrar profundamente en las vías \nrespiratorias y, por lo tanto, constituyen un riesgo para la salud al \naumentar la mortalidad por infecciones y enfermedades respiratorias, cáncer de \npulmón y determinadas enfermedades cardiovasculares.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.6.2&seriesCode=EN_ATM_PM25&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=CITY\"> Partikula finen urteko batez besteko mailak (populazioaren arabera haztatuak) hirietan (PM2,5 metro kubikoko mikrogramoak) EN_ATM_PM25</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-06-02.pdf\">Metadatuak 11-6-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g. PM<sub>2.5</sub> and PM<sub>10</sub>) in cities (population weighted)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_ATM_PM25 - Annual mean levels of fine particulate matter (population-weighted), by location [11.6.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>3.9.1: Mortality rate attributed to household and ambient air pollution</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The mean annual concentration of fine suspended particles of less than 2.5 microns in diameters (PM<sub>2.5</sub>) is a common measure of air pollution. The mean is a population-weighted average for urban population in a country, and is expressed in micrograms per cubic meter [&#xB5;g/m<sup>3</sup>].</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Micrograms per cubic meter [&#xB5;g/m<sup>3</sup>]</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The PM<sub>2.5</sub> concentrations are geographically classified according to the 2021 United Nations Statistics Division (UNSD) Degree of Urbanization classification: cities, towns and rural areas. Data is also provided for urban (aggregation of cities and towns) and all (aggregation of cities, towns and rural) areas.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Sources of data include ground measurements from monitoring networks, collected for 6,000 cities and localities (WHO, 2022) around the world, satellite remote sensing, population estimates, topography, information on local monitoring networks and measures of specific contributors of air pollution (WHO, 2022).</p>", "COLL_METHOD__GLOBAL"=>"<p>Data collection process for ground measurements include official reporting from countries to WHO (after request), and web searches. Measurements of PM<sub>10</sub> or PM<sub>2.5</sub> from official national/sub-national reports and websites or reported by regional networks such as Clean Air Asia for Asia and the European Environment Agency for Europe or data from UN agencies, development agencies, articles from peer reviewed journals and ground measurements are compiled in the framework of the Global Burden of Disease Project.</p>", "FREQ_COLL__GLOBAL"=>"<p>Ongoing</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The global database for indicator 11.6.2 is released every 2 to 3 years</p>", "DATA_SOURCE__GLOBAL"=>"<p>Ministry of Health, Ministry of the Environment</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Health Organization (WHO)</p>", "INST_MANDATE__GLOBAL"=>"<p>The World Health Organization (WHO) is the Custodian Agency or co-Custodian Agency for reporting on several SDG indicators, including indicator 11.6.2, annual mean levels of fine particulate matter (e.g. PM<sub>2.5</sub> and PM<sub>10</sub>) in cities (population weighted)).</p>", "RATIONALE__GLOBAL"=>"<p>Air pollution consists of many pollutants, among other particulate matter. These particles are able to penetrate deeply into the respiratory tract and therefore constitute a risk for health by increasing mortality from respiratory infections and diseases, lung cancer, and selected cardiovascular diseases.</p>", "REC_USE_LIM__GLOBAL"=>"<p><strong>Urban/rural data:</strong> while the data quality available for urban/rural population is generally good for high-income countries, it can be relatively poor for some low- and middle income areas. Furthermore, the definition of urban/rural may greatly vary by country.</p>", "DATA_COMP__GLOBAL"=>"<p>The annual urban mean concentration of PM<sub>2.5</sub> is estimated with improved modelling using data integration from satellite remote sensing, population estimates, topography and ground measurements (WHO, 2016; Shaddick et al, 2016).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Draft estimates are reviewed with Member States through a WHO country consultation process and SDG focal points every time new data are generated. In addition, the methods and data are published in a peer-reviewed journal.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Missing values are left blank.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Missing values are excluded from the regional and global averages.</p>", "REG_AGG__GLOBAL"=>"<p>The regional and global aggregates are population-weighted figures of the national estimates.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>C</mi>\n      </mrow>\n      <mrow>\n        <mi>a</mi>\n        <mi>g</mi>\n        <mi>g</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <msub>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </msub>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>C</mi>\n              </mrow>\n              <mrow>\n                <mi>n</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mo>,</mo>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n            <mo>&#x2219;</mo>\n            <msub>\n              <mrow>\n                <mi>P</mi>\n              </mrow>\n              <mrow>\n                <mi>n</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mo>,</mo>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mrow>\n          <munder>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>i</mi>\n            </mrow>\n          </munder>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>P</mi>\n              </mrow>\n              <mrow>\n                <mi>n</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mo>,</mo>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where:</p>\n<ul>\n  <li>C<sub>agg</sub> is the regional/global estimate,</li>\n  <li>C<sub>nat</sub> is the national estimate,</li>\n  <li>P<sub>nat</sub> is the country population.</li>\n  <li>The sum is done over the countries <em>i</em> in the region (regional aggregate) or all countries (global aggregate).</li>\n</ul>", "DOC_METHOD__GLOBAL"=>"<p>Countries which have air quality monitoring networks in place in urban areas can use the annual mean concentrations from the ground measurements and the corresponding number of inhabitants to derive the population-weighted exposure to particulate matter in cities.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>For information on data quality management, assurance, and assessment processes at WHO, please refer to: <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a></p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data inputs to the model are official or published data on air quality or other relevant topics. Modelled estimates are carefully crossed-checked and compared with official ground measurements.</p>\n<p>Consultation/validation process with countries for adjustments and estimates. Data inputs, methods and final estimates are shared with countries prior to publication via WHO official communication channels with WHO Member States.</p>\n<p>https://www.who.int/teams/environment-climate-change-and-health/air-quality-and-health</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>For information on data quality management, assurance, and assessment processes at WHO, please refer to: <a href=\"https://www.who.int/data/ddi\">https://www.who.int/data/ddi</a></p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The indicator is available for 232 countries. Missing countries include mostly Small State Islands in the Western Pacific and in the Latin American and the Caribbean regions.</p>\n<p><strong>Time series:</strong></p>\n<p>The indicator provides estimates from 2010 to most recent reporting period. Previous data estimates are updated with when there have been changes in modelling method and input data.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator is available by 0.1&#xB0; x 0.1&#xB0; grid size for the world. National, regional and global data are disaggregated into cities, towns, urban and rural areas. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p><strong>The source of differences between global and national figures:</strong> Modelled estimates versus annual mean concentrations obtained from ground measurements.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>[1]: https://www.who.int/data/gho/data/themes/air-pollution</p>\n<p><strong>References:</strong></p>\n<ul>\n  <li>Shaddick G et al (2016). <em>Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution. Royal Statistical Society</em>, arXiv: 1609.0014.</li>\n  <li>WHO (2016). <em>Ambient air pollution: a global assessment of exposure and burden of disease</em>, WHO Geneva.</li>\n  <li>WHO (2022). <em>WHO Urban ambient air quality database</em>, WHO Geneva.</li>\n</ul>", "indicator_sort_order"=>"11-06-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.7.1", "slug"=>"11-7-1", "name"=>"Proporción media de la superficie edificada de las ciudades que se dedica a espacios abiertos para uso público de todos, desglosada por sexo, edad y personas con discapacidad", "url"=>"/site/es/11-7-1/", "sort"=>"110701", "goal_number"=>"11", "target_number"=>"11.7", "global"=>{"name"=>"Proporción media de la superficie edificada de las ciudades que se dedica a espacios abiertos para uso público de todos, desglosada por sexo, edad y personas con discapacidad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción media de la superficie edificada de las ciudades que se dedica a espacios abiertos para uso público de todos, desglosada por sexo, edad y personas con discapacidad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción media de la superficie edificada de las ciudades que se dedica a espacios abiertos para uso público de todos, desglosada por sexo, edad y personas con discapacidad", "indicator_number"=>"11.7.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción media de la superficie edificada de las ciudades que se dedica a espacios abiertos para uso público de todos", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.7- De aquí a 2030, proporcionar acceso universal a zonas verdes y espacios públicos seguros, inclusivos y accesibles, en particular para las mujeres y los niños, las personas de edad y las personas con discapacidad", "definicion"=>"Proporción que representa la superficie construida de las ciudades destinada a espacios públicos abiertos (plazas, parques…) y calles respecto a la superficie total construida", "formula"=>"\n$$PSC_{espacios\\, públicos\\, abiertos\\, y\\, calles}^{t} = \\frac{SC_{espacios\\, públicos\\, abiertos}^{t} + SC_{calles}^{t}}{SC^{t}} \\cdot 100$$\n\ndonde:\n\n$SC_{espacios\\, públicos\\, abiertos}^{t} =$ superficie construida destinada a espacios públicos abiertos en el año $t$\n\n$SC_{calles}^{t} =$ superficie construida destinada a calles en el año $t$\n\n$SC^{t} =$ superficie total construida en el año $t$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Irregular / Aperiódica", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El valor de los espacios públicos suele ser ignorado o subestimado por \nlos responsables políticos, líderes, ciudadanos y promotores urbanos. \nEsto se debe a varias razones, como la falta de recursos, comprensión o \ncapacidad para utilizar el espacio público como un sistema urbano completo \ny multifuncional. \n\nA menudo, la falta de marcos propicios adecuados, la escasa voluntad política y \nla ausencia de mecanismos de participación ciudadana agravan la situación. \nSin embargo, fundamentalmente, la falta de un indicador de medición global ha \ndificultado la apreciación local y global del valor de los espacios públicos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-07-01.pdf\">Metadatos 11-7-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"Proporción media de la superficie edificada de las ciudades que se dedica a espacios abiertos para uso público de todos", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.7- De aquí a 2030, proporcionar acceso universal a zonas verdes y espacios públicos seguros, inclusivos y accesibles, en particular para las mujeres y los niños, las personas de edad y las personas con discapacidad", "definicion"=>"Proportion of the built surface area of cities used for open public spaces  (squares, parks, etc.) and streets in relation to the total built surface area ", "formula"=>"\n$$PSC_{open\\, public\\, spaces\\, and\\, streets}^{t} = \\frac{SC_{open\\, public\\, spaces}^{t} + SC_{streets}^{t}}{SC^{t}} \\cdot 100$$\n\nwhere:\n\n$SC_{open\\, public\\, spaces}^{t} =$ built area used for open public spaces in year $t$\n\n$SC_{streets}^{t} =$ built surface area used for streets in year $t$\n\n$SC^{t} =$ total built area in year $t$\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Irregular / Aperiódica", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The value of public spaces is often overlooked or underestimated by policy \nmakers, leaders, citizens and urban developers. There are several reasons \nfor this, such as the lack of resources, or understanding or capacity to use \npublic space as a complete, multi-functional urban system. \n\nOften the lack of appropriate enabling frameworks, weak political will and \nthe absence of the means of public engagement compound the situation. Nevertheless, \nfundamentally, the lack of a global measurement indicator has hindered the local \nand global appreciation of the value of the public spaces. \n\nSource: United Nations Statistics Division \n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-07-01.pdf\">Metadata 11-7-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción media de la superficie edificada de las ciudades que se dedica a espacios abiertos para uso público de todos", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.7- De aquí a 2030, proporcionar acceso universal a zonas verdes y espacios públicos seguros, inclusivos y accesibles, en particular para las mujeres y los niños, las personas de edad y las personas con discapacidad", "definicion"=>"Proporción que representa la superficie construida de las ciudades destinada a espacios públicos abiertos (plazas, parques…) y calles respecto a la superficie total construida", "formula"=>"\n$$PSC_{espazio\\, publiko\\, irekiak\\, eta\\, kaleak}^{t} = \\frac{SC_{espazio\\, publiko\\, irekiak}^{t} + SC_{kaleak}^{t}}{SC^{t}} \\cdot 100$$\n\nnon:\n\n$SC_{espazio\\, publiko\\, irekiak}^{t} =$ espazio publiko irekietarako azalera eraikia $t$ urtean\n\n$SC_{kaleak}^{t} =$ kaleetarako azalera eraikia $t$ urtean \n\n$SC^{t} =$ eraikitako azalera osoa $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Irregular / Aperiódica", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El valor de los espacios públicos suele ser ignorado o subestimado por \nlos responsables políticos, líderes, ciudadanos y promotores urbanos. \nEsto se debe a varias razones, como la falta de recursos, comprensión o \ncapacidad para utilizar el espacio público como un sistema urbano completo \ny multifuncional. \n\nA menudo, la falta de marcos propicios adecuados, la escasa voluntad política y \nla ausencia de mecanismos de participación ciudadana agravan la situación. \nSin embargo, fundamentalmente, la falta de un indicador de medición global ha \ndificultado la apreciación local y global del valor de los espacios públicos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-07-01.pdf\">Metadatuak 11-7-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 11.7: By 2030, provide universal access to safe, inclusive and accessible, green and public spaces, in particular for women and children, older persons and persons with disabilities</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 11.7.1: Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_URB_OPENSP - Average share of the built-up area of cities that is open space for public use for all [11.7.1]</p>\n<p>EN_ACS_URB_OPENSP - Average share of urban population with convenient access to open public spaces (%) [11.7.1] </p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.3.1: Ratio of land consumption rate to population growth rate</p>\n<p>11.2.1: Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions and Concepts:</strong></p>\n<p>Indicator 11.7.1 has several interesting concepts that required global consultations and consensus. These include; built-up area, cities, open spaces for public use, etc. As a custodian agency, UN-Habitat has worked on these concepts along with several other partners.</p>\n<ol>\n  <li><strong>City</strong>: A range of accepted definitions of the &#x201C;city&#x201D; exist, from those based on population data and extent of the built-up area to those that are based solely on administrative boundaries. These definitions vary within and between nations, complicating the task of international reporting for the SDGs. Definitions of cities, metropolitan areas and urban agglomerations also vary depending on legal, administrative, political, economic or cultural criteria in the respective countries and regions. Since 2016, UN-Habitat and partners organized global consultations and discussions to narrow down the set of meaningful definitions that would be helpful for the global monitoring and reporting process. Following consultations with 86 member states; the United Nations Statistical Commission, in its 51st Session (March 2020), endorsed the Degree of Urbanisation (DEGURBA) as a workable method to delineate cities, urban and rural areas for international statistical comparisons.<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup> This definition combines population size and population density thresholds to classify the entire territory of a country along the urban-rural continuum, and captures the full extent of a city, including the dense neighbourhoods beyond the boundary of the central municipality. DEGURBA is applied in a two-step process: First, 1 km<sup>2</sup> grid cells are classified based on population density, contiguity and population size. Subsequently, local units are classified as urban or rural based on the type of grid cells in which majority of their population resides. For the computation of indicator 11.7.1, countries are encouraged to adopt the degree of urbanisation to define the analysis area (city or urban area).</li>\n  <li><strong>Built-up area of cities: </strong>Conventionally, built up areas of cities are areas occupied by buildings and other artificial surfaces. For indicator 11.7.1, built up areas, as the indicator denominator has the same meaning as &#x201C;city&#x201D; (see definition of city above). </li>\n</ol>\n<p><strong>Public space:</strong> The Global Public Space toolkit defines Public Space as all places that are publicly owned or of public use, accessible and enjoyable by all, for free and without a profit motive, categorized into streets, open spaces and public facilities. Public space in general is defined as the meeting or gathering places that exist outside the home and workplace that are generally accessible by members of the public, and which foster resident interaction and opportunities for contact and proximity. This definition implies a higher level of community interaction and places a focus on public involvement rather than public ownership or stewardship. For the purpose of monitoring and reporting on indicator 11.7.1, public space is defined as all places of public use, accessible by all, and comprises open public space and streets. </p>\n<ol>\n  <li><strong>Open public space</strong>: is any open piece of land that is undeveloped or land with no buildings (or other built structures) that is accessible to the public without charge, and provides recreational areas for residents and helps to enhance the beauty and environmental quality of neighbourhoods. UN-Habitat recognizes that different cities have different types of open public spaces, which vary in both size and typology. Based on the size of both soft and hard surfaces, open public spaces are broadly classified into six categories: national/metropolitan open spaces, regional/larger city open spaces, district/city open spaces, neighbourhood open spaces, local/pocket open spaces and linear open spaces. Classification of open public space by typology is described by the function of the space and can include: green public areas, riparian reserves, parks and urban forests, playground, square, plazas, waterfronts, sports field, community gardens, parklets and pocket parks. </li>\n  <li><strong>Potential open public space: </strong> the identification of open public spaces across cities can be implemented through, among other sources, analysis of high to very high-resolution satellite imagery, from base-maps provided by different organizations (e.g. OpenStreetMap, Esri, etc) or as crowd-sourced and volunteered data. While these sources provide important baseline data for indicator 11.7.1, some of the identifiable spaces may not meet the criteria of being &#x201C;accessible to the public without charge&#x201D;. The term &#x201C;potential open public space&#x201D; is thus used to refer to open public spaces which are extracted from the above-mentioned sources (based on their spatial character), but which are not yet validated to confirm if they are accessible to the public without charge. </li>\n  <li><strong>Streets</strong> are defined thoroughfares that are based inside urban areas, towns, cities and neighbourhoods most commonly lined with houses or buildings used by pedestrians or vehicles in order to go from one place to another in the city, interact and to earn a livelihood. The main purpose of a street is facilitating movement and enabling public interaction. The following elements are considered as streets space: Streets, avenues and boulevards, pavements, passages and galleries, Bicycle paths, sidewalks, traffic island, tramways and roundabouts. Elements excluded from street space include plots (either built-up), open space blocks, railways, paved space within parking lots and airports and individual industries.</li>\n  <li><strong>Land allocated to streets</strong> refers to the total area of the city/urban area that is occupied by all forms of streets (as defined above). This indicator only includes streets available at the time of data collection and excludes proposed networks.</li>\n</ol>\n<p>For more details and illustrations on the definition of the different types of open spaces considered for indicator 11.7.1 see SDG 11.7.1 step by step training module (<a href=\"https://data.unhabitat.org/pages/guidance\">https://data.unhabitat.org/pages/guidance</a> ).</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> A recommendation on the method to delineate cities, urban and rural areas for international statistical comparisons. <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The indicator depends on international classifications on boundaries of countries and regions and city boundaries. Guidance on the city definitions is provided based on a harmonized global city definition, see: <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf</a>.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Satellite imagery (open sources), documentation outlining publicly owned land and community-based maps are the main sources of data.</p>\n<ul>\n  <li>For definition of the city as the unit of analysis, data on the built-up areas are required, which can be extracted from existing layers of satellite imagery ranging from open sources such as Google Earth, US Geological Survey/NASA Landsat imagery and Sentinel Imagery to higher resolution land cover data sets and commercial imagery. Images are to be analyzed for the latest available year. </li>\n  <li>Population data will be sourced from national censuses or other demographic surveys, which can be disaggregated to the smallest units possible through household information aggregation or through population modelling/gridding approaches.</li>\n  <li>For the Inventory of open public space - Information can be obtained from legal documents outlining publicly owned land and well-defined land use plans. In some cases, where this information is lacking, incomplete or outdated, open sources, key informants in the city and community-based maps, which are increasingly recognized as a valid source of information, can be a viable alternative.</li>\n  <li>The share of land occupied by public open spaces cannot be obtained directly from the use of high-resolution satellite imagery because it is not possible to determine the ownership or use of open spaces through remote sensing. However, fieldwork to validate and verify the open spaces derived from satellite imagery helps to map out land that is for public and non-public use.</li>\n</ul>", "COLL_METHOD__GLOBAL"=>"<p>Data collection is supposed to be done at the local city/urban level, with national aggregates made from all cities in the country, or from a sample of representative cities (selected using the National Sample of Cities Approach developed by UN-Habitat: <a href=\"https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf\">https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf</a>). </p>\n<p>At the Global level, data will be assembled and compiled for international consumption and comparison by UN-Habitat and other partners. UN-Habitat and partners will explore several capacity building options to ensure that uniform standards for generation, reporting and analysing data for this indicator are applied by all countries and regions.</p>\n<p>Validation of data on potential open public spaces, which are mapped from high resolution imagery or compiled from open sources (see method of computation section) requires ground truthing. UN-Habitat has developed a set of questions, which can be administered through mobile device-based applications such as KoboToolbox. The questions are available on this tool: https://ee.kobotoolbox.org/x/#IGFf6ubq</p>", "FREQ_COLL__GLOBAL"=>"<p>The monitoring of the indicator can be repeated at regular intervals of 3-5 years, allowing for three reporting points until the year 2030. However, annual updates to the existing database will be done and hence data releases based on annual updates will be available every year. Monitoring in 3-5-year intervals will allow cities to determine whether the shares of open public space in the built-up areas of cities are increasing significantly over time, as well as deriving the share of the global urban population living in cities where the open public space is below the acceptable minimum.</p>\n<p>UN-Habitat has developed a simple reporting template to collect city level data which will be sent to countries on an annual basis for reporting. This reporting template, which requests for information on the major components described in this metadata is expected to be used until 2030, but slight changes may be affected as data on more aspects become available. The template can be accessed <a href=\"https://data.unhabitat.org/datasets/template-for-compilation-of-sdg-indicator-11-7-1\">HERE</a>.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data for indicator 11.7.1 will be released on an annual basis, to cater for an anticipated increase in the number of cities/urban areas and countries reporting on the indicator. Changes in trends within individual cities and/or countries are likely to happen in spans of about 3-5 years, so a three-year window will be applied for comprehensive review of all data, with updates made based on availability of new data.</p>", "DATA_SOURCE__GLOBAL"=>"<p>See the &#x201C;Data compilers&#x201D; section below.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UN-Habitat is the lead agency on the global reporting for this indicator and as such, has since 2017 coordinated the efforts of various partners, on methodological developments and piloting of data collection. Key among these partners have included National Statistical Offices, New York University (NYU), ESRI, Food and Agriculture Organization of the United Nations (FAO), United Nations Committee of Experts on Global Geospatial Information Management (UNGGIM), United Cities and Local Governments (UCLG), Local government departments, the European Commission, UN regional commissions, KTH University-Sweden, Urban Observatories, etc. Working in partnership with these partners, UN-Habitat has undertaken trainings and capacity development activities in cities, countries and regions, which have contributed to enhanced data collection and setting up of systems to monitor and report on the indicator. </p>\n<p>In addition, UN-Habitat and other partners have held several consultations which have collectively contributed to the refinement of the indicator methodology, and its piloting. Some of the key activities include; </p>\n<ul>\n  <li>\n    <ul>\n      <li>\n        <ol>\n          <li>Internal consultations within UN-Habitat and the review of several toolkits of relevance to the subject of public space have provided an initial base of information on concepts and definitions. Lessons learned by UN-Habitat in field projects devoted to public space have proven particularly valuable. </li>\n          <li>A second important source and point of reference has been the Charter of Public Space adopted by the Biennial of Public Space, containing simple and actionable principles for the creation, management and enjoyment of public spaces in cities.</li>\n          <li>A third set of sources has been the contributions offered by a team of international experts, both during and immediately following the Expert Group Meeting (EGM) on Public Space held in Rome in 12-14 January 2014. Additionally, the contributions of over 300 practitioners from over 40 countries during the series of International Conferences on the Future of Places, which developed a set of key messages in advancing the public space agenda at the global level.</li>\n          <li>A fourth source has been global consultative meetings organized after the adoption of the 2030 Agenda in line with the SDG requirements for indicator 11.7.1 and global initiatives that have supported the data collection of this indicator. Specifically, these were:<ol>\n              <li>The first EGM in October 2016 focused mainly on methodological refinements and on concretising the institutional partnership arrangements for capacity development and data collection. Representatives from the NSOs, Urban Observatories, European Union, World Resources Institute, United Cities and Local Governments, Arab Urban Development Institute, World Health Organization, ESRI, NYU, among others participated in this EGM.</li>\n              <li>The second EGM held in February 2017 focused on the challenges of data collection and review of preliminary data made available through the efforts of collecting city-based human settlement data at local levels. </li>\n            </ol>\n          </li>\n        </ol>\n      </li>\n    </ul>\n  </li>\n  <li>It also focused on the technical aspects of computing the indicator using the proposed methodology. This helped in identifying the challenges and opportunities of improving the methodology as well as strategies to scale up and capacity building for NSOs. </li>\n  <li>Representatives attended the meeting from Urban Observatories, European Union, World Resources Institute, United Cities and Local Governments, ESRI, Arab Urban Development Institute, UNESCO, Women in Cities (WICI), Universities and private planning firms, senior statisticians from governments, academic institutions, urban planners, etc. </li>\n</ul>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Human Settlements Programme (UN-Habitat) is the specialized agency for sustainable urbanization and human settlements in the United Nations. The mandate derives from the priorities established in relevant General Assembly resolutions and decisions, including General Assembly resolution 3327 (XXIX), by which the General Assembly established the United Nations Habitat and Human Settlements Foundation, and resolution 32/162 by which the Assembly established the United Nations Center for Human Settlements (Habitat). In 2001, by its Resolution 56/206, the General Assembly transformed the Habitat into the secretariat of the United Nations Human Settlements Programme (UN-Habitat), with a mandate to coordinate human settlements activities within the United Nations System. As such, UN-Habitat has been designated the overall coordinator of SDG 11 and specifically as a custodian agency for 9 of the 15 indicators under SDG 11 including indicator 11.7.1. UN-Habitat also supports the monitoring and reporting of 4 urban specific indicators in other goals.</p>", "RATIONALE__GLOBAL"=>"<p>The value of public spaces is often overlooked or underestimated by policy makers, leaders, citizens and urban developers. There are several reasons for this, such as the lack of resources, or understanding or capacity to use public space as a complete, multi-functional urban system. Often the lack of appropriate enabling frameworks, weak political will and the absence of the means of public engagement compound the situation. Nevertheless, fundamentally, the lack of a global measurement indicator has hindered the local and global appreciation of the value of the public spaces. </p>\n<p>The SDGs have for the first time provided a platform where public spaces can be globally monitored. Indicator 11.7.1 measures the share of land allocated to public spaces and the total population with access to these spaces by age, gender and disability. The share of land that a city allocates to streets and open public spaces is not only critical to its productivity, but also contributes significantly to the social dimensions and health of its population. The size, distribution and quality of a city&#x2019;s overall public space act as a good indicator of shared prosperity.</p>\n<p>Cities that improve and sustain the use of public space, including streets, enhance community cohesion, civic identity, and quality of life. A prosperous city develops policies and actions for sustainable use of, and equitable access to public space. In cities, due to a neglect of public space both in quantity and quality, there is a need to revise and expand the ratio of land allocated to public spaces to make them more efficient, prosperous and sustainable. Uncontrolled rapid urbanization has created disorderly settlement patterns with alarmingly low shares of public space. Many cities in developed countries are also experiencing a dramatic reduction of public space. Reclaiming urban spaces for people is part of how we can humanize our cities and make our streets and public areas more communal. </p>\n<p>A well developed and properly designed network of streets increases connectivity, promotes walking and social interactions but also encourages development of other street activities that bring life to a city. Equally, a well distributed and hierarchical system of open public spaces that can be accessed by all regardless of income, gender, race or disability status and one that promotes multiple activities not only encourages their use, but also contributes to the urban character and quality of urban life.</p>", "REC_USE_LIM__GLOBAL"=>"<p>A major challenge for local monitoring of this indicator is the maintenance and the application/consistency of use of universal definition, which broadly does not consider existing operational/functional administrative demarcations. While urbanization has over the past decade resulted in big urbanized patches/regions which extend beyond existing urban area boundaries, the local operationalization and management of urban systems remain within defined authorities. These authorities are often in charge of governing the urban systems, ensuring effective and efficient functioning through such actions as provision of basic services, development control among others. While some countries have adopted dynamic administrative structures for their urban areas (which shift with expansions in built-up areas), others have maintained confined boundaries. Some of the most common types of boundaries include city, municipality, local authority, metropolitan, mega and meta region demarcations; all of which are set and defined based on prevailing operational dynamics (e.g. governance and service delivery structures). </p>\n<p>UN-Habitat has developed tools, programmes and guidelines to assist cities in measuring, and accounting for the available public space in cities. Some cities in the developing world lack formally recognized public spaces, that are publicly maintained. Understanding of the prevailing local contexts and primary data collection in collaboration with city authorities and local communities contribute significantly to collecting accurate and relevant data in these contexts. </p>\n<p>Similarly, the types of open public space vary across cities. The types of spaces listed in this indicator are however the most common and accepted variations of the open public space. Data collection processes using the methodology described in this metadata, which has been conducted by UN-Habitat in partnership with cities, as well as by other partners has revealed that there are no major overlaps or omissions in the described broad categories of open public spaces. </p>\n<p>Beyond quantifying the amount of open space in public use in cities, this indicator also attempts in minimal ways to capture the quality of the space that may impede its proper use. The qualitative data collected on this indicator strengthens the evidence that an open space exists, and that its public use is guaranteed, to allow city authorities and other stakeholders to further improve its quality and increase its use.</p>", "DATA_COMP__GLOBAL"=>"<p><strong>Computation Method:</strong></p>\n<p>The method to estimate the area of public space has been globally piloted in over 600 cities and this follows a series of methodological developments that go back to the last 7 years. The finalized methodology is a three-step process: </p>\n<ol>\n  <li>Spatial analysis to delimit the city/urban area which will act as the geographical scope for the spatial analysis and indicator computation; </li>\n  <li>Spatial analysis to identify potential open public spaces, field work to validate data and assess the quality of spaces and calculation of the total area occupied by the verified open public spaces; </li>\n  <li>Estimation of the total area allocated to streets;</li>\n  <li>Estimation of share of population with access to open public spaces within 400 meters walking distance out of the total population in the city/ urban area and disaggregation of the population with access by sex, age and persons with disabilities </li>\n  <li><strong>Spatial analysis to delimit the city/urban area </strong></li>\n</ol>\n<p>Following consultations with 86 member states, the United Nations Statistical Commission in its 51<sup>st</sup> Session (March 2020) endorsed the Degree of Urbanisation (DEGURBA) as a workable method to delineate cities, urban and rural areas for international statistical comparisons. Countries are thus encouraged to adopt this approach, which will help them produce data that is comparable across urban areas within their territories, as well as with urban areas and cities in other countries. More details on DEGURBA and its application are available here: <a href=\"https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf\">https://unstats.un.org/unsd/statcom/51st-session/documents/BG-Item3j-Recommendation-E.pdf</a></p>\n<ol>\n  <li><strong>Spatial analysis to identify potential open public spaces, ground verification and estimating their total area</strong></li>\n</ol>\n<p>This step involves mapping of potential open public spaces within the urban boundaries defined in step one above and estimation of their area. Identification of potential open public spaces is based on the spatial character of each space and is also informed by existing country/city land use maps and open space inventories. To compute this component of the indicator, follow these steps:</p>\n<ol>\n  <li>An inventory of Open Public Spaces should be the initial source of information. Additional legal documents, land use plans and other official sources of information can be used to complement the data from the inventory. If the focus urban area or city has a detailed and up-to-date database of its open public spaces, use the information to plot such spaces in GIS software and compute their areas. Where necessary, clean data to remove components which are not applicable in the computation of this sub-indicator (e.g. recreation areas which attract a fee such as golf courses, etc). </li>\n  <li>Since many cities and countries do not have an open public spaces inventory, satellite imagery can be used to extract information on potential open public spaces. The identification of such spaces from imagery should be based on careful evaluation of the character of each space against the known forms of open public spaces within that city/country. High resolution satellite imagery or Google Earth imagery can be used in this analysis. Open data sources such as OpenStreetMap (OSM) have some polygon data on open spaces in many cities. While this data may not be comprehensive for all cities, it can contribute to the data collection efforts and can be explored. </li>\n  <li>Using the data extracted from step 2 above, undertake validation to remove spaces which are not open for public use (e.g. private non-built-up land within the urban area), or to add new spaces that might have been omitted during the extraction stage. This can be achieved through analysing the character of spaces (e.g. size, shape, land cover, etc), comparison of identified spaces with known recreational areas within the city or with data from OpenStreetMap, or consultations with city leaders, local civil society groups, community representatives among others. UN-Habitat, in consultation with partners, experts and data producers have developed a detailed tool to facilitate the verification of each space and collection of additional data on the space quality and accessibility. This tool is freely available and allows for on-site definition/ editing of the space&#x2019;s boundaries. It also contains standard and extended questions which collect data relevant to the indicator, including location of the spaces, their ownership and management, safety, inclusivity and accessibility. This data provides basic information about each space, as well as information relevant for disaggregation - such as access issues linked to age, gender and disabilities, as requested for by the indicator. The tool is dynamic and allows cities to include extra questions which generate information that is useful for their decision making (Tool is available at https://ee.kobotoolbox.org/x/#IGFf6ubq). It should however be noted that the validation approaches which require primary data collection are capital intensive and may not be feasible for most countries in the short term. Validation based on existing city-level data and continuous stakeholder engagement should thus be adopted since they have been shown to produce reliable results at lower costs.</li>\n  <li>Calculate the total area covered by the verified open public spaces. Once all open public spaces (OPS) have been verified, calculate their area in GIS or other database management software. The share of land occupied by these spaces is then calculated using the formula<math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n      <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    </math></li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"bold\">S</mi>\n    <mi mathvariant=\"bold\">h</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">r</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">f</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">d</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">l</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">n</mi>\n    <mi mathvariant=\"bold\">d</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">b</mi>\n    <mi mathvariant=\"bold\">y</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">O</mi>\n    <mi mathvariant=\"bold\">P</mi>\n    <mi mathvariant=\"bold\">S</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi mathvariant=\"bold\">%</mi>\n    <mo>)</mo>\n    <mo>=</mo>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mfenced open=\"[\" close=\"]\" separators=\"|\">\n      <mrow>\n        <mfrac>\n          <mrow>\n            <mi mathvariant=\"bold\">T</mi>\n            <mi mathvariant=\"bold\">o</mi>\n            <mi mathvariant=\"bold\">t</mi>\n            <mi mathvariant=\"bold\">a</mi>\n            <mi mathvariant=\"bold\">l</mi>\n            <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold\">a</mi>\n            <mi mathvariant=\"bold\">r</mi>\n            <mi mathvariant=\"bold\">e</mi>\n            <mi mathvariant=\"bold\">a</mi>\n            <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold\">c</mi>\n            <mi mathvariant=\"bold\">o</mi>\n            <mi mathvariant=\"bold\">v</mi>\n            <mi mathvariant=\"bold\">e</mi>\n            <mi mathvariant=\"bold\">r</mi>\n            <mi mathvariant=\"bold\">e</mi>\n            <mi mathvariant=\"bold\">d</mi>\n            <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold\">b</mi>\n            <mi mathvariant=\"bold\">y</mi>\n            <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n            <mi mathvariant=\"bold\">O</mi>\n            <mi mathvariant=\"bold\">P</mi>\n            <mi mathvariant=\"bold\">S</mi>\n          </mrow>\n          <mrow>\n            <mtable>\n              <mtr>\n                <mtd>\n                  <mrow>\n                    <maligngroup></maligngroup>\n                    <mi>T</mi>\n                    <mi>o</mi>\n                    <mi>t</mi>\n                    <mi>a</mi>\n                    <mi>l</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>a</mi>\n                    <mi>r</mi>\n                    <mi>e</mi>\n                    <mi>a</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>o</mi>\n                    <mi>f</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>t</mi>\n                    <mi>h</mi>\n                    <mi>e</mi>\n                    <mi>&amp;nbsp;</mi>\n                    <mi>c</mi>\n                    <mi>i</mi>\n                    <mi>t</mi>\n                    <mi>y</mi>\n                    <mi>&amp;nbsp;</mi>\n                  </mrow>\n                </mtd>\n              </mtr>\n              <mtr>\n                <mtd>\n                  <mrow>\n                    <maligngroup></maligngroup>\n                    <mi>&amp;nbsp;</mi>\n                  </mrow>\n                </mtd>\n              </mtr>\n            </mtable>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfenced>\n  </math></p>\n<ol>\n  <li><strong>Computation of land allocated to streets (LAS) </strong></li>\n</ol>\n<p>Where street data by width and length fields is available/specified, the following methodology could be used:</p>\n<ol>\n  <li>Select only the streets included in the city/urban area (or clip streets to the city/urban boundary)</li>\n  <li>From GIS (or alternative software), calculate the total area occupied by each street by multiplying its length with width. Add up all individual street areas to attain the total amount of land occupied all streets within the defined urban area.</li>\n</ol>\n<p>Where detailed data on streets is not available, there is need to map out each street line (or the entire area covered by the streets), measure its length and width, which are required for the area computation. For small urban areas, it is possible to manually digitize all streets, but this is more complex for large urban areas and cities. For these large urban areas, an alternative technique for computing land allocated to the streets is one that adopts sampling principles. An approach that uses the Halton sampling sequence is recommended, specifically because the sequence generates equidistant points, increasing the degree of sample representativeness. To compute LAS using this method, follow the following steps: </p>\n<ol>\n  <li>Using the urban extent boundary identified earlier, generate a Halton sequence of sample points (Halton sequence refers to quasi-random sequence used to generate points in space that are ex-post evenly spread i.e. Equidistant). The number of points used for each city varies based on its area. In large study areas of more than 20 km<sup>2</sup>, a density of one circle per hectare is used while in small study areas of less than 20 km<sup>2</sup> a density of 0.5 circle per hectare is used.</li>\n  <li>Buffer the points to get sample areas with an area of 10 hectares each. </li>\n  <li>Within each 10-hectare sample area, digitize all streets in GIS software and compute the total amount of land they occupy. </li>\n  <li>Calculate the average land allocated to streets for all sample areas using the following formula:</li>\n</ol>\n<p>Land allocated to streets = <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">L</mi>\n        <mi mathvariant=\"normal\">A</mi>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">N</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Open source datasets such as OpenStreetMap (OSM) have a good amount of street data on many cities, which is increasingly being updated and extended to cover new areas. This data can also be used as a starting point to understand the pattern of streets in a city. Upon verification of the OSM street categorization for each city, sampling can be used to estimate the average width of each street category, which can in turn help to compute the share of land allocated to streets. </p>\n<p>The final computation of the indicator is done using the formula:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"bold\">S</mi>\n    <mi mathvariant=\"bold\">h</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">r</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">f</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">h</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">b</mi>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">l</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mo>-</mo>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">r</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">f</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">h</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">y</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">h</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">s</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">n</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">s</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">n</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">b</mi>\n    <mi mathvariant=\"bold\">l</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">s</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi mathvariant=\"bold\">%</mi>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"bold\">T</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">s</mi>\n        <mi mathvariant=\"bold\">u</mi>\n        <mi mathvariant=\"bold\">r</mi>\n        <mi mathvariant=\"bold\">f</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">c</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"bold\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"bold\">p</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mi mathvariant=\"bold\">n</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">p</mi>\n        <mi mathvariant=\"bold\">u</mi>\n        <mi mathvariant=\"bold\">b</mi>\n        <mi mathvariant=\"bold\">l</mi>\n        <mi mathvariant=\"bold\">i</mi>\n        <mi mathvariant=\"bold\">c</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">s</mi>\n        <mi mathvariant=\"bold\">p</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">c</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mo>+</mo>\n        <mi mathvariant=\"bold\">T</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">s</mi>\n        <mi mathvariant=\"bold\">u</mi>\n        <mi mathvariant=\"bold\">r</mi>\n        <mi mathvariant=\"bold\">f</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">c</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"bold\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">l</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">n</mi>\n        <mi mathvariant=\"bold\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">l</mi>\n        <mi mathvariant=\"bold\">l</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"bold\">c</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mi mathvariant=\"bold\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">s</mi>\n        <mi mathvariant=\"bold\">r</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">s</mi>\n      </mrow>\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>T</mi>\n                <mi>o</mi>\n                <mi>t</mi>\n                <mi>a</mi>\n                <mi>l</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>a</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>c</mi>\n                <mi>i</mi>\n                <mi>t</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n    </mfrac>\n  </math></p>\n<ol>\n  <li><strong>Estimation of share of population with access to open public spaces and disaggregation by population group</strong></li>\n</ol>\n<p>To help define an &#x201C;acceptable walking distance&#x201D; to open public spaces&#x201D;, UN-Habitat organized a series of consultations with national statistical officers, civil society and community groups, experts in diverse fields, representatives from academia, think tanks, other UN-agencies, and regional commissions among other partners. These consultations, which were held between 2016 and 2018 concluded that a walking distance of 400 meters - equivalent to 5 minutes&#x2019; walk was a practical and realistic threshold. Based on this, a street network-based service area is drawn around each public open space, using the 400 meters access threshold. All populations living within the service areas are in turn identified as having access to the open public spaces, based on the following key assumptions:</p>\n<ul>\n  <li>Equal access to each space by all groups of people &#x2013; i.e. children, the disabled, women, elderly can walk 400 meters (for 5 minutes) to access the spaces (in actual sense, these will vary significantly by group). </li>\n  <li>All streets are walkable &#x2013; where existing barriers are known (e.g. un-walkable streets, lack of pedestrian crossings, etc), these can be defined in the delimitation of the space service area. </li>\n  <li>All public open spaces have equal area of influence &#x2013; which is measured as 400 meters along street networks. In real life situations, bigger spaces have a much larger area of influence. </li>\n  <li>All buildings within the service area are habitable, and that the population is equally distributed in all buildings/built up areas. </li>\n</ul>\n<p>The estimation of total population with access to open public spaces is achieved using the two broad steps described below:</p>\n<ol>\n  <li>Create 400 meters walking distance service area from each open public space along the street network. This requires use of the network analysis tool in GIS software and street data (such as that from City Authorities or from Open Sources such as OpenStreetMap). A network service area is a region that encompasses all accessible areas via the streets network within a specified impedance/distance. The distance in each direction (and in turn the shape of the surface area) varies depending on, among other things, existence of streets, presence of barriers along each route (e.g. lack of foot bridges and turns) and walkability or availability of pedestrian walkways along each street section. In the absence of detailed information on barriers and walkability along each street network, the major assumption in creating the service areas is that all streets are walkable. Since the analysis is done at the city level, local knowledge can be used to exclude streets which are not walkable. The recommendation is to run the service area analysis for each OPS separately then merge all individual service areas to create a continuous service area polygon. Step by step guidance on how to create the service area is provided in the detailed SDG 11.7.1 training module (<a href=\"https://unhabitat.org/sites/default/files/2020/07/indicator_11.7.1_training_module_public_space.pdf\">https://unhabitat.org/sites/default/files/2020/07/indicator_11.7.1_training_module_public_space.pdf</a>) </li>\n  <li>In GIS, overlay the created service area with high resolution demographic data, which should be disaggregated by age, gender, and disability. The best source of population data for the analysis is individual dwelling or block level total population which is collected by National Statistical Offices through censuses and other surveys. Where this level of population data is not available, or where data is released at large population units, countries are encouraged to create population grids, which can help disaggregate the data from large and different sized census/population data release units to smaller uniform sized grids. For more details on the available methods for creation of population grids, explore the links provided under the references section on &#x201C;Some population gridding approaches&#x201D;. A generic description of the different sources of population data for the indicator computation is also provided in the detailed Indicator 11.7.1 training module (<a href=\"https://unhabitat.org/sites/default/files/2020/07/indicator_11.7.1_training_module_public_space.pdf\">https://unhabitat.org/sites/default/files/2020/07/indicator_11.7.1_training_module_public_space.pdf</a>). Once the appropriate source of population data is acquired, the total population with access to open public spaces in the city/urban area will be equal to the population encompassed within the combined service area for all open public spaces, calculated using the formula below. </li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"bold\">S</mi>\n    <mi mathvariant=\"bold\">h</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">r</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">f</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">l</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">n</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">w</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">h</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">s</mi>\n    <mi mathvariant=\"bold\">s</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">t</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">o</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">n</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">s</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">n</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">u</mi>\n    <mi mathvariant=\"bold\">b</mi>\n    <mi mathvariant=\"bold\">l</mi>\n    <mi mathvariant=\"bold\">i</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mi mathvariant=\"bold\">s</mi>\n    <mi mathvariant=\"bold\">p</mi>\n    <mi mathvariant=\"bold\">a</mi>\n    <mi mathvariant=\"bold\">c</mi>\n    <mi mathvariant=\"bold\">e</mi>\n    <mi mathvariant=\"bold\">s</mi>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi mathvariant=\"bold\">%</mi>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>=</mo>\n    <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"bold\">T</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">l</mi>\n        <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">p</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"bold\">p</mi>\n        <mi mathvariant=\"bold\">u</mi>\n        <mi mathvariant=\"bold\">l</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">i</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"bold\">n</mi>\n        <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">w</mi>\n        <mi mathvariant=\"bold\">i</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">h</mi>\n        <mi mathvariant=\"bold\">i</mi>\n        <mi mathvariant=\"bold\">n</mi>\n        <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n        <mn>400</mn>\n        <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">m</mi>\n        <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">s</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mi mathvariant=\"bold\">r</mi>\n        <mi mathvariant=\"bold\">v</mi>\n        <mi mathvariant=\"bold\">i</mi>\n        <mi mathvariant=\"bold\">c</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">r</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"bold\">T</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">l</mi>\n        <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">p</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"bold\">p</mi>\n        <mi mathvariant=\"bold\">u</mi>\n        <mi mathvariant=\"bold\">l</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">i</mi>\n        <mi mathvariant=\"bold\">o</mi>\n        <mi mathvariant=\"bold\">n</mi>\n        <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">w</mi>\n        <mi mathvariant=\"bold\">i</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">h</mi>\n        <mi mathvariant=\"bold\">i</mi>\n        <mi mathvariant=\"bold\">n</mi>\n        <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">h</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">c</mi>\n        <mi mathvariant=\"bold\">i</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">y</mi>\n        <mo>/</mo>\n        <mi mathvariant=\"bold\">u</mi>\n        <mi mathvariant=\"bold\">r</mi>\n        <mi mathvariant=\"bold\">b</mi>\n        <mi mathvariant=\"bold\">a</mi>\n        <mi mathvariant=\"bold\">n</mi>\n        <mi mathvariant=\"bold\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mi mathvariant=\"bold\">x</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi mathvariant=\"bold\">e</mi>\n        <mi mathvariant=\"bold\">n</mi>\n        <mi mathvariant=\"bold\">t</mi>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n    </mfrac>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>As part of the validation process, UN-Habitat developed a template to compile data generated by countries through the National Statistics Offices as well as other government agencies responsible for official statistics (see: <a href=\"https://data.unhabitat.org/datasets/template-for-compilation-of-sdg-indicator-11-7-1\">https://data.unhabitat.org/datasets/template-for-compilation-of-sdg-indicator-11-7-1</a>). Data compiled is then checked against several criteria including the data sources used, the application of internationally agreed definitions, classification and methodologies to the data from that source, etc. Once reviewed, appropriate feedback is then provided to individual countries for further discussion. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Any adjustments to the data is jointly agreed after consultations with the relevant national agencies that share the data points for reporting.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>This indicator is measured at city level, and aggregations from available cities made to represent national averages. UN-Habitat has proposed use of the <a href=\"https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf\">National Sample of Cities approach</a> to identify cities/urban areas for which data can be calculated in a manner that is nationally representative. Noting gaps in the availability of open public spaces data in many countries, particularly on smaller cities/urban areas which may impact negatively on the production of national aggregates, countries are requested to report on the individual city values without creating national aggregates. The data reporting template provided by UN-Habitat requests for both city and national values, allowing countries to report incrementally on the available data points. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>This indicator is measured at city level, and population weighted aggregates from available cities are undertaken to represent national, regional and global averages. Currently, there is adequate representative data on the indicator to undertake population weighted regional and global averages. The continued production of data on the indicator is also enhancing the accuracy of regional and global estimates and has eliminated the risk of missing data at this level. </p>", "REG_AGG__GLOBAL"=>"<p>Data at the global/regional levels will be estimated from national figures derived from an aggregation of performance for all cities/urban areas or a sample of nationally representative cities (selected using the national sample of cities approach developed by UN-Habitat). Regional estimates will incorporate national representations using a weighting by population sizes. Global monitoring will be led by UN-Habitat with the support of other partners and regional commissions.</p>", "DOC_METHOD__GLOBAL"=>"<p>The detailed tutorial on the indicator computation can be accessed here: <a href=\"https://data.unhabitat.org/pages/guidance\">https://data.unhabitat.org/pages/guidance</a> </p>\n<p>The guidance on implementation of the National Sample of Cities Approach is available here: <a href=\"https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf\">https://unhabitat.org/sites/default/files/2020/06/national_sample_of_cities_english.pdf</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>To ensure consistency in data production across countries, UN-Habitat has developed detailed step-by-step tutorials on the computation of indicator 11.7.1, which further explain the steps presented in this metadata. The detailed tutorials, which will be continuously updated are available at <a href=\"https://unhabitat.org/knowledge/data-and-analytics\">https://unhabitat.org/knowledge/data-and-analytics</a>, <a href=\"https://www.urbanagendaplatform.org/learning\">https://www.urbanagendaplatform.org/learning</a>, and <a href=\"https://data.unhabitat.org/\">https://data.unhabitat.org/</a>. </p>\n<p>Within its Data and Analytics Unit which is responsible for the indicator data compilation, UN-Habitat has a team of spatial data experts who check all submitted data and provide direct support to countries in the indicator computation. </p>\n<p>As part of its global custodianship of indicator 11.7.1, UN-Habitat has also established partnerships with major institutions and organizations involved in production of baseline data relevant for the indicator computation. The main aim of this is to create a common understanding on the approach for the indicator computation, and to encourage continuous production of high-quality global data that responds to the indicator computation needs. Examples of some ongoing initiatives with partners to manage quality of products and processes include, among others providing support to apply the Degree of Urbanisation at the local level for the indicator computation (in partnership with the European Commission), development of an Earth Observation Toolkit for SDG 11 (in partnership with EO4SDG and GEO), and continuous feedback to global products produced by partners. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data coming from the cities and countries will be verified through the local network of actors, who will also identify which open spaces meet the criteria defined in this metadata. Where information on streets and open public spaces is acquired from open sources and volunteered geospatial data channels, cities and countries will validate the accuracy of the information.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Once data is received from member states, UN-Habitat uses a checklist specific to each indicator to assess a) whether the data production process followed the metadata provisions, and b) confirm the accuracy of the data sources used for the indicator computation. Both components are captured in the reporting template shared with National Statistical Offices, which helps to assess whether computation was done using the proposed indicator inputs or proxies. The reporting template also requests for information that helps understand whether national data for the indicator was produced from a representative sample of the country&#x2019;s urban systems, or if estimates were done for only select cities/urban areas where data is easily available. </p>\n<p>In addition, the received data is also checked for other qualities such as data disaggregation, reporting period and consistency with other previously reported trends, which ensures reliable regional estimates. For indicator 11.2.1, one extra assessment is done is to check the completeness of open-source data (such as OpenStreetMap and General Transit Feeds Specification &#x2013; GTFS) for the specific country/city, where such is used for the indicator estimation. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>In 2024, data on indicator 11.7.1 is available for more than 1,500 cities from 187 countries. Some countries have also produced national averages based on the city level data. UN-Habitat has calculated populated weighted regional aggregates based on the M49 categories, as well as the UN regional commissions. UN-Habitat and partners are continuously supporting national statistical systems to increase data availability on the indicator, including disaggregation by gender and persons with disability. </p>\n<p><strong>Time series:</strong></p>\n<p>Annual based on data availability. Regional and global aggregates to be produced for 2020, 2025 and 2030. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Based on availability of high-resolution population data, population with access to open public spaces should be disaggregated by age, gender and disability.</p>\n<p>Wherever possible, it would also be useful to have information disaggregated by:</p>\n<ul>\n  <li>Location of public spaces (intra-urban), </li>\n  <li>Quality of the open public space by safety, inclusivity, accessibility, greenness, and comfort, </li>\n  <li>Type of open space as a share of the city area, </li>\n  <li>The share of open spaces in public use which are universally accessible, particularly for persons with disabilities, and </li>\n  <li>Type of human settlements.</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Applying the proposed methodology to an entire globe of different cities will be challenging, but there are some basic principles that cities can use to measure public space uniformly. Cities can inventory the spectrum of spaces, from natural areas to small neighbourhood parks owned by different government entities. For example, in some cities, cemeteries are publicly available spaces run by the city park and recreation department. UN-Habitat has developed a basic methodological guide and tools, which have enabled national statistical agencies and cities to apply these methods in a standard way and compile a comparable inventory of open public spaces.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>References:</strong></p>\n<ul>\n  <li>Axon Johnson Foundation, Public Spaces and Place making, Future of Places, http://futureofplaces.com/</li>\n  <li>UN-Habitat (2013) Streets as Public Spaces and Drivers of Urban Prosperity, Nairobi</li>\n  <li>UN-Habitat (2014) Methodology for Measuring Street Connectivity Index</li>\n  <li>UN-Habitat (2015) Spatial Capital of Saudi Arabian Cities, Street Connectivity as part of City Prosperity Initiative</li>\n  <li>UN-Habitat (2015) Global Public Space Toolkit from Global Principles to Local Policies and Practice</li>\n  <li>UN-Habitat (2018). SDG Indicator 11.7.1 Training Module: Public Space. United Nations Human Settlement Programme (UN-Habitat), Nairobi. Available at <a href=\"https://unhabitat.org/sites/default/files/2020/07/indicator_11.7.1_training_module_public_space.pdf\">https://unhabitat.org/sites/default/files/2020/07/indicator_11.7.1_training_module_public_space.pdf</a> </li>\n  <li>Kaw, Jon Kher, Hyunji Lee, and Sameh Wahba, editors. 2020. The Hidden Wealth of Cities: Creating, Financing, and Managing Public Spaces. Washington, DC: World Bank. doi:10.1596/978-1-4648-1449-5. License: Creative Commons Attribution CC BY 3.0 IGO</li>\n  <li>Some population gridding approaches: <a href=\"https://sedac.ciesin.columbia.edu/data/collection/usgrid/methods\">https://sedac.ciesin.columbia.edu/data/collection/usgrid/methods</a>; <a href=\"https://www.ciesin.columbia.edu/data/hrsl/\">https://www.ciesin.columbia.edu/data/hrsl/</a>; <a href=\"https://ec.europa.eu/eurostat/statistics-explained/index.php/Population_grids\">https://ec.europa.eu/eurostat/statistics-explained/index.php/Population_grids</a>; <a href=\"https://www.worldpop.org/methods\">https://www.worldpop.org/methods</a></li>\n</ul>", "indicator_sort_order"=>"11-07-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.7.2", "slug"=>"11-7-2", "name"=>"Proporción de personas que han sido víctimas de acoso no sexual o sexual en los últimos 12 meses, desglosada por sexo, edad, grado de discapacidad y lugar del hecho", "url"=>"/site/es/11-7-2/", "sort"=>"110702", "goal_number"=>"11", "target_number"=>"11.7", "global"=>{"name"=>"Proporción de personas que han sido víctimas de acoso no sexual o sexual en los últimos 12 meses, desglosada por sexo, edad, grado de discapacidad y lugar del hecho"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de personas que han sido víctimas de acoso no sexual o sexual en los últimos 12 meses, desglosada por sexo, edad, grado de discapacidad y lugar del hecho", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de personas que han sido víctimas de acoso no sexual o sexual en los últimos 12 meses, desglosada por sexo, edad, grado de discapacidad y lugar del hecho", "indicator_number"=>"11.7.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio del Interior", "periodicity"=>"Anual", "url"=>"https://www.interior.gob.es/opencms/es/archivos-y-documentacion/documentacion-y-publicaciones/anuarios-y-estadisticas/estadisticas-del-ministerio-del-interior/", "url_text"=>"Estadística de seguridad: actuaciones policiales", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Número de personas que han sido víctimas de acoso no sexual o sexual en los últimos 12 meses por cada 100.000 habitantes", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.7- De aquí a 2030, proporcionar acceso universal a zonas verdes y espacios públicos seguros, inclusivos y accesibles, en particular para las mujeres y los niños, las personas de edad y las personas con discapacidad", "definicion"=>"Número de personas que han sido víctimas de acoso contra la libertad de las personas, acoso inmobiliario, acoso laboral o funcionarial, o acoso sexual en los últimos 12 meses por cada 100.000 habitantes", "formula"=>"\n$$TVACOSO^{t} = \\frac{VACOSO^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$VACOSO^{t} =$ población que ha sido víctima de acoso no sexual o sexual en los últimos 12 meses, en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"", "observaciones"=>"\nSe contabiliza como acoso no sexual el acoso contra la libertad de las personas (art. 172 ter del código penal),  el acoso inmobiliario (art. 172,1.3 del código penal) y el acoso laboral o funcionarial (art. 173.1 del código penal).", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La experiencia del acoso sexual y no sexual puede tener efectos negativos de gran alcance en las \nvíctimas. \n\nAdemás del daño emocional y psicológico sufrido, el acoso puede tener consecuencias negativas \nen la capacidad de las víctimas para participar plenamente en la vida pública y para participar y \ncontribuir al desarrollo de sus comunidades. Por ejemplo, la incidencia generalizada del acoso sexual \nen el lugar de trabajo puede dar lugar a una menor participación de las mujeres en la fuerza laboral, \nespecialmente en ocupaciones dominadas por los hombres, y reducir su capacidad de generar ingresos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible no cumple con los metadatos de Naciones Unidas, pero aporta información complementaria.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-07-02.pdf\">Metadatos 11-7-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"Número de personas que han sido víctimas de acoso no sexual o sexual en los últimos 12 meses por cada 100.000 habitantes", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.7- De aquí a 2030, proporcionar acceso universal a zonas verdes y espacios públicos seguros, inclusivos y accesibles, en particular para las mujeres y los niños, las personas de edad y las personas con discapacidad", "definicion"=>"Victims of harassment against personal freedom, real estate harassment, workplace or  civil service harassment, or sexual harassment in the last 12 months per 100,000 inhabitants", "formula"=>"\n$$TVACOSO^{t} = \\frac{VACOSO^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$VACOSO^{t} =$ victims of non-sexual or sexual harassment in the last 12 months, in year $t$\n\n$P^{t} =$ population as of July 1 of the year $t$\n", "desagregacion"=>nil, "observaciones"=>"\nNon-sexual harassment includes harassment against the freedom of persons (Article 172 ter of the  Criminal Code), real estate harassment (Article 172.1.3 of the Criminal Code), and workplace or  public servant harassment (Article 173.1 of the Criminal Code). ", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The experience of non-sexual and sexual harassment can have far-reaching \nnegative impacts on the victims. \n\nBesides the emotional and psychological harm suffered, harassment can have \nnegative consequences on the ability of its victims to fully participate in \npublic life and to share in and contribute to the development of their communities. \nFor example, the widespread occurrence of sexual harassment in the workplace can \nlead to a lower participation of women in the workforce, especially in maledominated \noccupations, and lower their income-generating capacity. \n\nSource: United Nations Statistics Division \n", "comparabilidad"=>"The available indicator does not comply with the metadata of the United Nations indicator,  but provides complementary information. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-07-02.pdf\">Metadata 11-7-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Número de personas que han sido víctimas de acoso no sexual o sexual en los últimos 12 meses por cada 100.000 habitantes", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.7- De aquí a 2030, proporcionar acceso universal a zonas verdes y espacios públicos seguros, inclusivos y accesibles, en particular para las mujeres y los niños, las personas de edad y las personas con discapacidad", "definicion"=>"Número de personas que han sido víctimas de acoso contra la libertad de las personas, acoso inmobiliario, acoso laboral o funcionarial, o acoso sexual en los últimos 12 meses por cada 100.000 habitantes", "formula"=>"\n$$TVACOSO^{t} = \\frac{VACOSO^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$VACOSO^{t} =$ azken 12 hilabeteetan sexu-jazarpena edo jazarpen ez-sexuala pairatu duen biztanleria $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>nil, "observaciones"=>"\nSe contabiliza como acoso no sexual el acoso contra la libertad de las personas (art. 172 ter del código penal),  el acoso inmobiliario (art. 172,1.3 del código penal) y el acoso laboral o funcionarial (art. 173.1 del código penal).", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La experiencia del acoso sexual y no sexual puede tener efectos negativos de gran alcance en las \nvíctimas. \n\nAdemás del daño emocional y psicológico sufrido, el acoso puede tener consecuencias negativas \nen la capacidad de las víctimas para participar plenamente en la vida pública y para participar y \ncontribuir al desarrollo de sus comunidades. Por ejemplo, la incidencia generalizada del acoso sexual \nen el lugar de trabajo puede dar lugar a una menor participación de las mujeres en la fuerza laboral, \nespecialmente en ocupaciones dominadas por los hombres, y reducir su capacidad de generar ingresos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina informazio osagarria ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-07-02.pdf\">Metadatuak 11-7-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 11.7: By 2030, provide universal access to safe, inclusive and accessible, green and public spaces, in particular for women and children, older persons and persons with disabilities</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 11.7.2: Proportion of persons victim of non-sexual or sexual harassment, by sex, age, disability status and place of occurrence, in the previous 12 months<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Refinement of the indicator name approved by the IAEG-SDGs in its November 2023 meeting and pending final approval by the 55th session of the Statistical Commission in March 2024. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_VOH_SXPH - Proportion of persons victim of non-sexual or sexual harassment, in the previous 12 months [11.7.2]<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> Series description to be updated in the DSD and database by end of March 2024. Previous series description was &#x201C;Proportion of persons victim of physical or sexual harassment, in the previous 12 months&#x201D;. Data are the same. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "META_LAST_UPDATE__GLOBAL"=>"2024-01-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>5.2.1: Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age</p>\n<p>5.2.2: Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence</p>\n<p>16.1.3: Proportion of population subjected to (a) physical violence, (b) psychological violence and/or (c) sexual violence in the previous 12 months</p>\n<p>16.2.3: Proportion of young women and men aged 18&#x2013;29 years who experienced sexual violence by age 18</p>\n<p>16.3.1: Proportion of victims of (a) physical, (b) psychological and/or (c) sexual violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>\n<p></p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Number of persons who have been victims of non-sexual harassment or sexual harassment, as a percentage of the total population of the relevant area.</p>\n<p><strong>Concepts:</strong></p>\n<p>The operational definitions of non-sexual and sexual harassment are based on the <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">International Classification of Crime for Statistical Purposes (ICCS)</a>. While sexual harassment refers to a non-physical behaviour with a sexual connotation that is suitable to intimidate the victim, non-sexual harassment refers to all other non-sexual harassing behaviours that can cause fear for physical integrity and/or emotional distress. This type of non-sexual harassment formulated by the indicator overlaps to some extent with psychological violence. </p>\n<p>The internationally standardized and tested <a href=\"https://www.unodc.org/documents/data-and-analysis/sdgs/SDG16_Survey_Initiative_-_Questionnaire.pdf\">SDG 16 Survey questionnaire</a> and the accompanying <a href=\"https://www.unodc.org/documents/data-and-analysis/sdgs/SDG16_Survey_Initiative_-_Implementation_Manual.pdf\">Implementation Manual</a>, which can be used by countries for collecting data SDG indicator 11.7.2 on non-sexual and sexual harassment, provide a core set of questions about specific behaviours that allow for the measurement of the prevalence of sexual and non-sexual harassment in the population (see Figures 1 and 2 below). In addition, the regionally standardized and tested methodology, the Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI), offers a standardised methodology the measurement of indicator 11.7.2 and uses the same types of behaviours, both for non-sexual harassment and sexual harassment, but considering one additional behaviour indicative of sexual harassment: Somebody followed you and made you feel uncomfortable with sexual intentions.</p>\n<p>While the precise formulation and wording of the pertinent survey questions may need national customization, a core set of behaviours have been identified as forms of sexual and non-sexual harassment exercised towards a person: </p>\n<p>Figure 1: Types of non-sexual harassment included in the SDG 16 survey questionnaire</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>A.</p>\n      </td>\n      <td>\n        <p>Received non-sexual threatening or offensive MESSAGES, E-MAILS OR CALLS</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>B.</p>\n      </td>\n      <td>\n        <p>Somebody personally made OFFENSIVE, THREATENING OR HUMILIATING COMMENTS to you, such as insulting you or calling you names</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>C.</p>\n      </td>\n      <td>\n        <p>Somebody made OFFENSIVE OR THREATENING GESTURES to demean, insult or humiliate you</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>D.</p>\n      </td>\n      <td>\n        <p>Somebody posted OFFENSIVE, demeaning OR EMBARASSING COMMENTS, PHOTOS OR VIDEOS OF YOU ONLINE</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>E.</p>\n      </td>\n      <td>\n        <p>Somebody FOLLOWED YOU AGAINST YOUR WILL, EITHER PHYSICALLY OR ONLINE in a way that made you feel uncomfortable</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><em>Source: </em><a href=\"https://www.unodc.org/documents/data-and-analysis/sdgs/SDG16_Survey_Initiative_-_Questionnaire.pdf\"><em>SDG 16 Survey questionnaire</em></a></p>\n<p>Figure 2: Types of sexual harassment included in the SDG 16 survey questionnaire</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>A.</p>\n      </td>\n      <td>\n        <p>UNWANTED SEXUAL PROPOSITION or pressure for a date</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>B.</p>\n      </td>\n      <td>\n        <p>Unwanted MESSAGES, E-MAILS, CALLS OF A SEXUAL NATURE that offended you</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>C.</p>\n      </td>\n      <td>\n        <p>Embarrassing and SEXUALLY EXPLICIT MESSAGES about you and/or PHOTOS OR VIDEOS OF YOU POSTED ONLINE OR SENT TO ANYONE WITHOUT YOUR CONSENT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>D.</p>\n      </td>\n      <td>\n        <p>UNWANTED SEXUAL COMMENTS ABOUT YOUR PHYSICAL APPEARANCE OR BODY</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>E.</p>\n      </td>\n      <td>\n        <p>UNWANTED SEXUAL GESTURES, WHISTLING AND LEERING OR ANYONE GOT INAPPROPRIATELY CLOSE TO YOU</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>G.</p>\n      </td>\n      <td>\n        <p>Somebody INDECENTLY EXPOSED THEMSELVES TO YOU</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>H.</p>\n      </td>\n      <td>\n        <p>Receiving UNWANTED GIFTS of a sexual nature such as toys, accessories or underwear</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><em>Source: </em><a href=\"https://www.unodc.org/documents/data-and-analysis/sdgs/SDG16_Survey_Initiative_-_Questionnaire.pdf\"><em>SDG 16 Survey questionnaire</em></a><em>. Note: In relation to item E. &quot;inappropriately close&quot; means that the perpetrator is at a distance where he/she can touch the victim, but where physical contact does not take place.</em></p>\n<p>The SDG 16 survey instrument, developed by the United Nations Office on Drugs and Crime (UNODC), the United Nations Development Program (UNDP) and the Office of the United Nations High Commissioner for Human Rights (OHCHR), is designed as a flexible tool that can be adapted to national needs. It can either be applied as a standalone population survey or, if necessary, countries can implement selected modules as part of other ongoing survey operations.</p>\n<p>The LACSI survey instrument, developed by the United Nations Office on Drugs and Crime (UNODC) with the support of the Inter-American Development Bank (IADB), Organization of American States (OAS) and the United Nations Development Program (UNDP), is designed as an independent crime victimization survey that can be adapted to the national needs and covers a wide range of criminal behaviors, including sexual and non-sexual harassment.<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup> </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> Technical assistance for the implementation of LACSI methodology in the Latin America and the Caribbean region is provided by the UNODC-INEGI Center of Excellence in Statistical Information on Government, Crime, Victimization and Justice (CoE). For more information, visit: <a href=\"https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.cdeunodc.inegi.org.mx%2Findex.php%2Fen%2F&amp;data=05%7C01%7Cmaurice.dunaiski%40un.org%7Ceb3498bb23c84e42293b08db2659f3f7%7C0f9e35db544f4f60bdcc5ea416e6dc70%7C0%7C0%7C638145940021789602%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&amp;sdata=d0le6ksISowhh196Ld7SvLdJHgp9pf%2FpH1LrEMLG4dI%3D&amp;reserved=0\" target=\"_blank\">https://www.cdeunodc.inegi.org.mx/index.php/en/</a> <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">International Classification of Crime for Statistical Purposes (ICCS)</a></p>", "SOURCE_TYPE__GLOBAL"=>"<p>The indicator is based on a set of questions about experiences of 12 different forms of sexual and non-sexual harassment in the last 12 months to be included in a household survey. (see Section 4.c Method of computation). These questions can be part of an add-on module on sexual and non-sexual harassment, to be incorporated into other ongoing general population surveys (such as surveys on quality of life, public attitudes, or surveys on other topics) or be part of dedicated surveys on crime victimization.</p>\n<p>Data should be collected as part of a nationally representative sample of the adult population residing in the country, irrespective of legal residence status. The sampling frame and sample design should ensure that results can be disaggregated at sub-national level. The sample size should be sufficiently large to capture relevant events and compute needed disaggregations.</p>", "COLL_METHOD__GLOBAL"=>"<p>At international level, data are routinely collected by UNODC through the annual UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) data collection. As requested by the UN Commission on Crime Prevention and Criminal Justice, over 140 Member States have already appointed a UN-CTS national focal point that delivers UN-CTS data to UNODC. In most cases these focal points are national institutions responsible for data production in the area of crime and criminal justice (National Statistical Offices, Ministry of Interior, Ministry of Justice, etc.). For countries that have not appointed a focal point, the request for data is sent to permanent missions in Vienna. When a country does not report to UNODC, other official sources such as authoritative websites, publications, or other forms of communication are used. Once consolidated, data are shared with countries to check their accuracy and validity.</p>\n<p>The UN-CTS provides specific definitions of data to be collected. It also collects a set of metadata to identify possible discrepancies from standard definitions and to assess overall data quality (e.g. sample size, target population, agency responsible for the data collection, etc.).</p>", "FREQ_COLL__GLOBAL"=>"<p>Countries are encouraged to conduct crime victimization surveys which include harassment or a module within a national household survey through the proposed module in regular intervals, but at least every four years to reflect progress between each of the quadrennial reviews of Goal 11 at the High Level Political Forum (HLPF).</p>\n<p>UNODC collects data on this indicator according to the following schedule:</p>\n<p>III-IV quarter year n</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data on relevant SDG indicators are collected, compiled and sent back to countries for data review annually. Data are then reported to UN Statistics Division (UNSD) through the regular reporting channels annually.</p>\n<p>II quarter year n+1 (data for year n-1). For instance, data for the year 2023 are collected in III-IV quarter 2024 and released in II quarter 2025.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are collected through official nationally representative surveys. In most countries and most cases, such surveys are conducted by National Statistical Offices (NSOs). In some cases, other national institutions or other entities may conduct surveys on victimization which include harassment according to the same methodological standards.</p>\n<p>Data are sent to UNODC by Member States, usually through national UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) Focal Points, which in most cases are national institutions responsible for data production in the area of crime and criminal justice (National Statistical Offices, Ministry of Interior, Ministry of Justice, etc.).</p>", "COMPILING_ORG__GLOBAL"=>"<p><strong>Name:</strong></p>\n<p>United Nations Office on Drugs and Crime (UNODC)</p>\n<p><strong>Description:</strong></p>\n<p>At the international level, data are routinely collected and disseminated by the United Nations Office on Drugs and Crime (UNODC) through the annual UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) data collection. UNODC partners with regional organizations in the collection and dissemination of data, respectively with Eurostat in Europe and with the Organisation of American States in the Americas. </p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) &#x2013; as custodian of the UN standards and norms in crime prevention and criminal justice, UNODC assists Member States in reforming their criminal justice systems in order to be effective, fair and humane for the entire population. UNODC develops technical tools to assist Member States in implementing the UN standards and norms and supports Member States through the provision of technical assistance in crime prevention and criminal justice reform. It does so through several Global programmes and through the UNODC field office network.</p>\n<p>UNODC is responsible for carrying out the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS), which was introduced through the General Assembly Resolution <a href=\"https://undocs.org/en/A/RES/3021(XXVII)\">A/RES/3021(XXVII)</a> in 1972. The Economic and Social Council, in its resolution 1984/48 of 25 May 1984, further requested that the Secretary-General maintain and develop the United Nations crime-related database by continuing to conduct surveys of crime trends and the operations of criminal justice systems. </p>", "RATIONALE__GLOBAL"=>"<p>The experience of non-sexual and sexual harassment can have far-reaching negative impacts on the victims. Besides the emotional and psychological harm suffered, harassment can have negative consequences on the ability of its victims to fully participate in public life and to share in and contribute to the development of their communities. For example, the widespread occurrence of sexual harassment in the workplace can lead to a lower participation of women in the workforce, especially in male-dominated occupations, and lower their income-generating capacity.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Like other experience-based indicators on victimization, the indicator reflects the experience from the perspective of the victim. As such, the response provided by the victims reflects their experience as well as their subjective feeling of victimization, irrespective of whether actual harm was intended or not. The subjective feeling of victimization is an important component of safety and security across space and time (for example, in cities or in the domestic sphere) and a higher prevalence of experienced non-sexual or sexual harassment indicates a negative environment that warrants appropriate responses and interventions.</p>\n<p>Similar to other survey-based indicators, the scope of the indicator also relies on the design and sampling strategy of the survey. For example, most surveys set a lower age-limit for practical and ethical reasons (e.g. 18 years and older), which means that data are not representative for children (under 18 years).<sup><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></sup> Harassment specifically linked to disability requires relatively large sample sizes in order to obtain a sufficiently large number of disabled persons in the sample.</p>\n<p>The same behaviour can have different meanings and therefore have a different impact across cultural contexts and population groups. For this reason, the selection of &#x2018;harassment&#x2019; behaviours has been made also with the view of identifying situations of harassment that can be perceived as such across different social and cultural contexts.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> Other age limits (e.g. 15+ years) may be applied if consistent with national practices. Some surveys are also specifically designed to cover the youth and adolescent population. For example the Social Cohesion Survey to Prevent Violence and Crime (ECOPRED) conducted by the National Statistics Office of Mexico (INEGI) targets youth 12 years and older. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>This is a survey-based indicator that measures individuals&#x2019; experience of any of a set of behaviours that are collectively referred to as non-sexual harassment and sexual harassment. Questions on non-sexual and sexual harassment are to be measured separately. The results can then be combined. Both numerator and denominator are measured through sample surveys of the general population. The computation of this indicator requires the inclusion of a short module in a representative population survey that asks a set of questions about each type of non-sexual and sexual harassment included in Figures 1 and 2 respectively . </p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Content of question</strong></p>\n      </td>\n      <td>\n        <p><strong>Instruction</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Experience of sexual harassment in the past 12 months<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup>, by type of harassment (see Figure 2 for the set of behaviors indicative of sexual harassment) </li>\n        </ol>\n      </td>\n      <td>\n        <p>If no sexual harassment was experienced, skip to 4, otherwise go to 2.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Most recent type of sexual harassment experienced<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></li>\n        </ol>\n      </td>\n      <td>\n        <p>Continue with 3.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Place of most recent sexual harassment, by type of location</li>\n        </ol>\n      </td>\n      <td>\n        <p>Go to 4.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Experience of non-sexual harassment in the past 12 months, by type of harassment (see Figure 1 for the set of behaviors indicative of non-sexual harassment)</li>\n        </ol>\n      </td>\n      <td>\n        <p>If no non-sexual harassment was experienced, skip to END, otherwise go to 5.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Most recent type of non-sexual harassment experienced</li>\n        </ol>\n      </td>\n      <td>\n        <p>Continue with 6.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Place of most recent non-sexual harassment, by type of location</li>\n        </ol>\n      </td>\n      <td>\n        <p>Go to END.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Based on the responses to questions, the following indicators can be computed:</p>\n<p><strong>Indicator 11.7.2a:</strong> Proportion of persons victim of non-sexual harassment, by sex, age, disability status and place of occurrence, in the previous 12 months:</p>\n<p>This indicator in computed by taking the number of respondents who experienced at least one form of non-sexual harassment in the past 12 months and dividing by the total number of respondents. The result needs to be multiplied by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>11</mn>\n    <mo>.</mo>\n    <mn>7</mn>\n    <mo>.</mo>\n    <mn>2</mn>\n    <mi>a</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mo>-</mo>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>h</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>s</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n    <mi>X</mi>\n    <mn>100</mn>\n  </math> </p>\n<p><strong>Indicator 11.7.2b:</strong> Proportion of persons victim of sexual harassment, by sex, age, disability status and place of occurrence, in the previous 12 months</p>\n<p>This indicator in computed by taking the number of respondents who experienced at least one form of sexual harassment and dividing by the total number of respondents. The result needs to be multiplied by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>11</mn>\n    <mo>.</mo>\n    <mn>7</mn>\n    <mo>.</mo>\n    <mn>2</mn>\n    <mi>b</mi>\n    <mo>:</mo>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>h</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>s</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mi>X</mi>\n    <mn>100</mn>\n  </math> </p>\n<p><strong>Indicator 11.7.2: </strong>Proportion of persons victim of non-sexual or sexual harassment, by sex, age, disability status and place of occurrence, in the previous 12 months</p>\n<p>This indicator is computed by taking the number of respondents who experienced at least one form of non-sexual or sexual harassment and dividing by the total number of respondents. The result needs to be multiplied by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>11</mn>\n    <mo>.</mo>\n    <mn>7</mn>\n    <mo>.</mo>\n    <mn>2</mn>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>h</mi>\n        <mi>y</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>h</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>s</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mi>X</mi>\n    <mn>100</mn>\n  </math> </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> While not necessary for computing Indicator 11.7.2, it is recommended that the survey questionnaire first asks about experiences of harassment in the past 3 years (to reduce possible telescoping effects and capture relatively rare events). <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> While not necessary for computing Indicator 11.7.2, it is recommended that the survey questionnaire also asks how many times in the past 12 month the respondent has experienced each type of harassment, to understand the seriousness and severity of the harassment. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>The data for the indicator is collected through household surveys conducted by National Statistics Offices (NSOs) or other institutions following tight survey protocols and complying with the metadata. Data producers are encouraged to strictly follow the data quality practices, protocols and frameworks in relation of data quality. In addition to the data, countries are requested to report on the metadata which serves as one additional layer of validation and verification of the data. For survey-based indicators, metadata are assessed in relation to the representativeness and coverage of the survey as well as alignment of question wording and answer options with international standards.</p>\n<p>Before publication by custodian agencies, a standardised &#x201C;pre-publication process&#x201D; is implemented, where national stakeholders can verify and review the data before publication. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>&#x2022; <strong>At country level</strong></p>\n<p>Missing values are left blank.</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<p>In 2022, the United Nations Office on Drugs and Crime (UNODC) together with the United Nations Development Program (UNDP) and the Office of the United Nations High Commissioner on Human Rights (OHCHR) published the SDG 16 Survey Questionnaire and Implementation Manual, which contain internationally standardised survey question wording (in the five official UN languages) as well as implementation guidance related to this indicator. The questionnaire and manual are available at:</p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire</a></p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual</a> </p>\n<p>In 2013, the UNODC through its UNODC-INEGI Center of Excellence in Statistical Information on Government, Crime, Victimization and Justice (CoE) in Mexico, created the Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI), a regionally standardized methodology to measure comprehensively victimization, the perception of safety and the performance of authorities in a comparable manner in line with United Nations international standards. LACSI is led by UNODC, and it is supported by the Inter-American Development Bank (IDB), the United Nations Development Programme (UNDP) and the Organization of American States (OAS). The Initiative&apos;s Working Group (composed by 14 countries of the LAC region) meets periodically to review and update the main methodological tool. The meeting minutes, conceptual framework and methodological tools are available at: <a href=\"https://www.cdeunodc.inegi.org.mx/index.php/lacsi-initiative/\">https://www.cdeunodc.inegi.org.mx/index.php/lacsi-initiative/</a></p>\n<p>https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/</p>\n<p>In 2010, the United Nations Office on Drugs and Crime (UNODC) and the United Nations Economic Commission for Europe (UNODC-UNECE) published a Manual on Victimization Surveys that provides technical guidance on the implementation of such surveys, on the basis of good practices developed at the country-level. The UNODC-UNECE Manual on Victimization Surveys (2010) is available at: <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html\">https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) has a statistical section with dedicated staff to support the data collection through technical assistance, collating and verifying the received data and continuously improve data collection mechanisms including guidelines.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>It is recommended that National Statistics Offices (NSOs) serve as the main contact for compiling and assuring the quality of the necessary data to report on SDG 11.7.2, in close coordination with other relevant bodies in the country. Automated and substantive validation procedures are in place when data are processed by custodian agencies to assess their consistency and compliance with standards.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See section 4.d. Validation.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>While several countries, especially in the Americas under the LACSI methodology, have implemented national victimization surveys<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup>, at the global level, there continues to be limited availability of survey-based data for measuring non-sexual and sexual harassment prevalence.</p>\n<p>For this reason, UNODC partnered with UNDP and OHCHR to develop the internationally standardized and tested SDG 16 Survey questionnaire and the accompanying Implementation Manual, which countries can use for collecting data on 11 survey-based indicators under Goal 16 as well as two survey-based indicators under Goal 11, including indicator 11.7.2 on non-sexual and sexual harassment.</p>\n<p>Another important regional standard is the Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI), which countries can use to measure 4 survey-based indicators under Goal 16, as well as the survey-based indicator in Goal 11, including 11.7.2. LACSI goes beyond measuring SDG 2030 survey-based indicators and promotes the measurement of a wide range of dimensions to be measured in terms of safety and victimization that can be of use for policy makers and countries to better understand crime .</p>\n<p><strong>Time series:</strong></p>\n<p>The indicator has recently been included into the annual United Nations Crime Trends Survey (UN-CTS, the regular data collection used by UNODC to collect data from UN Member States. It is expected that countries will gradually report on this indicator as the methodological guidance is disseminated and relevant items are included in national surveys.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>When the proposed module on non-sexual and sexual harassment is part of a larger population survey, relevant disaggregations (e.g., income, sex, age group, geographic location, disability status, etc.) may not need to be included in the module since they are typically part of large socio-economic surveys. In contrast, disaggregations by place of occurrence, victim-perpetrator relationship,<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup> and reporting of the crime to the police or relevant authorities need to be included in the question module itself.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> To learn more about which countries have implemented national or subnational stand-alone crime victimization surveys, visit the UNODC-INEGI Center of Excellence Atlas on Victimization Surveys: https://www.cdeunodc.inegi.org.mx/index.php/atlas-on-cvs/ <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> The Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI) also recommends measuring the condition of both the victim and the perpetrator of being under the influence of alcohol or other drugs. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div></div>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Data for this indicator are based on a set of standardised survey questions. If data from more than one survey are available for the same country, discrepancies may be due to different wording of the questions, different structure of the questionnaire, different survey methods and operations, different sample design and sample size. As a rule, data from national surveys complying with recommended standards are used, when available.</p>", "OTHER_DOC__GLOBAL"=>"<p>UNODC-UNDP-OHCHR. 2022. SDG 16 Survey Questionnaire and Implementation Manual. Available at:</p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire</a></p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual</a> </p>\n<p>UNODC. 2013. Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI). Available at: </p>\n<p><a href=\"https://www.cdeunodc.inegi.org.mx/index.php/lacsi-initiative/\">https://www.cdeunodc.inegi.org.mx/index.php/lacsi-initiative/</a></p>\n<p><a href=\"https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/\">https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/</a></p>\n<p>UNODC-UNECE, <em>Manual on Victimization Surveys (2010)</em>. Available at: <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html\">https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html</a> </p>\n<p>EU Fundamental Rights Agency, <em>Violence against women: an EU-wide survey. Main results report (2014)</em>. Available at: <a href=\"https://fra.europa.eu/en/publication/2014/violence-against-women-eu-wide-survey-main-results-report\"><u>https://fra.europa.eu/en/publication/2014/violence-against-women-eu-wide-survey-main-results-report</u></a></p>\n<p>EU Fundamental Rights Agency,<em> What do fundamental rights mean for people in the EU? - Fundamental Rights Survey (2020)</em>. Available at: <a href=\"https://fra.europa.eu/en/publication/2020/fundamental-rights-survey-trust\">https://fra.europa.eu/en/publication/2020/fundamental-rights-survey-trust</a> </p>\n<p>Eurostat, <em>Methodological manual for the EU survey on gender-based violence against women and other forms of inter-personal violence (EU-GBV</em>), <em>2021 edition</em>. Available at: <a href=\"https://ec.europa.eu/eurostat/documents/3859598/13484289/KS-GQ-21-009-EN-N.pdf/1478786c-5fb3-fe31-d759-7bbe0e9066ad?t=1633004533458\">https://ec.europa.eu/eurostat/documents/3859598/13484289/KS-GQ-21-009-EN-N.pdf/1478786c-5fb3-fe31-d759-7bbe0e9066ad?t=1633004533458</a> </p>", "indicator_sort_order"=>"11-07-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.a.1", "slug"=>"11-a-1", "name"=>"Número de países que cuentan con políticas urbanas nacionales o planes de desarrollo regionales que a) responden a la dinámica de la población, b) garantizan un desarrollo territorial equilibrado y c) aumentan el margen fiscal local", "url"=>"/site/es/11-a-1/", "sort"=>"11aa01", "goal_number"=>"11", "target_number"=>"11.a", "global"=>{"name"=>"Número de países que cuentan con políticas urbanas nacionales o planes de desarrollo regionales que a) responden a la dinámica de la población, b) garantizan un desarrollo territorial equilibrado y c) aumentan el margen fiscal local"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población residente en municipios que cuentan con un plan de ordenación territorial, o que dispone de Plan General, Normas Subsidiarias o figuras equivalentes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que cuentan con políticas urbanas nacionales o planes de desarrollo regionales que a) responden a la dinámica de la población, b) garantizan un desarrollo territorial equilibrado y c) aumentan el margen fiscal local", "indicator_number"=>"11.a.1", "national_geographical_coverage"=>"", "page_content"=>"Las <a href=\"https://www.euskadi.eus/directrices-de-ordenacion-territorial-dot/web01-a3lurral/es/\">\nDirectrices de Ordenación Territorial</a> proporcionan los criterios relativos a la cuantificación residencial del planeamiento de los municipios vascos\natendiendo a la variación de la población residente, la variación del tamaño medio familiar o la demanda de vivienda.\nLos <a href=\"https://www.euskadi.eus/informacion/planes-territoriales-parciales-ptp/web01-a3lurral/es/\">\nPlanes Territoriales Parciales</a> desarrollan las Directrices de Ordenación Territorial en los quince ámbitos geográficos, \ndenominados Areas Funcionales, y definen los objetivos de la ordenación a partir del análisis del estado del territorio, \nde la situación socioeconómica y de sus posibilidades de evolución.\n", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Vivienda y Agenda Urbana", "periodicity"=>"Anual", "url"=>"https://www.mivau.gob.es/urbanismo-y-suelo/sistema-de-informacion-urbana", "url_text"=>"Sistema de información urbana", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de la población residente en municipios que cuentan con un plan de ordenación territorial, o que dispone de Plan General, Normas Subsidiarias o figuras equivalentes", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.a- Apoyar los vínculos económicos, sociales y ambientales positivos entre las zonas urbanas, periurbanas y rurales fortaleciendo la planificación del desarrollo nacional y regional", "definicion"=>"Este indicador se compone de dos series temporales:\n\n- Proporción de la población residente en municipios que cuentan con un plan de ordenación territorial\n- Proporción de la población residente en municipios que disponen de plan general o normas subsidiarias de planeamiento urbanístico\n", "formula"=>"\n<b>Proporción de la población residente en municipios que cuentan con un plan de ordenación territorial</b>\n\n$$PPTP^{t} = \\frac{PTP^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$PTP^{t} =$ población residente en municipios que cuentan con Plan Territorial Parcial el 1 de enero del año $t$\n\n$P^{t} =$ población a 1 de enero del año $t$\n\n\n<br>\n\n<b>Proporción de la población residente en municipios que disponen de plan general o normas subsidiarias de planeamiento urbanístico</b>\n\n$$PPPGNS^{t} = \\frac{PPG^{t}+PNS^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$PPG^{t} =$ población residente en municipios que disponen de plan general el 1 de enero del año $t$\n\n$PNS^{t} =$ población residente en municipios que disponen de normas subsidiarias el 1 de enero del año $t$\n\n$P^{t} =$ población a 1 de enero del año $t$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Este indicador se basa en la noción de que el desarrollo y la implementación de \nlas Políticas Urbanas Nacionales deben apoyar la participación, la asociación, la \ncooperación y la coordinación de los actores, así como facilitar el diálogo. Las Políticas \nUrbanas Nacionales (PNU) y los Planes de Desarrollo Regional (PDR) promueven un \ndesarrollo urbano coordinado y conectado. Un esfuerzo coordinado del \ngobierno a través de una PNU o un PDR brinda la mejor oportunidad para \nlograr una urbanización sostenible y un desarrollo territorial equilibrado al \nvincular las políticas sectoriales, conectar las políticas gubernamentales \nnacionales, regionales y locales, y fortalecer los vínculos urbanos, \nperiurbanos y rurales a través de un desarrollo territorial equilibrado.\n\nEste indicador proporciona un buen barómetro sobre el progreso global en materia \nde políticas urbanas nacionales sostenibles. Sirve como análisis de brechas \npara respaldar las recomendaciones de políticas. El indicador puede identificar \nbuenas prácticas y políticas entre los países que pueden promover la asociación \ny la cooperación entre todas las partes interesadas. Este indicador está orientado \na los procesos y es aspiracional y tiene el potencial de respaldar la validación \ndel Objetivo 11 y otros indicadores de los ODS con un componente urbano. El indicador \ntiene la capacidad de ser aplicable a niveles de múltiples jurisdicciones, es decir, \nabarcando varias áreas y al mismo tiempo atendiendo los desafíos urbanos de una manera \nnacional más integrada.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos de Naciones Unidas.", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.a.1&seriesCode=SD_CPA_UPRDP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Países que cuentan con políticas urbanas nacionales o planes de desarrollo regional que respondan a la dinámica poblacional, aseguren un desarrollo territorial equilibrado y aumenten el espacio fiscal local (1 = SÍ; 0 = NO) SD_CPA_UPRDP</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-0a-01.pdf\">Metadatos 11-a-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"Proporción de la población residente en municipios que cuentan con un plan de ordenación territorial, o que dispone de Plan General, Normas Subsidiarias o figuras equivalentes", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.a- Apoyar los vínculos económicos, sociales y ambientales positivos entre las zonas urbanas, periurbanas y rurales fortaleciendo la planificación del desarrollo nacional y regional", "definicion"=>"This indicator is composed of two time series:\n\n- Proportion of the population residing in municipalities that have a land use plan\n- Proportion of the population residing in municipalities that have a general plan \n  or subsidiary urban planning regulations\n", "formula"=>"\n<b>Proportion of the population residing in municipalities that have a land use plan</b>\n\n$$PPTP^{t} = \\frac{PTP^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$PTP^{t} =$ population residing in municipalities that have a Partial Territorial Plan on January 1 of year $t$\n\n$P^{t} =$ population as of January 1 of year $t$\n\n\n<br>\n\n<b>Proportion of the population residing in municipalities that have a general plan or subsidiary urban planning regulations</b>\n\n$$PPPGNS^{t} = \\frac{PPG^{t}+PNS^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$PPG^{t} =$ population residing in municipalities that have a general plan on January 1 of year $t$\n\n$PNS^{t} =$ population residing in municipalities that have subsidiary regulations on January 1 of year $t$\n\n$P^{t} =$ population as of January 1 of year $t$\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"This indicator is based on the notion that the development and implementation \nof National Urban Policies should support participation, partnership, cooperation \nand coordination of actors as well as facilitate dialogue. National Urban Policy \n(NUP) and Regional Development Plans (RDP) promote coordinated and connected urban \ndevelopment. A coordinated effort from government through a NUP or RDP provides the \nbest opportunity for achieving sustainable urbanization and balanced territorial \ndevelopment by linking sectorial policies, connecting national, regional and local \ngovernment policies, strengthening urban, peri-urban and rural links through balanced \nterritorial development. \n\nThis indicator provides a good barometer on global progress on sustainable national \nurban policies. It serves as gap analysis to support policy recommendations. The \nindicator can identify good practices and policies among countries that can promote \npartnership and cooperation between all stakeholders. This indicator is both process \noriented and aspirational and has the potential to support the validation of Goal 11 \nand other SDGs indicators with an urban component. The indicator has the ability to \nbe applicable at multi jurisdictions levels, i.e covering a number of areas while taking \ncare of urban challenges in a more integrated national manner. \n\nSource: United Nations Statistics Division \n", "comparabilidad"=>"The available indicator partially complies with the United Nations metadata.", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.a.1&seriesCode=SD_CPA_UPRDP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Countries that have national urban policies or regional development plans that respond to population dynamics; ensure balanced territorial development; and increase local fiscal space (1 = YES; 0 = NO) SD_CPA_UPRDP</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-0a-01.pdf\">Metadata 11-a-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de la población residente en municipios que cuentan con un plan de ordenación territorial, o que dispone de Plan General, Normas Subsidiarias o figuras equivalentes", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.a- Apoyar los vínculos económicos, sociales y ambientales positivos entre las zonas urbanas, periurbanas y rurales fortaleciendo la planificación del desarrollo nacional y regional", "definicion"=>"Este indicador se compone de dos series temporales:\n\n- Proporción de la población residente en municipios que cuentan con un plan de ordenación territorial\n- Proporción de la población residente en municipios que disponen de plan general o normas subsidiarias de planeamiento urbanístico\n", "formula"=>"\n<b>Lurralde-antolamenduko plana duten udalerrietako biztanleen proportzioa</b>\n\n$$PPTP^{t} = \\frac{PTP^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$PTP^{t} =$ Lurralde Plan Partziala duten udalerrietako biztanleak $t$ urteko urtarrilaren 1ean \n\n$P^{t} =$ guztizko biztanleria $t$ urteko urtarrilaren 1ean\n\n\n<br>\n\n<b>Hirigintza-plangintzako plan orokorra edo arau subsidiarioak dituzten udalerrietako biztanleen proportzioa</b>\n\n$$PPPGNS^{t} = \\frac{PPG^{t}+PNS^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$PPG^{t} =$ plan orokorra duten udalerrietako biztanleak $t$ urteko urtarrilaren 1ean \n\n$PNS^{t} =$ arau subsidiarioak dituzten udalerrietan bizi diren biztanleak $t$ urteko urtarrilaren 1ean\n\n$P^{t} =$ guztizko biztanleria $t$ urteko urtarrilaren 1ean\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Este indicador se basa en la noción de que el desarrollo y la implementación de \nlas Políticas Urbanas Nacionales deben apoyar la participación, la asociación, la \ncooperación y la coordinación de los actores, así como facilitar el diálogo. Las Políticas \nUrbanas Nacionales (PNU) y los Planes de Desarrollo Regional (PDR) promueven un \ndesarrollo urbano coordinado y conectado. Un esfuerzo coordinado del \ngobierno a través de una PNU o un PDR brinda la mejor oportunidad para \nlograr una urbanización sostenible y un desarrollo territorial equilibrado al \nvincular las políticas sectoriales, conectar las políticas gubernamentales \nnacionales, regionales y locales, y fortalecer los vínculos urbanos, \nperiurbanos y rurales a través de un desarrollo territorial equilibrado.\n\nEste indicador proporciona un buen barómetro sobre el progreso global en materia \nde políticas urbanas nacionales sostenibles. Sirve como análisis de brechas \npara respaldar las recomendaciones de políticas. El indicador puede identificar \nbuenas prácticas y políticas entre los países que pueden promover la asociación \ny la cooperación entre todas las partes interesadas. Este indicador está orientado \na los procesos y es aspiracional y tiene el potencial de respaldar la validación \ndel Objetivo 11 y otros indicadores de los ODS con un componente urbano. El indicador \ntiene la capacidad de ser aplicable a niveles de múltiples jurisdicciones, es decir, \nabarcando varias áreas y al mismo tiempo atendiendo los desafíos urbanos de una manera \nnacional más integrada.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen metadatuak.", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.a.1&seriesCode=SD_CPA_UPRDP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Populazio-dinamikari erantzuten dioten, lurralde-garapen orekatua ziurtatzen duten eta tokiko zerga-espazioa handitzen duten hiri-politika nazionalak edo eskualde-garapeneko planak dituzten herrialdeak (1 = BAI; 0 = EZ) SD_CPA_UPRDP</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-0a-01.pdf\">Metadatuak 11-a-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 11.a: Support positive economic, social and environmental links between urban, peri-urban and rural areas by strengthening national and regional development planning</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 11.a.1: Number of countries that have national urban policies or regional development plans that (a) respond to population dynamics; (b) ensure balanced territorial development; and (c) increase local fiscal space</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SD_CPA_UPRDP - Countries that have national urban policies or regional development plans that respond to population dynamics; ensure balanced territorial development; and increase local fiscal space (1 = YES; 0 = NO) [11.a.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>This Indicator is related to several Goals and Targets, particularly the following:</p>\n<p>1.4.2, 1.5.1, 2.3.1, 2.c.1, 5.2.1, 5a.1, 6.1.1, 6.2.1, 7.2.1, 7.3.1, 8.3.1, 8.5.1, 8.6.1, 9.1.1, 9.4.1, 9a.1, 10.4.1, 12.5.1, 13.1.1, 13b.1, 15.9.1, 16.7.1, 16a.1, 16b.1, 17.14.1, 17.17.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>\n<p>United Nations Population Fund (UNFPA)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>\n<p>United Nations Population Fund (UNFPA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p><em>National Urban Policies and regional development plans: </em>A National Urban Policy (NUP) is defined as a coherent set of decisions or principle of actions derived through a deliberate government led process of coordinating and rallying various actors for a common vision and goal that will promote more transformative, productive, inclusive, and resilient urban development for the long term.<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup> This standard definition is extended and adapted to country contexts and may include, where applicable terms such as National Urban <em>Plan</em>, <em>Framework</em>, or <em>Strategy</em> as long as they are aligned with the above qualifiers. Similarly, regional development plans (RDPs) follow the same definition, only applied at the subnational level.</p>\n<p><em>NUP that responds to population dynamics:</em> This first qualifier examines to what extent the NUP addresses issues to do with population composition, trends and projections in achieving development goals and targets.</p>\n<ul>\n  <li><em>Population composition </em>includes size, geographic distribution and density, household size and composition, mobility and migration, age and sex distribution and disaggregation, as specified in SDG target 17.18</li>\n  <li><em>Trends </em>are changes in composition of the population over time</li>\n  <li><em>Projections </em>are expected changes over time that the NUP needs to ensure that they are well addressed.</li>\n</ul>\n<p>Key questions for the assessment:</p>\n<ul>\n  <li>To what extent are quality and timely data on urban and rural population composition, trends and projections available for use in the development, implementation and monitoring of NUP or RDPs?</li>\n  <li>To what extent do the strategies/interventions of the NUP and/or RDPs refer to population composition, trends and projections over the timeframe of the plan?</li>\n</ul>\n<p><em>NUP that ensures balanced territorial development:</em> This second qualifier entails the promotion of a spatially coherent territory that includes a balanced system of human settlements including cities and towns and including urban corridors; that addresses social, economic, environmental and spatial disparities particularly considering the urban-rural continuum.</p>\n<p>Key questions for the assessment:</p>\n<ul>\n  <li>To what extent does the NUP consider the need for balanced development of the territory as a whole including the differentiated yet equivalent development of all types of settlements including villages, cities and towns, including urban corridors?</li>\n  <li>To what extent are the linkages &#x2013; social, economic, environmental and spatial &#x2013; between urban, peri-urban and rural areas considered with the ultimate goal of strengthening the urban-rural continuum?</li>\n</ul>\n<p><em>Increase local fiscal space:</em> Local fiscal space is understood as the sum of financial resources available for improved delivery of basic social and economic services at the local level as a result of the budget and related decisions by governments at all levels without any prejudice to the sustainability of a government&#x2019;s financial position.</p>\n<p>Key questions for the assessment:</p>\n<ul>\n  <li>To what extent has the policy made allowance for the provision of local financial resources to provide for the implementation of the policy and for the delivery of essential basic social and economic services?</li>\n  <li>To what extent has the policy assessed the status of human capacities required to effectively use financial resources for the implementation of the policy and the delivery of essential basic social and economic services?</li>\n</ul>\n<p><em>Developing:</em> Developing refers to the policy development pathways and processes that consider the feasibility and diagnosis of policy problems and opportunities, the formulation/drafting of the policy until the approval of the policy.</p>\n<p><em>Implementing:</em> Implementation refers to the realization of the policy proposal through legislative or financial action/commitments, including the continued monitoring and evaluation of that policy.</p>\n<p><strong>Concepts:</strong></p>\n<p>Introducing NUP: an appropriate framework to achieve target 11.a and more broadly a recognized tool of implementation and monitoring of global urban agendas &#x2013; along with RDPs, and adding three measurable qualifiers as requirements for successful plans and policies, makes indicator 11.a.1 not only a more adequate, measurable and implementable process indicator for target 11.a.1, but also will serve more broadly the progress of SDGs and the new urban agenda.</p>\n<p>This revised indicator is indeed suitable for all countries and regions, and lends itself to regional analyses, as well as other forms of aggregation and disaggregation, according to development level, for example. It is also applicable at multiple territorial levels.</p>\n<p>Moreover, monitoring this indicator will help more broadly with NUP monitoring and help increase awareness, capacity and knowledge of best practices for sustainable urban policy in the process. Also, due to the multidisciplinary dimension of NUPs and their role in global agendas, the enhanced data collection and analysis capacity that would be permitted by this indicator revision would participate in guiding the necessary steps to create a more enabling urban policy environment to support SDG 11 and urban dimensions of other SDGs. NUP monitoring according to SDGs would for instance serve as a gap analysis to help formulate tailored recommendations and identify best practices.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> UN-Habitat and Cities Alliance, 2014. The Evolution of National Urban Policy: A global overview. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Number (of countries)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The primary source of data is the official documents of NUPs and RDPs, available in or provided by national and regional administrations of the countries. All these will be derived from the national and global state of NUP survey results.</p>\n<p>The alignment of the policies and plans with proposed indicators are assessed by independent national level policy evaluators to avoid subjectivity and bias. The field of practice on NUP has developed a database of experts across the regions where evaluators are routinely drawn for undertaking these reviews.</p>\n<p>To help with this evaluation according to the three qualifiers, policy evaluators follow an agreed upon analysis framework. Other supporting tools such as expert opinion, baseline data, benchmarking, performance monitoring and reporting, and gap and content analysis could be used.</p>\n<p>Global, regional and national level compilations and analyses of NUP have already been undertaken by UN-Habitat and partners, which provide a solid foundation of evidence and expertise for the monitoring of this proposed proxy indicator for 11.a.1.</p>", "COLL_METHOD__GLOBAL"=>"<p>Tailor-made questionnaires are sent to relevant focal points in charge of indicator 11a.1 to fill in the status of the indicator components. The national level data is collected based on the training modules that have widely been disseminate to many national urban policy and statistics systems. The baseline data is derived from the country, regional and global assessments undertaken every year to inform the Global State of NUP. Additionally, the data collection process targeting specifically the three qualifiers of the indicator 11.a.1. is conducted for the Global State of NUP published every two years. The data collection process is ongoing. The results listed above are based on current findings, from 67 of the 194 countries who completed the 2020 survey as well as using baseline 2018 NUP data which included 79 countries which had not yet responded to the 2020 survey, but NUP data was available based on thematic focus areas.</p>\n<p>UN Habitat compiles and presents national urban policies into a National Urban Policy Database <a href=\"http://urbanpolicyplatform.org/wp-content/uploads/2018/09/13092018NUP-database.pdf\"><u>http://urbanpolicyplatform.org/wp-content/uploads/2018/09/13092018NUP-database.pdf</u></a><u>.</u></p>\n<p>This document gathers country level data on the presence of a NUP, their title and date, status of development, and focus. It also provides direct links to the national urban policy documents. It currently contains information on 150 NUPs worldwide and is frequently updated.</p>\n<p>Every year we conduct new rounds of data collection for indicator 11a.1. For example, the 2020 round of data collection for indicator 11.a.1. is now ongoing. Member States have been contacted to fill out the <em>2020 Global State of National Urban Policy Survey</em> which includes various questions regarding the individual countries&#x2019; status on NUPs, as well as a question specific to indicator 11.a.1.</p>", "FREQ_COLL__GLOBAL"=>"<p>Monitoring and reporting of the indicator is repeated at annual intervals, allowing several reporting points until 2030. Comprehensive reporting will be undertaken once every 2 years.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The data will be available annually, and updates on the global database will be conducted every 6 months. Data will be available online on the Urban Policy Platform.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Government departments in charge of urban, rural or territorial affairs fill in the survey. Additional information is gathered from National Statistical Offices (NSOs) and government official websites and UNDESA data are also consulted for population dynamics.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Human Settlements Programme (UN-Habitat)</p>\n<p>United Nations Population Fund (UNFPA)</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Human Settlements Programme (UN-Habitat) is the specialized agency for sustainable urbanization and human settlements in the United Nations. The mandate derives from the priorities established in relevant General Assembly resolutions and decisions, including General Assembly resolution 3327 (XXIX), by which the General Assembly established the United Nations Habitat and Human Settlements Foundation, and resolution 32/162 by which the Assembly established the United Nations Center for Human Settlements (Habitat). In 2001, by its Resolution 56/206, the General Assembly transformed the Habitat into the secretariat of the United Nations Human Settlements Programme (UN-Habitat), with a mandate to coordinate human settlements&#x2019; activities within the United Nations System. As such, UN-Habitat has been designated the overall coordinator of SDG 11 and specifically as a custodian agency for 9 of the 14 indicators under SDG 11 including indicator 11.a.1. UN-Habitat also supports the monitoring and reporting of 4 urban specific indicators in other goals.</p>", "RATIONALE__GLOBAL"=>"<p><em>National Urban Policies (NUP) can help achieve target 11.a.1</em></p>\n<p>This indicator is based on the notion that the development and implementation of NUPs should support participation, partnership, cooperation and coordination of actors as well as facilitate dialogue. NUP and Regional Development Plans (RDP) promote coordinated and connected urban development. A coordinated effort from government through a NUP or RDP provides the best opportunity for achieving sustainable urbanization and balanced territorial development by linking sectorial policies, connecting national, regional and local government policies, strengthening urban, peri-urban and rural links through balanced territorial development.</p>\n<p>This indicator provides a good barometer on global progress on sustainable NUPs. It serves as gap analysis to support policy recommendations. The indicator can identify good practices and policies among countries that can promote partnership and cooperation between all stakeholders. This indicator is both process oriented and aspirational and has the potential to support the validation of Goal 11 and other SDGs indicators with an urban component. The indicator has the ability to be applicable at multi jurisdictions levels, i.e. covering a number of areas while taking care of urban challenges in a more integrated national manner.</p>\n<p>The explicit introduction of NUP in the wording of indicator 11.a.1 brings emphasis to a policy process that can better satisfy the requirements of target 11.a through sectorial, territorial and jurisdictional integration and coordination steered by the national level. This is so because evidence shows that NUP can <em>support positive economic, social and environmental links</em> by ensuring at the highest level of government the coherent alignment of sectorial policies to support sustainable and inclusive urbanization<sup><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></sup>. With the World increasingly urbanizing, it is becoming clear today that how cities are managed and planned has ramifications well beyond their boundaries and that urbanization is a key force for national and sustainable development.</p>\n<p>Urbanization has indeed historically been a catalyst for economic growth and social progress, and even holds the possibility for the protection and more efficient use of natural resources, and climate change mitigation and adaptation. However, this positive impact is not automatic, particularly in developing countries &#x2013; where rapid and/or unplanned urbanization can bring about negative economic, social and environmental externalities with increasing congestion, sprawl, informality, social exclusion and conflict &#x2013; if the provision of services and infrastructure does not keep up with natural and internal population growth, equitable distribution, migration patterns to the city, etc. Governments need to be sensitive to the fact that urbanization is a nation-wide and multi-sectorial issue. Therefore, NUPs provide the framework to harness urbanization dividends and mitigate its negative externalities. A NUP calls attention to the impact of sectorial governmental policies on the sustainable development of cities and encourages and enables the vertical and horizontal coordination of government departments and their policies to best support it.</p>\n<p>This consideration in turn also encourages more cooperation and coordination between different levels of government to support the development and implementation of a national vision for urban development, effectively <em>strengthening national and regional development planning</em>. The urban policy process is led at the national level to ensure the articulation and coordination of different sectors and government levels but engages both top down and bottom up processes. For a successful implementation, a NUP must create an enabling, collaborative and cooperative institutional environment, mobilizing different levels, assessing and building their capacities, and establishing jointly defined and transparent responsibilities for implementation. Subnational governments are key implementation partners due to their proximity to citizens and role in delivering services and infrastructure. As such, a NUP does not replace regional and local development policies and plans but strengthens them and relies on their horizontal alignment and vertical articulation, especially to tackle cross boundary challenges such as sustainable resource management, infrastructure development, climate change adaptation and mitigation, or urban-rural linkages.</p>\n<p>Finally, NUP is an overarching framework articulating and aligning subnational and local plans and policies under a common vision for urbanization that also makes it particularly suited to consider the urban-peri-urban-rural continuum. This urban and rural consideration is a key element of data disaggregation and administrative delineation in territorial planning. However, the importance of urban-rural linkages (through flows of people, natural resources, capital, goods, ecosystem services, information, technology, ideas and innovation) is increasingly being acknowledged for sustainable and integrated territorial development. The New Urban Agenda (NUA) for instance stresses the need to reduce urban and rural disparities to foster equitable development and encourage connectivity. Target 11.a is the only one that explicitly considers <em>urban, peri-urban and rural areas</em> under a city-centric SDG 11. NUP is the adequate framework to strengthen and direct urban and rural flows towards the most sustainable patterns of consumption and equitable resource distribution, as they can strike the balance between competition and solidarity between territories of a country.</p>\n<p><em>Urban Policies are more broadly instrumental for the implementation and monitoring of global agendas</em></p>\n<p>NUPs therefore enable a cross-sectorial approach, and the horizontal and vertical institutional coordination needed to address the challenges and opportunities of urbanization, which are increasingly recognized as going beyond the boundaries of the city. Intergovernmental agreements have indeed shown a new interest in urbanization for sustainable development. This is illustrated of course in Agenda 2030 with its introduction of a standalone urban SDG-11, but many other SDGs also have clear urban dimensions and implications. Following the Agenda 2030, the United Nations Conference on Housing and Sustainable Development (Habitat III) adopted the New Urban Agenda, a roadmap for the next 20 years setting new global standards for sustainable urban development. Finally, although the Paris Agreement on Climate Change does not explicitly mention cities, the management of urbanization is still essential to addressing climate change, as is illustrated by the fact that two third of Intended Nationally Determined Contributions (NDCs) contain clear urban references and content.<sup><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></sup> As an instrument for governments to harness the dynamics of urbanization for national development, NUPs have therefore been identified as a key tool for the implementation and monitoring of such agendas.</p>\n<p>The Policy Paper on NUPs prepared for Habitat III for instance explained that <em>a NUP should constitute an important part of any serious attempt to implement the SDGs and should become a key instrument to measure the achievement of the SDGs. </em>As explained above, NUPs are a particularly appropriate framework to achieve target 11.a, and more generally can be instrumental in creating the necessary enabling framework to implement the urban development objectives of SDG 11. For instance, the NUA explicitly identifies NUPs as essential to achieve the urban paradigm shift it advocates for, recognizing the <em>leading role of national governments [&#x2026;]</em> <em>in the implementation of inclusive and effective urban policies and legislation for sustainable urban development </em>(NUA &#x2013; 15.b). Moreover, the Urban-Rural Linkages Guiding Principles provide practical approach and actions to enhance territorial cohesion including via policies.<sup><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></sup> Organisation for Economic Co-operation and Development (OECD) set of urban and rural policies are additional frameworks that are very important to enhance social, economic and environmental links across urban-rural and peri-urban territories.<sup><sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup></sup></p>\n<p>Finally, NUPs can also be an instrument to coordinate the urban components of NDCs across scales and sectors and mainstream the principles of climate change adaptation and mitigation for the implementation of the Paris Agreement.<sup><sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></sup></p>\n<p><em>Qualifiers for a measurable process indicator</em></p>\n<p>Given their instrumental role for the implementation and monitoring of global urban agendas, the adoption of a NUP by a national government can be considered as a strong indicator of political commitment to promoting sustainable urban development. It also makes them particularly well suited for measuring target 11.a through a <em>process</em> indicator. As a process indicator, 11.a.1 is indeed supposed to assess the progress made towards creating an enabling environment that will ensure achievement of the outcomes and impacts of the targets of the Sustainable Development Agenda. Its definition sets the foundation on <em>how </em>target 11.a can be achieved, through measurable means. The proposed revision of the indicator therefore supplements <em>national urban policies and regional development plans </em>with 3 qualifiers that indicate the means of successfully reaching the requirements of target 11.a.</p>\n<p>The first qualifier is that policies and plans should <em>respond to population dynamics</em>. Grounding policies and plans in the most current and comprehensive spatial and demographic data and projections is indeed a prerequisite for a successful implementation. The challenges posed by rapid urbanization indeed stem from the fact that policy and planning framework and their implementation are outpaced by population growth, coupled with policy priorities that may not prioritize inclusive development for all current and future urban residents, which together result in straining the provision of infrastructure and services, and causing socio-economic and environmental damages. Forecasting demographic trends and needs in the diagnosis phase of policies and plans enables governments to plan ahead for urbanization and provide adequate land and infrastructure in a more cost-efficient and less socially disruptive way than trying to catch up, repair and upgrade uncontrolled expansion. This process of developing urban policies and plans can also be the occasion to improve national data collection on urban areas, and serve other SDG 11 indicators, as well as provide a baseline for monitoring the outcomes of such interventions.</p>\n<p>The second qualifier requires policies and plans to <em>ensure balanced territorial development</em>, in a direct answer to target 11.a.1&#x2019;s reference to the urban, peri-urban and rural continuum. Policies and plans should adopt a broad territorial perspective and consider the linkages and flows from urban to rural areas not only to avoid and reduce social, economic and environmental disparities between territories but also to promote distinctive strengths and encourage beneficial interactions for the most efficient path to sustainable growth for the country. Such a perspective for policies and plans is achieved by higher territorial scale than cities, through regional plans and national policies.</p>\n<p>Finally, the third qualifier is to <em>increase local fiscal space</em>. As integrated NUPs and RDPs introduce a more coordinated and decentralized articulation of responsibilities for urban development, ensuring that subnational and local governments have the adequate financial resources to carry out their responsibilities is essential to the successful implementation of policies and plans. The transfer of competences from central to local levels must therefore be accompanied by a commensurate devolution of financial resources and autonomy. Moreover, in times of shrinking governmental budgets, the capacity of local governments to expand and diversify endogenous financial resources and revenues and not rely too heavily on central transfers should be increased. This involves more fiscal power and capacity, better land value capture mechanisms &#x2013; which go hand in hand with a clear and enforceable land policy framework &#x2013; and innovative financial partnerships, for instance collaborating with the private sector for service and infrastructure delivery. In all cases, fiscal policies and mechanisms must remain subordinated to the established urban policy and planning objectives: central transfers must be embedded within the NUP framework, and take into account territorial equity; and local fiscal systems must be closely tied to local territorial plans so as to incentivize sustainable patterns of development.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> UN-Habitat and OECD, 2018, Global State of National Urban Policy. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> UN-Habitat, 2016, <em>Sustainable Urbanization in the Paris Agreement. Comparative review for urban content in the Nationally Determined Contributions (NDCs).</em> <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> UN-Habitat, 2019, Urban-Rural Linkages, Guiding Principles: Framework for Action to Advance Integrated Territorial Development (https://urbanrurallinkages.files.wordpress.com/2019/09/url-gp-1.pdf). <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> OECD, 2019, OECD Principles on Urban Policy (<a href=\"https://www.oecd.org/cfe/Brochure-OECD-Principles-Urban-Policy.pdf\">https://www.oecd.org/cfe/Brochure-OECD-Principles-Urban-Policy.pdf</a>) and OECD Principles on Rural Policy (https://www.oecd.org/rural/rural-development-conference/documents/Rural-principles.pdf). <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> UN-Habitat, 2016, <em>Addressing Climate Change in National Urban Policies.</em> <a href=\"#footnote-ref-7\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>UN-Habitat and UNFPA, along with many other partners such as OECD and Cities Alliance are working together to collect updated information from Member States regarding the three qualifiers in addition to other questions pertinent to National Urban Policies (NUP) and their implementation process. The survey<sup><sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup></sup> results will inform the 2020 Global State of NUP Report. Many countries have filled in required information based on the specific qualifiers of indicator 11.a.1., which builds upon the 2018 NUP dataset.<sup><sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup></sup> The success of the indicator requires more capacity development and routine follow ups with ministries and NSOs at national levels, but sometimes also going beyond the national levels to ensure good understanding of the 3 sub-components.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> See question 27 of Global Survey on National Urban Policies at: <a href=\"https://drive.google.com/file/d/1-zn9d85GWJv1Tr039OtmoqPOfpwiowku/view?usp=sharing\">https://drive.google.com/file/d/1-zn9d85GWJv1Tr039OtmoqPOfpwiowku/view?usp=sharing</a>. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> UN-Habitat and OECD, 2018, Global State of National Urban Policy at: <a href=\"http://urbanpolicyplatform.org/wp-content/uploads/2019/11/Global-Report-NUP1.pdf\">http://urbanpolicyplatform.org/wp-content/uploads/2019/11/Global-Report-NUP1.pdf</a>. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>The methodology uses a policy evaluation framework that assesses and tracks progress on the extent to which country level national urban policy (NUP) or regional development plans (RDPs) are being developed or implemented to cover or satisfy the following criteria:</p>\n<ol>\n  <li>Responds to population dynamics</li>\n  <li>Ensures balanced regional and territorial development</li>\n  <li>Increases local fiscal space</li>\n</ol>\n<p>Essentially, in countries that already have NUPs and RDPs, the NUPs are examined for their consistency in covering the three above qualifiers. While for countries that do not have NUPs or are currently developing NUPs, these are noted and documented as steps towards developing a NUP. Such countries are counted with zero scores to ensure a full coverage of status on all countries.</p>\n<p>To maintain the objectivity and comparability in the policy analysis, five categories of assessment are used for each qualifier. These categories correspond to a progressive evaluation of the extent to which national and regional policies in plans integrate elements that contribute to the realization of each qualifier:</p>\n<ul>\n  <li>Category 1: policy document does not make any reference to the qualifier or the country is not developing or implementing a policy (no NUP exists)</li>\n  <li>Category 2: policy document makes some reference to the specific qualifier, but this qualifier is not integrated in the diagnosis and recommendations of the policy</li>\n  <li>Category 3: policy document integrates the specific qualifier, but this qualifier is poorly understood or misinterpreted</li>\n  <li>Category 4: policy document integrates in a cross-cutting perspective the specific qualifier without clear policy recommendations</li>\n  <li>Category 5: policy document integrates and mainstreams the specific qualifier with clear policy recommendations derived from the qualifier</li>\n</ul>\n<p>Each category is assigned a percentage bracket, as follows:</p>\n<ul>\n  <li>Category 1: 0 per cent</li>\n  <li>Category 2: 1-25 per cent</li>\n  <li>Category 3: 26-50 per cent</li>\n  <li>Category 4: 51-75 per cent</li>\n  <li>Category 5: 76-100 per cent</li>\n</ul>\n<p>For example, in Table 1, the evaluator provides a numeric value based on the category that corresponds to the qualifier analyzed, understanding that only one category per qualifier is selected:</p>\n<p><strong><em>Table 1: Evaluators Assessment of one of the qualifiers</em></strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Qualifier</p>\n      </td>\n      <td>\n        <p>Category 1</p>\n        <p>(0%)</p>\n      </td>\n      <td>\n        <p>Category 2</p>\n        <p>(1-25%)</p>\n      </td>\n      <td>\n        <p>Category 3</p>\n        <p>(26-50%)</p>\n      </td>\n      <td>\n        <p>Category 4</p>\n        <p>(51-75%)</p>\n      </td>\n      <td>\n        <p>Category 5</p>\n        <p>(76-100%)</p>\n      </td>\n      <td>\n        <p>Total</p>\n        <p>(max 100 per qualifier)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Qualifier (a)</p>\n        <p><em>national urban policies or regional development plans respond to population dynamics</em></p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>40%</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>a = 40%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Qualifier (b)</p>\n        <p><em>National urban policies or regional development plans ensure balanced regional and territorial development</em></p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>20%</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>b = 20%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Qualifier (c)</p>\n        <p><em>National urban policies or regional development plans increase local fiscal space</em></p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>75%</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>c = 75%</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>To reduce the bias of subjectivity in the overall assessment, independent policy evaluation will be undertaken by several evaluators. Once each qualifier is evaluated by all the evaluators, a final averaged value for the indicator 11.a.1 is calculated. The table 2 below provides a summary of the procedures for the computation of the final values (final averaged value for the indicator 11.a.1).</p>\n<p><strong><em>Table 2: Summary table for the computations of the indicator</em></strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>National Urban Policy </p>\n      </td>\n      <td>\n        <p>Evaluation 1 </p>\n      </td>\n      <td>\n        <p>Evaluation 2 </p>\n      </td>\n      <td>\n        <p>Evaluation 3 </p>\n      </td>\n      <td>\n        <p>Evaluation 4 </p>\n      </td>\n      <td>\n        <p>Total</p>\n        <p>(max 100 per qualifier)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Qualifier (a)</p>\n        <p><em>national urban policies or regional development plans respond to population dynamics</em></p>\n      </td>\n      <td>\n        <p>A1</p>\n      </td>\n      <td>\n        <p>A2</p>\n      </td>\n      <td>\n        <p>A3</p>\n      </td>\n      <td>\n        <p>A4</p>\n      </td>\n      <td>\n        <p>Qa = (A1+A2+A3+A4)/4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Qualifier (b)</p>\n        <p><em>National urban policies or regional development plans ensure balanced regional and territorial development</em></p>\n      </td>\n      <td>\n        <p>B1</p>\n      </td>\n      <td>\n        <p>B2</p>\n      </td>\n      <td>\n        <p>B3</p>\n      </td>\n      <td>\n        <p>B4</p>\n      </td>\n      <td>\n        <p>Qb = (B1+B2+B3+B4)/4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Qualifier (c)</p>\n        <p><em>National urban policies or regional development plans increase local fiscal space</em></p>\n      </td>\n      <td>\n        <p>C1</p>\n      </td>\n      <td>\n        <p>C2</p>\n      </td>\n      <td>\n        <p>C3</p>\n      </td>\n      <td>\n        <p>C4</p>\n      </td>\n      <td>\n        <p>Qc = (C1+C2+C3+C4)/4</p>\n      </td>\n    </tr>\n    <tr>\n      <td></td>\n      <td colspan=\"4\">\n        <p>Final value of the assessment (average values of all 3 qualifiers)</p>\n      </td>\n      <td>\n        <p>X = (Qa + Qb + Qc)/3</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Based on the final value of the assessment (X in Table 2 above), Category 1 countries are considered as countries that are not developing or implementing a national urban policy (i.e no NUP exists), countries that fall into categories 2 and 3, which correspond to 1-50 percentage points, are not counted as &#x201C;countries that are developing and implementing a NUP or RDPs&#x201D;. These countries are encouraged to deploy efforts in order to improve NUPs or RDPs.</p>\n<p>Countries that fall into categories 4 and 5, which correspond to 51 percentage points or more in the assessment, are considered as &#x201C;countries that are developing and implementing a NUP or regional development plan&#x201D; that contribute to the achievement of target 11.a. Countries that are counted as having NUPs or RDPs can still make efforts to improve the rating of the 3 qualifiers.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data compiled is checked against several criteria including the data sources used, the application of internationally agreed definitions, classification and methodologies to the data from that source, etc. Once reviewed, appropriate feedback is then provided to individual countries for further discussion.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Any adjustment to the data is jointly agreed after consultations with the relevant national agencies that share the data points for reporting.</p>", "IMPUTATION__GLOBAL"=>"<p>Measuring this process indicator entails a policy evaluation of governmental National Urban Policies (NUPs) or Regional Development Plans (RDPs), the data source as such is easily accessible for evaluation. Data from 2018 was also included in the table counts above based on thematic focus: economic development, spatial structure, human development, environmental sustainability, and climate resilience. Missing values for this process-oriented indicator is reported as 0 to signify that the country has no NUP.</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates can be a simple addition of the indicator&#x2019;s values for the countries representing the region.</p>", "DOC_METHOD__GLOBAL"=>"<p>As of May 2020, the qualifiers were collected by distributing the <em>Global State of NUP Survey<sup><sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup></sup></em> to Member States. Reporting is subjective to the Member State and will need to be verified against the Member States&#x2019; National Urban Policies (NUPs) or Regional Development Plans (RDPs) for quality assurance. 2018 data was also collected through national follow ups with relevant offices and additional follow ups with experts in various countries. A guide was developed for collection of NUP data and disseminated to many countries.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> See survey questionnaire at: <a href=\"https://drive.google.com/file/d/1zn9d85GWJv1Tr039OtmoqPOfpwiowku/view?usp=sharing\">https://drive.google.com/file/d/1zn9d85GWJv1Tr039OtmoqPOfpwiowku/view?usp=sharing</a>. <a href=\"#footnote-ref-10\">&#x2191;</a></p></div></div>", "QUALITY_MGMNT__GLOBAL"=>"<p>To ensure consistency in data production across countries, UN-Habitat has developed detailed step-by-step tutorials on the computation of indicator 11.a.1, which further explain the steps presented in this metadata. The detailed tutorials, which will be continuously updated are available at <a href=\"https://unhabitat.org/knowledge/data-and-analytics\">https://unhabitat.org/knowledge/data-and-analytics</a>, <a href=\"https://www.urbanagendaplatform.org/learning\">https://www.urbanagendaplatform.org/learning</a>, and <a href=\"https://data.unhabitat.org/\">https://data.unhabitat.org/</a>. Within its Data and Analytics Section which is responsible for the indicator data compilation, UN-Habitat has a team of data experts who check all submitted data and provide direct support to countries in the indicator computation in collaboration with the Agency&#x2019;s NUP experts.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UN-Habitat&#x2019;s work in the areas of national and regional development planning has developed a strong foundation of evidence that can be adapted to monitor this target and indicator.</p>\n<p>Monitoring of the indicator will also benefit from various ongoing initiatives of policy reviews undertaken by UN-Habitat for its country assistance, or the OECD in its Urban Policy Review series.</p>\n<p>For instance, UN-Habitat and the OECD jointly published the 2018 <em>Global State of National Urban Policy Report,</em> which identified 150 NUPs worldwide, and analysed them according to their development phase, thematic components and institutional arrangement, and aggregated them into regional and global analyses. The second edition of the Global Report on NUPs was published in 2020 and all subsequent editions have been aligned closely with the metadata and ambitions of indicator 11.a.1 and have all consistently assessed the three qualifiers.</p>\n<p>In 2019 and 2023, UN-Habitat also conducted in-depth analyses of the NUP trends and national case studies in global regions through National Urban Policy Reports in Arab States, Asia and the Pacific, Europe and North America, Latin America and the Caribbean, and Sub-Saharan Africa.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Once data is received from member states, UN-Habitat uses a checklist specific to each indicator to</p>\n<p>a) assess whether the data production process followed the metadata provisions, and</p>\n<p>b) confirm the accuracy of the data sources used for the indicator computation.</p>\n<p>In addition, the received data is also checked for other qualities such as reporting period and consistency with other previously reported trends, which ensures reliable regional estimates.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>. The updated data series is available online on the Urban Policy Platform and the website link of the<em> Global State of National Urban Policy Report</em> which is updated every 2 years calendar year.</p>\n<p><strong>Time series:</strong></p>\n<p>A comprehensive update on National Urban Policy is conducted every two years with the baselines having been set from 2018.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Not applicable</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>No differences between country produced data and international estimated data on the indicator are expected to arise. Where such discrepancies exist, these will be resolved through planned technical meetings and capacity development workshops.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>[1]: <a href=\"https://unhabitat.org/programme/national-urban-policy\">https://unhabitat.org/programme/national-urban-policy</a> </p>\n<p>[2]: <a href=\"https://www.worldbank.org/en/topic/urbandevelopment/publication/urbanization-reviews\">https://www.worldbank.org/en/topic/urbandevelopment/publication/urbanization-reviews</a> </p>\n<p>[3]: <a href=\"https://www.oecd-ilibrary.org/urban-rural-and-regional-development/oecd-urban-policy-reviews_23069341\">https://www.oecd-ilibrary.org/urban-rural-and-regional-development/oecd-urban-policy-reviews_23069341</a> </p>\n<p>[4]: <a href=\"https://unhabitat.org/sites/default/files/2020/10/nua-monitoring-framework-and-related-indicators_1.pdf\">https://unhabitat.org/sites/default/files/2020/10/nua-monitoring-framework-and-related-indicators_1.pdf</a> </p>\n<p>[5]: <a href=\"https://urbanpolicyplatform.org/national-urban-policy-database/\">https://urbanpolicyplatform.org/national-urban-policy-database/</a> </p>\n<p>[6]: <u>https://urbanpolicyplatform.org/</u></p>\n<p><strong>References:</strong></p>\n<p>OECD Urban Policy Review Series Available at: <a href=\"http://www.oecd.org/cfe/regional-policy/urbanmetroreviews.htm\"><u>http://www.oecd.org/cfe/regional-policy/urbanmetroreviews.htm</u></a><u>.</u></p>\n<p>UN Habitat (2015), <em>National Urban Policy: Framework for a Rapid Diagnostic</em>, United Nations Human Settlements Programme: Nairobi. Available at: <a href=\"https://unhabitat.org/national-urban-policy-framework-for-a-rapid-diagnostic/\"><u>https://unhabitat.org/national-urban-policy-framework-for-a-rapid-diagnostic/</u></a><u>.</u></p>\n<p>UN Habitat (2015), <em>National Urban Policy: A Guiding Framework, </em>United Nations Human Settlements Programme: Nairobi. Available at: <a href=\"https://unhabitat.org/national-urban-policy-framework-for-a-rapid-diagnostic/\"><u>https://unhabitat.org/national-urban-policy-framework-for-a-rapid-diagnostic/</u></a><u>.</u></p>\n<p>UN Habitat (2017a), <em>National Urban Policy, Arab States Report</em>, United Nations Human Settlements Programme: Nairobi.</p>\n<p>UN Habitat (2017b), <em>National Urban Policy, Africa Report</em>, United Nations Human Settlements Programme: Nairobi.</p>\n<p>UN Habitat (2017c), <em>National Urban Policy, Europe and North America Report</em>, United Nations Human Settlements Programme: Nairobi.</p>\n<p>UN Habitat (2018a), <em>National Urban Policy Database</em>,<em> </em>United Nations Human Settlements Programme: Nairobi. Available at: <a href=\"http://urbanpolicyplatform.org/wp-content/uploads/2018/09/13092018NUP-database.pdf\"><u>http://urbanpolicyplatform.org/wp-content/uploads/2018/09/13092018NUP-database.pdf</u></a><u>.</u></p>\n<p>UN Habitat (2018b), <em>National Urban Policy, Latin America and the Caribbean Report</em>, forthcoming, United Nations Human Settlements Programme: Nairobi.</p>\n<p>UN Habitat (2018c), <em>National Urban Policy, Asia and the Pacific Report,</em> forthcoming, United Nations Human Settlements Programme: Nairobi.UN-Habitat and OECD (2018), Global State of National Urban Policy, United Nations Human Settlements Programme, Nairobi. Available at: <a href=\"https://unhabitat.org/books/global-state-of-national-urban-policy/\"><u>https://unhabitat.org/books/global-state-of-national-urban-policy/</u></a><u>.</u></p>", "indicator_sort_order"=>"11-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.b.1", "slug"=>"11-b-1", "name"=>"Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030", "url"=>"/site/es/11-b-1/", "sort"=>"11bb01", "goal_number"=>"11", "target_number"=>"11.b", "global"=>{"name"=>"Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>true, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030", "indicator_number"=>"11.b.1", "national_geographical_coverage"=>"", "page_content"=>"La C.A. de Euskadi cuenta con leyes, estrategias y planes orientados a la prevención y respuesta a los desastres, \nque involucran tanto a los distintos Departamentos del Gobierno Vasco como a las Administraciones Locales:\n\n<a href=\"https://www.euskadi.eus/plan-proteccion-civil-euskadi/web01-a2blarri/es/\" target=\"_blank\"> Plan de Protección Civil de Euskadi, Larrialdiei Aurregiteko Bidea-LABI</a>: el marco para la protección civil en la C.A. de Euskadi\n\n<a href=\"https://www.euskadi.eus/directrices-de-ordenacion-territorial-dot/web01-a3lurral/es/\" target=\"_blank\"> Planes de Ordenación del Territorio</a>: recoge el objetivo de limitar los usos del suelo en función de las vulnerabilidades existentes \ny una utilización más correcta del mismo para evitar el incremento del riesgo.\n\n<a href=\"https://www.euskadi.eus/documentacion/2015/estrategia-vasca-de-cambio-climatico-2050/web01-a2ingkli/es/\" target=\"_blank\">  Estrategia vasca de cambio climático 2050</a>: recoge como objetivos a largo plazo la reducción de las emisiones de gases de efecto invernadero en un 80% para 2050, así como el aumento de la resiliencia del territorio vasco para hacer frente a los efectos esperados por el cambio de clima.\n", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nEl Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue adoptado por los Estados Miembros de las \nNaciones Unidas en marzo de 2015 como una política global de reducción del riesgo de desastres. \n\nEl resultado esperado del Marco de Sendai es lograr “La reducción sustancial del riesgo de desastres y las \npérdidas en vidas, medios de subsistencia y salud y en los activos económicos, físicos, sociales, culturales \ny ambientales de las personas, las empresas, las comunidades y los países”. Entre las metas globales del \nMarco de Sendai, la “Meta E: Aumentar sustancialmente el número de países con estrategias nacionales y \nlocales de reducción del riesgo de desastres para 2020” tiene como objetivo mejorar \nel progreso global y la cobertura de las estrategias y políticas nacionales y locales de \nreducción del riesgo de desastres. \n\nLos objetivos de los planes, estrategias y políticas nacionales \nde reducción del riesgo de desastres son prevenir nuevos riesgos de desastres y reducir los \nexistentes mediante la implementación de medidas económicas, estructurales, legales, \nsociales, de salud, culturales, educativas, ambientales, tecnológicas, políticas e \ninstitucionales integradas e inclusivas que prevengan y reduzcan la exposición a peligros y \nla vulnerabilidad a los desastres, aumenten la preparación para la respuesta y la recuperación y, \nde esa manera, fortalezcan la resiliencia. \n\nEl indicador creará un puente entre los ODS y el Marco de Sendai para la Reducción del \nRiesgo de Desastres. Un número cada vez mayor de gobiernos nacionales que adopten e \nimplementen estrategias nacionales y locales de reducción del riesgo de desastres, como \nlo exige el Marco de Sendai, contribuirá al desarrollo sostenible desde las perspectivas \neconómica, ambiental y social.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.b.1&seriesCode=SG_DSR_SFDRR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Número de países que informaron tener una Estrategia Nacional de RRD alineada con el Marco de Sendai SG_DSR_SFDRR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-0b-01.pdf\">Metadatos 11-b-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-04", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in \nMarch 2015 as a global policy of disaster risk reduction. \n\nThe expected outcome of the Sendai Framework is to realize “The substantial reduction of \ndisaster risk and losses in lives, livelihoods and health and in the economic, physical, \nsocial, cultural and environmental assets of persons, businesses, communities and countries”. \nAmong the Sendai Framework global targets, “Target E: Substantially increase the number of \ncountries with national and local disaster risk reduction strategies by 2020” aims to enhance \nthe global progress and coverage of national and local disaster risk reduction strategies and \npolicies. \n\nThe objectives of the national DRR plans, strategies and policies are to prevent new and \nreduce existing disaster risk through the implementation of integrated and inclusive economic, \nstructural, legal, social, health, cultural, educational, environmental, technological, \npolitical and institutional measures that prevent and reduce hazard exposure and vulnerability \nto disaster, increase preparedness for response and recovery, and thus strengthen resilience. \n\nThe indicator will build bridge between the SDGs and the Sendai Framework for Disaster Risk Reduction \n(DRR). Increasing number of national governments that adopt and implement national and local DRR \nstrategies, which the Sendai Framework calls for, will contribute to sustainable development from \neconomic, environmental and social perspectives. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.b.1&seriesCode=SG_DSR_SFDRR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Number of countries that reported having a National DRR Strategy which is aligned to the Sendai Framework SG_DSR_SFDRR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-0b-01.pdf\">Metadata 11-b-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>nil, "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nEl Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue adoptado por los Estados Miembros de las \nNaciones Unidas en marzo de 2015 como una política global de reducción del riesgo de desastres. \n\nEl resultado esperado del Marco de Sendai es lograr “La reducción sustancial del riesgo de desastres y las \npérdidas en vidas, medios de subsistencia y salud y en los activos económicos, físicos, sociales, culturales \ny ambientales de las personas, las empresas, las comunidades y los países”. Entre las metas globales del \nMarco de Sendai, la “Meta E: Aumentar sustancialmente el número de países con estrategias nacionales y \nlocales de reducción del riesgo de desastres para 2020” tiene como objetivo mejorar \nel progreso global y la cobertura de las estrategias y políticas nacionales y locales de \nreducción del riesgo de desastres. \n\nLos objetivos de los planes, estrategias y políticas nacionales \nde reducción del riesgo de desastres son prevenir nuevos riesgos de desastres y reducir los \nexistentes mediante la implementación de medidas económicas, estructurales, legales, \nsociales, de salud, culturales, educativas, ambientales, tecnológicas, políticas e \ninstitucionales integradas e inclusivas que prevengan y reduzcan la exposición a peligros y \nla vulnerabilidad a los desastres, aumenten la preparación para la respuesta y la recuperación y, \nde esa manera, fortalezcan la resiliencia. \n\nEl indicador creará un puente entre los ODS y el Marco de Sendai para la Reducción del \nRiesgo de Desastres. Un número cada vez mayor de gobiernos nacionales que adopten e \nimplementen estrategias nacionales y locales de reducción del riesgo de desastres, como \nlo exige el Marco de Sendai, contribuirá al desarrollo sostenible desde las perspectivas \neconómica, ambiental y social.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=11.b.1&seriesCode=SG_DSR_SFDRR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Sendaiko esparruarekin bat datorren HAM (Hondamendi Arriskua Murrizteko) Estrategia Nazionala dutela jakinarazi duten herrialdeen kopurua SG_DSR_SFDRR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-0b-01.pdf\">Metadatuak 11-b-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>nil, "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.5.3: Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015&#x2013;2030</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_DSR_SFDRR - Countries that reported having a National DRR Strategy which is aligned to the Sendai Framework to a certain extent (1 = YES; 0 = NO) [1.5.3, 11.b.1, 13.1.2]</p>\n<p>SG_DSR_LEGREG - Countries with legislative and/or regulatory provisions been made for managing disaster risk (1 = YES; 0 = NO) [1.5.3,11.b.1,13.1.2]</p>\n<p>SG_DSR_LGRGSR - Score of adoption and implementation of national DRR strategies in line with the Sendai Framework [1.5.3, 11.b.1, 13.1.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.b.1, 13.1.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator captures the number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015&#x2013;2030, and the coverage score for the level of implementation which Member States will report their status in the Sendai Framework Monitor (SFM).</p>\n<p><strong>Concepts:</strong></p>\n<p><strong>Disasters</strong>: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Disaster risk reduction strategies</strong>: define goals and objectives across different timescales and with concrete targets, indicators and time frames. In line with the Sendai Framework for Disaster Risk Reduction 2015-2030, the strategies should be aimed at preventing the creation of disaster risk, the reduction of existing risk and the strengthening of economic, social, health and environmental resilience.</p>\n<p><strong>Notes: </strong></p>\n<p>[a] An open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction established by the General Assembly (resolution 69/284) is developing a set of indicators to measure global progress in the implementation of the Sendai Framework. These indicators will eventually reflect the agreements on the Sendai Framework indicators.</p>\n<p>Detailed definitions, concepts, including composition and calculation for each of the data series, can be found in the SFM Technical Guidance (see below the Reference section)</p>", "UNIT_MEASURE__GLOBAL"=>"<p>SG_DSR_LGRGSR: index</p>\n<p>SG_DSR_SFDRR: number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are reported by national Sendai Framework focal points in the Sendai Framework Monitor (SFM). </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are reported in Sendai Framework Monitor (SFM) on an ongoing basis, and snapshotted once every year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released once a year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The coordinating lead institution chairing the National Disaster Risk Reduction (DRR) platform which is comprised of special purpose agencies including national disaster agencies, civil protection agencies, and meteorological agencies.</p>\n<p>The coordinating lead institution chairing the National DRR platform which is comprised of special purpose agencies including national disaster agencies, civil protection agencies, and meteorological agencies.</p>\n<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OIEWG) report, endorsed by the United Nations General Assembly in Resolution A/RES/71/276, requested the UNDRR to undertake technical work and provide technical guidance to develop minimum standards and metadata, the methodologies, and the global monitoring and measurements of the SFM global indicators.</p>\n<p>This indicator is recommended by the OIEWG for the measurement of global Target E of the Sendai Framework, which were endorsed by the UN General Assembly in its Resolution A/RES/71/276, <em>Report of the open-ended intergovernmental</em> <em>expert working group on indicators and terminology relating to disaster risk</em>.</p>", "RATIONALE__GLOBAL"=>"<p>The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. The expected outcome of the Sendai Framework is to realize &#x201C;The substantial reduction of disaster risk and losses in lives, livelihoods and health and in the economic, physical, social, cultural and environmental assets of persons, businesses, communities and countries&#x201D;. Among the Sendai Framework global targets, &#x201C;Target E: Substantially increase the number of countries with national and local disaster risk reduction strategies by 2020&#x201D; aims to enhance the global progress and coverage of national and local disaster risk reduction strategies and policies. The objectives of the national DRR plans, strategies and policies are to prevent new and reduce existing disaster risk through the implementation of integrated and inclusive economic, structural, legal, social, health, cultural, educational, environmental, technological, political and institutional measures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness for response and recovery, and thus strengthen resilience. </p>\n<p>The indicator will build bridge between the SDGs and the Sendai Framework for Disaster Risk Reduction (DRR). Increasing number of national governments that adopt and implement national and local DRR strategies, which the Sendai Framework calls for, will contribute to sustainable development from economic, environmental and social perspectives.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The Sendai Framework Monitoring (SFM) System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States report through the system since March 2018. The data for SDG indicators are compiled and reported by UNDRR.</p>\n<p>To the deliberations of the OEIWG as well as the IAEG-SDG, UNDRR proposed computation methodologies that allow the monitoring of improvement in national DRR strategies. </p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <msubsup>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>j</mi>\n              <mo>=</mo>\n              <mn>1</mn>\n            </mrow>\n            <mrow>\n              <mn>10</mn>\n            </mrow>\n          </msubsup>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>K</mi>\n                <mi>E</mi>\n              </mrow>\n              <mrow>\n                <mi>j</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mn>10</mn>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where:</p>\n<p>E<sub>1</sub>: National DRR strategy progress score; corresponding to Sendai Framework Indicator E-1.</p>\n<p>KE<sub>j</sub>: the level of achievement of the DRR national strategy Key Element j in the country.</p>\n<p>Member States will assess the level of implementation for ten key elements of the national DRR strategy, and enter key elements scores in the Sendai Framework Monitor. The national DRR strategy progress score E<sub>1</sub> would be calculated as the arithmetic average across ten national DRR strategy key elements (KE<sub>j</sub>).</p>\n<p>The national DRR strategy progress score will benchmark according to the following categories:</p>\n<ul>\n  <li>Comprehensive implementation: E<sub>1</sub> is higher than 0.75;</li>\n  <li>Substantial implementation, additional progress required: E<sub>1</sub> is higher than 0.5, but less than or equal to 0.75;</li>\n  <li>Moderate implementation, neither comprehensive nor substantial: E<sub>1</sub> is higher than 0.25, but less than or equal to 0.5;</li>\n  <li>Limited implementation: E<sub>1</sub> is higher than 0 but less than or equal to 0.25,</li>\n  <li>No national DRR strategy: If there is no implementation of national DRR strategy, or no existence of such plans, the score will be 0.</li>\n</ul>\n<p><strong>Note: </strong></p>\n<p>For the complete computation methodology, refer to the Technical Guidance, which provides a full detailed methodology for the indicator and sub-components.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are validation by UNDRR and national focal points.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction</li>\n  <li>ADPC Disaster and Climate Resilience e-Learning: An orientation to using the online Sendai Framework Monitor, https://courses.adpc.net/courses/course-v1:UNISDR+SFM001+2019Y1/about</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>2005-2023</p>\n<p><strong>Time series:</strong></p>\n<p>Annual</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Not applicable</p>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Internationally agreed methodology and guideline URL:</strong></p>\n<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017)</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf\">https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf</a></p>\n<p>Sendai Framework Monitor</p>\n<p><a href=\"https://sendaimonitor.undrr.org/\">https://sendaimonitor.undrr.org/</a></p>\n<p><strong>Other references:</strong></p>\n<ul>\n  <li>Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG). Endorsed by UNGA on 2<sup>nd</sup> February 2017. Available at:</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/publications/view/51748\">https://www.preventionweb.net/publications/view/51748</a></p>", "indicator_sort_order"=>"11-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"11.b.2", "slug"=>"11-b-2", "name"=>"Proporción de gobiernos locales que adoptan y aplican estrategias locales de reducción del riesgo de desastres en consonancia con las estrategias nacionales de reducción del riesgo de desastres", "url"=>"/site/es/11-b-2/", "sort"=>"11bb02", "goal_number"=>"11", "target_number"=>"11.b", "global"=>{"name"=>"Proporción de gobiernos locales que adoptan y aplican estrategias locales de reducción del riesgo de desastres en consonancia con las estrategias nacionales de reducción del riesgo de desastres"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"Los municipios que disponen de un plan (municipal o territorial) de emergencias tienen un valor de 100%", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de gobiernos locales que adoptan y aplican estrategias locales de reducción del riesgo de desastres en consonancia con las estrategias nacionales de reducción del riesgo de desastres", "indicator_number"=>"11.b.2", "national_geographical_coverage"=>"", "page_content"=>"En la C.A. de Euskadi, se dispone de un plan de emergencias a nivel autonómico y tres planes de emergencias territoriales, uno por cada territorio histórico. Por tanto, el 100% de los municipios se encuentran bajo el paraguas de un plan de emergencias territorial", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Seguridad", "periodicity"=>"Anual", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.b- De aquí a 2020, aumentar considerablemente el número de ciudades y asentamientos humanos que adoptan e implementan políticas y planes integrados para promover la inclusión, el uso eficiente de los recursos, la mitigación del cambio climático y la adaptación a él y la resiliencia ante los desastres, y desarrollar y poner en práctica, en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030, la gestión integral de los riesgos de desastre a todos los niveles", "definicion"=>"Porcentaje de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres mediante planes territoriales de emergencias y planes municipales de emergencias", "formula"=>"<b>Porcentaje de municipios que disponen de un plan municipal de emergencias</b>\n\n$$PMUN_{RRD\\, municipal}^{t} = \\frac{MUN_{RRD\\, municipal}^{t}}{MUN^{t}} \\cdot 100$$\n\ndonde:\n\n$MUN_{RRD\\, municipal}^{t} =$ número de gobiernos locales con planes municipales de emergencias en el año $t$\n\n$MUN^{t} =$ número de gobiernos locales en el año $t$\n\n <br>\n\n<b>Porcentaje de municipios cubiertos por un plan territorial de emergencias</b>\n\n$$PMUN_{RRD\\, territorial}^{t} = \\frac{MUN_{RRD\\, territorial}^{t}}{MUN^{t}} \\cdot 100$$\n\ndonde:\n\n$MUN_{RRD\\, territorial}^{t} =$ número de gobiernos locales con planes territoriales de emergencias en el año $t$\n\n$MUN^{t} =$ número de gobiernos locales en el año $t$\n", "desagregacion"=>"Territorio histórico/Comarca/Municipio", "observaciones"=>"\nEn Euskadi, según determina el Plan de Protección Civil de Euskadi – LABI, deben elaborar \ny aprobar planes municipales de emergencia (PEM) los municipios con población superior a 20.000 habitantes. \n\nPara los municipios de más de 5.000 habitantes, en coherencia con la normativa estatal reguladora de las \nBases del Régimen Local, esta directriz es recomendatoria. En 2024, el 96% de los municipios de 5.000 a 20.000 habitantes \ny el 25% de los municipios de 1.000 a 5.000 habitantes disponen de Plan de Emergencia Municipal homologado.\n", "periodicidad"=>"Anual", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue adoptado por los \nEstados Miembros de las Naciones Unidas en marzo de 2015 como una política global de \nreducción del riesgo de desastres. El resultado esperado del Marco de Sendai es lograr \n“la reducción sustancial del riesgo de desastres y de las pérdidas en vidas, medios de \nsubsistencia y salud y en los activos económicos, físicos, sociales, culturales y \nambientales de las personas, las empresas, las comunidades y los países”. \n\nEntre las metas globales del Marco de Sendai, la “Meta E: Aumentar sustancialmente \nel número de países con estrategias nacionales y locales de reducción del riesgo \nde desastres para 2020” tiene por objeto mejorar el progreso y la cobertura globales \nde las estrategias y políticas nacionales y locales de reducción del riesgo de desastres.\n\nLos objetivos de los planes, estrategias y políticas nacionales de reducción del riesgo \nde desastres son prevenir nuevos riesgos de desastres y reducir los existentes mediante \nla implementación de medidas económicas, estructurales, legales, sociales, de salud, culturales, \neducativas, ambientales, tecnológicas, políticas e institucionales integradas e inclusivas que \nprevengan y reduzcan la exposición a los peligros y la vulnerabilidad a los desastres, aumenten \nla preparación para la respuesta y la recuperación y, de ese modo, fortalezcan la resiliencia. \n\nAumentar la proporción de gobiernos locales que adoptan e implementan estrategias locales de \nreducción del riesgo de desastres, como lo exige el Marco de Sendai, contribuirá al desarrollo \nsostenible y fortalecerá la resiliencia económica, social, sanitaria y ambiental. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-0b-02.pdf\">Metadatos 11-b-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"Proporción de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.b- De aquí a 2020, aumentar considerablemente el número de ciudades y asentamientos humanos que adoptan e implementan políticas y planes integrados para promover la inclusión, el uso eficiente de los recursos, la mitigación del cambio climático y la adaptación a él y la resiliencia ante los desastres, y desarrollar y poner en práctica, en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030, la gestión integral de los riesgos de desastre a todos los niveles", "definicion"=>"Percentage of local governments that adopt and implement local disaster risk  reduction strategies through territorial emergency plans and municipal  emergency plans", "formula"=>"<b>Percentage of municipalities that have a municipal emergency plan</b>\n\n$$PMUN_{DRR\\, municipal}^{t} = \\frac{MUN_{DRR\\, municipal}^{t}}{MUN^{t}} \\cdot 100$$\n\nwhere:\n\n$MUN_{DRR\\, municipal}^{t} =$ number of local governments with municipal emergency plans in year $t$\n\n$MUN^{t} =$ number of local governments in year $t$\n\n <br>\n\n<b>Percentage of municipalities covered by a territorial emergency plan</b>\n\n$$PMUN_{DRR\\, territorial}^{t} = \\frac{MUN_{DRR\\, territorial}^{t}}{MUN^{t}} \\cdot 100$$\n\nwhere:\n\n$MUN_{DRR\\, territorial}^{t} =$ number of local governments with territorial emergency plans in year $t$\n\n$MUN^{t} =$ number of local governments in year $t$\n", "desagregacion"=>"Province/County/Municipality", "observaciones"=>"\nIn the Basque Country, according to the Basque Civil Protection Plan (LABI), municipalities \nwith a population of over 20,000 must prepare and approve municipal emergency plans (PEM). \n\nFor municipalities with more than 5,000 inhabitants, in accordance with the state regulations \ngoverning the Bases of the Local Government, this guideline is recommendatory. In 2024, 96% of \nmunicipalities with 5,000 to 20,000 inhabitants and 25% of municipalities with 1,000 to 5,000 \ninhabitants have an approved Municipal Emergency Plan.\n", "periodicidad"=>"Anual", "justificacion_global"=>"The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in \nMarch 2015 as a global policy of disaster risk reduction. The expected outcome of the Sendai Framework \nis to realize “The substantial reduction of disaster risk and losses in lives, livelihoods \nand health and in the economic, physical, social, cultural and environmental assets of persons, \nbusinesses, communities and countries”.\n\nAmong the Sendai Framework global targets, “Target E: Substantially increase the number of \ncountries with national and local disaster risk reduction strategies by 2020” aims to enhance \nthe global progress and coverage of national and local disaster risk reduction strategies and policies.\n\nThe objectives of the national DRR plans, strategies and policies are to prevent new and reduce \nexisting disaster risk through the implementation of integrated and inclusive economic, structural, \nlegal, social, health, cultural, educational, environmental, technological, political and institutional \nmeasures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness \nfor response and recovery, and thus strengthen resilience.\n\nIncreasing the proportion of local governments that adopt and implement local disaster risk reduction \nstrategies, which the Sendai Framework calls for, will contribute to sustainable development and \nstrengthen economic, social, health and environmental resilience.\n\nSource: United Nations Statistics Division\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-0b-02.pdf\">Metadata 11-b-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres", "objetivo_global"=>"11- Lograr que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles", "meta_global"=>"11.b- De aquí a 2020, aumentar considerablemente el número de ciudades y asentamientos humanos que adoptan e implementan políticas y planes integrados para promover la inclusión, el uso eficiente de los recursos, la mitigación del cambio climático y la adaptación a él y la resiliencia ante los desastres, y desarrollar y poner en práctica, en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030, la gestión integral de los riesgos de desastre a todos los niveles", "definicion"=>"Porcentaje de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres mediante planes territoriales de emergencias y planes municipales de emergencias", "formula"=>"<b>Larrialdietarako udal-plana duten udalerrien ehunekoa</b>\n\n$$PMUN_{udal\\, HAM}^{t} = \\frac{MUN_{udal\\, HAM}^{t}}{MUN^{t}} \\cdot 100$$\n\nnon:\n\n$MUN_{Udal\\, HAM}^{t} =$ larrialdietarako udal-planak dituzten udalerrien kopurua $t$ urtean\n\n$MUN^{t} =$ udalerrien kopurua $t$ urtean\n\n <br>\n\n<b>Larrialdietarako lurralde-plan batek estalitako udalerrien ehunekoa</b>\n\n$$PMUN_{lurralde\\, HAM}^{t} = \\frac{MUN_{lurralde\\, HAM}^{t}}{MUN^{t}} \\cdot 100$$\n\nnon:\n\n$MUN_{lurralde\\, HAM}^{t} =$ larrialdietako lurralde-planak dituzten udalerrien kopurua $t$ urtean\n\n$MUN^{t} =$ udalerrien kopurua $t$ urtean\n", "desagregacion"=>"Lurralde historikoa/Eskualdea/Udalerria", "observaciones"=>"\nEn Euskadi, según determina el Plan de Protección Civil de Euskadi – LABI, deben elaborar \ny aprobar planes municipales de emergencia (PEM) los municipios con población superior a 20.000 habitantes. \n\nPara los municipios de más de 5.000 habitantes, en coherencia con la normativa estatal reguladora de las \nBases del Régimen Local, esta directriz es recomendatoria. En 2024, el 96% de los municipios de 5.000 a 20.000 habitantes \ny el 25% de los municipios de 1.000 a 5.000 habitantes disponen de Plan de Emergencia Municipal homologado.\n", "periodicidad"=>"Anual", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue adoptado por los \nEstados Miembros de las Naciones Unidas en marzo de 2015 como una política global de \nreducción del riesgo de desastres. El resultado esperado del Marco de Sendai es lograr \n“la reducción sustancial del riesgo de desastres y de las pérdidas en vidas, medios de \nsubsistencia y salud y en los activos económicos, físicos, sociales, culturales y \nambientales de las personas, las empresas, las comunidades y los países”. \n\nEntre las metas globales del Marco de Sendai, la “Meta E: Aumentar sustancialmente \nel número de países con estrategias nacionales y locales de reducción del riesgo \nde desastres para 2020” tiene por objeto mejorar el progreso y la cobertura globales \nde las estrategias y políticas nacionales y locales de reducción del riesgo de desastres.\n\nLos objetivos de los planes, estrategias y políticas nacionales de reducción del riesgo \nde desastres son prevenir nuevos riesgos de desastres y reducir los existentes mediante \nla implementación de medidas económicas, estructurales, legales, sociales, de salud, culturales, \neducativas, ambientales, tecnológicas, políticas e institucionales integradas e inclusivas que \nprevengan y reduzcan la exposición a los peligros y la vulnerabilidad a los desastres, aumenten \nla preparación para la respuesta y la recuperación y, de ese modo, fortalezcan la resiliencia. \n\nAumentar la proporción de gobiernos locales que adoptan e implementan estrategias locales de \nreducción del riesgo de desastres, como lo exige el Marco de Sendai, contribuirá al desarrollo \nsostenible y fortalecerá la resiliencia económica, social, sanitaria y ambiental. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-11-0b-02.pdf\">Metadatuak 11-b-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere </p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.5.4: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_DSR_SILS - Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies [1.5.4, 11.b.2, 13.1.3]</p>\n<p>SG_DSR_SILN - Number of local governments that adopt and implement local DRR strategies in line with national strategies [1.5.4, 11.b.2, 13.1.3]</p>\n<p>SG_GOV_LOGV - Number of local governments [1.5.4, 11.b.2, 13.1.3]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.b.2, 13.1.3</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator captures the percentage of local governments that adopt and implement local disaster risk reduction strategies in line with national strategies.</p>\n<p><strong>Concepts:</strong></p>\n<p><strong>Disasters</strong>: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Disaster risk reduction strategies</strong>: define goals and objectives across different timescales and with concrete targets, indicators and time frames. In line with the Sendai Framework for Disaster Risk Reduction 2015-2030, the strategies should be aimed at preventing the creation of disaster risk, the reduction of existing risk and the strengthening of economic, social, health and environmental resilience.</p>\n<p><strong>Local Government</strong>: Form of sub-national public administration with responsibility for disaster risk reduction &#x2013; to be determined by countries for the purposes of monitoring Sendai Framework Target E.</p>\n<p><strong>Notes: </strong></p>\n<p>[a] An open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction established by the General Assembly (resolution 69/284) is developing a set of indicators to measure global progress in the implementation of the Sendai Framework. These indicators will eventually reflect the agreements on the Sendai Framework indicators.</p>\n<p>Detailed definitions, concepts, including composition and calculation for each of the data series, can be found in the SFM Technical Guidance (see below the Reference section)</p>", "UNIT_MEASURE__GLOBAL"=>"<p>SG_DSR_SILS: Percent (%) </p>\n<p>SG_DSR_SILN: Number </p>\n<p>SG_GOV_LOGV: Number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are reported by national Sendai Framework focal points in the Sendai Framework Monitor (SFM). </p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are reported in Sendai Framework Monitor (SFM) on an ongoing basis, and snapshotted once every year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released once a year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Sendai Framework Focal Points usually represent the coordinating lead institution chairing the National Disaster Risk Reduction (DRR) platform which is comprised of special purpose agencies including national disaster agencies, civil protection agencies, and meteorological agencies.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OIEWG) report, endorsed by the United Nations General Assembly in Resolution A/RES/71/276, requested the UNDRR to undertake technical work and provide technical guidance to develop minimum standards and metadata, the methodologies, and the global monitoring and measurements of the SFM global indicators.</p>\n<p>This indicator is recommended by the OIEWG for the measurement of global Target E of the Sendai Framework, which were endorsed by the UN General Assembly in its Resolution A/RES/71/276, Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk.</p>", "RATIONALE__GLOBAL"=>"<p>The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. The expected outcome of the Sendai Framework is to realize &#x201C;The substantial reduction of disaster risk and losses in lives, livelihoods and health and in the economic, physical, social, cultural and environmental assets of persons, businesses, communities and countries&#x201D;. Among the Sendai Framework global targets, &#x201C;Target E: Substantially increase the number of countries with national and local disaster risk reduction strategies by 2020&#x201D; aims to enhance the global progress and coverage of national and local disaster risk reduction strategies and policies. The objectives of the national DRR plans, strategies and policies are to prevent new and reduce existing disaster risk through the implementation of integrated and inclusive economic, structural, legal, social, health, cultural, educational, environmental, technological, political and institutional measures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness for response and recovery, and thus strengthen resilience. </p>\n<p>Increasing the proportion of local governments that adopt and implement local disaster risk reduction strategies, which the Sendai Framework calls for, will contribute to sustainable development and strengthen economic, social, health and environmental resilience. Their economic, environmental and social perspectives would include poverty eradication, urban resilience, and climate change adaptation.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The Sendai Framework Monitoring (SFM) System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States report through the system since March 2018. The data for SDG indicators are compiled and reported by UNDRR.</p>\n<p>To the deliberations of the OEIWG as well as the IAEG-SDG, UNDRR proposed computation methodologies that allow the monitoring of improvement in national DRR strategies. </p>", "DATA_COMP__GLOBAL"=>"<p>Member States count the number of local governments that adopt and implement local DRR strategies in line with the national strategy and express it as a percentage of the total number of local governments in the country.</p>\n<p>Local governments are determined by the reporting country for this indicator, considering sub-national public administrations with responsibility to develop local disaster risk reduction strategies. It is recommended that countries report on progress made by the lowest level of government accorded the mandate for disaster risk reduction, as the Sendai Framework promotes the adoption and implementation of local disaster risk reduction strategies in every local authority.</p>\n<p>Each Member State will calculate the ratio of the number of local governments with local DRR strategies in line with national strategies and the total number of local governments.</p>\n<p>Global Average will then be calculated as below through arithmetic average of the data from each Member State.</p>\n<p>For the complete computation methodology, refer to the Technical Guidance, which provides a full detailed methodology for the indicator.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are validation by UNDRR and national focal points.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction</li>\n  <li>ADPC Disaster and Climate Resilience e-Learning: An orientation to using the online Sendai Framework Monitor, https://courses.adpc.net/courses/course-v1:UNISDR+SFM001+2019Y1/about</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>2005-2023</p>\n<p><strong>Time series:</strong></p>\n<p>Annual</p>\n<p><strong>Disaggregation:</strong></p>\n<p>By local government (applying sub-national administrative unit)</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Internationally agreed methodology and guideline URL:</strong></p>\n<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017)</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf\">https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf</a></p>\n<p>Sendai Framework Monitor</p>\n<p><a href=\"https://sendaimonitor.undrr.org/\">https://sendaimonitor.undrr.org/</a></p>\n<p><strong>Other references:</strong></p>\n<ul>\n  <li>Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG). Endorsed by UNGA on 2<sup>nd</sup> February 2017. Available at:</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/publications/view/51748\">https://www.preventionweb.net/publications/view/51748</a></p>", "indicator_sort_order"=>"11-0b-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"12.1.1", "slug"=>"12-1-1", "name"=>"Número de países que elaboran, adoptan o aplican instrumentos de política destinados a apoyar la transición hacia modalidades de consumo y producción sostenibles", "url"=>"/site/es/12-1-1/", "sort"=>"120101", "goal_number"=>"12", "target_number"=>"12.1", "global"=>{"name"=>"Número de países que elaboran, adoptan o aplican instrumentos de política destinados a apoyar la transición hacia modalidades de consumo y producción sostenibles"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Se elaboran, adoptan o aplican instrumentos de política destinados a apoyar la transición hacia modalidades de consumo y producción sostenibles", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que elaboran, adoptan o aplican instrumentos de política destinados a apoyar la transición hacia modalidades de consumo y producción sostenibles", "indicator_number"=>"12.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio para la Transición Ecológica y el Reto Demográfico", "periodicity"=>"Anual", "url"=>"https://www.miteco.gob.es/es/calidad-y-evaluacion-ambiental/temas/economia-circular/estrategia.html", "url_text"=>"Estrategia española de economía circular, España circular 2030", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Se elaboran, adoptan o aplican instrumentos de política destinados a apoyar la transición hacia modalidades de consumo y producción sostenibles", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.1- Aplicar el Marco Decenal de Programas sobre Modalidades de Consumo y Producción Sostenibles, con la participación de todos los países y bajo el liderazgo de los países desarrollados, teniendo en cuenta el grado de desarrollo y las capacidades de los países en desarrollo", "definicion"=>"Valor lógico que indica si se elabora, adopta o aplica políticas destinadas a apoyar  la transición hacia modalidades de consumo y producción sostenibles", "formula"=>"\n$$PCPS^{t} =\\begin{cases}\n1 & \\text{si se elabora, adopta o aplica políticas destinadas al consumo y producción sostenibles en el año } t \\\\\n0 & \\text{si no se elabora, adopta ni aplica políticas destinadas al consumo y producción sostenibles en el año } t\n\\end{cases}$$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa incorporación del consumo y la producción sostenibles en la adopción de decisiones \na todos los niveles es una función fundamental del Marco Decenal, que se espera que \n“apoye la integración del consumo y la producción sostenibles en las políticas, \nprogramas y estrategias de desarrollo sostenible, según corresponda, incluidas, cuando \nproceda, las estrategias de reducción de la pobreza” (Documento Final de Río+20 – A/CONF.216/5).\n\nEl objetivo de este indicador es ayudar a evaluar el volumen y la distribución geográfica \nde los avances de los gobiernos en materia de consumo y producción sostenibles. Además, \nse está recopilando más información sobre los tipos, el enfoque y la orientación de \nlos instrumentos de política que se están elaborando y utilizando, para supervisar su \nprogreso a lo largo del tiempo, así como su contribución a otros Objetivos de \nDesarrollo Sostenible. \n\nEsto debería respaldar la evaluación de cuánto y con qué rapidez avanzan los gobiernos \nen el desarrollo y la aplicación de políticas que abordan el consumo y la producción \nsostenibles, ya sea a nivel transversal o sectorial. El indicador también tiene en \ncuenta los instrumentos de política vinculantes (leyes y reglamentos) y no \nvinculantes. \n\nLa primera categoría es esencial para el cambio, ya que los instrumentos \nvinculantes proporcionan la base legal para el consumo y la producción sostenibles, y \npueden utilizarse para hacer cumplir la ley o para proporcionar incentivos. La capacidad \nde elaborar, aprobar e implementar leyes es un indicador del compromiso de \nlas jurisdicciones en el cambio hacia el consumo y la producción sostenibles. Este \nindicador también puede ayudar a monitorear la evolución del panorama legislativo global. \n\nLa segunda categoría también es esencial para garantizar la participación, el compromiso y \nla apropiación institucional. En algunos casos, los instrumentos de política no vinculantes \npueden conducir a la creación de nuevos instrumentos legales. El desarrollo y la implementación \nde instrumentos no vinculantes en todos los sectores también proporciona información sobre \nel compromiso de los socios y otras partes interesadas en el consumo y la producción sostenibles.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.1.1&seriesCode=SG_SCP_CNTRY&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Países con planes de acción nacionales de consumo y producción sostenibles (CPS) o con CPS incorporado como prioridad u objetivo en las políticas nacionales (1 = SÍ; 0 = NO) SG_SCP_CNTRY</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-01-01.pdf\">Metadatos 12-1-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"Se elaboran, adoptan o aplican instrumentos de política destinados a apoyar la transición hacia modalidades de consumo y producción sostenibles", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.1- Aplicar el Marco Decenal de Programas sobre Modalidades de Consumo y Producción Sostenibles, con la participación de todos los países y bajo el liderazgo de los países desarrollados, teniendo en cuenta el grado de desarrollo y las capacidades de los países en desarrollo", "definicion"=>"Logical value that indicates whether policies are developed, adopted or applied  to support the transition towards sustainable consumption and production patterns ", "formula"=>"\n$$PCPS^{t} =\\begin{cases}\n1 & \\text{If policies for sustainable consumption and production are developed, adopted or applied in year } t \\\\\n0 & \\text{If policies for sustainable consumption and production are not developed, adopted or applied in year } t\n\\end{cases}$$\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nMainstreaming sustainable consumption and production in decision-making at all \nlevels is a core function of the 10-Year Framework, which is expected to “support \nthe integration of sustainable consumption and production into sustainable development \npolicies, programmes and strategies, as appropriate, including, where applicable, \ninto poverty reduction strategies” (Rio+20 Outcome Document – A/CONF.216/5). \n\nThe purpose of this indicator is to help assess the volume and geographical \nrepartition of governments progressing on sustainable consumption and production. \nIn addition, further information is being collected on the types, focus and \norientation of the policy instruments that are being developed and used, to monitor \ntheir progression over time as well as their contribution to other Sustainable \nDevelopment Goals. \n\nThis should support evaluation of how much / how fast governments progress in the \ndevelopment and application of policies addressing sustainable consumption and \nproduction, whether at cross-cutting or sectoral level. The indicator is also \nconsidering both binding (laws and regulations) and non-binding policy instruments. \n\nThe first category is essential to the shift, as binding instruments provide the \nlegal ground for sustainable consumption and production, and can be used for \nenforcement or to provide incentives. The ability to develop, pass and implement \nlegislation is an indication of jurisdictions’ engagement in the shift towards \nsustainable consumption and production. This indicator can also help monitor the \nevolution of the global legislative landscape. \n\nThe second category is also essential to ensure institutional engagement, commitment \nand ownership. In some cases, non-binding policy instruments can lead to the creation \nof new legal ones. The development and implementation of non-binding instruments across \nsectors also provides information on engagement of partners and other stakeholders in \nsustainable consumption and production. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.1.1&seriesCode=SG_SCP_CNTRY&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Countries with sustainable consumption and production (SCP) national action plans or SCP mainstreamed as a priority or target into national policies (1 = YES; 0 = NO) SG_SCP_CNTRY</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-01-01.pdf\">Metadata 12-1-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Se elaboran, adoptan o aplican instrumentos de política destinados a apoyar la transición hacia modalidades de consumo y producción sostenibles", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.1- Aplicar el Marco Decenal de Programas sobre Modalidades de Consumo y Producción Sostenibles, con la participación de todos los países y bajo el liderazgo de los países desarrollados, teniendo en cuenta el grado de desarrollo y las capacidades de los países en desarrollo", "definicion"=>"Valor lógico que indica si se elabora, adopta o aplica políticas destinadas a apoyar  la transición hacia modalidades de consumo y producción sostenibles", "formula"=>"\n$$PCPS^{t} =\\begin{cases}\n1 & \\text{kontsumo eta ekoizpen jasangarrirako politikak egin, hartu edo aplikatzen badira $t$ urtean } \\\\\n0 & \\text{ez bada kontsumo eta ekoizpen jasangarrirako politikarik egin, ez hartu eta ez aplikatzen $t$ urtean } \n\\end{cases}$$\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLa incorporación del consumo y la producción sostenibles en la adopción de decisiones \na todos los niveles es una función fundamental del Marco Decenal, que se espera que \n“apoye la integración del consumo y la producción sostenibles en las políticas, \nprogramas y estrategias de desarrollo sostenible, según corresponda, incluidas, cuando \nproceda, las estrategias de reducción de la pobreza” (Documento Final de Río+20 – A/CONF.216/5).\n\nEl objetivo de este indicador es ayudar a evaluar el volumen y la distribución geográfica \nde los avances de los gobiernos en materia de consumo y producción sostenibles. Además, \nse está recopilando más información sobre los tipos, el enfoque y la orientación de \nlos instrumentos de política que se están elaborando y utilizando, para supervisar su \nprogreso a lo largo del tiempo, así como su contribución a otros Objetivos de \nDesarrollo Sostenible. \n\nEsto debería respaldar la evaluación de cuánto y con qué rapidez avanzan los gobiernos \nen el desarrollo y la aplicación de políticas que abordan el consumo y la producción \nsostenibles, ya sea a nivel transversal o sectorial. El indicador también tiene en \ncuenta los instrumentos de política vinculantes (leyes y reglamentos) y no \nvinculantes. \n\nLa primera categoría es esencial para el cambio, ya que los instrumentos \nvinculantes proporcionan la base legal para el consumo y la producción sostenibles, y \npueden utilizarse para hacer cumplir la ley o para proporcionar incentivos. La capacidad \nde elaborar, aprobar e implementar leyes es un indicador del compromiso de \nlas jurisdicciones en el cambio hacia el consumo y la producción sostenibles. Este \nindicador también puede ayudar a monitorear la evolución del panorama legislativo global. \n\nLa segunda categoría también es esencial para garantizar la participación, el compromiso y \nla apropiación institucional. En algunos casos, los instrumentos de política no vinculantes \npueden conducir a la creación de nuevos instrumentos legales. El desarrollo y la implementación \nde instrumentos no vinculantes en todos los sectores también proporciona información sobre \nel compromiso de los socios y otras partes interesadas en el consumo y la producción sostenibles.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.1.1&seriesCode=SG_SCP_CNTRY&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Kontsumo eta ekoizpen jasangarriko ekintza-plan nazionalak dituzten herrialdeak edo kontsumo eta ekoizpen jasangarria politika nazionaletan lehentasun edo helburu gisa txertatuta dituztenak (1 = BAI; 0 = EZ) SG_SCP_CNTRY</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-01-01.pdf\">Metadatuak 12-1-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 12: Ensure sustainable consumption and production patterns</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 12.1: Implement the 10&#x2011;Year Framework of Programmes on Sustainable Consumption and Production Patterns, all countries taking action, with developed countries taking the lead, taking into account the development and capabilities of developing countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 12.1.1: Number of countries developing, adopting or implementing policy instruments aimed at supporting the shift to sustainable consumption and production</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_SCP_CNTRY - Countries with sustainable consumption and production (SCP) national action plans or SCP mainstreamed as a priority or target into national policies (1 = YES; 0 = NO) [12.1.1]</p>\n<p>SG_SCP_POLINS - Countries with policy instrument for sustainable consumption and production (1 = YES; 0 = NO) [12.1.1]</p>\n<p>SG_SCP_TOTLN - Number of policies, instruments and mechanism in place for sustainable consumption and production [12.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-01-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>The 10-year framework of programmes on Sustainable Consumption and Production is linked to all targets of SDG 12, literature research shows that SDG 12 is connected to a total of 16 other SDGs - making SCP the number one most cross-cutting theme across the SDGs.</p>\n<p>Main Associated SDG indicators: 12.7.1; </p>\n<p>Linked SDGs: 12.2.1/8.4.1, 12.2.2/8.4.2, 12.3.1, 12.5.1, 12.6.1, 12.8.1, 12.a.1, 12.b.1, 12.c.1, 13.2.1, 14.c.1, 14.6.1, 15.8.1 </p>\n<p>Considering that the development, adoption and implementation of policy instruments integrating SCP are creating the enabling environment for sustainable development, there are potentially many more associated SDGs, targets and indicators.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>This indicator allows for the quantification (#) and monitoring of countries making progress along the policy cycle of binding and non-binding policy instruments aimed at supporting Sustainable Consumption and Production. </p>\n<ul>\n  <li><strong>Sustainable Consumption and Production</strong>:<strong> </strong>the working definition of Sustainable Consumption and Production (SCP) used in the context of this framework is: &#x201C;The use of services and related products, which respond to basic needs and bring a better quality of life while minimising the use of natural resources and toxic materials as well as the emissions of waste and pollutants over the life cycle of the service or product so as not to jeopardise the needs of future generation.&#x201D;<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></li>\n  <li><strong>Policy:</strong> although quite flexible and contexts specific, a policy is usually defined as a course of action that has been officially agreed by an entity or an organization (governmental or non-governmental) and is effectively implemented to achieve specific objectives.</li>\n  <li><strong>Policy instruments for sustainable consumption and production: </strong>policy instruments refer to the means &#x2013; methodologies, measures or interventions &#x2013; that are used to achieve those objectives. In the case of SCP, such instruments are designed and implemented to reduce the environmental impacts of consumption and production patterns, with a view of generating economic and/or social benefits. </li>\n</ul>\n<p>Making progress along the policy cycle refers to the development, adoption, implementation or evaluation of such policy instruments. </p>\n<p><strong>Concepts:</strong></p>\n<p>As mentioned above, policy instruments are distinguished in legally binding policies and non-legally binding ones.</p>\n<ul>\n  <li><strong>Legally binding</strong>: a legally binding policy instrument refers to a system of rules, procedures and/or principles which are prescribed and enforced by a governing authority with the aim of requiring or preventing specific actions or providing incentives that lead to change in actions or preferences. It includes: laws, regulations, standards, by-laws, codes, etc. They can relate to different types of jurisdictions such as a ministry, state, municipality, or group of states. </li>\n  <li><strong>Non-binding</strong>: a non-binding policy instrument refers to a coherent set of decisions associated to a common vision, objective and/or direction, and to a proposed course of action to achieve these. It includes, for instance: action plans, policies, strategies, programmes, and projects. They can have different scopes of application (international, national, local, etc.).</li>\n  <li>At another level, different categories of policy instruments can be distinguished: <ul>\n      <li>Macro policies (e.g. national strategies/action plans, new institutions/entities)</li>\n      <li>Regulatory and legal instruments (e.g. laws, standards, enforcement measures)</li>\n      <li>Economic and fiscal instruments (taxes and tax incentives, grants, preferential loans, etc.) </li>\n      <li>Voluntary and self-regulation schemes (e.g. sectoral partnerships, codes of conduct, CSR initiatives)</li>\n    </ul>\n  </li>\n</ul>\n<p>It is important to note that, except for regulatory / legal instruments and voluntary / self-regulation schemes, the options above are not mutually exclusive: for instance, an economic instrument can be legally binding. </p>\n<p><strong>&#x201C;Policy cycle&#x201D;:</strong> this political science concept is widely used to analyse and inform public policy-making processes, but can be transposed to any recurrent pattern leading to the implementation of a policy or policy instrument. The following approach with regards to the various stages of the policy cycle is adopted: </p>\n<ul>\n  <li>Policy development, including Agenda setting (e.g. the problem identified is high enough on the public agenda that action becomes likely) and Policy design (e.g. setting objectives, identifying costs-benefits of potential policy instruments and selecting); </li>\n  <li>Policy adopted or officially launched (e.g. adopting or authorizing the preferred policy options through the legislative process and refined through the bureaucratic process); </li>\n  <li>Policy under implementation through specific actions (e.g. translating policy into concrete action and policy instruments); results and impacts are being monitored;</li>\n  <li>Policy and related action plan has reached its end date and has been evaluated.</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> <em>UNEP (2010). ABC of SCP: Clarifying Concepts on Sustainable Consumption and Production.</em> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Number</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<ul>\n  <li>Data is collected through an online survey based on this metadata sheet.</li>\n  <li>The survey may include additional questions, such as those on inter-ministerial and/or multi-stakeholder coordination mechanism for SCP.</li>\n  <li>The questions included in the survey can be revised as needed, in particular as data becomes available through the survey and alignment may be required with related ongoing work under the SDGs.</li>\n  <li>The 10YFP Global Survey on National SCP Policies and Initiatives, administered by the 10YFP Secretariat in 2015, and reported on by 10YFP National Focal Points, as well as the subsequent report, may complement information and data collected. </li>\n</ul>", "COLL_METHOD__GLOBAL"=>"<ul>\n  <li>Data is provided by 10YFP National Focal Points.</li>\n  <li>The survey is administered by the 10YFP Secretariat.</li>\n  <li>A pilot data collection and reporting was undertaken to test the methodology and reporting tools in 2017. </li>\n  <li>Since 2019, the data is collected through an online survey based on this metadata sheet.</li>\n</ul>", "FREQ_COLL__GLOBAL"=>"<ul>\n  <li>Reporting on this indicator should be done in accordance with the methodology presented here.</li>\n  <li>10YFP National Focal Points are responsible for relevance, accuracy and methodological rigour of any information reported.</li>\n  <li>The pilot reporting was conducted in 2017. </li>\n  <li>Since 2019, the data has been collected annually.</li>\n</ul>", "REL_CAL_POLICY__GLOBAL"=>"<ul>\n  <li>Pilot reporting data was released at the High-Level Political Forum on Sustainable Development in 2018. </li>\n  <li>Since then, data has been released annually at the High-Level Political Forum on Sustainable Development and in the official SDG database.</li>\n  <li>Data is uploaded to the official SDG database annually in February/March. </li>\n</ul>", "DATA_SOURCE__GLOBAL"=>"<p>National data provider: 10YFP National Focal Points &#x2013; <a href=\"https://www.oneplanetnetwork.org/10yfp-national-focal-points\">the full list of National Focal Points is available here.</a> In countries there is no nominated 10YFP national focal point, the survey is sent to the UNEP Focal Point and SDG Focal Point. </p>", "COMPILING_ORG__GLOBAL"=>"<p>Organisations responsible for data collection and compilation on this indicator at the global level: United Nations Environment Programme (UNEP), the 10YFP Secretariat administers the data collection through a dedicated online tool. UNEP, the 10YFP or the 10YFP Secretariat are not responsible for the quality of the data provided.</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) has been assigned the role of custodian agency for this indicator by the IAEG-SDG.</p>", "RATIONALE__GLOBAL"=>"<p>Mainstreaming sustainable consumption and production in decision-making at all levels is a core function of the 10-Year Framework, which is expected to <em>&#x201C;support the integration of sustainable consumption and production into sustainable development policies, programmes and strategies, as appropriate, including, where applicable, into poverty reduction strategies&#x201D; (Rio+20 Outcome Document &#x2013; A/CONF.216/5)</em>. The purpose of this indicator is to help assess the volume and geographical repartition of governments progressing on sustainable consumption and production. In addition, further information is being collected on the types, focus and orientation of the policy instruments that are being developed and used, to monitor their progression over time as well as their contribution to other Sustainable Development Goals. This should support evaluation of how much / how fast governments progress in the development and application of policies addressing sustainable consumption and production, whether at cross-cutting or sectoral level.</p>\n<p>The indicator is also considering both binding (laws and regulations) and non-binding policy instruments. The first category is essential to the shift, as binding instruments provide the legal ground for sustainable consumption and production, and can be used for enforcement or to provide incentives. The ability to develop, pass and implement legislation is an indication of jurisdictions&#x2019; engagement in the shift towards sustainable consumption and production. This indicator can also help monitor the evolution of the global legislative landscape. The second category is also essential to ensure institutional engagement, commitment and ownership. In some cases, non-binding policy instruments can lead to the creation of new legal ones. The development and implementation of non-binding instruments across sectors also provides information on engagement of partners and other stakeholders in sustainable consumption and production.</p>", "REC_USE_LIM__GLOBAL"=>"<p>the indicator quantifies and monitors countries&#x2019; progress along the policy cycle of binding and non-binding policy instruments aimed at supporting Sustainable Consumption and Production. It does not provide any qualitative information and whether policies were well-designed or if a proper background analysis had been conducted, the quality of implementation, level of enforcement, and its effects. These aspects will have to be looked at through narrative reports / qualitative analysis. </p>\n<p>The indicator encompasses policy instruments supporting the shift to SCP, including: policies which identify SCP as a key priority, policies focused on SCP and sectoral policies with SCP objectives. It is acknowledged that sectoral policies are also being reported under other SDG indicators and in particular 12.7.1 (# of countries implementing sustainable public procurement policies and action plans).</p>\n<p>Establishing baselines and targets can be time and resource intensive and depends on the willingness of 10YFP National Focal Points to communicate necessary information. </p>\n<p>Main aspects regarding precision, reliability, attribution and double counting are addressed above. If you come across additional issues, please inform the 10YFP Secretariat.</p>", "DATA_COMP__GLOBAL"=>"<p>To be reported under this indicator, a government should have moved through one or more new stage(s) of the &#x201C;Policy cycle&#x201D; on one or more policy instrument(s) during the reporting period.</p>\n<p>This indicator is calculated at relevant aggregation levels based on the information collected from the National Focal Points and other government officials; users of the data should be mindful of double counting one same policy, when aggregating data across reporting years.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Once data is received on the development, adoption and implementation of policy instruments, this data is reviewed to ensure that sufficient information is provided on the policy. In case insufficient information is provided, the National Focal Point is contacted to update the submission. However, the United Nations Environment Programme (UNEP), the 10YFP or 10YFP Secretariat is not responsible for the quality of the data provided and does not validate the quality of the policies submitted.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level:</strong></p>\n<p>A zero is imputed when no positive real value was officially recorded, in the base data sets used, for any of the underlying components which make up this aggregated total. Thus &#x201C;0.0&#x201D; can represent either NA, or a genuine 0.0, or (crucially) a combination of both, which is a common situation. This allows for values to be easily aggregated into further aggregations; however, it should be thus noted that due to imputing missing values as &#x2018;0.0&#x2019;, the aggregations may represent a lower value than actual situation. </p>\n<p><strong>&#x2022; At regional and global levels:</strong></p>\n<p>Similarly, missing values are imputed as zero in the regional and global aggregations. </p>\n<p>Note: the disaggregation categories above are indicative and some can be left empty when reporting on measures for which such data elements are not available.</p>", "REG_AGG__GLOBAL"=>"<p>The data will be aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see: <a href=\"http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a> </p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>INDICATORS OF SUCCESS: Demonstrating the shift to Sustainable Consumption and Production. Principles, process and methodology: <a href=\"https://sdg12hub.org/sites/default/files/2021-06/10yfp_indicators_of_success_2017_visual_update_impacts.pdf\">https://sdg12hub.org/sites/default/files/2021-06/10yfp_indicators_of_success_2017_visual_update_impacts.pdf</a> </li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Once data is received on the development, adoption and implementation of policy instruments, this data is reviewed by the United Nations Environment Programme (UNEP) and the 10YFP Secretariat to ensure that sufficient information is provided on the policy. In case insufficient information is provided, the National Focal Point is contacted to update the submission.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Once data is received on the development, adoption and implementation of policy instruments, this data is reviewed by the United Nations Environment Programme (UNEP) and the 10YFP Secretariat to ensure that sufficient information is provided on the policy. In case insufficient information is provided, the National Focal Point is contacted to update the submission.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See 4.d</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Between 2019 and 2022, 485 policy instruments supporting the shift to sustainable consumption and production were reported by 62 countries and the European Union under target 12.1.</p>\n<p><strong>Time series:</strong></p>\n<p>The data set covers each nation individually since 2002. </p>\n<p><strong>Disaggregation:</strong></p>\n<ul>\n  <li> Country (using the official SDG country list provided by UNDESA).</li>\n  <li>Ministry: Ministry of Environment / Sustainable Development / Natural Resources / Energy; Ministry of the Economy / Finance / Treasury; Ministry of Industry / Trade / Commerce / Labour; Ministry of Planning / Development / Infrastructures; Ministry of Foreign Affairs / Regional / International Cooperation; Ministry of Energy / Mineral Development / Power; Ministry of Science / Research / Technology / Innovation; Ministry of Agriculture / Livestock / Fisheries / Forestry / Food Security / Rural Affairs; Ministry of Tourism / Culture / Sports; Ministry of Transports / Roads / Works / Construction / Building; Ministry of Urban Development / Land Management / Housing; Ministry of Education / Higher Education / Youth; Ministry of Poverty Alleviation / Social Welfare / Families / Women.</li>\n  <li>Type of instrument: national strategy/roadmap/plan; regulatory/legal; economic/financial; voluntary/self-regulatory</li>\n  <li>Policy cycle stage: Under development (initial stage); just adopted; under implementation through specific actions; has reached its end date and has been evaluated. </li>\n  <li>Year of development, adoption, implementation and/or end-date: from 2002.</li>\n  <li>Legal status: binding/non-binding.</li>\n  <li>Sectors: Agriculture and fishery; Buildings and construction; Consumer goods; Culture and recreation; Financial sector; Education; Energy, Food &amp; Beverage; Forestry; Environmental protection; Environmental services; Government and Civil Society; Health; Housing; Industrial sector (Including SMEs); Information and Communications Technology (ICT); Plastics; Scientific Research, Development and Innovation; Textiles; Tourism; Transport; Waste (including Chemicals); Water.</li>\n  <li>Actors involved: national ministries or other specialized national agencies; local authorities; civil society organizations; scientific and technical organizations; United Nations/inter-governmental organizations; business sector. </li>\n  <li>Support received from non &#x2013; national partner: United Nations/inter-governmental organizations; multilateral financial institutions; bilateral organizations; international non-governmental organizations.</li>\n  <li>Support received from 10YFP: encouraged the development/implementation; technical support; financial support; capacity-building activities; experience and knowledge-sharing tools; no connection to 10YFP.</li>\n  <li>Link to other SDGs: SDG 1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 13; 14; 15; 16; 17.</li>\n  <li>Link to other SDG 12 Targets: SDG 12.2; 12.3; 12.4; 12.5; 12.6; 12.7; 12.8; 12.a; 12.b; 12.c.</li>\n  <li>Stages of the value chain being addressed: Finance / investment; Policy / regulation; Product / service design and planning; Research and development / Innovation; Extraction/production of raw materials; Processing of raw materials and making of product parts &amp; components; Production / manufacturing / construction; Packaging; Transportation; Distribution / retail; Service; Use / consumption; Disposal / treatment of waste / Recycling; Not targeting a specific step of the value chain.</li>\n  <li>Impact measured: Resource efficiency; environmental impact; human well-being. More detailed impact indicators in the 10YFP Indicators of Success.</li>\n  <li>Relevant links and attachments including electronic copies of the policies, or their drafts, relevant official reports, summary of consultations and any other relevant associated documents and web links should be attached to the reporting.</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<ul>\n  <li>INDICATORS OF SUCCESS: Demonstrating the shift to Sustainable Consumption and Production. Principles, process and methodology: https://sdg12hub.org/sites/default/files/2021-06/10yfp_indicators_of_success_2017_visual_update_impacts.pdf</li>\n  <li>SDG 12 Hub <a href=\"https://sdg12hub.org\">https://sdg12hub.org</a> </li>\n  <li>UNEP World Environment Situation Room (Natural Resources: DMC, Energy, GHG, Water Footprint) https://wesr.unep.org/ </li>\n  <li>SCP-HAT - http://scp-hat.lifecycleinitiative.org/</li>\n</ul>\n<p><strong>References:</strong></p>\n<ul>\n  <li>Sustainable Consumption and Production: A handbook for policy-makers. UNEP, 2015.</li>\n  <li>ABC for SCP: clarifying concepts on Sustainable Consumption and Production, UNEP, 2010</li>\n  <li>10YFP Secretariat&#x2019;s inventory of SCP National Action Plans and other strategies integrating SCP </li>\n  <li>Methodological note and questionnaire of the 10YFP Global Survey on National SCP Policies and Initiatives </li>\n  <li>Global Outlook on SCP Policies. UNEP, 2011 </li>\n  <li>Sustainable Consumption and Production indicators for the future SDGs. UNEP, 2015</li>\n  <li>Progress report on the 10-Year Framework of Programmes on Sustainable Consumption and Production Patterns. Note by the Secretary-General. 2020</li>\n</ul>", "indicator_sort_order"=>"12-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"12.2.1", "slug"=>"12-2-1", "name"=>"Huella material en términos absolutos, huella material per cápita y huella material por PIB", "url"=>"/site/es/12-2-1/", "sort"=>"120201", "goal_number"=>"12", "target_number"=>"12.2", "global"=>{"name"=>"Huella material en términos absolutos, huella material per cápita y huella material por PIB"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Huella material en términos absolutos, huella material per cápita y huella material por PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Huella material en términos absolutos, huella material per cápita y huella material por PIB", "indicator_number"=>"12.2.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La huella material del consumo informa la cantidad de materias primas necesarias \npara satisfacer la demanda final de un país y puede interpretarse como un indicador \ndel nivel de vida material/nivel de capitalización de una economía. La huella material \nper cápita describe el uso promedio de materiales para la demanda final.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-02-01.pdf\">Metadatos 12-2-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Material footprint of consumption reports the amount of primary materials \nrequired to serve final demand of a country and can be interpreted as an \nindicator of the material standard of living/level of capitalization of an \neconomy. Per-capita MF describes the average material use for final demand. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-02-01.pdf\">Metadata 12-2-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La huella material del consumo informa la cantidad de materias primas necesarias \npara satisfacer la demanda final de un país y puede interpretarse como un indicador \ndel nivel de vida material/nivel de capitalización de una economía. La huella material \nper cápita describe el uso promedio de materiales para la demanda final.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-02-01.pdf\">Metadatuak 12-2-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.4: Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation, in accordance with the 10-Year Framework of Programmes on Sustainable Consumption and Production, with developed countries taking the lead</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.4.1: Material Footprint, material footprint per capita, and material footprint per GDP</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_MAT_FTPRPC - Material footprint per capita [8.4.1, 12.2.1]</p>\n<p>EN_MAT_FTPRPG - Material footprint per unit of GDP [8.4.1, 12.2.1]</p>\n<p>EN_MAT_FTPRTN - Material footprint [8.4.1, 12.2.1] </p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>12.2.1, 8.4.2, 12.2.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Material Footprint (MF) is the attribution of global material extraction to domestic final demand of a country. The total material footprint is the sum of the material footprint for biomass, fossil fuels, metal ores and non-metallic minerals. </p>\n<p><strong>Concepts:</strong></p>\n<p>Domestic Material Consumption (DMC) and MF need to be looked at in combination, as they cover the two aspects of the economy, production and consumption. The DMC reports the actual amount of material in an economy, MF the virtual amount required across the whole supply chain to service final demand. A country can, for instance, have a very high DMC because it has a large primary production sector for export or a very low DMC because it has outsourced most of the material intensive industrial process to other countries. The material footprint corrects for both phenomena. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Tonnes;</p>\n<p>Kilograms per constant United States dollar;</p>\n<p>Tonnes per capita.</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>Material categories accordance to the global EW-MFA guide &#x201C;UNEP (2023). The use of natural resources in the economy: A Global Manual on Economy Wide Material Flow Accounting&#x201D; (<a href=\"https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y\">https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y</a>);</li>\n  <li><u>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)</u></li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>The global estimation for MF is based on data available from different national and international datasets in the domain of material flow accounts, agriculture, forestry, fisheries, mining and energy statistics. International statistical sources for MF include the International Energy Agency, the United Nations Statistical Division, the United States Geological Survey, the Food and Agriculture Organization and COMTRADE databases. </p>", "COLL_METHOD__GLOBAL"=>"<p>For global estimation, the International Resource Panel (IRP) Global Material Flows and Resource Productivity working group compiles the data from national and international databases.</p>\n<p>At the same time, country-provided indicators are collected through the QUESTIONNAIRE ON ECONOMY WIDE MATERIAL FLOW ACCOUNTS for the SDG indicators 8.4.1/12.2.1 and 8.4.2/12.2.2.</p>", "FREQ_COLL__GLOBAL"=>"<p> First data collection in 2022 and every 2 to 3 years after.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>First data release in 2017, the second in 2021 (fully estimated data). Then, in 2022 and every 2 to 3 years after (both globally estimated and country data).</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), Organization for Economic Co-operation and Development (OECD) and EUROSTAT</p>", "INST_MANDATE__GLOBAL"=>"<p>UNEP was mandated as a Custodian Agency for indicator 8.4.1 / 12.2.1 by the Inter-agency and Expert Group on SDG Indicators. UNEP IRP is the mechanism within UNEP supporting all work aspect in relation to Material Flow Accounting.</p>", "RATIONALE__GLOBAL"=>"<p>Material footprint of consumption reports the amount of primary materials required to serve final demand of a country and can be interpreted as an indicator of the material standard of living/level of capitalization of an economy. Per-capita MF describes the average material use for final demand.</p>", "REC_USE_LIM__GLOBAL"=>"<p>A footprint calculation uses the global Multi-Regional Input Output<strong> </strong>(MRIO) analysis, which compiles information from many countries national statistics to create a global multi-regional input-output table. This process requires a high level of computing capacity by supercomputers. Therefore, a limited number of countries can do the analysis on its own.</p>", "DATA_COMP__GLOBAL"=>"<p>Material footprint by type of raw material (tonnes) is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>M</mi>\n    <mi>F</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi>D</mi>\n        <mi>E</mi>\n        <mo>+</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>R</mi>\n        <mi>M</mi>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mi>I</mi>\n        <mi>M</mi>\n      </mrow>\n    </msub>\n    <mo>-</mo>\n    <mi>&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi>R</mi>\n        <mi>M</mi>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mi>E</mi>\n        <mi>X</mi>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n    </msub>\n  </math></p>\n<p>Where: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>M</mi>\n    <mi>F</mi>\n  </math> &#x2013; material footprint;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>E</mi>\n  </math><em> </em>&#x2013; domestic extraction of materials;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>R</mi>\n        <mi>M</mi>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mi>I</mi>\n        <mi>M</mi>\n      </mrow>\n    </msub>\n  </math> &#x2013; raw material equivalent of imports;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>R</mi>\n        <mi>M</mi>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mi>E</mi>\n        <mi>X</mi>\n      </mrow>\n    </msub>\n  </math> &#x2013; raw material equivalents of exports.</p>\n<p> </p>\n<p>For the attribution of the primary material needs of final demand a global, multi-regional input-output (MRIO) framework is employed. The attribution method based on I-O analytical tools is described in detail in Wiedmann et al. 2015. It is based on the Eora MRIO framework developed by the University of Sydney, Australia (Lenzen et al. 2013) which is an internationally well-established and the most detailed and reliable MRIO framework available to date. </p>\n<p>Material footprint per capita, by type of raw material (tonnes), is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>M</mi>\n    <mi>F</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>p</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>M</mi>\n        <mi>F</mi>\n      </mrow>\n      <mrow>\n        <mi>A</mi>\n        <mi>n</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Material footprint per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollar), is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>M</mi>\n    <mi>F</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>G</mi>\n    <mi>D</mi>\n    <mi>P</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>M</mi>\n        <mi>F</mi>\n      </mrow>\n      <mrow>\n        <mi>G</mi>\n        <mi>D</mi>\n        <mi>P</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>2015</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>U</mi>\n        <mi>n</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>S</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>D</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP) sends a prefilled questionnaire with estimated data to the National Statistical Office (NSO) Focal Points (FP) with a request to validate globally estimated data for this indicator and replace the data if needed/possible. The FPs coordinate data validation with stakeholders within their countries and report back the data to UNEP. For countries with no national data collected for this indicator, UNEP asks to agree on publishing and releasing the estimated data on UNEP&#x2019;s World Environment Situation Room and UNSD SDG Global database.</p>", "ADJUSTMENT__GLOBAL"=>"<p>UNEP replaces globally estimated data by national data if requested by the country. </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level:</strong></p>\n<p>A zero is imputed when no positive real value was officially recorded, in the base data sets used, for any of the underlying components which make up this aggregated total. Thus &#x201C;0.0&#x201D; can represent either NA, or a genuine 0.0, or (crucially) a combination of both, which is a common situation. This allows for values to be easily aggregated further; however, it should be thus noted that due to imputing missing values as &#x201C;0.0&#x201D;, the aggregations may represent a lower value than the actual situation. </p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels:</strong></p>\n<p>Similarly, missing values are imputed as zero in the regional and global aggregations. However, in the case where no data is available at all for a particular country, then the per capita and per GDP estimates are weighted averages of the available data. </p>", "REG_AGG__GLOBAL"=>"<p>The data are aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see: <a href=\"http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a></p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>United Nations Environment Programme (UNEP) jointly with the International Resource Panel (IRP), United Nations Statistics Division (UNSD), the Statistical Office of the European Union (Eurostat) and the Organisation for Economic Co-operation and Development (OECD) have developed a global manual on Economy-Wide Material Flow Accounting (EW-MFA) which brings in the European guidelines but provides a modular approach for countries looking to develop EW-MFA for the first time and it addresses specific issues related to resource extractive based economies. UNEP (2023). The use of natural resources in the economy - A Global Manual on Economy Wide Material Flow Accounting: <a href=\"https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y\">https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y</a></li>\n  <li>EUROSTAT (2018). The EU Economy-wide material flow accounts handbook 2018: <a href=\"https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006\">https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006</a> </li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), using the Global Manual on Economy-Wide Material Flow Accounting (UNEP, 2023).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), using the Global Manual on Economy Wide Material Flow Accounting (UNEP, 2023).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), in consultation with countries (nominated Focal Points) after receiving their feedback on the globally estimated indicators.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The data covers about 160 countries (either globally estimated or country data).</p>\n<p><strong>Time series:</strong></p>\n<p>The data set presented in the SDG database covers a time period of 24 years (2000-2023). </p>\n<p>The International Resource Panel (IRP) publishes estimated data series for 1970-2024 on its website. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The Material Footprint indicator is disaggregated into four main material categories (biomass, fossil fuels, metal ores and non-metallic minerals). </p>", "COMPARABILITY__GLOBAL"=>"<p>Material Footprint is calculated coherent with international standards, recommendations, and classifications such as the System of National Accounts 2008, the System of Environmental-Economic Accounting &#x2013; Central Framework 2012, the Balance of Payments and International Investment Position, the International Standard Industrial Classification of All Economic Activities (ISIC), the Central Product Classification (CPC) and the Framework for the Development of Environment Statistics.</p>\n<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>UNEP (2023), The use of National Resources in the Economy: a Global Manual on Economy Wide Material Flow Accounting. https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y</p>\n<p><strong>References:</strong></p>\n<p>EUROSTAT (2013). Economy-Wide Material Flow Accounts. Compilation guide 2013: https://ec.europa.eu/eurostat/documents/1798247/6191533/2013-EW-MFA-Guide-10Sep2013.pdf/54087dfb-1fb0-40f2-b1e4-64ed22ae3f4c</p>\n<p>EUROSTAT (2018). The EU Economy-wide material flow accounts handbook 2018: <a href=\"https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006\">https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006</a></p>\n<p>Wiedmann, T., H. Schandl, M. Lenzen, D. Moran, S. Suh, J. West, K. Kanemoto, (2013) The Material Footprint of Nations, Proc. Nat. Acad. Sci. Online before print.</p>\n<p>Lenzen, M., Moran, D., Kanemoto, K., Geschke, A. (2013) Building Eora: A global Multi-regional Input-Output Database at High Country and Sector Resolution, Economic Systems Research, 25:1, 20-49. </p>", "indicator_sort_order"=>"12-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"12.2.2", "slug"=>"12-2-2", "name"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB", "url"=>"/site/es/12-2-2/", "sort"=>"120202", "goal_number"=>"12", "target_number"=>"12.2", "global"=>{"name"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB", "indicator_number"=>"12.2.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso en la serie Consumo material interno por PIB", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/estadistica-de-flujo-de-materiales-090217/web01-s2ing/es/", "url_text"=>"Estadística de Flujo de Materiales", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.2- De aquí a 2030, lograr la gestión sostenible y el uso eficiente de los recursos naturales", "definicion"=>"El consumo doméstico de materiales (CDM), o consumo interno de materiales, es un indicador \nestándar de contabilidad de flujo de materiales (MFA) e informa el consumo aparente \nde materiales en la economía de la C.A. de Euskadi.\n\nEl CDM mide la cantidad total de material (biomasa, combustibles fósiles, minerales \nmetálicos y minerales no metálicos) utilizado directamente en una economía y basado \nen cuentas de flujos directos de materiales, es decir, material extraido, \nimportaciones y exportaciones físicas.\n\nLos datos se presentan en términos absolutos, por habitante y por unidad de PIB.\n", "formula"=>"<b>Consumo doméstico de materiales</b>\n\n$$CDM^t = Extr^t + Imp^t - Exp^t$$\n\ndonde:\n\n$Extr^t =$ Extracción doméstica en el año $t$\n\n$Imp^t =$ Importaciones en el año $t$\n\n$Exp^t =$ Exportaciones en el año $t$\n\n<br>\n\n<b>Consumo doméstico de materiales per cápita</b>\n\n$$CMDPC^{t} = \\frac{{CMD^{t}}}{P^{t}}$$\n\ndonde:\n\n$CMD^{t} =$ consumo doméstico de materiales en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n\n<br>\n\n<b>Consumo doméstico de materiales per cápita por unidad de PIB</b>\n\n$$CMDPIB^{t} = \\frac{{CMD^{t}}}{PIB_{2022}^{t}}$$\n\ndonde:\n\n$CMD^{t} =$ consumo doméstico de materiales en el año $t$\n\n$PIB_{2022}^{t} =$ Producto interior bruto en volumen encadenado con referencia 2022 \nen millones de euros en el año $t$ \n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"", "justificacion_global"=>"\nEl consumo doméstico de materiales (CDM) informa sobre la cantidad de materiales que se \nutilizan en una economía. Es un indicador territorial (del lado de la producción). \n\nEl CDM \ntambién presenta la cantidad de materiales que se deben manipular dentro de una economía, \nque se agregan a las existencias de materiales de los edificios y la infraestructura \nde transporte o se utilizan para impulsar la economía como producción de materiales. \nDescribe la dimensión física de los procesos e interacciones económicas. También se \npuede interpretar como equivalente de residuos a largo plazo. \n\nEl CDM per cápita \ndescribe el nivel promedio de uso de materiales en una economía (un indicador \nde presión ambiental) y también se lo conoce como perfil metabólico.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-02-02.pdf\">Metadatos 12-2-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.2- De aquí a 2030, lograr la gestión sostenible y el uso eficiente de los recursos naturales", "definicion"=>"Domestic material consumption (DMC), or internal material consumption, \nis a standard material flow accounting (MFA) indicator and reports the \napparent consumption of materials in the Basque Country economy. \n\nDMC measures the total amount of material (biomass, fossil fuels, metallic \nminerals, and non-metallic minerals) directly used in an economy and is based \non direct material flow accounts, i.e., domestic material extraction, and physical imports\nand exports. \n\nData are presented in absolute terms, per capita, and per unit of GDP.\n", "formula"=>"<b>Domestic material consumption</b>\n\n$$CDM^t = Extr^t + Imp^t - Exp^t$$\n\nwhere:\n\n$Extr^t =$ domestic extraction of materials in year $t$\n\n$Imp^t =$ imports in year $t$\n\n$Exp^t =$ exports in year $t$\n\n<br>\n\n<b>Domestic material consumption per capita</b>\n\n$$CMDPC^{t} = \\frac{{CMD^{t}}}{P^{t}}$$\n\nwhere:\n\n$CMD^{t} =$ domestic material consumption in year $t$\n\n$P^{t} =$ population as of 1 July of year $t$\n\n<br>\n\n<b>Domestic material consumption per unit of GDP</b>\n\n$$CMDPIB^{t} = \\frac{{CMD^{t}}}{PIB_{2022}^{t}}$$\n\nwhere:\n\n$CMD^{t} =$ domestic material consumption in year $t$\n\n$PIB_{2022}^{t} =$ Gross domestic product in chained volumes with reference to 2022\nin millions of euros for the year $t$ \n", "desagregacion"=>nil, "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nDomestic Material Consumption (DMC) reports the amount of materials that are used \nin a national economy. It is a territorial (production side) indicator. \n\nDMC also presents the amount of material that needs to be handled within an economy, \nwhich is either added to material stocks of buildings and transport infrastructure \nor used to fuel the economy as material throughput. \n\nPer-capita DMC describes the average level of material use in an economy – an \nenvironmental pressure indicator – and is also referred to as metabolic profile. \n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-02-02.pdf\">Metadata 12-2-2.pdf</a>", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Consumo material interno en términos absolutos, consumo material interno per cápita y consumo material interno por PIB", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.2- De aquí a 2030, lograr la gestión sostenible y el uso eficiente de los recursos naturales", "definicion"=>"Materialaren bertako kontsumoa (MBK), material-fluxuaren kontabilitate-adierazle estandarra \nda, eta Euskal AEko ekonomian materialen itxurazko kontsumoaren berri ematen du.\n\nMBKak ekonomia batean zuzenean erabilitako eta material-fluxu zuzenen kontuetan oinarritutako \nmaterial kantitate osoa neurtzen du (biomasa, erregai fosilak, mineral metalikoak eta mineral \nez-metalikoak), hau da, erauzitako materiala, inportazioak eta esportazio fisikoak.\n\nDatuak balio absolututan aurkezten dira, biztanleko eta BPGren unitateko.\n", "formula"=>"<b>Materialen bertako kontsumoa</b>\n\n$$CDM^t = Extr^t + Imp^t - Exp^t$$\n\nnon:\n\n$Extr^t =$ bertako erauzketa $t$ urtean\n\n$Imp^t =$ inportazioak $t$ urtean\n\n$Exp^t =$ esportazioak $t$ urtean\n\n<br>\n\n<b>Materialen bertako kontsumoa per capita</b>\n\n$$CMDPC^{t} = \\frac{{CMD^{t}}}{P^{t}}$$\n\nnon:\n\n$CMD^{t} =$ materialen bertako kontsumoa $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n\n<br>\n\n<b>Materialen bertako kontsumoa per capita BPG unitateko</b>\n\n$$CMDPIB^{t} = \\frac{{CMD^{t}}}{PIB_{2022}^{t}}$$\n\nnon:\n\n$CMD^{t} =$ materialen bertako kontsumoa $t$ urtean\n\n$PIB_{2022}^{t} =$ Barne-produktu gordina, kateatutako bolumenean, 2022ko erreferentziarekin, milioi eurotan $t$ urtean \n", "desagregacion"=>nil, "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nEl consumo doméstico de materiales (CDM) informa sobre la cantidad de materiales que se \nutilizan en una economía. Es un indicador territorial (del lado de la producción). \n\nEl CDM \ntambién presenta la cantidad de materiales que se deben manipular dentro de una economía, \nque se agregan a las existencias de materiales de los edificios y la infraestructura \nde transporte o se utilizan para impulsar la economía como producción de materiales. \nDescribe la dimensión física de los procesos e interacciones económicas. También se \npuede interpretar como equivalente de residuos a largo plazo. \n\nEl CDM per cápita \ndescribe el nivel promedio de uso de materiales en una economía (un indicador \nde presión ambiental) y también se lo conoce como perfil metabólico.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-02-02.pdf\">Metadatuak 12-2-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 8.4: Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation, in accordance with the 10-Year Framework of Programmes on Sustainable Consumption and Production, with developed countries taking the lead</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 8.4.2: Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDP</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_MAT_DOMCMPC - Domestic material consumption per capita, by type of raw material [8.4.2,12.2.2]</p>\n<p>EN_MAT_DOMCMPG - Domestic material consumption per unit of GDP [8.4.2,12.2.2]</p>\n<p>EN_MAT_DOMCMPT - Domestic material consumption [8.4.2,12.2.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>8.4.1, 12.2.1, 12.2.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>Domestic Material Consumption (DMC) is a standard material flow accounting (MFA) indicator and reports the apparent consumption of materials in a national economy. </p>\n<p>DMC measures the total amount of material (biomass, fossil fuels, metal ores and non-metallic minerals) directly used in an economy and based on accounts of direct material flows, i.e., domestic material extraction and physical imports and exports.</p>\n<p><strong>Concepts:</strong></p>\n<p>DMC and Material Footprint (MF) need to be looked at in combination, as they cover the two aspects of the economy, production and consumption. The DMC reports the actual amount of material in an economy, MF the virtual amount required across the whole supply chain to service final demand. A country can, for instance, have a very high DMC because it has a large primary production sector for export or a very low DMC because it has outsourced most of the material intensive industrial process to other countries. The material footprint corrects for both phenomena. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Tonnes;</p>\n<p>Kilograms per constant United States dollar;</p>\n<p>Tonnes per capita.</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>Material categories accordance to the global EW-MFA guide &#x201C;UNEP (2023). The use of natural resources in the economy: A Global Manual on Economy Wide Material Flow Accounting&#x201D; (<a href=\"https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y\">https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y</a>);</li>\n  <li>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>The global estimation of DMC is based on data available from different national and international datasets in the domain of agriculture, forestry, fisheries, mining and energy statistics. International statistical sources for DMC include the International Energy Agency, the United Nations Statistical Division, the United States Geological Survey, the Food and Agriculture Organisation and COMTRADE databases. </p>", "COLL_METHOD__GLOBAL"=>"<p>For global estimation, the International Resource Panel (IRP) Global Material Flows and Resource Productivity working group compiles the data from national and international databases.</p>\n<p>At the same time, country-provided indicators are collected through the QUESTIONNAIRE ON ECONOMY WIDE MATERIAL FLOW ACCOUNTS for the SDG indicators 8.4.1/12.2.1 and 8.4.2/12.2.2.</p>", "FREQ_COLL__GLOBAL"=>"<p> First data collection in 2022 and every 2 to 3 years after.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p> First data release in 2017, the second in 2021 (fully estimated data). Then, in 2022 and every 2 to 3 years after (both globally estimated and country data).</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices </p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), Organization for Economic Co-operation and Development (OECD) and EUROSTAT</p>", "INST_MANDATE__GLOBAL"=>"<p>UNEP was mandated as Custodian Agency for indicator 8.4.2 / 12.2.2 by the Inter-agency and Expert Group on SDG Indicators. UNEP IRP is the mechanism within UNEP supporting all work aspect in relation to Material Flow Accounting.</p>", "RATIONALE__GLOBAL"=>"<p>Domestic Material Consumption (DMC) reports the amount of materials that are used in a national economy. It is a territorial (production side) indicator. DMC also presents the amount of material that needs to be handled within an economy, which is either added to material stocks of buildings and transport infrastructure or used to fuel the economy as material throughput. It describes the physical dimension of economic processes and interactions. It can also be interpreted as long-term waste equivalent. Per-capita DMC describes the average level of material use in an economy &#x2013; an environmental pressure indicator &#x2013; and is also referred to as metabolic profile. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Domestic Material Consumption cannot be disaggregated to economic sectors which limits its potential to become a satellite account to the System of National Accounts (SNA). </p>", "DATA_COMP__GLOBAL"=>"<p>Domestic Material Consumption (DMC) is a standard material flow accounting (MFA) indicator. MFAs below to environmental-economic accounts and apply the accounting concepts, structures, rules and principles of the System of Environmental-Economic Accounting 2012 - Central Framework. It should be used in conjunction with reading the global EW-MFA guide The use of natural resources in the economy: A Global Manual on Economy Wide Material Flow Accounting (https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y).</p>\n<p>Domestic Material Consumption (DMC), by type of raw material (tonnes) is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>M</mi>\n    <mi>C</mi>\n    <mo>=</mo>\n    <mi>D</mi>\n    <mi>E</mi>\n    <mo>+</mo>\n    <mi>I</mi>\n    <mi>M</mi>\n    <mo>-</mo>\n    <mi>E</mi>\n    <mi>X</mi>\n    <mo>,</mo>\n  </math></p>\n<p>Where:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>M</mi>\n    <mi>C</mi>\n  </math> &#x2013; domestic material consumption;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>E</mi>\n  </math> &#x2013; domestic extraction of materials; </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>M</mi>\n  </math> &#x2013; direct imports;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>E</mi>\n    <mi>X</mi>\n  </math> &#x2013; direct exports.</p>\n<p>DMC measure the amount of materials that are used in economic processes. It does not include materials that are mobilized for the process of domestic extraction but do not enter the economic process. </p>\n<p>Domestic material consumption per capita, by type of raw material (tonnes), is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>M</mi>\n    <mi>C</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>p</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>D</mi>\n        <mi>M</mi>\n        <mi>C</mi>\n      </mrow>\n      <mrow>\n        <mi>A</mi>\n        <mi>n</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2015 United States dollars), is calculated as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>D</mi>\n    <mi>M</mi>\n    <mi>C</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>G</mi>\n    <mi>D</mi>\n    <mi>P</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>D</mi>\n        <mi>M</mi>\n        <mi>C</mi>\n      </mrow>\n      <mrow>\n        <mi>G</mi>\n        <mi>D</mi>\n        <mi>P</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>2015</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>U</mi>\n        <mi>n</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>S</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>D</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP) sends a prefilled questionnaire with estimated data to the National Statistical Office (NSO) Focal Points (FP) with a request to validate globally estimated data for this indicator and replace the data if needed/possible. The FPs coordinate data validation with stakeholders within their countries and report back the data to UNEP. For countries with no national data collected for this indicator, UNEP asks to agree on publishing and releasing the estimated data on UNEP&#x2019;s World Environment Situation Room and UNSD SDG Global database.</p>", "ADJUSTMENT__GLOBAL"=>"<p>UNEP replaces globally estimated data by national data if requested by the country.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level:</strong></p>\n<p>A zero is imputed when no positive real value was officially recorded, in the base data sets used, for any of the underlying components which make up this aggregated total. Thus &#x201C;0.0&#x201D; can represent either NA, or a genuine 0.0, or (crucially) a combination of both, which is a common situation. This allows for values to be easily aggregated further; however, it should be thus noted that due to imputing missing values as &#x201C;0.0&#x201D;, the aggregations may represent a lower value than the actual situation. </p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels:</strong></p>\n<p>Similarly, missing values are imputed as zero in the regional and global aggregations. However, in the case where no data is available at all for a particular country, the per capita and per GDP estimates are weighted averages of the available data. </p>", "REG_AGG__GLOBAL"=>"<p>The data are aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see: <a href=\"http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a></p>", "DOC_METHOD__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), jointly with the International Resource Panel (IRP) and United Nations Statistics Division (UNSD), the Statistical Office of the European Union (Eurostat) and the Organisation for Economic Co-operation and Development (OECD) have developed a global manual on Economy-Wide Material Flow Accounting (EW-MFA) which brings in the European guidelines, but provides a modular approach for countries looking to develop EW-MFA for the first time and it addresses specific issues related to resource extractive based economies. </p>\n<ul>\n  <li>UNEP (2023). The use of natural resources in the economy - A Global Manual on Economy Wide Material Flow Accounting: https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y</li>\n  <li>EUROSTAT (2018). The EU Economy-wide material flow accounts handbook 2018:https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), using the Global Manual on Economy-Wide Material Flow Accounting (UNEP, 2023).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), using the Global Manual on Economy Wide Material Flow Accounting (UNEP, 2023).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided by United Nations Environment Programme (UNEP), jointly with International Resource Panel (IRP), in consultation with countries (nominated Focal Points) after receiving their feedback on the globally estimated indicators.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The data covers 193 countries (either globally estimated or country data).</p>\n<p><strong>Time series:</strong></p>\n<p>The data set presented in the SDG database covers a time period of 24 years (2000-2023). </p>\n<p>The International Resource Panel (IRP) publishes estimated data series for 1970-2024 on its website.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The Domestic Material Consumption (DMC) indicator is disaggregated by main material categories (biomass, fossil fuels, metal ores and non-metallic minerals). </p>", "COMPARABILITY__GLOBAL"=>"<p>Domestic Material Consumption is calculated coherent with international standards, recommendations, and classifications such as the System of National Accounts 2008, the System of Environmental-Economic Accounting &#x2013; Central Framework 2012, the Balance of Payments and International Investment Position, the International Standard Industrial Classification of All Economic Activities (ISIC), the Central Product Classification (CPC) and the Framework for the Development of Environment Statistics.</p>\n<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>UNEP (2023), The use of National Resources in the Economy: a Global Manual on Economy Wide Material Flow Accounting. https://wedocs.unep.org/bitstream/handle/20.500.11822/36253/UNRE.pdf?sequence=3&amp;isAllowed=y</p>\n<p><strong>References:</strong></p>\n<p>EUROSTAT (2013). Economy-Wide Material Flow Accounts. Compilation Guide 2013: https://ec.europa.eu/eurostat/documents/1798247/6191533/2013-EW-MFA-Guide-10Sep2013.pdf/54087dfb-1fb0-40f2-b1e4-64ed22ae3f4c</p>\n<p>EUROSTAT (2018). The EU Economy-wide material flow accounts handbook 2018: <a href=\"https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006\">https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-006</a></p>\n<p>Wiedmann, T., H. Schandl, M. Lenzen, D. Moran, S. Suh, J. West, K. Kanemoto, (2013) The Material Footprint of Nations, Proc. Nat. Acad. Sci. Online before print.</p>\n<p>Lenzen, M., Moran, D., Kanemoto, K., Geschke, A. (2013) Building Eora: A global Multi-regional Input-Output Database at High Country and Sector Resolution, Economic Systems Research, 25:1, 20-49. </p>", "indicator_sort_order"=>"12-02-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"12.3.1", "slug"=>"12-3-1", "name"=>"a) Índice de pérdidas de alimentos y b) índice de desperdicio de alimentos", "url"=>"/site/es/12-3-1/", "sort"=>"120301", "goal_number"=>"12", "target_number"=>"12.3", "global"=>{"name"=>"a) Índice de pérdidas de alimentos y b) índice de desperdicio de alimentos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"a) Índice de pérdidas de alimentos y b) índice de desperdicio de alimentos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"a) Índice de pérdidas de alimentos y b) índice de desperdicio de alimentos", "indicator_number"=>"12.3.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La Agenda 2030 para el Desarrollo Sostenible ha enfatizado la importancia de los sistemas \nsostenibles de producción y consumo, ya que los sistemas alimentarios eficientes, \ntanto en el lado de la oferta como en el del consumo, contribuyen a la seguridad \nalimentaria y la sostenibilidad de los recursos naturales, dado que la agricultura \nes un importante consumidor de tierra y agua.\n\nEl índice de pérdida y desperdicio de alimentos analiza toda la cadena de suministro \ny la tendencia de las pérdidas estructurales. El Índice de Pérdida de Alimentos monitorea \nel progreso en el lado de la oferta de las cadenas alimentarias, ya que mide si la \nproporción de la producción agrícola que no llega a la etapa de venta minorista en \n2030 ha aumentado o disminuido con respecto al período base y en qué medida. El \nnumerador del indicador indica el nivel de pérdidas e informa sobre la magnitud del problema.\n\nUna mayor eficiencia en la cadena de suministro de alimentos también tiene \nimplicaciones para todos los productores, ya sea considerando la eficiencia de \nlos grandes productores para los mercados de exportación o de las pequeñas \nunidades de producción relevantes para los objetivos de reducción de la \npobreza y la inseguridad alimentaria.\n\nSegún una publicación de la FAO de 2011, aproximadamente un tercio \nde todos los alimentos se pierden o se desperdician. Esto genera pérdidas \neconómicas y aumenta la presión sobre los sistemas alimentarios. Reducir el \ndesperdicio de alimentos es fundamental para maximizar el valor de las \ntierras agrícolas y garantizar que los recursos naturales se utilicen \nde forma sostenible. Este indicador no solo ayudará a los países a identificar \ndónde se pierden y desperdician alimentos, sino que también puede proporcionar \ninformación que los gobiernos, la ciudadanía y el sector privado pueden \nutilizar para reducir el desperdicio de alimentos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.3.1&seriesCode=AG_FOOD_WST_PC&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">\nDesperdicio de alimentos per cápita (KG) AG_FOOD_WST_PC</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.3.1&seriesCode=AG_FOOD_WST&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">\nDesperdicio de alimentos (toneladas) AG_FOOD_WST</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.3.1&seriesCode=AG_FLS_INDEX&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nÍndice mundial de pérdida de alimentos AG_FLS_INDEX</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.3.1&seriesCode=AG_FLS_PCT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nPorcentaje de pérdida de alimentos (%) AG_FLS_PC</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-03-01A.pdf\">Metadatos 12-3-1(a).pdf</a> (solo en inglés)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-03-01B.pdf\">Metadatos 12-3-1(b).pdf</a> (solo en inglés)\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The 2030 Sustainable Development Agenda has emphasized the importance of sustainable production \nand consumption systems as efficient food systems, on the supply side and the consumption side, \ncontribute to food security and sustainability of natural resource since agriculture is a major \nuser of land and water. \n\nThe food loss and food waste index look at the entire supply chain and the trend in structural \nlosses. The Food Loss Index monitors progress on the supply side of food chains, as it measures \nif the share of agriculture production that does not reach the retail stage in 2030 has increased \nor decreased with respect to the base period and by how much. The numerator of the indicator \nindicates the level of losses and informs on the magnitude of the problem. \n\nA greater efficiency of the food supply chain also has implications for all producers whether \nlooking at efficiency in large-scale producers for export markets or in small-scale production \nunits relevant for poverty and food insecurity reduction goals. \n\nAccording to a 2011 FAO publication, approximately one-third of all food is lost or wasted. This \ngenerates economic losses and increases pressure on food systems. Reducing food waste is critical \nto maximizing the value of agricultural land and ensuring that natural resources are used sustainably. \nThis indicator will not only help countries identify where food is lost and wasted, but can also \nprovide information that governments, citizens, and the private sector can use to reduce food waste.  \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.3.1&seriesCode=AG_FOOD_WST_PC&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">\nFood waste per capita (KG) AG_FOOD_WST_PC</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.3.1&seriesCode=AG_FOOD_WST&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">\nFood waste (Tonnes) AG_FOOD_WST</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.3.1&seriesCode=AG_FLS_INDEX&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nGlobal food loss index AG_FLS_INDEX</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.3.1&seriesCode=AG_FLS_PCT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nFood loss percentage (%) AG_FLS_PC</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-03-01A.pdf\">Metadata 12-3-1(a).pdf</a>\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-03-01B.pdf\">Metadata 12-3-1(b).pdf</a>\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La Agenda 2030 para el Desarrollo Sostenible ha enfatizado la importancia de los sistemas \nsostenibles de producción y consumo, ya que los sistemas alimentarios eficientes, \ntanto en el lado de la oferta como en el del consumo, contribuyen a la seguridad \nalimentaria y la sostenibilidad de los recursos naturales, dado que la agricultura \nes un importante consumidor de tierra y agua.\n\nEl índice de pérdida y desperdicio de alimentos analiza toda la cadena de suministro \ny la tendencia de las pérdidas estructurales. El Índice de Pérdida de Alimentos monitorea \nel progreso en el lado de la oferta de las cadenas alimentarias, ya que mide si la \nproporción de la producción agrícola que no llega a la etapa de venta minorista en \n2030 ha aumentado o disminuido con respecto al período base y en qué medida. El \nnumerador del indicador indica el nivel de pérdidas e informa sobre la magnitud del problema.\n\nUna mayor eficiencia en la cadena de suministro de alimentos también tiene \nimplicaciones para todos los productores, ya sea considerando la eficiencia de \nlos grandes productores para los mercados de exportación o de las pequeñas \nunidades de producción relevantes para los objetivos de reducción de la \npobreza y la inseguridad alimentaria.\n\nSegún una publicación de la FAO de 2011, aproximadamente un tercio \nde todos los alimentos se pierden o se desperdician. Esto genera pérdidas \neconómicas y aumenta la presión sobre los sistemas alimentarios. Reducir el \ndesperdicio de alimentos es fundamental para maximizar el valor de las \ntierras agrícolas y garantizar que los recursos naturales se utilicen \nde forma sostenible. Este indicador no solo ayudará a los países a identificar \ndónde se pierden y desperdician alimentos, sino que también puede proporcionar \ninformación que los gobiernos, la ciudadanía y el sector privado pueden \nutilizar para reducir el desperdicio de alimentos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.3.1&seriesCode=AG_FOOD_WST_PC&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">\nAlferrik galdutako elikagaiak, per capita (KG) AG_FOOD_WST_PC</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.3.1&seriesCode=AG_FOOD_WST&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">\nAlferrik galdutako elikagaiak (tonak) AG_FOOD_WST</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.3.1&seriesCode=AG_FLS_INDEX&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nElikagaien galeraren munduko indizea AG_FLS_INDEX</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.3.1&seriesCode=AG_FLS_PCT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nAlferrik galdutako elikagaien ehunekoa (%) AG_FLS_PC</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-03-01A.pdf\">Metadatuak 12-3-1(a).pdf</a> (ingelesez bakarrik)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-03-01B.pdf\">Metadatuak 12-3-1(b).pdf</a> (ingelesez bakarrik)\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 12: Ensure sustainable consumption and production patterns</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 12.3: By 2030, halve per capita global food waste at the retail and consumer levels and reduce food losses along production and supply chains, including post-harvest losses</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 12.3.1: (a) Food loss index and (b) food waste index</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>AG_FOOD_WST - Food waste [12.3.1]</p>\n<p>AG_FOOD_WST_PC - Food waste per capita [12.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.6.1, 12.3.1(a), 12.5.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p><strong>Food waste</strong> is food and associated inedible parts removed from the human food supply chain in the following sectors: retail and other distribution of food; food service (restaurants, schools, hospitals, other canteens, etc.); and households. &#x201C;Removed from the human food supply chain&#x201D; means one of the following end destinations: landfill, controlled combustion, sewer, litter/discards/ refuse, co/anaerobic digestion, compost/aerobic digestion or land application.</p>\n<p>The indicator aims to measure the total amount of food that is wasted in tonnes. It complements SDG 12.3.1(a) on Food Loss (which is under the custodianship of FAO). Both indicators look to divide the food value chain and measure the efficiency of the food system. </p>\n<p>The food waste indicator is calculated at two levels, which are presented in Table 1 below.</p>\n<p><em>Table 1: Two levels of indicator 12.3.1(b) on food waste</em></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Name</strong></p>\n      </td>\n      <td>\n        <p><strong>Measurement</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Level I indicator:<em> </em></p>\n        <p><em>Food waste estimates for each sector </em></p>\n      </td>\n      <td>\n        <p>Existing data and extrapolation to other countries </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Level II indicator:</p>\n        <p><em>Food waste generation tracked at a national level </em></p>\n      </td>\n      <td>\n        <p>Direct measurement of food waste in retail, food service and households at the national level. Sufficiently accurate for tracking. </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Concepts:</strong></p>\n<p><em>Food: </em>Any substance &#x2014; whether processed, semi-processed, or raw &#x2014; that is intended for human consumption. &#x201C;Food&#x201D; includes drink and any substance that has been used in the manufacture, preparation, or treatment of food. &#x201C;Food&#x201D; also includes material that has spoiled and is therefore no longer fit for human consumption. It does not include cosmetics, tobacco, or substances used only as drugs. It does not include processing agents used along the food supply chain, for example, water to clean or cook raw materials in factories or at home.</p>\n<p><em>Inedible (or non-edible) parts: </em>Components associated with a food that, in a particular food supply chain, are not intended to be consumed by humans. Examples of inedible parts associated with food could include bones, rinds, and pits/stones. &#x201C;Inedible parts&#x201D; do not include packaging. What is considered inedible varies among users (e.g., chicken feet are consumed in some food supply chains but not others), changes over time, and is influenced by a range of variables including culture, socio-economic factors, availability, price, technological advances, international trade, and geography.<em> </em></p>\n<p><em>Municipal Solid Waste (MSW)</em> includes waste originating from households, commerce, and trade, small businesses, office buildings and institutions (schools, hospitals, government buildings). It also includes bulky waste (e.g., old furniture, mattresses) and waste from selected municipal services, e.g., waste from park and garden maintenance, waste from street cleaning services (street sweepings, the content of litter containers, market cleansing waste), if managed as waste. Further information on municipal solid waste is defined in the SDG indicator methodology for 11.6.1.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>\n<p>Tonnes</p>\n<p>KG</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>International Standard Industrial Classification of All Economic Activities (ISIC), Rev.4.</li>\n  <li>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions).</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>Level 1 indicator: Indicators estimated by international organisations using country data from different sources.</p>\n<p>Level 2 indicator: Data provided by national governments, including National Statistical Offices (NSOs), Ministries of Environment and other relevant organizations.</p>", "COLL_METHOD__GLOBAL"=>"<p>Level 2 indicator: The United Nations Environment Programme (UNEP) plans to pilot national data collection in 2023. </p>\n<ul>\n  <li>UNEP and UNSD are exploring the possibility of using the UNSD/UNEP Questionnaire on Environment Statistics for future data collection. </li>\n</ul>", "FREQ_COLL__GLOBAL"=>"<p>Level 2 indicator: First data collection in 2023. Thereafter, the data collection calendar will be harmonized with the UNSD/UNEP Questionnaire on Environment Statistics (every 2 years).</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Level 1 indicator: First reporting cycle in 2021. </p>\n<p>Level 2 indicator: First data reporting in 2023. Thereafter, the data collection calendar will be harmonized with the UNSD/UNEP Questionnaire on Environment Statistics (is every 2 years).</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices, relevant ministries and other oranizations</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Statistics Division (UNSD) and United Nations Environment Programme (UNEP)</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) was mandated as Custodian Agencies for indicator 12.3.1(b) by the Inter-agency and Expert Group on SDG Indicators. In addition, the United Nations Environment Assembly urged Member States to establish mechanisms for measuring food loss and waste, and requested support in providing technical assistance that would allow countries to make measure and make progress. </p>", "RATIONALE__GLOBAL"=>"<p>The 2030 Agenda for Sustainable Development has emphasized the importance of sustainable production and consumption systems as efficient food systems, on the supply side and the consumption side, contribute to food security and sustainability of natural resource since agriculture is a major user of land and water. </p>\n<p>According to an FAO publication in 2011, approximately one-third of all food is lost or wasted. This results in economic loss and increased pressure on food systems. Reducing food waste is critical to maximizing the value of agricultural land and ensuring that natural resources are used in a sustainable way. This indicator will not only help countries identify where food is lost and wasted but it can also provide information which Governments, citizens, and the private sector can use to reduce food waste.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The challenge resulting from the flexible approach to presenting a methodology is one of consistency and comparability. Can one compare between levels or across methods? Not directly and not without caveats. It is possible to compare at regional levels where the random error is relatively high (e.g. around 25%) for each country but it would not be appropriate to compare countries against each other unless there was a much greater difference in their estimates than the combined amount of error. The approach to consistency is one of transparency against a framework.</p>\n<p>Different methods of quantification can also be used for other relevant and related purposes (for example, &#x201C;where are the greatest opportunities within the waste that is produced to reduce it?&#x201D;). Taking in-home consumption as an example, it is difficult to obtain reasons for discarding food (and therefore the opportunities for influencing citizen behaviour) without the use of diaries or ethnography. However, direct weighing of waste volumes could give a significantly more accurate quantity.</p>", "DATA_COMP__GLOBAL"=>"<p>For the purpose of this indicator, the methodology aims to estimate the amount of food in total waste stream.</p>\n<p><strong>For level 1</strong>, the global modelling approach estimates a proportion of food in the total waste stream data (e.g., municipal solid waste (MSW)) and applies the proportion to the total. The work on this model utilizes the existing efforts to compile information for SDG 11.6.1 on MSW management and utilizes existing information on global waste, including World Bank publication &#x201C;What a Waste 2.0, A Global Snapshot of Solid Waste Management to 2050&#x201D;. Some countries publish data on the ratio of food waste to the total MSW. The existing data are used to create a regional coefficient for each SDG sub-region. These regional coefficients then applies to the data for 11.6.1 and &#x201C;What a Waste&#x201D; data to fill data gaps. </p>\n<p>Note that when a country reports data then no global estimation will be done, the country data will be used directly.</p>\n<p><strong>For level 2</strong>, countries should identify the scope of which stages of the supply chain can be covered and estimate the total amount of food wasted for each supply chain stream. The amount of food waste within a stage of the food supply chain shall be established by measuring food waste generated by a sample of food business operators or households in accordance with any of the following methods, or a combination of those methods, or any other method equivalent in terms of relevance, representativeness, and reliability.</p>\n<p><em>Table 2: Methods of measurement of food waste at different stages of the food supply chain</em></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Stages of the food supply chain</strong></p>\n      </td>\n      <td colspan=\"6\">\n        <p><strong>Methods of measurement</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Manufacturing / processing (if included) </p>\n      </td>\n      <td rowspan=\"4\">\n        <p>Direct measurement (for food-only waste streams) </p>\n      </td>\n      <td rowspan=\"4\">\n        <p>Waste composition analysis (for waste streams in which food is mixed with non-food) </p>\n      </td>\n      <td rowspan=\"4\">\n        <p>Volumetric assessment </p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Mass Balance </p>\n      </td>\n      <td></td>\n      <td rowspan=\"2\"></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Retail and other distribution of food </p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Counting/ scanning </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Food service (out-of-home consumption in restaurants, schools, hospitals, other canteens, etc.) </p>\n      </td>\n      <td rowspan=\"2\"></td>\n      <td rowspan=\"2\">\n        <p>Diaries (for material going down sewer, home composted or fed to animals </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Households</p>\n      </td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n<p>The food waste index is calculated according to the following approach:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>F</mi>\n        <mi>o</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>p</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>T</mi>\n            <mi>o</mi>\n            <mi>t</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>f</mi>\n            <mi>o</mi>\n            <mi>o</mi>\n            <mi>d</mi>\n            <mi>w</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>t</mi>\n            <mi>e</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n            <mi>n</mi>\n            <mi>n</mi>\n            <mi>u</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>A</mi>\n            <mi>v</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>a</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>P</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where: </p>\n<p><em>t</em> = year </p>\n<p><em>Total food waste</em> is the sum of waste in three sectors in a given year as per the formula below:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi>F</mi>\n        <mi>W</mi>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>H</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>h</mi>\n            <mi>o</mi>\n            <mi>l</mi>\n            <mi>d</mi>\n            <mi>s</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n    </msub>\n    <mo>+</mo>\n    <mi>&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi>F</mi>\n        <mi>W</mi>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>F</mi>\n            <mi>o</mi>\n            <mi>o</mi>\n            <mi>d</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>v</mi>\n            <mi>i</mi>\n            <mi>c</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </msub>\n    <mo>+</mo>\n    <mi>&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi>F</mi>\n        <mi>W</mi>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>R</mi>\n            <mi>e</mi>\n            <mi>t</mi>\n            <mi>a</mi>\n            <mi>i</mi>\n            <mi>l</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </msub>\n  </math></p>\n<p>The Food Waste Index for the year in question is then calculated as food waste per capita in that year divided by food waste per capita in a baseline year (<em>t<sub>0</sub></em>) multiplied by 100 to express the result as a percentage:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>F</mi>\n        <mi>o</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>W</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>I</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>F</mi>\n            <mi>o</mi>\n            <mi>o</mi>\n            <mi>d</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>w</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>t</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>a</mi>\n            <mi>p</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>a</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>F</mi>\n            <mi>o</mi>\n            <mi>o</mi>\n            <mi>d</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>w</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>t</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>a</mi>\n            <mi>p</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>a</mi>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>t</mi>\n              </mrow>\n              <mrow>\n                <mn>0</mn>\n              </mrow>\n            </msub>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mi>&amp;nbsp;</mi>\n    <mn>100</mn>\n  </math></p>\n<p>In countries where it is not possible to obtain the detailed data necessary to estimate total food waste using the formula above, a simplified approach to calculating food waste per capita may be taken:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>F</mi>\n        <mi>o</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>p</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n            <mi>i</mi>\n            <mi>m</mi>\n            <mi>p</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>M</mi>\n            <mi>S</mi>\n            <mi>W</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>e</mi>\n            <mi>d</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n        <mi>&amp;nbsp;</mi>\n        <mo>&#xD7;</mo>\n        <mi>&amp;nbsp;</mi>\n        <msub>\n          <mrow>\n            <mi>S</mi>\n            <mi>h</mi>\n            <mi>a</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>f</mi>\n            <mi>o</mi>\n            <mi>o</mi>\n            <mi>d</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>w</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>t</mi>\n            <mi>e</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n            <mi>n</mi>\n            <mi>n</mi>\n            <mi>u</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>A</mi>\n            <mi>v</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>a</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>P</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n          </mrow>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where:</p>\n<p><em>t</em> = year</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>M</mi>\n        <mi>S</mi>\n        <mi>W</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> is total municipal solid waste generated in a given year (as calculated for Indicator 11.6.1)</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>S</mi>\n        <mi>h</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n  </math> is the proportion of total MSW made up of food waste in the year, which can be estimated from waste composition studies</p>\n<p>The food waste index for the year is then calculated using the simplified estimate of food waste per capita in the same formula as above: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>F</mi>\n        <mi>o</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>W</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>I</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>t</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n            <mi>i</mi>\n            <mi>m</mi>\n            <mi>p</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>F</mi>\n            <mi>o</mi>\n            <mi>o</mi>\n            <mi>d</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>w</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>t</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>a</mi>\n            <mi>p</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>a</mi>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>t</mi>\n              </mrow>\n              <mrow>\n                <mi>s</mi>\n                <mi>i</mi>\n                <mi>m</mi>\n                <mi>p</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </msub>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>F</mi>\n            <mi>o</mi>\n            <mi>o</mi>\n            <mi>d</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>w</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>t</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>c</mi>\n            <mi>a</mi>\n            <mi>p</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>a</mi>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>t</mi>\n                  </mrow>\n                  <mrow>\n                    <mn>0</mn>\n                  </mrow>\n                </msub>\n              </mrow>\n              <mrow>\n                <mi>s</mi>\n                <mi>i</mi>\n                <mi>m</mi>\n                <mi>p</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mi>&amp;nbsp;</mi>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) and the United Nations Statistics Division (UNSD) carries out extensive data validation procedures that include built-in automated procedures, manual checks and cross-references to national sources of data. Communication is carried out with countries for clarification and validation of data. </p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made.</p>", "IMPUTATION__GLOBAL"=>"<p>Missing values are not imputed for national figures. However, UNEP is using a global modelling approach for level 1 (this is due to the lack of data on this topic and the interest in having data that can be used for high-level tracking).</p>", "REG_AGG__GLOBAL"=>"<p>The data will be aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see: <a href=\"http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a>. </p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li><a href=\"https://www.unep.org/resources/report/unep-food-waste-index-report-2021\">UNEP (2021). Food Waste Index Report 2021</a>.</li>\n  <li><a href=\"https://www.unep.org/resources/publication/global-chemicals-and-waste-indicator-review-document\">UNEP (2021). Global Chemicals and Waste Indicator Review Document</a>. </li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided by the United Nations Environment Programme (UNEP) and the United Nations statistics Division (UNSD).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided by the United Nations Environment Programme (UNEP) and the United Nations statistics Division (UNSD) in cooperation with the countries that provide these data.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided by the United Nations Environment Programme (UNEP) and the United Nations statistics Division (UNSD).</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Level 1 indicator: modelled data are available for all countries.</p>\n<p>Level 2 indicator: forthcoming.</p>\n<p><strong>Time series:</strong></p>\n<p>Level 1 indicator: The data sets presented in the SDG database for 2019.</p>\n<p>Level 2 indicators: Forthcoming.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Ideally, food waste would be disaggregated by edible and inedible parts (Note that it is important to consider the difference between countries in terms of inedible parts. Nicholes et al. 2019 provides some insight into differences between countries.</p>\n<p>Food waste also would be disaggregated by lifecycle stage (or sector): retail, food service, households.</p>\n<p>Disaggregation of food waste by destination is important for understanding the best way to optimize the use of food waste for fertilizer. This includes: </p>\n<ul>\n  <li>Co-digestion/anaerobic digestion,</li>\n  <li>Composting/aerobic process,</li>\n  <li>Controlled combustion,</li>\n  <li>Land application,</li>\n  <li>Landfill,</li>\n  <li>Refuse/discards/litter.</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>As mentioned earlier in 3.a, waste statistics involve a large number of national and sub-national stakeholders which may create discrepancies. Additionally, there are a number of challenges related to the following:</p>\n<ul>\n  <li>Variations in waste over time can have a significant impact on estimated quantities of waste when short studies (e.g. a week) are used to represent a longer time period (a year),</li>\n  <li>The specific time of year when a study takes place which may affect the waste produced,</li>\n  <li>Natural variation over time in amounts of waste generated by single entities (e.g., households or restaurants), </li>\n  <li>At a national level, countries may have to rely on other entities to measure their own waste and report to the government, which would then be collated and analysed to estimate the total amount. How the data is collected would vary by the food chain stage as the way food waste is generated in each stage varies. For example, a large formal retailer (supermarket chain) may keep records of stock unsold and discarded which could be reported. On the other hand, a government requesting reporting from households may have to issue guidance to local municipalities and prescribe a quantification method e.g. a food waste diary. The reported quantities may require scaling if a government cannot obtain reports from the entire population of the food chain stage i.e. it is unlikely that every household in the country would report.</li>\n</ul>", "OTHER_DOC__GLOBAL"=>"<p><a href=\"https://www.unep.org/resources/report/unep-food-waste-index-report-2021\">UNEP (2021). Food Waste Index Report 2021</a>.</p>\n<p><a href=\"https://www.unep.org/resources/publication/global-chemicals-and-waste-indicator-review-document\">UNEP (2021). Global Chemicals and Waste Indicator Review Document</a>.</p>\n<p>Nicholes, M. J., Quested, T. E., Reynolds, C., Gillick, S., &amp; Parry, A. D. (2019). Surely you don&#x2019;t eat parsnip skins? Categorising the edibility of food waste. Resources, Conservation and Recycling, 147, 179&#x2013;188. https://doi.org/10.1016/j.resconrec.2019.03.004</p>", "indicator_sort_order"=>"12-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"12.4.1", "slug"=>"12-4-1", "name"=>"Número de partes en los acuerdos ambientales multilaterales internacionales sobre desechos peligrosos y otros productos químicos que cumplen sus compromisos y obligaciones de transmitir información como se exige en cada uno de esos acuerdos", "url"=>"/site/es/12-4-1/", "sort"=>"120401", "goal_number"=>"12", "target_number"=>"12.4", "global"=>{"name"=>"Número de partes en los acuerdos ambientales multilaterales internacionales sobre desechos peligrosos y otros productos químicos que cumplen sus compromisos y obligaciones de transmitir información como se exige en cada uno de esos acuerdos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de partes en los acuerdos ambientales multilaterales internacionales sobre desechos peligrosos y otros productos químicos que cumplen sus compromisos y obligaciones de transmitir información como se exige en cada uno de esos acuerdos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de partes en los acuerdos ambientales multilaterales internacionales sobre desechos peligrosos y otros productos químicos que cumplen sus compromisos y obligaciones de transmitir información como se exige en cada uno de esos acuerdos", "indicator_number"=>"12.4.1", "national_geographical_coverage"=>"", "page_content"=>"El indicador se refiere al número de Estados (Partes) que han ratificado, aceptado, aprobado o se han adherido a los \nsiguientes cinco Acuerdos Ambientales Multilaterales (AAM). Estos acuerdos están regulados en el ámbito de la \nUnión Europea, aunque la información requerida por las Secretarías de las respectivas Convenciones se gestiona a \nnivel estatal. Los cinco acuerdos y las bases legales europeas son:\n\n 1. <a href=\"http://www.basel.int/\">Convenio de Basilea</a> sobre el control de los movimientos transfronterizos de los residuos peligrosos y su eliminación:\n  <a href=\"https://eur-lex.europa.eu/legal-content/ES/ALL/?uri=celex%3A32006R1013\">\nReglamento (CE)  1013/2006 del Parlamento Europeo y del Consejo</a>, de 14 de junio de 2006 , relativo a los traslados de residuos \n\n 2. <a href=\"http://www.pic.int/\">Convenio de Róterdam</a> sobre el procedimiento de consentimiento fundamentado previo aplicable a ciertos plaguicidas y productos químicos peligrosos objeto de comercio internacional: <a href=\"https://eur-lex.europa.eu/legal-content/ES/TXT/?qid=1616424284711&uri=CELEX%3A32012R0649\">Reglamento (UE) 649/2012 del Parlamento Europeo y del Consejo</a>, de 4 de julio de 2012 , relativo a la exportación e importación de productos químicos peligrosos\n\n 3. <a href=\"http://www.pops.int/\">Convenio de Estocolmo</a> sobre contaminantes orgánicos persistentes: <a href=\"https://eur-lex.europa.eu/legal-content/ES/TXT/?qid=1616424312742&uri=CELEX%3A32019R1021\">Reglamento (UE) 2019/1021 del Parlamento Europeo y del Consejo</a>, de 20 de junio de 2019, sobre contaminantes orgánicos persistentes\n\n 4. <a href=\"https://ozone.unep.org/treaties/montreal-protocol\">Protocolo de Montreal</a> relativo a las sustancias que agotan la capa de ozono: <a href=\"https://eur-lex.europa.eu/legal-content/ES/TXT/?qid=1616423953157&uri=CELEX%3A32009R1005\">Reglamento (CE) 1005/2009 del Parlamento Europeo y del Consejo</a>, de 16 de septiembre de 2009 , sobre las sustancias que agotan la capa de ozono\n\n 5. <a href=\"http://www.mercuryconvention.org/\">Convenio de Minamata</a> sobre el mercurio: <a href=\"https://eur-lex.europa.eu/legal-content/ES/TXT/?qid=1616424340862&uri=CELEX%3A32017R0852\">Reglamento (UE) 2017/852 del Parlamento Europeo y del Consejo</a>, de 17 de mayo de 2017, sobre el mercurio\n", "permalink"=>"", "precision"=>[], "progress_status"=>"notapplicable", "reporting_status"=>"notapplicable", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El indicador propuesto está orientado a procesos y se centra en el cumplimiento \nde las obligaciones que contribuyen al objetivo general de lograr la gestión \nambientalmente racional de las sustancias químicas y todos \nlos residuos a lo largo de su ciclo de vida. \n\nNo mide la cantidad de sustancias químicas presentes en los medios \nni cuantifica los impactos adversos en la salud humana y el medio ambiente. \nSin embargo, los acuerdos ambientales multilaterales se desarrollaron y adoptaron para abordar los \ndesafíos más urgentes para la salud humana y el medio ambiente y, \npor lo tanto, mediante su implementación, se avanzará en la reducción de\nlas emisiones al aire, el agua y el suelo, así como de la presencia de \nsustancias químicas peligrosas en los productos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRMNTRL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nPartes que cumplen sus compromisos y obligaciones en la transmisión de información según lo exige \nel Protocolo de Montreal sobre residuos peligrosos y otros productos químicos (%) SG_HAZ_CMRMNTRL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRROTDAM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nPartes que cumplen sus compromisos y obligaciones en la transmisión de información según lo exige \nel Convenio de Rotterdam sobre residuos peligrosos y otros productos químicos (%) SG_HAZ_CMRROTDAM</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRBASEL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nPartes que cumplen sus compromisos y obligaciones en la transmisión de información según lo exige \nel Convenio de Basilea sobre residuos peligrosos y otros productos químicos (%) SG_HAZ_CMRBASEL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRSTHOLM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nPartes que cumplen sus compromisos y obligaciones en la transmisión de información según lo exige \nel Convenio de Estocolmo sobre residuos peligrosos y otros productos químicos (%) SG_HAZ_CMRSTHOLM</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRMNMT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nPartes que cumplen sus compromisos y obligaciones en la transmisión de información según lo exige el \nConvenio de Minamata sobre residuos peligrosos y otros productos químicos (%) SG_HAZ_CMRMNMT</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-04-01.pdf\">Metadatos 12-4-1.pdf</a> (solo en inglés)\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The proposed indicator is process-oriented, focusing on compliance with the obligations \nthat contribute to the overall target of achieving the environmentally sound management \nof chemicals and all wastes throughout their life cycle. \n\nIt does not measure the quantity of chemicals in media and does not quantify adverse \nimpacts on human health and the environment. The MEAs, however, were developed and adopted \nto address the most urgent challenges for human health and the environment and therefore, \nthrough the implementation of MEAs progress will be made to reduce release to air, water \nand soil as well as presence of hazardous chemicals in products. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRMNTRL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nParties meeting their commitments and obligations in transmitting information as required by Montreal Protocol on hazardous waste, and other chemicals (%) SG_HAZ_CMRMNTRL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRROTDAM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nParties meeting their commitments and obligations in transmitting information as required by Rotterdam Convention on hazardous waste, and other chemicals (%) SG_HAZ_CMRROTDAM</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRBASEL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nParties meeting their commitments and obligations in transmitting information as required by Basel Convention on hazardous waste, and other chemicals (%) SG_HAZ_CMRBASEL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRSTHOLM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nParties meeting their commitments and obligations in transmitting information as required by Stockholm Convention on hazardous waste, and other chemicals (%) SG_HAZ_CMRSTHOLM</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRMNMT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nParties meeting their commitments and obligations in transmitting information as required by Minamata Convention on hazardous waste, and other chemicals (%) SG_HAZ_CMRMNMT</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-04-01.pdf\">Metadata 12-4-1.pdf</a>\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"El indicador propuesto está orientado a procesos y se centra en el cumplimiento \nde las obligaciones que contribuyen al objetivo general de lograr la gestión \nambientalmente racional de las sustancias químicas y todos \nlos residuos a lo largo de su ciclo de vida. \n\nNo mide la cantidad de sustancias químicas presentes en los medios \nni cuantifica los impactos adversos en la salud humana y el medio ambiente. \nSin embargo, los acuerdos ambientales multilaterales se desarrollaron y adoptaron para abordar los \ndesafíos más urgentes para la salud humana y el medio ambiente y, \npor lo tanto, mediante su implementación, se avanzará en la reducción de\nlas emisiones al aire, el agua y el suelo, así como de la presencia de \nsustancias químicas peligrosas en los productos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRMNTRL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nHondakin arriskutsuei eta beste produktu kimiko batzuei buruzko Montrealgo Protokoloak eskatzen duen bezala, informazioa transmititzeko konpromisoak eta betebeharrak betetzen dituzten alderdiak (%) SG_HAZ_CMRMNTRL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRROTDAM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nHondakin arriskutsuei eta beste produktu kimiko batzuei buruzko Rotterdameko Hitzarmenak eskatzen duen bezala, informazioa transmititzeko konpromisoak eta betebeharrak betetzen dituzten alderdiak (%) SG_HAZ_CMRROTDAM</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRBASEL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nHondakin arriskutsuei eta beste produktu kimiko batzuei buruzko Basileako Hitzarmenak eskatzen duen bezala, informazioa transmititzeko konpromisoak eta betebeharrak betetzen dituzten alderdiak (%) SG_HAZ_CMRBASEL</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRSTHOLM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nHondakin arriskutsuei eta beste produktu kimiko batzuei buruzko Stockholmeko Hitzarmenak eskatzen duen bezala, informazioa transmititzeko konpromisoak eta betebeharrak betetzen dituzten alderdiak (%) SG_HAZ_CMRSTHOLM</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.4.1&seriesCode=SG_HAZ_CMRMNMT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nHondakin arriskutsuei eta beste produktu kimiko batzuei buruzko Minamatako Hitzarmenak eskatzen duen bezala, informazioa transmititzeko konpromisoak eta betebeharrak betetzen dituzten alderdiak (%) SG_HAZ_CMRMNMT</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-04-01.pdf\">Metadatuak 12-4-1.pdf</a> (ingelesez bakarrik)\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 12: Ensure sustainable consumption and production patterns</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 12.4: By 2020, achieve the environmentally sound management of chemicals and all wastes throughout their life cycle, in accordance with agreed international frameworks, and significantly reduce their release to air, water and soil in order to minimize their adverse impacts on human health and the environment</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 12.4.1: Number of parties to international multilateral environmental agreements on hazardous waste, and other chemicals that meet their commitments and obligations in transmitting information as required by each relevant agreement</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_HAZ_CMRBASEL - Parties meeting their commitments and obligations in transmitting information as required by Basel Convention on hazardous waste, and other chemicals (%) [12.4.1]</p>\n<p>SG_HAZ_CMRMNMT - Parties meeting their commitments and obligations in transmitting information as required by Minamata Convention on hazardous waste, and other chemicals (%) [12.4.1]</p>\n<p>SG_HAZ_CMRMNTRL - Parties meeting their commitments and obligations in transmitting information as required by Montreal Protocol on hazardous waste, and other chemicals (%) [12.4.1]</p>\n<p>SG_HAZ_CMRROTDAM - Parties meeting their commitments and obligations in transmitting information as required by Rotterdam Convention on hazardous waste, and other chemicals (%) [12.4.1]</p>\n<p>SG_HAZ_CMRSTHOLM - Parties meeting their commitments and obligations in transmitting information as required by Stockholm Convention on hazardous waste, and other chemicals (%) [12.4.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>12.4.2, 12.5.1, 3.9.1, 3.9.2 and 3.9.3.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>The indicator refers to the number of Parties (= countries that have ratified, accepted, approved, or accessed), to the following Multilateral Environmental Agreements (MEAs):</p>\n<ol>\n  <li>The Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and their Disposal (Basel Convention); </li>\n  <li>The Rotterdam Convention on the prior informed consent procedure for certain hazardous chemicals and pesticides in international trade (Rotterdam Convention);</li>\n  <li>The Stockholm Convention on Persistent Organic Pollutants (Stockholm Convention);</li>\n  <li>The Montreal Protocol on Substances that Deplete the Ozone Layer (Montreal Protocol);</li>\n  <li>Minamata Convention on Mercury (Minamata Convention),</li>\n</ol>\n<p>which have submitted the information to the Secretariat of each MEA, as required by each of the agreements. </p>\n<p>The information required is as follows:</p>\n<p><strong><u>Basel Convention<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>:</u></strong></p>\n<ol>\n  <li>Designation of the Focal Point and one or more Competent Authorities;</li>\n  <li>Submission of the annual national reports.</li>\n</ol>\n<p><strong><u>Rotterdam Convention:</u></strong></p>\n<ol>\n  <li>Designation of the Designated National Authority(ies) and Official contact points;</li>\n  <li>Submission of the import responses. </li>\n</ol>\n<p><strong><u>Stockholm Convention:</u></strong></p>\n<ol>\n  <li>Designation of the Stockholm Convention official contact points and national focal points;</li>\n  <li>Submission of the national implementation plans; </li>\n  <li>Submission of the revised national implementation plan addressing amendments; </li>\n  <li>Submission of the national reports.</li>\n</ol>\n<p><strong><u>Montreal Protocol:</u></strong></p>\n<ol>\n  <li>Compliance with annual reporting requirements for production and consumption of controlled substances under Article 7 of the Montreal Protocol;</li>\n  <li>Submission of information on Licensing systems under (Article 4B of) the Montreal Protocol;</li>\n  <li>For each party, a percentage value is assigned to indicate how much of the required information has been submitted.</li>\n</ol>\n<p><strong><u>Minamata Convention:</u></strong></p>\n<ol>\n  <li>Designation of a national focal point for exchange of information under Article 17 of the Convention;</li>\n  <li>Submission of national reports as required under Article 21 of the Minamata Convention.</li>\n</ol>\n<p><strong>Concepts:</strong></p>\n<p>Parties to the <strong>Basel Convention</strong> have an obligation to present an annual national report as provided for by Article 13, paragraph 3 in order to enable monitoring of the implementation of the Basel Convention by its Parties. The reports are to contain, inter alia, information regarding transboundary movements of hazardous wastes or other wastes in which Parties have been involved, including the amount of hazardous wastes and other wastes exported, their category, characteristics, destination, any transit country and disposal method as stated on the response to notification, the amount of hazardous wastes and other wastes imported in their category, characteristics, origin, and disposal methods; information on accidents occurring during the transboundary movement and disposal of hazardous wastes and other wastes and on the measures undertaken to deal with them; information on disposal options operated within the area of their national jurisdiction; and other information as per reporting format.</p>\n<p>Import responses under the <strong>Rotterdam Convention</strong> are the decisions provided by Parties indicating whether or not they will consent to import the chemicals listed in Annex III of the Convention and subject to the prior informed consent (PIC) procedure. Article 10 of the Rotterdam Convention sets out the obligations of Parties with respect to the future import of chemicals listed in Annex III.</p>\n<p>Under the <strong>Stockholm Convention</strong>, a Party has an obligation to report on the measures it has taken to implement the provisions of the Convention and on the effectiveness of such measures in meeting the objectives of the Convention. The national reports include statistical data on the total quantities of production, import and export of each of the chemicals listed in Annex A and Annex B or a reasonable estimate of such data; and to the extent practicable, a list of the States from which it has imported each substance and the States to which it has exported each substance. A National Implementation Plan under the Stockholm Convention is a plan explaining how a Party is going to implement the obligations under the Convention and make efforts to put such a plan into operation (<a href=\"http://chm.pops.int/Portals/0/Repository/conf/UNEP-POPS-CONF-4-AppendixII.5206ab9e-ca67-42a7-afee-9d90720553c8.pdf#Article%207\">Article 7</a>). Changes in the obligations arising from amendments to the Convention or its annexes, for example when a new chemical is listed into the annexes of the Convention, will require that a Party is to review and update its implementation plan, and transmit the updated plan to the Conference of the Parties (COP) within two years of the entry into force of the amendment for it, consistent with paragraph 1 (b) of the Convention (according to paragraph 7 of the annex to decision SC-1/12). </p>\n<p>The <strong>Minamata Convention</strong> requires, under its article 17, paragraph 4, that each Party designates a National Focal Point for the exchange of information under it, including with regard to the consent of importing Parties under Article 3. Pursuant to Article 21 of the Minamata Convention, each party to the Convention shall report to the COP on the measures it has taken to implement the provisions of the Convention, on the effectiveness of such measures and on possible challenges in meeting the objectives of the Convention. In decision MC-1/8 on the Timing and format of reporting by the Parties, the COP at its first meeting (2017) agreed on the full format of reporting and decided that each Party shall report every four years using the full format and report every two years on four questions marked by an asterisk in the full format. The COP further decided on the following timing with regards to the short and full reporting: 31 December 2019 as the deadline for the first short national report; 31 December 2021 as the deadline for the first full national report. </p>\n<p>The <strong>Montreal Protocol</strong> requires, under its Article 7, that each Party provides to the Secretariat for each controlled substance statistical data on its annual production, amounts used for feedstocks, amounts destroyed by technologies approved by the Parties, imports from and exports to Parties and non-Parties respectively and amount of the controlled substance listed in Annex E used for quarantine and pre-shipment applications, for the year during which provisions concerning those substances entered into force for that Party and for each year thereafter. Each Party shall also provide to the Secretariat statistical data on its annual emissions of trifluoromethane (HFC-23) per facility. The calculation of control levels is provided in Article 3 of the Protocol. This reporting enables monitoring of the implementation of the Protocol, and compliance with the control measures under the protocol. Additionally, under Article 4B, each party is required to establish and implement a system for licensing the import and export of new, used, recycled and reclaimed controlled substances. Each Party is required, within three months of the date of introducing its licensing system, to report to the Secretariat on the establishment and operation of that system. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p><sup> </sup> The parameters presented below are based on the obligations of the Parties to transmit information to the Secretariat, whatever its national circumstances. Other information that only needs to be communicated to the Secretariat based on national circumstances, such as a possible national definitions of hazardous wastes, possible article 11 agreements under the Basel Convention, or a possible exemptions under the Stockholm Convention would not be included, either because the Secretariat is not in a position to assess whether the obligation to transmit information has materialized itself, or because Parties have the right not to make use of a right. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>For the <strong>Basel, Rotterdam and Stockholm Conventions</strong> the units of measurements are the transmission of information, such as the number of country contacts designated, number of national reports, national implementation plans and import responses. For each Party, a percentage value is assigned to indicate how much of the required information has been submitted.</p>\n<p>For the <strong>Minamata Convention</strong>, the units of measurement are the number of designated national focal points and the number of national reports received. For each Party, a percentage value is assigned to indicate how much of the required information has been submitted.</p>\n<p>For the <strong>Montreal Protocol</strong>, the units of measurement are the number of Parties that comply with their reporting obligations with regard to production and consumption of controlled substances (Article 7) and submission of information on licensing systems (Article 4B). For each party, a percentage value is assigned to indicate how much of the required information has been submitted.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>At the regional and global levels, the indicator is presented according to the Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions).</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Description:</strong></p>\n<ol>\n  <li>Basel Convention: national focal points, electronic reporting system for annual national reports;</li>\n  <li>Rotterdam Convention: official contact points and designated national authorities, PIC circular for import responses;</li>\n  <li>Stockholm Convention: official contact points; electronic reporting system for national reports every four years, National Implementation Plans;</li>\n  <li>Montreal Protocol: national focal points;</li>\n  <li>Minamata Convention: national focal points.</li>\n</ol>", "COLL_METHOD__GLOBAL"=>"<p>Data is collected by the Secretariat of the Basel, Rotterdam and Stockholm Conventions from Focal Points for the Basel Conventions, official contact points and designated national authorities for the Rotterdam Convention, official contact points for the Stockholm Convention, by the Ozone Secretariat from national focal points for the Montreal Protocol, and by the Secretariat of the Minamata Convention from national focal points for the Minamata Convention. </p>", "FREQ_COLL__GLOBAL"=>"<ol>\n  <li>First reporting cycle: 2017;</li>\n  <li>Second reporting cycle: 2020;</li>\n  <li>Third reporting cycle: 2025;</li>\n  <li>Fourth reporting cycle: 2030.</li>\n</ol>", "REL_CAL_POLICY__GLOBAL"=>"<ol>\n  <li>According to the result of the first reporting cycle: data for 2010-2014;</li>\n  <li>According to the result of the second reporting cycle: data for 2015-2019;</li>\n  <li>According to the result of the third reporting cycle: data for 2020-2024;</li>\n  <li>According to the result of the fourth reporting cycle: data for 2025-2029.</li>\n</ol>", "DATA_SOURCE__GLOBAL"=>"<ol>\n  <li>Focal Points and Competent Authorities for the Basel Conventions (189 Parties);</li>\n  <li>Designated National Authorities and Official contact points for the Rotterdam Convention (165 Parties);</li>\n  <li>Official contact points and national focal points for Stockholm Convention (185 Parties);</li>\n  <li>Focal points for Montreal Protocol (198 Parties);</li>\n  <li>Focal points for information exchange and national focal points for the Minamata Convention (currently 137 Parties).</li>\n</ol>", "COMPILING_ORG__GLOBAL"=>"<ol>\n  <li>Secretariat of the Basel, Rotterdam and Stockholm Conventions; </li>\n  <li>Secretariat for the Montreal Protocol (Ozone Secretariat);</li>\n  <li>Secretariat of the Minamata Convention.</li>\n</ol>", "INST_MANDATE__GLOBAL"=>"<p><strong>Basel Convention</strong>: Pursuant to article 5 of the Basel Convention, Parties are required to designate or establish one or more competent authorities and one focal point to facilitate the implementation of the Convention. Parties also have an obligation to inform the Secretariat of any changes regarding designations made by them. The Conference of the Parties (COP) has adopted a standard form for notification of designation of contacts (decision BC-11/21), which Parties are requested to use to transmit information to the Secretariat including modifications. </p>\n<p>A list of competent authorities and focal points to the Basel Convention is maintained and regularly updated on <a href=\"http://www.basel.int/Countries/CountryContacts/tabid/1342/Default.aspx\">the Convention website</a>.</p>\n<p>To enable monitoring of the implementation of the Basel Convention by its Parties and to present reports on this matter to the COP on a regular basis, the Convention establishes a mechanism for Parties to transmit information about implementation of the Convention. According to Article 13, the Parties, consistent with national laws and regulations, shall transmit, through the Secretariat, to the COP established under Article 15, before the end of each calendar year<strong><u>, a report</u></strong> on the previous calendar year.</p>\n<p>Article 13 mandates the Secretariat to receive and disseminate this and other types of information.</p>\n<p><strong>Rotterdam Convention:</strong> Pursuant to Article 4 of the Rotterdam Convention, each Party is required to designate one or more national authorities that shall be authorized to act on its behalf in the performance of the administrative functions required by the Convention. The Secretariat also communicates with an Official Contact Point (OCP) of a Party on official issues. Here too, the COP has adopted a standard form for notification of designation of contacts (decision RC-6/13), which Parties are requested to use to transmit information to the Secretariat. A contacts database is available on <a href=\"http://www.pic.int/Countries/CountryContacts/tabid/3282/language/en-US/Default.aspx\">the Rotterdam Convention website</a>. </p>\n<p>Article 10 of the Convention sets out the obligations of Parties with respect to the future import of chemicals listed in Annex III. Parties have an ongoing obligation to submit to the Secretariat, as soon as possible and in any event no later than nine months after the date of dispatch of a decision guidance document, their <strong><u>import response</u></strong><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> (whether a final or interim response) concerning the future import of the chemical. If a Party modifies its response, it has an obligation to immediately submit the revised response to the Secretariat.</p>\n<p>Article 14 in addition to other relevant Articles gives the mandate to the Secretariat to facilitate the information exchange. The Secretariat maintains various databases of information on the Convention website based on transmissions from Parties e.g. country profiles, database of import responses, national legislation collection.</p>\n<p><strong>Stockholm Convention:</strong> Pursuant to Article 9 of the Stockholm Convention, each Party shall designate a national focal point for the exchange of the information referred to in paragraph 1 of article 9. Pursuant to decision <a href=\"http://chm.pops.int/Portals/0/download.aspx?d=UNEP-POPS-COP.2-SC-2-16.English.PDF\">SC-2/16</a> of the second meeting of the COP of the Stockholm Convention, Parties are invited to nominate OCPs. A revised harmonised form for notification of designation of contacts has also been adopted by the COP to the Stockholm Convention for notification of contacts, including modifications (decision SC-6/26). The Secretariat also maintains for this <a href=\"http://chm.pops.int/Countries/CountryContacts/tabid/304/Default.aspx\">Convention a database of country contacts</a>.</p>\n<p>Parties to the Stockholm Convention are required to develop, endeavour to implement, update and review as appropriate, a plan explaining how they are going to implement the obligations under the Convention (Article 7) (&#x201C;national implementation plans&#x201D;). The plans are made available on the Convention website. </p>\n<p>Furthermore, Article 9 specifies that the Parties facilitate or undertake the exchange of information relevant to the reduction or elimination of the production, use and release of persistent organic pollutants and alternatives to them directly or through the Secretariat. </p>\n<p>A national report contains information on the measures taken by a Party in implementing the Stockholm Convention. The information provided in the national reports is one of the main references to be used for the evaluation of the effectiveness of the Convention in accordance with its Article 16. The COP decided at its first meeting that <strong><u>national reports</u></strong> shall be submitted every four years. The OCP has the authority to submit a national report to the Secretariat.</p>\n<p><strong>Minamata Convention:</strong> Parties requested the Secretariat of the Minamata Convention to facilitate cooperation in the exchange of information referred to in Article 17, including with respect to the designation of national focal points, pursuant to paragraph 3 of Article 17 of the Minamata Convention. Article 24 of the Convention further includes in the functions of the Secretariat, <em>inter alia</em>, to assist Parties in the exchange of information related to the implementation of the Convention, and to prepare and make to the Parties periodic reports based on information received pursuant to Article 21.</p>\n<p><strong>Montreal Protocol:</strong> Under the Montreal Protocol, the role of the Secretariat is stipulated in Article 12 of the Protocol including the obligation to receive data provided pursuant to Article 7. Additionally, under Article 4B, each Party is required, within three months of the date of introducing its licensing system, to report to the Secretariat on the establishment and operation of that system. </p>\n<p>Compliance of the Parties with their reporting obligations is considered by an Implementation Committee established under the Protocol&#x2019;s Non-Compliance Procedure and is determined by the Meeting of the Parties based on the Committee&#x2019;s recommendations (<a href=\"https://ozone.unep.org/list-of-implementation-committee-recommendations\">https://ozone.unep.org/list-of-implementation-committee-recommendations</a>).</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> The import response may consist of an interim response that is not necessarily a decision, see for example Article 10(4)(b)(ii)-(iv). <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "RATIONALE__GLOBAL"=>"<p>The proposed indicator is process-oriented, focusing on compliance with the obligations that contribute to the overall target of achieving the environmentally sound management of chemicals and all wastes throughout their life cycle. </p>\n<p>It does not measure the quantity of chemicals in media and does not quantify adverse impacts on human health and the environment. The MEAs, however, were developed and adopted to address the most urgent challenges for human health and the environment and therefore, through the implementation of MEAs progress will be made to reduce release to air, water and soil as well as presence of hazardous chemicals in products. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The transmission of information as required by the five Conventions follows a different timing. This is the reason why the reporting to this indicator has been scheduled for 5-year cycles, which would allow capturing the compliance of Parties with the transmission of information of all the Conventions.</p>\n<p>Please also note that the timing for submission of reporting for the Minamata Convention has been agreed upon under decision MC-1/8, with the deadlines for the short and full reporting: 31 December 2019 as the deadline for the first short national report and 31 December 2021 as the deadline for the first full national report. Based on the prescribed deadlines, it therefore follows that for the first short reports the reporting period covers 16 August 2017 (the date of entry into force of the Convention) to 31 December 2018 (to be submitted by 31 December 2019), and for the first full reports the reporting period covers 16 August 2017 to 31 December 2020 (to be submitted by 31 December 2021). The cycle will then be repeated, with the subsequent short reports covering 1 January 2021 to 31 December 2022 and the subsequent full reports covering 1 January 2021 to 31 December 2024, and so on.</p>", "DATA_COMP__GLOBAL"=>"<p>In the following methodology, reporting is to take place in 2017 for the period 2010-2014, in 2020 for the period 2015-2019, in 2025 for the period 2020-2024 and in 2030 for the period 2025-2029. Reporting parameters include the following: </p>\n<p>The Country Score depends on the amount of information that is sent to the Conventions&#x2019; Secretariat, and is calculated as follows (and communicated by the Secretariats):</p>\n<p><strong><u>Basel Convention:</u></strong></p>\n<ol>\n  <li>Designation of the Focal Point and one or more Competent Authorities (1 point);</li>\n  <li>Submission of the annual national reports during the reporting period (1 point per report).</li>\n</ol>\n<p><strong><u>Rotterdam Convention:</u></strong></p>\n<ol>\n  <li>Designation of the Designated National Authority(ies) and Official contact point (1 point);</li>\n  <li>Submission of the import responses during the reporting period (0.2 point per import response).</li>\n</ol>\n<p><strong><u>Stockholm Convention:</u></strong></p>\n<ol>\n  <li>Designation of the Stockholm Convention official contact point and national focal point (1 point);</li>\n  <li>Submission of the national implementation plan (1 point);</li>\n  <li>Submission of the revised national implementation plan(s) addressing the amendments adopted by the Conference of the Parties within the reporting period (1 point per revised and updated plan)<sup><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></sup>;</li>\n</ol>\n<p><strong><u>Montreal Protocol:</u></strong></p>\n<ol>\n  <li>Compliance with annual reporting requirements for production and consumption of controlled substances under Article 7 of the Montreal Protocol (15 points per report);</li>\n  <li>Submission of information on Licensing systems under (Article 4B of) the Montreal Protocol (5 points).</li>\n</ol>\n<p><strong><u>Minamata Convention:</u></strong></p>\n<ol>\n  <li>Designation of a national focal point (Article 17) (5 points);</li>\n  <li>Submission of national report (Article 21) (15 points).</li>\n</ol>\n<p>By completing the table below, countries can calculate their Country Scores for each convention and the total transmission rate.</p>\n<table>\n  <tbody>\n    <tr>\n      <td rowspan=\"2\">\n        <p>#</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Convention</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Maxi-mum Points (MP)</p>\n      </td>\n      <td colspan=\"5\">\n        <p>Points per year (p(t))*</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Country Score per Convention (CS)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1<sup>st</sup> year</p>\n      </td>\n      <td>\n        <p>2<sup>nd</sup> year</p>\n      </td>\n      <td>\n        <p>3<sup>rd</sup> year</p>\n      </td>\n      <td>\n        <p>4<sup>th</sup> year</p>\n      </td>\n      <td>\n        <p>5<sup>th</sup> year</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A</p>\n      </td>\n      <td>\n        <p>Basel Convention</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>C</mi>\n                <mi>S</mi>\n              </mrow>\n              <mrow>\n                <mi>A</mi>\n              </mrow>\n            </msub>\n            <mo>=</mo>\n            <mi>&amp;nbsp;</mi>\n            <mfrac>\n              <mrow>\n                <mi>p</mi>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mi>t</mi>\n                    <mn>1</mn>\n                  </mrow>\n                </mfenced>\n                <mo>+</mo>\n                <mi>p</mi>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mi>t</mi>\n                    <mn>2</mn>\n                  </mrow>\n                </mfenced>\n                <mo>+</mo>\n                <mi>p</mi>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mi>t</mi>\n                    <mn>3</mn>\n                  </mrow>\n                </mfenced>\n                <mo>+</mo>\n                <mi>p</mi>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mi>t</mi>\n                    <mn>4</mn>\n                  </mrow>\n                </mfenced>\n                <mo>+</mo>\n                <mi>p</mi>\n                <mo>(</mo>\n                <mi>t</mi>\n                <mn>5</mn>\n                <mo>)</mo>\n              </mrow>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>M</mi>\n                    <mi>P</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>A</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </math></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>B</p>\n      </td>\n      <td>\n        <p>Rotterdam Convention</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>&#x2026;</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>C</p>\n      </td>\n      <td>\n        <p>Stockholm Convention</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>&#x2026;</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>Montreal Protocol</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>&#x2026;</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>E </p>\n      </td>\n      <td>\n        <p>Minamata Convention</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <msub>\n              <mrow>\n                <mi>C</mi>\n                <mi>S</mi>\n              </mrow>\n              <mrow>\n                <mi>E</mi>\n              </mrow>\n            </msub>\n            <mo>=</mo>\n            <mi>&amp;nbsp;</mi>\n            <mfrac>\n              <mrow>\n                <mi>p</mi>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mi>t</mi>\n                    <mn>1</mn>\n                  </mrow>\n                </mfenced>\n                <mo>+</mo>\n                <mi>p</mi>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mi>t</mi>\n                    <mn>2</mn>\n                  </mrow>\n                </mfenced>\n                <mo>+</mo>\n                <mi>p</mi>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mi>t</mi>\n                    <mn>3</mn>\n                  </mrow>\n                </mfenced>\n                <mo>+</mo>\n                <mi>p</mi>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mi>t</mi>\n                    <mn>4</mn>\n                  </mrow>\n                </mfenced>\n                <mo>+</mo>\n                <mi>p</mi>\n                <mo>(</mo>\n                <mi>t</mi>\n                <mn>5</mn>\n                <mo>)</mo>\n              </mrow>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>M</mi>\n                    <mi>P</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>E</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </math></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>* Points provided once (e.g. for a designation of a national focal point) are cumulative with the first year.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>T</mi>\n    <mi>r</mi>\n    <mi>a</mi>\n    <mi>n</mi>\n    <mi>s</mi>\n    <mi>m</mi>\n    <mi>i</mi>\n    <mi>s</mi>\n    <mi>s</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>R</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n            <mi>S</mi>\n          </mrow>\n          <mrow>\n            <mi>A</mi>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n            <mi>S</mi>\n          </mrow>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n            <mi>S</mi>\n          </mrow>\n          <mrow>\n            <mi>C</mi>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n            <mi>S</mi>\n          </mrow>\n          <mrow>\n            <mi>D</mi>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>C</mi>\n            <mi>S</mi>\n          </mrow>\n          <mrow>\n            <mi>E</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mi>N</mi>\n        <mi>o</mi>\n        <mo>.</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>C</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n    <mi>%</mi>\n    <mo>.</mo>\n  </math></p>\n<p>The final indicator will be a number expressed as percent, where 100% is the maximum degree of compliance with the reporting obligations of the MEAs to which a Country is a Party, and 0% the least degree of compliance with those obligations.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p><u> Applicable to Parties bound by the amendments to the Stockholm Convention. Parties that are not bound by the amendments will by default receive one point for each such amendment.</u> <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>All the information mentioned below on the <strong>Basel, Rotterdam and Stockholm Conventions</strong> is submitted through the officially designated country contacts. </p>\n<ul>\n  <li>The databases of country contracts for the Basel, Rotterdam and Stockholm Conventions are: <a href=\"http://www.basel.int/Countries/CountryContacts/tabid/1342/Default.aspx\">http://www.basel.int/Countries/CountryContacts/tabid/1342/Default.aspx</a></li>\n</ul>\n<p><a href=\"http://www.pic.int/Countries/CountryContacts/tabid/3282/language/en-US/Default.aspx\">http://www.pic.int/Countries/CountryContacts/tabid/3282/language/en-US/Default.aspx</a></p>\n<p><a href=\"http://chm.pops.int/Countries/CountryContacts/tabid/304/Default.aspx\">http://chm.pops.int/Countries/CountryContacts/tabid/304/Default.aspx</a> </p>\n<ul>\n  <li>National annual reports under the Basel Convention: <a href=\"http://www.basel.int/Countries/NationalReporting/NationalReports/BC2018Reports/tabid/8202/Default.aspx\">http://www.basel.int/Countries/NationalReporting/NationalReports/BC2018Reports/tabid/8202/Default.aspx</a> </li>\n  <li>The prior informed consent Circular contains import responses under the Rotterdam Convention which is updated every six months: <a href=\"http://www.pic.int/Implementation/PICCircular/tabid/1168/language/en-US/Default.aspx\">http://www.pic.int/Implementation/PICCircular/tabid/1168/language/en-US/Default.aspx</a>. Please also see country profiles <a href=\"http://www.pic.int/Countries/CountryProfiles/tabid/1087/language/en-US/Default.aspx\">webpage</a> which contains various relevant information. </li>\n  <li>National reports under the Stockholm Convention: <a href=\"http://chm.pops.int/Countries/Reporting/NationalReports/tabid/3668/Default.aspx\">http://chm.pops.int/Countries/Reporting/NationalReports/tabid/3668/Default.aspx</a></li>\n  <li>Transmission of the National Implementation Plans under the Stockholm Convention: <a href=\"http://chm.pops.int/Implementation/NationalImplementationPlans/NIPTransmission/tabid/253/Default.aspx\">http://chm.pops.int/Implementation/NationalImplementationPlans/NIPTransmission/tabid/253/Default.aspx</a></li>\n</ul>\n<p>For the <strong>Minamata Convention</strong>: </p>\n<ul>\n  <li>A list of designated national focal points is available at <a href=\"https://www.mercuryconvention.org/Countries/Parties/Notifications/tabid/3826/language/en-US/Default.aspx\"> https://www.mercuryconvention.org/en/parties/focal-points</a>. </li>\n  <li>National reports submitted by the Parties to the Minamata Convention for the first reporting cycle are available at https://www.mercuryconvention.org/en/parties/reporting/2019</li>\n</ul>\n<p>Under the <strong>Montreal Protocol</strong>, the Secretariat does not carry out any validation, other than simple completeness and consistency checks which are communicated back to the reporting party. There is no consultation with countries on the national data submitted to the SDGs Indicators Database. </p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level:</strong></p>\n<p>Missing values are not imputed. </p>\n<p><strong>&#x2022; At regional and global levels:</strong></p>\n<p>Missing values are not imputed.</p>", "REG_AGG__GLOBAL"=>"<p>The data will be aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see <a href=\"http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a>. </p>", "DOC_METHOD__GLOBAL"=>"<p><strong>For the Basel Convention: </strong>information on mandate, frequency, format and procedures for the designation of the <a href=\"http://www.basel.int/Procedures/FocalPoint/tabid/1325/Default.aspx\">focal points</a>, <a href=\"http://www.basel.int/Procedures/CompetentAuthorities/tabid/1324/Default.aspx\">competent authorities</a>, as well as : </p>\n<p><a href=\"http://www.basel.int/Countries/NationalReporting/Guidance/tabid/1498/Default.aspx\">Format and manual for national reporting for the year 2018 and onwards</a>.: </p>\n<p><strong>For the Rotterdam Convention:</strong> information on mandate, frequency, format and procedures for the designation of the <a href=\"http://www.pic.int/Procedures/OfficialContactPoints/tabid/3285/language/en-US/Default.aspx\">official contact points</a>, <a href=\"http://www.pic.int/Procedures/DesignatedNationalAuthorities/tabid/1366/language/en-US/Default.aspx\">National Authorities</a>, as well as </p>\n<p><a href=\"http://www.pic.int/Procedures/ImportResponses/FormandInstructions/tabid/1165/language/en-US/Default.aspx\">Forms and Instructions for Parties on import responses</a>. </p>\n<p><strong>For the Stockholm Convention:</strong> </p>\n<ul>\n  <li>Information on mandate, frequency, format and procedures for the designation of the <a href=\"http://chm.pops.int/Procedures/OfficialContactPoint/tabid/3278/Default.aspx\">official contact points</a> and <a href=\"http://chm.pops.int/Procedures/NationalFocalPoint/tabid/3279/Default.aspx\">national focal points</a>.</li>\n  <li><a href=\"http://chm.pops.int/Countries/Reporting/Guidance/tabid/3670/Default.aspx\">User manual for the Electronic Reporting System of the Stockholm Convention on Persistent Organic Pollutants (POPs) and Manual for national reports</a> under Article 15 of the Stockholm Convention.</li>\n  <li>A set of <a href=\"http://chm.pops.int/Implementation/NationalImplementationPlans/Guidance/tabid/7730/Default.aspx\">guidance documents on developing and updating National Implementation Plans under the Stockholm Convention</a>.</li>\n</ul>\n<p>For transmission of notifications of designations of country contacts in accordance with the Basel, Rotterdam and Stockholm Conventions, the revised form has been harmonised and may be used to transmit information on designated contacts in accordance with the provisions of any or all three of the Conventions. This is intended to facilitate transmission of information to the Secretariat while respecting the legal autonomy of each Convention.</p>\n<p>For the <strong>Minamata Convention:</strong></p>\n<ul>\n  <li><a href=\"https://www.mercuryconvention.org/en/parties/focal-points\">States or regional economic integration are invited to designate national focal points using the form and the sample letter</a>. </li>\n</ul>\n<p><a href=\"https://www.mercuryconvention.org/en/parties/reporting\">Guidance for the submission of national reports</a>. </p>\n<p>The <strong>Montreal protocol</strong> does not provide any guidance to countries for the compilation of the data at the national level. However, the Parties adopted data reporting forms to guide them on the information to be reported to the Secretariat. Additionally, under the institutions of the protocol, developing countries get technical and financial assistance, part of which includes training manuals and other resources and guidance on compilation and reporting of data - <a href=\"https://www.unep.org/ozonaction/resources\">https://www.unep.org/ozonaction/resources</a>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The <strong>Basel, Rotterdam and Stockholm (BRS) Secretariat</strong> reviews the national reports for completeness and correctness and communicates with Parties with a view of addressing identified gaps, where possible.</p>\n<p>Under the Committee administering the Mechanism for Promoting Implementation and Compliance (ICC), which is a subsidiary body of the Basel Convention, there is a standing area of work on the national reporting which aims at improving timely and complete national reporting under paragraph 3 of Article 13 of the Convention. Activities in the biennium 2020-2021 include, inter alia, classifying and, as appropriate, publishing information on Parties&#x2019; compliance with their annual national reporting obligations for 2016 and 2017 based on the assumptions, criteria and categories adopted by the Conference of the Parties (COP) at its thirteenth meeting and the targets adopted by the COP at its fourteenth meeting; developing recommendations on the revision of targets referred to in paragraph 13 of decision BC-14/15 for the reports due for 2018 and subsequent years; and with a view to increasing the completeness and timeliness of national reporting under paragraph 3 of Article 13, exploring how individual Parties can integrate national reporting needs under the Basel Convention into the United Nations Development Assistance Framework.</p>\n<p><strong>The Minamata Secretariat</strong> uses an online reporting system with a database for collecting and managing the reported information. This is complemented by an internal system to (i) review the completeness and correctness of the reports received; and (ii) inform Parties about the outcomes of such review before the reports are published on the Minamata Convention website.</p>\n<p><strong>The Secretariat for the Montreal Protocol</strong> uses an online reporting system with a database for collecting and managing the reported information. The system includes a variety of checks and validation rules to ensure completeness and consistency of the reported information.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>For the <strong>Basel, Rotterdam and Stockholm Conventions</strong> the Electronic Reporting System is the tool to be used by Parties to submit their national reports. For guidelines, please see the responses to the question in 4(h).</p>\n<p>A reporting format for the <strong>Minamata Convention</strong> has been adopted by the first Conference of the Parties (COP) for the submission of national reports pursuant to Article 21. The Secretariat drafted guidance for the short reports (4 questions) to assist Parties. In decision MC-3/13, on guidance for completing the national reporting format, the COP, recognized the need for a complete and consistent national reporting to provide information for the effectiveness evaluation and for supporting compliance, and requested the secretariat to prepare draft guidance for the full national reporting format to clarify the information being sought. A draft of the Guidance was circulated on 20 May 2021, and has been provisionally used to inform the completion of the first full national reports due by 31 December 2021. The draft guidance is under consideration at the fourth meeting of the COP of the Minamata Convention in March 2022. . An online reporting tool was developed and launched by the Secretariat on 7 September 2021 to assist Parties and facilitate collecting the information for the reports. Under the Minamata Convention, the responsibility for quality assurance of the submitted data and information lies with the Parties. </p>\n<p>Under the <strong>Montreal Protocol</strong>, the responsibility for quality assurance of the submitted data and information lies with the Parties. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>For the <strong>Basel, Rotterdam and Stockholm Conventions</strong>, information transmitted by Parties to the Secretariat is made available to the Conference of the Parties for monitoring. </p>\n<p>For the <strong>Minamata Convention</strong>, the Secretariat reported a very high reporting rate: 89% of Parties have submitted <a href=\"https://www.mercuryconvention.org/en/parties/reporting/2019\">their first short national report</a>. ). The Secretariat can also report that 96% of Parties have designated <a href=\"https://www.mercuryconvention.org/en/parties/focal-points\">national focal points</a> in a timely and appropriate manner. </p>\n<p>Under the <strong>Montreal Protocol</strong>, the responsibility for the overall evaluation of fulfilling quality of the submitted data lies with the Parties. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<ol>\n  <li>Basel Conventions: 189 Parties;</li>\n  <li>Rotterdam Convention: 165 Parties;</li>\n  <li>Stockholm Convention: 185 Parties;</li>\n  <li>Focal points for Montreal Protocol: 198 Parties;</li>\n  <li>Minamata Convention: currently 137 Parties.</li>\n</ol>\n<p><strong>Time series:</strong></p>\n<p>The reporting on this indicator will follow a 5-year cycle.</p>\n<ol>\n  <li>First baseline reporting cycle in 2017: data collected from 2010 to 2014;</li>\n  <li>Second reporting cycle in 2020: data collected from 2015 to 2019;</li>\n  <li>Third reporting cycle in 2025: data collected from 2020 to 2024;</li>\n  <li>Fourth reporting cycle in 2030: data collected from 2025 to 2029.</li>\n</ol>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator is available at the global, regional and national levels. </p>\n<p>It is disaggregated by Convention, in addition to providing the average transmission rate of the five Conventions. </p>", "COMPARABILITY__GLOBAL"=>"<p>For the <strong>Basel, Rotterdam and Stockholm Conventions</strong>, the data are produced by Parties and then transmitted to the Secretariat which makes them publicly available on the Conventions&#x2019; website.</p>\n<p>For the <strong>Minamata Convention</strong>, the data reported are produced by Parties.</p>\n<p>Under the <strong>Montreal Protocol</strong>, the data and information reported are produced by Parties. </p>", "OTHER_DOC__GLOBAL"=>"<p>Relevant links to the <strong>Basel, Rotterdam and Stockholm Conventions</strong>: </p>\n<p><a href=\"http://www.brsmeas.org/Implementation/SustainableDevelopmentGoals/Overview/tabid/8490/language/en-US/Default.aspx\">Basel, Rotterdam and Stockholm Conventions and the 2030 Agenda for Sustainable Development</a> </p>\n<p><a href=\"http://www.basel.int/Countries/CountryContacts/tabid/1342/Default.aspx\">Country Contacts</a> for the Basel Convention;</p>\n<p><a href=\"http://www.pic.int/Countries/CountryContacts/tabid/3282/language/en-US/Default.aspx\">Country Contacts for the Rotterdam Convention</a>; </p>\n<p><a href=\"http://chm.pops.int/Countries/CountryContacts/tabid/304/Default.aspx\">Country Contacts for the Stockholm Convention</a>; </p>\n<p><a href=\"http://www.basel.int/Countries/NationalReporting/NationalReports/BC2018Reports/tabid/8202/Default.aspx\">Basel Convention National Reports - Year 2018</a>; </p>\n<p><a href=\"http://www.pic.int/Implementation/PICCircular/tabid/1168/language/en-US/Default.aspx\">Prior Informed Consent Circulars for the Rotterdam Convention</a>; </p>\n<p><a href=\"http://www.pic.int/Countries/CountryProfiles/tabid/1087/language/en-US/Default.aspx\">Country Profiles for the Rotterdam Convention</a>; </p>\n<p><a href=\"http://chm.pops.int/Countries/Reporting/NationalReports/tabid/3668/Default.aspx\">National Reports for the Stockholm Convention</a>; </p>\n<p><a href=\"http://chm.pops.int/Implementation/NationalImplementationPlans/NIPTransmission/tabid/253/Default.aspx\">National Implementation Plans for the Stockholm Convention</a>. </p>\n<p>Relevant links for the <strong>Minamata Convention</strong> relevant to this indicator: </p>\n<p><a href=\"https://www.mercuryconvention.org/en/parties/notifications\">Notifications under the Minamata Convention on Mercury</a>; </p>\n<p><a href=\"https://www.mercuryconvention.org/en/parties/focal-points\">National Focal Points</a>; </p>\n<p><a href=\"https://www.mercuryconvention.org/en/parties/reporting\">National Reporting pursuant to Article 21</a>; </p>\n<p><a href=\"https://www.mercuryconvention.org/en/documents/draft-guidance-completing-national-reporting-format-minamata-convention-mercury\">UNEP/MC/COP.4/17 - Draft guidance for completing the national reporting format for the Minamata Convention on Mercury</a>;</p>\n<p><a href=\"https://www.mercuryconvention.org/en/resources\">Resources</a>; </p>\n<p><a href=\"https://www.mercuryconvention.org/en/resources/getting-ready-online-reporting-tool\">Online Reporting Tool</a>. </p>\n<p>For the <strong>Montreal Protocol</strong>, relevant links can be found on the Secretariat&#x2019;s website at: </p>\n<p><a href=\"https://ozone.unep.org/\">The Ozon Secretariat webpage</a>. </p>", "indicator_sort_order"=>"12-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"12.4.2", "slug"=>"12-4-2", "name"=>"a) Desechos peligrosos generados per cápita y b) proporción de desechos peligrosos tratados, desglosados por tipo de tratamiento", "url"=>"/site/es/12-4-2/", "sort"=>"120402", "goal_number"=>"12", "target_number"=>"12.4", "global"=>{"name"=>"a) Desechos peligrosos generados per cápita y b) proporción de desechos peligrosos tratados, desglosados por tipo de tratamiento"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"GRAFICO.12-4-2-titulo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"a) Desechos peligrosos generados per cápita y b) proporción de desechos peligrosos tratados, desglosados por tipo de tratamiento", "indicator_number"=>"12.4.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso en la proporción de residuos peligrosos reciclados", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/estadistica-de-residuos-peligrosos-090209/web01-a2inghon/es/", "url_text"=>"Estadística de Residuos Peligrosos", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"a) Desechos peligrosos generados per cápita y b) proporción de desechos peligrosos tratados, desglosados por tipo de tratamiento", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.4- De aquí a 2020, lograr la gestión ecológicamente racional de los productos químicos y de todos los desechos a lo largo de su ciclo de vida, de conformidad con los marcos internacionales convenidos, y reducir significativamente su liberación a la atmósfera, el agua y el suelo a fin de minimizar sus efectos adversos en la salud humana y el medio ambiente", "definicion"=>"Residuos peligrosos generados per cápita y proporción de residuos peligrosos tratados  según operación de tratamiento (incinerados,  vertidos y reciclados) en relación al total de residuos peligrosos generados y tratados", "formula"=>"\n<b> Residuos peligrosos generados per cápita</b> \n\n$$RPPC^{t} = \\frac{RP^{t}}{P^{t}} $$\n\ndonde:\n\n$RP^{t} =$ cantidad de residuos peligrosos generados en el año $t$ \n\n$P^{t} =$ población a 1 de julio del año $t$\n\n<br>\n\n<b> Proporción de residuos peligrosos tratados, por tipo de tratamiento</b> \n\n$$PRP_{tratamiento}^{t} = \\frac{RP_{tratamiento}^{t}}{RP^{t}} \\cdot 100$$ <br> \n\n$RP_{tratamiento}^{t} =$ cantidad de residuos peligrosos tratados (según tipo de tratamiento) en el año $t$\n\n$RP^{t} =$ cantidad total de residuos peligrosos generados y tratados en el año $t$\n", "desagregacion"=>"Operación de tratamiento de residuos: reciclaje, incineración con recuperación de \nenergía, incineración sin recuperación de energía, depósito en vertedero.\n\nTerritorio histórico\n", "observaciones"=>"\nOperaciones de tratamiento R1 de acuerdo con la Directiva 2008/98/CE de residuos.", "periodicidad"=>"Anual", "justificacion_global"=>"Los productos químicos forman parte de la vida cotidiana. Se utilizan más de 140.000 \nsustancias diferentes en todos los sectores económicos del mundo. Sus beneficios son \nmuchos y también lo es su potencial para afectar negativamente a la \nsalud humana y al medio ambiente si no se gestionan adecuadamente. Todos los países, \nespecialmente los de ingresos medios y bajos, se enfrentan al complejo desafío de \ngestionar los residuos peligrosos de acuerdo con las normas internacionales de \nbuenas prácticas. La situación se complica por la limitación de los recursos \nhumanos, financieros y/o técnicos. \n\nPor ello, es necesario adoptar medidas para apoyar el uso sostenible de los productos \nquímicos y la gestión ambientalmente racional de los residuos peligrosos. También \nse está produciendo un rápido aumento de la generación de residuos peligrosos. \n\nSi bien la mayoría de los residuos peligrosos convencionales se producen en \noperaciones industriales y manufactureras, se generan cantidades significativas \nen sectores no industriales, incluidos los lodos del sector sanitario, las plantas \nde tratamiento de aguas residuales, los aceites usados y las baterías usadas. \n\nTambién hay un aumento en la complejidad de los productos y de componentes peligrosos \nno identificados, como recubrimientos y/o artículos que no son peligrosos \n(laminados y envases multicapa), pero que presentan peligrosidad de diversas \nmaneras cuando se desechan de manera inadecuada y terminan en el aire, el agua o se queman.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos de Naciones Unidas.", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-04-02.pdf\">Metadatos 12-4-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"a) Desechos peligrosos generados per cápita y b) proporción de desechos peligrosos tratados, desglosados por tipo de tratamiento", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.4- De aquí a 2020, lograr la gestión ecológicamente racional de los productos químicos y de todos los desechos a lo largo de su ciclo de vida, de conformidad con los marcos internacionales convenidos, y reducir significativamente su liberación a la atmósfera, el agua y el suelo a fin de minimizar sus efectos adversos en la salud humana y el medio ambiente", "definicion"=>"Hazardous waste generated per capita and proportion of hazardous waste treated  by treatment operation (incinerated, landfilled, and recycled) in relation to  total hazardous waste generated and treated.", "formula"=>"\n<b> Hazardous waste generated per capita</b> \n\n$$RPPC^{t} = \\frac{RP^{t}}{P^{t}} $$\n\nwhere:\n\n$RP^{t} =$ amount of hazardous waste generated in year $t$ \n\n$P^{t} =$ population as of July 1 of year $t$\n\n<br>\n\n<b> Proportion of hazardous waste treated, by type of treatment</b> \n\n$$PRP_{treatment}^{t} = \\frac{RP_{treatment}^{t}}{RP^{t}} \\cdot 100$$ <br> \n\n$RP_{treatment}^{t} =$ amount of hazardous waste treated, by type of treatment in year $t$\n\n$RP^{t} =$ total amount of hazardous waste generated and treated in year $t$\n", "desagregacion"=>"Waste treatment operations: recycling; incineration with energy recovery; \nincineration without energy recovery; landfill. \n\nProvince\n", "observaciones"=>"\nR1 treatment operations in accordance with Waste Directive 2008/98/EC.", "periodicidad"=>"Anual", "justificacion_global"=>"Chemicals are part of everyday life. There are over 140,000 different substances \nused in all economic sectors globally. Their benefits are many and so too are their \npotential to adversely impact human health and the environment if not properly managed. \nAll countries, especially middle- and low-income countries, are facing the complex \nchallenge of managing hazardous waste according to international standards of good \npractice. The situation is complicated by limited human, financial and/or technical \nresources. \n\nAs such, action is needed to support the sustainable use of chemicals and environmentally \nsound management of hazardous waste. There is also a rapid increase in the generation of \nhazardous waste. \n\nWhere most of the conventional hazardous wastes are produced in industrial and manufacturing \noperations, significantamounts are generated in non-industrial sectors, including sludge \nfrom the healthcare sector; waste-water treatment plants, waste oils, and waste batteries. \n\nThere is also an increase in the complexity of products and unidentified hazardous components \nlike coatings, and/or items which are not hazardous (laminates and multi-layer packaging), \nbut present hazardousness in a variety of ways when improperly discarded and end up in air, \nwater or are burned. \n\nSource: United Nations Statistics Division \n", "comparabilidad"=>"The available indicator partially complies with the United Nations metadata. ", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-04-02.pdf\">Metadata 12-4-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"a) Desechos peligrosos generados per cápita y b) proporción de desechos peligrosos tratados, desglosados por tipo de tratamiento", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.4- De aquí a 2020, lograr la gestión ecológicamente racional de los productos químicos y de todos los desechos a lo largo de su ciclo de vida, de conformidad con los marcos internacionales convenidos, y reducir significativamente su liberación a la atmósfera, el agua y el suelo a fin de minimizar sus efectos adversos en la salud humana y el medio ambiente", "definicion"=>"Residuos peligrosos generados per cápita y proporción de residuos peligrosos tratados  según operación de tratamiento (incinerados,  vertidos y reciclados) en relación al total de residuos peligrosos generados y tratados", "formula"=>"\n<b> Sortutako hondakin arriskutsuak per capita</b> \n\n$$RPPC^{t} = \\frac{RP^{t}}{P^{t}} $$\n\nnon:\n\n$RP^{t} =$ sortutako hondakin arriskutsuen kantitatea $t$ urtean \n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n\n<br>\n\n<b> Tratatutako hondakin arriskutsuen proportzioa, tratamendu motaren arabera</b> \n\n$$PRP_{tratamendua}^{t} = \\frac{RP_{tratamendua}^{t}}{RP^{t}} \\cdot 100$$ <br> \n\n$RP_{tratamendua}^{t} =$ tratatutako hondakin arriskutsuen kantitatea (tratamendu motaren arabera) $t$ urtean \n\n$RP^{t} =$ sortutako eta tratatutako hondakin arriskutsuen guztizko kantitatea $t$ urtean \n", "desagregacion"=>"Hondakinak tratatzeko eragiketa: birziklatzea; erraustea energia berreskuratuta; erraustea energia \nberreskuratu gabe; zabortegian uztea\n\nLurralde historikoa\n", "observaciones"=>"\nOperaciones de tratamiento R1 de acuerdo con la Directiva 2008/98/CE de residuos.", "periodicidad"=>"Anual", "justificacion_global"=>"Los productos químicos forman parte de la vida cotidiana. Se utilizan más de 140.000 \nsustancias diferentes en todos los sectores económicos del mundo. Sus beneficios son \nmuchos y también lo es su potencial para afectar negativamente a la \nsalud humana y al medio ambiente si no se gestionan adecuadamente. Todos los países, \nespecialmente los de ingresos medios y bajos, se enfrentan al complejo desafío de \ngestionar los residuos peligrosos de acuerdo con las normas internacionales de \nbuenas prácticas. La situación se complica por la limitación de los recursos \nhumanos, financieros y/o técnicos. \n\nPor ello, es necesario adoptar medidas para apoyar el uso sostenible de los productos \nquímicos y la gestión ambientalmente racional de los residuos peligrosos. También \nse está produciendo un rápido aumento de la generación de residuos peligrosos. \n\nSi bien la mayoría de los residuos peligrosos convencionales se producen en \noperaciones industriales y manufactureras, se generan cantidades significativas \nen sectores no industriales, incluidos los lodos del sector sanitario, las plantas \nde tratamiento de aguas residuales, los aceites usados y las baterías usadas. \n\nTambién hay un aumento en la complejidad de los productos y de componentes peligrosos \nno identificados, como recubrimientos y/o artículos que no son peligrosos \n(laminados y envases multicapa), pero que presentan peligrosidad de diversas \nmaneras cuando se desechan de manera inadecuada y terminan en el aire, el agua o se queman.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen metadatuak.", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-04-02.pdf\">Metadatuak 12-4-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 12: Ensure sustainable consumption and production patterns</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 12.4: By 2020, achieve the environmentally sound management of chemicals and all wastes throughout their life cycle, in accordance with agreed international frameworks, and significantly reduce their release to air, water and soil in order to minimize their adverse impacts on human health and the environment</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 12.4.2: (a) Hazardous waste generated per capita; and (b) proportion of hazardous waste treated, by type of treatment</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_EWT_COLLPCAP - Electronic waste collected, per capita (KG) [12.4.2]</p>\n<p>EN_EWT_COLLR - Proportion of electronic waste collected (%) [12.4.2]</p>\n<p>EN_EWT_COLLV - Electronic waste collected (Tonnes) [12.4.2]</p>\n<p>EN_EWT_GENPCAP - Electronic waste generated, per capita (Kg) [12.4.2]</p>\n<p>EN_EWT_GENV - Electronic waste generated (Tonnes) [12.4.2]</p>\n<p>EN_HAZ_EXP - Hazardous waste exported (Tonnes) [12.4.2]</p>\n<p>EN_HAZ_GENGDP - Hazardous waste generated, per unit of GDP [12.4.2]</p>\n<p>EN_HAZ_GENV - Hazardous waste generated (Tonnes) [12.4.2]</p>\n<p>EN_HAZ_IMP - Hazardous waste imported (Tonnes) [12.4.2]</p>\n<p>EN_HAZ_PCAP - Hazardous waste generated, per capita (Kg) [12.4.2]</p>\n<p>EN_HAZ_TREATV - Hazardous waste treated and by type of treatment (Tonnes) [12.4.2]</p>\n<p>EN_HAZ_TRTDISR - Proportion of hazardous waste treated or disposed (%) [12.4.2]</p>\n<p>EN_HAZ_TRTDISV - Hazardous waste treated or disposed (Tonnes) [12.4.2]</p>\n<p>EN_MWT_COLLV - Municipal waste collected (Tonnes) [12.4.2]</p>\n<p>EN_MWT_EXP - Municipal waste exported (Tonnes) [12.4.2]</p>\n<p>EN_MWT_GENV - Municipal waste generated (Tonnes) [12.4.2]</p>\n<p>EN_MWT_IMP - Municipal waste imported (Tonnes) [12.4.2]</p>\n<p>EN_MWT_TREATR - Proportion of municipal waste treated, by type of treatment (%) [12.4.2]</p>\n<p>EN_MWT_TREATV - Municipal waste treated by type of treatment (Tonnes) [12.4.2]</p>\n<p>EN_TWT_GENV - Total waste generation, by activity (Tonnes) [12.4.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.6.1, 12.5.1, 14.1.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), United Nations Statistics Division (UNSD), United Nations Institute for Training and Research (UNITAR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), United Nations Statistics Division (UNSD), United Nations Institute for Training and Research (UNITAR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>The indicator includes hazardous generated, hazardous waste generated by type (including e-waste as a sub-indicator) and the proportion of hazardous waste treated. </p>\n<p><strong>Hazardous waste </strong>is waste with properties capable of having a harmful effect on human health or the environment and is regulated and controlled by law.</p>\n<p><strong>Hazardous waste generated</strong>: refers to the quantity of hazardous waste generated within the country during the reported year, prior to any activity such as collection, preparation for reuse, treatment, recovery, including recycling, or export, no matter the destination of this waste.</p>\n<p><strong>Hazardous waste generated by type, including e-waste</strong>: A breakdown of hazardous waste generated by key type of waste, including e-waste.</p>\n<p><strong>Municipal waste: Municipal solid waste (MSW) </strong>includes waste originating from households, commerce and trade, small businesses, office buildings and institutions (schools, hospitals, government buildings). It also includes bulky waste (e.g., old furniture, mattresses) and waste from selected municipal services, e.g. waste from parks and gardens maintenance, waste from street cleaning services (street sweepings, litter containers content, market cleansing waste), if managed as waste.</p>\n<p><strong>E-waste:</strong> Electronic waste, or e-waste, refers to all items of electrical and electronic equipment (EEE) and its parts that have been discarded by its owner as waste without the intent of re-use.</p>\n<p><strong>Hazardous waste treated</strong>: Hazardous waste treated during reporting year, per each type of treatment (recycling, incineration with/without energy recovery, landfilling or other), including exports and excluding imports.</p>\n<p><strong>Concepts:</strong></p>\n<p><em>Hazardous waste</em> is waste with properties that make it hazardous or capable of having a harmful effect on human health or the environment. Hazardous waste is generated from many sources, ranging from industrial manufacturing process waste to domestic items such as batteries and may come in many forms, including liquids, solids, gases and sludge. They can be discarded as commercial products, like cleaning fluids or pesticides or the by-products of manufacturing processes, from Basel Convention (Article 1, paragraph 1(a)). Waste listed in Annex VIII of the Basel Convention is presumed to be hazardous, while waste listed in Annex IX is presumed not to be hazardous. For the purpose of this indicator, due to comparability reasons, additional waste considered hazardous as per national definitions, as provided by the Basel Convention under Article 1, paragraph 1(b), are excluded.</p>\n<p><em>Hazardous waste generated</em> refers to the quantity of hazardous waste (as per the definition above) that is generated within the country during the reported year, prior to any activity such as collection, preparation for reuse, treatment, recovery, including recycling, or export, no matter the destination of this waste. For waste that are not covered under the above definition, but are defined as, or are considered to be hazardous waste by national definitions and are included in the &#x201C;hazardous waste generated&#x201D; amount, a specific note should be added specifying the additional types/streams of hazardous waste included as well as their quantities.</p>\n<p> </p>\n<p>&#x201C;<em>Waste treated</em>&#x201D; and &#x201C;<em>type of treatment</em>&#x201D; are not defined in the Basel Convention. In this context, &#x201C;treatment&#x201D; will include all operations included under Annex IV of the Basel Convention, namely &#x201C;Disposal&#x201D; operations D1 to D15 and &#x201C;Recovery&#x201D; operations R1 to R13. This is also linked to the definitions of &#x201C;Recycling, Incineration, Incineration with energy recovery, Landfilling and other types of treatment or disposal&#x201D;.</p>\n<p>A full methodology for this indicator is available in the document entitled, &#x201C;<a href=\"https://www.unep.org/resources/publication/global-chemicals-and-waste-indicator-review-document#:~:text=The%20Global%20Chemicals%20and%20Waste,related%20SDG%20indicators%20across%20sectors.\">Global Chemicals and Waste Indicator Review Document</a>&#x201D; (UNEP, 2021).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Tonnes, Kilograms (Kg), kilograms per constant United States dollars, Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>International Standard Industrial Classification of All Economic Activities (ISIC), Rev.4.</li>\n</ul>\n<p>The hazardous waste generated should be reported as a total amount generated during the year, as well as by its distribution among wide categories of economic activities and by households. The economic activities included in the scope of hazardous waste are disaggregated by ISIC, Rev.4:</p>\n<ul>\n  <li>Agriculture, forestry and fishing (ISIC 01-03)</li>\n  <li>Mining and quarrying (ISIC 05-09)</li>\n  <li>Manufacturing (ISIC 10-33)</li>\n  <li>Electricity, gas, steam and air conditioning supply (ISIC 35)</li>\n  <li>Construction (ISIC 41-43)</li>\n  <li>Other economic activities excluding ISIC 38</li>\n  <li>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions).</li>\n  <li>Categories of hazardousness according to the Basel Convention.</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>Data provided by national governments, including National Statistical Offices (NSOs), Ministries of Environment and other relevant organisations.</p>", "COLL_METHOD__GLOBAL"=>"<ul>\n  <li>The custodian agencies collect national data through the UNSD/UNEP Questionnaire on Environment Statistics (waste section). </li>\n  <li>The United Nations Statistics Division (UNSD) carries out extensive data validation procedures that include built-in automated procedures, manual checks and cross-references to national sources of data. Communication is carried out with countries for clarification and validation of data. Only data that are considered accurate or those confirmed by countries during the validation process are included in UNSD&#x2019;s environment statistics database and disseminated on UNSD&#x2019;s website.</li>\n  <li>Additionally, data from the Basel Convention reporting may also be sent to countries for their consideration for SDG reporting. </li>\n</ul>\n<p>Data for the Organization for Economic Co-operation and Development (OECD) and European Union countries are collected through the biennial OECD/Eurostat Joint Questionnaire on the State of the Environment that is consistent with the UNSD/UNEP Questionnaire, so data are comparable. </p>", "FREQ_COLL__GLOBAL"=>"<ul>\n  <li>The UNSD/UNEP Questionnaire on Environment Statistics is sent every 2 years.</li>\n  <li>The biennial OECD/Eurostat Joint Questionnaire on the State of the Environment is also sent every 2 years.</li>\n</ul>", "REL_CAL_POLICY__GLOBAL"=>"<p>Every two years after the validation of national statistics from the UNSD/UNEP Questionnaire on Environment Statistics and the OECD/Eurostat Joint Questionnaire on the State of the Environment.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Systems and relevant ministries. </p>", "COMPILING_ORG__GLOBAL"=>"<ul>\n  <li>The United Nations Statistics Division (UNSD), the United Nations Environment Programme (UNEP), the Organization for Economic Co-operation and Development (OECD) and Eurostat for all waste indicators excepted global e-waste estimates.</li>\n  <li>The United Nations Institute for Training and Research (UNITAR) for global e-waste estimates.</li>\n</ul>", "INST_MANDATE__GLOBAL"=>"<p>UNEP and UNSD were mandated as Custodian Agencies for indicator 12.4.2 by the Inter-agency and Expert Group on SDG Indicators. </p>", "RATIONALE__GLOBAL"=>"<p>Chemicals are part of everyday life. There are over 140,000 different substances used in all economic sectors globally. Their benefits are many and so too are their potential to adversely impact human health and the environment if not properly managed. All countries, especially middle- and low-income countries, are facing the complex challenge of managing hazardous waste according to international standards of good practice. The situation is complicated by limited human, financial and/or technical resources. As such, action is needed to support the sustainable use of chemicals and environmentally sound management of hazardous waste. There is also a rapid increase in the generation of hazardous waste. Where most of the conventional hazardous wastes are produced in industrial and manufacturing operations, significant amounts are generated in non-industrial sectors, including sludge from the healthcare sector; waste-water treatment plants, waste oils, and waste batteries. There is also an increase in the complexity of products and unidentified hazardous components like coatings, and/or items which are not hazardous (laminates and multi-layer packaging), but present hazardousness in a variety of ways when improperly discarded and end up in air, water or are burned.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data on hazardous waste generation and treatment may be scarce in some countries, due to a series of factors, such as lack of, or insufficient, policies and regulations on management and/or reporting; limited human, financial and technical resources within government agencies, lack of clear disclosure and reporting rules and requirements, and unwillingness of generators and public officials in certain countries to disclose the quantities of hazardous waste generated. Some countries may have the data and monitoring systems needed to report, while for others there is a need for training and capacity development to enhance data collection, validation and reporting capacity. </p>\n<p>Limitations in terms of usable data for calculating the indicator(s) may arise due to differences in understanding of the terminology used in the indicator or differences between these definitions and those included in national legislation. This can lead to differences in reported values and difficulties in cross-checking of reported data. For example, by national legislation, countries may define additional types of waste to be considered as hazardous beyond the waste streams defined in the Basel Convention.</p>", "DATA_COMP__GLOBAL"=>"<p>A full methodology for this indicator is available in the document entitled, &#x201C;<a href=\"https://www.unep.org/resources/publication/global-chemicals-and-waste-indicator-review-document#:~:text=The%20Global%20Chemicals%20and%20Waste,related%20SDG%20indicators%20across%20sectors.\">Global Chemicals and Waste Indicator Review Document</a>&#x201D; (UNEP, 2021).</p>\n<p>For the purpose of this indicator, Hazardous waste generated should include collected hazardous waste (either by specialized companies or by municipal services), hazardous waste which is given by the generator directly to the treatment or disposal facility, as well as an estimation of the hazardous waste which is unaccounted for. Generated hazardous waste includes exported hazardous waste and excludes imports of hazardous waste.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>H</mi>\n    <mi>a</mi>\n    <mi>z</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>d</mi>\n    <mi>o</mi>\n    <mi>u</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>w</mi>\n    <mi>a</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>g</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n  </math>= <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>h</mi>\n    <mi>a</mi>\n    <mi>z</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>d</mi>\n    <mi>o</mi>\n    <mi>u</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>w</mi>\n    <mi>a</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>l</mi>\n    <mi>l</mi>\n    <mi>e</mi>\n    <mi>c</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>t</mi>\n    <mi>h</mi>\n    <mi>r</mi>\n    <mi>o</mi>\n    <mi>u</mi>\n    <mi>g</mi>\n    <mi>h</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>m</mi>\n    <mi>u</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>i</mi>\n    <mi>p</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>s</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>v</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>v</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>m</mi>\n    <mi>p</mi>\n    <mi>a</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>+</mo>\n    <mi>h</mi>\n    <mi>a</mi>\n    <mi>z</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>d</mi>\n    <mi>o</mi>\n    <mi>u</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>w</mi>\n    <mi>a</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>g</mi>\n    <mi>i</mi>\n    <mi>v</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>b</mi>\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>g</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>t</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>m</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>t</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>s</mi>\n    <mi>p</mi>\n    <mi>o</mi>\n    <mi>s</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>f</mi>\n    <mi>a</mi>\n    <mi>c</mi>\n    <mi>i</mi>\n    <mi>l</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mo>+</mo>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>m</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>h</mi>\n    <mi>a</mi>\n    <mi>z</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>d</mi>\n    <mi>o</mi>\n    <mi>u</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>w</mi>\n    <mi>a</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>u</mi>\n    <mi>n</mi>\n    <mi>a</mi>\n    <mi>c</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>u</mi>\n    <mi>n</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>f</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n  </math></p>\n<p></p>\n<p>The estimation of hazardous waste unaccounted for is the most difficult aspect of this methodology as it requires local-level knowledge and estimation. This aspect of the indicator is particularly important as hazardous waste that is unaccounted for is typically also untreated and has a high potential to impact the environment.</p>\n<p>The proportion of hazardous waste treated is presented below. Note that the total quantity of hazardous waste treated during the reported year in the reporting country is calculated by adding quantities of hazardous waste treated, per type of treatment (recycling, incineration with/without energy recovery, landfilling or other), including exports and excluding imports. This matches with the definition of recycling in SDG indicator 12.5.1.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>P</mi>\n    <mi>r</mi>\n    <mi>o</mi>\n    <mi>p</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>h</mi>\n    <mi>a</mi>\n    <mi>z</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>d</mi>\n    <mi>o</mi>\n    <mi>u</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>w</mi>\n    <mi>a</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>t</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>%</mi>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <malignmark></malignmark>\n                <mi>Q</mi>\n                <mi>u</mi>\n                <mi>a</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>t</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>h</mi>\n                <mi>a</mi>\n                <mi>z</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n                <mi>d</mi>\n                <mi>o</mi>\n                <mi>u</mi>\n                <mi>s</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>w</mi>\n                <mi>a</mi>\n                <mi>s</mi>\n                <mi>t</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <malignmark></malignmark>\n                <mi>d</mi>\n                <mi>u</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>g</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>g</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>y</mi>\n                <mi>e</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n                <mi>*</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n        <mo>&#xD7;</mo>\n        <mn>100</mn>\n      </mrow>\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <malignmark></malignmark>\n                <mi>T</mi>\n                <mi>o</mi>\n                <mi>t</mi>\n                <mi>a</mi>\n                <mi>l</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>q</mi>\n                <mi>u</mi>\n                <mi>a</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>t</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>h</mi>\n                <mi>a</mi>\n                <mi>z</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n                <mi>d</mi>\n                <mi>o</mi>\n                <mi>u</mi>\n                <mi>s</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>w</mi>\n                <mi>a</mi>\n                <mi>s</mi>\n                <mi>t</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>g</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <malignmark></malignmark>\n                <mi>d</mi>\n                <mi>u</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>g</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>g</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>y</mi>\n                <mi>e</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>* Hazardous waste treated in the country plus materials exported for treatment minus the materials imported for treatment. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The United Nations Statistics Division (UNSD) carries out extensive data validation procedures that include built-in automated procedures, manual checks and cross-references to national sources of data. Communication is carried out with countries for clarification and validation of data. Only data that are considered accurate or those confirmed by countries during the validation process are included in UNSD&#x2019;s environment statistics database and disseminated on its website.</p>\n<p>The Organization for Economic Co-operation and Development (OECD) and Eurostat carry out extensive data validation procedures on the biennial OECD/Eurostat Joint Questionnaire on the State of the Environment.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>The United Nations Statistics Division (UNSD), which conducts the data collection, validation and dissemination process via the UNSD/UNEP Questionnaire on Environment Statistics, does not make any estimation or imputation for missing values, so the number of data points provided are actual country data. However, UNEP is considering the possibility of global modelling. </p>\n<p>The Organization for Economic Co-operation and Development (OECD) and Eurostat also do not make any estimation or imputation for missing values.</p>", "REG_AGG__GLOBAL"=>"<p>The data will be aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see: <a href=\"http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a>. </p>", "DOC_METHOD__GLOBAL"=>"<p><a href=\"https://www.unep.org/resources/publication/global-chemicals-and-waste-indicator-review-document#:~:text=The%20Global%20Chemicals%20and%20Waste,related%20SDG%20indicators%20across%20sectors.\">Global Chemicals and Waste Indicator Review Document</a> (UNEP, 2021)</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided:</p>\n<ul>\n  <li>by the United Nations Statistics Division (UNSD) for non-OECD and non-European Union country data; </li>\n  <li>by the Organization for Economic Co-operation and Development (OECD) and Eurostat for OECD and European Union country data.</li>\n</ul>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided:</p>\n<ul>\n  <li>by the United Nations Statistics Division (UNSD) for non-OECD and non-European Union country data; </li>\n  <li>by the Organization for Economic Co-operation and Development (OECD) and Eurostat for OECD and European Union country data;</li>\n</ul>\n<p>in cooperation with the countries that provide these data.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided:</p>\n<ul>\n  <li>by the United Nations Statistics Division (UNSD) for non-OECD and non-European Union country data; </li>\n  <li>by the Organization for Economic Co-operation and Development (OECD) and Eurostat for OECD and European Union country data;</li>\n</ul>\n<p>in cooperation with the countries that provide these data.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>For national data: All countries that reply to the questionnaire.</p>\n<p>For global estimates: Regional and global level. </p>\n<p><strong>Time series:</strong></p>\n<p> For national data: The data sets presented in the SDG database cover a period since 2000 if countries report them.</p>\n<p>For global estimates: The data sets presented in the SDG database cover a period since 2000.</p>\n<p><strong>Disaggregation: </strong></p>\n<ul>\n  <li>Disaggregation by ISIC codes. Information on the generation and treatment of hazardous waste could be collected from industry or municipal level and treatment/disposal facilities. </li>\n  <li>Disaggregation by type of landfilling. As there is a significant difference between landfilling in controlled and uncontrolled landfills, further disaggregation on this type of treatment could be analysed.</li>\n  <li>Disaggregation by type of treatment per generating sector. </li>\n  <li>Disaggregation by type of recycling operation (R2 to R12 from Basel convention Annex IV).</li>\n  <li>Disaggregation by territorial division. Information on the hazardous waste generated can significantly vary throughout the territory of a country as there might be hotspots of hazardous waste generation, concentrated around industry intensive areas. </li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>As mentioned, waste statistics involve a large number of national and sub-national stakeholders which may create discrepancies. To address these possible discrepancies, inter-institutional stakeholder collaboration is always encouraged.</p>", "OTHER_DOC__GLOBAL"=>"<p><a href=\"https://www.unep.org/resources/publication/global-chemicals-and-waste-indicator-review-document#:~:text=The%20Global%20Chemicals%20and%20Waste,related%20SDG%20indicators%20across%20sectors.\">Global Chemicals and Waste Indicator Review Document</a> (UNEP, 2021)</p>\n<p><a href=\"https://unstats.un.org/unsd/envstats/questionnaire\">UNSD/UNEP Questionnaire on Environment Statistics (waste section)</a>. </p>\n<p><a href=\"https://collections.unu.edu/eserv/UNU:6477/RZ_EWaste_Guidelines_LoRes.pdf\">E-WASTE STATISTICS GUIDELINES ON CLASSIFICATION, REPORTING AND INDICATORS</a></p>\n<p><a href=\"https://globalewaste.org/\">Global and Regional E-waste Monitors</a></p>", "indicator_sort_order"=>"12-04-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"12.5.1", "slug"=>"12-5-1", "name"=>"Tasa nacional de reciclado, en toneladas de material reciclado", "url"=>"/site/es/12-5-1/", "sort"=>"120501", "goal_number"=>"12", "target_number"=>"12.5", "global"=>{"name"=>"Tasa nacional de reciclado, en toneladas de material reciclado"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de residuos municipales reciclados en relación al total de residuos municipales generados y tratados", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tasa nacional de reciclado, en toneladas de material reciclado", "indicator_number"=>"12.5.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/estadistica-de-residuos-solidos-urbanos-090218/web01-a2inghon/es/", "url_text"=>"Estadística de Residuos Urbanos", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de residuos municipales reciclados en relación al total de residuos municipales generados y tratados", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.5- De aquí a 2030, reducir considerablemente la generación de desechos mediante actividades de prevención, reducción, reciclado y reutilización", "definicion"=>"Proporción de residuos urbanos reciclados respecto del total de residuos urbanos  generados y tratados procedentes de los hogares y del sector servicios (comercios,  oficinas e instituciones públicas o privadas), cuya gestión es asumida por las entidades locales", "formula"=>"\n$$PRU_{reciclados}^{t} = \\frac{\\frac{RU_{reciclados}^{t}}{P^{t}}}{365}$$\n\ndonde: \n\n$RU_{reciclados}^{t} =$ cantidad de residuos urbanos reciclados en el año en el año $t$\n\n$RU^{t} =$ cantidad total de residuos urbanos generados y tratados en el año $t$\n", "desagregacion"=>"Territorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Minimizar la generación de residuos y maximizar su reciclaje es fundamental para el \nconcepto de economía circular. Sin embargo, actualmente, se estima que la cantidad \ntotal de materiales producidos que se reciclan es baja (según la literatura académica). \n\nSi los países comprenden mejor cómo se generan, recolectan y reciclan los residuos, \nesto les permitirá a los países y a otras partes interesadas determinar mejor cómo \nabordar los principales flujos de residuos, por ejemplo, los desechos electrónicos \no el plástico.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos de Naciones Unidas.", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-05-01.pdf\">Metadatos 12-5-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"Proporción de residuos municipales reciclados en relación al total de residuos municipales generados y tratados", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.5- De aquí a 2030, reducir considerablemente la generación de desechos mediante actividades de prevención, reducción, reciclado y reutilización", "definicion"=>"Proportion of urban waste recycled compared to the total amount of urban waste generated  by households and the services sector (businesses, offices and public or private institutions),  which is treated and managed by the Local Entities", "formula"=>"\n$$PRU_{recycled}^{t} = \\frac{\\frac{RU_{recycled}^{t}}{P^{t}}}{365} $$\n\nwhere: \n\n$RU_{recycled}^{t} =$ amount of urban  waste recycled in year $t$\n\n$RU^{t} =$ total amount of urban waste generated and treated in yea $t$\n", "desagregacion"=>"Province\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Minimizing waste generation and maximizing the recycling of waste is central to \nthe concept of circular economy. However, currently, the total amount of produced \nmaterials that are recycled are estimated to be low (based on academic literature). \n\nIf countries better understand how waste are generated, collected and recycled, \nthis will enable countries and other stakeholders to better determine how to deal \nwith major waste streams, for example e-waste or plastic. \n\nSource: United Nations Statistics Division \n", "comparabilidad"=>"The available indicator partially complies with the United Nations metadata. ", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-05-01.pdf\">Metadata 12-5-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de residuos municipales reciclados en relación al total de residuos municipales generados y tratados", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.5- De aquí a 2030, reducir considerablemente la generación de desechos mediante actividades de prevención, reducción, reciclado y reutilización", "definicion"=>"Proporción de residuos urbanos reciclados respecto del total de residuos urbanos  generados y tratados procedentes de los hogares y del sector servicios (comercios,  oficinas e instituciones públicas o privadas), cuya gestión es asumida por las entidades locales", "formula"=>"\n$$PRU_{birziklatuak}^{t} = \\frac{\\frac{RU_{birziklatuak}^{t}}{P^{t}}}{365}$$\n\nnon: \n\n$RU_{birziklatuak}^{t} =$ hiri-hondakin birziklatuen kantitatea $t$ urtean\n\n$RU^{t} =$ sortutako eta tratatutako hiri-hondakinen guztizko kantitatea $t$ urtean\n", "desagregacion"=>"Lurralde historikoa\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Minimizar la generación de residuos y maximizar su reciclaje es fundamental para el \nconcepto de economía circular. Sin embargo, actualmente, se estima que la cantidad \ntotal de materiales producidos que se reciclan es baja (según la literatura académica). \n\nSi los países comprenden mejor cómo se generan, recolectan y reciclan los residuos, \nesto les permitirá a los países y a otras partes interesadas determinar mejor cómo \nabordar los principales flujos de residuos, por ejemplo, los desechos electrónicos \no el plástico.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen metadatuak.", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-05-01.pdf\">Metadatuak 12-5-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 12: Ensure sustainable consumption and production patterns</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 12.5: By 2030, substantially reduce waste generation through prevention, reduction, recycling and reuse</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 12.5.1: National recycling rate, tons of material recycled</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_EWT_RCYPCAP - Electronic waste recycled, per capita (Kg) [12.5.1]</p>\n<p>EN_EWT_RCYR - Proportion of electronic waste recycled (%) [12.5.1]</p>\n<p>EN_EWT_RCYV - Electronic waste recycled (Tonnes) [12.5.1]</p>\n<p>EN_MWT_RCYR - Proportion of municipal waste recycled (%) [12.5.1]</p>\n<p>EN_MWT_RCYV - Municipal waste recycled (Tonnes) [12.5.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-05-24", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.6.1, 12.4.2, 12.3.1, 14.1.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), United Nations Statistics Division (UNSD), United Nations Institute for Training and Research (UNITAR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), United Nations Statistics Division (UNSD), United Nations Institute for Training and Research (UNITAR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p><strong>National Recycling Rate</strong> is defined as the quantity of material recycled in the country plus quantities exported for recycling minus material imported intended for recycling out of total waste generated in the country. Note that recycling includes codigestion/anaerobic digestion and composting/aerobic process, but not controlled combustion (incineration) or land application.</p>\n<p>National recycling rate can be presented by type of waste, including e-waste, plastic waste, municipal waste, and others.</p>\n<p><strong>Concepts:</strong><em> </em></p>\n<p><em>Material recycled</em> expressed in tons, reported at the last entity in the recycling chain, preferably when tons of material is bought as secondary resource to be used in production facilities during the course of the reporting year; Secondary mineral materials used in the construction sector are excluded; composting is considered recycling for the purposes of this indicator.</p>\n<p><em>Recycling</em> is defined under the UNSD/UNEP Questionnaire on Environment Statistics and further for the purpose of these indicators as &#x201C;Any reprocessing of waste material [&#x2026;] that diverts it from the waste stream, except reuse as fuel. Both reprocessing as the same type of product, and for different purposes should be included. Recycling within industrial plants i.e., at the place of generation should be excluded.&#x201D;</p>\n<p>For the purpose of consistency with the Basel Convention reporting and correspondence with EUROSTAT reporting system, Recovery operations R2 to R12 listed in Basel Convention Annex IV, are to be considered as &#x2018;Recycling&#x2019; under the UNSD reporting for hazardous waste. </p>\n<p><em>Total waste generated</em> is the total amount of waste (both hazardous and non-hazardous) generated in the country during the year. </p>\n<p><em>Municipal Solid Waste (MSW)</em> includes waste originating from households, commerce and trade, small businesses, office buildings and institutions (schools, hospitals, government buildings). It also includes bulky waste (e.g., old furniture, mattresses) and waste from selected municipal services, e.g., waste from park and garden maintenance, waste from street cleaning services (street sweepings, the content of litter containers, market cleansing waste), if managed as waste. Further information on MSW is defined in the SDG indicator methodology for 11.6.1. </p>\n<p><em>Electronic waste, or e-waste,</em> refers to all items of electrical and electronic equipment (EEE) and its parts that have been discarded by its owner as waste without the intent of re-use.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Tonnes, Percent (%), Kilograms (Kg)</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>International Standard Industrial Classification of All Economic Activities (ISIC), Rev.4.</li>\n  <li>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions).</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>Data provided by national governments, including National Statistical Offices (NSOs), Ministries of Environment and other relevant organizations.</p>", "COLL_METHOD__GLOBAL"=>"<p>The custodian agencies propose to collect national data through the UNSD/UNEP Questionnaire on Environment Statistics (waste section). </p>\n<p>The United Nations Statistics Division (UNSD) carries out extensive data validation procedures that include built-in automated procedures, manual checks and cross-references to national sources of data. Communication is carried out with countries for clarification and validation of data. Only data that are considered accurate or those confirmed by countries during the validation process are included in UNSD&#x2019;s environment statistics database and disseminated on its website (<a href=\"https://unstats.un.org/unsd/envstats/qindicators\">https://unstats.un.org/unsd/envstats/qindicators</a> and <a href=\"https://unstats.un.org/unsd/envstats/country_files\">https://unstats.un.org/unsd/envstats/country_files</a>).</p>\n<ul>\n  <li>Additionally, data from the Basel Convention reporting may also be sent to countries for their consideration for SDG reporting. </li>\n</ul>\n<p>Data for the Organization for Economic Co-operation and Development (OECD) and European Union countries are collected through the biennial OECD/Eurostat Joint Questionnaire on the State of the Environment that is consistent with the UNSD/UNEP Questionnaire, so data are comparable. </p>", "FREQ_COLL__GLOBAL"=>"<p>The UNSD/UNEP Questionnaire on Environment Statistics is sent every 2 years.</p>\n<p>The biennial OECD/Eurostat Joint Questionnaire on the State of the Environment is also sent every 2 years.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Every two years after the validation of national statistics from the UNSD/UNEP Questionnaire on Environment Statistics and the OECD/Eurostat Joint Questionnaire on the State of the Environment.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Systems and relevant ministries.</p>", "COMPILING_ORG__GLOBAL"=>"<p>The United Nations Statistics Division (UNSD), the United Nations Environment Programme (UNEP), the Organization for Economic Co-operation and Development (OECD) and Eurostat for all waste indicators excepted global e-waste estimates. </p>\n<p>The United Nations Institute for Training and Research (UNITAR) for global e-waste estimates.</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) and the United Nations Statistics Division (UNSD) were mandated as Custodian Agencies for indicator 12.5.1 by the Inter-agency and Expert Group on SDG Indicators. </p>", "RATIONALE__GLOBAL"=>"<p>Minimizing waste generation and maximizing the recycling of waste is central to the concept of circular economy. However, currently, the total amount of produced materials that are recycled are estimated to be low (based on academic literature). If countries better understand how waste are generated, collected and recycled, this will enable countries and other stakeholders to better determine how to deal with major waste streams, for example e-waste or plastic. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Most countries control large end-of-chain recycling facilities and export of recyclable materials, so data from these entities are feasible to collect. There may be recycling carried out in the informal sector that never enters the formal channels, in this case, countries can estimate the size of the informal recycling sector to properly account for all the recycling in the country. </p>\n<p>National recycling rate is part of measuring progress towards sustainable consumption and production, but it does not capture prevention, reduction, reuse and repair. Calculating additional intensity indicators against the Domestic Material Consumption and the Material Flow gives proxies and helps connect this indicator to resource efficiency in consumption and production.</p>\n<p>Additional research is needed to understand typical losses (due to transformation of materials, loss of humidity, percent of rejects) along the recycling chain for various recyclable materials. The losses would need to be known as percentages from the point of entry in the recycling value chain (i.e., Collection of source segregated material, or input to sorting facility) to the point of exit (i.e., when the material leaves the last recyclable processing unit to enter a facility as secondary raw material). This would allow connecting indicator 11.6.1. which will measure among other things the municipal recycling rate, to the national recycling rate. Municipal recycling rate is likely going to be measured at the beginning of the chain, while indicator 12.5.1 will likely be measured at the point of exit from the chain. Such studies may be done using the process flow and material mass balance approach. Another approach could be to follow transactions in the waste management process and introducing so called &#x201C;system of boundaries&#x201D; defining points of reporting of waste quantities. </p>", "DATA_COMP__GLOBAL"=>"<p>A full methodology for this indicator is available in the document entitled, &#x201C;<a href=\"https://www.unep.org/resources/publication/global-chemicals-and-waste-indicator-review-document#:~:text=The%20Global%20Chemicals%20and%20Waste,related%20SDG%20indicators%20across%20sectors.\">Global Chemicals and Waste Indicator Review Document</a>&#x201D; (UNEP, 2021). </p>\n<p>National Recycling Rate is defined as the quantity of material recycled in the country plus quantities exported for recycling minus material imported intended for recycling out of total waste generated in the country. Note that recycling includes codigestion/anaerobic digestion and composting/aerobic process, but not controlled combustion (incineration) or land application.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>R</mi>\n    <mi>e</mi>\n    <mi>c</mi>\n    <mi>y</mi>\n    <mi>c</mi>\n    <mi>l</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>g</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>r</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mo>(</mo>\n                <mi>M</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>a</mi>\n                <mi>l</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>c</mi>\n                <mi>y</mi>\n                <mi>c</mi>\n                <mi>l</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mo>+</mo>\n                <mi>M</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>a</mi>\n                <mi>l</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>e</mi>\n                <mi>x</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>t</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>d</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>f</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>c</mi>\n                <mi>y</mi>\n                <mi>c</mi>\n                <mi>l</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>g</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mo>-</mo>\n                <mi>&amp;nbsp;</mi>\n                <mi>M</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>a</mi>\n                <mi>l</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>i</mi>\n                <mi>m</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>t</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>d</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>f</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>c</mi>\n                <mi>y</mi>\n                <mi>c</mi>\n                <mi>l</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>g</mi>\n                <mo>)</mo>\n                <mo>&#xD7;</mo>\n                <mn>100</mn>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>T</mi>\n    <mi>o</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>w</mi>\n    <mi>a</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>g</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mo>=</mo>\n    <mi>W</mi>\n    <mi>a</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>f</mi>\n    <mi>r</mi>\n    <mi>o</mi>\n    <mi>m</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>m</mi>\n    <mi>a</mi>\n    <mi>n</mi>\n    <mi>u</mi>\n    <mi>f</mi>\n    <mi>a</mi>\n    <mi>c</mi>\n    <mi>t</mi>\n    <mi>u</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>g</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>I</mi>\n        <mi>S</mi>\n        <mi>I</mi>\n        <mi>C</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>10</mn>\n        <mo>-</mo>\n        <mn>33</mn>\n      </mrow>\n    </mfenced>\n    <mo>+</mo>\n    <mi>W</mi>\n    <mi>a</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>f</mi>\n    <mi>r</mi>\n    <mi>o</mi>\n    <mi>m</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>e</mi>\n    <mi>l</mi>\n    <mi>e</mi>\n    <mi>c</mi>\n    <mi>t</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>y</mi>\n    <mo>,</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>g</mi>\n    <mi>a</mi>\n    <mi>s</mi>\n    <mo>,</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>a</mi>\n    <mi>m</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mi>i</mi>\n    <mi>r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>g</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>s</mi>\n    <mi>u</mi>\n    <mi>p</mi>\n    <mi>p</mi>\n    <mi>l</mi>\n    <mi>y</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>I</mi>\n        <mi>S</mi>\n        <mi>I</mi>\n        <mi>C</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mn>35</mn>\n      </mrow>\n    </mfenced>\n    <mo>+</mo>\n    <mi>W</mi>\n    <mi>a</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>f</mi>\n    <mi>r</mi>\n    <mi>o</mi>\n    <mi>m</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>t</mi>\n    <mi>h</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>e</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>o</mi>\n    <mi>m</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mi>c</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>v</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>c</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>g</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>I</mi>\n        <mi>S</mi>\n        <mi>I</mi>\n        <mi>C</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>38</mn>\n      </mrow>\n    </mfenced>\n    <mo>+</mo>\n    <mi>M</mi>\n    <mi>u</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>i</mi>\n    <mi>p</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>w</mi>\n    <mi>a</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi>e</mi>\n    <mi>x</mi>\n    <mi>c</mi>\n    <mi>l</mi>\n    <mi>u</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>g</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>c</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>r</mi>\n    <mi>u</mi>\n    <mi>c</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi>m</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>g</mi>\n    <mo>)</mo>\n  </math></p>\n<p>It is proposed that recycling rate is disaggregated by type of waste, including e-waste and other waste types (such as packaging waste and metals). For the disaggregation by waste stream, the formula will be the same but particular waste types will be evaluated. (Existing data on e-waste and the importance of e-waste means that this disaggregation will be collected at the global level.)</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The United Nations Statistics Division (UNSD) carries out extensive data validation procedures that include built-in automated procedures, manual checks and cross-references to national sources of data. Communication is carried out with countries for clarification and validation of data. Only data that are considered accurate or those confirmed by countries during the validation process are included in UNSD&#x2019;s environment statistics database and disseminated on its website.</p>\n<p>The Organization for Economic Co-operation and Development (OECD) and Eurostat carry out extensive data validation procedures on the biennial OECD/Eurostat Joint Questionnaire on the State of the Environment.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>The United Nations Statistics Division (UNSD), which conducts the data collection, validation and dissemination process via the UNSD/UNEP Questionnaire on Environment Statistics, does not make any estimation or imputation for missing values so the number of data points provided are actual country data. </p>\n<p>However, UNEP is considering the possibility of global modelling towards at country, regional and global levels. </p>", "REG_AGG__GLOBAL"=>"<p>The data will be aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see <a href=\"https://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">here</a>. </p>", "DOC_METHOD__GLOBAL"=>"<p><a href=\"https://www.unep.org/resources/publication/global-chemicals-and-waste-indicator-review-document#:~:text=The%20Global%20Chemicals%20and%20Waste,related%20SDG%20indicators%20across%20sectors.\">Global Chemicals and Waste Indicator Review Document</a> (UNEP, 2021)</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided:</p>\n<ul>\n  <li>by the United Nations Statistics Division (UNSD) for non-OECD and non-European Union country data; </li>\n  <li>by the Organization for Economic Co-operation and Development (OECD) and Eurostat for OECD and European Union country data.</li>\n</ul>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided:</p>\n<ul>\n  <li>by the United Nations Statistics Division (UNSD) for non-OECD and non-European Union country data; </li>\n  <li>by the Organization for Economic Co-operation and Development (OECD) and Eurostat for OECD and European Union country data;</li>\n</ul>\n<p>in cooperation with the countries that provide these data.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided:</p>\n<ul>\n  <li>by the United Nations Statistics Division (UNSD) for non-OECD and non-European Union country data; </li>\n  <li>by the Organization for Economic Co-operation and Development (OECD) and Eurostat for OECD and European Union country data.</li>\n</ul>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>For national data: All countries that reply to the questionnaire. </p>\n<p>For global estimates: Regional and global level.</p>\n<p><strong>Time series:</strong></p>\n<p>For national data: The data sets presented in the SDG database cover a period since 2000 if countries report them.</p>\n<p>For global estimates: The data sets presented in the SDG database cover a period since 2010.</p>\n<p><strong>Disaggregation:</strong></p>\n<ul>\n  <li>By where recycling occurs (in-country and materials exported destined for recycling).</li>\n  <li>By material type (e-waste, plastics, metals, etc.) and for key groups of materials (e.g. e-waste and packaging waste).</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>As mentioned, waste statistics involve a large number of national and sub-national stakeholders which may create discrepancies. To address these possible discrepancies, inter-institutional stakeholder collaboration is always encouraged.</p>", "OTHER_DOC__GLOBAL"=>"<p><a href=\"https://www.unep.org/resources/publication/global-chemicals-and-waste-indicator-review-document#:~:text=The%20Global%20Chemicals%20and%20Waste,related%20SDG%20indicators%20across%20sectors.\">Global Chemicals and Waste Indicator Review Document</a> (UNEP, 2021)</p>\n<p><a href=\"https://unstats.un.org/unsd/envstats/questionnaire\">UNSD/UNEP Questionnaire on Environment Statistics (waste section)</a>. </p>\n<p><a href=\"https://collections.unu.edu/eserv/UNU:6477/RZ_EWaste_Guidelines_LoRes.pdf\">E-WASTE STATISTICS GUIDELINES ON CLASSIFICATION, REPORTING AND INDICATORS</a></p>\n<p><a href=\"https://globalewaste.org/\">Global and Regional E-waste Monitors</a></p>\n<p> </p>", "indicator_sort_order"=>"12-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"12.6.1", "slug"=>"12-6-1", "name"=>"Número de empresas que publican informes sobre sostenibilidad", "url"=>"/site/es/12-6-1/", "sort"=>"120601", "goal_number"=>"12", "target_number"=>"12.6", "global"=>{"name"=>"Número de empresas que publican informes sobre sostenibilidad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de organizaciones con EMAS o Ecolabel", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de empresas que publican informes sobre sostenibilidad", "indicator_number"=>"12.6.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"IHOBE", "periodicity"=>"Anual", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Ihobe.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Número de organizaciones con EMAS o Ecolabel", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.6- Alentar a las empresas, en especial las grandes empresas y las empresas transnacionales, a que adopten prácticas sostenibles e incorporen información sobre la sostenibilidad en su ciclo de presentación de informes", "definicion"=>"Número de organizaciones con Sistema comunitario de gestión y auditoría medioambiental  (EMAS) y Número de licencias de Etiqueta Ecológica Europea (EU Ecolabel)", "formula"=>"\n$$NOEMAS_{t}$$\n\ndonde: \n\n$NOEMAS_{t} $= número de organizaciones con Sistema comunitario de gestión \ny auditoría medioambiental en el año $t$\n\n$$NLEEE_{t}$$\n\ndonde:\n\n$NLEEE_{t} $= número de licencias de Etiqueta Ecológica Europea en el año $t$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nSi bien el sector privado tiene un papel fundamental que desempeñar en el logro de \nlos ODS, la Meta 12.6 y el Indicador 12.6.1 son los únicos que monitorean específicamente \nlas prácticas de las entidades del sector privado. \n\nEl Indicador 12.6.1 cuenta el número de empresas que producen \n“informes de sostenibilidad”. El indicador \nes una oportunidad importante no solo para hacer el seguimiento y promover el crecimiento de \nlos informes de sostenibilidad a nivel mundial, sino también para \npromover informes de alta calidad, promover la integración de información de \nsostenibilidad en el ciclo de informes anuales de las empresas y promover prácticas \nde sostenibilidad por parte de las empresas (como se menciona en la Meta en la que se \nenmarca el indicador).\n\nEMAS es un instrumento voluntario de la Unión Europea que ayuda a organizaciones de cualquier tamaño y sector a \nmejorar continuamente su desempeño ambiental. Contar con la certificación EMAS no implica automáticamente \nque las organizaciones o sus productos sean más respetuosos con el medio ambiente que organizaciones y \nproductos comparables. EMAS implica la obligación de informar a las organizaciones sobre sus principales \nimpactos ambientales, así como datos relativos a la eficiencia energética y de materiales, las emisiones, \nel agua, los residuos y el uso del suelo/la biodiversidad. \n\nLa etiqueta ecológica de la UE es un sistema voluntario que promueve bienes y servicios que \ndemuestran claramente una excelencia ambiental, basándose en procesos estandarizados y evidencia científica.\nLa Etiqueta Ecológica de la UE es el único sistema de ecoetiquetado ISO 14024 Tipo I a nivel de la UE. \nReconocido en toda Europa, aplica múltiples criterios y aborda los principales impactos ambientales de \nlos productos a lo largo de todo su ciclo de vida, desde la extracción de la materia prima hasta su \neliminación. La Etiqueta Ecológica de la UE está verificada por terceros, lo que significa que \nexpertos independientes son responsables de comprobar el cumplimiento de los criterios de la \nEtiqueta Ecológica.\n\nFuente: División de Estadísticas de las Naciones Unidas, Comisión Europea\n", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos de Naciones Unidas.", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.6.1&seriesCode=EN_SCP_FRMN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=TOTAL%20%7C%20TOTAL\"> Número de empresas que publican informes de sostenibilidad con divulgación por dimensión, por nivel de exigencia (Número) EN_SCP_FRMN</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-06-01.pdf\">Metadatos 12-6-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Número de organizaciones con EMAS o Ecolabel", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.6- Alentar a las empresas, en especial las grandes empresas y las empresas transnacionales, a que adopten prácticas sostenibles e incorporen información sobre la sostenibilidad en su ciclo de presentación de informes", "definicion"=>"Number of organizations with an Eco-Management and Audit Scheme (EMAS) and number  of European Ecolabel (EU Ecolabel) licenses", "formula"=>"\n$$NOEMAS_{t}$$\n\nwhere: \n\n$NOEMAS_{t}$ = Number of organizations with an Eco-Management and Audit Scheme in year $t$\n\n$$NLEEE_{t}$$\n\nwhere:\n\n$NLEEE_{t}$ = number of European Ecolabel licenses in year $t$\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nWhile the private sector has a critical role to play in the attainment of the \nSDGs, Target 12.6 and Indicator 12.6.1 are the only ones specifically monitoring \nthe practices of private sector entities. \n\nWhile Indicator 12.6.1 counts the number of companies producing “sustainability \nreports”, the custodian agencies consider the indicator an important opportunity \nnot only to monitor and promote the growth in sustainability reporting globally, \nbut also to monitor and promote high quality reporting, promote the integration \nof sustainability information into the annual reporting cycle of companies, and \npromote sustainability practices by companies (as mentioned in the Target under \nwhich the indicator falls). \n\nEMAS is a voluntary instrument of the European Union that helps organisations of any size \nand in any sector to continuously improve their environmental performance. Having EMAS \ncertification does not automatically mean that organisations or their products are \nmore environmentally friendly than comparable organisations and products. EMAS \ninvolves a reporting obligation requiring organisations to submit environmental statements. \nThese statements include reporting on the main environmental impacts of the organisation \nin question as well as data pertaining to energy and material efficiency, emissions, water, \nwaste and use of land/biodiversity.\n\nThe EU Ecolabel is a voluntary scheme promoting goods and services that clearly demonstrate \nenvironmental excellence, based on standardised processes and scientific evidence. EU Ecolabel is the only \nEU-wide ISO 14024 Type I ecolabelling scheme. Recognised throughout Europe, it is multi-criteria \nand tackles the main environmental impacts of products along their full lifecycle, from extraction \nof raw material to disposal. The EU Ecolabel is third-party verified, which means independent \nexperts are responsible for checking compliance with the EU Ecolabel criteria. \n\nSource: United Nations Statistics Division, European Commission\n", "comparabilidad"=>"The available indicator partially complies with the United Nations metadata.", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.6.1&seriesCode=EN_SCP_FRMN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=TOTAL%20%7C%20TOTAL\"> Number of companies publishing sustainability reports with disclosure by dimension, by level of requirement (Number) EN_SCP_FRMN</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-06-01.pdf\">Metadata 12-6-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Número de organizaciones con EMAS o Ecolabel", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.6- Alentar a las empresas, en especial las grandes empresas y las empresas transnacionales, a que adopten prácticas sostenibles e incorporen información sobre la sostenibilidad en su ciclo de presentación de informes", "definicion"=>"Número de organizaciones con Sistema comunitario de gestión y auditoría medioambiental  (EMAS) y Número de licencias de Etiqueta Ecológica Europea (EU Ecolabel)", "formula"=>"\n$$NOEMAS_{t}$$\n\nnon: \n\n$NOEMAS_{t} $= Batasunaren Ingurumen Kudeaketako eta Ikuskaritzako Sistema duten erakundeen kopurua $t$ urtean\n\n$$NLEEE_{t}$$\n\nnon:\n\n$NLEEE_{t} $= Europako Etiketa Ekologikoko lizentzien kopurua $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nSi bien el sector privado tiene un papel fundamental que desempeñar en el logro de \nlos ODS, la Meta 12.6 y el Indicador 12.6.1 son los únicos que monitorean específicamente \nlas prácticas de las entidades del sector privado. \n\nEl Indicador 12.6.1 cuenta el número de empresas que producen \n“informes de sostenibilidad”. El indicador \nes una oportunidad importante no solo para hacer el seguimiento y promover el crecimiento de \nlos informes de sostenibilidad a nivel mundial, sino también para \npromover informes de alta calidad, promover la integración de información de \nsostenibilidad en el ciclo de informes anuales de las empresas y promover prácticas \nde sostenibilidad por parte de las empresas (como se menciona en la Meta en la que se \nenmarca el indicador).\n\nEMAS es un instrumento voluntario de la Unión Europea que ayuda a organizaciones de cualquier tamaño y sector a \nmejorar continuamente su desempeño ambiental. Contar con la certificación EMAS no implica automáticamente \nque las organizaciones o sus productos sean más respetuosos con el medio ambiente que organizaciones y \nproductos comparables. EMAS implica la obligación de informar a las organizaciones sobre sus principales \nimpactos ambientales, así como datos relativos a la eficiencia energética y de materiales, las emisiones, \nel agua, los residuos y el uso del suelo/la biodiversidad. \n\nLa etiqueta ecológica de la UE es un sistema voluntario que promueve bienes y servicios que \ndemuestran claramente una excelencia ambiental, basándose en procesos estandarizados y evidencia científica.\nLa Etiqueta Ecológica de la UE es el único sistema de ecoetiquetado ISO 14024 Tipo I a nivel de la UE. \nReconocido en toda Europa, aplica múltiples criterios y aborda los principales impactos ambientales de \nlos productos a lo largo de todo su ciclo de vida, desde la extracción de la materia prima hasta su \neliminación. La Etiqueta Ecológica de la UE está verificada por terceros, lo que significa que \nexpertos independientes son responsables de comprobar el cumplimiento de los criterios de la \nEtiqueta Ecológica.\n\nFuente: División de Estadísticas de las Naciones Unidas, Comisión Europea\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen metadatuak.", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.6.1&seriesCode=EN_SCP_FRMN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=TOTAL%20%7C%20TOTAL\"> Dimentsiokako informazioa barneratzen duten jasangarritasun-txostenak argitaratzen dituzten enpresen kopurua, eskakizun-mailaren arabera (kopurua) EN_SCP_FRMN</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-06-01.pdf\">Metadatuak 12-6-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 12: Ensure sustainable consumption and production patterns</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 12.6: Encourage companies, especially large and transnational companies, to adopt sustainable practices and to integrate sustainability information into their reporting cycle</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 12.6.1: Number of companies publishing sustainability reports</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_SCP_FRMN - Number of companies publishing sustainability reports [12.6.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Conference on Trade and Development (UNCTAD)</p>\n<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Conference on Trade and Development (UNCTAD)</p>\n<p>United Nations Environment Programme(UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p><u>Sustainability Reports</u>: </p>\n<p>For the purposes of this indicator, &#x2018;sustainability reports&#x2019; will not be limited to stand-alone sustainability reports produced by companies, but will be considered as &#x2018;reporting sustainability information&#x2019; and expanded to other forms of reporting sustainability information, such as publishing sustainability information as part of the company&#x2019;s annual reports or reporting sustainability information to the national government. This is to ensure that the focus of the indicator is on tracking the publishing of sustainability information, rather than on the practice of publishing stand-alone sustainability reports. It also ensures that the indicator interpretation is aligned with the wording of Target 12.6 which refers to promoting &#x201C;the integration of sustainability information into the annual reporting cycle of companies&#x201D;. </p>\n<p><u>Company:</u></p>\n<p>While many companies report at the group level, many of their impacts will be local, and some subsidiaries or franchises produce separate sustainability reports. As a practice that should be encouraged, and one that is useful to monitor, it is therefore proposed to count both the group and subsidiary/franchise level separately, as separate entities. &#x201C;Company&#x201D; can therefore apply to either the parent company, or a franchise or subsidiary, depending on their reporting practices.</p>\n<p><strong>Concepts:</strong></p>\n<p>It is proposed that, to be counted towards the indicator, companies are encouraged to publish information that meets a &#x201C;Minimum requirement&#x201D; of disclosure. A core set of economic, environmental, social and governance disclosures of sustainability information is therefore identified. In defining these disclosure elements, the custodian agencies attempted to align with the disclosures that appear in existing related reporting frameworks, including the International Integrated Reporting Council (IIRC) reporting framework, the Global Reporting Initiative Standard (GRI), the Sustainability Accounting Standards Board (SASB) (see Annex I for a comparison of the various sustainability disclosures contained under each.</p>\n<p>It also attempts to align with the UNCTAD Core Indicators for company reporting on the contribution towards the attainment of the Sustainable Development Goals. UNCTAD has prepared Guidance on Core indicators for entity reporting on the contribution towards the attainment of the Sustainable Development Goals (SDGs) to support entities in the provision of information under indicator 12.6.1 and governments in assessing the private sector contribution to the SDGs. The Guidance reflects the Agreed Conclusions of the thirty-fourth session of the Intergovernmental Working Group of Experts on International Standards of Accounting and Reporting (ISAR), which in 2017 requested UNCTAD to develop the guiding document. The UNCTAD Guidance includes detailed definitions and data sources for the core indicators in the company accounts to assist the entities in the reporting.</p>\n<p>The purpose is not to create a new reporting standard or framework, but to ensure that the minimum reporting recommendations for Indicator 12.6.1 are aligned with existing global frameworks currently used by companies, so that they may continue to use these frameworks.</p>\n<p>While establishing a minimum recommendations in terms of reporting enables companies disclosing meaningful information on all aspects of sustainability to be counted towards the indicator, it could be perceived as giving the message that the minimum suffices and that companies do not need to go beyond it. </p>\n<p>Therefore, it is proposed that the methodology include an advanced level, with a further set of disclosure elements, which would further provide impetus for examining and reporting on the sustainability practices and impacts of the company. These include: 1) stakeholder engagement, 2) assessing impacts beyond the company boundaries and along the supply chain; 3) supplier and consumer engagement on sustainability issues; 4) procurement and sourcing practices; and 5) environmental performance information in the form of intensity values to be monitored over time, such as consumption of energy, water or materials per unit of production or per unit of profit.</p>\n<p>Having different levels will also allow for information to be collected on the degree of reporting of different companies, including whether the same companies produce more ambitious reports, and go further in their sustainability practices with time, such as through supplier engagement. It would allow for companies who are beginning to produce sustainability reports to provide incentive, through their inclusion in the indicator count, for them to work towards more ambitious reporting and demonstrate their progress over time.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of companies</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions);International Standard Industrial Classification of All Economic Activities (ISIC), Rev.4.</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<ul>\n  <li>National and international reports published on ESG rating platforms, global report aggregators (Refinitive)</li>\n</ul>", "COLL_METHOD__GLOBAL"=>"<ul>\n  <li>The Custodian Agencies will mine existing global report aggregators, to analyse the reports included in their databases in order to: </li>\n  <li>Provide country specific information.</li>\n  <li>Aggregate data at sub-regional, regional and global levels (avoiding double-counting of the same companies).</li>\n  <li>Disaggregate data (company size, per industry).</li>\n  <li>The platform monitored will enable to aggregate e data to obtain a global figure as well as data per UN sub-region and region for inclusion in the Global SDGs Database, and as a basis for the analysis of progress carried out annually for the United Nations <em>Sustainable Development Goals Report</em> and the Secretary General&#x2019;s Report on Progress towards the Sustainable Development Goals.</li>\n  <li>While common definitions of company size, industries (defined below), etc. are required to be used by the custodian agencies for analysis and aggregation at regional and global levels and reporting to the SDGs Report, national governments may choose to use different definitions for their own analysis and reporting, such as for their Voluntary National Reviews (VNRs). Filters will be included on the online platform for the database which will allow governments and other users to filter information according to their own national definitions.</li>\n</ul>", "FREQ_COLL__GLOBAL"=>"<p>First data collection: Expected in early 2020 for 2019 company reports, </p>\n<p>Annually thereafter</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>First reporting cycle: 2020, Annually thereafter.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National and international companies through ESG rating platforms and global report aggregators.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Conference on Trade and Development (UNCTAD) and United Nations Environment Programme (UNEP) </p>", "INST_MANDATE__GLOBAL"=>"<p>United Nations Conference on Trade and Development (UNCTAD) and United Nations Environment Programme (UNEP) were mandated as Custodian Agencies for indicator 12.6.1 by the Inter-agency and Expert Group on SDG Indicators. </p>", "RATIONALE__GLOBAL"=>"<p>While the private sector has a critical role to play in the attainment of the SDGs, Target 12.6 and Indicator 12.6.1 are the only ones specifically monitoring the practices of private sector entities. While Indicator 12.6.1 counts the number of companies producing &#x201C;sustainability reports&#x201D;, the custodian agencies consider the indicator an important opportunity not only to monitor and promote the growth in sustainability reporting globally, but also to monitor and promote high quality reporting, promote the integration of sustainability information into the annual reporting cycle of companies, and promote sustainability practices by companies (as mentioned in the Target under which the indicator falls). Attempts have therefore been made to integrate all of these aspects into the methodology, to the extent possible to encourage companies to advance the quality of sustainability reporting by disclosing baseline indicators across economic, environmental, social and institutional dimensions (for more details, please consult Minimum and Advanced recommendations below)</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator is limited by the number of reports published on ESG rating platforms and collected by global report aggregators.</p>\n<p>The analytics are carried out in all official UN languages and a variety of other languages, but not all national languages are covered. Therefore, there could be some reports that cannot be captured for this reason.</p>", "DATA_COMP__GLOBAL"=>"<p>Companies will be mostly counted towards the indicator by acknowledging publishing sustainability information covering the following sustainability disclosures:</p>\n<p><strong>Minimum reporting recommendations:</strong></p>\n<p><strong>Institutional and governance: </strong></p>\n<ul>\n  <li>Materiality assessment<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></li>\n  <li>Sustainability strategy and/or principles related to sustainability</li>\n  <li>Management approach to address materiality topics</li>\n  <li>Governance structure, including for economic, environmental and social issues</li>\n  <li>Key impacts, risks, opportunities </li>\n  <li>Anti-fraud, anti-corruption and anti-competitive behaviour practices</li>\n</ul>\n<p><strong>Economic:</strong></p>\n<ul>\n  <li>Direct measure of economic performance (revenue, net profit, value added, pay-outs to shareholders)</li>\n  <li>Indirect measure of economic performance (community investment, investment in infrastructure or other significant local economic impact)</li>\n</ul>\n<p><strong>Environmental:</strong></p>\n<ul>\n  <li>Energy consumption and energy efficiency</li>\n  <li>Water consumption, wastewater generation, integrated water resource management practices, or water recycling/re-use and efficiency</li>\n  <li>Greenhouse gas emissions</li>\n  <li>Other emissions and effluents, including Ozone-depleting substances, Nitrogen Oxides (NOX), Sulphur Oxides (SOX), and chemicals</li>\n  <li>Waste generation, including hazardous wastes</li>\n  <li>Waste minimisation and recycling practices</li>\n  <li>Use and/or production of hazardous chemicals and substances</li>\n</ul>\n<p><strong>Social:</strong></p>\n<ul>\n  <li>Occupational health and safety</li>\n  <li>Total number of employees, by contract type and gender</li>\n  <li>Employee training </li>\n  <li>Unfair and illegal labour practices and other human rights considerations</li>\n  <li>Diversity, equal opportunity and discrimination in governance bodies and among employees</li>\n  <li>Worker rights and collective agreements</li>\n</ul>\n<p>Advanced level reporting recommendations :</p>\n<p>As for minimum requirement, with the following additional disclosures and/or indicators: </p>\n<p><strong>Institutional and governance: </strong></p>\n<ul>\n  <li>Details of supply chain</li>\n  <li>Details of stakeholder engagement surrounding sustainability performance</li>\n  <li>Details of remuneration</li>\n</ul>\n<p><strong>Economic</strong></p>\n<ul>\n  <li>Sustainable public procurement policies and practices</li>\n  <li>Percentage or proportion of local suppliers/procurement</li>\n  <li>Charitable donations</li>\n</ul>\n<p><strong>Environmental</strong></p>\n<ul>\n  <li>Supplier environmental assessment</li>\n  <li>Material consumption, sourcing of materials and reclaimed or recycled materials used</li>\n  <li>Energy intensity and renewable energy sources</li>\n  <li>Water intensity and Integrated water resource management</li>\n  <li>GHG intensity</li>\n  <li>Waste intensity</li>\n  <li>Biodiversity impacts</li>\n  <li>Supplier and consumer/customer engagement on environmental issues</li>\n</ul>\n<p><strong>Social</strong></p>\n<ul>\n  <li>Supplier social assessment</li>\n  <li>Local community impacts</li>\n  <li>Supplier and consumer engagement on sustainability issues</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p><em>In the context of the SDG reporting, materiality should take on the broadest possible scope for all industries. Adoption of the Goals required multi-stakeholder consultations, and all parties agreed that certain aspects of economic, environmental and social activities were material to them. It is also consistent with the Task Force on Climate-related Financial Disclosures (TCFD) report on climate-related financial risk disclosure, which indicates climate-related risk as a non-diversifiable risk that affects nearly all industries. The notion that some baseline aspects of sustainability information have an intrinsic impact on material risks is also echoed by the European Commission action plan on financing sustainable growth.</em> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>The United Nations Conference on Trade and Development (UNCTAD) and United Nations Environment Programme (UNEP) validate the methodology used by data providers using sampling data approach and by using different type of report and compare against the minimum and advanced recommendations and data disaggregation set in this methodology.</p>", "ADJUSTMENT__GLOBAL"=>"<p>No further adjustments are made.</p>", "IMPUTATION__GLOBAL"=>"<p>No treatment of missing values is done at country level and at regional level.</p>\n<p>The analytics are carried out in all official UN languages and a variety of other languages, but not all national languages are covered. Therefore, there could be some reports that cannot be captured for this reason. </p>", "REG_AGG__GLOBAL"=>"<p>The data are aggregated at the sub-regional, regional and global levels. In doing so, double-counting is avoided, so a company may appear under several countries, but is only counted once at regional and global levels.</p>", "DOC_METHOD__GLOBAL"=>"<p>Since the data for this indicator is collected by the custodians directly through ESG rating platforms, no guidance methods or guidance were provided to countries wishing to compile national data for this indicator.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided by United Nations Conference on Trade and Development (UNCTAD) and United Nations Environment Programme (UNEP).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided by United Nations Conference on Trade and Development (UNCTAD) and United Nations Environment Programme (UNEP).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided by United Nations Conference on Trade and Development (UNCTAD) and United Nations Environment Programme (UNEP) and is based on sample checking of randomly selected reports and comparing them with the qualification criteria.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data on number of companies reports are available for all member states that have companies publishing sustainability information, as defined by the indicator. </p>\n<p><strong>Time series:</strong></p>\n<p>The reporting on this indicator is annual, given that most companies publish sustainability information on an annual basis.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The platform will generate the following information for each country, then aggregate per sub-region, region and globally (avoiding double-counting of companies during the aggregation): </p>\n<ul>\n  <li><strong>Total number of companies publishing reports that:</strong></li>\n  <li>Meet the minimum reporting recommendations </li>\n  <li>Meet the advanced level reporting recommendations</li>\n</ul>\n<ol>\n  <li><strong>Inclusion of a company under a specific country</strong></li>\n</ol>\n<p>It is proposed that:</p>\n<ul>\n  <li>Multi-national companies are included in the country in which they are listed, or in the country where the head office is found. </li>\n  <li>When a multinational company produces specific separate reports, with disaggregated information per country, for the different countries they operate in, these would be counted separately under the indicator count for each country.</li>\n  <li><strong>Data disaggregated per company size</strong></li>\n</ul>\n<p>Company sizes are currently defined differently in different jurisdictions. For Indicator 12.6.1, a simple split of &#x2018;large&#x2019; and &#x2018;small&#x2019; could be proposed, with large being more than 250 employees, and small and medium being less than 250 employees. This is in line with the Global Reporting Initiative (GRI), UN Global Compact definitions, and is the most frequent definition at the national level in terms of employee number. No minimum turnover requirement is prescribed due to the wide variation in turnover of companies of this size between countries. </p>\n<p>This is the definition of a company size that will be used by the custodian agencies for aggregation and comparability of data and analysis of trends at sub-regional, regional and global levels. </p>\n<ul>\n  <li><strong>Data disaggregated per sector</strong></li>\n  <li>UNCTAD and UNEPpropose to use the International Standard Industrial Classification of All Economic Activities (ISIC) (first level classification) to provide information on the number of companies publishing sustainability reports per industry.</li>\n</ul>\n<p>A. Agriculture, forestry and fishing</p>\n<p>B. Mining and quarrying</p>\n<p>C. Manufacturing</p>\n<p>D. Electricity, gas, steam and air conditioning supply</p>\n<p>E. Water supply; sewerage, waste management and remediation activities</p>\n<p>F. Construction</p>\n<p>G. Wholesale and retail trade; repair of motor vehicles and motorcycles</p>\n<p>H. Transportation and storage</p>\n<p>I. Accommodation and food service activities</p>\n<p>J. Information and communication</p>\n<p>K. Financial and insurance activities</p>\n<p>L. Real estate activities</p>\n<p>M. Professional, scientific and technical activities</p>\n<p>N. Administrative and support service activities</p>\n<p>O. Public administration and defense; compulsory social security</p>\n<p>P. Education</p>\n<p>Q. Human health and social work activities</p>\n<p>R. Arts, entertainment and recreation</p>\n<p>S. Other service activities</p>\n<p>T. Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use</p>\n<p>U. Activities of extraterritorial organizations and bodies</p>\n<ul>\n  <li><strong>Proportion of reports that have undergone verification/assurance of complete report</strong></li>\n  <li>Complete list of accepted assurance standards and tools to be defined.</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p>Not applicable</p>", "indicator_sort_order"=>"12-06-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"12.7.1", "slug"=>"12-7-1", "name"=>"Número de países que aplican políticas y planes de acción sostenibles en materia de adquisiciones públicas", "url"=>"/site/es/12-7-1/", "sort"=>"120701", "goal_number"=>"12", "target_number"=>"12.7", "global"=>{"name"=>"Número de países que aplican políticas y planes de acción sostenibles en materia de adquisiciones públicas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países que aplican políticas y planes de acción sostenibles en materia de adquisiciones públicas", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que aplican políticas y planes de acción sostenibles en materia de adquisiciones públicas", "indicator_number"=>"12.7.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La contratación pública posee un enorme poder adquisitivo, representando un promedio del 12% del \nproducto interno bruto (PIB) en los países de la OCDE y hasta el 30% del PIB en muchos países en desarrollo.\nAprovechar este poder adquisitivo mediante la adquisición de bienes y servicios más sostenibles \npuede contribuir a impulsar los mercados hacia la sostenibilidad, reducir los impactos negativos \nde una organización y generar beneficios positivos para el medio ambiente y la sociedad. \n\nEl fomento de prácticas de contratación pública sostenible (CPS) se reconoce como un componente \nestratégico clave de los esfuerzos globales para lograr patrones de consumo y producción \nmás sostenibles. Los actores involucrados en las CPS llevan mucho tiempo solicitando \ninformación fiable y actualizada sobre las actividades y organizaciones que participan en ellas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-07-01.pdf\">Metadatos 12-7-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Public procurement wields enormous purchasing power, accounting for an average \nof 12 percent of gross domestic product (GDP) in OECD countries, and up to 30 \npercent of GDP in many developing countries. Leveraging this purchasing power \nby buying more sustainable goods and services can help drive markets in the \ndirection of sustainability, reduce the negative impacts of an organization, \nand also produce positive benefits for the environment and society. \n\nThe advancement of sustainable public procurement (SPP) practices is recognized \nas being a key strategic component of the global efforts towards achieving more \nsustainable consumption and production patterns. SPP stakeholders have long \nrequested reliable and upto-date information on activities and organizations \ninvolved in SPP. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-07-01.pdf\">Metadata 12-7-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La contratación pública posee un enorme poder adquisitivo, representando un promedio del 12% del \nproducto interno bruto (PIB) en los países de la OCDE y hasta el 30% del PIB en muchos países en desarrollo.\nAprovechar este poder adquisitivo mediante la adquisición de bienes y servicios más sostenibles \npuede contribuir a impulsar los mercados hacia la sostenibilidad, reducir los impactos negativos \nde una organización y generar beneficios positivos para el medio ambiente y la sociedad. \n\nEl fomento de prácticas de contratación pública sostenible (CPS) se reconoce como un componente \nestratégico clave de los esfuerzos globales para lograr patrones de consumo y producción \nmás sostenibles. Los actores involucrados en las CPS llevan mucho tiempo solicitando \ninformación fiable y actualizada sobre las actividades y organizaciones que participan en ellas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-07-01.pdf\">Metadatuak 12-7-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 12: Ensure sustainable consumption and production patterns</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 12.7: Promote public procurement practices that are sustainable, in accordance with national policies and priorities</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 12.7.1: Number of countries implementing sustainable public procurement policies and action plans</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_SCP_PROCN - Countries implementing sustainable public procurement policies and action plans (1 = YES; 0 = NO) [12.7.1]</p>\n<p>SG_SCP_PROCN_HS - Number of countries implementing sustainable public procurement policies and action plans at higher subnational level by level of implementation (1 = YES; 0 = NO) [12.7.1]</p>\n<p>SG_SCP_PROCN_LS - Number of countries implementing sustainable public procurement policies and action plans at lower subnational level by level of implementation (1 = YES; 0 = NO) [12.7.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-01-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>12.1.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>The indicator measures the number of countries implementing Sustainable Public Procurement (SPP) policies and action plans, by assessing the degree of implementation through an index. To produce the index, countries self-assess the following main elements: </p>\n<p>- Public procurement legal and regulatory framework</p>\n<p>- Practical support delivered for the implementation of SPP</p>\n<p>- SPP priority products<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> and corresponding sustainable procurement criteria </p>\n<p>- Existence of SPP monitoring system</p>\n<p>- Measurement of actual SPP outcome</p>\n<p>More details are provided in the attached <a href=\"https://wedocs.unep.org/handle/20.500.11822/37332\"><em>SPP Index Methodology</em></a> (revised February 2021).</p>\n<p><strong>Concepts:</strong></p>\n<p><em>Sustainable Public Procurement (SPP): </em>Sustainable Public Procurement is a &#x201C;A process whereby public organizations meet their needs for goods, services, works and utilities in a way that achieves value for money on a whole life cycle basis in terms of generating benefits not only to the organisation, but also to society and the economy, whilst significantly reducing negative impacts on the environment&#x201D; (Definition updated by the Multistakeholder Advisory Committee of the 10YFP SPP Programme).</p>\n<p><em>Sustainable Public Procurement Action Plan:</em> A Sustainable Public Procurement (SPP) action plan is a policy document articulating the priorities and actions a public authority will adopt to support the implementation of SPP. </p>\n<p>Plans usually/should address the economic, environmental and social dimensions of SPP, and recognise the potential for SPP to realise SDGs&#x201D;. In some cases a country&#x2019;s action plan may focus on a single aspect of sustainability, being either environmental (e.g. &#x201C;Green&#x201D; public procurement action plan), social (e.g. reference to human rights, fair trade, focus on employment of minorities, etc.), or economic (e.g. promotion of SMEs&#x2019; participation in tenders, focus on employment of minorities, etc.). </p>\n<p><em>Best Value for Money:</em> can be defined as the &#x201C;optimum combination of whole-life cost and quality to meet the end-user&apos;s requirements&quot;.</p>\n<p><em>Life-cycle costing (LCC):</em> is used to evaluate costs which may not be reflected in the purchase price of a product, work or service, and which will be incurred during their lifetime.</p>\n<p><em>MEAT:</em> The Most Economically Advantageous Tender criterion enables the contracting authority to take account of criteria that reflect qualitative, technical and sustainable aspects of the tender submission as well as price when reaching an award decision.</p>\n<p>More reference about the above and their contextualization can be found in the attached <a href=\"https://wedocs.unep.org/handle/20.500.11822/37332\"><em>SPP Index Methodology</em></a>.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of countries</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>Based on the contact list of focal points identified in the drafting of the 2017 SPP country factsheets and of the One Planet 10-year framework of programmes on Sustainable Consumption and Production patterns, representatives from more than <strong>70 countries</strong> were contacted from September to November 2020<strong>, to identify relevant focal points for SDG 12.7.1 data collection.</strong></p>\n<p>As a result of this process, <strong>55+ national governments</strong> and <strong>8 subnational governments</strong> (reporting independently from their national government) <strong>set a specific team or designated a relevant focal point to report on SDG 12.7.1 indicator</strong>, most often originating either from National Procurement Agencies, Treasury Boards (Ministries of Finance), Ministries of Environment. In rarer cases, from the Focal point works for the Statistical Departments in charge of reporting on SDGs at national level.</p>\n<p>In 2020, the SDG 12.7.1 survey was sent out to those focal points and, as a result, submissions were received from 40 national/federal governments (some of which included subnational data as well from provinces or municipalities). 8 subnational governments also reported independently on their SPP policy and action plan implementation efforts. In 2022, 67 national governments reported to UNEP on the number of countries implementing Sustainable Public Procurement policies and action plans.</p>", "COLL_METHOD__GLOBAL"=>"<p>All individual components should be collected at the same source, i.e., focal points nominated to report on SDG 12.7.1. indicator, or SDG focal points, every two years, 2020 onwards<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>.</p>\n<p>To facilitate the data collection effort and reporting process, <strong>a Microsoft Excel&#xAE;-based calculation tool</strong> was designed <strong>to collect inputs</strong>, along with <strong>PDF Reporting Instructions</strong>, and <strong>Frequently Asked Questions</strong>. This Excel&#xAE;-based form provides <strong>a set of answers</strong> for each question, which need to be <strong>supported by evidence </strong>(policy document, procurement guidelines inclusive of sustainability criteria, enabling legislation, trainings, &#x2018;green&#x2019; contracts, etc.).</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> We consider the 2020 data collection exercise as a pilot exercise which will help to refine the metadata and collection method. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "FREQ_COLL__GLOBAL"=>"<p>First data collection: November 2020 &#x2013; February 2021 for 2018-2020 implementation of sustainable public procurement policies and action plans. Following data collection exercises: October-December 2022 and on a biennial mode thereafter.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>2020 data collection: data was released in March 2021 and on a biennial mode thereafter.</p>", "DATA_SOURCE__GLOBAL"=>"<p>SDG 12.7 Focal Points nominated by governments.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Environment Programme (UNEP).</p>", "INST_MANDATE__GLOBAL"=>"<p>UNEP has been nominated as the custodian of SDG 12 and SDG 12.7.1 indicator.</p>", "RATIONALE__GLOBAL"=>"<p>Public procurement wields enormous purchasing power, accounting for an average of 12 percent of gross domestic product (GDP) in OECD countries, and up to 30 percent of GDP in many developing countries. Leveraging this purchasing power by buying more sustainable goods and services can help drive markets in the direction of sustainability, reduce the negative impacts of an organization, and also produce positive benefits for the environment and society. The advancement of sustainable public procurement (SPP) practices is recognized as being a key strategic component of the global efforts towards achieving more sustainable consumption and production patterns. SPP stakeholders have long requested reliable and up-to-date information on activities and organizations involved in SPP.</p>\n<p>As very few countries are able to measure the proportion of their public procurement which is green or sustainable, the methodology tries instead to assess the means and efforts countries are devoting to the implementation of SPP policies or national SPP programmes. Countries scoring above a certain threshold will be considered as SPP implementing countries.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The index aims to measure not only Sustainable Public Procurement (SPP) but also GPP (Green Public Procurement) and SRPP (Socially Responsible Public Procurement). However, <strong>SPP, GPP and SRPP may be addressed in very different ways</strong> depending on the country. They may appear <strong>as a component of overarching policies</strong> such as Sustainable Development Strategies, Green Economy Roadmaps, etc. <strong>They may also be addressed directly with the adoption of a SPP action plan or policy, or through regulatory means</strong>, such as specific provisions in the Public Procurement legal framework.</p>\n<p> </p>\n<p>The main issues faced during the development of this indicator are:</p>\n<ul>\n  <li>Data on the proportion of sustainable public procurement are not available because there is no agreement on which products are green or sustainable and because data are very often not classified in terms of volumes and value of purchased products. </li>\n  <li>Another limitation is related to the existence of multiple layers and components of public procurement: central government, provinces in federal countries, municipal level, public enterprises, hospitals, defence, etc. Procurement data from these different sectors are very often not aggregated.</li>\n  <li>In addition, contracts below a certain threshold are not monitored.</li>\n</ul>\n<p>As a result, and in line with the comment in the rationale section, it was decided to focus on process sub-indicators which will measure the means and efforts countries are investing in the implementation of their SPP plans, policies and programmes.</p>", "DATA_COMP__GLOBAL"=>"<p>So as to evaluate the &#x2018;<em>number of countries implementing a sustainable public procurement policy and action plans</em>&#x2019;, <strong>a</strong> <strong>specific threshold</strong> <strong>above which a country will be</strong> <strong>considered as having a sound Sustainable Public Procurement (SPP) policy or action plan </strong>has been set, to determine whether this country will be considered compliant with the indicator in the final calculation of SDG Indicator 12.7.1.</p>\n<p>It is proposed that this assessment is <strong>based on the evaluation of a national government&#x2019;s SPP implementation level, scope and comprehensiveness</strong>, through the <strong>appraisal of 6 specific parameters</strong> (described in the table below), which will lead to the calculation of a <strong><em>Government SPP Implementation Score. </em></strong></p>\n<p>SPP Implementation Score <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi>A</mi>\n      </mrow>\n      <mrow></mrow>\n    </msub>\n    <mo>&#xD7;</mo>\n    <mrow>\n      <msubsup>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi>i</mi>\n          <mo>=</mo>\n          <mi>B</mi>\n        </mrow>\n        <mrow>\n          <mi>F</mi>\n        </mrow>\n      </msubsup>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>i</mi>\n          </mrow>\n          <mrow></mrow>\n        </msub>\n      </mrow>\n    </mrow>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>=</mo>\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi>A</mi>\n      </mrow>\n      <mrow></mrow>\n    </msub>\n    <mo>&#xD7;</mo>\n    <mrow>\n      <mo stretchy=\"false\">&#x2211;</mo>\n      <mrow>\n        <mfenced open=\"{\" close=\"}\" separators=\"|\">\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>B</mi>\n              </mrow>\n              <mrow></mrow>\n            </msub>\n            <mo>&#x22EF;</mo>\n            <msub>\n              <mrow>\n                <mi>F</mi>\n              </mrow>\n              <mrow></mrow>\n            </msub>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mrow>\n  </math></p>\n<p>Table 1: Description of parameters and scoring used for the assessment of SPP implementation</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Denoted as:</strong></p>\n      </td>\n      <td>\n        <p><strong>Parameter and sub-indicators</strong></p>\n      </td>\n      <td>\n        <p><strong>Scoring</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A</p>\n      </td>\n      <td>\n        <p>Existence of a SPP action plan/policy, and/or SPP regulatory requirements.</p>\n        <p>0 means no SPP policy in place, 1 means existence of SPP action plan, policy and/or SPP regulatory requirements at national, local or both levels.</p>\n      </td>\n      <td>\n        <p>0 or 1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>B</p>\n      </td>\n      <td>\n        <p>Public procurement regulatory framework conducive to sustainable public procurement.</p>\n      </td>\n      <td>\n        <p>0 to 1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>C</p>\n      </td>\n      <td>\n        <p>Practical support delivered to public procurement practitioners in the implementation of SPP.</p>\n      </td>\n      <td>\n        <p>0 to 1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>SPP purchasing criteria/ buying standards / requirements.</p>\n      </td>\n      <td>\n        <p>0 to 1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>E</p>\n      </td>\n      <td>\n        <p>Existence of a SPP monitoring system.</p>\n      </td>\n      <td>\n        <p>0 to 1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>F</p>\n      </td>\n      <td>\n        <p>Percentage of sustainable purchase of priority products/services.</p>\n      </td>\n      <td>\n        <p>0-100%</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>It is proposed that the <strong>specific threshold</strong> above which a country is considered as having a sound SPP policy or action plan and considered compliant with SDG 12.7.1. indicator <strong>is set at a score equal to 1.</strong></p>\n<p><strong>Five classification groups</strong> are proposed to classify submissions received, and reflect the <strong>different stages in the advancement of SPP implementation:</strong></p>\n<p><strong>SPP Implementation Classification Groups</strong></p>\n<p><strong>Level 0: Insufficient data or insufficient implementation of SPP policy/ action plan</strong> (SPP Implementation Score below 1), therefore not complying with the expected set level of implementation.</p>\n<p><strong>------------------------------------------ <em>Threshold</em> --------------------------------------------------------</strong></p>\n<p><strong>Level 1: </strong>Low level of SPP implementation<strong> </strong>(SPP Implementation Score ranging from 1 to 2).</p>\n<p><strong>Level 2: </strong>Medium-low level of SPP implementation<strong> </strong>(SPP Implementation Score ranging from 2 to 3).</p>\n<p><strong>Level 3: </strong>Medium-high level of SPP implementation<strong> </strong>(SPP Implementation Score ranging from 3 to 4).</p>\n<p><strong>Level 4: </strong>High level of SPP implementation<strong> </strong>(SPP Implementation Score larger than 4).</p>\n<p>The full calculations and explanation of the index can be found in the attached <a href=\"https://wedocs.unep.org/handle/20.500.11822/37332\"><strong><em>SPP Index methodology</em></strong></a>.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Firstly, <strong>Excel-based self-assessment forms</strong>, along with <strong>PDF Guidance, and Frequently Asked Questions</strong> are shared with reporting government focal points to provide relevant instructions on how to supply the required information and data. For each answer provided, it is required to provide relevant evidence, and precise references to that evidence, supporting the selected pre-set answer.</p>\n<p>Secondly, each report is verified to check whether relevant evidence is provided. Detailed feedback specific to each question is sent to the relevant focal points to request for clarifications or further details and evidence.</p>\n<p>Thirdly, further to those bilateral exchanges, the additional information or evidence provided are further checked. <strong>When it appears that the provided details/evidence do not sufficiently support the selected answer, those answers are not considered in the final evaluation</strong>. Final information and reports provided are deemed compliant or not compliant, leading to the calculation and validation of the final Sustainable Public Procurement (SPP) score.</p>\n<p>The calculation of this final score provides a basis for the classification of governments into five different categories reflecting the level of implementation of SPP, as described in section <em>4.c. Method of computation</em>.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Despite the fact that the evaluation methodology was developed in consultation with national governments and Sustainable Public Procurement (SPP) experts from different areas of the world, as the differences in public procurement systems and framework significantly differ, some governments had difficulties in applying some questions in section B to their own practice of SPP implementation (for example, GPP (Green Public Procurement) implementation through the use of ecolabels in Asian countries, rather than through (a strong) regulatory framework as in the European Union). As regards section B, equivalent systems of implementation were therefore accepted after deliberation.</p>", "IMPUTATION__GLOBAL"=>"<p>&#x2022; <strong>At country level</strong></p>\n<p>With regard to the developed Sustainable Public Procurement (SPP) government implementation assessment itself, missing values do not significantly impact the calculated score, as governments may report on some sub-indicators only (B, C, D, E, F), only sub-indicator A being mandatory (A: Existence of a SPP action plan/policy, and/or SPP regulatory requirements).</p>\n<p>With regard to the general assessment of SPP implementation at country level<strong>, it should however be noted that it had been</strong> <strong>originally planned to calculate a country-level SPP implementation Index based on the aggregation of three sub-indices reflecting three different levels of government</strong>, including <strong>a weighting representing the government&#x2019;s share of procurement </strong>in total public procurement value at country level (formula shown below), which would provide a fairer evaluation of SPP efforts at country level.</p>\n<p>The actual scope of the national/federal government&#x2019;s SPP implementation might indeed vary considerably from one country to another, as in some countries, SPP implementation when directed by the central government may apply to most public entities in the country, while in other countries, implementation conducted by the federal government might only represent a small share of public procurement at country level.</p>\n<p>The first data collection exercise however <strong>showed that the total public procurement value</strong>, at country level or at the level of the considered government, <strong>is not always available</strong>, therefore not allowing for the calculation of such an index.</p>\n<p>In the first reporting exercise, the assessed level of SPP implementation and further classification in groups, is therefore mainly based on the calculated SPP National/Federal Government Score, taking account of national/federal government SPP implementation efforts. </p>\n<p>Subnational submissions received may however also be evaluated following the same evaluation framework, and, through the calculation of a similar score, be classified according to their level of SPP implementation, and compared with similar-level governments (higher-level subnational government &#x2013; such as provinces, or states in the case of Brazil and the US &#x2013; and lower-level subnational government &#x2013; such as cities and municipalities).</p>\n<p>&#x2022; <strong>At regional and global levels:</strong></p>\n<p>As SDG 12.7.1. indicator measures the number of countries implementing SPP action plans and policies, therefore missing data (countries not submitting reports on their implementation of SPP) do not significantly impact the indicator measurement.</p>", "REG_AGG__GLOBAL"=>"<p>The data will be aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see: <a href=\"http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a>. </p>", "DOC_METHOD__GLOBAL"=>"<p>The following documents were made available to focal points and developed <u>in three languages</u> (English, Spanish and French):</p>\n<ul>\n  <li>2020 Excel-based calculation tool used for the collection of inputs;</li>\n  <li>2020 Reporting Instructions;</li>\n  <li>2020 Frequently Asked Questions.</li>\n</ul>\n<p>Public procurement systems differing significantly, and the responsibility of SPP/GPP/SRPP policy development or implementation belonging to different ministries or institutions in each country, it is not however in UNEP&#x2019;s capacity to provide more detailed assistance to national focal points on data collection specific to each sub-indicator, as those data may originate either from Public Procurement Agencies, Treasury Boards (Ministry of Finance), or Ministries of Environment.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided by the United Nations Environment Programme (UNEP).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided by the United Nations Environment Programme (UNEP) in cooperation with the countries that provide these data. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided by the United Nations Environment Programme (UNEP). </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data will be made available for all UN Member States which have sustainable public procurement policies and action plans and submit information on it, as defined by the indicator. </p>\n<p><strong>Time series:</strong></p>\n<p>The reporting on this indicator will be biennial, starting from 2021.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Administrative level of the public procurement: national, provincial, or local. </p>\n<p>Note: Information has been received at those three levels in the first data collection exercise, but only in rare occasions. Data can be provided <u>separately</u> by administrative level (whenever data was received from subnational governments) for some provincial or local governments only.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p>SPP index methodology</p>\n<p><a href=\"https://wedocs.unep.org/handle/20.500.11822/37332\">https://wedocs.unep.org/handle/20.500.11822/37332</a> </p>\n<p>EU publications <a href=\"https://publications.europa.eu/en/publication-detail/-/publication/cb70c481-0e29-4040-9be2-c408cddf081f/language-en\"><em>Buying Social &#x2013; A guide to taking account of social considerations in public procurement</em></a><em>, </em>accessible at <a href=\"https://publications.europa.eu/en/publication-detail/-/publication/cb70c481-0e29-4040-9be2-c408cddf081f/language-en\">https://publications.europa.eu/en/publication-detail/-/publication/cb70c481-0e29-4040-9be2-c408cddf081f/language-en</a></p>\n<p>United Nations Convention on the Rights of Persons with Disabilities: http://register.consilium.europa.eu/pdf/en/09/st15/st15540.en09.pdf</p>\n<p>European Commission Life-Cycle costing</p>\n<p><a href=\"https://ec.europa.eu/environment/gpp/lcc.htm\">https://ec.europa.eu/environment/gpp/lcc.htm</a></p>\n<p>Multistakeholder Advisory Committee of the 10YFP SPP Programme from: Procuring the Future &#x2013; the report of the UK Sustainable Procurement Task Force, June 2006</p>\n<p>EU Public Procurement Registration - Most economically advantageous tender (MEAT)</p>\n<p><a href=\"https://www.felp.ac.uk/content/most-economically-advantageous-tender-meat\">https://www.felp.ac.uk/content/most-economically-advantageous-tender-meat</a></p>\n<p>UNEP Global review of sustainable public procurement 2017</p>\n<p><a href=\"https://wedocs.unep.org/bitstream/handle/20.500.11822/20919/GlobalReview_Sust_Procurement.pdf?sequence=1&amp;isAllowed=y\"><u>https://wedocs.unep.org/bitstream/handle/20.500.11822/20919/GlobalReview_Sust_Procurement.pdf?sequence=1&amp;isAllowed=y</u></a></p>", "indicator_sort_order"=>"12-07-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"12.8.1", "slug"=>"12-8-1", "name"=>"Grado en que i) la educación para la ciudadanía mundial y ii) la educación para el desarrollo sostenible se incorporan en a) las políticas nacionales de educación, b) los planes de estudio, c) la formación de docentes y d) la evaluación de los estudiantes", "url"=>"/site/es/12-8-1/", "sort"=>"120801", "goal_number"=>"12", "target_number"=>"12.8", "global"=>{"name"=>"Grado en que i) la educación para la ciudadanía mundial y ii) la educación para el desarrollo sostenible se incorporan en a) las políticas nacionales de educación, b) los planes de estudio, c) la formación de docentes y d) la evaluación de los estudiantes"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Grado en que i) la educación para la ciudadanía mundial y ii) la educación para el desarrollo sostenible se incorporan en a) las políticas nacionales de educación, b) los planes de estudio, c) la formación de docentes y d) la evaluación de los estudiantes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Grado en que i) la educación para la ciudadanía mundial y ii) la educación para el desarrollo sostenible se incorporan en a) las políticas nacionales de educación, b) los planes de estudio, c) la formación de docentes y d) la evaluación de los estudiantes", "indicator_number"=>"12.8.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Para alcanzar las metas 4.7, 12.8 y 13.3 de los ODS, es necesario que los gobiernos garanticen que la \nEducación para el Desarrollo Sostenible (EDS) y la Educación para la Ciudadanía Global (ECG) y \nsus subtemas estén plenamente integrados en todos los aspectos de sus sistemas educativos. \n\nLos estudiantes no alcanzarán los resultados de aprendizaje deseados si la EDS y la ECG no se han identificado \ncomo prioridades en las políticas o leyes educativas, si los currículos no incluyen específicamente \nlos temas y subtemas de la EDS y la ECM, y si el profesorado no está capacitado para impartir estos \ntemas en todo el currículo.\n\nEste indicador busca ofrecer una evaluación sencilla de si existe la infraestructura básica que \npermita a los países impartir EDS y ECM de calidad a sus alumnos, para garantizar que sus \npoblaciones cuenten con información adecuada sobre desarrollo sostenible y estilos de vida \nen armonía con la naturaleza. Unas políticas educativas, currículos, formación docente y \nevaluación del alumnado adecuados son aspectos clave del compromiso y el esfuerzo nacionales \npara implementar la ECM y la EDS de forma eficaz y proporcionar un entorno de aprendizaje propicio.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-08-01.pdf\">Metadatos 12-8-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"In order to achieve SDG targets 4.7, 12.8 and 13.3, it is necessary for governments \nto ensure that ESD and GCED and their sub-themes are fully integrated in all aspects \nof their education systems. \n\nStudents will not achieve the desired learning outcomes if Education for Sustainable \nDevelopment (ESD) and Global Citizenship Education (GCED) have not been identified \nas priorities in education policies or laws, if curricula do not specifically include \nthe themes and sub-themes of ESD and GCED, and if teachers are not trained to teach \nthese topics across the curriculum. \n\nThis indicator aims to give a simple assessment of whether the basic infrastructure \nexists that would allow countries to deliver quality ESD and GCED to learners, to \nensure their populations have adequate information on sustainable development and \nlifestyles in harmony with nature. Appropriate education policies, curricula, teacher \neducation, and student assessment are key aspects of national commitment and effort to \nimplement GCED and ESD effectively and to provide a conducive learning environment. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-08-01.pdf\">Metadata 12-8-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Para alcanzar las metas 4.7, 12.8 y 13.3 de los ODS, es necesario que los gobiernos garanticen que la \nEducación para el Desarrollo Sostenible (EDS) y la Educación para la Ciudadanía Global (ECG) y \nsus subtemas estén plenamente integrados en todos los aspectos de sus sistemas educativos. \n\nLos estudiantes no alcanzarán los resultados de aprendizaje deseados si la EDS y la ECG no se han identificado \ncomo prioridades en las políticas o leyes educativas, si los currículos no incluyen específicamente \nlos temas y subtemas de la EDS y la ECM, y si el profesorado no está capacitado para impartir estos \ntemas en todo el currículo.\n\nEste indicador busca ofrecer una evaluación sencilla de si existe la infraestructura básica que \npermita a los países impartir EDS y ECM de calidad a sus alumnos, para garantizar que sus \npoblaciones cuenten con información adecuada sobre desarrollo sostenible y estilos de vida \nen armonía con la naturaleza. Unas políticas educativas, currículos, formación docente y \nevaluación del alumnado adecuados son aspectos clave del compromiso y el esfuerzo nacionales \npara implementar la ECM y la EDS de forma eficaz y proporcionar un entorno de aprendizaje propicio.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-08-01.pdf\">Metadatuak 12-8-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture&#x2019;s contribution to sustainable development</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.7.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessment</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SE_GCEDESD_CUR - Extent to which global citizenship education and education for sustainable development are mainstreamed in curricula [4.7.1,12.8.1,13.3.1]</p>\n<p>SE_GCEDESD_NEP - Extent to which global citizenship education and education for sustainable development are mainstreamed in national education policies [4.7.1,12.8.1,13.3.1]</p>\n<p>SE_GCEDESD_SAS - Extent to which global citizenship education and education for sustainable development are mainstreamed in student assessment [4.7.1,12.8.1,13.3.1]</p>\n<p>SE_GCEDESD_TED - Extent to which global citizenship education and education for sustainable development are mainstreamed in teacher education [4.7.1,12.8.1,13.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>12.8.1 and 13.3.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Education Sector, Division for Peace and Sustainable Development, Section of Education for Sustainable Development (UNESCO-ED/PSD/ESD)</p>\n<p>UNESCO Institute for Statistics (UNESCO-UIS)</p>\n<p>Global Education Monitoring Report</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Education Sector, Division for Peace and Sustainable Development, Section of Education for Sustainable Development (UNESCO-ED/PSD/ESD), UNESCO Institute for Statistics (UNESCO-UIS), and Global Education Monitoring Report.</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Indicator 4.7.1/12.8.1/13.3.1 measures the extent to which countries mainstream Global Citizenship Education (GCED) and Education for Sustainable Development (ESD) in their education systems. This is an indicator of characteristics of different aspects of education systems: education policies, curricula, teacher education and student assessment as reported by government officials, ideally following consultation with other government ministries, national human rights institutes, the education sector and civil society organizations. It measures government intentions and not necessarily what is implemented in practice in schools and classrooms.</p>\n<p>For each of the four components of the indicator (policies, curricula, teacher education, and student assessment), a number of criteria are measured, which are then combined to give a single score between zero and one for each component. (See methodology section for full details).</p>\n<p>The indicator and its methodology have been reviewed and endorsed by UNESCO&#x2019;s <a href=\"https://tcg.uis.unesco.org/\">Education Data and Statistics Commission (EDSC)</a> (former TCG), which is responsible for the development and maintenance of the thematic indicator framework for the follow-up and review of SDG 4. The EDSC also has an interest in education-related indicators in other SDGs, including global indicators 12.8.1 and 13.3.1. The EDSC is composed of 28 regionally representative experts from UNESCO Member States (nominated by the respective geographic groups of UNESCO) as per the revised <a href=\"https://ces.uis.unesco.org/wp-content/uploads/sites/23/2024/01/EDS-2.1.-TCG-TOR-_Final-WEB.pdf\">Terms of Reference </a> (November 2023), as well as international and regional partners and civil society. The <a href=\"http://uis.unesco.org/\">UNESCO Institute for Statistics</a> acts as the Secretariat.</p>\n<p><strong>Concepts:</strong></p>\n<p>Global Citizenship Education (GCED) and Education for Sustainable Development (ESD) nurture respect for all, build a sense of belonging to a common humanity, foster responsibility for a shared planet, and help learners become responsible and active global citizens and proactive contributors to a more peaceful, tolerant, inclusive, secure and sustainable world. They aim to empower learners of all ages to address and resolve local and global challenges and to take informed decisions and actions for environmental integrity, economic viability and a just society for present and future generations, while respecting cultural diversity.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>For Survey Index (between 0.000 and 1.000).</p>\n<p>For Greening Curriculum Index (between 0 and 100).</p>\n<p>For reporting on harmonised scale, 0-100 range values will be used.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>For the time period 2017-2020, responses to the quadrennial reporting by UNESCO Member States on the implementation of the 1974 <a href=\"http://portal.unesco.org/en/ev.php-URL_ID=13088&amp;URL_DO=DO_TOPIC&amp;URL_SECTION=201.html\"><em>Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms</em></a>. The last round of reporting took place in 2020-21. The results were published in the Global SDG Indicator Database in July and September 2021. In November 2023, the 1974 Recommendation was superseded by the 2023 Recommendation on Education for Peace, Human Rights and Sustainable Development. The first reporting on the new Recommendation will take place in 2026-2027 covering the period 2024-2026. It will be one data source for the global indicator. In 2024 a short, one-off survey is being considered by UNESCO to collect data for the global indicator covering the time period 2021-2023. (See methodology section for details of questions asked).</p>\n<p><strong>Greening</strong></p>\n<p>To measure the extent to which green content is integrated in the official intended curriculum of primary and (lower) secondary education, two types of documents were analysed to create a country&#x2019;s Greening Curriculum Indicator (GCI) score: 1) <strong>national curriculum frameworks</strong> and 2) <strong>subject curricula documents</strong> from science and social science subjects taught in grades 3, 6, and 9. The terms curriculum or syllabus here should be distinguished from related terms such as textbook, lesson plan, and teaching guidelines. A database of over 1,700 curriculum documents has been compiled for the 2025 data release.</p>\n<h3><em>National curriculum frameworks (NCFs)</em></h3>\n<p>NCFs<strong> </strong>are defined as national-level policy documents that overview a country&#x2019;s educational goals and priorities and set forth key parameters of the country&#x2019;s official intended curriculum. NCFs are written and approved by the relevant ministry of education or another officially designated body. A comprehensive NCF: 1) delineates the aims of the curriculum at various stages of schooling; 2) explains the educational philosophy underlying the curriculum and approaches to teaching, learning, and assessment that align with that philosophy; 3) describes curricular structures; 4) assigns names to subject/learning areas; 5) allocates time to each subject (or group of subjects) in each grade level (or set of grades); 6) provides guidelines to curriculum developers, teacher trainers, and textbook writers; 7) prescribes curricular standards and mechanisms for inspection and monitoring; and 8) refers to learning assessments to be conducted (UNESCO-IBE, 2017a; UNESCO-IBE, 2017b). </p>\n<p>To be considered an NCF for the purposes of the GCI, the document has to:</p>\n<ul>\n  <li>Be written by the ministry of education or other official designated body.</li>\n  <li>Cover primary, lower secondary, or upper secondary levels of formal education (categories 1, 2, and 3 according to the International Standard Classification of Education or ISCED).<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup></li>\n  <li>Have a title or opening matter that describes the document as a National Curriculum Framework.</li>\n  <li>Include content that aligns with the sections outlined in the document definitions above.</li>\n</ul>\n<p>In cases where an NCF matching the above criteria was not identified, other documents containing similar content to an NCF were considered for inclusion. For example:</p>\n<ul>\n  <li>The introductory or front matter of document(s) specifying the content of subject curricula similarly to an NCF.</li>\n  <li>Laws or regulations passed by legislative or executive bodies that specify curricular structures and contents of a national education system along the lines of an NCF.</li>\n  <li>Official websites of national governments or subnational political units that present in a similar manner to an NCF.</li>\n</ul>\n<h3><em>Subject curricula</em></h3>\n<p>Subject curricula or subject syllabi are defined as subject- and grade-specific documents that include most or all of the following information: 1) a general rationale for the teaching of the subject; 2) the intended aims and learning outcomes; 3) clearly defined content areas (topics and themes) to be included in the teaching of each subject; and 4) ideally, a weekly, monthly, or yearly timetable allocating instructional time to each topic/subject, pedagogical considerations, and possibly assessment guidelines. The name given to such documents varies by language &#x2013; for example, &#x201C;programme&#x201D; (French), &#x201C;Lehrplan&#x201D; (German), &#x201C;programma&#x201D; (Italian), &#x201C;plan de estudios&#x201D; (Spanish) and &#x201C;almanhaj&#x201D; (Arabic) &#x2013; and may have slightly different connotations. There are no international guidelines for subject curricula, partly because they reflect national traditions in the development and implementation of the official curriculum, the extent of teacher and school autonomy, and patterns of pre-service and in-service teacher training.</p>\n<p>Subject curricula were included for subjects in two broad knowledge domains: science and social science. Curricula for up to four subjects in each knowledge domain (thus up to eight subjects in total) were included at each grade level (3, 6, and 9) in each country or sub-national jurisdiction. Table 1 lists the typical subjects found in each knowledge domain internationally. Many countries organize instruction in a single general science and/or social science subject (more common in grades 3 and 6), rather than numerous specialized subjects (more common in grade 9). Some countries teach interdisciplinary subjects such as environmental education (EE) or education for sustainable development (ESD) or special hybrid subjects that combine science and social science content. Such interdisciplinary or hybrid subjects were included among the up to eight subjects per grade level. </p>\n<p>Table 1: List of typical science, social science and EE/ESD subjects included in GCI calculations</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Science Subjects</strong></p>\n      </td>\n      <td>\n        <p><strong>Social Science Subjects</strong></p>\n      </td>\n      <td>\n        <p><strong> EE/ESD subjects</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ul>\n          <li>General Science</li>\n          <li>Applied Science / Technology</li>\n          <li>Earth Science</li>\n          <li>Life Science</li>\n          <li>Physical Science</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>General Social Science</li>\n          <li>Geography</li>\n          <li>History</li>\n          <li>Civics/Citizenship</li>\n          <li>Economics</li>\n          <li>Religious, Moral, and Philosophy</li>\n          <li>Cultural and Art Studies</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Environmental Education</li>\n          <li>Environmental Education / Education for Sustainable Development</li>\n          <li>Environmental and Outdoor Education </li>\n          <li>Sustainability</li>\n        </ul>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><em>Note</em>. The number of science subjects never exceeded four subjects in any country, so all science subjects were collected for the countries included in the sample. Any curricula related to EE or ESD were also collected. In total, 17 countries had EE/ESD specific curricula.</p>\n<p><em>Green keywords</em></p>\n<p>A set of 13 green keywords were defined in relation to four themes: environment, sustainability, climate change, and biodiversity (see Table 2).</p>\n<p>Table 2: List of green keywords used in the analysis</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Themes </strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>Keywords</strong></p>\n      </td>\n      <td>\n        <p><strong>Total number of keywords</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Environment and </p>\n        <p>sustainability</p>\n      </td>\n      <td>\n        <ul>\n          <li>environmental*</li>\n          <li>sustainability</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>greening</li>\n          <li>&#x201C;sustainable development&#x201D;</li>\n        </ul>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Climate change</p>\n      </td>\n      <td>\n        <ul>\n          <li>&#x201C;climate change&#x201D;</li>\n          <li>&#x201C;global warming&#x201D;</li>\n          <li>&#x201C;greenhouse gas*&quot;</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>&quot;climate justice&quot;</li>\n          <li>&#x201C;renewable energy&#x201D;</li>\n        </ul>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Biodiversity</p>\n      </td>\n      <td>\n        <ul>\n          <li>biodiversity</li>\n          <li>ecosystem*</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>extinction*</li>\n          <li>invasive species</li>\n        </ul>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Total</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>13</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>All keywords were translated into 40 languages and then validated by language proficient experts. The keyword searches are carried out using a bespoke Python application.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> See <a href=\"https://uis.unesco.org/en/topic/international-standard-classification-education-isced\">https://uis.unesco.org/en/topic/international-standard-classification-education-isced</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Responses are submitted by national governments, typically by officials in Ministries of Education. Respondents are asked to consult widely across other government ministries, with national human rights institutes, the education sector and civil society organizations in compiling their responses. Respondents are also asked to submit supporting evidence in the form of documents or links (e.g. to education policies or laws, curricula, etc.), which will be made publicly available in UNESCO&#x2019;s <a href=\"https://en.unesco.org/themes/right-to-education/database\">Observatory on the Right to Education</a>. </p>\n<p><strong>Greening</strong></p>\n<p><em>National curriculum frameworks</em></p>\n<p>NCF documents are identified by searching ministry of education websites, as well as databases such as UNESCO IIEP Planipolis, UNESCO International Institute for Educational Planning (IIEP), Siteal, UNESCO Regional Comparative and Explanatory Study (ERCE), Eurydice, Organization for Economic Cooperation and Development (OECD) Policy Outlook, and the Educational Media Research (Edumeres), as well as consulting country experts. </p>\n<p><em>Subject curricula</em></p>\n<p>Subject curricula are included for subjects in two broad knowledge domains: science and social science. Curricula for up to four subjects in each knowledge domain (thus up to eight subjects in total) are included at each grade level (3, 6, and 9) in each country or sub-national jurisdiction. Table 1 above lists the typical subjects found in each knowledge domain internationally. Many countries organize instruction in a single general science and/or social science subject (more common in grades 3 and 6), rather than numerous specialized subjects (more common in grade 9). Some countries teach interdisciplinary subjects on environmental education (EE) or education for sustainable development (ESD) or special hybrid subjects that combine science and social science content. Such interdisciplinary or hybrid subjects are included among the up to eight subjects per grade level. </p>\n<p>Subject curricula documents are identified through a range of sources, including through manually reviewing ministry of education websites and searching archives of recent curriculum studies. National Commissions for UNESCO also provided subject curricula following a request by the UNESCO International Bureau of Education and UNESCO headquarters. In cases where these methods do not yield the relevant subject curricula, additional documents are collected through consultation with country education experts. </p>", "FREQ_COLL__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>2020-21 round (covering 2017-2020) completed in April 2020. Next round foreseen in 2024 (covering 2021-2023). Data for the period 2024-2026 are expected to be collected in 2026-2027, as the first reporting on the 2023 Recommendation on Education for Peace, Human Rights and Sustainable Development.</p>\n<p><strong>Greening</strong></p>\n<p>Data collection is expected to be annual. The collection of 2025 data was carried out between 2023 and 2024.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Q2 and Q3 of 2021 (from 2020-21 reporting round). 2025 (for 2024 reporting round).</p>\n<p><strong>Greening</strong></p>\n<p>2025 (for 2023-24 data).</p>", "DATA_SOURCE__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Requests for reports are submitted to Ministers Responsible for Relations with UNESCO who are typically Education Ministers. Reports are usually completed by government officials in Ministries of Education. Countries are requested to consult widely before submitting their reports. To assist with this, requests for reports are also copied to NGOs in official partnership with UNESCO and the Office of the High Commissioner for Human Rights (OHCHR). </p>\n<p><strong>Greening</strong></p>\n<p>Data were provided by the UNESCO ESD Section and the Monitoring and Evaluating Climate Communication and Education (MECCE) Project.</p>", "COMPILING_ORG__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>UNESCO&#x2019;s Sections for Education for Sustainable Development and Global Citizenship and Peace Education.</p>\n<p><strong>Greening</strong></p>\n<p>Global Education Monitoring Report and the Monitoring and Evaluating Climate Communication and Education (MECCE) Project.</p>", "INST_MANDATE__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>In 1974, UNESCO Member States adopted the <em>Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms</em>, which encapsulates many of the aims of SDG targets 4.7, 12.8 and 13.3. Every four years, countries report on the implementation of the Recommendation. This well-established formal mechanism is the data source for indicator 4.7.1/12.8.1/13.3.1. The seventh quadrennial reporting round took place in 2020-2021.</p>\n<p><strong>Greening</strong></p>\n<p>During the UN Transforming Education Summit (November 16-19, 2022), which sought to mobilize solutions to accelerate national and international efforts to achieve ADG 4, participants agreed to seven global initiatives, one of which is &#x201C;Greening Education: to get every learner climate ready.&#x201D; UNESCO established the Greening Education Partnership in 2022, which prioritized the greening of schools, curricula, teacher training and system capacities and communities. In December 2022 the SDG 4 High-level Steering Committee met in Paris and decided to &#x201C;add indicators for&#x2026;greening education and requested that its Data and Monitoring Technical Committee&#x2026;develop a methodology for these indicators&#x2026;&#x201D; The Steering committee also mandated UIS and the GEM Report to develop benchmark indicators on greening education.</p>", "RATIONALE__GLOBAL"=>"<p>In order to achieve SDG targets 4.7, 12.8 and 13.3, it is necessary for governments to ensure that ESD and GCED and their sub-themes are fully integrated in all aspects of their education systems. Students will not achieve the desired learning outcomes if Education for Sustainable Development (ESD) and Global Citizenship Education (GCED) have not been identified as priorities in education policies or laws, if curricula do not specifically include the themes and sub-themes of ESD and GCED, and if teachers are not trained to teach these topics across the curriculum. </p>\n<p>This indicator aims to give a simple assessment of whether the basic infrastructure exists that would allow countries to deliver quality ESD and GCED to learners, to ensure their populations have adequate information on sustainable development and lifestyles in harmony with nature. Appropriate education policies, curricula, teacher education, and student assessment are key aspects of national commitment and effort to implement GCED and ESD effectively and to provide a conducive learning environment.</p>\n<p><strong>Survey</strong></p>\n<p>Each component of the indicator is assessed on a scale of zero to one. The closer to one the value, the better mainstreamed are ESD and GCED in that component. By presenting results separately for each component, governments will be able to identify in which areas more efforts may be needed.</p>\n<p><strong>Greening</strong></p>\n<p>Greening related to environment, sustainability, climate change and biodiversity (SDG indicator 13.3.1) is captured under the component &#x201C;Curricula&#x201D; of the indicator. The measurement of greening follows a specific computation method and is presented separately. The goal is to assess the extent to which green content (related to environment, sustainability, climate and biodiversity) is prioritized and integrated into national curriculum policy frameworks and science and social science subject curricula (syllabi) in grades 3, 6, and 9.</p>", "REC_USE_LIM__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>The indicator is based on self-reporting by government officials. However, countries are asked to provide supporting evidence in the form of documents or links (e.g. education policies or laws, curricula, etc.) to back up their responses. In addition, UNESCO compares responses with available information from alternative sources and, if appropriate, raises queries with national respondents. At the end of the reporting cycle, country responses and the supporting documents will be made publicly available.</p>\n<p><strong>Greening</strong></p>\n<p>The greening indicator analyses the content of official policy and curriculum documents for themes related to sustainability, environment, climate change and biodiversity to determine the extent to which relevant green content is prioritized. As it is based on counts of keywords, it does not capture how these keywords are used.</p>", "DATA_COMP__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Information collected with the questionnaire for monitoring the implementation by UNESCO Member States of the 1974 <em>Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms</em> and from 2026, the 2023 <em>Recommendation on Education for Peace, Human Rights and Sustainable Development </em>is used for the construction of the global indicator. For each of the four components of the indicator (policies, curricula, teacher education, and student assessment), a number of criteria are measured, which are then combined to give a single score between zero and one for each component. Only information for primary and secondary education are used for calculation of indicator 4.7.1/12.8.1/13.3.1.</p>\n<ol>\n  <li><u>Laws and policies</u></li>\n</ol>\n<p>The following questions are used to calculate the policies component of the indicator:</p>\n<p><em>A2: Please indicate which global citizenship education (GCED) and education for sustainable development) ESD themes are covered in national or sub-national <u>laws, legislation or legal frameworks</u> on education. </em></p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) and two levels of government (national and sub-national) = 16 responses.</p>\n<p>Response categories are no = 0, yes = 1, unknown, which is treated as zero, and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank the question score is not calculated. </p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = simple mean of the 0 and 1 scores, <u>excluding not applicables </u>(i.e., if eight of the 16 responses are &#x2018;not applicable&#x2019;, the sum of the 0 and 1 scores is divided by 8 to get the mean and not by 16).</p>\n<p><em>A4. Please indicate which GCED and ESD themes are covered in national or sub-national <u>education policies, frameworks or strategic objectives</u>.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) = 8 responses. </p>\n<p>Response categories are no = 0, yes = 1, and unknown (treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>A5. Please indicate whether national or sub-national <u>education policies, frameworks or strategic objectives</u> on education provide a mandate to integrate GCED and ESD. </em></p>\n<p>There are two levels of government (national, sub-national) and five areas of integration (curricula, learning objectives, textbooks, teacher education, and student assessment) = 10 responses. </p>\n<p>Response categories are no = 0, yes = 1, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding not applicables</u> are unknown or blank, the question score is not calculated. </p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = simple mean of the 0 and 1 scores, <u>excluding not applicables </u>(i.e., if five of the 10 responses are &#x2018;not applicable&#x2019;, the sum of the 0 and 1 scores is divided by 5 to get the mean and not by 10).</p>\n<p><em>E1a. Based on your responses to questions in the previous section (laws and policies) please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed</em><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup><em> in education laws and policies in your country.</em> </p>\n<p>There are two levels of government (national, sub-national) = 2 responses. </p>\n<p>Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding not applicables</u> are unknown or blank, the question score is not calculated. </p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = half the simple mean of the 0, 1 and 2 scores, <u>excluding not applicables </u>(i.e., if one of the two responses is &#x2018;not applicable&#x2019;, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1 as do the scores for the other three questions in this section.</p>\n<p>Policy component score = simple mean of the scores for questions A2, A4, A5 and E1a. Where a question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.</p>\n<ol>\n  <li><u>Curricula</u></li>\n</ol>\n<p>The following questions are used to calculate the curricula component of the indicator:</p>\n<p><em>B2: Please indicate which global citizenship education (GCED) and education for sustainable development (ESD) themes are taught as part of the curriculum.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) = 8 responses.</p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated. </p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>B3. Please indicate in which subjects or fields of study GCED and ESD are taught in primary and secondary education.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) and twelve subjects in which they may be taught (arts; civics, civil or citizenship education; ethics/moral studies; geography; health, physical education and sports; history; languages; mathematics; religious education; science; social studies and integrated studies) = 96 responses. </p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank the question score is not calculated.</p>\n<p>Note that responses to &#x2018;other subjects, please specify&#x2019; in the question are ignored. If appropriate, during quality assurance answers in this category may be recoded to one of the other 12 subjects.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>B4. Please indicate the approaches used to teach GCED and ESD in primary and secondary education.</em> </p>\n<p>There are four teaching approaches (GCED/ESD as separate subjects, cross-curricular, integrated, whole school) = 4 responses</p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>E1b. Based on your responses to questions in the previous section (curricula) please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed</em><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup><em> in curricula in your country.</em> </p>\n<p>There are two levels of government (national, sub-national) = 2 responses.</p>\n<p>Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding &#x2018;not applicables&#x2019;</u> are unknown or blank, the question score is not calculated.</p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = half the simple mean of the 0, 1 and 2 scores, <u>excluding &#x2018;not applicables&#x2019; </u>(i.e., if one of the two responses is &#x2018;not applicable&#x2019;, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1, as do the scores for the other three questions in this section.</p>\n<p>Curricula component score = simple mean of the scores for questions B2, B3, B4 and E1b. Where a question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.</p>\n<ol>\n  <li><u>Teacher education</u></li>\n</ol>\n<p>The following questions are used to calculate the teacher education component of the indicator:</p>\n<p><em>C2: Please indicate whether teachers, trainers and educators are trained to teach global citizenship education (GCED) and education for sustainable development (ESD) during initial or pre-service training and/or through continuing professional development.</em> </p>\n<p>There are two types of training (initial/pre-service and continuing professional development) and two types of teachers (of selected subjects in which ESD/GCED are typically taught, and of other subjects) = 4 responses. </p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>C3. Please indicate on which GCED and ESD themes pre-service or in-service training is available for teachers, trainers and educators.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) = 8 responses.</p>\n<p>Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>C4. Please indicate whether teachers, trainers and educators are trained to teach the following dimensions of learning in GCED and ESD.</em> </p>\n<p>There are four learning dimensions (knowledge, skills, values, and attitudes/behaviours) = 4 responses. </p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>C5. Please indicate whether teachers, trainers and educators are trained to use the following approaches to teach GCED and ESD in primary and secondary education</em>. </p>\n<p>There are four teaching approaches (GCED/ESD as separate subjects, cross-curricular, integrated, whole school) = 4 responses. </p>\n<p>Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>E1c. Based on your responses to questions in the previous section (teacher education), please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed</em><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup><em> in teacher education in your country.</em> </p>\n<p>There are two levels of government (national, sub-national) = 2 responses. </p>\n<p>Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable (which is ignored). Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding &#x2018;not applicables&#x2019;</u> are unknown or blank, the question score is not calculated.</p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = half the simple mean of the 0, 1 and 2 scores, <u>excluding &#x2018;not applicables&#x2019; </u>(i.e., if one of the two responses is &#x2018;not applicable&#x2019;, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1, as do the scores for the other three questions in this section.</p>\n<p>Teacher education component score = simple mean of the scores for questions C2, C3, C4, C5 and E1c. Where component question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.</p>\n<ol>\n  <li><u>Student assessment</u></li>\n</ol>\n<p>The following questions are used to calculate the student assessment component of the indicator:</p>\n<p><em>D2: Please indicate whether the global citizenship education (GCED) and education for sustainable development (ESD) themes below are generally included in <u>student assessments or examinations</u>.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) = 8 responses. </p>\n<p>Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>D3. Please indicate which of the dimensions of learning in GCED and ESD below are generally included in <u>student assessments or examinations</u>.</em> </p>\n<p>There are four learning dimensions (knowledge, skills, values, and attitudes/behaviours) = 4 responses.</p>\n<p>Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated. </p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>E1d. Based on your responses to questions in the previous section (student assessment), please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed</em><sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup><em> in student assessment in your country.</em> </p>\n<p>There are two levels of government (national, sub-national) = 2 responses. </p>\n<p>Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding &#x2018;not applicables&#x2019;</u> are unknown or blank, the question score is not calculated. </p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = half the simple mean of the 0, 1 and 2 scores, <u>excluding &#x2018;not applicables&#x2019; </u>(i.e., if one of the two responses is &#x2018;not applicable&#x2019;, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1, as do the scores for the other three questions in this section.</p>\n<p>Student assessment component score = simple mean of the scores for questions D2, D3 and E1d. Where component question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.</p>\n<p>The component scores all lie between zero and one and are presented as a dashboard of four scores. They are not combined to create a single overall score for the indicator. The higher the score, the more GCED and ESD are mainstreamed in the given component. In this way, users can make a simple assessment in which component area more efforts may be needed. </p>\n<p><strong>Greening</strong></p>\n<ol>\n  <li><u>Curricula</u></li>\n</ol>\n<h3><em>Document preparation</em></h3>\n<p>All collected documents are added to a single database in a standardized fashion. Documents are downloaded if found online and converted to PDF if in another format. In many cases, subject curricula are part of a larger document, in which case, relevant subject- and grade-specific material are extracted into separate documents. Documents in the database are named using the following protocol:</p>\n<p><em> &#x201C;country_state/province_documenttype_region_year_language_grade_knowledgedomain&#x201D;</em></p>\n<p>Information about each document is stored in a database (one row per document), including document title, year of publication, subject, author, source, and language. </p>\n<p>For documents in languages for which there are fewer than three documents in that language (Burmese, Norwegian, Swedish, and Urdu), the documents are machine translated into English using Google Translate.</p>\n<h2><em>Keyword selection and analysis</em></h2>\n<p>The GCI measures the inclusion of green content in four document types (NCF, grade 3 subject curricula, grade 6 subject curricula, and grade 9 subject curricula). It counts the presence of 13 keywords corresponding to three themes of Environment/Sustainability, Climate Change, and Biodiversity. The selected keywords: 1) best represent the theme, 2) can be translated into all relevant languages, and 3) are sufficiently prevalent in the analysed documents to provide data for measuring components of the GCI (see Table 2 above). Additional sources such as recent UNESCO studies of greening education and the Greening Education Partnership curriculum guidance were also used to identify relevant green keywords.</p>\n<p>Each keyword includes its plural and singular as well as the many forms the word may take depending on the language.<sup><sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></sup> Some languages and/or countries employ distinctive language/culture-specific keywords to capture a theme. Thus, each theme includes space for a culture- or language-specific keyword to be added, if appropriate.<sup><sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup></sup> The keywords and their translations into 40 languages are reviewed and validated by native speaking experts who are familiar with greening education concepts.</p>\n<p>A Python-based application is used to bulk process text files and identify keywords in documents in all the required languages. To be read by the Python application, all the text documents are converted to UTF-8 text format and stored in a local folder. The Python application also requires a two-column spreadsheet with columns for &quot;File Name&quot; and &quot;Language&quot; and a second spreadsheet with columns for &#x201C;Keyword&#x201D; and the keyword&#x2019;s &#x201C;Language.&#x201D; These files and the folder location are then loaded into the Python application. The application uses the language file to determine which column from the keyword file to utilize in searching for keywords for each text file. The application then counts relevant keywords in every document (NCF and subject curricula) in the specified language. After completing the keyword search processing, the application outputs a spreadsheet file that contains a row for each curriculum document and columns for every keyword.<sup><sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup></sup> This output file becomes the raw data used in the calculation of the GCI. </p>\n<h2><em>Calculation of the greening curriculum indicator</em></h2>\n<p>After the prevalence of each keyword in each document is determined, keyword counts are compiled into an output spreadsheet which is then used to calculate a country&#x2019;s GCI score. The following specific steps are taken to calculate a country&#x2019;s GCI score:</p>\n<h3><em>Phase 1) Development of standardized keyword counts</em></h3>\n<p>The analysis of the green content of each country&#x2019;s NCFs and subject curricula is done at the country level.</p>\n<ul>\n  <li>For the NCF and each grade level (3, 6, and 9), the frequency of keywords belonging to the themes of Environment/Sustainability, Climate Change, and Biodiversity is calculated by summing up the counts of the keywords.</li>\n  <li>To account for varying document lengths, the number of keywords is standardized for each theme by dividing the keywords counts in that country&#x2019;s theme with the total number of words in the country&#x2019;s documents. </li>\n  <li>This standardized number is then multiplied by 1 million to transform the result into a number that is more easily interpreted (i.e., not a very small decimal). The result is a keyword count per million words for each theme at each grade level and NCF for each country. The standardization calculation is as follows:<ul>\n      <li>1,000,000*(Keywords in that theme for a country) / (Total words in documents for that country) </li>\n    </ul>\n  </li>\n</ul>\n<h3><em>Phase 2) Transformation of standardized keyword counts into an ordinal scale</em></h3>\n<p>The distribution of these standardized numbers presents a statistical challenge since it is both zero bounded<sup><sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup></sup> and has a long tail.<sup><sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup></sup> </p>\n<ul>\n  <li>To create a more normal distribution, the standardized numbers are transformed into an ordinal scale ranging from 0 to 10 in the following way: <ul>\n      <li>If there are no keywords, the score is 0, otherwise it ranges from 1 to 10 using a &#xBD; life logarithmic transformation.<sup><sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup></sup> </li>\n      <li>For the Environment/Sustainability &apos;core&apos; theme, the maximum score of 10 is achieved with 10,000 standardized keywords. The following formulas are used:<ul>\n          <li>&gt;10,000 standardized keywords are assigned a score of 10,</li>\n          <li>&lt;=20 standardized keywords are assigned a score of 1,</li>\n          <li>0 standardized keywords are assigned a score of 0,</li>\n          <li>Otherwise, 10-log.5(#/10,000)</li>\n          <li>Result multiplied by 10</li>\n        </ul>\n      </li>\n      <li>For the Climate Change and Biodiversity themes, the maximum score of 10 is achieved with 5,000 standardized keywords, given that these keywords are used less commonly. The following formulas are used:<ul>\n          <li>&gt;5,000 standardized keywords are assigned a score of 10, </li>\n          <li>&lt;=10 standardized keywords are assigned a score of 1, </li>\n          <li>0 standardized keywords are assigned a score of 0, </li>\n          <li>Otherwise, 10-log.5(#/5,000)</li>\n          <li>Result multiplied by 10</li>\n        </ul>\n      </li>\n    </ul>\n  </li>\n</ul>\n<h3><em>Phase 3) Calculating GCIs for federated countries</em></h3>\n<p>To calculate the GCI for federated countries (e.g., Australia, Canada, Switzerland, United Kingdom), all of the above mentioned steps are carried out for <u>each</u> sub-national jurisdiction, which results in a number of (sub-national) GCIs. The sub-national GCI scores for the country are then averaged into a national GCI score. The data for all federated countries are then added to the dataset produced in Phase 1.</p>\n<h3><em>Phase 4) Final calculation of the GCI</em></h3>\n<p>At this point, each country has either three or four document-specific scores (ranging from 0 to 10) for each of the three themes (i.e., 9 or 12 total scores, since countries are included if they have at least 3 of the 4 main document types (NCF, grade 3 subject curricula, grade 6 subject curricula, and grade 9 subject curricula).</p>\n<ul>\n  <li>Within each of the Environment/Sustainability, Climate Change, and Biodiversity themes, the three grade level scores and the NCF score are averaged together (i.e., each contributes &#xBC; of the total score per theme in a country). For countries with only three document types, the same procedure is done but each document score contributes &#x2153; of the total theme-focused score.</li>\n  <li>A single overall GCI score is now calculated based on a weighted mean, with the Environment/Sustainability core theme weighted 50% and the Climate Change and Biodiversity themes each weighted at 25%.</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities), and/or education professionals (e.g. teachers and lecturers), as appropriate. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities), and/or education professionals (e.g. teachers and lecturers), as appropriate. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities) and/or education professionals (e.g. teachers and lecturers), as appropriate. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities) and/or education professionals (e.g. teachers and lecturers) as appropriate. <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> Different forms of the word are included only due to genders, definite articles, etc. but not when they change the meaning or part of speech. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> For example, in China the phrase &#x2018;ecological civilization&#x2019; is now being used much more frequently than &#x2018;sustainable development&#x2019; or &#x2018;environmental.&#x2019; In Japan, the term &#x2018;sustainable societies&#x2019; is becoming more prevalent than the term &#x2018;sustainable development.&#x2019; At this point in time, no culture- or language-specific keywords are included in the GCI. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> To determine the accuracy of the counts generated by the Python program, a validation exercise was carried out in October 2024 by sampling 30 documents in English, Spanish, Arabic and French, the four most prevalent languages. A three-way comparison of results from NVivo (the software used for all related UNESCO consultancies), Python, and manual counts identified several minor issues (e.g., keywords split across lines or the lack of a definite article in the Arabic keyword list), which were immediately corrected in the Python program and the keyword list. Since then, the Python program has been reviewed by several experts and undergone further refinements to ensure its counting accuracy is comparable to NVivo and manual counting. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> While there are many documents lacking any keywords related to Environment/Sustainability, Climate Change and Biodiversity, there are no documents with a negative number of keywords. Such a situation represents a zero-bounded distribution and creates a lopsided and non-normal distribution. <a href=\"#footnote-ref-10\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> While more than half the document types have less than 120 standardized keywords in a theme, they range to over 9,000 (75+ times as much as the median). Log transformations are conceptually useful when dealing with such data. For example, going from 0 to 50 standardized keywords is more significant than going from 1000 to 1050 standardized keywords. <a href=\"#footnote-ref-11\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> This means that for each time the standardized counts are halved, the score goes down by 1. So, for example, if 10,000 standardized references is a score of 10, 5,000 is a score of 9, 2,500 is a score of 8, and so on. <a href=\"#footnote-ref-12\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Responses are reviewed by UNESCO for consistency and credibility and, if necessary, queries are raised with national respondents. Where feasible, reference is made to national documents and links supplied by respondents and to available alternative sources of information. </p>\n<p>Any proposed changes in response values in the questionnaire as a result of quality assurance procedures are communicated and verified with countries by UNESCO. Final results are shared before publication by UNESCO with the national data providers and with national SDG indicator focal points where they exist.</p>", "ADJUSTMENT__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>The only adjustments made are where question response categories are not valid and responses between different questions are inconsistent. In those circumstances, proposed changes are communicated to and verified with countries.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p><strong>At country level: </strong>A small number of missing values &#x2013; unknown responses and/or blanks &#x2013; are treated as zeros in the calculation of the question scores. Where they represent more than 50% of the responses to a single question, the component score is not calculated. In such cases, the component score is reported as not available when results are disseminated.</p>\n<p><strong>At regional level: </strong>Regional values are not calculated.</p>\n<p><strong>Greening</strong></p>\n<p>As previously noted, the GCI aligns with commitments made by parties to the UN Framework Convention on Climate Change (UN, 1992), by UN Member States in the 2030 Agenda for Sustainable Development (UN, 2015), and by attendees to the UN Transforming Education Summit (UN, 2022; 2023). As such, the focus of document compilation is all 193 UN Member States as well as 3 additional entities (i.e., Cook Islands, Niue, and Palestine), which are parties to the UNFCCC. Among these 196 possible countries, inclusion in the GCI is dependent on whether a sufficiently complete set of documents for that country has been compiled. A sufficient set of documents means having at least three of the following four types of documents that meet the previously outlined criteria:</p>\n<ul>\n  <li>Grade 3 subject curricula</li>\n  <li>Grade 6 subject curricula</li>\n  <li>Grade 9 subject curricula</li>\n  <li>National Curriculum Framework (NCF)</li>\n</ul>\n<p>A special notation (i.e., &quot;Qualifier of Data-Partial Data&quot;) is placed in the database to indicate cases where the GCI was calculated based on three of the four document types. When missing document types are obtained, a revised GCI score based on a complete set of document types is calculated for the bi-annual data releases.</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates are not calculated.</p>", "DOC_METHOD__GLOBAL"=>"<p><strong>Survey</strong></p>\n<ul>\n  <li>Countries wishing to calculate this indicator for themselves should follow the steps described in section 4.c. Method of computation above.</li>\n  <li>The questionnaires for the monitoring of the implementation of UNESCO Recommendations are approved by the Member States of the Executive Board of UNESCO. The questionnaire contains guidelines for completion and a glossary of key terms. In addition, UNESCO provides direct support to Member States in completing the questionnaire and responds to queries in a timely manner.</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>None related to the processing of qualitative data collected principally for non-statistical purposes.</p>", "QUALITY_ASSURE__GLOBAL"=>"<ul>\n  <li>UNESCO reviews country responses for consistency and credibility and, if necessary, raises queries with national respondents. To assist with this, countries are asked to provide, in addition to completed questionnaires, supporting evidence of their responses in the form of documents or links (e.g. to education policies, laws, curricula, etc.). These will be made publicly available during 2022 along with completed questionnaires. UNESCO also takes into account alternative sources of information, where available. These may include national responses to similar intergovernmental consultation processes, such as the Council of Europe&#x2019;s consultations on the <a href=\"https://www.coe.int/en/web/edc/2016-report-analysis\">Charter on Education for Democratic Citizenship and Human Rights Education</a>, the UN Economic Commission for Europe&#x2019;s consultations on the <a href=\"http://www.unece.org/env/esd/implementation.html\">Strategy for Education for Sustainable Development</a>, or other information on education for sustainable development (ESD) and global citizenship education (GCED) in countries&#x2019; national education systems.</li>\n  <li>Any proposed changes to response values in the questionnaire as a result of quality assurance procedures are communicated to and verified with countries by UNESCO. Final results are shared before publication by UNESCO with the national data providers and SDG indicator focal points.</li>\n  <li>Regarding greening, keywords and their translations were reviewed by native speakers who were also familiar with greening concepts. Documents were reviewed against a set of criteria before being included for analysis.</li>\n</ul>\n<p>Before data release and addition to the global SDG indicators database, the indicator&#x2019;s values and notes on methodology are submitted to National Statistical Offices, Ministries of Education or other relevant agencies in individual countries for their review and feedback.</p>\n<p> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>None related to the processing of qualitative data collected principally for non-statistical purposes.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p><strong>Data availability: </strong>During the last consultation on the implementation of the 1974 Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms carried out in 2020-2021, 75 countries provided reports: Central and Southern Asia (4), Eastern and South-Eastern Asia (7), Europe and Northern America (32), Latin America and the Caribbean (10), Northern Africa and Western Asia (14), Oceania (2), and sub-Saharan Africa (6).</p>\n<p><strong>Time series: </strong>The first data are available for the time period 2017-2020 (as a single time point). Data for the period 2021-2023 (from UNESCO one-off survey conducted in 2024) are expected in 2025. Data for the period 2024-2026 from the first reporting on the 2023 Recommendation on Education for Peace, Human Rights and Sustainable Development will be collected in 2026-2027.</p>\n<p><strong>Disaggregation: </strong>None</p>\n<p><strong>Greening</strong></p>\n<p>Data currently available refer to 2023-2024.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies: </strong>There should be no difference as the indicator values are calculated from the responses submitted by countries. If any changes are proposed to responses as a result of quality assurance procedures, these are communicated to and verified with countries.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<ul>\n  <li><a href=\"http://uis.unesco.org/\"><u>http://uis.unesco.org/</u></a>; <a href=\"https://databrowser.uis.unesco.org/\"><u>https://databrowser.uis.unesco.org/</u></a></li>\n  <li><a href=\"https://www.unesco.org/en/sustainable-development/education\"><u>https://www.unesco.org/en/sustainable-development/education</u></a></li>\n  <li>https://www.unesco.org/gem-report/en </li>\n  <li>https://tcg.uis.unesco.org/wp-content/uploads/sites/4/2025/02/EDSC.11.3.4.GCI-Methods.pdf</li>\n</ul>\n<p><strong>References: </strong></p>\n<p><a href=\"http://portal.unesco.org/en/ev.php-URL_ID=13088&amp;URL_DO=DO_TOPIC&amp;URL_SECTION=201.html\"><u>Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms</u></a>.</p>\n<p>Recommendation on Education for Peace, Human Rights and Sustainable Development.</p>", "indicator_sort_order"=>"12-08-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"12.a.1", "slug"=>"12-a-1", "name"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)", "url"=>"/site/es/12-a-1/", "sort"=>"12aa01", "goal_number"=>"12", "target_number"=>"12.a", "global"=>{"name"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)", "indicator_number"=>"12.a.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://eve.eus/Conoce-la-Energia/La-energia-en-Euskadi/Publicaciones?lang=es-es", "url_text"=>"Datos energéticos de la C. A. de Euskadi", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.a- Ayudar a los países en desarrollo a fortalecer su capacidad científica y tecnológica para avanzar hacia modalidades de consumo y producción más sostenibles", "definicion"=>"\nCapacidad instalada de las instalaciones que generan electricidad \na partir de fuentes de energía renovables dividida por la población\n", "formula"=>"\n$$CINSTPC_{renovable}^{t} = \\frac{CINST_{renovable}^{t}}{P^{t}} \\cdot 100$$ \n\ndonde:\n\n$CINST_{renovable}^{t} =$ capacidad instalada de generación de energía eléctrica procedente de fuentes renovables en el año $t$ \n\n$P^{t} =$ población total en el año  $t$\n", "desagregacion"=>"\nTipo de fuente de energía renovable: hidraúlica, fotovoltaica, eólica, solar térmica\n", "periodicidad"=>"Anual", "observaciones"=>"", "justificacion_global"=>"\nLa infraestructura y las tecnologías necesarias para suministrar \nservicios energéticos modernos y sostenibles abarcan una\namplia gama de equipos y dispositivos que se utilizan en numerosos \nsectores económicos. No existe un mecanismo fácilmente disponible \npara recopilar, agregar y medir la contribución de este grupo dispar de \nproductos a la prestación de servicios energéticos modernos y sostenibles.\n\nSin embargo, una parte importante de la cadena de suministro de energía \nque puede medirse fácilmente es la infraestructura utilizada para \nproducir electricidad. \n\nLas energías renovables se consideran una forma sostenible de suministro \nde energía, ya que su uso actual no suele agotar su disponibilidad para \nsu uso en el futuro. El enfoque de este indicador en la electricidad \nrefleja el énfasis del objetivo en las fuentes modernas de energía y \nes particularmente relevante para los países en desarrollo donde la \ndemanda de electricidad suele ser alta y su disponibilidad es limitada. \n\nAdemás, el enfoque en las energías renovables refleja el hecho de que las \ntecnologías utilizadas para producir electricidad renovable son \ngeneralmente modernas y más sostenibles que las no renovables, \nen particular en los subsectores de mayor crecimiento de la generación \nde electricidad a partir de energía eólica y solar.\n\nLa división de la capacidad de electricidad renovable por población \n(para producir una medida de vatios per cápita) propone escalar los datos \nde capacidad para tener en cuenta la gran variación de necesidades entre \npaíses. Utiliza la población en lugar del PIB para escalar los datos, \nporque este es el indicador más básico de la demanda de servicios \nenergéticos modernos y sostenibles en un país.\n\nEste indicador también debería complementar los indicadores \n7.1.1 y 7.2.1. Con respecto al acceso a la electricidad, proporcionará \ninformación adicional sobre la proporción de personas con acceso a la \nelectricidad al mostrar cuánta infraestructura está disponible \npara brindar ese acceso (en términos de la cantidad de capacidad por persona). \nEl enfoque en la capacidad renovable también agregará valor al indicador \nde energías renovables existente (7.2.1) al mostrar cuánto contribuye \nla energía renovable a la necesidad de un mejor acceso a la electricidad.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.b.1&seriesCode=EG_EGY_RNEW&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">Capacidad instalada de generación de electricidad renovable (vatios per cápita) EG_EGY_RNEW</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-0a-01.pdf\">Metadatos 12-a-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.a- Ayudar a los países en desarrollo a fortalecer su capacidad científica y tecnológica para avanzar hacia modalidades de consumo y producción más sostenibles", "definicion"=>"\nInstalled renewable electricity-generating capacity divided by population\n", "formula"=>"\n$$CINSTPC_{renewable}^{t} = \\frac{CINST_{renewable}^{t}}{P^{t}} \\cdot 100$$ \n\nwhere:\n\n$CINST_{renewable}^{t} =$ installed renewable electricity-generating capacity in year $t$ \n\n$P^{t} =$ total population in year $t$\n", "desagregacion"=>"\nType of renewable energy source: hydraulic, photovoltaic, wind, solar thermal\n", "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nThe infrastructure and technologies required to supply modern \nand sustainable energy services cover a wide range of equipment \nand devices that are used across numerous economic sectors. There \nis no readily available mechanism to collect, aggregate and measure \nthe contribution of this disparate group of products to the delivery \nof modern and sustainable energy services. \n\nHowever, one major part of the energy supply chain that can be readily \nmeasured is the infrastructure used to produce electricity. \n\nRenewables are considered a sustainable form of energy supply, as their \ncurrent use does not usually deplete their availability to be used in \nthe future. The focus of this indicator on electricity reflects the \nemphasis of the target on modern sources of energy and is particularly \nrelevant for developing countries where the demand for electricity is often \nhigh and its availability is constrained. \n\nFurthermore, the focus on renewables reflects the fact that the technologies \nused to produce renewable electricity are generally modern and more \nsustainable than non-renewables, particularly in the fastest growing sub-sectors \nof electricity generation from wind and solar energy. \n\nThe division of renewable electricity capacity by population (to produce a \nmeasure of Watts per capita) is proposing to scale the capacity data to account \nfor the large variation in needs between countries. It uses population rather \nthan GDP to scale the data, because this is the most basic indicator of the \ndemand for modern and sustainable energy services in a country. \n\nThis indicator should also complement indicators 7.1.1 and 7.2.1. With respect \nto electricity access, it will provide additional information to the proportion \nof people with electricity access by showing how much infrastructure is available \nto deliver that access (in terms of the amount of capacity per person). The focus \non renewable capacity will also add value to the existing renewables indicator \n(7.2.1) by showing how much renewable energy is contributing to the need for \nimproved electricity access. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.b.1&seriesCode=EG_EGY_RNEW&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">Installed renewable electricity-generating capacity (watts per capita) EG_EGY_RNEW</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-0a-01.pdf\">Metadata 12-a-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Capacidad instalada de generación de energía renovable en los países en desarrollo y en los países desarrollados (en vatios per cápita)", "objetivo_global"=>"12- Garantizar modalidades de consumo y producción sostenibles", "meta_global"=>"12.a- Ayudar a los países en desarrollo a fortalecer su capacidad científica y tecnológica para avanzar hacia modalidades de consumo y producción más sostenibles", "definicion"=>"\nCapacidad instalada de las instalaciones que generan electricidad \na partir de fuentes de energía renovables dividida por la población\n", "formula"=>"\n$$CINSTPC_{berriztagarria}^{t} = \\frac{CINST_{berriztagarria}^{t}}{P^{t}} \\cdot 100$$ \n\nnon:\n\n$CINST_{berriztagarria}^{t} =$ iturri berriztagarrietatik sortutako energia elektrikoa sortzeko ahalmen instalatua $t$ urtean \n\n$P^{t} =$ guztizko biztanleria $t$ urtean\n", "desagregacion"=>"\nEnergia berriztagarriaren iturri mota: hidraulikoa; fotovoltaikoa; eolikoa; eguzki-energia termikoa\n", "periodicidad"=>"Anual", "observaciones"=>nil, "justificacion_global"=>"\nLa infraestructura y las tecnologías necesarias para suministrar \nservicios energéticos modernos y sostenibles abarcan una\namplia gama de equipos y dispositivos que se utilizan en numerosos \nsectores económicos. No existe un mecanismo fácilmente disponible \npara recopilar, agregar y medir la contribución de este grupo dispar de \nproductos a la prestación de servicios energéticos modernos y sostenibles.\n\nSin embargo, una parte importante de la cadena de suministro de energía \nque puede medirse fácilmente es la infraestructura utilizada para \nproducir electricidad. \n\nLas energías renovables se consideran una forma sostenible de suministro \nde energía, ya que su uso actual no suele agotar su disponibilidad para \nsu uso en el futuro. El enfoque de este indicador en la electricidad \nrefleja el énfasis del objetivo en las fuentes modernas de energía y \nes particularmente relevante para los países en desarrollo donde la \ndemanda de electricidad suele ser alta y su disponibilidad es limitada. \n\nAdemás, el enfoque en las energías renovables refleja el hecho de que las \ntecnologías utilizadas para producir electricidad renovable son \ngeneralmente modernas y más sostenibles que las no renovables, \nen particular en los subsectores de mayor crecimiento de la generación \nde electricidad a partir de energía eólica y solar.\n\nLa división de la capacidad de electricidad renovable por población \n(para producir una medida de vatios per cápita) propone escalar los datos \nde capacidad para tener en cuenta la gran variación de necesidades entre \npaíses. Utiliza la población en lugar del PIB para escalar los datos, \nporque este es el indicador más básico de la demanda de servicios \nenergéticos modernos y sostenibles en un país.\n\nEste indicador también debería complementar los indicadores \n7.1.1 y 7.2.1. Con respecto al acceso a la electricidad, proporcionará \ninformación adicional sobre la proporción de personas con acceso a la \nelectricidad al mostrar cuánta infraestructura está disponible \npara brindar ese acceso (en términos de la cantidad de capacidad por persona). \nEl enfoque en la capacidad renovable también agregará valor al indicador \nde energías renovables existente (7.2.1) al mostrar cuánto contribuye \nla energía renovable a la necesidad de un mejor acceso a la electricidad.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=7.b.1&seriesCode=EG_EGY_RNEW&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALL\">Elektrizitate berriztagarria sortzeko ahalmen instalatua (watt per capita) EG_EGY_RNEW</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-0a-01.pdf\">Metadatuak 12-a-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 7.b: By 2030, expand infrastructure and upgrade technology for supplying modern and sustainable energy services for all in developing countries, in particular least developed countries, small island developing States and landlocked developing countries, in accordance with their respective programmes of support</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 7.b.1: Installed renewable energy-generating capacity in developing and developed countries (in watts per capita)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EG_EGY_RNEW - Installed renewable electricity-generating capacity (watts per capita) [7.b.1, 12.a.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>This indicator is also used as indicator 12.a.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Renewable Energy Agency (IRENA)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Renewable Energy Agency (IRENA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator is defined as the installed capacity of power plants that generate electricity from renewable energy sources divided by the total population of a country. Capacity is defined as the net maximum electrical capacity installed at the year-end and renewable energy sources are as defined in the IRENA Statute (see concepts below).</p>\n<p><strong>Concepts:</strong></p>\n<p>Electricity capacity is defined in the International Recommendations for Energy Statistics or IRES (UN, 2018) as the maximum active power that can be supplied continuously (i.e., throughout a prolonged period in a day with the whole plant running) at the point of outlet (i.e., after taking the power supplies for the station auxiliaries and allowing for the losses in those transformers considered integral to the station). This assumes no restriction of interconnection to the network. It does not include overload capacity that can only be sustained for a short period of time (e.g., internal combustion engines momentarily running above their rated capacity).</p>\n<p>The IRENA Statute defines renewable energy to include energy from the following sources: hydropower; marine energy (ocean, tidal and wave energy); wind energy; solar energy (photovoltaic and thermal energy); bioenergy; and geothermal energy.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Watts per capita</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Electricity capacity classifications follow the International Recommendations for Energy Statistics or IRES</p>", "SOURCE_TYPE__GLOBAL"=>"<p>IRENA&#x2019;s electricity capacity database contains information about the electricity generating capacity installed at the year-end, measured in megawatt (MW). The dataset covers all countries and areas from the year 2000 onwards. The dataset also records whether the capacity is on-grid or off-grid and is split into 36 different renewable energy types that can be aggregated into the six main sources of renewable energy.</p>\n<p><strong>Population data:</strong></p>\n<p>For the population part of this indicator, IRENA uses population data from the United Nations World Population Prospects. The population data reflects the residents in a country or area regardless of legal status or citizenship. The values are midyear estimates.</p>\n<p>The United Nations Department of Economic and Social Affairs published information about its methodology on the link below:</p>\n<p><a href=\"https://population.un.org/wpp/Methodology/\">https://population.un.org/wpp/Methodology/</a></p>", "COLL_METHOD__GLOBAL"=>"<p>The capacity data are collected as part of IRENA&#x2019;s annual questionnaire cycle. Questionnaires are sent to countries at the start of a year asking for renewable energy data for two years previously (i.e. at the start of 2019, questionnaires ask for data for the year 2017). The data are then validated and checked with countries and published in the IRENA Renewable Energy Statistics Yearbook at the end of June. To minimise reporting burden, the questionnaires for some countries are pre-filled with data collected by other agencies (e.g. Eurostat) and are sent to countries for them to complete any additional details requested by IRENA.</p>\n<p>At the same time as this, preliminary estimates of capacity for the previous year are also collected from official sources where available (e.g. national statistics, data from electricity grid operators) and from other unofficial sources (mostly industry associations for the different renewable energy sectors). These are published at the end of March.</p>", "FREQ_COLL__GLOBAL"=>"<p>Capacity data are recorded as a year-end figure. The data are collected in the first six months of every year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Estimates of generating capacity for a year are published at the end of March in the following year. Final figures for the previous year are published at the end of June.</p>", "DATA_SOURCE__GLOBAL"=>"<p><strong>Renewable energy generating capacity:</strong></p>\n<p>National Statistical Offices and National Energy Agencies of Ministries (the authority to collect this data varies between countries). Data for preliminary estimates may also be collected from industry associations, national utility companies or grid operators.</p>\n<p><strong>Population:</strong></p>\n<p>United Nations Population Division- World Population Prospects.</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Renewable Energy Agency (IRENA).</p>", "INST_MANDATE__GLOBAL"=>"<p>With a mandate from countries around the world, IRENA encourages governments to adopt enabling policies for renewable energy investments, provides practical tools and policy advice to accelerate renewable energy deployment, and facilitates knowledge sharing and technology transfer to provide clean, sustainable energy for the world&#x2019;s growing population. Renewable energy capacity statistics are in line with these aims.</p>", "RATIONALE__GLOBAL"=>"<p>The infrastructure and technologies required to supply modern and sustainable energy services cover a wide range of equipment and devices that are used across numerous economic sectors. There is no readily available mechanism to collect, aggregate and measure the contribution of this disparate group of products to the delivery of modern and sustainable energy services. However, one major part of the energy supply chain that can be readily measured is the infrastructure used to produce electricity.</p>\n<p>Renewables are considered a sustainable form of energy supply, as their current use does not usually deplete their availability to be used in the future. The focus of this indicator on electricity reflects the emphasis of the target on modern sources of energy and is particularly relevant for developing countries where the demand for electricity is often high and its availability is constrained. Furthermore, the focus on renewables reflects the fact that the technologies used to produce renewable electricity are generally modern and more sustainable than non-renewables, particularly in the fastest growing sub-sectors of electricity generation from wind and solar energy. </p>\n<p>The division of renewable electricity capacity by population (to produce a measure of Watts per capita) is proposing to scale the capacity data to account for the large variation in needs between countries. It uses population rather than GDP to scale the data, because this is the most basic indicator of the demand for modern and sustainable energy services in a country.</p>\n<p>This indicator should also complement indicators 7.1.1 and 7.2.1. With respect to electricity access, it will provide additional information to the proportion of people with electricity access by showing how much infrastructure is available to deliver that access (in terms of the amount of capacity per person). The focus on renewable capacity will also add value to the existing renewables indicator (7.2.1) by showing how much renewable energy is contributing to the need for improved electricity access.</p>", "REC_USE_LIM__GLOBAL"=>"<p>At present, electricity only accounts for about one-quarter of total energy use in the World and an even lower share of energy use in most developing countries. The focus of this indicator on electricity capacity does not capture any trends in the modernisation of technologies used to produce heat or provide energy for transport.</p>\n<p>However, with the growing trend towards electrification of energy end-uses, the focus here on electricity may become less of a weakness in the future and may also serve as a general indicator of the progress towards greater electrification in developing counties. That, in itself, should be seen as a shift towards the use of more modern technology to deliver sustainable energy services.</p>\n<p>Furthermore, as reflected in many national policies, plans and targets, increasing the production of electricity and, in particular, renewable electricity, is seen by many countries as a first priority in their transition to the delivery of more modern and sustainable energy services. Thus, this indicator is a useful first-step towards measuring overall progress on this target that reflects country priorities and can be used until other additional or better indicators can be developed.</p>", "DATA_COMP__GLOBAL"=>"<p>For each country and year, the renewable electricity generating capacity at the end of the year is divided by the total population of the country as of mid-year (July 1st).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>All countries are invited to provide their capacity data or at least review the data that IRENA has compiled (from other official and unofficial sources) through an annual process of data collection using the IRENA Renewable Energy Questionnaire. This process is reinforced through IRENA&#x2019;s renewable energy statistics training workshops, which are held twice a year in different (rotating) regions. To date, over 200 energy statisticians have participated in these workshops, many of whom provide renewable energy data to IRENA. In addition, IRENA&#x2019;s statistics are presented each year to member countries at one of IRENA&#x2019;s three governing body meetings, where discrepancies or other data issues can be discussed with country representatives.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>At country level:</strong></p>\n<p>At the country level, electricity capacity data are sometimes missing for two reasons:</p>\n<ol>\n  <li>Delays in responding to IRENA questionnaires or publication of official data. In such cases, estimates are made so that global and regional totals can be calculated. The most basic treatment is to repeat the value of capacity from the previous year. However, IRENA also checks unofficial data sources and collects data about investment projects (see Indicator 7.a.1). These other sources can be used to identify if any new power plants have been commissioned in a year and are used where available to update the capacity value at the end of a year. Any such estimates are eventually replaced by official or questionnaire data when that becomes available.</li>\n  <li>Off-grid capacity data are frequently missing from national energy statistics or is presented in non-standard units (e.g. numbers of mini-hydro plants in a country rather than their capacity in MW). Where official data are not available, off-grid capacity figures are collected by IRENA from a wide variety of other official and unofficial sources in countries (e.g. development agencies, government departments, NGOs, project developers and industry associations) and this information is added to the capacity database to give a more complete picture of developments in the renewable energy sector in a country. These data are peer reviewed each year through an extensive network of national correspondents (the REN21 Network) and is checked with IRENA country focal points when they attend IRENA meetings and training workshops.</li>\n</ol>\n<p>When capacity data are missing, mostly in non-state territories, these are excluded from the dataset. </p>\n<p><strong>At regional and global levels:</strong></p>\n<p>See above. Regional and global totals are only estimated to the extent that figures for some countries may be estimated in each year. (See also data availability below). </p>", "REG_AGG__GLOBAL"=>"<p>Regional and global averages are calculated by summing the renewable generating capacity for a region or the World and dividing that by the corresponding figure for the total population. </p>\n<p>This calculation excludes the population of those countries and/or territories that have missing capacity data. As such, the regional and global population values used in the calculation might differ from those reported in the UN World Population Prospects.</p>\n<p>Furthermore, the indicator is also aggregated by development regions: developed and developing regions as per the historical distinction of May 2022 from the United Nations Statistics Division.</p>\n<p><strong>Developed</strong></p>\n<p>Aland Islands, Albania, Andorra, Australia, Austria, Belarus, Belgium, Bermuda, Bosnia and Herzegovina, Bulgaria, Canada, Christmas Island, Cocos (Keeling) Islands, Croatia, Cyprus, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Greenland, Guernsey, Heard Island and McDonald Islands, Holy See, Hungary, Iceland, Ireland, Isle of Man, Israel, Italy, Japan, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Monaco, Montenegro, Netherlands, New Zealand, Norfolk Island, North Macedonia, Norway, Poland, Portugal, Republic of Korea, Republic of Moldova, Romania, Russian Federation, Saint Pierre and Miquelon, San Marino, Sark, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Mayen Islands, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland, United States of America</p>\n<p><strong>Developing</strong></p>\n<p>Afghanistan, Algeria, American Samoa, Angola, Anguilla, Antigua and Barbuda, Argentina, Armenia, Aruba, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belize, Benin, Bhutan, Bolivia (Plurinational State of), Bonaire, Sint Eustatius and Saba, Botswana, Bouvet Island, Brazil, British Indian Ocean Territory, British Virgin Islands, Brunei Darussalam, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Cayman Islands, Central African Republic, Chad, Chile, China, China, Hong Kong Special Administrative Region, China, Macao Special Administrative Region, Colombia, Comoros, Congo, Cook Islands, Costa Rica, C&#xF4;te d&#x2019;Ivoire, Cuba, Cura&#xE7;ao, Democratic People&apos;s Republic of Korea, Democratic Republic of the Congo, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Falkland Islands (Malvinas), Fiji, French Guiana, French Polynesia, French Southern Territories, Gabon, Gambia, Georgia, Ghana, Grenada, Guadeloupe, Guam, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, India, Indonesia, Iran (Islamic Republic of), Iraq, Jamaica, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Kyrgyzstan, Lao People&apos;s Democratic Republic, Lebanon, Lesotho, Liberia, Libya, Madagascar, Malawi, Malaysia, Maldives, Mali, Marshall Islands, Martinique, Mauritania, Mauritius, Mayotte, Mexico, Micronesia (Federated States of), Mongolia, Montserrat, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, New Caledonia, Nicaragua, Niger, Nigeria, Niue, Northern Mariana Islands, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Pitcairn, Puerto Rico, Qatar, R&#xE9;union, Rwanda, Saint Barth&#xE9;lemy, Saint Helena, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French Part), Saint Vincent and the Grenadines, Samoa, Sao Tome and Principe, Saudi Arabia, Senegal, Seychelles, Sierra Leone, Singapore, Sint Maarten (Dutch part), Solomon Islands, Somalia, South Africa, South Georgia and the South Sandwich Islands, South Sudan, Sri Lanka, State of Palestine, Sudan, Suriname, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Togo, Tokelau, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Turks and Caicos Islands, Tuvalu, Uganda, United Arab Emirates, United Republic of Tanzania, United States Minor Outlying Islands, United States Virgin Islands, Uruguay, Uzbekistan, Vanuatu, Venezuela (Bolivarian Republic of), Viet Nam, Wallis and Futuna Islands, Western Sahara, Yemen, Zambia, Zimbabwe</p>", "DOC_METHOD__GLOBAL"=>"<p>Guidance for the collection of electricity capacity data is provided by the International Recommendations for Energy Statistics. IRENA also produces methodological guidance for countries, specifically about how to measure renewable energy and collect renewable energy data. This is supported by a comprehensive programme of regional renewable energy statistics training workshops and ongoing communications with countries as part of the annual questionnaire cycle. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data for renewable energy capacity is validated by technology, year and country during the IRENA statistics cycle. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>IRENA data are compiled from national sources following the United Nations Fundamental Principles of Official Statistics: <a href=\"https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx\">https://unstats.un.org/unsd/dnss/gp/fundprinciples.aspx</a>.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The quality of the data are verified by automated validation routines for aggregates. Furthermore, official questionnaires guarantee the validity for each data point, where applicable. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The total number of capacity records in the database (all developing countries/areas, all years since 2000, all technologies) is 11,000. In terms of numbers of records, 3,120 (28%) are estimates and 740 (7%) are from unofficial sources. The remaining records (65%) are all from returned questionnaires or official data sources. </p>\n<p>However, in terms of the amount of capacity covered in the database, the shares of data from estimated and unofficial sources is only 5% and 1% respectively. The large difference between these measures is due to the inclusion of off-grid capacity figures in the database. The amount of off-grid generating capacity in a country is frequently estimated by IRENA, but the amounts of off-grid capacity recorded in each case is often relatively small.</p>\n<p><strong>Time series:</strong></p>\n<p>Renewable generating capacity data are available from 2000 onwards. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>IRENA&#x2019;s renewable capacity data are available for every country and area in the world from the year 2000 onwards. These figures can also be disaggregated by technology (solar, hydro, wind, etc.) and by on-grid and off-grid capacity.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The main source of discrepancies between different sources of electricity capacity data are likely to be due to the under-reporting or non-reporting of off-grid capacity data (see above) or slight variations in the definition of installed capacity. IRENA uses the IRES definition of capacity agreed by the Oslo Group on Energy Statistics, while some countries and institutions may use slightly different definitions of capacity to reflect local circumstances (e.g. the reporting of derated rather than maximum net installed capacity or the reporting of built rather than commissioned capacity at year-end).</p>", "OTHER_DOC__GLOBAL"=>"<p>UN, 2018. International Recommendations for Energy Statistics (IRES). New York City: United Nations. Retrieved from <a href=\"https://unstats.un.org/unsd/energystats/methodology/documents/IRES-web.pdf\">https://unstats.un.org/unsd/energystats/methodology/documents/IRES-web.pdf</a></p>\n<p>IRENA Statistical Yearbooks: <a href=\"https://www.irena.org/Statistics\">https://www.irena.org/Statistics</a></p>", "indicator_sort_order"=>"12-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"12.b.1", "slug"=>"12-b-1", "name"=>"Aplicación de instrumentos normalizados de contabilidad para hacer un seguimiento de los aspectos económicos y ambientales de la sostenibilidad del turismo", "url"=>"/site/es/12-b-1/", "sort"=>"12bb01", "goal_number"=>"12", "target_number"=>"12.b", "global"=>{"name"=>"Aplicación de instrumentos normalizados de contabilidad para hacer un seguimiento de los aspectos económicos y ambientales de la sostenibilidad del turismo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Aplicación de instrumentos normalizados de contabilidad para hacer un seguimiento de los aspectos económicos y ambientales de la sostenibilidad del turismo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Aplicación de instrumentos normalizados de contabilidad para hacer un seguimiento de los aspectos económicos y ambientales de la sostenibilidad del turismo", "indicator_number"=>"12.b.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La Meta 12.b insta a los países a desarrollar e implementar herramientas para monitorear \nel turismo sostenible. El turismo sostenible es aquel que tiene plenamente en cuenta sus \nimpactos económicos, sociales y ambientales actuales y futuros, atendiendo al mismo \ntiempo las necesidades de los visitantes, la industria, el medio ambiente y las comunidades anfitrionas. \n[...] Es un proceso continuo que requiere un monitoreo constante de los impactos.\n\nEl indicador 12.b.1 de los ODS mide el nivel de capacidad estadística a nivel nacional y \nmundial para monitorear de forma creíble y comparable la sostenibilidad del turismo, \nespecialmente las dimensiones económica y ambiental. Tiene la ventaja adicional de no \nsolo monitorear y fomentar el logro de la meta 12.b, sino también de apoyar un monitoreo \nmás general del turismo sostenible, incluyendo las demás metas relacionadas con el turismo, \nen particular la 8.9 y la 14.7.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.b.1&seriesCode=ST_EEV_STDACCT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Implementación de herramientas contables estandarizadas para el seguimiento de los aspectos económicos y ambientales del turismo (número de tablas) ST_EEV_STDACCT</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.b.1&seriesCode=ST_EEV_ACCSEEA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Implementación de herramientas contables estándar para monitorear los aspectos económicos y ambientales del turismo (tablas SEEA) ST_EEV_ACCSEEA</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.b.1&seriesCode=ST_EEV_ACCTSA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Implementación de herramientas contables estándar para el seguimiento de los aspectos económicos y ambientales del turismo (tablas de la Cuenta Satélite de Turismo) ST_EEV_ACCTSA</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-0b-01.pdf\">Metadatos 12-b-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nTarget 12.b calls on countries to develop and implement tools to \nmonitor sustainable tourism. Sustainable tourism is “tourism that \ntakes full account of its current and future economic, social and \nenvironmental impacts whilst addressing the needs of visitors, the \nindustry, the environment and host communities. [...] It is a continuous \nprocess and requires constant monitoring of impacts. \n\nSDG indicator 12.b.1 measures the level of statistical capacity at the \nnational and global levels to credibly and comparably monitor the \nsustainability of tourism, especially the economic and environmental \ndimensions. It has the added advantage of not only monitoring and \nencouraging attainment of target 12.b, but also of supporting more general \nmonitoring of sustainable tourism including the other targets related \nto tourism, notably 8.9 and 14.7. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.b.1&seriesCode=ST_EEV_STDACCT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism (number of tables) ST_EEV_STDACCT</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.b.1&seriesCode=ST_EEV_ACCSEEA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism (SEEA tables) ST_EEV_ACCSEEA</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.b.1&seriesCode=ST_EEV_ACCTSA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism (Tourism Satellite Account tables) ST_EEV_ACCTSA</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-0b-01.pdf\">Metadata 12-b-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La Meta 12.b insta a los países a desarrollar e implementar herramientas para monitorear \nel turismo sostenible. El turismo sostenible es aquel que tiene plenamente en cuenta sus \nimpactos económicos, sociales y ambientales actuales y futuros, atendiendo al mismo \ntiempo las necesidades de los visitantes, la industria, el medio ambiente y las comunidades anfitrionas. \n[...] Es un proceso continuo que requiere un monitoreo constante de los impactos.\n\nEl indicador 12.b.1 de los ODS mide el nivel de capacidad estadística a nivel nacional y \nmundial para monitorear de forma creíble y comparable la sostenibilidad del turismo, \nespecialmente las dimensiones económica y ambiental. Tiene la ventaja adicional de no \nsolo monitorear y fomentar el logro de la meta 12.b, sino también de apoyar un monitoreo \nmás general del turismo sostenible, incluyendo las demás metas relacionadas con el turismo, \nen particular la 8.9 y la 14.7.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.b.1&seriesCode=ST_EEV_STDACCT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Kontabilitateko tresna estandarizatuen ezarpena turismoaren alderdi ekonomikoen eta ingurumenekoen segimendua egiteko (taula kopurua) ST_EEV_STDACCT</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.b.1&seriesCode=ST_EEV_ACCSEEA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Kontabilitateko tresna estandarizatuen ezarpena turismoaren alderdi ekonomikoen eta ingurumenekoen segimendua egiteko (SEEA taulak) ST_EEV_ACCSEEA</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.b.1&seriesCode=ST_EEV_ACCTSA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Kontabilitateko tresna estandarizatuen ezarpena turismoaren alderdi ekonomikoen eta ingurumenekoen segimendua egiteko (Turismoaren kontu satelitearen taulak) ST_EEV_ACCTSA</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-0b-01.pdf\">Metadatuak 12-b-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 12: Ensure sustainable consumption and production patterns</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 12.b: Develop and implement tools to monitor sustainable development impacts for sustainable tourism that creates jobs and promotes local culture and products</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 12.b.1: Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism sustainability</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ST_EEV_ACCSEEA - Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism (SEEA tables) [12.b.1]</p>\n<p>ST_EEV_ACCTSA - Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism (Tourism Satellite Account tables) [12.b.1]</p>\n<p>ST_EEV_STDACCT - Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism (Total number of tables) [12.b.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>8.9.1 Tourism Direct GDP as a proportion of total GDP and in growth rate </p>\n<p>15.9.1 (a) Number of countries that have established national targets in accordance with or similar to Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011&#x2013;2020 in their national biodiversity strategy and action plans and the progress reported towards these targets; and (b) integration of biodiversity into national accounting and reporting systems, defined as implementation of the System of Environmental-Economic Accounting</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Tourism Organization (UNWTO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Tourism Organization (UNWTO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong> The indicator &#x201C;Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism sustainability&#x201D; relates to the degree of implementation in countries of the Tourism Satellite Account (TSA) and the System of Environmental and Economic Accounts (SEEA) tables that are to date considered most relevant and feasible for monitoring sustainability in tourism. These tables are:</p>\n<ul>\n  <li>TSA Table 1 on inbound tourism expenditure</li>\n  <li>TSA Table 2 on domestic tourism expenditure</li>\n  <li>TSA Table 3 on outbound tourism expenditure</li>\n  <li>TSA Table 4 on internal tourism consumption </li>\n  <li>TSA Table 5 on production accounts of tourism industries</li>\n  <li>TSA Table 6 domestic supply and internal tourism consumption</li>\n  <li>TSA Table 7 on employment in tourism industries</li>\n  <li>SEEA table water flows</li>\n  <li>SEEA table energy flows</li>\n  <li>SEEA table GHG emissions</li>\n  <li>SEEA table solid waste</li>\n</ul>\n<p>The TSA tables should be implemented following the <a href=\"https://unstats.un.org/unsd/publication/Seriesf/SeriesF_80rev1e.pdf\"><em>Tourism Satellite Account: Recommended Methodological Framework 2008</em></a> and the environmental tables should be implemented following the <a href=\"https://seea.un.org/content/seea-central-framework\"><em>System of Economic-Environmental Accounting 2012</em></a>.</p>\n<p><strong>Concepts:</strong></p>\n<p>The concepts and template presentation tables related to Tourism Satellite Accounts can be found in the <em><a href=\"https://unstats.un.org/unsd/tourism/methodology.asp\">Tourism Satellite Account: Recommended Methodological Framework 2008</a></em> (TSA: RMF 2008) which provides the common conceptual framework for constructing a TSA. It adopts the basic system of concepts, classifications, definitions, tables and aggregates of the System of National Accounts 2008 (SNA 2008). The UN Statistical Commission took note of the TSA: RMF 2008 document at its 39th session (26-29 February 2008), which updates and replaces the previous TSA: RMF 2000 that was approved by the United Nations Statistical Commission at its 31st session (29 February-3 March 2000).</p>\n<p>The concepts and template presentation tables related to water, energy, Greenhouse gas (GHG) emission and solid waste can be found in <a href=\"https://seea.un.org/content/methodology\"><em>System of Environmental-Economic Accounting - Central Framework</em></a><em> (SEEA-CF). </em>The SEEA-CF is an international statistical standard for measuring the environment and its relationship with the economy. It contains an internationally agreed set of standard concepts, definitions, classifications, accounting rules and tables to produce internationally comparable statistics. The UN Statistical Commission adopted the SEEA Central Framework at its 43<sup>rd</sup> session (28 February &#x2013; 2 March 2012). </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of Tables/Accounts compiled</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Tourism Satellite Account tables and related information can be found here: <a href=\"https://unstats.un.org/unsd/publication/seriesf/seriesf_80rev1e.pdf\">https://unstats.un.org/unsd/publication/seriesf/seriesf_80rev1e.pdf</a></p>\n<p>Information on water use, energy use, air emissions and solid waste SEEA accounts can be found here: https://seea.un.org/</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The indicator is sourced from countries&#x2019; Tourism Satellite Account and Environmental-Economic Accounts.</p>", "COLL_METHOD__GLOBAL"=>"<p>UNWTO sends an excel questionnaire to countries to obtain information on the number of relevant TSA and SEEA tables produced by countries.</p>", "FREQ_COLL__GLOBAL"=>"<p>The exercise to collect data on TSA and SEEA tables implementation directly from countries is done through an annual UNWTO questionnaire. The questionnaire is sent out to countries in September and data collection is closed in February of the following year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The data is released twice a year in the <a href=\"https://www.unwto.org/tourism-statistics/economic-contribution-SDG\">UNWTO&#x2019;s Tourism Statistics Database</a>, the first update is done in November and the second in January.</p>", "DATA_SOURCE__GLOBAL"=>"<p>For implementation of the TSA: all official entities, usually National Statistics Offices and/or National Tourism Administrations.</p>\n<p>For implementation the SEEA: all official entities, usually National Statistics Offices and/or environment ministries.</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Tourism Organization (UNWTO) with input and in coordination with the UN Statistics Division (UNSD) especially with respect to the data on the implementation SEEA tables.</p>", "INST_MANDATE__GLOBAL"=>"<p>As per the article 13 of the agreement between the United Nations and the World Tourism Organization: &#x201C;the United Nations recognizes the World Tourism Organization as the appropriate organization to collect, to analyse, to publish, to standardize and to improve the statistics of tourism, and to promote the integration of these statistics within the sphere of the United Nations system.&#x201D; The World Tourism Organization is the custodian agency for SDG indicator 12.b.1.</p>", "RATIONALE__GLOBAL"=>"<p>Target 12.b calls on countries to &quot;develop and implement tools to monitor sustainable [tourism]&#x201D;. Sustainable tourism is &#x201C;tourism that takes full account of its current and future economic, social and environmental impacts whilst addressing the needs of visitors, the industry, the environment and host communities. [...] It is a continuous process and requires constant monitoring of impacts&#x201D;.</p>\n<p>SDG indicator 12.b.1 measures the level of statistical capacity at the national and global levels to credibly and comparably monitor the sustainability of tourism, especially the economic and environmental dimensions. It has the added advantage of not only monitoring and encouraging attainment of target 12.b, but also of supporting more general monitoring of sustainable tourism including the other targets related to tourism, notably 8.9 and 14.7. </p>\n<p>It does so by tracking implementation of those tables and accounts from the <a href=\"https://unstats.un.org/unsd/tourism/methodology.asp\" target=\"_blank\"><em>Tourism Satellite Account: Recommended Methodological Framework 2008</em></a> (TSA: RMF 2008) and the <a href=\"https://seea.un.org/content/seea-central-framework\"><em>System of Environmental-Economic Accounting (SEEA)</em></a> that are deemed most relevant for deriving information on sustainable tourism. In fact, the TSA and SEEA have been identified as core pillars in the <a href=\"https://www.unwto.org/standards/statistical-framework-for-measuring-the-sustainability-of-tourism\"><em>Statistical Framework for Measuring the Sustainability of Tourism (SF-MST)</em></a> which is currently under development and which has been supported by the United Nations Statistical Commission as the main tool for monitoring the contribution of tourism to the SDG Agenda. The SF-MST integrates tourism statistics with other economic, social and environmental information and provides a coherent base for deriving indicators that are relevant for monitoring and analysing the sustainability of tourism. The level of implementation of the TSA and SEEA tables and accounts identified in this indicator provide a good indication of a country&#x2019;s statistical preparedness for monitoring the sustainability of tourism.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator in principle does not account for different degrees of consolidation in the implementation of TSA and SEEA (ranging from experimental to full-fledged implementation), which might vary between countries. </p>", "DATA_COMP__GLOBAL"=>"<p>Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism sustainability = total number of tables produced by countries out of the tables identified below:</p>\n<ul>\n  <li>TSA Table 1 on inbound tourism expenditure</li>\n  <li>TSA Table 2 on domestic tourism expenditure</li>\n  <li>TSA Table 3 on outbound tourism expenditure</li>\n  <li>TSA Table 4 on internal tourism consumption </li>\n  <li>TSA Table 5 on production accounts of tourism industries</li>\n  <li>TSA Table 6 domestic supply and internal tourism consumption </li>\n  <li>TSA Table 7 on employment in tourism industries</li>\n  <li>SEEA table water flows</li>\n  <li>SEEA table energy flows</li>\n  <li>SEEA table GHG emissions</li>\n  <li>SEEA table solid waste</li>\n</ul>", "DATA_VALIDATION__GLOBAL"=>"<p>Every year historical data is requested. If there are differences in the newly reported data for the country with respect to the data available previously, countries are consulted. Similarly, if other inconsistencies are found, there is ongoing follow-up with countries. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates correspond to the sum of the values (number of tables/accounts implemented) reported by the countries.</p>", "DOC_METHOD__GLOBAL"=>"<p>In relation to the TSA, the methodology is described in the <a href=\"https://unstats.un.org/unsd/tourism/methodology.asp\"><em>Tourism Satellite Account: Recommended Methodological Framework 2008</em></a><em>.</em> </p>\n<p>In relation to the SEEA, the methodology is described in the <a href=\"https://seea.un.org/content/seea-central-framework\"><em>System of Environmental-Economic Accounting (SEEA) Central Framework</em></a>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Recommendations on quality management for the underlying tourism data needed to compile a TSA are available in <a href=\"https://unstats.un.org/unsd/publication/Seriesm/SeriesM_83rev1e.pdf\">the International Recommendations for Tourism Statistics 2008</a> (IRTS 2008), the UN ratified methodological framework for measuring tourism.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data will be verified by UNWTO and any issues will be resolved through written communication with countries. In the case of the availability of TSA tables, it is also be possible to cross-validate with the information reported by countries to UNWTO on SDG indicator 8.9.1 (Tourism Direct GDP). The availability reported on SEEA tables can also be cross-checked with information collected by UNSD. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The data should comply with the recommendations provided in the international standards: the <a href=\"http://unstats.un.org/unsd/tradeserv/tourism/manual.html\">Tourism Satellite Account: Recommended Methodological Framework 2008</a> and the <a href=\"https://seea.un.org/content/seea-central-framework\">System of Environmental-Economic Accounting (SEEA)</a> Central Framework.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>While SEEA and TSA tables are currently not compiled everywhere, by construction it is possible for all countries to provide information on this indicator. Those countries where no tables are compiled report a value of zero (0). There are currently (as of March 2024) data available for over 180 countries, in all regions. </p>\n<p><strong>Time series:</strong></p>\n<p>Data is collected from the 2008 reference year onwards.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>It is possible to disaggregation by the different TSA tables and SEEA tables (water flows, energy flows, GHG emissions and solid waste), and disaggregation by standard (TSA and SEEA). </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Discrepancies might arise from the different degrees of consolidation in the implementation of TSA and SEEA in countries. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://www.unwto.org/standards/un-standards-for-measuring-tourism\">https://www.unwto.org/standards/un-standards-for-measuring-tourism</a> <a href=\"https://seea.un.org/content/seea-central-framework\">https://seea.un.org/content/seea-central-framework</a></p>\n<p><strong>References:</strong></p>\n<p>Commission of the European Communities, Organization for Economic Cooperation and Development, United Nations and World Tourism Organization (2010), <em>Tourism Satellite Account: Recommended Methodological Framework 2008 </em>(online) available at: <a href=\"https://www.unwto.org/standards/on-economic-contribution-of-tourism-tsa-2008\">https://www.unwto.org/standards/on-economic-contribution-of-tourism-tsa-2008</a> (29-03-2022)</p>\n<p>United Nations, European Commission, Food and Agriculture Organization, International Monetary Fund, Organization for Economic Cooperation and Development and World Bank (2014), <em>System of Environmental-Economic Accounting 2012: Central Framework</em> (online) available at: <a href=\"https://seea.un.org/content/seea-central-framework\" target=\"_blank\">https://seea.un.org/content/seea-central-framework</a> (29-03-2022) </p>", "indicator_sort_order"=>"12-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"12.c.1", "slug"=>"12-c-1", "name"=>"Cuantía de los subsidios a los combustibles fósiles (producción y consumo) por unidad del PIB", "url"=>"/site/es/12-c-1/", "sort"=>"12cc01", "goal_number"=>"12", "target_number"=>"12.c", "global"=>{"name"=>"Cuantía de los subsidios a los combustibles fósiles (producción y consumo) por unidad del PIB"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Cuantía de los subsidios a los combustibles fósiles (producción y consumo) por unidad del PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Cuantía de los subsidios a los combustibles fósiles (producción y consumo) por unidad del PIB", "indicator_number"=>"12.c.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La magnitud y el impacto de los subsidios a los combustibles fósiles presentan \ntanto desafíos como oportunidades para alcanzar los objetivos de la Agenda 2030 \npara el Desarrollo Sostenible. Por un lado, el uso de combustibles fósiles y su \npromoción mediante programas de subsidios afecta negativamente la capacidad de \nlos gobiernos para alcanzar objetivos clave, como la reducción de la pobreza, \nla mejora de la salud, la igualdad de género, el acceso a la energía y \nla lucha contra el cambio climático. \n\nAl mismo tiempo, es necesario garantizar \nque los hogares pobres, especialmente vulnerables a los aumentos de precios, \nobtengan o mantengan el acceso a la energía. Los sectores de la economía que \ndependen de la energía pueden verse afectados, en particular por cambios \nabruptos en los precios. Por lo tanto, cualquier reforma exitosa requiere \nun análisis cuidadoso y medidas de mitigación adaptadas. Por otro lado, \nla reasignación de los subsidios a los combustibles fósiles a sectores \nrelevantes para el desarrollo podría impulsar el logro de los ODS.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.c.1&seriesCode=ER_FFS_CMPT_GDP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Subsidios a los combustibles fósiles (consumo y producción) como proporción del PIB total (%) ER_FFS_CMPT_GDP</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-0c-01.pdf\">Metadatos 12-c-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-07", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The scale and impact of fossil fuel subsidies presents both challenges \nand opportunities for achieving the goals of the 2030 Agenda on Sustainable \nDevelopment. For one, the use of fossil fuels, and their promotion through \nsubsidy schemes, adversely affects the ability of governments to attain key \ngoals, such as reducing poverty, improving health, reaching gender equality, \nproviding access to energy, and addressing climate change. \n\nAt the same time, there is a need to ensure that poor households that are \nparticularly vulnerable to price increases obtain or retain access to energy. \nEnergy-dependent sectors of the economy can be affected, particularly by abrupt \nchanges in prices. Any successful reform therefore requires careful analysis \nand adapted mitigation measures. For another, reallocating fossil fuel subsidies \nto sectors that are relevant for development could give a boost to reaching the \nSDGs. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.c.1&seriesCode=ER_FFS_CMPT_GDP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Fossil-fuel subsidies (consumption and production) as a proportion of total GDP (%) ER_FFS_CMPT_GDP</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-0c-01.pdf\">Metadata 12-c-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La magnitud y el impacto de los subsidios a los combustibles fósiles presentan \ntanto desafíos como oportunidades para alcanzar los objetivos de la Agenda 2030 \npara el Desarrollo Sostenible. Por un lado, el uso de combustibles fósiles y su \npromoción mediante programas de subsidios afecta negativamente la capacidad de \nlos gobiernos para alcanzar objetivos clave, como la reducción de la pobreza, \nla mejora de la salud, la igualdad de género, el acceso a la energía y \nla lucha contra el cambio climático. \n\nAl mismo tiempo, es necesario garantizar \nque los hogares pobres, especialmente vulnerables a los aumentos de precios, \nobtengan o mantengan el acceso a la energía. Los sectores de la economía que \ndependen de la energía pueden verse afectados, en particular por cambios \nabruptos en los precios. Por lo tanto, cualquier reforma exitosa requiere \nun análisis cuidadoso y medidas de mitigación adaptadas. Por otro lado, \nla reasignación de los subsidios a los combustibles fósiles a sectores \nrelevantes para el desarrollo podría impulsar el logro de los ODS.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=12.c.1&seriesCode=ER_FFS_CMPT_GDP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Erregai fosilentzako dirulaguntzak (kontsumoa eta ekoizpena), guztizko BPGaren proportzio gisa (%) ER_FFS_CMPT_GDP</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-12-0c-01.pdf\">Metadatuak 12-c-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 12: Ensure sustainable consumption and production patterns</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 12.c: Rationalize inefficient fossil-fuel subsidies that encourage wasteful consumption by removing market distortions, in accordance with national circumstances, including by restructuring taxation and phasing out those harmful subsidies, where they exist, to reflect their environmental impacts, taking fully into account the specific needs and conditions of developing countries and minimizing the possible adverse impacts on their development in a manner that protects the poor and the affected communities</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 12.c.1: Amount of fossil-fuel subsidies (production and consumption) per unit of GDP</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_FFS_CMPT_CD - Fossil-fuel subsidies (consumption and production) [12.c.1]</p>\n<p>ER_FFS_CMPT_GDP - Fossil-fuel subsidies (consumption and production) as a proportion of total GDP [12.c.1]</p>\n<p>ER_FFS_CMPT_PC_CD - Fossil-fuel subsidies (consumption and production) per capita [12.c.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-01-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>12.1.1, 8.4.1/12.2.1, 8.4.2/12.2.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>In order to measure fossil fuel subsidies at the national, regional and global level, three sub-indicators are recommended for reporting on this indicator: 1) direct transfer of government funds; 2) induced transfers (price support); and as an optional sub-indicator 3) tax expenditure, other revenue foregone, and under-pricing of goods and services. The definitions of the International Energy Agency (IEA) Statistical Manual (IEA, 2005) and the Agreement on Subsidies and Countervailing Measures (ASCM) under the World Trade Organization (WTO) (WTO, 1994) are used to define fossil fuel subsidies. Standardised descriptions from the United Nations Statistical Office&#x2019;s Central Product Classification should be used to classify individual energy products. It is proposed to drop the wording &#x201C;as a proportion of total national expenditure on fossil fuels&#x201D; and thus this indicator is effectively &quot;Amount of fossil fuel subsidies per unit of GDP (production and consumption)&quot;.</p>\n<p><strong>Concepts:</strong></p>\n<p>The concepts and definitions used in the methodology have been based on existing international frameworks and glossaries.</p>\n<ul>\n  <li>Use definition of fossil fuels from the International Energy Agency (IEA) Statistics Manual, &#x201C;Fossil fuels are taken from natural resources which were formed from biomass in the geological past. By extension, the term fossil is also applied to any secondary fuel manufactured from a fossil fuel.&#x201D;</li>\n  <li>Use the terms set out in the Central Product Classification (CPC), Rev. 2.1 for the statistical classification of the individual products. No other commonly accepted definition identified.</li>\n  <li>Include electricity and heat generated from fossil fuels in the scope of fossil fuels. </li>\n  <li>Include non-energy uses with monitoring optional for the measuring of this indicator.</li>\n  <li>Additional details are provided in the methodological document entitled, Measuring Fossil Fuel Subsidies in the Context of the Sustainable Development Goals.</li>\n</ul>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) - for Fossil-fuel subsidies (consumption and production) as a proportion of total GDP;</p>\n<p>Billions of nominal United States dollars - for Fossil-fuel subsidies (consumption and production);</p>\n<p>Nominal United States dollars - for Fossil-fuel subsidies (consumption and production) per capita.</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>Direct transfers are generally reported in government budgets, and well documented in sectoral and Finance Ministries, broken down by programme if not by fuel. Those that meet the SNA definition of &#x201C;subsidies&#x201D; &#x2013; i.e., subsidies on products, and other subsidies on production &#x2013; can also be found in a country&#x2019;s System of National Accounts. Budget documents are publicly available for most countries. The degree to which information on individual programmes is itemized in those reports is highly variable, however. Support to corporations involved in energy production or transformation may sometimes be found in their annual reports, for example. In some cases, researchers may be able to obtain unpublished data from state-owned energy enterprises directly.</p>\n<p>Induced transfers are measured by calculating the price-gap between the producer or consumer price and a reference price and multiplying that differential by the affected volume produced or consumed. </p>\n<p>Measuring the value of special features introduced into the tax code to favour certain industries or activities of those industries (such as investment in productive capital) can be a complex endeavour. Some countries do this exercise already, and report the annual value of those tax features in their periodic tax-expenditure reports. Where that is not the case, the analysist must construct a model and estimate the difference in the revenues that would be owed to the government under the baseline conditions and with the special tax feature.</p>\n<p>Fossil fuel subsidies should be monitored on an annual basis.</p>", "COLL_METHOD__GLOBAL"=>"<p>The data will be collected by United Nations Environment Programme (UNEP) through electronic reporting being developed by UNEP. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data reporting on induced transfers started in 2018 and on direct transfers and tax revenue foregone in 2020 and annually thereafter.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Annual.</p>", "DATA_SOURCE__GLOBAL"=>"<ol>\n  <li>National Focal Points from National Statistical Systems.</li>\n  <li>International Estimate Providers &#x2013; the Organisation for Economic Co-operation and Development (OECD), the International Energy Agency (IEA) and the International Monetary Fund (IMF)</li>\n</ol>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) has been assigned the role of custodian agency for this indicator under the SDG process.</p>", "RATIONALE__GLOBAL"=>"<p>The scale and impact of fossil fuel subsidies presents both challenges and opportunities for achieving the goals of the 2030 Agenda on Sustainable Development. For one, the use of fossil fuels, and their promotion through subsidy schemes, adversely affects the ability of governments to attain key goals, such as reducing poverty, improving health, reaching gender equality, providing access to energy, and addressing climate change. At the same time, there is a need to ensure that poor households that are particularly vulnerable to price increases obtain or retain access to energy. Energy-dependent sectors of the economy can be affected, particularly by abrupt changes in prices. Any successful reform therefore requires careful analysis and adapted mitigation measures. For another, reallocating fossil fuel subsidies to sectors that are relevant for development could give a boost to reaching the SDGs. </p>\n<p>Awareness and understanding of existing subsidies based on credible data is necessary to increase transparency and inform decision-making. Reporting against a global indicator measuring consumer and producer fossil fuel subsidies provides a global picture that encompasses both consumer and producer subsidies. It allows for tracking of national and global trends and serve as an important guide for policy-making. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The monitoring and reporting of SDG Indicator 12.c.1 requires capacity within national statistical systems to evaluate direct and indirect transfers of government funds. Data collection by the statistical agencies from the sectoral ministries and state-owned enterprises, including at the sub-national level, which depends on their capacity. There is a need for additional training materials and sharing of experiences on the indicator.</p>\n<p>The indicator methodology utilizes a phased monitoring to allow for countries with different capacities to engage in monitoring 12.c.1. The two phases include global monitoring based on price gap estimates plus national monitoring of direct and indirect transfers with optional monitoring of tax expenditure foregone.</p>", "DATA_COMP__GLOBAL"=>"<p>It is proposed that countries report on the subsidy categories listed below as sub-indicators:</p>\n<p>- Direct transfers; </p>\n<p>- Induced transfers (reporting on regulated prices and calculation of the total amount);</p>\n<p>- Tax expenditure, other government revenue foregone and under-pricing of goods and services, including risk (optional). </p>\n<p>The last category should be included as an optional sub-indicator. Each sub-indicator should be expressed in national currency or United States dollars in current prices. The United Nations Environment Programme (UNEP) uses market exchange rates to calculate between national currency and United States dollar. </p>\n<p>Care should be given if a country chooses to aggregate across the three sub-indicators in order to avoid double counting and all three sub-indicators should be publicly available to ensure transparency. Care needs to be taken when aggregating estimates of induced transfers with data on direct transfers and some measures in under-pricing of goods and services. </p>\n<p>Estimates of subsidies to consumers observable through price-gaps (i.e., consumer price support) have been calculated by several international organizations (the Inter-American Development Bank (IADB), the International Energy Agency (IEA), and the International Monetary Fund (IMF)), covering different geographic regions and time-periods. The three organisations that produce these estimates use roughly the same approach, which can be summed up by the following equation:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>C</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>s</mi>\n    <mi>u</mi>\n    <mi>m</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>s</mi>\n    <mi>u</mi>\n    <mi>p</mi>\n    <mi>p</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>t</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi>a</mi>\n    <mi>d</mi>\n    <mi>j</mi>\n    <mi>u</mi>\n    <mi>s</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>n</mi>\n    <mi>e</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mi>x</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>f</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>c</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>u</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#x2013;</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>l</mi>\n    <mi>o</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>n</mi>\n    <mi>e</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mi>x</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>u</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>e</mi>\n    <mo>)</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>x</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>u</mi>\n    <mi>n</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>s</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>s</mi>\n    <mi>u</mi>\n    <mi>b</mi>\n    <mi>s</mi>\n    <mi>i</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>z</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n  </math></p>\n<p>Estimates are based on reference prices on import (or export) parity prices using the price of a product at the nearest international hub, adjusted for quality differences if necessary, plus (or minus) the cost of freight and insurance to the net importer (or back to the net exporter), plus the cost of internal distribution and marketing and any value-added tax (VAT). For tradable commodities (mainly coal, crude oil, and petroleum products), the reference prices are based on the spot price at the nearest international hub &#x2013; e.g., the United States, Northwest Europe, or Singapore.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data sent to the United Nations Environment Programme (UNEP) is monitored and verified for quality with the help of institutional partners, before being transmitted to the United Nations Statistics Division (UNSD).</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values are not imputed.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>A price gap method is used to create national, regional and global estimates.</p>", "REG_AGG__GLOBAL"=>"<p>The methodology used for the calculation of the regional/global aggregates from the country values is available at <a href=\"http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a>. </p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>UNEP (2019). Measuring Fossil Fuel Subsidies in the Context of the Sustainable Development Goals: https://www.unep.org/resources/report/measuring-fossil-fuel-subsidies-context-sustainable-development-goals</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided by the United Nations Environment Programme (UNEP).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided by the United Nations Environment Programme (UNEP) in cooperation with the countries that provide these data.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided by the United Nations Environment Programme (UNEP).</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>An initial baseline data assessment of data availability demonstrates that 99 countries have existing data which can be used to estimate fossil fuels from direct transfer and many of these countries also have information on tax revenue foregone. Data on induced transfers using a price gap approach is available for all UN Member States.</p>\n<p><strong>Time series:</strong></p>\n<p>Data reporting on induced transfers started in 2018 and on direct transfers and tax revenue foregone in 2020 and annually thereafter.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Because of the risk of double counting, the dataset should therefore provide disaggregated information on individual subsidy measures that will be reported as sub-indicators by category of subsidies.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Country level data and price gap data are shown separately, thus this should not apply. </p>", "OTHER_DOC__GLOBAL"=>"<p>UNEP (2019). Measuring Fossil Fuel Subsidies in the Context of the Sustainable Development Goals: https://www.unep.org/resources/report/measuring-fossil-fuel-subsidies-context-sustainable-development-goals</p>", "indicator_sort_order"=>"12-0c-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"13.1.1", "slug"=>"13-1-1", "name"=>"Número de personas muertas, desaparecidas y afectadas directamente atribuido a desastres por cada 100.000 personas", "url"=>"/site/es/13-1-1/", "sort"=>"130101", "goal_number"=>"13", "target_number"=>"13.1", "global"=>{"name"=>"Número de personas muertas, desaparecidas y afectadas directamente atribuido a desastres por cada 100.000 personas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[{"unit"=>"Por 100.000 habitantes", "minimum"=>0, "maximum"=>5}], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de personas muertas directamente atribuido a desastres por cada 100.000 habitantes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de personas muertas, desaparecidas y afectadas directamente atribuido a desastres por cada 100.000 personas", "indicator_number"=>"13.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_28/opt_0/tipo_1/ti_defunciones/temas.html", "url_text"=>"Estadística de defunciones", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Número de personas muertas directamente atribuido a desastres por cada 100.000 habitantes", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"13.1- Fortalecer la resiliencia y la capacidad de adaptación a los riesgos relacionados con el clima y los desastres naturales en todos los países", "definicion"=>"Defunciones atribuidas a desastres naturales por cada 100.000 habitantes", "formula"=>"\n$$TM_{desastres}^{t} = \\frac{D_{desastres}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$D_{desastres}^{t} =$ defunciones atribuidas a desastres naturales (códigos X30-X39 de la CIE-10) en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico/Comarca/Municipio\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue \nadoptado por los Estados miembros de las Naciones Unidas en marzo de 2015 \ncomo una política global de reducción del riesgo de desastres.\n\nEntre sus metas se encuentran:\n\nMeta A: Reducir sustancialmente la mortalidad global por desastres para \n2030, con el objetivo de reducir el promedio de mortalidad global por cada 100.000 habitantes entre \n2020-2030 en comparación con 2005-2015\n\nMeta B: Reducir sustancialmente el número de personas afectadas\na nivel mundial para 2030 , con el objetivo de reducir la cifra promedio \nmundial por cada 100.000 habitantes \nentre 2020 y 2030 en comparación con el período 2005-2015.\n\nLa consecución de sus metas contribuirá al desarrollo sostenible y fortalecerá la \nresiliencia económica, social, sanitaria y ambiental.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-01-01.pdf\">Metadatos 13-1-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"Número de personas muertas directamente atribuido a desastres por cada 100.000 habitantes", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"13.1- Fortalecer la resiliencia y la capacidad de adaptación a los riesgos relacionados con el clima y los desastres naturales en todos los países", "definicion"=>"Deaths attributed to natural disasters per 100.000 inhabitants", "formula"=>"\n$$TM_{disasters}^{t} = \\frac{D_{disasters}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$D_{disasters}^{t} =$ deaths attributed to natural disasters (codes X30-X39 of the ICD-10) in year $t$\n\n$P^{t} =$ population as of 1 July of year $t$\n", "desagregacion"=>"Sex\n\nProvince/County/Municipality\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted \nby UN Member States in March 2015 as a global policy of disaster risk \nreduction. \n\nIts targets include: \n\nTarget A: Substantially reduce global disaster mortality by 2030, aiming \nto lower average per 100,000 global mortality between 2020-2030 compared \nwith 2005-2015. \n\nTarget B: Substantially reduce the  number of affected people globally by \n2030, aiming to lower the average global figure per 100,000 between 2020-2030 \ncompared with 2005-2015. \n\nAchieving its targets will contribute to sustainable development and strengthen \neconomic, social, health, and environmental resilience. \n\nSource: United Nations Statistics Division\n", "comparabilidad"=>"The available indicator partially complies with the United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-01-01.pdf\">Metadata 13-1-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Número de personas muertas directamente atribuido a desastres por cada 100.000 habitantes", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"13.1- Fortalecer la resiliencia y la capacidad de adaptación a los riesgos relacionados con el clima y los desastres naturales en todos los países", "definicion"=>"Defunciones atribuidas a desastres naturales por cada 100.000 habitantes", "formula"=>"\n$$TM_{hondamendiak}^{t} = \\frac{D_{hondamendiak}^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$D_{hondamendiak}^{t} =$ hondamendi naturalei egotzitako heriotzak (GNS-10eko X30-X39 kodeak) $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Sexua\n\nLurraldea/Eskualdea/Udalerria\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue \nadoptado por los Estados miembros de las Naciones Unidas en marzo de 2015 \ncomo una política global de reducción del riesgo de desastres.\n\nEntre sus metas se encuentran:\n\nMeta A: Reducir sustancialmente la mortalidad global por desastres para \n2030, con el objetivo de reducir el promedio de mortalidad global por cada 100.000 habitantes entre \n2020-2030 en comparación con 2005-2015\n\nMeta B: Reducir sustancialmente el número de personas afectadas\na nivel mundial para 2030 , con el objetivo de reducir la cifra promedio \nmundial por cada 100.000 habitantes \nentre 2020 y 2030 en comparación con el período 2005-2015.\n\nLa consecución de sus metas contribuirá al desarrollo sostenible y fortalecerá la \nresiliencia económica, social, sanitaria y ambiental.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen metadatuak.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-01-01.pdf\">Metadatuak 13-1-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_DSR_AFFCT - Number of people affected by disaster [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_DAFF - Number of directly affected persons attributed to disasters per 100,000 population [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_IJILN - Number of injured or ill people attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_MISS - Number of missing persons due to disaster [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_MMHN - Number of deaths and missing persons attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_MORT - Number of deaths due to disaster [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_MTMP - Number of deaths and missing persons attributed to disasters per 100,000 population [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_PDAN - Number of people whose damaged dwellings were attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_PDLN - Number of people whose livelihoods were disrupted or destroyed, attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>\n<p>VC_DSR_PDYN - Number of people whose destroyed dwellings were attributed to disasters [1.5.1, 11.5.1, 13.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.5.1, 13.1.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator measures the number of people who died, went missing or were directly affected by disasters per 100,000 population. </p>\n<p><strong>Concepts:</strong></p>\n<p><strong>Disasters</strong>: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Death</strong>: The number of people who died during the disaster, or directly after, as a direct result of the hazardous event.</p>\n<p><strong>Missing persons</strong>: The number of people whose whereabouts is unknown since the hazardous event. It includes people who are presumed dead, for whom there is no physical evidence such as a body, and for which an official/legal report has been filed with competent authorities.</p>\n<p><strong>Disaster-affected persons</strong>: People who are affected, either directly or indirectly, by a hazardous event. Directly affected are those who have suffered injury, illness or other health effects. Indirectly affected are people who have suffered consequences, other than or in addition to direct effects (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Injured or ill persons</strong>: People suffering from a new or exacerbated physical or psychological harm, trauma or an illness as a result of a disaster.</p>\n<p><strong>Livelihood</strong>: The capacities, productive assets (both living and material) and activities required for securing a means of living, on a sustainable basis, with dignity. </p>\n<p><strong>People whose damaged or destroyed dwellings were attributed to disasters</strong>: The estimated number of inhabitants previously living in the dwellings (houses, or housing units) damaged or destroyed. These inhabitants are considered affected by the fact that their dwellings were damaged (asset property damage), and because in many cases they would be included in those evacuated, displaced, or relocated. The categories of <em>evacuated, displaced, or relocated</em> should not be included in the indicators.</p>\n<p><strong>Houses damaged</strong>: Houses (housing units) with minor damage, not structural or architectural, and which may continue to be habitable, although they may require repair and/or cleaning.</p>\n<p><strong>Houses destroyed</strong>: Houses (housing units) levelled, buried, collapsed, washed away or damaged to the extent that they are no longer habitable, or must be rebuilt.</p>\n<p><strong>Notes</strong>: </p>\n<p>1) The data on number of deaths and number of missing/presumed dead are mutually exclusive, so no-one should be double counted.</p>\n<p>2) It&#x2019;s important to remember that disasters are not natural, they result from human choices.</p>\n<p>Detailed definitions, concepts, including composition and calculation for each of the data series, can be found in the SFM Technical Guidance (see below the Reference section)</p>", "UNIT_MEASURE__GLOBAL"=>"<p>For VC_DSR_MTMP - Number of deaths and missing persons attributed to disasters per 100,000 population; and VC_DSR_DAFF - Number of directly affected persons attributed to disasters per 100,000 population: ratio</p>\n<p>For other data series: Number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Data sources and collection method:</strong></p>\n<p>Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are reported by national Sendai Framework focal points in the Sendai Framework Monitor (SFM) and national disaster loss database: DesInventar-Sendai. Data are consisted of official, national reporting exclusively. Number of deaths attributed to disasters, number of missing persons attributed to disasters, number of injured or ill people attributed to disasters, number of people whose damaged dwellings were attributed to disasters, number of people whose destroyed dwellings were attributed to disasters, and number of people whose livelihoods were disrupted or destroyed, attributed to disasters are reported in SFM and DesInventar-Sendai. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are reported in Sendai Framework Monitor (SFM) on an ongoing basis, and snapshotted once every year.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released once a year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OIEWG) report, endorsed by the United Nations General Assembly in Resolution A/RES/71/276, requested the UNDRR to undertake technical work and provide technical guidance to develop minimum standards and metadata, the methodologies, and the global monitoring and measurements of the SFM global indicators.</p>\n<p>This indicator is recommended by the OIEWG for the measurement of global Targets A and B of the Sendai Framework, which were endorsed by the UN General Assembly in its Resolution A/RES/71/276, <em>Report of the open-ended intergovernmental</em> <em>expert working group on indicators and terminology relating to disaster risk</em>.</p>", "RATIONALE__GLOBAL"=>"<p>The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. Among the global targets, &#x201C;Target A: Substantially reduce global disaster mortality by 2030, aiming to lower average per 100,000 global mortality between 2020-2030 compared with 2005-2015&#x201D; and &#x201C;Target B: Substantially reduce the number of affected people globally by 2030, aiming to lower the average global figure per 100,000 between 2020-2030 compared with 2005-2015&#x201D; will contribute to sustainable development and strengthen economic, social, health and environmental resilience. The economic, environmental and social perspectives would include poverty eradication, urban resilience, and climate change adaptation.</p>\n<p>The open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OIEWG) established by the General Assembly (resolution 69/284) has developed a set of indicators to measure global progress in the implementation of the Sendai Framework, which was endorsed by the United Nations General Assembly (UNGA) (OIEWG report A/71/644). The relevant global indicators for the Sendai Framework will be used to report for this indicator. </p>\n<p>Disaster loss, mortality and affected populations are greatly influenced by large-scale catastrophic events, as well as a high number of small-scale hazardous events. UNDRR recommends Member States to report the data by event in DesInventar-Sendai, and per the minimum reporting requirement of the Sendai Framework Monitor (SFM) using the Technical Guidance (see Reference and Documentation section), so complementary analysis can be done on the regional and global scale.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The Sendai Framework Monitoring (SFM) System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States report through the system since 2018. The data for SDG indicators are compiled and reported by UNDRR.</p>\n<p>Proxy, alternative and additional indicators:</p>\n<p>In most cases international data sources only record events that surpass some threshold of impact and use secondary data sources which usually have non uniform or even inconsistent methodologies, producing heterogeneous datasets</p>", "DATA_COMP__GLOBAL"=>"<p>Related indicators as of December 2017</p>\n<p>For death and missing perons:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mo>(</mo>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n          </mrow>\n          <mrow>\n            <mn>2</mn>\n            <mi>a</mi>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n          </mrow>\n          <mrow>\n            <mn>3</mn>\n            <mi>a</mi>\n          </mrow>\n        </msub>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n  </math></p>\n<p>Where:</p>\n<p>A<sub>1</sub>: Number of deaths and missing persions attributed to disasters per 100 000; corresponding to Sendai Framework Indicator A-1.</p>\n<p>A<sub>2a</sub>: Number of deaths attributed to disasters; </p>\n<p>A<sub>3a</sub>: Number of missing persons attributed to disasters; and </p>\n<p>Population: Represented population.</p>\n<p>* Detailed methodologies can be found in the Technical Guidance (see below the Reference section)</p>\n<p>For number of disaster-affected person:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>B</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mo>(</mo>\n        <msub>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n          <mrow>\n            <mn>2</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n          <mrow>\n            <mn>3</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n          <mrow>\n            <mn>4</mn>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>B</mi>\n          </mrow>\n          <mrow>\n            <mn>5</mn>\n          </mrow>\n        </msub>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n  </math></p>\n<p>Where:</p>\n<p>B<sub>1</sub>: Number of directly affected people attributed to disasters, per 100,000 population; corresponding to Sendai Framework Indicator B-1.</p>\n<p>B<sub>2</sub>: Number of injured or ill people attributed to disasters; corresponding to Sendai Framework Indicator B-2.</p>\n<p>B<sub>3</sub>: Number of people whose damaged dwellings were attributed to disasters; corresponding to Sendai Framework Indicator B-3.</p>\n<p>B<sub>4</sub>: Number of people whose destroyed dwellings were attributed to disasters; corresponding to Sendai Framework Indicator B-4.</p>\n<p>B<sub>5</sub>: Number of people whose livelihoods were disrupted or destroyed, attributed to disasters; corresponding to Sendai Framework Indicator B-5.</p>\n<p>Population: Represented population.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are validation by UNDRR and national focal points.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction</li>\n  <li>ADPC Disaster and Climate Resilience e-Learning: An orientation to using the online Sendai Framework Monitor, https://courses.adpc.net/courses/course-v1:UNISDR+SFM001+2019Y1/about</li>\n</ul>\n<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>2005-2023</p>\n<p><strong>Time series:</strong></p>\n<p>Annual</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Number of deaths attributed to disasters; </p>\n<p>Number of missing persons attributed to disasters; and </p>\n<p>Number of directly affected people attributed to disasters. </p>\n<p> [Optional Disaggregation]:</p>\n<p>Hazard types</p>\n<p>Geography (Administrative Unit)</p>\n<p>Sex</p>\n<p>Age (3 categories)</p>\n<p>Disability</p>\n<p>Income</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Official SDG Metadata URL: </strong><a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-01.pdf\">https://unstats.un.org/sdgs/metadata/files/Metadata-01-05-01.pdf</a></p>\n<p><strong>Internationally agreed methodology and guideline URL: </strong></p>\n<p><strong>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017)</strong></p>\n<p><a href=\"https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf\">https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf</a></p>\n<p>Sendai Framework Monitor</p>\n<p><a href=\"https://sendaimonitor.undrr.org/\">https://sendaimonitor.undrr.org/</a></p>\n<p><br>DesInventar-Sendai</p>\n<p><a href=\"https://www.desinventar.net/\">https://www.desinventar.net/</a></p>\n<p><strong>Other references:</strong></p>\n<p><strong>Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG</strong>). Endorsed by UNGA on 2nd February 2017. Available at: <a href=\"https://www.preventionweb.net/publications/view/51748\">https://www.preventionweb.net/publications/view/51748</a></p>", "indicator_sort_order"=>"13-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"13.1.2", "slug"=>"13-1-2", "name"=>"Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030", "url"=>"/site/es/13-1-2/", "sort"=>"130102", "goal_number"=>"13", "target_number"=>"13.1", "global"=>{"name"=>"Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que adoptan y aplican estrategias nacionales de reducción del riesgo de desastres en consonancia con el Marco de Sendái para la Reducción del Riesgo de Desastres 2015‑2030", "indicator_number"=>"13.1.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nEl Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue adoptado por los Estados Miembros de las \nNaciones Unidas en marzo de 2015 como una política global de reducción del riesgo de desastres. \n\nEl resultado esperado del Marco de Sendai es lograr “La reducción sustancial del riesgo de desastres y las \npérdidas en vidas, medios de subsistencia y salud y en los activos económicos, físicos, sociales, culturales \ny ambientales de las personas, las empresas, las comunidades y los países”. Entre las metas globales del \nMarco de Sendai, la “Meta E: Aumentar sustancialmente el número de países con estrategias nacionales y \nlocales de reducción del riesgo de desastres para 2020” tiene como objetivo mejorar \nel progreso global y la cobertura de las estrategias y políticas nacionales y locales de \nreducción del riesgo de desastres. \n\nLos objetivos de los planes, estrategias y políticas nacionales \nde reducción del riesgo de desastres son prevenir nuevos riesgos de desastres y reducir los \nexistentes mediante la implementación de medidas económicas, estructurales, legales, \nsociales, de salud, culturales, educativas, ambientales, tecnológicas, políticas e \ninstitucionales integradas e inclusivas que prevengan y reduzcan la exposición a peligros y \nla vulnerabilidad a los desastres, aumenten la preparación para la respuesta y la recuperación y, \nde esa manera, fortalezcan la resiliencia. \n\nEl indicador creará un puente entre los ODS y el Marco de Sendai para la Reducción del \nRiesgo de Desastres. Un número cada vez mayor de gobiernos nacionales que adopten e \nimplementen estrategias nacionales y locales de reducción del riesgo de desastres, como \nlo exige el Marco de Sendai, contribuirá al desarrollo sostenible desde las perspectivas \neconómica, ambiental y social.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=13.1.2&seriesCode=SG_DSR_SFDRR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Número de países que informaron tener una Estrategia Nacional de RRD alineada con el Marco de Sendai SG_DSR_SFDRR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-01-02.pdf\">Metadatos 13-1-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in \nMarch 2015 as a global policy of disaster risk reduction. \n\nThe expected outcome of the Sendai Framework \nis to realize “The substantial reduction of disaster risk and losses in lives, \nlivelihoods and health and in the economic, physical, social, cultural \nand environmental assets of persons, businesses, communities and \ncountries”. Among the Sendai Framework global targets, “Target E: Substantially \nincrease the number of countries with national and local disaster risk reduction \nstrategies by 2020” aims to enhance the global progress and coverage of national \nand local disaster risk reduction strategies and policies. \n\nThe objectives of the national DRR plans, strategies and policies are to prevent \nnew and reduce existing disaster risk through the implementation of integrated \nand inclusive economic, structural, legal, social, health, cultural, educational, \nenvironmental, technological, political and institutional measures that prevent and \nreduce hazard exposure and vulnerability to disaster, increase preparedness for \nresponse and recovery, and thus strengthen resilience. \n\nThe indicator will build bridge between the SDGs and the Sendai Framework for Disaster \nRisk Reduction (DRR). Increasing number of national governments that adopt and \nimplement national and local DRR strategies, which the Sendai Framework calls for, \nwill contribute to sustainable development from economic, environmental and social perspectives.\n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=13.1.2&seriesCode=SG_DSR_SFDRR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Number of countries that reported having a National DRR Strategy aligned with the Sendai Framework SG_DSR_SFDRR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-01-02.pdf\">Metadata 13-1-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>nil, "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nEl Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue adoptado por los Estados Miembros de las \nNaciones Unidas en marzo de 2015 como una política global de reducción del riesgo de desastres. \n\nEl resultado esperado del Marco de Sendai es lograr “La reducción sustancial del riesgo de desastres y las \npérdidas en vidas, medios de subsistencia y salud y en los activos económicos, físicos, sociales, culturales \ny ambientales de las personas, las empresas, las comunidades y los países”. Entre las metas globales del \nMarco de Sendai, la “Meta E: Aumentar sustancialmente el número de países con estrategias nacionales y \nlocales de reducción del riesgo de desastres para 2020” tiene como objetivo mejorar \nel progreso global y la cobertura de las estrategias y políticas nacionales y locales de \nreducción del riesgo de desastres. \n\nLos objetivos de los planes, estrategias y políticas nacionales \nde reducción del riesgo de desastres son prevenir nuevos riesgos de desastres y reducir los \nexistentes mediante la implementación de medidas económicas, estructurales, legales, \nsociales, de salud, culturales, educativas, ambientales, tecnológicas, políticas e \ninstitucionales integradas e inclusivas que prevengan y reduzcan la exposición a peligros y \nla vulnerabilidad a los desastres, aumenten la preparación para la respuesta y la recuperación y, \nde esa manera, fortalezcan la resiliencia. \n\nEl indicador creará un puente entre los ODS y el Marco de Sendai para la Reducción del \nRiesgo de Desastres. Un número cada vez mayor de gobiernos nacionales que adopten e \nimplementen estrategias nacionales y locales de reducción del riesgo de desastres, como \nlo exige el Marco de Sendai, contribuirá al desarrollo sostenible desde las perspectivas \neconómica, ambiental y social.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=13.1.2&seriesCode=SG_DSR_SFDRR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Sendaiko esparruarekin bat datorren HAM (Hondamendi Arriskua Murrizteko) Estrategia Nazionala dutela jakinarazi duten herrialdeen kopurua SG_DSR_SFDRR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-01-02.pdf\">Metadatuak 13-1-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>nil, "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.5.3: Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015&#x2013;2030</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_DSR_SFDRR - Countries that reported having a National DRR Strategy which is aligned to the Sendai Framework to a certain extent (1 = YES; 0 = NO) [1.5.3, 11.b.1, 13.1.2]</p>\n<p>SG_DSR_LEGREG - Countries with legislative and/or regulatory provisions been made for managing disaster risk (1 = YES; 0 = NO) [1.5.3,11.b.1,13.1.2]</p>\n<p>SG_DSR_LGRGSR - Score of adoption and implementation of national DRR strategies in line with the Sendai Framework [1.5.3, 11.b.1, 13.1.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.b.1, 13.1.2</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator captures the number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015&#x2013;2030, and the coverage score for the level of implementation which Member States will report their status in the Sendai Framework Monitor (SFM).</p>\n<p><strong>Concepts:</strong></p>\n<p><strong>Disasters</strong>: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Disaster risk reduction strategies</strong>: define goals and objectives across different timescales and with concrete targets, indicators and time frames. In line with the Sendai Framework for Disaster Risk Reduction 2015-2030, the strategies should be aimed at preventing the creation of disaster risk, the reduction of existing risk and the strengthening of economic, social, health and environmental resilience.</p>\n<p><strong>Notes: </strong></p>\n<p>[a] An open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction established by the General Assembly (resolution 69/284) is developing a set of indicators to measure global progress in the implementation of the Sendai Framework. These indicators will eventually reflect the agreements on the Sendai Framework indicators.</p>\n<p>Detailed definitions, concepts, including composition and calculation for each of the data series, can be found in the SFM Technical Guidance (see below the Reference section)</p>", "UNIT_MEASURE__GLOBAL"=>"<p>SG_DSR_LGRGSR: index</p>\n<p>SG_DSR_SFDRR: number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are reported by national Sendai Framework focal points in the Sendai Framework Monitor (SFM). </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are reported in Sendai Framework Monitor (SFM) on an ongoing basis, and snapshotted once every year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released once a year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The coordinating lead institution chairing the National Disaster Risk Reduction (DRR) platform which is comprised of special purpose agencies including national disaster agencies, civil protection agencies, and meteorological agencies.</p>\n<p>The coordinating lead institution chairing the National DRR platform which is comprised of special purpose agencies including national disaster agencies, civil protection agencies, and meteorological agencies.</p>\n<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OIEWG) report, endorsed by the United Nations General Assembly in Resolution A/RES/71/276, requested the UNDRR to undertake technical work and provide technical guidance to develop minimum standards and metadata, the methodologies, and the global monitoring and measurements of the SFM global indicators.</p>\n<p>This indicator is recommended by the OIEWG for the measurement of global Target E of the Sendai Framework, which were endorsed by the UN General Assembly in its Resolution A/RES/71/276, <em>Report of the open-ended intergovernmental</em> <em>expert working group on indicators and terminology relating to disaster risk</em>.</p>", "RATIONALE__GLOBAL"=>"<p>The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. The expected outcome of the Sendai Framework is to realize &#x201C;The substantial reduction of disaster risk and losses in lives, livelihoods and health and in the economic, physical, social, cultural and environmental assets of persons, businesses, communities and countries&#x201D;. Among the Sendai Framework global targets, &#x201C;Target E: Substantially increase the number of countries with national and local disaster risk reduction strategies by 2020&#x201D; aims to enhance the global progress and coverage of national and local disaster risk reduction strategies and policies. The objectives of the national DRR plans, strategies and policies are to prevent new and reduce existing disaster risk through the implementation of integrated and inclusive economic, structural, legal, social, health, cultural, educational, environmental, technological, political and institutional measures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness for response and recovery, and thus strengthen resilience. </p>\n<p>The indicator will build bridge between the SDGs and the Sendai Framework for Disaster Risk Reduction (DRR). Increasing number of national governments that adopt and implement national and local DRR strategies, which the Sendai Framework calls for, will contribute to sustainable development from economic, environmental and social perspectives.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The Sendai Framework Monitoring (SFM) System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States report through the system since March 2018. The data for SDG indicators are compiled and reported by UNDRR.</p>\n<p>To the deliberations of the OEIWG as well as the IAEG-SDG, UNDRR proposed computation methodologies that allow the monitoring of improvement in national DRR strategies. </p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>E</mi>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mrow>\n          <msubsup>\n            <mo stretchy=\"false\">&#x2211;</mo>\n            <mrow>\n              <mi>j</mi>\n              <mo>=</mo>\n              <mn>1</mn>\n            </mrow>\n            <mrow>\n              <mn>10</mn>\n            </mrow>\n          </msubsup>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>K</mi>\n                <mi>E</mi>\n              </mrow>\n              <mrow>\n                <mi>j</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mrow>\n      </mrow>\n      <mrow>\n        <mn>10</mn>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where:</p>\n<p>E<sub>1</sub>: National DRR strategy progress score; corresponding to Sendai Framework Indicator E-1.</p>\n<p>KE<sub>j</sub>: the level of achievement of the DRR national strategy Key Element j in the country.</p>\n<p>Member States will assess the level of implementation for ten key elements of the national DRR strategy, and enter key elements scores in the Sendai Framework Monitor. The national DRR strategy progress score E<sub>1</sub> would be calculated as the arithmetic average across ten national DRR strategy key elements (KE<sub>j</sub>).</p>\n<p>The national DRR strategy progress score will benchmark according to the following categories:</p>\n<ul>\n  <li>Comprehensive implementation: E<sub>1</sub> is higher than 0.75;</li>\n  <li>Substantial implementation, additional progress required: E<sub>1</sub> is higher than 0.5, but less than or equal to 0.75;</li>\n  <li>Moderate implementation, neither comprehensive nor substantial: E<sub>1</sub> is higher than 0.25, but less than or equal to 0.5;</li>\n  <li>Limited implementation: E<sub>1</sub> is higher than 0 but less than or equal to 0.25,</li>\n  <li>No national DRR strategy: If there is no implementation of national DRR strategy, or no existence of such plans, the score will be 0.</li>\n</ul>\n<p><strong>Note: </strong></p>\n<p>For the complete computation methodology, refer to the Technical Guidance, which provides a full detailed methodology for the indicator and sub-components.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are validation by UNDRR and national focal points.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction</li>\n  <li>ADPC Disaster and Climate Resilience e-Learning: An orientation to using the online Sendai Framework Monitor, https://courses.adpc.net/courses/course-v1:UNISDR+SFM001+2019Y1/about</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>2005-2023</p>\n<p><strong>Time series:</strong></p>\n<p>Annual</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Not applicable</p>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Internationally agreed methodology and guideline URL:</strong></p>\n<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017)</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf\">https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf</a></p>\n<p>Sendai Framework Monitor</p>\n<p><a href=\"https://sendaimonitor.undrr.org/\">https://sendaimonitor.undrr.org/</a></p>\n<p><strong>Other references:</strong></p>\n<ul>\n  <li>Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG). Endorsed by UNGA on 2<sup>nd</sup> February 2017. Available at:</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/publications/view/51748\">https://www.preventionweb.net/publications/view/51748</a></p>", "indicator_sort_order"=>"13-01-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"13.1.3", "slug"=>"13-1-3", "name"=>"Proporción de gobiernos locales que adoptan y aplican estrategias locales de reducción del riesgo de desastres en consonancia con las estrategias nacionales de reducción del riesgo de desastres", "url"=>"/site/es/13-1-3/", "sort"=>"130103", "goal_number"=>"13", "target_number"=>"13.1", "global"=>{"name"=>"Proporción de gobiernos locales que adoptan y aplican estrategias locales de reducción del riesgo de desastres en consonancia con las estrategias nacionales de reducción del riesgo de desastres"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"Los municipios que disponen de un plan (municipal o territorial) de emergencias tienen un valor de 100%", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de gobiernos locales que adoptan y aplican estrategias locales de reducción del riesgo de desastres en consonancia con las estrategias nacionales de reducción del riesgo de desastres", "indicator_number"=>"13.1.3", "national_geographical_coverage"=>"", "page_content"=>"En la C.A. de Euskadi, se dispone de un plan de emergencias a nivel autonómico y tres planes de emergencias territoriales, uno por cada territorio histórico. Por tanto, el 100% de los municipios se encuentran bajo el paraguas de un plan de emergencias territorial", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Seguridad", "periodicity"=>"Anual", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres", "objetivo_global"=>"13- Adoptar medidas urgentes para combatir el cambio climático y sus efectos", "meta_global"=>"13.1- Fortalecer la resiliencia y la capacidad de adaptación a los riesgos relacionados con el clima y los desastres naturales en todos los países", "definicion"=>"Porcentaje de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres mediante planes territoriales de emergencias y planes municipales de emergencias", "formula"=>"<b>Porcentaje de municipios que disponen de un plan municipal de emergencias</b>\n\n$$PMUN_{RRD\\, municipal}^{t} = \\frac{MUN_{RRD\\, municipal}^{t}}{MUN^{t}} \\cdot 100$$\n\ndonde:\n\n$MUN_{RRD\\, municipal}^{t} =$ número de gobiernos locales con planes municipales de emergencias en el año $t$\n\n$MUN^{t} =$ número de gobiernos locales en el año $t$\n\n <br><br>\n\n<b>Porcentaje de municipios cubiertos por un plan territorial de emergencias</b>\n\n$$PMUN_{RRD\\, territorial}^{t} = \\frac{MUN_{RRD\\, territorial}^{t}}{MUN^{t}} \\cdot 100$$\n\ndonde:\n\n$MUN_{RRD\\, territorial}^{t} =$ número de gobiernos locales con planes territoriales de emergencias en el año $t$\n\n$MUN^{t} =$ número de gobiernos locales en el año $t$\n", "desagregacion"=>"Territorio histórico/Comarca/Municipio", "observaciones"=>"\nEn Euskadi, según determina el Plan de Protección Civil de Euskadi – LABI, deben elaborar \ny aprobar planes municipales de emergencia (PEM) los municipios con población superior a 20.000 habitantes. \n\nPara los municipios de más de 5.000 habitantes, en coherencia con la normativa estatal reguladora de las \nBases del Régimen Local, esta directriz es recomendatoria. En 2024, el 96% de los municipios de 5.000 a 20.000 habitantes \ny el 25% de los municipios de 1.000 a 5.000 habitantes disponen de Plan de Emergencia Municipal homologado.\n", "periodicidad"=>"Anual", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue adoptado por los \nEstados Miembros de las Naciones Unidas en marzo de 2015 como una política global de \nreducción del riesgo de desastres. El resultado esperado del Marco de Sendai es lograr \n“la reducción sustancial del riesgo de desastres y de las pérdidas en vidas, medios de \nsubsistencia y salud y en los activos económicos, físicos, sociales, culturales y \nambientales de las personas, las empresas, las comunidades y los países”. \n\nEntre las metas globales del Marco de Sendai, la “Meta E: Aumentar sustancialmente \nel número de países con estrategias nacionales y locales de reducción del riesgo \nde desastres para 2020” tiene por objeto mejorar el progreso y la cobertura globales \nde las estrategias y políticas nacionales y locales de reducción del riesgo de desastres.\n\nLos objetivos de los planes, estrategias y políticas nacionales de reducción del riesgo \nde desastres son prevenir nuevos riesgos de desastres y reducir los existentes mediante \nla implementación de medidas económicas, estructurales, legales, sociales, de salud, culturales, \neducativas, ambientales, tecnológicas, políticas e institucionales integradas e inclusivas que \nprevengan y reduzcan la exposición a los peligros y la vulnerabilidad a los desastres, aumenten \nla preparación para la respuesta y la recuperación y, de ese modo, fortalezcan la resiliencia. \n\nAumentar la proporción de gobiernos locales que adoptan e implementan estrategias locales de \nreducción del riesgo de desastres, como lo exige el Marco de Sendai, contribuirá al desarrollo \nsostenible y fortalecerá la resiliencia económica, social, sanitaria y ambiental. Sus perspectivas económicas, \nambientales y sociales incluirían la erradicación de la pobreza, la resiliencia urbana y la adaptación al cambio climático.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-01-03.pdf\">Metadata 13-1-3.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Proporción de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres", "objetivo_global"=>"13- Adoptar medidas urgentes para combatir el cambio climático y sus efectos", "meta_global"=>"13.1- Fortalecer la resiliencia y la capacidad de adaptación a los riesgos relacionados con el clima y los desastres naturales en todos los países", "definicion"=>"Percentage of local governments that adopt and implement local disaster risk  reduction strategies through territorial emergency plans and municipal emergency plans", "formula"=>"<b>Percentage of municipalities that have a municipal emergency plan</b>\n\n$$PMUN_{DRR\\, municipal}^{t} = \\frac{MUN_{DRR\\, municipal}^{t}}{MUN^{t}} \\cdot 100$$\n\nwhere:\n\n$MUN_{DRR\\, municipal}^{t} =$ number of local governments with municipal emergency plans in the year $t$\n\n$MUN^{t} =$ number of local governments in the year $t$\n\n <br>\n\n<b>Percentage of municipalities covered by a territorial emergency plan</b>\n\n$$PMUN_{DRR\\, territorial}^{t} = \\frac{MUN_{DRR\\, territorial}^{t}}{MUN^{t}} \\cdot 100$$\n\nwhere:\n\n$MUN_{DDR\\, territorial}^{t} =$ number of local governments with territorial emergency plans in the year $t$\n\n$MUN^{t} =$ number of local governments in the year $t$\n", "desagregacion"=>"Province/County/Municipality", "observaciones"=>"\nIn the Basque Country, according to the Basque Civil Protection Plan (LABI), municipalities \nwith a population of over 20,000 must prepare and approve municipal emergency plans (PEM). \n\nFor municipalities with more than 5,000 inhabitants, in accordance with the state regulations \ngoverning the Bases of the Local Government, this guideline is recommendatory. In 2024, 96% of \nmunicipalities with 5,000 to 20,000 inhabitants and 25% of municipalities with 1,000 to 5,000 \ninhabitants have an approved Municipal Emergency Plan.\n", "periodicidad"=>"Anual", "justificacion_global"=>"The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in \nMarch 2015 as a global policy of disaster risk reduction. The expected outcome of the Sendai Framework \nis to realize “The substantial reduction of disaster risk and losses in lives, livelihoods \nand health and in the economic, physical, social, cultural and environmental assets of persons, \nbusinesses, communities and countries”.\n\nAmong the Sendai Framework global targets, “Target E: Substantially increase the number of \ncountries with national and local disaster risk reduction strategies by 2020” aims to enhance \nthe global progress and coverage of national and local disaster risk reduction strategies and policies.\n\nThe objectives of the national DRR plans, strategies and policies are to prevent new and reduce \nexisting disaster risk through the implementation of integrated and inclusive economic, structural, \nlegal, social, health, cultural, educational, environmental, technological, political and institutional \nmeasures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness \nfor response and recovery, and thus strengthen resilience.\n\nIncreasing the proportion of local governments that adopt and implement local disaster risk reduction \nstrategies, which the Sendai Framework calls for, will contribute to sustainable development and \nstrengthen economic, social, health and environmental resilience.\n\nSource: United Nations Statistics Division\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-01-03.pdf\">Metadata 13-1-3.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres", "objetivo_global"=>"13- Adoptar medidas urgentes para combatir el cambio climático y sus efectos", "meta_global"=>"13.1- Fortalecer la resiliencia y la capacidad de adaptación a los riesgos relacionados con el clima y los desastres naturales en todos los países", "definicion"=>"Porcentaje de gobiernos locales que adoptan e implementan estrategias locales de reducción del riesgo de desastres mediante planes territoriales de emergencias y planes municipales de emergencias", "formula"=>"<b>Larrialdietarako udal-plana duten udalerrien ehunekoa</b>\n\n$$PMUN_{udal\\, HAM}^{t} = \\frac{MUN_{udal\\, HAM}^{t}}{MUN^{t}} \\cdot 100$$\n\nnon:\n\n$MUN_{udal\\, HAM}^{t} =$ larrialdietarako udal-planak dituzten udalerrien kopurua $t$ urtean\n\n$MUN^{t} =$ udalerrien kopurua $t$ urtean\n\n <br><br>\n\n<b>Larrialdietarako lurralde-plan batek estalitako udalerrien ehunekoa</b>\n\n$$PMUN_{lurralde\\, HAM}^{t} = \\frac{MUN_{lurralde\\, HAM}^{t}}{MUN^{t}} \\cdot 100$$\n\nnon:\n\n$MUN_{lurralde\\, HAM}^{t} =$ larrialdietako lurralde-planak dituzten udalerrien kopurua $t$ urtean\n\n$MUN^{t} =$ udalerrien kopurua $t$ urtean\n", "desagregacion"=>"Lurralde historikoa/Eskualdea/Udalerria", "observaciones"=>"\nEn Euskadi, según determina el Plan de Protección Civil de Euskadi – LABI, deben elaborar \ny aprobar planes municipales de emergencia (PEM) los municipios con población superior a 20.000 habitantes. \n\nPara los municipios de más de 5.000 habitantes, en coherencia con la normativa estatal reguladora de las \nBases del Régimen Local, esta directriz es recomendatoria. En 2024, el 96% de los municipios de 5.000 a 20.000 habitantes \ny el 25% de los municipios de 1.000 a 5.000 habitantes disponen de Plan de Emergencia Municipal homologado.\n", "periodicidad"=>"Anual", "justificacion_global"=>"El Marco de Sendai para la Reducción del Riesgo de Desastres 2015-2030 fue adoptado por los \nEstados Miembros de las Naciones Unidas en marzo de 2015 como una política global de \nreducción del riesgo de desastres. El resultado esperado del Marco de Sendai es lograr \n“la reducción sustancial del riesgo de desastres y de las pérdidas en vidas, medios de \nsubsistencia y salud y en los activos económicos, físicos, sociales, culturales y \nambientales de las personas, las empresas, las comunidades y los países”. \n\nEntre las metas globales del Marco de Sendai, la “Meta E: Aumentar sustancialmente \nel número de países con estrategias nacionales y locales de reducción del riesgo \nde desastres para 2020” tiene por objeto mejorar el progreso y la cobertura globales \nde las estrategias y políticas nacionales y locales de reducción del riesgo de desastres.\n\nLos objetivos de los planes, estrategias y políticas nacionales de reducción del riesgo \nde desastres son prevenir nuevos riesgos de desastres y reducir los existentes mediante \nla implementación de medidas económicas, estructurales, legales, sociales, de salud, culturales, \neducativas, ambientales, tecnológicas, políticas e institucionales integradas e inclusivas que \nprevengan y reduzcan la exposición a los peligros y la vulnerabilidad a los desastres, aumenten \nla preparación para la respuesta y la recuperación y, de ese modo, fortalezcan la resiliencia. \n\nAumentar la proporción de gobiernos locales que adoptan e implementan estrategias locales de \nreducción del riesgo de desastres, como lo exige el Marco de Sendai, contribuirá al desarrollo \nsostenible y fortalecerá la resiliencia económica, social, sanitaria y ambiental. Sus perspectivas económicas, \nambientales y sociales incluirían la erradicación de la pobreza, la resiliencia urbana y la adaptación al cambio climático.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-01-03.pdf\">Metadatuak 13-1-3.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 1: End poverty in all its forms everywhere </p>", "SDG_TARGET__GLOBAL"=>"<p>Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 1.5.4: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_DSR_SILS - Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies [1.5.4, 11.b.2, 13.1.3]</p>\n<p>SG_DSR_SILN - Number of local governments that adopt and implement local DRR strategies in line with national strategies [1.5.4, 11.b.2, 13.1.3]</p>\n<p>SG_GOV_LOGV - Number of local governments [1.5.4, 11.b.2, 13.1.3]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.b.2, 13.1.3</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator captures the percentage of local governments that adopt and implement local disaster risk reduction strategies in line with national strategies.</p>\n<p><strong>Concepts:</strong></p>\n<p><strong>Disasters</strong>: A serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts (UNDRR, 2017, https://www.preventionweb.net/terminology/disaster). </p>\n<p><strong>Disaster risk reduction strategies</strong>: define goals and objectives across different timescales and with concrete targets, indicators and time frames. In line with the Sendai Framework for Disaster Risk Reduction 2015-2030, the strategies should be aimed at preventing the creation of disaster risk, the reduction of existing risk and the strengthening of economic, social, health and environmental resilience.</p>\n<p><strong>Local Government</strong>: Form of sub-national public administration with responsibility for disaster risk reduction &#x2013; to be determined by countries for the purposes of monitoring Sendai Framework Target E.</p>\n<p><strong>Notes: </strong></p>\n<p>[a] An open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction established by the General Assembly (resolution 69/284) is developing a set of indicators to measure global progress in the implementation of the Sendai Framework. These indicators will eventually reflect the agreements on the Sendai Framework indicators.</p>\n<p>Detailed definitions, concepts, including composition and calculation for each of the data series, can be found in the SFM Technical Guidance (see below the Reference section)</p>", "UNIT_MEASURE__GLOBAL"=>"<p>SG_DSR_SILS: Percent (%) </p>\n<p>SG_DSR_SILN: Number </p>\n<p>SG_GOV_LOGV: Number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data provider at national level is appointed Sendai Framework Focal Points. In most countries disaster data are collected by line ministries and national disaster loss databases are established and managed by special purpose agencies including national disaster management agencies, civil protection agencies, and meteorological agencies. The Sendai Framework Focal Points in each country are responsible of data reporting through the Sendai Framework Monitoring System.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data are reported by national Sendai Framework focal points in the Sendai Framework Monitor (SFM). </p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are reported in Sendai Framework Monitor (SFM) on an ongoing basis, and snapshotted once every year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released once a year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Sendai Framework Focal Points usually represent the coordinating lead institution chairing the National Disaster Risk Reduction (DRR) platform which is comprised of special purpose agencies including national disaster agencies, civil protection agencies, and meteorological agencies.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office for Disaster Reduction (UNDRR)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Related to Disaster Risk Reduction (OIEWG) report, endorsed by the United Nations General Assembly in Resolution A/RES/71/276, requested the UNDRR to undertake technical work and provide technical guidance to develop minimum standards and metadata, the methodologies, and the global monitoring and measurements of the SFM global indicators.</p>\n<p>This indicator is recommended by the OIEWG for the measurement of global Target E of the Sendai Framework, which were endorsed by the UN General Assembly in its Resolution A/RES/71/276, Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk.</p>", "RATIONALE__GLOBAL"=>"<p>The Sendai Framework for Disaster Risk Reduction 2015-2030 was adopted by UN Member States in March 2015 as a global policy of disaster risk reduction. The expected outcome of the Sendai Framework is to realize &#x201C;The substantial reduction of disaster risk and losses in lives, livelihoods and health and in the economic, physical, social, cultural and environmental assets of persons, businesses, communities and countries&#x201D;. Among the Sendai Framework global targets, &#x201C;Target E: Substantially increase the number of countries with national and local disaster risk reduction strategies by 2020&#x201D; aims to enhance the global progress and coverage of national and local disaster risk reduction strategies and policies. The objectives of the national DRR plans, strategies and policies are to prevent new and reduce existing disaster risk through the implementation of integrated and inclusive economic, structural, legal, social, health, cultural, educational, environmental, technological, political and institutional measures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness for response and recovery, and thus strengthen resilience. </p>\n<p>Increasing the proportion of local governments that adopt and implement local disaster risk reduction strategies, which the Sendai Framework calls for, will contribute to sustainable development and strengthen economic, social, health and environmental resilience. Their economic, environmental and social perspectives would include poverty eradication, urban resilience, and climate change adaptation.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The Sendai Framework Monitoring (SFM) System has been developed to measure the progress in the implementation of the Sendai Framework by UNGA endorsed indicators. Member States report through the system since March 2018. The data for SDG indicators are compiled and reported by UNDRR.</p>\n<p>To the deliberations of the OEIWG as well as the IAEG-SDG, UNDRR proposed computation methodologies that allow the monitoring of improvement in national DRR strategies. </p>", "DATA_COMP__GLOBAL"=>"<p>Member States count the number of local governments that adopt and implement local DRR strategies in line with the national strategy and express it as a percentage of the total number of local governments in the country.</p>\n<p>Local governments are determined by the reporting country for this indicator, considering sub-national public administrations with responsibility to develop local disaster risk reduction strategies. It is recommended that countries report on progress made by the lowest level of government accorded the mandate for disaster risk reduction, as the Sendai Framework promotes the adoption and implementation of local disaster risk reduction strategies in every local authority.</p>\n<p>Each Member State will calculate the ratio of the number of local governments with local DRR strategies in line with national strategies and the total number of local governments.</p>\n<p>Global Average will then be calculated as below through arithmetic average of the data from each Member State.</p>\n<p>For the complete computation methodology, refer to the Technical Guidance, which provides a full detailed methodology for the indicator.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are validation by UNDRR and national focal points.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not applicable</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction</li>\n  <li>ADPC Disaster and Climate Resilience e-Learning: An orientation to using the online Sendai Framework Monitor, https://courses.adpc.net/courses/course-v1:UNISDR+SFM001+2019Y1/about</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>2005-2023</p>\n<p><strong>Time series:</strong></p>\n<p>Annual</p>\n<p><strong>Disaggregation:</strong></p>\n<p>By local government (applying sub-national administrative unit)</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>Internationally agreed methodology and guideline URL:</strong></p>\n<ul>\n  <li>Technical guidance for monitoring and reporting on progress in achieving the global targets of the Sendai Framework for Disaster Risk Reduction (UNDRR 2017)</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf\">https://www.preventionweb.net/files/54970_collectionoftechnicalguidancenoteso.pdf</a></p>\n<p>Sendai Framework Monitor</p>\n<p><a href=\"https://sendaimonitor.undrr.org/\">https://sendaimonitor.undrr.org/</a></p>\n<p><strong>Other references:</strong></p>\n<ul>\n  <li>Report of the open-ended intergovernmental expert working group on indicators and terminology relating to disaster risk reduction (OEIWG). Endorsed by UNGA on 2<sup>nd</sup> February 2017. Available at:</li>\n</ul>\n<p><a href=\"https://www.preventionweb.net/publications/view/51748\">https://www.preventionweb.net/publications/view/51748</a></p>", "indicator_sort_order"=>"13-01-03", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"13.2.1", "slug"=>"13-2-1", "name"=>"Número de países con contribuciones determinadas a nivel nacional, estrategias a largo plazo, planes nacionales de adaptación y comunicaciones sobre la adaptación, notificadas a la secretaría de la Convención Marco de las Naciones Unidas sobre el Cambio Climático", "url"=>"/site/es/13-2-1/", "sort"=>"130201", "goal_number"=>"13", "target_number"=>"13.2", "global"=>{"name"=>"Número de países con contribuciones determinadas a nivel nacional, estrategias a largo plazo, planes nacionales de adaptación y comunicaciones sobre la adaptación, notificadas a la secretaría de la Convención Marco de las Naciones Unidas sobre el Cambio Climático"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>true, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países con contribuciones determinadas a nivel nacional, estrategias a largo plazo, planes nacionales de adaptación y comunicaciones sobre la adaptación, notificadas a la secretaría de la Convención Marco de las Naciones Unidas sobre el Cambio Climático", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países con contribuciones determinadas a nivel nacional, estrategias a largo plazo, planes nacionales de adaptación y comunicaciones sobre la adaptación, notificadas a la secretaría de la Convención Marco de las Naciones Unidas sobre el Cambio Climático", "indicator_number"=>"13.2.1", "national_geographical_coverage"=>"", "page_content"=>"La Unión Europea y sus Estados miembros presentaron su contribución determinada a nivel nacional (NDC) en octubre de 2016, \ncon un objetivo de reducción de al menos el 40% de las emisiones de gases de efecto invernadero en toda la economía para 2030, en comparación con los niveles de 1990. \nEn <a href=\"https://unfccc.int/sites/default/files/NDC/2023-10/ES-2023-10-17%20EU%20submission%20NDC%20update.pdf\" target=\"_blank\">octubre de 2023 se presentó \nla última actualización de la contribución determinada a nivel nacional</a> de la Unión Europea y sus Estados miembros.\n\nEn octubre de 2021, el Gobierno vasco adoptó el Plan de Transición Energética y Cambio Climático 2021-2024, en el que se fijan los objetivos climáticos para el año 2024:\nreducir en un 30% la emisión de gases de efecto invernadero; lograr que la cuota de energías renovables represente el 20% del consumo final de energía; y asegurar la resiliencia del territorio vasco al cambio climático.\n", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nEn virtud de la Convención Marco de las Naciones Unidas sobre el Cambio Climático (CMNUCC), \ntodas las Partes formularán, implementarán, publicarán y actualizarán periódicamente \nprogramas nacionales y regionales que contengan medidas para mitigar el cambio climático \ny facilitar una adaptación adecuada, teniendo en cuenta sus responsabilidades comunes \npero diferenciadas y sus prioridades, objetivos y circunstancias de desarrollo nacionales \ny regionales específicos. Estas políticas y medidas deberán ser apropiadas a las condiciones \nespecíficas de cada Parte e integrarse en los programas nacionales de desarrollo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=13.2.1&seriesCode=EN_NAD_CONTR&areaCode= 1&period=3&table=FIRST\">Número de países con contribuciones determinadas a nivel nacional (Número) EN_NAD_CONTR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-02-01.pdf\">Metadatos 13-2-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nUnder the United Nations Framework Convention on Climate Change (UNFCCC), all Parties shall \nformulate, implement, publish and regularly update national/regional programmes containing \nmeasures to mitigate climate change and to facilitate adequate adaptation, while taking into \naccount their common but differentiated responsibilities and their specific national and regional \ndevelopment priorities, objectives and circumstances. These policies and measures should be \nappropriate for the specific conditions of each Party and should be integrated with national \ndevelopment programmes.\n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=13.2.1&seriesCode=EN_NAD_CONTR&areaCode= 1&period=3&table=FIRST\">Number of countries with nationally determined contributions (Number) EN_NAD_CONTR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-02-01.pdf\">Metadata 13-2-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>nil, "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nEn virtud de la Convención Marco de las Naciones Unidas sobre el Cambio Climático (CMNUCC), \ntodas las Partes formularán, implementarán, publicarán y actualizarán periódicamente \nprogramas nacionales y regionales que contengan medidas para mitigar el cambio climático \ny facilitar una adaptación adecuada, teniendo en cuenta sus responsabilidades comunes \npero diferenciadas y sus prioridades, objetivos y circunstancias de desarrollo nacionales \ny regionales específicos. Estas políticas y medidas deberán ser apropiadas a las condiciones \nespecíficas de cada Parte e integrarse en los programas nacionales de desarrollo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=13.2.1&seriesCode=EN_NAD_CONTR&areaCode= 1&period=3&table=FIRST\">Nazio mailan zehaztutako kontribuzioak dituzten herrialdeen kopurua (kopurua) EN_NAD_CONTR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-02-01.pdf\">Metadatuak 13-2-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>nil, "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 13: Take urgent action to combat climate change and its impacts</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 13.2: Integrate climate change measures into national policies, strategies and planning</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 13.2.1: Number of countries with nationally determined contributions, long-term strategies, national adaptation plans and adaptation communications, as reported to the secretariat of the United Nations Framework Convention on Climate Change</p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2021-03-01</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UN Climate Change (UNFCCC Secretariat)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UN Climate Change (UNFCCC Secretariat)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>NDCs</p>\n<p>The Paris Agreement requires each Party to prepare, communicate and maintain successive <em>nationally determined contributions (NDCs)</em> including mitigation, adaptation and support measures. </p>\n<p>The <a href=\"https://unfccc.int/node/617\" target=\"_blank\">Paris Agreement</a> (Article 4, paragraph 2) requires each Party to prepare, communicate and maintain successive nationally determined contributions (NDCs) that it intends to achieve. Parties shall pursue domestic mitigation measures, with the aim of achieving the objectives of such contributions.</p>\n<p>Starting in 2023 and then every five years, governments will take stock of the implementation of the Agreement to assess the collective progress towards achieving the purpose of the Agreement and its long-term goals. The outcome of the global stocktake (GST) will inform the preparation of subsequent NDCs, in order to allow for increased ambition and climate action to achieve the purpose of the Paris Agreement and its long-term goals. <a href=\"https://unfccc.int/process-and-meetings/the-paris-agreement/nationally-determined-contributions-ndcs\">https://unfccc.int/process-and-meetings/the-paris-agreement/nationally-determined-contributions-ndcs</a></p>\n<p>NDC interim registry <a href=\"https://www4.unfccc.int/sites/ndcstaging/Pages/Home.aspx\">https://www4.unfccc.int/sites/ndcstaging/Pages/Home.aspx</a> </p>\n<p><strong>NAPs</strong></p>\n<p>The <em>national adaptation plan (NAP)</em> process was established under the <a href=\"https://unfccc.int/adaptation/items/5852.php\">Cancun Adaptation Framework</a> (CAF). It enables Parties to formulate and implement <em>national adaptation plans (NAPs)</em> as a means of identifying medium- and long-term adaptation needs and developing and implementing strategies and programmes to address those needs. It is a continuous, progressive and iterative process which follows a country-driven, gender-sensitive, participatory and fully transparent approach supported by technical guidelines and up to USD 3 million per developing country through the Green Climate Fund Readiness and Preparatory Support Programme, intended to support the formulation of NAPs. Technical guidelines for the NAP process are available at &lt;<a href=\"https://unfccc.int/topics/adaptation-and-resilience/workstreams/national-adaptation-plans-naps/guidelines-for-national-adaptation-plans-naps\">unfccc.int</a>&gt;; NAPs received by the UNFCCC secretariat are posted at &lt;<a href=\"https://www4.unfccc.int/sites/NAPC/News/Pages/national_adaptation_plans.aspx\">unfccc.int</a>&gt;.</p>\n<p><strong>Long term strategies </strong></p>\n<p>Under the Paris Agreement, all Parties should further strive to formulate and <em>communicate long-term low greenhouse gas emission development strategies</em> to provide a context and integrated long-term view to their NDCs. </p>\n<p>In accordance with Article 4, paragraph 19, of the Paris Agreement, all Parties should strive to formulate and communicate long-term low greenhouse gas emission development strategies, mindful of Article 2 taking into account their common but differentiated responsibilities and respective capabilities, in the light of different national circumstances.</p>\n<p>The COP, by its decision 1/CP 21, paragraph 35, invited Parties to communicate, by 2020, to the secretariat mid-century, long-term low greenhouse gas emission development strategies in accordance with Article 4, paragraph 19, of the Agreement. Further information is available at &lt;<a href=\"https://unfccc.int/process/the-paris-agreement/long-term-strategies\">unfccc.int</a>&gt;</p>\n<p><strong>Adaptation communications</strong></p>\n<p>Under the Paris Agreement&#x2019;s Article 7, paragraphs 10 and 11, each Party should, as appropriate, submit and update periodically an adaptation communication, which may include its priorities, implementation and support needs, plans and actions. The purpose of the adaptation communication is to strengthen the visibility and profile of adaptation, balance with mitigation, actions, support, learning and understanding. Parties may include information on e.g. their circumstances, institutions, vulnerabilities, adaptation priorities, plans, needs, progress achieved, co-benefits, other frameworks, gender aspects, and indigenous knowledge. The adaptation communications will be recorded in a public registry maintained by the secretariat, and they will provide input to the process of global stocktake every five years. The adaptation communications received so far are currently available at: <a href=\"https://unfccc.int/topics/adaptation-and-resilience/workstreams/adaptation-communications\">https://unfccc.int/topics/adaptation-and-resilience/workstreams/adaptation-communications</a>. </p>\n<p><strong>National communications</strong></p>\n<p>The Convention established several processes to foster transparency and accountability of countries&#x2019; actions to address climate change. Under Article 12, all Parties are asked to submit national inventories and national communications (NCs) to report on the implementation of the Convention. This reporting is required at different levels of stringency and with varying frequency for different Parties. National Communications received by the UNFCCC secretariat are available at &lt;<a href=\"https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-convention/national-communications-and-biennial-reports-annex-i-parties/seventh-national-communications-annex-i\">unfccc.int</a>&gt;.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of submissions received from Parties to UNFCCC</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Official documents and registries, as reported by Parties to the UNFCCC and the Paris Agreement, and published on &lt;unfccc.int&gt;.</p>\n<p>NDC interim registry available at &lt; https://www4.unfccc.int/sites/NDCStaging/Pages/Home.aspx &gt; </p>\n<p>Long term strategies received by the UNFCCC secretariat are available at &lt;unfccc.int&gt;.</p>\n<p>NAPs received by the UNFCCC secretariat are available at &lt;unfccc.int&gt;.</p>\n<p>Adaptation communications will be recorded in the future in a public registry maintained by the secretariat. Until the finalization of the design of the registry, the adaptation communications received so far are available at: <a href=\"https://unfccc.int/topics/adaptation-and-resilience/workstreams/adaptation-communications\">https://unfccc.int/topics/adaptation-and-resilience/workstreams/adaptation-communications</a></p>", "COLL_METHOD__GLOBAL"=>"<p>Submission of documents to the UNFCCC Secretariat from Parties to the UNFCCC and Paris Agreement.</p>", "FREQ_COLL__GLOBAL"=>"<p>Ongoing as Parties submit reports. Will be compiled annually in advance of preparation of annual SDG progress reports; in advance of the global stocktake.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Ongoing as Parties submit reports. Will be compiled annually in advance of preparation of annual SDG progress reports; in advance of the global stocktake.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Parties to the UNFCCC and Paris Agreement, aggregate, UN Climate Change (UNFCCC Secretariat); Further analysis on linkages across other SDGs may be undertaken in collaboration with other UN organisations, as relevant, to show how countries are utilising these tools for implementation of climate action and SDGs more broadly.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UN Climate Change (UNFCCC Secretariat).</p>", "RATIONALE__GLOBAL"=>"<p><strong>Rationale and concepts, comments and limitations:</strong></p>\n<p>Under the United Nations Framework Convention on Climate Change (UNFCCC), all Parties shall formulate, implement, publish and regularly update national/regional programmes containing measures to mitigate climate change and to facilitate adequate adaptation, while taking into account their common but differentiated responsibilities and their specific national and regional development priorities, objectives and circumstances. These policies and measures should be appropriate for the specific conditions of each Party and should be integrated with national development programmes.</p>\n<p>The Convention established several processes to foster transparency and accountability of countries&#x2019; actions to address climate change.</p>\n<p>The Paris Agreement<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> builds upon the Convention and brings all nations into a common cause to undertake ambitious efforts to combat climate change and adapt to its effects, with enhanced support to assist developing countries to do so, charting a new course in the global climate effort. The Paris Agreement&#x2019;s central aim is to strengthen the global response to the threat of climate change by keeping a global temperature rise this century well below 2 degrees Celsius above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5 degrees Celsius. Additionally, the agreement aims to strengthen the ability of countries to deal with the impacts of climate change. </p>\n<p>Materials are received from Parties on an ongoing basis. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> The Paris Agreement entered into force on 4 November 2016. Further information about the Paris Agreement may be found at &lt;http://unfccc.int/paris_agreement/items/9485.php&gt; <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>See 4.a</p>", "DATA_COMP__GLOBAL"=>"<p>Count of submitted reports annually in advance of preparation of SDG progress reports, based on most recent data.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>At country level</strong></p>\n<p>n/a</p>\n<p><strong>At regional and global levels</strong></p>\n<p>n/a</p>", "REG_AGG__GLOBAL"=>"<p>n/a</p>", "DOC_METHOD__GLOBAL"=>"<p>&#x2022; Data is compiled globally</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data reported is based on official information as documented and reported on at &lt;unfccc.int&gt;.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Number of Parties to the UNFCCC and Paris Agreement</p>\n<p>Currently, there are 197 Parties (196 States and 1 regional economic integration organization) to the United Nations Framework Convention on Climate Change.</p>\n<p>https://unfccc.int/process-and-meetings/the-convention/status-of-ratification/status-of-ratification-of-the-convention</p>\n<p>To this date, 191 Parties have ratified the Paris Agreement, of 197 Parties to the Convention.</p>\n<p>https://unfccc.int/process/the-paris-agreement/status-of-ratification </p>\n<p><strong>Time series:</strong></p>\n<p>Ongoing as Parties submit reports. Will be compiled annually in advance of preparation of annual SDG progress reports; NDCs are submitted in advance of the global stocktake (starting in 2023) every five years, with the next round of NDCs (new or updated) being submitted by 2020. </p>\n<p>https://unfccc.int/topics/science/workstreams/global-stocktake-referred-to-in-article-14-of-the-paris-agreement</p>\n<p><strong>Disaggregation:</strong></p>\n<p>n/a. Some analysis on linkages across other SDGs may be undertaken in collaboration with other UN organisations, as relevant, to show how countries are utilising these tools for implementation of climate action and SDGs more broadly.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>n/a</p>", "OTHER_DOC__GLOBAL"=>"<p>As included in links above;</p>\n<p>NDC interim registry available at &lt; https://www4.unfccc.int/sites/NDCStaging/Pages/Home.aspx &gt; </p>\n<p>Long term strategies received by the UNFCCC secretariat are available at &lt;unfccc.int&gt;.</p>\n<p>NAPs received by the UNFCCC secretariat are posted at &lt;unfccc.int&gt;.</p>\n<p>Adaptation communications will be recorded in the future in a public registry maintained by the secretariat. Until the finalization of the design of the registry, the adaptation communications received so far are available <a href=\"https://unfccc.int/topics/adaptation-and-resilience/workstreams/adaptation-communications\">at:</a> https://unfccc.int/topics/adaptation-and-resilience/workstreams/adaptation-communications. </p>", "indicator_sort_order"=>"13-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"13.2.2", "slug"=>"13-2-2", "name"=>"Emisiones totales de gases de efecto invernadero por año", "url"=>"/site/es/13-2-2/", "sort"=>"130202", "goal_number"=>"13", "target_number"=>"13.2", "global"=>{"name"=>"Emisiones totales de gases de efecto invernadero por año"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Emisiones totales de gases de efecto invernadero por año", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Emisiones totales de gases de efecto invernadero por año", "indicator_number"=>"13.2.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso en la serie Emisiones totales de gases de efecto invernadero", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/inventario-de-gases-de-efecto-invernadero-090205/web01-a2ingair/es/", "url_text"=>"Inventario de gases de efecto invernadero", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Emisiones totales de gases de efecto invernadero (GEI), por unidad de PIB real, per cápita, y tasas de variación respecto a 1990 y 2005", "objetivo_global"=>"13- Adoptar medidas urgentes para combatir el cambio climático y sus efectos", "meta_global"=>"13.2- Incorporar medidas relativas al cambio climático en las políticas, estrategias y planes nacionales", "definicion"=>"Emisiones totales de gases de efecto invernadero (GEI), emisiones totales de GEI por unidad de PIB real,  per cápita, y tasas de variación respecto a las emisiones en los años 1990 y 2005", "formula"=>"\n<b>Emisiones totales de gases de efecto invernadero por unidad de PIB</b>\n\n$$PPIBGEI^{t} = \\frac{GEI^{t}}{PIB_{2022}^{t}} $$\n\n<b>Emisiones totales de gases de efecto invernadero per cápita</b>\n\n$$GEIPC^{t} = \\frac{GEI^{t}}{P^{t}} $$\n\n<b>Emisiones de gases de efecto invernadero respecto al año 1990</b>\n\n$$TVGEI_{1990}^{t} = \\frac{GEI^{t}-GEI^{1990}}{GEI^{1990}} \\cdot 100$$ \n\n<b>Emisiones de gases de efecto invernadero respecto al año 2005</b>\n\n$$TVGEI_{2005}^{t} = \\frac{GEI^{t}-GEI^{2005}}{GEI^{2005}} \\cdot 100$$ \n\n\ndonde:\n\n$GEI^{t} =$ emisiones de gases de efecto invernadero en el año $t$\n\n$PIB_{2022}^{t} =$ producto interior bruto en volumen encadenado con referencia 2022 en el año $t$\n\n$P^{t} =$ población a 1 de julio en el año $t$\n\n$GEI^{1990} =$ emisiones de gases de efecto invernadero en el año 1990\n\n$GEI^{2005} =$ emisiones de gases de efecto invernadero en el año 2005\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El objetivo último de la Convención Marco sobre el Cambio Climático (CMNUCC) es lograr \nla estabilización de las concentraciones de gases de efecto invernadero en la atmósfera a \nun nivel que impida interferencias antropogénicas peligrosas en el sistema climático. \nLa estimación de los niveles de emisiones y absorciones de gases de efecto invernadero (GEI) \nes un elemento importante de los esfuerzos para lograr este objetivo.\n\nEl Acuerdo de París adoptado en 2015 marca el último paso en la evolución del régimen de \ncambio climático de la ONU y se basa en el trabajo realizado en el marco de la Convención. \nSu objetivo principal es fortalecer la respuesta mundial a la amenaza del cambio \nclimático manteniendo el aumento de la temperatura global en este siglo muy por debajo de los \n2 grados Celsius con respecto a los niveles preindustriales y proseguir los esfuerzos para \nlimitar el aumento de la temperatura aún más a 1,5 grados Celsius. El Acuerdo también tiene \ncomo objetivo fortalecer la capacidad de los países para hacer frente a los impactos del \ncambio climático.\n\nEl indicador forma parte del conjunto de indicadores de los Objetivos de Desarrollo \nSostenible (ODS) de la UE. La normativa europea del clima establece un marco para la acción \nclimática y aumenta la ambición de la UE para 2030, con un nuevo objetivo de reducir \nlas emisiones netas de gases de efecto invernadero (GEI) en al menos un 55 % para \nese año (en comparación con 1990) y lograr la neutralidad climática para 2050.\n\nEl año 1990 se utiliza como referencia en la mayoría de los acuerdos climáticos \ninternacionales (Kioto, Naciones Unidas). El año 2005 es el año base utilizado por la \nDirectiva del reparto del esfuerzo de mitigación de los gases de efecto \ninvernadero (ETS, sistema de comercio de emisiones de la UE), \ndebido a que existe mayor disponibilidad de datos detallados sectoriales a partir\nde esa fecha.\n\nFuente: División de Estadísticas de las Naciones Unidas, Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_13_10/default/table?lang=en&category=sdg.sdg_13\"> Emisiones netas de gases de efecto invernadero (sdg_13_10)</a> Eurostat", "comparabilidad"=>"La serie \"emisiones totales de gases de efecto invernadero por año\" cumple con los metadatos de  Naciones Unidas. Se presentan, además, otras series relacionadas con los objetivos europeos.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-02-02.pdf\">Metadatos 13-2-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Emisiones totales de gases de efecto invernadero (GEI), por unidad de PIB real, per cápita, y tasas de variación respecto a 1990 y 2005", "objetivo_global"=>"13- Adoptar medidas urgentes para combatir el cambio climático y sus efectos", "meta_global"=>"13.2- Incorporar medidas relativas al cambio climático en las políticas, estrategias y planes nacionales", "definicion"=>"Total greenhouse gas (GHG) emissions, total GHG emissions per unit of real GDP, per capita,  and rates of change relative to emissions in the years 1990 and 2005 ", "formula"=>"\n<b>Total greenhouse gas emissions per unit of GDP</b>\n\n$$PPIBGEI^{t} = \\frac{GEI^{t}}{PIB_{2022}^{t}} $$\n\n<b>Total greenhouse gas emissions per capita</b>\n\n$$GEIPC^{t} = \\frac{GEI^{t}}{P^{t}} $$\n\n<b>Greenhouse gas emissions compared to 1990</b>\n\n$$TVGEI_{1990}^{t} = \\frac{GEI^{t}-GEI^{1990}}{GEI^{1990}} \\cdot 100$$ \n\n<b>Greenhouse gas emissions compared to 2005</b>\n\n$$TVGEI_{2005}^{t} = \\frac{GEI^{t}-GEI^{2005}}{GEI^{2005}} \\cdot 100$$ \n\n\nwhere:\n\n$GEI^{t} =$ greenhouse gas emissions in year $t$\n\n$PIB_{2022}^{t} =$ gross domestic product in chained volume with reference to 2022 en el año $t$\n\n$P^{t} =$ population as of July 1 of year $t$\n\n$GEI^{1990} =$ greenhouse gas emissions in 1990\n\n$GEI^{2005} =$ greenhouse gas emissions in 2005\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The ultimate objective of the Climate Change Convention (UNFCCC) is to achieve the stabilization of \ngreenhouse gas concentrations in the atmosphere at a level that would prevent dangerous \nanthropogenic interference with the climate system. Estimating the levels of greenhouse gas (GHG) \nemissions and removals is an important element of the efforts to achieve this objective. \n\nThe Paris Agreement adopted in 2015 marks the latest step in the evolution of the UN climate change \nregime and builds on the work undertaken under the Convention. Its central aim is to strengthen the \nglobal response to the threat of climate change by keeping a global temperature rise this century well \nbelow 2 degrees Celsius above pre-industrial levels and to pursue efforts to limit the temperature \nincrease even further to 1.5 degrees Celsius. The Agreement also aims to strengthen the ability of \ncountries to deal with the impacts of climate change. \n\nThe indicator is part of the EU Sustainable Development Goals (SDG) indicator set. The European Climate \nLaw sets out a framework for climate action and increases the EU’s ambition for 2030, with a new goal \nto reduce net greenhouse gas (GHG) emissions by at least 55 % by that year (compared to 1990) and to \nachieve climate-neutrality by 2050. \n\nThe year 1990 is used as the reference year in most international climate agreements (Kyoto, United \nNations). The year 2005 is the base year used by the Greenhouse Gas Mitigation Effort Sharing Directive \n(ETS, EU Emissions Trading System) due to the greater availability of detailed sectoral data from that \ndate onwards. \n\nSource: United Nations Statistics Division, Eurostat \n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_13_10/default/table?lang=en&category=sdg.sdg_13\"> Net greenhouse gas emissions (sdg_13_10)</a> Eurostat", "comparabilidad"=>"The \"total annual greenhouse gas emissions\" series complies with United Nations metadata. Other series related to European targets are also presented.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-02-02.pdf\">Metadata 13-2-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Emisiones totales de gases de efecto invernadero (GEI), por unidad de PIB real, per cápita, y tasas de variación respecto a 1990 y 2005", "objetivo_global"=>"13- Adoptar medidas urgentes para combatir el cambio climático y sus efectos", "meta_global"=>"13.2- Incorporar medidas relativas al cambio climático en las políticas, estrategias y planes nacionales", "definicion"=>"Emisiones totales de gases de efecto invernadero (GEI), emisiones totales de GEI por unidad de PIB real,  per cápita, y tasas de variación respecto a las emisiones en los años 1990 y 2005", "formula"=>"\n<b>Berotegi-efektua eragiten duten gasen guztizko isurketak, BPG-unitateko</b>\n\n$$PPIBGEI^{t} = \\frac{GEI^{t}}{PIB_{2022}^{t}} $$\n\n<b>Berotegi-efektua eragiten duten gasen guztizko isurketak per capita</b>\n\n$$GEIPC^{t} = \\frac{GEI^{t}}{P^{t}} $$\n\n<b>Berotegi-efektua eragiten duten gasen isurketak, 1990. urtearekiko</b>\n\n$$TVGEI_{1990}^{t} = \\frac{GEI^{t}-GEI^{1990}}{GEI^{1990}} \\cdot 100$$ \n\n<b>Berotegi-efektua eragiten duten gasen isurketak, 2005. urtearekiko</b>\n\n$$TVGEI_{2005}^{t} = \\frac{GEI^{t}-GEI^{2005}}{GEI^{2005}} \\cdot 100$$ \n\n\nnon:\n\n$GEI^{t} =$ berotegi-efektua eragiten duten gasen isurketak $t$ urtean\n\n$PIB_{2022}^{t} =$ barne-produktu gordina, kateatutako bolumenean, 2022 erreferentziarekin $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n\n$GEI^{1990} =$ berotegi-efektua eragiten duten gasen isurketak 1990ean\n\n$GEI^{2005} =$ berotegi-efektua eragiten duten gasen isurketak 2005ean\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El objetivo último de la Convención Marco sobre el Cambio Climático (CMNUCC) es lograr \nla estabilización de las concentraciones de gases de efecto invernadero en la atmósfera a \nun nivel que impida interferencias antropogénicas peligrosas en el sistema climático. \nLa estimación de los niveles de emisiones y absorciones de gases de efecto invernadero (GEI) \nes un elemento importante de los esfuerzos para lograr este objetivo.\n\nEl Acuerdo de París adoptado en 2015 marca el último paso en la evolución del régimen de \ncambio climático de la ONU y se basa en el trabajo realizado en el marco de la Convención. \nSu objetivo principal es fortalecer la respuesta mundial a la amenaza del cambio \nclimático manteniendo el aumento de la temperatura global en este siglo muy por debajo de los \n2 grados Celsius con respecto a los niveles preindustriales y proseguir los esfuerzos para \nlimitar el aumento de la temperatura aún más a 1,5 grados Celsius. El Acuerdo también tiene \ncomo objetivo fortalecer la capacidad de los países para hacer frente a los impactos del \ncambio climático.\n\nEl indicador forma parte del conjunto de indicadores de los Objetivos de Desarrollo \nSostenible (ODS) de la UE. La normativa europea del clima establece un marco para la acción \nclimática y aumenta la ambición de la UE para 2030, con un nuevo objetivo de reducir \nlas emisiones netas de gases de efecto invernadero (GEI) en al menos un 55 % para \nese año (en comparación con 1990) y lograr la neutralidad climática para 2050.\n\nEl año 1990 se utiliza como referencia en la mayoría de los acuerdos climáticos \ninternacionales (Kioto, Naciones Unidas). El año 2005 es el año base utilizado por la \nDirectiva del reparto del esfuerzo de mitigación de los gases de efecto \ninvernadero (ETS, sistema de comercio de emisiones de la UE), \ndebido a que existe mayor disponibilidad de datos detallados sectoriales a partir\nde esa fecha.\n\nFuente: División de Estadísticas de las Naciones Unidas, Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_13_10/default/table?lang=en&category=sdg.sdg_13\"> Berotegi-efektua eragiten duten gasen isurketa garbiak (sdg_13_10)</a> Eurostat", "comparabilidad"=>"\"Berotegi-efektua eragiten duten gasen urteko guztizko isurketak\" serieak Nazio Batuen metadatuak betetzen ditu.  Horrez gain, Europako helburuekin zerikusia duten beste serie batzuk aurkezten dira. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-02-02.pdf\">Metadatuak 13-2-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 13: Take urgent action to combat climate change and its impacts</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 13.2: Integrate climate change measures into national policies, strategies and planning</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 13.2.2: Total greenhouse gas emissions per year</p>", "META_LAST_UPDATE__GLOBAL"=>"2021-03-01", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UN Climate Change (UNFCCC Secretariat)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UN Climate Change (UNFCCC Secretariat)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition, rationale and concepts:</strong></p>\n<p>The ultimate objective of the Climate Change Convention (UNFCCC) is to achieve the stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system. Estimating the levels of greenhouse gas (GHG) emissions and removals is an important element of the efforts to achieve this objective.</p>\n<p>In accordance with Articles 4 and 12 of the Climate Change Convention and the relevant decisions of the Conference of the Parties, countries that are Parties to the Convention submit national GHG inventories to the Climate Change secretariat. These submissions are made in accordance with the reporting requirements adopted under the Convention, such as the revised &#x201C;Guidelines for the preparation of national communications by Parties included in Annex I to the Convention, Part I: UNFCCC reporting guidelines on annual greenhouse gas inventories&#x201D; (decision 24/CP.19) for Annex I Parties and &#x201C;Guidelines for the preparation of national communications for non-Annex I Parties&#x201D; (decision 17/CP.8). The inventory data are provided in the annual GHG inventory submissions by Annex I Parties and in the national communications and biennial update reports by non-Annex I Parties.</p>\n<p>The Paris Agreement adopted in 2015 marks the latest step in the evolution of the UN climate change regime and builds on the work undertaken under the Convention. Its central aim is to strengthen the global response to the threat of climate change by keeping a global temperature rise this century well below 2 degrees Celsius above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5 degrees Celsius. The Agreement also aims to strengthen the ability of countries to deal with the impacts of climate change.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Mt CO<sub>2</sub>-equivalent</p>", "SOURCE_TYPE__GLOBAL"=>"<p>&#x2022; Annual GHG inventory submissions from Annex I Parties</p>\n<p>&#x2022; National communications (NC) and/or Biennial update reports (BUR) from non-Annex I Parties</p>", "COLL_METHOD__GLOBAL"=>"<p>&#x2022; Annex I GHG inventories are submitted through the CRF Reporter application. Information are automatically imported in the UNFCCC Data Warehouse.</p>\n<p>&#x2022; Information for non-Annex I Parties are manually extracted from their NC and/or BUR and stored in the UNFCCC Data Warehouse using Excel import sheets.</p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>See above</p>", "REL_CAL_POLICY__GLOBAL"=>"<p> The UNFCCC reporting guidelines on annual inventories for Annex I Parties require each Annex I Party to provide its annual GHG inventory by 15 April each year.</p>\n<p>The national communications (NCs) of non-Annex I Parties are usually submitted every four years; the biennial update reports (BURs) every two years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Parties to the UNFCCC</p>", "COMPILING_ORG__GLOBAL"=>"<p>UN Climate Change (UNFCCC secretariat)</p>", "INST_MANDATE__GLOBAL"=>"<ul>\n  <li>Reporting requirements for Annex I Parties &#x2013; <a href=\"https://unfccc.int/process-andmeetings/transparency-and-reporting/reporting-and-review-under-the-convention/greenhousegas-inventories-annex-i-parties/reporting-requirements\">https://unfccc.int/process-andmeetings/transparency-and-reporting/reporting-and-review-under-the-convention/greenhousegas-inventories-annex-i-parties/reporting-requirements</a></li>\n  <li>Reporting requirements for non-Annex I Parties &#x2013; <a href=\"https://unfccc.int/process-andmeetings/transparency-and-reporting/support-for-developing-countries/guidelines-andmanuals-for-the-preparation-of-non-annex-i-national-reports-and-international#eq-5\">https://unfccc.int/process-andmeetings/transparency-and-reporting/support-for-developing-countries/guidelines-andmanuals-for-the-preparation-of-non-annex-i-national-reports-and-international#eq-5</a></li>\n</ul>", "RATIONALE__GLOBAL"=>"<p>See 2a.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data is limited to Parties that submit their GHG inventories. As the reporting requirements for non-Annex I Parties are not as rigid as those for Annex I Parties, information for these Parties are available usually only for selected years.</p>\n<p>The annual timing of submission of updated inventory reports is very close to publication date of annual SDG progress reports.</p>", "DATA_COMP__GLOBAL"=>"<p>Total GHG emissions are calculated as the sum of emissions of direct GHGs: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), perfluorocarbons (PFCs), hydrofluorocarbons (HFCs), sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3), measured in units of CO2-equivalent, by using a common weighting factor, the so-called Global Warming Potentials (GWP). In accordance with the latest reporting guidelines for Annex I Parties under the UNFCCC, the GWP values to be used are those for the 100-year time horizon listed in Table 2.14 of the IPCC Fourth Assessment Report (https://www.ipcc.ch/report/ar4/wg1/). However, non-Annex I Parties should use the GWP provided in the IPCC Second Assessment Report (https://www.ipcc.ch/report/ipcc-second-assessment-full-report/) based on the effects of GHGs over a 100-year time.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>Availability of data depends only on what is received from Parties.</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>n/a</p>", "REG_AGG__GLOBAL"=>"<p>n/a</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>Reporting requirements for Annex I Parties &#x2013; <a href=\"https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-convention/greenhouse-gas-inventories-annex-i-parties/reporting-requirements\">https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-convention/greenhouse-gas-inventories-annex-i-parties/reporting-requirements</a></li>\n  <li>Reporting requirements for non-Annex I Parties &#x2013; <a href=\"https://unfccc.int/process-and-meetings/transparency-and-reporting/support-for-developing-countries/guidelines-and-manuals-for-the-preparation-of-non-annex-i-national-reports-and-international#eq-5\">https://unfccc.int/process-and-meetings/transparency-and-reporting/support-for-developing-countries/guidelines-and-manuals-for-the-preparation-of-non-annex-i-national-reports-and-international#eq-5</a></li>\n  <li>2006 IPCC Guidelines for National Greenhouse Gas Inventories &#x2013; https://www.ipcc-nggip.iges.or.jp/public/2006gl/</li>\n  <li>Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories &#x2013; https://www.ipcc-nggip.iges.or.jp/public/gl/invs1.html</li>\n</ul>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Based on national inventory reports submitted to the UNFCCC secretariat, total greenhouse gas (GHG) emissions in Gt CO2 eq of developed countries (43 Annex I Parties under UNFCCC) from 1990 onwards and developing countries (153 non-Annex I Parties under UNFCCC) from 2000 onwards. Annex I Parties submit their GHG inventories annually (submission deadline is 15 April), whereas non-Annex I Parties submit their national communications/biennial update reports only periodically.</p>\n<p><strong>Time series:</strong></p>\n<p>Data for Annex I Parties are available from the base year (usually 1990) up to two years before the inventory is due. Data available for non-Annex I Parties are usually only for selected years.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data is disaggregated by Annex I and Non-Annex I Parties to the UNFCCC</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>n/a</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li><a href=\"http://unfccc.int/resource/docs/2013/cop19/eng/10a03.pdf#page=2\" target=\"_blank\">UNFCCC reporting guidelines on annual inventories for Parties included in Annex I to the Convention</a></li>\n  <li><a href=\"https://unfccc.int/sites/default/files/resource/docs/cop8/07a02.pdf\">Guidelines for the preparation of national communications from Parties not included in Annex I to the Convention</a></li>\n  <li><a href=\"https://unfccc.int/process-and-meetings/transparency-and-reporting/greenhouse-gas-data/ghg-data-unfccc/ghg-data-from-unfccc\">Greenhouse gas data interface</a></li>\n</ul>", "indicator_sort_order"=>"13-02-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"13.3.1", "slug"=>"13-3-1", "name"=>"Grado en que i) la educación para la ciudadanía mundial y ii) la educación para el desarrollo sostenible se incorporan en a) las políticas nacionales de educación, b) los planes de estudio, c) la formación del profesorado y d) la evaluación de los estudiantes", "url"=>"/site/es/13-3-1/", "sort"=>"130301", "goal_number"=>"13", "target_number"=>"13.3", "global"=>{"name"=>"Grado en que i) la educación para la ciudadanía mundial y ii) la educación para el desarrollo sostenible se incorporan en a) las políticas nacionales de educación, b) los planes de estudio, c) la formación del profesorado y d) la evaluación de los estudiantes"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Grado en que i) la educación para la ciudadanía mundial y ii) la educación para el desarrollo sostenible se incorporan en a) las políticas nacionales de educación, b) los planes de estudio, c) la formación del profesorado y d) la evaluación de los estudiantes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Grado en que i) la educación para la ciudadanía mundial y ii) la educación para el desarrollo sostenible se incorporan en a) las políticas nacionales de educación, b) los planes de estudio, c) la formación del profesorado y d) la evaluación de los estudiantes", "indicator_number"=>"13.3.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Para alcanzar las metas 4.7, 12.8 y 13.3 de los ODS, es necesario que los gobiernos garanticen que la \nEducación para el Desarrollo Sostenible (EDS) y la Educación para la Ciudadanía Global (ECG) y \nsus subtemas estén plenamente integrados en todos los aspectos de sus sistemas educativos. \n\nLos estudiantes no alcanzarán los resultados de aprendizaje deseados si la EDS y la ECG no se han identificado \ncomo prioridades en las políticas o leyes educativas, si los currículos no incluyen específicamente \nlos temas y subtemas de la EDS y la ECM, y si el profesorado no está capacitado para impartir estos \ntemas en todo el currículo.\n\nEste indicador busca ofrecer una evaluación sencilla de si existe la infraestructura básica que \npermita a los países impartir EDS y ECM de calidad a sus alumnos, para garantizar que sus \npoblaciones cuenten con información adecuada sobre desarrollo sostenible y estilos de vida \nen armonía con la naturaleza. Unas políticas educativas, currículos, formación docente y \nevaluación del alumnado adecuados son aspectos clave del compromiso y el esfuerzo nacionales \npara implementar la ECM y la EDS de forma eficaz y proporcionar un entorno de aprendizaje propicio.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-03-01.pdf\">Metadatos 13-3-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"In order to achieve SDG targets 4.7, 12.8 and 13.3, it is necessary for \ngovernments to ensure that ESD and GCED and their sub-themes are fully \nintegrated in all aspects of their education systems. \n\nStudents will not achieve the desired learning outcomes if Education for Sustainable Development \n(ESD) and Global Citizenship Education (GCED) have not been identified as \npriorities in education policies or laws, if curricula do not specifically \ninclude the themes and sub-themes of ESD and GCED, and if teachers are not \ntrained to teach these topics across the curriculum. \n\nThis indicator aims to give a simple assessment of whether the basic \ninfrastructure exists that would allow countries to deliver quality ESD and \nGCED to learners, to ensure their populations have adequate information on \nsustainable development and lifestyles in harmony with nature. Appropriate \neducation policies, curricula, teacher education, and student assessment are \nkey aspects of national commitment and effort to implement GCED and ESD effectively \nand to provide a conducive learning environment. \n\n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-03-01.pdf\">Metadata 13-3-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Para alcanzar las metas 4.7, 12.8 y 13.3 de los ODS, es necesario que los gobiernos garanticen que la \nEducación para el Desarrollo Sostenible (EDS) y la Educación para la Ciudadanía Global (ECG) y \nsus subtemas estén plenamente integrados en todos los aspectos de sus sistemas educativos. \n\nLos estudiantes no alcanzarán los resultados de aprendizaje deseados si la EDS y la ECG no se han identificado \ncomo prioridades en las políticas o leyes educativas, si los currículos no incluyen específicamente \nlos temas y subtemas de la EDS y la ECM, y si el profesorado no está capacitado para impartir estos \ntemas en todo el currículo.\n\nEste indicador busca ofrecer una evaluación sencilla de si existe la infraestructura básica que \npermita a los países impartir EDS y ECM de calidad a sus alumnos, para garantizar que sus \npoblaciones cuenten con información adecuada sobre desarrollo sostenible y estilos de vida \nen armonía con la naturaleza. Unas políticas educativas, currículos, formación docente y \nevaluación del alumnado adecuados son aspectos clave del compromiso y el esfuerzo nacionales \npara implementar la ECM y la EDS de forma eficaz y proporcionar un entorno de aprendizaje propicio.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-03-01.pdf\">Metadatos 13-3-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture&#x2019;s contribution to sustainable development</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 4.7.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessment</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SE_GCEDESD_CUR - Extent to which global citizenship education and education for sustainable development are mainstreamed in curricula [4.7.1,12.8.1,13.3.1]</p>\n<p>SE_GCEDESD_NEP - Extent to which global citizenship education and education for sustainable development are mainstreamed in national education policies [4.7.1,12.8.1,13.3.1]</p>\n<p>SE_GCEDESD_SAS - Extent to which global citizenship education and education for sustainable development are mainstreamed in student assessment [4.7.1,12.8.1,13.3.1]</p>\n<p>SE_GCEDESD_TED - Extent to which global citizenship education and education for sustainable development are mainstreamed in teacher education [4.7.1,12.8.1,13.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>12.8.1 and 13.3.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNESCO Education Sector, Division for Peace and Sustainable Development, Section of Education for Sustainable Development (UNESCO-ED/PSD/ESD)</p>\n<p>UNESCO Institute for Statistics (UNESCO-UIS)</p>\n<p>Global Education Monitoring Report</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNESCO Education Sector, Division for Peace and Sustainable Development, Section of Education for Sustainable Development (UNESCO-ED/PSD/ESD), UNESCO Institute for Statistics (UNESCO-UIS), and Global Education Monitoring Report.</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Indicator 4.7.1/12.8.1/13.3.1 measures the extent to which countries mainstream Global Citizenship Education (GCED) and Education for Sustainable Development (ESD) in their education systems. This is an indicator of characteristics of different aspects of education systems: education policies, curricula, teacher education and student assessment as reported by government officials, ideally following consultation with other government ministries, national human rights institutes, the education sector and civil society organizations. It measures government intentions and not necessarily what is implemented in practice in schools and classrooms.</p>\n<p>For each of the four components of the indicator (policies, curricula, teacher education, and student assessment), a number of criteria are measured, which are then combined to give a single score between zero and one for each component. (See methodology section for full details).</p>\n<p>The indicator and its methodology have been reviewed and endorsed by UNESCO&#x2019;s <a href=\"https://tcg.uis.unesco.org/\">Education Data and Statistics Commission (EDSC)</a> (former TCG), which is responsible for the development and maintenance of the thematic indicator framework for the follow-up and review of SDG 4. The EDSC also has an interest in education-related indicators in other SDGs, including global indicators 12.8.1 and 13.3.1. The EDSC is composed of 28 regionally representative experts from UNESCO Member States (nominated by the respective geographic groups of UNESCO) as per the revised <a href=\"https://ces.uis.unesco.org/wp-content/uploads/sites/23/2024/01/EDS-2.1.-TCG-TOR-_Final-WEB.pdf\">Terms of Reference </a> (November 2023), as well as international and regional partners and civil society. The <a href=\"http://uis.unesco.org/\">UNESCO Institute for Statistics</a> acts as the Secretariat.</p>\n<p><strong>Concepts:</strong></p>\n<p>Global Citizenship Education (GCED) and Education for Sustainable Development (ESD) nurture respect for all, build a sense of belonging to a common humanity, foster responsibility for a shared planet, and help learners become responsible and active global citizens and proactive contributors to a more peaceful, tolerant, inclusive, secure and sustainable world. They aim to empower learners of all ages to address and resolve local and global challenges and to take informed decisions and actions for environmental integrity, economic viability and a just society for present and future generations, while respecting cultural diversity.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>For Survey Index (between 0.000 and 1.000).</p>\n<p>For Greening Curriculum Index (between 0 and 100).</p>\n<p>For reporting on harmonised scale, 0-100 range values will be used.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>For the time period 2017-2020, responses to the quadrennial reporting by UNESCO Member States on the implementation of the 1974 <a href=\"http://portal.unesco.org/en/ev.php-URL_ID=13088&amp;URL_DO=DO_TOPIC&amp;URL_SECTION=201.html\"><em>Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms</em></a>. The last round of reporting took place in 2020-21. The results were published in the Global SDG Indicator Database in July and September 2021. In November 2023, the 1974 Recommendation was superseded by the 2023 Recommendation on Education for Peace, Human Rights and Sustainable Development. The first reporting on the new Recommendation will take place in 2026-2027 covering the period 2024-2026. It will be one data source for the global indicator. In 2024 a short, one-off survey is being considered by UNESCO to collect data for the global indicator covering the time period 2021-2023. (See methodology section for details of questions asked).</p>\n<p><strong>Greening</strong></p>\n<p>To measure the extent to which green content is integrated in the official intended curriculum of primary and (lower) secondary education, two types of documents were analysed to create a country&#x2019;s Greening Curriculum Indicator (GCI) score: 1) <strong>national curriculum frameworks</strong> and 2) <strong>subject curricula documents</strong> from science and social science subjects taught in grades 3, 6, and 9. The terms curriculum or syllabus here should be distinguished from related terms such as textbook, lesson plan, and teaching guidelines. A database of over 1,700 curriculum documents has been compiled for the 2025 data release.</p>\n<h3><em>National curriculum frameworks (NCFs)</em></h3>\n<p>NCFs<strong> </strong>are defined as national-level policy documents that overview a country&#x2019;s educational goals and priorities and set forth key parameters of the country&#x2019;s official intended curriculum. NCFs are written and approved by the relevant ministry of education or another officially designated body. A comprehensive NCF: 1) delineates the aims of the curriculum at various stages of schooling; 2) explains the educational philosophy underlying the curriculum and approaches to teaching, learning, and assessment that align with that philosophy; 3) describes curricular structures; 4) assigns names to subject/learning areas; 5) allocates time to each subject (or group of subjects) in each grade level (or set of grades); 6) provides guidelines to curriculum developers, teacher trainers, and textbook writers; 7) prescribes curricular standards and mechanisms for inspection and monitoring; and 8) refers to learning assessments to be conducted (UNESCO-IBE, 2017a; UNESCO-IBE, 2017b). </p>\n<p>To be considered an NCF for the purposes of the GCI, the document has to:</p>\n<ul>\n  <li>Be written by the ministry of education or other official designated body.</li>\n  <li>Cover primary, lower secondary, or upper secondary levels of formal education (categories 1, 2, and 3 according to the International Standard Classification of Education or ISCED).<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup></li>\n  <li>Have a title or opening matter that describes the document as a National Curriculum Framework.</li>\n  <li>Include content that aligns with the sections outlined in the document definitions above.</li>\n</ul>\n<p>In cases where an NCF matching the above criteria was not identified, other documents containing similar content to an NCF were considered for inclusion. For example:</p>\n<ul>\n  <li>The introductory or front matter of document(s) specifying the content of subject curricula similarly to an NCF.</li>\n  <li>Laws or regulations passed by legislative or executive bodies that specify curricular structures and contents of a national education system along the lines of an NCF.</li>\n  <li>Official websites of national governments or subnational political units that present in a similar manner to an NCF.</li>\n</ul>\n<h3><em>Subject curricula</em></h3>\n<p>Subject curricula or subject syllabi are defined as subject- and grade-specific documents that include most or all of the following information: 1) a general rationale for the teaching of the subject; 2) the intended aims and learning outcomes; 3) clearly defined content areas (topics and themes) to be included in the teaching of each subject; and 4) ideally, a weekly, monthly, or yearly timetable allocating instructional time to each topic/subject, pedagogical considerations, and possibly assessment guidelines. The name given to such documents varies by language &#x2013; for example, &#x201C;programme&#x201D; (French), &#x201C;Lehrplan&#x201D; (German), &#x201C;programma&#x201D; (Italian), &#x201C;plan de estudios&#x201D; (Spanish) and &#x201C;almanhaj&#x201D; (Arabic) &#x2013; and may have slightly different connotations. There are no international guidelines for subject curricula, partly because they reflect national traditions in the development and implementation of the official curriculum, the extent of teacher and school autonomy, and patterns of pre-service and in-service teacher training.</p>\n<p>Subject curricula were included for subjects in two broad knowledge domains: science and social science. Curricula for up to four subjects in each knowledge domain (thus up to eight subjects in total) were included at each grade level (3, 6, and 9) in each country or sub-national jurisdiction. Table 1 lists the typical subjects found in each knowledge domain internationally. Many countries organize instruction in a single general science and/or social science subject (more common in grades 3 and 6), rather than numerous specialized subjects (more common in grade 9). Some countries teach interdisciplinary subjects such as environmental education (EE) or education for sustainable development (ESD) or special hybrid subjects that combine science and social science content. Such interdisciplinary or hybrid subjects were included among the up to eight subjects per grade level. </p>\n<p>Table 1: List of typical science, social science and EE/ESD subjects included in GCI calculations</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Science Subjects</strong></p>\n      </td>\n      <td>\n        <p><strong>Social Science Subjects</strong></p>\n      </td>\n      <td>\n        <p><strong> EE/ESD subjects</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ul>\n          <li>General Science</li>\n          <li>Applied Science / Technology</li>\n          <li>Earth Science</li>\n          <li>Life Science</li>\n          <li>Physical Science</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>General Social Science</li>\n          <li>Geography</li>\n          <li>History</li>\n          <li>Civics/Citizenship</li>\n          <li>Economics</li>\n          <li>Religious, Moral, and Philosophy</li>\n          <li>Cultural and Art Studies</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>Environmental Education</li>\n          <li>Environmental Education / Education for Sustainable Development</li>\n          <li>Environmental and Outdoor Education </li>\n          <li>Sustainability</li>\n        </ul>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><em>Note</em>. The number of science subjects never exceeded four subjects in any country, so all science subjects were collected for the countries included in the sample. Any curricula related to EE or ESD were also collected. In total, 17 countries had EE/ESD specific curricula.</p>\n<p><em>Green keywords</em></p>\n<p>A set of 13 green keywords were defined in relation to four themes: environment, sustainability, climate change, and biodiversity (see Table 2).</p>\n<p>Table 2: List of green keywords used in the analysis</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Themes </strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>Keywords</strong></p>\n      </td>\n      <td>\n        <p><strong>Total number of keywords</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Environment and </p>\n        <p>sustainability</p>\n      </td>\n      <td>\n        <ul>\n          <li>environmental*</li>\n          <li>sustainability</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>greening</li>\n          <li>&#x201C;sustainable development&#x201D;</li>\n        </ul>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Climate change</p>\n      </td>\n      <td>\n        <ul>\n          <li>&#x201C;climate change&#x201D;</li>\n          <li>&#x201C;global warming&#x201D;</li>\n          <li>&#x201C;greenhouse gas*&quot;</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>&quot;climate justice&quot;</li>\n          <li>&#x201C;renewable energy&#x201D;</li>\n        </ul>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Biodiversity</p>\n      </td>\n      <td>\n        <ul>\n          <li>biodiversity</li>\n          <li>ecosystem*</li>\n        </ul>\n      </td>\n      <td>\n        <ul>\n          <li>extinction*</li>\n          <li>invasive species</li>\n        </ul>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Total</p>\n      </td>\n      <td></td>\n      <td></td>\n      <td>\n        <p>13</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>All keywords were translated into 40 languages and then validated by language proficient experts. The keyword searches are carried out using a bespoke Python application.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> See <a href=\"https://uis.unesco.org/en/topic/international-standard-classification-education-isced\">https://uis.unesco.org/en/topic/international-standard-classification-education-isced</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Responses are submitted by national governments, typically by officials in Ministries of Education. Respondents are asked to consult widely across other government ministries, with national human rights institutes, the education sector and civil society organizations in compiling their responses. Respondents are also asked to submit supporting evidence in the form of documents or links (e.g. to education policies or laws, curricula, etc.), which will be made publicly available in UNESCO&#x2019;s <a href=\"https://en.unesco.org/themes/right-to-education/database\">Observatory on the Right to Education</a>. </p>\n<p><strong>Greening</strong></p>\n<p><em>National curriculum frameworks</em></p>\n<p>NCF documents are identified by searching ministry of education websites, as well as databases such as UNESCO IIEP Planipolis, UNESCO International Institute for Educational Planning (IIEP), Siteal, UNESCO Regional Comparative and Explanatory Study (ERCE), Eurydice, Organization for Economic Cooperation and Development (OECD) Policy Outlook, and the Educational Media Research (Edumeres), as well as consulting country experts. </p>\n<p><em>Subject curricula</em></p>\n<p>Subject curricula are included for subjects in two broad knowledge domains: science and social science. Curricula for up to four subjects in each knowledge domain (thus up to eight subjects in total) are included at each grade level (3, 6, and 9) in each country or sub-national jurisdiction. Table 1 above lists the typical subjects found in each knowledge domain internationally. Many countries organize instruction in a single general science and/or social science subject (more common in grades 3 and 6), rather than numerous specialized subjects (more common in grade 9). Some countries teach interdisciplinary subjects on environmental education (EE) or education for sustainable development (ESD) or special hybrid subjects that combine science and social science content. Such interdisciplinary or hybrid subjects are included among the up to eight subjects per grade level. </p>\n<p>Subject curricula documents are identified through a range of sources, including through manually reviewing ministry of education websites and searching archives of recent curriculum studies. National Commissions for UNESCO also provided subject curricula following a request by the UNESCO International Bureau of Education and UNESCO headquarters. In cases where these methods do not yield the relevant subject curricula, additional documents are collected through consultation with country education experts. </p>", "FREQ_COLL__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>2020-21 round (covering 2017-2020) completed in April 2020. Next round foreseen in 2024 (covering 2021-2023). Data for the period 2024-2026 are expected to be collected in 2026-2027, as the first reporting on the 2023 Recommendation on Education for Peace, Human Rights and Sustainable Development.</p>\n<p><strong>Greening</strong></p>\n<p>Data collection is expected to be annual. The collection of 2025 data was carried out between 2023 and 2024.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Q2 and Q3 of 2021 (from 2020-21 reporting round). 2025 (for 2024 reporting round).</p>\n<p><strong>Greening</strong></p>\n<p>2025 (for 2023-24 data).</p>", "DATA_SOURCE__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Requests for reports are submitted to Ministers Responsible for Relations with UNESCO who are typically Education Ministers. Reports are usually completed by government officials in Ministries of Education. Countries are requested to consult widely before submitting their reports. To assist with this, requests for reports are also copied to NGOs in official partnership with UNESCO and the Office of the High Commissioner for Human Rights (OHCHR). </p>\n<p><strong>Greening</strong></p>\n<p>Data were provided by the UNESCO ESD Section and the Monitoring and Evaluating Climate Communication and Education (MECCE) Project.</p>", "COMPILING_ORG__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>UNESCO&#x2019;s Sections for Education for Sustainable Development and Global Citizenship and Peace Education.</p>\n<p><strong>Greening</strong></p>\n<p>Global Education Monitoring Report and the Monitoring and Evaluating Climate Communication and Education (MECCE) Project.</p>", "INST_MANDATE__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>In 1974, UNESCO Member States adopted the <em>Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms</em>, which encapsulates many of the aims of SDG targets 4.7, 12.8 and 13.3. Every four years, countries report on the implementation of the Recommendation. This well-established formal mechanism is the data source for indicator 4.7.1/12.8.1/13.3.1. The seventh quadrennial reporting round took place in 2020-2021.</p>\n<p><strong>Greening</strong></p>\n<p>During the UN Transforming Education Summit (November 16-19, 2022), which sought to mobilize solutions to accelerate national and international efforts to achieve ADG 4, participants agreed to seven global initiatives, one of which is &#x201C;Greening Education: to get every learner climate ready.&#x201D; UNESCO established the Greening Education Partnership in 2022, which prioritized the greening of schools, curricula, teacher training and system capacities and communities. In December 2022 the SDG 4 High-level Steering Committee met in Paris and decided to &#x201C;add indicators for&#x2026;greening education and requested that its Data and Monitoring Technical Committee&#x2026;develop a methodology for these indicators&#x2026;&#x201D; The Steering committee also mandated UIS and the GEM Report to develop benchmark indicators on greening education.</p>", "RATIONALE__GLOBAL"=>"<p>In order to achieve SDG targets 4.7, 12.8 and 13.3, it is necessary for governments to ensure that ESD and GCED and their sub-themes are fully integrated in all aspects of their education systems. Students will not achieve the desired learning outcomes if Education for Sustainable Development (ESD) and Global Citizenship Education (GCED) have not been identified as priorities in education policies or laws, if curricula do not specifically include the themes and sub-themes of ESD and GCED, and if teachers are not trained to teach these topics across the curriculum. </p>\n<p>This indicator aims to give a simple assessment of whether the basic infrastructure exists that would allow countries to deliver quality ESD and GCED to learners, to ensure their populations have adequate information on sustainable development and lifestyles in harmony with nature. Appropriate education policies, curricula, teacher education, and student assessment are key aspects of national commitment and effort to implement GCED and ESD effectively and to provide a conducive learning environment.</p>\n<p><strong>Survey</strong></p>\n<p>Each component of the indicator is assessed on a scale of zero to one. The closer to one the value, the better mainstreamed are ESD and GCED in that component. By presenting results separately for each component, governments will be able to identify in which areas more efforts may be needed.</p>\n<p><strong>Greening</strong></p>\n<p>Greening related to environment, sustainability, climate change and biodiversity (SDG indicator 13.3.1) is captured under the component &#x201C;Curricula&#x201D; of the indicator. The measurement of greening follows a specific computation method and is presented separately. The goal is to assess the extent to which green content (related to environment, sustainability, climate and biodiversity) is prioritized and integrated into national curriculum policy frameworks and science and social science subject curricula (syllabi) in grades 3, 6, and 9.</p>", "REC_USE_LIM__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>The indicator is based on self-reporting by government officials. However, countries are asked to provide supporting evidence in the form of documents or links (e.g. education policies or laws, curricula, etc.) to back up their responses. In addition, UNESCO compares responses with available information from alternative sources and, if appropriate, raises queries with national respondents. At the end of the reporting cycle, country responses and the supporting documents will be made publicly available.</p>\n<p><strong>Greening</strong></p>\n<p>The greening indicator analyses the content of official policy and curriculum documents for themes related to sustainability, environment, climate change and biodiversity to determine the extent to which relevant green content is prioritized. As it is based on counts of keywords, it does not capture how these keywords are used.</p>", "DATA_COMP__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Information collected with the questionnaire for monitoring the implementation by UNESCO Member States of the 1974 <em>Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms</em> and from 2026, the 2023 <em>Recommendation on Education for Peace, Human Rights and Sustainable Development </em>is used for the construction of the global indicator. For each of the four components of the indicator (policies, curricula, teacher education, and student assessment), a number of criteria are measured, which are then combined to give a single score between zero and one for each component. Only information for primary and secondary education are used for calculation of indicator 4.7.1/12.8.1/13.3.1.</p>\n<ol>\n  <li><u>Laws and policies</u></li>\n</ol>\n<p>The following questions are used to calculate the policies component of the indicator:</p>\n<p><em>A2: Please indicate which global citizenship education (GCED) and education for sustainable development) ESD themes are covered in national or sub-national <u>laws, legislation or legal frameworks</u> on education. </em></p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) and two levels of government (national and sub-national) = 16 responses.</p>\n<p>Response categories are no = 0, yes = 1, unknown, which is treated as zero, and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank the question score is not calculated. </p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = simple mean of the 0 and 1 scores, <u>excluding not applicables </u>(i.e., if eight of the 16 responses are &#x2018;not applicable&#x2019;, the sum of the 0 and 1 scores is divided by 8 to get the mean and not by 16).</p>\n<p><em>A4. Please indicate which GCED and ESD themes are covered in national or sub-national <u>education policies, frameworks or strategic objectives</u>.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) = 8 responses. </p>\n<p>Response categories are no = 0, yes = 1, and unknown (treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>A5. Please indicate whether national or sub-national <u>education policies, frameworks or strategic objectives</u> on education provide a mandate to integrate GCED and ESD. </em></p>\n<p>There are two levels of government (national, sub-national) and five areas of integration (curricula, learning objectives, textbooks, teacher education, and student assessment) = 10 responses. </p>\n<p>Response categories are no = 0, yes = 1, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding not applicables</u> are unknown or blank, the question score is not calculated. </p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = simple mean of the 0 and 1 scores, <u>excluding not applicables </u>(i.e., if five of the 10 responses are &#x2018;not applicable&#x2019;, the sum of the 0 and 1 scores is divided by 5 to get the mean and not by 10).</p>\n<p><em>E1a. Based on your responses to questions in the previous section (laws and policies) please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed</em><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup><em> in education laws and policies in your country.</em> </p>\n<p>There are two levels of government (national, sub-national) = 2 responses. </p>\n<p>Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding not applicables</u> are unknown or blank, the question score is not calculated. </p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = half the simple mean of the 0, 1 and 2 scores, <u>excluding not applicables </u>(i.e., if one of the two responses is &#x2018;not applicable&#x2019;, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1 as do the scores for the other three questions in this section.</p>\n<p>Policy component score = simple mean of the scores for questions A2, A4, A5 and E1a. Where a question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.</p>\n<ol>\n  <li><u>Curricula</u></li>\n</ol>\n<p>The following questions are used to calculate the curricula component of the indicator:</p>\n<p><em>B2: Please indicate which global citizenship education (GCED) and education for sustainable development (ESD) themes are taught as part of the curriculum.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) = 8 responses.</p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated. </p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>B3. Please indicate in which subjects or fields of study GCED and ESD are taught in primary and secondary education.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) and twelve subjects in which they may be taught (arts; civics, civil or citizenship education; ethics/moral studies; geography; health, physical education and sports; history; languages; mathematics; religious education; science; social studies and integrated studies) = 96 responses. </p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank the question score is not calculated.</p>\n<p>Note that responses to &#x2018;other subjects, please specify&#x2019; in the question are ignored. If appropriate, during quality assurance answers in this category may be recoded to one of the other 12 subjects.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>B4. Please indicate the approaches used to teach GCED and ESD in primary and secondary education.</em> </p>\n<p>There are four teaching approaches (GCED/ESD as separate subjects, cross-curricular, integrated, whole school) = 4 responses</p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>E1b. Based on your responses to questions in the previous section (curricula) please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed</em><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup><em> in curricula in your country.</em> </p>\n<p>There are two levels of government (national, sub-national) = 2 responses.</p>\n<p>Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding &#x2018;not applicables&#x2019;</u> are unknown or blank, the question score is not calculated.</p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = half the simple mean of the 0, 1 and 2 scores, <u>excluding &#x2018;not applicables&#x2019; </u>(i.e., if one of the two responses is &#x2018;not applicable&#x2019;, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1, as do the scores for the other three questions in this section.</p>\n<p>Curricula component score = simple mean of the scores for questions B2, B3, B4 and E1b. Where a question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.</p>\n<ol>\n  <li><u>Teacher education</u></li>\n</ol>\n<p>The following questions are used to calculate the teacher education component of the indicator:</p>\n<p><em>C2: Please indicate whether teachers, trainers and educators are trained to teach global citizenship education (GCED) and education for sustainable development (ESD) during initial or pre-service training and/or through continuing professional development.</em> </p>\n<p>There are two types of training (initial/pre-service and continuing professional development) and two types of teachers (of selected subjects in which ESD/GCED are typically taught, and of other subjects) = 4 responses. </p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>C3. Please indicate on which GCED and ESD themes pre-service or in-service training is available for teachers, trainers and educators.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) = 8 responses.</p>\n<p>Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>C4. Please indicate whether teachers, trainers and educators are trained to teach the following dimensions of learning in GCED and ESD.</em> </p>\n<p>There are four learning dimensions (knowledge, skills, values, and attitudes/behaviours) = 4 responses. </p>\n<p>Response categories are no = 0, yes = 1, and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>C5. Please indicate whether teachers, trainers and educators are trained to use the following approaches to teach GCED and ESD in primary and secondary education</em>. </p>\n<p>There are four teaching approaches (GCED/ESD as separate subjects, cross-curricular, integrated, whole school) = 4 responses. </p>\n<p>Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>E1c. Based on your responses to questions in the previous section (teacher education), please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed</em><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup><em> in teacher education in your country.</em> </p>\n<p>There are two levels of government (national, sub-national) = 2 responses. </p>\n<p>Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable (which is ignored). Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding &#x2018;not applicables&#x2019;</u> are unknown or blank, the question score is not calculated.</p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = half the simple mean of the 0, 1 and 2 scores, <u>excluding &#x2018;not applicables&#x2019; </u>(i.e., if one of the two responses is &#x2018;not applicable&#x2019;, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1, as do the scores for the other three questions in this section.</p>\n<p>Teacher education component score = simple mean of the scores for questions C2, C3, C4, C5 and E1c. Where component question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.</p>\n<ol>\n  <li><u>Student assessment</u></li>\n</ol>\n<p>The following questions are used to calculate the student assessment component of the indicator:</p>\n<p><em>D2: Please indicate whether the global citizenship education (GCED) and education for sustainable development (ESD) themes below are generally included in <u>student assessments or examinations</u>.</em> </p>\n<p>There are eight GCED/ESD themes (cultural diversity and tolerance, gender equality, human rights, peace and non-violence, climate change, environmental sustainability, human survival and well-being, and sustainable consumption and production) = 8 responses. </p>\n<p>Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated.</p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>D3. Please indicate which of the dimensions of learning in GCED and ESD below are generally included in <u>student assessments or examinations</u>.</em> </p>\n<p>There are four learning dimensions (knowledge, skills, values, and attitudes/behaviours) = 4 responses.</p>\n<p>Response categories are no = 0, yes = 1 and unknown, which is treated as zero. Blanks are also treated as zeros. </p>\n<p>If more than half of responses are unknown or blank, the question score is not calculated. </p>\n<p>Question score = simple mean of the 0 and 1 scores.</p>\n<p><em>E1d. Based on your responses to questions in the previous section (student assessment), please indicate to what extent global citizenship education (GCED) and education for sustainable development (ESD) are mainstreamed</em><sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup><em> in student assessment in your country.</em> </p>\n<p>There are two levels of government (national, sub-national) = 2 responses. </p>\n<p>Response categories are not at all = 0, partially = 1, extensively = 2, unknown (treated as zero), and not applicable, which is ignored. Blanks are also treated as zeros. </p>\n<p>If more than half of responses <u>excluding &#x2018;not applicables&#x2019;</u> are unknown or blank, the question score is not calculated. </p>\n<p>Note that &#x2018;not applicable&#x2019; is used where only one level of government is responsible for education.</p>\n<p>Question score = half the simple mean of the 0, 1 and 2 scores, <u>excluding &#x2018;not applicables&#x2019; </u>(i.e., if one of the two responses is &#x2018;not applicable&#x2019;, the sum of the 0, 1 and 2 scores is divided by 2 to get half the mean and not by 4). The score is half the mean in order to ensure it lies between 0 and 1, as do the scores for the other three questions in this section.</p>\n<p>Student assessment component score = simple mean of the scores for questions D2, D3 and E1d. Where component question score could not be calculated because too many responses were unknown or blank, the component score is not calculated and is reported as not available.</p>\n<p>The component scores all lie between zero and one and are presented as a dashboard of four scores. They are not combined to create a single overall score for the indicator. The higher the score, the more GCED and ESD are mainstreamed in the given component. In this way, users can make a simple assessment in which component area more efforts may be needed. </p>\n<p><strong>Greening</strong></p>\n<ol>\n  <li><u>Curricula</u></li>\n</ol>\n<h3><em>Document preparation</em></h3>\n<p>All collected documents are added to a single database in a standardized fashion. Documents are downloaded if found online and converted to PDF if in another format. In many cases, subject curricula are part of a larger document, in which case, relevant subject- and grade-specific material are extracted into separate documents. Documents in the database are named using the following protocol:</p>\n<p><em> &#x201C;country_state/province_documenttype_region_year_language_grade_knowledgedomain&#x201D;</em></p>\n<p>Information about each document is stored in a database (one row per document), including document title, year of publication, subject, author, source, and language. </p>\n<p>For documents in languages for which there are fewer than three documents in that language (Burmese, Norwegian, Swedish, and Urdu), the documents are machine translated into English using Google Translate.</p>\n<h2><em>Keyword selection and analysis</em></h2>\n<p>The GCI measures the inclusion of green content in four document types (NCF, grade 3 subject curricula, grade 6 subject curricula, and grade 9 subject curricula). It counts the presence of 13 keywords corresponding to three themes of Environment/Sustainability, Climate Change, and Biodiversity. The selected keywords: 1) best represent the theme, 2) can be translated into all relevant languages, and 3) are sufficiently prevalent in the analysed documents to provide data for measuring components of the GCI (see Table 2 above). Additional sources such as recent UNESCO studies of greening education and the Greening Education Partnership curriculum guidance were also used to identify relevant green keywords.</p>\n<p>Each keyword includes its plural and singular as well as the many forms the word may take depending on the language.<sup><sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></sup> Some languages and/or countries employ distinctive language/culture-specific keywords to capture a theme. Thus, each theme includes space for a culture- or language-specific keyword to be added, if appropriate.<sup><sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup></sup> The keywords and their translations into 40 languages are reviewed and validated by native speaking experts who are familiar with greening education concepts.</p>\n<p>A Python-based application is used to bulk process text files and identify keywords in documents in all the required languages. To be read by the Python application, all the text documents are converted to UTF-8 text format and stored in a local folder. The Python application also requires a two-column spreadsheet with columns for &quot;File Name&quot; and &quot;Language&quot; and a second spreadsheet with columns for &#x201C;Keyword&#x201D; and the keyword&#x2019;s &#x201C;Language.&#x201D; These files and the folder location are then loaded into the Python application. The application uses the language file to determine which column from the keyword file to utilize in searching for keywords for each text file. The application then counts relevant keywords in every document (NCF and subject curricula) in the specified language. After completing the keyword search processing, the application outputs a spreadsheet file that contains a row for each curriculum document and columns for every keyword.<sup><sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup></sup> This output file becomes the raw data used in the calculation of the GCI. </p>\n<h2><em>Calculation of the greening curriculum indicator</em></h2>\n<p>After the prevalence of each keyword in each document is determined, keyword counts are compiled into an output spreadsheet which is then used to calculate a country&#x2019;s GCI score. The following specific steps are taken to calculate a country&#x2019;s GCI score:</p>\n<h3><em>Phase 1) Development of standardized keyword counts</em></h3>\n<p>The analysis of the green content of each country&#x2019;s NCFs and subject curricula is done at the country level.</p>\n<ul>\n  <li>For the NCF and each grade level (3, 6, and 9), the frequency of keywords belonging to the themes of Environment/Sustainability, Climate Change, and Biodiversity is calculated by summing up the counts of the keywords.</li>\n  <li>To account for varying document lengths, the number of keywords is standardized for each theme by dividing the keywords counts in that country&#x2019;s theme with the total number of words in the country&#x2019;s documents. </li>\n  <li>This standardized number is then multiplied by 1 million to transform the result into a number that is more easily interpreted (i.e., not a very small decimal). The result is a keyword count per million words for each theme at each grade level and NCF for each country. The standardization calculation is as follows:<ul>\n      <li>1,000,000*(Keywords in that theme for a country) / (Total words in documents for that country) </li>\n    </ul>\n  </li>\n</ul>\n<h3><em>Phase 2) Transformation of standardized keyword counts into an ordinal scale</em></h3>\n<p>The distribution of these standardized numbers presents a statistical challenge since it is both zero bounded<sup><sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup></sup> and has a long tail.<sup><sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup></sup> </p>\n<ul>\n  <li>To create a more normal distribution, the standardized numbers are transformed into an ordinal scale ranging from 0 to 10 in the following way: <ul>\n      <li>If there are no keywords, the score is 0, otherwise it ranges from 1 to 10 using a &#xBD; life logarithmic transformation.<sup><sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup></sup> </li>\n      <li>For the Environment/Sustainability &apos;core&apos; theme, the maximum score of 10 is achieved with 10,000 standardized keywords. The following formulas are used:<ul>\n          <li>&gt;10,000 standardized keywords are assigned a score of 10,</li>\n          <li>&lt;=20 standardized keywords are assigned a score of 1,</li>\n          <li>0 standardized keywords are assigned a score of 0,</li>\n          <li>Otherwise, 10-log.5(#/10,000)</li>\n          <li>Result multiplied by 10</li>\n        </ul>\n      </li>\n      <li>For the Climate Change and Biodiversity themes, the maximum score of 10 is achieved with 5,000 standardized keywords, given that these keywords are used less commonly. The following formulas are used:<ul>\n          <li>&gt;5,000 standardized keywords are assigned a score of 10, </li>\n          <li>&lt;=10 standardized keywords are assigned a score of 1, </li>\n          <li>0 standardized keywords are assigned a score of 0, </li>\n          <li>Otherwise, 10-log.5(#/5,000)</li>\n          <li>Result multiplied by 10</li>\n        </ul>\n      </li>\n    </ul>\n  </li>\n</ul>\n<h3><em>Phase 3) Calculating GCIs for federated countries</em></h3>\n<p>To calculate the GCI for federated countries (e.g., Australia, Canada, Switzerland, United Kingdom), all of the above mentioned steps are carried out for <u>each</u> sub-national jurisdiction, which results in a number of (sub-national) GCIs. The sub-national GCI scores for the country are then averaged into a national GCI score. The data for all federated countries are then added to the dataset produced in Phase 1.</p>\n<h3><em>Phase 4) Final calculation of the GCI</em></h3>\n<p>At this point, each country has either three or four document-specific scores (ranging from 0 to 10) for each of the three themes (i.e., 9 or 12 total scores, since countries are included if they have at least 3 of the 4 main document types (NCF, grade 3 subject curricula, grade 6 subject curricula, and grade 9 subject curricula).</p>\n<ul>\n  <li>Within each of the Environment/Sustainability, Climate Change, and Biodiversity themes, the three grade level scores and the NCF score are averaged together (i.e., each contributes &#xBC; of the total score per theme in a country). For countries with only three document types, the same procedure is done but each document score contributes &#x2153; of the total theme-focused score.</li>\n  <li>A single overall GCI score is now calculated based on a weighted mean, with the Environment/Sustainability core theme weighted 50% and the Climate Change and Biodiversity themes each weighted at 25%.</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities), and/or education professionals (e.g. teachers and lecturers), as appropriate. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities), and/or education professionals (e.g. teachers and lecturers), as appropriate. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities) and/or education professionals (e.g. teachers and lecturers), as appropriate. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> GCED and ESD are mainstreamed if they or their themes and sub-themes are mentioned explicitly in relevant documents and are expected to be implemented by the relevant authorities (e.g. Ministries, regional or local education authorities), educational institutions (e.g. schools, colleges and universities) and/or education professionals (e.g. teachers and lecturers) as appropriate. <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> Different forms of the word are included only due to genders, definite articles, etc. but not when they change the meaning or part of speech. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> For example, in China the phrase &#x2018;ecological civilization&#x2019; is now being used much more frequently than &#x2018;sustainable development&#x2019; or &#x2018;environmental.&#x2019; In Japan, the term &#x2018;sustainable societies&#x2019; is becoming more prevalent than the term &#x2018;sustainable development.&#x2019; At this point in time, no culture- or language-specific keywords are included in the GCI. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> To determine the accuracy of the counts generated by the Python program, a validation exercise was carried out in October 2024 by sampling 30 documents in English, Spanish, Arabic and French, the four most prevalent languages. A three-way comparison of results from NVivo (the software used for all related UNESCO consultancies), Python, and manual counts identified several minor issues (e.g., keywords split across lines or the lack of a definite article in the Arabic keyword list), which were immediately corrected in the Python program and the keyword list. Since then, the Python program has been reviewed by several experts and undergone further refinements to ensure its counting accuracy is comparable to NVivo and manual counting. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> While there are many documents lacking any keywords related to Environment/Sustainability, Climate Change and Biodiversity, there are no documents with a negative number of keywords. Such a situation represents a zero-bounded distribution and creates a lopsided and non-normal distribution. <a href=\"#footnote-ref-10\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> While more than half the document types have less than 120 standardized keywords in a theme, they range to over 9,000 (75+ times as much as the median). Log transformations are conceptually useful when dealing with such data. For example, going from 0 to 50 standardized keywords is more significant than going from 1000 to 1050 standardized keywords. <a href=\"#footnote-ref-11\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> This means that for each time the standardized counts are halved, the score goes down by 1. So, for example, if 10,000 standardized references is a score of 10, 5,000 is a score of 9, 2,500 is a score of 8, and so on. <a href=\"#footnote-ref-12\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>Responses are reviewed by UNESCO for consistency and credibility and, if necessary, queries are raised with national respondents. Where feasible, reference is made to national documents and links supplied by respondents and to available alternative sources of information. </p>\n<p>Any proposed changes in response values in the questionnaire as a result of quality assurance procedures are communicated and verified with countries by UNESCO. Final results are shared before publication by UNESCO with the national data providers and with national SDG indicator focal points where they exist.</p>", "ADJUSTMENT__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p>The only adjustments made are where question response categories are not valid and responses between different questions are inconsistent. In those circumstances, proposed changes are communicated to and verified with countries.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p><strong>At country level: </strong>A small number of missing values &#x2013; unknown responses and/or blanks &#x2013; are treated as zeros in the calculation of the question scores. Where they represent more than 50% of the responses to a single question, the component score is not calculated. In such cases, the component score is reported as not available when results are disseminated.</p>\n<p><strong>At regional level: </strong>Regional values are not calculated.</p>\n<p><strong>Greening</strong></p>\n<p>As previously noted, the GCI aligns with commitments made by parties to the UN Framework Convention on Climate Change (UN, 1992), by UN Member States in the 2030 Agenda for Sustainable Development (UN, 2015), and by attendees to the UN Transforming Education Summit (UN, 2022; 2023). As such, the focus of document compilation is all 193 UN Member States as well as 3 additional entities (i.e., Cook Islands, Niue, and Palestine), which are parties to the UNFCCC. Among these 196 possible countries, inclusion in the GCI is dependent on whether a sufficiently complete set of documents for that country has been compiled. A sufficient set of documents means having at least three of the following four types of documents that meet the previously outlined criteria:</p>\n<ul>\n  <li>Grade 3 subject curricula</li>\n  <li>Grade 6 subject curricula</li>\n  <li>Grade 9 subject curricula</li>\n  <li>National Curriculum Framework (NCF)</li>\n</ul>\n<p>A special notation (i.e., &quot;Qualifier of Data-Partial Data&quot;) is placed in the database to indicate cases where the GCI was calculated based on three of the four document types. When missing document types are obtained, a revised GCI score based on a complete set of document types is calculated for the bi-annual data releases.</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates are not calculated.</p>", "DOC_METHOD__GLOBAL"=>"<p><strong>Survey</strong></p>\n<ul>\n  <li>Countries wishing to calculate this indicator for themselves should follow the steps described in section 4.c. Method of computation above.</li>\n  <li>The questionnaires for the monitoring of the implementation of UNESCO Recommendations are approved by the Member States of the Executive Board of UNESCO. The questionnaire contains guidelines for completion and a glossary of key terms. In addition, UNESCO provides direct support to Member States in completing the questionnaire and responds to queries in a timely manner.</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>None related to the processing of qualitative data collected principally for non-statistical purposes.</p>", "QUALITY_ASSURE__GLOBAL"=>"<ul>\n  <li>UNESCO reviews country responses for consistency and credibility and, if necessary, raises queries with national respondents. To assist with this, countries are asked to provide, in addition to completed questionnaires, supporting evidence of their responses in the form of documents or links (e.g. to education policies, laws, curricula, etc.). These will be made publicly available during 2022 along with completed questionnaires. UNESCO also takes into account alternative sources of information, where available. These may include national responses to similar intergovernmental consultation processes, such as the Council of Europe&#x2019;s consultations on the <a href=\"https://www.coe.int/en/web/edc/2016-report-analysis\">Charter on Education for Democratic Citizenship and Human Rights Education</a>, the UN Economic Commission for Europe&#x2019;s consultations on the <a href=\"http://www.unece.org/env/esd/implementation.html\">Strategy for Education for Sustainable Development</a>, or other information on education for sustainable development (ESD) and global citizenship education (GCED) in countries&#x2019; national education systems.</li>\n  <li>Any proposed changes to response values in the questionnaire as a result of quality assurance procedures are communicated to and verified with countries by UNESCO. Final results are shared before publication by UNESCO with the national data providers and SDG indicator focal points.</li>\n  <li>Regarding greening, keywords and their translations were reviewed by native speakers who were also familiar with greening concepts. Documents were reviewed against a set of criteria before being included for analysis.</li>\n</ul>\n<p>Before data release and addition to the global SDG indicators database, the indicator&#x2019;s values and notes on methodology are submitted to National Statistical Offices, Ministries of Education or other relevant agencies in individual countries for their review and feedback.</p>\n<p> </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>None related to the processing of qualitative data collected principally for non-statistical purposes.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Survey</strong></p>\n<p><strong>Data availability: </strong>During the last consultation on the implementation of the 1974 Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms carried out in 2020-2021, 75 countries provided reports: Central and Southern Asia (4), Eastern and South-Eastern Asia (7), Europe and Northern America (32), Latin America and the Caribbean (10), Northern Africa and Western Asia (14), Oceania (2), and sub-Saharan Africa (6).</p>\n<p><strong>Time series: </strong>The first data are available for the time period 2017-2020 (as a single time point). Data for the period 2021-2023 (from UNESCO one-off survey conducted in 2024) are expected in 2025. Data for the period 2024-2026 from the first reporting on the 2023 Recommendation on Education for Peace, Human Rights and Sustainable Development will be collected in 2026-2027.</p>\n<p><strong>Disaggregation: </strong>None</p>\n<p><strong>Greening</strong></p>\n<p>Data currently available refer to 2023-2024.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies: </strong>There should be no difference as the indicator values are calculated from the responses submitted by countries. If any changes are proposed to responses as a result of quality assurance procedures, these are communicated to and verified with countries.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<ul>\n  <li><a href=\"http://uis.unesco.org/\"><u>http://uis.unesco.org/</u></a>; <a href=\"https://databrowser.uis.unesco.org/\"><u>https://databrowser.uis.unesco.org/</u></a></li>\n  <li><a href=\"https://www.unesco.org/en/sustainable-development/education\"><u>https://www.unesco.org/en/sustainable-development/education</u></a></li>\n  <li>https://www.unesco.org/gem-report/en </li>\n  <li>https://tcg.uis.unesco.org/wp-content/uploads/sites/4/2025/02/EDSC.11.3.4.GCI-Methods.pdf</li>\n</ul>\n<p><strong>References: </strong></p>\n<p><a href=\"http://portal.unesco.org/en/ev.php-URL_ID=13088&amp;URL_DO=DO_TOPIC&amp;URL_SECTION=201.html\"><u>Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedoms</u></a>.</p>\n<p>Recommendation on Education for Peace, Human Rights and Sustainable Development.</p>", "indicator_sort_order"=>"13-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"13.a.1", "slug"=>"13-a-1", "name"=>"Cantidades proporcionadas y movilizadas en dólares de los Estados Unidos al año en relación con el objetivo actual mantenido de movilización colectiva de 100.000 millones de dólares de aquí a 2025", "url"=>"/site/es/13-a-1/", "sort"=>"13aa01", "goal_number"=>"13", "target_number"=>"13.a", "global"=>{"name"=>"Cantidades proporcionadas y movilizadas en dólares de los Estados Unidos al año en relación con el objetivo actual mantenido de movilización colectiva de 100.000 millones de dólares de aquí a 2025"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Cantidades proporcionadas y movilizadas en dólares de los Estados Unidos al año en relación con el objetivo actual mantenido de movilización colectiva de 100.000 millones de dólares de aquí a 2025", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Cantidades proporcionadas y movilizadas en dólares de los Estados Unidos al año en relación con el objetivo actual mantenido de movilización colectiva de 100.000 millones de dólares de aquí a 2025", "indicator_number"=>"13.a.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notapplicable", "reporting_status"=>"notapplicable", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"En el marco del proceso de la CMNUCC, la COP solicitó al Comité Permanente de Finanzas (SCF) que elaborara \nuna Evaluación y Panorama Bienal de los Flujos de Financiación para el Clima (AB), basándose en las fuentes \nde información disponibles e incluyendo información sobre el equilibrio geográfico y temático de los flujos. \n\nNo existe una definición consensuada en la CMNUCC sobre qué elementos deben contabilizarse para evaluar \nel progreso hacia el compromiso de 100.000 millones de dólares. Los datos de la secretaría de la \nCMNUCC se refieren al apoyo financiero específico para el clima a las Partes que son países en \ndesarrollo, notificado por las Partes del Anexo I en sus Informes Bienales. Solo las Partes del Anexo \nII están obligadas a informar sobre el apoyo financiero proporcionado. Las Partes del Anexo I \ntambién proporcionan voluntariamente esta información. Por consiguiente, estos datos no deben \ninterpretarse como un indicador en relación con el logro del objetivo de movilización colectiva \ndel compromiso de 100.000 millones de dólares.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=13.a.1&seriesCode=DC_FIN_CLIMT&areaCode=1&period=3&table=Total\"> Apoyo financiero total específico para el clima proporcionado (miles de millones de dólares estadounidenses actuales) DC_FIN_CLIMT</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-0a-01.pdf\">Metadatos 13-a-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Under the UNFCCC process, the COP requested the Standing Committee on Finance (SCF) to prepare a \nBiennial Assessment and Overview of Climate Finance Flows (BA) (decision 2/CP.17 paragraph 121(f)), \ndrawing on the available sources of information, and including information on the geographical and \nthematic balance of flows.  \n\nThere is no agreed definition under the UNFCCC on what should count toward \nassessing progress toward the $100 billion commitment. Data from the UNFCCC secretariat refers to \nclimate-specific financial support to developing country Parties, reported by Annex I Parties in their \nBiennial Reports. Only Annex II Parties are obligated to report on financial support provided and other \nAnnex I Parties also voluntarily provide this information. Consequently, this data should not be \ninterpreted as an indicator in relation to the achievement of the collective mobilization goal of 100 \nbillion dollar commitment. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=13.a.1&seriesCode=DC_FIN_CLIMT&areaCode=1&period=3&table=Total\"> Total climate-specific financial support provided (billions of current US dollars) DC_FIN_CLIMT</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-0a-01.pdf\">Metadata 13-a-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"En el marco del proceso de la CMNUCC, la COP solicitó al Comité Permanente de Finanzas (SCF) que elaborara \nuna Evaluación y Panorama Bienal de los Flujos de Financiación para el Clima (AB), basándose en las fuentes \nde información disponibles e incluyendo información sobre el equilibrio geográfico y temático de los flujos. \n\nNo existe una definición consensuada en la CMNUCC sobre qué elementos deben contabilizarse para evaluar \nel progreso hacia el compromiso de 100.000 millones de dólares. Los datos de la secretaría de la \nCMNUCC se refieren al apoyo financiero específico para el clima a las Partes que son países en \ndesarrollo, notificado por las Partes del Anexo I en sus Informes Bienales. Solo las Partes del Anexo \nII están obligadas a informar sobre el apoyo financiero proporcionado. Las Partes del Anexo I \ntambién proporcionan voluntariamente esta información. Por consiguiente, estos datos no deben \ninterpretarse como un indicador en relación con el logro del objetivo de movilización colectiva \ndel compromiso de 100.000 millones de dólares.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=13.a.1&seriesCode=DC_FIN_CLIMT&areaCode=1&period=3&table=Total\">Klimarentzat emandako finantza-laguntza espezifiko osoa (egungo milaka milioi dolar estatubatuar) DC_FIN_CLIMT</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-0a-01.pdf\">Metadatuak 13-a-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 13: Take urgent action to combat climate change and its impacts</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 13.a: Implement the commitment undertaken by developed-country parties to the United Nations Framework Convention on Climate Change to a goal of mobilizing jointly $100 billion annually by 2020 from all sources to address the needs of developing countries in the context of meaningful mitigation actions and transparency on implementation and fully operationalize the Green Climate Fund through its capitalization as soon as possible</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 13.a.1: Amounts provided and mobilized in United States dollars per year in relation to the continued existing collective mobilization goal of the $100 billion commitment through to 2025</p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2021-03-01</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UN Climate Change (UNFCCC Secretariat)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UN Climate Change (UNFCCC Secretariat)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Under the UNFCCC process, the COP requested the Standing Committee on Finance (SCF) to prepare a Biennial Assessment and Overview of Climate Finance Flows (BA) (<a href=\"http://unfccc.int/resource/docs/2011/cop17/eng/09a01.pdf#page=4\" target=\"_blank\">decision 2/CP.17</a> paragraph 121(f)), drawing on the available sources of information, and including information on the geographical and thematic balance of flows. There is no agreed definition under the UNFCCC on what should count toward assessing progress toward the $100 billion commitment. Data from the UNFCCC secretariat refers to climate-specific financial support to developing country Parties, reported by Annex I Parties in their Biennial Reports. Only Annex II Parties are obligated to report on financial support provided and other Annex I Parties also voluntarily provide this information. Consequently, this data should not be interpreted as an indicator in relation to the achievement of the collective mobilization goal of $100 billion commitment. </p>\n<p>One of the functions of the SCF is to assist the COP with respect to the measurement, reporting and verification of the support provided to developing country Parties through activities such as the preparation of the Biennial Assessment and Overview of Climate Finance Flows (BA). Subsequently, the COP requested SCF to consider: </p>\n<ul>\n  <li>Relevant work by other bodies and entities on the MRV of support and the tracking of climate finance<br>(<a href=\"http://unfccc.int/resource/docs/2012/cop18/eng/08a01.pdf#page=3\" target=\"_blank\">decision 1/CP.18</a> paragraph 71);</li>\n  <li>Ways of strengthening methodologies for reporting climate finance (<a href=\"http://unfccc.int/resource/docs/2012/cop18/eng/08a01.pdf#page=26\" target=\"_blank\">decision 5/CP.18</a> paragraph 11);</li>\n  <li>Ongoing technical work on operational definitions of climate finance, including private finance mobilized by public interventions, to assess how adaptation and mitigation needs can most effectively be met by climate finance (<a href=\"http://unfccc.int/resource/docs/2013/cop19/eng/10a01.pdf#page=9\" target=\"_blank\">decision 3/CP.19</a>, paragraph 11).</li>\n</ul>\n<p>The SBSTA. by decision 18/CMA.1, paragraph 12a, was requested to develop the common tabular formats for the electronic reporting of the information referred to in chapters V and VI of the modalities, procedures and guidelines of enhanced framework, taking into account the existing the existing common tabular formats and common reporting formats.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>United States dollars per year </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The reporting of quantitative information on financial support through CTFs is guided by BR guidelines (decision 2/CP.17), CTF reporting parameters (19/CP.18) and footnotes to the CTF tables.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Biennial reports of Annex I Parties in the Convention submitted to the UNFCCC Secretariat. </p>\n<ul>\n  <li>Biennial Reports by Annex I Parties until 2022: <a href=\"https://unfccc.int/BRs\"><em>https://unfccc.int/BRs</em></a><em>, </em>and Biennial Transparency Reports by developed as well as developing country Parties to be reported under Paris Agreement from 2024 onwards: <a href=\"https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-paris-agreement\">https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-paris-agreement</a></li>\n  <li>Biennial Assessment and Overview of Climate Finance Flows: <a href=\"https://unfccc.int/topics/climate-finance/resources/biennial-assessment-of-climate-finance\"><em>https://unfccc.int/topics/climate-finance/resources/biennial-assessment-of-climate-finance</em></a><em>.</em></li>\n</ul>", "COLL_METHOD__GLOBAL"=>"<p>Annex I Parties are requested to submit their Biennial Reports (BRs) to the UNFCCC secretariat every two years (<a href=\"https://unfccc.int/decisions?f%5B0%5D=body%3A1343&amp;f%5B1%5D=conference%3A3461\" target=\"_self\">decision 2/CP.17</a>). Annex I Parties use the BR Common Tabular Format (CTF) application when preparing their BRs (<a href=\"https://unfccc.int/decisions?f%5B0%5D=body%3A1343&amp;f%5B1%5D=conference%3A3845\" target=\"_self\">decision 19/CP.18</a>). </p>\n<p><strong>Report preparers</strong>: Annex I Parties, collect data using their own data collection processes but follow BR guidelines and CTF reporting parameters and footnotes when reporting financial information to UNFCCC secretariat. </p>\n<p><strong>Users</strong>: UNFCCC secretariat, in preparing compilation and synthesis (C&amp;S), in particular the compilation of financial information from BR CTFs as submitted by Annex I Parties.<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[1]</a></sup></p>\n<p> </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">1</sup><p> Available at: <a href=\"https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-convention/national-communications-and-biennial-reports-annex-i-parties/compilation-and-synthesis-reports/compilation-and-synthesis-reports-of-parties-included-in-annex-i-to#eq-1\">https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-convention/national-communications-and-biennial-reports-annex-i-parties/compilation-and-synthesis-reports/compilation-and-synthesis-reports-of-parties-included-in-annex-i-to#eq-1</a> <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "FREQ_COLL__GLOBAL"=>"<p>The fourth Biennial Reports by Annex I Parties were submitted in 2020 and a C&amp;S of the information was published in October 2020. This includes data on financial support provided to developing countries in the years 2017-2018. </p>\n<p>The next (fifth) Biennial Reports by Annex I Parties (BR5) to the Convention should be submitted to the UNFCCC Secretariat by 1 January 2022.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>By fourth quarter of 2022 compilation of data on financial support provided during the years 2019 and 2020 will be released. The data, including in spreadsheet format (CTF), as submitted by Annex I Parties to UNFCCC secretariat is publicly available and accessible via UNFCCC website.<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[2]</a></sup> </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-4\">2</sup><p> Available at: https://unfccc.int/BRs <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "DATA_SOURCE__GLOBAL"=>"<p>National Governments of Annex I Parties to the UNFCCC. Only Annex II Parties report on financial support provided via CTF in accordance with guidelines for the preparation of the BRs and other Annex I Parties also voluntarily provide this information. </p>", "COMPILING_ORG__GLOBAL"=>"<p>UNFCCC secretariat for purposes of C&amp;S.</p>", "INST_MANDATE__GLOBAL"=>"<p>There isn&#x2019;t a formal set of instructions that would directly assign responsibility to an organisation for collection, processing, and dissemination of statistics for this indicator. However, the UNFCCC secretariat was requested by the COP 17 to prepare compilation and synthesis reports on the information reported by Parties in their BRs.<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[3]</a></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-5\">3</sup><p> Available at: <a href=\"https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-convention/national-communications-and-biennial-reports-annex-i-parties/compilation-and-synthesis-reports/compilation-and-synthesis-reports-of-parties-included-in-annex-i-to#eq-1\">https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-convention/national-communications-and-biennial-reports-annex-i-parties/compilation-and-synthesis-reports/compilation-and-synthesis-reports-of-parties-included-in-annex-i-to#eq-1</a> <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>There is no common agreement on to the methodology to measure progress towards the USD 100bn commitment under the UNFCCC. The UNFCCC secretariat, in preparing C&amp;S, compiles financial information on support provided and mobilised as reported by Annex I Parties.</p>", "DATA_COMP__GLOBAL"=>"<p>There is no common agreement on to the methodology to measure progress towards the USD 100bn commitment under the UNFCCC. Data provided through Biennial Reports reflects the reporting of financial support provided to developing countries by Annex I Parties to the Convention. Moreover, the Biennial Assessment and Overview of Climate Finance Flows is a report prepared under the Standing Committee on Finance by the UNFCCC and includes a compilation of the data on financial support provided to developing countries by Annex I Parties. Each Party reports climate-specific finance provided and their underlying assumption and methodologies in accordance with the guidance linked under 4.h below. Moreover, Parties are requested to include information on underlying assumptions and methodologies in documentation box in BR CTFs.</p>", "ADJUSTMENT__GLOBAL"=>"<p>The data is presented as reported by Annex I Parties to the Convention in their BRs, no adjustments with respect to use of standard classifications and harmonization of breakdowns or compliance with specific definitions are made.</p>", "IMPUTATION__GLOBAL"=>"<p>The data is presented as reported by Annex I Parties to the Convention in their BRs, no estimates are produced. Only Annex II Parties are obligated to report on financial support provided and other Annex I Parties also voluntarily provide this information. Some Parties have not reported across all reporting cycles. </p>", "DOC_METHOD__GLOBAL"=>"<p>UNFCCC biennial reporting guidelines for developed country Parties, Annex I, <a href=\"https://unfccc.int/resource/docs/2011/cop17/eng/09a01.pdf#page=4\" target=\"_blank\">Decision 2/CP.17</a></p>\n<p>Biennial Reports Common tabular format (CTF) for &#x201C;UNFCCC biennial reporting guidelines for developed country Parties&#x201D;, <a href=\"http://unfccc.int/resource/docs/2012/cop18/eng/08a03.pdf#page=3\" target=\"_blank\">Decision 19/CP.18</a></p>\n<p>Methodologies for the reporting of financial information by Parties included in Annex I of the Convention, <a href=\"https://unfccc.int/resource/docs/2015/cop21/eng/10a02.pdf#page=15\">Decision 9/CP.21</a></p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability</strong>:</p>\n<p>Biennial Reports of 41 Annex I Parties on financial support provided are available since 2011</p>\n<p><strong>Time series:</strong></p>\n<p>2011-2018. Data are annualised.</p>\n<p><strong>Years of BRs submissions:</strong></p>\n<p>In 2014, 43 Annex I Parties out of 44 submitted their Biennial Reports (BR1), including climate finance data for 2011 and 2012.</p>\n<p>In 2016, 43 Annex I Parties out of 44 submitted their Biennial Reports (BR2), including climate finance data for 2013 and 2014.</p>\n<p>In 2018, 42 Annex I Parties out of 44 submitted their Biennial Reports (BR3), including climate finance data for 2015 and 2016.</p>\n<p>In 2020, 42 Annex I Parties out of 44 submitted their Biennial Reports (BR4), including climate finance data for 2017 and 2018. </p>", "COMPARABILITY__GLOBAL"=>"<p>There is no agreed definition of climate finance or the methodology on how to account climate finance in order to measure progress towards the USD 100bn commitment under the UNFCCC.</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>UNFCCC biennial reporting guidelines for developed country Parties, Annex I: <a href=\"https://unfccc.int/resource/docs/2011/cop17/eng/09a01.pdf#page=4\" target=\"_blank\">Decision 2/CP.17</a></li>\n  <li>Biennial Reports Common tabular format (CTF) for &#x201C;UNFCCC biennial reporting guidelines for developed country Parties&#x201D;: <a href=\"http://unfccc.int/resource/docs/2012/cop18/eng/08a03.pdf#page=3\" target=\"_blank\">Decision 19/CP.18</a></li>\n  <li>Biennial Reports by Annex I Parties to be submitted until 2022: <a href=\"https://unfccc.int/BRs\"><em>https://unfccc.int/BRs</em></a>, and Biennial Transparency Reports by developed as well as developing country Parties to be reported under Paris Agreement from 2024 onwards: <a href=\"https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-paris-agreement\">https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-paris-agreement</a></li>\n  <li>Compilation and synthesis of the fourth Biennial Reports (BR4): <a href=\"https://unfccc.int/CandS-report-2020\">https://unfccc.int/CandS-report-2020</a></li>\n  <li>Biennial Assessment and Overview of Climate Finance Flows is a report prepared under the Standing Committee on Finance by the UNFCCC and includes a compilation of the data on financial support provided to developing countries by Annex I Parties. Each Party reports climate-specific finance provided and the underlying assumption and methodologies used: <a href=\"https://unfccc.int/topics/climate-finance/resources/biennial-assessment-of-climate-finance\"><em>https://unfccc.int/topics/climate-finance/resources/biennial-assessment-of-climate-finance</em></a><em>.</em></li>\n  <li>Statement by 18 Donor States Determined to Commit USD100 Billion for Climate Finance: <a href=\"https://unfccc.int/news/18-industrial-states-release-climate-finance-statement\">https://unfccc.int/news/18-industrial-states-release-climate-finance-statement</a></li>\n</ul>", "indicator_sort_order"=>"13-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"13.b.1", "slug"=>"13-b-1", "name"=>"Número de países menos adelantados y pequeños Estados insulares en desarrollo con contribuciones determinadas a nivel nacional, estrategias a largo plazo, planes nacionales de adaptación y comunicaciones sobre la adaptación, notificadas a la secretaría de la Convención Marco de las Naciones Unidas sobre el Cambio Climático", "url"=>"/site/es/13-b-1/", "sort"=>"13bb01", "goal_number"=>"13", "target_number"=>"13.b", "global"=>{"name"=>"Número de países menos adelantados y pequeños Estados insulares en desarrollo con contribuciones determinadas a nivel nacional, estrategias a largo plazo, planes nacionales de adaptación y comunicaciones sobre la adaptación, notificadas a la secretaría de la Convención Marco de las Naciones Unidas sobre el Cambio Climático"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países menos adelantados y pequeños Estados insulares en desarrollo con contribuciones determinadas a nivel nacional, estrategias a largo plazo, planes nacionales de adaptación y comunicaciones sobre la adaptación, notificadas a la secretaría de la Convención Marco de las Naciones Unidas sobre el Cambio Climático", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países menos adelantados y pequeños Estados insulares en desarrollo con contribuciones determinadas a nivel nacional, estrategias a largo plazo, planes nacionales de adaptación y comunicaciones sobre la adaptación, notificadas a la secretaría de la Convención Marco de las Naciones Unidas sobre el Cambio Climático", "indicator_number"=>"13.b.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notapplicable", "reporting_status"=>"notapplicable", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"En virtud de la Convención Marco de las Naciones Unidas sobre el Cambio Climático (CMNUCC), \ntodas las Partes formularán, implementarán, publicarán y actualizarán periódicamente programas \nnacionales y regionales que contengan medidas para mitigar el cambio climático y facilitar \nuna adaptación adecuada, teniendo en cuenta sus responsabilidades comunes pero diferenciadas \ny sus prioridades, objetivos y circunstancias de desarrollo nacionales y regionales \nespecíficos. Estas políticas y medidas deberán ser apropiadas a las condiciones específicas de \ncada Parte e integrarse en los programas nacionales de desarrollo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=13.b.1&seriesCode=EN_NAD_CONTR_DV&areaCode=199,722&period=3&table=FIRST\">Número de países menos adelantados y pequeños Estados insulares en desarrollo con contribuciones determinadas a nivel nacional (Número) EN_NAD_CONTR_DV</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-0b-01.pdf\">Metadatos 13-b-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Under the United Nations Framework Convention on Climate Change (UNFCCC), all Parties shall \nformulate, implement, publish and regularly update national/regional programmes containing \nmeasures to mitigate climate change and to facilitate adequate adaptation, while taking into \naccount their common but differentiated responsibilities and their specific national and regional \ndevelopment priorities, objectives and circumstances. These policies and measures should be \nappropriate for the specific conditions of each Party and should be integrated with national \ndevelopment programmes.\n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=13.b.1&seriesCode=EN_NAD_CONTR_DV&areaCode=199,722&period=3&table=FIRST\">Number of least developed countries and small island developing States with nationally determined contributions (Number) EN_NAD_CONTR_DV</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-0b-01.pdf\">Metadata 13-b-1.pdf</a> ", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"En virtud de la Convención Marco de las Naciones Unidas sobre el Cambio Climático (CMNUCC), \ntodas las Partes formularán, implementarán, publicarán y actualizarán periódicamente programas \nnacionales y regionales que contengan medidas para mitigar el cambio climático y facilitar \nuna adaptación adecuada, teniendo en cuenta sus responsabilidades comunes pero diferenciadas \ny sus prioridades, objetivos y circunstancias de desarrollo nacionales y regionales \nespecíficos. Estas políticas y medidas deberán ser apropiadas a las condiciones específicas de \ncada Parte e integrarse en los programas nacionales de desarrollo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=13.b.1&seriesCode=EN_NAD_CONTR_DV&areaCode=199,722&period=3&table=FIRST\">Aurrerapen txikiagoko herrialdeen eta garapen bidean dauden uharte-estatu txikien kopurua, nazio-mailan zehaztutako ekarpenak egiten dituztenak (kopurua) EN_NAD_CONTR_DV</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-13-0b-01.pdf\">Metadatuak 13-b-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 13: Take urgent action to combat climate change and its impacts</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 13.b: Promote mechanisms for raising capacity for effective climate change-related planning and management in least developed countries and small island developing States, including focusing on women, youth and local and marginalized communities</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 13.b.1: Number of least developed countries and small island developing States with nationally determined contributions, long-term strategies, national adaptation plans and adaptation communications, as reported to the secretariat of the United Nations Framework Convention on Climate Change</p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2021-03-01</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UN Climate Change (UNFCCC Secretariat)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UN Climate Change (UNFCCC Secretariat)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p><strong>SIDS: </strong><a href=\"http://unohrlls.org/about-sids/\"><strong>http://unohrlls.org/about-sids/</strong></a></p>\n<p><strong>LDCs: </strong><a href=\"http://unohrlls.org/about-ldcs/\"><strong>http://unohrlls.org/about-ldcs/</strong></a><strong> </strong></p>\n<p><strong>NDCs</strong></p>\n<p>The Paris Agreement requires each Party to prepare, communicate and maintain successive <em>nationally determined contributions (NDCs)</em> including mitigation, adaptation and support measures. </p>\n<p>The <a href=\"https://unfccc.int/node/617\" target=\"_blank\">Paris Agreement</a> (Article 4, paragraph 2) requires each Party to prepare, communicate and maintain successive nationally determined contributions (NDCs) that it intends to achieve. Parties shall pursue domestic mitigation measures, with the aim of achieving the objectives of such contributions.</p>\n<p>Starting in 2023 and then every five years, governments will take stock of the implementation of the Agreement to assess the collective progress towards achieving the purpose of the Agreement and its long-term goals. The outcome of the global stocktake (GST) will inform the preparation of subsequent NDCs, in order to allow for increased ambition and climate action to achieve the purpose of the Paris Agreement and its long-term goals. <a href=\"https://unfccc.int/process-and-meetings/the-paris-agreement/nationally-determined-contributions-ndcs\">https://unfccc.int/process-and-meetings/the-paris-agreement/nationally-determined-contributions-ndcs</a></p>\n<p>NDC interim registry <a href=\"https://www4.unfccc.int/sites/ndcstaging/Pages/Home.aspx\">https://www4.unfccc.int/sites/ndcstaging/Pages/Home.aspx</a> </p>\n<p><strong>NAPs</strong></p>\n<p>The <em>national adaptation plan (NAP)</em> process was established under the <a href=\"https://unfccc.int/adaptation/items/5852.php\">Cancun Adaptation Framework</a> (CAF). It enables Parties to formulate and implement <em>national adaptation plans (NAPs)</em> as a means of identifying medium- and long-term adaptation needs and developing and implementing strategies and programmes to address those needs. It is a continuous, progressive and iterative process which follows a country-driven, gender-sensitive, participatory and fully transparent approach supported by technical guidelines and up to USD 3 million per developing country through the Green Climate Fund Readiness and Preparatory Support Programme, intended to support the formulation of NAPs. Technical guidelines for the NAP process are available at &lt;<a href=\"https://unfccc.int/topics/adaptation-and-resilience/workstreams/national-adaptation-plans-naps/guidelines-for-national-adaptation-plans-naps\">unfccc.int</a>&gt;; NAPs received by the UNFCCC secretariat are posted at &lt;<a href=\"https://www4.unfccc.int/sites/NAPC/News/Pages/national_adaptation_plans.aspx\">unfccc.int</a>&gt;.</p>\n<p><strong>Long term strategies </strong></p>\n<p>Under the Paris Agreement, all Parties should further strive to formulate and <em>communicate long-term low greenhouse gas emission development strategies</em> to provide a context and integrated long-term view to their NDCs. </p>\n<p>In accordance with Article 4, paragraph 19, of the Paris Agreement, all Parties should strive to formulate and communicate long-term low greenhouse gas emission development strategies, mindful of Article 2 taking into account their common but differentiated responsibilities and respective capabilities, in the light of different national circumstances.</p>\n<p>The COP, by its decision 1/CP 21, paragraph 35, invited Parties to communicate, by 2020, to the secretariat mid-century, long-term low greenhouse gas emission development strategies in accordance with Article 4, paragraph 19, of the Agreement. Further information is available at &lt;<a href=\"https://unfccc.int/process/the-paris-agreement/long-term-strategies\">unfccc.int</a>&gt;</p>\n<p><strong>Adaptation communications</strong></p>\n<p>Under the Paris Agreement&#x2019;s Article 7, paragraphs 10 and 11, each Party should, as appropriate, submit and update periodically an adaptation communication, which may include its priorities, implementation and support needs, plans and actions. The purpose of the adaptation communication is to strengthen the visibility and profile of adaptation, balance with mitigation, actions, support, learning and understanding. Parties may include information on e.g. their circumstances, institutions, vulnerabilities, adaptation priorities, plans, needs, progress achieved, co-benefits, other frameworks, gender aspects, and indigenous knowledge. The adaptation communications will be recorded in a public registry maintained by the secretariat, and they will provide input to the process of Global Stocktake every five years. The adaptation communications received so far are currently available at https://unfccc.int/topics/adaptation-and-resilience/workstreams/adaptation-communications.</p>\n<p><strong>National communications</strong></p>\n<p>The Convention established several processes to foster transparency and accountability of countries&#x2019; actions to address climate change. Under Article 12, all Parties are asked to submit national inventories and national communications (NCs) to report on the implementation of the Convention. This reporting is required at different levels of stringency and with varying frequency for different Parties. National Communications received by the UNFCCC secretariat are available at &lt;<a href=\"https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-convention/national-communications-and-biennial-reports-annex-i-parties/seventh-national-communications-annex-i\">unfccc.int</a>&gt;.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of submissions received from Parties to UNFCCC</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Official documents and registries, as reported by Parties to the UNFCCC and the Paris Agreement, and published on &lt;unfccc.int&gt;.</p>\n<p>NDC interim registry available at &lt;https://www4.unfccc.int/sites/NDCStaging/Pages/Home.aspx &gt; </p>\n<p>Long term strategies received by the UNFCCC secretariat are available at &lt;<a href=\"https://unfccc.int/process/the-paris-agreement/long-term-strategies\">unfccc.int</a>&gt;.</p>\n<p>NAPs received by the UNFCCC secretariat are available at &lt;<a href=\"https://www4.unfccc.int/sites/NAPC/News/Pages/national_adaptation_plans.aspx\">unfccc.int</a>&gt;.</p>\n<p>Adaptation communications will be recorded in the future in a public registry maintained by the secretariat. Until the finalization of the design of the registry, the adaptation communications received so far are available at: https://unfccc.int/topics/adaptation-and-resilience/workstreams/adaptation-communications.</p>", "COLL_METHOD__GLOBAL"=>"<p>Submission of documents to the UNFCCC Secretariat from Parties to the UNFCCC and Paris Agreement.</p>", "FREQ_COLL__GLOBAL"=>"<p>Ongoing as Parties submit reports. Will be compiled annually in advance of preparation of annual SDG progress reports; in advance of the global stocktake.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Ongoing as Parties submit reports. Will be compiled annually in advance of preparation of annual SDG progress reports; in advance of the global stocktake.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Parties to the UNFCCC and Paris Agreement, aggregate, UN Climate Change (UNFCCC Secretariat); Further analysis on linkages across other SDGs may be undertaken in collaboration with other UN organisations, as relevant, to show how countries are utilising these tools for implementation of climate action and SDGs more broadly.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UN Climate Change (UNFCCC Secretariat).</p>", "RATIONALE__GLOBAL"=>"<p>Rationale and concepts, comments and limitations:</p>\n<p>Under the United Nations Framework Convention on Climate Change (UNFCCC), all Parties shall formulate, implement, publish and regularly update national/regional programmes containing measures to mitigate climate change and to facilitate adequate adaptation, while taking into account their common but differentiated responsibilities and their specific national and regional development priorities, objectives and circumstances. These policies and measures should be appropriate for the specific conditions of each Party and should be integrated with national development programmes.</p>\n<p>The Convention established several processes to foster transparency and accountability of countries&#x2019; actions to address climate change.</p>\n<p>The Paris Agreement<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> builds upon the Convention and brings all nations into a common cause to undertake ambitious efforts to combat climate change and adapt to its effects, with enhanced support to assist developing countries to do so, charting a new course in the global climate effort. The Paris Agreement&#x2019;s central aim is to strengthen the global response to the threat of climate change by keeping a global temperature rise this century well below 2 degrees Celsius above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5 degrees Celsius. Additionally, the agreement aims to strengthen the ability of countries to deal with the impacts of climate change. </p>\n<p>Materials are received from Parties on an ongoing basis. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> The Paris Agreement entered into force on 4 November 2016. Further information about the Paris Agreement may be found at &lt;http://unfccc.int/paris_agreement/items/9485.php&gt; <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p> see 4.a</p>", "DATA_COMP__GLOBAL"=>"<p>Count of submitted reports annually in advance of preparation of SDG progress reports, based on most recent data for SIDS and LDCs.</p>", "IMPUTATION__GLOBAL"=>"<p>&#x2022; <strong>At country level</strong></p>\n<p>n/a</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>n/a</p>", "REG_AGG__GLOBAL"=>"<p>n/a</p>", "DOC_METHOD__GLOBAL"=>"<p>&#x2022; Data is compiled globally</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data reported is based on official information as documented and reported on at &lt;unfccc.int&gt;.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Number of SIDS and LDCs; Number of Parties to the UNFCCC and Paris Agreement</p>\n<p>Currently, there are 197 Parties <strong>(196 States and 1 regional economic integration organization)</strong> to the United Nations Framework Convention on Climate Change.</p>\n<p><a href=\"https://unfccc.int/process-and-meetings/the-convention/status-of-ratification/status-of-ratification-of-the-convention\"><em>https://unfccc.int/process-and-meetings/the-convention/status-of-ratification/status-of-ratification-of-the-convention</em></a></p>\n<p><strong>To this date, 191 Parties have ratified the Paris Agreement,</strong> of 197 Parties to the Convention.</p>\n<p><a href=\"https://unfccc.int/process/the-paris-agreement/status-of-ratification\"><em>https://unfccc.int/process/the-paris-agreement/status-of-ratification</em></a><em> </em></p>\n<p><strong>Time series:</strong></p>\n<p>Ongoing as Parties submit reports. Will be compiled annually in advance of preparation of annual SDG progress reports; NDCs are submitted in advance of the global stocktake, (starting in 2023) every five years, with the next round of NDCs (new or updated) being submitted by 2020. </p>\n<p><a href=\"https://unfccc.int/topics/science/workstreams/global-stocktake-referred-to-in-article-14-of-the-paris-agreement\">https://unfccc.int/topics/science/workstreams/global-stocktake-referred-to-in-article-14-of-the-paris-agreement</a> </p>\n<p><strong>Disaggregation:</strong></p>\n<p>n/a. Some analysis on linkages across other SDGs may be undertaken in collaboration with other UN organisations, as relevant, to show how countries are utilising these tools for implementation of climate action and SDGs more broadly.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>n/a</p>", "OTHER_DOC__GLOBAL"=>"<p>As included in links above;</p>\n<p>NDC interim registry available at &lt;<a href=\"https://www4.unfccc.int/sites/NDCStaging/Pages/Home.aspx\">https://www4.unfccc.int/sites/NDCStaging/Pages/Home.aspx</a>&gt; </p>\n<p>Long term strategies received by the UNFCCC secretariat are available at &lt;<a href=\"https://unfccc.int/process/the-paris-agreement/long-term-strategies\">unfccc.int</a>&gt;.</p>\n<p>NAPs received by the UNFCCC secretariat are posted at &lt;<a href=\"https://www4.unfccc.int/sites/NAPC/News/Pages/national_adaptation_plans.aspx\">unfccc.int</a>&gt;.</p>\n<p>Adaptation communications will be recorded in the future in a public registry maintained by the secretariat. Until the finalization of the design of the registry, the adaptation communications received so far are available <a href=\"at:\">at:</a> https://unfccc.int/topics/adaptation-and-resilience/workstreams/adaptation-communications.</p>\n<p><strong>SIDS: </strong><a href=\"http://unohrlls.org/about-sids/\"><strong>http://unohrlls.org/about-sids/</strong></a></p>\n<p><strong>LDCs: </strong><a href=\"http://unohrlls.org/about-ldcs/\"><strong>http://unohrlls.org/about-ldcs/</strong></a><strong> </strong></p>", "indicator_sort_order"=>"13-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"14.1.1", "slug"=>"14-1-1", "name"=>"a) Índice de eutrofización costera; y b) densidad de detritos plásticos", "url"=>"/site/es/14-1-1/", "sort"=>"140101", "goal_number"=>"14", "target_number"=>"14.1", "global"=>{"name"=>"a) Índice de eutrofización costera; y b) densidad de detritos plásticos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"a) Índice de eutrofización costera; y b) densidad de detritos plásticos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"a) Índice de eutrofización costera; y b) densidad de detritos plásticos", "indicator_number"=>"14.1.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Las zonas costeras son áreas de alta productividad donde convergen los aportes de \nla tierra, el mar, el aire y las personas. Con más del 40% de la población humana \nresidiendo en zonas costeras, la degradación de los ecosistemas en estas áreas \npuede tener efectos desproporcionados en la sociedad (IGOS, 2006). \n\nUna de las mayores presiones sobre los entornos costeros es la eutrofización, \nresultante principalmente del aporte de nutrientes terrestres procedentes de la \nescorrentía agrícola y el vertido de aguas residuales domésticas. La eutrofización \ncostera puede provocar graves daños a los ecosistemas marinos y a los hábitats \nmarinos vitales, y puede provocar la propagación de floraciones de algas nocivas.\n\nLa basura marina se encuentra en todos los océanos y mares del mundo. Constituye un \nriesgo creciente para la salud de los ecosistemas y la biodiversidad, a la vez que \nconlleva importantes costes económicos por sus impactos en la salud pública, \nel turismo, la pesca y la acuicultura. Los plásticos marinos revisten especial \ninterés debido a que, en los últimos 50 años, la producción de plástico se ha \nmultiplicado por más de 22, mientras que la tasa mundial de reciclaje de \nplásticos en 2015 se estimaba en tan solo un 9 %. Este aumento en la producción \nde plástico y la falta de gestión de los residuos plásticos ha supuesto una \namenaza creciente para los entornos marinos, con un estimado de entre 5 y 13 \nmillones de toneladas de plástico procedente de fuentes terrestres que acaban en ellos.\n\nLa Meta 14.1 busca reducir el impacto de la contaminación mediante la prevención \ny la reducción de la contaminación marina de todo tipo, en particular la procedente \nde actividades terrestres, incluidos los detritos marinos y la contaminación por nutrientes.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.1.1&seriesCode=EN_MAR_BEALIT_PUSA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Elementos de basura de playa por unidad de superficie (Número de elementos por cada 100 metros cuadrados) EN_MAR_BEALIT_PUSA</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-01-01.pdf\">Metadatos 14-1-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Coastal areas are areas of high productivity where inputs from land, sea, air and people converge. With \nover 40 percent of the human population residing in coastal areas, ecosystem degradation in these areas \ncan have disproportionate effects on society (IGOS, 2006). \n\nOne of the largest pressures on coastal environments is eutrophication, resulting primarily from \nland-based nutrient input from agricultural runoff and domestic wastewater discharge. Coastal \neutrophication can lead to serious damage to marine ecosystems, vital sea habitats, and can cause \nthe spread of harmful algal blooms. \n\nMarine litter is found in all the world’s oceans and seas. It constitutes an increasing risk to \necosystem health and biodiversity while entailing substantial economic costs through its impacts \non public health, tourism, fishing and aquaculture. Marine plastics are of particular interest due \nto the fact that in the last 50 years, plastic production has increased more than 22-fold while the \nglobal recycling rate of plastics in 2015 was only an estimated 9%. This rise in plastic production \nand unmanaged plastic waste has resulted a growing threat to marine environments with an estimated \n5-13 million tons of plastic from land-based sources ending up in marine environments. \n\nTarget 14.1 aims to reduce the impacts of pollution through prevention and reduction of marine pollution \nof all kinds, in particular from land-based activities, including marine debris and nutrient pollution. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.1.1&seriesCode=EN_MAR_BEALIT_PUSA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Beach litter items per unit of surface area (Number of items per 100 square meters) EN_MAR_BEALIT_PUSA</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-01-01.pdf\">Metadata 14-1-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Las zonas costeras son áreas de alta productividad donde convergen los aportes de \nla tierra, el mar, el aire y las personas. Con más del 40% de la población humana \nresidiendo en zonas costeras, la degradación de los ecosistemas en estas áreas \npuede tener efectos desproporcionados en la sociedad (IGOS, 2006). \n\nUna de las mayores presiones sobre los entornos costeros es la eutrofización, \nresultante principalmente del aporte de nutrientes terrestres procedentes de la \nescorrentía agrícola y el vertido de aguas residuales domésticas. La eutrofización \ncostera puede provocar graves daños a los ecosistemas marinos y a los hábitats \nmarinos vitales, y puede provocar la propagación de floraciones de algas nocivas.\n\nLa basura marina se encuentra en todos los océanos y mares del mundo. Constituye un \nriesgo creciente para la salud de los ecosistemas y la biodiversidad, a la vez que \nconlleva importantes costes económicos por sus impactos en la salud pública, \nel turismo, la pesca y la acuicultura. Los plásticos marinos revisten especial \ninterés debido a que, en los últimos 50 años, la producción de plástico se ha \nmultiplicado por más de 22, mientras que la tasa mundial de reciclaje de \nplásticos en 2015 se estimaba en tan solo un 9 %. Este aumento en la producción \nde plástico y la falta de gestión de los residuos plásticos ha supuesto una \namenaza creciente para los entornos marinos, con un estimado de entre 5 y 13 \nmillones de toneladas de plástico procedente de fuentes terrestres que acaban en ellos.\n\nLa Meta 14.1 busca reducir el impacto de la contaminación mediante la prevención \ny la reducción de la contaminación marina de todo tipo, en particular la procedente \nde actividades terrestres, incluidos los detritos marinos y la contaminación por nutrientes.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.1.1&seriesCode=EN_MAR_BEALIT_PUSA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Hondartzako zabor-elementuak azalera-unitateko (elementu-kopurua 100 metro koadroko) EN_MAR_BEALIT_PUSA</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-01-01.pdf\">Metadatuak 14-1-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 14.1: By 2025, prevent and significantly reduce marine pollution of all kinds, in particular from land-based activities, including marine debris and nutrient pollution</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 14.1.1: (a) Index of coastal eutrophication; and (b) plastic debris density</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_MAR_BEALIT_BP - Beach litter originating from national land-based sources that ends in the beach (%) [14.1.1]</p>\n<p>EN_MAR_BEALIT_BV - Beach litter originating from national land-based sources that ends in the beach (Tonnes) [14.1.1]</p>\n<p>EN_MAR_BEALIT_EXP - Exported beach litter originating from national land-based sources (Tonnes) [14.1.1]</p>\n<p>EN_MAR_BEALIT_OP - Beach litter originating from national land-based sources that ends in the ocean (%) [14.1.1]</p>\n<p>EN_MAR_BEALIT_OV - Beach litter originating from national land-based sources that ends in the ocean (Tonnes) [14.1.1]</p>\n<p>EN_MAR_BEALIT_PUSA - Beach litter items per unit of surface area [14.1.1]</p>\n<p>EN_MAR_CHLANM - Chlorophyll-a anomaly, remote sensing (%) [14.1.1]</p>\n<p>EN_MAR_CHLDEV - Chlorophyll-a deviations, remote sensing [14.1.1]</p>\n<p>EN_MAR_COEUPO - Indicator for Coastal Eutrophication Potential (ICEP) (kilograms of carbon from algae biomass per sq. km. of river basin area per day) [14.1.1]</p>\n<p>EN_MAR_DSI - Coastal eutrophication: Dissolved silica (DSi) (kilograms of silica from algae biomass per sq. km. of river basin area per day) [14.1.1]</p>\n<p>EN_MAR_PLASDD - Floating plastic debris density (count per km2) [14.1.1]</p>\n<p>EN_MAR_TN - Coastal eutrophication: Total Nitrogen (TN) (kilograms of nitrogen from algae biomass per sq. km. of river basin area per day) [14.1.1]</p>\n<p>EN_MAR_TP - Coastal eutrophication: Total Phosphorus (TP) (kilograms of phosphorus from algae biomass per sq. km. of river basin area per day) [14.1.1]</p>\n<p>EN_MAR_DSI_MML - Coastal eutrophication: Total Dissolved Silica (micromole per liter) [14.1.1]</p>\n<p>EN_MAR_TN_MML - Coastal eutrophication: Total Nitrogen (micromole per liter) [14.1.1]</p>\n<p>EN_MAR_TP_MML - Coastal eutrophication: Total Phosphorus (micromole per liter) [14.1.1]</p>\n<p>EN_MAR_CHLCONC - Chlorophyll a concentration (milligram per liter) [14.1.1] </p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>11.6.1, 12.4.2, 12.5.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition: </strong></p>\n<p>The indicator 14.1.1 includes two sub-indicators:</p>\n<ul>\n  <li>14.1.1a Index of coastal eutrophication (ICEP), and </li>\n  <li>14.1.1b Plastic debris density. </li>\n</ul>\n<p>The indicator 14.1.1a &#x201C;Index of coastal eutrophication&#x201D; (ICEP) is based on loads and ratios of nitrogen, phosphorous and silica delivered by rivers to coastal waters (Garnier et al. 2010) which contribute to the ICEP. This indicator assumes that excess nitrogen or phosphorus relative to silica will result in increased growth of potentially harmful algae (ICEP&gt;0).</p>\n<p>The indicator 14.1.1b &#x201C;Plastic debris density&quot; includes potential measurement of plastics washed onto beaches or shorelines, floating on the water or in the water column, deposited on the seafloor/seabed, as well as ingested by biota; however, it is also important to note the importance of monitoring information on waste management and the sources of plastic pollution for understanding plastic pollution.</p>\n<p>Across the 14.1.1a and 14.1.1b, two mandatory levels are proposed: </p>\n<p>Level 1: Global Data Products (globally available data from earth observations and modelling), </p>\n<p>Level 2: National Data, which are collected from countries (through the relevant Regional Seas Programme for countries that are member of a Regional Seas Programme, or directly by UNEP).</p>\n<p>The tables 1 and 2 demonstrate the proposed parameters for sub-indicators 14.1.1a and 14.1.1b.</p>\n<p><em>Table 1: Monitoring parameters for eutrophication to track progress against SDG Indicator 14.1.1a.</em></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Monitoring parameters</p>\n      </td>\n      <td>\n        <p>Level 1</p>\n      </td>\n      <td>\n        <p>Level 2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Indicator for Coastal Eutrophication Potential (N and P loading)</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Chlorophyll-a deviations (remote sensing)</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Chlorophyll-a concentration (<em>remote sensing and in situ</em>)</p>\n      </td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>National modelling of indicator for Coastal Eutrophication Potential (ICEP) </p>\n      </td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Total Nitrogen </p>\n      </td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Total Phosphorus </p>\n      </td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Total Silica </p>\n      </td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><em>Table 2: Monitoring parameters for marine plastic litter to track progress against SDG Indicator 14.1.1b.</em></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Monitoring parameters (and methods)</p>\n      </td>\n      <td>\n        <p> Level 1</p>\n      </td>\n      <td>\n        <p>Level 2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Plastic patches greater than 10 meters*</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Beach litter originating from national land-based sources</p>\n      </td>\n      <td>\n        <p>X</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Beach litter (beach surveys)</p>\n      </td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Floating plastics (visual observation, manta trawls)</p>\n      </td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Water column plastics (demersal trawls)</p>\n      </td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Seafloor litter (benthic trawls (e.g. fish survey trawls), divers, video/camera tows, submersibles, remotely operated vehicles)</p>\n      </td>\n      <td></td>\n      <td>\n        <p>X</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Concepts:</strong></p>\n<p>One of the largest pressures on <strong>coastal environments is eutrophication</strong>, resulting primarily from land-based nutrient input from agricultural runoff and domestic wastewater discharge. Coastal eutrophication can lead to serious damage to marine ecosystems, vital sea habitats, and can cause the spread of harmful algal blooms. SDG Indicator 14.1.1a aims to measure the contribution to coastal eutrophication from countries and the state of coastal eutrophication.</p>\n<p>Eutrophication is an excess nutrient loading into coastal environments from anthropogenic sources, resulting in excessive growth of plants, algae and phytoplankton. The basis for these loads is collected from land-based assessments of land use including fertilizer use, population density, socioeconomic factors and other contributors to nutrient pollution runoff. Given the land-based nature of the indicator, it provides a modelled number indicating the risk of coastal eutrophication at a specific river mouth.</p>\n<p>One more important characteristic is Chlorophyll-a deviation. Chlorophyll-a concentrations for this indicator are obtained from the global ocean, 4 km spatial resolution per pixel monthly mean product of the OC-CCI project product for each pixel within the country&#x2019;s territorial sea and exclusive economic zone.</p>\n<p>Territorial sea is a belt of coastal waters extending at most 12 nautical miles from the baseline of a coastal state, as outlined by the United Nations Convention on the Law of the Sea.</p>\n<p>The Exclusive Economic Zone (EEZ) is an area beyond and adjacent to the territorial sea. The EEZ shall not extend beyond 200 nautical miles from the baselines from which the breadth of the territorial sea is measured, as outlined by the United Nations Convention on the Law of the Sea.</p>\n<p>Based on the existing internationally agreed <a href=\"https://wedocs.unep.org/handle/20.500.11822/30009\">Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP) guidelines</a> and the existing national data collections, it is recommended that the SDG reporting includes sub-indicators related to beach litter, floating plastic and plastic in the sea column, plastic on the sea floor and additional option indicators.</p>\n<p>Plastic litter is most obvious on shorelines, where litter accumulates due to current, wave and wind action, river outflows and by direct littering at the coast. However, plastic litter occurs on the ocean surface, suspended in the water column, on the seabed and in association with biota, due to entanglement or ingestion (GESAMP, 2019).</p>\n<p>Marine litter - any persistent, manufactured or processed solid material which is lost or discarded and ends up in the marine and coastal environment.</p>\n<p>The full methodology for this indicator is available in the document entitled &#x201C;<a href=\"https://wedocs.unep.org/handle/20.500.11822/35086\">Understanding the State of the Ocean: A Global Manual on Measuring SDG 14.1.1, SDG 14.2.1 and SDG 14.5.1</a>&#x201D; (UNEP, 2021). </p>", "UNIT_MEASURE__GLOBAL"=>"<ul>\n  <li>Chlorophyll-a deviations and Chlorophyll-a anomaly: Percent (%). </li>\n  <li>Beach litter: Number per 100 square meter, Percent (%), Tonnes.</li>\n  <li>Floating plastic debris density: Count per square kilometer (count per km<sup>2</sup>).</li>\n  <li>Indicator for Coastal Eutrophication Potential (ICEP): kilograms of carbon (from algae biomass) per square kilometre of river basin area per day (kg C km<sup>-2</sup> day<sup>-1</sup>).</li>\n</ul>", "CLASS_SYSTEM__GLOBAL"=>"<p>This indicator is classified by the Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>For Level 1 indicators:</p>\n<ul>\n  <li>Satellite data.</li>\n  <li>Global models, which are based on official data from national governments as collected from UN organizations. </li>\n</ul>\n<p>For Level 2 indicators:</p>\n<ul>\n  <li>Data provided by national governments.</li>\n</ul>", "COLL_METHOD__GLOBAL"=>"<p>National data are collected through the Regional Seas Programmes to reduce the reporting burden on countries. For countries that are not included in a Regional Seas Programme, UNEP contacts countries directly. </p>\n<p>For globally derived data, UNEP has established a partnership with NOAA and GEO Blue Planet, the Global Nutrient Management System (GNMS) and the Scientific Advisory Committee of the Ad hoc and Open Ended Expert Group on Marine Litter. This facilitates the production of global data products.</p>", "FREQ_COLL__GLOBAL"=>"<p>The first UNEP data collection from countries is planned in 2023. After that, direct data collection will be synchronised with the Regional Seas data collection calendar </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>For Level 1 data: </p>\n<ul>\n  <li>Chlorophyll-a: the first reporting cycle was in 2020 and then every two years.</li>\n  <li>Beach litter originating from national land-based sources: the first reporting cycle was in 2022.</li>\n</ul>\n<p>For Level 2 data: The first UNEP data collection is planned in 2023. After that, data collection will be synchronised with the Regional Seas data collection calendar.</p>", "DATA_SOURCE__GLOBAL"=>"<p>For Level 1 data: </p>\n<ul>\n  <li>14.1.1a: Geo Blue Planet, NOAA, Esri. </li>\n  <li>14.1.1b: Florida State University, EPA: European Environment Agency, Marine Litter Watch (MLW); OC: Ocean Conservancy: International Coastal Clean-up (ICC).</li>\n</ul>\n<p>For Level 2 data: National governments through the Regional Seas, or directly to UNEP. More information on the Regional Seas Programme is <a href=\"https://www.unep.org/explore-topics/oceans-seas/what-we-do/regional-seas-programme\">here</a>. </p>", "COMPILING_ORG__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP), in collaboration with partners mentioned in the other sections of this metadata.</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) was mandated as Custodian Agencies for indicator 14.1.1 by the Inter-agency and Expert Group on SDG Indicators.</p>\n<p>The UNEP Regional Seas Programme is UNEP&#x2019;s most important regional mechanism for conservation of the marine and coastal environment since its establishment in 1974. These Multilateral Environmental Agreements are governed by their own meetings of the Contracting Parties. The individual Regional Seas Conventions and Action Plans have both a normative and implementation mandate. They provide an expression of common regional priorities, including those in the delivery of global mandates such as the 2030 Agenda, provisions of Multilateral Environmental Agreements (MEAs) and United Nations Environment Assembly (UNEA) resolutions. They also provide platforms for acting, including through integrated assessment, policy development, capacity building and exchange, as well as through implementation of projects. By building on the mandates of Regional Seas in addressing adverse impacts to the marine and coastal environment, UNEP can enhance impact and sustainability of efforts by utilization of advantages of the Regional Seas under the programme of work at the regional level. </p>", "RATIONALE__GLOBAL"=>"<p>Coastal areas are areas of high productivity where inputs from land, sea, air and people converge. With over 40 percent of the human population residing in coastal areas, ecosystem degradation in these areas can have disproportionate effects on society (IGOS, 2006). One of the largest pressures on coastal environments is eutrophication, resulting primarily from land-based nutrient input from agricultural runoff and domestic wastewater discharge. Coastal eutrophication can lead to serious damage to marine ecosystems, vital sea habitats, and can cause the spread of harmful algal blooms. </p>\n<p>Marine litter is found in all the world&#x2019;s oceans and seas. It constitutes an increasing risk to ecosystem health and biodiversity while entailing substantial economic costs through its impacts on public health, tourism, fishing and aquaculture. Marine plastics are of particular interest due to the fact that in the last 50 years, plastic production has increased more than 22-fold while the global recycling rate of plastics in 2015 was only an estimated 9%. This rise in plastic production and unmanaged plastic waste has resulted a growing threat to marine environments with an estimated 5-13 million tons of plastic from land-based sources ending up in marine environments.</p>\n<p>Target 14.1 aims to reduce the impacts of pollution through prevention and reduction of marine pollution of all kinds, in particular from land-based activities, including marine debris and nutrient pollution.</p>", "REC_USE_LIM__GLOBAL"=>"<p>This methodology mobilizes the collection of widely available earth observation data and other data sources which will be validated by countries. The methodologies used to generate this data are technical in nature. The methodology employs internationally recognized methods, from expert communities such as the Group on Earth Observation (GEO) and international space agencies and technical experts. There is a need to provide training on these indicators over time.</p>\n<p> </p>\n<p>The Indicator is designed in a way to generate data to allow informed decision making towards identifying the state of pollution and pollution flows in oceans. It is assumed that countries would use the data to actively make decisions, but as oceans are transboundary, it makes this decision-making complex. Additionally, there is a need to consider data on pollution generation and waste in conjunction with these indicators.</p>", "DATA_COMP__GLOBAL"=>"<p>A full methodology for this indicator is available in the document entitled &#x201C;<a href=\"https://wedocs.unep.org/handle/20.500.11822/35086\">Understanding the State of the Ocean: A Global Manual on Measuring SDG 14.1.1, SDG 14.2.1 and SDG 14.5.1</a>&#x201D; (UNEP, 2021).</p>\n<p><strong>For 14.1.1a &#x201C;Index of coastal eutrophication&#x201D;:</strong></p>\n<ul>\n  <li><em><u>Level 1: Indicator for coastal eutrophication potential </u></em></li>\n</ul>\n<p>This indicator is based on loads and ratios of nitrogen, phosphorous and silica delivered by rivers to coastal waters (Garnier et al. 2010), which contribute to the ICEP, and assumes that excess nitrogen or phosphorus relative to silica will result in increased growth of potentially harmful algae (ICEP&gt;0). The basis for these loads is collected from land-based assessments of land use including fertilizer use, population density, socioeconomic factors and other contributors to nutrient pollution runoff. Given the land-based nature of the indicator, it provides a modelled number indicating the risk of coastal eutrophication at a specific river mouth. </p>\n<p>The indicator can be further developed by incorporating in situ monitoring to evaluate the dispersion of concentrations of nitrogen, phosphorous and silica to ground-truth the index. The indicator assumes that excess concentrations of nitrogen or phosphorus relative to silica will result in increased growth of potentially harmful algae (ICEP&gt;0). ICEP is expressed in kilograms of carbon (from algae biomass) per square kilometre of river basin area per day (kg C km<sup>-2</sup> day<sup>-1</sup>). </p>\n<p>The ICEP model is calculated using one of two equations depending on whether nitrogen or phosphorus is limiting. The equations (Billen and Garnier 2007) are:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>C</mi>\n    <mi>E</mi>\n    <mi>P</mi>\n    <mi>&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi>N</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>m</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>g</mi>\n      </mrow>\n    </mfenced>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mo>[</mo>\n    <mi>N</mi>\n    <mi>F</mi>\n    <mi>l</mi>\n    <mi>x</mi>\n    <mo>/</mo>\n    <mo>(</mo>\n    <mn>14</mn>\n    <mi>*</mi>\n    <mn>16</mn>\n    <mo>)</mo>\n    <mo>-</mo>\n    <mi>S</mi>\n    <mi>i</mi>\n    <mi>F</mi>\n    <mi>l</mi>\n    <mi>x</mi>\n    <mo>/</mo>\n    <mo>(</mo>\n    <mn>28</mn>\n    <mi>*</mi>\n    <mn>20</mn>\n    <mo>)</mo>\n    <mo>]</mo>\n    <mi>*</mi>\n    <mn>106</mn>\n    <mi>*</mi>\n    <mn>12</mn>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>C</mi>\n    <mi>E</mi>\n    <mi>P</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi>P</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>l</mi>\n    <mi>i</mi>\n    <mi>m</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>g</mi>\n    <mo>)</mo>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mo>[</mo>\n    <mi>P</mi>\n    <mi>F</mi>\n    <mi>l</mi>\n    <mi>x</mi>\n    <mo>/</mo>\n    <mn>31</mn>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#x2013;</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>S</mi>\n    <mi>i</mi>\n    <mi>F</mi>\n    <mi>l</mi>\n    <mi>x</mi>\n    <mo>/</mo>\n    <mo>(</mo>\n    <mn>28</mn>\n    <mi>*</mi>\n    <mn>20</mn>\n    <mo>)</mo>\n    <mo>]</mo>\n    <mi>*</mi>\n    <mn>106</mn>\n    <mi>*</mi>\n    <mn>12</mn>\n  </math></p>\n<p>where <em>PFlx</em>, <em>NFlx</em> and <em>SiFlx</em> are respectively the mean specific values of total nitrogen, total phosphorus and dissolved silica delivered at the mouth of the river basin, expressed in <em>kg P km<sup>-2</sup> day<sup>-1</sup></em>, in <br><em>kg N km<sup>-2</sup> day<sup>-1</sup></em> and in <em>kg Si km<sup>-2</sup> day<sup>-1</sup></em>. </p>\n<ul>\n  <li><em><u>Level 1: Chlorophyll-A deviation modelling</u></em></li>\n</ul>\n<p>Satellite-based assessments of ocean colour began in 1978 with the launch of the Coastal Zone Color Scanner (CZCS) aboard the NASA Nimbus 7 satellite. Following a decade long break in observations, there has been continuous satellite ocean colour since 1997 with SeaWiFS, followed by MERIS, MODIS (Terra, Aqua), VIIRS (NPP, N20) and now OLCI (S-3A, S-3B). Data gaps from individual sensors are common due to revisit cycles, cloud cover, and spurious retrievals resulting from a host of confounding atmospheric and aquatic conditions. This issue has been addressed by combining data from multiple sensors and creating a consistent, merged ocean colour product (e.g., chlorophyll-a). The ESA Ocean Colour CCI (OC-CCI) project, led by the Plymouth Marine Laboratory (PML), has produced a consistent, merged chlorophyll-a product from SeaWiFS, MODIS, MERIS and VIIRS, spanning 1997 to 2018 (Sathyendranath et al., 2018). A merged multi-sensor product will be updated in both time and with data from additional sensors (e.g., OLCI) under a forthcoming EUMETSAT initiative that will continue the time series on an operational basis.</p>\n<p> </p>\n<p>For SDG 14.1.1a, Chlorophyll-a (4 km resolution, monthly products) will be derived from the OC-CCI project is generated for each individual pixel within the country&#x2019;s territorial sea and EEZ. For generation of a climatological baseline, results are averaged by month over the time period of 2000 &#x2013; 2004. Pixels with differences from the baseline that are in the 90th percentile of values &gt;0 across the cumulative global EEZ and territorial sea. The percentage of pixels in a country&#x2019;s EEZ and territorial sea that are identified as deviating from the baseline (falling in the 90th percentile) will be calculated for each national EEZ and territorial sea by month. The annual average of these monthly values is then calculated.</p>\n<ul>\n  <li><em><u>Level 2: In situ monitoring of nutrients</u></em></li>\n</ul>\n<p>Where national capacity to do so exists, national level measurements of Chlorophyll-a and other parameters (including nitrogen, phosphate and silica) (in situ or from remote sensing), should be used to complement and ground truth global remote sensing and modelled data and enable a more detailed assessment of eutrophication. In particular, monitoring of supplementary eutrophication parameters is advisable to determine whether an increase in Chlorophyll-a concentration is directly linked to an anthropogenic increase in nutrients. </p>\n<ul>\n  <li><em><u>Level 2: National ICEP modelling</u></em></li>\n</ul>\n<p>Existing ICEP modelling at the national level is limited, but could be further developed following the model of a current study analysing basin level data in Chinese rivers (Strokal et al 2016). The study utilises Global NEWS &#x2013; 2 (Nutrient Export from WaterSheds) and Nutrient flows in Food chains, Environment and Resources use (NUFER) as models. The Global NEWS-2 model is basin-scale and quantifies river export of various nutrients (nitrogen, phosphorus, carbon and silica) in multiple forms (dissolved inorganic, dissolved organic and particulate) as functions of human activities on land and basin characteristics (Strokal et al 2016). Furthermore, the model shows past and future trends.</p>\n<p><strong>For 14.1.1b &#x201C;Plastic debris density&#x201D;:</strong></p>\n<ul>\n  <li><em><u>Level 1: Plastic patches greater than 10 meters</u></em></li>\n</ul>\n<p>Satellite-based global data products make up the statistics for this indicator. NASA and ESA both contribute satellite images to construct information on the plastic patches greater than 10 meters throughout the world&#x2019;s oceans. Multi-spectral satellite remote sensing of plastic in the water column is currently only possible for larger elements (more than 10m) and under good atmospheric conditions (no clouds). </p>\n<ul>\n  <li><em><u>Level 1: Beach litter originating from national land-based sources</u></em></li>\n</ul>\n<p>Modelling of litter movement through the oceans occurs through numerical models using inputs including ocean flow and marine plastic litter characteristics. UNEP and Florida State University are producing a global model of marine litter using OceanParcels v2.0, a state-of-the-art Lagrangian Ocean analysis framework to create customizable particle tracking simulation using outputs from ocean circulation models.</p>\n<ul>\n  <li><em><u>Level 2: Beach litter (average count of plastic items per 100m</u><sup><u>2</u></sup><u>)</u></em></li>\n  <li><em><u>Plastic in the sea column and floating plastic and plastic on the sea floor (average count of plastic items per km</u><sup><u>2</u></sup><u>)</u></em></li>\n</ul>\n<p>The details for collecting data for beach litter, plastic in the sea column and floating plastic and plastic on the sea floor are in the global manual and in the GESAMP Guidelines (GESAMP 2019). Beach litter is the most available type of data at the national level. National efforts to collect data on beach litter can be supported by campaigns to engage members of the public as volunteers in beach clean-ups (see for example the Ocean Conservancy&#x2019;s International Coastal Clean-up (ICC) initiative) or citizen science programmes (see for example NOAA&#x2019;s Marine Debris Monitoring and Assessment Citizen Science Project). Specific instructions on how to conduct citizen science beach surveys are included in the GESAMP Guidelines (GESAMP 2019). </p>\n<p>Beyond the tools used to conduct beach litter monitoring, it is important to consider the timing of surveys in order to properly plan effective surveys. The GESAMP Guidelines explain two main types of surveying beaches including rapid assessment surveys and routine shoreline monitoring. Rapid assessment surveys are best conducted in response to natural disasters, to build a baseline for future surveys and/or to identify beach litter hotspots.</p>\n<p>The average count of plastic items can be computed for each area sampled. A geospatial model is recommended in order to estimate the density across the coastline and to establish a national average.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The data validation for this indicator will differ according to the level classification of the indicator measured:</p>\n<p>For Level 1 data: All globally estimated or modelled data will be shared with national statistical offices and other relevant authorities for in-country validation and replacement with national data if possible.</p>\n<p>For Level 2 data: The United Nations Environment Programme (UNEP) and the Regional Seas will be carried out extensive data validation procedures that include built-in automated procedures, manual checks and cross-references to national sources of data. Communication will be carried out with countries for clarification and validation of data. Only data that are considered accurate or those confirmed by countries during the validation process will be reported by UNEP on the Global SDG Database. </p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made</p>", "IMPUTATION__GLOBAL"=>"<p>For Level 1 data: Not applicable.</p>\n<p>For Level 2 data: The United Nations Environment Programme (UNEP) and the Regional Seas do not make any estimation or imputation for missing values, so the number of data points provided are actual country data. </p>", "REG_AGG__GLOBAL"=>"<p>The data are aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see <a href=\"https://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">here</a>. </p>", "DOC_METHOD__GLOBAL"=>"<p>The full methodology for this indicator is available in the document entitled &#x201C;<a href=\"https://wedocs.unep.org/handle/20.500.11822/35086\">Understanding the State of the Ocean: A Global Manual on Measuring SDG 14.1.1, SDG 14.2.1 and SDG 14.5.1</a>&#x201D; (UNEP, 2021).</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided by the United Nations Environment Programme (UNEP) and the Regional Seas.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided by the United Nations Environment Programme (UNEP) and the Regional Seas in cooperation with the countries that provide these data.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided by the United Nations Environment Programme (UNEP) and the Regional Seas.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>For Level 1 data: All UN Member States.</p>\n<p>For Level 2 data: All UN member States reporting national data.</p>\n<p><strong>Time series:</strong></p>\n<p>For Level 1 data: </p>\n<ul>\n  <li>Chlorophyll-a: the first reporting cycle was in 2020 and then every two years.</li>\n  <li>Beach litter originating from national land-based sources: the first reporting cycle was in 2022.</li>\n</ul>\n<p>For Level 2 data: The first UNEP data collection is planned in 2023. After that, data collection will be synchronised with the Regional Seas data collection calendar.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>A geospatial disaggregation of the state of pollution is proposed. For the ICEP loading indicators, this disaggregation should be at the sub-basin level.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies: </strong></p>\n<p>There are a number of experiences in terms of collecting data on marine plastics and some do not follow a consistent methodology. Similarly, the underlying national nutrient data which feeds into national or global ICEP modelling may include discrepancies (for example, in some cases different national ministries maintain data on fertilizer, wastewater, etc.). It is recommended that national statistical systems review and work to eliminate discrepancies in the underlying data for these indictors.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>References: </strong></p>\n<p><a href=\"https://www.unep.org/explore-topics/oceans-seas/what-we-do/regional-seas-programme\">Regional Seas Programme website</a> </p>\n<p><a href=\"https://wedocs.unep.org/handle/20.500.11822/35086\">Understanding the State of the Ocean: A Global Manual on Measuring SDG 14.1.1, SDG 14.2.1 and SDG 14.5.1</a> (UNEP, 2021) </p>\n<p><a href=\"https://wesr.unep.org/media/docs/marine_plastics/une_science_dvision_gesamp_reports.pdf\">Guidelines for the Monitoring and Assessment of Plastic Litter in the Ocean</a> (GESAMP, 2019) </p>\n<p><a href=\"https://wedocs.unep.org/bitstream/handle/20.500.11822/26440/MSP_ICZM_Guidelines.pdf?sequence=1&amp;isAllowed=y\">Conceptual guidelines for the application of Marine Spatial Planning and Integrated Coastal Zone Management approaches to support the achievement of Sustainable Development Goal Targets 14.1 and 14.2</a> (UNEP, 2018)</p>", "indicator_sort_order"=>"14-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"14.2.1", "slug"=>"14-2-1", "name"=>"Número de países que aplican enfoques basados en los ecosistemas para gestionar las zonas marinas", "url"=>"/site/es/14-2-1/", "sort"=>"140201", "goal_number"=>"14", "target_number"=>"14.2", "global"=>{"name"=>"Número de países que aplican enfoques basados en los ecosistemas para gestionar las zonas marinas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países que aplican enfoques basados en los ecosistemas para gestionar las zonas marinas", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que aplican enfoques basados en los ecosistemas para gestionar las zonas marinas", "indicator_number"=>"14.2.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Los océanos son una parte importante del sistema global y cubren más del 70% de \nla superficie terrestre. Proporcionan alimento y sustento a miles de millones de \npersonas, absorben el calor atmosférico y más de una cuarta parte del dióxido de \ncarbono, y producen aproximadamente la mitad del oxígeno atmosférico. \n\nDebido a las actividades humanas, el cambio climático global y los problemas ambientales han \ngenerado amenazas para los ecosistemas y entornos marinos. Es importante identificar maneras \nde evaluar los planes existentes y desarrollar la capacidad para una planificación integrada.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-02-01.pdf\">Metadatos 14-2-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Oceans are an important part of the global system and covering more than 70 per cent of the Earth’s \nsurface. They provide food and livelihoods for billions of people, absorb atmospheric heat and more than \na quarter of carbon dioxide, and produce about half of the oxygen in the atmosphere. \n\nDue to human activities, global climate change and environmental problems have led to threats to \nmarine ecosystems and environments. It is important to identify ways to measure existing plans and to \nbuild capacity for integrated planning. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-02-01.pdf\">Metadata 14-2-1.pdf</a> ", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Los océanos son una parte importante del sistema global y cubren más del 70% de \nla superficie terrestre. Proporcionan alimento y sustento a miles de millones de \npersonas, absorben el calor atmosférico y más de una cuarta parte del dióxido de \ncarbono, y producen aproximadamente la mitad del oxígeno atmosférico. \n\nDebido a las actividades humanas, el cambio climático global y los problemas ambientales han \ngenerado amenazas para los ecosistemas y entornos marinos. Es importante identificar maneras \nde evaluar los planes existentes y desarrollar la capacidad para una planificación integrada.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-02-01.pdf\">Metadatuak 14-2-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 14.2: By 2020, sustainably manage and protect marine and coastal ecosystems to avoid significant adverse impacts, including by strengthening their resilience, and take action for their restoration in order to achieve healthy and productive oceans</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 14.2.1: Number of countries using ecosystem-based approaches to managing marine areas</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_SCP_ECSYBA - Number of countries using ecosystem-based approaches to managing marine areas (1 = YES; 0 = NO) [14.2.1] </p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>14.1.1, 14.5.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition: </strong></p>\n<p><strong>Concepts:</strong></p>\n<p>Regional Seas Coordinated Indicator 22 &#x2018;Integrated Coastal Zone Management&#x2019; (ICZM) is proposed as the primary indicator. For countries with Marine/Maritime Spatial Planning (MSP) in place, these plans can be helpful to assess ICZM. For other countries, it is important to identify ways to measure existing plans and to build capacity for integrated planning. </p>\n<p>An Integrated Coastal Zone Management (ICZM) plan covers the entire coastal zone. Marine and terrestrial areas are managed together. Plans are developed through coordination across different marine and terrestrial institutions and agencies.</p>\n<p>Marine Spatial Planning (MSP) is focused on the Exclusive Economic Zone (EEZ). It integrates the needs and policies of multiple marine sectors into one coherent planning framework.</p>\n<p>The Exclusive Economic Zone (EEZ) is an area beyond and adjacent to the territorial sea. The EEZ shall not extend beyond 200 nautical miles from the baselines from which the breadth of the territorial sea is measured, as outlined by the United Nations Convention on the Law of the Sea.</p>\n<p>Territorial sea is a belt of coastal waters extending at most 12 nautical miles from the baseline of a coastal state, as outlined by the United Nations Convention on the Law of the Sea.</p>\n<p>The full methodology for this indicator is available in the document entitled &#x201C;<a href=\"https://wedocs.unep.org/handle/20.500.11822/35086\">Understanding the State of the Ocean: A Global Manual on Measuring SDG 14.1.1, SDG 14.2.1 and SDG 14.5.1</a>&#x201D; (UNEP, 2021).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>For time series characterising the world or regions: number.</p>\n<p>For time series characterising selected countries: identification &#x201C;1&#x201D; meaning presence, or &#x201C;0&#x201D; meaning not present.</p>\n<p>The &#x201C;number&#x201D; represents the number of countries using ecosystem-based approaches to manage marine areas. </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data are provided by national governments.</p>", "COLL_METHOD__GLOBAL"=>"<p>National data are collected through the Regional Seas Programmes to reduce the reporting burden on countries. For countries that are not included in the Regional Seas Programme, UNEP contacts countries directly. </p>", "FREQ_COLL__GLOBAL"=>"<p>First data collection cycle: 2021.</p>\n<p>Second collection cycle: 2025.</p>\n<p>Third collection cycle: 2029.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>First reporting cycle: 2022</p>\n<p>Second collection cycle: 2026.</p>\n<p>Third collection cycle: 2030.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National governments, through the Regional Seas, or directly to the United Nations Environment Programme (UNEP). </p>\n<p>More information on the Regional Seas Programme is <a href=\"https://www.unep.org/explore-topics/oceans-seas/what-we-do/regional-seas-programme\">here</a>.</p>", "COMPILING_ORG__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP), in collaboration with the Reginal Seas Programme.</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) was mandated as Custodian Agencies for indicator 14.2.1 by the Inter-agency and Expert Group on SDG Indicators.</p>\n<p>The <a href=\"https://www.unep.org/explore-topics/oceans-seas/what-we-do/regional-seas-programme\">UNEP Regional Seas Programme</a> is UNEP&#x2019;s most important regional mechanism for conservation of the marine and coastal environment since its establishment in 1974. These Multilateral Environmental Agreements are governed by their own meetings of the Contracting Parties. The individual Regional Seas Conventions and Action Plans have both a normative and implementation mandate. They provide an expression of common regional priorities, including those in the delivery of global mandates such as the 2030 Agenda, provisions of Multilateral Environmental Agreements (MEAs) and United Nations Environment Assembly (UNEA) resolutions. They also provide platforms for acting, including through integrated assessment, policy development, capacity building and exchange, as well as through implementation of projects. By building on the mandates of Regional Seas in addressing adverse impacts to the marine and coastal environment, UNEP can enhance impact and sustainability of efforts by utilization of advantages of the Regional Seas under the programme of work at the regional level.</p>", "RATIONALE__GLOBAL"=>"<p> Oceans are an important part of the global system and covering more than 70 per cent of the Earth&#x2019;s surface. They provide food and livelihoods for billions of people, absorb atmospheric heat and more than a quarter of carbon dioxide, and produce about half of the oxygen in the atmosphere. </p>\n<p>Due to human activities, global climate change and environmental problems have led to threats to marine ecosystems and environments. It is important to identify ways to measure existing plans and to build capacity for integrated planning.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator only measures the policy formulation and not policy implementation.</p>", "DATA_COMP__GLOBAL"=>"<p>The full methodology for this indicator is available in the document entitled &#x201C;<a href=\"https://wedocs.unep.org/handle/20.500.11822/35086\">Understanding the State of the Ocean: A Global Manual on Measuring SDG 14.1.1, SDG 14.2.1 and SDG 14.5.1</a>&#x201D; (UNEP, 2021).</p>\n<p>This indicator aims to capture Integrated Coastal Zone Management (ICZM) and other area-based, integrated planning and management in place in waters under national jurisdiction, including exclusive economic zones (e.g. marine/maritime spatial planning, Marine Protected Areas (MPAs), marine zoning, sector specific management plans).</p>\n<p>To score this indicator, countries should: </p>\n<ol>\n  <li>Identify national authorities/agencies/organisations responsible for coastal and marine/maritime planning and management.</li>\n  <li>Identify and spatially map the boundaries of ICZM plans or other plans at national, sub-national and local level. Coordinate with the national authorities/agencies/organisations responsible for coastal and marine/maritime planning and management to complete a questionnaire on the ICZM plans.</li>\n  <li>Determine the status of implementation of each plan, and categorise the spatial map according to implementation stages:</li>\n</ol>\n<p>1) Initial plan preparation.</p>\n<p>2) Plan development.</p>\n<p>3) Plan adoption/designation.</p>\n<p>4) Implementation and adaptive management.</p>\n<p>It is recommended that the collected responses include a spatial map showing the boundaries of relevant plans. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) and the Regional Seas will be carried out extensive data validation procedures that include built-in automated procedures, manual checks and cross-references to national sources of data. Communication is carried out with countries for clarification and validation of data. Only data that are considered accurate or those confirmed by countries during the validation process are reported by UNEP on the SDG Global Database.</p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made.</p>", "IMPUTATION__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) and the Regional Seas do not make any estimation or imputation for missing values, so the number of data points provided are actual country data. </p>", "REG_AGG__GLOBAL"=>"<p>The data will be aggregated at the sub-regional, regional and global levels by counting the number of countries with a plan for each group.</p>", "DOC_METHOD__GLOBAL"=>"<p>The full methodology for this indicator is available in the document entitled &#x201C;<a href=\"https://wedocs.unep.org/handle/20.500.11822/35086\">Understanding the State of the Ocean: A Global Manual on Measuring SDG 14.1.1, SDG 14.2.1 and SDG 14.5.1</a>&#x201D; (UNEP, 2021).</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided by the United Nations Environment Programme (UNEP) and the Regional Seas.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided by the United Nations Environment Programme (UNEP) and the Regional Seas in cooperation with the countries that provide these data.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided by the United Nations Environment Programme (UNEP) and the Regional Seas.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are available for all UN Member States reporting national data.</p>\n<p><strong>Time series:</strong></p>\n<p>Time series have different lengths for different UN Member States (depending on the availability of data at the national level).</p>\n<p><strong>Disaggregation:</strong></p>\n<p>By implementation stage: </p>\n<ul>\n  <li>Initial plan preparation</li>\n  <li>Plan development</li>\n  <li>Plan adoption/designation</li>\n  <li>Implementation and adaptive management</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies: </strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><a href=\"https://www.unep.org/explore-topics/oceans-seas/what-we-do/regional-seas-programme\">Regional Seas Programme website</a> </p>\n<p><a href=\"https://wedocs.unep.org/handle/20.500.11822/35086\">Understanding the State of the Ocean: A Global Manual on Measuring SDG 14.1.1, SDG 14.2.1 and SDG 14.5.1</a> (UNEP, 2021) </p>\n<p><a href=\"https://wedocs.unep.org/bitstream/handle/20.500.11822/26440/MSP_ICZM_Guidelines.pdf?sequence=1&amp;isAllowed=y\">Conceptual guidelines for the application of Marine Spatial Planning and Integrated Coastal Zone Management approaches to support the achievement of Sustainable Development Goal Targets 14.1 and 14.2</a> (UNEP, 2018)</p>", "indicator_sort_order"=>"14-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"14.3.1", "slug"=>"14-3-1", "name"=>"Acidez media del mar (pH) medida en un conjunto convenido de estaciones de muestreo representativas", "url"=>"/site/es/14-3-1/", "sort"=>"140301", "goal_number"=>"14", "target_number"=>"14.3", "global"=>{"name"=>"Acidez media del mar (pH) medida en un conjunto convenido de estaciones de muestreo representativas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Acidez media del mar (pH) medida en un conjunto convenido de estaciones de muestreo representativas", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Acidez media del mar (pH) medida en un conjunto convenido de estaciones de muestreo representativas", "indicator_number"=>"14.3.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Mantenimiento", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>"", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Acidez media del mar (pH) medida en un conjunto convenido de estaciones de muestreo representativas", "definicion"=>"Acidez media del mar (pH) medida en un conjunto convenido de estaciones de muestreo representativas", "formula"=>"\n$${AMM}^t = \\overline{pH^t}$$\n\ndonde:\n\n$\\overline{pH^t}=$ media aritmética del pH in-situ recogido el día 15 de cada mes en las estaciones de medición en el año $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"La asignación de las estaciones de medición por comunidades autónomas se realiza atendiendo a su punto de tierra más cercano ", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El océano absorbe hasta el 30 % de las emisiones anuales de CO2 antropogénico a la atmósfera, \nlo que ayuda a mitigar los impactos del cambio climático en el planeta. Sin embargo, \nesto tiene un alto coste ecológico, ya que el CO2 absorbido reacciona con el agua \nde mar y provoca cambios en la composición química de los carbonatos disueltos, \nincluyendo un aumento de la acidez en el medio marino (disminución del pH del agua \nde mar). \n\nLa acidificación de los océanos tiene consecuencias potencialmente directas para la vida \nmarina y repercute en los servicios que prestan el océano abierto y las zonas costeras, \ncomo la alimentación y los medios de vida, el turismo, la protección costera, la identidad cultural, \nel transporte y la recreación. Los impactos de la acidificación de los océanos en los \nservicios oceánicos pueden reducirse mediante un monitoreo adecuado y una mejor comprensión \nde la variabilidad y las tasas de cambio, lo que ayuda a fundamentar las estrategias \nde mitigación y/o adaptación.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-03-01.pdf\">Metadatos 14-3-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Acidez media del mar (pH) medida en un conjunto convenido de estaciones de muestreo representativas", "definicion"=>"Average sea acidity (pH) measured at an agreed set of representative sampling stations", "formula"=>"\n$${AMM}^t = \\overline{pH^t}$$\n\nwhere:\n\n$\\overline{pH^t}=$ arithmetic mean of the in-situ pH collected on the 15th of each month at the measuring stations in year $t$\n", "desagregacion"=>nil, "periodicidad"=>"Anual", "observaciones"=>"The allocation of measuring stations by autonomous communities is carried out according to their nearest land point", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The ocean absorbs up to 30% of the annual emissions of anthropogenic CO2 to the atmosphere, helping \nto alleviate the impacts of climate change on the planet. However, this comes at a steep ecological cost, \nas the absorbed CO2 reacts with seawater and results in shifts in the dissolved carbonate chemistry \nincluding increased acidity levels in the marine environment (decreased seawater pH). \n\nOcean acidification has potentially direct consequences for marine life and cascades through to the \nservices provided by the open ocean and coastal areas including food and livelihood, tourism, coastal \nprotection, cultural identity, transportation and recreation. The impacts on ocean services from ocean \nacidification may be lessened through appropriate monitoring and improved understanding of variability \nand rates of change, helping to inform mitigation and/or adaptation strategies. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-03-01.pdf\">Metadata 14-3-1.pdf</a> ", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Acidez media del mar (pH) medida en un conjunto convenido de estaciones de muestreo representativas", "definicion"=>"Acidez media del mar (pH) medida en un conjunto convenido de estaciones de muestreo representativas", "formula"=>"\n$${AMM}^t = \\overline{pH^t}$$\n\nnon:\n\n$\\overline{pH^t}=$ neurketa-estazioetan hil bakoitzaren 15ean jasotako pH in situ neurketaren batez besteko aritmetikoa $t$ urtean\n", "desagregacion"=>nil, "periodicidad"=>"Anual", "observaciones"=>"La asignación de las estaciones de medición por comunidades autónomas se realiza atendiendo a su punto de tierra más cercano ", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El océano absorbe hasta el 30 % de las emisiones anuales de CO2 antropogénico a la atmósfera, \nlo que ayuda a mitigar los impactos del cambio climático en el planeta. Sin embargo, \nesto tiene un alto coste ecológico, ya que el CO2 absorbido reacciona con el agua \nde mar y provoca cambios en la composición química de los carbonatos disueltos, \nincluyendo un aumento de la acidez en el medio marino (disminución del pH del agua \nde mar). \n\nLa acidificación de los océanos tiene consecuencias potencialmente directas para la vida \nmarina y repercute en los servicios que prestan el océano abierto y las zonas costeras, \ncomo la alimentación y los medios de vida, el turismo, la protección costera, la identidad cultural, \nel transporte y la recreación. Los impactos de la acidificación de los océanos en los \nservicios oceánicos pueden reducirse mediante un monitoreo adecuado y una mejor comprensión \nde la variabilidad y las tasas de cambio, lo que ayuda a fundamentar las estrategias \nde mitigación y/o adaptación.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-03-01.pdf\">Metadatuak 14-3-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 14.3: Minimize and address the impacts of ocean acidification, including through enhanced scientific cooperation at all levels</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 14.3.1: Average marine acidity (pH) measured at agreed suite of representative sampling stations</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_OAW_MNACD - Average marine acidity (pH) measured at agreed suite of representative sampling stations [14.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>14.a.1 Increase scientific knowledge, develop research capacity and transfer marine technology, taking into account the Intergovernmental Oceanographic Commission Criteria and Guidelines on the Transfer of Marine Technology, in order to improve ocean health and to enhance the contribution of marine biodiversity to the development of developing countries, in particular small island developing States and least developed countries</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Intergovernmental Oceanographic Commission (IOC) of UNESCO</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Intergovernmental Oceanographic Commission (IOC) of UNESCO</p>", "STAT_CONC_DEF__GLOBAL"=>"<h2>Definitions:</h2>\n<p>Ocean acidification is the decrease of seawater pH over an extended period, typically of decades or longer, which is caused primarily by the uptake of carbon dioxide from the atmosphere<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>. </p>\n<p>This indicator is based on observations that constrain the ocean carbon system and which are required to describe the variability in ocean acidity. The carbon system in this context mainly refers to the four measurable parameters: pH (the concentration of hydrogen ions on a logarithmic scale), DIC (CT; total dissolved inorganic carbon), <em>p</em>CO<sub>2</sub> (carbon dioxide partial pressure), and TA (AT, total alkalinity). Average, as used here, is the equally weighed annual mean. </p>\n<p>An agreed suite of representative sampling stations are sites that have a measurement frequency that is adequate for describing variability and trends in carbonate chemistry in order to deliver critical information on the exposure of and impacts on marine systems to ocean acidification, and which provide data of sufficient quality and with comprehensive metadata information to enable integration with data from other sites in the country. </p>\n<p><strong>Concepts:</strong></p>\n<p>Ocean acidification is caused by an increase in the amount of dissolved atmospheric CO<sub>2</sub> in the seawater. The average marine acidity is expressed as pH, the concentration of hydrogen ions on a logarithmic scale. In order to be able to constrain the carbonate chemistry of seawater, it is necessary to measure at least two of the four parameters, i.e. pH, <em>p</em>CO<sub>2</sub>, DIC (CT), and TA (AT). pH (the concentration of hydrogen ions on a logarithmic scale, expressed on total scale), DIC (total dissolved inorganic carbon, in &#x3BC;mol kg<sup>-1</sup>), <em>p</em>CO<sub>2</sub> (carbon dioxide partial pressure, in ppt or &#x3BC;atm), and TA (AT, total alkalinity, in &#x3BC;mol kg<sup>-1</sup>).</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p>NOAA. What is ocean acidification? National Ocean Service website <a href=\"https://oceanservice.noaa.gov/facts/acidification.html\">https://oceanservice.noaa.gov/facts/acidification.html</a>, 06/25/18 <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>pH on total scale</p>\n<p>and/or pCO<sub>2</sub> [&#x3BC;atm or ppt], DIC [&#x3BC;mol kg<sup>-1</sup>], TA [&#x3BC;mol kg<sup>-1</sup>]</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The SDG indicator 14.3.1 methodology was endorsed by IOC Member States at the Fifty-first Session of the IOC Executive Council (IOC/EC-LI/2 Annex 6 rev). In November 2018, the SDG indicator 14.3.1 was upgraded to Tier II by the UN Inter&#x2011;agency and Expert Group on SDG indicators (IAEG-SDGs). The methodology was further community approved as an Ocean Best Practice (<a href=\"http://dx.doi.org/10.25607/OBP-655\">http://dx.doi.org/10.25607/OBP-655</a>). </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The general IOC data collection process is described in Document <a href=\"http://www.ioc-unesco.org/index.php?option=com_oe&amp;task=viewDocumentRecord&amp;docID=19589\">IOC-XXIX/2Annex 14</a>.</p>\n<p>The novelty of assessing ocean acidification at the global level, as in indicator 14.3.1, requires the IOC secretariat to collect the data via different pathways. Data collections are a mixture of:</p>\n<ul>\n  <li>direct requests to National Statistical Offices (NSOs), as new national reporting mechanisms are now installed allowing them to provide the required information (from the 2021 data collection onwards),</li>\n  <li>annual requests to the IOC national focal points,</li>\n  <li>collaboration with National Oceanographic Data Centres, international data centres and </li>\n  <li>directly with data providers via the GOA-ON data portal (Figure 1).</li>\n</ul>\n<p><img src=\"data:image/png;base64,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\"></p>\n<p>Figure 1. Scheme to illustrate the proposed data collection and publication process related to national contributions of data related to 14.3.1 (SDG: Sustainable Development Goal; IOC-UNESCO: Intergovernmental Oceanographic Commission of UNESCO; GOA-ON: Global Ocean Acidification &#x2013; Observing Network; JCOMM: WMO-IOC Joint Technical Commission for Oceanography and Marine Meteorology; WMO: World Meteorological Association; IODE: International Oceanographic Data and Information Exchange of IOC UNESCO; GDAC: Global Data Assembly Center; BGC ARGO: Biogeochemical Argo floats; QC: Quality Control; NODC: National Oceanographic Data Centre; DOI: Digital Object Identifier; BP: Best Practice; CD: Capacity Development; PI: Principal Investigator; RTC: Regional Training Centre).</p>\n<p>Global scientific efforts (<a href=\"http://www.go-ship.org/\">GO-SHIP</a>, <a href=\"https://www.socat.info/\">SOCAT</a>, <a href=\"https://public.wmo.int/en/programmes/global-climate-observing-system\">GCOS</a>) which host and feature data from various ocean observing efforts and/or focus on collecting measurements in international waters will also be queried for annual or more likely multi-year data sets representing status and change of ocean acidification variables in the open ocean. </p>\n<p>The data collection process takes place in close collaboration with the IOC Project Office for IODE Oostende, Belgium and relevant data providers/national archives, the GOA-ON data portal, and entities such as the marine chemistry part of the European Marine Observation and Data Network (EMODnet). Since 2019 IOC invites all data providers to use the newly established SDG 14.3.1 Data Portal (<a href=\"http://oa.iode.org\">http://oa.iode.org</a>). This SDG 14.3.1 Data Portal is a tool for the submission, collection, validation, storage and sharing of ocean acidification data and metadata submitted towards the Sustainable Development Goal 14.3.1 Indicator: Average marine acidity (pH) measured at agreed suite of representative sampling stations. Besides allowing for a direct submission of metadata and data, the portal further provides the full text of the SDG 14.3.1 Indicator Methodology, the data template, the metadata template and the metadata instructions file. Since 2020 a newly developed FAQ section facilitates the provision of 14.3.1 data. IOC is developing a federated data system to automatically harvest data from other relevant ocean carbon databases and repositories into the SDG 14.3.1 Indicator database.</p>\n<p>Furthermore the<a href=\"http://portal.goa-on.org/\"> GOA-ON data portal</a> features open access data, in addition to a global monitoring asset inventory. The portal is designed to offer two levels of access: 1) visualization and 2) download capabilities. Combining different open-access data sets may provide incentives to create new observing systems in under-sampled areas and to increase the application of open access data policies worldwide, according to the IOC Criteria and Guidelines for the Transfer of Marine Technology (2005) in the future.</p>\n<p>Furthermore, the GOA-ON website hosts a number of pages dedicated to the SDG 14.3.1 methodology: <a href=\"http://goa-on.org/sdg_14.3.1/sdg_14.3.1.php\">http://goa-on.org/sdg_14.3.1/sdg_14.3.1.php</a>. </p>", "COLL_METHOD__GLOBAL"=>"<p>The official counterparts are the IOC focal points. They, as well as National Oceanographic Data Centres (NODCs), are contacted by IOC to request relevant data from the appropriate national oceanographic data centres and/or relevant scientists, agencies or programmes. An annual data submission request is sent out via IOC Circular Letters directly to the member states asking for the respective data and metadata (through circular letter <a href=\"https://oceanexpert.org/document/26312\">2792</a> in 2019, circular letter <a href=\"https://oceanexpert.org/document/27502\">2815</a> in 2020 and circular letter <a href=\"https://unesdoc.unesco.org/ark:/48223/pf0000379637\">2859</a> in 2021). New updates and the inclusion of new features to the SDG 14.3.1 data portal to be developed in 2022 will facilitate with collaboration with other existing ocean carbon data centres and biogeochemical data platforms. </p>\n<p>Furthermore, IOC benefits from direct contributions from ocean acidification scientists organized within the Global Ocean Acidification Observing Network (GOA-ON) to the SDG 14.3.1 data portal. </p>\n<p>All contributors of data to SDG 14.3.1 are encouraged to read and follow the standard operating procedures provided in Dickson et al. 2007. This document covers ocean carbon chemistry, sample-handling techniques, quality assurance procedures, the use of Certified Reference Materials (CRMs) and Standard Operating Procedures (SOPs) for discrete sampling of pH, <em>p</em>CO<sub>2</sub>, TA, and DIC. Data contributors are also encouraged to read the Guide to Best Practices in Ocean Acidification Research and Data Reporting, which focuses on best practices for laboratory experiments, but also includes background on carbon chemistry (Riebesell et al. 2010). For coastal environments, which can be subject to large variability and a range of influences, such as nutrient and freshwater inputs, guidelines for the measurement of pH and carbonate chemistry can be found here (Pimenta and Grear 2018).</p>\n<p>All data submitted to SDG 14.3.1 must include an estimate of measurement uncertainty in the metadata. Autonomous sensors for pH and <em>p</em>CO<sub>2</sub> require calibration and maintenance to validate sensor performance and identify drift or sensor malfunction. Where possible, the analysis of discrete bottle samples analysed for pH, DIC or TA collected next to the sensors can be used to calculate pH and <em>p</em>CO<sub>2</sub>.</p>\n<p>All ocean acidity datasets submitted to SDG 14.3.1 must also include associated temperature (in situ [and temperature of measurement if different than in situ]), salinity, and pressure (sampling depth). If submitting pH values, all pH values must be on the total scale (Dickson et al. 2007).</p>", "FREQ_COLL__GLOBAL"=>"<p>National data sets should be reported annually (at the least), following the request by IOC Circular letters. However, experts, national focal points of Member States and NODCs are invited to submit data throughout the year via the SDG 14.3.1 data portal. The invitation via a Circular Letter will be sent during the second semester of each year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released in February each year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The general IOC data collection process is described in Document IOC-XXIX/2Annex 14.</p>\n<p>The novelty of assessing ocean acidification at the global level, as for this indicator 14.3.1, requires the IOC secretariat to collect the data via a range of different pathways. This will include direct requests to National Statistical Offices (NSOs), annual requests to the IOC national focal points, and NODCs and associated data agencies in the member states, as well as international data centres and individual data providers.</p>", "COMPILING_ORG__GLOBAL"=>"<p>The Intergovernmental Oceanographic Commission (IOC) of UNESCO is the custodian agency for this Indicator. In collaboration with the International Oceanographic Data and Information Exchange (IODE) of IOC, the data will be collected and stored in a transparent and traceable manner, allowing for the ocean acidification data to be shared. IOC welcomes data sets which can be freely shared without restrictions (CC0, CC BY), with restrictions for commercial use (CC BY-NC), as well as those which only allow for IOC-UNESCO to derive products used for the purpose of the SDG indicator 14.3.1 reporting (<a href=\"http://oa.iode.org\">http://oa.iode.org</a>). </p>", "INST_MANDATE__GLOBAL"=>"<p>IOC-UNESCO is the custodian agency for the SDG indicator 14.3.1. The purpose of the Commission is to promote international cooperation and to coordinate programmes in research, services and capacity-building, in order to learn more about the nature and resources of the ocean and coastal areas and to apply that knowledge for the improvement of management, sustainable development, the protection of the marine environment, and the decision-making processes of its Member States. In addition, IOC is recognized through the United Nations Convention on the Law of the Sea (UNCLOS) as a competent international organization in the fields of Marine Scientific Research (Part XIII) and Transfer of Marine Technology (Part XIV). </p>\n<p>According to its Statutes, the Commission may act also as a joint specialized mechanism of the organizations of the United Nations system that have agreed to use the Commission for discharging certain of their responsibilities in the fields of marine sciences and ocean services, and have agreed accordingly to sustain the work of the Commission. IOC is further one of the organizations supporting the Global Ocean Acidification Observing Network (GOA-ON) (<a href=\"http://goa-on.org\">http://goa-on.org</a>). The Commission hosts one part of the distributed GOA-ON Secretariat, fostering science collaboration and capacity building in ocean acidification observations. GOA-ON actively encourages its members to collect and report metadata and data relevant for the SDG indicator 14.3.1.</p>", "RATIONALE__GLOBAL"=>"<p>The ocean absorbs up to 30% of the annual emissions of anthropogenic CO<sub>2</sub> to the atmosphere, helping to alleviate the impacts of climate change on the planet. However, this comes at a steep ecological cost, as the absorbed CO<sub>2</sub> reacts with seawater and results in shifts in the dissolved carbonate chemistry including increased acidity levels in the marine environment (decreased seawater pH). The observed changes have been shown to cause a range of responses at the organism level that can affect biodiversity, ecosystem structure and food security. For example, a decrease in dissolved carbonate reduces the solubility of carbonate minerals including aragonite and calcite, the two main forms of calcium carbonate used by marine species to form shells and skeletal material (e.g. reef building corals and shelled molluscs). Aragonite is the more soluble form and its availability for shell building by organisms such as corals and oysters, called the aragonite saturation state [&#x3A9; (aragonite)], is used together with pH as an indicator in tracking the progression of ocean acidification. In addition, of equal importance to some key marine organisms, is the dissolved CO<sub>2</sub> and bicarbonate concentration. It is, therefore, of the upmost urgency that a full categorization of the changing carbonate system is delivered.</p>\n<p>Regular observations of marine acidity at open-ocean locations over the past 20-30 years have revealed a clear trend of decreasing pH and that present-day conditions are often outside preindustrial bounds. Observational trends in coastal areas have been reported to be more difficult to determine. In some regions, the changes are amplified by natural processes like upwelling (whereby cold, often CO<sub>2</sub> and nutrient rich, water from the deep rises toward the sea surface). In addition, other factors, including freshwater run-off, ice-melting, nutrients, biological activity, temperature change and large ocean oscillations influencing carbon dioxide levels, particularly in coastal waters, need to be taken into account when interpreting drivers of ocean acidification and the related impacts. Ocean acidification has potentially direct consequences for marine life and cascades through to the services provided by the open ocean and coastal areas including food and livelihood, tourism, coastal protection, cultural identity, transportation and recreation. The impacts on ocean services from ocean acidification may be lessened through appropriate monitoring and improved understanding of variability and rates of change, helping to inform mitigation and/or adaptation strategies.</p>\n<p>Although this indicator requests &#x201C;average acidity&#x201D; values from nations, the data which comprises the average ought to provide insight into the variability of the measurements, which is more relevant for the impact on marine life. In other words, species do not respond to &#x201C;average&#x201D; conditions, but to real time conditions. At a minimum, the total range (minimum and maximum values) should be reported in addition to the average.</p>\n<p>Coastal countries often have long-term monitoring of water quality, including information on nutrient concentrations, temperature, salinity and occasionally carbonate chemistry. These water quality monitoring sites provide historical context about biogeochemical variability of the system and should be considered ideal location for ocean acidification monitoring. Additional sites may also need to be established to characterize variability.</p>\n<p>The data variables associated with the monitoring of ocean acidification (variables include pH, carbon dioxide partial pressure [<em>p</em>CO<sub>2</sub>], total dissolved inorganic carbon [DIC], and total alkalinity [TA]) have the potential to serve global, national, regional, and local data needs, such as tracking the exposure of marine ecosystems and aquaculture sites to corrosive conditions, and identifying opportunities to reduce ecosystem and economic vulnerability to ocean acidification. For example, local monitoring of pH and aragonite saturation state on the Pacific coast of the United States has enabled shellfish farmers to adapt to damaging conditions present during upwelling events, which reduce pH and threaten brood stock.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The methodology for this indicator has been developed with the technical support of experts in the field of ocean acidification. It provides globally accepted and adapted guidelines and best practices established by scientists and published in peer-reviewed literature. </p>\n<p>As this is a highly complex indicator, the technical infrastructure necessary for the correct measurement is a potentially constraining factor. The Methodology for the indicator describes how to avoid comparability issues of the data, which have been problematic in the past, as well as measurement errors and advises on the most appropriate technical and methodological procedures to guarantee high-quality data that can be used for the global assessment of ocean acidification. The addition of metadata to the methodology for this indicator is crucial for adding traceability and transparency to the data, by providing information on the precise equipment and methodology used, as well as specifying the location, accompanying biogeochemical variables and the person taking the measurement.</p>", "DATA_COMP__GLOBAL"=>"<p>Detailed information in <a href=\"https://unesdoc.unesco.org/ark:/48223/pf0000265127?3=null&amp;queryId=f3a23122-f713-4f5c-aa88-5e73275ff5f6\">Attachment I IOC/EC-LI/2 Annex 6</a>.</p>\n<p>This indicator calls for the collection of multiple observations, in the form of individual data points, to capture the variability in ocean acidity. Individual data points for pH either are measured directly or can be calculated based on data for two of the other carbonate chemistry parameters, these being TA (AT), DIC (CT) and <em>p</em>CO<sub>2</sub>. Calculation tools developed by experts in the field are freely available, and they are introduced and linked in the methodology. Average pH is defined as the annual equally weighed mean of multiple data points at representative sampling stations. The exact number of samples and data points depends on the level of variability of ocean acidity at the site in question. The minimum number of samples should enable the characterisation of a seasonal cycle at the site. Detailed guidelines on the minimum number of observations required are provided in the Methodology (<a href=\"https://oa.iode.org\">https://oa.iode.org</a>). </p>\n<p>In addition to the data value, standard deviation and the total range (minimum and maximum values measured), as well as underlying data used to provide traceability and transparency (metadata information) should be reported. All reported values should have gone through a first level quality control by the data provider. If historical data is available, this should be released to enable calculations about the rate of change and to compare natural variability and anthropogenic effects.</p>\n<p>Relevant data from 2010 onwards are accepted.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The counterparts are invited to provide references (<a href=\"https://oceanexpert.org/document/26001\">metadata</a>) for the information provided. Data provided by experts, who are not representatives of NODCs or IOC Member States, are sent for national validation to the relevant official counterparts.</p>\n<p>Further IOC receives verified information by the identified representatives of its Member States directly, which entails the validation necessary to be published for the SDG indicator 14.3.1 assessments.</p>", "ADJUSTMENT__GLOBAL"=>"<p>The 14.3.1 data and metadata files give detailed information about the requested data and metadata to report. Data and metadata files contain compulsory variables to be reported and additional variables to be included if available.</p>\n<p>Data providers/Member States are encouraged to submit primary quality controlled data sets of two variables characterizing the carbonate system: pH, TA, DIC or <em>p</em>CO<sub>2</sub>, plus precise location, temperature, salinity and hydrostatic pressure (sampling depth) (see Quality control). Depending on data quality, different categories will be assigned to the submitted data sets. In addition, corresponding macro nutrient concentrations are requested, if nitrate, phosphate and silicate data are available (see Data quality). Further, data providers will be invited to submit all data, independent of where the data were collected within the water column; however, they are encouraged to provide surface data (&#x2264; 10 m). </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Some missing values may be modelled or calculated if established methodologies exist (see Recommendations for calculation of the carbonate system in IOC/EC-LI/2 Annex 6).</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Regional aggregates are permissible if more than 50% of coastal nations have reported values.</p>", "REG_AGG__GLOBAL"=>"<p>Every country or nominated IODE National Oceanographic Data Centre (NODC)/Associated Data Unit (ADU)<sup><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> </sup>will provide annually updated data sets. Aggregations across regions will require data of comparable quality and all relevant metadata with site-specific information be included in the data sets. Due to the variability of measurements and the prevalence of areas with high variability in ocean acidity, the aggregation of measurement averages (equally weighed annual mean) across coastal marine habitat and ecosystem types is difficult to interpret and is therefore discouraged. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> https://www.iode.org/index.php?option=com_oe&amp;task=viewGroupRecord&amp;groupID=349 <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "DOC_METHOD__GLOBAL"=>"<p>The SDG 14.3.1 Indicator Methodology presented in the document <a href=\"https://unesdoc.unesco.org/ark:/48223/pf0000265127?3=null&amp;queryId=f3a23122-f713-4f5c-aa88-5e73275ff5f6\">IOC-XXIX/2Annex 14, IOC/EC-LI/2 Annex 6</a> provides guidelines for the collection of measurements towards the Indicator. Data and metadata files in which all of the relevant measurements should be compiled will be provided to the data centre or data originator. This data will be collected by the relevant national data centers, such as National Statistical Offices (NSOs) and National Oceanographic Data Centers (NODCs), and shared with the Indicator&#x2019;s custodian agency, the IOC of UNESCO.</p>\n<p>The Indicator Methodology comprises an overview of statements on best practice and links to several Standard Operating Procedures (SOPs). These procedures represent the best practices compiled by the leading researcher in the field and have been made freely available. A list of relevant material, as referenced in the Indicator Methodology, can be found here: <a href=\"http://www.ioccp.org/index.php/documents/standards-and-methods\">http://www.ioccp.org/index.php/documents/standards-and-methods</a></p>\n<p>The collection of samples followed by their analysis according to the methods and standards included in the SDG 14.3.1 Indicator Methodology is of the greatest importance for the production of data that can be collated towards the global comparison of ocean acidification data of known quality under this indicator. Guidance on how to collect, analyse and manage the data is provided in the methodology and associated metadata and its metadata instruction file. </p>\n<p>The document <a href=\"https://unesdoc.unesco.org/ark:/48223/pf0000265127?3=null&amp;queryId=f3a23122-f713-4f5c-aa88-5e73275ff5f6\">IOC-XXIX/2Annex 14, IOC/EC-LI/2 Annex 6</a> further provides guidance on sampling strategies, sampling frequencies, recommendations for calculation of the carbonate system and uncertainty of measurement.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>For the purposes of SDG 14.3.1, three categories of measurement quality were established (adapted from Newton et al. 2015):</p>\n<p><u>Category 1: Climate quality</u></p>\n<p>The climate quality objective is typically used to determine trends in the open ocean, shelf and coastal waters, providing data on seasonal through interannual variability on regional scales. The climate quality objective requires that a change in the dissolved carbonate ion concentration to be estimated at a particular site with a relative standard uncertainty of 1%. The carbonate ion concentration is calculated from two of the four carbonate system parameters and implies an uncertainty of approximately 0.003 in pH; of 2 &#x3BC;mol kg<sup>&#x2013;1</sup> in measurements of TA and DIC; and a relative uncertainty of about 0.5% in the <em>p</em>CO<sub>2</sub>. Such precision is only currently achievable by a limited number of laboratories and is not typically achievable for all parameters by even the best autonomous sensors.</p>\n<p><u>Category 2: Weather quality </u></p>\n<p>The weather quality objective is suitable for many coastal and nearshore environments, particularly those with restricted circulation or where CO<sub>2</sub> system parameters are forced by processes like upwelling, pollution or freshwater inputs that can cause large variability. The weather objective requires the carbonate ion concentration (used to calculate saturation state) to have a relative standard uncertainty of 10%. This implies an uncertainty of approximately 0.02 in pH; of 10 &#x3BC;mol kg<sup>&#x2013;1</sup> in measurements of TA and DIC; and a relative uncertainty of about 2.5% in <em>p</em>CO<sub>2</sub>. Such precision should be achievable in competent laboratories, and is also achievable with the best autonomous sensors. </p>\n<p><u>Category 3: Measurements of undefined quality </u></p>\n<p>For SDG 14.3.1, pH measurements using glass electrodes will be considered Category 3 due to the challenges of using glass pH electrodes in seawater. It is intended that the methodology provided here gives useful information for countries building capacity towards Category 1 and 2 measurements. For example, carefully calibrated glass pH electrodes may help in the identification of coastal ocean acidification hot spots and help prioritize future monitoring plans. In annual SDG 14.3.1 summary products, Category 3 measurement sites will be presented as data collection sites only, no data values will be visualized. </p>\n<p>All those contributing data to SDG 14.3.1 are encouraged to adopt measurement quality Category 1 or 2. A variety of capacity development activities to support Member States&#x2019; capacity in this regard are conducted by different organizations (more information can be found here: e.g. <a href=\"http://www.iaea.org/ocean-acidification\">www.iaea.org/ocean-acidification</a>; <a href=\"http://ioccp.org\">http://ioccp.org</a>; <a href=\"http://www.ioc-cd.org/index.php\">http://www.ioc-cd.org/index.php</a>; <a href=\"http://www.whoi.edu/courses/OCB-OA/\">http://www.whoi.edu/courses/OCB-OA/</a>). </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data quality control and validation processes were developed in close consultation with experts in the field of ocean acidification, amongst them members of the Global Ocean Acidification Observing Network (GOA-ON) and data management experts, like the ones at IODE. Data quality control is a critical component of the data analysis, submission and processing. Scientists and technicians who collected the submitted data will be responsible for the primary quality control of the data and accompanying detailed metadata. The metadata submitted with the data must also describe the quality control standard operating procedures (SOPs) followed for each parameter.</p>\n<p><u>Primary quality control by data provider consists of:</u></p>\n<ul>\n  <li>Quality control that is attached to the methodology (CRMs, tris buffer calibration, SOPs are provided),</li>\n  <li>Quality control and quality assurance of the actual data (SOPs are provided) and usage of community agreed quality flags,</li>\n  <li>Identifying and flagging of outliers, </li>\n  <li>Making determinations regarding validity of those outlying points,</li>\n  <li>Estimating uncertainty of the measurement,</li>\n  <li>Identifying all the sources of uncertainty in the measurements,</li>\n  <li>Rolling up the individual uncertainties into overall uncertainty (error propagation).</li>\n</ul>\n<p><u>Secondary quality control by IOC Secretariat and experts:</u></p>\n<ul>\n  <li>Harmonization of the data and ensuring metadata completeness,</li>\n  <li>External quality control/audit &#x2013; Expert QC Group applying the weather and climate levels as defined by GOA-ON (following the example of SOCAT),</li>\n  <li>Feedback to data holders.</li>\n</ul>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Following the quality control management and assurance mechanisms described in 4.i and 4.j, three categories of measurement quality will be attributed to the individual data sets:</p>\n<ol>\n  <li>Established oceanographic climate quality (Category 1).</li>\n  <li>Weather quality data including that from sensors and capacity building simplified pH and alkalinity measurements, with appropriate uncertainty assessment (Category 2).</li>\n  <li>Measurements of undefined quality (Category 3) (will not be displayed in the visualization of annual weighted means and variance of pH).</li>\n</ol>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Metadata and data availability continuously increase. Since 2021, SDG 14.3.1 data from different national and national data bases are provided directly to a dedicated data portal (<a href=\"http://oa.iode.org\">http://oa.iode.org</a>). This data portal features a wide range of metadata and additional data characterizing the carbonate system of the seawater, not available at the global SDG database.</p>\n<p>In order to close existing data gaps to a) measure ocean acidification and b) to report SDG indicator 14.3.1 metadata and data, IOC, together with partners, conducts trainings and webinars. A new ocean acidification online course is now available. (https://classroom.oceanteacher.org/tag/index.php?tc=1&amp;tag=Ocean%20acidification). Past and future trainings are announced on the Ocean Expert (<a href=\"https://oceanexpert.org/events/calendar\">https://oceanexpert.org/events/calendar</a>) and GOA-ON website (<a href=\"http://goa-on.org/news/news.php\">http://goa-on.org/news/news.php</a>).</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Countries provide complete data sets with respective site-specific data and metadata files.</p>", "COMPARABILITY__GLOBAL"=>"<p>As this indicator only considers data submitted by Member States, there are no discrepancies between estimates and submitted data sets. In the past, differences between countries in the measurement of pH and other ocean acidification data were mainly attributable to technical difficulties and the lack of comprehensive guidelines for the best practice of measurements. The present Methodology and the guidelines contained within provide detailed instructions on the measurement, collection, treatment and quality control of data in a way that will enable countries to avoid future discrepancies. </p>", "OTHER_DOC__GLOBAL"=>"<h2>Main URLs:</h2>\n<p>IOC-UNESCO <a href=\"http://www.ioc-unesco.org/\">http://www.ioc-unesco.org/</a></p>\n<p>IODE <a href=\"https://www.iode.org/\">https://www.iode.org/</a>; https://oa.iode.org</p>\n<p>GOA-ON <a href=\"http://goa-on.org/\">http://goa-on.org/</a></p>\n<p>GOA-ON data portal <a href=\"http://portal.goa-on.org/\">http://portal.goa-on.org/</a></p>\n<p>Document IOC/EC-LI/2 Annex 6 -14.3.1 Methodology <a href=\"http://ioc-unesco.org/index.php?option=com_oe&amp;task=viewDocumentRecord&amp;docID=21938\">http://ioc-unesco.org/index.php?option=com_oe&amp;task=viewDocumentRecord&amp;docID=21938</a></p>\n<p>Document IOC-XXIX/2Annex 14 <a href=\"http://www.ioc-unesco.org/index.php?option=com_oe&amp;task=viewDocumentRecord&amp;docID=19589\">http://www.ioc-unesco.org/index.php?option=com_oe&amp;task=viewDocumentRecord&amp;docID=19589</a></p>\n<h2>References:</h2>\n<p>Dickson, A.G., Sabine, C.L. and Christian, J.R. (Eds.) (2007) Guide to best practices for ocean CO<sub>2</sub> measurements. PICES Special Publication 3, 191 pp.</p>\n<p>Feely, R. A., Byrne, R. H., Acker, J. G., Betzer, P. R., Chen, C. T. A., Gendron, J. F., &amp; Lamb, M. F. (1988). Winter-summer variations of calcite and aragonite saturation in the northeast Pacific. Marine Chemistry, 25(3), 227-241.</p>\n<p>Intergovernmental Oceanographic Commission. <em>IOC Criteria and Guidelines on the Transfer of Marine Technology (CGTMT)/ Crit&#xE8;res et principes directeurs de la COI concernant le Transfert de Techniques Marines (CPTTM)</em>. Paris, UNESCO, 2005. 68pp. (IOC Information document, 1203)</p>\n<p>McLaughlin, K., Weisberg, S.B., Dickson, A.G., Hofmann, G.E., Newton, J.A., Aseltine-Neilson, D., Barton, A., Cudd, S., Feely, R.A., R.A. Jefferds, R.A., Jewett, E.B., King, T., Langdon, C.J., McAfee, S., Pleschner-Steele, D. and Steele, B. (2015) Core principles of the California Current Acidification Network: Linking chemistry, physics, and ecological effects. Oceanography 28(2):160&#x2013;169, <a href=\"http://dx.doi.org/10.5670/oceanog.2015.39\">http://dx.doi.org/10.5670/oceanog.2015.39</a>. </p>\n<p>Newton J. A., Feeley, R. A., Jewett, E. B., Williamson, P. and Mathis, J. (2015) Global Ocean Acidification Observing Network: Requirements and Governance Plan (2<sup>nd</sup> edition)</p>\n<p>Pimenta, A.R. and Grear, J.S. (2018) EPA Guidelines for Measuring Changes in Seawater pH and Associated Carbonate Chemistry in Coastal Environments of the Eastern United States. Office of Research and Development, National Health and Environmental Effects Research Laboratory. EPA/600/R-17/483 </p>\n<p>Riebesell U., Fabry V. J., Hansson L. &amp; Gattuso J.-P. (Eds.) (2011) Guide to best practices for ocean acidification research and data reporting. Luxembourg, Publications Office of the European Union, 258pp. (EUR 24872 EN). </p>\n<p>Tilbrook, B., Jewett, E.B., DeGrandpre, M.D., Hernandez-Ayon, J.M., Feely, R.A., Gledhill, D.K., Hansson, L., Isensee, K., Kurz, M.L., Newton, J.A. and Siedlecki, S.A., 2019. An enhanced ocean acidification observing network: from people to technology to data synthesis and information exchange. Frontiers in Marine Science, 6, p.337.</p>", "indicator_sort_order"=>"14-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"14.4.1", "slug"=>"14-4-1", "name"=>"Proporción de poblaciones de peces cuyos niveles son biológicamente sostenibles", "url"=>"/site/es/14-4-1/", "sort"=>"140401", "goal_number"=>"14", "target_number"=>"14.4", "global"=>{"name"=>"Proporción de poblaciones de peces cuyos niveles son biológicamente sostenibles"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de poblaciones de peces cuyos niveles son biológicamente sostenibles", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de poblaciones de peces cuyos niveles son biológicamente sostenibles", "indicator_number"=>"14.4.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La Convención de las Naciones Unidas sobre el Derecho del Mar (UNCLOS), el Acuerdo de \nlas Naciones Unidas sobre las Poblaciones de Peces (UNFSA, 1995) y el Código de \nConducta para la Pesca Responsable de la FAO (FAO, 1995) exigen el mantenimiento o \nrestablecimiento de las poblaciones de peces a niveles que permitan alcanzar su \nrendimiento máximo sostenible (RMS). \n\nPara cumplir los objetivos de estos tratados internacionales, las autoridades de gestión \npesquera deben evaluar el estado de las poblaciones de peces y desarrollar \npolíticas y estrategias de gestión eficaces.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.4.1&seriesCode=ER_H2O_FWTL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de poblaciones de peces dentro de niveles biológicamente sostenibles (no sobreexplotadas) (%) ER_H2O_FWT</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-04-01.pdf\">Metadatos 14-4-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The United Nations (UN) Convention on the Law of the Sea (UNCLOS), the United Nations Fish Stocks \nAgreement (UNFSA [UN, 1995]) and FAO Code of Conduct for Responsible Fisheries (FAO, 1995a) all \nrequire maintaining or restoring fish stocks at levels that are capable of producing their maximum \nsustainable yield (MSY). \n\nTo fulfil the objectives of these international treaties, fishery management \nauthorities need to undertake assessment of the state of fish stocks and develop effective policies and \nmanagement strategies. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.4.1&seriesCode=ER_H2O_FWTL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of fish stocks within biologically sustainable levels (not overexploited) (%) ER_H2O_FWT</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-04-01.pdf\">Metadata 14-4-1.pdf</a> ", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La Convención de las Naciones Unidas sobre el Derecho del Mar (UNCLOS), el Acuerdo de \nlas Naciones Unidas sobre las Poblaciones de Peces (UNFSA, 1995) y el Código de \nConducta para la Pesca Responsable de la FAO (FAO, 1995) exigen el mantenimiento o \nrestablecimiento de las poblaciones de peces a niveles que permitan alcanzar su \nrendimiento máximo sostenible (RMS). \n\nPara cumplir los objetivos de estos tratados internacionales, las autoridades de gestión \npesquera deben evaluar el estado de las poblaciones de peces y desarrollar \npolíticas y estrategias de gestión eficaces.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.4.1&seriesCode=ER_H2O_FWTL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Biologikoki jasangarriak diren mailetan (gehiegi ustiatu gabe) dauden arrain-populazioen proportzioa (%) ER_H2O_FWT</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-04-01.pdf\">Metadatuak 14-4-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 14.4: By 2020, effectively regulate harvesting and end overfishing, illegal, unreported and unregulated fishing and destructive fishing practices and implement science-based management plans, in order to restore fish stocks in the shortest time feasible, at least to levels that can produce maximum sustainable yield as determined by their biological characteristics</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 14.4.1: Proportion of fish stocks within biologically sustainable levels</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_H2O_FWTL - Proportion of fish stocks within biologically sustainable levels (not overexploited) [14.4.1] </p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 14.7.1: Sustainable fisheries as a percentage of GDP in small island developing States, least developed countries and all countries</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator, &quot;Proportion of marine fish stocks within biologically sustainable levels&quot;, measures the sustainability of the world&apos;s marine capture fisheries by the abundance of the exploited fish stocks with respect to MSY levels.</p>\n<p>For each level of reporting (National, Regional, Global) the indicator is calculated as the ratio between the number of exploited fish stocks classified as &quot;within biologically sustainable levels&quot; and the total number of stocks in the reference list that were classified with a determined status (within/not within &quot;biologically sustainable levels&quot;).</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <mi>s</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mi>N</mi>\n      </mrow>\n    </mfrac>\n    <mi>x</mi>\n    <mn>100</mn>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>u</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n    <mi>x</mi>\n    <mn>100</mn>\n  </math></p>\n<p>where Ps is the percentage of stocks classified as &quot;within biologically sustainable levels&quot; for the reference list of stocks. Ns is the number of stocks in the reference list classified as &quot;within biologically sustainable levels&quot;, Nu is the number of stocks in the reference list classified as &quot;outside biologically sustainable levels&quot; and N = Ns + Nu is the total number of stocks in the reference list that have been classified as within or outside &quot;biologically sustainable levels&quot;.</p>\n<p>Classifying individual stocks as within/outside &quot;biologically sustainable levels&quot;:</p>\n<p>In order to keep consistency with the 14.4 target (&quot;at least to levels that can produce maximum sustainable yield as determined by their biological characteristics&quot; and other earlier international agreements, including the United Nations Convention on the Law of the Sea (UNCLOS), a fish stock is classified as &quot;within biologically sustainable levels&quot; if its abundance is estimated (considering uncertainty) to be equal to or greater than the level that can produce the Maximum Sustainable Yield (MSY). In contrast, when abundance falls below the MSY level, the stock is classified as &quot;outside biologically sustainable levels&quot;.</p>\n<p>A wide array of methods and approaches (including documented expert opinion) is used to classify stock status relative to the abundance producing MSY. This varies among countries, regions and stocks. Nevertheless, the reliability of the classification is assessed by FAO as part of the process of producing the index.</p>\n<p><strong>Maximum Sustainable Yield (MSY)</strong> is commonly defined as the greatest average amount of catch that can be harvested in the long-term from a stock under constant and current environmental conditions (e.g., habitat, water conditions, species composition and interactions, and anything that could affect birth, growth, or death rates of the stock), without affecting the long-term productivity of the stock. A stock can produce MSY if its abundance is above a certain level, usually around 50% of its unexploited abundance (but actual value can vary around that level, depending on the biological characteristics of the stock). See more at <a href=\"https://www.fao.org/faoterm/en/?defaultCollId=21\">https://www.fao.org/faoterm/en/?defaultCollId=21</a></p>\n<p>MSY-based reference points are the most common type of reference points used in fisheries management today. This is primarily because, for decades, reference points from surplus production models have most often been set based on the concept of MSY and they are the basic benchmarks for the sustainability of fisheries set by the UN Convention on the Law of the Sea (UNCLOS, Article 61(3)). For more on Reference Points in Fish Stock Assessment, see Caddy and Mahon (1995), Cadima (2003) or Haddon (2011).</p>\n<p><strong>BMSY</strong>: Biomass corresponding to Maximum Sustainable Yield from a production model or from an age-based analysis using a stock recruitment model. Often used as a biological reference point in fisheries management, it is the calculated long-term average biomass value expected if fishing at FMSY.</p>\n<p><strong>A population is:</strong> &#x201C;A group of individuals of the same species living in the same area at the same time and sharing a common gene pool, with little or no immigration or emigration.&#x201D;</p>\n<p><strong>A biological stock is: </strong>&#x201C;A subpopulation of a species inhabiting a particular geographic area, having similar biological characteristics (e.g. growth, reproduction, mortality) and negligible genetic mixing with other adjacent subpopulations of the same species.&quot; (FAO, 2004-2021).</p>\n<p>The <strong>reference list of Stocks:</strong> it is not possible to classify the sustainability of exploitation for all the exploited stocks from a country, region or the world. Therefore, the indicator must be calculated based on a subset of these stocks. The list of the stocks that are classified for status and used to calculate the indicators is called the &quot;reference list of Stocks&quot;.</p>\n<p>The reference list of Stocks is built differently for the Regional/Global and the National levels. The process of building the reference list of Stocks for regional and global level are described in FAO (2011). At National level, countries are requested to define a list of stocks, based on an agreed set of criteria (Appendix 1). National and shared stocks can be included, but not straddling stocks (stocks that are distributed both in national EEZ and Areas Beyond National Jurisdiction).</p>\n<p>At this moment, there is not a direct correspondence between the national level reference lists (that are defined by each country) and the regional and global Reference lists (that are defined by FAO).</p>\n<p>FAO is working on an update of its methodology at global/regional level (FAO, 2024) which also addresses needs for convergence with the national level. Full results of the application of this updated methodology are planned to be published in 2026.</p>\n<p><a id=\"move956633181111111111111111\"></a></p>\n<p>The detailed description of all necessary concepts can be found in the e-learning course (FAO 2019-2021).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>FAO Major Fishing Areas for Statistical Purposes</p>\n<p>ASFIS List of Species for Fishery Statistics Purposes</p>\n<p>UNFSA Stock Jurisdictional distribution</p>\n<p>FIRMS typology of stock units</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The classification of the status of exploited stocks relative to the abundance that can produce MSY is often established through a formal stock assessment process. The data to inform stock assessments can come from many different sources, including fishery-dependent and fishery-independent sources. Fishery-dependent data are collected from the fishery itself, using both commercial and recreational sources through reporting or sample-based surveys at sea, at landing sites, or within fishing communities. They can include information on removals of fish from the sea, which can include landings and discards, and information on the fleet such as number of boats, number of tows, time spent on the sea, as well as economic and social information like fish prices, fuel expenditures, total sales, employment or other.</p>\n<p>Fishery-independent data are obtained in ways not related to any fishing activity and are typically collected by scientists via surveys (often scientific cruises) designed to estimate species abundance and biomass over long time series, and over consistent seasons and geographic areas. Typically, fisheries-independent data also include biological information on the species (age, length, weight, maturity, etc.), and habitat and environmental information (temperature, salinity, depth, etc.).</p>\n<p>These data and other information are used by stock assessment scientists to classify the stock status. References on the methods most commonly used can be found in Cadima (2003), Haddon (2011), Sparre and Venema (1998) and other publications dealing with the methods of stock assessment.</p>\n<p>The information used for the indicator at the Global/Regional level is based on a different process and data sources than that used for the national level.</p>\n<p>Global/Regional:</p>\n<p>Because of the high data demands of classical stock assessment methods, only a limited number of fish stocks have been assessed. These species account for ca 50 percent of the global catch (Hilborn et al., 2020), and most are caught by industrial fisheries in developed countries. To balance the global representativeness of the assessment results and the goal of using the best available information, FAO uses a wide spectrum of data and methods to extend its assessment to the fish stocks that account for the majority (70-80 percent) of the global catch (FAO, 2011).</p>\n<p>National:</p>\n<p>The national level indicator, on the other hand, is based exclusively on the stock status reported by countries. A multiplicity of methods are used to classify the stock status, including model-based estimates, empirical indicators and documented expert opinion.</p>\n<p>For country reporting, a questionnaire is sent out to all FAO member states with marine boundaries (i.e. 165 states, 11 territories, and three Caspian Sea border countries) on a two-year basis. For the most recent and complete list of questions used to inform this indicator, please visit <a href=\"https://www.fao.org/fileadmin/user_upload/faoweb/statistics/questionnaires/SDG_14.4.1_questionnaire_2022_en.xlsx\">https://www.fao.org/fileadmin/user_upload/faoweb/statistics/questionnaires/SDG_14.4.1_questionnaire_2022_en.xlsx</a> .</p>", "COLL_METHOD__GLOBAL"=>"<p>At this moment, data collection is separate for the national and regional/global levels.</p>\n<p>Global/regional level: </p>\n<p>The fish stocks that FAO has monitored since 1974 represent a wide spectrum of data availability, ranging from data-rich and formally assessed stocks to those that have very little information apart from catch statistics by FAO major fishing area and those with no stock assessment at all. For the purposes of using the best available data and information and maintaining consistency among stocks and assessors, a procedure has been defined to identify relevant stock status information (FAO 2011).</p>\n<p>National level: </p>\n<p>FAO collects national data through a questionnaire sent to the Principal Focal Point (PFP) of each country. The PFP organizes an institutional set-up which identifies the competent authorities to develop a reference list of stocks and completes the questionnaire. </p>\n<p>During the initial stages of national data reporting, the information or data collected through the questionnaire from a country will initially only inform the indicator for the individual countries, also acknowledging the need for a learning curve along the few first questionnaire inquiries. As a result, the global/regional indicator remains separate from the national indicators during these initial stages. However, FAO is working on a convergence (where possible) of the two processes, and good-quality stock status assessments reported by countries for the national indicators will be included in the regional/global indicator calculations, depending on the evolution and further standardization of country reporting over the next 3-5 years.</p>\n<p>Despite this effort, due to the heterogeneity of reporting from countries in the same FAO Major Fishing Area, and the necessary inclusion of straddling and highly migratory stocks and fisheries in the regional and global indicator, it is unlikely that a full convergence will be achieved in a short time-frame.</p>\n<p>The indicator is applicable for countries with marine borders (or those bordering the Caspian Sea) and therefore excludes landlocked countries from data collection and processing. </p>", "FREQ_COLL__GLOBAL"=>"<p>National : Reporting every 2 years beginning in November 2019.</p>\n<p>Global/regional: every 2 years since 2013. The data collection calendar for the national level may be adjusted in the future according to requirements for a convergence between national and global/regional processes.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>National: biennially. </p>\n<p>Global/regional: biennially</p>", "DATA_SOURCE__GLOBAL"=>"<p>FAO provides global and regional data. National-level data are generally reported by the National Statistics Office or the Ministry of Fisheries and/or Agriculture.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "INST_MANDATE__GLOBAL"=>"<p>The Food and Agriculture Organization of the United Nations (FAO) is the lead United Nations agency for agriculture, forestry, fisheries and rural development. As part of its mandate, it fosters global, regional and national sustainable development initiatives to secure responsible fisheries worldwide, which in turn requires maintaining fish stocks at biologically sustainable levels, so that they can contribute fully, and in a sustainable way, to the food and nutrition security, as well as to social and economic development, of humankind.</p>\n<p>Specifically, the mission of FAO<a href=\"http://www.fao.org/fishery-aquaculture/en/\" target=\"_blank\"> Fisheries and Aquaculture Division (NFI)</a> is stated as &quot;To strengthen global governance and the managerial and technical capacities of members and to lead consensus-building towards improved conservation and utilization of aquatic resources&quot;.</p>\n<p>As part of its mandate, FAO is also tasked with collecting and disseminating data and information for improved planning and management of fisheries, aquaculture and the other food-producing sectors of the economy.</p>\n<p>Article I of FAO constitution requires that the organization collect, analyses, interpret and disseminate information relating to nutrition, food and agriculture (the term &#x201C;agriculture&#x201D; and its derivatives includes forestry, fisheries and aquaculture, <a href=\"http://www.fao.org/3/K8024E/K8024E.pdf\">http://www.fao.org/3/K8024E/K8024E.pdf</a>).</p>\n<p>The first session of FAO Conference in 1945 provided the basis and rationale for FAO mandate as a custodian agency of this indicator: &#x201C;If FAO is to carry out its work successfully it will need to know where and why hunger and malnutrition exist, what forms they take, and how widespread they are. Such data will serve as a basis for making plans, determining the efficacy of measures used, and measuring progress from time to time.&#x201D;</p>", "RATIONALE__GLOBAL"=>"<p>The United Nations (UN) Convention on the Law of the Sea (UNCLOS), the United Nations Fish Stocks Agreement (UNFSA [UN, 1995]) and FAO Code of Conduct for Responsible Fisheries (FAO, 1995a) all require maintaining or restoring fish stocks at levels that are capable of producing their maximum sustainable yield (MSY). To fulfil the objectives of these international treaties, fishery management authorities need to undertake assessment of the state of fish stocks and develop effective policies and management strategies. As a UN Agency with a mandate for fisheries, FAO endeavours to provide the international community with the best information on the state of marine fishery resources.</p>\n<p>Since 1974, FAO has been periodically assessing and reporting the state of marine fishery resources using a wide spectrum of methods from numerical models to data-limited approaches. FAO global and regional estimates were also used as an MDG indicator for Goal 7 on environment during the period 2000-2015. This facilitated its approval as a Tier I SDG indicator by the 2nd IAEG-SDG in October 2015.</p>\n<p>The indicator has a peculiar nature compared to more conventional SDG indicators. The indicator estimates the sustainability of fish stocks that often move across national boundaries. This led the indicator to be initially reported only at global and regional levels, with regions not corresponding to continental MDG or SDG regions but to marine regions termed &#x201C;FAO Major Fishing Areas&#x201D;.</p>\n<p>The Global SDG Indicator Framework is a voluntary mechanism, but countries are required to report if data are available. As a custodian agency, FAO works to put in action the 2030 Agenda&#x2019;s emphasis on country ownership and raise the incentive to take action at country, regional and global levels. FAO has developed, since 2019, a questionnaire approach to allow individual countries to report on the sustainability of fish stocks. The approach 1) provides a framework for meaningful country-level reporting that complements but does not alter the core methodology of SDG indicator 14.4.1 at the global/regional levels (FAO, 2011), and 2) provides countries with simplified methods to carry out fish stock assessment in data-limited contexts, to some extent overcoming the technical barriers that traditional methods have presented. This is because country-level reporting will be limited to the assessment of stocks that are found only within a country&#x2019;s EEZ and/or shared with neighbouring countries&#x2019; EEZs, and therefore do not include straddling stocks, highly migratory species, or stocks in Areas Beyond National Jurisdiction (ABNJ). As a result, national data alone cannot be meaningfully aggregated at global/regional levels, but it can be used to inform country progress on fish stock sustainability within the EEZ. </p>\n<p>In 2019, FAO began sending a questionnaire to countries to collect national data with the aim to help countries in the reporting process.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator measures the sustainability of fishery resources, and as an end-result, is a measure of Target 14.4. Its derivation requires catch and fishing effort data and/or other biological or technical data and parameters together with the scientific expertise necessary to correctly perform stock assessment. The indicator at the global level is estimated by FAO based on methodology developed in the 1980s. Although regular updates were carried out to incorporate technical advances and changes in major fish species, some discrepancies between regions may occur in the representativeness of the reference list. However, this will not pose a large impact on the reliability of the indicator&#x2019;s temporal trends.</p>\n<p>For the national level, the composition of stocks within the reference list of stocks and the selection criteria used to develop the list will vary between countries, making the indicator suitable for checking countries&#x2019; own progress over time. </p>", "DATA_COMP__GLOBAL"=>"<p>FAO currently reports the global and regional indicators calculated from FAO&#x2019;s assessment of a selected reference list of fish stocks around the world. The methodology is described in FAO Technical Paper (FAO 2011). </p>\n<p>FAO has been developing the new approach for country-level reporting since 2017, and has consulted with countries in three dedicated expert consultation workshops: in November 2017, FAO convened a workshop to exchange views with national practitioners on the proposed new analytical methods to produce Indicator 14.4.1 at country level<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>. In February 2019, FAO convened an expert consultation workshop<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> on development of the methodologies for the global assessment of fish stock status, with participants from countries and regional fisheries organizations. In order to help countries reporting on the indicator, FAO then organized a series of capacity development workshops on stock status assessment and estimation methods of SDG Indicator 14.4.1 for most regions. </p>\n<p>In November 2019, FAO dispatched the first SDG14.4.1 questionnaire calling countries to report on their national indicator. Eighty-three countries submitted their questionnaire and three reported independently. </p>\n<p>For each level of reporting (national, regional, global) the indicator is calculated as the ratio between the number of exploited fish stocks classified as &quot;within biologically sustainable levels&quot; and the total number of stocks in the reference list that were classified with a determined status (within/not within &quot;biologically sustainable levels&quot;).</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <mi>s</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mi>N</mi>\n      </mrow>\n    </mfrac>\n    <mi>x</mi>\n    <mn>100</mn>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n          </mrow>\n        </msub>\n        <mo>+</mo>\n        <msub>\n          <mrow>\n            <mi>N</mi>\n          </mrow>\n          <mrow>\n            <mi>u</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n    <mi>x</mi>\n    <mn>100</mn>\n  </math></p>\n<p>where Ps is the percent of stocks classified as &quot;within biologically sustainable levels&quot; for the reference list of stocks. Ns is the number of stocks in the reference list classified as &quot;within biologically sustainable levels&quot;, Nu is the number of stocks in the reference list classified as &quot;outside biologically sustainable levels&quot; and N = Ns + Nu is the total number of stocks in the reference list that have been classified as within or outside &quot;biologically sustainable levels&quot;.</p>\n<p>To classify individual stocks as within/outside &quot;biologically sustainable levels&quot;:</p>\n<p>To keep consistency with the 14.4 target (&quot;at least to levels that can produce maximum sustainable yield as determined by their biological characteristics&quot; and other earlier international agreements, including the United Nations Convention on the Law of the Sea (UNCLOS)), a fish stock is classified as &quot;within biologically sustainable levels&quot; if its abundance is estimated to be (considering uncertainty) at or greater than the level that can produce the Maximum Sustainable Yield (MSY). In contrast, when abundance falls below the MSY level, the stock is classified as &quot;outside biologically sustainable levels&quot;.</p>\n<p>A wide array of methods and approaches (including documented expert opinion) is used to classify stock status relative to the abundance producing MSY. This varies among countries, regions and stocks. Nevertheless, the reliability of the classification is assessed by FAO as part of the process of producing the index.</p>\n<p>Global/Regional:</p>\n<p>Global and regional estimates of stock sustainability have been performed for 584 fish stocks around the world since 1974, representing 70% of global landings. The status of each stock is estimated using the methodology described in FAO Technical Paper (FAO, 2011). </p>\n<p>National:</p>\n<p>Countries are requested to report the status of a reference list of fish stocks defined by each country on the basis of the criteria presented in Appendix 1.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Full report accessible here: http://www.fao.org/documents/card/en/c/I8714EN/ <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> Full report accessible here: http://www.fao.org/3/ca4355en/ca4355en.pdf <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>FAO carries out a series of validations to assure that the data and information are provided by countries in line with the questionnaire instructions. The validation process consists of: (i) identification of errors, mistakes and missing values in the data and, (ii) correcting errors, mistakes and missing values in close consultation with the countries concerned. Each country is asked either to confirm that the data provided are correct or to provide remarks and / or revise data accordingly if they identify any errors.</p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments were applied for the time series</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x25CF; At regional and global levels</strong></p>\n<p>To ensure completeness of regional and global information on stocks, FAO gathers additional information outside of what is provided by each country, in particular concerning the highly migratory and straddling fish stocks. For shared stocks, FAO may consult with regional fisheries bodies (RFBs), who are mandated to assess and manage stocks with their contracting parties, in order to receive information and data and conduct stock assessments when necessary. </p>\n<p><strong>&#x25CF; At country level</strong></p>\n<p>This indicator examines marine fish stocks. If a country has no marine capture fisheries, then the indicator is not calculated for that country and FAO reports it to UNSD with the flag &#x201C;N&#x201D; (Not relevant), with the exception of countries surrounding the Caspian Sea. When data are missing at the national level, no imputation is performed to derive estimates. The indicator at regional and global levels was estimated not based on country questionnaires, but by FAO through a systematic assessment of a reference list of stocks selected globally. </p>", "REG_AGG__GLOBAL"=>"<p>As explained in the &#x201C;Rationale&#x201D; section, national data alone cannot be meaningfully aggregated at global/regional levels because country-level reporting will be limited to the assessment of stocks that are found only within a country&#x2019;s EEZ (including stocks shared with neighbouring countries&#x2019; EEZs), and will therefore not include straddling stocks, highly migratory species, or stocks in Areas Beyond National Jurisdiction (ABNJ). Therefore, regional &#x201C;aggregates&#x201D; by FAO Major Fishing Area and the global indicator value are calculated with a specific approach, as described in the FAO Technical Paper (FAO 2011).</p>", "DOC_METHOD__GLOBAL"=>"<p>In each country, the data available for each stock and expertise level to conduct different types of assessments will differ. Some countries may have classic stock assessments already conducted for many of their stocks, while others may have very few or no assessments available. </p>\n<p>For some countries, little stock assessment has been done. To help these countries and to facilitate their reporting, FAO prepared online materials and tools, including a selection of methods that can be used to evaluate stock status with data limited methods such as length-based and catch-only methods and an online platform for hands-on practice. The strengths and limitations of these methods are discussed in an eLearning course<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup> (Lesson 4), and caveats were also provided to avoid misuse and encourage caution in their use. Furthermore, capacity development workshops have been organized to provide support to countries in stock assessment and reporting on the Indicator SDG 14.4.1.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p>eLearning course: <a href=\"https://elearning.fao.org/course/view.php?id=502\">https://elearning.fao.org/course/view.php?id=502</a> <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "QUALITY_MGMNT__GLOBAL"=>"<p>FAO has in place the necessary frameworks and procedures for quality assurance of the SDG indicators data, according to the Fundamental Principles of Official Statistics and the FAO Statistics Quality Assurance Framework (SQAF) available at: <a href=\"http://www.fao.org/docrep/019/i3664e/i3664e.pdf\">http://www.fao.org/docrep/019/i3664e/i3664e.pdf</a>.</p>\n<p> </p>\n<p>FAO is systematically carrying out quality assessments to ensure the quality of the SDG indicator data sets. </p>\n<p>For this indicator, a systematic cross-checking of the various source data was carried out during the overall compilation process of national and regional data.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>FAO carries out a quality assurance review to help with consistency and correctness of this reporting process. The review is performed in two steps to quantify the level of confidence that can be attributed to national reporting: 1) to verify that the questionnaire has been correctly and sufficiently filled out and complies with the reporting guidelines, and 2) to assess the reliability of the responses relative to the supporting information reported by the country. Reliability is based on the compliance to the guidelines in developing the reference list of stocks, the proportion of stocks with official assessments, the source of stock assessments (e.g. RFB, peer-reviewed, expert knowledge), the amount of data available at the stock level, and the consistency with regional assessments (for shared stocks). FAO provides feedback to respondents, who have an opportunity to adjust their submission. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment reveals that quality is highly dependent on the primary data which undergoes the applicable validation procedures before dissemination. The outcomes of the calculations are also controlled and compared inside and among FAO fishing areas. Global and regional aggregates are assessed by considering and evaluating the contributions of regional fisheries bodies while ensuring consistency of the entire time series for the global indicator, with reference to the published methodology (FAO, 2011). In addition, an internal summary report on the annual assessment of the quality of country data is also produced.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Global/regional: the indicator has global and regional data on a biennial basis from 1974 to 2021. Regional breakdown is by FAO major fishing area. The regional and global indicators were calculated based on the reference list of fish stocks FAO established in 1974. Countries are not directly involved in the computation of the indicator at global/regional level. </p>\n<p>National: the national-level questionnaire was dispatched for the first time in November 2019 and then on a biennial basis since 2022; FAO identifies 165 countries with a marine border, 11 territories with a marine border, and three countries with Caspian Sea border, as being eligible, in principle, to report. Of these, data for the indicator was reported for about half.</p>\n<p><strong>Time series:</strong></p>\n<p>Global/regional level: from 1974 to 2021.</p>\n<p>National level: On a biennial basis since 2019</p>\n<p><strong>Disaggregation:</strong></p>\n<p>By FAO major marine fishing areas for statistical purposes<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup>.</p>\n<p>Taxonomically, FAO publishes the indicator separately for straddling stocks (mostly tuna and tuna like species).</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p><a href=\"https://www.fao.org/fishery/en/area/search\">https://www.fao.org/fishery/en/area/search</a> and <a href=\"https://www.fao.org/cwp-on-fishery-statistics/handbook/general-concepts/main-water-areas/en/\">https://www.fao.org/cwp-on-fishery-statistics/handbook/general-concepts/main-water-areas/en/</a> <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The indicator is estimated by FAO based on the methodology developed in the 1980s (FAO, 2011). Although regular updates were carried out to incorporate technical advances and changes in major fish species, some discrepancies between regions may occur in the representativeness of the reference list of stocks. However, this will not pose a large impact on the reliability of the global indicator&#x2019;s temporal trends, which covers 75% of global landings.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<h2>FAO 2016-2021. Sustainable Development Goals. Indicator 14.4.1 - Proportion of fish stocks within biologically sustainable levels. <a href=\"http://www.fao.org/sustainable-development-goals/indicators/1441/en/\">http://www.fao.org/sustainable-development-goals/indicators/1441/en/</a> </h2>\n<p>FAO 2019-2021. SDG 14.4.1 eLearning course. <a href=\"https://elearning.fao.org/course/view.php?id=502\">https://elearning.fao.org/course/view.php?id=502</a></p>\n<p>FAO 2015-2021. CWP handbook of fishery statistical standards. Fishing areas for statistical purpose. <a href=\"https://www.fao.org/cwp-on-fishery-statistics/handbook/general-concepts/main-water-areas/en/\">https://www.fao.org/cwp-on-fishery-statistics/handbook/general-concepts/main-water-areas/en/</a> </p>\n<p>FAO 2015-2021. CWP handbook of fishery statistical standards. Identifiers for aquatic animals and plants: <a href=\"http://www.fao.org/cwp-on-fishery-statistics/handbook/general-concepts/identifiers-for-aquatic-animals-and-plants/en/\">http://www.fao.org/cwp-on-fishery-statistics/handbook/general-concepts/identifiers-for-aquatic-animals-and-plants/en/</a></p>\n<p>FAO 2004-2021. FIRMS Information Management Policy - Annex 1.2 - List of reference terms for Marine Resources. Updated June 2019. <a href=\"http://www.fao.org/3/a-ax530e.pdf\">http://www.fao.org/3/a-ax530e.pdf</a></p>\n<p>Questionnaire for national reporting of SDG indicator 14.4.1: https://www.fao.org/fileadmin/user_upload/faoweb/statistics/questionnaires/SDG_14.4.1_questionnaire_2022_en.xlsx</p>\n<p><strong>References:</strong></p>\n<p>Branch, T.A., Jensen, O.P., Ricard, D., Ye, Y. &amp; Hilborn, R. (2011) Contrasting global trends in marine fishery status obtained from catches and from stock assessments. Conservation Biology, 25: 777&#x2013;783. doi: 10.1111/j.1523-1739.2011.01687.x.</p>\n<p>Caddy, J.R. and Mahon, R. (1995). Reference Points for fisheries management. FAO Fisheries Technical Paper. No. 337. Rome, FAO. 83p.</p>\n<p>Cadima, E.L. (2003) Fish stock assessment manual. FAO Fisheries Technical Paper. No. 393. Rome, FAO. 161p.</p>\n<p>FAO (1995) Code of conduct for responsible fisheries. 41 pp.</p>\n<p>FAO (2005) Review of the state of world marine fishery resources. FAO Fisheries Technical Paper No. 457. Rome. 235 pp</p>\n<p>FAO (2011) Review of the state of world marine fishery resources. FAO technical paper 569: <a href=\"http://www.fao.org/docrep/015/i2389e/i2389e00.htm\">http://www.fao.org/docrep/015/i2389e/i2389e00.htm</a>. </p>\n<p>FAO (2024) The State of World Fisheries and Aquaculture &#x2013; Blue Transformation in Action</p>\n<p>Haddon, M. (2011). Modelling and Quantitative Methods in Fisheries 2nd Edition. Chapman and Hall/CRC. 465 p.</p>\n<p>Hilborn, R., R.O. Amoroso, C.M. Anderson, J.K. Baum, T.A. Branch, C. Costello, C.L. de Moor, et al. 2020. &#x201C;Effective Fisheries Management Instrumental in Improving Fish Stock Status.&#x201D; Proceedings of the National Academy of Sciences of the United States of America 117 (4): 2218&#x2013;24. <a href=\"https://doi.org/10.1073/\">https://doi.org/10.1073/</a> pnas.1909726116.</p>\n<p>Sparre P. &amp; Venema, S.C. (1998). Introduction to tropical fish stock assessment. Part 1. Manual. FAO Fisheries Technical Paper. No. 306.1, Rev. 2. Rome, FAO. 407p.</p>\n<p>UN (1995) Agreement for the implementation of the provisions of the United Nations Convention on the Law of the Sea of 10 December 1982 relating to the conservation and management of straddling fish stocks and highly migratory fish stocks. 40 pp.</p>\n<p>Appendix 1</p>\n<p>Guidelines to establish reference list of stocks.</p>\n<p>The reference list of stocks should include only marine species of national stocks, i.e. a biological stock distributed within a country&#x2019;s national jurisdiction area; or shared stocks if the biological stock is distributed across the national jurisdictions of neighbouring countries, and/or possibly the competence area of a regional fisheries management organization. This list of fish stocks will ideally include existing assessment units or management units, and also possibly other unassessed fish stocks that are fished in a given country. The list will exclude stocks straddling in the high seas, mostly tuna and tuna-like species.</p>\n<p>This list should:</p>\n<ol>\n  <li>Include finfish, crustaceans, molluscs and other aquatic animals, and exclude aquatic mammals, reptiles, seaweeds and other aquatic plants;</li>\n  <li>Represent at least 60% (a higher percent is preferred when possible) of the national total landed and/or reported catch (Total in Tonnes excluding landings from straddling stocks). Information should be provided on all of the stocks that contribute to this top 60% (or more) of landings regardless of whether their status is known. Stocks should be input from left to right on the spreadsheet in the order of the largest to smallest total landings for each stock, by Tonnes. Species with multiple different stocks should be input as separate stocks. </li>\n  <li>Contain stocks of major importance in terms of catch, ecosystem role, economic value, historical importance, representative geographic distribution, stocks from both industrial and artisanal fisheries, and social/cultural considerations. If possible, the list should represent stocks of each of these categories for a given country. For example, care should be taken to include fish stocks that are important to small-scale fisheries as well as large-scale industrial fisheries. Consideration for these different categories will vary between countries.</li>\n  <li>Remain unchanged (i.e. for at least 5 years) to better reflect changes in stock status at the national level and minimize the effect of changing the reference list of stocks (i.e., adding, deleting, merging stocks) into the SDG indicator. This will ensure consistency in the indicator calculation and better reflect fish stock sustainability over time.</li>\n</ol>", "indicator_sort_order"=>"14-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"14.5.1", "slug"=>"14-5-1", "name"=>"Cobertura de las zonas protegidas en relación con las zonas marinas", "url"=>"/site/es/14-5-1/", "sort"=>"140501", "goal_number"=>"14", "target_number"=>"14.5", "global"=>{"name"=>"Cobertura de las zonas protegidas en relación con las zonas marinas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Cobertura de las zonas protegidas en relación con las zonas marinas", "graph_titles"=>[], "graph_type"=>"bar", "indicator_name"=>"Cobertura de las zonas protegidas en relación con las zonas marinas", "indicator_number"=>"14.5.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Cobertura de las zonas protegidas en relación con las zonas marinas", "definicion"=>"Porcentaje de los lugares importantes para la biodiversidad marina (aquellos que contribuyen significativamente a la persistencia global de la biodiversidad) que está cubierto por áreas protegidas", "formula"=>"\n$$PKBA_{mar}^{t} = \\frac{APKBA_{mar}^{t}}{KBA_{mar}^{t}} \\cdot 100$$\n\ndonde:\n\n$APKBA_{mar}^{t} =$ superficie de los lugares importantes para la biodiversidad marina cubierta por áreas protegidas en el año $t$\n\n$KBA_{mar} =$ superficie de los lugares importantes para la biodiversidad marina en el año $t$\n", "desagregacion"=>"", "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>"", "justificacion_global"=>"La protección de lugares importantes es vital para frenar la pérdida de biodiversidad \ny garantizar el uso sostenible a largo plazo de los recursos naturales marinos. \nEl establecimiento de áreas protegidas es un mecanismo clave para lograr este objetivo, \ny este indicador sirve para medir el progreso hacia la conservación, la restauración y \nel uso sostenible de los ecosistemas marinos y sus servicios, de conformidad con las \nobligaciones contraídas en virtud de los acuerdos internacionales.\n\nEste indicador aporta información significativa, complementa y amplía las \nestadísticas simples tradicionalmente reportadas sobre el área marina cubierta \npor áreas protegidas. Se calcula dividiendo el área protegida total de un país \nentre su superficie territorial total y multiplicando por 100 (p. ej., Chape et al., 2005). \nEstas estadísticas de cobertura porcentual no reconocen la extrema variación de la importancia \nde la biodiversidad en el espacio (Rodrigues et al., 2004), por lo que corren el riesgo de \ngenerar resultados perversos al proteger áreas extensas en detrimento de las que requieren protección.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.5.1&seriesCode=ER_MRN_MPA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proporción promedio de áreas marinas clave para la biodiversidad (KBA) cubiertas por áreas protegidas (%) ER_MRN_MPA</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-05-01.pdf\">Metadatos 14-5-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Cobertura de las zonas protegidas en relación con las zonas marinas", "definicion"=>"Percentage of important sites for marine biodiversity (those that contribute significantly to the global persistence of biodiversity) that are covered by protected areas", "formula"=>"\n$$PKBA_{mar}^{t} = \\frac{APKBA_{mar}^{t}}{KBA_{mar}^{t}} \\cdot 100$$\n\nwhere:\n\n$APKBA_{mar}^{t} =$ area of important sites for marine biodiversity covered by protected areas in year $t$\n\n$KBA_{mar} =$ surface area of important sites for marine biodiversity in year $t$\n", "desagregacion"=>nil, "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>nil, "justificacion_global"=>"The safeguard of important sites is vital for stemming the decline in biodiversity and ensuring long term \nand sustainable use of marine natural resources. The establishment of protected areas is an important \nmechanism for achieving this aim, and this indicator serves as a means of measuring progress toward the \nconservation, restoration and sustainable use of marine ecosystems and their services, in line with \nobligations under international agreements. \n\nThis indicator adds meaningful information to, complements and builds from traditionally reported \nsimple statistics of marine area covered by protected areas, computed by dividing the total protected \narea within a country by the total territorial area of the country and multiplying by 100 (e.g., Chape et al. \n2005). Such percentage area coverage statistics do not recognise the extreme variation of biodiversity \nimportance over space (Rodrigues et al. 2004), and so risk generating perverse outcomes through the \nprotection of areas which are large at the expense of those which require protection. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.5.1&seriesCode=ER_MRN_MPA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Average proportion of Marine Key Biodiversity Areas (KBAs) covered by protected areas (%) ER_MRN_MPA</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-05-01.pdf\">Metadata 14-5-1.pdf</a> ", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Cobertura de las zonas protegidas en relación con las zonas marinas", "definicion"=>"Porcentaje de los lugares importantes para la biodiversidad marina (aquellos que contribuyen significativamente a la persistencia global de la biodiversidad) que está cubierto por áreas protegidas", "formula"=>"\n$$PKBA_{mar}^{t} = \\frac{APKBA_{mar}^{t}}{KBA_{mar}^{t}} \\cdot 100$$\n\nnon:\n\n$APKBA_{mar}^{t} =$ eremu babestuek estalitako itsas biodibertsitaterako leku garrantzitsuen azalera $t$ urtean\n\n$KBA_{mar} =$ itsas biodibertsitaterako leku garrantzitsuen azalera $t$ urtean\n", "desagregacion"=>nil, "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>nil, "justificacion_global"=>"La protección de lugares importantes es vital para frenar la pérdida de biodiversidad \ny garantizar el uso sostenible a largo plazo de los recursos naturales marinos. \nEl establecimiento de áreas protegidas es un mecanismo clave para lograr este objetivo, \ny este indicador sirve para medir el progreso hacia la conservación, la restauración y \nel uso sostenible de los ecosistemas marinos y sus servicios, de conformidad con las \nobligaciones contraídas en virtud de los acuerdos internacionales.\n\nEste indicador aporta información significativa, complementa y amplía las \nestadísticas simples tradicionalmente reportadas sobre el área marina cubierta \npor áreas protegidas. Se calcula dividiendo el área protegida total de un país \nentre su superficie territorial total y multiplicando por 100 (p. ej., Chape et al., 2005). \nEstas estadísticas de cobertura porcentual no reconocen la extrema variación de la importancia \nde la biodiversidad en el espacio (Rodrigues et al., 2004), por lo que corren el riesgo de \ngenerar resultados perversos al proteger áreas extensas en detrimento de las que requieren protección.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.5.1&seriesCode=ER_MRN_MPA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Babestutako eremuek estalitako biodibertsitaterako funtsezko itsas eremuen (KBA) batez besteko proportzioa (%) ER_MRN_MPA</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-05-01.pdf\">Metadatuak 14-5-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 14.5: By 2020, conserve at least 10 per cent of coastal and marine areas, consistent with national and international law and based on the best available scientific information</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 14.5.1: Coverage of protected areas in relation to marine areas</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_MRN_MPA - Average proportion of Marine Key Biodiversity Areas (KBAs) covered by protected areas [14.5.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Other relevant indicators include:</p>\n<p>SDG 15.1.2 Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type.</p>\n<p>SDG 15.4.1 Coverage by protected areas of important sites for mountain biodiversity.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>BirdLife International (BLI)</p>\n<p>International Union for Conservation of Nature (IUCN)</p>\n<p>UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)</p>\n<p>UN Environment Programme</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>BirdLife International (BLI)</p>\n<p>International Union for Conservation of Nature (IUCN)</p>\n<p>UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator Coverage of protected areas in relation to marine areas shows trends over time in the mean percentage of each important site for marine biodiversity (i.e., those that contribute significantly to the global persistence of biodiversity) that is covered by designated protected areas and Other Effective Area-based Conservation Measures (OECMs).</p>\n<p><strong>Concepts:</strong></p>\n<p>Protected areas, as defined by the IUCN (IUCN; Dudley 2008), are clearly defined geographical spaces, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) (Mean percentage of each marine KBA covered by (i.e. overlapping with) protected areas and/or OECM.)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Protected Areas are defined as described above by IUCN (IUCN; Dudley 2008) and documented in the World Database on Protected Areas (WDPA). (<a href=\"http://www.protectedplanet.net\">www.protectedplanet.net</a>).</p>\n<p>Importantly, a variety of specific management objectives are recognised within this definition, spanning conservation, restoration, and sustainable use:</p>\n<p>- Category Ia: Strict nature reserve</p>\n<p>- Category Ib: Wilderness area</p>\n<p>- Category II: National park</p>\n<p>- Category III: Natural monument or feature</p>\n<p>- Category IV: Habitat/species management area</p>\n<p>- Category V: Protected landscape/seascape</p>\n<p>- Category VI: Protected area with sustainable use of natural resources</p>\n<p>The status &quot;designated&quot; is attributed to a protected area when the corresponding authority, according to national legislation or common practice (e.g., by means of an executive decree or the like), officially endorses a document of designation. The designation must be made for the purpose of biodiversity conservation, not de facto protection arising because of some other activity (e.g., military).</p>\n<p>Data on protected areas are managed in the WDPA (www.protectedplanet.net) by UNEP-WCMC.</p>\n<p>OECMs are defined as described above by the Convention on Biological Diversity (CBD 2018) and documented in the World Database on Other Effective Area-based Conservation Measures (WDOECM) (<a href=\"http://www.protectedplanet.net/en/thematic-areas/oecms\">www.protectedplanet.net/en/thematic-areas/oecms</a>).</p>\n<p>OECMs are defined by the Convention on Biological Diversity (CBD) as &#x201C;A geographically defined area other than a Protected Area, which is governed and managed in ways that achieve positive and sustained long-term outcomes for the in-situ conservation of biodiversity, with associated ecosystem functions and services and where applicable, cultural, spiritual, socio&#x2013;economic, and other locally relevant values&#x201D; (CBD, 2018). Data on OECMs are managed in the WDOECM (<a href=\"http://www.protectedplanet.net/en/thematic-areas/oecms\">www.protectedplanet.net/en/thematic-areas/oecms</a>) by UNEP-WCMC.</p>\n<p>Key Biodiversity Areas (KBA) are defined as described above by IUCN (2016) and documented in the World Database of KBAs (WDKBA) (www.keybiodiversityareas.org/kba-data).</p>\n<p>Sites contributing significantly to the global persistence of biodiversity are identified following globally criteria set out in A Global Standard for the Identification of KBAs (IUCN 2016) applied at national levels. KBAs encompass (a) Important Bird &amp; Biodiversity Areas, that is, sites contributing significantly to the global persistence of biodiversity, identified using data on birds, of which more than13,000 sites in total have been identified from all of the world&#x2019;s countries (BirdLife International 2014, Donald et al. 2018); (b) Alliance for Zero Extinction sites (Ricketts et al. 2005), that is, sites holding effectively the entire population of at least one species assessed as Critically Endangered or Endangered on the IUCN Red List of Threatened Species, of which 853 sites have been identified for 1,483 species of mammals, birds, amphibians, reptiles, freshwater crustaceans, reef-building corals, conifers, cycads and other taxa; (c) KBAs identified under an earlier version of the KBA criteria (Langhammer et al. 2007), including those identified in Ecosystem Hotspot Profiles developed with support of the Critical Ecosystem Partnership Fund. These three subsets are being reassessed using the Global Standard, which unifies these approaches along with other mechanisms for identification of important sites for other species and ecosystems (IUCN 2016).</p>\n<p>Data on KBAs are managed in the WDKBA (www.keybiodiversityareas.org/kba-data) by BirdLife International on behalf of the KBAs Partnership.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Protected area data are compiled by ministries of environment and other ministries responsible for the designation and maintenance of protected areas. Protected Areas data for sites designated under the Ramsar Convention and the UNESCO World Heritage Convention are collected through the relevant convention international secretariats. Protected area data are aggregated globally into the WDPA by UNEP-WCMC, according to the mandate for production of the United Nations List of Protected Areas (Deguignet et al. 2014). They are disseminated through <a href=\"http://www.protectedplanet.net/\">Protected Planet</a>, which is jointly managed by UNEP-WCMC and IUCN and its World Commission on Protected Areas (UNEP-WCMC 2016).</p>\n<p>Other Effective Area-based Conservation Measures (OECMs) are collated in the WDOECM. This database can be regarded as a sister database to the WDPA as it is also hosted on Protected Planet. Furthermore, the databases share many of the same fields and have an almost identical workflow; differing only in what they list. OECMs are a quickly evolving area of work, as such for the latest information on OECMs and the WDOECM please contact UNEP-WCMC. </p>\n<p>KBAs are identified at national scales through multi-stakeholder processes, following standard criteria and thresholds. KBAs data are aggregated into the <a href=\"http://www.keybiodiversityareas.org/\">World Database on </a></p>\n<p><a href=\"http://www.keybiodiversityareas.org/\">KBAs</a>, managed by BirdLife International. </p>", "COLL_METHOD__GLOBAL"=>"<p>See information under other sections, and detailed information on the process by which KBAs are identified at <a href=\"http://www.keybiodiversityareas.org/working-with-kbas/proposing-updating\">www.keybiodiversityareas.org/working-with-kbas/proposing-updating</a>. Guidance on Proposing, Reviewing, Nominating and Confirming KBAs is available in KBA Secretariat (2019) at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>.</p>\n<p>The KBA identification process is highly inclusive and consultative. Anyone with appropriate data may propose a site. Consultation with stakeholders at the national level (both non-governmental and governmental organisations) is required during the proposal process. Any site proposal must undergo independent review. This is followed by the official site nomination with full documentation meeting the Documentation Standards for KBAs. Sites confirmed by the KBA Secretariat to qualify as KBAs are then published on the KBA Website.</p>\n<p>Submission of proposals for KBAs to the WDKBA follows a systematic review process to ensure that the KBA criteria have been applied correctly and that the sites can be recognised as important for the global persistence of biodiversity. Regional Focal Points have been appointed to help KBA proposers develop proposals and then ensure they are reviewed independently. Guidance on Proposing, Reviewing, Nominating and Confirming sites has been published to help guide proposers through the development of proposals and the review process, highlighting where they can obtain help in making a proposal. </p>", "FREQ_COLL__GLOBAL"=>"<p>UNEP-WCMC produces the UN List of Protected Areas every 5&#x2013;10 years, based on information provided by national ministries/agencies. In the intervening period between compilations of UN Lists, UNEP-WCMC works closely with national ministries/agencies and NGOs responsible for the designation and maintenance of protected areas, continually updating the WDPA as new data become available. The WDOECM is also updated on an ongoing basis. The WDKBA is also updated on an ongoing basis with updates currently released twice a year, as new national data are submitted.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The indicator of protected area coverage of important sites for biodiversity is updated each November-December using the latest versions of the datasets on protected areas, OECMs and KBAs.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Protected area data are compiled by ministries of environment and other ministries responsible for the designation and maintenance of protected areas. KBAs are identified at national scales through multi-stakeholder processes, following established processes and standard criteria and thresholds (see above for details).</p>", "COMPILING_ORG__GLOBAL"=>"<p>BirdLife International, IUCN, UNEP-WCMC</p>\n<p>Protected area data are aggregated globally into the WDPA by UNEP-WCMC, according to the mandate for production of the United Nations List of Protected Areas (Deguignet et al. 2014). They are disseminated through Protected Planet, which is jointly managed by UNEP-WCMC and IUCN and its World Commission on Protected Areas (UNEP-WCMC 2016). KBAs data are aggregated into the WDKBA, managed by BirdLife International (2019). </p>", "INST_MANDATE__GLOBAL"=>"<p>Protected area data and OECM data are aggregated globally into the WDPA and WDOECM by the UNEP-WCMC, according to the mandate for production of the United Nations List of Protected Areas (Deguignet et al. 2014). </p>\n<p>BirdLife International is mandated by the <a href=\"http://www.keybiodiversityareas.org/assets/dfbb558651f335617813f6c0c42f9e50\">KBAs Partnership Agreement</a> to manage data on KBAs in the WDKBAs on behalf of the KBAs Partnership.</p>\n<p>BirdLife International, IUCN and UNEP-WCMC collaborate to produce the indicator of coverage of KBAs by Protected Areas and OECMs.</p>", "RATIONALE__GLOBAL"=>"<p>The safeguard of important sites is vital for stemming the decline in biodiversity and ensuring long term and sustainable use of marine natural resources. The establishment of protected areas is an important mechanism for achieving this aim, and this indicator serves as a means of measuring progress toward the conservation, restoration and sustainable use of marine ecosystems and their services, in line with obligations under international agreements. Importantly, while it can be disaggregated to report on any given single ecosystem of interest, it is not restricted to any single ecosystem type.</p>\n<p>Levels of access to protected areas vary among the protected area management categories. Some areas, such as scientific reserves, are maintained in their natural state and closed to any other use. Others are used for recreation or tourism, or even open for the sustainable extraction of natural resources. In addition to protecting biodiversity, protected areas have high social and economic value: supporting local livelihoods; maintaining fisheries; harbouring an untold wealth of genetic resources; supporting thriving recreation and tourism industries; providing for science, research and education; and forming a basis for cultural and other non-material values.</p>\n<p>This indicator adds meaningful information to, complements and builds from traditionally reported simple statistics of marine area covered by protected areas, computed by dividing the total protected area within a country by the total territorial area of the country and multiplying by 100 (e.g., Chape et al. </p>\n<p>2005). Such percentage area coverage statistics do not recognise the extreme variation of biodiversity importance over space (Rodrigues et al. 2004), and so risk generating perverse outcomes through the protection of areas which are large at the expense of those which require protection.</p>\n<p>The indicator was used to track progress towards the 2011&#x2013;2020 Strategic Plan for Biodiversity (CBD 2014, Tittensor et al. 2014, CBD 2020a), and was used as an indicator towards the Convention on Biological Diversity&#x2019;s 2010 Target (Butchart et al. 2010). It has been proposed as an indicator for monitoring progress towards the post-2020 Global Biodiversity Framework (CBD 2020b).</p>", "REC_USE_LIM__GLOBAL"=>"<p>Quality control criteria are applied to ensure consistency and comparability of the data in the WDPA. New data are validated at UNEP-WCMC through a number of tools and translated into the standard data structure of the WDPA. Discrepancies between the data in the WDPA and new data are minimised by provision of a manual (UNEP-WCMC 2019) and resolved in communication with data providers. Similar processes apply for the incorporation of data into the WDKBAs (BirdLife International 2019).</p>\n<p>The indicator does not measure the effectiveness of protected areas in reducing biodiversity loss, which ultimately depends on a range of management and enforcement factors not covered by the indicator. A number of initiatives are underway to address this limitation. Most notably, numerous mechanisms have been developed for assessment of protected area management, which can be synthesised into an indicator (Leverington et al. 2010). This is used by the Biodiversity Indicators Partnership as a complementary indicator of progress towards Aichi Biodiversity Target 11 </p>\n<p>(http://www.bipindicators.net/pamanagement). However, there may be little relationship between these measures and protected area outcomes (Nolte &amp; Agrawal 2013). More recently, approaches to &#x201C;green listing&#x201D; have started to be developed, to incorporate both management effectiveness and the outcomes of protected areas, and these are likely to become progressively important as they are tested and applied more broadly.</p>\n<p>Data and knowledge gaps can arise due to difficulties in determining whether a site conforms to the IUCN definition of a protected area or the CBD definition of an OECM. However, given that both are incorporated into the indicator, misclassifications (as one or the other) do not impact the calculated indicator value.</p>\n<p>Regarding important sites, the biggest limitation is that site identification to date has focused mainly on specific subsets of biodiversity, for example birds (for Important Bird and Biodiversity Areas) and highly threatened species (for Alliance for Zero Extinction sites). While Important Bird and Biodiversity Areas have been documented to be good surrogates for biodiversity more generally (Brooks et al. 2001, Pain et al. 2005), the application of the unified standard for identification of KBA sites (IUCN 2016) across different levels of biodiversity (genes, species, ecosystems) and different taxonomic groups remains a high priority, building from efforts to date (Eken et al. 2004, Knight et al. 2007, Langhammer et al. 2007, Foster et al. 2012). Birds now comprise less than 50% of the species for which KBAs have been identified, and as KBA identification for other taxa and elements of biodiversity proceeds, such bias will become a less important consideration in the future.</p>\n<p>KBA identification has been validated for a number of countries and regions where comprehensive biodiversity data allow formal calculation of the site importance (or &#x201C;irreplaceability&#x201D;) using systematic conservation planning techniques (Di Marco et al. 2016, Montesino Pouzols et al. 2014).</p>\n<p>Future developments of the indicator will include: a) expansion of the taxonomic coverage of marine KBAs through application of the KBAs standard (IUCN 2016) to a wide variety of marine vertebrates, invertebrates, plants and ecosystem type; b) improvements in the data on protected areas by continuing to increase the proportion of sites with documented dates of designation and with digitised boundary polygons (rather than coordinates); and c) increased documentation of Other Effective Area-based Conservation Measures in the World Database of OECMs.</p>", "DATA_COMP__GLOBAL"=>"<p>This indicator is calculated from data derived from a spatial overlap between digital polygons for protected areas from the WDPA (UNEP-WCMC &amp; IUCN 2020), digital polygons for OECMs from the WDOECM and digital polygons for marine KBAs from the WDKBA, including Important Bird and Biodiversity Areas, Alliance for Zero Extinction sites, and other KBAs). Sites were classified as marine KBAs by undertaking a spatial overlap between the KBA polygons and an ocean raster layer (produced from the &#x2018;adm0&#x2019; layer from the database of Global Administrative Areas (GADM 2019)), classifying any KBA as a marine KBA where it had &#x2265;5% overlap with the ocean layer (hence some sites were classified as both marine and terrestrial). The value of the indicator at a given point in time, based on data on the year of protected area establishment recorded in the WDPA is computed as the mean percentage of each KBA currently recognised that is covered by protected areas and/or OECMs.</p>\n<p>Protected areas lacking digital boundaries in the WDPA, and those sites with a status of &#x2018;proposed&#x2019; or &#x2018;not reported&#x2019; are omitted. Degazetted sites are not kept in the WDPA and are also not included. Man and Biosphere Reserves are also excluded as these often contain potentially unprotected areas. Year of protected area establishment is unknown for ~12% of protected areas in the WDPA, generating uncertainty around changing protected area coverage over time. To reflect this uncertainty, a year was randomly assigned from another protected area within the same country, and then this procedure repeated 1,000 times, with the median plotted. </p>\n<p>Prior to 2017, the indicator was presented as the percentage of KBAs completely covered by protected areas. However, it is now presented as the mean % of each KBA that is covered by protected areas in order to better reflect trends in protected area coverage for countries or regions with few or no KBAs that are completely covered.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Protected Areas and OECMs are validated through dialogue with the governing authority, who signs a data contributor agreement that these sites are, to the best of their knowledge, an accurate depiction of the sites in question. Over time the data for sites may improve or other aspects of the sites may change, as and when this occurs a further data sharing agreement is required by the site&#x2019;s governing authority.</p>\n<p>Proposed KBAs undergo detailed checking by Regional Focal Points, formal Review of KBA Proposals by independent Reviewers, and validation of Nominated KBAs by the KBAs Secretariat. For further information, see the Guidance on Proposing, Reviewing, Nominating and Confirming KBAs available in KBA Secretariat (2019) at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>.</p>\n<p>When the indicators of protected area coverage of KBAs are updated each year, the updated indicators (and underlying numbers of protected areas, OECMs, and KBAs) are made available for review by countries prior to submission to the SDG Indicators Database. This is achieved through updating the country profiles in the Integrated Biodiversity Assessment Tool (<a href=\"https://ibat-alliance.org/country_profiles\">https://ibat-alliance.org/country_profiles</a>) and circulating these for consultation and review to CBD National Focal Points, SDG National Statistical Office Focal Points, and IUCN State Members.</p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made to the index with respect to harmonization of breakdowns or for compliance with specific international or national definitions.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Data are available for protected areas and KBAs in all of the world&#x2019;s countries, and so no imputation or estimation of national level data is necessary.</p>\n<p> </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Global indicators of protected area coverage of important sites for biodiversity are calculated as the mean percentage of each KBA that is covered by protected areas and Other Effective Area-based Conservation Measures. The data are generated from all countries, and so while there is uncertainty around the data, there are no missing values as such and so no need for imputation or estimation.</p>", "REG_AGG__GLOBAL"=>"<p>Regional indices are calculated as the mean percentage of each KBA in the region covered by (i.e. overlapping with) protected areas and/or OECMs: in other words, the percentage of each KBA covered by these designations, averaged over all KBAs in the particular region.</p>", "DOC_METHOD__GLOBAL"=>"<p><strong><em>PAs</em></strong></p>\n<p>Data on protected areas are submitted by government agencies to the WDPA and disseminated through Protected Planet. The WDPA has its origins in a 1959 UN mandate when the United Nations Economic and Social Council called for a list of national parks and equivalent reserves Resolution 713 (XXVIII). </p>\n<p>Protected areas data are therefore compiled directly from government agencies, regional hubs and other authoritative sources in the absence of a government source. All records have a unique metadata identifier (MetadataID) which links the spatial database to the Source table where all sources are described. The data is collated and standardised following the WDPA Data Standards and validated with the source. The process of collation, validation and publication of data as well as protocols and the WDPA data standards are regularly updated in the WDPA User Manual (<a href=\"https://www.protectedplanet.net/c/wdpa-manual\">https://www.protectedplanet.net/c/wdpa-manual</a>) made available through www.protectedplanet.net where all spatial data and the Source table are also published every month and can be downloaded. The WDPA User Manual (published in English, Spanish, and French) provides guidance to countries on how to submit protected areas data to the WDPA, the benefits of providing such data, and the data standards and quality checks that are performed. </p>\n<p><strong><em>OECMS</em></strong></p>\n<p>Guiding principles, common characteristics and criteria for identification of OECMs are available in CBD (2018) at <a href=\"https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-08-en.pdf\">https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-08-en.pdf</a>.</p>\n<p>Guidance on recognising and reporting other effective area-based conservation measures is available in IUCN-WCPA Task Force on OECMs (2019) at: <a href=\"https://portals.iucn.org/library/node/48773\">https://portals.iucn.org/library/node/48773</a>.</p>\n<p><strong><em>KBAs</em></strong></p>\n<p>The &#x201C;Global Standard for the Identification of KBAs&#x201D; (<a href=\"https://portals.iucn.org/library/node/46259\">https://portals.iucn.org/library/node/46259</a>) comprises the standard recommendations available to countries in the identification of KBAs. Guidelines for using A global standard for the identification of KBAs are available at https://portals.iucn.org/library/node/49131.</p>\n<p>Guidance on Proposing, Reviewing, Nominating and Confirming KBAs is available in KBA Secretariat (2019) at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>.</p>\n<p>A summary of the process by which KBAs are identified is available at <a href=\"http://www.keybiodiversityareas.org/working-with-kbas/proposing-updating\">www.keybiodiversityareas.org/working-with-kbas/proposing-updating</a>.</p>\n<p>The KBA identification process is highly inclusive, consultative and nationally driven. Anyone with appropriate data may propose a site as a KBA, although consultation with relevant stakeholders at the local and national level is required when identifying the site and needs to be documented in the proposal. In order to propose a site as a KBA, a proposer must apply the KBA criteria to data on biodiversity elements (species and ecosystems) at the site. Associated with the proposal process is the need to delineate the site accurately so that its boundaries are clear. Although anyone with appropriate scientific data may propose a site to qualify as a KBA, wide consultation with stakeholders at the national level (both non-governmental and governmental organizations) is required during the proposal process. The formal proposal is then made using a proposal process that ensures there is an independent review of the proposal before a site is incorporated in the WDKBA. This is important given that KBA status of a site may lead to changes in actions of governments, private sector companies and other institutions following consultation as appropriate. </p>\n<p>KBA identification builds off the existing network of KBAs, including those identified as (a) Important Bird &amp; Biodiversity Areas through the BirdLife Partnership of 120 national organisations (<a href=\"http://www.birdlife.org/worldwide/partnership/birdlife-partners\">http://www.birdlife.org/worldwide/partnership/birdlife-partners</a>), (b) Alliance for Zero Extinction sites by 93 national and international organisations in the Alliance (<a href=\"http://www.zeroextinction.org/partners.html\">http://www.zeroextinction.org/partners.html</a>), and (c) other KBAs by civil society organisations supported by the Critical Ecosystem Partnership Fund in developing ecosystem profiles, named in each of the profiles listed here (<a href=\"http://www.cepf.net/resources/publications/Pages/ecosystem_profiles.aspx\">http://www.cepf.net/resources/publications/Pages/ecosystem_profiles.aspx</a>), with new data strengthening and expanding expand the network of these sites.</p>\n<p>The main steps of the KBA identification process are the following: </p>\n<ol>\n  <li>submission of Expressions of Intent to identify a KBA to Regional Focal Points; </li>\n  <li>Proposal Development process, in which proposers compile relevant data and documentation and consult national experts, including organizations that have already identified KBAs in the country, either through national KBA Coordination Groups or independently;</li>\n  <li>review of proposed KBAs by Independent Expert Reviewers, verifying the accuracy of information within their area of expertise; and</li>\n  <li>a Site Nomination phase comprising the submission of all the relevant documentation for verification by the KBAs Secretariat. Sites confirmed by the KBAs Secretariat to qualify as KBAs are then published on the KBAs website (<a href=\"http://www.keybiodiversityareas.org/home\">http://www.keybiodiversityareas.org/home</a>).</li>\n</ol>\n<p>Once a KBA is identified, monitoring of its qualifying features and its conservation status is important. Proposers, reviewers and those undertaking monitoring can join the KBAs Community to exchange their experiences, case studies and best practice examples.</p>\n<p>The R code for calculating protected area coverage of KBAs is documented in Simkins et al. (2020). </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>For protected areas and OECMs please see the section on validation. Ensuring the WDPA and WDOECM remain an accurate and true depiction of reality is a never-ending task; however, over time the quality of the data (e.g. the proportion of sites with defined boundaries) is increasing.</p>\n<p>For KBAs, see above and below, plus the guidance on Proposing, Reviewing, Nominating and Confirming KBAs which is available in KBA Secretariat (2019) at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>. Data quality is ensured through wide stakeholder engagement in the KBA proposal process, data checking by Regional Focal Points, formal Review of KBA Proposals by independent Reviewers, and validation of Nominations by the KBAs Secretariat. Furthermore, an independent KBA Standards and Appeals Committee ensures the correct application of the Global Standard for the identification of KBAs, and oversees a formal Procedure for handling of appeals against the identification of KBAs (see http://www.keybiodiversityareas.org/assets/1b388c918e14c5f4c3d7a0237eb0d366).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Information on the process of how protected area data are collected, standardised and published is available in the WDPA User Manual at: <a href=\"https://www.protectedplanet.net/c/wdpa-manual\">https://www.protectedplanet.net/c/wdpa-manual</a> which is available in English, French and Spanish. Specific guidance is provided at <a href=\"https://www.protectedplanet.net/c/world-database-on-protected-areas\">https://www.protectedplanet.net/c/world-database-on-protected-areas</a> on, for example, predefined fields or look up tables in the WDPA: <a href=\"https://www.protectedplanet.net/c/wdpa-lookup-tables\">https://www.protectedplanet.net/c/wdpa-lookup-tables</a>, how WDPA records are coded how international designations and regional designations data is collected, how regularly is the database updated, and how to perform protected areas coverage statistics. </p>\n<p>Data quality in the process of identifying KBAs is ensured through processes established by the KBAs Partnership (<a href=\"http://www.keybiodiversityareas.org/kba-partners\">http://www.keybiodiversityareas.org/kba-partners</a>) and KBAs Secretariat. Data quality is ensured through wide stakeholder engagement in the KBA proposal process, data checking by Regional Focal Points, formal Review of KBA Proposals by independent Reviewers, and validation of Nominations by the KBAs Secretariat. </p>\n<p>In addition, the Chairs of the IUCN Species Survival Commission and World Commission on Protected Areas (both of whom are elected by the IUCN Membership of governments and non-governmental organisations), appoint the Chair of an independent KBAs Standards and Appeals Committee, which ensures the correct application of the Global Standard for the identification of KBAs, and oversees a formal Procedure for handling of appeals against the identification of KBAs (see http://www.keybiodiversityareas.org/assets/1b388c918e14c5f4c3d7a0237eb0d366).</p>\n<p>Before submission to the UN SDG Indicators database the annually updated indicators of coverage of KBAs by protected areas and Other Effective Area-based Conservation Measures are incorporated into updated Country Profiles on the Integrated Biodiversity Assessment Tool (<a href=\"https://ibat-alliance.org/country_profiles\">https://ibat-alliance.org/country_profiles</a>) and then sent for consultation to National Focal Points of the Convention on Biological Diversity (<a href=\"https://www.cbd.int/information/nfp.shtml\">https://www.cbd.int/information/nfp.shtml</a>), National Statistics Offices SDG Representatives and UN Permanent Missions (Geneva) representatives. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>High.</p>\n<p>Each custodian agency is responsible for quality management of their own database.<br>Quality assessment of the indicator is shared between he indicator custodian agencies.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>This indicator has been classified by the IAEG-SDGs as Tier 1. Current data are available for all countries in the world, and these are updated on an ongoing basis. Index values for each country are available in the UN SDG Indicators Database <a href=\"https://unstats.un.org/sdgs/indicators/database/\">https://unstats.un.org/sdgs/indicators/database/</a>. Graphs of Protected area coverage of KBAs are also available for each country in the BIP Indicators Dashboard (<a href=\"https://bipdashboard.natureserve.org/bip/SelectCountry.html\">https://bipdashboard.natureserve.org/bip/SelectCountry.html</a>), and the Integrated Biodiversity Assessment Tool Country Profiles (<a href=\"https://ibat-alliance.org/country_profiles\">https://ibat-alliance.org/country_profiles</a>).</p>\n<p>Underlying data on protected areas and Other Effective Area-based Conservation Measures are available at <a href=\"http://www.protectedplanet.net\">www.protectedplanet.net</a>. Data on KBAs are available at <a href=\"http://www.keybiodiversityareas.org\">www.keybiodiversityareas.org</a>. Data on subsets of KBAs are available for Important Bird and Biodiversity Areas at <a href=\"http://datazone.birdlife.org/site/search\">http://datazone.birdlife.org/site/search</a> and for Alliance for Zero Extinction sites at https://zeroextinction.org.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Given that data for the global indicator are compiled at national levels, it is straightforward to disaggregate to national and regional levels (e.g., Han et al. 2014), or conversely to aggregate to the global level. KBAs span all ecosystem types through the marine environment (Edgar et al. 2008) and beyond. The indicator can therefore be reported in combination across marine systems along with terrestrial or freshwater systems, or disaggregated among them. However, individual KBAs can encompass marine, terrestrial, and freshwater systems simultaneously, and so determining the results is not simply additive. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>National processes provide the data that are incorporated into the WDPA, the WDOECM, and the World Database of KBAs, so there are very few discrepancies between national indicators and the global one. One minor source of difference is that the WDPA incorporates internationally-designated protected areas (e.g., UNESCO World Heritage sites, Ramsar sites, etc), a few of which are not considered by their sovereign nations to be protected areas. </p>\n<p>Note that because countries do not submit comprehensive data on degazetted protected areas to the WDPA, earlier values of the indictor may marginally underestimate coverage. Furthermore, there is also a lag between the point at which a protected area is designated on the ground and the point at which it is reported to the WDPA. As such, current or recent coverage may also be underestimated.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>http://www.unep-wcmc.org/; http://www.birdlife.org/; http://www.iucn.org/</p>\n<p><strong>References:</strong></p>\n<p>These metadata are based on http://mdgs.un.org/unsd/mi/wiki/7-6-Proportion-</p>\n<p>of-terrestrial-and-marine-areas-protected.ashx, supplemented by http://www.bipindicators.net/paoverlays and the references listed below.</p>\n<p>BIRDLIFE INTERNATIONAL (2014). Important Bird and Biodiversity Areas: a global network for conserving nature and benefiting people. Cambridge, UK: BirdLife International. Available at datazone.birdlife.org/sowb/sowbpubs#IBA.</p>\n<p>BIRDLIFE INTERNATIONAL (2019) World Database of KBAs. Developed by the KBA Partnership: BirdLife International, International Union for the Conservation of Nature, Amphibian Survival Alliance, Conservation International, Critical Ecosystem Partnership Fund, Global Environment Facility, Global Wildlife Conservation, NatureServe, Rainforest Trust, Royal Society for the Protection of Birds, Wildlife Conservation Society and World Wildlife Fund. September 2019 version. Available at http://keybiodiversityareas.org/sites/search.</p>\n<p>BROOKS, T. et al. (2001). Conservation priorities for birds and biodiversity: do East African Important Bird Areas represent species diversity in other terrestrial vertebrate groups? Ostrich suppl. 15: 3&#x2013;12. Available </p>\n<p>from: http://www.tandfonline.com/doi/abs/10.2989/00306520109485329#.VafbVJPVq75.</p>\n<p>BROOKS, T.M. et al. (2016) Goal 15: Life on land. Sustainable manage forests, combat desertification, halt and reverse land degradation, halt biodiversity loss. Pp. 497&#x2013;522 in Dur&#xE1;n y Lalaguna, P., D&#xED;az Barrado, C.M. &amp; Fern&#xE1;ndez Liesa, C.R. (eds.) International Society and Sustainable Development Goals. Editorial Aranzadi, Cizur Menor, Spain. Available from: https://www.thomsonreuters.es/es/tienda/pdp/duo.html?pid=10008456</p>\n<p>BUTCHART, S. H. M. et al. (2010). Global biodiversity: indicators of recent declines. Science 328: 1164&#x2013;1168. Available from http://www.sciencemag.org/content/328/5982/1164.short.</p>\n<p>BUTCHART, S. H. M. et al. (2012). Protecting important sites for biodiversity contributes to meeting global conservation targets. PLoS One 7(3): e32529. Available from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0032529.</p>\n<p>BUTCHART, S. H. M. et al. (2015). Shortfalls and solutions for meeting national and global conservation area targets. Conservation Letters 8: 329&#x2013;337. Available from http://onlinelibrary.wiley.com/doi/10.1111/conl.12158/full.</p>\n<p>CBD (2014). Global Biodiversity Outlook 4. Convention on Biological Diversity, Montr&#xE9;al, Canada. Available from <a href=\"https://www.cbd.int/gbo4/\">https://www.cbd.int/gbo4/</a>.</p>\n<p>CBD (2018). Protected areas and other effective area-based conservation measures. Decision 14/8 adopted by the Conference of the Parties to the Convention on Biological Diversity. Available at <a href=\"https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-08-en.pdf\">https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-08-en.pdf</a>. </p>\n<p>CBD (2020a). Global Biodiversity Outlook 5. Convention on Biological Diversity, Montr&#xE9;al, Canada. Available from https://www.cbd.int/gbo5/. </p>\n<p>CBD (2020b). Post-2020 Global Biodiversity Framework: Scientific and technical information to support the review of the updated Goals and Targets, and related indicators and baselines. Document CBD/SBSTTA/24/3. Available at: https://www.cbd.int/doc/c/705d/6b4b/a1a463c1b19392bde6fa08f3/sbstta-24-03-en.pdf.</p>\n<p>CHAPE, S. et al. (2005). Measuring the extent and effectiveness of protected areas as an indicator for meeting global biodiversity targets. Philosophical Transactions of the Royal Society B 360: 443&#x2013;445. Available from http://rstb.royalsocietypublishing.org/content/360/1454/443.short.</p>\n<p>DEGUIGNET, M., et al. (2014). 2014 United Nations List of Protected Areas. UNEP-WCMC, Cambridge, UK. Available from http://unep-wcmc.org/system/dataset_file_fields/files/000/000/263/original/2014_UN_List_of_Protected_Areas_EN_web.PDF?1415613322.</p>\n<p>DI MARCO, M., et al. (2016). Quantifying the relative irreplaceability of Important Bird and Biodiversity Areas. Conservation Biology 30: 392&#x2013;402. Available from http://onlinelibrary.wiley.com/doi/10.1111/cobi.12609/abstract.</p>\n<p>DONALD, P. et al. (2018) Important Bird and Biodiversity Areas (IBAs): the development and characteristics of a global inventory of key sites for biodiversity. Bird Conserv. Internat. 29:177&#x2013;198.</p>\n<p>DUDLEY, N. (2008). Guidelines for Applying Protected Area Management Categories. International Union for Conservation of Nature (IUCN). Gland, Switzerland. Available from https://portals.iucn.org/library/node/9243.</p>\n<p>EDGAR, G.J. et al. (2008). KBAs as globally significant target sites for the conservation of marine biological diversity. Aquatic Conservation: Marine and Freshwater Ecosystems 18: 969&#x2013;983. Available from http://onlinelibrary.wiley.com/doi/10.1002/aqc.902/abstract.</p>\n<p>EKEN, G. et al. (2004). KBAs as site conservation targets. BioScience 54: 1110&#x2013;1118. Available from http://bioscience.oxfordjournals.org/content/54/12/1110.short.</p>\n<p>FOSTER, M.N. et al. (2012) The identification of sites of biodiversity conservation significance: progress with the application of a global standard. Journal of Threatened Taxa 4: 2733&#x2013;2744. Available from </p>\n<p>http://www.threatenedtaxa.in/index.php/JoTT/article/view/779.</p>\n<p>Global Administrative Areas (2019). GADM database of Global Administrative Areas, version 2.8. Available from www.gadm.org.</p>\n<p>HAN, X. et al. (2014). A Biodiversity indicators dashboard: addressing challenges to monitoring progress towards the Aichi Biodiversity Targets using disaggregated global data. PLoS ONE 9(11): e112046. Available from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112046.</p>\n<p>HOLLAND, R.A. et al. (2012). Conservation priorities for freshwater biodiversity: the KBA approach refined and tested for continental Africa. Biological Conservation 148: 167&#x2013;179. Available from </p>\n<p>http://www.sciencedirect.com/science/article/pii/S0006320712000298.</p>\n<p>IUCN (2016). A Global Standard for the Identification of KBAs. International Union for Conservation of Nature, Gland, Switzerland. Available from https://portals.iucn.org/library/node/46259.</p>\n<p>IUCN-WCPA Task Force on OECMs (2019). Recognising and reporting other effective area-based conservation measures. Gland, Switzerland: IUCN.</p>\n<p>JONAS, H.D. et al. (2014) New steps of change: looking beyond protected areas to consider other effective area-based conservation measures. Parks 20: 111&#x2013;128. Available from http://parksjournal.com/wp-content/uploads/2014/10/PARKS-20.2-Jonas-et-al-10.2305IUCN.CH_.2014.PARKS-20-2.HDJ_.en_.pdf.</p>\n<p>KBA Secretariat (2019). KBAs Proposal Process: Guidance on Proposing, Reviewing, Nominating and Confirming sites. Version 1.0. Prepared by the KBA Secretariat and KBA Committee of the KBA Partnership. Cambridge, UK. Available at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>. </p>\n<p>KNIGHT, A. T. et al. (2007). Improving the KBAs approach for effective conservation planning. BioScience 57: 256&#x2013;261. Available from </p>\n<p>http://bioscience.oxfordjournals.org/content/57/3/256.short.</p>\n<p>LANGHAMMER, P. F. et al. (2007). Identification and Gap Analysis of KBAs: Targets for Comprehensive Protected Area Systems. IUCN World Commission on Protected Areas Best Practice Protected Area Guidelines Series No. 15. IUCN, Gland, Switzerland. Available from https://portals.iucn.org/library/node/9055.</p>\n<p>LEVERINGTON, F. et al. (2010). A global analysis of protected area management effectiveness. Environmental Management 46: 685&#x2013;698. Available from http://link.springer.com/article/10.1007/s00267-010-</p>\n<p>9564-5#page-1.</p>\n<p>MONTESINO POUZOLS, F., et al. (2014) Global protected area expansion is compromised by projected land-use and parochialism. Nature 516: 383&#x2013;386. Available from http://www.nature.com/nature/journal/v516/n7531/abs/nature14032.html.</p>\n<p>NOLTE, C. &amp; AGRAWAL, A. (2013). Linking management effectiveness indicators to observed effects of protected areas on fire occurrence in the Amazon rainforest. Conservation Biology 27: 155&#x2013;165. Available from http://onlinelibrary.wiley.com/doi/10.1111/j.1523-1739.2012.01930.x/abstract.</p>\n<p>PAIN, D.J. et al. (2005) Biodiversity representation in Uganda&#x2019;s forest IBAs. Biological Conservation 125: 133&#x2013;138. Available from http://www.sciencedirect.com/science/article/pii/S0006320705001412.</p>\n<p>RICKETTS, T. H. et al. (2005). Pinpointing and preventing imminent extinctions. Proceedings of the National Academy of Sciences of the U.S.A. 102: 18497&#x2013;18501. Available from http://www.pnas.org/content/102/51/18497.short.</p>\n<p>RODRIGUES, A. S. L. et al. (2004). Effectiveness of the global protected area network in representing species diversity. Nature 428: 640&#x2013;643. Available from http://www.nature.com/nature/journal/v428/n6983/abs/nature02422.html.</p>\n<p>RODR&#xCD;GUEZ-RODR&#xCD;GUEZ, D., et al. (2011). Progress towards international targets for protected area coverage in mountains: a multi-scale assessment. Biological Conservation 144: 2978&#x2013;2983. Available from </p>\n<p>http://www.sciencedirect.com/science/article/pii/S0006320711003454.</p>\n<p>SIMKINS, A.T., PEARMAIN, E.J., &amp; DIAS, M.P. (2020). Code (and documentation) for calculating the protected area coverage of KBAs. <a href=\"https://github.com/BirdLifeInternational/kba-overlap\">https://github.com/BirdLifeInternational/kba-overlap</a>. </p>\n<p>TITTENSOR, D. et al. (2014). A mid-term analysis of progress towards international biodiversity targets. Science 346: 241&#x2013;244. Available from http://www.sciencemag.org/content/346/6206/241.short.</p>\n<p>UNEP-WCMC (2019). World Database on Protected Areas User Manual 1.6. UNEP-WCMC, Cambridge, UK. Available from http://wcmc.io/WDPA_Manual.</p>\n<p>UNEP-WCMC &amp; IUCN (2020). The World Database on Protected Areas (WDPA). UNEP-WCMC, Cambridge, UK. Available from http://www.protectedplanet.net.</p>", "indicator_sort_order"=>"14-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"14.6.1", "slug"=>"14-6-1", "name"=>"Grado de aplicación de instrumentos internacionales cuyo objetivo es combatir la pesca ilegal, no declarada y no reglamentada", "url"=>"/site/es/14-6-1/", "sort"=>"140601", "goal_number"=>"14", "target_number"=>"14.6", "global"=>{"name"=>"Grado de aplicación de instrumentos internacionales cuyo objetivo es combatir la pesca ilegal, no declarada y no reglamentada"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Grado de aplicación de instrumentos internacionales cuyo objetivo es combatir la pesca ilegal, no declarada y no reglamentada", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Grado de aplicación de instrumentos internacionales cuyo objetivo es combatir la pesca ilegal, no declarada y no reglamentada", "indicator_number"=>"14.6.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El propósito de este indicador es mostrar una visión general del estado de \nimplementación de los instrumentos para combatir la pesca ilegal, no \ndeclarada y no reglamentada (INDNR) a nivel nacional, \nregional y mundial. La primera edición del indicador proporcionará una base del \nestado actual de implementación de estos acuerdos. Las estimaciones posteriores del \nindicador permitirán mostrar el progreso realizado por los países.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.6.1&seriesCode=ER_REG_UNFCIM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Progreso de los países en el grado de implementación de los instrumentos internacionales destinados a combatir la pesca ilegal, no declarada y no reglamentada (nivel de implementación: 1 más bajo a 5 más alto) ER_REG_UNFCIM</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-06-01.pdf\">Metadatos 14-6-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The purpose of this indicator is to show a picture of the state of implementation of the instruments to \ncombat IUU fishing, at a national, regional and global level. The first edition of the indicator will provide a \nbaseline of the current state of implementation of these agreements. Subsequent indicator estimates will \nthen be able to show any progress made by countries. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.6.1&seriesCode=ER_REG_UNFCIM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Progress by countries in the degree of implementation of international instruments aiming to combat illegal, unreported and unregulated fishing (level of implementation: 1 lowest to 5 highest) ER_REG_UNFCIM</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-06-01.pdf\">Metadata 14-6-1.pdf</a> ", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"El propósito de este indicador es mostrar una visión general del estado de \nimplementación de los instrumentos para combatir la pesca ilegal, no \ndeclarada y no reglamentada (INDNR) a nivel nacional, \nregional y mundial. La primera edición del indicador proporcionará una base del \nestado actual de implementación de estos acuerdos. Las estimaciones posteriores del \nindicador permitirán mostrar el progreso realizado por los países.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.6.1&seriesCode=ER_REG_UNFCIM&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Herrialdeen aurrerapena legez kanpoko, aitortu gabeko eta arautu gabeko arrantzari aurre egiteko nazioarteko tresnen inplementazio-mailan (inplementazio-maila: 1 baxuenetik 5 altuenera) ER_REG_UNFCIM</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-06-01.pdf\">Metadatuak 14-6-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 14.6: By 2020, prohibit certain forms of fisheries subsidies which contribute to overcapacity and overfishing, eliminate subsidies that contribute to illegal, unreported and unregulated fishing and refrain from introducing new such subsidies, recognizing that appropriate and effective special and differential treatment for developing and least developed countries should be an integral part of the World Trade Organization fisheries subsidies negotiation</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 14.6.1: Degree of implementation of international instruments aiming to combat illegal, unreported and unregulated fishing</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_REG_UNFCIM - Progress by countries in the degree of implementation of international instruments aiming to combat illegal, unreported and unregulated fishing (level of implementation: 1 lowest to 5 highest) [14.6.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>SDG 1, SDG 2, SDG 5, SDG 12, SDG 13, SDG 14.2/4/5/6/7/c</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Progress by countries in the degree of implementation of international instruments aiming to combat illegal, unreported and unregulated fishing.</p>\n<p><strong>Concepts:</strong></p>\n<p>The definitions and concepts associated with the indicator and utilized in the methodology are defined in the FAO term portal: <a href=\"http://www.fao.org/faoterm/collection/fisheries/en/\">http://www.fao.org/faoterm/collection/fisheries/en/</a> </p>\n<p>This indicator is based on a country&#x2019;s implementation of the different international instruments that combat illegal, unreported and unregulated fishing (IUU fishing). IUU fishing undermines national and regional efforts to conserve and manage fish stocks and, as a consequence, inhibits progress towards achieving the goals of long-term sustainability and responsibility as set forth in, inter alia, Chapter 17 of Agenda 21 and the 1995 FAO Code of Conduct for Responsible Fisheries. Moreover, IUU fishing greatly disadvantages and discriminates against those fishers that act responsibly, honestly and in accordance with the terms of their fishing authorizations. This is a compelling reason why IUU fishing must be dealt with expeditiously and in a transparent manner. If IUU fishing is not curbed, and if IUU fishers target vulnerable stocks that are subject to strict management controls or moratoria, efforts to rebuild those stocks to healthy levels will not be achieved. To efficiently curb IUU fishing a number of different international instruments have been developed over the years that focus on the implementation of the different responsibilities of States. </p>\n<p>The instruments covered by this indicator and their role in combatting IUU fishing are as follows:</p>\n<p>&#x2022; <strong>The 1982 United Nations Convention on the Law of the Sea (UNCLOS)</strong></p>\n<p>This instrument is the basis upon which all the subsequent instruments are built upon. UNCLOS defines the rights and responsibilities of nations with respect to their use of the world&apos;s oceans, establishing guidelines for businesses, the environment, and the management of marine natural resources. It is a binding instrument, although its principles may also be applied by countries who are not party to it.</p>\n<p>&#x2022; <strong>The Agreement for the Implementation of the Provisions of the United Nations Convention on the Law of the Sea of 10 December 1982 Relating to the Conservation and Management of Straddling Fish Stocks and Highly Migratory Fish Stocks (UN Fish Stocks Agreement)</strong></p>\n<p>The UN Fish Stocks Agreement entered into force on 11 December 2001, and is the most comprehensive of the binding international instruments in defining the role of Regional Fisheries Management Organisations and elaborating measures that could be taken in relation to IUU fishing activities. Although the UN Fish Stocks Agreement applies primarily to the highly migratory and straddling fish stocks on the high seas, its broad acceptance and application is evidenced by the reinforcement of other international instruments, implementation at the regional level, and to some extent by State practice within areas of national jurisdiction.</p>\n<p>&#x2022; <strong>The International Plan of Action to Prevent, Deter and Eliminate Illegal, Unreported and Unregulated Fishing (IPOA-IUU)</strong></p>\n<p>The objective of the IPOA is to prevent, deter and eliminate IUU fishing by providing all States with comprehensive, effective and transparent measures by which to act, including through appropriate regional fisheries management organizations established in accordance with international law. This instrument covers all the aspects of a State&#x2019;s responsibilities including, flag State responsibilities, coastal State measures, port State measures, internationally agreed market-related measures, research and regional fisheries management organizations.</p>\n<p>&#x2022; <strong>The 2009 FAO Agreement on Port State Measures to Prevent, Deter and Eliminate Illegal, Unreported and Unregulated Fishing (PSMA)</strong></p>\n<p>The FAO Agreement on Port State Measures to Prevent, Deter and Eliminate Illegal, Unreported and Unregulated Fishing entered into force on the 5th of June 2016. The main purpose of the Agreement is to prevent, deter and eliminate illegal, unreported and unregulated (IUU) fishing through the implementation of robust port State measures. The Agreement envisages that parties, in their capacities as port States, will apply the Agreement in an effective manner to foreign vessels when seeking entry to ports or while they are in port. The application of the measures set out in the Agreement will, inter alia, contribute to harmonized port State measures, enhanced regional and international cooperation and block the flow of IUU-caught fish into national and international markets.</p>\n<p>&#x2022; <strong>The FAO Voluntary Guidelines for Flag State Performance (VG-FSP)</strong></p>\n<p>The FAO Voluntary Guidelines for Flag State Performance spell out a range of actions that countries can take to ensure that vessels registered under their flags do not conduct IUU fishing, including monitoring, control and surveillance (MCS) activities, such as vessel monitoring systems (VMS) and observers. They promote information exchange and cooperation among countries so that flag states are in a position to refuse to register vessels that are &quot;flag-hopping&quot; by attempting to register with another flag state or to refuse vessels that have been reported for IUU fishing. The Guidelines also include recommendations on how countries can encourage compliance and take action against non-compliance by vessels, as well as on how to enhance international cooperation to assist developing countries to fulfil their flag state responsibilities.</p>\n<p>&#x2022; <strong>The FAO Agreement to Promote Compliance with International Conservation and Management Measures by Fishing Vessels on the High Seas (Compliance Agreement)</strong></p>\n<p>The 1993 FAO Compliance Agreement entered into force on the 24th of April 2003. Its main purpose is to encourage countries to take effective action, consistent with international law, and to deter the reflagging of vessels by their nationals as a means of avoiding compliance with applicable conservation and management rules for fishing activities on the high seas. With respect to the role of RFBs, the preamble calls upon States which do not participate in global, regional or sub regional fishery organizations or arrangements to do so, with a view to achieving compliance with international conservation and management measures.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Degree of implementation of applicable international instruments categorised into 5 bands, reflected as following:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Score</p>\n      </td>\n      <td></td>\n      <td>\n        <p>Bands</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&gt;0 &#x2013;&lt; 0.2</p>\n      </td>\n      <td></td>\n      <td>\n        <p>Band 1: Very low implementation of applicable instruments to combat IUU fishing</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.2 &#x2013;&lt; 0.4</p>\n      </td>\n      <td></td>\n      <td>\n        <p>Band 2: Low implementation of applicable instruments to combat IUU fishing</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.4 &#x2013;&lt; 0.6</p>\n      </td>\n      <td></td>\n      <td>\n        <p>Band 3: Medium implementation of applicable instruments to combat IUU fishing</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.6 &#x2013;&lt; 0.8</p>\n      </td>\n      <td></td>\n      <td>\n        <p>Band 4: High implementation of applicable instruments to combat IUU fishing</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.8 &#x2013; 1.0</p>\n      </td>\n      <td></td>\n      <td>\n        <p>Band 5: Very high implementation of applicable instruments to combat IUU fishing</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>See more details for the determination of the bands under 4.a. and for the computation of the sub-indicators under 4.c. and the Annex.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>No applicable international standards for measuring degree of implementation of such applicable instruments to combat IUU fishing.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>For the complete list of questions used for this indicator, please refer to appendix 1. </p>\n<p>The questionnaire is sent out to all FAO member States on a biennial basis. The questions used for this indicator will be included into the Committee on Fisheries Questionnaire for monitoring the implementation of the 1995 FAO Code of Conduct for Responsible Fisheries and related instruments. </p>", "COLL_METHOD__GLOBAL"=>"<p>This questionnaire is run on a web-application, which automatically records the submissions from the countries onto a database. The indicator will be extracted automatically from their responses, with a report of the indicator shown to the respondent prior to final submission. This will ensure transparency of the process and will allow for final confirmation of the results.</p>\n<p>The sample size will differ from year to year depending on the number of respondents to the questionnaire. </p>", "FREQ_COLL__GLOBAL"=>"<p>The questionnaire is sent out on a biennial basis. It is expected to be sent out 8 months prior to the holding of the Committee on Fisheries and remain open for a 3-month period.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data for the indicator are expected to be released one week after closure of the questionnaire.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data is typically provided by the national fishery Ministries/departments.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "INST_MANDATE__GLOBAL"=>"<p>Article I of the FAO constitution requires that the Organization collect, analyses, interpret and disseminate information relating to nutrition, food and agriculture http://www.fao.org/3/K8024E/K8024E.pdf.</p>", "RATIONALE__GLOBAL"=>"<p>The purpose of this indicator is to show a picture of the state of implementation of the instruments to combat IUU fishing, at a national, regional and global level. The first edition of the indicator will provide a baseline of the current state of implementation of these agreements. Subsequent indicator estimates will then be able to show any progress made by countries. </p>\n<p>Although the exact score will be important from one reporting year to the next for determining the progress made by a country, to aid the interpretation of this indicator, the score will then be converted into one of five bands as following:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Score</p>\n      </td>\n      <td>\n        <p>Bands</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&gt;0 &#x2013;&lt; 0.2</p>\n      </td>\n      <td>\n        <p>Band 1: Very low implementation of applicable instruments to combat IUU fishing</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.2 &#x2013;&lt; 0.4</p>\n      </td>\n      <td>\n        <p>Band 2: Low implementation of applicable instruments to combat IUU fishing</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.4 &#x2013;&lt; 0.6</p>\n      </td>\n      <td>\n        <p>Band 3: Medium implementation of applicable instruments to combat IUU fishing</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.6 &#x2013;&lt; 0.8</p>\n      </td>\n      <td>\n        <p>Band 4: High implementation of applicable instruments to combat IUU fishing</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.8 &#x2013; 1.0</p>\n      </td>\n      <td>\n        <p>Band 5: Very high implementation of applicable instruments to combat IUU fishing</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Additionally, a State may receive an indicator score of &#x201C;N/A&#x201D;, in the case that none of the instruments are applicable. This would only be the case if the country is land locked and does not flag any vessels that conduct fishing or fishing related activities. </p>\n<p><u>Countries that do not submit a response to the questionnaire on which the indicator is based or do not approve the use of their responses to the questionnaire for use in this indicator, will not receive an indicator score.</u></p>", "REC_USE_LIM__GLOBAL"=>"<p>Aside from the status of a country as party or non-party to an international agreement which is available as public record, the indicator is a self-analysis by the country of their state of implementation of the various international instruments. Although questions in the questionnaire will be accompanied by pop up guides describing any technical aspects or terms, there may be a small variance in interpretation by different respondents.</p>\n<p>Additionally, due to the fact that responses are not provided by an independent source, responses could in theory be politically influenced.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is based upon responses by States to a certain sections of the questionnaire for monitoring the implementation of the Code of Conduct for Responsible Fisheries and related instruments (CCRF). These are sections covering the implementation of different international instruments used to combat IUU fishing. The responses will be converted using an algorithm to obtain a score for the indicator. Each instrument will be covered within a given variable, as follows:</p>\n<p><strong>Variable 1</strong> <strong>(V1)</strong> - Adherence and implementation of the 1982 United Nations Convention on the Law of the Sea</p>\n<p><strong>Variable 2</strong> <strong>(V2)</strong> - Adherence and implementation of the 1995 United Nations Fish Stocks Agreement </p>\n<p><strong>Variable 3 (V3)</strong> - Development and implementation of a national plan of action (NPOA) to combat IUU fishing in line with the IPOA-IUU </p>\n<p><strong>Variable 4 (V4)</strong> - Adherence and implementation of the 2009 FAO Agreement on Port State Measures (PSMA) </p>\n<p><strong>Variable 5 (V5)</strong> - Implementation of Flag State Responsibilities in the context of the 1993 FAO Compliance Agreement and FAO Voluntary Guidelines for Flag State Performance</p>\n<p>Depending on responses by FAO Members on the adherence and implementation of the above-mentioned instruments, States will score an indicator value between 0 and 1. Each variable is given a weighting, which takes into consideration the importance of the instrument in combating IUU fishing as well as the overlap between the instruments. The variable weightings are as follows:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Variable</strong></p>\n      </td>\n      <td>\n        <p><strong>Weighting*</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>V1</p>\n      </td>\n      <td>\n        <p>10%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>V2</p>\n      </td>\n      <td>\n        <p>10%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>V3</p>\n      </td>\n      <td>\n        <p>30%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>V4</p>\n      </td>\n      <td>\n        <p>30%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>V5</p>\n      </td>\n      <td>\n        <p>20%</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>(*) item on &#x201C;Applicability of instruments&#x201D;</p>\n<p>For binding agreements, States will still be able to score points if they are not party to the agreement but are implementing its provisions. States will also score points if they have initiated the process to becoming party to an agreement. </p>\n<p>This indicator is automatically computed within the web-application on which the countries will be responding to the questionnaire. Once the questionnaire is completed the respondent will be presented with a report of the indicator, describing the methodology and the score attained. The user will then be able to give a final confirmation of the indicator. The final scores from all the respondents will automatically be collected onto a database. This web-application will also allow the user to access in any the following languages: English, French, Spanish, Chinese, Arabic and Russian.</p>\n<p><u>Choice of weighting per variable:</u></p>\n<p>The weightings for each variable have been carefully selected. These have been determined based upon their importance of their role in combatting IUU fishing as well as in consideration of the overlap present in between the different instruments. It is also for this consideration of overlap that the VG-FSP and the Compliance Agreement have been combined into Variable 5.</p>\n<p><u>Applicability of instruments:</u></p>\n<p>A set of questions will be present to determine certain characteristics of States (coastal, port, flag and land-locked). This will ensure that the indicator scoring for a country is not negatively affected if an instrument is not applicable to them. <u>In such case, the weighing of the variable that is not applicable is redistributed into the remaining variables</u>. In cases where none of the instruments is applicable, the country will get an indicator score of &#x201C;N/A&#x201D;.</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Variable</strong></p>\n      </td>\n      <td>\n        <p><strong>Cases in which Instruments are not applicable</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>V1</strong></p>\n      </td>\n      <td>\n        <p>The only case where this instrument becomes not applicable, is when the State is landlocked and they are not a flag state. </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>V2</strong></p>\n      </td>\n      <td>\n        <p>Is not applicable if the country is land-locked and not a flag State or a coastal State but is not a flag State or Port State.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>V3</strong></p>\n      </td>\n      <td>\n        <p>Same as Variable 2.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>V4</strong></p>\n      </td>\n      <td>\n        <p>Same as Variable 2.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>V5</strong></p>\n      </td>\n      <td>\n        <p>Is not applicable if the country is not a flag State.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>For more details regarding the list of question, scoring and applicability, please refer to Appendix 1 and 2.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Upon completing the questionnaire, States are provided with a condensed report showing their responses to relevant questions within the questionnaire for the indicator and the resulting SDG indicator score for their validation. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>Indicator will only be available for responding countries who approve of the use of their responses to the CCRF questionnaire for this indicator.</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>Data will only be aggregated from responding countries.</p>", "REG_AGG__GLOBAL"=>"<p>The categorization into the respective bands will also apply in the case of regional and global aggregates for this indicator. Once the mean score for an SDG region has been calculated, the region will be classified into a particular band reflecting the degree of implementation of relevant instruments.</p>\n<p>Data is combined for the respective nations within a region, as a count of the number of countries by Band, and this can be further aggregated to the global level without the need for any weighting of national or regional scores.</p>", "DOC_METHOD__GLOBAL"=>"<p>Once the countries receive the questionnaire, they will have access to a manual that will guide the user along the best process for completing the questionnaire. Due to the various themes that are covered within the questionnaire, it is essential that the focal point or user gather the responses using a well-coordinated process involving all the relevant staff that are in charge of the work within the various themes contained within the questionnaire, such as the focal point for the indicator. Additionally, the manual will also have a section describing the methodology of the indicator.</p>\n<p>Within the questionnaire application, the user will be able to find pop up guides embedded in the application describing technical aspects or terms encountered.</p>\n<p>URL to the authenticated CCRF questionnaire application: <a href=\"http://www.fao.org/fishery/code/codequest/?locale=en&amp;lang=en\">FAO Questionnaire for Monitoring the Implementation of the Code of Conduct for Responsible Fisheries</a> and Related Instruments</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>FAO is responsible for the quality of the internal statistical processes used to compile the published datasets. The FAO Statistics Quality Assurance Framework (SQAF), available at: <a href=\"http://www.fao.org/docrep/019/i3664e/i3664e.pdf\">http://www.fao.org/docrep/019/i3664e/i3664e.pdf</a>, provides the necessary principles, guidelines and tools to carry out quality assessments. FAO is performing an internal bi-annual survey (FAO Quality Assessment and Planning Survey) designed to gather information on all of FAO&#x2019;s statistical activities, notably to assess the extent to which quality standards are being implemented with a view to increasing compliance with the quality dimensions of SQAF, documenting best practices and prepare quality improvement plans, where necessary. Domain-specific quality assurance activities are carried out systematically (e.g. quality reviews, self-assessments, compliance monitoring). </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The questionnaire was created upon the request of the Members to the Committee on Fisheries. Within this process, FAO would not be in a position to question the responses of countries. Equally, this would require independent analysis of the status of implementation in the field of all responding countries for every edition of the questionnaire, a task that would require a substantial outlay of resources. </p>\n<p>FAO does however use the indicator when carrying out its national and regional workshops under its global capacity development programme to support the implementation of international instruments to combat IUU fishing. During these workshops, the indicator is used as a tool to understand the situation within the countries, all the while ensuring that there is a clear understanding of the questions, reporting process or any other technical aspects relevant to this indicator. </p>\n<p>Furthermore, once the user has completed the questionnaire, the user is able to extract a report of the indicator detailing their responses to the relevant questions and the corresponding scoring. The questionnaire respondent will then be able to validate the indicator score, which will in turn be automatically stored onto FAO databases. This system has been put in place, not only to ensure that no mistakes were made during the completion of the questionnaire but also to ensure transparency of the indicator process.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>From 2022 data series onwards, questions of a factual nature, used to indicate applicability of the indicator or to calculate the score of the indicator, such as whether a country is landlocked or whether it is a Party to a relevant international instrument will be pre-compiled. Official sources will be used to conduct this activity such as the depository of the relevant international binding instrument. </p>\n<p>This activity will be conducted for the following questions, detailed within Appendix 1: A.1, 1.1, 2.1, 4.1 and 5.1</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The data required for this indicator is not currently available. It will become available in early 2018 after the closure of the 2017/18 edition of the Questionnaire for monitoring the implementation of the 1995 FAO Code of Conduct for Responsible Fisheries. Thereafter it will be collected regularly every two years through the Questionnaire for monitoring the implementation of the 1995 FAO Code of Conduct for Responsible Fisheries.</p>\n<p><strong>Time series:</strong></p>\n<p>2017 (When available will become baseline)</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Due to nature of indicator, there will only be one score per country which could then be aggregated regionally or globally.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Data for this indicator is not internationally estimated.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<p>SDG 14.6.1: <a href=\"http://www.fao.org/sustainable-development-goals/indicators/14.6.1/en/\">http://www.fao.org/sustainable-development-goals/indicators/14.6.1/en/</a></p>\n<p>Appendix 1: Questions and scoring</p>\n<table>\n  <thead>\n    <tr>\n      <th rowspan=\"2\">\n        <p><strong>Section not applicable if:</strong></p>\n      </th>\n      <th rowspan=\"2\">\n        <p><strong>Question not applicable if:</strong></p>\n      </th>\n      <th>\n        <p><strong>Questions:</strong></p>\n      </th>\n      <th rowspan=\"2\">\n        <p><strong>Response Type</strong></p>\n      </th>\n      <th rowspan=\"2\">\n        <p><strong>Total Possible Indicator Score per Question:</strong></p>\n      </th>\n      <th colspan=\"7\">\n        <p><strong>Indicator Score per Response Type:</strong></p>\n      </th>\n      <th rowspan=\"2\">\n        <p><strong>Variable Weighting Multiplier:</strong></p>\n      </th>\n    </tr>\n    <tr>\n      <th>\n        <p>(Note: when applicable &#x201C;1-5&#x201D; is a range representing extent of implementation starting from &#x201C;1&#x201D; being &#x201C;Not at all&#x201D; up to &#x201C;5&#x201D; being &#x201C;Fully&#x201D;)</p>\n      </th>\n      <th>\n        <p>Yes</p>\n      </th>\n      <th>\n        <p>No</p>\n      </th>\n      <th>\n        <p>1</p>\n      </th>\n      <th>\n        <p>2</p>\n      </th>\n      <th>\n        <p>3</p>\n      </th>\n      <th>\n        <p>4</p>\n      </th>\n      <th>\n        <p>5</p>\n      </th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong>General Questions to Determine a States Applicability to Instruments to Combat IUU Fishing</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"11\">\n        <p><strong>-</strong></p>\n      </td>\n      <td rowspan=\"2\">\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p>A.1) Is your country land-locked?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td rowspan=\"11\">\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.2) Does your country flag vessels conducting fishing and fishing related activities to operate in: </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>&quot;Yes&quot; to: A.1</strong></p>\n      </td>\n      <td>\n        <p>A.2.1) Areas within the national jurisdiction of your country including your Economic Exclusive Zone (e.g. internal waters, territorial sea and archipelagic waters of an archipelagic State)?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"5\">\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p>A.2.2) The High Seas?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.2.3) Waters under the jurisdiction of other coastal States?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.3) Are any of the vessels flying your flag conducting fishing and fishing related activities authorised by other States to operate in:</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.3.1) Waters under the jurisdiction of the concerned State(s)?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.3.2) The High Seas?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"3\">\n        <p><strong>&quot;Yes&quot; to: A.1</strong></p>\n      </td>\n      <td>\n        <p>A.3) Does your country authorise vessels flying the flag of other States and which conduct fishing and fishing related activities, to:</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.3.1) Enter and use the designated ports of your country?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.3.2) Operate within waters under the jurisdiction of your country including your Economic Exclusive Zone (e.g. internal waters, territorial sea and archipelagic waters of an archipelagic State)?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong>Variable 1. the 1982 United Nations Convention on the Law of the Sea - Weighting 10%</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"7\">\n        <p><strong>&quot;Yes&quot; to: A.1 and &quot;No&quot; to: A.2.2, A.2.3, A.3.1 and A.3.2</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p>1.1) Is your country a Party to the United Nations Convention on the Law of the Sea (UNCLOS)?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td rowspan=\"7\">\n        <p>x10 if Variable Applicable</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>&quot;Yes&quot; to: 1.1</strong></p>\n      </td>\n      <td>\n        <p>1.2) If no to 1.1, has your country initiated the process to becoming Party to UNCLOS?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"5\">\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p>1.3) To what extent is your country implementing the provisions of the UNCLOS in relation to coastal States and flag State responsibilities for the management of fisheries, with regard to:</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 1.3.1) Policy</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 1.3.2) Legislation</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 1.3.3) Institutional framework</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 1.3.4) Operations and procedures</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong>Variable 2. the 1995 United Nations Fish Stocks Agreement - Weighting 10%</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"8\">\n        <p><strong>&quot;Yes&quot; to: A.1 and &quot;No&quot; to: A.2.2, A.2.3, A.3.1 and A.3.2 or &quot;No&quot; to: A.2-A.4</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p>2.1) Is your country a Party to the Agreement for the Implementation of the Provisions of the United Nations Convention on the Law of the Sea of 10 December 1982 Relating to the Conservation and Management of Straddling Fish Stocks and Highly Migratory Fish Stocks (UN Fish Stocks Agreement)?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td rowspan=\"8\">\n        <p>x10 if Variable Applicable</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>&quot;Yes&quot; to: 2.1</strong></p>\n      </td>\n      <td>\n        <p>2.2) If no to 2.1, has your country initiated the process to becoming Party to the UN Fish Stocks Agreement?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"6\">\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p>2.3) To what extent is your country implementing the provisions of the UN Fish Stocks Agreement in relation to coastal State and flag State responsibilities for the management of fisheries, with regard to:</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 2.3.1) Policy</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.025</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 2.3.2) Legislation</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.025</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 2.3.3) Institutional framework</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.025</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 2.3.4) Operations and procedures</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.025</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.4) To what extent is your country engaged in sub-regional, regional and international cooperation in enforcement, as required by the UN Fish Stocks Agreement?</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.4</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.025</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong>Variable 3. National Plan of Action to Combat IUU Fishing in Line with IPOA-IUU - Weighting 30%</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"7\">\n        <p><strong>&quot;Yes&quot; to: A.1 and &quot;No&quot; to: A.2.2, A.2.3, A.3.1 and A.3.2 or &quot;No&quot; to: A.2-A.4</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p>3.1) Has your country developed a national plan of action to combat IUU fishing (NPOA-IUU)?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td rowspan=\"7\">\n        <p>x30 if Variable Applicable</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>&quot;Yes&quot; to: 3.1</strong></p>\n      </td>\n      <td>\n        <p>3.2) If no to 3.1, is there an intention to develop a national plan of action?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"5\">\n        <p><strong>&quot;No&quot; to: 3.1</strong></p>\n      </td>\n      <td>\n        <p>3.3) If yes to 3.1, to what extent has your country implemented its NPOA-IUU, with regard to:</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 3.3.1) Policy</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 3.3.2) Legislation</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 3.3.3) Institutional framework</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 3.3.4) Operations and procedures</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong>Variable 4. the 2009 FAO Agreement on Port State Measures - Weighting 30%</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"9\">\n        <p><strong>&quot;Yes&quot; to: A.1 and &quot;No&quot; to: A.2.2, A.2.3, A.3.1 and A.3.2 or &quot;No&quot; to: A.2-A.4</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p>4.1) Is your country Party to The FAO Agreement on Port State Measures to Prevent, Deter and Eliminate Illegal, Unreported and Unregulated Fishing (PSMA)?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>0.2</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td rowspan=\"9\">\n        <p>x30 if Variable Applicable</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>&quot;Yes&quot; to: 4.1</strong></p>\n      </td>\n      <td>\n        <p>4.2) If no to 4.1, has your country initiated the process to become a Party to the PSMA?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"7\">\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p>4.3) To what extent has your country implemented the provisions of the PSMA, with regard to: (even through relevant regional mechanisms)</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 4.3.1) Policy</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.0375</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1125</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 4.3.2) Legislation</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.0375</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1125</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 4.3.3) Institutional framework</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.0375</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1125</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 4.3.4) Operations and procedures</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.0375</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1125</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.4) Has your country designated ports to receive vessels flying the flag of other States that are conducting fishing and fishing related activities, as required under the PSMA?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.5) Has your country designated an authority that shall act as a contact point for the exchange of information, as required by the PSMA?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong>Variable 5. Flag State Responsibilities - Weighting 20%</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"13\">\n        <p><strong>&quot;No&quot; to: A.3 and A.4</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p>5.1) Has your country become a Party to The FAO Agreement to Promote Compliance with International Conservation and Management Measures by Fishing Vessels on the High Seas (the Compliance Agreement)?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td rowspan=\"13\">\n        <p>x20 if Variable Applicable</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>&quot;Yes&quot; to: 5.1</strong></p>\n      </td>\n      <td>\n        <p>5.2) If no to 5.1, has your country initiated the process to become a Party to the Compliance Agreement?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"10\">\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p>5.3) To what extent has the Compliance Agreement and/or other flag state responsibilities been implemented with regard to:</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 5.3.1) Policy</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.025</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 5.3.2) Legislation</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.025</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 5.3.3) Institutional framework</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.025</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> 5.3.4) Operations and procedures</p>\n      </td>\n      <td>\n        <p>1-5</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>0.025</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.4) Does your country maintain a record of vessels authorized by your country to operate on the high seas conducting fishing and fishing related activities and supply the record to the FAO or interested States at their request?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.08</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.5) Does your country ensure that vessels flying your flag, that are conducting fishing and fishing related activities, have not engaged in previous activities that has undermined the effectiveness of international conservation and management measures, unless it has satisfied certain requirements in line with the provisions of the FAO Compliance Agreement or the UN Fish Stocks Agreement?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.08</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.6) Does your country ensure that vessels flying your flag, that are conducting fishing and fishing related activities, provide your country with information on its operations as may be necessary to enable your country to fulfil its obligations as a flag State?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.08</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.7) Does your country ensure vessels flying your flag do not conduct unauthorised fishing or fishing related activities within areas under jurisdiction of other States?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.075</p>\n      </td>\n      <td>\n        <p>0.08</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.8) Has your country undertaken an assessment of your country&#x2019;s performance as a flag State in accordance with The FAO Voluntary Guidelines for Flag State Performance?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>0.15</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>&quot;Yes&quot; to: 5.8</strong></p>\n      </td>\n      <td>\n        <p>5.9) If no to 5.8, does your country intend to do so in the future?</p>\n      </td>\n      <td>\n        <p>Yes/No</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0.05</p>\n      </td>\n      <td>\n        <p>0</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n      <td>\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td colspan=\"9\">\n        <p>Final Indicator Score = Total of Variables / Total Multiplier of Applicable Variables</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Appendix 2: Example indicator scoring</p>\n<p>The general question ascertains the applicability of the instruments to a State.</p>\n<p>- Country A is a coastal State, port State and flag State with high levels of implementation of instruments to combat IUU fishing.</p>\n<p>- Country B is a coastal State, port State and flag State with very low levels of implementation of instruments to combat IUU fishing, however it still scores some points for initiating the processes of becoming a party to certain agreements and base implementation of UNCLOS.</p>\n<p>- Country C is a coastal State and port State but does not flag any vessels conducting fishing or fishing related activities. It is not a party to any of the agreements but has a high level of implementation of instruments to combat IUU fishing to which it is applicable.</p>\n<p>The table on the next page shows hypothetical responses for these three countries, the scores that they achieve with these responses and finally the bands that these scores translate into.</p>\n<table>\n  <tbody>\n    <tr>\n      <td rowspan=\"2\">\n        <p><strong>Questions</strong>:</p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>Country A</strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>Country B</strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>Country C</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Responses</strong></p>\n      </td>\n      <td>\n        <p><strong> Variable Score</strong></p>\n      </td>\n      <td>\n        <p><strong>Responses</strong></p>\n      </td>\n      <td>\n        <p><strong>Variable Score</strong></p>\n      </td>\n      <td>\n        <p><strong>Responses</strong></p>\n      </td>\n      <td>\n        <p><strong>Variable Score</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"7\">\n        <p><strong>General Questions</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.1</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td rowspan=\"8\">\n        <p>-</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td rowspan=\"8\">\n        <p>-</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td rowspan=\"8\">\n        <p>-</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.2.1</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.2.2</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.2.3</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.3.1</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.3.2</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.4.1</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.4.2</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"7\">\n        <p><strong>Variable 1. UNCLOS &#x2013; 10%</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.1</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td rowspan=\"6\">\n        <p>0.9</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td rowspan=\"6\">\n        <p>0.5</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td rowspan=\"6\">\n        <p>0.7</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.2</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.3.1</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.3.2</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.3.3</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.3.4</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"7\">\n        <p><strong>Variable 2. Fish Stocks Agreement &#x2013; 10%</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.1</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td rowspan=\"7\">\n        <p>0.85</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td rowspan=\"7\">\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td rowspan=\"7\">\n        <p>0.75</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.2</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.3.1</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.3.2</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.3.3</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.3.4</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.4</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"7\">\n        <p><strong>Variable 3. IPOA-IUU &#x2013; 30%</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3.1</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td rowspan=\"6\">\n        <p>0.9</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td rowspan=\"6\">\n        <p>0.1</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td rowspan=\"6\">\n        <p>0.95</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3.2</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3.3.1</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3.3.2</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3.3.3</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3.3.4</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"7\">\n        <p><strong>Variable 4. PSMA &#x2013; 30%</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.1</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td rowspan=\"8\">\n        <p>0.725</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td rowspan=\"8\">\n        <p>0</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td rowspan=\"8\">\n        <p>0.725</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.2</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.3.1</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.3.2</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.3.3</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.3.4</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.4</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4.5</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"7\">\n        <p><strong>Variable 5. Flag State Responsibilities &#x2013; 20%</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.1</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td rowspan=\"12\">\n        <p>0.975</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td rowspan=\"12\">\n        <p>0.175</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n      <td rowspan=\"12\">\n        <p>n/a*</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.2</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.3.1</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.3.2</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.3.3</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.3.4</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.4</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.5</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.6</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.7</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.8</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>No</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5.9</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n      <td>\n        <p>Yes</p>\n      </td>\n      <td>\n        <p>n/a</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Indicator Score: </strong></p>\n        <p><strong>(Weighted average)</strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>0.86</strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>0.13</strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>0.73</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Band</strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>5</strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>1</strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>4</strong></p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "indicator_sort_order"=>"14-06-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"14.7.1", "slug"=>"14-7-1", "name"=>"Proporción del PIB correspondiente a la pesca sostenible en los pequeños Estados insulares en desarrollo, en los países menos adelantados y en todos los países", "url"=>"/site/es/14-7-1/", "sort"=>"140701", "goal_number"=>"14", "target_number"=>"14.7", "global"=>{"name"=>"Proporción del PIB correspondiente a la pesca sostenible en los pequeños Estados insulares en desarrollo, en los países menos adelantados y en todos los países"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción del PIB correspondiente a la pesca sostenible en los pequeños Estados insulares en desarrollo, en los países menos adelantados y en todos los países", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción del PIB correspondiente a la pesca sostenible en los pequeños Estados insulares en desarrollo, en los países menos adelantados y en todos los países", "indicator_number"=>"14.7.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Si bien la meta 14.7 promueve el uso sostenible de los recursos marinos, \nincluidos la pesca, la acuicultura y el turismo, este indicador, seleccionado por \nel IAEG-ODS, se centra únicamente en el uso sostenible de los recursos marinos \npor parte de la pesca. La metodología propuesta por la FAO mide la pesca \nsostenible como porcentaje del PIB, de acuerdo con la formulación del indicador acordada. \n\nLa proporción del valor añadido de una industria en el PIB se utiliza comúnmente como \nindicador de su importancia económica. En consecuencia, el valor añadido de la pesca de \ncaptura marina indica la importancia de las actividades relacionadas con la pesca marina \nen la economía del país y su importancia para los medios de vida. Tanto el PIB como el \nVA se miden en precios constantes y en moneda nacional.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.7.1&seriesCode=EN_SCP_FSHGDP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Pesca sostenible como proporción del PIB EN_SCP_FSHGDP</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-07-01.pdf\">Metadatos 14-7-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Although target 14.7 promotes the sustainable use of marine resources “including of fisheries, \naquaculture and tourism”, this indicator as selected by the IAEG-SDG focuses only on the sustainable use \nof marine resources by fisheries. The methodology hereby proposed by FAO thus measures sustainable \nfisheries as a percentage of GDP, in accordance with the agreed indicator formulation. \n\nThe share of value added from an industry in GDP is commonly used as an indication of its economic \nimportance. Accordingly, the value added of marine capture fisheries indicates the prominence of marine \nfish related activities in the country’s economy and its importance for livelihoods. Both GDP and the VA \nare measured in constant prices and domestic currency. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.7.1&seriesCode=EN_SCP_FSHGDP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Sustainable fisheries as a proportion of GDP EN_SCP_FSHGDP</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-07-01.pdf\">Metadata 14-7-1.pdf</a> ", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Si bien la meta 14.7 promueve el uso sostenible de los recursos marinos, \nincluidos la pesca, la acuicultura y el turismo, este indicador, seleccionado por \nel IAEG-ODS, se centra únicamente en el uso sostenible de los recursos marinos \npor parte de la pesca. La metodología propuesta por la FAO mide la pesca \nsostenible como porcentaje del PIB, de acuerdo con la formulación del indicador acordada. \n\nLa proporción del valor añadido de una industria en el PIB se utiliza comúnmente como \nindicador de su importancia económica. En consecuencia, el valor añadido de la pesca de \ncaptura marina indica la importancia de las actividades relacionadas con la pesca marina \nen la economía del país y su importancia para los medios de vida. Tanto el PIB como el \nVA se miden en precios constantes y en moneda nacional.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.7.1&seriesCode=EN_SCP_FSHGDP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Arrantza jasangarria, BPGren proportzio gisa EN_SCP_FSHGDP</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-07-01.pdf\">Metadatuak 14-7-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 14.7: By 2030, increase the economic benefits to Small Island Developing States and least developed countries from the sustainable use of marine resources, including through sustainable management of fisheries, aquaculture and tourism</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 14.7.1: Sustainable fisheries as a proportion of GDP in small island developing States, least developed countries and all countries</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>EN_SCP_FSHGDP - Sustainable fisheries as a proportion of GDP [14.7.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Linkages with other goals and targets: SDG 1, SDG 2, SDG 8 (in particular 8.1 and 8.4), SDG 12, SDG 13, SDG 14 (in particular 14.4.1)</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>This indicator expresses the value added of sustainable marine capture fisheries as a proportion of Gross Domestic Product (GDP).</p>\n<h2>Concepts:</h2>\n<p>The GDP is the value of all final goods and services produced in an economy in a given period, which is equivalent to the sum of the value added (VA) from all sectors in an economy.</p>\n<p>The <u>value added of marine capture fisheries</u> measures the value of fish harvested from marine stocks, minus the value of goods and services that are used in the production process (such as raw materials and utilities). It includes activities that are normally integrated into the process of production and occur at sea, such as fishing vessels which process or preserve their catch on board. However, it does not include the processing or preserving of fish when it occurs in land-based facilities.</p>\n<p>A <u>fish stock</u> is a subset of a species (fish, crustacean, mollusc, etc.) or a population inhabiting a geographical area and participating in the same reproductive process.</p>\n<p><u>Maximum sustainable yield (MSY)</u> is the highest theoretical equilibrium yield that can be continuously taken (on average) from a stock under existing (average) environmental conditions without significantly affecting the reproduction process. A stock fished at <u>(MSY)</u> is referred to as <u>biologically sustainable</u>, as it may remain stable or grow while sustaining losses from fishing and natural sources of mortality.</p>\n<p><u>FAO Fishing Areas for Statistical Purposes</u> are arbitrary areas to facilitate comparison of data, improving the possibilities of cooperation in statistical matters.<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></p>\n<p>The basic concepts associated with this indicator are part of the following international instruments and classification schemes:</p>\n<p><u>The 1982 United Nations Convention on the Law of the Sea (UNCLOS)</u><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></p>\n<p><em>This instrument is the basis upon which all the subsequent instruments are built. UNCLOS defines the rights and responsibilities of nations concerning their use of the world&apos;s oceans, establishing guidelines for businesses, the environment, and the management of marine natural resources. It is a binding instrument, although its principles may also be applied by countries who are not a party to it.</em></p>\n<p><u>The 1995 FAO Code of Conduct for Responsible Fisheries (CCRF)</u><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></p>\n<p><em>This instrument provides the necessary framework for national and international efforts to ensure sustainable exploitation of aquatic living resources in harmony with the environment by establishing principles and standards applicable to the conservation, management, and development of all fisheries.</em></p>\n<p><em>The FAO Code of Conduct for Responsible Fisheries relies on the concept of MSY when setting general principles and standards for fisheries management. Article 7.2.1 details how management measures should be &#x201C;based on the best scientific evidence available&#x201D; and &#x201C;designed to maintain or restore stocks at levels capable of producing maximum sustainable yield, as qualified by relevant environmental and economic factors, including the special requirements of developing countries.&#x201D;</em></p>\n<p><u>United Nation&#x2019;s International Standard Classification of All Economic Activities (ISIC)</u><em> <sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></em></p>\n<p><em>All components of marine capture fisheries are clearly defined within section A 0311 ISIC revision</em></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> FAO fishing areas for statistical purposes: </p><p><a href=\"http://fao.org/cwp-on-fishery-statistics/handbook/general-concepts/main-water-areas/en/\">http://fao.org/cwp-on-fishery-statistics/handbook/general-concepts/main-water-areas/en/</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> UNCLOS: <a href=\"http://www.un.org/Depts/los/convention_agreements/texts/unclos/unclos_e.pdf\">http://www.un.org/Depts/los/convention_agreements/texts/unclos/unclos_e.pdf</a> <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> CCRF: <a href=\"http://www.fao.org/3/v9878e/V9878E.pdf\">http://www.fao.org/3/v9878e/V9878E.pdf</a> <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> ISIC revision 4: <a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf</a> <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%). The indicator measure the value added of sustainable marine capture fisheries as a percentage of GDP.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The United Nation&#x2019;s International Standard Classification of All Economic Activities (ISIC) and</p>\n<p>FAO Fishing Areas for Statistical Purposes.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The data series on the value added of fisheries and aquaculture and GDP are derived from UNSD National Accounts Official Country Data. In case of missing values, supplementary data is retrieved from OECD Annual National Accounts Database. </p>\n<p>Economic data are specifically taken from:</p>\n<ul>\n  <li>UNSD National Accounts Official Country Data<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup>\n    <ul>\n      <li>Table 2.1. Value added by industries at current prices (ISIC Rev. 3)</li>\n      <li>Table 2.4. Value added by industries at current prices (ISIC Rev. 4)</li>\n    </ul>\n  </li>\n  <li>OECD Annual National Accounts<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup>\n    <ul>\n      <li>Table 6. Value added and its components by activity, ISIC rev3</li>\n      <li>Table 6A. Value added and its components by activity, ISIC rev4</li>\n    </ul>\n  </li>\n</ul>\n<p>The base data from which stock status is modelled and a detailed description of the approach used by FAO is available in:</p>\n<ul>\n  <li>FAO Review of the State of World Marine Fishery Resources<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup></li>\n  <li>Tables D 1-D 19. State of exploitation and annual nominal catches.</li>\n  <li>SDG 14.4.1 proportion of fish stocks within biologically sustainable levels</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> <a href=\"http://data.un.org/Explorer.aspx\">http://data.un.org/Explorer.aspx</a> <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> <a href=\"http://stats.oecd.org/\">http://stats.oecd.org/</a> <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> <a href=\"http://www.fao.org/docrep/015/i2389e/i2389e.pdf\">http://www.fao.org/docrep/015/i2389e/i2389e.pdf</a> <a href=\"#footnote-ref-8\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p>All data used in the calculation of this indicator is already provided by countries or published by FAO. </p>\n<p>National accounts data:</p>\n<p>National accounts data is used for both GDP and the value added of fisheries and aquaculture. This data is obtained from UNSD and OECD databases, both available online. </p>\n<p>Stock status:</p>\n<p>The fish stocks that FAO has monitored since 1974 represent a wide spectrum of data availability, ranging from data-rich and formally assessed stocks to those that have very little information apart from catch statistics by FAO major fishing area and those with no stock assessment at all. For the purposes of using the best available data and information and maintaining consistency among stocks and assessors, a procedure has been defined to identify stock status information (FAO 2011).</p>\n<p>FAO collects national data through a questionnaire sent to the Principal Focal Point (PFP) of each country. The PFP organises an institutional set-up which identifies the competent authorities to develop a reference list of stocks and completes the questionnaire. The information or data collected through the questionnaire from a country will initially only inform individual country progress. FAO is working on a convergence (where possible) of the two processes under SDG indicator 14.4.1, and good-quality stock status assessments reported by countries for the national indicators will be included in the regional/global indicator calculations, depending on the evolution and further standardization of country reporting.</p>\n<p>The indicator is applicable for countries with marine borders (or those bordering the Caspian Sea) and therefore excludes landlocked countries from data collection and processing.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data for GDP and value added is retrieved by FAO from UNSD (or the OECD in case of missing values) once a year every February.</p>\n<p>FAO compiles stock status information biennially.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>New data for this indicator is expected to be released biennially in March.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National governmental agencies reporting to:</p>\n<p>&#x2022; Food and Agriculture Organization of the United Nations (FAO).</p>\n<p>&#x2022; United Nations Statistics Division (UNSD). </p>\n<p>&#x2022; The Organization for Economic Cooperation and Development (OECD).</p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "INST_MANDATE__GLOBAL"=>"<p>FAO is the sole custodian of indicator 14.7.1, as designated by the Inter-agency and Expert Group on Sustainable Development Goal Indicators (IAEG-SDGs).</p>", "RATIONALE__GLOBAL"=>"<p>Although target 14.7 promotes the sustainable use of marine resources &#x201C;including of fisheries, aquaculture and tourism&#x201D;, this indicator as selected by the IAEG-SDG focuses only on the sustainable use of marine resources by fisheries. The methodology hereby proposed by FAO thus measures sustainable fisheries as a percentage of GDP, in accordance with the agreed indicator formulation.</p>\n<p>The share of value added from an industry in GDP is commonly used as an indication of its economic importance. Accordingly, the value added of marine capture fisheries indicates the prominence of marine fish related activities in the country&#x2019;s economy and its importance for livelihoods. Both GDP and the VA are measured in constant prices and domestic currency.</p>\n<p>Stocks that are fished at sustainable levels are able to support the communities and industries which rely on them, without compromising reproduction and long-term sustainability. By contrast, a stock that is exploited to a point where it cannot replenish itself will ultimately provide sub-optimal long-term economic returns for stakeholders. </p>\n<p>The status of a fish stock is evaluated through various processes of assessment that commonly combine biological and statistical information to assess changes in its abundance in response to fishing, which also enables forecasting of future trends. </p>\n<p>FAO has been periodically analysing and compiling the status of marine fish stocks combining the results of formal stock assessments available, including the assessments carried out at the regional level and a finer scale by national institutions and scientific working groups. For stocks that do not have a formal stock assessment, effort is made to collect relevant data and information from the literature, or from local experts, that could be used to infer stock status (for instance trends in catch rates, size frequency distribution of the catch, occasional fishing mortality estimates through surveys, etc.). The information from various sources is analysed and synthesized to classify the exploitation status of fish stocks. FAO monitoring of stocks will be enhanced with the implementation of SDG indicator 14.4.1, which tracks progress towards more fish stocks within biologically sustainable levels at national, regional (across FAO Major Fishing Areas) and global levels.</p>\n<p>Based on FAO&#x2019;s monitoring of stocks at regional and global level, the percentage of fish resources that are within biologically sustainable levels has exhibited a downward trend from 90 percent in 1974 to 67 percent in 2015, while 33 percent are considered to be overexploited. Overexploitation not only has negative ecological consequences, but also reduces long-term fishery yields, which have adverse social and economic effects, particularly for dependent communities in developing countries and Small Island Developing States (SIDS).</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator measures the value added of sustainable marine capture fisheries as a proportion of GDP. However, the vast majority of countries report only aggregated data for value added for the fisheries and aquaculture sector. To overcome this problem it is necessary to separate the value added for marine capture fisheries from the aggregated data. Preferably this would be done using the value of marine capture fisheries as a proxy. However, in the absence of value data, the quantity of marine capture fisheries as a proportion of total production is used as a proxy for the proportion of value added.</p>\n<p>For marine capture fisheries, despite the expanded coverage of FAO&#x2019;s assessments in recent years, data deficiencies may lead to uncertainty as to the level of exploitation of a stock. While data limitations exist, the methodology employed by FAO seeks to eliminate discrepancies and provide a representative assessment of marine fish stocks. The time series for which stock assessment is available starts with the first public release of FAO stock assessment, in 2011 for each FAO Major Fishing area. FAO continues to release this information biennially.<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup></p>\n<p>National fish stock assessments are only available for a few countries, and therefore are not globally or regionally representative. Therefore, the sustainability multiplier used in the compilation of this indicator is based on the average fish stock sustainability calculated by FAO for each Major Fishing Area. For each country, the sustainability multiplier will be the average sustainability weighted by the proportion of the quantity of marine capture for each respective fishing area in which the country performs fishing activities. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> The most recent version of Review of the State of World Marine Fishery Resources which contains stock status is available at <a href=\"http://www.fao.org/docrep/015/i2389e/i2389e.pdf\">http://www.fao.org/docrep/015/i2389e/i2389e.pdf</a> <a href=\"#footnote-ref-9\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>The method of computation for 14.7.1 differs depending on the availability of data. Method 1 outlines the steps for calculating 14.7.1 using national sustainability. Method 2 gives the steps for calculating 14.7.1 using proxy regional sustainability data.</p>\n<p>Method 1: When national sustainability data is available from 14.4.1, the contribution of sustainable marine capture fisheries to GDP is calculated as follows </p>\n<ol>\n  <li>The percentage contribution of fisheries and aquaculture to GDP is estimated by simply dividing the value added of fisheries and aquaculture by national GDP.</li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">D</mi>\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">F</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">A</mi>\n    <mi mathvariant=\"normal\">q</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">V</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">A</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">F</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">A</mi>\n        <mi mathvariant=\"normal\">q</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">F</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">A</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi mathvariant=\"normal\">V</mi>\n            <mi mathvariant=\"normal\">A</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">F</mi>\n            <mi mathvariant=\"normal\">I</mi>\n            <mi mathvariant=\"normal\">A</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<ol>\n  <li>In order to disaggregate for the value added of marine capture fisheries and the value added of aquaculture, the quantity of fish produced from marine capture fisheries will be divided by total quantity of national production of fish, and then multiplied by the percentage of GDP from fisheries and aquaculture. As such, the quantity of production of marine capture fisheries is used as a proxy for the value of marine capture fisheries.</li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">V</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">F</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">x</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi mathvariant=\"normal\">%</mi>\n    <mo>)</mo>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">D</mi>\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">F</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">A</mi>\n    <mi mathvariant=\"normal\">q</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">Q</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">M</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">F</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">Q</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">F</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">h</mi>\n      </mrow>\n    </mfrac>\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">V</mi>\n        <mi mathvariant=\"normal\">A</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">F</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">F</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">A</mi>\n      </mrow>\n    </msub>\n    <mo>&#xD7;</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi mathvariant=\"normal\">Q</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">M</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi mathvariant=\"normal\">Q</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">T</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<ol>\n  <li>The value added of marine capture fisheries (b) will be adjusted by the sustainability multiplier. The sustainability multiplier is taken from national indicators for SDG 14.4.1, proportion of fish stocks within biologically sustainable levels</li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">S</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">b</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">F</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">%</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">D</mi>\n    <mi mathvariant=\"normal\">P</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">S</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">b</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mi mathvariant=\"normal\">V</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">A</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">F</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">s</mi>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">F</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">S</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mo>&#xD7;</mo>\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">V</mi>\n        <mi mathvariant=\"normal\">A</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">F</mi>\n      </mrow>\n    </msub>\n  </math></p>\n<p>In summary, the computation method for GDP from sustainable marine capture fisheries may also be expressed as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">F</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mrow>\n      <munderover>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi mathvariant=\"normal\">i</mi>\n          <mo>=</mo>\n          <mn>1</mn>\n        </mrow>\n        <mrow>\n          <mi mathvariant=\"normal\">n</mi>\n        </mrow>\n      </munderover>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi mathvariant=\"normal\">S</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">i</mi>\n          </mrow>\n        </msub>\n        <mfrac>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">Q</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">Q</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">N</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mrow>\n    <mo>&#xD7;</mo>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mfrac>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">Q</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">M</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">Q</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">T</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfrac>\n        <mo>&#xD7;</mo>\n        <mfrac>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">V</mi>\n                <mi mathvariant=\"normal\">A</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">F</mi>\n                <mi mathvariant=\"normal\">I</mi>\n                <mi mathvariant=\"normal\">A</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">G</mi>\n            <mi mathvariant=\"normal\">D</mi>\n            <mi mathvariant=\"normal\">P</mi>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>+</mo>\n  </math></p>\n<p>Method 2: When national sustainability data is not available from 14.4.1, the contribution of sustainable marine capture fisheries to GDP is calculated as follows using proxy regional sustainability data.</p>\n<ol>\n  <li>The percentage contribution of fisheries and aquaculture to GDP is estimated by simply dividing the value added of fisheries and aquaculture by national GDP.</li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">D</mi>\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">F</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">A</mi>\n    <mi mathvariant=\"normal\">q</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi mathvariant=\"normal\">%</mi>\n      </mrow>\n    </mfenced>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">V</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">A</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">F</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">A</mi>\n        <mi mathvariant=\"normal\">q</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">F</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">A</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi mathvariant=\"normal\">V</mi>\n            <mi mathvariant=\"normal\">A</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">F</mi>\n            <mi mathvariant=\"normal\">I</mi>\n            <mi mathvariant=\"normal\">A</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<ol>\n  <li>In order to disaggregate for the value added of marine capture fisheries and the value added of aquaculture, the quantity of fish produced from marine capture fisheries will be divided by total quantity of national production of fish, and then multiplied by the percentage of GDP from fisheries and aquaculture. As such, the quantity of production of marine capture fisheries is used as a proxy for the value of marine capture fisheries.</li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">V</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">F</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">x</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>(</mo>\n    <mi mathvariant=\"normal\">%</mi>\n    <mo>)</mo>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">D</mi>\n    <mi mathvariant=\"normal\">P</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">F</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">A</mi>\n    <mi mathvariant=\"normal\">q</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"normal\">Q</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">M</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">F</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">Q</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">F</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">h</mi>\n      </mrow>\n    </mfrac>\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">V</mi>\n        <mi mathvariant=\"normal\">A</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">F</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">F</mi>\n        <mi mathvariant=\"normal\">I</mi>\n        <mi mathvariant=\"normal\">A</mi>\n      </mrow>\n    </msub>\n    <mo>&#xD7;</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi mathvariant=\"normal\">Q</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">M</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi mathvariant=\"normal\">Q</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">T</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<ol>\n  <li>The sustainability multiplier will be calculated based on the average sustainability published periodically for each FAO major marine fishing area.</li>\n</ol>\n<p>For each country, the sustainability multiplier will be the average sustainability weighted by the proportion of the quantity of marine capture for each respective fishing area in which the country performs fishing activities. When a country fishes in only one FAO fishing area, its sustainability multiplier will be equal to the average sustainability of stocks in that area.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">S</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">b</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">S</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">E</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">c</mi>\n        <mi mathvariant=\"normal\">h</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">g</mi>\n        <mi mathvariant=\"normal\">i</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mo>&#xD7;</mo>\n        <mfrac>\n          <mrow>\n            <mi mathvariant=\"normal\">Q</mi>\n            <mi mathvariant=\"normal\">u</mi>\n            <mi mathvariant=\"normal\">a</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mi mathvariant=\"normal\">t</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">t</mi>\n            <mi mathvariant=\"normal\">y</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">f</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">s</mi>\n            <mi mathvariant=\"normal\">h</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">d</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">f</mi>\n            <mi mathvariant=\"normal\">r</mi>\n            <mi mathvariant=\"normal\">o</mi>\n            <mi mathvariant=\"normal\">m</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">E</mi>\n            <mi mathvariant=\"normal\">a</mi>\n            <mi mathvariant=\"normal\">c</mi>\n            <mi mathvariant=\"normal\">h</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">m</mi>\n            <mi mathvariant=\"normal\">a</mi>\n            <mi mathvariant=\"normal\">r</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">r</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">g</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">o</mi>\n            <mi mathvariant=\"normal\">n</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">T</mi>\n            <mi mathvariant=\"normal\">o</mi>\n            <mi mathvariant=\"normal\">t</mi>\n            <mi mathvariant=\"normal\">a</mi>\n            <mi mathvariant=\"normal\">l</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">Q</mi>\n            <mi mathvariant=\"normal\">u</mi>\n            <mi mathvariant=\"normal\">a</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mi mathvariant=\"normal\">t</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">t</mi>\n            <mi mathvariant=\"normal\">y</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">f</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">s</mi>\n            <mi mathvariant=\"normal\">h</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">d</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">f</mi>\n            <mi mathvariant=\"normal\">r</mi>\n            <mi mathvariant=\"normal\">o</mi>\n            <mi mathvariant=\"normal\">m</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">A</mi>\n            <mi mathvariant=\"normal\">l</mi>\n            <mi mathvariant=\"normal\">l</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">m</mi>\n            <mi mathvariant=\"normal\">a</mi>\n            <mi mathvariant=\"normal\">r</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">r</mi>\n            <mi mathvariant=\"normal\">e</mi>\n            <mi mathvariant=\"normal\">g</mi>\n            <mi mathvariant=\"normal\">i</mi>\n            <mi mathvariant=\"normal\">o</mi>\n            <mi mathvariant=\"normal\">n</mi>\n            <mi mathvariant=\"normal\">s</mi>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">S</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mo>=</mo>\n    <mrow>\n      <munderover>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi mathvariant=\"normal\">i</mi>\n          <mo>=</mo>\n          <mn>1</mn>\n        </mrow>\n        <mrow>\n          <mi mathvariant=\"normal\">n</mi>\n        </mrow>\n      </munderover>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi mathvariant=\"normal\">S</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">i</mi>\n          </mrow>\n        </msub>\n        <mo>&#xD7;</mo>\n        <mfrac>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">Q</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">Q</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">N</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mrow>\n  </math></p>\n<ol>\n  <li>The value added of marine capture fisheries (b) will be adjusted by the sustainability multiplier (c) to get the sustainable marine capture fisheries as a percentage of GDP</li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">S</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">b</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">c</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">F</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">%</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">f</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">G</mi>\n    <mi mathvariant=\"normal\">D</mi>\n    <mi mathvariant=\"normal\">P</mi>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">S</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">b</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">y</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">p</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mi mathvariant=\"normal\">V</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mi mathvariant=\"normal\">u</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">A</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">d</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">n</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mi mathvariant=\"normal\">F</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">s</mi>\n    <mi mathvariant=\"normal\">h</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">r</mi>\n    <mi mathvariant=\"normal\">i</mi>\n    <mi mathvariant=\"normal\">e</mi>\n    <mi mathvariant=\"normal\">s</mi>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">F</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mi mathvariant=\"normal\">S</mi>\n    <mi mathvariant=\"normal\">m</mi>\n    <mo>&#xD7;</mo>\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">V</mi>\n        <mi mathvariant=\"normal\">A</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">F</mi>\n      </mrow>\n    </msub>\n  </math></p>\n<p>In summary, the computation method for GDP from sustainable marine capture fisheries may also be expressed as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi mathvariant=\"normal\">S</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">G</mi>\n        <mi mathvariant=\"normal\">D</mi>\n        <mi mathvariant=\"normal\">P</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">F</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mrow>\n      <munderover>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi mathvariant=\"normal\">i</mi>\n          <mo>=</mo>\n          <mn>1</mn>\n        </mrow>\n        <mrow>\n          <mi mathvariant=\"normal\">n</mi>\n        </mrow>\n      </munderover>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi mathvariant=\"normal\">S</mi>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">i</mi>\n          </mrow>\n        </msub>\n        <mfrac>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">Q</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">i</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">Q</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">N</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mrow>\n    <mo>&#xD7;</mo>\n    <mfenced separators=\"|\">\n      <mrow>\n        <mfrac>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">Q</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">M</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">Q</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">T</mi>\n              </mrow>\n            </msub>\n          </mrow>\n        </mfrac>\n        <mo>&#xD7;</mo>\n        <mfrac>\n          <mrow>\n            <msub>\n              <mrow>\n                <mi mathvariant=\"normal\">V</mi>\n                <mi mathvariant=\"normal\">A</mi>\n              </mrow>\n              <mrow>\n                <mi mathvariant=\"normal\">F</mi>\n                <mi mathvariant=\"normal\">I</mi>\n                <mi mathvariant=\"normal\">A</mi>\n              </mrow>\n            </msub>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">G</mi>\n            <mi mathvariant=\"normal\">D</mi>\n            <mi mathvariant=\"normal\">P</mi>\n          </mrow>\n        </mfrac>\n      </mrow>\n    </mfenced>\n  </math></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>+</mo>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>The methodology relies on information which is already provided by countries or published by FAO. National statistical systems are the primary providers of data for each aspect of the indicator. Value added and GDP data are collected and validated by the countries themselves. All inputs are reviewed for consistency prior to calculation of the indicator to ensure the consistency of figures and methodologies</p>", "ADJUSTMENT__GLOBAL"=>"<p>National accounts data is harmonised to ensure that figures for GDP and the value added of fisheries and aquaculture are obtained from the same ISIC review and System of National Accounts (SNA) series.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>This indicator examines economic contribution from marine capture fisheries. If a country has no marine capture fisheries then the indicator is not calculated for that country.</p>\n<p>No imputation is performed to derive estimates for countries or years when the value added of fisheries and aquaculture is not available. </p>\n<p>FAO employs a wide spectrum of data and analysis to assess 500 fish stocks, which accounts for 70&#x2013;80 percent of global landings. A detailed description of the approach used by FAO is available at the Review of the State of World Marine Fishery Resources.<sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup> If national estimates of fish stocks are not available from SDG 14.4.1 , then regional stock status will be used.</p>\n<ul>\n  <li><strong>At regional and global level</strong></li>\n</ul>\n<p>When a country has not reported the value added of fishing and aquaculture in a given year, their most recent figure for the value added of fisheries and aquaculture will be used will be used as proxy. In such instances GDP data will be from the same year as the most recent figure for the value added of fisheries and aquaculture, while other components will be from the year for which the indicator is being calculated. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> The most recent version of Review of the State of World Marine Fishery Resources is available at <a href=\"http://www.fao.org/docrep/015/i2389e/i2389e.pdf\">http://www.fao.org/docrep/015/i2389e/i2389e.pdf</a> <a href=\"#footnote-ref-10\">&#x2191;</a></p></div></div>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates will be generated by taking the average value of the indicator for countries in each SDG region.</p>\n<p>When interpreting regional aggregates, it is important to consider that a country&#x2019;s geographic region is not necessarily indicative of how or where it fishes. Countries may fish in completely different fishing areas from others in their region, and therefore land-based regional aggregates can be inappropriate when dealing with marine resources.</p>", "DOC_METHOD__GLOBAL"=>"<p>All data used in the calculation of this indicator is drawn from already available international sources. As such there is no additional reporting burden for countries.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>In order to provide continuity of collection of data for value added for fisheries and aquaculture, and GDP across different versions of the Systems of National Accounts (SNA) and ISIC revisions, FAO Fisheries and Aquaculture Department ensures its consistency by the use of backwards and forwards linkages when collecting and validating the information.</p>\n<p>While SDG indicator 14.7.1 is completely constructed on data already provided by countries to FAO, to the United Nations Statistics Division (UNSD) and to the Organization for Economic Cooperation and Development (OECD), countries are invited to collaborate with FAO to increase the precision of their results, by providing otherwise unavailable inputs for the calculation of the indicator.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The indicator provides a clear framework for monitoring countries&#x2019; progress towards target 14.7. Inputs are robust, standardised, globally recognised and available for a wide range of countries, including many developing nations. As such there is comprehensive coverage for the majority of countries.</p>\n<p>There may be variation in the completeness of nationally reported data. Improvements in data collection by national statistics systems may improve the accuracy of this indicator. When regional stock status is used in the calculation of this indicator it may not fully reflect the sustainability of national fisheries.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The indicator may be calculated based on currently available data for over 120 countries which have marine capture fisheries and have reported the value added of fisheries and aquaculture at least once since 2011. </p>\n<p><strong>Time Series:</strong></p>\n<p>Regional state of the world&#x2019;s marine fish stock: every two years from 2011</p>\n<p>Value added from UNSD:, annually</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Currently there are no disaggregation dimensions for this indicator.</p>", "COMPARABILITY__GLOBAL"=>"<p>Stock status taken from 14.4.1 is estimated by FAO based on the methodologies developed in the 1980s. Although regular updates were carried out to incorporate technical advances and changes in major fish species, some discrepancies between regions may occur in the representativeness of the reference list in practical fisheries. However, this will not pose a large impact on the reliability of the indicator&#x2019;s temporal trends.</p>", "OTHER_DOC__GLOBAL"=>"<p>- Sustainable Development Goal 14.7.1: http://www.fao.org/sustainable-development-goals/indicators/1471/en</p>\n<p>- FAO. 2018. Fishery and Aquaculture Statistics. Global capture production 1950-2016 (FishstatJ). In: FAO Fisheries and Aquaculture Department [online]. Rome. Updated 2018. www.fao.org/fishery/statistics/software/fishstatj/en</p>\n<p>- FAO. 2018. FAO yearbook. Fishery and Aquaculture Statistics 2016. Rome: http://www.fao.org/fishery/static/Yearbook/YB2016_USBcard/index.htm</p>\n<p>- FAO. 2018. The State of World Fisheries and Aquaculture 2018 - Meeting the sustainable development goals. Rome: http://www.fao.org/3/i9540en/I9540EN.pdf</p>\n<p>- FAO. 2011. Review of the State of World Marine Fishery Resources. Rome: http://www.fao.org/docrep/015/i2389e/i2389e.pdf</p>\n<p>- FAO. 1995. Code of Conduct for Responsible Fisheries. Rome: http://www.fao.org/3/a-v9878e.pdf</p>\n<p>- ICTSD. 2018. Overfishing, Overfished Stocks, and the Current WTO Negotiations on Fisheries Subsidies: https://www.greengrowthknowledge.org/sites/default/files/downloads/resource/Overfishing,%20Overfished%20Stocks,%20and%20the%20Current%20WTO%20Negotiations%20on%20Fisheries%20Subsidies.pdf</p>\n<p>- OECD Annual National Accounts: http://stats.oecd.org/ </p>\n<p>- The United Nations International Standard Industrial Classification of All Economic Activities, revision 4: https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf</p>\n<p>- The United Nations International Standard Industrial Classification of All Economic Activities, revision 4: https://unstats.un.org/unsd/statcom/doc02/isic.pdf</p>\n<p>- System of National Accounts 2008 - 2008 SNA: https://unstats.un.org/unsd/nationalaccount/sna2008.asp</p>\n<p>- System of National Accounts 1993 - 1993 SNA: https://unstats.un.org/unsd/nationalaccount/sna1993.asp</p>\n<p>- System of National Accounts 1968 - 1968 SNA: https://unstats.un.org/unsd/nationalaccount/docs/1968SNA.pdf</p>", "indicator_sort_order"=>"14-07-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"14.a.1", "slug"=>"14-a-1", "name"=>"Proporción del presupuesto total de investigación asignada a la investigación en el campo de la tecnología marina", "url"=>"/site/es/14-a-1/", "sort"=>"14aa01", "goal_number"=>"14", "target_number"=>"14.a", "global"=>{"name"=>"Proporción del presupuesto total de investigación asignada a la investigación en el campo de la tecnología marina"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción del presupuesto total de investigación asignada a la investigación en el campo de la tecnología marina", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción del presupuesto total de investigación asignada a la investigación en el campo de la tecnología marina", "indicator_number"=>"14.a.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La inversión sostenida en investigación y desarrollo (I+D), incluida la \ninvestigación oceánica, sigue siendo esencial para el avance del conocimiento \ny el desarrollo de nuevas tecnologías necesarias para sustentar las economías modernas. \n\nLa economía oceánica genera diversos beneficios en términos de empleo, ingresos e innovación \nen numerosos ámbitos. Sus avances actuales se basan, en gran medida, en décadas de \ninversión en ciencia e I+D por parte de gobiernos de todo el mundo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.a.1&seriesCode=ER_RDE_OSEX&areaCode=1&period=3&table=Total\"> Gasto nacional en ciencias oceánicas como porcentaje de la financiación total de investigación y desarrollo (%) ER_RDE_OSEX</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-0a-01.pdf\">Metadatos 14-a-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Sustained investment in research and development (R&D), including ocean research, remains essential to \nadvance knowledge and to develop new technology needed to support modern economies. \n\nThe ocean economy yields various benefits in terms of employment, revenues and innovation in many domains. Its \ncurrent developments are largely based on decades of science and R&D investments by governments \naround the world. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.a.1&seriesCode=ER_RDE_OSEX&areaCode=1&period=3&table=Total\"> National ocean science expenditure as a share of total research and development funding (%) ER_RDE_OSEX</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-0a-01.pdf\">Metadata 14-a-1.pdf</a> ", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La inversión sostenida en investigación y desarrollo (I+D), incluida la \ninvestigación oceánica, sigue siendo esencial para el avance del conocimiento \ny el desarrollo de nuevas tecnologías necesarias para sustentar las economías modernas. \n\nLa economía oceánica genera diversos beneficios en términos de empleo, ingresos e innovación \nen numerosos ámbitos. Sus avances actuales se basan, en gran medida, en décadas de \ninversión en ciencia e I+D por parte de gobiernos de todo el mundo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.a.1&seriesCode=ER_RDE_OSEX&areaCode=1&period=3&table=Total\"> Zientzia ozeanikoetako gastu nazionala, ikerketa eta garapeneko finantzaketa osoaren ehuneko gisa (%) ER_RDE_OSEX</a> UNSTATS", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-0a-01.pdf\">Metadatuak 14-a-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 14.a: Increase scientific knowledge, develop research capacity and transfer marine technology, taking into account the Intergovernmental Oceanographic Commission Criteria and Guidelines on the Transfer of Marine Technology, in order to improve ocean health and to enhance the contribution of marine biodiversity to the development of developing countries, in particular small island developing States and least developed countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 14.a.1: Proportion of total research budget allocated to research in the field of marine technology</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_RDE_OSEX - National ocean science expenditure as a share of total research and development funding [14.a.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-08-02", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Links to SDG 17, SDG 5.</p>\n<p>Targets: to all other SDG 14 targets, as science is crucial to protect and conserve the oceans&#x2019; resources.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Intergovernmental Oceanographic Commission of UNESCO (IOC-UNESCO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Intergovernmental Oceanographic Commission of UNESCO (IOC-UNESCO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p>Indicator 14.a.1 shows the annual national research budget allocated by governments in the field of marine technology, relative to the overall national governmental research and development budget in general.</p>\n<p><strong>Definition:</strong></p>\n<p>Definitions and mechanisms used in the development of the SDG indicator 14.a.1 are based on the IOC Criteria and Guidelines on Transfer of Marine Technology &#x2013; IOCCGTMT (originally published and endorsed by IOC Member States in 2005. These guidelines provide an internationally agreed definition of what is understood by the term marine technology and have been referenced in various UN General Assembly Resolutions and specifically in the formulation of SDG target 14.a. These are further explained in the Global Ocean Science Report (GOSR) referenced below.</p>\n<p>Marine technology as defined in the IOCCGTMT refers to instruments, equipment, vessels, processes and methodologies required to produce and use knowledge to improve the study and understanding of the nature and resources of the ocean and coastal areas. Toward this end, marine technology may include any of the following components:</p>\n<ol>\n  <li>Information and data, in a user-friendly format, on marine sciences and related marine operations and services;</li>\n  <li>Manuals, guidelines, criteria, standards, reference materials;</li>\n  <li>Sampling and methodology equipment (e.g., for water, geological, biological, chemical samples);</li>\n  <li>Observation facilities and equipment (e.g. remote sensing equipment, buoys, tide gauges, shipboard and other means of ocean observation);</li>\n  <li>Equipment for in situ and laboratory observations, analysis and experimentation;</li>\n  <li>Computer and computer software, including models and modelling techniques;</li>\n  <li>Expertise, knowledge, skills, technical/scientific/legal know-how and analytical methods related to marine scientific research and observation.</li>\n</ol>\n<p><strong>Concepts:</strong></p>\n<p>The concepts used for the definition and calculation of the indicator 14.a.1 are based on similar concepts used in the UNESCO Science Report (2010, 2015).These reports present GERD data (gross domestic expenditure on research and experimental development) as a share of GDP (gross domestic product) and further provide the R&amp;D (research and development) expenditure by sector of performance in % (Table S2 in the 2015 UNESCO Science Report). In addition, UIS publishes science field specific R&amp;D, e.g. natural sciences (http://data.uis.unesco.org/).</p>\n<p>The definitions and classifications used to collect R&amp;D data are based on the &#x2018;Frascati Manual: Proposed Standard Practice for Surveys on Research and Experimental Development&#x2019; (OECD, 2002).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) (Ocean science expenditure as a share of GERD) </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable </p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data sources: regular direct submission to the GOSR questionnaire/GOSR portal (https://gosr.ioc-unesco.org).</p>\n<p>The questionnaire used for the first edition of the GOSR was reviewed by the Editorial Board of the GOSR2020 as well as by UIS in 2017/2018 prior to the data collection exercise started in 2018. Assessments from 2018 on were conducted with an improved questionnaire (https://gosr.ioc-unesco.org/methodology).</p>\n<p>The novelty of the GOSR published for the first time in 2017, and the respective data collection of the 14.a.1 related data, requires the IOC Secretariat to collect the data via its national focal point until now. Future data collections might explore data availability at NSOs. New national reporting mechanisms are being established, which facilitate the provision of the required information (e.g. Colombia, Canada, Italy; document IOC-XXIX/2 Annex 14). The GERD (gross domestic expenditure on research and development) data were obtained from the UNESCO Institute for Statistics/World Bank, based on information directly provided from NSOs.</p>", "COLL_METHOD__GLOBAL"=>"<p>(I) National Counterparts:</p>\n<p>As mentioned in the previous paragraph the official counterparts are the IOC focal points <a href=\"https://oceanexpert.org/document/17716\">https://oceanexpert.org/document/17716</a> and well as National Oceanographic and Statistical Data Centres <a href=\"https://www.iode.org/index.php?option=com_content&amp;view=article&amp;id=61&amp;Itemid=100057\">https://www.iode.org/index.php?option=com_content&amp;view=article&amp;id=61&amp;Itemid=100057</a>. </p>\n<p>(II) Validation and consultation process by IOC Secretariat.</p>\n<p>These counterparts are invited to provide metadata information for the data provided. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data are collected minimum every 4 years. The GOSR data portal will allow for data submission throughout the year. In addition, IOC Member States will receive regular invitations to submit to the portal via IOC Circular letters. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Biannually.</p>", "DATA_SOURCE__GLOBAL"=>"<p>IOC focal points </p>\n<p>National Statistical Offices (NSOs)</p>\n<p>UNESCO Institute for Statistics (UIS)/World Bank</p>", "COMPILING_ORG__GLOBAL"=>"<p>Intergovernmental Oceanographic Commission of UNESCO (IOC-UNESCO) </p>\n<p>UNESCO Institute for Statistics (UIS)/World Bank</p>", "INST_MANDATE__GLOBAL"=>"<p>IOC-UNESCO is the custodian agency for the SDG indicator 14.a.1. The purpose of the Commission is to promote international cooperation and to coordinate programmes in research, services and capacity-development, in order to learn more about the nature and resources of the ocean and coastal areas and to apply that knowledge for the improvement of management, sustainable development, the protection of the marine environment, and the decision-making processes of its Member States. In addition, IOC is recognized through the United Nations Convention on the Law of the Sea (UNCLOS) as a competent international organization in the fields of Marine Scientific Research (Part XIII) and Transfer of Marine Technology (Part XIV). According to its Statutes, the Commission may act also as a joint specialized mechanism of the organizations of the United Nations system that have agreed to use the Commission for discharging certain of their responsibilities in the fields of marine sciences and ocean services, and have agreed accordingly to sustain the work of the Commission. IOC&#x2019;s Member States agreed to submit information relevant to the SDG indicator 14.a.1 to the IOC Secretariat in 2014 IOC/EC-XLVII/2 Annex 8. </p>", "RATIONALE__GLOBAL"=>"<p>Sustained investment in research and development (R&amp;D), including ocean research, remains essential to advance knowledge and to develop new technology needed to support modern economies. The ocean economy yields various benefits in terms of employment, revenues and innovation in many domains. Its current developments are largely based on decades of science and R&amp;D investments by governments around the world. Baseline information on ocean science funding, as delivered by the indicator 14.a.1 can be used as a starting point for more directed, tailored investment and new capacity development strategies, and to support the case for ensuring maximum impact of ocean research, for example through marine technology and knowledge transfer from government-funded marine and maritime R&amp;D projects. Annual (2009-2013) baseline information for 24 countries is presented in the GOSR (Isensee, K., Horn, L. and Schaaper, M. 2017. The funding for ocean science. In: In: IOC UNESCO, <em>Global Ocean Science Report&#x2014;The current status of ocean science around the world</em>. L. Vald&#xE9;s et al. (eds). Paris, UNESCO, pp. 80&#x2013;97) and in the GOSR2020 for 27 countries (Jolly, C., Olivari, M., Isensee, K., Nurse, L., Roberts, S., Lee, Y.-H. and Escobar Briones, E. 2020. Funding for ocean science. IOC-UNESCO, <em>Global Ocean Science Report 2020&#x2013;Charting Capacity for Ocean Sustainability.</em> K. Isensee (ed.), Paris, UNESCO Publishing, pp 69-90.). </p>\n<p>Updates on the methodology and progress made was published in the <a href=\"http://legacy.ioc-unesco.org/index.php?option=com_oe&amp;task=viewDocumentRecord&amp;docID=24776\">IOC/INF-1368</a> and <a href=\"https://oceanexpert.org/event/2805#documents\">IOC/INF-1385</a>.</p>\n<p>In addition to the data related to ocean science funding the GOSR 2017, 2020 and the GOSR portal provide information about the impacts of ocean science funding, such as data about research output, i.e. bibliometric and technometric data, ocean science personal and ocean science technology. The GOSR reports ocean science investment and the resulting capacity in a transparent and inclusive manner, based on a unique collection of primary data, is an opportunity to support and measure progress in capacity development globally. This ambition of the 2030 Agenda is also evident in the UN Decade of Ocean Science for Sustainable Development (2021&#x2013;2030, hereafter &#x2018;the Ocean Decade&#x2019;), where the definition of &#x2018;ocean science&#x2019; encompasses natural and social science disciplines, including interdisciplinary approaches; the technology and infrastructure that supports ocean science; the application of ocean science for societal benefits, including knowledge transfer and applications in regions that are currently lacking science capacity; as well as science-policy and science-innovation interfaces. </p>\n<p>Data and information presented in the GOSR2020, in future editions of the report and in the new GOSR portal will form part of the monitoring and evaluation process to track the progress of the Ocean Decade in achieving its vision &#x2018;The science we need for the ocean we want&#x2019;, via the objectives, challenges and seven goals outlined in the Ocean Decade Implementation Plan. The baseline information collected and published in the GOSR2020 immediately before the start of Ocean Decade will guide all ocean science actors, support</p>\n<p>the involvement of all countries in the Ocean Decade and help to remove barriers related to gender, generation and origin for all participants.</p>", "REC_USE_LIM__GLOBAL"=>"<p>As of 2020 the SDG 14.a.1 methodology is an adopted mechanism to obtain related information. Due to the fact that no agreed procedure to assess ocean science capacity existed until the first edition of the Global Ocean Science Report in 2017, national reporting mechanisms had to be developed and require partly still to be harmonized. However, since the GOSR 2020 data collection more countries established a strategy to collect 14.a.1 related information, allowing for global and regional technology and knowledge transfer in a resource- and need-adapted manner based on national inventories, as well as global and regional comparisons. </p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>14</mn>\n    <mo>.</mo>\n    <mi>a</mi>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>N</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>g</mi>\n    <mi>o</mi>\n    <mi>v</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>n</mi>\n    <mi>m</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>s</mi>\n    <mi>e</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>c</mi>\n    <mi>h</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>e</mi>\n    <mi>x</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>u</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>m</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>e</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mi>c</mi>\n    <mi>h</mi>\n    <mi>n</mi>\n    <mi>o</mi>\n    <mi>l</mi>\n    <mi>o</mi>\n    <mi>g</mi>\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n    <mo>/</mo>\n    <mi>&amp;nbsp;</mi>\n    <mi>N</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>g</mi>\n    <mi>o</mi>\n    <mi>v</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>n</mi>\n    <mi>m</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>R</mi>\n    <mo>&amp;amp;</mo>\n    <mi>D</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>e</mi>\n    <mi>x</mi>\n    <mi>p</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>t</mi>\n    <mi>u</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n  </math></p>\n<p>National governmental R&amp;D expenditure data are assessed annually by the UNESCO Institute for Statistics (UIS).</p>\n<p>National governmental ocean science expenditures are envisaged to be assessed biannually via the GOSR portal (IOC-XXIX/2 Annex 10).</p>\n<p>The development of the GOSR data repository/data portal will take place in close collaboration with UIS and IOC (at Headquarters and at the IOC Project Office for IODE, Oostende, Belgium).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>IOC receives verified information directly from the identified representatives of its Member States directly (primary data), which entails the validation to be published for the SDG indicator 14.a.1 assessments.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Data are based on the GOSR2020 questionnaire and UNESCO Institute for Statistics database. Note that ocean science funding is not identified as such in GERD data, and can be found in natural sciences and other categories. </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>In case countries do not provide data, no estimate will be calculated.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>For regional and global estimates/averages, only data received from Member States will be taken into account, missing values are not imputed or otherwise estimated.</p>", "REG_AGG__GLOBAL"=>"<p>Each national contribution is weighted equally to calculate average values for the regional and global estimates.</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>No particular guidance for the national data compilation exists as the organization of ocean science differs among Member States. </li>\n  <li>The IOC Secretariat recommends that IOC national focal points (IOC official national designated Coordinating Bodies for Liaison with the IOC) consult with the respective ministry(ies) responsible for ocean science and national universities and institutions to obtain SDG indicator 14.3.1 data.</li>\n  <li>IOC is an intergovernmental body of 150 Member States, the IOC national focal points may act as national coordinating bodies for relevant government departments, universities and research institutions actively involved in marine science and technology and other related aspects of ocean affairs.</li>\n  <li>As mentioned in point 3.a, the novelty of the GOSR published for the first time in 2017, and the respective data collection of the 14.a.1 related data, requires the IOC secretariat to collect the data via its national focal point until now. Future data collections might explore data availability at NSOs. New national reporting mechanisms are being established, which facilitate the provision of the required information (e.g. Colombia, Canada, Italy; document IOC-XXIX/2 Annex 14). The GERD (gross domestic expenditure on research and development) data were obtained from the UNESCO Institute for Statistics/World Bank, based on information directly provided from NSOs.</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Automated quality control will be set up for future data collection via the GOSR portal. Currently information received from IOC Member States are quality controlled by the IOC Secretariat before publication, which involves contacting the respective focal points in case needed. The quality controlled information is then made freely available and open access at the GOSR portal (<a href=\"https://gosr.ioc-unesco.org/home\">https://gosr.ioc-unesco.org/home</a>). </p>", "QUALITY_ASSURE__GLOBAL"=>"<ul>\n  <li>IOC national focal points and experts from UIS assist in the data quality assessment, comparing indicator values with the national expenditure for Natural Sciences (UIS), this allows the identification of discrepancies. In the future new values will be compared to previously obtained information. In case of discrepancies, the IOC secretariat will consult the data providers individually.</li>\n  <li>Combination of: Automated quality control by data portal; National quality control; Automated quality control via GOSR portal, IOC Secretariat.</li>\n</ul>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See 4.i and 4.j.</p>", "COVERAGE__GLOBAL"=>"<p>All data collected so far are available at the GOSR portal, as well as in the Global Ocean Science Reports (see <a href=\"https://gosr.ioc-unesco.org/home\">https://gosr.ioc-unesco.org/home</a>, <a href=\"https://gosr.ioc-unesco.org/report\">https://gosr.ioc-unesco.org/report</a>) </p>\n<p><strong>Time series:</strong></p>\n<p>Annual data points provided by countries are available from 2009 on. The latest data points are having a minimum delay of one year. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Possibility for regional and global aggregation.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>As this indicator only takes into account data submitted by Member States, there are no discrepancies between estimates and submitted data sets.</p>", "OTHER_DOC__GLOBAL"=>"<p>IOC-UNESCO. 2017<em>., Global Ocean Science Report&#x2014;The current status of ocean science around the world</em>. L. Vald&#xE9;s et al. (eds), UNESCO Publishing, Paris.</p>\n<p>IOC-UNESCO. 2020. <em>Global Ocean Science Report 2020&#x2013;Charting Capacity for Ocean Sustainability</em>. K. Isensee (ed.), UNESCO Publishing, Paris.</p>\n<p>Isensee, K., Horn, L. and Schaaper, M. 2017. The funding for ocean science. In: In: IOC-UNESCO, Global</p>\n<p>Ocean Science Report&#x2014;The current status of ocean science around the world. L. Vald&#xE9;s et al. (eds). Paris, UNESCO, pp. 80&#x2013;97.</p>\n<p>Jolly, C., Olivari, M., Isensee, K., Nurse, L., Roberts, S., Lee, Y.-H. and Escobar Briones, E. 2020. Funding for ocean science. IOC-UNESCO, <em>Global Ocean Science Report 2020&#x2013;Charting Capacity for Ocean Sustainability.</em> K. Isensee (ed.), Paris, UNESCO Publishing, pp 69-90.</p>\n<p>GOSR portal</p>\n<p><a href=\"https://gosr.ioc-unesco.org/home\">https://gosr.ioc-unesco.org/home</a></p>\n<p>UNESCO Science Report 2010, 2015</p>\n<p>https://en.unesco.org/unesco_science_report</p>\n<p>IOC Assembly Decisions: IOC-XXIX/5.1. and IOC-XXIX/9.1.)</p>\n<p>http://www.ioc-unesco.org/index.php?option=com_oe&amp;task=viewDocumentRecord&amp;docID=19770</p>\n<p>IOC Information documents</p>\n<p><a href=\"http://legacy.ioc-unesco.org/index.php?option=com_oe&amp;task=viewDocumentRecord&amp;docID=24776\">IOC/INF-1368</a> and <a href=\"https://oceanexpert.org/event/2805#documents\">IOC/INF-1385</a></p>\n<p>IOC-XXIX/2 Annex 14</p>\n<p>http://ioc-unesco.org/index.php?option=com_oe&amp;task=viewDocumentRecord&amp;docID=19589</p>\n<p>R&amp;D relevant data</p>\n<p>http://data.uis.unesco.org/</p>\n<p>Definition/Concepts: Frascati Manual: Proposed Standard Practice for Surveys on Research and</p>\n<p>Experimental Development&#x2019; (OECD, 2002)</p>\n<p>https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2002_9789264199040-en</p>\n<p>IOC Criteria and Guidelines on the Transfer of Marine Technology</p>\n<p><a href=\"https://unesdoc.unesco.org/ark:/48223/pf0000139193.locale=en\">https://unesdoc.unesco.org/ark:/48223/pf0000139193.locale=en</a></p>\n<p>UNESCO. 2015. UNESCO Science Report: Towards 2030. Paris, UNESCO Publishing. </p>", "indicator_sort_order"=>"14-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"14.b.1", "slug"=>"14-b-1", "name"=>"Grado de aplicación de un marco jurídico, reglamentario, normativo o institucional que reconozca y proteja los derechos de acceso para la pesca en pequeña escala", "url"=>"/site/es/14-b-1/", "sort"=>"14bb01", "goal_number"=>"14", "target_number"=>"14.b", "global"=>{"name"=>"Grado de aplicación de un marco jurídico, reglamentario, normativo o institucional que reconozca y proteja los derechos de acceso para la pesca en pequeña escala"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Grado de aplicación de un marco jurídico, reglamentario, normativo o institucional que reconozca y proteja los derechos de acceso para la pesca en pequeña escala", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Grado de aplicación de un marco jurídico, reglamentario, normativo o institucional que reconozca y proteja los derechos de acceso para la pesca en pequeña escala", "indicator_number"=>"14.b.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La Meta 14.b se centra en el acceso a los recursos y mercados para la pesca en pequeña escala, \nen consonancia con el párrafo 175 del documento final de Río+20. Para garantizar \nun acceso seguro, es necesario un entorno propicio que reconozca y proteja los \nderechos de la pesca en pequeña escala. Este entorno propicio tiene tres características clave:\n\n 1. Marcos jurídicos, reglamentarios y de políticas adecuados;\n 2. Iniciativas específicas para apoyar la pesca en pequeña escala; y\n 3. Mecanismos institucionales conexos que permitan la participación de las organizaciones de pesca en pequeña escala en los procesos pertinentes.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.b.1&seriesCode=ER_REG_SSFRAR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Grado de aplicación de un marco jurídico/regulatorio/político/institucional que reconoce y protege los derechos de acceso para la pesca en pequeña escala (nivel de implementación: 1 más bajo a 5 más alto) ER_REG_SSFRAR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-0b-01.pdf\">Metadatos 14-b-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Target 14.b focuses on access to resources and markets for small-scale fisheries, in line with the Rio+20 \noutcome document para, 175. In order to guarantee secure access, an enabling environment is necessary \nwhich recognizes and protects small-scale fisheries rights. Such an enabling environment has three key \nfeatures: \n\n 1. Appropriate legal, regulatory and policy frameworks; \n 2. Specific initiatives to support small-scale fisheries; and \n 3. Related institutional mechanisms which allow for the participation of small-scale fisheries \norganisations in relevant processes. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.b.1&seriesCode=ER_REG_SSFRAR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Degree of application of a legal/regulatory/policy/institutional framework which recognizes and protects access rights for small-scale fisheries (level of implementation: 1 lowest to 5 highest) ER_REG_SSFRAR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-0b-01.pdf\">Metadata 14-b-1.pdf</a> ", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La Meta 14.b se centra en el acceso a los recursos y mercados para la pesca en pequeña escala, \nen consonancia con el párrafo 175 del documento final de Río+20. Para garantizar \nun acceso seguro, es necesario un entorno propicio que reconozca y proteja los \nderechos de la pesca en pequeña escala. Este entorno propicio tiene tres características clave:\n\n 1. Marcos jurídicos, reglamentarios y de políticas adecuados;\n 2. Iniciativas específicas para apoyar la pesca en pequeña escala; y\n 3. Mecanismos institucionales conexos que permitan la participación de las organizaciones de pesca en pequeña escala en los procesos pertinentes.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=14.b.1&seriesCode=ER_REG_SSFRAR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Eskala txikiko arrantzarako sarbide-eskubideak aitortzen eta babesten dituen esparru juridiko/arautzaile/politiko/instituzional baten inplementazio-maila (inplementazio-maila: 1 baxuenetik 5 altuenera) ER_REG_SSFRAR</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-0b-01.pdf\">Metadatuak 14-b-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 14.b: Provide access for small-scale artisanal fishers to marine resources and markets</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 14.b.1: Degree of application of a legal/regulatory/policy/institutional framework which recognizes and protects access rights for small&#x2010;scale fisheries</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_REG_SSFRAR - Degree of application of a legal/regulatory/policy/institutional framework which recognizes and protects access rights for small-scale fisheries (level of implementation: 1 lowest to 5 highest) [14.b.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Linkages with any other Goals and Targets: SDG 1, SDG 2 (in particular 2.3), SDG 5, SDG 12, SDG 13, SDG 14.2/4/5/6/7</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Progress by number of countries in the degree of application of a legal/regulatory/policy/institutional framework which recognizes and protects access rights for small-scale fisheries.</p>\n<p><strong>Concepts:</strong></p>\n<p>National Statistical Systems already collect fisheries-relevant data, with a focus on production, employment, and trade. Relevant concepts can be found at CWP Handbook of Fishery Statistical Standards of the Coordinating Working Party on Fisheries Statistics (CWP).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Degree of implementation of frameworks which recognize and protect access rights for small-scale fisheries, categorized into 5 bands, as follows:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Score</p>\n      </td>\n      <td>\n        <p>Bands</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&gt;0 &#x2013;&lt; 0.2</p>\n      </td>\n      <td>\n        <p>Band 1: Very low implementation of instruments for access to resources and markets for small-scale fisheries</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.2 &#x2013;&lt; 0.4</p>\n      </td>\n      <td>\n        <p>Band 2: Low implementation of instruments for access to resources and markets for small-scale fisheries</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.4 &#x2013;&lt; 0.6</p>\n      </td>\n      <td>\n        <p>Band 3: Medium implementation of instruments for access to resources and markets for small-scale fisheries</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.6 &#x2013;&lt; 0.8</p>\n      </td>\n      <td>\n        <p>Band 4: High implementation of instruments for access to resources and markets for small-scale fisheries</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.8 &#x2013; 1.0</p>\n      </td>\n      <td>\n        <p>Band 5: Very high implementation of instruments for access to resources and markets for small-scale fisheries</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>See more details for the determination of the bands under 4.a., for the computation of the sub-indicators under 4.c. and the Annex for the full original questions informing the sub-indicators. </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>No applicable international standards for measuring degree of implementation of frameworks which recognize and protect access rights for small-scale fisheries.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data are based on the replies to three questions of the CCRF questionnaire (see Annex). It is usually provided from administrative sources, as best identified by the national fisheries administration responsible for replying to the CCRF questionnaire. The data are based on the presence of relevant laws, regulations, policies, plans or strategies and how these have been implemented so both legislative, management, and other documentation must be consulted to respond to the queries.</p>", "COLL_METHOD__GLOBAL"=>"<p>The CCRF questionnaire is a web-based system, with related data processing tools and usability features. Data is collected from FAO member countries every two years to be reported at aggregated level on the occasion of the sessions of the FAO Committee on Fisheries (COFI), usually in the period November to March preceding the session of COFI. In 2016, for the 32nd Session of COFI, 92 countries and the European Union (EU) responded to the section on small-scale fisheries of the CCRF questionnaire, which includes the three questions providing the variables for indicator 14.b.1. </p>", "FREQ_COLL__GLOBAL"=>"<p>The questionnaire is sent out on a biennial basis. It is expected to be sent out towards the end of the year prior to the holding of the Committee on Fisheries and remain open for a 2-3 month period, alterations of this calendar are subject to changes in the timing of the Committee on Fisheries.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data for the indicator are expected to be released one week after closure of the questionnaire.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are typically provided by the National Fishery Ministries/departments.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "INST_MANDATE__GLOBAL"=>"<p>Article I of the FAO constitution requires that the Organization collect, analyses, interpret and disseminate information relating to nutrition, food and agriculture http://www.fao.org/3/K8024E/K8024E.pdf.</p>", "RATIONALE__GLOBAL"=>"<p>Target 14.b focuses on access to resources and markets for small-scale fisheries, in line with the Rio+20 outcome document para, 175. In order to guarantee secure access, an enabling environment is necessary which recognizes and protects small-scale fisheries rights. Such an enabling environment has three key features:</p>\n<ol>\n  <li>Appropriate legal, regulatory and policy frameworks; </li>\n  <li>Specific initiatives to support small-scale fisheries; and</li>\n  <li>Related institutional mechanisms which allow for the participation of small-scale fisheries organisations in relevant processes. </li>\n</ol>\n<p>The 32nd Session of the FAO Committee on Fisheries agreed that the data submitted through the Code of Conduct for Responsible Fisheries (CCRF) questionnaire could be used by Members for reporting on Sustainable Development Goals (SDGs) indicators.</p>\n<p>The indicator variables are therefore chosen from three of the five questions on small-scale fisheries of the CCRF questionnaire to reflect these three aspects:</p>\n<ol>\n  <li>Are there any laws, regulations, policies, plans or strategies that specifically target or address the small-scale fisheries sector?</li>\n  <li>Are there any ongoing specific initiatives to implement the SSF Guidelines?</li>\n  <li>Does your country have an advisory/consultative body to the Ministry/Department of Fisheries in which fishers/fish workers can participate and contribute to decision-making processes?</li>\n</ol>\n<p>The national indicator is calculated based on these questions specifically focusing on actual efforts of promoting and facilitating access rights to small scale fisheries.</p>\n<p>Although the exact score will be important from one reporting year to the next for determining the progress made by a country, to aid the interpretation of this indicator, the score will then be converted into one of 5 bands as following:</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p>Score</p>\n      </td>\n      <td>\n        <p>Bands</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>&gt;0 &#x2013;&lt; 0.2</p>\n      </td>\n      <td>\n        <p>Band 1: Very low implementation of instruments for access to resources and markets for small-scale fisheries</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.2 &#x2013;&lt; 0.4</p>\n      </td>\n      <td>\n        <p>Band 2: Low implementation of instruments for access to resources and markets for small-scale fisheries</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.4 &#x2013;&lt; 0.6</p>\n      </td>\n      <td>\n        <p>Band 3: Medium implementation of instruments for access to resources and markets for small-scale fisheries</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.6 &#x2013;&lt; 0.8</p>\n      </td>\n      <td>\n        <p>Band 4: High implementation of instruments for access to resources and markets for small-scale fisheries</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>0.8 &#x2013; 1.0</p>\n      </td>\n      <td>\n        <p>Band 5: Very high implementation of instruments for access to resources and markets for small-scale fisheries</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "REC_USE_LIM__GLOBAL"=>"<p>It should be noted that while target 14.b refers to access for small-scale artisanal fishers to marine resources and markets some landlocked countries with inland fisheries have taken the opportunity to report on this indicator.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated using three variables, which are given respective weightings for the final calculation. There has not been a change in the calculation, nor the use of mixed sources. </p>\n<p><strong>Variable 1</strong>: Existence of laws, regulations, policies, plans or strategies that specifically target or address the small-scale fisheries sector</p>\n<p><strong>Variable 2</strong>: Ongoing specific initiatives to implement the SSF Guidelines</p>\n<p><strong>Variable 3</strong>: Existence of mechanisms enabling small-scale fishers and fish workers to contribute to decision-making processes </p>\n<p>Performance is scored based on the country responses to the relevant portions of three questions included in the Code of Conduct for Responsible Fisheries Questionnaire (CCRF). These questions have been transformed into weighted variables for the purpose of calculating the country scores. <u>The target has been set at a positive (&#x2018;yes&#x2019;) response to all the sub-variables, resulting in a score of 1</u>. </p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><em>Sub-variables</em></p>\n      </td>\n      <td>\n        <p><em>Weight</em></p>\n      </td>\n      <td>\n        <p><em> </em></p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><em>Sub-variables</em></p>\n      </td>\n      <td>\n        <p><em>Weight</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"5\">\n        <p><strong><em>Variable 1</em></strong></p>\n      </td>\n      <td>\n        <p>1.1</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td rowspan=\"11\">\n        <p><strong><em>Variable 2</em></strong></p>\n      </td>\n      <td>\n        <p><em>2.1</em></p>\n      </td>\n      <td>\n        <p>0.03</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.2</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>2.2</p>\n      </td>\n      <td>\n        <p>0.03</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.3</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>2.3</p>\n      </td>\n      <td>\n        <p>0.03</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.4</p>\n      </td>\n      <td>\n        <p>0.1</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>2.4</p>\n      </td>\n      <td>\n        <p>0.03</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.5</p>\n      </td>\n      <td>\n        <p><em><sup>1</sup></em></p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>2.5</p>\n      </td>\n      <td>\n        <p>0.03</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong>Variable weight</strong></p>\n      </td>\n      <td>\n        <p><strong>0.4</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p>2.6</p>\n      </td>\n      <td>\n        <p>0.03</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"3\" rowspan=\"3\">\n        <p><em><sup>1</sup></em><sup> </sup>Sub-variable 1.5 is only weighted when a response of &apos;yes&apos; is provided along with supporting details in the text form. </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>2.7</p>\n      </td>\n      <td>\n        <p>0.03</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>2.8</p>\n      </td>\n      <td>\n        <p>0.03</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>2.9</p>\n      </td>\n      <td>\n        <p>0.03</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>2.10</p>\n      </td>\n      <td>\n        <p>0.03</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong>Indicator weight</strong></p>\n      </td>\n      <td>\n        <p><strong>0.3</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><em> </em></p>\n      </td>\n      <td>\n        <p>3.1</p>\n      </td>\n      <td>\n        <p>0.3</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong><em>Variable 3</em></strong></p>\n      </td>\n      <td>\n        <p><strong>Indicator weight</strong></p>\n      </td>\n      <td>\n        <p><strong>0.3</strong></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>The higher weighting assigned to Variable 1 reflects the slightly greater importance of that indicator for assessing the degree of application of a legal/regulatory/policy/institutional framework which recognizes and protects access rights for small-scale fishers. </p>\n<p>Each sub-variable is scored on the basis of a &#x2018;yes&#x2019; or &#x2018;no&#x2019; response and any &#x2018;blank&#x2019; or &#x2018;unknown&#x2019; responses are scored as a &#x2018;no&#x2019;, or zero. A response of yes results in a score that corresponds with the full weighting value for that variable category. For example, a &#x2018;yes&#x2019; response for variables 1.3, 2.1 and 3.1 are scored as 0.1, 0.03 and 0.3 respectively. All &#x2018;no&#x2019;, &#x2018;blank&#x2019; or &#x2018;unknown&#x2019; responses are scored as zero.</p>\n<p>One exception is made in the case of sub-variable 1.5. This question allows a response of &#x2018;other&#x2019; with an associated text field. A positive response in this field is only scored as a &#x2018;yes&#x2019; in the case where the text field is also completed AND at least one of the other prior sub-variable were scored as &#x2018;no&#x2019;. This allows the indicator weighting to remain consistent in all cases.</p>\n<p>Once the specific score has been determined for each country, countries will be classified into a number of bands, ranging from a low to a high degree of implementation, and thus effectively translate a synthetic score into a tangible and intuitive metric for countries.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Upon completing the questionnaire, States are provided with a condensed report showing their responses to relevant questions within the questionnaire for the indicator and the resulting SDG indicator score for their validation.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>The most appropriate methodology for producing estimates for the indicator when the country data are not available would be the use of expert consultation and judgement rather than the use of mathematical formula for data imputation. The use of expert judgement is a critical factor as the indicator asses the state of management/ policy implementation at a national level, not values that could be readily inputted.</p>\n<p> </p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>The categorization into the respective bands will also apply in the case of regional and global aggregates for this indicator. Once the mean score for an SDG region has been calculated, the region will be classified into a particular band reflecting the degree of implementation of relevant instruments.</p>\n<p>Data is combined for the respective nations within a region, as a count of the number of countries by Band, and this can be further aggregated to the global level without the need for any weighting of national or regional scores.</p>", "DOC_METHOD__GLOBAL"=>"<p>Data is collected through an electronic questionnaire submitted by FAO to the country focal points for the CCRF questionnaire, usually in the national fisheries administration. Data are validated upon intake of the questionnaires. No adjustments are required for the data for definitions nor for classification or demographic harmonization.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>FAO is responsible for the quality of the internal statistical processes used to compile the published datasets. The FAO Statistics Quality Assurance Framework (SQAF), available at: <a href=\"http://www.fao.org/docrep/019/i3664e/i3664e.pdf\">http://www.fao.org/docrep/019/i3664e/i3664e.pdf</a>, provides the necessary principles, guidelines and tools to carry out quality assessments. FAO is performing an internal bi-annual survey (FAO Quality Assessment and Planning Survey) designed to gather information on all of FAO&#x2019;s statistical activities, notably to assess the extent to which quality standards are being implemented with a view to increasing compliance with the quality dimensions of SQAF, documenting best practices and prepare quality improvement plans, where necessary. Domain-specific quality assurance activities are carried out systematically (e.g. quality reviews, self-assessments, compliance monitoring).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>&#x2022; Data are checked for their correctness, completeness, consistency along the process of data entry, and/or through a specific statistical analysis as the yearly data set is closed. </p>\n<p>&#x2022; The indicator relies on data generated through the CCRF questionnaire which is filled in by countries on a biannual basis. To facilitate reporting of the CCRF-based SDG indicators, a tailor-made data processing tool has been developed within the framework of the existing CCRF questionnaire online platform. Upon submission of the questionnaire by the user, an indicator report will automatically be generated for final validation by the country.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>From 2022 data series onwards, questions of a factual nature used to indicate applicability of the indicator or to calculate the score of the indicator, such as whether a country is landlocked or whether it is a Party to a relevant international instrument will be pre-compiled. Official sources will be used to conduct this activity such as the depository of the relevant international binding instrument.</p>\n<p>This activity will be conducted for the following questions, please refer to Appendix 1 for full text of referenced question: A.1, 1.1, 2.1, 4.1 and 5.1</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>In 2016, 92 countries and the European Union replied to the questionnaire section on the three indicators to measure target performance for 14.b.1. </p>\n<p>The below table indicates the scores for SDG 14.b.1 reporting that where validated by countries since 2018. </p>\n<table>\n  <tbody>\n    <tr>\n      <td></td>\n      <td colspan=\"2\">\n        <p>Indicator 14.b.1</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Reporting year</p>\n      </td>\n      <td>\n        <p>2018</p>\n      </td>\n      <td>\n        <p>2020</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Validated scores</p>\n      </td>\n      <td>\n        <p>113</p>\n      </td>\n      <td>\n        <p>92</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Non validated scores</p>\n      </td>\n      <td>\n        <p>7</p>\n      </td>\n      <td>\n        <p>16</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Not applicable scores</p>\n      </td>\n      <td>\n        <p>11</p>\n      </td>\n      <td>\n        <p>10</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Breakdown of the number of countries covered by region is as follows:</p>\n<table>\n  <thead>\n    <tr>\n      <th></th>\n      <th>\n        <p>Number of countries</p>\n      </th>\n      <th>\n        <p>Nature of data</p>\n      </th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>\n        <p>World</p>\n      </td>\n      <td>\n        <p>120</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Africa</p>\n      </td>\n      <td>\n        <p>26</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Northern Africa</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Sub-Saharan Africa</p>\n      </td>\n      <td>\n        <p>25</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Eastern Africa</p>\n      </td>\n      <td>\n        <p>9</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Middle Africa</p>\n      </td>\n      <td>\n        <p>6</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Southern Africa</p>\n      </td>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Western Africa</p>\n      </td>\n      <td>\n        <p>6</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Americas</p>\n      </td>\n      <td>\n        <p>27</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Latin America and the Caribbean</p>\n      </td>\n      <td>\n        <p>25</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Caribbean</p>\n      </td>\n      <td>\n        <p>9</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Latin America</p>\n      </td>\n      <td>\n        <p>14</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Northern America</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Asia</p>\n      </td>\n      <td>\n        <p>25</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Central Asia</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Eastern Asia</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Southern Asia</p>\n      </td>\n      <td>\n        <p>6</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>South-Eastern Asia</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Western Asia</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Europe</p>\n      </td>\n      <td>\n        <p>35</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Eastern Europe</p>\n      </td>\n      <td>\n        <p>8</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Northern Europe</p>\n      </td>\n      <td>\n        <p>9</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Southern Europe</p>\n      </td>\n      <td>\n        <p>9</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Western Europe</p>\n      </td>\n      <td>\n        <p>9</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Oceania</p>\n      </td>\n      <td>\n        <p>7</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Australia and New Zealand</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Melanesia</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Micronesia</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Polynesia</p>\n      </td>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>G</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Time series:</strong></p>\n<p>2016 (baseline)</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The disaggregation level is the national level. No demographic features are included in the indicators and are thus excluded from the consideration of level of disaggregation.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There might be differences between a national estimated based on an expert judgment, in case of country data is not available, and the answer a country would give via the self-assessment questionnaire. This can happen not only because the expert judgement represents the best approximation to the reality, but not the reality itself, and/or due to the well-known self-report bias verifiable in this type of surveys that means countries will by tendency report a better reality that the one indeed in place.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<p>&#x2022; SDG 14.b http://www.fao.org/sustainable-development-goals/indicators/14.b.1/en/</p>\n<p>&#x2022; e-learning course on SDG indicator 14.b.1: <a href=\"https://elearning.fao.org/course/view.php?id=348&amp;lang=en\">https://elearning.fao.org/course/view.php?id=348&amp;lang=en</a></p>\n<ul>\n  <li>06-08 July 2021 (Online event) <a href=\"https://www.fao.org/sustainable-development-goals/events/detail/en/c/1437632/\" target=\"_blank\">Asia Regional Workshop on SDG 14.b and its indicator 14.b.1</a></li>\n  <li>9-11 April 2019 (Nadi, Fiji)| <a href=\"https://www.fao.org/documents/card/en/c/ca7753en\" target=\"_top\">Pacific Regional Workshop on Exploring SDG Target 14.b and its Indicator 14.b.1</a>.</li>\n  <li>28-29 November 2017 (Gaeta, Italy) | <a href=\"https://www.fao.org/3/ca0140en/CA0140EN.pdf\" target=\"_blank\">Exploring Sustainable Development Goal 14.b and its Proposed Indicator 14.b.1</a></li>\n  <li><a href=\"https://www.fao.org/publications/card/en/c/CB4806EN\" target=\"_blank\">Reporting on Sustainable Development Goal Target 14.b and its indicator 14.b.1 - Guidance for Pacific Island countries</a></li>\n</ul>\n<p><strong>References:</strong></p>\n<p>32nd Session of the FAO Committee on Fisheries &#x2013; relevant documents:</p>\n<p>&#x2022; http://www.fao.org/3/a-mq663e.pdf</p>\n<p>&#x2022; http://www.fao.org/3/a-mq873e.pdf</p>\n<p>&#x2022; <a href=\"http://www.fao.org/3/a-bo076e.pdf\">http://www.fao.org/3/a-bo076e.pdf</a></p>\n<p><strong>ANNEX &#x2013; Relevant questions from the FAO CCRF questionnaire</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Variable 1. Existence of laws, regulations, policies, plans or strategies that specifically target or address the small-scale fisheries sector &#x2013; weighting 40%</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Are there any laws, regulations, policies, plans or strategies that specifically target or address the small-scale fisheries sector?</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.1) Law</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.2) Regulation</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.3) Policy</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.4) Plan/strategy</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1.5) Other*</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Variable 2. Ongoing specific initiatives to implement the SSF Guidelines - weighting 30%</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>In the case that your country has a specific initiative to implement the SFF guidelines. What specific activities are included in this initiative:</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.1) Improving tenure security for small-scale fishers and fish workers in accordance with SSF Guidelines paragraphs 5.2-5.12</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.2) Supporting small-scale fisheries actors to take an active part in sustainable resource management in accordance with SSF Guidelines paragraphs 5.13-5.20</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.3) Promoting social development, employment and decent work in small-scale fisheries in accordance with SSF Guidelines paragraphs 6.2-6.18</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.4) Enhancing small-scale fisheries value chains, post-harvest operations and trade in accordance with SSF Guidelines paragraphs 7.1-7.10</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.5) Ensuring gender equality in small-scale fisheries in accordance with SSF Guidelines paragraphs 8.1-8.4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.6) Addressing disaster risks and climate change in small-scale fisheries in accordance with SSF Guidelines paragraphs 9.1-9.9</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.7) Strengthening institutions in support of SSF and to promote policy coherence, coordination and collaboration in accordance with SSF Guidelines paragraphs 10.1-10.8</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.8) Improving information, research and communication on the contribution of SSF to food security and poverty eradication in accordance with SSF Guidelines paragraphs 11.1-11.11</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.9) Implementing capacity development of fisheries organizations and other stakeholders in accordance with SSF Guidelines paragraphs 12.1-12.4</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2.10) Establishing or improving monitoring mechanisms and promoting SSF Guidelines implementation in accordance with SSF Guidelines paragraphs 13.1-13.6</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Variable 3. Existence of mechanisms through which small-scale fishers and fish workers contribute to decision-making processes &#x2013; weighting 30%</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3.1) Does your country have an advisory/consultative body to the Ministry/Department of Fisheries in which fishers/fish workers can participate and contribute to decision-making processes? (representation at national or provincial level)</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "indicator_sort_order"=>"14-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"14.c.1", "slug"=>"14-c-1", "name"=>"Número de países que, mediante marcos jurídicos, normativos e institucionales, avanzan en la ratificación, la aceptación y la implementación de los instrumentos relacionados con los océanos que aplican el derecho internacional reflejado en la Convención de las Naciones Unidas sobre el Derecho del Mar para la conservación y el uso sostenible de los océanos y sus recursos", "url"=>"/site/es/14-c-1/", "sort"=>"14cc01", "goal_number"=>"14", "target_number"=>"14.c", "global"=>{"name"=>"Número de países que, mediante marcos jurídicos, normativos e institucionales, avanzan en la ratificación, la aceptación y la implementación de los instrumentos relacionados con los océanos que aplican el derecho internacional reflejado en la Convención de las Naciones Unidas sobre el Derecho del Mar para la conservación y el uso sostenible de los océanos y sus recursos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países que, mediante marcos jurídicos, normativos e institucionales, avanzan en la ratificación, la aceptación y la implementación de los instrumentos relacionados con los océanos que aplican el derecho internacional reflejado en la Convención de las Naciones Unidas sobre el Derecho del Mar para la conservación y el uso sostenible de los océanos y sus recursos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que, mediante marcos jurídicos, normativos e institucionales, avanzan en la ratificación, la aceptación y la implementación de los instrumentos relacionados con los océanos que aplican el derecho internacional reflejado en la Convención de las Naciones Unidas sobre el Derecho del Mar para la conservación y el uso sostenible de los océanos y sus recursos", "indicator_number"=>"14.c.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La Meta 14.c busca mejorar la conservación y el uso sostenible de los \nocéanos y sus recursos mediante la aplicación del derecho internacional \nreflejado en la Convención de las Naciones Unidas sobre el Derecho del Mar (CNUDM).\n\nLa CNUDM establece el marco jurídico dentro del cual deben llevarse a \ncabo todas las actividades en los océanos y mares, incluida la conservación \ny el uso sostenible de los océanos y sus recursos. Es un instrumento marco \nque prevé el desarrollo de otros instrumentos que se ajusten a sus disposiciones.\n\nPor lo tanto, el progreso en la aplicación del derecho internacional reflejado \nen la CNUDM solo puede medirse exhaustivamente si también se mide el progreso \nen la aplicación de los instrumentos relacionados con los océanos que implementan \nel derecho internacional reflejado en la CNUDM.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-0c-01.pdf\">Metadatos 14-c-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-08", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Target 14.c seeks to enhance the conservation and sustainable use of oceans and their resources by \nimplementing international law as reflected in UNCLOS. \n\nUNCLOS sets out the legal framework within which all activities in the oceans and seas must be carried \nout, including the conservation and sustainable use of oceans and their resources. It is a framework \ninstrument, which provides for the development of other instruments that conform toits provisions. \n\nTherefore, progress in the implementation of international law as reflected in UNCLOS can only be \ncomprehensively measured if progress in the implementation of ocean-related instruments that \nimplement international law as reflected in UNCLOS, is also measured. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-0c-01.pdf\">Metadata 14-c-1.pdf</a> ", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La Meta 14.c busca mejorar la conservación y el uso sostenible de los \nocéanos y sus recursos mediante la aplicación del derecho internacional \nreflejado en la Convención de las Naciones Unidas sobre el Derecho del Mar (CNUDM).\n\nLa CNUDM establece el marco jurídico dentro del cual deben llevarse a \ncabo todas las actividades en los océanos y mares, incluida la conservación \ny el uso sostenible de los océanos y sus recursos. Es un instrumento marco \nque prevé el desarrollo de otros instrumentos que se ajusten a sus disposiciones.\n\nPor lo tanto, el progreso en la aplicación del derecho internacional reflejado \nen la CNUDM solo puede medirse exhaustivamente si también se mide el progreso \nen la aplicación de los instrumentos relacionados con los océanos que implementan \nel derecho internacional reflejado en la CNUDM.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-14-0c-01.pdf\">Metadatuak 14-c-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 14.c: Enhance the conservation and sustainable use of oceans and their resources by implementing international law as reflected in the United Nations Convention on the Law of the Sea, which provides the legal framework for the conservation and sustainable use of oceans and their resources, as recalled in paragraph 158 of &#x201C;The future we want&#x201D;</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 14.c.1: Number of countries making progress in ratifying, accepting and implementing through legal, policy and institutional frameworks, ocean-related instruments that implement international law, as reflected in the United Nations Convention on the Law of the Sea, for the conservation and sustainable use of the oceans and their resources</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_UNCLOS_RATACC - Score for the ratification of and accession to UNCLOS and its two implementing agreements (%) [14.c.1]</p>\n<p>ER_UNCLOS_IMPLE - Score for the implementation of UNCLOS and its two implementing agreements (%) [14.c.1] </p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Implementation of target 14.c is linked to progress in all other targets of Sustainable Development Goal 14, and the other ocean-related Goals of the 2030 Agenda.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Division for Ocean Affairs and the Law of the Sea (DOALOS), Office of Legal Affairs (OLA), United Nations Secretariat</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Division for Ocean Affairs and the Law of the Sea (DOALOS), Office of Legal Affairs (OLA) United Nations Secretariat</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Sustainable Development Goal (SDG) indicator 14.c.1 measures the number of countries making progress in the ratification of, accession to and implementation of ocean-related instruments that implement international law, as reflected in the United Nations Convention on the Law of the Sea (UNCLOS), for the conservation and sustainable use of the oceans and their resources.</p>\n<p>There are two aspects to this indicator:</p>\n<p>&#x2022; the number of countries making progress in ratifying and acceding to ocean-related instruments that implement international law as reflected in UNCLOS for the conservation and sustainable use of the oceans and their resources, and </p>\n<p>&#x2022; the number of countries making progress in implementing such instruments through legal, policy and institutional frameworks.</p>\n<p><strong>Concepts:</strong></p>\n<p>N/A.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%): a score for the ratification of and accession to UNCLOS and its two implementing agreements and a score for the implementation of these instruments.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data will be collected through a questionnaire, which has been developed to facilitate measurement of the number of countries making progress in ratifying, accepting and implementing through legal, policy and institutional frameworks, ocean-related instruments that implement international law, as reflected in UNCLOS, for the conservation and sustainable use of the oceans and their resources. This is in line with the requirements of indicator 14.c.1.</p>", "COLL_METHOD__GLOBAL"=>"<p>OLA/DOALOS will coordinate distribution/completion of the indicator 14.c.1 questionnaire through the Permanent Missions of Member States to the United Nations in New York and through other appropriate channels to other States. The focal points of National Statistical Offices will also be informed of the distribution of the questionnaire. The Permanent Missions would coordinate distribution of the questionnaire amongst relevant government ministries, departments and agencies, and submit the completed questionnaires to OLA/DOALOS, as necessary. </p>", "FREQ_COLL__GLOBAL"=>"<p>Baseline data collection was administered in 2020-2021. Data collection will be repeated every two to three years.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p> Data will be published every two to three years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data will be provided by relevant government ministries, departments and agencies.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Division for Ocean Affairs and the Law of the Sea (DOALOS), Office of Legal Affairs (OLA), United Nations Secretariat</p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>Target 14.c seeks to enhance the conservation and sustainable use of oceans and their resources by implementing international law as reflected in UNCLOS.</p>\n<p>UNCLOS sets out the legal framework within which all activities in the oceans and seas must be carried out, including the conservation and sustainable use of oceans and their resources. It is a framework instrument, which provides for the development of other instruments that conform toits provisions. Therefore, progress in the implementation of international law as reflected in UNCLOS can only be comprehensively measured if progress in the implementation of ocean-related instruments that implement international law as reflected in UNCLOS, is also measured. </p>\n<p>Such instruments include, in particular, UNCLOS&#x2019;s two implementing agreements - the Agreement relating to the implementation of Part XI of the United Nations Convention on the Law of the Sea of 10 December 1982 (Part XI Agreement) and the Agreement for the Implementation of the Provisions of the United Nations Convention on the Law of the Sea of 10 December 1982 relating to the Conservation and Management of Straddling Fish Stocks and Highly Migratory Fish Stocks (UNFSA). </p>\n<p>Accordingly, following extensive consultation with Member States and other stakeholders, the methodology for indicator 14.c.1 measures the number of countries making progress in ratifying, acceding to and implementing UNCLOS, the Part XI Agreement and UNFSA through legal, policy and institutional frameworks. </p>\n<p>Data collected through the first administration of the questionnaire, which is based on the indicator, will provide a baseline of the current state of ratification of, accession to and implementation of UNCLOS and its two implementing agreements. Subsequent indicator-based data will then show progress made by countries.</p>\n<p>Countries that do not respond to the questionnaire, or do not approve the use of their responses to the questionnaire, will not receive indicator scores.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Implementation of UNCLOS and its implementing agreements through legal frameworks (for example, through national legislation or executive acts) as well as policy and institutional frameworks will be scored on the basis of a self-analysis by countries of the extent of implementation. Countries will be invited in the questionnaire to share information regarding their methods of implementation.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator measures the number of countries making progress in ratifying, acceding to and implementing UNCLOS and its two implementing agreements through legal, policy and institutional frameworks. </p>\n<p>This measurement of progress is computed on the basis of countries&#x2019; responses to the questionnaire, which contains three questions in respect to each of the three instruments. </p>\n<p>Countries will be invited to respond to questions which relate to ratification of or accession to UNCLOS and its two implementing agreements (Questions 1.1, 2.1 and 3.1). They are coded with simple &#x201C;Yes/No&#x201D; answers, with a score of &#x201C;1&#x201D; for &#x201C;Yes&#x201D; and &#x201C;0&#x201D; for &#x201C;No&#x201D;. Each country&#x2019;s overall score for ratification of or accession to these instruments will therefore be a number between 0 and 3, which will be reported as a percentage (with &#x201C;100&#x201D; representing a score of &#x201C;3&#x201D;, and &#x201C;0&#x201D; representing a score of &#x201C;0&#x201D;).</p>\n<p>Countries will also be invited to respond to questions which relate to implementation of UNCLOS and its two implementing agreements through legal frameworks (Questions 1.2, 2.2 and 3.2) by evaluating their own national implementation and assigning a score of between 1 and 9 &#x2013; with &#x201C;1&#x201D; being &#x201C;not at all&#x201D; and &#x201C;9&#x201D; being &#x201C;fully&#x201D; &#x2013; or indicating that the question of implementation is not applicable (&#x201C;N/A&#x201D;). </p>\n<p>Countries will further be invited to indicate whether they have a national policy and/or a national institution or another mechanism, such as a national focal point or an inter-agency or inter-departmental working group, with responsibility for ensuring that the problems of ocean space, matters related to the Part XI Agreement and matters related to UNFSA are considered through an integrated, interdisciplinary and inter-sectoral approach (Questions 1.3, 2.3 and 3.3). These questions are coded with simple &#x201C;Yes&#x201D;, &#x201C;No&#x201D; and &#x201C;N/A&#x201D; answers, with a score of &#x201C;1&#x201D; for &#x201C;Yes&#x201D; and &#x201C;0&#x201D; for &#x201C;No&#x201D;. </p>\n<p>The scoring methodology regarding implementation is the total of the scores reported by States regarding implementation through legal frameworks for UNCLOS and each of its two implementing agreements (in response to Questions 1.2, 2.2 and 3.2), added to the relevant scores achieved regarding implementation through national policy and/or national institutions for UNCLOS and each of its implementing agreements (in respect to Questions 1.3, 2.3 and 3.3). Pursuant to this scoring methodology, each State could achieve a maximum score of 30 points for implementation. These scores which will be reported as a percentage (with 100 representing a score of 30, 80 representing a score of 24, and so on). </p>\n<p>&#x201C;N/A&#x201D; responses will not be included as part of the overall score calculation. If a State is not a party to any of the three instruments, a score for the implementation (score 2) will not be calculated. </p>\n<p><strong>Formulas:</strong></p>\n<p><strong>Score 1: Score for the ratification of and accession to UNCLOS and its two implementing agreements (%); </strong></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>[</mo>\n    <mo>(</mo>\n    <mi mathvariant=\"bold-italic\">A</mi>\n    <mo>+</mo>\n    <mi mathvariant=\"bold-italic\">B</mi>\n    <mo>+</mo>\n    <mi mathvariant=\"bold-italic\">C</mi>\n    <mo>)</mo>\n    <mo>/</mo>\n    <mn>3</mn>\n    <mo>]</mo>\n    <mi mathvariant=\"bold-italic\">*</mi>\n    <mn>100</mn>\n  </math></p>\n<p>Where: </p>\n<ul>\n  <li>A &#x2013; Party to UNCLOS = 1; not a party to UNCLOS =0</li>\n  <li>B &#x2013; Party to UNFSA = 1; not party to UNFSA = 0</li>\n  <li>C &#x2013; Party to Part XI Agreement = 1; not party to Part XI Agreement = 0</li>\n</ul>\n<p><strong>Score 2: Score for the implementation of UNCLOS and its two implementing agreements (%)</strong></p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mo>{</mo>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"bold-italic\">A</mi>\n        <mo>+</mo>\n        <mi mathvariant=\"bold-italic\">B</mi>\n        <mo>+</mo>\n        <mi mathvariant=\"bold-italic\">C</mi>\n        <mo>+</mo>\n        <mi mathvariant=\"bold-italic\">D</mi>\n        <mo>+</mo>\n        <mi mathvariant=\"bold-italic\">E</mi>\n        <mo>+</mo>\n        <mi mathvariant=\"bold-italic\">F</mi>\n      </mrow>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi mathvariant=\"bold-italic\">X</mi>\n            <mi mathvariant=\"bold-italic\">*</mi>\n            <mn>10</mn>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mfrac>\n    <mo>}</mo>\n    <mi mathvariant=\"bold-italic\">*</mi>\n    <mn>100</mn>\n  </math></p>\n<p>Where:</p>\n<ul>\n  <li>A is self-reported score from 0 to 9 in response to survey question 2, on level of implementation of UNCLOS</li>\n  <li>B is 1 if response to question 5 on existence of National policy/ mechanism responsible for ocean space is &#x2018;yes&#x2019;, and 0 if the response is &#x2018;no&#x2019; or &#x2018;n/a&#x2019;</li>\n  <li>C is self-reported score from 0 to 9 in response to survey question 9, on level of implementation of Part XI Agreement </li>\n  <li>D is 1 if response to survey question 12 on the existence of National policy/ mechanism responsible for Part XI Agreement is &#x2018;yes&#x2019;; and 0 if the response is &#x2018;no&#x2019; or &#x2018;n/a&#x2019;</li>\n  <li>E is self-reported score from 0 to 9 in response to survey question 16, on level of implementation of UNFSA</li>\n  <li>F &#x2013; is 1 if response to survey question 19 on the existence of National policy/ mechanism responsible for UNFSA is &#x2018;yes&#x2019;; and 0 if the response is &#x2018;no&#x2019; or &#x2018;n/a&#x2019;</li>\n  <li>X is the number of the three instruments that the State is a party to. If a State is not a party to any of the three instruments, a score for the implementation will not be calculated. </li>\n</ul>", "DATA_VALIDATION__GLOBAL"=>"<p>The completed questionnaire is expected to be submitted through the Permanent Missions in New York. If other government ministries, departments and agencies submit data, the Permanent Missions will be informed and provided with a copy of the completed questionnaire. In case there are ambiguities or a need for correction, the Permanent Missions will be requested to clarify or confirm, or be otherwise informed of the relevant query. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Not imputed. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not imputed. Data will only be aggregated from responding countries.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global data regarding ratification of, accession to and implementation of UNCLOS and its implementing agreements would be aggregated by calculating the unweighted average of the scores of each country in that region (or globally) with respect to ratification/accession and with respect to implementation.</p>", "DOC_METHOD__GLOBAL"=>"<p>A questionnaire, with accompanying instructions regarding its completion is used to collect national-level data.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data on ratification of and accession to UNCLOS and its two implementing agreements is available, and may be verified. OLA/DOALOS will verify data on ratification of and accession to UNCLOS and its two implementing agreements submitted by countries, in light of information available to the Secretary-General as the depositary for those instruments. </p>\n<p>UNCLOS and UNFSA do not provide for a secretariat. OLA/DOALOS performs the role of secretariat for these instruments <em>de facto</em>. It has received no mandate from the General Assembly to review or assess the status of implementation of these instruments. </p>\n<p>Respondent countries will be invited to assess their level of implementation and share relevant information regarding the implementation of UNCLOS and its implementing agreements in their responses to the questionnaire.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>If the verification mentioned above indicates any discrepancy between the data submitted and information available to the depositary, OLA/DOALOS will contact the country concerned to update the information received to ensure that accurate data will be included in the SDG Indicators Database.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The data on indicator 14.c.1 is available at <a href=\"https://unstats.un.org/sdgs/dataportal/database\">https://unstats.un.org/sdgs/dataportal/database</a>. </p>\n<p><strong>Time series:</strong></p>\n<p>Not applicable</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data will not be disaggregated within each country. Two scores per country &#x2013; one score for the ratification of or accession to UNCLOS and its implementing agreements, and one score for the implementation of these instruments &#x2013; will be aggregated regionally or globally.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p>URL: <a href=\"https://www.un.org/Depts/los/convention_agreements/convention_overview_convention.htm\">https://www.un.org/Depts/los/convention_agreements/convention_overview_convention.htm</a> </p>\n<p><a href=\"https://www.un.org/Depts/los/convention_agreements/convention_overview_part_xi.htm\">https://www.un.org/Depts/los/convention_agreements/convention_overview_part_xi.htm</a> </p>\n<p><a href=\"https://www.un.org/Depts/los/convention_agreements/convention_overview_fish_stocks.htm\">https://www.un.org/Depts/los/convention_agreements/convention_overview_fish_stocks.htm</a> </p>", "indicator_sort_order"=>"14-0c-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.1.1", "slug"=>"15-1-1", "name"=>"Superficie forestal en proporción a la superficie total", "url"=>"/site/es/15-1-1/", "sort"=>"150101", "goal_number"=>"15", "target_number"=>"15.1", "global"=>{"name"=>"Superficie forestal en proporción a la superficie total"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>true, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Superficie forestal en proporción a la superficie total", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Superficie forestal en proporción a la superficie total", "indicator_number"=>"15.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Mantenimiento o ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Alimentación, Desarrollo Rural, Agricultura y Pesca", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/gobierno-vasco/-/informacion/inventarios-forestales/", "url_text"=>"Inventario Forestal", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Superficie forestal en proporción a la superficie total", "objetivo_global"=>"15- Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad", "meta_global"=>"15.1- De aquí a 2020, asegurar la conservación, el restablecimiento y el uso sostenible de los ecosistemas terrestres y los ecosistemas interiores de agua dulce y sus servicios, en particular los bosques, los humedales, las montañas y las zonas áridas, en consonancia con las obligaciones contraídas en virtud de acuerdos internacionales", "definicion"=>"Superficie forestal arbolada en proporción a la superficie total", "formula"=>"\n$$PSFA^{t} = \\frac{SFA^{t}}{ST^{t}} \\cdot 100$$\n\ndonde:\n\n$SFA^{t} =$ superficie forestal arbolada en el año $t$ \n\n$ST^{t} =$ superficie total en el año $t$\n", "desagregacion"=>"Territorio histórico/Comarca/Municipio\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Los bosques cumplen una serie de funciones vitales para la humanidad, entre ellas \nla provisión de bienes (madera y productos forestales no madereros) y servicios como \nhábitats para la biodiversidad, secuestro de carbono, protección costera y conservación del \nsuelo y el agua.\n\nEl indicador proporciona una medida de la extensión relativa de los bosques en un país. \nLa disponibilidad de datos precisos sobre la superficie forestal de un país es un elemento \nclave para la política y la planificación forestales en el contexto del desarrollo sostenible.\n\nLos cambios en la superficie forestal reflejan la demanda de tierras para otros usos y \npueden ayudar a identificar prácticas no sostenibles en el sector forestal y agrícola.\n\nLa superficie forestal como porcentaje de la superficie terrestre total puede utilizarse \ncomo un indicador aproximado del grado en que se están conservando o restaurando los bosques \nde un país, pero es sólo en parte una medida del grado en que se gestionan de forma sostenible.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.1.1&seriesCode=AG_LND_FRST&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Superficie forestal como proporción de la superficie total del terreno (%) AG_LND_FRST</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-01-01.pdf\">Metadatos 15-1-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Superficie forestal en proporción a la superficie total", "objetivo_global"=>"15- Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad", "meta_global"=>"15.1- De aquí a 2020, asegurar la conservación, el restablecimiento y el uso sostenible de los ecosistemas terrestres y los ecosistemas interiores de agua dulce y sus servicios, en particular los bosques, los humedales, las montañas y las zonas áridas, en consonancia con las obligaciones contraídas en virtud de acuerdos internacionales", "definicion"=>"Wooded forest area in proportion to total area", "formula"=>"\n$$PSFA^{t} = \\frac{SFA^{t}}{ST^{t}} \\cdot 100$$\n\nwhere:\n\n$SFA^{t} =$ wooded forest area in year $t$ \n\n$ST^{t} =$ total land area in year $t$\n", "desagregacion"=>"Province/County/Municipality\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Forests fulfil a number of functions that are vital for humanity, including the provision of goods (wood \nand non-wood forest products) and services such as habitats for biodiversity, carbon sequestration, \ncoastal protection and soil and water conservation. \n\nThe indicator provides a measure of the relative extent of forest in a country. The availability of accurate \ndata on a country's forest area is a key element for forest policy and planning within the context of \nsustainable development. \n\nChanges in forest area reflect the demand for land for other uses and may help identify unsustainable \npractices in the forestry and agricultural sector. \n\nForest area as percentage of total land area may be used as a rough proxy for the extent to which the \nforests in a country are being conserved or restored, but it is only partly a measure for the extent to \nwhich they are sustainably managed. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.1.1&seriesCode=AG_LND_FRST&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Forest area as a proportion of total land area (%) AG_LND_FRST</a> UNSTATS \n", "comparabilidad"=>"The available indicator complies with United Nations metadata. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-01-01.pdf\">Metadata 15-1-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Superficie forestal en proporción a la superficie total", "objetivo_global"=>"15- Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad", "meta_global"=>"15.1- De aquí a 2020, asegurar la conservación, el restablecimiento y el uso sostenible de los ecosistemas terrestres y los ecosistemas interiores de agua dulce y sus servicios, en particular los bosques, los humedales, las montañas y las zonas áridas, en consonancia con las obligaciones contraídas en virtud de acuerdos internacionales", "definicion"=>"Superficie forestal arbolada en proporción a la superficie total", "formula"=>"\n$$PSFA^{t} = \\frac{SFA^{t}}{ST^{t}} \\cdot 100$$\n\nnon:\n\n$SFA^{t} =$ zuhaiztien baso-azalera $t$ urtean \n\n$ST^{t} =$ guztizko azalera $t$ urtean\n", "desagregacion"=>"Lurralde historikoa/Eskualdea/Udalerria\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Los bosques cumplen una serie de funciones vitales para la humanidad, entre ellas \nla provisión de bienes (madera y productos forestales no madereros) y servicios como \nhábitats para la biodiversidad, secuestro de carbono, protección costera y conservación del \nsuelo y el agua.\n\nEl indicador proporciona una medida de la extensión relativa de los bosques en un país. \nLa disponibilidad de datos precisos sobre la superficie forestal de un país es un elemento \nclave para la política y la planificación forestales en el contexto del desarrollo sostenible.\n\nLos cambios en la superficie forestal reflejan la demanda de tierras para otros usos y \npueden ayudar a identificar prácticas no sostenibles en el sector forestal y agrícola.\n\nLa superficie forestal como porcentaje de la superficie terrestre total puede utilizarse \ncomo un indicador aproximado del grado en que se están conservando o restaurando los bosques \nde un país, pero es sólo en parte una medida del grado en que se gestionan de forma sostenible.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.1.1&seriesCode=AG_LND_FRST&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Baso-azalera, lursailaren azalera osoaren proportzio gisa (%) AG_LND_FRST</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-01-01.pdf\">Metadatuak 15-1-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.1: By 2020, ensure the conservation, restoration and sustainable use of terrestrial and inland freshwater ecosystems and their services, in particular forests, wetlands, mountains and drylands, in line with obligations under international agreements</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.1.1: Forest area as a proportion of total land area</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>AG_LND_FRST - Forest area as a proportion of total land area [15.1.1]</p>\n<p>AG_LND_FRSTN - Forest area (hectares) [15.1.1]</p>\n<p>AG_LND_TOTL - Land area (hectares) [15.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>15.2.1: Progress towards sustainable forest management</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Forest area as a proportion of total land area</p>\n<p><strong>Concepts:</strong></p>\n<p>To provide a precise definition of the indicator, it is crucial to provide a definition of its two components:</p>\n<p>&#x201C;Forest&#x201D; and &#x201C;Land Area&#x201D;. </p>\n<p>According to the FAO, <strong><u>Forest</u></strong> is defined as: &#x201C;land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use&#x201D;. More specifically:</p>\n<ul>\n  <li>Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters.</li>\n  <li>It includes areas with young trees that have not yet reached but which are expected to reach a canopy cover of at least 10 percent and tree height of 5 meters or more. It also includes areas that are temporarily unstocked due to clear-cutting as part of a forest management practice or natural disasters, and which are expected to be regenerated within 5 years. Local conditions may, in exceptional cases, justify that a longer time frame is used.</li>\n  <li>It includes forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific environmental, scientific, historical, cultural or spiritual interest.</li>\n  <li>It includes windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters.</li>\n  <li>It includes abandoned shifting cultivation land with a regeneration of trees that have, or are expected to reach, a canopy cover of at least 10 percent and tree height of at least 5 meters.</li>\n  <li>It includes areas with mangroves in tidal zones, regardless of whether this area is classified as land area or not.</li>\n  <li>It includes rubberwood, cork oak and Christmas tree plantations.</li>\n  <li>It includes areas with bamboo and palms provided that land use, height and canopy cover criteria are met.</li>\n  <li>It excludes tree stands in agricultural production systems, such as fruit tree plantations, oil palm plantations, olive orchards and agroforestry systems when crops are grown under tree cover. Note: Some agroforestry systems such as the &#x201C;Taungya&#x201D; system where crops are grown only during the first years of the forest rotation should be classified as forest.</li>\n</ul>\n<p><strong><u>Land area</u></strong> is the country area excluding area under inland waters and coastal waters.</p>\n<ul>\n  <li><strong>Country area</strong>: Area under national sovereignty. It is the sum of land area, inland waters and coastal waters. It excludes the exclusive economic zone.</li>\n  <li><strong>Inland waters</strong>: Areas corresponding to natural or artificial water courses, serving to drain natural or artificial bodies of water, including lakes, reservoirs, rivers, brooks, streams, ponds, inland canals, dams, and other land-locked waters. The banks constitute limits whether the water is present or not.</li>\n  <li><strong>Coastal waters</strong>: Waters located in-between the land territory and the outer limit of the territorial sea. They comprise &apos;&apos;Internal waters&apos;&apos; and &apos;&apos;Territorial sea,&quot; and where applicable, &apos;&apos;Archipelagic waters.&quot; </li>\n</ul>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Forest area:</strong></p>\n<p>Data on Forest area are collected by FAO through the Global Forest Resources Assessment (FRA). This assessment has been carried out at regular intervals since 1946 and are now produced every five year. The latest of these assessments, FRA 2020, contains information for 236 countries and territories on about 60 variables related to the extent of forests, their conditions, uses and values for several points in time. </p>\n<p><strong>Land area:</strong></p>\n<p>Data on land area are collected from FAO members through the annual FAO Questionnaire on Land Use, Irrigation and Agricultural Practices. Missing data may be sourced from national statistical yearbooks and other official government data portals. Supplemental information for further gap filling may be derived from national and international sectoral studies and reports, as well as from land cover statistical information compiled by FAO and disseminated in FAOSTAT.</p>", "COLL_METHOD__GLOBAL"=>"<p><strong>Forest area:</strong></p>\n<p>Officially nominated national correspondents and their teams prepare the country reports for the Global Forest Resources Assessment. Some countries prepare more than one report as they also report on dependent territories. For the remaining countries and territories where no information is provided, a report is prepared by FAO using existing information, literature search, remote sensing or a combination of two or more of them.</p>\n<p>All data are provided to FAO by countries in the form of a country report through an online platform following a standard format, which includes the original data and reference sources and descriptions of how these have been used to estimate the forest area for different points in time. The online platform is used for all data entry, review and quality control.</p>\n<p><strong>Land area:</strong></p>\n<p>The Land Use, Irrigation and Agricultural Practices FAO Questionnaire, <a href=\"http://www.fao.org/economic/ess/ess-home/questionnaires/en/\">http://www.fao.org/economic/ess/ess-home/questionnaires/en/</a>, is sent annually to 205 countries and territories reaching out the National Focal Points in National Institutions, typically National Statistical Offices, Ministries of Agriculture or other relevant Agencies. The questionnaire is sent in Excel format together with accompanying cover letter explaining FAO mandate and scope of the data collection. </p>\n<p>Data returned in questionnaire are checked against previous reports and for consistency with the other land categories reported in questionnaire. Depending on questionnaire completeness and in case of non-reporting, Land area data may be derived by subtracting the Inland waters area and the Coastal waters area from the Country area. Missing Land area data are also imputed by carry-forward of the latest value officially reported by the country. </p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p><strong>Forest area:</strong></p>\n<p>Data collection process for FRA 2020 was launched in 2018 and data collection took place in 2018-2019. Data collection for FRA 2025 is expected to start in 2023. </p>\n<p><strong>Land area:</strong></p>\n<p>The FAO Land Use, Irrigation and Agricultural Practices questionnaire is part of the joint dispatch of three questionnaires on agri-environmental statistics. Questionnaires are dispatched annually on 4th October with deadline after 4 weeks; first and second follow-ups are sent within 5 and 10 weeks respectively from the dispatch date. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p><strong>Forest area:</strong></p>\n<p>Data with updated time series and including year 2020 was released July 2020. Next release of a complete FRA dataset is scheduled for 2025. The possibilitiy of a more frequent reporting on forest area and other key indicators is currently being evaluated. </p>\n<p><strong>Land area:</strong></p>\n<p>Data release in year 2022 is planned for June 2022.</p>", "DATA_SOURCE__GLOBAL"=>"<p><strong>Forest area:</strong></p>\n<p>Data on forest area are provided by the countries and reported to FAO through a global network of officially nominated national correspondents. For the countries and territories which do not have a national correspondent, a desk study is prepared by FAO using previously reported information, literature search, remote sensing or their combination.</p>\n<p><strong>Land area:</strong></p>\n<p>Data are provided by the National Focal Points in National Institutions, typically National Statistical Offices, Ministries of Agriculture or other relevant Agencies. Records of National Focal Points is maintained up to date through the questionnaire cover where countries are requested to confirm the focal point contact detail (e.g. Name, Title, Administration and Office, Email and Web site address) as well as through official communications from countries to FAO, or information provided to FAO during meetings, conferences or commissions. </p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO).</p>", "INST_MANDATE__GLOBAL"=>"<p>Article 1 of FAO&#x2019;s constitution specifies that, &#x201C;The Organization shall collect, analyze, interpret, and disseminate information related to nutrition, food and agriculture.&#x201D; In this regard, FAO collects national level data from member countries, which it then standardizes and disseminates through corporate statistical databases. FAO is the custodian UN agency for 21 SDG indicators, including 15.1.1.</p>", "RATIONALE__GLOBAL"=>"<p>Forests fulfil a number of functions that are vital for humanity, including the provision of goods (wood and non-wood forest products) and services such as habitats for biodiversity, carbon sequestration, coastal protection and soil and water conservation. </p>\n<p>The indicator provides a measure of the relative extent of forest in a country. The availability of accurate data on a country&apos;s forest area is a key element for forest policy and planning within the context of sustainable development.</p>\n<p>Changes in forest area reflect the demand for land for other uses and may help identify unsustainable practices in the forestry and agricultural sector.</p>\n<p>Forest area as percentage of total land area may be used as a rough proxy for the extent to which the forests in a country are being conserved or restored, but it is only partly a measure for the extent to which they are sustainably managed.</p>\n<p>The indicator was included among the indicators for the Millennium Development Goals (MDG indicator</p>\n<p>7.1 &#x201C;Proportion of land covered by forest&#x201D;).</p>", "REC_USE_LIM__GLOBAL"=>"<p>Assessment of forest area is carried out at infrequent intervals in many countries. Although the improved access to remote sensing data can help some countries to update their forest area estimates more frequently, estimation of forest area using remote sensing techniques has certain challenges. In particular the assessment of forest area relates to land use, while remote sensing primarily assesses land cover. Furthermore, gradual changes, such as forest regrowth, require several years to become detectable in satellite imagery. In addition, forest areas with low canopy cover density (e.g. 10-30%) are still difficult to detect at large scale with affordable remote sensing techniques.</p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi>F</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>f</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>y</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>L</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>f</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>y</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>All data submitted by countries to FRA, including the FAO estimates made in case of desk studies, are available at the FRA online platform (<a href=\"https://fra-data.fao.org/\">https://fra-data.fao.org/</a>). The platform also includes the calculated indicator for 15.1.1. A request for validation was sent to the Head of Forestry of each country before finalization and publishing of data.</p>", "ADJUSTMENT__GLOBAL"=>"<p>When FAOSTAT land area data indicate variations in land area that are inconsistent and do not reflect real changes but are the effect of changes in assessment methodology or countries not having revised historical data points, inconsistent data points are imputed by FAO.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>For countries and territories where no information was provided to FAO for FRA 2020 (47 countries and territories representing 0.5 percent of the global forest area), FAO made estimates of forest area based on existing information from previous assessments, literature search, remote sensing or a combination of these data sources. </p>\n<p>For countries/territories not included in FAOSTAT, land area data is collected from other sources (national Web sites, etc.). In a few cases where land area for a specific reference year is not available in FAOSTAT, land area is imputed by using data for closest available reference year.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>See above</p>", "REG_AGG__GLOBAL"=>"<p>Since information is available for all countries and territories, regional and global estimates are produced by aggregating country-level data. </p>", "DOC_METHOD__GLOBAL"=>"<p><strong>Forest area:</strong></p>\n<p>Detailed methodology and guidance on how to prepare the country reports through an online web platform and to convert national data according to national categories and definitions to FAO&#x2019;s global categories and definitions is found in the document &#x201C;<em>FRA 2020</em> <em>Guidelines and Specifications</em>&#x201D; (<a href=\"http://www.fao.org/3/I8699EN/i8699en.pdf\"><u>www.fao.org/3/I8699EN/i8699en.pdf</u></a>) .</p>\n<p><strong>Land area:</strong></p>\n<p>Detailed classification and definition are provided in sections &#x201C;<em>Instructions</em>&#x201D; and &#x201C;<em>Definitions</em>&#x201D;, of the FAO Land Use, Irrigation and Agricultural Practices Questionnaire of which a copy is available on the FAO Statistics website, Data Collection subpage (<a href=\"http://www.fao.org/statistics/data-collection/en/\">http://www.fao.org/statistics/data-collection/en/</a>). </p>\n<p>Definitions are also provided together with data in the FAOSTAT Land Use domain in section &#x201C;<em>Definitions and Standards</em>&#x201D; (<a href=\"http://www.fao.org/faostat/en/#data/RL\">http://www.fao.org/faostat/en/#data/RL</a>). </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>FAO is responsible for the quality of the internal statistical processes used to compile the published datasets. The FAO Statistics Quality Assurance Framework (SQAF), available at: <a href=\"http://www.fao.org/docrep/019/i3664e/i3664e.pdf\">http://www.fao.org/docrep/019/i3664e/i3664e.pdf</a>, provides the necessary principles, guidelines and tools to carry out quality assessments. FAO is performing an internal bi-annual survey (FAO Quality Assessment and Planning Survey) designed to gather information on all of FAO&#x2019;s statistical activities, notably to assess the extent to which quality standards are being implemented with a view to increasing compliance with the quality dimensions of SQAF, documenting best practices and prepare quality improvement plans, where necessary. Domain-specific quality assurance activities are carried out systematically (e.g. quality reviews, self-assessments, compliance monitoring).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data on forest area reported by countries to FAO are subject to a rigorous review process to ensure correct use of definitions and methodology as well as internal consistency. A comparison is made with past assessments and other existing data sources. Regular contacts between national correspondents and FAO staff by e-mail, through the FRA online platform and during regional/sub-regional review workshops form part of this review process.</p>\n<p>Data on land area are reported by FAO members through the FAO Land Use, Irrigation and Agricultural Practices questionnaire. Collected data are routinely checked for internal consistency (e.g. outliers and significant variation in time series). Observed discrepancies are routinely checked and validated with countries.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p> Quality of statistics produced and disseminated by the FAO is evaluated in terms of fitness for use i.e. the degree to which statistics meet the user&#x2019;s requirements. The quality dimensions assessed are: Relevance; Accuracy and Reliability; Timeliness and Punctuality; Coherence and Comparability; Accessibility and Clarity. Quality dimensions definitions are provided in the FAO Statistical Quality Assurance Framework (SQAF), which provides the definition of quality and describes quality principles for statistical outputs; statistical processes; institutional environment (http://www.fao.org/docrep/019/i3664e/i3664e.pdf). The SQAF is based on the Fundamental Principles of Official Statistics and the Principles Governing International Statistical Activities (CCSA). Adherence to these principles ensures the quality of FAO statistical production processes and of statistical outputs. Regular quality assessments are conducted through the FAO Quality Assessment and Planning Survey (QAPS), a bi-annual survey designed to gather information on all of FAO&#x2019;s statistical activities, which is used to assess the extent to which quality standards are being met with a view to increasing compliance with the SQAF, and to document best practices and provide guidance for improvement where necessary.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Forest area data are available for all 236 countries and territories for the years , and2000, 2010, 2015, and every year since.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>No further disaggregation of this indicator.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The national figures in the database are reported by the countries themselves following standardized format, definitions and reporting years, thus eliminating any discrepancies between global and national figures. The reporting template requests that countries provide the full reference for original data sources as well as national definitions and terminology. Separate sections in the template country reports deal with the analysis of data (including any assumptions made and the methods used for estimates and projections to the common reporting years); calibration of data to the official land area as held by FAO; and reclassification of data to the classes used in FAO&#x2019;s Global Forest Resources Assessments.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.fao.org/forest-resources-assessment/\">http://www.fao.org/forest-resources-assessment/</a></p>\n<p><a href=\"http://www.fao.org/faostat\">http://www.fao.org/faostat</a></p>\n<p><strong>References:</strong></p>\n<p>Global Forest Resources Assessment 2020, Guidelines and Specifications (<a href=\"http://www.fao.org/3/I8699EN/i8699en.pdf\"><u>www.fao.org/3/I8699EN/i8699en.pdf</u></a>)</p>\n<p>Global Forest Resources Assessment 2020, Terms and Definitions </p>\n<p>(<a href=\"http://www.fao.org/3/I8661EN/i8661en.pdf\"><u>www.fao.org/3/I8661EN/i8661en.pdf</u></a>).</p>", "indicator_sort_order"=>"15-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}, {"type"=>"map", "label"=>"indicator.map"}]}, {"number"=>"15.1.2", "slug"=>"15-1-2", "name"=>"Proporción de lugares importantes para la biodiversidad terrestre y del agua dulce incluidos en zonas protegidas, desglosada por tipo de ecosistema", "url"=>"/site/es/15-1-2/", "sort"=>"150102", "goal_number"=>"15", "target_number"=>"15.1", "global"=>{"name"=>"Proporción de lugares importantes para la biodiversidad terrestre y del agua dulce incluidos en zonas protegidas, desglosada por tipo de ecosistema"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[{"field"=>"Territorio histórico", "value"=>"Araba/Álava"}, {"field"=>"Territorio histórico", "value"=>"Gipuzkoa"}, {"field"=>"Territorio histórico", "value"=>"Bizkaia"}], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de lugares importantes para la biodiversidad terrestre y del agua dulce incluidos en zonas protegidas", "graph_titles"=>[], "graph_type"=>"bar", "indicator_name"=>"Proporción de lugares importantes para la biodiversidad terrestre y del agua dulce incluidos en zonas protegidas, desglosada por tipo de ecosistema", "indicator_number"=>"15.1.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Geoeuskadi", "url"=>"https://www.geo.euskadi.eus/", "url_text"=>"Geoeuskadi", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de lugares importantes para la biodiversidad terrestre y del agua dulce incluidos en zonas protegidas", "definicion"=>"Porcentaje de los lugares importantes para la biodiversidad terrestre y del agua dulce (aquellos que contribuyen significativamente a la persistencia global de la biodiversidad) que está cubierto por áreas protegidas", "formula"=>"\n$$PKBA_{terrestre}^{t} = \\frac{APKBA_{terrestre}^{t}}{KBA_{terrestre}^{t}} \\cdot 100$$\n\ndonde:\n\n$APKBA_{terrestre}^{t} =$ superficie de los lugares importantes para la biodiversidad terrestre y de agua dulce cubierta por áreas protegidas en el año $t$\n\n$KBA_{terrestre} =$ superficie de los lugares importantes para la biodiversidad terrestre y de agua dulce en el año $t$\n", "desagregacion"=>"", "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>"", "justificacion_global"=>"La protección de lugares importantes es vital para frenar la pérdida de biodiversidad y garantizar \nel uso sostenible a largo plazo de los recursos naturales terrestres y de agua dulce. El establecimiento \nde áreas protegidas es un mecanismo importante para lograr este objetivo, y este indicador sirve para \nmedir el progreso hacia la conservación, la restauración y el uso sostenible de los ecosistemas \nterrestres y de agua dulce y sus servicios, de conformidad con las obligaciones contraídas \nen virtud de los acuerdos internacionales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.1.2&seriesCode=ER_PTD_TERR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProporción promedio de áreas clave para la biodiversidad terrestre (ACB) cubiertas por áreas protegidas (%) ER_PTD_TERR</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.1.2&seriesCode=ER_PTD_FRHWTR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProporción promedio de áreas clave para la biodiversidad (KBA) de agua dulce cubiertas por áreas protegidas (%) ER_PTD_FRHWTR</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-01-02.pdf\">Metadatos 15-1-2.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Proporción de lugares importantes para la biodiversidad terrestre y del agua dulce incluidos en zonas protegidas", "definicion"=>"Percentage of important sites for terrestrial and freshwater biodiversity (those that contribute significantly to the global persistence of biodiversity) that are covered by protected areas", "formula"=>"\n$$PKBA_{terrestrial}^{t} = \\frac{APKBA_{terrestrial}^{t}}{KBA_{terrestrial}^{t}} \\cdot 100$$\n\nwhere:\n\n$APKBA_{terrestrial}^{t} =$ area of important sites for terrestrial and freshwater biodiversity covered by protected areas in year $t$\n\n$KBA_{terrestrial} =$ area of important sites for terrestrial and freshwater biodiversity in year $t$\n", "desagregacion"=>nil, "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>nil, "justificacion_global"=>"The safeguard of important sites is vital for stemming the decline in biodiversity and ensuring long term \nand sustainable use of terrestrial and freshwater natural resources. The establishment of protected areas \nis an important mechanism for achieving this aim, and this indicator serves as a means of measuring \nprogress toward the conservation, restoration and sustainable use of terrestrial and freshwater \necosystems and their services, in line with obligations under international agreements. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.1.2&seriesCode=ER_PTD_TERR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nAverage proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas (%) ER_PTD_TERR</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.1.2&seriesCode=ER_PTD_FRHWTR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nAverage proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas (%) ER_PTD_FRHWTR</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-01-02.pdf\">Metadata 15-1-2.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de lugares importantes para la biodiversidad terrestre y del agua dulce incluidos en zonas protegidas", "definicion"=>"Porcentaje de los lugares importantes para la biodiversidad terrestre y del agua dulce (aquellos que contribuyen significativamente a la persistencia global de la biodiversidad) que está cubierto por áreas protegidas", "formula"=>"\n$$PKBA_{lurrekoa}^{t} = \\frac{APKBA_{lurrekoa}^{t}}{KBA_{lurreko}^{t}} \\cdot 100$$\n\nnon:\n\n$APKBA_{lurrekoa}^{t} =$ lurreko eta ur gezaren biodibertsitaterako garrantzitsuak diren lekuen azalera, babestutako eremuek estalitakoa $t$ urtean\n\n$KBA_{lurrekoa} =$ lurreko eta ur gezaren biodibertsitaterako garrantzitsuak diren lekuen azalera $t$ urtean\n", "desagregacion"=>nil, "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>nil, "justificacion_global"=>"La protección de lugares importantes es vital para frenar la pérdida de biodiversidad y garantizar \nel uso sostenible a largo plazo de los recursos naturales terrestres y de agua dulce. El establecimiento \nde áreas protegidas es un mecanismo importante para lograr este objetivo, y este indicador sirve para \nmedir el progreso hacia la conservación, la restauración y el uso sostenible de los ecosistemas \nterrestres y de agua dulce y sus servicios, de conformidad con las obligaciones contraídas \nen virtud de los acuerdos internacionales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.1.2&seriesCode=ER_PTD_TERR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nEremu babestuek estalitako Lurreko Biodibertsitaterako (ACB) funtsezko eremuen batez besteko proportzioa (%) ER_PTD_TERR</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.1.2&seriesCode=ER_PTD_FRHWTR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nBabestutako eremuek estalitako ur gezako biodibertsitaterako funtsezko eremuen (KBA) batez besteko proportzioa (%) ER_PTD_FRHWTR</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-01-02.pdf\">Metadatuak 15-1-2.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.1: By 2020, ensure the conservation, restoration and sustainable use of terrestrial and inland freshwater ecosystems and their services, in particular forests, wetlands, mountains and drylands, in line with obligations under international agreements</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.1.2: Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_PTD_FRHWTR - Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas [15.1.2]</p>\n<p>ER_PTD_TERR - Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas [15.1.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Other relevant indicators include:</p>\n<p>SDG 14.5.1 Coverage of protected areas in relation to marine areas.</p>\n<p>SDG 15.4.1 Coverage by protected areas of important sites for mountain biodiversity.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>BirdLife International (BLI)</p>\n<p>International Union for Conservation of Nature (IUCN)</p>\n<p>UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)</p>\n<p>UN Environment Programme</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>BirdLife International (BLI)</p>\n<p>International Union for Conservation of Nature (IUCN)</p>\n<p>UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type shows temporal trends in the mean percentage of each important site for terrestrial and freshwater biodiversity (i.e., those that contribute significantly to the global persistence of biodiversity) that is covered by designated protected areas and Other Effective Area-based Conservation Measures (OECMs).</p>\n<p><strong>Concepts:</strong></p>\n<p>Protected areas, as defined by the IUCN (IUCN; Dudley 2008), are clearly defined geographical spaces, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) (Mean percentage of each terrestrial/freshwater Key Biodiversity Area (KBA) covered by (i.e. overlapping with) protected areas and/or OECMs.)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Protected Areas are defined as described above by IUCN (IUCN; Dudley 2008) and documented in the World Database on Protected Areas (WDPA). (<a href=\"http://www.protectedplanet.net\">www.protectedplanet.net</a>).</p>\n<p>Importantly, a variety of specific management objectives are recognised within this definition, spanning conservation, restoration, and sustainable use:</p>\n<p>- Category Ia: Strict nature reserve</p>\n<p>- Category Ib: Wilderness area</p>\n<p>- Category II: National park</p>\n<p>- Category III: Natural monument or feature</p>\n<p>- Category IV: Habitat/species management area</p>\n<p>- Category V: Protected landscape/seascape</p>\n<p>- Category VI: Protected area with sustainable use of natural resources</p>\n<p>The status &quot;designated&quot; is attributed to a protected area when the corresponding authority, according to national legislation or common practice (e.g., by means of an executive decree or the like), officially endorses a document of designation. The designation must be made for the purpose of biodiversity conservation, not de facto protection arising because of some other activity (e.g., military).</p>\n<p>Data on protected areas are managed in the WDPA (www.protectedplanet.net) by UNEP-WCMC.</p>\n<p>OECMs are defined as described above by the Convention on Biological Diversity (CBD 2018) and documented in the World Database on Other Effective Area-based Conservation Measures (WDOECM) (<a href=\"http://www.protectedplanet.net/en/thematic-areas/oecms\">www.protectedplanet.net/en/thematic-areas/oecms</a>).</p>\n<p>OECMs are defined by the Convention on Biological Diversity (CBD) as &#x201C;A geographically defined area other than a Protected Area, which is governed and managed in ways that achieve positive and sustained long-term outcomes for the in-situ conservation of biodiversity, with associated ecosystem functions and services and where applicable, cultural, spiritual, socio&#x2013;economic, and other locally relevant values&#x201D; (CBD, 2018). Data on OECMs are managed in the WDOECM (<a href=\"http://www.protectedplanet.net/en/thematic-areas/oecms\">www.protectedplanet.net/en/thematic-areas/oecms</a>) by UNEP-WCMC.</p>\n<p>Key Biodiversity Areas (KBAs) are defined as described below by IUCN (2016) and documented in the World Database of KBAs (WDKBA) (www.keybiodiversityareas.org/kba-data).</p>\n<p>Sites contributing significantly to the global persistence of biodiversity are identified following globally criteria set out in A Global Standard for the Identification of KBAs (IUCN 2016) applied at national levels. KBAs encompass (a) Important Bird &amp; Biodiversity Areas, that is, sites contributing significantly to the global persistence of biodiversity, identified using data on birds, of which more than13,000 sites in total have been identified from all of the world&#x2019;s countries (BirdLife International 2014, Donald et al. 2018); (b) Alliance for Zero Extinction sites (Ricketts et al. 2005), that is, sites holding effectively the entire population of at least one species assessed as Critically Endangered or Endangered on the IUCN Red List of Threatened Species, of which 853 sites have been identified for 1,483 species of mammals, birds, amphibians, reptiles, freshwater crustaceans, reef-building corals, conifers, cycads and other taxa; (c) KBAs identified under an earlier version of the KBA criteria (Langhammer et al. 2007), including those identified in Ecosystem Hotspot Profiles developed with support of the Critical Ecosystem Partnership Fund. These three subsets are being reassessed using the Global Standard, which unifies these approaches along with other mechanisms for identification of important sites for other species and ecosystems (IUCN 2016).</p>\n<p>Data on KBAs are managed in the WDKBA (www.keybiodiversityareas.org/kba-data) by BirdLife International on behalf of the KBA Partnership.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Protected area data are compiled by ministries of environment and other ministries responsible for the designation and maintenance of protected areas. Protected Areas data for sites designated under the Ramsar Convention and the UNESCO World Heritage Convention are collected through the relevant convention international secretariats. Protected area data are aggregated globally into the WDPA by UNEP-WCMC, according to the mandate for production of the United Nations List of Protected Areas (Deguignet et al. 2014). They are disseminated through <a href=\"http://www.protectedplanet.net/\">Protected Planet</a>, which is jointly managed by UNEP-WCMC and IUCN and its World Commission on Protected Areas (UNEP-WCMC 2016).</p>\n<p>Other Effective Area-based Conservation Measures (OECMs) are collated in the WDOECM. This database can be regarded as a sister database to the WDPA as it is also hosted on Protected Planet. Furthermore, the databases share many of the same fields and have an almost identical workflow; differing only in what they list. OECMs are a quickly evolving area of work, as such for the latest information on OECMs and the WDOECM please contact UNEP-WCMC. </p>\n<p>KBAs are identified at national scales through multi-stakeholder processes, following standard criteria and thresholds. KBAs data are aggregated into the <a href=\"http://www.keybiodiversityareas.org/\">World Database on </a></p>\n<p><a href=\"http://www.keybiodiversityareas.org/\">KBAs</a>, managed by BirdLife International. </p>", "COLL_METHOD__GLOBAL"=>"<p>See information under other sections, and detailed information on the process by which KBAs are identified at <a href=\"http://www.keybiodiversityareas.org/working-with-kbas/proposing-updating\">www.keybiodiversityareas.org/working-with-kbas/proposing-updating</a>. Guidance on Proposing, Reviewing, Nominating and Confirming KBAs is available in KBA Secretariat (2019) at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>.</p>\n<p>The KBA identification process is highly inclusive and consultative: anyone with data on the biodiversity importance of a site may propose it as a KBA if it meets the <a href=\"https://portals.iucn.org/library/sites/library/files/documents/2016-048.pdf\">KBA criteria</a>, and consultation with stakeholders at the national level (both non-governmental and governmental organisations) is required during the proposal process. Any site proposal must undergo independent review. This is followed by the official site nomination with full documentation meeting the Documentation Standards for KBAs. Sites confirmed by the KBA Secretariat to qualify as KBAs are then published on the KBA Website.</p>\n<p>Submission of proposals for KBAs to the WDKBA follows a systematic review process to ensure that the KBA criteria have been applied correctly and that the sites can be recognised as important for the global persistence of biodiversity. Regional Focal Points have been appointed to help KBA proposers develop proposals and then ensure they are reviewed independently. Guidance on Proposing, Reviewing, Nominating and Confirming sites has been published to help guide proposers through the development of proposals and the review process, highlighting where they can obtain help in making a proposal. </p>", "FREQ_COLL__GLOBAL"=>"<p>UNEP-WCMC produces the UN List of Protected Areas every 5&#x2013;10 years, based on information provided by national ministries/agencies. In the intervening period between compilations of UN Lists, UNEP-WCMC works closely with national ministries/agencies and NGOs responsible for the designation and maintenance of protected areas, continually updating the WDPA as new data become available. The WDOECM is also updated on an ongoing basis. The WDKBA is also updated on an ongoing basis with updates currently released twice a year, as new national data are submitted.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The indicator of protected area coverage of important sites for biodiversity is updated each November-December using the latest versions of the datasets on protected areas, OECMs and KBAs.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Protected area data are compiled by ministries of environment and other ministries responsible for the designation and maintenance of protected areas. KBAs are identified at national scales through multi-stakeholder processes, following established processes and standard criteria and thresholds (see above for details).</p>", "COMPILING_ORG__GLOBAL"=>"<p>BirdLife International, IUCN, UNEP-WCMC</p>\n<p>Protected area data are aggregated globally into the WDPA by UNEP-WCMC, according to the mandate for production of the United Nations List of Protected Areas (Deguignet et al. 2014). They are disseminated through Protected Planet, which is jointly managed by UNEP-WCMC and IUCN and its World Commission on Protected Areas (UNEP-WCMC 2016). KBAs data are aggregated into the WDKBA, managed by BirdLife International (2019). </p>", "INST_MANDATE__GLOBAL"=>"<p>Protected area data and OECM data are aggregated globally into the WDPA and WDOECM by the UNEP-WCMC, according to the mandate for production of the United Nations List of Protected Areas (Deguignet et al. 2014). </p>\n<p>BirdLife International is mandated by the <a href=\"http://www.keybiodiversityareas.org/assets/dfbb558651f335617813f6c0c42f9e50\">KBAs Partnership Agreement</a> to manage data on KBAs in the WDKBAs on behalf of the KBAs Partnership.</p>\n<p>BirdLife International, IUCN and UNEP-WCMC collaborate to produce the indicator of coverage of KBAs by Protected Areas and OECMs.</p>", "RATIONALE__GLOBAL"=>"<p>The safeguard of important sites is vital for stemming the decline in biodiversity and ensuring long term and sustainable use of terrestrial and freshwater natural resources. The establishment of protected areas is an important mechanism for achieving this aim, and this indicator serves as a means of measuring progress toward the conservation, restoration and sustainable use of terrestrial and freshwater ecosystems and their services, in line with obligations under international agreements. Importantly, while it can be disaggregated to report on any given single ecosystem of interest, it is not restricted to any single ecosystem type.</p>\n<p>Levels of access to protected areas vary among the protected area management categories. Some areas, such as scientific reserves, are maintained in their natural state and closed to any other use. Others are used for recreation or tourism, or even open for the sustainable extraction of natural resources. In addition to protecting biodiversity, protected areas have high social and economic value: supporting local livelihoods; maintaining fisheries; harbouring an untold wealth of genetic resources; supporting thriving recreation and tourism industries; providing for science, research and education; and forming a basis for cultural and other non-material values.</p>\n<p>This indicator adds meaningful information to, complements and builds from traditionally reported simple statistics of terrestrial and freshwater area covered by protected areas, computed by dividing the total protected area within a country by the total territorial area of the country and multiplying by 100 (e.g., Chape et al. 2005). Such percentage area coverage statistics do not recognise the extreme variation of biodiversity importance over space (Rodrigues et al. 2004), and so risk generating perverse outcomes through the protection of areas which are large at the expense of those which require protection.</p>\n<p>The indicator was used to track progress towards the 2011&#x2013;2020 Strategic Plan for Biodiversity (CBD 2014, Tittensor et al. 2014, CBD 2020a), and was used as an indicator towards the Convention on Biological Diversity&#x2019;s 2010 Target (Butchart et al. 2010). It has been proposed as an indicator for monitoring progress towards the post-2020 Global Biodiversity Framework (CBD 2020b).</p>", "REC_USE_LIM__GLOBAL"=>"<p>Quality control criteria are applied to ensure consistency and comparability of the data in the WDPA. New data are validated at UNEP-WCMC through a number of tools and translated into the standard data structure of the WDPA. Discrepancies between the data in the WDPA and new data are minimised by provision of a manual (UNEP-WCMC 2019) and resolved in communication with data providers. Similar processes apply for the incorporation of data into the WDKBA (BirdLife International 2019).</p>\n<p>The indicator does not measure the effectiveness of protected areas in reducing biodiversity loss, which ultimately depends on a range of management and enforcement factors not covered by the indicator. A number of initiatives are underway to address this limitation. Most notably, numerous mechanisms have been developed for assessment of protected area management, which can be synthesised into an indicator (Leverington et al. 2010). This is used by the Biodiversity Indicators Partnership as a complementary indicator of progress towards Aichi Biodiversity Target 11 </p>\n<p>(http://www.bipindicators.net/pamanagement). However, there may be little relationship between these measures and protected area outcomes (Nolte &amp; Agrawal 2013). More recently, approaches to &#x201C;green listing&#x201D; have started to be developed, to incorporate both management effectiveness and the outcomes of protected areas, and these are likely to become progressively important as they are tested and applied more broadly.</p>\n<p>Data and knowledge gaps can arise due to difficulties in determining whether a site conforms to the IUCN definition of a protected area or the CBD definition of an OECM. However, given that both are incorporated into the indicator, misclassifications (as one or the other) do not impact the calculated indicator value.</p>\n<p>Regarding important sites, the biggest limitation is that site identification to date has focused mainly on specific subsets of biodiversity, for example birds (for Important Bird and Biodiversity Areas) and highly threatened species (for Alliance for Zero Extinction sites). While Important Bird and Biodiversity Areas have been documented to be good surrogates for biodiversity more generally (Brooks et al. 2001, Pain et al. 2005), the application of the unified standard for identification of KBA sites (IUCN 2016) across different levels of biodiversity (genes, species, ecosystems) and different taxonomic groups remains a high priority, building from efforts to date (Eken et al. 2004, Knight et al. 2007, Langhammer et al. 2007, Foster et al. 2012). Birds now comprise less than 50% of the species for which KBAs have been identified, and as KBA identification for other taxa and elements of biodiversity proceeds, such bias will become a less important consideration in the future.</p>\n<p>KBA identification has been validated for a number of countries and regions where comprehensive biodiversity data allow formal calculation of the site importance (or &#x201C;irreplaceability&#x201D;) using systematic conservation planning techniques (Di Marco et al. 2016, Montesino Pouzols et al. 2014).</p>\n<p>Future developments of the indicator will include: a) expansion of the taxonomic coverage of KBAs through application of the KBA standard (IUCN 2016) to a wide variety of vertebrates, invertebrates, plants and ecosystem type; b) improvements in the data on protected areas by continuing to increase the proportion of sites with documented dates of designation and with digitised boundary polygons (rather than coordinates); and c) increased documentation of Other Effective Area-based Conservation Measures in the World Database of OECMs.</p>", "DATA_COMP__GLOBAL"=>"<p>This indicator is calculated from data derived from a spatial overlap between digital polygons for protected areas from the World Database on Protected Areas (UNEP-WCMC &amp; IUCN 2020), digital polygons for Other Effective Area-based Conservation Measures from the World Database on OECMs and digital polygons for terrestrial and freshwater Key Biodiversity Areas (from the World Database of Key Biodiversity Areas, including Important Bird and Biodiversity Areas, Alliance for Zero Extinction sites, and other Key Biodiversity Areas). </p>\n<p>Sites were classified as terrestrial Key Biodiversity Areas by undertaking a spatial overlap between the Key Biodiversity Area polygons and an ocean raster layer (produced from the &#x2018;adm0&#x2019; layer from the database of Global Administrative Areas (GADM 2019)), classifying any Key Biodiversity Area as a terrestrial Key Biodiversity Area where it had &#x2264;95% overlap with the ocean layer (hence some sites were classified as both terrestrial and marine). </p>\n<p>Sites were classified as freshwater Key Biodiversity Areas if the resident species for which they were identified were documented in the IUCN Red List as dependent on &#x2018;Inland Water&#x2019; systems. For non-resident or migrant species, or species that shift habitats during the annual cycle, the site was tagged as freshwater if the species occurred at the site in the appropriate season of water-dependence (e.g. some species are only dependent on water during the breeding season). Sites were then screened (using the satellite imagery base layer within ArcGIS) as to whether they lay wholly in the Coastal Zone (defined here as within 10 km of the coast), and these sites were then untagged as Freshwater and instead tagged as Marine if the wetland habitats present at the site fell purely within the IUCN Habitat Classification Scheme class &#x2018;Marine Supratidal&#x2019; (i.e. estuaries, lagoons, etc.). If the site was within the Coastal Zone, but contained a mixture of Marine Supratidal and Inland Water classes, then it was tagged as both Freshwater and Marine. Each site was then manually cross-checked against other (less comprehensively available) site attributes, such as the habitat preferences of its trigger species, the site&#x2019;s name (Delta, River, Humedal, etc.), its areal coverage by different habitat types, its overlap with Ramsar Sites, and its &#x2018;shadow&#x2019; Ramsar status, so as to confirm or remove the Freshwater tag appropriately. The value of the indicator at a given point in time, based on data on the year of protected area establishment recorded in the World Database on Protected Areas, is computed as the mean percentage of each Key Biodiversity Area currently recognised that is covered by protected areas and/or Other Effective Area-based Conservation Measures.</p>\n<p>Protected areas lacking digital boundaries in the World Database of Protected Areas, and those sites with a status of &#x2018;proposed&#x2019; or &#x2018;not reported&#x2019; are omitted. Degazetted sites are not kept in the WDPA and are also not included. Man and Biosphere Reserves are also excluded as these often contain potentially unprotected areas. Year of protected area establishment is unknown for ~12% of protected areas in the World Database on Protected Areas, generating uncertainty around changing protected area coverage over time. To reflect this uncertainty, a year was randomly assigned from another protected area within the same country, and then this procedure repeated 1,000 times, with the median plotted. </p>\n<p>Prior to 2017, the indicator was presented as the percentage of Key Biodiversity Areas completely covered by protected areas. However, it is now presented as the mean % of each Key Biodiversity Area that is covered by protected areas in order to better reflect trends in protected area coverage for countries or regions with few or no Key Biodiversity Areas that are completely covered.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Protected Areas and OECMs are validated through dialogue with the governing authority, who signs a data contributor agreement that these sites are, to the best of their knowledge, an accurate depiction of the sites in question. Over time the data for sites may improve or other aspects of the sites may change, as and when this occurs a further data sharing agreement is required by the site&#x2019;s governing authority.</p>\n<p>Proposed KBAs undergo detailed checking by Regional Focal Points, formal Review of KBA Proposals by independent Reviewers, and validation of Nominated KBAs by the KBAs Secretariat. For further information, see the Guidance on Proposing, Reviewing, Nominating and Confirming KBAs available in KBA Secretariat (2019) at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>.</p>\n<p>When the indicators of protected area coverage of KBAs are updated each year, the updated indicators (and underlying numbers of protected areas, OECMs, and KBAs) are made available for review by countries prior to submission to the SDG Indicators Database. This is achieved through updating the country profiles in the Integrated Biodiversity Assessment Tool (<a href=\"https://ibat-alliance.org/country_profiles\">https://ibat-alliance.org/country_profiles</a>) and circulating these for consultation and review to CBD National Focal Points, SDG National Statistical Office Focal Points, and IUCN State Members.</p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made to the index with respect to harmonization of breakdowns or for compliance with specific international or national definitions.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Data are available for protected areas and KBAs in all of the world&#x2019;s countries, and so no imputation or estimation of national level data is necessary.</p>\n<p> </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Global indicators of protected area coverage of important sites for biodiversity are calculated as the mean percentage of each KBA that is covered by protected areas and Other Effective Area-based Conservation Measures. The data are generated from all countries, and so while there is uncertainty around the data, there are no missing values as such and so no need for imputation or estimation.</p>", "REG_AGG__GLOBAL"=>"<p>Regional indices are calculated as the mean percentage of each KBA in the region covered by (i.e. overlapping with) protected areas and/or OECMs: in other words, the percentage of each KBA covered by these designations, averaged over all KBAs in the particular region.</p>", "DOC_METHOD__GLOBAL"=>"<p><strong><em>PAs</em></strong></p>\n<p>Data on protected areas are submitted by government agencies to the WDPA and disseminated through Protected Planet. The WDPA has its origins in a 1959 UN mandate when the United Nations Economic and Social Council called for a list of national parks and equivalent reserves Resolution 713 (XXVIII). </p>\n<p>Protected areas data are therefore compiled directly from government agencies, regional hubs and other authoritative sources in the absence of a government source. All records have a unique metadata identifier (MetadataID) which links the spatial database to the Source table where all sources are described. The data is collated and standardised following the WDPA Data Standards and validated with the source. The process of collation, validation and publication of data as well as protocols and the WDPA data standards are regularly updated in the WDPA User Manual (<a href=\"https://www.protectedplanet.net/c/wdpa-manual\">https://www.protectedplanet.net/c/wdpa-manual</a>) made available through www.protectedplanet.net where all spatial data and the Source table are also published every month and can be downloaded. The WDPA User Manual (published in English, Spanish, and French) provides guidance to countries on how to submit protected areas data to the WDPA, the benefits of providing such data, and the data standards and quality checks that are performed. </p>\n<p><strong><em>OECMS</em></strong></p>\n<p>Guiding principles, common characteristics and criteria for identification of OECMs are available in CBD (2018) at <a href=\"https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-08-en.pdf\">https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-08-en.pdf</a>.</p>\n<p>Guidance on recognising and reporting other effective area-based conservation measures is available in IUCN-WCPA Task Force on OECMs (2019) at: <a href=\"https://portals.iucn.org/library/node/48773\">https://portals.iucn.org/library/node/48773</a>.</p>\n<p><strong><em>KBAs</em></strong></p>\n<p>The &#x201C;Global Standard for the Identification of KBAs&#x201D; (<a href=\"https://portals.iucn.org/library/node/46259\">https://portals.iucn.org/library/node/46259</a>) comprises the standard recommendations available to countries in the identification of KBAs. Guidelines for using A global standard for the identification of KBAs are available at https://portals.iucn.org/library/node/49131.</p>\n<p>Guidance on Proposing, Reviewing, Nominating and Confirming KBAs is available in KBA Secretariat (2019) at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>.</p>\n<p>A summary of the process by which KBAs are identified is available at <a href=\"http://www.keybiodiversityareas.org/working-with-kbas/proposing-updating\">www.keybiodiversityareas.org/working-with-kbas/proposing-updating</a>.</p>\n<p>The KBA identification process is highly inclusive, consultative and nationally driven. Anyone with appropriate data may propose a site as a KBA, although consultation with relevant stakeholders at the local and national level is required when identifying the site and needs to be documented in the proposal. In order to propose a site as a KBA, a proposer must apply the KBA criteria to data on biodiversity elements (species and ecosystems) at the site. Associated with the proposal process is the need to delineate the site accurately so that its boundaries are clear. Although anyone with appropriate scientific data may propose a site to qualify as a KBA, wide consultation with stakeholders at the national level (both non-governmental and governmental organizations) is required during the proposal process. The formal proposal is then made using a proposal process that ensures there is an independent review of the proposal before a site is incorporated in the WDKBA. This is important given that KBA status of a site may lead to changes in actions of governments, private sector companies and other institutions following consultation as appropriate. </p>\n<p>KBA identification builds off the existing network of KBAs, including those identified as (a) Important Bird &amp; Biodiversity Areas through the BirdLife Partnership of over 115 national organisations (https://www.birdlife.org/who-we-are/), (b) Alliance for Zero Extinction sites by 93 national and international organisations in the Alliance (<a href=\"http://www.zeroextinction.org/partners.html\">http://www.zeroextinction.org/partners.html</a>), and (c) other KBAs by civil society organisations supported by the Critical Ecosystem Partnership Fund in developing ecosystem profiles, named in each of the profiles listed here (<a href=\"http://www.cepf.net\">http://www.cepf.net</a> ), with new data strengthening and expanding expand the network of these sites.</p>\n<p>The main steps of the KBA identification process are the following: </p>\n<ol>\n  <li>submission of Expressions of Intent to identify a KBA to Regional Focal Points; </li>\n  <li>Proposal Development process, in which proposers compile relevant data and documentation and consult national experts, including organizations that have already identified KBAs in the country, either through national KBA Coordination Groups or independently;</li>\n  <li>review of proposed KBAs by Independent Expert Reviewers, verifying the accuracy of information within their area of expertise; and</li>\n  <li>a Site Nomination phase comprising the submission of all the relevant documentation for verification by the KBAs Secretariat. Sites confirmed by the KBAs Secretariat to qualify as KBAs are then published on the KBAs website (<a href=\"http://www.keybiodiversityareas.org/home\">http://www.keybiodiversityareas.org/home</a>).</li>\n</ol>\n<p>Once a KBA is identified, monitoring of its qualifying features and its conservation status is important. Proposers, reviewers and those undertaking monitoring can join the KBAs Community to exchange their experiences, case studies and best practice examples.</p>\n<p>The R code for calculating protected area coverage of KBAs is documented in Simkins et al. (2020). </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>For protected areas and OECMs, please see the section on validation. Ensuring the WDPA and WDOECM remain an accurate and true depiction of reality is a never-ending task; however, over time the quality of the data (e.g. the proportion of sites with defined boundaries) is increasing.</p>\n<p>For KBAs, see above and below, plus the guidance on Proposing, Reviewing, Nominating and Confirming KBAs which is available in KBA Secretariat (2019) at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>. Data quality is ensured through wide stakeholder engagement in the KBA proposal process, data checking by Regional Focal Points, formal Review of KBA Proposals by independent Reviewers, and validation of Nominations by the KBAs Secretariat. Furthermore, an independent KBA Standards and Appeals Committee ensures the correct application of the Global Standard for the identification of KBAs, and oversees a formal Procedure for handling of appeals against the identification of KBAs (see http://www.keybiodiversityareas.org/assets/1b388c918e14c5f4c3d7a0237eb0d366).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Information on the process of how protected area data are collected, standardised and published is available in the WDPA User Manual at: <a href=\"https://www.protectedplanet.net/c/wdpa-manual\">https://www.protectedplanet.net/c/wdpa-manual</a> which is available in English, French and Spanish. Specific guidance is provided at <a href=\"https://www.protectedplanet.net/c/world-database-on-protected-areas\">https://www.protectedplanet.net/c/world-database-on-protected-areas</a> on, for example, predefined fields or look up tables in the WDPA: <a href=\"https://www.protectedplanet.net/c/wdpa-lookup-tables\">https://www.protectedplanet.net/c/wdpa-lookup-tables</a>, how WDPA records are coded how international designations and regional designations data is collected, how regularly is the database updated, and how to perform protected areas coverage statistics. </p>\n<p>Data quality in the process of identifying KBAs is ensured through processes established by the <a href=\"https://www.keybiodiversityareas.org/working-with-kbas/programme/partnership\">KBA Partnership</a> and KBA Secretariat. Data quality is ensured through wide stakeholder engagement in the KBA proposal process, data checking by Regional Focal Points, formal Review of KBA Proposals by independent Reviewers, and validation of Nominations by the KBA Secretariat. </p>\n<p>In addition, the Chairs of the IUCN Species Survival Commission and World Commission on Protected Areas (both of whom are elected by the IUCN Membership of governments and non-governmental organisations), appoint the Chair of an independent KBA Standards and Appeals Committee, which ensures the correct application of the Global Standard for the identification of KBA, and oversees a formal Procedure for handling of appeals against the identification of KBAs (see http://www.keybiodiversityareas.org/assets/1b388c918e14c5f4c3d7a0237eb0d366).</p>\n<p>Before submission to the UN SDG Indicators database the annually updated indicators of coverage of KBAs by protected areas and Other Effective Area-based Conservation Measures are incorporated into updated Country Profiles on the Integrated Biodiversity Assessment Tool (<a href=\"https://ibat-alliance.org/country_profiles\">https://ibat-alliance.org/country_profiles</a>) and then sent for consultation to National Focal Points of the Convention on Biological Diversity (<a href=\"https://www.cbd.int/information/nfp.shtml\">https://www.cbd.int/information/nfp.shtml</a>), National Statistics Offices SDG Representatives and UN Permanent Missions (Geneva) representatives. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>High.</p>\n<p>Each custodian agency is responsible for quality management of their own database.<br>Quality assessment of the indicator is shared between the indicator custodian agencies.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>This indicator has been classified by the IAEG-SDGs as Tier 1. Current data are available for all countries in the world, and these are updated on an ongoing basis. Index values for each country are available in the UN SDG Indicators Database <a href=\"https://unstats.un.org/sdgs/indicators/database/\">https://unstats.un.org/sdgs/indicators/database/</a>. Graphs of Protected area coverage of KBAs are also available for each country in the BIP Indicators Dashboard (<a href=\"https://bipdashboard.natureserve.org/bip/SelectCountry.html\">https://bipdashboard.natureserve.org/bip/SelectCountry.html</a>), and the Integrated Biodiversity Assessment Tool Country Profiles (<a href=\"https://ibat-alliance.org/country_profiles\">https://ibat-alliance.org/country_profiles</a>).</p>\n<p>Underlying data on protected areas and Other Effective Area-based Conservation Measures are available at <a href=\"http://www.protectedplanet.net\">www.protectedplanet.net</a>. Data on KBAs are available at <a href=\"http://www.keybiodiversityareas.org\">www.keybiodiversityareas.org</a>. Data on subsets of KBAs are available for Important Bird and Biodiversity Areas at <a href=\"http://datazone.birdlife.org/site/search\">http://datazone.birdlife.org/site/search</a> and for Alliance for Zero Extinction sites at https://zeroextinction.org.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Given that data for the global indicator are compiled at national levels, it is straightforward to disaggregate to national and regional levels (e.g., Han et al. 2014), or conversely to aggregate to the global level. KBAs span all ecosystem types through the marine environment (Edgar et al. 2008) and beyond. The indicator can therefore be reported in combination across marine systems along with terrestrial or freshwater systems, or disaggregated among them. However, individual KBAs can encompass marine, terrestrial, and freshwater systems simultaneously, and so determining the results is not simply additive. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>National processes provide the data that are incorporated into the WDPA, the WDOECM, and the World Database of KBAs, so there are very few discrepancies between national indicators and the global one. One minor source of difference is that the WDPA incorporates internationally-designated protected areas (e.g., UNESCO World Heritage sites, Ramsar sites, etc), a few of which are not considered by their sovereign nations to be protected areas. </p>\n<p>Note that because countries do not submit comprehensive data on degazetted protected areas to the WDPA, earlier values of the indictor may marginally underestimate coverage. Furthermore, there is also a lag between the point at which a protected area is designated on the ground and the point at which it is reported to the WDPA. As such, current or recent coverage may also be underestimated.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.unep-wcmc.org/\">http://www.unep-wcmc.org/</a>; <a href=\"http://www.birdlife.org/\">http://www.birdlife.org/</a>; <a href=\"http://www.iucn.org/\">http://www.iucn.org/</a> </p>\n<p><strong>References:</strong></p>\n<p>BIRDLIFE INTERNATIONAL (2014). Important Bird and Biodiversity Areas: a global network for conserving nature and benefiting people. Cambridge, UK: BirdLife International. Available at datazone.birdlife.org/sowb/sowbpubs#IBA.</p>\n<p>BIRDLIFE INTERNATIONAL (2019) World Database of Key Biodiversity Areas.Developed by the KBA Partnership: BirdLife International, International Union for the Conservation of Nature, Amphibian Survival Alliance, Conservation International, Critical Ecosystem Partnership Fund, Global Environment Facility, Global Wildlife Conservation, NatureServe, Rainforest Trust, Royal Society for the Protection of Birds, Wildlife Conservation Society and World Wildlife Fund. September 2019 version. Available at http://keybiodiversityareas.org/sites/search.</p>\n<p>BROOKS, T. et al. (2001). Conservation priorities for birds and biodiversity: do East African Important Bird Areas represent species diversity in other terrestrial vertebrate groups? Ostrich suppl. 15: 3&#x2013;12. Available </p>\n<p>from: http://www.tandfonline.com/doi/abs/10.2989/00306520109485329#.VafbVJPVq75.</p>\n<p>BROOKS, T.M. et al. (2016) Goal 15: Life on land. Sustainable manage forests, combat desertification, halt and reverse land degradation, halt biodiversity loss. Pp. 497&#x2013;522 in Dur&#xE1;n y Lalaguna, P., D&#xED;az Barrado, C.M. &amp; Fern&#xE1;ndez Liesa, C.R. (eds.) International Society and Sustainable Development Goals. Editorial Aranzadi, Cizur Menor, Spain. Available from: https://www.thomsonreuters.es/es/tienda/pdp/duo.html?pid=10008456</p>\n<p>BUTCHART, S. H. M. et al. (2010). Global biodiversity: indicators of recent declines. Science 328: 1164&#x2013;1168. Available from https://www.science.org/doi/10.1126/science.1187512.</p>\n<p>BUTCHART, S. H. M. et al. (2012). Protecting important sites for biodiversity contributes to meeting global conservation targets. PLoS One 7(3): e32529. Available from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0032529.</p>\n<p>BUTCHART, S. H. M. et al. (2015). Shortfalls and solutions for meeting national and global conservation area targets. Conservation Letters 8: 329&#x2013;337. Available from http://onlinelibrary.wiley.com/doi/10.1111/conl.12158/full.</p>\n<p>CBD (2014). Global Biodiversity Outlook 4. Convention on Biological Diversity, Montr&#xE9;al, Canada. Available from <a href=\"https://www.cbd.int/gbo4/\">https://www.cbd.int/gbo4/</a>.</p>\n<p>CBD (2018). Protected areas and other effective area-based conservation measures. Decision 14/8 adopted by the Conference of the Parties to the Convention on Biological Diversity. Available at <a href=\"https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-08-en.pdf\">https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-08-en.pdf</a>. </p>\n<p>CBD (2020a). Global Biodiversity Outlook 5. Convention on Biological Diversity, Montr&#xE9;al, Canada. Available from https://www.cbd.int/gbo5/. </p>\n<p>CBD (2020b). Post-2020 Global Biodiversity Framework: Scientific and technical information to support the review of the updated Goals and Targets, and related indicators and baselines. Document CBD/SBSTTA/24/3. Available at: https://www.cbd.int/doc/c/705d/6b4b/a1a463c1b19392bde6fa08f3/sbstta-24-03-en.pdf.</p>\n<p>CHAPE, S. et al. (2005). Measuring the extent and effectiveness of protected areas as an indicator for meeting global biodiversity targets. Philosophical Transactions of the Royal Society B 360: 443&#x2013;445. Available from http://rstb.royalsocietypublishing.org/content/360/1454/443.short.</p>\n<p>DEGUIGNET, M., et al. (2014). 2014 United Nations List of Protected Areas. UNEP-WCMC, Cambridge, UK. Available from http://unep-wcmc.org/system/dataset_file_fields/files/000/000/263/original/2014_UN_List_of_Protected_Areas_EN_web.PDF?1415613322.</p>\n<p>DI MARCO, M., et al. (2016). Quantifying the relative irreplaceability of Important Bird and Biodiversity Areas. Conservation Biology 30: 392&#x2013;402. Available from http://onlinelibrary.wiley.com/doi/10.1111/cobi.12609/abstract.</p>\n<p>DONALD, P. et al. (2018) Important Bird and Biodiversity Areas (IBAs): the development and characteristics of a global inventory of key sites for biodiversity. Bird Conserv. Internat. 29:177&#x2013;198.</p>\n<p>DUDLEY, N. (2008). Guidelines for Applying Protected Area Management Categories. International Union for Conservation of Nature (IUCN). Gland, Switzerland. Available from https://portals.iucn.org/library/node/9243.</p>\n<p>EDGAR, G.J. et al. (2008). KBAs as globally significant target sites for the conservation of marine biological diversity. Aquatic Conservation: Marine and Freshwater Ecosystems 18: 969&#x2013;983. Available from http://onlinelibrary.wiley.com/doi/10.1002/aqc.902/abstract.</p>\n<p>EKEN, G. et al. (2004). KBAs as site conservation targets. BioScience 54: 1110&#x2013;1118. Available from http://bioscience.oxfordjournals.org/content/54/12/1110.short.</p>\n<p>FOSTER, M.N. et al. (2012) The identification of sites of biodiversity conservation significance: progress with the application of a global standard. Journal of Threatened Taxa 4: 2733&#x2013;2744. Available from </p>\n<p>https://threatenedtaxa.org/index.php/JoTT/article/view/779.</p>\n<p>Global Administrative Areas (2019). GADM database of Global Administrative Areas, version 2.8. Available from www.gadm.org.</p>\n<p>HAN, X. et al. (2014). A Biodiversity indicators dashboard: addressing challenges to monitoring progress towards the Aichi Biodiversity Targets using disaggregated global data. PLoS ONE 9(11): e112046. Available from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112046.</p>\n<p>HOLLAND, R.A. et al. (2012). Conservation priorities for freshwater biodiversity: the key biodiversity area approach refined and tested for continental Africa. Biological Conservation 148: 167&#x2013;179. Available from </p>\n<p>http://www.sciencedirect.com/science/article/pii/S0006320712000298.</p>\n<p>IUCN (2016). A Global Standard for the Identification of Key Biodiversity Areas. International Union for Conservation of Nature, Gland, Switzerland. Available from https://portals.iucn.org/library/node/46259.</p>\n<p>IUCN-WCPA Task Force on OECMs (2019). Recognising and reporting other effective area-based conservation measures. Gland, Switzerland: IUCN.</p>\n<p>JONAS, H.D. et al. (2014) New steps of change: looking beyond protected areas to consider other effective area-based conservation measures. Parks 20: 111&#x2013;128. Available from http://parksjournal.com/wp-content/uploads/2014/10/PARKS-20.2-Jonas-et-al-10.2305IUCN.CH_.2014.PARKS-20-2.HDJ_.en_.pdf.</p>\n<p>KBA Secretariat (2019). Key Biodiversity Areas Proposal Process: Guidance on Proposing, Reviewing, Nominating and Confirming sites. Version 1.0. Prepared by the KBA Secretariat and KBA Committee of the KBA Partnership. Cambridge, UK. Available at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>. </p>\n<p>KNIGHT, A. T. et al. (2007). Improving the Key Biodiversity Areas approach for effective conservation planning. BioScience 57: 256&#x2013;261. Available from </p>\n<p>http://bioscience.oxfordjournals.org/content/57/3/256.short.</p>\n<p>LANGHAMMER, P. F. et al. (2007). Identification and Gap Analysis of Key Biodiversity Areas: Targets for Comprehensive Protected Area Systems. IUCN World Commission on Protected Areas Best Practice Protected Area Guidelines Series No. 15. IUCN, Gland, Switzerland. Available from https://portals.iucn.org/library/node/9055.</p>\n<p>LEVERINGTON, F. et al. (2010). A global analysis of protected area management effectiveness. Environmental Management 46: 685&#x2013;698. Available from http://link.springer.com/article/10.1007/s00267-010-</p>\n<p>9564-5#page-1.</p>\n<p>MONTESINO POUZOLS, F., et al. (2014) Global protected area expansion is compromised by projected land-use and parochialism. Nature 516: 383&#x2013;386. Available from http://www.nature.com/nature/journal/v516/n7531/abs/nature14032.html.</p>\n<p>NOLTE, C. &amp; AGRAWAL, A. (2013). Linking management effectiveness indicators to observed effects of protected areas on fire occurrence in the Amazon rainforest. Conservation Biology 27: 155&#x2013;165. Available from http://onlinelibrary.wiley.com/doi/10.1111/j.1523-1739.2012.01930.x/abstract.</p>\n<p>PAIN, D.J. et al. (2005) Biodiversity representation in Uganda&#x2019;s forest IBAs. Biological Conservation 125: 133&#x2013;138. Available from http://www.sciencedirect.com/science/article/pii/S0006320705001412.</p>\n<p>RICKETTS, T. H. et al. (2005). Pinpointing and preventing imminent extinctions. Proceedings of the National Academy of Sciences of the U.S.A. 102: 18497&#x2013;18501. Available from http://www.pnas.org/content/102/51/18497.short.</p>\n<p>RODRIGUES, A. S. L. et al. (2004). Effectiveness of the global protected area network in representing species diversity. Nature 428: 640&#x2013;643. Available from http://www.nature.com/nature/journal/v428/n6983/abs/nature02422.html.</p>\n<p>RODR&#xCD;GUEZ-RODR&#xCD;GUEZ, D., et al. (2011). Progress towards international targets for protected area coverage in mountains: a multi-scale assessment. Biological Conservation 144: 2978&#x2013;2983. Available from </p>\n<p><a href=\"http://www.sciencedirect.com/science/article/pii/S0006320711003454\">http://www.sciencedirect.com/science/article/pii/S0006320711003454</a>.</p>\n<p>SIMKINS, A.T., PEARMAIN, E.J., &amp; DIAS, M.P. (2020). Code (and documentation) for calculating the protected area coverage of Key Biodiversity Areas. <a href=\"https://github.com/BirdLifeInternational/kba-overlap\">https://github.com/BirdLifeInternational/kba-overlap</a>. </p>\n<p>TITTENSOR, D. et al. (2014). A mid-term analysis of progress towards international biodiversity targets. Science 346: 241&#x2013;244. Available from https://www.science.org/doi/10.1126/science.1257484.</p>\n<p>UNEP-WCMC (2019). World Database on Protected Areas User Manual 1.6. UNEP-WCMC, Cambridge, UK. Available from <a href=\"http://wcmc.io/WDPA_Manual\">http://wcmc.io/WDPA_Manual</a>.</p>\n<p>UNEP-WCMC &amp; IUCN (2020). The World Database on Protected Areas (WDPA). UNEP-WCMC, Cambridge, UK. Available from http://www.protectedplanet.net.</p>", "indicator_sort_order"=>"15-01-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.1.E1", "slug"=>"15-1-E1", "name"=>"Superficie de las áreas terrestres protegidas (km2 y porcentaje) (Indicador UE sdg_15_20)", "url"=>"/site/es/15-1-E1/", "sort"=>"1501E1", "goal_number"=>"15", "target_number"=>"15.1", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Superficie de las áreas terrestres protegidas (km2 y porcentaje) (Indicador UE sdg_15_20)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Superficie de las áreas terrestres protegidas (km2 y porcentaje) (Indicador UE sdg_15_20)", "indicator_number"=>"15.1.E1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Mantenimiento o ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/indicadores-ambientales-090207/web01-a2inguru/es/", "url_text"=>"Estadística de Indicadores Ambientales", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}, {"organisation"=>"Geoeuskadi", "url"=>"https://www.geo.euskadi.eus/", "url_text"=>"Geoeuskadi", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Superficie de las áreas terrestres protegidas (km2 y porcentaje) (Indicador UE sdg_15_20)", "definicion"=>"Superficie terrestre incluida en áreas protegidas (Red Natura 2000 y Espacios Naturales Protegidos), y porcentaje sobre el total de la superficie terrestre", "formula"=>"\n\n$$PSTP^{t} = \\frac{STP^{t}}{ST^{t}} \\cdot 100$$\n\ndonde:\n\n$STP^{t} =$ superficie terrestre incluida en áreas protegidas en el año $t$\n\n$ST^{t} =$ superficie terrestre en el año $t$\n\nLa desagregación por tipo de ecosistema se realiza sobre la superficie total del ecosistema, según\nla siguiente fórmula:\n\n$$PSTP_{ecosistema}^{t} = \\frac{STP_{ecosistema}^{t}}{ST_{ecosistema}^{t}} \\cdot 100$$\n\ndonde:\n\n$STP_{ecosistema}^{t} =$ superficie de un ecosistema determinado incluida en áreas protegidas en el año $t$\n\n$ST_{ecosistema}^{t} =$ superficie total de un ecosistema determinado en el año $t$\n", "desagregacion"=>"Tipo de ecosistema: bosques, humedales, pastizales, tierras de cultivo,  asentamientos, otras tierras", "periodicidad"=>"Trienal", "observaciones"=>"", "justificacion_global"=>"Este indicador mide la extensión de las áreas terrestres protegidas, que incluyen las designadas \na nivel nacional y los espacios Natura 2000. Un área designada a nivel nacional es un \nárea protegida por la legislación nacional.\n\nLos espacios Natura 2000 se designan para salvaguardar las especies y los hábitats de \nEuropa, incluidos en las Directivas de Hábitats y de Aves de la UE.\n\nFuente: Eurostat\n", "dato_global"=>"\n<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_15_20/default/table?lang=en&category=sdg.sdg_15\">\nSuperficie de las áreas terrestres protegidas(sdg_15_20)</a> Eurostat\n", "comparabilidad"=>"El indicador disponible es comparable con el indicador europeo.\n", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_15_20_esmsip2.htm\">Metadatos Eurostat (sdg_15_20)</a> (solo en inglés)", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Superficie de las áreas terrestres protegidas (km2 y porcentaje) (Indicador UE sdg_15_20)", "definicion"=>"Land area included in protected areas (Natura 2000 Network and Protected Natural Areas), and percentage of the total land area", "formula"=>"\n\n$$PSTP^{t} = \\frac{STP^{t}}{ST^{t}} \\cdot 100$$\n\nwhere:\n\n$STP^{t} =$ land area included in protected areas in year $t$\n\n$ST^{t} =$ total land area in year $t$\n\nThe disaggregation by type of ecosystem is carried out on the total surface of the ecosystem, \naccording to the following formula: \n\n$$PSTP_{ecosystem}^{t} = \\frac{STP_{ecosystem}^{t}}{ST_{ecosystem}^{t}} \\cdot 100$$\n\nwhere:\n\n$STP_{ecosystem}^{t} =$ surface area of a given ecosystem included in protected areas in year $t$\n\n$ST_{ecosystem}^{t} =$ total surface area of a given ecosystem in year $t$\n", "desagregacion"=>"Ecosystem type: forests; wetlands; grasslands; croplands; settlements; other lands", "periodicidad"=>"Trienal", "observaciones"=>nil, "justificacion_global"=>"This indicator measures the extent of terrestrial protected areas, comprising nationally designated \nprotected areas and Natura 2000 sites. A nationally designated area is an area protected by national legislation. \n\nThe Natura 2000 sites are designated to safeguard Europe's species and habitats, listed under the EU \nHabitats and Birds Directives. \n\nSource: Eurostat\n", "dato_global"=>"\n<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_15_20/default/table?lang=en&category=sdg.sdg_15\">\nSurface of the terrestrial protected areas (sdg_15_20)</a> Eurostat\n", "comparabilidad"=>"The available indicator is comparable with the European indicator.\n", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_15_20_esmsip2.htm\">Metadata Eurostat (sdg_15_20)</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Superficie de las áreas terrestres protegidas (km2 y porcentaje) (Indicador UE sdg_15_20)", "definicion"=>"Superficie terrestre incluida en áreas protegidas (Red Natura 2000 y Espacios Naturales Protegidos), y porcentaje sobre el total de la superficie terrestre", "formula"=>"\n\n$$PSTP^{t} = \\frac{STP^{t}}{ST^{t}} \\cdot 100$$\n\nnon:\n\n$STP^{t} =$ eremu babestuetan sartutako lur-azalera $t$ urtean\n\n$ST^{t} =$ lur-azalera $t$ urtean\n\nEkosistema motaren araberako bereizketa ekosistemaren azalera osoan egiten da, formula honen arabera:\n\n$$PSTP_{ekosistema}^{t} = \\frac{STP_{ekosistema}^{t}}{ST_{ekosistema}^{t}} \\cdot 100$$\n\nnon:\n\n$STP_{ekosistema}^{t} =$ ekosistema jakin baten azalera, eremu babestuetan dagoena $t$ urtean\n\n$ST_{ekosistema}^{t} =$ ekosistema jakin baten guztizko azalera $t$ urtean\n", "desagregacion"=>"Ekosistema mota: basoak; hezeguneak; larreak; laborantza-lurrak; asentamenduak; beste lur batzuk ", "periodicidad"=>"Trienal", "observaciones"=>nil, "justificacion_global"=>"Este indicador mide la extensión de las áreas terrestres protegidas, que incluyen las designadas \na nivel nacional y los espacios Natura 2000. Un área designada a nivel nacional es un \nárea protegida por la legislación nacional.\n\nLos espacios Natura 2000 se designan para salvaguardar las especies y los hábitats de \nEuropa, incluidos en las Directivas de Hábitats y de Aves de la UE.\n\nFuente: Eurostat\n", "dato_global"=>"\n<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_15_20/default/table?lang=en&category=sdg.sdg_15\">\nLurreko eremu babestuen azalera (sdg_15_20)</a> Eurostat\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazlea Europako adierazlearekin aldera daiteke.\n", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_15_20_esmsip2.htm\">Metadatuak Eurostat (sdg_15_20)</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "indicator_sort_order"=>"15-01-E1", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.2.1", "slug"=>"15-2-1", "name"=>"Avances hacia la gestión forestal sostenible", "url"=>"/site/es/15-2-1/", "sort"=>"150201", "goal_number"=>"15", "target_number"=>"15.2", "global"=>{"name"=>"Avances hacia la gestión forestal sostenible"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Avances hacia la gestión forestal sostenible", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Avances hacia la gestión forestal sostenible", "indicator_number"=>"15.2.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio para la Transición Ecológica y el Reto Demográfico", "periodicity"=>"Anual", "url"=>"https://www.miteco.gob.es/es/ministerio/servicios/estadisticas/estadisticas-forestales/estadistica-sobre-gestion-forestal-sostenible.html", "url_text"=>"Estadística sobre gestión forestal sostenible", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Avances hacia la gestión forestal sostenible", "objetivo_global"=>"15- Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad", "meta_global"=>"15.2- De aquí a 2020, promover la puesta en práctica de la gestión sostenible de todos los tipos de bosques, detener la deforestación, recuperar los bosques degradados y aumentar considerablemente la forestación y la reforestación a nivel mundial", "definicion"=>"Superficie forestal con instrumentos de ordenación vigentes, y porcentaje respecto a la superficie forestal", "formula"=>"\n$$PSF_{ordenada}^{t} = \\frac{SF_{ordenada}^{t}}{SF^{t}} \\cdot 100$$\n\ndonde: \n\n$SF_{ordenada}^{t} =$ superficie forestal con instrumentos de ordenación vigentes en el año $t$  \n\n$SF^{t} =$ superficie forestal en el año $t$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La definición de la gestión forestal sostenible de la Asamblea General de las Naciones \nUnidas contiene varios aspectos clave, en particular que la gestión forestal sostenible \nes un concepto que varía a lo largo del tiempo y entre países, cuyas \ncircunstancias –ecológicas, sociales y económicas– varían ampliamente, pero que \nsiempre debe abordar una amplia gama de valores forestales, incluidos los valores \neconómicos, sociales y ambientales, y tener en cuenta la equidad intergeneracional.\n\nEs evidente que una simple medida de la superficie forestal no es suficiente para hacer \nel seguimiento de la gestión forestal sostenible en su conjunto. El indicador de Naciones\nUnidas se compone de cinco subindicadores que miden el progreso hacia todas las \ndimensiones de la gestión forestal sostenible. Los subindicadores son:\n\n1. Tasa anual de cambio de la superficie forestal\n2. Biomasa sobre el suelo en los bosques\n3. Proporción de la superficie forestal dentro de las zonas protegidas establecidas legalmente\n4. Proporción de la superficie forestal sujeta a un plan de gestión a largo plazo\n5. Superficie forestal sujeta a un sistema de certificación de la gestión forestal \nverificado de forma independiente\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.2.1&seriesCode=AG_LND_FRSTMGT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proporción de superficie forestal con un plan de gestión a largo plazo (%) AG_LND_FRSTMGT</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible responde al subindicador 4 del indicador propuesto por Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-02-01.pdf\">Metadatos 15-2-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Avances hacia la gestión forestal sostenible", "objetivo_global"=>"15- Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad", "meta_global"=>"15.2- De aquí a 2020, promover la puesta en práctica de la gestión sostenible de todos los tipos de bosques, detener la deforestación, recuperar los bosques degradados y aumentar considerablemente la forestación y la reforestación a nivel mundial", "definicion"=>"Forest area with management instruments in force and percentage with respect to forest area", "formula"=>"\n$$PSF_{managed}^{t} = \\frac{SF_{managed}^{t}}{SF^{t}} \\cdot 100$$\n\nwhere: \n\n$SF_{managed}^{t} =$ forest area with management instruments in force in year $t$  \n\n$SF^{t} =$ forest area in year $t$\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The definition of SFM by the UN General Assembly contains several key aspects, notably that sustainable \nforest management is a concept which varies over time and between countries, whose circumstances – \necological, social and economic – vary widely, but that it should always address a wide range of forest \nvalues, including economic, social and environmental values, and take intergenerational equity into \naccount. \n\nClearly a simple measure of forest area is insufficient to monitor sustainable forest management as a \nwhole. The United Nations indicator consists of five sub-indicators that measure progress toward all \ndimensions of sustainable forest management. Sub-indicators are: \n\n1. Annual forest area change rate \n2. Above-ground biomass in forest \n3. Proportion of forest area within legally established protected areas \n4. Proportion of forest area under a long-term management plan \n5. Forest area under an independently verified forest management certification scheme \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.2.1&seriesCode=AG_LND_FRSTMGT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proportion of forest area with a long-term management plan (%) AG_LND_FRSTMGT</a> UNSTATS\n", "comparabilidad"=>"The available indicator responds to sub-indicator 4 of the indicator proposed by the United Nations. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-02-01.pdf\">Metadata 15-2-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Avances hacia la gestión forestal sostenible", "objetivo_global"=>"15- Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad", "meta_global"=>"15.2- De aquí a 2020, promover la puesta en práctica de la gestión sostenible de todos los tipos de bosques, detener la deforestación, recuperar los bosques degradados y aumentar considerablemente la forestación y la reforestación a nivel mundial", "definicion"=>"Superficie forestal con instrumentos de ordenación vigentes, y porcentaje respecto a la superficie forestal", "formula"=>"\n$$PSF_{antolatua}^{t} = \\frac{SF_{antolatua}^{t}}{SF^{t}} \\cdot 100$$\n\nnon: \n\n$SF_{antolatua}^{t} =$ antolamendu-tresnak indarrean dituen baso-azalera $t$ urtean\n\n$SF^{t} =$ baso-azalera $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La definición de la gestión forestal sostenible de la Asamblea General de las Naciones \nUnidas contiene varios aspectos clave, en particular que la gestión forestal sostenible \nes un concepto que varía a lo largo del tiempo y entre países, cuyas \ncircunstancias –ecológicas, sociales y económicas– varían ampliamente, pero que \nsiempre debe abordar una amplia gama de valores forestales, incluidos los valores \neconómicos, sociales y ambientales, y tener en cuenta la equidad intergeneracional.\n\nEs evidente que una simple medida de la superficie forestal no es suficiente para hacer \nel seguimiento de la gestión forestal sostenible en su conjunto. El indicador de Naciones\nUnidas se compone de cinco subindicadores que miden el progreso hacia todas las \ndimensiones de la gestión forestal sostenible. Los subindicadores son:\n\n1. Tasa anual de cambio de la superficie forestal\n2. Biomasa sobre el suelo en los bosques\n3. Proporción de la superficie forestal dentro de las zonas protegidas establecidas legalmente\n4. Proporción de la superficie forestal sujeta a un plan de gestión a largo plazo\n5. Superficie forestal sujeta a un sistema de certificación de la gestión forestal \nverificado de forma independiente\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.2.1&seriesCode=AG_LND_FRSTMGT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Epe luzerako kudeaketa-plana duen baso-azaleraren proportzioa (%) AG_LND_FRSTMGT</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuek proposatutako adierazlearen 4. azpiadierazleari erantzuten dio.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-02-01.pdf\">Metadatuak 15-2-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.2: By 2020, promote the implementation of sustainable management of all types of forests, halt deforestation, restore degraded forests and substantially increase afforestation and reforestation globally</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.2.1: Progress towards sustainable forest management</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Annual forest area change rate (%) (AG_LND_FRSTCHG)</p>\n<p>Above-ground biomass in forest (tonnes per hectare) (AG_LND_FRSTBIOPHA)</p>\n<p>Proportion of forest area within legally established protected areas (%) (AG_LND_FRSTPRCT)</p>\n<p>Proportion of forest area with a long-term management plan (%) (AG_LND_FRSTMGT)</p>\n<p>Forest area under an independently verified forest management certification scheme (thousands of hectares) (AG_LND_FRSTCERT)</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-05-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>15.1.1: Forest area as a proportion of total land area </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition: </strong></p>\n<p>&#x201C;Sustainable forest management&#x201D; (SFM) is a central concept for Goal 15 and target 15.1 as well as for target 15.2. It has been formally defined, by the UN General Assembly, as follows: </p>\n<p><em>[a] dynamic and evolving concept [that] aims to maintain and enhance the economic, social and environmental values of all types of forests, for the benefit of present and future generations</em>&#x201D; (Resolution A/RES/62/98) </p>\n<p>The indicator is composed of five sub-indicators that measure progress towards all dimensions of sustainable forest management. The environmental values of forests are covered by three sub-indicators focused on the extension of forest area, biomass within the forest area and protection and maintenance of biological diversity, and of natural and associated cultural resources. Social and economic values of forests are reconciled with environmental values through sustainable management plans. The sub-indicator provides further qualification to the management of forest areas, by assessing areas which are independently verified for compliance with a set of national or international standards. </p>\n<p>The sub-indicators are: </p>\n<ol>\n  <li>Annual forest area change rate </li>\n  <li>Above-ground biomass in forest </li>\n  <li>Proportion of forest area within legally established protected areas </li>\n  <li>Proportion of forest area under a long-term management plan </li>\n  <li>Forest area under an independently verified forest management certification scheme </li>\n</ol>\n<p>A dashboard is used to assess progress related to the five sub-indicators. The adoption of the dashboard approach aims at ensuring consideration of all dimensions of sustainable forest management and provides for clear view of areas where progress has been achieved. </p>\n<p><strong>Concepts: </strong></p>\n<p>See Annex 1 with Terms and Definitions. </p>", "UNIT_MEASURE__GLOBAL"=>"<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>SUB-INDICATOR</strong></p>\n      </td>\n      <td>\n        <p><strong>UNIT</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Annual forest area change rate </p>\n      </td>\n      <td>\n        <p>Percent (%)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Above-ground biomass in forest </p>\n      </td>\n      <td>\n        <p>Tonnes per hectare</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Proportion of forest area within legally established protected areas </p>\n      </td>\n      <td>\n        <p>Percent (%)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Proportion of forest area under a long-term management plan </p>\n      </td>\n      <td>\n        <p>Percent (%)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Forest area under an independently verified forest management certification scheme </p>\n      </td>\n      <td>\n        <p>1000 hectares</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Sub-indicators 1 to 4</p>\n<p>Data are collected by FAO through the Global Forest Resources Assessment (FRA). Assessments have been carried out at regular intervals since 1946 and are now produced every five year. The latest of these assessments, FRA 2020, contains information for 236 countries and territories on about 60 variables related to the extent of forests, their conditions, uses and values for several points in time. </p>\n<p>Sub-indicator 5</p>\n<p>Currently, forest certification by the Forest Stewardship Council (FSC) and the Programme for the Endorsement of Forest Certification (PEFC) are included in the data submissions. The latter includes several national/regional certification schemes that have been endorsed according to the PEFC standards. </p>\n<p>Data on forest certification are submitted annually to FAO by the head offices of the respective forest certification scheme. Data include the area certified by each scheme, as well as areas that are double-certified by the two schemes. That allows for estimating the total certified forest area, adjusted for double certified area. </p>", "COLL_METHOD__GLOBAL"=>"<p>Sub-indicators 1 to 4</p>\n<p>Data on these sub-indicators are collected through FAO&#x2019;s Global Forest Resources Assessment (FRA) programme. Officially nominated national correspondents and their teams prepare the country reports for the assessment. Some prepare more than one report as they also report on dependent territories. For the remaining countries and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. </p>\n<p>All data are provided to FAO by countries in the form of a country report through an online platform following a standard format, which includes the original data and reference sources and descriptions of how these have been used to estimate the forest area for different points in time. The online platform was used for all data entry, review and quality control.</p>\n<p>In order to obtain internationally comparable data, countries are requested to provide national categories and definitions, and in case these are different than the FAO categories and definitions, countries are requested to perform a reclassification of national data to correspond to the FAO categories and definitions and to document this step in the country report. Countries are also requested to use interpolation or extrapolation of national data in order to provide estimates for the specific reporting years. </p>\n<p>Sub-indicator 5</p>\n<p>Data are annually reported by the certification bodies to FAO and consolidated into estimates of total certified forest area, which are made available to the countries through the FRA online platform where country officials can view the data that are being submitted. </p>", "FREQ_COLL__GLOBAL"=>"<p>Source data collection for sub-indicators 1 to 4 was initiated in 2018 and concluded in 2019. Data collection for the next FRA is expected to start in 2022. </p>\n<p>Data on sub-indicator 5 is reported by the certification bodies to FAO by the end of each calendar year, referring to the status of certified forest area by end of June that year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data with updated time series and including year 2020 was released in July 2020. Next release of a complete FRA dataset is scheduled for 2025. The possibilities of a more frequent reporting on forest area and other key indicators are currently being evaluated. Data on forest certification is updated annually.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The data for sub-indicators 1 to 4 are provided by the countries through a global network of officially nominated national correspondents. For the countries and territories which do not have a national correspondent, a report is prepared by FAO using previously reported information, literature search, remote sensing or their combination.</p>\n<p> </p>\n<p>For sub-indicator 5, forest certification, data are provided by head offices of respective forest certification scheme.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO) </p>", "INST_MANDATE__GLOBAL"=>"<p>Article 1 of FAO&#x2019;s constitution specifies that, &#x201C;The Organization shall collect, analyse, interpret, and disseminate information related to nutrition, food and agriculture.&#x201D; In this regard, FAO collects national level data from member countries, which it then standardizes and disseminates through corporate statistical databases. FAO is the custodian UN agency for 21 SDG indicators, including 15.2.1.</p>", "RATIONALE__GLOBAL"=>"<p>The definition of SFM by the UN General Assembly contains several key aspects, notably that sustainable forest management is a concept which varies over time and between countries, whose circumstances &#x2013; ecological, social and economic &#x2013; vary widely, but that it should always address a wide range of forest values, including economic, social and environmental values, and take intergenerational equity into account. </p>\n<p>Clearly a simple measure of forest area is insufficient to monitor sustainable forest management as a whole. The significance of the five sub-indicators can be briefly explained as follows: </p>\n<ol>\n  <li>Trends in forest area are crucial for monitoring SFM. The first sub-indicator focuses on both the direction of change (whether there is a loss or gain in forest area) and how the change rate varies over time; the latter is important to capture progress among countries that are losing forest area but have managed to reduce the rate of annual forest area loss. </li>\n  <li>Changes in the above-ground biomass stock in forest indicate the balance between gains in biomass stock due to forest growth and losses due to wood removals, natural losses, fire, wind, pests and diseases. At country level and over a longer period, sustainable forest management would imply a stable or increasing biomass stock per hectare, while a long-term reduction of biomass stock per hectare would imply either unsustainable management of the forests and degradation or unexpected major losses due to fire, wind, pests or diseases. </li>\n  <li>The change in forest area within legally protected areas is a proxy for trends in conservation of forest biodiversity as well as cultural and spiritual values of forests and thus a clear indication of the political will to protect and conserve forests. This indicator is related to the CBD Aichi Target 11 which calls for each country to conserve at least 17 per cent of terrestrial and inland water areas. </li>\n  <li>The fourth sub-indicator looks at the forest area that is under a long-term forest management plan. The existence of a documented forest management plan is the basis for long term and sustainable management of the forest resources for a variety of management objectives such as for wood and non-wood forest products, protection of soil and water, biodiversity conservation, social and cultural use, and a combination of two or several of these. An increasing area under forest management plan is therefore an indicator of progress towards sustainable forest management. </li>\n  <li>The fifth sub-indicator is the forest area that is certified by an independently verified forest management certification scheme. Such certification schemes apply standards that generally are higher than those established by the countries&#x2019; own normative frameworks, and compliance is verified by an independent and accredited certifier. An increase in certified forest area therefore provides an additional indication of progress towards sustainable forest management. It should however be noted that there are significant areas of sustainably managed forest which are not certified, either because their owners have chosen not to seek certification (which is voluntary and market-based) or because no credible or affordable certification scheme is in place for that area. </li>\n</ol>", "REC_USE_LIM__GLOBAL"=>"<p>The five sub-indicators chosen to illustrate progress towards sustainable forest management do not fully cover all aspects of sustainable forest management. In particular, social and economic aspects are summarized under the sub-indicators on areas under sustainable forest management plans. Furthermore, as the trends are calculated using only those countries which have data complete time series, different sub-indicators may reflect different sets of countries. </p>\n<p>While the dashboard illustrates the progress on the individual sub-indicators, there is no weighting of the relative importance of the sub-indicators. </p>", "DATA_COMP__GLOBAL"=>"<p>National data on forest area, biomass stock, forest area within protected areas, and forest area under management plan are reported directly by countries to FAO for pre-established reference years. Based on the country reported data, FAO then makes country-level estimates of the forest area net change rate using the compound interest formula. The proportion of forest area within protected area and under management plan is calculated using the reported areas for each reference year and the forest area for year 2015. Data on forest area under an independently verified forest management certification scheme are reported to FAO by the head offices of respective forest certification scheme, who are jointly adjusting the figures to remove any double accounting. </p>\n<p>No dashboard traffic lights are made at country level. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>All data submitted by countries to FRA, including the FAO estimates made in case of desk studies, are available at the FRA online platform (<a href=\"https://fra-data.fao.org/\">https://fra-data.fao.org</a>). The platform also includes the sub-indicators for 15.2.1. A request for validation was sent to the respective Head of Forestry before finalization and publishing of data. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level </strong></li>\n</ul>\n<p>For countries and territories where no information was provided to FAO for FRA 2020 (47 countries and territories representing 0.5 percent of the global forest area), a report was prepared by FAO using existing information from previous assessments, literature search, remote sensing or a combination of two or more of them. </p>\n<p>For the above-ground biomass sub-indicator, imputation of the missing values has been carried out by FAO for those countries with at least one data point in the time series. The value of the data point closest in time was used as imputed value. For those countries where no value was reported for any of the reporting years, no imputation was done and the values for all years were set as &#x201C;Not Available&#x201D;. </p>\n<ul>\n  <li><strong>At regional and global levels </strong></li>\n</ul>\n<p>See above. </p>", "REG_AGG__GLOBAL"=>"<p>See Annex 2 &#x2013; Methodology. It should be noted that for those sub-indicators where there are gaps in the data set, only the countries with complete data for the relevant years (either provided by the countries or estimated by FAO) are included in the regional and global aggregates. Annex 2 also shows how the dashboard traffic lights are applied at global and regional level. </p>", "DOC_METHOD__GLOBAL"=>"<p>Detailed methodology and guidance on how to prepare the country reports through an online reporting platform and to convert national data according to national categories and definitions to FAO&#x2019;s global categories and definitions is found in the documents </p>\n<p>&#x201C;<em>Guidelines and Specifications</em>&#x201D; (<a href=\"http://www.fao.org/3/I8699EN/i8699en.pdf\"><u>www.fao.org/3/I8699EN/i8699en.pdf</u></a>) and </p>\n<p>&#x201C;<em>Terms and Definitions</em>&#x201D; (<a href=\"http://www.fao.org/3/I8661EN/i8661en.pdf\"><u>www.fao.org/3/I8661EN/i8661en.pdf</u></a>). </p>\n<p>FAO supports the reporting process through capacity development on reporting methodology and remote sensing. The reporting platform provides easy access to relevant and freely available global remote sensing data sets and products.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>FAO is responsible for the quality of the internal statistical processes used to compile the published datasets. The FAO Statistics Quality Assurance Framework (SQAF), available at: <a href=\"http://www.fao.org/docrep/019/i3664e/i3664e.pdf\">http://www.fao.org/docrep/019/i3664e/i3664e.pdf</a>, provides the necessary principles, guidelines and tools to carry out quality assessments. FAO is performing an internal bi-annual survey (FAO Quality Assessment and Planning Survey) designed to gather information on all of FAO&#x2019;s statistical activities, notably to assess the extent to which quality standards are being implemented with a view to increasing compliance with the quality dimensions of SQAF, documenting best practices and prepare quality improvement plans, where necessary. Domain-specific quality assurance activities are carried out systematically (e.g. quality reviews, self-assessments, compliance monitoring).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Date reported by countries to FAO are subject to a rigorous review process to ensure correct use of definitions and methodology as well as internal consistency. A comparison is made with past assessments and other existing data sources. Regular contacts between national correspondents and FAO staff by e-mail and regional/sub-regional review workshops form part of this review process. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality of statistics produced and disseminated by the FAO is evaluated in terms of fitness for use i.e. the degree to which statistics meet the user&#x2019;s requirements. The quality dimensions assessed are: Relevance; Accuracy and Reliability; Timeliness and Punctuality; Coherence and Comparability; Accessibility and Clarity. Quality dimensions definitions are provided in the FAO Statistical Quality Assurance Framework (SQAF), which provides the definition of quality and describes quality principles for statistical outputs; statistical processes; institutional environment (http://www.fao.org/docrep/019/i3664e/i3664e.pdf). The SQAF is based on the Fundamental Principles of Official Statistics and the Principles Governing International Statistical Activities (CCSA). Adherence to these principles ensures the quality of FAO statistical production processes and of statistical outputs. Regular quality assessments are conducted through the FAO Quality Assessment and Planning Survey (QAPS), a bi-annual survey designed to gather information on all of FAO&#x2019;s statistical activities, which is used to assess the extent to which quality standards are being met with a view to increasing compliance with the SQAF, and to document best practices and provide guidance for improvement where necessary. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The Global Forest Resources Assessment collects data from 236 countries and territories. </p>\n<p><strong>Time series:</strong></p>\n<p>2000, 2010, 2015, and every year since.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>No further disaggregation of this indicator. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The national figures in the database are reported by the countries themselves following a standardized format, definitions and reporting years, thus eliminating any discrepancies between global and national figures. The reporting template requests that countries provide the full reference for original data sources as well as national definitions and terminology. Separate sections in the template country reports deal with the analysis of data (including any assumptions made and the methods used for estimates and projections to the common reporting years); calibration of data to the official land area as held by FAO; and reclassification of data to the classes used in FAO&#x2019;s Global Forest Resources Assessments. </p>\n<p>Regarding the data on forest area under an independently verified forest management certification scheme, these are usually not part of official national statistics, and are maintained by local offices of the respective certification schemes. They in turn report their data to their head offices. As certified forest area is dynamic and can change monthly as some certificates expire and new certificates come. Therefore, the data are requested to correspond to the end of June each year. However, data are not always reported by the local offices according to that date. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong><a href=\"http://www.fao.org/forest-resources-assessment/en/\"><u>http://www.fao.org/forest-resources-assessment/en/</u></a> </p>\n<p><strong>References:</strong></p>\n<p>Global Forest Resources Assessment 2020, Guidelines and Specifications (<a href=\"http://www.fao.org/3/I8699EN/i8699en.pdf\"><u>www.fao.org/3/I8699EN/i8699en.pdf</u></a>)</p>\n<p>Global Forest Resources Assessment 2020, Terms and Definitions (<a href=\"http://www.fao.org/3/I8661EN/i8661en.pdf\"><u>www.fao.org/3/I8661EN/i8661en.pdf</u></a>).</p>\n<p>United Nations. Resolution adopted by the General Assembly on 17 December 2007 (<a href=\"https://undocs.org/en/A/RES/62/98\">https://undocs.org/en/A/RES/62/98</a>). </p>\n<p><strong>Annex 1 &#x2013; Terms and Definitions </strong></p>\n<p><strong>FOREST</strong></p>\n<p>Land spanning more than 0.5 hectares with <u>tree</u>s higher than 5 meters and a <u>canopy cover</u> of more than 10 percent, or trees able to reach these thresholds <em>in situ</em>. It does not include land that is predominantly under agricultural or urban land use. </p>\n<p><u>Explanatory notes</u></p>\n<ol>\n  <li>Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters. </li>\n  <li>Includes areas with young trees that have not yet reached but which are expected to reach a canopy cover of at least 10 percent and tree height of 5 meters or more. It also includes areas that are temporarily unstocked due to clear-cutting as part of a forest management practice or natural disasters, and which are expected to be regenerated within 5 years. Local conditions may, in exceptional cases, justify that a longer time frame is used.</li>\n  <li>Includes forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific environmental, scientific, historical, cultural or spiritual interest.</li>\n  <li>Includes windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters.</li>\n  <li>Includes abandoned shifting cultivation land with a regeneration of trees that have, or are expected to reach, a canopy cover of at least 10 percent and tree height of at least 5 meters.</li>\n  <li>Includes areas with mangroves in tidal zones, regardless whether this area is classified as land area or not.</li>\n  <li>Includes rubberwood, cork oak and Christmas tree plantations. </li>\n  <li>Includes areas with bamboo and palms provided that land use, height and canopy cover criteria are met.</li>\n  <li><u>Excludes</u> tree stands in agricultural production systems, such as fruit tree plantations, oil palm plantations, olive orchards and agroforestry systems when crops are grown under tree cover. <u>Note:</u> Some agroforestry systems such as the &#x201C;Taungya&#x201D; system where crops are grown only during the first years of the forest rotation should be classified as forest.</li>\n</ol>\n<p><strong>ABOVE-GROUND BIOMASS</strong></p>\n<p>All living biomass above the soil including stem, stump, branches, bark, seeds, and foliage.</p>\n<p><u>Explanatory note </u></p>\n<ol>\n  <li>In cases where forest understorey is a relatively small component of the aboveground biomass carbon pool, it is acceptable to exclude it, provided this is done in a consistent manner throughout the inventory time series.</li>\n</ol>\n<p><strong>PROTECTED AREAS</strong></p>\n<p>Areas especially dedicated to the protection and maintenance of biological diversity, and of natural and associated cultural resources, and managed through legal or other effective means.</p>\n<p><strong>FOREST AREA WITHIN PROTECTED AREAS</strong></p>\n<p>Forest area within formally established protected areas independently of the purpose for which the protected areas were established. </p>\n<p><u>Explanatory notes</u></p>\n<ol>\n  <li>Includes IUCN Categories I &#x2013; IV</li>\n  <li><u>Excludes</u> IUCN Categories V-VI</li>\n</ol>\n<p><strong>FOREST AREA WITH MANAGEMENT PLAN</strong></p>\n<p>Forest area that has a long-term documented management plan, aiming at defined management goals, which is periodically revised. </p>\n<p><u>Explanatory notes</u></p>\n<ol>\n  <li>A forest area with management plan may refer to forest management unit level or aggregated forest management unit level (forest blocks, farms, enterprises, watersheds, municipalities, or wider units).</li>\n  <li>A management plan must include adequate detail on operations planned for individual operational units (stands or compartments) but may also provide general strategies and activities planned to reach management goals.</li>\n  <li>Includes forest area in protected areas with management plan.</li>\n</ol>\n<p><strong>INDEPENDENTLY VERIFIED FOREST MANAGEMENT CERTIFICATION</strong></p>\n<p>Forest area certified under a forest management certification scheme with published standards and is independently verified by a third-party.</p>\n<p><strong>Annex 2 &#x2013; Methodology</strong></p>\n<p><strong>Sub-indicator 1 - Annual forest area change rate</strong></p>\n<p><u>Unit</u>: Percent</p>\n<p><u>Reference period:</u> 2010-2020</p>\n<p><u>Method of estimation:</u> Compound annual change rate formula as follows:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>r</mi>\n    <mo>=</mo>\n    <mfenced open=\"[\" close=\"]\" separators=\"|\">\n      <mrow>\n        <msup>\n          <mrow>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mfrac>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>A</mi>\n                        <mi>F</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>t</mi>\n                        <mn>2</mn>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>A</mi>\n                        <mi>F</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>t</mi>\n                        <mn>1</mn>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                </mfrac>\n              </mrow>\n            </mfenced>\n          </mrow>\n          <mrow>\n            <mfrac bevelled=\"true\">\n              <mrow>\n                <mn>1</mn>\n              </mrow>\n              <mrow>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mi>t</mi>\n                    <mn>2</mn>\n                    <mo>-</mo>\n                    <mi>t</mi>\n                    <mn>1</mn>\n                  </mrow>\n                </mfenced>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </msup>\n        <mo>-</mo>\n        <mn>1</mn>\n      </mrow>\n    </mfenced>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>where: </p>\n<p><em>r</em> = compound annual change rate for the period <em>t<sub>1 - </sub>t<sub>2</sub></em><sub> </sub></p>\n<p><em>t<sub>i</sub></em> = time i (year)</p>\n<p><em>AF<sub>t1</sub></em> = forest area at <em>t<sub>1</sub></em> </p>\n<p><em>AF<sub>t2</sub></em> = forest area at <em>t<sub>2</sub></em></p>\n<p><u>Translation to dashboard/traffic light:</u></p>\n<p>The following flowchart explains the logic behind the translation of this indicator to a dashboard/traffic light:</p>\n<p>Forest area change direction</p>\n<p>Forest area stable </p>\n<p>or increasing</p>\n<p>Forest area decreasing</p>\n<p>Change in forest area loss rate </p>\n<p>Loss rate</p>\n<p>decreasing</p>\n<p>Loss rate stable</p>\n<p>or increasing</p>\n<p>The forest area change direction is determined by examining the value of the forest area change rate for the most recent period, a negative value indicate a loss of forest area, a zero value means that forest area is stable, and a positive value means that forest area has increased. The change in forest area loss rate<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> is based on a comparison of the annual forest area change rate for the period 2010-2020 with the annual <u>forest area change rate for the period 2000-2010 </u>(<u>baseline).</u></p>\n<p><u>Comments:</u></p>\n<p>This traffic light takes into consideration both the direction of forest area change (if forest area increases or decreases) as well as changes in the rate of forest area loss &#x2013; the latter important in order to indicate progress among countries that are losing forest area but manage to reduce the loss rate. </p>\n<p>The baseline should be updated every 5 years. In 2020 a new baseline was calculated for the period 2000-2010 based on updated country data. </p>\n<p><strong>Sub-indicator 2 &#x2013; Above-ground biomass in forest </strong></p>\n<p><u>Unit</u>: tonnes/hectare</p>\n<p><u>Reference year:</u> Latest reporting year</p>\n<p><u>Method of estimation:</u> Reported directly by countries</p>\n<p><u>Translation to dashboard/traffic light:</u></p>\n<p>The indicator value for the latest reporting year is compared with the indicator value reported for 2010.</p>\n<p>The ratio (r) between the current indicator value and the value reported for 2010 is calculated; r&gt;1 means an increase in stock per hectare, r&lt;1 means a decrease while 1 indicates no change. A narrow interval for r has been established to indicate a stable condition, and traffic-light colors are assigned as follows:</p>\n<p> r &#x2265; 1.01 </p>\n<p> 0.99 &lt; r &lt; 1.01</p>\n<p> r &#x2264; 0.99</p>\n<p><strong>Sub-indicator 3 &#x2013; Proportion of forest area within legally established protected areas.</strong></p>\n<p><u>Unit</u>: Percent</p>\n<p><u>Reference year:</u> Latest reporting year</p>\n<p><u>Method of estimation:</u> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>r</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n            <mi>F</mi>\n            <mi>P</mi>\n          </mrow>\n          <mrow>\n            <mfenced open=\"[\" close=\"]\" separators=\"|\">\n              <mrow>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>f</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>c</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>y</mi>\n                <mi>e</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n              </mrow>\n            </mfenced>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n            <mi>F</mi>\n          </mrow>\n          <mrow>\n            <mn>2015</mn>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Where:</p>\n<p><em>AFP</em> = Forest area within legally established protected areas</p>\n<p><em>AF</em> = Total forest area</p>\n<p><u>Translation to dashboard/traffic light:</u></p>\n<p>The indicator value for latest reporting year is compared with the indicator value reported for 2010.</p>\n<p>The ratio (r) between the current indicator value and the value reported for 2010 is calculated; r&gt;1 means an increase in forest area within protected areas, r&lt;1 means a decrease while 1 indicates no change. A narrow interval for r has been established to indicate a stable condition, and traffic-light colors are assigned as follows:</p>\n<p> r &#x2265; 1.01 </p>\n<p> 0.99 &lt; r &lt; 1.01</p>\n<p> r &#x2264; 0.99</p>\n<p><u>Comment:</u></p>\n<p>Using forest area in 2015 as denominator for estimating this indicator ensures that the time series of percentages reflect real changes in the forest area within legally established protected areas and is not affected by changes (losses or gains) in total forest area. </p>\n<p><strong>Sub-indicator 4 &#x2013; Proportion of forest area under a long-term management plan.</strong></p>\n<p><u>Unit</u>: Percent</p>\n<p><u>Reference year:</u> Latest reporting year</p>\n<p><u>Method of estimation:</u> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>r</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n            <mi>F</mi>\n            <mi>M</mi>\n            <mi>P</mi>\n          </mrow>\n          <mrow>\n            <mfenced open=\"[\" close=\"]\" separators=\"|\">\n              <mrow>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>f</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>c</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>y</mi>\n                <mi>e</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n              </mrow>\n            </mfenced>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n            <mi>F</mi>\n          </mrow>\n          <mrow>\n            <mn>2015</mn>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Where:</p>\n<p><em>AFMP</em> = Forest area under a long-term management plan</p>\n<p><em>AF</em> = Total forest area</p>\n<p><u>Translation to dashboard/traffic light: </u>The indicator value for latest reporting year is compared with the indicator value reported for 2010.</p>\n<p>The ratio (r) between the current indicator value and the value reported for 2010 is calculated; r&gt;1 means an increase in areas under management plan, r&lt;1 means a decrease while 1 indicates no change. A narrow interval for r has been established to indicate a stable condition, and traffic-light colors are assigned as follows:</p>\n<p> r &#x2265; 1.01 </p>\n<p> 0.99 &lt; r &lt; 1.01</p>\n<p> r &#x2264; 0.99</p>\n<p><u>Comment:</u></p>\n<p>Using forest area in 2015 as denominator for estimating this indicator ensures that the time series of percentages reflect real changes in the forest area under management plan and is not affected by changes (losses or gains) in total forest area. </p>\n<p><strong>Sub-indicator 5 &#x2013; Forest area under an independently verified forest management certification scheme.</strong></p>\n<p><u>Unit</u>: Thousand hectares</p>\n<p><u>Reference year:</u> Latest reporting year (as of June 30)</p>\n<p><u>Method of estimation:</u> Data is collected directly from the databases of each certification scheme and provided to countries for validation.</p>\n<p><u>Translation to dashboard/traffic light: </u>The indicator value for latest reporting year is compared with the indicator value for previous reporting year for assessment of continuity of progress since last report.</p>\n<p>The ratio (r) between the current indicator value and the previously reported value is calculated; r&gt;1 means an increase in areas under an independent forest management certification scheme, r&lt;1 means a decrease while 1 indicates no change. A small interval for r has been established to indicate a stable condition, and traffic-light colors are assigned as follows:</p>\n<p> r &#x2265; 1.01 </p>\n<p> 0.99 &lt; r &lt; 1.01</p>\n<p> r &#x2264; 0.99</p>\n<p><u>Comments:</u></p>\n<p>Using June 30 as the date for reporting, allows for the certification bodies to have their databases updated so they can provide information to FAO by end of the year, and then be included in the annual reporting to SDG in the beginning of the following year.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> If forest area change rate is negative (= forest loss) then: annual forest area loss rate = <strong>-</strong> (annual forest area change rate) <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "indicator_sort_order"=>"15-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.3.1", "slug"=>"15-3-1", "name"=>"Proporción de tierras degradadas en comparación con la superficie total", "url"=>"/site/es/15-3-1/", "sort"=>"150301", "goal_number"=>"15", "target_number"=>"15.3", "global"=>{"name"=>"Proporción de tierras degradadas en comparación con la superficie total"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Superficie de suelos potencialmente contaminados y recuperados para nuevos usos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de tierras degradadas en comparación con la superficie total", "indicator_number"=>"15.3.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/indicadores-ambientales-090207/web01-a2inguru/es/", "url_text"=>"Estadística de Indicadores Ambientales", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Superficie de suelos potencialmente contaminados y recuperados para nuevos usos", "objetivo_global"=>"15- Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad", "meta_global"=>"15.3- De aquí a 2030, luchar contra la desertificación, rehabilitar las tierras y los suelos degradados, incluidas las tierras afectadas por la desertificación, la sequía y las inundaciones, y procurar lograr un mundo con efecto neutro en la degradación de las tierras", "definicion"=>"Superficie de suelos potencialmente contaminados y recuperados para nuevos usos acumulado desde el año 2005 y porcentaje respecto a la superficie potencialmente contaminada\n\nSe considera que un suelo ha sido recuperado si dispone de la Declaración de la calidad \ndel suelo según lo previsto en la Ley 4/2015, de 25 de junio, para la prevención y \ncorrección de la contaminación del suelo.\n", "formula"=>"\n$$PSPC_{recuperado}^{t} = \\frac{SPC_{recuperado}^{t}}{SPC^{t}} \\cdot 100$$\n\ndonde: \n\n$SPC_{recuperado}^{t} =$ superficie de suelos potencialmente contaminados y recuperados para nuevos usos desde el año 2005 hasta el año $t$ \n\n$SPC^{t} =$ superficie de suelos potencialmente contaminados en el año $t$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"La degradación de la tierra se define como la reducción o pérdida de la productividad \nbiológica o económica y de la complejidad de las tierras de cultivo de secano, \nlas tierras de cultivo de regadío o los pastizales, pastizales, bosques y zonas \nboscosas como resultado de una combinación de presiones, incluidas las prácticas de \nuso y gestión de la tierra. \n\nEl indicador 15.3.1 de los ODS es una cuantificación binaria -degradado/no degradado- basada \nen el análisis de los datos disponibles para tres subindicadores: tendencias en la \ncobertura terrestre, productividad de la tierra y reservas de carbono.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.3.1&seriesCode=AG_LND_DGRD&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proporción de tierra degradada sobre la superficie total (%) AG_LND_DGRD</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible no cumple con los metadatos de Naciones Unidas, pero aporta información complementaria.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-03-01.pdf\">Metadatos 15-3-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-05-08", "en"=>{"indicador_disponible"=>"Superficie de suelos potencialmente contaminados y recuperados para nuevos usos", "objetivo_global"=>"15- Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad", "meta_global"=>"15.3- De aquí a 2030, luchar contra la desertificación, rehabilitar las tierras y los suelos degradados, incluidas las tierras afectadas por la desertificación, la sequía y las inundaciones, y procurar lograr un mundo con efecto neutro en la degradación de las tierras", "definicion"=>"Area of potentially contaminated soils recovered for new uses, accumulated since 2005 \nand percentage with respect to the potentially contaminated area. \n\nA soil is considered to have been recovered if it has a Declaration of Soil Quality \nas provided for in Law 4/2015, of June 25, for the prevention and correction of soil \ncontamination. \n", "formula"=>"\n$$PSPC_{recovered}^{t} = \\frac{SPC_{recovered}^{t}}{SPC^{t}} \\cdot 100$$\n\nwhere: \n\n$SPC_{recovered}^{t} =$ area of potentially contaminated soils recovered for new uses from 2005 to year $t$\n\n$SPC^{t} =$ area of potentially contaminated soils in year $t$\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"Land degradation is defined as the reduction or loss of the biological or economic productivity and \ncomplexity of rain fed cropland, irrigated cropland, or range, pasture, forest and woodlands resulting \nfrom a combination of pressures, including land use and management practices. \n\nSDG indicator 15.3.1 is a binary - degraded/not degraded - quantification based on the analysis of \navailable data for three sub-indicators: Trends in Land Cover, Land Productivity and Carbon Stocks. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.3.1&seriesCode=AG_LND_DGRD&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Proportion of land that is degraded over total land area (%) AG_LND_DGRD</a> UNSTATS\n", "comparabilidad"=>"The available indicator does not comply with the metadata of the United Nations indicator, but provides complementary information.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-03-01.pdf\">Metadata 15-3-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Superficie de suelos potencialmente contaminados y recuperados para nuevos usos", "objetivo_global"=>"15- Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad", "meta_global"=>"15.3- De aquí a 2030, luchar contra la desertificación, rehabilitar las tierras y los suelos degradados, incluidas las tierras afectadas por la desertificación, la sequía y las inundaciones, y procurar lograr un mundo con efecto neutro en la degradación de las tierras", "definicion"=>"Superficie de suelos potencialmente contaminados y recuperados para nuevos usos acumulado desde el año 2005 y porcentaje respecto a la superficie potencialmente contaminada\n\nSe considera que un suelo ha sido recuperado si dispone de la Declaración de la calidad \ndel suelo según lo previsto en la Ley 4/2015, de 25 de junio, para la prevención y \ncorrección de la contaminación del suelo.\n", "formula"=>"\n$$PSPC_{berreskuratua}^{t} = \\frac{SPC_{berreskuratua}^{t}}{SPC^{t}} \\cdot 100$$\n\nnon: \n\n$SPC_{berreskuratua}^{t} =$ kutsatuta egon daitezkeen eta erabilera berrietarako berreskuratuta dauden lurzoruen azalera 2005etik $t$ urtera arte\n\n$SPC^{t} =$ kutsatuta egon daitezkeen lurzoruen azalera $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>"", "periodicidad"=>"Anual", "justificacion_global"=>"La degradación de la tierra se define como la reducción o pérdida de la productividad \nbiológica o económica y de la complejidad de las tierras de cultivo de secano, \nlas tierras de cultivo de regadío o los pastizales, pastizales, bosques y zonas \nboscosas como resultado de una combinación de presiones, incluidas las prácticas de \nuso y gestión de la tierra. \n\nEl indicador 15.3.1 de los ODS es una cuantificación binaria -degradado/no degradado- basada \nen el análisis de los datos disponibles para tres subindicadores: tendencias en la \ncobertura terrestre, productividad de la tierra y reservas de carbono.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.3.1&seriesCode=AG_LND_DGRD&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Lur degradatuaren proportzioa azalera osoarekiko (%) AG_LND_DGRD</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu betetzen Nazio Batuen adierazlearen metadatuak, baina informazio osagarria ematen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-03-01.pdf\">Metadatuak 15-3-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.3: By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.3.1: Proportion of land that is degraded over total land area</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>AG_LND_DGRD - Proportion of land that is degraded over total land area [15.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>2.4.1; 6.6.1; 11.3.1; 15.1.1; 15.2.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Convention to Combat Desertification (UNCCD) and partners, including: Conservation International (CI), European Space Agency (ESA), Food and Agriculture Organization of the United Nations (FAO), Group on Earth Observation Land Degradation Neutrality Initiative (GEO-LDN), International Soil Reference and Information Centre (ISRIC), International Union for Conservation of Nature (IUCN), Joint Research Centre of the European Commission (JRC), United Nations Statistics Division (UNSD), United Nations Development Programme (UNDP), United Nations Environment (UNEP), World Resources Institute (WRI), United Nations Framework Convention on Climate Change (UNFCCC) and Convention on Biological Diversity (CBD).</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Convention to Combat Desertification (UNCCD).</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p><strong><em>Land degradation</em></strong> is defined as the reduction or loss of the biological or economic productivity and complexity of rain fed cropland, irrigated cropland, or range, pasture, forest and woodlands resulting from a combination of pressures, including land use and management practices. This definition was adopted by and is used by the 196 countries that are Party to the UNCCD.<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup> (see also Figure 1)</p>\n<p><strong><em>Land Degradation Neutrality</em></strong> (LDN) is defined as a state whereby the amount and quality of land resources necessary to support ecosystem functions and services and enhance food security remain stable or increase within specified temporal and spatial scales and ecosystems (decision 3/COP12).<sup><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></sup></p>\n<p><strong><em>Total land area</em></strong> is the total surface area of a country excluding the area covered by inland waters, like major rivers and lakes.<sup><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></sup></p>\n<p><strong><em>SDG indicator 15.3.1</em></strong> is a binary - degraded/not degraded - quantification based on the analysis of available data for three sub-indicators to be validated and reported by national authorities. The sub-indicators (Trends in Land Cover, Land Productivity and Carbon Stocks) were adopted by the UNCCD&#x2019;s governing body in 2013 as part of its monitoring and evaluation approach.<sup><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></sup> </p>\n<p><strong><em>The method of computation</em></strong> for this indicator follows the &#x201C;One Out, All Out&#x201D; statistical principle and is based on the baseline assessment and evaluation of change in the sub-indicators to determine the extent of land that is degraded over total land area.</p>\n<p><strong><em>The One Out, All Out (1OAO)</em></strong><em><sup><sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup></sup></em> principle is applied taking into account changes in the sub-indicators which are depicted as (i) positive or improving, (ii) negative or declining, or (iii) stable or unchanging. If one of the sub-indicators is negative (or stable when degraded in the baseline or previous monitoring year) for a particular land unit, then it would be considered as degraded subject to validation by national authorities.</p>\n<p><strong>Concepts:</strong></p>\n<p>The assessment and quantification of land degradation is generally regarded as context-specific, making it difficult for a single indicator to fully capture the state or condition of the land. While necessary but not sufficient, the sub-indicators address changes in different yet highly relevant ways: for example, land cover or productivity trends can capture relatively fast changes while changes in carbon stocks reflect slower changes that suggest a trajectory or proximity to thresholds.<sup><sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></sup> </p>\n<p>As proxies to monitor the key factors and driving variables that reflect the capacity to deliver land-based ecosystem services, the sub-indicators are globally agreed upon in definition and methodology of calculation, and deemed both technically and economically feasible for systematic observation under both the Global Climate Observation System (GCOS) and the integrated measurement framework of the System of Environmental-Economic Accounting (SEEA). The ultimate determination of the extent of degraded land made by national authorities should be contextualized with other indicators, data and ground-based information.</p>\n<p>An operational definition of land degradation along with a description of the linkages among the sub-indicators is given in Figure 1. </p>\n<p><strong>Figure 1: Operational definition of land degradation and linkage with the sub-indicators.</strong></p>\n<p><img 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\"></p>\n<p><strong><em>Land cover</em></strong> refers to the observed physical cover of the Earth&#x2019;s surface which describes the distribution of vegetation types, water bodies and human-made infrastructure.<sup><sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup></sup> It also reflects the use of land resources (i.e., soil, water and biodiversity) for agriculture, forestry, human settlements and other purposes.<sup><sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup></sup> This sub-indicator serves two functions for SDG indicator 15.3.1: (1) changes in land cover may point to land degradation when there is a loss of ecosystem services that are considered desirable in a local or national context; and (2) a land cover classification system can be used to disaggregate the other two sub-indicators, thus increasing the indicator&#x2019;s policy relevance. This sub-indicator is also expected to be used for reporting on SDG indicators 6.6.1, 11.3.1 and 15.1.1.</p>\n<p><strong><em>Land productivity</em></strong> refers to the total above-ground net primary production (NPP) defined as the energy fixed by plants minus their respiration which translates into the rate of biomass accumulation that delivers a suite of ecosystem services.<sup><sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup></sup> This sub-indicator points to changes in the health and productive capacity of the land and reflects the net effects of changes in ecosystem functioning on plant and biomass growth, where declining trends are often a defining characteristic of land degradation.<sup><sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup></sup></p>\n<p><br><strong><em>Carbon stock</em></strong> is the quantity of carbon in a &#x201C;pool&#x201D;: a reservoir which has the capacity to accumulate or release carbon and is comprised of above- and below-ground biomass, dead organic matter, and soil organic carbon.<sup><sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup></sup> In UNCCD decision 22/COP.11, <em>soil organic carbon (SOC) stock</em> was adopted as the metric to be used with the understanding that this metric will be replaced by <em>total terrestrial system carbon stocks</em>, once operational. SOC is an indicator of overall soil quality associated with nutrient cycling and its aggregate stability and structure with direct implications for water infiltration, soil biodiversity, vulnerability to erosion, and ultimately the productivity of vegetation, and in agricultural contexts, yields. SOC stocks reflect the balance between organic matter gains, dependent on plant productivity and management practices, and losses due to decomposition through the action of soil organisms and physical export through leaching and erosion.<sup><sup><a href=\"#footnote-13\" id=\"footnote-ref-13\">[12]</a></sup></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> United Nations Convention to Combat Desertification. 1994. Article 1 of the Convention Text<br><a href=\"http://www2.unccd.int/sites/default/files/relevant-links/2017-01/UNCCD_Convention_ENG_0.pdf\">http://www2.unccd.int/sites/default/files/relevant-links/2017-01/UNCCD_Convention_ENG_0.pdf</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> <a href=\"http://www2.unccd.int/sites/default/files/sessions/documents/ICCD_COP12_20_Add.1/20add1eng.pdf\">http://www2.unccd.int/sites/default/files/sessions/documents/ICCD_COP12_20_Add.1/20add1eng.pdf</a> <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> Food and Agriculture Organization of the United Nations <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> By its decision 22/COP.11, the Conference of the Parties established a monitoring and evaluation approach consisting of: (a) indicators; (b) a conceptual framework that allows for the integration of indicators; and (c) indicators sourcing and management mechanisms at the national/local level.<br><a href=\"https://www.unccd.int/sites/default/files/sessions/documents/ICCD_COP11_23_Add.1/23add1eng.pdf\">https://www.unccd.int/sites/default/files/sessions/documents/ICCD_COP11_23_Add.1/23add1eng.pdf</a> <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> <a href=\"https://circabc.europa.eu/sd/a/06480e87-27a6-41e6-b165-0581c2b046ad/Guidance%20No%2013%20-%20Classification%20of%20Ecological%20Status%20(WG%20A).pdf\">https://circabc.europa.eu/sd/a/06480e87-27a6-41e6-b165-0581c2b046ad/Guidance%20No%2013%20-%20Classification%20of%20Ecological%20Status%20(WG%20A).pdf</a> <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> <a href=\"https://www.unccd.int/publications/scientific-conceptual-framework-land-degradation-neutrality-report-science-policy%20\">https://www.unccd.int/publications/scientific-conceptual-framework-land-degradation-neutrality-report-science-policy%20</a> <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> Di Gregorio, A. 2005. Land cover classification system (LCCS): classification concepts and user manual. Food and Agriculture Organization of the United Nations, Rome. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> FAO-GTOS. 2009. Land Cover: Assessment of the status of the development of the standards for the Terrestrial Essential Climate Variables. Global Terrestrial Observing System, Rome. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> Millennium Ecosystem Assessment. 2005. Ecosystems and human wellbeing: a framework for assessment. Island Press, Washington, DC. <a href=\"#footnote-ref-10\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> Joint Research Centre of the European Commission. 2017. World Atlas of Desertification, 3<sup>rd</sup> edition. JRC, Ispra. <a href=\"https://wad.jrc.ec.europa.eu/\">https://wad.jrc.ec.europa.eu/</a> <a href=\"#footnote-ref-11\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> IPCC. 2006. IPCC Guidelines for National Greenhouse Gas Inventories: Agriculture, Forestry and other Land Use. Prepared by the National Greenhouse Gas Inventories Programme: Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). IGES, Japan. <a href=\"#footnote-ref-12\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-13\">12</sup><p> Smith, P., Fang, C., Dawson, J. J., &amp; Moncrieff, J. B. 2008. Impact of global warming on soil organic carbon. Advances in agronomy, 97: 1-43. <a href=\"#footnote-ref-13\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) (The measurement unit for this indicator is the spatial extent (hectares or km<sup>2</sup>) expressed as the proportion (percentage or %) of land that is degraded over total land area.)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>There is an international standard for the sub-indicator on land cover<sup><sup><a href=\"#footnote-14\" id=\"footnote-ref-14\">[13]</a></sup></sup> which includes the Land Cover Meta Language (LCML), a common reference structure (statistical standard) for the comparison and integration of data for any generic land cover classification system. LCML is also used for defining land cover and ecosystem functional units used in the SEEA, and closely linked to the Intergovernmental Panel on Climate Change (IPCC) classification on land cover/land use.</p>\n<p>The international standard for calculating NPP (gC/m&#xB2;/day) from remotely-sensed, multi-temporal surface reflectance data, accounting for the global range of climate and vegetation types, was established in 1999 by the U.S. National Aeronautics and Space Administration (NASA) in anticipation of the launch of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor.<sup><sup><a href=\"#footnote-15\" id=\"footnote-ref-15\">[14]</a></sup></sup> The Land Productivity Dynamics (LPD) methodology and dataset, developed by the Joint Research Centre of the European Commission<sup><sup><a href=\"#footnote-16\" id=\"footnote-ref-16\">[15]</a></sup></sup> and used in the UNCCD pilot programme, employs this international standard to calculate NPP time series trends and change analyses.</p>\n<p>For carbon stocks, IPCC (2006 and 2019) contains the most relevant definitions and standards, especially with regard to reference values applicable for Tier 2 and 3 GHG reporting.<sup><sup><a href=\"#footnote-17\" id=\"footnote-ref-17\">[16]</a></sup></sup> In this regard, the technical soil infrastructure, data transfer and provision of national reporting data is also standards-based.<sup><sup><a href=\"#footnote-18\" id=\"footnote-ref-18\">[17]</a></sup></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-14\">13</sup><p> <a href=\"https://www.iso.org/standard/44342.html\">https://www.iso.org/standard/44342.html</a> <a href=\"#footnote-ref-14\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-15\">14</sup><p> Running et al. 1999. MODIS Daily Photosynthesis (PSN) and Annual Net Primary Production (NPP) Product (MOD17): Algorithm Theoretical Basis Document <a href=\"https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd_mod16.pdf\">https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd_mod16.pdf</a> <a href=\"#footnote-ref-15\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-16\">15</sup><p> Ivits and Cherlet. 2013. Land-productivity dynamics towards integrated assessment of land degradation at global scales. European Commission JRC Technical Report. <a href=\"https://publications.europa.eu/en/publication-detail/-/publication/1e2aceac-b20b-45ab-88d9-b3d187ae6375/language-en/format-PDF/source-49343336\">https://publications.europa.eu/en/publication-detail/-/publication/1e2aceac-b20b-45ab-88d9-b3d187ae6375/language-en/format-PDF/source-49343336</a> <a href=\"#footnote-ref-16\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-17\">16</sup><p> IPCC. 2006. ibid and IPCC. 2019<em>. </em>Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. In: Buendia<em>, </em>E<em>.</em>, Tanabe<em>,</em> K<em>.</em>, Kranjc<em>, </em>A<em>.</em>, Baasansuren<em>, </em>J<em>.</em>, Fukuda<em>, </em>M<em>.</em>, Ngarize<em>, </em>S<em>.</em>, Osako<em>, </em>A<em>.</em>, Pyrozhenko<em>, </em>Y<em>.</em>, Shermanau<em>, </em>P<em>.</em>, Federici<em>, </em>S<em>. </em>(eds). Intergovernmental Panel on Climate Change, Geneva, Switzerland. <a href=\"#footnote-ref-17\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-18\">17</sup><p> <a href=\"https://www.iso.org/standard/44595.html\">https://www.iso.org/standard/44595.html</a> <a href=\"#footnote-ref-18\">&#x2191;</a></p></div></div>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Description:</strong></p>\n<p>National data on the three sub-indicators is and can be collected through existing sources (e.g., databases, maps, reports), including participatory inventories on land management systems as well as remote sensing data collected at the national level. Datasets that complement and support existing national indicators, data and information are likely to come from multiple sources, including statistics and estimated data for administrative or national boundaries, ground measurements, Earth observation and geospatial information. A comprehensive inventory of all data sources available for each sub-indicator is contained in the Good Practice Guidance for SDG Indicator 15.3.1.<sup><a href=\"#footnote-19\" id=\"footnote-ref-19\">[18]</a></sup> </p>\n<p>The most accessible and widely used regional and global data sources for each of the sub-indicators are briefly described here.</p>\n<p><strong>1) Land cover and land cover change data</strong> are available in the: </p>\n<p><strong>(1) ESA-CCI-LC,</strong><sup><sup><a href=\"#footnote-20\" id=\"footnote-ref-20\">[19]</a></sup></sup> containing annual land cover area data at 300 m spatial resolution for the period from 1992 to present, produced by the Catholic University of Louvain Geomatics as part of the Climate Change Initiative of the European Space Agency (ESA); or </p>\n<p><strong>(2) SEEA-MODIS,</strong><sup><sup><a href=\"#footnote-21\" id=\"footnote-ref-21\">[20]</a></sup></sup> containing annual land cover area data at 500 m spatial resolution for the period 2001-2019, derived from the International Geosphere-Biosphere Programme (IGBP) type of the MODIS land cover dataset (MCD12Q1).</p>\n<p><strong>2) Land productivity data</strong> represented as vegetation indices (i.e., direct observations), and their derived products are considered the most independent and robust option for the analyses of land productivity, offering the longest consolidated time series and a broad range of operational data sets at different spatial scales. The most accurate and reliable datasets are available in the: </p>\n<p><strong>(1) MODIS data products,</strong><sup><sup><a href=\"#footnote-22\" id=\"footnote-ref-22\">[21]</a></sup></sup><strong> </strong>averaged at 250 m pixel resolution, integrated over each calendar year since 2000; and </p>\n<p><strong>(2) Copernicus Global Land Service products,</strong><sup><sup><a href=\"#footnote-23\" id=\"footnote-ref-23\">[22]</a></sup></sup> averaged at 1 km pixel resolution and integrated over each calendar year since 1998.</p>\n<p><strong>3) Soil organic carbon stock data</strong> are available in the: </p>\n<p><strong>(1) Harmonized World Soil Database (HWSD), Version 1.2,</strong><sup><sup><a href=\"#footnote-24\" id=\"footnote-ref-24\">[23]</a></sup></sup> the latest update being the current de facto standard soil grid with a spatial resolution of about 1 km; </p>\n<p><strong>(2) SoilGrids250m,</strong><sup><sup><a href=\"#footnote-25\" id=\"footnote-ref-25\">[24]</a></sup></sup> a global 3D soil information system at 250m resolution containing spatial predictions for a selection of soil properties (at six standard depths) including SOC stock (t ha<sup>-1</sup>);</p>\n<p><strong>(3) Global SOC Map, Version 1.0,</strong><sup><sup><a href=\"#footnote-26\" id=\"footnote-ref-26\">[25]</a></sup></sup> which consists of national SOC maps, developed as 1 km soil grids, covering a depth of 0-30 cm.</p>\n<p>In the absence of, to enhance, or as a complement to national data sources, good practice suggests that the data and information derived from global and regional data sets should be interpreted and validated by national authorities. The most common validation approach involves the use of national, sub-national or site-based indicators, data and information to assess the accuracy of the sub-indicators derived from these regional and global data sources. This could include a mixed-methods approach which makes use of multiple sources of information or combines quantitative and qualitative data, including the ground truthing of remotely sensed data using Google Earth images, field surveys or a combination of both.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-19\">18</sup><p> <a href=\"https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land\">https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land</a> <a href=\"#footnote-ref-19\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-20\">19</sup><p> <a href=\"https://www.esa-landcover-cci.org/\">https://www.esa-landcover-cci.org/</a> and <a href=\"https://cds.climate.copernicus.eu/cdsapp%23!/dataset/satellite-land-cover?tab=overview\">https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview</a> <a href=\"#footnote-ref-20\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-21\">20</sup><p> <a href=\"https://modis.gsfc.nasa.gov/data/dataprod/mod12.php\">https://modis.gsfc.nasa.gov/data/dataprod/mod12.php</a> <a href=\"#footnote-ref-21\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-22\">21</sup><p> <a href=\"https://modis.gsfc.nasa.gov/data/dataprod/mod13.php\">https://modis.gsfc.nasa.gov/data/dataprod/mod13.php</a> <a href=\"#footnote-ref-22\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-23\">22</sup><p> <a href=\"https://land.copernicus.eu/global/products/ndvi\">https://land.copernicus.eu/global/products/ndvi</a> <a href=\"#footnote-ref-23\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-24\">23</sup><p> <a href=\"http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/\">http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/</a> <a href=\"#footnote-ref-24\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-25\">24</sup><p> <a href=\"https://soilgrids.org/\">https://soilgrids.org/</a> <a href=\"#footnote-ref-25\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-26\">25</sup><p> <a href=\"http://54.229.242.119/GSOCmap/\">http://54.229.242.119/GSOCmap/</a> <a href=\"#footnote-ref-26\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p>Data on the indicator and sub-indicators will be provided by national authorities (&#x201C;main reporting entity&#x201D;) to the UNCCD in their national reports following a standard format every four years beginning in 2018 or through other national data platforms and mechanisms endorsed by the UN Statistical Commission. This will include the original data and reference sources, and descriptions of how these have been used to derive the indicator and sub-indicators. Eligible (i.e. developing) countries will receive financial and technical assistance in preparing their national reports from the UNCCD and its partners.</p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>The data collection process for UNCCD reporting has begun with the first reporting period scheduled for 2018 and subsequent reporting every four years.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data from the 2018 reporting period will be released by February 2019 and every four years thereafter in national, sub-regional, regional and global formats. </p>", "DATA_SOURCE__GLOBAL"=>"<p>The ministries or agencies (&#x201C;main reporting entity&#x201D;) that host the UNCCD National Focal Points, in conjunction with National Statistical Offices and specialized agencies, will prepare UNCCD national reports that include indicator 15.3.1 and the sub-indicators. Otherwise, national data will be procured through national data platforms and mechanisms endorsed by the UN Statistical Commission.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Convention to Combat Desertification (UNCCD) </p>", "INST_MANDATE__GLOBAL"=>"<p>The 13<sup>th</sup> meeting of the Conference of the Parties gave the UNCCD secretariat the mandate to continue working with the Interagency and Expert Group on Sustainable Development Goal Indicators in its role as the custodian agency to finalize the methodology and data management protocols for Sustainable Development Goal indicator 15.3.1 and begin coordination related to national, regional and global reporting according to the protocols established within the Sustainable Development Goal indicator framework.<sup><a href=\"#footnote-27\" id=\"footnote-ref-27\">[26]</a></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-27\">26</sup><p> <a href=\"https://www.unccd.int/sites/default/files/sessions/documents/2019-08/9COP13_0.pdf\">https://www.unccd.int/sites/default/files/sessions/documents/2019-08/9COP13_0.pdf</a> <a href=\"#footnote-ref-27\">&#x2191;</a></p></div></div>", "RATIONALE__GLOBAL"=>"<p>In the last decade, there have been a number of global/regional targets and initiatives to halt and reverse land degradation and restore degraded land. Starting in 2010, these include the Aichi Biodiversity Targets, one of which aims to restore at least 15% of degraded ecosystems; the Bonn Challenge and its regional initiatives to restore more than 150 million hectares; and most recently the Sustainable Development Goals (SDGs), in particular SDG target 15.3. </p>\n<p>For each of the sub-indicators, countries can access a wide range of data sources, including Earth observation and geospatial information, while at the same time ensuring national ownership.<sup><sup><a href=\"#footnote-28\" id=\"footnote-ref-28\">[27]</a></sup></sup> The use of the existing national reporting templates of the UNCCD,<sup><sup><a href=\"#footnote-29\" id=\"footnote-ref-29\">[28]</a></sup></sup> which include the indicator and sub-indicators, provides a practical and harmonized approach to reporting on this indicator beginning in 2018 and every four years thereafter.<sup><sup><a href=\"#footnote-30\" id=\"footnote-ref-30\">[29]</a></sup></sup> The quantitative assessments and corresponding mapping at the national level, as required by this indicator, would help countries to set policy and planning priorities among diverse land resource areas, in particular:</p>\n<ul>\n  <li>to identify hotspots and plan actions of redress, including through the conservation, rehabilitation, restoration and sustainable management of land resources; and</li>\n  <li>to address emerging pressures to help avoid future land degradation.</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-28\">27</sup><p> United Nations General Assembly. 2015. Transforming our world: the 2030 Agenda for Sustainable Development. Resolution adopted by the General Assembly on 25 September 2015 (A/RES/70/1). <a href=\"#footnote-ref-28\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-29\">28</sup><p> <a href=\"https://www.unccd.int/conventionreporting-process-and-prais/prais-4-reporting-platform\">https://www.unccd.int/conventionreporting-process-and-prais/prais-4-reporting-platform</a> <a href=\"#footnote-ref-29\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-30\">29</sup><p> <a href=\"https://www.unccd.int/sites/default/files/sessions/documents/2019-08/15COP13_0.pdf\">https://www.unccd.int/sites/default/files/sessions/documents/2019-08/15COP13_0.pdf</a> <a href=\"#footnote-ref-30\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>SDG indicator 15.3.1 is a binary -- degraded/not degraded -- quantification based on the analysis of available data that is validated and reported by national authorities. Reporting on the sub-indicators should be based primarily, and to the largest extent possible, on comparable and standardized national official data sources. To a certain extent, national data on the three sub-indicators is and can be collected through existing sources (e.g., databases, maps, reports), including participatory inventories on land management systems as well as remote sensing data collected at the national level. </p>\n<p>Regional and global datasets derived from Earth observation and geospatial information can play an important role in the absence of, to complement, or to enhance national official data sources. These datasets can help validate and improve national statistics for greater accuracy by ensuring that the data are spatially-explicit. Recognizing that the sub-indicators cannot fully capture the complexity of land degradation (i.e., its degree and drivers), countries are strongly encouraged to use other relevant national or sub-national indicators, data and information to strengthen their interpretation. </p>\n<p>As regards slow changing variables, such as soil organic carbon stocks, reporting every four years may not be practical or offer reliable change detection for many countries. Nevertheless, this sub-indicator captures important data and information that will become more available in the future via improved measurements at the national level, such as those being facilitated by the FAO&#x2019;s Global Soil Partnership and others.</p>\n<p>While access to remote sensing imagery has improved dramatically in recent years, there is still a need for essential historical time series that is currently only available at coarse to medium resolution. The expectation is that the availability of high-resolution, locally-calibrated datasets will increase rapidly in the near future. National capacities to process, interpret and validate geospatial data still need to be enhanced in many countries; good practice guidance for the monitoring and the reporting of the sub-indicators in other processes will assist in this regard. </p>", "DATA_COMP__GLOBAL"=>"<p>By analysing changes in the sub-indicators in the context of local assessments of climate, soil, land use and any other factors influencing land conditions, national authorities can determine which land units are to be classified as degraded, sum the total, and report on the indicator. A conceptual framework, endorsed by the UNCCD&#x2019;s governing body in September 2017,<sup><sup><a href=\"#footnote-31\" id=\"footnote-ref-31\">[30]</a></sup></sup> underpins a universal methodology for deriving the indicator. The methodology helps countries to select the most appropriate datasets for the sub-indicators and determine national methods for estimating the indicator. In order to assist countries with monitoring and reporting, Good Practice Guidance for SDG Indicator 15.3.1<sup><sup><a href=\"#footnote-32\" id=\"footnote-ref-32\">[31]</a></sup></sup> has been developed by the UNCCD and its partners. </p>\n<p>The indicator is derived from a binary classification of land condition (i.e., degraded or not degraded) based primarily, and to the largest extent possible, on comparable and standardized national official data sources. However, due to the nature of the indicator, Earth observation and geospatial information from regional and global data sources can play an important role in its derivation, subject to validation by national authorities. </p>\n<p>Quantifying the indicator is based on the evaluation of changes in the sub-indicators in order to determine the extent of land that is degraded over total land area. The sub-indicators are few in number, complementary and non-additive components of land-based natural capital and sensitive to different degradation factors. As a result, the 1OAO principle is applied in the method of computation where changes in the sub-indicators are depicted as (i) positive or improving, (ii) negative or declining, or (iii) stable or unchanging. If one of the sub-indicators is negative (or stable when degraded in the baseline or previous monitoring year) for a particular land unit, then normally it would be considered as degraded subject to validation by national authorities. </p>\n<p>The baseline year for the indicator is 2015 and its value (t<sub>0</sub>) is derived from an initial quantification and assessment of time series data for the sub-indicators for each land unit during the period 2000-2015. Subsequent values for the indicator during each monitoring period (t<sub>1-n</sub>) are derived from the quantification and assessment of changes in the sub-indicators as to whether there has been positive, negative or no change for each land unit relative to the baseline value. Although the indicator will be reported as a single figure quantifying the area of land that is degraded as a proportion of land area, it can be spatially disaggregated by land cover class or other policy&#x2010;relevant units.</p>\n<p>As detailed in the Good Practice Guidance for SDG indicator 15.3.1, deriving the indicator for the baseline and subsequent monitoring years is done by summing all those areas where any changes in the sub-indicators are considered negative (or stable when degraded in the baseline or previous monitoring year) by national authorities. This involves the:</p>\n<p>(1) assessment and evaluation of <strong>land cover and land cover changes</strong>;<br>(2) analysis of <strong>land productivity</strong> status and trends based on net primary production; and <br>(3) determination of <strong>carbon stock</strong> values and changes, with an initial assessment of soil organic carbon as the proxy.</p>\n<p>It is good practice to assess change for interim and final reporting years in relation to the baseline year for each sub-indicator and then the indicator. This facilitates the spatial aggregation of the results from the sub-indicators for each land unit to determine the proportion of land that is degraded for the baseline and each monitoring year. Furthermore, it ensures that land classified as degraded will retain that status unless it has improved relative to the baseline or previous monitoring year. </p>\n<p>Land degradation (or improvement) as compared to the baseline may be identified with reference to parameters describing the slope and confidence limits around the trends in the sub-indicators, or to the level or distribution of conditions in space and/or time as shown during the baseline period. The evaluation of changes in the sub-indicators may be determined using statistical significance tests or by interpretation of results in the context of local indicators, data and information. The method of computation for SDG indicator 15.3.1 is illustrated in Figure 2.</p>\n<p><strong>Figure 2: Steps to derive the indicator from the sub-indicators, where ND is not degraded and D is degraded.</strong></p>\n<p><strong><img 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\" alt=\"Timeline Description automatically generated\"><br></strong><br></p>\n<p>The area degraded in the monitoring period <em>t<sub>n</sub></em> within land cover class <em>i</em> is estimated by summing all the area units within the land cover class determined to be degraded plus all area units that had previously been defined as degraded and that remain degraded, minus area units that have improved from a degraded to a non-degraded state:</p>\n<p> <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>D</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mo>,</mo>\n        <mi mathvariant=\"normal\">n</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n        <mo>,</mo>\n        <mi>n</mi>\n      </mrow>\n    </msub>\n    <mo>+</mo>\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mo>,</mo>\n        <mi>n</mi>\n      </mrow>\n    </msub>\n    <mo>-</mo>\n    <mi>&amp;nbsp;</mi>\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>i</mi>\n        <mi>m</mi>\n        <mi>p</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mn>1</mn>\n        <mo>,</mo>\n        <mi>n</mi>\n      </mrow>\n    </msub>\n  </math> (1)</p>\n<p>Where: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>D</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mo>,</mo>\n        <mi>n</mi>\n      </mrow>\n    </msub>\n  </math> is the total area degraded in the land cover class <em>i</em> in the year of monitoring <em>n</em> (ha);</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mo>,</mo>\n        <mi>n</mi>\n      </mrow>\n    </msub>\n  </math> is the area defined as degraded in the current monitoring year following 1OAO assessment of the sub-indicators (ha);</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mo>,</mo>\n        <mi>n</mi>\n      </mrow>\n    </msub>\n  </math> is the area previously defined as degraded which remains degraded in the monitoring year following the 1OAO assessment of the sub-indicators (ha);</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>i</mi>\n        <mi>m</mi>\n        <mi>p</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mo>,</mo>\n        <mi>n</mi>\n      </mrow>\n    </msub>\n  </math>is the area that has improved from a degraded to a non-degraded state following the 1OAO assessment of the sub-indicators (ha).</p>\n<p>The proportion of land cover type <em>i</em> that is degraded is then given by:</p>\n<p> <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mo>,</mo>\n        <mi>n</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mi>D</mi>\n                <mi>e</mi>\n                <mi>g</mi>\n                <mi>r</mi>\n                <mi>a</mi>\n                <mi>d</mi>\n                <mi>e</mi>\n                <mi>d</mi>\n              </mrow>\n            </mfenced>\n          </mrow>\n          <mrow>\n            <mi>i</mi>\n            <mo>,</mo>\n            <mi mathvariant=\"normal\">n</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mi>t</mi>\n                <mi>o</mi>\n                <mi>t</mi>\n                <mi>a</mi>\n                <mi>l</mi>\n              </mrow>\n            </mfenced>\n          </mrow>\n          <mrow>\n            <mi>i</mi>\n            <mo>,</mo>\n            <mn>0</mn>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math> (2)</p>\n<p>Where</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mo>,</mo>\n        <mi mathvariant=\"normal\">n</mi>\n      </mrow>\n    </msub>\n    <mi>&amp;nbsp;</mi>\n  </math>is the proportion of degraded land in that land cover type <em>i</em> in the monitoring period <em>n</em>;<em> </em></p>\n<p> <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>D</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mo>,</mo>\n        <mi mathvariant=\"normal\">n</mi>\n      </mrow>\n    </msub>\n  </math> is the total area degraded in the land cover type <em>i</em> in the year of monitoring <em>n </em>(ha);</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>t</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mo>,</mo>\n        <mn>0</mn>\n      </mrow>\n    </msub>\n  </math> is the total area of land cover type <em>i</em> within the national boundary (ha). </p>\n<p>The total area of land that is degraded over total land area is the accumulation across all land cover classes within the monitoring period <em>n </em>is given by:</p>\n<p> <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>D</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">n</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mrow>\n      <munderover>\n        <mo stretchy=\"false\">&#x2211;</mo>\n        <mrow>\n          <mi>i</mi>\n        </mrow>\n        <mrow></mrow>\n      </munderover>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n            <mo>(</mo>\n            <mi>D</mi>\n            <mi>e</mi>\n            <mi>g</mi>\n            <mi>r</mi>\n            <mi>a</mi>\n            <mi>d</mi>\n            <mi>e</mi>\n            <mi>d</mi>\n            <mo>)</mo>\n          </mrow>\n          <mrow>\n            <mi>i</mi>\n            <mo>,</mo>\n            <mi mathvariant=\"normal\">n</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mrow>\n  </math> (3)</p>\n<p>Where</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>D</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">n</mi>\n      </mrow>\n    </msub>\n  </math> is the total area degraded in the year of monitoring <em>n </em>(ha);</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>D</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>i</mi>\n        <mo>,</mo>\n        <mi mathvariant=\"normal\">n</mi>\n      </mrow>\n    </msub>\n  </math> is the total area degraded in the land cover type <em>i</em> in the year of monitoring <em>n</em>.</p>\n<p>The total proportion of land that is degraded over total land area is given by:</p>\n<p> <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">n</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>A</mi>\n            <mo>(</mo>\n            <mi>D</mi>\n            <mi>e</mi>\n            <mi>g</mi>\n            <mi>r</mi>\n            <mi>a</mi>\n            <mi>d</mi>\n            <mi>e</mi>\n            <mi>d</mi>\n            <mo>)</mo>\n          </mrow>\n          <mrow>\n            <mi mathvariant=\"normal\">n</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">A</mi>\n        <mo>(</mo>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n  </math> (4)</p>\n<p>Where</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">n</mi>\n      </mrow>\n    </msub>\n  </math> is the proportion of land that is degraded over total land area;</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>A</mi>\n        <mo>(</mo>\n        <mi>D</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">n</mi>\n      </mrow>\n    </msub>\n  </math> is the total area degraded in the year of monitoring n (ha);</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"normal\">A</mi>\n    <mo>(</mo>\n    <mi mathvariant=\"normal\">T</mi>\n    <mi mathvariant=\"normal\">o</mi>\n    <mi mathvariant=\"normal\">t</mi>\n    <mi mathvariant=\"normal\">a</mi>\n    <mi mathvariant=\"normal\">l</mi>\n    <mo>)</mo>\n  </math> is the total area within the national boundary (ha).</p>\n<p><br>The proportion is converted to a percentage value by multiplying by 100. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-31\">30</sup><p> <a href=\"https://www.unccd.int/sites/default/files/sessions/documents/2019-08/18COP13_0.pdf\">https://www.unccd.int/sites/default/files/sessions/documents/2019-08/18COP13_0.pdf</a> <a href=\"#footnote-ref-31\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-32\">31</sup><p> <a href=\"https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land\">https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land</a> <a href=\"#footnote-ref-32\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>Once received, national reports will undergo a review process by the UNCCD and its partners to ensure the correct use of definitions and methodology as well as internal consistency. A comparison can be made with past assessments and other existing data sources. Regular contacts between the main reporting entity and UNCCD secretariat via a help desk system, and through regional, sub-regional, and national workshops, will form part of this review process, enable data adjustments when needed, and contribute to building national capacities. The data will then be aggregated at sub-regional, regional and global levels by the UNCCD and its partners.</p>\n<p>By leveraging an already established reporting mechanism, this data flows and validation mechanism increases the efficiency with which UNCCD can gather data from countries. In addition, since the definitions and methodologies for reporting on SDG Indicator 15.3.1 are aligned with those adopted by the UNCCD, the reporting burden on countries and the need for harmonization/validation of the indicator values is reduced. Since national data and information to report on SDG Indicator 15.3.1 generally comes from outside the National Statistical Offices (NSOs), prior to submitting the data to the UN Statistical Division (UNSD), the UNCCD consults with the NSOs and requests them to review and validate the data submitted by their country as part of their national report. For those countries that have not submitted a national report, the UNCCD provides the NSOs with national estimates derived from global data sources for review and validation. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>For countries where no data or information is available, the UNCCD and its partners can provide default estimates from regional or global data sources that would then be validated by national authorities.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>The land area of countries with missing values (i.e., there is no default data) would be excluded from regional and global aggregation.</p>", "REG_AGG__GLOBAL"=>"<p>The indicator can be aggregated to the regional and global level by summing the spatial extent of land that is degraded over total land area for all countries reporting in a specific region or globally.</p>", "DOC_METHOD__GLOBAL"=>"<p>All data are provided to UNCCD by countries in the form of a national report following a standard reporting template,<sup><sup><a href=\"#footnote-33\" id=\"footnote-ref-33\">[32]</a></sup></sup> which includes the quantitative data for the indicator and sub-indicators as well as a qualitative assessment of indicator trends. The reporting template ensures that countries provide the full reference for original data sources as well as national definitions and methodology.</p>\n<p>Detailed guidance on how to prepare the country reports and how to compute the indicator and sub-indicators is contained in the UNCCD reporting manual<sup><a href=\"#footnote-34\" id=\"footnote-ref-34\">[33]</a></sup> and in the Good Practice Guidance for SDG indicator 15.3.1,<sup><sup><a href=\"#footnote-35\" id=\"footnote-ref-35\">[34]</a></sup></sup> respectively. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-33\">32</sup><p> <a href=\"https://www.unccd.int/conventionreporting-process-and-prais/prais-4-reporting-platform\">https://www.unccd.int/conventionreporting-process-and-prais/prais-4-reporting-platform</a> <a href=\"#footnote-ref-33\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-34\">33</sup><p> <a href=\"https://prais4-reporting-manual.readthedocs.io/en/latest/index.html\">https://prais4-reporting-manual.readthedocs.io/en/latest/index.html</a> <a href=\"#footnote-ref-34\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-35\">34</sup><p> <a href=\"https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land\">https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land</a> <a href=\"#footnote-ref-35\">&#x2191;</a></p></div></div>", "QUALITY_MGMNT__GLOBAL"=>"<p>The UNCCD reporting system, the Performance Review and Assessment of the Implementation System (PRAIS),<sup><a href=\"#footnote-36\" id=\"footnote-ref-36\">[35]</a></sup> has a set of validation checks on the reported SDG indicator 15.3.1 and its sub-indicators. Should the checks fail, the user is notified that:</p>\n<ul>\n  <li>The area reported as degraded should not exceed the total land area of the country;</li>\n  <li>The proportion of degraded land is a read-only field that is dynamic with the area of degraded land and the total land area reported by the country, this should stop spurious values being entered by mistake and ensure integrity across the national report;</li>\n  <li>The number of decimal places in the reported percent value is limited to one, to strike a balance between the precision of the reported value and relevance of additional numeric precision.</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-36\">35</sup><p> See <a href=\"https://reporting.unccd.int/\">https://reporting.unccd.int/</a> and <a href=\"https://www.unccd.int/conventionreporting-process-and-prais/prais-4-reporting-platform\">https://www.unccd.int/conventionreporting-process-and-prais/prais-4-reporting-platform</a> for more information <a href=\"#footnote-ref-36\">&#x2191;</a></p></div></div>", "QUALITY_ASSURE__GLOBAL"=>"<p>In addition to the PRAIS built-in quality check functionalities (see 4.1. Quality Management for more information), once received, national reports undergo a review process by the UNCCD and its partners to ensure data integrity, correctness and completeness, the correct use of definitions and methodology as well as internal consistency. </p>\n<p>A help-desk system<sup><sup><a href=\"#footnote-37\" id=\"footnote-ref-37\">[36]</a></sup></sup> has been set up as a single point of contact for countries to get answers to questions and gain assistance on reporting issues.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-37\">36</sup><p> <a href=\"http://support.unccd.int/\">http://support.unccd.int/</a> <a href=\"#footnote-ref-37\">&#x2191;</a></p></div></div>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The UNCCD has developed guidelines for the technical review of national reports, which include information on SDG indicator 15.3.1 and its sub-indicators.<sup><a href=\"#footnote-38\" id=\"footnote-ref-38\">[37]</a></sup> The technical review of each national report is conducted as a desk review. Experts assess the completeness, transparency, consistency, comparability and accuracy in reported data and methods, as well as how well country Parties have adhered to the Good Practice Guidance for SDG Indicator 15.3.1. The technical review of national reports is conducted in PRAIS, leveraging its in-built revision and review system.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-38\">37</sup><p> See <a href=\"https://www.unccd.int/official-documentscric-20-abidjan-cote-divoire-2022/iccdcric209\">https://www.unccd.int/official-documentscric-20-abidjan-cote-divoire-2022/iccdcric209</a> and <a href=\"https://www.unccd.int/official-documentscric-20-abidjan-cote-divoire-2022/iccdcric20inf1\">https://www.unccd.int/official-documentscric-20-abidjan-cote-divoire-2022/iccdcric20inf1</a> <a href=\"#footnote-ref-38\">&#x2191;</a></p></div></div>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are currently available in 123 countries. Additionally, 40 national estimates prepared by the UNCCD in its capacity as custodian agency and based on global data sources have been used for the calculation of regional and global aggregates in 2019. Communication and coordination with national statistical systems, NSO representatives and UNCCD national focal points in a transparent manner will include an assessment of data needs and capacity building for monitoring and reporting on the indicator when necessary.</p>\n<p><strong>Time series:</strong></p>\n<p>Annual since the year 2000.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator can be disaggregated by land cover class or other spatially explicit land unit.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Data reported by the countries themselves will follow a standardized format for UNCCD national reporting<sup><sup><a href=\"#footnote-39\" id=\"footnote-ref-39\">[38]</a></sup></sup> that will include the indicator and sub-indicators as well as their data sources and explanatory notes. Differences between global and national figures may arise due to differences in spatial resolution of datasets, classification approaches (i.e. definition of land cover classes) and/or contextualization with other indicators, data and information. </p>\n<p>The UNCCD reporting format helps to ensure that countries provide references for national data sources as well as associated definitions and terminology. In addition, the reporting format can accommodate more detailed analysis of the data, including any assumptions made and the methods used for estimating the indicator and sub-indicators.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-39\">38</sup><p> <a href=\"https://www.unccd.int/conventionreporting-process-and-prais/prais-4-reporting-platform\">https://www.unccd.int/conventionreporting-process-and-prais/prais-4-reporting-platform</a> <a href=\"#footnote-ref-39\">&#x2191;</a></p></div></div>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>PRAIS 4 portal, data collection tool for SDG 15.3.1: <a href=\"https://reporting.unccd.int/\">https://reporting.unccd.int/</a> </p>\n<p>Trends.Earth, data calculation tool for SDG 15.3.1: <a href=\"https://trends.earth/docs/en/\">https://trends.earth/docs/en/</a></p>\n<p><strong>References</strong>:</p>\n<p>Di Gregorio, A. 2005. Land cover classification system (LCCS): classification concepts and user manual. Food and Agriculture Organization of the United Nations, Rome.</p>\n<p>European Communities. (2013). Overall Approach to the Classification of Ecological Status and Ecological Potential, Guidance Document No 13. Luxembourg: European Union. <a href=\"https://circabc.europa.eu/sd/a/06480e87-27a6-41e6-b165-0581c2b046ad/Guidance%20No%2013%20-%20Classification%20of%20Ecological%20Status%20(WG%20A).pdf\">https://circabc.europa.eu/sd/a/06480e87-27a6-41e6-b165-0581c2b046ad/Guidance%20No%2013%20-%20Classification%20of%20Ecological%20Status%20(WG%20A).pdf</a></p>\n<p>FAO-GTOS. 2009. Land Cover: Assessment of the status of the development of the standards for the Terrestrial Essential Climate Variables. Global Terrestrial Observing System, Rome.</p>\n<p>IPCC. 2006. IPCC Guidelines for National Greenhouse Gas Inventories: Agriculture, Forestry and other Land Use. Prepared by the National Greenhouse Gas Inventories Programme: Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). IGES, Japan.</p>\n<p>IPCC. 2019<em>. </em>Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. In: Buendia<em>, </em>E<em>.</em>, Tanabe<em>,</em> K<em>.</em>, Kranjc<em>, </em>A<em>.</em>, Baasansuren<em>, </em>J<em>.</em>, Fukuda<em>, </em>M<em>.</em>, Ngarize<em>, </em>S<em>.</em>, Osako<em>, </em>A<em>.</em>, Pyrozhenko<em>, </em>Y<em>.</em>, Shermanau<em>, </em>P<em>.</em>, Federici<em>, </em>S<em>. </em>(eds). Intergovernmental Panel on Climate Change, Geneva, Switzerland.</p>\n<p>Ivits and Cherlet. 2013. Land-productivity dynamics towards integrated assessment of land degradation at global scales. European Commission JRC Technical Report. <a href=\"https://publications.europa.eu/en/publication-detail/-/publication/1e2aceac-b20b-45ab-88d9-b3d187ae6375/language-en/format-PDF/source-49343336\">https://publications.europa.eu/en/publication-detail/-/publication/1e2aceac-b20b-45ab-88d9-b3d187ae6375/language-en/format-PDF/source-49343336</a></p>\n<p>Joint Research Centre of the European Commission. 2017. World Atlas of Desertification, 3<sup>rd</sup> edition. JRC, Ispra. <a href=\"https://wad.jrc.ec.europa.eu/\">https://wad.jrc.ec.europa.eu/</a></p>\n<p>Millennium Ecosystem Assessment. 2005. Ecosystems and human wellbeing: a framework for assessment. Island Press, Washington, DC.</p>\n<p>Orr, B.J., Cowie, A.L., Castillo Sanchez, V.M., Chasek, P., Crossman, N.D., Erlewein, A., Louwagie, G., Maron, M., Metternicht, G.I., Minelli, S., Tengberg, A.E., Walter, S., Welton, S., 2017. Scientific Conceptual Framework for Land Degradation Neutrality. A Report of the Science Policy Interface. United Nations Convention to Combat Desertification (UNCCD), Bonn, Germany. <a href=\"https://www.unccd.int/publications/scientific-conceptual-framework-land-degradation-neutrality-report-science-policy\">https://www.unccd.int/publications/scientific-conceptual-framework-land-degradation-neutrality-report-science-policy</a></p>\n<p>Running et al. 1999. MODIS Daily Photosynthesis (PSN) and Annual Net Primary Production (NPP) Product (MOD17): Algorithm Theoretical Basis Document <a href=\"https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd_mod16.pdf\">https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd_mod16.pdf</a></p>\n<p>Sims, N.C., Newnham, G.J., England, J.R., Guerschman, J., Cox, S.J.D., Roxburgh, S.H., Viscarra Rossel, R.A., Fritz, S. and Wheeler, I. 2021. Good Practice Guidance. SDG Indicator 15.3.1, Proportion of Land That Is Degraded Over Total Land Area. Version 2.0. United Nations Convention to Combat Desertification, Bonn, Germany. <a href=\"https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land\">https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land</a></p>\n<p>Smith, P., Fang, C., Dawson, J. J., &amp; Moncrieff, J. B. 2008. Impact of global warming on soil organic carbon. Advances in agronomy, 97: 1-43.</p>\n<p>United Nations Convention to Combat Desertification. 1994. Convention Text<br><a href=\"http://www2.unccd.int/sites/default/files/relevant-links/2017-01/UNCCD_Convention_ENG_0.pdf\">http://www2.unccd.int/sites/default/files/relevant-links/2017-01/UNCCD_Convention_ENG_0.pdf</a></p>", "indicator_sort_order"=>"15-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.4.1", "slug"=>"15-4-1", "name"=>"Lugares importantes para la biodiversidad de las montañas incluidos en zonas protegidas", "url"=>"/site/es/15-4-1/", "sort"=>"150401", "goal_number"=>"15", "target_number"=>"15.4", "global"=>{"name"=>"Lugares importantes para la biodiversidad de las montañas incluidos en zonas protegidas"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Lugares importantes para la biodiversidad de las montañas incluidos en zonas protegidas", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Lugares importantes para la biodiversidad de las montañas incluidos en zonas protegidas", "indicator_number"=>"15.4.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Lugares importantes para la biodiversidad de las montañas incluidos en zonas protegidas", "definicion"=>"Porcentaje de los lugares importantes para la biodiversidad de las montañas (aquellos que contribuyen significativamente a la persistencia global de la biodiversidad) que está cubierto por áreas protegidas", "formula"=>"\n$$PKBA_{montaña}^{t} = \\frac{APKBA_{montaña}^{t}}{KBA_{montaña}^{t}} \\cdot 100$$\n\ndonde:\n\n$APKBA_{montaña}^{t} =$ superficie de los lugares importantes para la biodiversidad de las montañas cubierta por áreas protegidas en el año $t$\n\n$KBA_{montaña}^{t}  =$ superficie de los lugares importantes para la biodiversidad de las montañas en el año $t$\n", "desagregacion"=>"", "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>"", "justificacion_global"=>"La protección de lugares importantes es vital para frenar la pérdida de biodiversidad \ny garantizar el uso sostenible a largo plazo de los recursos naturales de montaña. \nEl establecimiento de áreas protegidas es un mecanismo clave para lograr este objetivo, \ny este indicador sirve para medir el progreso hacia la conservación, la restauración y \nel uso sostenible de los ecosistemas de montaña y sus servicios, de conformidad con las \nobligaciones contraídas en virtud de los acuerdos internacionales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.4.1&seriesCode=ER_PTD_MTN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProporción promedio de áreas clave para la biodiversidad (KBA) de montaña cubiertas por áreas protegidas (%) ER_PTD_MTN</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-04-01.pdf\">Metadatos 15-4-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Lugares importantes para la biodiversidad de las montañas incluidos en zonas protegidas", "definicion"=>"Percentage of important sites for mountain biodiversity (those that contribute significantly to the overall persistence of biodiversity) that are covered by protected areas", "formula"=>"\n$$PKBA_{mountain}^{t} = \\frac{APKBA_{mountain}^{t}}{KBA_{mountain}^{t}} \\cdot 100$$\n\nwhere:\n\n$APKBA_{mountain}^{t} =$ area of important sites for mountain biodiversity covered by protected areas in year $t$\n\n$KBA_{mountain}^{t}  =$ area of important sites for mountain biodiversity in year $t$\n", "desagregacion"=>nil, "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>nil, "justificacion_global"=>"The safeguard of important sites is vital for stemming the decline in biodiversity and ensuring long term \nand sustainable use of mountain natural resources. The establishment of protected areas is an important \nmechanism for achieving this aim, and this indicator serves as a means of measuring progress toward the \nconservation, restoration and sustainable use of mountain ecosystems and their services, in line with \nobligations under international agreements. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.4.1&seriesCode=ER_PTD_MTN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nAverage proportion of Mountain Key Biodiversity Areas (KBAs) covered by protected areas (%) ER_PTD_MTN</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-04-01.pdf\">Metadata 15-4-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Lugares importantes para la biodiversidad de las montañas incluidos en zonas protegidas", "definicion"=>"Porcentaje de los lugares importantes para la biodiversidad de las montañas (aquellos que contribuyen significativamente a la persistencia global de la biodiversidad) que está cubierto por áreas protegidas", "formula"=>"\n$$PKBA_{mendia}^{t} = \\frac{APKBA_{mendia}^{t}}{KBA_{mendia}^{t}} \\cdot 100$$\n\nnon:\n\n$APKBA_{mendia}^{t} =$ mendien biodibertsitaterako leku garrantzitsuen azalera, eremu babestuek estalia $t$ urtean\n\n$KBA_{mendia}^{t}  =$ mendien biodibertsitaterako leku garrantzitsuen azalera $t$ urtean\n", "desagregacion"=>nil, "periodicidad"=>"Irregular / Aperiódica", "observaciones"=>nil, "justificacion_global"=>"La protección de lugares importantes es vital para frenar la pérdida de biodiversidad \ny garantizar el uso sostenible a largo plazo de los recursos naturales de montaña. \nEl establecimiento de áreas protegidas es un mecanismo clave para lograr este objetivo, \ny este indicador sirve para medir el progreso hacia la conservación, la restauración y \nel uso sostenible de los ecosistemas de montaña y sus servicios, de conformidad con las \nobligaciones contraídas en virtud de los acuerdos internacionales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.4.1&seriesCode=ER_PTD_MTN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nEremu babestuek estalitako mendietako biodibertsitaterako funtsezko eremuen (KBA) batez besteko proportzioa (%) ER_PTD_MTN</a> UNSTATS\n", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-04-01.pdf\">Metadatuak 15-4-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.4: By 2030, ensure the conservation of mountain ecosystems, including their biodiversity, in order to enhance their capacity to provide benefits that are essential for sustainable development</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.4.1: Coverage by protected areas of important sites for mountain biodiversity</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_PTD_MTN - Average proportion of Mountain Key Biodiversity Areas (KBAs) covered by protected areas [15.4.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Other relevant indicators include:</p>\n<p>SDG 14.5.1 Coverage of protected areas in relation to marine areas.</p>\n<p>SDG 15.1.2 Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>BirdLife International (BLI)</p>\n<p>International Union for Conservation of Nature (IUCN)</p>\n<p>UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)</p>\n<p>UN Environment Programme</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>BirdLife International (BLI)</p>\n<p>International Union for Conservation of Nature (IUCN)</p>\n<p>UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator Coverage by protected areas of important sites for mountain biodiversity shows temporal trends in the mean percentage of each important site for mountain biodiversity (i.e., those that contribute significantly to the global persistence of biodiversity) that is covered by designated protected areas and Other Effective Area-based Conservation Measures (OECMs).</p>\n<p><strong>Concepts:</strong></p>\n<p>Protected areas, as defined by the IUCN (IUCN; Dudley 2008), are clearly defined geographical spaces, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) (Mean percentage of each mountain Key Biodiversity Area (KBA) covered by (i.e. overlapping with) protected areas and/or OECMs.)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Protected Areas are defined as described above by IUCN (IUCN; Dudley 2008) and documented in the World Database on Protected Areas (WDPA). (<a href=\"http://www.protectedplanet.net\">www.protectedplanet.net</a>).</p>\n<p>Importantly, a variety of specific management objectives are recognised within this definition, spanning conservation, restoration, and sustainable use:</p>\n<p>- Category Ia: Strict nature reserve</p>\n<p>- Category Ib: Wilderness area</p>\n<p>- Category II: National park</p>\n<p>- Category III: Natural monument or feature</p>\n<p>- Category IV: Habitat/species management area</p>\n<p>- Category V: Protected landscape/seascape</p>\n<p>- Category VI: Protected area with sustainable use of natural resources</p>\n<p>The status &quot;designated&quot; is attributed to a protected area when the corresponding authority, according to national legislation or common practice (e.g., by means of an executive decree or the like), officially endorses a document of designation. The designation must be made for the purpose of biodiversity conservation, not de facto protection arising because of some other activity (e.g., military).</p>\n<p>Data on protected areas are managed in the WDPA (www.protectedplanet.net) by UNEP-WCMC.</p>\n<p>OECMs are defined as described above by the Convention on Biological Diversity (CBD 2018) and documented in the World Database on Other Effective Area-based Conservation Measures (WDOECM) (<a href=\"http://www.protectedplanet.net/en/thematic-areas/oecms\">www.protectedplanet.net/en/thematic-areas/oecms</a>).</p>\n<p>OECMs are defined by the Convention on Biological Diversity (CBD) as &#x201C;A geographically defined area other than a Protected Area, which is governed and managed in ways that achieve positive and sustained long-term outcomes for the in-situ conservation of biodiversity, with associated ecosystem functions and services and where applicable, cultural, spiritual, socio&#x2013;economic, and other locally relevant values&#x201D; (CBD, 2018). Data on OECMs are managed in the WDOECM (<a href=\"http://www.protectedplanet.net/en/thematic-areas/oecms\">www.protectedplanet.net/en/thematic-areas/oecms</a>) by UNEP-WCMC.</p>\n<p>Key Biodiversity Areas (KBAs) are defined as described below by IUCN (2016) and documented in the World Database of KBAs (WDKBA) (www.keybiodiversityareas.org/kba-data).</p>\n<p>Sites contributing significantly to the global persistence of biodiversity are identified following globally criteria set out in A Global Standard for the Identification of KBAs (IUCN 2016) applied at national levels. KBAs encompass (a) Important Bird &amp; Biodiversity Areas, that is, sites contributing significantly to the global persistence of biodiversity, identified using data on birds, of which more than13,000 sites in total have been identified from all of the world&#x2019;s countries (BirdLife International 2014, Donald et al. 2018); (b) Alliance for Zero Extinction sites (Ricketts et al. 2005), that is, sites holding effectively the entire population of at least one species assessed as Critically Endangered or Endangered on the IUCN Red List of Threatened Species, of which 853 sites have been identified for 1,483 species of mammals, birds, amphibians, reptiles, freshwater crustaceans, reef-building corals, conifers, cycads and other taxa; (c) KBAs identified under an earlier version of the KBA criteria (Langhammer et al. 2007), including those identified in Ecosystem Hotspot Profiles developed with support of the Critical Ecosystem Partnership Fund. These three subsets are being reassessed using the Global Standard, which unifies these approaches along with other mechanisms for identification of important sites for other species and ecosystems (IUCN 2016).</p>\n<p>Data on KBAs are managed in the WDKBA (www.keybiodiversityareas.org/kba-data) by BirdLife International on behalf of the KBA Partnership.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Protected area data are compiled by ministries of environment and other ministries responsible for the designation and maintenance of protected areas. Protected Areas data for sites designated under the Ramsar Convention and the UNESCO World Heritage Convention are collected through the relevant convention international secretariats. Protected area data are aggregated globally into the WDPA by UNEP-WCMC, according to the mandate for production of the United Nations List of Protected Areas (Deguignet et al. 2014). They are disseminated through <a href=\"http://www.protectedplanet.net/\">Protected Planet</a>, which is jointly managed by UNEP-WCMC and IUCN and its World Commission on Protected Areas (UNEP-WCMC 2016).</p>\n<p>Other Effective Area-based Conservation Measures (OECMs) are collated in the WDOECM. This database can be regarded as a sister database to the WDPA as it is also hosted on Protected Planet. Furthermore, the databases share many of the same fields and have an almost identical workflow; differing only in what they list. OECMs are a quickly evolving area of work, as such for the latest information on OECMs and the WDOECM please contact UNEP-WCMC. </p>\n<p>KBAs are identified at national scales through multi-stakeholder processes, following standard criteria and thresholds. KBAs data are aggregated into the <a href=\"http://www.keybiodiversityareas.org/\">World Database on </a></p>\n<p><a href=\"http://www.keybiodiversityareas.org/\">KBAs</a>, managed by BirdLife International. </p>", "COLL_METHOD__GLOBAL"=>"<p>See information under other sections, and detailed information on the process by which KBAs are identified at <a href=\"http://www.keybiodiversityareas.org/working-with-kbas/proposing-updating\">www.keybiodiversityareas.org/working-with-kbas/proposing-updating</a>. Guidance on Proposing, Reviewing, Nominating and Confirming KBAs is available in KBA Secretariat (2019) at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>.</p>\n<p>The KBA identification process is highly inclusive and consultative: anyone with data on the biodiversity importance of a site may propose it as a KBA if it meets the <a href=\"https://portals.iucn.org/library/sites/library/files/documents/2016-048.pdf\">KBA criteria</a>, and consultation with stakeholders at the national level (both non-governmental and governmental organisations) is required during the proposal process. Any site proposal must undergo independent review. This is followed by the official site nomination with full documentation meeting the Documentation Standards for KBAs. Sites confirmed by the KBA Secretariat to qualify as KBAs are then published on the KBA Website.</p>\n<p>Submission of proposals for KBAs to the WDKBA follows a systematic review process to ensure that the KBA criteria have been applied correctly and that the sites can be recognised as important for the global persistence of biodiversity. Regional Focal Points have been appointed to help KBA proposers develop proposals and then ensure they are reviewed independently. Guidance on Proposing, Reviewing, Nominating and Confirming sites has been published to help guide proposers through the development of proposals and the review process, highlighting where they can obtain help in making a proposal. </p>", "FREQ_COLL__GLOBAL"=>"<p>UNEP-WCMC produces the UN List of Protected Areas every 5&#x2013;10 years, based on information provided by national ministries/agencies. In the intervening period between compilations of UN Lists, UNEP-WCMC works closely with national ministries/agencies and NGOs responsible for the designation and maintenance of protected areas, continually updating the WDPA as new data become available. The WDOECM is also updated on an ongoing basis. The WDKBA is also updated on an ongoing basis with updates currently released twice a year, as new national data are submitted.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The indicator of protected area coverage of important sites for biodiversity is updated each November-December using the latest versions of the datasets on protected areas, OECMs and KBAs.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Protected area data are compiled by ministries of environment and other ministries responsible for the designation and maintenance of protected areas. KBAs are identified at national scales through multi-stakeholder processes, following established processes and standard criteria and thresholds (see above for details).</p>", "COMPILING_ORG__GLOBAL"=>"<p>BirdLife International, IUCN, UNEP-WCMC</p>\n<p>Protected area data are aggregated globally into the WDPA by UNEP-WCMC, according to the mandate for production of the United Nations List of Protected Areas (Deguignet et al. 2014). They are disseminated through Protected Planet, which is jointly managed by UNEP-WCMC and IUCN and its World Commission on Protected Areas (UNEP-WCMC 2016). KBAs data are aggregated into the WDKBA, managed by BirdLife International (2019). </p>", "INST_MANDATE__GLOBAL"=>"<p>Protected area data and OECM data are aggregated globally into the WDPA and WDOECM by the UNEP-WCMC, according to the mandate for production of the United Nations List of Protected Areas (Deguignet et al. 2014). </p>\n<p>BirdLife International is mandated by the <a href=\"http://www.keybiodiversityareas.org/assets/dfbb558651f335617813f6c0c42f9e50\">KBAs Partnership Agreement</a> to manage data on KBAs in the WDKBAs on behalf of the KBAs Partnership.</p>\n<p>BirdLife International, IUCN and UNEP-WCMC collaborate to produce the indicator of coverage of KBAs by Protected Areas and OECMs.</p>", "RATIONALE__GLOBAL"=>"<p>The safeguard of important sites is vital for stemming the decline in biodiversity and ensuring long term and sustainable use of mountain natural resources. The establishment of protected areas is an important mechanism for achieving this aim, and this indicator serves as a means of measuring progress toward the conservation, restoration and sustainable use of mountain ecosystems and their services, in line with obligations under international agreements. Importantly, while it can be disaggregated to report on any given single ecosystem of interest, it is not restricted to any single ecosystem type.</p>\n<p>Levels of access to protected areas vary among the protected area management categories. Some areas, such as scientific reserves, are maintained in their natural state and closed to any other use. Others are used for recreation or tourism, or even open for the sustainable extraction of natural resources. In addition to protecting biodiversity, protected areas have high social and economic value: supporting local livelihoods; maintaining fisheries; harbouring an untold wealth of genetic resources; supporting thriving recreation and tourism industries; providing for science, research and education; and forming a basis for cultural and other non-material values.</p>\n<p>This indicator adds meaningful information to, complements and builds from traditionally reported simple statistics of mountain area covered by protected areas, computed by dividing the total protected area within a country by the total territorial area of the country and multiplying by 100 (e.g., Chape et al. </p>\n<p>2005). Such percentage area coverage statistics do not recognise the extreme variation of biodiversity importance over space (Rodrigues et al. 2004), and so risk generating perverse outcomes through the protection of areas which are large at the expense of those which require protection.</p>\n<p>The indicator was used to track progress towards the 2011&#x2013;2020 Strategic Plan for Biodiversity (CBD 2014, Tittensor et al. 2014, CBD 2020a), and was used as an indicator towards the Convention on Biological Diversity&#x2019;s 2010 Target (Butchart et al. 2010). It has been proposed as an indicator for monitoring progress towards the post-2020 Global Biodiversity Framework (CBD 2020b).</p>", "REC_USE_LIM__GLOBAL"=>"<p>Quality control criteria are applied to ensure consistency and comparability of the data in the WDPA. New data are validated at UNEP-WCMC through a number of tools and translated into the standard data structure of the WDPA. Discrepancies between the data in the WDPA and new data are minimised by provision of a manual (UNEP-WCMC 2019) and resolved in communication with data providers. Similar processes apply for the incorporation of data into the WDKBA (BirdLife International 2019).</p>\n<p>The indicator does not measure the effectiveness of protected areas in reducing biodiversity loss, which ultimately depends on a range of management and enforcement factors not covered by the indicator. A number of initiatives are underway to address this limitation. Most notably, numerous mechanisms have been developed for assessment of protected area management, which can be synthesised into an indicator (Leverington et al. 2010). This is used by the Biodiversity Indicators Partnership as a complementary indicator of progress towards Aichi Biodiversity Target 11 </p>\n<p>(http://www.bipindicators.net/pamanagement). However, there may be little relationship between these measures and protected area outcomes (Nolte &amp; Agrawal 2013). More recently, approaches to &#x201C;green listing&#x201D; have started to be developed, to incorporate both management effectiveness and the outcomes of protected areas, and these are likely to become progressively important as they are tested and applied more broadly.</p>\n<p>Data and knowledge gaps can arise due to difficulties in determining whether a site conforms to the IUCN definition of a protected area or the CBD definition of an OECM. However, given that both are incorporated into the indicator, misclassifications (as one or the other) do not impact the calculated indicator value.</p>\n<p>Regarding important sites, the biggest limitation is that site identification to date has focused mainly on specific subsets of biodiversity, for example birds (for Important Bird and Biodiversity Areas) and highly threatened species (for Alliance for Zero Extinction sites). While Important Bird and Biodiversity Areas have been documented to be good surrogates for biodiversity more generally (Brooks et al. 2001, Pain et al. 2005), the application of the unified standard for identification of KBA sites (IUCN 2016) across different levels of biodiversity (genes, species, ecosystems) and different taxonomic groups remains a high priority, building from efforts to date (Eken et al. 2004, Knight et al. 2007, Langhammer et al. 2007, Foster et al. 2012). Birds now comprise less than 50% of the species for which KBAs have been identified, and as KBA identification for other taxa and elements of biodiversity proceeds, such bias will become a less important consideration in the future.</p>\n<p>KBA identification has been validated for a number of countries and regions where comprehensive biodiversity data allow formal calculation of the site importance (or &#x201C;irreplaceability&#x201D;) using systematic conservation planning techniques (Di Marco et al. 2016, Montesino Pouzols et al. 2014).</p>\n<p>Future developments of the indicator will include: a) expansion of the taxonomic coverage of mountain KBAs through application of the KBA standard (IUCN 2016) to a wide variety of mountain vertebrates, invertebrates, plants and ecosystem type; b) improvements in the data on protected areas by continuing to increase the proportion of sites with documented dates of designation and with digitised boundary polygons (rather than coordinates); and c) increased documentation of Other Effective Area-based Conservation Measures in the World Database of OECMs.</p>", "DATA_COMP__GLOBAL"=>"<p>This indicator is calculated from data derived from a spatial overlap between digital polygons for protected areas from the World Database on Protected Areas (UNEP-WCMC &amp; IUCN 2020), digital polygons for Other Effective Area-based Conservation Measures from the World Database on OECMs and digital polygons for mountain Key Biodiversity Areas (from the World Database of Key Biodiversity Areas, including Important Bird and Biodiversity Areas, Alliance for Zero Extinction sites, and other Key Biodiversity Areas). Sites were classified as mountain Key Biodiversity Areas by undertaking a spatial overlap between the Key Biodiversity Area polygons and a mountain raster layer (UNEP-WCMC 2002), classifying any Key Biodiversity Area as a mountain Key Biodiversity Area where it had &#x2265;5% overlap with the mountain layer. The value of the indicator at a given point in time, based on data on the year of protected area establishment recorded in the World Database on Protected Areas, is computed as the mean percentage of each Key Biodiversity Area currently recognised that is covered by protected areas and/or Other Effective Area-based Conservation Measures.</p>\n<p>Protected areas lacking digital boundaries in the World Database of Protected Areas, and those sites with a status of &#x2018;proposed&#x2019; or &#x2018;not reported&#x2019; are omitted. Degazetted sites are not kept in the WDPA and are also not included. Man and Biosphere Reserves are also excluded as these often contain potentially unprotected areas. Year of protected area establishment is unknown for ~12% of protected areas in the World Database on Protected Areas, generating uncertainty around changing protected area coverage over time. To reflect this uncertainty, a year was randomly assigned from another protected area within the same country, and then this procedure repeated 1,000 times, with the median plotted. </p>\n<p>Prior to 2017, the indicator was presented as the percentage of Key Biodiversity Areas completely covered by protected areas. However, it is now presented as the mean % of each Key Biodiversity Area that is covered by protected areas in order to better reflect trends in protected area coverage for countries or regions with few or no Key Biodiversity Areas that are completely covered.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Protected Areas and OECMs are validated through dialogue with the governing authority, who signs a data contributor agreement that these sites are, to the best of their knowledge, an accurate depiction of the sites in question. Over time the data for sites may improve or other aspects of the sites may change, as and when this occurs a further data sharing agreement is required by the site&#x2019;s governing authority.</p>\n<p>Proposed KBAs undergo detailed checking by Regional Focal Points, formal Review of KBA Proposals by independent Reviewers, and validation of Nominated KBAs by the KBAs Secretariat. For further information, see the Guidance on Proposing, Reviewing, Nominating and Confirming KBAs available in KBA Secretariat (2019) at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>.</p>\n<p>When the indicators of protected area coverage of KBAs are updated each year, the updated indicators (and underlying numbers of protected areas, OECMs, and KBAs) are made available for review by countries prior to submission to the SDG Indicators Database. This is achieved through updating the country profiles in the Integrated Biodiversity Assessment Tool (<a href=\"https://ibat-alliance.org/country_profiles\">https://ibat-alliance.org/country_profiles</a>) and circulating these for consultation and review to CBD National Focal Points, SDG National Statistical Office Focal Points, and IUCN State Members.</p>\n<p>When the indicators of protected area coverage of Key Biodiversity Areas are updated each year, the updated indicators (and underlying numbers of protected areas, Other Effective Area-based Conservation Measures, and Key Biodiversity Areas) are made available for review by countries prior to submission to the SDG Indicators Database. This is achieved through updating the country profiles in the Integrated Biodiversity Assessment Tool (<a href=\"https://ibat-alliance.org/country_profiles\">https://ibat-alliance.org/country_profiles</a>) and circulating these for consultation and review to CBD National Focal Points, SDG National Statistical Office Focal Points, and IUCN State Members.</p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made to the index with respect to harmonization of breakdowns or for compliance with specific international or national definitions.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Data are available for protected areas and KBAs in all of the world&#x2019;s countries, and so no imputation or estimation of national level data is necessary.</p>\n<p> </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Global indicators of protected area coverage of important sites for biodiversity are calculated as the mean percentage of each KBA that is covered by protected areas and Other Effective Area-based Conservation Measures. The data are generated from all countries, and so while there is uncertainty around the data, there are no missing values as such and so no need for imputation or estimation.</p>", "REG_AGG__GLOBAL"=>"<p>Regional indices are calculated as the mean percentage of each KBA in the region covered by (i.e. overlapping with) protected areas and/or OECMs: in other words, the percentage of each KBA covered by these designations, averaged over all KBAs in the particular region.</p>", "DOC_METHOD__GLOBAL"=>"<p><strong><em>PAs</em></strong></p>\n<p>Data on protected areas are submitted by government agencies to the WDPA and disseminated through Protected Planet. The WDPA has its origins in a 1959 UN mandate when the United Nations Economic and Social Council called for a list of national parks and equivalent reserves Resolution 713 (XXVIII). </p>\n<p>Protected areas data are therefore compiled directly from government agencies, regional hubs and other authoritative sources in the absence of a government source. All records have a unique metadata identifier (MetadataID) which links the spatial database to the Source table where all sources are described. The data is collated and standardised following the WDPA Data Standards and validated with the source. The process of collation, validation and publication of data as well as protocols and the WDPA data standards are regularly updated in the WDPA User Manual (<a href=\"https://www.protectedplanet.net/c/wdpa-manual\">https://www.protectedplanet.net/c/wdpa-manual</a>) made available through www.protectedplanet.net where all spatial data and the Source table are also published every month and can be downloaded. The WDPA User Manual (published in English, Spanish, and French) provides guidance to countries on how to submit protected areas data to the WDPA, the benefits of providing such data, and the data standards and quality checks that are performed. </p>\n<p><strong><em>OECMS</em></strong></p>\n<p>Guiding principles, common characteristics and criteria for identification of OECMs are available in CBD (2018) at <a href=\"https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-08-en.pdf\">https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-08-en.pdf</a>.</p>\n<p>Guidance on recognising and reporting other effective area-based conservation measures is available in IUCN-WCPA Task Force on OECMs (2019) at: <a href=\"https://portals.iucn.org/library/node/48773\">https://portals.iucn.org/library/node/48773</a>.</p>\n<p><strong><em>KBAs</em></strong></p>\n<p>The &#x201C;Global Standard for the Identification of KBAs&#x201D; (<a href=\"https://portals.iucn.org/library/node/46259\">https://portals.iucn.org/library/node/46259</a>) comprises the standard recommendations available to countries in the identification of KBAs. Guidelines for using A global standard for the identification of KBAs are available at https://portals.iucn.org/library/node/49131.</p>\n<p>Guidance on Proposing, Reviewing, Nominating and Confirming KBAs is available in KBA Secretariat (2019) at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>.</p>\n<p>A summary of the process by which KBAs are identified is available at <a href=\"http://www.keybiodiversityareas.org/working-with-kbas/proposing-updating\">www.keybiodiversityareas.org/working-with-kbas/proposing-updating</a>.</p>\n<p>The KBA identification process is highly inclusive, consultative and nationally driven. Anyone with appropriate data may propose a site as a KBA, although consultation with relevant stakeholders at the local and national level is required when identifying the site and needs to be documented in the proposal. In order to propose a site as a KBA, a proposer must apply the KBA criteria to data on biodiversity elements (species and ecosystems) at the site. Associated with the proposal process is the need to delineate the site accurately so that its boundaries are clear. Although anyone with appropriate scientific data may propose a site to qualify as a KBA, wide consultation with stakeholders at the national level (both non-governmental and governmental organizations) is required during the proposal process. The formal proposal is then made using a proposal process that ensures there is an independent review of the proposal before a site is incorporated in the WDKBA. This is important given that KBA status of a site may lead to changes in actions of governments, private sector companies and other institutions following consultation as appropriate. </p>\n<p>KBA identification builds off the existing network of KBAs, including those identified as (a) Important Bird &amp; Biodiversity Areas through the BirdLife Partnership of over 115 national organisations (https://www.birdlife.org/who-we-are/), (b) Alliance for Zero Extinction sites by 93 national and international organisations in the Alliance (<a href=\"http://www.zeroextinction.org/partners.html\">http://www.zeroextinction.org/partners.html</a>), and (c) other KBAs by civil society organisations supported by the Critical Ecosystem Partnership Fund in developing ecosystem profiles, named in each of the profiles listed here (<a href=\"http://www.cepf.net\">http://www.cepf.net</a> ), with new data strengthening and expanding expand the network of these sites.</p>\n<p>The main steps of the KBA identification process are the following: </p>\n<ol>\n  <li>submission of Expressions of Intent to identify a KBA to Regional Focal Points; </li>\n  <li>Proposal Development process, in which proposers compile relevant data and documentation and consult national experts, including organizations that have already identified KBAs in the country, either through national KBA Coordination Groups or independently;</li>\n  <li>review of proposed KBAs by Independent Expert Reviewers, verifying the accuracy of information within their area of expertise; and</li>\n  <li>a Site Nomination phase comprising the submission of all the relevant documentation for verification by the KBAs Secretariat. Sites confirmed by the KBAs Secretariat to qualify as KBAs are then published on the KBAs website (<a href=\"http://www.keybiodiversityareas.org/home\">http://www.keybiodiversityareas.org/home</a>).</li>\n</ol>\n<p>Once a KBA is identified, monitoring of its qualifying features and its conservation status is important. Proposers, reviewers and those undertaking monitoring can join the KBAs Community to exchange their experiences, case studies and best practice examples.</p>\n<p>The R code for calculating protected area coverage of KBAs is documented in Simkins et al. (2020). </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>For protected areas and OECMs, please see the section on validation. Ensuring the WDPA and WDOECM remain an accurate and true depiction of reality is a never-ending task; however, over time the quality of the data (e.g. the proportion of sites with defined boundaries) is increasing.</p>\n<p>For KBAs, see above and below, plus the guidance on Proposing, Reviewing, Nominating and Confirming KBAs which is available in KBA Secretariat (2019) at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>. Data quality is ensured through wide stakeholder engagement in the KBA proposal process, data checking by Regional Focal Points, formal Review of KBA Proposals by independent Reviewers, and validation of Nominations by the KBAs Secretariat. Furthermore, an independent KBA Standards and Appeals Committee ensures the correct application of the Global Standard for the identification of KBAs, and oversees a formal Procedure for handling of appeals against the identification of KBAs (see http://www.keybiodiversityareas.org/assets/1b388c918e14c5f4c3d7a0237eb0d366).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Information on the process of how protected area data are collected, standardised and published is available in the WDPA User Manual at: <a href=\"https://www.protectedplanet.net/c/wdpa-manual\">https://www.protectedplanet.net/c/wdpa-manual</a> which is available in English, French and Spanish. Specific guidance is provided at <a href=\"https://www.protectedplanet.net/c/world-database-on-protected-areas\">https://www.protectedplanet.net/c/world-database-on-protected-areas</a> on, for example, predefined fields or look up tables in the WDPA: <a href=\"https://www.protectedplanet.net/c/wdpa-lookup-tables\">https://www.protectedplanet.net/c/wdpa-lookup-tables</a>, how WDPA records are coded how international designations and regional designations data is collected, how regularly is the database updated, and how to perform protected areas coverage statistics. </p>\n<p>Data quality in the process of identifying KBAs is ensured through processes established by the <a href=\"https://www.keybiodiversityareas.org/working-with-kbas/programme/partnership\">KBA Partnership</a> and KBA Secretariat. Data quality is ensured through wide stakeholder engagement in the KBA proposal process, data checking by Regional Focal Points, formal Review of KBA Proposals by independent Reviewers, and validation of Nominations by the KBA Secretariat. </p>\n<p>In addition, the Chairs of the IUCN Species Survival Commission and World Commission on Protected Areas (both of whom are elected by the IUCN Membership of governments and non-governmental organisations), appoint the Chair of an independent KBA Standards and Appeals Committee, which ensures the correct application of the Global Standard for the identification of KBA, and oversees a formal Procedure for handling of appeals against the identification of KBAs (see http://www.keybiodiversityareas.org/assets/1b388c918e14c5f4c3d7a0237eb0d366).</p>\n<p>Before submission to the UN SDG Indicators database the annually updated indicators of coverage of KBAs by protected areas and Other Effective Area-based Conservation Measures are incorporated into updated Country Profiles on the Integrated Biodiversity Assessment Tool (<a href=\"https://ibat-alliance.org/country_profiles\">https://ibat-alliance.org/country_profiles</a>) and then sent for consultation to National Focal Points of the Convention on Biological Diversity (<a href=\"https://www.cbd.int/information/nfp.shtml\">https://www.cbd.int/information/nfp.shtml</a>), National Statistics Offices SDG Representatives and UN Permanent Missions (Geneva) representatives. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>High.</p>\n<p>Each custodian agency is responsible for quality management of their own database.<br>Quality assessment of the indicator is shared between the indicator custodian agencies.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>This indicator has been classified by the IAEG-SDGs as Tier 1. Current data are available for all countries in the world, and these are updated on an ongoing basis. Index values for each country are available in the UN SDG Indicators Database <a href=\"https://unstats.un.org/sdgs/indicators/database/\">https://unstats.un.org/sdgs/indicators/database/</a>. Graphs of Protected area coverage of Key Biodiversity Areas are also available for each country in the BIP Indicators Dashboard (<a href=\"https://bipdashboard.natureserve.org/bip/SelectCountry.html\">https://bipdashboard.natureserve.org/bip/SelectCountry.html</a>), and the Integrated Biodiversity Assessment Tool Country Profiles (<a href=\"https://ibat-alliance.org/country_profiles\">https://ibat-alliance.org/country_profiles</a>).</p>\n<p>Underlying data on protected areas and Other Effective Area-based Conservation Measures are available at <a href=\"http://www.protectedplanet.net\">www.protectedplanet.net</a>. Data on Key Biodiversity Areas are available at <a href=\"http://www.keybiodiversityareas.org\">www.keybiodiversityareas.org</a>. Data on subsets of KBAs are available for Important Bird and Biodiversity Areas at <a href=\"http://datazone.birdlife.org/site/search\">http://datazone.birdlife.org/site/search</a> and for Alliance for Zero Extinction sites at https://zeroextinction.org.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Given that data for the global indicator are compiled at national levels, it is straightforward to disaggregate to national and regional levels (e.g., Han et al. 2014), or conversely to aggregate to the global level. Key Biodiversity Areas span all ecosystem types through the marine environment (Edgar et al. 2008) and beyond. The indicator can therefore be reported in combination across marine systems along with terrestrial or freshwater systems, or disaggregated among them. However, individual Key Biodiversity Areas can encompass marine, terrestrial, and freshwater systems simultaneously, and so determining the results is not simply additive. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>National processes provide the data that are incorporated into the WDPA, the WDOECM, and the World Database of KBAs, so there are very few discrepancies between national indicators and the global one. One minor source of difference is that the WDPA incorporates internationally-designated protected areas (e.g., UNESCO World Heritage sites, Ramsar sites, etc), a few of which are not considered by their sovereign nations to be protected areas. </p>\n<p>Note that because countries do not submit comprehensive data on degazetted protected areas to the WDPA, earlier values of the indictor may marginally underestimate coverage. Furthermore, there is also a lag between the point at which a protected area is designated on the ground and the point at which it is reported to the WDPA. As such, current or recent coverage may also be underestimated.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.unep-wcmc.org/\">http://www.unep-wcmc.org/</a> ; <a href=\"http://www.birdlife.org/\">http://www.birdlife.org/</a> ; <a href=\"http://www.iucn.org/\">http://www.iucn.org/</a> </p>\n<p><strong>References:</strong></p>\n<p>BIRDLIFE INTERNATIONAL (2014). Important Bird and Biodiversity Areas: a global network for conserving nature and benefiting people. Cambridge, UK: BirdLife International. Available at datazone.birdlife.org/sowb/sowbpubs#IBA.</p>\n<p>BIRDLIFE INTERNATIONAL (2019) World Database of Key Biodiversity Areas. Developed by the KBA Partnership: BirdLife International, International Union for the Conservation of Nature, Amphibian Survival Alliance, Conservation International, Critical Ecosystem Partnership Fund, Global Environment Facility, Global Wildlife Conservation, NatureServe, Rainforest Trust, Royal Society for the Protection of Birds, Wildlife Conservation Society and World Wildlife Fund. September 2019 version. Available at http://keybiodiversityareas.org/sites/search.</p>\n<p>BROOKS, T. et al. (2001). Conservation priorities for birds and biodiversity: do East African Important Bird Areas represent species diversity in other terrestrial vertebrate groups? Ostrich suppl. 15: 3&#x2013;12. Available </p>\n<p>from: http://www.tandfonline.com/doi/abs/10.2989/00306520109485329#.VafbVJPVq75.</p>\n<p>BROOKS, T.M. et al. (2016) Goal 15: Life on land. Sustainable manage forests, combat desertification, halt and reverse land degradation, halt biodiversity loss. Pp. 497&#x2013;522 in Dur&#xE1;n y Lalaguna, P., D&#xED;az Barrado, C.M. &amp; Fern&#xE1;ndez Liesa, C.R. (eds.) International Society and Sustainable Development Goals. Editorial Aranzadi, Cizur Menor, Spain. Available from: https://www.thomsonreuters.es/es/tienda/pdp/duo.html?pid=10008456</p>\n<p>BUTCHART, S. H. M. et al. (2010). Global biodiversity: indicators of recent declines. Science 328: 1164&#x2013;1168. Available from https://www.science.org/doi/10.1126/science.1187512.</p>\n<p>BUTCHART, S. H. M. et al. (2012). Protecting important sites for biodiversity contributes to meeting global conservation targets. PLoS One 7(3): e32529. Available from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0032529.</p>\n<p>BUTCHART, S. H. M. et al. (2015). Shortfalls and solutions for meeting national and global conservation area targets. Conservation Letters 8: 329&#x2013;337. Available from http://onlinelibrary.wiley.com/doi/10.1111/conl.12158/full.</p>\n<p>CBD (2014). Global Biodiversity Outlook 4. Convention on Biological Diversity, Montr&#xE9;al, Canada. Available from <a href=\"https://www.cbd.int/gbo4/\">https://www.cbd.int/gbo4/</a>.</p>\n<p>CBD (2018). Protected areas and other effective area-based conservation measures. Decision 14/8 adopted by the Conference of the Parties to the Convention on Biological Diversity. Available at <a href=\"https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-08-en.pdf\">https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-08-en.pdf</a>. </p>\n<p>CBD (2020a). Global Biodiversity Outlook 5. Convention on Biological Diversity, Montr&#xE9;al, Canada. Available from https://www.cbd.int/gbo5/. </p>\n<p>CBD (2020b). Post-2020 Global Biodiversity Framework: Scientific and technical information to support the review of the updated Goals and Targets, and related indicators and baselines. Document CBD/SBSTTA/24/3. Available at: https://www.cbd.int/doc/c/705d/6b4b/a1a463c1b19392bde6fa08f3/sbstta-24-03-en.pdf.</p>\n<p>CHAPE, S. et al. (2005). Measuring the extent and effectiveness of protected areas as an indicator for meeting global biodiversity targets. Philosophical Transactions of the Royal Society B 360: 443&#x2013;445. Available from http://rstb.royalsocietypublishing.org/content/360/1454/443.short.</p>\n<p>DEGUIGNET, M., et al. (2014). 2014 United Nations List of Protected Areas. UNEP-WCMC, Cambridge, UK. Available from http://unep-wcmc.org/system/dataset_file_fields/files/000/000/263/original/2014_UN_List_of_Protected_Areas_EN_web.PDF?1415613322.</p>\n<p>DI MARCO, M., et al. (2016). Quantifying the relative irreplaceability of Important Bird and Biodiversity Areas. Conservation Biology 30: 392&#x2013;402. Available from http://onlinelibrary.wiley.com/doi/10.1111/cobi.12609/abstract.</p>\n<p>DONALD, P. et al. (2018) Important Bird and Biodiversity Areas (IBAs): the development and characteristics of a global inventory of key sites for biodiversity. Bird Conserv. Internat. 29:177&#x2013;198.</p>\n<p>DUDLEY, N. (2008). Guidelines for Applying Protected Area Management Categories. International Union for Conservation of Nature (IUCN). Gland, Switzerland. Available from https://portals.iucn.org/library/node/9243.</p>\n<p>EDGAR, G.J. et al. (2008). Key Biodiversity Areas as globally significant target sites for the conservation of marine biological diversity. Aquatic Conservation: Marine and Freshwater Ecosystems 18: 969&#x2013;983. Available from http://onlinelibrary.wiley.com/doi/10.1002/aqc.902/abstract.</p>\n<p>EKEN, G. et al. (2004). Key biodiversity areas as site conservation targets. BioScience 54: 1110&#x2013;1118. Available from http://bioscience.oxfordjournals.org/content/54/12/1110.short.</p>\n<p>FOSTER, M.N. et al. (2012) The identification of sites of biodiversity conservation significance: progress with the application of a global standard. Journal of Threatened Taxa 4: 2733&#x2013;2744. Available from </p>\n<p>https://threatenedtaxa.org/index.php/JoTT/article/view/779.</p>\n<p>Global Administrative Areas (2019). GADM database of Global Administrative Areas, version 2.8. Available from www.gadm.org.</p>\n<p>HAN, X. et al. (2014). A Biodiversity indicators dashboard: addressing challenges to monitoring progress towards the Aichi Biodiversity Targets using disaggregated global data. PLoS ONE 9(11): e112046. Available from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112046.</p>\n<p>HOLLAND, R.A. et al. (2012). Conservation priorities for freshwater biodiversity: the key biodiversity area approach refined and tested for continental Africa. Biological Conservation 148: 167&#x2013;179. Available from </p>\n<p>http://www.sciencedirect.com/science/article/pii/S0006320712000298.</p>\n<p>IUCN (2016). A Global Standard for the Identification of Key Biodiversity Areas. International Union for Conservation of Nature, Gland, Switzerland. Available from https://portals.iucn.org/library/node/46259.</p>\n<p>IUCN-WCPA Task Force on OECMs (2019). Recognising and reporting other effective area-based conservation measures. Gland, Switzerland: IUCN.</p>\n<p>JONAS, H.D. et al. (2014) New steps of change: looking beyond protected areas to consider other effective area-based conservation measures. Parks 20: 111&#x2013;128. Available from http://parksjournal.com/wp-content/uploads/2014/10/PARKS-20.2-Jonas-et-al-10.2305IUCN.CH_.2014.PARKS-20-2.HDJ_.en_.pdf.</p>\n<p>KBA Secretariat (2019). Key Biodiversity Areas Proposal Process: Guidance on Proposing, Reviewing, Nominating and Confirming sites. Version 1.0. Prepared by the KBA Secretariat and KBA Committee of the KBA Partnership. Cambridge, UK. Available at <a href=\"http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a\">http://www.keybiodiversityareas.org/assets/35687f50ac0bcad155ab17447b48885a</a>. </p>\n<p>KNIGHT, A. T. et al. (2007). Improving the Key Biodiversity Areas approach for effective conservation planning. BioScience 57: 256&#x2013;261. Available from </p>\n<p>http://bioscience.oxfordjournals.org/content/57/3/256.short.</p>\n<p>LANGHAMMER, P. F. et al. (2007). Identification and Gap Analysis of Key Biodiversity Areas: Targets for Comprehensive Protected Area Systems. IUCN World Commission on Protected Areas Best Practice Protected Area Guidelines Series No. 15. IUCN, Gland, Switzerland. Available from https://portals.iucn.org/library/node/9055.</p>\n<p>LEVERINGTON, F. et al. (2010). A global analysis of protected area management effectiveness. Environmental Management 46: 685&#x2013;698. Available from http://link.springer.com/article/10.1007/s00267-010-</p>\n<p>9564-5#page-1.</p>\n<p>MONTESINO POUZOLS, F., et al. (2014) Global protected area expansion is compromised by projected land-use and parochialism. Nature 516: 383&#x2013;386. Available from http://www.nature.com/nature/journal/v516/n7531/abs/nature14032.html.</p>\n<p>NOLTE, C. &amp; AGRAWAL, A. (2013). Linking management effectiveness indicators to observed effects of protected areas on fire occurrence in the Amazon rainforest. Conservation Biology 27: 155&#x2013;165. Available from http://onlinelibrary.wiley.com/doi/10.1111/j.1523-1739.2012.01930.x/abstract.</p>\n<p>PAIN, D.J. et al. (2005) Biodiversity representation in Uganda&#x2019;s forest IBAs. Biological Conservation 125: 133&#x2013;138. Available from http://www.sciencedirect.com/science/article/pii/S0006320705001412.</p>\n<p>RICKETTS, T. H. et al. (2005). Pinpointing and preventing imminent extinctions. Proceedings of the National Academy of Sciences of the U.S.A. 102: 18497&#x2013;18501. Available from http://www.pnas.org/content/102/51/18497.short.</p>\n<p>RODRIGUES, A. S. L. et al. (2004). Effectiveness of the global protected area network in representing species diversity. Nature 428: 640&#x2013;643. Available from http://www.nature.com/nature/journal/v428/n6983/abs/nature02422.html.</p>\n<p>RODR&#xCD;GUEZ-RODR&#xCD;GUEZ, D., et al. (2011). Progress towards international targets for protected area coverage in mountains: a multi-scale assessment. Biological Conservation 144: 2978&#x2013;2983. Available from </p>\n<p>http://www.sciencedirect.com/science/article/pii/S0006320711003454.</p>\n<p>SIMKINS, A.T., PEARMAIN, E.J., &amp; DIAS, M.P. (2020). Code (and documentation) for calculating the protected area coverage of Key Biodiversity Areas. <a href=\"https://github.com/BirdLifeInternational/kba-overlap\">https://github.com/BirdLifeInternational/kba-overlap</a>. </p>\n<p>TITTENSOR, D. et al. (2014). A mid-term analysis of progress towards international biodiversity targets. Science 346: 241&#x2013;244. Available from https://www.science.org/doi/10.1126/science.1257484.</p>\n<p>UNEP-WCMC (2019). World Database on Protected Areas User Manual 1.6. UNEP-WCMC, Cambridge, UK. Available from <a href=\"http://wcmc.io/WDPA_Manual\">http://wcmc.io/WDPA_Manual</a>.</p>\n<p>UNEP-WCMC &amp; IUCN (2020). The World Database on Protected Areas (WDPA). UNEP-WCMC, Cambridge, UK. Available from http://www.protectedplanet.net.</p>", "indicator_sort_order"=>"15-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.4.2", "slug"=>"15-4-2", "name"=>"a) Índice de cobertura verde de las montañas y b) proporción de terreno montañoso degradado", "url"=>"/site/es/15-4-2/", "sort"=>"150402", "goal_number"=>"15", "target_number"=>"15.4", "global"=>{"name"=>"a) Índice de cobertura verde de las montañas y b) proporción de terreno montañoso degradado"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"a) Índice de cobertura verde de las montañas y b) proporción de terreno montañoso degradado", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"a) Índice de cobertura verde de las montañas y b) proporción de terreno montañoso degradado", "indicator_number"=>"15.4.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Mantenimiento o ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/indicadores-ambientales-090207/web01-a2inguru/es/", "url_text"=>"Estadística de Indicadores Ambientales", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}, {"organisation"=>"Geoeuskadi", "url"=>"https://www.geo.euskadi.eus/", "url_text"=>"Geoeuskadi", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Índice de cobertura verde de las montañas", "definicion"=>"Superficie de cobertura verde (bosques, pastizales y tierras de cultivo) en zonas de montaña sobre el total de la superficie de zonas de montaña", "formula"=>"\n$$PSMV^{t} = \\frac{SMV^{t}}{SM^{t}} \\cdot 100$$\n\ndonde:\n\n$SMV^{t} =$ superficie de zonas de montaña cubierta por bosques, pastizales y tierras de cultivo en el año $t$\n\n$SM^{t} =$ superficie de zonas de montaña en el año $t$\n", "desagregacion"=>"", "periodicidad"=>"Trienal", "observaciones"=>"", "justificacion_global"=>"Los ecosistemas montañosos son importantes centros de biodiversidad que brindan \nvaliosos servicios ecosistémicos a las zonas río arriba y río abajo. Sin embargo, \nlas montañas son muy frágiles y se ven fácilmente afectadas por factores tanto naturales \ncomo antropogénicos. Estos pueden incluir el cambio climático, la expansión \nagrícola no planificada, la urbanización no planificada, la extracción de madera, \nlas actividades recreativas y los peligros naturales como deslizamientos de tierra e inundaciones.\n\nLa degradación de los ecosistemas montañosos, como la pérdida de la cubierta glaciar, \nla biodiversidad montañosa y la cubierta vegetal, afectará la capacidad del ecosistema \npara suministrar agua río abajo. La pérdida de la cubierta forestal y vegetal reducirá \nla capacidad del ecosistema para retener el suelo y prevenir deslizamientos de tierra \ne inundaciones río abajo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.4.2&seriesCode=ER_MTN_GRNCVI&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=MGCI%20%7C%20MONTANE\">\nÍndice de cobertura verde de montaña ER_MTN_GRNCVI, MGCI Montano</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible no cumple con los metadatos del indicador de Naciones Unidas, pero aporta información similar", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-04-02.pdf\">Metadatos 15-4-2.pdf</a> (solo en inglés)", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Índice de cobertura verde de las montañas", "definicion"=>"Area of green cover (forests, grasslands and croplands) in mountain areas relative to the total area of mountain areas", "formula"=>"\n$$PSMV^{t} = \\frac{SMV^{t}}{SM^{t}} \\cdot 100$$\n\nwhere:\n\n$SMV^{t} =$ area of mountain areas covered by forests, grasslands and croplands in year $t$\n\n$SM^{t} =$ area of mountain areas in year $t$\n", "desagregacion"=>nil, "periodicidad"=>"Trienal", "observaciones"=>nil, "justificacion_global"=>"Mountain ecosystems are important biodiversity centres that provide valuable ecosystem services to \nupstream and downstream areas. Yet, mountains are very fragile and impacted easily by both natural and \nanthropogenic factors. These can include climate change, unplanned agricultural expansion, unplanned \nurbanization, timber extraction, recreational activities and natural hazards such as landslides and flooding. \n\nThe degradation of mountain ecosystems such as loss of the glacial cover, mountain biodiversity and green \ncover will affect the ability of the ecosystem to supply water downstream. The loss of forest and vegetative \ncover will reduce the ability of the ecosystem to retain soil and prevent landslides and flooding \ndownstream. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.4.2&seriesCode=ER_MTN_GRNCVI&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=MGCI%20%7C%20MONTANE\">\nMountain Green Cover Index ER_MTN_GRNCVI, MGCI Montano</a> UNSTATS\n", "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-04-02.pdf\">Metadata 15-4-2.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Índice de cobertura verde de las montañas", "definicion"=>"Superficie de cobertura verde (bosques, pastizales y tierras de cultivo) en zonas de montaña sobre el total de la superficie de zonas de montaña", "formula"=>"\n$$PSMV^{t} = \\frac{SMV^{t}}{SM^{t}} \\cdot 100$$\n\nnon:\n\n$SMV^{t} =$ basoek, larreek eta laborantza-lurrek estalitako mendi-eremuen azalera $t$ urtean\n\n$SM^{t} =$ mendi-eremuen azalera $t$ urtean\n", "desagregacion"=>nil, "periodicidad"=>"Trienal", "observaciones"=>nil, "justificacion_global"=>"Los ecosistemas montañosos son importantes centros de biodiversidad que brindan \nvaliosos servicios ecosistémicos a las zonas río arriba y río abajo. Sin embargo, \nlas montañas son muy frágiles y se ven fácilmente afectadas por factores tanto naturales \ncomo antropogénicos. Estos pueden incluir el cambio climático, la expansión \nagrícola no planificada, la urbanización no planificada, la extracción de madera, \nlas actividades recreativas y los peligros naturales como deslizamientos de tierra e inundaciones.\n\nLa degradación de los ecosistemas montañosos, como la pérdida de la cubierta glaciar, \nla biodiversidad montañosa y la cubierta vegetal, afectará la capacidad del ecosistema \npara suministrar agua río abajo. La pérdida de la cubierta forestal y vegetal reducirá \nla capacidad del ecosistema para retener el suelo y prevenir deslizamientos de tierra \ne inundaciones río abajo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.4.2&seriesCode=ER_MTN_GRNCVI&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=MGCI%20%7C%20MONTANE\">\nMendiko estaldura berdearen indizea ER_MTN_GRNCVI, MGCI Montano</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen adierazlearen metadatuak betetzen, baina antzeko informazioa eskaintzen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-04-02.pdf\">Metadatuak 15-4-2.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.4: By 2030, ensure the conservation of mountain ecosystems, including their biodiversity, in order to enhance their capacity to provide benefits that are essential for sustainable development</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.4.2: (a) Mountain Green Cover Index and (b) proportion of degraded mountain land</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Primary series: </p>\n<p>ER_MTN_DGRDP - Proportion of degraded mountain land (%) [15.4.2] </p>\n<p>ER_MTN_GRNCVI - Mountain Green Cover Index [15.4.2] </p>\n<p>Supplementary series: </p>\n<p>ER_MTN_GRNCOV - Mountain green cover area (square kilometres) [15.4.2] </p>\n<p>ER_MTN_TOTL - Mountain area (square kilometres) [15.4.2] </p>\n<p>ER_MTN_DGRDA - Area of degraded mountain land (square kilometres) [15.4.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>6.6.1, 15.1.1, 15.2.1, 15.3.1, 15.4.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>The indicator is composed of two sub-indicators to monitor progress towards the conservation of mountain ecosystems:</p>\n<p>Sub-indicator 15.4.2a, Mountain Green Cover Index (MGCI), is designed to measure the extent and changes of green cover - i.e. forest, shrubs, trees, pasture land, cropland, etc. &#x2013; in mountain areas. MGCI is defined as the percentage of green cover over the total surface of the mountain area of a given country and for given reporting year. The aim of the index is to monitor the evolution of green cover and thus assess the status of conservation of mountain ecosystems.</p>\n<p>Sub-indicator 15.4.2b, Proportion of degraded mountain land, is designed to monitor the extent of degraded mountain land as a result of land cover change in a given country and for given reporting year. Similarly to sub-indicator &#x2018;&#x2019;trends in land cover&#x201D; under SDG Indicator 15.3.1 (Sims <em>et al.</em> 2021), mountain ecosystem degradation and recovery is assessed based on the definition of land cover type transitions that indicate improving, stable or degrading conservation status. The definition of degradation adopted for the computation of this indicator is the one established Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES)<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>.</p>\n<p><strong>Concepts:</strong></p>\n<p><strong><em>Mountain area</em> </strong>is defined according to the UNEP-WCMC (2002) method. It defines total global mountain area as the sum of seven classes (commonly known as &#x2018;Kapos mountain classes&#x2019;), based on elevation, slope and local elevation ranges (Table 1).</p>\n<p><strong>Table 1.</strong> Global mountain classes as defined by UNEP-WCMC (2002)</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Kapos Mountain Class</strong></p>\n      </td>\n      <td>\n        <p><strong>Description</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Class 1</p>\n      </td>\n      <td>\n        <p>Elevation &gt;= 4500 meters</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Class 2</p>\n      </td>\n      <td>\n        <p>Elevation &gt;= 3500 &amp; &lt; 4500 meters</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Class 3</p>\n      </td>\n      <td>\n        <p>Elevation &gt;= 2500 &amp; &lt; 3500 meters</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Class 4</p>\n      </td>\n      <td>\n        <p>Elevation &gt;= 1500 &amp; &lt; 2500 meters &amp; slope &gt;= 2 degrees</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Class 5</p>\n      </td>\n      <td>\n        <p>Elevation&gt;= 1000 &amp; &lt; 1500 meters &amp; slope &gt;= 5 degrees OR local (7 km radius) elevation range &gt; 300 meters</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Class 6</p>\n      </td>\n      <td>\n        <p>Elevation &gt;= 300 &amp; &lt; 1000 meters &amp; local (7 km radius) elevation range &gt; 300 meters</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Class 7</p>\n      </td>\n      <td>\n        <p>Inner isolated areas (&lt;=25 Km<sup>2</sup> in size) that do not meet criteria but surrounded by mountains</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Prior to the methodological refinement of this indicator approved by the Inter-agency and Expert Group on SDG Indicators (IAEG-SDG) in June 2022, the UNEP-WCMC classification was used to disaggregate the indicator by Kapos mountain classes. This is no longer the case, with Kapos mountain classes having been replaced by bioclimatic belts (see section 2.c below).</p>\n<p><strong><em>Land cover </em></strong>refers to the observed physical cover of the Earth&#x2019;s surface. It includes vegetation and man-made features as well as bare rock, bare soil and inland water surfaces (FAO-GTOS, 2009). The primary units for characterizing land cover are categories (e.g. Forest or Open Water). These categories must be defined following a standardized land cover classification in order to identify land cover changes consistently over time. Several global standards of land cover classifications have been developed by international initiatives for this purpose. </p>\n<p>For the purposes of standardization and harmonization when reporting on SDG Indicator 15.4.2, this indicator has adapted the land cover classification established by the United Nations Statistical Commission&#x2019;s System of Environmental and Economic Accounting (UN-SEEA) (UN Statistical Division, 2014) by selecting the most relevant SEEA classes for mountain ecosystems and aggregating all croplands classes (Table 2).</p>\n<p><strong>Table 2.</strong> Left: Land cover classification established by the UN-SEEA (Source: UN Statistical Division, 2014). Right: Adapted land cover classification for the computation and aggregate reporting on SDG Indicator 15.4.2.</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Original UN &#x2013; SEEA land cover classification (n=14)</strong></p>\n      </td>\n      <td>\n        <p><strong>SDG Indicator 15.4.2 land cover classification (n=10)</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1 Artificial surfaces </p>\n      </td>\n      <td>\n        <p>1 Artificial surfaces</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2 Herbaceous crops<em> </em></p>\n      </td>\n      <td rowspan=\"3\">\n        <p>2 Croplands</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3 Woody crops<em> </em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4 Multiple or layered crops<em> </em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5 Grassland<em> </em></p>\n      </td>\n      <td>\n        <p>3 Grasslands</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>6 Tree-covered areas<em> </em></p>\n      </td>\n      <td>\n        <p>4 Tree-covered areas<em> </em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>7 Mangroves<em> </em></p>\n      </td>\n      <td>\n        <p>Discarded. Not relevant for mountains</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>8 Shrub-covered areas<em> </em></p>\n      </td>\n      <td>\n        <p>5 Shrub-covered areas<em> </em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>9 Shrubs and/or herbaceous vegetation, aquatic or regularly flooded<em> </em></p>\n      </td>\n      <td>\n        <p>6 Shrubs and/or herbaceous vegetation, aquatic or regularly flooded<em> </em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>10 Sparsely natural vegetated areas<em> </em></p>\n      </td>\n      <td>\n        <p>7 Sparsely natural vegetated areas<em> </em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>11 Terrestrial barren land<em> </em></p>\n      </td>\n      <td>\n        <p>8 Terrestrial barren land<em> </em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>12 Permanent snow and glaciers<em> </em></p>\n      </td>\n      <td>\n        <p>9 Permanent snow and glaciers<em> </em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>13 Inland water bodies<em> </em></p>\n      </td>\n      <td>\n        <p>10 Inland water bodies<em> </em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>14 Coastal water bodies and intertidal areas<em> </em></p>\n      </td>\n      <td>\n        <p>Discarded. Not relevant for mountains</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Land cover serves different functions for SDG Indicator 15.4.2:</p>\n<p>In sub-indicator 15.4.2a, land cover is used to categorize land into green and non-green cover areas. As showed in Table 3, green cover includes areas covered by both natural vegetation and vegetation resulting from anthropic activity. Non-green areas include non-vegetated areas such as bare land, water, permanent ice/snow, urban areas and sparsely vegetated areas. In addition, land cover is used to disaggregate the indicator into the 10 land cover classes included in Table 2, thus increasing the indicator&#x2019;s policy relevance.</p>\n<p><strong>Table 3. </strong>Classification of SEEA land cover classes into green and non-green cover.</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>SEEA land cover classes</strong></p>\n      </td>\n      <td>\n        <p><strong>Green/Non-green</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Croplands</p>\n      </td>\n      <td>\n        <p>Green</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Grasslands</p>\n      </td>\n      <td>\n        <p>Green </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Tree-covered areas</p>\n      </td>\n      <td>\n        <p>Green</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Shrub-covered areas</p>\n      </td>\n      <td>\n        <p>Green</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Shrubs and/or herbaceous vegetation, aquatic or regularly flooded<em> </em></p>\n      </td>\n      <td>\n        <p>Green</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Artificial surfaces</p>\n      </td>\n      <td>\n        <p>Non-green</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Sparsely natural vegetated areas<em> </em></p>\n      </td>\n      <td>\n        <p>Non-green</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Terrestrial barren land</p>\n      </td>\n      <td>\n        <p>Non-green</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Permanent snow and glaciers</p>\n      </td>\n      <td>\n        <p>Non-green</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Inland water bodies</p>\n      </td>\n      <td>\n        <p>Non-green</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>In sub-indicator 15.4.2b, land cover is used to identify areas where changes in the type of land cover (land cover transitions) may indicate a decline or loss of biodiversity, mountain ecosystem functions or services that are considered desirable in a local or national context. A transition that indicates a decline or loss of biodiversity and mountain ecosystem services of the land is considered degradation. The definition of land cover transitions is documented in a transition matrix that specifies the land cover changes occurring in a given land unit (pixel) as being either degradation, improvement or neutral transitions.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> IPBES defines land degradation as &#x201C;the many human-caused processes that drive the decline or loss in biodiversity, ecosystem functions or ecosystem services in any terrestrial and associated aquatic ecosystems&#x201D; (IPBES, 2018) <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Both sub-indicators are expressed as proportions (percent) and area (KM2).</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>This indicator uses two established classifications: (1) the simplified UN-SEEA land cover classification included in Table 2, and (2) the mountain bioclimatic belt classification established by K&#xF6;rner <em>et al. </em>(2011). The latter is used for data disaggregation only.</p>\n<p>K&#xF6;rner <em>et al. </em>(2011) subdivides mountains vertically into seven bioclimatic belts based on average temperatures, therefore accounting for the latitudinal change in elevation of thermally similar areas in the world&#x2019;s mountains. For the purposes of this indicator, these seven bioclimatic belts are aggregated into four (Nival, Alpine, Montane and Remaining Mountain Areas), as illustrated in Table 4.</p>\n<p><strong>Table 4.</strong><strong> </strong>Mountain bioclimatic belts as defined by K&#xF6;rner et al. (2011) and reclassification for data disaggregation of SDG Indicator 15.4.2. Growing season is defined as the number of days between daily mean temperature exceeds 0.9 &#xB0;C then falls below 0.9 &#xB0;C</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Bioclimatic belts</strong></p>\n      </td>\n      <td>\n        <p><strong>Growing season mean temperature</strong></p>\n      </td>\n      <td>\n        <p><strong>Growing season length</strong></p>\n      </td>\n      <td>\n        <p><strong>Bioclimatic belts adopted for SDG Indicator 15.4.2</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Nival</p>\n      </td>\n      <td>\n        <p>&lt; 3.5 &#xB0;C</p>\n      </td>\n      <td>\n        <p>&lt; 10 days</p>\n      </td>\n      <td>\n        <p>Nival</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Upper alpine</p>\n      </td>\n      <td>\n        <p>&lt; 3.5 &#xB0;C</p>\n      </td>\n      <td>\n        <p>&gt; 10 days &amp; &lt; 54 days</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Alpine</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Lower alpine</p>\n      </td>\n      <td>\n        <p>&lt; 6.4&#xB0;C</p>\n      </td>\n      <td>\n        <p>&lt; 54 days</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"4\">\n        <p>THE TREELINE</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Upper montane</p>\n      </td>\n      <td>\n        <p>&gt; 6.4&#xB0;C &amp; &#x2264; 10 &#xB0;C</p>\n      </td>\n      <td>\n        <p>---</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Montane</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Lower montane</p>\n      </td>\n      <td>\n        <p>&gt; 10 &#xB0;C &amp; &#x2264; 15 &#xB0;C</p>\n      </td>\n      <td>\n        <p>---</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Remaining mountain area with frost</p>\n      </td>\n      <td>\n        <p>&gt; 15 &#xB0;C</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>---</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Remaining mountain areas</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Remaining mountain area without frost</p>\n      </td>\n      <td>\n        <p>&gt; 15 &#xB0;C</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "SOURCE_TYPE__GLOBAL"=>"<p>Land cover maps developed by appropriate national authorities generally provide the most relevant data source to compute this indicator. However, in certain cases, such data may not be available. In those cases, various regional or global products provide a viable alternative.</p>\n<p>The default sources of land cover data for this indicator are the <a href=\"https://land.copernicus.eu/en/products/corine-land-cover\">CORINE Land Cover (CLC) product</a> for all countries and territories covered by this dataset<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>, the Global 2000-2020 Land Cover and Land Use Change Dataset (GLCLUC2020) (Potapov <em>et al.</em> 2022) for countries and territories not covered by CORINE, and the European Space Agency Climate Change Initiative (ESA-CCI) Land Cover product (ESA, 2017) for small island countries and territories not covered by any of the above products. The selection of the land cover product used for each country and territory was based on the following criteria: temporal coverage (at least from 2000 onwards), reported accuracy (products with higher reported accuracies were preferred as values derived from those products are expected to be closer to true land cover condition), spatial resolution and minimum mapping unit (higher resolution was preferred to allow capturing finer scale land cover changes), thematic resolution (higher thematic coverage was preferred to allow capturing finer detailed land cover changes) and future continuity of the product (regular updates and improvements of the products are expected or already underway). Table 5 includes a summary of the key characteristics of each of the above-mentioned land cover data sources.</p>\n<p><strong>Table 5. </strong>Summary of the key characteristics of each of the 3 land cover data sources used to estimate global default values.</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Product</strong></p>\n      </td>\n      <td>\n        <p><strong>Measurement method</strong></p>\n      </td>\n      <td>\n        <p><strong>Geographical coverage </strong></p>\n      </td>\n      <td>\n        <p><strong>Spatial resolution</strong></p>\n      </td>\n      <td>\n        <p><strong>Thematic resolution</strong></p>\n      </td>\n      <td>\n        <p><strong>Temporal coverage</strong></p>\n      </td>\n      <td>\n        <p><strong>Reported accuracy</strong></p>\n      </td>\n      <td>\n        <p><strong>Link</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>ESA&#x2011;CCI&#x2011;LC</p>\n      </td>\n      <td>\n        <p>Based on AVHRR, SPOT, PROBA-V</p>\n        <p>and Sentinel-3 satellite imagery</p>\n      </td>\n      <td>\n        <p>Global</p>\n      </td>\n      <td>\n        <p>300 m</p>\n      </td>\n      <td>\n        <p>22 classes</p>\n      </td>\n      <td>\n        <p>Every year from 1992 to 2022</p>\n      </td>\n      <td>\n        <p>Aprox. 73%</p>\n      </td>\n      <td>\n        <p><a href=\"http://maps.elie.ucl.ac.be/CCI/viewer/index.php\">http://maps.elie.ucl.ac.be/CCI/viewer/index.php</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>GLCLUC 2020</p>\n      </td>\n      <td>\n        <p>Based on Landsat 5, 7, and 8 imagery.</p>\n      </td>\n      <td>\n        <p>Global (except small islands, Arctic islands and Greenland).</p>\n      </td>\n      <td>\n        <p>30 m</p>\n      </td>\n      <td>\n        <p>13 classes</p>\n      </td>\n      <td>\n        <p>2000, 2005, 2010, 2015, and 2020 </p>\n      </td>\n      <td>\n        <p>Above 85%</p>\n      </td>\n      <td>\n        <p><a href=\"https://glad.umd.edu/dataset/GLCLUC2020\">https://glad.umd.edu/dataset/GLCLUC2020</a></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>CORINE LC</p>\n      </td>\n      <td>\n        <p>Based on Landsat 5, 7, 8, SPOT 4/5, IRS P6, and Sentinel 2.</p>\n      </td>\n      <td>\n        <p>EEA38 and the UK</p>\n      </td>\n      <td>\n        <p>100 m</p>\n      </td>\n      <td>\n        <p>44 classes</p>\n      </td>\n      <td>\n        <p>1990, 2000, 2006, 2012, and 2018 </p>\n      </td>\n      <td>\n        <p>Above 85%</p>\n      </td>\n      <td>\n        <p><a href=\"https://land.copernicus.eu/pan-european/corine-land-cover\">https://land.copernicus.eu/pan-european/corine-land-cover</a></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>A global mountain area map sub-divided by bioclimatic belts has been developed by FAO and made available to national authorities to facilitate the compute this indicator<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup>. This map is the result of combining a global mountain area map developed from the Global Multi-Resolution Terrain Elevation Data (GMTED2010), following the UNEP-WCMC methodology (Ravilious <em>et al. </em>2021) and a mountain bioclimatic belt map created by the Global Mountain Biodiversity Assessment<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup>. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> European Environment Agency member and cooperating countries (EEA38) and the United Kingdom of Great Britain and Northern Ireland. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> Available at: https://mgci-docs.readthedocs.io/en/latest/annexes/annex4.html <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> https://ilias.unibe.ch/goto.php?target=file_2171234 <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p>Data on both sub-indicators are provided by National Statistics Office (NSO) SDG focal points to the FAO following a standard format every three years. This includes the original data and reference sources, and descriptions of how these have been used to derive sub-indicators values. </p>\n<p>In addition, global estimates of both sub-indicators for all countries and territories having mountain areas are computed by FAO using the above-mentioned land cover data sources when national official data do not exist or are incomplete. In such cases, FAO shares country figures with NSO SDG focal points for their validation before publication, in accordance to the IAEG-SDG guidelines of Global Data Flows and Reporting. These figures are calculated through a Python code in a SEPAL<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup> environment. Copies of this code are openly available in a GitHub repository and executable in Google Colab for transparency and reproducibility purposes.<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></p>\n<p> </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> https://openforis.org/solutions/sepal/ <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> <strong>Sub-Indicator A:</strong> https://github.com/sepal-contrib/sepal_mgci/blob/main/Colab_SDG_15_4_2_Sub_A_Default_values.ipynb</p><p><strong> Sub-Indicator B:</strong> https://github.com/sepal-contrib/sepal_mgci/blob/main/Colab_SDG_15_4_2_Sub_B_Default_values.ipynb <a href=\"#footnote-ref-7\">&#x2191;</a></p></div></div>", "FREQ_COLL__GLOBAL"=>"<p>SDG indicator 15.4.2 is updated every three years.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>March of every year, in line with the annual SDG reporting cycle.</p>", "DATA_SOURCE__GLOBAL"=>"<p>NSO SDG focal points provide reports that include values for both sub-indicators, including the original data and reference sources, and descriptions of how these have been used to derive sub-indicators values. FAO provide country-specific values for both sub-indicators when national official data do not exist or are incomplete, in consultation with concerned countries</p>", "COMPILING_ORG__GLOBAL"=>"<p>Food and Agriculture Organization of the United Nations (FAO)</p>", "INST_MANDATE__GLOBAL"=>"<p>Article 1 of FAO&#x2019;s constitution specifies that &#x201C;The Organization shall collect, analyse, interpret, and disseminate information related to nutrition, food and agriculture.&#x201D; In this regard, FAO collects national level data from member countries, which it then standardizes and disseminates through corporate statistical databases. FAO is the custodian UN agency for 21 SDG indicators, including 15.4.2.</p>", "RATIONALE__GLOBAL"=>"<p>Mountain ecosystems are important biodiversity centres that provide valuable ecosystem services to upstream and downstream areas. Yet, mountains are very fragile and impacted easily by both natural and anthropogenic factors. These can include climate change, unplanned agricultural expansion, unplanned urbanization, timber extraction, recreational activities and natural hazards such as landslides and flooding. The degradation of mountain ecosystems such as loss of the glacial cover, mountain biodiversity and green cover will affect the ability of the ecosystem to supply water downstream. The loss of forest and vegetative cover will reduce the ability of the ecosystem to retain soil and prevent landslides and flooding downstream. </p>\n<p>Therefore, monitoring mountain vegetation changes and its estimated impact in terms of ecosystem degradation and recovery provides information on the status of mountain ecosystems. Assessing the changes in land cover differentiated by bioclimatic belts is important in understanding the role that environmental factors, such as climate, play in explaining variations of mountain green cover across regions and helps to better interpret the direction of those changes. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator can be calculated using freely available Earth Observation data and simple Geographic Information Science (GIS) operations that can be processed in free and open source software (FOSS) GIS. Regional and global land cover data derived from Earth observation can play an important role in the absence of, to complement, or to enhance national official data sources. These datasets can help validate and improve national statistics for greater accuracy by ensuring that the data</p>\n<p>Recognizing that this indicator cannot fully capture the complexity of mountain ecosystems across the world, countries are strongly encouraged to use other relevant national or sub-national indicators, data and information to strengthen their interpretation, as well as taking into account the following limitations: </p>\n<ul>\n  <li>Sub-indicator &#x2018;&#x2019;a&#x2019;&#x2019; should be interpreted with care given that: 1) lack of green cover does not necessarily mean that a particular mountain area is degraded (i.e. areas of permanent snow and ice, scree slopes and natural sparsely vegetated areas above the tree line, 2) it does not capture significant drivers of change such as conversion of natural areas to cropland or pastureland, and 3) increase in green cover may due to impacts of climate change in mountain areas (i.e. increase in green cover due to snow and glacier retreat due to global warming). </li>\n  <li>Because land cover refers to the naturally stable aspects of land and the structure of its key elements, transient aspects such as vegetation phenology, snow or flooding cannot be captured by land cover transitions as measured in sub-indicator 15.4.2b. In the context of SDG Target 15.4, this is particularly relevant for snow cover dynamics (snow cover duration within a year), which has been highlighted as a key impact of global warming in mountain ecosystems with direct impacts to water provision (Notarnicola, 2020). </li>\n  <li>Decisions about which land cover transitions are linked to degradation processes would sometimes require information on the use of land, not only land cover. For example, the conversion of tree-covered areas to grassland may be a result of deforestation (change in land cover and land use) or just the result of certain management practices and natural disturbance (change in land cover only). The former could be identified as a negative transition, while the latter could be considered as stable or unchanging. The use of land use information would help to better characterize those changes in the context of sub-indicator &#x201C;b&#x2019;&#x2019;. </li>\n  <li>Both sub-indicators are not able to capture ecosystem degradation drivers that do not necessarily result in changes in land cover. Some examples of this include conversions of natural forests to intensively managed production systems such as plantation forests, orchards and oil palm plantations; conversion of natural and semi-natural grasslands to intensively used pastures, forest and grassland degradation or invasive species invasion, among others. However, the use of more detailed national land use maps may be able to overcome some of these gaps for sub-indicator 15.4.2b.</li>\n  <li>While access to remote sensing imagery has improved dramatically in recent years, there is still a need for essential historical time series that is currently only available at coarse to medium resolution. Therefore, if countries have national land cover maps of higher spatial resolution and comparable or better quality, FAO advises using them, following the same methodology presented here, for the generation of the indicator&#x2019;s values. </li>\n  <li>Area estimations based on remote-sensing-derived land cover maps such as the ESA-CCI product via pixel counting may lead to biased area estimates due to map errors (Olofsson et al. 2014). Countries are encouraged to further refine those estimates by comparing them against reference datasets and applying bias corrections.</li>\n</ul>", "DATA_COMP__GLOBAL"=>"<p><strong>Sub-indicator 15.4.2a</strong>, Mountain Green Cover Index, is defined as: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>M</mi>\n    <mi>G</mi>\n    <mi>C</mi>\n    <mi>I</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>M</mi>\n            <mi>o</mi>\n            <mi>u</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mi>a</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>G</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>C</mi>\n            <mi>o</mi>\n            <mi>v</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>A</mi>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>a</mi>\n          </mrow>\n          <mrow>\n            <mi>n</mi>\n          </mrow>\n        </msub>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>M</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>A</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Where: </p>\n<ul>\n  <li><em>Mountain Green Cover Area<sub>n</sub> </em>= Sum of areas (in km<sup>2</sup>) covered by (1) tree-covered areas, (2) croplands, (3) grasslands, (4) shrub-covered areas and (5) shrubs and/or herbaceous vegetation, aquatic or regularly flooded classes in the reporting period <em>n.</em> </li>\n  <li><em>Total mountain area</em> = Total area of mountains (in km<sup>2</sup>). In both the numerator and denominator, mountain area is defined according to UNEP-WCMC<em> </em>(2002). </li>\n</ul>\n<p><strong>Sub-indicator 15.4.2b</strong>, Proportion of degraded mountain area, is reported as a binary quantification (degraded/non-degraded) of the extent of degraded land over total mountain area, given by:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>P</mi>\n    <mi>r</mi>\n    <mi>o</mi>\n    <mi>p</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>n</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>o</mi>\n    <mi>f</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>d</mi>\n    <mi>e</mi>\n    <mi>g</mi>\n    <mi>r</mi>\n    <mi>a</mi>\n    <mi>d</mi>\n    <mi>e</mi>\n    <mi>d</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>m</mi>\n    <mi>o</mi>\n    <mi>u</mi>\n    <mi>n</mi>\n    <mi>t</mi>\n    <mi>a</mi>\n    <mi>i</mi>\n    <mi>n</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>a</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>a</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>D</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n      </mrow>\n    </mfrac>\n    <mi>&amp;nbsp;</mi>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n  </math></p>\n<p>Where: </p>\n<ul>\n  <li><em>Degraded mountain area<sub>n</sub> </em>= Total degraded mountain area (in km<sup>2</sup>) in the reporting period <em>n.</em> This is, the sum of the areas where land cover change is considered to constitute degradation from the baseline period. Land cover changes that constitute degradation (as well as improvement and neutral transitions) are defined through a land cover change matrix. The generic land cover change matrix used to produce the FAO global default estimates is included in Annex 1. <em>Total mountain area</em> = Total area of mountains (in km<sup>2</sup>). In both the numerator and denominator, mountain area is defined according to UNEP-WCMC<em> </em>(2002). </li>\n</ul>\n<p>If the country/region has no mountain area, it is assigned the value NA.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Once received, national reported indicator values undergo a review process by FAO to ensure the correct use of definitions and methodology as well as internal consistency.</p>\n<p>For those countries that have not submitted national indicator values, FAO will provide the NSO SDG focal points with national estimates derived from available global or regional data sources for review and validation.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>For countries where data is not available or incomplete, FAO will provide default estimates derived from global or regional data sources that would then be validated by national focal points.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Not applicable, as the indicator has a universal coverage.</p>", "REG_AGG__GLOBAL"=>"<p>The indicator is aggregated to the regional and global level by, in the case of sub-indicator 15.4.2a, summing the spatial extent of green cover and total mountain area, and in the case of 15.4.2b, summing the spatial extent of degraded over total mountain area for all countries and territories reporting in a specific region or globally. </p>", "DOC_METHOD__GLOBAL"=>"<p>Detailed guidance and computation tools to support countries to compute the indicator and report its values using standardised reporting tables is available online at https://mgci-docs.readthedocs.io/en/latest/.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>FAO is responsible for the quality of the internal statistical processes used to compile the published datasets. The FAO Statistics Quality Assurance Framework (SQAF), available at: <a href=\"http://www.fao.org/docrep/019/i3664e/i3664e.pdf\">http://www.fao.org/docrep/019/i3664e/i3664e.pdf</a>, provides the necessary principles, guidelines and tools to carry out quality assessments. FAO is performing an internal bi-annual survey (FAO Quality Assessment and Planning Survey) designed to gather information on all of FAO&#x2019;s statistical activities, notably to assess the extent to which quality standards are being implemented with a view to increasing compliance with the quality dimensions of SQAF, documenting best practices and prepare quality improvement plans, where necessary. Domain-specific quality assurance activities are carried out systematically (e.g. quality reviews, self-assessments, compliance monitoring).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Date reported by countries to FAO are subject to a rigorous review process to ensure correct use of definitions and methodology as well as internal consistency. A comparison is made with past assessments and other existing data sources. Regular contacts between national correspondents and FAO staff by e-mail form part of this review process.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality of statistics produced and disseminated by the FAO is evaluated in terms of fitness for use i.e. the degree to which statistics meet the user&#x2019;s requirements. The quality dimensions assessed are: Relevance; Accuracy and Reliability; Timeliness and Punctuality; Coherence and Comparability; Accessibility and Clarity. Quality dimensions definitions are provided in the FAO Statistical Quality Assurance Framework (SQAF), which provides the definition of quality and describes quality principles for statistical outputs; statistical processes; institutional environment (http://www.fao.org/docrep/019/i3664e/i3664e.pdf). The SQAF is based on the Fundamental Principles of Official Statistics and the Principles Governing International Statistical Activities (CCSA). Adherence to these principles ensures the quality of FAO statistical production processes and of statistical outputs. Regular quality assessments are conducted through the FAO Quality Assessment and Planning Survey (QAPS), a bi-annual survey designed to gather information on all of FAO&#x2019;s statistical activities, which is used to assess the extent to which quality standards are being met with a view to increasing compliance with the SQAF, and to document best practices and provide guidance for improvement where necessary.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The indicator is generated by geospatial data and therefore has almost universal coverage. Countries or territories with no values on the global SDG database are either A) countries or territories with no mountains where the indicator is not applicable (indicated as NA), B) countries or territories that have not validated FAO&#x2019;s estimates and yet have not provided figures of their own, or C) countries or territories where available land cover data is deemed inadequate for the purposes of this indicator (at the time of writing this is the case of Greenland and some small island archipelagos close to the North and South Poles).</p>\n<p><strong>Time series:</strong></p>\n<p>Country, regional and global figures are available since the year 2000. </p>\n<p>For sub-indicator 15.4.2a, data is available for the years 2000, 2005, 2010, 2015 and 2018, and subsequently every three years. </p>\n<p>For sub-indicator 15.4.2b, data is available for the reporting period 2000-2015 (baseline), 2018, and subsequently every three years. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>In the global SDG database, both sub-indicators are disaggregated by mountain bioclimatic belt as defined by K&#xF6;rner <em>et al. </em>(2011) (see section 2c. Classifications). In addition, sub-indicator 15.4.2a is disaggregated by the 10 SEEA classes included in Table 2. Those values are reported both as proportions (percent) and area (in square kilometres). </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The global default sources of land cover data for this indicator have varying reported accuracies. The CORINE and the GLCLUC2020 land cover products have overall reported accuracies of &#x2265; 85%. The ESA-CCI Land Cover product has an overall accuracy of 73.2%. However, these accuracy estimates were calculated using the original land cover legends of these products. As the methodology presented here is based on use of aggregate classes, the accuracy can be expected to be higher. The accuracy of the global land cover products can vary regionally and by land cover type. For the same reason, the presented indicator values may differ from those derived using national land cover maps. </p>\n<p>The reporting format help to ensure that countries provide references for national data sources used, associated definitions and terminology as well as more detailed analysis of the data based on more detailed land cover classifications. </p>", "OTHER_DOC__GLOBAL"=>"<p>ESA (2017) Land Cover CCI Product User Guide Version 2. Tech. Rep. Available at:<strong> </strong><a href=\"http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf\"><u>maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf</u></a></p>\n<p>FAO-GTOS. (2009). <em>Land Cover: Assessment of the status of the development of the standards for the Terrestrial Essential Climate Variables</em>. Global Terrestrial Observing System, Rome. </p>\n<p>IPBES (2018): <em>Summary for policymakers of the assessment report on land degradation and restoration of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services</em>. R. Scholes, L. Montanarella, A. Brainich, N. Barger, B. ten Brink, M. Cantele, B. Erasmus, J. Fisher, T. Gardner, T. G. Holland, F. Kohler, J. S. Kotiaho, G. Von Maltitz, G. Nangendo, R. Pandit, J. Parrotta, M. D. Potts, S. Prince, M. Sankaran and L. Willemen (eds.). IPBES secretariat, Bonn, Germany. 44 pages</p>\n<p>K&#xF6;rner, C., Paulsen, J., &amp; Spehn, E. (2011). A definition of mountains and their bioclimatic belts for global comparisons of biodiversity data. <em>Alpine Botany, 121</em>, 73-78.</p>\n<p>Notarnicola, C. (2020) Hotspots of snow cover changes in global mountain regions over 2000-2018. <em>Remote Sensing of Environment </em>243, 111781.</p>\n<p>Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., Wulder, M. A. (2014): Good practices for estimating area and assessing accuracy of land change<em>. Remote Sensing of Environment</em>, 148, 42-57.</p>\n<p>Potapov, P., Hansen, MC., Pickens, A., Hernandez-Serna, A., Tyukavina, A., Turubanova, S., Zalles, V., Li, X., Khan, A., Stolle, F., Harris, N., Song, X-P., Baggett, A., Kommareddy, I., and Kommareddy, A. (2022) The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results. <em>Frontiers in Remote Sensing</em> 3: 856903. doi: 10.3389/frsen.2022.856903.</p>\n<p>Ravilious, C., Tshwene-Mauchaza, B. and Kapos, V. (2021). <em>Validation and implementation of the Kapos Mountain Classification: Assessing the impact of DEM resolution on the mapping of mountain classes following the Kapos methodology</em>. UNEP-WCMC, Cambridge, UK.</p>\n<p>Santoro, M., Kirches, G., Wevers, J., Boettcher, M., Brockmann, C., Lamarche, C., . . . Defourny, P. (2015). <em>Land Cover CCI PRODUCT USER GUIDE VERSION 2.0.</em> European Spatial Agency. European Spatial Agency. Retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf</p>\n<p>Sims, N.C., Newnham, G.J., England, J.R., Guerschman, J., Cox, S.J.D., Roxburgh, S.H., Viscarra Rossel, R.A., Fritz, S. and Wheeler, I. (2021). <em>Good Practice Guidance. SDG Indicator 15.3.1, Proportion of Land That Is Degraded Over Total Land Area. Version 2.0.</em> United Nations Convention to Combat Desertification, Bonn, Germany </p>\n<p>UN Statistical Division (2014). <em>System of Environmental Economic Accounting 2012 &#x2014; Central Framework. </em>New York, USA.</p>\n<p>UNEP-WCMC (2002). <em>Mountain Watch: Environmental change and sustainable development in mountains.</em> Cambridge, UK</p>\n<p><strong>Annex 1. </strong>Generic land cover change matrix used to produce the FAO global default estimates for Sub-indicator 15.4.2b). Land cover change processes are colour coded as improvement (green), stable (yellow) or degradation (red).</p>\n<table>\n  <tbody>\n    <tr>\n      <td></td>\n      <td colspan=\"10\">\n        <p><strong>FINAL CLASS</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td></td>\n      <td>\n        <p><strong>Artificial surfaces</strong></p>\n      </td>\n      <td>\n        <p><strong>Cropland</strong></p>\n      </td>\n      <td>\n        <p><strong>Grassland</strong></p>\n      </td>\n      <td>\n        <p><strong>Tree-covered areas</strong></p>\n      </td>\n      <td>\n        <p><strong>Shrub-covered areas</strong></p>\n      </td>\n      <td>\n        <p><strong>Wetland</strong></p>\n      </td>\n      <td>\n        <p><strong>Sparsely vegetated areas</strong></p>\n      </td>\n      <td>\n        <p><strong>Barren land</strong></p>\n      </td>\n      <td>\n        <p><strong>Permanent snow &amp; glaciers</strong></p>\n      </td>\n      <td>\n        <p><strong>Water bodies</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>ORIGINAL CLASS</strong></p>\n      </td>\n      <td colspan=\"10\"></td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Artificial Surfaces</strong></p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Cropland</strong></p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I </p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Grassland</strong></p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Tree-covered areas</strong></p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Shrub-covered areas</strong></p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Wetlands</strong></p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Sparsely vegetated areas</strong></p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Barren land</strong></p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Permanent snow &amp; glaciers</strong></p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Water bodies</strong></p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D </p>\n      </td>\n      <td>\n        <p>D </p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>D</p>\n      </td>\n      <td>\n        <p>I</p>\n      </td>\n      <td>\n        <p>S</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "indicator_sort_order"=>"15-04-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.5.1", "slug"=>"15-5-1", "name"=>"Índice de la Lista Roja", "url"=>"/site/es/15-5-1/", "sort"=>"150501", "goal_number"=>"15", "target_number"=>"15.5", "global"=>{"name"=>"Índice de la Lista Roja"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Índice de la Lista Roja", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Índice de la Lista Roja", "indicator_number"=>"15.5.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Las especies del mundo se ven afectadas por diversos procesos amenazantes, \ncomo la destrucción y degradación del hábitat, la sobreexplotación, las especies \nexóticas invasoras, las perturbaciones humanas, la contaminación y el \ncambio climático. \n\nEste indicador puede utilizarse para evaluar los cambios generales en \nel riesgo de extinción de grupos de especies como resultado de estas \namenazas y el grado en que se están mitigando.\n\nEl valor del Índice de la Lista Roja va de 1 (todas las especies se clasifican \ncomo \"Preocupación Menor\") a 0 (todas las especies se clasifican como \"Extintas\") e \nindica el grado de avance general del conjunto de especies hacia la extinción. \nPor lo tanto, el Índice de la Lista Roja global permite comparar conjuntos \nde especies tanto en su nivel general de riesgo de extinción \n(es decir, su grado de amenaza promedio) como en la velocidad a la \nque este riesgo cambia con el tiempo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.5.1&seriesCode=ER_RSK_LST&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Índice de la Lista Roja ER_RSK_LST</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-05-01.pdf\">Metadatos 15-5-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The world’s species are impacted by a number of threatening processes, including habitat destruction \nand degradation, overexploitation, invasive alien species, human disturbance, pollution and climate \nchange. \n\nThis indicator can be used to assess overall changes in the extinction risk of groups of species as a \nresult of these threats and the extent to which threats are being mitigated. \n\nThe Red List Index value ranges from 1 (all species are categorized as ‘Least Concern’) to 0 (all species are \ncategorized as ‘Extinct’), and so indicates how far the set of species has moved overall towards \nextinction. Thus, the global Red List Index allows comparisons between sets of species in both their \noverall level of extinction risk (i.e., how threatened they are on average), and in the rate at which this risk \nchanges over time. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.5.1&seriesCode=ER_RSK_LST&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Red List Index ER_RSK_LST</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-05-01.pdf\">Metadata 15-5-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Las especies del mundo se ven afectadas por diversos procesos amenazantes, \ncomo la destrucción y degradación del hábitat, la sobreexplotación, las especies \nexóticas invasoras, las perturbaciones humanas, la contaminación y el \ncambio climático. \n\nEste indicador puede utilizarse para evaluar los cambios generales en \nel riesgo de extinción de grupos de especies como resultado de estas \namenazas y el grado en que se están mitigando.\n\nEl valor del Índice de la Lista Roja va de 1 (todas las especies se clasifican \ncomo \"Preocupación Menor\") a 0 (todas las especies se clasifican como \"Extintas\") e \nindica el grado de avance general del conjunto de especies hacia la extinción. \nPor lo tanto, el Índice de la Lista Roja global permite comparar conjuntos \nde especies tanto en su nivel general de riesgo de extinción \n(es decir, su grado de amenaza promedio) como en la velocidad a la \nque este riesgo cambia con el tiempo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.5.1&seriesCode=ER_RSK_LST&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Zerrenda Gorriaren indizea ER_RSK_LST</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-05-01.pdf\">Metadatuak 15-5-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.5: Take urgent and significant action to reduce the degradation of natural habitats, halt the loss of biodiversity and, by 2020, protect and prevent the extinction of threatened species</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.5.1: Red List Index</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Red List Index (Value, UpperBound, LowerBound) </p>", "META_LAST_UPDATE__GLOBAL"=>"2022-12-16", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Disaggregations of the Red List Index are also of particular relevance as indicators towards the following SDG targets (Brooks et al. 2015): SDG 2.4 Red List Index (species used for food and medicine); SDG 2.5 Red List Index (wild relatives and local breeds); SDG 12.2 Red List Index (impacts of utilisation) (Butchart 2008); SDG 12.4 Red List Index (impacts of pollution); SDG 13.1 Red List Index (impacts of climate change); SDG 14.1 Red List Index (impacts of pollution on marine species); SDG 14.2 Red List Index (marine species); SDG 14.3 Red List Index (reef-building coral species) (Carpenter et al. 2008); SDG 14.4 Red List Index (impacts of utilisation on marine species); SDG 15.1 Red List Index (terrestrial &amp; freshwater species); SDG 15.2 Red List Index (forest-specialist species); SDG 15.4 Red List Index (mountain species); SDG 15.7 Red List Index (impacts of utilisation) (Butchart 2008); and SDG 15.8 Red List Index (impacts of invasive alien species) (Butchart 2008, McGeoch et al. 2010).</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Union for Conservation of Nature (IUCN)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Union for Conservation of Nature (IUCN)</p>\n<p>BirdLife International (BLI)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The Red List Index measures change in aggregate extinction risk across groups of species. It is based on genuine changes in the number of species in each category of extinction risk on The IUCN Red List of Threatened Species (www.iucnredlist.org) is expressed as changes in an index ranging from 0 to 1.</p>\n<p><strong>Concepts:</strong></p>\n<p>Threatened species are those listed on The IUCN Red List of Threatened Species in the categories Vulnerable, Endangered, or Critically Endangered (i.e., species that are facing a high, very high, or extremely high risk of extinction in the wild in the medium-term future). Changes over time in the proportion of species threatened with extinction are largely driven by improvements in knowledge and changing taxonomy. The indicator excludes such changes to yield a more informative indicator than the simple proportion of threatened species. It therefore measures change in aggregate extinction risk across groups of species over time, resulting from genuine improvements or deteriorations in the status of individual species. It can be calculated for any representative set of species that have been assessed for The IUCN Red List of Threatened Species at least twice (Butchart et al. 2004, 2005, 2007). To calculate the Red List Index for individual countries and regions, each species contributing to the index is weighted by the proportion of its global range within the particular country or region. The resulting index therefore shows the aggregate extinction risk for species within the country or region relative to its potential contribution to global species extinction risk (within the taxonomic groups included).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Index.</p>\n<p>The Red List Index for a particular country or region is an index of the aggregate extinction risk for species within the country or region relative to its potential contribution to global species extinction risk (within the taxonomic groups included).</p>\n<p>It is measured on a scale of 0 to 1, where 1 is the maximum contribution that the country or region can make to global species survival, equating to all species being classified as Least Concern on the IUCN Red List, and 0 is the minimum contribution that the country or region can make to global species survival, equating to all species in the country or region having gone extinct.</p>\n<p> </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The Red List Index is based on categorisations of species on the IUCN Red List of Threatened Species (<a href=\"http://www.iucnredlist.org\">www.iucnredlist.org</a>), defined following IUCN (2012a).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The Red List Index is based on data from The IUCN Red List of Threatened Species (<a href=\"http://www.iucnredlist.org\">www.iucnredlist.org</a>), in particular the numbers of species in each Red List category of extinction risk, and changes in these numbers over time resulting from genuine improvements or deteriorations in the status of species. Data on species&#x2019; distribution, population size, trends and other parameters that underpin Red List assessments are gathered from published and unpublished sources, species experts, scientists, and conservationists through correspondence, workshops, and electronic fora. </p>", "COLL_METHOD__GLOBAL"=>"<p>A detailed description of the Red List Assessment process is provided at <a href=\"https://www.iucnredlist.org/assessment/process\">https://www.iucnredlist.org/assessment/process</a>. See also information under other categories.</p>", "FREQ_COLL__GLOBAL"=>"<p>The IUCN Red List of Threatened Species is updated at least three times per year. Red List Indices for sets of species that have been comprehensively reassessed are usually released alongside the relevant update of the IUCN Red List. Data are stored and managed in the Species Information Service database, and are made freely available for non-commercial use through the IUCN Red List website (www.iucnredlist.org). Re-assessments of extinction risk are required for every species assessed on The IUCN Red List of Threatened Species once every ten years, and ideally undertaken once every five years. A Red List Strategic Plan details a calendar of upcoming re-assessments for each taxonomic group. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The Red List Index is updated annually in November-December using the latest data from reassessments on the IUCN Red List.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National agencies producing relevant data include government, non-governmental organisations (NGOs), and academic institutions working jointly and separately. Data are gathered from published and unpublished sources, species experts, scientists, and conservationists through correspondence, workshops, and electronic fora. Data are submitted by national agencies to IUCN, or are gathered through initiatives of the Red List Partnership. The members of the Red List Partnership are listed at <a href=\"https://www.iucnredlist.org/about/partners\">https://www.iucnredlist.org/about/partners</a>, and currently include: ABQ BioPark; Arizona State University Centre for Biodiversity Outcomes; BirdLife International; Botanic Gardens Conservation International; Conservation International; Global Wildlife Conservation; Missouri Botanical Garden; NatureServe; Royal Botanic Gardens, Kew; Sapienza University of Rome; Texas A&amp;M University; and Zoological Society of London.</p>", "COMPILING_ORG__GLOBAL"=>"<p><strong>Name:</strong></p>\n<p>International Union for Conservation of Nature (IUCN)</p>\n<p><strong>Description:</strong></p>\n<p>Compilation and reporting of the Red List Index at the global level is conducted by the International Union for Conservation of Nature (IUCN) and BirdLife International, on behalf of the Red List Partnership. </p>", "INST_MANDATE__GLOBAL"=>"<p>Responsibility for overseeing Red List assessments, which underpin the Red List Index, is assigned to</p>\n<p>Red List Authorities according to the IUCN Red List Rules of Procedure (https://nc.iucnredlist.org/redlist/content/attachment_files/Rules_of_Procedure_for_IUCN_Red_List_2017-2020.pdf). The role of Red List Authorities is to ensure that all species within their remit are correctly assessed against the IUCN Red List Categories and Criteria at least once every ten years and, if possible, every five years. Further details of the roles and responsibilities of Red List Authorities are provided at <a href=\"https://www.iucnredlist.org/assessment/authorities\">https://www.iucnredlist.org/assessment/authorities</a>, and the full list and contact details for all appointed Red List Authorities are available at https://www.iucn.org/commissions/ssc-groups.</p>\n<p> </p>", "RATIONALE__GLOBAL"=>"<p>The world&#x2019;s species are impacted by a number of threatening processes, including habitat destruction and degradation, overexploitation, invasive alien species, human disturbance, pollution and climate change. This indicator can be used to assess overall changes in the extinction risk of groups of species as a result of these threats and the extent to which threats are being mitigated.</p>\n<p>The Red List Index value ranges from 1 (all species are categorized as &#x2018;Least Concern&#x2019;) to 0 (all species are categorized as &#x2018;Extinct&#x2019;), and so indicates how far the set of species has moved overall towards extinction. Thus, the global Red List Index allows comparisons between sets of species in both their overall level of extinction risk (i.e., how threatened they are on average), and in the rate at which this risk changes over time. A downward trend in the global Red List Index over time means that the expected rate of future species extinctions is worsening (i.e., the rate of biodiversity loss is increasing). An upward trend means that the expected rate of species extinctions is abating (i.e., the rate of biodiversity loss is decreasing), and a horizontal line means that the expected rate of species extinctions is remaining the same, although in each of these cases it does not mean that biodiversity loss has stopped. An upward global Red List Index trend would indicate that the SDG Target 15.5 of reducing the degradation of natural habitats and protecting threatened species is on track. A global Red List Index value of 1 would indicate that biodiversity loss has been halted.</p>\n<p>The name &#x201C;Red List Index&#x201D; should not be taken to imply that the indicator is produced as a composite indicator of a number of disparate metrics (in the same way that, e.g., the Multidimensional Poverty Index is compiled). The Red List Index provides an indicator of trends in species&#x2019; extinction risk, as measured using the IUCN Red List Categories and Criteria (Mace et al. 2008, IUCN 2012a), and is compiled from data on changes over time in the Red List Category for each species, excluding any changes driven by improved knowledge or revised taxonomy.</p>\n<p>The Red List Index was used as an indicator towards the 2011&#x2013;2020 Strategic Plan for Biodiversity (CBD 2014, Tittensor et al. 2014, CBD 2020a), the Convention on Biological Diversity&#x2019;s 2010 Target (Butchart et al. 2010) and Millennium Development Goal 7. It has been proposed as a Headline Indicator in the draft post-2020 Global Biodiversity Framework (CBD 2020b).</p>", "REC_USE_LIM__GLOBAL"=>"<p>There are four main sources of uncertainty associated with Red List Index values and trends.</p>\n<ol>\n  <li>Inadequate, incomplete or inaccurate knowledge of a species&#x2019; status. This uncertainty is minimized by assigning estimates of extinction risk to categories that are broad in magnitude and timing.</li>\n  <li>Delays in knowledge about a species becoming available for assessment. Such delays apply to a small (and diminishing) proportion of status changes, and can be overcome in the Red List Index through back-casting (Butchart et al. 2007).</li>\n  <li>Inconsistency between species assessments. These can be minimized by the requirement to provide supporting documentation detailing the best available data, with justifications, sources, and estimates of uncertainty and data quality, which are checked and standardized by IUCN through Red List Authorities, a Red List Technical Working Group and an independent Standards and Petitions Sub-committee. Further, detailed Guidelines on the Application of the Categories and Criteria are maintained (IUCN SPSC 2019), as is an online training course (in English, Spanish and French).</li>\n  <li>Species that are too poorly known for the Red List Criteria to be applied are assigned to the Data Deficient category. For birds, only 0.8% of extant species are evaluated as Data Deficient, compared with 24% of amphibians. If Data Deficient species differ in the rate at which their extinction risk is changing, the Red List Index may give a biased picture of the changing extinction risk of the overall set of species. The degree of uncertainty this introduces is estimated through a bootstrapping procedure that randomly assigns each Data Deficient species a category based on the numbers of non-Data Deficient species in each Red List category for the set of species under consideration, and repeats this for 1,000 iterations, plotting the 2.5 and 97.5 percentiles as lower and upper confidence intervals for the median.</li>\n</ol>\n<p>The main limitation of the Red List Index is related to the fact that the Red List Categories are relatively broad measures of status, and thus the Red List Index for any individual taxonomic group can practically be updated at intervals of at least four years. However, as the overall index is aggregated across multiple taxonomic groups, with groups reassessed asynchronously, it can be updated annually. A further limitation is that the Red List Index does not reflect particularly well the deteriorating status of common species that remain abundant and widespread but are declining slowly.</p>", "DATA_COMP__GLOBAL"=>"<p>The Red List Index is calculated at a point in time by first multiplying the number of species in each Red List Category by a weight (ranging from 1 for &#x2018;Near Threatened&#x2019; to 5 for &#x2018;Extinct&#x2019; and &#x2018;Extinct in the Wild&#x2019;) and summing these values. This is then divided by a maximum threat score, which is the total number of species multiplied by the weight assigned to the &#x2018;Extinct&#x2019; category. This final value is subtracted from 1 to give the Red List Index value.</p>\n<p>Mathematically this calculation is expressed as: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>R</mi>\n    <mi>L</mi>\n    <msub>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mi>t</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mn>1</mn>\n    <mo>-</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>&#x3A3;</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n          </mrow>\n        </msub>\n        <mi>&amp;nbsp;</mi>\n        <msub>\n          <mrow>\n            <mi>W</mi>\n          </mrow>\n          <mrow>\n            <mi>c</mi>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mi>t</mi>\n                <mo>,</mo>\n                <mi>s</mi>\n              </mrow>\n            </mfenced>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <mo>(</mo>\n        <msub>\n          <mrow>\n            <mi>W</mi>\n          </mrow>\n          <mrow>\n            <mi>E</mi>\n            <mi>X</mi>\n          </mrow>\n        </msub>\n        <mi>*</mi>\n        <mi>N</mi>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where Wc(t,s) is the weight for category (c) at time (t) for species (s) (the weight for &#x2018;Critically Endangered&#x2019; = 4, &#x2018;Endangered&#x2019; = 3, &#x2018;Vulnerable&#x2019; = 2, &#x2018;Near Threatened&#x2019; = 1, &#x2018;Least Concern&#x2019; = 0. &#x2018;Critically Endangered&#x2019; species tagged as &#x2018;Possibly Extinct&#x2019; or &#x2018;Possibly Extinct in the Wild&#x2019; are assigned a weight of 5); WEX = 5, the weight assigned to &#x2018;Extinct&#x2019; or &#x2018;Extinct in the Wild&#x2019; species; and N is the total number of assessed species, excluding those assessed as Data Deficient in the current time period, and those considered to be &#x2018;Extinct&#x2019; in the year the set of species was first assessed.</p>\n<p>The formula requires that:</p>\n<ul>\n  <li>Exactly the same set of species is included in all time periods, and</li>\n  <li>The only Red List Category changes are those resulting from genuine improvement or deterioration in status (i.e., excluding changes resulting from improved knowledge or taxonomic revisions), and</li>\n  <li>Data Deficient species are excluded (or treated according to the procedure described above).</li>\n</ul>\n<p>In many cases, species lists will change slightly from one assessment to the next (e.g., owing to taxonomic revisions). The conditions can therefore be met by retrospectively adjusting earlier Red List categorizations using current information and taxonomy. This is achieved by assuming that the current Red List Categories for the taxa have applied since the set of species was first assessed for the Red List, unless there is information to the contrary that genuine status changes have occurred. Such information is often contextual (e.g., relating to the known history of habitat loss within the range of the species). If there is insufficient information available for a newly added species, it is not incorporated into the Red List Index until it is assessed for a second time, at which point earlier assessments are retrospectively corrected by extrapolating recent trends in population, range, habitat and threats, supported by additional information. To avoid spurious results from biased selection of species, Red List Indices are typically calculated only for taxonomic groups in which all species worldwide have been assessed for the Red List, or for samples of species that have been systematically or randomly selected.</p>\n<p>The methods and scientific basis for the Red List Index were described by Butchart et al. (2004, 2005, 2007, 2010). </p>\n<p>Butchart et al. (2010) also described the methods by which Red List Indices for different taxonomic groups are aggregated to produce a single multi-taxon Red List Index. Specifically, aggregated Red List Indices are calculated as the arithmetic mean of modelled Red List Indices. Red List Indices for each taxonomic group are interpolated linearly for years between data points and extrapolated linearly (with a slope equal to that between the two closest assessed points) to align them with years for which Red List Indices for other taxa are available. The Red List Indices for each taxonomic group for each year are modelled to take into account various sources of uncertainty: </p>\n<ol>\n  <li>Data Deficiency: Red List categories (from Least Concern to Extinct) are assigned to all Data Deficient species, with a probability proportional to the number of species in non-Data Deficient categories for that taxonomic group; </li>\n  <li>Extrapolation uncertainty: although RLIs were extrapolated linearly based on the slope of the closest two assessed point, there is uncertainty about how accurate this slope may be. To incorporate this uncertainty, rather than extrapolating deterministically, the slope used for extrapolation is selected from a normal distribution with a probability equal to the slope of the closest two assessed points, and standard deviation equal to 60% of this slope (i.e., the CV is 60%); </li>\n  <li>Temporal variability: the &#x2018;true&#x2019; Red List Index likely changes from year to year, but because assessments are repeated only at multi-year intervals, the precise value for any particular year is uncertain. </li>\n</ol>\n<p>To make this uncertainty explicit, the Red List Index value for a given taxonomic group in a given year is assigned from a moving window of five years, centred on the focal year (with the window set as 3-4 years for the first two and last two years in the series). Note that assessment uncertainty cannot yet be incorporated into the index. Practically, these uncertainties are incorporated into the aggregated Red List Indices as follows: Data Deficient species were allotted a category as described above, and a Red List Index for each taxonomic group was calculated interpolating and extrapolating as described above. A final Red List Index value was assigned to each taxonomic group for each year from a window of years as described above. Each such &#x2018;run&#x2019; produced a Red List Index for the complete time period for each taxonomic group, incorporating the various sources of uncertainty. Ten thousand such runs are generated for each taxonomic group, and the mean is calculated.</p>\n<p>Methods for generating national disaggregations of the Red List Index are described below in section 5 on Data availability and disaggregation.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Red List Assessments are checked before submission to IUCN by Assessors and Red List Authority Coordinators, to ensure that all of the required supporting information is provided in the appropriate format, distribution maps follow the required mapping standards (<a href=\"https://www.iucnredlist.org/resources/mappingstandards\">https://www.iucnredlist.org/resources/mappingstandards</a>), and the IUCN Red List Criteria have been applied appropriately and consistently following IUCN Guidelines (IUCN SPSC 2019). For further details, see <a href=\"https://www.iucnredlist.org/assessment/process\">https://www.iucnredlist.org/assessment/process</a>. All submitted assessments must be reviewed by at least one Reviewer designated by the Red List Authority. For more details on the review process, see the Rules of Procedure (<a href=\"https://nc.iucnredlist.org/redlist/content/attachment_files/Rules_of_Procedure_for_IUCN_Red_List_2017-2020.pdf\">https://nc.iucnredlist.org/redlist/content/attachment_files/Rules_of_Procedure_for_IUCN_Red_List_2017-2020.pdf</a>).</p>\n<p>When Red List Indices are updated each year, the updated index (and underlying numbers of species in each Red List Category) are made available for review by countries prior to submission to the SDG Indicators Database. This is achieved through updating the country profiles in the Integrated Biodiversity Assessment Tool (<a href=\"https://ibat-alliance.org/country_profiles\">https://ibat-alliance.org/country_profiles</a>) and circulating these for consultation and review to CBD National Focal Points, SDG National Statistical Office Focal Points, and IUCN State Members.</p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made to the index with respect to harmonization of breakdowns or for compliance with specific international or national definitions.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Red List Indices for each taxonomic group are interpolated linearly for years between data points and extrapolated linearly (with a slope equal to that between the two closest assessed points, except for corals) back to the earliest time point and forwards to the present for years for which estimates are not available. The start year of the aggregated index is set as ten years before the first assessment year for the taxonomic group with the latest starting point. Corals are not extrapolated linearly because declines are known to have been much steeper subsequent to 1996 (owing to extreme bleaching events) than before. Therefore, the rate of decline prior to 1996 is set as the average of the rates for the other taxonomic groups.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>The Red List Index is calculated globally based on assessments of extinction risk of each species included, because many species have distributions that span many countries. Thus, while there is certainly uncertainty around the Red List Index, there are no missing values as such, and so no imputation is necessary.</p>", "REG_AGG__GLOBAL"=>"<p>The Red List Categories and Criteria are applied for each species on The IUCN Red List of Threatened Species and are determined globally and provided principally by the Specialist Groups and stand-alone Red List Authorities of the IUCN Species Survival Commission, IUCN Secretariat-led initiatives, and Red List partner organizations. The staff of the IUCN Global Species Programme compile, validate, and curate these data, and are responsible for publishing and communicating the results. Each individual species assessment is supported by the application of metadata and documentation standards (IUCN 2013), including classifications of, for example, threats and conservation actions (Salafsky et al. 2008). </p>\n<p>Red List assessments are undertaken either through open workshops or open-access web-based discussion fora. Assessments are reviewed by the appropriate Red List Authority (an individual or organization appointed by the IUCN Species Survival Commission to review assessments for specific species or groups of species) to ensure standardisation and consistency in the interpretation of information and application of the criteria. A Red List Technical Working Group and the IUCN Red List Unit work to ensure consistent categorization between species, groups and assessments. Finally, a Standards and Petitions Sub-committee monitors the process and resolves challenges and disputes over Red List assessments.</p>\n<p>In addition, IUCN publishes guidelines on applying the IUCN Red List Categories and Criteria at regional or national scales (IUCN 2012b). Based on these, many countries have initiated programmes to assess the extinction risk of species occurring within their borders. These countries will be able to implement the Red List Index based on national extinction risk, once they have carried out at least two national Red Lists using the IUCN system in a consistent way (Bubb et al. 2009). An increasing number of countries have now completed national Red List Indices for a range of taxa (e.g., G&#xE4;rdenfors 2010, Pihl &amp; Flensted 2011).</p>\n<p>While global Red List Indices can be disaggregated to show trends for species at smaller spatial scales, the reverse is not true. National or regional Red List Indices cannot be aggregated to produce Red List Indices showing global trends. This is because a taxon&#x2019;s global extinction risk has to be evaluated at the global scale and cannot be directly determined from multiple national scale assessments across its range (although the data from such assessments can be aggregated for inclusion in the global assessment).</p>\n<p>Methods for generating regional disaggregations of the Red List Index are described below in section 5 on Data availability and disaggregation.</p>", "DOC_METHOD__GLOBAL"=>"<p><strong>Methods and guidance available to countries for the compilation of the data at the national level:</strong></p>\n<p>See above. In sum: the data underlying the Red List Index are compiled under the authority of the IUCN Red List Committee, through application of the IUCN Red List Categories &amp; Criteria (<a href=\"https://portals.iucn.org/library/node/10315\">https://portals.iucn.org/library/node/10315</a>). This includes submissions of endemics from national red list processes, where these have been conducted following the &#x201C;Guidelines for application of IUCN Red List Criteria at Regional and National Levels&#x201D; (<a href=\"https://portals.iucn.org/library/node/10336\">https://portals.iucn.org/library/node/10336</a>) and following the &#x201C;Required and Recommended Supporting Information for IUCN Red List Assessments&#x201D; (<a href=\"https://www.iucnredlist.org/resources/supporting-information-guidelines\">https://www.iucnredlist.org/resources/supporting-information-guidelines</a>). Assessments may be submitted in all three IUCN languages (English, French and Spanish) and Portuguese. All assessments are peer reviewed through the relevant Red List Authority for the species or species group in question, as documented in the Red List Rules of Procedure (<a href=\"https://www.iucnredlist.org/resources/rules-of-procedure\">https://www.iucnredlist.org/resources/rules-of-procedure</a>); see in particular Annex 3, the &#x201C;Details of the Steps Involved in the IUCN Red List Process&#x201D; (<a href=\"https://nc.iucnredlist.org/redlist/content/attachment_files/Details_of_the_Steps_Involved_in_the_IUCN_Red_List_Process.pdf\">https://nc.iucnredlist.org/redlist/content/attachment_files/Details_of_the_Steps_Involved_in_the_IUCN_Red_List_Process.pdf</a>).</p>\n<p>The key document providing international recommendations and guidelines to countries and all involved in application of the IUCN Red List Categories &amp; Criteria (<a href=\"https://portals.iucn.org/library/node/10315\">https://portals.iucn.org/library/node/10315</a>) is the &#x201C;Guidelines for Using the IUCN Red List Categories and Criteria&#x201D; (<a href=\"https://www.iucnredlist.org/resources/redlistguidelines\">https://www.iucnredlist.org/resources/redlistguidelines</a>; available in English, French, Spanish, and Portuguese) accompanied by the &#x201C;Required and Recommended Supporting Information for IUCN Red List Assessments&#x201D;. For countries (and regions), this is supplemented by the &#x201C;Guidelines for application of IUCN Red List Criteria at Regional and National Levels&#x201D; (<a href=\"https://portals.iucn.org/library/node/10336\">https://portals.iucn.org/library/node/10336</a>). To support the calculation of Red List Indices for any given country (or region), &#x201C;Code (and documentation) for calculating and plotting national RLIs weighted by the proportion of each species&#x2019; distribution within a country or region&#x201D; is posted online (Dias et al. 2020; <a href=\"https://github.com/BirdLifeInternational/rli-codes\">https://github.com/BirdLifeInternational/rli-codes</a>). </p>\n<p>Methods for generating national disaggregations of the Red List Index are described below in section 5 on Data availability and disaggregation.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>See above and below, and full documentation in the Red List Rules of Procedure (<a href=\"https://www.iucnredlist.org/resources/rules-of-procedure\">https://www.iucnredlist.org/resources/rules-of-procedure</a>) in particular Annex 3, the &#x201C;Details of the Steps Involved in the IUCN Red List Process&#x201D; (<a href=\"https://cmsdocs.s3.amazonaws.com/keydocuments/Details_of_the_Steps_Involved_in_the_IUCN_Red_List_Process.pdf\">https://cmsdocs.s3.amazonaws.com/keydocuments/Details_of_the_Steps_Involved_in_the_IUCN_Red_List_Process.pdf</a>).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>See above, and full documentation in the Red List Rules of Procedure (<a href=\"https://www.iucnredlist.org/resources/rules-of-procedure\">https://www.iucnredlist.org/resources/rules-of-procedure</a>) in particular Annex 3, the &#x201C;Details of the Steps Involved in the IUCN Red List Process&#x201D; (<a href=\"https://cmsdocs.s3.amazonaws.com/keydocuments/Details_of_the_Steps_Involved_in_the_IUCN_Red_List_Process.pdf\">https://cmsdocs.s3.amazonaws.com/keydocuments/Details_of_the_Steps_Involved_in_the_IUCN_Red_List_Process.pdf</a>). In sum: all Red List assessments are peer reviewed through the relevant Red List Authority for the species or species group in question; and all Red List assessments undergo consistency checks (to ensure consistency with assessments submitted for other taxonomic groups, regions, processes, etc.) by the Red List Unit before publication on the Red List website (<a href=\"http://www.iucnredlist.org/\">http://www.iucnredlist.org/</a>). Finally, the Chair of the IUCN Species Survival Commission (elected each four years by the government and non-governmental Members of IUCN) appoints a Chair for a Standards and Petitions Sub-Committee (<a href=\"https://www.iucn.org/our-union/commissions/group/iucn-ssc-standards-and-petitions-committee\">https://www.iucn.org/our-union/commissions/group/iucn-ssc-standards-and-petitions-committee</a>), which is responsible for ensuring the quality and standards of the IUCN Red List and for ruling on petitions against the listings of species on the IUCN Red List. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The IUCN Red List is governed by a Red List Committee (<a href=\"https://www.iucn.org/our-union/commissions/group/iucn-ssc-red-list-committee\">https://www.iucn.org/our-union/commissions/group/iucn-ssc-red-list-committee</a>), comprising representatives from the Red List Partnership, the IUCN Species Survival Commission, and the IUCN Secretariat. This committee establishes and maintains the Red List Strategic Plan, including ongoing evaluation of fitness for use i.e. the degree to which the IUCN Red List meets user&#x2019;s requirements. This encompasses, inter alia, considerations of relevance, accuracy, timeliness, consistency, comprehensiveness, and accessibility. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The Red List Index has been classified by the IAEG-SDGs as Tier 1. Current data are available for all countries in the world, and these are updated annually. Index values for each country are available in the UN SDG Indicators Database <a href=\"https://unstats.un.org/sdgs/indicators/database/\">https://unstats.un.org/sdgs/indicators/database/</a>. Red List Index graphs and underlying index data are available for each country, SDG regions, IPBES region, CMS region and various thematic disaggregations at <a href=\"https://www.iucnredlist.org/search\">https://www.iucnredlist.org/search</a>. Red List Index graphs are also available for each country in the BIP Indicators Dashboard (<a href=\"https://bipdashboard.natureserve.org/bip/SelectCountry.html\">https://bipdashboard.natureserve.org/bip/SelectCountry.html</a>), the Integrated Biodiversity Assessment Tool Country Profiles (<a href=\"https://ibat-alliance.org/country_profiles\">https://ibat-alliance.org/country_profiles</a>), and (for birds) on the BirdLife International Data Zone (http://datazone.birdlife.org/species/dashboard).</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The Red List Index can be downscaled to show national and regional Red List Indices, weighted by the fraction of each species&#x2019; distribution occurring within the country or region, building on the method published by Rodrigues et al. (2014) PLoS ONE 9(11): e113934. These show an index of how well species are conserved in a country or region to its potential contribution to global species conservation (for the taxonomic groups of species included). The index is calculated as: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>R</mi>\n    <mi>L</mi>\n    <msub>\n      <mrow>\n        <mi>I</mi>\n      </mrow>\n      <mrow>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mi>t</mi>\n            <mo>,</mo>\n            <mi>u</mi>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mn>1</mn>\n    <mo>-</mo>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>&#x3A3;</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n          </mrow>\n        </msub>\n        <mfenced separators=\"|\">\n          <mrow>\n            <msub>\n              <mrow>\n                <mi>W</mi>\n              </mrow>\n              <mrow>\n                <mfenced separators=\"|\">\n                  <mrow>\n                    <mi>t</mi>\n                    <mo>,</mo>\n                    <mi>s</mi>\n                  </mrow>\n                </mfenced>\n              </mrow>\n            </msub>\n            <mi>*</mi>\n            <mfenced separators=\"|\">\n              <mrow>\n                <mfrac>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>r</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>s</mi>\n                        <mi>u</mi>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                  <mrow>\n                    <msub>\n                      <mrow>\n                        <mi>R</mi>\n                      </mrow>\n                      <mrow>\n                        <mi>s</mi>\n                      </mrow>\n                    </msub>\n                  </mrow>\n                </mfrac>\n              </mrow>\n            </mfenced>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>W</mi>\n          </mrow>\n          <mrow>\n            <mi>E</mi>\n            <mi>X</mi>\n          </mrow>\n        </msub>\n        <mi>*</mi>\n        <mi>&amp;nbsp;</mi>\n        <msub>\n          <mrow>\n            <mi>&#x3A3;</mi>\n          </mrow>\n          <mrow>\n            <mi>s</mi>\n          </mrow>\n        </msub>\n        <mfenced separators=\"|\">\n          <mrow>\n            <mfrac>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>r</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>s</mi>\n                    <mi>u</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n              <mrow>\n                <msub>\n                  <mrow>\n                    <mi>R</mi>\n                  </mrow>\n                  <mrow>\n                    <mi>s</mi>\n                  </mrow>\n                </msub>\n              </mrow>\n            </mfrac>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>where t is the year of comprehensive reassessment, u is the spatial unit (i.e. country), W_((t,s)) is the weight of the global Red List category for species s at time t (Least Concern =0, Near Threatened =1, Vulnerable =2, Endangered =3, Critically Endangered =4, Critically Endangered (Possibly Extinct) =5, Critically Endangered (Possibly Extinct in the Wild) =5, Extinct in the Wild =5 and Extinct =5), WEX = 5 is the weight for Extinct species, r_su is the fraction of the total range of species s in unit u, and R_s is the total range size of species s.</p>\n<p>The index varies from 1 if the country has contributed the minimum it can to the global RLI (i.e., if the numerator is 0 because all species in the country are Least Concern) to 0 if the country has contributed the maximum it can to the global RLI (i.e., if the numerator equals the denominator because all species in the country are Extinct or Possibly Extinct). </p>\n<p>The taxonomic groups included are those in which all species have been assessed for the IUCN Red List more than once. Red List categories for years in which comprehensive assessments (i.e. those in which all species in the taxonomic group have been assessed) were carried out are determined following the approach of Butchart et al. 2007; PLoS ONE 2(1): e140, i.e. they match the current categories except for those taxa that have undergone genuine improvement or deterioration in extinction risk of sufficient magnitude to qualify for a higher or lower Red List category.</p>\n<p>The indicator can also be disaggregated by ecosystems, habitats, and other political and geographic divisions (e.g., Han et al. 2014), by taxonomic subsets (e.g., Hoffmann et al. 2011), by suites of species relevant to particular international treaties or legislation (e.g., Croxall et al. 2012), by suites of species exposed to particular threatening processes (e.g., Butchart 2008), and by suites of species that deliver particular ecosystem services, or have particular biological or life-history traits (e.g., Regan et al. 2015). In each case, information can be obtained from The IUCN Red List of Threatened Species to determine which species are relevant to particular subsets (e.g., which occur in particular ecosystems, habitats, and geographic areas of interest). These disaggregations are available on the IUCN Red List website at https://www.iucnredlist.org/search.</p>\n<p>Disaggregations of the Red List Index are also of particular relevance as indicators towards the following SDG targets (Brooks et al. 2015): SDG 2.4 Red List Index (species used for food and medicine); SDG 2.5 Red List Index (wild relatives and local breeds); SDG 12.2 Red List Index (impacts of utilisation) (Butchart 2008); SDG 12.4 Red List Index (impacts of pollution); SDG 13.1 Red List Index (impacts of climate change); SDG 14.1 Red List Index (impacts of pollution on marine species); SDG 14.2 Red List Index (marine species); SDG 14.3 Red List Index (reef-building coral species) (Carpenter et al. 2008); SDG 14.4 Red List Index (impacts of utilisation on marine species); SDG 15.1 Red List Index (terrestrial &amp; freshwater species); SDG 15.2 Red List Index (forest-specialist species); SDG 15.4 Red List Index (mountain species); SDG 15.7 Red List Index (impacts of utilisation) (Butchart 2008); and SDG 15.8 Red List Index (impacts of invasive alien species) (Butchart 2008, McGeoch et al. 2010).</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Some countries have assessed the national extinction risk of species occurring in the country, and have repeated such assessments, allowing a national Red List Index to be produced. This may differ from the indicator described here because (a) it considers national rather than global extinction risk, and (b) because it takes no account of the national responsibility for the conservation of each species, treating as equal both those species that occur nowhere outside the country (i.e. national endemics) and those with large ranges that occur in many other countries. Any such differences will be smaller for countries within which a high proportion of species are endemic (i.e., only found in that country), as in many island nations and mountainous countries, especially in the tropics. The differences will be larger for countries within which a high proportion of species have widespread distributions across many nations.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://www.iucn.org/assessment/red-list-index\">https://www.iucn.org/assessment/red-list-index</a> </p>\n<p><strong>References:</strong></p>\n<p>These metadata are based on <a href=\"https://www.bipindicators.net/indicators/red-list-index\">https://www.bipindicators.net/indicators/red-list-index</a> and the references listed below.</p>\n<p>BAILLIE, J. E. M. et al. (2004). 2004 IUCN Red List of Threatened Species: a Global Species Assessment. IUCN, Gland, Switzerland and Cambridge, United Kingdom. Available from <a href=\"https://portals.iucn.org/library/node/9830\">https://portals.iucn.org/library/node/9830</a>. </p>\n<p>BROOKS, T. M. et al. (2015). Harnessing biodiversity and conservation knowledge products to track the Aichi Targets and Sustainable Development Goals. Biodiversity 16: 157&#x2013;174. Available from <a href=\"http://www.tandfonline.com/doi/pdf/10.1080/14888386.2015.1075903\">http://www.tandfonline.com/doi/pdf/10.1080/14888386.2015.1075903</a>. </p>\n<p>BUBB, P.J. et al. (2009). IUCN Red List Index - Guidance for National and Regional Use. IUCN, Gland, Switzerland. Available from <a href=\"https://portals.iucn.org/library/node/9321\">https://portals.iucn.org/library/node/9321</a>.</p>\n<p>BUTCHART, S. H. M. et al. (2010). Global biodiversity: indicators of recent declines. Science 328: 1164&#x2013;1168. Available from <a href=\"https://www.science.org/doi/10.1126/science.1187512\">https://www.science.org/doi/10.1126/science.1187512</a>. </p>\n<p>BUTCHART, S. H. M. (2008). Red List Indices to measure the sustainability of species use and impacts of invasive alien species. Bird Conservation International 18 (suppl.): 245&#x2013;262. Available from <a href=\"http://journals.cambridge.org/action/displayJournal?jid=BCI\">http://journals.cambridge.org/action/displayJournal?jid=BCI</a>. </p>\n<p>BUTCHART, S. H. M. et al. (2007). Improvements to the Red List Index. PLoS ONE 2(1): e140. Available from <a href=\"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000140\">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000140</a>. </p>\n<p>BUTCHART, S. H. M. et al. (2006). Biodiversity indicators based on trends in conservation status: strengths of the IUCN Red List Index. Conservation Biology 20: 579&#x2013;581. Available from <a href=\"http://onlinelibrary.wiley.com/doi/10.1111/j.1523-1739.2006.00410.x/abstract\">http://onlinelibrary.wiley.com/doi/10.1111/j.1523-1739.2006.00410.x/abstract</a>. </p>\n<p>BUTCHART, S. H. M. et al. (2005). Using Red List Indices to measure progress towards the 2010 target and beyond. Philosophical Transactions of the Royal Society of London B 360: 255&#x2013;268. Available from <a href=\"http://rstb.royalsocietypublishing.org/content/360/1454/255.full\">http://rstb.royalsocietypublishing.org/content/360/1454/255.full</a>. </p>\n<p>BUTCHART, S. H. M. et al. (2004). Measuring global trends in the status of biodiversity: Red List Indices for birds. PLoS Biology 2(12): e383. Available from <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020383\">http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020383</a>. </p>\n<p>CARPENTER, K. E. et al. (2008). One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321: 560&#x2013;563. Available from <a href=\"https://www.science.org/doi/10.1126/science.1159196\">https://www.science.org/doi/10.1126/science.1159196</a> . </p>\n<p>CBD (2014). Global Biodiversity Outlook 4. Convention on Biological Diversity, Montr&#xE9;al, Canada. Available from <a href=\"https://www.cbd.int/gbo4/\">https://www.cbd.int/gbo4/</a>. </p>\n<p>CBD (2020a). Global Biodiversity Outlook 5. Convention on Biological Diversity, Montr&#xE9;al, Canada. Available from <a href=\"https://www.cbd.int/gbo5/\">https://www.cbd.int/gbo5/</a>. </p>\n<p>CBD (2020b). Post-2020 Global Biodiversity Framework: Scientific and technical information to support the review of the updated Goals and Targets, and related indicators and baselines. Document CBD/SBSTTA/24/3. Available at: <a href=\"https://www.cbd.int/doc/c/705d/6b4b/a1a463c1b19392bde6fa08f3/sbstta-24-03-en.pdf\">https://www.cbd.int/doc/c/705d/6b4b/a1a463c1b19392bde6fa08f3/sbstta-24-03-en.pdf</a>.</p>\n<p>DIAS, M.P, SIMKINS, A.T., &amp; PEARMAIN, E.J. (2020). Code (and documentation) for calculating and plotting national RLIs weighted by the proportion of each species&#x2019; distribution within a country or region. <a href=\"https://github.com/BirdLifeInternational/rli-codes\">https://github.com/BirdLifeInternational/rli-codes</a>. </p>\n<p>CROXALL, J. P. et al. (2012). Seabird conservation status, threats and priority actions: a global assessment. Bird Conservation International 22: 1&#x2013;34. </p>\n<p>G&#xC4;RDENFORS, U. (ed.) (2010). R&#xF6;dlistade arter i Sverige 2010 &#x2013; The 2010 Red List of Swedish Species. ArtDatabanken, SLU, Uppsala.</p>\n<p>HAN, X. et al. (2014). A Biodiversity indicators dashboard: addressing challenges to monitoring progress towards the Aichi Biodiversity Targets using disaggregated global data. PLoS ONE 9(11): e112046. Available from <a href=\"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112046\">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112046</a>. </p>\n<p>HOFFMANN, M. et al. (2010). The impact of conservation on the status of the world&#x2019;s vertebrates. Science 330: 1503&#x2013;1509. Available from <a href=\"https://www.science.org/doi/10.1126/science.1194442\">https://www.science.org/doi/10.1126/science.1194442</a>. </p>\n<p>HOFFMANN, M. et al. (2011). The changing fates of the world&#x2019;s mammals. Philosophical Transactions of the Royal Society of London B 366: 2598&#x2013;2610. Available from <a href=\"http://rstb.royalsocietypublishing.org/content/366/1578/2598.abstract\">http://rstb.royalsocietypublishing.org/content/366/1578/2598.abstract</a>. </p>\n<p>IUCN SPSC (2019) Guidelines for Using the IUCN Red List Categories and Criteria. Version 14. International Union for Conservation of Nature &#x2013; Standards and Petitions Subcommittee, Gland, Switzerland. Available from <a href=\"https://www.iucnredlist.org/resources/redlistguidelines\">https://www.iucnredlist.org/resources/redlistguidelines</a>. </p>\n<p>IUCN (2012a). IUCN Red List Categories and Criteria: Version 3.1. Second edition. International Union for Conservation of Nature, Gland, Switzerland. Available from <a href=\"https://portals.iucn.org/library/node/10315\">https://portals.iucn.org/library/node/10315</a>. </p>\n<p>IUCN (2012b). Guidelines for Application of IUCN Red List Criteria at Regional and National</p>\n<p>Levels: Version 4.0. International Union for Conservation of Nature, Gland, Switzerland. Available from <a href=\"https://portals.iucn.org/library/node/10336\">https://portals.iucn.org/library/node/10336</a>. </p>\n<p>IUCN (2013). Documentation Standards and Consistency Checks for IUCN Red List assessments and species accounts. International Union for Conservation of Nature, Gland, Switzerland. Available from <a href=\"https://www.iucnredlist.org/resources/supporting-information-guidelines\">https://www.iucnredlist.org/resources/supporting-information-guidelines</a>.</p>\n<p>IUCN (2015). IUCN Red List of Threatened Species. Version 2015.1. International Union for Conservation of Nature, Gland, Switzerland. Available from <a href=\"http://www.iucnredlist.org\">http://www.iucnredlist.org</a>. </p>\n<p>MACE, G. M. et al. (2008) Quantification of extinction risk: IUCN&#x2019;s system for classifying threatened species. Conservation Biology 22: 1424&#x2013;1442. Available from <a href=\"http://onlinelibrary.wiley.com/doi/10.1111/j.1523-1739.2008.01044.x/full\">http://onlinelibrary.wiley.com/doi/10.1111/j.1523-1739.2008.01044.x/full</a>. </p>\n<p>MCGEOCH, M. A. et al. (2010) Global indicators of biological invasion: species numbers, biodiversity impact and policy responses. Diversity and Distributions 16: 95&#x2013;108. Available from <a href=\"http://onlinelibrary.wiley.com/doi/10.1111/j.1472-4642.2009.00633.x/abstract\">http://onlinelibrary.wiley.com/doi/10.1111/j.1472-4642.2009.00633.x/abstract</a>. </p>\n<p>PIHL, S. &amp; FLENSTED, K. N. (2011). A Red List Index for breeding birds in Denmark in the period 1991-2009. Dansk Ornitologisk Forenings Tidsskrift 105: 211-218.</p>\n<p>REGAN, E. et al. (2015). Global trends in the status of bird and mammal pollinators. Conservation Letters. doi: 10.1111/conl.12162. Available from <a href=\"http://onlinelibrary.wiley.com/doi/10.1111/conl.12162/abstract\">http://onlinelibrary.wiley.com/doi/10.1111/conl.12162/abstract</a>. </p>\n<p>RODRIGUES, A. S. L. et al. (2014). Spatially explicit trends in the global conservation status of vertebrates. PLoS ONE 9(11): e113934. Available from <a href=\"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0113934\">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0113934</a>. </p>\n<p>SALAFSKY, N., et al. (2008) A standard lexicon for biodiversity conservation: unified classifications of threats and actions. Conservation Biology 22: 897&#x2013;911. Available from <a href=\"http://onlinelibrary.wiley.com/doi/10.1111/j.1523-1739.2008.00937.x/full\">http://onlinelibrary.wiley.com/doi/10.1111/j.1523-1739.2008.00937.x/full</a>. </p>\n<p>TITTENSOR, D. et al. (2014). A mid-term analysis of progress towards international biodiversity targets. Science 346: 241&#x2013;244. Available from <a href=\"https://www.science.org/doi/10.1126/science.1257484\">https://www.science.org/doi/10.1126/science.1257484</a>. </p>\n<p>VISCONTI, P. et al. (2015) Projecting global biodiversity indicators under future development scenarios. Conservation Letters. doi: 10.1111/conl.12159. Available from <a href=\"http://onlinelibrary.wiley.com/doi/10.1111/conl.12159/abstract\">http://onlinelibrary.wiley.com/doi/10.1111/conl.12159/abstract</a>.</p>", "indicator_sort_order"=>"15-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.6.1", "slug"=>"15-6-1", "name"=>"Número de países que han adoptado marcos legislativos, administrativos y normativos para asegurar una distribución justa y equitativa de los beneficios", "url"=>"/site/es/15-6-1/", "sort"=>"150601", "goal_number"=>"15", "target_number"=>"15.6", "global"=>{"name"=>"Número de países que han adoptado marcos legislativos, administrativos y normativos para asegurar una distribución justa y equitativa de los beneficios"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países que han adoptado marcos legislativos, administrativos y normativos para asegurar una distribución justa y equitativa de los beneficios", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que han adoptado marcos legislativos, administrativos y normativos para asegurar una distribución justa y equitativa de los beneficios", "indicator_number"=>"15.6.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Para que el Protocolo de Nagoya entre en vigor, se requieren ciertas condiciones \npropicias a nivel nacional para su aplicación efectiva. En particular, los países \ndeberán, según sus circunstancias específicas, revisar las medidas legislativas, \nadministrativas o de política vigentes o desarrollar nuevas medidas para cumplir \ncon las obligaciones establecidas en el Protocolo.\n\nEn particular, el Protocolo de Nagoya establece que las Partes adoptarán medidas \nlegislativas, administrativas o de política, según corresponda, para garantizar la \ndistribución justa y equitativa de los beneficios derivados de la utilización de los \nrecursos genéticos, incluidos los recursos genéticos que se encuentran en posesión de las \ncomunidades indígenas, y los beneficios derivados de la utilización de los \nconocimientos tradicionales asociados a los recursos genéticos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.6.1&seriesCode=ER_CBD_SMTA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Número total notificado de acuerdos estándar de transferencia de material (ANTM) que transfieren recursos fitogenéticos para la alimentación y la agricultura al país (número) ER_CBD_SMTA</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-06-01.pdf\">Metadatos 15-6-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The Nagoya Protocol, to be operational, requires that certain enabling conditions are met at the national \nlevel for its effective implementation. In particular, countries will need, depending on their specific \ncircumstances, to revise legislative, administrative or policy measures already in place or develop new \nmeasures in order to meet the obligations set out under the Protocol. \n\nIn particular, the Nagoya Protocol provides that Parties are to take legislative, administrative or policy \nmeasures, as appropriate, to ensure the fair and equitable sharing of the benefits arising from the \nutilization of genetic resources, including for genetic resources that are held by indigenous communities, \nand benefits arising from the utilization of traditional knowledge associated with genetic resources. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.6.1&seriesCode=ER_CBD_SMTA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Total reported number of Standard Material Transfer Agreements (SMTAs) transferring plant genetic resources for food and agriculture to the country (number) ER_CBD_SMTA</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-06-01.pdf\">Metadata 15-6-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Para que el Protocolo de Nagoya entre en vigor, se requieren ciertas condiciones \npropicias a nivel nacional para su aplicación efectiva. En particular, los países \ndeberán, según sus circunstancias específicas, revisar las medidas legislativas, \nadministrativas o de política vigentes o desarrollar nuevas medidas para cumplir \ncon las obligaciones establecidas en el Protocolo.\n\nEn particular, el Protocolo de Nagoya establece que las Partes adoptarán medidas \nlegislativas, administrativas o de política, según corresponda, para garantizar la \ndistribución justa y equitativa de los beneficios derivados de la utilización de los \nrecursos genéticos, incluidos los recursos genéticos que se encuentran en posesión de las \ncomunidades indígenas, y los beneficios derivados de la utilización de los \nconocimientos tradicionales asociados a los recursos genéticos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.6.1&seriesCode=ER_CBD_SMTA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Elikadurarako eta nekazaritzarako baliabide fitogenetikoak herrialdera transferitzeko akordio estandarren (ANTM) guztizko kopurua (kopurua) ER_CBD_SMTA</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-06-01.pdf\">Metadatuak 15-6-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.6: Promote fair and equitable sharing of the benefits arising from the utilization of genetic resources and promote appropriate access to such resources, as internationally agreed</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.6.1: Number of countries that have adopted legislative, administrative and policy frameworks to ensure fair and equitable sharing of benefits</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Countries that are contracting Parties to the International Treaty on Plant Genetic Resources for Food and Agriculture (PGRFA) (1 = YES; 0 = NO) ER_CBD_PTYPGRFA</p>\n<p>Countries that are parties to the Nagoya Protocol (1 = YES; 0 = NO) ER_CBD_NAGOYA</p>\n<p>Countries that have legislative, administrative and policy framework or measures reported through the Online Reporting System on Compliance of the International Treaty on Plant Genetic Resources for Food and Agriculture (PGRFA) (1 = YES; 0 = NO) ER_CBD_ORSPGRFA</p>\n<p>Countries that have legislative, administrative and policy framework or measures reported to the Access and Benefit-Sharing Clearing-House (1 = YES; 0 = NO) ER_CBD_ABSCLRHS</p>\n<p>Total reported number of Standard Material Transfer Agreements (SMTAs) transferring plant genetic resources for food and agriculture to the country (number) <strong>ER_CBD_SMTA</strong></p>", "META_LAST_UPDATE__GLOBAL"=>"2022-04-12", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable. </p>\n<p>An indicator on numbers of permits and numbers of Material Transfer Agreements issued would provide complementary information.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Secretariat of the Convention on Biological Diversity (CBD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Secretariat of the Convention on Biological Diversity (CBD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition</strong></p>\n<p>The indicator is defined as the number of countries that have adopted legislative, administrative and policy frameworks to ensure fair and equitable sharing of benefits. It refers to the efforts by countries to implement the Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from their Utilization to the Convention on Biological Diversity<em> </em>(2010) and the International Treaty on Plant Genetic Resources for Food and Agriculture (2001).</p>\n<p>The Nagoya Protocol covers genetic resources and traditional knowledge associated with genetic resources, as well as the benefits arising from their utilization by setting out core obligations for its contracting Parties to take measures in relation to access, benefit-sharing and compliance. The objectives of the International Treaty are the conservation and sustainable use of plant genetic resources for food and agriculture and the fair and equitable sharing of the benefits arising out of their use, in harmony with the Convention on Biological Diversity. </p>\n<p>The Protocol provides greater legal certainty and transparency for both providers and users of genetic resources and associated traditional knowledge, and therefore, encourages the advancement of research on genetic resources which could lead to new discoveries for the benefit of all. </p>\n<p>The Nagoya Protocol also creates incentives to conserve and sustainably use genetic resources, and thereby enhances the contribution of biodiversity to development and human well-being. In addition, Parties to the Protocol are to encourage users and providers to direct benefits arising from the utilization of genetic resources towards the conservation of biological diversity and the sustainable use of its components.</p>\n<p>The International Treaty has established the Multilateral System of Access and Benefit-sharing, which facilitates exchanges of plant genetic resources for purposes of agricultural research and breeding to contribute to sustainable agriculture and food security, by providing a transparent and reliable framework for the exchange of crop genetic resources. The Multilateral System is instrumental to achieving the conservation and sustainable use of plant genetic resources as well as the fair and equitable sharing of benefits arising from their use. The Standard Material Transfer Agreement is a mandatory standard contract for parties wishing to provide and receive material under the Multilateral System. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>For data series ER CBD PTYPGRFA, ER CBD NAGOYA, ER CBD ORSPGRFA, CBD ABSCLRHS the unit of measurement is a binary measure (1 = YES; 0 = NO).</p>\n<p>For data series ER CBD SMTA the unit of measurement is number of Standard Material Transfer Agreements (SMTAs). The total number of SMTAs transferring plant genetic resources for food and agriculture to the country is a cumulative figure.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The Access and Benefit-sharing Clearing-House Country Profiles: https://absch.cbd.int/en/countries</p>\n<p>The Online Reporting System on Compliance of the International Treaty on PGRFA, <a href=\"http://www.fao.org/plant-treaty/areas-of-work/compliance/compliance-reports/en/\">http://www.fao.org/plant-treaty/areas-of-work/compliance/compliance-reports/en/</a> </p>\n<p>Easy-SMTA, <a href=\"https://mls.planttreaty.org\">https://mls.planttreaty.org</a> </p>", "COLL_METHOD__GLOBAL"=>"<p>Data is collected from the existing online platforms of the two instruments (see 3.a above). </p>\n<p>For the ABS Clearing-House, a country must complete and publish a common format for legislative, administrative, or policy measures. The common format can be downloaded at the following link: <a href=\"https://www.cbd.int/abs/common-formats/en/ABSCH-MSR-en.doc\">https://www.cbd.int/abs/common-formats/en/ABSCH-MSR-en.doc</a>. Once the format is completed, it is published as a national record on the ABS Clearing-House and the country will be henceforth counted as having a measure in place.</p>\n<p>For the International Treaty, countries (Contracting Parties) submit a national report regarding their implementation of the provisions of the International Treaty under the compliance procedure, using the standard reporting format. The submitted national reports are available at the following link: https://www.fao.org/plant-treaty/areas-of-work/compliance/compliance-reports/en/</p>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>Data is collected on an ongoing basis, as new information is made available by countries (CBD ABSCLRHS and ER_CBD_ORSPGRFA) or by users of plant genetic resources (ER_CBD_SMTA).</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data for the Nagoya Protocol and International Treaty is compiled and provided as of 15 February every year, to meet the SDGs annual reporting requirement.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Publishing authorities for the ABS Clearing-House as designated by the CBD national focal points or the ABS focal points. Publishing authorities for the Online Reporting System on compliance of the International Treaty on PGRFA are the officially nominated national focal points or nominated reporting authorities.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Secretariat of the Convention on Biological Diversity and Secretariat of the International Treaty on Plant Genetic Resources for Food and Agriculture.</p>", "INST_MANDATE__GLOBAL"=>"<p>The ABS Clearing-House is a platform for exchanging information on access and benefit-sharing established by Article 14 of the Protocol, The ABS Clearing-House is a key tool for facilitating the implementation of the Nagoya Protocol, by enhancing legal certainty and transparency on procedures for access, and for monitoring the utilization of genetic resources along the value chain. The Protocol requires Parties to make information on legislative, administrative and policy measures available to the ABS Clearing-House. Non-Parties are also encouraged to make this information available in the same manner.</p>\n<p>In order to promote compliance with all the provisions of the International Treaty, including access and benefit sharing obligations, and to address issues of non-compliance, the Governing Body of the International Treaty has approved the procedures and operational mechanisms by Resolution 2/2011. Under V. 1, it is noted that each Contracting Party is to submit to the Compliance Committee, established by the Governing Body by Resolution 3/2006, through the Secretary, a report on the measures it has taken to implement its obligations under the International Treaty in one of the six languages of the United Nations. Contracting Parties have submitted their report by using a standard reporting format approved by the Governing Body, sending it to the Secretary. The Secretariat of the International Treaty prepares an analysis of the reports received from Contracting Parties for consideration by the Compliance Committee. </p>\n<p>Relevant Resolutions of the Governing Body: <a href=\"http://www.fao.org/3/a-be452e.pdf\">http://www.fao.org/3/a-be452e.pdf</a> (Resolution 2/2011); <a href=\"http://www.fao.org/3/a-mn566e.pdf\">http://www.fao.org/3/a-mn566e.pdf</a> (Resolution 9/2013)</p>\n<p>Regarding the number of SMTAs, the Secretariat of the International Treaty has developed a system called &#x201C;Easy-SMTA&#x201D; (the link is provided under 3.a,Data sources) to assist users of plant genetic resources with compiling and generating SMTAs in the six official languages of the International Treaty and reporting on SMTAs concluded in accordance with the guidance provided by the Governing Body of the International Treaty.</p>", "RATIONALE__GLOBAL"=>"<p>The Nagoya Protocol, to be operational, requires that certain enabling conditions are met at the national level for its effective implementation. In particular, countries will need, depending on their specific circumstances, to revise legislative, administrative or policy measures already in place or develop new measures in order to meet the obligations set out under the Protocol. </p>\n<p>In particular, the Nagoya Protocol provides that Parties are to take legislative, administrative or policy measures, as appropriate, to ensure the fair and equitable sharing of the benefits arising from the utilization of genetic resources, including for genetic resources that are held by indigenous communities, and benefits arising from the utilization of traditional knowledge associated with genetic resources. </p>\n<p>The ABS Clearing-House is a platform for exchanging information on access and benefit-sharing established by Article 14 of the Protocol, The ABS Clearing-House is a key tool for facilitating the implementation of the Nagoya Protocol, by enhancing legal certainty and transparency on procedures for access, and for monitoring the utilization of genetic resources along the value chain. The Protocol requires Parties to make information on legislative, administrative and policy measures available to the ABS Clearing-House. Non-Parties are also encouraged to make this information available in the same manner. The goal is to allow users of genetic resources and associated traditional knowledge to easily find information on the ABS Clearing-House on how to access these resources and knowledge in an organized manner, and all in one convenient location.</p>\n<p>The International Treaty stipulates that Contracting Parties ensure the conformity of its laws, regulations and procedures with their obligations under the International Treaty (Article 4). Under the Multilateral System of Access and Benefit-sharing (Articles 10-13), countries grant each other facilitated access to a selection of their plant genetic resources for food and agriculture, while users are encouraged to share their benefits with the Multilateral System. Such benefits should primarily flow to farmers in developing countries who promote the conservation and sustainable use of plant genetic resources. </p>\n<p>Pursuant to Article 21, the Governing Body adopted the Procedures and operational mechanism to promote compliance and address issues of non-compliance. Under the monitoring and reporting in the Procedures, each Contracting Party is requested to submit a report on the measures it has taken to implement its obligations under the International Treaty, including the access and benefit-sharing measures. Contracting Parties report using an agreed standard format and through the Online Reporting System on Compliance. Additionally, information on the number of Standard Material Transfer Agreements is gathered from the Data Store of the International Treaty through Easy-SMTA. SMTA is a mandatory contract that Contracting Parties of the International Treaty have agreed to use whenever plant genetic resources falling under the Multilateral System are made available. </p>\n<p>Indicator 15.6.1 directly measures progress made by countries in establishing legislative, administrative or policy frameworks on access and benefit-sharing (ABS). By developing their ABS frameworks, countries are contributing to the achievement of SDG Target 15.6 and to the conservation and sustainable use of biological and genetic diversity. Progress in this indicator is assessed through measuring the increase in the number of countries that have adopted ABS legislative, administrative and policy measures and that have made available this information in the ABS Clearing-House and through the Online Reporting System on Compliance of the International Treaty in relation to plant genetic resources for food and agriculture. </p>\n<p><u>The indicator consists of 4 sub-indicators: </u></p>\n<ul>\n  <li>Countries that are Contracting Parties to the International Treaty on Plant Genetic Resources for Food and Agriculture;</li>\n  <li>Countries that are Parties to the Nagoya Protocol to the Convention on Biological Diversity;</li>\n  <li>Countries that have legislative, administrative and policy measures reported through the Online Reporting System on Compliance of the International Treaty on Plant Genetic Resources for Food and Agriculture;</li>\n  <li>Countries that have legislative, administrative or policy measures reported to the Access and Benefit-Sharing Clearing-House of the Secretariat of the Convention on Biological Diversity;</li>\n</ul>\n<p>An additional sub-indicator provides complementary information on the number, by country, of Standard Material Transfer Agreements (SMTAs) transfering plant genetic resources for food and agriculture.</p>", "REC_USE_LIM__GLOBAL"=>"<p>This indicator can be used to measure progress in adopting ABS legislative, administrative and policy frameworks over time.</p>\n<p>This indicator does not assess the scope or effectiveness of ABS legislative, administrative and policy frameworks. </p>\n<p>The notion of framework suggests that there is a complete set of rules established on access and benefit-sharing. However, it is difficult to have a predefined idea of what constitutes an ABS framework. In the context of this indicator, the publication by a country of one or more ABS legislative, administrative and policy measure in the ABS Clearing-House would be considered progress by that country on having an ABS legislative, administrative and policy framework, and through the Online Reporting System on Compliance of the International Treaty in relation to plant genetic resources for food and agriculture.</p>", "DATA_COMP__GLOBAL"=>"<p>For CBD ABSCLRHS, the indicator is calculated based on national information made available to the Access and Benefit-sharing Clearing-House. If a country has published at least one legislative, administrative or policy measure to ensure fair and equitable benefit-sharing, the data compilers will indicate 1 (1=YES). For ER_CBD_ORSPGRFA, the method of computation is the same but is calculated based on information from national reports submitted to the Secretariat of the International Treaty on PGRFA.</p>\n<p><br>For ER_CBD_NAGOYA and ER_CBD_PTYPGRFA, the indicator is calculated based on the status of ratifications to the Nagoya Protocol and the International Treaty on PGRFA, respectively. If a country has ratified/acceded/accepted the respective treaty, the data compilers will indicate 1 (1=YES). </p>\n<p>For ER_CBD_SMTA (complementary sub-indicator), the indicator is calculated based on information generated through the Easy-SMTA platform. The data is the number of SMTA reported through the online system of Easy-SMTA for each country. SMTA is a mandatory contract that Contracting Parties of the International Treaty have agreed to use whenever plant genetic resources falling under the Multilateral System are made available through transfer.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The sub-indicator data may be validated independently by consulting the following websites:</p>\n<ul>\n  <li>ER_CBD_PTYPGRFA: Website of the International Treaty on PGRFA (http://www.fao.org/plant-treaty/countries/membership/en/)</li>\n  <li>ER_CBD_NAGOYA: United Nations Treaty Collection (<a href=\"https://treaties.un.org/Pages/ViewDetails.aspx?src=TREATY&amp;mtdsg_no=XXVII-8-b&amp;chapter=27\">https://treaties.un.org/Pages/ViewDetails.aspx?src=TREATY&amp;mtdsg_no=XXVII-8-b&amp;chapter=27</a>) </li>\n  <li>CBD ABSCLRHS: Access and Benefit-Sharing Clearing-House Country Profiles (<a href=\"https://absch.cbd.int/countries\">https://absch.cbd.int/countries</a>) </li>\n  <li>ER_CBD_ORSPGRFA: National Reports of the International Treaty (<a href=\"http://www.fao.org/plant-treaty/areas-of-work/compliance/compliance-reports/en/\">http://www.fao.org/plant-treaty/areas-of-work/compliance/compliance-reports/en/</a>) </li>\n  <li>ER_CBD_SMTA: Easy-SMTA (<a href=\"https://mls.planttreaty.org\">https://mls.planttreaty.org</a>) </li>\n</ul>\n<p>As these sub-indicators are based on nationally reported information, there is no additional consultation or validation process in place with national focal points or authorities.</p>", "ADJUSTMENT__GLOBAL"=>"<p>For CBD ABSCLRHS and ER_CBD_NAGOYA, and ER_CBD_PTYPGRFA and ER_CBD_ORSPGRFA, there is a need for the data compilers to subtract the European Union from regional and global aggregations. The European Union is a Party to the Nagoya Protocol and the International Treaty, but is not counted as a country for these indicators.</p>\n<p>For ER_CBD_SMTA, the SMTA is a private contract, therefore it is reported by users, not by a government focal point. Users have also a two-year period for reporting their SMTAs, and therefore the number reported for a specific year would be fixed in two years (may also change during the two years). </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Regarding the number of SMTA, the data is the number of SMTA reported through the online system of the International Treaty (Easy-SMTA) for each country, while the actual number of SMTA issued (signed) could be higher, as all SMTAs signed may not be reported through the online system and therefore not recorded. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>", "REG_AGG__GLOBAL"=>"<p>For CBD ABSCLRHS and ER_CBD_NAGOYA, regional aggregations can be generated using the appropriate filters on the Access and Benefit-sharing Clearing-House Country Profiles page. These filters follow UN regional groupings. ER_CBD_PTYPGRFA, ER_CBD_ORSPGRFA and ER_CBD_SMTA, regionally aggregated data are provided every year as required by the UN SDG indicator reporting. </p>", "DOC_METHOD__GLOBAL"=>"<p>The Nagoya Protocol requires its Parties to make certain types of information available to the Access and Benefit-sharing Clearing-House, including information on legislative, administrative or policy measures in place on access and benefit-sharing. Further information on this requirement and on steps to publish information on the Clearing-House is available at: <a href=\"https://absch.cbd.int/en/kb/tags/getting-started/Getting-started-using-the-ABS-Clearing-House-for-Governments/5bbe211fb899670001de9bb9\">https://absch.cbd.int/en/kb/tags/getting-started/Getting-started-using-the-ABS-Clearing-House-for-Governments/5bbe211fb899670001de9bb9</a>. </p>\n<p>The International Treaty has the Procedures to promote compliance and address issues of non-compliance. Under the monitoring and reporting in the Procedures, each Contracting Party is requested to submit a report on the measures it has taken to implement its obligations under the International Treaty, including the access and benefit-sharing measures. Contracting Parties report using an agreed standard format and through the Online Reporting System on Compliance. The below link on the website of the International Treaty provides the relevant information on how to report under the Compliance Procedures. </p>\n<p>https://www.fao.org/plant-treaty/areas-of-work/compliance/howtoreport/en/</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Regular maintenance and updating of the online platforms hosted by the Secretariats of the CBD and of the International Treaty on PGRFA.</p>", "COVERAGE__GLOBAL"=>"<p>For CBD data series, data are available for 196 Parties (195 countries plus the European Union) to the Convention on Biological Diversity. For CBD ABSCLRHS, availability of data is dependent on countries making information on their legislative, administrative or policy measures available to the ABS Clearing-House. As the ABS Clearing-House was established in October 2014, data are available for the 2015 calendar year thereon. Only regional aggregations are available.</p>\n<p>For International Treaty, data are available for 148 Contracting Parties (147 countries plus the European Union) that have ratified, accepted, approved or acceded to the International Treaty on Plant Genetic Resources for Food and Agriculture. For ER_CBD_ORSPGRFA, availability of data is dependent on countries providing information on their legislative, administrative or policy measures in their national report submitted under the Compliance Procedures.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Reliability of the indicator is dependent on countries making information available to the ABS Clearing-House of the Nagoya Protocol and to the Online Reporting System on Compliance of the International Treaty on ABS legislative, administrative or policy measures.</p>\n<p>In addition to the information made available by countries to the ABS Clearing-House, the CBD Secretariat collects information from other sources: national biodiversity strategies and actions plans, national reports submitted under the CBD, national reports on the implementation of the Nagoya Protocol,and official communications to the SCBD (responses to notifications, email communications, etc.). The information collected from these sources inform the Secretariat&#x2019;s inputs to other processes under the Protocol, in particular the consideration by the Conference of the Parties serving as the meeting of the Parties to the Protocol (COP-MOP) of national reports (Article 29) and assessment and review (Article 31). The resulting information on the number of countries with ABS legislative, administrative or and policy measures may differ from the number of countries that have made available this information in the ABS Clearing-House. </p>\n<p> </p>\n<p>In addition to the information made available by countries to the Online Reporting System on Compliance of the International Treaty, FAO collects information from countries, submitted through their national reports, on conservation and use of PGRFA and their efforts in this regard for the preparation of the <em>State of the World&#x2019;s Plant Genetic Resources for Food and Agriculture</em>. </p>", "OTHER_DOC__GLOBAL"=>"<p>Text of the Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from their Utilization to the Convention on Biological Diversity: <a href=\"https://www.cbd.int/abs/text/default.shtml\"><u>https://www.cbd.int/abs/text/default.shtml</u></a> </p>\n<p>The Access and Benefit-sharing Clearing-House: <a href=\"http://absch.cbd.int\"><u>http://absch.cbd.int</u></a> </p>\n<p>International Treaty on Plant Genetic Resources for Food and Agriculture, https://www.fao.org/plant-treaty/en/</p>\n<p>Data Store of the International Treaty on PGRFA, Easy-SMTA, <a href=\"https://mls.planttreaty.org\"><u>https://mls.planttreaty.org</u></a></p>", "indicator_sort_order"=>"15-06-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.7.1", "slug"=>"15-7-1", "name"=>"Proporción de especímenes de flora y fauna silvestre comercializados procedentes de la caza furtiva o el tráfico ilícito", "url"=>"/site/es/15-7-1/", "sort"=>"150701", "goal_number"=>"15", "target_number"=>"15.7", "global"=>{"name"=>"Proporción de especímenes de flora y fauna silvestre comercializados procedentes de la caza furtiva o el tráfico ilícito"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de especímenes de flora y fauna silvestre comercializados procedentes de la caza furtiva o el tráfico ilícito", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de especímenes de flora y fauna silvestre comercializados procedentes de la caza furtiva o el tráfico ilícito", "indicator_number"=>"15.7.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Hay más de 35.000 especies bajo protección internacional, por lo que es imposible \nmonitorear toda la caza furtiva. Sin embargo, el comercio ilegal es un indicador indirecto \nde la caza furtiva. Las incautaciones de fauna silvestre representan casos concretos de \ncomercio ilegal, pero la proporción que representan de los delitos contra la vida silvestre \nen general es desconocida y variable. Además, el número de especies bajo protección \ninternacional sigue creciendo. \n\nEl comercio internacional legal de especies protegidas, por definición, \nse refleja al 100 % en la Base de Datos sobre el Comercio de Especies Amenazadas \nde Fauna y Flora Silvestres (CITES), que actualmente contiene más de 16 millones \nde registros de comercio de especies incluidas en la CITES. \n\nPara fundamentar los datos de comercio ilegal en un indicador completo, se estima \nla proporción de incautaciones agregadas con respecto al comercio total. Un aumento en \nla proporción del comercio total de fauna silvestre que es ilegal se interpretaría \ncomo un indicador negativo, y una disminución como positivo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-07-01.pdf\">Metadatos 15-7-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"There are over 35,000 species under international protection, so it is impossible to monitor all poaching. \nIllegal trade, however, is an indirect indicator of poaching. Wildlife seizures represent concrete instances \nof illegal trade, but the share of overall wildlife crime they represent is unknown and variable. In addition, \nthe number of species under international protection continues to grow. \n\nLegal international trade in protected species, by definition, is 100% captured in the Convention on International \nTrade in Endangered Species of Wild Fauna and Flora (CITES) Trade Database, which now contains over 16 million\nrecords of trade in CITES-listed species. \n\nTo ground the illegal trade data in a complete indicator, the ratio of aggregated seizures to total trade is \nestimated. An increase in the share of total wildlife trade that is illegal would be interpreted as a negative \nindicator, and a decrease as a positive one. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-07-01.pdf\">Metadata 15-7-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Hay más de 35.000 especies bajo protección internacional, por lo que es imposible \nmonitorear toda la caza furtiva. Sin embargo, el comercio ilegal es un indicador indirecto \nde la caza furtiva. Las incautaciones de fauna silvestre representan casos concretos de \ncomercio ilegal, pero la proporción que representan de los delitos contra la vida silvestre \nen general es desconocida y variable. Además, el número de especies bajo protección \ninternacional sigue creciendo. \n\nEl comercio internacional legal de especies protegidas, por definición, \nse refleja al 100 % en la Base de Datos sobre el Comercio de Especies Amenazadas \nde Fauna y Flora Silvestres (CITES), que actualmente contiene más de 16 millones \nde registros de comercio de especies incluidas en la CITES. \n\nPara fundamentar los datos de comercio ilegal en un indicador completo, se estima \nla proporción de incautaciones agregadas con respecto al comercio total. Un aumento en \nla proporción del comercio total de fauna silvestre que es ilegal se interpretaría \ncomo un indicador negativo, y una disminución como positivo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-07-01.pdf\">Metadatuak 15-7-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.7: Take urgent action to end poaching and trafficking of protected species of flora and fauna and address both demand and supply of illegal wildlife products</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.7.1: Proportion of traded wildlife that was poached or illicitly trafficked</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_WLD_TRPOACH - Proportion of traded wildlife that was poached or illicitly trafficked [15.7.1,15.c.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The share of all trade in wildlife detected as being illegal</p>\n<p><strong>Concepts:</strong></p>\n<p>&#x201C;All trade in wildlife&#x201D; is the sum of the values of legal and illegal trade</p>\n<p>&#x201C;Legal trade&#x201D; is the sum of the value of all shipments made in compliance with the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), using valid CITES permits and certificates.</p>\n<p>&#x201C;Illegal trade&#x201D; is the sum of the value of all CITES/listed specimens seized.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The Checklist of Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) is used to assign values to each of the species and specimens traded, either legally or seized. Species. More information at <a href=\"https://checklist.cites.org/#/en\">https://checklist.cites.org/#/en</a>. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The legal trade data are reported annually by Parties to Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and stored in the CITES Trade Database, managed by the United Nations Environmental Programme (UNEP) World Conservation Monitoring Centre in Cambridge.</p>\n<p>The detected illegal trade data is collected through two main databases: a) the United Nations Office on Drugs and Crime (UNODC) database called &#x201C;World WISE&#x201D;, which combines a variety of data sources on individual illicit wildlife seizures, and b) the CITES Illegal Trade Database, which contains data reported by CITES Parties through the Annual Illegal Trade Reports (see https://cites.org/eng/resources/reports/Annual_Illegal_trade_report). </p>\n<p>The US LEMIS price data for CITES-listed species are also provided to UNEP-WCMC within the U.S. annual report to CITES, and are used for valuation<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> See <a href=\"https://www.fws.gov/library/collections/office-law-enforcement-importexport-data\"><u>https://www.fws.gov/library/collections/office-law-enforcement-importexport-data</u></a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p>Some adjustment/validation is necessary between countries, but standardized codes for the legal wildlife trade have been developing since 1975. The basic fields necessary for the global indicator (species, product, and unit) are well established and present in every seizure. Some unit conversions (e.g. logs to MT to m3 for timber) are necessary for some products. To do regional or national breakdowns, however, data on the source of the shipment are necessary (as the impact of poaching pertains to the source country, not the seizure country), and these data are not available for every seizure.</p>", "FREQ_COLL__GLOBAL"=>"<p>The first data collection cycle for the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Annual Illicit Trade Reports (AITR) took place in 2017. Since then, these reports are collected every year, with a deadline of October 31 for submission by CITES Parties. Data reported through AITR have been processed and put together into the CITES Illegal Trade Database<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> in 2023-2024, and the database was launched in November 2023 and is now available for CITES Parties.</p>\n<p>As for the data collected for the World WISE database, these are collected in an ad-hoc basis based on sources availability and partnerships. Data from the Environmental Investigation Agency (EIA), for example, are collected regularly and included into World WISE.</p>\n<p><u>Data for legal trade are collected by the CITES Secretariat through the CITES Annual Reports and processed by UNEP-WCMC on an annual basis. </u></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> See <a href=\"https://citesdata.un.org/\"><u>https://citesdata.un.org/</u></a> <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "REL_CAL_POLICY__GLOBAL"=>"<p>After a first submission of data in Q2 2024, data will be published annually in Q1. </p>", "DATA_SOURCE__GLOBAL"=>"<p>The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Management Authority of each country provide data for both the CITES Legal and Illegal Trade Databases, through the Annual Reports and AITR. In addition, other sources are used for World WISE, such as the Environmental Investigation Agency, TRAFFIC, the World Customs Organization (WCO), amongst others.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC) for illegal trade data and United Nations Environmental Programme (UNEP) World Conservation Monitoring Centre for legal trade data.</p>", "INST_MANDATE__GLOBAL"=>"<p>Annual Reports (article VIII of the Convention) and AITR (Conf.11.17 &#x2013; Rev CoP19) are collected by the CITES Secretariat as per their mandate. CITES Secretariat works together with UNODC on the data processing for AITR data, and wile UNEP-WCMC for the processing of Annual Reports data.</p>", "RATIONALE__GLOBAL"=>"<p><strong>Rationale:</strong></p>\n<p>There are over 35,000 species under international protection, so it is impossible to monitor all poaching. Illegal trade, however, is an indirect indicator of poaching. Wildlife seizures represent concrete instances of illegal trade, but the share of overall wildlife crime they represent is unknown and variable. In addition, the number of species under international protection continues to grow. Legal international trade in protected species, by definition, is 100% captured in the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Trade Database, which now contains over 16 million records of trade in CITES-listed species. To ground the illegal trade data in a complete indicator, the ratio of aggregated seizures to total trade is estimated. An increase in the share of total wildlife trade that is illegal would be interpreted as a negative indicator, and a decrease as a positive one.</p>\n<p>Because the illegal wildlife trade represents thousands of distinct products, a means of aggregation is necessary. The legal trade value does not represent the true black market value of the items seized, nor the true value of the legal shipments, because it is derived from a single market source (US LEMIS). It does, however, present a logical and consistent means of aggregating unlike products.</p>", "REC_USE_LIM__GLOBAL"=>"<p>- Seizures are an incomplete indicator of trafficking, and subject to considerable volatility, and depend on external factors that may not be directly linked to the volume of flows, such as law enforcement priorities. </p>\n<p>- Universal coverage is not presently available, although data are available for a growing number of countries (see section 5). </p>\n<p>- Since the indicator looks at the relationship between two values (legal vs illegal trade), changes in the relationship could be due to changes in either value.</p>", "DATA_COMP__GLOBAL"=>"<p>The value of a species-product unit is derived from the median price declared for legal imports of analogous species product units, as acquired from United States Law Enforcement Monitoring and Information System of the Fish and Wildlife Service (US LEMIS). Particularly, the median values for each TAXON/DESCRIPTION OF SPECIMEN/UNIT OF MEASUREMENT possible combination were used to assign a value to each legal trade and seizure record. Some additional sources (for cases where US LEMIS data were not available or were unreliable) were used, such as estimates based on field research from UNEP-WCMC and UNODC. Also, median values of combinations that were outliers, based on small sample sizes or had high variability were excluded from the calculations.</p>\n<p>The value of legal trade is the sum of all species-product units documented in Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) export permits as reported in the CITES Annual Reports times the species-product unit prices as specified above.</p>\n<p>The value of illegal trade is the sum of all species-product units documented in the World WISE seizure and CITES Illegal Trade databases times the species-product unit prices as specified above.</p>\n<p>The indicator is defined as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi>V</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n      </mrow>\n      <mrow>\n        <mi>V</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mo>+</mo>\n        <mi>V</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n      </mrow>\n    </mfrac>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data received go through a validation process that includes consistency checks, standardization of variables and conversion of measurement units. Due to the variety of sources involved, a deduplication process takes place, where records representing events identified as being reported by multiple sources are clustered, and only one is kept, to avoid double-counting. Large seizures are individually analysed to ensure there is no double-counting that could significantly affect totals.</p>\n<p>Finally, the data used are periodically shared with countries for their review as part of the process of &#x201C;pre-publication&#x201D; for UNODC World Wildlife Crime Reports (as done in October 2023), and the compiled 15.7.1 indicators are shared with countries for their review before submission to the United Nations Statistical Department (UNSD). </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>For legal trade, data reported by other countries is used to impute the totals exported and imported at the national level.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>As above</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries can provide data through the CITES. Information and guidelines can be found at <a href=\"https://cites.org/eng/resources/reports/Annual_Illegal_trade_report\">https://cites.org/eng/resources/reports/Annual_Illegal_trade_report</a> and https://cites.org/eng/imp/reporting_requirements/annual_report</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>A thorough validation process is implemented to ensure high quality of the data, as described in Section 4.d. When inconsistencies are found, UNODC contacts the data providers, either countries in the case of AITR (with the support of CITES) or other organizations in the case of data in World WISE. </p>\n<p>The prepublication process described in Section 4.d is part of UNODC&#x2019;s general quality management process.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The deduplication process described in Section 4.d is implemented not only for wildlife-related seizures databases, but also for other databases within UNODC, such as the Drugs Monitoring Platform. In this sense, a comprehensive deduplication protocol is implemented to ensure high quality in all datasets and to take into account the singularities of each.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>A specific assessment of the deduplication process was implemented in June 2022, and it was found that the proportion of false negatives and false positives was relatively small in the dataset at the time.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>As of July 2024, data for the indicator are available for 98 countries and territories. </p>\n<p><strong>Time series:</strong></p>\n<p>Data for 2016 onwards are available for this indicator.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Where source data are available, the data are disaggregated by plant and animal species. As a form of trade data, issues of gender, age, and disability status are not applicable.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The global figure is the ratio of the aggregate of national figures provided by countries for the numerator and denominator of the indicator as defined in 4.c.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.unodc.org\">www.unodc.org</a> </p>\n<p><strong>References:</strong></p>\n<p><a href=\"http://www.unodc.org/documents/data-and-analysis/wildlife/Methodological_Annex_final.pdf\">http://www.unodc.org/documents/data-and-analysis/wildlife/Methodological_Annex_final.pdf</a> </p>\n<p><a href=\"http://trade.cites.org/cites_trade_guidelines/en-CITES_Trade_Database_Guide.pdf\"><u>http://trade.cites.org/cites_trade_guidelines/en-CITES_Trade_Database_Guide.pdf</u></a></p>\n<p>CITES Annual Reports: <a href=\"https://cites.org/eng/imp/reporting_requirements/annual_report\">https://cites.org/eng/imp/reporting_requirements/annual_report</a></p>\n<p>CITES AITR: https://cites.org/eng/resources/reports/Annual_Illegal_trade_rep</p>", "indicator_sort_order"=>"15-07-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.8.1", "slug"=>"15-8-1", "name"=>"Proporción de países que han aprobado la legislación nacional pertinente y han destinado recursos suficientes para la prevención o el control de las especies exóticas invasoras", "url"=>"/site/es/15-8-1/", "sort"=>"150801", "goal_number"=>"15", "target_number"=>"15.8", "global"=>{"name"=>"Proporción de países que han aprobado la legislación nacional pertinente y han destinado recursos suficientes para la prevención o el control de las especies exóticas invasoras"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Se dispone de legislación nacional pertinente y se destinan recursos para la prevención o el control de las especies exóticas invasoras", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de países que han aprobado la legislación nacional pertinente y han destinado recursos suficientes para la prevención o el control de las especies exóticas invasoras", "indicator_number"=>"15.8.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Se dispone de legislación nacional pertinente y se destinan recursos para la prevención o el control de las especies exóticas invasoras", "objetivo_global"=>"15- Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad", "meta_global"=>"15.8- De aquí a 2020, adoptar medidas para prevenir la introducción de especies exóticas invasoras y reducir significativamente sus efectos en los ecosistemas terrestres y acuáticos y controlar o erradicar las especies prioritarias", "definicion"=>"Valor lógico que indica si se dispone de legislación pertinente y se destinan recursos para la prevención o el control de las especies exóticas invasoras", "formula"=>"\n$$LCEEI^{t} = \\begin{cases} 1 & \\text{Sí se dispone de legislación pertinente y se destinan recursos en el año 𝑡} \\\\ 0 & \\text{No se dispone de legislación pertinente o no se destinan recursos en el año 𝑡} \\end{cases} $$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La Meta 9 de Aichi para la Diversidad Biológica establece: “Para 2020, se habrán \nidentificado y priorizado las especies exóticas invasoras y sus vías de introducción, \nse habrán controlado o erradicado las especies prioritarias y se habrán establecido \nmedidas para gestionar las vías de introducción a fin de impedir su \nintroducción y establecimiento”.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.8.1&seriesCode=ER_IAS_NBSAPP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> \nProporción de países con metas de la Estrategia y Plan de Acción Nacionales sobre Diversidad Biológica (EPANB) alineadas con la meta 9 de Aichi para la Diversidad Biológica establecida en el Plan Estratégico para la Diversidad Biológica 2011-2020 (%) ER_IAS_NBSAPP</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con la serie principal (1a) del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-08-01.pdf\">Metadatos 15-8-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Se dispone de legislación nacional pertinente y se destinan recursos para la prevención o el control de las especies exóticas invasoras", "objetivo_global"=>"15- Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad", "meta_global"=>"15.8- De aquí a 2020, adoptar medidas para prevenir la introducción de especies exóticas invasoras y reducir significativamente sus efectos en los ecosistemas terrestres y acuáticos y controlar o erradicar las especies prioritarias", "definicion"=>"Logical value that indicates whether relevant legislation is in place and resources are allocated  for the prevention or control of invasive alien species. ", "formula"=>"\n$$LCEEI^{t} = \\begin{cases} 1 & \\text{Relevant legislation is available and resources are allocated in year 𝑡} \\\\ 0 & \\text{There is no relevant legislation or no resources are allocated in year 𝑡} \\end{cases} $$\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Aichi Biodiversity Target 9 states: “By 2020, invasive alien species and pathways are identified and \nprioritized, priority species are controlled or eradicated, and measures are in place to manage pathways \nto prevent their introduction and establishment”. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.8.1&seriesCode=ER_IAS_NBSAPP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> \nProportion of countries with National Biodiversity Strategy and Action Plan (NBSAP) targets alignment to Aichi Biodiversity target 9 set out in the Strategic Plan for Biodiversity 2011-2020 (%) ER_IAS_NBSAPP</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with the main series (1a) of the United Nations indicator. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-08-01.pdf\">Metadata 15-8-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"indicador_disponible"=>"Se dispone de legislación nacional pertinente y se destinan recursos para la prevención o el control de las especies exóticas invasoras", "objetivo_global"=>"15- Proteger, restablecer y promover el uso sostenible de los ecosistemas terrestres, gestionar sosteniblemente los bosques, luchar contra la desertificación, detener e invertir la degradación de las tierras y detener la pérdida de biodiversidad", "meta_global"=>"15.8- De aquí a 2020, adoptar medidas para prevenir la introducción de especies exóticas invasoras y reducir significativamente sus efectos en los ecosistemas terrestres y acuáticos y controlar o erradicar las especies prioritarias", "definicion"=>"Valor lógico que indica si se dispone de legislación pertinente y se destinan recursos para la prevención o el control de las especies exóticas invasoras", "formula"=>"\n$$LCEEI^{t} = \\begin{cases} 1 & \\text{Legeria egokia dago eta baliabideak bideratzen dira 𝑡 urtean} \\\\ 0 & \\text{Ez dago legeria egokirik edo ez da baliabiderik bideratzen 𝑡 urtean} \\end{cases} $$\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La Meta 9 de Aichi para la Diversidad Biológica establece: “Para 2020, se habrán \nidentificado y priorizado las especies exóticas invasoras y sus vías de introducción, \nse habrán controlado o erradicado las especies prioritarias y se habrán establecido \nmedidas para gestionar las vías de introducción a fin de impedir su \nintroducción y establecimiento”.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.8.1&seriesCode=ER_IAS_NBSAPP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> \n2011-2020 Aniztasun Biologikorako Plan Estrategikoan ezarritako Aniztasun Biologikorako Aichiko 9. helburuarekin lerrokatutako Aniztasun Biologikoari buruzko Estrategia eta Ekintza Plan Nazionalak (EPANB) dituzten herrialdeen proportzioa (%) ER_IAS_NBSAPP</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen serie nagusia (1a) betetzen du.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-08-01.pdf\">Metadatuak 15-8-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.8: By 2020, introduce measures to prevent the introduction and significantly reduce the impact of invasive alien species on land and water ecosystems and control or eradicate the priority species</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.8.1: Proportion of countries adopting relevant national legislation and adequately resourcing the prevention or control of invasive alien species</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Part 1a:</p>\n<p>ER_IAS_LEGIS - Legislation, Regulation, Act related to the prevention of introduction and management of Invasive Alien Species (1 = YES, 0 = NO) [15.8.1]</p>\n<p>including specific components obtained from the following questions used in the annual survey on invasive alien species under the following 11 themes: Animal_Health; Plant_Health; Environment; Protected_Areas; Specific_Species; Biosecurity; Fisheries; Hunting; Wetlands; Marine; IAS</p>\n<p>Part 1b: </p>\n<p>ER_IAS_NBSAP - National Biodiversity Strategy and Action Plan (NBSAP) targets alignment to Aichi Biodiversity target 9 set out in the Strategic Plan for Biodiversity 2011-2020 (1 = YES, 0 = NO) [15.8.1]</p>\n<p>Part 2: Proxies for resource allocation towards the management of IAS (1 = YES, 0 = NO), which encompasses 18 specific components obtained from the following questions used in the annual survey on invasive alien species related to the functions, legal mandate, necessary powers, and resourcing of IAS-related national institutions, including:</p>\n<p>ER_IAS_NATBUD - Countries with an allocation from the national budget to manage the threat of invasive alien species (1 = YES, 0 = NO) [15.8.1]</p>\n<p>ER_IAS_GLOFUN - Recipient countries of global funding with access to any funding from global financial mechanisms for projects related to invasive alien species management (1 = YES, 0 = NO) [15.8.1]</p>\n<p>In addition, regional and global aggregate series are provided for Part 1b and Part 2 as follows:</p>\n<p>ER_IAS_NBSAPP - Proportion of countries with National Biodiversity Strategy and Action Plan (NBSAP) targets alignment to Aichi Biodiversity target 9 set out in the Strategic Plan for Biodiversity 2011-2020 (%) [15.8.1]</p>\n<p>ER_IAS_NATBUDP - Proportion of countries with allocation from the national budget to manage the threat of invasive alien species (%) [15.8.1]</p>\n<p>ER_IAS_GLOFUNP - Proportion of recipient countries of global funding with access to any funding from global financial mechanisms for projects related to invasive alien species management (%) [15.8.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Policy Response Indicator within the suite of Invasive alien species Indicators within the Biodiversity Indicator Partnership (BIP) of the UNEP-WCMC (<a href=\"https://www.bipindicators.net/indicators/adoption-of-national-legislation-relevant-to-the-prevention-or-control-of-invasive-alien-species\">https://www.bipindicators.net/indicators/adoption-of-national-legislation-relevant-to-the-prevention-or-control-of-invasive-alien-species</a>) </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Union for Conservation of Nature (IUCN)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Union for Conservation of Nature (IUCN)- Invasive Species Specialist Group</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator aims to quantify trends in: </p>\n<p>Commitment by countries to relevant multinational agreements, specifically:</p>\n<p>(1) National adoption of invasive alien species relevant policy. </p>\n<p>Percentage of countries with </p>\n<p>(a) national legislation and policy relevant to invasive alien species. </p>\n<p>(b) targets and objectives within national strategies for preventing and controlling invasive alien species are aligned with Aichi Target 9.</p>\n<p>The translation of policy arrangements into action by countries to implement policy and actively prevent and control invasive alien species (IAS) and the resourcing of this action, specifically:</p>\n<p>(2) National allocation of resources towards the prevention or control of IAS.</p>\n<p><strong>Concepts:</strong></p>\n<p>An &#x201C;Alien&#x201D; species is described as one which has been introduced outside its natural distribution range because of intentional or accidental dispersion by human activity. An alien species which has become established in a natural or semi-natural ecosystem or habitat, is an agent of change, and threatens native biological diversity is known as an &#x201C;Invasive alien species&#x201D; (Convention on Biological Diversity 2016).</p>\n<p>The introduction of an alien species can be intentional or unintentional/accidental. Alien species have been introduced intentionally for forestry, ornamental purposes, for aquaculture/mariculture, hunting, fisheries etc. Examples of unintentional or accidental introductions include: alien species that have escaped from gardens, aquaculture containment facilities, forestry, horticulture; pets and aquarium species that are released in the wild; transport contaminants and stowaways including in ballast water or as hull fouling organisms, and seeds carried in soil, equipment, vehicles etc.</p>\n<p>Mechanisms of impact of invasive species include competition, predation, hybridisation, and disease transmission, parasitism, herbivory and trampling and rooting. The outcomes of these impacts lead to biodiversity loss, habitat degradation, and loss of ecosystem services.</p>\n<p><strong>Comments and limitations:</strong></p>\n<p>The adoption of legislation does not necessarily indicate the existence of regulations or policy to implement the legislation, nor how successful such implementation has been on the ground. There remains a need for further indicator development to make this link clearer. Legislation does not necessarily capture all efforts against invasive alien species that are happening at the national level.</p>\n<p>Allocation of resources to facilitate the implementation of IAS management action is difficult to measure, particularly in a way that is comparable across countries. Proxies used to measure allocation of resources included- allocation of a budget line to invasive species management activities (including prevention, rapid response, and active management); appointed staff to carry out any IAS related activities; active programmes/projects etc.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>For four series within this indicator, a Boolean measure is used (1 = YES, 0 = NO), specifically:</p>\n<p>Part 1a: Legislation, Regulation, Act related to the prevention of introduction and management of Invasive Alien Species (ER_IAS_LEGIS)</p>\n<p>Part 1b: National Biodiversity Strategy and Action Plan (NBSAP) targets alignment to Aichi Biodiversity target 9 set out in the Strategic Plan for Biodiversity 2011-2020 (ER_IAS_NBSAP)</p>\n<p>Part 2: Proxies for resource allocation towards the management of IAS, including:</p>\n<p>Countries with an allocation from the national budget to manage the threat of invasive alien species (ER_IAS_NATBUD)</p>\n<p>Recipient countries of global funding with access to any funding from global financial mechanisms for projects related to invasive alien species management (ER_IAS_GLOFUN) </p>\n<p>In addition, regional and global aggregate series are provided for Part 1b and Part 2, which use percent (%) as a unit, specifically:</p>\n<p>Proportion of countries with National Biodiversity Strategy and Action Plan (NBSAP) targets alignment to Aichi Biodiversity target 9 set out in the Strategic Plan for Biodiversity 2011-2020 (ER_IAS_NBSAPP)</p>\n<p>Proportion of countries with allocation from the national budget to manage the threat of invasive alien species (ER_IAS_NATBUDP)</p>\n<p>Proportion of recipient countries of global funding with access to any funding from global financial mechanisms for projects related to invasive alien species management (ER_IAS_GLOFUNP)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>To collate and record data and information on national legislation and regulations enacted related to the prevention of introduction of alien and invasive species and their management if already established was mainly by consulting two databases FAOLEX<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> and ECOLEX<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>. For supplemental information, national government websites were also consulted.</p>\n<p>Data related to country strategies and NBSAPS to confirm if their targets were aligned to Aichi Target 9, all NBSAP documents were consulted from the CBD website.<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> An FAO-compiled database of &#x201C;national laws and regulations on food, agriculture and renewable natural resources <a href=\"http://www.fao.org/faolex/en/\">http://www.fao.org/faolex/en/</a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> ECOLEX has been designed to be the most comprehensive global source of information on national and international environmental law. It is a web-based environmental law information service, operated jointly by FAO, IUCN and UNEP since 2001. It is a platform that synergizes information on environmental law collected through FAOLEX (FAO), ELIS (IUCN) and InforMEA (UNEP). <a href=\"http://www.ecolex.org\">www.ecolex.org</a> &gt; <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> <a href=\"https://www.cbd.int/countries/\">https://www.cbd.int/countries/</a> <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p>Desktop literature review and relevant databases were consulted to collate data on legal responses by national governments and to confirm the alignment of national targets to the Aichi Target 9.</p>\n<p>Data to compile resource allocation by countries towards invasive alien species management including prevention, eradication, control, and outreach was compiled through an online survey. NSOs, NBSAP nodes and officials from the Dept of Environment of 196 parties to the CBD were the target of this survey which was open for 6 months from March 2020 to August 2020. A total of 142 countries completed the survey. The survey questionnaire can be accessed at <a href=\"https://url6.mailanyone.net/v1/?m=1kQmRb-0000WJ-5C&amp;i=57e1b682&amp;c=avaY8OCfig_yWtnIpsardYqtmcmV6gXq_RtsH3TIfTqisnI1Hi1yVTz18UB_lR9PQxuJZM5FOOsOvYDXQDsG9-hL6RrpWm3C6ikbPikxIfZz9INhDy462KDCg6EdbWIrL3dnMltkcsnIW_ImyPhOYcH9zVDEYBBSEkxUJUxzuC_ycvYniUNcJCTTzT1mfPWAGMRsuyEgegErVG4YuFURt0Kfep9h1EGSSIFyy3lGwpjthJtA3aRvQnfGqWvGhGTz\"><u>Pagad, Shyama; Affleck, Saxbee; McGeoch, Melodie (2020): Factsheet. La Trobe. Report</u></a><u> </u><a href=\"https://opal.latrobe.edu.au/articles/report/Factsheet/13065152?file=24997454\">https://opal.latrobe.edu.au/articles/report/Factsheet/13065152?file=24997454</a><u> </u></p>", "FREQ_COLL__GLOBAL"=>"<p>National agencies producing relevant data include government, non-governmental organizations (NGOs), and academic institutions working jointly and separately. Data are gathered from published and unpublished sources, species experts, scientists, and conservationists through correspondence, workshops, and electronic fora. This indicator was calculated in 2010 and 2016, and now includes the current 2020 update. Next updates are anticipated to be the Beginning at the first quarter of 2022 till the end of the second quarter of 2022.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>End of fourth quarter of 2022</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data were collected through a survey submitted to all listed NSOs; and, in the absence of NSOs or their response to relevant national agencies (Ministries of Environment or similar agencies).</p>\n<p>Data on national legislation was obtained from the two key databases/ repositories of Environmental Law- ECOLEX and FAOLEX. Information related to national targets was obtained from the latest NBSAPs and national reports submitted to the CBD.</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Union for Conservation of Nature (IUCN) Species Survival Commission (SSC) Invasive Species Specialist Group (ISSG)</p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>Aichi Biodiversity Target 9 states: &#x201C;By 2020, invasive alien species and pathways are identified and prioritized, priority species are controlled or eradicated, and measures are in place to manage pathways to prevent their introduction and establishment&#x201D;.</p>\n<p>Under sub-indicator (1)(a), Effective national policy and legislation underpins effective national strategies and action for preventing and controlling invasive alien species. </p>\n<p>Measurement of sub-indicator (1) (a) was first undertaken in 2010, and published in Butchart et al. (2010), CBD (2014), McGeoch et al. (2010), and Tittensor et al. (2014). Sub-indicator (1) indicators have now also been added to include (b) national commitment (mandate and legal authority) to key invasive alien species related themes, specifically if targets and objectives within national strategies for preventing and controlling invasive alien species are aligned with Aichi Target 9. </p>\n<p>The indicator now also addresses (2) resourcing by national governments for the prevention and control of invasive alien species, as identified by the Sustainable Development Goals indicator 15.8.1 (&#x201C;Proportion of countries adopting relevant national legislation and adequately resourcing the prevention or control of invasive alien species&#x201D;). Adequate resourcing is vital to ensure implementation and effective delivery of targets set.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The adoption of legislation does not necessarily indicate the existence of regulations or policy to implement the legislation or how successful such implementation has been on the ground. There remains a need for further indicator development to make this link clearer. Legislation does not necessarily capture all efforts against invasive alien species that are happening at the national level.</p>\n<p>Allocation of resources to facilitate the implementation of IAS management action is difficult to measure, particularly in a way that is comparable across countries. Proxies used to measure allocation of resources included- allocation of a budget line to invasive species management activities (including prevention, rapid response, and active management); appointed staff to carry out any IAS related activities; active programmes/projects etc.</p>\n<p>Comments on the feasibility, suitability, relevance and limitations of the indicator. Also includes data comparability issues, presence of wide confidence intervals (such as for maternal mortality ratios); provides further details on additional non-official indicators commonly used together with the indicator.</p>", "DATA_COMP__GLOBAL"=>"<p>This indicator is calculated from data derived from two annually updated datasets.</p>\n<p>(1) (a) National Legislation considered relevant to the prevention of introduction of invasive alien species and control. </p>\n<p>All countries currently party to the Convention on Biological Diversity were considered in the analysis (n = 195), excluding the European Union as an entity. Data for five countries were not comparable and were not included.</p>\n<p>This indicator analysed national legislation relevant to IAS. Across countries, IAS relevant policies are found in legislations, regulations and acts related to the Environment, Forestry, Plant health, Animal health, Fisheries, Water, Species including Wild Fauna and Flora and Genetically Modified Organism (GMO). Most countries adopt a sectoral approach to IAS management. A few have adopted a more focused approach- one example is the 2014 Regulation (EU) No 1143/2014 of the European Parliament on the prevention and management of the introduction and spread of invasive alien species.</p>\n<p>The 2010 and 2016 data considered national legislation related to invasive alien species in an overall perspective. The 2020 update included thematic sectors. To quantify adoption of IAS relevant policies, seven national legislation sectors were considered; animal health, plant health, environment (including protected areas and wildlife protection), biosecurity, fisheries and aquaculture (including wetlands and marine legislation), invasive alien species, and others (including hunting well as policy on particular species, such as the Giant African Snail, <em>Achatina fulica</em>). Examples of national legislation focused on IAS specifically were noted. </p>\n<p>(1) (b) National Biodiversity Strategy and Action Plan (NBSAP) targets alignment to Aichi Biodiversity target 9 set out in the Strategic Plan for Biodiversity 2011-2020.</p>\n<p>All countries currently party to the Convention on Biological Diversity were considered in the analysis (n = 195), excluding the European Union as an entity. This indicator measured whether countries firstly had targets related to IAS management in their NBSAPS, and secondly, whether these targets were aligned to Aichi Biodiversity Target 9.</p>\n<p>NBSAPs are a key policy instrument that reflect, how national biodiversity strategies intend to fulfil the obligations of the CBD, and how the related action plans outline the steps to be taken to meet these goals. All parties to the CBD are obligated to revise their NBSAPs to reflect compliance with the revised Strategic Plan and Aichi Targets.</p>\n<p>Part (1a) and (1b) were calculated as follows:</p>\n<p>National strategies for preventing and controlling invasive alien species, underpinned by national policy and legislation for effective management of biological invasions. </p>\n<p>The components of this sub-indicator are calculated as the number of countries with (a) national legislation and policy relevant to Invasive alien species concerns; and (b) national strategies for preventing and controlling invasive alien species, each divided by the total number of countries (196 to date) for which data are available. The first data point for component (1) (a) of this sub-indicator is 2010; the first data point for component (1)(b) is 2016.</p>\n<p>Both Part 1a and Part 1b are incorporated in the SDG Database as ER_IAS_LEGIS and ER_IAS_NBSAP respectively. Regional and global series are also incorporated for the latter, as ER_IAS_NBSAPP.</p>\n<p>Part (2) Indicator: The translation of policy arrangements into action by countries to implement policy and actively prevent and control invasive alien species and the resourcing of this action.</p>\n<p>(2) Online survey on Policy responses, mandate, legal authority, and resourcing to manage the threat of invasive alien species.</p>\n<p>An online survey was developed and submitted to all listed NSOs, CBD National focal points (in cases of absence of NSOs or lack of response) to obtain an insight into the allocation of resources to the management of invasive alien species. 142 of the 196 countries completed the survey. Considering the difficulty in obtaining information on the level of national investment on invasive alien species issues, proxy indicators were used to measure the allocation of resources by individual countries, such as &#x201C;does the country have a dedicated and staffed program for invasive alien species management&#x201D;. </p>\n<p>This sub-indicator is calculated as the number of national respondents to the annual survey on invasive alien species response financing reporting availability of sufficient resources, divided by the total number of countries (142 to date) for which data are available. The first data point for this sub-indicator is 2016. Part 2 encompasses 18 specific components obtained from the following questions used in the annual survey on invasive alien species, as follows:</p>\n<p>Does your country have a Government Department, National agency or agencies (including supranational institutions/organizations, e.g. EU) responsible for managing IAS that impact the natural environment, economic sectors (e.g. agriculture, forestry, tourism, etc.) or human health? </p>\n<p>Are there institutions (including supranational institutions/organizations, e.g. EU) with a clear legal mandate and the necessary powers to develop national plans and policies in relation to invasive alien species?</p>\n<p>Are there institutions (including supranational institutions/organizations, e.g. EU) with a clear legal mandate and the necessary powers to undertake risk analyses of potentially invasive species?</p>\n<p>Are there institutions (including supranational institutions/organizations, e.g. EU) with a clear legal mandate and the necessary powers to prevent the intentional introduction of species assessed as potentially invasive (including importation for the purposes of agriculture, aquaculture, the nursery trade, farming and animal breeding, the pet trade etc.)?</p>\n<p>Are there institutions (including supranational institutions/organizations, e.g. EU) with a clear legal mandate and the necessary powers to minimise the unintentional introduction of alien species?</p>\n<p>Are there institutions (including supranational institutions/organizations, e.g. EU) with a clear legal mandate and the necessary powers to promote public awareness of IAS issues?</p>\n<p>Are there institutions (including supranational institutions/organizations, e.g. EU) with a clear legal mandate and the necessary powers to monitor and conduct surveillance programmes to detect founder populations of IAS at an early stage?</p>\n<p>Are there institutions (including supranational institutions/organizations, e.g. EU) with a clear legal mandate and the necessary powers to contain and eradicate populations of IAS within the country?</p>\n<p>Are there institutions (including supranational institutions/organizations, e.g. EU) with a clear legal mandate and the necessary powers to record and maintain information on IAS?</p>\n<p>Are there institutions (including supranational institutions/organizations, e.g. EU) with a clear legal mandate and the necessary powers to enforce the relevant legal provisions regarding the control of IAS?</p>\n<p>Are there any existing legal provisions or institutional arrangements to facilitate cooperation between different government agencies in making decisions regarding IAS?</p>\n<p>Does your country have an allocation from the National budget to manage the threat of IAS?</p>\n<p>If your country is a recipient of global funding (such as the Global Environment Facility (GEF) - has your country accessed any funding from global financial mechanisms for projects related to IAS management? </p>\n<p>Does your Biodiversity Strategy (at the local, national, regional, or supranational level) include objective(s) and actions related to IAS management?</p>\n<p>Is there a budget allocation or are there any financial tools (for e.g. dedicated financial programmes) available for this implementation?</p>\n<p>Has your country developed a National Invasive Alien Species Strategy and Action Plan (NISSAP)?</p>\n<p>Is there a budget allocation or are there any financial tools (for e.g. dedicated financial programmes) available for this implementation?</p>\n<p>Do you know of any non-governmental agencies (NGO) or civil society groups involved in IAS management in your country?</p>\n<p>Two of these, national budget allocations and recipients of global funding, are incorporated in the SDG Database as ER_IAS_NATBUD and ER_IAS_GLOFUN respectively. Regional and global series are also incorporated for each, as ER_IAS_NATBUDP and ER_IAS_GLOFUNP respectively.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Authoritative and reliable sources were used to collate data. In some cases, cross referencing with National government websites was completed for supplemental data. The survey was targeted towards NSOs or national nodes.</p>\n<p>Description of process of monitoring the results of data compilation and ensuring the quality of the statistical results, including consultation process with countries on the national data submitted to the SDGs Indicators Database. Descriptions and links to all relevant reference materials should be provided.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>Countries for which no data are available are omitted from the indicator.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>The indicator is calculated as the simple proportion of countries (for which data are available) that have a given invasive alien species response (treaties, strategy, legislation, financing) in place.</p>", "DOC_METHOD__GLOBAL"=>"<p>The national questionnaire circulated to national agencies (NSOs; and relevant national agencies eg Ministries of Environment or similar) is supported by clear definitions and guidance to support documentation of the YES/NO answers to each question. The IUCN Invasive Species Specialist Group also provides case-by-case response to queries. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The indicator is managed under the IUCN Invasive Species Specialist Group, of which the Chair is appointed by the Chair of the IUCN Species Survival Commission, elected every four years by the IUCN Membership of governments and civil society. The Invasive Species Specialist Group undertakes ongoing evaluation of fitness for use of the indicator i.e. the degree to which it meets user&#x2019;s requirements. This encompasses, inter alia, considerations of relevance, accuracy, timeliness, consistency, comprehensiveness, and accessibility.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data sources and data collection:</strong></p>\n<p>Two datasets were updated/developed for the measurement of this indicator.</p>\n<p>Part (1) (a) </p>\n<p>National Legislation considered relevant to the prevention of introduction of invasive alien species and control (used for &#x201C;National strategies for preventing and controlling invasive alien species&#x201D;). The data format is a spreadsheet of countries vs inclusion of invasive alien species in legislation, with year of legislation in each cell. Key information sources included ECOLEX (<a href=\"https://www.ecolex.org/\">https://www.ecolex.org/</a>), FAOLEX (<a href=\"http://www.fao.org/faolex/en/\">http://www.fao.org/faolex/en/</a>) and national government websites with information on Legislation. Country experts were also contacted for clarifications.</p>\n<p>Part (1)(b) National Biodiversity Strategy and Action Plan (NBSAP) targets alignment to Aichi Biodiversity target 9 set out in the Strategic Plan of Biodiversity Conservation 2011-2020 and status of implementation of targets as described in the 5th National reports (used for &#x201C;National strategies for preventing and controlling invasive alien species&#x201D;). The information source was the CBD website, which features country profiles (<a href=\"https://www.cbd.int/countries/\">https://www.cbd.int/countries/</a>). 196 countries were included. The data format is a spreadsheet of countries vs inclusion of IAS in NBSAP, and Aichi Target 9 alignment.</p>\n<p>Part (2) Results of online survey, disseminated to all CBD national focal points, on Policy responses, mandate, legal authority and resourcing to manage the threat of invasive alien species (used for &#x201C;National legislation and policy relevant to invasive alien species&#x201D; and &#x201C;National allocation of resources towards the prevention or control of invasive alien species&#x201D;). The data format is a spreadsheet of countries vs each of nine IAS management related themes, for both mandate and legal authority; and with an additional dataset indicating funding received from global funding mechanisms for invasive alien species related projects.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>196 countries that are party to the CBD. All datasets developed for the measurement of this indicator used the country name as the qualifier. Datasets can be aggregated regionally if desired.</p>", "COMPARABILITY__GLOBAL"=>"<p>All data sources are national, and so there are no differences between global and national figures.</p>", "OTHER_DOC__GLOBAL"=>"<p>Biodiversity Indicators Partnership. (2017). Legislation for prevention and control of invasive alien species (IAS), encompassing &#x201C;Trends in policy responses, legislation and management plans to control and prevent spread of invasive alien species&#x201D; and &#x201C;Proportion of countries adopting relevant national legislation and adequately resourcing the prevention or control of invasive alien species&#x201D;. Retrieved from <a href=\"https://www.bipindicators.net/indicators/adoption-of-national-legislation-relevant-to-the-prevention-or-control-of-invasive-alien-species\">https://www.bipindicators.net/indicators/adoption-of-national-legislation-relevant-to-the-prevention-or-control-of-invasive-alien-species</a>.</p>\n<p> </p>\n<p>McGeoch, M.A., Butchart, S.H.M., Spear, D., Marais, E., Kleynhans, E.J., Symes, A., Chanson, J. &amp; Hoffmann, M. (2010) Global indicators of biological invasion: species numbers, biodiversity impact and policy responses. Diversity and Distributions, 16, 95-108.</p>\n<p>Tittensor, D. P., M. Walpole, S. L. L. Hill, D. G. Boyce, G. L. Britten, N. D. Burgess, S. H. M. Butchart, P. W. Leadley, E. C. Regan, R. Alkemade, R. Baumung, C. Bellard, L. Bouwman, N. J. Bowles-Newark, A. M. Chenery, W. W. L. Cheung, V. Christensen, H. D. Cooper, A. R. Crowther, M. J. R. Dixon, A. Galli, V. Gaveau, R. D. Gregory, N. L. Gutierrez, T. L. Hirsch, R. Hoeft, S. R. Januchowski-Hartley, M. Karmann, C. B. Krug, F. J. Leverington, J. Loh, R. K. Lojenga, K. Malsch, A. Marques, D. H. W. Morgan, P. J. Mumby, T. Newbold, K. Noonan-Mooney, S. N. Pagad, B. C. Parks, H. M. Pereira, T. Robertson, C. Rondinini, L. Santini, J. P. W. Scharlemann, S. Schindler, U. R. Sumaila, L. S. L. Teh, J. van Kolck, P. Visconti, and Y. Ye. 2014. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241-244.</p>\n<p>Turbelin, A. J., Malamud, B. D., &amp; Francis, R. A. (2017). Mapping the global state of invasive alien species: Patterns of invasion and policy responses. Global Ecology and Biogeography, 26(1), 78&#x2013;92.</p>", "indicator_sort_order"=>"15-08-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.9.1", "slug"=>"15-9-1", "name"=>"a) Número de países que han establecido metas nacionales de conformidad con la segunda Meta de Aichi del Plan Estratégico para la Diversidad Biológica 2011-2020 o metas similares en sus estrategias y planes de acción nacionales en materia de diversidad biológica y han informado de sus progresos en el logro de estas metas; y b) integración de la biodiversidad en los sistemas nacionales de contabilidad y presentación de informes, definidos como implementación del Sistema de Contabilidad Ambiental y Económica", "url"=>"/site/es/15-9-1/", "sort"=>"150901", "goal_number"=>"15", "target_number"=>"15.9", "global"=>{"name"=>"a) Número de países que han establecido metas nacionales de conformidad con la segunda Meta de Aichi del Plan Estratégico para la Diversidad Biológica 2011-2020 o metas similares en sus estrategias y planes de acción nacionales en materia de diversidad biológica y han informado de sus progresos en el logro de estas metas; y b) integración de la biodiversidad en los sistemas nacionales de contabilidad y presentación de informes, definidos como implementación del Sistema de Contabilidad Ambiental y Económica"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"a) Número de países que han establecido metas nacionales de conformidad con la segunda Meta de Aichi del Plan Estratégico para la Diversidad Biológica 2011-2020 o metas similares en sus estrategias y planes de acción nacionales en materia de diversidad biológica y han informado de sus progresos en el logro de estas metas; y b) integración de la biodiversidad en los sistemas nacionales de contabilidad y presentación de informes, definidos como implementación del Sistema de Contabilidad Ambiental y Económica", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"a) Número de países que han establecido metas nacionales de conformidad con la segunda Meta de Aichi del Plan Estratégico para la Diversidad Biológica 2011-2020 o metas similares en sus estrategias y planes de acción nacionales en materia de diversidad biológica y han informado de sus progresos en el logro de estas metas; y b) integración de la biodiversidad en los sistemas nacionales de contabilidad y presentación de informes, definidos como implementación del Sistema de Contabilidad Ambiental y Económica", "indicator_number"=>"15.9.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El objetivo de la Meta 2 de Aichi para la Diversidad Biológica es garantizar que los \ndiversos valores de la biodiversidad y las oportunidades derivadas de su conservación \ny uso sostenible se reconozcan y reflejen en todos los procesos de toma de \ndecisiones públicos y privados pertinentes.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.9.1&seriesCode=ER_BDY_ABT2NP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=_T\">\nPaíses que establecieron metas nacionales de conformidad con la Meta 2 de Aichi para la Diversidad Biológica del Plan Estratégico para la Diversidad Biológica 2011-2020 en su Estrategia y Planes de Acción Nacionales sobre Diversidad Biológica (1 = SÍ; 0 = NO) ER_BDY_ABT2NP</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.9.1&seriesCode=ER_BDY_SEEA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=_T\">\nPaíses con valores de biodiversidad integrados en los sistemas nacionales de contabilidad y presentación de informes, definidos como la implementación del Sistema de Contabilidad Ambiental-Económica (1 = SÍ; 0 = NO) ER_BDY_SEEA</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-09-01.pdf\">Metadatos 15-9-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The objective of Aichi Biodiversity Target 2 is to ensure that the diverse values of biodiversity \nand the opportunities arising from its conservation and sustainable use are recognized and reflected \nin all relevant public and private decision-making processes. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.9.1&seriesCode=ER_BDY_ABT2NP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=_T\">\nCountries that established national targets in accordance with Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011-2020 in their National Biodiversity Strategy and Action Plans (1 = YES; 0 = NO) ER_BDY_ABT2NP</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.9.1&seriesCode=ER_BDY_SEEA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=_T\">\nCountries with integrated biodiversity values into national accounting and reporting systems, defined as implementation of the System of Environmental-Economic Accounting (1 = YES; 0 = NO) ER_BDY_SEEA</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-09-01.pdf\">Metadata 15-9-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"El objetivo de la Meta 2 de Aichi para la Diversidad Biológica es garantizar que los \ndiversos valores de la biodiversidad y las oportunidades derivadas de su conservación \ny uso sostenible se reconozcan y reflejen en todos los procesos de toma de \ndecisiones públicos y privados pertinentes.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.9.1&seriesCode=ER_BDY_ABT2NP&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=_T\">\n2011-2020 Aniztasun Biologikorako Plan Estrategikoaren Aniztasun Biologikorako Aichiko 2. helburuari jarraiki jomuga nazionalak ezarri zituzten herrialdeak, Aniztasun Biologikoari buruzko Ekintza Plan eta Estrategia Nazionalean (1 = BAI; 0 = EZ) ER_BDY_ABT2NP</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.9.1&seriesCode=ER_BDY_SEEA&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=_T\">\nKontabilitateko eta txostenen aurkezpeneko sistema nazionaletan integratutako biodibertsitate-balioak dituzten herrialdeak, Ingurumen eta Ekonomia Kontabilitateko Sistemaren inplementazio gisa definituak (1 = BAI; 0 = EZ) ER_BDY_SEEA</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-09-01.pdf\">Metadatuak 15-9-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.9: By 2020, integrate ecosystem and biodiversity values into national and local planning, development processes, poverty reduction strategies and accounts</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.9.1: (a) Number of countries that have established national targets in accordance with or similar to Kunming-Montreal Global Biodiversity Framework Target 14 in their national biodiversity strategy and action plans and the progress reported towards these targets; and (b) integration of biodiversity into national accounting and reporting systems, defined as implementation of the System of Environmental-Economic Accounting</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Until 2022: ER_BDY_ABT2NP - Countries that established national targets in accordance with Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011-2020 in their National Biodiversity Strategy and Action Plans (1 = YES; 0 = NO) [15.9.1]</p>\n<p>From 2023: Countries that established national targets in accordance with Kunming-Montreal Global Biodiversity Framework Target 14 </p>\n<p>ER_BDY_SEEA - Countries with integrated biodiversity values into national accounting and reporting systems, defined as implementation of the System of Environmental-Economic Accounting (1 = YES; 0 = NO) [15.9.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>15.a.1, 15.b.1 </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), Convention on Biological Diversity (CBD), United Nations Statistics Division (UNSD) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP), Convention on Biological Diversity (CBD), United Nations Statistics Division (UNSD) </p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Until 2022: The indicator measures the progress towards national targets established in accordance with Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011-2020: By 2020, at the latest, biodiversity values have been integrated into national and local development and poverty reduction strategies and planning processes and are being incorporated into national accounting, as appropriate, and reporting systems.</p>\n<p>From 2023: The indicator measures the progress towards national targets established in accordance with Kunming-Montreal Global Biodiversity Framework Target 14: By 2030, Ensure the full integration of biodiversity and its multiple values into policies, regulations, planning and development processes, poverty eradication strategies, strategic environmental assessments, environmental impact assessments and, as appropriate, national accounting, within and across all levels of government and across all sectors, in particular those with significant impacts on biodiversity, progressively aligning all relevant public and private activities, and fiscal and financial flows with the goals and targets of this framework. </p>\n<p>The indicator is divided in two sub-indicators: </p>\n<ul>\n  <li><strong>15.9.1(a): </strong><em>Number of countries that established national targets in accordance with </em>Kunming-Montreal Global Biodiversity Framework Target 14 <em>in their national biodiversity strategy and action plans and the progress reported towards these targets.</em></li>\n  <li><strong>15.9.1(b):</strong><em> Integration of biodiversity into national accounting and reporting systems, defined as implementation of the System of Environmental-Economic Accounting.</em></li>\n</ul>\n<p><strong>Concepts:</strong></p>\n<p><strong><u>Biodiversity</u></strong></p>\n<p>The 1992 United Nations Earth Summit defined &quot;biological diversity&quot; as &quot;the variability among living organisms from all sources, including, <em>inter alia</em>, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part: this includes diversity within species, between species and of ecosystems&quot;.</p>\n<p><strong><u>Until 2022: Aichi Biodiversity Target 2</u></strong></p>\n<p>Aichi Biodiversity Target 2 is under Strategic Goal A of the Strategic Plan for Biodiversity 2011-2020, which addresses the underlying causes of biodiversity loss by mainstreaming biodiversity across government and society.</p>\n<p>Aichi Biodiversity Target 2: By 2020, at the latest, biodiversity values have been integrated into national and local development and poverty reduction strategies and planning processes and are being incorporated into national accounting, as appropriate,<strong> </strong>and reporting systems.</p>\n<p><strong>From 2023: Kunming-Montreal Global Biodiversity Framework </strong></p>\n<p>The Kunming-Montreal Global Biodiversity Framework supports the 2030 Agenda for Sustainable Development. Achieving the Sustainable Development Goals and sustainable development in environmental, social, and economic aspects is crucial to meet the Framework&apos;s goals and targets. </p>\n<p>The Kunming-Montreal Global Biodiversity Framework has four long-term goals for 2050 related to the 2050 Vision for biodiversity and has 23 action-oriented global targets for urgent action over the decade to 2030.</p>\n<p><strong>TARGET 14</strong> Ensure the full integration of biodiversity and its multiple values into policies, regulations, planning and development processes, poverty eradication strategies, strategic environmental assessments, environmental impact assessments and, as appropriate, national accounting, within and across all levels of government and across all sectors, in particular those with significant impacts on biodiversity, progressively aligning all relevant public and private activities, and fiscal and financial flows with the goals and targets of this framework.</p>\n<p><strong><u>NBSAPs</u></strong></p>\n<p>In accordance with Article 6 of the Convention on Biological Diversity, Parties are obligated to develop national biodiversity strategies and action plans, and integrate biodiversity considerations into relevant sectoral or cross-sectoral plans, programmes and policies. The National Biodiversity Strategy and Action Plan (NBSAP) is intended to define the current status of biodiversity, the threats leading to its degradation and the strategies and priority actions to ensure its conservation and sustainable use within the framework of the socio-economic development of the country.</p>\n<p><strong><u>National Reports</u></strong></p>\n<p>In accordance with Article 26 of the Convention on Biological Diversity, Parties are obligated to provide information on measures taken towards the implementation of the Convention and its strategic plans, as reflected in the National Biodiversity Strategy and Action Plan (NBSAP), as well as on the effectiveness of these measures. The format for the sixth national reports requested that Parties, among other things, provide an assessment of their progress towards their national targets and/or the Kunming-Montreal Global Biodiversity Framework (GBF) Targets. These national reports are publicly available on the Convention&#x2019;s Clearing-House Mechanism, which is constantly being improved to enhance usability by Parties and better contribute to assessment of the implementation of the Kunming-Montreal Global Biodiversity Framework (GBF) and the achievement of GBF 23 Targets.</p>\n<p><strong>The system of environmental-economic accounting</strong> is presented by two international statistical standards: the System for Environmental-Economic Accounting Central Framework (SEEA-CF), adopted in 2012, and the System for Environmental-Economic Accounting-Ecosystem Accounting (SEEA-EA), adopted in 2021.</p>\n<p><strong><u>SEEA-CF </u></strong></p>\n<p>The System for Environmental-Economic Accounting Central Framework (SEEA-CF) is an international statistical standard for measuring the environment and its relationship with the economy. It integrates economic and environmental data to provide a more comprehensive and multipurpose view of the interrelationships between the economy and the environment and the stocks and changes in stocks of environmental assets, as they bring benefits to humanity.</p>\n<p><strong><u>SEEA-EA </u></strong></p>\n<p>The System for Environmental-Economic Accounting-Ecosystem Accounting (SEEA-EA) is an integrated statistical framework for organizing biophysical data, measuring ecosystem services in physical and monetary terms, tracking changes in the condition and extent of ecosystem assets and linking this information to economic and other human activity. The SEEA-EA takes the perspective of ecosystems and considers how individual environmental assets interact as part of natural processes within a given spatial area. </p>\n<p><strong><u>The Global Assessment of Environmental-Economic Accounting and Supporting Statistics</u></strong></p>\n<p>The Global Assessment of Environmental-Economic Accounting and Supporting Statistics is a survey administered by the UNSD under the auspices of the UN Committee of Experts on Environmental Economic Accounting (UNCEEA). The aim of the Global Assessment is to assess the progress in reaching the implementation targets of the UNCEEA.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>For time series characterising the world or regions: number.</p>\n<p>For time series characterising selected countries: identification &#x201C;1&#x201D; meaning presence, or &#x201C;0&#x201D; meaning not present.</p>\n<p>For indicator 15.9.1a, the &#x201C;number&#x201D; represents the number of countries that established national targets in accordance with:</p>\n<ul>\n  <li>Until 2022: Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011-2020; </li>\n  <li>From 2023: Kunming-Montreal Global Biodiversity Framework Target 14 </li>\n</ul>\n<p>in their National Biodiversity Strategy and Action Plans.</p>\n<p>For indicator 15.9.1b, the &#x201C;number&#x201D; represents the number of countries with integrated biodiversity values into national accounting and reporting systems, defined as implementation of the System of Environmental-Economic Accounting.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions).</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Description:</strong></p>\n<p>National Statistical Systems and other relevant agencies contribute directly to the National Biodiversity Strategy and Action Plan (NBSAP) reporting and to the reporting on SEEA implementation.</p>\n<p>Sub-indicator (a): NBSAPs and National Reports. </p>\n<p>Sub-indicator (b): Global Assessments of Environmental-Economic Accounting and Supporting Statistics.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data collection is through submission of reports (sub-indicator (a)) and a dedicated survey on SEEA implementation (sub-indicator (b)).</p>\n<p>The data for <strong>sub-indicator (a)</strong> is currently collected by the Secretariat of the Convention on Biological Diversity. Collection of NBSAPs and National Reports is regularly updated by the Secretariat of the Convention on Biological Diversity and is available here:</p>\n<ol>\n  <li><a href=\"https://www.cbd.int/nbsap/\"><u>https://www.cbd.int/nbsap/</u></a> </li>\n  <li><a href=\"https://www.cbd.int/reports/\"><u>https://www.cbd.int/reports/</u></a></li>\n</ol>\n<p>The number of Parties to the Convention on Biological Diversity considered to have submitted post-2020 NBSAPs that take Kunming-Montreal Global Biodiversity Framework (GBF) into account is regularly updated as well. </p>\n<p>The data source for <strong>sub-indicator (b)</strong> is the results of the Global Assessments of Environmental-Economic Accounting and Supporting Statistics administered under the auspices of the UN Committee of Experts on Environmental Economic Accounting (UNCEEA), for which reports can be found here: <a href=\"https://seea.un.org/content/global-assessment-environmental-economic-accounting\"><u>https://seea.un.org/content/global-assessment-environmental-economic-accounting</u></a>. </p>", "FREQ_COLL__GLOBAL"=>"<p>Existing reporting to the Convention on Biological Diversity (CBD) and to the United Nations Statistics Division (UNSD).</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released in the year following the data collection. </p>", "DATA_SOURCE__GLOBAL"=>"<ol>\n  <li>Ministries of Environment or other relevant agencies through the National Focal Points to the Convention on Biological Diversity. </li>\n  <li>National Statistical Offices through the UNCEEA focal points.</li>\n</ol>", "COMPILING_ORG__GLOBAL"=>"<ol>\n  <li>The Secretariat of the Convention on Biological Diversity (CBD) collects data on sub-indicator (a). </li>\n  <li>The United Nations Statistics Division (UNSD) collects data on sub-indicator (b).</li>\n</ol>", "INST_MANDATE__GLOBAL"=>"<p><strong>Sub-indicator (a)</strong>: In decision XV/4, the Conference of the Parties to the Convention on Biological Diversity, urged Parties to develop national and regional targets, using the Kunming-Montreal Global Biodiversity Framework (GBF), as a flexible framework, in accordance with national priorities and capacities and taking into account both the global targets and the status and trends of biological diversity in the country, and the resources provided through the strategy for resource mobilization, with a view to contributing to collective global efforts to reach the global targets, and report thereon to the Conference of the Parties at its sixteenth meeting. In the same decision, the Conference of the Parties requested the Executive Secretary of the Secretariat of the Convention on Biological Diversity to prepare an analysis/synthesis of national, regional and other actions, including targets as appropriate, established in accordance with the Strategic Plan, to enable the Conference of Parties at its sixteenth and subsequent meetings to assess the contribution of such national and regional targets towards the global targets.</p>\n<p><strong>Sub-indicator (b)</strong>: For sub-indicator (b), the UNCEEA was established by the UN Statistical Commission at its 36<sup>th</sup> session in March 2005. The UNCEEA functions as an intergovernmental body to provide overall vision, coordination, prioritization and direction in the field of environmental economic accounting and supporting statistics. As Secretariat to the UNCEEA, UNSD administers the Global Assessment on Environmental-Economic Accounting and Supporting Statistics. </p>", "RATIONALE__GLOBAL"=>"<p>The objective of Kunming-Montreal Global Biodiversity Framework Target 14 is to ensure the full integration of biodiversity and its multiple values into policies, regulations, planning and development processes, poverty eradication strategies, strategic environmental assessments, environmental impact assessments and, as appropriate, national accounting, within and across all levels of government and across all sectors, in particular those with significant impacts on biodiversity, progressively aligning all relevant public and private activities, and fiscal and financial flows with the goals and targets of this framework.</p>\n<p><strong>Sub-indicator (a)</strong>:<strong> </strong>National Biodiversity Strategies and Action Plans are described in Article 6 of the Convention on Biological Diversity on General Measures for Conservation and Sustainable Use. Under this article, it is stated that &#x201C;each Party to the Convention shall, in accordance with its particular conditions and capabilities: (a) Develop national strategies, plans or programmes for the conservation and sustainable use of biological diversity or adapt for this purpose existing strategies, plans or programmes which shall reflect, <em>inter alia</em>, the measures set out in this Convention relevant to the Contracting Party concerned; and (b) Integrate, as far as possible and as appropriate, the conservation and sustainable use of biological diversity into relevant sectoral or cross-sectoral plans, programmes and policies&#x201D;. Further, under Article 26, it is stated that &#x201C;each Contracting Party shall, at intervals to be determined by the Conference of the Parties, present to the Conference of the Parties, reports on measures which it has taken for the implementation of the provisions of this Convention and their effectiveness in meeting the objectives of this Convention&#x201D;.</p>\n<p><strong>Sub-indicator (b)</strong>:<strong> </strong>Integration of biodiversity values into national accounting and reporting systems can be achieved through implementation of the international statistical standard, the System for Environmental-Economic Accounting (SEEA). The SEEA Central Framework (SEEA CF) was adopted by the UN Statistical Commission in 2012 as the first international standard for environmental-economic accounting. In addition, the SEEA Ecosystem Accounting (SEEA EA) was endorsed by the UN Statistical Commission in 2021. Results of the Global Assessment of Environmental-Economic Accounting and Supporting Statistics provide the data needed for Sub-indicator (b) of the indicator.</p>", "REC_USE_LIM__GLOBAL"=>"<p><strong>Sub-indicator (a)</strong>: The assessment of national targets has several limitations stemming from the different approaches Parties have taken in setting national targets and in reporting against them. Parties have mapped their national targets to the GBF Targets in different ways and based on different information. The different approaches in national target-setting and reporting present challenges to undertaking analysis in a systematic manner. </p>\n<p><strong>Sub-indicator (b)</strong>: The SEEA EA was adopted in March 2021, and the way that the SEEA EA is implemented by countries is expected to develop over time. In addition, the extent to which specific SEEA accounts relate to biodiversity differs, and some accounts relate more directly to biodiversity than others. Thus, the extent to which certain SEEA accounts directly integrate biodiversity into national accounting and reporting systems will also differ.</p>", "DATA_COMP__GLOBAL"=>"<p><strong>Sub-indicator (a)</strong>: The seventh and eighth national reports provide progress made in achieving the national targets and/or the Kunming-Montreal Global Biodiversity Framework, The progress assessment for Kunming-Montreal Global Biodiversity Framework Target 14 would thus provide critical information for indicator 15.9.1. </p>\n<p>Parties are also requested to use the monitoring framework of the Kunming-Montreal Global Biodiversity Framework, in their national reports, supplemented by optional component and complementary indicators and also other national indicators.</p>\n<p><strong>Sub-indicator (b)</strong>: The Global Assessment of Environmental-Economic Accounting and Supporting Statistics collects information on whether countries are currently planning or implementing SEEA accounts, the specific accounts being implemented and plans for new/future accounts. Sub-indicator (b) is defined as the number of countries, which indicate they have implemented any SEEA Central Framework or SEEA Ecosystem Accounting accounts in their response to the Global Assessment. The sub-indicator uses the definition of implementation put forth by the UNCEEA, which disaggregates implementation into three progressive stages:</p>\n<ol>\n  <li>Compilation: A country falls into this stage if it has compiled at least one account (which is consistent with the SEEA) over the past five years.</li>\n  <li>Dissemination: A country falls into this stage if it has compiled and published at least one account within the past five years. </li>\n  <li>Regular compilation and dissemination: A country falls into this stage if it regularly publishes at least one account. Regularly published accounts are compiled and published according to a scheduled production cycle (which may differ by account).</li>\n</ol>\n<p>These stages will be scored as follows:</p>\n<ol>\n  <li>No compilation</li>\n  <li>Compilation</li>\n  <li>Dissemination</li>\n  <li>Regular compilation and dissemination</li>\n</ol>", "DATA_VALIDATION__GLOBAL"=>"<p><strong>Sub-indicator (a)</strong>: Information is provided directly by Parties to the Convention on Biological Diversity. The data is provided to the meetings of the Conference of the Parties to the Convention on Biological Diversity, as well as to relevant meetings of the Convention&#x2019;s subsidiary bodies.<s> </s></p>\n<p> </p>\n<p><strong>Sub-indicator (b)</strong>: For sub-indicator (b), the data is derived from the Global Assessment for Environmental-Economic Accounting and Supporting Statistics, which is sent to all national statistical offices. UNSD validates the data and consults with countries in the case of any discrepancies.</p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p><strong>Sub-indicator (a)</strong>: Missing values are not imputed. </p>\n<p><strong>Sub-indicator (b)</strong>: Missing values will occur if a country does not respond to the Global Assessment. If a country does not respond, missing values will be imputed if the custodian agency can find evidence of implementation, such as online publications of SEEA accounts, or based on information gathered from international organizations on the compilation of SEEA accounts. In particular:</p>\n<p> -If a national statistical office or other government institution has published a SEEA account which is easily accessible online, this country will be imputed as compiling the SEEA. Since no assumption can be made that the country regularly compiles and publishes the account, this country would fall under Stage II.</p>\n<p> -If the custodian agency finds that a country compiles SEEA accounts through a project or other implementation programme and verifies this with the international organizations involved, this country will be imputed as compiling the SEEA under Stage I or Stage II as appropriate.</p>\n<p>In all cases, imputation is only be done as a secondary step after first contacting countries. All imputations will be clearly flagged for users as imputations by UNSD. </p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p><strong>Sub-indicator (a)</strong>: Missing values are considered to be 0 as this indicator refers to reporting processes. Thus if a country does not report it is assumed that there is no corresponding national target.</p>\n<p><strong>Sub-indicator (b)</strong>: A simple count of countries will be used.</p>", "REG_AGG__GLOBAL"=>"<p>For <strong>sub-indicator (a)</strong>, weighted averages will be developed using the method described here:</p>\n<p><a href=\"http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a>.</p>\n<p>For <strong>sub-indicator (b)</strong>, a simple count of countries will be used.</p>", "DOC_METHOD__GLOBAL"=>"<p><strong>Sub-indicator (a)</strong>: The reporting guidelines (decision XV/6), including reporting templates, and associated guidance for the preparation of the seventh and eighth national reports to the Convention on Biological Diversity are available at: <a href=\"https://www.cbd.int/doc/nr/Annex%20II%20(decision15-6).pdf\">https://www.cbd.int/doc/nr/Annex%20II%20(decision15-6).pdf</a> </p>\n<p><strong>Sub-indicator (b)</strong>: SEEA methodology is available <a href=\"https://seea.un.org/content/methodology\">here</a>.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p><strong>For sub-indicator (a)</strong>, the information is provided by Parties to the Convention on Biological Diversity directly through their seventh and eighth national reports. The information can only be submitted by the National Focal Point to the Convention on Biological Diversity. Given that the information is submitted directly by the Party, there are no significant issues related to quality management. </p>\n<p><strong>For sub-indicator (b)</strong>, the UNCEEA evaluates the Global Assessment survey with each administration to ensure the survey is clear and obtains the information needed. The UNCEEA also reviews all reports related to the Global Assessment survey results.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p><strong>Sub-indicator (a)</strong>: The information is provided by Parties to the Convention on Biological Diversity directly through their seventh and eighth national reports. The information can only be submitted by the National Focal Point to the Convention on Biological Diversity. Given that the information is submitted directly by the Party, there are no significant issues related to quality assurance. </p>\n<p><strong>Sub-indicator (b)</strong>: When the information is provided by countries directly through the Global Assessment, there are no significant issues related to quality assurance. In the case of imputation, this is only used as a secondary step after first contacting countries. If UNSD finds that a country compiles SEEA accounts through a project or implementation, this information is verified with the appropriate persons within the international organizations involved. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p><strong>Sub-indicator (a)</strong>: The information is provided by Parties to the Convention on Biological Diversity directly through their seventh and eighth national reports. The information can only be submitted by the National Focal Point to the Convention on Biological Diversity. Ultimately, the quality of the assessment is dependent on the quality of the information being provided by Parties. The limitations noted under section 4.b should be kept in mind. </p>\n<p><strong>Sub-indicator (b)</strong>: The quality of the Global Assessment responses is dependent on the quality of information provided by respondents. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p><strong>For sub-indicator (a)</strong>, there have been six rounds of national reporting to date. </p>\n<p><strong>For sub-indicator (b)</strong>, the Global Assessment was last sent to national statistical offices in August 2020. A Global Assessment will be administered annually.</p>\n<p><strong>Time series:</strong></p>\n<ol>\n  <li>Collection of NBSAPs and National Reports is regularly updated by the CBD Secretariat (see <a href=\"https://www.cbd.int/nbsap/\">https://www.cbd.int/nbsap/</a> and <a href=\"https://www.cbd.int/reports/\"><u>https://www.cbd.int/reports/</u></a>). Under the Convention, national reporting typically occurs every 4 years.</li>\n  <li>The reports for previous Global Assessments can be found here: <a href=\"https://seea.un.org/content/global-assessment-environmental-economic-accounting\"><u>https://seea.un.org/content/global-assessment-environmental-economic-accounting</u></a>. Data on SEEA implementation will be collected every year, with the full detailed questionnaire being sent approximately every three years.</li>\n</ol>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator is available at the global, reginal and country levels. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong></p>\n<p>All information on national reporting to the Convention on Biological Diversity can be found <a href=\"https://www.cbd.int/reports/\">here</a>. </p>\n<p>All information on the SEEA can be found <a href=\"https://seea.un.org/content/homepage\">here</a>.</p>\n<p><strong>References:</strong></p>\n<p><a href=\"https://www.bipindicators.net/bip-dashboard-of-indicator-visualisations-is-now-live\">Biodiversity Indicators Partnership</a></p>\n<p><a href=\"https://seea.un.org/content/seea-central-framework\">SEEA Central Framework</a></p>\n<p><a href=\"https://seea.un.org/ecosystem-accounting\">SEEA Ecosystem Accounting</a></p>\n<p><a href=\"https://www.cbd.int/doc/nr/Annex%20II%20(decision15-6).pdf\">CBD 7<sup>th</sup> and 8 th National Reporting Guidelines</a> </p>\n<p><a href=\"https://www.cbd.int/convention/text/\">Text of the Convention on Biological Diversity</a></p>\n<p><a href=\"https://www.cbd.int/doc/decisions/cop-15/cop-15-dec-04-en.pdf\">Kunming-Montreal Global Biodiversity Framework</a></p>", "indicator_sort_order"=>"15-09-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.a.1", "slug"=>"15-a-1", "name"=>"a) Asistencia oficial para el desarrollo destinada concretamente a la conservación y el uso sostenible de la biodiversidad y b) ingresos generados y financiación movilizada mediante instrumentos económicos pertinentes para la biodiversidad", "url"=>"/site/es/15-a-1/", "sort"=>"15aa01", "goal_number"=>"15", "target_number"=>"15.a", "global"=>{"name"=>"a) Asistencia oficial para el desarrollo destinada concretamente a la conservación y el uso sostenible de la biodiversidad y b) ingresos generados y financiación movilizada mediante instrumentos económicos pertinentes para la biodiversidad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"a) Asistencia oficial para el desarrollo destinada concretamente a la conservación y el uso sostenible de la biodiversidad y b) ingresos generados y financiación movilizada mediante instrumentos económicos pertinentes para la biodiversidad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"a) Asistencia oficial para el desarrollo destinada concretamente a la conservación y el uso sostenible de la biodiversidad y b) ingresos generados y financiación movilizada mediante instrumentos económicos pertinentes para la biodiversidad", "indicator_number"=>"15.a.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"a) Los flujos totales de Ayuda oficial al desarrollo (AOD) cuantifican el esfuerzo \npúblico que los donantes realizan en favor de la biodiversidad.\n\nb) Los instrumentos de política económica pueden generar ingresos (p. ej., \nimpuestos relevantes para la biodiversidad) o movilizar financiación \ndirectamente para la conservación y el uso sostenible de la biodiversidad \n(p. ej., tasas y cargos relevantes para la biodiversidad; subsidios positivos; \npago por servicios ambientales -PSA- y compensaciones), que es financiación \nmovilizada a nivel nacional.\nLos datos se recopilan de forma coherente y comparable entre países.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.a.1&seriesCode=DC_ODA_BDVL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nAsistencia oficial total para el desarrollo destinada a la biodiversidad, por países receptores (millones de dólares de los Estados Unidos constantes de 2022) DC_ODA_BDVL</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-0a-01.pdf\">Metadatos 15-a-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"a) Total ODA flows to developing countries on the DAC List of ODA Recipients quantify the public effort \nthat donors provide to developing countries for biodiversity. \n\nb) Economic policy instruments can either generate revenue (e.g. biodiversity-relevant taxes) or mobilise \nfinance directly for biodiversity conservation and sustainable use (e.g. biodiversity-relevant fees and \ncharges; positive subsidies; PES and offsets) which is finance mobilised at domestic level. The data are collected  \nin a consistent and comparable way across countries. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.a.1&seriesCode=DC_ODA_BDVL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nTotal official development assistance for biodiversity, by recipient countries (millions of constant 2022 United States dollars) DC_ODA_BDVL</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-0a-01.pdf\">Metadata 15-a-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"a) Los flujos totales de Ayuda oficial al desarrollo (AOD) cuantifican el esfuerzo \npúblico que los donantes realizan en favor de la biodiversidad.\n\nb) Los instrumentos de política económica pueden generar ingresos (p. ej., \nimpuestos relevantes para la biodiversidad) o movilizar financiación \ndirectamente para la conservación y el uso sostenible de la biodiversidad \n(p. ej., tasas y cargos relevantes para la biodiversidad; subsidios positivos; \npago por servicios ambientales -PSA- y compensaciones), que es financiación \nmovilizada a nivel nacional.\nLos datos se recopilan de forma coherente y comparable entre países.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.a.1&seriesCode=DC_ODA_BDVL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nBiodibertsitatera bideratutako garapenerako laguntza ofizial osoa, herrialde hartzaileen arabera (2022ko Estatu Batuetako dolar konstante milioiak) DC_ODA_BDVL</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-0a-01.pdf\">Metadatuak 15-a-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.a: Mobilize and significantly increase financial resources from all sources to conserve and sustainably use biodiversity and ecosystems</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.a.1: (a) Official development assistance on conservation and sustainable use of biodiversity; and (b) revenue generated and finance mobilized from biodiversity-relevant economic instruments</p>", "META_LAST_UPDATE__GLOBAL"=>"2020-04-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>A related indicator is that on public expenditure on biodiversity. Public expenditure on biodiversity is currently a Tier III indicator and is to be improved. For expenditure the methodology is agreed upon, i.e. SEEA Environmental Expenditure Accounts and National accounts COFOG. </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Cooperation and Development (OECD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Cooperation and Development (OECD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This is a twin indicator consisting of:</p>\n<p>a) Official development assistance on conservation and sustainable use of biodiversity, defined as gross disbursements of total Official Development Assistance (ODA) from all donors for biodiversity. </p>\n<p>b) revenue generated and finance mobilised from biodiversity-relevant economic instruments, defined as revenue generated and finance mobilised from biodiversity-relevant economic instruments, covering biodiversity-relevant taxes, fees and charges, and positive subsidies. (New on-going work is underway to collect data on payments for ecosystem services and biodiversity offsets -- including the finance they mobilise for biodiversity).</p>\n<p><strong>Concepts:</strong></p>\n<p>a) The Development Assistance Committee (DAC) defines ODA as those flows to countries and territories on the DAC list of ODA recipients and multilateral institutions which are:</p>\n<ol>\n  <li>Provided by official agencies, including state and local governments, or by their executive agencies; and</li>\n  <li>Each transaction of which:</li>\n  <li> is administered with the promotion of the economic development and welfare of developing countries as its main objective; and</li>\n  <li>is concessional in character.</li>\n</ol>\n<p><em> </em>(See <a href=\"http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm\"><u>http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm</u></a>).</p>\n<p>b) The Environmental Policy Committee (EPOC) collects data on Policy Instruments for the Environment (to the OECD PINE database), including biodiversity-relevant economic instruments. Currently more than 110 countries are contributing data. For 2020 data, see <u> </u><a href=\"https://www.oecd.org/environment/resources/tracking-economic-instruments-and-finance-for-biodiversity-2020.pdf\"><u>Tracking Economic Instruments and Finance for Biodiversity -2020</u></a>. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>a) The OECD/DAC has been collecting data on official and private resource flows from 1960 at an aggregate level and 1973 at an activity level through the CRS (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements). The Rio marker for biodiversity was introduced in 2002. The data are provided by DAC donors, other bilateral providers of development cooperation and multilateral organizations. </p>\n<p>b) Information for the OECD PINE database is collected via a network of 200 country experts, including in government agencies (Ministries of Finance and Environment, statistical institutes) as well as research institutes and international organisations. Data is collected systematically for 37 OECD members as well as the active accession countries. A growing number of non-member countries also provide information. Currently, more than 110 countries are contributing data. Registered experts are asked to update data at least once a year, typically in January or February, through a password-protected interface. The data collection method may result in some reporting bias, as OECD members and active accession countries are likely to report more data on a regular basis, and all figures should be interpreted in this context.</p>", "COLL_METHOD__GLOBAL"=>"<p>a) Via and annual questionnaire reported by national statistical reporters in aid agencies, ministries of foreign affairs, etc.</p>\n<p>b) Via questionnaire and directly via the network of contacts.</p>", "FREQ_COLL__GLOBAL"=>"<p>a) On an annual basis. </p>\n<p>b) On an on-going basis. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>a) The data are published at the end of each year for year -1.</p>\n<p>b) An updated and expanded brochure on &#x201C;Tracking Economic Instruments and Finance for Biodiversity&#x201D; is planned to be released in mid-2020. </p>\n<p>The 2020 version is available here: OECD (2020), Tracking Economic Instruments and Finance for Biodiversity -2020.</p>", "DATA_SOURCE__GLOBAL"=>"<p>a) A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc.</p>\n<p>b) Information for the PINE database is collected via a network of 200 country experts, including in government agencies (Ministries of Finance and Environment, statistical institutes) as well as research institutes and international organisations. Data is collected systematically for 37 OECD members as well as the active accession countries. A growing number of non-member countries also provide information. Registered experts are asked to update data at least once a year, typically in January or February, through a password-protected interface. The data collection method may result in some reporting bias, as OECD members and active accession countries are likely to report more data on a regular basis, and all figures should be interpreted in this context. </p>\n<p>The OECD Secretariat, in consultation with countries, validates the data before they are published online. The management of PINE is overseen by OECD Committees and Working Parties.</p>", "COMPILING_ORG__GLOBAL"=>"<p>a) OECD, Development Cooperation Directorate. The OECD is the only International Organisation collecting this data.</p>\n<p>b) OECD, Environment Directorate. The OECD is the only International Organisation collecting this data.</p>", "RATIONALE__GLOBAL"=>"<p>a) Total ODA flows to developing countries quantify the public effort that donors provide to developing countries for biodiversity.</p>\n<p>b) Economic policy instruments can either generate revenue (e.g. biodiversity-relevant taxes) or mobilise finance directly for biodiversity conservation and sustainable use (e.g. biodiversity-relevant fees and charges; positive subsidies; PES and offsets) which is finance mobilised at domestic level. </p>\n<p>The data are collected in a consistent and comparable way across countries.</p>", "REC_USE_LIM__GLOBAL"=>"<p>a) OECD CRS data are available since 1973. However, the data coverage at an activity level is considered complete from 1995 for commitments and 2002 for disbursements. The Rio biodiversity marker was introduced in 2002.</p>\n<p>b) The OECD PINE database tracks the biodiversity-relevant economic instruments that countries have put in place, and countries are encouraged to also provide information on the revenue and finance channelled via each of the instruments. The comprehensiveness of data provided currently varies across the biodiversity-relevant economic instruments. The data on revenue generated by biodiversity-relevant taxes is currently the most comprehensive. For the data on biodiversity-relevant fees and charges, for example, of the total number of these instruments currently reported to the PINE database, 42% also include data on the finance they generate. </p>\n<p>Like all data provided by a diffuse set of respondents, the data is subject to missing values, human error, and differences in interpretation of the provided definitions. However, all possible efforts have been made to ensure that the data is complete, accurate, and comparable across countries.</p>", "DATA_COMP__GLOBAL"=>"<p>a) This indicator is calculated as the sum of all ODA flows from all donors to developing countries that have biodiversity as a principal or significant objective, thus marked with the Rio marker for biodiversity.</p>\n<p>b) Countries are requested to report on when the policy instrument was introduced, what it applies to, the geographical coverage, the environmental domain, the industries concerned; the revenues, costs or rates; whether the revenue is earmarked; and exemptions.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>a) and b) No attempt is made to estimate missing values.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>a) and b) No attempt is made to estimate missing values.</p>", "REG_AGG__GLOBAL"=>"<p>a) Data are reported at a country level. </p>\n<p>b) Data are reported at national and sub-national level, depending on the scope of the policy instrument.</p>", "DOC_METHOD__GLOBAL"=>"<p>a) The DAC statistical Reporting Directives govern the reporting of DAC statistics, and are reviewed and agreed by the DAC Working Party of Development Finance Statistics, see: <a href=\"https://one.oecd.org/document/DCD/DAC/STAT(2018)9/FINAL/en/pdf\"><u>https://one.oecd.org/document/DCD/DAC/STAT(2018)9/FINAL/en/pdf</u></a></p>\n<p>b) The OECD provides instructions and a formatted questionnaire for countries to provide data.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>a) The data collected by the OECD/DAC Secretariat are official data provided by national statistical reporters in each providing country/agency. The OECD/DAC Secretariat is responsible for checking, validating and publishing these data. </p>\n<p>b) Data are provided by competent national authorities. The OECD Secretariat conducts regular checks to identify errors or missing data.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>a) The Rio biodiversity marker was introduced in 2002 and data are available since then for most DAC members, with improvements in reporting over time. Not all other providers report their data at an activity level though.</p>\n<p>Provisional data classification: Tier I</p>\n<p>b) Currently more than 110 countries are contributing data to the PINE database. As of March 2020, the database contained more than 3 500 policy instruments for the environment, of which 3 100 were in force. The environmental domains covered by the database include biodiversity, climate, air pollution, among others.</p>\n<p><strong>Time series:</strong></p>\n<p>a) The data are available since 1996 on an annual basis, with time series since 1950.</p>\n<p>b) The data series is annual and data is available from before 1980. </p>\n<p>The PINE database exists since 1996, with the added feature of tagging biodiversity-relevant instruments introduced in 2017. The biodiversity-relevant information in the PINE database is being used to monitor progress towards Aichi Target 3 on positive incentives, under the Convention on Biological Diversity. For more information on this, see Aichi Target 3 under the website of the Biodiversity Indicators Partnership (BIP).</p>\n<p><strong>Disaggregation:</strong></p>\n<p>a) This indicator can be disaggregated by donor, by recipient country (or region), by type of finance, by type of aid, by sub-sector, by policy marker (e.g. gender), etc.</p>\n<p>b) Information is available by country at the individual policy instrument level.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>a) DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries. Some countries provide more comprehensive information than others.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>a) See all links here:<strong> </strong><a href=\"http://www.oecd.org/dac/stats/methodology.htm\"><strong><u>http://www.oecd.org/dac/stats/methodology.htm</u></strong></a></p>\n<p><strong>References: </strong></p>\n<p>a) See all links here:<strong> </strong><a href=\"http://www.oecd.org/dac/stats/methodology.htm\"><strong><u>http://www.oecd.org/dac/stats/methodology.htm</u></strong></a></p>\n<p>b) OECD (2020), <a href=\"https://www.oecd.org/environment/resources/tracking-economic-instruments-and-finance-for-biodiversity-2020.pdf\"><u>Tracking Economic Instruments and Finance for Biodiversity - 2020</u></a>. </p>\n<p>The brochure also highlights on-going work to scale up the policy instruments to include Payments for Ecosystem Services, and Biodiversity Offsets, and the finance these two policy instruments mobilise. The PINE data is available at <a href=\"https://oe.cd/pine\"><u>https://oe.cd/pine</u></a></p>\n<p> </p>\n<p>Additional information extracted from the PINE database is reported in OECD (2019) <a href=\"https://www.oecd.org/env/resources/biodiversity/biodiversity-finance-and-the-economic-and-business-case-for-action.htm\"><em><u>Biodiversity: Finance and the Economic and Business Case for Action</u></em></a></p>", "indicator_sort_order"=>"15-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.b.1", "slug"=>"15-b-1", "name"=>"a) Asistencia oficial para el desarrollo destinada concretamente a la conservación y el uso sostenible de la biodiversidad y b) ingresos generados y financiación movilizada mediante instrumentos económicos pertinentes para la biodiversidad", "url"=>"/site/es/15-b-1/", "sort"=>"15bb01", "goal_number"=>"15", "target_number"=>"15.b", "global"=>{"name"=>"a) Asistencia oficial para el desarrollo destinada concretamente a la conservación y el uso sostenible de la biodiversidad y b) ingresos generados y financiación movilizada mediante instrumentos económicos pertinentes para la biodiversidad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"a) Asistencia oficial para el desarrollo destinada concretamente a la conservación y el uso sostenible de la biodiversidad y b) ingresos generados y financiación movilizada mediante instrumentos económicos pertinentes para la biodiversidad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"a) Asistencia oficial para el desarrollo destinada concretamente a la conservación y el uso sostenible de la biodiversidad y b) ingresos generados y financiación movilizada mediante instrumentos económicos pertinentes para la biodiversidad", "indicator_number"=>"15.b.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"a) Los flujos totales de Ayuda oficial al desarrollo (AOD) cuantifican el esfuerzo \npúblico que los donantes realizan en favor de la biodiversidad.\n\nb) Los instrumentos de política económica pueden generar ingresos (p. ej., \nimpuestos relevantes para la biodiversidad) o movilizar financiación \ndirectamente para la conservación y el uso sostenible de la biodiversidad \n(p. ej., tasas y cargos relevantes para la biodiversidad; subsidios positivos; \npago por servicios ambientales -PSA- y compensaciones), que es financiación \nmovilizada a nivel nacional.\nLos datos se recopilan de forma coherente y comparable entre países.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.b.1&seriesCode=DC_ODA_BDVL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nAsistencia oficial total para el desarrollo destinada a la biodiversidad, por países receptores (millones de dólares de los Estados Unidos constantes de 2022) DC_ODA_BDVL</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-0b-01.pdf\">Metadatos 15-b-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"a) Total ODA flows to developing countries on the DAC List of ODA Recipients quantify the public effort \nthat donors provide to developing countries for biodiversity. \n\nb) Economic policy instruments can either generate revenue (e.g. biodiversity-relevant taxes) or mobilise \nfinance directly for biodiversity conservation and sustainable use (e.g. biodiversity-relevant fees and \ncharges; positive subsidies; PES and offsets) which is finance mobilised at domestic level. The data are collected  \nin a consistent and comparable way across countries. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.b.1&seriesCode=DC_ODA_BDVL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nTotal official development assistance for biodiversity, by recipient countries (millions of constant 2022 United States dollars) DC_ODA_BDVL</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-0b-01.pdf\">Metadata 15-b-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"a) Los flujos totales de Ayuda oficial al desarrollo (AOD) cuantifican el esfuerzo \npúblico que los donantes realizan en favor de la biodiversidad.\n\nb) Los instrumentos de política económica pueden generar ingresos (p. ej., \nimpuestos relevantes para la biodiversidad) o movilizar financiación \ndirectamente para la conservación y el uso sostenible de la biodiversidad \n(p. ej., tasas y cargos relevantes para la biodiversidad; subsidios positivos; \npago por servicios ambientales -PSA- y compensaciones), que es financiación \nmovilizada a nivel nacional.\nLos datos se recopilan de forma coherente y comparable entre países.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=15.b.1&seriesCode=DC_ODA_BDVL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nBiodibertsitatera bideratutako garapenerako laguntza ofizial osoa, herrialde hartzaileen arabera (2022ko Estatu Batuetako dolar konstante milioiak) DC_ODA_BDVL</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-0b-01.pdf\">Metadatuak 15-b-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.a: Mobilize and significantly increase financial resources from all sources to conserve and sustainably use biodiversity and ecosystems</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.a.1: (a) Official development assistance on conservation and sustainable use of biodiversity; and (b) revenue generated and finance mobilized from biodiversity-relevant economic instruments</p>", "META_LAST_UPDATE__GLOBAL"=>"2020-04-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>A related indicator is that on public expenditure on biodiversity. Public expenditure on biodiversity is currently a Tier III indicator and is to be improved. For expenditure the methodology is agreed upon, i.e. SEEA Environmental Expenditure Accounts and National accounts COFOG. </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Cooperation and Development (OECD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Cooperation and Development (OECD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This is a twin indicator consisting of:</p>\n<p>a) Official development assistance on conservation and sustainable use of biodiversity, defined as gross disbursements of total Official Development Assistance (ODA) from all donors for biodiversity. </p>\n<p>b) revenue generated and finance mobilised from biodiversity-relevant economic instruments, defined as revenue generated and finance mobilised from biodiversity-relevant economic instruments, covering biodiversity-relevant taxes, fees and charges, and positive subsidies. (New on-going work is underway to collect data on payments for ecosystem services and biodiversity offsets -- including the finance they mobilise for biodiversity).</p>\n<p><strong>Concepts:</strong></p>\n<p>a) The Development Assistance Committee (DAC) defines ODA as those flows to countries and territories on the DAC list of ODA recipients and multilateral institutions which are:</p>\n<ol>\n  <li>Provided by official agencies, including state and local governments, or by their executive agencies; and</li>\n  <li>Each transaction of which:</li>\n  <li> is administered with the promotion of the economic development and welfare of developing countries as its main objective; and</li>\n  <li>is concessional in character.</li>\n</ol>\n<p><em> </em>(See <a href=\"http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm\"><u>http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm</u></a>).</p>\n<p>b) The Environmental Policy Committee (EPOC) collects data on Policy Instruments for the Environment (to the OECD PINE database), including biodiversity-relevant economic instruments. Currently more than 110 countries are contributing data. For 2020 data, see <u> </u><a href=\"https://www.oecd.org/environment/resources/tracking-economic-instruments-and-finance-for-biodiversity-2020.pdf\"><u>Tracking Economic Instruments and Finance for Biodiversity -2020</u></a>. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>a) The OECD/DAC has been collecting data on official and private resource flows from 1960 at an aggregate level and 1973 at an activity level through the CRS (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements). The Rio marker for biodiversity was introduced in 2002. The data are provided by DAC donors, other bilateral providers of development cooperation and multilateral organizations. </p>\n<p>b) Information for the OECD PINE database is collected via a network of 200 country experts, including in government agencies (Ministries of Finance and Environment, statistical institutes) as well as research institutes and international organisations. Data is collected systematically for 37 OECD members as well as the active accession countries. A growing number of non-member countries also provide information. Currently, more than 110 countries are contributing data. Registered experts are asked to update data at least once a year, typically in January or February, through a password-protected interface. The data collection method may result in some reporting bias, as OECD members and active accession countries are likely to report more data on a regular basis, and all figures should be interpreted in this context.</p>", "COLL_METHOD__GLOBAL"=>"<p>a) Via and annual questionnaire reported by national statistical reporters in aid agencies, ministries of foreign affairs, etc.</p>\n<p>b) Via questionnaire and directly via the network of contacts.</p>", "FREQ_COLL__GLOBAL"=>"<p>a) On an annual basis. </p>\n<p>b) On an on-going basis. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>a) The data are published at the end of each year for year -1.</p>\n<p>b) An updated and expanded brochure on &#x201C;Tracking Economic Instruments and Finance for Biodiversity&#x201D; is planned to be released in mid-2020. </p>\n<p>The 2020 version is available here: OECD (2020), Tracking Economic Instruments and Finance for Biodiversity -2020.</p>", "DATA_SOURCE__GLOBAL"=>"<p>a) A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc.</p>\n<p>b) Information for the PINE database is collected via a network of 200 country experts, including in government agencies (Ministries of Finance and Environment, statistical institutes) as well as research institutes and international organisations. Data is collected systematically for 37 OECD members as well as the active accession countries. A growing number of non-member countries also provide information. Registered experts are asked to update data at least once a year, typically in January or February, through a password-protected interface. The data collection method may result in some reporting bias, as OECD members and active accession countries are likely to report more data on a regular basis, and all figures should be interpreted in this context. </p>\n<p>The OECD Secretariat, in consultation with countries, validates the data before they are published online. The management of PINE is overseen by OECD Committees and Working Parties.</p>", "COMPILING_ORG__GLOBAL"=>"<p>a) OECD, Development Cooperation Directorate. The OECD is the only International Organisation collecting this data.</p>\n<p>b) OECD, Environment Directorate. The OECD is the only International Organisation collecting this data.</p>", "RATIONALE__GLOBAL"=>"<p>a) Total ODA flows to developing countries quantify the public effort that donors provide to developing countries for biodiversity.</p>\n<p>b) Economic policy instruments can either generate revenue (e.g. biodiversity-relevant taxes) or mobilise finance directly for biodiversity conservation and sustainable use (e.g. biodiversity-relevant fees and charges; positive subsidies; PES and offsets) which is finance mobilised at domestic level. </p>\n<p>The data are collected in a consistent and comparable way across countries.</p>", "REC_USE_LIM__GLOBAL"=>"<p>a) OECD CRS data are available since 1973. However, the data coverage at an activity level is considered complete from 1995 for commitments and 2002 for disbursements. The Rio biodiversity marker was introduced in 2002.</p>\n<p>b) The OECD PINE database tracks the biodiversity-relevant economic instruments that countries have put in place, and countries are encouraged to also provide information on the revenue and finance channelled via each of the instruments. The comprehensiveness of data provided currently varies across the biodiversity-relevant economic instruments. The data on revenue generated by biodiversity-relevant taxes is currently the most comprehensive. For the data on biodiversity-relevant fees and charges, for example, of the total number of these instruments currently reported to the PINE database, 42% also include data on the finance they generate. </p>\n<p>Like all data provided by a diffuse set of respondents, the data is subject to missing values, human error, and differences in interpretation of the provided definitions. However, all possible efforts have been made to ensure that the data is complete, accurate, and comparable across countries.</p>", "DATA_COMP__GLOBAL"=>"<p>a) This indicator is calculated as the sum of all ODA flows from all donors to developing countries that have biodiversity as a principal or significant objective, thus marked with the Rio marker for biodiversity.</p>\n<p>b) Countries are requested to report on when the policy instrument was introduced, what it applies to, the geographical coverage, the environmental domain, the industries concerned; the revenues, costs or rates; whether the revenue is earmarked; and exemptions.</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>a) and b) No attempt is made to estimate missing values.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>a) and b) No attempt is made to estimate missing values.</p>", "REG_AGG__GLOBAL"=>"<p>a) Data are reported at a country level. </p>\n<p>b) Data are reported at national and sub-national level, depending on the scope of the policy instrument.</p>", "DOC_METHOD__GLOBAL"=>"<p>a) The DAC statistical Reporting Directives govern the reporting of DAC statistics, and are reviewed and agreed by the DAC Working Party of Development Finance Statistics, see: <a href=\"https://one.oecd.org/document/DCD/DAC/STAT(2018)9/FINAL/en/pdf\"><u>https://one.oecd.org/document/DCD/DAC/STAT(2018)9/FINAL/en/pdf</u></a></p>\n<p>b) The OECD provides instructions and a formatted questionnaire for countries to provide data.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>a) The data collected by the OECD/DAC Secretariat are official data provided by national statistical reporters in each providing country/agency. The OECD/DAC Secretariat is responsible for checking, validating and publishing these data. </p>\n<p>b) Data are provided by competent national authorities. The OECD Secretariat conducts regular checks to identify errors or missing data.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>a) The Rio biodiversity marker was introduced in 2002 and data are available since then for most DAC members, with improvements in reporting over time. Not all other providers report their data at an activity level though.</p>\n<p>Provisional data classification: Tier I</p>\n<p>b) Currently more than 110 countries are contributing data to the PINE database. As of March 2020, the database contained more than 3 500 policy instruments for the environment, of which 3 100 were in force. The environmental domains covered by the database include biodiversity, climate, air pollution, among others.</p>\n<p><strong>Time series:</strong></p>\n<p>a) The data are available since 1996 on an annual basis, with time series since 1950.</p>\n<p>b) The data series is annual and data is available from before 1980. </p>\n<p>The PINE database exists since 1996, with the added feature of tagging biodiversity-relevant instruments introduced in 2017. The biodiversity-relevant information in the PINE database is being used to monitor progress towards Aichi Target 3 on positive incentives, under the Convention on Biological Diversity. For more information on this, see Aichi Target 3 under the website of the Biodiversity Indicators Partnership (BIP).</p>\n<p><strong>Disaggregation:</strong></p>\n<p>a) This indicator can be disaggregated by donor, by recipient country (or region), by type of finance, by type of aid, by sub-sector, by policy marker (e.g. gender), etc.</p>\n<p>b) Information is available by country at the individual policy instrument level.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>a) DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries. Some countries provide more comprehensive information than others.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>a) See all links here:<strong> </strong><a href=\"http://www.oecd.org/dac/stats/methodology.htm\"><strong><u>http://www.oecd.org/dac/stats/methodology.htm</u></strong></a></p>\n<p><strong>References: </strong></p>\n<p>a) See all links here:<strong> </strong><a href=\"http://www.oecd.org/dac/stats/methodology.htm\"><strong><u>http://www.oecd.org/dac/stats/methodology.htm</u></strong></a></p>\n<p>b) OECD (2020), <a href=\"https://www.oecd.org/environment/resources/tracking-economic-instruments-and-finance-for-biodiversity-2020.pdf\"><u>Tracking Economic Instruments and Finance for Biodiversity - 2020</u></a>. </p>\n<p>The brochure also highlights on-going work to scale up the policy instruments to include Payments for Ecosystem Services, and Biodiversity Offsets, and the finance these two policy instruments mobilise. The PINE data is available at <a href=\"https://oe.cd/pine\"><u>https://oe.cd/pine</u></a></p>\n<p> </p>\n<p>Additional information extracted from the PINE database is reported in OECD (2019) <a href=\"https://www.oecd.org/env/resources/biodiversity/biodiversity-finance-and-the-economic-and-business-case-for-action.htm\"><em><u>Biodiversity: Finance and the Economic and Business Case for Action</u></em></a></p>", "indicator_sort_order"=>"15-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"15.c.1", "slug"=>"15-c-1", "name"=>"Proporción de especímenes de flora y fauna silvestre comercializados procedentes de la caza furtiva o el tráfico ilícito", "url"=>"/site/es/15-c-1/", "sort"=>"15cc01", "goal_number"=>"15", "target_number"=>"15.c", "global"=>{"name"=>"Proporción de especímenes de flora y fauna silvestre comercializados procedentes de la caza furtiva o el tráfico ilícito"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de especímenes de flora y fauna silvestre comercializados procedentes de la caza furtiva o el tráfico ilícito", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de especímenes de flora y fauna silvestre comercializados procedentes de la caza furtiva o el tráfico ilícito", "indicator_number"=>"15.c.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Hay más de 35.000 especies bajo protección internacional, por lo que es imposible \nmonitorear toda la caza furtiva. Sin embargo, el comercio ilegal es un indicador indirecto \nde la caza furtiva. Las incautaciones de fauna silvestre representan casos concretos de \ncomercio ilegal, pero la proporción que representan de los delitos contra la vida silvestre \nen general es desconocida y variable. Además, el número de especies bajo protección \ninternacional sigue creciendo. \n\nEl comercio internacional legal de especies protegidas, por definición, \nse refleja al 100 % en la Base de Datos sobre el Comercio de Especies Amenazadas \nde Fauna y Flora Silvestres (CITES), que actualmente contiene más de 16 millones \nde registros de comercio de especies incluidas en la CITES. \n\nPara fundamentar los datos de comercio ilegal en un indicador completo, se estima \nla proporción de incautaciones agregadas con respecto al comercio total. Un aumento en \nla proporción del comercio total de fauna silvestre que es ilegal se interpretaría \ncomo un indicador negativo, y una disminución como positivo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-0c-01.pdf\">Metadatos 15-c-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"There are over 35,000 species under international protection, so it is impossible to monitor all poaching. \nIllegal trade, however, is an indirect indicator of poaching. Wildlife seizures represent concrete instances \nof illegal trade, but the share of overall wildlife crime they represent is unknown and variable. In addition, \nthe number of species under international protection continues to grow. \n\nLegal international trade in protected species, by definition, is 100% captured in the Convention on International \nTrade in Endangered Species of Wild Fauna and Flora (CITES) Trade Database, which now contains over 16 million\nrecords of trade in CITES-listed species. \n\nTo ground the illegal trade data in a complete indicator, the ratio of aggregated seizures to total trade is \nestimated. An increase in the share of total wildlife trade that is illegal would be interpreted as a negative \nindicator, and a decrease as a positive one. \n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-0c-01.pdf\">Metadata 15-c-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Hay más de 35.000 especies bajo protección internacional, por lo que es imposible \nmonitorear toda la caza furtiva. Sin embargo, el comercio ilegal es un indicador indirecto \nde la caza furtiva. Las incautaciones de fauna silvestre representan casos concretos de \ncomercio ilegal, pero la proporción que representan de los delitos contra la vida silvestre \nen general es desconocida y variable. Además, el número de especies bajo protección \ninternacional sigue creciendo. \n\nEl comercio internacional legal de especies protegidas, por definición, \nse refleja al 100 % en la Base de Datos sobre el Comercio de Especies Amenazadas \nde Fauna y Flora Silvestres (CITES), que actualmente contiene más de 16 millones \nde registros de comercio de especies incluidas en la CITES. \n\nPara fundamentar los datos de comercio ilegal en un indicador completo, se estima \nla proporción de incautaciones agregadas con respecto al comercio total. Un aumento en \nla proporción del comercio total de fauna silvestre que es ilegal se interpretaría \ncomo un indicador negativo, y una disminución como positivo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-15-0c-01.pdf\">Metadatuak 15-c-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 15.7: Take urgent action to end poaching and trafficking of protected species of flora and fauna and address both demand and supply of illegal wildlife products</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 15.7.1: Proportion of traded wildlife that was poached or illicitly trafficked</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>ER_WLD_TRPOACH - Proportion of traded wildlife that was poached or illicitly trafficked [15.7.1,15.c.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The share of all trade in wildlife detected as being illegal</p>\n<p><strong>Concepts:</strong></p>\n<p>&#x201C;All trade in wildlife&#x201D; is the sum of the values of legal and illegal trade</p>\n<p>&#x201C;Legal trade&#x201D; is the sum of the value of all shipments made in compliance with the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), using valid CITES permits and certificates.</p>\n<p>&#x201C;Illegal trade&#x201D; is the sum of the value of all CITES/listed specimens seized.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>The Checklist of Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) is used to assign values to each of the species and specimens traded, either legally or seized. Species. More information at <a href=\"https://checklist.cites.org/#/en\">https://checklist.cites.org/#/en</a>. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The legal trade data are reported annually by Parties to Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and stored in the CITES Trade Database, managed by the United Nations Environmental Programme (UNEP) World Conservation Monitoring Centre in Cambridge.</p>\n<p>The detected illegal trade data is collected through two main databases: a) the United Nations Office on Drugs and Crime (UNODC) database called &#x201C;World WISE&#x201D;, which combines a variety of data sources on individual illicit wildlife seizures, and b) the CITES Illegal Trade Database, which contains data reported by CITES Parties through the Annual Illegal Trade Reports (see https://cites.org/eng/resources/reports/Annual_Illegal_trade_report). </p>\n<p>The US LEMIS price data for CITES-listed species are also provided to UNEP-WCMC within the U.S. annual report to CITES, and are used for valuation<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> See <a href=\"https://www.fws.gov/library/collections/office-law-enforcement-importexport-data\"><u>https://www.fws.gov/library/collections/office-law-enforcement-importexport-data</u></a> <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p>Some adjustment/validation is necessary between countries, but standardized codes for the legal wildlife trade have been developing since 1975. The basic fields necessary for the global indicator (species, product, and unit) are well established and present in every seizure. Some unit conversions (e.g. logs to MT to m3 for timber) are necessary for some products. To do regional or national breakdowns, however, data on the source of the shipment are necessary (as the impact of poaching pertains to the source country, not the seizure country), and these data are not available for every seizure.</p>", "FREQ_COLL__GLOBAL"=>"<p>The first data collection cycle for the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Annual Illicit Trade Reports (AITR) took place in 2017. Since then, these reports are collected every year, with a deadline of October 31 for submission by CITES Parties. Data reported through AITR have been processed and put together into the CITES Illegal Trade Database<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> in 2023-2024, and the database was launched in November 2023 and is now available for CITES Parties.</p>\n<p>As for the data collected for the World WISE database, these are collected in an ad-hoc basis based on sources availability and partnerships. Data from the Environmental Investigation Agency (EIA), for example, are collected regularly and included into World WISE.</p>\n<p><u>Data for legal trade are collected by the CITES Secretariat through the CITES Annual Reports and processed by UNEP-WCMC on an annual basis. </u></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> See <a href=\"https://citesdata.un.org/\"><u>https://citesdata.un.org/</u></a> <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "REL_CAL_POLICY__GLOBAL"=>"<p>After a first submission of data in Q2 2024, data will be published annually in Q1. </p>", "DATA_SOURCE__GLOBAL"=>"<p>The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Management Authority of each country provide data for both the CITES Legal and Illegal Trade Databases, through the Annual Reports and AITR. In addition, other sources are used for World WISE, such as the Environmental Investigation Agency, TRAFFIC, the World Customs Organization (WCO), amongst others.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC) for illegal trade data and United Nations Environmental Programme (UNEP) World Conservation Monitoring Centre for legal trade data.</p>", "INST_MANDATE__GLOBAL"=>"<p>Annual Reports (article VIII of the Convention) and AITR (Conf.11.17 &#x2013; Rev CoP19) are collected by the CITES Secretariat as per their mandate. CITES Secretariat works together with UNODC on the data processing for AITR data, and wile UNEP-WCMC for the processing of Annual Reports data.</p>", "RATIONALE__GLOBAL"=>"<p><strong>Rationale:</strong></p>\n<p>There are over 35,000 species under international protection, so it is impossible to monitor all poaching. Illegal trade, however, is an indirect indicator of poaching. Wildlife seizures represent concrete instances of illegal trade, but the share of overall wildlife crime they represent is unknown and variable. In addition, the number of species under international protection continues to grow. Legal international trade in protected species, by definition, is 100% captured in the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Trade Database, which now contains over 16 million records of trade in CITES-listed species. To ground the illegal trade data in a complete indicator, the ratio of aggregated seizures to total trade is estimated. An increase in the share of total wildlife trade that is illegal would be interpreted as a negative indicator, and a decrease as a positive one.</p>\n<p>Because the illegal wildlife trade represents thousands of distinct products, a means of aggregation is necessary. The legal trade value does not represent the true black market value of the items seized, nor the true value of the legal shipments, because it is derived from a single market source (US LEMIS). It does, however, present a logical and consistent means of aggregating unlike products.</p>", "REC_USE_LIM__GLOBAL"=>"<p>- Seizures are an incomplete indicator of trafficking, and subject to considerable volatility, and depend on external factors that may not be directly linked to the volume of flows, such as law enforcement priorities. </p>\n<p>- Universal coverage is not presently available, although data are available for a growing number of countries (see section 5). </p>\n<p>- Since the indicator looks at the relationship between two values (legal vs illegal trade), changes in the relationship could be due to changes in either value.</p>", "DATA_COMP__GLOBAL"=>"<p>The value of a species-product unit is derived from the median price declared for legal imports of analogous species product units, as acquired from United States Law Enforcement Monitoring and Information System of the Fish and Wildlife Service (US LEMIS). Particularly, the median values for each TAXON/DESCRIPTION OF SPECIMEN/UNIT OF MEASUREMENT possible combination were used to assign a value to each legal trade and seizure record. Some additional sources (for cases where US LEMIS data were not available or were unreliable) were used, such as estimates based on field research from UNEP-WCMC and UNODC. Also, median values of combinations that were outliers, based on small sample sizes or had high variability were excluded from the calculations.</p>\n<p>The value of legal trade is the sum of all species-product units documented in Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) export permits as reported in the CITES Annual Reports times the species-product unit prices as specified above.</p>\n<p>The value of illegal trade is the sum of all species-product units documented in the World WISE seizure and CITES Illegal Trade databases times the species-product unit prices as specified above.</p>\n<p>The indicator is defined as:</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mi>V</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n      </mrow>\n      <mrow>\n        <mi>V</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mo>+</mo>\n        <mi>V</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>g</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n      </mrow>\n    </mfrac>\n  </math></p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data received go through a validation process that includes consistency checks, standardization of variables and conversion of measurement units. Due to the variety of sources involved, a deduplication process takes place, where records representing events identified as being reported by multiple sources are clustered, and only one is kept, to avoid double-counting. Large seizures are individually analysed to ensure there is no double-counting that could significantly affect totals.</p>\n<p>Finally, the data used are periodically shared with countries for their review as part of the process of &#x201C;pre-publication&#x201D; for UNODC World Wildlife Crime Reports (as done in October 2023), and the compiled 15.7.1 indicators are shared with countries for their review before submission to the United Nations Statistical Department (UNSD). </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>For legal trade, data reported by other countries is used to impute the totals exported and imported at the national level.</p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>As above</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries can provide data through the CITES. Information and guidelines can be found at <a href=\"https://cites.org/eng/resources/reports/Annual_Illegal_trade_report\">https://cites.org/eng/resources/reports/Annual_Illegal_trade_report</a> and https://cites.org/eng/imp/reporting_requirements/annual_report</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>A thorough validation process is implemented to ensure high quality of the data, as described in Section 4.d. When inconsistencies are found, UNODC contacts the data providers, either countries in the case of AITR (with the support of CITES) or other organizations in the case of data in World WISE. </p>\n<p>The prepublication process described in Section 4.d is part of UNODC&#x2019;s general quality management process.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The deduplication process described in Section 4.d is implemented not only for wildlife-related seizures databases, but also for other databases within UNODC, such as the Drugs Monitoring Platform. In this sense, a comprehensive deduplication protocol is implemented to ensure high quality in all datasets and to take into account the singularities of each.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>A specific assessment of the deduplication process was implemented in June 2022, and it was found that the proportion of false negatives and false positives was relatively small in the dataset at the time.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>As of July 2024, data for the indicator are available for 98 countries and territories. </p>\n<p><strong>Time series:</strong></p>\n<p>Data for 2016 onwards are available for this indicator.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Where source data are available, the data are disaggregated by plant and animal species. As a form of trade data, issues of gender, age, and disability status are not applicable.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The global figure is the ratio of the aggregate of national figures provided by countries for the numerator and denominator of the indicator as defined in 4.c.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.unodc.org\">www.unodc.org</a> </p>\n<p><strong>References:</strong></p>\n<p><a href=\"http://www.unodc.org/documents/data-and-analysis/wildlife/Methodological_Annex_final.pdf\">http://www.unodc.org/documents/data-and-analysis/wildlife/Methodological_Annex_final.pdf</a> </p>\n<p><a href=\"http://trade.cites.org/cites_trade_guidelines/en-CITES_Trade_Database_Guide.pdf\"><u>http://trade.cites.org/cites_trade_guidelines/en-CITES_Trade_Database_Guide.pdf</u></a></p>\n<p>CITES Annual Reports: <a href=\"https://cites.org/eng/imp/reporting_requirements/annual_report\">https://cites.org/eng/imp/reporting_requirements/annual_report</a></p>\n<p>CITES AITR: https://cites.org/eng/resources/reports/Annual_Illegal_trade_rep</p>", "indicator_sort_order"=>"15-0c-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.1.1", "slug"=>"16-1-1", "name"=>"Número de víctimas de homicidios intencionales por cada 100.000 habitantes, desglosado por sexo y edad", "url"=>"/site/es/16-1-1/", "sort"=>"160101", "goal_number"=>"16", "target_number"=>"16.1", "global"=>{"name"=>"Número de víctimas de homicidios intencionales por cada 100.000 habitantes, desglosado por sexo y edad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de víctimas de homicidios intencionales por cada 100.000 habitantes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de víctimas de homicidios intencionales por cada 100.000 habitantes, desglosado por sexo y edad", "indicator_number"=>"16.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Seguridad", "periodicity"=>"Anual", "url"=>"https://www.ertzaintza.euskadi.eus/lfr/web/ertzaintza/estadisticas-delictivas", "url_text"=>"Estadísticas delictivas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Número de víctimas de homicidios intencionales por cada 100.000 habitantes", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.1- Reducir significativamente todas las formas de violencia y las correspondientes tasas de mortalidad en todo el mundo", "definicion"=>"Número de víctimas de homicidio doloso/asesinato consumado por cada 100.000 habitantes.", "formula"=>"$$THD^{t} = \\frac{HD^{t}}{P^{t}} \\cdot 100.000$$\n\ndonde:\n\n$HD^{t} =$ número de infracciones penales conocidas por homicidio doloso/asesinato consumado en el año $t$\n\n$P^{t} =$ población a 1 de julio del año $t$\n", "desagregacion"=>"\nTerritorio histórico\n", "periodicidad"=>"Anual", "observaciones"=>"Datos recogidos por la Ertzaintza y por las Policías Locales. Los datos solo incluyen los homicidios dolosos consumados registrados en poblaciones de más de 15.000 habitantes.\nSe utilizan las tipologías comunes básicas establecidas en la Unión Europea (a través de su oficina estadística -EUROSTAT-) sobre las infracciones penales conocidas por los cuerpos policiales de los distintos países.", "justificacion_global"=>"Este indicador se utiliza ampliamente a nivel nacional e internacional para medir la forma más extrema de delito violento \ny también proporciona una indicación directa de la falta de seguridad. \n\nLa seguridad frente a la violencia es un requisito previo para que las personas disfruten de una vida segura \ny activa y para que las sociedades y las economías se desarrollen libremente. Los homicidios intencionales ocurren \nen todos los países del mundo y este indicador tiene una aplicabilidad global. El seguimiento de los homicidios \nintencionales es necesario para evaluar mejor sus causas, factores impulsores y consecuencias y, a \nlargo plazo, para desarrollar medidas preventivas eficaces. Si los datos se desglosan \nadecuadamente (como se sugiere en la Clasificación Internacional del Delito con Fines Estadísticos), el indicador puede \nidentificar los diferentes tipos de violencia asociados con el homicidio: interpersonal (incluida la violencia relacionada \ncon la pareja y la familia), criminal (incluido el crimen organizado y otras formas de actividades delictivas) y \nsociopolítica (incluido el terrorismo y los delitos motivados por el odio).\n\nEl homicidio intencional se define como la muerte ilegal infligida a una persona con la intención de \ncausarle la muerte o lesiones graves (Fuente: Clasificación Internacional de Delitos con Fines Estadísticos, ICCS 2015).\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.1.1&seriesCode=VC_IHR_PSRC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Número de víctimas de homicidio intencional por cada 100.000 habitantes, por sexo (víctimas por cada 100.000 habitantes) VC_IHR_PSRC</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-01-01.pdf\">Metadatos 16-1-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Número de víctimas de homicidios intencionales por cada 100.000 habitantes", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.1- Reducir significativamente todas las formas de violencia y las correspondientes tasas de mortalidad en todo el mundo", "definicion"=>"Number of victims of intentional homicide/completed murder per 100,000 inhabitants.", "formula"=>"$$THD^{t} = \\frac{HD^{t}}{P^{t}} \\cdot 100.000$$\n\nwhere:\n\n$HD^{t} =$ number of known criminal offenses for intentional homicide/completed murder in year $t$\n\n$P^{t} =$ population as of July 1 of year $t$\n", "desagregacion"=>"\nProvince\n", "periodicidad"=>"Anual", "observaciones"=>"Data collected by the Ertzaintza (Basque Police) and local police forces. The data only include  intentional homicides recorded in towns with more than 15,000 inhabitants. \nThe basic common typologies established by the European Union (through its statistical office,   Eurostat) for criminal offenses known to police forces in different countries are used. ", "justificacion_global"=>"This indicator is widely used at national and international level to measure the most extreme \nform of violent crime and it also provides a direct indication of lack of security. \n\nSecurity from violence is a prerequisite for individuals to enjoy a safe and active life and \nfor societies and economies to develop freely. Intentional homicides occur in all countries \nof the world and this indicator has a global applicability. Monitoring intentional homicides \nis necessary to better assess their causes, drivers and consequences and, in the longer term, \nto develop effective preventive measures. If data are properly disaggregated (as suggested in \nthe International Classification of Crime for Statistical Purposes), the indicator can identify \nthe different type of violence associated with homicide: inter-personal (including partner and \nfamilyrelated violence), crime (including organized crime and other forms of criminal activities) \nand sociopolitical (including terrorism, hate crime). \n\nIntentional homicide is defined as the unlawful death inflicted upon a person with the intent \nto cause death or serious injury (Source: International Classification of Crime for Statistical \nPurposes, ICCS 2015). \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.1.1&seriesCode=VC_IHR_PSRC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Number of victims of intentional homicide per 100,000 population, by sex (victims per 100,000 population) VC_IHR_PSRC</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-01-01.pdf\">Metadata 16-1-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Número de víctimas de homicidios intencionales por cada 100.000 habitantes", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.1- Reducir significativamente todas las formas de violencia y las correspondientes tasas de mortalidad en todo el mundo", "definicion"=>"Número de víctimas de homicidio doloso/asesinato consumado por cada 100.000 habitantes.", "formula"=>"$$THD^{t} = \\frac{HD^{t}}{P^{t}} \\cdot 100.000$$\n\nnon:\n\n$HD^{t} =$ gauzatutako dolozko homizidioagatik/hilketagatik ezagutzen diren zigor-arloko arau-hausteen kopurua $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"\nLurralde historikoa\n", "periodicidad"=>"Anual", "observaciones"=>"Datos recogidos por la Ertzaintza y por las Policías Locales. Los datos solo incluyen los homicidios dolosos consumados registrados en poblaciones de más de 15.000 habitantes.\nSe utilizan las tipologías comunes básicas establecidas en la Unión Europea (a través de su oficina estadística -EUROSTAT-) sobre las infracciones penales conocidas por los cuerpos policiales de los distintos países.", "justificacion_global"=>"Este indicador se utiliza ampliamente a nivel nacional e internacional para medir la forma más extrema de delito violento \ny también proporciona una indicación directa de la falta de seguridad. \n\nLa seguridad frente a la violencia es un requisito previo para que las personas disfruten de una vida segura \ny activa y para que las sociedades y las economías se desarrollen libremente. Los homicidios intencionales ocurren \nen todos los países del mundo y este indicador tiene una aplicabilidad global. El seguimiento de los homicidios \nintencionales es necesario para evaluar mejor sus causas, factores impulsores y consecuencias y, a \nlargo plazo, para desarrollar medidas preventivas eficaces. Si los datos se desglosan \nadecuadamente (como se sugiere en la Clasificación Internacional del Delito con Fines Estadísticos), el indicador puede \nidentificar los diferentes tipos de violencia asociados con el homicidio: interpersonal (incluida la violencia relacionada \ncon la pareja y la familia), criminal (incluido el crimen organizado y otras formas de actividades delictivas) y \nsociopolítica (incluido el terrorismo y los delitos motivados por el odio).\n\nEl homicidio intencional se define como la muerte ilegal infligida a una persona con la intención de \ncausarle la muerte o lesiones graves (Fuente: Clasificación Internacional de Delitos con Fines Estadísticos, ICCS 2015).\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.1.1&seriesCode=VC_IHR_PSRC&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Nahita egindako hilketen biktimen kopurua 100.000 biztanleko, sexuaren arabera (biktimak 100.000 biztanleko) VC_IHR_PSRC</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-01-01.pdf\">Metadatuak 16-1-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.1: Significantly reduce all forms of violence and related death rates everywhere</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.1.1: Number of victims of intentional homicide per 100,000 population, by sex and age</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_IHR_PSRC - Number of victims of intentional homicide per 100,000 population [16.1.1]</p>\n<p>VC_IHR_PSRCN - Number of victims of intentional homicide [16.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>5.2.1: Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age</p>\n<p>16.1.3: Proportion of population subjected to (a) physical violence, (b) psychological violence and (c) sexual violence in the previous 12 months</p>\n<p>16.1.4: Proportion of population that feel safe walking alone around the area they live after dark</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC), World Health Organization (WHO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator is defined as the total count of victims of intentional homicide divided by the total population, expressed per 100,000 population.</p>\n<p>Intentional homicide is defined as the unlawful death inflicted upon a person with the intent to cause death or serious injury (Source: International Classification of Crime for Statistical Purposes, ICCS 2015); population refers to total resident population in a given country in a given year.</p>\n<p><strong>Concepts:</strong></p>\n<p>The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide.</p>\n<p>Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury.</p>\n<p>The statistical definition contains three elements that characterize the killing of a person as &#x201C;intentional homicide&#x201D;:</p>\n<p>1. The killing of a person by another person (objective element);</p>\n<p>2. The intent of the perpetrator to kill or seriously injure the victim (subjective element);</p>\n<p>3. The unlawfulness of the killing, which means that the law considers the perpetrator liable for the unlawful death (legal element).</p>\n<p>This definition states that, for statistical purposes, all killings corresponding to the three criteria above should be considered as intentional homicides, irrespective of definitions provided by national legislations or practices.</p>\n<p>In the International Classification of Diseases (ICD), deaths coded with ICD-10 codes X85-Y09 (injuries inflicted by another person with intent to injure or kill) and ICD-10 code Y87.1 (sequelae of assault), or ICD-11 codes PD50-PF2Z and PJ20-PJ2Z, generally correspond to the definition of intentional homicide discussed above.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of homicide deaths per 100,000 population</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">International Classification of Crime for Statistical Purposes</a>, ICCS 2015</p>\n<p>International Classification of Diseases</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Two separate sources exist at country level: a) criminal justice system; b) civil registration/vital statistics. The United Nations Office on Drugs and Crime (UNODC) collects and publishes data from criminal justice systems through its annual data collection mandated by the UN General Assembly (UN Survey of Crime Trends and Operations of Criminal Justice Systems, UN-CTS); the World Health Organization (WHO) collects and publishes death certificate data (civil registration/vital statistics). The data collection through the UN-CTS is facilitated by a network of over 140 national Focal Points appointed by responsible authorities.</p>", "COLL_METHOD__GLOBAL"=>"<p>At international level, data on intentional homicides are routinely collected by UNODC through the annual UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) data collection. As requested by the UN Commission on Crime Prevention and Criminal Justice, over 140 Member States have already appointed a UN-CTS national focal point that delivers UN-CTS data to UNODC. In most cases these focal points are national institutions responsible for data production in the area of crime and criminal justice (National Statistical Offices, Ministry of Interior, Ministry of Justice, etc.). For countries that have not appointed a focal point, the request for data is sent to permanent missions in Vienna. When a country does not report to UNODC, other official sources such as authoritative websites, publications, or other forms of communication are used. Homicide estimates from WHO are used when no other source on homicide is available. Once consolidated, data are shared to countries to check their accuracy.</p>\n<p>When data and related metadata are available, some adjustments are made to data in order to assure compliance with the definition of intentional homicide as provided by the International Classification of Crime for Statistical Purposes (ICCS). National data on types of killings that are considered as intentional homicide by the ICCS, while being classified under a different crime at country level, are added to national figures of intentional homicide. This can be done only when detailed data on such types of killings (e.g. serious assault leading to death, honor killing, etc.) are available.</p>\n<p>As for UNODC data dissemination policy, data for SDG monitoring are sent to countries for consultation prior to publication. </p>", "FREQ_COLL__GLOBAL"=>"<p>III-IV quarter </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>II quarter year n+1 (data for year n-1). For instance, data for year of data 2022 are collected in III-IV quarter 2023 and released in II quarter 2024. </p>", "DATA_SOURCE__GLOBAL"=>"<p>Data on intentional homicide are sent to UNODC by member states, usually through national UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) Focal Points, which in most cases are national institutions responsible for data production in the area of crime and criminal justice (National Statistical Offices, Ministry of Interior, Ministry of Justice, etc.). The primary source on intentional homicide is usually an institution of the criminal justice system (Police, Ministry of Interior, general Prosecutor Office, etc.). Data produced by public health/civil registration system are sent to WHO through national statistics offices and/or ministries of health.</p>", "COMPILING_ORG__GLOBAL"=>"<p><strong>Name:</strong></p>\n<p>United Nations Office on Drugs and Crime (UNODC), World Health Organization (WHO)</p>\n<p><strong>Description:</strong></p>\n<p>At international level, data on intentional homicides are routinely collected and disseminated by the United Nations Office on Drugs and Crime (UNODC) through the annual UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) data collection. UNODC partners with regional organizations in the collection and dissemination of homicide data, respectively with Eurostat in Europe and with the Organisation of American States in the Americas. WHO collects data on intentional homicide in the framework of regular data collection on causes of death. In this context, data on deaths by assault are considered as intentional homicides.</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) was introduced through the General Assembly Resolution <a href=\"https://undocs.org/en/A/RES/3021(XXVII)\">A/RES/3021(XXVII)</a> in 1972. The Economic and Social Council, in its resolution 1984/48 of 25 May 1984, requested that the Secretary-General maintain and develop the United Nations crime-related database by continuing to conduct surveys of crime trends and the operations of criminal justice systems. </p>\n<p>According to Article 64 of its constitution, WHO is mandated to request each Member State to provide statistics on mortality. In support of this, the first World Health Assembly endorsed the sixth revision of the International List of Causes of Death, now called the International Statistical Classification of Diseases, Injuries and Causes of death (ICD). The <a href=\"https://apps.who.int/iris/bitstream/handle/10665/89478/WHA20.18_eng.pdf\" target=\"_blank\">WHO Nomenclature Regulations</a> of 1967 affirms the importance of compiling and publishing statistics of mortality and morbidity in comparable form. Member States are obliged to provide WHO with the statistics in accordance with the Regulations.</p>", "RATIONALE__GLOBAL"=>"<p>This indicator is widely used at national and international level to measure the most extreme form of violent crime and it also provides a direct indication of lack of security. Security from violence is a pre-requisite for individuals to enjoy a safe and active life and for societies and economies to develop freely. Intentional homicides occur in all countries of the world and this indicator has a global applicability.</p>\n<p>Monitoring intentional homicides is necessary to better assess their causes, drivers and consequences and, in the longer term, to develop effective preventive measures. If data are properly disaggregated (as suggested in the International Classification of Crime for Statistical Purposes), the indicator can identify the different type of violence associated with homicide: inter-personal (including partner and family-related violence), crime (including organized crime and other forms of criminal activities) and socio-political (including terrorism, hate crime).</p>", "REC_USE_LIM__GLOBAL"=>"<p>The International Classification of Crime for Statistical Purposes (ICCS) provides important clarifications on the definition of intentional homicide. In particular, it states that the following killings are included in the count of homicide:</p>\n<p>- Murder</p>\n<p>- Honour killing</p>\n<p>- Serious assault leading to death</p>\n<p>- Death as a result of terrorist activities</p>\n<p>- Dowry-related killings</p>\n<p>- Femicide</p>\n<p>- Infanticide</p>\n<p>- Voluntary manslaughter</p>\n<p>- Extrajudicial killings</p>\n<p>- Killings caused by excessive force by law enforcement/state officials</p>\n<p>Furthermore, the <a href=\"https://www.unodc.org/documents/data-and-analysis/statistics/crime/ICCS/Unlawful_killings_conflict_situations_ICCS.pdf\">ICCS Briefing Note on Unlawful killings in conflict situations</a> provides indications on how to distinguish between intentional homicides, killings directly related to war/conflict and other killings that amount to war crimes.</p>\n<p>The complete recording of homicide deaths in death-registration systems requires good linkages with coronial and police systems, but can be impeded by delays in determining intent of injury deaths. Less than one half of WHO Member States have well-functioning death-registration systems that record causes of death. </p>\n<p>The fact that homicide data are typically produced by two separate and independent sources at national level (criminal justice and public health) represents a specific asset of this indicator, as the comparison of the two sources is a tool to assess accuracy of national data. Usually, for countries where data from both sources exist, a good level of matching between the sources is recorded (see UNODC Global Study on Homicide, 2013).</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated as the total number of victims of intentional homicide recorded in a given year divided by the total resident population in the same year, multiplied by 100,000.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>R</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mo>=</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n    <mi>*</mi>\n    <mfrac>\n      <mrow>\n        <mi>V</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>m</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>For the rate by sex, the number of victims of that sex should be divided by the population of the same sex. </p>\n<p>In several countries, two separate sets of data on intentional homicide are produced, respectively from criminal justice and public health/civil registration systems. When existing, figures from both data sources are reported. Population data are derived from annual estimates produced by the UN Population Division.</p>\n<p>In cases where data on intentional homicide victims are not available, the number of intentional homicide offences, that is the number of incidents involving one or more victims recorded by the police, can be used as a proxy.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Following the submission of the CTS questionnaire, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation. The Organization for American States is also reviewing the responses of its Member States. All data submitted by Member States through other means or taken from other sources are added to the dataset after review and validation by Member States. </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>When national data on victims of intentional homicide are not available from neither criminal justice nor from public health/civil registration, the number of intentional homicide offences is considered. If no data are available, missing values are left blank.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>See section 4.g. Regional aggregations for more information. </p>", "REG_AGG__GLOBAL"=>"<p>The method used for estimating the number of victims of intentional homicides at the global and regional level aims to make the best possible use of available data. For each regional aggregate, the number of victims of intentional homicides should correspond to the sum of all national data of such killings in the region, in each year. However, for many countries, data on intentional homicides are not available, or data are available only for some years. As a result, the sample of countries with available data is different for each year. If left unaddressed, this issue would result in inconsistencies, as regional aggregates would be drawn from a different set of countries each year.</p>\n<p>Imputations for intentional homicides victims are performed on the country-level rate of intentional homicide victims per 100,000 population. If a country has just one available data point since the year 2000, all missing values are set equal to this single available data point. This approach therefore accounts for population growth over time and does not mean that the series is constant in absolute terms. If a country has two to eight available data points, the missing values between two data points are estimated by linear interpolation, and if there are missing values that are temporally before (or after) the earliest (or latest) available data point, the values at the beginning (or end) of the series are filled with the earliest (or latest) available data point. If a country has more than eight available data points in the respective time series, the missing values between two data points are estimated by linear interpolation, and if there are missing values that are temporally before (or after) the earliest (or latest) available data point, the values at the end of the time series are imputed using an exponential smoothing approach (for more information, see <a href=\"https://afit-r.github.io/ts_exp_smoothing\">this page</a>). Finally, the regional rate is applied to countries without any data point </p>\n<p>Once the series have been imputed at the national level, they are aggregated at the regional level. Regional homicide totals are calculated for each year by multiplying the regional homicide rate per 100,000 population with the total population of the respective region (divided by 100,000). The regions are the ones from the United Nations &#x201C;<a href=\"https://unstats.un.org/unsd/methodology/m49/\">Standard Country or Area Codes for Statistical Use</a>&#x201D;. Each country or area is included in one region only.</p>\n<p>Finally, regional estimates are aggregated to compute the global number of intentional homicides. </p>", "DOC_METHOD__GLOBAL"=>"<p>The <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">International Classification of Crime for Statistical Purposes</a> (ICCS) and its <a href=\"https://www.unodc.org/documents/data-and-analysis/statistics/crime/ICCS/Unlawful_killings_conflict_situations_ICCS.pdf\">briefing note on unlawful killings in conflict situations</a> include information on the definition and disaggregations.</p>\n<p>The <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/United-Nations-Surveys-on-Crime-Trends-and-the-Operations-of-Criminal-Justice-Systems.html\">United Nations Survey of Crime Trends an the Operations of Criminal Justice Systems</a> (UN-CTS) includes further information on counting rules used for homicide victims.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>See section 4.d Validation</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>See section 4.d Validation</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>UNODC developed a quality assessment score for homicide data at country level. Based on a set of standard quality dimensions for statistical data, a quality assessment framework was developed to evaluate global homicide data based on five main criteria:</p>\n<ol>\n  <li>Comparability</li>\n  <li>Completeness</li>\n  <li>Timeliness</li>\n  <li>Internal Consistency</li>\n  <li>External Consistency</li>\n</ol>\n<p>For each of these criteria quality indicators are defined and a qualitative score is computed per country (on a scale of 0-100), which is then converted to a qualitative score in three categories (good; fair; low). A total score encompasses all five criteria.</p>\n<p>More information on the quality assessment score can be found in the <a href=\"https://www.unodc.org/documents/data-and-analysis/gsh/Meth_Annex_GHS.pdf\">methodological annex</a> of the 2019 Global Study on Homicide. The next update data quality assessment will be presented in the 2023 Global Study on Homicide.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Considering data collected by both UNODC and WHO, national data on homicide are available for most Member States. However, data availability is lower in Africa, Asia and the Pacific and Western Asia than in the Americas and Europe. Furthermore, data availability for crucial disaggregations (by sex, or victim-perpetrator relationship) is more limited than for total homicide counts.</p>\n<p><strong>Time series:</strong></p>\n<p>1990-present day</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Recommended disaggregation for this indicator are:</p>\n<p>- sex and age of the victim and the perpetrator (suspected offender)</p>\n<p>- relationship between victim and perpetrator (intimate partner, other family member, acquaintance, etc.)</p>\n<p>- means of perpetration (firearm, sharp object, etc.)</p>\n<p>- situational context/motivation (organized crime, inter-personal violence, etc.)</p>\n<p>Tables III, IV and V of the International Classification of Crime for Statistical Purposes contains more information on these disaggregations, including definitions for each of the categories.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Discrepancies might exist between country produced and internationally reported counts of intentional homicides as national data might refer to national definition of intentional homicide while data reported by UNODC aim to comply with the definition provided by the International Classification of Crime for Statistical Purposes (ICCS) (approved in 2015 by Member States in the UN Statistical Commission and the UN Commission on Crime Prevention and Criminal Justice). The United Nations Office on Drugs and Crime (UNODC) makes special efforts to count all killings falling under the ICCS definition of intentional homicide, while national data may still be compiled according to national legal systems rather than the statistical classification. The gradual implementation of ICCS by countries should improve quality and consistency of national and international data.</p>\n<p>Intentional homicide rates may also differ due to the use of different population figures.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.unodc.org\">www.unodc.org</a> </p>\n<p><strong>References:</strong></p>\n<p>UNODC Homicide Database (<a href=\"https://dataunodc.org/\">https://dataunodc.org/</a>), UNODC, Global Study on Homicide 2019; WHO-UNDP-UNODC, Global Status Report on Violence Prevention 2014; UNODC, International Classification of Crime for Statistical Purposes - ICCS, 2015</p>", "indicator_sort_order"=>"16-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.1.2", "slug"=>"16-1-2", "name"=>"Muertes relacionadas con conflictos por cada 100.000 habitantes, desglosadas por sexo, edad y causa", "url"=>"/site/es/16-1-2/", "sort"=>"160102", "goal_number"=>"16", "target_number"=>"16.1", "global"=>{"name"=>"Muertes relacionadas con conflictos por cada 100.000 habitantes, desglosadas por sexo, edad y causa"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Muertes relacionadas con conflictos por cada 100.000 habitantes, desglosadas por sexo, edad y causa", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Muertes relacionadas con conflictos por cada 100.000 habitantes, desglosadas por sexo, edad y causa", "indicator_number"=>"16.1.2", "national_geographical_coverage"=>"", "page_content"=>"La C.A. de Euskadi no se encuentra en la lista de situaciones de conflicto armado considerada para este indicador por la Oficina del Alto Comisionado de las Naciones Unidas para los Derechos Humanos (ACNUDH)", "permalink"=>"", "precision"=>[], "progress_status"=>"notapplicable", "reporting_status"=>"notapplicable", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nEste indicador mide la prevalencia de los conflictos armados y su impacto en términos \nde pérdida de vidas. Junto con el indicador 16.1.1 sobre homicidio intencional, miden \nlas muertes violentas que ocurren en todos los países del mundo (homicidios intencionales) \ny en situaciones de conflicto armado (muertes relacionadas con conflictos). \n\nLa Agenda 2030 para el Desarrollo Sostenible busca fortalecer la paz universal y se compromete \na redoblar los esfuerzos para resolver o prevenir los conflictos. Reconoce que no puede haber \ndesarrollo sostenible sin paz ni paz sin desarrollo sostenible. Por lo tanto, el recuento de \nlas muertes que ocurren en situaciones de conflicto armado es esencial para la medición de la \nAgenda, incluido y más allá de su Objetivo 16. \n\nEl seguimiento de las muertes relacionadas \ncon los conflictos también es necesario para ayudar a proteger a los civiles y otras posibles \nvíctimas, garantizar el respeto de las normas humanitarias y de derechos humanos y comprender \nlos patrones y las consecuencias de los conflictos armados a fin de prevenir conflictos armados futuros.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.1.2&seriesCode=VC_DTH_TOCVN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAGE%20%7C%20BOTHSEX%20%7C%20_T\">Número de muertes relacionadas con conflictos (civiles), por sexo, edad y causa de muerte (Número) VC_DTH_TOCVN</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-01-02.pdf\">Metadatos 16-1-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThis indicator measures the prevalence of armed conflicts and their impact in terms of loss of life. \nTogether with the indicator 16.1.1 on intentional homicide, they measure violent deaths that occur in all \ncountries of the world (intentional homicides) and in situations of armed conflict (conflict-related \ndeaths). \n\nThe 2030 Agenda for Sustainable Development seeks to strengthen universal peace and commits to \nredouble efforts to resolve or prevent conflict. It recognizes that there can be no sustainable \ndevelopment without peace and no peace without sustainable development. Counting deaths occurring \nin situations of armed conflict is therefore essential to the measurement of the Agenda, including and \nbeyond its Goal 16. \n\n Monitoring conflict-related deaths is also necessary to help protect civilians and other \npotential victims, ensure respect of humanitarian and human rights standards, and understand the \npatterns and consequences of armed conflicts in order to prevent future armed conflicts. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.1.2&seriesCode=VC_DTH_TOCVN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAGE%20%7C%20BOTHSEX%20%7C%20_T\">Number of conflict-related deaths (civilians), by sex, age and cause of death (Number) VC_DTH_TOCVN</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-01-02.pdf\">Metadata 16-1-2.pdf</a> ", "informacion_interes"=>nil, "national_data_updated_date"=>nil, "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nEste indicador mide la prevalencia de los conflictos armados y su impacto en términos \nde pérdida de vidas. Junto con el indicador 16.1.1 sobre homicidio intencional, miden \nlas muertes violentas que ocurren en todos los países del mundo (homicidios intencionales) \ny en situaciones de conflicto armado (muertes relacionadas con conflictos). \n\nLa Agenda 2030 para el Desarrollo Sostenible busca fortalecer la paz universal y se compromete \na redoblar los esfuerzos para resolver o prevenir los conflictos. Reconoce que no puede haber \ndesarrollo sostenible sin paz ni paz sin desarrollo sostenible. Por lo tanto, el recuento de \nlas muertes que ocurren en situaciones de conflicto armado es esencial para la medición de la \nAgenda, incluido y más allá de su Objetivo 16. \n\nEl seguimiento de las muertes relacionadas \ncon los conflictos también es necesario para ayudar a proteger a los civiles y otras posibles \nvíctimas, garantizar el respeto de las normas humanitarias y de derechos humanos y comprender \nlos patrones y las consecuencias de los conflictos armados a fin de prevenir conflictos armados futuros.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.1.2&seriesCode=VC_DTH_TOCVN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ALLAGE%20%7C%20BOTHSEX%20%7C%20_T\">Gatazkekin erlazionatutako heriotzen kopurua (zibilak), sexuaren, adinaren eta heriotzaren arrazoiaren arabera (kopurua) VC_DTH_TOCVN</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-01-02.pdf\">Metadatuak 16-1-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>nil, "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.1: Significantly reduce all forms of violence and related death rates everywhere</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.1.2: Conflict-related deaths per 100,000 population, by sex, age and cause</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_DTH_TOCVN - Number of conflict-related deaths (civilians), by sex, age and cause of death [16.1.2]</p>\n<p>VC_DTH_TOTR - Number of total conflict-related deaths per 100,000 population [16.1.2]</p>\n<p>VC_DTH_TOTN - Number of total conflict-related deaths, by sex, age and cause of death [16.1.2]</p>\n<p>VC_DTH_TOUNN - Number of conflict-related deaths (unknown), by sex, age and cause of death [16.1.2]</p>\n<p>VC_DTH_TONCVN - Number of conflict-related deaths (non-civilians), by sex, age and cause of death [16.1.2]</p>\n<p>VC_DTH_TOTPT - Conflict-related total death rate, by sex, age and cause of death (%) [16.1.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>16.1.1 Number of victims of intentional homicide per 100,000 population, by sex and age</p>\n<p>16.1.3 Proportion of population subjected to physical, psychological or sexual violence in the previous 12 months</p>\n<p>16.1.4 Proportion of population that feel safe walking alone around the area they live</p>\n<p>16.4.2 Proportion of seized, found or surrendered arms whose illicit origin or context has been traced or established by a competent authority in line with international instruments</p>\n<p>16.a.1 Existence of independent national human rights institutions in compliance with the Paris Principles</p>\n<p>10.3.1 and 16.b.1: Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights law</p>\n<p>5.2.2: Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Office of the United Nations High Commissioner for Human Rights (OHCHR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Office of the United Nations High Commissioner for Human Rights (OHCHR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definitions:</strong></p>\n<p>This indicator is defined as the total count of conflict-related deaths divided by the total population, expressed per 100,000 population.</p>\n<p>&#x2018;Conflict&#x2019; is defined as &#x2018;armed conflict&#x2019; in reference to a terminology enshrined in International Humanitarian Law (IHL), and applied to situations based on the assessment of the United Nations (UN) and other internationally mandated entities. &#x2018;Conflict-related deaths&#x2019; refers to direct and indirect deaths associated to armed conflict. &#x2018;Population&#x2019; refers to total resident population in a given situation of armed conflict included in the indicator, in a given year. Population data are derived from annual estimates produced by the UN Population Division.</p>\n<p><strong>Concepts:</strong></p>\n<p><em>&#x2018;Conflict&#x2019;</em></p>\n<p>According to IHL, the branch of international law, which specifically focuses on armed conflicts, two types of armed conflicts exist: international armed conflicts (IAC) and non-international armed conflicts (NIAC). </p>\n<p>IAC exist whenever there is resort to armed force between two or more States. An IAC does not exist in cases in which use of force is the result of an error (e.g. involuntary incursion into foreign territory, wrongly identifying the target); and when the territorial State has given its consent to an intervention. </p>\n<p>NIAC are protracted armed confrontations occurring between governmental armed forces and the forces of one or more armed groups, or between such groups arising on the territory of a State. The armed confrontation must reach a &#x201C;minimum level of intensity&#x201D; and the parties involved in the conflict must show a &#x201C;minimum of organisation&#x201D;. </p>\n<p><em>&#x2018;Conflict-related deaths&#x2019;</em></p>\n<p>Direct deaths are deaths where there are reasonable grounds to believe that they resulted directly from war operations and that the acts, decisions and/or purposes that caused these deaths were in furtherance of or under the guise of armed conflict. </p>\n<p>These deaths may have been caused by (i) the use of weapons or (ii) other means and methods. Deaths caused by the use of weapons, include but are not limited to those inflicted by firearms, missiles, mines, and bladed weapons. It may also include deaths resulting from aerial attacks and bombardments (e.g. of military bases, cities and villages), crossfire, explosive remnants of war, targeted killings or assassinations, force protection incidents. Deaths caused by other means and methods may include deaths from torture or sexual and gender-based violence, intentional killing using starvation, depriving prisoners of access to health care or denying access to essential goods and services (e.g. an ambulance stopped at a check point).</p>\n<p>Indirect deaths are deaths resulting from a loss of access to essential goods and services (e.g. economic slowdown, shortages of medicines or reduced farming capacity that result in lack of access to adequate food, water, sanitation, health care and safe conditions of work) that are caused or aggravated by the situation of armed conflict.</p>\n<p>By definition, these deaths should be separated from other violent deaths which are, in principle, not connected to the situation of armed conflict (e.g. intentional and non-intentional homicides, self-defence, self-inflicted), but are still relevant to the implementation and measurement of SDG target 16.1. The International Classification of Crime for Statistical Purposes (ICCS) provides definitional elements and classification of violent deaths both related and not related to armed conflict. The ICCS provides indications on how to distinguish between intentional homicides, killings directly related to war/armed conflict and killings that amount to war crimes.</p>\n<p><em>&#x2018;Cause&#x2019;</em> refers to the weapons, means and methods that caused the conflict-related deaths. The categories for the disaggregation of the &#x2018;c<em>ause of death</em>&#x2019; for direct deaths build on the WHO International Classification of Diseases (ICD-11), ICCS, the International Committee of the Red Cross (ICRC) overview of weapons regulated by IHL, UN practice and OHCHR casualty recording. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number</p>\n<p>Per 100,000 population for VC_DTH_TOTR</p>\n<p>Percent (%) for VC_DTH_TOTPT</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>International Classification of Crime for Statistical Purposes (ICCS); International Classification of Diseases (ICD-11).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Examples of sources include eyewitnesses; hospital records; community elders, religious and civil leaders; security forces and conflict parties; local authorities; prosecution offices, police and other law enforcement agencies, health authorities; government departments and officials; UN and other international organizations; detailed media reports and other relevant civil society organizations. </p>", "COLL_METHOD__GLOBAL"=>"<p>Data will be compiled from data providers that have been systematically assessed by the Office of the United Nations High Commissioner for Human Rights (OHCHR) for their application of the methodology for the indicator, including their ability to provide credible and reliable data and apply the verification standard based on the technical guidance.</p>\n<p>The mechanisms, bodies and institutions that have the mandate, capacity and independence to document and investigate alleged killings related to conflict will be prioritized. From this perspective, UN entities working on casualty recording in the framework of their operations (e.g. peacekeeping operations, commissions of inquiry, humanitarian operations and human rights offices), national human rights institutions and national statistical offices will generally be prioritized. OHCHR will conduct capacity-building activities and collaborate, including in validating data, with relevant stakeholders at national, regional and international levels. </p>", "DATA_SOURCE__GLOBAL"=>"<p>National and international data providers that have been assessed by OHCHR for their application of the indicator&#x2019;s associated methodology, including UN entities working on casualty recording in the framework of their operations (e.g. peacekeeping operations, commissions of inquiry, humanitarian operations and human rights offices), national human rights institutions, national statistical offices and relevant civil society organizations. </p>", "COMPILING_ORG__GLOBAL"=>"<p>Office of the United Nations High Commissioner for Human Rights (OHCHR)</p>", "INST_MANDATE__GLOBAL"=>"<p>As part of its global mandate to promote and protect human rights, OHCHR carries out casualty recording in situations of armed conflict</p>", "RATIONALE__GLOBAL"=>"<p>This indicator measures the prevalence of armed conflicts and their impact in terms of loss of life. Together with the indicator 16.1.1 on intentional homicide, they measure violent deaths that occur in all countries of the world (intentional homicides) and in situations of armed conflict (conflict-related deaths). </p>\n<p>The 2030 Agenda for Sustainable Development seeks to strengthen universal peace and commits to redouble efforts to resolve or prevent conflict. It recognizes that there can be no sustainable development without peace and no peace without sustainable development. Counting deaths occurring in situations of armed conflict is therefore essential to the measurement of the Agenda, including and beyond its Goal 16. Monitoring conflict-related deaths is also necessary to help protect civilians and other potential victims, ensure respect of humanitarian and human rights standards, and understand the patterns and consequences of armed conflicts in order to prevent future armed conflicts.</p>", "REC_USE_LIM__GLOBAL"=>"<p>In situations of armed conflict, a large share of deaths may not be reported. Often, normal registration systems are heavily affected by the presence of armed conflict. Additionally, actors on both sides of an armed conflict may have incentives for misreporting, deflating or inflating casualties. In most instances, the number of cases reported will depend on access to conflict zones, access to information, motivation and perseverance of both international and national actors, such as UN peace missions and other internationally mandated entities, national institutions (e.g. national statistical offices, national human rights institutions) and relevant civil society organizations. </p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated as the total count of conflict-related deaths divided by the total resident population in a given situation of armed conflict for the year, expressed per 100,000 population, occurring within the preceding 12 months. </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>16</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mo>.</mo>\n    <mn>2</mn>\n    <mi>&amp;nbsp;</mi>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>f</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>x</mi>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>x</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#xD7;</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n  </math></p>\n<p>The total count of conflict-related deaths includes first the total number of documented direct deaths, using all potentially relevant data sources (e.g. UN peace missions, national statistical offices, national human rights institutions, civil society organisations). Documented cases provide verified information on each direct conflict-related death. </p>\n<p>Depending on the magnitude of conflict-related deaths, capacity of data providers, and other contextual and practical considerations, the methodology will seek to produce statistical estimates of undocumented deaths directly linked to the armed conflict. Further work will be needed to cover deaths indirectly linked to the armed conflict, e.g. loss of access to essential goods and services. Existing data must be updated regularly and retrospectively reflecting the emergence of new data over time.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Validation based on assessment of record completeness (e.g. name of deceased, date and place of death) and further conformity with OHCHR casualty recording methodology (see <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_1_2_Guidance_Note.pdf\">Guidance note</a>). </p>", "ADJUSTMENT__GLOBAL"=>"<p>Categories for the disaggregation of the &#x2018;cause of death&#x2019; for direct deaths were specifically developed for the purpose of this indicator (see <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_1_2_Guidance_Note.pdf\">Guidance note</a>). </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>As a starting point, the indicator only includes documented conflict-related direct deaths. If there are no documented conflict-related direct deaths for a particular situation of armed conflict, no estimate of missing values is computed. Specific to the nature of this indicator, it is worth noting that depending on the availability and quality of data over the course of the armed conflict, statistical surveys and techniques may be used to estimate undocumented direct conflict-related deaths, adding the statistical estimates to the documented cases.</p>\n<p>National datasets with sufficiently well documented direct deaths constitute an essential source for further statistical analysis and estimations of undocumented direct deaths. As indirect deaths would typically fall outside the scope of common casualty recording practices (that rather focus on direct deaths), they may be captured using additional administrative records and/or statistical surveys allowing the measurement of excess mortality, namely all the deaths (direct and indirect) that would not have occurred in time of peace, as defined and measured by epidemiologists. </p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>Same as country level.</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates are calculated as the total number of documented direct conflict-related deaths, divided by the total resident population of armed conflict, for the region, expressed in 100, 000 population. The global aggregate is calculated as the total number of documented direct conflict-related deaths for all the situations of armed conflict, divided by the total resident population of all situations of armed conflict, included in the indicator, expressed in 100, 000 population.</p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li> <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_1_2_Guidance_Note.pdf\">OHCHR Guidance note</a></li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not available</p>", "QUALITY_ASSURE__GLOBAL"=>"<ul>\n  <li>l <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_1_2_Guidance_Note.pdf\">OHCHR guidance note</a></li>\n  <li>OHCHR will conduct a validation process of the list of situations of armed conflict to be considered for the indicator every year. OHCHR will systematically assess each potentially relevant data provider for its application of the methodology for the indicator, including its ability to provide credible and reliable data and apply verification standards. This will be done through metadata exchange, capacity building and continued exchange with data providers.</li>\n</ul>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not available</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data on documented direct conflict-related deaths of civilians have been collected for most of the deadliest situations of armed conflict in the SDG regions of Southern Asia, Western Asia, Sub-Saharan Africa, Northern Africa, Latin America and Europe. </p>\n<p><strong>Time series:</strong></p>\n<p>Global data available since 2015</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The recommended disaggregation for this indicator are:</p>\n<ul>\n  <li>Sex of person killed (Man, Woman, Unknown)</li>\n  <li>Age group of person killed (Adult (18 and above), Child (below 18), Unknown)</li>\n  <li>Cause of death (Heavy weapons and explosive munitions; Planted explosives and unexploded ordnance (UXO); Small arms and light weapons;; Incendiary; Chemical, Biological, Radiological, Nuclear (CBRN); Electromagnetic weapons; Less lethal weapons; Denial of access to/destruction of objects indispensable to survival; Accidents related to conflict; Use of objects and other means; Unknown)</li>\n  <li>Status of the person killed (Civilian, Other protected person, Member of armed forces, Person directly participating in hostilities, Unknown) </li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Discrepancies might exist between national definitions, international statistical and legal standards, coverage and quality of data, according to the mandate, methods and capacity of data providers. Capacity building for the implementation of the methodology for this indicator by data providers contributes tol improving quality and consistency across data sets.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://www.ohchr.org/en/instruments-and-mechanisms/human-rights-indicators/sdg-indicators-under-ohchrs-custodianship\">https://www.ohchr.org/en/instruments-and-mechanisms/human-rights-indicators/sdg-indicators-under-ohchrs-custodianship</a> </p>\n<p><strong>References:</strong></p>\n<p>INTERNATIONAL COMMITTEE OF THE RED CROSS (2009). Typology of Armed Conflicts in International Humanitarian Law: Legal Concepts and Actual Situations. Volume 91 Numbers 873. Available from <a href=\"https://www.icrc.org/en/doc/assets/files/other/irrc-873-vite.pdf\">https://www.icrc.org/en/doc/assets/files/other/irrc-873-vite.pdf</a>.</p>\n<p> </p>\n<p>INTERNATIONAL COMMITTEE OF THE RED CROSS (2008). How is the Term &#x2018;Armed Conflict&#x2019; Defined in International Humanitarian Law? Opinion Paper. Available from https://www.icrc.org/sites/default/files/external/doc/en/assets/files/other/opinion-paper-armed-conflict.pdf </p>\n<p>INTERNATIONAL COMMITTEE OF THE RED CROSS (2015). Report of the 32nd International Conference of the Red Cross and the Red Crescent, International Humanitarian Law and the Challenges of Contemporary Armed Conflicts. Geneva. Available from <a href=\"http://rcrcconference.org/wp-content/uploads/2015/10/32IC-Report-on-IHL-and-challenges-of-armed-conflicts.pdf\">http://rcrcconference.org/wp-content/uploads/2015/10/32IC-Report-on-IHL-and-challenges-of-armed-conflicts.pdf</a> </p>\n<p>INTERNATIONAL COMMITTEE OF THE RED CROSS (2011). Overview of Weapons Regulated by IHL. Available from <a href=\"https://www.icrc.org/en/document/weapons\">https://www.icrc.org/en/document/weapons</a> .</p>\n<p>UNITED NATIONS (2015). International Classification of Crime for Statistical Purposes (ICCS), Version 1.0. Vienna. Available from: <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html</a> .</p>\n<p>UNITED NATIONS. Guidance on Casualty Recording. Available from: https://www.ohchr.org/sites/default/files/Documents/Publications/Guidance_on_Casualty_Recording.pdf.</p>\n<p>WORLD HEALTH ORGANIZATION (2018). International Classification of Diseases 11<sup>th</sup> Revision. Available from <a href=\"https://icd.who.int/\">https://icd.who.int/</a> .</p>\n<p>UNITED NATIONS (2012). Human Rights Indicators: A Guide to Measurement and Implementation. New York and Geneva. Available from <a href=\"http://www.ohchr.org/EN/Issues/Indicators/Pages/HRIndicatorsIndex.aspx\">http://www.ohchr.org/EN/Issues/Indicators/Pages/HRIndicatorsIndex.aspx</a> .</p>\n<p>HUMAN RIGHTS DATA AND ANALYSIS GROUP (2014). Updated Statistical Analysis of Documentation of Killings in the Syrian Arab Republic, Commissioned by the Office of the UN High Commissioner for Human Rights. Available from <a href=\"https://www.ohchr.org/Documents/Countries/SY/HRDAGUpdatedReportAug2014.pdf\">https://www.ohchr.org/Documents/Countries/SY/HRDAGUpdatedReportAug2014.pdf</a> .</p>", "indicator_sort_order"=>"16-01-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.1.3", "slug"=>"16-1-3", "name"=>"Proporción de la población que ha sufrido a) violencia física, b) violencia psicológica o c) violencia sexual en los últimos 12 meses", "url"=>"/site/es/16-1-3/", "sort"=>"160103", "goal_number"=>"16", "target_number"=>"16.1", "global"=>{"name"=>"Proporción de la población que ha sufrido a) violencia física, b) violencia psicológica o c) violencia sexual en los últimos 12 meses"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población que ha sufrido a) violencia física, b) violencia psicológica o c) violencia sexual en los últimos 12 meses", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población que ha sufrido a) violencia física, b) violencia psicológica o c) violencia sexual en los últimos 12 meses", "indicator_number"=>"16.1.3", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Este indicador mide la prevalencia de la victimización por violencia física, \npsicológica y sexual. Es de relevancia mundial, ya que la violencia en diversas formas \nocurre en todas las regiones y países del mundo. \n\nDado que el número de actos de violencia denunciados ante las autoridades está muy por debajo de la realidad, este \nindicador debe basarse en datos recopilados mediante encuestas de muestreo de la población adulta.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-01-03.pdf\">Metadatos 16-1-3.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"This indicator measures the prevalence of victimization from physical, psychological, and sexual violence. \nIt is globally relevant as violence in various forms occurs in all regions and countries of the world. \n\nGiven that acts of violence are heavily underreported to the authorities, this indicator needs to be based on \ndata collected through sample surveys of the adult population. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-01-03.pdf\">Metadata 16-1-3.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Este indicador mide la prevalencia de la victimización por violencia física, \npsicológica y sexual. Es de relevancia mundial, ya que la violencia en diversas formas \nocurre en todas las regiones y países del mundo. \n\nDado que el número de actos de violencia denunciados ante las autoridades está muy por debajo de la realidad, este \nindicador debe basarse en datos recopilados mediante encuestas de muestreo de la población adulta.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-01-03.pdf\">Metadatuak 16-1-3.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.1: Significantly reduce all forms of violence and related death rates everywhere</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.1.3: Proportion of population subjected to (a) physical violence, (b) psychological violence and/or (c) sexual violence in the previous 12 months</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_VOV_PHYL - Proportion of population subjected to physical violence in the previous 12 months [16.1.3]</p>\n<p>VC_VOV_ROBB - Proportion of population subjected to robbery in the previous 12 months [16.1.3]</p>\n<p>VC_VOV_SEXL - Proportion of population subjected to sexual violence in the previous 12 months [16.1.3]</p>\n<p>VC_VOV_PSYCHL - Proportion of population subjected to psychological violence in the previous 12 months, by sex (%) [16.1.3]</p>\n<p>VC_VOV_PHY_ASLT - Proportion of population subjected to physical assault in the previous 12 months, by sex (%) [16.1.3]</p>\n<p>VC_VOV_SEX_ASLT - Proportion of population subjected to sexual assault in the previous 12 months, by sex (%) [16.1.3]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 5.2.1: Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age</p>\n<p>Indicator 5.2.2: Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence</p>\n<p>Indicator 11.7.2: Proportion of persons victim of non-sexual or sexual harassment, by sex, age, disability status and place of occurrence, in the previous 12 months</p>\n<p>Indicator 16.2.3: Proportion of young women and men aged 18&#x2013;29 years who experienced sexual violence by age 18</p>\n<p>Indicator 16.3.1: Proportion of victims of (a) physical, (b) psychological and/or (c) sexual violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms</p>\n<p>Indicator 16.a.1: Existence of independent national human rights institutions in compliance with the Paris</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The total number of persons who have been victim of (a) physical, (b) psychological and (c) sexual violence in the previous 12 months, as a share of the total population.</p>\n<p>Three separate indicators should be computed, one for each type of violence.</p>\n<p><strong>Concepts:</strong></p>\n<p>This indicator measures the prevalence of victimization from (a) physical, (b) psychological and (c) sexual violence respectively.</p>\n<p><strong>(a) Physical violence:</strong> This concept largely corresponds to physical assault and robbery.</p>\n<p>Assault is defined in the International Classification of Crime for Statistical Purposes (ICCS) as: the intentional or reckless application of physical force inflicted upon the body of a person.<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> This includes the intentional or reckless application of serious physical force resulting in serious bodily injury, and the intentional or reckless application of minor physical force resulting in no injury or minor bodily injury. According to the ICCS, these are defined as:</p>\n<ul>\n  <li>Serious bodily injury, at minimum, includes gunshot or bullet wounds; knife or stab wounds; severed limbs; broken bones or teeth knocked out; internal injuries; being knocked unconscious; and other severe or critical injuries.</li>\n  <li>Serious physical force, at minimum, includes being shot; stabbed or cut; hit by an object; hit by a thrown object; poisoning and other applications of force with the potential to cause serious bodily injury.</li>\n  <li>Minor bodily injury, at minimum, includes bruises, cuts, scratches, chipped teeth, swelling, black eye and other minor injuries.</li>\n  <li>Minor physical force, at minimum, includes hitting, slapping, pushing, tripping, knocking down and other applications of force with the potential to cause minor bodily injury.</li>\n</ul>\n<p>In addition to acts of assault, acts amounting to serious physical threats are also included in the definition of physical violence. As defined in the ICCS, serious physical threats refer to threats with the intention to cause death or serious bodily injury.<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></p>\n<p>Furthermore, physical violence also covers acts of robbery, defined in the ICCS as unlawfully taking or obtaining property with the use of force or threat of force against a person with intent to permanently or temporarily withhold it from a person or organization.<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></p>\n<p>Physical violence only counts as such when it is non-consensual, for example, acts of physical violence (punching, kicking, etc.) experienced while exercising a regulated combat sport or combat training will not count towards victimization prevalence.</p>\n<p>In the absence of suitable data on physical violence, it is possible to use data on physical assault or robbery, given they are both component of physical violence. </p>\n<p><strong>Psychological violence:</strong> There is no consensus at the international level on the precise definition of psychological violence. Psychological violence may be defined as any intentional and reckless act that causes psychological distress to an individual. Psychological violence can take the form of, for example, coercion, defamation, humiliation, intimidation, credible threats of violence, excessive verbal attacks or bullying, or harassment. Often, psychological violence is a pattern of behaviours, but it may be a distinct incident as well. Psychological violence is often experienced in domestic contexts. The internationally standardized and tested SDG 16 survey questionnaire provides a methodology and a core set of questions to measure psychological violence (see Section 4.c. Method of computation). </p>\n<p><strong>Sexual violence</strong>: As defined in the International Classification of Crime for Statistical Purposes (ICCS), sexual violence includes unwanted sexual acts or attempts to obtain a sexual act without valid consent or with consent as a result of intimidation, force, fraud, coercion, threat, deception, use of drugs or alcohol, or abuse of power or of a position of vulnerability. This includes rape and other forms of sexual assault, excluding non-physical sexual assault (e.g. sexual harassment). Sexual violence may be perpetrated by casual partners, by acquaintances or by strangers, but such acts also occur in established or even in formalized intimate partnerships, including in marriages. Sexual violence most often, but not exclusively, targets women. Sexual violence may also take place in same-sex contexts.</p>\n<p>More details on the set of behaviours to be used to measure physical, psychological and sexual violence are provided in Section 4.c. Method of computation.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> See ICCS 02011 Assault. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> As per ICCS, a &#x201C;threat&#x201D; refers to any type of threatening behaviour if it is believed that the threat</p><p>could be enacted. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> See ICCS 0401 Robbery. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>UNODC. 2015. <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">International Classification of Crime for Statistical Purposes</a> (ICCS)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Acts of violence are heavily underreported to the authorities, so this indicator should be derived from population surveys, not administrative data sources.</p>\n<p>Experience of violent victimization is collected through a series of questions on concrete acts of violence suffered by the respondent (see Section 4.c. Method of computation)</p>\n<p>These questions can be part of an add-on module on physical, psychological and sexual violence, to be incorporated into other ongoing general population surveys (such as surveys on quality of life, public attitudes, or surveys on other topics) or be part of dedicated surveys on crime victimization.</p>\n<p>Data should be collected as part of a nationally representative sample of the adult population residing in the country, irrespective of legal residence status. The sampling frame and sample design should ensure that results can be disaggregated at sub-national level. The sample size should be sufficiently large to capture relevant events and compute needed disaggregations.</p>", "COLL_METHOD__GLOBAL"=>"<p>At international level, data on physical, psychological and sexual violence are routinely collected by UNODC through the annual UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) data collection. As requested by the UN Commission on Crime Prevention and Criminal Justice, over 140 Member States have already appointed a UN-CTS national focal point that delivers UN-CTS data to UNODC. In most cases these focal points are national institutions responsible for data production in the area of crime and criminal justice (National Statistical Offices, Ministry of Interior, Ministry of Justice, etc.). For countries that have not appointed a focal point, the request for data is sent to permanent missions in Vienna. When a country does not report to UNODC, other official sources such as authoritative websites, publications, or other forms of communication are used. Once consolidated, data are shared with countries to check their accuracy and validity.</p>\n<p>The UN-CTS provides specific definitions of data to be collected in line with the International Classification of Crime for Statistical Purposes (ICCS). It also collects a set of metadata to identify possible discrepancies from standard definitions and to assess overall data quality (e.g. sample size, target population, agency responsible for the data collection, etc.).</p>", "FREQ_COLL__GLOBAL"=>"<p>Countries are encouraged to conduct surveys on crime victimisation in regular intervals, but at least every four years to reflect progress between each of the quadrennial reviews of Goal 16 at the High Level Political Forum (HLPF).</p>\n<p>UNODC collects data on this indicator according to the following schedule:</p>\n<p>III-IV quarter year n</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data on relevant SDG indicators are collected, compiled and sent back to countries for data review annually. Data are then reported to UN Statistics Division (UNSD) through the regular reporting channels annually.</p>\n<p>II quarter year n+1 (data for year n-1). For instance, data for the year 2023 are collected in III-IV quarter 2024 and released in II quarter 2025.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are collected through official nationally representative surveys. In most countries and most cases, such surveys are conducted by National Statistical Offices (NSOs). In some cases, other national institutions or other entities may conduct surveys on crime victimisation according to the same methodological standards.</p>\n<p>Data are sent to UNODC by Member States, usually through national UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) Focal Points, which in most cases are national institutions responsible for data production in the area of crime and criminal justice (National Statistical Offices, Ministry of Interior, Ministry of Justice, etc.).</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) &#x2013; as custodian of the UN standards and norms in crime prevention and criminal justice, UNODC assists Member States in reforming their criminal justice systems in order to be effective, fair and humane for the entire population. UNODC develops technical tools to assist Member States in implementing the UN standards and norms and supports Member States through the provision of technical assistance in crime prevention and criminal justice reform. It does so through several Global programmes and through the UNODC field office network.</p>\n<p>UNODC is responsible for carrying out the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS), which was introduced through the General Assembly Resolution <a href=\"https://undocs.org/en/A/RES/3021(XXVII)\">A/RES/3021(XXVII)</a> in 1972. The Economic and Social Council, in its resolution 1984/48 of 25 May 1984, further requested that the Secretary-General maintain and develop the United Nations crime-related database by continuing to conduct surveys of crime trends and the operations of criminal justice systems. </p>", "RATIONALE__GLOBAL"=>"<p>This indicator measures the prevalence of victimization from physical, psychological, and sexual violence. It is globally relevant as violence in various forms occurs in all regions and countries of the world. Given that acts of violence are heavily underreported to the authorities, this indicator needs to be based on data collected through sample surveys of the adult population.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Crime victimization surveys are able to capture experience of violence suffered by adult population of both sexes; however, due to the complexity of collecting information on experiences of violence, it is likely that not all experiences of violence are duly covered by these surveys, which aim to cover several types of crime experience. Other dedicated surveys on violence usually focus on selected population groups (typically women, children or the elderly) or specific contexts (domestic violence, schools, prisons, etc.), but they are not able to portray levels and trends of violence in the entire population.</p>\n<p>Victimization surveys (as dedicated surveys or as modules of household surveys) are usually restricted to the general population living in households above a certain age (typically 15 or 18 years of age and older), while sometimes an upper age limit is also applied (typically 65, 70 or 75 years of age).</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated as the number of survey respondents who have been victim of (a) physical, (b) psychological, and(c) sexual violence in the previous 12 months, divided by the total number of survey respondents.</p>\n<p>Three separate indicators should be computed, one for each type of violence.</p>\n<p>The indicators refer to the individual (&#x201C;direct&#x201D;) experience of the respondent, who should be randomly selected among eligible household members. Experiences of violence by other members of the household should not be included in the computation.</p>\n<p>The internationally standardized and tested <a href=\"https://www.unodc.org/documents/data-and-analysis/sdgs/SDG16_Survey_Initiative_-_Questionnaire.pdf\">SDG 16 Survey questionnaire</a> and the accompanying <a href=\"https://www.unodc.org/documents/data-and-analysis/sdgs/SDG16_Survey_Initiative_-_Implementation_Manual.pdf\">Implementation Manual</a>, which can be used by countries for collecting data SDG indicator 16.1.3, provide a core set of questions about specific behaviours that allow for the measurement of the prevalence of physical, sexual and psychological violence in the population. The Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI) also offers a standardised methodology to measure violence. </p>\n<p>While the precise formulation and wording of the pertinent survey questions may need national customization, a core set of behaviours have been identified as indicative of physical, psychological and sexual violence exercised towards a person.</p>\n<p>Questions on physical, psychological and sexual violence are to be measured separately. Both numerator and denominator are measured through sample surveys of the general population.</p>\n<p>The computation of this indicator requires the inclusion of a short module<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup> in a representative population survey, which elicits whether the respondent has, in the past 12 months, personally experienced any of the following acts or behaviours indicative of physical, psychological and sexual violence (see Table 1): </p>\n<p><strong>Table 1: Types of acts or behaviours indicative of physical, psychological and sexual violence.</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Physical violence<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup></strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.</p>\n      </td>\n      <td>\n        <p>THREATEN TO HURT PHYSICALLY WITH A WEAPON (stick, knife, firearm, etc.)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>B.</p>\n      </td>\n      <td>\n        <p>THREATEN TO HURT PHYSICALLY WITHOUT A WEAPON, but in a really frightening way</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>C.</p>\n      </td>\n      <td>\n        <p>PUSH, SHOVE or SHAKE</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>D.</p>\n      </td>\n      <td>\n        <p>SLAP or PUNCH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>E.</p>\n      </td>\n      <td>\n        <p>THROW A HARD OBJECT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>F.</p>\n      </td>\n      <td>\n        <p>GRAB, PULL HAIR or DRAG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>G.</p>\n      </td>\n      <td>\n        <p>BEAT WITH FIST OR A HARD OBJECT, OR KICK</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>H.</p>\n      </td>\n      <td>\n        <p>BURN</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>I.</p>\n      </td>\n      <td>\n        <p>Try to SUFFOCATE or STRANGLE</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>J.</p>\n      </td>\n      <td>\n        <p>CUT OR STAB</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>K.</p>\n      </td>\n      <td>\n        <p>SHOOT at</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>L.</p>\n      </td>\n      <td>\n        <p>BEAT HEAD AGAINST SOMETHING</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>X. </p>\n      </td>\n      <td>\n        <p>SOMETHING ELSE TO PHYSICALLY HURT, NOT COUNTING A SEXUAL ATTACK</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Psychological violence<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>A.</strong></p>\n      </td>\n      <td>\n        <p>HURT, THREATEN TO HURT, OR THREATEN TO TAKE AWAY <u>CHILDREN</u></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>B.</strong></p>\n      </td>\n      <td>\n        <p>LIMIT CHOICES ABOUT FAMILY PLANNING, for example, by forbidding use of contraception or misleading about own use of contraception</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>C.</strong></p>\n      </td>\n      <td>\n        <p>EXPECT TO BE ASKED PERMISSION TO SEE A DOCTOR</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>D.</strong></p>\n      </td>\n      <td>\n        <p>TRY TO PREVENT TALKING TO OTHER MEN/WOMEN out of jealousy, OR INSIST ON KNOWING WHEREABOUTS at all times</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>E.</strong></p>\n      </td>\n      <td>\n        <p>CONTROL WHAT CLOTHES ALLOWED TO WEAR AND TELL HOW TO DRESS</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>F.</strong></p>\n      </td>\n      <td>\n        <p>SCARE OR INTIMIDATE ON PURPOSE, for example, by yelling and smashing things, using threatening expressions/words. </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>G.</strong></p>\n      </td>\n      <td>\n        <p>DAMAGE OR DESTROY POSSESSIONS OR PROPERTY, including pets, to scare or hurt</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>H.</strong></p>\n      </td>\n      <td>\n        <p>HARM, OR THREATEN TO HARM, SOMEONE CLOSE (apart from the cases already discussed)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>I.</strong></p>\n      </td>\n      <td>\n        <p>RESTRICT FREEDOM OF MOVEMENT, for example, by locking up or taking away I.D. or passport</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>J.</strong></p>\n      </td>\n      <td>\n        <p>Try to LIMIT CONTACT WITH FAMILY OR FRIENDS or restrict use of social media sites such as Facebook, Instagram or Twitter</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Sexual violence</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>A.</strong></p>\n      </td>\n      <td>\n        <p>FORCED SEXUAL INTERCOURSE by threatening, holding down or hurting in some way. Sexual intercourse means vaginal or anal penetration, including with objects, or oral sex.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>B.</strong></p>\n      </td>\n      <td>\n        <p>ATTEMPT to FORCE SEXUAL INTERCOURSE by threatening, holding down or hurting in some way, but intercourse DOES NOT OCCUR.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>C.</strong></p>\n      </td>\n      <td>\n        <p>FORCED SEXUAL INTERCOURSE when UNABLE TO REFUSE owing to the influence of alcohol or drugs</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>D.</strong></p>\n      </td>\n      <td>\n        <p>FORCED or attempted to FORCE or THREATEN or BLACKMAIL TO HAVE SEXUAL INTERCOURSE WITH SOMEONE, including forced to have sex in exchange for money, goods or favours.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>E.</strong></p>\n      </td>\n      <td>\n        <p>Unwanted sexual intercourse BECAUSE AFRAID OF WHAT MIGHT HAPPEN IF REFUSED</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>F.</strong></p>\n      </td>\n      <td>\n        <p>STRIP, TOUCH INTIMATE PARTS &#x2013; GENITALS OR BREASTS &#x2013;OR KISSED when not wanted.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>G.</strong></p>\n      </td>\n      <td>\n        <p>Do something or forced to do something else of sexual nature that is perceived as DEGRADING OR HUMILIATING.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>H.</strong></p>\n      </td>\n      <td>\n        <p>THREATEN WITH VIOLENT SEXUAL ACTS, SUCH AS RAPE (OR FORCED PREGNANCY) in a really frightening way </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Based on the responses about experiences of different types of violent acts or behaviours listed in Table 1, the following indicators can be computed:</p>\n<p><strong>Indicator 16.1.3a: </strong>Proportion of population subjected to physical violence in the previous 12 months.</p>\n<p>This indicator in computed by taking the number of respondents who experienced at least one form of physical violence in the past 12 months and dividing by the total number of respondents. The result needs to be multiplied by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>16</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mo>.</mo>\n    <mn>3</mn>\n    <mi>a</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>h</mi>\n        <mi>y</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n    <mi>X</mi>\n    <mn>100</mn>\n  </math> </p>\n<p><strong>Indicator 16.1.3b: </strong>Proportion of population subjected to psychological violence in the previous 12 months.</p>\n<p>This indicator in computed by taking the number of respondents who experienced at least one form of psychological violence in the past 12 months and dividing by the total number of respondents. The result needs to be multiplied by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>16</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mo>.</mo>\n    <mn>3</mn>\n    <mi>b</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>s</mi>\n        <mi>y</mi>\n        <mi>c</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>o</mi>\n        <mi>g</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n    <mi>X</mi>\n    <mn>100</mn>\n  </math> </p>\n<p><strong>Indicator 16.1.3c: </strong>Proportion of population subjected to sexual violence in the previous 12 months.</p>\n<p>This indicator in computed by taking the number of respondents who experienced at least one form of sexual violence in the past 12 months and dividing by the total number of respondents. The result needs to be multiplied by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>16</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mo>.</mo>\n    <mn>3</mn>\n    <mi>c</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n    <mi>X</mi>\n    <mn>100</mn>\n  </math> </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> See SDG 16 Survey Questionnaire (available in English, Arabic, Spanish, French, and Chinese): https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> In cases where survey data on physical violence are not available, survey data on robbery can be used as a suitable proxy measure. For suitable survey questions to measure experiences of robbery, please refer to Items C2.5a/b in the LACSI Initiative Core Questionnaire, available at: <a href=\"https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/\">https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/</a> <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> Please note that the provided list of acts indicative of psychological violence is not exhaustive. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>The data for the indicator are collected through household surveys conducted by National Statistics Offices (NSOs) or other institutions following tight survey protocols and complying with the metadata. Data producers are encouraged to strictly follow the data quality practices, protocols and frameworks in relation of data quality. In addition to the data, countries are requested to report on the metadata which serves as one additional layer of validation and verification of the data. For survey-based indicators, metadata are assessed in relation to the representativeness and coverage of the survey as well as alignment of question wording and answer options with international standards.</p>\n<p>Before publication by custodian agencies, a standardised &#x201C;pre-publication process&#x201D; is implemented, where national stakeholders can verify and review the data before publication. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values are left blank.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Global estimates are currently not produced.</p>", "DOC_METHOD__GLOBAL"=>"<p>In 2022, the United Nations Office on Drugs and Crime (UNODC) together with the United Nations Development Program (UNDP) and the Office of the United Nations High Commissioner on Human Rights (OHCHR) published the SDG 16 Survey Questionnaire and Implementation Manual, which contain internationally standardised survey question wording (in the five official UN languages) as well as implementation guidance related to this indicator. The questionnaire and manual are available at:</p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire</a></p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual</a> </p>\n<p>In 2013, the UNODC through its UNODC-INEGI Center of Excellence in Statistical Information on Government, Crime, Victimization and Justice (CoE) in Mexico, created the Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI), a regionally standardized methodology to measure comprehensively victimization, the perception of safety and the performance of authorities in a comparable manner in line with United Nations international standards. LACSI is led by UNODC, and it is supported by the Inter-American Development Bank (IDB), the United Nations Development Programme (UNDP) and the Organization of American States (OAS). The Initiative&apos;s Working Group (composed by 14 countries of the LAC region) meets periodically to review and update the main methodological tool. The meeting minutes, conceptual framework and methodological tools are available at: <a href=\"https://www.cdeunodc.inegi.org.mx/index.php/lacsi-initiative/\">https://www.cdeunodc.inegi.org.mx/index.php/lacsi-initiative/</a></p>\n<p>https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/</p>\n<p>In 2010, the United Nations Office on Drugs and Crime (UNODC) and the United Nations Economic Commission for Europe (UNODC-UNECE) published a Manual on Victimization Surveys that provides technical guidance on the implementation of such surveys, on the basis of good practices developed at the country-level. The UNODC-UNECE Manual on Victimization Surveys (2010) is available at: <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html\">https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) has a statistical section with dedicated staff to support the data collection through technical assistance, collating and verifying the received data and continuously improve data collection mechanisms including guidelines.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>It is recommended that National Statistics Offices (NSOs) serve as the main contact for compiling and assuring the quality of the necessary data to report on SDG 16.1.3, in close coordination with other relevant bodies in the country. Automated and substantive validation procedures are in place when data are processed by custodian agencies to assess their consistency and compliance with standards.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See section 4.d. Validation.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>While several countries, especially in the Americas under the LACSI methodology, have implemented national victimization surveys<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup>, at the global level, there continues to be limited availability of survey-based data for measuring physical, psychological and sexual violence prevalence.</p>\n<p>For this reason, UNODC partnered with UNDP and OHCHR to develop the internationally standardized and tested SDG 16 Survey questionnaire and the accompanying Implementation Manual, which countries can use for collecting data on 11 survey-based indicators under Goal 16 as well as two survey-based indicators under Goal 11.</p>\n<p>Another important regional standard is the Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI), which countries can use to measure 4 survey-based indicators under Goal 16 including indicator 16.1.3, as well as the survey-based indicator in Goal 11. LACSI goes beyond measuring SDG 2030 survey-based indicators and promotes the measurement of a wide range of dimensions to be measured in terms of safety and victimization that can be of use for policy makers and countries to better understand crime .<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup></p>\n<p><strong>Time series:</strong></p>\n<p>The indicator has recently been included into the annual United Nations Crime Trends Survey (UN-CTS, the regular data collection used by UNODC to collect data from UN Member States. It is expected that countries will gradually report on this indicator as the methodological guidance is disseminated and relevant items are included in national surveys.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Type of crime</p>\n<p>Sex and age</p>\n<p>Income level</p>\n<p>Educational attainment</p>\n<p>Victim-perpetrator relationship (current or former intimate partner, other family member, work colleague, school peer, other).</p>\n<p>When the proposed module on physical, psychological and sexual violence is part of a larger population survey, relevant disaggregations (e.g., sex, age, etc.) may not need to be included in the module since they are typically part of large socio-economic surveys. In contrast, disaggregations by type of crime and victim-perpetrator relationship need to be included in the question module itself.<sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> To learn more about which countries have implemented national or subnational stand-alone crime victimization surveys, visit the UNODC-INEGI Center of Excellence Atlas on Victimization Surveys: https://www.cdeunodc.inegi.org.mx/index.php/atlas-on-cvs/ <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> Technical assistance for the implementation of LACSI methodology in the Latin America and the Caribbean region is provided by the UNODC-INEGI Center of Excellence in Statistical Information on Government, Crime, Victimization and Justice (CoE). For more information, visit: <a href=\"https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.cdeunodc.inegi.org.mx%2Findex.php%2Fen%2F&amp;data=05%7C01%7Cmaurice.dunaiski%40un.org%7Ceb3498bb23c84e42293b08db2659f3f7%7C0f9e35db544f4f60bdcc5ea416e6dc70%7C0%7C0%7C638145940021789602%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&amp;sdata=d0le6ksISowhh196Ld7SvLdJHgp9pf%2FpH1LrEMLG4dI%3D&amp;reserved=0\" target=\"_blank\">https://www.cdeunodc.inegi.org.mx/index.php/en/</a> <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> The Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI) also recommends measuring the condition of both the victim and the perpetrator of being under the influence of alcohol or other drugs. <a href=\"#footnote-ref-10\">&#x2191;</a></p></div></div>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Data for this indicator are based on a set of standardised survey questions. If data from more than one survey are available for the same country, discrepancies may be due to different wording of the questions, different structure of the questionnaire, different survey methods and operations, different sample design and sample size. As a rule, data from national surveys complying with recommended standards are used, when available.</p>", "OTHER_DOC__GLOBAL"=>"<p>UNODC-UNDP-OHCHR. 2022. SDG 16 Survey Questionnaire and Implementation Manual. Available at:</p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire</a></p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual</a> </p>\n<p>UNODC. 2013. Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI). Available at: </p>\n<p><a href=\"https://www.cdeunodc.inegi.org.mx/index.php/lacsi-initiative/\">https://www.cdeunodc.inegi.org.mx/index.php/lacsi-initiative/</a></p>\n<p><a href=\"https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/\">https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/</a></p>\n<p>UNODC-UNECE, <em>Manual on Victimization Surveys (2010)</em>. Available at: <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html\">https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html</a></p>\n<p>EU Fundamental Rights Agency, <em>Violence against women: an EU-wide survey. Main results report (2014)</em>. Available at: <a href=\"https://fra.europa.eu/en/publication/2014/violence-against-women-eu-wide-survey-main-results-report\"><u>https://fra.europa.eu/en/publication/2014/violence-against-women-eu-wide-survey-main-results-report</u></a></p>\n<p>EU Fundamental Rights Agency,<em> What do fundamental rights mean for people in the EU? - Fundamental Rights Survey (2020)</em>. Available at: <a href=\"https://fra.europa.eu/en/publication/2020/fundamental-rights-survey-trust\">https://fra.europa.eu/en/publication/2020/fundamental-rights-survey-trust</a> </p>\n<p><em>Eurostat, Methodological manual for the EU survey on gender-based violence against women and other forms of inter-personal violence (EU-GBV), 2021 edition. </em>Available at: <a href=\"https://ec.europa.eu/eurostat/documents/3859598/13484289/KS-GQ-21-009-EN-N.pdf/1478786c-5fb3-fe31-d759-7bbe0e9066ad?t=1633004533458\">https://ec.europa.eu/eurostat/documents/3859598/13484289/KS-GQ-21-009-EN-N.pdf/1478786c-5fb3-fe31-d759-7bbe0e9066ad?t=1633004533458</a> </p>", "indicator_sort_order"=>"16-01-03", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.1.4", "slug"=>"16-1-4", "name"=>"Proporción de la población que se siente segura al caminar sola en su zona de residencia después de que oscurece", "url"=>"/site/es/16-1-4/", "sort"=>"160104", "goal_number"=>"16", "target_number"=>"16.1", "global"=>{"name"=>"Proporción de la población que se siente segura al caminar sola en su zona de residencia después de que oscurece"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población que se siente segura al caminar sola en su zona de residencia después de que oscurece", "graph_titles"=>[], "graph_type"=>"bar", "indicator_name"=>"Proporción de la población que se siente segura al caminar sola en su zona de residencia después de que oscurece", "indicator_number"=>"16.1.4", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Irregular / Aperiódica", "url"=>"https://www.eustat.eus/estadisticas/tema_632/opt_1/ti_encuesta-de-bienestar-personal/temas.html", "url_text"=>"Encuesta de bienestar personal", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de la población que se siente segura al caminar sola en su zona de residencia después de que oscurece", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.1- Reducir significativamente todas las formas de violencia y las correspondientes tasas de mortalidad en todo el mundo", "definicion"=>"Proporción de personas de 16 y más años que se sienten seguras (muy o bastante seguras) al caminar solas de noche en la zona en la que vive", "formula"=>"\n$$PPSSC_{16+}^{t} = \\frac{PSSC_{16+}^{t}}{P_{16+}^{t}} \\cdot 100$$\n\ndonde:\n\n$PSSC_{16+}^{t} =$ población de 16 y más años que se siente segura (muy o bastante segura) al caminar sola de noche en la zona en la que vive en el año $t$ \n\n$P^{t} =$ población de 16 y más años en el año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La percepción de seguridad se considera un indicador subjetivo de bienestar. \nAfecta la forma en que los seres humanos interactúan con su entorno, su salud y, \nen consecuencia, su calidad de vida. \n\nEl indicador 16.1.4 se basa en el concepto de “miedo al delito”, que se ha obtenido en \ndocenas de encuestas de victimización por delitos, y la formulación estándar utilizada \naquí ha demostrado ser aplicable en diferentes contextos culturales.\n\nEs importante señalar que el miedo al delito es un fenómeno que está separado de la \nprevalencia del delito y que el miedo al delito puede incluso ser en gran medida \nindependiente de la experiencia real. La percepción del delito y el miedo resultante \nal mismo están influenciados por varios factores, como la conciencia del delito, \nel discurso público, el discurso de los medios de comunicación y las circunstancias \npersonales. \n\nSin embargo, el miedo al delito es un indicador importante en sí mismo, ya que \nlos altos niveles de miedo pueden influir negativamente en el bienestar y conducir a \nuna reducción de los contactos con el público, una menor confianza y compromiso en \nla comunidad y, por lo tanto, representar un obstáculo para el desarrollo. El miedo \nal delito también difiere entre los grupos demográficos y este indicador ayuda a \nidentificar a los grupos vulnerables.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-01-04.pdf\">Metadatos 16-1-4.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Proporción de la población que se siente segura al caminar sola en su zona de residencia después de que oscurece", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.1- Reducir significativamente todas las formas de violencia y las correspondientes tasas de mortalidad en todo el mundo", "definicion"=>"Proportion of people aged 16 and over who feel safe (very or fairly safe) walking alone at night in the area they live in", "formula"=>"\n$$PPSSC_{16+}^{t} = \\frac{PSSC_{16+}^{t}}{P_{16+}^{t}} \\cdot 100$$\n\nwhere:\n\n$PSSC_{16+}^{t} =$ people aged 16 and over who feel safe (very or fairly safe) walking alone at night in the area they live in, in year $t$ \n\n$P^{t} =$ people aged 16 and over in year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Perception of safety is considered a subjective wellbeing indicator. It affects the way in which human \nbeings interact with their surroundings, their health, and consequently, their quality of life. \n\nIndicator 16.1.4 taps into the concept of ‘fear of crime’, which has been elicited in dozens of crime victimization \nsurveys, and the standard formulation used here has been shown to be applicable in different cultural \ncontexts. \n\nIt is important to note that fear of crime is a phenomenon that is separate from the prevalence \nof crime and that fear of crime may be even largely independent from actual experience. The perception \nof crime and the resulting fear of it is influenced by several factors, such as the awareness of crime, the \npublic discussion, the media discourse, and personal circumstances. \n\nNevertheless, fear of crime is an important indicator in itself as high levels of fear can negatively influence \nwell-being and lead to reduced contacts with the public, reduced trust and engagement in the community, and thus \nrepresent an obstacle to development. Fear of crime also differs across demographic groups and this indicator helps to \nidentify vulnerable groups. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-01-04.pdf\">Metadata 16-1-4.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de la población que se siente segura al caminar sola en su zona de residencia después de que oscurece", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.1- Reducir significativamente todas las formas de violencia y las correspondientes tasas de mortalidad en todo el mundo", "definicion"=>"Proporción de personas de 16 y más años que se sienten seguras (muy o bastante seguras) al caminar solas de noche en la zona en la que vive", "formula"=>"\n$$PPSSC_{16+}^{t} = \\frac{PSSC_{16+}^{t}}{P_{16+}^{t}} \\cdot 100$$\n\nnon:\n\n$PSSC_{16+}^{t} =$ bizi den inguruan gauez bakarrik ibiltzean seguru (oso edo nahiko seguru) sentitzen den 16 urteko eta gehiagoko biztanleria $t$ urtean \n\n$P^{t} =$ 16 urte eta gehiagoko biztanleria $t$ urtean \n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La percepción de seguridad se considera un indicador subjetivo de bienestar. \nAfecta la forma en que los seres humanos interactúan con su entorno, su salud y, \nen consecuencia, su calidad de vida. \n\nEl indicador 16.1.4 se basa en el concepto de “miedo al delito”, que se ha obtenido en \ndocenas de encuestas de victimización por delitos, y la formulación estándar utilizada \naquí ha demostrado ser aplicable en diferentes contextos culturales.\n\nEs importante señalar que el miedo al delito es un fenómeno que está separado de la \nprevalencia del delito y que el miedo al delito puede incluso ser en gran medida \nindependiente de la experiencia real. La percepción del delito y el miedo resultante \nal mismo están influenciados por varios factores, como la conciencia del delito, \nel discurso público, el discurso de los medios de comunicación y las circunstancias \npersonales. \n\nSin embargo, el miedo al delito es un indicador importante en sí mismo, ya que \nlos altos niveles de miedo pueden influir negativamente en el bienestar y conducir a \nuna reducción de los contactos con el público, una menor confianza y compromiso en \nla comunidad y, por lo tanto, representar un obstáculo para el desarrollo. El miedo \nal delito también difiere entre los grupos demográficos y este indicador ayuda a \nidentificar a los grupos vulnerables.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-01-04.pdf\">Metadatuak 16-1-4.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.1: Significantly reduce all forms of violence and related death rates everywhere</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.1.4: Proportion of population that feel safe walking alone around the area they live after dark</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_SNS_WALN_DRK - Proportion of population that feel safe walking alone around the area they live after dark [16.1.4]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>16.1.1: Number of victims of intentional homicide per 100,000 population, by sex and age</p>\n<p>16.1.2: Conflict-related deaths per 100,000 population, by sex, age and cause</p>\n<p>16.1.3: Proportion of population subjected to (a) physical violence, (b) psychological violence and (c) sexual violence in the previous 12 months</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator refers to the proportion of the adult population who feel safe walking alone in their neighbourhood after dark.</p>\n<p><strong>Concepts:</strong></p>\n<p>&#x201C;Neighbourhood&#x201D; &#x2013; the indicator aims to capture fear of crime in the context of people&#x2019;s everyday lives. It does so by limiting the area in question to the &#x201C;neighbourhood&#x201D; or &#x201C;area they live in&#x201D;. Various other formulations of local neighbourhood may be appropriate depending on cultural, physical and language context. Providing a universally applicable definition of neighborhood is challenging, as one&#x2019;s neighbourhood is a subjective concept that will mean different things to different people.<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> </sup></p>\n<p>&#x201C;After dark&#x201D;- the indicator should specifically capture respondent&#x2019;s feelings and perceptions when walking alone after dark. The specific reference to darkness is important because according to research,<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> darkness is one of the factors individuals perceive as important when assessing whether a situation is dangerous. While the specific reference to &#x201C;after dark&#x201D; is the preferrable wording and widely used in crime victimisation surveys,<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup> a suitable alternative wording is &#x201C;at night&#x201D;.<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup> Specifying an exact time of the day (e.g. 6pm), however, is not advisable as darkness (not time of day per se) is the factor that affects individuals perception of safety, and cross-national as well as seasonal variation in the onset of darkness makes it problematic to establish a universally suitable threshold to define nighttime.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Ferraro, K. F., &amp; LaGrange, R. L.. 1987. The measurement of fear of crime. Sociological Inquiry, 57(1), 70&#x2013;101. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> See e.g. Warr, Mark. 1990. &quot;Dangerous Situations: Social Context and Fear of Victimization&quot;. Social Forces. 68 (3): 891-907. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> UNODC-UNECE (2010) Manual on Victimization Surveys, p. 57; <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> Roberts B. (2014) Fear of Walking Alone at Night. In: Michalos A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. <a href=\"https://doi.org/10.1007/978-94-007-0753-5_1023\">https://doi.org/10.1007/978-94-007-0753-5_1023</a> <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The indicator is based on a single survey question (&#x2018;How safe do you feel walking alone in your area/neighbourhood after dark?&#x2019;) to be included in a general population survey. The question can be an add-on survey module to be incorporated into other ongoing general population surveys (such as surveys on corruption, governance, quality of life, public attitudes or surveys on other topics) or be part of dedicated surveys on crime victimisation.</p>\n<p>Data should be collected as part of a nationally representative probability sample of adult population (this typically refers to the population aged 18 years and above) residing in the country, irrespective of legal residence status. The sampling frame and sample design should ensure that results can be disaggregated at sub-national level. It is recommended that the sample size is sufficiently large to allow for disaggregation by age, gender, ethnicity, and other relevant covariates.</p>\n<p>The survey documentation should provide the specific wording used to collect data on perceptions of safety, enable the identification of possible discrepancies from standard definitions (e.g. no reference to &#x201C;after dark&#x201D; or &#x201C;neighbourhood&#x201D;), and allow an assessment of the overall data quality (e.g. sample size, target population, agency responsible for the data collection, etc.).</p>", "COLL_METHOD__GLOBAL"=>"<p>At the international level, data are routinely collected by UNODC through the annual UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) data collection. As requested by the UN Commission on Crime Prevention and Criminal Justice, over 140 Member States have appointed a UN-CTS national focal point that submits UN-CTS data to UNODC. In most cases these focal points are national institutions responsible for data production in the area of crime and criminal justice (National Statistical Offices, Ministry of Interior, Ministry of Justice, etc.). For countries that have not appointed a focal point, the request for data is sent to permanent missions in Vienna. When a country does not report to UNODC, other official sources such as authoritative websites, publications, or other forms of communication are used. Once consolidated, data are shared with countries to check their accuracy and validity.</p>\n<p>The UN-CTS provides specific definitions of data to be collected. It also collects a set of metadata to identify possible discrepancies from standard definitions and to assess overall data quality (e.g. sample size, target population, agency responsible for the data collection, etc.).</p>", "FREQ_COLL__GLOBAL"=>"<p>Countries are encouraged to conduct surveys on crime victimisation in regular intervals, but at least every four years to reflect progress between each of the quadrennial reviews of Goal 16 at the High-Level Political Forum (HLPF).</p>\n<p>UNODC collects data on this indicator according to the following schedule:</p>\n<p>III-IV quarter year n</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data on relevant SDG indicators are collected, compiled and sent back to countries for data review annually. Data are then reported to UN Statistics Division (UNSD) through the regular reporting channels annually.</p>\n<p>II quarter year n+1 (data for year n-1). For instance, data for the year 2022 are collected in III-IV quarter 2023 and released in II quarter 2024.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are collected through official nationally representative surveys. In most cases, such surveys are conducted by National Statistical Offices (NSOs). In some cases, other national institutions or other entities may conduct such surveys according to the same methodological standards.</p>\n<p>Data are sent to UNODC by Member States, usually through national UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) Focal Points, which in most cases are national institutions responsible for data production in the area of crime and criminal justice (National Statistical Offices, Ministry of Interior, Ministry of Justice, etc.).</p>", "COMPILING_ORG__GLOBAL"=>"<p><strong>Name:</strong></p>\n<p>United Nations Office on Drugs and Crime (UNODC)</p>\n<p><strong>Description:</strong></p>\n<p>At international level, data are routinely collected and disseminated by the United Nations Office on Drugs and Crime (UNODC) through the annual UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) data collection. UNODC partners with regional organizations in the collection and dissemination of data, respectively with Eurostat in Europe and with the Organisation of American States in the Americas. </p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) &#x2013; as custodian of the UN standards and norms in crime prevention and criminal justice, UNODC assists Member States in reforming their criminal justice systems in order to be effective, fair and humane for the entire population. UNODC develops technical tools to assist Member States in implementing the UN standards and norms and supports Member States through the provision of technical assistance in crime prevention and criminal justice reform. It does so through several Global programmes and through the UNODC field office network.</p>\n<p>UNODC is responsible for carrying out the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS), which was introduced through the General Assembly Resolution <a href=\"https://undocs.org/en/A/RES/3021(XXVII)\">A/RES/3021(XXVII)</a> in 1972. The Economic and Social Council, in its resolution 1984/48 of 25 May 1984, further requested that the Secretary-General maintain and develop the United Nations crime-related database by continuing to conduct surveys of crime trends and the operations of criminal justice systems. </p>", "RATIONALE__GLOBAL"=>"<p>Perception of safety is considered a subjective wellbeing indicator. It affects the way in which human beings interact with their surroundings, their health, and consequently, their quality of life. Indicator 16.1.4 taps into the concept of &#x2018;fear of crime&#x2019;, which has been elicited in dozens of crime victimization surveys, and the standard formulation used here has been shown to be applicable in different cultural contexts.<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup> It is important to note that fear of crime is a phenomenon that is separate from the prevalence of crime and that fear of crime may be even largely independent from actual experience. The perception of crime and the resulting fear of it is influenced by several factors, such as the awareness of crime, the public discussion, the media discourse, and personal circumstances. Nevertheless, fear of crime is an important indicator in itself as high levels of fear can negatively influence well-being and lead to reduced contacts with the public, reduced trust and engagement in the community, and thus represent an obstacle to development. Fear of crime also differs across demographic groups and this indicator helps to identify vulnerable groups.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> UNODC-UNECE (2010) Manual on Victimization Surveys, p. 56. <a href=\"#footnote-ref-6\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>Victimization surveys (as dedicated surveys or as modules of household surveys) are usually restricted to the general population living in households above a certain age (typically 15 or 18 years of age), while sometimes an upper age limit is also applied (typically 65, 70 or 75 years of age).</p>\n<p>There are several limitations associated with the wording of the survey question used to measure fear of crime (&#x2018;How safe do you feel walking alone in your area/neighbourhood after dark?&#x2019;). First, the question assumes that respondents do the following: (1) go out, (2) go out alone, (3) go out in their neighbourhood, and (4) go out after dark. For many respondents, the reasons for not going out alone in their neighbourhood after dark may have nothing or little to do with crime and more to do with personal and circumstantial issues, such as lack of mobility, childcare commitments, or the use of a car that allows them to travel further afield. Second, the question does not define the meaning of &#x201C;neighbourhood&#x201D;, which may mean different things to different respondents, even those living in the same street. Third, the question does not explicitly refer to &#x2018;crime&#x2019;, but rather it is implicit in the question. There may be other reasons unrelated to crime (e.g. wild animals, traffic, etc.) why respondents may not feel safe walking around their neighbourhood after dark.<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> UNODC-UNECE (2010) Manual on Victimization Surveys, p. 57. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>The question used in victimization surveys is: How safe do you feel walking alone in your area/neighbourhood after dark?<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup> Answer options are typically: (1) Very safe, (2) safe, (3) unsafe (4), very unsafe, (5) I never go out alone at night/does not apply, (99) don&#x2019;t know.<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup> The proportion of population that feel safe is calculated by summing up the number of respondents who feel &#x201C;very safe&#x201D; and &#x201C;safe&#x201D; and dividing the total by the total number of respondents, and multiplying by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mn>16</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mo>.</mo>\n    <mn>4</mn>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>e</mi>\n        <mi>e</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>a</mi>\n        <mi>f</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>a</mi>\n        <mi>f</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>k</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>g</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>f</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>d</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>k</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>i</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>i</mi>\n        <mi>g</mi>\n        <mi>h</mi>\n        <mi>b</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>o</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>v</mi>\n        <mi>e</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n    <mi>X</mi>\n    <mn>100</mn>\n  </math></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> This question is intended to capture respondents&#x2019; perception of safety when thinking about crime, although it does not explicitly mention crime or prime respondents to think about crime. Where the respondent&#x2019;s answer is &#x201C;Unsafe&#x201D; or &#x201C;Very unsafe&#x201D;, the following probing question may be asked to further understand why respondents feel unsafe: &#x201C;Why do you feel unsafe walking alone in your area/neighbourhood at night after dark?&#x201D; Possible answer options should be tailored to the specific country context and, in addition to crime-related reasons could also include options that are not crime-related. To avoid biasing respondents answers, it is recommended that answer options are not revealed to the respondent. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> It is recommended that where the respondent&#x2019;s answer is &#x201C;I never go out alone after dark&#x201D;, the following probing question is asked: &#x201C;How safe <em>would </em>you feel if you went outside after dark?&#x201D;.. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>The data for the indicator is collected through household surveys conducted by National Statistics Offices (NSOs) or other institutions following tight survey protocols and complying with the metadata. Data producers are encouraged to strictly follow the data quality practices, protocols and frameworks in relation of data quality. In addition to the data, countries are requested to report on the metadata which serves as one additional layer of validation and verification of the data. For survey-based indicators, metadata are assessed in relation to the representativeness and coverage of the survey as well as alignment of question wording and answer options with international standards. Before publication by custodian agencies, a standardised &#x201C;pre-publication process&#x201D; is implemented, where national stakeholders can verify and review the data before publication. </p>", "ADJUSTMENT__GLOBAL"=>"<p> Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>&#x2022; <strong>At country level</strong></p>\n<p>Missing values are left blank.</p>\n<p>&#x2022; <strong>At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregations refer to 3-year averages weighted by countries&#x2019; population size.</p>", "DOC_METHOD__GLOBAL"=>"<p>In 2010, the United Nations Office on Drugs and Crime (UNODC) and the United Nations Economic Commission for Europe (UNODC-UNECE) published a Manual on Victimization Surveys that provides technical guidance on the implementation of such surveys, on the basis of good practices developed at the country-level. The UNODC-UNECE Manual on Victimization Surveys (2010) is available at: <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html\">https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html</a> </p>\n<p>In 2022, the United Nations Office on Drugs and Crime (UNODC) together with the United Nations Development Program (UNDP) and the Office of the United Nations High Commissioner on Human Rights (OHCHR) published the SDG 16 Survey Questionnaire and Implementation Manual, which contain internationally standardised survey question wording (in the five official UN languages) as well as implementation guidance related to this indicator. The questionnaire and manual are available at:</p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire</a></p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) has a statistical section with dedicated staff to support the data collection through technical assistance, collating and verifying the received data and continuously improve data collection mechanisms including guidelines.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>It is recommended that National Statistics Offices (NSOs) serve as the main contact for compiling and assuring the quality of the necessary data to report on SDG 16.1.4, in close coordination with other relevant bodies in the country. Automated and substantive validation procedures are in place when data are processed by custodian agencies to assess their consistency and compliance with standards.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See section 4.d Validation </p>", "COVERAGE__GLOBAL"=>"<p>A growing number of countries are implementing surveys using similar methodologies in order to assess the population&#x2019;s perception of safety and fear of crime. However, the scale and methods of administration vary. Many of these surveys contain the question needed to compute indicator 16.1.4. (&#x2018;How safe do you feel walking alone in your area/neighbourhood after dark?&#x2019;). This suggests that data on this indicator will become more widely available over the next few years.</p>\n<p>Recommended disaggregation for this indicator:</p>\n<p>- time of day (perception of safety &#x201C;during the day&#x201D; and &#x201C;after dark&#x201D;)</p>\n<p>- age</p>\n<p>- sex</p>\n<p>- disability status</p>\n<p>- ethnicity</p>\n<p>- migration background</p>\n<p>- citizenship</p>", "COMPARABILITY__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) only compiles data from national sources, therefore no differences between nationally produced estimates and international estimates should exist. If data from more than one survey are available for the same country, discrepancies may arise due to different wording of the questions, different structure of the questionnaire, different survey methods and operations, different sample design and sample size. As a rule, data from national surveys complying with recommended international standards are used, when available.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL &amp; References:</strong></p>\n<p><a href=\"http://www.unodc.org\">www.unodc.org</a></p>\n<p><a href=\"https://dataunodc.un.org/sdgs\">https://dataunodc.un.org/sdgs</a></p>\n<p>Ferraro, K. F., &amp; LaGrange, R. L.. 1987. &#x201C;The measurement of fear of crime&#x201D;. Sociological Inquiry, 57(1), 70&#x2013;101.</p>\n<p>Roberts B. 2014. &#x201C;Fear of Walking Alone at Night&#x201D;. In: Michalos A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. <a href=\"https://doi.org/10.1007/978-94-007-0753-5_1023\">https://doi.org/10.1007/978-94-007-0753-5_1023</a> </p>\n<p>UNODC-UNECE. 2010. Manual on Victimization Surveys. Available at : <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html\">https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html</a> </p>\n<p>UNODC-UNDP-OHCHR. 2022. SDG 16 Survey Questionnaire and Implementation Manual. Available at:</p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire</a></p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual</a> </p>\n<p>Warr, Mark. 1990. &quot;Dangerous Situations: Social Context and Fear of Victimization&quot;. Social Forces. 68 (3): 891-907.</p>", "indicator_sort_order"=>"16-01-04", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.1.E1", "slug"=>"16-1-E1", "name"=>"Población que reporta ocurrencia de delitos, violencia o vandalismo en su área (Indicador UE sdg_16_20)", "url"=>"/site/es/16-1-E1/", "sort"=>"1601E1", "goal_number"=>"16", "target_number"=>"16.1", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"Población que reporta ocurrencia de delitos, violencia o vandalismo en su área (Indicador UE sdg_16_20)", "graph_titles"=>[], "graph_type"=>"bar", "indicator_available"=>"", "indicator_name"=>"Población que reporta ocurrencia de delitos, violencia o vandalismo en su área (Indicador UE sdg_16_20)", "indicator_number"=>"16.1.E1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Salud", "periodicity"=>"Quinquenal", "url"=>"https://www.euskadi.eus/encuesta-salud/inicio/", "url_text"=>"Encuesta de salud", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>false, "tags"=>"", "x_axis_label"=>"", "indicador_disponible"=>"Población que reporta ocurrencia de delitos, violencia o vandalismo en su área (Indicador UE sdg_16_20)", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.1- Reducir significativamente todas las formas de violencia y las correspondientes tasas de mortalidad en todo el mundo", "definicion"=>"Proporción de personas que declaran que viven en una zona con problemas de delincuencia o vandalismo", "formula"=>"\n$$PPDVZ^{t} = \\frac{PDVZ^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$PDVZ^{t} =$ población que declara vivir en una zona con problemas de delincuencia o vandalismo en el año $t$ \n\n$P^{t} =$ población total en el año $t$\n", "desagregacion"=>"\nTerritorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador muestra la proporción de la población que declaró que \nenfrenta problemas de delincuencia, violencia o vandalismo en su área local. \nEsto describe la situación en la que la persona encuestada siente que la delincuencia, la violencia o el \nvandalismo en la zona son un problema para el hogar, aunque esta percepción no se basa \nnecesariamente en la experiencia personal.\n\nEl 24 de julio de 2020, la Comisión Europea presentó una nueva Estrategia de la Unión de la Seguridad de la \nUE para el período 2020-2025, que sustituye a la anterior estrategia de seguridad establecida en la Agenda \nEuropea de Seguridad (2015-2020). Como componente central de la estrategia, la Comisión define un nuevo camino a \nseguir en materia de seguridad interior con acciones en áreas clave: delincuencia organizada; terrorismo y prevención \nde la radicalización violenta; resiliencia de nuestras infraestructuras críticas y espacios públicos; ciberdelincuencia, \nincluida la lucha contra el abuso sexual infantil; cooperación policial e intercambio de información; e investigación e innovación.\n\nLa seguridad es un aspecto crucial en la vida de las personas. La inseguridad de cualquier tipo es una fuente \nde miedo y preocupación, que afectan negativamente a la calidad de vida. La inseguridad física incluye \ntodos los factores externos que podrían poner en peligro la integridad física del individuo. \nLas acciones delictivas son uno de los abusos más evidentes de la inseguridad. Los análisis de la inseguridad \nfísica suelen combinar aspectos tanto subjetivos como objetivos: la percepción subjetiva de la inseguridad y la \nfalta objetiva de seguridad medida a partir de las estadísticas de delincuencia. Por tanto, este indicador \ncomplementa al indicador sobre tasas de homicidios al centrarse en la percepción de inseguridad.\n\nFuente: Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_16_20/default/table?lang=en&category=sdg.sdg_16\">Población que reporta ocurrencia de delitos, violencia o vandalismo en su área según estado de pobreza (sdg_16_20)</a> Eurostat", "comparabilidad"=>"El indicador disponible cumple con los metadatos del indicador de la UE.", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_16_20_esmsip2.htm\">Metadatos del indicador sdg_16_20</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Población que reporta ocurrencia de delitos, violencia o vandalismo en su área (Indicador UE sdg_16_20)", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.1- Reducir significativamente todas las formas de violencia y las correspondientes tasas de mortalidad en todo el mundo", "definicion"=>"Proportion of people who report living in an area with crime or vandalism problems", "formula"=>"\n$$PPDVZ^{t} = \\frac{PDVZ^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$PDVZ^{t} =$ people who report living in an area with crime or vandalism problems in year $t$ \n\n$P^{t} =$ total population in year $t$\n", "desagregacion"=>"\nProvince\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The indicator shows the share of the population who reported facing a problem of crime, violence or \nvandalism in their local area. This describes the situation where the respondent feels crime, violence \nor vandalism in the area to be a problem for the household, although this perception is not necessarily \nbased on personal experience. \n\nOn 24 July 2020, the European Commission presented a new EU Security Union Strategy for the period 2020-2025, \nwhich replaces the previous security strategy set out in the European Agenda on Security (2015-2020). As a \ncentral component of the strategy, the Commission defines a new way forward on internal security with actions \nin key areas: organized crime; terrorism and preventing violent radicalization; resilience of our critical \ninfrastructure and public spaces; cybercrime, including the fight against child sexual abuse; police cooperation \nand information sharing; and research and innovation. \n\nSafety is a crucial aspect in people’s lives. Insecurity of any kind is a source of fear and worry, which \nnegatively affect quality of life. Physical insecurity includes all the external factors that could potentially \nput the individual’s physical integrity in danger. Criminal actions are one of the most obvious abuses of insecurity. \nAnalyses of physical insecurity usually combine both subjective and objective aspects – the subjective perception \nof insecurity and the objective lack of safety as measured by crime statistics. This indicator therefore complements \nthe indicator on homicide rates by focussing on the perception of insecurity. \n\nSource: Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_16_20/default/table?lang=en&category=sdg.sdg_16\">Population reporting occurrence of crime, violence or vandalism in their area by poverty status (sdg_16_20)</a> Eurostat", "comparabilidad"=>"The available indicator complies with the EU indicator metadata.", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_16_20_esmsip2.htm\">Metadata sdg_16_20</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Población que reporta ocurrencia de delitos, violencia o vandalismo en su área (Indicador UE sdg_16_20)", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.1- Reducir significativamente todas las formas de violencia y las correspondientes tasas de mortalidad en todo el mundo", "definicion"=>"Proporción de personas que declaran que viven en una zona con problemas de delincuencia o vandalismo", "formula"=>"\n$$PPDVZ^{t} = \\frac{PDVZ^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$PDVZ^{t} =$ delinkuentzia- edo bandalismo-arazoak dituen eremu batean bizi dela adierazten duen biztanleria $t$ urtean \n\n$P^{t} =$ guztizko biztanleria $t$ urtean \n", "desagregacion"=>"\nLurralde historikoa\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador muestra la proporción de la población que declaró que \nenfrenta problemas de delincuencia, violencia o vandalismo en su área local. \nEsto describe la situación en la que la persona encuestada siente que la delincuencia, la violencia o el \nvandalismo en la zona son un problema para el hogar, aunque esta percepción no se basa \nnecesariamente en la experiencia personal.\n\nEl 24 de julio de 2020, la Comisión Europea presentó una nueva Estrategia de la Unión de la Seguridad de la \nUE para el período 2020-2025, que sustituye a la anterior estrategia de seguridad establecida en la Agenda \nEuropea de Seguridad (2015-2020). Como componente central de la estrategia, la Comisión define un nuevo camino a \nseguir en materia de seguridad interior con acciones en áreas clave: delincuencia organizada; terrorismo y prevención \nde la radicalización violenta; resiliencia de nuestras infraestructuras críticas y espacios públicos; ciberdelincuencia, \nincluida la lucha contra el abuso sexual infantil; cooperación policial e intercambio de información; e investigación e innovación.\n\nLa seguridad es un aspecto crucial en la vida de las personas. La inseguridad de cualquier tipo es una fuente \nde miedo y preocupación, que afectan negativamente a la calidad de vida. La inseguridad física incluye \ntodos los factores externos que podrían poner en peligro la integridad física del individuo. \nLas acciones delictivas son uno de los abusos más evidentes de la inseguridad. Los análisis de la inseguridad \nfísica suelen combinar aspectos tanto subjetivos como objetivos: la percepción subjetiva de la inseguridad y la \nfalta objetiva de seguridad medida a partir de las estadísticas de delincuencia. Por tanto, este indicador \ncomplementa al indicador sobre tasas de homicidios al centrarse en la percepción de inseguridad.\n\nFuente: Eurostat\n", "dato_global"=>"<a href=\"https://ec.europa.eu/eurostat/databrowser/view/sdg_16_20/default/table?lang=en&category=sdg.sdg_16\">Bere eremuan delituak, indarkeria edo bandalismoa gertatu direla jakinarazten duten biztanleak, pobrezia-egoeraren arabera (sdg_16_20)</a> Eurostat", "comparabilidad"=>"EAEn erabilgarri dagoen adierazleak EBko adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/sdg_16_20_esmsip2.htm\">Metadatuak sdg_16_20</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "indicator_sort_order"=>"16-01-E1", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.2.1", "slug"=>"16-2-1", "name"=>"Proporción de niños de entre 1 y 17 años que han sufrido algún castigo físico o agresión psicológica a manos de sus cuidadores en el último mes", "url"=>"/site/es/16-2-1/", "sort"=>"160201", "goal_number"=>"16", "target_number"=>"16.2", "global"=>{"name"=>"Proporción de niños de entre 1 y 17 años que han sufrido algún castigo físico o agresión psicológica a manos de sus cuidadores en el último mes"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de niños de entre 1 y 17 años que han sufrido algún castigo físico o agresión psicológica a manos de sus cuidadores en el último mes", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de niños de entre 1 y 17 años que han sufrido algún castigo físico o agresión psicológica a manos de sus cuidadores en el último mes", "indicator_number"=>"16.2.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Con demasiada frecuencia, los niños son criados utilizando métodos que recurren a la fuerza \nfísica o la intimidación verbal para castigar conductas no deseadas y alentar las deseadas. \n\nEl uso de la disciplina violenta con los niños representa una violación de sus derechos. La disciplina \nfísica y la agresión psicológica tienden a superponerse y con frecuencia ocurren juntas, lo que \nexacerba el daño a corto y largo plazo que infligen. Las consecuencias de la disciplina violenta \nvarían desde efectos inmediatos hasta daños a largo plazo que los niños llevan consigo hasta bien \nentrada la edad adulta. La disciplina violenta es el tipo de violencia contra los niños más extendido y socialmente aceptado.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.2.1&seriesCode=VC_VAW_PHYPYV&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=1-14\">Proporción de niños de 1 a 14 años que experimentaron castigo físico y/o agresión psicológica por parte de sus cuidadores en el último mes (% de niños de 1 a 14 años) VC_VAW_PHYPYV</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-02-01.pdf\">Metadatos 16-2-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"All too often, children are raised using methods that rely on physical force or verbal intimidation to \npunish unwanted behaviours and encourage desired ones. \n\nThe use of violent discipline with children represent a violation of their rights. Physical discipline \nand psychological aggression tend to overlap and frequently occur together, exacerbating the short- and \nlong-term harm they inflict. The consequences of violent discipline range from immediate effects to \nlong-term damage that children carry well into adulthood. Violent discipline is the most widespread, and \nsocially accepted, type of violence against children. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.2.1&seriesCode=VC_VAW_PHYPYV&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=1-14\">Proportion of children aged 1-14 years who experienced physical punishment and/or psychological aggression by caregivers in last month (% of children aged 1-14 years) VC_VAW_PHYPYV</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-02-01.pdf\">Metadata 16-2-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Con demasiada frecuencia, los niños son criados utilizando métodos que recurren a la fuerza \nfísica o la intimidación verbal para castigar conductas no deseadas y alentar las deseadas. \n\nEl uso de la disciplina violenta con los niños representa una violación de sus derechos. La disciplina \nfísica y la agresión psicológica tienden a superponerse y con frecuencia ocurren juntas, lo que \nexacerba el daño a corto y largo plazo que infligen. Las consecuencias de la disciplina violenta \nvarían desde efectos inmediatos hasta daños a largo plazo que los niños llevan consigo hasta bien \nentrada la edad adulta. La disciplina violenta es el tipo de violencia contra los niños más extendido y socialmente aceptado.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.2.1&seriesCode=VC_VAW_PHYPYV&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=1-14\">Azken hilabetean zaintzaileen zigor fisikoa eta/edo eraso psikologikoa jasan duten 1 eta 14 urte bitarteko haurren proportzioa (1 eta 14 urte bitarteko haurren %) VC_VAW_PHYPYV</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-02-01.pdf\">Metadatuak 16-2-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.2: End abuse, exploitation, trafficking and all forms of violence against and torture of children</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.2.1: Proportion of children aged 1&#x2013;17 years who experienced any physical punishment and/or psychological aggression by caregivers in the past month</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_VAW_PHYPYV - Proportion of children aged 1-14 years who experienced physical punishment and/or psychological aggression by caregivers in last month [16.2.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of children aged 1-17 years who experienced any physical punishment and/or psychological aggression by caregivers in the past month is currently being measured by the Proportion of children aged 1-14 years who experienced any physical punishment and/or psychological aggression by caregivers in the past month.</p>\n<p><strong>Concepts:</strong></p>\n<p>In Multiple Indicator Cluster Surveys (MICS), psychological aggression refers to the action of shouting, yelling or screaming at a child, as well as calling a child offensive names, such as &#x2018;dumb&#x2019; or &#x2018;lazy&#x2019;. Physical (or corporal) punishment is an action intended to cause physical pain or discomfort, but not injuries. Physical punishment is defined as shaking the child, hitting or slapping him/her on the hand/arm/leg, hitting him/her on the bottom or elsewhere on the body with a hard object, spanking or hitting him/her on the bottom with a bare hand, hitting or slapping him/her on the face, head or ears, and beating him/her over and over as hard as possible.</p>\n<p>&apos;Past month&apos; typically refers to the 30 days prior to the interview/data collection (in other words, has the child experienced violent discipline at any point in the 30 days prior to data collection). &apos;Caregiver&apos; refers to any adult household member with caregiving responsibilities for the child (not just the primary caregiver or the respondent to the questionnaire).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) of children aged 1-14 years</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><a href=\"https://data.unicef.org/resources/international-classification-of-violence-against-children/\">International Classification of Violence against Children</a></p>", "SOURCE_TYPE__GLOBAL"=>"<p>Household surveys such as UNICEF-supported Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS) that have been collecting data on this indicator in low- and middle-income countries since around 2005. In some countries, such data are also collected through other national household surveys.</p>\n<p>MICS, the source of the majority of comparable data, includes a module on disciplinary methods. The module, developed for use in MICS, is adapted from the parent-child version of the Conflict Tactics Scale (CTSPC), a standardized and validated epidemiological measurement tool that is widely accepted and has been implemented in a large number of countries, including high-income countries. The MICS module includes a standard set of questions covering non-violent forms of discipline, psychological aggression and physical means of punishing children. Data are collected for children ranging from age 1 to age 14. Some DHS have included the standard, or an adapted version of, the MICS module on child discipline.</p>", "COLL_METHOD__GLOBAL"=>"<ul>\n  <li>\n    <ol>\n      <li>United Nations Children&apos;s Fund (UNICEF) undertakes a wide consultative process of compiling and assessing data from national sources for the purposes of updating its global databases on the situation of children. Up until 2017, the mechanism UNICEF used to collaborate with national authorities on ensuring data quality and international comparability on key indicators of relevance to children was known as Country Data Reporting on the Indicators for the Goals (CRING).</li>\n      <li>As of 2018, UNICEF launched a new country consultation process with national authorities on selected child-related global SDG indicators for which it is custodian or co-custodian to meet emerging standards and guidelines on data flows for global reporting of SDG indicators, which place strong emphasis on technical rigour, country ownership and use of official data and statistics. The consultation process involves soliciting feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the latest available national data source and estimates for each indicator. Countries have the opportunity to submit new or updated data sources and estimates to be considered for official SDG reporting. A thorough technical assessment is conducted by UNICEF as the custodian agency, in consultation with the country, and feedback is made available to countries on whether or not specific data sources and data points are accepted for official SDG reporting, and if not, the reasons why. </li>\n    </ol>\n  </li>\n</ul>", "FREQ_COLL__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF) will undertake an annual country consultation likely between December and January every year to allow for review and processing of the feedback received in order to meet global SDG reporting deadlines.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Annually in March.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (for the most part)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "INST_MANDATE__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF) is responsible for global monitoring and reporting on the wellbeing of children. It provides technical and financial assistance to Member States to support their efforts to collect quality data on violence, including through the UNICEF-supported Multiple Indicator Cluster Surveys (MICS) household survey programme. UNICEF also compiles violence statistics with the goal of making internationally comparable datasets publicly available, and it analyses violence statistics which are included in relevant data-driven publications, including in its flagship publication, <em>The State of the World&#x2019;s Children.</em></p>", "RATIONALE__GLOBAL"=>"<p>All too often, children are raised using methods that rely on physical force or verbal intimidation to punish unwanted behaviours and encourage desired ones. The use of violent discipline with children represent a violation of their rights. Physical discipline and psychological aggression tend to overlap and frequently occur together, exacerbating the short- and long-term harm they inflict. The consequences of violent discipline range from immediate effects to long-term damage that children carry well into adulthood. Violent discipline is the most widespread, and socially accepted, type of violence against children.</p>", "REC_USE_LIM__GLOBAL"=>"<p>In the third and fourth rounds of Multiple Indicator Cluster Surveys (MICS), the standard indicator referred to the percentage of children aged 2-14 years who experienced any form of violent discipline (physical punishment and/or psychological aggression) within the past month. Beginning with the fifth round of MICS (MICS5), the age group covered was expanded to capture children&#x2019;s experiences with disciplinary practices between the ages of 1 and 14 years. Therefore, current data availability does not capture the full age range specified in the SDG indicator since data are not collected for adolescents aged 15-17 years and further methodological work is needed to identify additional items on disciplinary practices relevant for older adolescents.</p>", "DATA_COMP__GLOBAL"=>"<p>Number of children aged 1-17 years who are reported to have experienced any physical punishment and/or psychological aggression by caregivers in the past month divided by the total number of children aged 1-17 in the population multiplied by 100</p>\n<p>Proxy indicator:</p>\n<p>Number of children aged 1-14 years who are reported to have experienced any physical punishment and/or psychological aggression by caregivers in the past month divided by the total number of children aged 1-14 in the population multiplied by 100</p>", "DATA_VALIDATION__GLOBAL"=>"<p>A wide consultative process is undertaken to compile, assess and validate data from national sources. </p>\n<p>The consultation process involves soliciting feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the latest available national data source and estimates for each indicator. Countries have the opportunity to submit new or updated data sources and estimates to be considered for official SDG reporting. A thorough technical assessment is conducted by UNICEF as the custodian agency, in consultation with the country, and feedback is made available to countries on whether or not specific data sources and data points are accepted for official SDG reporting, and if not, the reasons why. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>When data for a country are entirely missing, United Nations Children&apos;s Fund (UNICEF) does not produce modelled estimates and therefore no country-level estimates are published.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>When internationally comparable country-level data are available for at least 50 per cent of the regional poulation for the relevant age group, the regional average is applied to countries with missing values for the purposes of calculating regional aggregates, but imputed country-level values are not published. When internationally comparable country-level data are not available for at least 50 per cent of the regional population for the relevant age group, other national surveys and studies are used to inform regional aggregates, but such country-level values are not published. </p>", "REG_AGG__GLOBAL"=>"<p>The global aggregate is a weighted average of all countries with available data. Global aggregates are published regardless of population coverage, but the number of countries and the proportion of the relevant population group represented by the available data are clearly indicated.</p>\n<p>Regional aggregates are weighted averages of all the countries within the region.</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries gather data on child discipline through household surveys such as UNICEF-supported Multiple Indicator Cluster Surveys (MICS) or Demographic and Health Surveys. In some countries, such data are also collected through other national household surveys.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The process behind the production of reliable statistics on violence is well established within UNICEF. The quality and process leading to the production of the SDG indicator 16.2.1 is ensured by working closely with the statistical offices and other relevant stakeholders through a consultative process.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF) maintains the global database on child discipline that is used for SDG and other official reporting. Before the inclusion of any data point in the database, it is reviewed by technical focal points at UNICEF headquarters to check for consistency and overall data quality. This review is based on a set of objective criteria to ensure that only the most recent and reliable information are included in the databases. These criteria include the following: data sources must include proper documentation; data values must be representative at the national population level; data are collected using an appropriate methodology (e.g., sampling); data values are based on a sufficiently large sample; data conform to the standard indicator definition including age group and concepts, to the extent possible; data are plausible based on trends and consistency with previously published/reported estimates for the indicator. </p>\n<p>As of 2018, UNICEF undertakes an annual consultation with government authorities on 10 of the child-related SDG indicators in its role of sole or joint custodian, and in line with its global monitoring mandate and normative commitments to advancing the 2030 Agenda for children. This includes indicator 16.2.1. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Nationally representative and comparable prevalence data are currently available for a sub-sample of children aged 1-14 years for nearly 100 mostly low- and middle-income countries</p>\n<p><strong>Time series:</strong></p>\n<p>Not available</p>\n<p><strong>Disaggregation:</strong></p>\n<p>None</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The estimates compiled and presented at global level come directly from nationally produced data and are not adjusted or recalculated.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://data.unicef.org/\">https://data.unicef.org/</a> </p>\n<p><strong>References:</strong></p>\n<p><a href=\"http://data.unicef.org/child-protection/violent-discipline.html\">http://data.unicef.org/child-protection/violent-discipline.html</a></p>\n<p><a href=\"https://data.unicef.org/resources/a-generation-to-protect/\">https://data.unicef.org/resources/a-generation-to-protect/</a> </p>", "indicator_sort_order"=>"16-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.2.2", "slug"=>"16-2-2", "name"=>"Número de víctimas de la trata de personas por cada 100.000 habitantes, desglosado por sexo, edad y tipo de explotación", "url"=>"/site/es/16-2-2/", "sort"=>"160202", "goal_number"=>"16", "target_number"=>"16.2", "global"=>{"name"=>"Número de víctimas de la trata de personas por cada 100.000 habitantes, desglosado por sexo, edad y tipo de explotación"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de víctimas detectadas de tráfico de personas por cada 100.000 habitantes, desglosado por tipo de explotación", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de víctimas de la trata de personas por cada 100.000 habitantes, desglosado por sexo, edad y tipo de explotación", "indicator_number"=>"16.2.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio del Interior", "periodicity"=>"Anual", "url"=>"https://www.interior.gob.es/opencms/es/servicios-al-ciudadano/trata/situacion-en-espana/", "url_text"=>"Trata y explotación de seres humanos en España. Balance estadístico", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Número de víctimas detectadas de tráfico de personas por cada 100.000 habitantes, desglosado por tipo de explotación", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.2- Poner fin al maltrato, la explotación, la trata y todas las formas de violencia y tortura contra los niños", "definicion"=>"Número de víctimas detectadas de trata de seres humanos con fines de explotación sexual, laboral, obtención de órganos, matrimonios  forzados, criminalidad forzada y/o mendicidad por cada 100.000 habitantes", "formula"=>"\n$$TVTP^{t} = \\frac{VTP_{exp\\, sexual}^{t}+VTP_{exp\\, laboral}^{t}+VTP_{obtención\\, órganos}^{t}+VTP_{otros\\, fines}^{t}}{P^{t}} \\cdot 100.000$$ \n\ndonde:\n\n$VTP_{exp\\, sexual}^{t} =$ número de víctimas detectadas de trata de seres humanos con fines de explotación sexual en el año $t$ \n\n$VTP_{exp\\, laboral}^{t} =$ número de víctimas detectadas de trata de seres humanos con fines de explotación laboral en el año $t$ \n\n$VTP_{obtención\\, órganos}^{t} =$ número de víctimas detectadas de trata de seres humanos para la obtención de órganos en el año $t$ \n\n$VTP_{otros\\, fines}^{t} =$ número de víctimas detectadas de trata de seres humanos con fines de matrimonios forzados, criminalidad forzada y/o mendicidad en el año $t$ \n\n$P^{t} =$ población a 1 de julio del año $t$ \n", "desagregacion"=>"Tipo de explotación: explotación sexual; explotación laboral; obtención de órganos; otros fines (matrimonios forzados, criminalidad forzada y/o mendicidad) \n", "observaciones"=>"\nSe puede producir cierta sobrestimación del indicador, ya que una misma persona ha \npodido ser víctima detectada de tráfico de personas en dos o más tipos de explotación \ny no es posible su identificación por motivos de confidencialidad. Así mismo, \nse puede producir cierta sobrestimación del indicador a nivel estatal, dado que una \nmisma persona ha podido ser víctima detectada de tráfico de personas en dos o más \ncomunidades autónomas diferentes y no es posible su identificación por motivos \nde confidencialidad.\n\nTrata de seres humanos: captación, transporte, traslado, acogida o recepción de \npersonas, recurriendo a la amenaza o al uso de la fuerza u otras formas de coacción, \nal rapto, al fraude, al engaño, al abuso de poder o de una situación de vulnerabilidad \no a la concesión o recepción de pagos o beneficios para obtener el consentimiento \nde una persona que tenga autoridad sobre otra, con fines de explotación. Esa \nexplotación incluirá, como mínimo, la explotación de la prostitución ajena u otras \nformas de explotación sexual, los trabajos o servicios forzados, la esclavitud o \nlas prácticas análogas a la esclavitud, la servidumbre o la extracción de órganos.\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La trata de personas es un delito grave y una grave violación de los derechos humanos. \nTodos los días, las víctimas son explotadas en restaurantes, granjas, sitios de \nconstrucción, burdeles, fábricas, mercados, minas y en los hogares de personas en todas \npartes, pero se sabe poco sobre la prevalencia y las características de este delito. \nSe necesitan mejores datos para fundamentar respuestas más eficaces y proporcionar \na los responsables de las políticas y a los profesionales la información y el \nanálisis que necesitan para afinar las medidas contra la trata y mejorar la prevención.\n\nEste indicador tiene por objeto medir la prevalencia de la trata de personas según \nel perfil de las víctimas y las formas de explotación, y hacer un seguimiento de \nlos avances mundiales, regionales y nacionales en la lucha contra este delito.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-02-02.pdf\">Metadatos 16-2-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Número de víctimas detectadas de tráfico de personas por cada 100.000 habitantes, desglosado por tipo de explotación", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.2- Poner fin al maltrato, la explotación, la trata y todas las formas de violencia y tortura contra los niños", "definicion"=>"Number of detected victims of human trafficking for sexual exploitation, labour exploitation, removal of organ, forced marriages, forced crime and/or begging per 100,000 inhabitants", "formula"=>"\n$$TVTP^{t} = \\frac{VTP_{sexual\\, exp}^{t}+VTP_{labour\\, exp}^{t}+VTP_{organ\\, removal}^{t}+VTP_{other\\, purposes}^{t}}{P^{t}} \\cdot 100.000$$ \n\nwhere:\n\n$VTP_{sexual\\, exp}^{t} =$ number of detected victims of human trafficking for sexual exploitation in year $t$ \n\n$VTP_{labour\\, exp}^{t} =$ number of detected victims of human trafficking for labour exploitation in year $t$ \n\n$VTP_{organ\\, removal}^{t} =$ number of detected victims of human trafficking for removal of organ in year $t$ \n\n$VTP_{other\\, purposes}^{t} =$ number of detected victims of human trafficking for forced marriages, forced crime and/or begging in year $t$ \n\n$P^{t} =$ population as of July 1 of the year $t$ \n", "desagregacion"=>"Type of exploitation: sexual exploitation; labour exploitation; removal of organ; other purposes (forced marriages, forced crime and begging) \n", "observaciones"=>"\nThe indicator may be somewhat overestimated, as the same person may have been detected as a victim of human \ntrafficking in two or more different types of exploitation, and their identification is not possible for \nconfidentiality reasons. Likewise, the indicator may be somewhat overestimated at the state level, as the same \nperson may have been detected as a victim of human trafficking in two or more different autonomous communities, \nand their identification is not possible for confidentiality reasons. \n\nHuman trafficking: recruitment, transportation, transfer, harbouring or receipt of persons, by means of the threat \nor use of force or other forms of coercion, abduction, fraud, deception, abuse of power or a position of vulnerability, \nor the giving or receiving of payments or benefits to achieve the consent of a person having control over another, \nfor the purpose of exploitation. Such exploitation shall include, as a minimum, the exploitation of the prostitution \nof others or other forms of sexual exploitation, forced labour or services, slavery or practices similar to slavery, \nservitude or the removal of organs. \n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Trafficking in persons is a serious crime and a grave violation of human rights. Every day, victims are \nexploited in restaurants, farms, construction sites, brothels, factories, markets, mines and in people’s \nhomes everywhere, but little is known about the prevalence and characteristics of the crime. Better data \nis needed to inform more effective responses, and provide policymakers and practitioners with the \ninformation and analysis they need to sharpen anti-trafficking action and improve prevention. \n\nThis indicator aims to measure the prevalence of trafficking in persons according to the victims profile \nand the forms of exploitation, and to track global, regional, and national progress in combatting this \ncrime. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-02-02.pdf\">Metadata 16-2-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Número de víctimas detectadas de tráfico de personas por cada 100.000 habitantes, desglosado por tipo de explotación", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.2- Poner fin al maltrato, la explotación, la trata y todas las formas de violencia y tortura contra los niños", "definicion"=>"Número de víctimas detectadas de trata de seres humanos con fines de explotación sexual, laboral, obtención de órganos, matrimonios  forzados, criminalidad forzada y/o mendicidad por cada 100.000 habitantes", "formula"=>"\n$$TVTP^{t} = \\frac{VTP_{sexu\\, esplotazioa}^{t}+VTP_{lan\\, esplotazioa}^{t}+VTP_{organoak}^{t}+VTP_{bestelako\\, helburuak}^{t}}{P^{t}} \\cdot 100.000$$ \n\nnon:\n\n$VTP_{sexu\\, esplotazioa}^{t} =$ sexu-esplotaziorako gizakien salerosketan detektatutako biktima-kopurua $t$ urtean \n\n$VTP_{lan\\, esplotazioa}^{t} =$ lan-esplotaziorako gizakien salerosketan detektatutako biktima-kopurua $t$ urtean \n\n$VTP_{organoak}^{t} =$ organoak lortzeko gizakien salerosketan detektatutako biktima-kopurua $t$ urtean \n\n$VTP_{bestelako\\, helburuak}^{t} =$ gizakien salerosketan detektatutako biktima-kopurua, ezkontza behartuetarako, behartutako kriminalitaterako eta/edo eskean aritzeko $t$ urtean \n\n$P^{t} =$ biztanleria $t$ urteko uztailaren 1ean\n", "desagregacion"=>"Esplotazio mota: sexu-esplotazioa; lan-esplotazioa; organoak lortzea; bestelako helburuak (ezkontza behartuak, behartutako kriminalitatea eta/edo eskean aritzea)\n", "observaciones"=>"\nSe puede producir cierta sobrestimación del indicador, ya que una misma persona ha \npodido ser víctima detectada de tráfico de personas en dos o más tipos de explotación \ny no es posible su identificación por motivos de confidencialidad. Así mismo, \nse puede producir cierta sobrestimación del indicador a nivel estatal, dado que una \nmisma persona ha podido ser víctima detectada de tráfico de personas en dos o más \ncomunidades autónomas diferentes y no es posible su identificación por motivos \nde confidencialidad.\n\nTrata de seres humanos: captación, transporte, traslado, acogida o recepción de \npersonas, recurriendo a la amenaza o al uso de la fuerza u otras formas de coacción, \nal rapto, al fraude, al engaño, al abuso de poder o de una situación de vulnerabilidad \no a la concesión o recepción de pagos o beneficios para obtener el consentimiento \nde una persona que tenga autoridad sobre otra, con fines de explotación. Esa \nexplotación incluirá, como mínimo, la explotación de la prostitución ajena u otras \nformas de explotación sexual, los trabajos o servicios forzados, la esclavitud o \nlas prácticas análogas a la esclavitud, la servidumbre o la extracción de órganos.\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"La trata de personas es un delito grave y una grave violación de los derechos humanos. \nTodos los días, las víctimas son explotadas en restaurantes, granjas, sitios de \nconstrucción, burdeles, fábricas, mercados, minas y en los hogares de personas en todas \npartes, pero se sabe poco sobre la prevalencia y las características de este delito. \nSe necesitan mejores datos para fundamentar respuestas más eficaces y proporcionar \na los responsables de las políticas y a los profesionales la información y el \nanálisis que necesitan para afinar las medidas contra la trata y mejorar la prevención.\n\nEste indicador tiene por objeto medir la prevalencia de la trata de personas según \nel perfil de las víctimas y las formas de explotación, y hacer un seguimiento de \nlos avances mundiales, regionales y nacionales en la lucha contra este delito.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-02-02.pdf\">Metadatuak 16-2-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.2: End abuse, exploitation, trafficking and all forms of violence against and torture of children</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.2.2: Number of victims of human trafficking per 100,000 population, by sex, age and form of exploitation</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_HTF_DETV - Detected victims of human trafficking (number) [16.2.2]</p>\n<p>VC_HTF_DETVFL - Detected victims of human trafficking for forced labour, servitude and slavery (number) [16.2.2]</p>\n<p>VC_HTF_DETVFLR - Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (per 100,000 population) [16.2.2]</p>\n<p>VC_HTF_DETVOG - Detected victims of human trafficking for removal of organ (number) [16.2.2]</p>\n<p>VC_HTF_DETVOGR - Detected victims of human trafficking for removal of organ, by age and sex (per 100,000 population) [16.2.2]</p>\n<p>VC_HTF_DETVOP - Detected victims of human trafficking for other purposes (number) [16.2.2]</p>\n<p>VC_HTF_DETVOPR - Detected victims of human trafficking for other purposes, by age and sex (per 100,000 population) [16.2.2]</p>\n<p>VC_HTF_DETVR - Detected victims of human trafficking, by age and sex (per 100,000 population) [16.2.2]</p>\n<p>VC_HTF_DETVSX - Detected victims of human trafficking for sexual exploitation, by age and sex (number) [16.2.2]</p>\n<p>VC_HTF_DETVSXR - Detected victims of human trafficking for sexual exploitation, by age and sex (per 100,000 population) [16.2.2] </p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p><strong>Indicator 5.2.1:</strong> Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age </p>\n<p><strong>Indicator 5.2.2:</strong> Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator is defined as the ratio between the total number of victims of trafficking in persons in a country and the population resident in that country, expressed per 100,000 population.</p>\n<p>According to Article 3, paragraph (a) of the UN Trafficking in Persons Protocol, trafficking in persons is defined as &#x201C;the recruitment, transportation, transfer, harbouring or receipt of persons, by means of the threat or use of force or other forms of coercion, of abduction, of fraud, of deception, of the abuse of power or of a position of vulnerability or of the giving or receiving of payments or benefits to achieve the consent of a person having control over another person, for the purpose of exploitation. Exploitation shall include, at a minimum, the exploitation of the prostitution of others or other forms of sexual exploitation, forced labour or services, slavery or practices similar to slavery, servitude or the removal of organs&#x201D;.</p>\n<p>Article 3, (b) states &#x201C;the consent of a victim of trafficking in persons to the intended exploitation set forth in subparagraph (a) of this article shall be irrelevant where any of the means set forth in subparagraph (a) have been used&#x201D;;</p>\n<p>Article 3, (c) states &#x201C;the recruitment, transportation, transfer, harbouring or receipt of a child for the purpose of exploitation shall be considered trafficking in persons even if this does not involve any of the means set forth in subparagraph (a);&quot;</p>\n<p><strong>Concepts:</strong></p>\n<p>According to the definition given in the Trafficking in Persons Protocol, trafficking in persons has three constituent elements; The Act (Recruitment, transportation, transfer, harbouring or receipt of persons), the Means</p>\n<p>(Threat or use of force, coercion, abduction, fraud, deception, abuse of power or of a position of vulnerability, or giving payments or benefits to a person in control over another person) and the Purpose (at minimum exploiting the prostitution of others, sexual exploitation, forced labour, slavery or similar practices and the removal of organs).</p>\n<p>The definition implies that the exploitation does not need to be in place, as the intention by traffickers to exploit the victim is sufficient to define a trafficking offence. Furthermore, the list of exploitative forms is not limited, which means that other forms of exploitation may emerge and they could be considered to represent additional forms of trafficking offences.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of persons per 100,000 population </p>", "CLASS_SYSTEM__GLOBAL"=>"<p><a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">International Classification of Crime for Statistical Purposes</a>, ICCS 2015 </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The data are sourced from the designated authorities for the identification of victims of trafficking, including law enforcement, criminal justice system, and National Referral Mechanisms (NRMs) when available. </p>", "COLL_METHOD__GLOBAL"=>"<p>Data are collected by the United Nations Office on Drugs and Crime (UNODC) from national authorities with the annual Questionnaire for the Global Report on Trafficking in Persons (GLOTIP). National focal points working in national agencies responsible for trafficking in persons, statistics on crime and/or the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the GLOTIP questionnaire to UNODC. Following the submission, UNODC checks the data for consistency and coherence with other data sources. Member States that are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union channel their responses through Eurostat. Data submitted by Member States through other means or taken from other sources, namely official websites of national authorities or governments&#x2019; reports are added to the dataset after review by Member States.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data collection is conducted every year, starting in the second quarter. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The data are published on a biennual basis on the UNODC data portal. </p>", "DATA_SOURCE__GLOBAL"=>"<p>National focal points working in national agencies responsible for trafficking in persons, statistics on crime and/or the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the GLOTIP questionnaire to UNODC.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "INST_MANDATE__GLOBAL"=>"<p>In 2010, the General Assembly mandated UNODC to &#x201C;collect information and report biennially &#x2026;on patterns and flows of trafficking in persons at the national, regional and international levels.&#x201D; (Para 60, A/RES/64/293 &#x2013; United Nations Global Plan of Action against Trafficking in Persons). </p>", "RATIONALE__GLOBAL"=>"<p>Trafficking in persons is a serious crime and a grave violation of human rights. Every day, victims are exploited in restaurants, farms, construction sites, brothels, factories, markets, mines and in people&#x2019;s homes everywhere, but little is known about the prevalence and characteristics of the crime. Better data is needed to inform more effective responses, and provide policymakers and practitioners with the information and analysis they need to sharpen anti-trafficking action and improve prevention.</p>\n<p>This indicator aims to measure the prevalence of trafficking in persons according to the victims profile and the forms of exploitation, and to track global, regional, and national progress in combatting this crime. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The total number of victims of trafficking in persons is defined as the total number of victims officially detected by national authorities plus the number of undetected victims of trafficking. </p>\n<p>Unfortunately, as data on the number of undetected victims is available only for a very limited number of countries and is not regularly monitored, the current computation of the indicator 16.2.2. only focuses on the number of detected victims of trafficking. The count of detected victims of trafficking has the benefit of referring to victims as defined by the UN Protocol, where the act, the mean and the purpose of trafficking have been identified by the national authorities. While information on detected victims can provide valuable information to monitor sex and age profile of detected victims, as well as on forms of exploitation, and trafficking flows, the number of detected victims per se does not monitor the level of trafficking of persons. Interpretation of trends should be done with caution, as changes in detected victims of trafficking can be due to multiple factors such as changes of law enforcement practices, changes in legislation, or changes in trafficking severity or patterns. A decrease over time in a given country may not necessarily reflect a reduced incidence of the crime, but rather a change in detection patterns that may be due to a number of underlying reasons. No clear target can be defined for this indicator until data availability will allow for the inclusion of undetected victims of trafficking. </p>", "DATA_COMP__GLOBAL"=>"<p>The numerator of this indicator is composed of two parts: detected and undetected victims of trafficking in persons. The detected part of trafficking victims, as resulting from investigation and prosecution activities of criminal justice system, is counted and reported by national law enforcement authorities. Ideally, the indicator shall be calculated as the ratio between the sum of detected and undetected victims of trafficking over the total population resident in the country, multiplied by 100,000. </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>R</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>e</mi>\n    <mo>=</mo>\n    <mn>100</mn>\n    <mo>,</mo>\n    <mn>000</mn>\n    <mi>*</mi>\n    <mfrac>\n      <mrow>\n        <mi>D</mi>\n        <mi>e</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>+</mo>\n        <mi>u</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>m</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>r</mi>\n        <mi>a</mi>\n        <mi>f</mi>\n        <mi>f</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>k</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>g</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>P</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>New methodologies to estimate the number of undetected victims of trafficking in persons are currently being tested. At the moment, however, the indicator shall be interpreted as number of detected victims of trafficking in persons per 100,000 populations and calculated as follows: </p>\n<p>The numerator is composed of the number of detected victims of trafficking. </p>\n<p>The denominator is composed of the country population, and the result multiplied per 100,000. </p>\n<p>While Member States are requested to provide data on the number of detected victims, by age group, sex and form of exploitation to UNODC, the computation of the indicator is conducted by UNODC, on the basis of the data submitted by Member States and population estimates from the UN World Population Prospects. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>Following the submission of the GLOTIP questionnaire, UNODC checks for consistency and coherence with other data sources. Data on the number of detected victims of trafficking are shared with Member States for validation prior to publication. </p>", "ADJUSTMENT__GLOBAL"=>"<p>No standardised adjustments are applied to the data. </p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; </strong>Missing values on detected victims of trafficking are not imputed or estimated, neither for country level analysis nor for regional or global aggregates, when not provided by national authorities. Methods to estimate undetected victims of trafficking are currently being tested by United Nations Office on Drugs and Crime (UNODC).</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates of the number of victims of trafficking are currently not produced. As the data available would only provide an overview of the detected victims of trafficking in persons, regional and global aggregates alone would not provide an accurate overview of the phenomenon. </p>", "DOC_METHOD__GLOBAL"=>"<p>UNODC joinly with IOM has produced the International Classification Standards on Administrative Data on Trafficking in Persons (ICS-TIP). </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data quality management is ensured by UNODC. See section 4.d Validation.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UNODC is responsible for the quality assurance process. See section 4.d Validation.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>UNODC regularly perfoms data quality assessments informing the reporting on this indicator. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Currently the United Nations Office on Drugs and Crime (UNODC) has regular data collection on detected victims of trafficking in persons for about 140 countries.</p>\n<p><strong>Time series:</strong></p>\n<p>Information available since 2003 (limited to detected victims of trafficking).</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Recommended disaggregations for this indicator are:</p>\n<p>- sex of victims</p>\n<p>- age of victims</p>\n<p>- form of exploitation</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL &amp; References:</strong></p>\n<p>www.unodc.org </p>\n<p><a href=\"https://dataunodc.un.org/sdgs\">https://dataunodc.un.org/sdgs</a></p>\n<p><a href=\"http://www.unodc.org/glotip.html\">www.unodc.org/glotip.html</a> </p>\n<p><a href=\"https://dataunodc.un.org/dp-trafficking-persons\">https://dataunodc.un.org/dp-trafficking-persons</a> </p>\n<p>UNODC, Global Report on Trafficking in Persons, 2022</p>", "indicator_sort_order"=>"16-02-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.2.3", "slug"=>"16-2-3", "name"=>"Proporción de mujeres y hombres jóvenes de entre 18 y 29 años que sufrieron violencia sexual antes de cumplir los 18 años", "url"=>"/site/es/16-2-3/", "sort"=>"160203", "goal_number"=>"16", "target_number"=>"16.2", "global"=>{"name"=>"Proporción de mujeres y hombres jóvenes de entre 18 y 29 años que sufrieron violencia sexual antes de cumplir los 18 años"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de mujeres y hombres jóvenes de entre 18 y 29 años que sufrieron violencia sexual antes de cumplir los 18 años", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de mujeres y hombres jóvenes de entre 18 y 29 años que sufrieron violencia sexual antes de cumplir los 18 años", "indicator_number"=>"16.2.3", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La violencia sexual es una de las violaciones de los derechos de la infancia más \ninquietantes. Las experiencias de violencia sexual en la infancia obstaculizan todos \nlos aspectos del desarrollo: físico, psicológico/emocional y social. Además de las \nlesiones físicas que pueden derivar, los investigadores han constatado sistemáticamente \nque el abuso sexual infantil se asocia a una amplia gama de consecuencias para la salud \nmental y comportamientos adversos en la edad adulta. \n\nEl problema es de relevancia universal y el indicador capta una de las formas más \ngraves de violencia contra la infancia. El derecho de la infancia a la protección \ncontra todas las formas de violencia está consagrado en la Convención sobre \nlos Derechos del Niño (CDN) y sus Protocolos Facultativos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.2.3&seriesCode=VC_VAW_SXVLN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=18-29%20%7C%20FEMALE\">\nProporción de la población de 18 a 29 años que sufrió violencia sexual antes de los 18 años, por sexo (% de la población de 18 a 29 años) VC_VAW_SXVLN</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-02-03.pdf\">Metadatos 16-2-3.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Sexual violence is one of the most unsettling of children's rights violations. Experiences of sexual violence \nin childhood hinder all aspects of development: physical, psychological/emotional and social. Apart from \nthe physical injuries that can result, researchers have consistently found that the sexual abuse of children \nis associated with a wide array of mental health consequences and adverse behavioural outcomes in \nadulthood. \n\nThe issue is universally relevant and the indicator captures one of the gravest forms of violence against \nchildren. The right of children to protection from all forms of violence is enshrined in the Convention on \nthe Rights of the Child (CRC) and its Optional Protocols. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.2.3&seriesCode=VC_VAW_SXVLN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=18-29%20%7C%20FEMALE\">\nProportion of population aged 18-29 years who experienced sexual violence by age 18, by sex (% of population aged 18-29) VC_VAW_SXVLN</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-02-03.pdf\">Metadata 16-2-3.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La violencia sexual es una de las violaciones de los derechos de la infancia más \ninquietantes. Las experiencias de violencia sexual en la infancia obstaculizan todos \nlos aspectos del desarrollo: físico, psicológico/emocional y social. Además de las \nlesiones físicas que pueden derivar, los investigadores han constatado sistemáticamente \nque el abuso sexual infantil se asocia a una amplia gama de consecuencias para la salud \nmental y comportamientos adversos en la edad adulta. \n\nEl problema es de relevancia universal y el indicador capta una de las formas más \ngraves de violencia contra la infancia. El derecho de la infancia a la protección \ncontra todas las formas de violencia está consagrado en la Convención sobre \nlos Derechos del Niño (CDN) y sus Protocolos Facultativos.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.2.3&seriesCode=VC_VAW_SXVLN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=18-29%20%7C%20FEMALE\">\n18 urte bete aurretik sexu-indarkeria pairatu zuten 18-29 urteko biztanleen proportzioa, sexuaren arabera (18-29 urteko biztanleen %) VC_VAW_SXVLN</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-02-03.pdf\">Metadatuak 16-2-3.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.2: End abuse, exploitation, trafficking and all forms of violence against and torture of children</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.2.3: Proportion of young women and men aged 18&#x2013;29 years who experienced sexual violence by age 18</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_VAW_SXVLN - Proportion of population aged 18-29 years who experienced sexual violence by age 18 [16.2.3]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of young women and men aged 18-29 years who experienced sexual violence by age 18</p>\n<p><strong>Concepts:</strong></p>\n<p>Within the <a href=\"https://data.unicef.org/resources/international-classification-of-violence-against-children/\">International Classification of Violence against Children</a> (ICVAC), sexual violence is defined as &#x2018;Any deliberate, unwanted and non-essential act of a sexual nature, either completed or attempted, that is perpetrated against a child, including for exploitative purposes, and that results in or has a high likelihood of resulting in injury, pain or psychological suffering.&#x2019; Sexual violence against a child can take many forms including rape, sexual assault or non-contact sexual violence (see ICVAC for detailed definitions). </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) of population aged 18-29</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><a href=\"https://data.unicef.org/resources/international-classification-of-violence-against-children/\">International Classification of Violence against Children</a></p>", "SOURCE_TYPE__GLOBAL"=>"<p>Household surveys such as Demographic and Health Surveys (DHS) have been collecting data on this indicator in low- and middle-income countries since the late 1990s.</p>\n<p>The DHS includes a standard module that captures information on rape (ICVAC category 301) and sexual assault of a child (ICVAC category 302) but does not collect information about non-contact sexual violence against a child (ICVAC category 303). . In recent years, some countries conducting Multiple Indicator Cluster Surveys (MICS) have included the standard, or an adapted, version of the DHS module. Respondents are asked whether, at any time in their lives (as children or adults), anyone ever forced them &#x2013; physically or in any other way &#x2013; to have sexual intercourse or to perform any other sexual acts against their will. Those responding &#x2018;yes&#x2019; to this question are then asked how old they were the first time this happened. </p>\n<p>It is important to flag that the DHS module was not specifically designed to capture experiences of sexual violence in childhood and while it produces data that can be used to report on 16.2.3, methodological work is underway to develop standard questions specifically designed to measure sexual violence against children in all its forms (i.e., both contact and non-contact).</p>", "COLL_METHOD__GLOBAL"=>"<ul>\n  <li>\n    <ol>\n      <li>United Nations Children&apos;s Fund (UNICEF) undertakes a wide consultative process of compiling and assessing data from national sources for the purposes of updating its global databases on the situation of children. Up until 2017, the mechanism UNICEF used to collaborate with national authorities on ensuring data quality and international comparability on key indicators of relevance to children was known as Country Data Reporting on the Indicators for the Goals (CRING).</li>\n      <li>As of 2018, UNICEF launched a new country consultation process with national authorities on selected child-related global SDG indicators for which it is custodian or co-custodian to meet emerging standards and guidelines on data flows for global reporting of SDG indicators, which place strong emphasis on technical rigour, country ownership and use of official data and statistics. The consultation process involves soliciting feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the latest available national data source and estimates for each indicator. Countries have the opportunity to submit new or updated data sources and estimates to be considered for official SDG reporting. A thorough technical assessment is conducted by UNICEF as the custodian agency, in consultation with the country, and feedback is made available to countries on whether or not specific data sources and data points are accepted for official SDG reporting, and if not, the reasons why.</li>\n    </ol>\n  </li>\n</ul>\n<p> </p>", "FREQ_COLL__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF) will undertake an annual country consultation likely between December and January every year to compile the latest available national data source and estimate for the indicator. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Annually in March for updated national estimates. It is expected that global and regional estimates will be produced every four years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (for the most part) or line ministries/other government agencies that have conducted national surveys on sexual violence against women and men.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "INST_MANDATE__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF) is responsible for global monitoring and reporting on the wellbeing of children. It provides technical and financial assistance to Member States to support their efforts to collect quality data on violence against children, including through the UNICEF-supported Multiple Indicator Cluster Surveys (MICS) household survey programme. UNICEF also compiles violence statistics with the goal of making internationally comparable datasets publicly available, and it analyses violence statistics which are included in relevant data-driven publications, including in its flagship publication, <em>The State of the World&#x2019;s Children.</em></p>", "RATIONALE__GLOBAL"=>"<p>Sexual violence is one of the most unsettling of children&apos;s rights violations. Experiences of sexual violence in childhood hinder all aspects of development: physical, psychological/emotional and social. Apart from the physical injuries that can result, researchers have consistently found that the sexual abuse of children is associated with a wide array of mental health consequences and adverse behavioural outcomes in adulthood.</p>\n<p>The issue is universally relevant and the indicator captures one of the gravest forms of violence against children. The right of children to protection from all forms of violence is enshrined in the Convention on the Rights of the Child (CRC) and its Optional Protocols.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The availability of comparable data remains a serious challenge in this area as many data collection efforts have relied on different study methodologies and designs, definitions of sexual violence, samples and questions to elicit information. Data on the experiences of boys are particularly sparse. A further challenge in this field is underreporting, especially when it comes to reporting on experiences of sexual violence among boys and men.</p>", "DATA_COMP__GLOBAL"=>"<p>Number of young women and men aged 18-29 years who report having experienced any sexual violence by age 18 divided by the total number of young women and men aged 18-29 years, respectively, in the population multiplied by 100.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>A wide consultative process is undertaken to compile, assess and validate data from national sources. </p>\n<p>The consultation process involves soliciting feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the latest available national data source and estimates for each indicator. Countries have the opportunity to submit new or updated data sources and estimates to be considered for official SDG reporting. A thorough technical assessment is conducted by UNICEF as the custodian agency, in consultation with the country, and feedback is made available to countries on whether or not specific data sources and data points are accepted for official SDG reporting, and if not, the reasons why.</p>", "ADJUSTMENT__GLOBAL"=>"<p>The last two decades has witnessed a proliferation of measurement activities aimed at improving data availability and filling data gaps. That said, such efforts have remained sporadic and limited in coverage and the resulting data are often not fully comparable. In order to produce global and regional estimates, a number of statistical corrections and systematic adjustments are applied to the available national data to improve accuracy and comparability. Such corrections and adjustments are applied at the level of the available data sources and reflected in the global and regional estimates. However, national estimates are published without corrections and adjustments. For technical details on the corrections and adjustments, see: <a href=\"https://data.unicef.org/resources/when-numbers-demand-action/\">UNICEF, <em>When Numbers Demand Action</em></a>.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>When data for a country are entirely missing, United Nations Children&apos;s Fund (UNICEF) does not produce modelled estimates and therefore no country-level estimates are published.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>The regional average is applied to those countries within the region with missing values for the purposes of calculating regional aggregates only, but imputed country-level values are not published. Regional aggregates are only published when at least 50 per cent of the regional population for the relevant age group are covered by the available data.</p>", "REG_AGG__GLOBAL"=>"<p>The global aggregate is a weighted average of the aggregates for all the sub-regions that make up the world. </p>\n<p>Regional aggregates are weighted averages of all the countries with available data within the region.</p>", "DOC_METHOD__GLOBAL"=>"<p>Countries gather data on childhood experiences of sexual violence through household surveys such as the Demographic and Health Surveys. In some countries, such data are also collected through other national household surveys, including dedicated surveys on violence. This indicator captures all experiences of sexual violence that occurred during childhood (i.e. prior to the age of 18 years) regardless of the legal age of consent stipulated in relevant national legislation.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The process behind the production of reliable statistics on violence is well established within UNICEF. The quality and process leading to the production of the SDG indicator 16.2.3 is ensured by working closely with the statistical offices and other relevant stakeholders through a consultative process.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF) maintains the global database on sexual violence in childhood that is used for SDG and other official reporting. Before the inclusion of any data point in the database, it is reviewed by technical focal points at UNICEF headquarters to check for consistency and overall data quality. This review is based on a set of objective criteria to ensure that only the most recent and reliable information are included in the databases. These criteria include the following: data sources must include proper documentation; data values must be representative at the national population level; data are collected using an appropriate methodology (e.g., sampling); data values are based on a sufficiently large sample; data conform to the standard indicator definition including age group and concepts, to the extent possible; data are plausible based on trends and consistency with previously published/reported estimates for the indicator. </p>\n<p>As of 2018, UNICEF undertakes an annual consultation with government authorities on 10 of the child-related SDG indicators in its role of sole or joint custodian, and in line with its global monitoring mandate and normative commitments to advancing the 2030 Agenda for children. This includes indicator 16.2.3. More details on the process for the country consultation are outlined below. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Nationally representative and comparable data are currently available for women from around 68 low- and middle-income countries and for men from around 12 low- and middle-income countries.</p>\n<p><strong>Time series:</strong></p>\n<p>Not available</p>\n<p><strong>Disaggregation:</strong></p>\n<p>None (the indicator is already sex-specific)_</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The country estimates compiled and presented in the global SDG database have been re-analyzed by UNICEF in order to obtain estimates for the standard age group for reporting (i.e., ages 18-29 years) since data for this age group are not typically available in published survey reports. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://data.unicef.org/\">https://data.unicef.org/</a> </p>\n<p><strong>References:</strong></p>\n<p><a href=\"http://data.unicef.org/child-protection/sexual-violence.html\">http://data.unicef.org/child-protection/sexual-violence.html</a></p>\n<p>https://data.unicef.org/resources/when-numbers-demand-action/</p>\n<p><a href=\"https://data.unicef.org/resources/a-generation-to-protect/\">https://data.unicef.org/resources/a-generation-to-protect/</a> </p>", "indicator_sort_order"=>"16-02-03", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.3.1", "slug"=>"16-3-1", "name"=>"Proporción de víctimas de a) violencia física, b) violencia psicológica o c) violencia sexual en los últimos 12 meses que han notificado su victimización a las autoridades competentes u otros mecanismos de resolución de conflictos reconocidos oficialmente", "url"=>"/site/es/16-3-1/", "sort"=>"160301", "goal_number"=>"16", "target_number"=>"16.3", "global"=>{"name"=>"Proporción de víctimas de a) violencia física, b) violencia psicológica o c) violencia sexual en los últimos 12 meses que han notificado su victimización a las autoridades competentes u otros mecanismos de resolución de conflictos reconocidos oficialmente"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de víctimas de a) violencia física, b) violencia psicológica o c) violencia sexual en los últimos 12 meses que han notificado su victimización a las autoridades competentes u otros mecanismos de resolución de conflictos reconocidos oficialmente", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de víctimas de a) violencia física, b) violencia psicológica o c) violencia sexual en los últimos 12 meses que han notificado su victimización a las autoridades competentes u otros mecanismos de resolución de conflictos reconocidos oficialmente", "indicator_number"=>"16.3.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Denunciar ante las autoridades competentes es el primer paso para que las víctimas \nde delitos busquen justicia: si no se alerta a las autoridades competentes, no \npodrán realizar investigaciones adecuadas ni administrar justicia. \n\nSin embargo, la falta de confianza en la capacidad de la policía u otras autoridades \npara brindar una reparación efectiva, o las dificultades objetivas y subjetivas \npara acceder a las autoridades, pueden influir negativamente en la denuncia \nde las víctimas de delitos. Por lo tanto, las tasas de denuncia proporcionan \nuna medida directa de la confianza de las víctimas de delitos en la capacidad \nde la policía u otras autoridades para brindar asistencia y llevar a los \nperpetradores ante la justicia. \n\nLas tasas de denuncia también proporcionan una medida de la \"cifra oculta\" de la \ndelincuencia, es decir, la proporción de delitos que no se denuncian a la policía. \nLas tendencias en las tasas de denuncia de delitos violentos pueden utilizarse para \nmonitorear la confianza pública en las autoridades competentes basándose en \ncomportamientos reales y no en percepciones.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.2.3&seriesCode=VC_VAW_SXVLN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=18-29%20%7C%20FEMALE\">\nProporción de la población de 18 a 29 años que sufrió violencia sexual antes de los 18 años, por sexo (% de la población de 18 a 29 años) VC_VAW_SXVLN</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-03-01.pdf\">Metadatos 16-3-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Reporting to competent authorities is the first step for crime victims to seek justice: If competent \nauthorities are not alerted, they are not in a position to conduct proper investigations and administer \njustice. \n\n However, lack of trust and confidence in the ability of the police or other authorities to provide \neffective redress, or objective and subjective difficulties in accessing the authorities, can negatively \ninfluence the reporting behaviour of crime victims. As such, reporting rates provide a direct measure of \nthe confidence of victims of crime in the ability of the police or other authorities to provide assistance \nand bring perpetrators to justice. \n\nReporting rates also provide a measure of the “hidden figure” of crime, \nthat is, the proportion of crimes not reported to the police. Trends in reporting rates of violent crime can \nbe used to monitor public trust and confidence in competent authorities on the basis of actual \nbehaviours and not perceptions. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.2.3&seriesCode=VC_VAW_SXVLN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=18-29%20%7C%20FEMALE\">\nProportion of population aged 18-29 years who experienced sexual violence by age 18, by sex (% of population aged 18-29) VC_VAW_SXVLN</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-03-01.pdf\">Metadata 16-3-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Denunciar ante las autoridades competentes es el primer paso para que las víctimas \nde delitos busquen justicia: si no se alerta a las autoridades competentes, no \npodrán realizar investigaciones adecuadas ni administrar justicia. \n\nSin embargo, la falta de confianza en la capacidad de la policía u otras autoridades \npara brindar una reparación efectiva, o las dificultades objetivas y subjetivas \npara acceder a las autoridades, pueden influir negativamente en la denuncia \nde las víctimas de delitos. Por lo tanto, las tasas de denuncia proporcionan \nuna medida directa de la confianza de las víctimas de delitos en la capacidad \nde la policía u otras autoridades para brindar asistencia y llevar a los \nperpetradores ante la justicia. \n\nLas tasas de denuncia también proporcionan una medida de la \"cifra oculta\" de la \ndelincuencia, es decir, la proporción de delitos que no se denuncian a la policía. \nLas tendencias en las tasas de denuncia de delitos violentos pueden utilizarse para \nmonitorear la confianza pública en las autoridades competentes basándose en \ncomportamientos reales y no en percepciones.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.2.3&seriesCode=VC_VAW_SXVLN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=18-29%20%7C%20FEMALE\">\n18 urte bete aurretik sexu-indarkeria pairatu zuten 18-29 urteko biztanleen proportzioa, sexuaren arabera (18-29 urteko biztanleen %) VC_VAW_SXVLN</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-03-01.pdf\">Metadatuak 16-3-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.3: Promote the rule of law at the national and international levels and ensure equal access to justice for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.3.1: Proportion of victims of (a) physical, (b) psychological and/or (c) sexual violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_PRR_PHYV - Police reporting rate for physical assault in the previous 12 months, by sex (%) [16.3.1]</p>\n<p>VC_PRR_SEXV - Police reporting rate for sexual assault in the previous 12 months, by sex (%) [16.3.1]</p>\n<p>VC_PRR_ROBB - Police reporting rate for robbery in the previous 12 months, by sex (%) [16.3.1]</p>\n<p>VC_PRR_PSYCHV - Police reporting rate for psychological violence in the previous 12 months, by sex (%) [16.3.1]</p>\n<p>VC_PRR_PHY_VIO - Police reporting rate for physical violence in the previous 12 months, by sex (%) [16.3.1]</p>\n<p>VC_PRR_SEX_VIO - Police reporting rate for sexual violence in the previous 12 months, by sex (%) [16.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 5.2.1: Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age</p>\n<p>Indicator 5.2.2: Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence</p>\n<p>Indicator 11.7.2: Proportion of persons victim of non-sexual or sexual harassment, by sex, age, disability status and place of occurrence, in the previous 12 months</p>\n<p>Indicator 16.2.3: Proportion of young women and men aged 18&#x2013;29 years who experienced sexual violence by age 18</p>\n<p>Indicator 16.1.3: Proportion of population subjected to (a) physical violence, (b) psychological violence and/or (c) sexual violence in the previous 12 months</p>\n<p>Indicator 16.a.1: Existence of independent national human rights institutions in compliance with the Paris</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Number of victims of violent crime in the previous 12 months who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms, as a percentage of all victims of violence in the previous 12 months.</p>\n<p>Reporting rates to be computed separately for physical, sexual and psychological violence. For each of the indicators of violence (physical, psychological and sexual), countries should calculate the share of victims who reported their victimization. Those reporting rates are published separately. </p>\n<p><strong>Concepts:</strong></p>\n<p><strong>Competent authorities </strong>include police, prosecutors or other authorities with competencies to investigate relevant crimes, while &#x201C;other officially recognized conflict resolution mechanisms&#x201D; may include a variety of institutions with a role in the informal justice or dispute resolution process (i.e. tribal or religious leaders, village elders, community leaders), provided their role is officially recognized by state authorities. The operationalization of these concepts is to be provided by national implementation teams by adding appropriate response categories for the authorities and mechanisms to which victims may report the violence they have experienced. </p>\n<p><strong>Physical violence:</strong> This concept corresponds to physical assault and robbery.</p>\n<p>Assault is defined in the International Classification of Crime for Statistical Purposes (ICCS) as: the intentional or reckless application of physical force inflicted upon the body of a person. <sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> This includes the intentional or reckless application of serious physical force resulting in serious bodily injury, and the intentional or reckless application of minor physical force resulting in no injury or minor bodily injury. According to the ICCS, these are defined as:</p>\n<ul>\n  <li>Serious bodily injury, at minimum, includes gunshot or bullet wounds; knife or stab wounds; severed limbs; broken bones or teeth knocked out; internal injuries; being knocked unconscious; and other severe or critical injuries.</li>\n  <li>Serious physical force, at minimum, includes being shot; stabbed or cut; hit by an object; hit by a thrown object; poisoning and other applications of force with the potential to cause serious bodily injury.</li>\n  <li>Minor bodily injury, at minimum, includes bruises, cuts, scratches, chipped teeth, swelling, black eye and other minor injuries.</li>\n  <li>Minor physical force, at minimum, includes hitting, slapping, pushing, tripping, knocking down and other applications of force with the potential to cause minor bodily injury.</li>\n</ul>\n<p>In addition to acts of assault, acts amounting to serious physical threats are also included in the definition of physical violence. As defined in the ICCS, serious physical threats refer to threats with the intention to cause death or serious bodily injury.<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></p>\n<p>Furthermore, physical violence also covers acts of robbery, defined in the ICCS as unlawfully taking or obtaining property with the use of force or threat of force against a person with intent to permanently or temporarily withhold it from a person or organization.<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></p>\n<p>Physical violence only counts as such when it is non-consensual, for example, acts of physical violence (punching, kicking, etc.) experienced while exercising a regulated combat sport or combat training will not count towards victimization prevalence.</p>\n<p>In the absence of suitable data on physical violence, it is possible to use data on physical assault or robbery , given they are both component of physical violence. </p>\n<p><strong>Psychological violence:</strong> There is no consensus at the international level on the precise definition of psychological violence. Psychological violence may be defined as any intentional and reckless act that causes psychological distress to an individual. Psychological violence can take the form of, for example, coercion, defamation, humiliation, intimidation, credible threats of violence, excessive verbal attacks or bullying, or harassment. Often, psychological violence is a pattern of behaviours, but it may be a distinct incident as well. Psychological violence is often experienced in domestic contexts. The internationally standardized and tested SDG 16 survey questionnaire provides a methodology and a core set of questions to measure psychological violence (see Section 4.c. Method of computation). </p>\n<p><strong>Sexual violence</strong>: As defined in the International Classification of Crime for Statistical Purposes (ICCS), sexual violence includes unwanted sexual act, attempt to obtain a sexual act without valid consent or with consent as a result of intimidation, force, fraud, coercion, threat, deception, use of drugs or alcohol, or abuse of power or of a position of vulnerability. This includes rape and other forms of sexual assault, excluding non-physical sexual assault (e.g. sexual harassment). Sexual violence may be perpetrated by casual partners, by acquaintances or by strangers, but such acts also occur in established or even in formalized intimate partnerships, including in marriages. Sexual violence most often, but not exclusively, targets women. Sexual violence may also take place in same-sex contexts.</p>\n<p>More details on the set of behaviours to be used to measure physical, psychological and sexual violence are provided in Section 4.c. Method of computation.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> See ICCS 02011 Assault. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> As per ICCS, a &#x201C;threat&#x201D; refers to any type of threatening behaviour if it is believed that the threat</p><p>could be enacted. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> See ICCS 0401 Robbery. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>UNODC. 2015. <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">International Classification of Crime for Statistical Purposes</a> (ICCS)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Acts of violence are heavily underreported to the authorities, so this indicator should be derived from population surveys, not administrative data sources.</p>\n<p>Experience of violent victimization and the reporting of such an experience to competent authorities are collected through a series of questions on concrete acts of violence suffered by the respondent and whether these were reported (see Section 4.c. Method of computation)</p>\n<p>The questions can be part of an add-on module on physical, psychological and sexual violence, to be incorporated into other ongoing general population surveys (such as surveys on quality of life, public attitudes, or surveys on other topics) or be part of dedicated surveys on crime victimization.</p>\n<p>Data should be collected as part of a nationally representative sample of the adult population residing in the country, irrespective of legal residence status. The sampling frame and sample design should ensure that results can be disaggregated at sub-national level. The sample size should be sufficiently large to capture relevant events and compute needed disaggregations.</p>", "COLL_METHOD__GLOBAL"=>"<p>At the international level, data on reporting of physical, psychological and sexual violence are routinely collected by UNODC through the annual UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) data collection. As requested by the UN Commission on Crime Prevention and Criminal Justice, over 140 Member States have already appointed a UN-CTS national focal point that delivers UN-CTS data to UNODC. In most cases these focal points are national institutions responsible for data production in the area of crime and criminal justice (National Statistical Offices, Ministry of Interior, Ministry of Justice, etc.). For countries that have not appointed a focal point, the request for data is sent to permanent missions in Vienna. When a country does not report to UNODC, other official sources such as authoritative websites, publications, or other forms of communication are used. Once consolidated, data are shared with countries to check their accuracy and validity.</p>\n<p>The UN-CTS provides specific definitions of data to be collected in line with the International Classification of Crime for Statistical Purposes (ICCS). It also collects a set of metadata to identify possible discrepancies from standard definitions and to assess overall data quality (e.g. sample size, target population, agency responsible for the data collection, etc.).</p>", "FREQ_COLL__GLOBAL"=>"<p>Countries are encouraged to conduct surveys on crime victimisation in regular intervals, but at least every four years to reflect progress between each of the quadrennial reviews of Goal 16 at the High Level Political Forum (HLPF).</p>\n<p>UNODC collects data on this indicator according to the following schedule:</p>\n<p>III-IV quarter year n</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data on relevant SDG indicators are collected, compiled and sent back to countries for data review annually. Data are then reported to UN Statistics Division (UNSD) through the regular reporting channels annually.</p>\n<p>II quarter year n+1 (data for year n-1). For instance, data for the year 2023 are collected in III-IV quarter 2024 and released in II quarter 2025.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are collected through official nationally representative surveys. In most countries and most cases, such surveys are conducted by National Statistical Offices (NSOs). In some cases, other national institutions or other entities may conduct surveys on crime victimisation according to the same methodological standards.</p>\n<p>Data are sent to UNODC by Member States, usually through national UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) Focal Points, which in most cases are national institutions responsible for data production in the area of crime and criminal justice (National Statistical Offices, Ministry of Interior, Ministry of Justice, etc.).</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) &#x2013; as custodian of the UN standards and norms in crime prevention and criminal justice, UNODC assists Member States in reforming their criminal justice systems in order to be effective, fair and humane for the entire population. UNODC develops technical tools to assist Member States in implementing the UN standards and norms and supports Member States through the provision of technical assistance in crime prevention and criminal justice reform. It does so through several Global programmes and through the UNODC field office network.</p>\n<p>UNODC is responsible for carrying out the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS), which was introduced through the General Assembly Resolution <a href=\"https://undocs.org/en/A/RES/3021(XXVII)\">A/RES/3021(XXVII)</a> in 1972. The Economic and Social Council, in its resolution 1984/48 of 25 May 1984, further requested that the Secretary-General maintain and develop the United Nations crime-related database by continuing to conduct surveys of crime trends and the operations of criminal justice systems. </p>", "RATIONALE__GLOBAL"=>"<p>Reporting to competent authorities is the first step for crime victims to seek justice: If competent authorities are not alerted, they are not in a position to conduct proper investigations and administer justice. However, lack of trust and confidence in the ability of the police or other authorities to provide effective redress, or objective and subjective difficulties in accessing the authorities, can negatively influence the reporting behaviour of crime victims. As such, reporting rates provide a direct measure of the confidence of victims of crime in the ability of the police or other authorities to provide assistance and bring perpetrators to justice. Reporting rates also provide a measure of the &#x201C;hidden figure&#x201D; of crime, that is, the proportion of crimes not reported to the police. Trends in reporting rates of violent crime can be used to monitor public trust and confidence in competent authorities on the basis of actual behaviours and not perceptions.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Crime victimization surveys are able to capture experience and reporting of violence suffered by adult population of both sexes; however, due to the complexity of collecting information on experiences of violence, it is likely that not all experiences and reporting of violence are duly covered by these surveys, which aim to cover several types of crime experience. Other dedicated surveys on violence usually focus on selected population groups (typically women, children or the elderly) or specific contexts (domestic violence, schools, prisons, etc.), but they are not able to portray levels and trends of violence in the entire population.</p>\n<p>Victimization surveys (as dedicated surveys or as modules of household surveys) are usually restricted to the general population living in households above a certain age (typically 15 or 18 years of age and older), while sometimes an upper age limit is also applied (typically 65, 70 or 75 years of age).</p>\n<p>Questions on violence reporting require national adaptations of the formal authorities that in the national context are eligible and normally function as law enforcement agents (various branches of police, specialized branches of military responsible for law enforcement, or religious police) or other nationally relevant mechanisms, including informal authorities that are widely used to obtain redress for victims of violence. NSOs are advised to keep the police and medical services in first two positions and should be included by default. Among informal competent authorities, NSOs may consider mechanisms in public and private institutions for addressing the experience of violence (e.g. offices of internal affairs or internal disciplinary control) or traditional leadership structures such as tribal or religious leaders or community elders.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated as the number of survey respondents who were victims of (a) physical, (b) psychological, and(c) sexual violence in the previous 12 months and who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms, divided by the total number of survey respondents who were victims of (a) physical, (b) psychological, and(c) sexual violence in the previous 12 months (also called the &#x2018;crime reporting rate&#x2019;).</p>\n<p>Three separate indicators should be computed, one for each type of violence.</p>\n<p>The indicators refer to the individual (&#x201C;direct&#x201D;) experience and reporting of the respondent, who should be randomly selected among eligible household members. Experiences and reporting of violence by other members of the household should not to be included in the computation.</p>\n<p>The internationally standardized and tested <a href=\"https://www.unodc.org/documents/data-and-analysis/sdgs/SDG16_Survey_Initiative_-_Questionnaire.pdf\">SDG 16 Survey questionnaire</a> and the accompanying <a href=\"https://www.unodc.org/documents/data-and-analysis/sdgs/SDG16_Survey_Initiative_-_Implementation_Manual.pdf\">Implementation Manual</a>, which can be used by countries for collecting data SDG indicator 16.3.1, provide a core set of questions about specific behaviours that allow for the measurement of the reporting rate of physical, sexual and psychological violence in the population. The Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI) also offers a standardised methodology to measure violence reporting. While the precise formulation and wording of the pertinent survey questions may need national customization, a core set of behaviours have been identified as indicative of physical, psychological and sexual violence exercised towards a person.</p>\n<p>Questions on physical, psychological and sexual violence are to be measured separately. Both numerator and denominator are measured through sample surveys of the general population.</p>\n<p><strong>Table 1: Question schedule for measuring experiences and reporting of violence </strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Content of question</strong></p>\n      </td>\n      <td>\n        <p><strong>Instruction</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Experience of physical violence in the 12 months, by type of physical violence (see Table 2 for the set of acts/behaviors indicative of physical violence) </li>\n        </ol>\n      </td>\n      <td>\n        <p>If no physical violence was experienced, skip to 4, otherwise go to 2.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Most recent type of physical violence experienced</li>\n        </ol>\n      </td>\n      <td>\n        <p>Continue with 3.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Did you report this last incident to the police or to any other competent authority where you could seek assistance or justice?</li>\n        </ol>\n      </td>\n      <td>\n        <p>Go to 4.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Experience of psychological violence in the 12 months, by type of psychological violence (see Table 2 for the set of acts/behaviors indicative of psychological violence) </li>\n        </ol>\n      </td>\n      <td>\n        <p>If no psychological violence was experienced, skip to 7, otherwise go to 5.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Most recent type of psychological violence experienced</li>\n        </ol>\n      </td>\n      <td>\n        <p>Continue with 6.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Did you report this last incident to the police or to any other competent authority where you could seek assistance or justice?</li>\n        </ol>\n      </td>\n      <td>\n        <p>Go to 7.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Experience of sexual violence in the 12 months, by type of sexual violence (see Table 2 for the set of acts/behaviors indicative of sexual violence) </li>\n        </ol>\n      </td>\n      <td>\n        <p>If no sexual violence was experienced, skip to END, otherwise go to 8.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Most recent type of sexual violence experienced</li>\n        </ol>\n      </td>\n      <td>\n        <p>Continue with 9.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Did you report this last incident to the police or to any other competent authority where you could seek assistance or justice?</li>\n        </ol>\n      </td>\n      <td>\n        <p>Go to END.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Table 2: Types of acts or behaviours indicative of physical, psychological and sexual violence.</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Physical violence<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>A.</p>\n      </td>\n      <td>\n        <p>THREATEN TO HURT PHYSICALLY WITH A WEAPON (stick, knife, firearm, etc.)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>B.</p>\n      </td>\n      <td>\n        <p>THREATEN TO HURT PHYSICALLY WITHOUT A WEAPON, but in a really frightening way</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>C.</p>\n      </td>\n      <td>\n        <p>PUSH, SHOVE or SHAKE</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>D.</p>\n      </td>\n      <td>\n        <p>SLAP or PUNCH</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>E.</p>\n      </td>\n      <td>\n        <p>THROW A HARD OBJECT</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>F.</p>\n      </td>\n      <td>\n        <p>GRAB, PULL HAIR or DRAG</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>G.</p>\n      </td>\n      <td>\n        <p>BEAT WITH FIST OR A HARD OBJECT, OR KICK</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>H.</p>\n      </td>\n      <td>\n        <p>BURN</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>I.</p>\n      </td>\n      <td>\n        <p>Try to SUFFOCATE or STRANGLE</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>J.</p>\n      </td>\n      <td>\n        <p>CUT OR STAB</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>K.</p>\n      </td>\n      <td>\n        <p>SHOOT at</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>L.</p>\n      </td>\n      <td>\n        <p>BEAT HEAD AGAINST SOMETHING</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>X. </p>\n      </td>\n      <td>\n        <p>SOMETHING ELSE TO PHYSICALLY HURT, NOT COUNTING A SEXUAL ATTACK</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Psychological violence<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup></strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>A.</strong></p>\n      </td>\n      <td>\n        <p>HURT, THREATEN TO HURT, OR THREATEN TO TAKE AWAY <u>CHILDREN</u></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>B.</strong></p>\n      </td>\n      <td>\n        <p>LIMIT CHOICES ABOUT FAMILY PLANNING, for example, by forbidding use of contraception or misleading about own use of contraception</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>C.</strong></p>\n      </td>\n      <td>\n        <p>EXPECT TO BE ASKED PERMISSION TO SEE A DOCTOR</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>D.</strong></p>\n      </td>\n      <td>\n        <p>TRY TO PREVENT TALKING TO OTHER MEN/WOMEN out of jealousy, OR INSIST ON KNOWING WHEREABOUTS at all times</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>E.</strong></p>\n      </td>\n      <td>\n        <p>CONTROL WHAT CLOTHES ALLOWED TO WEAR AND TELL HOW TO DRESS</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>F.</strong></p>\n      </td>\n      <td>\n        <p>SCARE OR INTIMIDATE ON PURPOSE, for example, by yelling and smashing things, using threatening expressions/words. </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>G.</strong></p>\n      </td>\n      <td>\n        <p>DAMAGE OR DESTROY POSSESSIONS OR PROPERTY, including pets, to scare or hurt</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>H.</strong></p>\n      </td>\n      <td>\n        <p>HARM, OR THREATEN TO HARM, SOMEONE CLOSE (apart from the cases already discussed)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>I.</strong></p>\n      </td>\n      <td>\n        <p>RESTRICT FREEDOM OF MOVEMENT, for example, by locking up or taking away I.D. or passport</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>J.</strong></p>\n      </td>\n      <td>\n        <p>Try to LIMIT CONTACT WITH FAMILY OR FRIENDS or restrict use of social media sites such as Facebook, Instagram or Twitter</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Sexual violence</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>A.</strong></p>\n      </td>\n      <td>\n        <p>FORCED SEXUAL INTERCOURSE by threatening, holding down or hurting in some way. Sexual intercourse means vaginal or anal penetration, including with objects, or oral sex.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>B.</strong></p>\n      </td>\n      <td>\n        <p>ATTEMPT to FORCE SEXUAL INTERCOURSE by threatening, holding down or hurting in some way, but intercourse DOES NOT OCCUR.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>C.</strong></p>\n      </td>\n      <td>\n        <p>FORCED SEXUAL INTERCOURSE when UNABLE TO REFUSE owing to the influence of alcohol or drugs</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>D.</strong></p>\n      </td>\n      <td>\n        <p>FORCED or attempted to FORCE or THREATEN or BLACKMAIL TO HAVE SEXUAL INTERCOURSE WITH SOMEONE, inlcuding forced to have sex in exchange for money, goods or favours.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>E.</strong></p>\n      </td>\n      <td>\n        <p>Unwanted sexual intercourse BECAUSE AFRAID OF WHAT MIGHT HAPPEN IF REFUSED</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>F.</strong></p>\n      </td>\n      <td>\n        <p>STRIP, TOUCH INTIMATE PARTS &#x2013; GENITALS OR BREASTS &#x2013;OR KISSED when not wanted.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>G.</strong></p>\n      </td>\n      <td>\n        <p>Do something or forced to do something else of sexual nature that is perceived as DEGRADING OR HUMILIATING.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>H.</strong></p>\n      </td>\n      <td>\n        <p>THREATEN WITH VIOLENT SEXUAL ACTS, SUCH AS RAPE (OR FORCED PREGNANCY) in a really frightening way </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>The computation of this indicator requires the inclusion of a short question schedule (Table 1) in a representative population survey, which elicits whether the respondent has, in the past 12 months, personally experienced and reported any acts or behaviours indicative of physical, psychological and sexual violence (Table 2).<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup> </p>\n<p>Based on the responses about experiences and reporting of different types of violent acts or behaviours listed in Table 2, the following indicators can be computed:</p>\n<p><strong>Indicator 16.3.1a: </strong>Proportion of victims of physical violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms.</p>\n<p>This indicator in computed by taking the number of respondents who experienced at least one form of physical violence<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup> in the past 12 months and who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms, divided by the total number of survey respondents who were victims of at least one form of physical violence in the past 12 months. The result needs to be multiplied by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>16</mn>\n    <mo>.</mo>\n    <mn>3</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mi>a</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>h</mi>\n        <mi>y</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>h</mi>\n        <mi>y</mi>\n        <mi>s</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n    <mi>X</mi>\n    <mn>100</mn>\n  </math> </p>\n<p><strong>Indicator 16.3.1b: </strong>Proportion of victims of psychological violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms.</p>\n<p>This indicator in computed by taking the number of respondents who experienced at least one form of psychological violence in the past 12 months and who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms, divided by the total number of survey respondents who were victims of at least one form of psychological violence in the past 12 months. The result needs to be multiplied by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>16</mn>\n    <mo>.</mo>\n    <mn>3</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mi>b</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>s</mi>\n        <mi>y</mi>\n        <mi>c</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>o</mi>\n        <mi>g</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>s</mi>\n        <mi>y</mi>\n        <mi>c</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>o</mi>\n        <mi>g</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n    <mi>X</mi>\n    <mn>100</mn>\n  </math> </p>\n<p><strong>Indicator 16.3.1c: </strong>Proportion of victims of sexual violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms.</p>\n<p>This indicator in computed by taking the number of respondents who experienced at least one form of sexual violence in the past 12 months and who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms, divided by the total number of survey respondents who were victims of at least one form of sexual violence in the past 12 months. The result needs to be multiplied by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>I</mi>\n    <mi>n</mi>\n    <mi>d</mi>\n    <mi>i</mi>\n    <mi>c</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>o</mi>\n    <mi>r</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>16</mn>\n    <mo>.</mo>\n    <mn>3</mn>\n    <mo>.</mo>\n    <mn>1</mn>\n    <mi>c</mi>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n      </mrow>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>u</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>12</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>s</mi>\n      </mrow>\n    </mfrac>\n    <mi>X</mi>\n    <mn>100</mn>\n  </math> </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>X</mi>\n    <mn>100</mn>\n  </math></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> In cases where survey data on physical violence are not available, survey data on robbery can be used as a suitable proxy measure. For suitable survey questions to measure experiences of robbery, please refer to Items C2.5a/b in the LACSI Initiative Core Questionnaire, available at: <a href=\"https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/\">https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/</a> <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> Please note that the provided list of acts indicative of psychological violence is not exhaustive. <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> See SDG 16 Survey Questionnaire (available in English, Arabic, Spanish, French, and Chinese): https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> In many cases victims of violence may experience several acts of violence at the same time. When reported to the police or other competent authorities, only the most serious act(s) may be recorded depending on the laws or registration rules. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>The data for the indicator are collected through household surveys conducted by National Statistics Offices (NSOs) or other institutions following tight survey protocols and complying with the metadata. Data producers are encouraged to strictly follow the data quality practices, protocols and frameworks in relation of data quality. In addition to the data, countries are requested to report on the metadata which serves as one additional layer of validation and verification of the data. For survey-based indicators, metadata are assessed in relation to the representativeness and coverage of the survey as well as alignment of question wording and answer options with international standards.</p>\n<p>Before publication by custodian agencies, a standardised &#x201C;pre-publication process&#x201D; is implemented, where national stakeholders can verify and review the data before publication. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values are left blank.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Global estimates are currently not produced.</p>", "DOC_METHOD__GLOBAL"=>"<p>In 2022, the United Nations Office on Drugs and Crime (UNODC) together with the United Nations Development Program (UNDP) and the Office of the United Nations High Commissioner on Human Rights (OHCHR) published the SDG 16 Survey Questionnaire and Implementation Manual, which contain internationally standardised survey question wording (in the five official UN languages) as well as implementation guidance related to this indicator. The questionnaire and manual are available at:</p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire</a></p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual</a> </p>\n<p>In 2013, the UNODC through its UNODC-INEGI Center of Excellence in Statistical Information on Government, Crime, Victimization and Justice (CoE) in Mexico, created the Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI), a regionally standardized methodology to measure comprehensively victimization, the perception of safety and the performance of authorities in a comparable manner in line with United Nations international standards. LACSI is led by UNODC, and it is supported by the Inter-American Development Bank (IDB), the United Nations Development Programme (UNDP) and the Organization of American States (OAS). The Initiative&apos;s Working Group (composed by 14 countries of the LAC region) meets periodically to review and update the main methodological tool. The meeting minutes, conceptual framework and methodological tools are available at: <a href=\"https://www.cdeunodc.inegi.org.mx/index.php/lacsi-initiative/\">https://www.cdeunodc.inegi.org.mx/index.php/lacsi-initiative/</a></p>\n<p>https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/</p>\n<p>In 2010, the United Nations Office on Drugs and Crime (UNODC) and the United Nations Economic Commission for Europe (UNODC-UNECE) published a Manual on Victimization Surveys that provides technical guidance on the implementation of such surveys, on the basis of good practices developed at the country-level. The UNODC-UNECE Manual on Victimization Surveys (2010) is available at: <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html\">https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) has a statistical section with dedicated staff to support the data collection through technical assistance, collating and verifying the received data and continuously improve data collection mechanisms including guidelines.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>It is recommended that National Statistics Offices (NSOs) serve as the main contact for compiling and assuring the quality of the necessary data to report on SDG 16.3.1, in close coordination with other relevant bodies in the country. Automated and substantive validation procedures are in place when data are processed by custodian agencies to assess their consistency and compliance with standards.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See section 4.d. Validation.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>While several countries, especially in the Americas under the LACSI methodology, have implemented national victimization surveys<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup>, at the global level, there continues to be limited availability of survey-based data for measuring the reporting rate of physical, psychological and sexual violence.</p>\n<p>For this reason, UNODC partnered with UNDP and OHCHR to develop the internationally standardized and tested SDG 16 Survey questionnaire and the accompanying Implementation Manual, which countries can use for collecting data on 11 survey-based indicators under Goal 16 as well as two survey-based indicators under Goal 11.</p>\n<p>Another important regional standard is the Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI), which countries can use to measure 4 survey-based indicators under Goal 16 including indicator 16.3.1, as well as the survey-based indicator in Goal 11. LACSI goes beyond measuring SDG 2030 survey-based indicators and promotes the measurement of a wide range of dimensions to be measured in terms of safety and victimization that can be of use for policy makers and countries to better understand crime<sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup>.</p>\n<p><strong>Time series:</strong></p>\n<p>The indicator has recently been included into the annual United Nations Crime Trends Survey (UN-CTS, the regular data collection used by UNODC to collect data from UN Member States. It is expected that countries will gradually report on this indicator as the methodological guidance is disseminated and relevant items are included in national surveys.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Recommended disaggregations for this indicator are:</p>\n<p>- sex</p>\n<p>- age</p>\n<p>- type of violence (physical, psychological, sexual)</p>\n<p>- Victim-perpetrator relationship (current or former intimate partner, other family member, work colleague, school peer, other)</p>\n<p>- type of reporting authority</p>\n<p>When the proposed module on experience and reporting of physical, psychological and sexual violence is part of a larger population survey, relevant disaggregations (e.g., sex, age, etc.) may not need to be included in the module since they are typically part of large socio-economic surveys. In contrast, disaggregations by type of violence, victim-perpetrator relationship and type of reporting authority need to be included in the question module itself.</p>\n<p>To promote not only the measurement of the indicator, but to also better understand crime reporting and recording practices. The LACSI questionnaire recommends an additional follow-up question to confirm if the victim, after reporting the crime to the police/competent authority, signed a legal paper or a formal document which states that his/her report was recorded. This follow-up question is useful for comparing violence reporting data derived from the survey against police/competent authority records, and for identifying possible gaps between violence reporting and violence recording by the police/competent authority.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> To learn more about which countries have implemented national or subnational stand-alone crime victimization surveys, visit the UNODC-INEGI Center of Excellence Atlas on Victimization Surveys: https://www.cdeunodc.inegi.org.mx/index.php/atlas-on-cvs/ <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> Technical assistance for the implementation of LACSI methodology in the Latin America and the Caribbean region is provided by the UNODC-INEGI Center of Excellence in Statistical Information on Government, Crime, Victimization and Justice (CoE). For more information, visit: <a href=\"https://www.cdeunodc.inegi.org.mx/index.php/en/\" target=\"_blank\">https://www.cdeunodc.inegi.org.mx/index.php/en/</a> <a href=\"#footnote-ref-10\">&#x2191;</a></p></div></div>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Data for this indicator are based on a set of standardised survey questions. If data from more than one survey are available for the same country, discrepancies may be due to different wording of the questions, different structure of the questionnaire, different survey methods and operations, different sample design and sample size. As a rule, data from national surveys complying with recommended standards are used, when available.</p>", "OTHER_DOC__GLOBAL"=>"<p>UNODC-UNDP-OHCHR. 2022. SDG 16 Survey Questionnaire and Implementation Manual. Available at:</p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire</a></p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual</a> </p>\n<p>UNODC. 2013. Latin America and the Caribbean Crime Victimization Survey Initiative (LACSI). Available at: </p>\n<p><a href=\"https://www.cdeunodc.inegi.org.mx/index.php/lacsi-initiative/\">https://www.cdeunodc.inegi.org.mx/index.php/lacsi-initiative/</a></p>\n<p><a href=\"https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/\">https://www.cdeunodc.inegi.org.mx/index.php/questionnaire/</a></p>\n<p>UNODC-UNECE, <em>Manual on Victimization Surveys (2010)</em>. Available at: <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html\">https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html</a></p>\n<p>EU Fundamental Rights Agency, <em>Violence against women: an EU-wide survey. Main results report (2014)</em>. Available at: <a href=\"https://fra.europa.eu/en/publication/2014/violence-against-women-eu-wide-survey-main-results-report\"><u>https://fra.europa.eu/en/publication/2014/violence-against-women-eu-wide-survey-main-results-report</u></a></p>\n<p>Eurostat, <em>Methodological manual for the EU survey on gender-based violence against women and other forms of inter-personal violence (EU-GBV), 2021 edition.</em> Available at: <a href=\"https://ec.europa.eu/eurostat/documents/3859598/13484289/KS-GQ-21-009-EN-N.pdf/1478786c-5fb3-fe31-d759-7bbe0e9066ad?t=1633004533458\">https://ec.europa.eu/eurostat/documents/3859598/13484289/KS-GQ-21-009-EN-N.pdf/1478786c-5fb3-fe31-d759-7bbe0e9066ad?t=1633004533458</a> </p>", "indicator_sort_order"=>"16-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.3.2", "slug"=>"16-3-2", "name"=>"Proporción de detenidos que no han sido condenados en el conjunto de la población reclusa total", "url"=>"/site/es/16-3-2/", "sort"=>"160302", "goal_number"=>"16", "target_number"=>"16.3", "global"=>{"name"=>"Proporción de detenidos que no han sido condenados en el conjunto de la población reclusa total"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de detenidos que no han sido condenados en el conjunto de la población reclusa total", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de detenidos que no han sido condenados en el conjunto de la población reclusa total", "indicator_number"=>"16.3.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Descenso", "permalink"=>"", "precision"=>[], "progress_status"=>"retroceso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio del Interior", "periodicity"=>"Anual", "url"=>"https://www.interior.gob.es/opencms/es/archivos-y-documentacion/documentacion-y-publicaciones/anuarios-y-estadisticas/estadisticas-del-ministerio-del-interior/", "url_text"=>"Estadística General de la Población Reclusa", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de detenidos que no han sido condenados en el conjunto de la población reclusa total", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.3- Promover el estado de derecho en los planos nacional e internacional y garantizar la igualdad de acceso a la justicia para todos", "definicion"=>"Proporción de personas en prisión preventiva respecto al total de personas reclusas", "formula"=>"\n$$PPR_{preventiva}^{t} = \\frac{PR_{preventiva}^{t}}{PR^{t}} \\cdot 100$$ \n\ndonde: \n\n$PR_{preventiva}^{t} =$ población reclusa en prisión preventiva a 31 de diciembre del año $t$ \n\n$P^{t} =$ población reclusa total a 31 de diciembre del año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador refleja el respeto generalizado del principio de que las personas \nen espera de juicio no deben ser detenidas innecesariamente. Esto, a su vez, se basa \nen aspectos del derecho a la presunción de inocencia hasta que se demuestre su \nculpabilidad. \n\nDesde una perspectiva de desarrollo, el uso extensivo de la detención previa a la \nsentencia cuando no es necesaria por razones como la prevención de fugas, \nla protección de víctimas o testigos o la prevención de la comisión de nuevos delitos, \npuede desviar recursos del sistema de justicia penal y generar cargas financieras y de \ndesempleo para el acusado y su familia. Medir el grado relativo en que se utiliza la \ndetención previa a la sentencia puede proporcionar la evidencia para ayudar a los países \na reducir esas cargas y garantizar su uso proporcionado.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.3.2&seriesCode=VC_PRS_UNSNT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\"> Detenidos no sentenciados como proporción de la población carcelaria total (%) VC_PRS_UNSNT</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-03-02.pdf\">Metadatos 16-3-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Proporción de detenidos que no han sido condenados en el conjunto de la población reclusa total", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.3- Promover el estado de derecho en los planos nacional e internacional y garantizar la igualdad de acceso a la justicia para todos", "definicion"=>"Proportion of people in pretrial detention compared to the total prison population ", "formula"=>"\n$$PPR_{pretrial}^{t} = \\frac{PR_{pretrial}^{t}}{PR^{t}} \\cdot 100$$ \n\nwhere: \n\n$PR_{pretrial}^{t} =$ prison population in pretrial detention a 31 de diciembre del año $t$ \n\n$P^{t} =$ total prison population as of December 31 of the year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The indicator signifies overall respect for the principle that persons awaiting trial shall not be detained in \ncustody unnecessarily. This, in turn, is premised on aspects of the right to be presumed innocent until \nproven guilty. \n\nFrom a development perspective, extensive use of pre-sentence detention when not \nnecessary for reasons such as the prevention of absconding, the protection of victims or witnesses, or the \nprevention of the commission of further offences, can divert criminal justice system resources, and exert \nfinancial and unemployment burdens on the accused and his or her family. Measuring the relative extent \nto which pre-sentence detention is used can provide the evidence to assist countries in lowering such \nburdens and ensuring its proportionate use. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.3.2&seriesCode=VC_PRS_UNSNT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\"> Unsentenced detainees as a proportion of overall prison population (%) VC_PRS_UNSNT</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-03-02.pdf\">Metadata 16-3-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de detenidos que no han sido condenados en el conjunto de la población reclusa total", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.3- Promover el estado de derecho en los planos nacional e internacional y garantizar la igualdad de acceso a la justicia para todos", "definicion"=>"Proporción de personas en prisión preventiva respecto al total de personas reclusas", "formula"=>"\n$$PPR_{behin-behinekoa}^{t} = \\frac{PR_{behin-behinekoa}^{t}}{PR^{t}} \\cdot 100$$ \n\nnon: \n\n$PR_{behin-behinekoa}^{t} =$ behin-behineko espetxealdian dauden presoak $t$ urteko abenduaren 31n \n\n$P^{t} =$ espetxe-biztanleria osoa $t$ urteko abenduaren 31n\n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador refleja el respeto generalizado del principio de que las personas \nen espera de juicio no deben ser detenidas innecesariamente. Esto, a su vez, se basa \nen aspectos del derecho a la presunción de inocencia hasta que se demuestre su \nculpabilidad. \n\nDesde una perspectiva de desarrollo, el uso extensivo de la detención previa a la \nsentencia cuando no es necesaria por razones como la prevención de fugas, \nla protección de víctimas o testigos o la prevención de la comisión de nuevos delitos, \npuede desviar recursos del sistema de justicia penal y generar cargas financieras y de \ndesempleo para el acusado y su familia. Medir el grado relativo en que se utiliza la \ndetención previa a la sentencia puede proporcionar la evidencia para ayudar a los países \na reducir esas cargas y garantizar su uso proporcionado.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.3.2&seriesCode=VC_PRS_UNSNT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\"> Epaitu gabeko atxilotuak espetxe-biztanleria osoaren proportzio gisa (%) VC_PRS_UNSNT</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-03-02.pdf\">Metadatuak 16-3-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.3: Promote the rule of law at the national and international levels and ensure equal access to justice for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.3.2: Unsentenced detainees as a proportion of overall prison population</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_PRS_UNSNT - Unsentenced detainees as a proportion of overall prison population [16.3.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-01-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Several other SDG indicators are related to access to justice and the efficiency of the criminal justice system: Indicator 16.3.1 on reporting experiences of violence to the authorities; Indicator 16.3.3. on access to dispute resolution mechanism</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p><a href=\"https://www.unodc.org/\">United Nations Office on Drugs and Crime</a> (UNODC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The total number of persons held in detention who have not yet been sentenced, as a percentage of the total number of persons held in detention, on a specified date.</p>\n<p><strong>Concepts:</strong></p>\n<p><u>&#x2018;Persons held in detention&#x2018;</u> refers to persons held in Prisons, Penal Institutions or Correctional Institutions on a specified day and should exclude non-criminal prisoners held for administrative purposes, for example, persons held pending investigation into their immigration status or foreign citizens without a legal right to stay. Also, people under house arrest, persons under other forms of sanctions or supervision, such as electronic surveillance or community-based surveillance should be excluded from the prison population (persons held in prison).</p>\n<p><u>&#x2018;Sentenced&#x2019;</u> refers persons held in prisons, penal institutions or correctional institutions after a first instance decision or a final decision on their case has been made by a competent authority. This includes sentenced prisoners with a final decision and persons held who are awaiting the outcome of an appeal in respect of verdict or sentence or who are within the statutory limits for appealing . Persons held who have received a custodial sentence for one crime but are still under trial and unsentenced for another crime should be counted as sentenced persons held. Furthermore, for the purpose of international comparability, persons held who have been convicted of a crime (in a first instance decision) but who have not yet received a sentence should also be treated as &#x2018;sentenced&#x2019;, even if national definitions of sentenced detainees are narrower. &#x2019;.</p>\n<p><u>&#x2018;Unsentenced&#x2019;</u> refers to persons held in prisons, penal institutions or correctional institutions who are untried, pre-trial or awaiting a first instance decision on their case from a competent authority regarding their conviction or acquittal. Persons held before and during the trial should be included. Sentenced persons held awaiting the outcome of an appeal in respect of verdict or sentence or who are within the statutory limits for appealing their sentence should be excluded.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>UNODC collects data on prisons directly from national prison authorities through its annual data collection on crime and criminal justice (<a href=\"https://www.unodc.org/unodc/en/data-and-analysis/United-Nations-Surveys-on-Crime-Trends-and-the-Operations-of-Criminal-Justice-Systems.html\">UN Survey of Crime Trends and Operations of Criminal Justice Systems</a>, UN-CTS). In addition, UNODC collects data on prisons from National Statistical Offices through the annual SDG pre-publication validation exercise. Furthermore, prison data are augmented periodically by consulting national data compiled by independent research initiatives (such as the <a href=\"https://prisonstudies.org\">World Prison Brief</a>) or non-governmental sources. </p>\n<p>The population data are sourced from the <a href=\"https://population.un.org/wpp\">World Population Prospect</a>, Population Division, United Nations Department of Economic and Social Affairs.</p>", "COLL_METHOD__GLOBAL"=>"<p>There is a consolidated system of annual data collection on crime and criminal justice (UN-CTS) which represents the basis of data on unsentenced detainees. The UN-CTS data collection is largely based on the network of national Focal Points, which are institutions/officials appointed by countries and having the technical capacity and role to produce data on crime and criminal justice (around 200 appointed Focal Points from more than 140 countries/territories as of 2022). In addition, these data are supplemented for countries with missing values with official data collected by the Institute for Criminal Policy Research (<a href=\"https://www.prisonstudies.org/\">World Prison Brief</a>), which collects data directly from national prison administrations or from the websites of Ministries of Justice or other official agencies. For future SDG reporting data will be sent to countries for consultation prior to publication.</p>", "FREQ_COLL__GLOBAL"=>"<p>III-IV quarter year n</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>II quarter year n+1 (data for year n-1). For instance, data for the year 2023 are collected in III-IV quarter 2024 and released in II quarter 2025.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National prison authority, through the UNCTS Focal Points</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>\n<p>At international level, data on prisons are routinely collected and disseminated by the United Nations Office on Drugs and Crime (UNODC) through the annual UN-CTS data collection. UNODC partners with regional organizations in the collection and dissemination of homicide data, respectively with Eurostat in Europe and with the Organisation of American States in the Americas. In case of missing data, UNODC considers national data compiled by the World Prison Brief and other national sources.</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) was introduced through the General Assembly Resolution <a href=\"https://undocs.org/en/A/RES/3021(XXVII)\">A/RES/3021(XXVII)</a> in 1972. The Economic and Social Council, in its resolution 1984/48 of 25 May 1984, requested that the Secretary-General maintain and develop the United Nations crime-related database by continuing to conduct surveys of crime trends and the operations of criminal justice systems.</p>", "RATIONALE__GLOBAL"=>"<p>The indicator signifies overall respect for the principle that persons awaiting trial shall not be detained in custody unnecessarily. This, in turn, is premised on aspects of the right to be presumed innocent until proven guilty. From a development perspective, extensive use of pre-sentence detention when not necessary for reasons such as the prevention of absconding, the protection of victims or witnesses, or the prevention of the commission of further offences, can divert criminal justice system resources, and exert financial and unemployment burdens on the accused and his or her family. Measuring the relative extent to which pre-sentence detention is used can provide the evidence to assist countries in lowering such burdens and ensuring its proportionate use. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The target relates to the multidimensional concepts of rule of law and access to justice and at least two indicators are required to cover the main elements of access to justice and efficiency of the justice system. The proposed indicator 16.3.2 covers the efficiency of the justice system.</p>\n<p>Furthermore, it is not straightforward to define a concrete target for Indicator 16.3.2. This is because pre-sentence detention is a constitutive part of the criminal justice process and a very low share of of unsentenced detainees (e.g. close to zero) is not necessarily reflective of an accessible and fair criminal justice process.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated as the total number of unsentenced persons held in detention divided by the total number of persons held in detention on a specified date, multiplied by 100</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <msub>\n      <mrow>\n        <mi>P</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n      </mrow>\n      <mrow>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n    </msub>\n    <mo>=</mo>\n    <mn>100</mn>\n    <mi>*</mi>\n    <mfrac>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>P</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>s</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>s</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>h</mi>\n            <mi>e</mi>\n            <mi>l</mi>\n            <mi>d</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>u</mi>\n            <mi>n</mi>\n            <mi>s</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>c</mi>\n            <mi>e</mi>\n            <mi>d</mi>\n          </mrow>\n          <mrow>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </msub>\n      </mrow>\n      <mrow>\n        <msub>\n          <mrow>\n            <mi>P</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>s</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>s</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>h</mi>\n            <mi>e</mi>\n            <mi>l</mi>\n            <mi>d</mi>\n          </mrow>\n          <mrow>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </msub>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>For the percentage by sex, the number of persons held unsentenced of that sex should be divided by the number of persons held of the same sex.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Following the submission of the UN-CTS questionnaire, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union send their responses to the UN-CTS to Eurostat for validation. The Organization for American States also reviews the responses of its Member States. All data submitted by Member States through other means or taken from other sources are added to the dataset after review and validation by Member States.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>If values for a given period and country are missing, then the missing values are left blank. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>See section 4.g. Regional aggregations for more information.</p>", "REG_AGG__GLOBAL"=>"<p>The methods used for estimating the number of persons held, total, by sex, sentenced and unsentenced, at the global and regional level aim to make the best possible use of available data. For each regional aggregate, the number persons held should correspond to the sum of all national data of such in the region, in each year. However, for many countries, data on persons held are not available, or data are available only for some years . As a result, the sample of countries with available data is different for each year. If left unaddressed, this issue would result in inconsistencies, as regional aggregates would be drawn from a different set of countries each year.</p>\n<p>Imputations for total persons held are performed on the country-level rate of total persons held per 100,000 population. If a country has just one available data point since the year 2000, all missing values are set equal to this single available data point. This approach therefore accounts for population growth over time and does not mean that the series is constant in absolute terms.</p>\n<p>If a country has two to eight available data points, the missing values between two data points are estimated by linear interpolation, and if there are missing values that are temporally before (or after) the earliest (or latest) available data point, the values at the beginning (or end) of the series are filled with the earliest (or latest) available data point. If a country has more than eight available data points in the respective time series, the missing values between two data points are estimated by linear interpolation, and if there are missing values that are temporally before (or after) the earliest (or latest) available data point, the values at the end of the time series are imputed using an exponential smoothing approach (for more information, see <a href=\"https://afit-r.github.io/ts_exp_smoothing\">https://afit-r.github.io/ts_exp_smoothing</a>).<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> </p>\n<p>Once the series have been computed at the national level, they are aggregated at the regional level. Regional counts for persons held are calculated for each year by multiplying the average regional rate per 100,000 population with the total population of the respective region (divided by 100,000).<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> The regions are the ones from the United Nations &#x201C;<a href=\"https://unstats.un.org/unsd/methodology/m49/\">Standard Country or Area Codes for Statistical Use</a>&#x201D;. Each country or area is included in one region only. </p>\n<p>Finally, regional estimates are aggregated to compute the global number of persons held.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Imputations for disaggregated series (e.g. female persons held, or unsentenced detainees) follow the same approach is the one for total persons held, except that the imputations are done on the ratio of the disaggregation over the total number of persons held, rather than the rate per 100,000 population. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> For countries without any data points since the year 2000, this means that the regional rate is applied. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "DOC_METHOD__GLOBAL"=>"<p>The <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">International Classification of Crime for Statistical Purposes</a> (ICCS) provides a comprehensive framework for producing statistics on crime and criminal justice. Its primary unit of classification is the act or event that constitutes a criminal offence and the description of the criminal acts is based on behaviours and not on legal provisions.</p>\n<p>The ICCS is a tool to understand crime extent and drivers, but can also be used to improve quality of data on crime and criminal justice at national level and to support national efforts to monitor SDG targets in the areas of public security and safety, trafficking, corruption, and access to justice.</p>\n<p>The UN-CTS questionnaire is fully consistent with the concepts, categories, and definitions of the ICCS and responsive to data needs at national and international level, including data needed to monitor progress on several <a href=\"http://www.unodc.org/unodc/en/about-unodc/sustainable-development-goals/sdgs-index.html\">Sustainable Development Goals</a> (SDGs) in the areas of crime, violence, justice and the rule of law under UNODC mandate. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>See section 4.d. Validation</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>See section 4.d. Validation</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See section 4.d. Validation</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data on unsentenced and total detainees from the UN-CTS are available for around 110 countries/territories (2022). The country coverage can improve if other sources (research institutions and NGOs) are included (data for additional 80 countries/territories are available, bringing the total for the period 2010-2021 to more than 190 countries/territories).</p>\n<p><strong>Time series:</strong></p>\n<p>2003-to present day</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Recommended disaggregation for this indicator are:</p>\n<p>- age status (juvenile vs adult) and sex (male/female)</p>\n<p>- length of pre-trial/unsentenced detention (e.g. less than 6 months, 6-12 months, more than 1 year)</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Discrepancies might exist between country produced and internationally reported counts of sentenced and unsentenced detainees as national data might refer to national definition while data reported by UNODC aim to comply with the definition provided in the UN-CTS questionnaire.</p>\n<p>Furthermore, there might be some discrepancies between number reported for the total prison population and the different disaggregations (e.g. sex disaggregation) which are often only available for the adult prison population. Adult and juvenile detention are are often collected by separate authorities.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.unodc.org\">www.unodc.org</a> </p>\n<p><strong>References:</strong></p>\n<p>Definitions and other metadata are provided in the <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/United-Nations-Surveys-on-Crime-Trends-and-the-Operations-of-Criminal-Justice-Systems.html\">UN Survey of Crime Trends and Operationsof Criminal Justice Systems</a>(UN-CTS), <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">International Classification of Crime for Statistical purpose</a> (ICCS), Guidance on collection of information on detained persons, as well as example data collection sheets, are provided in the United Nations Manual for the Development of a System of Criminal Justice Statistics (<a href=\"https://unstats.un.org/unsd/demographic-social/Standards-and-Methods/files/Handbooks/crime/seriesf_89-E.pdf\">ST/ESA/STAT/SER.F/89</a>), as well as (for children), in the <a href=\"https://www.unodc.org/documents/nigeria/publications/Otherpublications/UNODCUNICEF_Manual_for_the_Measurement_of_juvenile_justice_indicators1.pdf\">UNODC/UNICEF Manual</a> for the Measurement of Juvenile Justice Indicators.</p>", "indicator_sort_order"=>"16-03-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.3.3", "slug"=>"16-3-3", "name"=>"Proporción de la población que se ha visto implicada en alguna controversia en los dos últimos años y ha accedido a algún mecanismo oficial u oficioso de solución de controversias, desglosada por tipo de mecanismo", "url"=>"/site/es/16-3-3/", "sort"=>"160303", "goal_number"=>"16", "target_number"=>"16.3", "global"=>{"name"=>"Proporción de la población que se ha visto implicada en alguna controversia en los dos últimos años y ha accedido a algún mecanismo oficial u oficioso de solución de controversias, desglosada por tipo de mecanismo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población que se ha visto implicada en alguna controversia en los dos últimos años y ha accedido a algún mecanismo oficial u oficioso de solución de controversias, desglosada por tipo de mecanismo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población que se ha visto implicada en alguna controversia en los dos últimos años y ha accedido a algún mecanismo oficial u oficioso de solución de controversias, desglosada por tipo de mecanismo", "indicator_number"=>"16.3.3", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.3- Promover el estado de derecho en los planos nacional e internacional y garantizar la igualdad de acceso a la justicia para todos", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Si bien no existe una definición estándar de acceso a la justicia, en términos generales se refiere \na “la capacidad de las personas para defender y hacer valer sus derechos y obtener una resolución \njusta de los problemas justiciables en cumplimiento de las normas de derechos humanos; de ser necesario, \na través de instituciones imparciales de justicia formales o informales y con el apoyo jurídico adecuado”.  \n\nEn el caso de los ciudadanos que necesitan justicia, se deben cumplir una serie de \ncondiciones para que se reconozcan sus derechos, como el acceso a información adecuada, \nel acceso a servicios de justicia y asesoramiento jurídico, y el acceso a instituciones de \njusticia que proporcionen un trato justo e imparcial. La razón de ser de este indicador es \ncentrarse en un paso del proceso y, en particular, en la accesibilidad de las instituciones y \nlos mecanismos de justicia (tanto formales como informales) por parte de quienes han experimentado un problema justiciable. \n\nEl indicador puede proporcionar información importante sobre la accesibilidad general de las \ninstituciones y los procesos de justicia civil, las barreras y las razones de exclusión de algunas personas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-03-03.pdf\">Metadatos 16-3-3.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.3- Promover el estado de derecho en los planos nacional e internacional y garantizar la igualdad de acceso a la justicia para todos", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"While there is no standard definition of access to justice, it is broadly concerned with “the ability of \npeople to defend and enforce their rights and obtain just resolution of justiciable problems in compliance \nwith human rights standards; if necessary, through impartial formal or informal institutions of \njustice and with appropriate legal support.”  \n\nFor citizens in need of justice, a number of conditions should \nbe met for their rights to be recognised, such as access to adequate information, access to justice services \nand legal advice, and access to institutions of justice that provide fair and impartial treatment. The rationale \nof this indicator is to focus on one step of the process and in particular on the accessibility of justice institutions \nand mechanisms (both formal and informal) by those who have experienced a justiciable problem. \n\nThe indicator can provide important information about the overall accessibility of civil justice institutions \nand processes, barriers, and reasons for exclusion of some people. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-03-03.pdf\">Metadata 16-3-3.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.3- Promover el estado de derecho en los planos nacional e internacional y garantizar la igualdad de acceso a la justicia para todos", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Si bien no existe una definición estándar de acceso a la justicia, en términos generales se refiere \na “la capacidad de las personas para defender y hacer valer sus derechos y obtener una resolución \njusta de los problemas justiciables en cumplimiento de las normas de derechos humanos; de ser necesario, \na través de instituciones imparciales de justicia formales o informales y con el apoyo jurídico adecuado”.  \n\nEn el caso de los ciudadanos que necesitan justicia, se deben cumplir una serie de \ncondiciones para que se reconozcan sus derechos, como el acceso a información adecuada, \nel acceso a servicios de justicia y asesoramiento jurídico, y el acceso a instituciones de \njusticia que proporcionen un trato justo e imparcial. La razón de ser de este indicador es \ncentrarse en un paso del proceso y, en particular, en la accesibilidad de las instituciones y \nlos mecanismos de justicia (tanto formales como informales) por parte de quienes han experimentado un problema justiciable. \n\nEl indicador puede proporcionar información importante sobre la accesibilidad general de las \ninstituciones y los procesos de justicia civil, las barreras y las razones de exclusión de algunas personas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-03-03.pdf\">Metadatuak 16-3-3.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.3: Promote the rule of law at the national and international levels and ensure equal access to justice for all</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.3.3: Proportion of the population who have experienced a dispute in the past two years and who accessed a formal or informal dispute resolution mechanism, by type of mechanism</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Applies to all series</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>This indicator complements the other indicators of 16.3 which focus on rates of pretrial detention and reporting of victimization and thereby provides a more holistic picture of people&#x2019;s ability to access justice mechanisms across a wide range of disputes. </p>\n<p>This indicator also relates to several other targets under SDG 16 on issues that may require access to justice. For instance, people need to access justice institutions and mechanisms when they are subject to (or a witness of) corruption (target 16.5), when they have problems in accessing government payments (such as social safety net assistance) or public services (target 16.6), when they have difficulty in obtaining legal identify, such as birth registration (target 16.9), or when they experience discrimination (target 16.B). </p>\n<p>In addition, the indicator relates to other Goals that have targets conveying aspirations for more just and fair societies. For instance, people may need to access justice institutions and mechanisms when faced with discrimination in education (target 4.5), when subject to discrimination against women and girls (target 5.1), when seeking &#x2018;equal pay for work of equal value&#x2019; (target 8.5), when wanting their labor rights to be upheld (target 8.8), or when demanding that equal opportunity laws be respected (target 10.3).</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Development Programme (UNDP), United Nations Office on Drugs and Crime (UNODC) and Organization for Economic Cooperation and Development (OECD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Development Programme (UNDP), United Nations Office on Drugs and Crime (UNODC) and Organization for Economic Cooperation and Development (OECD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Number of persons who experienced a dispute during the past two years who accessed a formal or informal dispute resolution mechanism, as a percentage of all those who experienced a dispute in the past two years, by type of mechanism. </p>\n<p><strong>Concepts:</strong></p>\n<p>A <u>dispute</u> can be understood as a <u>justiciable problem</u> between individuals or between individual(s) and an entity. Justiciable problems can be seen as the ones giving rise to legal issues, whether or not the problems are perceived as being &#x201C;legal&#x201D; by those who face them, and whether or not any legal action was taken as a result of the problem.<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup></p>\n<p>Categories of disputes can vary between countries depending on social, economic, political, legal, institutional and cultural factors. There are, however, a number of categories that have broad applicability across countries, such as problems or disputes related to:<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></p>\n<ul>\n  <li>Land or buying and selling property</li>\n  <li>Family and relationship break-ups</li>\n  <li>Injuries or illnesses caused by an intentional or unintentional act or omission of another person or entity</li>\n  <li>Occupation/employment</li>\n  <li>Commercial transactions (including defective or undelivered goods or services)</li>\n  <li>Government and public services (including abuse by public officials)</li>\n  <li>Government payments </li>\n  <li>Housing (Tenancy and landlord) </li>\n  <li>Debt, damage compensation, and other financial matters</li>\n  <li>Environmental damage (land or water pollution, waste dumping, etc.)</li>\n</ul>\n<p><u>Dispute resolution mechanisms</u> vary across countries around the world. While in many countries courts represent the main institution dealing with disputes of civil nature, the same may not be true in countries or societies where the first point of reference in such cases are informal systems, traditional or religious leaders. The formulation of the indicator, and the formulation of the questions in the survey, have to account for these differences and make sure to include all relevant institutions or mechanisms that are generally recognized and used.</p>\n<p>A list of dispute resolution mechanisms could include: </p>\n<ul>\n  <li>Lawyer or third-party mediation</li>\n  <li>Community or religious leaders or other customary law mechanisms </li>\n  <li>A court or tribunal</li>\n  <li>The police</li>\n  <li>A government office or other formal designated authority or agency</li>\n  <li>Other formal complaints or appeal procedure </li>\n</ul>\n<p>To improve the accuracy of the indicator it is important to define precisely the denominator (the population at &#x2018;risk&#x2019; of experiencing the event of interest, i.e. accessing a dispute-resolution mechanism) by identifying the &#x2018;demand&#x2019; of dispute resolution mechanisms. This demand is composed of those who use dispute resolution mechanisms (users) and those who - despite needing them - do not have &#x201C;access&#x201D; to such mechanisms for various reasons such as lack of knowledge on how to access them, lack of trust in institutions, lack of legal advice/assistance, lack of awareness about justice mechanisms, geographical distance or financial costs, to mention a few. It is important to exclude from the demand those who experience disputes and do not turn to dispute resolution mechanisms because they do not need them (voluntarily self-excluded). This refers to cases where the dispute is simple or when respondents solve the issue with the other party through direct negotiation.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Genn, G, <em>Paths to Justice: What People Do and Think About Going to Law </em>(Oxford: Hart, 1999), 12. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> See <a href=\"https://www.oecd.org/governance/legal-needs-surveys-and-access-to-justice-g2g9a36c-en.htm\"><em>Legal Needs Surveys and Access to Justice</em></a><em> </em>, OECD (2019) <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The Indicator is based on four questions to be included in a household survey. The four questions can be part of an add-on access to justice survey module, to be incorporated into other ongoing general population surveys (such as surveys on crime victimization, corruption, governance, quality of life, public attitudes or surveys on other topics) or be part of dedicated surveys on access to justice and legal needs.</p>\n<p>Data should be collected as part of a nationally representative probability sample of adult population residing in the country, irrespective of legal residence status. The sampling frame and sample design should ensure that results can be disaggregated at sub-national level. The sample size should be sufficiently large to capture relevant events and compute needed disaggregation.</p>", "COLL_METHOD__GLOBAL"=>"<ul>\n  <li>Data are collected by the United Nations Development Programme (UNDP) through a standardised questionnaire sent to countries. This questionnaire provides specific definitions of data to be collected and it collects a set of metadata to identify possible discrepancies from standard definitions and to assess overall data quality (e.g. sample size, target population, agency responsible for the data collection, etc.). </li>\n  <li>Data for multiple years are collected to assess data consistency across time.</li>\n  <li>Countries can use the collected data to calculate the indicators based on the proposed module or using other data sources (e.g. SDG 16 Survey Initiative, crime victimization surveys among others). </li>\n</ul>", "FREQ_COLL__GLOBAL"=>"<p>Countries are encouraged to conduct surveys on access to justice through the proposed module in regular intervals, but at least every four years to reflect progress between each of the quadrennial reviews of Goal 16 at the High Level Political Forum (HLPF).</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data on relevant SDG indicators are collected, compiled and sent back to countries for data review annually. Data are then reported to United Nations Statistics Division (UNSD) through the regular reporting channels annually.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are collected through official nationally representative surveys. In most countries and most cases, such surveys are conducted by National Statistical Offices (NSOs). In some cases, other national institutions or other entities may conduct surveys on access to justice according to the same methodological standards.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Data will be compiled by the co-custodians for this indicator- United Nations Office on Drugs and Crime (UNODC), United Nations Development Programme (UNDP) and Organization for Economic Cooperation and Development (OECD).</p>", "INST_MANDATE__GLOBAL"=>"<p><a href=\"https://www.undp.org/content/undp/en/home/2030-agenda-for-sustainable-development/peace/rule-of-law--justice--security-and-human-rights/access-to-justice.html\"><strong>UNDP</strong></a><strong> - </strong>Strengthening the rule of law and promoting human rights are cornerstones of UNDP&#x2019;s work to achieve structural transformation for sustainable human development, build resilience to prevent and withstand shocks and eradicate extreme poverty. UNDP supports national partners to expand access to justice, especially for women, youth, persons with disabilities, marginalized groups and displaced communities. This includes advancing legal aid mechanisms and the use of mobile courts to resolve criminal and civil matters in hard-to-reach areas.</p>\n<p>UNODC &#x2013; as custodian of the UN standards and norms in crime prevention and criminal justice, UNODC assists Member States in reforming their criminal justice systems in order to be effective, fair and humane for the entire population. UNODC develops technical tools to assist Member States in implementing the UN standards and norms and supports Member States through the provision of technical assistance in crime prevention and criminal justice reform. It does so through a number of Global programmes and through the UNODC field office network.</p>\n<p>OECD &#x2013; The OECD supports its Member and partner countries in achieving more responsive and people-centred justice services and access to justice as core components of inclusive growth, sound democracies and a thriving investment climate. Enhanced access to justice is also a fundamental piece of the OECD&#x2019;s work to shape policies that foster equality, opportunity and well-being for all, given its significant impacts on people&#x2019;s ability to participate in the economy, health, employment and relationships. Additional areas of support include digital and data-driven transformation of justice, justice for businesses, child-friendly justice, justice for women and people-centred measurement of justice performance.</p>", "RATIONALE__GLOBAL"=>"<p>While there is no standard definition of access to justice, it is broadly concerned with &#x201C;the ability of people to defend and enforce their rights and obtain just resolution of justiciable problems in compliance with human rights standards; if necessary, through impartial formal or informal institutions of justice and with appropriate legal support.&#x201D;<sup><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></sup> For citizens in need of justice, a number of conditions should be met for their rights to be recognised, such as access to adequate information, access to justice services and legal advice, and access to institutions of justice that provide fair and impartial treatment. The rationale of this indicator is to focus on one step of the process and in particular on the accessibility of justice institutions and mechanisms (both formal and informal) by those who have experienced a justiciable problem. The indicator can provide important information about the overall accessibility of civil justice institutions and processes, barriers, and reasons for exclusion of some people. The disaggregation by type of dispute resolution mechanism provides additional information about the channels used by citizens in need of enforcing or defending their rights. </p>\n<p>This indicator has several advantages: </p>\n<ol>\n  <li>It is people-centred, as it measures the experience of justiciable problems from the perspective of those who face them.</li>\n  <li>It provides a broad assessment of people&#x2019;s approach to address problems they face, both inside and outside of formal institutions or mechanisms.</li>\n  <li>It focuses on the experience of accessing justice mechanisms or institutions when in need</li>\n  <li>It is easy to interpret.</li>\n  <li>It can be produced on the basis of few survey questions, which can be easily incorporated into ongoing national surveys.</li>\n  <li>It is well suited to monitor public policies aimed at improving the functioning of formal or informal dispute resolution mechanisms (top-down policies) and to those aimed at empowering the population (bottom-up policies).</li>\n  <li>It can be disaggregated by various socio-demographic (such as age, sex, migratory background, etc.) and geographical variables and thus be used to identify vulnerable groups/areas.</li>\n  <li>It draws on methodological guidelines derived from a comprehensive review of more than 60 national surveys conducted by governments and civil society organizations in more than 30 jurisdictions in the last 25 years. </li>\n</ol><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> <em>Praia Group Handbook on Governance Statistics: Access to and Quality of Justice </em>(forthcoming 2019). <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p>A major challenge is that the concept of dispute (justiciable problem) is subject to different interpretations and the propensity to consider a disagreement or conflict in terms of a justiciable problem can vary greatly across individuals and between societies. A way to address this issue is to focus on a number of possible disputes that can be considered of justiciable nature across most countries, as for example the one listed in the section above<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup>. Standardised descriptions of the most common types of disputes are also to be used in surveys in order to maximise comparability across different legal systems and countries. </p>\n<p>In order to identify the group of people in demand of dispute resolution mechanism it is necessary to identify the group of people voluntarily self-excluded. A way to identify this group is by including an additional question about the reasons why people did not use a dispute resolution mechanism. This question would allow to differentiate cases of voluntary and involuntary exclusion and define the denominator as the population who experienced a problem minus the voluntarily self-excluded.</p>\n<p>Another challenge refers to identifying possible dispute resolution mechanisms as they vary considerably in different countries around the world. The formulation of the questions in the survey has to take into account these different possibilities and make sure to include all relevant institutions generally recognized in the community. This proposed list of dispute resolution mechanisms identifies those that are common in most countries in the world but it can be adapted to the country context.</p>\n<p>The share of population experiencing the disputes under investigation can be of relatively small size and this can influence the statistical significance of results. A way to address this is to increase the question&#x2019;s reference period, recognizing that respondents&#x2019; ability to recall specific issues becomes increasingly unreliable the further back in time it extends. For these reasons, this proposal follows the recommendation from the Legal Needs Surveys and Access to Justice methodological guidance and suggests a reference interval of two years. With such reference period resulting data would be suitable for monitoring recent changes in contexts/policies while being based on a sufficient number of cases to ensure statistical significance of analyses.<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup> Possible telescoping effects (the effect of misplacement in time of events taking place in the past) need to be addressed properly by bounding in clear terms the time interval of reference in relevant questions. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> These types of disputes have broad applicability across countries as reflected in <a href=\"https://www.oecd.org/governance/legal-needs-surveys-and-access-to-justice-g2g9a36c-en.htm\"><em>Legal Needs Surveys and Access to Justice</em></a><em> </em>, OECD (2019), which builds upon a review of more than 60 large-scale legal need surveys conducted over the past 25 years. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> Experimental evidence indicates that increasing a legal needs survey&#x2019;s reference period from one to three years has only &#x201C;a fairly modest&#x201D; impact on problem reporting [Pleasence et al. (2016)] <a href=\"#footnote-ref-6\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<p>Number of persons who experienced a dispute during the past two years who accessed a formal or informal dispute resolution mechanism (numerator), divided by the number of those who experienced a dispute in the past two years minus those who are voluntarily self-excluded (denominator). The result would be multiplied by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mn>16</mn>\n    <mo>.</mo>\n    <mn>3</mn>\n    <mo>.</mo>\n    <mn>3</mn>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>c</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>c</mi>\n        <mi>h</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>i</mi>\n        <mi>s</mi>\n        <mi>m</mi>\n        <mi>&amp;nbsp;</mi>\n      </mrow>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>l</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>o</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>p</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>c</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>s</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>2</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>y</mi>\n        <mi>e</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>d</mi>\n        <mi>i</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>v</mi>\n        <mi>o</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>y</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>l</mi>\n        <mi>f</mi>\n        <mo>-</mo>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>c</mi>\n        <mi>l</mi>\n        <mi>u</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n      </mrow>\n    </mfrac>\n    <mi>X</mi>\n    <mn>100</mn>\n  </math></p>\n<p>This is a survey-based indicator that emphasizes citizens&#x2019; experiences over general perceptions. Both numerator and denominator are measured through sample surveys of the general population.</p>\n<p>The computation of this indicator requires the inclusion of a short module of four questions in a representative population survey. The following table illustrates the content of the four questions needed to compute the indicator.</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Content of question</strong></p>\n      </td>\n      <td>\n        <p><strong>Instruction</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Experience of a dispute over past 2 years, by type of dispute</li>\n        </ol>\n      </td>\n      <td>\n        <p>If no dispute was experienced, skip to END, otherwise go to 2.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Most recent dispute experienced, by type of dispute</li>\n        </ol>\n      </td>\n      <td>\n        <p>Continue with 3.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Access to dispute resolution mechanism, by type of mechanism</li>\n        </ol>\n      </td>\n      <td>\n        <p>If no DRM was accessed go to 4., otherwise skip to END</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Reason why no dispute resolution mechanism was accessed</li>\n        </ol>\n      </td>\n      <td>\n        <p>Go to END.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "DATA_VALIDATION__GLOBAL"=>"<p>The data for the indicator is collected through Household Surveys conducted by National Statistics Offices (NSOs) or other institutions following tight survey protocols and complying with the metadata. Data producers are encouraged to strictly follow the data quality practices, protocols and frameworks in relation of data quality. In addition to the data, countries are requested to report on the metadata which serves as one additional layer of validation and verification of the data by confronting with the metadata used and the recommended for global reporting. For survey-based indicators, metadata are assessed in relation to the representativeness and coverage of the survey as well as alignment of question wording and answer options with international standards. Before publication by custodian agencies, a standardised &#x201C;pre-publication process&#x201D; is implemented, where national stakeholders can verify and review the data before publication. </p>\n<p> </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>&#x2022; <strong>At country level</strong></p>\n<p>National data are not imputed if data derived from surveys conducted at country level are not available</p>\n<p>&#x2022; <strong>At regional and global levels</strong></p>\n<p>There is no imputation of missing values.</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates are produced only when available data cover at least a certain percentage of countries of the region and the population of these countries account for a certain percentage of the regional population.</p>", "DOC_METHOD__GLOBAL"=>"<p>Methodological documentation from surveys conducted at national level is available (e.g. household survey in Nigeria conducted by the National Bureau of Statistics (NBS) and UNODC; Governance, Public Safety and Justice Survey conducted by Statistics South Africa in 2019, Kenya Integrated Household Budget Survey 2015-2016 conducted by KNBS; Argentina - Unmet Legal Needs and Access to Justice conducted by the Subsecretar&#xED;a de Acceso a la Justicia Ministerio de Justicia y Derechos Humanos; or Colombia &#x2013; Survey of Citizen Security and Coexistence conducted by DANE). </p>\n<p>Furthermore, the Legal Needs Surveys and Access to Justice methodological guidance published by OECD in 2019 provides methodological guidance for developing the questionnaires and conducting such surveys. This guide brings together the experience gained through more than 60 national surveys conducted by governments and civil society organizations in more than 30 jurisdictions in the last 25 years.</p>\n<p>In 2022, the United Nations Office on Drugs and Crime (UNODC) together with the United Nations Development Program (UNDP) and the Office of the United Nations High Commissioner on Human Rights (OHCHR) published the SDG 16 Survey Questionnaire<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup> and Implementation Manual<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup>, which contain internationally standardised survey question wording (in the five official UN languages) as well as implementation guidance related to this indicator. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> See <a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire\">SDG 16 Initiative Questionnaire</a> <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> See <a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual\">SDG 16 Initiative Implementation Manual</a> <a href=\"#footnote-ref-8\">&#x2191;</a></p></div></div>", "QUALITY_MGMNT__GLOBAL"=>"<p>The three custodian agencies have statistical units with dedicated staff to support the data collection through technical assistance, collating and verifying the received data and continuously improve data collection mechanisms including guidelines.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>It is recommended that NSOs serve as the main contact for compiling and quality assuring the necessary data to report on SDG 16.3.3, in close coordination with Ministries of Justice and/or other relevant bodies in the country. Automated and substantive validation procedures are in place when data are processed by custodian agencies to assess their consistency and compliance with standards.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See Section 4.d Validation</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>A growing number of countries are implementing surveys using similar methodologies in order to assess legal needs, improve justice services, and strengthen linkages across sectors. However, the scale and methods of administration have varied. More than 60 national legal needs surveys have been conducted in more than 30 countries over the course of the last 25 years. </p>\n<p>Many of those surveys contain the questions needed to compute this indicator (experience of dispute, use of resolution mechanism - either formal or informal &#x2013; and reasons for not taking action to resolve the dispute).</p>\n<p><strong>Time series:</strong></p>\n<p>Not applicable</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Recommended disaggregation for this indicator are:</p>\n<p>- type of dispute resolution mechanism</p>\n<p>- sex</p>\n<p>- disability status</p>\n<p>- ethnicity</p>\n<p>- migration background</p>\n<p>- citizenship</p>\n<p>The disaggregation by type of dispute resolution mechanism is of fundamental importance to assess the type of justice institutions and mechanisms available for citizens and for this reason it is part of the indicator itself.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Data for this indicator are based on four standardised survey questions. If data from more than one survey are available for the same country, discrepancies may be due to different wording of the questions, different structure of the questionnaire, different survey methods and operations, different sample design and sample size. As a rule, data from national surveys complying with recommended standards are used, when available.</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li><strong>URL:</strong>UNODC-UNECE. 2010. Manual on Victimization Surveys. Available at : <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html\">https://www.unodc.org/unodc/en/data-and-analysis/Manual-on-victim-surveys.html</a></li>\n  <li>UNODC-UNDP-OHCHR. 2022. SDG 16 Survey Questionnaire and Implementation Manual. Available at:</li>\n</ul>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire</a></p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual</a> </p>\n<ul>\n  <li>Legal Needs Survey and Access to Justice. Available at: </li>\n</ul>\n<p><a href=\"https://www.oecd.org/governance/legal-needs-surveys-and-access-to-justice-g2g9a36c-en.htm\">https://www.oecd.org/governance/legal-needs-surveys-and-access-to-justice-g2g9a36c-en.htm</a></p>", "indicator_sort_order"=>"16-03-03", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.4.1", "slug"=>"16-4-1", "name"=>"Valor total de las corrientes financieras ilícitas entrantes y salientes (en dólares corrientes de los Estados Unidos)", "url"=>"/site/es/16-4-1/", "sort"=>"160401", "goal_number"=>"16", "target_number"=>"16.4", "global"=>{"name"=>"Valor total de las corrientes financieras ilícitas entrantes y salientes (en dólares corrientes de los Estados Unidos)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Valor total de las corrientes financieras ilícitas entrantes y salientes (en dólares corrientes de los Estados Unidos)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Valor total de las corrientes financieras ilícitas entrantes y salientes (en dólares corrientes de los Estados Unidos)", "indicator_number"=>"16.4.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Un desafío importante para el desarrollo sostenible de las sociedades de todo el mundo, \nen particular de los países en desarrollo, está representado por varias actividades delictivas \ny prácticas comerciales e impositivas ilícitas que son la causa o están asociadas a los flujos \nfinancieros ilícitos. \n\nLos ingresos provenientes de actividades delictivas suelen transferirse entre países para blanquearlos, \nutilizarlos y reinvertirlos en actividades lícitas o ilícitas. Los flujos financieros ilícitos \ntambién pueden tener su origen en actividades económicas legales, pero convertirse en ilícitos cuando \nlos flujos financieros se gestionan o transfieren de manera ilícita; por ejemplo, para evadir \nimpuestos o financiar actividades ilegales. Los flujos financieros ilícitos drenan recursos \ndel desarrollo sostenible. \n\nPor lo tanto, combatirlos es un componente crucial del objetivo de promover la paz, la justicia y \nlas instituciones sólidas, como se establece en el Objetivo 16 de la Agenda 2030 para el Desarrollo Sostenible.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-04-01.pdf\">Metadatos 16-4-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"A major challenge to sustainable development of societies around the world, particularly in developing \ncountries, is represented by several criminal activities and tax and commercial illicit practices which are at \nthe origin or associated with illicit financial flows (IFFs). \n\nProceeds from criminal activities are often transferred between countries to be laundered, utilized and \nreinvested in licit or illicit activities. IFFs can also originate from legal economic activities but \nbecome illicit when financial flows are managed or transferred illicitly; for instance, to evade taxes or \nto finance illegal activities. IFFs drain resources from sustainable development. \n\nCombatting IFFs is therefore a crucial component of the goal to promote peace, justice and strong institutions, \nas set out in Goal 16 of its 2030 Agenda for Sustainable Development. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-04-01.pdf\">Metadata 16-4-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Un desafío importante para el desarrollo sostenible de las sociedades de todo el mundo, \nen particular de los países en desarrollo, está representado por varias actividades delictivas \ny prácticas comerciales e impositivas ilícitas que son la causa o están asociadas a los flujos \nfinancieros ilícitos. \n\nLos ingresos provenientes de actividades delictivas suelen transferirse entre países para blanquearlos, \nutilizarlos y reinvertirlos en actividades lícitas o ilícitas. Los flujos financieros ilícitos \ntambién pueden tener su origen en actividades económicas legales, pero convertirse en ilícitos cuando \nlos flujos financieros se gestionan o transfieren de manera ilícita; por ejemplo, para evadir \nimpuestos o financiar actividades ilegales. Los flujos financieros ilícitos drenan recursos \ndel desarrollo sostenible. \n\nPor lo tanto, combatirlos es un componente crucial del objetivo de promover la paz, la justicia y \nlas instituciones sólidas, como se establece en el Objetivo 16 de la Agenda 2030 para el Desarrollo Sostenible.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-04-01.pdf\">Metadatuak 16-4-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.4: By 2030, significantly reduce illicit financial and arms flows, strengthen the recovery and return of stolen assets and combat all forms of organized crime</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.4.1: Total value of inward and outward illicit financial flows (in current United States dollars)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>DI_ILL_IN - Total value of inward illicit financial flows [16.4.1]</p>\n<p>DI_ILL_OUT - Total value of outward illicit financial flows [16.4.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 16.4.2 refers to the illicit trade in firearms</p>\n<p>Indicator 16.2.2 to trafficking in persons</p>\n<p>Indicators 16.5.1 and 16.5.2 to corruption and bribery in all forms</p>\n<p>Indicator 10.7.2 is concerned with migration policies</p>\n<p>Indicators 15.7.1 and 15.c.1 Proportion of traded wildlife that was poached or illicitly trafficked</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC) and United Nations Conference on Trade and Development (UNCTAD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC) and United Nations Conference on Trade and Development (UNCTAD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator measures the total value of inward and outward illicit financial flows (IFFs) in current United States dollars. IFFs are defined as &#x201C;financial flows that are illicit in origin, transfer or use, that reflect an exchange of value and that cross country borders&#x201D;.</p>\n<p><strong>Concepts:</strong></p>\n<p>IFFs have the following features: </p>\n<ul>\n  <li><strong>Illicit in origin, transfer or use</strong>. A flow of value is considered illicit if it is illicitly generated (e.g., originates from criminal activities or tax evasion), illicitly transferred (e.g., violating currency controls) or illicitly used (e.g., for financing terrorism). The flow can be legallygenerated, transferred or used, but it must be illicit in at least one of these aspects. Some flows that are not strictly illegal may fall within the definition of IFFs for statistical purposes, for example, cross-border aggressive tax avoidance which erodes the tax base of a country where that income was generated. </li>\n  <li><strong>Exchange of value</strong>, rather than purely financial transfers. Exchange of value includes exchange of goods and services, and financial and non-financial assets. For instance, illicit cross-border bartering, meaning the illicit exchange of goods and services for other goods and services, is a common practice in illegal markets and it is considered as IFF.</li>\n  <li>IFFs measure <strong>a flow of value over a given time</strong> - as opposed to a stock measure, which would be the accumulation of value. </li>\n  <li><strong>Flows that cross a border</strong>.<sup><sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></sup> This includes assets that cross borders and assets where the ownership changes from a resident of a country to a non-resident, even if the assets remain in the same jurisdiction. </li>\n</ul>\n<p>There are four main types of activities that can generate IFFs:</p>\n<ul>\n  <li><em><u>Illicit tax and commercial practices</u></em>: These include illicit practices by legal entities as well as arrangements and individuals with the objective of concealing revenues and reducing tax burden through evading controls and regulations. This category can be divided into two components: <ul>\n      <li><strong>IFFs from illegal commercial activities and tax evasion</strong>. These include illegal practices such as tariff, duty and revenue offences, tax evasion, competition offences and market manipulation amongst others, as included in the International Classification of Crime for Statistical Purposes (ICCS)<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>. Most of these activities are non-observed, hidden or part of the shadow, underground or informal economy that may generate IFFs. </li>\n      <li><strong>IFFs from aggressive tax avoidance</strong>. Illicit flows can also be generated from legal economic activities through aggressive tax avoidance. This can take place through the manipulation of transfer pricing, strategic location of debt and intellectual property, tax treaty shopping and the use of hybrid instruments and entities. These flows need to be carefully considered, as they generally arise from legal business transactions and only the illicit part of the cross-border flows belongs within the scope of IFFs.</li>\n    </ul>\n  </li>\n  <li><em><u>IFFs from illegal markets</u></em>: These include trade in illicit goods and services when the corresponding financial flows cross borders. The focus is on criminal activities where income is generated through exchange (trade) of illegal goods or services. Such processes often involve a degree of criminal organisation aimed at creating profit. They include any type of trafficking in goods, such as drugs and firearms, or services, such as smuggling of migrants. IFFs emerge from transnational trade in illicit goods and services, as well as from cross-border flows from managing the illicit income from such activities.</li>\n  <li><em><u>IFFs from corruption</u></em>: The United Nations Convention against Corruption (UNCAC) defines acts considered as corruption, and they are consistently defined in the ICCS, such as bribery, embezzlement, abuse of functions, trading in influence, illicit enrichment and other acts of corruption in the scope. When these acts &#x2013; directly or indirectly - generate cross-border flows, they generate IFFs. </li>\n  <li><em><u>Exploitation-type activities, and financing of crime and terrorism:</u></em> Exploitation-type activities are non-productive activities that entail a forced, involuntary and illicit transfer of economic resources between two actors. Terrorism financing and financing of crime are illicit, voluntary transfers of funds between two actors. Examples of exploitation-type activities are sexual exploitation, theft, extortion, illicit enrichment, and kidnapping. When the related financial flows cross country borders, they constitute IFFs.</li>\n</ul>\n<p>Other relevant concepts include:</p>\n<ul>\n  <li><em><u>Inward IFFs</u></em>: IFFs entering a country.</li>\n  <li><em><u>Outward IFFs</u></em>: IFFs leaving a country.</li>\n  <li><em><u>Illicit income generation</u></em>: This refers to the set of transactions that either directly generate illicit income for an actor during a productive or non-productive illicit activity, or that are performed in the context of the production of illicit goods and services. A transaction constitutes an IFF when it crosses country borders.</li>\n  <li><em><u>Illicit income management</u></em>: These transactions use illicit income to invest in (legal or illegal) financial and non-financial assets or to consume (legal or illegal) goods and services. A transaction constitutes an IFFs when it crosses country borders.</li>\n  <li><em><u>Illegal markets</u></em> comprise all transactions related to the production and the trade with a certain illicit good or service. Regardless of the illicit nature, these market activities are considered as being economically productive, because value added is generated at each transaction. The value added describes the net increase in value (price times quantity) of the product at each transaction.</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> The proposed bottom-up measurement approach described below considers domestic IFFs as part of the illegal economy too. These flows would not fall under the definition of IFFs for SDG 16.4.1, but are of high relevance to understanding organised cross-border illicit flows. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> See section 2.c <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Current United States dollars </p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Illicit financial flows (IFFs) are measured by identifying activities and behaviours that may generate them, such as those that are listed in the <em><u>UNODC (2015) International Classification of Crime for Statistical Purposes</u></em> (ICCS) <sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup> and those that relate to the area of aggressive tax avoidance in addition. ICCS provides definitions of a number of behaviours, events and activities which may generate IFFs such as exploitation-type activities and terrorism, trafficking activities and corruption, as well as many activities related to illicit tax and commercial practices. The ICCS, however, focuses solely on actions and behaviours that are attributable to different types of crime. The classification will be extended to cover all IFFs related to tax and commercial activities, namely IFFs related to aggressive tax avoidance. A draft of classifying tax and commercial activities extending from, but not indicating their inclusion in the ICCS, is presented below. Please note that codes 080413, 080414 and 080415 are not covered by the ICCS as they are clearly not criminal activities. Only an excerpt is shown for illustrative purposes as a more exhaustive classification is being developed. </p>\n<p><strong>Draft classification of tax and commercial IFFs</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Code</strong></p>\n      </td>\n      <td>\n        <p><strong>Description</strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>Inclusion/exclusion</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>08041<strong><u>1</u></strong></p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Acts of concealing revenues or wealth in order to evade taxation</p>\n      </td>\n      <td>\n        <p>Inclusion</p>\n      </td>\n      <td>\n        <p>Outright undeclared (concealed e.g., in secrecy jurisdictions); Undeclared via instruments (Phantom corporations or shell companies, tax havens)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Exclusion</p>\n      </td>\n      <td>\n        <p>Fraud, deception or corruption (07)</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>08041<strong><u>2</u></strong></p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Acts of fraudulently misdeclaring the object, the quantity or the value of traded goods in invoicing transactions</p>\n      </td>\n      <td>\n        <p>Inclusion</p>\n      </td>\n      <td>\n        <p>Under/over reporting prices; Multiple invoicing; Over/under reporting of quantities; Misclassification of tariff categories</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Exclusion</p>\n      </td>\n      <td>\n        <p>Transfer misprincing (08041<strong><u>3</u></strong>)</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>08041<strong><u>3*</u></strong></p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Acts departing from the arm&#x2019;s length principle</p>\n      </td>\n      <td>\n        <p>Inclusion</p>\n      </td>\n      <td>\n        <p>Setting up over/under priced exchange of goods and services with the intent of moving profits among MNEs units</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Exclusion</p>\n      </td>\n      <td>\n        <p>Misinvoicing (08041<strong><u>2</u></strong>)</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>08041<strong><u>4*</u></strong></p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Acts related to strategic location of debt, other financial assets, risks, or other corporate activities</p>\n      </td>\n      <td>\n        <p>Inclusion</p>\n      </td>\n      <td>\n        <p>Intracompany loans; Interest payments</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Exclusion</p>\n      </td>\n      <td>\n        <p>Transfer misprincing (08041<strong><u>3</u></strong>)</p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>08041<strong><u>5*</u></strong></p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Acts related to strategic location of intellectual property products and other non-financial assets</p>\n      </td>\n      <td>\n        <p>Inclusion</p>\n      </td>\n      <td>\n        <p>Strategic location of intellectual property; Strategic location of other assets; Cost-sharing agreements; Royalty payments</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Exclusion</p>\n      </td>\n      <td>\n        <p>Transfer misprincing (08041<strong><u>3</u></strong>)</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>* Although extending from ICCS code 08041, these categories are not covered in ICCS (not criminal activities).</p>\n<p>A number of activities and behaviours are identified as potentially generating IFFs, both from tax and commercial, and illegal IFFs categories. Examples of such behaviours as based directly on ICCS are shown below, but a more exhaustive classification is being developed. </p>\n<p><strong>Examples of activities that may generate IFFs from crime, by ICCS categories</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td></td>\n      <td>\n        <p><em>Examples</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Tax and commercial practices</p>\n      </td>\n      <td>\n        <p>08041 Tariff, taxation, duty and revenue offences </p>\n        <p>08042 Corporate offences including competition and import/export offences; acts against trade regulations</p>\n        <p>08045 Market manipulation or insider trading, price fixing </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Exploitation-type activities and terrorism financing (parts of sections 02, 04, 09)</p>\n      </td>\n      <td>\n        <p>020221 Kidnapping </p>\n        <p>0203 Slavery and exploitation</p>\n        <p>0204 Trafficking in persons</p>\n        <p>0302 Sexual exploitation</p>\n        <p>02051 Extortion</p>\n        <p>0401 Robbery</p>\n        <p>0501 Burglary</p>\n        <p>0502 Theft</p>\n        <p>09062 Financing of terrorism</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Illegal markets</p>\n      </td>\n      <td>\n        <p>ICCS includes a long list of activities, including for example drug trafficking (060132), firearm trafficking (090121), illegal mining (10043), smuggling of migrants (08051), smuggling of goods (08044), wildlife trafficking (100312)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Corruption (section 0703)</p>\n      </td>\n      <td>\n        <p>07031 Bribery </p>\n        <p>07032 Embezzlement </p>\n        <p>07033 Abuse of functions </p>\n        <p>07034 Trading in influence </p>\n        <p>07035 Illicit enrichment </p>\n        <p>07039 Other acts of corruption</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>In developing a more exhaustive classification of IFFs, each activity is being analysed considering three aspects:</p>\n<ul>\n  <li>Change in income: whether the activity is economic (directly or indirectly generating a change of income) or non-economic; </li>\n  <li>Direct or indirect flows: activity generating a change of income with or without direct exchange of resources;</li>\n  <li>Productive or non-productive activities: falling within or outside the production boundary as defined in the System of National Accounts (SNA).</li>\n</ul>\n<p>Such taxonomy allows for addressing not only whether each activity generates IFFs, but also which part, i.e., income generation or income management, thus guiding IFF measurement.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> <a href=\"about:blank\">https://www.unodc.org/documents/data-and-analysis/statistics/crime/ICCS/ICCS_English_2016_web.pdf</a> <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "SOURCE_TYPE__GLOBAL"=>"<p>The measurement of illicit financial flows (IFFs) requires combining data held by different entities of the national statistical system and beyond, especially national statistical offices, customs and tax authorities, financial intelligence units and central banks. The balance of payments and system of national accounts data on illegal economic activities and non-observed economy provide a good starting point for the measurement of IFFs. Trade transactions data, held by customs, are essential for analysing the commercial IFFs, including trade misinvoicing. Statistics on international trade in goods and services, financial and business statistics as well as foreign affiliates statistics collate relevant data for estimating commercial IFFs. Similarly, tax returns at individual (person or firm) level can be used for analysing IFFs related to tax avoidance and evasion. Additionally, where established, large case units (LCUs) of national statistical offices offer indispensable expert knowledge particularly in the field of profit shifting and related tax and commercial IFFs. </p>\n<p>Given the transnational nature of the indicator, data availability in other countries can support the calculation of national measures.<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup> The following existing data collection systems collect data relevant to IFFs from countries globally and can also be resources for countries to measure their IFFs.</p>\n<p>UNODC Annual Reports Questionnaire (ARQ) collects the following data, which allow to understand current scale of drug supply market:</p>\n<ul>\n  <li>Annual seizures of drugs in amounts and number of cases</li>\n  <li>Trafficking routes (origin, transit and destination countries) and main transportation modes (air, land, sea and mail)</li>\n  <li>Range and typical prices of drugs in retail and wholesale levels of supply market</li>\n  <li>Range and typical purities of drugs in retail and wholesale levels of supply market</li>\n  <li>Illicit cultivation, eradication and production of drug crops</li>\n  <li>Illicit manufacture of plant-based or synthetic drug-related end products (clandestine laboratories detected and dismantled)</li>\n</ul>\n<p>The global data collection on firearm trafficking collects data on seizures, prices and trafficking routes and it is an essential tool to understanding the dynamics of illegal firearms markets and flows.<sup><sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup></sup></p>\n<p>UNODC collects data on trafficking victims identified in their respective countries using a common questionnaire with a standard set of indicators, including official information on detected cases and on origin-destination of trafficking flows. </p>\n<p>UNODC, in partnership with the <strong>Convention on International Trade in Endangered Species of Wild Fauna and Flora</strong> (CITES) also maintains a global database of wildlife seizure incidents. This is mainly based on data submitted by the Parties to CITES.<sup><sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></sup>. Thanks to these data and to data collected by other official and open source data sources, UNODC compiles the WISE (World Wildlife Seizure Database), which provides key information on detected trafficking volumes and origin-destination routes, and estimated monetary value of the items seized. Such data are a key source to understand and identify IFFs generated by wildlife trafficking activities.</p>\n<p>Other global data sources can be used to directly support, or supplement existing national data sources in measuring IFFs, particularly tax and commercial IFFs. These include, among others: </p>\n<ul>\n  <li>United Nations International Trade Statistics Database (United Nations Comtrade) or IMF Direction of Trade Statistics (DOTS) for international trade data; </li>\n  <li>Global Transport Costs Dataset for International Trade by UNCTAD, the World Bank, and Equitable Maritime Consulting, or OECD International Transport and Insurance Costs of Merchandise Trade for addressing different valuation of international trade flows; </li>\n  <li>OECD Country-by-Country Reporting, OECD Analytical Database on Individual Multinationals and Affiliates, OECD Activity of Multinational Enterprises, Global Groups Register and other for tracking activities and aggressive tax avoidance by multinational enterprise groups. </li>\n  <li>The locational banking statistics from the Bank of International Settlements to estimate the flows related to undeclared offshore wealth, i.e., IFFs from tax evasion. </li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> For example, drug price in destination countries can help estimating illicit flows entering the country where the drug is produced. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> https://www.unodc.org/unodc/en/firearms-protocol/index.html <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> These data were shared with UNODC through International Consortium on Combating Wildlife Crime (ICCWC) for more information see: https://www.unodc.org/documents/data-and-analysis/wildlife/WLC16_Chapter_2.pdf <a href=\"#footnote-ref-7\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p>The indicator builds on existing data, but its exhaustiveness may require extensions to national data collection. This includes both administrative and statistical data. Central banks, tax and customs authorities and national statistical offices often have the strongest mandate to access necessary data. This may be considered in the division of work for the compilation of different parts of indicator 16.4.1. The country-by-country reporting data of tax authorities, and other incentives to share economic data in statistically safe environments may prove useful for the measurement of illicit financial flows (IFFs) in the future. </p>\n<p>The agency in charge of data collection and compilation will vary across countries depending on the national division of labour and on the type of IFFs prominent in the country. As the coordinator of the national statistics system, the national statistical office is expected to act as the official counterpart and coordinator of work for most countries. </p>\n<p>If there are major inconsistencies across countries, with other existing data, or in relation to standard classifications and concepts, the custodian agencies will contact the designated Focal Points regarding any need for clarification, correction or additional metadata. Indicators are reviewed prior to global release following the procedures set by the IAEG-SDGs.</p>", "FREQ_COLL__GLOBAL"=>"<p>UNODC and UNCTAD are in the process of supporting Member States to strengthen their national capacity to measure the Indicator. Until 2022, 22 countries globally have tested methodologies to measure specific elements of the indicator with some preliminary statistics on IFFs being compiled. Some of them are ready for global reporting in early 2023. The work to expand the scope, i.e., coverage of both other elements of IFFs and other countries, is underway by custodian agencies and United Nations Regional Commissions. More detailed data collection plans will be made based on the outcomes of current consultations, pilot testing and ongoing capacity-strengthening projects.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>It is expected that preliminary calculations for the annual indicator at the national, regional and sub-regional levels will be carried out in autumn every year for the preceding year. Considering the wide range of source data needed, the compilers will have to strike a balance between exhaustiveness and timeliness. </p>", "DATA_SOURCE__GLOBAL"=>"<p>Data providers are natural (individuals) or legal persons (businesses or institutions) who report their data for different purposes. Thus, relevant data are held by national statistical offices, central banks, tax authorities, customs, financial intelligence centres, criminal justice institutions, including courts, police, military, etc. They collect primary data from individuals, businesses, institutions and other statistical units either for statistical purposes or for their administrative work. Focal points at the national level are responsible for compiling the indicator and submitting it in collaboration with the national statistical office. </p>", "COMPILING_ORG__GLOBAL"=>"<p>At the national level, national statistical offices have a coordinating role in the national statistical system, and are, thus, well placed to lead the compilation work and bring the stakeholders together to measure illicit financial flows (IFFs). National statistical offices may either collate all relevant data to compile the SDG indicator, or coordinate the compilation of different types of IFFs among national authorities to form the overall SDG Indicator 16.4.1. UNODC and UNCTAD will collate the indicator data and report it globally.</p>", "INST_MANDATE__GLOBAL"=>"<p>As described earlier, the national division of work varies across countries. Data relevant for illicit financial flows (IFFs) are collected or accessed by different national authorities to fulfil their mandate. Often the national statistical office has the mandate to access data necessary for statistical production, including confidential data held by other national authorities, or to collect the data directly from respondents. The compilation of aggregates for different IFFs can also be decentralised reflecting the mandates of the relevant agencies. </p>", "RATIONALE__GLOBAL"=>"<p>A major challenge to sustainable development of societies around the world, particularly in developing countries, is represented by several criminal activities and tax and commercial illicit practices which are at the origin or associated with illicit financial flows (IFFs). Proceeds from criminal activities are often transferred between countries to be laundered, utilized and reinvested in licit or illicit activities. IFFs can also originate from legal economic activities but become illicit when financial flows are managed or transferred illicitly; for instance, to evade taxes or to finance illegal activities. IFFs drain resources from sustainable development. Combatting IFFs is therefore a crucial component of the goal to promote peace, justice and strong institutions, as set out in Goal 16 of its 2030 Agenda for Sustainable Development.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The statistical definition of illicit financial flows (IFFs) provides a comprehensive definition of the phenomenon to be measured. It does not focus on a specific measurement approach only, like trade asymmetry, but relies on a combination of methods to estimate different types of IFFs. </p>\n<p>The disaggregated and bottom-up measurement approach is in line with existing frameworks such as the System of National Accounts (SNA) and the Balance of Payments (BOP) and it follows international efforts to measure non-observed or illegal economic activities. </p>\n<p>SDG Indicator 16.4.1 calls for the measurement of the &#x201C;total value&#x201D; of inward and outward IFFs. While this is useful as an indication of the overall size of the problem and for measuring progress, a more granular measurement of IFFs helps to identify the main sources and channels of IFFs and can guide interventions targeting IFFs. </p>\n<p>Countries are affected by different types of IFFs and it is suggested that main types of IFFs are defined at country level. This limits the possibility of measuring all types of IFFs in a comprehensive manner and comparability may be affected by different coverage from one country to another. However, the goal is to capture the most significant flows at country level and a gradual process of improving the exhaustiveness of the indicator is expected, following the model of measuring illegal economic activities and the non-observed economy in the balance of payments and national accounts.</p>\n<p>There is a risk of double-counting when adding together explicit estimates of activities generating IFFs. Estimates for IFFs should not be simply added together, because they may already include parts of others (e.g., drug trafficking and bribery) and there may be double-counting. During the expert consultations, double counting was discussed and will be addressed in the comprehensive guidelines, namely the upcoming <em>Statistical Framework to measure IFFs</em>, issued to Member States.</p>", "DATA_COMP__GLOBAL"=>"<p>A bottom-up and direct measurement approach is preferred for constructing the indicator. Bottom-up methods estimate illicit financial flows (IFFs) directly in relation to the four main activities and build them up departing from the overall economic income that illicit activities generate. Direct refers to the fact that data referring to the various stages of the economic processes generating IFFs are individually measured (via surveys, administrative data or other transparent methods) and are not the exclusive result of model-based procedures. The measurement approach is in line with the &#x201C;Eurostat Handbook on the compilation of statistics on illegal economic activities in national accounts and balance of payments&#x201D;<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup> for the estimation of the contribution of illegal activities to the GDP.<sup><sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup></sup> The proposed compilation methods follow the principles developed in economic measurement frameworks such as the System of National Accounts and the Balance of Payments.</p>\n<p>In 2021, UNCTAD released a draft <a href=\"https://unctad.org/webflyer/methodological-guidelines-measure-tax-and-commercial-illicit-financial-flows-methods-pilot\">Methodological guidelines to measure tax and commercial illicit financial flows</a>. They identify a suite of methods for the measurement of the main types of tax and commercial IFFs, specifically two methods for each of the three main types of tax and commercial IFFs:</p>\n<ol>\n  <li>Trade misinvoicing by entities <ul>\n      <li>Method #1 - Partner Country Method Plus</li>\n      <li>Method #2 - Price Filter Method Plus</li>\n    </ul>\n  </li>\n  <li>Aggressive tax avoidance or profit shifting by multinational enterprise groups (MNEs)<ul>\n      <li>Method #3 &#x2013; Global distribution of MNEs&#x2019; profits and corporate taxes</li>\n      <li>Method #4 &#x2013; MNE vs comparable non-MNE profit shifting</li>\n    </ul>\n  </li>\n  <li>Transfer of wealth to evade taxes by individuals <ul>\n      <li>Method #5 &#x2013; Flows of undeclared offshore assets indicator</li>\n      <li>Method #6 &#x2013; Flows of offshore financial wealth by country</li>\n    </ul>\n  </li>\n</ol>\n<p>UNODC has developed and continues to enhance methods to address IFFs from criminal activities, such as smuggling of migrants, drugs trafficking, illegal mining, wildlife trafficking, trafficking in persons, and corruption, providing guidance and expert support to national authorities undertaking measurement.</p>\n<p>The methodology foresees:</p>\n<ol>\n  <li>A risk assessment that identifies the major and most relevant sources of IFFs in a country. This risk assessment can follow and build on existing risk assessments, e.g., the ones mandated by the Financial Action Task Force (FATF).<sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup> </li>\n  <li>Once the activities that generate the most important flows are identified, the flows are estimated in a disaggregated manner and then summed up for the indicator. </li>\n</ol>\n<p>Given the broad scope of activities generating IFFs, each type of flow needs to be treated in a separate manner. </p>\n<p>A two-step process was developed that aids Member States in calculating Indicator 16.4.1.</p>\n<p>As a first step in constructing the IFFs Indicator is to focus, for each IFF type, on IFFs generated during the <em><u>illicit income generation</u></em>: this refers to the set of transactions &#x2013; such as those related to international trade of illicit goods - that either directly generate illicit income for an actor during a productive or non-productive illicit activity, or that are performed in the context of the illicit production of goods and services. </p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong><u>Examples of income generation IFFs related to selected illegal activities</u></strong></p>\n        <p><strong><u>IFFs from drug trafficking</u></strong></p>\n        <p>In a drug producing country, the method to estimate IFFs derived from drug trafficking can be broadly described as follows:</p>\n        <p>All drug produced in the country (P) is either consumed domestically (C), seized by law enforcement (S), exported (E) or lost (L).</p>\n        <p>With that <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mi>P</mi>\n            <mo>=</mo>\n            <mi>C</mi>\n            <mo>+</mo>\n            <mi>S</mi>\n            <mo>+</mo>\n            <mi>E</mi>\n            <mo>+</mo>\n            <mi>L</mi>\n          </math>.</p>\n        <p>Countries with extended illicit drug cultivation, normally collect data on P, C, and S (losses cannot be estimated and are excluded from the calculations) and annual exports of drugs can be estimated. </p>\n        <p>The value of exports can be measured by the wholesale value of the relevant drug in countries of destination of the drug produced in the country. These data can be retrieved from international data on seizures reported by other Member States (which provide information on the country of origin) and price data, which is as well reported annually through the mandated Annual Report Questionnaire (ARQ) submitted to UNODC (see <a href=\"about:blank\">https://dataunodc.un.org/</a>)</p>\n        <p>This methodology has been applied in Peru, Mexico and Afghanistan<sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup> where certain portions of the income generated from drug production and trafficking are accounted for in the national accounts.</p>\n        <p><strong><u>IFFs from smuggling of migrants<sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup></u></strong></p>\n        <p>Following the Eurostat manual &#x201C;Handbook on the compilation of statistics on illegal economic activities in national accounts and balance of payments&#x201D; four types of smuggling transactions can be distinguished, two of which create IFF:</p>\n        <p><strong>Type I: Resident smugglers and resident migrants </strong>does not cover transnationality and illegal entry and does not create IFFs </p>\n        <p> </p>\n        <p><strong>Type II: Resident smugglers and non-resident migrants</strong></p>\n        <p>Constitutes an export of services and does incur an inward IFF:</p>\n        <p>Export of transportation services = number of non-resident migrants smuggled by resident smugglers * prices</p>\n        <p><strong>Type III: Non-resident smugglers and resident migrants</strong></p>\n        <p>Estimations recorded as import of illegal services and constitute and outward IFF:</p>\n        <p>Import of illegal transportation services = number of residents smuggled by non-resident smugglers * </p>\n        <p>prices</p>\n        <p><strong>Type IV: Non-resident smugglers and non-resident migrants</strong></p>\n        <p>No estimations recorded</p>\n        <p>The pilot studies found the methodology to be feasible, however, limitations on data exist, in particular on pricing.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>At a second stage, IFFs in relation to <em><u>illicit income management</u></em> are estimated. These refer e.g., to IFFs generated when income generated from illegal activities is invested abroad (e.g., into property). To assess these flows, quantitative and qualitative information held by financial authorities, central banks and other entities concerned with money laundering and financial crimes can be used. Further methodological deliberations on income generation / income management are being undertaken by the custodian agencies to be refined, finalized and included in a comprehensive Statistical Framework for the measurement of IFFs. </p>\n<p>The methodological work of custodians on aggregation to measure IFFs as a single SDG indicator proposes a matrix approach, allowing activities identified to be analysed with respect to an aggregated income generation (IG) and income management (IM) approach as well as according to methods used to measure IFFs from these activities (see Figure 1). Using such a matrix, areas of (potential) overlap between different methods and types of IFFs can be identified &#x2013; in the figure, by observing which areas are covered by a specific method (marked in green; light green indicates merely partial coverage by a particular method). Further practical studies in countries will be needed to design suitable and robust aggregation methods in the future. </p>\n<p>Figure 1. Activity-method matrix for aggregated IG-IM representation of IFF measurement</p>\n<p> <img src=\"data:image/png;base64,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\"></p>\n<p>Source: Deliberations by Task Force on the Statistical Measurement</p>\n<p>It is advised that the estimates of IFFs are reported as the (best) estimate, accompanied by a lower and an upper bound estimate to account for uncertainties in the data sources and methods. Custodian agencies are currently developing further guidance to Member States to be included in the Statistical Framework for measurement of IFFs. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> Available here: <a href=\"about:blank\">https://ec.europa.eu/eurostat/documents/3859598/8714610/KS-05-17-202-EN-N.pdf/eaf638df-17dc-47a1-9ab7-fe68476100ec</a> <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> With one principle difference. The mere transfer of funds (exploitation-type activities and terrorism financing) are not considered in the GDP estimates, as they are not productive transactions and may not be carried out with the mutual agreement of both parties. Such activities can, however, generate noteworthy amounts of illicit income and subsequent IFFs. The present framework includes activities that are not considered as being productive in the framework of the System of National Accounts. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> https://www.fatf-gafi.org/ <a href=\"#footnote-ref-10\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> See e.g., National Statistics and Information Authority, Afghanistan and UNODC, &#x201C;Afghanistan Opium Survey 2018 &#x2013; Challenges to sustainable development, peace and security&#x201D;, July 2019. <a href=\"#footnote-ref-11\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> The Protocol against the Smuggling of Migrants, supplementing the United Nations Convention against Transnational Organized Crime (the Migrant Smuggling Protocol) defines migrant smuggling as: &#x201D;in order to obtain, directly or indirectly, a financial or other material benefits, of the illegal entry of a person into a State Party of which the person is not a national or a permanent resident&#x201D;. See as well ICCS. <a href=\"#footnote-ref-12\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>UNODC and UNCTAD request Member States to provide sufficient metadata accompanying their compiled IFFs estimates. UNODC annually reviews methods used to compile crime-related IFFs estimates and to make sure they are compatible with the definition and concepts presented in the <em>Conceptual Framework for the Statistical Measurement of IFFs</em>.<sup><a href=\"#footnote-13\" id=\"footnote-ref-13\">[12]</a></sup> In addition, in Q1 2023 UNODC started to include estimates on SDG indicator 16.4.1 in the annual SDG Pre-Publication, a process that allows countries to comment or review the data of each indicator UNODC is custodian of, before such data are submitted to UNSD. Deviations to account for national circumstances will clearly need to be identified, justified and their impact on international comparability and methodological comprehensiveness be estimated. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-13\">12</sup><p>https://www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_Conceptual_Framework_for_publication_FINAL_16Oct_print.pdf <a href=\"#footnote-ref-13\">&#x2191;</a></p></div></div>", "ADJUSTMENT__GLOBAL"=>"<p>Given the compilation process outlined above, national circumstances will come at play when measuring the IFFs. The need for adjustment can be assessed based on information on the breakdowns included in the reported IFFs estimates (in the accompanying metadata). The goal is to base the indicator on nationally compiled and reported data. Ongoing work on classification and aggregation of IFFs will result in further guidance on how to adjust for potential duplication and to harmonise breakdowns. </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li><strong>At country level</strong></li>\n</ul>\n<p>When national data are missing, transnational data sources or alternative data sources can be examined. It is important to provide comprehensive metadata explaining current issues related to missing data and exhaustiveness of the indicator. Although national data may only partially cover IFFs, they are still valuable for assessing the significance of IFFs globally and regionally. UNCTAD and UNODC may support countries to assess alternative sources for obtaining the missing information. </p>\n<ul>\n  <li><strong>At regional and global levels</strong></li>\n</ul>\n<p>In order to calculate regional and global aggregates, missing data may be estimated using information from international sources. As historical data for countries become available with time, it will be possible to impute using the same country&#x2019;s data as well. Estimated indicators are not to be released at the country level, but only in aggregated form at regional and global levels. There will be certain thresholds to be met for the regional and global estimates to be acceptable. If these thresholds are not met, the regional and global estimates will not be published.</p>", "REG_AGG__GLOBAL"=>"<p>Once values of country indicators have been released, missing indicators estimated, any sub-regional, regional and global estimates will be obtained by aggregating the country indicators within a specific sub-region and region. The global value would be calculated by aggregating the regional values in a similar manner. National differences in the comprehensiveness of IFF coverage will influence the quality of regional aggregates. Regional aggregations will be further methodologically developed once sufficient country-level statistics on IFFs become fully available. </p>", "DOC_METHOD__GLOBAL"=>"<ul>\n  <li>UNCTAD and UNODC published a <a href=\"https://unctad.org/publication/conceptual-framework-statistical-measurement-illicit-financial-flows\">Conceptual Framework for the Statistical Measurement of Illicit Financial Flows</a> as a joint publication in October 2020. It details the concepts, definitions and types of IFFs, and discusses the challenges of statistical production. The Conceptual Framework has been endorsed by Member States at 53<sup>rd</sup> session of the United Nations Statistical Commission in March 2022.<sup><a href=\"#footnote-14\" id=\"footnote-ref-14\">[13]</a></sup></li>\n  <li>At the national level, data sources need to be identified separately for the major IFFs originating from tax and commercial practices, corruption, exploitation-type and terrorism activities, and illegal markets. These sources should cover the major flows relevant to the country and provide information for estimating total inward and outward flows separately. The <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">ICCS</a> provides a useful listing of behaviours, events and activities that may generate IFFs, and an extended classification of IFFs from aggressive tax avoidance is being discussed. </li>\n  <li>UNCTAD/UNODC Task Force is finalising methodological guidelines on the measurement of selected types of IFFs. To date, methodologies to measure IFFs have been tested by 22 countries on three continents in efforts coordinated by UN regional commissions (ESCAP, ECA) and UNODC field Offices (on crime related IFFs), alongside UNCTAD and UNODC statistics. This includes 12 African countries, 4 Latin American and 6 Asian countries that have produced first estimates of commercial or crime-related IFFs. Custodian agencies are now refining methodological guidelines and materials prepared and made publicly available<sup><a href=\"#footnote-15\" id=\"footnote-ref-15\">[14]</a></sup>. UNCTAD and UNODC are working towards a comprehensive Statistical Framework for the Measurement of Illicit Financial Flows, providing practical guidance to national statistical authorities including suggested methodologies to measure different types of IFFs, to be submitted to the United Nations Statistical Commission for its review once finalised. </li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-14\">13</sup><p> https://unstats.un.org/unsd/statcom/53rd-session/documents/2022-14-CrimeStats-E.pdf <a href=\"#footnote-ref-14\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-15\">14</sup><p> For methodological guidelines to measure tax and commercial IFFs, see: <a href=\"https://unctad.org/webflyer/methodological-guidelines-measure-tax-and-commercial-illicit-financial-flows-methods-pilot\">https://unctad.org/webflyer/methodological-guidelines-measure-tax-and-commercial-illicit-financial-flows-methods-pilot</a>. UNODC has developed guidance on measuring IFFs from trafficking in persons, drug trafficking, smuggling of migrants and wildlife trafficking. <a href=\"#footnote-ref-15\">&#x2191;</a></p></div></div>", "QUALITY_MGMNT__GLOBAL"=>"<p>Compilation of indicator 16.4.1 must be conducted in full adherence to the Fundamental Principles of Official Statistics. Moreover, national statistical authorities will follow established quality assurance frameworks for official statistics. Once fully developed, methodological material will allow for integrated quality management, with methods selection based on quality aspects related to: </p>\n<ul>\n  <li>source data (timeliness, availability, fit-for-purpose, coverage, granularity, and interoperability), </li>\n  <li>methods (relevance of scope, clarity of concepts, robustness, transferability, equivalence, statistical alignment, capacity requirements) and </li>\n  <li>results (relevance for use, accuracy, timeliness, clarity, comparability, coherence). </li>\n</ul>", "QUALITY_ASSURE__GLOBAL"=>"<ul>\n  <li>Statistics received from Member States will go through a validation process. </li>\n  <li>The data for the indicator are externally validated by comparing to other available sources. </li>\n  <li>Once the information has been validated and information from additional sources incorporated, any questions for clarification or proposals are shared with Member States for their review. </li>\n  <li>In case any adjustment is needed, after Member States have reviewed the values, indicators are ready to be published and sub-regional, regional and global totals, where appropriate, can be estimated.</li>\n</ul>", "QUALITY_ASSMNT__GLOBAL"=>"<p>UNCTAD and UNODC will review the quality of reported national data jointly with the national focal points. The methodological guidelines provide instructions and quality criteria for the selection of source data, methods and assessment of results, as detailed above in 4.i. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>It is expected that first and preliminary statistics on IFFs will be reported globally in early 2023. It is further expected that the number of countries for which this indicator is available will gradually start increasing over time. According to inventories, over 60 per cent of countries globally already collect some data that can be used in the estimation of IFFs. However, notably efforts are planned to support countries in building their capacity to measure Indicator 16.4.1. Currently, 22 pioneering countries have pilot tested the indicator compilation with some in the final stages of producing early estimates of IFF statistics. Estimates will also be prepared in countries participating in UNCTAD and UNODC capacity building projects, carried out jointly with United Nations Regional Commissions in 2023-2026.</p>\n<p>It is expected that the estimates of IFFs are available as the (best) estimate, accompanied by a lower and an upper bound estimate. </p>\n<p><strong>Time series:</strong></p>\n<p>Availability of time series would be useful for the analysis of development over time. Feasibility of constructing historical time series data will be reviewed.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>At the indicator level, the IFFs are to be reported separately as inward and outward IFFs. </p>\n<p>In addition, a disaggregated measurement approach is proposed. As a minimum, disaggregation of the index by relevant types of IFFs, should be published separately for the main elements. Furthermore, depending on data availability, each should be disaggregated to reflect specific IFFs categories (following the ones identified in the Conceptual Framework<sup><a href=\"#footnote-16\" id=\"footnote-ref-16\">[15]</a></sup>), for example: </p>\n<p>&#x2022; IFFs from illicit tax and commercial practices (additionally, e.g., trade misinvoicing, tax evasion, aggressive tax avoidance by MNEs), </p>\n<p>&#x2022; IFFs from illegal markets (additionally, e.g., drug trafficking, smuggling of migrants, wildlife trafficking), </p>\n<p>&#x2022; IFFs from corruption, and</p>\n<p>&#x2022; IFFs from exploitation-type and financing of crime and terrorism (additionally, e.g., trafficking in persons).</p>\n<p>Moreover, where possible and relevant, further disaggregation of IFF indicator is to be made in reference to:</p>\n<p>&#x2022; Sector (e.g., as defined by economic sector or activity within the International Standard Industrial Classification of All Economic Activities)</p>\n<p>&#x2022; Regions/Countries of origin/destination of the flows (to construct a country-flow matrix).</p>\n<p>Other possible disaggregation might be considered by countries regarding:&#x2022; type of payment method (cash / trade flows / crypto currencies)</p>\n<p>&#x2022; resulting assets (offshore wealth / real estate etc.)</p>\n<p>&#x2022; actors (characters of individuals / types of businesses etc.)</p>\n<p>&#x2022; industries, commodities or service categories.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-16\">15</sup><p> See page 13 at <a href=\"https://www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_Conceptual_Framework_for_publication_FINAL_16Oct_print.pdf\">https://www.unodc.org/documents/data-and-analysis/statistics/IFF/IFF_Conceptual_Framework_for_publication_FINAL_16Oct_print.pdf</a> <a href=\"#footnote-ref-16\">&#x2191;</a></p></div></div>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>As mentioned above, countries are affected by different types of IFFs and varying data availability. Therefore, the coverage of different types of IFFs in the indicator may vary from one country to another, thus affecting comparability. However, the goal is to capture the largest flows even when country-specific solutions are applied. Furthermore, based on the country metadata, the custodian agencies may discuss necessary corrections or adjustments for producing regional and global aggregates with countries. A gradual process of improving the exhaustiveness of the indicator is expected, following the model of measuring illegal economic activities and the non-observed economy in the balance of payments and system of national accounts.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"about:blank\">www.unodc.org</a></p>\n<p>https://unctad.org/statistics/illicit-financial-flows<a href=\"https://sdgpulse.unctad.org/illicit-financial-flows/\">https://sdgpulse.unctad.org/illicit-financial-flows/</a> and https://sdgpulse.unctad.org/unctad-leads-global-efforts-to-measure-illicit-financial-flows-with-unodc/</p>\n<p><a href=\"about:blank\">https://dataunodc.un.org/</a></p>\n<p><a href=\"about:blank\">https://unctadstat.unctad.org</a></p>\n<p>UNCTAD Stat Youtube Channel: https://www.youtube.com/channel/UCbRSDgH8NS-U6aAJ_Q6B14w</p>\n<p><a href=\"https://www.unodc.org/unodc/en/data-and-analysis/iff.html\">https://www.unodc.org/unodc/en/data-and-analysis/iff.html</a></p>\n<p>UNCTAD-UNODC Conceptual Framework for the Statistical Measurement of Illicit Financial Flows (2020) <a href=\"https://unctad.org/publication/conceptual-framework-statistical-measurement-illicit-financial-flows\"><u>https://unctad.org/publication/conceptual-framework-statistical-measurement-illicit-financial-flows</u></a> <br>UNCTAD Methodological Guidelines to Measure Tax and Commercial Illicit Financial Flows &#x2013; Methods for pilot testing (2021). https://unctad.org/webflyer/methodological-guidelines-measure-tax-and-commercial-illicit-financial-flows-methods-pilot</p>\n<p>UNODC-UNCTAD project on Latin America (2017-2020): <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/iff_Lac.html\">https://www.unodc.org/unodc/en/data-and-analysis/iff_Lac.html</a> </p>\n<p>UNCTAD-ECA project on Africa (2018-2022):<br><a href=\"https://unctad.org/project/defining-estimating-and-disseminating-statistics-illicit-financial-flows-africa\">https://unctad.org/project/defining-estimating-and-disseminating-statistics-illicit-financial-flows-africa</a></p>\n<p>UNODC-ESCAP-UNCTAD project on Asia-Pacific (2020-2022): <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/iff_Asia.html\">https://www.unodc.org/unodc/en/data-and-analysis/iff_Asia.html</a> </p>\n<p>UNODC (2020) - Supply and value chains and illicit financial flows from the trade in ivory and rhinoceros horn (Chapter 8 &#x2013; Second World Wildlife Crime Report) <a href=\"https://www.unodc.org/documents/data-and-analysis/wildlife/2020/WWLC20_Chapter_8_Value_chains.pdf\">https://www.unodc.org/documents/data-and-analysis/wildlife/2020/WWLC20_Chapter_8_Value_chains.pdf</a> </p>", "indicator_sort_order"=>"16-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.4.2", "slug"=>"16-4-2", "name"=>"Proporción de armas incautadas, encontradas o entregadas cuyo origen o contexto ilícitos han sido determinados o establecidos por una autoridad competente, de conformidad con los instrumentos internacionales", "url"=>"/site/es/16-4-2/", "sort"=>"160402", "goal_number"=>"16", "target_number"=>"16.4", "global"=>{"name"=>"Proporción de armas incautadas, encontradas o entregadas cuyo origen o contexto ilícitos han sido determinados o establecidos por una autoridad competente, de conformidad con los instrumentos internacionales"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de armas incautadas, encontradas o entregadas cuyo origen o contexto ilícitos han sido determinados o establecidos por una autoridad competente, de conformidad con los instrumentos internacionales", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de armas incautadas, encontradas o entregadas cuyo origen o contexto ilícitos han sido determinados o establecidos por una autoridad competente, de conformidad con los instrumentos internacionales", "indicator_number"=>"16.4.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Si bien la Meta 16.4 tiene por objeto reducir significativamente los flujos \nilícitos de armas, la medición directa de estos tipos de flujos es extremadamente \ndifícil debido a la naturaleza clandestina del tráfico ilícito de armas. \n\nPor lo tanto, el indicador no tiene por objeto medir estos flujos, sino \nla eficiencia con la que la comunidad internacional combate el fenómeno del \ntráfico ilícito de armas. \n\nAdemás del indicador 16.4.2, otros indicadores \nno oficiales pueden ser de ayuda para interpretar los valores de los informes para \nla Meta 16.4. En particular, se recopila información sobre el número de solicitudes \nde rastreo internacionales y nacionales presentadas y respondidas, y el número total \nde armas incautadas, encontradas y entregadas, independientemente de si están \nmarcadas de manera única o no, y el número total de armas que han sido marcadas, \nregistradas o destruidas. \n\nAdemás, se dispone de datos sobre el número de personas en contacto con \nla policía, procesadas y condenadas, en relación con el tráfico ilícito de armas. \nTodos estos indicadores podrían ayudar a completar el panorama sobre el alcance de \nlas actividades de aplicación de la ley a nivel nacional para combatir el tráfico ilícito de armas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-04-02.pdf\">Metadatos 16-4-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"While Target 16.4 aims at significantly reducing illicit arms flows, directly measuring these types of flows \nis extremely difficult due to the underground nature of illicit arms trafficking. \n\nTherefore, the indicator does not aim at measuring these flows, but the efficiency with which the international \ncommunity combats the phenomenon of illicit arms trafficking. \n\nIn addition to indicator 16.4.2 as defined in this document, other non-official indicators may be of \nassistance when interpreting the reporting values for Goal 16.4. In particular, information is collected on \nthe number of international and national tracing requests placed and responded to, and the total number \nof arms seized, found and surrendered by whether they are uniquely marked or not, the total number of \narms that have been marked, recorded or destroyed. \n\nIn addition, data on the number of individuals in\ncontact with the police, prosecuted and convicted, in relation to illicit trafficking of arms is available. All \nthese indicators could help complete the picture regarding the extent of Law Enforcement activities at \nthe national level to counter illicit trafficking in arms. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-04-02.pdf\">Metadata 16-4-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Si bien la Meta 16.4 tiene por objeto reducir significativamente los flujos \nilícitos de armas, la medición directa de estos tipos de flujos es extremadamente \ndifícil debido a la naturaleza clandestina del tráfico ilícito de armas. \n\nPor lo tanto, el indicador no tiene por objeto medir estos flujos, sino \nla eficiencia con la que la comunidad internacional combate el fenómeno del \ntráfico ilícito de armas. \n\nAdemás del indicador 16.4.2, otros indicadores \nno oficiales pueden ser de ayuda para interpretar los valores de los informes para \nla Meta 16.4. En particular, se recopila información sobre el número de solicitudes \nde rastreo internacionales y nacionales presentadas y respondidas, y el número total \nde armas incautadas, encontradas y entregadas, independientemente de si están \nmarcadas de manera única o no, y el número total de armas que han sido marcadas, \nregistradas o destruidas. \n\nAdemás, se dispone de datos sobre el número de personas en contacto con \nla policía, procesadas y condenadas, en relación con el tráfico ilícito de armas. \nTodos estos indicadores podrían ayudar a completar el panorama sobre el alcance de \nlas actividades de aplicación de la ley a nivel nacional para combatir el tráfico ilícito de armas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-04-02.pdf\">Metadatuak 16-4-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.4: By 2030, significantly reduce illicit financial and arms flows, strengthen the recovery and return of stolen assets and combat all forms of organized crime</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.4.2: Proportion of seized, found or surrendered arms whose illicit origin or context has been traced or established by a competent authority in line with international instruments</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_ARM_SZTRACE - Proportion of seized, found or surrendered arms whose illicit origin or context has been traced or established by a competent authority in line with international instruments [16.4.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC) and United Nations Office for Disarmement Affairs (UNODA)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC) and United Nations Office for Disarmement Affairs (UNODA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<h2>Definition:</h2>\n<p>Proportion of seized, found or surrendered arms whose illicit origin or context has been traced or established by a competent authority in line with international instruments</p>\n<h2>Concepts:</h2>\n<p><em><u>Arms</u>:</em> arms refer to &#x2018;small arms and light weapons&#x2019;, defined as any portable lethal weapon that expels or launches, is designed to expel or launch, or may be readily converted to expel or launch a shot, bullet or projectile by the action of an explosive, excluding antique small arms and light weapons or their replicas. Antique small arms and light weapons and their replicas will be defined in accordance with domestic law, and in no case will they include those manufactured after 1899. Arms include all firearms, as defined in the &#x201C;Protocol against the illicit manufacturing of and trafficking in firearms, their parts and components and ammunition&#x201D;. </p>\n<p>In particular, &#x2018;small arms&#x2019; are, broadly speaking, weapons for individual use, including revolvers, pistols, rifles and carbines, shotguns, sub-machine guns and light machine guns. &#x2018;Light weapons&#x2019; are, broadly speaking, weapons designed for use by two or three persons serving as a crew, although some may be carried and used by a single person. They include, heavy machine guns, hand-held under-barrel and mounted grenade launchers, portable anti-aircraft guns, portable anti-tank guns, recoilless rifles, portable launchers of anti-tank missile and rocket systems, portable launchers of anti-aircraft missile systems, and mortars of a calibre of less than 100 millimetres.</p>\n<p><em><u>Seized</u></em>: arms that have been physically apprehended during the reported period by a competent authority, whether temporarily or not, in relation to a suspected criminal offence or administrative violation related to these arms. For the purpose of the calculation of indicator 16.4.2, only arms that were seized due to criminal offences are considered.</p>\n<p><em><u>Found</u></em>: arms apprehended by authorities that are not linked to an intentional or planned investigation or inspection, neither attributable to any apparent holder or owner, regardless of whether the items were reported lost or stolen. </p>\n<p><em><u>Surrendered</u></em>: arms willingly handed over to authorities that are not linked to a planned investigation or inspection. The surrender may occur as a personal initiative of a citizen in the context of a voluntary surrender campaign and disarmament, demobilisation and reintegration processes, inter alia.</p>\n<p><em><u>Illicit origin</u></em>: Earliest point in time in the life of an arm where it was of an illicit nature. In order to establish the illicit origin, it is necessary to identify the point of diversion of the arm and the circumstances around it.</p>\n<p><em><u>Point of diversion</u></em>: the point in space and time and/or circumstances when arms left the licit circuit and entered the illicit one. If identified through tracing, the last legal record needs to be found. For arms illicitly manufactured, the point of diversion is the manufacture itself. </p>\n<p><em><u>Last legal record</u></em>: last recorded information available about the item, its status (deactivated, stolen, lost, seized, found, surrendered, sent for destruction, confiscated, in transit, etc.) and its legal end-user. The identification of the last legal record may require the initiation of several individual tracing requests.</p>\n<p><em><u>Tracing</u></em>: the systematic tracking of weapons and, where possible, their parts and components, and ammunition, at the national and/or international level for the purpose of assisting the competent authorities of States parties in detecting, investigating and analysing illicit manufacturing and illicit trafficking.</p>\n<p><em><u>Illicit origin established by a competent authority in line with international instruments</u></em>: illicit origin established through means other than tracing, e.g. through intelligence. In the case of arms that are not traceable, this is the only mean to establishing the illicit origin. </p>\n<p><em><u>Marking:</u></em> A uniquely marked item has a unique marking providing the name of the manufacturer, the country or place of manufacture and the serial number, or maintain any alternative unique user-friendly marking with simple geometric symbols in combination with a numeric and/or alphanumeric code, permitting ready identification by all States of the country of manufacture.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>At national level data are produced by Law Enforcement or other Agencies responsible for firearms issues.</p>\n<p>Such data are reported at international level through tables 5.1 to 5.3 of the UN-IAFQ. Please refer to the following link for detailed information: <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/data-collections.html\">https://www.unodc.org/unodc/en/data-and-analysis/data-collections.html</a>.</p>\n<p>These data will also be supplemented by data collected through the Programme of Action (PoA) national reports; in particular, Section 6 of its reporting form (national reports submitted by States are available at: <a href=\"http://www.smallarms.un-arm.com/sustainable-development-goals\">www.smallarms.un-arm.com/sustainable-development-goals</a>).</p>\n<p>Additional data sources include national official publications, as well as data from international organizations such as the World Customs Organization and INTERPOL. </p>", "COLL_METHOD__GLOBAL"=>"<p>The IAFQ is sent to Member States every year (first data cycle in 2018). The official counterparts at the country level are designated Focal Points that are in charge of coordinating the data collection among different national institutions. </p>\n<p>Relevant data are also collected on a biennial basis through the PoA National Reports (as revised in 2022). </p>\n<p>Data from alternative sources is collected throughout the year and incorporated into the internal database in parallel to the data collections above. Both UNODC and UNODA carry-out data validation processes of their respective dataset with Member States, and cooperate on data consolidation. </p>\n<p>After data is consolidated, UNODC will share its dataset with Member States for their review before publication. </p>", "FREQ_COLL__GLOBAL"=>"<p>The first data collection cycle for compiling the indicator through the IAFQ started in 2018, covering the reporting years of 2016 and 2017. Since then, main data from the IAFQ is collected directly from Member States every year between May and July. Data are validated and reported for inclusion in the global database yearly in Q1.</p>\n<p>Regarding supplementary data, the first data collection cycle for the PoA National Reports started in 2018, covering the reporting years of 2016 and 2017. The 2020 data collection cycle covered the period 2018-2019,data received in 2022 covered the reporting period 2020-2021 and data received in 2024 covered the reporting period 2022-2023. Data collection for the PoA National Reports is to continue on a similar biennial basis, where Member States submit their report ahead of PoA process meetings and conferences. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>It is expected that preliminary calculations for the annual indicator at the national, regional and sub-regional levels will be shared in March of every year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Most of the data providers are Law Enforcement Agencies, including National Police, Regional/State Police, Customs, Military, etc. Focal Points at the national level are responsible for compiling the data and submitting it.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC) and United Nations Office for Disarmement Affairs (UNODA)</p>", "INST_MANDATE__GLOBAL"=>"<p><strong>CTOC/COP/2020, Res 10/2, The Conference of the Parties to the United Nations Convention</strong></p>\n<p><strong>against Transnational Organized Crime:</strong></p>\n<p>8. Calls upon States to develop or strengthen their national capacity for the collection and analysis of data on illicit trafficking in firearms, with a view to identifying trends and patterns, fostering the exchange of information and enabling the global monitoring of progress on indicator 16.4.2 of the Sustainable Development Goals, and invites States parties to participate in and contribute to the upcoming data-collection cycle of the United Nations Office on Drugs and Crime by providing quantitative and qualitative data and information.</p>\n<p>A/RES/72/57, The illicit trade in small arms and light weapons in all its aspects: </p>\n<p>8. Underlines the importance of the full and effective implementation of the Programme of Action and the International Tracing Instrument for attaining Goal 16 and target 16.4 of the Sustainable Development Goals;</p>\n<p>And subsequent resolutions on the illicit trade in small arms and light weapons in all its aspects, including paragraph 10, <strong>A/RES/77/71.</strong></p>\n<p><strong>A/CONF.192/BMS/2022/1: BMS8 Outcome document: </strong></p>\n<p>25. To highlight, as appropriate and on a voluntary basis, progress in data collection efforts under indicator 16.4.2 of the 2030 Agenda for Sustainable Development as part of national reports on the implementation of the Programme of Action and the International Tracing Instrument, optimizing the use of national reports.</p>", "RATIONALE__GLOBAL"=>"<p>While Target 16.4 aims at significantly reducing illicit arms flows, directly measuring these types of flows is extremely difficult due to the underground nature of illicit arms trafficking. Therefore, the indicator does not aim at measuring these flows, but the efficiency with which the international community combats the phenomenon of illicit arms trafficking. </p>\n<p>In addition to indicator 16.4.2 as defined in this document, other non-official indicators may be of assistance when interpreting the reporting values for Goal 16.4. In particular, information is collected on the number of international and national tracing requests placed and responded to, and the total number of arms seized, found and surrendered by whether they are uniquely marked or not, the total number of arms that have been marked, recorded or destroyed. In addition, data on the number of individuals in contact with the police, prosecuted and convicted, in relation to illicit trafficking of arms is available. All these indicators could help complete the picture regarding the extent of Law Enforcement activities at the national level to counter illicit trafficking in arms.</p>", "REC_USE_LIM__GLOBAL"=>"<p>There are certain limitations to the methodology used in the calculation of indicator 16.4.2:</p>\n<ul>\n  <li>Information on the illicit circumstances of arms not uniquely identifiable, which include illicitly manufactured firearms and firearms with erased or altered markings, is scarce, and these arms are very difficult to trace. Therefore, information on the establishment of the illicit origin for these arms is excluded from the calculation, with the indicator focusing on arms that are potentially traceable (i.e., uniquely identifiable through marking or unknown status with respect to marking).</li>\n  <li>The values for indicator 16.4.2 may be affected by whether the country has a significant proportion of apprehended arms that are traceable, which is usually a consequence of the context of illicit arms trafficking in the country and is not related to its Law Enforcement efforts.</li>\n  <li>The process of tracing firearms can be notably long, especially if several requests are involved. Therefore, the information on tracing results provided on the questionnaire for the reference year may be incomplete. While the fact that countries are requested to review the figures reported during the previous data collection cycle may partially correct for this, there may still be a bias in the calculation.</li>\n  <li>Due to the year-to-year volatility of of the estimates, the data is published as a 2-year moving average to increase robustness.</li>\n</ul>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated as a proportion, and can be computed separately for each of seized, found and surrendered arms, as well as all three taken together.</p>\n<p>The denominator of the proportion is the total number of arms seized, found and surrendered that are potentially traceable (i.e., uniquely identifiable through marking or unknown status with respect to marking). </p>\n<p>The numerator includes all those arms for which the point of diversion was established / identified, either through tracing or by a competent authority (e.g. through intelligence).</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>S</mi>\n    <mi>D</mi>\n    <mi>G</mi>\n    <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n    <mn>16</mn>\n    <mo>.</mo>\n    <mn>4</mn>\n    <mo>.</mo>\n    <mn>2</mn>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mi>N</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>h</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>h</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>l</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>c</mi>\n        <mi>i</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>i</mi>\n        <mi>g</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>c</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>e</mi>\n        <mi>x</mi>\n        <mi>t</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>a</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>e</mi>\n        <mi>s</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>b</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>s</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>/</mo>\n        <mi>i</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>f</mi>\n        <mi>i</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>A</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>+</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>B</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>+</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>C</mi>\n        <mo>)</mo>\n      </mrow>\n      <mrow>\n        <mi>T</mi>\n        <mi>o</mi>\n        <mi>t</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>u</mi>\n        <mi>m</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>m</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>e</mi>\n        <mi>i</mi>\n        <mi>z</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mo>,</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>f</mi>\n        <mi>o</mi>\n        <mi>u</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>s</mi>\n        <mi>u</mi>\n        <mi>r</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>e</mi>\n        <mi>r</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>(</mo>\n        <mi>A</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>+</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>B</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>+</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>C</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>+</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>D</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>+</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>E</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>+</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>F</mi>\n        <mi>&amp;nbsp;</mi>\n        <mo>+</mo>\n        <mi>&amp;nbsp;</mi>\n        <mi>G</mi>\n        <mo>)</mo>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p><strong>A:</strong> Weapon seized/found/surrendered from <u>illegitimate</u> owner and weapon found in <u>national</u> registry (e.g., lost or stolen) (national tracing)</p>\n<p><strong>B:</strong> Point of diversion of the weapon (last legal record) <u>identified</u> through tracing and weapon found in <u>foreign</u> registry (international tracing)</p>\n<p><strong>C:</strong> Point of diversion otherwise established by a competent authority</p>\n<p><strong>D:</strong> Tracing attempted, but not enough information to identify point of diversion</p>\n<p><strong>E:</strong> Tracing procedure still pending</p>\n<p><strong>F:</strong> No tracing procedure initiated</p>\n<p><strong>G:</strong> Unknown status with respect to marking</p>\n<p>Data are published as a two-year moving average to increase robustness. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>Any discrepancies in a data set from a specific country will be verified through follow-up correspondences with national authorities of the country. Data for the indicator are shared with countries for their review before submission to UNSD, both with regards to the compiled indicator and supplementary data.</p>\n<p>UNODC and UNODA are responsible for carrying-out data validation processes of their respective datasets and verification with Member States. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>A first step to follow when there is missing data to produce these estimates is to consult and follow up with the Member States. In particular, United Nations Office on Drugs and Crime (UNODC) and United Nations Office for Disarmement Affairs (UNODA) will request further information directly to the relevant Focal Points.</p>\n<p>In the absence of feedback, alternative sources would be consulted to obtain the missing information. This information will be shared with the Member State for their approval. Finally, if no additional information is available through these two channels, the country&#x2019;s indicator will not be published. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>In order to calculate regional and global levels, the indicator for those Member States that were not published after treatment at the country level, will be estimated using information from alternative sources and/or from similar countries. The selection of these &#x201C;similar countries&#x201D; will be based on geographical location (e.g. regional or sub-regional averages), and/or structural similarities, such as the proportion of uniquely marked arms seized or the total number of arms seized, found and surrendered per capita. As historical data for countries becomes available with time, it will be possible to impute using the same country&#x2019;s data as well.</p>", "REG_AGG__GLOBAL"=>"<p>Once values of indicators for countries have been imputed, the sub-regional, regional and global estimates will be obtained by separately adding the numerator and denominator values for countries within a specific sub-region and region, and calculating the proportion. The global value would be calculated by aggregating the regional values in a similar manner. </p>\n<p>There are certain thresholds to be met for the regional and global estimates to be acceptable. If these thresholds are not met, the estimates will not be published.</p>", "DOC_METHOD__GLOBAL"=>"<p>Guidelines for countries on the compilation of these data are available at: <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/crime/iafq.html\">https://www.unodc.org/unodc/en/data-and-analysis/statistics/crime/iafq.html</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) has a statistical section with dedicated staff to support the data collection through technical assistance, collating and verifying the received data and continuously improve data collection mechanisms including guidelines.</p>", "QUALITY_ASSURE__GLOBAL"=>"<ul>\n  <li>The data received from Member States goes through a thorough internal validation process. The IAFQ already has a built-in validation procedure that allows the respondent to see on the spot whether the reported values add up to the corresponding totals reported in other parts of the questionnaire. </li>\n  <li>Internal validation is also performed automatically in the internal database system.</li>\n  <li>The data is also externally validated by comparing it to other (preferably official) available sources. </li>\n  <li>Once the information has been validated and information from additional sources incorporated, it is shared with Member States for their approval. After Member States have approved the corresponding values, data are ready to be published and sub-regional, regional and global totals are ready to be estimated.</li>\n</ul>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Country level estimates will not be published where there is insufficient information available for either the nominator or denominator.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The IAFQ data collection started in 2018 and countries are expected to submit their responses yearly between May and July. Betwen 2018 and 2023, 115 Member States have submitted at least once the IAFQ. Out of these, 29 countries have provided data relevant for the calculation of the indicator. With ongoing capacity building activities taking place, it is expected that this number will rise in the future. </p>\n<p>In 2024, 100 States have provided information in their PoA National reports, which will be also used as information for the calculation of the denominator of indicator 16.4.2 (see at: <a href=\"http://www.smallarms.un-arm.com/sustainable-development-goals\">www.smallarms.un-arm.com/sustainable-development-goals</a>)</p>\n<p>Between 2018 and 2024, States have submitted a total of 411 PoA national reports from 145 countries, in an average of 103 reports in each biennial cycle. </p>\n<p><strong>Time series:</strong></p>\n<p>2016 &#x2013; most recent year </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The collected data allows for the annual calculation of indicator 16.4.2 at the national level, which can be aggregated to sub-regional, regional and global levels. Disaggregating the indicator by a number of variables is also possible:</p>\n<ul>\n  <li>By arms seized, arms found and arms surrendered.</li>\n  <li>By different &#x201C;levels of tracing&#x201D; in cases where tracing was not successful. For example, cases where tracing is still pending or there was not enough information to establish the point of diversion, could be disaggregated from the cases where there was no attempt to trace the weapon whatsoever. </li>\n  <li>By whether the illicit origin was determined through tracing or established by a competent authority. </li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><a href=\"http://www.unodc.org\">www.unodc.org</a></p>\n<p><a href=\"https://dataunodc.un.org/sdgs\">https://dataunodc.un.org/sdgs</a></p>\n<p><a href=\"http://www.unodc.org/unodc/en/data-and-analysis/statistics/crime/iafq.html\">http://www.unodc.org/unodc/en/data-and-analysis/statistics/crime/iafq.html</a></p>\n<p>https://smallarms.un-arm.org/sustainable-development-goals</p>\n<p>https://smallarms.un-arm.org/national-reports<a href=\"http://www.unodc.org/unodc/en/data-and-analysis/statistics/crime/iafq.html\">http://www.unodc.org/unodc/en/data-and-analysis/statistics/crime/iafq.html</a></p>\n<p><a href=\"https://unstats.un.org/sdgs/tierIII-indicators/files/Tier3-16-04-02.pdf\">02.pdf</a></p>", "indicator_sort_order"=>"16-04-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.5.1", "slug"=>"16-5-1", "name"=>"Proporción de personas que han tenido al menos un contacto con un funcionario público y que han pagado un soborno a un funcionario público, o a las que un funcionario público les ha pedido un soborno, durante los últimos 12 meses", "url"=>"/site/es/16-5-1/", "sort"=>"160501", "goal_number"=>"16", "target_number"=>"16.5", "global"=>{"name"=>"Proporción de personas que han tenido al menos un contacto con un funcionario público y que han pagado un soborno a un funcionario público, o a las que un funcionario público les ha pedido un soborno, durante los últimos 12 meses"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de personas que han tenido al menos un contacto con un funcionario público y que han pagado un soborno a un funcionario público, o a las que un funcionario público les ha pedido un soborno, durante los últimos 12 meses", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de personas que han tenido al menos un contacto con un funcionario público y que han pagado un soborno a un funcionario público, o a las que un funcionario público les ha pedido un soborno, durante los últimos 12 meses", "indicator_number"=>"16.5.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La corrupción es antónimo de la igualdad de acceso a los servicios públicos y del correcto \nfuncionamiento de la economía; como tal, tiene un impacto negativo en la distribución justa \nde los recursos y las oportunidades de desarrollo. \n\nAdemás, la corrupción erosiona la confianza pública en las autoridades y el estado de derecho; \ncuando el soborno administrativo se convierte en una experiencia recurrente de amplios sectores de la \npoblación y las empresas, sus efectos negativos tienen un impacto negativo duradero en el estado de derecho, \nlos procesos democráticos y la justicia. \n\nAl proporcionar una medida directa de la experiencia del soborno, este indicador proporciona \nuna métrica objetiva de la corrupción, un criterio para monitorear el progreso en la lucha contra la corrupción.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-05-01.pdf\">Metadatos 16-5-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Corruption is an antonym of equal accessibility to public services and of correct functioning of the \neconomy; as such, it has a negative impact on fair distribution of resources and development \nopportunities. \n\nBesides, corruption erodes public trust in authorities and the rule of law; when \nadministrative bribery becomes a recurrent experience of large sectors of the population and businesses, \nits negative effects have an enduring negative impact on the rule of law, democratic processes and \njustice. \n\nBy providing a direct measure of the experience of bribery, this indicator provides an objective \nmetric of corruption, a yardstick to monitor progress in the fight against corruption. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-05-01.pdf\">Metadata 16-5-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La corrupción es antónimo de la igualdad de acceso a los servicios públicos y del correcto \nfuncionamiento de la economía; como tal, tiene un impacto negativo en la distribución justa \nde los recursos y las oportunidades de desarrollo. \n\nAdemás, la corrupción erosiona la confianza pública en las autoridades y el estado de derecho; \ncuando el soborno administrativo se convierte en una experiencia recurrente de amplios sectores de la \npoblación y las empresas, sus efectos negativos tienen un impacto negativo duradero en el estado de derecho, \nlos procesos democráticos y la justicia. \n\nAl proporcionar una medida directa de la experiencia del soborno, este indicador proporciona \nuna métrica objetiva de la corrupción, un criterio para monitorear el progreso en la lucha contra la corrupción.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-05-01.pdf\">Metadatuak 16-5-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.5: Substantially reduce corruption and bribery in all their forms</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.5.1: Proportion of persons who had at least one contact with a public official and who paid a bribe to a public official, or were asked for a bribe by those public officials, during the previous 12 months</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>IU_COR_BRIB - Prevalence rate of bribery [16.5.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 16.5.2: Proportion of businesses that had at least one contact with a public official and that paid a bribe to a public official, or were asked for a bribe by those public officials during the previous 12 months</p>\n<p>Indicator 16.4.1: Total value of inward and outward illicit financial flows (in current United States dollars)</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office on Drugs and Crime (UNODC)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator is defined as the percentage of persons who paid at least one bribe (gave a public official money, a gift or counter favour) to a public official, or were asked for a bribe by these public officials, in the last 12 months, as a percentage of persons who had at least one contact with a public official in the same period.</p>\n<p><strong>Concepts:</strong></p>\n<p>In the International Classification of Crime for Statistical Purposes (ICCS), bribery is defined as: &#x2018;Promising, offering, giving, soliciting, or accepting an undue advantage to or from a public official or a person who directs or works in a private sector entity, directly or indirectly, in order that the person act or refrain from acting in the exercise of his or her official duties&#x2019; (ICCS Category 07031). This definition is based on definitions of bribery of national public officials, bribery of foreign public officials and official of international organisations and bribery in the private sector that are contained in the United Nations Convention against Corruption (articles 15, 16, and 21).</p>\n<p>While the concept of bribery is broader, as it includes also actions such as promising or offering, and it covers both public and private sector, this indicator focuses on specific forms of bribery that are more measurable (the giving and/or requesting of bribes) and it limits the scope to the public sector.</p>\n<p>The concept of undue advantage is operationalized by reference to giving of money (in addition to an official fee), gifts or provision of a service requested/offered by/to a public official in exchange for a special treatment.</p>\n<p>This indicator captures the often called &#x2018;administrative bribery&#x2019;, which is often intended as the type of bribery affecting citizens in their dealings with public administrations and/or civil servants.</p>\n<p>For this indicator, public official refers to persons holding a legislative, executive, administrative or judicial office. In the operationalization of the indicator, a list of selected officials and civil servants is used.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>UNODC. 2015. <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">International Classification of Crime for Statistical Purposes</a> (ICCS)</p>\n<p>UNODC. 2023. Statistical framework to measure corruption</p>", "SOURCE_TYPE__GLOBAL"=>"<p>This indicator is derived from household surveys on corruption experience and/or victimisation surveys with a module on bribery.</p>\n<p>The indicator refers to individual (&#x201C;direct&#x201D;) experiences of the respondent, who is randomly selected among the household members, while (&#x201C;indirect&#x201D;) experiences of bribery by other members should not be included. However, direct bribery experiences of the respondent can include instances where the giving of money (in addition to an official fee), gifts or provision of a service is done through someone else (e.g. middlemen). Experience of bribery is collected through a series of questions on concrete contacts and experiences of bribery with a list of public official and civil servants.</p>\n<p>The denominator refers only to those persons that had at least one direct interaction with a public official/civil servant as they form the population group at risk of experiencing bribery.</p>\n<p>United Nations Office on Drugs and Crime (UNODC) collects data on the prevalence of bribery through its annual data collection: the UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). The data collection through the UN-CTS is facilitated by a network of over 140 national Focal Points appointed by responsible authorities.</p>", "COLL_METHOD__GLOBAL"=>"<p>At international level, data are collected by United Nations Office on Drugs and Crime (UNODC) through the annual UN-CTS data collection. Data are on bribery indicator are sent to UNODC by member states, usually through national UN-CTS Focal Points (over 140 appointed Focal Points) which in most cases are national institutions responsible for data production in the area of crime and criminal justice (National Statistical Offices, Ministry of Interior, Ministry of Justice, etc.). For countries that have not appointed a focal point, the request for data is sent to the permanent mission in Vienna. When a country does not report to UNODC, other official sources such as authoritative websites, publications, or other forms of communication are used. Once consolidated, data are shared with countries to check their accuracy and validity.</p>", "FREQ_COLL__GLOBAL"=>"<p>III-IV quarter year n</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>II quarter year n+1 (data for year n-1). For instance, data for the year 2022 are collected in III-IV quarter 2023 and released in II quarter 2024.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The primary source of data on the indicator of bribery experience is usually the institution responsible for surveys on corruption/victimisation surveys (National Statistical Office, Anti-Corruption Agency, etc.).</p>\n<p>Data on bribery are sent to UNODC by member states, usually through national UN-CTS Focal Points which in most cases are national institutions responsible for data production in the area of crime and criminal justice (National Statistical Offices, Ministry of Interior, Ministry of Justice, etc.). </p>", "COMPILING_ORG__GLOBAL"=>"<p><strong>Name:</strong></p>\n<p>United Nations Office on Drugs and Crime (UNODC)</p>\n<p><strong>Description:</strong></p>\n<p>At the international level, data are routinely collected and disseminated by the United Nations Office on Drugs and Crime (UNODC) through the annual UN Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS) data collection. UNODC partners with regional organizations in the collection and dissemination of data, respectively with Eurostat in Europe and with the Organisation of American States in the Americas. </p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) &#x2013; as custodian of the UN standards and norms in crime prevention and criminal justice, UNODC assists Member States in reforming their criminal justice systems in order to be effective, fair and humane for the entire population. UNODC develops technical tools to assist Member States in implementing the UN standards and norms and supports Member States through the provision of technical assistance in crime prevention and criminal justice reform. It does so through several Global programmes and through the UNODC field office network.</p>\n<p>UNODC is responsible for carrying out the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS), which was introduced through the General Assembly Resolution <a href=\"https://undocs.org/en/A/RES/3021(XXVII)\">A/RES/3021(XXVII)</a> in 1972. The Economic and Social Council, in its resolution 1984/48 of 25 May 1984, further requested that the Secretary-General maintain and develop the United Nations crime-related database by continuing to conduct surveys of crime trends and the operations of criminal justice systems. </p>", "RATIONALE__GLOBAL"=>"<p>Corruption is an antonym of equal accessibility to public services and of correct functioning of the economy; as such, it has a negative impact on fair distribution of resources and development opportunities. Besides, corruption erodes public trust in authorities and the rule of law; when administrative bribery becomes a recurrent experience of large sectors of the population and businesses, its negative effects have an enduring negative impact on the rule of law, democratic processes and justice. By providing a direct measure of the experience of bribery, this indicator provides an objective metric of corruption, a yardstick to monitor progress in the fight against corruption</p>", "REC_USE_LIM__GLOBAL"=>"<p>Bribery prevalence in the SDG indicator framework is defined as the percentage of persons who paid at least one bribe (gave a public official money, a gift or counter favour) to a public official, <u>or were asked for a bribe by these public officials</u>, in the last 12 months, as a percentage of persons who had at least one contact with a public official in the same period.</p>\n<p>In this formulation, the share of the population in contact with public officials who was asked to pay a bribe (but did not give it) is to beincluded in the numerator of the indicator. However, several historical and on-going survey programmes implemented at the national and international level do not include experiences of bribery refusal in the formulation and prevalence computation. It is expected that data according to the preferred definition (which includes bribery refusal) will become increasingly available at national and global level, as standardised question wording and indicator computation are applied.</p>\n<p>On a more general level, it should be noted that this indicator provides information on the experiences of bribery occurring during interactions between citizens and the public sector, while it does not cover other forms of corruption, such as &#xB4;grand corruption&#xB4;, trading in influence, or abuse of power.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated as the total number of persons who paid at least one bribe to a public official (or were asked for a bribe) in the last 12 months, over the total number of persons who had at least one contact with a public official in the same period, multiplied by 100.</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>B</mi>\n    <mi>r</mi>\n    <mi>i</mi>\n    <mi>b</mi>\n    <mi>e</mi>\n    <mi>r</mi>\n    <mi>y</mi>\n    <mi>&amp;nbsp;</mi>\n    <mi>p</mi>\n    <mi>r</mi>\n    <mi>e</mi>\n    <mi>v</mi>\n    <mi>a</mi>\n    <mi>l</mi>\n    <mi>e</mi>\n    <mi>n</mi>\n    <mi>c</mi>\n    <mi>e</mi>\n    <mo>=</mo>\n    <mn>100</mn>\n    <mi>*</mi>\n    <mfrac>\n      <mrow>\n        <mi>B</mi>\n      </mrow>\n      <mrow>\n        <mi>C</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>Where <em>B</em> refers to the number of people who paid a bribe to or were asked for a bribe by public official in the last 12 months, and <em>C </em>refers to the total number of people who had contact with public officials in the last 12 months.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Following the submission of the CTS questionnaire, UNODC checks the submitted data for consistency and coherence with other data sources. For survey-based indicators, metadata are assessed in relation to the representativeness and coverage of the survey as well as alignment of question wording and answer options with international standards. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation. The Organization for American States is also reviewing the responses of its Member States. All data submitted by Member States through other means or taken from other sources are added to the dataset after review and validation by Member States.</p>\n<p> </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values are not imputed.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<p>In 2018, UNODC together with the United Nations Development Program (UNDP) and the UNODC-INEGI Centre of Excellence published the Manual on Corruption Surveys, which provides Member States with detailed guidelines not only for planning and implementing corruption surveys, but for also analyzing and reporting on corruption survey data. The Manual deals with deals with corruption surveys among the general population as well as businesses. The Manual is available in multiple UN languages at: <a href=\"https://www.unodc.org/unodc/data-and-analysis/corruption-manuals.html\">https://www.unodc.org/unodc/data-and-analysis/corruption-manuals.html</a> </p>\n<p>In 2022, the United Nations Office on Drugs and Crime (UNODC) together with the United Nations Development Program (UNDP) and the Office of the United Nations High Commissioner on Human Rights (OHCHR) published the SDG 16 Survey Questionnaire and Implementation Manual, which contain internationally standardised survey question wording (in the five official UN languages) as well as implementation guidance related to this indicator. The questionnaire and manual are available in multiple languages at:</p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire</a></p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual</a> </p>\n<p>UNODC. 2023. Statistical framework to measure corruption</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The United Nations Office on Drugs and Crime (UNODC) has a statistical section with dedicated staff to support the data collection through technical assistance, collating and verifying the received data and continuously improve data collection mechanisms including guidelines.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>It is recommended that National Statistics Offices (NSOs) serve as the main contact for compiling and assuring the quality of the necessary data to report on SDG 16.5.1, in close coordination with other relevant bodies in the country. Automated and substantive validation procedures are in place when data are processed by custodian agencies to assess their consistency and compliance with standards.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See section 4.d Validation</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>More than 120 countries have at least one data point on bribery prevalence based on a nationally representative survey. A growing number of countries are implementing surveys using similar methodologies in order to assess the prevalence of bribery experiences in the population. However, the scale and methods of administration of the surveys vary. </p>\n<p><strong>Time series:</strong></p>\n<p>The indicator has recently been included into the annual United Nations Crime Trends Survey (UN-CTS, the regular data collection used by UNODC to collect data from UN Member States. It is expected that countries will gradually report on this indicator as the methodological guidance is disseminated and relevant items are included in national surveys.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Recommended disaggregation for this indicator are by:</p>\n<ul>\n  <li>age and sex of the bribe-givers</li>\n  <li>type of official (police officer, health care worker, customs officer, etc.)ethe bribe-givers</li>\n  <li>educational attainment of the bribe-givers</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>If data from more than one survey are available for the same country, discrepancies may arise due to different wording of the questions, different structure of the questionnaire, different survey methods and operations, different sample design and sample size. As a rule, data from national surveys complying with recommended international standards are used, when available.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL and References:</strong></p>\n<p><a href=\"http://www.unodc.org\">www.unodc.org</a> </p>\n<p><a href=\"https://dataunodc.un.org/sdgs\">https://dataunodc.un.org/sdgs</a></p>\n<p>General information on UNODC&#x2019;s work related to corruption surveys: <a href=\"https://www.unodc.org/unodc/data-and-analysis/corruption.html\">https://www.unodc.org/unodc/data-and-analysis/corruption.html</a></p>\n<p>Platform for accessing micro-data on corruption surveys: <a href=\"https://dataunodc.un.org/content/microdata\">https://dataunodc.un.org/content/microdata</a> </p>\n<p>UNODC. 2015. International Classification of Crime for Statistical Purposes (ICCS)</p>\n<p><a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\">https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html</a> </p>\n<p>UNODC-UNDP-UNODC-INEGI CoE. 2018. Manual on Corruption Surveys.</p>\n<p><a href=\"https://www.unodc.org/unodc/data-and-analysis/corruption-manuals.html\">https://www.unodc.org/unodc/data-and-analysis/corruption-manuals.html</a> </p>\n<p>UNODC-UNDP-OHCHR. 2022. SDG 16 Survey Questionnaire and Implementation Manual. Available at:</p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-questionnaire</a></p>\n<p><a href=\"https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual\">https://www.sdg16hub.org/topic/sdg-16-survey-initiative-implementation-manual</a> </p>\n<p>UNODC. 2023. Statistical framework to measure corruption</p>", "indicator_sort_order"=>"16-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.5.2", "slug"=>"16-5-2", "name"=>"Proporción de negocios que han tenido al menos un contacto con un funcionario público y que han pagado un soborno a un funcionario público, o a los que un funcionario público les ha pedido un soborno, durante los últimos 12 meses", "url"=>"/site/es/16-5-2/", "sort"=>"160502", "goal_number"=>"16", "target_number"=>"16.5", "global"=>{"name"=>"Proporción de negocios que han tenido al menos un contacto con un funcionario público y que han pagado un soborno a un funcionario público, o a los que un funcionario público les ha pedido un soborno, durante los últimos 12 meses"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de negocios que han tenido al menos un contacto con un funcionario público y que han pagado un soborno a un funcionario público, o a los que un funcionario público les ha pedido un soborno, durante los últimos 12 meses", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de negocios que han tenido al menos un contacto con un funcionario público y que han pagado un soborno a un funcionario público, o a los que un funcionario público les ha pedido un soborno, durante los últimos 12 meses", "indicator_number"=>"16.5.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nLa razón de ser de este indicador es determinar si se solicitan obsequios o \npagos informales (es decir, sobornos) a las empresas al realizar transacciones \nque involucran a funcionarios públicos. \n\nSolicitar licencias regulatorias, obtener conexiones de servicios públicos y \npagar impuestos se exigen formalmente en la mayoría de los países; por lo tanto, \nla razón de ser de este indicador es medir la incidencia de la corrupción \nen estas transacciones rutinarias.\n\nLa principal fortaleza de la Encuesta Empresarial reside en que la mayoría de las preguntas \nse refieren a la experiencia cotidiana de la empresa; estas preguntas sobre corrupción \nno se basan en opiniones, sino en la realidad cotidiana de la empresa.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.5.2&seriesCode=IC_FRM_BRIB&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Incidencia del soborno (porcentaje de empresas que experimentaron al menos una solicitud de pago de soborno) IC_FRM_BRIB</a> UNSTATS", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-05-02.pdf\">Metadatos 16-5-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-05-20", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe rationale for this indicator is to ascertain whether firms are solicited for gifts or informal payments \n(i.e. bribes) when undertaking transactions that involve public officials. \n\nApplying for regulatory licenses, \nobtaining utility connections, and paying taxes are required of formal forms in most countries and hence \nthe rational for this indicator is to measure the incidence of corruption during these routine transactions. \n\nThe key strength of the Enterprise Survey is that most of the questions in the survey pertain to the actual, \nday-to-day experiences of the firm; these questions regarding corruption are not opinion-based question \nbut rather are grounded in the firm’s day-to-day reality. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.5.2&seriesCode=IC_FRM_BRIB&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Bribery incidence (% of firms experiencing at least one bribe payment request) IC_FRM_BRIB</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-05-02.pdf\">Metadata 16-5-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nLa corrupción es antónimo de la igualdad de acceso a los servicios públicos y \ndel correcto funcionamiento de la economía; por lo tanto, tiene un impacto \nnegativo en la distribución justa de los recursos y las oportunidades de desarrollo. \n\nAdemás, la corrupción erosiona la confianza pública en las autoridades y el Estado \nde derecho; cuando el soborno administrativo se convierte en una experiencia \nrecurrente para amplios sectores de la población y las empresas, sus efectos \nnegativos tienen un impacto duradero en el Estado de derecho, los procesos democráticos \ny la justicia. \n\nAl proporcionar una medida directa de la experiencia de soborno, este indicador \nproporciona una métrica objetiva de la corrupción, un criterio para \nmonitorear el progreso en la lucha contra la corrupción.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.5.2&seriesCode=IC_FRM_BRIB&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Eroskeriaren intzidentzia (gutxienez eroskeria ordaintzeko eskaera bat izan zuten enpresen ehunekoa) IC_FRM_BRIB</a> UNSTATS", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-05-01.pdf\">Metadatuak 16-5-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.5: Substantially reduce corruption and bribery in all their forms</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.5.2: Proportion of businesses that had at least one contact with a public official and that paid a bribe to a public official, or were asked for a bribe by those public officials during the previous 12 months</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Bribery incidence (% of firms experiencing at least one bribe payment request) </p>", "META_LAST_UPDATE__GLOBAL"=>"2023-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>16.5.1: Proportion of persons who had at least one contact with a public official and who paid a bribe to a public official, or were asked for a bribe by those public officials, during the previous 12 months</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank (WB)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank (WB)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The percent of firms experiencing at least one bribe payment request across 6 public transactions dealing with utilities access, permits, licenses, and taxes. </p>\n<p>In every Enterprise Survey (<a href=\"http://www.enterprisesurveys.org\">www.enterprisesurveys.org</a>), there are standard questions which ask the survey respondent if they were expected to give a gift or informal payment during a transaction with a public official. There are six, separate transactions which make up this indicator, they include an application for an electrical connection, an application for a water connection, an application for a construction-related permit, an application for an import license, an application for an operating license, and during an inspection/meeting with tax officials. In all of these transactions, if the respondent indicates &#x2018;yes&#x2019; they had the transaction (e.g. they applied for an import license), then there is a follow-up question which asks if the respondent was expected to provide a gift or an informal payment during this transaction (an application or meeting). The response options include &#x201C;yes&#x201D;, &#x201C;no&#x201D;, &#x201C;don&#x2019;t know&#x201D;,and &#x201C;refuse&#x201D;. Note that refusals are accepted and recorded but for the purposes of indicator construction, refusals are considered as a &#x2018;yes&#x2019;. The indicator 16.5.2 is measuring whether the respondent indicated &#x2018;yes&#x2019; to a bribe payment for any of these six transactions.</p>\n<p>Enterprise Surveys are firm-level surveys conducted in World Bank client countries. The survey focuses on various aspects of the business environment as well as firm&#x2019;s outcome measures such as annual sales, productivity, etc. The surveys are conducted via face-to-face interviews with the top manager or business owner. For each country, the survey is conducted approximately every 4-5 years.</p>\n<p><strong>Concepts:</strong></p>\n<p>The respondents to the Enterprise Survey are firms- either manufacturing or services establishments. These are registered (formal) firms with 5+ employees. The firms are either fully or partially private (100% state-owned firms are ineligible for the Enterprise Survey). More information on the survey methodology can be found on the Methodology page of the website: <a href=\"http://www.enterprisesurveys.org/methodology\">www.enterprisesurveys.org/methodology</a></p>\n<p>A gift or an informal payment is considered a &#x2018;bribe&#x2019;.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) of firms experiencing at least one bribe payment request</p>", "CLASS_SYSTEM__GLOBAL"=>"<p><a href=\"https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf\">International Standard Industrial Classification of all Economic Activities (ISIC) Revision 4</a></p>", "SOURCE_TYPE__GLOBAL"=>"<p>The website for Enterprise Surveys (<a href=\"http://www.enterprisesurveys.org\">www.enterprisesurveys.org</a>) provides all metadata, including survey questionnaires and implementation reports for all Enterprise Surveys. The implementation reports indicate the sample size, sample frame used, dates/duration of fieldwork, the response rates, etc.</p>\n<p>Registration to the Enterprise Survey&#x2019;s website is free and the website&#x2019;s data portal allows users to access the raw data and survey documentation for each survey.</p>", "COLL_METHOD__GLOBAL"=>"<p>The World Bank conducts the Enterprise Surveys in client countries. The surveys are comparable as the survey methodology is applied in a consistent manner across countries: obtaining suitable sample frames, eligibility criteria for respondent firms, survey sample design, core questionnaire elements across every country, standardized QC checks on the received data, standardized computation of sampling weights, etc. </p>", "FREQ_COLL__GLOBAL"=>"<p>The Surveys are ongoing. Information on current projects can be found at: <a href=\"http://www.enterprisesurveys.org/Methodology/Current-projects\">http://www.enterprisesurveys.org/Methodology/Current-projects</a></p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The indicators on the Enterprise Surveys website are updated whenever a new survey has been completed and uploaded to the website. For each country, only the most recently completed survey is used when calculating the indicator.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The indicator is derived from Enterprise Surveys which are conducted by the World Bank. The World Bank usually hires a private contractor (typically a market research company) to conduct the survey fieldwork.</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Bank</p>", "INST_MANDATE__GLOBAL"=>"<p>The World Bank conducts Enterprise Surveys across the world, but mostly in developing countries. There is an institutional mandate that this data be collected and released for the public good of information. All of the Enterprise Surveys data is anonymized and published on the Enterprise Surveys website. The data can be downloaded, free of charge, for all registered users of the website&#x2019;s data portal. </p>", "RATIONALE__GLOBAL"=>"<p>The rationale for this indicator is to ascertain whether firms are solicited for gifts or informal payments (i.e. bribes) when undertaking transactions that involve public officials. Applying for regulatory licenses, obtaining utility connections, and paying taxes are required of formal forms in most countries and hence the rational for this indicator is to measure the incidence of corruption during these routine transactions. The key strength of the Enterprise Survey is that most of the questions in the survey pertain to the actual, day-to-day experiences of the firm; these questions regarding corruption are not opinion-based question but rather are grounded in the firm&#x2019;s day-to-day reality.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The key strength of the Enterprise Survey is that most of the questions in the survey pertain to the actual, day-to-day experiences of the firm; these questions regarding corruption are not opinion-based question but rather are grounded in the firm&#x2019;s day-to-day reality.</p>\n<p>The limitations include that some countries&#x2019; data is almost 10 years old (e.g. Burkina Faso and Brazil). This is due to the fact that these face-to-face survey projects can be expensive in some countries and hence due to budget limitations, the World Bank hasn&#x2019;t been able to update some of the Enterprise Surveys data in a subset of countries. Another limitation is that the surveys are done mostly in World Bank client countries and hence several high-income countries are not covered by the surveys (US, Canada, UK, Singapore, Japan, GCC countries, etc.).</p>\n<p>Another limitation may be the sensitive nature of corruption. In some countries/cultures, firms may not be comfortable answering questions on corruption. Although the data is collected under the context of confidentiality, firms may refuse to answer the question if they have been subject to bribery solicitations. Hence, in some countries, the actual incidence of this particular type of corruption may be higher than the calculated indicator value.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated for each country, by looking at the proportion of firms which answered &#x2018;yes&#x2019; to the survey questions. For all Enterprise Survey projects conducted since 2006, the resulting dataset has sampling weights. Hence the indicator value, which is computed using Stata, incorporates these sampling weights as well as the design strata.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>This indicator is computed using data collected from the World Bank&#x2019;s Enterprise Surveys. A detailed manual and guide on the Enterprise Surveys implementation is found here (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise%20Surveys_Manual%20and%20Guide.pdf). Section 4.4 &#x201C;Data Collection Cycle&#x201D; of this document describes the processes in place used to validate or check the survey data which is collected to ensure quality.</p>", "ADJUSTMENT__GLOBAL"=>"<p>For any given survey, during the quality checks outlined in the Enterprise Surveys manual and guide (section 4.4), if inconsistencies or mistakes are found in the data, the World Bank transmits this feedback to the fieldwork team that is conducting the survey in the first place. The fieldwork team should make sure that any data mistakes are corrected (or if the data is indeed correct, provide the justification to the World Bank) when submitting the final survey dataset.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>The indicator value is not imputed for countries which do not have an Enterprise Survey.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Regional and global aggregates of the indicator are derived from completed surveys. A single point estimate is created for each country and a global/regional aggregate takes a simple average of every country&#x2019;s point estimate (when there is available data for that country). For example the East Asia Pacific average (point estimate) for the indicator does not include Japan since there is no Enterprise Survey for Japan.</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates are computed by taking the simple average of the indicator value for all relevant countries. When producing regional and global aggregates as presented on the Enterprise Surveys website, note that only surveys posted during years 2010 onwards are used.</p>", "DOC_METHOD__GLOBAL"=>"<p>We recommend users consult the Enterprise Surveys website to learn about the overall survey methodology and learn which countries are available for benchmarking purposes. <a href=\"http://www.enterprisesurveys.org/methodology\">http://www.enterprisesurveys.org/methodology</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>A detailed manual and guide on the Enterprise Surveys implementation is found here (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise%20Surveys_Manual%20and%20Guide.pdf). This manual provides a comprehensive overview of the quality management of the Enterprise Surveys.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The process of quality assurance includes the review of survey questionnaires/documentations/metadata, examination of reliability of data, and making sure they comply with international standards (e.g. workforce concepts in the survey questions correspond to International Labour Organization (ILO) standards), and examining the consistency and coherence within the data set as well as with the time series of data and the resulting indicators.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>When conducting our survey projects, the implementing fieldwork team must send periodic batches of completed interviews to the World Bank so that we can run our own quality control programs on the data. After running these programs, we provide the QC feedback to the implementing fieldwork team so that survey data, which has been flagged, can be verified and continuously improved. This is how we continuously monitor the survey data while the projects are in the field.</p>\n<p>The World Bank collects this survey data for the public good of information. For an individual survey project, once the data is collected and considered finalized (after our own internal QC processes), the survey data is published on the World Bank&#x2019;s Enterprise Surveys website.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data for around 154 economies were collected.</p>\n<p><strong>Time series:</strong></p>\n<p>Surveys are implemented in around 50 countries every year. Data frequency for each country is around 4-5 years.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The Enterprise Survey captures several descriptive characteristics of the respondent firms including: gender of top manager, primary business activity of the firm, subnational location of the firm, exporting status, number of employees, degree of foreign ownership, and several other characteristics. Hence the indicator can be disaggregated by the levels of these individual characteristics.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>We are unaware of any country-produced data on this indicator.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.worldbank.org\">www.worldbank.org</a> </p>\n<p><strong>References:</strong></p>\n<ul>\n  <li><a href=\"http://www.enterprisesurveys.org\">www.enterprisesurveys.org</a> </li>\n  <li><a href=\"http://www.enterprisesurveys.org/methodology\">www.enterprisesurveys.org/methodology</a> </li>\n  <li><a href=\"http://www.enterprisesurveys.org/data/exploretopics/corruption\">http://www.enterprisesurveys.org/data/exploretopics/corruption</a> </li>\n</ul>", "indicator_sort_order"=>"16-05-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.6.1", "slug"=>"16-6-1", "name"=>"Gastos primarios del gobierno en proporción al presupuesto aprobado originalmente, desglosados por sector (o por códigos presupuestarios o elementos similares)", "url"=>"/site/es/16-6-1/", "sort"=>"160601", "goal_number"=>"16", "target_number"=>"16.6", "global"=>{"name"=>"Gastos primarios del gobierno en proporción al presupuesto aprobado originalmente, desglosados por sector (o por códigos presupuestarios o elementos similares)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Gastos primarios del gobierno en proporción al presupuesto aprobado originalmente, desglosados por sector (o por códigos presupuestarios o elementos similares)", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Gastos primarios del gobierno en proporción al presupuesto aprobado originalmente, desglosados por sector (o por códigos presupuestarios o elementos similares)", "indicator_number"=>"16.6.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Gasto liquidado en proporción al gasto inicial de los presupuestos de las administraciones públicas autonómicas y locales", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.6- Crear a todos los niveles instituciones eficaces y transparentes que rindan cuentas", "definicion"=>"Gasto liquidado en proporción al gasto inicial de los presupuestos de las  administraciones públicas autonómicas y locales", "formula"=>"\n$$PG_{administración\\, pública}^{t} = \\frac{GL_{administración\\, pública}^{t}}{GI_{administración\\, pública}^{t}} \\cdot 100$$ <br>\n\ndonde:\n\n$GL_{administración\\, pública}^{t} =$ gasto total en la liquidación de presupuestos consolidados de la administración pública (administración autonómica o administración local) en el año $t$\n\n$GI_{CCAA}^{t} =$ gasto total en el presupuesto inicial consolidado de la administración pública (administración autonómica o administración local) en el año $t$\n", "desagregacion"=>"Administración pública: administración autonómica y administración local\n", "observaciones"=>"Los datos consolidados se toman depurados de IFL (intermediación financiera local) y PAC (política agrícola común).\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador intenta captar la fiabilidad de los presupuestos gubernamentales: \n¿los gobiernos gastan lo que tienen previsto y recaudan lo que se han propuesto recaudar?\n\nEs un indicador sencillo e intuitivo que se entiende fácilmente y la metodología es \ntransparente y cada calificación es fácilmente verificable.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-06-01.pdf\">Metadatos 16-6-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Gasto liquidado en proporción al gasto inicial de los presupuestos de las administraciones públicas autonómicas y locales", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.6- Crear a todos los niveles instituciones eficaces y transparentes que rindan cuentas", "definicion"=>"Liquidated expenditure in proportion to the initial expenditure of the budgets of regional and  local public administrations ", "formula"=>"\n$$PG_{public\\, aministration}^{t} = \\frac{GL_{public\\, administration}^{t}}{GI_{public\\, administration}^{t}} \\cdot 100$$ <br>\n\nwhere:\n\n$GL_{public\\, administration}^{t} =$ total expenditure on the settlement of consolidated budgets of the public administration (autonomous administration or local administration) in year $t$\n\n$GI_{public\\, administration}^{t} =$ total expenditure in the initial consolidated budget of the public administration (autonomous administration or local administration) in year $t$\n", "desagregacion"=>"Public administration: autonomous administration; local administration\n", "observaciones"=>"The consolidated data are taken after being cleaned from IFL (local financial intermediation) and CAP (common agricultural policy).\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The indicator attempts to capture the reliability of government budgets: do governments spend what they \nintend to and do they collect what they set out to collect? \n\nIt is a simple and intuitive indicator that is easily \nunderstood and the methodology is transparent and every rating easily verifiable. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-06-01.pdf\">Metadata 16-6-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Gasto liquidado en proporción al gasto inicial de los presupuestos de las administraciones públicas autonómicas y locales", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.6- Crear a todos los niveles instituciones eficaces y transparentes que rindan cuentas", "definicion"=>"Gasto liquidado en proporción al gasto inicial de los presupuestos de las  administraciones públicas autonómicas y locales", "formula"=>"\n$$PG_{administrazio\\, publikoa}^{t} = \\frac{GL_{administrazio\\, publikoa}^{t}}{GI_{administrazio\\, publikoa}^{t}} \\cdot 100$$ <br>\n\nnon:\n\n$GL_{administrazio\\, publikoa}^{t} =$ administrazio publikoaren (administrazio autonomikoa edo toki-administrazioa) aurrekontu bateratuen likidazioko guztizko gastua $t$ urtean\n\n$GI_{administrazio\\, publikoa}^{t} =$ administrazio publikoaren (autonomia-administrazioa edo toki-administrazioa) hasierako aurrekontu bateratuko guztizko gastua $t$ urtean\n", "desagregacion"=>"Administrazio publikoa: autonomia-administrazioa; toki-administrazioa\n", "observaciones"=>"Los datos consolidados se toman depurados de IFL (intermediación financiera local) y PAC (política agrícola común).\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador intenta captar la fiabilidad de los presupuestos gubernamentales: \n¿los gobiernos gastan lo que tienen previsto y recaudan lo que se han propuesto recaudar?\n\nEs un indicador sencillo e intuitivo que se entiende fácilmente y la metodología es \ntransparente y cada calificación es fácilmente verificable.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-06-01.pdf\">Metadatuak 16-6-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.6: Develop effective, accountable and transparent institutions at all levels</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.6.1: Primary government expenditures as a proportion of original approved budget, by sector (or by budget codes or similar)</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>GF_XPD_GBPC - Primary government expenditures as a proportion of original approved budget [16.6.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank (WB)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank (WB)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Primary government expenditures as a proportion of original approved budget</p>\n<p>This indicator measures the extent to which aggregate budget expenditure outturn reflects the amount originally approved, as defined in government budget documentation and fiscal reports. The coverage is budgetary central government (BCG) and the time period covers every fiscal year for the countries..</p>\n<p><strong>Concepts:</strong></p>\n<p>Aggregate expenditure includes actual expenditures incorporating those incurred as a result of unplanned or exceptional events&#x2014;for example, armed conflicts or natural disasters. Expenditures financed by windfall revenues, including privatization, should be included and noted in the supporting fiscal tables and narrative. Expenditures financed externally by loans or grants should be included, if covered by the budget, along with contingency vote(s) and interest on debt. Expenditure assigned to suspense accounts is not included in the aggregate. However, if amounts are held in suspense accounts at the end of any year that could affect the scores if included in the calculations, they can be included. In such cases the reason(s) for inclusion must be clearly stated.</p>\n<p>Actual expenditure outturns can deviate from the originally approved budget for reasons unrelated to the accuracy of forecasts&#x2014;for example, as a result of a major macroeconomic shock. The calibration of this indicator accommodates one unusual or &#x201C;outlier&#x201D; year and focuses on deviations from the forecast which occur in two of the three years covered by the assessment.</p>\n<p>Very detailed resources are available at <a href=\"http://www.pefa.org\">www.pefa.org</a>. The document directly related to the SDG Indicator 16.6.1 is the &#x201C;PEFA Framework for assessing public financial management&#x201D; : <a href=\"https://www.pefa.org/resources/pefa-2016-framework\">https://www.pefa.org/resources/pefa-2016-framework</a>). There are seven Pillars in this document containing a total of 31 indicators. The pillar containing the indicator PI-1 corresponding to SDG 16.6.1 is part of Pillar I which measures Budget reliability. </p>\n<p>The SDG 16.6.1 Indicator follows the definition and concept for PEFA PI-1 Indicator in PEFA Framework with the only difference that the budget deviations for PI-1 are computed based on three years country performance, while the SDG 16.6.1 indicator is based on the annual budgets deviations.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The raw Data collected in order to calculate indicator 16.6.1 are the initially Approved and Executed Budgets. Budget Laws of countries is the usual source of the approved budget of countries. The end-of-year fiscal reports (/budget execution reports) are the sources of the actual spending. This data is typically obtained from websites of the Ministry of Finance (MoF) or the national Parliament, or data are collected through communication with the MoF. Based on the SDG Data collected since 2017 the main sources of information are the Ministry of Finances in countries and additional sources could be: </p>\n<ul>\n  <li>End of the year Fiscal reports</li>\n  <li>Annual Financial Statements</li>\n  <li>Controller General Accounts</li>\n  <li>Federal Government Data</li>\n  <li>Department of Budget and Management</li>\n  <li>Supreme Audit Institutions</li>\n  <li>Federal Finance Administration /FFA</li>\n  <li>Statistics Institutions</li>\n</ul>", "COLL_METHOD__GLOBAL"=>"<p>PEFA Secretariat, a unit hosted by the WB, is collecting the data in cooperation with the WB Data department and using a collaborative approach using the WB network, that involves the WB Governance Practice managers, in charge of the WB regions, and country economists from the WB country offices, that have better access to the budgets data in the countries and better knowledge where to find the information (sometimes is available only in local languages). This method proved to be very successful.</p>", "FREQ_COLL__GLOBAL"=>"<p>The data is collected in the beginning of the fiscal year, as requested by UN. Additional provided data during the year is updated and shared during the next year cycle of data collection.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The data is updated to meet the deadlines for submission to UN. </p>", "DATA_SOURCE__GLOBAL"=>"<p>WB country offices obtain the data mainly form the Ministry of Finances of countries. Additional sources are: </p>\n<ul>\n  <li>End of the year Fiscal reports</li>\n  <li>Annual Financial Statements</li>\n  <li>Controller General Accounts</li>\n  <li>Federal Government Data</li>\n  <li>Department of Budget and Management</li>\n  <li>Supreme Audit Institutions</li>\n  <li>Federal Finance Administration /FFA</li>\n  <li>Statistics Institutions</li>\n</ul>\n<p>The provided raw data is processed by PEFA Secretariat/WB/ and submitted to UN</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Bank</p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>The indicator attempts to capture the reliability of government budgets: do governments spend what they intend to and do they collect what they set out to collect. It is a simple and intuitive indicator that is easily understood and the methodology is transparent and every rating easily verifiable.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Although not all countries have used the PEFA methodology on an annual basis for the PEFA PI-1 indicator, the methodology relies on standard data sets for approved and final budget outturns which are commonly produced at least annually in every country. The countries that have not used the methodology to date are primarily highly developed countries which would have less difficulty in providing the necessary data than those in the lower and middle income categories that have been primary users of Public Expenditure and Financial Accountability (PEFA) to date.</p>\n<p>One limitation of the indicator is that it is an aggregate indicator of budget reliability. While it can be disaggregated across regions, it is not disaggregated across various budget subcomponents. Different indicators are used for assessing changes in expenditure composition in the PEFA framework. Also, while this indicator is intended to measure budget reliability it should be understood that actual expenditure outturns can deviate from the originally approved budget for reasons unrelated to the accuracy of forecasts&#x2014;for example, as a result of a major macroeconomic shock. However, the calibration of this indicator accommodates one unusual or &#x201C;outlier&#x201D; year and focuses on deviations from the forecast which occur in two of the three years covered by the assessment. Therefore, single year shocks are discounted allowing a more balanced assessment.</p>\n<p>The broader context in which the indicator was developed is as follows. PEFA is a tool for assessing the status of public financial management and reporting on the strengths and weaknesses of Public Financial Management (PFM). A PEFA assessment provides a thorough, consistent and evidence-based analysis of PFM performance at a specific point in time and can be reapplied in successive assessments to track changes over time. The PEFA framework provides the foundation for evidence-based measurement of countries&#x2019; PFM systems using 31 performance indicators that are further disaggregated into 94 dimensions. A PEFA assessment measures the extent to which PFM systems, processes and institutions contribute to the achievement of desirable budget outcomes: aggregate fiscal discipline, strategic allocation of resources, and efficient service delivery.</p>", "DATA_COMP__GLOBAL"=>"<p>The PEFA PI-1 Indicator (described below) is used as a basis for the SDG 16.6.1 Indicator, following the measurement guidance and coverage. In order to make the computation and the analysis of data over time easy and applicable for all countries, it was decided that SDG 16.6.1 indicator will be based on the annual data collection on approved and executed budgets for all countries and will be calculated annually. </p>\n<p>The simple calculation for every year for every country in the submitted excel sheet is for the</p>\n<p>Aggregate expenditure outturn = Executed Budget/Approved Budget*100</p>\n<p>In the countries and regional groupings, analysis of the deviations are done according regions/years/countries, using the requirements of PEFA PI-1 indictor below.</p>\n<p>Although the computation and scoring used for PI-1 indicator are not applied for the SDG 16.6.1 indicator, the categorization described below is applied and is the basis for the SDG 16.6.1 indicator.</p>\n<p>PEFA Methodology </p>\n<p>The methodology for calculating the PEFA PI-1 indicator is provided in a spreadsheet (titled &#x201C;<a href=\"https://www.pefa.org/resources/calculation-sheets-pefa-performance-indicators-pi-1-pi-2-and-pi-23-november-2018\">En PI-1 and PI-2 Exp Calculation-Feb 1 2016 (xls</a>)&#x201D;) and is based on the PEFA <a href=\"https://www.pefa.org/resources/pefa-2016-framework\">Public Expenditure and Financial Accountability (PEFA) Framework</a>. </p>\n<p>Scoring is at the heart of the indicator. A country is scored separately on a four-point ordinal scale: A, B, C, or D, according to precise criteria: </p>\n<p>(A) Aggregate expenditure outturn was between 95% and 105% of the approved aggregate budgeted expenditure in at least two of the last three years.</p>\n<p>(B) Aggregate expenditure outturn was between 90% and 110% of the approved aggregate budgeted expenditure in at least two of the last three years.</p>\n<p>(C Aggregate expenditure outturn was between 85% and 115% of the approved aggregate budgeted expenditure in at least two of the last three years.</p>\n<p>(D) Performance is less than required for a C score.</p>\n<p>In order to justify a particular score, every aspect specified in the scoring requirements must be fulfilled. If the requirements are only partly met, the criteria are not satisfied and a lower score should be given that coincides with achievement of all requirements for the lower performance rating. A score of C reflects the basic level of performance for each indicator and dimension, consistent with good international practices. A score of D means that the feature being measured is present at less than the basic level of performance or is absent altogether, or that there is insufficient information to score the dimension.</p>\n<p>The D score indicates performance that falls below the basic level. &#x2018;D&#x2019; is applied if the performance observed is less than required for any higher score. For this reason, a D score is warranted when sufficient information is not available to establish the actual level of performance. A score of D due to insufficient information is distinguished from D scores for low-level performance by the use of an asterisk&#x2014;that is, D* at the dimension level. The asterisk is not included at the indicator level.</p>\n<p>The coverage is budgetary central government (BCG) and requires data for three consecutive years as a basis for assessment. The data would cover the most recent completed fiscal year for which data is available and the two immediately preceding years.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The collected data cannot be directly validated, however using the WB network of experts at local level to collect the information form local sources gives a high potential of the credibility of the collected data. Another approach that confirms the validity of data is sharing the existing information during the following years of annual circulation to double check if the provided data is correct.</p>", "ADJUSTMENT__GLOBAL"=>"<p>If additional information is provided from the data source the data is adjusted every year.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>As the data collection is a complex process in finding the sources of information, the number of countries increases every year and the missing values are constantly filled. The target is to collect data for new countries and to fill the gap years for existing countries. Example: in 2018 the available data was for 60 countries, in 2023 the data is available for 171. In the current 2023 annual data collection - 16 new countries were added to the existing pool of data. It is expected that based on the created network, in the future data will be collected only for the last fiscal year.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>The regional aggregation according the UN regions is done annually, based on the collected data.</p>", "REG_AGG__GLOBAL"=>"<p>Not applicable</p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data Availability 2010 to present as at February 2023 (in terms of how many countries have at least 1 data point after 2010 for this indicator)</p>\n<p>New Zealand and Australia:1; Oceania:13; Central and Southern Asia:13; Eastern and South Eastern Asia: 12; Europe and North America: 37; Western Asia and Northern Africa: 18; Latin America and Carrebean: 30</p>\n<p>Sub-Saharan Africa: 47</p>\n<p>Data Availability 2000-2009:</p>\n<p>New Zealand and Australia:1; Oceania:13; Central and Southern Asia:13; Eastern and South Eastern Asia: 11; Europe and North America: 28; Western Asia and Northern Africa: 16; Latin America and Carrebean: 30</p>\n<p>Sub-Saharan Africa: 45</p>\n<p><strong>Time series:</strong></p>\n<p>On average all data available for 171 countries is for an average of 12 year period of time.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>This is an aggregate national level figure. However, subnational figures can be obtained for countries with decentralized government systems.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable as all figures are obtained from national budget data.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.worldbank.org\">www.worldbank.org</a> </p>\n<p><a href=\"https://data.worldbank.org/indicator/GF.XPD.BUDG.ZS\">World Bank Data Portal for SDG 16.6.1</a></p>\n<p><strong>References:</strong></p>\n<p>Very detailed resource on which is based the methodology of the indicator is the latest 2016 version of the &#x201C;<a href=\"https://www.pefa.org/resources/pefa-2016-framework\">PEFA Framework for Assessing Public Financial Management</a>&#x201D; that is available on PEFA Website: <a href=\"http://www.pefa.org\">www.pefa.org</a></p>\n<p>Data about PEFA Assessments could be found on <a href=\"https://www.pefa.org/assessments\">PEFA Assessments Portal</a></p>\n<p>Additional source of information on budget reliability is the <a href=\"https://www.pefa.org/global-report-2022/en/\">PEFA Global Report on PFM</a></p>\n<p>Publications on the SDG 16.6.1 Indicator are:</p>\n<p><a href=\"https://datatopics.worldbank.org/world-development-indicators/stories/government-budget-credibility-and-the-impact-of-covid-19.html\">Government Budget credibility and the Impact of COVID-19</a> &#x2013; published on the WB Story Site</p>\n<p>SDG Indicator 16.6.1 Speaks how Budgets are Affected by COVID-19 Pandemic</p>", "indicator_sort_order"=>"16-06-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.6.2", "slug"=>"16-6-2", "name"=>"Proporción de la población que se siente satisfecha con su última experiencia de los servicios públicos", "url"=>"/site/es/16-6-2/", "sort"=>"160602", "goal_number"=>"16", "target_number"=>"16.6", "global"=>{"name"=>"Proporción de la población que se siente satisfecha con su última experiencia de los servicios públicos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población que se siente satisfecha con el funcionamiento de los servicios públicos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población que se siente satisfecha con su última experiencia de los servicios públicos", "indicator_number"=>"16.6.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Centro de Investigaciones Sociológicas (CIS)", "periodicity"=>"Anual", "url"=>"https://www.cis.es/catalogo-estudios/avance-resultados", "url_text"=>"Opinión pública y política fiscal", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/CIS.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de la población que se siente satisfecha con el funcionamiento de los servicios públicos", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.6- Crear a todos los niveles instituciones eficaces y transparentes que rindan cuentas", "definicion"=>"Proporción media de la población que se siente muy o bastante satisfecha con el funcionamiento de los servicios públicos en materia de enseñanza, asistencia sanitaria, gestión de pensiones, administración de justicia, seguridad ciudadana, servicios sociales, transporte público, obras públicas y ayuda a personas dependientes.", "formula"=>"\n$$PPS^t = \\frac{1}{n} \\sum_{i=1}^{n} PPS_{i}^{t}$$\n\ndonde:\n\n$PPS_{i}^{t}$ representa la proporción de la población satisfecha con el funcionamiento de los servicios públicos en el año $t$ \npara las siguientes áreas: enseñanza, asistencia sanitaria, gestión de pensiones, \nadministración de justicia, seguridad ciudadana, servicios sociales, transporte \npúblico, obras públicas y ayuda a personas dependientes\n", "desagregacion"=>"\nServicios públicos: enseñanza, asistencia sanitaria, gestión de pensiones, \nadministración de justicia, seguridad ciudadana, servicios sociales, transporte \npúblico, obras públicas y ayuda a personas dependientes\n\nSexo\n\nTerritorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Los gobiernos tienen la obligación de proporcionar una amplia gama de servicios públicos \nque satisfagan las expectativas de sus ciudadanos en términos de acceso, capacidad de \nrespuesta y fiabilidad/calidad. \n\nCuando los ciudadanos no pueden permitirse algunos servicios esenciales, cuando su \nacceso geográfico o electrónico a los servicios y a la información es difícil, cuando \nlos servicios prestados no responden a sus necesidades y son de mala calidad, los \nciudadanos tenderán naturalmente a manifestar una menor satisfacción no solo con \nestos servicios, sino también con las instituciones públicas y los gobiernos. \n\nEn este sentido, se ha demostrado que la experiencia de los ciudadanos con los \nservicios públicos de primera línea afecta a su confianza en las instituciones públicas \n(OCDE 2017, Confianza y políticas públicas: cómo una mejor gobernanza puede ayudar a \nreconstruir la confianza pública; Eurofound 2018, Cambio social y confianza en \nlas instituciones).\n\nConscientes de esta estrecha relación entre la prestación y el rendimiento de los servicios, \nla satisfacción de los ciudadanos y la confianza pública, los gobiernos están cada vez \nmás interesados en comprender mejor las necesidades, experiencias y preferencias de \nlos ciudadanos para poder proporcionar servicios más específicos, incluso para las \npoblaciones desatendidas.\n\nMedir la satisfacción con los servicios públicos es el núcleo de un enfoque centrado en \nlos ciudadanos para la prestación de servicios y un indicador de resultados importante \ndel desempeño general del gobierno.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-06-02.pdf\">Metadatos 16-6-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Proporción de la población que se siente satisfecha con el funcionamiento de los servicios públicos", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.6- Crear a todos los niveles instituciones eficaces y transparentes que rindan cuentas", "definicion"=>"Average proportion of the population that feels very or fairly satisfied with the functioning of public services in the areas of education, healthcare, pension management, administration of justice, public security, social services, public transport, public works and assistance to dependent persons. ", "formula"=>"\n$$PPS^t = \\frac{1}{n} \\sum_{i=1}^{n} PPS_{i}^{t}$$\n\nwhere:\n\n$PPS_{i}^{t} =$ proportion of the population satisfied with the functioning of public services in year $t$ for the \nfollowing areas: education, health care, pension management, administration of justice, public security, social \nservices, public transport, public works and assistance to dependent persons\n", "desagregacion"=>"Public services: education; health care; pension management; administration of justice; public security; social \nservices; public transport; public works; assistance to dependent persons \n\nSex\n\nProvince\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Governments have an obligation to provide a wide range of public services that should meet the \nexpectations of their citizens in terms of access, responsiveness and reliability/quality. \n\nWhen citizens cannot afford some essential services, when their geographic or electronic access to services and \ninformation is difficult, when the services provided do not respond to their needs and are of poor quality, \ncitizens will naturally tend to report lower satisfaction not only with these services, but also with public \ninstitutions and governments. \n\nIn this regard, it has been shown that citizens’ experience with front-line \npublic services affects their trust in public institutions (OECD 2017, Trust and Public Policy – How Better \nGovernance Can Help Rebuild Public Trust; Eurofound 2018, Societal change and trust in institutions). \n\nMindful of this close connection between service provision/performance, citizen satisfaction and public \ntrust, governments are increasingly interested in better understanding citizens’ needs, experiences and \npreferences to be able to provide better targeted services, including for underserved populations. \n\nMeasuring satisfaction with public services is at the heart of a citizen-centered approach to service delivery \nand an important outcome indicator of overall government performance. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-06-02.pdf\">Metadata 16-6-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de la población que se siente satisfecha con el funcionamiento de los servicios públicos", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.6- Crear a todos los niveles instituciones eficaces y transparentes que rindan cuentas", "definicion"=>"Proporción media de la población que se siente muy o bastante satisfecha con el funcionamiento de los servicios públicos en materia de enseñanza, asistencia sanitaria, gestión de pensiones, administración de justicia, seguridad ciudadana, servicios sociales, transporte público, obras públicas y ayuda a personas dependientes.", "formula"=>"\n$$PPS^t = \\frac{1}{n} \\sum_{i=1}^{n} PPS_{i}^{t}$$\n\nnon:\n\n$PPS_{i}^{t} =$ $t$ urtean zerbitzu publikoen funtzionamenduarekin pozik dagoen biztanleriaren \nproportzioa honako arlo hauetarako: hezkuntza, osasun-arreta, pentsioen kudeaketa, \njustizia-administrazioa, herritarren segurtasuna, gizarte-zerbitzuak, garraio publikoa, herri-lanak \neta mendeko pertsonentzako laguntza \n", "desagregacion"=>"\nZerbitzu publikoak: hezkuntza; osasun-arreta; pentsioen kudeaketa; justizia-administrazioa; \nherritarren segurtasuna; gizarte-zerbitzuak; garraio publikoa; herri-lanak; mendeko pertsonentzako \nlaguntza\n\nSexua\n\nLurralde historikoa\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Los gobiernos tienen la obligación de proporcionar una amplia gama de servicios públicos \nque satisfagan las expectativas de sus ciudadanos en términos de acceso, capacidad de \nrespuesta y fiabilidad/calidad. \n\nCuando los ciudadanos no pueden permitirse algunos servicios esenciales, cuando su \nacceso geográfico o electrónico a los servicios y a la información es difícil, cuando \nlos servicios prestados no responden a sus necesidades y son de mala calidad, los \nciudadanos tenderán naturalmente a manifestar una menor satisfacción no solo con \nestos servicios, sino también con las instituciones públicas y los gobiernos. \n\nEn este sentido, se ha demostrado que la experiencia de los ciudadanos con los \nservicios públicos de primera línea afecta a su confianza en las instituciones públicas \n(OCDE 2017, Confianza y políticas públicas: cómo una mejor gobernanza puede ayudar a \nreconstruir la confianza pública; Eurofound 2018, Cambio social y confianza en \nlas instituciones).\n\nConscientes de esta estrecha relación entre la prestación y el rendimiento de los servicios, \nla satisfacción de los ciudadanos y la confianza pública, los gobiernos están cada vez \nmás interesados en comprender mejor las necesidades, experiencias y preferencias de \nlos ciudadanos para poder proporcionar servicios más específicos, incluso para las \npoblaciones desatendidas.\n\nMedir la satisfacción con los servicios públicos es el núcleo de un enfoque centrado en \nlos ciudadanos para la prestación de servicios y un indicador de resultados importante \ndel desempeño general del gobierno.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-06-02.pdf\">Metadatuak 16-6-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.6: Develop effective, accountable and transparent institutions at all levels</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.6.2: Proportion of population satisfied with their last experience of public services</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Applies to all series</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>SDG indicator 16.6.2, measured from citizen surveys, is an important complement to other SDG indicators assessing various aspects of public service provision that draw from administrative sources, such as SDG 3.8.1 on coverage of essential health services<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> and SDG 4.a.1 on school facilities<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup>. While these indicators focus on similar attributes as those measured by SDG 16.6.2, such as &#x2018;accessibility&#x2019; and &#x2018;quality of facilities&#x2019;, they may not reflect people&#x2019;s actual experience of education facilities or healthcare services due to the methodological challenges of collecting quality data from administrative sources. </p>\n<p>Amongst SDG indicators assessing various aspects of public service provision, indicator 1.4.1, which measures the &#x201C;proportion of population living in households with access to basic services&#x201D; has particular relevance to indicator 16.6.2:</p>\n<p>&#x2022; Indicator 1.4.1 measures &#x2018;Access to Basic Health Care Services&#x2019; by drawing on readily available data reported on SDG indicator 3.7.1 on access to reproductive health (Proportion of women of reproductive age (aged 15-49 years) who have their need for family planning satisfied with modern methods). Indicator 16.6.2 therefore provides important additional information by (1) broadening the scope of measurement from reproductive health to &#x2018;basic healthcare services&#x2019; as internationally defined, and (2) by assessing five key attributes of healthcare service provision not assessed by 1.4.1, namely access, affordability, quality of facilities, equal treatment for everyone and doctor&#x2019;s attitude, and (3) by using survey data to measure people&#x2019;s satisfaction with healthcare services based on their last experience.</p>\n<p>&#x2022; Indicator 1.4.1 also measures &#x2018;Access to Basic Education&#x2019; by drawing on readily available data reported on SDG indicator 4.1.1 on educational achievements (Percentage of children/young people: (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics). Indicator 16.6.2 therefore provides important additional information by (1) assessing four key attributes of education service provision not assessed by 1.4.1, namely access, affordability, quality of facilities and equal treatment for everyone, and (2) by using survey data (SDG 4.1.1 uses test scores) to measure people&#x2019;s satisfaction with education services based on their first-hand experience with such services.</p>\n<p>Indicator 16.6.2 can also be used to complement SDG target 10.2 on the promotion of the &#x201C;social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status&#x201D;, which only has one indicator measuring economic exclusion (SDG 10.2.1 &#x2013; Proportion of people living below 50 per cent of median income, by age, sex and persons with disabilities). Indicator 16.6.2 therefore provides important additional information to measure progress against this target by providing data on social inclusion. </p>\n<p>Similarly, 16.6.2 can also be used to complement SDG target 10.3 on &#x201C;Ensuring equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regard&#x201D;, which only has one indicator measuring felt discrimination on various grounds (SDG 10.3.1 Proportion of the population reporting having personally felt discriminated against or harassed within the previous 12 months on the basis of a ground of discrimination prohibited under international human rights law). Thus indicator 16.6.2 provides important additional information to measure progress against this target by helping to identify in which service area the incidence of discrimination is highest. </p>\n<p>Finally, SDG 16.6.2, with its focus on &#x2018;accessibility&#x2019;, &#x2018;equal treatment&#x2019; and other important attributes of public services, provides important complementary information to analyze results on SDG 16.5.1 on the &#x2018;Proportion of persons who had at least one contact with a public official and who paid a bribe to a public official, or were asked for a bribe by those public officials, during the previous 12 months&#x2019;. In other words, people may resort to bribery when the quality of public service provision is too poor, as revealed by SDG 16.6.2. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> 3.8.1 Coverage of essential health services (defined as the average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access, among the general and the most disadvantaged population) <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> 4.A.1 Proportion of schools with access to: (a) electricity; (b) the Internet for pedagogical purposes; (c) computers for pedagogical purposes; (d) adapted infrastructure and materials for students with disabilities; (e) basic drinking water; (f) single-sex basic sanitation facilities; and (g) basic handwashing facilities (as per the WASH indicator definitions) <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Development Programme (UNDP) </p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNDP Oslo Governance Centre</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator measures levels of public satisfaction with people&#x2019;s last experience with public services, in the three service areas of healthcare, education and government services (i.e. services to obtain government-issued identification documents and services for the civil registration of life events such as births, marriages and deaths)<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup>. This is a survey-based indicator which emphasizes citizens&#x2019; <em>experiences</em> over general perceptions, with an eye on measuring the availability and quality of services <em>as they were actually delivered to survey respondents</em>. </p>\n<p>Respondents are asked to reflect on their last experience with each service, and to provide a rating on five &#x2018;attributes&#x2019;, or service-specific standards, of healthcare, education and government services (such as access, affordability, quality of facilities, etc.). A final question asks respondents for their overall satisfaction level with each service. </p>\n<p>It is recommended that survey results, at a minimum, be disaggregated by sex, income and place of residence (urban/rural, administrative regions). To the extent possible, all efforts should be made to also disaggregate results by disability status and by &#x2018;nationally relevant population groups&#x2019;.</p>\n<p>A detailed questionnaire and implementation manual to produce the indicator is defined in the SDG 16 Survey Initiative<a href=\"https://www.undp.org/publications/sdg16-survey-initiative\"><strong><sup><sup></sup></sup></strong></a><strong><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a>:</strong> The questions for 16.6.2 on healthcare, education and government services can be inserted into existing surveys, using these surveys&#x2019; additional batteries on demographics for subsequent disaggregation of results. This modular &#x2018;add-on&#x2019; technique also allows for the cross-tabulation of satisfaction levels with other socioeconomic variables found in the larger survey, such as the health conditions of the respondent. This enables a more comprehensive analysis of disparities in the provision of services, and helps to pinpoint specific factors that influence satisfaction levels. </p>\n<p><strong>Concepts:</strong></p>\n<ul>\n  <li><strong>Public services:</strong> As stated by the United Nations High Commissioner for Human Rights, &#x201C;States are responsible for delivering a variety of services to their populations, including education, health and social welfare services. The provision of these services is essential to the protection of human rights such as the right to housing, health, education and food. The role of the public sector as service provider or regulator of the private provision of services is crucial for the realization of all human rights, particularly social and economic rights.&#x201D;<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup> </li>\n</ul>\n<p>While several definitions of &#x2018;public services&#x2019; exist, they tend to have in common a focus on &#x2018;common interest&apos; and on &#x2018;government responsibility&#x2019;. For instance, the European Commission defines such services as &#x201C;Services that public authorities of the Member States clarify as being of general interest and, therefore, subject to specific public service obligations.&#x201D;<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup> Similarly, the African Charter on Values and Principles of Public Service and Administration (African Union, 2011) defines a public service as &#x201C;Any service or public-interest activity that is under the authority of the government administration&#x201D;. </p>\n<ul>\n  <li><strong>Public services <em>&#x2018;of general interest&#x2019;: </em></strong>The methodology for SDG 16.6.2 carefully defines the scope of healthcare and education services to ensure that the focus is placed on services that are truly <em>of general interest</em>. In the case of healthcare services, for instance, preventive and primary healthcare services can be said to be truly &#x2018;of general interest&#x2019;: these services are relevant to everyone and they are most commonly found in both urban and rural areas. This might not be the case for hospitals that provide tertiary care, and as such hospital and specialist care is excluded from the questions on healthcare services. Likewise, in the case of education services, primary and lower secondary education services can be said to be truly &#x2018;of general interest&#x2019;, given their universality. University education, however, is excluded from the questions on education services. </li>\n  <li><strong>&#x2018;Last experience&#x2019; of public services in the past 12 months:</strong> Indicator 16.6.2 focuses on respondents&#x2019; &#x2018;last experience of public services&#x2019;, and specifies a reference period of &#x201C;the past 12 months&#x201D; to avoid telescoping effects and to minimize memory bias effects. This means that only respondents who will have used healthcare, education and government services in the past 12 months will proceed to answer the survey questions. </li>\n  <li><strong>Service-specific standards &#x2013; or &#x2018;attributes&#x2019;: </strong>The United Nations High Commissioner for Human Rights explains that &#x201C;A human rights-based approach to public services is integral to the design, delivery, implementation and monitoring of all public service provision. Firstly, the normative human rights framework provides an important legal yardstick for measuring how well public service is designed and delivered and whether the benefits reach rights-holders&#x201D;<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup>. For instance, the Committee on Economic, Social and Cultural Rights specifies that &#x201C;The availability, accessibility, acceptability and quality of health-related services should be facilitated and controlled by States. This duty extends to a variety of health-related services ranging from controlling the spread of infectious diseases to ensuring maternal health and adequate facilities for children.&#x201D;<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup> Similarly, with respect to education services, the same Committee underlines that &#x201C;States should adopt a human rights approach to ensure that [education services are] of an adequate standard and do not exclude any child on the basis of race, religion, geographical location or any other defining characteristic.&#x201D;<sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup> </li>\n  <li><strong>Healthcare services: </strong>The questions on healthcare services focus on respondents&#x2019; experiences (or that of a child in their household who needed treatment and was accompanied by the respondent) with <em>primary </em>healthcare services (over the past 12 months) &#x2013; that is, basic health care services provided by a government/public health clinic, or covered by a public health system. It can include health care services provided by private institutions, as long as such services are provided at reduced (or no) cost to beneficiaries, under a public health system. Respondents are specifically asked <em>not </em>to include in their answers any experience they might have had with hospital or specialist medical care services (for example, if they had a surgery), or with dental care and teeth exams (because in many countries, dental care is not covered by publicly funded healthcare systems). Attributes-based questions on healthcare services focus on 1) Accessibility (related to geographic proximity, delay in getting appointment, waiting time to see doctor on day of appointment); 2) Affordability; 3) Quality of facilities; 4) Equal treatment for everyone; and 5) Courtesy and treatment (attitude of healthcare staff).</li>\n  <li><strong>Education services: </strong>The questions on education services focuses on respondents&#x2019; experience with the <em>public school system</em> over the past 12 months, that is, if there are children in their household whose age falls within the age range spanning primary and secondary education in the country. Public schools are defined as &#x201C;those for which no private tuition fees or major payments must be paid by the parent or guardian of the child who is attending the school; they are state-funded schools.&#x201D; Respondents are asked to respond separately for primary and secondary schools if children in their household attend school at different levels. Attributes-based questions on education services focus on 1) Accessibility (with a focus on geographic proximity); 2) Affordability; 3) Quality of facilities; 4) Equal treatment for everyone; and 5) Effective delivery of service (Quality of teaching).</li>\n  <li><strong>Government services: </strong>The battery on government services focuses exclusively on two types of government services: 1) Services to obtain government-issued identification documents (such as national identity cards, passports, driver&#x2019;s licenses and voter&#x2019;s cards) and 2) services for the civil registration of life events such as births, marriages and deaths. This particular focus on these two types of services arises from the high frequency of use of these services. Attributes-based questions on government services focus on 1) Accessibility; 2) Affordability; 3) Equal treatment for everyone; 4) Effective delivery of service (delivery process is simple and easy to understand); and 5) Timeliness. </li>\n</ul>\n<p><strong>Selection of relevant disaggregation dimensions </strong></p>\n<ul>\n  <li><em>Relevant international legal frameworks: </em>Indicator 16.6.2 aims to provide a better understanding of how access to services and the quality of services differ across localities and across various demographic groups. This aim is supported by international human rights law: </li>\n  <li>Article 25 (c) of the International Covenant on Civil and Political Rights provides for the right to <em>equal access</em> to public service. In its report on the role of the public services as an essential component in the promotion and protection of human rights, the United Nations High Commissioner for Human Rights reminds that &#x201C;States must bear in mind that there are demographic groups in every society that may be disadvantaged in their access to public services, namely women, children, migrants, persons with disabilities, indigenous persons and older persons. States need to ensure that the human rights of these groups are not undermined and that they receive adequate public services.&#x201D;<sup><sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup></sup> The High Commissioner also calls attention to the fact that &#x201C;Poverty acts as a major barrier in relation to public services.&#x201D; </li>\n  <li>The obligations to ensure equality and non-discrimination are recognized in article 2 of the Universal Declaration of Human Rights and are encountered in many United Nations human rights instruments, such as the International Covenant on Civil and Political Rights (arts. 2 and 26), the International Covenant on Economic, Social and Cultural Rights (art. 2 (2)), the Convention on the Rights of the Child (art. 2), the International Convention on the Protection of the Rights of All Migrant Workers and Members of Their Families (art. 7) and the Convention on the Rights of Persons with Disabilities (art. 5). In terms of public services, this means that States have an immediate obligation to ensure that deliberate, targeted measures are put into place to secure substantive equality and that all individuals have an equal opportunity to enjoy their right to access public services.</li>\n  <li><em>Empirical analysis: </em>Statistical analysis of available datasets on citizen satisfaction with healthcare and education services<sup><sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup></sup> shows that the demographic variables that are most strongly correlated with satisfaction with healthcare and education services are (1) income (by far the strongest determinant of satisfaction levels), (2) sex, and (3) place of residence (rural/urban). There is no statistically significant association between the age of respondents and satisfaction levels. </li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> The formulation &#x2018;government services&#x2019; (also commonly called &#x2018;administrative services&#x2019;) is used in this metadata to mirror this more colloquial language used in the survey questionnaire. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> <em>Good Governance Practices for the Protection of Human Rights</em> (United Nations publication, Sales No. E.07.XIV.10), p. 38 &#x2013; cited in Report of the United Nations High Commissioner for Human Rights on the role of the public service as an essential component of good governance in the promotion and protection of human rights, Human Rights Council, 25<sup>th</sup> Session, 23 December 2013, A/HRC/25/27 <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> European Commission&#x2019;s 2011 Communication regarding &#x2018;A Quality Framework for Services of General Interest in Europe&#x2019;, p. 3 <a href=\"#footnote-ref-7\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> Report of the United Nations High Commissioner for Human Rights on the role of the public service as an essential component of good governance in the promotion and protection of human rights, Human Rights Council, 25<sup>th</sup> Session, 23 December 2013, A/HRC/25/27 <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> Committee on Economic, Social and Cultural Rights, General Comment No. 14 (2000) on the right to the highest attainable standard of health, para. 4. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> Committee on Economic, Social and Cultural Rights, general comment No. 13 (1999) on the right to education, para. 1. <a href=\"#footnote-ref-10\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> Report of the United Nations High Commissioner for Human Rights on the role of the public service as an essential component of good governance in the promotion and protection of human rights, Human Rights Council, 25<sup>th</sup> Session, 23 December 2013, A/HRC/25/27 <a href=\"#footnote-ref-11\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> From the European Social Survey, the European Quality of Life Survey and the Afrobarometer &#x2013; see more information in the section on &#x201C;Data Availability&#x201D;. <a href=\"#footnote-ref-12\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>This indicator needs to be measured on the basis of data collected by National Statistical Offices (NSOs) through official household surveys.</p>", "COLL_METHOD__GLOBAL"=>"<p>NSOs should identify suitable survey vehicles to incorporate the 16.6.2 batteries of question. Some countries may not have an integrated or unified survey covering various public services. In countries where each Ministry/Department/Agency conducts its respective satisfaction survey, the NSO should liaise with each entity to harmonize existing survey questions with this metadata. </p>", "FREQ_COLL__GLOBAL"=>"<p>To ensure timely capture of changes in levels of citizen satisfaction with public services, NSOs should report data on indicator 16.6.2 at least once every two years. NSOs will need to choose the most appropriate time/period for administering the 16.6.2 batteries of questions. Electoral periods should be avoided, and NSOs should aim for the middle of an electoral term. Experience shows that surveys conducted at the beginning of an electoral term generate more positive responses than surveys conducted at the end of a term. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data will be reported at the international level in the first half of each year. </p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Development Programme (UNDP)</p>", "INST_MANDATE__GLOBAL"=>"<p>Recent evidence shows that citizens call for responsive and inclusive public institutions with capacity to efficiently deliver services. To advance these aspirations from societies, UNDP helps countries to strengthen responsive and accountable institutions. UNDP recognizes the foundational importance of effective and responsive governance to achieve sustainable development.</p>", "RATIONALE__GLOBAL"=>"<p>Governments have an obligation to provide a wide range of public services that should meet the expectations of their citizens in terms of access, responsiveness and reliability/quality. When citizens cannot afford some essential services, when their geographic or electronic access to services and information is difficult, when the services provided do not respond to their needs and are of poor quality, citizens will naturally tend to report lower satisfaction not only with these services, but also with public institutions and governments. In this regard, it has been shown that citizens&#x2019; experience with front-line public services affects their trust in public institutions (OECD 2017, <em>Trust and Public Policy &#x2013; How Better Governance Can Help Rebuild Public Trust; Eurofound 2018, Societal change and trust in institutions</em>). Mindful of this close connection between service provision/performance, citizen satisfaction and public trust, governments are increasingly interested in better understanding citizens&#x2019; needs, experiences and preferences to be able to provide better targeted services, including for underserved populations. </p>\n<p>Measuring satisfaction with public services is at the heart of a citizen-centered approach to service delivery and an important outcome indicator of overall government performance. Yet while a large number of countries have experience with measuring citizen satisfaction with public services, there is also large variability in the ways national statistical offices and government agencies in individual countries collect data in this area, in terms of the range of services included, the specific attributes of services examined, question wording and response formats, among other methodological considerations. This variability poses a significant challenge for cross-country comparison of such data.</p>\n<p>SDG indicator 16.6.2 aims to generate globally comparable data on satisfaction with public services. To this end, SDG 16.6.2 focuses global reporting on the three service areas of (1) healthcare, (2) education and (3) government services (i.e. services to obtain government-issued identification documents and services for the civil registration of life events such as births, marriages and deaths.)</p>\n<p>The rationale for selecting these three public services, (1) healthcare, (2) education and (3) government services, is threefold: </p>\n<ul>\n  <li>First, these are &#x2018;services of consequence&#x2019;<sup><a href=\"#footnote-13\" id=\"footnote-ref-13\">[12]</a></sup>, salient for all countries and for both rural and urban populations within countries. They are also among the most common service areas<strong> </strong>covered by national household or citizen surveys on satisfaction with public services<sup><a href=\"#footnote-14\" id=\"footnote-ref-14\">[13]</a></sup>.</li>\n  <li>Second, while healthcare and education services are covered by other SDG indicators<sup><sup><a href=\"#footnote-15\" id=\"footnote-ref-15\">[14]</a></sup></sup>, most of these other indicators rely on administrative sources (i.e. they do not measure people&#x2019;s direct experiences and level of satisfaction with services) and are mainly focused on measuring the national coverage of a given service. </li>\n  <li>Third, government services are not monitored under other Goals. This is a gap that indicator 16.6.2 can usefully fill, especially since Goal 16 is dedicated to enhancing governance. While Goal 16 does consider birth registration services under indicator 16.9.1, it falls short of measuring satisfaction with the services provided. </li>\n</ul>\n<p>With the aim of generating harmonized statistics, indicator 16.6.2 is measured through five attributes-based questions under each service area (e.g. on the accessibility and affordability of the service, the quality of facilities, etc.):</p>\n<ul>\n  <li>The attributes-based questions are asked <em>before</em> the overall satisfaction question. This is based on the intention to enhance the accuracy of the proposed statistical measure on overall satisfaction &#x2013; that is, to ensure that it correctly reflects the underlying concept that it is intended to capture (based on the specific attributes selected for each service). Experts in governance measurements have found that citizen satisfaction with public services is influenced not only by citizens&#x2019; previous experiences with the services, but also by citizens&#x2019; expectations<sup><a href=\"#footnote-16\" id=\"footnote-ref-16\">[15]</a></sup>. These can be influenced by cultural assumptions about the extent to which service providers should be responsive to citizens&#x2019; preferences; by broad public perception of services as communicated through the media; by individual experiences of friends, family and acquaintances; and by how service providers themselves communicate about the type of services they commit to delivering. For instance, national experiences with different question formats have shown that more highly educated respondents who interact more frequently with government (and who possibly have higher awareness of their own rights and of their government&#x2019;s obligations) have higher expectations in terms of what constitutes a public service of &#x2018;good quality&#x2019;, compared to the rest of the population<sup><a href=\"#footnote-17\" id=\"footnote-ref-17\">[16]</a></sup>. </li>\n  <li>Given these multiple influences over citizen expectations of public services, which differ across different national contexts and across different demographic groups, it is essential for this methodology to foster a common understanding among respondents of which aspects of &#x2018;good quality&#x2019; service provision are measured. To this end, this methodology &#x2018;primes&#x2019; respondents with a common set of attributes of &#x2018;good quality&#x2019; service provision prior to asking about their overall satisfaction. </li>\n  <li>National experiences have also shown that asking attributes-based questions prior to an overall satisfaction question helps respondents recall their last experience with more specificity.<sup><a href=\"#footnote-18\" id=\"footnote-ref-18\">[17]</a></sup></li>\n  <li>A key reference used to identify relevant attributes for each service area covered by SDG 16.6.2 is the OECD Serving Citizens Framework (OECD 2015, Government at a Glance), which measures the quality of public services delivered to citizens by assessing three key dimensions of service provision, namely Access<sup><a href=\"#footnote-19\" id=\"footnote-ref-19\">[18]</a></sup>, Responsiveness<sup><a href=\"#footnote-20\" id=\"footnote-ref-20\">[19]</a></sup> and Reliability/Quality<sup><a href=\"#footnote-21\" id=\"footnote-ref-21\">[20]</a></sup>. Each one of these three dimensions is then further assessed with specific attributes. </li>\n  <li>The list of attributes in the OECD Serving Citizens Framework is comprehensive and more than a global indicator can feasibly and usefully cover. SDG 16.6.2, therefore, focuses on a limited subset of attributes. The specific set of five attributes used by SDG 16.6.2 to measure satisfaction with healthcare and education service areas was selected on the basis of statistical analysis performed on accessible datasets on satisfaction with these two services, namely from the Afrobarometer and the European Quality of Life Survey. Regression and cluster analysis were conducted on these two datasets to determine the main &#x2018;drivers&#x2019; of overall satisfaction among several such attributes, for healthcare and education services<sup><a href=\"#footnote-22\" id=\"footnote-ref-22\">[21]</a></sup>. The below table presents the results of this empirical analysis &#x2013; that is, the subset of five attributes used by SDG 16.6.2 to assess satisfaction in each service area:</li>\n</ul>\n<p><strong>Attributes of public services found to be the biggest &#x2018;drivers&#x2019; of satisfaction with healthcare and education services (in Europe and Africa)</strong></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Attributes</strong></p>\n      </td>\n      <td>\n        <p><strong>Healthcare service</strong></p>\n      </td>\n      <td>\n        <p><strong>Education service</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1</p>\n      </td>\n      <td>\n        <p>Accessibility <em>(includes a range of issues such as geographic proximity, delay in getting appointment, waiting time to see doctor on day of appointment)</em></p>\n      </td>\n      <td>\n        <p>Accessibility <em>(geographic proximity)</em> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2</p>\n      </td>\n      <td>\n        <p>Affordability</p>\n      </td>\n      <td>\n        <p>Affordability</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3</p>\n      </td>\n      <td>\n        <p>Quality of facilities </p>\n      </td>\n      <td>\n        <p>Quality of facilities </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4</p>\n      </td>\n      <td>\n        <p>Equal treatment for everyone</p>\n      </td>\n      <td>\n        <p>Equal treatment for everyone</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5</p>\n      </td>\n      <td>\n        <p>Courtesy and treatment <em>(Attitude of healthcare staff)</em></p>\n      </td>\n      <td>\n        <p>Effective delivery of service <em>(Quality of teaching)</em></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><u>Source</u>: Statistical analysis by the UNDP Oslo Governance Centre, 2019</p>\n<ul>\n  <li>Attributes-specific questions aim to be specifically informative for national policymaking. The specificity of the information generated by such questions, as well as the focus on citizen <em>experiences</em> rather than simply perceptions, have greater policy use than stand-alone perception data on overall satisfaction, which may not reveal &#x201C;what needs to be fixed&#x201D;. </li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-13\">12</sup><p> While drinking water and sanitation services are also &#x2018;services of consequence&#x2019;, they are already well covered by SDG indicator 6.1.1 &#x201C;Proportion of population using safely managed drinking water services&#x201D; and SDG indicator 6.2.1 &#x201C;<em>Proportion of population using safely managed sanitation services, including a hand-washing facility with soap and water&#x201D; </em>which also draw from citizen surveys (Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) supported by UNICEF and WHO) and look at access, availability and quality. <a href=\"#footnote-ref-13\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-14\">13</sup><p> See UNDP Oslo Governance Centre (Nov 2017), A Review of National Statistics Offices&#x2019; Practices </p><p>and Methodological Considerations in Measuring Citizen Satisfaction with Public Services &#x2013; Inputs for SDG Indicator 16.6.2 Measurement Methodology <a href=\"#footnote-ref-14\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-15\">14</sup><p><sup> </sup>For health care services, 3.8.1, 3.5.1, 3.b.1 and 1.4.1, and for education services, 4.a.1 and 4.c.1. <a href=\"#footnote-ref-15\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-16\">15</sup><p> See Ellen Lust et al., 2015; Nick Thijs, 2011, Van Ryzin, 2004, for instance. <a href=\"#footnote-ref-16\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-17\">16</sup><p> Evidence from Mexico, National Survey of Quality and Governmental Impact (ENCIG) 2017 <a href=\"#footnote-ref-17\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-18\">17</sup><p> Ibid. <a href=\"#footnote-ref-18\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-19\">18</sup><p> Under the &#x2018;Access&#x2019; dimension, three attributes are considered: &#x2018;Affordability&#x2019;, &#x2018;Geographic proximity&#x2019; and &#x2018;Accessibility of information&#x2019;. <a href=\"#footnote-ref-19\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-20\">19</sup><p> Under the &#x2018;Responsiveness&#x2019; dimension, three attributes are considered: &#x2018;Citizen-centered approach (courtesy, treatment and integrated services)&#x2019;, &#x2018;Match of services to special needs&#x2019; and &#x2018;Timeliness&#x2019;. <a href=\"#footnote-ref-20\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-21\">20</sup><p> Under the &#x2018;Reliability/Quality&#x2019; dimension, three attributes are considered: &#x2018;Effective delivery of services and outcomes&#x2019;, &#x2018;Consistency in service delivery and outcomes&#x2019; and &#x2018;Security/safety&#x2019;. <a href=\"#footnote-ref-21\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-22\">21</sup><p> In the absence of regional or global datasets on satisfaction with government services, the same empirical analysis could not be performed in this service area. To the extent possible, similar attributes are used to assess satisfaction with government services as those used for healthcare and education services, with a distinct focus on the attribute of &#x2018;timeliness&#x2019; in the case of government services. <a href=\"#footnote-ref-22\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p><strong>Recommended set of complementary questions to address selection 16.6.2 bias towards &#x2018;<em>users&#x2019;</em> of public services </strong></p>\n<ul>\n  <li>Since SDG 16.6.2 refers to people&#x2019;s &#x2018;last experience&#x2019; with public services, the indicator needs to focus on user experiences rather than on non-user perceptions. The experience of users is important, but it is equally important to understand the experiences and perceptions of those who turn elsewhere for services, or who do not access services altogether. </li>\n  <li>For each service area, NSOs are therefore strongly encouraged to administer three complementary questions (see Methodology section) <em>prior </em>to the two &#x2018;priority questions&#x2019; to be used for global 16.6.2 reporting. These additional questions will help capture the experience of <em>both</em> users <em>and </em>non-users of public services. They will help identify which population sub-groups who needed healthcare, education and government services did <em>not </em>access the services they needed, and what barriers prevented them from doing so. While the information generated by these additional questions is critical for policymakers to design service provision programmes that &#x2018;leave no one behind&#x2019;, it is left to the discretion of each country to integrate them or not, as some may already be collecting similar information through existing surveys.</li>\n</ul>\n<p>Otherwise, the selection bias inherent in SDG 16.6.2, with its focus on users, can result in mismeasurement due to underlying inequalities in the propensity of various groups to interact with state institutions. In other words, a focus on &#x2018;the last experience with public services&#x2019; implicitly means that this indicator includes only those respondents who were privileged enough to access public services in the past year. This means that those (such as ethnic minorities, migrants, the elderly, undocumented workers) who have <em>not </em>been able &#x2013; or willing &#x2013; to access the healthcare, education or government services they needed in the past 12 months, often as a consequence of multiple social and economic barriers arising from overlapping forms of marginalization will be undercounted by this indicator. There is a risk therefore that overall satisfaction levels reported on 16.6.2 will over-represent the experience of more privileged groups for whom access to public services is easier, because they have the financial, logistical and intellectual means to do so, and they trust that it is in their interest to do so.</p>", "DATA_COMP__GLOBAL"=>"<p>Reporting on SDG 16.6.2 should be done separately for each of the three service areas. (NB: questions on education may refer to either primary or secondary education &#x2013; and separate computation of results is recommended for the two levels, resulting in <em>de facto</em> four service areas). Computation involves the computation and reporting of the following three estimates, for each service area:</p>\n<ol>\n  <li>The share of respondents who responded positively (i.e. &#x2018;strongly agree &#x2018; or &#x2018;agree&#x2019;) to each of the five attributes questions;</li>\n  <li>The simple average of positive responses for the five attribute questions combined; and</li>\n  <li>The share of respondents who say they are satisfied (i.e. those who responded &#x2018;very satisfied&#x2019; or &#x2018;satisfied&#x2019;) in the overall satisfaction question. </li>\n</ol>\n<p>For instance: </p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Attributes of healthcare services</strong></p>\n      </td>\n      <td>\n        <p><strong>Positive responses</strong></p>\n      </td>\n      <td>\n        <p><strong>Attributes of primary education services</strong></p>\n      </td>\n      <td>\n        <p><strong>Positive responses</strong></p>\n      </td>\n      <td>\n        <p><strong>Attributes of secondary education services</strong></p>\n      </td>\n      <td>\n        <p><strong>Positive responses</strong></p>\n      </td>\n      <td>\n        <p><strong>Attributes of government services</strong></p>\n      </td>\n      <td>\n        <p><strong>Positive responses</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Accessibility</p>\n      </td>\n      <td>\n        <p><em>50% respondents &apos;strongly agree&apos; or &apos;agree&apos;</em></p>\n      </td>\n      <td>\n        <p>Accessibility </p>\n      </td>\n      <td></td>\n      <td>\n        <p>Accessibility </p>\n      </td>\n      <td></td>\n      <td>\n        <p>Accessibility </p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Affordability</p>\n      </td>\n      <td>\n        <p><em>60% respondents &apos;strongly agree&apos; or &apos;agree&apos;</em></p>\n      </td>\n      <td>\n        <p>Affordability</p>\n      </td>\n      <td></td>\n      <td>\n        <p>Affordability</p>\n      </td>\n      <td></td>\n      <td>\n        <p>Affordability</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Quality of facilities </p>\n      </td>\n      <td>\n        <p><em>73% respondents &apos;strongly agree&apos; or &apos;agree&apos;</em></p>\n      </td>\n      <td>\n        <p>Quality of facilities </p>\n      </td>\n      <td></td>\n      <td>\n        <p>Quality of facilities </p>\n      </td>\n      <td></td>\n      <td>\n        <p>Effective service delivery process</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Equal treatment for everyone </p>\n      </td>\n      <td>\n        <p><em>55% respondents &apos;strongly agree&apos; or &apos;agree&apos;</em></p>\n      </td>\n      <td>\n        <p>Equal treatment for everyone</p>\n      </td>\n      <td></td>\n      <td>\n        <p>Equal treatment for everyone</p>\n      </td>\n      <td></td>\n      <td>\n        <p>Equal treatment for everyone</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Courtesy and treatment (Attitude of healthcare staff)</p>\n      </td>\n      <td>\n        <p><em>42% respondents &apos;strongly agree&apos; or &apos;agree&apos;</em></p>\n      </td>\n      <td>\n        <p>Effective delivery of service (Quality of teaching) </p>\n      </td>\n      <td></td>\n      <td>\n        <p>Effective delivery of service (Quality of teaching) </p>\n      </td>\n      <td></td>\n      <td>\n        <p>Timeliness</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Average share of positive responses on attributes of healthcare services </strong></p>\n      </td>\n      <td>\n        <p><em>(50+60+73+55+42)/5 = 56%</em></p>\n        <p><em> </em></p>\n      </td>\n      <td>\n        <p><strong>Average share of positive responses on attributes of primary education services</strong> </p>\n      </td>\n      <td></td>\n      <td>\n        <p><strong>Average share of positive responses on attributes of secondary education services </strong></p>\n      </td>\n      <td></td>\n      <td>\n        <p><strong>Average share of positive responses on attributes of government services </strong></p>\n      </td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Share of respondents satisfied with healthcare services overall</strong></p>\n      </td>\n      <td>\n        <p><em>(23% &apos;very satisfied&apos; + 37% &apos;satisfied&apos;) = 60%</em></p>\n      </td>\n      <td>\n        <p><strong>Share of respondents satisfied with primary education services overall</strong></p>\n      </td>\n      <td></td>\n      <td>\n        <p><strong>Share of respondents satisfied with secondary education services overall</strong></p>\n      </td>\n      <td></td>\n      <td>\n        <p><strong>Share of respondents satisfied with government services overall</strong></p>\n      </td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n<p><em>*<u>Note</u>: It is important for NSOs to clearly report, for each question, the number of respondents who selected &#x201C;don&#x2019;t know&#x201D; (DK), &#x201C;not applicable&#x201D; (NA) or &#x201C;refuse to answer&#x201D; (RA), and to exclude such respondents from the calculation of shares of positive responses. For instance, if 65 respondents out of 1000 respondents responded DK, NA or RA on the first attribute-based question, the share of positive responses for this attribute will be calculated out of a total of 935 respondents, and the reporting sheet will indicate that for this particular question, 65 respondents responded DK/NA/RA.</em></p>\n<p>While national-level reporting should cover all three estimates described above, global reporting on SDG indicator 16.6.2 will focus on the last two estimates (i.e. the average share of positive responses across the five attribute questions; and the share of respondents who say they are satisfied in the overall satisfaction question). Additionally, global reporting will also consider the share of positive responses of the five service attributes by the share of people who are satisfied for each of the four service areas (i.e.., primary and secondary education, healthcare, and government services). </p>\n<p><strong> Answer scales:</strong></p>\n<ul>\n  <li>To ensure the consistency of measurement in an international context, a standardised approach to response format is required. Available evidence from piloting and other NSO experiences suggests that a four-point Likert-scale with verbal scale anchors is preferable over the alternatives. A four-point scale offers the optimal range of response options for the concepts at hand, in terms of capturing as much meaningful variation between responses as there exists, while remaining understandable for respondents who are not very numerate or literate. Piloting experiences have revealed that offering too few response options (such as a &#x2018;yes/no&#x2019; binary response format) would not reveal much variation and might even frustrate some respondents, who might feel their satisfaction level cannot be accurately expressed. Furthermore, the Guidelines on Measuring Subjective Well-Being (OECD, 2013) caution against using &#x201C;agree/disagree, true/false, and yes/no response formats in the measurement of subjective well-being due to the heightened risk of acquiescence and socially desirable responding&#x201D;. Meanwhile, piloting experiences have shown that respondents would be equally burdened by too many response categories (such a 7- or 10-point scale), especially if the categories are too close to distinguish between them cognitively. </li>\n  <li>There are different schools of thought on whether an odd or even number of categories is best when using Likert scales. While taking away the middle category forces respondents to voice a positive or negative opinion, and some respondents might find this approach frustrating, several NSOs in developing country contexts favor a Likert scale <em>without </em>a neutral value (such as &#x201C;neither satisfied nor dissatisfied&#x201D;). Their preference is motivated by their long-standing survey experience which has shown that when a neutral value is provided, a large proportion (often a majority) of respondents will refrain from expressing their opinion &#x2018;hiding&#x2019; behind this middle-point. </li>\n  <li>The survey methodology for 16.6.2 therefore uses a 4-point bipolar Likert scale for all questions (for internal consistency), with the following scale labels: &#x201C;strongly agree, agree, disagree, strongly disagree&#x201D; for attributes-based questions, and &#x201C;very satisfied, satisfied, dissatisfied, very dissatisfied&#x201D; for overall satisfaction questions. &#x201C;Don&#x2019;t know&#x201D; and &#x201C;refuse to answer&#x201D; options are also available, but <em>should not be read out loud</em>, so as to not provide an easy way for respondents to disengage from the subjects of the various questions. When respondents say they &#x201C;don&#x2019;t know&#x201D;, enumerators should repeat the question and simply ask them to provide their best guess. The &#x201C;don&#x2019;t know&#x201D; and &#x201C;refuse to answer&#x201D; options should be used only as a last resort.<strong> </strong></li>\n</ul>", "DATA_VALIDATION__GLOBAL"=>"<p>The countries are requested to input the indicators&#x2019; data and metadata in a reporting platform following the guidelines in the present metadata sheet. The platform encourages to provide separate information on the survey metadata, namely the source of information for the statistics, the survey instruments, the methodology and protocols and possible. Countries are also requested to insert the statistics on the two questions disaggregated by the pre-specified fields. All inputted information is verified for conformity with the metadata prior to submission.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>There is no treatment of missing values.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>There is no imputation of missing values.</p>", "REG_AGG__GLOBAL"=>"<p>Data points will be provided for each region, and globally (i.e. two data points for each service area: combined average % of those who responded positively to the five attributes questions, and % satisfied with the service overall).</p>", "DOC_METHOD__GLOBAL"=>"<p><strong>Methods and guidance available to countries for the compilation of data at national level:</strong></p>\n<p>See <a href=\"http://documents.worldbank.org/curated/en/775701527003544796/pdf/126399-WP-PUBLIC-CitizenCentricGovernanceIndicatorsFinalReport.pdf\"><u>Indicators of Citizen-Centric Public Service Delivery</u>, </a>World Bank (2018)</p>\n<p>To disaggregate survey results by disability status, it is recommended that countries use the <a href=\"http://www.washingtongroup-disability.com/washington-group-question-sets/short-set-of-disability-questions/\"><u>Short Set of</u></a> <a href=\"https://www.washingtongroup-disability.com/question-sets/wg-short-set-on-functioning-wg-ss/\"><u>Questions on Disability elaborated by the Washington Group.</u></a></p>\n<p><strong>Methods and guidance available to countries for the compilation of data at international level:</strong></p>\n<p>See <a href=\"http://documents.worldbank.org/curated/en/775701527003544796/pdf/126399-WP-PUBLIC-CitizenCentricGovernanceIndicatorsFinalReport.pdf\"><u>Indicators of Citizen-Centric Public Service Delivery</u>, </a>World Bank (2018)</p>\n<p>To disaggregate survey results by disability status, it is recommended that countries use the <a href=\"http://www.washingtongroup-disability.com/washington-group-question-sets/short-set-of-disability-questions/\"><u>Short Set of</u></a> <a href=\"https://www.washingtongroup-disability.com/question-sets/wg-short-set-on-functioning-wg-ss/\"><u>Questions on Disability elaborated by the Washington Group.</u></a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Statistics for this indicator is inputted in the reporting platform (<a href=\"https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsdg16reporting.undp.org%2Flogin&amp;data=02%7C01%7Cmariana.neves%40undp.org%7C307a2d2600d64d5872e908d812bea69e%7Cb3e5db5e2944483799f57488ace54319%7C0%7C0%7C637279957333850920&amp;sdata=AI9rb2m1dE62v7zxpoPS6Kgk6m1Nvs3bspt4M4wATWw%3D&amp;reserved=0\">https://sdg16reporting.undp.org/login</a>). UNDP has dedicated staff to verify the collected data and liaise with the data officers in the agency in the countries.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>NSOs have the main responsibility to ensure the statistical quality of the data compiled for this indicator. One possible quality assurance mechanism would be to compare results obtained by the NSO with readily available survey results on satisfaction with public services generated by relevant national, regional or global non-official data producers (see potential non-official sources below).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>UNDP will make available a quality assessment protocol for national statistics office to be used at national level and intended to assess the alignment of data produced with users&#x2019; needs, the compliance with guidelines in terms of computations, the timeliness of data production, the accessibility of statistics produced, the consistent use of methodology both in terms of geographic representation and through time, the coherence in terms of data production, and the architecture of data production.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<ul>\n  <li>This indicator needs to be measured on the basis of data collected by NSOs through official household surveys.</li>\n</ul>\n<p><strong>Description and time series:</strong></p>\n<p>There is no existing globally comparable official dataset on the &#x201C;Proportion of the population satisfied with their last experience of public services.&#x201D; There is a large variability in the ways NSOs and government agencies in individual countries collect data on citizen satisfaction with public services, in terms of the range of services included, the specific attributes examined, question wording and response formats, etc. This variability poses a significant challenge for cross-country comparability of such data. Several global and regional sources provide comparable data on some measures of citizen satisfaction with public services: </p>\n<ul>\n  <li>The <a href=\"https://www.gallup.com/analytics/232838/world-poll.aspx\">Gallup World Poll</a> surveys people&#x2019;s satisfaction with local education and healthcare public services in over 150 countries. However, the Gallup World Poll questions do not ask specifically about satisfaction <em>with the last experience of public services</em>, questions do not refer to specific attributes of public services and data is not publicly available.</li>\n  <li>Since launching its first round in 1999/2001, the <a href=\"https://www.afrobarometer.org/surveys-and-methods/\">Afrobarometer</a><sup><a href=\"#footnote-23\" id=\"footnote-ref-23\">[22]</a></sup> has been collecting data biennially on citizens&#x2019; satisfaction with healthcare and education services in more than 35 countries in Africa. The Afrobarometer, however, also does not ask about specific attributes of public services and does not ask specifically about satisfaction <em>with the last experience of public services</em>.</li>\n  <li>Starting from 2002, the biennial <a href=\"https://www.europeansocialsurvey.org/methodology/ess_methodology/survey_specifications.html\">European Social Survey</a><sup><a href=\"#footnote-24\" id=\"footnote-ref-24\">[23]</a></sup> provides time series data on perception of education and health services in Europe. Once again, these survey questions do not ask specifically about satisfaction <em>with the last experience of public services</em> and do not ask respondents to consider specific attributes of public services when providing their assessment.</li>\n  <li>In its 2016 editions, the European Quality of Life Survey<sup><a href=\"#footnote-25\" id=\"footnote-ref-25\">[24]</a></sup> (EQLS) notably introduced questions on specific attributes of service provision in healthcare and education, in additions to questions on overall satisfaction, several of which match the attributes selected for global reporting on 16.6.2. With this focus on the quality of public service provision, this survey could therefore become an appropriate source of data for reporting on SDG 16.6.2 for the 33 participating countries. More specifically, the following corresponding questions in the EQLS have been identified, jointly with Eurofound experts, to report on SDG 16.6.2:</li>\n</ul>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"3\">\n        <p><strong>Healthcare services<sup><a href=\"#footnote-26\" id=\"footnote-ref-26\">[25]</a></sup></strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Attributes</strong></p>\n      </td>\n      <td>\n        <p><strong>SDG 16.6.2 questions</strong></p>\n      </td>\n      <td>\n        <p><strong>Corresponding EQLS questions</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Access</strong></p>\n      </td>\n      <td>\n        <p><em>Q 4.1 It was easy to get to the place where I received medical treatment. (0-3)</em></p>\n      </td>\n      <td>\n        <p>Q61 - Thinking about the last time you needed to see or be treated by a GP, family doctor or health centre, to what extent did any of the following make it difficult or not for you to do so? [Very difficult (1); a little difficult (2); not difficult at all (3)]: </p>\n        <p>a. Distance to GP/doctor&#x2019;s office / health centre</p>\n        <p>b. Delay in getting appointment</p>\n        <p>c. Waiting time to see doctor on day of appointment</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Affordability</strong></p>\n      </td>\n      <td>\n        <p><em>Q 4.2 Expenses for healthcare services were affordable to you/your household. (0-3)</em></p>\n      </td>\n      <td>\n        <p>Q61 &#x2013; Same as above: </p>\n        <p>d. Cost of seeing the doctor</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Quality of facilities </strong></p>\n      </td>\n      <td>\n        <p><em>Q 4.3 The healthcare facilities were clean and in good condition. (0-3)</em></p>\n      </td>\n      <td>\n        <p>Q62 - You mentioned that you used GP, family doctor or health centre services. On a scale of 1 to 10 where 1 means very dissatisfied and 10 means very satisfied, tell me how satisfied or dissatisfied you were with each of the following aspects the last time that you used the service. </p>\n        <ol>\n          <li>Quality of the facilities (building, room, equipment)</li>\n        </ol>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Equal treatment for everyone</strong></p>\n      </td>\n      <td>\n        <p><em>Q 4.4 All people are treated equally in receiving healthcare services in your area.</em> <em>(0-3)</em></p>\n      </td>\n      <td>\n        <p>Q63 - To what extent do you agree or disagree with the following about GP, family doctor or health centre services in your area? [on a scale of 1 to 10, where 1 means completely disagree and 10 means completely agree]: </p>\n        <p>a. All people are treated equally in these services in my area</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Courtesy and treatment (Doctor&#x2019;s attitude) </strong></p>\n      </td>\n      <td>\n        <p><em>The doctor or other healthcare staff you saw spent enough time with you [or a child in your household] during the consultation. (0-3)</em></p>\n      </td>\n      <td>\n        <p>Q62 - Satisfaction with the following aspects [on a scale of 1 to 10 where 1 means very dissatisfied and 10 means very satisfied]: </p>\n        <p>c. Personal attention you were given, including staff attitude and time devoted</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Overall satisfaction</strong></p>\n      </td>\n      <td>\n        <p><em>Overall, how satisfied or dissatisfied were you with the quality of the healthcare services you [or a child in your household] received on that last consultation? (i.e. the last time you [or a child in your household] had a medical examination or treatment in the past 12 months)</em></p>\n        <p><em>Very dissatisfied (0) - Dissatisfied (1) &#x2013; Satisfied (2) &#x2013; Very satisfied (3)</em></p>\n      </td>\n      <td>\n        <p>Q58 - In general, how would you rate the quality of each of the following public services in [COUNTRY]? [on a scale of one to 10, where 1 means very poor quality and 10 means very high quality]</p>\n        <p>a. Health services</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"3\">\n        <p><strong>Education services</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Attributes</strong></p>\n      </td>\n      <td>\n        <p><strong>SDG 16.6.2 questions</strong></p>\n      </td>\n      <td>\n        <p><strong>Corresponding EQLS questions</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Access</strong></p>\n      </td>\n      <td>\n        <p><em>Q. 9.1 The school can be reached by public or private transportation, or by walk, in less than 30 minutes and without difficulties. (0-3)</em></p>\n      </td>\n      <td>\n        <p><u>No relevant EQLS question </u></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Affordability</strong></p>\n      </td>\n      <td>\n        <p><em>Q. 9.2 School-related expenses (including administrative fees, books, uniforms and transportation) are affordable to you/your household. (0-3)</em></p>\n      </td>\n      <td>\n        <p><u>No relevant EQLS question</u><sup><a href=\"#footnote-27\" id=\"footnote-ref-27\">[26]</a></sup> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Quality of facilities </strong></p>\n      </td>\n      <td>\n        <p><em>Q. 9.3 School facilities are in good condition. (0-3)</em></p>\n      </td>\n      <td>\n        <p>Q85 - You mentioned that your child or someone in your household attended school. On a scale of 1 to 10 where 1 means very dissatisfied and 10 means very satisfied, please tell me how satisfied or dissatisfied you were with each of the following aspects. </p>\n        <p>a. Quality of the facilities (building, room, equipment)</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Equal treatment for everyone</strong></p>\n      </td>\n      <td>\n        <p><em>Q. 9.4 All children are treated equally in the school attended by the child/children in your household. (0-3)</em></p>\n      </td>\n      <td>\n        <p>Q86 - To what extent do you agree or disagree with the following statements about school services in your area? Please tell me on a scale of 1 to 10, where 1 means completely disagree and 10 means completely agree.</p>\n        <p>a. All people are treated equally in these services in my area</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Effective delivery of service (Quality of teaching)</strong></p>\n      </td>\n      <td>\n        <p><em>Q. 9.5 The quality of teaching is good. (0-3)</em></p>\n      </td>\n      <td>\n        <p>Q85 - You mentioned that your child or someone in your household attended school. On a scale of 1 to 10 where 1 means very dissatisfied and 10 means very satisfied, please tell me how satisfied or dissatisfied you were with each of the following aspects.</p>\n        <p>b. Expertise and professionalism of staff/teachers</p>\n        <p>e. The curriculum and activities</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Overall satisfaction</strong></p>\n      </td>\n      <td>\n        <p><em>Q 10. Overall, how satisfied or dissatisfied are you with the quality of education services provided by the primary and/or secondary public schools attended by this child/children in your household? </em></p>\n        <p><em>Are you reporting on: </em></p>\n        <ol>\n          <li><em>Primary school in your area ___</em></li>\n          <li><em>Secondary school in your area ___</em></li>\n        </ol>\n        <p><em>Very dissatisfied (0) - Dissatisfied (1) &#x2013; Satisfied (2) &#x2013; Very satisfied (3)</em></p>\n      </td>\n      <td>\n        <p>Q58 - In general, how would you rate the quality of each of the following public services in [COUNTRY]? [on a scale of one to 10, where one means very poor quality and 10 means very high quality]</p>\n        <p>b. Education system </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong>Disaggregation categories</strong></p>\n<p>Indicator 16.6.2 aims to measure how access to services and how the quality of services differs across various demographic groups. Empirical analysis to identify the strongest demographic determinants of citizen satisfaction with public services reveals that the most relevant disaggregation categories for SDG indicator 16.6.2 are (1) income, (2) sex and (3) place of residence (urban/rural, and by administrative region e.g., by province, state, district, etc.)</p>\n<p>At a minimum, results <em>for each one of the three service areas</em> covered by this indicator (healthcare, education and government services)<em> </em>should be disaggregated by these three variables:</p>\n<ul>\n  <li><strong>Income:</strong> Income (or expenditure) quintiles </li>\n  <li><strong>Sex:</strong> Male/Female</li>\n  <li><strong>Place of residence:</strong> Living in urban/rural areas and/or living in which administrative region (province, state, district, etc.)<sup><a href=\"#footnote-28\" id=\"footnote-ref-28\">[27]</a></sup> </li>\n</ul>\n<p>To the extent possible, all efforts should be made to also disaggregate results by disability status and by &#x2018;nationally relevant population groups&#x2019;:</p>\n<ul>\n  <li><strong>Disability status:</strong> &#x2018;Disability&#x2019; is an umbrella term covering long-term physical, mental, intellectual or sensory impairments which in interaction with various barriers may hinder the full and effective participation of disabled persons in society on an equal basis with others<sup><sup><a href=\"#footnote-29\" id=\"footnote-ref-29\">[28]</a></sup></sup>. If possible, NSOs are encouraged to add the <a href=\"https://www.washingtongroup-disability.com/question-sets/wg-short-set-on-functioning-wg-ss/\"><u>Short Set of Questions on Disability developed by the Washington Group</u> </a>to the survey vehicle used to administer the 16.6.2 batteries to disaggregate results by disability status.</li>\n  <li><strong>Nationally relevant population groups</strong>: groups with a distinct ethnicity, language, religion, indigenous status, nationality or other characteristics.<sup><a href=\"#footnote-30\" id=\"footnote-ref-30\">[29]</a></sup> </li>\n  <li><strong>Age:</strong> Empirical analysis shows that there is no statistically significant association between the age of respondents and satisfaction levels. However, if countries choose to also disaggregate results by age, it is recommended to follow UN standards for the production of age-disaggregated national population statistics, using the following age groups: (1) below 25 years old, (2) 25-34, (3) 35-44, (4) 45-54, (5) 55-64 and (6) 65 years old and above. </li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-23\">22</sup><p> The Afrobarometer is conducting its public attitude surveys on democracy, governance, economic conditions, and related issues in more than 35 countries in Africa. <a href=\"#footnote-ref-23\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-24\">23</sup><p> In total, 37 countries have taken part in at least one round of the ESS since its inception. Surveys are conducted by leading academics and social research professionals. <a href=\"#footnote-ref-24\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-25\">24</sup><p> EQLS 2016 &#x2013; the fourth survey in the series &#x2013; covered the 28 EU Member States and 5 candidate countries (Albania, the former Yugoslav Republic of Macedonia, Montenegro, Serbia and Turkey). <a href=\"#footnote-ref-25\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-26\">25</sup><p> <u>Note</u>: For healthcare services, EQLS data would allow for the separate reporting of results (across all questions) on (1) primary care services (GP / doctor&#x2019;s office / health centre) and (2) hospital or medical specialist services. Separate reporting on these two types of health care would be particularly relevant for the &#x2018;affordability&#x2019; attribute, given in European countries, primary care services typically cost little; more relevant would be to assess the affordability of hospital or medical specialist services, using question 67.e. <a href=\"#footnote-ref-26\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-27\">26</sup><p> However, question HC100 on &#x2018;Affordability of formal education&#x2019; could be used in the European Union Statistics on Income and Living Conditions (EU-SILC) ad hoc module 2016. <a href=\"#footnote-ref-27\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-28\">27</sup><p> Based on the premise that decentralization efforts are aimed at extending local rights and responsibilities across the national territory, indicator 16.6.2 can help detect unequal access to services and disparities in the quality of services across localities. There is a risk for erroneous conclusions to be drawn from national aggregates unable to detect variations at sub-national level. <a href=\"#footnote-ref-28\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-29\">28</sup><p> UN General Assembly, Convention on the Rights of Persons with Disabilities: resolution / adopted by the General Assembly, 24 January 2007, A/RES/61/106, available at: http://www.refworld.org/docid/45f973632.html <a href=\"#footnote-ref-29\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-30\">29</sup><p> The population of a country is a mosaic of different population groups that can be identified according to racial, ethnic, language, indigenous or migration status, religious affiliation, or sexual orientation, amongst other characteristics. For the purpose of this indicator, particular focus is placed on minorities. <em>Minority groups are </em>groups that are numerically inferior to the rest of the population of a state, in a non-dominant position, whose members&#x2014;being nationals of the state&#x2014;possess ethnic, religious or linguistic characteristics differing from those of the rest of the population and show, even if only implicitly, a sense of solidarity directed towards preserving their culture, traditions, religion or language. While the nationality criterion included in the above definition has often been challenged, the requirement to be in a non-dominant position remains important (OHCHR, 2010). Collecting survey data disaggregated by population groups should be subject to the legality of compiling such data in a particular national context and to a careful assessment of the potential risks of collecting such data for the safety of respondents. <a href=\"#footnote-ref-30\">&#x2191;</a></p></div></div>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There is no internationally estimated data for this indicator.</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>African Barometer (1999-2017). An African-led series of national public attitude surveys on democracy and governance in Africa. <a href=\"http://www.afrobarometer.org/surveys-and-methods/questionnaires\">Survey Questionnaires</a></li>\n  <li>American Customer Satisfaction Index LLC (2016). ACSI Federal Government Report 2016 <em>[in which 2,380 users randomly selected, contacted via email and asked about their recent experiences with federal government services]</em>. Available at <a href=\"http://www.theacsi.org/images/stories/images/reports/17jan-Gov-report-2016.pdf\">http://www.theacsi.org/images/stories/images/reports/17jan-Gov-report-2016.pdf</a> </li>\n  <li>Alvarez, R. Michael, and Brehm, John (2000). &#x201C;<u>Binding the frame: how important are </u><a href=\"http://polmeth.wustl.edu/media/Paper/alvar00b.pdf\"><u>frames for</u></a> <a href=\"http://polmeth.wustl.edu/media/Paper/alvar00b.pdf\"><u>survey response?</u></a>&#x201D; Paper presented at the annual meeting of the American Political Science Association, Washington, DC, August 31&#x2013;September 2</li>\n  <li>Basab, Dasgupta, Ambar Narayan, Emmanuel Skoufias (2009). Measuring the Quality of Education and Health Services: The Use of Perception Data from Indonesia <a href=\"http://documents.worldbank.org/curated/en/814671468040542129/pdf/WPS5033.pdf\">http://documents.worldbank.org/curated/en/814671468040542129/pdf/WPS5033.pdf</a> </li>\n  <li>Bo, Rothstein, Centre for Public Sector Research and Jan Teorell (2005). What Is Quality of Government? A Theory of Impartial Political Institutions. Available at <a href=\"http://unpan1.un.org/intradoc/groups/public/documents/un-dpadm/unpan044549.pdf\">http://unpan1.un.org/intradoc/groups/public/documents/un-dpadm/unpan044549.pdf</a></li>\n  <li>Charron, Nicholas (2013). European Quality of Government Index 2013: <a href=\"https://nicholascharron.files.wordpress.com/2013/09/2013-eqi-survey-questions.docx\"><em>Survey questions</em></a>.</li>\n  <li>Cl&#xE1;udia, Carvalho &amp; Carlos, Brito (2012). Assessing Users&apos; Perceptions on how to Improve Public Services Quality. Public Management Review. Vol. 14, 2012. Issue 4, pp. 451-472</li>\n  <li><a href=\"https://www.qualtrics.com/blog/author/dave-vannette/\">Dave, Vannette</a> (April 22, 2015). Three Tips for Effectively Designing Rating Scales. Available at <a href=\"https://www.qualtrics.com/blog/three-tips-for-effectively-using-scale-point-questions/\">https://www.qualtrics.com/blog/three-tips-for-effectively-using-scale-point-questions/</a> </li>\n  <li>Druckman, James (2001). &#x201C;<a href=\"https://link.springer.com/article/10.1023/A:1015006907312\">The Implications of Framing Effects for Citizen Competence.&#x201D;</a> Political Behavior 23(3) September: 227-256</li>\n  <li>Ellen Lust, <a href=\"http://gld.gu.se/en/collaborators/scholars/lindsay-j-benstead/\" target=\"_blank\">Lindsay J. Benstead</a>, <a href=\"http://gld.gu.se/en/collaborators/scholars/pierre-f-landry/\" target=\"_blank\">Pierre F. Landry</a>, and <a href=\"http://gld.gu.se/en/collaborators/scholars/dhafer-malouche/\" target=\"_blank\">Dhafer Malouche</a> (2015). The Local Governance Performance Index (LGPI): Report Paper. Information about LGPI available at <a href=\"https://www.venice.coe.int/images/SITE%20IMAGES/Publications/14th_UniDemMed_Thijs_EUPAN_Measures_to_improve_public_sector_performance_English_FINAL.pdf\"><u>https://www.venice.coe.int/images/SITE%20IMAGES/Publications/14th_UniDemMed_Thijs_EUPAN_Measures_to_improve_public_sector_performance_English_FINAL.pdf</u></a></li>\n  <li>European Commission (2011). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: A Quality Framework for Services of General Interest in Europe. Available at <a href=\"http://eur-lex.europa.eu/procedure/EN/201238\">http://eur-lex.europa.eu/procedure/EN/201238</a> </li>\n  <li>Giulia Megellini (2017). Critical Review of Existing Best Practices to Measure the Experience of Corruption. Centre of Excellence in Statistical Information on Government, Crime, Victimisation and Justice. A Report for UNODC. </li>\n  <li>Gregg G. Van Ryzin (2004). The Measurement of Overall Citizen Satisfaction, Public Performance &amp; Management Review, 27:3, 9-28. Available at <a href=\"https://www.tandfonline.com/doi/abs/10.1080/15309576.2004.11051805\"><u>https://www.tandfonline.com/doi/abs/10.1080/15309576.2004.11051805</u></a> </li>\n  <li>Hiil (2017). Justice Needs in Tunisia - 2017 Available at <a href=\"https://www.hiil.org/projects/justice-needs-and-satisfaction-in-tunisia/\">https://www.hiil.org/projects/justice-needs-and-satisfaction-in-tunisia/</a> </li>\n  <li>Institute for Citizen-Centred Service (2015). Citizens First 7. Report for the Government of Northwest Territories, Canada. Available at <a href=\"http://www.assembly.gov.nt.ca/sites/default/files/td253-175.pdf\">http://www.assembly.gov.nt.ca/sites/default/files/td253-175.pdf</a> </li>\n  <li>Ireland Department of Public Expenditure &amp; Reform (2015) <a href=\"https://www.gov.ie/pdf/?file=https://assets.gov.ie/7429/5ef76a4ac3424982a678a4e48a2f8920.pdf#page=1\"><em>Irish Civil Service Customer Satisfaction Survey 2015 Report of Findings</em></a>. IPSOS MRBI</li>\n  <li>Janet M. Kelly &amp; David Swindell (2003). The case for inexperienced user: Rethinking Filter Questions in Citizen Satisfaction Surveys. American Review of Public Administration, Vol. 33 No. 1, March 2003 91-108. DOI: 10.1177/0275074002250256. Available at <a href=\"http://journals.sagepub.com/doi/abs/10.1177/0275074002250256?journalCode=arpb\">http://journals.sagepub.com/doi/abs/10.1177/0275074002250256?journalCode=arpb</a> </li>\n  <li>Marcus Tannenberg (2017). The Autocratic Trust Bias: Politically Sensitive Survey Items and Self-censorship. Working Paper Series 2017:49. The Varieties of Democracy Institute, University of Gothenburg. Available at <a href=\"https://www.afrobarometer.org/wp-content/uploads/2022/02/afropaperno176_autocratic_trust_bias.pdf\">https://www.afrobarometer.org/wp-content/uploads/2022/02/afropaperno176_autocratic_trust_bias.pdf</a> </li>\n  <li>Mitchel N. Herian and Alan J. Tomkins (2012). Citizen Satisfaction Survey Data: A Mode Comparison of the Derived Importance&#x2013;Performance Approach. The American Review of Public Administration 42(1) 66&#x2013;86. P. 67</li>\n  <li>New Zealand Government (2016). New Zealanders&#x2019; satisfaction with public services: <a href=\"http://www.ssc.govt.nz/kiwis-count\">&#x2018;Kiwis Count&#x2019; Annual Report</a>.</li>\n  <li>Nick Thijs (2011). Measure to Improve: Improving public sector performance by using citizen - user satisfaction information.<strong> </strong>Available at <a href=\"https://www.venice.coe.int/images/SITE%20IMAGES/Publications/14th_UniDemMed_Thijs_EUPAN_Measures_to_improve_public_sector_performance_English_FINAL.pdf\"><u>https://www.venice.coe.int/images/SITE%20IMAGES/Publications/14th_UniDemMed_Thijs_EUPAN_Measures_to_improve_public_sector_performance_English_FINAL.pdf</u></a> </li>\n  <li>OECD (2015), &#x201C;The OECD serving citizens&apos; framework&#x201D;, in Government at a Glance 2015, OECD Publishing, Paris </li>\n  <li>OECD (2017), &#x201C;Serving Citizens Scorecards&#x201D;, in Government at a Glance 2017, OECD Publishing, Paris. DOI: <a href=\"http://dx.doi.org/10.1787/gov_glance-2017-en\">http://dx.doi.org/10.1787/gov_glance-2017-en</a></li>\n  <li>OECD (2017), &#x201C;Citizen satisfaction with public services and institutions&#x201D;, in <em>Government at a Glance 2017</em>, OECD Publishing, Paris. DOI: <a href=\"http://dx.doi.org/10.1787/gov_glance-2017-82-en\">http://dx.doi.org/10.1787/gov_glance-2017-82-en</a></li>\n  <li>Parasuraman, A, Ziethaml, V. and Berry, L.L., &quot;SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality&apos; <em>Journal of Retailing,</em> Vol. 62, no. 1, 1988, p. 22, 25 and 29. Available at <a href=\"https://www.researchgate.net/profile/Valarie_Zeithaml/publication/225083802_SERVQUAL_A_multiple-_Item_Scale_for_measuring_consumer_perceptions_of_service_quality/links/5429a4540cf27e39fa8e6531/SERVQUAL-A-multiple-Item-Scale-for-measuring-consumer-perceptions-of-service-quality.pdf\">https://www.researchgate.net/profile/Valarie_Zeithaml/publication/225083802_SERVQUAL_A_multiple-_Item_Scale_for_measuring_consumer_perceptions_of_service_quality/links/5429a4540cf27e39fa8e6531/SERVQUAL-A-multiple-Item-Scale-for-measuring-consumer-perceptions-of-service-quality.pdf</a></li>\n  <li><a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Al-Abri%20R%5BAuthor%5D&amp;cauthor=true&amp;cauthor_uid=24501659\">Rashid Al-Abri</a> and <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Al-Balushi%20A%5BAuthor%5D&amp;cauthor=true&amp;cauthor_uid=24501659\">Amina Al-Balushi</a> (2014). Patient Satisfaction Survey as a Tool Towards Quality Improvement. <a href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3910415/\">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3910415/</a></li>\n  <li>Survey Monkey (2017). Five tips on how to use Likert scales. Available at<strong> </strong><a href=\"https://www.surveymonkey.com/mp/likert-scale/\">https://www.surveymonkey.com/mp/likert-scale/</a> </li>\n  <li>The Program for East Asia Democratic Studies Asian (<em>date unknown</em>). Asian Barometer&#x2019;s Survey of Democracy, Governance and Development &#x2013; Fourth Wave. Available at <a href=\"http://www.asianbarometer.org/data/core-questionnaire\"><u>http://www.asianbarometer.org/data/core-questionnaire</u></a></li>\n  <li>The World Bank (2010). Citizen-centric Governance Indicators: Measuring and Monitoring Governance by Listening to the People and Not the Interest Groups. World Bank Institute Research Working Paper No. 5181. Available at <a href=\"http://documents.worldbank.org/curated/en/190241468340284322/Citizen-centric-governance-indicators-measuring-and-monitoring-governance-by-listening-to-the-people-and-not-the-interest-groups\">http://documents.worldbank.org/curated/en/190241468340284322/Citizen-centric-governance-indicators-measuring-and-monitoring-governance-by-listening-to-the-people-and-not-the-interest-groups</a> </li>\n  <li>The World Bank Group (2011). World Bank Governance and Anti-corruption (GAC) Diagnostic Survey. Available at <a href=\"http://web.worldbank.org/archive/website00818/WEB/PDF/01_SUR-2.PDF\"><u>http://web.worldbank.org/archive/website00818/WEB/PDF/01_SUR-2.PDF</u></a></li>\n  <li>The World Bank Group (2017). Indicators of Citizen-Centric Public Service Delivery. June 2017 Final Draft. </li>\n  <li>The United Nations (11 May 2017). Progress towards the Sustainable Development Goals. Report of the Secrecretary-General at High-level political forum on sustainable development, convened under the auspices of the Economic and Social Council, 2017 Session. Available at <a href=\"https://unstats.un.org/sdgs/files/report/2017/secretary-general-sdg-report-2017--EN.pdf\">https://unstats.un.org/sdgs/files/report/2017/secretary-general-sdg-report-2017--EN.pdf</a> </li>\n  <li>United Nations Development Programme (UNDP) (2016). Citizen Engagement in Service Delivery &#x2013; The Critical Role of Public Officials. Global Center for Public Service Excellence. Available at <a href=\"https://www.undp.org/sites/g/files/zskgke326/files/publications/GCPSE_CitizenEngagement_Summary_2016.pdf\">https://www.undp.org/sites/g/files/zskgke326/files/publications/GCPSE_CitizenEngagement_Summary_2016.pdf</a> </li>\n  <li>UNDP, VFF-CRT &amp; CECODES (2011-2017). The Viet Nam Governance and Public Administration Performance Index (PAPI): Measuring Citizens&#x2019; Experiences. Available at <a href=\"http://www.papi.org.vn/eng\">www.papi.org.vn/eng</a> </li>\n  <li>UNDP, VLA &amp; CECODES (2012, 2015). Viet Nam&#x2019;s Justice Index (VJI): Towards a justice system for the people. Available at <a href=\"http://www.chisocongly.vn/en/\">www.chisocongly.vn/en/</a> </li>\n  <li>UNDP (2016). Human Development Index. Available at <a href=\"http://hdr.undp.org/en/content/human-development-index-hdi\">http://hdr.undp.org/en/content/human-development-index-hdi</a> </li>\n  <li>UNDP (2015). From Old Public Administration to New Public Service &#x2013; Implications for Public Sector Reform in Developing Countries. Global Center for Public Service Excellence. Available at <a href=\"http://www.undp.org/content/undp/en/home/librarypage/capacity-building/global-centre-for-public-service-excellence/PS-Reform.html\">http://www.undp.org/content/undp/en/home/librarypage/capacity-building/global-centre-for-public-service-excellence/PS-Reform.html</a> </li>\n  <li>UNDP (2015). Citizen Satisfaction with Public Services in Georgia: 2015. United Nations Development Programme (UNDP), Swiss Cooperation Office (SCO) for the South Caucasus, Austrian Development Cooperation (ADC): November 2015. Available at <a href=\"https://www.undp.org/georgia/publications/citizen-satisfaction-public-services-georgia-2015\">https://www.undp.org/georgia/publications/citizen-satisfaction-public-services-georgia-2015</a></li>\n  <li>World Justice Project (2016). WJP Rule of Law Index 2016. Available at<strong> </strong><a href=\"https://worldjusticeproject.org/our-work/wjp-rule-law-index/wjp-rule-law-index-2016\">https://worldjusticeproject.org/our-work/wjp-rule-law-index/wjp-rule-law-index-2016</a><strong> </strong></li>\n</ul>", "indicator_sort_order"=>"16-06-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.7.1", "slug"=>"16-7-1", "name"=>"Proporciones de plazas en las instituciones nacionales y locales, entre ellas: a) las asambleas legislativas, b) la administración pública y c) el poder judicial, en comparación con la distribución nacional, desglosadas por sexo, edad, personas con discapacidad y grupos de población", "url"=>"/site/es/16-7-1/", "sort"=>"160701", "goal_number"=>"16", "target_number"=>"16.7", "global"=>{"name"=>"Proporciones de plazas en las instituciones nacionales y locales, entre ellas: a) las asambleas legislativas, b) la administración pública y c) el poder judicial, en comparación con la distribución nacional, desglosadas por sexo, edad, personas con discapacidad y grupos de población"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Ratio de la proporción de mujeres en el sector público respecto a la proporción de mujeres en la población entre 16 y 64 años, por institución pública", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporciones de plazas en las instituciones nacionales y locales, entre ellas: a) las asambleas legislativas, b) la administración pública y c) el poder judicial, en comparación con la distribución nacional, desglosadas por sexo, edad, personas con discapacidad y grupos de población", "indicator_number"=>"16.7.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Bienal", "url"=>"https://www.eustat.eus/estadisticas/tema_515/opt_0/ti_indice-de-igualdad-de-genero/temas.html", "url_text"=>"Índice de Igualdad de Género", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Ministerio de Política Territorial y Memoria Democrática", "periodicity"=>"Anual", "url"=>"https://digital.gob.es", "url_text"=>"Boletín estadístico del personal al servicio de las administraciones públicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}, {"organisation"=>"Consejo General del Poder Judicial", "periodicity"=>"Anual", "url"=>"https://www.poderjudicial.es/cgpj/es/Temas/Estadistica-Judicial/", "url_text"=>"Estadística Judicial"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Ratio de la proporción de mujeres en el sector público respecto a la proporción de mujeres en la población entre 16 y 64 años, por institución pública", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.7- Garantizar la adopción en todos los niveles de decisiones inclusivas, participativas y representativas que respondan a las necesidades", "definicion"=>"Ratio de la proporción de mujeres en el sector público respecto a la proporción de mujeres en la población entre 16 y 64 años, por institución pública", "formula"=>"\n$$RM_{SP}^{t} = \\frac{PM_{SP}^{t}}{PM_{16-64}^{t}}$$\n\nsiendo:\n\n$$PM_{SP}^{t} = \\frac{P_{SP\\, mujeres}^{t}}{P_{SP}^{t}} \\cdot 100 $$\n\n$$PM_{16-64}^{t} = \\frac{P_{16-64\\, mujeres}^{t}}{P_{16-64}^{t}} \\cdot 100 $$\n\ndonde: \n\n$P_{SP\\, mujeres}^{t} =$ mujeres al servicio del sector público a 1 de enero del año $t$ \n\n$P_{SP}^{t} =$ personas al servicio del sector público a 1 de enero del año $t$ \n\n$P_{16-64\\, mujeres}^{t} =$ mujeres entre 16 y 64 años a 1 de enero del año $t$ \n\n$P_{16-64}^{t} =$ personas entre 16 y 64 años a 1 de enero del año $t$ \n", "desagregacion"=>"\nInstitución pública: sector público (total), sector público autonómico, sector público local, sector público estatal, instituciones sanitarias autonómicas, centros educativos no universitarios autonómicos, diputaciones forales, ayuntamientos, universidades, policía autonómica, administración de justicia autonómica, administración de justicia del Estado, fuerzas armadas, fuerzas y cuerpos de seguridad del Estado.\n", "observaciones"=>"El valor de referencia de este indicador es 1, que se tiene cuando la representación de la \nmujer en la administración pública analizada es perfectamente proporcional a su presencia \nen la población entre 16 y 64 años. \n\nSi el indicador toma un valor menor que 1 significa que la mujer está \ninfrarepresentada, mientras que si toma un valor mayor que 1 significa que \nestá sobrerepresentada.\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El servicio público es la base del gobierno, donde se desarrollan e implementan las \npolíticas y programas públicos y donde la sociedad interactúa con el gobierno. En la \nmayoría de los países, el servicio público es también el mayor empleador. Es en este \ncontexto que el ODS 16, en su meta 16.7, alienta a los países a garantizar que el \nservicio público sea representativo de las personas a las que sirve “en todos \nlos niveles”. \n\nEl indicador 16.7.1 se centra en la representación proporcional en las instituciones \npúblicas; mide el grado en que las instituciones públicas de un país son representativas \nde la población general. La representación proporcional (también conocida como \n“representación descriptiva”) en el servicio público se relaciona con el grado en que \nla composición del servicio público refleja los diversos grupos sociodemográficos de \nla población nacional. El supuesto subyacente es que cuando el servicio público \nrefleja la diversidad social de una nación, esto puede conducir a una mayor \nlegitimidad del servicio público a los ojos de los ciudadanos, ya que los \nfuncionarios públicos se asemejan a las personas a las que prestan servicios. \n\nSe ha descubierto que la representación proporcional está asociada con niveles más \naltos de confianza en las instituciones públicas, ya que las personas perciben \nprocesos de formulación de políticas más inclusivos para mejorar la calidad y la equidad \nde las decisiones políticas y ayudar a frenar la influencia indebida de intereses \ncreados sobre la toma de decisiones.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.7.1&seriesCode=SG_DMK_PARLMP_LC&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Ratio de mujeres miembros de los parlamentos (Relación de la proporción de mujeres en el parlamento con la proporción de mujeres en la población nacional, con la edad de elegibilidad como límite inferior), Cámara Baja o Unicameral SG_DMK_PARLMP_LC</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos de Naciones Unidas. Se centra en el subindicador b).", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-07-01b.pdf\">Metadatos 16-7-1(b).pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Ratio de la proporción de mujeres en el sector público respecto a la proporción de mujeres en la población entre 16 y 64 años, por institución pública", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.7- Garantizar la adopción en todos los niveles de decisiones inclusivas, participativas y representativas que respondan a las necesidades", "definicion"=>"Ratio of the proportion of women in the public sector to the proportion of women in the population aged 16 to 64, by public institution", "formula"=>"\n$$RM_{SP}^{t} = \\frac{PM_{SP}^{t}}{PM_{16-64}^{t}}$$\n\nbeing:\n\n$$PM_{SP}^{t} = \\frac{P_{SP\\, women}^{t}}{P_{SP}^{t}} \\cdot 100 $$\n\n$$PM_{16-64}^{t} = \\frac{P_{16-64\\, women}^{t}}{P_{16-64}^{t}} \\cdot 100 $$\n\nwhere: \n\n$P_{SP\\, women}^{t} =$ women in public service as of January 1 of year $t$ \n\n$P_{SP}^{t} =$ people in public service as of January 1 of year $t$ \n\n$P_{16-64\\, women}^{t} =$ women aged 16-64 as of 1 January of year $t$ \n\n$P_{16-64}^{t} =$ people aged 16-64 as of 1 January of year $t$ \n", "desagregacion"=>"\nPublic institution: public sector (total); autonomous public sector; local public sector; state public sector; autonomous health institutions; autonomous non-university educational centers; provincial governments; town councils; universities; autonomous police; autonomous justice administration; state justice administration; armed forces; state security forces and bodies\n", "observaciones"=>"The reference value of this indicator is 1, which means the representation of women in the public \nadministration analyzed is perfectly proportional to their presence in the population between 16 \nand 64 years old. \n\nIf the indicator takes a value less than 1 it means that women are underrepresented, while if it \ntakes a value greater than 1 it means that they are overrepresented. \n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The public service is the bedrock of government – where the development and implementation of public \npolicies and programmes takes place and where society interacts with the government. In most countries, \nthe public service is also the single largest employer. It is in this context that SDG 16, under its target 16.7, \nencourages countries to ensure that the public service is representative of the people it serves “at all \nlevels”. \n\nIndicator 16.7.1 focuses on proportional representation in public institutions; it measures the extent to \nwhich a country’s public institutions are representative of the general population. Proportional \nrepresentation (also known as ‘descriptive representation’) in the public service is concerned with the \nextent to which the composition of the public service mirrors the various socio-demographic groups in the \nnational population. The underlying assumption is that when the public service reflects the social diversity \nof a nation, this may lead to greater legitimacy of the public service in the eyes of citizens, as public servants \nresemble the people they provide services to.  \n\nProportional representation has been found to be associated\nwith higher levels of trust in public institutions, as people perceive more inclusive policymaking processes \nto improve the quality and fairness of policy decisions, and to help curb the undue influence of vested \ninterests over decision-making. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.7.1&seriesCode=SG_DMK_PARLMP_LC&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Ratio for female members of parliaments (Ratio of the proportion of women in parliament in the proportion of women in the national population with the age of eligibility as a lower bound boundary), Lower Chamber or Unicameral SG_DMK_PARLMP_LC</a> UNSTATS", "comparabilidad"=>"The available indicator partially complies with the United Nations metadata. It focuses on sub-indicator b). ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-07-01b.pdf\">Metadata 16-7-1(b).pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Ratio de la proporción de mujeres en el sector público respecto a la proporción de mujeres en la población entre 16 y 64 años, por institución pública", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.7- Garantizar la adopción en todos los niveles de decisiones inclusivas, participativas y representativas que respondan a las necesidades", "definicion"=>"Ratio de la proporción de mujeres en el sector público respecto a la proporción de mujeres en la población entre 16 y 64 años, por institución pública", "formula"=>"\n$$RM_{SP}^{t} = \\frac{PM_{SP}^{t}}{PM_{16-64}^{t}}$$\n\nizanik:\n\n$$PM_{SP}^{t} = \\frac{P_{SP\\, emakumeak}^{t}}{P_{SP}^{t}} \\cdot 100 $$\n\n$$PM_{16-64}^{t} = \\frac{P_{16-64\\, emakumeak}^{t}}{P_{16-64}^{t}} \\cdot 100 $$\n\nnon: \n\n$P_{SP\\, emakumeak}^{t} =$ sektore publikoan zerbitzua ematen duten emakumeak $t$ urteko urtarrilaren 1ean\n\n$P_{SP}^{t} =$ sektore publikoan zerbitzua ematen duten pertsonak $t$ urteko urtarrilaren 1ean\n\n$P_{16-64\\, emakumeak}^{t} =$ 16 eta 64 urte bitarteko emakumeak $t$ urteko urtarrilaren 1ean\n\n$P_{16-64}^{t} =$ 16 eta 64 urte bitarteko pertsonak $t$ urteko urtarrilaren 1ean\n", "desagregacion"=>"\nErakunde publikoa: sektore publikoa (guztira); sektore publiko autonomikoa; tokiko sektore publikoa; \nestatuko sektore publikoa; osasun-erakunde autonomikoak; unibertsitateaz kanpoko ikastetxe autonomikoak; \nforu-aldundiak; udalak; unibertsitateak; polizia autonomikoa; justizia-administrazio autonomikoa; \nestatuko justizia-administrazioa; indar armatuak; estatuko segurtasun-indar eta -kidegoak.\n", "observaciones"=>"El valor de referencia de este indicador es 1, que se tiene cuando la representación de la \nmujer en la administración pública analizada es perfectamente proporcional a su presencia \nen la población entre 16 y 64 años. \n\nSi el indicador toma un valor menor que 1 significa que la mujer está \ninfrarepresentada, mientras que si toma un valor mayor que 1 significa que \nestá sobrerepresentada.\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El servicio público es la base del gobierno, donde se desarrollan e implementan las \npolíticas y programas públicos y donde la sociedad interactúa con el gobierno. En la \nmayoría de los países, el servicio público es también el mayor empleador. Es en este \ncontexto que el ODS 16, en su meta 16.7, alienta a los países a garantizar que el \nservicio público sea representativo de las personas a las que sirve “en todos \nlos niveles”. \n\nEl indicador 16.7.1 se centra en la representación proporcional en las instituciones \npúblicas; mide el grado en que las instituciones públicas de un país son representativas \nde la población general. La representación proporcional (también conocida como \n“representación descriptiva”) en el servicio público se relaciona con el grado en que \nla composición del servicio público refleja los diversos grupos sociodemográficos de \nla población nacional. El supuesto subyacente es que cuando el servicio público \nrefleja la diversidad social de una nación, esto puede conducir a una mayor \nlegitimidad del servicio público a los ojos de los ciudadanos, ya que los \nfuncionarios públicos se asemejan a las personas a las que prestan servicios. \n\nSe ha descubierto que la representación proporcional está asociada con niveles más \naltos de confianza en las instituciones públicas, ya que las personas perciben \nprocesos de formulación de políticas más inclusivos para mejorar la calidad y la equidad \nde las decisiones políticas y ayudar a frenar la influencia indebida de intereses \ncreados sobre la toma de decisiones.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.7.1&seriesCode=SG_DMK_PARLMP_LC&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Parlamentuetako emakume kideen ratioa (Parlamentuko emakumeen proportzioaren eta estatuko biztanleriako emakumeen proportzioaren arteko erlazioa, hautagarritasun-adina beheko muga gisa hartuta), Behe Ganbera edo Ganbera Bakarra SG_DMK_PARLMP_LC</a> UNSTATS", "comparabilidad"=>"EAEn erabilgarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen metadatuak. b) azpiadierazlean zentratzen da.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-07-01b.pdf\">Metadatuak 16-7-1(b).pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.7: Ensure responsive, inclusive, participatory and representative decision-making at all levels</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.7.1: Proportions of positions in national and local institutions, including (a) the legislatures; (b) the public service; and (c) the judiciary, compared to national distributions, by sex, age, persons with disabilities and population groups</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_DMK_PARLCC_JC - Number of chairs of permanent committees, by age sex and focus of the committee, Joint Committees [16.7.1]</p>\n<p>SG_DMK_PARLCC_LC - Number of chairs of permanent committees, by age, sex and focus of the committee, Lower Chamber or Unicameral [16.7.1]</p>\n<p>SG_DMK_PARLCC_UC - Number of chairs of permanent committees, by age, sex and focus of the committee, Upper Chamber [16.7.1]</p>\n<p>SG_DMK_PARLMP_LC - Ratio for female members of parliaments (Ratio of the proportion of women in parliament in the proportion of women in the national population with the age of eligibility as a lower bound boundary), Lower Chamber or Unicameral [16.7.1]</p>\n<p>SG_DMK_PARLMP_UC - Ratio for female members of parliaments (Ratio of the proportion of women in parliament in the proportion of women in the national population with the age of eligibility as a lower bound boundary), Upper Chamber [16.7.1]</p>\n<p>SG_DMK_PARLSP_LC - Number of speakers in parliament, by age and sex , Lower Chamber or Unicameral [16.7.1]</p>\n<p>SG_DMK_PARLSP_UC - Number of speakers in parliament, by age and sex, Upper Chamber [16.7.1]</p>\n<p>SG_DMK_PARLYN_LC - Number of youth in parliament (age 45 or below), Lower Chamber or Unicameral [16.7.1]</p>\n<p>SG_DMK_PARLYN_UC - Number of youth in parliament (age 45 or below), Upper Chamber [16.7.1]</p>\n<p>SG_DMK_PARLYP_LC - Proportion of youth in parliament (age 45 or below), Lower Chamber or Unicameral [16.7.1]</p>\n<p>SG_DMK_PARLYP_UC - Proportion of youth in parliament (age 45 or below), Upper Chamber [16.7.1]</p>\n<p>SG_DMK_PARLYR_LC - Ratio of young members in parliament (Ratio of the proportion of young members in parliament (age 45 or below) in the proportion of the national population (age 45 or below) with the age of eligibility as a lower bound boundary), Lower Chamber or Unicameral [16.7.1]</p>\n<p>SG_DMK_PARLYR_UC - Ratio of young members in parliament (Ratio of the proportion of young members in parliament (age 45 or below) in the proportion of the national population (age 45 or below) with the age of eligibility as a lower bound boundary), Upper Chamber [16.7.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 5.5.1(a) looks at the proportion of seats held by women in national parliaments while indicator 5.5.1(b) considers the proportion of women in local governments. The metadata developed for the latter only considers elected positions in legislative bodies of local government, thus focusing on the same positions that would be covered by indicator 16.7.1(a) at sub-national level. The Methodology Development Narrative Report for the present indicator recommends building on the methodology elaborated for indicator 5.5.1(b) for future reporting on indicator 16.7.1(a) at local level.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Inter-Parliamentary Union (IPU)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Inter-Parliamentary Union (IPU)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This metadata sheet is focused only on the first sub-component of indicator 16.7.1, namely on positions in national legislatures held by individuals of each target population (sex, age, persons with disabilities, and contextually relevant population groups). </p>\n<p>The legislative sub-component of indicator 16.7.1 aims to measure how representative of the general population are the individuals occupying key decision-making positions in national legislatures. More specifically, this indicator measures the proportional representation of various demographic groups (women, age groups) in the national population amongst individuals occupying the following positions in national legislatures: (1) Members, (2) Speakers and (3) Chairs of permanent committees in charge of the following portfolios: Foreign Affairs, Defence, Finance, Human Rights and Gender Equality. Furthermore, it looks at the electoral and constitutional provisions adopted by countries to secure representation in national legislatures of persons with disabilities and contextually relevant population groups. </p>\n<p><strong>Concepts:</strong></p>\n<p>The indicator is based on the following key concepts and terms: </p>\n<ul>\n  <li><em>National legislature: </em>A legislature (alternatively called &#x2018;assembly&#x2019; or &#x2018;parliament&#x2019;) is the multi-member branch of government that considers public issues, makes laws and oversees the executive. <ul>\n      <li><em>Unicameral / bicameral parliaments:</em> A legislature may consist of a single chamber (unicameral parliament) or two chambers (bicameral parliament). The organization of a country&#x2019;s legislature is prescribed by its constitution. Around the world, about 59% of all countries have unicameral legislatures, while the remaining 41% are bicameral<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup>. To allow for a comprehensive analysis, this indicator will consider both chambers in bicameral parliaments.</li>\n    </ul>\n  </li>\n  <li><em>Member of Parliament (MP): </em>A person who is formally an elected or appointed member of a national legislature. This metadata considers all members of lower and upper chamber regardless of the selection modality (direct election, indirect election and appointment).</li>\n  <li>Speaker: A Speaker (alternatively called &#x2018;president&#x2019; or &#x2018;chairperson&#x2019; of the legislature) is the presiding officer of the legislature.</li>\n  <li><em>Permanent committee </em>(alternatively called &#x2018;standing committee&#x2019;): established for the full duration of the legislature and generally aligned with the specific policy areas of key government departments. For the purpose of SDG indicator 16.7.1(a), the permanent committees in charge of five portfolios are being considered: Foreign Affairs, Defence, Finance, Human Rights and Gender Equality.</li>\n  <li><em>Permanent Committee Chair:</em> A person designated to preside over the work of a permanent committee, selected through nomination by political parties, election by MPs, appointment by the Speaker, or other means. </li>\n  <li><em>Disability: </em>long-term physical, mental, intellectual or sensory impairments which in interaction with various barriers may hinder the full and effective participation of disabled persons in society on an equal basis with others.<sup><sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></sup></li>\n  <li><em>Population group: </em>The population of a country is a mosaic of different population groups that can be identified according to racial or ethnic, language, migration status, religious affiliation, sexual orientation, as well as disability status (UNECE). The indicator adopts a broad definition of population groups, not limited to minorities<sup><sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup></sup> and indigenous peoples<sup><sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></sup>, in order to capture all nationally relevant groups tracked by a given parliament, which depends on the constitutional and electoral measures in place to guarantee the representation of certain groups. Such measures sometimes extend to groups other than &#x2018;minorities&#x2019;, such as, for instance, occupational groups.<sup><sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup></sup></li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Source: Structure of Parliaments, IPU New Parline database on national parliaments &lt;https://data.ipu.org/compare?field=country%3A%3Afield_structure_of_parliament#pie&gt; <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> UN General Assembly, Convention on the Rights of Persons with Disabilities: resolution / adopted by the General Assembly, 24 January 2007, A/RES/61/106, available at: http://www.refworld.org/docid/45f973632.html <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> <em>Minority group: </em>a group numerically inferior to the rest of the population of a State, in a non-dominant position, whose members&#x2014;being nationals of the State&#x2014;possess ethnic, religious or linguistic characteristics differing from those of the rest of the population and show, if only implicitly, a sense of solidarity, directed towards preserving their culture, traditions, religion or language. Source: UN Office of the High Commissioner for Human Rights (OHCHR), Minority Rights: International Standards and Guidance for Implementation, 2010, HR/PUB/10/3, &lt;http://www.refworld.org/docid/4db80ca52.html&gt; <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> <em>Indigenous peoples: </em>peoples in independent countries who are regarded as indigenous on account of their descent from the populations which inhabited the country, or a geographical region to which the country belongs, at the time of conquest or colonization or the establishment of present state boundaries and who, irrespective of their legal status, retain some or all of their own social, economic, cultural and political institutions. Source: C169 - Indigenous and Tribal Peoples Convention, 1989 (No. 169) <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> For example, Egypt&apos;s electoral law reserves 50 per cent of seats in the People&apos;s Assembly for &#x201C;workers and farmers&#x201D;. <a href=\"#footnote-ref-6\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Number, Ratio, Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The multiple data points pertaining to the parliamentary sub-component of indicator 16.7.1 will be compiled by the Inter-Parliamentary Union (IPU) based on information gathered in its New PARLINE database on national parliaments:</p>\n<p><u>Data on age and sex of Members and Speakers: </u></p>\n<p>The IPU already collects data from secretariats of national parliaments on an ongoing basis for New PARLINE. The Platform already provides up-to-date and disaggregated data on the following positions: </p>\n<ul>\n  <li><em>Members</em>: data disaggregated by sex and age. </li>\n  <li><em>Speakers</em>: data disaggregated by sex and age. </li>\n  <li><em>Chairs of permanent committees on Human Rights and Gender Equality: </em>data disaggregated by sex and age. </li>\n</ul>\n<p><u>Data on age and sex of Chairs of permanent committee </u>on Foreign Affairs, Defense and Finance<u>: </u></p>\n<p>Data on the sex and age of Chairs of permanent committees on Foreign Affairs, Defense and Finance New Parline, will be added to Parline in 2020 . This is building on the successful attempt made by the IPU in 2011 to collect sex-disaggregated data on committee Chairs, broken down by area of competence (see IPU, Gender-sensitive parliaments, 2011).</p>\n<p><u>Data on disability and population group status of Members:</u></p>\n<p>In the immediate future, data on the disability and population group status of individual members will not be collected. As explained above, (1) such characteristics are very rarely tracked by parliaments in a systematic way; (2) confidentiality and data protection concerns are likely to make such data collection challenging, if not legally impossible; (3) data on the representation of persons with disabilities or various population groups will likely be of limited potential use. </p>\n<p>Instead, lists of electoral or constitutional provisions guaranteeing representation of persons with disabilities and various population groups in parliament are already compiled in the New PARLINE database (see &#x2018;Reserved seats and quotas&#x2019; section) and will be used to report on this indicator. </p>\n<p>In the future, it is recommended that the &#x2018;Inclusion Survey&#x2019; (see Annex) be considered by the IPU&#x2019;s network of national parliaments. In this survey, each member is asked to self-report on (1) levels of difficulty in performing activities in five<sup><sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup></sup> core functional domains &#x2013; namely seeing, hearing, walking, cognition and communication (the &#x2018;Inclusion Survey&#x2019; is an adapted version of the standardized Short Set of Questions on<strong> </strong>Disability<strong> </strong>elaborated by the Washington Group), and (2) his/her affiliation to a national, ethnic, religious or linguistic minority group, or to an indigenous or occupational group, in keeping with the UN principle of self-identification with regards to indigenous peoples and minorities. </p>\n<p>Given the potential sensitivity of disclosing information on population groups and disability, declaring and being transparent as to who is the sponsor of the Inclusion Survey can make respondents more comfortable. It is important for the sponsor to be a neutral entity independent from the employer institution, and to be able to protect the confidentiality of survey respondents. In this regard, organisations such as IPU and National Statistical Offices are particularly well positioned to administer the Inclusion Survey in national parliaments, and to perform subsequent data analysis. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> It was advised by the Washington Group to omit the sixth domain of &#x2018;self-care&#x2019; from the Short Set of Questions on Disability, as this question does not capture additional disability cases but acts more like a &#x2018;severity indicator&#x2019;. Given the target population for this survey (members of parliament), this question was found unnecessary. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div></div>", "COLL_METHOD__GLOBAL"=>"<p>The compilation of data by the Inter-Parliamentary Union uses the following mechanisms:</p>\n<ul>\n  <li>data collection forms sent to Parliaments<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup> </li>\n  <li>internal review and validation of data obtained from national parliaments by the IPU </li>\n  <li>on-line dissemination of data by IPU on New PARLINE</li>\n</ul>\n<p>The IPU will apply the data validation procedures developed for New Parline, plus additional checks specifically for SDG indicator 16.7.1(a), prior to submitting data at the international level for SDG reporting. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> In case of bicameral parliaments, data will be obtained separately from the secretariat of each chamber, except where the two chambers share a secretariat / contact point. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div></div>", "FREQ_COLL__GLOBAL"=>"<p>Data should be collected at least once every legislative term (preferably within 6 months of the opening of a new parliament). If possible, data should be updated annually. This will ensure timely capturing of changes in the composition of parliament and/or permanent committees which may come as a consequence of the electoral cycle, snap elections and by-elections held in selected constituencies to fill vacancies arising from the death or resignation of members. </p>\n<ul>\n  <li>Sex and age of members: updated after every election</li>\n  <li>Sex and age of Speakers: updated on a daily basis, every time a change occurs</li>\n  <li>Sex and age of permanent committee Chairs: updated after every election</li>\n  <li>Data on electoral or constitutional provisions guaranteeing representation of persons with disabilities and various population groups: updated at the time of every election</li>\n  <li> In addition, all data will be reviewed and updated annually by parliaments.</li>\n</ul>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data will be reported at the international level in February each year, and will provide a snapshot of the situation as at 1 January of that year.</p>\n<p>The first full release of data for the indicator will take place in February 2020, on the basis of data as at 1 January 2020.</p>\n<p>The IPU will have a rolling schedule of publication of parts of the data for the indicator in the New Parline database. For example, data on the sex of members of parliament is already available; whereas data on the age and sex of the Chairs of permanent committees on Foreign Affairs, Defence and Finance will be published in the database in 2020.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The Inter-Parliamentary Union is responsible for the provision of data on all dimensions of the indicator. Data is directly provided by national parliaments and then made available on New Parline.</p>", "COMPILING_ORG__GLOBAL"=>"<p>The Inter-Parliamentary Union is responsible for the compilation of all data points required by this indicator and for the computation of the two ratios for each parliamentary chamber of each country. </p>", "INST_MANDATE__GLOBAL"=>"<p>The IPU is a global organization of national parliaments founded in 1889 that promotes democracy and sustainable development and helps parliaments to become stronger, younger, gender-balanced and more diverse. The IPU has a historical record of collecting reference data on parliaments since the 1960s. It also maintains the flaghsip Parline database on national parliaments - an authoritative and up-to-date resource containing over 600 data fields for every functioning parliament in the world. </p>\n<p>In 2017 UNDP approached the IPU to jointly develop metadata for the 16.7.1a component of this indicator and in November 2018 the UN-IAEG approved the metadata and confirmed IPU as custodian.</p>", "RATIONALE__GLOBAL"=>"<p><em><u>The concept of representation</u></em></p>\n<p>There are different approaches to the concept of representation in parliament, with two of the most widely-known being descriptive and substantive representation (Bird, 2003; Floor Eelbode, 2010). Descriptive representation is concerned with the extent to which the composition of parliament mirrors the various socio-demographic groups in the national population. Substantive representation, meanwhile, is concerned with the extent to which parliament acts in the interest of certain population groups (irrespective of whether or not members of parliament consider themselves as members of those groups).</p>\n<p>Indicator 16.7.1 focuses on descriptive representation. The underlying assumption is that when parliament reflects the social diversity of a nation, this may lead to greater legitimacy of the parliament in the eyes of the electorate, as members resemble the people they represent in respect to gender, age, ethnicity and disability. Descriptive representation has been found to be associated with higher levels of trust in public institutions, as people feel closer to elected representatives who resemble them and perceive more visibly representative political bodies with better quality and fairness of policy decisions, and with less undue influence of vested interests over decision-making.<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup> Such descriptive representation should then enhance the substantive influence of population groups.</p>\n<p>The methodology for this indicator measures representation in parliamentary decision-making with respect to the sex and age of members of parliament. It identifies the extent to which the proportion of women members of parliament, and the proportion of young members of parliament, corresponds to the proportion of these groups in society as a whole. </p>\n<p>A different approach is taken with regard to disability and population group status, which focuses on electoral and constitutional provisions guaranteeing the representation of persons with disabilities and various population groups in national parliaments (see &#x2018;Comments and limitations&#x2019;).</p>\n<p><em><u>&#x2018;Decision-making positions&#x2019; in national parliaments</u></em></p>\n<p>Target 16.7 focuses on &#x2018;decision-making&#x2019; and the extent to which it is responsive, inclusive, participatory and representative. For the purpose of this indicator, three positions were identified for their importance in decision-making and leadership: Members of parliament, the Speaker of parliament and permanent committee Chairs. Broadly speaking, the decision-making power of individuals holding these positions can be described as follows: </p>\n<ul>\n  <li><em>Members of parliament</em> play important roles in public decision-making by voting on laws and holding the government to account. </li>\n  <li><em>The Speaker</em><strong> </strong>of a legislature presides over the proceedings of parliament and typically plays a significant role in setting the parliamentary agenda and organizing the business of parliament. The Speaker is responsible for ensuring parliamentary business is conducted fairly and effectively, and for protecting the autonomy of the legislature in relation to the other branches of government. </li>\n  <li><em>Committee Chairs</em> preside over the work of parliamentary committees, and typically have great influence over the committee agenda and business, including the legislative and oversight work carried out. In addition, committee Chairs often participate in the management boards or bureau that guide the overall work of parliament. As the number and mandates of permanent committees vary between parliaments, for the sake of better-quality data and greater comparability, this indicator only considers five Permanent Committees : Foreign Affairs, Defence, Finance, Human Rights and Gender Equality (see &#x2018;Comments and limitations&#x2019;). </li>\n</ul>\n<p><em><u>Political representation and disaggregation dimensions</u></em></p>\n<p>The indicator calls for disaggregation of positions by age, sex, contextually relevant population groups and disability status. The following international human rights instruments contain provisions on enhancing opportunities for political participation by individuals and groups holding such characteristics: </p>\n<p><strong><em>The right and opportunity to participate in public affairs</em></strong></p>\n<p>Article 25 of the International Covenant on Civil and Political Rights (ICCPR) recognizes &#x201C;the right and opportunity, without distinction of any kind such as race, color, sex, language, religion, political or other opinion, national or social origin, property, birth or other status to take part in the conduct of public affairs, directly or through freely chosen representatives&#x201D;.</p>\n<p><strong><em>Age</em></strong></p>\n<p>The 2015 Security Council Resolution 2250 urges Member States to consider ways to increase inclusive representation of youth in decision-making at all levels in local, national, regional and international institutions and mechanisms to prevent and resolve conflict and counter violent extremism. </p>\n<p><strong><em>Sex</em></strong></p>\n<p>The 2000 Security Council Resolution 1325 and the six supporting resolutions between 2000-2013 on Women, Peace and Security urge member states to increase the numbers of women at all levels of decision-making institutions. The 1979 Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) provides the basis for realizing equality between women and men through ensuring women&apos;s equal access to, and equal opportunities in, political and public life, including the right to vote and to stand for election, as well as to hold public office at all levels of government (Article 7). States parties agree to take all appropriate measures to overcome historical discrimination against women and obstacles to women&#x2019;s participation in decision-making processes (Article 8), including legislation and temporary special measures (Article 4). </p>\n<p><strong><em>Ethnic or minority status</em></strong></p>\n<p>The Declaration on the Rights of Persons belonging to National or Ethnic, Religious and Linguistic Minorities (1992) and the Declaration on the Rights of Indigenous Peoples (2007) provide that persons belonging to minorities and indigenous peoples have the right to participate in the political, economic, social and cultural life of the State. </p>\n<p><strong><em>Disability status</em></strong></p>\n<p>The United Nations Convention on the Rights of Persons with Disabilities (2006) calls upon State Parties to ensure that persons with disabilities can effectively and fully participate in political and public life on an equal basis with others, directly or through freely chosen representatives, including the right and opportunity for persons with disabilities to vote and be elected. Resolution 2155 (2017) of the Parliamentary Assembly of the Council of Europe (PACE) on the political rights of persons with disabilities recommends for countries to consider the establishment of quotas for the participation of persons with disabilities in parliamentary and local elections, with a view to increasing participation and representation.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> See OECD (2017) <a href=\"#footnote-ref-9\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p><em><u>Measuring representation</u></em></p>\n<ul>\n  <li>The significance of descriptive representation has been challenged in different ways. First, there is the question of what and who should be mirrored in the representative body; why be attentive to some groups (women, young people, minorities etc) but not others (the poor, LGBTI, &quot;ethnic&quot; groups who might not be officially recognized etc)? Second, the mirror notion of descriptive representation may be deemed dangerous if it precludes citizens from choosing representatives who do not look like them. One of the base tenets of democracy is freedom of choice at the ballot box and if one is corralled into having to vote for a candidate of your own sex or ethnicity, then that intrinsic liberty is constrained. Third, descriptive representation has the danger of ultimately becoming an end in itself. Concerns about effective representation should not end once parliament has the appropriate number of members for each minority groups. Indeed, at this stage concerns about adequate political representation should be just beginning. These members should be able to articulate minority concerns and have the same opportunities to influence policy as other members. Nevertheless, if a parliament includes none, or very few, women, young people, minorities etc., that is probably a worrying sign that their interests are not being heard. <sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup> </li>\n  <li>Representation needs to go hand in hand with participation, with both concepts being part of target 16.7. Without meaningful opportunities for citizens to participate in parliamentary decision-making, representation alone is unlikely to automatically lead to effective popular control of the government - one of the fundamental principles of democracy (International IDEA, 2013). </li>\n  <li>The age and sex of individuals holding decision-making positions in parliament provide an indication at the symbolic level of the way in which power is shared within this institution. However, there is no certainty that because a Speaker or committee Chair is young (or old), a woman (or a man), or belongs to a minority group, s/he will bring to the fore issues of interest to groups with the same socio-demographic profile.</li>\n  <li>Tracking the age of MPs over time offers some measure of youth representation in parliament. However, in most parliaments around the world, leadership positions such as Speaker and permanent committee Chairs are considered senior functions which require considerable experience, and are awarded in recognition of parliamentary achievement. This means that such positions are by nature unlikely to be held by members below the &#x2018;youth&#x2019; age bracket of &#x2018;45 years old and under&#x2019;. As such, for the positions of Speaker and committee Chairs, more relevant insights will be generated on the basis of sex disaggregation. </li>\n  <li>IPU studies on women in parliaments<sup><sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup></sup> have found that committees representing the three &#x2018;hard&#x2019; policy portfolios of Foreign Affairs, Defence and Finance are traditionally male-dominated. The two other committees tracked by this indicator, representing cross-cutting portfolios of Human Rights and Gender Equality, are also of interest given their specific areas of focus. Although not found in every parliament, the very existence of these two committees suggests a particular commitment within parliament to safeguarding human rights and promoting gender equality. </li>\n  <li>In certain countries, particularly Small Island Developing States, the number of members of parliament may be very small. Consequently, there may not be a committee system, or the committee system may not contain the same distribution by areas of responsibility as observed in the majority of parliaments. In addition, in parliaments with a very small number of members, the addition or reduction of just one or two people to the number of women or the number of young MPs may have a significant impact on the overall percentage of representation of these groups.</li>\n</ul>\n<p><em><u>Methodology</u></em></p>\n<ul>\n  <li>As regards the scope of &#x2018;population groups&#x2019;, while representation of minorities and indigenous peoples may be more often tracked by national parliaments due to the availability of internationally accepted definitions, the indicator also invites reporting on any other tracked population groups, including, for instance, occupational groups.</li>\n  <li>An obvious limitation of this metadata is that it only considers members of parliament, in keeping with the focus of target 16.7 on &#x2018;decision-making&#x2019;. However, some parliaments may find it useful to also look at the composition of various staff categories such as clerks of the parliament, committee clerks or researchers, etc. </li>\n  <li>Who holds the Chairs of parliamentary committees is largely tributary to the overall distribution of seats within the parliament. For example, parliaments with no members under the age of 30 will not have any committee Chairs under that age. Since committee chairs are typically awarded on the basis of experience and seniority,<sup><sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup></sup> higher age groups are expected to be common among committee Chairs and Speakers. </li>\n</ul>\n<p><em><u>Data collection</u></em></p>\n<ul>\n  <li>In between reporting dates, it may be difficult to maintain up-to-date information on the results of by-elections held in selected constituencies to fill vacancies arising from the death or resignation of members.</li>\n  <li>From one year to another during any given parliamentary term (typically 4 or 5 years), some Members may fall into a different age group amongst those considered for this indicator. For this reason, age of Members is collected at the time of their election to parliament. </li>\n  <li>Age of Speakers and permanent committee Chairs is collected at the time of their appointment to the position, then verified and updated as of 1 January each year. </li>\n</ul>\n<p><em><u>Recommended approach to monitoring disability and population groups:</u></em></p>\n<p><em><u>1) Sensitivity of disability and population group data</u></em></p>\n<ul>\n  <li>Efforts to promote inclusive parliaments presuppose recognition of ethno-cultural diversity<sup><a href=\"#footnote-13\" id=\"footnote-ref-13\">[12]</a></sup>. In certain contexts, population group status may prove to be a sensitive and politically charged variable. For example, several countries actively restrict or ban identification of ethnic or religious status, in order to protect vulnerable populations or discourage inter-ethnic conflict. In addition, definitions of groups that constitute a minority vary greatly between countries. </li>\n  <li>Furthermore, there is a strong human rights principle that individuals must be able to choose to identify themselves as members of a minority, or not. It would not be appropriate for parliaments (or any other body) to assume or to assign MPs&#x2019; membership of a particular population group. </li>\n  <li>Similarly, discriminatory perceptions and implicit bias against disability can make the collection of data by parliaments on this characteristic equally sensitive. This is partly because parliamentarians with disabilities, like everyone else, have a right to privacy and therefore are not under an obligation to reveal a disability. Moreover, in many states, information concerning disability falls under the umbrella of health data and is therefore confidential, thus preventing parliaments to release this information even on an anonymous basis.<sup><a href=\"#footnote-14\" id=\"footnote-ref-14\">[13]</a></sup> </li>\n  <li>As a result, currently, next to no countries systematically collect data on disability among members of parliaments. As pointed out by the European Union Agency for Fundamental Rights (FRA), while collecting reliable and accurate statistical data regarding the experiences of persons with disabilities presents numerous challenges, the lack of comparable data hinders the understanding of barriers to political participation.<sup><a href=\"#footnote-15\" id=\"footnote-ref-15\">[14]</a></sup> </li>\n</ul>\n<p><em><u>2) Limitations of the descriptive representation approach to tracking disability and population group status </u></em></p>\n<ul>\n  <li>Unlike for sex and age, monitoring the descriptive representation of members of parliament based on disability or population group status would be neither feasible nor meaningful.</li>\n  <li>Considering how broad the concept of disability is, encompassing various types of impairments and various degrees of severity, it would be unrealistic and unwarranted to expect a one-to-one ratio of representation in parliament. Furthermore, since national-level disability statistics are not always up-to-date, let alone available, the comparison between the share of disabled in the national population and in parliament could be unsound, or difficult to establish. </li>\n  <li>There are similar concerns with respect to monitoring the representation of various population groups. In countries whose populations are a mosaic of many diverse groups (some of which may account for less than 1 percent of the population) an exact reflection of such pluralism in the composition of parliament would be impossible and unnecessary. </li>\n  <li>For ethical reasons, data on disability and population group status of MPs could only be collected through individual surveys that meet required standards of confidentiality. Seeing that such practice is currently not in place, the testing of this approach will be explored in the future to establish whether surveying the world&#x2019;s 46,000 parliamentarians is feasible. </li>\n</ul>\n<p><em>3) Adopting an incremental approach </em></p>\n<ul>\n  <li>Given the perceived sensitivity of collecting data on disability and population group status and concerns related to the feasibility and usefulness of monitoring descriptive representation, it is proposed to take stock instead of electoral and constitutional provisions guaranteeing the representation of persons with disabilities and various population groups in national parliaments. </li>\n  <li><strong>Reserved seats and quotas</strong> are among the most commonly utilized electoral means to ensure representation of certain groups in the political process. Above and beyond guaranteeing a minimum number of seats held by persons with disabilities and certain population groups, the existence of such provisions substantiates a country&#x2019;s commitment to the right to equal participation in public and political life.</li>\n  <li>Provisions on quotas can be found in countries&#x2019; constitutions or electoral laws (i.e. legislated quotas).<sup><a href=\"#footnote-16\" id=\"footnote-ref-16\">[15]</a></sup> Such electoral measures are used to achieve equal or balanced access to political power by increasing access to political decision-making processes of certain sociodemographic groups. In 2010, the constitutions or electoral laws of more than 30 countries included electoral quotas for various groups (e.g. ethnic, religious) that commonly go under the name of &#x2018;minority groups&#x2019;. A few countries have similar provisions for persons with disabilities<sup><a href=\"#footnote-17\" id=\"footnote-ref-17\">[16]</a></sup>. </li>\n  <li>The impracticality of looking at descriptive representation does not mean there is no merit in producing statistics on disability or population groups in parliament. Even an indicative number of MPs self-reporting disability could help parliamentary administrations around the world to better accommodate their special needs. It could also provide valuable information on the actual exercise (and not only the legal status) of the human right to equal opportunity to participate in the public and political life. When supported by concrete figures, such information can be valuable to a broad range of actors trying to identify and address barriers to political participation, including civil society, community advocates, researchers, development partners and political institutions themselves. </li>\n  <li>In line with the proposed incremental approach, an &#x2018;Inclusion Survey&#x2019; (see Annex and Data Sources) was developed to facilitate the collection of self-reported data on disability (using the Short Set of Questions on<strong> </strong>Disability<strong> </strong>elaborated by the Washington Group) and population group status by parliaments. This short survey module of 8 questions, developed specifically for the purpose of reporting on indicator 16.7.1(a), could be administered directly to all Members by a neutral sponsor such as a national statistical office or the IPU itself. Importantly, the introduction to the survey reassures respondents of the anonymity and confidentiality of their responses, which is essential to overcome individual reluctance to disclose sensitive personal information. </li>\n</ul>\n<p><em><u>Recommendations for reporting also on the composition of local parliaments </u></em></p>\n<p>While at present the indicator looks only at national parliaments, broadening its scope to include legislative bodies of local governments could be considered in the future, in line with target 16.7 which calls for decision-making to be representative &#x201C;at all levels&#x201D;. Local councils or assemblies hold important decision-making powers, including the ability to issue by-laws that influence the lives of their respective local communities. While it is premature at this stage to propose a global methodology to report on representation in local legislatures due to the varying quality of data collection systems in place at the local level, and to a number of methodological complexities (notably with regards to the need for disaggregated population statistics to be available for each administrative division, in order to compute representation ratios in each local parliament), countries should nonetheless be encouraged to track diversity in local parliaments, using methodologies appropriate to their local context. As far as global SDG reporting is concerned, a recommendation for the future inclusion of local legislatures in indicator 16.7.1(a) can be found in Annex 1 to the Methodology Development Narrative. A custodian for this part of the indicator on local legislatures remains to be identified.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p> IPU and UNDP, &#x201C;Frequently Asked Questions on the representation of minorities and indigenous peoples in parliament&#x201D; (2008) in &#x201C;Promoting inclusive parliaments: The representation of minorities and indigenous peoples in parliament&#x201D; <a href=\"#footnote-ref-10\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> See, for example: IPU, &#x201C;Gender-Sensitive Parliaments&#x201D; (2011), &#x201C;Equality in Politics: A Survey of Women and Men in Parliaments&#x201D; (2008), &#x201C;Women in Parliament: 20 Years in Review&#x201D; (2016), &#x201C;Women in Politics&#x201D; (2017) <a href=\"#footnote-ref-11\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> See e.g. IPU &#x201C;Gender-sensitive Parliaments&#x201D;, p. 18 (on committee chairs: &#x201C;All leaders, irrespective of gender, need to demonstrate their capabilities before they can be accepted as credible and legitimate authority bearers&#x201D;). <a href=\"#footnote-ref-12\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-13\">12</sup><p> IPU and UNDP, &#x201C;The representation of minorities and indigenous peoples in parliament: A global overview&#x201D; (2010). <a href=\"#footnote-ref-13\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-14\">13</sup><p> See, for example, the EU General Data Protection Regulation (GDPR, 2016/679) which introduced a particularly broad definition of health data and a range of restrictions on processing it. GDPR took effect in all EU Member States in May 2018. <a href=\"#footnote-ref-14\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-15\">14</sup><p> European Union Agency for Fundamental Rights, &#x201C;The right to political participation for persons with disabilities: human rights indicators&#x201D; (2014): <a href=\"http://fra.europa.eu/en/publication/2014/right-political-participation-persons-disabilities-human-rights-indicators\">http://fra.europa.eu/en/publication/2014/right-political-participation-persons-disabilities-human-rights-indicators</a> <a href=\"#footnote-ref-15\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-16\">15</sup><p> Voluntary party quotas fall outside the scope of this indicator. <a href=\"#footnote-ref-16\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-17\">16</sup><p> Countries with constitutional or electoral provisions guaranteeing the representation of persons with disabilities in parliaments include Uganda, India, Afghanistan and Rwanda. <a href=\"#footnote-ref-17\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<ul>\n  <li><em>Members:</em></li>\n</ul>\n<p>Indicator 16.7.1(a) aims to compare the proportion of various demographic groups (by sex and age) represented in national parliaments, relative to the proportion of these same groups in the national population above the age of eligibility. </p>\n<p>To report on indicator 16.7.1(a), two ratios must be calculated, namely: </p>\n<ul>\n  <li>For &#x2018;young&#x2019; MPs (aged 45 and below) </li>\n  <li>For female MPs </li>\n</ul>\n<p>When comparing ratios of &#x2018;young&#x2019; MPs and female MPs with corresponding shares of the national population that is aged 45 and below (for the first ratio) and female (for the second ratio), <em>it is important to consider the population <u>of, or above, the age of eligibility</u></em>, the latter being, by definition, the lowest possible age of members of parliament. In other words, if the age of eligibility in a given country is 18 years old, the national population to be used as a comparator for the first ratio (for &#x2018;young&#x2019; MPs) will be the national population aged 18-45 (<em>not </em>0-45), and for the second ratio (for female MPs), the female population aged 18 and above. </p>\n<ol>\n  <li>To calculate the ratio for &#x2018;young&#x2019; MPs (aged 45 and below), the following formula is to be used: </li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi mathvariant=\"bold-italic\">R</mi>\n    <mi mathvariant=\"bold-italic\">a</mi>\n    <mi mathvariant=\"bold-italic\">t</mi>\n    <mi mathvariant=\"bold-italic\">i</mi>\n    <mi mathvariant=\"bold-italic\">o</mi>\n    <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n    <mn>1</mn>\n    <mo>=</mo>\n    <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi mathvariant=\"bold-italic\">P</mi>\n        <mi mathvariant=\"bold-italic\">r</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">p</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">r</mi>\n        <mi mathvariant=\"bold-italic\">t</mi>\n        <mi mathvariant=\"bold-italic\">i</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">n</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">f</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">M</mi>\n        <mi mathvariant=\"bold-italic\">P</mi>\n        <mi mathvariant=\"bold-italic\">s</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">a</mi>\n        <mi mathvariant=\"bold-italic\">g</mi>\n        <mi mathvariant=\"bold-italic\">e</mi>\n        <mi mathvariant=\"bold-italic\">d</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mn>45</mn>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">a</mi>\n        <mi mathvariant=\"bold-italic\">n</mi>\n        <mi mathvariant=\"bold-italic\">d</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">b</mi>\n        <mi mathvariant=\"bold-italic\">e</mi>\n        <mi mathvariant=\"bold-italic\">l</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">w</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">i</mi>\n        <mi mathvariant=\"bold-italic\">n</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">p</mi>\n        <mi mathvariant=\"bold-italic\">a</mi>\n        <mi mathvariant=\"bold-italic\">r</mi>\n        <mi mathvariant=\"bold-italic\">l</mi>\n        <mi mathvariant=\"bold-italic\">i</mi>\n        <mi mathvariant=\"bold-italic\">a</mi>\n        <mi mathvariant=\"bold-italic\">m</mi>\n        <mi mathvariant=\"bold-italic\">e</mi>\n        <mi mathvariant=\"bold-italic\">n</mi>\n        <mi mathvariant=\"bold-italic\">t</mi>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"bold-italic\">P</mi>\n        <mi mathvariant=\"bold-italic\">r</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">p</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">r</mi>\n        <mi mathvariant=\"bold-italic\">t</mi>\n        <mi mathvariant=\"bold-italic\">i</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">n</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">f</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">t</mi>\n        <mi mathvariant=\"bold-italic\">h</mi>\n        <mi mathvariant=\"bold-italic\">e</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">n</mi>\n        <mi mathvariant=\"bold-italic\">a</mi>\n        <mi mathvariant=\"bold-italic\">t</mi>\n        <mi mathvariant=\"bold-italic\">i</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">n</mi>\n        <mi mathvariant=\"bold-italic\">a</mi>\n        <mi mathvariant=\"bold-italic\">l</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">p</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">p</mi>\n        <mi mathvariant=\"bold-italic\">u</mi>\n        <mi mathvariant=\"bold-italic\">l</mi>\n        <mi mathvariant=\"bold-italic\">a</mi>\n        <mi mathvariant=\"bold-italic\">t</mi>\n        <mi mathvariant=\"bold-italic\">i</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">n</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">a</mi>\n        <mi mathvariant=\"bold-italic\">g</mi>\n        <mi mathvariant=\"bold-italic\">e</mi>\n        <mi mathvariant=\"bold-italic\">d</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mn>45</mn>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">a</mi>\n        <mi mathvariant=\"bold-italic\">n</mi>\n        <mi mathvariant=\"bold-italic\">d</mi>\n        <mi mathvariant=\"bold-italic\">&amp;nbsp;</mi>\n        <mi mathvariant=\"bold-italic\">b</mi>\n        <mi mathvariant=\"bold-italic\">e</mi>\n        <mi mathvariant=\"bold-italic\">l</mi>\n        <mi mathvariant=\"bold-italic\">o</mi>\n        <mi mathvariant=\"bold-italic\">w</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p><strong><em>(with the age of eligibility as a lower boundary)</em></strong></p>\n<p>where: </p>\n<ul>\n  <li>The numerator is the number of seats held by MPs aged 45 and below, divided by the total number of members in parliament</li>\n  <li>The denominator can be computed using national population figures as follows: </li>\n</ul>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mfenced open=\"[\" close=\"]\" separators=\"|\">\n          <mrow>\n            <mi>S</mi>\n            <mi>i</mi>\n            <mi>z</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>n</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mo>&#x2264;</mo>\n            <mn>45</mn>\n          </mrow>\n        </mfenced>\n        <mo>-</mo>\n        <mi>&amp;nbsp;</mi>\n        <mfenced open=\"[\" close=\"]\" separators=\"|\">\n          <mrow>\n            <mi>S</mi>\n            <mi>i</mi>\n            <mi>z</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>n</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mo>&amp;lt;</mo>\n            <mi>a</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>l</mi>\n            <mi>i</mi>\n            <mi>g</mi>\n            <mi>i</mi>\n            <mi>b</mi>\n            <mi>i</mi>\n            <mi>l</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>y</mi>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <mi>S</mi>\n        <mi>i</mi>\n        <mi>z</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p>The resulting ratio can then be interpreted as follows: </p>\n<ul>\n  <li>0 means no representation at all of &#x2018;youth&#x2019; (45 years and below) in parliament </li>\n  <li>1 means perfectly proportional representation of &#x2018;youth&#x2019; (45 years and below) in parliament </li>\n  <li>&lt;1 means under-representation of &#x2018;youth&#x2019; (45 years and below) in parliament </li>\n  <li>&gt;1 means over-representation of &#x2018;youth&#x2019; (45 years and below) in parliament </li>\n</ul>\n<p><strong>Example:</strong></p>\n<p>Say in country A, 30% of the national population is aged 45 or younger (but above the age of eligibility), but only 25% of MPs fall in this age category: </p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>R</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>1</mn>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>P</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>M</mi>\n        <mi>P</mi>\n        <mi>s</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>45</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>l</mi>\n        <mi>o</mi>\n        <mi>w</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>a</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow>\n        <mi>P</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>g</mi>\n        <mi>e</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mn>45</mn>\n        <mi>&amp;nbsp;</mi>\n        <mi>a</mi>\n        <mi>n</mi>\n        <mi>d</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>b</mi>\n        <mi>e</mi>\n        <mi>l</mi>\n        <mi>o</mi>\n        <mi>w</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p><em>(with the age of eligibility as a lower boundary)</em></p>\n<p>Ratio = 0.25 / 0.3 = <strong>0.83</strong> </p>\n<p><em>(&lt;1 since MPs aged 45 or younger are under-represented amongst MPs compared to the proportion of this age group in the national population. The ratio is close to 1 as the share of &#x2018;young&#x2019; MPs is not too far from the corresponding share of the national population falling in this age group.)</em></p>\n<p><strong>While a simple proportion of &#x2018;young&#x2019; MPs in parliament is not internationally comparable, a ratio computed using the above formula is.</strong> For instance, 48% of &#x2018;young&#x2019; MPs (45 years old or younger) may be an overrepresentation of youth in country A where only 30% of the national population above eligibility age falls in this age bracket (Ratio = 48/30 = 1.6), but in country B where 70% of the national population is 45 years old or younger, the same 48% would be interpreted as under-representation (Ratio = 48/70 = 0.69). In this example, the figure of 48% is not internationally comparable in relation to the national population (it means over-representation in one country and under-representation in another), but the ratios 1.6 and 0.69 <em>are </em>internationally comparable. They help us understand whether 48% of MPs aged 45 years old or less is close to, or far from, proportional representation of this age group in the national population. </p>\n<ol>\n  <li>To calculate the ratio for female MPs, the following formula is to be used: </li>\n</ol>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>R</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>2</mn>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>P</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>o</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>a</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow>\n        <mi>P</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>o</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p><em>(with the age of eligibility as a lower boundary)</em></p>\n<p>where: </p>\n<ul>\n  <li>The numerator is the number of seats held by female MPs, divided by the total number of members in parliament</li>\n  <li>The denominator can be computed using national population figures as follows: </li>\n</ul>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mfrac>\n      <mrow>\n        <mfenced open=\"[\" close=\"]\" separators=\"|\">\n          <mrow>\n            <mi>S</mi>\n            <mi>i</mi>\n            <mi>z</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>f</mi>\n            <mi>e</mi>\n            <mi>m</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>n</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mo>&#x2265;</mo>\n            <mi>a</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>l</mi>\n            <mi>i</mi>\n            <mi>g</mi>\n            <mi>i</mi>\n            <mi>b</mi>\n            <mi>i</mi>\n            <mi>l</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>y</mi>\n          </mrow>\n        </mfenced>\n      </mrow>\n      <mrow>\n        <mfenced open=\"[\" close=\"]\" separators=\"|\">\n          <mrow>\n            <mi>S</mi>\n            <mi>i</mi>\n            <mi>z</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>t</mi>\n            <mi>h</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>n</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>a</mi>\n            <mi>l</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mo>&#x2265;</mo>\n            <mi>a</mi>\n            <mi>g</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>l</mi>\n            <mi>i</mi>\n            <mi>g</mi>\n            <mi>i</mi>\n            <mi>b</mi>\n            <mi>i</mi>\n            <mi>l</mi>\n            <mi>i</mi>\n            <mi>t</mi>\n            <mi>y</mi>\n          </mrow>\n        </mfenced>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p><u>Note</u>: This denominator can be set at 50 in most countries, as women generally represent around 50% of the national population in any given age bracket. </p>\n<p>The resulting ratio can be:</p>\n<ul>\n  <li>0, when there is no representation of women at all in parliament</li>\n  <li>&lt;1, when the proportion of women in parliament is lower than that in the national population </li>\n  <li>=1, when the proportion of women in parliament equals that in the national population</li>\n  <li>&gt;1, when the proportion of women in parliament is higher than that in the national population</li>\n</ul>\n<p><strong>Example:</strong></p>\n<p>Say in the same country A, 10% of seats are held by women MPs and women represent 50% of the national population in the given age bracket):</p>\n<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mi>R</mi>\n    <mi>a</mi>\n    <mi>t</mi>\n    <mi>i</mi>\n    <mi>o</mi>\n    <mi>&amp;nbsp;</mi>\n    <mn>2</mn>\n    <mo>=</mo>\n    <mi>&amp;nbsp;</mi>\n    <mfrac>\n      <mrow>\n        <mi>P</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>o</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>a</mi>\n        <mi>r</mi>\n        <mi>l</mi>\n        <mi>i</mi>\n        <mi>a</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>t</mi>\n      </mrow>\n      <mrow>\n        <mi>P</mi>\n        <mi>r</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>r</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>o</mi>\n        <mi>f</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>w</mi>\n        <mi>o</mi>\n        <mi>m</mi>\n        <mi>e</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>i</mi>\n        <mi>n</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>t</mi>\n        <mi>h</mi>\n        <mi>e</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>n</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n        <mi>a</mi>\n        <mi>l</mi>\n        <mi>&amp;nbsp;</mi>\n        <mi>p</mi>\n        <mi>o</mi>\n        <mi>p</mi>\n        <mi>u</mi>\n        <mi>l</mi>\n        <mi>a</mi>\n        <mi>t</mi>\n        <mi>i</mi>\n        <mi>o</mi>\n        <mi>n</mi>\n      </mrow>\n    </mfrac>\n  </math></p>\n<p><em>(with the age of eligibility as a lower boundary)</em></p>\n<p>Ratio = 0.10 / 0.50 = <strong>0.2</strong> </p>\n<p><em>(&lt;1 since women are under-represented amongst MPs, but this time the ratio is much smaller as sex-based representation in parliament is far from parity.)</em></p>\n<ul>\n  <li><em>Speakers:</em> No computation, as most parliaments will only have one Speaker per parliament in unicameral parliaments or one Speaker per chamber in bicameral parliaments<sup><sup><a href=\"#footnote-18\" id=\"footnote-ref-18\">[17]</a></sup></sup>. Personal characteristics of the individual(s) holding the position of Speaker are recorded (i.e. age group and sex).</li>\n  <li><em>Chairs of permanent committees on Foreign Affairs, Defence, Finance, Human Rights and Gender Equality:</em> No computation, as data is collected only on five committee Chairs. Personal characteristics of the five individuals chairing these three committees are recorded (i.e. age group and sex).</li>\n</ul>\n<p> </p>\n<p><em><u>Computation in bicameral legislatures</u></em></p>\n<p>In bicameral parliaments, data will be collected and computed separately for the same set of positions in each chamber. </p>\n<p><strong>Regional/global aggregates:</strong></p>\n<p>Regional and global aggregates can be calculated on the basis of the data compiled for the indicator. </p>\n<ul>\n  <li>Members: Regional and global aggregates should be calculated using raw data, not the ratio</li>\n  <li>Speakers: Regional and global aggregates can be calculated</li>\n  <li>Committee chairs: When calculating regional and global aggregates, attention must be paid to committees that cover more than one portfolio and/or that are joint committees of both chambers in a bicameral parliament. </li>\n</ul>\n<p><em>Effect of the age of eligibility for upper chambers on the age ratio </em></p>\n<p>While in many bicameral legislatures, the age of eligibility for the upper chamber is significantly higher than that for the lower chamber, some have adopted an equal or similar age requirement for both chambers.<sup> </sup>However, regardless of the minimum age of eligibility set for upper chambers, members of these chambers throughout the world are older on average than members of lower chambers (see New Parline). As such, those upper chambers that have a low eligibility age are likely to have a lower ratio for &#x2018;young&#x2019; MPs than upper chambers that have a higher eligibility age. In other words, in upper chambers where the eligibility age is lower, the share of MPs who are 45 or younger is likely to be considerably less than the corresponding proportion of the national population that falls between the eligibility age and 45 years old. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-18\">17</sup><p> In very rare cases, there are two or more speakers per parliament / chamber. For the sake of clarity and consistency of the analysis, this metadata does not introduce computation for such cases. <a href=\"#footnote-ref-18\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>IPU member parliaments provide information on changes and updates to the IPU secretariat via IPU Groups within each parliamentary chamber or via the Parline Correspondent&#x2019;s Network. </p>\n<p>Parline Correspondents are staff members of national parliaments who act as the IPU focal point for IPU&#x2019;s Parline database within each chamber or parliament. Their main role is to make sure that all the data in Parline for their parliament is up&#x2011;to&#x2011;date and correct, including for this indicator. If no response is provided to questionnaires, other methods are used to obtain the information, such as from the electoral management body, parliamentary web sites or internet searches. Additional information gathered from other sources is regularly crosschecked with parliaments. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>There is no treatment of missing values.</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>There is no imputation of missing values.</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregations are a simple sum of country and chamber level data. A weighting structure is not applied.</p>", "DOC_METHOD__GLOBAL"=>"<p><strong>Methods and guidance available to countries for the compilation of data at national level:</strong></p>\n<p>Data on the age and sex of Members, Speakers and Committee Chairs, as well as of electoral or constitutional provisions guaranteeing representation of persons with disabilities and various population groups in parliament, will be reported directly by the IPU. The IPU already compiles this data in the New Parline database on national parliaments (<a href=\"https://data.ipu.org\">https://data.ipu.org</a>). </p>\n<p>New Parline contains data on the composition, structure and working methods of all national parliaments. New Parline was launched in September 2018, as the successor to the Parline database on national parliaments that was established by the IPU in 1996. New Parline contains some 450 different fields, which are collected or updated at varying intervals, depending on the nature of the data. Data is collected by the IPU directly from national parliaments and other official sources (such as electoral commissions). Data is collected using questionnaires and surveys that are distributed via national IPU Groups in parliament (via the Secretary General of non-member parliaments. As at 19 September 2018, the IPU has 177 members; a further 16 national parliaments are not members). Data is then processed by the IPU prior to inclusion in the database. Some fields are updated daily, while others are updated annually, after each election, or when the constitutional or legal powers of parliament are changed. Parliaments are invited to check and update their data at least annually.</p>\n<p>The IPU will inform parliaments that part of the data they provide will be used for the purpose of monitoring this indicator and will provide appropriate guidelines to respondents. In addition, the IPU will extend its data collection to include information on the age and sex of the Chairs of permanent committees on Foreign Affairs, Defense and Finance (data on Chairs of permanent committees on women and human rights is already collected within the scope of New Parline). </p>\n<p><strong>Methods and guidance available to countries for the compilation of data at international level:</strong></p>\n<p>The Declaration on Parliamentary Openness calls on parliaments to make publicly available information &#x201C;about the backgrounds, activities and affairs of members, including sufficient information for citizens to make informed judgments regarding their integrity and probity, and potential conflicts of interest.&#x201D; </p>\n<p>The Commonwealth Parliamentary Association (CPA)&#x2019;s Study Group on &#x2018;The Financing and Administration of Parliament&#x2019; recommended for parliaments to have in place an information strategy detailing how the membership of the Legislature will be communicated to the general public.</p>\n<p>Inter-Parliamentary Union (IPU)&#x2019;s &#x201C;Guidelines for the Content and Structure of Parliamentary Websites&#x201D; (2000) recommend that for the sake of informing the electorate about Members, official parliamentary websites should feature biodata of the current speaker and a list of members and permanent committee Chairs as recommended minimum. Biodata of members is a much-welcomed optional element. </p>\n<p>Under Article 31 of the Convention on the Rights of Persons with Disabilities, State Parties undertake to collect disaggregated information, including statistical and research data to give effect to the Convention, and assume responsibility for the dissemination of these statistics. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data for this indicator is input and housed within the Parline database (data.ipu.org).</p>\n<p>IPU has dedicated staff for data collection and management, a Network of Parline Correspondents to provide data updates, and a constant exchange with parliaments via IPU groups housed within member parliaments.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Data for the indicator will follow the quality assurance measures put in place by IPU for Parline data. Data is collected directly from national parliaments. Quality controls and &#x201C;sanity checks&#x201D; are carried out by the IPU, using comparison against historical records for the same country and comparison between countries. In the case of any inconsistencies, a dialogue is opened with the parliament to clarify and, where necessary, correct the data. In addition, parliaments are invited to review all of their data on New Parline at regular intervals, at least annually and following elections.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>IPU data is housed within the Parline database which automatically generates calculations on number and percentage of women to ensure accuracy. Exports from the database are utilised for SDG reporting.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Description and time series:</strong></p>\n<p><u>Data on age and sex: </u></p>\n<p>As a general rule, (nearly) all parliamentary secretariats keep records of basic information on all members. While the format and scope of information provided vary, most feature the MPs&#x2019; date of birth and sex. As such, parliamentary secretariats are the primary source of data for the <strong>age </strong>and<strong> sex</strong> dimensions of this indicator. </p>\n<p>The IPU publishes data points on the sex and age of Members, Speakers and committee Chairs for the following number of countries:</p>\n<ul>\n  <li><em>Members</em>: Sex-disaggregated data available for parliaments in 193 countries and split between chambers in case of bicameral parliaments. Data on age is collected at the start of each new legislature, following parliamentary elections. The New PARLINE database provides information on the number of MPs in each parliament across 10 statistical intervals (age 18-20; age 21-30; age 31-40; age 41-45; age 46-50; age 51-60; age 61-70; age 71-80; age 81-90; age 91 and over) and the percentage of members age 45 and younger, with 45 being the cut-off age for &#x2018;young&#x2019; MPs. </li>\n  <li><em>Speakers</em>: Sex and age of Speakers available on New PARLINE for all parliamentary chambers in 193 countries. This data is updated on a daily basis, every time a change occurs. </li>\n  <li><em>Permanent committee Chairs</em>: Sex and age of chairs on committees on Human Rights and Gender Equality are featured on New PARLINE and sex and age data of foreign affairs, defence, and finance committees will be added in 2020. This data is updated after every election and checked with parliaments at the start of each year. In addition, New PARLINE provides information on the age of eligibility in all countries with national parliaments (i.e. the age of eligibility will be the cut-off age above which the demographic profile of the national population will be compared to that of members in parliament). This is required for defining the national population to be used as a comparator for the share of &#x2018;young&#x2019; MPs in parliament (see Ratio 1). This data is updated every time a change occurs. </li>\n  <li><em>National population statistics: </em>National population statistics are required to calculate the denominator of Ratio 1 (see &#x2018;Computation Method&#x2019;), namely to calculate the &#x201C;size of national population &#x2264; to 45&#x201D; and the &#x201C;size of national population &lt; age of eligibility&#x201D;, for the current year, and for both sexes combined.<em> </em><a href=\"https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpopulation.un.org%2Fwpp%2FDataQuery%2F&amp;data=02%7C01%7C%7C0db59f4fb1304ea7528a08d629f1b842%7Cb3e5db5e2944483799f57488ace54319%7C0%7C0%7C636742515482684081&amp;sdata=lMZjxUY5rXE8%2FU3InZgWWkvsR60dibpPC%2FvjJ6xIH34%3D&amp;reserved=0\">The World Population Prospects 2017 database</a> is the most recent official United Nations population estimates and projections<sup><a href=\"#footnote-19\" id=\"footnote-ref-19\">[18]</a></sup>. It presents population estimates for 233 countries and areas.<sup><a href=\"#footnote-20\" id=\"footnote-ref-20\">[19]</a></sup> Estimates are available in annually interpolated series <a href=\"https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpopulation.un.org%2Fwpp%2FDVD%2FFiles%2F1_Indicators%2520(Standard)%2FEXCEL_FILES%2F5_Interpolated%2FWPP2017_INT_F03_1_POPULATION_BY_AGE_ANNUAL_BOTH_SEXES.xlsx&amp;data=02%7C01%7C%7C0db59f4fb1304ea7528a08d629f1b842%7Cb3e5db5e2944483799f57488ace54319%7C0%7C0%7C636742515482684081&amp;sdata=OehpBQLSBdr1tx9omSp5mgEO1Ja%2BnRLGGFv6GCFuQwk%3D&amp;reserved=0\">graduated into single age distributions</a> (0, 1, 2, ..., 99, 100), for both sexes, as of 1 July of the year indicated. </li>\n</ul>\n<p><u>Data on electoral and constitutional measures for guaranteeing representation of persons with disabilities and population groups in parliament:</u></p>\n<p>The &#x2018;Reserved seats and quotas&#x2019; section of New PARLINE provides details of electoral and constitutional measures in each parliament regarding women, youth, indigenous peoples, minorities, persons with disabilities and other groups. This data is updated every time a change occurs.</p>\n<p><strong>Disaggregation:</strong></p>\n<ul>\n  <li>Sex (Male/Female)</li>\n  <li>Age: Cut-off age of 45 years of age or younger at the time of election, for members of the current legislature. For the Speaker and permanent committee Chairs, same cut-off age of 45 years of age or younger at the time of nomination to the position.<sup><a href=\"#footnote-21\" id=\"footnote-ref-21\">[20]</a></sup> </li>\n  <li>Disability: List of electoral or constitutional provisions guaranteeing representation of persons with disabilities in parliament.</li>\n  <li>Contextually relevant population groups (e.g. indigenous/linguistic/ethnic/religious/occupational groups): List of electoral or constitutional provisions guaranteeing representation of various population groups in parliament. </li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-19\">18</sup><p> The Population Division of the Department of Economic and Social Affairs of the United Nations issues a new Revision of the <em>World Population Prospects</em> every two years, with the next one due in the first half of 2019. Estimates from the <em>World Population Prospects</em> sometimes differ from official statistics as &#x201C;official demographic statistics are affected by incompleteness of coverage, lack of timeliness and errors in the reporting or coding of the basic information. The analysis carried out by the Population Division takes into account those deficiencies and seeks to establish past population trends by resolving the inconsistencies affecting the basic data. Use of the cohort-component method to reconstruct populations is the major tool to ensure that the population trends estimated by the Population Division are internally consistent.&#x201D; The availability of data gathered by major survey programs, such as the Demographic and Health Surveys or the Multiple-Indicator Cluster Surveys, are useful in generating some of the data that is not currently being produced by official statistics. For more information on the methodology used by the United Nations Population Division to produce the estimates and projections for the <em>World Population Prospects</em>, please refer to the publication on Methodology. <a href=\"#footnote-ref-19\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-20\">19</sup><p> About half of those countries or areas do not report official demographic statistics with the detail necessary for the preparation of cohort-component population projections, hence this estimation work undertaken by the Population Division in order to close those gaps. <a href=\"#footnote-ref-20\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-21\">20</sup><p> In an attempt to maximize data availability and minimize gaps in submissions of data on age and sex, this indicator is aligned with existing data collection practices of the IPU with regards to age, and adopts IPU&#x2019;s definition of young MPs as those under 45 years old. <a href=\"#footnote-ref-21\">&#x2191;</a></p></div></div>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There is no internationally estimated data for this indicator.</p>", "OTHER_DOC__GLOBAL"=>"<p>Arnesen and Peters, &#x201C;The Legitimacy of Representation: How Descriptive, Formal, and Responsiveness Representation Affect the Acceptability of Political Decisions&#x201D;, Comparative Political Studies 2018, Vol. 51(7) 868&#x2013;899. </p>\n<p>Bird, &#x201C;Comparing the political representation of ethnic minorities in advanced democracies. Annual meeting of the Canadian Political Science Association Winnipeg&#x201D; (2003)</p>\n<p>Commonwealth Parliamentary Association (CPA)&#x2019;s Study Group on &#x2018;Administration and Financing of Parliament&#x2019;, Zanzibar, Tanzania on May 25-29, 2005, in CPA &#x201C;Benchmarks for Democratic Legislatures&#x201D; (2006): <a href=\"https://www.cpahq.org/media/awydqld2/administration-and-financing-of-parliament-study-group-report-1.pdf\">https://www.cpahq.org/media/awydqld2/administration-and-financing-of-parliament-study-group-report-1.pdf</a>. </p>\n<p>Congleton, On the Merits of Bicameral Legislatures: Policy Stability within Partisan Polities (2012): <a href=\"https://www.researchgate.net/publication/228527163_On_the_merits_of_bicameral_legislatures_Policy_stability_within_partisan_polities\">https://www.researchgate.net/publication/228527163_On_the_merits_of_bicameral_legislatures_Policy_stability_within_partisan_polities</a> </p>\n<p>Declaration on Parliamentary Openness (2012): <a href=\"https://www.openingparliament.org/static/pdfs/english.pdf\">https://www.openingparliament.org/static/pdfs/english.pdf</a> </p>\n<p>Eelbode, &#x201C;The political representation of ethnic minorities: A framework for a comparative analysis of ethnic minority representation&#x201D; (2010), available from: <a href=\"http://hdl.handle.net/1854/LU-2001816\" target=\"_parent\">http://hdl.handle.net/1854/LU-2001816</a></p>\n<p>Hague, Harrop, McCormick, &#x201C;Comparative Government and Politics: An Introduction&#x201D;, 10<sup>th</sup> Edition, Palgrave, London (2016).</p>\n<p>Heywood, &#x201C;Politics&#x201D;, 4<sup>th</sup> Edition, Palgrave Macmillan, Basingstoke (2013).</p>\n<p>Institute for International Law and Human Rights, &#x201C;Minority Representation in Electoral Legislation&#x201D; (2009), <a href=\"http://lawandhumanrights.org/documents/compreviewminorityrepinelectoralleg.pdf\">http://lawandhumanrights.org/documents/compreviewminorityrepinelectoralleg.pdf</a> </p>\n<p>International IDEA, &#x201C;Inclusive Political Participation and Representation. The Role of Regional Organizations&#x201D; (2013): <a href=\"https://www.idea.int/sites/default/files/publications/inclusive-political-participation-and-representation.pdf\">https://www.idea.int/sites/default/files/publications/inclusive-political-participation-and-representation.pdf</a> </p>\n<p>International IDEA, &#x201C;Bicameralism&#x201D;, International IDEA Constitution-Building Primer 2 (2016): <a href=\"https://www.idea.int/sites/default/files/publications/bicameralism-primer.pdf\">https://www.idea.int/sites/default/files/publications/bicameralism-primer.pdf</a></p>\n<p>International Republican Institute (IRI) 2016, Women&#x2019;s Political Empowerment, Representation and Influence in Africa: A Pilot Study of Women&#x2019;s Leadership in Political Decision-Making: <a href=\"https://www.iri.org/sites/default/files/wysiwyg/womens_political_index_0.pdf\">https://www.iri.org/sites/default/files/wysiwyg/womens_political_index_0.pdf</a> </p>\n<p>Inter-Parliamentary Union &#x201C;Equality in Politics: A Survey of Women and Men in Parliaments&#x201D; (2008): <a href=\"https://www.ipu.org/resources/publications/reports/2016-07/equality-in-politics-survey-women-and-men-in-parliaments\">https://www.ipu.org/resources/publications/reports/2016-07/equality-in-politics-survey-women-and-men-in-parliaments</a> </p>\n<p>Inter-Parliamentary Union &#x201C;Gender-Sensitive Parliaments&#x201D; (2011): <a href=\"http://archive.ipu.org/pdf/publications/gsp11-e.pdf\">http://archive.ipu.org/pdf/publications/gsp11-e.pdf</a> </p>\n<p>IPU&#x2019;s &#x201C;Guidelines for the Content and Structure of Parliamentary Websites&#x201D; (2000): <a href=\"http://archive.ipu.org/cntr-e/web.pdf\">http://archive.ipu.org/cntr-e/web.pdf</a> </p>\n<p>Inter-Parliamentary Union former PARLINE database on national parliaments: <a href=\"http://archive.ipu.org/parline/parlinesearch.asp\">http://archive.ipu.org/parline/parlinesearch.asp</a></p>\n<p>Inter-Parliamentary Union New Parline database on national parliaments: <a href=\"https://data.ipu.org/\">https://data.ipu.org/</a> </p>\n<p>Inter-Parliamentary Union, &#x201C;Women in Parliament: 20 Years in Review&#x201D; (2016): <a href=\"https://www.ipu.org/resources/publications/reports/2016-07/women-in-parliament-20-years-in-review\">https://www.ipu.org/resources/publications/reports/2016-07/women-in-parliament-20-years-in-review</a></p>\n<p>Inter-Parliamentary Union and UNDP, &#x201C;The representation of minorities and indigenous peoples in parliament: A global overview&#x201D; (2010) <a href=\"https://ipu.org/resources/publications/reports/2016-07/representation-minorities-and-indigenous-peoples-in-parliament-global-overview\">https://ipu.org/resources/publications/reports/2016-07/representation-minorities-and-indigenous-peoples-in-parliament-global-overview</a> </p>\n<p>Inter-Parliamentary Union and UN Women, &#x201C;Women in Politics&#x201D; (2017): <a href=\"https://www.ipu.org/resources/publications/infographics/2017-03/women-in-politics-2017\">https://www.ipu.org/resources/publications/infographics/2017-03/women-in-politics-2017</a></p>\n<p>Inter-Parliamentary Union, &#x201C;Youth participation in national parliaments&#x201D; (2016), <a href=\"https://www.ipu.org/resources/publications/reports/2016-07/youth-participation-in-national-parliaments\">https://www.ipu.org/resources/publications/reports/2016-07/youth-participation-in-national-parliaments</a> </p>\n<p>Krook &amp; O&#x2019;Brien, &#x201C;The politics of group representation: Quotas for women and minorities worldwide&#x201D; (2010), Comparative Politics, 42 (3), 253&#x2013;272.</p>\n<p>Kreppel in Martin, Saalfeld and Str&#xF8;m (ed) 2014, The Oxford Handbook of Legislative Studies, Oxford University Press.</p>\n<p>Lupu, &#x201C;Class and Representation in Latin America&#x201D; (2015), Swiss Political Science Review 21(2): 229&#x2013;236. </p>\n<p>Norton; Parliament in British Politics, 2<sup>nd</sup> Edition, Palgrave Macmillan, Basingstoke (2013).</p>\n<p>OECD (2017), <em>Trust and Public Policy: How Better Governance Can Help Rebuild Public Trust</em>, OECD Public Governance Reviews, OECD Publishing, Paris, <a href=\"https://doi.org/10.1787/9789264268920-en\">https://doi.org/10.1787/9789264268920-en</a>.</p>\n<p>Reynolds, &#x201C;Reserved seats in national legislatures: A research note&#x201D; (2005), Legislative Studies Quarterly, 301&#x2013;310.</p>\n<p>UNDP, GOPAC, IDB, &quot;Parliament&apos;s Role in Implementing the Sustainable Development Goals: A Parliamentary Handbook&quot; (2017). See <a href=\"http://www.undp.org/content/undp/en/home/librarypage/democratic-governance/parliamentary_development/parliament-s-role-in-implementing-the-sustainable-development-go.html\">http://www.undp.org/content/undp/en/home/librarypage/democratic-governance/parliamentary_development/parliament-s-role-in-implementing-the-sustainable-development-go.html</a> </p>\n<p>UN Women, Methodological Note on SDG Indicator 5.5.1b &#x201C;Proportion of seats held by women in local governments&#x201D; (October 2017). See<a href=\"https://unstats.un.org/sdgs/iaeg-sdgs/metadata-compilation/\"> https://unstats.un.org/sdgs/iaeg-sdgs/metadata-compilation/</a></p>\n<p>Zhanarstanova &amp; Nechayeva, &#x201C;Contemporary Principles of Political Representation of Ethnic Groups&#x201D; (2016): doi:<a href=\"http://dx.doi.org/10.1016/S2212-5671(16)30243-X\" target=\"_blank\">10.1016/S2212-5671(16)30243-X</a></p>", "indicator_sort_order"=>"16-07-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.7.2", "slug"=>"16-7-2", "name"=>"Proporción de la población que considera que la adopción de decisiones es inclusiva y responde a sus necesidades, desglosada por sexo, edad, discapacidad y grupo de población", "url"=>"/site/es/16-7-2/", "sort"=>"160702", "goal_number"=>"16", "target_number"=>"16.7", "global"=>{"name"=>"Proporción de la población que considera que la adopción de decisiones es inclusiva y responde a sus necesidades, desglosada por sexo, edad, discapacidad y grupo de población"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población que considera que la adopción de decisiones es inclusiva y responde a sus necesidades, desglosada por sexo, edad, discapacidad y grupo de población", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población que considera que la adopción de decisiones es inclusiva y responde a sus necesidades, desglosada por sexo, edad, discapacidad y grupo de población", "indicator_number"=>"16.7.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El indicador 16.7.2 de los ODS se refiere al concepto de «eficacia política», \nque se remonta a la década de 1950, cuando se debatió junto con la confianza política \ncomo una medida clave de la salud general de un sistema democrático (Craig et al., 1990). \nPuede definirse como la «sensación de que el cambio político y social es posible y de \nque cada ciudadano puede contribuir a lograrlo» (Campbell, Gurin y Miller, 1954, p. 187). \n\nEsta percepción de que las personas pueden influir en la toma de decisiones es \nimportante, ya que justifica el cumplimiento de sus deberes cívicos (Acok et al., 1985).\n\nLa capacidad de participar en la sociedad, de opinar en la formulación de políticas \ny de disentir sin temor son libertades esenciales. La voz política también \nproporciona un correctivo a las políticas públicas: puede garantizar la \nrendición de cuentas de los funcionarios y las instituciones públicas, \nrevelar lo que la gente necesita y valora, y llamar la atención sobre \nprivaciones significativas. \n\nLa voz política también reduce el potencial \nde conflictos y mejora la posibilidad de generar consenso sobre temas \nclave, con beneficios para la eficiencia económica, la equidad social y \nla inclusión en la vida pública.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-07-02.pdf\">Metadatos 16-7-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"SDG indicator 16.7.2 refers to the concept of ‘political efficacy’, which dates back to the 1950s, when the \nconcept was discussed jointly with political trust as a key measure of the overall health of a democratic \nsystem (Craig et al, 1990). It can be defined as the “feeling that political and social change is possible and \nthat the individual citizen can play a part in bringing about this change\" (Campbell, Gurin and Miller, 1954, \np.187). \n\nThis perception that people can impact decision-making is important as it makes it worthwhile for \nthem to perform their civic duties (Acok et al, 1985). \n\nThe ability to participate in society, to have a say in the shaping of policies and to dissent without fear are \nessential freedoms. Political voice also provides a corrective to public policy: it can ensure the \naccountability of officials and public institutions, reveal what people need and value, and call attention to \nsignificant deprivations. \n\nPolitical voice also reduces the potential for conflicts and enhances the prospect \nof building consensus on key issues, with payoffs for economic efficiency, social equity, and inclusiveness \nin public life.\n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-07-02.pdf\">Metadata 16-7-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"El indicador 16.7.2 de los ODS se refiere al concepto de «eficacia política», \nque se remonta a la década de 1950, cuando se debatió junto con la confianza política \ncomo una medida clave de la salud general de un sistema democrático (Craig et al., 1990). \nPuede definirse como la «sensación de que el cambio político y social es posible y de \nque cada ciudadano puede contribuir a lograrlo» (Campbell, Gurin y Miller, 1954, p. 187). \n\nEsta percepción de que las personas pueden influir en la toma de decisiones es \nimportante, ya que justifica el cumplimiento de sus deberes cívicos (Acok et al., 1985).\n\nLa capacidad de participar en la sociedad, de opinar en la formulación de políticas \ny de disentir sin temor son libertades esenciales. La voz política también \nproporciona un correctivo a las políticas públicas: puede garantizar la \nrendición de cuentas de los funcionarios y las instituciones públicas, \nrevelar lo que la gente necesita y valora, y llamar la atención sobre \nprivaciones significativas. \n\nLa voz política también reduce el potencial \nde conflictos y mejora la posibilidad de generar consenso sobre temas \nclave, con beneficios para la eficiencia económica, la equidad social y \nla inclusión en la vida pública.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-07-02.pdf\">Metadatuak 16-7-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.7: Ensure responsive, inclusive, participatory and representative decision-making at all levels</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.7.2: Proportion of population who believe decision-making is inclusive and responsive, by sex, age, disability and population group</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>Applies to all series</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-03-31", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>SDG indicator 16.7.2 complements indicator <strong>16.7.1</strong> (under the same target 16.7 -- &#x201C;Ensure responsive, inclusive, participatory and representative decision-making at all levels&#x201D;) which draws on administrative data sources to measure the proportional representation of various population groups in public institutions. The two indicators are highly complementary as proportional representation alone is no guarantee that all population groups represented in public institutions have equal decision-making power, or that all population groups in the national population have equal opportunities to voice their interests and preferences and to influence public decision-making. Indicator 16.7.2 provides important additional information by focusing on the inclusiveness and responsiveness of decision-making, as perceived by the population (drawing from population surveys).</p>\n<p>Indicator 16.7.2 can also be used to complement SDG target 10.2 on the promotion of the &#x201C;social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status&#x201D;, which only has one indicator measuring economic exclusion (<strong>SDG 10.2.1</strong> &#x2013; Proportion of people living below 50 per cent of median income, by age, sex and persons with disabilities). Indicator 16.7.2 therefore provides important additional information to measure progress against this target by providing data on <em>political</em> inclusion. </p>\n<p>Similarly, 16.7.2 can also be used to complement SDG target 10.3 on &#x201C;Ensuring equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regard&#x201D;, which only has one indicator measuring felt discrimination on various grounds (<strong>SDG 10.3.1</strong> Proportion of the population reporting having personally felt discriminated against or harassed within the previous 12 months on the basis of a ground of discrimination prohibited under international human rights law). Indicator 16.7.2 therefore provides relevant additional information to measure progress against this target by helping to identify whether certain population groups might feel discriminated against in terms of their inclusion in public decision-making and the extent to which political institutions are responsive to their demands/preferences.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>UNDP Oslo Governance Centre</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>UNDP Oslo Governance Centre</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This survey-based indicator measures self-reported levels of &#x2018;external political efficacy&#x2019;, that is, the extent to which people think that politicians and/or political institutions will listen to, and act on, the opinions of ordinary citizens. </p>\n<p>To address both dimensions covered by this indicator, SDG indicator 16.7.2 uses two well-established survey questions, namely: 1) one question measuring the extent to which people feel they <em>have a say</em> in what the government does (focus on <em>inclusive</em> participation in decision-making) and 2) another question measuring the extent to which people feel the political system allows them to have an <em>influence </em>on politics (focus on <em>responsive </em>decision-making).</p>\n<p>All efforts should be made to disaggregate survey results on these two questions by sex, age group, income level, education level, place of residence (administrative region e.g. province, state, district; urban/rural), disability status, and nationally relevant population groups. A detailed questionnaire and implementation manual to produce the indicator is defined in the SDG 16 Survey Initiative<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> </p>\n<p><strong>Concepts</strong></p>\n<p><strong>Decision-making:</strong> It is implicit in indicator 16.7.2 that &#x2018;decision-making&#x2019; refers to decision-making in the public governance realm (and not all decision-making). </p>\n<p><strong><em>Inclusive </em>decision-making: </strong>Decision-making processes which provide people with an opportunity to &#x2018;have a say&#x2019;, that is, to voice their demands, opinions and/or preferences to decision-makers. </p>\n<p><strong><em>Responsive </em>decision-making:</strong> Decision-making processes where politicians and/or political institutions listen to and act on the stated demands, opinions and/or preferences of people.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> The SDG 16 Survey Initiative jointly developed by UNDP, UNODC and OHCHR provides a high quality, well tested tool that countries can use to measure progress on many of the survey-based indicators under SDG16. It can support data production on peace, justice and inclusion (SDG 16). The methodology was welcomed by the 53<sup>rd</sup> United Nations Statistical Commission (E/2022/24-E/CN.3/2022/41). <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>This indicator needs to be measured on the basis of data collected by National Statistical Offices (NSOs) through official household surveys.</p>", "COLL_METHOD__GLOBAL"=>"<p>NSOs should identify suitable survey vehicles to incorporate the two questions for measuring SDG indicator 16.7.2, keeping in mind the guidelines on survey methodology provided above. </p>", "FREQ_COLL__GLOBAL"=>"<p>To ensure timely capture of changes in levels of external political efficacy, NSOs should report data on indicator 16.7.2 at least once every two years. </p>\n<p>NSOs will need to choose the most appropriate time/period for administering the 16.7.2 questions. Electoral periods should be avoided, and NSOs should aim for the middle of an electoral term. Experience shows that surveys conducted at the beginning of an electoral term generate more positive responses than surveys conducted at the end of a term. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data will be reported at the international level in the first half of each year. </p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices </p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Development Programme (UNDP)</p>", "INST_MANDATE__GLOBAL"=>"<p>UNDP helps national and local government partners to build capable, responsive, open, inclusive and accountable core governance institutions that reinforce the dynamic <u>relationship between the State and the people</u> across all developmental contexts, by supporting inclusive political processes and strengthening multi-stakeholder engagement at the local level for more community participation and capacity and the inclusion of marginalized groups.</p>", "RATIONALE__GLOBAL"=>"<p>SDG indicator 16.7.2 refers to the concept of &#x2018;political efficacy&#x2019;, which dates back to the 1950s, when the concept was discussed jointly with political trust as a key measure of the overall health of a democratic system (Craig et al, 1990). It can be defined as the &#x201C;feeling that political and social change is possible and that the individual citizen can play a part in bringing about this change&quot; (Campbell, Gurin and Miller, 1954, p.187). This perception that people can impact decision-making is important as it makes it worthwhile for them to perform their civic duties (Acok et al, 1985).</p>\n<p>The ability to participate in society, to have a say in the shaping of policies and to dissent without fear are essential freedoms. Political voice also provides a corrective to public policy: it can ensure the accountability of officials and public institutions, reveal what people need and value, and call attention to significant deprivations. Political voice also reduces the potential for conflicts and enhances the prospect of building consensus on key issues, with payoffs for economic efficiency, social equity, and inclusiveness in public life.<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></p>\n<p>Since the seminal studies of Campbell, Gurin and Miller (1954) and Campbell, Converse, Miller and Stokes (1960), the political efficacy construct has been regarded both as an important predictor of political participation and as a positive outcome of participation (Finkel, 1985). High levels of political efficacy among citizens are regarded as desirable for democratic stability. Individuals that are confident about their ability to influence the actions of their government are more likely to support the democratic system of government (Easton, 1965). </p>\n<p>There are two dimensions to political efficacy. First, subjective competence, or &#x2018;internal efficacy&#x2019;, can be defined as the confidence of the individual in his or her own abilities to understand politics and to act politically. Second, system responsiveness, or &#x2018;external efficacy&#x2019;, can be defined as the individual&#x2019;s belief in the responsiveness of the political system, i.e. policymaking processes and government decisions that respond to public demands or preferences (Lane 1959; Converse 1972; Balch 1974). SDG indicator 16.7.2 focuses only on this second dimension, &#x2018;external efficacy&#x2019;.<strong> </strong></p>\n<p>Levels of external efficacy across various population groups are important to measure as they are correlated with trust in government and government evaluations (Finkel, 1985; Quintilier &amp; Hooghe, 2012), as well as perceptions of the legitimacy of public institutions (Mcevoy, 2016). Higher levels of system responsiveness are also expected to be associated with higher levels of political participation, including voting in elections (Abramson and Aldrich, 1982), and with people&#x2019;s own life satisfaction (Flavin and Keane, 2011).</p>\n<p>The OECD monitors levels of external political efficacy &#x2013; &#x201C;the personal feeling of having a say in what the government does&#x201D; &#x2013; as part of its biennial report on Measuring Well-Being (<a href=\"https://read.oecd-ilibrary.org/economics/how-s-life-2017/governance-and-well-being_how_life-2017-8-en#page26\">OECD, How&#x2019;s Life? 2017: Measuring Well-Being</a>, p.182). A survey question on system responsiveness, sourced from the OECD Adult Skills Survey (PIAAC)<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup>, is used by the OECD to produce one of two &#x2018;headline indicators&#x2019; of civic engagement and governance for close to 40 OECD countries and/or partner countries (the other headline indicator used by the OECD is voter turnout). The specific question used by the OECD asks respondents: &#x201C;<em>To what extent do you agree or disagree with the following statements? People like me don&#x2019;t have any say in what the government does&#x201D;, </em>which is answered through a 5-point Likert-type scale (ranging from 1 for &#x201C;strongly agree&#x201D; to 5 for &#x201C;strongly disagree&#x201D;).</p>\n<p>Since 2016, the European Social Survey<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup> has integrated in its core module two questions on system responsiveness, namely <em>&#x201C;How much would you say the political system in [country] allows people like you to have a say in what the government does?&#x201D;</em> and <em>&#x201C;How much would you say that the political system in [country] allows people like you to have an influence on politics?&#x201D;</em>, each answered through a 5-point Likert scale ranging from &#x2018;Not at all&#x2019;, &#x2018;Very little&#x2019;, &#x2018;Some&#x2019;, &#x2018;A lot&#x2019;, &#x2018;A great deal&#x2019;, in its last Round 9 in 2018. In its last round 9 in 2018, the ESS was conducted in 29 European countries.<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup> </p>\n<p> </p>\n<p>As part of its 7<sup>th</sup> wave (2018-19), the <a href=\"http://www.worldvaluessurvey.org/WVSContents.jsp\">World Values Survey Association</a> (WVSA) administered in 15 countries worldwide<sup><a href=\"#footnote-7\" id=\"footnote-ref-7\">[6]</a></sup> the first question on external political efficacy used by the ESS (<em>&#x201C;How much would you say the political system in [country] allows people like you to have a say in what the government does?&#x201D;</em>). This question has since been incorporated in the core WVS questionnaire for all countries, and the WVSA will incorporate the second question used by the ESS (<em>&#x201C;How much would you say that the political system in [country] allows people like you to have an influence on politics?&#x201D;) </em>in its next survey wave. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> See OECD, &#x201C;Final report of the expert group on quality of life indicators&#x201D;, 2017. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> The question on external political efficacy was included in the past two rounds of the OECD Adult Skills Survey (PIAAC), with each data collection round including different countries: in 2008-2013, the PIAAC covered 20 OECD countries plus 3 OECD sub-entities, namely Flanders, England and Northern Ireland, and the Russian Federation; and in 2012-2016, the PIAAC covered 6 additional countries, as well as Lithuania (an OECD accession country). <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> See <a href=\"https://www.europeansocialsurvey.org/methodology/ess_methodology/source_questionnaire/\">https://www.europeansocialsurvey.org/methodology/ess_methodology/source_questionnaire/</a> <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> The European Social Survey in its Round 9 (2018) was run in Albania, Austria, Belgium, Bulgaria, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Montenegro, Netherlands, Norway, Poland, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland and United Kingdom. <a href=\"#footnote-ref-6\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-7\">6</sup><p> The World Values Survey Association administered the first question on external political efficacy used by the ESS in the following 15 countries: Andorra, Argentina, Australia, Bangladesh, Brazil, Egypt, Indonesia, Iraq, Kazakhstan, Jordan, Lebanon, Malaysia, Nigeria, Pakistan, Peru. <a href=\"#footnote-ref-7\">&#x2191;</a></p></div></div>", "REC_USE_LIM__GLOBAL"=>"<p><strong>Excludes measurement of &#x2018;internal political efficacy&#x2019; </strong></p>\n<p>As discussed in detail above, there are two dimensions to political efficacy. First, subjective competence, or &#x2018;internal efficacy&#x2019;, and second, system responsiveness, or &#x2018;external efficacy&#x2019;. This methodology stops short of measuring &#x2018;internal political efficacy&#x2019; (also called &#x2018;subjective competence&#x2019;), which can be defined as the confidence or belief that an individual has in his or her own abilities to understand politics and to participate in the political process. Subjective competence is expected to be correlated with political interest (ESS, 2016). Higher levels of subjective competence are also expected to be associated with higher levels of political participation, including voting in elections. As such, policymakers interested in identifying factors driving high or low levels of political participation should not base their diagnostics solely on levels of external efficacy measured by SDG 16.7.2, as levels of internal efficacy (<em>not </em>measured by SDG 16.7.2) also come into play. </p>\n<p><strong>Translation challenges</strong></p>\n<p>The idiom &#x2018;having a say&#x2019; can be difficult to translate into other languages, given it can also have various meanings in English (such as expressing one&#x2019;s views, or being in command, among others). To ensure global comparability of results on this question, getting good quality local language translations is a critical step in the measurement of SDG 16.7.2. To ensure the best possible quality of local language translations, NSOs should be cautious not to use formal or &#x2018;academically correct&#x2019; versions of the local languages; rather, they should focus on the everyday (colloquial) use of the language. </p>\n<p>To ensure equivalence of meaning during translation, the following protocol is recommended: </p>\n<ul>\n  <li>NSOs should make sure that translators understand the concepts, rationale and meaning behind each question before they embark on translating. </li>\n  <li>Initial drafts of each local language translations should be given to independent reviewers for blind back translation back into the national language. These translators should not have seen the original language version of the questionnaire. </li>\n  <li>The original team of translators should then further refine their translations based on the review of the back translations. </li>\n  <li>These revised translations should then be pre-tested. Feedback from the pre-tests should lead to final refinements of the translations to produce the final versions that will go to the field. </li>\n</ul>\n<p>It is important to recognize that it takes time to go through these steps and get good quality translations. NSOs should start this process well ahead of the planned fieldwork dates so that the procedures can be carefully followed. </p>\n<p>Translation for the two questions is readily available in all languages used by the 29 European countries covered by the European Social Survey, as well as in Arabic, Catalan, Malay, Chinese/ Mandarin, Hausa, Igbo, Yoruba, Indonesian, Urdu, Bengali, Russian, Swahili and Kazakh languages.</p>\n<p><strong>Social desirability bias</strong></p>\n<p>Surveys are the most common and most reliable method of gathering public opinion data representative of the population from which the sample is drawn. However, when studying public opinion with surveys, the researcher assumes that respondents answer truthfully to the questions that interviewers pose. It has been shown that this assumption does not hold in many instances. Survey measures of self-reported voter turnout for example are highly biased in that a significant portion of survey respondents in the US have been found to state they have voted, when they have in fact not.<sup><a href=\"#footnote-8\" id=\"footnote-ref-8\">[7]</a></sup> Similarly, social scientists have determined that many common survey items are plagued by such bias such as those that probe for an individual&#x2019;s attitude towards race relations<sup><a href=\"#footnote-9\" id=\"footnote-ref-9\">[8]</a></sup>, corruption, and electoral support.</p>\n<p>&#x2018;Social desirability bias&#x2019;, as this is known in the literature, arises whenever survey respondents do not reveal their true beliefs but rather provide a response that they believe to be more socially acceptable, or the response that they believe the interviewers wish to hear. Naturally, this poses a threat to the reliability and validity of survey items.</p>\n<p>It is possible that the two questions used to measure SDG indicator 16.7.2 could be affected by social desirability bias. However, pilot-testing of the two questions across all regions and diverse national contexts, as well as statistical analysis of existing survey results on these two questions (using national datasets from the ESS), have not detected any systematic occurrence of social desirability bias. A useful way of detecting more positive results inflated by social desirability bias is to compare the results obtained by an NSO to results obtained by different entities (e.g. by independent researchers from the WVSA or the ESS), provided the time lag between the two data collection efforts is not too wide. It is useful also to keep in mind that high levels of &#x2018;don&#x2019;t know&#x2019; or &#x2018;refuse to answer&#x2019; in a national dataset may be a possible sign that respondents do not feel comfortable revealing their true opinion on the questions posed.</p>\n<p><strong>Normative framework for selection of disaggregation dimensions</strong></p>\n<p>People&#x2019;s perceived capacity to shape government decisions is affected by their personal characteristics and socio-economic background. As such, the indicator calls for disaggregation of survey results by age, sex, nationally relevant population groups and disability status. The following international human rights instruments contain provisions on enhancing opportunities for participation by individuals and groups holding such characteristics: </p>\n<ul>\n  <li><em>The universal right and opportunity to participate in public affairs: </em>Article 25 of the International Covenant on Civil and Political Rights (ICCPR) recognizes &#x201C;the right and opportunity, without distinction of any kind such as race, color, sex, language, religion, political or other opinion, national or social origin, property, birth or other status to take part in the conduct of public affairs, directly or through freely chosen representatives&#x201D;. </li>\n  <li><em>Sex: </em>The 1979 Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) provides the basis for realizing equality between women and men through ensuring women&apos;s equal access to, and equal opportunities in, political and public life, including the right to participate in the formulation of government policy and the implementation thereof and to hold public office and perform all public functions at all levels of government (Article 7). States parties agree to take all appropriate measures to overcome historical discrimination against women and obstacles to women&#x2019;s participation in decision-making processes (Article 8), including legislation and temporary special measures (Article 4). The Beijing Declaration and Platform for Action also call for women&#x2019;s equal access to public service jobs, by setting a target of a minimum of 30 percent of women in leadership positions.</li>\n  <li><em>Age: </em>The 2015 Security Council Resolution 2250 urges Member States to consider ways to increase inclusive representation of <em>youth</em> in decision-making at all levels in local, national, regional and international institutions and mechanisms to prevent and resolve conflict and counter violent extremism. Furthermore, the Madrid International Plan of Action on Ageing and the Political Declaration, adopted by the international community at the Second World Assembly on Ageing in April 2002, recognize for the first time in history that &#x201C;ageing has profound consequences for every aspect of individual, community, national and international life&#x201D;.<sup><sup><a href=\"#footnote-10\" id=\"footnote-ref-10\">[9]</a></sup></sup> The Madrid Plan of Action in particular stresses the importance of research, data collection and analysis in supporting policy and programme development as a key priority for national Governments and international assistance. Following the adoption of the Plan of Action, the General Assembly, at successive sessions, has called for the international community and the United Nations system to &#x201C;support national efforts to provide funding for research and data-collection initiatives on ageing&#x201D; (see, e.g., Assembly resolution 69/146, para. 38).</li>\n  <li><em>&#x2018;Population group&#x2019; status: </em>The Declaration on the Rights of Persons belonging to National or Ethnic, Religious and Linguistic Minorities (1992) and the Declaration on the Rights of Indigenous Peoples (2007) provide that persons belonging to minorities and indigenous peoples have the right to participate in the political, economic, social and cultural life of the State. </li>\n  <li><em>Disability status: </em>The United Nations Convention on the Rights of Persons with Disabilities (2006) calls upon State Parties to ensure that persons with disabilities can effectively and fully participate in political and public life on an equal basis with others. Under Article 31 of the Convention, State Parties commit to collecting disaggregated information, including statistical and research data to give effect to the Convention, and assume responsibility for the dissemination of these statistics. </li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-8\">7</sup><p> See Holbrook, A. L., &amp; Krosnick, J. A. (2010). Social desirability bias in voter turnout reports tests using the item count technique. Public Opinion Quarterly, 74 (1), 37{67}. <a href=\"#footnote-ref-8\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-9\">8</sup><p> See Kuklinski, J. H., Cobb, M. D., &amp; Gilens, M. (1997). Racial attitudes and the new south. The Journal of Politics, 59 (02), 323{349}. <a href=\"#footnote-ref-9\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-10\">9</sup><p><sup> </sup>See https://www.un.org/development/desa/ageing/wp-content/uploads/sites/24/2018/03/Report-of-the-United-Kingdom-of-Great-Britain-and-Northern-Ireland-on-ageing-related-statistics-and-age-disaggregated-data.pdf <a href=\"#footnote-ref-10\">&#x2191;</a></p></div></div>", "DATA_COMP__GLOBAL"=>"<ol>\n  <li>NSOs first need to calculate the share of respondents who responded positively to each question (i.e. the cumulative percentage of respondents who responded 3-&apos;some&apos;, 4-&apos;a lot&apos; or 5-&apos;a great deal&apos;).<sup><a href=\"#footnote-11\" id=\"footnote-ref-11\">[10]</a></sup> </li>\n</ol>\n<p>For instance: </p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"3\">\n        <p><em>1. How much would you say the political system in [country X] allows people like you to <u>have a say</u> in what the government does? </em></p>\n      </td>\n      <td colspan=\"2\">\n        <p><em>2. And how much would you say that the political system in [country] allows people like you to <u>have an influence</u> on politics?</em></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>1- <em>Not at all</em></p>\n      </td>\n      <td>\n        <p>8%</p>\n      </td>\n      <td colspan=\"2\">\n        <p>1- <em>Not at all</em></p>\n      </td>\n      <td>\n        <p>16%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>2- <em>Very little</em></p>\n      </td>\n      <td>\n        <p>22%</p>\n      </td>\n      <td colspan=\"2\">\n        <p>2- <em>Very little</em></p>\n      </td>\n      <td>\n        <p>30%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>3- <em>Some</em></p>\n      </td>\n      <td>\n        <p>26%</p>\n      </td>\n      <td colspan=\"2\">\n        <p>3- <em>Some</em></p>\n      </td>\n      <td>\n        <p>26%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>4- <em>A lot</em></p>\n      </td>\n      <td>\n        <p>34%</p>\n      </td>\n      <td colspan=\"2\">\n        <p>4- <em>A lot</em></p>\n      </td>\n      <td>\n        <p>14%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>5- <em>A great deal</em></p>\n      </td>\n      <td>\n        <p>10%</p>\n      </td>\n      <td colspan=\"2\">\n        <p>5- <em>A great deal</em></p>\n      </td>\n      <td>\n        <p>14%</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>% of those who responded positively (i.e. answer choices 3, 4 or 5)</strong></p>\n      </td>\n      <td>\n        <p><strong>(26%+34%+10%) = 70% </strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><strong>% of those who responded positively (i.e. answer choices 3, 4 or 5)</strong></p>\n      </td>\n      <td>\n        <p><strong>(26%+14%+14%) = 54%</strong></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<ol>\n  <li>Secondly, NSOs need to calculate the simple average of these two cumulative percentages. Continuing with the above example: </li>\n</ol>\n<p>(70% + 54%) / 2 = 62%</p>\n<p><em>*<u>Note:</u> It is important for NSOs to clearly report, for each question, the number of respondents who selected &#x201C;don&#x2019;t know&#x201D; (DK), &#x201C;no answer&#x201D; (NA) or &#x201C;refuse to answer&#x201D; (RA), and to exclude such respondents from the calculation of cumulative shares of positive responses. For instance, if 65 out of 1000 respondents responded either one of these three options on the first question, the cumulative share of positive responses on this first question will be calculated out of a total of 935 respondents, and the reporting sheet will indicate that for this particular question, x respondents responded DK, y responded NA, and z responded RA.</em></p>\n<p>Overall, global reporting on SDG 16.7.2 will require: </p>\n<ul>\n  <li>Distributions of answers across all answer options, for each one of the two questions;</li>\n  <li>Cumulative share of respondents who responded positively to each question (i.e. the cumulative percentage of respondents who responded 3-&apos;some&apos;, 4-&apos;a lot&apos; or 5-&apos;a great deal&apos;); and </li>\n  <li>simple average of these two cumulative percentages.</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-11\">10</sup><p> If this indicator is being calculated from an existing survey that uses a non-standard response scale, please contact UNDP at sdg16indicators@undp.org for guidance on identifying &#x201C;positive&#x201D; responses in non-standard response scales. <a href=\"#footnote-ref-11\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>The countries are requested to input the indicators&#x2019; data and metadata in a reporting platform following the guidelines in the present metadata sheet. The platform encourages to provide separate information on the survey metadata, namely the source of information for the statistics, the survey instruments, the methodology and protocols and possible. Countries are also requested to insert the statistics on the two questions disaggregated by the pre-specified fields. All inputted information is verified for conformity with the metadata prior to submission. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>There is no treatment of missing values.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>There is no imputation of missing values. </p>", "REG_AGG__GLOBAL"=>"<p>The average share of respondents who responded positively to the two questions selected to measure SDG 16.7.2 will be provided for each region, and globally.</p>", "DOC_METHOD__GLOBAL"=>"<p><strong>Methods and guidance available to countries for the compilation of data at national level:</strong> </p>\n<p>To disaggregate survey results by disability status, it is recommended that countries use the <a href=\"https://www.washingtongroup-disability.com/question-sets/wg-short-set-on-functioning-wg-ss/\">Short Set of Questions on<strong> </strong>Disability<strong> </strong>elaborated by the Washington Group</a>. </p>\n<p><strong>Methods and guidance available to countries for the compilation of data at international level:</strong></p>\n<p>European Social Survey: Source questionnaire and accompanying guidance, in various languages: </p>\n<p><a href=\"https://www.europeansocialsurvey.org/methodology/ess_methodology/source_questionnaire/\">https://www.europeansocialsurvey.org/methodology/ess_methodology/source_questionnaire/</a> </p>\n<p>OECD&#x2019;s Adult Skills Survey (PIAAC): Questionnaire and accompanying guidance, in various languages: <a href=\"http://www.oecd.org/skills/piaac/samplequestionsandquestionnaire.htm\">http://www.oecd.org/skills/piaac/samplequestionsandquestionnaire.htm</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Statistics for this indicator is inputted in the reporting platform (<a href=\"https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsdg16reporting.undp.org%2Flogin&amp;data=02%7C01%7Cmariana.neves%40undp.org%7C307a2d2600d64d5872e908d812bea69e%7Cb3e5db5e2944483799f57488ace54319%7C0%7C0%7C637279957333850920&amp;sdata=AI9rb2m1dE62v7zxpoPS6Kgk6m1Nvs3bspt4M4wATWw%3D&amp;reserved=0\">https://sdg16reporting.undp.org/login</a>). UNDP has dedicated staff to verify the collected data and liaise with the data officers in the agency in the countries.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>NSOs have the main responsibility to ensure the statistical quality of the data compiled for this indicator. One possible quality assurance mechanism would be to compare results obtained by the NSO with readily available survey results on external political efficacy generated by relevant national, regional or global unofficial data producers (see potential global and regional unofficial sources below).</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>UNDP will make available a quality assessment protocol for national statistics office to be used at national level and intended to assess the alignment of data produced with users&#x2019; needs, the compliance with guidelines in terms of computations, the timeliness of data production, the accessibility of statistics produced, the consistent use of methodology both in terms of geographic representation and through time, the coherence in terms of data production, and the architecture of data production.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Description and time series:</strong></p>\n<ul>\n  <li>There is no existing globally comparable official dataset on the &#x201C;Proportion of population who believe decision-making is inclusive and responsive, by sex, age, disability and population group.&#x201D; While a large number of countries have experience with measuring external political efficacy, there is large variability in the ways NSOs and government agencies in individual countries collect data on this concept, in terms of question wording and response formats, etc. This variability poses a significant challenge for cross-country comparability of such data.</li>\n  <li>However, a number of non-official global and regional survey data producers have already incorporated the two questions for 16.7.2 reporting in their questionnaires, and are already producing the necessary data. In line with the <a href=\"https://unstats.un.org/unsd/accsub-public/Principles_stat_activities/Ltr-CoChairs-Principles.pdf\">2017 Guiding Principles of Data Reporting and Data Sharing for the Global Monitoring of the 2030 Agenda for Sustainable Development</a> (Version 1) developed by the Committee for the Coordination of Statistical Activities (CCSA) which states that &#x201C;non-official sources may be used by international organizations in compiling official statistics to reach the following objectives: &#x2026;d) to construct international data series in fields which are not covered by existing official sources; and&#x2026;e) to impute national data where national official data do not exist or are of proven poor quality&#x201D;, it is suggested to consider using these non-official sources for countries where the NSO has not yet incorporated the two questions selected for 16.7.2. <strong>As outlined in the above-cited Guiding Principles, NSOs would need to validate this unofficial data before it is submitted to the international level for SDG reporting:</strong>\n    <ul>\n      <li><u>For OECD/EU countries</u>:<ul>\n          <li>The <a href=\"https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.europeansocialsurvey.org%2Fdata%2Fthemes.html%3Ft%3Dpolitics&amp;data=02%7C01%7Cmarie.laberge%40undp.org%7C472afd856d184d49e57408d661f51d72%7Cb3e5db5e2944483799f57488ace54319%7C0%7C0%7C636804102720880715&amp;sdata=T0zCoilm0m8m9J7zXnOvSmKQF9lUwGPE4QxFZoGrZ%2Fk%3D&amp;reserved=0\"><u>European Social Survey</u></a> has integrated in its core module &#x2013; a core set of key questions used to generate time series to track trends over time<sup><a href=\"#footnote-12\" id=\"footnote-ref-12\">[11]</a></sup> &#x2013; the two questions selected for SDG indicator 16.7.2 since 2016. The ESS was conducted in 29 European countries<sup><a href=\"#footnote-13\" id=\"footnote-ref-13\">[12]</a></sup> in its last Round 9 in 2018. The ESS is conducted every two years, which is ideal for SDG reporting.</li>\n          <li>The OECD Adult Skills Survey (<a href=\"https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.oecd.org%2Fskills%2Fpiaac%2F&amp;data=02%7C01%7Cmarie.laberge%40undp.org%7C472afd856d184d49e57408d661f51d72%7Cb3e5db5e2944483799f57488ace54319%7C0%7C0%7C636804102720880715&amp;sdata=0nhpDYftNkv2c6%2FfNtYawhpFKrLvZQOFrGztGjyatbA%3D&amp;reserved=0\"><u>PIAAC</u></a>) is already producing data on the first question (on &#x201C;having a say in what the government does&#x201D;) and has committed to aligning the wording of this particular question with the formulation to be used for reporting on SDG 16.7.2. The PIAAC was run in 39 countries (incl. OECD member states and OECD &#x2018;partners&#x2019; in other regions) in its last round, which span three waves from 2008 to 2019<sup><a href=\"#footnote-14\" id=\"footnote-ref-14\">[13]</a></sup>. However, the PIAAC in any given country is conducted only once every 10 years (with three &#x2018;waves&#x2019; of the PIAAC survey taking place during that 10-year period, each one covering a different subset of countries).</li>\n          <li>Both sources are highly regarded by the OECD and the EU for their high-quality standards, and both sources are already used by the OECD in its flagship publication &#x201C;How&#x2019;s Life? Measuring Well-Being&#x201D;. </li>\n        </ul>\n      </li>\n      <li><u>Globally,</u> the World Values Survey Association pilot-tested in 2018-19 and incorporated the first question (on &#x201C;having a say in what the government does&#x201D;) in its standard questionnaire, and plans to also incorporate the second question starting next year. </li>\n    </ul>\n  </li>\n</ul>\n<p><strong>Disaggregation:</strong></p>\n<p>Indicator 16.7.2 aims to measure how individual beliefs in the inclusiveness and responsiveness of the political system differ across various demographic groups, including by sex, age, disability status and nationally relevant population groups. While empirical analysis confirmed the effect of these demographic variables on self-reported levels of external efficacy, other influential variables were identified, including income and education level. Moreover, since target 16.7 focuses on &#x2018;decision-making <em>at all levels</em>&#x2019;, disaggregation by place of residence (by administrative region e.g. by province, state, district; urban/rural) is also important to help identify areas in a given country where people feel most excluded from decision-making. </p>\n<ul>\n  <li><strong>Sex:</strong> Male/Female</li>\n  <li><strong>Age groups: </strong>It is recommended to follow UN standards for the production of age-disaggregated national population statistics, using the following age groups: (1) below 25 years old, (2) 25-34, (3) 35-44, (4) 45-54, (5) 55-64 and (6) 65 years old and above. Since age exhibits a negative relationship with external efficacy (evidence shows that older respondents report lower levels of political efficacy than younger respondents), a particular focus should be placed on older age brackets. </li>\n  <li><strong>Disability status:</strong> &#x2018;Disability&#x2019; is an umbrella term covering long-term physical, mental, intellectual or sensory impairments which in interaction with various barriers may hinder the full and effective participation of disabled persons in society on an equal basis with others<sup><sup><a href=\"#footnote-15\" id=\"footnote-ref-15\">[14]</a></sup></sup>. If possible, NSOs are encouraged to add the <a href=\"https://www.washingtongroup-disability.com/question-sets/wg-short-set-on-functioning-wg-ss/\">Short Set of Questions on Disability developed by the Washington Group</a> to the survey vehicle used to administer the two questions selected for 16.7.2 to disaggregate results by disability status.</li>\n  <li><strong>Nationally relevant population groups</strong> (groups with a distinct ethnicity, language, religion, indigenous status, nationality or other characteristics): The population of a country is a mosaic of different population groups that can be identified according to racial, ethnic, language, indigenous or migration status, religious affiliation, or sexual orientation, amongst other characteristics. For the purpose of this indicator, particular focus is placed on minorities. <em>Minority groups are </em>groups that are numerically inferior to the rest of the population of a state, in a non-dominant position, whose members&#x2014;being nationals of the state&#x2014;possess ethnic, religious or linguistic characteristics differing from those of the rest of the population and show, even if only implicitly, a sense of solidarity directed towards preserving their culture, traditions, religion or language.<sup><sup><a href=\"#footnote-16\" id=\"footnote-ref-16\">[15]</a></sup></sup> While the nationality criterion included in the above definition has often been challenged, the requirement to be in a non-dominant position remains important (United Nations, 2010).<sup><sup><a href=\"#footnote-17\" id=\"footnote-ref-17\">[16]</a></sup></sup> Collecting survey data disaggregated by population groups should be subject to the legality of compiling such data in a particular national context and to a careful assessment of the potential risks of collecting such data for the safety of respondents.</li>\n  <li><strong>Income level: </strong>By income quintile</li>\n  <li><strong>Education level:</strong> Primary education, Secondary education, Tertiary education</li>\n  <li><strong>Place of residence:</strong> by administrative region e.g. by province, state, district; urban/rural</li>\n</ul><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-12\">11</sup><p> The ESS was primarily designed as a time series that could monitor changing attitudes and values across Europe. For this reason, its questionnaire comprises a core module, containing items measuring a range of topics of enduring interest to the social sciences as well as the most comprehensive set of socio-structural (&apos;background&apos;) variables of any cross-national survey. The exact number of items can change from round to round, but each question has a unique variable name to assist users working with data over time. <a href=\"#footnote-ref-12\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-13\">12</sup><p> The European Social Survey in its Round 9 (2018) was run in Albania, Austria, Belgium, Bulgaria, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Montenegro, Netherlands, Norway, Poland, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland and United Kingdom. <a href=\"#footnote-ref-13\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-14\">13</sup><p> In 2008-2013 (round 1), the PIAAC covered 20 OECD countries plus 3 OECD sub-entities, namely Flanders, England and Northern Ireland, and the Russian Federation; in 2012-2016 (round 2), the PIAAC covered 6 additional countries, as well as Lithuania (an OECD accession country); in 2016-19, the PIAAC is covering Ecuador, Hungary, Kazakhstan, Mexico, Peru and the United States. <a href=\"#footnote-ref-14\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-15\">14</sup><p> UN General Assembly, Convention on the Rights of Persons with Disabilities: resolution / adopted by the General Assembly, 24 January 2007, A/RES/61/106, available at: http://www.refworld.org/docid/45f973632.html <a href=\"#footnote-ref-15\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-16\">15</sup><p> Francesco Capotorti, Special Rapporteur of the United Nations Sub-Commission on Prevention of Discrimination and Protection of Minorities (1977). <a href=\"#footnote-ref-16\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-17\">16</sup><p> UN Office of the High Commissioner for Human Rights (OHCHR), Minority Rights: International Standards and Guidance for Implementation, 2010, HR/PUB/10/3, &lt;http://www.refworld.org/docid/4db80ca52.html&gt; <a href=\"#footnote-ref-17\">&#x2191;</a></p></div></div>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There is no internationally estimated data for this indicator.</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>Abramson, P. R., &amp; Aldrich, J. H. (1982). The decline of electoral participation in America. <em>American Political Science Review</em>, 76, (3), 502-521 </li>\n  <li>Abramson, P. R., &amp; Finifter, A. W. (1981). On the Meaning of Political Trust: New Evidence from Items Introduced in 1978. <em>American Journal of Political Science</em>. 25, (2), 297-307. </li>\n  <li>Balch, G. I. (1974). Multiple Indicators in Survey Research: The Concept of &#x201C;Sense of Political Efficacy&#x201D;. <em>Political Methodology</em>, 1, (2), 1-43 </li>\n  <li>Campbell, A., Gurin, G., &amp; Miller, W. E. (1954). The Voter Decides. Evanston, IL, Row, Peterson. </li>\n  <li>Campbell, A., Converse, P. E., Miller, W. E., &amp; Stokes, D. E. (1960). The American Voter. New York: John Wiley &amp; Sons. </li>\n  <li>Condon, M. and Holleque, M. (2013), Entering Politics: General Self-Efficacy and Voting Behavior Among Young People. <em>Political Psychology</em>, 34: 167&#x2013;181. doi:10.1111/pops.12019 </li>\n  <li>Converse, P. E. (1972). Change in the American Electorate. In: A. Campbell &amp; P. E. Converse (Eds.), The Human Meaning of Social Change. New York: Russell Sage. </li>\n  <li>Easton, D. (1965). A Systems Analysis of Political Life. New York: John Wiley. </li>\n  <li>Finkel, Steven E. 1985. &#x201C;Reciprocal Effects of Participation and Political Efficacy: A Panel Analysis.&#x201D; <em>American Journal of Political Science </em>29(4): 891-913. </li>\n  <li>Lane, R. E. (1959). Political life: why and how people get involved in politics. Chicago, Markham. </li>\n  <li>Niemi, R. G., Craig, S. C., &amp; Mattei, F. (1991). Measuring Internal Political Efficacy in the 1988 National Election Study. <em>The American Political Science Review</em>, 85,(4), 1407-1413. </li>\n  <li>Quintelier, E. And Hooghe, M. (2012). Political attitudes and political participation: A panel study on socialization and self-selection effects among late adolescents. International Political Science Review, 33 (1), 63-81. DOI: 10.1177/0192512111412632 </li>\n  <li>Saris, W.E. and Revilla, M. (2012). ESS-DACE Deliverable 4.6: Evaluation of the experiments in the supplementary questionnaire of Round 5 of the ESS </li>\n  <li>Saris, W. E. and Torcal, M (2009). Alternative measurement procedures and models for Political Efficacy. http://hdl.handle.net/10230/28300 </li>\n  <li>Vecchione, M., &amp; Caprara, G. V. (2009). Personality determinants of political participation: The contribution of traits and self-efficacy beliefs. <em>Personality and Individual Differences</em>, <em>46</em>(4), 487-492. DOI: 10.1016/j.paid.2008.11.021 </li>\n</ul>\n<p><strong>Guidelines on survey methodology</strong></p>\n<ul>\n  <li><strong>Two questions:</strong> SDG indicator 16.7.2 aims to measure both the inclusiveness <em>and </em>the responsiveness of decision-making. As such, the methodology for 16.7.2 consists in two separate survey questions addressing these two distinct dimensions, namely: </li>\n</ul>\n<ol>\n  <li><strong>To measure <u>inclusive</u> participation in decision-making: </strong><em>How much would you say the political system in [country X] allows people like you to <u>have a say</u> in what the government does?</em><strong><em> </em></strong></li>\n  <li><strong>To measure <u>responsive</u> decision-making: </strong><em>And how much would you say that the political system in [country] allows people like you to <u>have an influence</u> on politics?</em><strong><em> </em></strong></li>\n</ol>\n<ul>\n  <li><strong>Questions to be incorporated in a support survey:</strong> These two questions to measure SDG 16.7.2 can be inserted into existing national surveys run by NSOs, using these surveys&#x2019; additional batteries on demographics for subsequent disaggregation of results. </li>\n  <li><strong>Target population: </strong>Residents of the country aged 18 or older.</li>\n  <li><strong>Sampling approach: </strong>Data should be collected on the basis of a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 18 and over within the household are eligible for the question set. The sampling frame as well as methods of sample selection should ensure that every individual and household in the target population is assigned a known probability of selection that is not zero.<em> </em>(integrating the questions for SDG 16.7.2 in a household survey that targets household heads or &#x201C;most informed household member&#x201D; only should be avoided at all costs).</li>\n  <li><strong>Refer to interviewer instructions for additional guidance on terminology: </strong>Interviewers should refer to the specific wording provided below if respondents do not understand certain terms. To ensure consistency in the way this methodology is applied across countries, interviewers should <em>not </em>try to explain the meaning of certain words in their own terms. </li>\n  <li><strong>&#x201C;Don&#x2019;t know&#x201D;, &#x201C;refuse to answer&#x201D; or &#x201C;not applicable&#x201D; should not be read out loud to respondents: </strong>Providing a &#x201C;don&#x2019;t know&#x201D; or &#x201C;refuse to answer&#x201D; option provides an easy way for respondents to avoid engaging with the subject of the question. As such, when respondents say they &#x201C;don&#x2019;t know&#x201D;, enumerators should repeat the question and simply ask them to provide their best guess. The &#x201C;don&#x2019;t know&#x201D; and &#x201C;refuse to answer&#x201D; options should be used only as a last resort.<strong> </strong>Interviewers should use<strong> </strong>separate coding for &#x201C;not applicable&#x201D; (NA &#x2013; 97), &#x201C;don&#x2019;t know&#x201D; (DK &#x2013; 98) and &#x201C;not applicable&#x201D; (NA &#x2013; 99), as indicated in the questionnaire. </li>\n</ul>\n<p><strong>Questions </strong></p>\n<ol>\n  <li><strong><em>How much would you say the political system in [country X] allows people like you to <u>have a say</u> in what the government does? </em></strong></li>\n  <li><strong><em>Not at all</em></strong></li>\n  <li><strong><em>Very little</em></strong></li>\n  <li><strong><em>Some</em></strong></li>\n  <li><strong><em>A lot</em></strong></li>\n  <li><strong><em>A great deal</em></strong></li>\n  <li><strong><em>Refusal</em></strong></li>\n  <li><strong><em>Don&#x2019;t know</em></strong></li>\n  <li><strong><em>No answer</em></strong></li>\n  <li><strong><em>And how much would you say that the political system in [country] allows people like you to <u>have an influence</u> on politics? </em></strong></li>\n  <li><strong><em>Not at all</em></strong></li>\n  <li><strong><em>Very little</em></strong></li>\n  <li><strong><em>Some</em></strong></li>\n  <li><strong><em>A lot</em></strong></li>\n  <li><strong><em>A great deal</em></strong></li>\n  <li><strong><em>Refusal</em></strong></li>\n  <li><strong><em>Don&#x2019;t know</em></strong></li>\n  <li><strong><em>No answer</em></strong></li>\n</ol>\n<p><u>Clarifications on question wording</u></p>\n<p><strong><em>&#x201C;The <u>political system</u> in [country]&#x201D;: </em></strong>A particular form of government. For example, democracy is a political system in which citizens govern themselves. Other political systems include republics, monarchies, communist systems and dictatorships.</p>\n<p><strong><em>&#x201C;<u>Having a say</u> in what the government does&#x201D; </em></strong>means having a channel to express one&#x2019;s demands, opinions or preferences about what the government does, and feeling listened to.</p>\n<p><strong><em>&#x201C;<u>Have an influence</u> on politics&#x201D;</em></strong> means feeling that decision-makers listen to and act on one&#x2019;s demands, opinions or preferences. </p>", "indicator_sort_order"=>"16-07-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.8.1", "slug"=>"16-8-1", "name"=>"Proporción de miembros y derechos de voto de los países en desarrollo en organizaciones internacionales", "url"=>"/site/es/16-8-1/", "sort"=>"160801", "goal_number"=>"16", "target_number"=>"16.8", "global"=>{"name"=>"Proporción de miembros y derechos de voto de los países en desarrollo en organizaciones internacionales"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de miembros y derechos de voto de los países en desarrollo en organizaciones internacionales", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de miembros y derechos de voto de los países en desarrollo en organizaciones internacionales", "indicator_number"=>"16.8.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notapplicable", "reporting_status"=>"notapplicable", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La ONU se basa en el principio de igualdad soberana de todos sus Estados Miembros \n(Artículo 2 de la Carta de la ONU). Este indicador busca medir el grado de representación \nequitativa de los Estados en las organizaciones internacionales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-08-01.pdf\">Metadatos 16-8-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The UN is based on a principle of sovereign equality of all its Member States (Article 2, UN Charter). \nThis indicator aims to measure the degree to which States enjoy equal representation in international \norganizations.\n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-08-01.pdf\">Metadata 16-8-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La ONU se basa en el principio de igualdad soberana de todos sus Estados Miembros \n(Artículo 2 de la Carta de la ONU). Este indicador busca medir el grado de representación \nequitativa de los Estados en las organizaciones internacionales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-08-01.pdf\">Metadatuak 16-8-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.6: Ensure enhanced representation and voice for developing countries in decision-making in global international economic and financial institutions in order to deliver more effective, credible, accountable and legitimate institutions</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.6.1: Proportion of members and voting rights of developing countries in international organizations</p>", "META_LAST_UPDATE__GLOBAL"=>"2022-07-07", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Financing for Sustainable Development Office (FSDO), United Nations Department of Economic and Social Affairs (UN-DESA)</p>\n<p></p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Financing for Sustainable Development Office (FSDO), United Nations Department of Economic and Social Affairs (UN-DESA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<h3>Definition:</h3>\n<p>The indicator <em>Proportion of members and voting rights of developing countries in international organizations </em>has two separate components: the developing country proportion of voting rights and the developing country proportion of membership in international organisations. In some institutions, these two components are identical.</p>\n<p>The indicator is calculated independently for eleven different international institutions: The United Nations General Assembly, the United Nations Security Council, the United Nations Economic and Social Council, the International Monetary Fund, the International Bank for Reconstruction and Development, the International Finance Corporation, the African Development Bank, the Asian Development Bank, the Inter-American Development Bank, the World Trade Organisation, and the Financial Stability Board.</p>\n<h3>Concepts:</h3>\n<p>There is no established convention for the designation of &quot;developed&quot; and &quot;developing&quot; countries or areas in the United Nations system. The aggregation across all institutions is currently done according to the &#x201C;historical&#x201D; classification of &#x201C;Developed regions&#x201D; and &#x201C;Developing regions&#x201D; as of December 2021 in the United Nations M49 statistical standard. The removal of this classification from the M49 standard at the end of 2021 makes it more urgent to reach agreement on how to define these terms for the purposes of SDG monitoring. The designations &quot;developed&quot; and developing&quot; are intended for statistical convenience and do not necessarily express a judgement about the stage reached by a particular country or area in the development process.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percentage</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Classification of countries as least developed countries (LDCs), landlocked developing countries (LLDCs), and small island developing States (SIDS) according to the United Nations M49 standard. The classification of developing countries and developed countries is based on the &#x201C;historical&#x201D; classification of &#x201C;Developed regions&#x201D; and &#x201C;Developing regions&#x201D; as of December 2021 in the United Nations M49 statistical standard). </p>", "SOURCE_TYPE__GLOBAL"=>"<h3>Description:</h3>\n<p>Annual reports, as presented on the website of the institution in question, are used as sources of data. Sources of information by institution:</p>\n<p><u>United Nations General Assembly (UNGA):</u> website of the General Assembly (http://www.un.org/en/member-states/index.html)</p>\n<p><u>United Nations Security Council (UNSC):</u> Report of the Security Council for the respective year (https://www.un.org/securitycouncil/content/sc_annual_reports)</p>\n<p><u>United Nations Economic and Social Council (ECOSOC):</u> Report of the Economic and Social Council for the respective year (https://www.un.org/ecosoc/en/documents/reports-general-assembly)</p>\n<p><u>International Monetary Fund (IMF):</u> Annual Report for the respective year (https://www.imf.org/en/Publications/AREB)</p>\n<p><u>International Bank for Reconstruction and Development (IBRD):</u> 2000: The World Bank Annual Report 2000: Financial Statement and Appendixes to the Annual Report; from 2005: International Bank for Reconstruction and Development Management&#x2019;s Discussion &amp; Analysis and Financial Statements for the respective year (https://www.worldbank.org/en/about/annual-report/world-bank-group-downloads)</p>\n<p><u>International Finance Corporation (IFC):</u> IFC Annual Report (volume 2) for the respective year (<a href=\"https://openknowledge.worldbank.org/handle/10986/2128\">https://openknowledge.worldbank.org/handle/10986/2128</a>) </p>\n<p><u>African Development Bank (AFDB):</u> African Development Bank Group Annual Report for the respective year (https://www.afdb.org/en/documents-publications/annual-report)</p>\n<p><u>Asian Development Bank (ADB):</u> 2000-2017: Annual Report for the respective year; from 2018: Financial Report for the respective year (https://www.adb.org/documents/series/adb-annual-reports)</p>\n<p><u>Inter-American Development Bank (IADB):</u> Inter-American Development Bank Annual Report for the respective year (https://www.iadb.org/en/about-us/annual-reports) </p>\n<p><u>World Trade Organisation (WTO):</u> WTO Annual Report for the respective year (https://www.wto.org/english/res_e/reser_e/annual_report_e.htm)</p>\n<p><u>Financial Stability Board (FSB):</u> 2010, 2015: charter of the Financial Stability Board; 2016-2018: Financial Stability Board Financial Report for the respective year; from 2019: Financial Stability Board Financial Statements for the respective year (<a href=\"https://www.fsb.org/publications/\">https://www.fsb.org/publications/</a>)</p>\n<h3>List:</h3>\n<p>Website of the General Assembly; Report of the Security Council for the respective year; Report of the Economic and Social Council for the respective year; IMF Annual Report for the respective year; IBRD Management&#x2019;s Discussion &amp; Analysis and Financial Statements for the respective year; IFC Annual Report (volume 2) for the respective year; AFDB Annual Report for the respective year; AFDB Group Annual Report for the respective year; ADB Financial Report for the respective year; IADB Annual Report for the respective year; WTO Annual Report for the respective year; FSB Financial Statements for the respective year</p>", "COLL_METHOD__GLOBAL"=>"<p>Desk review, annually, pulling data from the above-mentioned sources.</p>", "FREQ_COLL__GLOBAL"=>"<p>Annually in March</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>United Nations General Assembly: continuous</p>\n<p>United Nations Security Council: annually in September</p>\n<p>United Nations Economic and Social Council: annually in August </p>\n<p>International Monetary Fund: annually in October</p>\n<p>International Bank for Reconstruction and Development: annually in September </p>\n<p>International Finance Corporation: annually in September</p>\n<p>African Development Bank: annually in June</p>\n<p>Asian Development Bank: annually in April</p>\n<p>Inter-American Development Bank: annually in March</p>\n<p>World Trade Organisation: annually in May </p>\n<p>Financial Stability Board: annually in August</p>\n<p>Next release: UNGA continuous; UNSC September 2022; ECOSOC August 2022; IMF October 2022; IBRD September 2022; IFC September 2022; AFDB June 2022; ADB April 2022; IADB March 2022; WTO May 2022; FSB August 2022.</p>", "DATA_SOURCE__GLOBAL"=>"<h3>Name:</h3>\n<p>UNGA, UNSC, ECOSOC, IMF, IBRD, IFC, AfDB, ADB, IADB, WTO, FSB</p>\n<h3>Description:</h3>\n<p>The United Nations General Assembly, the United Nations Security Council, the United Nations Economic and Social Council, the International Monetary Fund, the International Bank for Reconstruction and Development, the International Finance Corporation, the African Development Bank, the Asian Development Bank, the Inter-American Development Bank, the World Trade Organisation, and the Financial Stability Board</p>", "COMPILING_ORG__GLOBAL"=>"<h3>Name:</h3>\n<p>FSDO/UN-DESA</p>\n<h3>Description:</h3>\n<p>The data is compiled and the proportions calculated by the Financing for Sustainable Development Office, United Nations Department of Economic and Social Affairs.</p>", "INST_MANDATE__GLOBAL"=>"<p>At its second meeting in October 2015, the Inter-agency and Expert Group on SDG Indicators (IAEG-SDG) agreed to a draft indicator and to UN-DESA being designated as the compiling entity. The Statistical Commission, at its 47<sup>th</sup> session in March 2016, approved the report of the IAEG-SDG containing the proposed set of indicators. </p>", "RATIONALE__GLOBAL"=>"<p>The UN is based on a principle of sovereign equality of all its Member States (Article 2, UN Charter). This indicator aims to measure the degree to which States enjoy equal representation in international organizations.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Cross institutional comparisons need to pay attention to the different membership of the institutions. Voting rights and membership in their institutions are agreed by the Member States themselves. As a structural indicator, there will be only small changes over time to reflect agreement on new States joining as Members, suspension of voting rights, membership withdrawal and negotiated voting rights changes. The indicator is not intended for use at country-level or for cross-country comparisons.</p>", "DATA_COMP__GLOBAL"=>"<p>The computation uses each institutions&#x2019; own published membership and voting rights data from their respective annual reports. The ratio of voting rights is computed as the number of voting rights allocated to developing countries (as classified by the &#x201C;historical&#x201D; classification of &#x201C;Developed regions&#x201D; and &#x201C;Developing regions&#x201D; as of December 2021 in the United Nations M49 statistical standard), divided by the total number of voting rights. The ratio of membership is calculated by taking the number of developing country members (using the same classification), divided by the total number of members. Both ratios are expressed as percentages.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Not applicable</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Countries which are not a member of the specific international organisation/body will not have a figure for the related sub-indicator. These are intentionally left blank.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>", "REG_AGG__GLOBAL"=>"<p>Aggregations are additive, with no weighting.</p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Internal review undertaken by data compiler, FSDO/UN-DESA</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Available for all countries.</p>\n<p><strong>Time series:</strong></p>\n<p>2000, 2005, 2010, 2015, and annually thereafter</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data is calculated and presented separately for each international organization.</p>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<h3>URL:</h3>\n<p><a href=\"https://www.un.org/development/desa/en/\"><u>https://www.un.org/development/desa/en/</u></a></p>\n<h3>Data Sources:</h3>\n<p><u>United Nations General Assembly (UNGA):</u> http://www.un.org/en/member-states/index.html</p>\n<p><u>United Nations Security Council (UNSC):</u> https://www.un.org/securitycouncil/content/sc_annual_reports</p>\n<p><u>United Nations Economic and Social Council (ECOSOC):</u> https://www.un.org/ecosoc/en/documents/reports-general-assembly</p>\n<p><u>International Monetary Fund (IMF):</u> https://www.imf.org/en/Publications/AREB</p>\n<p><u>International Bank for Reconstruction and Development (IBRD):</u> https://www.worldbank.org/en/about/annual-report/world-bank-group-downloads</p>\n<p><u>International Finance Corporation (IFC):</u> <a href=\"https://openknowledge.worldbank.org/handle/10986/2128\">https://openknowledge.worldbank.org/handle/10986/2128</a> </p>\n<p><u>African Development Bank (AFDB):</u> https://www.afdb.org/en/documents-publications/annual-report</p>\n<p><u>Asian Development Bank (ADB):</u> https://www.adb.org/documents/series/adb-annual-reports</p>\n<p><u>Inter-American Development Bank (IADB):</u> https://www.iadb.org/en/about-us/annual-reports </p>\n<p><u>World Trade Organisation (WTO):</u> https://www.wto.org/english/res_e/reser_e/annual_report_e.htm</p>\n<p><u>Financial Stability Board (FSB):</u> https://www.fsb.org/publications/</p>", "indicator_sort_order"=>"16-08-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.9.1", "slug"=>"16-9-1", "name"=>"Proporción de niños menores de 5 años cuyo nacimiento se ha registrado ante una autoridad civil, desglosada por edad", "url"=>"/site/es/16-9-1/", "sort"=>"160901", "goal_number"=>"16", "target_number"=>"16.9", "global"=>{"name"=>"Proporción de niños menores de 5 años cuyo nacimiento se ha registrado ante una autoridad civil, desglosada por edad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de niños menores de 5 años cuyo nacimiento se ha registrado ante una autoridad civil, desglosada por edad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de niños menores de 5 años cuyo nacimiento se ha registrado ante una autoridad civil, desglosada por edad", "indicator_number"=>"16.9.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de niños menores de 5 años cuyo nacimiento se ha registrado ante una autoridad civil, desglosada por edad", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.9- De aquí a 2030, proporcionar acceso a una identidad jurídica para todos, en particular mediante el registro de nacimientos", "definicion"=>"Proporción de menores de 5 años cuyo nacimiento se ha inscrito en el Registro Civil", "formula"=>"\n$$PPRC_{0-4}^{t} = \\frac{PRC_{0-4}^{t}}{P_{0-4}^{t}}\\cdot 100 $$\n\ndonde:\n\n$PRC_{0-4}^{t} =$ población de menos de 5 años cuyo nacimiento ha sido inscrito en el Registro Civil en el año $t$\n\n$P_{0-4}^{t} =$ población de menos de 5 años en el año $t$\n", "desagregacion"=>"", "observaciones"=>"El indicador es del 100% ya que la legislación sobre el Registro Civil establece la obligatoriedad de inscribir los nacimientos\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El registro de los niños al nacer es el primer paso para garantizar su reconocimiento ante \nla ley, salvaguardar sus derechos y garantizar que cualquier violación de estos derechos \nno pase desapercibida.\n\nA los niños que no tienen documentos de identificación oficiales se les puede negar \nla atención médica o la educación. Más adelante en la vida, la falta de dicha \ndocumentación puede significar que un niño pueda contraer matrimonio o ingresar al \nmercado laboral, o ser reclutado en las fuerzas armadas, antes de la edad legal. \n\nEn la edad adulta, los certificados de nacimiento pueden ser necesarios para obtener \nasistencia social o un trabajo en el sector formal, para comprar o demostrar el \nderecho a heredar propiedades, para votar y para obtener un pasaporte. El derecho de los \nniños a un nombre y una nacionalidad está consagrado en la Convención sobre los \nDerechos del Niño (CDN) en el artículo 7.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.9.1&seriesCode=SG_REG_BRTH&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y\"> Proporción de niños menores de 5 años cuyos nacimientos han sido registrados ante una autoridad civil (% de niños menores de 5 años) SG_REG_BRTH</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-09-01.pdf\">Metadatos 16-9-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"Proporción de niños menores de 5 años cuyo nacimiento se ha registrado ante una autoridad civil, desglosada por edad", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.9- De aquí a 2030, proporcionar acceso a una identidad jurídica para todos, en particular mediante el registro de nacimientos", "definicion"=>"Proportion of children under 5 years of age whose birth has been registered in the Civil Registry", "formula"=>"\n$$PPRC_{0-4}^{t} = \\frac{PRC_{0-4}^{t}}{P_{0-4}^{t}}\\cdot 100 $$\n\nwhere:\n\n$PRC_{0-4}^{t} =$ children under 5 years of age whose birth has been registered in the Civil Registry in year $t$\n\n$P_{0-4}^{t} =$ children under 5 years of age in year $t$\n", "desagregacion"=>nil, "observaciones"=>"The indicator is 100% since the legislation on the Civil Registry establishes the obligation to register births\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Registering children at birth is the first step in securing their recognition before the law, safeguarding \ntheir rights, and ensuring that any violation of these rights does not go unnoticed. \n\nChildren without official identification documents may be denied health care or education. Later in life, \nthe lack of such documentation can mean that a child may enter into marriage or the labour market, or \nbe conscripted into the armed forces, before the legal age. \n\nIn adulthood, birth certificates may be required to obtain social assistance or a job in the formal sector, \nto buy or prove the right to inherit property, to vote and to obtain a passport. Children’s right to a name \nand nationality is enshrined in the Convention on the Rights of the Child (CRC) under Article 7. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.9.1&seriesCode=SG_REG_BRTH&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y\"> Proportion of children under 5 years of age whose births have been registered with a civil authority (% of children under 5 years of age) SG_REG_BRTH</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-09-01.pdf\">Metadata 16-9-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de niños menores de 5 años cuyo nacimiento se ha registrado ante una autoridad civil, desglosada por edad", "objetivo_global"=>"16- Promover sociedades pacíficas e inclusivas para el desarrollo sostenible, facilitar el acceso a la justicia para todos y construir a todos los niveles instituciones eficaces e inclusivas que rindan cuentas", "meta_global"=>"16.9- De aquí a 2030, proporcionar acceso a una identidad jurídica para todos, en particular mediante el registro de nacimientos", "definicion"=>"Proporción de menores de 5 años cuyo nacimiento se ha inscrito en el Registro Civil", "formula"=>"\n$$PPRC_{0-4}^{t} = \\frac{PRC_{0-4}^{t}}{P_{0-4}^{t}}\\cdot 100 $$\n\nnon:\n\n$PRC_{0-4}^{t} =$ 5 urtetik beherako biztanleak, jaiotza Erregistro Zibilean inskribatutakoak $t$ urtean\n\n$P_{0-4}^{t} =$ 5 urtetik beherako biztanleria $t$ urtean\n", "desagregacion"=>nil, "observaciones"=>"El indicador es del 100% ya que la legislación sobre el Registro Civil establece la obligatoriedad de inscribir los nacimientos\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El registro de los niños al nacer es el primer paso para garantizar su reconocimiento ante \nla ley, salvaguardar sus derechos y garantizar que cualquier violación de estos derechos \nno pase desapercibida.\n\nA los niños que no tienen documentos de identificación oficiales se les puede negar \nla atención médica o la educación. Más adelante en la vida, la falta de dicha \ndocumentación puede significar que un niño pueda contraer matrimonio o ingresar al \nmercado laboral, o ser reclutado en las fuerzas armadas, antes de la edad legal. \n\nEn la edad adulta, los certificados de nacimiento pueden ser necesarios para obtener \nasistencia social o un trabajo en el sector formal, para comprar o demostrar el \nderecho a heredar propiedades, para votar y para obtener un pasaporte. El derecho de los \nniños a un nombre y una nacionalidad está consagrado en la Convención sobre los \nDerechos del Niño (CDN) en el artículo 7.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.9.1&seriesCode=SG_REG_BRTH&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=%3C5Y\"> 5 urtetik beherako haurren proportzioa, jaiotza agintaritza zibil baten aurrean erregistratutakoak (5 urtetik beherako haurren %) SG_REG_BRTH</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-09-01.pdf\">Metadatuak 16-9-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.9: By 2030, provide legal identity for all, including birth registration</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.9.1: Proportion of children under 5 years of age whose births have been registered with a civil authority, by age</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_REG_BRTH - Proportion of children under 5 years of age whose births have been registered with a civil authority [16.9.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Proportion of children under 5 years of age whose births have been registered with a civil authority.</p>\n<p><strong>Concepts:</strong></p>\n<p>&#x2022; Birth registration: Birth registration is defined as &#x2018;the continuous, permanent and universal recording, within the civil registry, of the occurrence and characteristics of births in accordance with the legal requirements of a country&#x2019;.</p>\n<p>&#x2022; Birth certificate: A birth certificate is a vital record that documents the birth of a child. The term &#x2018;birth certificate&#x2019; can refer either to the original document certifying the circumstances of the birth, or to a certified copy or representation of the registration of that birth, depending on the practices of the country issuing the certificate.</p>\n<p>&#x2022; Civil authority: Official authorized to register the occurrence of a vital event and to record the required details.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%) of children under 5 years of age</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Description:</strong></p>\n<p>Censuses, household surveys such as Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS) and national civil registration systems.</p>\n<p><strong>Civil registration systems</strong>: Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. These data normally refer to live births that were registered within a year or the legal time frame for registration applicable in the country.</p>\n<p><strong>Household or other population-based surveys</strong>: In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. The standard indicator used in DHS and MICS to report on birth registration refers to the percentage of children under age 5 (0-59 months) with a birth certificate, regardless of whether or not it was seen by the interviewer, or whose birth was reported as registered with civil authorities at the time of survey. Depending on the country, surveys collecting these data may be conducted every 3-5 years, or possibly at more frequent intervals.</p>\n<p><strong>Censuses </strong>can also provide data on children who have acquired their right to a legal identity. However, censuses are conducted only every ten years (in most countries) and are therefore not well-suited for routine monitoring.</p>", "COLL_METHOD__GLOBAL"=>"<ul>\n  <li>\n    <ol>\n      <li>United Nations Children&apos;s Fund (UNICEF) undertakes a wide consultative process of compiling and assessing data from national sources for the purposes of updating its global databases on the situation of children. Up until 2017, the mechanism UNICEF used to collaborate with national authorities on ensuring data quality and international comparability on key indicators of relevance to children was known as Country Data Reporting on the Indicators for the Goals (CRING).</li>\n      <li>As of 2018, UNICEF launched a new country consultation process with national authorities on selected child-related global SDG indicators for which it is custodian or co-custodian to meet emerging standards and guidelines on data flows for global reporting of SDG indicators, which place strong emphasis on technical rigour, country ownership and use of official data and statistics. The consultation process involves soliciting feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the latest available national data source and estimates for each indicator. Countries have the opportunity to submit new or updated data sources and estimates to be considered for official SDG reporting. A thorough technical assessment is conducted by UNICEF as the custodian agency, in consultation with the country, and feedback is made available to countries on whether or not specific data sources and data points are accepted for official SDG reporting, and if not, the reasons why. </li>\n    </ol>\n  </li>\n</ul>", "FREQ_COLL__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF) will undertake an annual country consultation likely between December and January every year to compile the latest available national data source and estimate for the indicator. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Annually in March for updated national estimates. It is expected that global and regional estimates will be produced every four years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (for the most part) and line ministries/other government agencies responsible for maintaining national vital registration systems</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF) </p>", "INST_MANDATE__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF) is responsible for global monitoring and reporting on the wellbeing of children. It provides technical and financial assistance to Member States to support their efforts to collect quality data on birth registration, including through the UNICEF-supported Multiple Indicator Cluster Surveys (MICS) household survey programme. UNICEF also compiles birth registration statistics with the goal of making internationally comparable datasets publicly available, and it analyses birth registration statistics which are included in relevant data-driven publications, including in its flagship publication, <em>The State of the World&#x2019;s Children.</em></p>", "RATIONALE__GLOBAL"=>"<p>Registering children at birth is the first step in securing their recognition before the law, safeguarding their rights, and ensuring that any violation of these rights does not go unnoticed.</p>\n<p>Children without official identification documents may be denied health care or education. Later in life, the lack of such documentation can mean that a child may enter into marriage or the labour market, or be conscripted into the armed forces, before the legal age. In adulthood, birth certificates may be required to obtain social assistance or a job in the formal sector, to buy or prove the right to inherit property, to vote and to obtain a passport. </p>\n<p>Children&#x2019;s right to a name and nationality is enshrined in the Convention on the Rights of the Child (CRC) under Article 7.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The number of children who have acquired their right to a legal identity is collected mainly through censuses, civil registration systems and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.</p>\n<p>Data from household surveys like Multiple Indicator Cluster Surveys (MICS) or Demographic and Health Surveys (DHS) sometimes refer only to children with a birth certificate. UNICEF methodically notes this difference when publishing country-level estimates for global SDG monitoring.</p>", "DATA_COMP__GLOBAL"=>"<p>Number of children under age of five whose births are reported as being registered with the relevant national civil authorities divided by the total number of children under the age of five in the population multiplied by 100.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>A wide consultative process is undertaken to compile, assess and validate data from national sources. </p>\n<p>The consultation process involves soliciting feedback directly from National Statistical Offices, as well as other government agencies responsible for official statistics, on the latest available national data source and estimates for each indicator. Countries have the opportunity to submit new or updated data sources and estimates to be considered for official SDG reporting. A thorough technical assessment is conducted by UNICEF as the custodian agency, in consultation with the country, and feedback is made available to countries on whether or not specific data sources and data points are accepted for official SDG reporting, and if not, the reasons why. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>When data for a country are entirely missing, United Nations Children&apos;s Fund (UNICEF) does not produce modelled estimates and therefore no country-level estimates are published.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>The regional average is applied to those countries within the region with missing values for the purposes of calculating regional aggregates only, but imputed country-level values are not published. Regional aggregates are only published when at least 50 per cent of the regional population for the relevant age group are covered by the available data.</p>", "REG_AGG__GLOBAL"=>"<p>The global aggregate is a weighted average of the aggregates for all the sub-regions that make up the world. Regional aggregates are weighted averages of all the countries with available data within the region. </p>", "DOC_METHOD__GLOBAL"=>"<p>Substantial differences can exist between Civil Registration and Vital Statistics (CRVS) coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The process behind the production of reliable statistics on birth registration is well established within United Nations Children&apos;s Fund (UNICEF). The quality and process leading to the production of the SDG indicator 16.9.1 is ensured by working closely with the statistical offices and other relevant stakeholders through a consultative process.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>United Nations Children&apos;s Fund (UNICEF) maintains the global database on birth registration that is used for SDG and other official reporting. Before the inclusion of any data point in the database, it is reviewed by technical focal points at UNICEF headquarters to check for consistency and overall data quality. This review is based on a set of objective criteria to ensure that only the most recent and reliable information are included in the databases. These criteria include the following: data sources must include proper documentation; data values must be representative at the national population level; data are collected using an appropriate methodology (e.g., sampling); data values are based on a sufficiently large sample; data conform to the standard indicator definition including age group and concepts, to the extent possible; data are plausible based on trends and consistency with previously published/reported estimates for the indicator. </p>\n<p>As of 2018, UNICEF undertakes an annual consultation with government authorities on 10 of the child-related SDG indicators in its role of sole or joint custodian, and in line with its global monitoring mandate and normative commitments to advancing the 2030 Agenda for children. This includes indicator 16.9.1. More details on the process for the country consultation are outlined below.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Data consistency and quality checks are regularly conducted for validation of the data before dissemination</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Nationally representative and comparable data are currently available for around 170 countries</p>\n<p><strong>Time series:</strong></p>\n<p>Not available</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Age</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The estimates compiled and presented at global level come directly from nationally produced data and are not adjusted or recalculated.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>https://data.unicef.org/</p>\n<p><strong>References:</strong></p>\n<p><a href=\"http://data.unicef.org/child-protection/birth-registration.html\">http://data.unicef.org/child-protection/birth-registration.html</a></p>\n<p><a href=\"https://data.unicef.org/resources/a-generation-to-protect/\">https://data.unicef.org/resources/a-generation-to-protect/</a> </p>\n<p>https://data.unicef.org/resources/the-right-start-in-life-2024-update/</p>", "indicator_sort_order"=>"16-09-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.10.1", "slug"=>"16-10-1", "name"=>"Número de casos verificados de asesinato, secuestro, desaparición forzada, detención arbitraria y tortura de periodistas, miembros asociados de los medios de comunicación, sindicalistas y defensores de los derechos humanos, en los últimos 12 meses", "url"=>"/site/es/16-10-1/", "sort"=>"161001", "goal_number"=>"16", "target_number"=>"16.10", "global"=>{"name"=>"Número de casos verificados de asesinato, secuestro, desaparición forzada, detención arbitraria y tortura de periodistas, miembros asociados de los medios de comunicación, sindicalistas y defensores de los derechos humanos, en los últimos 12 meses"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de casos verificados de asesinato, secuestro, desaparición forzada, detención arbitraria y tortura de periodistas, miembros asociados de los medios de comunicación, sindicalistas y defensores de los derechos humanos, en los últimos 12 meses", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de casos verificados de asesinato, secuestro, desaparición forzada, detención arbitraria y tortura de periodistas, miembros asociados de los medios de comunicación, sindicalistas y defensores de los derechos humanos, en los últimos 12 meses", "indicator_number"=>"16.10.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Este indicador busca medir el disfrute de las libertades fundamentales \n(por ejemplo, la libertad de opinión, la libertad de expresión y el acceso a la \ninformación, el derecho de reunión pacífica y la libertad de asociación) partiendo \nde la premisa de que los asesinatos, las desapariciones forzadas, la tortura, las \ndetenciones arbitrarias, los secuestros y otros actos lesivos contra periodistas, \nsindicalistas y defensores de los derechos humanos tienen un efecto inhibidor \nen el ejercicio de estas libertades fundamentales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.10.1&seriesCode=VC_VAW_MTUHRA&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">\nNúmero de casos de asesinatos de defensores de derechos humanos, periodistas y sindicalistas VC_VAW_MTUHRA</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.10.1&seriesCode=VC_VOC_ENFDIS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">\nNúmero de casos de desaparición forzada de defensores de derechos humanos, periodistas y sindicalistas (Número) VC_VOC_ENFDIS</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.10.1&seriesCode=VC_VAW_MTUHRAN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">\nPaíses con al menos un caso verificado de asesinatos de defensores de derechos humanos, periodistas y sindicalistas (1 = SÍ, 0 = NO) VC_VAW_MTUHRAN</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-10-01.pdf\">Metadatos 16-10-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"This indicator seeks to measure enjoyment of fundamental freedoms (e.g. freedom of opinion, freedom \nof expression and access to information, the right to peaceful assembly and freedom of association) on \nthe premise that killing, enforced disappearance, torture, arbitrary detention, kidnapping and other \nharmful act against journalists, trade unionists and human rights defenders have a chilling effect on the \nexercise of these fundamental freedoms. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.10.1&seriesCode=VC_VAW_MTUHRA&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">\nNumber of cases of killings of human rights defenders, journalists and trade unionists VC_VAW_MTUHRA</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.10.1&seriesCode=VC_VOC_ENFDIS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">\nNumber of cases of enforced disappearance of human rights defenders, journalists and trade unionists (Number) VC_VOC_ENFDIS</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.10.1&seriesCode=VC_VAW_MTUHRAN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">\nCountries with at least one verified case of killings of human rights defenders, journalists, and trade unionists (1 = YES, 0 = NO) VC_VAW_MTUHRAN</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-10-01.pdf\">Metadata 16-10-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Este indicador busca medir el disfrute de las libertades fundamentales \n(por ejemplo, la libertad de opinión, la libertad de expresión y el acceso a la \ninformación, el derecho de reunión pacífica y la libertad de asociación) partiendo \nde la premisa de que los asesinatos, las desapariciones forzadas, la tortura, las \ndetenciones arbitrarias, los secuestros y otros actos lesivos contra periodistas, \nsindicalistas y defensores de los derechos humanos tienen un efecto inhibidor \nen el ejercicio de estas libertades fundamentales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.10.1&seriesCode=VC_VAW_MTUHRA&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">\nGiza eskubideen defendatzaileen, kazetarien eta sindikalisten hilketa-kasuen kopurua VC_VAW_MTUHRA</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.10.1&seriesCode=VC_VOC_ENFDIS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">\nGiza eskubideen defendatzaileen, kazetarien eta sindikalisten desagertze behartu kasuen kopurua (kopurua) VC_VOC_ENFDIS</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=16.10.1&seriesCode=VC_VAW_MTUHRAN&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">\nGiza eskubideen defendatzaileen, kazetarien eta sindikalisten hilketen kasu egiaztatu bat gutxienez duten herrialdeak (1 = BAI, 0 = EZ) VC_VAW_MTUHRAN</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-10-01.pdf\">Metadatuak 16-10-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.10: Ensure public access to information and protect fundamental freedoms, in accordance with national legislation and international agreements</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.10.1: Number of verified cases of killing, kidnapping, enforced disappearance, arbitrary detention and torture of journalists, associated media personnel, trade unionists and human rights advocates in the previous 12 months</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_VAW_MTUHRA - Number of cases of killings of human rights defenders, journalists and trade unionists [16.10.1]</p>\n<p>VC_VOC_ENFDIS - Number of cases of enforced disappearance of human rights defenders, journalists and trade unionists [16.10.1]</p>\n<p>VC_VAW_MTUHRAN - Countries with at least one verified case of killings of human rights defenders, journalists, and trade unionists (1 = YES, 0 = NO) [16.10.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>16.1.1 Number of victims of intentional homicide per 100,000 population, by sex and age</p>\n<p>16.1.2 Conflict-related deaths per 100,000 population, by sex, age and cause</p>\n<p>16.1.3 Proportion of population subjected to physical, psychological or sexual violence in the previous 12 months</p>\n<p>16.1.4 Proportion of population that feel safe walking alone around the area they live</p>\n<p>16.10.2 Number of countries that adopt and implement constitutional, statutory and/or policy guarantees for public access to information</p>\n<p>16.3.1 Proportion of victims of violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized conflict resolution mechanisms</p>\n<p>16.3.2 Un-sentenced detainees as a proportion of overall prison population</p>\n<p>16.a.1 Existence of independent national human rights institutions in compliance with the Paris Principles</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Office of the United Nations High Commissioner for Human Rights (OHCHR) United Nations Educational, Scientific and Cultural Organization (UNESCO) International Labour Organization (ILO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Office of the United Nations High Commissioner for Human Rights (OHCHR) United Nations Educational, Scientific and Cultural Organization (UNESCO) International Labour Organization (ILO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator is defined as the number of verified cases of killing, enforced disappearance, torture, arbitrary detention, kidnapping and other harmful acts committed against journalists, trade unionists and human rights defenders on an annual basis.</p>\n<p><em>&#x2018;Journalists&#x2019; </em>refers to everyone who observes, describes, documents and analyses events, statements, policies, and any propositions that can affect society, with the purpose of systematizing such information and gathering of facts and analyses to inform sectors of society or society as a whole, and others who share these journalistic functions, including all media workers and support staff, as well as community media workers and so-called &#x201C;citizen journalists&#x201D; when they momentarily play that role,<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> professional full-time reporters and analysts, as well as bloggers and others who engage in forms of self-publication in print, on the internet or elsewhere.<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></p>\n<p><em>&#x2018;Trade unionists&#x2019; </em>refers to everyone exercising their right to form and to join trade unions for the protection of their interests.<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup> A trade union is an association of workers organized to protect and promote their common interests.<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></p>\n<p><em>&#x2018;Human rights defenders&#x2019; </em>refers to everyone exercising their right, individually and in association with others, to promote and to strive for the protection and realization of human rights and fundamental freedoms at national and international levels,<sup><a href=\"#footnote-6\" id=\"footnote-ref-6\">[5]</a></sup> including some journalists and trade unionists. While the term &#x2018;human rights advocate&#x2019; is broadly speaking a synonymous of &#x2018;human rights defender,&#x2019; the latter is preferred as it is more consistent with internationally agreed human rights standards and established practice.</p>\n<p>The different categories of violations tracked by the indicator have been defined in accordance with international law and methodological standards and monitoring practices developed by the OHCHR and other international mechanisms and classified drawing on the International Classification of Crime for Statistical Purposes (ICCS) disseminated by the UN Office of Drugs and Crime (UNODC). As such:</p>\n<ul>\n  <li><em>&#x2018;Killing&#x2019; </em>is defined as any extrajudicial execution or other unlawful killing by State actors or other actors acting with the State&#x2019;s permission, support or acquiescence that were motivated by the victim, or someone associated with the victim, engaging in activities as a journalist, trade unionist or human rights defender; or while the victim was engaged in such activities; or by persons or groups not acting with the support or acquiescence of the State whose harmful acts were either motivated by the victim engaging in activities as a journalist, trade unionist or human rights defender, and/or met by a failure of due diligence on the part of the State in responding to these harmful acts, such a failu re motivated by the victim or associate engaging in activities as a journalist, trade unionist or human rights defender; and other unlawful attacks and destruction in violation of international humanitarian law leading to or intending to cause the victim&#x2019;s death., corresponding to ICCS codes 0101, 0102 and 110139 and coded herein as A [0101, 0102</li>\n</ul>\n<p>and 110139].</p>\n<ul>\n  <li><em>`Enforced disappearance&#x2019; </em>refers to the arrest, detention, abduction or any other form of deprivation of liberty of a victim by agents of the State or by persons or groups of persons acting with the authorization, support or acquiescence of the State, motivated by the victim, or someone associated with the victim, engaging in activities as a journalist, trade unionist or human rights defender, followed by a refusal to acknowledge the deprivation of liberty or by concealment of the fate or whereabouts of the victim, which places the victim outside the protection of the law, corresponding to ICCS code 020222 (forced disappearance) and coded herein as B [02022ED]</li>\n  <li><em>&#x2018;Torture&#x2019; </em>refers to any act by which severe pain or suffering, whether physical or mental, is intentionally inflicted on a journalist, trade unionist or human rights defender, for such purposes as obtaining from them or a third person information or a confession, punishing them, intimidating them or coercing them, or for any reason based on discrimination of any kind, when such pain or suffering is inflicted by or at the instigation of or with the consent or acquiescence of a public official or other persons acting in an official capacity, corresponding to ICCS code 11011 and coded herein as C [11011].</li>\n  <li><em>&#x2018;Arbitrary detention&#x2019; </em>refers to any arrest or detention not in accordance with national laws, because it is not properly based on grounds established by law, or does not conform to the procedures established by law, or is otherwise deemed arbitrary in the sense of being inappropriate, unjust, unreasonable or unnecessary in the circumstances, and motivated by the victim, or someone associated with the victim, engaging in activities as a journalist, trade unionist or human rights defender, corresponding to ICCS code 020222 (unlawful deprivation of liberty) and coded herein as D [020222AD]</li>\n  <li><em>&#x2018;Kidnapping&#x2019; </em>refers to unlawfully detaining, taking away and/or confining a victim without their consent by persons or groups not acting with the support or acquiescence of the State, and the unlawful detention and/or confinement was met by a failure of due diligence on the part of the State in responding to the unlawful detention, such a failure motivated by the victim or associate engaging in activities as a journalist, trade unionist or human rights defender, corresponding to ICCS codes 020221 and coded herein as E [020221]</li>\n  <li><em>&#x2018;Other harmful acts&#x2019; </em>refers to other acts by State actors or other actors acting with the</li>\n</ul>\n<p>State&#x2019;s permission, support or acquiescence causing harm or intending to cause harm and motivated by the victim engaging in activities as a journalist, trade unionist or human rights defender, corresponding to ICCS codes 0301, 0219, 110133, 02012, 0205, 0208,</p>\n<p>0210 and 0211, and coded herein as F [0301, 0219, 110133, 02012, 0205, 0208, 0210 and</p>\n<p>0211].</p>\n<p><em>&#x2018;Verified cases&#x2019; </em>refer to reported cases that contain a minimum set of relevant information on particular persons and circumstances, which have been reviewed by mandated bodies, mechanisms, and institutions, and provided them with reasonable grounds to believe those persons were victims of the above-mentioned human rights violations or abuses.</p>\n<p><strong>Concepts:</strong></p>\n<p>The operational definitions of the cases, victims and other elements of the indicator have been patterned as far as practicable after corresponding categories in ICCS. The task of classifying cases entails observing events from both statistical standards and international law perspectives. For example, intentional homicide (ICCS code 0101) is included as a component of the violation type &#x2018;killing&#x2019; and is in turn supplemented by applicable human rights standards:</p>\n<ul>\n  <li>0101 Intentional homicide. Inclusions: murder; serious assault leading to death; femicide; honour killing; voluntary manslaughter; killings caused by excessive use of force by law enforcement officials; <em>extrajudicial and extra-legal, summary or arbitrary executions</em>. [<em>human rights standards added in italics</em>]</li>\n</ul>\n<p>This conceptual approach is necessitated by the confluence of three factors. First is the principle that all the violent acts tracked by the indicator are motivated by the exercise of fundamental freedoms that are guaranteed by human rights law to all persons. Second, while human rights abuses are not always explicitly criminalized in domestic jurisdictions, ICCS has achieved a certain level of success in terms of integrating human rights elements in the classification of crimes.</p>\n<p>Third, irrespective of definitions provided by national legislation or practices, all events &#x2013; whether ordinary crimes or human rights violations &#x2013; that meet the elements provided in the definitional framework will be counted for statistical purposes.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> A/HRC/20/17, para 4 <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> Human Rights Committee, General Comment 34, para 44 <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> UDHR, Art. 23, 4, supplemented by ICESCR, Article 8 <a href=\"#footnote-ref-4\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> ILO, Glossary on Labour Law and Industrial Relations (with special reference to the European Union)(Geneva, 2005) p 250 <a href=\"#footnote-ref-5\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-6\">5</sup><p> Article 1, Declaration on the Right and Responsibility of Individuals, Groups and Organs of Society to Promote and Protect Universally Recognized Human Rights and Fundamental Freedoms, UNGA Res 53/144, A/RES/53/1 <a href=\"#footnote-ref-6\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>See <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_10_1_Guidance_Note.pdf\">OHCHR Guidance note on SDG indicator 16.10.1</a>.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data are incrementally collected from global, regional and national mandated bodies, mechanisms and institutions maintain administrative data whether in aggregated form or at micro-level:</p>\n<ul>\n  <li>Global mechanisms<ul>\n      <li>OHCHR<ul>\n          <li>Data from OHCHR monitoring work</li>\n          <li>Data from the work of the Special Procedures of the Human Rights Council</li>\n          <li>Data from the Treaty Bodies reporting system</li>\n          <li>Press Releases and Statements of the UN High Commissioner for Human Rights</li>\n          <li>Other reports and publications, such as the UN Secretary General&#x2019;s Report on Reprisals</li>\n          <li>Other mandated reports and publications</li>\n        </ul>\n      </li>\n      <li>UNESCO<ul>\n          <li>Journalists Killings Condemned by the UNESCO Director General</li>\n          <li>Other mandated reports and publications</li>\n        </ul>\n      </li>\n      <li>ILO<ul>\n          <li>Cases reviewed by the Committee on Freedom of Association</li>\n          <li>Other mandated reports and publications</li>\n        </ul>\n      </li>\n      <li>Other UN agencies or entities producing relevant reports</li>\n      <li>Regional mechanisms</li>\n      <li>National mechanisms<ul>\n          <li>National Human Rights Institutions</li>\n          <li>National monitoring and protection mechanisms for journalists, trade unionists and/or human rights defenders</li>\n          <li>Justice sector institutions such as Ministries of Justice, Interior etc</li>\n          <li>National Statistical Offices in their general role to coordinate national statistical systems</li>\n        </ul>\n      </li>\n    </ul>\n  </li>\n</ul>\n<p>Integration of data from the different sources is based on the methodology described in <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_10_1_Guidance_Note.pdf\">OHCHR guidance note on SDG 16.10.1</a>. </p>", "COLL_METHOD__GLOBAL"=>"<p>Data is compiled from administrative data produced by OHCHR, ILO, UNESCO and other UN agencies or entities in accordance with their respective mandates and procedures.</p>\n<p>For example, with the support of OHCHR, the various Special Procedures of the UN Human Rights Council undertake country visits and act on individual cases by sending communications to States and occasionally, to non-State actors, in which they bring alleged violations or abuses to their attention for action, among other functions. Special Procedures report annually to the Human Rights Council and the majority of the mandate-holders also report to the General Assembly.</p>\n<p>According to Section 40 of the Manual of Operations of Special Procedures, a decision to take action on a case or situation rests on the discretion of the mandate-holder. That discretion should be exercised in light of the mandate entrusted to him or her as well as criteria generally relating to the reliability of the source; the credibility of information received; the details provid ed; and the scope of the mandate. Every effort is made to determine the probable validity of alleged incidents and the reliability of the source before the special rapporteur makes contact with the Government of the State where the alleged abuse is thought to have occurred. Contact is usually conducted through an &#x201C;urgent appeal&#x201D; or &#x201C;allegation&#x201D; letter addressed to the State&#x2019;s diplomatic mission with the United Nations in Geneva for transmission to capitals. These communications are used to ask the Government to take all appropriate action to investigate and address the alleged events and to communicate the results of its investigation and actions to the Special Rapporteur. Communications as well as State replies are kept confidential until the end of the reporting period. The mandate-holder then reports these cases to the Human Rights Council or the General Assembly.</p>\n<p>Regarding UNESCO&#x2019;s statistics on the killings of journalists, UNESCO&#x2019;s data on the killings of journalists corresponds to all of the cases of journalists&#x2019; killings that have been condemned by the UNESCO Director-General. These cases are identified based on reports from multiple sources, including from international, regional and local monitoring groups; UNESCO field offices; UNESCO Permanent Delegations; and other UN bodies. This follows the methodology requested by the IPDC Council through the 2012 IPDC Decision on the Safety of Journalists and the Issue of Impunity, which states that the report should be the result of &#x201C;analysis and comparison o f information from a broad and diverse range of sources for the sake of ensuring objectivity, including updated information provided by the relevant Member States on a voluntary basis on the killing of journalists, and non-responses, and be made widely available&#x201D;.</p>\n<p>As concerns the status of judicial enquiries into the killings of journalists, UNESCO&#x2019;s data is based solely on information provided by the Member States in which ki llings of journalists condemned by UNESCO&#x2019;s Director-General have occurred. Each year, UNESCO sends out a letter to the Permanent Delegations of these Member States requesting them for an official update on the judicial follow-up to the cases of killed journalists. It is the Permanent Delegation&#x2019;s responsibility to transfer the letter to the competent authorities at national level. On the basis of the information provided, UNESCO prepares the Director-General&#x2019;s Report on the Safety of Journalists or the World Trends in Freedom of Expression and Media Development Report, depending on the year.</p>\n<p>To a large extent, these procedures are typical of monitoring mechanisms under international law. OHCHR, UNESCO, ILO and other agencies that are responsible for these mechanisms take particular care to integrate in these standard operating procedures the requirement of consultation with the Member States concerned.</p>\n<p>Similarly, ILO is able to verify reported violations and abuses committed against trade unionists using data from its stakeholders.</p>\n<p>As a result of these processes, administrative data on violence against journalists, trade unionists and other human rights defenders are generated by international organizations. OHCHR will compile and integrate the data using a common data management tool.</p>\n<p>National Human Rights Institutions (NHRIs), National Statistical Offices, other government agencies as well as civil society organizations and networks can also play an important role in the collection of complementary data. NHRIs, including internationally accredited NHRIs (SDG indicator 16.a.1) on the basis of their own mandate, are able to investigate cases of violations and abuses brought to their attention. Several NHRIs have also institutionalized the provision of legal advice and other forms of support to victims of abuses who wish to access international mechanisms. NSOs, on the other hand, can complement this work by ensuring the implementation of internationally-accepted statistical standards, including on data exchange and dissemination for this indicator. </p>\n<p>Within existing capacity, OHCHR, UNESCO and ILO work jointly with national stakeholders to build capacity, harmonize data collection procedures and produce globally comparable results. </p>", "FREQ_COLL__GLOBAL"=>"<p>Daily</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Yearly</p>", "DATA_SOURCE__GLOBAL"=>"<p>Name:</p>\n<p>International data providers: OHCHR, UNESCO and ILO</p>\n<p>National data providers: </p>\n<p>National human rights institutions (NHRIs) compliant with the Paris Principles and other relevant institutions at national level.</p>\n<p>Description:</p>\n<p>Global data on violence against journalists, trade unionists and other human rights defenders are collected by OHCHR, UNESCO and ILO using a common template and integrated into a single dataset, eliminating double-counting. Complementary national data are provided to OHCHR, UNESCO and ILO, as relevant, by member states, through their national human rights institutions, in collaboration with national statistical offices (NSOs), as applicable. At country level, the primary sources will be generally NHRIs working with civil society organizations and networks.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Name:</p>\n<p>A troika composed of OHCHR, UNESCO, ILO</p>\n<p>Description:</p>\n<p>At international level, data on violence against journalists, trade unionists and other human rights defenders will be regularly compiled and disseminated by the troika (OHCHR, UNESCO and ILO)</p>\n<p>through the Secretary General&#x2019;s Annual SDG Report and the proposed Annual Global Report on Violence Against Human Rights Defenders. The troika will seek to work with further partners, to enhance dissemination of the indicator.</p>", "INST_MANDATE__GLOBAL"=>"<p><u>International mandates from OHCHR, as part of its mandate to promote and protect human rights globally, and from UNESCO and ILO, and related mandated mechanisms (e.g. Special Procedures of the Human Rights Council).</u></p>", "RATIONALE__GLOBAL"=>"<p>This indicator seeks to measure enjoyment of fundamental freedoms (e.g. freedom of opinion, freedom of expression and access to information, the right to peaceful assembly and freedom of association) on the premise that killing, enforced disappearance, torture, arbitrary detention, kidnapping and other harmful act against journalists, trade unionists and human rights defenders have a chilling effect on the exercise of these fundamental freedoms. </p>", "REC_USE_LIM__GLOBAL"=>"<p>As for other crime statistics and other statistics based on administrative sources, this indicator is sensitive to the completeness of reporting of individual events. There is a real but manageable risk of underreporting. Moreover, reporting rates and statistical accuracy are influenced by various factors, including changes and biases in victim reporting behaviour, changes in police and recording practices or rules, new laws, processing errors and non-responsive institutions.</p>\n<p>Regional and global aggregates may underestimate the true incidence and volume of victimization, overcompensate for robust and inclusive national data collection systems. In most instances, the number of cases reported will depend on the access to information, motivation and perseverance of national stakeholders, of human rights defenders themselves, and the corresponding support of the international community.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator is calculated as the total count of victims of reported incidents occurring within the preceding 12 months.</p>\n<p>Drawing on the International Classification of Crime for Statistical Purposes (ICCS), which is an incidents-based international classification system, the indicator counts victims on the basis of cases of violations or abuses using a classification framework developed for the purposes of the indicator (see <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_10_1_Guidance_Note.pdf\">OHCHR guidance on SDG indicator 16.10.1</a>).</p>\n<p>For normative and practical purposes, the recorded offences are ordered as follows:</p>\n<ol>\n  <li>Killing</li>\n  <li>Enforced disappearance</li>\n  <li>Arbitrary detention</li>\n  <li>Torture</li>\n  <li>Kidnapping</li>\n  <li>Other harmful acts</li>\n</ol>\n<p>If an incident incorporates elements of more than one category, it is coded to the higher category. Thus, for an incident in which the victim was subjected to prolonged incommunicado detention without medical access in the course of an unlawful detainment, the violation would be counted under torture.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data received by OHCHR, UNESCO and ILO are systematically reviewed and assessed against international legal and data availability criteria (see, for instance, <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_10_1_Guidance_Note.pdf\">OHCHR guidance note on SDG indicator 16.10.1</a>).</p>", "ADJUSTMENT__GLOBAL"=>"<p>See <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_10_1_Guidance_Note.pdf\">OHCHR guidance note on SDG indicator 16.10.1</a></p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Estimates are not produced for missing values.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Estimates are not be produced for missing values.</p>", "REG_AGG__GLOBAL"=>"<p>Regional aggregates are produced but not estimated in respect of missing data.</p>", "DOC_METHOD__GLOBAL"=>"<p>See <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_10_1_Guidance_Note.pdf\">OHCHR guidance note on SDG indicator 16.10.1</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>See <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_10_1_Guidance_Note.pdf\">OHCHR guidance note on SDG indicator 16.10.1</a></p>", "QUALITY_ASSURE__GLOBAL"=>"<p>See <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_10_1_Guidance_Note.pdf\">OHCHR guidance note on SDG indicator 16.10.1</a></p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See <a href=\"https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16_10_1_Guidance_Note.pdf\">OHCHR guidance note on SDG indicator 16.10.1</a></p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Global and regional aggregates have been available since 2014, country data since 2023. . Between 2015 and 2023, data on killings and disappearances have been recorded for 119 countries, including global monitoring and national monitoring data. </p>\n<p><strong>Time series:</strong></p>\n<p>Data available since 2014</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Data disaggregated by sex is available.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Considering common challenges in the field of other crime statistics and administrative data sources, the indicator suffers from underreporting in some country contexts. Global data providers rely on reports from national sources with varying capacities to document incidents and to engage with international mechanisms. The on-going development of national data collection frameworks comprised of internationally accredited national human rights institutions (SDG indicator 16.a.1), national statistical offices and civil society organizations has been contributed to enhancing data availability and mitigating discrepancies between national and international data providers and standards..</p>\n<p>While national data may still be compiled according to national legal systems rather than International Classification of Crime for Statistical Purposes (ICCS), OHCHR and its partner agencies support UNODC as it undertakes special efforts to ensure the gradual implementation of ICCS by countries. Over time, this should help improve quality and consistency of national and international data.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.ohchr.org/EN/Issues/Indicators/Pages/HRIndicatorsIndex.aspx\"><u>http://www.ohchr.org/EN/Issues/Indicators/Pages/HRIndicatorsIndex.aspx</u></a></p>\n<p><strong>References:</strong></p>\n<p>Declaration on the Right and Responsibility of Individuals, Groups and Organs of Society to Promote and Protect Universally Recognized Human Rights and Fundamental Freedoms (frequently abbreviated &#x201C;The Declaration on human rights defenders&#x201D;): <a href=\"http://www.ohchr.org/EN/Issues/SRHRDefenders/Pages/Declaration.aspx\">http://www.ohchr.org/EN/Issues/SRHRDefenders/Pages/Declaration.aspx</a> </p>\n<p>UNITED NATIONS (2004). Human Rights Defenders: Protecting the Right to Defend Human Rights. Geneva. Available from <a href=\"http://www.ohchr.org/EN/Issues/SRHRDefenders/Pages/SRHRDefendersIndex.aspx\">http://www.ohchr.org/EN/Issues/SRHRDefenders/Pages/SRHRDefendersIndex.aspx</a>. </p>\n<p>UNITED NATIONS (2012). Human Rights Indicators: A Guide to Measurement and Implementation. New York and Geneva. Available from <a href=\"http://www.ohchr.org/EN/Issues/Indicators/Pages/HRIndicatorsIndex.aspx\">http://www.ohchr.org/EN/Issues/Indicators/Pages/HRIndicatorsIndex.aspx</a>. </p>\n<p>United Nations (20142016). The Safety of Journalists and the Danger of Impunity: Report by the Director- General to the Intergovernmental Council of the IPDC (Twenty-NinthThirtieth Session). Paris. Available from <a href=\"http://en.unesco.org/dg-report/2016-report\">http://en.unesco.org/dg-report/2016-report</a> <a href=\"http://unesdoc.unesco.org/images/0023/002301/230101E.pdf\"><u>http://unesdoc.unesco.org/images/0023/002301/230101E.pdf</u></a></p>\n<p>UNITED NATIONS (2015) World Trends in Freedom of Expression and Media Development. Paris. Available from: <a href=\"http://www.unesco.org/new/en/world-media-trends\">http://www.unesco.org/new/en/world-media-trends</a> </p>\n<p>UNITED NATIONS (2015) International Classification of Crime for Statistical Purposes (ICCS), Version 1.0. Vienna. Available from: <a href=\"https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html\"><u>https://www.unodc.org/unodc/en/data-and-analysis/statistics/iccs.html</u></a></p>\n<p>United Nations (2012). Manual on Human Rights Monitoring. Available from: <a href=\"http://www.ohchr.org/EN/PublicationsResources/Pages/MethodologicalMaterials.aspx\"><u>http://www.ohchr.org/EN/PublicationsResources/Pages/MethodologicalMaterials.aspx</u></a></p>\n<p><u>United Nations (2018). </u><a href=\"https://www.ohchr.org/en/documents/tools-and-resources/human-rights-based-approach-data-leaving-no-one-behind-2030-agenda\">Human Rights-Based Approach to Data</a><u>.</u></p>", "indicator_sort_order"=>"16-10-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.10.2", "slug"=>"16-10-2", "name"=>"Número de países que adoptan y aplican garantías constitucionales, legales o normativas para el acceso público a la información", "url"=>"/site/es/16-10-2/", "sort"=>"161002", "goal_number"=>"16", "target_number"=>"16.10", "global"=>{"name"=>"Número de países que adoptan y aplican garantías constitucionales, legales o normativas para el acceso público a la información"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países que adoptan y aplican garantías constitucionales, legales o normativas para el acceso público a la información", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que adoptan y aplican garantías constitucionales, legales o normativas para el acceso público a la información", "indicator_number"=>"16.10.2", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El propósito de este indicador es informar el número total de países que adoptan \ngarantías legales sobre el acceso a la información, así como las principales \ntendencias en la implementación de estas garantías.\n\nEl derecho de acceso a la información pública forma parte del derecho fundamental a la libertad \nde expresión, consagrado en el artículo 19 de la Declaración Universal de Derechos Humanos (1948) \ny el posterior Pacto Internacional de Derechos Civiles y Políticos. Estos instrumentos establecen \nque el derecho fundamental a la libertad de expresión abarca la libertad de \"buscar, \nrecibir y difundir información e ideas por cualquier medio de comunicación y \nsin limitación de fronteras\". Buscar y recibir es la dimensión \ndel derecho que resulta inmediatamente relevante para este indicador de los ODS, \nmientras que el derecho a difundir información e ideas constituye la otra cara de la moneda.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-10-02.pdf\">Metadatos 16-10-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The purpose of this indicator is to report the total of number of countries that adopted legal guarantees \non ATI, as well as the main tendencies in the implementation of these guarantees. \n\nThe right of access to public information (RTI) is a component of the fundamental right of freedom of \nexpression as set forth by Article 19 of the Universal Declaration of Human Rights (1948), and the \nsubsequent International Covenant on Civil and Political Rights. These state that the fundamental right of \nfreedom of expression encompasses the freedom \"to seek, receive and impart information and ideas \nthrough any media and regardless of frontiers”. Seeking and receiving is the dimension of the \nright that is immediately relevant to this SDG indicator, with the right to impart information and ideas \nconstituting the other side of the coin. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-10-02.pdf\">Metadata 16-10-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"El propósito de este indicador es informar el número total de países que adoptan \ngarantías legales sobre el acceso a la información, así como las principales \ntendencias en la implementación de estas garantías.\n\nEl derecho de acceso a la información pública forma parte del derecho fundamental a la libertad \nde expresión, consagrado en el artículo 19 de la Declaración Universal de Derechos Humanos (1948) \ny el posterior Pacto Internacional de Derechos Civiles y Políticos. Estos instrumentos establecen \nque el derecho fundamental a la libertad de expresión abarca la libertad de \"buscar, \nrecibir y difundir información e ideas por cualquier medio de comunicación y \nsin limitación de fronteras\". Buscar y recibir es la dimensión \ndel derecho que resulta inmediatamente relevante para este indicador de los ODS, \nmientras que el derecho a difundir información e ideas constituye la otra cara de la moneda.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-10-02.pdf\">Metadatuak 16-10-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.10: Ensure public access to information and protect fundamental freedoms, in accordance with national legislation and international agreements</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.10.2: Number of countries that adopt and implement constitutional, statutory and/or policy guarantees for public access to information</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>None</p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2021-07-01</p>", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>None</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Educational, Scientific and Cultural Organization (UNESCO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Educational, Scientific and Cultural Organization (UNESCO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Number of countries that adopt and implement constitutional, statutory and/or policy guarantees for public access to information.</p>\n<p>The purpose of this indicator is to report the total of number of countries that adopted legal guarantees on ATI, as well as the main tendencies in the implementation of these guarantees, which are presented in global aggregates.</p>\n<p>Based on the definition above, the indicator has two components:</p>\n<p>1. Adoption</p>\n<p>2. Implementation</p>\n<p>Under each component, key questions were identified based on what can be called &#x201C;Principles of Access to Information&#x201D;, and which highlight essential components for effective implementation of Access to Information implementation at the country level. These Principles are synthesized from existing frameworks and documents recognised internationally.<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> For the purpose of this survey, the principles of relevance are as follows:</p>\n<p>1. Legal frameworks for Access to Information </p>\n<p>2. Limited exemptions</p>\n<p>3. Oversight mechanism </p>\n<p>4. Appeals mechanism</p>\n<p>5. Record keeping and reporting</p>\n<p>Each question values between 0 and 2. Upon the completion of the survey, a country can get a total score of 0-9. The total score of each country will not be assigned to any level category (e.g.: low, medium or high). However, it will contribute to global aggregates. </p>\n<p>More details on the computation method are under the section Methodology.</p>\n<p><strong>Concepts:</strong></p>\n<ol>\n  <li><em>Access to Information</em></li>\n</ol>\n<p>&#x201C;Public access to information&#x201D; is based upon the established human right to the fundamental freedom of expression (FOE) and association. States are duty-bearers for this right and measuring the fulfilment of this duty allows for assessment of progress.</p>\n<p>In terms of defining what is being measured, Access to Information (ATI) has two principle components: the obligation for states to have a legal framework that is also implemented in practice, that: </p>\n<ul>\n  <li>Entitles public to request access to information (documents and other information recorded in any format) and to respond to such requests in a timely fashion. </li>\n  <li>Obliges authorities to ensure that information of public interest is put into the public domain proactively, without the need for requests.</li>\n</ul>\n<ol>\n  <li><em>Right to Information</em></li>\n</ol>\n<p>The right of access to public information (RTI) is a component of the fundamental right of freedom of expression as set forth by Article 19 of the Universal Declaration of Human Rights (1948), and the subsequent International Covenant on Civil and Political Rights. These state that the fundamental right of freedom of expression encompasses the freedom &quot;to seek, receive and impart information and ideas through any media and regardless of frontiers&#x201D; (our italics). Seeking and receiving is the dimension of the right that is immediately relevant to this SDG indicator, with the right to impart information and ideas constituting the other side of the coin. </p>\n<p>RTI is an umbrella term that refers to the legal right to access information held by public bodies. It is often used in the same way as terms such as Freedom of Information (FOI). </p>\n<ol>\n  <li><em>Implementation</em></li>\n</ol>\n<p>This refers primarily to efforts to give practical effect to the provisions of the law, policy or regulation. Implementation thus designates government bodies providing information to the public (on request as well as proactively). Implementation is important to ensure that the benefits of the law, policy or regulation are realized.</p>\n<ol>\n  <li><em>Monitoring </em></li>\n</ol>\n<p>Monitoring the implementation of access to information refers to the supervision and examination conducted by the dedicated Access to Information oversight institution to ensure effective application of the legal guarantee(s). This includes a role in assessing efforts made by public bodies with a view to advance access to information in the country. </p>\n<ol>\n  <li><em>Enforcement </em></li>\n</ol>\n<p>Enforcement of compliance with Access to Information legal guarantee(s) refers to the actions of obliging adherence by duty-bearers to the respective requirements and the implementation of sanctions when violations are found. Enforcement is a disciplinary function that seeks to ensure that there are consequences to the violation of rules, involving a set of tools used to punish breaches of laws and regulations, and to deter future violations.</p>\n<ol>\n  <li><em>Mediation</em></li>\n</ol>\n<p>Mediation is a negotiation facilitated by a neutral third party (a mediator). Mediation does not involve decision making by the neutral third party. Unlike a judge or an arbitrator, therefore, the mediator is not a decision-maker. In mediation, the disputing parties work with the mediator to resolve their disputes. The mediator assists the parties in reaching their own decision on a settlement of the dispute by supervising the exchange of information and the bargaining process. </p>\n<ol>\n  <li><em>Dedicated oversight </em></li>\n</ol>\n<p>This specialist function covers the process of supervision, monitoring, evaluation of performance and review, to ensure compliance with laws, regulations and policies. It entails assessing and enforcing implementation. Oversight of implementation is thus different to executing the actual implementation itself in regard to the direct provision of information. </p>\n<p>An oversight institution refers to the body charged with ensuring Oversight and therefore accountability for the implementation of ATI. The same body or another may also do appeals, although appeals is a distinct function from oversight and are sometimes done by a separate body. This is why some countries, there exists more than one oversight institution, depending on the different tasks performed. </p>\n<p>The oversight function can be exercised by the following (indicative) institutions: </p>\n<ul>\n  <li>Information Commission/ Commissioner;</li>\n  <li>Data Protection or Privacy Commission / Commissioner</li>\n  <li>Human Rights Commission</li>\n  <li>Ombudsman</li>\n  <li>Department/ Ministry/ Agency</li>\n</ul>\n<ol>\n  <li><em>Appeals </em></li>\n</ol>\n<p>An appeal is an application for a decision (or lack of a decision) relating to a request for information, to be reviewed by the Access to Information oversight institution that is tasked with this. Appeals normally involve requests to reconsider failures by duty-bearers to provide information. Ideally, an independent and impartial review body will be established with the power to compel disclosure. While in some jurisdictions, courts may be an effective alternative to a review body, they can be slow and expensive, and therefore may prevent many people from seeking review. Appeals to a court should normally be a last resort once institutional appeal processes are exhausted, and this realm is treated as outside the scope of this indicator. </p>\n<ol>\n  <li><em>Limited exemptions </em></li>\n</ol>\n<p>Exemptions (or exceptions) allow the withholding of certain categories of information. Limited exemptions mean that such withholding must be based on narrow, proportionate, necessary and clearly defined limitations. Exceptions should apply only where there is a risk of substantial harm to the protected interest and where the harm is greater the overall public interest in having access to the information. Bodies should provide reasons for any refusal to provide access to information.</p>\n<p>Several permissible exemptions include:</p>\n<ul>\n  <li>national security; </li>\n  <li>international relations; </li>\n  <li>public health and safety;</li>\n  <li> the prevention, investigation and prosecution of legal wrongs; </li>\n  <li>privacy; </li>\n  <li>legitimate commercial and other economic interests; </li>\n  <li>management of the economy; </li>\n  <li>fair administration of justice and legal advice privilege; </li>\n  <li>conservation of the environment; and </li>\n  <li>legitimate policy making and other operations of public bodies.</li>\n</ul>\n<ol>\n  <li><em>Record-keeping and reporting</em></li>\n</ol>\n<p>Record-keeping is part of a records management system, which plays an important role in fostering accountability and good governance. Without adequate and reliable records of requests and/or appeals received and how they are processed, it would be difficult to measure, and report progress on access to information. In the implementation of access to information, reporting is an essential tool for transparency and accountability purposes, as well as for gathering evidence and data in mapping any gaps and needs as a precondition for making targeted improvements. </p>\n<p><strong>Comments and limitations:</strong></p>\n<p>The indicator allows for reporting the total number of countries that adopted constitutional, statutory and/or policy guarantees for public access to information globally. Data on the implementation of these guarantees comes from entities that responded to UNESCO survey.</p>\n<p>In some countries, the oversight institutions for Access to Information that are the entities best placed to provide data for this survey, directly or indirectly, might not have an explicit monitoring role, or may have weak record-keeping situations. Hence, they might not be able to provide detailed information that could help contextualize the analysis. </p>\n<p>The indicator does not enter into whether the national measures taken do lead to further impacts. It focuses on the implementation of the regulatory environment and on the mandate and supporting systems that is are preconditions for effective implementation.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> These include Article 10 of the United Nations Convention against Corruption; resolutions of the UN General Assembly and Human Rights Council; the Commonwealth&#x2019;s Model Freedom of Information Bill; Organization of American States (OAS)&#x2019;s Model Law on Access to Information; African Union&#x2019;s Model Law on Access to Information and reports from the UN the Special Rapporteur on the promotion and protection of the right to freedom of opinion and expression. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of countries.</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>None</p>", "SOURCE_TYPE__GLOBAL"=>"<p><strong>Description:</strong></p>\n<p>Data on the number of countries that adopted the guarantees will be obtained through the responses from countries to the Survey on Public Access to Information (SDG Indicator 16.10.2), </p>\n<p>Data on the implementation at national level, which will contribute to UNESCO&#x2019;s global reporting, will be obtained through the responses from countries and their territories to the same survey. </p>", "COLL_METHOD__GLOBAL"=>"<p>In collecting data at national level, UNESCO invites countries to participate in UNESCO Survey on Public Access to Information (SDG Indicator 16.10.2). The survey will include an instruction manual. </p>\n<p>Countries that answer the overarching questions that will be scored accordingly. In addition, where applicable, supplementary data will be collected through follow-up questions, which will not be scored and will be used to contextualize UNESCO&#x2019;s analysis.</p>", "FREQ_COLL__GLOBAL"=>"<p>UNESCO anticipates the collection of data on an annual basis.</p>\n<p> </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>UNESCO plans to release data for indicator 16.10.2 in Q1 of each year as part of its reporting to the UN Secretary-General Progress Report towards the SDGs. </p>", "DATA_SOURCE__GLOBAL"=>"<p><strong>Name:</strong></p>\n<p>Countries </p>\n<p><strong>Description:</strong></p>\n<p>Each country completes the survey in consultation with relevant line departments/ ministries/ agencies/ oversight bodies for access to information (e.g. Information Commissions, Data Protection or Privacy Commission, Ombudsman, National Human Rights Institutions), and National Statistical Offices.</p>", "COMPILING_ORG__GLOBAL"=>"<p>UNESCO</p>", "INST_MANDATE__GLOBAL"=>"<p>UNESCO is the UN specialized agency building peace in the minds of people through education, the sciences, culture, communication and information. In the field of communication and information, UNESCO defends and promotes freedom of expression, media independence and pluralism, and the building of inclusive knowledge societies underpinned by universal access to information and the innovative use of digital technologies. Since 2017, UNESCO has been designated as the custodian agency for indicator 16.10.2. In this context, UNESCO, via its International Programme for the Development of Communication (IPDC), has been mandated by its Member States to monitor and report progress on this indicator worldwide.</p>", "RATIONALE__GLOBAL"=>"<p>To report on the number of countries that adopted the guarantees, data collected through the survey instrument are triangulated by a desk research. The data, which include years of adoption of such guarantees, are monitored and updated annually to reflect changes, such as: </p>\n<p>&#x2022; whether a country just passed a guarantee for Access to Information; </p>\n<p>&#x2022; whether a country amended its existing guarantee(s) for Access to Information. </p>\n<p>In parallel, to link the data on adoption above with the implementation aspect, and to measure the component of implementation at national level, UNESCO collects data directly from countries and their territories via the Survey on Public Access to Information (SDG Indicator 16.10.2). </p>", "REC_USE_LIM__GLOBAL"=>"<p>This indicator does not assess the totality of &#x201C;public access to information&#x201D; component of the full Target of 16.10. Nevertheless, it focusses on a key determinant of the wider information environment.</p>", "DATA_COMP__GLOBAL"=>"<p>Responses to the survey will be computed using a weighted system, where each question values between 0 and 2. There is a total of 8 key questions (4 for the component on &#x201C;Adoption&#x201D; and 3 for the component on &#x201C;Implementation&#x201D;). A country can obtain a total score between 0-9 points. </p>\n<p>The total score of each country will not be assigned to any level category (e.g.: low, medium or high). However, it will contribute to global aggregates, in which data will be interpreted using the sum formula to show overall trends. The trends will illustrate the state of Access to Information implementation as per &#x201C;Principles of Access to Information&#x201D;, as cited in the Rationale section above.</p>\n<p>The table below show how questions are computed.</p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"3\">\n        <p><strong>UNESCO Survey on Public Access to Information </strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"3\">\n        <p><strong>Indicator: 16.10.2</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"3\">\n        <p><strong>Components: Adoption + Implementation; Score: 0-9</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"3\">\n        <p><strong>Component 1: ADOPTION; Score: 0-5</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Survey Question based on Principles of Access to Information</strong></p>\n      </td>\n      <td>\n        <p><strong> Score </strong></p>\n      </td>\n      <td>\n        <p><strong>Description of the calculation for global aggregates</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Whether a constitutional, statutory and/or other legal guarantee that recognises access to information as a fundamental right exists in your country? </li>\n        </ol>\n      </td>\n      <td>\n        <p>Yes = 1</p>\n        <p>No = 0</p>\n        <p>In progress: 0.5</p>\n      </td>\n      <td>\n        <p>The sum of countries that responded &#x201C;yes&#x201D; and &#x201C;in progress&#x201D;</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Whether the legal guarantee on Access to Information specifies the need of a dedicated oversight institution [or institutions]?</li>\n        </ol>\n      </td>\n      <td>\n        <p>Yes = 1</p>\n        <p>No = 0</p>\n      </td>\n      <td>\n        <p>The sum of countries that responded &#x201C;yes&#x201D;</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Whether the legal guarantee on Access to Information specifies the need for national public bodies (Ministry/Agency/Department) to appoint public information officers or a specific unit to handle Access to Information requests from the public? </li>\n        </ol>\n      </td>\n      <td>\n        <p>Yes, to <em>ALL</em> public bodies being required to appoint = 1 </p>\n        <p>Yes, but only to <em>some</em> public bodies = 0.5 </p>\n        <p>No = 0</p>\n      </td>\n      <td>\n        <p>The sum of countries that responded &#x201C;yes, all&#x201D; and &#x201C;yes, some&#x201D;</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Whether the legal guarantee on Access to Information mandates the following roles for the dedicated Access to Information oversight institution/s :</li>\n          <li>Oversight (legal responsibility to ensure implementation of the guarantee)</li>\n          <li>Appeals</li>\n          <li>Monitoring of Access to Information implementation</li>\n          <li>Enforcement of compliance with Access to Information legal guarantee(s)</li>\n          <li>Mediation</li>\n        </ol>\n      </td>\n      <td>\n        <p>0.2 for each role selected</p>\n        <p>Total point: 1 </p>\n      </td>\n      <td>\n        <p>The sum of countries that responded, &#x201C;option a&#x201D;, &#x201C;option b&#x201D;, &#x201C;option c&#x201D;, &#x201C;option d&#x201D; and &#x201C;option e&#x201D; </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Does the legal guarantee on Access to Information explicitly mentions permissible exemptions that are elaborated in well-defined categories whereby requests for information may be legally denied. that are consistent with international standards?</li>\n        </ol>\n      </td>\n      <td>\n        <p>Yes = 1</p>\n        <p>No = 0</p>\n      </td>\n      <td>\n        <p>The sum of countries that responded &#x201C;yes&#x201D;</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Score for Component 1</strong></p>\n      </td>\n      <td>\n        <p><strong>0-5</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"3\">\n        <p><strong>Component 2: IMPLEMENTATION; Score: 0-4</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Survey Question based on Principles of Access to Information</strong></p>\n      </td>\n      <td>\n        <p><strong>Score</strong></p>\n      </td>\n      <td>\n        <p><strong>Description of the calculation for global aggregates</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Whether the dedicated Access to Information oversight institution/s in practice during the reporting year has carried out the following activities: </li>\n          <li>Published an Annual Report</li>\n          <li>Provided implementation guidance and/or offer training to officials from public bodies (Ministry/Agency/Department)</li>\n          <li>Raised public awareness</li>\n          <li>Kept statistics on requests and/or appeals </li>\n          <li>Requested public bodies to keep statistics of their activities and decisions</li>\n        </ol>\n      </td>\n      <td>\n        <p>0.4 for each activity selected</p>\n        <p>Total point: 2 </p>\n      </td>\n      <td>\n        <p>The sum of countries that responded &#x201C;option a&#x201D;; &#x201C;option b&#x201D; ; &#x201C;option c&#x201D;; &#x201C;option d&#x201D;; &#x201C;option e&#x201D; </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Whether in practice the dedicated Access to Information oversight institution/s at the national level receive/s reports from public bodies (Ministry/Agency/Department) on the processing of Access to Information requests?</li>\n        </ol>\n      </td>\n      <td>\n        <p>Yes = 1</p>\n        <p>No = 0</p>\n      </td>\n      <td>\n        <p>The sum of countries that responded &#x201C;yes&#x201D;.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <ol>\n          <li>Whether the dedicated Access to Information oversight institution/s keep/s statistics of appeals at the national level?</li>\n        </ol>\n      </td>\n      <td>\n        <p>Yes = 1</p>\n        <p>No = 0</p>\n      </td>\n      <td>\n        <p>The sum of countries that responded &#x201C;yes&#x201D;.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Score for Component 2</strong></p>\n      </td>\n      <td>\n        <p><strong>0-4</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Total Score for the Survey (component 1 and 2)</strong></p>\n      </td>\n      <td>\n        <p><strong>0-9</strong></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>The scenario below can provide an example of how a country obtains its score:</p>\n<p>Country X responded to the survey and based on its responses, it obtained points, as in below:</p>\n<ul>\n  <li>Question 1: responded &#x2018;YES&#x2019; and obtained 1 point</li>\n  <li>Question 2: responded &#x2018;YES&#x2019; and obtained 1 point</li>\n  <li>Question 3: responded &#x2018;NO&#x2019; and obtained 0 point</li>\n  <li>Question 4: selected three of five options provided. Each answer has 0.2 point, so it obtained 0.6 point. </li>\n  <li>Question 5: responded &#x2018;NO&#x2019; and obtained 0 point. </li>\n  <li>Question 6: selected four of five options provided. Each answer has 0.4 point and obtained 1.6 point. </li>\n  <li>Question 7: responded &#x2018;NO and obtained 0 point</li>\n  <li>Question 8: responded &#x2018;YES and obtained 1 point</li>\n</ul>\n<p>Therefore, Country X obtained a total score of 5.2. This score will not be assigned to any level category (e.g.: low, medium or high). However, it will contribute to global aggregates, in which data will be interpreted using the sum formula to show trends. </p>\n<p>Below is an example of how responses to the survey are used in the interpretation of a global aggregate that illustrate a trend in the &#x201C;Record keeping and reporting&#x201D; principle: </p>\n<p>Out of 100 countries that responded to UNESCO Survey on Public Access to Information (SDG Indicator 16.10.2), 80% have oversight institutions on Access to Information (ATI). However, only 50% of them keep records of appeals with regards RTI requests. This flags the need for improvement, as good record-keeping is vital for evidence-based reporting, which can provide many advantages for improving ATI. Without adequate and reliable records of the requests received and how they are processed, it is difficult to produce evidence and measure progress. </p>\n<p>In addition, where applicable, supplementary data will be collected through follow-up questions, which will not be scored and will be used to contextualize UNESCO&#x2019;s analysis. The follow-up questions are as follow:</p>\n<ul>\n  <li>Question 1<ul>\n      <li>If responded &#x2018;YES&#x2019;: What are the guarantees (by type &#x2013; primary legislation, secondary legislation/regulation, binding policy document, etc)?</li>\n      <li>If responded &#x2018;NO&#x2019;: Are there still any non-binding policies on Access to Information (Public Statement such Open Government Partnership Action Plan; Strategy such as in Open Government/Open Data/ Open Access; Master or Action Plan/ SOP/ protocols/ digital or e-government policies relating to implementation of ATI; or Others) - then &#x2018;End survey&#x2019;. </li>\n      <li>If responded &#x2018;IN PROGRESS&#x2019;: Please explain - then &#x2018;End survey&#x2019;</li>\n    </ul>\n  </li>\n  <li>Question 2, if responded &#x2018;YES&#x2019;: <ul>\n      <li>\n        <ol>\n          <li>What is it / are they? (by type: Information Commission or Commissioner/ Data Protection or privacy Commission or Commissioner/ Converged body that combines data/privacy protection and Access to Information/ Human Rights Commission/ Ombudsman/ Department or Ministry or/ Agency or Other; and specify where appropriate at national or subnational levels).</li>\n          <li>Who appointed the Head of the oversight institution? (Executive/ Legislative/ Judiciary/ Other (e.g. a Committee): ________________ please explain)</li>\n          <li>Who approved the budget of the oversight institution [or institutions]? (Executive/ Legislative/ Judiciary/ Other (e.g. a multistakeholder committee): ________________ please explain)</li>\n          <li>To whom does/do the oversight institution/s directly report about their activities? (Executive/ Legislative/ Other (e.g. a Committee): ________________ please explain)</li>\n        </ol>\n      </li>\n    </ul>\n  </li>\n  <li>Question 5, if responded &#x2018;YES&#x2019;: Which of the following exemptions is/are mentioned: national security; international relations; public health and safety; the prevention, investigation and prosecution of legal wrongs; privacy; legitimate commercial and other economic interests; management of the economy; fair administration of justice and legal advice privilege; conservation of the environment; and legitimate policy making and other operations of public bodies.</li>\n  <li>Question 6, if one of the options is selected: Any other initiatives/activities that you would like to add?</li>\n  <li>Question 7, if responded &#x2018;YES&#x2019;: <ol>\n      <li>Choose reference year</li>\n      <li>How many formal requests made under the Access to Information guarantee(s)&#x2026; Received; Granted (fully; partially; total); Denied; Dismissed as ineligible?</li>\n      <li>Do you keep disaggregated data on the reasons for non-disclosure and partial disclosure on the basis of the permissible exemptions as stipulated in your country&#x2019;s legal guarantee? (Yes/No): </li>\n    </ol>\n  </li>\n  <li>Question 8, if responded &#x2018;YES&#x2019;:</li>\n</ul>\n<ol>\n  <li>Choose reference year</li>\n  <li>How many appeals that your institution&#x2026; Received?; Granted (fully; partially; total)?; Denied; Dismissed as ineligible? </li>\n  <li>Do you keep disaggregated data on the reasons for non-disclosure and partial disclosure on the basis of the permissible exemptions as stipulated in your country&#x2019;s legal guarantee? (Yes/No). </li>\n</ol>", "DATA_VALIDATION__GLOBAL"=>"<p>Data will be validated with countries during the processing stage to ensure its quality and accuracy.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>Missing values are not computed.</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>Data will only be aggregated from responding countries</p>", "REG_AGG__GLOBAL"=>"<p>For the reporting to the UN, regional aggregates follow the regional grouping outlined by the UN Statistics Department for the UN Secretary-General Progress Report towards the SDGs. As regards UNESCO reporting to its Member States, this follows UNESCO&#x2019;s regional grouping based on its definition of regions.<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup> </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> UNESCO&#x2019;s definition of regions with a view to the execution by the Organization of regional activities: unesdoc.unesco.org/in/rest/annotationSVC/DownloadWatermarkedAttachment/attach_import_b8a0c1c2-bc9b-4433-9742-c568fc7c0d19?_=372956eng.pdf&amp;to=142&amp;from=140 <a href=\"#footnote-ref-3\">&#x2191;</a></p></div></div>", "DOC_METHOD__GLOBAL"=>"<p>Once countries receive an invitation to participate in the survey, they will have access to a manual that will guide the user. It is essential that the user/person in charge gathers the responses using a well-coordinated process involving all the relevant staff that oversee the work within the various key issues contained within the survey. During the data collection period, UNESCO will mobilise a team to support countries in filling the survey and respond to their queries in a quality and timely manner.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>UNESCO puts in place a dedicated team for the management of the survey. The team provides a help desk service and online workshops to ensure relationship management with countries. The team is also responsible for quality control that includes data cleaning, processing, as well as verification. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>UNESCO ensures quality by validating data collected via its survey with countries in the case where a clarification is needed on the responses. UNESCO also proposes online workshops with countries in three languages (English, French and Spanish) to assist them in completing the survey, with a view to avoid errors in respondent comprehension and interpretation, as well as ensuring the quality of data that will be collected. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment will be done by evaluating data quality, comparability and harmonization against the principles of Access to Information setforth earlier in this document. As part of the evaluation mechanism, UNESCO will also collect feedback directly from countries and experts, with a view to improve the data collection process and the survey tool, as necessary.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>National data on adoption and implementation of legal guarantees on Access to Information should be available following the participation of States in UNESCO&#x2019;s survey. Other data are available from various monitoring and research initiatives around the world which can be used for triangulation and as supplementary sources.</p>\n<p><strong>Time series: </strong></p>\n<p>Not applicable.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Regional and global aggregates for this indicator will count the number of countries within a region or globally that adopt and implement constitutional, statutory and/or policy guarantees for public access to information.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable because the indicator is only calculated from data submitted by Member States to UNESCO in response to the Survey on Public Access to Information (SDG Indicator 16.10.2).</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>https://en.unesco.org/themes/monitoring-and-reporting-access-information</p>\n<p><strong>References:</strong></p>\n<p><strong>UNESCO 2020 Report on SDG Indicator 16.10.2 (Public Access to Information):</strong></p>\n<ul>\n  <li>\n    <ul>\n      <li>From promise to practice: access to information for sustainable development (publication version): https://unesdoc.unesco.org/ark:/48223/pf0000375022 </li>\n      <li>First global report on the implementation of access to Information laws (version submitted to the 32nd Session the Intergovernmental Council of the International Programme for the Development of Communication): https://unesdoc.unesco.org/ark:/48223/pf0000374637.locale=env </li>\n    </ul>\n  </li>\n</ul>\n<p><strong>Powering sustainable development with access to information: highlights from the 2019 UNESCO monitoring and reporting of SDG indicator 16.10.2:</strong> https://unesdoc.unesco.org/ark:/48223/pf0000369160?posInSet=2&amp;queryId=d806d9b7-15e1-4d94-95a2-6dfd9967e6c6 </p>\n<p><strong>Access to information: a new promise for sustainable development:</strong> https://unesdoc.unesco.org/ark:/48223/pf0000371485 </p>\n<p><strong>The Commonwealth&#x2019;s Model Freedom of Information Bill: </strong>https://thecommonwealth.org/sites/default/files/key_reform_pdfs/P15370_12_ROL_Model_Freedom_Information.pdf </p>\n<p><strong>Organization of American States (OAS)&#x2019;s Model Law on Access to Information:</strong> https://www.oas.org/dil/AG-RES_2607-2010_eng.pdf </p>\n<p><strong>African Union&#x2019;s Model Law on Access to Information:</strong> https://archives.au.int/handle/123456789/2062</p>\n<p><strong>United Nations Convention against Corruption:</strong> https://www.unodc.org/documents/brussels/UN_Convention_Against_Corruption.pdf </p>\n<p><strong>Resolution of the UN General Assembly and Human Rights Council 31/32:</strong> https://undocs.org/A/HRC/RES/31/32 </p>\n<p><strong>2013 Report of the Special Rapporteur on the promotion and protection of the right to freedom of opinion and expression:</strong> https://ap.ohchr.org/documents/dpage_e.aspx?si=A/68/362 </p>", "indicator_sort_order"=>"16-10-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.a.1", "slug"=>"16-a-1", "name"=>"Existencia de instituciones nacionales independientes de derechos humanos, en cumplimiento de los Principios de París", "url"=>"/site/es/16-a-1/", "sort"=>"16aa01", "goal_number"=>"16", "target_number"=>"16.a", "global"=>{"name"=>"Existencia de instituciones nacionales independientes de derechos humanos, en cumplimiento de los Principios de París"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Existencia de instituciones nacionales independientes de derechos humanos, en cumplimiento de los Principios de París", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Existencia de instituciones nacionales independientes de derechos humanos, en cumplimiento de los Principios de París", "indicator_number"=>"16.a.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Este indicador mide los esfuerzos continuos de los países a nivel mundial \npara establecer instituciones nacionales independientes mediante la \ncooperación internacional, con el fin de promover sociedades inclusivas, \npacíficas y responsables.\n\nLa creación y el fomento de una Institución Nacional de Derechos Humanos (INDH) \nindican el compromiso de un Estado con la promoción y protección de los derechos \nhumanos consagrados en los instrumentos internacionales de derechos humanos.\n\nEl cumplimiento de los Principios de París confiere a las INDH un amplio mandato, \ncompetencia y facultades para investigar, informar sobre la situación nacional \nde los derechos humanos y difundirlos mediante la información y la educación.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-0a-01.pdf\">Metadatos 16-a-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"This indicator measures the global continual efforts of countries in setting up independent national \ninstitutions, through international cooperation, to promote inclusive, peaceful and accountable societies. \n\nThe creation and fosterage of a National Human Rights Institution (NHRI) indicates a State’s commitment \nto promote and protect the human rights provided in international human rights instruments. \n\nCompliance with the Paris Principles vest NHRIs with a broad mandate, competence and power to \ninvestigate, report on the national human rights situation, and publicize human rights through \ninformation and education. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-0a-01.pdf\">Metadata 16-a-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Este indicador mide los esfuerzos continuos de los países a nivel mundial \npara establecer instituciones nacionales independientes mediante la \ncooperación internacional, con el fin de promover sociedades inclusivas, \npacíficas y responsables.\n\nLa creación y el fomento de una Institución Nacional de Derechos Humanos (INDH) \nindican el compromiso de un Estado con la promoción y protección de los derechos \nhumanos consagrados en los instrumentos internacionales de derechos humanos.\n\nEl cumplimiento de los Principios de París confiere a las INDH un amplio mandato, \ncompetencia y facultades para investigar, informar sobre la situación nacional \nde los derechos humanos y difundirlos mediante la información y la educación.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-0a-01.pdf\">Metadatuak 16-a-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 16.a: Strengthen relevant national institutions, including through international cooperation, for building capacity at all levels, in particular in developing countries, to prevent violence and combat terrorism and crime</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 16.a.1: Existence of independent national human rights institutions in compliance with the Paris Principles</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_NHR_CMPLNC - Countries with National Human Rights Institutions in compliance with the Paris Principles (0 = No status; 1 = Status B, partially compliant; 2 = Status A, fully compliant) [16.a.1]</p>\n<p>SG_NHR_IMPL - Proportion of countries with independent National Human Rights Institutions in compliance with the Paris Principles (%) [16.a.1]</p>\n<p>SG_NHR_INTEXST - Proportion of countries that applied for accreditation as independent National Human Rights Institutions in compliance with the Paris Principles [16.a.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 10.3.1<u> / 16.b.1</u>: Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights law </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Office of the High Commissioner for Human Rights</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Office of the High Commissioner for Human Rights</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator Existence of independent national human rights institutions in compliance with the Paris Principles measures the compliance of existing national human rights institutions with the Principles relating to the Status of National Institutions (The Paris Principles), which were adopted by the General Assembly (resolution 48/134) based on the rules of procedure of the Global Alliance of National Human Rights Institutions (GANHRI), formerly the International Coordinating Committee of National Institutions for the Promotion and Protection of Human Rights (or ICC).</p>\n<p><strong>Concepts:</strong></p>\n<p>A National Human Rights Institution (NHRI) is an independent administrative body set up by a State to promote and protect human rights. NHRIs are State bodies with a constitutional and/or legislative mandate to protect and promote human rights. They are part of the State apparatus and are funded by the State. However, they operate and function independently from government. While their specific mandate may vary, the general role of NHRIs is to address discrimination in all its forms, as well as to promote the protection of civil, political, economic, social and cultural rights. Core functions of NHRIs include complaint handling, human rights education and making recommendations on law reform. Effective NHRIs are an important link between government and civil society, in so far as they help bridge the &apos;protection gap&apos; between the rights of individuals and the responsibilities of the State. Six models of NHRIs exist across all regions of the world today, namely: Human rights commissions, Human rights ombudsman institutions, Hybrid institutions, Consultative and advisory bodies, Institutes and centers and multiple institutions. </p>\n<p>To be effective in their work to promote and protect human rights, national human rights institutions must be credible and independent. The Paris Principles set out internationally agreed minimum standards that NHRIs must meet to be considered credible, requiring NHRIs to be independent in law, membership, operations, policy and control of resources. </p>\n<p>The Global Alliance of National Human Rights Institutions (GANHRI), through the Sub-Committee on Accreditation (SCA), is responsible for reviewing and accrediting NHRIs in compliance with the Paris Principles: NHRIs that are assessed as complying with the Paris Principles are accredited with &#x2018;A status&#x2019;, while those that partially comply are accredited with &#x2018;B status&#x2019;.</p>\n<p>&#x2018;A status&#x2019; NHRIs have independent participation rights at the UN Human Rights Council, its subsidiary bodies and some General Assembly bodies and mechanisms. They are eligible for full membership of GANHRI, including the right to vote and hold governance positions. NHRIs accredited with &#x2018;B status&#x2019; participate in GANHRI meetings but are unable to vote or hold governance positions.</p>\n<p>Accreditation by the GANHRI entails a determination whether the NHRI is compliant, both in law and practice, with the Paris principles, the principal source of the normative standards for NHRIs, as well as with the General Observations developed by the SCA. Other international standards may also be taken into account by the SCA, including the provisions related to the establishment of national mechanisms in the Optional Protocol to the Convention against Torture and other Cruel, Inhuman or Degrading Treatment or Punishment as well as in the International Convention on the Rights of Persons with Disabilities. Likewise, the SCA looks at any NHRI-related recommendation from the international human rights mechanisms, notably, the Treaty Bodies, Universal Periodic Review (UPR) and special procedures. The process also looks into the effectiveness and level of engagement with international human rights systems.</p>\n<p>The Principles relating to the Status of National Institutions (The Paris Principles) adopted by General Assembly, Resolution 48/134 of 20 December 1993 provide the international benchmarks against which NHRIs can be accredited by the GANHRI.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>\n<p>Number</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The main source of data on the indicator is administrative records of the Sub- Committee on Accreditation reports of the Global Alliance of National Human Rights Institutions (GANHRI). OHCHR compiles the data into a global directory of National Human Rights Institution (NHRI) status accreditation updated every six months, after the Sub-committee on Accreditation submits its report.</p>", "COLL_METHOD__GLOBAL"=>"<p>An international survey is sent to national human rights institution, which fill it in and send it back to the international mechanism. The latter also use complementary information, if available, received from civil society organizations.</p>\n<p>National human rights institutions seeking accreditation have to submit detailed information about their practices and how they directly promote compliance with the Paris Principles, namely the Principles relating to the Status of National Institutions that were adopted by the General Assembly (resolution 48/134). Information to be submitted relates to: </p>\n<p>1) Guarantee of tenure for members of the National Human Rights Institution decision-making body; </p>\n<p>2) full-time members of a National Human Rights Institution; </p>\n<p>3) Guarantee of functional immunity; </p>\n<p>4) Recruitment and retention of National Human Rights Institution staff; </p>\n<p>5) Staffing of the National Human Rights Institution by secondment; </p>\n<p>6) National Human Rights Institutions during the situation of a coup d&#x2019;&#xE9;tat or a state of emergency; </p>\n<p>7) Limitation of power of National Human Rights Institutions due to national security; </p>\n<p>8) Administrative regulation of National Human Rights Institutions; </p>\n<p>9) Assessing National Human Rights Institutions as National Preventive and National Monitoring Mechanisms; </p>\n<p>10) The quasi-judicial competency of National Human Rights Institutions (complaints-handling). </p>\n<p>Based on the information received, the process of accreditation is conducted through peer review by the Sub-Committee on Accreditation (SCA) of Global Alliance of National Human Rights Institutions (GANHRI).</p>", "FREQ_COLL__GLOBAL"=>"<p>Every six months, following the report submitted by the Sub-committee on Accreditation.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Yearly</p>", "DATA_SOURCE__GLOBAL"=>"<p><strong>Name:</strong></p>\n<p>National human rights institutions</p>\n<p><strong>Description:</strong></p>\n<p>National human rights institutions (e.g. national human rights commissions, human rights ombudsman institutions, hybrid institutions, consultative and advisory bodies, institutes and centers and multiple institutions)</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Office of the High Commissioner for Human Rights (OHCHR) and the Sub-Committee on Accreditation (SCA) of the Global Alliance of National Human Rights Institutions (GANHRI).</p>", "INST_MANDATE__GLOBAL"=>"<p>The Office of the United Nations High Commissioner for Human Rights (OHCHR) acts as secretariat for the <a href=\"https://www.ohchr.org/EN/Countries/NHRI/Pages/About-GANHRI.aspx\"><strong>Global Alliance of National Human Rights Institutions (GANHRI)</strong></a> and its <a href=\"https://www.ohchr.org/EN/Countries/NHRI/Pages/GANHRISSubCommitteeAccreditation.aspx\"><strong>Sub-Committee on Accreditation</strong></a>, on which the Office is a permanent observer. . As the international custodian of this indicator and with a global mandate to promote and protect human rights, OHCHR has the responsibility and mandated authority to collect, process, and disseminate statistics for this indicator.</p>", "RATIONALE__GLOBAL"=>"<p>This indicator measures the global continual efforts of countries in setting up independent national institutions, through international cooperation, to promote inclusive, peaceful and accountable societies. The creation and fosterage of a National Human Rights Institution (NHRI) indicates a State&#x2019;s commitment to promote and protect the human rights provided in international human rights instruments. Compliance with the Paris Principles vest NHRIs with a broad mandate, competence and power to investigate, report on the national human rights situation, and publicize human rights through information and education. While NHRIs are essentially state funded, they are to maintain independence and pluralism. When vested with a quasi-judicial competence, NHRIs handle complaints and assist victims in taking their cases to courts making them an essential component in the national human rights protection system. These fundamental functions that NHRIs play and their increasing participation in the international human rights fora make them important actors in the improvement of the human rights situation, including the elimination of discriminatory laws and the promotion and enforcement of non-discriminatory laws. At the national level reporting, the better the accreditation classification of the NHRI reflects that it is credible, legitimate, relevant and effective in promoting human rights at the national level.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The important and constructive role of national institutions for the promotion and protection of human rights has been acknowledged in different United Nations instruments and resolutions, including the Final Document and Programme of Action of the 1993 World Conference on Human Rights in Vienna, GA resolutions A/RES/63/172 (2008) and A/RES/64/161 (2009) on National institutions for the promotion and protection of human rights. In addition, creation and strengthening of National Human Rights Institutions (NHRIs) have also been encouraged. For example, the 1993 GA resolution 48/134 &#x2018;affirms the priority that should be accorded to the development of appropriate arrangements at the national level to ensure the effective implementation of international human rights standards&#x2019; while the 2008 GA resolution A/RES/63/169 encouraged states &#x2018;to consider the creation or the strengthening of independent and autonomous Ombudsman, mediator and other national human rights institutions&#x2019;. The Human Rights Council (HRC resolution 5/1, 2007) also called for the effective participation of national human rights institutions in its institution building package, which provides elements to guide its future work. </p>\n<p>UN treaty bodies have also recognized the crucial role that NHRIs represent in the effective implementation of treaty obligations and encouraged their creation (e.g. CERD General Comment 17, A/48/18 (1993); CESCR General Comment 10, E/C.12/1998/25; and CRC General Comment 2, CRC/GC/2002/2). A compilation of various recommendations and concluding observations relevant to NHRIs emanating from the international human rights mechanisms in the United Nations is available at: http://www.universalhumanrightsindex.org/. </p>\n<p>The Global Alliance of National Human Rights Institutions (GANHRI) is an international association of NHRIs which promotes and strengthens NHRIs to be in accordance with the Paris Principles and provides leadership in the promotion and protection of human rights (ICC Statute, Art. 5). Decisions on the classifications of NHRIs are based on their submitted documents such as: 1) copy of legislation or other instrument by which it is established and empowered in its official or published format (e.g. statute, and /or constitutional provisions, and/or presidential decree, 2) outline of organizational structure including details of staff and annual budget, 3) copy of recent published annual report; 4) detailed statement showing how it complies with the Paris Principles. NHRIs that hold &#x2018;A&#x2019; and &#x2018;B&#x2019; status are reviewed every five years. Civil society organizations may also provide relevant information to OHCHR pertaining to any accreditation matter. </p>\n<p>Accreditation of NHRIs shows that the government supports human rights work in the country. However their effectiveness should also be measured based on their ability to gain public trust and the quality of their human rights work. In this context, it would also be worthwhile to look into the responses of the NHRI to the recommendations of the GANHRI. Likewise, the inputs from the NHRI while engaging with the international human rights mechanisms (i.e. submissions to the Human Rights Council, including UPR, and to the treaty bodies) represent a valuable source of information on how NHRIs carry out their mandate in reference to international human rights instruments.</p>", "DATA_COMP__GLOBAL"=>"<p>In terms of method of computation, the indicator is computed as the accreditation classification, namely A (complying with the Paris Principles), or B (partially complying with the Paris Principles). </p>", "DATA_VALIDATION__GLOBAL"=>"<p>Not applicable</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>All country data are available and there is no Treatment of missing values.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>All country data are available and there is no Treatment of missing values.</p>", "REG_AGG__GLOBAL"=>"<p>Numbers and proportions of countries with independent National Human Rights Institutions in full and partial compliance with the Paris Principles are available per region.</p>", "DOC_METHOD__GLOBAL"=>"<p>See ganhri.org/accreditation </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data is available for all countries.</p>\n<p><strong>Time series:</strong></p>\n<p>Data available since 2000.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>While disaggregation of information is not applicable for this indicator, it may be desirable to highlight the type of National Human Rights Institution (NHRI), whether Ombudsman, human rights commission, advisory body, research-based institute, etc.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The country counterpart has the possibility to appeal the decision on the level of compliance with the Paris Principles received from the international mechanism. The appeal needs to be supported by at least 4 other national human rights institutions (all members of the international bureau) and 2 regional networks of national human rights institutions.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.ohchr.org/EN/Issues/Indicators/Pages/HRIndicatorsIndex.aspx\">http://www.ohchr.org/EN/Issues/Indicators/Pages/HRIndicatorsIndex.aspx</a> </p>\n<p><strong>References:</strong></p>\n<p><a href=\"http://www.ohchr.org/Documents/Issues/HRIndicators/Metadata_16.a.1_3_March2016.pdf\">http://www.ohchr.org/Documents/Issues/HRIndicators/Metadata_16.a.1_3_March2016.pdf</a> </p>\n<p><a href=\"http://www.ohchr.org/EN/ProfessionalInterest/Pages/StatusOfNationalInstitutions.aspx\">http://www.ohchr.org/EN/ProfessionalInterest/Pages/StatusOfNationalInstitutions.aspx</a> </p>\n<p>https://ganhri.org/accreditation/ </p>", "indicator_sort_order"=>"16-0a-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"16.b.1", "slug"=>"16-b-1", "name"=>"Proporción de la población que declara haberse sentido personalmente discriminada o acosada en los últimos 12 meses por motivos de discriminación prohibidos por el derecho internacional de los derechos humanos", "url"=>"/site/es/16-b-1/", "sort"=>"16bb01", "goal_number"=>"16", "target_number"=>"16.b", "global"=>{"name"=>"Proporción de la población que declara haberse sentido personalmente discriminada o acosada en los últimos 12 meses por motivos de discriminación prohibidos por el derecho internacional de los derechos humanos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de la población que declara haberse sentido personalmente discriminada o acosada en los últimos 12 meses por motivos de discriminación prohibidos por el derecho internacional de los derechos humanos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de la población que declara haberse sentido personalmente discriminada o acosada en los últimos 12 meses por motivos de discriminación prohibidos por el derecho internacional de los derechos humanos", "indicator_number"=>"16.b.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El compromiso de no dejar a nadie atrás y eliminar la discriminación \nes un elemento central de la Agenda 2030 para el Desarrollo Sostenible. \n\nLa eliminación de la discriminación también está consagrada en la Declaración \nUniversal de Derechos Humanos y en los principales tratados internacionales \nde derechos humanos. \n\nEl propósito de este indicador es medir la prevalencia de la \ndiscriminación a partir de la experiencia personal relatada por las personas. \nSe considera un indicador de resultados que ayuda a \nmedir la eficacia de las leyes, políticas y prácticas no discriminatorias \npara los grupos de población afectados.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-0b-01.pdf\">Metadatos 16-b-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-09", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The pledge to leave no-one behind and eliminate discrimination is at the centre of the 2030 Agenda for \nSustainable Development. \n\nThe elimination of discrimination is also enshrined in the Universal Declaration \nof Human Rights and the core international human rights treaties. \n\nThe purpose of this indicator is to measure a prevalence of discrimination based on the personal \nexperience reported by individuals. It is considered an outcome indicator helping to measure the \neffectiveness of non-discriminatory laws, policy and practices for the concerned population groups. \n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-0b-01.pdf\">Metadata 16-b-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"El compromiso de no dejar a nadie atrás y eliminar la discriminación \nes un elemento central de la Agenda 2030 para el Desarrollo Sostenible. \n\nLa eliminación de la discriminación también está consagrada en la Declaración \nUniversal de Derechos Humanos y en los principales tratados internacionales \nde derechos humanos. \n\nEl propósito de este indicador es medir la prevalencia de la \ndiscriminación a partir de la experiencia personal relatada por las personas. \nSe considera un indicador de resultados que ayuda a \nmedir la eficacia de las leyes, políticas y prácticas no discriminatorias \npara los grupos de población afectados.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-16-0b-01.pdf\">Metadatuak 16-b-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 10: Reduce inequality within and among countries</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 10.3: Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regard</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 10.3.1: Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights law</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>VC_VOV_GDSD - Proportion of population reporting having felt discriminated against [10.3.1, 16.b.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>5.1.1 Whether or not legal frameworks are in place to promote, enforce and monitor equality and non-discrimination on the basis of sex</p>\n<p>16.1.3 Proportion of population subjected to physical, psychological or sexual violence in the previous 12 months</p>\n<p>16.a.1 Existence of independent national human rights institutions in compliance with the Paris Principles</p>\n<p>16.6.2 Proportion of population satisfied with their last experience of public services</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Office of the United Nations High Commissioner for Human Rights (OHCHR)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Office of the United Nations High Commissioner for Human Rights (OHCHR)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator is defined as the proportion of the population (adults) who self-report that they personally experienced discrimination or harassment during the last 12 months based on ground(s) prohibited by international human rights law (IHRL). IHRL refers to the body of international legal instruments aiming to promote and protect human rights, including the Universal Declaration of Human Rights (UDHR) and subsequent international human rights treaties adopted by the United Nations (UN).</p>\n<p><strong>Concepts:</strong></p>\n<p>Discrimination is any distinction, exclusion, restriction or preference or other differential treatment that is directly or indirectly based on prohibited grounds of discrimination, and which has the intention or effect of nullifying or impairing the recognition, enjoyment or exercise, on an equal footing, of human rights and fundamental freedoms in the political, economic, social, cultural or any other field of public life.<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> Harassment is a form of discrimination when it is also based on prohibited grounds of discrimination. Harassment may take the form of words, gestures or actions, which tend to annoy, alarm, abuse, demean, intimidate, belittle, humiliate or embarrass another or which create an intimidating, hostile or offensive environment. While generally involving a pattern of behaviours, harassment can take the form of a single incident.<sup><a href=\"#footnote-3\" id=\"footnote-ref-3\">[2]</a></sup></p>\n<p>IHRL provides lists of the prohibited grounds of discrimination. The inclusion of &#x201C;other status&#x201D; in these lists indicate that they are not exhaustive and that other grounds may be recognized by international human rights mechanisms. A review of the international human rights normative framework helps identify a list of grounds that includes race, colour, sex, language, religion, political or other opinion, national origin, social origin, property, birth status, disability, age, nationality, marital and family status, sexual orientation, gender identity, health status, place of residence, economic and social situation, pregnancy, indigenous status, afro-descent and other status.<sup><a href=\"#footnote-4\" id=\"footnote-ref-4\">[3]</a></sup> In practice, it will be difficult to include all potentially relevant grounds of discrimination in household survey questions. For this reason, it is recommended that data collectors identify contextually relevant and feasible lists of grounds, drawing on the illustrative list and formulation of prohibited grounds of discrimination outlined in the methodology section below, and add an &#x201C;other&#x201D; category to reflect other grounds that may not have been listed explicitly.</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> See, for instance, Art. 1 of the International Convention on the Elimination of All Forms of Racial Discrimination (ICERD); Art. 1 of the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW); Art. 2 of the Convention on the Rights of Persons with Disabilities (CRPD); General Comment 18 of the Human Rights Committee (paragraphs 6 and 7) and General Comment 20 of the Committee on Economic, Social and Cultural Rights (paragraph 7). <a href=\"#footnote-ref-2\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-3\">2</sup><p> See, for instance, General Comment 20 of the Committee on Economic, Social and Cultural Rights, and United Nations Secretary-General&#x2019;s bulletin (ST/SGB/2008/5) on Prohibition of discrimination, harassment, including sexual harassment, and abuse of authority. <a href=\"#footnote-ref-3\">&#x2191;</a></p></div><div><sup class=\"footnote-number\" id=\"footnote-4\">3</sup><p> More information on the grounds of discrimination prohibited by IHRL is available at: <a href=\"http://www.ohchr.org/Documents/Issues/HRIndicators/HumanRightsStandards.pdf\">http://www.ohchr.org/Documents/Issues/HRIndicators/HumanRightsStandards.pdf</a>. <a href=\"#footnote-ref-4\">&#x2191;</a></p></div></div>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>International Classification of Crime for Statistical Purposes </p>", "SOURCE_TYPE__GLOBAL"=>"<p>Household surveys, such as Multiple Indicator Cluster Surveys (MICS), victimisation surveys and other social surveys, are the main data sources for this indicator.</p>", "COLL_METHOD__GLOBAL"=>"<p>See Guidance Note for Implementation of Survey Module on SDG Indicator 16.b.1 &amp; 10.3.1</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are requested annually in October.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Yearly</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (NSOs). If the data are not collected by the NSO but another source, they will be sent to the NSO for consultation prior to their publication in global SDG databases.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Office of the United Nations High Commissioner for Human Rights (OHCHR)</p>", "INST_MANDATE__GLOBAL"=>"<p>As the international custodian of this indicator and with a global mandate to promote and protect human rights, OHCHR has the responsibility and mandated authority to collect, process, and disseminate statistics for this indicator.</p>", "RATIONALE__GLOBAL"=>"<p>The pledge to leave no-one behind and eliminate discrimination is at the centre of the 2030 Agenda for Sustainable Development. The elimination of discrimination is also enshrined in the Universal Declaration of Human Rights and the core international human rights treaties. The purpose of this indicator is to measure a prevalence of discrimination based on the personal experience reported by individuals. It is considered an outcome indicator (see <a href=\"https://www.ohchr.org/EN/Issues/Indicators/Pages/documents.aspx\">HR/PUB/12/5</a>) helping to measure the effectiveness of non-discriminatory laws, policy and practices for the concerned population groups.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator measures an overall population prevalence of discrimination and harassment in the total adult population at the national level. The indicator will not necessarily inform on the prevalence of discrimination within specific population groups. This will depend on sample frames. For example, if disability is included within the selected grounds, the resulting data for discrimination on the ground of disability will represent only the proportion of the total population who feel that they had personally experienced discrimination against on the ground of disability. Unless the sample design provides adequate coverage of people with disability to allow disaggregation on this characteristic, the data cannot be understood as an indication of the prevalence of discrimination (on the ground of disability) within the population of people with a disability.</p>\n<p>The indicator is not measuring a general perception of respondents on the overall prevalence of discrimination in a country. It is based on personal experience self-reported by individual respondents. The indicator does not provide a legal determination of any alleged or proven cases of discrimination. The indicator will also not capture the cases of discrimination or harassment the respondents are not personally aware off or willing to disclose to data collectors. The indicator should be a starting point for further efforts to understand patterns of discrimination and harassment (e.g. location/context of incidents, relationship of the respondent to the person or entity responsible for discrimination or harassment, and frequency and severity of incidents). More survey questions will be needed for examining policy and legislative impact and responses.</p>\n<p>OHCHR advises that data collectors engage in participatory processes to identify contextually relevant grounds and formulations. The process should be guided by the principles outlined in OHCHR&#x2019;s <a href=\"https://www.ohchr.org/en/documents/tools-and-resources/human-rights-based-approach-data-leaving-no-one-behind-2030-agenda\">Human Rights-Based Approaches to Data</a> (HRBAD), which stems from internationally agreed human rights and statistics standards. National Institutions with mandates related to human rights or non-discrimination and equality are ideal partners for these activities. Data collectors are also strongly encouraged to work with civil society organisations that are the representatives of or have better access to groups more are risk of being discriminated or left behind.</p>", "DATA_COMP__GLOBAL"=>"<p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n    <mtable>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>P</mi>\n            <mi>r</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>p</mi>\n            <mi>u</mi>\n            <mi>l</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>r</mi>\n            <mi>e</mi>\n            <mi>p</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>g</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>e</mi>\n            <mi>x</mi>\n            <mi>p</mi>\n            <mi>e</mi>\n            <mi>r</mi>\n            <mi>i</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>c</mi>\n            <mi>e</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>a</mi>\n            <mi>n</mi>\n            <mi>y</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>&amp;nbsp;</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>f</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n            <mi>m</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>f</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>d</mi>\n            <mi>i</mi>\n            <mi>s</mi>\n            <mi>c</mi>\n            <mi>r</mi>\n            <mi>i</mi>\n            <mi>m</mi>\n            <mi>i</mi>\n            <mi>n</mi>\n            <mi>a</mi>\n            <mi>t</mi>\n            <mi>i</mi>\n            <mi>o</mi>\n            <mi>n</mi>\n            <mi>&amp;nbsp;</mi>\n            <mi>o</mi>\n            <mi>r</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n      <mtr>\n        <mtd>\n          <mrow>\n            <maligngroup></maligngroup>\n            <mi>h</mi>\n            <mi>a</mi>\n            <mi>r</mi>\n            <mi>a</mi>\n            <mi>s</mi>\n            <mi>s</mi>\n            <mi>m</mi>\n            <mi>e</mi>\n            <mi>n</mi>\n            <mi>t</mi>\n          </mrow>\n        </mtd>\n      </mtr>\n    </mtable>\n    <mo>=</mo>\n    <mfrac>\n      <mrow>\n        <mtable>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>N</mi>\n                <mi>u</mi>\n                <mi>m</mi>\n                <mi>b</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>s</mi>\n                <mi>u</mi>\n                <mi>r</mi>\n                <mi>v</mi>\n                <mi>e</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>s</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>d</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>s</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>w</mi>\n                <mi>h</mi>\n                <mi>o</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>r</mi>\n                <mi>e</mi>\n                <mi>p</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>t</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>e</mi>\n                <mi>x</mi>\n                <mi>p</mi>\n                <mi>e</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>c</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>a</mi>\n                <mi>n</mi>\n                <mi>y</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>f</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>m</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>f</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>d</mi>\n                <mi>i</mi>\n                <mi>s</mi>\n                <mi>c</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>m</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>a</mi>\n                <mi>t</mi>\n                <mi>i</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>o</mi>\n                <mi>r</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>h</mi>\n                <mi>a</mi>\n                <mi>r</mi>\n                <mi>a</mi>\n                <mi>s</mi>\n                <mi>s</mi>\n                <mi>m</mi>\n                <mi>e</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>&amp;nbsp;</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n          <mtr>\n            <mtd>\n              <mrow>\n                <maligngroup></maligngroup>\n                <mi>&amp;nbsp;</mi>\n                <mi>d</mi>\n                <mi>u</mi>\n                <mi>r</mi>\n                <mi>i</mi>\n                <mi>n</mi>\n                <mi>g</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>e</mi>\n                <mi>&amp;nbsp;</mi>\n                <mi>l</mi>\n                <mi>a</mi>\n                <mi>s</mi>\n                <mi>t</mi>\n                <mi>&amp;nbsp;</mi>\n                <mn>12</mn>\n                <mi>&amp;nbsp;</mi>\n                <mi>m</mi>\n                <mi>o</mi>\n                <mi>n</mi>\n                <mi>t</mi>\n                <mi>h</mi>\n                <mi>s</mi>\n              </mrow>\n            </mtd>\n          </mtr>\n        </mtable>\n      </mrow>\n      <mrow>\n        <mi mathvariant=\"normal\">T</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">a</mi>\n        <mi mathvariant=\"normal\">l</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">m</mi>\n        <mi mathvariant=\"normal\">b</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">f</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">u</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">v</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">y</mi>\n        <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n        <mi mathvariant=\"normal\">r</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">s</mi>\n        <mi mathvariant=\"normal\">p</mi>\n        <mi mathvariant=\"normal\">o</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">d</mi>\n        <mi mathvariant=\"normal\">e</mi>\n        <mi mathvariant=\"normal\">n</mi>\n        <mi mathvariant=\"normal\">t</mi>\n        <mi mathvariant=\"normal\">s</mi>\n      </mrow>\n    </mfrac>\n    <mo>&#x2219;</mo>\n    <mn>100</mn>\n    <mi>%</mi>\n  </math></p>\n<p>To minimize the effect of <em>forward telescoping<sup><a href=\"#footnote-5\" id=\"footnote-ref-5\">[4]</a></sup></em>, the module asks two questions: a first question about the respondent&#x2019;s experience over the last 5 years, and a second question about the last 12 months:</p>\n<ul>\n  <li>Question 1: In [COUNTRY], do you feel that you personally experienced any form of discrimination or harassment during the last 5 years, namely since [YEAR OF INTERVIEW MINUS 5] (or since you have been in the country), on the following grounds?</li>\n  <li>Question 2: In [COUNTRY], do you feel that you personally experienced any form of discrimination or harassment during the past 12 months, namely since [MONTH OF INTERVIEW] [YEAR OF INTERVIEW MINUS 1], on any of these grounds?</li>\n</ul>\n<p>The proposed survey module recommends that interviewer reads or the data collection mechanism provides a short definition of discrimination/harassment to the respondent before asking the questions. Providing respondents with a basic introduction to these notions helps improve their comprehension and recall of incidents. Following consultations with experts and complementary cognitive testing, the following introductory text is recommended:</p>\n<p><em>Discrimination happens when you are treated less favourably compared to others or harassed because of the way you look, where you come from, what you believe or for other reasons. You may be refused equal access to work, housing, healthcare, education, marriage or family life, the police or justice system, shops, restaurants, or any other services or opportunities. You may also encounter comments, gestures or other behaviours that make you feel offended, threatened or insulted, or have to stay away from places or activities to avoid such behaviours.</em></p>\n<p>The proposed survey module also recommends that a list of grounds is provided to respondents to facilitate comprehension and recall of incidents. As a starting point, OHCHR recommends the use of the following list of grounds prohibited by international human rights law and adding an &#x201C;any other ground&#x201D; category to capture grounds that are not explicitly listed. The module recommends that the following illustrative list is reviewed and contextualised at national level through a participatory process (see HRBAD and accompanying guidance) to reflect specific population groups and data collection/disaggregation needs:</p>\n<p>1. SEX: such as being a woman or a man</p>\n<p>2. AGE: such as being perceived to be too young or too old</p>\n<p>3. DISABILITY OR HEALTH STATUS: such as having difficulty in seeing, hearing, walking or moving, concentrating or communicating, having a disease or other health conditions and no reasonable accommodation provided for it</p>\n<p>4. ETHNICITY, COLOUR OR LANGUAGE: such as skin colour or physical appearance, ethnic origin or way of dressing, culture, traditions, native language, indigenous status, or being of African descent</p>\n<p>5. MIGRATION STATUS: such as nationality or national origin, country of birth, refugees, asylum seekers, migrant status, undocumented migrants or stateless persons</p>\n<p>6. SOCIO-ECONOMIC STATUS: such as wealth or education level, being perceived to be from a lower or different social or economic group or class, land or home ownership or not</p>\n<p>7. GEOGRAPHIC LOCATION OR PLACE OF RESIDENCE: such as living in urban or rural areas, formal or informal settlements</p>\n<p>8. RELIGION: such as having or not a religion or religious beliefs</p>\n<p>9. MARITAL AND FAMILY STATUS: such as being single, married, divorced, widowed, pregnant, with or without children, orphan or born from unmarried parents</p>\n<p>10. SEXUAL ORIENTATION OR GENDER IDENTITY: such as being attracted to person of the same sex, self-identifying differently from sex assigned at birth or as being either sexually, bodily and/or gender diverse</p>\n<p>11. POLITICAL OPINION: such as expressing political views, defending the rights of others, being a member or not of a political party or trade union</p>\n<p>12. OTHER GROUNDS</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-5\">4</sup><p> Pattern of reporting events as having occurred more recently that they actually did. This is a phenomenon commonly observed in crime victimization surveys. <a href=\"#footnote-ref-5\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>If the data are not collected by the NSO but another source, they will be sent to the NSO for consultation prior to their publication in global SDG databases.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Estimates will not be produced for missing values.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Estimates will not be produced for missing values.</p>", "REG_AGG__GLOBAL"=>"<p>Not available</p>", "DOC_METHOD__GLOBAL"=>"<p>See Guidance Note for Implementation of Survey Module on SDG Indicator 16.b.1 &amp; 10.3.1</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>See Guidance Note for Implementation of Survey Module on SDG Indicator 16.b.1 &amp; 10.3.1</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>See Guidance Note for Implementation of Survey Module on SDG Indicator 16.b.1 &amp; 10.3.1</p>\n<p>OHCHR consults NSOs focal points for the SDG indicator framework (list maintained by the UNSD) on the availability of national data for the SDGs indicators database.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See Guidance Note for Implementation of Survey Module on SDG Indicator 16.b.1 &amp; 10.3.1</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>As of 2024, at least one data point available for more than 40% countriess</p>\n<p><strong>Time series:</strong></p>\n<p>Data available since 2015</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregation for this indicator is in line with SDG target 17.18 (income, gender/sex, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts). </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>OHCHR compiles data from national sources only, possibly regional sources, if available/appropriate. Therefore, there should not be discrepancies.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong><a href=\"http://www.ohchr.org\">www.ohchr.org</a><strong> </strong></p>\n<p><strong>References: </strong></p>\n<p>https://www.ohchr.org/en/instruments-and-mechanisms/human-rights-indicators/sdg-indicators-under-ohchrs-custodianship </p>\n<p> </p>\n<p>https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/SDG_Indicator_16b1_10_3_1_Guidance_Note_.pdf </p>\n<p>https://www.ohchr.org/sites/default/files/Documents/Issues/HRIndicators/GuidanceNoteonApproachtoData.pdf </p>", "indicator_sort_order"=>"16-0b-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.1.1", "slug"=>"17-1-1", "name"=>"Total de ingresos del gobierno en proporción al PIB, desglosado por fuente", "url"=>"/site/es/17-1-1/", "sort"=>"170101", "goal_number"=>"17", "target_number"=>"17.1", "global"=>{"name"=>"Total de ingresos del gobierno en proporción al PIB, desglosado por fuente"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Total de ingresos de las Administraciones Públicas Autonómicas y Locales en proporción al PIB ", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Total de ingresos del gobierno en proporción al PIB, desglosado por fuente", "indicator_number"=>"17.1.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_190/opt_1/ti_cuentas-economicas-de-las-administraciones-vascas/temas.html", "url_text"=>"Cuentas económicas de las administraciones vascas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Total de ingresos del gobierno en proporción al PIB, desglosado por fuente", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.1- Fortalecer la movilización de recursos internos, incluso mediante la prestación de apoyo internacional a los países en desarrollo, con el fin de mejorar la capacidad nacional para recaudar ingresos fiscales y de otra índole", "definicion"=>"Total de ingresos consolidados de las administraciones públicas vascas en proporción al PIB a precios corrientes", "formula"=>"\n$$PPIBIAP^{t} = \\frac{ICN_{CCAA+EELL}^{t}}{PIB_{CCAA}^{t}} \\cdot 100$$\n\ndonde:\n\n$ICN_{CCAA+EELL}^{t} =$ ingresos consolidados netos de las administraciones públicas vascas en el año $t$\n\n$PIB_{CCAA}^{t} =$ producto interior bruto de la Comunidad Autónoma de Euskadi a precios corrientes en el año $t$\n\n<br>\n\nPara las desagregaciones territoriales se utiliza esta fórmula:\n\n$$PPIBIEL^{t} = \\frac{IN_{EELL}^{t}}{PIB_{TH}^{t}} \\cdot 100$$\n\ndonde:\n\n$IN_{EELL}^{t} =$ ingresos netos de las entidades locales en un determinado territorio histórico en el año $t$\n\n$PIB_{TH}^{t} =$ producto interior bruto de un determinado territorio histórico a precios corrientes en el año $t$\n", "desagregacion"=>"", "observaciones"=>"Los ingresos consolidados de las administraciones vascas se obtienen sumando los ingresos de la \ncomunidad autónoma y las \nentidades locales, restando las transferencias entre ellas (tanto corrientes como de capital), \npara evitar doble contabilización.\n\nLos indicadores territorializados Araba/Álava, Bizkaia y Gipuzkoa, se corresponden con los \ndatos de las entidades locales de cada territorio histórico, sin incluir los ingresos \nde la administración autonómica ni consolidación de transferencias.\n", "periodicidad"=>"Anual", "justificacion_global"=>"La política fiscal es el uso del nivel y la composición del gasto y los ingresos del \ngobierno general y del sector público, así como la acumulación conexa de activos y \npasivos gubernamentales, para alcanzar objetivos como la estabilización de la economía, \nla reasignación de recursos y la redistribución del ingreso. \n\nAdemás de la movilización de ingresos, las unidades gubernamentales también pueden \nfinanciar una parte de sus actividades en un período específico mediante préstamos o \nadquiriendo fondos de fuentes distintas de las transferencias obligatorias, por \nejemplo, ingresos por intereses, ventas incidentales de bienes y servicios o la renta \nde activos del subsuelo. \n\nEl indicador 17.1.1 Ingresos gubernamentales totales como proporción del PIB, por \nfuente, permite comprender la movilización de ingresos internos de los países en \nforma de fuentes tributarias y no tributarias. El indicador proporcionará a los analistas \nun conjunto de datos comparables entre países que resalta la relación entre los cuatro \ntipos principales de ingresos, así como la \"carga tributaria\" relativa (ingresos en \nforma de impuestos) y la \"carga fiscal\" (ingresos en forma de impuestos más \ncontribuciones sociales).\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.1.1&seriesCode=GR_G14_GDP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Ingresos totales del gobierno (gobierno central presupuestario) como proporción del PIB (%) GR_G14_GDP</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos de Naciones Unidas. El indicador se ha adaptado al contexto de la C.A. de Euskadi.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-01-01.pdf\">Metadatos 17-1-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Total de ingresos del gobierno en proporción al PIB, desglosado por fuente", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.1- Fortalecer la movilización de recursos internos, incluso mediante la prestación de apoyo internacional a los países en desarrollo, con el fin de mejorar la capacidad nacional para recaudar ingresos fiscales y de otra índole", "definicion"=>"Total consolidated revenue of the Basque public administrations as a proportion of GDP at current prices", "formula"=>"\n$$PPIBIAP^{t} = \\frac{ICN_{CCAA+EELL}^{t}}{PIB_{CCAA}^{t}} \\cdot 100$$\n\nwhere:\n\n$ICN_{CCAA+EELL}^{t} =$ consolidated revenue of the Basque public administrations in year $t$\n\n$PIB_{CCAA}^{t} =$ gross domestic product of the Autonomous Community of the Basque Country at current prices in year $t$\n\n<br>\n\nFor territorial disaggregations this formula is used:\n\n$$PPIBIEL^{t} = \\frac{IN_{EELL}^{t}}{PIB_{TH}^{t}} \\cdot 100$$\n\nwhere:\n\n$IN_{EELL}^{t} =$ net income of local entities of a province in year $t$\n\n$PIB_{TH}^{t} =$ gross domestic product of a province at current prices in year $t$\n", "desagregacion"=>nil, "observaciones"=>"The consolidated income of the Basque administrations is obtained by adding the income of the autonomous \ncommunity and local entities, subtracting the transfers between them (both current and capital), to avoid \ndouble counting. \n\nThe territorialized indicators for Araba/Álava, Bizkaia and Gipuzkoa correspond to the data of the local \nentities of each province, without including the income of the autonomous administration or consolidation \nof transfers. \n", "periodicidad"=>"Anual", "justificacion_global"=>"Fiscal policy is the use of the level and composition of the general government and public sectors’ \nspending and revenue—and the related accumulation of government assets and liabilities—to achieve \nsuch goals as the stabilization of the economy, the reallocation of resources, and the redistribution of \nincome. \n\nIn addition to revenue mobilization, government units may also finance a portion of their \nactivities in a specific period by borrowing or by acquiring funds from sources other than compulsory \ntransfers—for example, interest revenue, incidental sales of goods and services, or the rent of subsoil \nassets. \n\nIndicator 17.1.1 Total government revenue as a proportion of GDP, by source supports \nunderstanding countries’ domestic revenue mobilization in the form of tax and non-tax sources. The \nindicator will provide analysts with a cross-country comparable dataset that highlights the relationship \nbetween the four main types of revenue as well as the relative \"tax burden\" (revenue in the form of \ntaxes) and “fiscal burden” (revenue in the form of taxes plus social contributions). \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.1.1&seriesCode=GR_G14_GDP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Total government revenue (budgetary central government) as a proportion of GDP (%) GR_G14_GDP</a> UNSTATS", "comparabilidad"=>"The available indicator partially complies with the United Nations metadata. The indicator has been adapted to the context of the Basque Country.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-01-01.pdf\">Metadata 17-1-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Total de ingresos del gobierno en proporción al PIB, desglosado por fuente", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.1- Fortalecer la movilización de recursos internos, incluso mediante la prestación de apoyo internacional a los países en desarrollo, con el fin de mejorar la capacidad nacional para recaudar ingresos fiscales y de otra índole", "definicion"=>"Total de ingresos consolidados de las administraciones públicas vascas en proporción al PIB a precios corrientes", "formula"=>"\n$$PPIBIAP^{t} = \\frac{ICN_{AAEE+TTEE}^{t}}{PIB_{AAEE}^{t}} \\cdot 100$$\n\nnon:\n\n$ICN_{AAEE+TTEE}^{t} =$ euskal administrazio publikoen diru-sarrera kontsolidatu garbiak $t$ urtean\n\n$PIB_{AAEE}^{t} =$ Euskal Autonomia Erkidegoko barne-produktu gordina, uneko prezioetan $t$ urtean\n\n<br>\n\nLurralde-desagregazioetarako formula hau erabiltzen da::\n\n$$PPIBIEL^{t} = \\frac{IN_{TTEE}^{t}}{PIB_{LH}^{t}} \\cdot 100$$\n\nnon:\n\n$IN_{TTEE}^{t} =$ lurralde historiko jakin bateko toki-erakundeen diru-sarrera garbiak $t$ urtean \n\n$PIB_{LH}^{t} =$ lurralde historiko jakin bateko barne-produktu gordina, uneko prezioetan $t$ urtean \n", "desagregacion"=>nil, "observaciones"=>"Los ingresos consolidados de las administraciones vascas se obtienen sumando los ingresos de la \ncomunidad autónoma y las \nentidades locales, restando las transferencias entre ellas (tanto corrientes como de capital), \npara evitar doble contabilización.\n\nLos indicadores territorializados Araba/Álava, Bizkaia y Gipuzkoa, se corresponden con los \ndatos de las entidades locales de cada territorio histórico, sin incluir los ingresos \nde la administración autonómica ni consolidación de transferencias.\n", "periodicidad"=>"Anual", "justificacion_global"=>"La política fiscal es el uso del nivel y la composición del gasto y los ingresos del \ngobierno general y del sector público, así como la acumulación conexa de activos y \npasivos gubernamentales, para alcanzar objetivos como la estabilización de la economía, \nla reasignación de recursos y la redistribución del ingreso. \n\nAdemás de la movilización de ingresos, las unidades gubernamentales también pueden \nfinanciar una parte de sus actividades en un período específico mediante préstamos o \nadquiriendo fondos de fuentes distintas de las transferencias obligatorias, por \nejemplo, ingresos por intereses, ventas incidentales de bienes y servicios o la renta \nde activos del subsuelo. \n\nEl indicador 17.1.1 Ingresos gubernamentales totales como proporción del PIB, por \nfuente, permite comprender la movilización de ingresos internos de los países en \nforma de fuentes tributarias y no tributarias. El indicador proporcionará a los analistas \nun conjunto de datos comparables entre países que resalta la relación entre los cuatro \ntipos principales de ingresos, así como la \"carga tributaria\" relativa (ingresos en \nforma de impuestos) y la \"carga fiscal\" (ingresos en forma de impuestos más \ncontribuciones sociales).\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.1.1&seriesCode=GR_G14_GDP&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\"> Gobernuaren guztizko diru-sarrerak (aurrekontuen gobernu zentrala), BPGren proportzio gisa (%) GR_G14_GDP</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen metadatuak. Adierazlea Euskal AEko testuingurura egokitu da. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-01-01.pdf\">Metadatuak 17-1-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.1: Strengthen domestic resource mobilization, including through international support to developing countries, to improve domestic capacity for tax and other revenue collection</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.1.1: Total government revenue as a proportion of GDP, by source</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>GR_G14_GDP - Total government revenue as a proportion of GDP [17.1.1]</p>\n<p>GR_G14_XDC - Total government revenue, in local currency [17.1.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 17.1.2: Proportion of domestic budget funded by domestic taxes </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Monetary Fund (IMF) Statistics Department (Government Finance Division)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Monetary Fund (IMF) Statistics Department (Government Finance Division)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Revenue is defined in Chapter 4 (paragraph 4.23) of Government Finance Statistics Manual (GFSM) 2014 as an increase in net worth resulting from a transaction. It is a fiscal indicator for assessing the sustainability of fiscal activities. General government units have four types of revenue. The major types of revenue are taxes (GFS code 11), social contributions (GFS code 12), grants (GFS code 13), and other revenue (GFS code 14). Of these, compulsory levies and transfers are the main sources of revenue for most general government units. In particular, taxes are compulsory, unrequited amounts receivable by government units from institutional units. Social contributions are actual or imputed revenue receivable by social insurance schemes to make provision for social insurance benefits payable. Grants are transfers receivable by government units from other resident or non-resident government units or international organizations, and that do not meet the definition of a tax, subsidy, or social contribution. Other revenue is all revenue receivable excluding taxes, social contributions, and grants. Other revenue comprises: (i) property income; (ii) sales of goods and services; (iii) fines, penalties, and forfeits; (iv) transfers not elsewhere classified; and (v) premiums, fees, and claims related to non-life insurance and standardized guarantee schemes.</p>\n<p><strong>Concepts:</strong></p>\n<p>The transactions and the associated classifications are detailed in Chapter 5 of GFSM 2014 and are structured to demonstrate how general government (and public sector) units raise revenue. Only those taxes and social insurance contributions that are evidenced by tax assessments and declarations, customs declarations, and similar documents are considered to create revenue for government units. Thus, the difference between assessments and expected collections represents a claim that has no real value and should not be recorded as revenue (see GFSM 2014 paragraph 5.20). The analytic framework of GFSM 2014 (like that of the GFSM 2001) builds on the GFSM 1986 framework, and extends it by incorporating additional elements that are useful in assessing fiscal policy. An important example is the treatment of non-financial assets, where the sale of such assets is no longer included in revenue. The disposal of a non-financial asset by sale or barter is not revenue because it has no effect on net worth. Rather, it changes the composition of the balance sheet by exchanging one asset (the non-financial asset) for another (the proceeds of the sale). </p>\n<p>Similarly, amounts receivable from loan repayments and loan disbursements are not revenue. In general, transactions that increase net worth result from current operations. Capital transfers are an exception. In GFSM 2014, capital transfers receivables are classified as revenue because they increase the recipient&#x2019;s net worth and they are often indistinguishable from current transfers in their effect on government operations. In recording cash-based accounting revenue transactions, data representing the tax payments received by government, net of refunds paid out during the period covered should be reported. These data will include taxes paid after the original assessment, taxes paid or refunds deducted from taxes after subsequent assessments, and taxes paid or refunds deducted after any subsequent reopening of the accounts. Therefore, total tax revenue could be presented on a gross basis as the total amount of all taxes accrued, or on a net basis as the gross amount minus tax refunds. Revenue categories are presented as gross of expense categories for the same or related category. In particular, interest revenue is presented as gross rather than as net interest expense or net interest revenue. </p>\n<p>Similarly, social benefits and social contributions, grant revenue and expense, and rent revenue and expense are presented gross. Also, sales of goods and services are presented gross of the expenses incurred in their production. In cases of erroneous or unauthorized transactions, revenue categories are presented net of refunds of the relevant revenue, and expense categories are presented net of inflows from the recovery of the expense. For example, refunds of income taxes may be paid when the amount of taxes withheld or otherwise paid in advance of the final determination exceeds the actual tax due. Such refunds are recorded as a reduction in tax revenue. For this reason, tax revenue is presented as net of non-payable tax credits (see GFSM 2014 paragraphs 5.29&#x2013;5.32).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>\n<p>Local currency (millions)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>See 2.a.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The actual and recommended sources of data for deriving this indicator are the fiscal statistics reported to the IMF&#x2019;s Statistics Department. These come from various national agencies (Ministries of Finance, Central Banks, National Statistics Offices, etc.) and are compiled according to a standardized method for data collection: the annual <a href=\"https://www.imf.org/external/pubs/ft/gfs/manual/gfs-qtca.htm\">GFS Questionnaire</a>. In the 2020 annual reporting cycle, approximately 130 countries reported the relevant series for monitoring indicator 17.1.1. For current non-reporting countries that have demonstrated the capacity to compile and report the relevant GFS revenue series, the IMF Statistics Department is engaged in outreach to the national authorities, in consultation with the respective IMF Area Departments and Offices of the Executive Director, as needed. Capacity Development activities will seek to address data deficiencies, including through regional workshops. The steps outlined above should allow, over time, for covering virtually the entire IMF membership.</p>", "COLL_METHOD__GLOBAL"=>"<p>See 3.a. </p>", "FREQ_COLL__GLOBAL"=>"<p>The data collection cycle normally runs from September through December of the next year from the reference year (T+9 to 12 months). IMF Statistics Department normally completes a round of annual GFS collection in February of the following year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Country data are disseminated as they are processed following the data collection. Summary World Tables and other indicators including 17.1.1 are planned for release early in the second year from the reference year. For most countries, the latest data will be the reference year, including five or more most recent years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>See 3.a.</p>", "COMPILING_ORG__GLOBAL"=>"<p>The International Monetary Fund (IMF) Statistics Department (Government Finance Division) is the organization responsible for the compilation and reporting on this indicator at the global level.</p>", "INST_MANDATE__GLOBAL"=>"<p>See 3.a.</p>", "RATIONALE__GLOBAL"=>"<p>Fiscal policy is the use of the level and composition of the general government and public sectors&#x2019; spending and revenue&#x2014;and the related accumulation of government assets and liabilities&#x2014;to achieve such goals as the stabilization of the economy, the reallocation of resources, and the redistribution of income. In addition to revenue mobilization, government units may also finance a portion of their activities in a specific period by borrowing or by acquiring funds from sources other than compulsory transfers&#x2014;for example, interest revenue, incidental sales of goods and services, or the rent of subsoil assets. Indicator 17.1.1 Total government revenue as a proportion of GDP, by source supports understanding countries&#x2019; domestic revenue mobilization in the form of tax and non-tax sources. The indicator will provide analysts with a cross-country comparable dataset that highlights the relationship between the four main types of revenue as well as the relative &quot;tax burden&quot; (revenue in the form of taxes) and &#x201C;fiscal burden&#x201D; (revenue in the form of taxes plus social contributions).</p>", "REC_USE_LIM__GLOBAL"=>"<p>In principle, GFS should cover all entities that materially affect fiscal policies. Cross-country comparisons are ideally made with reference to the consolidated general government sector. However, for most developing and many emerging market economies, compiling data for the consolidated general government and its sub-sectors is problematic owing to limitations in the availability and/or timeliness of source data. For example, a country may have one central government; several state, provincial, or regional governments; and many local governments. Countries may also have social security funds. The <em>GFSM 2014</em> recommends that statistics should be compiled for all such general government units. This reporting structure is illustrated below:</p>\n<p>Structure of the general government sector and its subsectors</p>\n<p><img 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\"></p>\n<p>Some countries report data for the consolidated general government with one or more sub-sectors not separately reported. Similarly, there are some countries that report &#x201C;consolidated central government&#x201D; without necessarily providing the budgetary central government sub-sector separately. To address this, and allow the derivation of regional and world aggregates, the country data are presented for the budgetary central government, the consolidated central government (with and without social security funds), and for consolidated general government, as reported by the national authorities. </p>\n<p>For many emerging market and low-income countries with limited statistical capacity, budgetary central government is considered the most appropriate level of institutional coverage for comparison purposes. Budgetary central government, as described in <em>GFSM 2014</em> (paragraph 2.81), is an institutional unit of the general government sector particularly important in terms of size and power, particularly the power to exercise control over many other units and entities. This component of general government is usually covered by the main (or general) budget. The budgetary central government&#x2019;s revenue (and expense) are normally regulated and controlled by a ministry of finance, or its functional equivalent, by means of a budget approved by the legislature. </p>", "DATA_COMP__GLOBAL"=>"<p>Indicator 17.1.1 will be derived using series that are basic to the GFS reporting framework. GFS revenue series maintained by the IMF Statistics Department are collected in Table 1 of the standard annual data questionnaire. Each revenue transaction is classified according to whether it is a tax or another type of revenue. GFS revenue aggregates are summations of individual entries and elements in this particular class of flows and allow for these data to be arranged in a manageable and analytically useful way. For example, tax revenue is the sum of all flows that are classified as taxes. Conceptually, the value for each main revenue aggregate is the sum of the values for all items in the relevant category. The annual GFS series for monitoring Indicator 17.1.1 will be derived from the data reported by the national authorities (in national currency) expressed as a percent of Gross Domestic Product (GDP), where GDP is derived from the IMF <em>World Economic Outlook</em> database (no adjustments and/or weighting techniques will be applied). Mixed sources are not being used nor will the calculation change over time (i.e., there are no discontinuities in the underlying series as these are key aggregates/ components in all country reported GFS series). The presentation will closely align with that currently contained in World Table 4 from the hard-copy <em>GFS Yearbook</em>: </p>\n<p>Revenue categories</p>\n<p><img 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\"></p>\n<p><br>Historic series have been aligned with <em>GFSM 2014</em> classifications. This enhances the comparability of data across countries and ensures establishing robust analytical findings to support SDG monitoring using fiscal data.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>See 4.c.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>The IMF plans to rely exclusively on officially reported data provided by the national authorities using the standard GFS questionnaire based on GFSM 2014 methodology. No country data estimates for missing values will be calculated by the IMF Statistics Department. Where country data are not available due to a lack of reporting to the IMF Statistics Department, we plan to engage in outreach to the national authorities, in consultation with the respective IMF Area Departments and Offices of the Executive Director, as needed, to ensure that the key GFS series are reported.</p>", "REG_AGG__GLOBAL"=>"<p>The IMF Statistics Department will leverage the existing GFS database to provide cross-country comparable series in a standardized presentation format.</p>", "DOC_METHOD__GLOBAL"=>"<p>See 4.c. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>See 4.c.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>See 4.c.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See 4.c.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Classification of the indicator into one of the three tiers: We recommend that 17.1.1 (like 17.1.2) remain classified as Tier 1: The indicator is conceptually clear and internationally agreed standards for compiling components and aggregates are available. The underlying data are regularly produced by countries, and there is current data available. From the IAEG-SDGs Tier Classification description at https://unstats.un.org/sdgs/iaeg-sdgs/tier-classification/, a key criterion is that &#x201C;data are regularly produced by countries for at least 50 percent of countries&#x201D;. The IMF GFS database, with 130+ regular annual reporting countries using the same reporting format meets this key criterion. Apart from conflict countries, all IMF member countries produce revenue (and expenditure) data for surveillance purposes. In recent rounds of soliciting annual GFS series from countries, we have specifically encouraged those countries that were non-reporters over the past few years to (at a minimum) provide the key revenue and expenditure series needed to monitor 17.1.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The detailed GFS revenue classification structure in the annual questionnaire that is used by countries to report data allows for compiling 17.1.1. The four types of revenue: Taxes, Social contributions, Grants and Other revenue are further disaggregated in the annual GFS questionnaire in order to encompass all possible forms of revenue administrations. Taxes are disaggregated as follows:<br></p>\n<p><img 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riatqWsCm8Ymc1JtGWufmKnuEzPtTX5nhXwIPgcfReuyj8Un48M5++yzHcT1KfmgrCSZb21V8el5etZZZymZVaX/xHTZx6v+fGJa9GlrvX1ivgK0WQQsBRYEy4LFAf0Wijbf9M4yoqeWFAuLvltksqqky/jjFBJeWZQsTbhnmcJJUyhd9olZynDbsobz+G8ULHfWeV3OQmpVMV5GzdgZQeNoNI1puhwUs6pYWi2w5oCZYMk1FiDc/R64/9TMPAByy5A72lCLkdA3084d//rtjvue+laNMaboVQpYj9BnviLC92Nm67YfPjPfqkcKKOa3V3TY6yox7ZT3VivQGvLZGCGXHz0lfUNeDyUqVK3KGyV+axoByECMHwr4bn2ZrUfu6EIa2rTLoWTTLqNkti43SubYZZQMeJsuh/nb7HLyxWHv2C5r2v0wf6NRnsB8o2y+Id5829IoD5hvrPsuq0qFqt1oYrdRbgUG823Q5cF8m9zlLc23jT6xNrHn+4kZi426PPkTa132iY1+y3Pp8jKvKht1eS6fWFtI2yeW+ZaFtH1iExbS/hOb10I6+ROb+6oytsuzrSpWA0ATFGuryuiSPsMnllVlFMUCl3NcVXCDnBSBu7/21vEzywpJajSjyR7aYKmuFgeKA8WB4sDKcoChgtVhcI58Kmc32j3TMStQi/KNCeR3RkJmB4a1VeEJIB+cDWOlYShjF2KKzMVg8tjHPpbpZpCdbFX6WHQWB4oDxYHiwLpyIHHBB9cmQM6KYhfBZqpdB7tBcVhz2d6wp2J3zaaXnbPVZZnND9sY9gLtjeX65Cc/aQfFbk2l8V7dYS3KiwPFgeLA3uHAJNO6HWV77Jw+soWcLXfOLGCP44Ptfff5bnDoePOb37wSLBuN7MYphr3BNgn85nfAHYaNgScChwjePTxliDJir37uc5/TWX4KXITwIa4T+MO5hnzEO4ZnEJ8RsdO5wzzrWc/iYoYhRB/vxv1EbfT+uEIkUxmnmJtuuik7K9xYOM5w3FgJNhaRxYHiQHGgOLAQDoyN7DZ0dmMnb1vocIjHKVc6TnegiBsh10pPeahywONrB3u4YtLLBxvvS/vnIGlKT+cb3vAGTraDvgBpDOFb2AZMLHTo60XoywWRK6ZHxBqunvx7/Waoj4MVGwYTfXtRSS6OQD2N8uYlAPXzgBclL/SlZV0RVhwoDhQHigML58DYpCmTTOsOeDid4qAFCM+RhgCPkwMO4TihwbOfwroQqWS+jbI35LghJ8O+Zr3jI6qblGynHRwnsI/g7FZORDiQAPid9gHtGBKF3rEH5we41DlR4JwGXHf+x7ElqO9UEq1d/Yz2zABvf/vbqe/kIWlRuCBQ1in98+1X1VYcKA4UB4oDa8+BSUDuOJ2Da4zGCT6AF/n3R3/0Rx3ZBGA5rbvGPGJC5wrgYHGO5DKPE2icCcmZfRejOhY55AqVccMZSkdISQOsH6Cafd4d0E4eIv04+WbrPa4KRAQHWJ0WPfLIIx2+tH/h4J+DrWvMzOpacaA4UBwoDuwEBzb3Wh/rIxe1cicIWqo6CS7c+oRnEV+FBULoBmht47yJL36IFSMUALJFKqB/+0Ffz9lEUVOEKBEOAoqL/oFj8TYgDdDyCUNs6TYv6PGir/C5c1p0qbpfxBQHigPFgeLA8nNgcyBf/j7sHIUCYNn5hrLAW5Sf1772tQlw1rdox8LOtzscCOyU+wHdk6SctVzYI6YLwaEEA3LZhk+kXH5tFH376OLoiXUl9BUnO3cGle9c16rm4kBxoDhQHFgPDhSQTxpHnvli5gncSHtmDHdWLYFX2zv2wgE8Y7t98RjYhQ1iM7d9DsIF/+MVb5ddqCDe6R6JPigWoNfp+hR0BWyuO+1GVnBT4EmOcusxsaoXxYHiQHGgOLA7HCggn8RnejMdGhgzgPMVsI3NbE7bjlYN2oWhhr6i6bK6CyZDwxaI21Ne/TwJxNITggdy0+Ntt/N+h/ReZF0XwpeW7776qez83qnvzrY5h7Y7A1+tFAeKA8WB4sB6cGAqILfdC7RsD/eG3/h4J8nVGvBCLxIRt+8L7zPeao6Pc/eT2MBxebvgIvVLYKD7wtyLlmO3W5IYKM6zXax8R+1th8Nj2+qSNygmlp4LivNj5/qufuCd3ADAO09Z3fm0S4Cx3s6DazBPqgvFgeJAcWDZOLB5rHVQRIOUNYv6KM+Pg+PpQ9KlsBuzKvNvX7aOjaVnNCBMirFyxwBOOU4sl3ZxW5PHxovSDYFb2+HuQGXo7hUxYaBy2IJLzpipCjeUxDcvMqdjVJKtuQ/pW83Ky9UTMYi+7qkyK8HGIrI4UBwoDhQHFsKBsQFhNgfyhdC6Q41uBOTbby7x2rZfT9VQHCgOFAeKA8WBjTgwFsinMq0XTzflQKH4piyqAsWB4kBxoDiwExwYAnncuNb1yqmwuooDxYHiQHGgOLCKHBiLYvuY1nlNCyZ69NFH275dxR5OppnSzOfcMTCBUdevd9Wj4kBxoDhQHFhvDvCmkgPlCU94wh//8R/3Pd0HyIUYE4VU0tJ1DfoNy5PQbL0Hu3pXHCgOFAeKA+vHAelGnZkaxeghkDteJTjJAMjZ2+Ef12su2S11ClB0n5rvfh/GNWCJgwovVZwyhOWwHNfx1osZRjodHN0UT4yXperyDL0bvGJ8ky/HEG+HadunZGlr6OeDCZDvon0UCfPnzyRNWpJehCr/7v6MzWLSz6iYCsOiTfmTCTlYizZ9a9MCbRBzxmTT8lWgOLAQDgByzm6OOg8weui1/rKXvUzw8AGJJrdjVA49O1XVA5iT5Yzw3/Ed3+FkWnvF1yhmuAXCYSqtLqS3GzXKtP7rv/7rcplshyrfuRNoquor0Wsn0Jawy9vpqXcdoNdZ4+j03TarWtfXzQf7Ncw85oAPxA+BAXwUCeHnYgQzN9q5zSXhg48UqeIP7vJHmu0tYHzPe94zi4k55l82w2k8RrPmoBmr58vJZB92ABUl8625aisOzJEDNHJBTSwyfZ1D17ZRadSXIwb4/e53P2m+hBNvLzsDLSGYeGcnn3xyX+PnPve5gw8+WESzm2++eY7Uz6WqucjaMpkeeOCBgrD2l1Pgy9nlbfJNxLoHPvCBPCfWdbdlm/wxo2xZYZEJILqAGEE+B8HzheVPViGJ56W/u+222waS3zbb3ebrsJNQL96RWITbrGqrr4uTyBdHZmRhGLzrRKhQSFdccYWQDJtWRVQScEmkBxPSurRp+ekLkFblNxLcSTSn6d+qksWB3efAWBTbxEdd1pBjjz3WNy+cOI28icyCkAtbdu6555Ks3U9nyLMCjz/taU+T9YvIHAP7+l2Y0Lrc987N9fP5l0zdGvfRj360N7qs35jO3CNKrcC9lLlLL72U1eqVr3zlJZdcQtm9+OKLfRq///u/7w50j7195lbm/iJ6WAh8pLt/juOxj30stkg19J3f+Z36dfrpp4t4KJLSlHqwVQXZczdvUMSlGBYSyvI1d25XhcWBnebApMVFFHHBR4nABNXQESC3ZskJxlJPHW+74yTrF7zgBccddxyjoqhnc9F9d7rzs9UvaeknPvEJq7ZYrWK6MdTfcccdzH2//Mu/TKiPQPOpT31KrHXWP/ZVmvpHPvKRKGT0WvqHVePQQw/FMXKSqOyWMEyOymuBw0Z2bC+yn2ioJ1KoOGcKWAJw2L82QQhYY3thZaQICrqHBvqiGO999Fny1kMe8hANsamIJquY3GsSto5uUl5//fXf/d3f/dSnPpV9WFS7ZzzjGSaDyPA0KlbZe9/73roT02gufT/11FOpp6bB/e9//+uuu65Pd6t+QW0xEFXODpxwwgm/8Ru/kRd1hFyIMzr16le/Ggce/vCHf/nLX5aoRqPuExxti6SwFvVIv9SDeC1On2zm05/+tKE54IADaIHS4eAzxVTCm9R80003PfShD5V2Vvx82eI9kjXHfQP6uMc9TnMosdb7000GKj4lN9xwg9/GhaPoJz/5yTe84Q1URl0jzir5oQ99CE+e/exnS10fHRSp9HUsIhthgn4xNU8zFb/xjW/4Ho2X0TfrfIa956rKhfhFvLkkGY/R0TtSRYugTCB7+ctfntiFP/MzP2O+Za96o6bJIuxwHEpkHBBOuNXjFbmCTHW2cZQYZbX1lJilbHIoIeJgdSi55ppr0pAp7c/9999fsEhseetb34o/CX7sM1GAbERPMPkNDYZLc3DXXXdN6aAq47CxIyKY/L5KGYTZEZsxCQdOOukkw2pyYsKRRx6J1FBF9jK+eiqbsD/NW0N/yCGHnHfeeT402QvJH0xxbVlTlXRHPg2DaESUbFVNM5RVpjgwZw7EByeXxd3q0P4E1RQyX+m1115L3ZRv28rlqe/TsmX9oqyT6I855hg3mex9GG9605vY4ZnOTHFrYl/5MvwGANbleVGiKosCOAEDfZ2WocEJN8tZK3P77bfDP2sN/siwQjaC9BKTq+EDH/iAZagfYGwEpfExtJZR73rNGPOFgrfCDnokWYt0bf2Oo+XbkBmjlDzrrLPgHx2xqYkytr3qVa8C9oOqaJMePfrRjyapZLEzE/pAs8zIZkLesrqBKwt364JuwpI8BVf8KNvOsTIodI7iM5/5jKcEIxDoZr9l+yM/8iM4kNosvsA1Mw2pfT2ePv/5z7fOTjOyUCH54/tewHUWcq9bwRNvOJxxjkOnrOawpx+XBz3oQVdffTVrMKho97/ru76LYZYsSzLzLUhq14hXRjR+cpsmoPiAeIbiNjRjuwA/fHo+z54GKEsqauVVrhew2Xi1YtAItikjwS6kbIosJuugeSh/7miLZpqPOhpzLgTrV+YhADbne0pgeZsDChAiTXiTs6eklWHdAc/+NOfNq1YPmZid3+u2IYgIff2KEXBDJxnUTCCyjHKMfLnffvvlRUNgFSIOkjgJoF40t8lMfbWewvt0yuQ01j6u0GBwjabCzfaGsVyFzdKQQbKB7n1thF0fyDQzsMoUB7bDAd+CL2tQwySN3OJlbZXew5QNKnjZv/QMgq2vy1LYRFRfjtTaVmrf0mRJv5/9q/sbK2KN8KNXOt2hs1oOIDdt0jJqrYcxVrd0li5y1FFHWRGIQdhLp6EWY7WF6f3vf7/1GlzdfffdpISrrrqKokO0iiZnreHmYC2Wcs267DcIpLwOtjDsHYITwgSIfcc73kEBlb7F0kxu8ErGyyIL/qka9GnAxsPRW9TfwdlEJXMwoRlg/VAVTKJowjw9pSVHUVazJZ4MoYAl1VaoahlRb7nllvRdp9BgPdVrT/0Gh9DOhYEtpxz9Bj6RHelMbB4UuxtvvDEUUknpPV/84hcJizakMRmXcqpSl6NLbXqZyYZMc4RUijWeYz56KHCasHB76l9mADXTI63O0A7DwYBW7rzzTj/wKiu+AqBCowbCHeojPmCaRywKRtAdf7Iey3ena8Yd/Z76gui1RkQrfk/2OEMzbCNSG3HitcHVZVz96le/mk/SFUd0cxI9VFtQpF/Ijl+LGchaQJ5TCRsySchaYA6MPXzB/cU4mm8Iw6LXve517EkYpTlVAXgCaCgxduR7thx96Tmvy8b0oIMOIrbimDJGyrdA9DFdM6MQwChC8lOYgcpcZdJQCRsV95p8Phq1nuD2pvoueYuubyoyWui+ITN/cLtNXWNqgdJ31TKW+K1Tfre99lAVhvgXJ617OGBu+FT9iYwYwEwJv3GPYK3jBtQgvv71r/ctbzr9qkBxYCc4MAnIfWk+BlJqf+oMEZQ5wqylx+RuC4GvUWFTOae8doLWVanTcmn5tgpY+KxEVCXffNuDoE+QhA4//HArrBXTggLaMc26zM4M+aAXSyORn6OZVcZaHE2OHgN6Ye273vUuKrXVnO7LxDrYfIX62tUKoyI9lVLrXzqfSgBhLPxq8xYYJqURyCyjaEZhf4xwI26TJNjh2U69i3LCXOr0rh6xG/OocHlkEYTinL9UTkzREdhgsSbtecoa+ZKXvMTsssTHjTnThjKHWvUDDDef+MQnwlSLeyjUfYs1voEESz8uEVPodnY33Z8mkJG+wxLwQJzSFkUKopvMlmbEY4uxoC9ao6lofhgXNZPGKGSQiYn1xS9+MXpAI5yg8kZ1MxAsK0akScq6Rin0saiQMk2LVXly55BmoL5BJD34ylS+6V41QVmjLEDowVVMy6QafGuKmV2AEw9Zd/SLXMUQxecOc7DdphirNRs7CclQjv1UsRc26yyRBYtIlqYxS3t0dCOup4wNisFmfBilRJctEXZJSBU4hsMUbhOAqNfkTj+IGupRmG0AJ5M0yIs+H7PdiJDYzGcTbNOZ6cMx/YhKBlT3TVGzWoVN0MF8fDYQvinyB9T3Q7UDKbxNe+WRh42Y5l99b0cKVetL9K6BIKIRanHS+K6rV9CqLLx7mc7NHXA2mugboXX7cvYsWy1tTHZ2fGkJ1g6ffeChMcQiZeXyJy3BYhELpMU9R5hoCQyVVFJGSEsw8R96GQWLMnQB/OycjPC8hAChVgbu0FZVKxQUobOmRc3RfY1XS0Trd/KtgRkFyByRwKY5/wOrUjPs6c/qoBxaW900nR4R7JhzrYP+zKEsN21DtpNsmKM2veu7QPUMu9Rv9Qdd3nIHfkSZjgpl4aaC4xKxJiYBQDVlRl216TJKwh+mi5ARDdWFY3maThkaC3coccEew5f8sw1j/GheCI2NKsznk8HNiBs11nWbLwDAPiu0Y2KZ7IMd2y9LhhdJLfgPV8KlwZCZGzxXNJQDWv5VAA1hPpkjQ2Po8XkjyxliEBxnl0wP0Ogy3Cgh7rAhnXLKKYBZW5G3Rj92LA0lLpMcA01mZDSCs1CELf5t3GOlYN4gnxlQ6IiGwecz2pY7qdy3Bstbo306QQPEQOIL8lnpUQyHG034WGWwC9lq03GzupGhvwwJmGCSMDnYT2QxwhA2/LG01c3iwE5zYHMg32kK1qx+4E19sQxZVnzt1ILoZL18Q9Ugy+u4FRYOxWxo3bTsWj7sQ1uvLe6g2mWtBxtty5YFj58XwynVymYJ3d3K3vMQ4Fl0wP+XvvSlNEpbpeWrwf3eSA7Ls2k6fbAXFXolDsbZU+g1Huump4yuWldAv9CJWqqbV7K7j6p20Ij5lFJrxfe01dM2LEgbXonNuam56E89eHLEEUfgRhhlVWWBwMNNp1OADW24l8IIDhktUpACTbbQnPtUyRiWXX5QiN2f3pSKsLbbSqgyvkAFZyj3xD7SWFw3NiIeztkBofwRWeia5hVojAF/8EobnRYyJRYIQ6PjTD7x/GKbodnr41gkS9cIlBkpr9jd4Oug4zhDnTUnVUX3RQmpYrQSjWKpzZSQB5tRjgnIGFUAFDYBMnbmqrZsQ6CNHs9sA0Q3HdN8Psj29ZlsKc9X15/tXcONz4hnisB2Sv+EHUAkmWnmXqbfaNgcBhgiNQ8Sg8higRsqZ2PfSO2ZpgtVpjgwMwcKyGdm3fgX4TcTn9XK4ktaZ/sFtP5sCMpoySYMwzjssDPTrWE5HYj2yQmLrkBVYpJlR2XateiwbVILrNp0MnuHNmVhIVRQXmGwN1DmqHr0GK1T6xlv/QvsGXIplNyj+nM7U8bSGvQzmDraecuiJuhDtk4t+pZjwEPdhFg0FSu1tQ9tAAlP7K9brO3X0lkBG1lnrIFnQGHECLoU8ACuKsQiW9HspdZl/1I37SCwx+J876U/oFa1cAX8W4ht51Prrb9eR/zAhAuitKUhyMe/2p4u0cQrQAKT9Xey/cnTGG/t2toKMShYwZBgAqhB5QZR3xWIEmw72UiZIQOCVQIqYJtpQCaDGTZu/TmqU46OTmCS7khSseFNxDTfDA0CtDt6KA5VTA6sKVhk7Ozv0DiZ4rlTwGasY6XXES+qzSNOGP5sLg6NctvJF1xwAd80TMY3erx9CvLHAOq8qxdOfBBtyX+YoPsRpDDKVEFGYHXyh0p7JtuhUO9s4ZuBtsl7JwAA7ymyGRK465qBbDC9/8do/dodOy0Noj0RM4GkogBe+WZj8Zo8H+a81lR1xYHGgabr+DHwWs8jXxqwUZ6Lk9WklfdlMiC7b6+xr4T0bWm2Jo51iO1L7v7vOXqt+24thZAJrljLWl+sDlaudijWMm1ZJNdbOyzH1ikrqacQiJLHR4ad2SoAeNRAXbCh2E9OcMUvPd6w9CHblv1T+o2N21H3Xb481NPeZ5iRkxbVnNJhmHZptFGpLaNoYANnrx4MijURdnoUr3XO8KQHblNZ4yglFKYTTzwxb1ngeDvHGpkLCoLJPLWy00RBV3tKhaJ1mTCeWmSdIvOIlTJ/mo3WfcDvTyxFMDJo0pZ4ing2BdplyuWUBHOImyBWycgBg8vmKJO+EWmu0TpoGz4+aKh13+5yDmjk0jq5qm/OtxBnN2s3hwCPwHwGgvEWWtD5/CZ7tTiJiKfgQilt9VVRJZ3CwjqF43TdONYIyImsxljqsknlRa1Ql1OMBMMfAvHQxZ9GVtOYRpjzp3axupmas92jQmciRllkorJzxLc/l88Z9iuJEtJGO5RhBFFipplLZn6qouvzn3ez93t3Ig4we0qm8ZbKccOf5LkMZc5xaNpmfBM3dceEMVh4EruREwSA39b76LTXFxJwCCa1sGyhk1dgvNbJ02Zv65FqVW7aAPiMFKHZ55ZZRPLwLr+5HAkhxkFru/g5XRJm9oOIQq4JzAmjzKw7xYH5cmCs1/o92CTbjKQhmaxUnH6OIiIBHEx6X0I7/mQ9BUU+AyuUFaS9YsWHcNYahdsmWV/hAn9bC8DJAANmpscaCol98JaMtmZZg6x6NGZMo8lRKbhrcW21cOAtdtEXkeF4kvBw1Ajg6qmtTehiszarpwKWWlqUzUILaMQCi7JXLN/a9S+sYrfnUdwf91LMeFl0uLApQ9gCtBDRumzpac441iPkWfWo+zQwPIHlmrM+9vCvNuoXDUZH9EJJxSgxPNFQ7k97BBCX65NLYcuZnVRDr3JrImc6x6lZR6NO6QX6qU0gB21QRO84xKHWU9OJmGhG5ZC3P4kj2MJooS2w6hGxgKCDM+YbOrHOvyYk2uiLWaZ1mU1YGVsPYy3tekSdMl6kAY5U6OTKhMnZFDdAVEBEYkWznGOUC1CBMZMH2/HTtFeerMACYQUHGLiEwwQCfwL+OAfopgKI5GJGPjANCAF2r6GLp/5koFZbjCuiIvp8OBZQXvtpCXRNKt3BOj/4DxrNQLiGEqwUN0A4JrO9I9IjnyG0Y6sAUSYJTmoCYoFeIri3cAmrByfrVKU5901shdWjX3DalZBHjRI8tDHMIU5fTAaU5FgdwohTOGNT331Mw0wTNYfNDBDC0OAkuh4ZUwzH2xifEKNabDQ0PgfD6l37DlYSf+odTDUZmEPc753LCC56TTggRhhK3xdk9a3phRloZuIw8hjbkcTVlAxqXpnbGuUxkPmmGFkZSZowT1iwzDezFxvtvyhmeqsEM8kl6CTaGkT3fRG+9F5wmXlVqReLA5M5YHVlDRoEVP3/dx99sWM18u1IE2NVou1UuM1356iRb5OSen2HOGDNdUgsx9XGNkHFBwkkD5C5QzTMXK39ckbs3sAzc1ULfBGUwjkyyhxjNmzaHSJOrFkMJGxXEJ2Y6E/iwhIO9KbdqQLFgY04sOVz5NuXjKbxgt5+K1VDcSAcIDhyPmAdZeHv472M8sdHMmWwsF3jLduGzW/WnWYf3rWmd6ihwbHVHWol1dK82WlYreyR06QZQriG0J5jItrRpqvy4sDCOVDObgsfgiJgbhxg+KUL2iGasKcTCN/0XPLcaJq6IpsgHClsE0wWQaaub2EFw+GcpdxNIhgzbBlkp8blB/cdlv9SJ3ZzFKqthXBgmMbUIVfxOhZCyi40ah/RRuw205juAp3VxM5xwG4oW2sOi4/6bO9cu3unZoq4XXP/4vBuxkghN+Q0eQJVJXjcNCcS987QVE/XgAN8bhifNkljut5L26aHWNZgmKsLkznAG5EBluvTek/1BU6DRArierabKK6/wBtya5eZnd+ZH4XiC5wG1fQOcWAsiu2jkTsTxWOWRr6Wyad95/ZQnSdpcT92iNFVbXGgOFAcKA4UB+bOAQcraOQ8OgcYvQ+Q87JxFEdChWliVs+dxJ2uMCdhnKipUyI7zeqqvzhQHCgOFAfmzgEnMJ155kkzSHA13CN3bqeFsJg7EQuv0P6oQ6u1R77wgSgCigPFgeJAcWAGDohUISLFJnvkG6VCEY9JFJH+xI6TmmI8JcNmLtkIkgopf4qoIFSZ4Bgz0LpDr2zUux1qrq+WJ46IEyJHimopzEUuYa02zc+4Vdo0JHammCdJudhfhk+yZ2M05ckr4V8k9nAsaks08CgU1E8r03uGO/ONOdryiiAbwniZXRsNlogi8pwmCF0jjKHJAXHJN2ceYi+iwRnuCbFdt8SHKlwcKA4UB+bOgbHx/Kc6fmZ9FNcJKnMKDVkM1EBIXCdO4LkjbKdoSvAj2SYAvGCKQjslHXJdcEI4HpHOBD9P8mwpIEWjxEbINEf+aEgiB4FgR0Uowyf+lHGcJksYmBSFVMgzoTa2RB4pQWw1QDs9ppIusUVbSd3GdsTteSNnNDYlBQZbRODfqTNR0GfOWoFaMT6xbi0dRLY0glW4OFAcWC0OTAJy6pGowvBGjCR4zAfVZc0VmFPkCmdek8WBTknPE4JD9ERBHCmCIjqJ7G1NtKb3WTpWizVzp9YmvXiczBiEHkrzL/zCL8iAIpi5BBhBVgEjaYQeuUQgb3GeRSdt+ARmCE/BfqyOEulSW1RnrYiA4aLUCvkpxwZTTPrikaicdllyshZYQlyte3eQQi3lEwCVQKAeTXCyCDabFUhtth3TQDAvBpi8xVVYvHGBfs0WEU/NHOWRoQaxOfuMpSaM+4Jfah1VCQAuAqjwmcJk6qYCTXaE3xpVlfPBxx13nMBhekHvV4MgMFoPtQhAGO61nJL+NCF709FGfEsNpj2ysQXBJIZS0Of+IVSFxYHiwHw5MAnIAYP8RWJWWzqty/zloLLVTaRiabiEVhYRWhnpIp75zGfyIHNHFGIKnyAM4EoUTHE5prevzrdjS1hbvO16i7dYoZgGZqAF8ejoo48WQVqsbFGpRW9mKwZjrBoiXbSEjG4yhJCcwDw5QHkHDVxOFtLvk98JgjKZSPXBJ8JlBJPqqvFEGZSIRM37UQEtapr3wECHds4BtXTlNCHGOERXiZQS4g0A4FToRXIbbTh/Uo6FuZZoB/QyDAjK4akmQqq3Qgk5g/AntLUo5XTxSIrMAGoT3UwYQnOPuwZITrUmnkZ1CroL10WqEKPeHa/rgtYJlEkV6l01kJbSHcnidJM1PvVg0YBv9jvCNwPkIveIKyIyOWoJXswnTZhYwklVJBUHigPFgUlAbmGV5cL6KAWCg6EWWUswlYUSaQ21QEcjByGsr7IQJgskLLf0AyeJuabci91Tw9DHmYLf8FhESYyVQ0zeCKotzIYlsqfIM5HkWvTOtkNBR3QGWvwyWI7PcB1wumAPGwmYhF7kLfkzxKekTLtkQiMW0FmzqRy4gl6w1itUfGZwGUfIZ4MNexNA2Go0UGc1YXtFPii0+RflLYU26KXCEuYyjipP+m1zgyhgX4a2TbBQRgIPSXqciTBzzB9qt/r1F8zblInxxlvMCSpRoUdmWqrVnOlERNBBBVDlnKT5qVqVSzkD6QPkyFag5f9Ggz+TWNrFkwPf5EAL34499tjLLruMgGIIEIDPcowSQYyLp0lOQHTY5SBle+qLqM4WB4oD2+TAJCAHGJQSi2BsvFqy/lqLZbXKb2sftKAPQXd/JjSj5E5J4dUbUbdJ5Vq+jj/stzRO8UlYlUlCsosmfxSRCEThJ0ySdA4mgU/YCZXJTMpLHgXSmJRbXG6qbbJguWiQ1N9XvOIVQTV5I9QQszaADDwTCGR/kuRKGVD3vOc9D4wxU/fapxa5R9rXVwYxWmR8pgprHQ0M/gDe3CAiqBOUjg6T2sgQnCc8Uj8iSSps3Yz5EJo4ggaPAHnSnLQaoLUKCYWKAVSiBku7Rgk9Hpl4YNsd3UxqTlIIg8EgNfvYaUMwwjcyU55S6Bvf/Bn++DcWdZo9KUqurQrzuZbfYHWqOLAeHJjK2W1LXa0lbyN2EYlsSZByQJrLn/aSZfiQSLHFwGKFhkyCUlHH6bXUU3sWNilsVVCdoT6bh1SPLbgPnLN/Qd1kCgarABvOiXjjLUaUIBM4BL3Uyha6UkkCAe8wJUMMQLUnwgrdjCiQmyatHvCZeohrcqfCb4RJyqlCAgGIdQe+bhRFq82HfmLY7VZtanZhBQLU3yu+yCY1kmBoydCUuECqQFKSArnjlYbBCovnhYYpVecB38J//LH1Tryg/eOM+okLW5r8Vbg4UBwoDuw+B+YP5FOupLvf1YW3SF8Et2zIYAxUuGjAUByOghAb4WDJMT9OgrRelmfqLGbCbNoqRRaegXPKerRwv/fbbz8qNRUTADMIw8V4JCTQdPP6zq5576xA3VSYDyM8CyVwkYXcTnmfKmq0HoKCemjh7N4EDi/S7IkFNq03Ym+bD+1HfAV6CltDg8lD4WZdZ/emwXuFlSKtxPajU62PG9UwShX+h29iH4VvyQWeaolBtHA7FPwNSS0yYzIM8CgsV4+Ffz5FQHGgOLARB+YP5MXrjTjAIAz86NP0Y2q0C1RkK5enm71e+7WcuViwhazxCKgEqOxlsGnHlZ3joa1l1maqqh9UfJH41Mlvru0xkwAUaO7WOX0gsWP0eNXS+CGfyrUSSlz3uc99+jwiMS/39aCfMEHxVY/CfgDy7EzLZD/9uMNLveB43yzhKORVx+TQa+3oVK2OO85OyEAq58q0Esq5y7VzdHYN1MBorwYvaqIdl2CuUD4iArEAeIdvzCH4BqcViN9AZCAiDv4QsBxnd7SdnYAjQp1Jm358q2RxoDiwyxwoIN89hgdjxroOACF6re1wyqJi9svFjWku7rTk/fffn6bOGQ3iwlFQbb/cDyZuCiWnayeyoCy88RZk5WLW/LSZ0KEaM7V/4RnUZAknK3D4es973pP+O4dmK33g3E7lpZtyiEsZP7zOvp08m/ztCRacusFhtvanvFIJvBQ3JqfCUAI1kT0AcnKP7XNe6MiAuPgQPI6LpT/xJJv6Dkm61NBc20gYEYPcJ8fEeA74CRBj+WZcvI4eeazPOusshbFa9gEsJQPVKcopB7eKFQeKA7vPgamA3OppOWsub6HSwse5t5kcR8sMCux+35atRfoijo0Nk0LnBq5C6PDYYrXmWU0jBM8QKL1g4+WbbX8davrznve8J4yhkjofxUTMm4y9HT4Fy40FmGQtf/azn83DnF3aGTY6NJjkMQ757CUfdthhsBBiaU6jnN7vfe97w+NmrI6d2R274JpQ5vbbb3fyjVqcMk4k0laBH2GiZ3VmQpwf6dx63U6+mUJElpgEOAHQjzmpOazIjZxwEMoVRmSbV9zplde15tbnkZI27/kG6iP6na/DMZQk/jBl3T6FOrnrP//5z4fcdtbDdhsTyO759rCHPSx8Qydq4+n2rne9i11EzYcccggCmPQZBpZtOhU9xYHiQHEgHLiH40CNF1Q365qVbpQ7NBIrKWtqc8uy3llMKZH8jFI+JlbqS8oo4DeP34TpWIYrJlwoshBiAAYUxJBRVIA0XKso0y7YCTy4kfNW41wdnzXYSfd1iMDmbk52GQsqqbAnUJmt3oa3OwQCVmv10+C9C5/U9qQnPQkMB32NozGKCxvtH3Cyn6sHYSeddBKc63ViBFO7iRQczZTh4A102wTww+41azyZoO+RGhirzRatIJXqz+yfAl65173uxTagv+DWu4Fe1NK80YPmHB5LTAJV6Z1iDtbnrdjAPeIAaK56CtTRaTed/4FeUNwV84PQgJkKgHOzVK/xJ86DJmTPN3fwjZsbshXTFiOHAmj2Cgs8Unu2LGTyVKPFgeJAcQAHaFY5FttzY5g0RYQNx3PXlV9Ay5mlJUyaAlAnQwXbspgnvN5yYGzT8puO4PZrwMlLL72U/MFFbhea27SJaQpsv9fTtFJligPFgeLADnGAKzRj6iZJU3ao7ap2Mgc2QnHAQzX8+Mc/7pSaA/rN22v7CuJ2aiAPCThz3XXX0ZUn+KvvIzB+25Vs4dd2er1w4ouA4kBxoDgwlgPDPfL1XulWsXe2yXmzO6vtWFQ7eL3Y2cyV3ea6k3IM0U22WCxJ1XpxoDhQHNgLHBiLYkPTuriqPKfW8tRszlPZNuYytkKH3W0AZ+S4a02fT2xHJzR6WkSatZwqO8q9qrw4UBwoDszGAS5ErKE2WAem9X2AnCM0S6lt8jU+NQuEVgjFZxvseqs4UBwoDhQH1o8DHHIdEnbYZ4DRUx0/Wz92VI+KA6u4zzL9qC1V73LWYHriq2RxoDiwJQ5MZVpvplRnfJs6m2NIOfjrR1+m2V1DSv/Wloibb2FHjxyhXpRpvTdHD/o1d/5kaEZN8WPHcQKTB0M83+FYYG3pF697R/W2ypPdJ7tROP3eig46o0J4d04vxw7bd7r79CeprnYdpJzv3pCuJWZfYvntfteqxeLALnNgI9P6n37h7eK7JKVjf6f9FmFD1kt2+XZHxFD5pMX2anecuJXwVCQQ6aVFF3Fc2/lmObjGVriQmy355u63Dq3PPfdcJ9+c18rljLLoLuJ4z5cYixr+i9sKqAY1Gz4H2N773vcmivuml9NlfCbEY9+05GoVEFlWoBhR1pEt+IyD5lKzT8mT3e8pwgSUdYgfNk/ZujOmr33ta5N9VYA/AXPEAJ7y3bkXMxuPP/54EYXnXrPAvdgi2j9HjblXXhUWB5aTA46fCXExoG1oWvd4VMSACldccYV8WS1El2BhIpDIP9ECjgoRKp6MyCEKi6npbDHAEAjM6vPSl760T465yyJM39zY3u0OPZqGiJQSEXiuueYaJ/pFPBUVlaBDGJpSU5mGflVxdOflPtov653FXWzXNo4T+q4tIy6/y6BwT8M09OwOe7fUCjXORI2bnogxRkTGs6UNwiq+jWMCQsQnWN40PTVwTi0mBbugs1/72tdaUrtpXp9vGQSIUZicAlPSPyUBghWKpyskYgtHOOWL8yVjykarWHFgLhwYO3snBYShrNDCQY6QlpQ5ETGJ9qTgK6+8UhqJxAY/8cQThcAE4XJ+KCOwJfx+8YtfTCewjgAVeg8t3zFo4bHm0o3tVLLAgDDgUKQdQUDPOeccAe/SC3ArohmtF8cIWRdccIGcZkqypoqGRjYC/JdccomlXKC3RDgnDXzyk5+0hIn+RjQTezVCEhxSXrRUixrTCCwXLk1JcKUqjSrDP0LYUdKDkKsAjJJ08cUXG1BNC9/m9YHQY8TVIAqsDKcCpYk9LoishpwgJw2gHBlesUw76a7yF7zgBakBn8844wxx35hkEgu2r/nss89O4nP2CUneEnSWliw7O1Ow8Ol6zdiDFajStPThOEBvRnarx/k30qSGUgnaPIJ2QqyL0J5ccO4L4W4zJZsXOk4Rx5nXv/71ws0K5SZQHeYrI/a7UHTARsx5k5m4oxIH99WgWqSaw9jIXxRDWLcEbXW4I0ip79ho8vuT/OqtvrPsIkLmgVUEGFZviUCXoPf6xazFw9TrJgaCCb7C2+V1gfxk0NG0DwdWsY5ghZnTNpu9i1ckEuzio2oKectwn3/++WREFYp2x5BGqlZMT4XfV5vgfYLjto9RlHtP5dYTAdD5RjK3SqA+nih56623kuTC+aTFUy0NW51YKoieXPWiBesLfuIz8tCMBvH7MFnIPF0zgc0QHCOzGlAzxwQwUiaqQH4WiiTANdyWFE8lqtGWWfGMZzzjrW99q3cF2jv11FMdNlGM/s3Zx7vXX3+9F81AfTT6TA5YJK+uUaCmH3HEEQ3dTUWsNlXUaYAy3yxuFiVxfJXHeR8IC9l21pZ6tziwOxwYGxBmUohWegDA9lGBZ+k6fHUiWaoF/AjjanHxxVpGQYuPQRmIbhmyHFgxfcN6ZdGhkavEp+WD351+TmhlgSFaLXaWZquJtczCFCLxzfolGReQuOiii5jZrYBOZgtcCh4smvhs+Rb6++CDD05iEpsXsArzIYclVQ3WU2uQYtZc8V/xGcNdWjQ6oBFGGiOA4XVilpuCm0JxeGw507SavWuISWYtAqvCwskxuoBwJCV4u4VS8FRzwEJvDc0hclUBA+u+sKmBAcsiLdD2ivKAE7XpMrCHMVZqE8nia0UmRtioRjOMFLouUwjEWrVVElS28qJBYSqpF9EMVmGMnSA0ZEKSGGzlyJNm6QcGXvHijTfe6BW/XSjRndSmUX/6AXS1DiT8q+9QXM1mLHkIY2G21933L/AzUmQpfREiHjBAEdzWOpECgBlExRDsfgNjHFN5BkKniC8ADM2J+WqIjSC2o8RWiyHzxRkORJJpDDqw1F99N7hmDmHOowC5wuRp84QIon7t+hepHlHHdQSpgtoaLPyXNsZcQgMWqcpbSqITAaaTFxPX1iQhcGgxI6iPJoZ+6aZ/TQyjwzKHdarSZbI7ThplcYGAouHzuoHzp1YIDQbIHcTAVHSSLPVFDSA/k5aUQxDxCmLsbuiyLzTDLaEO8QKjjKwusFr5NLxiUNSgWkwmeKH/oIMO0gWquS8CYTpiQqJN990x34gdSTmIfiKaSog+mkMzMnTEFj5W+Ci2lPtn4atZEbA3OTA2ROtwj5xU24zvlt22s+VrsVj7MHy6vgdlrKQSclhQ4EReEdP7qquuivaTyxdLDKcu+FTazQX+sLg0aneZDCsUnYlJwzrbmg6cMHsAJ8quBTeP8NkhQKstvlFBLGFQRA0u2rb4MO7Td9mE6St5hbnekqQG/LfrYeVtQ2l9hKPKw4knPvGJxojqQ/+mncO8vE4zozdbc9t2Y/baLX+AKmUAsHqMuDlAHKHoZF+ZWmN6uJliINz6C+n9BjBWUujltwrBHpygoqWkqizuMBIEynoCdVCVR1ZqC2tIyp/sGRplHILoKmnOHJZg91kmwA+9UCV2ZL2CD1RhhVOhH+znzAN+k1qoxUxNfou9z7ZBvfODfmkOY3heAWZg2yfgkQDsaoav7pMb5IBnkECVjwIwJN0LrDWIt912W3yvXIBcnQApf0pqR3whauRPu9cJcJs/SXIEI2CvNnYCsJ2pAuro1nALOKVmDZlLhi873/7EQ7NFv1KVp3a4lPebTkyGoOvnkax60M5A6JQatN6cAwh5ugnq3CGXa9RQ5i2XMbJVz26REXHhg+D/SUBHydbKmWeemUdsOfoS7wqzkWdGaGPtMOKtWhODpo4t+oUwYK82xTRhMrMiZDZCcfKN+YYGk9lAmJnuW9HIjuQzsYl8RwTltA7dL7zwQlSZfqAa9ue+ae8DBPnGJaaR3LdMmXj4/P/1tf4vDiwvBwimm++R9zIOXdz3E0VKt/ywONInknSEjO+mGhO63Gfja0zqqlQC7y1VvhmrUtNB96YMlV5TIKAdPRjy+ReQwForO9DFausdFLH2SS0KlWE5NYUaZIUCFVYrHLZ0AmNrHH5S1i3fFG7gjc9qZrDVhEExCpZLdpG0SzVRA0ACdfQ5dVpVCTRwAnx6FzHuWBYtfC2LeYbbwt0M2vRUmqvFl/KNZis+VQmY0avQQzNLc5ZdVJEA/IbErLUWU7+pWVZMPZWTLSXBhkeaRjzCVAKS88gKTo0zc0CpP2mi+us0hEkMn2Q9aZUkci2+qcQrKokl32/LPXjTKeodw7L5mdwtVFKsyAR2aVphmjHeAoaYLlw67hHFFH4oD9vopu7rglFLGjfo5U/jiI1wwiAyPjXrN+Kjr8MYg44zmB+DfProEWt//mTxcgeXjBR5JePuvn8ZirPHnAsHfJLkklhEVCgpDgqRGocGBBvHJKRRJ7kKZvvtqVY8UiDmEFWBPcQjD5KZEtgV93hsbPmQvMsOweCBS4ZAeReJR5fdjKRiKBnDlUSJVkzgbPpoS6c0aoJ5Vw0mc2rAEKuEP+PWnvTwflhDKM1k1px/wXaLDNq860/SDyz3w9yLZIONuETQtKXCtGMzyLvIULNXiDtpjhyGYDRo1AQmBLNqEKNjWs+Mras4sIocmP0ceVarAHy7/JmtqWTUYCu2FssFuYqsmTvNgJYugi30FeoFJ3ZLjKWEtISZ8Ixyad2nUFIaAE/yc1tM2dUpatYdK7VKsthBHZvE1GvgAbGIBRbunMZhVvVW895Sj2XOipZVXhnvWmQt5cjwrn9trFo6G8ZkZP0JQdtNIGQNtRxbXm2gWHzRT7YAzxbWhl5AyMqYLdX+yr6G+/2R4uywAgk3EdwS07kJrfvCeuRPwGP5HlSuEgt6KkFtNnGs4/RO+jH/bfooPRi1G3m04UlQIY4IudxkC9Ff9/0GRXEZw8bIGdoC/IwcFHoKtIaYqfC5uS4aEUMGI2WJZTOgj4KQfvNCVeSJNAfwctKMmUTNTfZ1k0hnIBphSE1P250MDagb6zWpleZtqhWUpxW9oK1mDiDP9jYRLSzCf2PRN6H7XtG0eau8y66BDrbUt2rr+9IiEjZmgnYTAJEmeWrQqI4YaCSh3A9meeW9a7ijduR1W3W64HXlGc/7WMVe9AWxDhKArDaEHpht/dEc2CZDMHUwFWjOAJE5NKE2ZkLSGAJ4ALCj+L5ID4PVbO4rQFVYHNghDswO5GMJ8kFaNWh7Vi7icL6T+jzCKyuLRR9bstnsX2gdxzcrFB2Crc86a22l7PIibMsipc2KiauQHrQwLHsFRDFL0iRYNdVGLwdgFlMrnSEAOc3b3EpnFbb89RBlmeZnpE7vuvxge2fPj+dRLi/29RhHi6nF3eLIeGDR9xZhwgooXXp7CxbqxWjoVnODSIGqHmxU5cXm0twe6YUatN4mj9/uqFzhWFxbiyqJHBANO5UQL2hmph+VHavpYezAg7iGrQZvRWzqyXYzfHN/dA6nLeziokgkIijQTTXBREzkUrOeEmRJPBz3CFuYbOeYjp4uT7jiJdrOg2gluyrtlbCrP02QIcacXkia3ArsJ94xvLc5gEt0VrsGcDRs7HutZsIEe0Nmby6yETmAlW7TTsXyAfWZUloNflDK7T6QWjLWGTs/ktK3H2JUjc6TFMAKbom8/1iYwL+4Vy5YThpg48m31ua5vXz7FIwZTF/EUHPeXo/tlbiDTGZaPS0OLCcH5g/kwQOWK5Yuhlndnn5xWU4ezYsqfLA2ta2HvloLooUVNtPtmPhodQGDrGvAHn7bCATwngJsaiKgYi9lHWXwsPB5pcGbNZ3mGjukR/QSikicg/xp1WMQBntWtwZsCKNs9WiBWusa/cm7rR7V0mMgDZ2YwRYGEDuo8vEEzsV3Ke7HoZ840tAacuh+fCw81TpzLizUu9ETcUrqXbMPa5oYwRqh+wzLrRIFki4dl8IulPtBP2PtV4MNCLyNOTodaaQ2q5LyUUCZGaK8KobJrAv41uvQ/bvBG3R6ne80bthqVUPO/rnPymL7QD3x51cbVkyWa/EB2QizbRG+GU3jTtRrTROholKngNaNNZ4PDmJN+O60Eu88NDf08qc5sNHRRChuY0WPMh9c2UfzFgImtJX+hsMuRqkY0lODFkePxrkfNuZdfc/2jaEfS54asl1CtOV4wZGNTAO5jZ0JkK360KCY8Y2JwvTzRZDAzA3SngmTyVZXcWDlODBPIKdy+Wx4GNmdpe35SLLZNhDtV45Hu0AwqLAQW7sDb+CcomOFBVfueMqbjMEQKMYjOihrpQNOViU2ZEDiT/Bv3QeNlD96Bs5DEXZFb1FNorNa7/wp7L7dREb+QAVHJys79brHcquhfVAqprYs2Q4UWcrtvke5V4NlnZccC0EPdbal2UiJJjpiw5WlgQeT8uaDJtCmnkgz1tAckwPwo6u5Vtg81a+kPWZWawZ8G9gECIRx9MuyS8ECLSphL22VxEZN1iGIKIONmGAz2PJtW6EFO8v6HuD3OvIQzE3PTdDoFQJK+DZqss4Q8P/im40PXoHTcDGnxfwZ65RBiUehMlRzPAddzdYyOrXw3+jYn2aTNw0U4PmFYz2HATklmGefHikA5lkFiDicIbK30gSUjaYuwpijtaIGBn/FjIgftqIN3FhgpkwbcZznuZbhI7JzXGC41p1BMMe+3dgzDI1ZYV/frKA950y8wUW2rQf09DX4bWLHT1BVZqA/DTEsH2vpwStPadgKK8lA4gfjOa8O4h0vjQi1oJrRyz6LscBSy1RAXXmyIG70+xe78NVXE8WBeXFgWiD34Q1k4dhve/XC9x8ndnZ1WpqFm4rjm6T/MdnNi+LVrQe7NorpYauVFzTd0aqXg17gxCrTtB9rtOWb3pCj236zGxsCS4+11QYhNx8AALfAm0rYeNkYPQJO2rVW2ho0WDF6u174whfajOcDrDB1lioJkyx8DTBiOqZQ5kCRFsEhaGmH4C3BWoQHaOsHRWEyh9pUa930IjtnCtC8OdaRLdzUrk1Z7dpd9khfotu1qtyxFiugJDdsVeU0vOmkfj2l3nnEOcCKH9+3VOJf94k+MNjq7Dc89gN+wFSXGkgbzlMgDK6EIS4yB7bYTM0r7uBPz7c22z2KKZupGZipzTzXO9wGG/ER868NCFvXOutbgIKEM/wEKrB8tMvIJhnkK/MFYQvIAcxENP7bcc1rzCHHcL4jGSCVez+MxNi48rnDns8V3KwgAJGHVNsY60drRSV2bcgrXtFNw0E4MKAazTwZGA+ILLhN4vR1myf6ggCnCQy0OtvcjtWnNcpqzeiNGMKi3wQv5nRUEY9AKRGEpx7uKW+iNtM63nJix0+08UezrW5bSu+y4dKKhU7ygQkfbvgiSLE6hfPmqhmLHkYOTTAQcjehaZC0fG7EERxDhmlvJ8sALW1QoNVd9Iry3eHApIAwjQJfAkuXSe9qNjQri8tn07xjqINtd7YtAX745q25/R7t7vRttJUFBoRBDG8s3LC+jzXVAmAmRIuUZdS6THOlO1KDwjfKpTU3h46iu1i/VBjdDk5AC/y3/HlRVVGq4hzkTpyD3FSmjSNuqCEbzKlhQBjlxkLpEUq87sXmjOZPledQE715MLgagpcEkSDEQNHxCNnKxKUrHlUxdep4/kSYRV9nLdBwAk+wovdxm6YSZOuChuLNHnRRj25axNGPM8jTVuMJMvwZj7DGN32MA1rcstSDw6qKJKF8jnW4IFw76h2GexrLgVHDYe1qXT3+1N84i6XLhgPB6Akzscif5oPvS8cVZlju1VZ3UsBNBTTdJnx6EeajQUmt5DtNK82xsVWiMDJQGJ3ePEEGtrSwKqm8jWwkBiTFPVC/wtts32gFe806f6ZFTEMMlpq3FpMcjlC5FtN9NUQw1R2auu05XnUkpBCWqhRDsHcxUHdUhc4w0BA0K71WDHfjVaZKDBXuZxbhG6rU5qYLGcuwQC1qYax2V4gDYwPCTAXkK9TJyaQuFsi3w0YmUGoNfTEHApfh4tuFKgeNcuhovleAnCZqQR91gJ9vW1XbsnEgQO58R+9EuWxEFj3FgYVwYCyQT2taXwjF1SgFiM8OCy2DJFtxCxm2WM6wsbNLM/naVxY2ZCeIoT9RsGiWzXl7J1qpOpeTA9RlCj2xe2DbX05qi6riwMI5UEC+8CGYRECsozZi7UTaMlySPTzWVNvP7Py2LRk5d4KDtHCWVXb1/jjcTjRUdS4hB7hfcIS0Jb+EtBVJxYEl5MDQtM4lNYkT1vIi6XPxTSi6uooDxYHiQHGgOLBaHHD4glI3CImxD5Dz6hQOQpTmsWedV6u3o9TSbgE57xgG4TLZrfpoFv3FgeJAcWCvcYBLJuckptBBzIN9gNw5FqcyWHETOmP9rpy96cNwLlsfcyig5IxlG5eipzhQHCgOLJwDDmgIUuk4zyQg54FcpvWFD1UL6LFwSoqA4kBxoDhQHFgqDow1rQ+d3UYjWKUPObLca4pUWwcxe6fidqyZsCBeh6BdYl9sFN16IazZKPzkbhKDbyKiiArpEu8lCV5DAF4JJ5lgZFx2OYdX8OfdHJpqqzhQHCgOLDkHxqLYVF7r9pUFZhLlqlfnb7rpJsGnxI9Mtx0WkqBCyC3nRiQDFslBVHAqvt9LzpddJk8IeqHQBBfDHwGnBLcSRzpYLseMECuiRfothYNgZJIy7TJ51VxxoDhQHCgOrBYHNgdyKM4iL0SDOEotrJvAhxI6JQd2UFzyZrm8RK0S50uAbhm1gZOgj14UPHK1mLJz1ArSKZ7okUceiTn4yRdBDhKuCQJqatT+BwYmIpWgV3g7iKu1c4RVzcWB4kBxoDiwohyYBOSsvnKf8PEWXVlAMTDjEjVdaGLBQARNdFMZsanFqmRI96dIopJbyGyR+NsOeglrLODzaKqDFeXXdshmnLC9QdAhAwWquSAKX4XJl19+uTjqslYIii4UqywjzmdjmpynQkYrhsNU+Z6NfBdF+Qb8TlqLIJ3w3S5JLKj1LCikLrHKW7T27VBe7xYHigPFgeLA0nJgEpDbL2f7lQNbWiH5qfSBHukEl+QETL5HH300qGCvl5ngzjvvlBFBaiyoz7ou+YQwJsqLcuzctqRVY6OLLy1TdoIw/gRyRYgL7QhgX79w1tKBMHs4L+BQgYvWzgRid5zPwY3fvrwoViXIbxsZGP7mN79ZMXHfJL22kcFLMZitKpKTjGQyr1x55ZWCSO9Ed6rO4kBxoDhQHFgSDkwCcmAsw5L8gDJuBYm5tkFl8bbuf//7JwuFJAfScMk+JE+RMhIbsBW7CX7clJOKQsmSvCS9XSAZZBoQizPJX9JfBCAiEQU9WUmSppPAJL0KeBZDTWRWOxfxH/QigJdljjccgQm3obg8ZjRyaSWTQAzPiQtsIXbik2qiruJAcaA4UBxYVw5MAnIYkJRKfYJCJt+kCUqWQ2WStLGlNI0fO3Q54ogjjj32WKDCh6siZidLI1ln1DhhLxyQcyQMDCepNt7ic0uRkrRjecTpnXM7pVzoVtq8S9qSj370o7KJJw0XOwoIX9cpW/0qDhQHigPFgZ4Dmzu7bcSvsaFLOGol+DZ1kBO72DIc1zlgf+1rX9vjfBc8nIcBqA4Y95dTZ/A7mRn7A35+t+Nn8TQMz50D9EM8aso6X4Rcp59+OrO8VphJSABJDVlXcaA4UBwoDqw9B2YH8lHWMA5zdrOVa+O2PbVHC93L5epe97rX93zP9zCPf+lLX4rC3VhEvfan02hgeKNz/D23Gd6hPk+3U0899fXfvk477bQTTzyRuxz8BuQqqdhwa//pVgeLA8WB4kA4ME8gpyYy7Z5//vm33XZbAxJhT2iQtoH3OMftQXBno1hfdtll4Lwd5Lv77rs5q0v0xIoeq/umyjRmur7whS989rOfDVftmnNIzA56nVjb4zOtul8cKA7sNQ5MC+QMwoOAMv5s++LhGvcr9l5RYuRcES7mk5/85M0330zddBSq7fXuNf72/RXghfMghpx11lmf+tSnfvZnf/YjH/nIa1/7WsfxpQTlUcjbgHM7eHaoD9Kzqzd5iJKdHXQV8piTBZz7Ao3cTrnd8UsuuYRpPZq9cYn7wl5mdfW9OFAcKA7sHQ5MBeSUPK7UnNh7bQ/qsAa3BCSAx59Mvsy8zMjsvU960pOOO+44r8CbvcPQyT2VXO6KK66w0cDz3+/DDz8cHnNBdxbAiwceeCCl3JkxAM+7jbN6y8btB8t8/ApdjvKfeeaZfjiB9tznPvfd374cUTMK97znPZ0VXJLM5TXuxYHiQHGgOLDTHBjmI3/JS17CQ22nW11U/WwG4qFWPvJF8b/aLQ4UB4oDxYHtcOBXf/VXGXcHSUym0si302q9WxwoDhQHigPFgeLAznGggHzneFs1FweKA8WB4kBxYMc5MATy9fZ5Xu/e7fhkqQaKA8WB4kBxYKEcGIti++yRS3bCCcseuVNkCyV1pxpPiJVEpqurOFAcKA4UB4oDK8QBh5M///nPi9g9wOghkIv3+aIXvWhdgZxTN7fw0dhqKzSQRWpxoDhQHCgO7E0OAHI5tEYxeh8g/4Ef+AFpTqQ1azkxMQv4CWbi+JPTyUK7tNPkycKZsKPxoFPSnabv0n15iU8Tqmx3hgR5IFzyEuHftTjzSWuB6oRgG7VvqPBP/uRPWlDV3enU3mnFvHLW0fQjZWK+g44JQLRzGXKTyUZDRtYM37mG2iBq0dQia5pIvp1+cD3KxEsoIU9dM8/hvTNtqqfFgXXigGVQAuuDDjpIbo591od+LRAXXbqtl770pYOe/97v/Z5jylKUynudUOq5pNsS9UVUMnp87gg3Ju2pNF/ydSYG+FIxEZBLq7rN42d6J9CNs+AtOps+AhjBaB0E32blS8WupSJGClehb4S0E8OfQHnttdeSIB2jj1i2Q5ev5ZprriGtSvEn3d8OtdKqNYtE6BPn+HGPe9zDHvawQXOkFr0Wv880FqRBpP3KDrzTI1L1FweWjQNf/OIXJbccHD/7U8W0XSBZpPT+Dn1a6vEXvOAFOiOfqRhkeSoA2Q033GApcR/256aSIpe1bj/60Y9OrNbluSg6v/Zrv7ZNehzjk4J9dHQFXHv729++zcrr9Y04cNFFF+E50VDcQNKYgDmEUwHydpRjxDXy6/d+7/eSUKdvKLHuZ7t8Nbop2fzo6744yC3TvFBCUs6bzLM1UW8VB4oDq8sBe+QSXg/onxQQxoopxJjY6YnpTZ0XJ1z4NjFVqClAi3LA1ieGjHWH4ZGq9P73v59KKtqoMiBTPi4ld0GVmVJomktAGNHj8UQ8VOoaKweDp5ytDBWC39mYiHBjb8JORnYoPBK5FhNi2//mN7/pXfAg6wmd8hd/8RdFSldDcoerE0r97u/+rnEiGbjPttF653XvRidTmDL6wAc+cKwTowJf//rXf/M3fxOoGHVQ1AsfmlCJoHIe+a0tWdKJcaNh3v/gD/7gK1/5irZE65MLB8Gooiy2kHNok1ZVGZSjRPg/NJsV7psAOoItYtLRNfFNwphMD0/x51vf+pZ2kYoDGOLF5Nfxoob8luReStakVBeR/jWveQ0gF/1XCFs6qxaZRging9H/oz/6I5Wbve6jFvP1tJlPGFS+/OUvI9VM1iNbIajVdDL2uoi6CqBNH3WHX4lKrr76avX0DWmFSKdfxlEBViu9U61i+oJCgrMuiNlH6lWhuedP3ND9xufMEzQYSjUoKXyvVIHnnnsun1M0/NIv/ZK3jLIP6uSTTya4vOpVr/KhIdjYocf4Eq8NIkpUKxqgiZTOuu8txVzGSHd4s9qeMDE8wmH3DcdAKjVMCECtvpg2+F9nPaZcXqpYcWAXODA2IMwkjZzl3ArCmHnooYdaGsT3ZvSz6J9zzjlu2lCnPdCKjjnmGDcBg4DqlgZY7s8bb7xRrFBlrFPLI/vMRSNv3cENizINyQre9xH4WYgt7hlUQE7QsTevDJtwQqtCTRwjW6WYsKyWTthAPLLBkRcBzHnnnQcSWuU/93M/Jw5rmyuPeMQj5CAfZa9Wbr311sc85jEpCQle/vKXGwhrfQq/7nWvE+3Vsm6vJGUAgEixCdLeX5ASxrhilXF5kWoI7VIMhJgAba2H954iwKNTTjnFfJCWXrvgEwx7/fLLL/enCxp5C/eSOp2WCcaAHwNP6yDybr/99qieXsTJxz/+8RCaMHTf+94XXhKDBgRDrDe96U1tAwh7L7zwwp6HotNrkULPYSQNQTgSKkxVlX/f8Y53ECDCN2hqqgPjwRAr6XPydQiFGxqYsggcoPSd73ynPzGBBAYI0Q96W49sQn3wgx9M2ngXEdkU8nG1zSklMe1tb3sbbkBuf8Ls9773vX22AkKhSrxOAvjEJz7B7N/qF/RXnUmCIPy+qoxdrPRqINiZBnIftPJHHXUUZoYYoy+Fj7D/7SkDm96NzorRKVd3igPFgd3hwFiNfBKQU/5gNjiXUNyCZQ0NkINqi52FzEphDXr1q1/tJp3D0uz0mvv+fNe73gUb5PsKgC3JNUcgpwlZEylY1scBmvISgAQWUDqffxOF3v5umMBIC2IBjBD0WOfpC1/4QhDuEUXfRjtWQxo1R4mXVSVuTbDTku0ONZGeBDJhgGU3uVX666qrrhJu3XKsNjgUiwgVloaXYqCOKhxUVk+ECes7HXdQFRMuyPSUJqdkqlUnOExJIog/dRPNkBV5gCfpW6iPoE5yVSogJY+tQlUeYR1s0OvolECXWANK6ceJOY9gdarNb1iYtqYEcnweMB8n5adp/YJ88WMgg+pR+ADq6L7pTlRzPaKqJmS9p6b3gDNEE0PD1s19RI+4iBIIYP/FF18Mg2UcwFuCiA0pNahTj8I9oxZh1wU4iTJuqkeLCmOj8bWPQIbzQ1B9TWsLpsbOgV2K8U3x+oc+9KHIHIRm9fvXb0YLDPeU+Jg7xg4bX/nKV5Idew6nQkJephCYf9SjHuUOOjEtAtYBBxzw6U9/ekm+3yKjOFAcGAvkkyK7sYjKemIVa2m4MNG3DU6e//znWwQpXhbu3Lz//e9vRbvlllssTNJ00ggtZ8yeWSj31AW6Yh63MsIVii+cZj0OE+Ac/IYQb3jDGyy1xB2pxC36LKsf+9jHFKAuExGsxYQhYHbdddclmztlkSZnkSVd0aKYQBlIpWAZ5EchOVnl1UaKok5Zu42FoSRtgI0cvTNkxpS9BE6rhznXTRQOPCFDMO3ZIBIOlDSH4IQf1HcNeUqv1YTOIomjFgCg/saszchsMmAC5wlSCCbAs0gnqRkxmIMG1cJ1gIFCOPe+970v5gpbMzhJIND3KR27qLaN+QRQfdSuqdjPQD0Ch3RoHTFp9U4BfXfZI1CeTGMIsJ3Es1G7ahDxmAxkzus+OQBL1az7/mQYgO459OlLMZR6xN7wjGc8gzRsgLQSkoyFJnTfW4SMaPkGXdNePOOMM3xZ+IYnjBbKG32SDXxVAwuNOQDXyY7q9y+RyHYPpZyoxOCBwyp561vfSkhimTAWykNoEoDykuYZL/ToKWqJ7HqBgTbUTD9jgQ9uEoOW5+zJnlpJqrPFgSk5sHmI1o2+4eD36MWw/IpXvML6yMoXa+peu3CGCsX4qfusFBZE63u/0QgLTzjhBGyxZAPRqD5esTRTmul5Fm4YwAQCD0AglqoBhNuOJTxxSqBL0bABZ39QMHxWCUGK6Zh1hOLujsWdYRxKuZ8zVCjUNC2NGOFP8pblHr72fvipzegryeoAU/1JxVeV1q3+QWtVgUNAdeSRRx522GE2Zd1JPdkvB3ggClQgVcfbVrRi+kjRRJ7CIU83n/CEJ0QvJMSYQlDWu56O0jZ2XqkWlH7gAx849thjMZ+v+4D53qI3qzyb6+RUTECJ+vUIqRRrdBI4VEVmpfIau9HZzpBOPIKyhkyvNWrXA8PZHrRODiCRGFnDZ0/KcQZtqVOSX6qzHjXuaRcxjopogunFn6oCqAZd36PEu1oeYT9sb7vTqDXQ2b9naSBXGTK9yDFIPdUXBfx2h3hHXEMDst2RNw+um6hIRaeOmF2kSZs7GGKsSQ/Z8Qm1dRUHigPLyYHNgXxLdNtKpDdQLN74xjfSLK0jW3p9DQqDDQoNi+hb3vIWSMxXAFRQuXoksDhaagOTFKP0Oiaj/HC/eT7bDsjetkf2Mqj4bPKWWms9uwj3hQGW53XYEx+xrObR2hsNAVE4AWtTZgJM5gRzq01V7mgir9Dq4C7oguI2hml4vchC8oDc5gPFTuH73e9+gyFu0kPI9rTXgDWatppwMHmGYBTzA+ajim8mtzga+YD5qkKhDeyYnUNA+tKGIDS4iYAJrl7UcTykZzNXQF94SZ8G5BRucEtAYZyPwt061feo70vPf71goSGKkULYbFrkhtG+ZxRa5UgdDHTrYD/HUBjDjHe9Muh7m35Ngic3bCS1r8EHW10oDqwBB+YJ5FY0+230Kg7Gxx9/fHZA99pF6WG3pLCyu1LRYrNtsIcb1mXAxpnfmg4g7YaSftwHeJRXShsfMRoezQk2MF8zWYPDiET+9Vuud9ZOIgKNkBGV8bZfZ2EAXYp2RZ6g1XmLWdX5Y1vCoKst+v0iPmGMLPQ0ORTS2xSjmXHmYuEHUUjlXs4w7qaeUnD1PW5uTWig+VET2eHZdQkNo3EFGnYSLLIpTlJhw0hbJAMcoJXSkqex7hKPYjTGfI4IjBxIHX0xfR8gtGL4jwZdIHaQn9Cgd8Zio6ZZJogLmGwsoB21mB+ARpm+8Z9MQ0E3ZP60r6E2dTKiMGDoUR+Poed/ThmwynB7JBYYaNwbO0BaR63ZZaDNN2VYzs0rP9yH6P1AZPKYAKYH23s2a/xgLdAF5EUo8ZaNIXsc6Df9XHrHPbN5bu61z7n6WxxYCQ7MDcitmDZ9P/OZz1j4KI60NJooIzDb3UowYl5EWgqt0ZZ12AlORCyx7Rq1LE1AKahGJeLaRrfGrrPPPtu6CRptfFpt+Vsxfh5yyCF2KJ/ylKcwbIBS2AM13WSstidK1+e+gLcWX4DR63NQXLtQ0+6G/U6/7cvysDMc1NOmWDes3bTjAA95tks4yVvlwRIIN74gQWehDujimUUP5nBu3Y+OGJIgR6zrLLfs8ySPjZoD1WQUZXTTiS+Q70wE2MMWv3VngmLa6mzM54yp44xDcbvre53CTYBo70JQcGW7gWsYL3FM1l/yaCKsjbVYED74jlGdITQUZ0jHcJZz5d00RpRyXgKGie1EL8A8wjxCW9tiGDAkBgP9JRuBWGKx/fKxTFMDnnORI/rwqlOnf7kj4CHPA10ecMygcDY0W0w5nNFBk5MbAXpiKfEWgn3COeFm2qjQ7oad+D1oWtv0u6gCxYHl4cBUQA6WAM9AubHw0V3yr/7QC/nighyXDUIfP8Og/bbm1LM8fZ4XJRZKvWPZ7oN3Wl7tXtOlKEkUHcv3c57zHKyj/WAUZZHnkbXSEsnh3H4zJKDPOW4E6ng8wUIs5d7FcSmeyfaMc+zYNjYchSsUJjq90wG2b8lMQY7+skCTEnL2Dw1MqWp2pwVBAzxMBRk4F/r9SQMeVT0RAB2t/nCCnmpMKdkEETKHFzXNnqxaeKlT1DurP7UYbdFoXZ6SNqAjSO7T1egvbTJHy1ISFrJPqEFt3PTMKFq1tvTaUyWxMZu16IwL2yBsqobAp3rCfMCJ+V7B/LYBYSZn1NKuqvx2J5vK9uw5nMNR8pb+Gh00bxQIFuLiTPanjQUVGUNy7t85BXPAU/SLLsD/To9wDxsNRBwOwvm03nwXtIUtaDbo3BXBOWlGYCVtxRWxj8yK4eon8TCZ6C//dkPvDnEkHGsGoTRHU9e6IWOkMYtIGLjtBFqkCtSSxcE5dplgLAGKkf/6k2/9NKvfxYHiwJJwYFJAmJBopbMssjECEj47zSRombBeW+9I8VZMYj4TH8hvFktrU7BneRzX5xIQpo0cTdSOLNUHW2IWzmXBtYENRGmZNFfKHFS2tjI+W6CJOBRcAB8nr0g81kpPIQHrtHdZPuENCLEXnvNCufCcLd3yqiOeUrAA6sBrPdvJYEk9FPEomorF8S2XUfPI69QyhZmvdQQCQcHBhggI4bJHaQMA6EQAINeppuN6F0mcnCnfUCreUgqwqIcwZKgBzSQYsBECEIkn3gIbdNZeY1aSHKODNEg6MQ6gUHkoBXsYpUk2yLArj0UQsee8mgGhFu3yYGac5linEaOhdI2cQUJSuSineIhRrPH+xaKIRPEGt6MBzrVuHHUEoI7VobEX5qEN4LmQ6l2SUwwn6axu6hHBhV6LDMWagsuAgTydJdhhsk/Gh0YLx0lyAEmRNENC0hEzBMc01HibgUaA0dRlhXVQo3FgdBkvl2/QKwSpNvo+WzIKeoy42WVAm71BnUaTUSSx5TnBqXAj40GrsH4UB4oDu8aBsQFhNgfyXaNvFxqaL5Bvk+ApHbi22co2XwfkDsvRqklpA4lhypppiky1OmuXfUfjok9JTxUrDhQHigOry4GxQD6VaX11+7zMlI/ddl02gul51N/RZFzT0En3Bd4XXHABHZeVuFB8GqZVmeJAcaA4sFUOFJBvlWN7qzyDuS1YTlVTxmPpucMhwPlydmYbt33gz73FweptcaA4UBzYYQ4MTesCU/TBrne49d2u3laovc/KNLrbfK/2igPFgeJAcWAeHODexMVqkMZ0COQiQwnRNXAGnkfri6+DKdseOcc9fmcV4GLx41EUFAeKA8WB4sBWOMAyyrmVZ+4kIOdS60CLw99cf7dSeZUtDhQHigPFgeJAcWBnOeBUKo1cGM0BRg81cnuZ8k6upcJKI+e0JfoV0/padnBnZ1DVXhwoDhQHigML5QAUcyTVee9NTOvUcTmYF0rqDjZee+Q7yNyqujhQHCgOFAd2mANj98in8loX6UmMLZq6UBWNSLFfhIMmGsi3mJuOKskU4o44EhIriSyxwz3acvVzUcTFw+KGLUCHkB3tsu8uwIiAl1umqV7YNgccdhcQl61l2zVVBcWB4kBxYKk5MBbFNgFyKqy0zVBZVCmBM5M/0SXkpHBdQoXYeE8CBv5x4juKuS3asyhvAkRL/iGKyFKzZCbi2DSE2xRjS1zMdmGOuGOyjs5U5Zxfmou8Mmeadqw6KH7yyScLf7ZRGpIda7kqLg4UB4oDS8GBSZHdqDiA2VFgdnnhSKVwuPzyy2WGoHfKrijKoxiZArI6saYYaKenAjaKuziaAkS7c/TRR8v5sRQd/TYRc4nsJjg2zJYYSsAT0U6E+ZRYQrhTSUREGxWwWkMOuUmPwUMepoqRKeK3vBR4JYLp7bffLryoOJ2vfOUrOSzIR45RnBfEshVik81DxFCwxLwhYqhg7IJ4NwaSIW6++WZBQP0Q/1V8Tdk4BhlBEjBOgVtuuUWCE/UIkSuEuKifrR4BWQUWlcLEMXG/hfkUp9Ouii4MBosZx1AizOuygoo+iyoJVFpgVMFB3RdzVA3kG9wAq+K2eovpQpf1lK1C7HE1S+lmCokPI7Qq2vBHGFQJPJxT79vVQXH7xQlXrYzgAscmqJwj6TRvjg68MrVl+um7sRDqSCB3CWYwmSwlOCsR0yOxUZmLcEn3JXpZnnlYlBQHigPFgdk4MDay2/9NA5VkUKJ/07/z22WNFgxExmtJFS2pHNqDTGAb6gBs2SAsl8ccc4ybgoRLcCmBKayyXlt/rfX+bLUtww8YII70vChheLCJALyF4+7r9KfQ2f0gOdSXNFwgU3IzTBP5nCTEYoGxYqFDd0+96Oxfsm7kEpzcvkarHD9bJG1PReeWZYQtZNAjAMYcQuRq9YiYzUaSxNIuiVA1Kk9aC8CuKhDbCrQKhWaDkSJ1JzK8i5Qg8RqBI2VkGvWu++QV/4J52yvytrWmKco8KHVWYaKhVC66DN1bAQCcpy4DZL9GiPX2lIR02WWXmVGeMgtFIIi0xIGTNNNKkkKwy4wVQo4MkYnqKVaQuuY16FVPcaA4UBxYIAc4u9GsBgRMMq1bmo899thzzz1X0gWLb960MjrE9sY3vpFp3cZwO3EOtumdtDr6pbycslZQpKhiswkdy/8WViQdHJU3aan6C4iCZAYJKjWFmHIsF0hQEFvcBITSPIPhIE0SfMFaiixFmWwkgSZYortT+pO5i4YqvSZZikR10kknKSPdJ5kgg9Jf73vf+2AYxVcN5C1ZRiTtMI4kjxQDzAZUtdR9BhXASYeG2S0fWqtN/fASviof0c0cuvjiiyn0GfpkMvXDvzR1Igt5DgEA229Z10wM2Mw+gVcqAb04pmlmDAAPkvXRK2maCMLYg05PcUCQdvel8wLhniImbakK2bZy2DNkrHG2kkTijj0gc1XlIY98QOOnkfey0fJPraKwOFAcKA5sjQM9sA808vYIDllDIRCNqt2kJJ144olsp9HI2yUpJ/y2jhMCoNcCJZfRpueokcMSCjQc4vUmL8hoWwr89m//NmBm76WVyg7eynAsaEonfTT6IvRKcjmAZPNChi4AxiAMCG1YKGB3QwJKyATeGJap+KO6uGJcFijExsXpAwb2VrMRJAdwenDnnHPOoZHDPMZqf7oPj+my7NKDjtiBZnUAk0QBj6jsLOFmGMyOUs6uTguntTN6+5PZB4X+NGdS1dVXX01FpoIDaZsRbAPECLiep2Q+1nimdWImgIfoKGc5V4CBXSowPHEHqOss9wtHB2neBKBG5yWXXIJp9iaSmRQ9dnb8iV0kTuUZlpZqEhYxxYHiQHFgZg5sWSOPRLBRlDfLbi8y0P/gDWCgBdLtqOZybFNJWx7orckXq1waK+SmBDb2Ka666iohx/WmZXf1myWDMh11ltqaNJEynNI+afns52QmKAuwoVG2KnBbMalHINMVV1whRB9pif+BowGDJOLMA96y+e34QPRX5gHigh1l91vea/AJoZPIhLke+KFwNI+L2YZI9u1IHqQB7RIC5N9sBxm9yOItCztKiHo6Qox7+MMfnjG0uQ7FdcFMUL9Lo0ln7iI9yPjpUfrI6qASuwmQWBm2BLq4O9iS5rxuR6DfucAfRBJQsD19sVvPbkEwIkiRTUkVqzybivbiQHGgOLAJB6Y6fja5jqz+tFLL6/Oe9zxw7k+qJD2JhmdJ3VODwJ+OrZjqzGcNWtMsbaJnY6LxgT8Bq68/QRRNNzF62IeTmATrQCwDO/j0L4W+7QSzVNN0CQfkJIVtcAC8gau8tkAv1Rk0pkUQKAO6YXI/g4UYkEy1Tc0wfiAN9EPmkRoIAbmpKuXZ21tSbTdhsI77oQmEwfLAqisQnvtpWkmW/DCENUL3EeMKhW6SPwgfuu9i/McNckbL2oIeNfT89Ip3IyqREuj3fPSY97UL1Ikse2oGVmeLA8WBvcaBOQB5WMb6ai1mS/+VX/kVOih8Alcsz829eY9wFs6xCcNRcKL7FEomaNjTlF0gB4xZ2mEV5thvxitASNF0wSGKOGvwV77yFX55DrmRkOIgpkwwj0781re+lbrvPiYr2aOaLXljAT4hGQt8NozZtyn0Wuxd3FE1gMPRMYKOYNsmAulBWwCSocUOBQljdONZYX4VuoBmJm6GdHZ+px6QgSSdTXPuq8RUyVP0M867QLXX0Yl1hCFuFmECMw93S+i+kXEIb2nkkYdIJ4985CP9aftcB5ku6ljaHvn0qpvFgT3LgTkAeVZnJl9niiydDkQxtHJdtqQybLLx7inmMjsDOaBLXQYhHADj6t+YkM1vKiZlnfkdKPIh5yFIEWdJpunyffMbqlFPbSc79RdtGJqyNkeN9shvG9I0zoc+9KG9SRy+Pvaxj6XHw28w5k/AT2t3k4l7hmykXmGuB6UQ2oCyuHALECDIXvjoyIqQwxePuHDqqadC5TixmwN6QUzJdgxque6j3NPrr78eK2wlxBGdY5o9BRsTZBR9j6sdIYC7XF4cO5ecNyO1mH6kDa/g/wMe8AAl0Umnn6HLe2rGVmeLA8WBVefAVEBu/aWW5WxS67DfNCTI7VFunnHGGVy1Y61lEeUmzbl61Rk0gf5YiSl/vV0avLGr28z2IijiVoYPWEdJpTLSLx2mopICeJ7q4I1LAf2bem3XmTc7PRKYpVEKrh10UJdNdIBtd9y5vkaS122o52l/HXLIITTdbMO7iAXQlBLcjokbOJS3gYuxejC+edcj0Khp4kXs3qQNR85ovSkQo3rPBG2hCoKmAAGCN/5RRx2VP4Exw8Bpp52Wg2SwlnWhRQVmTiDc8GZvajRExzHSYWtrYFd3PDJH44hELj90k9DgBwGi1PHB3Kg/iwPFgfXjwKSAMK23/IlsatprjKbYVnBWYpZbOmjDktyBEAqDNEi2VCybS0CY1iPIB30hE/ihOLb7YNIOMdjGK0xIOB1aI+XYI0ZvsOd3YNUjTIM3SrKfoxAD/ZvDWnjbW7Chpprpx56CeTITzvdudI2GHPFSszvIUCyOb7nyKK/7U0fsCABsJ8L7nW+PeK07fQAXSQaIV62O9I3aPk/kFu+2uYF+3UwcQK3oQkz6vM9IMHZeKNDqzF672nruEY/SR235bQopEBlCtdjlpnfbHgFWuKl8dv1xUtfEgWERoaA7L9dXvlSzsYgpDhQHigNb5cDYgDBTAflWW1ra8vMF8qXt5hwJA+SipDGq893ro6/M1kSA3F6DSDiM/7NVsulbBAv4zZ/fLgYD/lhBZ9NKqkBxoDhQHFhCDowF8qlM60vYmSJpdzhAaaYf0+AHpw1na532rCoVqnZTP7sZmuCO52C6A28OBZA/uLsXis/AxnqlOFAcWC0OFJCv1njtNrV2x4WW47k2l10SWzN2xznq84PbyHNtOz20feCwn/hu559/vqN6/W7Cdqqtd4sDxYHiwDJzYGhaf9nLXiay2DJTvB3a7NqKws3BfjuV1LvFgeJAcaA4UBxYCAecy+Vr3OJxhYZ9gJyHMFdn55jjJLVmFxWQRVcEkr12tH3NxrG6UxwoDhQH9iYHeJE75eSo7SDBxz5ALsa1o1POAiW2xppdgNweLY9xVuKd2KBdM3aN7Y4t54QIbk9bqLi90P3q4wwcqBkyA9PqleLAWA44g8PZTcKLAUbvLdM6jVzolTKtz/yR5Njb4HW4vhMb3jMTWS8WB4oDxYF15cBY0/pwUd4o5rYjPaJt9KpY0lTE/dhBXoFIxTlJ6osBB9XpdeUXztm5uF7P3IvEPMclvGoXjk2Icz5zW3N/Mce1hXXr46TmOJ9EKXNvbkcrNBXtsGwU8HVHm55cuU/JfEh63EFJUxf/czR/ha4EQWph/1eI8iK1OLCcHBiLYlN5rfsOZcYUz6u3y8t9KUWKyOoOBzPIC9fFFVlMMZGxB/0XMFyU0DvvvHOxOLrwURGhTCCzBC51+YFpOAPaF07bpgQgUlA5x8pbPDiv6JGQbSKib/r68hQAlqLsmc/ywC4PVaFE5CWfklA2Ag31tKFZFJ0f//Efl8i15/+y0T+ghzgiu4/IfcJJLTmpRV5xYKU5sDmQQ3EW+bvuuosG0yyoQN05XYsLFfPkk0+WlxPG08+E3BJGlGd4Y4o0G44DycBdJ3qZNAg6lNoW1hQ6cl645pprWq6wpZ1MYrcJnSZ2Wz+OyVo2CAa3tF0IYeaw4LgC0YsVs4SkhsmjLEW2+6u1hWGqJOvdas2QJZwVRVJxYDIHJgE586NUGXy82YFlobDN7pKqSyqOl7/85UJyyvTsTyDNPw4geSphBktaVDQaD5VdHmuCudO9NRI4YGlLbNFAIB4eccQRn/70p8lDbgozLgJ5MnaLbd44RmDih5iY5x/72MfafZHLJArzb7Y8pPGmtIml6rcEa04fHHPMMWoTkY2kJVMLMcvRagXe/OY3i4XukcE94YQTWoWitQhdThSTv9yAKkyjYh39+te/LiY8Ye7EE08UNC2JU1Vy5pln3nzzzUKj+51KlOSC4EWv66zwrk5AML/78YpXvEIBf6qqVSIwu3QpKJE6pe+ykmqW/gQGcE4kLCaOert0+fDDD0+wWJlamArySMIVRg4B6k1OL+qyR70V3YsYLhi+9GuEKgnRxcMH6mjw24tSBmQITH4FpHU/77zzxJZxU871G2+8ERP8ZlMRGL/Rky/CfRHrMgTqF8ZOJa2M8k6FGCZ3NGpccMnFNiNjXlwN9NcoYCkeCjfrOKjCwe/+X6OZqTLKNyXtHbCU0OzlPsAEUXV/8id/MpOE0m8gvG6YQCwyCOWC1WMjZor2H2rJ5T5eJ/4J8SgUKjjDl8sXTTpPUGFGpg9/+MO5zz6Hct30inZd+KkSN1HiZL+guSnJriAgv/sWmVZt/SgOFAdm48AkIPe5WmTvuOMOOTetaxqwFlgjXvnKVwIPK4XP1dIm3KZza2leRC0lrXF+28878MADqT4Wa2tWOYqHRT0frJ7itCeTt5BkvBhgBnuG5GCMq695zWvct9JJOs6szawKJKyMjBypCocZLZvzQQ7XWak9gpdkLLvycqaJrioIuX8lL/n85z/vdQ1Z5TUkAxtjwNFHH91oI7SJ2SIFGQyzytsoMXzyj4leTpgAV16EWMpb9F3ADwBbkd355je/yeQAX8ESeY4kcfvtt1u7gQ3KyQH8LdWPQo/IELqMGDWjRDFdhvehROZTyWYIGciAnWoDn23nWO+kkyFPiNmuUeZo9tv3vOc9XsRM7xKJoCkRE3kS0uh1/3m0PXIc07Qpeskll+APfspEB5/w57jjjoNPyCOP4uS11177/ve/3y61AkDdfRLYrbfeKtysFv0mwbCs+BPCydWGsXq93377ffSjH41/qUbtLsFsqVzIChp95zvfqSGfjwxDemqLytwgDaOcxAYUjbXRkTAmMyQX9IWUbarIhdOmSt9HM0EkH9IAJqDcp2o3ATxrQkP2xYwyrqpNilunWbDRTaHxDGWOqGLv6aefTkjSWd0naxpZHNMdZfQCf0wPfTevSANhvvnmeAyOWQo+85nPyOtDNMQ0KE6csm6ESJUQsKTjm0ugob7j9bs4sAc5MAnIrWjwgx2y5YK00tGwaTw+76zmytjopS1Ryh/0oAdZngjy0b+td153dF2BPcjZ0S5bQ1ka+yQo+GatpA5aJa3ykJK2im8W1mc+85kMG1ZhHgawxCjIimYFB3XJ+OmKAtds3bG+5k/We+DtFSNlOOh2Rgc8U86gGsGLiqYhFhTAoyGwkToNMVykkRv0gw8+GBRZl9VMOaaWEdGgTiylhh5WUePAvEfJZUL9osTriMppk9TibB/Igfa0pz0NdLn0DlA5PgDDLOUWeoUlVaP8oQTYq9yGDsinKVIc0aPjkDsqKTjnXqcYURJbcMMP8hChwSu6r+NgQ5pXjKX6wy0mjX44lEkXVAhdcMms1lnShh75TcvUBSDqRZxk9lDGtNd3HFAAM3UBkShRxjDBNslYTXXxlPCKWAD1vQVQqebagvRGWc2awHziC4YoSdQgD+FSHNlgoXpMA8RglF6oHF5mWJFNsiEcPPzhD++nCkgO39rEUI9QtXiLCaRtgAqPFQsDWWjUrHVojfnUZWwkdhPUyFtGHFtIHl5EpMKYozYzR+uekinRjC0mCSBXgJlBVYgkiJhdyMvEE8uPxMZ44KYmjEVcI/0wWBYHc6MWh+JAcWCbHJgE5Gy5vsMs7tEj/QsSIkTH6Sb+a/DJMkfNIrNTksjjbtIGkuBrhdxztsnNya9jyMc//nHCjVXVGk15tc5STSz9Fn3KHIMnPMtTGiGdRuYPiy84TFp3S6E9jpaNdEJzFmuSVraBo88lGbnVnKEb3LaG+DpoJfberPIgJFnRYlO1oFvWDaJHfvSO00Y/KW4Vjg1///33J6kI6Qp9rfWAOXFSUa5F6prlHjagxCOrOXW/7zJK4JbyMABkkg5JABRTJIHAADkavKitZC91gQoFWKQJB7AE2VAkj2LunjADSTYKJ2+b2U5uiD0pbelsZCB/qjkFYp1KAbwF5FpETIQDIgW2U2oNKKrUxqblPvT1iGCUjOkaxQ2IroPMCW6q0PelKoJCZF8VKomf5kCaQwOxQPcHU6XxLb2OyGjEvZ47fmBFA3LU6hcKCSuAPD1ykU4MPcJ03GcOqsMN/EG5iWf+eGoKwX4TlSBooC+//HIE0OmzD9LscyaM6ZGcuSrJ/kWUcoKjKW1W1PZ5OF9XcWA7HNjc2W1C7T7LCNTEcF+1hZteQuK2B7n83lvb4dps71rUaLFWcE4GljC7ubRwmAe02Dyt137Q4eiClkL8ZLL2AyehafJ4Tn9ZymFDH2zcwg0G1KYhyNQaonW97nWv02ir3CKeTWULsWt6ByuHDBnek3mMlqanYFINkQIRw7RAMcUEfyoMM/ouQwVdBj+eMkHbF4dzCH7DG97APsHAHjxQG5g05XrCNAQSsDFw2KSNCJoTuqCebOfniLw/48QQmnPH5YeqNKFwUL+dnkeM0emb0FOVsP8zUVC4ATnrN0M0nicLO0cHQGj7XJ200txMnY2e3ImnAl7lT61gyIBvmSrh26CeBpPoMQHUk34Rg/TCHkfmSXtRN41OcuRk/771C2EoIXESPRGsOWq3i0zABkPkMqUJTFo0FmMnqlcUps2zTDDdMQBsP5/e9F9ElSwOrDEHZgdy3zzLMMy2pdfWTTK7VYbJ0VqwxlybrWtUWwuZ/ULLrk1ZO9D+pM/hHqi2SrIe27lkbHdBL+5FVnn83OgUvlXY1bKAB07aHrwf/XHkpPc2QBqy6d43xJTStKjZuuYt2M/uantYd3hR2OznCgfL/YkqGEyVZInVU6e/lE+6ejbwnhIvNj0brrMWcBcA595VcxRKtcEkFfbeBlATAXq3VVeM8LBn2qCGyQVCjJHt3yJPZEx1kEWancC2fSzJ1GKj+cEPfhCSUX85Cdovbx5tavNNudrAqVnXYhgL0mcEB1Ol51tYhD9wuh34xBztqqdtT2hCPf7sTSxu2gsw5eBxKGn9CiWMCp66bHaYopmrpjS3Pmp9okpsFBfBxgRRxoSH5eS5xzzmMaWOz/y51YvFgZ4DswO5JcAyYduMYtFOQhPY+ctYwqYx/+61kYiNt4k49Bi+00zo9lPZjWk8dnlxL2yhvFruLb5s6TAgjuL+pOyyiKYM5CZOMbRmdbabTluacMzPeEFWb9ER25CBGc5QmwpeFmiv0976TU13NI1yrcMDxhhAddNNN7GWu8ObCQDHvgqD2VQBM+TmO8lHT5eJIIMuf+ADH0icf3ygwtI+oR3AsPugX3nkLZZnKAUPwgcM0XcoxYC8m2Fe8ARV1FMgRzoJaqKc/Rm6s0n4k3XdD35wDCF+Ix7njSNZCv551+AaaNyLvcEMsUttUPwmEBhuP9gtgo6aYE2BfxvxrX1T+MDXLKKPypHn3yYkualCWxLotAHfQvqQyzFZMZNEp0gbrOgKm1c2evyAxAgA9gwkYXVc2IxFLBaDj9pNXc4MMVVo7aYQ9zrrA6mujqQO2FV/Fgdm48C0QG59GYRzcYdE77yTDV1LNkOx7xkkWK3c7F26YupcifhlszFxyrcsea5edWOQtCFKJ/ud3/kdbLTy8jS2Jjp5RaOVi9OqCsjZvS3c7PCcsflgt5A77M8WVg7DmG/3ncFW+ebs1uf8zg63EQSBvJftiKvfqg04KYWuFnvLSLWBRmp2x3XQ+mtM4aU97LblbDkGP7yfGEst1izJBALubKCIq4TK7Zsiya4q+gkcjMB8rAh//L1t/HMRoKrqZuuy7seMDJv50tt8hSWqgiJEkNj/g51OhVHWOVp7pAa6Iy8N265o6yebXo/OPTfDnMzMXm1tA5RH8QloUzcjmOFOzYrZRWY/57nNNIUVxgiW84TIxjOwpK0aUHCebD3UWR8Osg2oi1iDXZASwKscghod/cITnvz8/42XbyrNIVU9vCsAcz9VGt/aVCRvaYIA4as0Io4jck2AwcH1TAZlGAwI315XWKN84Pm1Mc8YbpRgPn91Li/uJ4oOFwFPzVVzxsQzhTRB0tLBnJ7ITAsZ8Sowt7Elrvv249FvuWCcIENM+eFUseJAcWATDjTLoR8kcTjR38lvmoH9b995jq/kojDZ52NN9YlycqaKMZbyIubENKjBCR+u0QkpM1r5bt6x3NNRdrPFvi0+TXajcaO/aQG1tFkiYaE1EVyxZzBgAjmgmJKUNg7n8Q+n7/avU5hgufKwzZrL2ZB+r4BigBDu+k2bx39aeCzALoejrOlpCORk69SlRUPpgG/ueJGQQYe29HvXag60cgQ85THTIQV6MPTyp/F1jAqAmQnsqNy7vH788cdDOOYH/lkpc9JJJ9GwU4k6W5e5YbcuewSeOVipCuyZabaZBwPnJlEGHhCGIGKekmbY5IlE+ROEQCZyQ3sXzJAebLqTh9CvsFcip9rsoCWTaRSGpuDZkHFGA1eBcLsAvM1DJIDkXgDeUrOzgobA9jCcI8625lTrG0Ghccl2gAtAEnZRTvTBf1PChjEiNc1v3Dl+viY6rvsgNq+AQ0isXxFBCDFMGqNTJYU1BOzVDHHtQxsLlOcRSwazvKN6BKzcMUzIQwxmaj03fdS6b3AJXmqA39wSW6f84KyuPCIJT0Y/Gzcms4WiMT+Vm5+kLoKmP80iUoVpzNLQ11a/iwPFgSk5QBGyJA4KD5OmxG9lXcWfxAavpCnrOr7Vr3AAXnIsIHXxxsh5h61eRHYiDmmDOEhc2OrrG5UnRpByqPLsEHOsdl7kVT3FgeXngIOmDGmDfOTTmtaXv3tFYXGgONBzIBrwzDyxpc160ceimbmq9iK7AvMD+1Oh+PaZWTUUBxoHCshrMhQH1pADLPC9k8RWe0gIyJa8H1t9d2x5lnxesY6r2VIRq24udVYlxYHiQDiwt/KRW5soBGVar9m/9hzg5cCH3M739GEABjzhBMeBcaND4TMwEJaz2Kuw1PEZuFevFAfCAb7DIjUNTOv7ADmvYHGa7JHHxXTNrpgKGRud7p2XnrFmLKrurAcHTPWcBJtZpVaDY2OJCDSXjyWh8VCV+IDrwefqRXFglzlAtrZHLpr1AKOHQM69hVPrWgL5n9ofvr08VcjYmSefhXhwCjGn3fbI2cLd72zwb8DzmYevXiwOFAdWmgOAnEY+itH7ALmTM9Is8lNN5I12WU0YxNilW9B1jxwzdfGFsYgnQKabZG2Oqf6NQU+BFoWRXK/aBa74CWIlloWsFQsZy0HMy9BgjWZvXCBbpmQFODGUzoKzi7Y4HmwbJoBhTdKUKata0WImueNh5vYupPJLbN2c9jZjneA3ezHZx7Wdne8V5XyRXRwoDoQDQMSRWhG3oMY+GN2vv6JoiRbpjO8o1wTlcKo1OYzzNEmf+ME7Qe4AtCPLFh3hlJ12bakJbUg7Nmqh95bjxbJULTaWk0VQzKxF7ZE7muwwfVKP50o6Z3aSxtWlna+2ZAT9EAFGUJdGbZKzOQhu5iwt5fMizHFzZ9lFMhHVZF51blSPtsyN5C/RnK+MkcynJJyA2bvYj2in+171FweKAxM4IIWxVWiT42djVUMruGDOAjwl17VLiBLJpAXQEFJDgEkhRACk+MlCcclNmUMv4mDIsiWkF8gXcNTJUeE1eNAscJAWa6IUQkesLvKUiJhCnrmwKBHWln/X0Lwxmoa7RxFTQrAXKqMxXXuNXEAkkznfz452lripLfFhRE0Ruse3pkUyhPw6Jk+h+AIXkGq6OLBwDoxFsXsI+dQog8dJapQ7DNGiXwnuKPsFMOYHR/GykFlN5PwQ1UH8RcGTL7vsMgGk3GGWt9ALm0V3p6aDeeGcxFWmiAukJQiU8ydCvwkINbMn7TaZCC+F704m9d2/xALTd2GwRL8Sr8MlSpowPZRaPOdgDNpxjy+DqDUkKmHX2kB4V+ZHR3eSXiX3SUW0fJbe3NE1ZWKDVZK4YPX3IilK+E/3SQza8lSoc0G4NK0hPGkMASFuJjS6UTb6ZDLRxwhwydWmElQxrtAXmVuADekN5WKPJKcZSzurg2DjAvwlxp9J4nVVIUZVmqPHo8d9mj0CBAJLl7XbD43yCPYoIWlHRw2pekE+FRIVDdnE0TV9RKeOk5OwFGGDLFss5DRdXSCIeF0Zlobmns3fW7w2nMQ3QdwMmWqRITQba4qSOpvCvgXF2J/wX8eNnX+ZwdNflAjGkkByhglL9Ugv+kR2KvSNIBivcLKRgXjDx5SFyeoXzpZJTXhzIdJ0Vr/QFpV9UZ/S7n8+1WJxoDiAA9Z2qCo55D7c6CO9DUK0euGYY46xaoNh/u6UAws9pUTOaXgvtLVwnmKOCiYqzkPqETlSQChxGb1LRDjxxBNb/dYscEUrbYEqp4xIN8diiw3RKiS1wJbyiLQeWeUt2aJgWsrxR5hP4Vqt2oKecmewlOc4L5uHiKq29gUBZfNoDMRM0OjfuBaDJSIUucpvZljSFUFBfG8VwjkBTaWpYKcV+lS6M8mh3XdHVFdIFpJIBow2tlfYz8XR9BbnR5ingF0ZfhaQhhSSaKka8jogEQ0eVe4ANrnD0SCiu9fNM+FRoY7A6VoX0JR92MSQxVXYVzjnGtvlVCWtlhjjycUuxCzYbnwjDQBgwVO1AlaRwbCRyKl4hXsmHhM0KRP+iQquXz3PzU9RS0WKRbky+kVURUzcOFhNJCmB3yrXfQINaUZhnISdrNwJgutihSKY+qjQA1m1i3XsE8QL4WYZsdw3KMZXK+j01YiN2uIcczgQEdYIGtZkMtUK3hpN3x1Bh0wA9ZEh0i1JWjeFbhXx1B39MhCDcL9z/FKqquJAcWA5OTA2ROukgDASJ1h8qUpWZ2uKXlmYKAEUdxqGNYWVD5xbm6y21kElqWi0dmhkeYLx1sdIDZQtK2/SSO9l22CckJskhS100zhPHXvssZZ4GEMqoqRS3VgycMxyD/4ZM5hYuS9gI9RPDQYIsrZ80hRc2nYyTbm8SAijwNH2rP7+NWqWfpKEao2UhtyBWFA2rhNoc/GHcAQReAvPjjBBtiEHyQAgEc7gXJRjoCWbmShdTO4IUxKF5557rj8ht9dNG/DGfwKuQ3RAS61HPKQHriZS3Cq1ghJmZNXK3JrtG/KNhkRi9wjB+uXPZlMy2cQSVq3aIBycQ7ONm/CEwh0Dhgkp0rt0JmwAjec5mqVdrKYrK8NkzZSN58qQVoWp12Xc0wURy42C7iTPOvlDIHR90VmFTXLSkr0kAi4+c3cgdWkdGFOjH//4xyMPJHtLK6HT60xcIUZCFHSKu05icxE+9NFXGl9Rg854QywmeJG+c9PeObkBYeg0oLarSkcpDhQHigOTgNxSazGiE1j3s3JZsEBCnMVot7njX9ZI0Zos0FYry5wVdsBZyzEFi0ZOKWnAswe5j12Y6V9w5Qecs0YDSLDBGAsJgBmFm+UW53HYQs9MTf3CW+yy1oPJaXKHW/eNAkust+jBhg8kQBf6JSsxdZx+qSF33KdxMmJnOPwGfnmRmsgSYOzcRIw6/Wh+EgrALX3JTAAzVEY4RItVzH3zxJ1US5EF0qAdrjPhAGZqKyjiKEfhRglpBiXJcK88cUSvzStcUliv4XQ7GUEcSfKVuFXapJD2A1ona6eu2dZJulWSKPgfeGa4Y26TOZKAi2bvX3KGalVCZsVhlOgpmxOzOUt7zmLob+usV0hORBwqOCIZxjGcfEAyg+XkIYMIqj2SeNu/apAZRXkmCp+J121FkWxYHdSpAEOXpr0Ya3njeQuSSlMnGbMleIrCSGnLf95hD37m1eXiwC5zYPYQrf3mnG1IGp612045FYQi1XeDBVIaJajAGLuX85Rb2Rl+WSzIRn5DEXsZEIihGwAwrdOMwQZwAkJyT9EpQQv1i1V2q3yzvsM/LxqICGEAgAgFQgAPVbg1BGKhe9JO54JYgQcv+rHVXVh9geV6AWxokHqqHp0lEwAeffdUndRclEi31Sjhks1CkNzYqMIokO91EkDSsecCw3ilwnY4wk02IRBI5gD2MR2lcDoytgvJxZcKo6ajB4XUXJWjimxB54a1ZCwQPloJaUOntAuG4TfTOiRmLeCsYB+EqQO6ky380Au1GVafhjt99nejTNxRwOYFAiafX2g0ZzNlq0Ozy4tLNVccKA7sDgdmB/KsI029jsXYDisdy6LmyjLK8A6TnvzkJ0PxfuXdne4tVSuWew6DTLg0MJor3AK00mJa32ldVnP+g5Z46iMwsKZDcQKQtbtpadvpjsHKSXrb5AyzfUMw1YbrdirPuzCYaELZZVpgbGAtt4Xcjkmw3jOSU7ijH6NEl9m9e0pILQnEDSP5W9DIGXKYzWmfLPlJVZ55le40mucSqgVJiLdfAIkRaQhcfsghBqFHnUURAPIJTMQjWjjd2uegC7qsHiKLP9nefQupKsNqk8LePModBiEosAeQ5NyXFsy++PKfX9j+PKkaigPFgflyYHYgpzpY78AzkG6R4KywlCcLrqfAiYWTF5KVy45m87WebwdWqDbMgU8Wa8jNnYqDGGeC5zznOfRL5gqKbBzOYYAyLlq4P/lJUTQHx/8bnhGnmiwFOeBZtMzRK+o10SGD1TfEoNJ21mfmJ1OwXQDV2g6AvsQFWmncxfULUNH7DzvsMLMl2cH1S5cRvBEl3kUVkGPuBqXgkGSgNq/YgNCLHvPMOn9qcWZTsxdVi5mowvk2Cn5EAB3LWCXJLjqOPGwEzBwFVAXUYb9dAyq+fzOguSjfumDQKfrM6TrFI8F9RgVWgVKyZ56B9WJxYM9yYHYgt1pRRyzH9nepX+EgnQx+szd6RO3jmcXYTi2bY+qF1R0qazQwaIodFOezzR+K5xeO+ZMXISfndJB2TpnDT5Cf7Vs3OZDzc+allTIgEHg77pXV3ysgf4JtVtPC6bOxc5jyVirh22U/nh45mbGG29ZJjlq1ku5oOoOrcoIdfGISB0t6ykv/tttuo+a6zw4B4PnE2c9mu2Zehn/AnvOXjeTWZeblmNZtS3P8tomucqbpgGg84WGtF9HDFyywzd1M/dRc18zxd8kBbOOogqyXXnppSCJDMJwgRu9ceJtt9Qbq7nBfYFaB96QxnwNQZ+GwX04QcdaD9YWKn9pgvM0ClSMS5RR0zNGoPnpkl4Fmb6ybGT+R0gdn5yYPUz0tDhQH9iAHpgJyqwk1hcrVKyUA2yIFGHinW8hYCDn1WLOcVePrazlmKhQthH8WnPDUxcae42d7kNG6HAe33kLrIJYNYLIOb/C4EfgTo/iacWDmNujQNvTylGGDPER9x/M4vrmofRRW7mNeiSs1SEj9BosSn98ww/CBBz8YA7jXwRsvGi8aIZtKGgo+ARJNZKCVRzPdN6YCLnIcxAwoM3IjgIc2rwhe3wDexjbtMzOBlxmnMwJKjko7A402tnTyisoDZoQSaii3cOVblwkBKlcS/XjikTkGHfl+t46rmduX3WVP7bhz6dcRUgJY1XFyQ4NzPwKN/ZRzsy8DwtNrCjEHNLOXYx3HNJXTlbMD4hGJipe7Q2Ukj6Y3063tJcWmAuMp5X5je6K/8RLwdei+StBpY4Wazt8TnbqAXYRgHffIGZCnPvWpxDIfDp67fCakAfXzPSTuaBHNLUayJozLILrT3vysqtfFgeLApIAwjTsWEVZHq5JlOp7DLsoZ3Y4jOiXG+mKht+5YpJxHslpZHBX2yAJHo8pJWZdFOedxF8L6xQaEsdDDQhpY8xWwoANj9lg3+WwDOayDqf4EhGAM7ipD0TQEdD7spad6Gu556rdqjQ689wh7DZMawKpHmqPP4Tabs/swMnZpMKO2NASuWLxDkqeIoUcaMu16URkaNv9zVm54pgZEojmmeH+qGRi7Y/TVGU3U6+4QO1Rlv9yLiiUWkGr96xWobOcb2Y0SeGaDPN7mKoHlDNdmC2qdcsSfZmxAmLkXM7UCYJKin6PYCFOzsxXJlQkavc6jMPJBLmVUrgxupwzJRhmcUaEO6rVHaDO9HQMzkxXTkB9K4mruuLJNjhi9w7qQ7RhC3PXVjHWqTRgZrBAegECQDf6wTovq9KXAcsNhiLEFtQoTg3RKtSpJgAGSn9oMU2IeI8+7ZY1fyGJSjRYHFsKBsQFhhvnIbWbTQrZJ39L609JyuGcvKtb6Nrm6y69vOoiDApuWR/8Mr+xyrzdqbu6UT8OuKYlZEhYVGcWB4sAucMD5Yca8TWKtz4WOUhHmwsbFVrLpIA4KbFo++mvfqWleWSwTWutzp3w7fd/Ou0vCzyKjOFAcmCMHptojn2N7VVVxoDhQHCgOFAeKA3PkwBDI11vYX+/ezXFaVFXFgeJAcaA4sIQcGItiwz1yzjjii61lVAouQjyTHQ3iN2R4NjpvvYQjVyQVB4oDxYHiQHGAl6vTOg7pDPbI9wFyPs98g/m7tQAvxbjiQHGgOFAcKA4UB5aBA07cOIvrnNEAo4cauTMwztvMHB5rGbq6EQ3RyJ3TdTJqJdTxmFCmJHVLhRc7TCtE6i4zapQzxatdHoJqrjiwzByAYsJ7Cz4xSSN3Yngux8+WlhGihQhhtkLHz0Rqc+p6Gn4KDeZw8+SUG9PUswtldMoZ6L2czXYjJovn6gS50+etADHOsVEnzndhXKqJ4kBxYPk5QCMXT2LG42cCgcmj3L/88z//88cdd5xYm26K4yEwhdVZdC2RrRovnHg76KCDLEMiXQhPtnAeTand7gSdjBwSSyfEh6gpufw2JLLDjRImGit/BcmqNyVGwC9sF1utTzC66VspoF3jKJaZPF0bvSL9lwJ9Su8pK+/RSFhZ+bxFRiMpirU+CLW21QqXp7wIawK9CY0nOek0VAkqJ7KeSIji1olqICqfQDG+DuFmDaJIiL3FTO44qWLke52m5ipTHCgO7AUOjEWxqY6fCQkuw4e0EM3kLrS1mxYaN+GTqNEXXHCBwJaCRctnJWwnhgq0Lng4DUNkb6mjhdoW1ptOvBd4PdpH3Bc5XOQv0o+oqHj1lm9f5Buhu8Y6Iibr9qbsEgqNKGDXIEG5tySsaFfYuFNPPXU0hXxrV0BWUsV2AutqRXRewUoJLiQGcyDh29bgEmMO9BJYx2a1Ge0gk8nhhx8uBp+AbkRegppQbj4Z0Vu5YaqkzxCI53aCJg/ohKdbmglrMBbVheLAnuXApMhuVihaOKVBNDRLzBlnnCHopt9vf/vbQTgzIPdvAT6pEZJTCbGOiUBdNg7Bty3comHffvvtZ599NoO+NQXwQ6aLL75YpMlFsXuBkd2obkLmgVsZ4ZguBhyAlIQevOXL8LSnPU34bsHVZRwRXpRfg3MENLk3vvGNiiURNfaKPS6MKJkJhyGB3xJyCNVOLKAjyjjHgu2muKcTUpSqCrIKgG8c5UoXFV9VyAC0AsRSNNEp26bxlcIc6pgS6jSUScTZxDUbFsQI8YZYblrXPvShD9G81U+G85aYqUiiXwoyKqS5oOWCm5oP5lIERHV6Hf1+M/MQd0RR1agk5RKlMO0w/CTVG2uz+OrER3FMMafnp0cmIZkJxNoqOuGEEyYndtOQkO+y/oSxnD11HGHmqgy8uIcMmUyTFl0Y14hcBsIAidAumCubiqkuE4yAqf2wSm521llnyQSojAiyDBtMLAZUDgLcEJhWzTK0+hyMphf9FljX6As+T03XcYo7G4bJYKz1nemFyEVWDruUJBBguyZ8aOQt3i3IU62S7B/EJglgfK1e0SOttKCNrHPmGykBc8R4f+5zn1snMxe1KFW7xYEtcWDLkd2scRZocaQBMwcxK6N9TR8/NBL7GqhbjASgtsTL0xVSPLLUuqw1VmGLtWjb7lsmGH5pG4uKsr4lTu1cYXwY5YDMGRdeeKG8JoJ4YylAJTxZ/a3Rtr0Bm4EApSwcFn2RujHZ6gyq0Wk/FYwBAOWTNlTCb2Z2qrySzCHwQMkJyhnY1hyd/m1vexul2cBBDiSxo6hKEwgGFeAHABPdYAPzjLc8chBC5fDMlgHagDR4aEYXr0uOAsg5JRAE1YDORqqGYCF4Q7nXIYrEKnA9VnciF2jUa3v/6DF5rr/+ejb5oDjg1KjdHLICuE2Ccxeq5BeBW1pkr7bvw/Ihg+pGA4rnbEX6DjiTZc7ryFCenIoYwlOyxcS8JFWdR6qV2U+jWgHw6EwWuL4VwofeEbOSJEYiOPiKcvIc0NVrn5KBg+iGzBD7jnAmsesjeQjPntjvnvphCAglJCG9Rqqq5NohZyuZhMKM8zqCKlxKZkJ5Y9FmtmCvSuyhmDYIwB/zzYjolywvBDW0lfq+c1991Vwc2HEO+IDbJXODlbf9Sf2ieeRP+SWtAhDFYsEk6I71gk5gKffb4stsTjWkncADL7ZKFPaifUGILg0UgOlb3OXfFjWr8C43muZ0XJ5KmUuos/KWUrao5pKJYaPl2OpMjU5JiAVOuCbSj0GsO/gpBVYrAK7oyjRUaOepTtEjVWX5NkBMI/RayJfa4BxVjH4ZjXNwuQlUmEzQIEcIgQxCKGNL21FFqqrfBAsgbbM271r0qc533HFHEnQ+61nPAld5RLc2AQiMyb+pp3R0r/utFQRDep2yoeCOMhRWamjeRbkTEzCbZulPgOQwpNoyweCoROYUR1IC2sg3WnEfoF577bVA12/cgG06SxVOnXgonwrgTNKw0Qs6HnvssajKI3ND6jbas/JSlwJavUvSP4NCv9eWR+a5YkA9ZFOLOSS2StyEkUQNxGey4Q8yIDQoVRu9WQY52M8MwPmAaSGtE44ZvVTY6GQpMdBhoO+IxIwPeao5H5RBCasJ3KpiiiB+2bvRIm7Q43kkkE7yiiE2ppjD0kNZR0zuE8WIO4v9MMeOTt0sDhQHRjlgWSP0D+5P2iOnDVhYiRKAJNY8qwYFPamf4loV/cmabuGDSVY06ni/X2hdht/0QuK/Yq4dl02WtQGqLf3yzjvvpJt+4NuXNZrqFrWbcmmTAjwwO4M0Kz7ehskGAnhYhaEX/Un6bTeNZbRA3LYKJ/Elk2kkLRhAswSTgNlFOd70SKHa+FsxCagzSclggLdcNEWoEL6S9qIxM1+7YAYJQ1skOfoic7e2sqFutpg/JkymEOjKv5kGVFIqJlmQkIEVdEf9pZ42b3YlWX2S6g0BZB0KNNVToyA2GzQMztCdOus33uIPevyLHlc0VNbplnd1MDUoxAQgJnGjgLHJ7J5dar1GDFt0fMjJLhRlZKsTAENl89l9heFrMq35tFI/xxEiAvEo5yPo0wBVyVbATb2D2bocXjXO9N+OMVUgHqYmCZy2leBDM384qWBU21Dn4kCoMhDBfi0SwdFpJphvWMFIgz/+lPDeU/PN3EtieEwmYu5xU9myrhlFV3FgKg5M5ew2oaZ8/xYyq4w1gpZJK7rhhhtaQmg2eXqMxd0GIZygYPUJuaeicV0KWZftuVIocYM+ZEXGMdZsO6/UTbIOCLfja82FiI2BAZWvfvWrYIapmbZN2/Pu6NYvEAJjXqQuw0hGY3o8ZYv5dMpluqVLVwnUaZiaxOQZB9CiIeZcWOUm1TBt0TIplMDPo0gY3srW+EYDSI/ECnq57WfGZyDUn7zKdkz4QFIxbdSsdX2E4qPdh4VIYq5g/kGPC2Hest+/0QawaiEZQwLi4TFKIHpzxIvCHQEoAmt6jaS2K6+MLjOD96fpSBtejEgUPoBhBXog3+qk1ih1nAzHzgGzfWhkvsYE42v3HVSTA4C9yqOU807I6PiXqR+dShLF7IsTr9kbQLj5xs9uz36VWx2IKl8cWEIOzA7kFkcrlHWNEta0vWRWdtMmK/2p356knFFQKDQznJJaQsbNQBKOWY6t8qPvAhKIyPhJEWeqpcBhYDsUTpFiMCcEWKyjadFEx7pJW6aBHOu3nWmsdvkB9bk+TYnlU/YLJukOfOJ137fF7ENca5piFPrROqEa+uE3M4N5QpXkewGAJ8+NyBbAHpdGq0UPkYVnX6NH9+E03rb04T0laqM6MwnYNuI6TtFXGPMRthHwe4XBCQ394QvSQOzSrXJlcLs/Yredwxohht3CxgdqbaP4zepgk6LNAdzQF396pCPKJwk98O5HxxzjO4k8uG4HnSEEw3XZkJlvUw59FSsOFAeWjQOzA7mVwhJsT86uZANsigjp3rJCIaA6AKS2hDFvehSPuWXjwm7SMwoSlCF8AwbOILED244F4RARr1KYwZxHGAjn0ESpsmTTPj3qwSNyFTs2hlPfG4oQHazvm9rVt8oBNDPnolNbbbtEF7Q1jW5nDjh47XXbMQy/LPns/xFxJqitHhERFIb6zP4Ka5HemRZZjCnB9oDJkemO++jpbRt9N/EES8k9oM7OhZqDhRNoUCF0ZGNAQEq6w67AgK+2NrLkBpQwq4Qwj4gISJ3GM3y0TO5g133ve19wyyqDe5RvGNzmgI+LFGj3nXspWzrZWuEYctoHmJmAJJRkvrEDMdEzmJlp5ttW50CVLw4UB5aEA7NjqhXBzuWP/diP8fiN55Eusf6xTPJXf+QjH8kZh/IUR1+rhjIWTRpYjjvX1TjAvMy6zgXJHQiNh/iGge0QuXXWCs6lSwHCE2cxHoUArHlrh/nKs3lgO2Mpdzl3LNx8rTlOxzNrjjzXFqHBNjAk4NqtZqKDdplkyCIbYXlPg07pEez0rr7YjqHcQ50EIRh7QSaeAUw76jedlOFVYPM+IVPslPMpwxO7DxFcbEbst99+BIWNFGKISLgkQygM/nkgIgMxcHGsuKnXvED02iscv73ldfsXXunLm+ccxRn54wAPdK+66qq4BWxpCAZDRnoIc/yLV5wqYLn5gCqjAL+f/vSn89SzR8OibiB8azZZQqfWATZ0xzdPzbd4WnjdfGOiN9+2RFsVLg4UB5aHA9OuLJbCgf3Qn1YHG3I2xU8//XT4bWlmo7OM8r/VQ+ZffjSMuvQGh2HADx9j6tTydH6XKQHP3It4b/nRLlopPINAfAgs9LQ9bgQ2jKEFpOSWzPGNzeNxj3ucZRongYSNZwUAD4TQBa8ceOCBPMydIwcexgLb/auwkldeeaUVn6+WP20DAwCaYt9xoBilNv8GPPxrxKPLtkd5K39GyOALxusbQqg8sW74YRHjYsYH7dlr91t5f9JT9QvywRt80CMuAt51H5xw0YdMjby00ujxOpIU1hfSCXYxETPO89ZuB7g5xvPVgOse4bD6uQg85SlPAVSglP8BG37rOyKZqXGeC7pqIR8xiBmJeSmu+6Z3k0hIBugJQ/CZqzlq1UCQZZr2dCC7+ARIGIYPJTCSSZw4lS3qxlhvxaVRnfwZbZ2whKutBYnTU/eNJku4CqGvUw8miarY1dVvz8U0wDG9w0+iDMuNp0aEgd1w+B5ZcfTOJCGmcHBzdE3MANPJcXlVIQnnzYqKArvLq0E1VxyYIwcmBYRpzcRUbqXunYpZ5yh8diUtVfQqq561D5xbTZrLkrXPWpP117ujgVDm2JNpqlpgQBjkURYZhAOWzYLqN7ZYzT0K7FlzE+uNXmiZJgP50w8XYLP4WqwTUIxFJO5O9CrWVMxn1DUcHmkrXmluwv7oghDOziivZqePGrsMnJpBPgADqIYpe/lqUJUmDHSimsQLDA+VNO6xrHjd3MgQK09hbVv7agiEuKMSRKpNeaRSrCGH+aOMd5GHAD1VszKs3KYcMcUdl6c6zvDu33BG10w5RKoZ9/o4cW6CYa0o5kX0hEX2y1k+WI/AWD9VEKBmg6Iw7uE/YSJ+avqFGKT6jUi0ISa+e/40sbFCl+Pej7BR/zs6NHoMVqz3ZFnmBPVoCwM9MsRhsjqNGs74duwRpCr9VQDfMAf/vYhUnFGh5iInhdQ2WH57BXnuqwTb1ZmZ4E8zITJWm076mwPr83WhmOZjrDLFgeLADBwYGxBmKiCfobHlfGWxQL5wnjghBpac7QYMCydmlwlwTMD2hLPpzMs73TSMt7UBxSnKaYtWDS/p8aX47jTzq/7iwHpzYMuR3dabHXuwd/FdoFbuwb6zPB9wwAE08l3oOw2b5s0GYKec6d5uAvmJmX2s8/wu0FNNFAeKA+vNgaFGbrmxr7aufWaKtLyuUBrTdR2Ite8XazanP2YAVnT2f1vaia1UV3GgOFAc2A4HOLXQxyblI+dKkwCftuW209JyvputXzuIpRgt5wCtDVU2nm0527nPVrc/7elsJ33c2nCmOlIcKA5shwOWFBml+TkN4ojso5Hz/uXByxt2ypyM2yFoIe/CcurRRgeLF0LSHBsdnCyfY81VVXGgOFAcKA4snAOcYUW/cJSJb2xPzD5ALr6VszrCSgwLfdshmWcvj9l2tpXO4coBoYS7AiTKtBNH/lSg+TBHNUn6ioWwI+S1vN0LoUGjeJJcnLkSfHQ2nsRs24aAsSWDslFtcSwfjGOrZKtBY9KLwaHERXG12i0OFAeKA2vPARq52E1OwE7SyOV32miP3GkfB296P6kcW3IqhowgLEYSqzhU48BPO1vltIxDRO2IrQNLDj4t8KDLMuyRGwB5JMOTnI/izzzb/DMEzqBz4IKp+OzYlcj2OS610eXMsXHs458z+Tpn5RBXL3JNQ4+mDWXtU0zDqypTHCgOFAfmwoGxe+TDgDBj1TLndoTdEEWr7Z0rJvuhuNaSTAj9TUCw/e5sj/AvCbnlAu0Cdwhq4VEuBoFE8lrUNU300B2lDU8cTJLgkgO5+B4u2S8mxDKbQAy126Fwgpfw2o4+C7/TD9DYFw2fMCBCwDZpTiVipAhXIrLpljpOJBJgRCygFg91S69X4eJAcaA4UByYgQNjUWyTyG7eocM5BSsGZFx40rAU43JRUAEdmZXrQqIU2MBsTuEWSiyoYImncYo4xuSbS1JqAbpnIH09Xkn0UHHWBGahTENfobBlGuWUkPCrCX+Wq+1QYLvfJCf/5lEGktnDeSoxOEXy8ohdJN584ZUyrSo/Ip8pINYK9b2ZTNw0pm4mYkwjIC26o/UY/9sQJFg39f2CCy4wuGwwKdbIa5S31wc9Wo/RrF4UB4oDxYEl4cAkIBcTSmxIllsgLbIj6zy0EL+ac7tEikJaeuSADdi2rZ59WcdskrDZb/G9qfJSNi1JVxdOhgDpdGICkADgIUZ4EPmwnVP67Gc/yzYutTYm58Lk2Dbc54FIchJE3X12+De/+c15XdBsccoELBPXU3ZUVbGIcIWAtaeeemqrSqw9OSs33YZnY3dmQf1edEJPEg5NEOBUDrAb9wQxlW5VK36IcBKNPNYeL3LVFi+2hVllM5BbLJQI/V0pthY+CYuA4kBxYP04MAnILf0Ov1qypTsUEjIKlkuSDNmcKJdwXcIuSbUVi5InYQOlXJBwv+ltokaLqm1xtynLjW5w9G39uDmhR469CX/NpYD1ohWjDQsPTi8XTV2mOAHXBFonNkmgIsg2yM9ehticcl6xfKiEYZxhg4AVx0PBOL0F5gX9lluaOUT0exBr1MC80KGkAVsewm4D1wlOcPCYGEHC8KK3ILeGhOym7t///vdXVVKccXowxE43mA+JzU6/lyaV3MBoT/IjdkBuUc2Jd9KCiXzudZYbfxLppMFuiV721OhXZ4sDxYHiwM5xYBKQC1Bl5QXS4CemVzZS+7u0QL5RCbXNtYoOZ+uXLiiSMwXdZq212yNgDzwo67zsIIENdcu9tX7nOrPMNes4GOYtmMDa7QKE2AuMBeKWu4K/IcbKnwH24Hfs24zV0pOIEcaODSaBK97Kj8K+bVxIA4lPTloSYdsd9hK71wzv7jCl2BmB0xv5ByR0Oc0bhB9++OEioHnLaBI4CAGQWw3QN9slZJHcQTCxAAFA2i67bQIU6gjtnxihU3Rx/hAEkcSKJyLIsCm32Myefcs8uEVbcaA4UBxYIAcmATmEiJt6y6eZtCjJidJyLQfOLdMiSx9zzDGXX345M6+b7LTXXXcd/GanhU+AwYYu9X1vRsYAltiY42Fjx/s+97kPwMMxohJe8Ylr+V4hK62aju5F/I/Bg49bqyonylwZKRIVXwRCFUWfHEa9HuyL9wQAfrTBbBWymhgpF537zDPPpGrzgSCWISxZLz/84Q/LXydcOVN5sncE4xkJOO7lXT/kQGPwB+oONFLr1cDegDYiyFZ94xf4bVTTxYHiQHFgJTgwbRrTsZ1Jci2P4AQ3bEkh7Z6yn9PG6G1s7G62/BzQiDZm3V/XeCyTx1v3iTuEmNio29W4QYembcv0KjQ32IPcTX7KHnPzUItvGj73PmupMHfkBWdKsYHtNBqLCFV+8gY5SGbPV5jUlfOH/O/4sskxCnqhOGwmhNGw/WsLnOadCjWnR/61f08jz7uI5w3AMEOYOOyww+TDZva//fbbpc6U85RksBIfRhFZHCgOFAdWhQOzAzltDJZQ1KR8tlObDlMiQTuDqv3Rm2++mSm1ZwRVnnF4gefIFzgq/NpoqOSYJBFvyIpXNrztbXNfxx8IKtc48wbYg/HZ0bBzwXbdiPdKcnuPnhWMivyxj32MJZ93Al0cvjoc2DB+lAM5jACtDZz03pAYHrvgsf17rcQewIeRU57tc8nTjHKa9m9M+jBb+bwbOSCueYwHRAoZPJ2yY2aw988Ov9XIMwsctWq6OFAcKA4sPwdmB/Jgg9gmNnebNzL8cCpaemO7tpZsZtV29Dw7phzd++zRy8+geVEIpFks4DHMtmMd1ZlJ/OKLL3b+m1cgTf2oo4561rOehT+s4nDd0+A3OUkGUlvmfpOQxNrFYfbqftub+q4JEKsYLHc0HKaCZwbza665Rg2qgtmjSrybEJctHUijLWYAMgQ5zEk5Cren2qKaiwrAN8ImegsehwB37H/bO2d1D6+0ftlllzmz4EwdN0kO+TZomGqo42wSzcwwL8ZWPcWB4kBxYI9z4B4crBoLrNQ2Na3pA6bQ/+zdCu3iSh4I1xe+8AUuV09+8pMZWp03E9zN2g2846DOTgtU2FEdTLKg83ezoENxPlxt63f3WQ9F2Pyh4O43rUVby8CSAwHYo2TjiVNhzOAvetGLaM/4I4O1AnCab6CnoNdxPlvjmA+/gTGQFofHu/zXKLi0ZBYRQ6BHXoHZeEv1JxZgu1EzRu6rkBlAVUwC7373uw2N9JpxdHCphAbPzc0dIoVGiWVU5xtvvNGOiUNl7gNg9xn/yQdGMHFenU9LXi/iwte//nXA709zwAa5R47P2UkhpqCQCo4GP0we4sXezKO6kClXjRYHigNrxgH+xVdffbVDTPv0KyfKcsGGSy+9tL+T30zoDpvdeuutfrSnOQTF0Ykq6egRjY27FggHJ60MoCIZuM/fLe5Oo5Xv5h2kEj52s8XRtmwz4wmbM1x3VOw973lPytC5bT9zLCAtOeAnZB4tVugYnv94yxWcsZoF25nyO++8M68og6sw3m8KMS85AgGJio8CxFUYZpMSWE2e/vSnGy+WAA7ttORo5y4QqzBjOBnCn8cffzyMN15a5LrYiDdwzsWxKDAVJN5Li+xG8vAn7EetHtHX2eG5zuVdYoQpIeQAFzmu+PB+scyv1osDxYHiwEpzgI5Hsxp0YZiPHDDYVV0zEaZ1h6HYqa0lzEduVEaN3iEbkAN4nmukpdUalwmdWq2OFLXFgeJAcWBJOEBN4qg0CMqyrT3yJenYGpCxEYrDQr3zL5145cLpbNSpNRiv6kJxoDhQHFgeDhSQL89YjKcEHCZF7LITWvQVB4oDxYHiwCI4UEC+CK5P3Sb85hZuf5p32+T8pFNXWQWLA8WB4kBxYK04MATyHFxe12sVewfL+aJPCM22roNV/SoOFAeKA8WBAQfGBmLZx9mNv/HBBx/sgJADTmvJvrhbt5NXa9nH6lRxoDhQHCgOrCUHnODl7CYXxgCj9wFyZ3+ZcMVLd6Zo/biQ7CDi1dzrXveKE9kSXrEZVOwzUmfSu/VjFHeBjbK/LOFoLg9JxbrlGYuipDgwMweEC3Ng2ElgQbX3WRv7tVLGi4TYnLmZJX8RkDvmvoTHzxrfDIczcuKeLjknd5o88ubYuDEb3d9petag/mLdGgxidaE4IE2lmCKDQ0zTniMXgEwkTgFGml1aCBEn09UorJhwYwKtUJXo9AKA2NPNGWIRSLzokS1ecb6cUBe7e4EjsQznyJ0LF/tFBBVKJxYJ/yK/SHgifIrgLTY4iBpipwinQ/ISImaBHNv9pkmaH/zgB00hMeN6gUbWdrlWBdLZf//9d5+qlW5RjKC77rpLXEUJiFe6I0V8caA4MPs5crHAjj76aIkpWwZSUVfPPfdcmTAsryK+ie0qxJvfcpiKOyaYKIgSc1uoTn8KPeaRCJ1eEV5uL48Emea8884TzU1cGlCNacKSv/nNbw5bRGfDsdhMiF2yp7Af7DV2sZoI7IpRg4Sn4s6+/e1v3+PzZ7bJAMhlxW0JEcZWsrSbTbN1ud4qDuwpDkyKtW5JBTYyZ4gYSlPkB8f2TqMXN1vYbQHAqddCb4Ii+TfFdrUDL9R21CaHpgTrJgFI5CWSq1wgQriLwi0kjdjsi2LxYmOtE2guuugiLDrllFNEypWvHVvwEzMTvTwaucSvoqsSg/wppSnI94p/3e/D1LOUSkfGKCLQPZ8GOchz1lxJqWu0ZSBYRNhLrNFQkBAmwKow74LhK2CjhWTHrIInffB5ZIj6LvirJDf2Y5p9m3jhvjtOwWlI68K5C3lrDmhFwHY0iLSPGIChgBrIIlwzhINNW4QSv8V7R6RG3fGv8p76V1X3vOc9/Ut3FI7eXrh5Im584FyseFKOV3CAZukRYVFzmiAjupAtT4+bqhLhX/h3dzBHL1CIvSohfeKJ+PMo0VAoceEGTqrcuzbmR0PxawWReIgMoe/bKEgC5C2VS9yOG6rFHOxt01tgf83pAs57HbcR2cwMf/iHf9j45hGaCXb4piSyVZUM9NircvYbkrRHUFn3W+YhbA/xPtVMg7SOGFPIvygX6FcB7eInSUh/8SS+r14xVQyutISL+iqr3eJAcWB6Dmw51roXKOI+fhG8mdClyubyLbE0dGFC/4mf+AlWdCmrn/GMZzAXJ/Sr9FmswRZiS5hsVx5Z7JYnsO1iY62Ll44hiUMeT65cEp1J+M1iDKIsryQeqU3gGSHJZoTtDLgOESnulua8YuWV4IRcZeFme3/0ox8NSxIFnZRARJCB1HJv4GCPS5xzKUoFURf2/Pzzzxc5n/XeyBK5pEuB/Y0Y9nyUADMugUwCicqOWmRb/REGLN1xtF0+FbHZ/WZ0Ub+g7qARPQDjxS9+8QMf+EA1IEYqVb8JdrojpSlJQm2mR9KuS8GCfuKCnOUwL/KizrojJC08C2E2I1RCPkA/sVJPWTVwRhPu+AbMOq+bhzLIiWhr1kkwoyO2LVIJ1GRASq9Rcsghh4B/lJgSsqy6j3i9xjpda9wgDSA4Gxy6hl1nnXUWZA1PSDZYKnrugx/8YAXsOp144om4ndcV+MQnPuEVBWygGERNMza0QRR1v/FN70jGGtIdvPLW6aefnoYEyfcnya9VQmjWHY+IU1LbaT3TQBx+sJ1pYLIRrCXDJR+QkNCmjEj7Us7jD2kgRDKnKTY2w0JjQv0oDhQHlocDY2OtTzo1TtWwEFjlr7/+euqgnlChSO4M6ZRyizW16UlPehLN2xph3bQKy5ol84pVyUptEbS4KGN5tZjSk/ayMzZ1kKoKLaAI4OkjtZGK8AqqwV3ohb1+QFDLNPiEK5ZdVg2jgO0R3PCczARU8JYy5y1rNHN0nvohaQrFC+4CDwPHuEKt97rRBGDMAAwtIIf+Z+yOOOKI2PO59B955JFAzg8oHrU+dZIwzIGoti5aJqyNcpltFPihj+gxZyiOpBP9VcxvIqAJQGWU1oUrJcK84pEyhx12GPpJD2omYRBldPNRj3oUAVHWFpCW5pAEvxl+LrjggpNOOkkrZ555pk0KTegRbqhERiB9MSGhOPDTLpRVJpUoj5OkH72m+JKW+HUim1rP7AxfEY9CsAfVWq5VeGlcACGlXKNEAchHokqv8YSdg53JthEC7ByRaUixoTmO4gYRqz/1qU8ZRD1661vfKrdv+xB8NdrSNNpsHJC9jBFeYYV6NBdWq4RBglqvkre97W0XXnjhDTfcYFjxyqVwpoHE8+QYWn4IwAETgzjO54Co96Y3vQk3nvvc5+JYRDdltEvv546QV+oqDhQHVpEDk4CcBkZFiHU0n73Fmu4oO7Xf2S93x79UB0sGvcFqQlMHVxYji6A/6fRWZ4jOqG4B3bNYjhsAhr101Bk7dk5rsQIQnY6IpX7AD0t5wBI6WvGTkhx6MZaqCi4aBeWZRlh0gS48xmEqmrxq/XRUv6H0SqzcRhDm0a1ZiaEjmAcSyoMr//Jq9C9NHbRQlDed1kjVhThSIUBtVEPaLYmBzkpppu9qC/3ohG1MxKaNO4QP2qG3lGFdwAF9cSnpaXPICAH+dD83qa16QZvXlptytblSzPx0fhJgAzzwH0O3frm8pWtqxiJ47z5K0IPDLMwmOUiDkSSk2PP1i2laeVJU6pFEjpZPIiYV+VM93oXrscaTP3CYwJqPJQVIP4DWv/7EIgnifBSQO09Z2sM33cG64447Lt6gyMN5DTEzoNDrWuEu6hHe+tDMAcSDYYT94A/+YKYBg4cBZWnXF5UTEHNAw5+NddR6RLLVu2k6EWtID7Fq1FUcKA6sKAdmj+PW65TUdGqHVdV2r2WUlkDBoqCDDTZe65pFir5ieQpU7M1rhnjpTTW0Lns9NZAJyElUQ4s4ILEuW/3ZaRmBLei4TQJwNSYHV6z+GRTLN+nKi/ZZWVxJA8QCyrcyUUm3erJA/WqLpcHlT78hCpCg2dOhUagtlySn2UeHkfri3wh2MVttlT/sCqQQ1RJc7PiAYbX5l0wAKWNPyiGLUEIqapRAOECIEhsEssoyLyOYjh6IzaU26my/Ke4m/R7MY2MrluS8Ke/fNkxukp9Addt0RwyJlrjGJpHy4ZuSGkJAH4WXKYUMTQXXEVgbXwdvpRIfFDnJW06FxDTiXWWklnc/woqh1PrgW1PGpgabv1kEzpUkA61ixMO9uYZUr4sDYzkwO5Bn5W221hymsizSBcEMtYPOR0V48pOfrGGrLX3FAkSVWbksXnOZOtARJFCDBgf5Vc7AC9KssJbjaSwW1FnKWSzDIAFEufxgfLbix0YyegVgCFsMzlRtRpT2LkMr/dsryJg5QUsPw/kNJKBRNpVbW0jl/wiipunpRpynu7ONYwIUtI9AELHZbF6lXT/YflizY8AIJWYjUbKnBAryGzAopE+oxvZONmIbeOELX+jPhuUD8WKrEXuU72HS70gtrWupvxfU8qg1lIEbrUSnTCrMRG0/DexKEKyJF02kGLDR7oaZQ3SzAUHXx8m5zPCqpDhQHFgUB2YHcnqJjXDnzdhLW7g4K5SV2jJhgaAQwO+2ANEYLN/RFRbV2wW2C9JIOVk9kdEv5TaqyUM0ZqrVNMyhZmEs5lvHo6u5/JicVUWLKrfNYbBirW3vpkJUwUWW4bFx/fI6dTDA48fYkL+NwwpTXsEJ0WHQFnllq8r3APYYohFsQ5qUwFoOpLEukiVUY1u2E8xLwL6y1r2rQIzwo5R4CsvVBtRtBHDNo5TzHAyUMmxgV7OLuIk/qmqb9xNmVEwO6MHP1l8m8VhQ+hc91RAJrxfCYDP7CsrdJH/Q4/tKEKzjvkH3wfb000C7rBFkIAYJdvs4IS7wu6imiwPFge1zYHYgt1JbPfnisv4xyYYUv+3VWWLo3zCDqtdslVxvmPKog3s2i5fDZtjFG5lLYFuU+R/Fm51BGGAAj2yi90g/GGY7mrY/8dPmax75beuUy5hBmWAmhSvMyIaAXxjjat5lm7X9YaT8ZqMGHjm8znrMPYq7nN+oZfhFGF/62GD4TyXOz0ZTUF/YZmzGU3M5o6WYRm2Ko3aCvKLjoAvY5/BVu2KHz03QSF7EhBxlBOcOOnqLcEMqIirxxMRt7cYBUL+4uCvDMS0VMkHbnPaUY4GSZqaajQ7uaQWLlCGp5AdXuEAsDsM/VQ1cEDZigtqQRDggvyrD6Q9Xka2h9koGWkMKsw3EQ8IGBF75jh70oAdFGjjttNNCg7EjDeiRTXRk6K+3Uhv5BntROJgGYV07NUcI4PvGXY7ZTBPE643or/vFgeLASnBgKiC31hD8c+6o9Sr+NexyTHlWfI6vzJvcl3hc83ejsfEMshvHAuy+y1El53D4tE/W5FaCa7MRyUThSB62AGDbt7kcrMpJIUoS2LP1YAObx3IMHk0XzAn46JdglbpJ6eSWBY3w1pkiCA3ggQHFLr5OjUi/DZ93synrIBMgcQJN6+QtTtr84LKZSj8DGDfffLP7tkUgugMIqYcDo+GDyqDCCTG7J15J2HM1U1sDM22qeGSg+cBzcuQAj0gvqoHAgQ9KAmPzJ3T6F82I9Bugwic76yZM9pJz2ZfRcTd5sfE4Qw9nN7WZeCQMYhBoB37wCVDZ942flyMATOgEDibozNVQAlz9oAczhCAeA/3JUY42z7QOqtMoU4cNC44dWsElrYu79/KXvzwSjEGhK2dQ8qcuDHz0EhzeMOE2l3Ve+jxFIsbhWFP3MVNDdgTs+mvIPMF5YwRls9eucsitErCNWsXQwE3dRc7AB/SzmbP6EKDbNMgAGVazC/MRkE9YVT5n3Ww9bXyuH8WB4sDKcWBSQJjWGeuOhYYmYQluMGy5ZJTjMWsNtQqT960XynAVjp7EFcuaYpnzwypsBXTUuA9psvvMWmxAGP1lBbVAY50FGpriD6HHuhy2xEgeN++c+cbe+CpbtfNneAvtLMFqi1HEEECXBANRFSCETy1miCEzcIbGIMZcbOBU63U/oBroagfJCBPKBE2JC97KMCEAOqqcdRqF9lOEFiCWGXR9QZt+GWjvshu3qaIJDm5e0ZwyHOucjPc78V6gDuLVjEIl42yfE89eoSwipvXCW9rylpvqxzrt2rBQ/0/91E8RFzDNNMNAPVItSchTVSFbJajiMd4oIWKiBMF6hNXYGOdtwoGrxSFGp6fexR/c85aD5njbeKJFfEB8/EVwhrjQorJQeZn3sRf/EQNrvd4c0AZ8MxZoSEO6QyDWNQ0xlnDla5XAcpXEOI9svcs0QAm22CBIoJuMoDq1oo9IUq05g6sRIxyZA/8kjAlmld3/QqvF4kBxYDIHxgaEmTbW+nowd+Gx1if4Zs/gtr0eg7ITHd+JOmfgtth8jPysAtOc4tuofvZ8ModK2MNnoGEsK3gD8G6R6rCir8/A0nqlOLBADswea32BRK9Z0xOcvGb2/1p1Fu1Ex3eizhn4nKgAbQdhhhq8kn0HlUxwm5hQc88KNdhu5yggKA2lnwlkNpLqreJAcWCpODDVHvlSUbwdYpZkfd9OF+rdFeIAa3bi3W7noLZKDjjgAJVsf/aqwZG8RHi1AbHArAcrNIhFanFgqTgwdh0Ymtb5y3BPS/qypaJ++8ToEdM6j197pduvrWooDmzKAfv0cYPvD7Bt+tagwFwqaXVyR4ibC2tBXOHqKg4UB1aFA9x3hFmzy7ZJPnJAzh3acbJ19S3Pwroqw1Z0FgeKA8WB4kBxIBzguyr8A//oFrsl9/fRyDmWO/oiS4QjTLNtyC0zu2nk6VT7sczUFm3FgeJAcaA4UBzoORAgd4p1EpA7xcTJVi6stdRZ6eI8hhzmcU5p/cSUmu7FgeJAcaA4sN4cgGJyKTkHu7lp3R75uvLC3r8gqQkVUldxoDhQHCgOFAdWiwM0cgE5BkA+9Fofq6rKliiylVgTski1PotL5RCLA7JJz9xf4lcIW0EgELF1qXg0F0WcKOAALuuF+BvtSvgU0b+Xqr+rRYxUqkKmyJ6XMKWLukSfFSlFhLtF2aX4oImFR9w89dRTyx9tUdOg2i0OLCcHxqLYJsfPxGUTieL5z3++oKGyZLZolIJ3CldpzRXScoDWglYCeJAmR+QgXOVy8mWrVHEgkFnLqXwmjnYJrSoOeS/obLXaOZafi7wyF3q2RAnGcsiUhXNRCJouO5qFDDN8S8TPhV2phJ8pSfGKK6543vOet51za3MkqaoqDhQHlpkDkyK7QWhRwQWvtrBa3aTWEA1K8Esg7T4Ih+sWHc5xTSl3RxTrCy+8UAgLoS4VExJyefo/l8huYp6L2CUMONgWvJr3gQVX2E7hPynoySUlNidpRoYMYCA6prQcyfpM1xQaU+72BFVNmvZvfOMbTgoIby6djBwzgnoKvEUVE4VUzcnbkYs55ZZbbpEVww9xNzknetHxpJ7DieSlgNQs0oeox3FhyWRFEm3FNEEWQbPTyX57hf4nCKiQpaODpb/vete7JOcQ4FNM+Kuuusp8gDQtUqmzVbKha87oixUqlrhzzyZGKNFH/SXleEtzYrsKad5aIQkJ7e5MoGivooqaPILGX3fddYlZK3S5WPSaxjc12xmSXWYU2xBgTpI1CZF4KM4Jhvd9kRgGNktAgld4oga/OYy0/D24gUhnrIVJMdXf8pa3sA0IZT96dkMfRXQXc56ziaMgeMKtpOV34YGhHilYdDbsSp405ish4oVS166Y8NKwCjQrNqr8MYZYl8MQg+tytsSoEWiMr4j3idZuCMw6P1SI8v4IJeaI/sYMZnqL8KpRsW/VFv5jjjSvOGAckwYGwXWCfHkWpaKkOLAlDoyN7PanX3u7LKaCRbQ/rX1WZEk+TjrpJEsAh3bQ7qmEXe7Db8ulxYhe3l65/vrrIRnwFv+Zdd3K1de/8N+WPAmv5kUGtLYsgg0Ld1+n1Rlc9WPjUJ8TfcpY31k4wIkX4RP88xsMQEFPvXjUUUf1OS5F1bav0SoHBgSCVrOo6RJmWKkHPaJNsgyLHN5KJtsVOElJeAnnBOVuYKwqKmAr0Cq0j3DiiScmPDhUsF0CjBEvPVfKkDyIaw94wANaWwLCqwrOearX7MN9si9xSGRUC80wFVDlRaAIzzSEdTk0AbRIhP27ppZM6oPOYinZopcXRR2XWaTvi4wpMNt9sdDTnN/EhVTFlt4ilZq3sFYfHfAYZazCRqoXicx/rJYAxiOirbTuiVrv8skYd3Ysj8i10py42cKwyzvgszL6p5xySsgge/nE3PF9kc8URkNSn7nTJxv13dkny1vEAmJ07/Zh/4w0oMUUIPb5EvvZ6IMVrnnAxvqzOFAcWAkOUH6sZgNSJ5nWLTqymVkmbIRHwXJZEQSrOvPMM0844QTLRx/pguAvoaQlkrpmre+TK29J4liJwlhBX4QiltrBSQB3IAG9h85N3wJOlE4KVrDQYk2jkj0Mr2h+6rGUB0je+c53glsA7EWrLUh73/veJw9YEoRTN6mndD47uLJjcVkgYyEgg9Jf1K9zzjlHVngwiQzrOCVShrrkKnUBGwOqQko/tKD5wQNZPgcOFGqmBSLSUFKFkU0yU8Y0amYAqvAZZ5zhvpRo2qKOmwbastegIQT7TRfkQOApNZfuyAhBBSdUwXuNmkXJCYYnQKtFIqIfkwv9K6+M/kqkBgsDrn1nE9iEJi0guQMXmpAyHBtJA60Ygum1GEIP1pb8LkRS+E29RhtxgUiBCWwSlH7qsptjbdpopvjKfCozik+DfOCVsAJhRDR1qsd9KewIT6wyJON8CGwMSuI5dxMQDvJ9VsQUuc8zvrHEEJHJOtRuNHvF+BLv8JB1x3BrjshCOxdmFVR7y+sGyAzEXnOGfOA7x+Hkb9VBIhdbBR8OT6VTI1uk2pX4yorI4kBxYCoO9MA+0MjbIwI+zcMCRAdtN625dDXrXTRyax/AAAxUFrDByAyc+vLLIOzMUSO3FMpuCXQtkRbTsb1je4fEgIpZmBraytDFLfcZHijFSu8RlLWCU5ShEbRTJ22bEEBvtpQrEEijr0M1NdCVAdhou3DIig8OLdlRbSnoQMUIAvjkogXzGoIZxAt/glsKsTIDRc0QgxxDLBNXXmRSRhI4TJeRxISgFyQGokDugGSvAGnKKPz2GwZHQeQ2oVFtsT/jHjikAduv8QgQ8vBCpKpCNu4RGZkK3L/77rtjDZpwGVyMkqhU12jV+hIpx2WiSoCGsGx2oA2m2vqBoGDPIAJOIoVH7pCibJeAzFGNHF7CQqMp3xo4xz3W7DQBOJklUKst0g/HPWQnSZ1GdZ8kpFHKtH7lFeOiHp8MZgL7GCckG2XkkNmWsIW36JGA3IuGnuCYwdIRezq+OPQweEQ6JEMwCeiF0cHha665RmGjdvbZZyPDvhjVHH/IYZPZWE+LA8WBZebAljXyIM1G0SWbP23iqwAJgj8lw0IPkyw6Fho2Z2AwlUCxRoUsvpZySy1ViUJGmtG5XsOzuMd2asEFt7Gl4xgo8q9XIDoDKUShRLqJh7iNsbReqS0BPDkADrGQQ/SBdgUFlZc3E2Rmp5mNBKCCCrVlNI0XrGJXzw4uJU+STRQO4vJqGjBDEbiYzJ428tlawK0a/Amf1KkV9xkh3EGktqj77iODAkoQQUkMzuzDzAzURI9SgNUnKbG9QhtWSWp2IcnWvgI0fpDvRTYM8D+YKcqbbwSmi759mYc4oLa+L1hEwdXN7A3bnsAfBVxoMFF1KrvOhACWcyMyVmeFiMRTewcEEfvQtH92KWOd9LhAnTSDSNxgIQCxKMnwZV0gTBB2WxpT4xKbCgcCMoHv0yPjrnXDHfKMGjlPGS8SIFAoTTDMNjfkdQXkQXeOF3GDwCUiRSwNRseo4Sp66Pp+mFfEbhs6A9PLGn181ZXiwF7kwHySplhNmIWtO2yJFjhmQw5ctChbvHYr9xRfrebWWUZgxk/qF7awc2RjovGBsgiAA6hUt6yq8DKuVZzmaGlW+Vwwpvlk8UsAIXbK6YvepfM5UMCS3HNYJYAWAADR3IclwMDoWNYDb971GxLESwsOjcUt0B7MbmZqdUYHTc1pC+q0tvzWlkfu5wIq7SloIRmgoT2lMqb76oQ9g+0Y5mvWoMsuu8xmDfGIeYD9fCAawk5aMsMyIkE+BZSW33O772/kCa20/qIE53Uqlmr3NTRhV4gsZQgMq60N0gCzPDC2s4BXGT5gzGBu4Pzrou7D78Z23GgSsPKkBxcjFrM8gY8czJRlaBp5XiRy+dOGRYQwo0mnx8bMmRj2k3E8EyaNmkVplEiBh/YaGHJIgSYP1Z/7S1nX99S6VJ1dbw7MB8itYhYO2kw29qxcUUApWFEj9s4FVu0sWK8pQNZN6hpLqUWzKYi0KEhgm9PKS0GkuLN5WtxZZbGLisysSu+0rDO9ushDvMAs+iqxiGMvnYxx2Ips0bevHPe9xmFAYh2n75IkrNdg0i4pdzDwRj6AW63k2P31fqSMXXzN9AJJkNIGAfM4REl3iALagoIsunoNCxmc0aYVbfEs8zrIoS7rBUqU4a1G4/TIhQPMzm7CftUqFoEjNCR9p8JQ/B3veAdG0d2xJd4G7aJrMvyoCsPxQWc1NDAtNBwdnYf0fjy3C6CPIJxrodHBvbF75EjSWf8yZXPxM4gUZYWZrNFpBEldb3zjG/2JTnsfLto2A8CAnkaGpqnR2UZxk/yHpe1plHiMMugYy8GN3GPczSuyMj4YazV4hTpOIsyE0ah/OWeQWmJdN20o5fxacBiK+5MHQynle2dRqp6uPwf6zYCxe+S0k7F75Nbcfo88bl/ZteU6xJ/Inmu2YJfn2oU9cvqipdy8sXY3MICItjbDB9vM4A3wfOxjH3NTMaZaMOaR805xNfdiDN0QEVTAQk9hjENr7Wl+sH/kaX+95z3viXN7q8dve7Q04xRj/kWALdiMF5SFu+yupJBBVZTFeLanOyAEYnHUb24BgCqnp1IgiMWdgrlbVRRBrwxoZqeJTzszb3CrvUj7bHvkmsjRu/QiP9Scd9tFwogbeasEDUQcfWl75GIesPAzYOQtnIffdGuWJNM7mNfXADjH7pGzM9k46BmrLUOW0xmQEpb3BKuWe4FZRyIhfqmW5t38yb0SVZtm7y0kGQg3DQobA5mY/TzOboNx9yhb4Bk7OndrNIyyTZ5jBeS8BGoMD/MUkcSCwUDXn8WB4sBKcGDGPXJfvpXFehd3p15diIkyUWKyjjfoorisZS7UXrLDDWt0VLR2n+LLL8nlKRi2uPMsU4bCBIFofvYa+Gw77Gu3lQZmfbeUU8iYiLkcKw8XVRgQ4mJttyKb6FZzRma6V54GL3mKtT3XRsOhhx7K3xuEpCRjCQswpdyPlDFw4KSF94nu29vMW1UopApTChHDmYvjVXT0NhOIa9pi8Q4sMQvDSzRHIrG54Kk+hhIWCNvY5JWY9HVWYQDmEeEgB7JbECHwrDniRd41wdSjI+3QdogEWhzvUaWMrXQeBuoh9AQUU0ZPdbAZzNPfzGemaSNy3HHHsSe5T3NFMwAeG8uITMN6DxrN8wwQiQGmRuDgqW5fnG9jCAa3zO+Qm1btqeZ6GkKYTqGcAO13O5qv2hDsFfVgO8mMU0WqhfoOMvB3Sw1mBZ6YFX6ngEMNSLLX7g59nRBjIz/2eRcJD1dxuw1x/SgOFAdWnQOTAsKkb5YVa4ptXasbWb6dO4LuNjUpeeBhEF8im7Kswcr3ttyFM2suAWFaL6y2zObWYt3MYp2L5Zlezk6LVzijAJCmlENcWEKBRobfgVWMwkaKKbRWHuuw1L/WXAxUpj9WrmZPCQSY7ykNT/1jE85m3zrnmw2BYnF8y5VHEDEDBzB0RDFqa18b6ytIBl2KMTPAbFv7toQRz0IeRTAzRBfi0uV1hfv9lJyEzgmrUUrimocAsGeCJcoKrIpEqL8M18qEG+HhqNEbnSrBVX3EMT/8ST5oXVYJ5TtMVi320tdhW2vIU69oDk+MghbRE/V6cKFEXzBQv1Ay+ChQ4lEAWD2kuowyzmjRdxTm9JZ2JU0PLXqEwjwySbRCtogLodfjORgeutncJtxBSeOSP9XfzwrvpnfZmzdR9Suui3UVB4oDK8eBsQFhNgfylevnBILnC+TrxJmN+iJqiuNw8InWCFe4WdkFYKVnnG/BZPYCH6qPxYHiQHFgGTgwFsjn4+y2DN0rGnaCA7Rwtlk6HGM1rzQo7uSbLQB76jvRXNVZHCgOFAeKA1vlwFAjt//nZPNWa1mV8izb/JUqjemWxouZV6QRzmssyQyzuMc7b6l2TLbUnSpcHCgOFAdWlwMO6fBxnpSPnJsVPykBJm2qrW4/N6I84TXs5lbGiC0NLsyG3/GNiB/WwL9vS7VV4eJAcaA4UByYjQM8e7jx8nXtQ1Crah+NHJA7/GNPdBA8fLYml/CtuCsP3J6XkM5lIGnC2etlIK9oKA4UB4oDe40DgNzxM15Kk4DcUZYyre+1mTGhv/TvjSKZFJeKA8WB4kBxYPc5MNa0PnR2o7OOpUwqDudh+qds9MJNOMnTyju9I6AV2/WgEudenJweSBC7338ttuiYC2k9jTJN228Wpct4uJwsd1ppgfRomqncwPUnp+2Lo9CgL5awpWrdMIlDlwC0u3DZBnJizYfTB2/YiXaNtVbWO1fhTvCt6iwOLIQDY1FsKq91gG3v/MYbb2xgDKoF7RKIgyt864wYW5IsieLZQ4JlKIePZfRaBhxdCOv7RoW9E6rFLgaXsUc84hECi4oJusC9DAMkYKdNFxHEmgTm1LhoLQXk/cAJ3yYqi6y+O42saVTkBuFlfGItbs9OTF198SFLGZfg7XUVB4oDq8iBTYDcyg7FhSHjt5ygHOmk2Nfis1romexzh4IucgjRfpB4SihscaGBRIsks4psmgvN5BgrpmhfYoKyXiRyp6BdclkKdpaQKZhMN8rVyz3Ja+Lf0UehLdH3XH0GFMt04ubmUV9DSubdBDbpA60kG0cjoKeqZRxJ5cF+v9XWE9wa7TOUDDq4kfmndbPPPq5wmmv8GQuobrYC6W8rNoGBG1GL4BCTPnL6axO+f2UsJWOZlpHqB2WQYT3VhlHaSuzY/prQi7F8G8yBfozCUoFgxXoTPyd9bLNo0KkMcXvaSNro/lw+maqkOFAcmIYDk4CcFVFwR+E5ObGL6WhZERAqsbVFyXZfdMyAgRVBqK9EuWo5LhmNqZ40TvdFspyGmvUuI8cG4wTWWTpbTwXUPO+880Szpxa7iY0CreCzEGbinqaYsFxivorPKldHRkEatD4JGKWNNCAumEcyi0i/lhcd/pYe7fTTT0+4NOu1uKeGI5VIeSnBxuguuFCjBAuKGglMmi/1iA8vSJm3IJkXhR017rKMiCoqPqgCDDM6QkZp/ZL6JRHEhCBNGRfrtFwjAq65LwCZ6K2j6qYgZTK0ilymjJkmKngAhuzIxUNmWJQklYiot6M2HgYPcUmR6qT7m970JglY0ZnWNSfwu2rFd3MUvhcjSKX4E2pR2Hoh4J2pi2NoNjRMTYno7r5w6MqHEtLqgBI9FRSPW2WY5iKxJfUZzogIaxroo6esMrZaGn+EoXXfRyRZGdluNGyf/DSGWNMuDI/856JSi6c74Jv7t99+u9mi0fDc7+R5c4nay7lVhD4TMjn0sDTck3mWBaLxwQ+h75OZDSsMUJt+grqLa+u+t4SvZ0jo36rfxYHiwC5wYBKQWz2///u/3xp98803J6+DOy7AQMO2EjWZHWb7ninu4KetaH7IMCE3l3jgFrLdMUjuAstmawJs2HoQlDthsftLbHCZygTHPvfccwVdEY2cnwH4sSjT3a3+WCc8p+zjwoBbQOW/chpeCPRUYiln8pXqG2wrdtppp5EYrNEe0Z++/vWv2+32rwFSsyXYxjzJDBgzn0j6qf4BlhtfCG2J14QxJWS89rWvBWNsMyK7aUusb3gJgQy31G1S44ABAIDCTBIuk6oFIWgjPeg4qmA/EAJv0AIQ2qmByhEU2mWrnvQghJzIM3oq0Popp5wiz32ODtqd+fCHP6w75A8CouhyyfXeLmAJklGLTu0Koi76aXonmr07kI/pSNIUBItjn1C4YIwjqBh2/hQYH9mo9QpB1iMcA2mmsQ1yHAZj6lSVOY+rCMBD0pIaekpEOyd4SY4O2OA0Zooqj3LM0Qo5yQ8GLdzDQ3Ya4IoS5ImS6yZWy57uu2sGgFTOMIZCPEG/39jrFS/+1m/9FoQGySYGsoWCIMTglVeIFGhT0iMDp3LMzzAZCBFkxXLHE5X40h09Jcdgvm7qY9tbIcm5T7IkLug1cZDNH5MxwaSVl12deidVEtl9gftEs32b9VZxYNU5MAnIKS4WILo41Sp2V6szvKF70UJoG9YC96EChAA5ciH7swE27YceaT2iQq06m7ZPP1gCz1gakai/KHzuWzSBN2ULz+nWThBYmsEAbc8iazkGPFwQrLyyVktiDY9Bo4vOagn21LuewmDvwk4V0ueIUKIHKE8mgytWahFdjB2vBUiczGkDejylurXA5pQzoIsq1lfEEztY3S3WyDboqoX3pASqZAKDy51KezP02gXJaKYpwoZkMuVmATnAlV15Sn8v1kTsAFEnn3wyucS0oUdqi9AAO9FJDJKxjcXCTOO0oXC/swvAYJUyMEb9ctKQOQASIMd87BKTjpRAPJJbBUIDQryFsmwhOmJW45Ve+MHsBPw8BU6Ep7ToWCagMhCJRmBQcJXej+EQl3WqZyN2kQxoqL4dWj4DgBfjO+JfI6hFzGQwkPnG8GELvkFWrXOe8JYccWSI3pnUu4qR1VSoAPay5djPUgY86zugRacwCUj1hRI7dFz3zQqF9YLqrHVCCSFbx4kLctsk1K6azRnfcmYXQZDJIdHmMd/MVA/5jNihCeOCCZL9GCyscBPDMYcUJYltHwd++x9O1VAcKA5syoFJQG4NTfaLtpmabcJkXLAWZEHnqxV0cb9HBcpEPunyccOEbBXjWIylAWAYyXoMV6z4FmXIRCSCN276F6hYQFnjYwSGGckCEsZmJ9W/IIqGqrC3XOCZn5rKWyw2aJF5IPMVdKTI0qg0Tc3aKFu8FrMX6y2DS0SAwbAfVYAcfGYOgByCGiD0J+ROK3CCDAFL0hFlSA+gnfKKNsS746IWE/X64DwwhmJHiFFnqqLlA2YITfkDJLqf0LBmFKoahSkMVJjlvZtOaYjQGY3WIzgKgSJTEjf1CPwkcTg7dkzuHmEsLhkmKEgRh0/yvaZ+UE28SI4cQ4bnoAsMS/7t0QC9yDTqxAdgzCKNA3oXNxFk63XLyKK/8YdADLkHSMeE4AchaWDKIqLBXQq3pm1YEK0UU7N3TQMZb9scYAYzB8K3jGCmECkK543spz71KXzwZzLJAnITBq6HRarF/PAE90gJDDN6nfpNHuo+6UF5gj5FH6n2AnAb98Zm8QkP6yoOFAd2ggNTea2PbdgqYHFkW6NPEN6TeMo3TJ8rkXyUY9AFYFhzsyuMexCCykh9tPNtFaYPKUN5hZRu0h1pjWyw7Ja4GpeiIHpQNsu9HyDW6kkvlELbi1Q9ZlUOz25CCA1lODTNuks/JhyAH+UhTaSBja40gTbVUjFVFauAsW4bzOZAJIkWtJUVFwzYzw49zM5MwQywUBzKgh/WYErkkUceqU5m80aDOhEJDHpTv+kE5JR3M+5jodZNd/qSKMHb5h2GY1HHXVGFe3dLfcFt6MWm7X7v6BcXM73AWMjaYEkxsKqknlL37QtQmlka2AawhTTTb7oTXHBY38EkPtCzm+9IiI90G0+0tE4m1tnGRjfjytCPDgmAHZ7ln8INPm090IDp6NhCXEMGhmcOMJP4Kg2ER7oQtpgJREZ6v/G68847STYAOGSbXViBJ6MwHBbtv//+7Aqp36VdHTeHjaPNBcKKfQ1zFW2998ZOrFlVZ3GgODDgwOxA7oOnAPGrsiPrS/Y9WzgsEBY4J9Bqn2zAaGu0VQ9/WGsD5NQjmI1vXJ+oWQytFm4rPn3LTRelNrbr0cSdrfKkDQUwdD7l8yITK0WQztdcEZWnX7KdUEMVsNrSnyDr6AZ5T7bKwQzFjhXanjThgGyBvGjDSlKgrdpUNASwHORdyqh1H4Yho3UErtPeoBQZQg12rxVAEj3PVn1e9BYuDWII0/7dB6ib+liAbWSw9Ka2WNTR6cVsA/WOde6zGGOdviTraOs43GJhpovTTeMClgsiAv5Ugo22BmwBsKCQbHg23HHHHS2wMSHAXoOBNpr8BjABloPSCV1ALb7pbC+pYEVzTGtkMJJzbPSVmTYG6H3vex913FjriA8wDHcxg2sd3/o5oBXKNAsHOcOWv6kFy+M2T2JQA56MynYYi4F2ZNTZ6jd1E3KfNQKiO5Wnp+QGuwwEhVpniwPFgd3kwOxA7uO3wkIgS7OlITmnLQrRcvqVcTf7s8xtsXwijwm9P3wPRaAac3R2uD/ykY/wHUsv4ByHqWwPj+1X1l84Gp+yLMGGADredtttUcfbi/yh/GZ1jw2cXd1OMJymrY46rqvEu1ZwT+2kWsRp89kh5j1u5x6eGWL6NODkXMbNihdbFF/0eGT/u2lmrLj5Uzfj3QZOTjrpJLZ3JdvOCxgm3KhQtSGbcGC7gUEbQrT9nY2GmJwElpRPTzFHH01FP1iJQZFtb4KUR6FcF5BqAiuAFYmRgNtwCCV2HxiNYTOETouGhu0BW5QEhDkaAM55n9GPczQuJanIJACIq4/MHnrEVdC/+BNDwqALGV/E+JdDX/bFUeu3R3156GvrnfykWpgK0ZUkIiDVTW75qVmvMRzb48XSmvNbfxFG1NZ9w0oFD+V+qFP9OKA8RjGeJ/oNrhLCkv4uVSljZirDdB9JghRCTSdNYt1kM89Gw1f3iwPFgZk5cI846ObiyZwt20F1vnnLh+00V3OjpXCwK1IL+GTZoHWxN1o6OcJQtnzPrRLLATndWm+lmKBcztyH6V8EBtbT+GTt/gVpaDC0W6yzWDPGMqfjmIt7UTzP7TtmU5yn1U033QRUwD9msmRarI1OrL6AB1rwEFYh7VCnLM3qtLBaf/22+mO4xZ0FlVHUTjMU0RBkgjHclBjYQQVbK+cGbOEHB+DtuQY2EngOGe7ALZUYOIKFpRxJfqicvugtSi2LLoDnS4+x9DN3UMJBXetI5aqGHoQhw0YMSwA90nYsXDR/ACHIjDk32dXIDaQQf9p7ttFL18QZQIIzBCBbD9gInwC2Jmzbw/gMZQzRyFYSAbAWEOq1mQm99DHOazqlO97VX6cqCE84aduYdIJa9mG9YzOXv5UHIpsTpnkX5U4WqJxlgvzhdTqosXDfb5o0GMsOOkr86z4C9IjsgmkGXXeILwaaXQR2Yrv68U2nqO9Udq8jG21gEjGK6QgRgTGjbQqQUXxxWIRUT42FDRRmG5z3CvDWNHwlyekIG489ddNME8hWFdpMGIPOT4JYxo8PDVpnFMENAxenAb0mF2qLwVwBjCUGqce44LBJC7w1oU61qQpjgTdnPWzET8p6ba7t/vJSLe4RDvjSfZ7Mgfv015rYroc85CGXXnppfye/Ldm8ha0sfrSnPlqZqn357Q5xHoRb16xHfSWWBlZf6471a7Ty3byDMAvrbrY42hbQtWcMjazpLsINVG7FwIaNT+DkEZSCMR5ZtR0Ds0yDYX8aSLvgEDEn03I58c9SCvBUCPVzMweKSAb50+ACoeyM0oxJV6zcFmiuTLbkWQLaAFG/SAmgFz1aZ1FXM5LAGwwmulH+6GROnFNY1QynEWD7ttFjLzxteYtiGlLhVjbvIStcodwPpooyoBdVdtO966Q7qlInacAebYYPnTAMDOdAV3+BGZKB+m0cMHUQLjEhBZCKt9q1nYyM/q1Qa1AIKGTW/hEGMpYQUJhSbBaEt6Y6IuPyrTuwMDb8dpFRQLtxNByED9BLewauBpRtgxXaHYV9UOQDpukcH3CRD+LRhrc80onFmNbXnGAAmmZm4G1ASs5TfGP6RgyhjaoNaHOf7cHQGOVWicEytVhQiGhu6q8JGQKwF9+0DqT7VzwimmB4pgECdDAVmiFscnrKmmK8wH9Pbf0uDhQH5suBKGCDOof5yK3RLQ7J+gk4Fjs4tNL5yI3fqG12sSO1JCTF3AJQm0M4jHSMigMH7XM3WTQlQ6YstpuUb7WtNejCVrtc5YsDi+UAzSQG0Z6M2ffIF9uZPdv6sqF4LMnLMBx0SiHiOU6zEtE1Hf2yVWTGt/Nju0bklAyZstiukT1DQ2vQhRl6Xa8UB5aNAwXkyzYiRc+MHOBnbgOIhZz+zXWLsdeeNMO4Xd4Za6zXigPFgeLAKnCggHwVRqlonI4DttWFYrUPnYsbQULo1FUcKA4UB9aYA0MgX6xX+U4zer17t9PcW/76k8aN47RLJJZBoPLlp78oLA4UB4oDkzkwNnLiPs5uzJICP/F9XddwLnxznJMZRMuqeVMcKA4UB4oDxYHl5wDlhLObQ0wDjN4HyB0bFVfSuZcWo2r5OzY9hRxzHIJySHf3vZ+mJ7JKbokDSdLj2tJbVbg4UBwoDqwiB0TaELBBVO8EsGrX8PgZddwB2VXs4TQ0A3JxOVb6+Nk03dw7ZYQwS/zzvdPl6mlxoDiwlzmQUBmbHD/bKLSqWE7CWvXRqkV+EPtFcInGU6uqKBNidAxiNDq9LQ+mqCNNcxJWzCXSyC6Px2oFjnWeyjFoEdAGGqf7Ymwl2/TYS3nBtoQEGUhtu8ZtR7/ELXEMbNCiQC6JO9unH52GKiYigU0EkmvxXM0xf2JCC64+TT1VpjhQHCgOrDQHxqLYVKoMSJCPQejmBNN2CS4m0JXA3YlrnQuuC0SlWA/kXrGm8x+2rOfUqahh8haLU8aveKUZutPEk7luv/12ITAH2yEiwYnRRq6aAORgD9sXBXJmhSPdhIkBhdw0yHlis5P/tmQPJyMKi6bXLeK6qgRwZWXa6VGo+osDxYHiwJJzYBKQ07/pT5RsYV0ht4DPQitTo6XDsky7Tz1qjmNM1mI9gm2mzgA2HGIEkE5RaC2/lbS+0y+huCWeObRPK7nkbFoIeWJwCrQndLYYt00KE9OUMQOMibCNKsMBsxOwnZqeTGL4z3nbWWpMJgQI+i16doCTagv8WsIxTwleSbIyDSiaEsKRaku8XpeInk1oQxgJwyPRwolo8m41p3HR3YVH9ShJS/mToz+TxJQQ61cCEgVE/RzwWTBChCVbiXmlzhRAhoi/7A3MPHoXK5MZqDCqNCTAe7P3EBrUExsADwlzrzUkeK1XdF+nml+IyoUjRVLqSQqTuooDxYHiwPJyoA/ZOoi1Drw5vgl4KW2DjEkiY1uF6Yg//MM/LIGHCNLCcVsW1WAlpWYJxyHUs0RPCclO91JAXA6BncW4tiyKI+2kr416kZyFd4ZP8w1Cu2ltyxBrfVMi+wICyoryLb53i0kOk2QBEedEMfnEhBOX14T7XlJiCK+bYNfyf/NbFNAbRMmoAZYSR132DplvWFP8Vie7iKEUSsXr4qhLptLHbx+QSkoT9JSHgRYF1tacMGpeSVXsMW4mhjxPCzHGVe4RqwBJDg0eaUvOLvG6E8cbrEoBorw5JmWIUOokjNaoKOsScXqkWrlVzDcdR0M6Lq+JTB6kQ4/URtyRq0PsF61I/kGGoK+HaULK21KS08VvEeblBDr//PP9vvvuu0ViV7nui77uUeYtOVWidDlMCVJIdZ+4s6VRq8LFgeJAcWCHODA21vokjZz+baWWH0LGcesashg2/YArPOAt1tHG3E96Y8mUKFttF5NSaNWGK9bNqF+WbKsnUUAm48p1OI1wJ4HVox71KCpp2+2GxLJokKW8bhTYz4G0PWkQCC8pl5RI6nvz/2L2MCiGMs0lt7eh8duwel3+FWnKvA74yQdsMGMJC+7K9sHUT8c13NwmCArEO81RbbXuEUqos1KT0YBz3lEBJgSZ3DwC9pRyVZkP5pINlyQtpbKzBwBgEyYZuPUXMeYJ7VlbhAxZUpoJxyQkJYB5aXtYhsg6rO5EEIgeVpAgZRyJA8eTn/xkeMw4RImnpsv8QTxFJKGHhKpy3TdFpYHhyeEVP9BAVtA7/CESReKcZryqTHGgOFAc2H0OTAJy+s1BBx1EuUlSZ8RBX8mRqHR+tzzW1jvQbnEUhSMySLpBJ5M+y49mnJQqUc5NdzbNLb37jFjOFjGfdR0yxRiO/xBIMq6kNpcKE4JSMWEYloorTrmMzDQZeCAiAYsyTan1ltcJBwwnMJ6gMNYJUbWUV6IDXEQG8GZ9gaDQGmaT5NxMpnOV23mhYfvN9E10Q1VmAnUZLnqFnAG59YWO7qZ3TZ4nPvGJpgrAVtK/4JMhhxTiT8AssV4wPpeSREaU56ZqSQzkHlWZmVRtrENzhAbp12w0MAiRGNCvs0Cd/Z9xSGGV4CRLA3rY5/FEhZigKvcZliSLq6Diy/mBFFXFgeIADkzl7LYRp7KI80KC7gJcj40407+7Wk7jyzA/AB5EBHJ2o+ENXLSVC1DdaeTBeJZhW8jAkj49TTgzI2XXnARGRYZw3qWjk9tsVAOz/mzCKBMgH2u2VsAbAwDsT45zdEbRd9Gt2dKTXJyEgeBYCPwLawlzCCBJSMFJBVdMbWwG6Cdb2E2IBMCAT/NOhSKtkgaQOllAMRU17S1ZWbXV0BepaNNlCToBdjbXmQ3QLCEgArR+wQUXEFOgOEXfIxzWIvPGMkyDoqE4UBwoDkzgwOxAbi22p8gF3fYkK6g/rYlWz6zLxfR5cQB60S95hFE9bXgDJK4MqZz2bLcCMlGm4aKzWP6kbk7TNLSm7DIvM5izLeeCx+ecc45qpcQ2oOBNhdwVU6ECTProAbH0VKBLo82+MhGt+a8FsIPcydLdn/Nu+IoAsMowDs55Y6jcv/CVE59XaNutEvWA5B6Y+w4GrVn1EXbaaaehlnncv2m6ldQ1BDOwu+M+agPY6bhXtE4OII+SJ+xCAXW/WT6Y5anvZUOaZlJVmeJAcWAhHNgWkFPHbZTanuTCxjPI6g/XKUPWU/CwkP6sX6MAzA4xaQmc0JjpzUzrusn260QfnZLb+atf/WoGaqOQIwO9HTgo2w4X+KFCMEktVpgbFxWWlpyLZxnw1ty73/1uJnF2ezo31wfNsTMDb/jHzdt2OKM6PZs6rnIIiiqSRLO4UHntMRMp1IkYSNnGBdls2gjwutY1hxLthgDEoEphfQGuzZYO9VWo2lETd7YJXHzluF9wgiNnxPWvCRCf+MQntCLSEYIZ7UkwykBu/7a++0ELz9EMxdj8ncJQWFW88dcy1uH6fSzVo+LA3uTA7EBOR7E40sWZH8Ut4RZEK4IrlksKYjOK7k22zrfXQueCPYCN5w972MNYrdXvN1sxbCM/wVGgyHeMHulOc1+AZFCWEg+Kgmqczuw9+3Gf+9zHi7D5oosuCrXEAkqqkuzY9r/p/cAMYAPXaLEc08hnAvLDXXd4yXFih7Xqd6gBAVzoU5U9cpKHV/bbbz96rVZ45LmP4Ne97nVQGeTb2qfsXnnllQSUvEUhZrQHydCa6q9dk8orHjnywLutD5KvcvXkTBohg6wAnk0/ZWjexxxzDFMB7PeuH9z6zFX8iZcc/jg3QZgQ9iBaO5H0hS98IVYgmzbvhKSbIByXPPJvxIu6igPFgeLAEnJgKiC32FmmKVi9rdJqaCm0+8gjiYnVxcUdWoAHN5ubtD4rRqHp3dStrXFWWkKOLCFJmMnrEAIBbOAaCmEStANmFGhP6c3QFPgBQojrviGDl0AIqkEyA/SUpzwFzIN2Y0G3dpqL/vozP/Mzj3/84z2l0ZIYHBcc69iFBvWzu6hEeS26Y6zt0IM6uAgUteI+lzdChiYMMQWXqdxNPuR82TRBCqGUK2A32pFFerBTiwpwKwPqJImIgIrR+9VMKJTIx+lwfe/VYshK1GCZF6pIbezhjAf2IJxS44J+6KGH6imRRV+cH7PxTxJywWbyCk569/TTT6dw5xW0qcRNhg16ObEjs1pfyBZos220hBOjSCoOFAeKAzhwD2bwxghrOoWPqjRgjdXQ6mbV5v7TPNpshNOobNAChpQHG3S1qIz9tqhXrNdRHAMSdD6u77uv6FisgVw02tW6cB7TQA4VOaewcBK3GYexF77Sofm3Gw5Ib1yMF23YTXjpX7BnmKAU8KZSA2zvesRKT5GlUnsExthX/BgL5JkDgFDrxAVjJwPPAQcckFZUomloRyFmFfdIKw6PYTWN2V54/OkMOlz3r0cKmxVIRQbavAVTqcXNY45I4fXY3u1Vg3M+fVppfnN6rXIvqg0ZaNAKtOZ66Uy5jqABqeAfT9zUiksXPEI/TpqQXtGK1zWtjN4pgKvaVT/+4GoOzddVHCgOFAcWzgF7gkyVorT1lAyTpli2OEAtnNYdIoAVgVN0JU2ZO3tzymvu1c63wp7IjQheiY7Mly1VW3GgOLBCHLB/6szwJklTVqg/RerycGD5UTw2jMaxjQheiY4sz7gXJcWB4sAycGC4R77eC9l6924Z5lPRUBwoDhQHigM7x4HxW5+9/5pt7Be84AWveMUrJocE2TkSd7Rm/eevJySnkHMVcXNHWV2VFweKA8WB4sDcOcAJyalgcbsHpvV99sg5B3FI5ig0yJs5d2oWWCFXqQowt0D+V9PFgeJAcaA4MBsH+Jg7zUvfHmD0EMgdueHvtsZAPhv76q3iQHGgOFAcKA4slgMB8sMOO2wSkDOtU8ePOOKIUZ21Rdxs3WgHzFrhFlNs8Hq7P+oSvJvKMTKa1/pqmdb7s3zh/6Z8yyt9DpsZ5p9KGHAEYnMia9MWZ6i/XikOFAeKA8WB6TlgTRZAWtyOgWn9/671WfGdJL7sssvyu78s4nT5/5e9e/216KrOg6+3vN8qJa3USk1J1NCGJopoE1VCNBRSCPebDeHiADZ2wKQJ4W7jgMEGbIKxsTHYYFPb3I2hYAfCHRwCoYFAIlVqCiqipB8KqVpVyYf+Be+PPNF4p9feZ5999jln385YH472WWuuOcccc675jDHmmGMICibIRt0X0M1BNZXWHTE1Rc8QFaSSZ+eR+1JkChkGR0XYcHjXkWhHdcV2nW3rVO8gQPaRU23iNCoXaCwpQxx6FpxEKLTFrRgvIQEEWhFg/Dj0CNTq2LeYrMeppN9tDjQHmgPNgZPigONngnBMapt6rXs8kQ7ckXpcCC1htkotE7xa3lJ5psdkEm9+85uhtbhdYyXyXwnaKsuWEGCidAm+IdKWiFrASbgusTxnW1xePDlqyXW2dVTa5pbnnSdiz3e/+138JzlhuxAl119/vRzeC+rXTWo0FF+Q6mMZVvB5VE9lAT+RHnUlzYHmQHOgObAyB+Yu3YtCtILkxHr7wz/8Qwo0fzkGevmjmN9BuyDYIl5VpQJkcginaldAbP++8Y1vFJPrO9/5jlhaknCo52lPe5rwZCJtya0iIpi0m6Bi5S7t/YtCiN97770XXHCB8Lci7gk9JhSr4HRy1aTvMF58U7Faf+mXfkmUALFFxUZlfhHurQAYHsuqwhojAhrLSmKt22ggmZEJRHnzuoTfkzw3d999t6jjpIcrrrjife97n1dYVohiaUu4VtGFtGWUJQBlyFHAto3M36KpV0Zz2ryQrqYQ+t/5znfWeIkbz3ijR6gioywjVez9WHcHmwPNgebAahxYBOQM0VCWMfyqq66SzjJ5JK3mzLzMtiBBgMwswW5+4hOfgCiCbkbldzMIDYR+53d+R7RLcTpvu+02MbFDKMhnK4Yx7PCrkX4W3mICsReSFN25gOiVV14JzjGZ18Nll11mAxu+ipqOk8KMf+Mb3whO+wvOjYLMpKSuc8899znPeY5BBKhijyvMcs6mImirRwKjSg1uHKshUUvFUiWBgWE/fvjDHzqXaMQVtlEigikhD04LsKqJD3zgAxCdDYCthXyWPLZkO8lOjPuFF14I/kly2bixu3HDDTeYUXpBNCGURA7oqznQHGgONAdW4MCiWOsWfQBsoZcE5WMf+5jVXHBTC7cI264kI/dDKOw77riDzdz2rTySPOYkvLLQQxEvSkcB3WmKAIA6XlmkWIlhv1WeZlbx21fowJFe2blY69gI+SjE/+W//BfKMQCW4At7hSjXcYqv5GO0bfouFRmi82MwRnwdvvWtb4FVo5P8ZrAc2CtDDpBkDMwbR7nLVH7TTTdRi40ajZzKTsAKS42aISYBgG31cI9QIQHOgCrGwg/FFfavv7R8acX9ZbnhOcEq419ZScRLl/tEu+aGRt///vf7rRcmDHlC+HTHHd0n0qmww/UcaTJ34eZAc+AMcmBurPVFGrkdddoVTsXHzY+kkeat5jeVDtLbxP2zP/szxnOaN61u9G0G7dZxJeNfxwxb6c64yDHMSmhBJxvzpJ3BUVncZXDrEAHAg7Ks6EBXLk5onVyfRoe1Q/oTDoZwkXqttmjDxsvoyBciegBlmn0enHsdeH//+983ZMzgdG66/lvf+tYPf/jDBksU/kk6GSYZJpMkBSfSKSBDCfGLlyIUN6ARyxjnZRv7gz/4Azo3E30c5iUZU7m/CmtXhlPZRd1hMCCFaMidt7/97Rw32BIIi7Oe+T0ZmgPNgeZAc2AZDiyVxnRuRYAEWkCUL3zhC7Jdsbdb1t2kjR2EzTRvr9gfZQGGN9zlYEDvjy4eJ8zkRWjH+nOf+xyFmFeB7J8MJNHIITSkVIBxm/Og7fMybySSnY1w8pPy0PrOO++UOV6FdHcCWXKp2e/gjcjrUFUTR8WRMHDOk5FtQFsq4b1o/z4DbTMeMDOtk9jIFlGsaeQx/vNt1C7yID2jjiylPCQIcIQ/+wLkErXp1AK/vGXmcZdpDjQHmgNnlgOrAznAsO7TAul8VmdKHpWL7s5wSvGazTVufbfQQyP4zQB7zTXXyEpprW+D6kGTj3njr//6r+0oFzPl3sZtmxQs59Fu6bJ8zejK+G//wsbzGF6XmmtXm19CRifXn/zJn3BCBOQAFb7yXpQmHNz+6q/+qv2OMW18CMsAse1TnTm+KUzzhsq2t+PfAIMp/Yj01+hHMiPPGWU69ze/+c20i2a75nzfzByGBDXYpPcuUzz6nW44sx9hd7w50BxoDhyHA6sDOZBgnqVXAYlf+ZVfkUyaTxPVyp4oHYsiOCELwFjH6ZRQ5LrrrosVt1F8weDh2Be/+EWMtRNRexaAFlL6yzENluP8f/tv/w3EqofNnN6sZMQjP1jm7am7b1zSELhlpWdyVwP1naBgQwTcAmA730aTOb1ICiTDXVWpwd68XXDqe9pSSQp4RLyAzeINweagO7s9+mnkscy7iHGUeO1qwna7HxEdOL6hqig8zmzud5sDzYHmwBnkwOpAbplmobXzyh/Kmu4CAzycOTTxcuIqNXLTSg2NnB13oknAdyZfy70l3o82rR807WxVQHFuaC972cu+973vwT+8Yt+GjpwEObVRZ2E5XRYzKdavfvWr6c3gGWQCcjz/8R//cX5qdj1ovYrBTuq4Oo0aJGaiJ3sZOHYUejMfCi5pHCOKnuxba9eL4Nw2yl/8xV/417tOt9si0a6bThUyG3CJ53PH4kL5RhijvaZtyTPdIxty28WnxNvpB9587vw1hSjxKCfVifN/Bj+/7nJzoDnQHDg+B5YF8uh5k/ZAxbi1CZX9a3We7Hdms1YME4s+J2fGXlogV2eXbVor+/G7sa81cGRjMAeKnAkwDbK+4AUv4FDGyYDLIRs78zVxCicFyQfPj3jEI0BmwvAaMgP0T//pP2U2NyIcD5m7FQDqr3zlK8kBz3zmM6nIwJ7TuEOGtsmh6Rj+hVimHsHdOMSx4aPBJjcy6O5c8DhGaIjNAJDbJSF2kN7skTuWRpjwIrAnKLDQIJswwUePOZ1aj1TTgDrOT4KFnzxHwosHZV/NgeZAc6A5cFQO3CdpCuVPxhQL/aQWEE5pAwOuMoYDbPBsWa+lH2woZkEfi6nKfTqiSvwY3drdUZL5d23HzyrW+lHZtNnyuBd1HBl4VVF3csrcKLif7XAqsgKGIOIU9dp9L0Lc7J3n4Fk82xXwejbF3ff6xEvReEFZLxplVRncGN7VmRgAoUcN5oAyGoptH4WlzSMpe/wq10Tmj2IVxzeVt9f6ZudYt94caA7sBAec9BH7a1Ea04OAfCe6twyROwrky3StyzQHmgPNgebA3nNgLpAva1rfe+50B5sDzYHmQHOgObCLHGgg38VRa5qbA82B5kBzoDnwtxyYAvl+b1WubTO+51dzoDnQHGgONAdOnANzUew+zm68lHlEO+rtSNKJN7/xCrlZce+S4cNhpz7ztvHhaAKaA82B5kBz4Egc4MgsHpdDRlyPxxenQC7wlixY+wrk3Kc5S3PbbiA/0uzZbOE43p/NISN9JrbPZoegW28ONAe2gQOAXK4yuS0WATmvdYnGE7prLy8HpQQzcYR6L3u3x506y6F8SZ+9JbTHc7u71hw4EgdE/ZJDcsXjZwKCUmRdtYnuKLBY66KsV2ZSK45izgrXceHQ576oYX/v7/09x4v9EC2Eidtv8UPGOGJH6sxqhXfr+Bk9TNQ82Ud0lloWxdRNl2PcQqzkOPiWX6gVr1dcWGfNBZ8RweZIBOOAmHQS5Um/5jS8mLKC5Dh6fqRKdqKwzyTxeXxlOW3vW9X3hMnbiS5MiDR1jRf60x35jicBH1OeeK2bNAzz2UBbUg7qrDXHAsKols9BhZN8fbvIpaa5OXAkDsw9fvajfJd1yWMtldZ4J799Zg996EPF2kx2rMCJlNKSScvbUeWp/JTdW2+9NeFfqqSYnWKBiVDmw5YVG8yj29okJddsW6d6xxIgmOipNnGCleO2uPRi5ljgKlSLf3FPcDQR8U6wrdOriglENDo0wye554lxR2rLRo9UaeL5e0uIugc+8IHy9Byphl0pTNwhaOsjuArNMseLZyx33K50Yfzqbc8x7wnYZ+qatIwKMgKYDLN9EetX0EAzBCq//vWvJ9Ac1F/MscJAcXWSeJ7//OePS83OcakJbg6swAHBzinAkxcPOX6mNC1BME6ADbzLxCeguiQZMleWUmj1edOb3uSOMmMqFCgulwZrAAlazkp/hWRXrToB+Ze//OUjCSNnqjCJ58orrxS4jSFBrHWyFDErstTnP/95WUnGuLmRrvzFovxOiNZc7oy/i43j/dqIHatKydRWv2ermrQYMlxcC0XXp0lTrCVWsbibOXN3fOdSogZLNlU+ryR+XPLnVr8W9yVPR/LG1qsj6WCRnfJ5OqF2Lm8PpSGVV4Xj0GR01KBfY5zE3IncVlcRPCFsMkDjTBj7eCidY1uT+TNy8qDpVGUEY4bNtGcfe0JA/uAHP/DtC8lsGRpbkSxAjgBLBMX92muvNUmkVpr7mZv8d911l/x7CFMntYFYQFYY0/ycqfWhO9scKA4sAnIKtG+MSZMWJZg2zLasyHUhIrdUpPe///1p1Qmr7tOSqZqNndnTypuFSUItMjj4cV/GTB/w1772tQsuuEAZT6XNsE6RD3owluFA4CQokvJCl+OhJCj5F28dOhD53O/3vve9BsjiCPkwX4B0ycjFWjc01GJQmqiuLq9bcAOQ4p/DXTdlHBeG3UCnjLxkEqIIz+43VDCCJAyvUI6TFt0l8YmQ7KK1myFMo+SMBHCN2GgFV762Bub2l1rGIKQYYZPiHnfLl7zkJfpCD1O5LmjXfBPanQFWQyyrGqq8qxK0iPSuBvsOCB7zsSJMhpgnPOEJnqqcbKpy+VoEfjerbfpcfvnl8sjJAhfazNWEnff00Y9+tPD1ua/7Qse/8IUvvOqqq+wN4efP/dzPwaoaFKzzvXiRDZkQlrd8PnTH66+/XlR8ZLsvFaxxUcwlPV0NYspHDv7gBz9I8JUknk3iVa96VQbCF6eGCDd8Wexkua874I3/S9Lb4JUg/N/+9rc9gnwy22ZTAw0lqeCV4XNfVQedUjGF8FmZf/JP/gkQrYFmJDAW0vZ412CxwE2glJbMksThhg3PBlBetKVyySWXmLHvete7xglQM4QCsMCrUb8oA+pkBawK9Yh5hr2w+jV3avXN5sDec2ARkPs8LBwk30984hP5IAMkkmFYuLOy54LZviirp7U1gbVdflhQLHM+dR+8RfOP//iPfYdRMqTwsij7MveexSfSwdGQkgpxz07BqCj7N3KVlRSuk7Rg1d133w2eAcw999xDQ7L2wcWAtExlQAWAMeracYBMniqTO4Y4DUmTanwf+9jHMroACUAlVRoNmwHGxHj3u98dYqhHDOCGW2E1ZC+AZVWaFslP5XhcOxAAAP/0SURBVD07//zzTQNpVGZjFdh2AY3mhmqt12YIKQRKSVpPFpRe5aabbvIDYAAtZJtsbD8kD3liglgy5+qpmakGZJBaNJfkMS5mjC984Qv2I9QpBTtRUn8huvmpd8ASu6RLD2GYA/gRgBWyvaGf2qeYRwqggQjFQOKyWcUuwhocAYjUq6cs4f5FDDlY2jr3jZ2qKJpyvFJAVWU/WBY7+8faBeRwmg9BzZPAkv4aHRnkMEG/6LhPe9rT5EGwpeJFCQa//vWvy0+jJKEBkXRZXVCMCKVCnxvIxCuSB54o7NskhJkk5AZ5hNWJmeQnT9E5Jjrym5SgIYIaUkkJgJ+6rC3zTRcMllmEZkIGbuDtOMkx2buxq4/3SfPGSwKe8aakPmYLyCfG3Xjjjco4WjP7yZhO5DNyvz7mKdHTWVkGP3JGZ0Oe5VjfOVMcWATkvkNrB6y1UmSNs0aQqa3pFkrfVZQtypN11rr50z/905Gpw0GqgLWSyqIeXxrlBgZYo60LNCcfLV3nyU9+8pli9wl2Nv4+Y4X+zYpGfSF4wU5Y7geEfvnLX/7gBz8YgFG1wTwUhMrAjDJKT7LEGyxaqTsw0tpKq4ZhAEMxiEUts87Cb0cYYTmQMKbkNnXC3eynGF8tGm6NUjdrbSULIsDKDpNocrNZztAD/il/VEBPQaNkaKq1alusqZIUcZQnO4upqNiDHvQgrVjHSSrwktkW+uqpSagGFghaJnyy6Ecvh0w6RcmmF+qsaaxFlQMbOGpn2ovwwNQlweAAi4X0btpy87LLLoNeACPJYT3yRVDKfQL2ax3XRKpPwF+o/PCHP1wuV1yFrMiDeZT77AWwfOCbt4Ao7fxhD3sYJiuvmC6Ugb0GNCYHoo+/GKh3tsxJWjRp9SPYD31Em3FnjGG9ALdUZ3xGIc6QFXyS6scTAEkIYFrQEXKGkfVInbIvQFafZO2aIZU0gD88G/RC0wbaVy9znZmgLYQBfmNtyMhYeGuqFNkYTmrRHYydzE9NGM1ZXzbjQqSI3EO8GPPvjdPbuFs3bMnJmIeT5Mgk3Duo/Al+a11Vc2DLObAIyH0hcUwrad1HbvmLSu1bVYDS85nPfEYxYrX7o43LZ2YVSMnI8vkhISYU98WCDTmqt5xBu0gePpOZQFdA3QLqd1ZVI+VfY2EpZ/JNFtT0EQZ7C+ICD6gDHf22KIMiwABK6eiQyUrKddFl08RiCt2t4yoH5NBiwq5Pf/rTrKzWdCjCJE4ZVS2UBRU1VVBCY9Y0bM7rADKUVD7WzECvaEVtKQYSYqhQG1WYER66IMxfWp3mRiD3ljrzIkhmP2AcBqu5A0uy4wNOwJUdCr1LN1mSKdO02CIGjoZUbEQD2tCAWtjJgB8aCAfAnixCaw+QY04+HMKBdmX19clgrG8hG/+zMy0b4flqdBx6GT7aNvQlAetdvi9Xctpir/FiqCBk6yC2MIYVT/iZEncAuW0yfZfnkHDGg8EEMD1GAowRUklUMauozdzQBbYWA+3yeoA/08kcq9H0g5wUf7TQZnxf+9rX4jCxj+BCcKytCk8l5LUrhxggzTygaXY7bJycrlHSXCVUsQTYAaEhRBiiQrRdfXbm9J2zxoHVY637nmknvm0LPYXP5qsFzlftx9xDZfFcxV8rGp+XX//1X7cIWunoc2eN6Wvor2V0PJxWS21M8dbZ5Dmd+FJZly2gkIMqpgD0sgMCvI2XV1hZKe50euqgi6rHxMr8C3fVr8Bk3IEKuytb9Itf/GI2WMBgEaeU83BM+fAhaewnGVRDySSxvcJW7dr8RmfMEggDaY985CNZCxBGgzS7mJ2RXR2Mh0da9KJXUFs0+E0q9W+ONoFhVmhVkVpc1Fye87TJvI6HJViEBhelnIILX/Oi8j4KkEMgCNJoIvPfvjVZlh6MIQpTpms3asHEIHUx3dvmJ/7qF1MZuWTkDw4AcsJWOIkePHzUox5VPLExbx+EOY0UYtQYYHiJ33DDDUwIeMX0Uq37HZ+GuoN7LkyLUFLTKZSPQkDUbveTptZTAj3ERYZee8TgUSBN7wfMFgTmBCYQkhCq7ErYJpirZxM4iCkkEhMJ5V60kRFP+DV8U91Ec2BrObA6kPtiWfay80c6ZhbzdVn0Kdm21mp7spZOe+2MiuNeoK/aksGYubXc2WbCooyOQDWup3l6EP2xrFjxRzclr1OVSGPeoqsZHYZ0QA4hAJulmSnVW/CJSu0y4tZfiiAAK2AbW2QvtWSzylpwmbsVppxRv+z+lg9UEG5CiZuhpBwnFwyEvkAshEEL4kII85dCTN8tPBgZoloqLAtzsYhJgODiX8RQQEEsaqsq5gRKsCbm+ttHvMg5Kx5npJZiDkSnaKaV2MCzx+8rgGocFzia+OFRpIG53cx9rgC+NRjMwkzBpZdjaclDfhgsdxjMaepaZJAHvZT+4okesZYbCxUSLyCrTxUBRpa8xXJQrRt60tXYWePoUudBHKh3LQskDDy0cR5RAEvJH3jinAv+jPITmwGmmUIs5OAZSJMd2QyIYpMYCTzauEGUbyxu64sFhwWFZDM6Nm7zN9u0NQdOiQOrA7nFwipp4SD1+8x4uPiorIbWR9rA+MFnJXIT3ttKTE+sFN7Kxvkp9W2/q6XoYPJ//+//Pd20/JGKDgKDCSu8aFEGzKziACBP+XwBWnZXyA0agbcBYqCOodtY0x3JZ+X0DgA4jdtYMeiz/msqpPCpSp2pn3HbvwzXdLWRHmQghl5VB5NQYhYRJlh3sjWQCDBzRRMyBLBkGKfqFRox89A+zcm5r6iWmVrvqkXbyYy6moCU7MZmaR2MJK3efvvtObU/t5vewk/UwiTef6pN71SOVzTp6qzXkURoAEK8yaARELLzrY+lv46cycZ5+q5HuMc2wLPEv1wXScYK5HSAnQUb5Lb84SVBAbV4gqt6EWdAl6lC8yYhgc+PfOQjfiijPJMDwlKPyxTyUauZHT5yHuGDcACbMe1QIM+42zvnVKj7I8QCaZ2l1tegmFeaY7mpdokLBnHWRKFd0gYzhq4Vi1gg8G2UDEbu9e/mwNnhwLJAHvvnuCy6Y7kn7/tcqeA+Mz8sAYxjjHhlh8RKn6V3wYCdMB8za5j1zo4mbZKddlTOzg7fj9pTnMfwcYGjNWIpnlvBrY+AvNZiq6ffWXP9nTi3W509xXy6MhBlJqHqsffSz1iGbanGNsvHWxmQZsfXv9Z6GE+ZNnBKfvKTn+SB7LKvGUPrhDyv2HyNNKAY4AGHhDlaF2rtnReca4WCaMKEEtPDzi4tzZYqCEch8APt4CRO44UluJG+QCz6nH/tx2tIDURGQAKDM2OVQWS9qEWs4wdgf1rHoRrueUqsBFec5/mRqYqpycRWFZ9qOJrX1aOn9SGEhvh20b85zPPuxhydtftrnsdknc9HDTZ6fTW6gwnadUEj4hExwtMJD/EEzpGlGMztrGsI531odFMIDYlZsyI3a5EkZDvf/gJSTQmCF73WI/7t/mXBZouORz22cGXXL0ZsdBrBbGDXnESkmUAg4PdOVvMio7fNAlKO7qe/KZxxn+yAYC+nPOYQzZE8EIwA8aMMK8xmySdI5XVb75qmu7PTYAh28R5ACaq42Y/fCAc3YopxxF5kG2XH8/y1yxAR6qgfVJdvDuwVB2JyzHVQZDdiMoOhRXaMo2Qxcs7VQl+vk/Tt+fkao17URV3wEfJhcYd5jRszSZwh9LOf/exYbA2/dyuy28gQC719X7hSYb88tcXI5Et4smg6wUV+Iie5D925odnm8JteCBQti1HvvOJglUUzlfuXXxjEtflNzApE5dIQBYiB2vCNlHApZ8K1sNqw5GoEfjw1DYh0MepOLhBixDWByMQNdLiLIgiHxpKwCsYrBkTNlggHLtBIveOJDQxADklCj/LITiq7dAV68wNIqAGmUlsBf9Vv9QeB4x2PwCeTr14gD0lkStiQVxgqSAZARVXwvia5zhJKbC7QbhXDLm5xHK/iu+eKwx3A8yJ+ZrCo4DaAIWL+hVtkBWCppz4rp8O5viOe6YKarkJImdrIQNy+cJsR3r/+so7gPIlKMSjLtmxiuJjNiUF5y8a8ajmv+B2XutCDvWxmKQMpyWeqYtP2bt0vjvkB7Nnh49leQRhzbA+15eJnhphv44v1W09xQytaV49hzXbG5EItVmAI2dFEtchYWAhSsyVNb61bPYg1hAADMbfdvtkc2GMOzI3sdp/sZ5Zaq48dwb0SVYbO7Fas9X0dhY33Cx7DVNpqbetACEIJmYYWe6rkWV+W3P44PhnrbOv41B5aw55159D+doHmwFwOzI21vqxpvXnaHNgbDjDMMJtTN538Bud2bWmo7PMsUqfdx7WhuI6ss63T5tv+dWcNHOsmzg4HGsjPzlh3T/+WA7zGmOhZdJ2ysw9tX9zuOzP1jiYZ63FtDjQHzjgHpkC+Z1L8ZHQP8jo+45PgDHafvxWvTFu2vMr9tYNecWbOIDe6y82B5sCucGAuit1nj5wHKW9VPimTU+C70sND6bTNxotqEgXl0Le6wF5yoCLaEl7tmi9zsGov+dCdag40B3aFA07W2CN3ZHSC0VMg5/fL+dmu4a507Eh0WrKdaJqNF3akSrpwc2BrOUA6WUEiSTiaFV5cDx9W69R6aOtWmgPr5AAt1AEfGL0IyJ20cRZWsKckkazLR24f0WGbRL/KfTWSDirjljsw0u7jpFjdz5HfvOvLTGr0HMhZDyP0AgFOPTkPs7ZGj9M1BE8SVI+1JZbqcepfz7vGWi8SnZQI5XyzQV+nLIWNpmUOfM8ddwUSeWY8IH585qg2cWGJxaeHkWkiQU/1wllBEfEWbCF5pFgSnoYbRsd34XB5Ytcfv+MnWENWHqfYhT/S0wSlH+MinGBbXVVzYPs54ENwIleg5UkygunxM3Z1h1ln+yNig7hOHH0r9oIAFCImCkABF1PeAs37VwwQJvpKpuS+VcNhYvkSHP1MSauqqA6qEs8BZWtjnwXLEWGHcdfW4nEagiuCcjiXb/HK+hshCT+JXEJs7kQsHcuuQ8+GG8AIHiD+CcqdAz4OZ470rskmGonz5Q6XTwJ/ph5ShfnpKHMFwzlS/QcVNlg+Od+Ig9qJznYal4Nz8tZIDqtrydDqEN3cbqZ13794MljhYwzeO46vBkdPnRc/DQqPU6ex0ymSikPw5AyxBEhF4gpUSJnjVN7vNgd2kQM0crG8JkA+dXabqzoIlCFhg+hXFY2L4C9ilNATFZASR6yGbloXLGHFIO0JcSVJg8AXpUFa2ZUU3XNM1bAGnp6eYnQaxKOWPETQcWGjYCNCAUTBXSaU1ca1qxAgdoqzXiYfscOKbDnWhXXSpi3B1OYm8smoma4ohIiT2LHHHFPilwg5wracnseJrsFsjvfsHMQRCWmI1wvSeirP2AbIBTot85igQKIJJaX6tl3mv+BFFdfZIDaEb9sYNT1r5sBcFPt/FxBB8xOoWYRI6rjFCHi4BIEiv4v0JMqmWM2FKEqKEwmqKxe1gFa25YVvE4fSClIlYb93hdnyTe63k/wxBxhgi2PlUg/LJ0VEcDdx2apanBS6Kyuy5VvQe1He3KT1CnUihqu3iF9grJQt0CLmF/nJ6i8WpjKpzU2h90wRw8RuU7lN89RMEBOUhdNvjzRklI2pQVeV31rxr6PYgnNVbk2D6xVSoGnATsP6QguUhQU97LpUVRHHSISQHrSbS4iMIUedwgiCnOA9Aw9TkA4KYaZr+uj8N/TVEJcOsclCJFnBzCQrsLjQrYtLKneuTKNqEJhM7LAkKYEKIqwJDB7bO0SczEbYRtZMmEKVIA+RKESDDRqxYxMvT816nWQzLp1NFHQ2Yf/i86yhm6aujPGVWJ1xpUgFxgYU00KhcHuC1yrGtd7A6Rem+aww3BAYCGWIR46/4xjh2HATR3xc6FEGeUA6VdHUvYJUxCDMKKezEF3N5ox04ypUlaFBHnkLhdRfAeCMvnrQ5hX38dxfs079rHFMXAYxTDbKGUER9Y0RJtS0TB/JGUK4J2Uchptp6A8lumYEDYSxFrrOHUMsNpwEM9IBmA/8cE1XwxFnVXSiWTxao4NyAeyyW2eIzRx9NBn0Hani9wX+VYVmKw+qDKVwgb3+1NzrH7vNgTGU3SREq8DIYiVafG2tW8WSAkGkSV8yi7romBJZJiqnL0eqYJfCFMdEcrVki9iqTvDjM4PoiRIqyLNqrVA24xMNe23X7oZotQIypzgllZwTLqunLFgijFr1svAl7qkF0TpuLHCbECaouPijeYVmkxER9ZMplZUlsUVVboyMnXpU6L7VuQaFDCdctrfSkB/+hRxWSQF3UZU8Y14XZZP+NMZ5hS7OaluyLbUiBqLNAgpsBAlnZueQIXysCr1oVU18WXNJJFcI51ItyLEQR1CgN8M2yc2IICixWJtLmUJsFWYpjFe/7ic8bS6kakXIT9ywc+SYGahALaquueYavTNdCUlir0Z0yGXRF/QUcqTLVnyeool7ysLkppC0MBiRcp8w3avHIxgvIQoeasJnItmXLkwCoAIY8eOQoXKBYMF2WvRxkY+xBW3egkA2swBYemczQt4zT9XsXSYuYo1H1Gsh1nVNLHQoCLDx2TSAl3qNXapKKFYlgbGP2res2gyT8VIANOpRAugSX4hcxhSTEcBoT5S074AJ+kUukcIctGM7jPeUJd8jeOwbz2pgZnqd2OF1rzigD9fdB7GmaPhvBsL+K664wkzwiBBpPVFYQ/icYLpEFtt8+KBRN60nwgObwHqHAFtOKM+cVKGY+Vl2cAblXvQUDRjCUKG8S9B4ogb2asWkspTVcPeP5sCucGBuiNZFAWF83qY7YdzON1lePy0EPgwfvIWDapgsihZ0q4bvP/mGy35OChZzQ6us6Ip53V9frO/cN2/BLePebotCG6IeY+lMVBYLnEtwdThnJYWsLqu8R0ACnDgwjUYDZDiwXRmaNGsqPQ+SWZFpgUaQKqkeWo4Rtz9S3YKvonwT2tKQHwZUeQXMENhmoFHiLSt+kDXvGnEqF2nDLCIcmDagN5qxRdxfWr7sGjpiSVUJK7S5BEGtvAoTO1Rrgaay2xyNtUBAcns0cB0lBEG7OcliKcCq7qiK6GCKispe9MdhyrseueldPUU2sAHewdHZMSQKUMexVEOqlfCDiolghfWaIghmaIqIBGYELPWY+eKToxMPNQGlsHqijgMbPKc7YpecHwir7CBENFhL1ECbjstlQMuXww0CUXNVrnWuBmpm4kIbFgFjmjfWAWwfKZgPn1kLbC3DMwOtKnTyUAFjo/08vSZqkPkgN6klsZnzRWMypKQis5pAPjVggtqEhVcP9FXMCOqpMPIeMQPgsOxqOuimuafvXjdPcJLsojkMNLiGRhdUBeCVN7jkJ4nt2ZB03NBjrymHElNF9+kMRDedAthxS8RSNgOSGfErc5JEKJw+jmV0yA04puPmhkWJ4GVOeoVlkb+CVvSOlwBb0ZjGd0PfcTfbHDgBDiwCcrIwxPXZRNuIOYsWYtXwO0kzffO0OuupBAY+vNF8T75O4qwcZlPYOmK5oSgQ1TuF8DFHz5LKFY5uYVWlo2Ap1TCmQkohRRz2WMvgbjAVYIAfGiTAs56CEIqUF5Uhfln+WFn9S3WmrlnfQ56xI3jRmKmDuQM+DbSlmW5nECnclvLMDYsjYipPZYhhBUmazrxexkxrqDkDYNykz1mmZTlzUxZR0p77ZgvY1rTkJdZlL5IjKZcW/VSlObKgyWld1ik6K+TzF4bR20b2xrxs6ccTZRBJ0DGTAepBDt6mrtQgtDo0KI8qRMZuDD6xDmT6DV8JK6DI50D6ASE6ktgy3iXETOY5JoM6BWJXBy3FaoTBv5wQ1SiYpFBmQ8rwAWw5bCC0f20ceJfa6tPLjrgOYnv47IdOMYxp3YswDHOids+1JHsFG0dvlUhgcdBjzoGRrBeYYMQxrfYg8AEYkxoVg/eUbAVUBWtRixX+ZQkgrxgOTdOSiSAGVFWM+apSUgchq3G3XKCQ8IHypz3taRm+HIrxNK498frEH3PSnojWU8xEMvnNSVNIeQIN7HcfMTqCA17RHfPWDEeV2kxjMuI6PW3HCdm/mwMny4HVQ7T6qKxrIIF1jl3UDll5qs9dLyx5PlHFYIwFdLf8zk6W6SdSm9XWOkXPgBy0GeKRRSrrvlXVis+iaxlN/HCFKUZWNPAPkFwWPspfsoMT19yxsPoL6iqDuBet71RPSF8uDklCrwx4sKoa1mBVDDaGfvl9R/THfpN4LBWhRYVWW2oi3dqiTJMrVzWtlCtlmkMPVPBjQWL7Eik0Z18AZhAOmOLn6uIZnbDXFoM9IGxh2KDeZd1Xm3pCRvXa5AerkAnNqQFhRgE8j62QpQDhQccNVAuGacYZON9LNndxxni5auaQt3DJiB8kiOAnCunZJgMbPoPzAne/yYREMHkoW9HqwQeqM47hG/wb4yl5lJOEGUEvaoWpX2FGbJjN5gG2R5ayi6AH/fYXzD1cgsrZFFjmu9Bfb5l745zEfHPSRCX8ZU6WtS+TBG2WHdIhuQRV1HQWkQzfMo12mebAlnNgdSCPaEwLgRA5sRa1KYbTSbd9WoyQUIfCQdGPZB376pYzaGvJozdb+2Aw02h8naz+pf9ZPRk2MbxWNGsZtcx4uWMpdFF0qON8iLzILKkS2GbbW7VsxWs+UBCAtLDCTuqm1RYSULD0gnF1ctbiqIOSmmmWAENiUGYh3WdGWiBzsMDTm+1E2HsGD4y9qDpZt/ZJL4ATG0MCNug1W71UhCt4vEfOMJTEDrPClT2UIw0oduUrftWrXkXy4ATAqk/IMDEW74h5kZhirwSvuBG84Q1vMJ1Y442gSqCvzRT/mns0dVryiQj0y8iOCIPivgvW+8wxyvp46Oaok6rLNwe2hwOrAznYpuHxVL/ttttgM9WBbQ1OcJuyO0hXqE5aEQAMZze+PEzBSpLH7T7a15S7gl10e9ixK5QwhFCmrUS8qyjWOYMUQUoX4DcwYAznBATvg5HUKcbYOBIrn4t2FT2Ywu3fG2+8kVppUEhd5DMvUrDUTNepEC5qoELRDie65vFZhwzruy1PurU9VDvxMAD0HmQTTovooXRapqH+QTTosr1YBfAkWcx1lqH1IPsBUzmNzW4FN0COmcDVpZKUn6vGASRqKKhjCAkZOAY2gOgIM8oYGvaGWVLtHGsCBFLEGdUT/yRjp3ImkNFSAuYVJoHNAqFpgHskAMYVtdGPDW40+2UArwhTj40A3zghgFGEo4OBiCv+gnrySIvIIzmZThYEc9UGP/7zJ0CbLe3wPwcRKdNg3pReZgrpL7aYe8ao5qQfGjJRLUQHSQYIi8lQ4nlzwAa5mWP3ZJlGu0xzYMs5sDqQ+3isI0x2vkybmi4bYBYgO3MUdDtY1XOfliWAimCpotlwXYYubJW+KIs1gNlyHm0heZhvWacp0n4oXhQLcE7PsHy7yZHNymibkNpB7yRUWcX4KgPI17/+9VZVPQKZfsNsCEfAYsfO7qYlEt5TiGP+dcc66w6MDx+4DllDqctG/8QDtFnWYaHuADxNI4kCTQTM1u9c/LAcc9qwghMv4nYOwLg3j6PmRVVlU19JCzpZB8egLKDKaaixvDtkF+X1nSShWruwvLfQgJK51myvMN7yC0OtGa42s52T4MTmpIAKCUngXFUiMdi5T9P+tXPvwwlY6gX/cGRnC0N5TovBcmNqXzneKjUEOq53QM588CPR0ICl8j43O/pq8PqsFJLdjTo0WHzAE69AX63DSP9ycTWp0JmTArPDoSS9n5ecc31moDpxOzvlZDKzyFNnFMG8fQr2BuBN7iHcI9U81Bespq8THUJGNjigfgQR/1pM8Me4eFfJFGPARxLXATO8nG3HATXi/PWcEeBWonXdyb57jnv01RzYdQ4sBeS+Hx/YJIClL8EdzkcUcft5OZ7kU7QEuzkudtn5s0JREFPMkufThRm+4WVim+w6l49Pv/VrHAILFoOzxYvPgQWd+ZeDm+XJosnVC0pRUi2d1ju4Dg8QQIdmAjEutsnZ2A0EpKEhZQStp26qyv6li4xlwQ3ZvJnAAAuKpy4/yGr0PFUZVqJDls6suVBnspK6ifIYpU2PKPeZURVrU++sqh7BITUjnqCAQhvkZBG/bS2jsF4PYeBKc+ahucQb2XRy4tzsQmEO3xcYYAJWqJlDtc6afuCT0RhtMFU9OjIGcAU/2KvanNMDSwQdqEntU1V6VN0MGW6ayWxRXgFRmuCUh3jlRx0Rttm8gGf5Cti9Hd8KnSQtH4iwdx7piBcNqwEFybGaZCcFW+x826LiQZYNY8RoAmYjlfWYQZscxgmRcxwCtEK2cO5LhWQdJfEZtYXotg9UYmPeRnjG0ZgqoF/osa9sRoVptiTYJ+JrFlknh+y9pdq4U2CaRv3gLWi2cBI0V5lADKKqTLl4yRAIbLfrFPw26GRKEoMXgTHtXxy38MQjL2rXEQNWosh52uITh9XOMmROkmxsMNENVKhAjU48KoyOvmiXy4JGdcoMZ2BnntGv43+bXUNzYOMcmIZotQblFMp4+Qx8XZZCIF24a1GzCPr24knr8v04W+Ijsbk4KiLu02Z8SD7pSPEWTWBD0PZRLQgneeLc0S6adyVE69h9qyebB+uFlTG8tWDR/HLSBoSDLsw3RllPoY7B8pZdcAUgYmqz9ZsoLm4CjASKsSiryn0Ln992ai3uoyiGbzbRoanCqrViJiCMyg2ulTTHDimUTiJlo7eIh0OIpJNZNC2pGrJYK8DWCrA1lIj9VDT6oqAl2mW2sUkMtq3jQJQmp4Cp4nWGd6/HMQrI6U7OSfs39h4TjKMWO3ARkN4hQG0I1gsMNBX1wrk7TEAGh2cF8Lamd0LWxCUK+oJbQGKjwTTWTZMflpCNMIH9wGSGUvkQbHaozRDYPzJeStoBmUxynwMFUU99LLC5SM1RN6OmPPmJGmrjCezpKWdGEkB865BKzkhzgBCs4gnaFHY2gSlFi9iODLZ9PTLhfafmD/kAzezJ8UKIqdkIIkY9thKgLwj3saMcV72LXahFgN9uglUDhyQ0kIqwmiyOWjMKr1SV7mQ64TxqkRpP2MSr8QHiP7EeWptUTBTY7inKs7ngDpGreKK8iY0V5iotnMBHlDH90Gn4KiCM0Yw3HwaaSCgk2IF8g6V35pUpah4aqYhW+pIYMie+yHSFzYFT5YDPxLdwSKz1uUB+qmSts/LdBfIVuDTX+LlCPcd5ZaThIHq2gc7j9HEN7wJ4Z6YZVEpVXUOjmlhyaJYptkyZU+3Uxgk41d515WeHA3OBfCnT+tnh0T719EieTafU8ZGGg+jZBjpPqfsnUm3MJNmeX3P0hSWHZpliy5Q5EXYdVMnGCTjV3nXlZ5wDDeRnfAJ097edAxCIPVyMlInrybbT3fQ1B5oD6+LAFMj3W27d796ta850O+vmANcHwUpthI/ZgddNRLfXHGgObAEH5qLY1NlNuAYOwHsZBV3/7ZFzfuGeM/co8BaMUZPQHGgONAeaA82B+RxwkJvDpjMpi5zdOLXKHMXfLdHR9/Jqn5e9HNbuVHOgOdAc2HsOOCHC2U3GsknMx6WOn+0Nd86U1/rejFp3pDnQHGgONAfCgfZa75nQHGgONAeaA82BHebA3D3ypbzWmaMFWJU1YbTLf+UrXxFDinRQLBG4g2+tZGiVW0JaRoEXhGIQvEIWqcpUvcNcXJV0J4gE7BQqJ2mscomeJs6JOBVH2rNXWDhuQdk65cOqo9HvNQeaA82BneTAXLA4HMi9JuG0MI2iRFWwScGhBDv0d4y2/Za3vEU05soSIQiiuNys+SJAieYtHpPEheJe7STzToJoEbi4H8sfJeCz8NcuQdGlhBKFLULWknCuMIHpTW9600HZME+C2K6jOdAcaA40B3aDA4uAXJhJUY5FbYTEMIO/nJ12gSrlRJFwQuzrZJ1KRz/zmc8IpijkoWLiLAqmKP4iDV5hwSxFQxQtWdhITuMnnmljNzj9N1TimNzt4lyKmZoLc+C63DNSnkBoYbGFgAbwCmO1UKPKC3Uu+GV1E8+lb1IGk8UVF65PfFAlBcWsMsJqEpscWBIwlSWg7gtgKZq3MfWKjB1zM0zsED+b1OZAc6A50BxYBORcwxjDBbuWVNiRrWRuYM5lJ6ejy07IZh4gZ1SXXRiWCIKdQFQQHWDIGyEmc7jsR5IjnWXwwIHZ2PIM7CwWQmE7LABoxYIWfVrcbLkxAL8w0ckAO7opeipjhDRTLCWEJJHAlZTLxMkErBab+q677hJr+rzzziMHKMw0IigYk4AfQo5LAyWUt5DajCgnkhC6P6TmQHOgOdAc2BQH7gekq23pjyQboCPmDtiWW+JZz3qWxBJ2viW9kH1BAgkpDZxjA8lSUMAJuM4+DI0gik1xSSykZFDMfnClKAXet9xyC7u6lE2VOmX9fWYMYPkfU6yujQbyjWwTkkmAVakj5HtgsXCHYQNUk4RkepC4gnuBRFtQFkjjub1w2TJkjCAGMcuzdoRgyrr427oj18jHP/5xSrmBoKDLe6GMlKPqgdlGymVo3vWud9HOmUwMMf3eKEgykcyVNunn5uVcG2e6oeZAc6A50BxYkgMyGMEFattYfpFGbqGXiUhpumA0b3gs+VUSXCYxsyyBtEC4wveKqjdXvYM3FERqpWgzRIEzG14Nu3CSCR36fve735VS2l/ZGzFEumuCkV1zUg6jtwzitGeppWLPYNuA7knJnAuf2TyISnh77bXXEqTE8JEdC3tJS2SF73//+3feeSfhSVprZnZJoqjp9kco7tLGS79BmHjyk5/Mh3GSMHvJydTFmgPNgeZAc2BLOHC4s9tBhIIf6C5L4+c//3nbsUBCbkE3aYRjinEa5/vf/34Jg6VvkiC1dMot6f86yQDVLBkUaNvbMPvTn/70Jz/5yUsuuQS7cI9dnV7OvIGNvBNcbh6UrB2QywRK26bNA2YxfCTYVqGtdD6JrOiw/D1/c8F4mj1bvXzVbCpSXHNToJRLCE07t62+5jwc62R4t9UcaA40B84CB1YHcmGfgY19VgZ2W+M2d+X9hUYgBLpHg6dWMuq+4x3vePGLX8zL/SwwdEEfSTn0Zlvgs2Wo4GDeIzo6ZGXbIPGweRzkT5C81HYubr75ZicA+SJwSHzqU58K1L1FArjiiitYSoyIi2ec0wTnn38+sYAs9cUvfpF1xOE3WO4AIXv+GR+X7n5zoDnQHNhpDqwO5DEUM6p/85vftEHLIPzc5z4XDvGRvu222+z4gnNW369+9avcuM4555ydZtMJEj93Q5rzOU9yWjUUt3Fu84KN3ZZ2nNVhM4wfQT3mEDcjFrzuda8jPz3zmc9kUaep53BBOcdxWrStQoawD8JA4t9HPvKRvBmcDyQxKHmCveuqmgPNgeZAc2DNHFgdyAHDT/7kT9pthdkwgy7+ne98R7T2973vfddccw3lz4FpVlxGdXvtwCPXWXZZN7S6Hz27GOIHtzUu6La3HQSAtQzv8JuDIZ2bMq08qwbVGTNrcsQcwjbOVm+XPZXQrR3W53LoVBvpKh7pkB5gc4KjuLvpqcA+xAWecUwpzPgc5dY857q55kBzoDnQHDhBDiwL5LBn4sgW6+64wwra/euidFINQQsTLuc4VmKeWS76H5ifpG05wc5seVU4ALBhKiknDMkFfXmc8VyjZ4Nz+xFc1h/72Me+/vWvF/iFGMQL/dJLL+X7Vh3EZFo7twOyFP+1VMJ4zn8N853yt/VOhIL3iv3u7/4uczoZy566VzjWGRFudJzgPPqJn/iJLedbk9ccaA40B5oDCziwVNIUmE1HZLB1lc853dEFQsrtGdhTzSl5LhgPsBWYtM3wG5+4jYzKZpOmYIiN8Lnh2zCWUzrcxTFEAlpCD7R2WE559x0KH3NRK5OtdBX6HWZyZ6sBIlQZC6OA1V78+3//78ekr7yLTKZaA2E4+uzZRqZiN9ocaA40B1bgwNykKUsB+QqNbecrmwXy7eRJU9UcaA40B5oDu8KBzn62KyPVdDYHmgPNgeZAc2BZDiy7R75sfV2uOdAcaA40B5oDzYE1cmAK5Pu9YzpuM6+Ryd1Uc6A50BxoDjQHToADc1HsPnvkAq1LsyFOyJhr6wRa3o4quH1xEHMcayOx1reDB01Fc6A50BxoDuwqB7gzi/ElS5bz3mMfpkAuOpjMZmOirV3t8Ty6eWs7lp1kIft3JUrM/vWre9QcaA40B5oDOADIBVGVyWwRkEtc5giyDOL7yjIauQPuzmrvZQeJKfu9M7KXo9adag40B5oDy3NALM5//a//9SQcy3SP/KDs1PJ5UNPHp44jC/xZh5jR4Rz5D3/4Q2b50gs9Fd5Emi8BRIUSW57WUyq52bhy0pkQI0Sxret73/seC8GROutoOLZjqThuIrQL9+bouXFx07GEiZh2pJpTWG1i68rPZigzuCoXRc4IZhANqFZOVvV3XB79urYCwZt9hWhoCDAKl/7yL//Sxk3nd9/siHTrzYH95sBcFFvKax2Ki+MtHnhJAdZx/wopSs0vrpEUxCOTsbTASdbtRz/60ZJu/at/9a9e+tKX7uXW+5KTBveFUn/gAx/48z//81KAu4R4cwmYeiQsh99ynanENolQuC984QvdASRSncqYcvw9EeHehH01ZILCirmrd0bt3e9+t4S2BvH666+X6e4rX/nKbKifJfkwWwzySdFmQweWr1zJRl70FRBrZJj90z/9U5yXFkiiOQLuRojpRpsDzYEzy4HDgdzyKhvK17/+9dFy+4EPfED2a6obk314Z/26+uqr5b3mU5fAbcKCyp4iB7kX5f+gj0rVdXyVcXeHSrS1X/u1X5MvPEHx4DfoFTD1G9/4RiLdAga8Sti1+p071WtpxWmuEstD2be//e1yn9gpUEAwOJwvRXl8fZZj49OJbi3SuzDvMqf5wfnRuxRNycvlryPGXXfddaglQ4gKF2W9rrGVujmpfNKpeio44Bg0MFWFD2NVI39S5qCOzL47YcJcysP2g1g3ty1TXXw9OxocL6C4nSkR+g6iagHbd3dWN+XNgebAxjmwCMhlyoLWYnEzGFrcLbXQQjazBz/4weDHfXphzKGWRUnHFeAQLpA4RAHY9Db3BQBX4Md//Mcf97jHyeulzo33ebMEjOglZZwY6aARRhKDBFdnwMA9kpO0pNKZ+I2rj3nMY6Kt3nLLLTKWSgAvG+mFF16In7gqudwznvEMxg+55qQbZwBneIe13jUWv/RLv0S6mnSZRCUGvgLG61WvelU9ZTWRiEWudEq5ttznIQmcgPqtt95KTWeAYXSR+Nwjamio1QoKmW1SD6GNBOC+8oLtV+VsEqQQGW/Noogdb33rW6n7c/f17T6g4corr8QfJQWKR9gNN9yQcLOveMUrUi09Xj5WN2V0FcY/N2WakRtGqhjR6dEGWU3O0YSgcrwlPSBDIrjwJyYT8opUcrAZ2+WeGW1Ib3jDG+SH1ZbEdGwhXqlIw34YQcwnuapHqHymFyVNe5aSksMwltQVjqH8LBuoNvsNduvNgT3jwCIgBznWHcviPffcYwnT86gUllfbsWN+cSsjgAc/UnRYyFxWz1e/+tUf/OAHAUaQ/o/+6I9k6rTs7hkHj9qdUUmNbARv/CUtgT1JTex8P/3pT4fKhCfoSIMHt8zpflj9iVAQTrITyAo/CEzQgulbQhTo7gecADPqZPX98z//8yc96UlOIjCHFJ1vetObADNYAiQGSBnoFaqkoCV4gWfKJaO9OyBZbcDbfXID6S3h4mXEAcNEOigoi5qRJUzY6v6rv/orJZnfCRMPfehD1UMUSNPkFTIB4zxHAVq+Hr3nPe/53Oc+p7ZZLDdh9J2MIoceewD4J8Homg1pUoj06qpl7CE0QE1b+KAdcst8ryHCJecDLNK6hmwAyR9DrAwZUsApbGJzArArIesMrvIJULmuKSmbnNd1Su+e8IQnREAh7jBKEW4w7W1vexsC4P049HqRLRJmEiShlsyKfslpLrjgAjXbs5CPTj3a/drXvqYemxdjzqGjTqQu3xxoDjQHwoFFQA50Zd+ylFNBstQyDvOXs6z/9E//NPzIEmx3UDJTKO5mrLtBBXk+/sE/+Aff/va3rYb//J//c6s8EPLWmWU9tlDFsKU4AA7hCi65j7f/7J/9s4c85CFAS2ZY0M4cQn7C1de+9rUghyTkRaBOSAIP0tUAcvzHc/jqX+NlpEAgCJENBZbTiUUFoI8W23mrEcJAtYAByhhKA0cII66pXM1K+gvekBQ60eMOshFDi9WiiwJKONDiT/3UT9nvh0+U0WQ3h7XMAPbUQSkd92d/9mdTj2L8Kmwu6BH6CQqqHV0jx4mha5lspBl/dcRbqkUYxwJTjnJPBCEZsGrIFnPRRRfR3e+9996/+Iu/QB7Lv44TL9DMvQOOgvxMYJgN6V/zmtfgFSkTHgdZzU+wSvVXFdZpxRkPm0d65F1tqUdyOUzzUZjSTCYejSKIISOjkBjko8NVw6rLhCSSGeuFMXXphafsAQQ1AnElHDqzH0V3vDnQHDg+BxYBuVUmAFB6AygCGNkXtxoqYEn9zGc+Y0V+1KMeZSGb7IkqZtmSUpNRlK6ZbfXjE72jNdhGjfcfVRUmwWxgw5wOb6Amfv7Mz/wMSLbcW/pBHXNxegoUARiIonrGKDJx8Pavm0lzThoAaR/72Mf4ptHR3YSFhco0VHeUAUhqdt/+iOEjb6Wt5KsdNcXciatkxtdvJIE64K0V46seWOsvmASQsF8xIKoLKk/N4DaUQEpIRlEG+dlrn7005EWtpAAxAs2x7sBOT0GmeUixDhaaqAoj284CycaFmQmB5JEf6RG5gfyBPKbvNErEJIuQk7BdW8SOOp2oOY9UCMg1qmSIIa9gmo5wUxiBXCvqUZgHQ4bYX8YVchJ1/EEPehCSXvKSl5CMCb4GFA2bSgO4o19Qk90caA7M5cDhzm4HMc4SZt+RUZ0Rkqodpc1a5gewr7fsaIIuyg09hlpDHRxPrJ2pUYEllm9OzrRh6p2/1LWLL76Y6haMhBOEoaR192NkDjRiuXV/8dIPcQ0BpKQNAwyb2TbLL7/8cpJBMFglUeKrckBoyIgIy4+FVqAybZgBGX6zYDPg2+O3kQ8piQglN4x1QjgwRqm1icDaT/JTyYIDgTgAqoOU/vodzK6OKDByI9yz15DAOHhVYoe3UpLEM5e35iS2I1slpSX7oS96xPzg98g0DZnkk01uRLpDXPvlX/5lhgdDbLvBRsO73vUuPgQkWpaVyy67zPYT4zznBvsgk8Ogyw9Bl2wONAeaA8WB1YHcukaJ4b9jHbcvaMvcImWxto/4e7/3e7ZOPXK2qloCV9Zia70V82wOgI7T5Ag9FHHsctnlLYUVT+JKDUswajyTBoSYxIGK+7M2j1lmUis5Xt144422w+nEH//4x5nl43IV7BwVesBmHI8Utja7J/rCgGxbOl5gpoFdcPXYJp89nAYpecLzsUcbPGOKt1HN/ICYg0ST2B7Su/yuvnslks3IDRDuDmv/6OQ/YU6Eg5G30BdvjQI9G5GQtZijHo/gLgnV2I0yhxqIPpHA6lJeSQDPgY7bYIZYN0VYYrfQEAWdHQKumwMqxDFy7dn8FrrXzYHmwAlyYHUgzwY52yP9g0WRaxWzreXJBq01Djzwjbr99ttL1WNHpYpRSQ+ypp5gr7azqgR7X+yrHDBg+8Uu2+fpCNcqKjXdF5YfhFLgh0JJHQRF3NrteQMnqrldauBdyIr/wMyWrQrVDJCC8ZBmeaaR4bzOT42JBXAyKlD6AZjRZ0Amq/EXUxtEt2HMccxvVNmKtkNM5rOVYPKIN2CfJeaHo1qYlVeVLqs/NnOV6zJ0x6UFWr4CZA4TlRta+qsG1DKnQ2UV8qGz+55HJrDNdeEOmcS16JVMZtOYyIKNtjDG4fA64UY96qytCn10Sj6he+x3cJ1jUQfkEB0bz6x1avnJ1iWbA82BQzmwLJBDgsm+rDuWRcbSO++8kwru4vFreeU9ZJGyo2lT3LLOL5qPNNWcmfecc87huDQxGh9K4t4UAFqug1Rqj3A4IMTlygYtt3BMc2SAbk3JcxrKI8VwPsWgSMK6gQR76sADKgMMGGnrHc85l0ERg+I4VmzUpITHP/7xNkQAKtcw5hN/Kda1MYwGTYz45HfgNgOhRV2g/pLSmNORpzlzANqxlquH/zxjDNBy6Fy8gXhFgHl75/DMITFTxeY6J3BI7w7JJk2MnPE73czNmMTzO72GxzzOVOVYGs8DThgkSLsVtHwli0VVPrMXo6AvKOU8j0ju5V7EWMIoGYj9nEnJNGbDsM/thy0hDmvq1BaCudBjlx1ux/B45rN2oDBDoHJSEaqY0/2w6aB+cQLUzzlfT+3N8//37yc/+UlMMFJEBDLN3kzv7khzoDmwKQ7cj1202rYm2lh16mZCjdWKDZCnEnguGKZ4UU0sRvFscrHQUtEsfA94wAOsiXFOZkjkNW3ZoiAyw46W5PX32WrOOflIZuQTJJJvFOWYD9Rc130wDDAolFgEDzBWRB1gyTuanmdXNWRTPYEoRzM2agwHk+DTIwwnMLGL2IRWCbUSFEEdMAmzQU5pvWr2m10XnDCl2K4ej5I7NpbT58UlzYE3qjzAA7dmghniYnPmxmVw4SjCUMg8w3DtdFxQkEhhHx09nqpTDe7YGNYvR+mAIjJMHtTqCPRVf+Wz0Sh2sUWbctCXa5sa/MsArkeJIUhe1F+ygoNn0JTDAc8ybZmWesEzAz2aoEZrTq+JlZ5qkZyEM3iLci5swBXngTEbA3HHbyfceH6QSEBvtswJoOohsugv4cOJMsfn3KeyGzhdxg0/iAiECdvhNhEQhj/KkGl8C6r17ZA8cF67iCHy8iA5wQnWVTUHmgN7zwHIa4G1QTn29D75six2lieuzvvKC5ZMmLS7SVOopIXH4+99Ha919guQ29i2Kc4Q4vjZOpvutpoDzYHmwJIcoEo59XNI0pQl6+piG+HAuJd81H3ljRC8c42C89633rlRa4KbA2ecA8vukZ9xNnX3zwIH4o1/FnrafWwONAf2iQNTIN9vPa/Tde/T3D3ZvpgbNstt7ds1P9mau7bmQHOgOXBSHJiLYvfZI+cExDWJg9Lxs2GeFNEnW0/ihJxZt/mTZeb+1eYLIciOB9b3r4/do+ZAc2B3OcCb2B65c0YTjJ4CuRBUjgXvcbJRTsiTc3S7O6hNeXOgOdAcaA6cHQ7QQh0Ng9GLgFxUEBElhaOaDVoiFElOMNcmYjJ38QxKQA/3E581ybbLpzo/wKeTxM7qANENulvrArdk56DWPPC6jFeOTs1tN/FBN8iWNXPjoObCAWqxWWSqmFemTR1Iy1lt9xcEgzuljoQwVJm9OTW+3ztQp8TGrrY50Bw4JgfArhCZYoFMlO3p8TN2dRElZxtzKNbJ1zH9hhwe8ks6a+u0bsqLgyH2hVwgBIIxMLVHjt6qQQDq5L3Y1AUvkb2R42eyiYjQ4kBzsnEbD+cHEvAEwx3LnnBsUyzaeLskLeFiEm5FHBXTxpFuc0/ENLwSwHxMH7c2ao2UuW32isyTpEF9NQeaA82B9XOARi7OxyHHz+ZGABW3S+IyGZwqSLU4JKJ6iTgm2mX1RDHhZYT/nITapt8LCyONh/AdmzVrL4jCfdrjEUsGdZOiSaQSe0cUlGzKHtVZeo89q6P70sLj0CG7qOB0iTMvIMyYjGc94zW2wqAyN7/faVPS9TcHmgPNgeLAXBRbFBAGHouuJXA0XUQcFZEpGd5p9HBIdCpxxAQXE+NTMDhtKHnppZfS0bn+UqfKjAy31CD9syjTVFIxKTeYg3lTAWEmZnNha4UVE6pTOLAMj7ER2VummYC0QGDCnGEUrgpF5/i/3yQkvxlFxH1Txm/x8oAcVZ5RRCCz2HvFNROkXVVeEdiEBjnR9QUgI9PZYhFmThg+YeO8ZViNnUygIpbTg6EmIwHTxcSGbBoIOY6NKkdVHLy15T4aBDFl81CtZGgFugkajyqKrDBtCTSbLosh70UatilUOVsZzwVu0zTbiUCzREYJYERSS4B3U06sfsSLwuavsK8q1OL4neOhGLTCobupI9w+RFVLATWgXwdBMiaLyJb7EsOrH6maE9IOW0SczSPR1wm/YiXpJglDN5OuFPMxR7HqUcoLQucVo4MDqK0grCrHH0It1vEqNY4jzf27OdAcaA4sw4G5AWF+lFeqLmGoAXP9KxQcbLZYv+AFLxAdU3BKqCwhB8xgURcOUzRKarryliepz0T6hASix1n4qhJLqtiZQrJ7ZCFLxOxNXSwKTNybaj3tYg4mC/lpxc8dZltBv4GKAJ8gRBoSvALzwFVIcEAL0lAu7rc43kDCK3BRgFW7GOKbCtcqSqtIrgQv7KXrgxZVGTh1qicG/Fxs+8973vPU6UVgaWRhkvuyboNSadmMLIwRRFZmNvlO6kW4CwXFPvOi16GgCIBkAgXIJRDLu7ZU2MA1jTYUekR6E+XUfa+oVipbgkLogcTI1qjLzEl5rRgglINhEdYAP9jGDcZ2gWal/gS02KKPiHcfjoqLTrAY6dSXCy+8EBmIES34iiuu8JYCgrELEgxEEYNppI2akPpC+lQ/yYD9HD2pENttR3mEb3IHyEBDeiB8+C4ISQhg4yJsFYeBvdCJviPk6S/nUkJJxuvVr361O+4jyRAQyzY7D7v15kBzYBc5INEDJWFC+aKAMLQWq5iFVeINK7s3GcbpK3BIEGmRruPmZvGlnTAU279UbMw9JVEEBYgKAoQ2aNZeRszZYBnQTkG36ANO0hbe0iBBDvMyUcmYuUNdxnkgkTRl0MuJ52uvvTbqNTwWuBuAeR2IghxV+ZcFBQTWbgqkt0UCikT89qJKYBvQAle0TOMrQjjrC/UUUhp3xYotsaywqSgDughwqhU13Vu0XoYBUEfIgFtvfvObJUNDs9kCfU07SO8V1crdIvEXgUB57TrrSEaRW4UgaCM8baEkkw1Y6pdsJUKpv/CFL4To3M3c18SVV16JQgHV9VT3k2wtl4joRAQI7amSKESqpr2YwPWCvSPGW4LMX3LJJaDXW6YxiVPKUaHsCQGkjSQ6o0arh8zKdsLIRCPHQ1tIhsPEZpoiarD/Z8PIX0hPdiGW4Z5KSBLq9BUo4y18cB9DmBCUWZClbYOzsZtuDjQHdo4Di4BcRgc6k4XV0m8d1DerOSsoXcdvJtCgOHWHR5IFy/I0QWvrneXMo7ze11wO2AD+7Gc/60QBaGSShd/0RdIP9lIQoTUE5WTAKEKZUwNXAxBCzKIRAglgTI2GMbhNN2Wppv9BSporIFSz+tMuVRI+aYgC7V+6NekBlDJ9Z7ceBsd1kSVZwhuIWwOarWtSBQ1e5Vox6A44uA/baMZyn6cVwp+sIaqFXiBTkhJ7AazZLnQqAALNCvMKCvoXTAJXGD8yJyb92BjKPxORWEQEMTMzCTFHnrGwJZdq0Um2IMfgjEew/ClPeYp/yRnIpnAnFxlzhWIgnxwDU5k3PNKuV8xtSK/v6LQjQIMnRiTLHGgnN+ggYYtgQZziH5rIBGQROK0SDNcEsh/+8Ifba9DfCAGEBqxToaQsWN3ujb0gNAeaAyfCgdVDtFryrEQWJvvlskCyGdbClFXYIuiyQFvaWh1fPFqx4tJWqd3UOLANDNwEXfaqoQ5tj8E8adPgNLWPZgxLlIeUDtTZYqeFGwUMp62CYXvDFHTlS4oCzFBkPAUHEXmDA/gMWSUwja14DCEE88AS0UG1KgefyWiuGBhTSTmTawIWopmOzk4jy5mnudiWiSAkP4+QbRPhSJM4QgMoZdg/yPEN37gF6BcxRRM2enKWEqi74K6bKME3ogAUJ5fgrfmJmWEvHuogtd59aWRtchOnMrcxxIQ3Uqb0LAxz/NRf/p7prN4ZMsYAWj5/AqYU9hU1k3504Ugd78LNgeZAc2ABB1YHcosanYOWAwYoJTks7q8V38pOibfN6Qd7u+bdzKPWQmYHA6+e+MQnQiYgAefgAZeCOu+PyVRwkJPE3i4AA6h4HtC/6Y4ueqq/dqOBB/sHiJUMWw0Ua5u4BmhsdPYM9JL2Ejgn+7hG2efBMz2bPR9MHjSmlGkQyPnRD+AXUkkhkAzYrybbhdTFx7gp+mwY+GYrGichN+M8OkE17znCShGjjN0K2B9iUrk9e6DLnEApt5NNmyf6LMMi3SRh2NoY+8vRxNdhcLmY6D5FHA/JAXblsaLXpuZAc6A5cHwOrA7klm9al60+u7Dww2pIB6KCX3TRRdQ1u+ZURgs9e7tH/OMoYYzD7LGWs+PTvU81ZAfXhjR9kZ9UzLA6iMM0Y5BMGJJeFqhkM5i2pwy0TpCZuiIt5VCArWUpyZ0sUIZzeNgF0mi0dYbQHZZ8g0iBXgaolFebJriPsZyzH1Bn7UO7z5KcTeXIGUSHuOxBTU0giWm66CQCwlr68VGRDJHqYX4g2ZhFB9GMQnTiIQu2kuecc45piRL8iUP+SEzYNVaFvZRy7IXlZBfCkFdSAN7jodep1LM73OCZREU4GPurcJqI+R3TbLrT2tlUCG37NI27L82B5sCmOLA6kFuvLWrWdDhEAeLcBIes3U6a0YTshlLaLFUe3XvvvZyerL+2QgHMQQHONsWCjbcLkwAD/MgxKtbgaLGgzqJv58IuMiBn4aCXww+4y95uO9bR/BDPf4oFmHXdD4IUBRR40NoBD7Csk05eJFSpn8O8t1R+1113uWl7+FD9GD02m/l1QzgYyfkLKKrN64ATrosrEGIQT1xAIRO33WWb9Jy88kgNnMZNGDKfjoSMxCRA1aEDgUiqra0HFmwtaleXOayVo5wa2CG4lZ177rkK4ycmgEzGA7s/+ACbuWqmIYIOhw/+ASgZNxH8dt+xAhYOAkfiAAbIlfQvMwNrOfOJO7qGgGQ+pceTALRO9koT3AkJr9zofB1cB+x9mPxEHEIP4SDb/H01B5oDzYFjcmApILeKsUyO8Vm1msCitBDro9XZ2kd3CcxwmLLEM1HmvjXRAud1SzDsP+PhLTET6/CzVDprPda96EUv8hceUyL5oFGUAR7UAclcyRx2AgCio9jLwHzQzjbLvAE8MNkxa9BFigKQdn/5nakKt/mCUQH9ziyhHTLt+usYm7cE2rOx/Za3vAUwE8uozokUG8SCiGPwIGSo3CAyrngXwilDaOD2BePhq8JahN8OvHHe1hEWBZbtxz72segxDZDKQuOwgznDyg0mYbzJowAvMBvwIVK1cE6dfjMeICO/w7R4AhIfdR8ZrNas2a76DDigmWwEIwf5EEOS8ANzSB6oZdsgU6IEPWjDZx2JO6e2SppBnkExBASXiuMWhuCe02h6x1dOJQ4XKFM6N0O9mxzy/WVc0RZ/QyzCGZTzsUcz/DbEwvmNPnrH/Iz79eZAc+Asc2BRQJjiS44eWXesmDSJ3LcYsaxaVSFH7lhnWdR5RIPwMeqL9ZH6Qpu0WCu8QSDfVECYcYaBTJqoi9Gi/MltxzL/YiADLP2Yds43G3AqRocDKpgG1ymXGA4YVAh3HaayoQ4hIDGQiLuZQaFlRl8ESHbfq5WQQWunwgLLRGKBcxm7L33pS/6NG5fKSQw0WsAzjpeaOdDBe73gwAUptU4rpYXbm8++vklC+6x5AqTpvqwLqtUvdAYaAbbNFx3UZbiYsEIuhNGbMYHMh1R6PxSkUnM711n3dRa1diL8BduOg/MgGzlsunIUx8C0ghhzMr2wY0059hcxuk8eitOc8qpyfj3/mrHaYgKB1hE91aMMWYqUYG7TrdmZ4iLncPzoJWALyfEBNgM3jRTDfr4F/MT2xIrXWb4LnYXvLK+83ffmwGocmBsQZikgX629LXxrG4B8NbZQlAtQx9+T2hY8Wq3dZd5iGLAbzVW+wHiZt45UZiP9OhKFXbg50BxoDqyBA3OBfCnT+hqI6yYWc2BUixeYNDZi7chR79GH7sRHcyP9OvFedIXNgeZAc+A0ONBAfhpcPVt12knhw8hsfra63b1tDjQHmgPbwYEpkO+36rPfvdvUjBKCRh48rnabIqDbbQ40B5oDZ4QDc1FsukfOyVakEd5M+8cUbln2yPlecRte8tj0/jGhe9QcaA40B5oDO8oBHrLcojlKT/KR3wfIuShz8XVkuaJb72hvF5DdblP7N6bdo+ZAc6A5cBY4AMgdX3KStkJ/ptfttX4WRr/72BxoDjQHmgP7wIH2Wt+HUew+NAeaA+FAUtD21RwYOSD05ERbXZI/lcxiyfKbKjZ3j3xZr3X2dskeRru8wBpiYyW/Vi5xNkTY+NjHPpaIlf6KuynKmzAa4mrxahac5MzmfRJRR05YwUzwQYxPUVNEGsEWMUSLY5uaGVvbruAqcpazJslcIhbNGui0Cgh8+/znP18MuzU054P6yEc+IradgDm75bchbo/YPmLQLn/sMAkFnvWsZx1/xWRdFF+vkgisYaTW34TIvmJd33TTTVs4MXwdn/nMZ8Q7ElVJfOWjJk04JWYK0CQhk5gWYjJqQmSLF77whYJozW1O4be97W0vfelLk6fRdBLiKTFMBb4UQ3P5iX1K3Tmo2rnz4XAg95rooXI3CRZWMSzF3sIy4CTCV7Un2KdYYwY1LeGI+FaikgngJbeKb1h80AoDt+bOb7w5ATvf+ta3fv7znxfe3FoGnMTvFM3UxXNB4M/lKVzwYa/tm19PQ6aTCWYtk9dVSLXlWbRySVHnYCpV79Dg8ys3Mb6YKHVJpXoiFa6tEoHwrINy2i6fOoEmQZa97rrrhMM7Jp0cV6kNK1Sy5LxdstgKBBzpFWpPMGnbLvy3lIn97Au1X2synBSFx+E8qny8gicmdiSt8sorr5yEfaz6FT7//PMlcqRZ+Q3v4z4lDub111/PV6xiM59U1061nkVATui+4447REonqmCHjrlkuxLmWoxruktiheYioFmPRLiswJNiaoqvSeQRgFPKChcwOxJinWrP11w5zVuIb0yw9gmzSh0X/lPMWiiOtyRcyWb8dkcSa7+xzk0ZOAi8sXAAM9OOLhLTigxj5E1xUuUmUT5I4JGgp6KlCmWqNvdnNUsq4FOf+lQFxBa9++67wwfL4qtf/WphdMVCD5EejWBmlvtIfvu3f5slRp0akorNjCf2et0xB5laUK5R2jNlotgr0ClpV0RSMckt4nPZLnbphRdeqAzdVHz4lJFVTyRziriaZd9JsvBchGXHKzBQneLKCb1OTMwjVDl2gRJP9SisyyXGquDq5rOvVM0jJURMh+hwUus4aZ4n6qqs5GLLGxQMEX54lniQb73QVmY4QT4MpywKJm+x0xaeEN2MSy0ibDAod//iiy9mBkxOuUnl4rxaaEItyMxTi5TA9aoVtV5zCMbSyRBjDpFaJnUzB+Wwc7azuoPb6q8MbMjTCvpVixXi8Jsn1mjzBD91B80i9wmIm/Q2GlXAJPzOd75Dps98E2q+iPGKwgZCbZVFRqYf9QgAnO5gUSjRkImdm0iSbdbrjDHqNFeTHrD4Y7bovmL6rqcJxf+e97zHxEOGcPpGbWSmdcwqRJ3APXymkPiUHJisZAf4iYyMMuXEu4mInDlpAkjnODv07picGsUxMxzDrYplbJBHACXoUUBm2/F1KqO4C0bWWzSi8ZGx81FnYiMYwCQ3NO9jHxpiMIS+OGvXNArMSKG20hep2bdjmC699FKLMKbRrNz0jVhJfOw0fr02TOPcJjoYcff1S6YGgmYoFMIZHNBGtOKpqYJFphbyxGP2b60q7DR5xdw2qeRglOxAp7yO7RYNA4ceFh15tth0TVEVSgloaMYP4Vvf+hY+IEOvDY1Iz1nlTAZhod1HuVUibZl4po3VyaPLLruMazeemAmmnK/b9Egx9TMtY5Hwzy5MtixYJ5VHmPtYhH4wh/kmoS/XSBkvo1YjZdzdp6PqlME9jvAxd16tcPN+2FevJcqmANq54wMw2MTnTB0wLMWFwQPhOCt+tQ/DoIJz4oxvA2et4OYuoDK04ocbe3zEF2NppUjM8BWoPKlXkhpks1YB36qVyPQVhdumg64BG5ADErAX8xOyHjSa6xZKs9BEp/fgsLGwMgqBbo5aT33egFw+FcVE//a6L4eib4DcF59cijDJT822Cn3ve2MSYNt3k10XWkARmUWMIBrMbMNK0DZYBo6EIdhLMR+QWxE8Vd5bopHffvvtJgnyZCHz22TwSAetYupBrXXK6JsMHpFdEClvGIFmDE4O6aXFUw/EwhCfot/K4IB46b5PMwq1aM7kQSpBW+s+fnsTsffQ1/2LbCsLym3i2LzQujULSRRf3yQKLfqWBv/isNmrLz5XnFQyHTcKHmnL+osbcD3TXruUD4bEMWWZ5iwZhFejJjC+MTWy9pKUIftarXDMIMrLolMkM2HtyfsIJnaQS4yRPkIRC4G0N9Sa+jqUt0LpO554xb8GC3mmioQ0KjeHscVN40vsQGQNkzLmjCWM7Kh1MwdhBDW/NScNj0XKF6pT5o+JpH4B9ondFlm81Rd3wK2nihkRK515ojax6JEE6vAQqVY93JPRzoz1aZtvTJQWRAOhWjsUWIowUwVLjTgW+WEGJgWi7IiINDGsOSaGsTZhLCZWSTWbJw7RqMEShHiDmI/FZRA9xXzkoQS34YEkgXILaRqf8cQQozaLvk/evPWvLlMkfB3Weq8DWk8VNlIeZZTNZCqa1dxnAumNkcmgsE9MXyqDgJIWN/NW73ySGEvmNpPPO+88fADq1ncDClaVhEBkYqNv/TGsjHDaAg96YcJgbNLnYLjZayAw0x08t4Sq3IvWT9+R33qB274vTCuG4Llh1S+M9ZVhMiaEWnPefDPZMBO7BHtAsHZ97Cr3r8p9bqQiHMNJgohOAVTjgp8+B0u6UTAlSGNkAnSi3DAZFNVqESUekRiyqvisUK5pQ4ZUkpbv0eepyxYTHcE3803T2oKpplDq933pHRryFZjzhAbM1JwvyBeqHp2yKGnLV6M2n57x8qWHgQbaN4uf2G4am6hYYdobYhwjm2b44H1mrDo9Mmd8R1JByungc5Pe0PRTOb0U6zSBFeo0LrpsXTLJzW0foCYMMWkDt7VyUiB1aD3IsGQRmu9T0lSuC5tMiPrXxMK7/GvATE0dliuFyOaOyUqkMgN822QZwjWMt7iYOpZyBW699VajRRYjqMr1RAY0D9Qwtrjm3wirHq256WoOBzDZ7IyK5jL1AYZZlX99Rc9+9rOJSu5bUKgF1mXMvPzyy/OKGnDbnPNB5hWcV96stWSYUoTH3LcIqjmpu112PbxFf9KEf00IGjaFCSiCfB8D5TKPfB4qhJdFtpXIh+fbpgFnEM1sSyE/Cas/cdCHmsKmhy/Bh6E56kVOM+aRb9VnA0gskbljBSQOQ02LUe5Yy3zYUoz4reOZY0WG8r4uZPjOcxMBvm1ZW6ySaLPygoc8shabgVYZ8o1ihGho4b6S2GKhgTc+Vxwj/bjpkcoteXQLvbDmJreb+2aOMtVo6vctWBQsdvkX02CVlQuvfAUQnakgj7yLEn3EE3qYzyGb4gbUlxVBylspDIlf9rKXkZ4ttf7FKyuapRDxXtdBPLR85HVTBbUhMhdu09RNMPPBvz5P45gEPJY5P0BvShpHqipZxCsq9Aouua9HFkcTz8roX6qedc1S6Lex8FtP0Y9yqhvCTM5UaPohFTBYai3B6SP6aQhe9yMojhKTx2oDy/Oi9ZTKZWZaYeEcttMF88gooDniTl2mkPL2+9wxY6kQWO1d/xoU+r0ZlZmMqxAOh310CHMHJOsRvqHHp2GkIFBSO+I8koCN1LQK+PrSIoQGoogsPhsIZWiEGX1/fS/wCWEmm3fNfJR4hCoz3KQy06hDJpuPN2kGrUVGmV1EbegkMJn51VOLqk+SkKFyw+Q7DTG+VrPOx5h/kcRIQ6BBZO5AYnAITXXQAAFX312xTkO+TaRqGgK5ryrM9J1aELDUBCNXpTzQYiRjnFMVOQ+kYZf7+Ma4ZRr7rb/GlBVwXFXMQDK0jwjZvrtw3jSDpsYdxPqX/E2uQnwqhB2+yuzMutCv75mBLrKXWeHzNKlQznCo4wZIZ8266p2hNy6adgfBvjVsZy3O0LiJmcDYIoNsA6EelZgSOJCuRaNgSKBoSWOYkXJZRnSKWJP5GSuRC6iRI2tBK0pO74evmHgxqX+RaZ2Mlo1JffaaH8il/1GD/La0EaZ8AJYnK47PnpQ9GmN9HoABs3zn2G3UsY9yUBatQ0WPM1IA3yymVmecJGoRmHz8hopkShKkTBDbzWCfBGzAE/htUpqRFkdLpMukt0ZYCAyZj993Qn232pI61Vy7mFYWcEVbjU0C/vn+tQU1teViaksKNfPeD48O3So2MdKoGQ9NLQReZ8SDWxZrFxHBXEek78QUDJ0xh7oAmE+XUF8JV3z27AcBb3W6xqgGsMcqYy2otKfKw3WTSj1qQ4llJZX74GktJid2edEyBBLcpx2amfpraab/mcMqjJEAmCWXOeEdmGGdxQWWWKSAq8V6nJO+BRKMQbH0MxsGOLO1poN4SD/zW22knPQFMaqyXieiLfHfHsGYKtBNy5DVJJYt//rKdAS1sFa1RhByJ44ePQaRhmmM4JRPFVuSvjbJ5YyRUcZVlZtmmTaUPMsc+QCiaIV6ARG9knSxOJxtMsuWGugrIcbfLAj65RvXTdaOzDdoZ7qGKvPH9+7DB9tAwqzzrld0RC9MRbxFZPhJuUGtmwZXAcwBb3mkXW1Nwl/4Fx/ijmQVVsCymyx/BsWCC6JGP9xx1Oq3tzLKvjWYYZQj2hos6zXOADBcch/HMm9rpwapfutvPkl8ACGmEOJ9mGDJ14qSjBHaDJCZhlRjbe5FNbS6+kJNWvxXQCsmTxlEM4K6yQJnVusRcZCEpEKzDtvTEWIKvESz6Z07XgSQxhoZGG5uh8jx0mWeT9muUpWZhqUo9KXoKeDXcWNnSvvXffCpJBku0wC+GsRkQfSB+z2uKoBwXFV8Nfm4jBf+6LX571/rDxWcPS9zSRkE10z2JcJRdFo9UIIwTNMv08kHa9Rw0vIIVoxCumYR8LqnmS3aUq1XiDUwK9sHdBv1aNdsz+KmC8rrQvRPl5p9FBoix9ANsMLXDbmNrNcxE+UWaqKhm0QTczXfxQavHyVYXO3Cpgg41BoSKLOY3276+NMr09dVlWOc7z9mvUlizdUI2Ju3zCfrO7nYpPFJm9CEROt4VmQfj/UFY9mXsrD6SGCbFdn8ThlPzS0fvAlq0VcPKDV3KVhmOfSKawKoU8MIG74TUJfsqAokyaaJHs1jNIAfxG3ljSap3xSH4iaDRrOzZX3xkcBykly+GXRakpQvG7Ip4dHoMOUR5LA8qXl2IwZhgBDN9QhgpHeIt7ZCrCJVF1QFFNWm/OifoWtmLMbiBmtkspe63DdR4+oRUAfS1kTfarYALdn10RoFMpbV2QqrrVhQskZrLgpfqrW4eEvl1CmdtQDlvmIINnAje7MrOaFWu6gNEFpxst5pAoeDH2OvdQGTa/g0oUXyX+ixMtYc8CIAgB8oDCtSj9dR5U7WNb8zRv71SjigdSKUVygr5hv0rflGLDMPATw1Ed8INBCrWKcXpuI4UmozXXESXAXvi3V6Wi3OnYR4jrZxroZ4g37QpM39iGtkFBSyL5LqaJlRxK3sgNz3VfMW2oG94jP6EVZ+G4rpfqRhIIdjY45av3HJB2teucZF3+xFPJg0jv4S7GpovJUh0Aq1mFziO8IidgumYxz2ruZMwoB9dTZfUOo0XoZ+wmoF1OlmVg8X4n1WMNvcQ3900zwyl8j92KuPfkfQHy+sMH/mriphl75ndCLmmtiZ2/41UsGCWseKw+iPCK7XmIYwU0I96NRWlMllLtz2pZNK6eUUFQNNksgGWfVxUo9XTB7cI/uW32tWYBywGWE+M5wQXlUbR4pJ5u5lCDvZMqvLEXia1NHULJwim5hqPgBSjCXDkmri+lHkGkLTCyM2LrycLAePX5upafeBFYisR7cmDNJgcDJzmuxvycBMGBlFFgMhGU0OisRlww8rJjOaj9MCarLSHkji7I2q9WKItDT4Gkcl22S1gheMLdOXGj4/QqEKLXPkVl8dKy47AQHOnqU5wL7KUcj3UHRa3C2XtcxZSVUy+mwHEa1usyievlsF1FwfIfpj4/F5m5MT929VaSsgND5CMzjMwqq2shK5j8mRY+K/an3nNm96Y6w9i7gLubyC7RwGbXlQbkx+egyeR+04iJMoid6QAlpJc2P5iAJzqV1Qc9UQfmqiBrpG2fSwDOlRhiMzx3pEn4uT2siHmPrVFoVp7qqnFfOQrMO8qRL7dplvPnbbK2Q75iWLgJt2SbIEa0WdCgRmiuwQsIKrsFeQPc5qVGFpSYdRs4p1E7EABNoOYO1gVDeB7RYxJKjNLvI4by10DPKFiBo1FUdDSJ1WMt80N9od/Y6IOUtqZq8pHUEzu2A1wdSfrtkaICWzEzAL2TDiBghIYh4wW1AyNhcxKHXm85wdu7BownyvAHgGg1pYzBCzhVUP0uejGPmc12c7FWW9VpWYnautkZ7JclRlzA3SoW4SKYwOMjhtoMH92H2XPx2qMEpIQtZVNg8fMgHdurRgrUMtrirDxDjOAXtSrB0WGQxh9cyOg/nPcjAi3TKr6ImXWR3IzR7qFL74XInbLnKK3Rd7HlwEfRK8ctipyhhFcAP8IH+UVU+8P7tYoRlmNbEUWkGIz1SBGLLMJ3BuBltBLCsMXKxY7tNsyMVYTX1Jf6Onmtx+4LPPmBZunpltCvssU4wgZeaRCfIN+8sMoAl22tnvc5aTWQFrwfIj7usBxSAcox+TFHhjI7UoxEhVtnGTAZ3jCkLY92W6mTK67JNAIeObD2Z2AVKYFqhAWOSi8WsaGXiIe3hSj1gadDDeZ2ojZUfPQ7BWKLg6zlSIY+TuLIXexeQUQ23cQSialAP7lDmZlnZj5KfPUbJtiLpDW4pN7yBmaoJQr0AMs35oCP2+i5HbjCu+EdRWbUXtXJ5MRipamlHI6V5rHyZQaBhdGGzQry/pLDqpX2l9GRFhdkoYSt3XF2zkuERK84Gbb9ZxrNMKgzN7hlmBAIwN/WaFaRmrUobYb9wDJMzRS269FTbTYiPpRvTxW3/9jbNbJq12S/gzaf2u/pq6SMUKi5XVzChnQkLNGhe1uTPCtk+JnFfTBh9wNbCEAyo3u0o2UswUBcZINeJIzQxBhpngB33OiJulmfzhs0f+1WjWAaOpWqiGUfYOfP4Je0I3JSIgr9zL1WBQdB8/54pfbqpNB/Mlhvnw2Oax79H9oiF2gsUnqmdXFf1V55Kryuykcsfkj/xHKKQVmBIZU/Rkw4LxIPMEZ6rjVdU4mXXW6GO+9YGGg1fm6miNGN/6G5PKj2xpIR6KlWUIE2LkMKwmDNYx1zHXx6uMJXVuR9Z2c3Ugx1ZDSFmxFuCmS2cYfMjgfDHs9DDq4ld2qrDD521lN2VHz8+19XObG/INgxz4bQLBIXtyrEnYaypbBLlu2fRl8cNJC6UvhKjIlG2liC9GvMTNTs7MRFcCdTy/zG8SqMXCdl267xuwt0S0p6/7F8zTm0ERT5xDgdz8tnJZHdBj1fA6PUz9htiiQJvRShY7q6G+2N5zh+OMuU6vVd6yyGMFnaOfBOi1fqkn50MsQJwqoHj2vWapihOpR8pbyFwEHc3BBsYeVfnYcoBNx+GHZU5VZp2LqxptxiOIgkUoIX2i0PqlNjR4ZNuY+7GqcBUymcYsTNkItMDpfuDBFYOEFdAS46tWjGpO2MrGh6eza6h6LBBaxH9kq4QdhXPcRLRFs44ogxJlNGH5thJ50bdzkD1wnOG4RJUxQG5SJdnSdcTrWIcAfmRgwCMfLOEbgBmaZUSENFEEREpjl+ZVpGsqwUMTAMNNEpPKQLtvzVWMOEWSiFbnRcOhm4T+xOsw50lFDJXm58Q+MffLDTwr6ZPxisqtp1lqLNZaN7LZLXIZTTOKvSRqE3cTPIlKbVF2ggCpGWUTCR5DDidsrVdUETPQaJoV7LEGq8wkPiXzB80miTq9zuMp09WqSFxTbU7BUemMhTkWX3Qw73PwokfGJW50hhtPdF8BFiyLgOU06qCPzmeFUYRjHEawNQFLvRUrt0XVnAR4OOBfheN0rYA+HmRKUdIqDaj8sINgeiMPkSYJlpJc9Rp7LThu2ph3c3aGxNTBGmcIbOdlVfGVWVVsQsGCZeZqje9YGDdMJMMBXHQZHzjTkQ8APGEI84kaaHbHGPnuUklmBVJrCoXCDIqjnrbASCpxL5isLd4tU5xRNnlijQ9XjQiLI3sbPlvQfEemk4bQY7kzEOv0Wp/7Rfzos6xr4rVe961NdmUsdvELzWW0iCRMwXWHbmH59k3Gq1k/LdyJFWBg1JBD9xu8tsFr3RcC9rJ2FCso3Nk3JURz72ftNDN4dcJvu7NRJvi+8igpx2m+RfEZMQXNJIc64nlr5pUjjFW7fFnTlkHhjpQlQIvqzH0ri7d8sfH4MF+1ZcWPWSyXqW8EDbEWDainDk2pzSMfVU7aRBbWwfipukybijdAmbY+jnWmDHwNzZYey2i5qtp5JcRYTcY5gwzUWiu1RXli1jYP48DsYh+KR5tP2vdWRwM88ilSBD3yFgCrAxS+W2CWnXt+yPQAjWKm2c7QWpuveEV4GimxaGo63xVYYgxEPO8bupS2DHF8y/XXmm6xq7HDnxj3GLpzMipa2lg5/5rwDbWs+vH8z8E8a30cwsk9dkzpZ3UwwU0liVDwFfZnlFkCU7m/akBkWbDJgnFdhm1G0KTy2/oOt8BMZpTtEs691iy/oYiRIiLoO2mS1YeQoWthghazIHiRLl6t2DnKCQsTTCtmi9/kP9xOGUuz9STd90PrxSvipnmuoZE5BsJGkhe14uswgtie3VYrNVlwLOw3Fcq6nIWe+QRO271K74xy7KXpAl6lp8A7/fKWaaNfkULqMqzmdgz4JAm4iCHlTO6EUvwZUaW5etdImWAxOwP18ZSQmnGV+7SPyzrgLYsAoHLfVwNoQ6GL0DCOuALWipwUJRTyUIklycXw6yOqgxWZA8Q4EoNHWZz91tnqF9Q0LdNxvUvwPk9tjjANWhn8Nl0dJ/H5h4fqnLuq6A5PNxMp1PrXK9oNeWyQ5EtA6HWjoLNmWpjvQgapAhmGTNesYyQnMroJbH2w+OSR6VSO7t4i+9qSV5K45hv04WTiGSxT17dAR4qrpjmDt3bNfUo5g6BCu37KGwLqqF77TiueDGgnj+Y70no5JVCcysl/MutO6d+5XutLJU1BEPaZW766slrEQmVGloaNWYoZ+9qeUQbTdd5b3i0fn5qRa/6BYPrNemKEHdQ1zESGy1So5SMmDQw0Nem4vuTs4MYGmEkT18oKuRMdqFyB7JpnA9Irkab9NjQwe+IUXW1pXVvZ9tO0kco2nhZVrpKouWNHEB83Hzc98q/C8f/0KdY2SjmgKRZEVGHgH52zThLK+LrQnC3w8iGadHmkBBne0kFiu7MivmcLhAIjW/QlDkF5EXlaUUArZunoH6SzwXWtYwtuRNaJi40fHimfowRFhpsKxPCoTtM7G+SKYZHmcCZfh381rYb8W5RYdrPl790JW0Zqiyfhv36hv5zC9Gj0w9IRggiFzCJFvPBKjXIoN21Unq8SzGQCYL4XM7tizvE7kyEzM94tYW/6pUw2LDNMKvEuvmVW62+BjfI5JZ+ZX4540bfUoDbMiWUiXsfKjKzz+rh97hWVK4YzXlRzmnO/3BUnH2AmjJtxN8OWOrg/d5QNmfv6pXJ1mrdzfT/rkBtrBB2aYhrbL2JMqnzRWhzdezNnkOpzcH8SIwtP8tV7C7fxxACpJ99Xpp/ZMnEmGKlVYbmkYUtme80uHadxEdNp+TmYoF/F/CwFWVj81py2Mi7xhFUVshGv5lqaPJ27qmRZKOCIecNbehSDin9xID6VOh53lpA6djn8DwbFe8aL2YybwEpWm0xszemFH6kQMzWRiRQ21sTOJHd5hBWhOR9mVgwFzMb6SDUdbmQ6VROTKXdK/85NmrKURn5KksX6q90GjXz9vd6nFn1sBPzsTKdfDGvsFsx6+9TN4/TFKmOB5udYatlxaut353KA5s3+nIPULoDBBEIrLUPU1vKtNHKqdvwN+9otDhz5HPkpCRRdbXNgZQ6Qx9mW2TxhlZ0gtlw7FHTxze9Rrdylk34xdgIawzKbzSfd+Fmpz66HGcjJgNXXPLQlbE5mg2b7WRDzG+W1tvy3n+amcDEHpqb1RGHbV65Z2myabta0vq+8XWe/DGICL1uS4Dq3mrlmz3WStD1tAXJeBfb2bC23Y+npjQsgZE5PUG4mWYETEih0+y9mrXixjckytp/spjAcMOWcoMuRlrruA+QWRH4fDmDMOvTvAROz9UsUXT6YwB70ev+6YByztaxrfltPs8+6fz1drUfZg8w5GaC+WiX91qEcIDvC75waV9h+6vKHmw+t/FQLoDz+NzGtn2pbXfnJcsC6R3zkYzvGu/zRSjgOJB/COA1NCp0sKRusLf5WZ83kGK+oDbJ9zU0b5V6e1szzbq450BxYAwcIjk5IOnmRKAIHauQOFdjpmRRSOpGYcnAoCyVEDCi6GSfAuKYrFkEvqDmekVUy8Q3S/NxKTpUXCGCRmETEPNUWj1k5Hkb1HJXO8C1HihcoAZ4SvV3xLj4mJcu8HmoTjmpSHhk5G3oQxB5a4FACwpZ4tE48gQ99tws0B5oDzYHt5wCEdRYx51EPBHJHhA/aI3f0k/N9Jc5TBUR05s9RyAKJHPB1jrNs185mOB0U932vQFAnm8d9O9Q4xudM7XqQZuf2yCGfOCQgyr7+KBLBZn6zBrXik8ydgrz0k9Ig+TNO+9IQap2pnQ3IbEeDn7nAFwfF9XM6yIHm5EJYmU7TyXwz2SoYyMpV9YvNgeZAc2ALOTB3j3wa2W2uDZZjsL1zxxVq7xzACIZAwR9TDAlrJTqueA45ekutlyJGHAMHM8SgYNYX6kQkh2KNSkS9UImYAOvh16ymuJ52V2sFfvPqstnBj4YUNg6NkEOiB4tVtHgTRFAFZYzLagQc6S3UEhqEIhFmbrJ54ZF4GiJmwPK5dSogOSBSSSdHanRSWDwT8WEqJO1xqup3mwPNgebAFnJgLootCtFqeeUQAcWFT8riGN9g5lxBB63LULniEjhAKeyOO0mDoVgSGYkH5Dyr3/6Kb5WEekhRicg7gmhS61fIlLCF/D0Nkqin4ksILJVEnGlC3GZMY/ZInLJchKfEPEoEA3cyfDTghO5JMTp6ipHJKnZ0wmso5lHFggDGblYZtXk3ItrcStw33IlyoxL1J6Kk++aDaFMkPAp34jwgIOEyiv4EvqiwFbPdqR55N/RPJnSO85p+da4mYStS3pUAVW7iRoVQTlyREpL0Oq8gvspoGkurqtRzGsPddTYHmgPNgRU4sAjIGb2lM2K8tYqxzcZFk9Lj0KRwE2JkioUZzPCX8i28lEMvFQ5aMD+hAaWbzRI8rn0C1Tr4C8XF7HQUuI8zHjRyYZqUtwJVVooUgZlgDFNHKb6gSCRXzLf9waoszrO3hF7n7uCYFkOIHwkSKbigYMWKGVaBYNOuYRIFTEpmNYgr+dGPftRNBhg7MYY1ZcAYjVlgSzZwcY9TiQNOQh5OiEee0xEmAwM7MS42A+FLYblHAiVq2rvipya8+XhFBATSQqgKtaiYQ7rMDymTiKRuusgEooqOqj9BQcBRoT1FW0zARaEo+W+mvEvCQTWQNcWQESoS6uMb3gqZySCfJvRafEeFbfcoX1iOpShJPbafSKUrfGz9SnOgOdAcOA0OLAJyq6oDaeyl8hAkUXEiIMqRYOGzVpZKJAgt26nF0SZ6+b4p7D4wSJBCVveyzKtE/Ho1g4Q40J1G3/ajTqMAQsBn0ku44C7Rh7dB+O+R0GZ4mDDIAk4JEcxkwvYOZYWtgNAJ6QytvUuK8lvmBuAq2rAa/MubQTIANVD31eamUOGU/kQLD4hKaKhOWyHCHMJj90U2BsYiJCuAzsCwpkUqRqF0II6rwlc3ExQTkJMGRFTVHCFAyGh7B5NhIq+IeGziiViuEnGzRewX+dLkSb5zfKAxM+eQKkZRACTrlMDj6hcsE8E8N3EjEf5JLWajqcuHQyxlFg7RmzVBPBUWm0CJDBmAWPjloMNYpBI4sIIEY27jIV7RxRHATEIwGnNh7cdM6140B5oDO8qBRUBuHZdyR+giNs+s0RQggexhA2UrXkvuW1WtfdZ34QWyXqdw1lzpQISht4bKpJu0Ex6pYaxkR3m3NrJ5FFIQ7XEAFXjmoje7iEqgjlAVPidrAniDQDR4SJbg3gmaDbTEmbeFDCMVE+sf5lFquafFud2I2PioGNeiW/hXdoS4KwpilTSChtWsIEaohLmFnq1AUp4H8iVpEGtChR6pjbiGThODQRt8wkL6OjSVXEfyjwSmzpUysp5wmZSlQ69VwjmDHKP1EKAJrEjWZMr3xIlPT6Fs/rIusB5J85D0JLw0JMzGOgxhUgLwDA/km8c//vGJ46EXmkYPlw6USA0iLQeNHIonUBohlTM8AxIpgajUsVbWNv+7oeZAc2AxBxYBeaLte792EC2j1q/4FSfovzVO4h2btZI0lFE9CzrLJIMkk6+n7PDJaMTSaymHK1VJj9ChHDAQ9NdkOAZy1NkcHyAw0R0BNkkL8ER+gp0QHSJCpshVGQ7iFLYrFs4bWcUgpQoVczEpj3k7jGZ8vw2ZdunxciKRJDQnJxgV1kViYGcGdYSM6gWQThMZ5Vhc1GzcqbmQMrlzdME1wiH6QTXAVpukSWlCx23BgFiPCJEsCrpgX8ZhSoLj5JhZJmr+apr9XzdhtrlHepAgLvldtJscXMz+T3nKU5I1RGpIlVPKudan6SQrZHvQcTWQkFRILycoMFCF2301B5oDzYGNc2D1fOTRn6hitG1mditmEh8BksA/jcomegUf9iMZbHpH/KijDgvtiIMZWA7AvA5XRo+2cjBMzeAtozM25F+jM0I1m4oXaxt49uw1AKOUM48De67vMI9JBs5JDsFsbv9b5mNWdLo1mKy2aq8kMkSZ3HOIH2ouCKcaTzTtMter30VrZ5zXHKkCE5jZPSJPsAqgh1v+QScRGIokgrRHzhOeZwCLhdlYwc7APFOE1wuP0UbRZ5/ns5mmmdPl6yS1UO6RwV2AeGHjgBjBGj8ba+Gow9rlmwPNgebAiXBgdSC3HNvOpI7bubRlaAXk3G7Flx3Pjqa9TzugTpcVldZ0r9DGjnNQ+ET6vHOVYB0Yo5IyTdNW4+oVAAPM5KdJ3F275mCe2BRdPH+9leSV1f2kAqRAp8DsyUM2Z6YUKrLdcYDH6ALStMv4zPWB0ZtLGpxjYGe+Xuzo4Cm5wetkkQW7y2YIZVen+Fuov5rQNPrJgk7i2WVnEjflsIKSDbAnA1o7O0wFjsPx9ZM+gH2Ico8tmsAHEE7hZoSwuRB61E+sccfOepp2McizOqiQEMA/gDwh/bY70dR3biI1wc2B5sBecmB1ILc0s51yiRLSNVu2AWn7sklrTXe85557ap1l2mWHZxftw2ZHnUlYHT/zu+66C3qx+sZE7D6GA3XIRHdMtcBenjtmZ7iVIGtMwe5DZa845J382fRUNnNgT/09SKllWTG+4JMerFGm+CQZo5XyiUtzHCH5nSU58YJ+IRUZ4NA0iNceyzYXcTv39RbytGgLX202oUMVHZqkaI/GhgLbABnRTbv7rAJJwT626xWVpL+2ADyyYc8g798Pf/jDvPkUYIFg4XcSkijAWq7muKwzctgDsq0O/kMSqZTJgSGEVEp0QCr7BPe3Jz3pSWruNC1HncZdvjnQHDglDiwL5Fb/nF8qOoCHFZPmxAJ58803W3k5ATGns2dS1yx5/locGSE5DPOG+9jHPsYcSsMbrbs0pImaeEr93NFqc8I7ujLDck7zczQzEIbDoGAmVtOMsZc1GMRiONRnIAH2JCowzBMbBJK0eIMDM+DEUuIvuYpPmftGQVVz4ZyjmYuDNzgkgREOaLfQzlYxl3hWbi2SLWpzmm2gjoS5qdpYsxPKF5DzgDNVbEWD2DhMZGhybB06EixQBbBZs8Gng44SkIN/ZRjDObEzfXvdOTRmAFvdYxS5QDvxkZ5tCwBz2IdMTq/wbyfWsFWQciIWcO7DUnhPnnCcTD3OoeGYrmUyawuom59oY15yX1UqpI4zDDDa7+ikarKbA82BPePA/XgSVZeoVvSh2RXKikyBs6DT6so7iaptgeMJDAlSg/1vC242VuNqBEtoQlZDJ3qZVS2C0ZbqsrCqXKNVyanyN9nPGJNPtZUTrBwK8hsHG/hjB9e/j3jEI6jFoJFODKg8osWyPFMcv/CFL4BGZw0c6gNRyLDtrctgmFlbSS8aI+fL4bpxdNDANrNiqmXxNu45dzDS70V3FLZvkjFSTHlI+cd//Md0WacKwTkakAQpkYFCdpdYsAEkQwLfckIDzKbLsnUDZs5rqr3sssuYstOcmQDpiQsAFcSih7jg8JsZRXXmfA5izSjD59Qcq4P7kB4xI8FmF0nCRgDxhYXc1MITu/sc2bjrRxZhukck/ph1ZimqNG3SYi/bBl88SI9FZqzZa19coyaMp2jWa03rDqeQHYrYf4ITsqtqDjQHNssBa6N1SYiXkYxpPnLGRir1Zgk9vdZBiyNYnY/89Dh8ejXXscZTamJB/afd9Cn1qKttDjQH9o8DwqLTSSZ+Ucua1vePHd2j3eLAaR/3WlD/aTe9WwPR1DYHmgPbxoEG8m0bkaanOdAcaA40B5oDR+BAA/kRmNVFmwPNgeZAc6A5sG0cmAL5flsRJ55c2zYYTU9zoDnQHGgONAcWcGAuit3H2c2h4Sc+8YmCbu5r1CpeS1yjOyJNfyfNgeZAc6A5sHMccO6Gs5tT3xOMvg+Qy3UhQZZ4lkk9uWdXQnmLRuKI0eIwZLvYcce9EiJmF4lvmpsDzYHmQHPgUA44USympPgZE6/16fEz4T5o5IdWt6MFALmAYnt5/MxBbYen93tnZEdnXZPdHGgONAdOigNCpgovfQiQH3SOXKQtYblE/K4Aq+JWCrUhzIjoHyFR1VJoiNFBsxdCRLgYoWCEzXI/mTOEdVO/iCUKf+YznxEmUzAvFQrN4f5J9XNBPbt7jlxwVgFJsIvOLaSJaCeVpwR7Rd0RsUR0FxFR5AjHUiJbkoz11RxoDjQHmgN7w4HVz5FDEZknBMwS0ivsEBtLQNAbb7xR3LFikIhd4sSJhZkgnezzYopBa5vzdqaFGKu0HFBc+G7Bq21XC/4lZOZ73vOeBbk09mYMVugI1r373e+WYQyjvM6ETiITFlcg0gqqipnipCbonlxhYrJW6NMVWuxXmgPNgeZAc2CHOLDo+BnYFq4SWgutKrQnnHCBk89//vOickJfoFIRW+XIAi3QmhE/Bl5BNCE6vBfZW94Ll6hy1HER5gT6FtQz6dEECac+Siol/PUOMW49pBoCqCwg32Me8xjiFIMHiUckcPFKxf0W9Btgk4oEGSU/GRcRTKnm2UehmuP5F7/4xVFCUoz6HrazqaQXdHq2E6lEjEgy1x2URmU9ve5WmgPNgeZAc2B5DiwCcqhg0Zfy5CEPeQhredJXQ4Lrr79ekG1Bs6XDyopP+aYUipstaUplhRIMFSTA8q985SuCe//n//yfQ5Yg4aJVS4UZG7vyrMQibLvfvlqTkSMMkaLsO5CBwlgskojMEAicLm0JiwikFwlfGHCJyPAQihsdw0Frf/3rX//Sl74U/8NY8cOlG0kuTrlVyAdJRvK///f/Vv/b3/52aULUTCbrnPHLf0JdsjnQHGgObJYDi4Accj/+8Y+nqFnx6XnAwPoucwb8ABuylwJpyjeDuZxaNHWpqRVTJhq5DWnIwdjuvrxbsl1BCMD/8z//82zFMqGl53R9GK8SWmb7ao2zIa55hCRSVKnIYRG5B5zzwHef2i3LyGte8xpcJVrhZLwh/JV1m/+B3F90bmKZxCFyglHWjanUI1CflKAGex+G0k6H3RdJQSTh7lSzm/0su/XmQHOgObA8BxYBOW8pRm+KICyJSme5d9ZcPiu/OV4Fxe2XwxIZt6B47YIrQFnkewX1YTzIoQjaaE/u57pUK2EXI/ATnvAErnDL030WSoLY//N//g8Wyc45CQIAaGXfYni3HwGkk4fUj6Q9fdazniVtNhYpxhvRXoZHkpUZKbYQBZjimUNgPF2ciSX5zQxBC1JnYV51H5sDzYE948DqIVot+jAe0vCuohHSswtsAufOo3/1q1+1ues3NCIWfO9736PKj4fU7777bjZeECLZ855x9kS6E2QdxaNRBvJ7LvTC9fgbJgBOxsVOh8Hi2Q7CSQbOqrHYU83BvALqoc03kJ/IqHUlzYHmQHNgnRxYHcit/iCZ8RZCyHwKA5ji/aXbzYZOc192Z/mePYpyT0e0fcu0Kyk117l19nlX2sKrf/SP/hE+/9Vf/dUEy2nVTOUMHrK8L9MdPFcDE70M39Hdc/2n//SfGNLjDdcOCstwsss0B5oDzYFt48DqQE4dz8mxf//v/z0lj6XXQXO4ftFFF1111VU2YhnSbcoWAtkyF1XOFjtE/8u//Mvzzz8fPvGdBiTbxpQtoYdIxBLO1MFTvbzPArdYR5n+yZ/8SbLRMgBMkHrAAx5gg8MP3nAkgFw5YrBMDVvCkyajOdAcaA40ByYcWB3IQYvIJLydHVFzDsplp9z2OfyG5b/4i7/4D//hPwTYrjQJeNh7bYRTJSG98GocpH/2Z3+2vNx7bGY5wO3g8ssvd4SMC0KOhsNdDH/+859vb/tVr3oVrZ2ohLGO85GQ5sbTh9PekovemQK+h06cq8f+ulGwlc7e3kPQc6850BxoDuwuB5YCckggAqilf1TdaNhMsvQ8mO0olIsfHFAB1YKZ0yZ5qj/0oQ+1KeuRA2YOPjlTDunFg3P02ak2juucq51t86KtdAFnWjWcyll/5+9wSeMMSPpx3g8nsevXf/3XcdhpMY+Uh99s5qSiF7/4xTzPDZMtjxhC8NMwedcPVhOvuC8qnHq8y3zynOc8h+8Cmcz4Zlu9r+ZAc6A50BzYLQ5MY63PDdFqiXcQnDMUQ25FgLE1zgX9X/7Lf0nzTp+BgSPLP/dzPwfLs03ufLMoJVRJOp93Y0V3pAqEcLfmhxXk9pdCKfFaRXs9JSbubohW+rcLJ/HK2TPqNWNGuOQOa4cTgPbL/+2//bfOj9HLSVcAPgfYnPp70pOexIquMCnKYT+740ZEGeUzcOK/2hlhq+9Mr6c097ra5kBzoDlwfA7MDdG6FJAfv+0tqWF3gXwFBsaivsKL/UpzoDnQHGgObCcHVo+1vp39aaoWc6BRvGdIc6A50Bw4CxyY7pHv9+rfduOzMKe7j82B5kBzYF85MBfF7mNa5zPF4dzxbibo/eMCGUW/eNHb7N+/3nWPmgPNgeZAc2C/OSBYJ69wjs+L8pHzHn/sYx/7whe+0IHvvWQHLE9O1b3s3aGW9j4UcAbHvbvcHGgO7A0HADlvZYlOJhh9H40ckIuWynF9jKK6NyzQEUjm2mYDe7D2NDY4HDzb5o7v0zTrvjQHmgPNgdPgACDn7HbhhRcuAnJxxOYePzsNgjZS5/Z7rSdLikg7J84f59CcPp+NnnviDXWFzYHmQHOgOXBKHFjda13AkF/7tV/7yEc+MmrqDojbUHcivMj9wQ9+IK7Lhz70ocQgc0ljylafsKBQBAWbte6ehqZ71NFy1N7he8ox2crp+Sc/+ckSuaYSkedlI02CuC984Qui5UhZdtT6Z8tLi3LuuedKW56Y6n3hAFZgCMk1ce7WcH3jG98QH0kopNNuy0cq1NLtt98+2UU77Xa7/uZAc2ANHJiLYodHdhMNRigxQdELb9CaoKECqhfdEPrqq69OTo7c/JM/+RM5TEEIZEo8UbHehCtZQ1cPamKzYgSqMORlL3uZLO9CsGEFBgqw8+AHP1g4F09t3gOYRG4RoE2y0bl5z47KQBVa04kOgqsv+e7IqIOYtnFmLtmXucUk+CGe4nBFJTpObcu8i11C7BnWZQofqcxkIAQCeu973+tb+7t/9+8eqZ4u3BxoDmw/B+YuvIuA3DInqKdIbdzkxGWz9rmo4Oeddx4LPDO1m4U099xzD7Ow5JiQCWYkGJzob3R0YVwf9KAHPe95z7O++L39nDolCkVapWHzJRQ+XQw70Wqx5YYbbhDS7t3vfvcFF1yggJywL3nJSzATJ0lef/7nf06NE5c+2vmI63LCPv3pT3/gAx+okt/8zd8shFDJLbfc4uiB0RGSnTh18cUXUweli1VMthXzQLo5dYoN53rmM59pmNLlj370o178/ve/r2m/vf7xj3/cbyOLPDPB5Yd/FXZf5L5XvvKVGpLZRT0mzCzrpLLleOGkgDMRr3/961OAYCHrnSS2mlOnoHJ4IqXb7OukQwYM9QtAe/PNN/tbkqI4/1RqNYtbJzJdvfvXf/3XKFet3pl1cq7Xo8997nPC03pElvqf//N/Cm+nFy6zGjNNTmxByazpQqPGJUwzcFjqddU6BOFbkGhA1ELVCpb3gQ98oDLcUPf5l2b4JJpTfuKmoMuGXoXKCGNsuIvUL3/5y6xZ4dsVV1yR+8SOG2+8URNSzmP7U5/6VIaxSOhovuaaa/CEAUYZoiEmC4SMYGF93ZnwFjO1qMsy1psz49PPfvazHmVq3XXXXTstsZ3St9zVNge2igP3k0WjCJLHTAhuCmLuiNwZIJFaQxDQX/iFX7Ckfuc737EYPeMZzxCHFSp4ev/7359RHXhYHL/97W9bKC1nHrEhMx1bSVMbS7IIoP5usP9WWFpRxZRdJyVW1dtuu82aK+XrmHtUqHOXlRR7k+yVDYP0I7OcNVS+MmniHv3oR/NUhNygItvnLBxqE6uV4mWZxn+ADdXo3KD393//97HaqBkdIGEUjKNxEVndwv26173OfrkY+IKuS0ZnxWce0AQJjDRw7bXXQgjXpz/9aewitLHECOQO87xu0Rdbl1iAElLFO9/5zv/xP/4H1DRnoLjNF9ijuWIsSGDj1Yo8eJoGTkiFalDnuuuuY2cGFao1K9CvOUTWu0AL8Ou4eYUhxIv3ve99yijvEbnEK/rIjAxNkYQbYI/5R0h/EsZv//ZvC0BLBrWhA8ykYNc66cS8xTQk2QMijJqfKPfKP/7H/1jNAJWlRM0YUrlkFCNAMFkbGtjJwoFpBugRj3iEv2984xsZohDpERDVnLawHcHkYNzgmUIQYdPSdzVUbF1TApORweIF71ULoTHWB4Vvn/jEJ37mZ34mJBEIVGs0zRBGna997WuA1rjbukI8QRDTNG1fBur7+pQxxxApv44RkSyHCU1KXNgc9kJxUwJ/zAHUqlxHzB+MNbXwluxlhuAJQYG0kaj+fTUHmgMb54AvnUpMZL8PJXHkzmUJgMf1rw/Ysuhfn7cP2zJqLaOIJAnHpz71KasA4zBlxVomJ0rQwsKtvPXXSmr5trjbX7eWUXSSdmWDF1IFHt8IAZZ1C/0ll1wyywTQCAhpZi9/+cuBItUNhZZ4XgVAN9R++MMf/hf/4l9IGuu3UaCnUkOjXrsAMySwrQ7nvPLIRz7S6FQ3aaiA8G1vexvodROYWeLz1EjRjMFMVQU74QGUNab/8T/+R2UkUQW9oDGvmEaWeJXASD/QnPs6CD/4Ula75g+FGxSRAnNTH8EY9Rf8POxhDzOp0lkmAYCqNgBfrxNclIGFsX5DfbIOfCVffve734U6CEtheAYg2RtwUsdNQqzII+BtVt9xxx3mLVKZDfxwHwG0avIKjGQ6Ap+AM69Q7olQTEpFiSGT2c9Uzx2SDbOKcPc663UCitMgYSDGYj4emmkadQzER5G3jAszTLXiDmqZRpBX1ZKkCVUMHr4XooPRzCNiCtDFK5SQQvhVkKfdl2kQThsRI6sXJACDomtmGgEdJSrBPR8sMQKopzY0YxGhoTqoXZwhZ2jRBwv4mTHylHGIRJ6Z01dzoDmwcQ74kClsEzIWmdYpE7GEUx285ofv2Ucut6bf1BQqi78WPmoZtKZSpFhCr/zwhz8k3VvItKqkxRc8EPw3LtFshACoZhWm9dqemBCAP4wckACfraQxHeMk7dySncL0yHDVbwooy4el/B3veAejqwvM+NdAADljBJbGoDcqVK3WY7umZFOsoSnFC4qrymZqOVAwGKgE/mkRfoM96pqlnwEgbVn0SWnaMqYakoXl1a9+NUxlAwClaK7eeRfUQVzqaW5C7uzOgEkAQ/gInTgAhHR/zMBGM2Z+oIbGHK2eRz3qUZBeX0gVJqfaUm1s2rDHfIOOVE/wlkeaQBj6bUwoQDYygd23c5FdZHxGg/vkVBIMcSEmqNG9H82QFSrbJuCQCKdJLXoapvnLrq5Cv1X+Yz/2Y4aJKcWEZ/uxaxBK1Jmm6/IKzug18Y5VnHzGSo8S1LpAMnENwzXHyqI2PNd340s/Zn5QDw3bHfYAfCN1eTf5igwN4ckjkgru+WDhNBalaQsBzKZqq1n9WvnSl75EwMU9L6qTiYjY4RHRgbDO8LANXqIj6/p3c6A5MHLgcGe3g/jl27YW06Us+tzXrVkBJHCeuCtgw5pCIbPKu6gIDAIWkbOZLhNboLi1tTZQi7HJaQaZsCuSUC6/a9sY07Kb6z50hMrqoSPWBXRZYjVhoYcxcx2d8jq7MasJaznlFUgbwTEfOZMsBQ5Sghn3oYvm/IC+aYt2SDOmWBPyrPLwlR0YBLqY9AkBRb93dWHcTDEroCyNE5GRSzIZTCSXpyNggGQ8KbnHj0iQ3qUTm2xjYcCm76BanRFXiww9SkQ/vZDLtXibDO5Kwj8TWOUf/OAHSTZMERTr0StNiyCcOYF5wC47OQYQjkwzTIkypDsKG0d3XJouIvEhLC3aTAl6tl0qo6BR6jVhgqTr8pZRCMPhNBSnXic3ndd1NmwxEJq49957dTx76nqReZI088H7yRXGGvfUTz5AKkqy5WT7LAYGneWganOdLD47aXsZbQ40B7aHA6sDuWXCWmP5yEJgE87FrGfdoQ3QnBgSGUJtgqa3lA8mXLp7OSttDxfWQAlt7Kd+6qes0Wynk+bcsTpbpqHvMm7qcMICzd0JqLAhu2hXbLas05ApStvceqzv2Q8GuiDN+SteWgzOcK7wxgB5VyUs2+4DG21RhTWRtlwkAPsm9DY7tfZigBztli777/7dvyMlVOA80KXaUW5TM7EguuyItXP5nzSsBSHqyczxLqpihK8XPdJufC0nrmpadFMvtF6CkUoUyzaHStiToLjtDMquHgEwGmpVznZtRwrWmsC6z4htV1tVB+mp6oxn6HhcM0aRybgYdMPhI2IdwXBbURwAFWOKuPTSS0lIYbiBZvFmMFdnBjdkE91YI2jS8BiRj3vc44L0Mef4O/dbM83YP8jWNX/84BZns0CdeMWBkUWdmUdz9HKbMvHs66s50BzYTg6sDuSWDMsE+7mln0LjYmIl5tso5XpNK7JIQfpauehqllSLyNmMSWJh5Q9lu9pu94g0dCkYTOjh00RPsviOqt7cSWMV5qvF8ozzKWAgKG3RhmfRJSu78bJGk7GUsT/KT41sQYbIBnbw0g/kWdCvuuoqJl+IpSHyhzJ50QVmQI6OZPThB5mA3gbOWWU9qhGnLEJcZeLNrnJPaYqszcvMAcWAZck9GrJ9gDkuJCGP/Tl4pn62btyjTXLdok1yGgi1JiTrgp4yjJt77nvqPhmFjcH89FtVfEFwj6cY1zOITiBAajHfHrz5DOaBLsu5f1P/QXoqkQJPEKnpOhHAFmWsY4pIzYrhRgqwrpOAbRYYU5oxGQh5IdWFNo1i+Pi6+6QTsoXpBPJZ0YF6Kldz/NoilvmhoQJjsrWOE7kK5hGGgRG5tIWxfnPjJ72xEyjGAlHc6B/NgebAtnFgWSAPGIzUWz6sjyy0ViJrhE1BSxVDK0Ge+mJDzhJjvQAG1lkFKBYsohaR5U8zbxuzjkmPDW8GUm5Hb3nLW+hq1lb2Xu5gOGZzFGfAmwUURmK1FXmivdW/wNXWrxosspZgCEd/UjMozUI/GSkQTrXCfDVDOys1IYBtwCpP5CJ4QTWV6B3woFhDUBu6btK8FWZWgV6OvyuPZtobzY82D8xojZRXNOiIgfYK3b0EESq7TtlntaVitrBIs9Zy66PH6+nkAPfsBDOX7Phqzu6vicTlyj53dihY9V3mHmgnWNi2h8S2cqGXV3SWgquD+gt6oTKnOZTwDDAPqZ5YBzUd/IvYxEsAaNFQAad67B2Qupiaa7hJAB6h31+znRgUj3c26tlhilREnlADoEWJt8AwB5GYu6tavGVLNysAp6r4omMj/nhRXyjZXtFxogzaMJaRoJzOqhJbKl7BYWb2GNJDAP7gRr5KlNsIx668xeqgCZIcCZJszXJuR5w5Bw0GxbaC2YUYDDRPMAdLSUjHnPz9enOgOXCKHMgCmmvitV73fe18pK1flp66yQbIg8aCWHcoMdZoiw5FKjfjD4x6WMK0Xp7PY6Pr/L1Br/XqJicm7lQZUcu6Bbo8hCExd2JnfuxhJwCcO3kRhll/4W7VY0eDH7tKYAn9D9oFGqG7jd4cN8gFcizi8A9mAAZuawwAXgR4nORpXUCOdVdJnm4s9pQzv6nm3MhhpN/UcSMbmslhCRLnPiW1lnib31b/2dFUD2UxL3KYyIukCno/lE1IWnjDnMNiUTMn9RBrWHqgvj6SJ8gN/g2Y6RRSEzyHtxp3sGoa/eAt1OLneE4BwczR7gMnEGtOxiDBeozteQXew91JR/i45eCfFnHYcUGnvwwBPqvEuMTRQVuOw+F/iGQmUcxbcJ1lnoSEdWPNQDr+jNGkfTtxLHVpInzzyEekKjdz+o79A+immHa9hQPFfF8rH343SS3ErEwzrUx46xFJK13GyQyEC+Us/HWG0Ol5ozM7rH2nOdAc2AgH5nqt30d7WxBr3fobq2a+fBfY8M27UzEusvKOxRTIMZ6sR5DjoG3FqvZUf2xDrPXsQJd6HYt32JJH2BXowrpE16lHB/FWAbxNydh7lRxZHSe7qOYGLq2kmKsainE1rccBLQUyskVztTUZ36J2HMRJc8rkqRmFntCp/kynWZd+N5VEEpsBmYD6COHStbi+Z2rFVTs1j9SO7A0bMyG9orzXMydHIievpE4cKxNC8Sc/Ql5aD9aGngklMUJMWDRhrKfVi5Gk8duJ2+M4vik51pyvNYn+Mh8y9OO4jH6XCte4jIzNVEw3T/XD7MqbA82BJTkwN9b6skC+ZBtbXmwbgHzLWbQ95DnYxqWORkgLRxVXSpooG7gz3NtDZFPSHGgONAfWyYHVk6ask8puqzkQDnA9s1XvgDWfAGZth55ZgCtQYHOpOdAcaA40B8KBZZ3dml/NgTVzIFv43AVs99rXt83v2PpmQ/yumQPdXHOgOdAcWIYDUyCfJHVYpoodKjPu8e8Q2WeWVD5iXM8S4Zzn+dzwJmeWOd3x5kBz4AxyYC6K3WePnF+0OFMORI3xufaJU3x/dK2cdfepa92X5kBzoDnQHNhvDjBJOtBLsRmDTenyFMhZLyVacIJlL9kByB3vcSp6L3vXnWoONAeaA82BPeZAgJzP7yIgd/xM3guBNveVEY7uOGQshtq+drD71RxoDjQHmgN7zAFhmkT1oJGOfVzq+Bnwh38CekgvMXobOdor6ASn4gQYEWNLsWrA2VOPBLXYnkOoJ3L8TAgOYUAmfHTiNim8bOvu0xwSrEZnBUcj/SzpYWBKmCT2s5csf7LscoRabBMEs7s4Ki30m/ES46zSpZxsc1WbE+omP1OWyMRjvvkqIKxK8r4sE552MZEmm0gyguoIrTi3rVPq42y1KBHqTq9RkoA5s5cIcSxh5sNuOSqKnyN6klmUhH7mVcIIShfUe3Nrm2Dd0CwH5h4/+1EIi7rmRnYTQ0oaCR+hsI4VL8yXKXajE71WJeHAUoOwVmP2TBQ4PjRmmN5IHJyx0ROJ7CYmSeXlHLlsLRO5c+N9PFkCxGAR7FO0sjHR+IImBO+T8VPQ2TEI4MmStKA2yC0NjCNq8nIqJkSrhGCCuIk7e9o0EHZFyTUf3vrWt862JeB8PPCFKz4+JbrpywKNPPmPX9txamDi4lUD2ATfnVuP/gqEJ4heMgPtygWwzXzmSRHp0UxKI9FaAH34iYHYV3NgUxw4cj5y01cAy1e84hVCNprZyVJqqbKOiMItAZSFSSjHiuaYJJL0sIixdNPTVoM2Iq+JgQWzkyoUQ1x+EHTE+KzkodmMB2auZBkPqcYeJ91MGi7/AgD/Vi4vL5I2Zl+s13EYpiYDTcKCzmWC+1VSE5PUsZpz0+uIUY8LPZPQ7qlWWjCRdwVATTYtxbzodT9Cw5juU1BxibOE8rV8J3W6S7UaCs3eHRONKONOMn6GhvAtTYQzE+K9PpexaUvMVATIpqoelYM62UQEN63M5RO2TPKkTdiSYSq2ZOwOYj6zkw8El2b1TqqzeKjCoTMVhC2GQ790JJzxIznNRkZN0rt5KwPqxWSlY3UYg7Uhr5iWcTlobowNzZY8dJQPpaQmpKpE4RUxXjD5jHuNb/UlSe2Kq7Nke6rL7teU8251bfzQjjRVambWtzbOf5F0xQyG2W5qwuCyN5ARKeUR1/LxHvSJoTbD5Jr7qU6GYOzRRta0bnTnOTCKFRONXAoNmRhM4sQGtxomWLoAW2zmLrGjLV6WzlQC1wXclrY5ALOF14lo5OmXT5FqbuNAoHJpNsbO+iwTwDyTgxQvqUkiXVuwpK8g68g74gtnloxyf9NNN0WFFTKdv2GM0iJsT1RJFhG5oiM5+QtlD4qDTQKLvURJabje8Y53sBMWkaKsoFxDpLSEBaVteKVyclRJ8AMwBIRHHngWDt02isDjCb0OKeXbiLI+Rjj3SIX4475o/MKDhxLKPVZUK2aO9VHluEEGOueccyCxGOx0aBlN/PUKmUm898woPJTng20zjDX94HQWQZdEn7Z+8gjCETQxR6hzRGo0ZTCQL2fCkapZK4SP6iwuMSlJK0CJhMdENDHbWZ4SnFVtUr7mCJxpj/nSxtS7wNiLgNxQjpNB3rCHP/zhtbukd3xVyDrYKFQ+xRp7xasHdRp64hOfGDlAArQ3vOENjNJVFesC05d+KQBjlETq2JbCFHSd8roO6gIzwNxvkGSD1QlTT9SW4qgitytvnqgceTLg5cO3HXD55ZcTR1KbhuT40YQavGvsjBSj3aQtQ1bB8DOZjYLJIJeS3YeLL75Y035IGyNlQ8zXeIvD45Q24vx6TGANPeYxj1EGuwT7S2IhwoFkBLiX70Xfa/HR+jhVNDpOlZBqr1E2AS8aHZVQvhO6GBtrm0DfRc530zaixRDNPu28PvnEjEV9YuQ2DBSRUDcTHFesezmHikWEY/MnQ6DvPuqKdT93yPpmc6A4MFcjX2RaN9HFyKSNyY/iu/XFBsgt7lYZaal83qZpgNyXY1UyZSET3dSSysYY+Xp7rhME8iwEAfLY3+q69957JzvlvAhlPUkBSw8EslKwP0OOpAPJR86a51GgKJd6wFjWF/qWdSEffy7oeP75588yWWoQWTvHerxlsSjz+LXXXjvZ0QQPgH9WAhOMxdIpHVYB+SRauO1n8wF5oEg3q1EICtqlvCMdjpSozZIXmuXurKPhqmX4scB5MbHE6y19ianc0jw5cSAnCnEzjLWvQY6st0gPdutHIKdRVTKVFDNdL7jggqCCC2EBwpHDdgrwjeKowklHcCyR210HAbmRRWS96ISn9OEypVaOFo/Y/4HrJMMYkDM9UrmsaESoMRy6t9Qgg1kKAFczYbL7DioIB8PE/FsTMRrGjqhn3A4IkFc4/ZQE1XfccYeqdAe1k4a4hkDQsSG/zSUBAGocfSlSy8gYlCxKkWy8OJmHCeFXVQXIzd7RzyaCaaYchB4f6YvPyiMq9YKpooCkRON0RYwlKx8yMbempe+CbOEm8SJJcgPks58YwdonRhH3lBQ12Uc37QnN6RfOnHfeeaM1RStcjCMv9tUcWMyBI5vWqT50L191fUgaMONJ0KRUETST7iKfuoWMggJsrMUWUJDve4D3cw224zqyo7/D6xBfP/IvPQa6WPLwAX5bL3DDQpCndFmfNP3GX/zxwVu5rE0WUKsDrZfy4V1LM9yyYgLyvEtmx2EqArzxuo0MCj2dcrK+Q1xhyaGFhghbdFCwQd+l3NMFK8uI1UQeMKseCLHuQNZ4LU2GI6tkrZVJ5ULTUj9w1QSNk+anDAWRykXL95vV4aMf/aiuuYN4op7K6b5w3YomTFsyqatNu7BT8jG8spOahvCT9ikFmZSvllfcSy5tOpmMnFoEbOQhi7hqsSU0kyOTzVO7WrfWWyszNODECgvIrdSGw8YQtoRCLNKXYgv9ni6FQtP4yiuvJELFrcxAINJv3fEuSUidWDfxeZydzKCFHGzh9oj1gtkAjmoFG6liQIKqRwTBPQOkp0m7DpXNh2j8viDEyGoKRZREg8yzYN4Ihl2IzHCYeOZbkrHSNd0xE0aSlKdTArk0hMN27k0YIlc4kCuZYGx+w11MU60hziiHEtY4QidKSBI6WNsoY1vAieGB9mx+Gprf+73fU2E2EUj/fiNSOH1NhBgKrggWkFsT45YHYtTP2qFrMrwRI0yG5Fk3DZL41dKm1+7T+DMDCcezU4XhIRSqkLmIlAObGYGUJHMggz0MN3wOjI4mHiHVNyhtXXiSy7grg7eTT4yRxgiiJB3UETPN0BP37bX7sszVzJY4kBJZWCPyIeO/T2a/g3HNfhp95wQ5sChEqy+QZArFYwutVi1AxE9Lm/uZ3B5Z4CzKvmqyudlM2TJrfXim7wmSuxNV+aTpH1YWnzFJyAo17nda4MhGFjiY7TNmwIyGhIFWJYuRJZIuxb8MkFjNLaAeYTX/ZMu9t9TpKQi3/FkORo1EPSQA6wK8J2/ZDWHNZkelVlrFrLzZLFeb4WNLAI3ENXY/9WSb9lAOmw8WU3o2kzgF12RInZYh4kjUINgMDq13QNc6hQ/Pfe5zBTFgE8aW9CjIgQxEwjnYYLKZZrqDeCRR3TBKQziWJogjGKvXdDtMMLXGnGxWWBqemekHSlRYfUGbdrFFDTQ8IIctmmB+x0/1ZKdf697VLuOTRVwxKn52ST3Ff70johGk4s23DMdCjB6lBr/9UCGSiMJs1G5ivm/NIxUCTunq/S0HCJMHbjEbsC5gCICBi8BVTzNeiAdCxhdmSBuPz7hNZPEKVk+EM/QQAvRUimENEa3S0Dj0/jXK5gYKbQlri/gVtwOU4JhxDyWsFIQ5hSeybJBPr8kNekpg0js/smJ4EWYbApT4bQ5AUFYBEkacEmaJQQbAtkkh0p9qkzpPnRYcI0LuMZTkM1UhtaaKuZepAjjHqRJYxUD81ztwbkxJFfjm40WhCZxdjEzpsXfaJTuaS6ZrfWKmSj6xzEkEGFDftbzyvjLWFEJY5Tw0qXzIaCOym40333yzyWkz5dBPrws0Bw7iwOGx1g9a3Cf6qKlJ22B3IuNbfCGNLUDrNfPXmeI+tlB8rQgWJkuAH9aCyUEsi0UAz6JgVYq13NqUJQDc2nS3tFmX3fSRZ3VTyWte8xruwZZRXKWpW7yYRizEI4e9YsG1SJXx1tJmdBDmfkYzK7UCWT4UCA0TmWDuwKEcmiZLJhQfLYS1ViZFJoBJj6z+6ZGDE26STuIxEA5Y60crKBoskUnFbSUFvVbnKCsqgWHWXCBt7QaBEyUmBKh5dKlLvzyKj1ixRc3YErejYosW60xXBFbvagVXDai5DSAFi6Uglo/nodM7KVAV86O8DlWr15E2EEz8gm3EXz9oh0ALEoRFGTh/MSp7yQFIbEkBxEcnBrFQDZ8RqR53SBujn6Py0J0ZgDWIPAR+wHA1NHYEbSC2RjlWn1DiR1ECoUFUUTLLinS8sqmmgFHIFgbyyHb279geyHP8KjKdJvNQrxGTR+OU82Ux+FOgUUtfJ/iSgKn+iuGAqeJjyVSBneNUMRAxitTiRu4kJ5kb2TXIZtbsRBrn0viJIYBcUiPlh6fEypjo831Vp8wxWr6deCo7owirBiEA8ROP1EPnVRdoDhQHDgfyQ5mVCUoUZcK18xR/7LiY+njGPd1Dq9qDAhZQSwk9wNdrBbE6gO0sgtU7FgueB1mCWYOpU/W1x4XNTYZfgpHLDzpiliFblWzODL9M1kBdW6xzTv2NfINPlg+PGC2zNNDymVgtyuPZ7vjgZNmqTNuH8l8vrOlZUqPGzapiHqEhZ6Z1xw/WTgtu9Qhz4qCUtVLro13X69VEKWfopAbFXQOK2Ni2cX4QlEbCGPFAK0ZB90EdtgSN6E/0UWRAo5K0RrZU656yJ3MXwGrearQoEsAyQs+En2iIvhimFbzl1BxfUZ8MfZrBY9w5DoV4wrRDjcu35neZ1jEnGj/RmYs4i45pc9ddd0ncbue1TlJkvvHE1hBZCorTFA86hq7Ocr/PKLtCiUf2FGKmppiyhOPnAm5Eahw7XgD5R3/0R4ghUqCEbBQZazKj/DtLTBiLJ/RpCrcdB18E9VeF9qfIi75Bdw6aKujxCNm08FRFDmAksxdA0KlR09+QPV7oqU/MrBg/MXSaY6ZQeIWBmVcGeqIOMYOR23wU6GREYTjRNK4e+gF2gebAXA6cAJCnXoZ0MiaV4tJLL/WD4Zdmab0u5+0zMgBUN2uBT1f3MYHGbPWPKTscsPZB4mxP2mwm+kAItj4SPXZZOCyyudST5dLqaXVgeGeFs6FugYZGHuXw20TdpweQ9N23lNucdv6HBc/uHVsxT7rxcNRBMLx4pLKmH1QmNmpQzfhscWddtLRNeuR1Nz09qJ7ZJhRWMxiGeRhoi5omxyyRVb6IiRnchgV44KWcg0NZSamADEWkCvvu2MKBgyTElovtpugoE8xtXRdyTgn2e9H+kU13TS8Z9MasQIlV29ptWEczBvK06CZcYX3BN1+QnXv9Vb+3FCY3gHbaNr0N5ZyiaZw6G/EO8UaWk6lvUN/DbdODME10GEc8DbmvIeCnIeZo7ebkwgSuZgE1lLDi2B6+4oorUGJ6m4pFyeys0HG9sMWGY+zbk7N5BtTM99eE0SlDE2JG7TnzZAKEbpoJANvcNhmAN8cC/fKi5jSqThBL0ffUeE2mCgWaTIxv/FIp9L4RZWjGADUTJhYdG0PcEQgHo5hiLhE44ihHZqpPzHzjdGIgyjPjIFsmAxV/C0q5L5QoYCzyFS85l87IQtrdPBoHsmzlmhsQxvcQa5XdKapAFbZSgG33nedx0yLiq7YsVvM2Gq1HY/0b/32CXuu+UuuI5dXSVh7pOmg18XmXzyr45H7lE+U+Y32BBJDYd2u3z74DdxvmQeojbdW71kROQOOqwTAIsXIyDfOp+OMaZ0m1DNX5q2IvRx7mynGTmKmWKb6C81hEQCkfnOjicaRisacbTcbIW1qk90AIGEDpofxxNMvaavG1JqIhb8EeDmtZj0SCQ4bl1ebCuEJp19qXQ19mjkdIrTAplBLsArpZeVVIQ9VNoo/fULA0JFYKcg/a3Cxne29pLjOQZ0BEScAAJ9SmclblUQe1HFu+63XGc3zA5Pii29rQNNZZ0C2+hiY1o9mXYqqz/9OM03e4zmnAU9LbhIdQgaIc05S13mzRHXyz70s4U9gogMbyWtcowjBK33MCDX/4RSZ+oksBSiGdEh6kLV+fdrMBX5dNbor+SIyGWIPKW1sNfusys3adQNMWwoxyRT4xA1EL9VVlDhAm7G2nFfOfrGCmETHnft3EhZCtO2QUZn//uki3ysfCkao0gRij407Y4vI12Yw36Jag3LGv73thaTCZbfNPTnmw04giYEDnThXiXY01Sdr8H33L7Whk1rl8iZavEOZsgjvMWr5lg0hoyNyY+4ll5lCy0Vzu9zYc9Y7/XT5kNox45NVFMjC+dQJiLif7ZnMgHJjrtX4/E6jmk1WbCmXlGieZN82/eIeCnxgtXZYnq4wPw+Lu+7FcempRsMz5y5MIzNv0Gqva+G80U1ZOyq9E31nFaUIWvqoTHyxGli2fJbZgr1XS4mgFt1IzjfKSDYg6lRQvJxhpD089Vgr4ZA3FZGul8lYEbIwe4y38BHJ0Dn9zCtlm52g7Vcx4ZTW0vOovsPGbvzSvhQJUMoRHCDCm5AYwAKusiToy2Qph0Db6Vlgn0KAmaLT6M4TGyS6ezCSAnDFTgFhDGlCG2Kf7HMtVi3JEghm/WRRpP9GAoaNKkKf+KKnQWp2MClm+9YX4giG2pXEGl/zGWC1irJXRDNQE1oVsT/UaAThpWpIJNOEti3Lc30xRlKvBnqtdD2zhr1Q6vdFBgE/AzrG+ZHxJJKpKCNL4NHBNwHYqHVQj4pRBmMbpKTjPCau69FFhlXuKLfpLBsJz5Pk32/AqxzTgZLxApk1TPcITPAQ2fqMBZEJincVDTRhlfEhbZpGh9N3FCcDk4T8ISCYnzTRkouKDhqApiUpfDI2JYTLkzGSMSX5jWo7zBVbxxDChFiX+hhLz01+U4EOd7x/7Ttwxf7SrgM6aCSQGk0TlGSz0mIrYy7LN9QwTjI62IoZiVCYAoSRiSsYItcqoHDFoQ62nuKFHJie+1TeoGLG1pgoiM1XUbyDMMbWZLd4ioPgR4g2HPhI3fYOa8K1phX5vZiqJwwd9YpGzfU1+qN+L/o1ruuH2/RopffSh+ZsoUu7bKiLUnrVdyI3DwY4SYH2z98oWdZ9FxgSt/32oviW7sDvaw0PJprxaF7YkaQrOr7DPemgfu8BBHGiG99w4KQ70XDopTnY9R+XA3FjrJ7ZHflRqunyj+JrnQDN8zQzf4+Z6Lu3x4O5i1xrId3HUmubmQHOgOdAcaA78LQcayHsqNAeaA82B5kBzYIc5MAXySYSNHe7ZPNL3u3d7NljdneZAc6A50ByYcGAuit3nLC8XWWEZHO2Yjbm9H9zkosLbdpLyYT+61r1oDjQHmgPNgf3mgJMOnN0cc5hg9H2AXGhlJRytcSh2/9iRoCJ89+s87h70kXR2UNyJ/ehdjk7udF9OcIxOsKqdZmkT3xw4mxxwRlGQA6cxJ+ma7gPkjp9Rxx1x3lceOT7rMOiWHD87ESY7oprwnHt5iQdScWR3t4NCF1RazGP2wplsR5/bZfqYbOzXmwO7ywGBtoRMmAD5dI/8IO1HhCYRIhOAMxdEFNJZ7AhxMIT4IAE4gy5httiN4nmNbLIcCwkurtbGdceNE4AtciYKoyFcFHYJDiXW5mpTCv/Fj5S3cbXX1/yWE/wi4Qhvd2jezxBmB0SAPPG8EvtarHgxEBJp/DiX6W0ay3BayU+PU9uh7yJYaFIBShdMPOFQTAmh34QfWVChj0i0MgFik29GmF4hzJLXoK/mQHPg7HBg7mKylNe6AKLCr8KMAnKhi0RaToZmCG1ZYY0XCcvKYskec2C4c8sttwihlWCWZ4fdc3sqsjpuJG8mSBMzCxsFQp+kqFqGSyKgYXulOV/mlbll1jMolEi5y2SjWhJ7xBojJsojF+0TgAnwOSa0WLm/KlT5JIn7bG0nwhb7WMgWXW6BlyWPDSI2IE++srpmCUhejQRgT5j9lZnQLzYHmgP7xIGpaX2M7AZsRCSuhAfCKQtDbSGm04B2yYVEx5ROWxngDeknfLGKiSUu3Lq1TFX+isu42cQAm43sBpbAtoiY73rXuxJXFVtETiUMMXigTUDmxHO2BQDswbyolhZuSCBNEzkpoTQFxYTfNFRALujjC17wgkTVNS6CRSsmqCd3B64AZDc+AUbBb9G8YYNYoYLFMpmwo4hDKQKlYKXe9SJBTXkIp87K9ZkxFVZTXOvKOy5gqgrBiaHXHIJ1LZEyxZ5MtrdcmoZSHrmpRfnxRPAeg8BrVwEWYwyx5ZGQlhoSzp1pB0MEKxUU09xD3ote9CLTD3NYqoWMrdi0ZESxr803dxKDPZXwClFS/aRMDFF/sqhhrPua1gtMoxMLGorzgmCrStBQNQj/qUJMw21/bWFwMxF0s8KMo0TUdOzFB5E7BX+NcKCbXvRUbRoyskaHpUqM0tHL0vja69IXnNF9KbxYHcRnJe4I3mmYcNX0YHdBsAi4oqaLSmvsBFXFZLq+ySAuqTpJhD7SJNExcIYvDaHZuJhd/hVs1Vvt5rlPa3f35WxyYG5ktx+F5q5rkjTFIiXQtzXOYmoZgsQWRwkArAhWNAgE16lN0m4K0m6hd0m/YVlJhQyGMlgAe8Usu7IDVZbfsdF1/j7BpClHJRtGiqGNV4ZhfBeXABUzqSMDLO1MHZ5alMGYuNB0UIqaUYBPkAaK8GAAnzR7QwA5LP3WdyYQ2CwxpVcU89dmh0qMl3whcJ0x30BAUIGd5YMSiNvKDi2S1wGoQBoD7UXYwHxi9S8iodHVV18N79GQyN7CYodO6VKQQfgTRN3r6pRmFPzkXZBGyFAhBDUBRIbnhAGWqmYCii4Xzc95znNkNvMU7tqpIRYAZjlDYa1UGd41nWQqQzYRRL+SLUY4dBG23U/uKR1J6gs70wK2q8eLHtmGiFFayimgnvwogpYTHbDF66D0ec97ni7oCKlUScIBODdqCWmuIwYlHQfVCPAu4l0qNL1Dj5ql35AVw7suIbvViUKdqo6Tw4RKNpqq9WXJg6dRfVetqnibStGhsA8QJYaMeGHmJHsmICci+KtOMfxNaVK1uPEEDrWR/AgESTxPhvCvgcMxXbBPsfEP8KhfTZdvDjQHJhyYmzRlkXUOTsAYi4gdbh5VqrMQWPVs+9FFrNEWEcsTpLH3KSOnNdcSL8ERPcNyb92xTAMYauiCnJVnRKqydsMA2p4le+wy5Y9sBHLoYZSw2Ev9BQB4DqFhNmkJTli+QRqeW5ElcpDtCjrKwAiMqXRYbZW3D62YjF623j0C5JSw5L02EEYNjsJa2I8e+EHNpbBa+inKtGeWW9IYBXTcuTdv3GRZIUC4AJ684FHfeaJBO2R7V7sy5TARxwCuUZnQlKFbS3IlI4WJMVqDFYCLwB7eeBcTSADmjxll5phj0nIDWjIibIuJgoKuOYBHiISX+uI+qPPil770Jfit+7JZBDVNOa3r9Sc+8QmPCEBYag6jWT3JNYmZuk8zhvFyaMqNhi2EHt33ivmPbzgPs3Vcr1n47a+jXGpL8hAJFcdcDm1efvnl8VfQLlIZLXQt8pakHQZrzG2jUziGMNUymBuXmhJoQ2cYpapk8gb5xGioLBGZzGlkNcOaCu3LMPAYUN+p2uTe0JxOuXy25gmZDMcYM1g1zJMz8rl1N5sDZ4oDi4CcyiJrkCUjykTMlRRHsr/f0T+Y7zjpSGdpVbWCwxirnkVfgegBfizp37TffE/qZXbUSh839nfcIx/vW8q94rIjjr10aws6zAhXvQWKYs41HACMNOBfo2YXA+bR/hlgvQ75DBad0qDQF2m0igEJr9NckQTYrPiIpETCVPpfkQGlADmFHgwYbgm1tBKk0ShhAvpmboBeZoAccGTUhXDElAguwJgOOnaTJm3mULKzL6Bdmj0a2Jb9C6EVVn/21P2AiCA2j/SO+OgR7CQE0ET1EW06y7DsPkSP7IikuQxXjy5jS9JLU9nZM2I2j007rmfKkBjk5vKb1qs7OoirhoOmC/jDJfgK9TWKBo2iR3/dxwqiwMRrhFjDDMOq71NSxg/2hsWO6MmWlqr8SJ51HybREAeMheaMo+EzHOQ5oommCYK+XB3BEDKHQRyzdu7359a9aw6cKQ6s7i8TaLc1aMmm24VrVgo6Dd2LbfBM8fHQzlp5s1iHb8tcSlqscZhpGjaDT8o3IBnfTZ14Tj9LbtBcFFPYBheTNjFZVgEA9c6jKgkhqIDAj6EFVBPdKIuz4YAUo0GyPwMJWVnBWLKOps6CZ7+BSkjSdNpKMWSgYTyFZZe31MqUATys0HaF5zIHDWkIW4Jk2oJk5BuShJpdWoTcYNL9lLT1kCSwk8u72II8P9SGe+FYimWwXBiCM4WykB4ZlHUAidpigt6RV0qI8VYSgM69fBoarb12vzXhlaMeKvMiucFYMA8g3qcXaonRZAVaO1Dn+Z98nT7Sg+jp+82B5sCuc+BYQB4H4JEFtBlqn2VxdFzfdR6dCP0WWZBJnZqYN2lLluOAx9yGgBOrL9WZ2mej15Lth7cWuw0uLzRAJqBy7733wu+LLrrIfjzE1VB0fRfDO1iS15n1HqnMy3y7snuy4Iq8MnZqbgcnN4+6BQPaIRajN2rRn4sMwatjGYFpLDO3fIlfBfB+zJY8kpSWHa8JZ46K4qGHjm5SEaMJFtV9PxwnYXThH0DCM16aY0dxEUFOZDJ3Jc2B5sBWcWB1IKdOsebZn6OiFThZ360jsMe1Vf3cODGwkPWY0jZZTN2hEN9+++2YScOrPXJKXgEG8QhsM00zVrOf24KldMZHOmWs2kSEEVztdAB7QtWhR+cDIXR0JmjHw5DHUYv1mLnefRZsO68csmjk9l/BBg1P0xOEm2Uvi7GZUEeqCHZUZ32sksQFMDyeRoNGgAeXlhwsfWffVqd6sAj9uciRh54uW6YJ9aOZvboGgt+fFynfcTUvadUPg4Izpv2hDGeoV6bODapcExpKK6oy1pkGOSZ3kESiElq4Ftk2dLm67wcjhAHKMD3qUY/i8U5NR7Mfy8g3yzCnyzQHmgPbw4HVgTxWWfvlFmveNOmSjU8Lq6XqpEJZbQ+njk8JNdHSTJ/miJTaoC8l2LLrIIBtWvCZ9R0/aeHuU7ksvlzh3LdAU8fhhxUZgIFtQxCPJzvfVm3eTFHuHUbi/GWnlsO2GhZQDjDAJ6+rpz71qeCQudjYkS2IHbGKex3GqJaNWlse0cvJE8iwkbxAj7Rra25wxbJfqx5dsxM84qsK4RCPa0KJAkzE73nPe+j68auANzqIA9nhngs/MM9hPH3nGce8nG7yIQBdlPLjj5fe4Qkf/uyXGxEDZ5vcZrltdU0ICZBWaMAkEkYL0360RRkgDDReo/nEFoZKsOVTn/qUdwkHmgj8E6fIZLwLE6+GCMXpJAb8MCTyltr8q2aCGg6QIey5hBJGdXsxHPR4SHBwIyASuEkYOOk7zem+vpoDzYE948BSQG7VyMI9rqdxRAIV3IYt7pQ2SzB3JH5SdPSRTZY2rx/kz7VnDF3QHcDGT81Os9Nfti2xix8yszZHaDcd5YJzHlmInQJwkspKDUtskOMzluYQFE7CfkCSbVGHo3hjAeDrr78eJIjBayA4e4MK4xK/LaJV0CXjGC9F/8bTDU4gzKYyfPIumAGNDiDktDdEZzAA5IDZU4PreIJ2wRiFW1X+VuWQhnLpX/WrlgmB3ZsLm87ydfd71G4BDADTCyep1EyS0F+9iAgIeECULV5oBJNCeWGkhlAejBTJgFijGP6wHjuqLl8AdX/upFWzd1WVvodUV/oStvihvyUA2RMhSAFppwA4xzmipgyesE84yo9yl5gK3PIjghisOKb5bRue+s4539OyPRCeGL3t5buPM2L84UyIid3FDw2ZG/wVDEeIJEUZAhPD4DKW+Jq0ojzafHQ8TLELJeaPF73lUJzWledLiC2OIyKYe91qNvyz85F2T5sDu8iBRQFhqj9WDXqh9Z3Zs5Qqqwm/JEBuiXE210pEq6DPOYM0Ucc94tBLG6BAbHYd2WxAmPAT37iFW9Zj/AS9lt08wqXEwYWsFnfmDcolRHQaypEw98GDc00Q14tqoLopD+lV4nVaKcyjYYMxq7kaYICz2t5VD0Ayjo6ZUYVp/wkoBo9VCJgdlMoBd68QGogRdVQMYKjZOS53SAyA3L8kCe2yt8Nm9EQTVRtFk5c7U016RHfn+WgCsLSjh+7oKfGiphaC9Y7S7xV4o6p6lONbdFCoyRLgUDU0RaqGcIa3Njoz01KJVgCbWXrOOecgFWCbtBrV39ESgAZkw1fKK0MItMsZP1OXnIQt9GBHuYAiRRy1nAbwTW0K4HN5seEzezVKtOW7MO2jOhsUHItjoBcx2bCiyrvlHKcYC4Sx0C+DBaERA5LDN+w1Ft5FDxmFMQadupD4AUQfh8pQ6A5xjcRDCjGjDLQZhV3PfOYz49toOBxsi9spRZ9wsMdh+Xdx8W2amwMrcGBuQJilgHyFxrbzlW0A8hPhzMRb6kTq7EomHCC7kAkotf6uwJzNjtFmW1+BXf1Kc6A5sAwH5gL5Uqb1ZWrvMuvkwGYNG+vs6abaAoQ5vU34O9RFfy6Rmx2jzba+qVHrdpsDZ5MDUyDf7++/80yczVm+Qq99CGzjzN2cGDabIGAF4vuV5kBzYF85MBfF7mNat1fHkYo3DS1k/7hgabbNbG/ShmUfwtm/8T3xHuX0F718NY38xOnpCpsDzYEzzgEORvyQOM1M4qXeB8g5tXKm5UY7G9trP9gHyxPJaz+6c0Z6cdQoMWeELd3N5kBz4KxxgHOr87oSnUwwegrkPIGdF5oF8tlIYYuN8FF5Z8tsUBUOHmzKCWgZdp21SblMfwvFG86XYVeXaQ40B/aYAwFyIUkWAbmzZGM+cuyYhb1C4mWQaW6ZTUGp7mzKa32ZLi9TZo8n6GzXShZM7u0xi/mZ4kN3tjnQHGgOFAdW8VoPEju+LFe0ZJrJDplLDGcKvuO8jsk6IO4AK/O9Y7jOiwvcnTJiVzl36/iswB2SnVuR56rpaxukTbnyadeZZkeTc9JXWE3nofEExzDHKXwSxqZoWxvzj9pQppCgMXJ0Oq591Ne7fHOgOdAc2D8OzEWKw4+fQXFZGYSOSvjPXCKAvuIVr7Drzg9I6Mdf+IVf8FsoDFq/eJ/iVSkj7pXQleKLiQoiN6WYJIJ4C2qxQc5u0LAvmMmdd94p7ocYHdJf8oUWfRPHiFeTGCmH8mdBL9bfwdNuMcFYxhgyh/KnCzQHmgPNgX3lwNwldxGQC5Uleqi4Y9/85jdF5qI7uuRDfPazny3GpMhiwmMJ7CV0F/84irhQXyKCiRpG1xQuVIAqTbLmK0YdF4mTQi/O1NmM1SpENj5gEVZgphheeOVf7BVvCzOFuRUOzL9uCjgq5hc1/bbbbpPrPdE9r7vuut/6rd8Spi0SmTzcYngJ/kWKUj6e1R4RlQhePPPVps4xK0lmtpDm4r4ZJgHCBDTNTeOicrHGxP9CWGKdzo3TLrAop0gR0xzN+upXv6pFY01iu/rqq9WDDLFmSSoJKCYKuikks44YZC4mnPHr0jvxaFHCCVM01jwyo974xjeKwU4cxA3U+i28KJMGem666SbBR0024d5wYF+/1e5Xc6A50BxYngP3Y7es0mI7W8EFZ84dKjh9UQxnEbYtmhDIWizcFXi23Ft/rbniX7KuK3bHHXd84AMfAPlwBTJxDvdIFm3RRlObJf5DH/oQ8IBnLPDLk3iCJSEB6SQBLDd1geco4ueee66wqciAtYBK9G+quUif9oPJQOgUPJW1Q64RoEUlZeEAsX6LiSsyq3wYpAGxtcGq8JxGwelBIyL0ugGyFQLIBemUhNRJhIpRCjtZAozpc5/7XGMk5Ce5ysgKgCrxhkoYTtAgLCgKhS8F2MUoI3jttdeKyv74v7lA+J/92Z+R7VClMPIMtzLAW0f4WxhlRghIrAzhDz1Sq2lRv3RHsU9+8pPik3uEYDKB0wQmj1lHLCCOEBSENRUn1XRiy0EkLJexVLRz8K91ooZWBB/d1FB2u82B5kBzYM0csFZbbCVlGNtdBOQQhQXYemo1l/opGqRV1eILCSy4tHMuSCJOg0YSgLjQoMV2pn9FhPauK41Zo63O8MY5dTGr19zzam4bgBxw2oBwAacAuZPKAJhs5A60S85Q8cMl/8BAqq2Rsz1Bp4d58oMxbABIQbxlHTUW8JvmSggjMxkU8Aa5f/M3f5Mxn8xkEEFgJCeg+Lu/+7uAn5bvrxdVa/goxJ4Ss9yUXUPAc2MENYHlr/zKr9QIIomuTOEmPRDpQL4I5xCX6owqB/SRCqp5RbC+kDb0S+VMC/7VF9UiQ1oU8OyHHCSIF8nczIHHBET7NUQBj+6++26VaItM6T6XAtOGHwYvDaIJCQDlasPJSI2bmk7dbnOgOdAcWDMH5gL5ItM6tyxIgEpnz2OXh8eyZSRTRbJFwWwaktVfEiorr3WZyiWLIv2yumfBBTNULnpnKehr7vw2N+dEwcUXXwzPsO6aa66BteAKbMNRKPWSl7wEZstMClMjGIFM8AZBmazx3MVdzr/2OJRnKfn0pz/NRwwcgnA1qz/dJ2MZQfVwtfMvhZuBxEixEMSzjDCRvB1G2Q/jXtm1DSL9W9PU6DRKOwfkJBJiB0u+PRRujyz/z3jGM+ytqCRzhlwC8kMAwCYiKC/djqfuB4bNIpYGzaGEnQAl0bk9ouK7PFKnyUMWkQeWUIgwGwfQfZtHtmlrDjQHmgNr4MDhzm4HEZHjUjQzqTnLGh+nJAmmklA5qGOXlGbGlkstG/NQraF7O9EEcCXlMHrLz8HPn9sgxE3OafTDKpvQbN1QPNxLzk1R6gAn17lclGlb71CcUR1YUtmp3fazbXvXNnmSlo4BR8lqxDJKc1BTyUTLAdtaH2MBAk7FoCnkTrtqVhvbTLCfDo1s4074qxfNBxJJ7aSgnyVcPdKdES9GStQM1D1SFWIIGWPrqPIvfZ3RXtPMSmRH4gvh9LS97XZiCjWRzYHmwFnmwOpAbrW13EunaH+0YqXlvC+FL1ogy+3NN99sU1bYV/5xZ5nRC/oOpNmrpawmFTm5R/mm3SbPqbd4J8A/5nSW7Xi0+Rf6clyw/628i1GaBMCCDQ5thbz//e93k777vve9jx8c6E3rYNtIjYHtjCCZANYeOjSIYeK2yUIm01zaRQC9HNnqkXUUrpMVzIfS4+1lEDiqRfdJIZpD54QSXVOyKFFyROj8pvezzANyAgrVn7GB7WESqvDQjnSB5kBzoDmwZxxYHchhNrBx2EyC6hwQd9mj5Z8FZlwwngkU/FDd7NruGeNOsDus4gAMKL7pTW/ywxE1pnX1g7r/+3//r4N/EBrSs2PHT5vdm7TEQM3aETKAsc1pYM9OzhnNjjvE5X6Yc/x15I9ODyBVDjK95a+3vGKLvaD3oH7RqjlJ2AV3kjDe7F5hKggNvB/guhOJzN2mhDngJuzXBCs6C41/gfH3v/99KrvmOELCfvcjmqDfI4U5YUwoieEnOr139c4PRiBGeFv7bAB6fYJj0VU1B5oDzYGd48CyQJ7gpmP3rObQmuMShYySZJGljfFap4tbji3Kt956K8xwXshvK3uu7KzvHJtOkOBwcmQCvzCcsREOgHGMRyK/M0wDWsCb0xkxiLQE4P3GZNZp+9ww3hl9LzJHs3nYVKbKK8NlgeM62zWINS6jwxorveNqLNJAFwF0axveNqdtpU804ODuSCQzAN80G/ls2kYZfNoypxPbsUazpjnSc9bj8c7l3m49+hnDad7+5Z5mb5vkYe9ADfHCc4pMNwl5WvnM31woca4slFTT2gX29HgT7J3vfKdd/Bjw+ROwZHCAZ4Q/wdHpqpoDzYHmwM5xYFkgp39PsqfFPYp2xTnr0ksvhS4s6qDlla98pb1MuqMLYFAN3efNlMve7ezJ5p3j2nEIxjScHA/gcQeDT9RZTuyA1r6yo2WQjEmDCzo5Cd+IR1y1//AP/zB2ciHxGZlhfw4IcIhzLutxj3scmOSI4OA1eAPb9t0ZRai/RTC5ir83sEeD0//gH+rnKZIqZpAfsyPOWgB6DSjpjZkdHqON9Z4Z5vd///eRp4B2ebRxL4ey2XxBEsWdCUG73jIB4sfHU88rdvRR4gg70cRBu1Ci6aJEWySA888/n0BAbjCXdCeijFnHWa+9Lo4zG/vd5kBzYA84cJ+kKbOx1quHtCLL6+idlD3OrP7Ur6hQFmULq7/0Kir7bJ4xTzd1iBx5m4q1Ppko4Ywd5YIrd1zhIf5goH/9UEyZYJU7uJqn/vUbTMYQrYzact8rcVVzuVP3iwZP01bkiQyHfw2ihjLE/lWsmh7pNxNqWL2bRJ8qrMJpXbuKOafANuCEOodz91Xu/qGUpGQoSTdVmHml9bK9Txz39uBr7C40B5oDzYHFHFgl1nrVOLtoZlHOYSFKJEcqF8UxcBIIyc3x2iCKb8/8wASMKhQPeBcPg6Zx6sbD0jiVGVEZh5UJb71b9hJvparEcp/NQq/CelrDkUEsQS2SwVxldxzWFMhYV2E/Cq0BcKrSkEZHFPfiQZSMky3dDG2qqi6rbRQrt2dwm5LmQHOgObBmDixrWl8zWd3cfnDANord9LmhXvejg92L5kBzoDmwcQ40kG98CPaWAMo0Z3suFA64720nu2PNgeZAc2DTHJgC+awldtMUnmT7bYw9SW4eVhdLuBNroqgm0FtfzYHmQHOgOXBMDsxFsfs4u3EwllCLC7SjTcdsbAtfhytsvLrGOfyMH4HbwtFpkpoDzYHmQHNgMQd4Czno+9SnPnUSP2MK5I4C8zTeVyDnRy0QmENQezldWFMmh793vZu72KP4MLakuOtzr+lvDmwhB3j7ylUmn8UiIHf8TNasPY6l6iCTCGLi1WzhCB2fJGLKnu0dOGm2i3s9O0r28Wdg19AcaA6cNgcESxXDYxKaetlz5AJtJvBqQYVgW8KKCUhC+RDp0wHfqIM2RIXsSIY0jQnBraS3xAqV5/S0O7m4/g2eI8cZcVEk+ajz4kh1KItUsUyo8+oXWUTEN/Uk34kAqw94wAOIacKrCfFmT/qY9gYVGmsxXmxACClzJNpOcHCxSwQ3oX8dQE+uthUurBZUx8RTw3oEAmFwBNQTomcX/ft8raaWj9S0FM1pnXxbYXD7lebA2eTA3HPkfxuJMxgsVOctt9yS3+MFjCXXkorU+p77YFu8TFG3pO0SJByoSF3lEm1UuC6RRJWhIMpiLh+Xm1bSpz/96YB/tvJ13oFSgpius8VqC6iIX2bm2eQIr5I7RPhSRpLlSUK/DGDq4dAgv9m5554rM41TXlKkiHQGSJavam5JYxd5i7cEG84xa1v5dfKK7C92ecD5ypWICytkrDC0xn3lSo70Iv8SfBNY90hvbUNhVgQBfYXINegi4OKbaH1r49s2cKBpaA7sBAekmQAcE1IXHT8LYENxET0lm/Jv1HGL7Hve8x4ZqOiF/rVcytYlBrgLJtmKT/5pobyFDWeod1Ml9t3FB6Wdn00xSq9huYikdMTwCiskRBGr3AKKpS7ctnQmSykxCDz7nVQomB++iTGupIQoRuS666675557xExVMhHWkgY+P/KuR/XuqNZTv1IgCVRyKQn2rObf+ta3CGG2WnJfhTkRngoTWw2F6ERMtZiqVKJ19/3NK2MTCnjLTa1X1tSqLWHVPTVbEmFmTGw/oWRki+a0ouZUGwoZFXTnnHPOYbHI0xTQdw0ZjpBX8f+Tgc3f6uk4V5MWvV4vjoUJHmmX3u9yZ3wxQ1mvIztJ4SZVecXNcEZtdfje63nFzRA2nsufzJNqNx1JhenjSNLYUEbQ5esmc/uLb1D8KU95SrISp+mMy1jPeH8yxGNb/bs50Bw4dQ6MwD7RyJlqL7nkEhRQxz0SVdvSIMuF34y6CZQtxPfzn//8L3zhCxMBAVCBKGsoe7JHvn+oI10VRN+g1LNZjfw3fuM35B5ltCwOMFGIS//GN75RuhRpTJ26Fvwcw6VIkUSEzu035yk3o5iygjB3uwnkhB9nC5FoRBByppHEaFOSyi4Ge6WbM0zcAiY8F1+d+Vd5hgFKZD2Vu8xS7j4Ti/yzdV/+NHnHMxflnvevR3R3PhfSmPpNqvg3/+bfMDn4LSar1m+44YbIcwjTRDYU8lSg+IC0vCk1c8wr04lRIV2usHcitBN0ihJx183GUGKjqJjJXMS949nPfrb75mfuY69g7DLK2HeQQQ611GUFAJXI9u7H3o4eediUl1TNZGbnSOBhDdFNq2nzOUZ+8eQFnc1938h5553nrLw661uVblUm2RTQcVKRlARGmbFaGT0i4OJJKJFbPSV9XOLk56ieUVYm9yWkka5G+H0R5jP0HpWubBTE5w+fBduP0ctT/ZX44KKLLkpsf1YcYFwk6WY2Te5///uzCeU+QwKWCuavU/h29913R8TRnLx5CqMNhbWJ9uEPfziz0WXojWzxqn80B5oDp8SBI2vkvk86GQ1S1ues+xQd6wIYgA3W3GhdPmDLYkKKWvtY8JX8sR/7Mcgk0Zacm/4lsFuprfWdq6qWez+SmEQqMNyj8QBmCyWWPve5zyX3SKZCk7M0s3XLFwI+AS04Adtvf/vb5S+xpsMJYCATCYHg5S9/eW7CFRhJOJBdFMw4qwCQqt3Xve51n/3sZyVTMc8oXsb3BS94QcKnq9YSD9hYU5i188qf/umfGmsYaWPFBW7lxZH6jFj2Ez/xE1Z/4Grpt1V/zTXX5BWqGxqIGuiHNLaNGXU0wfLvJjywDW/ykFfe/OY3U/29oiP2CKRJVRUbD2MPKVAydShi2qRaGVThH/kGr0iK3Avgh2ntEchBCecMFEI+UJpXkkvNpAVjZiY8s++ua8QUGX3wFsq6493o8WwkWkcDaAdmWI14NXhFl0GX38xR0rtFxvWvSogjuokq5OkUqYKNqhieMixVX//61yWOwwf5amGzLviBDF+T1nHGp6SPyhPpPvCBDxiONEH8NV5S5ugFeYWUgACPYO1VV13lpAlwJaz46PAZD80rX5xtMgDMtgGbfYaGFXMwyhwzEEw7aiaRyHBjso3TsvhmLmkXharSBAZaB0wAUxQB1gFP9doQ6ItFoEP4TdjY/zYH1sOBRaZ1CAEDeBtZIKIhWXytqrRAeMzsZkmyalihJO+yWCQBuXXBYqQ8rY56577yVlhL1cte9jJVradjW9iKddPKOHLAfqSVXWawJOuETBRojMVVxazmeGgUiERWXvikU3zZhCIvH7ToWwqrAUD6AY2srXGFozBBIHJY6YsAkkBgB9TIqs1oUt3QAK39m5Dv6qeph4EWaEs2ALBFonIXcYFiqrwBJQGI9wIUuUpQdustCz3Jg16ODE3Q8+AiO4G/RAqSBMK0ZT5wloQogFNHVK6wO+o3wRLUvXz31GmCeYverPvasrlDWMGWbB9QLpkNVOKVcm0r5R6wEW5ow/RaxQAb0UdDbiqD4SoBcgiQFM59whMNHqjzEYWj3DkBrZRrePL4xz/ehMcWczsNQWWiA6qSbmDW39CXQiZAIUlXSfKxgdYFPnEGi4sZzkBiWGhfQ4XkAGVwFcYjT80Ud9PDEINtpCKYtIQhOGlEjL7KlUEnAwa81xf+E8Qv73KBJMF4JUPgInBk3wQqGx0Nhbf11WjUv6w7arN3w8yuCT9ITiaDCRNLO2aqnwnErhkxbs8OTWzhGtIkNQfmcmARkGfPzGuV7cpyWekxctNyACesyOx7/NqsU/SzL33pS1bAtEfdZGVlgacaWiKpYmd2JDCTKmzdtCJjFwiBskQfdyyatBlKLesr1uU4gKU5vAIq1mvrL1i1whqFygCWAsYiN0kDBC8gQXdUG83egkuKqhXWaq6k2mLC1S5cgSjf+c53UlVU2BpxLVrireYGGs0uqMDQYimHvlDcoCuga4SDGlmLO0CKIVoTCFDAxCBGADmEpSS0Nj0AAySDiN6qk4HVo+ppTkkQesohHFv8ZnWIURfrqubJHPMUxCqMqx7pOyBHeYphqeY0BINxA0m5j/8YxUoPYjFQcwq4n2IqJBIFyB/0oAclH0y4Nxkd9wlVUDCj4LcmQknJx/R+FWJUKsQKTZgSgDyJ4Bg/8ggnMwfME9o2FTlVuXQfzQiG9IqZYLGHKa8jSgJmrCarKRliFNOQ2kgAExj2r44r7/s1LrlIP0xrdlLMBwyk3OuLjLqGEoVjHqDJEPS/zYHmwOlxYPVY67FYWims5tm9yxJJ+aM8lY+ShRu62y7NfjklZuK+dHp927aaoay1lbmYKkl5opgyLNOZclSvhKQ4vo2Zx/AZG+lYh5ouAyEUZeZfNl4AjPNaDH57pBK1jUs2oDVqk1OJxbokqKWn0oPR7GLbJyVQ6bLbosIYY0oJTkfGJK2Qz7+QOJlSx+U+8kRcqNQQ76q5V9KzThKyATx1JuNqssMd9HpSyVXH1TOpCtnJSleKaQTZ+PcFTatyVSnpUe7A4MWTLbQFgHWz0sXWWz6K5GmtO0n4punMjTq1WIlr9RpPJvMEQ7yisPvZ7UqFyT5nCOY25Km3JjDsXx0kjbG1sAmZAC6WfFshvmVik9nLGCMn/Tve8Q7CqI2Ag2bRtn2JTU9zYM84sDqQWyN8tzZZbbCVL6sVx33iOV2Bjd1mXvGL2ucpNe5QNNozFld3rLy0HxZa9kyqjIt9e4xDHmUuaUmzgueypDKtMxq7HzxefFHs+D3Z/rS2UqHYrl0BAKMQRKwaiBfM3UHl2SsmegNq8zU0u9DPOg3kaMOM22BetXT0ojbgV6SaJ+YDjY04MvZLeUq2v5pI4QW9SwLTCVuo+DnFF1V4MXPGp7OFMTk+7cUcdaqfvks2jZd78QcZbBXk19Q5q4LPcnIsM2ld0wkJMFLIUEFGYYGY2yk39RpaT8Ri88Rg4VVc3MuyolNGOUfDvTISY2jc0dCkF5pQGMYba+Jmht7sdY40+xGUcojOpGSnHyv4VdgqOmxu9vPmQHPg5DmwOpCjhQJhpXMUimEwpMHp2D992JyDYLmFPo/4XVslGeIWaF0n379tqtHalyNPC4iymAJFCy52Qd+U5MFuFab6AK1amieVqJnOBymtyzy2uLJb6G3l8o+rJPFeYV72L02ddcS/lnsedvFSnEuVcWRLtyESlzQXOzb5wB45bLCHYnyp6fzyOGcVyiKSH5a9AOWzu08KgQd8r8wBezGpioWWdZrYYVZMxDsoon49KkEHW5h/zDSN5vXPf/7zph+ztvl2EFuWH//spusXt7W8hU5QijOs4h4xKeu7+2a4oUGYLYwJ+OkFZobmZUSuNOQ7YusmK9h5SUcYun0+Yf5cwVe7JAwdZwvhr1cEG334ap4ooCNxfYDT3PG0Ygg0hLFAV3MeGSMSGOzHxgkPtat1NJgh8Sh0cUh0ToGBh82fu7sfxs6OvkP/6m/T+vLzrUs2B06QA/+/e8viSnNYdlybLASkdZ691uhbb73V1qwvmQOwzVF+xZYYm+JO8fJ3tRZYI6y55HpLycSkeYKd2fKqcpT5oPU9B3+zmOKSJdjWo3XWTaDI2yjO2/4tG3tOGFuyDYTNUQs6hmM+N2O7mzZErcV2TI0ID/OYbSE9bymb3FyTmEPBAMDgu1Abxqm/8InUZYj5M7K7GFxCAOileNks5+kNUx2s4uoFvVhZtU5Z14ohtiP+rne9C9RxdYbl1noUUuJ1hCO6/V3ADCkJFswSVO0wp7BEDVBfQw6JUQFNJ02oDazeeOONLAHYyPGCBxlXyjhpu2Z1ypzAdl/l2SZAXvEtcEv+yNzWKFI/+tGP0ra9SFDgCQjM9Jp52ZH9t7zlLcQpVOGq/QsU4kmayNwjanAoQ/PoCqD1nJhP6xno/PZivOW9aCuEfYugYz+bg7qbmAli0TY5cK/FbFL88i//Mj4bGlz1Iv4732gCEIBMCZOHScxfcAutuR/6SLWoIV0jwwF1Ap/anFLzkcJmv0NnmkAVNwuWc2fn+EAgw2j6kI2I35zzeQLaWSPM+fGwhz0sByb7ag40B9bMgfu94Q1vqCZ9mT5FB4UnRFhTqAi+WF9+wbC12NfOcsuVl1Ju6bGye9Eeag62WuZYU2GG9do2qrUYeBy6m3iq/beKWaPLOehU25qt3JKKCRyn57ruc422gvMngnZWVZ7AFl+QADboWHVSOXsT4AT0Ur9yVDqHCCi+/nXKCPaAE8s6ALYoU6HgZWlLdC/IRM361Kc+pTxct4le1LIEACGSBFJzk9UXzfAbzBAv2AZo5CCElgY87MHHcwoxjA0OsoNM2rn7hAlDj1qYlwPciCRScKz7xCc+QalleHjNa14T92njAlrMvfhboBYfHH+AlAg2kdzUTUCOBmzx1/2rr746mwKwih6sqjgc5NI0W4I6NQShMSEgx2pt9uKbwnCLluk+c7E5rF0qJpOGF81tfgzZMidDADb4R2k27WNndh/KGlavqxwTsA6jVMgzToUhQ7/YP3wmyFA/tsSzBCV6bQg8IuWwaiiGY4YGo3ghEFO8jlpMUMZb2bfWF52Cyt7yl/88fuIqico88YnpOCnHuGiCFGjOkJZynpA8p5gKNURN1zuO6PRpj4ym+9iiF+Fb6s8hQx33FTO2iyeBEmzHcEo/prGsqJPz/DHDA6/5e+zmmgO7yAGSNF2IHXQkftlY67vY4VmaYYxld3eTpkDlwuPx92mMzvL1T0rSz6C4zdSXvvSl44mm4xC5PDErt6IJ2AnzOGlXnJMj1TYhcg00H0QemYM9jGxBTB8j1RypOym8wV6sQG2/0hzYew7MjbV+rD3yvWfZtnVw3IM87f3I5eufLWn1pxSeYDLc5YlZecjigJaIp6tVMuv1vVo9J/JW+nJ8N/I1cP5E+tuVNAfOMgcayM/y6J9W37NHu3MYkDNpO0f27Cjqgo7s4hCc1ozsepsDe82BqWldAC8xIPe1y0yOtqJ317S+K+Ni59iG8Xoyh54gT+LndVLbASdI2ApVsYiA8w61tgLr+pXmwDZzgKsKf5pF+ch5EnF7AeTcXra5JyvTFnvjfqzUKzNhDS+C8Fiq19DWCTYRXXznyJ7LgX3qywkOcVfVHNhpDvBTtkfO03aC0ffRyAG5+NiOqewrkEdHObMRaXZxBu+oTLCLrG6amwPNgS3nACB3Isk5oEVA7oyNAF6OkUzV9v/n/3HUBP45/+ooGjiktajRPlzyECcGdV2KuUon8CPBLxVOpMmNbENqFAGO7jgatObRSpfxcG7Hx/PThxKWqhK/MwfKN8LMQ+lcvoAeQWuHpsYJk6niBJoDUZ76sR+K8vJs6ZLNgeZAc2DCATAq8oeTsYtM647hsqvbJp9ln0zYCcdR4awdlhVNwkEj0C4kRTI3M1w79KIGx6DHSgTkEhmGHFH5JTcyQsQLB2Q3tUcuOJoYbY4FB4zz16F20TYqP8qSbBHyxXA64lUZRJZ8cTuLOZntmLLoJU7YOzztSLTc7eKpOY/uke2e9cte28mopqo50Bw44xygkQvbMAHyqdf63KjRArcJ3M00H/3bJT6MPAq33XabEBZ+C0Ui/IUgXy7xNMb44QqLeiGEiDhfCm/WrL1MTOxTmiVEHHHQxEYVaEUkEzFzkk5KXJfKSbq80ilKDLEgEUN3+kqXRWoTvsYZbhNU+BGh6PCElCPYiFm7PFt2mhVNfHOgOdAcOJQDc1FsUUAYypCYTaJZCU4pcpbQmFRA4CEml9BaFlkLrvgyojOy9CZu1OxFCQZgAlOIRwN7hAzboK/ZBgPCAHIhtBAg3sgktBwGClMDw9z3W2hVUhGTBp7jGOGJbiqf9Lh/IWqmR7LHKqOACGWJYxr+GyCBz7IJIgKXvNSigmjaQAjUxemR675RYF8R3zS+5fzM77zzTlBqu4RLpA0IccFiViGHiQHHpsKYT0oTCW7iCy20J/MMW4tIYYjRltiikU50x2lyRggx5lQo6hwrDskvlhuBRQVqFemMvceM0pwYc6LIiSMm8JnAdkKiIkzX7PjoiHc9QqH+Ch1aAtChU78LNAeaA82B/eDA3IAwi0K0CmP5wQ9+EGALD0krErLR8molTU4zaqX1NHGhhdW0HFOe6NziUI6QI5q3rJfwQ+xPiMKGvMFTSRsM0YqBrMfEGjFKJyFaqelCgUJrXJJ+FKQxbEBlhhBZZ4AuWYoGj9WFoHAR2/k7+CEmqDCcwpcqg/MKy2YhbKexSPR1zdkWAai2NhQWWpXtGqgbGvYA1XokwPhNN91kjDyC9GpQoXir5DaVmwaiiwNgQ6w2hu6K1Jt+ibQq1KiIquoEt6SEGGbYxskH6iQRmk5q0yk9gv1f/epXbdMkIwsilRF8lORhhnC3JPABcpyxR57U2t4iSuqv8rYVSJnePbMJePZjSepeNAeaA0flwNwQrYsCwlglZUqg2zGMW76zBS7MshU/6TEspjCA9vahD32Izs3YTt0EMGW+twon85U0XBs0ax+VU6dUHryFIYQhQOsCVJAPk9nY4SuNFpekMHEHHoNP+AodHSWg8o5p0wCeSKhgG7dV8vznP//222//wQ9+YIBsnwv9DcLp5bCQEkzfhaDx2Hf/Va96lUdq1pYm/CXiGTtgjCT4mohg4mZ7JL46OUzmaY9QLurqW9/6VqSWuTuejIihzSNGGeG+XerBRkKb6SF8N0pkYYfrtHBlXJRvWzPsCnK3iM9va1yiNjONju7i+8Z1APF0cTfJKAQC9INw/bVNo5skg7a6n9Jc7WqbA82BHeLAIiCncNPJdIbmFxhm56SUyzvpdxJOw3JLOQygsVGVwAM7PBVNAU+BEB3O6n/8LJM7xNODSCUYgXDuhHBaEgv2CWZq6ZyVl9GZ/Zk/IPikssNdOivbRnzZaKJ4OAbNptnD/ksuucRTvOVyCObVAOpUePnllyvARkKHpuVrN1YQNy+99FIJV/ymiHvERZEmTZiA/Yhxn5YMYlVuxG1d06T9YB4wuEiiGStAGhi359k5mBN4UaTjPCqp7OYDm7w5AIPZA/wgLhDpXvnKV6YYeQUNNGw05JhDUmsXNptsScVm+qGfhcBvijuqGIdYFBjzd91jfw9mdXehOdAc2DgHVg/RmgVXbk26l+SJ6Qkcsi6DK3/pba7HPe5xlPgUtuye5ZUX1sJvUESVjJ2ZFivjHMUa2sEt5g0/7JR7pAA4XGA6TmrOsN27GJsj135DdFosMIagLCW105EDePE3BP+JjeOO/W9CQ1nL/WbNViZ5t+ji5Z3Hmv31r3/dxnyyWQd3vUjIqMxX/iV/mAD666l8WXZeqPi0dqr2uL+umPs6u3i3BcEqIR9Q7lGCBsm+8Kqlw40vH01Ac6A5sA0cWB3IA8mTxRRmWJ0t69Z66rhVm96Zs+NZ9M+yLRTHsKvQdxx+yiiU4osO5t0HbDmfdiR2eQWreR0ypTjnRmfVFkSf22LBcJojFlRb9Ru1qmLxBtvkg6jI/kqUnryiB105WVdN5N/0aHwl7niHynZ5i0TIumP7gIMe9zdSDvv8Zg9BbMMH3DQ0B5oDzYHVgRxIs53af33Ywx5G2QorqXduAnIKGQM7dZAlltMTdYqZl/puU/b4GZn2b9j4HDBuX3bZZRjIKYEKSy/nyh5r85KXEYnLGBM9kLMtAufwfwFYwki70Uzc8X5PQxT07MezqPMM5wG3QJ5QOXQ33GVsVw/KdYEhPTsyXmdaYBun4pdUQUpQs/q1srz/hCT3Mm3bNWBssFk++g0syaUu1hxoDjQH9owDqwO5xZe6JoaMZdoma/hiJ9WOpqWZfxPn9hxMcgYJfttZ/w//4T/wepucMt8zhi7oDkYxZZcJukrapeZQxmJMmYZMvNaBHBu7+1y1FeN5YGfd3vBiXkUjz2H9xO353Oc+94pXvII0oKq57xpEcMvdDAzzkE8ZxGRAGVccikMDZ7Q8smXOuM2yXaZ1N9nSleEulzLOjCGA0KbmGGy0Qshw/MzcuOaaa1KMyyQjhKaJfQcp1rR/HGOQJwHww0ioIpIHWQGWm3t+n5350z1tDjQHmgNzObAUkEMgS7DVc9TM/Mvfje+bo1Mcl7g7cT6CNzyoheKy/jr4645jSDyknCkCJzQ/i/KhptR9HSodJ8rQvHmZYVddoNFmOUaBNNjJMZuCjods2ny8FeO8xn+bjlucodfaJC78y764m46GOyt48803qwfb+YRfddVVSoI9mJoyGUTgavgMK6o0wXZy7bXXOo3m5Jv9EUSS0hRDAAQlW4BhxZ75zGc6wwbdK8CfMioknajH+CrD316kP+fHPNIEA0zgXNQBHu9f/vKXldGQg3b858855xyPiAUkmJgfFE7XyAekAW7zz372sxnVGX6o4PplUnHuwxx3JrGB93XmdL+aA82B5sACDiwKCFOvWVWhCzDmtlbhXBhjKX9xinYWyEIMG5hPLb6TSB0Wegofp2UqFK+lDQL5BgPCYIJIL/YXCkrHUWGuIBKxVeAqgzO7Oqjj78aREMhRbbmyj5YMlg8lQSO4VTM5gNmDtZkoYFDAnncBoY1kwGzsqPs4zwkREIJbj6Amx3LSGA/wjMgf/MEfUNwhNE+39773vUSHiy66yH0jy02d15tixpqgQBobiWdu4Sr/O7/zO9kFN0nMgcwTTegvGSVTwgwBxij3g1xCIIhXHfpZ41FoOmmOrwDhRl/c91thAooadITZ344++jHEZOtvuznQHGgOnCkOzA0IsxSQ7w2bNgjky/Bw9BFbpvxsmRVqwBNuiZzjqMtxKXdKm6c6zdvZ7mXIcDqcnZz7PbhdpvykzAo0r9BKv9IcaA40B/aAA3OBfCnT+h50fie6cHxbxQo1BLw5x4m/y9btnLfQbI9//OOXR2WGfSb6I/nljcOxAs07MZpNZHOgOdAcWA8HGsjXw+ftbSVmarHYmNl5JrL/M6rbC2foXvLwmwPrtrEXH0jb3v43Zc2B5kBzYMc5MDWtcwzmsLbjnTqQ/M2mMd1Xrna/mgPNgeZAc2A9HJibxvQ+QM5bin+TaKALQoish9bTaIUJV7/4gvG9Oo36u87mQHOgOdAcaA6cHgec0xFohMfxJBzLfYDctiizKqXclufpkbLZmrlAbzD92mb73q03B5oDzYHmwO5yAJBzdrvwwgsnGH22TOu2gYUusae7uwPZlDcHmgPNgebAmeWANNMPfehDJxr51NltSf+mHWXifvdug4OSZGsbJKCbbg40B5oDZ4EDc1FsKa91AWGe+9znChQ6hrb+4he/ePHFFwsVIh2WMCPClYiXyQValkzxYTDUeaR3v/vdnJ9FEfFIAZb9Q+OM7utICFgm0acoKFgRbuSv+Gt8xY8qYTi0/aQnPYlotg3skoac17ppsA3ENA3NgeZAc+CsceBwIIdAABuKy4ZS3AHVgnhDcaqYcGBCjAkqIvCW9NJyWgigraRAm0ncKeiY/XmFb7/9dikyzxqLq79Ci4ttJ2yqaGW4EZ4IQQrLc5Z6eThP9s8tOYHtJLoNmwr5d6TxXb7LR6q2CzcHmgPNgbPDgUVADrlF7BLjE0jTuQXvdEkxTjvnEAdLuH/Leybopljrv/iLvyisJnUcLCUvyH/9r/9VUiyH2cTWhvQuUbLHGN1nh8vVU+lkhGK1SR+GuCRHkYNEGBZ5SgCzqOa/8Ru/QRjyClbbC1FeMpVRivIoqCmoKpkJz8VvEQw1oEjwEqPNW4bD9au/+qtS17gv0LpWpKRTv3aJFCKiV8YzSCw3qLdUSN1/yUteIix8yBY8lcJtGnj6W7/1W5PAL2h47WtfK4aMkPtJiGJKyE8fArz1xje+UeVCxppLIrojkoFH4Ze//OVqjiwidqyg61ghXqzA8sUuoiGqzCh9vPrqqztp6Rn8ZLrLzYHmwKEcWATkVnkKN3Xcig+zkyRb+GursxUZeLMM07ltjsqXxd39sY99rFQWomGnVcu36CJSccgkzQ4sO9by2SoPpXtHCwDg2YRdIqJLU8Y0DWtxDJNJRd/+9rfh4kMe8hBJaIRdu+uuu1g4qtfq4bUnAQn3ReDKKPKOd7xDOHQFAB5cfMYzniH4ORQnTgn24l1DCW6BLmFCBDdArlHmAa9I8k1WoFirSno6adBkrnPTI2QAYGHPL7300he96EWCuAHmNJRLdHTxZMSKl+/OBbBNDBHUkS3bConhnnvued/73sfZ0s0777xTB00hlhuPEiydZd599cixdsEFF/yv//W/dMHcE09eCHd5SzVNMrCPAP4by3d05jfZzYHmwOlx4H4guWqnikl7RSvKHSu7ZfTJT36yRdaKL/EGVVLCDCkxAAxsYCK2Fst2ZaGngSmc9NXynknmwfzuUdKBsKjTxS3NqtqgUg4GqLZSkpweQw+qmSYKdyVBgWF+sKh/6lOfEuEcKBKAUAXeKOXgVt4zo0A1lwONk4GndtaT7KRYxyZPXz/vvPMc+jdeYI8CTWDCeVBKj4ej2A5iVUsLf97znkcO+9CHPkRogIuyhzGTqITURZv/5je/CbmJa+eee66UYsBbcheoTKVWLbEAlrvvdKI7Jgli1BOrgPmQPOiEBlWxB+iL5ohu1GiRCZhz7AIglQChOfsLKjcEIhaYJNR3Ke3Rf8MNN6jfvJKb59Zbb8UBJ/5lSNNB78qtjgkER5OwTw+uf/Z2i82B5sCWcICqI6kVlWykZ5FGTndkDFeaFb3MtnJbJcFlsnhZnVna6V5vetObKFJUva985SuQQ/5KijttngJHVvCIsgUDwNiZVaqo2mwSlFpoXX9xAzJRdpms+Q9Ca5AJ7chDwDIyB/MySE6isFzeAniA02/YScBSoRpURReH6JCVgd1g++HFWLC9RUogbwWASVdGFu5CVqCbxKMubg3EL4MLSvk3EC9uu+02I+iirHPNg82jqV+7ad27dlWY5RnDiSkSk99yyy22VwCw2nTNfCAWkBs0IaWb8sz+NgWcjKR8q98sstGAHllbzDQSgynLSZBQSLB4znOes9pO/JZ8gU1Gc6A50Bw4DQ4c7ux2UKtJWpV85IUBHNTdtzpTwviow/WIAi7KeqJ5r5xd4zT6v846dZwx44477mBqljFM9k8/+BAAJ4DNwsGaDd54/tuVcFGv/f7/2ruTVzmLrw/gL7z7rHXlQnGTheCwcKFm8caFIA5oHDdx2GhEE0WjoguVCEFREFEUxRlFIYoIKi7cq//T+3nf749D+XTfTifpe7tv3/MsLn2foeoMVWeqU6d2gpD+LkqyDPAiriqlyPN+6623ROOlL1Dk5cJ6J0o36t+la/9y4m0uqI7o+Jzzbd2EQYZrAgnsM5cwOJXvbPKdAAMJpctiE+r/+eefrRc4tFQvLBi90822LVDtWshhLQwCYABAy2mfmrdOr3K7JXOeva+cxiaKYI3GI+3vJcu6r6ZAU6ApsPkUuHhFTsLSAZwnThtZHFRJZzpJvJ3AlcrEmSsSCAtzAXlsBzY0imLIIpg8Oyz4vh5RmTnAmwtLldKjRdhlRhLC0osUngVv73Ntk4MWNT+3BVYXlumOwq4Ecso+URMKmIJ3gIqMNirWZVHAyrpVcIspkwbTBaZbaOdYy5mgxX0ixq5xvdDl1lZYACigXHBQ86+mJL6NXYD82LFjeqfIxfYZBFx8eD377LPjiFqGJv1OU6Ap0BTYegpcvCInmgloqUwCv5X9JF2LdyXeThOcOXOG5C2v0X1qhk+2xjXytbOTtosnOrmErC0PW4ZgG1FpdJvIOaWInt7krAu2x5NecGmZHYDyHGK+vvA4bzjZ7DstZ3gk8G69nCXh5TRujRyz/GB4Mcv49El8czEyKGPRgtltY8FLLhvIJU/wodMUyL1Mi/thzcWa99NPPy05wEqPFyRRWsERRZCvly5E+0V0DBuji/vu3xtvvFGKBoyA2op87WO4AWgKNAU2jQLLKnKyeCK7eVTctaNHj2YTOdFP38if4mNRIZLaPPKCNVGepZQr26bFignuBeHiTaPOyuGhCC06IIi/ubinIsZy30SSnQhOW/vtvsR1Hjan1m/pYPRiKdpANbIj3HHxrXFBpjqbiaaUMSfGTin6Xe+MSGGiJQ+hbAEAu/wZZNStr2Sl6V1+mbV5MOudosViK9+W8H/99dcxxE2Fe1m/VC+trAVBGiYIfSxLjj8t0mBrg9x7g8HquJ1mYv7+9b5VcEsw3pdazz7wrXUBCxBcdukUEi39qykZA8YPUPW+cqZ0g02BpkBTYH9ToOS7H5SH7KTxTn6T0SKlEpv9qKf2OsurooQse8tjSmTVAipdXu9YpiV83RcmpddpgtnG9/IOlcZl3Mseqy8+MXW4UzRCeppFCi8rvCMWHS7QmhQz6klPs/lqBPvdd9+l0v7++283tczT5VVTk/6V6W0FOjQXFRfcFpcWD2dL4a+la6aD16hGMXDRFCrZv/zsq666ylc0MY9Z+rotYekRi9lqGeWWz0XXWRsjMJqSxUaX23zovr8BmwMNEamO2dImWq7lfCj47yaDI/8aS4HZxQqkxXnw7gvqMCZyX6Z9vb8WDnanTYGmQFNg7RSwXynpw+M1PTRFtRDyfWKb+IBTRTHwvWq1lf7gz3Gvk0LlHV+R5ny7WgX3TpUc8a1UuPVaPZxL8ep1HZqSZPW5FEAcdENJFHP5F2GRN+fJuo90Y26BdjxlFuSm3xoP5X3ud5LLsEw7WJA3teaOyyP8ij5OXpvL0zCUHpXxLr/dJnX3NaWFqgFA00+yHDSiR/1qWWtB0029+NcP38JI4+7ElAmEI/zpwgsjstpxpes0viHF7NY7jLv3pkBT4MBSwEKkmOV5Dk2ZS53I1iiAeiGCNVViGAjcL9dEynsn911r1+JrZzy9VdSY/ECc0BbF/M7qA+WX15B3oju9MKp2bxblteB9gRAfhkEexQJLMfx0FJ3qBQNCCr0N4uLqPnFTHMV9uwdDsbRQAM/mKkZhpzvvB80AEEvC/YyWCkj4MYG/YB6R9W3uu4pEa+djA9AUaAo0BTaKAsuukW8U0A3MCilA3d58880KsNhDKKwtG9FCtaiMOyvspZtqCjQFmgJNgV2iwFKh9V3qe++bFaeVgbWu0Pre47t8j/LDLb0IYguA88Vl3h3YXYLLE63fbAo0BZoCe0wBqcHSkyeh9akiV2LMLuEkLe8xfLvdXVagpUanMl1fRQGUSQA8d1J1rgvj9whpCjQFmgIbRQFS2uZemcLjkeIg/JciF01V11qKk13F26fIZV1BKrXMNoo3DUxToCnQFGgKNAXOSwHZRZKROds80vHlqSKn6pXsmGj787a+X14QN95wjzyZaNkCsNormeqrbXNDWts9ok0Q3LOOZgm7TezbWFw2FrANmWgNxtopsJRH7mhLpb+dNb52cHcJgH2xRi6mvRvhkGwP2yXCrr3Z7HbbAzD2rKMJLtvEvo3FZZem3h4My+7i4FBg7hr5dCF8p5VR26+VGBtLf3Ntleuy5M59t7NN4S2XajAc/xQWdeWUFBVFPPJOdkWv8bqg0uW7BCdyKVwamrjUgamObABTziz1UNFQ/ZOVhEZSEt/Kyv5aU8As9WXVsVm8Wm8Z6K+//krB1129VNbTkZR+vShtpFhs1UhYVb8wdcy8InojymaNSWT6XHRABSWRUSN7Of4Vj1J8aTKAM/hdq6LYqtpBW3WN8DfH+hlUJN7+mi+rIkW3s+EUmDuLl8poo7Ctnf/www+VKcemVvmL7664mPqsDsD+n/+/VPR0zrT5EFqoNabCjJseqQ3nePIDe/RZCEIoKxuubpo6ZWjirHGnu1LeqXiqnKpaZoSI3yqy5aTwSx9VThtz+sjvv/++vw6QRSsHmKrKvnjMOBXmvffeo6gunVCLW1CiTkG9HCvgNBpHxEapr/Ayv8ypr7/+ekxJpYClrShqe9EhBza3SvW33377ePjsCsGe2xSAjXCW/fjU6bQQZFPudu8X2r6pYculCgqxPLDAubpzzze60Jb7/abAHlBgkSJP8S9anLKhm/2bgzFYBOqAliDjndATDPBU2zZLTWCvqTDqHOtTp04RxB6pOnL69GlS6aIdiz0gx652QVurKO6Ubqe7ktRoQrBee+21io0rb+5OaufFz5NGnnPEc3yIa2KIcdqoujzySVE11dDc0WDKpflrZcWP8djTfOjSSPl/qcSXf/32KLpf49VXzqEPodK4jtLOXM7muNW8UxXaUysw3c2ikMahT54eP37cjzq+PS9rMEC6j2hWIso5HskygpSCdyO07sylrftFnCJ7osGpjqffu+++m0Fm231ADRZw1GCxyY/gWK2NHAR53a+vUltJPsuos72JfVXmL4XzgKG7/J6FNlDlERi8bDgpqlPN+rAAnmvejRzPQApzQ96RdOPn4XVa1pceJ6s5Xoadv2Ff4M8nfgeqCYsXD5WRvCMkc5kYslQv5XDrAgypVpQ5YoY6JiDlgSeDf5wsI/1rTPokzMqYH+dmAOirKbB6Coz1Wie11nk5ji/TpWx2jxj1Bq5S237bwWVB/cEHH1Ql+5FHHnGKxqT0KxX14osvcj3tZMsjEyATePLmXv67xlrr0ORtK37uuO4JygqS0+W2Bjr+FbWdK+P8EodzK5/OC8yBoeqaOdAzmikXj+fw4cMe0f0+5ynmPkPh5MmTCqx6xKLSnZYzbvhknEiswbgaSXrh9+dbkAgVxGh7++23lXMHid/Mrzpy3o9YYy6xBJXh05R67DlpbXKJ3FxxxRVeUB7ugQceiFwDgwNRnExan1ON48ku3uFHGj9sQasMSsaCmYMeaau2v641ZexR8+7Y++4gPl8B/pprrnGHIrnpppusUwQezvRTTz2ltrxH9LGN8g6Gd7abf7XpaJbQlvDVVyk8+o+N5ZHckZwa4EN+mzgBCguD5xPAZ/Me3nk/PTot5siRIx988EG2O9LQHgV9Ntyrr75aLHC2r2L4pD930IkyuJDz5Vwjlx966CE+onr1vnWogc/RhD1d9fJoTWGzdKE1NnRKBDo+Tp1/JmMOO/Co6Oaoug8//HCWawLLjuMrCJ0FEOYqNuA+UYAFGXuq96NAWlDzP8g6Hun1119HDaGLahxsqaiPZQryw1GkwYm3jhJw01G2ond1NqC54BhA4gL3J0NFQm6GCkyN0lDeh+hGcbrvrwMARxuCRZgy0tYHkwPk1AA0CWzpImREK2EDzTo+IL0IuRtIGRJEn4HnJsC47KYz+APA5ZdfLvxAxHkq9OjNUC+TbpbCfacpcHEUmFtr/TyhdcPRSi3f2sw0KHVsQJ89e9asNg1iTZsbREZquN5www2O9/AmSe2pczhIigxoH46l2ktGHJAfZCj9RzxFBI/XLbfcQqhxPa1fOIeG6CdEck4a0e8ANMQUFHHfQkY+pCToGzLdh0LxFi+IZsLXI1JMWJ4K8ZXP/XDaDUn0yiuvUF3OoyM9SVJsIgeZC9yOF154IUFXCon1Zq2EiqJsSEyQ6BSv77///hzXBkjsNpjUkKEd8deaLmGnWSebRfYVds5tYwqAk4Aj5kDrhLe4a/6++eab1CGAiXgtw2hCGSonBdiNJYPQUKSYne9CJ1HqtJqgurNYaDj6HvCszNdee40+IJohonFS21eaNVCdt2Y8+wpZwAx46EMTjihjGchrbtJGCEinikVp4aWXXtIjyU5rIoKOmCN1VLzW9A5OqSGAfPTRRyk2BNcUyN0Uu5IJ4TV0A21mByXH4KCkEcGowJ3nn38eDUfShRT0Cp2ktDKFQVFR+ZoCIW76YX7RlMxohMUOOoNez0mvTEBLKt4EKiIAI4oK040l6hZUfmscWZiYI+WNVayn21RPghR0qCLMSkCFaQ4YZhPgDQy8o7fc/+233wxRMSfEt47DgPPCmLapTYtHyggy4NDTJ5jOJPImdviKMWq04xS6cR5y4l8U8zhUIO5zwJsFGOcTdxCBNeZfzQL4008/ZRvl/F/qFjUszOOIoQ4LXMBHLKDgvY+qPrHOpYoGQlkoBHkOCMAdw4MlajghMkaACsWMSUzXqUaME7xjLjuGGCIaJyGNc3PK2LvyyisRHAoHRNA1mmuhwCJFzhc097ggpn0MUnPAUKYbuDIcLEPZTCPoeU4MVQdXk8X8FVI+p5oa1uTgoUOH2AHcu4O85kSqQp/IiNs9Xmx55CVwySwqFrk8pfm8iXroz3ug4Em9nGSqKaKElCFiuIlom8AJTZmDT3hC119/va90x7ryI5FVLfMeqBPSlj1BsmMKFzDnoWlBU5acqQSSyFN+GKlKVRBJXFJGgIsW1yCm06nEEwMOnE4jtcRIsDqKvtxZzUp3IpEZGaBVzl2owMoLlZmgunQK2sJA4uSBx/iZpENqKq15mfLmxjE79GVYkrCg4uYCwF8wEL5A1TJFDlOQIAssaKyEPYljhqbBjCz+ihjx4/1mAZDRyTGEJn1ghGOE90GYSLIuwOlvuINcOZ+GxeND6kFgA6kRwVfsg7QWI8kj3VEnGBcOitxSOaaSBp1NR+jHMphV5HpBbViAU9cpaoRWiKlNXqxj5ZhxmMtFBm1WRpCdWwm7IEJF0eUZG6IvWAAYTzUrIiKcwy5HzxqTWmbr2IZKCUEKj9gTGSQ56sYambAH4E38qHYzXY96Z+uAU6wIeb0wribABRY+zzj0oac4ggLYJ5LEDjASMFR0h7WEKTHjsG8yVGjlBPD95eUDWPBJyIpRpVkwWPhwR3eXXXYZEzmxQCmf+IVQ2md0MiCg5n1dIHLOC/ADnDA1YjXuE70zmwwnRIYUmP/880/4alM7jFRdCCoI9iAjKvmK6CM8NWXysjgZB33SxFrU28HpdJEiz6EdmW+hiPFtHiZel5sC7FwlAsXUNSHFXc0x9jjPjCzj2xnijG7CkXNDYHFuDg5xR0xNexeSVvCwnuYgkyiMyMqQmgxV+TyvJa6bVUDOAVfpxx9/JM2JYxe1zfeiyBPkpOoqEOL9LGf4G5YRZOww9pavCBoeZLici1cHEk4GSY2n+uLh8R3p4/SlU8rDHcIXu0l84p7o5FExIMZ4JgdLUyAh0SIueZB0G4UdTNkNdcotNVkHnc2OEMB7M2uWfhPQcdZDqPygJold3iE9F1D536hEdeX4XcREmQxdXPBvrAStFW3JXyOWR8hxRBwD28txK/WSK7jkEzoMYCiQOwwdv3FBdMRXOFsc1AsYwkH0d59Bw/kza+jFBcGqKPjq2r9XX311Iti6Ay21LagDWqpX+zrFMg1CNlPVy2gCgOh4hhp+YS4SUUWsK5CMuyd8SztSt8IbaIjL586dC5Vc8MLTDOOccAM8jq+hosesBHnqQ8ovT4uhGYqjPAEG1nsBc1m0YiFcWwhaPgjwYbGnNVR0yqMgiAAGKn818tNPPxnY6R1ZQIIFbBT1mFlXyRIwa/xFMczyuR8ZmRkSI5FjQVLJxrDW6oxdiEuMCLJQM3EyJn2eMWkMx8yiv40EFpuOALMbG0qLqv2jKbBU1vpcMiXMTh7R5XVotDlmLiW7zSjnRbH9ycSkZ5sVROTaN6GthevmOaucy1JpWQVGPA/SNod+juBV/k5ck2gL8oV0sJjN2xOSJSxc3AsphwRTRHkJwbG1fM4jdzY8j9BfH4rrjttsSFVeo/hKwidZcOWQpRcXN/eLL76I68ltEpvFYpqA+y4yKc5fKOTbUbUTZ8RxrI04l6WMc6e8+VkelRYszT1BzaijP7inI1lYkOIWxmTIUsvJvo0xESlcXQuW8jK5/vSidoQrtDnrKKdrwEdDjGCbAnEi/S2Yq5cIdNYPX1D75o6/bNyduphLh5wm55FoNs+St83p1w7Io1/59znaLp+DJEfP6cVg87LAQ0aO+IqsLgsKpmr1BXghet48tmKx3AKaaUxULNKFfUk2dDO61hWL/7zHznrNpAjMLEt0EJ2mmOFiOKXxXLN2m07ZhdZuWBtEDXOEkKHOgSFexT82HQxRTYm4hO/JJ13SOUY0ncJrHDMagWPiSV4o4ziDIRwHDNq+//77Zta3335L+mUZaJaVfacpsCoKXLwiN/1MDO541qtqArufADJ/i8hIPpHLvyxuIfeSCKvCYV+0wyrnN4hSZO/NKLiziEvokBGj8NoJL+QlLLi/RJWQo8ihv4KKtAI5FadnrmIg+7jacoh0RBNzeugA6pxgKlUkEI1NAp5UsrAKy4CFgYl6qYtNxm2i8vlAhK8FyHfeeYf3Y4maR1X+Fjg1O7IbbNrU2sRRu3QORoByIrlZVuuLLIJAYgl07QKypHekEy+lAzhblsMRB74W4Bf4yh6hTxKsCgUiW1/cuLks8AkKiL1TqNinF+sONE0siQV2zEgiLWtHCAS0vEPuNVWRHAuYxn+ly2sPmzt+u4PsHukFB1EpDEUrW0P5mgHYX2F2OZImL+1IETJrBG9Yezu5lYmRUHi1CVAXTJxYqIuZ6wW4MPGNHGIEPHCRasAoAcliRxbLhCIwi9YUO7H2JB5j0VpoyqI13iEv7LyQXRsGni6W3IMXWyR258gXOGpt8VQV6hDZkgpgvYBFCKpkYPTVFNglCly8IgeQ8U2O8MkEGAOfUU4aEv2kJ2edMihp4h1ahDovR2GXUNrMZtGKvCZneT8inyUaqFWWkBCriByx5T6XbjEKbAJRO4kIljDyJt2gkSxzzsq++IXIzhfBL+KVoORG08Tkizg51ZtkHK6YXYKUtJVyMRXpQhQV+8OCiJym9JUUMy67pkRc6STNQo1vF2e0gGcQ0KwMl1p/Ff+n12mF6JuVcApqBHoCmHSPuL2lnLQs2M7CUAgpY3Vxd+jGyjSAuVCsHAZBxnYWYkGbgHy4E1NJvwKtnsIrjetLrhOlKDhc0ZSxX72ASkeKBAgVmA4iwPIBs7tvrtpL1+Px7eEyvQVaC+0ULYuKLgQt5rqygm75I0vy5p3dJeDxFRONSrZqi79BBPDiFtR8kYi5CRJjgI/rHcsTXH8wJMwwS0aPkAvxrUDr1AvuCNtAc0L5LBAkll4XqAAPHdEFepcPYIKA0FBJ8Ga2R2DAwioelOEFKcsKggoYZHxqjfxxR0hcs5beKW8I8uA1RccHcYv68KrGNWhYFn/ByTTESvwyEfKaJAzEtBpCuI3L/yMuBB3cLWFgLvOauTa7mrZ4KPbTpsCFUmDZmp21glsdmGbGvfgbY9x0IrlMSBqF3DeLBDMFOc0fgTvJICaJyWmmWTk7mIoc3XjMJraYthRc/mtWzWXosO5pVrKYt8SZIDepT6JqlOzRtdENZI0cJfSU/BypRH9kX5Df+DJJm0JwLJChQwCxADiLtDJOac3KKEnHtyN9yCaRSRqa6NEUn4wnYaVckJOXw72gNqgTmpLXTkSClhykpDVC6oGH84HpJbbIa9/aqsQv8YNVJ3BNUlMzVILLqCgvENiTnegeuZOwf3ZOl0wHeW3bBbZUDEnUyYci3KXyUWDAQBboZJtAdqJHU4aYJYjd1BpfDUZakwVtRxYlDeDYPRaVDWkyndeIO6LN8Tj9ZT0kbYpe5PBRislMpEc1OG4r10t2S7NviHhaxBwxZTSejGg/9O6rcaUj7NYao40eYvBl7zIKZKGdU05RmYmghbV2DAnRaQ439WykYSU60Csxs6wZG2yytcGPX/jOmKODeZA1tQV7NMjNNWCggKSwM06yJWGClHbcofOYJsaANElmH22KKXTnxDRJ4h69yAAiKBKQxwiIQEdEJ8IBCtRnMhAttOsC1rNDBTwQ5DkgKdz9EJyAbyonSvLXl3dQpsgigJTBrFPLfBz6YI3d+GsSmQImV8YeIgCSYZeKQ/5lDzFD2TfwhbV3CkGIxOCDFMob6ha/GCKAYWewty5UNPf7TYHlKfDf4qL1Nn1MYBl2k+8NesKaTCe+a/HVtDGUBWbpJB+S+4xx41jmTg78pplMMDIu6sEdVnZF2pcHcYVvmmxsizXCwNyx3oxQyEKgyP0mF2yVQSho0ujC7GZ+5dTQi3lEElEqxEHybihmaUrkDp9YI56ibbau8hjIPsqyspOwjJyiCQgaOsZTSt2aCPnOtJKIS9LxwglTN0lYAiiZO1hMYJGMnoIqiWMko/w4gBHKFBKAmQVwIdTsWQDe6LTRELQjAe0d8t1osfhKxWrch0aUICTjgJjmRVH8Xqg1dSJSdhJEGB9wJ5Shn5A1JoKNUE4KOqffmyA3dGHEJ0YWJhF1YmTGvqEmhQeQyF+fw8K4TSYa6lEDvkVSYwNgcp61CRIanS7RJkR8qE6qzGQdYQrdRsRTxmxZNy17ozCaWHimbjXLHwUAQmGWf7MVCgpmB6cTkGiiQZ9Y6MU12hff/UAT3dVESzWYbKq2y4CSo9EDD9oypAItmwCyWMletEzDfPEhlekCJAsS3YTQUdsIBAwrzZyFLENENLu8ZOxLyovARl4QmvYOnWSssgaEECDFBdejm0inL6Tzlb/GNqOKfXDixAkASx0P+rn0gtqGX7hJqvgWPREBZ0kJZEQTn9iljRR4h0fI6zU6NUMlwW3cRwc2DTLq1LcMFEvjIYvBxkROzUSmM3ZrAdF0Cn4eM7pp3MJBAIML1FhX3jTkDFFdGBLGg0+0oxdPDUWr+AwdnxgYeIQjyJsxyfhgsHrHHSAhoEEIfjLWpFuhHOumDjIFDFEqgyAdiTA9j9zWHXHFbSUTZWMax85Yy2XC7xTjXfBo70EdgdkJsFUBvKp2UOkSm7rEz5dk06p6WVU7S4K9q68ticsyry3zzibgsqswdONbTAGWLuOy1qz/Y4luMcIbiNqCldrzLuLuJTojMDsBtiqAV9UO+lxiU5f4+ZIMWlUvq2pnSbB39bUlcVnmtWXe2QRcdhWGbvygUeCSkt0OGrEa36ZAU6Ap0BRoCmwaBaaKfO327K4SaLux21XSdeNNgaZAU6ApsHYKzNVi/1ojl8wiHcbeYgkpawd3lwCQgNNVlnaJtt1sU6Ap0BRoCuweBeRgWiO3J2Wio/+lyGXn2sNj/2XO8tuyiyFjZ4i0Xkmnczenbhm+jU5ToCnQFGgKbBMFbNyw+9eOm0my2zRrnTuufOM2YT7iYi+KfSlrzFrfVsI2Xk2BpkBToCmwBxRQ/8AO0vNkrc91VSk/FbaVJLN5twD1pm2m3Hf1hOum/cT2Waoo4q/KIXuA1QV1cd6Ckcu3ptiFQhx26ykqqeClEhNqbsz9XNUUNUa876ndpeow+3Bu2a/le9/pTeVBlFpLRa2+mgJNgaZAU2DLKDBfi9HHdSmXoRzSeEclCsWqHB2BFspfKMGYp8p3qHakcoL1ZvvOc1PpCUvsRTXqXMWSsbW1/7ZkQN1eOhhqQThJYjyQVPEQla1UjUhVtboUhZB5oEhWCpMpMOIrpaMmr106SFpQP1KlKtUn6sTJlTTbjTQFmgJNgabAhlCAVlW8aALMospu/O/PPvtMwUtBeVZATgJQS4trr+6SMlL0uspfyn6pZ8TFVCaJ3qLvRfBV/VSj0cvKhG2OQbSSym5qVcKdC67+FILYm68ClBpPXGEEEbdXVwvKjAbFs1RRVXmNueNS6k45GsXRlLJKDVS1otgE/q0yXupDoZsaYWIbKlspLDWeMapQlCqhqVqvO+v92vQtvFTL0pe/fiubpZJUTrpUBouBxVMHht7VnFI/LukC7ijlRuurRaXom4JcSrCpveWd1KwGCSBBoprYBBKfK+3pqb8qxIk3gKQ3BWzOUG9ImgJNga2kwNzKbv85/iHqfeKR09/KPSpt6IAmeojHSaB7jQ4G7SCaAAANpklEQVRThJmG5n9LohN1d1OZZQpJ0U1q3r/Kecqs44DmLMsNuVbikVNvKmxTbEo/Fl4yCRX6lokgPpES4rSsipU1kmTY8ZhV01TEVFKh11KTnNakQeOv09AsJIUk8xUv/6WXXlJbtHp5+umnqW2FPGMrsJlE0T2Vweh39QU2xbTdp++fe+65sfg5ZZ+K2Xo8c+aMT7BMfVMx/5S5doYKUwDwb731FgWfNj165ZVX2BwFyc8//6ykaPVokLBa6oSxDWF3g9EUaAo0BbaMAnM98kUFYXh11oCpBKdEUCrIEcFNatMBQus5wiE3lU0m/akTZZzVkXYeA7dV6tzkNN/9biIxBWBqZQGm1g4KHerQgQ3C5mo4o4P7lLo6zCk0rSY5xx090RDFVMamvH1Cu/PvldpO8oES0A6B4AoLcnifHvWv87tyNJmLd+7SPvNI2EM8gB0QlS/9IdpdzWr9KoXtN0XOF9eRBllp4vmYomp6dLMVE0F+bjoAHG0CMDZZTuBmozjpBCsDCcMFx5VYz1ny7AafcOhBqC8aHVQiBHPPg9rvHG/4mwJNgabAplNgtFZm18jzVACWIyh+TjHU+3xB523w1OORc8hIc2u0OXHBxemMB78516V75ELfjgaBHROH5zqi5mwYipmVM2YGSAb0MmWc45ssVaCPwx4Esf0rNE3Ti8zHDBLzoOydYME1p86RnQVAJYt2pCMOOiJLTUDtWapKMMQmh3ZQtJ6O/rEggfgKje50CjZEfSsRLyrfBQwHYHgkBiDcAk7uO6hA4igL/zIU+O5e8FpOHJFRIXlyo4IumzPYGpKmQFOgKbByClywRx75vlN+9WzuHE9O9JgWcSAVjSI325HGpPym2zIXAp9l4BzPNTkqtO4gQoWy4397lNOm/XAHZaxBSDjwryg6WmUFnb7kAXPN6WPKlWPNOeb6+9apSnF20dznzp7K+VqTK7YCgmdnQs5s5nB/8803UuWdDWUx2wvj6cgcbuF6L1salyXHbvBbkAATLa4Lpwvjg8Rx4/5lBoEEDM7+uummm7z5xBNPsAyccOU0Hkd7XQgh+92mQFOgKdAUWA0FVllrndrgqMl+opAcQy7ZSoq4IwhXA+lmtCIizROVL8YrzY6yuqLqKLkFRxZGkVONruj1+pzenV97b+Y+pb7MPjrskKlA5YuTO97Rb2vztPjYqZxETnZMBAdWxmhbcEqKR/mc1hdOkC2B4z5kJVhZEK7YDC41FE2BpkBT4ABRYGWKXIjYei1nLgVQaXEp8kuqnH1Eb4rc8j+Xmhp78sknsxzu4vWKn7NgLE/kBPHxkiiQVLJRbY//0qOox7sVwX7jjTcsWyAdD9slzC7FfXSjF5OLoZC+fChfnaNP1+bsZ79HC4Dv/uuvv3755ZfC9b6SZye5wYcMEZBgqGVyDC1IhNyF3wsSQQWOvuC8TXcWFLj7Mv72ESsb1KZAU6ApsB0UWIEij4smQ1vIV6rX0aNHRYwpOT6rH9aDt4NShQWMJAdwXlXCEZqW8OWOhAAr2ZaW+aYjvjQxLS5GzWGVz8+4ydPRLfYvZUlx+pwZJK1M+phtbDQlo0FKHftgGRpSzCLkYiH33HOPfWhsAqFyN1XyAeS9996rsI92sjTgUqbGI1pcBpxNhviVnHYhB5BQ+f6luUGSS/neFPilsJMMAfGrr776wQcfFHqxHADaZeDsd5oCTYGmQFNghRRYSpEL0roI8dGfo4pEkt3J6i9HzeIuWW9ZV7SWJ0e+y9sat0GvEO41NkUZ22lGi1sb5vXaO05BCra//PLLkszluxVsAtES/g8dOoRE6txZYLZ6LZpdMXn6Wzians4SOOpJVaMgNWhJAhmpxlOnTmk8bSK497U2sQPylP6mg3GKc2yrGMpLSqeJ+feAVH7uxIkTerf2oQXt0+IUsKj7Qw89BE55+DaVyV8DjzK99q1Jwg8k/rIqQBJugsoLNLf7HH0WgF5sV8sSe19NgaZAU6ApsJcUmNZal4zNuRwhoDPoZqVI7E4WNy6fkq9J05DyvL3aMC1N3YopReU1vtqmueMSxyR/raTWOn0ZDZflA9qXekOiCfP0aN+5mLZ49eHDh2lQQQvrDsq9cb79yy3msl933XXR1pSuZi23a9Z9BBzdXNYA+JVeoVarhkz1iCOKt3CpBcZ9RdFS/LrzCU9d9Nubdr5ZF0ABZpkIAZ0Nqhgf4uoY6ren9s5JuwNJ8uxAIriieED1xdFnBLBjEnLQlzFQvv5ejuDuqynQFGgKHBwKCIhaKj3PoSmzinybCLRCRb5NZGGrdVG2bWJo49IUaApsKwXmKvKlQuvbSpHGKxRoLd4joSnQFGgK7F8KtCLfv7xryJsCTYGmQFOgKfBfB0uR25c1N02sB0JToCnQFGgKNAU2nwISmGa12L8UuS1VMrCkQUli8td+KnekRMFNJpTfMqfclxGtjJcELrlRcp3kSOcFOV8SoCRwqeQqSUq2lDQr+VZStHQssu+vQt/ypOSISWuXlmVrssw4+XEyqrKJWXfJI5NfbZe2yw//ulmQeDkpdT6XiKcpDWpW47rQjr861bVsbWDIHQOSf8EgRywdARvwkq7lgUMHUlCDIDTzwizK3vT+TijrRV96tBK/E8qglQ8oT+G8KCP+TiijG5QRv1CGF+IHZTQHBvpDGS9wxKMRZR/i3YjySHzo61oSnDEgHX0ByhCBclLioTwS3293MMXTEeWCZOSyXvSlx8l4AxV2hMtQzniDcurTTVCGr6vGm46yqd3LPgnKGsFlDWp2AcrGGy6PxE+1HFyDMnQgBbVCuQa2O0jhKbKEyyPxoVzjbTHKmWKFcsZbBna4jK0wnZ1iGfmZYtk5smCKhcuLp1ihPJliQRkkQbmm2NzxVlMsUiVTbIFUWTDFgrIeS6qMKEeqYNClS5VMsVGqFMrLTLHUbJgrVSCeKRYx653zSpWaYhOpMneKzRWkxhtGz51iJUhHqVLjrQRpSRU/sgdnrlTJFAuXl5EqNQyiOzLeZqXKOMUiVWqKgX+ExLTNeDuvVJmdYqXFSpDOSpWMt4lUmTveSpCiPGBmtdhiqVIDeyJVfPXPP//McUfHSrAqhygYrqC3CuFO0pRZDRMp1t6xh8oUchiaIuEKcyrs9d1339ltjK82HeGZjUnYIMvaWRr2TDtgQ4lWW5uwynlfoJHHDjf7oxDasWnqkNgw/dhjjznKU8Iz0itWqiN1UaDtCBAnotoBZTuWkuPKkyGHR9CQ3Y0ocq0d2GXTth1fzhq57bbbzA31RDFSRwSZ3Gzkvuuuu2ySVqrFXjgZ3RLpjUIvJM+cqoPy999/b5+3rVl//PGH2mfQlPUNEn/9lk+u9oun3vGmvfKGmm8LZW0qY24Hl14++ugjPZKkegeDjsCTYul2gn3++edKrBw/flymOvjNK7iAxC4v2Dl9BKZKrKhdD3c71E1yBeBAgjLog0po9fjjj6sYg3pK36Nk9pvpiHxBZ9S2CQ3l0R8XsBxHgjKAcQq/cA3vcND2BNzEUxMDf4OyGWIvmTIvxoANdQqz2zJupErID/EpQunrdjHYz/b2228rG3fHHXcY+g5WGVE2wWzSU7r17NmzzsGTui8ZvlCWw28iqfN67tw5m/JPnjypdo26NwRBoUySKg5vQ7wETDvaHd5z6623msbq7RhIQdmOCZWHjx075sR357j4IS3fTY+Cst35PlHbQMWe1157zSk+DnfRrMaDsu6Ifhv3la9/5plnAAMkO/GAhy9BGdiAt20PItCBlONqIGj7AC2b8QZ9kkhVXQRBFrv/nVdr7wChFi4HZcREUlNMZVxERmoEzxRDfIywMcRJtXb32czpJDpbHOktjAvKWAk7bL3vvvsUDLBXELuJOXsNgrLBYEjQ4obHV199ZagYMAonGzxUi4GU0kOGlgH2yy+/GGyGnIFn+NErUAZJTTED1XA1aJ0RYABnihXKau8b6nfeeadhb/A//PDDJoLpQA6QBohvmpgspoyJo1rzU089ZSqZUCVVMsVMOlNP5Qm7VR0PaEqamKNUsWPC5LVD0oZJByqaYqa2KRapAmVQsfUJgRw7RCwQDo4kICjgUlKFGLHBkkghWPwgZIga1Kgp5hNTjFAimkwxYkqzGg/xdZcpRqwBAzBAIu6AVygDO1IFItCBlCkGwZpieA19opWARRBkUU/pyJEjmWKRKpliiImkyjR9/PHHiIzUCJ4phgWgoixNMccdKbBo56opZjNLoYyVGIqtRLoDpu0dxW5MN8UKZUPCZhbDwxQzVAwYw8bgGaeYoWWAqUJhsDkoy8Az/HLwcUQ6lA1RA9VwNWgNXQPYMB5RNsUMdeWnDHuD39kNJoLpYFIYkFA2TUwWU8bEcZjFJ598YiqZUDXFoOy3SWfqeeodb5qS2YCjhUwxKJu82s8UM6lNbb0juxdM+UgVQoAoIBCIBcIB/ARFiG8ekZnECEyJFIIF7oTMiDL6oBJxRCgRTahHTKFkplikCjoTaMQayp8+fRoXiDvgBeVIFfzCNbzDQXzETTylL0apQrQSsMaAkUDkxj+hPkbF/X/Zyh1q3vxYSkPYFGgKNAWaAk2BnShwsNbIexw0BZoCTYGmQFNgyyjQinzLGNroNAWaAk2BpsDBokAr8oPF78a2KdAUaAo0BbaMAv8L3pxp8My6aY8AAAAASUVORK5CYII=\"></p>\n<p><br>Social contributions differentiate between social security and other social contributions, as follows:</p>\n<p><img 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u+kZPDzBiG3153RGY8ZJ+oVF/GVSDjuyMNN66e7ZslWeWszeS49MmFL6Ys6g0w4FZhe4m2V7UNBxhX2YbVlJjmy0T5HeSQ/eOwnlsJyzi9gLz6kzvmbc0JpTnsYw6QuoHvc3NmfnDB0P4+Q4yTTVQcTp08zvY5+CkslgP5sJQWFb9cBkKtAv/0JSgWjthBgaPDGoYQIx0gxsB00ainGXY3WnrGfvgDilX2pjhx/bPK/Qd64GxoINiX8A4Ywj3tjuVIFCoBAoBBYDgTGkHocpC2t0PqSC1H2hA3G84jHOcdre7WCYtrlABwdgEX5t3KmcyGd44A2HO6nReD1qIoO8pAWKOYLF/ZuVAlnahnddAfvrPNTk2JDMxkV5OJBcl9EDIB0aeYv6zqGMcKAevmlIy7uUl9UjSmcM5kPB9KytZbBL1OFxDZbVpkkDhAxvZwN31txdO/dqY3hIQH7FPNiOsdk18L2FAtRaPSIWkEU8yC3OWOdEWS9cIO3ZxezEq8R3EgYfApoxhZjhgYN94hmAheDC7YBjoH0NtNo97Eebhx6V2ra33Ccxj3uXMl6BucW0cbaNoMB+zvmA7AJkZzFo8JlX2pDCvntW71r9usOYZLC4DfKtc1pBbbWnPhe/x2pkIVAIrByBOxK6OKlFHXTkqVspPrB3Tm2VbMrSL5EXddO/bNSUTtFaKGEYhetW7K4z/qG3Ua+budtaz/qN+eiUKA2/4nLcyaO70YB+wcReNa2UHizwHK9ybtXpKVcvLuU4EhdyfXfIivubMg2HAGijHYB0UB8kxKEMz1GjebTRWTG9vQxs5CLe8joU29P1/WuDgGqrDZRRI8VRMem5InzYB1G5W3z39IIRJQfK3SWRcCBP6Bib8UDQBr3GwTG0UJo1A7kiaaOpJJr0lPNsqm1Q6IhdAH5njpPZ4Va5aIOuMFToMmFCnamQeOHjmBzHeFOF5s0DAALN0T3OdxBDxir0r8rd5WyoFwrrLJoXO5ZIBCUiV8Qsn9g8tFn7vVE7vYjQw0lQ7yCpMQYUtRs70oBUcguwQzTjv6xqXiFQCKw9AhQelmOc1X31JgldnCdm1B3VMkww74sjocRCv15hy2YBwGrD2v/w6o2FQCFQCGwOBCShFtujd4h6GXvq887omwPTZdU5CwBWG5Y1ZFW4ECgECoH5QmAZpD5fHavWFgKFQCFQCBQCGw2BIvWNNuLV30KgECgECoGFReCOPXWRR7lo8UiaJMDZPOIRX3ct58xV0UjmcQSrzYVAIVAIFAJBwEEhMctF2hZAs4vJJqTO1VmCskUl9Ubnk5wLr3lTCBQChUAhUAjMLAJI3RlsIUlGkroDQmKJC9nhOFO3Gw47+dfJ4CRNF6Kke3rNWeFelFNKcIsr4jzS7OjEjmY52aVtDj6tnNd7OCTk+KoMP8QgHJAnAZAFIge42xitSjNWvZJMJCjNaVSDAGJ0nGPMyT2/FF8SQscBvJVPKvWoPLFvW9K5VR+IqrAQKAQWAAErqozVAoH0UoNucqRNCjKnjXu9FV1EspC9995bRBG3hPoSg5PKj+MF+XJoezD2uxPPF110EY3/kEMOsUjNDnyOTSPLBEhf4ce5dpnBnAu3rEMGDo68r7DO9rgsMpK5CfQm+krCuSzxMRAinAvcKxWNkC+r1YZVr0ecGWf9hYJxLn/VK1/LCkXFufHGGx3WB7iT/YRlZ+hFrxsb8G6SRgrII5aRCAQyz05SvsoUAoXAhkVADFMEvdSRtp5WLaaKcKTHHnusOGW4HHCio5x++unijlEKRUO75pprhAxzq6ujiJgmvpinuimuZwT0pYOZT95I0fQs5ULCkQ+IS/orsl5ywKzKh6wgOJ2g5ZOoaxRfYpY86Hh9Vd6+OSphzjFbJJcTE0b9BJHEydkc71pJnaLWkDwy20d9dMHxUGmN9EI0QFGbZJBruXOW9XYT0rsY0NovSCQf+2Q333zzquj9y2pMFS4ECoH5QmCoIXyk97uAcRYXEUAFrKFtxxpsFaOUyImJ5rGOBOS4zdLcLM9il4oJ7y5lneIyC6eiN8cg6TKNilqMSunr4sgKerpa5ncNpvqLO6vaSewcMb9De5YT4DIpS4V31FFHiR+ng+xFeFGA4c0xOlPX6RdCEjV7lxaPqNEm+a677pp8dwwqBLuhqfnGtoQgKJwtcaeF7ZPGl6C87bbbLupvZywmVaAQKARWgsDwMLFWN/qKQKTim1q50BVjoDzc1i9O8uJ9iutpN92SRJUXhlOYWIyimNDoN9xwg6fUgGYsfJPQ0ko6sKxne2Fil/VsK6xr9iOQur15jgiCmxJuYCJAabdCG65CujKMUEl9t0nRi6RL7lGP+LKi7VJkpUJpKzswhb0T7jQJZthFiBEC7wtmzhBiuVdbW/RJWrRG14VTFZC12wYjIvatNvirmNCt3QQ8WiXPLDXRXeq+9nfvssq4pXk2GtSps92ayXwS0lBPdZ9OqXkqwW3ZOBe5T+YbTWLSkAqdUYcrgzljCokaSzdlNZKwzhV7FlrlcSV1ymzREt1Eq64Pbj0AX4Vw03L1KzBo9BY11uxVRjheCDRgtdNTXiTKrEq00BcvbW8RrF5Mfv1yUdxZD9pCgp4XJYqwd2kz/tZsHdFCXhqXXXYZkU7gXuJvtmOSUg8O6qfWSwMPJdcl6vVJAXej5bN7KQxeI64l5LMEyk0ZW/XqJAMl964GdDFRszFSjxf54vdo5iSbjmeZeSCpAcA0W3SnBIXpfvL1VCEwmwgMDRP7v44/+Qj0bX3J9+QRp6ZbFlGFRfmmm25qJfPFSkHrsnzY/bX0uIJ7Lr744ne84x3SaklfxpfeotN7an3/RSfija+8DUg0YcwlUUUe+K9lL03leEXSdJHMMaX1mm5nR9nK214t1DkjBw6GrXVcSfW0SiSFE2Bf/HPED1WaHKjRDC6x9PNvwB+tKrskNqpd5zHR7Ro+Q8mRNhIpnSdEK2BoNEm1iZdOLpGqJ355PraKTzzxRLylWjz95Cc/GT10+3jeeefhNrHuk4RG/hV8LFt8HqeFoy5dMAfUrDaNiYRHD8aC8c/AhVRS/Eo9JfGQkzyra1oFGY4FgyOF/gU6BgVCNQR2spMPpn2QH2I2/ZAi4XKHHXZg0CbQKGCWcgXFef4K76/9ClDN27NHHHFEZBe7KpLwQt5vRnkXBep/8IMffNtttynMgqXlaibXkgxUIj0dpwq77H4O7Fgupk5WdOjpjqH3m5IoyEsJNLlLeiAie50y6veLc/HQQw81H/hS+G4vQMY52OqpMZKchvOEmdMaTIAGaXIO3fWud1WV1AN+swpovO0hXi8EcWW22WYbk38Qz7pSCBQC84sAykbcvfYPN79bmyy79oktJT3n9ggsaMzSzNhu+aNYxPDrr4V7l112QSGraIueQREJjpyk8BZetC5L70Ypb11GfgwYBxxwAOioWRZrC66caS4GTAUISeBFLVReZKyYqpJWNUjSySzHgVpmNnSOIfAfI7YddH6IY2FBJFLK3nLLLdkj4CRI2EoeUg1A4Y4vYkcsSHnVMCloYw83RUgtqIv0QAZCe+iHYt3NYYqDsWa87mP/92+z/6MuBeSG0VRuZdhdGdSSPQJiInpDVPwo9Z36Dgf14EI0mfSyUtoMhujHytLTUUk5MVBPPa6dQh9rcNDwOHdQKdpIIVrrLk2a4BVzujZgaJVgQaKn4TBkIGrHPQRpkJ4HQxPIjKmmxrpuoGFIQoqLX0ZHl6P4eru3SCdjDmgeKj377LOJGm75ISiJs1MyKDVV2/YNCicf+NVAOIKOuwFW28gQJBjjGAODhjGe6W+baVqS3POEGwW0QafAqB5dJgGYMIZPNkXynMGdxEVj7LyqAoVAITDLCIyJKDfUW8ciSKmyP2pNYXunwfSyqrdFdpZ7vpK2YSnd5/ZFX7TC+uLggHU5dcb8CxwpwkhFTOgIHkQILOxCS1YAj9Kf/Pv4xz8ejfFRoPN1WwVGZECVZ0G1+lPfKW0MtlZ5r0gK1CU+CCl7H561mrMEUGGjiWoAlVcNLARIxQd/YzvyB6VW5e5qv2ynClOjXUEMLcfaWOhMG2SGtFpqte4jSe0abTKkKOoRuQfraACTAO1f+rWewV9J+xSIkxKvZs2TlZXaCuRmVUZsHmfhYLpAqEzi+sV4zoSA+QACc3IM+sSdwCGSQt58zozVdwV8ktQ1bTNwXuepUb1WrQJJb2iXyqYJQc1GzyQoxYTgb++Yn5cSC4gRGoOwyTdm2n777UfCoJ0DoTWP8MQsQbDWDI3Ul8heuq+wgXZI1eMs87L2tdy1Y9tWBQqBQmBOEVh2mFhLP69sWp2F2/rFqWfsmas5hWbpZlsxWVyZiKl0uAEbMfOiW09Zjm0q08/sjKaSyEaW3dCPAvTmsYHtFI4WSHKihCEnKrucqpbmSVyj0QBRg03Ygm68pElt5xHYdanghlIXDB9iUC0qov66qBe+57T0SsZuwsd1k06PmexbkzxYiZETu/HgqxmWmfqXaBjYYYvDouVrQIAaupccm9XYbeYUWxqHVsbrDCsZCLwrgU4f7aDrL5uHTZBG4elLr83t7d3ueIr9Aw4kA3ROMpPHNqEm6lMIFAILjMCySZ05ly2XjmUhZt+jIoxdFhcVPsoQqzi7ur1Y31v4EeZT6jXlGDWm7+3sQDiGFmu1pYQt7a+euLYWZRZsdlcuDkz9DMjZLR6bwB4x0FnRJKs7EzRLrP1siqwGkEio3eic6zUSZT+g8uJyqnx8weL+tkTzQiQRU/ztmWrGUmC251sx1LXPPvtEDSUnERaZ3wcrgXaM3tAe+goWFO2nnka60qqgFMPA2Hk4CX8PrUR70iSI6Vr2+yNVdFFqjpCtklHCmevxImQz6G7KpC9Dd8R6DYMSjwdgMtcTChl7+HCQNsaCUAUKgUJgrhFYHqlbLu3sWinsg9o45P+1ARndykg7t+nAEm7s0QymRB6OALBju2JBZ49lCGXSwPQc2WzcWp1p2yQhBRiWFWBxtbPrX65hNulx9qB6hxLUE4dwpmy6rM1RsgJ9dKyyjtg4rDEI20ImEyC8eG6nATZocZ69fK0lmdkzptgJe+IuiQSnMiDjA/+SKggH9qqNfpvresp8rSVojC0hNvNJJoMeQczjEOseA7OZ7b1mF+sxbIfmvL/3ve+tL8SUHHaHnpL+NsJ2l3FeAVvLCpBjOJrZAbHN4V1jeV2rQKRh8eGf8INiOZfY/1ZeUBoGFWZ/kPqXhKFOrIzy9Ys5J5aG1Aw6bfM60lXP3GVwOaubJCo3zWjt5g+3BuNux6R3xmFoO5lnbE9wm+CmZ/9F94kao4ShCXtaxQqBQmD2ERhD6tZBy5ClJIY7x3uofb5YMqin/IYsGairF1lWYQZerDCWeGYfoMEWWhwZhy3WjpLbSeVVbkG3gHLzzqLpL0ctxA8Bd4k+HJ2cbOZ0nbVbAU7p3LJwT6LzQgyrRSbwaQDiXY9vt912wptgVm5T7mJ3ckCjWGNEboiffLe1+Annue5x3ov4g3Mf2ksDbM9z8EaiHNS1gTc7wSJs4aUesVfNF5LHNTdv9KN5Kmz14y077kwIEGCw8WrzpDUAA3lvb1b4l5zBgOHthAbCBAd7+8QkA12IhzkOQ4eaPXRiUOVf9rKXuWuf2Ka7rR878WBpwoQWColoK8F5AYzIAQ2D0lBJMMqYjZnM2TKn8hqg7izF5aBgtLCNTR6CqkfMZJzancmtd5qdSjxo84XQxtMCnhCLhUBfqMskHj8TfqqqIuT5pHfaRjgGBbdHgJOVXUzlXg1zMwpEMHnMYx5jdMhzphlPyXZM1DxRWKcirwThbM/bEjJG6tQqvvGw3WOPPUzdefzFVZsLgUJgcgQ2CRO74447WjS7D1umrfuWOSRkp5OnEiXJ2pTVJ2ZArIPkuqoGZYXuYlVyYGm6oByTd2BZJe25Wm2HaoHLqsdiypXMeSfLKLZgDIdPL/oslPhpc0VWgGeWZb1xdt5FL8cEsLK4W7KRQVusmcTF6M9JJA3mVeddlm/MZ5/eiu8Re+RRKDXGngh1mQ4XS0D7uKUeHny4B8kZqe6pbrojH3icirFYepF0168NVjg7ujjmRupdg7+aTQbSDJJWs7/62xqAS7wUYxH7mou4wjaJTRUUqy8oisOXHnFqy7F7PEr00Qzu66OUUU1lOQAs9spZQdTVGztvIX2yaugsysfTEbZMV0DxG9AXIwIxTfJe3vgNedIqxAyNRnJgRNiUbwOXM3t5kd5pA8SQrmbYMiBt6ELO4GkS/4Bg5Y2G0kv1F3mHg+Ngn6oQsB+USQJkvG4i6RqJjScEcUoB8gTDgxMBOu6iyrv+gzwzVG7UMLcajJfXmUtxpNd4++gESrfAbhBnOTzRsn6AVbgQKAQgYM1kT+2FiR1D6l3gJvEqmnGgV4vUZ7ybc9o8tmLBi/goUMFXJY76EjgMncwLMMPndOir2YVAITAFAkNJfRl76pPsmE7RrHqkEGAPYMN35I+KSf3d3IwO8KGTuWZ4TcVCoBCYdwSWQerz3tVq/2wiQD9mNLavbFskrgmz2c5qVSFQCBQCs49Akfrsj9GCt5B+bNvb7i+/Aa5tXXe8Be95da8QKAQKgdVGYJM9dYeanH6Z5BTsajdjLerj1E0j5MHk6NSi9nEtcKx3FAKFQCFQCKw3AhhNoBHhMkc6yglswuGWm9J0maHXu4P1/kKgECgECoFCYKMg4NDNqaeeitEd2On2uTT1jTIDqp+FQCFQCBQCC4PAeE2dmj54Tn1h+p+O1JG2BRvQ6k4hUAgUAhsWgSmPtAlRwiafFGQclR09EkZDbBDhOIQf6aEpyIkwZIKHtOBiYn3IJpK4HBsWeh0XwEewM7FQnNoSLyUfQUiEl0lItenAEZNEajJ5WZJhfbpKZv8pAV+Fr+dJJ+mqCPYtHPoULRckxy9BapzKbjIFevVIIVAIzDgC41OvOmUku1cyZAhDLYKVKJjihIhjJfwc3bfLJTbtpZQWbS1HfgXMUkbOUCvpBj8ELDgoyUZ8eNFhYYLgffGRG9Sx7JWAwweihfmb8dm2RPMIf2RBtD1UNBEIXcY5IerIkRLALysvuArNUrHqPKgBIs+L7if43bKS0MwvsNXyQqAQ2FAIjCR1JHTdddeJGS6GpVCvPolJaXkV8Ouggw4SH1skTp+ExZasQuDr008/3YIrrCamEX97t912w2Tia4rTucB65IQzxmEt+bLEVBecXFIygdz9JSFNd4ir4SlkrNFpeV2Xbkx3FGZqRJxHuOmmm1D7UPnGjGLScJBdoFOdXRZiKmQ3EmZfeF3gOP4gIq+YqYM50yYcxypWCBQChcDMInAn2TbTOFo4g7DFLv/iZpHJ2TllYnYSTGxz2SDQswIJMJ6FUkxppnjrI/u8CNW+OzBm5XXy+IMf/KDw2tZi8bQlOBHXeiX66KogKBo2/WxZlLAq71WJVwu0TlMXGLxXJz6TJocxQ+zua6+9lpwkqLvyMqfxbISzqPuOcTOWKHb11VfLlMrvUYIW9SBmRGgg4Ax5BZhVBN5X2FgkeauLhiDj4or9FIPlKSMidv0FF1wgvL8I4fTgbnYymwJS9WgS5djEGJrgy/iaNspI7yZBiya1IRbXXc3mgHQmsqslNYBJRRd3y+u00xeTipgoj8CFF174gQ98QJYaFiAJ5RTWZtWKhS5KvDKqEl9dIHTh0AOFx70xSUrEVHcF36N/iInNTso0Uc1MwGokAD2oy6AzUcWHl23F9HZLBHiSq+5nYmiMx11h87/00kt9YUrRu9Y19ieNkTNNX4wFnCum+mr9TKqeQqAQmBwBS5CN1yT6ap+RpG4JoxhJouWv+J0slvaD7aaH0VEFN3rX6eIWSkundW2nnXaSZcQ77B/LPGaVpJXKP4Gokg9mI5M6RmH5QFr0RRChOjYMtIRWQcQOjzzwkNwtiByfKQleY4aBZBZ53eteR8ZyCx36i5iJTUknGlInNtn1QFEeYWfGxBhaChAjhVw5OsitEjkAh0kFS7iR6Mx2ifxsxAj0SWKTLYZ1WhlsjXTlLUVaBhrRGs0edWF9r1OGcZv9X0Za5J1X6Mi5557LVOMWAUIjtURrNVvyOhlQsDJRQFOTBgZNahIqhRK5xzTjnKEYm5BqUS8C9t11+W/gxo0gtK2d8peoGWIwlIjd4xDWKYlYZEgji4TUQU2kSCo8fiGEA5KKl0Im7dcGKexQvjbYuVcDYxWsdF9PoddSE5EP9A6ve6nxcl1VZcyffCWqkoVAIbAqCAwl9ZHmd6qVJdKLLW2x03attZZUSzYisUpmOUP2Fn3rKf+j5MxGA77k8XWn81VBcCWVxAjMvUDedDlP82EmccX+BV0Q4R177LEoRIpMOj0kEe3ll1+OUUgAnpX73LO4n98i5fWqq65q7VE5KpKmk+cdusKjog7IgE5/NSgHHHCAx+mmCbmjNlRnI8AgcjrzodnbN5Hd1ZfUSXe31eIKApPnVNpQxN/tPtLVVLnMkbqntMdAY1xldIc5QRuwssc9q2F53Htxp6nFpcDbuWsQRzRS922oMwK96U1vsiURtV5JzbYXjndJG8rEWoCbQaR+hck9SZ3ulgIRO6BBQGn2GFIpzzgJWxkepA0k66Bhr9A2HVS59mN3d+MNqiq9I6fuvPPO7ASnnHJKkuBl/rsFedM7qfPIKHBIttP6FAKFQCGw7giMz6c+2ERrn5XaCi6czSjD46AcsO5dXd8GoDpMgIcYdTEcDdgXKjsywyLYS55s+qhGohyq+bOe9SzfKfEMIfRalInLiVAuOnzIcNI9eoDGsA7rNF05W8U0YyZrSrzvDO/yhNJrOTpQfNlRXJEo1q4KilLAF2/RDGPqRYwH4TCGaFos2U7zlOlSF0MCKUHSVZqxGqi/SJHO7TuSJj3QepPi1uOynisfkQIIFG499WoNZgOIwOelxEGiTJs5ppZGKuNuiwCoSfpOZHHRXWnCyR9N3BwqO7qrcq9LVCX/gsiLXCQraJuLBFAfglH6iPIh6ZxC5C0vaoq4ZwGrZPLSKkPvX5cNnfWdz/X2QqAQmE0Elh37HaPTTqz+lJtugu3Z7N6stQonjXJPQ2mhLgUwUA5cIZWQkO+uhIPRjA9ubr3LTjniaZviviDplDFM9HIWZhzPdu2W44jIVc3+xYuxWtM78ahKvM4t5msX7373u7NvG261dSkTI6K0Lt22xiA8yjQ7dhRrSrM3EiZiwiGy2AXHgunaEs56WoLRu+nD1aYNQAgOqtLyKSahSsgu2qmRsQqoynfiiAEKYsQUb9e8tLP1DjjsK7aTSF1EJaYRBon4itanECgECoF1R2AZpG4pZI20ouEDjsrxZqrPmiEA/9AqrZfujlHaq7EOtZhMwL0uF31Bom2MnvGMZyAee8BkMgRGV3ZXbRidCk5tDck50YDP3EJdNqS9CD1HcbfV0qVPFEjHHUpm6BCL28lOrAKVqzmO6/5d2uV+UNUeLN9w0DA2D/vZg9UOVdnbRXVqoXZCSSUe91eDCRBEkIgaSwscgDIEBCkHOKVL+PrXv750v9ZsktSLCoFCYIMjMCmpU5usgDx+cYl4s3Gnqs9yEUCog3wz1LG8VzNCtYEdkzvvLRvSWLZL6uzYzMvcwdAzVmbYN14upgzl2MDxsFPAReyFYhmcNQbT01YVZv+3F64wMwCvRoZoCrpiLPBbb721LeQub3GI8zivPS3xCNa3ZcCDnabrXZ5Ctx50yxt5uilvCi3BfG5pv2ZEUBhVEoCc5Jm+leFe4AtlXS90yrM5iY5i3cLTDWp1Eiwi8WiGf5E6a4G+Z7MfsIQbHYfz0vSMyJ3nfMlLXqJynfJD0OzS1Jf7Q6jyhUAhsJkQGE/q9D/LFuLhH8Sryw4iN3h7orRAh3n4EMUWmo8F0bJOvWsrYx7vltlMPZn9ahnDaXjIFWXmYyebgyGXQ4Zfem1A8yV7wL5jC+jllvJc3tiEnbPiTdYOH0IYY1ExTzrpJKxGgfYKnmv277FUg8V5BL7u+D4b0j72gznrOeCgZn5ktup51OcWcz3zMrcyLSQ9iOYW7my1UeUf85jH4DYKvcftQDv8LRV6XMRlUMXxavO4eIL81EwV1zXV3CB2ZKrol96l12aUlvMbEJkHUMSC7t02iyADAdIDPfuII4448MADHbjwOEnCQYyzzjqLCUEN6tT3cK1mA5mpQPgaENmDUDORxYOGw1FA7Rct4LDDDkv3vUsLW3ybbMY32iYP6R1xRzPgfPLJJ3upyssVdPZ/gNXCQmAjILBJQpehsd9zBMuqjXj4Rvm37YZieufUm68TvKyANB5rIsLITrAFMY9bhdcd0HWM/U7W4XZOo+1lfUUGjpZhGjDGRo0wQI2H8BPuse3NUQvbgdomdyztbM4hbP9mgNxS3qk2+LuOTcle3fgqYgdxGqdzO/qVjWQf9XMCVwP9FSs3zd4tbchBLzxNDhjqC8ZkbSc+MofmkfMat9HOqcvRiT1O5sjrnEbz3Va9W7ReM4o8EcM+iYT/oEr0RQsp3Lbt3TXNcLk+8muLz2B29M0onnpOkKcvaoOVAtwA4wnPWS8IaB7wvcsVyKjZKGRKs14QdDSAkBSnPP8q6W98APXRuQMvatYpk1xTXdQwwgRhaIp9/XX/LVQDCoFCYN4RGBr7fTypz3u3u+1fR1JfCYw4ibxFDxaKbuqND+xrBtAy6dYraUw9WwgUAoVAITALCEyZ0GUWmr7B20BrpJtGQ10uFAQCiqnYNQ7B84/jGbfcGqp8IVAIFAKFwLwgsMme+sLvCzbH6XkZnrSTJfnpT386Pu6GcZ2wCzkDxibPrE3dz1H4+hQChUAhUAjMOwLDj/k0jzZeQnyphA9L3ovF+9g2tqVtH5SvU29je8Y7a+TQOW62e71cR2vP6nj81zzeUuLOeJereYVAIVAIFAJLIMDPSdjQRBPvFrtjT503E1cpOVW5Ui8qlDlVtVxeXFQ0ql+FQCFQCBQCc4oAd+A3v/nNgseIVD2S1DkAc43ukXpM1onFEWW/G8Sjd6i3Zw3olvTg+gbo0BgKur+JmLbGA7mE5X/pOCdj29kdoLGF56hA61ebflM3flEhmhqQerAQKATmHQGk7kit08sjSd3JnKFH2vScatsOR7XzbLHrDuISigpxtkAiSs7Chv36er/3Ao426Fae4IuwsvJKZm2Kt4kEt5U7QywkRLM2ZNWeQqAQWEsEpvR+F3JLKJLE8OKALUysM8RctxwaJiP0OmA/XpYXIbf4actN6dyz3VyFhayZwnN7LdHZ3O9yblusVke0nWkGYD7+5ZHOi21qywHCE9btiU98ouRsU1eyufs+Rf1iv1x88cUiwzgOzklQ1KNEZZ/iAxbn1yWbkY1tkSCaAop6pBAoBBYegTER5ZD03nvvLURoPMtsy0v29b73vU8kEGemxTXzpbtFjbyROv4Wt0SILkHNBOAUXFZkWWkuN3KGSm5uGEUiUdFXhFjx1wdjiaIvMNlKzBiGBuDz5fpnLpkMMswyDgkaM/gzI+44Ty9pEGcQBL+siIT2j2w12UjKfIOtODOS2AqPsxKcF34tqA4WAoXAAiAwktRFgjvttNNELkuuSQo3BqK1CxcqvqZgW9ttt52QXgzaIXV6udxf5513Hg3eYiqaGD9zZUQIF3BUBDF5uhbYBW+SqQAQwc6S7jO5Pn2S83SSx3tlmtIZL4Gw19h6uqrq+qqt6bXzCENj1emL60LpKaZ3jD1x4M9n6ZazhVDrRYJrgfNcgXy3hrFAjX3LJDVUmUKgECgE1hiBkaRu3RQCk2YpvjdSF//ECkv5Fi47TbRQIv4WIhu1ywgiwsmjHvWo5PqknSc0rMXUIkvpnzttctUHYyivwPbEE0+84oorHD1wBoH9g4QkfCmLsaiuZ599tnNo5557LhM08KVU32uvvViSexSO/NTDTCIku4+ByOk1Ou4b3vAGJhMhUUP88rirjebquzxsYsxtueWWNOakNvGh90uakrDw7DECrzYcjPKZZ56pPA8MJhyhZ9stQ+w8ZN5u6DPW/jLVuH7BBRfYfZDFVf745Gzl3GF2iWtL5mPvERyXmUfYdi2Rmt1GDxDkQMsBS8XMKKczdFBkeC0nOH7oQx86+uijU8Duhu+f+tSnzFuZb3TQSQ9wCbzjrg0OgeJJpdA45ZRT0shXvvKVUeU9QjaFmKd23313Cv3LX/5ydpSGsBCzNgJMbOLs9ddfv77C0KrPyaqwECgEFgmBkaQuHCnN25ou6jUnLKszhhaIm6Kp/wiDgd2q12K82xvGRk66kwAQDFUe/QQpJTGKB0kJi4TdsvpC3UQh2E7eETHYkYcv4H3rW98KTAHYURrMURcSlbGeaVpOFBFjMBy6xbL2PtC2ApiYESUZ29rHLXRlowRxCjmAwPBfvP2pvJLxYM0Uln1VnjftkbXs9NNPT3p1bHfOOedgsjCxIbvyyiuFVSdkdAObK4NKRZXPI2SRSHW6gK1xp/LGnUSC+/M6FhpdQLca9qAHPcjeDV50XeXsQGaLMxemlnouuugiPVXmPve5jzb4fvPNNyewPHzMQ64DfDuII9lfl2PN5kWImRHId0KG/jKEmGw2NbQkaeNp7fAkTOgv5wO0rZ2EG4H2krceFLbwiUrms3ayM8mak/YLy3/GGWcI2iOxDYOTt7///e9f1tBX4UKgECgE1gyBkaSOmJPew6IZ1aQpKNbB6667TsovObJk34rtV0QXfylY2TzOWuljD56aiAOELm/m0DXr3qy9CKo4svtxBQ9BhiGEZkx9RF2UUXR+1P/7YDX8jcYgLOPZcccdRxTAUgiv9U4NrhgUohUux7U0aaoq+ieQUYUVQIEGxdBIRuJ1WI2HnUqoy5RyTxEI8L06o/Q7LxG3iWYep/WiRiNOeqBD032RXGaFF1177bWkENRuuO3R3HDDDZjS3NAAf+9///srj0T1gkuBR+xzM+3YgKCd6ywDO8MPQmUNQvMe0Qw76/F7R/M8NI8//nhfJAYkIsDQIzg7+rTyvucIwKMf/WgYYu599903OVrcJbuoX6u0n8uodpqQ2qzl7RyHZkuO7i5qh1LmsALS6DFLGBqCF5U9yWnqUwgUAoXADCIwxlFu0NJopbPG0cPoLjKBdvOAdbuX63JpW4uZcy2vGzxAKcM1KiUGUVtp55wPfGFaZ0tHRVBlbI9ghKvkbWPs9R2hEq1YmIlW22+/PdZxkc6KsZq13BW0hHERWOwoPr5QgmN8lvYbF9I+ET87M25DqN5lPzuMxR+NrZsiq3w0b9sEhAxp37pjykiOaJNE1QfzcUZL5FrPqhBD55YvWq7+ULIG6JHrsfd4xLwi/MUDTnvcynaPzja/y3a43F3bECFsyreppe9DHQhyEVZkIMJQ12HeU9m/bxBppIZlL8Anhihv8RSBxieT33U6OrsFRmdLiJbfhaW+FwKFQCEwOwiMz6fe2mrFtCjT/3AS26bU1KO6kZL0S/ofqy/Vpy33s9PzdWkJ+Wbojmw2iVsOdRyfHXEwJkiAAq6kQJTUbjRfd9Xsr+vply+eyjFCRGVbxMYwzrY5jdjsDceojuc4ihtK6jUhwDC5roUeUaxH6snsPjQenxd5pL09lTd/Czp0qvJsy3Gedoa5ffFSkgS5ZKg5ByB5UD2kh6Sq80h7PBD1xrR3RXt6EGlzIMrbVa5AGtmGiRWBBGYa2/IgxDBsfOYzn1mXyVMvLQQKgUJgLAKTknrWRxuidlVthV544YVDnZbzPmsljVCwG7xuAzJ5qesTZMZyz1CgPEXPjkHYzjH9smnM4STcg3FtYOdxX2yFkL3yL00X0doyZ7jGiDR18gEas4Ftj5kKbpfkxhtvZFumi8fHLbFfuo2RjNyzLd6AL1oS4YMPv3/zdk/ZPlCJJrWNm6GigLuKtbf4Mio+Dz+DFCOU+GvX3F9EDpBgorPZXEiDe8ScVnGA12uNTDFf0LyW55HW394AqRmj22J32N32hInNQWGoZFYzvBAoBAqBdUdgUlLHRpY2zkRWRv7wVnA7pryTBg8QK5kdXx5VlBsFFFO47c2ve5/XqwGYhnoNHCovTNonvmC9VnWpzi2UjEv4r6mBaPXVr371qU99antEzfzO6Li4GXPbPsdA9GYXU8YtpnuOYLzDMD2VFHm7QjLjX6ZJNtRtvTPFhywHGd11NnxygFDDlH4sK/wAh3YVuuVFxl0lOI+Pno+SjNuN1Lu9S+XhThODLT3E3AOhFfOF87xiZEQOg+SACCvqt30gAhIkdY0dIuKI9gOEYAGlLkRUfDjYElIPnz7yARAaRK0BrRkxOCFyO/Q2473FK9TQM2Cs13Sq9xYChUAhMIjApKRuLeOHJRsMHZ2eZF2z10gV4znV43VcYv214tP/ZAtVLB+uSZbgDTsGOI9eSB6yc8zIHEx8wUyoFK8P3STucpIdXz5lkEfDzlyxl7S7eIgOzdGBOZ0Gb9sYu9sliZk6HzEDbJ2gMQfScgVJGxR1MrxzrNtzzz1Z6V0flfBGF5yOUwN3PM1msSe0qcQjWmViOKVmbjDM2JbmTKfkqKo02DxREvc7XcZhjcw3yj9D/d7IySCuAC996UtD6gAh2YhRA0Z+bZw24utOUuF2YPpxz0T5zbzPwc1eA4lKm+92t7uRkMgKCo9Su13XZUcJOB4++9nP9hYueOrkGLH0YG3YSV4dLwQKgXVH4I4sbaNivydgmeUSeVusuwZSS5vVvO2kRtexOvu4FbJvK6YarODruxquY+x3uIGxayLO2AMEr0AsLvFtcx1WGCVPYTun4LAaYkbA8fRuIWsyQLzPsomeI16qUm0XbaZybMQOz72xBYpXPwW0nXzLXngG0fCpszdebhFN4oCmAV7RqnKLFSHWeI1vfulxiFNVUuT5rp1xIE/LFUg9bpEdWxtgpRnqySOx9KjZs6F//2pJ8+zTl8Qkdsuz2SZQczYUMofzlrS/C1EKqNnjqVZtLXt9rE2xLrhYhzjWfdmqBhQChQAEhsZ+H0/qi4TdOpL6SmBEKk5/0bCdbp/6rL8tc4qpo1lOuq+kMfVsIVAIFAKFwCwgMGVCl1lo+gZvQzbjEwNguVDQL+2yb7vtttR0YoEj4MutocoXAoVAIVAIzAsCk+6pz0t/FrKdDMjiw7C9Ozw9RQftf4sFK1wMV7gEY6lPIVAIFAKFwEIisAmpL+GptBidb8ea56s7xoU3GfeuKXZzddk2/M4778x07+TCfHW8WlsIFAKFQCEwCoGhlH3HnjovX5Gz5N5wymghQdR/B7G4StFWR3llL2THq1OFQCFQCBQCC4YAIpMQxAE0p9K6XbuD1O222nM95JBDFjhBatzyuxlKFmyYqzuFQCFQCBQCGwEBu7HOGIvw7eDucFJ3pI2LtUCYCwyHY10s2ElUU59CoBAoBAqBQmB+ERC2Vd5IWTG7XdhkT32oUdoR3m9+85s5/exDjxdaDjuKrtVSsbUac1hZhDJfeGsrprBc1+3x9YVPq9bX8O4wNNzA4gOWSYwiQBbQNFA7Yy28zIQPri7UpoHYQcZ0davtzhybI6Ni42+ml65vtTkQ78e1+eaksAHqH/ydrlnH00fR/RzcyPiupLMq0Z0WqHjNelEvKgRmE4Ghv6bxWdok6BRoTMxXvfKz5EQtdJePpNriuveicVn3BSmj7uMhub8UE7pLjLN3v/vdm48PZhPuoa0S0QxuAVBIONk8l17jUKlopk6WJ+a5LGHSlEljqp417rW4qrvssss73/nOUfHXVtgesiBXPifpu4lqVljnjD9O0hV91o+rhaNf3QYbKXY58fxFxt1Mo5YGm8NDQx27RZ4QZ9DBDeGEM77f/va3J++m+d+tmYYgHbCkz5u1O5M3r0oWAjOIwBhSJ2LLDSoBduJpS+aduOJ0cS51z3ve81B+l5bO+H8fovT1118vVaV0n75fffXVEmmLWD6D/V/LJjGVWOCEVrUkWaqIRNKrOD6+9OlzTgCc+3xwADwFc7VGkwbWsuV5VzKjb6b3pptDk61tpjfOQrV67bzi5guzmLT0m2/UYGj2Sgfw+Mc/XsD/Qa7lvyKnsG2/u9zlLmnJ5J1ll2JaJBBYbTJYghBjdOczJ69kFka52lAIrCUCI9doPvA860QRF+Bdau1kwmD7ElWbisnvTvpOGUGYgkPqOFvObMmshOl2hkqqDzldJAHz4BZbbCFtBiV+I9vNGCFROG/ErbbaClzCkYJRGHPbIVKujdU8kiQUrwt7fuc733ktp0j3XQmVOvTt3S6M7c5gDUK0bpyVuuHTujzY96UxnAThtfEJxdN+4zTyUSOoAGpPOGF/u/kIlu4FISAhftsjeuSH0yITr9evoN5bCMwyAiNJ3U/xfve7H/Og3CEonGYp2TbTOs0y/fFjs7/bNsvFxHaXee2Rj3ykX7h/5d6IisB0j9KwUQumPcuIbKa2Jao5c6hsN3mFQOXs6nQRJJ01nfGDFEVUYtjoNgOMEo4xPLJ+29rwhb1kiXbK5yZXio0PBvOuGUDedJ+DDjqI8Z+kJeMLQY2UZmRlmiGxqVN7lDHo5DDyGetC90UWWRWeffbZKleJQHUt2asuuP7CF76QNSLdoWB5hbA5dl523HFHj9g4kFSmVagqkp8uq4pt1nQaejCB/4FzGbS0u9/97pdddlmESHXeeuut++23n4QuouJDNQzBussW8qIXvUiPeEQSNG39tDearkDO9gc7U0z9HmSLEhIf7ERVsXo0hgmqPUVlfPrTn64BhNRbbrmlCwiDsDaY9sQ1WVxF4ZWqjksqUdgZUUlu5I857LDD7JvA2UDvtttucs80Ck+b/fuNb3xDchqtMhayGycav1GGp3Qy7Y233Xab9vvrroR16oSqtnX3LIyjJD16IZ3SqaeemjD7vQ8MNQme2qxTxM1WwC/9mGOOCUTMQtqfWxpj5hD0pQ9Q+U477aSnrptC6TWNXKohgJgGuvnYxz7WKJBE6fHwJP0jY30n1AZhOYf0wlohD57yHlQb0+Dhhx9ON4CMxefQQw+l5QvSIDGgu8Rfdbpu5px77rmw0ki4eSqN5LACn6OOOkrzvEVTs2+YD1sCi4JoDUCzWM2Io8/g6NSVQmAlCIwkddKxH4DFFBnH9u6KJSCpKi2pfi1I2qIZwdnS4CfkrxQaWXb9LK2MeOvFL34xduFdv5KGzvuz1i96OSJ3xOARj3iEhRjZJLV5mEwUX6vM0Ucfvf/++1vCpFxzMas/JK1QcrpAW7qwffbZZwkHfhnPLPcqOfbYYw2NBa55M1g3r7zySiIa2sNeVnx7JQq84AUvQJBZ2a2b559/vh1QadaMGlZWoQFN3B5fLNkKo0xkQ/7AOm2XlBDgFZ/5zGcyWEhawHlmG6/gV/H617/e+ksucTEFLPF4VJw770KZXo0DejorMrCOQwxT6hSZA68HGdv8zmhidJsRjEl5UAsRjMmpbTwPMKtuZtF3i0eIFpIzfDGrcUZOOYIIIWkPpoebHkkzmEZyZtR+9GmAgCb7MHjbbCStmuRaRdglDJE/HApVodZC44orrpDmVTo4fSeKuajNZ511VssJmzYzYumjmaAGX8gNtq5cl8cP77KBNYdK/QWRzjqZyhCNGpGTdACmTbNRq5+M4orfHfILWfY+emf6Pf/5z4c8qUgH4ZyZhrZJSEccccRrXvMaAoFJm/kDKA3DxOahbTgXiTLKE19wP3JVEkpQVYyM4qIum4FsfjbsyApusUsRBLWQdGh0FFOA3GDCJ8uOYhAzEyCjth122AEshilp77UBMXsKoxMHYQUxBUzCZIAkIkjCS0qwFsGcXuF3FPKGIfypHOYDyZgsQngaRKauFALzjsBIUvdjS1BS60L0iWYr83t+xzveQR20ONoqy8KUJNN+k8lpHTutHxX2wusIo5vcet5Rm6799FQrvrVv++23R3UUOKsepURt9F1LIe2ZB4Ml7OEPf7gdd26JjdTBS1+hmmApC9MomwedkqJMSkC6VtV9990XAUtj0zRCVGeT0kcK0aRUp0NTUgkNBig50yzQRAc7lzQwewQaFsXRomxlxGHIxiOi1HHywqAW2Yhx5DYiIBbRcv/m7INXoFi1eRGeYLFgrkgqPwxBxQQIXtdaC3eP0b3X+m7Fp3ilnQrIqdrytKpNsnNBk1qO87STNuaip2JLCC2hak4JAvMRDuBMt/Zv7BPZe9ZZb8HrRBCdSko9GiqeAAWC0X49xUxhIB+vw3yqIgD5+Dn41RCFfQeO9xKPVE4aUC191EX0qQttCqmQ9ANGg6LBhAN9wYLGAltL9qoS0fu1BPge1DakbpSNtaliFIz1Rz7ykSS2T+WS0mJ07Kv7g8YPgBC8zEAP6lRKksUDkVskCVC7aMr55cYYo6cgYgHyiL4w42k2iDSSUu6XrmtMEcq4SBUmK2iGvsc+73Gjyc0TCGaXmpG3qdUKxKqnWLPVm4dmlKFUsykXxIjFOg4fKGkkxOz0ZRJmHH20H5h+XB6nu4fUDZnXGX1v12W/RL+y6X7I9VQhMMsIjPd+77XeL4pbO2WOtc1avLQPDineDw97WT35b6/j0ZoZGQOrDBsmQKiD0KNBnn766RZii6lF/JJLLnGL+ZGboRWT7tVIztJvVcK4WaHcovJalBWmuSbhqY9K0CQSYj9XFfMvRm+HpjxOrYnS4+MLMcIXy6il2egoYOVVxuLrOn5Cjcg+pGisre+aRKtOwFoUjmCaXwVBEC9idDRsnlD4yDGWY1eY8TWJRoiNrL8qpJ9Z1s2Q2H7IK6GE7j6rJsFBGR9lrOMWehdVGBpmsUBO3cFNAla1aZu+6JpeBEbrO9OIGlIeAWtbNFQf3xGVOpPvNfu4ajM0AKT2ab9Rw+hGrXmHaK1egzHNRvBONyTdO5r0gYMCYNQqF/G9XuSlzfhBcAFjngIseH2JdQGlYfHY/FEX7gSpTlF/6aNIXauorSSPUC9FFj5tiD07GO1fYY/DIWlqDT1qTDFo60IXIvBqnls6CEkQaWEgimQJZDPT3+TAhZgOxgunOy4pieYjft3znvfUyLYV1SuZf5PrudWci5mEvkApkMbLRCO9WiNhbgIbTc/qS5P2XGeRgiS1Hm5+FxvcdjgU87q4AAgsw5nZGuR3S0dnuCObW01G9V/JbpJ1WqAfOeLZyJtY1ujrrruOXTSgWZJYnhk/qV8o0MplPaLXUrPor1ZS+pDVdpQnkYUbRSnMYtz1s2vmXLd8cD/FKBsoeW+YO2u0l2ZErIbNwJ5/Y27xQXIGrh1wSDtbbVGMutv2SMtSS4DTF7ojhcnCqoCOaw+bpx0Eu6r4wMUIec0ONLSzinlFE26yE5Tm+a7yqJjdjzajHM1QYRb63FVV9Pj8G05taGDKRuTdpwJaaz8m0Meu65b6k+s9fQFX6kwlaXlwC56+dBHLv70+ZnSUNwcYukhC5glZjcRAV1ZYAcQMTx9MzBASUUBT29ilPYOoRnTrgdYgCrD5tze+eHoURAEzT+mj8Q3j9j4N2BgCrQxpZBuX3gmIVDtov+lOwu44qi3jmHnSHUcXaedEIhIVpwRmefsXQ0Goi4XAXCMwKannd4WWMDpboo23UclFlMzGGAeZQGOh8RvzO+8y/VyjNkXjqRf0PPuIzalHJVZq65pFMCqF7Qx+ZD4MIbYtRpG6pYp2S+GwDey4IPN1G4usaMz7Rko9li1GyEGz9hLtN1K2J2PMjNJvbzjEmfcaX8ti+FgxWqz6Gw3Q5Di+UYOYbem4NH5dI9LZv9Qk2/NUXiZlM0F/6ZHelQ3jWCZ6ZKMYHR1iAY1+TILRWaBFzsjC3etOuHzwuqpob9E780bWgqbUNv7rUojv+u7B8847L+0nijG2D/U+G4qqGtTchDmKvn/Zh0NmPmiM1AXGYO63o4XwjAHDi5jECA3kJMKfB/VdnUbZvkNG2UaY3XHGCeVpwB7JnoKPkRqMD8E2Tmo0IQMRVFnv4xihp2lARAGCI9EhJoTW4MFuqqdJfkEvQ9ArqVNcCrJzQcLzFhPAd6NMW4iibzIYlDYNfMkmSKsqk1BVHo+BKpMwkmsgHXy1YqrliOBXAy6krhKGsbIdDp20dXGuERhP6lEaLD14mpcQARypUxBta/ll+ql0f3IRul1kQLbxhhKsRJYMaw0ZeYokY3MNbrfx7NL2WQk6VATaFQDBwgMZBVL+rLPcghhU+fp+4hOfsHd7/PHHt43b6HkZiKELVnsR7Uc9VkaylwHi5sba38SIru6S2trYtZqJBZpn4WMwIDRosM1Ro6+kFZCR1uoZDzi0bXFkEGYD7+7C2ES3yHqWfRtJ6BrlEh2mPG2JsUdthDzX1cnLzCS55pprTDDLdJdTvZdAwJSqNqxmUoVj8Fb6MlTj7DK6FzW1GJNpD+T5K9B9oc1sG+70aWg0mSDqoAFizgWIzWYucrY8usFPRsGowjQvtdkr4VjgjWiYcZu5Ii9NU/URjMABEWDBq4/NbK77yA8DQYM67q/uk7TUHOjgidQJUirUWmIKrLjU6aO/xqL7Cw1zkwAY4XXKJFEzuzRLuJYEIlv4zNQo0OhrWyDqTb8GvvHyuzZXzWeCQiZSd5Y2HICpdxzcOFVA0nXLgpqNgluQ4a4BJTaePK6n+J7YZxlpTvgmjEkOH1yuNvt6vEYyCVsjW3+7M4QQQPxlG7OLoakRrZbePVyYxac6sqEQGEPqfrH0SCojfcXvjaDth8TJhRmQEkbl8nvrLqx+JH7hfi38crl5o3/eKHiF0w2y2VDIDnaWz63NPOyF4B0ToIujKCpg9iapgLaKeTZRBGlIb3rTm2LAhL+lNsdzLfTdY75D8YQ8ZzoSlRNNBAgbrtmQ9qEce1dOKyBvbBEvKqOcmn0xmobPsk4IczrInr32xDqqjMexMk4yuPz1LMQcLBov5i3eazG1cHM6y0s521MozRaWZM+aD2Fus8heO/9tF3GSDddeVcqgMRAxXdi3ZnXgFcX5P8s3fMzMnm3Wv97YbPI6CL3ocJDk/+wvJ3wnO7Aa+wGCjLockNWssMfbXqzrmIAfu/7y2KI3a0B7qQe1oUmr+MazoYqGsMJ6CjG/BSKOoUFCgd2D6sdqYGR08Qrb24Q89NMG1yu81zgy+0cd93EFFBy+oM0CZKDJUrn1qle9CmgCyTlrB0+v1uXeVCEE2ObnnAh5fvIMSNoWY7t5qBfmoS02gh25MGBmHUhPtdxMMB8i95gwhAy+6GSX+PdlLqVkPDY8HmdPw62bDAPOLKQ7hD+uiw5T+FGQaBl7eBKYQoYjPhm8Gi0+rqgkk9BPCUqcBE1O8iv0CB+qgpXy+eHEnuHftIRkBiVitC6Dziuc3Ksj772JUf8uAAJ3ZGnzI7EKO+G6AL0a1QXMYYlZx+Ats48tIwGbLbbgJzxda1nRef8RC5zL2si2maBnyvGcZ/XFstNtPyEzmjeveLIRCu8JMdONUT1VCBQCC4CAc7ZOKS+V0GUBOlldWDkCPWvtciukAbM0UIyK0UHXwJwaVaowczGjOptzMfpyZ2OVLwQ2GgLj99Q3GiIbvL98snKOaAocbN7TSlmJKZcMvFPUsHiPgIJFuuftNWE3eRjY7WKdZv9nQquUwRPiVsUKgY2MwCbm98qnvpGnQvpOycYldhyHRmxdGh+iAKs7zwC+YM2rfINDmuN88Ewop+V+7GXwW7RJbL+5vLqWi16VLwQWG4Gh+dTvIHUuJ5yAnD6fJMn3PCLFdElnyuHmqW2h89jxZbXZvi+gUNHSbvZD64xXFOcjj7d4OMt6++IVDiZLHA1fusvAtIthurKgTDEii4dn9agQKASCAD1BmGSnP3gldzG5g9S5pPKA5dHTTo8sGHbxf9apBCFZsN5VdwqBQqAQKAQ2DgI5WOTAiMOrw0k9WUElcnDOqlsiqi1FIVyYbGO+0BvEtejlAo9e4pEW8oKqwXg49TbtKo6QtjFC5MDS2mvq3guHngpLyIAnrKZTwvK4qB261sZoFRFTVWq2JTwYpoNO76Pxaw/mEn3MFFXAlIua20K8TYFMQ3im+jhFR+qRQqAQWDAEHO90QFRIiZGauiNtQ/fU8ZAYFJyZE9NRcGkBH+yeqtFh38HMIoks7cxxECQNECVy3nTdMZVixCq/Lg5HcBBbw2naFlgbGgmT7gT2dI7iYn0IpWLgMK6QGkJ5DJ7zXiHmajZ8QpoMngMUzUNYeI2fqcO+pquAKqjdDBQRz3ew5Fz4FB8eBgaI698UHgZTvK4eKQQKgUJgcgSG7qlv4v0+qC9SUARL4dJsZfcmC5zwHfbdBY5wlFnMhwR3bB9OPWJZCCKbaGgeFzpKzBAHbWdB10l4r8khW8WSKFAsl6QxTbU0SKgKu9EN3j75G0kJBDT5cvicE6SE/mCEWXWQVS7QhxBsvWijxl1KUOHVWsbPyVu+8pJkSmHXRKAb9NK3ecRjXCQ1UBBARXFxiHPyQSfEAJPwlwEiMwl9Q0hddWBXDkLVUAgUAhscgaEr28gjbUrTIx2QZZC3cMdKTC5gwaYUUtFEKRGSiSaUhRWLuyg0ldiT1Pd46loNRaRyixlzg29jJ/p9di7aRGS+5uzg1nTgJIK6Z+nKvkwX22TpX0V2K4z+oDquL3n72v+uiBRS1hIrB1MECV6WQHgapvFamP2gCRsp0pkAbYLnmP8e5DoqCV4i3k9YQxUrBAqBQmAdERi52CFvkUoZ2B/xiEeIeoEwnMyxvfqoRz0qdnhedSK/0uSixNDbBJ6kFYkuqZhF0IJ7ww03UKcY6nspNdexw+v46iWIIQnTfNAVXdPfrg7qO79913Or68cwtE4qJsuzj8JdFdN3ApbrqacXoDuvHvoKbzQftMHjo7ReMl9eOipJhrenjFf03Ajc8vY83nWb13El0x1PtWRoyUVmUmlSrC8apqR/kwywVeKuwhqvhm4uNRdbaP14h6RfyiTQfSwTaUAwbO1PVW0itUZmjHoQgS798uk5oKzjVKxXFwKFwKIiMJLUsfjWW28tGwSlh4ppqRKxnAYj90OwoMcLBd9wcS4Zr0u8YZNVYSugZ+WosJPv8emCmSwq6L1+IWYmX0G/mdBt39LdOS0yILdiAuwLr+26D3jFUR+FTI7twZyG6iM+PwNy43W5Mfbdd1/X1eOwA4tLq4fbhJPQeYXdaDlC2i2PSzMjtDiVXRRuFQ6+HZmJRu5Zu9esNUObZ39aCvm8XWhxtvFWzHcyYtqs8Tl/gTjNKDKivR7Vego+MYxLLCQ1u8jzhE5ZhTgrSC4g0YDQtoxJbEseQdLoGbvbPxJfXc0C72uDx3G8cC5CuIeA+Yiox9aSNzI+SUIvlZw9I9Nb9pEnPelJyZ8m0Yig8Wmk8PXmv4vxOPFqbxTXXSNlQVVVukZS0dQ8wpNDlPKNnH14g/ycq5uFwPoiMJLU5UKQF4SS3TNd5l8rmgVXrCuKe86JYQJpJFgsc3KMBi8HBgerbjLv9e3qLL89vG6b4+yzz8ZSkBc4PQRgx13cbwHF8JlNdK52uMG+xqA93NCgKISEpeybICFh3eRTD5OhH+cVCVhJdi7vC14MfRpNr5BETgZ0icJIbxKRtfSdCmBNPpZiwqvkrLPOsp/dNfXTZeUOQerinJMGPvaxj5166qk9tNEbmmTaIRPoKanF9k10Zc1DhGrQKs3WeF1wXXcgwD6EkvGrFG1uERPdUl7aEhRr+5wLJ/SkFJOlAz7YHQjRiV1H6naFvFQ9HpfPA3TmZzd/l5LJLWaXATernBsB/PkG5ny5Ke0pNctHIpuZdG0uSmzjr1f466I6QUf6ITHwnEj3lXz7298uGQmXRshccMEFPT/VWZ6T1bZCoBCYRwRGkrrVajC5U3pI4aMP0Rcp7s2nPRxj+csyZ93HENJAzZRr9MyOENwceJNQSzZSqavofHCLecOGLpWUlskWguxptDljNmh4dwX3kAyUVIw4xTFeJUm9yocR+6oBs7olV9gZZ5yRwwtEN/m1cLxXy2FFiXe9awbHZ5hetjeqv2FF243UtRxJo3OE571SuesCEjVJumhTXrGmMjhY77CmbGBI1EXN00hN1SrNJinqAmrUHR+MTnAkyqjWfnnqTPp5iNHgM0vJoKpNVtbQub8qd52bJzMD+VLXeCayCrTcdK2FDUyqtkdU3sTZ3MLKJAw1MJkwaegFeUgf4w+hPDHFuQbXWw5QTzG5235KO2WwReo2s2Z2ElbDCoFCYAEQGONA1NuR1WFLsOUYox955JEW+kFqsVJbN+WR3G677UTHzIa6vxvc1QiSPl2fuHhy5boP4zZTB0ahHUKssaYvKI1ijfLRiSSkRmGo55eLuAffKx/ewiWIkIbtu0FBSy0nKfbCgqmHAIGP6fF5BV8K7WnSmHYiqgyftJjEuBbJwEUlVWtWyIPpcR+W7TSj+/NgflCym88UeUf+o6BrZA7jabbGe1aF2paMqNoZngZFGpx99GjbmaImWNJy9zZ61KDmlEHJCuRY/6ifbksH3p35Cmsk6NLIAKJmNoZIHjBJI2Omyq68f8koxx9/PCsIWJiyKO7TnV1cgIWmulAIFAJrg8CkXsEhkuT55vHO+MncOthEC1wOplPllZEynLJOS/P3kksuWdQAtJMMFRK11ieGTyvP/oyoaHJwc7er/sbmoSQlmKWXlmw/21YunmicMfjeuKk3QkVRXhflFScN9QP3IsPqmCJ5wjB5hcTnKuk6xLXDC1TPxm3t7WrwCtZvG/as6LJW8z9vWnWKoXN1Dsoi+hg5psGi8QlYFF5s8k2PZTMhW4WKjTpK3lAli2Bl9B/ij9DgSyLjNqbP98grrY8a2az6LgbhFm+4K4TlkTxLImHksHUCFvYPppEyv0/yY6kyhUAhMDUCk5K6Nc6ixkjL+spfiS1xlF3dislfyXEjPl/3ute9aDDWPn+bpXTqts71g9Z3dgt7xtS1dIQy6qA/awe+D6MM8hYw6bhcvXADSDlkoSKG8aGaugECsr/GKM7btuGp6d7ruxeRIWwt5+121qNS+85E7CAiy4pz52Q1FNV9hVY5p5622dLG65TsUH4kD0zJXG+Hm0rq3LyxVm3vfB0DAKN6ezsc7M07P+ZdmqeR8YDTbI1vHckrBmFxBa0mRkKDbrBYREyNTxm+h4QeZzei8Wukvvii+8TN1iMoxZtBsVYnocp4Nc9QOw7ezjaQAoONjM3ALj6QuUCCxfCRbhsCcz2Zq/GFQCEwswiMJ/Uc+EHhFmJeP6y4X/ziFy1VotDwc84S3LoXbYaxUfwZH2Xs1Fq1/eVFTN2ZWSDWoGEC9mEX0LF2QObEE0/koYatCT1wa2fVMAROzQkC5am8PkKgeJATA58slMPFXYNB3c505egXcrUDjU44fNklMV5gD6ljYjIBZy77JmzCnNFYXPIK1mPSgAPZaRsxwiviQ6eAmgkiRtM5CJvl6NnWO0719tCq+nfcccfLL79cGYe8RSZAfj0Tt0cEIXY4gnv5q1/9aiGLUWkwR40ayUtcgzVbtbpAU89caqfyAlFTkbUZs8LQu/S3HQLMdkBY2XdszYeOU54oNNB+6EMfynKOcYWZg/Bhhx2mvwxL7bRbPNXlSDj33HMZMNSQBsDnKU95Cmf44447jh3CS4FJGssWQDsfmB9L3BEgAEOSBCH4jW98ox+LfXeeAWsw0+oVhUAhsGERuJNFNp23FlPmrDtdLCxM1j46ChcndmBf/IvU8QG1A8Fw/LEd2xRHC5ylk4WWR1WsxGyeTgQxGlsWewv92oMep/G2r7yWDQCFLXMEhkJE2YvjmMNXbN2akW1pm77KuE6FxfQMttlQ12zR4lhuoc3TDUFy18JqOAOpYxfAejwRBUTkxYVsKriZVoq8myVcAUQoeABvdhyPnxL81V86K/c3Z+fMARFXaNucyzyORKnRZgU+ptw7k8bvTIUGGl/6m7AEWkUh9iHzKcPRffDQhEoIB3jR24l9qDEBD/C3W/zYvd27bAHYno/1O0ftnTfzCkypj3amdV/lkKHcEzJ0ihWErz5RgM1Ak6jg5Az8jV/NRgWcFyB9soETPiJZejWUvBQg+msOe5xlPsc3VAtzw+G92qABmN5wkA+cAvCIkAxcEOISkbcog+PVqZFGh/OBu1znDCj/OD8ZFRJtnUhcy1lX7yoECoEFRsCKZJ3n9bwJazcDI/djS579vwWGgFxi1R4MY742XY5avDbv2qxvWZiObFaUqvJCoBAoBDYrAkyAV155pZhv3beMN79v1jZtqMoXg9EN2cJ0ZENNv+psIVAIbAQEitQ3wihXHwuBQqAQKAQ2BAJF6htimKuThUAhUAgUAhsBgU1IfeHNqt3jyBthdKuPhUAhUAgUAouKwFDKviPCBudeHyd/FjVEjP4nwkmljFvUKV79KgQKgUJggyDguI0TuQ5G+XS7fAepO5W0zTbbOCi8qKTuiJHjRhjdCaju2foNMgOqm4VAIVAIFAILgwBSF4FDpisH24aTuiNtz372s2nqC9PnwY442Zzo4gvcx+paIVAIFAKFwEZAgKYuG2rvSNsmwWdEzOgFn4GL6FpidCQ1iH+lX5N0VUWicAi5JflVN+Y21lTYXU+JAeJuL1zo+gK9WsFnKPqSnomhJrqqSDKCowGHAWDp3nlKCBfoOect2sn6QjH520V9EbxFN9scWPpZEWak8zE3hK8ZGs528leveklhbqW80yOxd4ROko9AZlvR5VarnVBSvwA4xnfhPVRWfXSqwkKgEFgWAiKSDQafWcr7XfA44c9E/hKRBod5mRBmF154oThoVH63pM2WvUPAuESwEQftnHPOOfroo1EXg4CYo5JSC7Y1GJR7We2etcK6I2KucGziqoq6CgepsoU+9X2wpyKdofxkEEHqWESwVbw+a51aoj2mgRAHAqiJBDdJs6UPN0MEKOymrpnkwRWWsW10++23twxyQ2szZDpCRjFSAuRJaGvGdpPMTt4GvSMQ9GK5mwmCworVP3k9VbIQKAQKgVVEYCSpI+NLL73Uqke1FywzKSNdpKkLn0kXx0wCcKIoZJaI3FRzBC/uKUVNLFK5peX+ElC2lw1zFVu/LlXhNkaPD37wg6J+CnrqQ+i59tpriThIpdskjE6sueiii9oWvtjsYpEmi/m8fNhaxGaXlWRC64KpIoStWLxrqaqaY+bk2WefLUfqEsAKVywCvyC12qZfaed0xiShZEm0Pf4WBVb9YtnOy+BWOwuBQmDBEBge+50eg6pl0vRFaG59FtBbzG2LoKjXO+ywA2szBQ7H429X2O05oImnLdeqZF8WSgGxeaVR6GX9kqttVEq3NUZz5eZ34gvd7l3vepdA4vBJ+2XpYG0Wro9RV8jx2HJZNRjnZU0lEj3gAQ9I6Ht7E6Kjy2InPLhHxBUHb5dUtJD+5+OLepI1NR9jQWaCqnRnxAVB0QUk7xKnR8ROV61b6uxui5AqPJW7SU2WYW0fpKhy7/XX455trTKyWHDrrbcWxrzF88edqjIBTAON7I7vJz/5SZYMUEjG2ht3XfjWt77lWaIhfNQ8mF/cdTVrqgZrtsrTR981zxtNLZX47tV6kbMM3khFtvsDWxM1fdcRV8CiPMRUJbI9AeWBD3ygAljZ/JR+zaEPb0zKOO1pkIqrr6leFyEs4OuRMtovyxzZTshh4CiQQTfV1S/8fhqQePXaQN713YNeEcwz0F5hOLzFvxmy9nbpALzRLV12cYMnQ1rjhaJeVwjMBQJDY7//X9ZIyyIlQ+qwJJG0xNsZff7zn8/qvu2220qPwaKeW+1j2ZW3ynLDpGzh691lc5bqwyJFlbGc9e6u17+SayX11tQfeUHINxJ14INuJRjdnrrA+lbhXKfQs2SYGZZ7Oi70CQSylUs6glSwi1uAJR8w5OYRkMqfhu/dkkyMmQTTtLdY5XfddVeKPk8FvKUZSdGWD4azIyChi2fdJXm0W/EAYE5I0Ht3bRZgmm77DTeBjDulAkwscsK2u8zaGJ08ly6jole96lWqQm9oku7L3q5rrTy2Q/+SsiT1WffDWG1SxflAB7lu9gpAD1MGHDIisUkqFO1XTGeTcW777bc3tRTAnckJ6xb9OAIQQcdmQarl9gEu4gWm1yQHPw4++GDyqCMeZjjjCpdJ096ooUzdkWKuOz0U8LhJTs4wh70CPueff76aiWs8BrwOEzNctUFn2/eIJElpFXuVn08St0sbY7sdhadtUFIb71QJcpIMXho3ckwDROPj4OIVcMD6Pazq30KgENjgCKBsK1gPhOHmd8sQE6J1hx7jgUGZxTJHR6d3WqARSdM/UhIV4SpOSRZuNayWF9IsiE4UL/oThuvZotE8OkF4lMK0EzUiPyXRG18Gy3euU7yQFo8qyiWRCPtmGxhtUOttRcu9rZIzzjhDNlLpzpJlNR+qHuRRmo18CHe1NzRz8sknM6KQonh+cWj47Gc/6xHDR03cbbfd5Apz0dslTcfZ6I0ckGq9Dm3Qaw0Zaczj6O0973lPey/KiUUBv6qEh6D8rWQRdI4FcRg9eOzoeJ3tGC3XEhjusssuJ5xwgpSy7UFCgM14mxqsO9opbyxzEbM/tThl0LYHNc/cszuuXx6HGxyIKWQdLGg3pJ3gQKXaTODgCEKecFdHoi5HIfZGlZMStB9ny2in2gZ4pJZm8FBVs0mwTu2///4mPxg1MrKUT8ok1Zukq4Qw9fPIg2pkPj+ZVhKAMNFlad+ICBpJZMxdm1ZGE7aEaSKIPLAEgjZeY6GuAoVAIbBhEZgmTKzFyIIlFzW2lnGSptIldYssvrHQW+BkBZV+e0Zs76syxpGJBiPTRXDpCkDdMs2mSkGkHdLUiTv4nsTDeJDM5czyXLfkPJV3HAFQ7CTNY8ruZqyHvEc8nqSl3Y+a0RW2du5AYnIPQj5NYjOgqT/zmc8UioCean9EmlEiBT2yW0B2XW9HmThbS2iovVdgdN2Uwpx+TFO3AeEMJCX1q1/9avTIpRFOB+3UsFfrPkYnA+2+++7tKczHYOAum4F2ai1SJA9JI4uYYaiPFG4KOm0YAlBCh+Hgofv3mmQSwgFlDk5CdyHmdczvmFh3/GXASJL4sZ8lPAa8i5SA7P0WEDnTCIHjwAMP1H4+evGtA6ZdqiSnJw5CPtsZea+7KtG7k046SRbjyBy9HZOxLawChUAhsAERWDapW17Z21/wghcwJDJs0qu6bl9WMa5hFDI6EHqgz03nhTSzI2FrnP2cmdQS3G0kbgMIqmvJy9vdyAHt3xhjw0O+x7bsX2s9bFEyOsd50GOPxTFYf+jjPYhYyBkDkCtNmhLMBM1eEnqweWw4mKBxoStGxId8EPdGladA29Cl+KKZnvUlDVaY/ZlJmVcgYY5+rMuqmiSYD4WVJd+r030Uxf7c3fiHAFZj84gVJEZpNcc7PW/voudK771DBQuvW8JW5G56Gg0e2s3WsvQkXEKIUaeOkMYgma2E9FeD9aW12b/emLZ1Z4J/DZZIUB5nhGefJxAYowVzOJ3Z33g1rBCYawSWTeof/vCH+RizfFrNWQitWV2VhUu8RR+dUE2Y+zHTXKMz2HhyDLc4uvXHP/7x7l37tTgSGi2yjUW/EXa35CgyIC64ZdeDckafY2GmttpRplVPsn9B1GDWZq21rUt5ZbDlDR4u1CR/cVXOmKFVXIIhQuo+aXOj21FDhqsIGRqmNkZjajRFExeiYfQ81tddB71CXwbFhbxRAbWRS6J8e13s2L0jZz1zSJ4F9SjBYgn2jQ985M4IEMwnTUhNnZqhWOP+vK5tYg3FylP4mCGBqNREhIgvMO9ycxP4Yv5ptcFhn332cfDEj4iZAeannXba0qf1FuyHVt0pBAqB6RBYHqnTtNA2ny+biJYbFt0u39BO7PyhIsuQu4P22+maOFNPWW05dbOIYk0MmrbBxIboE5/4RJ5cTfVEXRgCYXiE4ju2F2QFXmD2TVEsg7BXoHZ7xhAey5cqt0nPLoJObIvQpPFiyMPjqBdf2nRn33bFrvbHPvYx7njs2/5VebYDdCfmX1q4vfxelKJURZphCmao0DWdIuFRbc2KJh8s0U2Gd8Zzb2eHV0x7eADYx2mPxHFdOAXHBV1UM9c8lg+mi3jdL1E5IwScgR9fv0k+Os7BUzc//elPK2+327/OKYAijzOPI3i7+JRsyEBPgYxFxtTgDjWJQ56MpSpwcYwgSyWAj0GBOblwbPNo+X5BINpzzz0NqGEtNX0saFWgECgE/nehXhoFKymrskU8q7YVOZ4+VjqLFF8hW4Y8syx2LnKbstvqi71SDkfu+ig/yYo/R4OBbvmaoajXvva11tw99tjD+sv0/eIXv7jZWkOBVFhX8JbdCuZTSzOg6FvxeLJMW/QxYlRMm8S8rtAeJ3ZKMAcxrgm0va7YhBU8Tv8bhMvjmICDm7blzAJnLsWQkP1afG8znqMDHtVa3GmLJG72Cnipf1lfOOfbcecgqZ1tHyFzwHtdVJVqNYMTO986GiQmRtJGvLEOAou23aNhPKd+0gDPL5vxTPfe2HUOIHkosOWWWwrhop383VTC2ZCdP9bpNCOKu0kFuoYeSci4aIkjAwCPTcJf5eNwHsTslwd/V9QAf3ft2TNxE2U4iHh7O2UHJU4htsYNBysIaUbhvJ2QwRHSS3nzwby58oHIj8VbkL1nnbIjmuisHXFOiBBzZiGWAIOYkr73fmWRJzTePpdH+Ek4J2k+uDhHP5NqaiFQCKwLAsPPqXebYrHGTBY7ay59hQKKz3Ia2+LortWNmZ3WYo22YPFjiotWTjRRQIe6Ka1Lb1d+Tj1sjQ71ywoe1VZ/sZHDXT2VmuMV0sqeLsQACC5UIT6P7Qng5Ag4D7VsuKqTmqha5OFBjnKkga4uaKEHNYM8Gu4BaDdEnUZEH7WHtGEgUkYDvAUfYxE108v32muvUH4r4BGqpwJayN6As7F1K+CiQddOkoFXaxuK1Xh6J5aiejItNPlDI/WUq7k6exsH6SCqRmYAwXk59dc+CnivGjConXUHB/gHNOOH66ai7muGJplsHtcGcw96WpVTbXBIaIQcc4/ZIzZ2RK618AejGgguT33qU8leXNJA5NyB7Y/mUqd+bYA/Itcqt4gXQIivO7g8nj0Cp0oSbEdJ+KjfnNce5ZVxUpGg4EUk4DZw+qKAkhT6+ADqmnMNoFO5V2shqwPXDUYIo8lnYsHcU9ZlBaiXFgKLhMDQc+p3ZGmja2IRp5lH9bm37dcrtvTdGcHxa1/7miU4x7XrMwUCMzLK696MdW/AFGNXjxQChcCCIcAhWoiU3lbpMvbUl97ZnWTfd8EA3YDdmZFRXvdmrHsDNuDcqy4XAoXAJAgsg9Qnqa7KFAKFQCFQCBQChcB6IbCJ+Z13kqBmk5w5Xq/mruS99neZ321McjJf1D6uBJ96thAoBAqBQmBeEMBovJ75+fbM73eQOn8iPkdcuBPgbCE/8YIu2+lCDm51qhAoBAqBjYMAX11ndvjKiZLS7fUmpM6Jl0PyApN66HzpE88bZ05UTwuBQqAQKATmFAGkLuqooCMjSZ33u2AyIl30jkEnlpYjNw7hIMVuXM9cDCK9W66nfMOrFRh6zHoNYGWscLiI+d2pqrU3v3v7qCNJzlmtpD0ZIGgvcHySoKeDOW+WuGzTWVzWfR6uwVSvVxQChcDCI+CgrEwcAsaMNL8jdVEyWoarhoiYrxKEODHsnK6LQqedeeaZOY8rS6YzwSmJVARjkdoLZztJLM5Ji8yVAh6RkUwEEoE4pluOVz5IopwSSlok15VXOHkNwoXa/xCfBz6t+744JH366aev5JSdmDNiiDpZLmDA5O2Zr5JyxkgA45y3lDBaLrTO1IiJV2MOO9Bvqi9SqqH5GtBqbSFQCKwcARQsHPhSR9oG7dKuiPItbJYQcloguJXUXsJ24nhiAq+6lknamiuel6SZElm6ePTRRyehSPugLi0Q7Gwdrd8t1PbK0VxuDaK+iMeHiuQlE4TO33xEcOtmUF1utVR8EWOkgSE0rCOwy232YHk5bU0PEA3thTAsoq+IOSMnKV5f1g6RCk1IEmei5Jq3YsKIWrNekuXKsaoaCoFCoBCAwNDVcuSRNqqMkOZigIvFTbsVmYvtWsxXYT6tj6KKyhz1hS98IQvl7bffLuq7kGq4XFxPqTPF06ZdBXdpQAXvlNCTst5N6bbRRsUWiA0OrohyrvBd8PEFr08erryLWBtOpmmjM2GI+O4kmCkhgEX92muvxb5DuVa4OqHrhHJDyT7LCpiaGO9i7iYhisf5hIqQOEmanI02Rau/hUAhMO8IjAwTS7MUQztRxC21spXgnkQGTcxw9nYcLwCnkJyUcuus0NkxuVPrETm3OyFR/St9pM185n3fBStVw3qtp6sSJna6IfdqeIo2mhi63Y/tYYHBFeDvIKsKYYjuzkrMQEI2wnY0VEnhgKyYShSAdmKzI2ZoS3wiCqmjegrYAWGNUZudEXxGlafHO8tn0yHhVIW2peyK6as8thOlXwIVacttBHRtBqp1S9p15n2G/aGWauq1OO2a7Y2CnmpSo2RynsddF/RezNS4YnidUOpumVco3Bcyoo8tCYXlpEn6Wh/9slficfmBzCVlOHmaga4oQ1O3kUGgRPbJJ6u/uimkq3/JjhE3BXk104KkjrjuCrFSgnmVExHEbBdfndjqiu63qOwed0XYdvtVbAPQ1rvWNfV4xEkSDTMWfg4VwHW6H0U9VQgUAitBYGiY2JGkbgmztlIlrX14wjY5o6Vt9Wyi4yHLq1QuQnNbKO2Us2eG0an4mMla7FauWGqp9ejfwmpdnjCX6Eq6OurZdSR11MKYgZAIQ8jMVgW2k2JcFHRcSGvH34zPuApKpCiki4qQNCSlBjn++ONxnnw5GNSHyV1w9WQmDanLiYeYzzjjDOCrSuUIT/xwr+PKcOmll9JNkz0FhUv+huxZVmRkkX/9s5/9rJoRp8FN+HRvF31Qjl3cprwh5kLRoy7vleZEGQwdoYTIElFD797ylrdov1ux9JD8qMgiusvjYjpJAIPUJXkDiIDnyF4WGc1WpzaLTm+CgUseGswKLm9/xSteITo6c5F0tD7eqNkEFMKi+kkeMExMfnV6u8fZRYihNiZs/WiJtwvwDhbvMnu95aKLLgKOW1qi2YQP/G2klFeDHmkkWQEaic+feeWHxK3ELYDjdWKEjtf2/Ob4wVadhUAhsAQCQ0l9pPndEmwhjnYVO23XWkt9saghG8TQW85wgJVOvpCWsiwOXEkGumE3MpPtg15LvmkfpnhXwELWwXPys6FwGcNIS/RyHM9oTKeENl0Wvx511FEYSB42INMU22CrHBW95jWvgTktnKIZX0Xcj4llhSFp0SnjY682Q0az9y7HHDGc8UJjdNZwm4+dFyYBlE+x5lTBYYL80Z1b2kz4QOp2VTzugzulRVHGWzSbbebmm2/2+Mknn6xfeRzFaom/vDGIHYcffrieIma8yIuQVZzHn8x+oU9dVtizmoGtYyhyXU85JSBUxzkkJyXQpGYFInZAg2jSTPTsHEoSSWVBlTaXOq5+05v8pMuaqvFukU6YQ1JVUrGRSmEFGVBIrJL5T0gi7qhE+wGoeaSopN2rTyFQCBQC647A+NSrg02kIb3+9a+3TPNs6p5wCxnkIkZZ977NVANQHeM2EqVr4mm6uC9f/vKXIYmKsAgKsTehzQiJtCS6n+9MHbKKoT2iFW/t5DSzfyG7F/21dRCN0XGjMob5fKGSxr0RqzG3YEF0SFt1kSneBwfjZq1yRXk8p2FeRPzCYepku3bLfoFiZILuWUQU7opbyQXnXXhRs30nSWA+Wq/3+pfmzbKtfE7ceR0VmeatDLVeByPnsVi40hK2YlCwsBXFut5O62kDpZnI4qLtA6neTLkmbg4VGd1N5TR4T/lXv7zIv2SFYO6LrSJW/fQR65NEJaYzamQFWDXJ1bNJZ8cCYVzYn0g2rszUZKvGFAKFwIZFYNmx32knEkhjI+eCullBIUhl4SUndySi2rCALt3xpKMdWiZnr8M6LQAAUmkJwVoBdnJU13J4ewSZMREjnqae+oLdQ+pkLK7jDM62wKmetEz/+qtmaj3lHvviNto/xsrHLeZrF93CwYZb5V3KxIgkgC7dtk6xWiPRplhjcY3xau1XLdGBBIAF07UlnPW8UTeTirT7AU6AMv1IDBFilvXRkSRW18jMYX99R9LRuckTBA44p4XdRgKHRcQBEPsdtjNYs7pSxbKaUYULgUKgEFh1BJZB6pZChkq0bV3G3zTIbms4zTEOS8Vtv9aqveoNrQqbfzutl3GentowwTq0SVRHF89FX5BoK2N3mapKNWcPR2DUa3cNKKO03Wj0Fu8zh90NrlvexVKNiV1UlS/2U7pWGaxM224CR3d0cCGJQZ0xYtOJVRI3vbGDOMnuTMQOVSFmjc8uT3g3j/s7tJ52UWEtRNsUbpWkKg1ORva22bSEwGFHwyiwZ/g5ONlh62SJwmN7XQUKgUKgEFgtBCYldWqTxZ2JFU+wIfdOYVn0bUk6ci0gXU99X62GLkY9OGOQbybRNREqZzcaMxyo1HzKsGyP1A2QHXfGAOzuC8Zq8X/QFQu8fW5GeLqv6ygWK2sMZqJJe2rPPffkwqZOg6swSsZb9GyD64wiFb/LWyqkx1P0Ew1GGdo/zziaLrLUI7sDthjcsvdMp1feFFqC+dyKANE4deiIkzl4rkHAXUYjzoPs/HrBVRMrc9BznQ3DLcJEg5pVQM053a4Z/kXqPqQcYoGLWms3Qcfje7jEZIMJnwZEriV2B6LfryQg4GJM7OpFIVAIzAgC40kdQ1gNLYWO9/Aq4hXM75rZk6c0hc+qqie0c7o7s6RN1uyhOt6GeLqdtPBZZ23ZzkjP174ZmI+LnO1bPJSP7/ZuUSliQKKhE/wKqMTfDRvlFgrxOJWXexcC5laWLhggfEZcED8Al2efmIf8SSedlA3pfLbZZhvDp6r2IB2XELbffvtpic1pY8ftMYXZlmnqTjOqjUvdjjvuqM6uOEJXtrdNYXV83OOqesADHmBiRKQTm4izvY1/jztAISJhnC41tc0BPdIYUyuMyMGe7efggw8WAsFWQvwM3A0mmYQwoVIjY9vYcOAPv++++5ppCjAO7bTTTpz+iCwYV/lY+N3SbOILOUarOHzYkoCwjweFYdBH7U8sxZy69KxZGg3ex0sNQaNt0onwSkQBL/ITIODypNOYSWwMaz/l6o2FQCGw0RDYJPWqtftlL3tZDwKLKc84/E1H4RznX6teNhopZFyWEufL0SPrYDY7/UUn/Lm6G6J0OGoipRDrr9cK6Lg2VWzqCKMrmRx4gvpIee1GaIchdgQU+61WITZkRmUENaaBIdAYzOnNKAfv4kgc419Hs1i5tUcNKIe6bOdbeQ7ztHkXcTCK6sYDoC7vtttu9GlVNWuK+kURiNCGpLvIaAP/cAXiuDfUF4ws4mhZHAXMBKzcRhbUjlvgQiPu8ZyAV9Ic8J0o4xZ+NaN4/9F63TWLqPUkQn3RQlhpMzkDJlqoj57C96aZzQL1I3J3W9BftZEjFdAM4HjK8bY4uGmeW97F9w0yqjUKpjS3QUq/7QYNsE2e83iECSX9TUQBfXSagENci5ukvCs+5j+OdySv5y66knlSzxYChUAhMCECb3zjGx3GWSr2+1BSn7D2uSi2jqS+EnxwkqFBHjTLqWPKsr1zYORjT7deSWPq2UKgECgECoFZQGAoqY83v89C0zd4G6iGsVr7u1woCARUWFZi4WvoqTzjlltDlS8ECoFCoBCYFwSK1OdgpFjRhellLp7OCZG9mvGZFd1mcI5l16cQKAQKgUJgIRHYhNTXa6t7LZGdxz7au7URznltiiAn+iusiqACQr8Nhp1fS+TrXYVAIVAIFAKriMDws7vtAA+/KqeZuUN3A4et4uvXvSo+U3yv+E/lSPe6t2dZDcjgTX0YuntEe1nvrcKFQCFQCBQCM4gA994TTzzRqbSRjnJOOnHDFo18WcmqZ7CrSzSpG6JkvlperS0ECoFCoBAoBBoCDLecpRwycoC5C8v4I22LBOKcer8v0hBUXwqBQqAQKARWBYHyfl8VGKuSQqAQKAQKgUJgRhEY7/0uLoqgJXJS6YHIJ0QDe9LCdDgcJVpI6xYXa/HFROtkExAvViyadkuID+HMhPQSvkOITf/OKBibs1lil+61117ixoAIFD6+QNKWh1goU2+WC+EiipyAaGKzTF3J5uz3lHWbTjz79EscGOHzZBaY2tUDLCLemIHsVIsE0ZTI1mOFQCGw0AiMIXUsjqoFHcsJaXmp7ckLB8ubWkxQUUsTvwy18LATfUyBc889VxAx3tqionokaUJECX3rW996yimnfOlLX3JgeoG37UfNFpzkvLjAL+Lni/Pqr0zq/sojLu7eSnzywW4UWljTeZmuQrZJ3y6IrOQ0g23mAyLCnYTrvgixJw7u5PHVxZu76KKLBM1NRhnYilIn84r0sivBeV6ArXYWAoXARkZgJKljcawjPra035RvJ6RlvxCVTEIXAbdl+Nh9991xc/JhfPGLX5TATcxO4bWF4BYtHKPbwHZLbFQKvfI+HhTrVIW0/w0IOgxFGxWodauttpLOzsfpc/6JTpBPgUZTOhGVMKWjUpP1au6qquurtjqMgH0FwR3afXfNNNYgXXNgAXTdYku33LMmmIizLXorKVMw2kSBnfyzvvhM3s4qWQgUAoVAQ+BOxxxzTP6hSUvLQf/Ov+KX4enk3pb8CrtbZH0UEOxdAaqhBCEOwonILeSZqCaif2fdROf88dCVE9K0JSG4G3Wp012VdDOHrtl4UPi6ScfX7L1e5NXvfe97BZAR2Lz3XuYQ8pNdCYcTzj//fDKQ3F9Cr1MupcmhiIuWL+kLRV+iNsMk6rjY5onTnoBx119/PfOyK2wAZ5xxhsKEKqfSRa1xeM9TpC4DROvF/QhPGh42f5HkxY1nOLnssstsrximJN/zRsb8a6+91tuFdhdnPrHZ8zqN5HJ51VVXff7znxeirhsRXbIfdhqJ2M0Nc8m7qNeOEWqARrrlCxyEuBfsVkIB6VV0VgE6tCnEICQ3+TXXXONxrXWL1V1h3dFf1vgrrriCrEntRvDKK+CWZmN9Id/1XWOYPVwEo0lL6NRC77L3IYOczQ7iqXpOO+00ZQiXD37wgxMeX/Nchwko3va2t2mSSd7S4RBnX/3qV5vtmmeG95IOr+UsqncVAoVAIdAQsKVob/F5z3teF5ORmrp1E0+IFo7OLZqIB2dI90nV9rx/LcqIPxHKLHMMyyjcd+oXCrH6W3z9i5DsjIbsMY0tUjVHLNhQHwyHhxCDlGXypzlf6AvbO94i92A7OxfYGnqo1C2sI3cq3sJ8HBSQjV0P1IsUPYK5EVUXQPSpEoyIs1EUZmWCTt4d5mvpy5II1UfSF+yIwJA3YUKwOeSHvJXHcwpoz/vf/37vtVeCX7t2b4yO/Ihl2vaRj3xEvt1sdZsPeFTDDLEX6aY0AxECFD7uuOOIHcQUdh3FkLdbSYbmry57hbwpECBJ6JeLsKLEk2A0UiVSy+iXMrgZMgr4MAIRFJL3j0hEpKCdK+xZrfJX44OAOiFgk0LHdUE3tVNrZXtLdiL/murvfOc7JaINtetC4PJSzeC4ACX123j6+Mc/vqGmbnW2ECgE5giBkaTOdBlWzqqaBTod88WCbnHE1vTOJMLKx0pqzbXaPuc5z6EIdoGgY1mR/UX/LbPWHCG1wqYidcwXDbh9UB3ZCJ7sw9iFXwI6kbuFPUMUOSR9zjnnIBWMgmjRubSkV199NS8H0plKWpNU7q7C8p/CH7UzjaAxz6qHg57KUXvSuRK5DK4tAK+2qY+86bgIlTBB7lPAI5iSgnvkkUeecMIJSQfng5VpsWaFV9PUKa+2w0PqXkTOEPNO8zSAqIe8sbWGmR7eTrZzxVPEu+TktRMhJaAGa7aUdIwBmFXCVtwsCBIFmigJlmwrUJq9Wr9kOzXxtFwXPJICalOeFu5d/pU0dtddd7W7wQjBG9Fd1xkhcDx69i4t1E4InHnmmVqeRpq6rAKU+BtuuMGsBoUrniUQEH1izyAq8Q7ZmJ6eK5z/9XghUAisDQJjHOWGbivS6k4++WQ0wz6MAFpDrd30M8oTH28OUN0OkAxodTQhObZFIF+bvs3UWyijTBSIkLZNJLI94cutt956xBFHwBA1oqIYMHAVlPgl+I5QgUktxrI8EmK6R4c4m82jS+q4R2HcmYu+yAoabZ4IxYjCWs7YThNFY4iZ+z2VWrXInvOjAdIGKmwc7uxhM8ygzC6GpAFt04YM+hZbbEEgiMVetSrEl+FXzIdiGQx8R5laorBbQGAedyv6NAnPX+KIW65IpaqSFuwvdJ4ZSEbE8b7YGieakGm6WWW7OPiuI/rlr061W8prj9a2zL++aEwzeEBP5RkLLfFvXs2kb3eA6UKSVqaUffbZJ0NTn0KgECgEZhCB8UfaeiumpY3R2MJHo+otrCyWFD4rIHNrt6sWbkxPSeJDZ02cQRTWrElobKichL1AGk0a80EsntsYzr+hxpiUQzM+uLk7NJRm6mbzJvPFvymDqOyksGYjMNSOqmmxyQ3DwL7DDjtwm9h2223ZySnECmuhv743HT0vSi7zoV7oMQm0fDNpBlkkD3pjqvJsHm9e6N34tZ5C/0OT1kAjuOVAYAjeleYeOIlbu/Yo1iDyIvUEIlWxTGhkvBA0sg3TXe96VxOb+s5+gPUZS7ri1JrNnHpRIVAIFAKTILAMUledrU0nhWh+fL6sv90X2K20Vcy0zqTZy/mN/k8//XR7lmLQZjnesJ9JuGcUOCg/TEPjZKlGNq2k6xwPFWhM7wsipOWnzNOe9jSvtqkcb3PuYwpjfR4SOTDmYy/ZiXBm7ejK2Y3uNobpm6YbycNHMXJGyngRtTiH01zx9jSpbdwMFWVSuNuLLpt2X02aSUnub5qt72H0JiVka7xXW7cGt9gesnmfYlqrzQ2i9nhvjDxCXmGd8iBN/fbbb7dbMao7G3ZiV8cLgUJgRhCYlNStpLYSbYpbT9Fzr/X4AHPbYrfd271lQWSzdYsoQB2ckT6vYzOic/caMAnTs3vjEqTiWaZgX7pGYBxDf0Wx7NK4h5kd7EzQLuZddGUDZ58YKSZ2EKJC0oYVzStATafN2x/xfahl23UO7RidrZ6x3b/EOLsAfMd8VycjhFb5S6HnBICGGbeXJj9MTHteeos6Fnh7N4rpGmnSVj1xIRvtvOeCie0MuwnNdE+gUbiZCiI9QMN1beMcoJK0tkE0OCu8QoX2Svj/x9ugPoVAIVAIzDgC40kdMaMHGjYvJw7DDOl2ZJEEhY8t1BU9ZFq3Q8zPyxLplsIcoHhZe9buO5OvPVFEErcmlt4wwYb66DgucfaAGQMO+bCiYybAUpSbBowOaZBNXWbxdguSjg4K46Me1MvmkWMIPm4hJ8ZkTl50cfqoceGhjQi7m+LC/HkRbhPUJQ8iaRsl+++/v2LGSyAB5+ZTIfIz6D1KpsRz2tc2fnCkAR4VXATstnhEL8wBJ77sRnM3Q6giDyqvKh1pRnsVkjx8XHfXrra7OsJtTbU+DQQl476e6ed4hWKMQ57irpEzFw6kPe5xj3vsYx8LRs53ues6c4JWmX7OWBJBotCDyF2zkZ2D74LCHBpMZoW9K2/J2/2r+zFC6CYPBhPbuY+EGfC6jMKGmr3V2UKgEJgXBMYndLG60YHoRlZYSlic4bPcU+loe1GYrOOxkeaD8q28CIwKSHGM6Tirp51La/TQrdPNjdo6JnQBDnvGYGQ09AAo17ELZLIRjoEQM4jgps1uoV4hAZzvByxHM8TZ9oaNiAFiRlZe4Si+yFW1Xe5RhkOD3fQXvehFbRNEq2yrG2IlnVaI15sxIn8YbsPU09qNYM6DKYNKjX47wu6K89xRjsl2WhglG1maHv5Nd+zrJ/utYt4LE0fF+PHpjmIkv7b7DhO1IWBv9Bb9wrUeNBXTfgKBi+7GtO4u0CLHmHL6q0lq1s5ApCpv8SWxDr3I3XRQAfWoPMcQzHN/lQ+AhsM+usYrTKLtbTxt7klb9RcChUAhMBSBoQldxpP6IqG5jqS+EhgRjK0NOqLDaRh9uqp4OFLl99hjDxr5dDXUU4VAIVAIFAKzg0BlaZudsVheSxKhhcrbPaM1YRW0WLFrmKwPPfRQcXz5b0/4YBUrBAqBQqAQmDsENtlTX/idwnYCar7GyWYwyzlNfQo1XZfZvRmWRYah7ie4bH0KgUKgECgE5h2BoZR9h/ldDlAO1a985SsTDGTxPjZE7ZXy+rZxO/Sw9Sx32V6vPd12hGxZTU24e8NvmzzH3+tTCBQChUAhMNcI8GYTO1wIjd7ZnDtI3fEk5lleVJyM5rqrSzQ+kVXawac56maLrTZdmyPQ1enq6dCrpwqBQqAQmDUE+DWL9uaYjwO93bZtQuqOAAnH3SN1fEDHbVFBfG8e0Yn40apL5O0WEqTdcqXFh2/RQtYFIM7eGtMNbbuWzQBC/P/X8qVLv6s3uOvSsNYG82rl+MwgyOuCar20ECgEFhsBpM5RLkHHh5O6E1P2XF/60pcOAuHQkcNpIWa+Wk74+JIkGd0jT67nwJs9YLu/zdyPxggK0Y/VM1368FUZHgeotG290snAx1msluR7VXq0wkoMFhc8h75GBZxZYf2TPG56aIMNApPHpGpn5CZ5drAMkFWyLgcmp2twPVUIFAKFwHQIyL4hCJhoHN3HN3GUGzTPuiL7Jx8rkTo85tzw0UcfLVKYj/ge0nK3RxwydlzKdWeFn/CEJ0jU1l4juKzYJnlK/pJu0PLpejL1U+2E/dQ1TP0gF/RddtlFqrHNZwMfFWN1VJu1RAQ6qXVFqmlRX6bu4NgHhzaPqCdujNPzIhQ5iy+tgIk0tqpuga5yL9SdmIbSvc+UOWRZ3anChUAhUAhMiMBQNhkTUQ4BiwcuPkn2dAUgE7MMYfM4k9mCSzbKd50DlzBbYpW4K5a4GCkWaIfCtcxi7TyVMLH872TMFHuEWDCPu9oTorxEsc16uIDQIF+OJKHLFRo2a6saGoK3CGkndWmvebTqhzzkIaRDYt8U1gKyyMc+9jFx5UxRNYtLI47ewQcfPEVVKx/fqqEQKAQKgXVHYCSpM5i/7nWvE3IrebEYRQXNFmJTbFHhOenctCvhxMXh0gdszezpDLQQYy7S1BUQusstCr143XKEs9XL2inWKVvrRs5IPZZEl8vKmUMMzjzb49Yw4azKabcJC6+wmBeR/Ow+DDYPAbfNHe23FzD5uzxrZrLbqyE1e9FMbXBM3pcqWQgUAoXAyhEYSeqWSKG55Uc/5JBDBNREGNiapn7ggQe2tyZqLLVb1q+zzjqrpRjhj+ZWTk9ZYRPB1HfqlEiipASflTd97mqIj57I7aKmC6J+//vf/0EPepDUdgQjyVTIQ3vvvTd7uGL2SIRVl9pO6Nb73ve+bBsBk+FENh158BA/JVW8fUnWSUicHqinEr2ICa9mYpPCcro40UCKErecNNbgIq5xrxC3VZ6S448/flTevJtvvpkQZg4Q4z784Q834wrLtgw9ZDW3JOUTULbVrCW641Qks41B32qrrTQpzTZteHM897nPPeecc1hrrr/+erYcyUyFt+Po4bq5gdFZd3bbbTfPbr/99p/85Cdbzcw/BxxwQLaO9E7MdrYfmEgiAAHWIFHrvYvp3hkP5gqtZRWAsMzu9heYMVpVLEYe5zty+OGHi/lKEhV5vglSIsgedNBBwPGgmhf4JMjc/XyqwYVAITAJAiNJnepD4ZZaTQjxeL9ToSjuiV6Cyy30qJrWbi3mFmcRTNRudC7xl/jYltTWAmuliGbPeMYzeMkx2m9Y66iOozR8I4PnK17xip133lniOxyPw2wGS6HWWFAObxIV7kF+vlx88cUiyhkF5PeVr3wlzgEIFdQ0YAMhuSqWZeJGpZzOkhzPCB555JFEqBNOOCE2FR+1eYtRcHyRZMCgoqqeAi01yxlnnIFxyRPahkG9N49jxFtuucWmDFJk7iZMJOOqD2EOQ4tHy5Z+1FFHuWt7O454+NvkYbB59KMfTc6zj87TYsstt9Q7ndILxUg8rhMQvc40I3lwbEzN7EBuxRtDm4mG7EaazedRPnhB2mXKIWeoilSEwq+55hruC1qu+yQngmk2g3yyi09k8ewxxxxjAmtkhB63pKVRCbmEeOQKEWFjbhVNsnZUmUKgEJhBBEaSOqoOSbdU1k2bweiXX3451pG9ivrePQNt2cUlFlxH41jgW4exDr0Q36iNrX4GgVizJoELndi84DcnljucJV+hnkKMVt3SjZCT+Cdi+pe85CVbbLEFEQq2aI9i3XTr+Iqr0BX6aDY+ZDMLTfJvIDfwXkRRzjIaFH0kB3zuc5+jiWJrhgG0lxq6Nn+6LNrDncZ3991395fwkcmAetVsJ4W0QSxA1ba0MWIc03KgkWsF6Y2yTiLUHbKIV9CkvVSPcLZ/TQMhgBRjokD5pgdxxxshAAoSAxkFN5N+Mi4xs+f8RfobSz7Z0VtsDzm4Qb+Hj2I0eK4e3kvgUBWQeYG0xKxppEcYMFgIyBnErBzo0AASifZoPOTh5pDn5NsZazaF6kWFQCFQCIxCYIyj3OD+rrWS1kihcQSOZ1bTua19NDZn4elAGMun+0o0gMOYjq25Vmrm4g07JEn/xbIdFqFGi/8a5sBzviQzKeJvGejp38jY0YNBlbrBmMyh/rriL2Lmm0bRpIxeeOGFCD6p1ei4TMrMziFIFEuk6I0FIzaeIyIkKi2SIxlQu31XiRF3KwfP8DSVWvmQuuYpzGaTzOWoWsyjdK3bPIXdUiZpT9scw/RU/Bw4VLN9B2r30vMEmGr2N6ysKs0zD7NbFEEE2mojeWQLQxlvZzZQwIPMBhqZaUxQkCMYzgQpuCF4UsiGtSpt2F9odbwQmGsExudTb92zOkdHdzDOTiQ9pttzSyHHYwol9YtNdSgoFlPqlGV0I5M6ZHBJIxgMl8P9rjd9N9zTjplFtU36Wrda/J+eW1x0UAXyF7fZxmYPp1uTD+5xj3t4kMTQTC9546Dc5kWjosmmDS2UUFqiwgy3W3jRxo2/vvcqb81TMg5xPt23K9AeSRSEZuyJNBN+zZeeAt3+9SXp51uIIYXV1jU4uaWRZmO3kREI6O7kUfIQ0ZObCLNTmd/neoGrxhcCGw2BSUk9iyb/JqRuW9R+Z89x+qKLLrrpppv2228/Wk5TbiyUjMY8nqJBZg314Jo5Xc/pcEIbYtkFBxqVkW6N4chDAKSVYhpEhbYboSqJzMJAYVbB/O2d2/+2Qc7DkRLsVtKBt1ABNPIMTfdD+SZ7KRNq95dYEIc4arTBdRw876W4u65wd8QHpYT0wiNpXuZSV0fP2+NwEC8/nnF28RkSckvHNUPs+rQnsYxaPU3ciVjA9pDs7KmKk4d2sjR0veIHIxt6Vs18EWxhYHSTlmWe5WlRUyHM6U+jml0IFAJLIzCe1MPEKIT6wvDOJMsabM3l6sWoG7OnPUt+SQzyrMoMvG7ZEE32Ebuzu+6667vf/W7uXXZ2Ldw2fQdNvhthnEIkPRW2q842pqFKMhdfeumlNsKp2jgG0yBjHnBMygYCVXP55g0e/EN7eA4nuYvU2a7x+mWXXRalk/3cVrpiNrx9DBD5zMbztddei0e9t6v42hHgio8UuYXncdvSxg53slTjdbecLPcuhhlXGNIbqXfJUk+bmqt5Htdme9vh4HYr3/3Va9USDTXMJg6pQi/SO3IGcwUoOGQIlcMsH4Ej++vI3kUijrerigFAecSvqvge4niNbLp7F/NuS7yR0yJvA28xb8krzBujTgdshBlbfSwECoG5Q2A8qSfHl4XSgmuJvOqqq+R9sW/K2Rg933bbbfrM2Zg+JBwYT2O+Tu6y92J6az3nOAulSDVOZ/mOaThYzR1MK28wKGCI25BTO5adi402cI9PzhqQe9gzqKpczHi0cRyLeYNHGHLi5MVJTWHnzUKodo6NC3MxTiJRif/D1cuJA7wIcJq6u+mFI1t28Ulm7MwaQ4OPtbzbRy7l9lDYoo0yZwiPkypCiobSKGuPv47nsdDgyzyrJTFr51/VJjuc7zawH/awhznwJvgBRT8zKsVyeiJOf+TC8847j2hIXHCorx2gUF4II9OMu+Vpp53G3kDsgJI34l2N1FQSTDztwcK/j3Mfnzs1mJY89h0NyOu0Jzi3Rnq7eiBAFOD2weXeWzgnsk9ozBTpblc+W6qGQqAQKASmQ+AOt2frKXsjH+zBipoml03f7ie7m0O3ZtvOa3TTtu6vozuxc01odb1yijcYs30bQHIxmLTvDBvIEiXjZqpnbwu5oZ2nulV1956XgL3dUkN3j7w7spM8HkruPtXtTgzsQ5vXu5XdhEySiBfBpDtVum1uc6lhmBdlKubBbvsHG9lwa4ao1osuvOUlN92yUk8VAoXAGiBgH9wR5aViv49qRJZLH4tj79N4ZfBWq6374Doy+hpAvPQrGoxdqugSdvveiC2s2QMtnl8+vvSqysU0YwnY261UNbTZkzw++Gy3O2oY1bzerdST7rSu9XrdbfPQjrepmC/d9vc62H08xboFum1Y9zlTDSgECoFCYFkIjDe/L6u6KrwqCPAps1m+BklWVqW1VUkhUAgUAoXAjCBQpD4jA7FJM0SSYVexaT2Ljas2FQKFQCFQCMwqAneQOpMvd6RZbefqtKvrxrU6NW6eWvif819zUHvzVF+1FgKFQCFQCMw9AkPPD29C6nKBJFYJh2EHhETZ1GkRwpmCxSZzjsghdd7vDibxYRZAJvGxHUxPSjeHpnh4cUJ2EIu/tINGnvVWIUXVpk5nf52PEu3ckSoRuwRd4YmtgNDcnOedCf7oRz/qUPX555/vyJzgNqzQCvCiEqmUy7REJgKGc8KXFMTJKLHH3VKAmVqadk7LCUuuC1KhOBLG0YzTkwKOPwlFrs5LLrlEAcefTjnlFNlHeAV6O7dqZ7WPPfZYB6PlAnHKy3EmXtDC6XQRkPvEIS6Bd8THdeZKNHW+1l0EoOcolGNgjqJJNMJ5GwIqBxEEHJeS0N7BMGfJOME5DehQgFN/DQFnqSEgXIzTXJyuebCLW95FwEEy4VAcN/AR58e/QYCXu2IKcwMU6c/jKuEi3kXAi3jFe6nocjfccAMENEaTNKwhoMFON4BIF5xc4KnuiLbBzRzQWSHZv/CFL/B455oBCoC46FbmALg84kE+6ipRlQoB2xBwQuzkk0/26htvvFEzHHE0KIYmCBgsDTZwGm+MdMSACnNrcDMHdNbBfR2/9dZbHdw3GSBgYmSIIWDCAM3kcZJNqhtgmlSmlsoVgADAndAz/XIMnS0EAhlijTRYJq2pawKbxiazKW1iZ4j9NeENuqE3AUyDIOCnEQTUAwGT3OQxhUyk3iQ32Uw5E49Xi7yFF1xwgcOfDQG9cKzA8UIh93n+O4bnIEMPAZPctHfdT0AZCCjvqS4C6lSzH5G3iGnfneR+bn50WuVg6nve855F/ZlDwPFac0wA4/qZ1898gX/mlkEnkgYjud1B6qJkO41mmbP8WZ6sFEmQamW0LCYUiUXcR4GcCXYrBXxPbM4U8KVXwKKsTg9aBDXCEmyJV21q8KIUsO5YW62kgwUs7jgGSSQGqvIJh5IafFfAgx5vBQbbHw8pBdK8XgEv1bYlOqj9rYMAGUSgdRACCnQ7GIh0cEIEdKfXQV3uIqBAa38wbAh4CzCV73ZQbemgBmSIewUyxK1ADyLtH5wDOphX+Ns6CKKlh1gDMsQNotSTOWB0DKKetjlA3EmBwTngolspkA5mDgxFwOu6BZaYA4FoEAFXMgd0MEO8rDnQm+QZ4kyShoCOt19BG+LWwSCQSQ6r7iQJApkD2p8CbYhbDd0hXsif+YRDPO8/c+NbP/MN/jO3+jl56/huz+Dwf0farAgb2S997q0w1YFCoBAoBAqBDYlAj743Sc+1IQGpThcChUAhUAgUAguCQHm/L8hAVjcKgUKgECgECoEi9ZoDhUAhUAgUAoXAgiDw/wNKT4ScYz4towAAAABJRU5ErkJggg==\"></p>\n<p><br>Grants can be disaggregated by source as follows:</p>\n<p><img src=\"data:image/png;base64,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\"></p>\n<p><br></p>\n<p>And Other revenue is disaggregated into five main types, with additional component detail as follows:</p>\n<p><img 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\"><br><br></p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Where the relevant aggregates and component detail in series disseminated by the national authorities are found to differ from GFS due to unreported revisions, the IMF Statistics Department will solicit revised time series in GFS format from the national authorities.</p>", "OTHER_DOC__GLOBAL"=>"<p>The GFSM 2014 is available at <a href=\"http://www.imf.org/external/np/sta/gfsm/\">http://www.imf.org/external/np/sta/gfsm/</a>. A series of videos that discuss the GFS analytical framework are available at: <a href=\"http://www.youtube.com/playlist?list=PLmYAE4wV1YQzimXjDWMQ-4GwConTsP7-a\">IMF Statistics E-Learning Videos - YouTube</a>. Although not foreseen under the reporting of 17.1.1, analysts can also use the detailed IMF GFS Revenue database to supplement this indicator with measures of direct, indirect and capital taxes (see GFSM 2014, Annex to Chapter 4).</p>", "indicator_sort_order"=>"17-01-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.1.2", "slug"=>"17-1-2", "name"=>"Proporción del presupuesto nacional financiado por impuestos internos", "url"=>"/site/es/17-1-2/", "sort"=>"170102", "goal_number"=>"17", "target_number"=>"17.1", "global"=>{"name"=>"Proporción del presupuesto nacional financiado por impuestos internos"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de los ingresos de las administraciones públicas autonómicas y locales provenientes de impuestos", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción del presupuesto nacional financiado por impuestos internos", "indicator_number"=>"17.1.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_151/opt_1/ti_estadisticas-presupuestarias-del-sector-publico/temas.html", "url_text"=>"Estadística presupuestaria del sector público", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Proporción de los ingresos de las administraciones públicas autonómicas y locales provenientes de impuestos", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.1- Fortalecer la movilización de recursos internos, incluso mediante la prestación de apoyo internacional a los países en desarrollo, con el fin de mejorar la capacidad nacional para recaudar ingresos fiscales y de otra índole", "definicion"=>"\nSeries disponibles: \n\n - Proporción de los ingresos no financieros  de las administraciones públicas vascas que provienen de impuestos\n\n - Proporción de los ingresos totales de las administraciones públicas vascas que provienen de impuestos", "formula"=>"\n<b>Proporción de los ingresos no financieros que provienen de impuestos</b>\n\n$$PI_{imp}^{t} = \\frac{I_{imp}^{t}}{I_{no\\_fin}^{t}} \\cdot 100$$\n\ndonde: \n\n$I_{imp}^{t}$ = ingresos por impuestos directos e indirectos de la CAE y EELL en el año $t$ \n\n$I_{no_fin}^{t}$ = ingresos no financieros consolidados de la CAE y EELL en el año $t$ (capítulos 1 a 5, eliminando las transferencias internas entre administraciones)\n\n<br>\n\n<b>Proporción de los ingresos totales de las administraciones públicas vascas que provienen de impuestos</b>\n\n$$PIT_{imp}^{t} = \\frac{I_{imp}^{t}}{I_{tot}^{t}} \\cdot 100$$\n\ndonde: \n\n$I_{imp}^{t}$ = ingresos por impuestos directos e indirectos de la CAE y EELL en el año $t$\n\n$I_{tot}^{t}$ = ingresos totales consolidados de la CAE y EELL en el año $t$ (capítulos 1 a 9, eliminando transferencias internas entre administraciones)\n", "desagregacion"=>"", "observaciones"=>"\nLos datos consolidados de las liquidaciones presupuestarias de las comunidades autónomas \nse toman depurados de IFL (Intermediación Financiera Local) y PAC (política agrícola común).\n\nEn la primera serie se consideran ingresos por impuestos los incluidos en los capítulos 1 \n(impuestos directos) y 2 (impuestos indirectos) de la clasificación económica. Los ingresos \nno financieros corresponden a los capítulos 1 a 5. Se elimina la doble contabilización de \ntransferencias entre la comunidad autónoma y las entidades locales.\n\nLa segunda serie incluye tanto los ingresos no financieros como los financieros (capítulo 9). También \nse consolidan las transferencias internas para evitar duplicidades.\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador 17.1.2, proporción del gasto presupuestario interno del gobierno central \nfinanciado con impuestos, respalda una comprensión de hasta qué punto los gastos \nrecurrentes y de capital de los países están realmente cubiertos por la movilización de \ningresos internos en forma de impuestos. \n\nEl indicador, que puede derivarse directamente de las series de EFP comunicadas por \nlas autoridades nacionales al Departamento de Estadística del FMI, proporcionará a los \nanalistas un conjunto de datos comparable entre países que destaca la relación entre \nel presupuesto nacional ejecutado y la administración de ingresos/impuestos. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.1.2&seriesCode=GC_GOB_TAXD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción del presupuesto interno financiado por impuestos internos (%) GC_GOB_TAXD</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos de Naciones Unidas, pero aporta información complementaria. Mientras que el indicador de Naciones Unidas relaciona los  ingresos tributarios con el total del gasto público, el indicador de la C.A. de Euskadi mide  la proporción de los ingresos totales consolidados de las administraciones públicas  vascas que provienen de impuestos. Por tanto, se centra en la estructura de  financiación del sector público vasco y no en su ejecución presupuestaria.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-01-02.pdf\">Metadatos 17-1-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-05-20", "en"=>{"indicador_disponible"=>"Proporción de los ingresos de las administraciones públicas autonómicas y locales provenientes de impuestos", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.1- Fortalecer la movilización de recursos internos, incluso mediante la prestación de apoyo internacional a los países en desarrollo, con el fin de mejorar la capacidad nacional para recaudar ingresos fiscales y de otra índole", "definicion"=>"\nAvailable series: \n\n - Proportion of non-financial income of the Basque public administrations that comes from taxes\n\n - Proportion of total income of the Basque public administrations that comes from taxes", "formula"=>"\n<b>Proportion of non-financial income that comes from taxes</b>\n\n$$PI_{tax}^{t} = \\frac{I_{tax}^{t}}{I_{non\\_fin}^{t}} \\cdot 100$$\n\nwhere: \n\n$I_{tax}^{t}$ = direct and indirect tax revenues of the Autonomous Community and Local Entities in year $t$ \n\n$I_{non_fin}^{t}$ = consolidated non-financial income of the Autonomous Community and Local Entitites in year $t$ (chapters 1 to 5, eliminating internal transfers between administrations)\n\n<br>\n\n<b>Proportion of total income of the Basque public administrations that comes from taxes</b>\n\n$$PIT_{tax}^{t} = \\frac{I_{tax}^{t}}{I_{tot}^{t}} \\cdot 100$$\n\nwhere: \n\n$I_{imp}^{t}$ = direct and indirect tax revenues of the Autonomous Community and Local Entities in year $t$\n\n$I_{tot}^{t}$ = consolidated non-financial income of the Autonomous Community and Local Entitites in year $t$ (chapters 1 to 9, eliminating internal transfers between administrations)\n", "desagregacion"=>nil, "observaciones"=>"\nThe consolidated data on the budget settlements of the autonomous communities are taken after being filtered \nfrom the IFL (Local Financial Intermediation) and CAP (Common Agricultural Policy). \n\nIn the first series, tax revenues are considered to be those included in Chapters 1 (direct taxes) and 2 \n(indirect taxes) of the economic classification. Non-financial revenues correspond to Chapters 1 to 5. Double \ncounting of transfers between the autonomous community and local entities is eliminated. \n\nLa segunda serie incluye tanto los ingresos no financieros como los financieros (capítulo 9). También \nse consolidan las transferencias internas para evitar duplicidades. \n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Indicator 17.1.2, Proportion of domestic budgetary central government expenditure funded by taxes, \nsupports an understanding of the extent to which countries’ recurrent and capital outlays are actually \ncovered by domestic revenue mobilization in the form of taxation. \n\nThe indicator, which can be directly derived from GFS series reported by national authorities to the \nIMF Statistics Department, will provide analysts with a cross-country comparable dataset that highlights \nthe relationship between the executed national budget and the revenue/tax administration. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.1.2&seriesCode=GC_GOB_TAXD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of domestic budget funded by domestic taxes (%) GC_GOB_TAXD</a> UNSTATS", "comparabilidad"=>"The available indicator does not comply with the United Nations metadata, but provides complementary information.  While the United Nations indicator relates tax revenue to total public spending, the Basque Country indicator measures  the proportion of the total consolidated revenue of the Basque public administrations that comes from taxes. Therefore,  it focuses on the financing structure of the Basque public sector and not on its budget execution. ", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-01-02.pdf\">Metadata 17-1-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de los ingresos de las administraciones públicas autonómicas y locales provenientes de impuestos", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.1- Fortalecer la movilización de recursos internos, incluso mediante la prestación de apoyo internacional a los países en desarrollo, con el fin de mejorar la capacidad nacional para recaudar ingresos fiscales y de otra índole", "definicion"=>"\nSeries disponibles: \n\n - Proporción de los ingresos no financieros  de las administraciones públicas vascas que provienen de impuestos\n\n - Proporción de los ingresos totales de las administraciones públicas vascas que provienen de impuestos", "formula"=>"\n<b>Zergetatik datozen sarrera ez-finantzarioen proportzioa</b>\n\n$$PI_{zerga}^{t} = \\frac{I_{zerga}^{t}}{I_{ez\\_fin}^{t}} \\cdot 100$$\n\nnon: \n\n$I_{zerga}^{t}$ = autonomia-erkidegoen eta toki-erakundeen zuzeneko eta zeharkako zergengatiko diru-sarrerak $t$ urtean \n\n$I_{ez_fin}^{t}$ = Autonomia-erkidegoen eta toki-erakundeen diru-sarrera ez-finantzario kontsolidatuak $t$ urtean \n(1. kapitulutik 5.era, administrazioen arteko barne-transferentziak ezabatuta) \n\n\n<br>\n\n<b>Zergetatik datozen euskal administrazio publikoen guztizko diru-sarreren proportzioa</b>\n\n$$PIT_{zerga}^{t} = \\frac{I_{zerga}^{t}}{I_{guzt}^{t}} \\cdot 100$$ \n\nnon:  \n\n$I_{zerga}^{t}$ = autonomia-erkidegoen eta toki-erakundeen zuzeneko eta zeharkako zergengatiko diru-sarrerak $t$ urtean \n\n$I_{guzt}^{t}$ = Autonomia-erkidegoen eta toki-erakundeen guztizko diru-sarrera kontsolidatuak $t$ urtean (1. kapitulutik 9.era, administrazioen arteko barne-transferentziak ezabatuta) \n", "desagregacion"=>nil, "observaciones"=>"\nLos datos consolidados de las liquidaciones presupuestarias de las comunidades autónomas \nse toman depurados de IFL (Intermediación Financiera Local) y PAC (política agrícola común).\n\nEn la primera serie se consideran ingresos por impuestos los incluidos en los capítulos 1 \n(impuestos directos) y 2 (impuestos indirectos) de la clasificación económica. Los ingresos \nno financieros corresponden a los capítulos 1 a 5. Se elimina la doble contabilización de \ntransferencias entre la comunidad autónoma y las entidades locales.\n\nLa segunda serie incluye tanto los ingresos no financieros como los financieros (capítulo 9). También \nse consolidan las transferencias internas para evitar duplicidades.\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador 17.1.2, proporción del gasto presupuestario interno del gobierno central \nfinanciado con impuestos, respalda una comprensión de hasta qué punto los gastos \nrecurrentes y de capital de los países están realmente cubiertos por la movilización de \ningresos internos en forma de impuestos. \n\nEl indicador, que puede derivarse directamente de las series de EFP comunicadas por \nlas autoridades nacionales al Departamento de Estadística del FMI, proporcionará a los \nanalistas un conjunto de datos comparable entre países que destaca la relación entre \nel presupuesto nacional ejecutado y la administración de ingresos/impuestos. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.1.2&seriesCode=GC_GOB_TAXD&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Barne-zergen bidez finantzatutako barne-aurrekontuaren proportzioa (%) GC_GOB_TAXD</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos de Naciones Unidas, pero aporta información complementaria. Mientras que el indicador de Naciones Unidas relaciona los  ingresos tributarios con el total del gasto público, el indicador de la C.A. de Euskadi mide  la proporción de los ingresos totales consolidados de las administraciones públicas  vascas que provienen de impuestos. Por tanto, se centra en la estructura de  financiación del sector público vasco y no en su ejecución presupuestaria.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-01-02.pdf\">Metadatuak 17-1-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.1: Strengthen domestic resource mobilization, including through international support to developing countries, to improve domestic capacity for tax and other revenue collection</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.1.2: Proportion of domestic budget funded by domestic taxes</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>GC_GOB_TAXD - Proportion of domestic budget funded by domestic taxes [17.1.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2023-12-15", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 17.1.1: Total government revenue as a proportion of GDP, by source </p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Monetary Fund (IMF) Statistics Department (Government Finance Division)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Monetary Fund (IMF) Statistics Department (Government Finance Division)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition: </strong></p>\n<p>The precise definition of the indicator is the Proportion of domestic budgetary central government expenditure funded by taxes. Budgetary central government, described in the Government Finance Statistics Manual (GFSM) 2014 (paragraph 2.81) is an institutional unit of the general government sector particularly important in terms of size and power, particularly the power to exercise control over many other units and entities. The budgetary central government is often a single unit of the central government that encompasses the fundamental activities of the national executive, legislative, and judiciary powers. This component of general government is usually covered by the main (or general) budget. </p>\n<p>The budgetary central government&#x2019;s revenue (and expense) are normally regulated and controlled by a ministry of finance, or its functional equivalent, by means of a budget approved by the legislature. Most of the ministries, departments, agencies, boards, commissions, judicial authorities, legislative bodies, and other entities that make up the budgetary central government are not separate institutional units. This is because they generally do not have the authority to own assets, incur liabilities, or engage in transactions in their own right (see GFSM 2014 paragraph 2.42). including references to standards and classifications, preferably relying on international agreed definitions. The indicator definition should be unambiguous and expressed in universally applicable terms. It must clearly express the unit of measurement (proportion, dollars, number of people, etc.).</p>\n<p><strong>Concepts:</strong></p>\n<p>The key concepts and terms associated with the indicator are outlined in GFSM 2014, as are the associated classifications. Revenue is defined in Chapter 4 (paragraph 4.23) and the associated classifications are detailed in Chapter 5. Expenditure is also defined in Chapter 4 (paragraph 4.21) while the associated detailed classifications and concepts used for calculating this aggregate are outlined in Chapter 6 - 8.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>See 2.a.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The actual and recommended sources of data for deriving this indicator are the fiscal statistics reported to the IMF&#x2019;s Statistics Department. These come from various agencies (Ministries of Finance, Central Banks, National Statistics Offices, etc.) and are compiled according to a standardized method for data collection: the annual GFS Questionnaire. In the 2020 annual reporting cycle, approximately 130 countries reported the relevant series for monitoring indicator 17.1.2. For current non-reporting countries that have demonstrated the capacity to compile and report the relevant GFS revenue series, we are engaged in outreach to the national authorities, in consultation with the respective IMF Area Departments and Offices of the Executive Director, as needed. The steps outlined above should allow, over time, for covering virtually the entire IMF membership.</p>", "COLL_METHOD__GLOBAL"=>"<p>See 3.a. </p>", "FREQ_COLL__GLOBAL"=>"<p>The data collection cycle normally runs from September through December of the next year from the reference year (T+9 to 12 months). IMF Statistics Department normally completes a round of annual GFS collection in February of the following year. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Country data are disseminated as they are processed following the data collection. Summary World Tables and other indicators including 17.1.2 are planned for release early in the second year from the reference year. For most countries, the latest data will be the reference year, including five or more most recent years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>See 3.a.</p>", "COMPILING_ORG__GLOBAL"=>"<p>The International Monetary Fund (IMF) Statistics Department (Government Finance Division) is the organization responsible for the compilation and reporting on this indicator at the global level.</p>", "INST_MANDATE__GLOBAL"=>"<p>See 3.a.</p>", "RATIONALE__GLOBAL"=>"<p>Indicator 17.1.2, Proportion of domestic budgetary central government expenditure funded by taxes, supports an understanding of the extent to which countries&#x2019; recurrent and capital outlays are actually covered by domestic revenue mobilization in the form of taxation. The indicator, which can be directly derived from GFS series reported by national authorities to the IMF Statistics Department, will provide analysts with a cross-country comparable dataset that highlights the relationship between the executed national budget and the revenue/tax administration As outlined in the Annex to Chapter 4 of GFSM 2014, a variety of indicators can be observed or derived directly from the GFS framework, while others can be derived using a combination of GFS with other macroeconomic data (i.e., GDP). 17.1.2 will be derived using series that are basic to the GFS reporting framework. This enhances the comparability of data across countries and ensures establishing robust analytical findings to support SDG monitoring using fiscal data. There are also complementarities with Indicator 17.1.1, which facilitates an understanding of the &quot;tax burden&quot;. Both indicators are important in relation to achieving longer-term development objectives.</p>", "REC_USE_LIM__GLOBAL"=>"<p>At this time, the IMF recommends no regional and global aggregates be established. While we see no issues in terms of the feasibility and suitability of 17.1.2 for cross-country comparisons, we question the relevance of one single global indicator that combines data for advanced economies with those of emerging market and low-income countries. </p>\n<p>For reporting this indicator, budgetary central government is considered the most appropriate level of institutional coverage as it will encompass all countries. In principle, GFS should cover all entities that materially affect fiscal policies. However, for most developing and many emerging market economies compiling data for the consolidated general government and its subsectors is problematic owing to limitations in the availability and/or timeliness of source data. A country may have one central government; several state, provincial, or regional governments; and many local governments, and the GFSM 2014 recommends that statistics should be compiled for all such general government units. This reporting structure is illustrated below:</p>\n<p>Structure of the general government sector and its subsectors</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td colspan=\"2\">\n        <p>General Government</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td rowspan=\"3\">\n        <p>Memorandum: Central Govt. (incl. SSF of central level)</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"4\">\n        <p>Central Government (excluding social security funds)</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Social Security Funds</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>State Governments</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Local Governments</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Consolidation Column</p>\n      </td>\n      <td rowspan=\"2\">\n        <p>General Government</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Budgetary</p>\n      </td>\n      <td>\n        <p>Extrabudgetary</p>\n      </td>\n      <td>\n        <p>Consolidation Column</p>\n      </td>\n      <td>\n        <p>Central Government</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>BA = GL1</p>\n      </td>\n      <td>\n        <p>EA</p>\n      </td>\n      <td>\n        <p>CC</p>\n      </td>\n      <td>\n        <p>CG</p>\n      </td>\n      <td>\n        <p>SSF</p>\n      </td>\n      <td>\n        <p>SG</p>\n      </td>\n      <td>\n        <p>LG</p>\n      </td>\n      <td>\n        <p>CT</p>\n      </td>\n      <td>\n        <p>GG = GL3</p>\n      </td>\n      <td>\n        <p>GL2</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>There are some countries that report &#x201C;consolidated central government&#x201D; without necessarily providing the budgetary central government sub-sector separately. The IMF intends to provide data for the budgetary central government and will work to address this issue, where needed, as outlined under section 5, above.</p>", "DATA_COMP__GLOBAL"=>"<p>GFS budgetary central government revenue series - collected in Table 1 of the annual <a href=\"https://www.imf.org/external/pubs/ft/gfs/manual/gfs-qtca.htm\">GFS Questionnaire</a> provided to all countries - will be combined with series on budgetary central government expenditure (actual execution of the main budget) on &#x201C;expense&#x201D; plus the &#x201C;net acquisition of non-financial assets&#x201D;, as defined in <em>GFSM 2014</em>). GFS Expenditure series are reported by the economic classification in Tables 2, and 3 (items under code 31). Alternatively, for those countries that report total expenditure according to the functional classification (COFOG) in GFS Table 7, a similar calculation can be made. The <em>Proportion of domestic budgetary central government expenditure funded by taxes</em> will be calculated as (Taxes / Expenditure expressed as a %) using the following data series:</p>\n<p>An Example: Calculation of SDG Indicator 17.1.2<br></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Total Revenue</strong></p>\n      </td>\n      <td>\n        <p><strong>963</strong></p>\n      </td>\n      <td>\n        <p><strong> </strong></p>\n      </td>\n      <td>\n        <p><strong>Expenditure</strong></p>\n      </td>\n      <td>\n        <p><strong>1200</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Taxes</p>\n      </td>\n      <td>\n        <p>800</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p>Expense</p>\n      </td>\n      <td>\n        <p>950</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Social contributions</p>\n      </td>\n      <td>\n        <p>105</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td rowspan=\"2\">\n        <p>Net acquisition of nonfinancial assets</p>\n      </td>\n      <td>\n        <p>250</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Grants</p>\n      </td>\n      <td>\n        <p>25</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Other revenue</p>\n      </td>\n      <td>\n        <p>33</p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p> </p>\n      </td>\n      <td>\n        <p><strong>SDG Indicator 17.1.2</strong></p>\n      </td>\n      <td>\n        <p><strong>67%</strong></p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Consistency across countries will be ensured through the underlying structure of the IMF GFS database and application of one simple mathematical formula to make computations on the country reported source data used to produce the indicator (no adjustments and/or weighting techniques will be applied). Mixed sources are not being used nor will the calculation change over time (i.e., there are no discontinuities in the underlying series as these are key aggregates/components in all country reported GFS series).</p>", "DATA_VALIDATION__GLOBAL"=>"<p>See 4.c.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>The IMF plans to rely exclusively on officially reported data provided by the national authorities using the standard GFS questionnaire based on GFSM 2014 methodology. When country data are not available due to a lack of reporting to the IMF Statistics Department, we plan to engage in outreach to the national authorities, in consultation with the respective IMF Area Departments and Offices of the Executive Director, as needed, to ensure that the key GFS series are reported. No country data estimates for missing values will be calculated by the IMF Statistics Department.</p>", "REG_AGG__GLOBAL"=>"<p>The IMF Statistics Department will leverage the existing GFS database to provide cross-country comparable series in a standardized presentation format. </p>", "DOC_METHOD__GLOBAL"=>"<p>See 4.c. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>See 4.c.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>See 4.c.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>See 4.c.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Classification of the indicator into one of the following three tiers:</p>\n<p>We recommend that 17.1.2 remain classified as Tier 1: The indicator is conceptually clear and standards are available. The underlying data are regularly produced by countries, and there is current data available. From the IAEG-SDGs Tier Classification description at https://unstats.un.org/sdgs/iaeg-sdgs/tier-classification/, a key criterion is that &#x201C;data are regularly produced by countries for at least 50 per cent of countries&#x201D;. The IMF GFS database, with 130+ regular annual reporting countries using the same reporting format certainly meets this key criterion. All IMF member countries produce revenue (and expenditure) data for surveillance purposes. In recent rounds of soliciting annual GFS series from countries, we have specifically encouraged those countries that were non-reporters over the past few years to (at a minimum) provide the key revenue and expenditure series needed to monitor 17.1.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>General government units have four types of revenue: (i) compulsory levies in the form of taxes and certain types of social contributions; (ii) property income derived from the ownership of assets; (iii) sales of goods and services; and (iv) other transfers receivable from other units. Of these, compulsory levies and transfers are considered the main sources of revenue for most general government units (GFSM 2014 paragraph 5.1). These four types of revenue are represented by the following aggregates: Taxes, Social contributions, Grants, Other revenue. Similarly, the economic classification of expense identifies eight types of expense incurred according to the economic process involved. For example, compensation of employees, use of goods and services, and consumption of fixed capital all relate to the costs of producing non-market (and, in certain instances, market) goods and services by government. Subsidies, grants, social benefits, and transfers other than grants relate to transfers in cash or in kind, and are aimed at redistributing income and wealth. The functional classification of expense provides information on the purpose for which an expense was incurred. Examples of functions are education, health, and environmental protection. The detailed GFS classification structure used in the annual questionnaire that is used by countries to report data allows for sufficient disaggregation for compiling 17.1.2.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>The IMF Statistics Department plans to rely on officially reported national data as reported by the national authorities using the standard IMF GFS annual data questionnaire that is based on the GFSM 2014 methodology.</p>", "OTHER_DOC__GLOBAL"=>"<p>The GFSM 2014 is available at <a href=\"http://www.imf.org/external/np/sta/gfsm/\">http://www.imf.org/external/np/sta/gfsm/</a>. A series of videos that discuss the GFS analytical framework are available at: <a href=\"http://www.youtube.com/playlist?list=PLmYAE4wV1YQzimXjDWMQ-4GwConTsP7-a\">IMF Statistics E-Learning Videos - YouTube</a>.</p>", "indicator_sort_order"=>"17-01-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.2.1", "slug"=>"17-2-1", "name"=>"Asistencia oficial para el desarrollo neta, total y para los países menos adelantados en proporción al ingreso nacional bruto (INB) de los donantes del Comité de Asistencia para el Desarrollo de la Organización de Cooperación y Desarrollo Económicos (OCDE)", "url"=>"/site/es/17-2-1/", "sort"=>"170201", "goal_number"=>"17", "target_number"=>"17.2", "global"=>{"name"=>"Asistencia oficial para el desarrollo neta, total y para los países menos adelantados en proporción al ingreso nacional bruto (INB) de los donantes del Comité de Asistencia para el Desarrollo de la Organización de Cooperación y Desarrollo Económicos (OCDE)"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Ayuda Oficial al Desarrollo neta de las administraciones públicas autonómicas y locales, y en proporción al PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Asistencia oficial para el desarrollo neta, total y para los países menos adelantados en proporción al ingreso nacional bruto (INB) de los donantes del Comité de Asistencia para el Desarrollo de la Organización de Cooperación y Desarrollo Económicos (OCDE)", "indicator_number"=>"17.2.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Asuntos Exteriores, Unión Europea y Cooperación", "periodicity"=>"Anual", "url"=>"https://www.aecid.es/la-aecid/en-cifras#:~:text=La%20Ayuda%20Oficial%20al%20Desarrollo,gestion%C3%B3%20307%20millones%20de%20euros.", "url_text"=>"Estadística de Ayuda Oficial al Desarrollo de España", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_10/opt_1/ti_cuentas-economicas/temas.html", "url_text"=>"Cuentas económicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Ayuda Oficial al Desarrollo neta de las administraciones públicas autonómicas y locales, y en proporción al PIB", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.2- Velar por que los países desarrollados cumplan plenamente sus compromisos en relación con la asistencia oficial para el desarrollo, incluido el compromiso de numerosos países desarrollados de alcanzar el objetivo de destinar el 0,7% del ingreso nacional bruto a la asistencia oficial para el desarrollo de los países en desarrollo y entre el 0,15% y el 0,20% del ingreso nacional bruto a la asistencia oficial para el desarrollo de los países menos adelantados; se alienta a los proveedores de asistencia oficial para el desarrollo a que consideren la posibilidad de fijar una meta para destinar al menos el 0,20% del ingreso nacional bruto a la asistencia oficial para el desarrollo de los países menos adelantados", "definicion"=>"Desembolso neto de las administraciones públicas autonómicas y locales en donaciones y préstamos a los países en desarrollo y en proporción al PIB", "formula"=>"\n$$PPIBAODNAP^{t} = \\frac{AODN_{CCAA}^{t}+AODN_{EELL}^{t}}{PIB^{t}} \\cdot 100$$ \n\ndonde:\n\n$AODN_{CCAA}^{t} =$ Ayuda oficial al desarrollo neta de la comunidad autónoma en el año $t$ \n\n$AODN_{EELL}^{t} =$ Ayuda oficial al desarrollo neta de las entidades locales de la comunidad autónoma en el año $t$\n\n$PIB^{t} =$ producto interior bruto a precios corrientes en el año $t$\n", "desagregacion"=>"", "observaciones"=>"\nEl Comité de Ayuda al Desarrollo (CAD) de la Organización para la Cooperación y el  Desarrollo Económicos (OCDE) establece la lista de países elegibles para recibir Ayuda  oficial al desarrollo. Se trata de los países de ingresos bajos y medianos en base  al ingreso nacional bruto per cápita publicado por el Banco Mundial, con la excepción  de los miembros del G8, miembros de la UE y países con fecha firme para entrar en la UE.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Los flujos totales de ayuda oficial al desarrollo (AOD) a los países en desarrollo cuantifican el esfuerzo público \nque los donantes proporcionan a esos países. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-02-01.pdf\">Metadatos 17-2-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"Ayuda Oficial al Desarrollo neta de las administraciones públicas autonómicas y locales, y en proporción al PIB", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.2- Velar por que los países desarrollados cumplan plenamente sus compromisos en relación con la asistencia oficial para el desarrollo, incluido el compromiso de numerosos países desarrollados de alcanzar el objetivo de destinar el 0,7% del ingreso nacional bruto a la asistencia oficial para el desarrollo de los países en desarrollo y entre el 0,15% y el 0,20% del ingreso nacional bruto a la asistencia oficial para el desarrollo de los países menos adelantados; se alienta a los proveedores de asistencia oficial para el desarrollo a que consideren la posibilidad de fijar una meta para destinar al menos el 0,20% del ingreso nacional bruto a la asistencia oficial para el desarrollo de los países menos adelantados", "definicion"=>"Net disbursement of regional and local public administrations in grants and loans to developing countries and in proportion to GDP", "formula"=>"\n$$PPIBAODNAP^{t} = \\frac{AODN_{CCAA}^{t}+AODN_{EELL}^{t}}{PIB^{t}} \\cdot 100$$ \n\nwhere:\n\n$AODN_{CCAA}^{t} =$ net official development aid from the autonomous community in year $t$ \n\n$AODN_{EELL}^{t} =$ net official development aid from local public administrations in year $t$\n\n$PIB^{t} =$ gross domestic product at current prices in year $t$\n", "desagregacion"=>"", "observaciones"=>"\nThe Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD)  establishes the list of countries eligible to receive Official Development Assistance. These are low- and  middle-income countries based on gross national income per capita as published by the World Bank, with the  exception of G8 members, EU members, and countries with a firm EU entry date. ", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Total ODA flows to developing countries quantify the public effort that donors provide to developing\ncountries. \n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "comparabilidad"=>"The available indicator partially complies with the United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-02-01.pdf\">Metadata 17-2-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Ayuda Oficial al Desarrollo neta de las administraciones públicas autonómicas y locales, y en proporción al PIB", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.2- Velar por que los países desarrollados cumplan plenamente sus compromisos en relación con la asistencia oficial para el desarrollo, incluido el compromiso de numerosos países desarrollados de alcanzar el objetivo de destinar el 0,7% del ingreso nacional bruto a la asistencia oficial para el desarrollo de los países en desarrollo y entre el 0,15% y el 0,20% del ingreso nacional bruto a la asistencia oficial para el desarrollo de los países menos adelantados; se alienta a los proveedores de asistencia oficial para el desarrollo a que consideren la posibilidad de fijar una meta para destinar al menos el 0,20% del ingreso nacional bruto a la asistencia oficial para el desarrollo de los países menos adelantados", "definicion"=>"Desembolso neto de las administraciones públicas autonómicas y locales en donaciones y préstamos a los países en desarrollo y en proporción al PIB", "formula"=>"\n$$PPIBAODNAP^{t} = \\frac{AODN_{AAEE}^{t}+AODN_{TTEE}^{t}}{PIB^{t}} \\cdot 100$$ \n\nnon:\n\n$AODN_{AAEE}^{t} =$ Autonomia Erkidegoaren garapenerako laguntza ofizial garbia $t$ urtean \n\n$AODN_{TTEE}^{t} =$ Autonomia Erkidegoko toki-erakundeen garapenerako laguntza ofizial garbia $t$ urtean\n\n$PIB^{t} =$ barne produktu gordina uneko prezioetan $t$ urtean\n", "desagregacion"=>"", "observaciones"=>"\nEl Comité de Ayuda al Desarrollo (CAD) de la Organización para la Cooperación y el  Desarrollo Económicos (OCDE) establece la lista de países elegibles para recibir Ayuda  oficial al desarrollo. Se trata de los países de ingresos bajos y medianos en base  al ingreso nacional bruto per cápita publicado por el Banco Mundial, con la excepción  de los miembros del G8, miembros de la UE y países con fecha firme para entrar en la UE.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Los flujos totales de ayuda oficial al desarrollo (AOD) a los países en desarrollo cuantifican el esfuerzo público \nque los donantes proporcionan a esos países. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen metadatuak.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-02-01.pdf\">Metadatuak 17-2-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.2: Developed countries to implement fully their official development assistance commitments, including the commitment by many developed countries to achieve the target of 0.7 per cent of gross national income for official development assistance (ODA/GNI) to developing countries and 0.15 to 0.20 per cent of ODA/GNI to least developed countries; ODA providers are encouraged to consider setting a target to provide at least 0.20 per cent of ODA/GNI to least developed countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.2.1: Net official development assistance, total and to least developed countries, as a proportion of the Organization for Economic Cooperation and Development (OECD) Development Assistance Committee donors&#x2019; gross national income (GNI)</p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2020-07-08</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator Net official development assistance, total and to least developed countries, as a proportion of the Organization for Economic Cooperation and Development (OECD) Development Assistance Committee donors&apos; gross national income (GNI) is defined as Net ODA disbursements as a per cent of GNI.</p>\n<p><strong>Concepts:</strong></p>\n<p>ODA: The DAC defines ODA as &#x201C;those flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are i) provided by official agencies, including state and local governments, or by their executive agencies; and ii) each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and</p>\n<p>is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent). (See http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm)</p>\n<p>GNI is obtained by DAC reporters from their national statistical offices.</p>\n<p>Note: Since 2018, the Development Assistance Committee (DAC) of the OECD measures the headline ODA data as of 2018 on a grant equivalent basis. See references for more details.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The OECD/DAC has been collecting data on official and private resource flows from 1960 at an aggregate level and 1973 at an activity level through the Creditor Reporting System (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements).</p>\n<p>The data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm).</p>\n<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.</p>", "COLL_METHOD__GLOBAL"=>"<p>A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data are published on an annual basis in December for flows in the previous year. Detailed 2015 flows will be published in December 2016.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>December 2016</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.</p>", "COMPILING_ORG__GLOBAL"=>"<p>OECD</p>", "RATIONALE__GLOBAL"=>"<p>Total ODA flows to developing countries quantify the public effort that donors provide to developing countries.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data are available from 1960.</p>", "DATA_COMP__GLOBAL"=>"<p>Net ODA disbursements as a per cent of GNI.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>None</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>None</p>", "REG_AGG__GLOBAL"=>"<p>Total net ODA as per cent of GNI is a total donor figure.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>On a donor basis for all DAC countries and many non-DAC providers (bilateral and multilateral) that report to the DAC.</p>\n<p><strong>Time series:</strong></p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator can be disaggregated by donor, recipient country, type of finance, type of aid, sub-sector, etc.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>www.oecd.org/dac/stats</p>\n<p><strong>References:</strong></p>\n<p>See all links here: http://www.oecd.org/dac/stats/methodology.htm</p>\n<p>In addition, see: http://www.oecd.org/dac/financing-sustainable-development/development-financestandards/officialdevelopmentassistancedefinitionandcoverage.htm</p>", "indicator_sort_order"=>"17-02-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.3.1", "slug"=>"17-3-1", "name"=>"Recursos financieros adicionales movilizados para los países en desarrollo procedentes de múltiples fuentes", "url"=>"/site/es/17-3-1/", "sort"=>"170301", "goal_number"=>"17", "target_number"=>"17.3", "global"=>{"name"=>"Recursos financieros adicionales movilizados para los países en desarrollo procedentes de múltiples fuentes"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Inversión directa neta en los países receptores de Ayuda Oficial al Desarrollo, y en proporción al PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Recursos financieros adicionales movilizados para los países en desarrollo procedentes de múltiples fuentes", "indicator_number"=>"17.3.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Economía, Comercio y Empresa", "periodicity"=>"Anual", "url"=>"https://comercio.gob.es/es-es/inversiones_exteriores/Paginas/default.aspx", "url_text"=>"Registro de inversiones exteriores", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_10/opt_1/ti_cuentas-economicas/temas.html", "url_text"=>"Cuentas económicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Ayuda Oficial al Desarrollo neta de las administraciones públicas autonómicas y locales, y en proporción al PIB", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.3- Movilizar recursos financieros adicionales de múltiples fuentes para los países en desarrollo", "definicion"=>"Proporción que representan los recursos financieros netos destinados a participar en el capital de  las empresas de los países receptores de Ayuda oficial al desarrollo respecto al PIB a  precios corrientes y su valor total.", "formula"=>"\n$$PPIBIDN_{AOD}^{t} = \\frac{IDN_{AOD}^{t}}{PIB^{t}} \\cdot 100$$\n\ndonde: \n\n$IDN_{AOD}^{t} =$ inversión directa neta en los países receptores de Ayuda oficial al desarrollo en el año $t$ \n\n$PIB^{t} =$ producto interior bruto a precios corrientes en el año $t$\n", "desagregacion"=>"", "observaciones"=>"\nEl Comité de Ayuda al Desarrollo (CAD) de la Organización para la Cooperación y el  Desarrollo Económicos (OCDE) establece la lista de países elegibles para recibir Ayuda  Oficial al Desarrollo. Se trata de los países de ingresos bajos y medianos en base  al ingreso nacional bruto per cápita publicado por el Banco Mundial, con la excepción  de los miembros del G8, miembros de la UE y países con fecha firme para entrar en la UE.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador mide los recursos financieros adicionales para los países en \ndesarrollo provenientes de múltiples fuentes. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.3.1&seriesCode=GF_FRN_FDI&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Entradas de inversión extranjera directa (IED) (millones de dólares estadounidenses) GF_FRN_FDI</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-03-01.pdf\">Metadatos 17-3-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"Ayuda Oficial al Desarrollo neta de las administraciones públicas autonómicas y locales, y en proporción al PIB", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.3- Movilizar recursos financieros adicionales de múltiples fuentes para los países en desarrollo", "definicion"=>"Proportion of net financial resources allocated to participate in the capital of companies  in countries receiving Official Development Assistance with respect to GDP at current prices  and its total value.", "formula"=>"\n$$PPIBIDN_{AOD}^{t} = \\frac{IDN_{AOD}^{t}}{PIB^{t}} \\cdot 100$$\n\nwhere: \n\n$IDN_{AOD}^{t} =$ net direct investment in countries receiving Official Development Assistance in year $t$ \n\n$PIB^{t} =$ gross domestic product at current prices in year $t$\n", "desagregacion"=>"", "observaciones"=>"\nThe Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD)  establishes the list of countries eligible to receive Official Development Assistance. These are low- and  middle-income countries based on gross national income per capita as published by the World Bank, with the  exception of G8 members, EU members, and countries with a firm EU entry date.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The indicator measures additional financial resources for developing countries from multiple sources. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.3.1&seriesCode=GF_FRN_FDI&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Foreign direct investment (FDI) inflows (millions of United States dollars) GF_FRN_FDI</a> UNSTATS", "comparabilidad"=>"The available indicator partially complies with the United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-03-01.pdf\">Metadata 17-3-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Ayuda Oficial al Desarrollo neta de las administraciones públicas autonómicas y locales, y en proporción al PIB", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.3- Movilizar recursos financieros adicionales de múltiples fuentes para los países en desarrollo", "definicion"=>"Proporción que representan los recursos financieros netos destinados a participar en el capital de  las empresas de los países receptores de Ayuda oficial al desarrollo respecto al PIB a  precios corrientes y su valor total.", "formula"=>"\n$$PPIBIDN_{AOD}^{t} = \\frac{IDN_{AOD}^{t}}{PIB^{t}} \\cdot 100$$\n\nnon: \n\n$IDN_{AOD}^{t} =$ zuzeneko inbertsio garbia garapenerako laguntza ofiziala jasotzen duten herrialdeetan $t$ urtean \n\n$PIB^{t} =$ barne produktu gordina uneko prezioetan $t$ urtean\n", "desagregacion"=>"", "observaciones"=>"\nEl Comité de Ayuda al Desarrollo (CAD) de la Organización para la Cooperación y el  Desarrollo Económicos (OCDE) establece la lista de países elegibles para recibir Ayuda  Oficial al Desarrollo. Se trata de los países de ingresos bajos y medianos en base  al ingreso nacional bruto per cápita publicado por el Banco Mundial, con la excepción  de los miembros del G8, miembros de la UE y países con fecha firme para entrar en la UE.", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador mide los recursos financieros adicionales para los países en \ndesarrollo provenientes de múltiples fuentes. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.3.1&seriesCode=GF_FRN_FDI&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Atzerriko inbertsio zuzeneko sarrerak (milioika dolar estatubatuar) GF_FRN_FDI</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen metadatuak.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-03-01.pdf\">Metadatuak 17-3-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.3: Mobilize additional financial resources for developing countries from multiple sources</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.3.1: Additional financial resources mobilized for developing countries from multiple sources </p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>GF_FRN_FDI - Foreign direct investment (FDI) inflows [17.3.1]</p>\n<p>DC_OSSD_GRT - Gross receipts by developing countries of official sustainable development grants [17.3.1]</p>\n<p>DC_OSSD_OFFCL - Gross receipts by developing countries of official concessional sustainable development loans [17.3.1]</p>\n<p>DC_OSSD_OFFNL - Gross receipts by developing countries of official non-concessional sustainable development loans [17.3.1]</p>\n<p>DC_OSSD_MPF - Gross receipts by developing countries of mobilised private finance (MPF) - on an experimental basis [17.3.1]</p>\n<p>DC_OSSD_PRVGRT - Gross receipts by developing countries of private grants [17.3.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>10.b.1, 17.2.1, 17.3.2, 17.4.1, 17.5.1 17.9.1 (and others)</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Forum on TOSSD (independent entity hosted by the OECD) and UNCTAD</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>OECD Development Co-operation Directorate</p>\n<p>UNCTAD Statistics Service</p>", "STAT_CONC_DEF__GLOBAL"=>"<p>Annual gross receipts by developing countries of: a. Official sustainable development grants, b. Official concessional sustainable development loans, c. Official non-concessional sustainable development loans, d. Foreign direct investment, e. Mobilised private finance (MPF) on an experimental basis, and f. Private grants.</p>\n<p>a. Official sustainable development grants</p>\n<p>Grants are transfers in cash or in kind for which no legal debt is incurred by the recipient.</p>\n<p>b. Official concessional sustainable development loans</p>\n<p>Loans are transfers in cash or in kind for which the recipient incurs legal debt. A concessional transfer is one which gives something of value away. For the purposes of this indicator, a loan will be regarded as concessional if it embodies at least a 35% grant element when its service payments are discounted at 5% p.a. This test is derived from the World Bank-IMF Debt Sustainability Framework for Low Income Countries and has also been adopted in the TOSSD framework.</p>\n<p>See: </p>\n<ul>\n  <li><a href=\"https://www.imf.org/en/Publications/Policy-Papers/Issues/2018/02/14/pp122617guidance-note-on-lic-dsf\">https://www.imf.org/en/Publications/Policy-Papers/Issues/2018/02/14/pp122617guidance-note-on-lic-dsf</a></li>\n  <li><a href=\"https://www.tossd.org/docs/reporting-instructions.pdf\">https://www.tossd.org/docs/reporting-instructions.pdf</a></li>\n</ul>\n<p>c. Official non-concessional sustainable development loans</p>\n<p>These are loans (see above) which bear a grant element of less than 35% when their service payments are discounted at 5% p.a.</p>\n<p>d. Foreign direct investment</p>\n<p>Foreign direct investment (FDI) is a category of investment that reflects the objective of establishing a lasting interest by a resident enterprise in one economy (direct investor) in an enterprise (direct investment enterprise) that is resident in an economy other than that of the direct investor. The lasting interest implies the existence of a long-term relationship between the direct investor and the direct investment enterprise and a significant degree of influence on the management of the enterprise. The direct or indirect ownership of 10% or more of the voting power of an enterprise resident in one economy by an investor resident in another economy is taken as evidence of such a relationship. For OECD Benchmark Definition of Foreign Direct Investment - 4th Edition and UNCTAD work on Foreign Direct Investment Statistics.</p>\n<p>See:</p>\n<ul>\n  <li><a href=\"https://www.oecd.org/investment/fdibenchmarkdefinition.htm\">https://www.oecd.org/investment/fdibenchmarkdefinition.htm</a></li>\n  <li><a href=\"https://unctad.org/topic/investment/investment-statistics-and-trends\">https://unctad.org/topic/investment/investment-statistics-and-trends</a></li>\n</ul>\n<p>This sub-indicator is not restricted to developing countries.</p>\n<p>e. Mobilised private finance (MPF) on an experimental basis</p>\n<p>Mobilised private finance (MPF) consists of private resource flows for activities in developing countries which have been mobilised by interventions of multilateral development banks (MDBs), bilateral development finance institutions, or other bilateral agencies, i.e. where a direct causal link between the official intervention and the private resources can be demonstrated. The OECD method for counting MPF is used; see <a href=\"https://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/mobilisation.htm\">https://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/mobilisation.htm</a>. MPF is a &#x201C;memorandum item&#x201D; because it would likely include and overlap with some finance that would also be found in the FDI sub-indicator. MPF data are typically collected on a commitment basis, rather than in terms of developing country receipts. This indicator excludes private flows mobilised in recipient countries themselves as they do not constitute additional resources. The indicator is included on an experimental basis, and it is recommended that it be reviewed during the 2025 review of SDG indicators.</p>\n<p>f. Private grants</p>\n<p>Private grants are here taken to mean grants for developmental purposes from private institutions outside the recipient country, excluding commercial flows and personal transactions such as remittances. They essentially comprise grants from philanthropic foundations and other non-governmental organizations.</p>\n<p>Sustainable development criteria</p>\n<p>Based on the Group&#x2019;s discussions, and building on the work of the TOSSD Task Force, the following cascading approach will be used to identify flows that can be considered as supporting sustainable development:</p>\n<p>1. Flows within the proposed indicators and sub-indicators detailed below and identified individually, such as a specific activity in provider reporting systems, should be included if they directly support either (i) at least one of the SDG targets or (ii) an objective in the recipient country&#x2019;s development plan as long as this is directed towards supporting or achieving sustainable development, with the following exceptions:</p>\n<p>a. Flows for activities where a substantial detrimental effect is anticipated on one or more of the other targets. </p>\n<p>b. Flows where the recipient country, after discussion with the custodian agency and/or the reporting provider country, objects to their characterization as supporting its sustainable development. </p>\n<p>2. Flows, or portions of flows within the proposed indicators and sub-indicators detailed below for which data are only available at the aggregate country-to-country level are also considered as supporting sustainable development, subject to the same exceptions as under 1.a and 1.b.</p>\n<p>Note that some sub-indicators may contain a mixture of activity-specific and aggregate-level flow data and therefore require assessment against 1 and 2 respectively. Also note that further specific exclusions are proposed, as detailed below, that may in some cases be considered to reinforce the focus of the proposed indicators on the sustainable development of developing countries.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>US dollar</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>TOSSD classifications are available at: <a href=\"http://www.tossd.org/methodology\">www.tossd.org/methodology</a> (See &#x201C;TOSSD code lists&#x201D;)</p>\n<p>Modalities of South-South cooperation as defined in the initial conceptual framework. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>Existing databases established at the International Forum on TOSSD (hosted by the OECD), the OECD and UNCTAD will serve as a data source. At the OECD and the International Forum on TOSSD, this includes data collected through TOSSD reporting as well as traditional OECD-DAC-CRS reporting, with certain adjustments to the data in accordance with the requirements of this proposal. At the UNCTAD, this includes existing data on foreign direct investment, and pilot studies towards reporting on South-South cooperation.</p>", "COLL_METHOD__GLOBAL"=>"<p>International Forum on TOSSD: Data submission by countries following agreed contents and formats. See </p>\n<ul>\n  <li>TOSSD Reporting Instructions, code lists and data forms are available at: <a href=\"https://www.tossd.org/methodology%20\">https://www.tossd.org/methodology </a> </li>\n  <li>Converged Statistical Reporting Directives for the Creditor Reporting System (CRS) and the Annual DAC Questionnaire, DAC Working Party on Development Finance Statistics, 27 April 2023, available at: https://one.oecd.org/document/DCD/DAC/STAT(2023)9/FINAL/en/pdf </li>\n</ul>\n<p>UNCTAD: </p>\n<ul>\n  <li>Data submission by countries following format for reporting South-South cooperation to be piloted and fully developed. See attached: Outcome document of the sub-group on South-South cooperation, September 2021 (link to be provided later)</li>\n  <li>UNCTAD Training Manual on Statistics for FDI and the Operations of TNCs - Volume I FDI Flows and Stocks, UNCTAD, 2009, available at: <a href=\"https://unctad.org/system/files/official-document/diaeia20091_en.pdf\">https://unctad.org/system/files/official-document/diaeia20091_en.pdf</a></li>\n</ul>", "FREQ_COLL__GLOBAL"=>"<p>TOSSD and OECD-DAC-CRS data collection on YEAR N is launched in April of year N+1 and finalised by December of year N+1. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>TOSSD and OECD-DAC-CRS data on YEAR N are released in December of year N+1.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National development co-operation agencies, national ministries, national statistical offices, development finance institutions, multilateral institutions, philanthropic foundations and central banks</p>", "COMPILING_ORG__GLOBAL"=>"<p>National development co-operation agencies, national ministries, national statistical offices, multilateral institutions and central banks</p>", "INST_MANDATE__GLOBAL"=>"<p>Countries&#x2019; membership agreement with the International Forum on TOSSD, OECD, UNCTAD and the United Nations.</p>", "RATIONALE__GLOBAL"=>"<p>The indicator measures additional financial resources for developing countries from multiple sources. It fully complies with the Addis Ababa Action Agenda by distinguishing flows of different nature and concessionality that have different impacts on development, thus creating transparency. It follows the recipient perspective, and all data represent new financing flows to developing countries. It builds on existing work, in particular standard OECD and UNCTAD data collections and the work of the TOSSD Task Force and the newly established International Forum on TOSSD on its measurement of Total Official Support for Sustainable Development (TOSSD). It is underpinned by an initial conceptual framework on South-South cooperation, allowing reporting by countries that practice South-South cooperation.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The indicator is feasible, suitable and relevant. </p>\n<p>Some providers will be reporting on sub-indicators 17.3.1.a, 17.3.1.b and 17.3.1.c to OECD and the International Forum on TOSSD while some providers will report on these sub-indicators to UNCTAD according to the agreed conceptual framework on South-South cooperation developed by the sub-Group on South-South cooperation. </p>\n<p>Sub-indicator 17.3.1.d (FDI) is reported to UNCTAD by recipients according to the current reporting arrangements. </p>\n<p>Some multilateral and bilateral providers are reporting on sub-indicator 17.3.1.e mobilized private finance to OECD. Mobilized private finance is not part of the conceptual framework of South-South cooperation. Some providers that are engaging in this form of development finance may approach UNCTAD regarding the pilot testing and further development of this indicator for wider and global application. </p>\n<p>Some countries will report on 17.3.1.f to OECD. Private grants are not part of the conceptual framework of South-South cooperation. Some providers can report on private grants to UNCTAD on a voluntary basis as part of a pilot exercise.</p>\n<p>UNCTAD and the International Forum on TOSSD (the Secretariat of which is hosted by the OECD) as co-custodians have undertaken to ensure that there are no overlaps in global reporting for this indicator in cases where countries or multilaterals provide their information to both organizations. </p>\n<p>The indicator does not include debt relief, in-donor refugee costs, administrative costs not allocated to specific development activities, or peace and security expenditures other than those reportable as official development assistance (ODA). Furthermore, it does not include private non-concessional loans; portfolio investment; export credits, whether official, officially-supported, or private; short-term flows with an original maturity of 1 year or less; or any other flows that are not within the scope of the proposed sub-indicators. These exclusions sharpen the focus of the indicator on transfers of new resources to developing countries for sustainable development purposes, while excluding commercially-motivated debt-creating flows. While there was broad support for all exclusions during the discussions of the Working Group and the open consultation, and while there were relatively few objections to specific exclusions, some countries nevertheless believe that all exclusions should be reviewed in the context of the 2025 review.</p>", "DATA_COMP__GLOBAL"=>"<p>While the sub-indicators follow the recipient perspective, the data for all proposed sub-indicators except foreign direct investment are reportable by the providers and subsequently aggregated by recipient. Foreign direct investment is as reported by recipients.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Extensive validation and quality assurance procedures are in place and being further developed at the International Forum on TOSSD, OECD and UNCTAD. Flows where the recipient country, after discussion with the custodian agency and/or the reporting provider country, objects to their characterization as supporting its sustainable development may be excluded. The custodian agencies are requested to establish mechanisms for validation based on the sustainable development criteria applied for this indicator which will adequately support concerns of the recipient countries.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable.</p>", "IMPUTATION__GLOBAL"=>"<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Summation of US dollar values across countries of a specific region, as applicable.</p>", "DOC_METHOD__GLOBAL"=>"<p>See </p>\n<ul>\n  <li>TOSSD Methodology, including Reporting Instructions, code lists and data collection form: <a href=\"https://www.tossd.org/methodology\">https://www.tossd.org/methodology</a></li>\n  <li>Converged Statistical Reporting Directives for the Creditor Reporting System (CRS) and the Annual DAC Questionnaire, DAC Working Party on Development Finance Statistics, 27 April 2023, available at https://one.oecd.org/document/DCD/DAC/STAT(2023)9/FINAL/en/pdf</li>\n  <li>Outcome document of the sub-group on South-South cooperation, September 2021 (attached, link to be provided later)</li>\n  <li>UNCTAD Training Manual on Statistics for FDI and the Operations of TNCs - Volume I FDI Flows and Stocks, UNCTAD, 2009, available at: <a href=\"https://unctad.org/system/files/official-document/diaeia20091_en.pdf\">https://unctad.org/system/files/official-document/diaeia20091_en.pdf</a>.</li>\n  <li>UNCTAD project website (Quantifying South-South cooperation to mobilize funds for the Sustainable Development Goals) to feature developed, drafted and pilot tested methodological guidance and material, available at: https://unctad.org/project/quantifying-south-south-cooperation-mobilize-funds-sustainable-development-goals</li>\n</ul>", "QUALITY_MGMNT__GLOBAL"=>"<p>UNCTAD Statistics Quality Assurance Framework (SQAF), see <a href=\"https://unctad.org/webflyer/statistics-quality-assurance-framework\">https://unctad.org/webflyer/statistics-quality-assurance-framework</a></p>\n<p><a href=\"file:///\\\\main.oecd.org\\sdataDCD\\Data\\SDF\\Work%20streams\\Aid%20Architecture\\TOSSD\\UN%20WORKING%20GROUP\\2021-10-08%20Metadata%20files%20to%20UNSD\\1%20-%20Commented%20versions\\United%20Nations%20Quality%20Assurance%20Framework\">United Nations Quality Assurance Framework</a>; </p>\n<p><a href=\"https://www.oecd.org/sdd/qualityframeworkforoecdstatisticalactivities.htm\">Quality Framework for OECD Statistical Activities</a></p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Best practices are being followed.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Best practices are being followed.</p>", "COVERAGE__GLOBAL"=>"<p>Existing databases established at the International Forum on TOSSD, OECD and UNCTAD will serve as a data source. At the International Forum on TOSSD and the OECD, this includes data collected through TOSSD reporting as well as traditional OECD-DAC-CRS reporting, assuming the data will be adjusted in accordance with the requirements of this proposal. Pilot exercises are being conducted or are being planned. In its pilot data collection, the OECD was able to provide data as applicable for sub-indicators a, b, c, e and f for 140 reporters covering all recipient countries across all regions. At the UNCTAD, existing databases include data on foreign direct investment. Multiple countries practicing South-South cooperation agreed to the conduct of pilot studies while UNCTAD is committed to support others in their reporting.</p>\n<p>Mobilized private finance should cover and be disaggregated by flows originating in (i) high-income, (ii) low- and middle and (iii) multiple/unknown countries but should exclude flows known to be mobilized in recipient countries.</p>\n<p>The following countries have submitted their data for sub-indicators 17.3.1a, 17.3.1b, 17.3.1c, 17.3.1e and 17.3.1f: Australia, Austria, Belgium, Bulgaria, Canada, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Latvia, Liechtenstein, Lithuania, Malta, Monaco, New Zealand, Norway, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, and United States.<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup> Please note that all countries provide support in the form of official sustainable development grants, but only a few provide support in all the mentioned categories. In addition, the data includes multilateral providers. Data gaps linked to providers reporting to the OECD DAC (Creditor Reporting System) but not to TOSSD have been estimated (i.e. for the European Bank for Reconstruction and Development, the IMF concessional trust funds, Luxembourg, the Netherlands and the World Bank).</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Data submitted to TOSSD by 19 provider countries and territories have been excluded as they relate to South-South cooperation (SSC) and UNCTAD is responsible for global reporting on SSC. The providers excluded are: Azerbaijan, Brazil, Chile, Costa Rica, Dominican Republic, Ecuador, Indonesia, Kazakhstan, Kuwait, Mexico, Nigeria, Peru, the Palestinian International Co-operation Agency, Qatar, Saudi Arabia, Thailand, T&#xFC;rkiye, United Arab Emirates, and Uruguay. <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<ul>\n  <li>Inter-Agency and Expert Group on Sustainable Development Goal Indicators: Working Group on Measurement of Development Support, Finalized draft indictor proposal for SDG Target 17.3 and its reporting, (4 October 2021).</li>\n  <li>Outcome document of the sub-group on South-South cooperation, September 2021</li>\n  <li>The TOSSD Methodology, including Reporting Instructions, code lists and data collection form: <a href=\"https://www.tossd.org/methodology\">https://www.tossd.org/methodology</a> </li>\n  <li>Converged Statistical Reporting Directives for the Creditor Reporting System (CRS) and the Annual DAC Questionnaire, DAC Working Party on Development Finance Statistics, 27 April 2023, available at https://one.oecd.org/document/DCD/DAC/STAT(2023)9/FINAL/en/pdf</li>\n  <li>Links to data<ul>\n      <li><a href=\"https://tossd.online/\">TOSSD Visualisation Tool</a> - <a href=\"https://tossd.online/\">https://tossd.online/</a></li>\n      <li><a href=\"https://www.oecd.org/dac/financing-sustainable-development/development-finance-data/\">Development finance data - OECD</a> - <a href=\"https://www.oecd.org/dac/financing-sustainable-development/development-finance-data/\">https://www.oecd.org/dac/financing-sustainable-development/development-finance-data/</a></li>\n      <li>Investment statistics and trends (UNCTAD):<ul>\n          <li>https://unctadstat.unctad.org/</li>\n          <li><a href=\"https://unctadstat.unctad.org/wds/TableViewer/tableView.aspx?ReportId=96740\">Beyond 20/20 WDS - Table view - Foreign direct investment: Inward and outward flows and stock, annual (unctad.org)</a> - <a href=\"https://unctadstat.unctad.org/wds/TableViewer/tableView.aspx?ReportId=96740\">https://unctadstat.unctad.org/wds/TableViewer/tableView.aspx?ReportId=96740</a></li>\n          <li><a href=\"https://unctad.org/topic/investment/world-investment-report\">World Investment Report | UNCTAD</a> - <a href=\"https://unctad.org/topic/investment/world-investment-report\">https://unctad.org/topic/investment/world-investment-report</a></li>\n        </ul>\n      </li>\n    </ul>\n  </li>\n  <li>Amounts mobilised from the private sector for development (OECD method for counting MPF), see <a href=\"https://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/mobilisation.htm\">https://www.oecd.org/dac/financing-sustainable-development/development-finance-standards/mobilisation.htm</a></li>\n  <li>GUIDANCE NOTE ON THE BANK-FUND DEBT SUSTAINABILITY FRAMEWORK FOR LOW INCOME COUNTRIES, IMF, February 2018, available at: </li>\n</ul>\n<p><a href=\"https://www.imf.org/en/Publications/Policy-Papers/Issues/2018/02/14/pp122617guidance-note-on-lic-dsf\">https://www.imf.org/en/Publications/Policy-Papers/Issues/2018/02/14/pp122617guidance-note-on-lic-dsf</a></p>\n<ul>\n  <li>OECD Benchmark Definition of Foreign Direct Investment - 4th Edition, available at <a href=\"https://www.oecd.org/investment/fdibenchmarkdefinition.htm\">https://www.oecd.org/investment/fdibenchmarkdefinition.htm</a></li>\n  <li>UNCTAD Training Manual on Statistics for FDI and the Operations of TNCs - Volume I FDI Flows and Stocks, UNCTAD, 2009, available at: <a href=\"https://unctad.org/system/files/official-document/diaeia20091_en.pdf\">https://unctad.org/system/files/official-document/diaeia20091_en.pdf</a></li>\n  <li>UNCTAD Statistics Quality Assurance Framework (SQAF), see <a href=\"https://unctad.org/webflyer/statistics-quality-assurance-framework\">https://unctad.org/webflyer/statistics-quality-assurance-framework</a> </li>\n  <li>United Nations Quality Assurance Framework, see <a href=\"https://unstats.un.org/unsd/methodology/dataquality/\">https://unstats.un.org/unsd/methodology/dataquality/</a> </li>\n  <li>Quality Framework for OECD Statistical Activities, see <a href=\"https://www.oecd.org/sdd/qualityframeworkforoecdstatisticalactivities.htm\">https://www.oecd.org/sdd/qualityframeworkforoecdstatisticalactivities.htm</a></li>\n  <li>UNCTAD project website (Quantifying South-South cooperation to mobilize funds for the Sustainable Development Goals) to feature developed, drafted and pilot tested methodological guidance and material, available at: https://unctad.org/project/quantifying-south-south-cooperation-mobilize-funds-sustainable-development-goals.</li>\n</ul>", "indicator_sort_order"=>"17-03-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.3.2", "slug"=>"17-3-2", "name"=>"Volumen de remesas (en dólares de los Estados Unidos) en proporción al PIB total", "url"=>"/site/es/17-3-2/", "sort"=>"170302", "goal_number"=>"17", "target_number"=>"17.3", "global"=>{"name"=>"Volumen de remesas (en dólares de los Estados Unidos) en proporción al PIB total"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Volumen de remesas enviadas al extranjero, y en proporción al PIB", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Volumen de remesas (en dólares de los Estados Unidos) en proporción al PIB total", "indicator_number"=>"17.3.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Banco de España", "periodicity"=>"Anual", "url"=>"https://www.bde.es/webbe/es/estadisticas/otras-clasificaciones/publicaciones/boletin-estadistico/capitulo-17.html", "url_text"=>"Balanza de pagos", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/BE.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_10/opt_1/ti_cuentas-economicas/temas.html", "url_text"=>"Cuentas económicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Volumen de remesas enviadas al extranjero, y en proporción al PIB", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.3- Movilizar recursos financieros adicionales de múltiples fuentes para los países en desarrollo", "definicion"=>"Volumen de remesas enviadas al extranjero, y en proporción al PIB", "formula"=>"\n$$PPIBVR^{t} = \\frac{VR^{t}}{PIB^{t}} \\cdot 100 $$ \n\ndonde: \n\n$VR^{t} =$ volumen de remesas enviadas al extranjero en el año $t$\n\n$PIB^{t} =$ producto interior bruto a precios corrientes en el año $t$\n\nSi denotamos: \n\n$VR_{España,p}^{t} =$ volumen de remesas enviadas desde España al país $p$ en el año $t$\n\n$P_{16-64,España,p}^{t} =$ población extranjera entre 16 y 64 años del país $p$ residente en España a 1 de enero del año $t$ \n\n$P_{16-64,p}^{t} =$ población extranjera entre 16 y 64 años del país $p$ residente en la comunidad autónoma a 1 de enero del año $t$ \n\nEntonces:  \n\n$$ VR^{t} = \\displaystyle \\sum_{p \\epsilon Países} VR_{España,p}^{t} = \\frac{\\frac{P_{16-64,p}^{t}+P_{16-64,p}^{t+1}}{2}}{\\frac{P_{16-64,España,p}^{t}+P_{16-64,España,p}^{t+1}}{2}} $$ \n\nsiendo los países: Colombia, Marruecos, Ecuador, República Dominicana, Honduras, Bolivia, Senegal, Paraguay, Pakistán, Rumanía, resto de países. No se considera a los países pertenecientes a la Unión Europea en \"resto de países\".\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Disponer de datos precisos sobre los flujos de remesas es crucial para que los responsables \nde las políticas, los gobiernos y las organizaciones internacionales elaboren \nestrategias eficaces para promover el desarrollo sostenible y alcanzar los \nObjetivos de Desarrollo Sostenible (ODS). \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.3.2&seriesCode=BX_TRF_PWKR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Volumen de remesas (en dólares estadounidenses) como proporción del PIB total (%) BX_TRF_PWKR</a> UNSTATS", "comparabilidad"=>"El indicador disponible no cumple con los metadatos de Naciones Unidas, pero aporta  información similar. Los datos se presentan en euros y no en dólares de los Estados Unidos", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-03-02.pdf\">Metadatos 17-3-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"Volumen de remesas enviadas al extranjero, y en proporción al PIB", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.3- Movilizar recursos financieros adicionales de múltiples fuentes para los países en desarrollo", "definicion"=>"Volume of remittances sent abroad, and in proportion to GDP ", "formula"=>"\n$$PPIBVR^{t} = \\frac{VR^{t}}{PIB^{t}} \\cdot 100 $$ \n\nwhere: \n\n$VR^{t} =$ volume of remittances sent abroad in year $t$ \n\n$PIB^{t} =$ gross domestic product at current prices in year $t$ \n\nIf we denote: \n\n$VR_{Spain,c}^{t} =$ volume of remittances sent from Spain to country $c$ in year $t$ \n\n$P_{16-64,Spain,c}^{t} =$ foreign population between 16 and 64 years of age from the country $c$ residing in Spain as of 1 January of year $t$ \n\n$P_{16-64,p}^{t} =$ foreign population between 16 and 64 years of age of the country $c$ resident in the autonomous community as of January 1 of year $t$ \n\nThen:  \n\n$$ VR^{t} = \\displaystyle \\sum_{p \\epsilon Countries} VR_{Spain,c}^{t} = \\frac{\\frac{P_{16-64,c}^{t}+P_{16-64,c}^{t+1}}{2}}{\\frac{P_{16-64,Spain,c}^{t}+P_{16-64,Spain,c}^{t+1}}{2}} $$ \n\nCountries are: Colombia, Morocco, Ecuador, the Dominican Republic, Honduras, Bolivia, Senegal, Paraguay, Pakistan, Romania, and other countries. Countries belonging to the European Union are not included in \"other countries\". \n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Accurate data on remittance flows is crucial for policymakers, governments, and \ninternational organizations to develop effective strategies for to promoting sustainable development and \nachieving the Sustainable Development Goals (SDGs). \n\nSource: United Nations Statistics Division\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.3.2&seriesCode=BX_TRF_PWKR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Volumen de remesas (en dólares estadounidenses) como proporción del PIB total (%) BX_TRF_PWKR</a> UNSTATS", "comparabilidad"=>"The available indicator does not comply with United Nations metadata but provides similar information. Data are presented in euros and not US dollars.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-03-02.pdf\">Metadata 17-3-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Volumen de remesas enviadas al extranjero, y en proporción al PIB", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.3- Movilizar recursos financieros adicionales de múltiples fuentes para los países en desarrollo", "definicion"=>"Volumen de remesas enviadas al extranjero, y en proporción al PIB", "formula"=>"\n$$PPIBVR^{t} = \\frac{VR^{t}}{PIB^{t}} \\cdot 100 $$ \n\nnon: \n\n$VR^{t} =$ atzerrira bidalitako diru-igorpenen bolumena $t$ urtean\n\n$PIB^{t} =$ barne produktu gordina uneko prezioetan $t$ urtean\n\nhonakoa adierazten badugu: \n\n$VR_{Espainia,h}^{t} =$ Espainiatik $h$ herrialdera bidalitako diru-igorpenen bolumena $t$ urtean\n\n$P_{16-64,Espainia,h}^{t} =$ Espainian bizi den $h$ herrialdeko 16 eta 64 urte bitarteko biztanleria atzerritarra $t$ urteko urtarrilaren 1ean \n\n$P_{16-64,h}^{t} =$ Autonomia Erkidegoan bizi den $h$ herrialdeko 16 eta 64 urte bitarteko biztanleria atzerritarra $t$ urteko urtarrilaren 1ean \n\norduan:  \n\n$$ VR^{t} = \\displaystyle \\sum_{h \\epsilon Herrialdeak} VR_{Espainia,h}^{t} = \\frac{\\frac{P_{16-64,h}^{t}+P_{16-64,h}^{t+1}}{2}}{\\frac{P_{16-64,Espainia,h}^{t}+P_{16-64,Espainia,h}^{t+1}}{2}} $$ \n\nherrialdeak ondokoak izanda: Kolonbia, Maroko, Ekuador, Dominikar Errepublika, Honduras, Bolivia, Senegal, Paraguay, Pakistan, Errumania, gainerako herrialdeak. Europar Batasuneko herrialdeak ez dira aintzat hartzen \"gainerako herrialdeak\" kategorian.\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Disponer de datos precisos sobre los flujos de remesas es crucial para que los responsables \nde las políticas, los gobiernos y las organizaciones internacionales elaboren \nestrategias eficaces para promover el desarrollo sostenible y alcanzar los \nObjetivos de Desarrollo Sostenible (ODS). \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.3.2&seriesCode=BX_TRF_PWKR&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Diru-igorpenen bolumena (dolar estatubatuarretan), BPG osoaren proportzio gisa (%) BX_TRF_PWKR</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak ez ditu Nazio Batuen adierazlearen metadatuak betetzen, baina antzeko informazioa  eskaintzen du. Datuak eurotan aurkezten dira, ez Estatu Batuetako dolarretan.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-03-02.pdf\">Metadatuak 17-3-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.3: Mobilize additional financial resources for developing countries from multiple sources</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.3.2: Volume of remittances (in United States dollars) as a proportion of total GDP</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>BX_TRF_PWKR - Volume of remittances (in United States dollars) as a proportion of total GDP [17.3.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>The related indicator to SDG indicator 17.3.2 is indicator 17.3.1, which measures the proportion of a country&apos;s total domestic budget that comes from foreign direct investments, official development assistance, and South-South Cooperation.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank (WB)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank (WB)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Personal remittances received as proportion of GDP is the inflow of personal remittances expressed as a percentage of Gross Domestic Product (GDP). </p>\n<p><strong>Concepts:</strong></p>\n<p>Personal remittances comprise of personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from non-resident households. Personal transfers thus include all current transfers between resident and non-resident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by non-resident entities. Data are the sum of two items defined in the sixth edition of the IMF&apos;s Balance of Payments Manual: personal transfers and compensation of employees.</p>\n<p>The concepts used are in line with the Sixth Edition of the IMF&apos;s Balance of Payments and International Investment Position Manual (BPM6).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not Applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Volume of personal remittances data are sourced from IMF&#x2019;s Balance of Payments Statistics database and then gap-filled with World Bank staff estimates. </p>\n<p>GDP data, sourced from the World Bank&#x2019;s World Development Indicators (WDI) database is used as the denominator. GDP data collection is conducted from national and international sources through an annual survey of economists in the Bank&#x2019;s country office network &#x2013; the World Bank&#x2019;s principal mechanism for gathering quantitative macroeconomic information on its member countries.</p>", "COLL_METHOD__GLOBAL"=>"<p>IMF&#x2019;s Balance of Payments (BOP) constitute the core source of information and are used to obtain information on remittances from transactions involved money transfer activities. Depending on the frequency of release of remittance volumes, quarterly or annual data can be used. Furthermore, data gathered from governments&#x2019; bodies, such as the Central Bank and National Statistical Offices, are often useful to complement the data reported in BOP. The scope and capacity for standardized data collection is expected to rise with progressive data collection and implementation of the methodology.</p>", "FREQ_COLL__GLOBAL"=>"<p>This is done on an annual basis.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>No fixed releases; depends on release of the latest balance of payments data.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The national data provider of personal remittances is the institution in charge of the collection and compilation of the Balance of Payments statistics. This responsibility varies and is country specific (i.e. Central Bank). World Bank staff estimates for personal remittances data are used for gap-filling purposes. Personal remittances data are not reported directly to the World Bank from the national data provider. They are reported to the International Monetary Fund (IMF), which is the institution in charge of overseeing balance of payment stability as part of its institutional mandate. </p>\n<p>GDP data are sourced from the World Bank&#x2019;s World Development Indicators (WDI) database and are compiled in accordance to the System of National Accounts, 2008 (2008 SNA) methodology. GDP data collection is conducted through the Unified Survey process, the World Bank&#x2019;s principal mechanism for gathering quantitative macroeconomic information on its member countries.</p>", "COMPILING_ORG__GLOBAL"=>"<p>The government agency in charge of the collection and compilation of the Balance of Payments statistics is the responsible organization for compilation and reporting of the personal remittances data. This information gets reported by the countries&#x2019; government agencies to the International Monetary Fund. The World Bank is the responsible agency for compilation and reporting of the GDP data.</p>", "INST_MANDATE__GLOBAL"=>"<p>No set of rules or instructions available.</p>", "RATIONALE__GLOBAL"=>"<p>There is room for improvement in the quality of statistical data on remittances. Even though many countries implemented direct reporting and dedicated models to estimate remittances flows, there are challenges in accurately measuring and compiling remittance data. Problems include, but are not limited to, countries&#x2019; adoption of heterogeneous concepts and definitions heterogeneously, deficiencies in coverage and scope, and discrepancies in gross/net recordings for components such as the compensation of employees. Accurate data on remittance flows is crucial for policymakers, governments, and international organizations to develop effective strategies for to promoting sustainable development and achieving the Sustainable Development Goals (SDGs).</p>", "REC_USE_LIM__GLOBAL"=>"<p>The accurate measurement and compilation of remittance data continue to pose challenges for several reasons. </p>\n<p>(i) Remittance transactions through unofficial channels: A significant portion of remittances flows through informal channels, such as cash hand-carried by migrants when they return home. These transactions are difficult to track and often go unrecorded in official statistics. Furthermore, digitalization through Fintech solutions have significantly impacted the remittance industry but are difficult to track. Data on mobile wallets that receive remittances as well as blockchain and cryptocurrencies to facilitate cross-border remittances have yet to be incorporated into countries&#x2019; data compilation frameworks. </p>\n<p>(ii) Inadequate adherence to the residency concept: With increasing numbers of refugees, including transit migrants (who do not establish permanent residency in their transit countries) as well as the new phenomenon of remote workers with dual residency status, further clarification and practical guidelines are needed to ensure comprehensive coverage. </p>\n<p>(iii) Misclassification: Classifications used for remittance statistics are often not in conformity with guidelines outlined in the IMF&#x2019;s Remittances Guide for Compilers or its Balance of Payments Manual (RCG/BPM6). Some non-remittance transactions, travel revenues and expenditures, or inward direct investment flows are often misclassified as inward remittances. And small trade payments, gifts to charitable organizations, or bank deposits are also sometimes misclassified as personal remittances, further complicating data accuracy.</p>\n<p>(iv) Incompatibility of source data: There are several key limitations in the source data, including its lack of comprehensiveness, susceptibility to country-specific conditions, and incompleteness for proper transaction classification according to defined criteria in terms of scope, classifications, valuation, and required timing of recording.</p>", "DATA_COMP__GLOBAL"=>"<p>Personal remittances are the sum of two items defined in the sixth edition of the IMF&apos;s Balance of Payments Manual: personal transfers and compensation of employees. World Bank staff estimates on the volume of personal remittances data are used for gap-filling purposes. GDP data, sourced from the World Bank&#x2019;s World Development Indicators (WDI) database, are then used to express the indicator as a percentage of GDP.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Not applicable</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>World Bank staff estimates for personal remittances data are based on data from IMF Balance of Payments Statistics database and data releases from central banks, national statistical agencies, and World Bank country desks.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Regional and global estimates are calculated as the GDP weighted average. </p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Only use of balance of payments data released by the International Monetary Fund or government agencies; compiled and estimated by World Bank staff.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Standard quality criteria are met.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data for 207 countries are already currently available on a regular basis for this indicator.</p>\n<p><strong>Time series: </strong>Data are available from 2000 onwards.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>None</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.worldbank.org\">www.worldbank.org</a> </p>\n<p><strong>References:</strong></p>\n<p>Data are compiled in accordance with the sixth edition of the Balance of Payments and International Investment Position Manual (BPM6). The manual is available at: <a href=\"https://www.imf.org/external/pubs/ft/bop/2007/bopman6.htm\">https://www.imf.org/external/pubs/ft/bop/2007/bopman6.htm</a> </p>\n<p>GDP data are compiled in accordance to the System of National Accounts, 2008 (2008 SNA) methodology. The manual is available at: <a href=\"http://unstats.un.org/unsd/nationalaccount/docs/SNA2008.pdf\">http://unstats.un.org/unsd/nationalaccount/docs/SNA2008.pdf</a> </p>\n<p>Metadata also available at:</p>\n<p><a href=\"http://databank.worldbank.org/data/reports.aspx?source=2&amp;type=metadata&amp;series=BX.TRF.PWKR.DT.GD.ZS\">http://databank.worldbank.org/data/reports.aspx?source=2&amp;type=metadata&amp;series=BX.TRF.PWKR.DT.GD.ZS</a> </p>", "indicator_sort_order"=>"17-03-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.4.1", "slug"=>"17-4-1", "name"=>"Servicio de la deuda en proporción a las exportaciones de bienes y servicios", "url"=>"/site/es/17-4-1/", "sort"=>"170401", "goal_number"=>"17", "target_number"=>"17.4", "global"=>{"name"=>"Servicio de la deuda en proporción a las exportaciones de bienes y servicios"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Servicio de la deuda en proporción a las exportaciones de bienes y servicios", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Servicio de la deuda en proporción a las exportaciones de bienes y servicios", "indicator_number"=>"17.4.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"De conformidad con la Política del Banco Mundial sobre Informes de Deuda Externa \ny Estados Financieros, los informes de deuda externa son necesarios para satisfacer \nlas necesidades del Banco Mundial de información fiable y oportuna sobre \nla deuda externa a fin de (a) evaluar la situación de la deuda externa, la solvencia \ny la gestión económica de un país prestatario; y (b) realizar su labor económica \nnacional y evaluar los problemas regionales y mundiales de endeudamiento y \nservicio de la deuda.\n\nEl endeudamiento externo de los países informantes se realiza a través \ndel Sistema de Información de Deudores (SID), establecido en 1951, que \nrecopila información detallada a nivel de préstamo mediante un conjunto \nestandarizado de formularios.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.4.1&seriesCode=DT_TDS_DECT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nServicio de la deuda como proporción de las exportaciones de bienes, servicios e ingresos primarios (%) DT_TDS_DECT</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-04-01.pdf\">Metadatos 17-4-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"In accordance with the World Bank’s Bank Policy on External Debt Reporting and Financial Statements \n(which includes IBRD and IDA General Conditions), external debt reporting is required to fulfil the World \nBank’s needs for reliable and timely external debt information to (a) assess a borrowing country's \nexternal debt situation, creditworthiness, and economic management; and (b) conduct its country \neconomic work and assess regional and global indebtedness and debt servicing problems. \n\nExternal borrowing of reporting countries is performed through the Debtor Reporting System (DRS), \nwhich was established in 1951 and captures detailed information at loan level by using standardized set \nof forms. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.4.1&seriesCode=DT_TDS_DECT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nDebt service as a proportion of exports of goods, services and primary income (%) DT_TDS_DECT</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-04-01.pdf\">Metadata 17-4-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"De conformidad con la Política del Banco Mundial sobre Informes de Deuda Externa \ny Estados Financieros, los informes de deuda externa son necesarios para satisfacer \nlas necesidades del Banco Mundial de información fiable y oportuna sobre \nla deuda externa a fin de (a) evaluar la situación de la deuda externa, la solvencia \ny la gestión económica de un país prestatario; y (b) realizar su labor económica \nnacional y evaluar los problemas regionales y mundiales de endeudamiento y \nservicio de la deuda.\n\nEl endeudamiento externo de los países informantes se realiza a través \ndel Sistema de Información de Deudores (SID), establecido en 1951, que \nrecopila información detallada a nivel de préstamo mediante un conjunto \nestandarizado de formularios.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.4.1&seriesCode=DT_TDS_DECT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nZorraren zerbitzua, ondasunen, zerbitzuen eta lehen mailako sarreren esportazioen proportzio gisa (%) DT_TDS_DECT</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-04-01.pdf\">Metadatuak 17-4-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.4: Assist developing countries in attaining long-term debt sustainability through coordinated policies aimed at fostering debt financing, debt relief and debt restructuring, as appropriate, and address the external debt of highly indebted poor countries to reduce debt distress</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.4.1: Debt service as a proportion of exports of goods, services and primary income</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>DT_TDS_DECT - Debt service as a proportion of exports of goods, services and primary income [17.4.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank (WB)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank (WB)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Debt service as proportion of exports of goods, services and primary income is the percentage of debt services (principal and interest payments) to the exports of goods, services and primary income. Debt services covered in this indicator refer only to public and publicly guaranteed debt.</p>\n<p><strong>Concepts:</strong></p>\n<p>Concepts of public and publicly guaranteed external debt data are in accordance with the sixth edition of the Balance of Payments and International Investment Position Manual (BPM6) methodology.</p>\n<p>&#x201C;Exports of goods, services and primary income&#x201D; data concepts are in accordance with the sixth edition of the Balance of Payments and International Investment Position Manual (BPM6).</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>World Bank Debtor Reporting System Manual:</p>\n<p>https://databankfiles.worldbank.org/public/ddpext_download/site-content/kb/debt/DRS_Manual_English.pdf</p>\n<p>IMF Balance of Payments Manual 6 for the External Sector:</p>\n<p><a href=\"https://www.imf.org/external/pubs/ft/bop/2007/pdf/bpm6.pdf\">https://www.imf.org/external/pubs/ft/bop/2007/pdf/bpm6.pdf</a> </p>", "SOURCE_TYPE__GLOBAL"=>"<p>In accordance with the World Bank&#x2019;s Bank Policy on External Debt Reporting and Financial Statements (which includes IBRD and IDA General Conditions), external debt reporting is required to fulfil the World Bank&#x2019;s needs for reliable and timely external debt information to (a) assess a borrowing country&apos;s external debt situation, creditworthiness, and economic management; and (b) conduct its country economic work and assess regional and global indebtedness and debt servicing problems. </p>\n<p>External borrowing of reporting countries is performed through the Debtor Reporting System (DRS), which was established in 1951 and captures detailed information at loan level by using standardized set of forms.</p>", "COLL_METHOD__GLOBAL"=>"<p>Public and publicly guaranteed debt is reported on a quarterly basis through form 1 and form 2. Specifically, the new loan commitments are reported on Form 1 and when appropriate, Form 1a (Schedule of Drawings and Principal and Interest Payments); the loan transactions are reported once a year on Form 2 (Current Status and Transactions). Form 3 is used to report corrections to data originally reported in Forms 1 and 2. Forms 1 and 1A are submitted quarterly, within 30 days of the close of the quarter. Form 2 is submitted annually, by March 31 of the year following that for which the report is made.</p>", "FREQ_COLL__GLOBAL"=>"<p>Loan transactions are reported once a year on Form 2 (Current Status and Transactions). Forms 1 and 1A are submitted quarterly, within 30 days of the close of the quarter. Form 2 is submitted annually, by March 31 of the year following that for which the report is made.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The annual publication of new data for this indicator is planned for mid-December through the World Bank annual publication - International Debt Statistics (IDS) database and International Debt Report (IDR) available online (see link: https://www.worldbank.org/en/programs/debt-statistics/ids)</p>", "DATA_SOURCE__GLOBAL"=>"<p>The agency in charge of producing the debt statistics at the national level is the World Bank with the data sourced by government agencies on a loan by loan basis. The national data provider of &#x201C;Exports of Goods, Services and Primary Income&#x201D; is the institution in charge of the collection and compilation of the Balance of Payments statistics. This responsibility varies and is country specific (i.e. Central Bank). World Bank staff estimates for &#x201C;Exports of Goods, Services and Primary Income&#x201D; data are used for gap filling purposes. &#x201C;Exports of Goods, Services and Primary Income&#x201D; data are not reported directly to the World Bank from the national data provider. They are reported to the International Monetary Fund (IMF), which is the institution in charge of overseeing balance of payment stability as part of its institutional mandate.</p>", "COMPILING_ORG__GLOBAL"=>"<p>World Bank</p>", "INST_MANDATE__GLOBAL"=>"<p>The World Bank has a mandate to collect accurate and timely public debt data to facilitate the effective discharge of its duties and to disseminate the data to the public, as collected through the Debtor Reporting System (DRS) and published in IDS. The data collection and dissemination mandate of the World Bank is based on the Bank Policy on &#x201C;<a href=\"https://ppfdocuments.azureedge.net/8fcc4c7a-0e1c-47e0-84b3-0950b0fc4d9b.pdf\">External Debt Reporting and Financial Statements</a>&#x201D;. This was amended in July 2005 and provides the institutional framework for the requirement that a borrowing or guaranteeing member country provides reliable and timely external debt data to the Bank. The Bank&#x2019;s General Conditions require such member country to &#x201C;furnish to the Bank all such information as the Bank shall reasonably request with respect to financial and economic conditions in its territory, including its balance of payments and external debt&#x201D;. As a condition of Board presentation of loans and credits, the borrowing country must submit a complete report (or an acceptable plan of action for such reporting) on its external debt.</p>", "RATIONALE__GLOBAL"=>"<p>The World Bank&#x2019;s rationale for collecting data on the external debt obligations of its borrowers comes from the need to ensure their debt servicing capacity and to support the assessment of their overall macroeconomic health. As a global public good, the World Bank disseminates aggregate data series derived from borrowers&#x2019; submissions on an annual basis to the Debtor Reporting System (DRS).</p>", "REC_USE_LIM__GLOBAL"=>"<p>The methodologies for selected indicators follow long-established international standards as listed in 2. However, the implementation at the national level may vary. For more information on individual indicators, please visit IDS Data Sources and Methodology at</p>\n<p><a href=\"https://www.worldbank.org/en/programs/debt-statistics/ids/technical-guide\">https://www.worldbank.org/en/programs/debt-statistics/ids/technical-guide</a></p>", "DATA_COMP__GLOBAL"=>"<p>Public and publicly guaranteed external debt data are compiled by the World Bank based on the World Bank Debtor Reporting System Manual, dated January 2000 which sets out the reporting procedures to be used by countries. The data are provided by the countries on a loan by loan basis. </p>\n<p>&#x201C;Exports of goods, services and primary income&#x201D; data are sourced from IMF&#x2019;s Balance of Payments Statistics database and then gap-filled with World Bank staff estimates in accordance with the sixth edition of the Balance of Payments and International Investment Position Manual (BPM6) </p>\n<p>Both components are used to express the indicator in percentage terms.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Not Applicable</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not Applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>Not Applicable </strong></p>", "REG_AGG__GLOBAL"=>"<p>Aggregate (global, regional and income group) figures are composed of Debtor Reporting System (DRS) member countries only.</p>", "DOC_METHOD__GLOBAL"=>"<p>World Bank Debtor Reporting System Manual:</p>\n<p><a href=\"https://databankfiles.worldbank.org/public/ddpext_download/site-content/kb/debt/DRS_Manual_English.pdf\">https://databankfiles.worldbank.org/public/ddpext_download/site-content/kb/debt/DRS_Manual_English.pdf</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not Applicable </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Not Applicable</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not Applicable </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data for about 122 countries are currently available on a regular basis for this indicator.</p>\n<p><strong>Time series:</strong></p>\n<p>Data are available on annual basis from 1970 for low- and middle-income countries that report public and publicly guaranteed external debt to the World Bank&#x2019;s Debtor Reporting System (DRS).</p>\n<p><strong>Disaggregation:</strong></p>\n<p>None</p>", "COMPARABILITY__GLOBAL"=>"<p>The World Bank Debtor Reporting System Manual is in line with international standards such as the International Investment Position (IIP), the Balance of Payments (BoP), and External Debt Statistics: Guide for Compilers and Users manuals. </p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.worldbank.org/debtstatistics\">www.worldbank.org/debtstatistics</a> </p>\n<p><strong>References:</strong></p>\n<p><a href=\"http://databank.worldbank.org/data/reports.aspx?source=2&amp;type=metadata&amp;series=%20DT.TDS.DPPF.XP.ZS\">http://databank.worldbank.org/data/reports.aspx?source=2&amp;type=metadata&amp;series= DT.TDS.DPPF.XP.ZS</a> </p>\n<p><a href=\"https://databank.worldbank.org/source/international-debt-statistics\">https://databank.worldbank.org/source/international-debt-statistics</a></p>\n<p><a href=\"https://www.worldbank.org/en/programs/debt-statistics/idr/products\">https://www.worldbank.org/en/programs/debt-statistics/idr/products</a></p>\n<p>https://www.worldbank.org/en/programs/debt-statistics/ids/technical-guide</p>\n<p><a href=\"https://databankfiles.worldbank.org/public/ddpext_download/site-content/kb/debt/DRS_Manual_English.pdf\">https://databankfiles.worldbank.org/public/ddpext_download/site-content/kb/debt/DRS_Manual_English.pdf</a></p>\n<p>https://ppfdocuments.azureedge.net/8fcc4c7a-0e1c-47e0-84b3-0950b0fc4d9b.pdf</p>", "indicator_sort_order"=>"17-04-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.5.1", "slug"=>"17-5-1", "name"=>"Número de países que adoptan y aplican sistemas de promoción de las inversiones en favor de los países en desarrollo, entre ellos los países menos adelantados", "url"=>"/site/es/17-5-1/", "sort"=>"170501", "goal_number"=>"17", "target_number"=>"17.5", "global"=>{"name"=>"Número de países que adoptan y aplican sistemas de promoción de las inversiones en favor de los países en desarrollo, entre ellos los países menos adelantados"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países que adoptan y aplican sistemas de promoción de las inversiones en favor de los países en desarrollo, entre ellos los países menos adelantados", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que adoptan y aplican sistemas de promoción de las inversiones en favor de los países en desarrollo, entre ellos los países menos adelantados", "indicator_number"=>"17.5.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Los regímenes de promoción de la inversión pueden definirse como aquellos instrumentos que tienen como objetivo \ndirecto incentivar la inversión extranjera, tanto en el exterior como en el interior, mediante medidas específicas \nde los países de origen o de acogida de la inversión. \n\nLos regímenes de promoción de la inversión para los Países Menos Adelantados (PMA) son aquellos instrumentos que \nlos países de origen de los inversores han establecido para incentivar la inversión en el exterior de los PMA, ya \nsea directamente o mediante medidas dirigidas a los países en desarrollo. \n\nLa adopción de regímenes de promoción de la inversión para los PMA es un medio importante, aunque insuficiente, para \nfortalecer la alianza mundial en pos de los ODS (Objetivo 17). La implementación posterior de estos regímenes es \nnecesaria para que la herramienta sea eficaz.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.5.1&seriesCode=SG_CPA_OFDI&areaCode=1&period=3&table=ALL\">\nNúmero de países con un programa de promoción de inversiones en el exterior que pueda beneficiar a los países en desarrollo, incluidos los PMA (1 = SÍ, 0 = NO) SG_CPA_OFDI</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-05-01.pdf\">Metadatos 17-5-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-05-20", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Investment promotion regimes can be defined as those instruments that directly aim at encouraging \noutward or inward foreign investment through particular measures of the home or host countries of \ninvestment. \n\nInvestment promotion regimes for LDCs are those instruments that home countries of investors have put \nin place to encourage outward investment in LDCs directly or through measures intended for developing \ncountries. \n\nThe adoption of investment promotion regimes for LDCs is an important yet not \nsufficient means for strengthening the global partnership for the SGDs (Goal 17). Subsequent \nimplementation of these regimes is necessary for making the tool effective. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.5.1&seriesCode=SG_CPA_OFDI&areaCode=1&period=3&table=ALL\">\nNumber of countries with an outward investment promotion scheme which can benefit developing countries, including LDCs (1 = YES, 0 = NO) SG_CPA_OFDI</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-05-01.pdf\">Metadata 17-5-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"De conformidad con la Política del Banco Mundial sobre Informes de Deuda Externa \ny Estados Financieros, los informes de deuda externa son necesarios para satisfacer \nlas necesidades del Banco Mundial de información fiable y oportuna sobre \nla deuda externa a fin de (a) evaluar la situación de la deuda externa, la solvencia \ny la gestión económica de un país prestatario; y (b) realizar su labor económica \nnacional y evaluar los problemas regionales y mundiales de endeudamiento y \nservicio de la deuda.\n\nEl endeudamiento externo de los países informantes se realiza a través \ndel Sistema de Información de Deudores (SID), establecido en 1951, que \nrecopila información detallada a nivel de préstamo mediante un conjunto \nestandarizado de formularios.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.4.1&seriesCode=DT_TDS_DECT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nZorraren zerbitzua, ondasunen, zerbitzuen eta lehen mailako sarreren esportazioen proportzio gisa (%) DT_TDS_DECT</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-05-01.pdf\">Metadatuak 17-5-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.5: Adopt and implement investment promotion regimes for least developed countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.5.1: Number of countries that adopt and implement investment promotion regimes for developing countries, including the least developed countries</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_CPA_OFDI - Number of countries with an outward investment promotion scheme which can benefit developing countries, including LDCs [17.5.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Indicator 10.b.1: Total resource flows for development, by recipient and donor countries and type of flow (e.g. official development assistance, foreign direct investment and other flows) (metadata).</p>\n<p>Indicator 17.3.1: Additional financial resources mobilized for developing countries from multiple sources</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Conference on Trade and Development (UNCTAD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Conference on Trade and Development (UNCTAD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator provides the number of countries that have adopted and implemented investment promotion regimes for developing countries, including least developed countries (LDCs).</p>\n<p><strong>Concepts:</strong></p>\n<p><u>Investment promotion regimes</u> can be defined as those instruments that directly aim at encouraging outward or inward foreign investment through particular measures of the home or host countries of investment. </p>\n<p>Investment promotion regimes for LDCs are those instruments that <em>home countries</em> of investors have put in place to <em>encourage outward investment in LDCs directly or through measures intended for developing countries. </em></p>\n<p><u>Home country</u> refers to donor countries that put in place investment promotion regimes to encourage outward investment which can benefit developing countries, including LDCs. </p>\n<p><u>Foreign direct investment</u> involves a long-term relationship and reflects a lasting interest and control by a resident entity in one economy (foreign direct investor or parent enterprise) in an enterprise resident in an economy other than that of the foreign direct investor (FDI enterprise or affiliate enterprise or foreign affiliate).</p>\n<p><u>Adoption</u> means that a country has put in place such a system i.e. through the formal adoption of a law, regulation or programme to encourage investment in developing countries, including LDCs. </p>\n<p><u>Implementation</u> means that a country has actually started to promote individual investments in developing countries, including LDCs, on the basis of the relevant legislation. </p>\n<p><u>Instruments</u> used under investment promotion regimes include investment guarantees, financial or fiscal support for outward investors. Besides these legal instruments, countries often also provide information and other advisory and investment facilitation services for their outward investors. </p>\n<p><u>Investment guarantee</u> is an insurance, offered by governments of the home country or other institutions, to investors to protect against certain political risks in host countries, such as the risk of discrimination, expropriation, transfer restrictions or breach of contract<u>.</u></p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of countries</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Target economies and their numerical codes applied as in the UNCTADStat and in line with the ISO 3166-1 standard and the Standard Country or Area Codes for Statistical Use (M49) of the United Nations Statistics Division (UNSD).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data sources include the following: </p>\n<p>&#x25CF; Survey responses on investment guarantee schemes for outward investment in LDCs specifically or developing countries in general;</p>\n<p>&#x25CF; Survey responses on fiscal or financial support for outward investors in LDCs specially or developing countries in general;</p>\n<p>&#x25CF; Internet research carried out by UNCTAD to complement survey responses. UNCTAD research complements survey responses in cases where information from official government websites confirms that an outward investment promotion scheme is in place, but the concerned country has not responded to the survey.</p>", "COLL_METHOD__GLOBAL"=>"<p>Preliminary data are collected through the following means: </p>\n<p>&#x25CF; An in-depth online questionnaire circulated to SDG focal points in statistical offices worldwide; </p>\n<p>&#x25CF; Internet research carried out by UNCTAD;</p>\n<p>The agency in charge of outward investment promotion may vary across countries depending on the national structure. The SDG focal points are encouraged to route the questionnaire to the relevant counterparts at the country level, e.g. national ministries of investment, industry, economic development, or outward investment promotion agencies.</p>", "FREQ_COLL__GLOBAL"=>"<p>Annual data collection in Q4 of the year preceding the reporting.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p> Annual release, in Q1 of the reporting year.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data providers include national ministries, outward investment promotion agencies and other international organisations.</p>", "COMPILING_ORG__GLOBAL"=>"<p>The United Nations Conference on Trade and Development (UNCTAD) will compile the national data to report it globally.</p>", "INST_MANDATE__GLOBAL"=>"<p>UNCTAD is the focal point for international investment issues within the United Nations system. Through this survey, UNCTAD seeks to collect data relevant to measure indicator 17.5.1 specifically, which refers to the efforts made by member States of the United Nations to promote outward investment to developing countries, including least developed countries (LDCs). </p>", "RATIONALE__GLOBAL"=>"<p>Target 17.5 aims to adopt and implement investment promotion regimes for the least developed countries (LDCs). For the purpose of target 17.5, it is necessary to find out how many countries have put in place investment promotion regimes that may benefit LDCs directly. Therefore, SDG indicator 17.5.1, the number of countries that adopt and implement investment promotion regimes for developing countries, including least developed countries, has been selected onto the indicator framework to assess the achievement of this target.</p>", "REC_USE_LIM__GLOBAL"=>"<p>SDG indicator 17.5.1 calls for the measurement of both adoption and implementation of investment promotion regimes. The adoption of investment promotion regimes for LDCs is an important yet not sufficient means for strengthening the global partnership for the SGDs (Goal 17). Subsequent implementation of these regimes is necessary for making the tool effective. However, getting comprehensive and reliable data on the implementation stage (i.e. how many investments in LDCs have actually been promoted through the promotion regime?) will be difficult. These data are usually not publicly available. However, to some extent, data may exist in aggregate form (see below). </p>\n<p>Furthermore, UNCTAD research indicated that many developed countries and some emerging economies have national investment promotion regimes in place that encourage investment abroad. Usually, however, these promotion regimes are available for outward investment in any country &#x2013; not only for investment in LDCs or other developing economies. Some types of investment policy tools can be more country-specific, like bilateral investment treaties (BITs). The indicator reporting started with preliminary estimates covering BITs, relying on comprehensive and country specific UNCTAD data on BITs. Over the past decade, broad consensus formed on the need to reform the BIT regime. UNCTAD provides detailed policy guidance to support the reform action of countries and regions. Data on the actual number of countries with an outward FDI promotion scheme that can benefit developing countries, including LDCs, constitutes a more accurate measurement for this indicator. </p>", "DATA_COMP__GLOBAL"=>"<p>The proposed computation method includes the following in the compilation of SDG indicator 17.5.1: </p>\n<ol>\n  <li><strong><em>Target countries of outward investment promotion regimes </em></strong></li>\n</ol>\n<p>The indicator methodology covers both: </p>\n<ul>\n  <li>Specific investment promotion regimes targeted for LDCs only; </li>\n  <li>Investment promotion regimes for developing countries in general, including LDCs. </li>\n</ul>\n<p>The measurement should include outward investment promotion regimes that do not exclude developing countries. Only this approach ensures getting a full picture of outward investment promotion with LDCs as beneficiaries, which is better aligned with Target 17.5. By contrast, limiting the research to specific promotion regimes for LDCs only would result in partial information, because the number of LDCs that receive support through investment promotion regimes for all developing countries is likely to be much higher than the number of LDCs that benefit from LDC-specific promotion regimes. Therefore, both types are included when identifying the countries that have adopted and implemented investment promotion regimes for developing countries, including least developed countries.</p>\n<ol>\n  <li><strong><em>Types of outward investment promotion regimes </em></strong></li>\n</ol>\n<p>Based on consultations and feasibility studies on <em>what types</em> of investment promotion regimes to look at, the following methodology is suggested: </p>\n<p>Countries use various means to promote foreign investment abroad (see above &#x201C;Concepts&#x201D;). Indicator 17.5.1 will focus on the legal investment instruments, since relevant information is &#x2013; to various degrees - usually publicly available, and thus feasible to compile. </p>\n<p>Information is less frequently available on informal and ad-hoc means of outward investment promotion, such as advisory services. The availability of reliable information on such measures would vary greatly across countries. Thus, including such information would hamper the international comparability of the indicator.</p>\n<p>To be included in the number of countries that have adopted and implemented investment promotion regimes, the existence of at least one type of promotion instrument (e.g. an investment guarantee scheme or financial support for outward investment that can benefit developing countries, including LDCs) would be sufficient.</p>\n<ol>\n  <li><strong><em>Adoption vs. implementation of outward investment promotion regimes </em></strong></li>\n</ol>\n<p>Consultations and feasibility studies were carried out on whether &#x2013; in addition to the existence of an outward investment promotion regime, i.e., whether such tools were signed or otherwise adopted &#x2013; it would also be feasible to examine as to what extent the regime was actually <em>implemented,</em> i.e., whether the regime is in force or even if an LDC <em>actually benefitted</em> from it, e.g., by receiving a foreign investment promoted by an investment guarantee. It was concluded to focus the research on the <em>adoption</em> of a promotion system as such Otherwise information on the actual stage of implementation in individual countries is usually not publicly available; scattered data about the situation in some countries could not provide a comprehensive and reliable picture of the overall situation. However, it may be possible to come up with some aggregate data at the regional or global level (see below).</p>\n<ol>\n  <li><strong><em>Coverage of home countries of outward investment promotion regimes </em></strong></li>\n</ol>\n<p>There is also a question of <em>which countries</em> should be included in the measure as home countries of outward investment promotion regimes. The indicator will not only include measures put in place by developed countries but also by emerging economies, thus measuring South-South cooperation in this respect in addition. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>UNCTAD&#x2019;s research is complemented by annual surveys to ensure the accuracy of the reported information (see 4.f). The information collected through internet research is then verified with the national authorities. </p>", "ADJUSTMENT__GLOBAL"=>"<p>The 2023 survey included new questions on the criteria for accessing the investment promotion schemes.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>UNCTAD&#x2019;s research complements annual surveys in cases where information from official government websites confirms that an outward investment promotion scheme is in place, but the concerned country has not responded to the survey.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>In order to calculate regional and global levels, missing data may be estimated using information from international sources (e.g. OECD and UNCTAD databases).</p>\n<p> Such data may only be available in an aggregated form.</p>", "REG_AGG__GLOBAL"=>"<p>As explained under data sources, UNCTAD will combine data collected from national authorities with information sourced from international databases and Internet research, as necessary. Once country data have been completed and verified with member States, the indicator will be calculated by aggregating the country data within a specific sub-region, region and globally. Each country that has at least one type of investment promotion regime in place that supports LDCs either directly or through measures intended for developing countries will be counted once for indicator 17.5.1.</p>", "DOC_METHOD__GLOBAL"=>"<p>UNCTAD has published guidance documents specifically on outward investment promotion, related definitions and data: </p>\n<ul>\n  <li>The UNCTAD Investment Policy Hub provides definitions and data at: https://investmentpolicy.unctad.org/</li>\n  <li>Investment Policy Framework for Sustainable Development, New York and Geneva, 2015.</li>\n  <li>Outward Investment Agencies: Partners in Promoting Sustainable Development, IPA Observer No. 4 &#x2013; 2015.</li>\n  <li>Promoting investment in the sustainable development goals, Investment Advisory Series, Series A, number 8, 2019. </li>\n  <li>Handbook on outward investment agencies and institutions, New York and Geneva, 1999.</li>\n</ul>\n<p>Specific guidance for countries on how to compile data at the national level is contained in the questionnaire that UNCTAD has developed and sent to outward investment promotion agencies.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>To manage the quality of the indicator data, UNCTAD works in close collaboration with member States international investment policies and their reform, the provision of technical assistance and intergovernmental meetings as part of UNCTAD&#x2019;s broader mandate in this area. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The data received from member States will go through a thorough validation process. Once the information has been validated and information from additional sources incorporated, any questions for clarification or proposals are shared with member States for their review.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Work continues to improve reporting on the type of outward investment regimes, as well as the eligibility criteria to access those schemes, jointly with member States. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Currently, results are available based on a detailed online questionnaire on existing outward investment promotion regimes which can benefit developing countries, including LDCs. Replies have been received from 35 countries. The answers received vary considerably in their degree of substance and detail. </p>\n<p>Further data will be available on the government websites of home countries. In 2022, in-house internet research provided information on 15 additional countries.</p>\n<p><strong>Time series:</strong></p>\n<p>In light of the change in the metric utilized for the measurement of this indicator in 2023, the baseline year is 2022. Data on international investment agreements (IIA) concluded with LDCs, which constituted the original measurement for this indicator, can still be accessed on UNCTAD&#x2019;s IIA navigator, at: https://investmentpolicy.unctad.org/international-investment-agreements</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Indicator 17.5.1 can be disaggregated by type of investment promotion regimes that home countries adopt for developing countries, including LDCs (e.g. investment guarantees, fiscal and financial aid and investment facilitation).</p>\n<p>A geographical breakdown of the adoption of investment promotion schemes would also be possible. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>As mentioned above, countries have different outward investment promotion regimes in operation. Differences exist concerning: </p>\n<p>&#x25CF; the specificity of the system (does it target exclusively investment in LDCs or investment in any developing country?);</p>\n<p>&#x25CF; the type and number of investment promotion instruments (investment guarantees, fiscal or financial support, IIAs);</p>\n<p>&#x25CF; the degree of investment promotion (how much support does the individual promotion measure provide?), and </p>\n<p>&#x25CF; the actual impact of the investment promotion regime (how many investments have been made under the promotion regime and what effect do they have in the LDCs?).</p>\n<p>Indicator 17.5.1 measures the number of countries that have an investment promotion regime in place which can benefit developing countries, including LDCs. Counting this number cannot provide a complete picture of the content and impact of these regimes. Likewise, it does not differentiate between countries with different types of regimes &#x2013; except for the distinction between countries that promote outward investment in LDCs through an LDC-specific promotion system and those with a more general promotion regime. Therefore, disaggregation of the indicator should be gradually increased as more data become available. Further work to measure the &#x201C;implementation&#x201D; of investment promotion regimes for developing countries, including LDCs, could be pursued in the longer-term.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"https://unctad.org/statistics\">https://unctad.org/statistics</a> </p>\n<p><a href=\"https://investmentpolicy.unctad.org/international-investment-agreements\">https://investmentpolicy.unctad.org/international-investment-agreements</a></p>\n<p><a href=\"https://unctadstat.unctad.org/\">https://unctadstat.unctad.org</a> </p>\n<p><a href=\"https://unctad.org/en/pages/DIAE/DIAE.aspx\">https://unctad.org/en/pages/DIAE/DIAE.aspx</a> </p>", "indicator_sort_order"=>"17-05-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.6.1", "slug"=>"17-6-1", "name"=>"Número de abonados a servicios de banda ancha fija por cada 100 habitantes, desglosado por velocidad", "url"=>"/site/es/17-6-1/", "sort"=>"170601", "goal_number"=>"17", "target_number"=>"17.6", "global"=>{"name"=>"Número de abonados a servicios de banda ancha fija por cada 100 habitantes, desglosado por velocidad"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de abonados a servicios de banda ancha fija por cada 100 habitantes, desglosado por velocidad", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de abonados a servicios de banda ancha fija por cada 100 habitantes, desglosado por velocidad", "indicator_number"=>"17.6.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"progreso", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Comisión Nacional de los Mercados y la Competencia (CNMC)", "periodicity"=>"Anual", "url"=>"https://data.cnmc.es/telecomunicaciones-y-sector-audiovisual", "url_text"=>"Telecomunicaciones y sector audiovisual", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/CNMC.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Número de abonados a servicios de banda ancha fija por cada 100 habitantes, desglosado por velocidad", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.6- Mejorar la cooperación regional e internacional Norte-Sur, Sur-Sur y triangular en materia de ciencia, tecnología e innovación y el acceso a estas, y aumentar el intercambio de conocimientos en condiciones mutuamente convenidas, incluso mejorando la coordinación entre los mecanismos existentes, en particular a nivel de las Naciones Unidas, y mediante un mecanismo mundial de facilitación de la tecnología", "definicion"=>"Número de líneas de banda ancha fija respecto al número de habitantes", "formula"=>"\n$$TLBAF^{t} = \\frac{LBAF^{t}}{P^{t}}  \\cdot 100$$\n\ndonde: \n\n$LBAF^{t} =$  líneas de banda ancha fija en el año $t$\n\n$P^{t} =$ población a 1 de enero del año $t$\n", "desagregacion"=>"Tecnología de acceso: DSL, fibra óptica (FTTH), híbrido de fibra coaxial (HFC), otra tecnología\n\nTerritorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Internet se ha convertido en una herramienta cada vez más importante para proporcionar \nacceso a la información y puede ayudar, fomentar y mejorar la cooperación regional \ne internacional y el acceso a la ciencia, la tecnología y la innovaciones y mejorar \nel intercambio de conocimientos. \n\nEl acceso a Internet de alta velocidad es importante \npara garantizar que los usuarios de Internet tienen acceso de calidad a Internet y \npueden aprovechar la creciente cantidad de contenido de Internet –incluido el contenido \ngenerado por el usuario–, servicios e información.\n\nSi bien el número de suscripciones a banda ancha fija ha aumentado sustancialmente en \nlos últimos años y si bien los proveedores de servicios ofrecen velocidades cada vez más \naltas, la banda ancha fija de Internet puede variar enormemente por la velocidad, afectando \nasí la calidad y funcionalidad del acceso a Internet. \n\nMuchos países, especialmente en el mundo en desarrollo, no sólo tienen una cantidad muy \nlimitada de suscripciones a banda ancha fija, sino también velocidades bajas. Esta limitación \nes una barrera para la Meta 17.6 y el indicador destaca el potencial de \nla Internet (especialmente a través del acceso de alta velocidad) para mejorar la \ncooperación, mejorar el acceso a la ciencia, tecnología e innovación, y compartir \nconocimientos. \n\nEl indicador también destaca la importancia de Internet como facilitador del desarrollo y \nayuda a medir la brecha digital que, si no se aborda adecuadamente, agravará las \ndesigualdades en todos los ámbitos del desarrollo. Información sobre suscripciones \nde banda ancha fija por la velocidad contribuirá al diseño de políticas específicas \npara superar esas divisiones.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.6.1&seriesCode=IT_NET_BBND&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ANYS\">Suscripciones a banda ancha fija por cada 100 habitantes, por velocidad (por cada 100 habitantes) IT_NET_BBND</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-06-01.pdf\">Metadatos 17-6-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"Número de abonados a servicios de banda ancha fija por cada 100 habitantes, desglosado por velocidad", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.6- Mejorar la cooperación regional e internacional Norte-Sur, Sur-Sur y triangular en materia de ciencia, tecnología e innovación y el acceso a estas, y aumentar el intercambio de conocimientos en condiciones mutuamente convenidas, incluso mejorando la coordinación entre los mecanismos existentes, en particular a nivel de las Naciones Unidas, y mediante un mecanismo mundial de facilitación de la tecnología", "definicion"=>"Number of fixed broadband lines in relation to the number of inhabitants", "formula"=>"\n$$TLBAF^{t} = \\frac{LBAF^{t}}{P^{t}}  \\cdot 100$$\n\nwhere: \n\n$LBAF^{t} =$  fixed broadband lines in year $t$\n\n$P^{t} =$ population as of January 1 of the year $t$\n", "desagregacion"=>"Access technology: DSL; fiber optic (FTTH); hybrid fiber coaxial (HFC); other technology\n\nProvince\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The Internet has become an increasingly important tool to provide access to information, and can help \nfoster and enhance regional and international cooperation on, and access to, science, technology and \ninnovations, and enhance knowledge sharing. \n\nHigh-speed Internet access is important to ensure that \nInternet users have quality access to the Internet and can take advantage of the growing amount of \nInternet content – including user-generated content –, services and information. \n\nWhile the number of fixed-broadband subscriptions has increased substantially over the last years and \nwhile service providers offer increasingly higher speeds, fixed Internet broadband can vary tremendously \nby speed, thus affecting the quality and functionality of Internet access. \n\nMany countries, especially in the \ndeveloping world, have not only a very limited amount of fixed-broadband subscriptions, but also at very \nlow speeds. This limitation is a barrier to the Target 17.6 and the indicator highlights the potential of the \nInternet (especially through high-speed access) to enhance cooperation, improve access to science, \ntechnology and innovation, and share knowledge. \n\nThe indicator also highlights the importance of Internet \nuse as a development enabler and helps to measure the digital divide, which, if not properly addressed, \nwill aggravate inequalities in all development domains. Information on fixed broadband subscriptions by \nspeed will contribute to the design of targeted policies to overcome those divides. \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.6.1&seriesCode=IT_NET_BBND&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ANYS\">Fixed broadband subscriptions per 100 inhabitants, by speed (per 100 inhabitants) IT_NET_BBND</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-06-01.pdf\">Metadata 17-6-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Número de abonados a servicios de banda ancha fija por cada 100 habitantes, desglosado por velocidad", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.6- Mejorar la cooperación regional e internacional Norte-Sur, Sur-Sur y triangular en materia de ciencia, tecnología e innovación y el acceso a estas, y aumentar el intercambio de conocimientos en condiciones mutuamente convenidas, incluso mejorando la coordinación entre los mecanismos existentes, en particular a nivel de las Naciones Unidas, y mediante un mecanismo mundial de facilitación de la tecnología", "definicion"=>"Número de líneas de banda ancha fija respecto al número de habitantes", "formula"=>"\n$$TLBAF^{t} = \\frac{LBAF^{t}}{P^{t}}  \\cdot 100$$\n\nnon: \n\n$LBAF^{t} =$ banda zabal finkoko lineak $t$ urtean\n\n$P^{t} =$ biztanleria $t$ urteko urtarrilaren 1ean\n", "desagregacion"=>"Sarbide-teknologia: DSL; zuntz optikoa (FTTH); zuntz koaxialeko hibridoa (HFC); beste teknologia bat\n\nLurralde historikoa\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Internet se ha convertido en una herramienta cada vez más importante para proporcionar \nacceso a la información y puede ayudar, fomentar y mejorar la cooperación regional \ne internacional y el acceso a la ciencia, la tecnología y la innovaciones y mejorar \nel intercambio de conocimientos. \n\nEl acceso a Internet de alta velocidad es importante \npara garantizar que los usuarios de Internet tienen acceso de calidad a Internet y \npueden aprovechar la creciente cantidad de contenido de Internet –incluido el contenido \ngenerado por el usuario–, servicios e información.\n\nSi bien el número de suscripciones a banda ancha fija ha aumentado sustancialmente en \nlos últimos años y si bien los proveedores de servicios ofrecen velocidades cada vez más \naltas, la banda ancha fija de Internet puede variar enormemente por la velocidad, afectando \nasí la calidad y funcionalidad del acceso a Internet. \n\nMuchos países, especialmente en el mundo en desarrollo, no sólo tienen una cantidad muy \nlimitada de suscripciones a banda ancha fija, sino también velocidades bajas. Esta limitación \nes una barrera para la Meta 17.6 y el indicador destaca el potencial de \nla Internet (especialmente a través del acceso de alta velocidad) para mejorar la \ncooperación, mejorar el acceso a la ciencia, tecnología e innovación, y compartir \nconocimientos. \n\nEl indicador también destaca la importancia de Internet como facilitador del desarrollo y \nayuda a medir la brecha digital que, si no se aborda adecuadamente, agravará las \ndesigualdades en todos los ámbitos del desarrollo. Información sobre suscripciones \nde banda ancha fija por la velocidad contribuirá al diseño de políticas específicas \npara superar esas divisiones.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.6.1&seriesCode=IT_NET_BBND&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=ANYS\">Banda zabal finkoko harpidetzak 100 biztanleko, abiaduraren arabera (100 biztanleko) IT_NET_BBND</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-06-01.pdf\">Metadatuak 17-6-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.6: Enhance North-South, South-South and triangular regional and international cooperation on and access to science, technology and innovation and enhance knowledge-sharing on mutually agreed terms, including through improved coordination among existing mechanisms, in particular at the United Nations level, and through a global technology facilitation mechanism</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.6.1: Fixed broadband subscriptions per 100 inhabitants, by speed</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>IT_NET_BBND - Fixed broadband subscriptions per 100 inhabitants [17.6.1]<sup><a href=\"#footnote-1\" id=\"footnote-ref-1\">[1]</a></sup></p>\n<p>IT_NET_BBNDN - Fixed broadband subscriptions (number) [17.6.1]</p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-1\">1</sup><p> In March 2023, the series description was updated from &#x201C;Fixed Internet broadband subscriptions per 100 inhabitants, by speed&#x201D; to &#x201C;Fixed broadband subscriptions per 100 inhabitants, by speed&#x201D;; content in the series is the same. <a href=\"#footnote-ref-1\">&#x2191;</a></p></div></div>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>4.4.1, 4.5.1, 9.c.1, 17.8.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator fixed broadband subscriptions, by speed, refers to the number of fixed-broadband subscriptions to the public Internet, broken down by advertised download speed.</p>\n<p>The indicator is currently broken down by the following subscription speeds:</p>\n<p>- 256 kbit/s to less than 2 Mbit/s subscriptions: Refers to all fixed broadband Internet subscriptions with advertised downstream speeds equal to, or greater than, 256 kbit/s and less than 2 Mbit/s.</p>\n<p>- 2 Mbit/s to less than 10 Mbit/s subscriptions: Refers to all fixed -broadband Internet subscriptions with advertised downstream speeds equal to, or greater than, 2 Mbit/s and less than 10 Mbit/s.</p>\n<p>- Equal to or above 10 Mbit/s subscriptions (4213_G10). Refers to all fixed -broadband Internet subscriptions with advertised downstream speeds equal to, or greater than, 10 Mbit/s.</p>\n<p><strong>Concepts:</strong></p>\n<p>Fixed broadband subscriptions refer to subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. This includes cable modem, DSL, fibre-to-the-home/building, other fixed -broadband subscriptions, satellite broadband and terrestrial fixed wireless broadband. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It should include fixed WiMAX and any other fixed wireless technologies. It includes both residential subscriptions and subscriptions for organizations.</p>\n<p>The Internet is a worldwide public computer network. It provides access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Per 100 inhabitants</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Speed tiers as defined in the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Since data for this indicator are based on administrative data from operators, no information on individual subscribers is available and therefore the data cannot be broken down by any individual characteristics. Data could in theory be broken down by geographic location and urban/rural, but the International Telecommunication Union (ITU) does not collect this information.</p>", "COLL_METHOD__GLOBAL"=>"<p>ITU collects data for this indicator through a questionnaire from national regulatory authorities or Information and Communication Technology Ministries, who collect the data from Internet service providers. </p>", "FREQ_COLL__GLOBAL"=>"<p>International Telecommunication Union (ITU) collects data twice a year from Member States, in the 1<sup>st</sup> quarter and in 3<sup>rd</sup> quarter.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released twice a year, In July and December, in the <a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/wtid.aspx\">Wor&#x200B;ld Telecommun&#x200B;ic&#x200B;ation/ICT Indicators Database&#x200B;</a>&#x200B; and the ITU DataHub, see https://datahub.itu.int/.</p>", "DATA_SOURCE__GLOBAL"=>"<p>The telecommunication/ICT regulatory authority or the Ministry in charge of Information and Communication Technology (ICTs) within each country, who collect the data from Internet Service Providers (ISPs).</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "INST_MANDATE__GLOBAL"=>"<p>As the UN specialized agency for Information and Communication Technology (ICTs), International Telecommunication Union (ITU) is the official source for global ICT statistics, collecting ICT data from its Member States. </p>", "RATIONALE__GLOBAL"=>"<p>The Internet has become an increasingly important tool to provide access to information, and can help foster and enhance regional and international cooperation on, and access to, science, technology and innovations, and enhance knowledge sharing. High-speed Internet access is important to ensure that Internet users have quality access to the Internet and can take advantage of the growing amount of Internet content &#x2013; including user-generated content &#x2013;, services and information.</p>\n<p>While the number of fixed-broadband subscriptions has increased substantially over the last years and while service providers offer increasingly higher speeds, fixed Internet broadband can vary tremendously by speed, thus affecting the quality and functionality of Internet access. Many countries, especially in the developing world, have not only a very limited amount of fixed-broadband subscriptions, but also at very low speeds. This limitation is a barrier to the Target 17.6 and the indicator highlights the potential of the Internet (especially through high-speed access) to enhance cooperation, improve access to science, technology and innovation, and share knowledge. The indicator also highlights the importance of Internet use as a development enabler and helps to measure the digital divide, which, if not properly addressed, will aggravate inequalities in all development domains. Information on fixed broadband subscriptions by speed will contribute to the design of targeted policies to overcome those divides.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Since most Internet service providers offer plans linked to download speed, the indicator is relatively straightforward to collect. Countries may use packages that do not align with the speeds used for this group of indicators. Countries are encouraged to collect the data in more speed categories so as to allow aggregation of the data according to the split shown above. In the future, the International Telecommunication Union (ITU) might start to include higher-speed categories, reflecting the increasing demand and availability of higher-speed broadband subscriptions.</p>", "DATA_COMP__GLOBAL"=>"<p>International Telecommunication Union (ITU) collects data for this indicator through an annual questionnaire from national regulatory authorities or Information and Communication Technology (ICT) Ministries, who collect the data from national Internet service providers. The data can be collected by asking each Internet service provider in the country to provide the number of their fixed-broadband subscriptions by the speeds indicated. The data are then added up to obtain the country totals.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are submitted by Member States to International Telecommunication Union (ITU). ITU checks and validates the data, in consultation with the Member States. </p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made to the data submitted by countries.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values are not estimated (Not applicable).</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Missing values are not estimated (Not applicable).</p>", "REG_AGG__GLOBAL"=>"<p>Not calculated for the speed breakdowns.</p>", "DOC_METHOD__GLOBAL"=>"<p>International Telecommunication Union (ITU) Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020: <a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/handbook.aspx\"><u>https://www.itu.int/en/ITU-D/Statistics/Pages/publications/handbook.aspx</u></a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data are checked and validated by the Information and Communication Technology (ICT) Data and Analytics (IDA) Division of the ITU. Countries are contacted to clarify and correct their submissions.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The guidelines of the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020 are followed.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The guidelines of the ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020 are followed.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data for this indicator exist for more than 160 economies.</p>\n<p><strong>Time series:</strong></p>\n<p>2000 onwards.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Since data for this indicator are based on administrative data from Internet Service Providers (ISPs), no information on individual subscribers is available and therefore the data cannot be broken down by any individual characteristics. Data could in theory be broken down by geographic location and urban/rural, but ITU does not collect this information.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Differences between global and national figures may arise when countries do not use the same definition for fixed-broadband subscriptions, or when speed tiers differ. Differences for each data point will be explained in a note.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx\"><u>http://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx</u></a> </p>\n<p><strong>References:</strong></p>\n<p>ITU Handbook for the Collection of Administrative Data on Telecommunications/ICT 2020: <a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/handbook.aspx\"><u>https://www.itu.int/en/ITU-D/Statistics/Pages/publications/handbook.aspx</u></a> </p>", "indicator_sort_order"=>"17-06-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.7.1", "slug"=>"17-7-1", "name"=>"Total de los fondos destinados a los países en desarrollo a fin de promover el desarrollo, la transferencia y la difusión de tecnologías ecológicamente racionales", "url"=>"/site/es/17-7-1/", "sort"=>"170701", "goal_number"=>"17", "target_number"=>"17.7", "global"=>{"name"=>"Total de los fondos destinados a los países en desarrollo a fin de promover el desarrollo, la transferencia y la difusión de tecnologías ecológicamente racionales"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Total de los fondos destinados a los países en desarrollo a fin de promover el desarrollo, la transferencia y la difusión de tecnologías ecológicamente racionales", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Total de los fondos destinados a los países en desarrollo a fin de promover el desarrollo, la transferencia y la difusión de tecnologías ecológicamente racionales", "indicator_number"=>"17.7.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La gestión ambiental racional implica optimizar el uso de los recursos \npara satisfacer las necesidades humanas básicas sin destruir la capacidad \nde sustentabilidad y regeneración de los sistemas naturales. Esto requiere \nuna buena comprensión de los elementos que se intersectan en el marco general \ndel desarrollo e implica la adopción y el uso de estrategias de desarrollo \nalternativas y ambientalmente racionales, así como de tecnologías relacionadas. \n\nLas tecnologías ecológicamente racionales (TER) desempeñan un papel importante \npara mejorar la eficiencia de los recursos (materiales y energía) y reducir \nla contaminación y los residuos en diferentes sectores. La importancia de \nlas tecnologías ecológicamente racionales (TER) se destacó por primera vez \ndurante la Cumbre de la Tierra de Río en 1992 y, desde entonces, se ha \nconvertido en un componente fundamental de la cooperación ambiental \ninternacional. \n\nEl acceso a las TER también desempeña un papel central en el acuerdo pionero, \nla Agenda de Acción de Addis Abeba, que constituye un mecanismo de \nimplementación para los Objetivos de Desarrollo Sostenible \n(Agenda 2030 para el Desarrollo Sostenible) globales. El acuerdo fue \nalcanzado por los 193 Estados miembros de la ONU.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.7.1&seriesCode=DC_ENVTECH_TT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nComercio total de tecnologías ambientalmente racionales monitoreadas (dólares estadounidenses actuales) DC_ENVTECH_TT</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-07-01.pdf\">Metadatos 17-7-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Rational environmental management means making the best use of resources to meet basic human needs \nwithout destroying the sustaining and regenerative capacity of natural systems. This requires a good \nunderstanding of the intersecting elements within the larger frame of development and implies the \nadoption and use of alternative, environmentally sound development strategies and related technologies.  \n\nESTs play an important role to improve efficiency of resources (materials and energy), and reduce pollution \nand waste from different sectors. The importance of Environmentally Sound Technology was first \nemphasized during Rio Earth Summit in 1992 and ever since it has become a major component of \ninternational environmental cooperation. \n\nAccess to ESTs also plays a central role in the ground-breaking \nagreement, the Addis Ababa Action Agenda – which is an implementing mechanism for the global \nSustainable Development Goals (2030 Agenda for Sustainable Development). The agreement was reached \nby the 193 UN Member States. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.7.1&seriesCode=DC_ENVTECH_TT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nTotal trade of tracked Environmentally Sound Technologies (current United States dollars) DC_ENVTECH_TT</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-07-01.pdf\">Metadata 17-7-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La gestión ambiental racional implica optimizar el uso de los recursos \npara satisfacer las necesidades humanas básicas sin destruir la capacidad \nde sustentabilidad y regeneración de los sistemas naturales. Esto requiere \nuna buena comprensión de los elementos que se intersectan en el marco general \ndel desarrollo e implica la adopción y el uso de estrategias de desarrollo \nalternativas y ambientalmente racionales, así como de tecnologías relacionadas. \n\nLas tecnologías ecológicamente racionales (TER) desempeñan un papel importante \npara mejorar la eficiencia de los recursos (materiales y energía) y reducir \nla contaminación y los residuos en diferentes sectores. La importancia de \nlas tecnologías ecológicamente racionales (TER) se destacó por primera vez \ndurante la Cumbre de la Tierra de Río en 1992 y, desde entonces, se ha \nconvertido en un componente fundamental de la cooperación ambiental \ninternacional. \n\nEl acceso a las TER también desempeña un papel central en el acuerdo pionero, \nla Agenda de Acción de Addis Abeba, que constituye un mecanismo de \nimplementación para los Objetivos de Desarrollo Sostenible \n(Agenda 2030 para el Desarrollo Sostenible) globales. El acuerdo fue \nalcanzado por los 193 Estados miembros de la ONU.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.7.1&seriesCode=DC_ENVTECH_TT&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nIngurumenaren aldetik arrazionalak diren teknologia monitorizatuen guztizko merkataritza (egungo dolar estatubatuarrak) DC_ENVTECH_TT</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-07-01.pdf\">Metadatuak 17-7-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.7: Promote the development, transfer, dissemination and diffusion of environmentally sound technologies to developing countries on favourable terms, including on concessional and preferential terms, as mutually agreed</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.7.1: Total amount of funding for developing and developed countries to promote the development, transfer, dissemination and diffusion of environmentally sound technologies</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>DC_ENVTECH_INV - Total investment in Environment Sound Technologies, by sector [17.7.1]</p>\n<p>DC_ENVTECH_EXP - Amount of tracked exported Environmentally Sound Technologies [17.7.1]</p>\n<p>DC_ENVTECH_IMP - Amount of tracked imported Environmentally Sound Technologies [17.7.1]</p>\n<p>DC_ENVTECH_REXP - Amount of tracked re-exported Environmentally Sound Technologies [17.7.1]</p>\n<p>DC_ENVTECH_RIMP - Amount of tracked re-imported Environmentally Sound Technologies [17.7.1]</p>\n<p>DC_ENVTECH_TT - Total trade of tracked Environmentally Sound Technologies [17.7.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>United Nations Environment Programme (UNEP) has identified a number of SDGs where uptake of Environmentally Sound Technologies (ESTs) contributes to their achievement: Goal 7 on ensuring access to affordable, reliable, sustainable and modern energy for all; Goal 8 on the promotion of sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all; Goal 12 on sustainable consumption and production patterns, and Goal 13 on taking urgent action to combat climate change and its impacts.</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Environmentally Sound Technologies (ESTs) are technologies that have the potential for significantly improved environmental performance relative to other technologies. ESTs protect the environment, are less polluting, use resources in a sustainable manner, recycle more of their wastes and products, and handle all residual wastes in a more environmentally acceptable way than the technologies for which they are substitutes. ESTs are not just individual technologies. They can also be defined as total systems that include know-how, procedures, goods and services, and equipment, as well as organizational and managerial procedures for promoting environmental sustainability. </p>\n<p>The purpose of this indicator is to track the total amount of approved funding to promote the development, transfer, dissemination, and diffusion of environmentally sound technologies. A two-pronged approach is suggested: </p>\n<ul>\n  <li>Level 1 (globally estimated). Use globally available data to create a proxy of funding flowing to countries for environmentally sound technologies, or of trade in environmentally sound technologies </li>\n  <li>Level 2 (national). Collect national data on environmental goods and services sector (EGSS).</li>\n</ul>\n<p><strong>Concepts:</strong></p>\n<p>There are five crucial elements which make up Goal 17 - finance, capacity building, systemic issues, technology, and trade - all of which must be aligned for the Goal to be achieved. One of the key lessons over the last couple of decades has been that in order to achieve potential growth, measurement of financial flows (in terms of amount, type, geography, donor, recipient, and investors) is a necessary step in such a transformation. To understand systemic issues, trade, capacity building, technology lock-in, innovation and deployment, we must understand how, why and where finance is being deployed. Only then we can begin to realign its flows.</p>\n<p>In deciding which technologies are most appropriate, there will always be trade-offs between cost and a range of economic, social, health and environmental impacts, to be determined based on national or local contexts and priorities. It would also not be feasible for all countries to strive towards the best available technologies globally if these are not appropriate in a domestic context. Given the highly contextual nature of ESTs, it is therefore something that is better defined at the national level, taking into account the national context and mainstream technologies nationally. However, there is a real need to support national, sub-national governments and other actors with decision-making and defining the most nationally or locally appropriate technologies.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Current United States Dollars</p>", "CLASS_SYSTEM__GLOBAL"=>"<ul>\n  <li>International Standard Industrial Classification of All Economic Activities (ISIC), Rev.4.</li>\n  <li>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions).</li>\n  <li>Harmonized Commodity Description and Coding Systems (HS).</li>\n  <li>Classification of Environmental Purposes (CEP).</li>\n</ul>", "SOURCE_TYPE__GLOBAL"=>"<p>Level 1: the United Nations Comtrade database.</p>\n<p>Level 2: National Statistical Offices (NSOs) and other members of the National Statistical System (NSS).</p>", "COLL_METHOD__GLOBAL"=>"<p>National data are collected through the UNEP Questionnaire on Environmentally Sound Technologies every two years. </p>", "FREQ_COLL__GLOBAL"=>"<p>First data collection in 2021, then every 2 years.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>First reporting cycle in 2022, then every two years.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (NSOs) and other members of the National Statistical System (NSS), complemented by global modelling</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) was mandated as the Custodian Agency for indicator 17.7.1 by the Inter-agency and Expert Group on SDG Indicators. </p>", "RATIONALE__GLOBAL"=>"<p>Rational environmental management means making the best use of resources to meet basic human needs without destroying the sustaining and regenerative capacity of natural systems. This requires a good understanding of the intersecting elements within the larger frame of development and implies the adoption and use of alternative, environmentally sound development strategies and related technologies. ESTs play an important role to improve efficiency of resources (materials and energy), and reduce pollution and waste from different sectors. The importance of Environmentally Sound Technology was first emphasized during Rio Earth Summit in 1992 and ever since it has become a major component of international environmental cooperation. Access to ESTs also plays a central role in the ground-breaking agreement, the Addis Ababa Action Agenda &#x2013; which is an implementing mechanism for the global Sustainable Development Goals (2030 Agenda for Sustainable Development). The agreement was reached by the 193 UN Member States.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Various definitions of &#x2018;environmentally sound technology&#x2019; exist and are in use. Terms such as &#x2018;environmental technology&#x2019;, &#x2018;clean technology&#x2019;, &#x2018;and cleantech &#x2019;or &#x2018;low-carbon technology&#x2019; are sometimes used, although low-carbon technology can be considered as a sub-set of green technology. Other less commonly used terms include climate-smart and climate-friendly technology.</p>\n<p>Additional limitations include the different baseline years in numerous available databases, and the different purposes of available databases.</p>\n<p>Many national statistical systems lack the capacity to compile information on &#x201C;Total amount of approved funding to promote the development, transfer, dissemination and diffusion of environmentally sound technologies&#x201D;. Compiling data on this indicator presents a challenge in terms of consistent definitions and approaches. However, this methodology recognizes these difficulties and provides an approach that can allow a comparability among countries.</p>", "DATA_COMP__GLOBAL"=>"<p>The methodology for tracking the total amount of approved funding to promote the development, transfer, dissemination, and diffusion of environmentally sound technologies has a two-pronged approach: </p>\n<p><strong>Level 1</strong>. Use globally available data to create a proxy of funding flowing to countries for environmentally sound technologies, or of trade in environmentally sound technologies:</p>\n<p>Total trade of tracked Environmentally Sound Technologies (ESTs) that provides the closest indicator of investment flows is that of trade (e.g. traded goods and services that have been internationally agreed to have a positive environmental benefit), using HS codes of the Harmonized Commodity Description and Coding Systems, preferably more than 6-digit level. </p>\n<p>Total trade of tracked Environmentally Sound Technologies (ESTs) is calculated as the sum of tracked exported, imported, re-exported and re-imported ESTs.</p>\n<p>The sectors deemed to be ESTs through historical research include:</p>\n<ul>\n  <li>Air pollution control,</li>\n  <li>Wastewater management,</li>\n  <li>Solid and Hazardous waste management,</li>\n  <li>Renewable Energy,</li>\n  <li>Environmentally Preferable Products,</li>\n  <li>Water Supply &amp; Sanitation,</li>\n  <li>Energy Storage &amp; Distribution,</li>\n  <li>Land &amp; Water Protection &amp; Remediation.</li>\n</ul>\n<p><strong>Level 2.</strong> Collect national data on environmental goods and services sector (EGSS):</p>\n<p>ESTs can be considered as the environmental goods and services sector (EGSS), described in the System of Environmental-Economic Accounting - Central Framework (SEEA CF). The EGSS consists of producers of all environmental goods and services. Thus, all products that are produced, designed, and manufactured for purposes of environmental protection and resource management are within scope of the EGSS. This aligns with the intent of the EGSS to provide information on the extent to which the economy may become more environmentally friendly and resource efficient. The types of environmental goods and services in scope of the EGSS are environmental specific services, environmental sole-purpose products, adapted goods and environmental technologies.</p>\n<p>Environmental specific services are environmental protection and resource management specific services produced by economic units for sale or own use. Examples of environmental specific services are waste and wastewater management and treatment services, and energy and water-saving activities. Environmental specific services are those services that have the main purpose of:</p>\n<p>(a) Preventing or minimizing pollution, degradation or natural resources depletion (including the production of energy from renewable sources);</p>\n<p>(b) Treating and managing pollution, degradation and natural resource depletion;</p>\n<p>(c) Repairing damage to air, soil, water, biodiversity and landscapes;</p>\n<p>(d) Carrying out other activities such as measurement and monitoring, control, research and development, education, training, information, and communication related to environmental protection or resource management.</p>\n<p>Environmental sole-purpose products are goods (durable or non-durable) or services whose use directly serves an environmental protection or resource management purpose and that have no use except for environmental protection or resource management. Examples of these products include catalytic converters, septic tanks (including maintenance services), and the installation of renewable energy production technologies (e.g., solar panels).</p>\n<p>Adapted goods are goods that have been specifically modified to be more &#x201C;environmentally friendly&#x201D; or &#x201C;cleaner&#x201D; and whose use is therefore beneficial for environmental protection or resource management. For the purposes of the EGSS, adapted goods are either:</p>\n<p>(a) &#x201C;Cleaner&#x201D; goods, which help to prevent pollution or environmental degradation because they are less polluting at the time of their consumption and/or scrapping, compared with equivalent &#x201C;normal&#x201D; goods. Equivalent normal goods are goods that provide similar utility except for the impact on the environment. Examples include mercury-free batteries and cars or buses with lower air emissions;</p>\n<p>(b) &#x201C;Resource-efficient&#x201D; goods, which help to prevent natural resource depletion because they contain fewer natural resources in the production stage (e.g., recycled paper and renewable energy, heat from heat pumps and solar panels); and/or in the use stage (e.g., resource efficient appliances and water-saving devices such as tap filters).</p>\n<p>Adapted goods differ from environmental specific services and sole-purpose products because, while they serve an environmental protection or resource management purpose (through being cleaner or more resource-efficient), these are not the primary reasons for their production (e.g., the primary purpose for manufacturing buses with lower air emissions is transportation).</p>\n<p>Environmental technologies are technical processes, installations and equipment (goods), and methods or knowledge (services), whose technical nature or purpose is environmental protection or resource management. Environmental technologies can be classified as either:</p>\n<p>(a) End-of-pipe (pollution treatment) technologies, which are mainly technical installations and equipment produced for measurement, control, treatment and restoration/correction of pollution, environmental degradation, and/or resource depletion. Examples include plants within which to treat sewage, equipment for measuring air pollution, and facilities for the containment of high-level radioactive waste;</p>\n<p>(b) Integrated (pollution prevention) technologies, which are technical processes, methods or knowledge used in production processes that are less polluting and less resource-intensive than the equivalent &#x201C;normal&#x201D; technology used by other producers. Their use is less environmentally harmful than that of relevant alternatives.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Level 1 indicators: UNEP uses a random sampling for few countries and calculates the total of HS codes for export, import, re-export and re-import and compares with the automated produced amounts for those countries. The value per HS is also compared with the data on the COMTRADE database.</p>\n<p>Level 2 indicators: UNEP carries out data validation procedures and contacts countries for clarification if needed. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) does not make any imputation for missing values.</p>", "REG_AGG__GLOBAL"=>"<p>The data will be aggregated at the sub-regional, regional, and global levels. For the aggregation methods, please see: <a href=\"http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">http://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a>.</p>", "DOC_METHOD__GLOBAL"=>"<p>To define total ESTs produced, it is proposed to use statistics on environmental good and services, based on the System of Environmental-Economic Accounting &#x2013; Central Framework (Chapter IV Environmental activity accounts and related flows). The System of Environmental-Economic Accounting - Central Framework (SEEA CF) can be found on: https://seea.un.org/content/seea-central-framework</p>\n<p>General recommendations are provided in the <a href=\"https://wedocs.unep.org/xmlui/bitstream/handle/20.500.11822/38265/SDG17.7.1_Method.pdf\">INDICATOR METHODOLOGY FOR SDG 17.7.1</a>. </p>\n<p>To expand the coverage of national EGSS data, UNEP is preparing a step-by-step methodology based on the SEEA CF. The methodology will be available to countries in early 2025. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided by the United Nations Environment Programme (UNEP).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided by the United Nations Environment Programme (UNEP) in cooperation with the countries that provide these data.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided by the United Nations Environment Programme (UNEP).</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Level 1 indicators: All UN Member States.</p>\n<p>Level 2 indicators: All countries that provided country data to the UNEP Questionnaire on Environmentally Sound Technologies. </p>\n<p>According to the 2023 Global Assessment of Environmental-Economic Accounting and Supporting Statistics, organized by UNSD, 42 countries compile EGSS statistics. Data on EGSS are available for 27 EU countries in the Eurostat Database. The data will be reported by UNEP to the Global SDG Database in 2025.</p>\n<p>UNEP currently reports data on trade flows (proxy indicator) for 184 countries calculated using the COMTRADE database.</p>\n<p><strong>Time series:</strong></p>\n<p>Level 1 indicators: The data sets presented in the SDG database covers a period since 2010.</p>\n<p>Level 2 indicators: The data sets presented in the SDG database presented according to country responses.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>According to the Classification of Environmental Purposes (CEP):</p>\n<ul>\n  <li>Reduction and control of greenhouse gases and other air pollutions</li>\n  <li>Energy from renewable sources, energy savings and management</li>\n  <li>Wastewater management, water savings and management of natural water resources</li>\n  <li>Waste management, materials recovery and savings</li>\n  <li>Protection of soil, surface and groundwater, biodiversity and landscape</li>\n  <li>Other environmental purposes</li>\n</ul>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies</strong>:</p>\n<p>Possible sources of discrepancies are caused by the highly contextual nature of Environmentally Sound Technologies (ESTs).</p>", "OTHER_DOC__GLOBAL"=>"<p>The System of Environmental-Economic Accounting - Central Framework (SEEA CF) can be found on: https://seea.un.org/content/seea-central-framework</p>\n<p>General recommendations are provided in the <a href=\"https://wedocs.unep.org/xmlui/bitstream/handle/20.500.11822/38265/SDG17.7.1_Method.pdf\">INDICATOR METHODOLOGY FOR SDG 17.7.1</a>.</p>\n<p><a href=\"https://wedocs.unep.org/bitstream/handle/20.500.11822/27595/TradeEnvTech.pdf?sequence=1&amp;isAllowed=y\">UNEP (2018). Trade in environmentally sound technologies: Implications for Developing Countries.</a></p>\n<p><a href=\"https://www.unep.org/resources/report/trade-environmentally-sound-technologies-implications-developing-countries\">More information on Trade in Environmentally Sound Technologies on the UNEP website</a>.</p>", "indicator_sort_order"=>"17-07-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.8.1", "slug"=>"17-8-1", "name"=>"Proporción de personas que utilizan Internet", "url"=>"/site/es/17-8-1/", "sort"=>"170801", "goal_number"=>"17", "target_number"=>"17.8", "global"=>{"name"=>"Proporción de personas que utilizan Internet"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Proporción de personas entre 16 y 74 años que han utilizado Internet en los últimos tres meses", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de personas que utilizan Internet", "indicator_number"=>"17.8.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_15/opt_1/ti_encuesta-sobre-la-sociedad-de-la-informacion-familias/temas.html", "url_text"=>"Encuesta sobre la sociedad de la información. Familias", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>"", "indicador_disponible"=>"Proporción de personas entre 16 y 74 años que han utilizado Internet en los últimos tres meses", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.8- Poner en pleno funcionamiento, a más tardar en 2017, el banco de tecnología y el mecanismo de apoyo a la creación de capacidad en materia de ciencia, tecnología e innovación para los países menos adelantados y aumentar la utilización de tecnologías instrumentales, en particular la tecnología de la información y las comunicaciones", "definicion"=>"Proporción de personas entre 16 y 74 años que han utilizado Internet en los últimos tres meses", "formula"=>"\n$$PPINT_{16-74}^{t} = \\frac{PINT_{16-74}^{t}}{P_{16-74}^{t}} \\cdot 100$$\n\ndonde:\n\n$PINT_{16-74}^{t} =$ población entre 16 y 74 años que en los últimos tres meses ha utilizado Internet en el año $t$\n\n$P^{t} =$ población entre 16 y 74 años en el año $t$\n", "desagregacion"=>"Sexo\n\nTerritorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nInternet se ha convertido en una herramienta cada vez más importante para acceder a \nla información pública, lo que constituye un medio pertinente para proteger las \nlibertades fundamentales. \n\nEl número de usuarios de Internet ha aumentado sustancialmente en el último decenio y \nel acceso a Internet ha cambiado la forma en que las personas viven, se comunican, \ntrabajan y hacen negocios. La adopción de Internet es un indicador clave que siguen \nlos responsables políticos y otros para medir el desarrollo de la sociedad de la \ninformación y el crecimiento del contenido de Internet, incluido el contenido generado \npor los usuarios, proporciona acceso a cantidades cada vez mayores de información y \nservicios.\n\nA pesar del crecimiento de las redes, los servicios y las aplicaciones, el acceso \ny el uso de las tecnologías de la información y la comunicación (TIC) todavía \ndistan de estar distribuidos de manera equitativa, y muchas personas aún no pueden \nbeneficiarse del potencial de Internet. \n\nEste indicador destaca la importancia del uso de Internet como facilitador del desarrollo \ny ayuda a medir la brecha digital, que, si no se aborda adecuadamente, agravará \nlas desigualdades en todos los ámbitos del desarrollo. Las variables clasificatorias de \nlas personas que utilizan Internet, como la edad, el sexo, el nivel de educación o la \nsituación en la fuerza laboral, pueden ayudar a identificar las brechas digitales en \nlas personas que utilizan Internet. Esta información puede contribuir al diseño de \npolíticas específicas para superar esas brechas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.8.1&seriesCode=IT_USE_ii99&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Proporción de personas que utilizan Internet (%) IT_USE_ii99</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-08-01.pdf\">Metadatos 17-8-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"Proporción de personas entre 16 y 74 años que han utilizado Internet en los últimos tres meses", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.8- Poner en pleno funcionamiento, a más tardar en 2017, el banco de tecnología y el mecanismo de apoyo a la creación de capacidad en materia de ciencia, tecnología e innovación para los países menos adelantados y aumentar la utilización de tecnologías instrumentales, en particular la tecnología de la información y las comunicaciones", "definicion"=>"Proportion of people aged 16-74 who have used the Internet in the last three months", "formula"=>"\n$$PPINT_{16-74}^{t} = \\frac{PINT_{16-74}^{t}}{P_{16-74}^{t}} \\cdot 100$$\n\nwhere:\n\n$PINT_{16-74}^{t} =$ people aged 16-74 who have used the Internet in the last three months in year $t$\n\n$P^{t} =$ population aged 16-74 in year $t$\n", "desagregacion"=>"Sex\n\nProvince\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nThe Internet has become an increasingly important tool to access public information, which is a relevant \nmeans to protect fundamental freedoms. \n\nThe number of Internet users has increased substantially over \nthe last decade and access to the Internet has changed the way people live, communicate, work and do \nbusiness. Internet uptake is a key indicator tracked by policy makers and others to measure the \ndevelopment of the information society and the growth of Internet content – including user-generated \ncontent – provides access to increasing amounts of information and services. \n\nDespite growth in networks, services and applications, information and communication technology (ICT) \naccess and use is still far from equally distributed, and many people cannot yet benefit from the potential \nof the Internet. \n\nThis indicator highlights the importance of Internet use as a development enabler and \nhelps to measure the digital divide, which, if not properly addressed, will aggravate inequalities in all \ndevelopment domains. Classificatory variables for individuals using the Internet –such as age, sex, \neducation level or labour force status – can help identify digital divides in individuals using the Internet. \nThis information can contribute to the design of targeted policies to overcome those divides. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.8.1&seriesCode=IT_USE_ii99&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Proportion of individuals using the Internet (%) IT_USE_ii99</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-08-01.pdf\">Metadata 17-8-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Proporción de personas entre 16 y 74 años que han utilizado Internet en los últimos tres meses", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.8- Poner en pleno funcionamiento, a más tardar en 2017, el banco de tecnología y el mecanismo de apoyo a la creación de capacidad en materia de ciencia, tecnología e innovación para los países menos adelantados y aumentar la utilización de tecnologías instrumentales, en particular la tecnología de la información y las comunicaciones", "definicion"=>"Proporción de personas entre 16 y 74 años que han utilizado Internet en los últimos tres meses", "formula"=>"\n$$PPINT_{16-74}^{t} = \\frac{PINT_{16-74}^{t}}{P_{16-74}^{t}} \\cdot 100$$\n\nnon:\n\n$PINT_{16-74}^{t} =$ azken hiru hilabeteetan Internet erabili duten 16-74 urteko biztanleak $t$ urtean\n\n$P^{t} =$ 16-74 urteko biztanleak $t$ urtean\n", "desagregacion"=>"Sexua\n\nLurralde historikoa\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nInternet se ha convertido en una herramienta cada vez más importante para acceder a \nla información pública, lo que constituye un medio pertinente para proteger las \nlibertades fundamentales. \n\nEl número de usuarios de Internet ha aumentado sustancialmente en el último decenio y \nel acceso a Internet ha cambiado la forma en que las personas viven, se comunican, \ntrabajan y hacen negocios. La adopción de Internet es un indicador clave que siguen \nlos responsables políticos y otros para medir el desarrollo de la sociedad de la \ninformación y el crecimiento del contenido de Internet, incluido el contenido generado \npor los usuarios, proporciona acceso a cantidades cada vez mayores de información y \nservicios.\n\nA pesar del crecimiento de las redes, los servicios y las aplicaciones, el acceso \ny el uso de las tecnologías de la información y la comunicación (TIC) todavía \ndistan de estar distribuidos de manera equitativa, y muchas personas aún no pueden \nbeneficiarse del potencial de Internet. \n\nEste indicador destaca la importancia del uso de Internet como facilitador del desarrollo \ny ayuda a medir la brecha digital, que, si no se aborda adecuadamente, agravará \nlas desigualdades en todos los ámbitos del desarrollo. Las variables clasificatorias de \nlas personas que utilizan Internet, como la edad, el sexo, el nivel de educación o la \nsituación en la fuerza laboral, pueden ayudar a identificar las brechas digitales en \nlas personas que utilizan Internet. Esta información puede contribuir al diseño de \npolíticas específicas para superar esas brechas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.8.1&seriesCode=IT_USE_ii99&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=BOTHSEX\">Internet erabiltzen duten pertsonen proportzioa (%) IT_USE_ii99</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-08-01.pdf\">Metadatuak 17-8-1.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.8: Fully operationalize the technology bank and science, technology and innovation capacity-building mechanism for least developed countries by 2017 and enhance the use of enabling technology, in particular information and communications technology</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.8.1: Proportion of individuals using the Internet</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>IT_USE_ii99 - Proportion of individuals using the Internet [17.8.1]<sup><a href=\"#footnote-1\" id=\"footnote-ref-1\">[1]</a></sup></p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-1\">1</sup><p> In March 2023, the series description was updated from &#x201C;Internet users per 100 inhabitants&#x201D; to &#x201C;Proportion of individuals using the Internet (%)&#x201D; for clarity; the unit of measure was also updated from &#x201C;PER_100_POP&#x201D; to &#x201C;PERCENT&#x201D;; content in the series is the same. <a href=\"#footnote-ref-1\">&#x2191;</a></p></div></div>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>4.4.1, 4.5.1, 9.c.1, 17.6.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator proportion of individuals using the Internet is defined as the proportion of individuals who used the Internet from any location in the last three months.</p>\n<p><strong>Concepts:</strong></p>\n<p>The Internet is a worldwide public computer network. It provides access to a number of communication services including the World Wide Web and carries e-mail, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile telephone, tablet, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>For countries that collect this data on the proportion of individuals using the Internet through an official survey, and if data allow breakdown and disaggregation, the indicator can be broken down by region (urban/rural), by sex, by age group, by educational level (ISCED), by labour force status (ILO), and by occupation (ISCO). ITU collects data for all of these breakdowns from countries.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The indicator proportion of individuals using the Internet is based on an internationally agreed definition and methodology, which have been developed under the coordination of the International Telecommunication Union (ITU), through its Expert Groups and following an extensive consultation process with countries. It is also a core indicator of the Partnership on Measuring ICT for Development&apos;s Core List of Indicators, which has been endorsed by the UN Statistical Commission (last time in 2020). Data on individuals using the Internet are collected through an annual questionnaire that ITU sends to national statistical offices (NSO). In this questionnaire ITU collects absolute values. The percentages are calculated a-posteriori. The survey methodology is verified to ensure that it meets adequate statistical standards. The data are verified to ensure consistency with previous years&#x2019; data and situation of the country for other related indicators (ICT and economic).</p>\n<p>The percentage of individuals using the Internet data are based on methodologically sound household surveys conducted by national statistical agencies. If the NSO has not collected Internet user statistics, then ITU estimates the percentage of individuals using the Internet.</p>\n<p>Data are usually not adjusted, but discrepancies in the definition, age scope of individuals, reference period or the break in comparability between years are noted in a data note. For this reason, data are not always strictly comparable.</p>\n<p>Some countries conduct a household survey where the question on Internet use is included every year. For others, the frequency is every two or three years. </p>\n<p>ITU makes the indicator available for each year for 200 economies by using survey data and estimates for almost all countries of the world.</p>", "COLL_METHOD__GLOBAL"=>"<p>Data on individuals using the Internet are collected through an annual questionnaire that International Telecommunication Union (ITU) sends to national statistical offices (NSO). In this questionnaire ITU collects absolute values. The percentages are calculated a-posteriori. The survey methodology is verified to ensure that it meets adequate statistical standards. The data are verified to ensure consistency with previous years&#x2019; data and situation of the country for other related indicators (ICT and economic).</p>", "FREQ_COLL__GLOBAL"=>"<p>Various. Each survey has its own data collection cycle. International Telecommunication Union (ITU) collects data twice a year from Member States, in Q1 and in Q3.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are released twice a year, In July and December, in the <a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/wtid.aspx\"><u>Wor&#x200B;ld Telecommun&#x200B;ic&#x200B;ation/ICT Indicators Database&#x200B;</u></a>&#x200B;.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Office (NSO).</p>", "COMPILING_ORG__GLOBAL"=>"<p>International Telecommunication Union (ITU)</p>", "INST_MANDATE__GLOBAL"=>"<p>As the UN specialized agency for information and communication technology (ICTs), International Telecommunication Union (ITU) is the official source for global ICT statistics, collecting ICT data from its Member States. </p>", "RATIONALE__GLOBAL"=>"<p>The Internet has become an increasingly important tool to access public information, which is a relevant means to protect fundamental freedoms. The number of Internet users has increased substantially over the last decade and access to the Internet has changed the way people live, communicate, work and do business. Internet uptake is a key indicator tracked by policy makers and others to measure the development of the information society and the growth of Internet content &#x2013; including user-generated content &#x2013; provides access to increasing amounts of information and services.</p>\n<p>Despite growth in networks, services and applications, information and communication technology (ICT) access and use is still far from equally distributed, and many people cannot yet benefit from the potential of the Internet. This indicator highlights the importance of Internet use as a development enabler and helps to measure the digital divide, which, if not properly addressed, will aggravate inequalities in all development domains. Classificatory variables for individuals using the Internet &#x2013;such as age, sex, education level or labour force status &#x2013; can help identify digital divides in individuals using the Internet. This information can contribute to the design of targeted policies to overcome those divides.</p>", "REC_USE_LIM__GLOBAL"=>"<p>While the data on the percentage of individuals using the Internet are very reliable for countries that have collected the data through official household surveys, they are less reliable in cases where the number of Internet users is estimated by the International Telecommunication Union (ITU). ITU is encouraging all countries to collect data on this indicator through official surveys and the number of countries with official data for this indicator is increasing.</p>", "DATA_COMP__GLOBAL"=>"<p>For countries that collect data on this indicator through an official survey, this indicator is calculated by dividing the total number of in-scope individuals using the Internet (from any location) in the last 3 months by the total number of in-scope individuals. For countries that have not carried out a survey, data are estimated (by ITU) based on the number of Internet subscriptions and other socioeconomic indicators (GNI per capita) and on the time series data.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Data are submitted by Member States to International Telecommunication Union (ITU). ITU checks and validates the data, in consultation with the Member States. </p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made to the data submitted by countries.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>In the absence of official household surveys, International Telecommunication Union (ITU) estimates the percentage of individuals using the Internet (Internet users as a percentage of total population) using various techniques, such as hot-deck imputation, regression models and time series forecast, using data such as income, education and other ICT indicators.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>In the absence of official household surveys, ITU estimates the percentage of individuals using the Internet (Internet users as a percentage of total population) using various techniques, such as hot-deck imputation, regression models and time series forecast, using data such as income, education and other ICT indicators.</p>", "REG_AGG__GLOBAL"=>"<p>Country-level data on the percentage of individuals using the Internet (Internet users as a percentage of total population) are first estimated using various techniques, such as hot-deck imputation, regression models and time series forecast. Hot-deck imputation uses data from countries with &#x201C;similar&#x201D; characteristics, such as GNI per capita and geographic location. In cases when it is not possible to find an adequate imputation based on similar cases, regression models based on a set of countries with relatively similar characteristics are applied.</p>\n<p>Once the country-level percentages are available for all countries, the number of Internet users are calculated by multiplying the percentages to the population of the country. The regional and world total Internet users were calculated by summing the country-level data. The aggregate percentages were calculated by dividing the regional totals by the population of respective groups.</p>", "DOC_METHOD__GLOBAL"=>"<p>ITU Manual for Measuring ICT Access and Use by Households and Individuals 2020:</p>\n<p><a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx\"><u>https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx</u></a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Data are checked and validated by the ICT Data and Analytics (IDA) Division of the ITU. Countries are contacted to clarify and correct their submissions.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The guidelines of the Manual for Measuring ICT Access and Use by Households and Individuals 2020 are followed.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The guidelines of the Manual for Measuring ICT Access and Use by Households and Individuals 2020 are followed.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Overall, the indicator is available for more than 130 countries at least from one survey.</p>\n<p>ITU makes the indicator available for each year for 200 economies by using survey data and estimates for almost all countries of the world.</p>\n<p><strong>Time series:</strong></p>\n<p>2000 onwards</p>\n<p><strong>Disaggregation:</strong></p>\n<p>For countries that collect this data on the proportion of individuals using the Internet through an official survey, and if data allow breakdown and disaggregation, the indicator can be broken down by region (geographic and/or urban/rural), by sex, by age group, by educational level, by labour force status, and by occupation. ITU collects data for all of these breakdowns from countries. Estimates of regional aggregates by sex are also calculated.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Differences between global and national figures may arise when countries use a different definition than the one agreed internationally and used by ITU. Discrepancies may also arise in cases where the age scope of the surveys differs, or when the country only provides data for a certain age group and not the total population.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>http://www.itu.int/en/ITU-D/Statistics/Pages/default.aspx</p>\n<p><strong>References:</strong></p>\n<p>ITU Manual for Measuring ICT Access and Use by Households and Individuals 2020:</p>\n<p><a href=\"https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx\"><u>https://www.itu.int/en/ITU-D/Statistics/Pages/publications/manual.aspx</u></a> </p>", "indicator_sort_order"=>"17-08-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.9.1", "slug"=>"17-9-1", "name"=>"Valor en dólares de la asistencia financiera y técnica (incluso mediante la cooperación Norte-Sur, Sur‑Sur y triangular) prometida a los países en desarrollo", "url"=>"/site/es/17-9-1/", "sort"=>"170901", "goal_number"=>"17", "target_number"=>"17.9", "global"=>{"name"=>"Valor en dólares de la asistencia financiera y técnica (incluso mediante la cooperación Norte-Sur, Sur‑Sur y triangular) prometida a los países en desarrollo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Valor en dólares de la asistencia financiera y técnica (incluso mediante la cooperación Norte-Sur, Sur‑Sur y triangular) prometida a los países en desarrollo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Valor en dólares de la asistencia financiera y técnica (incluso mediante la cooperación Norte-Sur, Sur‑Sur y triangular) prometida a los países en desarrollo", "indicator_number"=>"17.9.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Los flujos totales de Ayuda oficial al desarrollo (AOD) y de otros flujos oficiales \na los países en desarrollo cuantifican el esfuerzo público (excluidos los \ncréditos a las exportaciones) que los donantes proporcionan a los países en desarrollo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.9.1&seriesCode=DC_FTA_TOTAL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nAsistencia oficial total para el desarrollo (desembolso bruto) para cooperación técnica (millones de dólares de los Estados Unidos de 2022) DC_FTA_TOTAL</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-09-01.pdf\">Metadatos 17-9-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Total ODA and OOF flows to developing countries quantify the public effort (excluding export credits) \nthat donors provide to developing countries. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.9.1&seriesCode=DC_FTA_TOTAL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nTotal official development assistance (gross disbursement) for technical cooperation (millions of 2022 United States dollars) DC_FTA_TOTAL</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-09-01.pdf\">Metadata 17-9-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Los flujos totales de Ayuda oficial al desarrollo (AOD) y de otros flujos oficiales \na los países en desarrollo cuantifican el esfuerzo público (excluidos los \ncréditos a las exportaciones) que los donantes proporcionan a los países en desarrollo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.9.1&seriesCode=DC_FTA_TOTAL&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nLankidetza teknikorako garapenerako laguntza ofizial osoa (ordainketa gordina) (2022ko Estatu Batuetako milioika dolar) DC_FTA_TOTAL</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-09-01.pdf\">Metadatuak 17-9-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.9: Enhance international support for implementing effective and targeted capacity-building in developing countries to support national plans to implement all the Sustainable Development Goals, including through North-South, South-South and triangular cooperation</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.9.1: Dollar value of financial and technical assistance (including through North-South, South&#x2011;South and triangular cooperation) committed to developing countries</p>", "META_LAST_UPDATE__GLOBAL"=>"2017-07-09", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Gross disbursements of total ODA and other official flows from all donors for capacity building and national planning.</p>\n<p><strong>Concepts:</strong></p>\n<p>ODA: The DAC defines ODA as &#x201C;those flows to countries and territories on the DAC List of ODA Recipients and to multilateral institutions which are </p>\n<p>i) provided by official agencies, including state and local governments, or by their executive agencies; and </p>\n<p>ii) each transaction is administered with the promotion of the economic development and welfare of developing countries as its main objective; and</p>\n<p>is concessional in character and conveys a grant element of at least 25 per cent (calculated at a rate of discount of 10 per cent). </p>\n<p>(See http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm)</p>\n<p>Other official flows (OOF): Other official flows (excluding officially supported export credits) are defined as transactions by the official sector which do not meet the conditions for eligibility as ODA, either because they are not primarily aimed at development, or because they are not sufficiently concessional.</p>\n<p>(See http://www.oecd.org/dac/stats/documentupload/DCDDAC(2016)3FINAL.pdf, Para 24).</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The OECD/DAC has been collecting data on official and private resource flows from 1960 at an aggregate level and 1973 at an activity level through the Creditor Reporting System (CRS data are considered complete from 1995 for commitments at an activity level and 2002 for disbursements). </p>\n<p>The data are reported by donors according to the same standards and methodologies (see here: http://www.oecd.org/dac/stats/methodology.htm). </p>\n<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.</p>", "COLL_METHOD__GLOBAL"=>"<p>A statistical reporter is responsible for the collection of DAC statistics in each providing country/agency. This reporter is usually located in the national aid agency, Ministry of Foreign Affairs or Finance etc. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data are published on an annual basis in December for flows in the previous year.</p>\n<p>Detailed 2015 flows was published in December 2016.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are reported on an annual calendar year basis by statistical reporters in national administrations (aid agencies, Ministries of Foreign Affairs or Finance, etc.</p>", "COMPILING_ORG__GLOBAL"=>"<p>OECD</p>", "RATIONALE__GLOBAL"=>"<p>Total ODA and OOF flows to developing countries quantify the public effort (excluding export credits) that donors provide to developing countries.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Data in the Creditor Reporting System are available from 1973. However, the data coverage is considered complete since 1995 for commitments at an activity level and 2002 for disbursements.</p>", "DATA_COMP__GLOBAL"=>"<p>The sum of ODA and OOF flows from all donors to developing countries for capacity building and national planning.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>None</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>None</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional figures are based on the sum of ODA and OOF flows.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>On a recipient basis for all developing countries eligible for ODA.</p>\n<p><strong>Time series:</strong></p>\n<p><strong>Disaggregation:</strong></p>\n<p>This indicator can be disaggregated by type of flow (ODA or OOF), by donor, recipient country, type of finance, type of aid, sector, etc.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>DAC statistics are standardized on a calendar year basis for all donors and may differ from fiscal year data available in budget documents for some countries.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>www.oecd.org/dac/stats</p>\n<p><strong>References:</strong></p>\n<p>See all links here: http://www.oecd.org/dac/stats/methodology.htm</p>", "indicator_sort_order"=>"17-09-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.10.1", "slug"=>"17-10-1", "name"=>"Promedio arancelario mundial ponderado", "url"=>"/site/es/17-10-1/", "sort"=>"171001", "goal_number"=>"17", "target_number"=>"17.10", "global"=>{"name"=>"Promedio arancelario mundial ponderado"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Promedio arancelario mundial ponderado", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Promedio arancelario mundial ponderado", "indicator_number"=>"17.10.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notapplicable", "reporting_status"=>"notapplicable", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El nivel promedio de los aranceles aduaneros aplicados en todo el mundo puede \nutilizarse como indicador del grado de éxito alcanzado por las negociaciones \nmultilaterales y los acuerdos comerciales regionales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-10-01.pdf\">Metadatos 17-10-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The average level of customs tariff rates applied worldwide can be used as an indicator of the degree of \nsuccess achieved by multilateral negotiations and regional trade agreements. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-10-01.pdf\">Metadata 17-10-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"El nivel promedio de los aranceles aduaneros aplicados en todo el mundo puede \nutilizarse como indicador del grado de éxito alcanzado por las negociaciones \nmultilaterales y los acuerdos comerciales regionales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-10-01.pdf\">Metadatuak 17-10-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.10: Promote a universal, rules-based, open, non&#x2011;discriminatory and equitable multilateral trading system under the World Trade Organization, including through the conclusion of negotiations under its Doha Development Agenda</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.10.1: Worldwide weighted tariff-average</p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2016-07-19</p>", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Linkages with the decrease of agricultural market distortions (target 2.b), improvements in the transfer of environmental goods and services (target 17.7) and better access to essential medicines (3.b).</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Trade Centre (ITC)</p>\n<p>United Nations Conference on Trade and Development (UNCTAD)</p>\n<p>The World Trade Organization (WTO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Trade Centre (ITC)</p>\n<p>United Nations Conference on Trade and Development (UNCTAD)</p>\n<p>The World Trade Organization (WTO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Value in percentage of weighted average tariffs applied to the imports of goods in HS chapter 01-97.</p>\n<p><strong>Concepts:</strong></p>\n<p>Weighted average: In order to aggregate tariff value for country groups it is recommended to make use of a weighting methodology based on the value of goods imported.</p>\n<p>Tariffs: Tariffs are customs duties on merchandise imports, levied either on an ad valorem basis (percentage of value) or on a specific basis (e.g. $7 per 100 kg). Tariffs can be used to create a price advantage for similar locally-produced goods and for raising government revenues. Trade remedy measures and taxes are not considered to be tariffs.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The main information used to calculate indicators 17.10.1 is import tariff data. Information on import tariffs might be retrieved by contacting directly National statistical offices, permanent country missions to the UN, regional organizations or focal points within the customs, ministries in charge of customs revenues (Ministry of economy/finance and related revenue authorities) or, alternatively, the Ministry of trade. Tariff data for the calculation of this indicator are retrieved from ITC (MAcMap) - http://www.macmap.org/ - WTO (IDB) - http://tao.wto.org - and UNCTAD (TRAINS) databases. Import tariff data included in the ITC (MAcMap) database are collected by contacting directly focal points in line national agencies or regional organizations (in the case of custom unions or regional economic communities). When available, data are downloaded from national or regional official websites. In some cases, data are purchased from private companies. Import tariff data included in the WTO (IDB) database are sourced from official notifications of WTO members. Import tariff included in the UNCTAD (TRAINS) database are collected from official sources, including official country or regional organizations websites.</p>", "FREQ_COLL__GLOBAL"=>"<p>Continuously update all year round</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Indicatively the indicators calculations can be ready by March every year. However, the date of release will depend on the period envisaged for the launching of the SDG monitoring report.</p>", "COMPILING_ORG__GLOBAL"=>"<p>ITC, WTO and UNCTAD will jointly report on this indicator</p>", "RATIONALE__GLOBAL"=>"<p>The average level of customs tariff rates applied worldwide can be used as an indicator of the degree of success achieved by multilateral negotiations and regional trade agreements.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Tariffs are only part of the factors that can explain the degree of openness and transparency in the international trade arena. However, accurate estimates on non-tariff measures or of transparency indicator do not exist.</p>\n<p>To further refine the quality of the information, additional sub-measurements could be calculated including: a) Tariff peaks (i.e. % of tariffs on some products that are considerably higher than usual, defined as above 15 per cent) and b) Tariff escalation (i.e. wherein a country applies a higher tariff rate to products at the later stages of production). These calculations were already provided by ITC as part of the MDG Gap Task Force Report. See the report for further information on the methodology at http://www.un.org/en/development/desa/policy/mdg_gap/mdg_gap2014/2014GAP_FULL_EN.pdf</p>", "DATA_COMP__GLOBAL"=>"<p>In order to include all tariffs into the calculation, some rates which are not expressed in ad valorem form (e.g., specific duties) are converted in ad valorem equivalents (i.e. in per cent of the import value), The conversion is made at the tariff line level for each importer by using the unit value method. Import unit values are calculated from import values and quantities. Only a limited number of non-ad valorem tariff rates (i.e. technical duties) cannot be provided with ad valorem equivalents (AVE) and are excluded from the calculation. This methodology also allows for cross-country comparisons.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values are calculated using the most recent year available.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Missing values are calculated using the most recent year available.</p>", "REG_AGG__GLOBAL"=>"<p>HS 6-digit tariff averages weighted with HS 6-digit bilateral import flows for traded national tariff lines.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Asia and Pacific: 42</p>\n<p>Africa: 49</p>\n<p>Latin America and the Caribbean: 34</p>\n<p>Europe, North America, Australia, New Zealand and Japan: 48</p>\n<p><strong>Time series:</strong></p>\n<p>Yearly data from 2005 to latest year </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregation is available by product sector (e.g. Agriculture, Textile, Environmental goods), geographical regions and country income level (e.g. Developed, Developing, LDCs)</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>http://www.intracen.org / www.wto.org / http://unctad.org/en/Pages/Home.aspx</p>\n<p><strong>References:</strong></p>\n<p>Not references</p>", "indicator_sort_order"=>"17-10-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.11.1", "slug"=>"17-11-1", "name"=>"Participación de los países en desarrollo y los países menos adelantados en las exportaciones mundiales", "url"=>"/site/es/17-11-1/", "sort"=>"171101", "goal_number"=>"17", "target_number"=>"17.11", "global"=>{"name"=>"Participación de los países en desarrollo y los países menos adelantados en las exportaciones mundiales"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Participación de los países en desarrollo y los países menos adelantados en las exportaciones mundiales", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Participación de los países en desarrollo y los países menos adelantados en las exportaciones mundiales", "indicator_number"=>"17.11.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notapplicable", "reporting_status"=>"notapplicable", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El indicador propuesto permite monitorear el aumento de las \nexportaciones de los países en desarrollo y los países menos\nadelandados (PMA), según lo estipulado en la meta 17.11. \n\nEl uso de las proporciones de las exportaciones mundiales \nproporciona información sobre el tamaño relativo de las \nexportaciones de los países en desarrollo y los PMA en \ncomparación con las exportaciones mundiales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.11.1&seriesCode=TX_IMP_GBMRCH&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nParticipación de los países en desarrollo y los países menos adelantados en las importaciones mundiales de mercancías (%) TX_IMP_GBMRCH</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.11.1&seriesCode=TX_EXP_GBMRCH&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nParticipación de los países en desarrollo y los países menos adelantados en las exportaciones mundiales de mercancías (%) TX_EXP_GBMRCH</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.11.1&seriesCode=TX_EXP_GBSVR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nParticipación de los países en desarrollo y los países menos adelantados en las exportaciones mundiales de servicios (%) TX_EXP_GBSVR</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.11.1&seriesCode=TX_IMP_GBSVR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nParticipación de los países en desarrollo y los países menos adelantados en las importaciones mundiales de servicios (%) TX_IMP_GBSVR</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-11-01.pdf\">Metadatos 17-11-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The indicator proposed allows tracking the increase of exports from developing countries and least \ndeveloped countries (LDCs) prescribed by target 17.11. \n\nThe use of world export shares provides information on the relative size of developing and LDC exports \ncompared to world exports. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.11.1&seriesCode=TX_IMP_GBMRCH&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nDeveloping countries’ and least developed countries’ share of global merchandise imports (%) TX_IMP_GBMRCH</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.11.1&seriesCode=TX_EXP_GBMRCH&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nDeveloping countries’ and least developed countries’ share of global merchandise exports (%) TX_EXP_GBMRCH</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.11.1&seriesCode=TX_EXP_GBSVR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nDeveloping countries’ and least developed countries’ share of global services exports (%) TX_EXP_GBSVR</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.11.1&seriesCode=TX_IMP_GBSVR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nDeveloping countries’ and least developed countries’ share of global services imports (%) TX_IMP_GBSVR</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-11-01.pdf\">Metadata 17-11-1.pdf</a> ", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"El indicador propuesto permite monitorear el aumento de las \nexportaciones de los países en desarrollo y los países menos\nadelandados (PMA), según lo estipulado en la meta 17.11. \n\nEl uso de las proporciones de las exportaciones mundiales \nproporciona información sobre el tamaño relativo de las \nexportaciones de los países en desarrollo y los PMA en \ncomparación con las exportaciones mundiales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.11.1&seriesCode=TX_IMP_GBMRCH&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nGarapen bidean dauden herrialdeen eta aurrerapen gutxien duten herrialdeen parte-hartzea salgaien munduko inportazioetan (%) TX_IMP_GBMRCH</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.11.1&seriesCode=TX_EXP_GBMRCH&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nGarapen bidean dauden herrialdeen eta aurrerapen gutxien duten herrialdeen parte-hartzea salgaien munduko esportazioetan (%) TX_EXP_GBMRCH</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.11.1&seriesCode=TX_EXP_GBSVR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nGarapen bidean dauden herrialdeen eta aurrerapen gutxien duten herrialdeen parte-hartzea zerbitzuen munduko esportazioetan (%) TX_EXP_GBSVR</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.11.1&seriesCode=TX_IMP_GBSVR&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nGarapen bidean dauden herrialdeen eta aurrerapen gutxien duten herrialdeen parte-hartzea zerbitzuen munduko inportazioetan (%) TX_IMP_GBSVR</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-11-01.pdf\">Metadatuak 17-11-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.11: Significantly increase the exports of developing countries, in particular with a view to doubling the least developed countries&#x2019; share of global exports by 2020</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.11.1: Developing countries&#x2019; and least developed countries&#x2019; share of global exports</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>TX_EXP_GBMRCH - Developing countries&#x2019; and least developed countries&#x2019; share of global merchandise exports [17.11.1]</p>\n<p>TX_EXP_GBSVR - Developing countries&#x2019; and least developed countries&#x2019; share of global services exports [17.11.1]</p>\n<p>TX_IMP_GBMRCH - Developing countries&#x2019; and least developed countries&#x2019; share of global merchandise imports [17.11.1]</p>\n<p>TX_IMP_GBSVR - Developing countries&#x2019; and least developed countries&#x2019; share of global services imports [17.11.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>2.b.1 Agricultural export subsidies</p>\n<p>8.a.1 Aid for Trade commitments and disbursements</p>\n<p>10.a.1 Proportion of tariff lines applied to imports from least developed countries and developing countries with zero-tariff</p>\n<p>17.10.1 Worldwide weighted tariff-average</p>\n<p>17.12.1 Weighted average tariffs faced by developing countries, least developed countries and small island developing States</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Trade Centre (ITC)</p>\n<p>United Nations Conference on Trade and Development (UNCTAD)</p>\n<p>The World Trade Organization (WTO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Trade Centre (ITC)</p>\n<p>United Nations Conference on Trade and Development (UNCTAD)</p>\n<p>The World Trade Organization (WTO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator is calculated as the ratio of the value of exports of developing countries and of LDCs, respectively, to the value of global exports. This ratio is calculated separately for merchandise exports and services exports. </p>\n<p><strong>Concepts:</strong></p>\n<p>International merchandise trade statistics records all goods which add or subtract from the stock of material resources of an economy by entering or leaving its territory. Goods are generally defined as physical, produced objects for which a demand exists, over which ownership rights can be established and whose ownership can be transferred from one institutional unit to another by engaging in transactions on markets.</p>\n<p>Services are understood as the result of a production activity that changes the conditions of the consuming units, or facilitates the exchange of products or financial assets, as defined in the <em>Manual on Statistics of International Trade in Services</em> <em>2010</em>, in accordance with the concepts of the balance of payments and of the national accounts. They can be differentiated into change-effecting services and margin services. Change-effecting services are outputs produced to order and typically result in changes in the condition of the consuming units, such as haircuts or education. Margin services are charges for the facilitation of change in ownership of goods, services, knowledge-capturing products, or financial assets. The production of services is generally simultaneous with their consumption.</p>\n<p>International trade in services takes place when a service is supplied in any of the following modes: 1) from one economy to another (services cross the border); 2) within an economy to a service consumer of another economy (consumer crosses the border); 3) by establishing affiliates in another economy (suppling company establishes a commercial presence abroad); 4) through the presence of natural persons of one economy in another economy (supplier crosses the border). </p>", "UNIT_MEASURE__GLOBAL"=>"<p>United States dollars</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Countries and territories are defined based on the UNCTADstat classification of economies which is almost identical to the M49 Classification with a few exceptions or deviations marked with footnotes where needed. Regions and country groups, including the groups of the developing and developed countries, are classified in accordance with the regional classification established in the data request from the UNSD SDG Team. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The input data are collected from datasets published by national agencies, international and regional organizations, as well as a private data provider. </p>\n<p>For the merchandise export share, the data sources comprise:</p>\n<ul>\n  <li>UN DESA Statistics Division: International Trade Statistics Yearbook; Monthly Bulletin of Statistics; UN Comtrade</li>\n  <li>WTO: Statistics Database</li>\n  <li>IMF: International Financial Statistics; Direction of Trade Statistics; Balance of Payments Statistics</li>\n  <li>Eurostat: <em>online database</em></li>\n  <li>World Bank: World Development Indicators</li>\n  <li>OECD: OECD.Stat </li>\n  <li>OPEC: Annual Statistical Bulletin</li>\n  <li>AfDB: African Development Bank</li>\n  <li>ASEAN: Association of South-East Asian Nations</li>\n  <li>BEAC: Bank of Central African States</li>\n  <li>BCEAO: Central Bank of West African States</li>\n  <li>CIS: Commonwealth of Independent States Statcommittee Online database</li>\n  <li>ECCB: Eastern Caribbean Central Bank</li>\n  <li>AFRISTAT: Economic and Statistical Observatory for Sub-Saharan Africa</li>\n  <li>PRISM: Secretariat of Pacific Community database</li>\n  <li>UNECE: Statistical Database: Labor Force &amp; Wages</li>\n  <li>national customs</li>\n  <li>national statistical offices</li>\n  <li>national central banks</li>\n  <li>national ministries</li>\n  <li><em>Trade Data Monitor</em></li>\n</ul>\n<p><br></p>\n<p>For the services export share, the data sources comprise:</p>\n<ul>\n  <li>Eurostat: <em>online database</em></li>\n  <li>IMF: Balance of Payments Statistics, Article iv staff reports, World Economic Outlook</li>\n  <li>OECD: OECD.Stat</li>\n  <li>BEAC: Bank of Central African States</li>\n  <li>BCEAO: Central Bank of West African States</li>\n  <li>ECCB: Eastern Caribbean Central Bank</li>\n  <li>national statistical offices</li>\n  <li>national central banks</li>\n  <li>national ministries</li>\n</ul>\n<p>In selected cases, estimates from the Economist Intelligence Unit (&quot;Country Data&quot;) are used for both the merchandise and services export shares.</p>", "COLL_METHOD__GLOBAL"=>"<p>The input data are collected from different published sources (see above) and, if necessary, converted from national currencies into United States dollars and/or aggregated from monthly or quarterly to annual frequency. The values collected from the primary source, established for the individual economy, are cross-validated with the values collected from other sources. In cases of significant deviations from international standards, identified by analysing the metadata and consulting the data providers, the values from the primary source are adjusted or substituted with imputed values from other sources. </p>", "FREQ_COLL__GLOBAL"=>"<p>Data on merchandise exports and imports up to year t-1 are collected in a first collection round in February and March, and in a second collection round in July and August of year t. </p>\n<p>Data on services exports and imports up to year t-1 are collected in April to June of year t.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>For merchandise exports, a first version of the data up to year t-1 is released on the UNCTADstat Data Centre and the WTO data portal in April of year t. The final version of these data is released in September of year t.</p>\n<p>For services exports, preliminary data (based on previously published quarterly values) up to year t-1 are released on the UNCTADstat Data Centre and the WTO data portal (see above) in April of year t. The final data are published in July of year t.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Already answered above in 3.a Data sources.</p>", "COMPILING_ORG__GLOBAL"=>"<p><strong>Name:</strong></p>\n<p>International Trade Centre (ITC), World Trade Organization (WTO) and United Nations Conference on Trade and Development (UNCTAD)</p>\n<p><strong>Description:</strong></p>\n<p>ITC, WTO and UNCTAD jointly report on this indicator to UNSD.</p>", "INST_MANDATE__GLOBAL"=>"<p>International Trade Centre (ITC), World Trade Organization (WTO) and United Nations Conference on Trade and Development (UNCTAD) have a mandate to organise for the collection, processing, and dissemination of statistics for this indicator. Respective areas of work and related mandates support joint co-custodianship of the indicator, ensuring effective use of resources and managing response burdens of the reporting countries.</p>", "RATIONALE__GLOBAL"=>"<p>The indicator proposed allows tracking the increase of exports from developing countries and least developed countries (LDCs) prescribed by target 17.11. Increasing export shares indicate a growing integration into world trade. This is considered as an important means for raising a country&apos;s economic output and welfare.</p>", "REC_USE_LIM__GLOBAL"=>"<p>While the aggregation of economies to groups of economies follows in general the M49 standard, slight deviations had to be made in cases where an M49 country is covered by the territory of the economy for which data have been collected. For example, the exports of the French overseas territories are included in the exports of France and the exports of the United States Virgin Islands and Puerto Rico are included in the exports of the United States of America. They are therefore not counted in the calculation of the developing countries&apos; export share. These cases are flagged in the dataset by a comment.</p>\n<p>By definition, international merchandise trade does not include: goods transported through a country; goods temporarily admitted or dispatched; goods for repair and maintenance; monetary gold, issued banknotes, securities and coins in circulation; goods consigned to and from the territorial enclaves; non-financial assets, ownership of which has been transferred from residents to non-residents without crossing borders; goods treated as part of trade in services, such as goods acquired by travellers, embassies and international organizations; goods under merchanting or operational lease; goods which got lost or destroyed on international transport; satellites moved to and launched from another country without change of ownership; goods functioning as means of transport; content delivered electronically; waste and scrap; goods entering or leaving the economic territory of a country illegally.</p>\n<p>Limitations of the trade-in-services statistics arise from difficulties to statistically capture certain service categories. Services are intangible products and activities, such as international property charges or audio-visual products. Services can be embedded in goods in varying degrees and not easily distinguishable from goods, like software. Furthermore, services provided by foreign affiliates within the economy in which the affiliates are established (services supplied by mode 3) are not covered by the balance-of-payments framework. They are therefore not measured by indicator 17.11.1. Intra-corporate services transactions represent another category difficult to seize.</p>", "DATA_COMP__GLOBAL"=>"<p>Share of global trade is intended of a particular group of country fraction of total trade.</p>\n<p>While for merchandise trade data are consistent through he time series (2000-current), for services trade there might be difficulties related to lack of harmonization for data previous to 2005. Before 2005 data are reported according the 5th Balance of Payments Manual. After 2005, data have been converted according to the categories and principles established by the 6th edition of the Balance of Payments Manual.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Validation is performed for each series observing the reported values using outlier detection and validation with additional sources; and using aggregations (for regional aggregates) to ensure quality of the reported statistics. Data are collected primarily from national and international sources with integrated quality assurance and management processed, hence no further dedicated steps are taken in compilation of this indicator&#x2019;s statistics. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Adjustments to the country classifications are marked with footnotes. No further adjustments are processed. </p>", "IMPUTATION__GLOBAL"=>"<ul>\n  <li>At country level</li>\n</ul>\n<p>Previous year is used when latest year is not available. Alternatively mirror statistics can be used. Mirror statistics is the term used to define statistics that are calculated using partner country information when data for the analysed country are not available. For instance, the export of a country X could be recalculated using mirror statistics through the imports from country X of all its partners. It has to be kept in mind however imports are often reported CIF (i.e. including the cost of freight and insurance) while export FoB (i.e. free on board). The difference between these two reporting systems should be taken into account when using import data to estimate exports.</p>\n<ul>\n  <li>At regional and global levels</li>\n</ul>\n<p>Answered above (in At country level section).</p>", "REG_AGG__GLOBAL"=>"<p>Country exports at the 6 digit level of the Harmonized System (HS) classification are summed together at the regional level and then divided by the total amount of exports.</p>", "DOC_METHOD__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Provided above in section 4.d Validation.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Provided above in section 4.d Validation.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p>Data availability:</p>\n<p>Asia and Pacific: 40</p>\n<p>Africa: 36</p>\n<p>Latin America and the Caribbean: 29</p>\n<p>Europe, North America, Australia, New Zealand and Japan: 31</p>\n<p>Time series:</p>\n<p>Yearly data from 2000 to latest year </p>\n<p>Disaggregation:</p>\n<p>Disaggregation is available by product sector (e.g. Agriculture, Textile, Environmental goods), level of goods processing, geographical region and country income level (e.g. Developed, Developing, LDCs).</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable to this indicator. Global data are calculated uniquely by international agencies. At the national level, the data used are the same provided by national authorities and statistical offices.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>https://intracen.org/; www.wto.org; https://unctadstat.unctad.org/EN/Index.html</p>\n<p><strong>References:</strong></p>\n<p>The calculation of trade in goods statistics is based on well-established international and national practices.</p>\n<p>For trade in goods refer to the manual on International Merchandise Trade Statistics (IMTS) <a href=\"https://unstats.un.org/unsd/trade/imts/methodology.asp\">https://unstats.un.org/unsd/trade/imts/methodology.asp</a> </p>\n<p>For trade in services, refer to the Manual on Statistics of International Trade in Services <a href=\"https://unstats.un.org/unsd/tradeserv/TFSITS/msits2010.htm\">https://unstats.un.org/unsd/tradeserv/TFSITS/msits2010.htm</a> </p>\n<p>Fifth Edition of the Balance of Payments Manual: </p>\n<p><a href=\"https://www.imf.org/en/Publications/Books/Issues/2016/12/30/Balance-of-Payments-Manual-157\">https://www.imf.org/en/Publications/Books/Issues/2016/12/30/Balance-of-Payments-Manual-157</a></p>\n<p>Sixth Edition of the IMF&apos;s Balance of Payments and International Investment Position Manual (BPM6):</p>\n<p> <a href=\"https://www.imf.org/external/pubs/ft/bop/2007/bopman6.htm\">https://www.imf.org/external/pubs/ft/bop/2007/bopman6.htm</a> </p>\n<p>System of national account (SNA) 2008: <a href=\"https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf\">https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf</a> </p>\n<p>UNCTAD classifications page: <a href=\"https://unctadstat.unctad.org/EN/Classifications.html\">https://unctadstat.unctad.org/EN/Classifications.html</a> </p>", "indicator_sort_order"=>"17-11-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.12.1", "slug"=>"17-12-1", "name"=>"Promedio ponderado de los aranceles que enfrentan los países en desarrollo, los países menos adelantados y los pequeños Estados insulares en desarrollo", "url"=>"/site/es/17-12-1/", "sort"=>"171201", "goal_number"=>"17", "target_number"=>"17.12", "global"=>{"name"=>"Promedio ponderado de los aranceles que enfrentan los países en desarrollo, los países menos adelantados y los pequeños Estados insulares en desarrollo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Promedio ponderado de los aranceles que enfrentan los países en desarrollo, los países menos adelantados y los pequeños Estados insulares en desarrollo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Promedio ponderado de los aranceles que enfrentan los países en desarrollo, los países menos adelantados y los pequeños Estados insulares en desarrollo", "indicator_number"=>"17.12.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notapplicable", "reporting_status"=>"notapplicable", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"El nivel promedio de los aranceles aduaneros que enfrentan los \npaíses en desarrollo y los países menos adelandados (PMA) permite \nobservar a qué ritmo avanza el sistema multilateral hacia la \nimplementación del acceso a los mercados libre de derechos y de cuotas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-12-01.pdf\">Metadatos 17-12-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The average level of customs tariff rates faced by developing countries and LDCs allows observing at \nwhich pace the multilateral system is advancing toward the implementation of duty-free and quota-free \nmarket access. \n\nSource: United Nations Statistics Division\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-12-01.pdf\">Metadata 17-12-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"El nivel promedio de los aranceles aduaneros que enfrentan los \npaíses en desarrollo y los países menos adelandados (PMA) permite \nobservar a qué ritmo avanza el sistema multilateral hacia la \nimplementación del acceso a los mercados libre de derechos y de cuotas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-12-01.pdf\">Metadatuak 17-12-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.12: Realize timely implementation of duty-free and quota-free market access on a lasting basis for all least developed countries, consistent with World Trade Organization decisions, including by ensuring that preferential rules of origin applicable to imports from least developed countries are transparent and simple, and contribute to facilitating market access</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.12.1: Weighted average tariffs faced by developing countries, least developed countries and small island developing States</p>", "META_LAST_UPDATE__GLOBAL"=>"<p>2016-07-19</p>", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Linkages with the decrease of agricultural market distortions (target 2.b), improvements in the transfer of environmental goods and services (target 17.7) and better access to essential medicines (3.b).</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>International Trade Centre (ITC)</p>\n<p>United Nations Conference on Trade and Development (UNCTAD)</p>\n<p>The World Trade Organization (WTO)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>International Trade Centre (ITC)</p>\n<p>United Nations Conference on Trade and Development (UNCTAD)</p>\n<p>The World Trade Organization (WTO)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>Average import tariffs (in per cent) faced by products exported from developing countries and least developed countries.</p>\n<p><strong>Concepts:</strong></p>\n<p>Tariffs: Tariffs are customs duties on merchandise imports, levied either on an ad valorem basis (percentage of value) or on a specific basis (e.g. $7 per 100 kg). Tariffs can be used to create a price advantage for similar locally-produced goods and for raising government revenues. Trade remedy measures and taxes are not considered to be tariffs. Tariff in HS chapters 01-97 is taken into consideration.</p>\n<p>Tariff line or National Tariff lines (NTL): National Tariff Line codes refer to the classification codes, applied to merchandise goods by individual countries that are longer than the HS six digit level. Countries are free to introduce national distinctions for tariffs and many other purposes.</p>\n<p>The national tariff line codes are based on the HS system but are longer than six digits. For example, the six digit HS code 010120 refers to Asses, mules and hinnies, live, whereas the US National Tariff line code 010120.10 refers to live purebred breeding asses, 010120.20 refers to live asses other than purebred breeding asses and 010120.30 refers to mules and hinnies imported for immediate slaughter.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The main information used to calculate indicators 17.12.1 is import tariff data. Information on import tariffs might be retrieved by contacting directly National statistical offices, permanent country missions to the UN, regional organizations or focal points within the customs, ministries in charge of customs revenues (Ministry of economy/finance and related revenue authorities) or, alternatively, the Ministry of trade. Tariff data for the calculation of this indicator are retrieved from ITC (MAcMap) - http://www.macmap.org/ - WTO (IDB) - http://tao.wto.org - and UNCTAD (TRAINS) databases. Import tariff data included in the ITC (MAcMap) database are collected by contacting directly focal points in line national agencies or regional organizations (in the case of custom unions or regional economic communities). When available, data are downloaded from national or regional official websites. In some cases, data are purchased from private companies. Import tariff data included in the WTO (IDB) database are sourced from official notifications of WTO members. Import tariff included in the UNCTAD (TRAINS) database are collected from official sources, including official country or regional organizations websites.</p>", "FREQ_COLL__GLOBAL"=>"<p>Continuously updated all year round</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Indicatively the indicator&#x2019;s calculations can be ready by March every year. However, the date of release will depend on the period envisaged for the launching of the SDG monitoring report.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Already under sources.</p>", "COMPILING_ORG__GLOBAL"=>"<p>ITC, WTO and UNCTAD</p>\n<p><strong>Description:</strong></p>\n<p>ITC, WTO and UNCTAD will jointly report on this indicator</p>", "RATIONALE__GLOBAL"=>"<p>The average level of customs tariff rates faced by developing countries and LDCs allows observing at which pace the multilateral system is advancing toward the implementation of duty-free and quota-free market access.</p>", "REC_USE_LIM__GLOBAL"=>"<p>&quot;The reduction of average tariffs on key sector as agriculture can represent a proxy of the level of commitment of developed country to improve market access conditions.</p>\n<p>In terms of limitations:</p>\n<p>Tariffs are only part of the trade limitation factors to the implementation of duty-free and quota-free market access, especially when looking at exports of developing or least developed countries under non-reciprocal preferential treatment that set criteria for eligibility. Accurate estimates on non-tariff measures do not exist, thus the calculations on market access are limited to tariffs only.</p>\n<p>A full coverage of preferential schemes of developed countries has been used for the computation, but preferential treatment may not be fully used by developing countries&apos; exporters for different reasons such as the inability of certain exporters to meet eligibility criteria (i.e., complying with rules of origin).&quot;</p>", "DATA_COMP__GLOBAL"=>"<p>Some tariff rates which are not expressed in ad valorem form (e.g., specific duties) need to be converted in ad valorem equivalents (i.e. in per cent of the import value). The conversion is made at the tariff line level for each importer by using the unit value method. Import unit values are calculated from import values and quantities. Only a limited number of non-ad valorem tariff rates (i.e. technical duties) cannot be provided with ad valorem equivalents (AVE) and are excluded from the calculation. This methodology also allows for cross-country comparisons.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Missing values are calculated using the most recent year available.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Answered under question 11.1</p>", "REG_AGG__GLOBAL"=>"<p>This fixed weighting scheme, referred to as &quot;Standard Import Structure&quot; is the same for all developed market imports originating from developing countries and least developed countries. This structure is calculated at the HS6-digit level by averaging total imports of Developed countries from Developing countries and least developed countries. </p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Asia and Pacific: 42</p>\n<p>Africa: 49</p>\n<p>Latin America and the Caribbean: 34</p>\n<p>Europe, North America, Australia, New Zealand and Japan: 48</p>\n<p><strong>Time series:</strong></p>\n<p>Yearly data from 2005 to latest year </p>\n<p><strong>Disaggregation:</strong></p>\n<p>Disaggregation is available by product sector (e.g. Agriculture, Textile, Environmental goods), geographical regions and country income level (e.g. Developed, Developing, LDCs)</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable. The same national data are used at the global level.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p>http://www.intracen.org; www.wto.org; http://unctad.org/en/Pages/Home.aspx</p>\n<p><strong>References:</strong></p>\n<p>This indicator was already calculated under MDG Target 8.A (Indicator 8.7). For reference purposes see The Millennium Development Goals Report 2015 available at http://www.un.org/millenniumgoals/2015_MDG_Report/pdf/MDG%202015%20rev%20(July%201).pdf (p. 64)</p>", "indicator_sort_order"=>"17-12-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.13.1", "slug"=>"17-13-1", "name"=>"Tablero macroeconómico", "url"=>"/site/es/17-13-1/", "sort"=>"171301", "goal_number"=>"17", "target_number"=>"17.13", "global"=>{"name"=>"Tablero macroeconómico"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Tablero macroeconómico", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Tablero macroeconómico", "indicator_number"=>"17.13.1", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> En indicadores basados cuyos valores son tasas de crecimiento, se utiliza el criterio experto para la evaluación", "permalink"=>"", "precision"=>[], "progress_status"=>"noevaluado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_10/opt_1/ti_cuentas-economicas/temas.html", "url_text"=>"Cuentas económicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://es.eustat.eus/estadisticas/tema_57/opt_1/ti_encuesta-de-poblacion-en-relacion-con-la-actividad/temas.html", "url_text"=>"Encuesta de población en relación con la actividad", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Anual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176802&menu=ultiDatos&idp=1254735976607", "url_text"=>"Índice de precios de consumo", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}, {"organisation"=>"Banco de España", "periodicity"=>"Anual", "url"=>"https://www.bde.es/webbe/es/estadisticas/otras-clasificaciones/publicaciones/boletin-estadistico/capitulo-17.html", "url_text"=>"Balanza de pagos", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/BE.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_190/opt_1/ti_cuentas-economicas-de-las-administraciones-vascas/temas.html", "url_text"=>"Cuentas económicas de las administraciones vascas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Banco de España", "periodicity"=>"Anual", "url"=>"https://www.bde.es/webbe/es/estadisticas/temas/administraciones-publicas.html", "url_text"=>"Estadísticas de Administraciones Públicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/BE.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Tablero macroeconómico", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.13- Aumentar la estabilidad macroeconómica mundial, incluso mediante la coordinación y coherencia de las políticas", "definicion"=>"Panel macroeconómico que incluye indicadores macroeconómicos importantes que abarcan \nlos sectores externo, financiero, fiscal y real:\n - Tasa de variación interanual del PIB real\n - Tasa de desempleo\n - Tasa de variación interanual del índice de precios de consumo\n - Volumen de remesas enviadas al extranjero en proporción al PIB\n - Deuda bruta de las administraciones públicas autonómicas según el protocolo de déficit excesivo en proporción al PIB\n - Ingresos fiscales de las administraciones públicas autonómicas en proporción al PIB\n - Saldo fiscal de las administraciones públicas autonómicas en proporción al PIB\n", "formula"=>"\n<b>Tasa de variación interanual del PIB real</b>\n\n$$TVPIB^{t} = \\left(\\frac{PIB_{2022}^{t}}{PIB_{2022}^{t-1}} - 1 \\right) \\cdot 100$$\n\ndonde: \n\n$PIB_{2022}^{t} =$ producto interior bruto en volumen encadenado con referencia 2022 en el año $t$\n\n$PIB_{2022}^{t-1} =$ producto interior bruto en volumen encadenado con referencia 2022 en el año $t-1$\n\n<br>\n\n<b>Tasa de desempleo</b>\n\n$$TD^{t} = \\frac{D^{t}}{A^{t}}  \\cdot 100$$\n\ndonde: \n\n$D^{t} =$ personas desempleadas en el año $t$\n\n$A^{t} =$ personas económicamente activas en el año $t$, teniendo en cuenta que cada una de estas poblaciones se calcula como la media aritmética de los cuatro trimestres del año.\n\n<br>\n\n<b>Tasa de variación interanual del índice de precios de consumo</b>\n\n$$TVIPC^{t} = \\left(\\frac{IPC^{t}}{IPC^{t-1}} - 1 \\right) \\cdot 100$$\n\ndonde:\n\n$IPC^{t} =$ indice de precios de consumo del año $t$\n\n$IPC^{t-1} =$ indice de precios de consumo del año $t-1$, teniendo en cuenta que los índices de precios de consumo anuales se calculan como la media aritmética de los índices de precios de consumo mensuales\n\n<br>\n\n<b>Volumen de remesas enviadas al extranjero en proporción al PIB</b>\n\n$VR^{t} =$ volumen de remesas enviadas al extranjero en el año $t$<br> \n\n$PIB^{t} =$ producto interior bruto a precios corrientes en el año $t$\n\nSi denotamos: \n\n$VR_{España,p}^{t} =$ volumen de remesas enviadas desde España al país $p$ en el año $t$\n\n$P_{16-64,España,p}^{t} =$ población extranjera entre 16 y 64 años del país $p$ residente en España a 1 de enero del año $t$ \n\n$P_{16-64,p}^{t} =$ población extranjera entre 16 y 64 años del país $p$ residente en la comunidad autónoma a 1 de enero del año $t$ \n\nEntonces:  \n\n$$ VR^{t} = \\displaystyle \\sum_{p \\epsilon Países} VR_{España,p}^{t} = \\frac{\\frac{P_{16-64,p}^{t}+P_{16-64,p}^{t+1}}{2}}{\\frac{P_{16-64,España,p}^{t}+P_{16-64,España,p}^{t+1}}{2}} $$ \n\nsiendo los países: Colombia, Marruecos, Ecuador, República Dominicana, Honduras, Bolivia, Senegal, Paraguay, Pakistán, Rumanía, resto de países. No se considera a los países pertenecientes a la Unión Europea en \"resto de países\".\n\n<br>\n\n<b>Deuda bruta de la administración pública autonómica según el protocolo de déficit excesivo en proporción al PIB</b>\n\n$$PPIBDBPDE^{t} = \\frac{DBPDE^{t}}{PIB^{t}} \\cdot 100$$\n\ndonde:\n\n$DBPDE^{t} =$ deuda bruta pública según el protocolo de déficit excesivo en el año $t$\n\n$PIB^{t} =$ producto interior bruto a precios corrientes en el año $t$\n\n<br>\n\n<b>Ingresos fiscales de la administración pública autonómica en proporción al PIB</b>\n\n$$PPIBIF^{t} = \\frac{D.211^{t} + D.212^{t} + D.214^{t} + D.29^{t} + D.51^{t} + D.59^{t} + D.91^{t} + D.611^{t} + D.613^{t}}{PIB^{t}} \\cdot 100$$\n\ndonde:\n\n - $D.211^{t} =$ impuestos del tipo de valor añadido IVA en el año $t$\n - $D.212^{t} =$ impuestos y derechos sobre las importaciones excluido el IVA en el año $t$\n - $D.214^{t} =$ impuestos sobre los productos excluido el IVA e importaciones en el año $t$\n - $D.29^{t} =$ otros impuestos sobre la producción en el año $t$\n - $D.51^{t} =$ impuestos sobre la renta en el año $t$\n - $D.59^{t} =$ otros impuestos corrientes en el año $t$\n - $D.91^{t} =$ impuestos sobre el capital en el año $t$\n - $D.611^{t} =$ cotizaciones sociales efectivas a cargo de los empleadores en el año $t$\n - $D.613^{t} =$ cotizaciones sociales efectivas a cargo de los hogares en el año $t$\n\n$PIB^{t} =$ producto interior bruto a precios corrientes en el año $t$\n\n<br>\n\n<b>Saldo fiscal de la administración pública autonómicas en proporción al PIB</b>\n\n$$PPIBSF^{t} = \\frac{SF^{t}}{PIB^{t}} \\cdot 100$$\n\ndonde:\n\n$SF^{t} =$ saldo fiscal (capacidad o necesidad de financiación) de las administraciones autonómicas en el año $t$\n\n$PIB^{t} =$ producto interior bruto a precios corrientes en el año $t$\n", "desagregacion"=>"Territorio histórico\n", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\n1. Sector externo: Se incluyen indicadores de las cuentas corriente y de capital y \nfinanciera para hacer el seguimiento del comercio y la balanza de pagos de cada país. \nLa sostenibilidad de la balanza de pagos depende tanto de los saldos de la cuenta corriente \ncomo de la cuenta de capital y financiera, incluidas las reservas de divisas.\n\n2. Sector fiscal: Para que el crecimiento económico sea sostenible, un país necesita \nuna política fiscal sostenible. El tablero de indicadores monitorea los \ningresos gubernamentales, el balance fiscal y la deuda pública como porcentaje \ndel PIB para fundamentar la toma de decisiones políticas.\n\n3. Sector real: El PIB mide la producción total de bienes y servicios de una nación. \nDurante muchas décadas, ha sido una medida integral de la actividad del mercado \nutilizada para una amplia variedad de propósitos analíticos, como medir la \nproductividad, aplicar la política monetaria y proyectar los ingresos fiscales. \nEn esta sección, se hace el seguimiento de las tendencias de crecimiento del PIB; \nla formación bruta de capital; las exportaciones de bienes y servicios; \nlas importaciones de bienes y servicios; el consumo de los hogares; el consumo del \ngobierno; y el índice de precios al consumidor para monitorear las tendencias de los precios.\n\n4. Sector financiero: Los indicadores del sector financiero son esenciales para medir \nla estabilidad de los mercados financieros y la estabilidad  económica de los países. \nLas instituciones financieras más sólidas desempeñan un papel importante en el \ndesempeño económico de un país. \n\n5. Desempleo: Las tendencias en los datos sobre la tasa de desempleo son un indicador \nvital para analizar el desarrollo económico a largo plazo de un país (ODS 8.5.2). \nUn crecimiento económico más fuerte y sostenible suele dar lugar a tasas de desempleo \nmás bajas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.13.1&seriesCode=NY_GDP_MKTP_KD_ZG&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Crecimiento anual del PIB (%) NY_GDP_MKTP_KD_ZG</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.13.1&seriesCode=FP_CPI_TOTL_ZG&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Inflación anual, precios al consumidor (%) FP_CPI_TOTL_ZG</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.13.1&seriesCode=GC_BAL_CASH_GD_ZS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Superávit/déficit de caja como proporción del PIB (%) GC_BAL_CASH_GD_ZS</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple parcialmente con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-13-01.pdf\">Metadatos 17-13-1.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-05-20", "en"=>{"indicador_disponible"=>"Tablero macroeconómico", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.13- Aumentar la estabilidad macroeconómica mundial, incluso mediante la coordinación y coherencia de las políticas", "definicion"=>"Macroeconomic panel that includes important macroeconomic indicators covering the external, financial, fiscal and real sectors: \n - Year-on-year rate of change in real GDP \n - Unemployment rate \n - Year-on-year rate of change in the consumer price index \n - Volume of remittances sent abroad as a proportion of GDP \n - Gross debt of autonomous public administrations according to the excessive deficit protocol as a proportion of GDP \n - Tax revenues of autonomous public administrations as a proportion of GDP \n - Fiscal balance of autonomous public administrations as a proportion of GDP \n", "formula"=>"\n<b>Year-on-year rate of change in real GDP</b>\n\n$$TVPIB^{t} = \\left(\\frac{PIB_{2022}^{t}}{PIB_{2022}^{t-1}} - 1 \\right) \\cdot 100$$ \n\nwhere: \n\n$PIB_{2022}^{t} =$ gross domestic product in chained volume with reference to 2022 in year $t$ \n\n$PIB_{2022}^{t-1} =$ gross domestic product in chained volume with reference to 2022 in year $t-1$ \n\n<br>\n\n<b>Unemployment rate</b>\n\n$$TD^{t} = \\frac{D^{t}}{A^{t}}  \\cdot 100$$ \n\nwhere: \n\n$D^{t} =$ unemployed people in year $t$ \n\n$A^{t} =$ economically active people in year $t$, taking into account that each of these populations is calculated as the arithmetic mean of the four quarters of the year \n\n<br>\n\n<b>Year-on-year rate of change in the consumer price index</b>\n\n$$TVIPC^{t} = \\left(\\frac{IPC^{t}}{IPC^{t-1}} - 1 \\right) \\cdot 100$$ \n\nwhere:\n\n$IPC^{t} =$ consumer price index in year $t$ \n\n$IPC^{t-1} =$ consumer price index in year $t-1$, taking into account that the annual consumer price indices are calculated as the arithmetic mean of the monthly consumer price indices \n\n<br>\n\n<b>Volume of remittances sent abroad as a proportion of GDP</b>\n\n$VR^{t} =$ volume of remittances sent abroad in year $t$  \n\n$PIB^{t} =$ gross domestic product at current prices in year $t$ \n\nIf we denote: \n\n$VR_{Spain,c}^{t} =$ volume of remittances sent from Spain to country $c$ in year $t$ \n\n$P_{16-64,Spain,c}^{t} =$ foreign population between 16 and 64 years of age from the country $c$ residing in Spain as of 1 January of year $t$ \n\n$P_{16-64,c}^{t} =$ foreign population between 16 and 64 years of age from the country $c$ resident in the autonomous community as of January 1 of year $t$ \n\nThen:  \n\n$$ VR^{t} = \\displaystyle \\sum_{p \\epsilon Countries} VR_{Spain,c}^{t} = \\frac{\\frac{P_{16-64,c}^{t}+P_{16-64,c}^{t+1}}{2}}{\\frac{P_{16-64,Spain,c}^{t}+P_{16-64,Spain,c}^{t+1}}{2}} $$ \n\nCountries are: Colombia, Morocco, Ecuador, the Dominican Republic, Honduras, Bolivia, Senegal, Paraguay, Pakistan, Romania, and other countries. Countries belonging to the European Union are not included in \"other countries\". \n\n<br>\n\n<b>Gross debt of autonomous public administrations according to the excessive deficit protocol as a proportion of GDP</b>\n\n$$PPIBDBPDE^{t} = \\frac{DBPDE^{t}}{PIB^{t}} \\cdot 100$$ \n\nwhere: \n\n$DBPDE^{t} =$ gross public debt according to the excessive deficit protocol in year $t$ \n\n$PIB^{t} =$ gross domestic product at current prices in year $t$ \n\n<br>\n\n<b>Tax revenues of autonomous public administrations as a proportion of GDP</b>\n\n$$PPIBIF^{t} = \\frac{D.211^{t} + D.212^{t} + D.214^{t} + D.29^{t} + D.51^{t} + D.59^{t} + D.91^{t} + D.611^{t} + D.613^{t}}{PIB^{t}} \\cdot 100$$ \n\nwhere:\n\n - $D.211^{t} =$ value-added taxes (VAT) in year $t$ \n - $D.212^{t} =$ taxes and duties on imports excluding VAT in year $t$ \n - $D.214^{t} =$ taxes on products excluding VAT and imports in year $t$ \n - $D.29^{t} =$ other taxes on production in year $t$ \n - $D.51^{t} =$ income taxes in year $t$ \n - $D.59^{t} =$ other current taxes in year $t$ \n - $D.91^{t} =$ capital taxes in year $t$ \n - $D.611^{t} =$ effective social contributions paid by employers in year $t$ \n - $D.613^{t} =$ effective social contributions paid by households in year $t$ \n\n$PIB^{t} =$ gross domestic product at current prices in year $t$ \n\n<br>\n\n<b>Fiscal balance of autonomous public administrations as a proportion of GDP</b>\n\n$$PPIBSF^{t} = \\frac{SF^{t}}{PIB^{t}} \\cdot 100$$ \n\nwhere:\n\n$SF^{t} =$ fiscal balance (capacity or need for financing) of autonomous public administrations as a proportion of GDP in year $t$ \n\n$PIB^{t} =$ gross domestic product at current prices in year $t$ \n", "desagregacion"=>"Province\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\n1. External sector: Indicators for the current and capital & financial accounts are included \nto monitor each country's trade and balance of payments. The sustainability of the balance of \npayments depends on both the current account and the capital and financial account balances, \nincluding foreign reserves. \n\n2. Fiscal sector: For a sustainable economic growth path, a country needs a sustainable fiscal \npolicy. The dashboard monitors government revenues, fiscal balance, and public debt as a share \nof GDP to inform policy-decision making. \n\n3. Real sector: GDP measures the nation's total output of goods and services. For many decades, \nit has been a comprehensive measure of market activity used for a wide variety of analytical \npurposes such as measuring productivity, conducting monetary policy, and projecting tax revenues. \nIn this section, we monitor growth trends of GDP; Gross capital formation; Exports of goods and \nservices; Imports of goods and services; Household consumption; Government consumption; and \nConsumer Price Index to monitor the price trends. \n\n4. Financial sector: Financial sector indicators are essential for measuring countries' financial \nmarket stability and economic stability. Stronger financial institutions play a significant role \nin a country's economic performance. \n\n5. Unenmployment: Trends in unemployment rate data are a vital indicator for analyzing the long-term \neconomic development of a country (SDG 8.5.2). Stronger and sustainable economic growth often results \nin lower unemployment rates. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.13.1&seriesCode=NY_GDP_MKTP_KD_ZG&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Annual GDP growth  (%) NY_GDP_MKTP_KD_ZG</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.13.1&seriesCode=FP_CPI_TOTL_ZG&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Annual inflation, consumer prices (%) FP_CPI_TOTL_ZG</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.13.1&seriesCode=GC_BAL_CASH_GD_ZS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Cash surplus/deficit as a proportion of GDP (%) GC_BAL_CASH_GD_ZS</a> UNSTATS\n", "comparabilidad"=>"The available indicator partially complies with the United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-13-01.pdf\">Metadata 17-13-1.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Tablero macroeconómico", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.13- Aumentar la estabilidad macroeconómica mundial, incluso mediante la coordinación y coherencia de las políticas", "definicion"=>"Panel macroeconómico que incluye indicadores macroeconómicos importantes que abarcan \nlos sectores externo, financiero, fiscal y real:\n - Tasa de variación interanual del PIB real\n - Tasa de desempleo\n - Tasa de variación interanual del índice de precios de consumo\n - Volumen de remesas enviadas al extranjero en proporción al PIB\n - Deuda bruta de las administraciones públicas autonómicas según el protocolo de déficit excesivo en proporción al PIB\n - Ingresos fiscales de las administraciones públicas autonómicas en proporción al PIB\n - Saldo fiscal de las administraciones públicas autonómicas en proporción al PIB\n", "formula"=>"\n<b>BPG errealaren urte arteko aldakuntza-tasa</b>\n\n$$TVPIB^{t} = \\left(\\frac{PIB_{2022}^{t}}{PIB_{2022}^{t-1}} - 1 \\right) \\cdot 100$$\n\nnon: \n\n$PIB_{2022}^{t} $ barne-produktu gordina, kateatutako bolumenean, 2022 erreferentziarekin $t$ urtean\n\n$PIB_{2022}^{t-1} $ barne-produktu gordina, kateatutako bolumenean, 2022 erreferentziarekin $t-1$ urtean\n\n<br>\n\n<b>Langabezia tasa</b>\n\n$$TD^{t} = \\frac{D^{t}}{A^{t}}  \\cdot 100$$\n\nnon: \n\n$D^{t} $ langabetuak $t$ urtean\n\n$A^{t} $ $t$ urtean ekonomikoki aktiboak diren pertsonak, kontuan hartuta populazio horietako bakoitza urteko lau hiruhilekoen batez besteko aritmetiko gisa kalkulatzen dela.\n\n<br>\n\n<b>Kontsumoko prezioen indizearen urte arteko aldakuntza-tasa</b>\n\n$$TVIPC^{t} = \\left(\\frac{IPC^{t}}{IPC^{t-1}} - 1 \\right) \\cdot 100$$\n\nnon:\n\n$IPC^{t} $ kontsumoko prezioen indizea $t$ urtean \n\n$IPC^{t-1} $ Kontsumoko prezioen indizearen $t-1$ urtean, kontuan hartuta urteko kontsumo-prezioen indizeak hileko kontsumo-prezioen indizeen batez besteko aritmetiko gisa kalkulatzen direla \n\n\n<br>\n\n<b>Atzerrira bidalitako diru-igorpenen bolumena, BPGarekiko proportzioan</b>\n\n$VR^{t} =$ atzerrira bidalitako diru-igorpenen bolumena $t$ urtean<br> \n\n$PIB^{t} =$ producto interior bruto a precios corrientes en el año $t$\n\nhonakoa adierazten badugu: \n\n$VR_{Espainia,h}^{t} =$ Espainiatik $h$ herrialdera bidalitako diru-igorpenen bolumena $t$ urtean\n\n$P_{16-64,Espainia,h}^{t} =$ Espainian bizi den $h$ herrialdeko 16 eta 64 urte bitarteko biztanleria atzerritarra $t$ urteko urtarrilaren 1ean \n\n$P_{16-64,h}^{t} =$ Autonomia Erkidegoan bizi den $h$ herrialdeko 16 eta 64 urte bitarteko biztanleria atzerritarra $t$ urteko urtarrilaren 1ean \n\norduan:  \n\n$$ VR^{t} = \\displaystyle \\sum_{h \\epsilon Herrialdeak} VR_{Espainia,h}^{t} = \\frac{\\frac{P_{16-64,h}^{t}+P_{16-64,h}^{t+1}}{2}}{\\frac{P_{16-64,Espainia,h}^{t}+P_{16-64,Espainia,h}^{t+1}}{2}} $$ \n\nherrialdeak ondokoak izanda: Kolonbia, Maroko, Ekuador, Dominikar Errepublika, Honduras, Bolivia, Senegal, Paraguay, Pakistan, Errumania, gainerako herrialdeak. Europar Batasuneko herrialdeak ez dira aintzat hartzen \"gainerako herrialdeak\" kategorian. \n\n<br>\n\n<b>Administrazio publiko autonomikoaren zor gordina, gehiegizko defizitaren protokoloaren arabera, BPGrekiko proportzioan</b>\n\n$$PPIBDBPDE^{t} = \\frac{DBPDE^{t}}{PIB^{t}} \\cdot 100$$\n\nnon:\n\n$DBPDE^{t} =$ zor gordin publikoa, gehiegizko defizitaren protokoloaren arabera $t$ urtean\n\n$PIB^{t} =$ barne produktu gordina uneko prezioetan $t$ urtean\n\n<br>\n\n<b>Administrazio publiko autonomikoaren diru-sarrera fiskalak, BPGrekiko proportzioan</b>\n\n$$PPIBIF^{t} = \\frac{D.211^{t} + D.212^{t} + D.214^{t} + D.29^{t} + D.51^{t} + D.59^{t} + D.91^{t} + D.611^{t} + D.613^{t}}{PIB^{t}} \\cdot 100$$\n\nnon:\n\n - $D.211^{t}$ balio erantsiaren tasaren zergak BEZ $t$ urtean\n - $D.212^{t}$ inportazioen gaineko zergak eta eskubideak, BEZik gabe $t$ urtean\n - $D.214^{t}$ produktuen gaineko zergak, BEZik eta inportaziorik gabe $t$ urtean\n - $D.29^{t}$ ekoizpenaren gaineko beste zerga batzuk $t$ urtean\n - $D.51^{t}$ errentaren gaineko zerga $t$ urtean\n - $D.59^{t}$ bestelako zerga arruntak $t$ urtean\n - $D.91^{t}$ kapitalaren gaineko zergak $t$ urtean\n - $D.611^{t}$ enplegatzaileen konturako gizarte-kotizazio efektiboak $t$ urtean\n - $D.613^{t}$ familien konturako gizarte-kotizazio efektiboak $t$ urtean\n\n$PIB^{t} =$ barne produktu gordina uneko prezioetan $t$ urtean\n\n<br>\n\n<b>Administrazio publiko autonomikoaren zerga-saldoa, BPGrekiko proportzioan</b>\n\n$$PPIBSF^{t} = \\frac{SF^{t}}{PIB^{t}} \\cdot 100$$\n\nnon:\n\n$SF^{t} =$ autonomia-erkidegoetako administrazioen zerga-saldoa (finantzatzeko gaitasuna edo beharra) $t$ urtean \n\n$PIB^{t} =$ barne produktu gordina uneko prezioetan $t$ urtean\n", "desagregacion"=>"Lurralde historikoa\n", "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\n1. Sector externo: Se incluyen indicadores de las cuentas corriente y de capital y \nfinanciera para hacer el seguimiento del comercio y la balanza de pagos de cada país. \nLa sostenibilidad de la balanza de pagos depende tanto de los saldos de la cuenta corriente \ncomo de la cuenta de capital y financiera, incluidas las reservas de divisas.\n\n2. Sector fiscal: Para que el crecimiento económico sea sostenible, un país necesita \nuna política fiscal sostenible. El tablero de indicadores monitorea los \ningresos gubernamentales, el balance fiscal y la deuda pública como porcentaje \ndel PIB para fundamentar la toma de decisiones políticas.\n\n3. Sector real: El PIB mide la producción total de bienes y servicios de una nación. \nDurante muchas décadas, ha sido una medida integral de la actividad del mercado \nutilizada para una amplia variedad de propósitos analíticos, como medir la \nproductividad, aplicar la política monetaria y proyectar los ingresos fiscales. \nEn esta sección, se hace el seguimiento de las tendencias de crecimiento del PIB; \nla formación bruta de capital; las exportaciones de bienes y servicios; \nlas importaciones de bienes y servicios; el consumo de los hogares; el consumo del \ngobierno; y el índice de precios al consumidor para monitorear las tendencias de los precios.\n\n4. Sector financiero: Los indicadores del sector financiero son esenciales para medir \nla estabilidad de los mercados financieros y la estabilidad  económica de los países. \nLas instituciones financieras más sólidas desempeñan un papel importante en el \ndesempeño económico de un país. \n\n5. Desempleo: Las tendencias en los datos sobre la tasa de desempleo son un indicador \nvital para analizar el desarrollo económico a largo plazo de un país (ODS 8.5.2). \nUn crecimiento económico más fuerte y sostenible suele dar lugar a tasas de desempleo \nmás bajas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.13.1&seriesCode=NY_GDP_MKTP_KD_ZG&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">BPGaren urteko hazkundea (%) NY_GDP_MKTP_KD_ZG</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.13.1&seriesCode=FP_CPI_TOTL_ZG&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Urteko inflazioa, kontsumitzailearentzako prezioak (%) FP_CPI_TOTL_ZG</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.13.1&seriesCode=GC_BAL_CASH_GD_ZS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Kutxako superabita/defizita, BPGaren proportzio gisa (%) GC_BAL_CASH_GD_ZS</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak partzialki betetzen ditu Nazio Batuen metadatuak.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-13-01.pdf\">Metadatuak 17-13-1.pdf</a> (inglesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.13: Enhance global macroeconomic stability, including through policy coordination and policy coherence</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.13.1: Macroeconomic Dashboard</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>BN_CAB_XOKA_GD_ZS - Current account balance as a proportion of GDP (%) [17.13.1]</p>\n<p>BN_KLT_PTXL_CD - Portfolio investment, net (Balance of Payments, current United States dollars) [17.13.1]</p>\n<p>BX_KLT_DINV_WD_GD_ZS - Foreign direct investment, net inflows, as a proportion of GDP (%) [17.13.1]</p>\n<p>DP_DOD_DLD2_CR_CG_Z1 - Gross public sector debt, Central Government, as a proportion of GDP (%) [17.13.1]</p>\n<p>DT_DOD_DECT_GN_ZS - External debt stocks as a proportion of GNI (%) [17.13.1]</p>\n<p>FB_BNK_CAPA_ZS - Bank capital to assets ratio (%) [17.13.1]</p>\n<p>FI_RES_TOTL_MO - Total reserves in months of imports (ratio) [17.13.1]</p>\n<p>FM_LBL_BMNY_IR_ZS - Broad money to total reserves ratio [17.13.1]</p>\n<p>FM_LBL_BMNY_ZG - Annual broad money growth (%) [17.13.1]</p>\n<p>FP_CPI_TOTL_ZG - Annual inflation, consumer prices (%) [17.13.1]</p>\n<p>GC_BAL_CASH_GD_ZS - Cash surplus/deficit as a proportion of GDP [17.13.1]</p>\n<p>GC_TAX_TOTL_GD_ZS - Tax revenue as a proportion of GDP (%) [17.13.1]</p>\n<p>NE_CON_GOVT_KD_ZG - Annual growth of the general government final consumption expenditure (%) [17.13.1]</p>\n<p>NE_CON_PRVT_KD_ZG - Annual growth of households and NPISHs final consumption expenditure [17.13.1]</p>\n<p>NE_EXP_GNFS_KD_ZG - Annual growth of exports of goods and services (%) [17.13.1]</p>\n<p>NE_GDI_TOTL_KD_ZG - Annual growth of the gross capital formation (%) [17.13.1]</p>\n<p>NE_IMP_GNFS_KD_ZG - Annual growth of imports of goods and services (%) [17.13.1]</p>\n<p>NY_GDP_MKTP_KD_ZG - Annual GDP growth (%) [17.13.1]</p>\n<p>PA_NUS_ATLS - DEC alternative conversion factor (in local currency unit per United States dollar) [17.13.1]</p>\n<p>TG_VAL_TOTL_GD_ZS - Merchandise trade as a proportion of GDP (%) [17.13.1]</p>\n<p>Series that are part of the dashboard but already associated with other indicators are listed below.</p>\n<p>FI_FSI_FSANL - Non-performing loans to total gross loans (%) [10.5.1] </p>\n<p>DT_TDS_DECT - Debt service as a proportion of exports of goods,services and primary income [17.4.1]</p>\n<p>BX_TRF_PWKR - Volume of remittances (in United States dollars) as a proportion of total GDP [17.3.2] </p>\n<p>SL_TLF_UEM - Unemployment rate, by sex and age - 13th ICLS (%) [8.5.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2025-04-23", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>8.1.1, 8.5.2, 10.1.1, 17.1.1, 17.3.1, 17.3.2, and 17.4.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>1. External Sector</strong></p>\n<p>Indicators for the current and capital &amp; financial accounts are included to monitor each country&apos;s trade and balance of payments. The sustainability of the balance of payments depends on both the current account and the capital and financial account balances, including foreign reserves.</p>\n<p><u>Current Account:</u> The current account balance is an important indicator of an economy&apos;s health. It is defined as the sum of the resource balance (exports less imports of goods and services), net primary income and secondary income. In addition, the dashboard includes indicators such as merchandise trade as a share of GDP to monitor the trade openness of the country and data on personal remittances, which have become an important integral part of many developing economies, since any changes to these flows may have a major impact on developing countries&apos; current account balances (defined as the savings-investment gap for an economy).</p>\n<p><u>Capital and Financial Accounts:</u> Data on capital and financial flows are key for monitoring vulnerability to shocks and constraints on fiscal and monetary policies. Financing trade deficits or other current imbalances through capital and financial flows is a reasonable way to achieve consumption smoothing of emerging economies. Foreign Direct Investments (FDI) equity is a preferred method of financing external current account deficits since these flows are non&#x2013;debt&#x2013;creating. Portfolio investment inflows measure the exposure of foreign investors to developing country bond and equity markets. </p>\n<p>External indebtedness affects a country&apos;s creditworthiness and investor perceptions. Nonreporting countries might have outstanding debts with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries&apos; ability to obtain foreign exchange through exports of goods, services, primary income, and personal remittances. Debt ratios are used to assess the sustainability of a country&apos;s debt service obligations, but no absolute rules determine what values are too high. </p>\n<p><u>Exchange Rates:</u> Sharp devaluations are usually associated with significant declines in equity markets, capital flows, and reserves. The dashboard will present official average exchange rates.</p>\n<ol>\n  <li><em>Merchandise trade as a proportion of GDP (%)</em></li>\n</ol>\n<p>This indicator is used as measurement for the Trade Openness of a country. Merchandise trade as a share of GDP is the sum of merchandise exports and imports divided by the value of GDP.</p>\n<ol>\n  <li><em>Personal remittances, received, as a proportion of GDP (%)</em></li>\n</ol>\n<p>Comprise personal transfers and compensation of employees, as defined in the sixth edition of the International Monetary Fund (IMF)s Balance of Payments Manual. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from non-resident households. Personal transfers thus include all current transfers between resident and non-resident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by non-resident entities. </p>\n<ol>\n  <li><em>Current account balance as a proportion of GDP (%)</em></li>\n</ol>\n<p>Current account balance is the sum of net exports of goods and services, net primary income, and net secondary income. </p>\n<ol>\n  <li><em>Foreign direct investment, net inflows, as a proportion of GDP (%)</em></li>\n</ol>\n<p>Comprises the net inflows of foreign direct investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. FDI is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net inflows (new investment inflows less disinvestment) in the reporting economy from foreign investors and is divided by GDP.</p>\n<ol>\n  <li><em>Portfolio investment, net (BoP, current US$)</em></li>\n</ol>\n<p>Portfolio investment covers transactions in equity securities and debt securities. Data are in current US dollars. </p>\n<ol>\n  <li><em>Total reserves in months of imports</em></li>\n</ol>\n<p>Total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices. This item shows reserves expressed in terms of the number of months of imports of goods and services they could pay for [Reserves/(Imports/12)].</p>\n<ol>\n  <li><em>External debt stocks as a proportion of GNI (%)</em></li>\n</ol>\n<p>Total external debt is debt owed to non-residents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad.</p>\n<ol>\n  <li><em>Debt service (PPG and IMF only, % of exports of goods, services and primary income)</em></li>\n</ol>\n<p>Debt service is the sum of principle repayments and interest actually paid in currency, goods, or services. This series differs from the standard debt to exports series. It covers only long-term public and publicly guaranteed (PPG) debt and repayments (repurchases and charges) to the IMF. Data for Heavily Indebted Poor Countries (HIPC) are from HIPC Initiative&apos;s Status of Implementation Report. </p>\n<ol>\n  <li><em>DEC alternative conversion factor (LCU per US$)</em></li>\n</ol>\n<p>The Development Economics (DEC) alternative conversion factor is the underlying annual exchange rate used for the World Bank Atlas method. As a rule, it is the official exchange rate reported in the IMF&apos;s International Financial Statistics. Exceptions arise where further refinements are made by World Bank staff. It is expressed in local currency units per US dollar.</p>\n<p><strong>2. Fiscal Sector</strong></p>\n<p>For a sustainable economic growth path, a country needs a sustainable fiscal policy. The dashboard monitors government revenues, fiscal balance, and public debt as a share of GDP to inform policy-decision making. </p>\n<ol>\n  <li><em>Tax revenue as a proportion of GDP (%)</em></li>\n</ol>\n<p>Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue. </p>\n<ol>\n  <li><em>Cash surplus/deficit as a proportion of GDP (%)</em></li>\n</ol>\n<p>Cash surplus or deficit is revenue (including grants) minus expense, minus net acquisition of nonfinancial assets. In the 1986 Government Finance Statistics (GFS) manual nonfinancial assets were included under revenue and expenditure in gross terms. This cash surplus or deficit is closest to the earlier overall budget balance (still missing is lending minus repayments, which are now a financing item under net acquisition of financial assets).</p>\n<ol>\n  <li><em>Gross PSD, Central Gov.-D2, All maturities, D1+ SDRs + currency and deposits, Nominal Value, as a proportion of GDP (%)</em></li>\n</ol>\n<p>The D2 coverage of instruments according to this classification includes (1) debt securities, (2) loans, (3) special drawing rights and (4) currency and deposits as percentage of GDP. </p>\n<p><strong>3. Real Sector</strong></p>\n<p>GDP measures the nation&apos;s total output of goods and services. For many decades, it has been a comprehensive measure of market activity used for a wide variety of analytical purposes such as measuring productivity, conducting monetary policy, and projecting tax revenues. In this section, we monitor growth trends of GDP; Gross capital formation; Exports of goods and services; Imports of goods and services; Household consumption; Government consumption; and Consumer Price Index to monitor the price trends.</p>\n<ol>\n  <li><em>Annual GDP growth (%)</em></li>\n</ol>\n<p>GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.</p>\n<ol>\n  <li><em>Annual growth of the gross capital formation (%)</em></li>\n</ol>\n<p>Gross capital formation (formerly gross domestic investment) consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories. Fixed assets include dwellings, other buildings and structures (including land improvements), machinery and equipment, weapons systems, cultivated biological resources, and intellectual property products (R&amp;D, mineral exploration, software, etc.). Inventories are stocks of goods held by firms to meet temporary or unexpected fluctuations in production or sales, and &quot;work in progress.&quot; According to the 2008 SNA, net acquisitions of valuables are also considered capital formation. </p>\n<ol>\n  <li><em>Annual growth of households and NPISHs final consumption expenditure (%)</em></li>\n</ol>\n<p>Household final consumption expenditure (formerly private consumption) is the market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. In WDI, household consumption expenditure includes the expenditures of non-profit institutions serving households, even when reported separately by the country. </p>\n<ol>\n  <li><em>Annual growth of the general government final consumption expenditure (%)</em></li>\n</ol>\n<p>General government final consumption expenditure (formerly general government consumption) includes all government current expenditures for purchases of goods and services (including compensation of employees and consumption of fixed capital). </p>\n<ol>\n  <li><em>Annual growth of exports of goods and services (%)</em></li>\n</ol>\n<p>Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. </p>\n<ol>\n  <li><em>Annual growth of imports of goods and services (%)</em></li>\n</ol>\n<p>Imports of goods and services represent the value of all goods and other market services received from the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. </p>\n<ol>\n  <li><em>Annual inflation, consumer prices (%)</em></li>\n</ol>\n<p>Consumer price index reflects changes in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is generally used. Data are period averages. </p>\n<p><strong>4. Financial Sector</strong></p>\n<p>Financial sector indicators are essential for measuring countries&apos; financial market stability and economic stability. Stronger financial institutions play a significant role in a country&apos;s economic performance. The strength of those institutions is measured through the following indicators. </p>\n<ol>\n  <li><em>Bank capital to assets ratio (%)</em></li>\n</ol>\n<p>Bank capital to assets is the ratio of bank capital and reserves to total assets. Capital and reserves include funds contributed by owners, retained earnings, general and special reserves, provisions, and valuation adjustments. Capital consists of tier 1 capital (paid-up shares and common stock), which is a common feature in all countries&apos; banking systems, and total regulatory capital, which includes several specified types of subordinated debt instruments that need not be repaid if the funds are required to maintain minimum capital levels (these comprise tier 2 and tier 3 capital). Total assets include all nonfinancial and financial assets. </p>\n<ol>\n  <li><em>Bank nonperforming loans to total gross loans (%)</em></li>\n</ol>\n<p>Bank nonperforming loans to total gross loans is the value of nonperforming loans divided by the total value of the loan portfolio (including nonperforming loans before the deduction of specific loan-loss provisions). The loan amount recorded as nonperforming should be the gross value of the loan as recorded on the balance sheet, not just the amount that is overdue. </p>\n<ol>\n  <li><em>Annual broad money growth (%)</em></li>\n</ol>\n<p>Broad money is the sum of currency outside banks; demand deposits other than those of the central government; the time, savings, and foreign currency deposits of resident sectors other than the central government; bank and traveler&apos;s checks; and other securities such as certificates of deposit and commercial paper. </p>\n<ol>\n  <li><em>Broad money to total reserves ratio</em></li>\n</ol>\n<p>Broad money (IFS line 35L.. ZK) is the sum of currency outside banks; demand deposits other than those of the central government; the time, savings, and foreign currency deposits of resident sectors other than the central government; bank and traveler&apos;s checks; and other securities such as certificates of deposit and commercial paper. </p>\n<p><strong>5. Unemployment</strong></p>\n<p>Trends in unemployment rate data are a vital indicator for analyzing the long-term economic development of a country (SDG 8.5.2). Stronger and sustainable economic growth often results in lower unemployment rates. </p>\n<p><em>Total unemployment out of total labor force (national estimate) (%)</em></p>\n<p>Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of the labor force and unemployment differ by country. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%), except for these: </p>\n<ul>\n  <li>Portfolio investment, net (BoP, current US$)</li>\n  <li>Total reserves in months of imports (ratio)</li>\n  <li>Broad money to total reserves ratio (ratio)</li>\n  <li>DEC alternative conversion factor (LCU per US$) (ratio)</li>\n</ul>", "CLASS_SYSTEM__GLOBAL"=>"<p>IMF Balance of Payments Manual 6 for the External Sector:</p>\n<p><a href=\"https://www.imf.org/external/pubs/ft/bop/2007/pdf/bpm6.pdf\">https://www.imf.org/external/pubs/ft/bop/2007/pdf/bpm6.pdf</a> </p>\n<p>IMF International Financial Statistics for the Financial sector: </p>\n<p><a href=\"http://data.imf.org/?sk=4C514D48-B6BA-49ED-8AB9-52B0C1A0179B&amp;sId=1537997141415\">http://data.imf.org/?sk=4C514D48-B6BA-49ED-8AB9-52B0C1A0179B&amp;sId=1537997141415</a> </p>\n<p>IMF Government Financial Statistics for the Fiscal sector: </p>\n<p><a href=\"https://www.imf.org/external/pubs/ft/gfs/manual/gfs.htm\">https://www.imf.org/external/pubs/ft/gfs/manual/gfs.htm</a> </p>\n<p>System of National Accounts for the Real sector: </p>\n<p><a href=\"https://unstats.un.org/unsd/nationalaccount/sna.asp\">https://unstats.un.org/unsd/nationalaccount/sna.asp</a> </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The data source is the World Development Indicators (<a href=\"http://wdi.worldbank.org/\">http://wdi.worldbank.org/</a>)&#x2014;a compilation of development data from countries and international agencies, covering 1,400 time-series indicators for 217 economies for many indicators going back 60 years. </p>", "COLL_METHOD__GLOBAL"=>"<p>The data and relevant information is collected from the data sources listed above. </p>", "FREQ_COLL__GLOBAL"=>"<p>Ongoing process</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Every July and December. However, data can be updated when countries revise their economic data monthly or quarterly, change methodology or coverage, or introduce new weights.</p>", "DATA_SOURCE__GLOBAL"=>"<p>International Labour Organization (ILO), International Monetary Fund (IMF), Organisation for Economic Co-operation and Development (OECD), World Bank, and World Trade Organization (WTO) </p>", "COMPILING_ORG__GLOBAL"=>"<p>World Bank</p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>To provide a standardized instrument to monitor the macroeconomic stability of countries, the World Bank has designed a Macroeconomic dashboard including important macroeconomic indicators covering the external, financial, fiscal, and real sectors. The indicator selection builds on existing macroeconomic monitoring frameworks developed and used by international and regional agencies, such as IMF, WB, ECB, and OECD. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The methodologies for selected indicators follow long-established international standards as listed in 2.c. Classifications. For example, National Statistical Offices compile real sector data according to the System of National Accounts 1993 / 2008. Similarly, Central Banks and Ministries of Finance collect balance payments according to the IMF Balance of Payments Manual, financial indicators following the IMF International Financial Statistics, and fiscal indicators following the IMF Government Financial Statistics. However, the implementation at the national level may vary. For more information on individual indicators, please visit World Development Indicators (WDI) at <a href=\"https://databank.worldbank.org/source/world-development-indicators\">https://databank.worldbank.org/source/world-development-indicators</a>. </p>", "DATA_COMP__GLOBAL"=>"<p>Refer to the Manuals listed in 2.c. Classifications.</p>", "DATA_VALIDATION__GLOBAL"=>"<p> A variety of checks are conducted by the subject matter experts, including anomaly detection, time series comparisons, and checks for missing values or other inconsistencies.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>Weighted average when available and median for Annual inflation, consumer prices (%). </p>", "DOC_METHOD__GLOBAL"=>"<p>Refer to the Manuals listed in 2.c. Classifications.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p> Throughout the data handling process, from acquisition to distribution, all activities adhere to internal policies designed to regulate data visibility for observations. Data usage also follows the terms established with the source agencies. Additionally, all information stored in the in-house system is safeguarded against unauthorized access and backed up daily to prevent data loss or system failures.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p> The data is structured using a standardized data model to analyze results derived from diverse data sources. Quality assurance processes include comparing different data versions and performing checks to verify that all inputs are functioning as intended, and to secure accurate calculations within the system. </p>\n<p>A detailed description of several of the transformations that take place are described in this technical page on the World Bank website: https://datatopics.worldbank.org/world-development-indicators/sources-and-methods.html</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>The series undergoes regular internal reviews, taking into account quantitative metrics such as completeness and comparability. The World Bank aims to apply uniform methodologies across various regions and time periods for indicators. Geographic identifiers are also adjusted to ensure consistency of national boundaries over time whenever feasible. Moreover, qualitative aspects, including development relevance, use of accurate and reliable data, and adherence to internationally agreed standards, are also taken into consideration.</p>", "COVERAGE__GLOBAL"=>"<p>The number of economies with at least 1 data point by indicator</p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Indicator</strong></p>\n      </td>\n      <td>\n        <p><strong>Number of economies</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Current account balance as a proportion of GDP (%)</p>\n      </td>\n      <td>\n        <p>196</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Portfolio investment, net (BoP, current US$)</p>\n      </td>\n      <td>\n        <p>196</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Foreign direct investment, net inflows, as a proportion of GDP (%)</p>\n      </td>\n      <td>\n        <p>203</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Personal remittances, received, as a proportion of GDP (%)</p>\n      </td>\n      <td>\n        <p>196</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Gross PSD, Central Gov.-D2, All maturities, D1+ SDRs + currency and deposits, Nominal Value, as a proportion of GDP (%)</p>\n      </td>\n      <td>\n        <p>44</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>External debt stocks as a proportion of GNI (%)</p>\n      </td>\n      <td>\n        <p>119</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Debt service (PPG and IMF only, % of exports of goods, services and primary income)</p>\n      </td>\n      <td>\n        <p>122</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Bank nonperforming loans to total gross loans (%)</p>\n      </td>\n      <td>\n        <p>141</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Bank capital to assets ratio (%)</p>\n      </td>\n      <td>\n        <p>137</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Total reserves in months of imports</p>\n      </td>\n      <td>\n        <p>180</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Broad money to total reserves ratio</p>\n      </td>\n      <td>\n        <p>160</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Annual broad money growth (%)</p>\n      </td>\n      <td>\n        <p>170</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Annual inflation, consumer prices (%)</p>\n      </td>\n      <td>\n        <p>194</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Cash surplus/deficit as a proportion of GDP (%)</p>\n      </td>\n      <td>\n        <p>153</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Tax revenue as a proportion of GDP (%)</p>\n      </td>\n      <td>\n        <p>156</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Annual growth of the general government final consumption expenditure (%)</p>\n      </td>\n      <td>\n        <p>173</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Annual growth of households and NPISHs final consumption expenditure (%)</p>\n      </td>\n      <td>\n        <p>175</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Annual growth of exports of goods and services (%)</p>\n      </td>\n      <td>\n        <p>180</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Annual growth of the gross capital formation (%)</p>\n      </td>\n      <td>\n        <p>171</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Annual growth of imports of goods and services (%)</p>\n      </td>\n      <td>\n        <p>180</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Annual GDP growth (%)</p>\n      </td>\n      <td>\n        <p>219</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>DEC alternative conversion factor (LCU per US$)</p>\n      </td>\n      <td>\n        <p>220</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Total unemployment out of total labour force (national estimate) (%)</p>\n      </td>\n      <td>\n        <p>220</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p>Merchandise trade as a proportion of GDP (%)</p>\n      </td>\n      <td>\n        <p>209</p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "COMPARABILITY__GLOBAL"=>"<p>The macroeconomic data are organized by international standards such as the System of National Accounts (SNA) and the Balance of Payments (BoP). However, the implementation at the national level may vary.</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.worldbank.org\">www.worldbank.org</a> </p>\n<p><strong>References:</strong></p>\n<p>World Development Indicators (WDI), The World Bank </p>\n<p>(<a href=\"https://databank.worldbank.org/source/world-development-indicators\">https://databank.worldbank.org/source/world-development-indicators</a>)</p>", "indicator_sort_order"=>"17-13-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.14.1", "slug"=>"17-14-1", "name"=>"Número de países que cuentan con mecanismos para mejorar la coherencia de las políticas de desarrollo sostenible", "url"=>"/site/es/17-14-1/", "sort"=>"171401", "goal_number"=>"17", "target_number"=>"17.14", "global"=>{"name"=>"Número de países que cuentan con mecanismos para mejorar la coherencia de las políticas de desarrollo sostenible"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países que cuentan con mecanismos para mejorar la coherencia de las políticas de desarrollo sostenible", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que cuentan con mecanismos para mejorar la coherencia de las políticas de desarrollo sostenible", "indicator_number"=>"17.14.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Mejorar la coherencia de las políticas para el desarrollo sostenible es fundamental \npara lograrlo en sus tres dimensiones (económica, social y ambiental) de forma \nequilibrada e integrada; para garantizar la coherencia entre las políticas en los \ndistintos niveles de gobierno; y para asegurar que las políticas de los diferentes \nsectores se complementen y no se contrapongan. También es importante para abordar los \nimpactos de las políticas nacionales a nivel internacional. \n\nLa coherencia de las políticas busca, como mínimo, identificar las compensaciones \ny mitigar los impactos negativos entre las políticas. A un nivel más ambicioso, \ntambién debería buscar fomentar sinergias y generar políticas que se refuercen \nmutuamente, y garantizar que las políticas implementadas sean implementables \ny sostenibles, ya que incluyen las perspectivas de las partes interesadas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-14-01.pdf\">Metadatos 17-14-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Enhancing policy coherence for sustainable development is important for achieving sustainable \ndevelopment in its three dimensions (economic, social and environmental) in a balanced and integrated \nmanner; for ensuring coherence between policies at various levels of government; and for ensuring that \npolicies in different sectors are mutually supportive and do not work against each other. It is also important \nin addressing the impacts of domestic policy internationally. \n\nPolicy coherence aims, as a minimum, to identify trade-offs and mitigate negative impacts between \npolicies. At a more ambitious level, it should also aim to foster synergies and produce policies that mutually \nreinforce each other and to ensure that policies put in place are implementable and sustainable as they \nare inclusive of the concerned stakeholders’ perspectives. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-14-01.pdf\">Metadata 17-14-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Mejorar la coherencia de las políticas para el desarrollo sostenible es fundamental \npara lograrlo en sus tres dimensiones (económica, social y ambiental) de forma \nequilibrada e integrada; para garantizar la coherencia entre las políticas en los \ndistintos niveles de gobierno; y para asegurar que las políticas de los diferentes \nsectores se complementen y no se contrapongan. También es importante para abordar los \nimpactos de las políticas nacionales a nivel internacional. \n\nLa coherencia de las políticas busca, como mínimo, identificar las compensaciones \ny mitigar los impactos negativos entre las políticas. A un nivel más ambicioso, \ntambién debería buscar fomentar sinergias y generar políticas que se refuercen \nmutuamente, y garantizar que las políticas implementadas sean implementables \ny sostenibles, ya que incluyen las perspectivas de las partes interesadas.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-14-01.pdf\">Metadatuak 17-14-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.14: Enhance policy coherence for sustainable development</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.14.1: Number of countries with mechanisms in place to enhance policy coherence of sustainable development</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_CPA_SDEVP - Mechanisms in place to enhance policy coherence for sustainable development (Percent) [17.14.1 ]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-07-29", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<ul>\n  <li><strong>Definition: </strong></li>\n</ul>\n<p>For the purpose of this methodology &#x2018;policy coherence of sustainable development&#x2019; has been interpreted as the coherence between policies in general that cover the dimensions of sustainable development. This indicator is a composite indicator which covers mechanisms related to: </p>\n<ol>\n  <li>Institutionalization of Political Commitment </li>\n  <li>Long-term considerations in decision-making</li>\n  <li>Inter-ministerial and cross-sectoral coordination </li>\n  <li>Participatory processes</li>\n  <li>Policy linkages</li>\n  <li>Alignment across government levels</li>\n  <li>Monitoring and reporting for policy coherence</li>\n  <li>Financing for policy coherence</li>\n</ol>\n<p><strong>Concepts:</strong></p>\n<p><em>Scope of &#x201C;Sustainable Development&#x201D;: </em>For the purpose of this methodology &#x2018;policy coherence of sustainable development&#x2019; has been interpreted as the coherence between policies in general that cover the dimensions of sustainable development, rather than adopting a narrower definition of mechanisms put in place to support the coherent implementation of Agenda 2030 and the Sustainable Development Goals (SDGs), so as to promote coherent policy for sustainable development well beyond the current agenda&#x2019;s timeframe. The policy coherence mechanisms set out in this methodology may therefore include mechanisms already in place before the adoption of the 2030 Agenda in 2015, and any mechanisms established during the next decade leading up to 2030 should aim to continue well beyond that timeframe. However, given the role of Agenda 2030 and the individual goals in defining the specific parameters of sustainable development, it is likely that governments will focus, in implementing this methodology, on bringing coherence in their policy approaches to implement the goals. </p>\n<p><em>The concept of Policy Coherence: </em>The textual formulation of the indicator covers &#x201C;policy coherence&#x201D;. In order to make the indicator universally applicable and adaptable to various national contexts, the mechanisms measured by the methodology cover a wide range of mechanisms that, although aiming to achieve the same objective, use slightly different language. In order to properly assess and report on this indicator, similar concepts such as &#x201C;whole of government approach or &#x201C;integrated approach&#x201D; will be interpreted in the same spirit as the concept of &#x201C;policy coherence&#x201D;. However, it is important that the used concept considers policies that cover the various dimensions of sustainable development. Hence, a mechanism focusing solely on the concept of policy coherence for development (which is often limited to coherence between Official Development Assistance (ODA) and other policies, in the spirit of the Millennium Development Goals) will not be considered by this framework. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Standard Country or Area Codes for Statistical Use (UN M49 classification of countries and regions)</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Data provided by national governments, including entities responsible for SDG implementation.</p>", "COLL_METHOD__GLOBAL"=>"<p> National data are collected through the UNEP Questionnaire on the mechanism in place to enhance policy coherence of sustainable development.</p>", "FREQ_COLL__GLOBAL"=>"<p>First data collection in 2020. Biennially thereafter.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>First reporting cycle: 2021. Biennially thereafter.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Data are provided by national governments, including entities responsible for SDG implementation.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Environment Programme (UNEP)</p>", "INST_MANDATE__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) was mandated as Custodian Agency for indicator 17.14.1 by the Inter-agency and Expert Group on SDG Indicators and supports all work aspect in relation to Policy Coherence for Sustainable Development.</p>", "RATIONALE__GLOBAL"=>"<p>Enhancing policy coherence for sustainable development is important for achieving sustainable development in its three dimensions (economic, social and environmental) in a balanced and integrated manner; for ensuring coherence between policies at various levels of government; and for ensuring that policies in different sectors are mutually supportive and do not work against each other. It is also important in addressing the impacts of domestic policy internationally. </p>\n<p>Policy coherence aims, as a minimum, to identify trade-offs and mitigate negative impacts between policies. At a more ambitious level, it should also aim to foster synergies and produce policies that mutually reinforce each other and to ensure that policies put in place are implementable and sustainable as they are inclusive of the concerned stakeholders&#x2019; perspectives.</p>", "REC_USE_LIM__GLOBAL"=>"<p>There are many mechanisms that could be useful to assess at the national level which would be relevant to enhance policy coherence for sustainable development. This methodology aims to provide a basis for countries to engage in discussions around what policy coherence means at the national level and how it could be improved. Such discussions and strategies to improve policy coherence that may results from it could feed into a country Voluntary National Review (VNR) or National Development Strategy or Plan development, to inform further efforts by the country to improve its ability to implement Agenda 2030 through better policy coherence. This document should be considered a living document which is regularly updated with the country experiences in putting in place and assessing mechanisms for policy coherence. These experiences, and related challenges, lessons learned and solutions, can be shared so that UNEP as custodian agency, with partners, can further refine this methodology and disseminate it not only as a tool to enable effective reporting but also to support national efforts toward policy coherence.</p>", "DATA_COMP__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) has developed a composite indicator framework for SDG 17.14.1 based on initial research on existing work, literature, partners and existing indicators on similar issues. This indicator includes 8 domains. Each domain is scored on a scale from 0 to 10, where 0 means none of the requested mechanisms are implemented, 10 means all the requested mechanisms are in place. The percentage of points out of the total 80 points is then computed for each country. It is recommended that Governments convene a stakeholder group for self-scoring. The below table is used for scoring. Full details are in the document &#x201C;Methodology for SDG-indicator 17.14.1: Mechanisms in place to enhance policy coherence for sustainable development&#x201D;.</p>\n<p><em>Table 1: Scoring for mechanisms in place to enhance policy coherence for sustainable development</em></p>\n<table>\n  <tbody>\n    <tr>\n      <td>\n        <p><strong>Theme</strong></p>\n      </td>\n      <td>\n        <p><strong>Domain</strong></p>\n      </td>\n      <td>\n        <p><strong>Points</strong></p>\n      </td>\n      <td>\n        <p><strong>National Score</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>1. Institutionalized political commitment</p>\n      </td>\n      <td>\n        <p>Political commitment expressed/endorsed by the highest level</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Additional specific commitments (1 point each, maximum of 5 points): </p>\n        <p>&#x2022; Set timelines for the achievement of policy coherence objectives;</p>\n        <p>&#x2022; A dedicated budget;</p>\n        <p>&#x2022; Defined roles and responsibilities;</p>\n        <p>&#x2022; Regular reporting mechanism;</p>\n        <p>&#x2022; Explicit consideration of international commitments; </p>\n        <p>&#x2022; Other nationally relevant commitment.</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>2. Long-term considerations </p>\n      </td>\n      <td>\n        <p>Long-term objectives going beyond the current electoral cycle included in national strategies</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Additional specific mechanisms (1 point each, maximum of 5 points): </p>\n        <p>&#x2022; A commissioner, council or ombudsperson for future generations; </p>\n        <p>&#x2022; Other mechanisms of scrutiny or oversight on possible future effects; </p>\n        <p>&#x2022; Mechanisms for regular appraisal of policies; </p>\n        <p>&#x2022; Impact assessment mechanisms; and </p>\n        <p>&#x2022; Other nationally relevant factors.</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>3. Inter-ministerial and cross-sectoral coordination</p>\n      </td>\n      <td>\n        <p>National mechanism for regular coordination </p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Additional elements (maximum of 5 points): </p>\n        <p>&#x2022; A mandate to make decisions regarding trade-offs (2 points);</p>\n        <p>&#x2022; Coordination body is convened by a centralized government body (1 point);</p>\n        <p>&#x2022; Coordination at both political level and technical level (1 point);</p>\n        <p>&#x2022; Mandate for aligning internal and external policies (1 point);</p>\n        <p>&#x2022; Other nationally relevant mechanism (1 point).</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>4. Participatory processes</p>\n      </td>\n      <td>\n        <p>Relevant stakeholders are consulted at the early stages of development of laws, policies, plans, etc.</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Additional elements (scored as follows): </p>\n        <p>&#x2022; Consultations take place in a comprehensive manner at various stages of the policy cycle (1 point);</p>\n        <p>&#x2022; Institutions disclose the rationale for not including inputs from consultations (2 points);</p>\n        <p>&#x2022; An accountability mechanism that allows public intervention (2 points).</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td rowspan=\"2\">\n        <p>5. Integration of the three dimensions of Sustainable Development, assessment of policy effects and linkages</p>\n      </td>\n      <td>\n        <p>A mechanism for assessing and addressing issues in terms of the contribution of a policy (new or existing) to broader sustainable development, including transboundary elements. </p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Additional mechanisms (1 point each, maximum of 5 points): </p>\n        <p>&#x2022; The application of the above mechanisms at all levels of government;</p>\n        <p>&#x2022; An indicator framework for tracking policy effectiveness towards sustainable development; </p>\n        <p>&#x2022; Cost-benefit analysis of policy impacts across all sectors;</p>\n        <p>&#x2022; The identification of measures to mitigate potentially negative effects and to optimize synergies as part of policy and planning;</p>\n        <p>&#x2022; The consideration of international spill-overs, such as cross-border and international impacts; and </p>\n        <p>&#x2022; Other nationally relevant mechanisms.</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>6. Consultation and coordination across government levels</p>\n      </td>\n      <td>\n        <p>Any of following mechanisms (5 points each, 10 points total &#x2013; two mechanisms are enough for 10 points):</p>\n        <p>&#x2022; Mechanisms to systematically collect the inputs of sub-national government entities; </p>\n        <p>&#x2022; Arrangements for regular formal exchange between central government and subnational levels;</p>\n        <p>&#x2022; Mechanisms to ensure enhance substantive coherence (templates &amp; checklists);</p>\n        <p>&#x2022; Planning cycle timeframes that facilitate alignment.</p>\n      </td>\n      <td>\n        <p>10</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td rowspan=\"3\">\n        <p>7. Monitoring and reporting for policy coherence</p>\n      </td>\n      <td>\n        <p>Monitoring and evaluation framework for policy coherence for sustainable development.</p>\n      </td>\n      <td>\n        <p>5</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Aspects of policy coherence for sustainable development are integrated into reporting processes.</p>\n      </td>\n      <td>\n        <p>2</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>Data and information management system for sustainable development data.</p>\n      </td>\n      <td>\n        <p>3</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td>\n        <p>8. Financial resources and tools</p>\n      </td>\n      <td>\n        <p>Any of following (5 points each, 10 points total):</p>\n        <p>&#x2022; Check-lists to ensure that plans and budgets reflect policy coherence for sustainable development;</p>\n        <p>&#x2022; Integrated financial information systems;</p>\n        <p>&#x2022; Mechanisms to ensure that cooperation funds are aligned with national policies and priorities;</p>\n        <p>&#x2022; Additional points for mechanisms that could promote alignment between internal and external policy coherence.</p>\n      </td>\n      <td>\n        <p>10</p>\n      </td>\n      <td></td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>TOTAL</strong></p>\n      </td>\n      <td>\n        <p>80</p>\n      </td>\n      <td>\n        <p>Sum</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Mechanisms in place to enhance policy coherence for sustainable development (%)</strong></p>\n      </td>\n      <td colspan=\"2\">\n        <p><math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mfrac>\n              <mrow>\n                <mi mathvariant=\"normal\">S</mi>\n                <mi mathvariant=\"normal\">u</mi>\n                <mi mathvariant=\"normal\">m</mi>\n              </mrow>\n              <mrow>\n                <mn>80</mn>\n              </mrow>\n            </mfrac>\n            <mi mathvariant=\"normal\">&amp;nbsp;</mi>\n            <mo>&#xD7;</mo>\n            <mn>100</mn>\n            <mi mathvariant=\"normal\">%</mi>\n          </math></p>\n      </td>\n    </tr>\n  </tbody>\n</table>", "DATA_VALIDATION__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) carries out data validation procedures and contact countries for clarification if needed.</p>\n<p> </p>", "ADJUSTMENT__GLOBAL"=>"<p>No adjustments are made.</p>", "IMPUTATION__GLOBAL"=>"<p>The United Nations Environment Programme (UNEP) does not make any imputation for missing values. </p>", "REG_AGG__GLOBAL"=>"<p>The data are aggregated at the sub-regional, regional and global levels. For the aggregation methods, please see: <a href=\"https://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf\">https://wesr.unep.org/media/docs/graphs/aggregation_methods.pdf</a>.</p>", "DOC_METHOD__GLOBAL"=>"<p>The methodology for calculating this indicator and guiding the reporting process is available in the UNEP document &#x201C;<a href=\"https://wedocs.unep.org/xmlui/bitstream/handle/20.500.11822/38262/SDG17.14.1_methodology.pdf\">Methodology for SDG-indicator 17.14.1: Mechanisms in place to enhance policy coherence for sustainable development</a>&#x201D;.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Quality management is provided by the United Nations Environment Programme (UNEP).</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Quality assurance is provided by the United Nations Environment Programme (UNEP) in cooperation with the countries that provide these data.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality assessment is provided by the United Nations Environment Programme (UNEP).</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Data are available for all countries that provide country data to the UNEP Questionnaire on the mechanism in place to enhance policy coherence of sustainable development.</p>\n<p><strong>Time series:</strong></p>\n<p>The data sets presented in the SDG database presented according to country responses.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Not applicable </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p>The methodology for calculating this indicator is available in the UNEP document &#x201C;<a href=\"https://wedocs.unep.org/xmlui/bitstream/handle/20.500.11822/38262/SDG17.14.1_methodology.pdf\">Methodology for SDG-indicator 17.14.1: Mechanisms in place to enhance policy coherence for sustainable development</a>&#x201D;.</p>", "indicator_sort_order"=>"17-14-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.15.1", "slug"=>"17-15-1", "name"=>"Grado de utilización de los marcos de resultados y las herramientas de planificación de los propios países por los proveedores de cooperación para el desarrollo", "url"=>"/site/es/17-15-1/", "sort"=>"171501", "goal_number"=>"17", "target_number"=>"17.15", "global"=>{"name"=>"Grado de utilización de los marcos de resultados y las herramientas de planificación de los propios países por los proveedores de cooperación para el desarrollo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Grado de utilización de los marcos de resultados y las herramientas de planificación de los propios países por los proveedores de cooperación para el desarrollo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Grado de utilización de los marcos de resultados y las herramientas de planificación de los propios países por los proveedores de cooperación para el desarrollo", "indicator_number"=>"17.15.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"La medición de la alineación del apoyo de los socios para el desarrollo con \nlas prioridades nacionales, en términos del diseño de las intervenciones \ny el tipo de mecanismos de presentación de informes de resultados, \nproporciona una evaluación relevante del grado de respeto al espacio político \ny liderazgo de cada país para establecer e implementar políticas nacionales \nde erradicación de la pobreza y desarrollo sostenible.\n\nEn particular, para las intervenciones aprobadas en el año de referencia \n(es decir, el comportamiento más reciente), la evaluación mide hasta qué punto \nel apoyo de otros países y organizaciones internacionales establece prioridades \ny condiciones exógenas para los países socios que reciben cooperación para el \ndesarrollo, que no se reflejan en los mecanismos de establecimiento de \nprioridades o las herramientas de planificación existentes a nivel nacional.\n\nLa información recopilada a lo largo del indicador proporciona una visión bidireccional: \nproporciona una estimación a nivel nacional sobre el espacio político existente de un \npaís y una estimación a nivel de socio para el desarrollo sobre su grado de alineación \ncon los marcos de resultados y los mecanismos de establecimiento de prioridades \nexistentes en los países socios donde opera.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.15.1&seriesCode=SG_PLN_PRVRIMON&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProporción de indicadores de resultados que serán monitoreados utilizando fuentes gubernamentales y sistemas de monitoreo - datos por proveedor (%) SG_PLN_PRVRIMON</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.15.1&seriesCode=SG_PLN_RECRIMON&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProporción de indicadores de resultados que serán monitoreados utilizando fuentes gubernamentales y sistemas de monitoreo - datos por receptor (%) SG_PLN_RECRIMON</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-15-01.pdf\">Metadatos 17-15-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Measuring the alignment of development partners’ support to country priorities in terms of intervention \ndesign and type of results-reporting mechanisms provides a relevant assessment regarding the degree of \nrespect for each country’s policy space and leadership to establish and implement country-owned \npolicies for poverty eradication and sustainable development. \n\nIn particular, for interventions approved in the year of reference (i.e. most recent behaviour), the \nassessment measures the extent to which support from other countries and international organizations \nset exogenous priorities and conditions to partner countries receiving development co-operation that are \nnot reflected in existing country-led priority-setting mechanisms or planning tools. \n\nThe information collected throughout the indicator provides a two-way mirror, providing both a \ncountry-level estimate on a country’s existing policy space, and a development partner-level estimate on \nits degree of alignment with existing results frameworks and priority-setting mechanisms in partner \ncountries where it operates. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.15.1&seriesCode=SG_PLN_PRVRIMON&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProportion of results indicators which will be monitored using government sources and monitoring systems - data by provider (%) SG_PLN_PRVRIMON</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.15.1&seriesCode=SG_PLN_RECRIMON&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nProportion of results indicators which will be monitored using government sources and monitoring systems - data by recipient (%) SG_PLN_RECRIMON</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-15-01.pdf\">Metadata 17-15-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"La medición de la alineación del apoyo de los socios para el desarrollo con \nlas prioridades nacionales, en términos del diseño de las intervenciones \ny el tipo de mecanismos de presentación de informes de resultados, \nproporciona una evaluación relevante del grado de respeto al espacio político \ny liderazgo de cada país para establecer e implementar políticas nacionales \nde erradicación de la pobreza y desarrollo sostenible.\n\nEn particular, para las intervenciones aprobadas en el año de referencia \n(es decir, el comportamiento más reciente), la evaluación mide hasta qué punto \nel apoyo de otros países y organizaciones internacionales establece prioridades \ny condiciones exógenas para los países socios que reciben cooperación para el \ndesarrollo, que no se reflejan en los mecanismos de establecimiento de \nprioridades o las herramientas de planificación existentes a nivel nacional.\n\nLa información recopilada a lo largo del indicador proporciona una visión bidireccional: \nproporciona una estimación a nivel nacional sobre el espacio político existente de un \npaís y una estimación a nivel de socio para el desarrollo sobre su grado de alineación \ncon los marcos de resultados y los mecanismos de establecimiento de prioridades \nexistentes en los países socios donde opera.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.15.1&seriesCode=SG_PLN_PRVRIMON&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nGobernu-iturriak eta monitorizazio-sistemak erabiliz monitorizatuko diren emaitzen adierazleen proportzioa - datuak hornitzaileko (%) SG_PLN_PRVRIMON</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.15.1&seriesCode=SG_PLN_RECRIMON&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nGobernu-iturriak eta monitorizazio-sistemak erabiliz monitorizatuko diren emaitzen adierazleen proportzioa - datuak hartzaileko (%) SG_PLN_RECRIMON</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-15-01.pdf\">Metadatuak 17-15-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.15: Respect each country&#x2019;s policy space and leadership to establish and implement policies for poverty eradication and sustainable development</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.15.1: Extent of use of country-owned results frameworks and planning tools by providers of development cooperation</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_PLN_PRPOLRES - Extent of use of country-owned results frameworks and planning tools by providers of development cooperation - data by provider (%) [17.15.1]</p>\n<p>SG_PLN_PRVNDI - Proportion of project objectives of new development interventions drawn from country-led result frameworks - data by provider [17.15.1]</p>\n<p>SG_PLN_PRVRICTRY - Proportion of results indicators drawn from country-led results frameworks - data by provider (%) [17.15.1]</p>\n<p>SG_PLN_PRVRIMON - Proportion of results indicators which will be monitored using government sources and monitoring systems - data by provider (%) [17.15.1]</p>\n<p>SG_PLN_RECNDI - Proportion of project objectives in new development interventions drawn from country-led result frameworks - data by recipient [17.15.1]</p>\n<p>SG_PLN_RECRICTRY - Proportion of results indicators drawn from country-led results frameworks - data by recipient (%) [17.15.1]</p>\n<p>SG_PLN_RECRIMON - Proportion of results indicators which will be monitored using government sources and monitoring systems - data by recipient (%) [17.15.1]</p>\n<p>SG_PLN_REPOLRES - Extent of use of country-owned results frameworks and planning tools by providers of development cooperation - data by recipient (%) [17.15.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>17.16.1 and 5.c.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>\n<p>United Nations Development Programme (UNDP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>\n<p>United Nations Development Programme (UNDP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This indicator measures the extent to which, and the ways in which, all concerned development partners use country-owned results frameworks (CRFs) to plan development cooperation efforts and assess their performance.</p>\n<p>The indicator assesses the degree to which providers of development cooperation (i.e. development partners) design their interventions by relying on objectives and results indicators that are drawn from country government-owned results frameworks reflecting the country&#x2019;s development priorities and goals.</p>\n<p><strong>Concepts:</strong></p>\n<p>Country-owned results frameworks (CRFs) define a country&#x2019;s approach to results and its associated monitoring and evaluation systems focusing on performance and achievement of development results. Using a minimal definition, these results frameworks include agreed objectives and results indicators (i.e. output, outcome, and/or impact). They also set targets to measure progress in achieving the objectives defined in the government&#x2019;s planning documents. </p>\n<p>The definition of country-owned results framework used for this indicator allows the possibility to use equivalent priority-setting mechanisms at the country level since not all countries articulate their priorities through consistent, integrated CRFs.</p>\n<p>In practice, country-owned results frameworks defined at the country level are often broadly stated (e.g. long-term vision plans, national development strategies) and operationalised in more detail at the sector level (e.g. sector strategies), where specific targets and indicators are set for a given timeframe.</p>\n<p>Some examples of CRFs are long-term vision plans; national development strategies; joint government-multi-donor plans; government&#x2019;s sector strategies, policies and plans; subnational planning instruments, as well as other frameworks (e.g. budget support performance matrices &amp; sector-wide approaches). In contrast, planning and priority setting documents produced outside the government, such as country strategies prepared by development partners, are not considered CRFs.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>For developing countries, classification is based on SDG grouping provided by the UN Statistical Office (regional classification, Least Developed Countries (LDCs), Landlocked Developing Countries (LLDCs), Small Island Developing States (SIDS)). </p>\n<p>For development partners, classification is based on SDG grouping. In addition, bilateral partners can be distinguished between members of the Development Assistance Committee (DAC) and non-members. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The monitoring is a voluntary and country-led process. Country governments lead and coordinate data collection and validation. At country level, data are reported by relevant government entities (e.g. the Ministry of Finance/budget department for national budget information) and by development partners and stakeholders. OECD and UNDP support countries in collecting relevant data through the Global Partnership monitoring exercise, and these organisations lead data aggregation and quality assurance at the global level.</p>", "COLL_METHOD__GLOBAL"=>"<p>(i) For the data collection process of the Global Partnership&apos;s monitoring exercise, a national coordinator is assigned by the country government. S/he typically comes from the Ministry of Finance, the Ministry of Planning, or the Ministry of Foreign Affairs, a Ministry that has a role for managing development cooperation and partnerships in accordance with the respective institutional structure of each country. </p>\n<p>(ii) The national coordinator collects inputs from development partners. The data is submitted to the OECD and UNDP and subsequently undergoes a review round with the headquarters offices of development partners. </p>\n<p>(iii) No adjustments are made to the data after they have undergone the validation process. However, inconsistencies or possible problematic values are highlighted and sent back to national coordinators for revision.</p>", "FREQ_COLL__GLOBAL"=>"<p>The data collection calendar was on a biennial cycle prior to 2020. Data has been reported based on the data collected in 2016 and 2018. The next monitoring round will take place starting from 2023 with data collection occurring on a rolling basis. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data release is scheduled for the first quarter in the year that immediately follows the national data gathering processes.</p>", "DATA_SOURCE__GLOBAL"=>"<p><strong>Name:</strong></p>\n<p>Leading central ministry from reporting countries. Typically, the Ministry of Finance, the Ministry of Planning, or the Ministry of Foreign Affairs, depending on the division of labour within each government.</p>\n<p><strong>Description:</strong></p>\n<p>Representatives from the leading ministry in country governments are responsible for leading the national data gathering process and country-level validation. These representatives coordinate the data collection process at the national level by consolidating data and inputs from providers of development co-operation, civil society organisations, the private sector, and trade unions. For calculation of indicator 17.15.1, country governments submit the data to the OECD/UNDP Joint Support Team of the Global Partnership.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD) and United Nations Development Programme (UNDP) jointly compile and report the data at the global level.</p>", "INST_MANDATE__GLOBAL"=>"<p>As custodians of this SDG indicator, OECD and UNDP are responsible for providing technical guidance and supporting countries to collect data, compiling and verifying country data, and for submitting the country data and aggregate data for this indicator. Drawing on their institutional support provided to the Global Partnership for Effective Development Co-operation, OECD and UNDP leverage country participation in the Global Partnership monitoring exercise, which since 2013 has tracked progress towards the effectiveness principles and is the recognised source of data and evidence on upholding effectiveness commitments, to aggregate global data for this indicator. Countries not participating in the Global Partnership monitoring exercise are able to submit their country data directly to OECD and UNDP. </p>", "RATIONALE__GLOBAL"=>"<p>Measuring the alignment of development partners&#x2019; support to country priorities in terms of intervention design and type of results-reporting mechanisms provides a relevant assessment regarding the degree of &#x201C;respect for each country&#x2019;s policy space and leadership to establish and implement country-owned policies for poverty eradication and sustainable development&#x201D;. </p>\n<p>In particular, for interventions approved in the year of reference (i.e. most recent behaviour), the assessment measures the extent to which support from other countries and international organizations set exogenous priorities and conditions to partner countries receiving development co-operation that are not reflected in existing country-led priority-setting mechanisms or planning tools.</p>\n<p>The information collected throughout the indicator provides a &#x201C;two-way mirror&#x201D;, providing both a country-level estimate on a country&#x2019;s existing policy space, and a development partner-level estimate on its degree of alignment with existing results frameworks and priority-setting mechanisms in partner countries where it operates. </p>", "REC_USE_LIM__GLOBAL"=>"<p>The Global Partnership monitoring exercise collects data beyond the scope of the proposed indicator, including additional aspects such as quality of national development planning, the enabling environment of civil society organisations, the quality of public-private dialogue, the predictability of development co-operation, and the use of country public financial management systems by providers of development co-operation. Data generated from the Global Partnership monitoring provide evidence for two additional SDG indicators: 17.16.1 and 5.c.1. </p>", "DATA_COMP__GLOBAL"=>"<p>To provide a comprehensive measure on the extent of use of country-owned results frameworks and other government-led planning tools, the indicator calculates the degree to which objectives, results, indicators and monitoring frameworks associated with new development interventions are drawn from government sources &#x2013; including national, sector and subnational planning tools.</p>\n<p>For each development intervention of significant size (US$ 100,000 and above) approved during the year of reference, the following dimensions are assessed:</p>\n<ul>\n  <li>Q1. Whether objectives are drawn from country-owned results frameworks, plans and strategies 0/1</li>\n  <li>Q2. Share of results (outcome) indicators that are drawn from country-owned results frameworks, plans and strategies %</li>\n  <li>Q3. Share of results (outcome) indicators that will rely on sources of data provided by existing country-led monitoring systems or national statistical services to track project progress %</li>\n</ul>\n<p>Global aggregates for the indicator (for partner countries and providers) are obtained by averaging the three dimensions of alignment with country&#x2019;s priorities and goals across all new interventions for the reporting year. </p>\n<p>Aggregated averages per partner country will provide the extent to which CRFs and planning tools are used by providers of development co-operation operating in that specific country in the design and monitoring of new development projects. </p>\n<p>All formulas are available at: <a href=\"http://unstats.un.org/sdgs/files/metadata-compilation/Metadata-Goal-17.pdf\">http://unstats.un.org/sdgs/files/metadata-compilation/Metadata-Goal-17.pdf</a></p>\n<p>Aggregated averages per development partner will indicate the extent to which that development partner uses CRFs and planning tools in the design and monitoring of new development projects in countries in which it operates. Formulas are available at: <a href=\"http://unstats.un.org/sdgs/files/metadata-compilation/Metadata-Goal-17.pdf\">http://unstats.un.org/sdgs/files/metadata-compilation/Metadata-Goal-17.pdf</a></p>\n<p>When aggregating, the size (budget amount) of the project/ intervention is not considered as weight in order to give the same level of importance to the extent of use of country-owned results frameworks and planning tools in medium-sized vs. larger projects, as the indicator tries to capture the overall behaviour of development partners in designing new interventions in a given country. Weighting by project size would otherwise overrepresent infrastructure projects and underrepresent interventions focused on influencing policies and institutional arrangements. Nevertheless, data on project size is available.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The national coordinator has the main responsibility to validate the project level data reported by respective government institutions development partners and stakeholders. </p>\n<p>At the global level, the OECD and UNDP review the project level data submitted by partner countries in consultation and coordination with countries&#x2019; national coordinators and with providers of development co-operation.</p>\n<p>Details on the validation process can be found at <a href=\"https://www.effectivecooperation.org/content/2018-monitoring-guide-national-co-ordinators\">https://www.effectivecooperation.org/content/2018-monitoring-guide-national-co-ordinators</a>. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022;</strong> <strong>At country level</strong></p>\n<p>There is no treatment of missing values. However, a validation process involving representatives of country governments and country offices as well as headquarters offices of development partners takes place. Missing values are highlighted during this validation process, and attempts are made to fill in these gaps.</p>\n<p><strong>&#x2022;</strong> <strong>At regional and global levels</strong></p>\n<p>There is no imputation of missing values. Attempts are made to minimize gaps in data submissions during the data validation process including triangulation with headquarters offices of development partners.</p>", "REG_AGG__GLOBAL"=>"<p>Global and regional estimates are constructed by making a simple average across all countries/providers globally and for a specific region. It was decided not to use a weighted average to give equal consideration to small and large projects (although project amounts and type are captured in the data to allow for more advanced tabulations).</p>", "DOC_METHOD__GLOBAL"=>"<p>A monitoring guide is available to national coordinators in English, French and Spanish. A separate guide in English is also available to providers of development cooperation. The guidance is updated regularly. The guide for national coordinators is available at https://www.effectivecooperation.org/content/2018-monitoring-guide-national-co-ordinators. The guide for providers is available at https://www.effectivecooperation.org/content/2018-monitoring-round-mini-guide-development-partners. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The national coordinator has the main responsibility to ensure the quality and comprehensiveness of data for this indicator. OECD and UNDP provide helpdesk and guidance materials to support the national coordinator managing the quality of data. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The national coordinator has the main responsibility to ensure the quality and comprehensiveness of data for this indicator. OECD and UNDP support the quality assurance through joint review of data with the national coordinator and by engaging development partners at HQ level, UN development system and UNDP country offices as needed, and cross checking with data set submitted for previous monitoring rounds.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>OECD and UNDP support the quality assessment through joint review of data with the national coordinator and by engaging development partners at HQ level, UN development system and UNDP country offices as needed, and cross checking with data set submitted for previous monitoring rounds.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>This indicator is relevant for a subset of countries. Series related to providers of development cooperation are relevant for countries that provide development co-operation, including but not limited to members of the OECD Development Assistance Committee (31 countries). Series related to recipients of development cooperation are only relevant for developing countries receiving development cooperation. </p>\n<p>Data collected in the 2016 and 2018 monitoring round generated data for a total of 96 recipient countries and for above 100 development partners &#x2013;including the 29 countries that are members of the OECD&#x2019;s Development Assistance Committee and the six major multilateral organizations in terms of development finance (i.e. the World Bank, the International Monetary Fund, the United Nations Development Programme, African Development Bank, Asian Development Bank, and the Inter-American Development Bank). </p>\n<p><strong>Time series:</strong></p>\n<p>Data for countries have been compiled in 2016 and 2018. From 2023, data will be available on a rolling basis with all countries encouraged to report at least once within a four-year cycle.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>Given the bottom-up approach in generating the indicator, disaggregation is possible at the country level and at the development partner level.</p>\n<p>While data collection is led at the country level, in a bottom-up approach, global and regional aggregates can be used for monitoring internationally-agreed commitments related to strengthening country ownership and better partner alignment with nationally-set development goals. </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>NA</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://effectivecooperation.org/\">http://effectivecooperation.org/</a></p>\n<p>Internationally agreed methodology and guideline URL: https://www.effectivecooperation.org/system/files/2020-09/2018_Monitoring_Guide_National_Coordinator.pdf</p>\n<p><strong>References:</strong></p>\n<p>Ocampo, Jose Antonio (2015). A Post-2015 Monitoring and Accountability Framework. UNDESA: CDP Background Paper No. 27. ST/ESA/2015/CDP/27.</p>\n<p>Espey, Jessica; K. Walecik and M. K&#xFC;hner (2015). Follow-up and Review of the SDGs: Fulfilling our Commitments. Sustainable Development Solutions Network: A Global Initiative for the United Nations. New York: SDSN.</p>\n<p>Coppard, D. and C. Culey (2015). The Global Partnership for Effective Development Co-operation&#x2019;s Contribution to the 2030 Agenda for Sustainable Development. Plenary Session 1 Background Paper. Busan Global Partnership Forum, Korea.</p>\n<p>GPEDC (2018). 2018 Monitoring Guide. /Paris/New York. Available at: https://www.effectivecooperation.org/system/files/2020-09/2018_Monitoring_Guide_National_Coordinator.pdf</p>", "indicator_sort_order"=>"17-15-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.16.1", "slug"=>"17-16-1", "name"=>"Número de países que informan de sus progresos en los marcos de múltiples interesados para el seguimiento de la eficacia de las actividades de desarrollo que apoyan el logro de los Objetivos de Desarrollo Sostenible", "url"=>"/site/es/17-16-1/", "sort"=>"171601", "goal_number"=>"17", "target_number"=>"17.16", "global"=>{"name"=>"Número de países que informan de sus progresos en los marcos de múltiples interesados para el seguimiento de la eficacia de las actividades de desarrollo que apoyan el logro de los Objetivos de Desarrollo Sostenible"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Número de países que informan de sus progresos en los marcos de múltiples interesados para el seguimiento de la eficacia de las actividades de desarrollo que apoyan el logro de los Objetivos de Desarrollo Sostenible", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que informan de sus progresos en los marcos de múltiples interesados para el seguimiento de la eficacia de las actividades de desarrollo que apoyan el logro de los Objetivos de Desarrollo Sostenible", "indicator_number"=>"17.16.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Alcanzar los Objetivos de Desarrollo Sostenible requiere movilizar y \nfortalecer alianzas multiactor que puedan aportar y utilizar eficazmente \ntodos los conocimientos, la experiencia, la tecnología y los recursos \nfinancieros disponibles para el desarrollo sostenible. La calidad de la \nrelación entre todos los socios relevantes define \nla solidez de la alianza mundial para el desarrollo sostenible.\n\nEl indicador mide los esfuerzos de los países para fortalecer estas alianzas multiactor \ny, por extensión, la Alianza Mundial para el Desarrollo Sostenible, analizando \nel progreso logrado en un conjunto de indicadores que dan seguimiento al \ntrabajo conjunto de los gobiernos nacionales y los socios para el desarrollo \nen pos del desarrollo sostenible.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.16.1&seriesCode=SG_PLN_MSTKSDG_P&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nNúmero de países que informan sobre avances en los marcos de seguimiento de la eficacia del desarrollo con participación de múltiples partes interesadas que apoyan el logro de los objetivos de desarrollo sostenible, Proveedor (1 = SÍ; 0 = NO) SG_PLN_MSTKSDG_P</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.16.1&seriesCode=SG_PLN_MSTKSDG_R&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nNúmero de países que informan sobre avances en los marcos de seguimiento de la eficacia del desarrollo con participación de múltiples partes interesadas que apoyan el logro de los objetivos de desarrollo sostenible, Receptor (1 = SÍ; 0 = NO) SG_PLN_MSTKSDG_R</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-16-01.pdf\">Metadatos 17-16-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-05-20", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Achieving the Sustainable Development Goals requires mobilizing and strengthening multi stakeholder \npartnerships that can bring and effectively use all the available knowledge, expertise, technology and \nfinancial resources for sustainable development. The quality of the relationship between all the relevant \npartners defines the strength of the global partnership for sustainable development. \n\nThe indicator provides a measure of countries’ efforts to enhance these multi stakeholder partnerships, \nand by extension the Global Partnership for Sustainable Development, by looking at progress made on a \nset of indicators that track how well country governments and development partners are working \ntogether towards sustainable development. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.16.1&seriesCode=SG_PLN_MSTKSDG_P&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nNumber of countries reporting progress in multi-stakeholder development effectiveness monitoring frameworks that support the achievement of the sustainable development goals, Provider (1 = YES; 0 = NO) SG_PLN_MSTKSDG_P</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.16.1&seriesCode=SG_PLN_MSTKSDG_R&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nNumber of countries reporting progress in multi-stakeholder development effectiveness monitoring frameworks that support the achievement of the sustainable development goals, Receiver (1 = YES; 0 = NO) SG_PLN_MSTKSDG_R</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-16-01.pdf\">Metadata 17-16-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Alcanzar los Objetivos de Desarrollo Sostenible requiere movilizar y \nfortalecer alianzas multiactor que puedan aportar y utilizar eficazmente \ntodos los conocimientos, la experiencia, la tecnología y los recursos \nfinancieros disponibles para el desarrollo sostenible. La calidad de la \nrelación entre todos los socios relevantes define \nla solidez de la alianza mundial para el desarrollo sostenible.\n\nEl indicador mide los esfuerzos de los países para fortalecer estas alianzas multiactor \ny, por extensión, la Alianza Mundial para el Desarrollo Sostenible, analizando \nel progreso logrado en un conjunto de indicadores que dan seguimiento al \ntrabajo conjunto de los gobiernos nacionales y los socios para el desarrollo \nen pos del desarrollo sostenible.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.16.1&seriesCode=SG_PLN_MSTKSDG_P&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nGarapen iraunkorreko helburuak lortzen laguntzen duten alderdi interesdun askoren garapenaren eraginkortasunaren jarraipen-esparruetan egindako aurrerapenei buruzko informazioa ematen duten herrialdeen kopurua, hornitzailea (1 = BAI; 0 = EZ) SG_PLN_MSTKSDG_P</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.16.1&seriesCode=SG_PLN_MSTKSDG_R&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nGarapen iraunkorreko helburuak lortzen laguntzen duten alderdi interesdun askoren garapenaren eraginkortasunaren jarraipen-esparruetan egindako aurrerapenei buruzko informazioa ematen duten herrialdeen kopurua, hartzailea (1 = BAI; 0 = EZ) SG_PLN_MSTKSDG_R</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-16-01.pdf\">Metadatuak 17-16-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.16: Enhance the Global Partnership for Sustainable Development, complemented by multi-stakeholder partnerships that mobilize and share knowledge, expertise, technology and financial resources, to support the achievement of the Sustainable Development Goals in all countries, in particular developing countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.16.1: Number of countries reporting progress in multi-stakeholder development effectiveness monitoring frameworks that support the achievement of the sustainable development goals</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_PLN_MSTKSDG_P - Number of countries reporting progress in multi-stakeholder development effectiveness monitoring frameworks that support the achievement of the sustainable development goals, Provider (1 = YES; 0 = NO) [17.16.1]</p>\n<p>SG_PLN_MSTKSDG_R - Number of countries reporting progress in multi-stakeholder development effectiveness monitoring frameworks that support the achievement of the sustainable development goals, Recipient (1 = YES; 0 = NO) [17.16.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>17.15.1 and 5.c.1</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>\n<p>United Nations Development Programme (UNDP)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD)</p>\n<p>United Nations Development Programme (UNDP)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator tracks the number of countries reporting progress in multi-stakeholder monitoring frameworks that track the implementation of development effectiveness commitments supporting the achievement of sustainable development goals (SDGs). </p>\n<p><strong>Concepts:</strong></p>\n<p>&#x201C;Multi-stakeholder development effectiveness monitoring frameworks&#x201D; that track effective development cooperation are monitoring frameworks: </p>\n<p>&#x2022; whose indicators have been agreed on a voluntary basis; whose indicators measure the strength of the relationship between development actors; </p>\n<p>&#x2022; where data collection and review are led by the countries themselves; and where participation in data collection and review involves relevant stakeholders representing, at minimum, the public sector, the private sector and civil society organizations.</p>\n<p>The indicator takes into account the need to capture the respective roles and responsibilities of all parties involved in multi-stakeholder partnerships for development. It does so by looking at development effectiveness frameworks that are led by countries but include the participation of all relevant development partners. </p>\n<p>The Global Partnership for Effective Development Cooperation (Global Partnership) monitoring framework is an example of existing development effectiveness monitoring frameworks. There are other complementary efforts, such as the United Nations Economic and Social Council (ECOSOC) Development Cooperation Forum (DCF) mutual accountability survey. Emerging and future monitoring frameworks that fit the above definition, such as recent efforts to track South-South Cooperation by the Ibero-American General Secretariat (SEGIB), could also be considered.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number of countries</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>For developing countries, classification is based on SDG grouping provided by the UN Statistical Office (regional classification, Least Developed Countries (LDCs), Landlocked Developing Countries (LLDCs), Small Island Developing States (SIDS)). </p>\n<p>For development partners, classification is based on SDG grouping (regional). In addition, bilateral partners can be distinguished between members of the Development Assistance Committee (DAC) and non-members. </p>", "SOURCE_TYPE__GLOBAL"=>"<p>The monitoring is a voluntary and country led process. Country governments lead and coordinate data collection and review. At country level, data are reported by relevant government entities (e.g. the Ministry of finance/budget department for national budget information) and by development partners and stakeholders. OECD and UNDP are supporting developing countries in collecting relevant data through the Global Partnership monitoring exercise, and these organisations lead data aggregation and quality assurance at the global level. </p>\n<p>Complementarily, the United Nations Department of Economic and Social Affairs has been conducting a regular survey for the Development Cooperation Forum, in cooperation with UNDP, to identify national progress in mutual accountability and transparency. Survey results are assessed in comprehensive studies, informing global monitoring and providing practical suggestions for improving development results. Synergies with the measurement of Accountability mechanisms of the Global Partnership monitoring framework are being used. Other complementary sources of data (i.e. additional multi-stakeholder frameworks) may be incorporated in the future to provide a broader picture of progress made by countries towards development effectiveness in support of SDG implementation. </p>", "COLL_METHOD__GLOBAL"=>"<p>(i) For the data collection process of the Global Partnership&apos;s monitoring exercise, a national coordinator is assigned from the country government. S/he typically comes from the Ministry of Finance, the Ministry of Planning, or the Ministry of Foreign Affairs. </p>\n<p>(ii) The national coordinator in turn consults with other stakeholders (including country offices of providers of development co-operation, Civil Society Organisations, the private sector, and trade unions) to gather and review data.</p>\n<p>Headquarters/offices of providers of development co-operation can review the data before it is submitted to the national coordinator by the country office.</p>\n<p>(iii) No adjustments are made to submitted data, given that the reporting process needs to stay at country level. However, inconsistencies or possible problematic values are highlighted and sent back to national coordinators for revision.</p>", "FREQ_COLL__GLOBAL"=>"<p>The data collection calendar for global data aggregation was on a biennial cycle prior to 2020. Data has been reported based on data collected in 2016 and 2018. Starting from 2023, the monitoring round takes place with data collection occurring on a rolling basis. </p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data for this indicator is released following the final data submission by countries and calculations by the OECD and UNDP on a rolling basis.</p>", "DATA_SOURCE__GLOBAL"=>"<p>Leading central ministry from reporting countries. Typically, the Ministry of Finance, the Ministry of Planning, or the Ministry of Foreign Affairs, depending on the division of labour within each government.</p>\n<p><strong>Description:</strong></p>\n<p>Representatives from the leading ministry in country governments &#x2013;- are responsible for leading the national data gathering and review process, and final data submission. These representatives coordinate the data collection process at the national level by consolidating data and inputs from providers of development co-operation, Civil Society Organisations, the private sector, and trade unions. For calculation of indicator 17.16.1, country governments submit the data to the OECD-UNDP Joint Support Team of the Global Partnership.</p>", "COMPILING_ORG__GLOBAL"=>"<p>Organisation for Economic Co-operation and Development (OECD) and United Nations Development Programme (UNDP) jointly compile and report the data at the global level.</p>", "INST_MANDATE__GLOBAL"=>"<p>As custodians of this SDG indicator, OECD and UNDP are responsible for providing technical guidance and supporting countries to collect data, compiling and reviewing country data, and for submitting the country data and aggregate data for this indicator. Drawing on their institutional support provided to the Global Partnership for Effective Development Co-operation, OECD and UNDP leverage country participation in the Global Partnership monitoring exercise, which since 2013 has tracked progress towards the effectiveness principles and is the recognised source of data and evidence on upholding effectiveness commitments, to aggregate global data for this indicator. </p>", "RATIONALE__GLOBAL"=>"<p>Achieving the Sustainable Development Goals requires mobilizing and strengthening multi stakeholder partnerships that can bring and effectively use all the available knowledge, expertise, technology and financial resources for sustainable development. The quality of the relationship between all the relevant partners defines the strength of the global partnership for sustainable development.</p>\n<p>The indicator provides a measure of countries&#x2019; efforts to enhance these multi stakeholder partnerships, and by extension the Global Partnership for Sustainable Development, by looking at progress made on a set of indicators that track how well country governments and development partners are working together towards sustainable development. </p>\n<p>Reflecting the spirit of the global partnership for sustainable development, and the universal nature of the SDGs, the indicator monitors the contribution and behaviour of both provider and recipient countries in establishing more effective, inclusive multi-stakeholder partnerships to support and sustain the implementation of the 2030 Agenda. It does so by measuring their respective but differentiated commitments to strengthen the quality of their development partnerships.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The design of the indicator has practical benefits: </p>\n<p>&#x2022; the indicator allows for relevant monitoring frameworks to be updated in line with evolving commitments and country specific context without affecting the spirit of the indicator;</p>\n<p>&#x2022; the indicator does not presume a globally-set multi-stakeholder framework, acknowledging the diversity of complementary efforts supporting effective development cooperation;</p>\n<p>&#x2022; the indicator allows participating countries to choose whether they would like to report as a provider of development co-operation, as a recipient, or both. </p>\n<p>Data collection for the Global Partnership monitoring framework is led by countries receiving development co-operation. Progress of countries providing development co-operation in implementing development effectiveness commitments is captured through their partnership behaviour in those countries. Depending on each case, countries that currently are both recipient and providers of development cooperation opt to report in their role as recipient and/or provider of development cooperation.</p>", "DATA_COMP__GLOBAL"=>"<p>To reflect the universal nature of target 17.16, this indicator is presented as the global aggregate number of countries reporting progress. For any country reporting towards one (or more) multi-stakeholder development effectiveness framework(s), the country is considered to be reporting progress when, since the last reporting cycle, the number of indicators within the framework(s) that show a positive trend is greater than the number of indicators that show a negative trend.</p>\n<p><u>Countries providing development co-operation funding</u> and reporting in multi-stakeholder development effectiveness monitoring frameworks are assessed against the following elements: </p>\n<ul>\n  <li><em>Extent of use of country-owned results frameworks and planning tools by providers of development co-operation (SDG 17.15.1). This element is the average of the following three sub-elements: </em>\n    <ul>\n      <li><em>Aligning to country-defined development objectives: </em>Percentage of new development interventions whose objectives are drawn from country-led results frameworks.</li>\n      <li><em>Using country-led results frameworks: </em>Percentage of results indicators contained in new development interventions which are drawn from country-owned results frameworks.</li>\n      <li><em>Using national monitoring and statistical systems: </em>Percentage of results indicators in new development interventions which will be monitored using government sources and monitoring systems.</li>\n    </ul>\n  </li>\n  <li><em>Transparency of development cooperation</em>: Public availability of information on development cooperation according to international reporting standards and systems. </li>\n  <li><em>Annual predictability of development cooperation</em>: Proportion of development cooperation disbursed as development partners had scheduled at the beginning of the year. </li>\n  <li><em>Medium-term predictability of development cooperation: Proportion of countries for which a development partner has provided </em>forward-looking spending plans to the partner government (indicative annual amounts of development cooperation support to be provided over the one-to-three years). </li>\n  <li><em>Development cooperation on budgets subject to parliamentary oversight: </em>Share<em> </em>of development cooperation funds planned to/for the country&#x2019;s public sector that are recorded in the national budget submitted for legislative approval.</li>\n  <li><em>Development cooperation delivered through country systems: </em>Proportion of development cooperation disbursed to a given country according to national regulations and systems for public financial management (i.e. budgeting, financial reporting, auditing) and procurement.</li>\n  <li><em>Untied aid: </em>Proportion of development cooperation that is untied.<sup><a href=\"#footnote-2\" id=\"footnote-ref-2\">[1]</a></sup></li>\n</ul>\n<p><u>Countries receiving development cooperation funding</u> and reporting in multi-stakeholder development effectiveness monitoring frameworks are assessed against the following elements:<u> </u></p>\n<ol>\n  <li><em>Leading in setting up national priorities: Quality of a country&#x2019;s </em>national development strategy and results frameworks.</li>\n  <li><em>Creating an enabling environment for civil society organisations: </em>Civil society organizations operate within an environment that maximises their engagement in and contribution to development.</li>\n  <li><em>Development cooperation on budgets subject to parliamentary oversight</em>: Share of development cooperation funds planned to/for the country&#x2019;s public sector that are recorded in the national budget submitted for legislative approval.</li>\n  <li><em>Strengthening mutual accountability: A country has inclusive, regular, transparent, result-focused accountability mechanisms captured in a policy framework. </em></li>\n  <li><em>Strengthening gender equality and women&#x2019;s empowerment: A country has a system in place to track &#x2013; and make public &#x2013; budget </em>allocations for gender equality and women&#x2019;s empowerment. </li>\n  <li><em>Strengthening domestic institutions: </em>Quality of a country&#x2019;s budgetary and public financial management system.</li>\n</ol>\n<p>Countries providing and receiving development cooperation funding are invited to select whether they would like to report against provider-specific commitments, against recipient-specific commitments, or against both sets of commitments.</p>\n<p>For countries reporting both as providers and recipients of development cooperation, progress is calculated separately based on the respective set of indicators described above. Disaggregated results will show the detailed performance in each category. For the ultimate count of the number of countries making progress, dual countries are accounted as making progress if progress is made as recipient <strong>or</strong> as provider of development cooperation. </p>\n<p>The baseline for assessing progress is the latest measurement available for each specific count. When no baseline exists for a country, the first measurement available for an indicator constitutes the baseline for future measurements of progress.</p>\n<p>When a country meets and sustains all targets for the indicators it reports on (i.e. it is logically impossible to make further progress) it is considered as &#x201C;making progress&#x201D;. </p><div class=\"footnotes\"><div><sup class=\"footnote-number\" id=\"footnote-2\">1</sup><p> Estimates currently available for countries that are members of the OECD Development Assistance Committee. Data can be found at https://stats.oecd.org/Index.aspx?DataSetCode=TABLE7B <a href=\"#footnote-ref-2\">&#x2191;</a></p></div></div>", "DATA_VALIDATION__GLOBAL"=>"<p>The OECD and UNDP review the data submitted by partner countries in consultation and coordination with countries&#x2019; national coordinators, who in turn coordinate with providers of development co-operation.</p>\n<p>Details on the review process can be found athttps://www.effectivecooperation.org/book-page/technical-guide-national-co-ordinators-and-other-participants. </p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>There is no treatment done for missing values. However, missing information is highlighted during data review processes and stakeholders are asked to fill in these gaps. </p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>No imputation is done for missing values. However, missing information is highlighted during data review processes and stakeholders are asked to fill in these gaps.</p>", "REG_AGG__GLOBAL"=>"<p>Global estimates are calculated as the simple sum of the number of countries in the world who have made progress in multistakeholder development effectiveness frameworks.</p>", "DOC_METHOD__GLOBAL"=>"<p>A monitoring guide is available to national coordinators in English, French and Spanish. A dedicated annex to this guide in English is also available to providers of development co-operation. The guidance is updated regularly. The guide for national coordinators is available at https://www.effectivecooperation.org/book-page/technical-guide-national-co-ordinators-and-other-participants. </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>The national coordinator has the main responsibility to ensure the quality and comprehensiveness of data for this indicator. OECD and UNDP provide a helpdesk and guidance materials to support the national coordinator managing the quality of data. </p>", "QUALITY_ASSURE__GLOBAL"=>"<p>The national coordinator has the main responsibility to ensure the quality and comprehensiveness of data for this indicator. OECD and UNDP support the quality assurance of data through joint review of data with the national coordinator who in turn coordinate<u>s</u> with development partners as needed, and cross checking with data set submitted for previous monitoring rounds. </p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>OECD and UNDP support the quality assessment through joint review of data with the national coordinator who in turn coordinate<u>s</u> with development partners, and cross checking with data set submitted for previous monitoring rounds.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>Global aggregates are available for the 2016 and 2018 Global Partnership monitoring rounds. New data will be available after 2023. </p>\n<p>This indicator is relevant for a subset of countries. Series related to providers of development cooperation are relevant for countries that provide development co-operation, including but not limited to members of the OECD Development Assistance Committee (31 countries). Series related to recipients of development cooperation are only relevant for developing countries receiving development cooperation. </p>\n<p><strong>Time series:</strong></p>\n<p>Data for countries have been compiled in 2016 and 2018. From 2023, data will be available on a rolling basis with all countries encouraged to report at least once within a four-year cycle.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator presented as a global aggregate is generated through a bottom-up approach whereby data is collected at the country level and can therefore be disaggregated back at the level of countries (for both development cooperation providers and recipients) for national analysis and mutual dialogue. The data can also be further disaggregated according to individual indicators (i.e. specific dimensions of effective development cooperation) that are included within the multi-stakeholder frameworks.</p>\n<p>To foster regional policy dialogue, disaggregation at the regional level is possible and encouraged. Some existing platforms are already using the evidence for regional monitoring, learning and policy discussions (e.g. NEPAD in Africa, the Asia-Pacific Development Effectiveness Facility in Asia-Pacific, the Pacific Islands Forum Secretariat, the UN Regional Economic Commissions). </p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>", "OTHER_DOC__GLOBAL"=>"<h2>URL:</h2>\n<p><a href=\"http://effectivecooperation.org/\">http://effectivecooperation.org/</a> </p>\n<p><strong>Internationally agreed methodology and guideline URL: </strong></p>\n<p>https://www.effectivecooperation.org/book-page/annex-5-methodological-note</p>\n<h2>References:</h2>\n<p>Coppard, D. and C. Culey (2015). The Global Partnership for Effective Development Co-operation&#x2019;s Contribution to the 2030 Agenda for Sustainable Development. Plenary Session 1 Background Paper. Busan Global Partnership Forum, Korea.</p>\n<p>Espey, Jessica; K. Walecik and M. K&#xFC;hner (2015). Follow-up and Review of the SDGs: Fulfilling our Commitments. Sustainable Development Solutions Network: A Global Initiative for the United Nations. New York: SDSN.</p>\n<ul>\n  <li>GPEDC (2023). <a href=\"https://www.effectivecooperation.org/book-page/technical-guide-national-co-ordinators-and-other-participants\">Technical Guide for National Co-ordinators and Other Participants</a></li>\n</ul>\n<p>. /Paris/New York. Available at: https://www.effectivecooperation.org/book-page/technical-guide-national-co-ordinators-and-other-participants 09/2018_Monitoring_Guide_National_Coordinator.pdf </p>\n<p>Hazlewood, P. (2015). Global Multi-stakeholder Partnerships: Scaling Up Public-Private Collective Impact for SDGs. Independent Research Forum, Background Paper 4: IRF2015.</p>\n<p>Ocampo, J.A. and G&#xF3;mez, N. (2014). Accountable and Effective Development Cooperation in a Post-2015 era. Background Study 3: Accountability for Development Cooperation. ECOSOC: DCG Germany High-Level Symposium.</p>\n<p>Ocampo, Jose Antonio (2015). A Post-2015 Monitoring and Accountability Framework. UNDESA: CDP Background Paper No. 27. ST/ESA/2015/CDP/27.</p>", "indicator_sort_order"=>"17-16-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.17.1", "slug"=>"17-17-1", "name"=>"Suma en dólares de los Estados Unidos prometida a las alianzas público-privadas centradas en la infraestructura", "url"=>"/site/es/17-17-1/", "sort"=>"171701", "goal_number"=>"17", "target_number"=>"17.17", "global"=>{"name"=>"Suma en dólares de los Estados Unidos prometida a las alianzas público-privadas centradas en la infraestructura"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Suma en dólares de los Estados Unidos prometida a las alianzas público-privadas centradas en la infraestructura", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Suma en dólares de los Estados Unidos prometida a las alianzas público-privadas centradas en la infraestructura", "indicator_number"=>"17.17.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"Las brechas de infraestructura son significativas y requerirían un \naumento en la financiación del sector privado. La razón de ser de \neste indicador es medir los cambios en el volumen de las \nasociaciones público-privadas en infraestructura, evaluar \nlas tendencias e identificar las limitaciones para la participación \ndel sector privado.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-17-01.pdf\">Metadatos 17-17-1.pdf</a> (solo en inglés)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"The infrastructure gaps is significant and it would require to increase private sector financing. The \nrationale behind the indicator is to measure the changes in the volume of public private partnerships in \ninfrastructure and assess trends and identify constraints for private sector participation. \n\nSource: United Nations Statistics Division \n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-17-01.pdf\">Metadata 17-17-1.pdf</a>", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"Las brechas de infraestructura son significativas y requerirían un \naumento en la financiación del sector privado. La razón de ser de \neste indicador es medir los cambios en el volumen de las \nasociaciones público-privadas en infraestructura, evaluar \nlas tendencias e identificar las limitaciones para la participación \ndel sector privado.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>nil, "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-17-01.pdf\">Metadatuak 17-17-1.pdf</a> (ingelesez bakarrik)", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.17: Encourage and promote effective public, public-private and civil society partnerships, building on the experience and resourcing strategies of partnerships</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.17.1: Amount in United States dollars committed to public-private partnerships for infrastructure</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>GF_COM_PPPI - Amount of United States dollars committed to public-private partnerships for infrastructure, USD nominal [17.17.1]</p>\n<p>GF_COM_PPPI_KD - Amount of United States dollars committed to public-private partnerships for infrastructure, USD real [17.17.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-08-02", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>World Bank</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>World Bank</p>", "STAT_CONC_DEF__GLOBAL"=>"<h2>Definition:</h2>\n<p>Indicator based on World Bank data<em>: </em>&#x201C;Amount of United States dollars committed to public-private partnerships in infrastructure<em>.&#x201D;</em></p>\n<p>The indicator by the World Bank defines the term Public-Private Partnership (PPPs) as &#x201C;<em>any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility</em>.&#x201D;<strong> </strong></p>\n<p>The term infrastructure refers to:</p>\n<ul>\n  <li>Energy: electricity generation, transmission, and distribution, and natural gas transmission and distribution pipelines</li>\n  <li>Information and communications technology (ICT): ICT backbone infrastructure</li>\n  <li>Transport: Airports, railways, ports, and roads.</li>\n  <li>Water: potable water treatment and distribution, and sewerage collection and treatment.</li>\n</ul>\n<p><strong>Concepts:</strong></p>\n<p>PPPs is defined as &#x201C;any contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.&#x201D;</p>\n<p> </p>\n<p>The term infrastructure refers to:</p>\n<p>&#x2022; Energy: electricity generation, transmission, and distribution, and natural gas transmission and distribution pipelines</p>\n<p>&#x2022; Information and communications technology (ICT): ICT backbone infrastructure</p>\n<p>&#x2022; Transport: Airports, railways, ports, and roads.</p>\n<p>&#x2022; Water: potable water treatment and distribution, and sewerage collection and treatment.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Millions of current United States dollars</p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>The indicator has a established methodology that is available at the website <a href=\"http://ppi.worldbank.org/methodology/ppi-methodology\">http://ppi.worldbank.org/methodology/ppi-methodology</a> and the data collection process is as follows:</p>\n<ul>\n  <li>Team of researcher gather data for each of the regions using public sources; commercial news databases as well as from commercial specialized and industry publications/subscriptions, specialist portal, sponsor information and multilateral development agencies.</li>\n  <li>Data is uploaded to an administrative website through a template to make sure data is standardized.</li>\n  <li>Data is validated by a group of experts in Singapore first (PPI team), then for the World Bank Group focal points colleagues.</li>\n  <li>Data is later uploaded to the public website (www.ppi.worldbank.org) and make it available free of charge.</li>\n</ul>\n<p>The dataset is known as the Private Participation in Infrastructure (PPI) database. Updates are provided every six months (usually April and October) and the data is publicly available at <a href=\"http://www.ppi.worldbank.org\">www.ppi.worldbank.org</a>. This indicator is also available at the World Development Indicators at http://databank.worldbank.org/data/reports.aspx?source=world-development-indicators</p>", "COLL_METHOD__GLOBAL"=>"<p>A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit&#x2019;s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial&#x2019;s Project Finance International, Euromoney&#x2019;s Project Finance, Media Analytics&#x2019; Global Water Intelligence, Pisent Masons&#x2019; Water Yearbooks, and Platt&#x2019;s Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their Web sites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their Websites, annual reports, and other studies.</p>\n<p>Data is uploaded to an administrative website through a template to make sure data is standardized.</p>\n<p>Data is validated by a group of experts in Singapore first (PPI team), then for the World Bank focal points colleagues.</p>\n<p>Data is later uploaded to the public website (www.ppi.worldbank.org) and make it available free of charge. The website has a mechanism for challenges the data and welcome all PPP units to challenges the information about any project.</p>", "FREQ_COLL__GLOBAL"=>"<p>Data is collected in an ongoing basis. Updates are provided every six months (usually April and October).</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Data for the first half of the calendar year is released in October and for the full year is usually released around April.</p>", "DATA_SOURCE__GLOBAL"=>"<p>While the data is currently collected by the World Bank , Public-Private Partnership (PPP) units at national and subnational level are identified as national data providers that could directly provide data on projects financially closed each year or they could actively validate data collected by World Bank .</p>", "COMPILING_ORG__GLOBAL"=>"<p>The World Bank</p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>The infrastructure gaps is significant and it would require to increase private sector financing. The rationale behind the indicator is to measure the changes in the volume of public private partnerships in infrastructure and assess trends and identify constraints for private sector participation.</p>", "REC_USE_LIM__GLOBAL"=>"<p>The limitations of the proposed indicator is that it does not account for other sectors such as education and health may account for a significant part of Public-Private Partnership (PPPs) but they are not captured by the database. </p>\n<p>The database only covers low and middle income countries (World Bank classification) and it does not collect the indicator for high income countries. Expanding the data to include high income countries as well as PPPs in other sector beyond infrastructure is something that the World Bank is considering but it is currently limited by budget constraints.</p>\n<p>Unfortunately, the Private Participation in Infrastructure (PPI) database does not collect data on civil society partnerships and this will not fit the currently methodology of data gathering and is outside the present work&#x2019;s scope.</p>", "DATA_COMP__GLOBAL"=>"<p>The indicator has an established methodology that is available at the website <a href=\"http://ppi.worldbank.org/methodology/ppi-methodology\">http://ppi.worldbank.org/methodology/ppi-methodology</a> and the data collection process is as follows:</p>\n<ul>\n  <li>Team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases ( such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit&#x2019;s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial&#x2019;s Project Finance International, Euromoney&#x2019;s Project Finance, Media Analytics&#x2019; Global Water Intelligence, Pisent Masons&#x2019; Water Yearbooks, and Platt&#x2019;s Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or PPP agencies, and regulatory agencies) sponsor information (primarily through their Web sites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their Websites, annual reports, and other studies.</li>\n  <li>Data is uploaded to an administrative website through a template to make sure data is standardized.</li>\n  <li>Data is validated by a group of experts in Singapore.</li>\n  <li>Data is later uploaded to the public website (www.ppi.worldbank.org) and make it available free of charge.</li>\n</ul>\n<p>The limitations of the proposed indicator is that it does not account for other sectors such as education and health may account for a significant part of PPPs but they are not captured by the database. Expanding the data to include PPPs in other sector beyond infrastructure is something that the World Bank is considering but it is currently limited by budget constraints.</p>\n<p>Unfortunately, the Private Participation in Infrastructure (PPI) database does not collect data on civil society partnerships and this will not fit the currently methodology of data gathering and is outside the present work&#x2019;s scope.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>Not applicable</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>Data is collected semi-annually for all emerging markets and development economies. When there are no Public-Private Partnership (PPP) projects in a country, the data shows a value of zero investments. If there is information of a project in a country but the information on investments is not available, then it is reported as missing value. No imputations are performed for missing values.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>No imputations are carried out for missing values in the database. </p>", "REG_AGG__GLOBAL"=>"<p>Regional and global aggregates are calculated by adding investment values of all countries in that region or globally without any weight assigned. The only adjustment that the data does is to account only once cross border projects, i.e. projects that involve more than one country and therefore have a unique project investment value. Cross border projects are counted for each country when data is presented at country level but only once when data is aggregated at regional or global level.</p>", "DOC_METHOD__GLOBAL"=>"<p>&#x2022; Currently countries do not compile this indicator. However, methodology for the Private Participation in Infrastructure (PPI) database can be used by any country to provide information on the Public-Private Partnership (PPP) projects that reached financial closure a particular year, or they could validate the list of projects provided by the PPI database.</p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<ul>\n  <li>The semi-annual list of projects goes through an extensive quality control process first by the Private Participation in Infrastructure (PPI) database team, then by the InfrastructureFinance Practice Group at the World Bank, and finally, it is shared for comment to focal points in the World Bank, the International FInance Corporation ( IFC) and the Multilateral Investment Guarantee Agency (MIGA). In addition, when data is released public ally, any project can be challenged in the website and therefore data will be reassessed and corrected if necessary.</li>\n  <li>No consultation project with countries on the national data is ongoing but the PPI database welcomes feedback and data contributors in the website.</li>\n</ul>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Not applicable</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The existing Private Participation in Infrastructure (PPI) database includes data on 6,400 projects over 28 years (1990-2023), with over 50 variables per project. It covers projects in 139 low and middle income countries as classified by the World Bank. The list of countries is reviewed every five years in order to maintain continuity in the data. </p>\n<p><strong>Time series:</strong></p>\n<p>The indicator is available since 1990 and data can be disaggregated on the monthly basis (i.e total investments in infrastructure PPP projects that reached financial closure in a particular month since 2000).</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The unit of analysis is the Public-Private Partnership (PPP) project; therefore, data can be disaggregated at the project level. There is data on sector and subsector as well as geographical location of the project at the subnational level and therefore it can be later aggregated by municipality, province, country or region.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>To our knowledge, countries do not produce estimates of this indicator. Some Public-Private Partnership (PPP) units in very few countries have data available in their website but it is neither presented in a cross- country comparable approach nor annually reported.</p>", "OTHER_DOC__GLOBAL"=>"<p>The data is publicly available at <a href=\"https://ppi.worldbank.org\">https://ppi.worldbank.org</a> . This indicator is also available at the World Development Indicators at <a href=\"http://databank.worldbank.org/data/reports.aspx?source=world-development-indicators\">http://databank.worldbank.org/data/reports.aspx?source=world-development-indicators</a></p>", "indicator_sort_order"=>"17-17-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.18.1", "slug"=>"17-18-1", "name"=>"Indicadores de la capacidad estadística", "url"=>"/site/es/17-18-1/", "sort"=>"171801", "goal_number"=>"17", "target_number"=>"17.18", "global"=>{"name"=>"Indicadores de la capacidad estadística"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Indicadores de la capacidad estadística", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Indicadores de la capacidad estadística", "indicator_number"=>"17.18.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nLos nuevos Indicadores de Desempeño Estadístico (IPS) sustituyen al \nÍndice de Capacidad Estadística (ICA), que el Banco Mundial publica \nperiódicamente desde 2004. Si bien los objetivos son los mismos: ofrecer \nuna mejor herramienta para medir los sistemas estadísticos de los países, \nel nuevo marco de los IPS se ha ampliado a nuevas áreas, como el uso de datos, \nlos datos administrativos, los datos geoespaciales, los servicios de datos y \nla infraestructura de datos. Los IPS proporcionan un marco que puede ayudar a \nlos países a medir su situación en diversas dimensiones y ofrece una ambiciosa \nagenda de medición para la comunidad internacional.\n\nEl pilar de fuentes de datos (Pilar 4) se segmenta en tres tipos de fuentes \ngeneradas por (i) la oficina de estadística (censos y encuestas) y fuentes a \nlas que se accede desde otros lugares, como (ii) datos administrativos y (iii) \ndatos geoespaciales. El equilibrio adecuado entre estos tipos de fuentes variará en \nfunción del contexto institucional de cada país y la madurez de su sistema estadístico.\n\nLas puntuaciones altas deben reflejar hasta qué punto las fuentes utilizadas \npermiten generar los indicadores estadísticos necesarios. Por ejemplo, una \npuntuación baja en estadísticas ambientales (en el pilar de producción de datos) \npuede reflejar una falta de uso (y una puntuación baja) de datos geoespaciales \n(en el pilar de fuentes de datos).\n\nPor otro lado, el Índice de Cobertura del Inventario de Datos Abiertos (ODIN) es un \nindicador de la capacidad de un país para producir un conjunto de \nestadísticas oficiales en 22 categorías a partir de bases de datos \nnacionales para apoyar los ODS. \n\nEl cálculo del índice total en los cinco elementos de cada una de las \n22 categorías de datos permite evaluar la capacidad revelada \ndel país para producir datos importantes para los esfuerzos \nde desarrollo nacionales, regionales y globales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.18.1&seriesCode=IQ_SPI_PIL4&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nÍndice de rendimiento de las fuentes de datos (Pilar 4 de indicadores de rendimiento estadístico) (Índice) IQ_SPI_PIL4</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.18.1&seriesCode=SG_STT_ODIN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nÍndice de cobertura del Inventario de Datos Abiertos (ODIN) SG_STT_ODIN</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-18-01a.pdf\">Metadatos 17-18-1(a).pdf</a> (solo en inglés)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-18-01b.pdf\">Metadatos 17-18-1(b).pdf</a> (solo en inglés)\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe new Statistical Performance Indicators (SPI) replaces the Statistical Capacity Index (SCI), which the \nWorld Bank has regularly published since 2004. Although the goals are the same, to offer a better tool to \nmeasure the statistical systems of countries, the new SPI framework has expanded into new areas \nincluding data use, administrative data, geospatial data, data services, and data infrastructure. The SPI \nprovides a framework that can help countries measure where they stand in several dimensions and offers \nan ambitious measurement agenda for the international community. \n\nThe data sources overall score (Pillar 4 score) is a composite measure of whether countries have data \navailable from the following sources: Censuses and surveys, administrative data and geospatial data. The \ndata sources (input) pillar is segmented by the following sources generated by (i) the statistical office \n(censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, and (iii) \ngeospatial data. The appropriate balance between these source types will vary depending on a \ncountry’s institutional setting and the maturity of its statistical system. \n\nHigh scores should reflect the \nextent to which the sources being utilized enable the necessary statistical indicators to be generated. For \nexample, a low score on environment statistics (in the data production pillar) may reflect a lack of use of \n(and low score for) geospatial data (in the data sources pillar). \n\nOn the other hand, The Open Data Inventory (ODIN) Coverage Index is an indicator of a country’s capacity to produce a set of \nofficial statistics across 22 categories from national databases to support the SDGs. \n\nComputing the total\nindex across the five elements for each of the 22 data categories allows for an assessment of the \ncountry’s revealed capacity to produce data that are important for national, regional, and global \ndevelopment efforts. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.18.1&seriesCode=IQ_SPI_PIL4&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nData Sources performance index (Statistical Performance Indicators Pillar 4) (Index) IQ_SPI_PIL4</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.18.1&seriesCode=SG_STT_ODIN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nOpen Data Inventory (ODIN) Coverage Index SG_STT_ODIN</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-18-01a.pdf\">Metadata 17-18-1(a).pdf</a>\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-18-01b.pdf\">Metadata 17-18-1(b).pdf</a>\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nLos nuevos Indicadores de Desempeño Estadístico (IPS) sustituyen al \nÍndice de Capacidad Estadística (ICA), que el Banco Mundial publica \nperiódicamente desde 2004. Si bien los objetivos son los mismos: ofrecer \nuna mejor herramienta para medir los sistemas estadísticos de los países, \nel nuevo marco de los IPS se ha ampliado a nuevas áreas, como el uso de datos, \nlos datos administrativos, los datos geoespaciales, los servicios de datos y \nla infraestructura de datos. Los IPS proporcionan un marco que puede ayudar a \nlos países a medir su situación en diversas dimensiones y ofrece una ambiciosa \nagenda de medición para la comunidad internacional.\n\nEl pilar de fuentes de datos (Pilar 4) se segmenta en tres tipos de fuentes \ngeneradas por (i) la oficina de estadística (censos y encuestas) y fuentes a \nlas que se accede desde otros lugares, como (ii) datos administrativos y (iii) \ndatos geoespaciales. El equilibrio adecuado entre estos tipos de fuentes variará en \nfunción del contexto institucional de cada país y la madurez de su sistema estadístico.\n\nLas puntuaciones altas deben reflejar hasta qué punto las fuentes utilizadas \npermiten generar los indicadores estadísticos necesarios. Por ejemplo, una \npuntuación baja en estadísticas ambientales (en el pilar de producción de datos) \npuede reflejar una falta de uso (y una puntuación baja) de datos geoespaciales \n(en el pilar de fuentes de datos).\n\nPor otro lado, el Índice de Cobertura del Inventario de Datos Abiertos (ODIN) es un \nindicador de la capacidad de un país para producir un conjunto de \nestadísticas oficiales en 22 categorías a partir de bases de datos \nnacionales para apoyar los ODS. \n\nEl cálculo del índice total en los cinco elementos de cada una de las \n22 categorías de datos permite evaluar la capacidad revelada \ndel país para producir datos importantes para los esfuerzos \nde desarrollo nacionales, regionales y globales.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.18.1&seriesCode=IQ_SPI_PIL4&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nDatu-iturrien errendimendu-indizea (estatistika-errendimenduaren adierazleen 4. zutabea) (indizea) IQ_SPI_PIL4</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.18.1&seriesCode=SG_STT_ODIN&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nDatu Irekien Inbentarioaren (ODIN) estaldura-indizea SG_STT_ODIN</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-18-01a.pdf\">Metadatuak 17-18-1(a).pdf</a> (ingelesez bakarrik)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-18-01b.pdf\">Metadatuak 17-18-1(b).pdf</a> (ingelesez bakarrik)\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17. Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.18: By 2020, enhance capacity-building support to developing countries, including for least developed countries and small island developing states, to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.18.1: Statistical capacity indicators</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_STT_ODIN - Open Data Inventory (ODIN) Coverage Index [17.18.1]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-03-28", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>17.18.2, 17.18.3</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Open Data Watch (ODW)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Open Data Watch (ODW)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p>Definitions:</p>\n<p>The Open Data Inventory (ODIN) is an evaluation of the coverage and openness of data provided on the websites maintained by national statistical offices (NSOs) and any official government website that is accessible from the NSO site, as well as a country&#x2019;s official SDG portal. </p>\n<p>Capacity to produce a set of official statistics from national databases to support the SDGs: The ODIN Coverage Index refers to the availability of important statistical indicators in <a href=\"https://docs.google.com/document/d/1q1h0_z0TUGayO-qN9o3ablmo_qVdSGgPgU_Ptq5xrdU\">22 categories of social, economic, and environmental statistics</a>. Each data category is assessed on five elements of coverage (see below) that measure how complete the country&#x2019;s data offerings are. </p>\n<p>Information on all elements are collected for each dataset assessed in ODIN, except for elements 4 and 5 (see below) that are not included for some data categories or for small countries.</p>\n<p>The five coverage elements (further described in 4.c) are:</p>\n<ol>\n  <li>Availability of indicators and disaggregations</li>\n  <li>Availability of data in the last five years</li>\n  <li>Availability of data in the last ten years</li>\n  <li>Availability of data at the first administrative geographic level</li>\n  <li>Availability of data at the second administrative geographic level</li>\n</ol>\n<p>Scores are assigned for each element of each data category, not indicator. In addition, each data category cannot score higher on coverage elements 2-5 than coverage element 1. Aggregate scores are computed across categories and elements.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>The availability of official statistics is expressed as an index from 0 to 100&#x2014;0 expressing no availability and 100 expressing complete availability.</p>\n<p></p>", "CLASS_SYSTEM__GLOBAL"=>"<p>Not applicable</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Assessments by Open Data Watch assessors of National Statistical Office (NSO) websites (and any official government sites or portals that are linked to the NSO website).</p>", "COLL_METHOD__GLOBAL"=>"<p><strong><em>Stage One: Initial Assessment</em></strong></p>\n<p>Assessors evaluate the NSO website (and any official government sites or portals that are one click away from the NSO website or SDG portals) for data on the <a href=\"https://docs.google.com/document/d/1q1h0_z0TUGayO-qN9o3ablmo_qVdSGgPgU_Ptq5xrdU\">65 Open Data Inventory (ODIN) indicators</a>.</p>\n<p><strong><em>Stage Two: First Round Internal Review</em></strong></p>\n<p>During this stage, internal reviewers meticulously review all the information provided by assessors. They download and view all recorded datasets to confirm the information provided by the assessor and make adjustments, as necessary.</p>\n<p><strong><em>Stage Three: NSO Review (Second Round)</em></strong></p>\n<p>NSOs are contacted at least three times by email inviting them to participate in the NSO Review process. If they accept, they are sent the results from their assessment (excluding scores). NSOs are then given one month to provide feedback on their assessment, including comments on datasets used. They can suggest new datasets, sources, or provide other information relevant to the assessment. During this stage, any adjustments to the assessment that result from the feedback received are completed.</p>\n<p><strong><em>Stage Four: Third Round Internal Review</em></strong></p>\n<p>During this stage, a final review is conducted that primarily focuses on scores. Each country&#x2019;s scores are reviewed for accuracy and adherence to the ODIN methodology.</p>\n<p><strong><em>Stage Five: Analysis and Release of Results</em></strong></p>\n<p>During this stage, results are analyzed, scores are published on the ODIN website, and the ODIN annual report is released. </p>", "FREQ_COLL__GLOBAL"=>"<p>June to September every two years. The latest data collection period was June &#x2013; October 2022 and the next data collection will begin June 2024.</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>The latest data release is July 2023 and the next data release will be Q1 2025.</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices (NSOs) websites and linked websites of the National Statistical System (NSS)</p>", "COMPILING_ORG__GLOBAL"=>"<p>Open Data Watch (ODW)</p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>The Open Data Inventory (ODIN) Coverage Index is an indicator of a country&#x2019;s capacity to produce a set of official statistics across 22 categories from national databases to support the SDGs. Computing the total index across the five elements for each of the 22 data categories allows for an assessment of the country&#x2019;s revealed capacity to produce data that are important for national, regional, and global development efforts.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Limitations:</p>\n<p>Data produced by supranational entities, such as multilateral organizations may contain national level data but may not be published by national statistical offices. Depending on the governance of a country&#x2019;s national statistical system, the national statistical office website may not be the central repository of datasets or necessarily link to data portals.</p>", "DATA_COMP__GLOBAL"=>"<p>Criteria of Coverage Elements</p>\n<p><strong><em>Availability of indicators &amp; disaggregations (coverage element 1)</em></strong></p>\n<p>This element measures whether indicators are available in each data category and what disaggregations are available.</p>\n<p>Because there are a unique number of indicators in each data category, the number of indicators and disaggregations required to receive full credit for this element vary by data category. See the section of the official ODIN methodology, <a href=\"https://docs.google.com/document/d/1q1h0_z0TUGayO-qN9o3ablmo_qVdSGgPgU_Ptq5xrdU/edit#heading=h.83hsxks91ejv\">Data Categories &amp; Indicators</a>, for the criteria required in each category for full or partial credit on this element. </p>\n<p><strong><em>Availability of data in the last 5 years (coverage element 2)</em></strong></p>\n<p>This element measures whether the data identified in coverage element 1 are available over the last five years. ODIN 2022/23 includes the years 2017-2021 (or 2016/2017- 2020/2021 for non-calendar years).</p>\n<p>If data are presented on a quarterly or monthly basis, a majority of quarters or months for a given year must have data available to receive credit for that year. For example, at least 3 out of 4 quarters or 7 out of 12 months for a particular year must be present to award credit for that year.</p>\n<p>The following table shows how each data category is scored for this element. Full credit only requires publication of data for 3 of the last 5 years, since not all indicators have enough variability from year to year to warrant more frequent data collection in many countries.</p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Coverage Element 2: How to receive credit</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Score</strong></p>\n      </td>\n      <td>\n        <p><strong>Minimum Criteria</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>1 point</strong></p>\n      </td>\n      <td>\n        <p>All indicators available in the category must have national data for at least 3 of the last 5 years for all available disaggregations.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>.5 point</strong></p>\n      </td>\n      <td>\n        <p>One indicator in the category has national data for at least 1 year of the last 5 years for any number of disaggregations.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>0 points</strong></p>\n      </td>\n      <td>\n        <p>No indicators in the category have any national data for any of the last 5 years.</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Note: Coverage element 2 cannot score higher than coverage element 1. This is to prevent inflated coverage scores based on the publication of just a few indicators.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong><em>Availability of data in the last 10 years (coverage element 3)</em></strong></p>\n<p>This element measures whether the data identified in coverage element 1 are available over the last ten years. For ODIN 2022/23, this includes the years 2012-2021 (or 2011/2012- 2020/2021 for non-calendar years)</p>\n<p>If data are presented on a quarterly or monthly basis, the majority of quarters or months for a given year must have data available to receive credit for that year. For example, at least 3 out of 4 quarters or 7 out of 12 months for a particular year must be present to award credit for that year.</p>\n<p>The following table shows how each data category is scored for this element. Full credit only requires publication of data for 6 of the last 10 years, since not all indicators have enough variability from year to year to warrant more frequent data collection in many countries.</p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Coverage Element 3: How to receive credit</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Score</strong></p>\n      </td>\n      <td>\n        <p><strong>Minimum Criteria</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>1 point</strong></p>\n      </td>\n      <td>\n        <p>All indicators available in the category must have national data for at least 6 of the last 10 years for all available disaggregations.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>.5 point</strong></p>\n      </td>\n      <td>\n        <p>One indicator in the category has national data for at least 3 years of the last 10 years for any number of disaggregations.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>0 points</strong></p>\n      </td>\n      <td>\n        <p>No indicators in the category have any national data for at least 2 of the last 10 years.</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Note: Coverage element 3 cannot score higher than coverage element 1. </p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong><em>Availability of data at the first administrative geographic level (coverage element 4)</em></strong></p>\n<p>This element records whether the data identified in coverage element 1 are also available at the subnational level defined as the first administrative geographic level. To identify the first administrative levels, ODIN largely draws on the ISO 3166-2 standard. For a full list of first administrative levels used in ODIN, see<a href=\"https://drive.google.com/file/d/1VMvB3yqX8IlHL-7URk2MDIDyu2k8agVf/view?usp=sharing\"> this file</a>.</p>\n<p>If data are presented at the first administrative level, the majority of first administrative level divisions must have data available to receive credit for that year. Credit will only be given for less than a majority of first administrative divisions if there are methodological reasons for them not to exist (sample size is too small, indicator not relevant to those divisions, or other reasons) and this is stated in the metadata of the dataset.</p>\n<p>In certain data categories, all indicators in the category are not scored for data at the first administrative level because how these indicators are calculated often do now allow for geographic disaggregation in most countries. These categories are Money &amp; Banking, International Trade, Balance of Payments, and Energy.</p>\n<p>The following table shows how each data category is scored for this element.</p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Coverage Element 4: How to receive credit</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Score</strong></p>\n      </td>\n      <td>\n        <p><strong>Minimum Criteria</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>1 point</strong></p>\n      </td>\n      <td>\n        <p>All indicators available in the category must have first administrative level data for all available disaggregations.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>.5 point</strong></p>\n      </td>\n      <td>\n        <p>One indicator in the category has first administrative data for any number of disaggregations.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>0 points</strong></p>\n      </td>\n      <td>\n        <p>No indicators in the category have any data available at the first administrative level.</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Note: Coverage element 4 cannot score higher than coverage element 1. In addition, ODIN designates certain countries as &#x201C;<a href=\"https://docs.google.com/document/d/1q1h0_z0TUGayO-qN9o3ablmo_qVdSGgPgU_Ptq5xrdU/edit#heading=h.24shir1680ap\">Small Countries</a>&#x201D; which are not scored for publishing data at the first administrative level for the indicators 2.3, 4.3, 6.1, 6.2, 7.3, 7.4, 7.5, 8.2, 9.3, 10.2, and 18.2.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p><strong><em>Availability of data at the second administrative geographic level (coverage element 5)</em></strong></p>\n<p>This element measures whether the data identified in coverage element 1 are also available at the subnational level defined as the second administrative geographic level. Second administrative levels are defined by the country but must be a further division of their first administrative levels. For a full list of the possible second administrative levels used in ODIN, see<a href=\"https://drive.google.com/file/d/1VMvB3yqX8IlHL-7URk2MDIDyu2k8agVf/view?usp=sharing\"> this file</a>.</p>\n<p>If data are presented at the second administrative level, the majority of second administrative level divisions must have data available to award credit for that year. Credit will only be given for less than a majority of second administrative divisions if there are methodological reasons for them not to exist (sample size is too small, indicator not relevant to those divisions, or other reasons) and this is stated in the metadata of the dataset.</p>\n<p>In certain data categories, all indicators in the category are not scored at the second administrative level because administrative units responsible for producing these data do not exist in many countries or the indicators are not typically defined for small administrative units. These categories are Money &amp; Banking, International Trade, Balance of Payments, National Accounts, Government Finance, Pollution, Energy, Price Indexes, and Resource Use.</p>\n<p>In addition, the following indicators in other categories are not scored at the second administrative level for the same reasons. These include:</p>\n<p>(2.3) Education expenditures</p>\n<p>(4.3) Health expenditures</p>\n<p>(6.1) Maternal mortality rate</p>\n<p>(6.2) Infant mortality rate or neonatal mortality rate</p>\n<p>(7.3) Prevalence of obesity</p>\n<p>(7.4) Prevalence of stunting</p>\n<p>(7.5) Prevalence of wasting</p>\n<p>(8.2) Proportion of women in government</p>\n<p>(9.3) Data on prison population</p>\n<p>(10.2) Distribution of income by deciles or Gini coefficient</p>\n<p>(18.2) Data on protected lands<u> </u></p>\n<p>The following table shows how each data category is scored for this element. </p>\n<table>\n  <tbody>\n    <tr>\n      <td colspan=\"2\">\n        <p><strong>Coverage Element 5: How to receive credit</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>Score</strong></p>\n      </td>\n      <td>\n        <p><strong>Minimum Criteria</strong></p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>1 point</strong></p>\n      </td>\n      <td>\n        <p>All indicators available in the category must have second administrative level data for all available disaggregations.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>.5 point</strong></p>\n      </td>\n      <td>\n        <p>One indicator in the category has second administrative data for any number of disaggregations or years.</p>\n      </td>\n    </tr>\n    <tr>\n      <td>\n        <p><strong>0 points</strong></p>\n      </td>\n      <td>\n        <p>No indicators in the category have any data available at the second administrative level.</p>\n      </td>\n    </tr>\n    <tr>\n      <td colspan=\"2\">\n        <p>Note: Coverage element 5 cannot score higher than coverage element 1. In addition, ODIN designates certain countries as &#x201C;<a href=\"https://docs.google.com/document/d/1q1h0_z0TUGayO-qN9o3ablmo_qVdSGgPgU_Ptq5xrdU/edit#heading=h.24shir1680ap\">Small Countries</a>&#x201D; which are not scored for publishing data at the second administrative level for any category.</p>\n      </td>\n    </tr>\n  </tbody>\n</table>\n<p>Coverage scores are the average scores across the five coverage elements. Each element receives a score of 0, .5 or 1. Some category coverage scores will be based on 3 or 4 elements, if first or second administrative level data are not required. You can read more about the coverage elements and how to score them <a href=\"https://docs.google.com/document/d/1q1h0_z0TUGayO-qN9o3ablmo_qVdSGgPgU_Ptq5xrdU/edit#heading=h.jvsdl8vbl84l\">here</a>.</p>\n<p>Coverage elements 2-5 cannot score higher than coverage element 1 within any given category. </p>", "DATA_VALIDATION__GLOBAL"=>"<p>Open Data Watch conducts three rounds of review:</p>\n<p>After the initial assessment, internal reviewers review all the information provided by assessors. They download and view all recorded datasets to confirm the information provided by the assessor and make adjustments, as necessary.</p>\n<p>NSOs are contacted at least three times by email inviting them to participate in the NSO Review process. If they accept, they are sent the results from their assessment (excluding scores). NSOs are then given one month to provide feedback on their assessment, including comments on datasets used. They are able to suggest new datasets, sources, or provide other information relevant to the assessment. During this stage, any adjustments to the assessment that result from the feedback received are completed.</p>\n<p>After the previous two rounds of review, a final review is conducted that primarily focuses on scores. Each country&#x2019;s scores are reviewed for accuracy and adherence to the ODIN methodology.</p>", "ADJUSTMENT__GLOBAL"=>"<p>See methodological changes over time in <a href=\"https://docs.google.com/document/d/1q1h0_z0TUGayO-qN9o3ablmo_qVdSGgPgU_Ptq5xrdU/edit#bookmark=id.ntj327l45ozo\">ODIN&#x2019;s official methodology</a>.</p>", "IMPUTATION__GLOBAL"=>"<p>No Imputations are made for missing values at country level.</p>", "REG_AGG__GLOBAL"=>"<p>ODIN coverage score is assessed at the national level. Regional aggregates represent the median values of all countries in a region with values.</p>", "DOC_METHOD__GLOBAL"=>"<p>ODIN methodology can be used to compile data at national level: <a href=\"https://docs.google.com/document/d/1q1h0_z0TUGayO-qN9o3ablmo_qVdSGgPgU_Ptq5xrdU\">https://docs.google.com/document/d/1q1h0_z0TUGayO-qN9o3ablmo_qVdSGgPgU_Ptq5xrdU</a> </p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Rigorous training of assessors through multiple workshops, detailed assessor&#x2019;s guide and availability of multiple full-time staff dedicated to answer questions and review issues.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Review with countries of datasets being included in assessments.</p>\n<p>Multiple rounds of internal review on scoring and datasets. Review of reviews.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data Availability:</strong></p>\n<p>165 countries have been included in all six assessments since 2016. ODIN 2022/23 assessed 195 countries.</p>\n<p><strong>Time series:</strong></p>\n<p>2016-2018 annually, and biennially thereafter starting with 2020.</p>", "COMPARABILITY__GLOBAL"=>"<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><a href=\"https://odin.opendatawatch.com/\">ODIN website</a></p>\n<p><a href=\"https://docs.google.com/document/d/1q1h0_z0TUGayO-qN9o3ablmo_qVdSGgPgU_Ptq5xrdU\">ODIN methodology</a></p>", "indicator_sort_order"=>"17-18-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.18.2", "slug"=>"17-18-2", "name"=>"Número de países cuya legislación nacional sobre estadísticas cumple los Principios Fundamentales de las Estadísticas Oficiales", "url"=>"/site/es/17-18-2/", "sort"=>"171802", "goal_number"=>"17", "target_number"=>"17.18", "global"=>{"name"=>"Número de países cuya legislación nacional sobre estadísticas cumple los Principios Fundamentales de las Estadísticas Oficiales"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"La legislación sobre estadísticas cumple los Principios Fundamentales de las Estadísticas Oficiales", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países cuya legislación nacional sobre estadísticas cumple los Principios Fundamentales de las Estadísticas Oficiales", "indicator_number"=>"17.18.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"La legislación sobre estadísticas cumple los Principios Fundamentales de las Estadísticas Oficiales", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.18- De aquí a 2020, mejorar el apoyo a la creación de capacidad prestado a los países en desarrollo, incluidos los países menos adelantados y los pequeños Estados insulares en desarrollo, para aumentar significativamente la disponibilidad de datos oportunos, fiables y de gran calidad desglosados por ingresos, sexo, edad, raza, origen étnico, estatus migratorio, discapacidad, ubicación geográfica y otras características pertinentes en los contextos nacionales", "definicion"=>"\nValor lógico que indica si la legislación en materia estadística cumple con los  Principios Fundamentales de las Estadísticas Oficiales adoptados por la Comisión  Estadística de Naciones Unidas en la sesión especial de 11-15 de abril de 1994", "formula"=>"\n$$LAPFEO^{t} = \\begin{cases} 1 & \\text{Sí cumple los Principios Fundamentales de las Estadísticas Oficiales en el año 𝑡} \\\\ 0 & \\text{No cumple los Principios Fundamentales de las Estadísticas Oficiales en el año 𝑡} \\end{cases} $$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Se considerará que la ley de estadística cumple con los Principios \nFundamentales de las Estadísticas Oficiales de las Naciones Unidas si \ncontiene disposiciones relacionadas con los diez Principios. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.18.2&seriesCode=SG_STT_FPOS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Países con legislación estadística nacional existente que cumple con los Principios Fundamentales de las Estadísticas Oficiales (1 = SÍ; 0 = NO) SG_STT_FPOS</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-18-02.pdf\">Metadatos 17-18-2.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"La legislación sobre estadísticas cumple los Principios Fundamentales de las Estadísticas Oficiales", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.18- De aquí a 2020, mejorar el apoyo a la creación de capacidad prestado a los países en desarrollo, incluidos los países menos adelantados y los pequeños Estados insulares en desarrollo, para aumentar significativamente la disponibilidad de datos oportunos, fiables y de gran calidad desglosados por ingresos, sexo, edad, raza, origen étnico, estatus migratorio, discapacidad, ubicación geográfica y otras características pertinentes en los contextos nacionales", "definicion"=>"\nLogical value indicating whether the legislation on statistics complies with the Fundamental  Principles of Official Statistics adopted by the United Nations Statistical Commission at the  special session of 11-15 April 1994 ", "formula"=>"\n$$LAPFEO^{t} = \\begin{cases} 1 & \\text{It complies with the Fundamental Principles of Official Statistics in year 𝑡} \\\\ 0 & \\text{It does not comply with the Fundamental Principles of Official Statistics in year 𝑡} \\end{cases} $$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"A country’s statistics law will be considered compliant with the UN Fundamental Principles of Official \nStatistics if the law has provisions relating to all ten Principles.  \n\nSource: United Nations Statistics Division\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.18.2&seriesCode=SG_STT_FPOS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Countries with national statistical legislation exists that complies with the Fundamental Principles of Official Statistics (1 = YES; 0 = NO) SG_STT_FPOS</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-18-02.pdf\">Metadata 17-18-2.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"La legislación sobre estadísticas cumple los Principios Fundamentales de las Estadísticas Oficiales", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.18- De aquí a 2020, mejorar el apoyo a la creación de capacidad prestado a los países en desarrollo, incluidos los países menos adelantados y los pequeños Estados insulares en desarrollo, para aumentar significativamente la disponibilidad de datos oportunos, fiables y de gran calidad desglosados por ingresos, sexo, edad, raza, origen étnico, estatus migratorio, discapacidad, ubicación geográfica y otras características pertinentes en los contextos nacionales", "definicion"=>"\nValor lógico que indica si la legislación en materia estadística cumple con los  Principios Fundamentales de las Estadísticas Oficiales adoptados por la Comisión  Estadística de Naciones Unidas en la sesión especial de 11-15 de abril de 1994", "formula"=>"\n$$LAPFEO^{t} = \\begin{cases} 1 & \\text{Estatistika Ofizialen Oinarrizko Printzipioak betetzen ditu 𝑡 urtean} \\\\ 0 & \\text{Ez ditu betetzen Estatistika Ofizialen Oinarrizko Printzipioak 𝑡 urtean} \\end{cases} $$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"Se considerará que la ley de estadística cumple con los Principios \nFundamentales de las Estadísticas Oficiales de las Naciones Unidas si \ncontiene disposiciones relacionadas con los diez Principios. \n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.18.2&seriesCode=SG_STT_FPOS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Estatistika Ofizialen Oinarrizko Printzipioak betetzen dituen legeria nazionala duten herrialdeak (1 = BAI; 0 = EZ) SG_STT_FPOS</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-18-02.pdf\">Metadatuak 17-18-2.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.18: By 2020, enhance capacity-building support to developing countries, including for least developed countries and small island developing States, to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.18.2: Number of countries that have national statistical legislation that complies with the Fundamental Principles of Official Statistics</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_STT_FPOS - Countries with national statistical legislation exists that complies with the Fundamental Principles of Official Statistics (1 = YES; 0 = NO) [17.18.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>Not applicable</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Partnership in Statistics for Development in the 21st Century (PARIS21)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Partnership in Statistics for Development in the 21st Century (PARIS21)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator refers to the number of countries that have national statistical legislation that complies with the Fundamental Principles of Official Statistics. This refers to the number of countries that have a statistical legislation which respects the principles of United Nations Fundamental Principles of Official Statistics (UNFPOS).</p>\n<p><strong>Concepts:</strong></p>\n<p>National statistical legislation: The statistics law defines rules, regulation, measures with regard to the</p>\n<p>organization, management, monitoring and inspection of the statistical activities in a systematic way, strength, effectiveness and efficiency to assure the full coverage, accuracy and consistency with facts in order to provide reference for policy direction, socio economic planning, and contribute to the</p>\n<p>country&#x2019;s development to achieve wealth, culture, well-being and equity.</p>\n<p>UN Fundamental Principles of Official Statistics </p>\n<p>The Fundamental Principles for Official Statistics adopted by the United Nations Statistical Commission, in its Special Session of 11-15 April 1994 are:</p>\n<p>Principle 1. Official statistics provide an indispensable element in the information system of a society, serving the government, the economy and the public with data about the economic, demographic, social and environmental situation. To this end, official statistics that meet the test of practical utility are to be compiled and made available on an impartial basis by official statistical agencies to honour citizens&#x2019; entitlement to public information.</p>\n<p>Principle 2. To retain trust in official statistics, the statistical agencies need to decide according to strictly professional considerations, including scientific principles and professional ethics, on the methods and procedures for the collection, processing, storage and presentation of statistical data.</p>\n<p>Principle 3. To facilitate a correct interpretation of the data, the statistical agencies are to present information according to scientific standards on the sources, methods and procedures of the statistics.</p>\n<p>Principle 4. The statistical agencies are entitled to comment on erroneous interpretation and misuse of statistics. </p>\n<p>Principle 5. Data for statistical purposes may be drawn from all types of sources, be they statistical surveys or administrative records. Statistical agencies are to choose the source with regard to quality, timeliness, costs and the burden on respondents. </p>\n<p>Principle 6. Individual data collected by statistical agencies for statistical compilation, whether they refer to natural or legal persons, are to be strictly confidential and used exclusively for statistical purposes. </p>\n<p>Principle 7. The laws, regulations and measures under which the statistical systems operate are to be made public. </p>\n<p>Principle 8. Coordination among statistical agencies within countries is essential to achieve consistency and efficiency in the statistical system. </p>\n<p>Principle 9. The use by statistical agencies in each country of international concepts, classifications and methods promotes the consistency and efficiency of statistical systems at all official levels. </p>\n<p>Principle 10. Bilateral and multilateral cooperation in statistics contributes to the improvement of systems of official statistics in all countries.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number</p>", "SOURCE_TYPE__GLOBAL"=>"<p>Partnership in Statistics for Development in the 21st Century (PARIS21) SDG Survey (Send questionnaire(s) to country)</p>\n<p>Obtain data directly from country database/website</p>\n<p>Joint survey/compilation with national agency and international entity</p>\n<p>Coverage: All countries</p>", "COLL_METHOD__GLOBAL"=>"<p>Online survey </p>", "FREQ_COLL__GLOBAL"=>"<p>First quarter of calendar year</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Second quarter of calendar year</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistics Offices (NSO) of countries</p>", "COMPILING_ORG__GLOBAL"=>"<p>Partnership in Statistics for Development in the 21st Century (PARIS21)</p>", "RATIONALE__GLOBAL"=>"<p>A country&#x2019;s statistics law will be considered compliant with the UN Fundamental Principles of Official Statistics if the law has provisions relating to all ten Principles.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Not applicable</p>", "DATA_COMP__GLOBAL"=>"<p>Indicator 17.18.2 = &#x2211;countries of which the law has provisions relating to all the ten Principles</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>No treatment of missing values at country level.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>No treatment of missing values at regional and global levels.</p>", "REG_AGG__GLOBAL"=>"<p>No treatment of missing values at regional and global levels.</p>", "DOC_METHOD__GLOBAL"=>"<p>&#x2022; Methodology used for the compilation of data at national level</p>\n<p>PARIS21 SDG Survey through online form. </p>\n<p>&#x2022; International recommendations and guidelines available to countries </p>\n<p>PARIS21 pre-filled the survey for countries compliant with the European Statistics Code of Practice. The European Statistics Code of Practice is coherent with the Fundamental Principles of Official Statistics. Therefore, compliance with the ESS Code of Practice equates with compliance with all 10 principles.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>&#x2022; Practices and guidelines for quality assurance followed at the compiling agency. </p>\n<p>Consultation with countries to check information available online.</p>\n<p>&#x2022; Consultation process with countries on the national data submitted to the SDGs Indicators Database.</p>\n<p>Consultation through phone calls and emails.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>All countries and territories on the M49 list (https://unstats.un.org/unsd/methodology/m49/) with SDG focal points were surveyed. Data are available for 189 countries and territories in 2023.</p>\n<p><strong>Time series:</strong></p>\n<p>Available from 2019. </p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator can be disaggregated by geographical area.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong><a href=\"https://unstats.un.org/unsd/dnss/gp/FP-New-E.pdf\">https://unstats.un.org/unsd/dnss/gp/FP-New-E.pdf</a><strong> </strong></p>\n<p><strong>References</strong>: The Fundamental Principles of Official Statistics</p>", "indicator_sort_order"=>"17-18-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.18.3", "slug"=>"17-18-3", "name"=>"Número de países que cuentan con un plan estadístico nacional plenamente financiado y en proceso de aplicación, desglosado por fuente de financiación", "url"=>"/site/es/17-18-3/", "sort"=>"171803", "goal_number"=>"17", "target_number"=>"17.18", "global"=>{"name"=>"Número de países que cuentan con un plan estadístico nacional plenamente financiado y en proceso de aplicación, desglosado por fuente de financiación"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Se dispone de un plan estadístico autonómico plenamente financiado y en fase de aplicación", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Número de países que cuentan con un plan estadístico nacional plenamente financiado y en proceso de aplicación, desglosado por fuente de financiación", "indicator_number"=>"17.18.3", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"Se dispone de un plan estadístico autonómico plenamente financiado y en fase de aplicación", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.18- De aquí a 2020, mejorar el apoyo a la creación de capacidad prestado a los países en desarrollo, incluidos los países menos adelantados y los pequeños Estados insulares en desarrollo, para aumentar significativamente la disponibilidad de datos oportunos, fiables y de gran calidad desglosados por ingresos, sexo, edad, raza, origen étnico, estatus migratorio, discapacidad, ubicación geográfica y otras características pertinentes en los contextos nacionales", "definicion"=>"\nValor lógico que indica si la comunidad autónoma dispone de plan estadístico autonómico o instrumento similar plenamente financiado y en fase de aplicación", "formula"=>"\n$$PEAFA^{t} = \\begin{cases} 1 & \\text{Sí se dispone de un plan estadístico plenamente financiado y en fase de aplicación en el año 𝑡} \\\\ 0 & \\text{No se dispone de un plan estadístico plenamente financiado y en fase de aplicación en el año 𝑡} \\end{cases} $$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador «Número de países con un plan estadístico nacional plenamente financiado y en fase de implementación» \nse basa en el Informe Anual sobre la Situación de las Estrategias Nacionales para el Desarrollo de la \nEstadística (ENDE). En colaboración con sus socios, PARIS21 informa sobre el progreso de los países en el \ndiseño e implementación de sus planes estadísticos nacionales. \n\nEste indicador contabiliza los países que (i) están implementando una estrategia, \n(ii) la están diseñando o (iii) están a la espera de su adopción durante el año en curso.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.18.3&seriesCode=SG_STT_NSDSFND&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Países con planes estadísticos nacionales totalmente financiados (1 = SÍ; 0 = NO) SG_STT_NSDSFND</a> UNSTATS", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-18-03.pdf\">Metadatos 17-18-3.pdf</a> (solo en inglés)", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"Se dispone de un plan estadístico autonómico plenamente financiado y en fase de aplicación", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.18- De aquí a 2020, mejorar el apoyo a la creación de capacidad prestado a los países en desarrollo, incluidos los países menos adelantados y los pequeños Estados insulares en desarrollo, para aumentar significativamente la disponibilidad de datos oportunos, fiables y de gran calidad desglosados por ingresos, sexo, edad, raza, origen étnico, estatus migratorio, discapacidad, ubicación geográfica y otras características pertinentes en los contextos nacionales", "definicion"=>"\nLogical value that indicates whether the autonomous community has a fully funded regional statistical plan or similar instrument in the implementation phase", "formula"=>"\n$$PEAFA^{t} = \\begin{cases} 1 & \\text{A fully funded statistical plan is available and is being implemented in year 𝑡} \\\\ 0 & \\text{There is no fully funded statistical plan in place in the implementation phase in year 𝑡} \\end{cases} $$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"The indicator Number of countries with a national statistical plan that is fully funded and under \nimplementation is based on the annual Status Report on National Strategies for the Development of \nStatistics (NSDS). In collaboration with its partners, PARIS21 reports on country progress in designing and \nimplementing national statistical plans. \n\nThe indicator is a count of countries that are either (i) \nimplementing a strategy, (ii) designing one or (iii) awaiting adoption of the strategy in the current year. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.18.3&seriesCode=SG_STT_NSDSFND&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Countries with national statistical plans that are fully funded (1 = YES; 0 = NO) SG_STT_NSDSFND</a> UNSTATS", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-18-03.pdf\">Metadata 17-18-3.pdf</a>", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"Se dispone de un plan estadístico autonómico plenamente financiado y en fase de aplicación", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.18- De aquí a 2020, mejorar el apoyo a la creación de capacidad prestado a los países en desarrollo, incluidos los países menos adelantados y los pequeños Estados insulares en desarrollo, para aumentar significativamente la disponibilidad de datos oportunos, fiables y de gran calidad desglosados por ingresos, sexo, edad, raza, origen étnico, estatus migratorio, discapacidad, ubicación geográfica y otras características pertinentes en los contextos nacionales", "definicion"=>"\nValor lógico que indica si la comunidad autónoma dispone de plan estadístico autonómico o instrumento similar plenamente financiado y en fase de aplicación", "formula"=>"\n$$PEAFA^{t} = \\begin{cases} 1 & \\text{Badago guztiz finantzatutako eta aplikazio-fasean dagoen estatistika-plan bat 𝑡 urtean} \\\\ 0 & \\text{Ez dago guztiz finantzatutako eta aplikazio-fasean dagoen estatistika-plan bat 𝑡 urtean} \\end{cases} $$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"El indicador «Número de países con un plan estadístico nacional plenamente financiado y en fase de implementación» \nse basa en el Informe Anual sobre la Situación de las Estrategias Nacionales para el Desarrollo de la \nEstadística (ENDE). En colaboración con sus socios, PARIS21 informa sobre el progreso de los países en el \ndiseño e implementación de sus planes estadísticos nacionales. \n\nEste indicador contabiliza los países que (i) están implementando una estrategia, \n(ii) la están diseñando o (iii) están a la espera de su adopción durante el año en curso.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.18.3&seriesCode=SG_STT_NSDSFND&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Erabat finantzatutako estatistika-plan nazionalak dituzten herrialdeak (1 = BAI; 0 = EZ) SG_STT_NSDSFND</a> UNSTATS", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-18-03.pdf\">Metadatuak 17-18-3.pdf</a> (ingelesez bakarrik)", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.18: By 2020, enhance capacity-building support to developing countries, including for least developed countries and small island developing States, to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.18.3: Number of countries with a national statistical plan that is fully funded and under implementation, by source of funding</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_STT_NSDSFDDNR - Countries with national statistical plans with funding from donors (1 = YES; 0 = NO) [17.18.3]</p>\n<p>SG_STT_NSDSFDGVT - Countries with national statistical plans with funding from Government (1 = YES; 0 = NO) [17.18.3]</p>\n<p>SG_STT_NSDSFDOTHR - Countries with national statistical plans with funding from others (1 = YES; 0 = NO) [17.18.3]</p>\n<p>SG_STT_NSDSFND - Countries with national statistical plans that are fully funded (1 = YES; 0 = NO) [17.18.3]</p>\n<p>SG_STT_NSDSIMPL - Countries with national statistical plans that are under implementation (1 = YES; 0 = NO) [17.18.3]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-12-20", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>17.19.1: Dollar value of all resources made available to strengthen statistical capacity in developing countries</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Partnership in Statistics for Development in the 21st Century (PARIS21)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Partnership in Statistics for Development in the 21st Century (PARIS21)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator Number of countries with a national statistical plan that is fully funded and under implementation is based on the annual Status Report on National Strategies for the Development of Statistics (NSDS). In collaboration with its partners, PARIS21 reports on country progress in designing and implementing national statistical plans. The indicator is a count of countries that are either (i) implementing a strategy, (ii) designing one or (iii) awaiting adoption of the strategy in the current year.</p>", "UNIT_MEASURE__GLOBAL"=>"<p>Number</p>", "SRC_TYPE_COLL_METHOD__GLOBAL"=>"<p>3.a. Data sources (SOURCE_TYPE).</p>\n<p>Partnership in Statistics for Development in the 21st Century (PARIS21) SDG Survey (Send questionnaire(s) to country)</p>\n<p>Joint survey/compilation with national agency and international entity.</p>\n<p>Data obtained directly from country database/website, collected through PARIS21&#x2019;s engagement with countries through its workstream on NSDS (National Strategy for Development of Statistics), or PARIS21&#x2019;s annual update on the status of National Strategies for the Development of Statistics (NSDS) (https://statisticalcapacitymonitor.org/indicator/165). Data collected through this source are prefilled in questionnaire and verified by countries through the PARIS21 SDG survey. </p>\n<p>Coverage: All countries</p>\n<p><strong>List:</strong></p>\n<p>National Statistical Offices</p>", "COLL_METHOD__GLOBAL"=>"<p>Online survey and direct reporting from countries</p>", "FREQ_COLL__GLOBAL"=>"<p>First quarter of calendar year</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Second quarter of calendar year</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistics Offices (NSO) of countries</p>", "COMPILING_ORG__GLOBAL"=>"<p>Partnership in Statistics for Development in the 21st Century (PARIS21)</p>", "RATIONALE__GLOBAL"=>"<p>If a country&#x2019;s national statistical plan is fully funded, the plan is considered to be under implementation. </p>", "REC_USE_LIM__GLOBAL"=>"<p>Not applicable</p>", "DATA_COMP__GLOBAL"=>"<p>Simple count of countries that are either (i) implementing a strategy, (ii) designing one or (iii) awaiting adoption of the strategy in the current year.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>No treatment of missing values at country level.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>No treatment of missing values at country level.</p>", "REG_AGG__GLOBAL"=>"<p>Regional-level aggregates are based on the total count of national strategies.</p>", "DOC_METHOD__GLOBAL"=>"<p>Partnership in Statistics for Development in the 21st Century (PARIS21) SDG Survey through online form + PARIS21&#x2019;s annual update on the status of National Strategies for the Development of Statistics (NSDS) (https://statisticalcapacitymonitor.org/indicator/165). </p>\n<p>NSDS Guideline (https://nsdsguidelines.paris21.org/ )</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>&#x2022; Practices and guidelines for quality assurance followed at the compiling agency. </p>\n<p>Consultation with countries to check information available online.</p>\n<p>&#x2022; Consultation process with countries on the national data submitted to the SDGs Indicators Database.</p>\n<p>Consultation through phone calls and emails.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>All countries and territories on the M49 list (https://unstats.un.org/unsd/methodology/m49/) with SDG focal points were surveyed. Data are available for 193 countries and territories in 2023.</p>\n<p><strong>Time series:</strong></p>\n<p>Available from 2019.</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The indicator can be disaggregated by geographical area.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>Not applicable</p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL: </strong>https://nsdsguidelines.paris21.org/</p>\n<p><strong>References: </strong>Guidelines for National Strategy for the Development of Statistics (NSDS)</p>", "indicator_sort_order"=>"17-18-03", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.19.1", "slug"=>"17-19-1", "name"=>"Valor en dólares de todos los recursos proporcionados para fortalecer la capacidad estadística de los países en desarrollo", "url"=>"/site/es/17-19-1/", "sort"=>"171901", "goal_number"=>"17", "target_number"=>"17.19", "global"=>{"name"=>"Valor en dólares de todos los recursos proporcionados para fortalecer la capacidad estadística de los países en desarrollo"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"Valor en dólares de todos los recursos proporcionados para fortalecer la capacidad estadística de los países en desarrollo", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Valor en dólares de todos los recursos proporcionados para fortalecer la capacidad estadística de los países en desarrollo", "indicator_number"=>"17.19.1", "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"", "precision"=>[], "progress_status"=>"notstarted", "reporting_status"=>"notstarted", "standalone"=>false, "tags"=>[], "indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>"", "formula"=>"", "desagregacion"=>"", "periodicidad"=>"", "observaciones"=>"", "justificacion_global"=>"\nEl indicador busca brindar una instantánea del valor en dólares \nestadounidenses del apoyo estadístico continuo en los países en desarrollo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.19.1&seriesCode=SG_STT_CAPTY&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nValor en dólares de todos los recursos puestos a disposición para fortalecer la capacidad estadística en los países en desarrollo (dólares estadounidenses actuales) SG_STT_CAPTY</a> UNSTATS\n", "comparabilidad"=>"", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-19-01.pdf\">Metadatos 17-19-1.pdf</a> (solo en inglés)\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nThe indicator aims to provide a snapshot of the US dollar value of ongoing statistical support in \ndeveloping countries. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.19.1&seriesCode=SG_STT_CAPTY&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nDollar value of all resources made available to strengthen statistical capacity in developing countries (current United States dollars) SG_STT_CAPTY</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-19-01.pdf\">Metadata 17-19-1.pdf</a>\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"No hay datos disponibles para este indicador", "definicion"=>nil, "formula"=>nil, "desagregacion"=>nil, "periodicidad"=>nil, "observaciones"=>nil, "justificacion_global"=>"\nEl indicador busca brindar una instantánea del valor en dólares \nestadounidenses del apoyo estadístico continuo en los países en desarrollo.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.19.1&seriesCode=SG_STT_CAPTY&areaCode=276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">\nGarapen bidean dauden herrialdeen ahalmen estatistikoa indartzeko eskura jarritako baliabide guztien balioa dolarretan (egungo dolar estatubatuarrak) SG_STT_CAPTY</a> UNSTATS\n", "comparabilidad"=>nil, "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-19-01.pdf\">Metadatuak 17-19-1.pdf</a> (ingelesez bakarrik)\n", "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.19: By 2030, build on existing initiatives to develop measurements of progress on sustainable development that complement gross domestic product, and support statistical capacity-building in developing countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.19.1: Dollar value of all resources made available to strengthen statistical capacity in developing countries</p>", "META_LAST_UPDATE__GLOBAL"=>"2017-07-11", "SDG_RELATED_INDICATORS__GLOBAL"=>"<p>17.18.3: Number of countries with a national statistical plan that is fully funded and under implementation, by source of funding</p>", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>Partnership in Statistics for Development in the 21st Century (PARIS21)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>Partnership in Statistics for Development in the 21st Century (PARIS21)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>The indicator Dollar value of all resources made available to strengthen statistical capacity in developing countries is based on the Partner Report on Support to Statistics (PRESS) that is designed and administered by PARIS21 to provide a snapshot of the US dollar value of ongoing statistical support in developing countries.</p>", "SOURCE_TYPE__GLOBAL"=>"<p>To provide a full picture of international support to statistics, the indicator draws on three distinct data sources. The first source of data is the OECD Creditor Reporting System (CRS), which records data from OECD Development Assistance Committee (DAC) members and some non-DAC donors, and provides a comprehensive accounting of ODA. Donors report specific codes for the sector targeted by their aid activity. Statistical capacity building (SCB) is designated by code 16062. </p>\n<p>Second, when SCB is a component of a larger project, it is not identified by this code, causing the CRS figures to underestimate actual levels of support for international aid. PARIS21 seeks to reduce this downward bias by searching project descriptions in the CRS for terms indicating a component of SCB. The methodology is presented at http://www.paris21.org/PRESS2015.</p>\n<p>Third, and finally, the PARIS21 Secretariat supplements this data with an online questionnaire completed by a global network of reporters. The questionnaire covers a subset of the variables collected in the CRS and some additional variables specific to statistical capacity building. Reporting to the questionnaire is voluntary, offering an opportunity for actors to share information on their statistical activities. Reporters to this questionnaire are countries that do not report to the CRS, as well as multilateral institutions with large portfolios of statistical projects that have requested to report to the PARIS21 Secretariat directly.</p>\n<p><strong>List:</strong></p>\n<p>OECD Creditor Reporting System (CRS), PARIS21</p>", "FREQ_COLL__GLOBAL"=>"<p>From Sep-16</p>", "DATA_SOURCE__GLOBAL"=>"<p>PARIS21/OECD</p>", "COMPILING_ORG__GLOBAL"=>"<p>PARIS21</p>", "RATIONALE__GLOBAL"=>"<p>The indicator aims to provide a snapshot of the US dollar value of ongoing statistical support in developing countries</p>", "REC_USE_LIM__GLOBAL"=>"<p>Measuring support to statistics comes with many methodological challenges. The financial figures presented in the PRESS therefore need to be interpreted with these challenges in mind. For instance, PRESS numbers rely on the Creditor Reporting System (CRS) for ODA commitments supplemented by voluntary reporting from additional donors. Yet, full coverage of all programs cannot be guaranteed. Furthermore, the reported commitments can be seen as an upper bound to the actual support to statistics for mainly three reasons. First, double counting of projects may occur when the donor and project implementer report on the same project or when all project co-financers report project totals. Second, the reported numbers may be inflated by working with project totals for multi-sector projects, which comprise only a small statistics component. Finally, the PRESS reports on donor-side commitments which do not always translate to actual disbursements to the recipient countries.</p>\n<p>The indicator only captures international support to statistics and does not account for domestic resources.</p>", "DATA_COMP__GLOBAL"=>"<p>The financial amounts were converted to US dollars by using the period average exchange rate of the commitment year of the project/program. In cases where the disbursement amounts were reported, the exchange rate used was the period average of the disbursement year.</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p><strong>&#x2022; At regional and global levels</strong></p>", "REG_AGG__GLOBAL"=>"<p>Regional-level aggregates are based on the sum of national commitments, sub-regional and regional commitments.</p>", "DOC_METHOD__GLOBAL"=>"<p>2016 Partner Report on Support to Statistics (PRESS) published by PARIS21 (<a href=\"http://www.paris21.org/press\">www.paris21.org/press</a>) based on data from Creditor Reporting System (<a href=\"https://stats.oecd.org/Index.aspx?DataSetCode=CRS1\">https://stats.oecd.org/Index.aspx?DataSetCode=CRS1</a>) and PARIS21 PRESS online survey.</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Inviting donors to check and validate information available online (<a href=\"http://www.paris21.org/press\">www.paris21.org/press</a>).</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>The current time series for 2006-2013 covers 132 developing countries.</p>\n<p><strong>Time series:</strong></p>\n<p>From 2006 to 2013</p>\n<p><strong>Disaggregation:</strong></p>\n<p>The commitment amount can be disaggregated by geographical area, ODA sectors, area of statistics and method of financing (grant vs loan).</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>", "OTHER_DOC__GLOBAL"=>"<p><strong>URL:</strong></p>\n<p><a href=\"http://www.paris21.org\">www.paris21.org</a> </p>\n<p><strong>References:</strong></p>\n<p>OECD (2007). Reporting Directives for the Creditor Reporting System. available at <a href=\"http://www.oecd.org/dac/stats/1948102.pdf\">http://www.oecd.org/dac/stats/1948102.pdf</a> </p>\n<p>PARIS21 (2015). Partner Report on Support to Statistics. Available at <a href=\"http://www.paris21.org/PRESS\">http://www.paris21.org/PRESS</a> </p>", "indicator_sort_order"=>"17-19-01", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"17.19.2", "slug"=>"17-19-2", "name"=>"Proporción de países que a) han realizado al menos un censo de población y vivienda en los últimos diez años; y b) han registrado el 100% de los nacimientos y el 80% de las defunciones", "url"=>"/site/es/17-19-2/", "sort"=>"171902", "goal_number"=>"17", "target_number"=>"17.19", "global"=>{"name"=>"Proporción de países que a) han realizado al menos un censo de población y vivienda en los últimos diez años; y b) han registrado el 100% de los nacimientos y el 80% de las defunciones"}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>[], "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_title"=>"a) Se ha realizado al menos un censo de población y vivienda en los últimos diez años, b) Se ha registrado el 100% de los nacimientos y al menos el 80% de las defunciones", "graph_titles"=>[], "graph_type"=>"line", "indicator_name"=>"Proporción de países que a) han realizado al menos un censo de población y vivienda en los últimos diez años; y b) han registrado el 100% de los nacimientos y el 80% de las defunciones", "indicator_number"=>"17.19.2", "national_geographical_coverage"=>"", "page_content"=>"<b>Dirección deseada:</b> Ascenso", "permalink"=>"", "precision"=>[], "progress_status"=>"alcanzado", "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_25/opt_1/ti_censos-de-poblacion-y-viviendas/temas.html", "url_text"=>"Censos de población y viviendas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>false, "tags"=>[], "indicador_disponible"=>"a) Se ha realizado al menos un censo de población y vivienda en los últimos diez años, b) Se ha registrado el 100% de los nacimientos y al menos el 80% de las defunciones", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.19- De aquí a 2030, aprovechar las iniciativas existentes para elaborar indicadores que permitan medir los progresos en materia de desarrollo sostenible y complementen el producto interno bruto, y apoyar la creación de capacidad estadística en los países en desarrollo", "definicion"=>"\nValor lógico que indica si se ha realizado a) al menos un censo de población y vivienda en los últimos diez años, b) se ha registrado el 100% de los nacimientos y al menos el 80% de las defunciones", "formula"=>"\n$$CPV^{t} = \\begin{cases} SÍ & \\text{Se ha realizado al menos un censo de población y vivienda en [t, t − 9]} \\\\ NO & \\text{No se ha realizado al menos un censo de población y vivienda en [t, t − 9]} \\end{cases} $$\n\n$$RND^{t} = \\begin{cases} 1 & \\text{Sí se ha registrado el 100% de los nacimientos y al menos el 80% de las defunciones del año 𝑡} \\\\ 0 & \\text{No se ha registrado el 100% de los nacimientos y al menos el 80% de las defunciones del año 𝑡} \\end{cases} $$\n", "desagregacion"=>"", "observaciones"=>"", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLos censos de población y vivienda son una de las principales fuentes de datos necesarios para \nformular, implementar y monitorear políticas y programas orientados al desarrollo \nsocioeconómico inclusivo y la sostenibilidad ambiental. \nLos censos de población y vivienda son una fuente importante para el suministro de datos \ndesagregados necesarios para la medición del progreso de la Agenda 2030 para el Desarrollo \nSostenible, especialmente en el contexto de la evaluación de la situación de las personas \nen función de los ingresos, el sexo, la edad, la raza, el origen étnico, el estatus migratorio, \nla discapacidad y la ubicación geográfica u otras características. \n\nLa introducción del indicador 17.19.2b como parte del marco global de los ODS refleja el \nreconocimiento del papel fundamental del sistema de registro civil para el \nfuncionamiento de las sociedades, y las ventajas jurídicas y protectoras que ofrece a las \npersonas. \n\nEl propósito esencial del sistema de registro civil es proporcionar documentos legales de \ninterés directo para las personas. Aparte de la importancia directa y general del registro civil \npara las autoridades públicas, en el sentido de que la información recopilada mediante el \nmétodo de registro proporciona datos esenciales para la preparación y planificación nacional y \nregional de programas médicos y de atención de la salud, el papel que desempeña el registro civil \nen la comprobación, el establecimiento, la implementación y la realización de muchos de los \nderechos humanos consagrados en declaraciones y convenciones internacionales refleja una \nde sus contribuciones más importantes al funcionamiento normal de las sociedades.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.19.2&seriesCode=SG_REG_CENSUS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de países que han realizado al menos un censo de población y vivienda en los últimos 10 años (%) SG_REG_CENSUS</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.19.2&seriesCode=SG_REG_BRTH90&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de países con datos de registro de nacimientos que están completos al menos en un 90 por ciento (%) SG_REG_BRTH90</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.19.2&seriesCode=SG_REG_DETH75&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proporción de países con datos de registro de defunciones que están completos al menos en un 75 por ciento (%) SG_REG_DETH75</a> UNSTATS\n", "comparabilidad"=>"El indicador disponible cumple con los metadatos de Naciones Unidas.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-19-02a.pdf\">Metadatos 17-19-2a.pdf</a> (solo en inglés)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-19-02b.pdf\">Metadatos 17-19-2b.pdf</a> (solo en inglés)\n", "informacion_interes"=>"", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"indicador_disponible"=>"a) Se ha realizado al menos un censo de población y vivienda en los últimos diez años, b) Se ha registrado el 100% de los nacimientos y al menos el 80% de las defunciones", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.19- De aquí a 2030, aprovechar las iniciativas existentes para elaborar indicadores que permitan medir los progresos en materia de desarrollo sostenible y complementen el producto interno bruto, y apoyar la creación de capacidad estadística en los países en desarrollo", "definicion"=>"\nLogical value that indicates whether a) at least one population and housing census has been carried out in the last ten years, b) 100% of births and at least 80% of deaths have been registered", "formula"=>"\n$$CPV^{t} = \\begin{cases} YES & \\text{At least one population and housing census has been conducted In [t, t − 9]} \\\\ NO & \\text{no population and housing census has been carried out in [t, t − 9]} \\end{cases} $$\n\n$$RND^{t} = \\begin{cases} 1 & \\text{Yes, 100% of births and at least 80% of deaths have been registered in year 𝑡} \\\\ 0 & \\text{No, 100% of births and at least 80% of deaths have not been registered in year 𝑡} \\end{cases} $$\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nPopulation and housing censuses are one of the primary sources of data needed for formulating, \nimplementing and monitoring policies and programmes aimed at inclusive socioeconomic development \nand environmental sustainability. Population and housing censuses are an important source for supplying \ndisaggregated data needed for the measurement of progress of the 2030 Agenda for Sustainable \nDevelopment, especially in the context of assessing the situation of people by income, sex, age, race, \nethnicity, migratory status, disability and geographic location, or other characteristics. \n\nThe introduction of indicator 17.19.2b as part of the SDG global framework reflects the recognition of the \nfundamental role of the civil registration system to the functioning of societies, and the legal and \nprotective advantages that it offers to individuals. \n\nThe essential purpose of civil registration system is to \nfurnish legal documents of direct interest to individuals. Aside from the direct and overarching \nimportance of civil registration to the public authorities, in that the information compiled using the \nregistration method provides essential data for national and regional preparation and planning for \nmedical and health-care programmes, the role played by civil registration in proving, establishing, \nimplementing and realizing many of the human rights embodied in international declarations and \nconventions reflects one of its most important contributions to the normal functioning of societies. \n\nSource: United Nations Statistics Division \n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.19.2&seriesCode=SG_REG_CENSUS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of countries that have conducted at least one population and housing census in the last 10 years (%) SG_REG_CENSUS</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.19.2&seriesCode=SG_REG_BRTH90&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of countries with birth registration data that are at least 90 percent complete (%) SG_REG_BRTH90</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.19.2&seriesCode=SG_REG_DETH75&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Proportion of countries with death registration data that are at least 75 percent complete (%) SG_REG_DETH75</a> UNSTATS\n", "comparabilidad"=>"The available indicator complies with United Nations metadata.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-19-02a.pdf\">Metadata 17-19-2a.pdf</a>\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-19-02b.pdf\">Metadata 17-19-2b.pdf</a>\n", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"indicador_disponible"=>"a) Se ha realizado al menos un censo de población y vivienda en los últimos diez años, b) Se ha registrado el 100% de los nacimientos y al menos el 80% de las defunciones", "objetivo_global"=>"17- Fortalecer los medios de implementación y revitalizar la Alianza Mundial para el Desarrollo Sostenible", "meta_global"=>"17.19- De aquí a 2030, aprovechar las iniciativas existentes para elaborar indicadores que permitan medir los progresos en materia de desarrollo sostenible y complementen el producto interno bruto, y apoyar la creación de capacidad estadística en los países en desarrollo", "definicion"=>"\nValor lógico que indica si se ha realizado a) al menos un censo de población y vivienda en los últimos diez años, b) se ha registrado el 100% de los nacimientos y al menos el 80% de las defunciones", "formula"=>"\n$$CPV^{t} = \\begin{cases} BAI & \\text{Biztanleriaren eta etxebizitzaren errolda bat egin da, gutxienez [t, t − 9]an} \\\\ EZ & \\text{Ez da egin biztanleriaren eta etxebizitzaren errolda bat, gutxienez [t, t − 9]an} \\end{cases} $$\n\n$$RND^{t} = \\begin{cases} 1 & \\text{Jaiotzen % 100 eta gutxienez heriotzen % 80 erregistratu dira 𝑡 urtean} \\\\ 0 & \\text{Ez dira erregistratu jaiotzen % 100 eta gutxienez heriotzen % 80 𝑡 urtean} \\end{cases} $$\n", "desagregacion"=>nil, "observaciones"=>nil, "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "justificacion_global"=>"\nLos censos de población y vivienda son una de las principales fuentes de datos necesarios para \nformular, implementar y monitorear políticas y programas orientados al desarrollo \nsocioeconómico inclusivo y la sostenibilidad ambiental. \nLos censos de población y vivienda son una fuente importante para el suministro de datos \ndesagregados necesarios para la medición del progreso de la Agenda 2030 para el Desarrollo \nSostenible, especialmente en el contexto de la evaluación de la situación de las personas \nen función de los ingresos, el sexo, la edad, la raza, el origen étnico, el estatus migratorio, \nla discapacidad y la ubicación geográfica u otras características. \n\nLa introducción del indicador 17.19.2b como parte del marco global de los ODS refleja el \nreconocimiento del papel fundamental del sistema de registro civil para el \nfuncionamiento de las sociedades, y las ventajas jurídicas y protectoras que ofrece a las \npersonas. \n\nEl propósito esencial del sistema de registro civil es proporcionar documentos legales de \ninterés directo para las personas. Aparte de la importancia directa y general del registro civil \npara las autoridades públicas, en el sentido de que la información recopilada mediante el \nmétodo de registro proporciona datos esenciales para la preparación y planificación nacional y \nregional de programas médicos y de atención de la salud, el papel que desempeña el registro civil \nen la comprobación, el establecimiento, la implementación y la realización de muchos de los \nderechos humanos consagrados en declaraciones y convenciones internacionales refleja una \nde sus contribuciones más importantes al funcionamiento normal de las sociedades.\n\nFuente: División de Estadísticas de las Naciones Unidas\n", "dato_global"=>"\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.19.2&seriesCode=SG_REG_CENSUS&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Azken 10 urteetan gutxienez biztanleriaren eta etxebizitzen errolda bat egin duten herrialdeen proportzioa (%) SG_REG_CENSUS</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.19.2&seriesCode=SG_REG_BRTH90&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Gutxienez ehuneko 90ean osatuta dauden jaiotzen erregistro-datuak dituzten herrialdeen proportzioa (%) SG_REG_BRTH90</a> UNSTATS\n\n<a href=\"https://unstats.un.org/sdgs/dataportal/analytics/SingleSeries?indicators=17.19.2&seriesCode=SG_REG_DETH75&areaCode=  276,40,56,100,196,191,208,703,705,724,233,246,250,300,528,348,372,380,428,440,442,470,616,620,203,642,752,1,202,14,17,18,11,747,15,145,62,143,34,753,30,35,419,29,13,5,9,54,57,61,53,513,150,21,432,199,722&period=3&table=Total\">Gutxienez ehuneko 75ean osorik dauden heriotzen erregistro-datuak dituzten herrialdeen proportzioa (%) SG_REG_DETH75</a> UNSTATS\n", "comparabilidad"=>"EAEn eskuragarri dagoen adierazleak Nazio Batuen adierazlearen metadatuak betetzen ditu.", "indicador_meta_enlace"=>"<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-19-02a.pdf\">Metadatuak 17-19-2a.pdf</a> (ingelesez bakarrik)\n\n<a href=\"https://unstats.un.org/sdgs/metadata/files/Metadata-17-19-02b.pdf\">Metadatos 17-19-2b.pdf</a> (ingelesez bakarrik)\n", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "SDG_GOAL__GLOBAL"=>"<p>Goal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development</p>", "SDG_TARGET__GLOBAL"=>"<p>Target 17.19: By 2030, build on existing initiatives to develop measurements of progress on sustainable development that complement gross domestic product, and support statistical capacity-building in developing countries</p>", "SDG_INDICATOR__GLOBAL"=>"<p>Indicator 17.19.2: Proportion of countries that (a) have conducted at least one population and housing census in the last 10 years; and (b) have achieved 100 percent birth registration and 80 percent death registration</p>", "SDG_SERIES_DESCR__GLOBAL"=>"<p>SG_REG_BRTH90 - Proportion of countries with birth registration data that are at least 90 percent complete [17.19.2]</p>\n<p>SG_REG_BRTH90N - Countries with birth registration data that are at least 90 percent complete (1 = YES; 0 = NO) [17.19.2]</p>\n<p>SG_REG_DETH75 - Proportion of countries with death registration data that are at least 75 percent complete [17.19.2]</p>\n<p>SG_REG_DETH75N - Countries with death registration data that are at least 75 percent complete (1 = YES; 0 = NO) [17.19.2]</p>", "META_LAST_UPDATE__GLOBAL"=>"2024-09-27", "SDG_CUSTODIAN_AGENCIES__GLOBAL"=>"<p>United Nations Statistics Division (UNSD), Department of Economic and Social Affairs, United Nations (UN DESA)</p>", "CONTACT_ORGANISATION__GLOBAL"=>"<p>United Nations Statistics Division (UNSD), Department of Economic and Social Affairs, United Nations (UN DESA)</p>", "STAT_CONC_DEF__GLOBAL"=>"<p><strong>Definition:</strong></p>\n<p>This information refers only to 17.19.2b: Proportion of countries that have achieved 100 percent birth registration and 80 percent death registration.</p>\n<p>According to the Principles and Recommendations for a Vital Statistics System, Revision 3 (https://unstats.un.org/unsd/demographic/standmeth/principles/M19Rev3en.pdf), a complete civil registration is defined as: &#x201C;The registration in the civil registration system of every vital event that has occurred to the members of the population of a particular country (or area), within a specified period as a result of which every such event has a vital registration record and the system has attained 100 percent coverage.&#x201D;</p>\n<p>In a given country or area, the level of completeness of birth registration can be different from the level of completeness of death registration. </p>\n<p>There exist several methods for the evaluation of completeness of birth or death registration systems. </p>\n<p>An elaboration of these methods is available at Principles and Recommendations for a Vital Statistics System, Revision 3. The evaluation and monitoring of quality and completeness of birth and death registration systems are addressed in Part three, sub-Chapters: (D). Quality assessment methods; (E). Direct versus indirect assessment, and (F). Choosing appropriate methods for assessing completeness and qualitative accuracy of registration and register-based vital statistics (para 579 to 622).</p>\n<p>Indicator 17.19.2b has two parts; the first concerning the birth registration and the second concerning the death registration of each individual country or area. </p>", "UNIT_MEASURE__GLOBAL"=>"<p>Percent (%)</p>\n<p>Number</p>", "SOURCE_TYPE__GLOBAL"=>"<p>National demographic analysis; National dual record check; Questions in population census; Questions in sample surveys; National Register; Other (as specified by the national statistical authorities &#x2013; please see 3.b. below)</p>", "COLL_METHOD__GLOBAL"=>"<p>The national level of completeness of birth and death registration is provided by the National Statistical Offices (NSOs) of all countries and areas to the United Nations Statistics Division (UNSD) as part of the annual data collection for the United Nations Demographic Yearbook. This information is usually reported as part of the metadata worksheets of the Vital Statistics questionnaire. The template of this questionnaire is available at: <a href=\"https://unstats.un.org/unsd/demographic-social/products/dyb/index.cshtml#questionnaires\">https://unstats.un.org/unsd/demographic-social/products/dyb/index.cshtml#questionnaires</a> </p>", "FREQ_COLL__GLOBAL"=>"<p>The first quarter of each year</p>", "REL_CAL_POLICY__GLOBAL"=>"<p>Annually</p>", "DATA_SOURCE__GLOBAL"=>"<p>National Statistical Offices of all countries and areas.</p>", "COMPILING_ORG__GLOBAL"=>"<p>United Nations Statistics Division (UNSD), Department of Economic and Social Affairs, United Nations (UN DESA)</p>", "INST_MANDATE__GLOBAL"=>"<p>Not applicable</p>", "RATIONALE__GLOBAL"=>"<p>The introduction of indicator 17.19.2b as part of the SDG global framework reflects the recognition of the fundamental role of the civil registration system to the functioning of societies, and the legal and protective advantages that it offers to individuals. The essential purpose of civil registration system is to furnish legal documents of direct interest to individuals. Aside from the direct and overarching importance of civil registration to the public authorities, in that the information compiled using the registration method provides essential data for national and regional preparation and planning for medical and health-care programmes, the role played by civil registration in proving, establishing, implementing and realizing many of the human rights embodied in international declarations and conventions reflects one of its most important contributions to the normal functioning of societies.</p>", "REC_USE_LIM__GLOBAL"=>"<p>Not applicable</p>", "DATA_COMP__GLOBAL"=>"<p>The two sub-indicators of the indicator 17.19.2b are expressed as proportions: at the global level, the proportion of countries that have achieved 100 percent birth registration is measured as the number of countries that have achieved 100 percent birth registration divided by the total number of countries. The computation is done in an analogous manner for the death registration part as well as for the regional measurements of both birth and death registration sub-indicators. </p>\n<p>The latest compiled data for this indicator are part of the Statistical Annex to the annual Secretary-General&#x2019;s progress report, available at https://unstats.un.org/sdgs. These data are compiled using the country-reported information on availability and completeness of birth and death registration data at the country level, to the United Nations Demographic Yearbook, via the Demographic Yearbook Vital Statistics questionnaire and accompanying metadata. United Nations Demographic Yearbook collection and associated online compilations are published by the United Nations Statistics Division of the Department of Economic and Social Affairs. Please refer to: <a href=\"https://unstats.un.org/unsd/demographic-social/products/dyb/index.cshtml#overview\">https://unstats.un.org/unsd/demographic-social/products/dyb/index.cshtml#overview</a> </p>\n<p>At the present time, the thresholds used for compiling the data for the indicator 17.19.2b are 90 percent for birth registration and 75 percent for death registration, due to the classification that has been used in the Demographic Yearbook metadata questionnaire on vital statistics. This classification is modified to enable reporting according to the exact formulation of the indicator 17.19.2b.</p>", "DATA_VALIDATION__GLOBAL"=>"<p>The available sources for the levels of completeness are reviewed thoroughly prior to updates in the SDG database.</p>", "ADJUSTMENT__GLOBAL"=>"<p>Not applicable</p>", "IMPUTATION__GLOBAL"=>"<p><strong>&#x2022; At country level</strong></p>\n<p>No attempts are made to provide estimates of completeness of birth and death registration when such information is not reported via the United Nations Demographic Yearbook data collection.</p>\n<p><strong>&#x2022; At regional and global levels</strong></p>\n<p>Not applicable</p>", "REG_AGG__GLOBAL"=>"<p>The regional values of this indicator are compiled as follows:</p>\n<p>17.19.2 (b.1) Number and proportion of countries with birth registration data that are at least 90 percent complete: The number of countries or areas on each of the listed regions with birth registration data that are at least 90 percent complete, and the proportion of such countries or areas to the total number of countries or areas in the respective region.</p>\n<p>17.19.2 (b.2) Number and proportion of countries with death registration data that are at least 75 percent complete: The number of countries or areas on each of the listed regions with death registration data that are at least 75 percent complete, and the proportion of such countries or areas to the total number of countries or areas in the respective region.</p>", "DOC_METHOD__GLOBAL"=>"<p><em>Principles and Recommendations for a Vital Statistics System, Revision 3 , </em>United Nations, New York, 2014 <a href=\"https://unstats.un.org/unsd/demographic/standmeth/principles/M19Rev3en.pdf\">https://unstats.un.org/unsd/demographic/standmeth/principles/M19Rev3en.pdf</a></p>", "QUALITY_MGMNT__GLOBAL"=>"<p>Not applicable</p>", "QUALITY_ASSURE__GLOBAL"=>"<p>Principles and Recommendations for a Vital Statistics System, Revision 3, Part three, I, &#x201C;Quality assurance and assessment of civil registration and register based vital statistics&#x201D;. </p>\n<p>Follow up with National Statistical Offices as part of the annual United Nations Demographic Yearbook data collection, validation and processing.</p>", "QUALITY_ASSMNT__GLOBAL"=>"<p>Quality is assured as part of Validation work.</p>", "COVERAGE__GLOBAL"=>"<p><strong>Data availability:</strong></p>\n<p>For the current availability please refer to the Statistical Annex SG&#x2019;s progress reports, available at <a href=\"https://unstats.un.org/sdgs\">https://unstats.un.org/sdgs</a>.</p>\n<p><strong>Time series:</strong></p>\n<p><strong>Disaggregation:</strong></p>\n<p>By their definition, the sub-indicators of the indicator 17.19.2b refer to the national levels of completeness of birth and death registration. </p>\n<p>However, knowledge of the birth and death registration completeness at sub-national administrative areas, as well as by income, sex, age group, disability status, etc. is very important for monitoring and improving the functioning of birth and death registration systems.</p>", "COMPARABILITY__GLOBAL"=>"<p><strong>Sources of discrepancies:</strong></p>\n<p>There is no deviation from international standards.</p>", "OTHER_DOC__GLOBAL"=>"<p>Principles and Recommendations for a Vital Statistics System, Revision 3, United Nations, New York, 2014. <a href=\"https://unstats.un.org/unsd/demographic/standmeth/principles/M19Rev3en.pdf\">https://unstats.un.org/unsd/demographic/standmeth/principles/M19Rev3en.pdf</a> </p>\n<p>United Nation Demographic Yearbook, United Nations, New York, annual.</p>\n<p><a href=\"https://unstats.un.org/unsd/demographic-social/products/dyb/index.cshtml\">https://unstats.un.org/unsd/demographic-social/products/dyb/index.cshtml</a> </p>", "indicator_sort_order"=>"17-19-02", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"OCECA.1.2.1.1A", "slug"=>"OCECA-1-2-1-1A", "name"=>"Proporción de personas en riesgo de pobreza relativa, considerando el umbral nacional de pobreza", "url"=>"/site/es/OCECA-1-2-1-1A/", "sort"=>"OCECA0102011A", "goal_number"=>"OCECA", "target_number"=>"OCECA.1", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción de personas en riesgo de pobreza relativa, considerando el umbral nacional de pobreza", "indicator_number"=>"OCECA-1-2-1-1A", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"OCECA-1-2-1-1A", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Anual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176807&menu=ultiDatos&idp=1254735976608", "url_text"=>"Encuesta de condiciones de vida", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>true, "tags"=>[], "x_axis_label"=>"", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"Proporción de personas con ingresos por unidad de consumo por debajo del  60% de la mediana nacional (escala OCDE modificada)", "formula"=>"$$PPRPR_{NAC}^{t} = \\frac{PRPR_{NAC}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$PRPR_{NAC}^{t} =$ población en riesgo de pobreza relativa considerando el umbral nacional de pobreza (60% de la mediana nacional de los ingresos por unidad de consumo (escala OCDE modificada) en el año $t$\n\n$P^{t} =$ población total en el año  $t$\n", "periodicidad"=>"Anual", "observaciones"=>"\nLos ingresos que se utilizan en el cálculo de este indicador corresponden \nal año anterior al de la encuesta.\n\nEl número de unidades de consumo de un hogar\nse calcula utilizando la escala OCDE modificada, que asigna un peso de 1 a la\nprimera persona de 14 o más años, un peso de 0,5 al resto de personas de 14 o\nmás años y un peso de 0,3 a las personas de menos de 14 años.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-15", "en"=>{"objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"Proportion of people with income per consumption unit below 60% of the national median (modified OECD scale)", "formula"=>"$$PPRPR_{NAC}^{t} = \\frac{PRPR_{NAC}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$PRPR_{NAC}^{t} =$ population at risk of relative poverty considering the national poverty threshold (60% of the national median income per consumption unit (modified OECD scale) in year $t$\n\n$P^{t} =$ total population in year $t$\n", "periodicidad"=>"Anual", "observaciones"=>"\nThe income used in the calculation of this indicator corresponds to the year prior to the survey. \n\nThe number of consumption units in a household \nis calculated using the modified OECD scale, which assigns a weight of 1 to the \nfirst person aged 14 or over, a weight of 0.5 for other people aged 14 or over \nand a weight of 0.3 for people under 14 years of age.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"Estatuko medianaren % 60tik beherako kontsumo-unitate bakoitzeko diru-sarrerak dituzten pertsonen proportzioa (ELGA eskala aldatua)", "formula"=>"$$PPRPR_{NAC}^{t} = \\frac{PRPR_{NAC}^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$PRPR_{NAC}^{t} =$ pobrezia-arrisku erlatiboan dagoen biztanleria, pobrezia-atalase nazionalaren arabera (kontsumo-unitateko diru-sarreren mediana nazionalaren % 60) (ELGA eskala aldatua) $t$ urtean\n\n$P^{t} =$ guztizko biztanleria $t$ urtean\n", "periodicidad"=>"Anual", "observaciones"=>"\nAdierazle hau kalkulatzeko erabiltzen diren diru-sarrerak inkestaren aurreko urtekoak dira.\n\nEtxeko kontsumo-unitateen kopurua kalkulatzeko, ELGA eskala aldatua erabiltzen da. \nEskala horrek 1 pisua esleitzen dio 14 urte edo gehiagoko lehen pertsonari, 0,5 pisua 14 urte edo \ngehiagoko gainerako pertsonei eta 0,3 pisua 14 urtetik beherako pertsonei.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "indicator_sort_order"=>"OCECA-1-2-1-1A", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"OCECA.1.2.1.2A", "slug"=>"OCECA-1-2-1-2A", "name"=>"Proporción de personas en riesgo de pobreza relativa con alquiler imputado, considerando el umbral nacional de pobreza", "url"=>"/site/es/OCECA-1-2-1-2A/", "sort"=>"OCECA0102012A", "goal_number"=>"OCECA", "target_number"=>"OCECA.1", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción de personas en riesgo de pobreza relativa con alquiler imputado, considerando el umbral nacional de pobreza", "indicator_number"=>"OCECA-1-2-1-2A", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"OCECA-1-2-1-2A", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Anual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176807&menu=ultiDatos&idp=1254735976608", "url_text"=>"Encuesta de condiciones de vida", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>true, "tags"=>[], "x_axis_label"=>"", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"Proporción de personas con ingresos con alquiler imputado por unidad de consumo por debajo del 60% de la mediana nacional de los ingresos con alquiler imputado por unidad de consumo (escala OCDE modificada)", "formula"=>"\n$$PPRPRAI_{NAC}^{t} = \\frac{PRPRAI_{NAC}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$PRPRAI_{NAC}^{t} =$ población en riesgo de pobreza relativa con alquiler imputado considerando el umbral nacional de pobreza (60% de la mediana nacional de los ingresos con alquiler imputado por unidad de consumo -escala OCDE modificada-) en el año $t$\n\n$P^{t} =$ población total en el año $t$\n", "periodicidad"=>"Anual", "observaciones"=>"\nLos ingresos que se utilizan en el cálculo de este indicador corresponden \nal año anterior al de la encuesta.\n\nEl número de unidades de consumo de un hogar\nse calcula utilizando la escala OCDE modificada, que asigna un peso de 1 a la\nprimera persona de 14 o más años, un peso de 0,5 al resto de personas de 14 o\nmás años y un peso de 0,3 a las personas de menos de 14 años.\n\nEl alquiler imputado se aplica a los hogares que no pagan un alquiler completo por\n ser propietarios o por ocupar una vivienda alquilada a un precio inferior al de \nmercado o a título gratuito para así equiparar las rentas con aquellos que sí lo pagan. \n\nEl valor que se imputa es el equivalente al alquiler que se pagaría en el mercado por una \nvivienda similar a la ocupada, menos cualquier alquiler realmente abonado. En esta \nvariable también se incluye el alquiler imputado de la vivienda cuando la misma \nestá proporcionada por la empresa en la que trabaja algún miembro del hogar. \nAsimismo se deducen de los ingresos totales del hogar los intereses de los \npréstamos solicitados para la compra de la vivienda principal.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-15", "en"=>{"objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"Proportion of people with  income with imputed rent per unit of consumption below 60% of the national median imputed rent per unit income (modified OECD scale)", "formula"=>"\n$$PPRPRAI_{NAC}^{t} = \\frac{PRPRAI_{NAC}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$PRPRAI_{NAC}^{t} =$ Population at risk of relative poverty with imputed rent considering the national poverty threshold (60% of the national median income with imputed rent per consumption unit - modified OECD scale) in year $t$\n\n$P^{t} =$ total population in year $t$\n", "periodicidad"=>"Anual", "observaciones"=>"\nThe income used in the calculation of this indicator corresponds to the year prior to the survey.\n\nThe number of consumption units in a household \nis calculated using the modified OECD scale, which assigns a weight of 1 to the \nfirst person aged 14 or over, a weight of 0.5 for other people aged 14 or over \nand a weight of 0.3 for people under 14 years of age. \n\nImputed rent is applied to households that do not pay full rent because they own or occupy a dwelling \nrented at below market rate or rent-free, in order to equalize rents with those that do pay full rent. \n\nThe imputed value is the equivalent of the market rent that would be paid for a dwelling similar to \nthe one occupied, less any rent actually paid. This variable also includes the imputed rent for the \ndwelling when it is provided by the company where a household member works. Interest on loans taken \nout to purchase the primary residence is also deducted from the household's total income. \n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"Alokairua inputatutako kontsumo-unitate bakoitzeko diru-sarrerak Estatuko medianaren % 60tik beherakoak dituzten pertsonen proportzioa (ELGA eskala aldatua)", "formula"=>"\n$$PPRPRAI_{NAC}^{t} = \\frac{PRPRAI_{NAC}^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$PRPRAI_{NAC}^{t} =$ pobrezia-arrisku erlatiboan dagoen biztanleria, pobrezia-atalase nazionalaren arabera \n(alokairua inputatutako kontsumo-unitate bakoitzeko diru-sarrerak Estatuko medianaren % 60 -ELGA eskala aldatua-) $t$ urtean \n\n$P^{t} =$ biztanleria $t$ urtean\n", "periodicidad"=>"Anual", "observaciones"=>"\nAdierazle hau kalkulatzeko erabiltzen diren diru-sarrerak inkestaren aurreko urtekoak dira.\n\nEtxeko kontsumo-unitateen kopurua kalkulatzeko, ELGA eskala aldatua erabiltzen da. \nEskala horrek 1 pisua esleitzen dio 14 urte edo gehiagoko lehen pertsonari, 0,5 pisua 14 urte edo \ngehiagoko gainerako pertsonei eta 0,3 pisua 14 urtetik beherako pertsonei.\n\nInputatutako alokairua aplikatzen zaie alokairu osoa ordaintzen ez duten familiei, jabeak direlako \nedo merkatuko prezioa baino merkeago edo doan alokatutako etxebizitza bat okupatzen dutelako,  \nalokairu osoa ordaintzen dutenekiko errentak parekatzeko.\n\nOkupatuaren antzeko etxebizitza batengatik merkatuan ordainduko litzatekeen alokairuaren baliokidea da inputatzen \nden balioa, benetan ordaindutako edozein alokairu kenduta. Aldagai horretan sartzen da, halaber, etxebizitzaren \nalokairu inputatua, etxebizitza hori etxeko kideren batek lan egiten duen enpresak ematen duenean. Era berean, \netxeko guztizko diru-sarreretatik kentzen dira etxebizitza nagusia erosteko eskatutako maileguen interesak.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "indicator_sort_order"=>"OCECA-1-2-1-2A", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"OCECA.1.2.1.2B", "slug"=>"OCECA-1-2-1-2B", "name"=>"Proporción de personas en riesgo de pobreza relativa con alquiler imputado, considerando el umbral autonómico de pobreza", "url"=>"/site/es/OCECA-1-2-1-2B/", "sort"=>"OCECA0102012B", "goal_number"=>"OCECA", "target_number"=>"OCECA.1", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción de personas en riesgo de pobreza relativa con alquiler imputado, considerando el umbral autonómico de pobreza", "indicator_number"=>"OCECA-1-2-1-2B", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"OCECA-1-2-1-2B", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Bienestar, Juventud y Reto Demográfico", "periodicity"=>"Bienal", "url"=>"https://www.euskadi.eus/encuesta-de-pobreza-y-desigualdades-sociales-epds/web01-s2enple/es/", "url_text"=>"Encuesta de pobreza y desigualdades sociales (EPDS)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}, {"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Anual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176807&menu=ultiDatos&idp=1254735976608", "url_text"=>"Encuesta de condiciones de vida", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>true, "tags"=>[], "x_axis_label"=>"", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.a- Garantizar una movilización significativa de recursos procedentes de diversas fuentes, incluso mediante la mejora de la cooperación para el desarrollo, a fin de proporcionar medios suficientes y previsibles a los países en desarrollo, en particular los países menos adelantados, para que implementen programas y políticas encaminados a poner fin a la pobreza en todas sus dimensiones", "definicion"=>"Proporción de personas con ingresos con alquiler imputado por unidad de consumo por debajo del 60% de la mediana autonómica de los ingresos con alquiler imputado por unidad de consumo (escala OCDE modificada)", "formula"=>"\n$$PPRPRAI_{CCAA}^{t} = \\frac{PRPRAI_{CCAA}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$PRPRAI_{CCAA}^{t} =$ población en riesgo de pobreza relativa con alquiler imputado considerando el umbral autonómico de pobreza (60% de la mediana autonómica de los ingresos con alquiler imputado por unidad de consumo -escala OCDE modificada-) en el año $t$\n\n$P^{t} =$ población total en el año  $t$\n", "periodicidad"=>"Cuatrienal", "observaciones"=>"El número de unidades de consumo de un hogar se calcula utilizando la escala OCDE modificada, \nque asigna un peso de 1 a la primera persona de 14 o más años, un peso de 0,5 al resto \nde personas de 14 o más años y un peso de 0,3 a las personas de menos de 14 años.\n\nEl alquiler imputado se aplica a los hogares que no pagan un alquiler completo por\n ser propietarios o por ocupar una vivienda alquilada a un precio inferior al de \nmercado o a título gratuito para así equiparar las rentas con aquellos que sí lo pagan. \n\nEl valor que se imputa es el equivalente al alquiler que se pagaría en el mercado por una \nvivienda similar a la ocupada, menos cualquier alquiler realmente abonado. En esta \nvariable también se incluye el alquiler imputado de la vivienda cuando la misma \nestá proporcionada por la empresa en la que trabaja algún miembro del hogar. \nAsimismo se deducen de los ingresos totales del hogar los intereses de los \npréstamos solicitados para la compra de la vivienda principal.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-15", "en"=>{"objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.a- Garantizar una movilización significativa de recursos procedentes de diversas fuentes, incluso mediante la mejora de la cooperación para el desarrollo, a fin de proporcionar medios suficientes y previsibles a los países en desarrollo, en particular los países menos adelantados, para que implementen programas y políticas encaminados a poner fin a la pobreza en todas sus dimensiones", "definicion"=>"Proportion of people with income with imputed rent per unit of consumption under 60% of the median income for the autonomous community with  imputed rent per unit of consumption (modified OECD scale)", "formula"=>"\n$$PPRPRAI_{CCAA}^{t} = \\frac{PRPRAI_{CCAA}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$PRPRAI_{CCAA}^{t} =$ population at risk of relative poverty with imputed rent considering the poverty line of the autonomous community (60% of median income in the autonomous community with imputed rent per unit of consumption (modified OECD scale) in year $t$ \n\n$P^{t} =$ total population in year $t$\n", "periodicidad"=>"Cuatrienal", "observaciones"=>"The number of consumption units in a household \nis calculated using the modified OECD scale, which assigns a weight of 1 to the \nfirst person aged 14 or over, a weight of 0.5 for other people aged 14 or over \nand a weight of 0.3 for people under 14 years of age. \n\nImputed rent is applied to households that do not pay full rent because they own or occupy a dwelling \nrented at below market rate or rent-free, in order to equalize rents with those that do pay full rent. \n\nThe imputed value is the equivalent of the market rent that would be paid for a dwelling similar to \nthe one occupied, less any rent actually paid. This variable also includes the imputed rent for the \ndwelling when it is provided by the company where a household member works. Interest on loans taken \nout to purchase the primary residence is also deducted from the household's total income. \n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.a- Garantizar una movilización significativa de recursos procedentes de diversas fuentes, incluso mediante la mejora de la cooperación para el desarrollo, a fin de proporcionar medios suficientes y previsibles a los países en desarrollo, en particular los países menos adelantados, para que implementen programas y políticas encaminados a poner fin a la pobreza en todas sus dimensiones", "definicion"=>"Alokairua inputatutako kontsumo-unitate bakoitzeko diru-sarrerak autonomia erkidegoko medianaren % 60tik beherakoak dituzten pertsonen proportzioa (ELGA eskala aldatua) ", "formula"=>"\n$$PPRPRAI_{CCAA}^{t} = \\frac{PRPRAI_{CCAA}^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$PRPRAI_{CCAA}^{t} =$ pobrezia-arrisku erlatiboan dagoen biztanleria, pobrezia-atalase autonomikoaren arabera  \n(alokairua inputatutako kontsumo-unitate bakoitzeko diru-sarrerak autonomia erkidegoko medianaren % 60 -ELGA eskala aldatua-) $t$ urtean \n\n$P^{t} =$ biztanleria $t$ urtean\n", "periodicidad"=>"Cuatrienal", "observaciones"=>"Etxeko kontsumo-unitateen kopurua kalkulatzeko, ELGA eskala aldatua erabiltzen da. \nEskala horrek 1 pisua esleitzen dio 14 urte edo gehiagoko lehen pertsonari, 0,5 pisua 14 urte edo \ngehiagoko gainerako pertsonei eta 0,3 pisua 14 urtetik beherako pertsonei.\n\nInputatutako alokairua aplikatzen zaie alokairu osoa ordaintzen ez duten familiei, jabeak direlako \nedo merkatuko prezioa baino merkeago edo doan alokatutako etxebizitza bat okupatzen dutelako,  \nalokairu osoa ordaintzen dutenekiko errentak parekatzeko.\n\nOkupatuaren antzeko etxebizitza batengatik merkatuan ordainduko litzatekeen alokairuaren baliokidea da inputatzen \nden balioa, benetan ordaindutako edozein alokairu kenduta. Aldagai horretan sartzen da, halaber, etxebizitzaren \nalokairu inputatua, etxebizitza hori etxeko kideren batek lan egiten duen enpresak ematen duenean. Era berean, \netxeko guztizko diru-sarreretatik kentzen dira etxebizitza nagusia erosteko eskatutako maileguen interesak.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "indicator_sort_order"=>"OCECA-1-2-1-2B", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"OCECA.1.2.2.1A", "slug"=>"OCECA-1-2-2-1A", "name"=>"Proporción de personas en riesgo de pobreza o exclusión social: indicador AROPE, considerando el umbral nacional de pobreza", "url"=>"/site/es/OCECA-1-2-2-1A/", "sort"=>"OCECA0102021A", "goal_number"=>"OCECA", "target_number"=>"OCECA.1", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción de personas en riesgo de pobreza o exclusión social: indicador AROPE, considerando el umbral nacional de pobreza", "indicator_number"=>"OCECA-1-2-2-1A", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"OCECA-1-2-2-1A", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Anual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176807&menu=ultiDatos&idp=1254735976608", "url_text"=>"Encuesta de condiciones de vida", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>true, "tags"=>[], "x_axis_label"=>"", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"\nProporción de personas que están en al menos uno de los tres criterios del riesgo de  pobreza o exclusión social: en riesgo de pobreza relativa (considerando  el umbral nacional de pobreza), viviendo en hogares con carencia material  severa o viviendo en hogares con baja intensidad de trabajo", "formula"=>"$$AROPE_{NAC}^{t} = \\frac{PRPES_{NAC}^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$PRPES_{NAC}^{t} =$ población en riesgo de pobreza o exclusión social el año $t$, considerando el umbral nacional de pobreza\n\n$P^{t} =$ población total en el año  $t$\n", "periodicidad"=>"Bienal", "observaciones"=>"El indicador mide la proporción de personas que cumplen al menos uno de los siguientes criterios:\n\n<b>Personas en riesgo de pobreza relativa:</b> personas con ingresos por unidad de consumo por\ndebajo del 60% de la mediana (escala OCDE modificada).\n\nEl número de unidades de consumo de un hogar se calcula utilizando la escala OCDE modificada, \nque asigna un peso de 1 a la primera  persona de 14 o más años, un peso de 0,5 al resto de personas de \n14 o más años y un peso de 0,3 a las personas de menos de 14 años.\n\n<b>Personas en situación de carencia material y social severa:</b> personas que padecen al menos \nsiete de la siguiente lista de trece limitaciones (siete definidas a nivel de hogar y seis a nivel de persona):\n\nA nivel de hogar:\n 1. No puede permitirse ir de vacaciones al menos una semana al año.\n 2. No puede permitirse una comida de carne, pollo o pescado al menos cada dos días.\n 3. No puede permitirse mantener la vivienda con una temperatura adecuada.\n 4. No tiene capacidad para afrontar gastos imprevistos.\n 5. Ha tenido retrasos en el pago de gastos relacionados con la vivienda principal \n(hipoteca o alquiler, recibos de gas, comunidad, etc) o en\ncompras a plazos en los últimos 12 meses.\n 6. No puede permitirse disponer de un automóvil.\n 7. No puede sustituir muebles estropeados o viejos.\n\nA nivel de persona:\n 8. No puede permitirse sustituir ropa estropeada por otra nueva.\n 9. No puede permitirse tener dos pares de zapatos en buenas condiciones.\n 10. No puede permitirse reunirse con amigos/familia para comer o tomar algo al menos una vez al mes.\n 11. No puede permitirse participar regularmente en actividades de ocio.\n 12. No puede permitirse gastar una pequeña cantidad de dinero en sí mismo.\n 13. No puede permitirse conexión a internet.\n\nEn el caso de menores de 16 años no se dispone de los seis conceptos enumerados anteriormente a \nnivel de persona. Para estos menores los valores de esos elementos se imputan a partir de los valores\nrecogidos para los miembros de su hogar con 16 o más años.\n\n<b>Baja intensidad en el empleo:</b> son los hogares en los que sus miembros en edad de trabajar \n(personas de 18 a 64 años, excluyendo los estudiantes de 18 a 24 años, los jubilados o retirados, \nasí como las personas inactivas entre 60 y 64 cuya fuente principal de ingresos del hogar sean las pensiones)\nlo hicieron menos del 20% del total de su potencial de trabajo durante el año de referencia.\nEsta variable no se aplica en el caso de las personas de 65 y más años.\n\nLos datos a partir del 2022 del Indicador de pobreza y exclusion AROPE se calculan con la nueva \ndefinicion de la tasa (metodología Eurostat del año 2021), mientras que los datos anteriores corresponden \na la definición antigua.\n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "national_data_updated_date"=>"2025-05-08", "national_metadata_updated_date"=>"2025-05-20", "en"=>{"objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"\nProportion of people who are in at least one of the three situations of risk of poverty or social  exclusion: at risk of relative poverty (considering the national poverty line), living in households  with severe material deprivation or living in households with low work intensity ", "formula"=>"$$AROPE_{NAC}^{t} = \\frac{PRPES_{NAC}^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$PRPES_{NAC}^{t} =$ population at risk of poverty or social exclusion in year $t$, considering the national poverty line \n\n$P^{t} =$ total population in year $t$\n", "periodicidad"=>"Bienal", "observaciones"=>"The indicator measures the proportion of people who meet at least one of the following criteria:\n\n<b>People at risk of relative poverty:</b> people with income per consumption unit below 60% of the median (modified OECD scale) \n\nThe number of consumption units in a household \nis calculated using the modified OECD scale, which assigns a weight of 1 to the \nfirst person aged 14 or over, a weight of 0.5 for other people aged 14 or over \nand a weight of 0.3 for people under 14 years of age. \n\n<b>People in situations of severe material and social deprivation:</b> people who suffer from at least seven \nof the following list of thirteen limitations (seven defined at the household level and six at the individual level): \n\nAt the household level:\n 1. You can't afford to go on vacation for at least one week a year.\n 2. You cannot afford a meal of meat, chicken or fish at least every other day.\n 3. You cannot afford to keep the home at an adequate temperature.\n 4. You do not have the capacity to face unforeseen expenses.\n 5. You have had delays in the payment of expenses related to the main home (mortgage or rent, gas bills, community...) \n or in installment purchases in the last 12 months.\n 6. You can't afford a car.\n 7. You cannot replace damaged or old furniture.\n\nAt the individual level:\n 8. You cannot afford to replace damaged clothes with new ones.\n 9. You can't afford to have two pairs of shoes in good condition.\n 10. You can't afford to meet friends/family for food or drinks at least once a month.\n 11. You cannot afford to regularly participate in leisure activities.\n 12. You can't afford to spend a small amount of money on yourself.\n 13. Cannot afford internet connection.\n\nIn the case of minors under 16 years of age, the six concepts listed above are not available at the individual level. \nFor these minors, the values of these elements are imputed from the values collected for the members of their household aged 16 or over. \n\n<b>Low work intensity:</b> these are households in which their working age members (people from 18 to 64 years old, excluding \nstudents from 18 to 24 years old, retirees or retirees, as well as inactive people between 60 and 64 whose main source of household income \nare pensions) worked less than 20% of their total work potential during the reference year. \nThis variable does not apply for people aged 65 and over. \n\nData from 2022 onwards for the AROPE Poverty and Exclusion Indicator are calculated using the new definition of the rate (Eurostat \nmethodology from 2021), while previous data correspond to the old definition. \n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.2- De aquí a 2030, reducir al menos a la mitad la proporción de hombres, mujeres y niños de todas las edades que viven en la pobreza en todas sus dimensiones con arreglo a las definiciones nacionales", "definicion"=>"Pobreziako edo gizarte-bazterketako arriskuaren hiru irizpideetako bat gutxienez  betetzen duten pertsonen proportzioa: pobrezia erlatiboko arriskuan egotea  (pobrezia-atalase nazionalaren arabera), gabezia material eta sozial larriko  egoeran egotea, edo lan-intentsitate txikiko etxe batean bizitzea ", "formula"=>"$$AROPE_{NAC}^{t} = \\frac{PRPES_{NAC}^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$PRPES_{NAC}^{t} =$ pobreziako edo gizarte-bazterketako arrisku erlatiboan dagoen biztanleria $t$ urtean, pobrezia-atalase nazionalaren arabera \n\n$P^{t} =$ biztanleria osoa $t$ urtean\n", "periodicidad"=>"Bienal", "observaciones"=>"Adierazleak irizpide hauetako bat gutxienez betetzen duten pertsonen proportzioa neurtzen du:\n\n<b>Pobrezia erlatiboaren arriskuan dauden pertsonak:</b> kontsumo-unitate bakoitzeko diru-sarrerak \nmedianaren % 60tik behera dituzten pertsonak (ELGA eskala aldatua).\n\nEtxeko kontsumo-unitateen kopurua kalkulatzeko, ELGA eskala aldatua erabiltzen da. \nEskala horrek 1 pisua esleitzen dio 14 urte edo gehiagoko lehen pertsonari, 0,5 pisua 14 urte edo \ngehiagoko gainerako pertsonei eta 0,3 pisua 14 urtetik beherako pertsonei.</p>\n\n<b>Gabezia material eta sozial larria duten pertsonak:</b> ondoko hamahiru egoeretatik gutxienez \nzazpi pairatzen dituzten pertsonak; zazpi egoera etxeari dagokizkonak dira eta sei pertsonari dagozkionak:\n\n Etxeari dagozkionak: \n 1. Ezin du urtean gutxienez astebetez oporretara joan. \n 2. Ezin du gutxienez bi egunetik behin haragi-, oilasko- edo arrain-otordurik egin.\n 3. Ezin du etxebizitza tenperatura egokian mantendu.\n 4. Ez du ustekabeko gastuei aurre egiteko gaitasunik.\n 5. Atzerapenak izan ditu etxebizitza nagusiarekin lotutako gastuen ordainketan \n(hipoteka edo alokairua, gas-ordainagiriak, komunitatea, etab.) edo epeka egindako \nerosketetan azken 12 hilabeteetan.\n 6. Ezin du kotxerik eduki.\n 7. Ezin ditu ordezkatu hondatutako altzariak edo zaharrak.\n\nPertsonari dagozkionak:\n 8. Ezin du hondatutako arroparen ordez berria erosi.\n 9. Ezin ditu egoera onean dauden bi zapata pare eduki.\n 10. Ezin du zerbait jateko edo edateko lagunekin/familiarekin elkartu hilean behin gutxienez.\n 11. Ezin du aldizka aisialdiko jardueretan parte hartu.\n 12. Ezin du bere buruarentzako gauzetan diru kopuru txiki bat gastatu.\n 13. Ezin du Interneteko konexiorik ordaindu.\n\n16 urtetik beherakoen kasuan, pertsonari dagozkion azken sei kontzeptuak ez daude eskuragarri. \nAdingabe horien kasuan, elementu horien balioak 16 urte edo gehiago dituzten etxeko kideentzat \njasotako balioetatik abiatuta egozten dira.\n\n<b>Lan-intentsitate txikia:</b> etxe horietako lan egiteko adinean dauden kideek beren lan-potentzial osoaren % 20 \nbaino gutxiago lan egin zuten erreferentziako urtean; lan egiteko adinean dauden kidetzat hartzen dira 18 eta 64 \nurteko arteko pertsonak (salbu eta 18 eta 24 urte bitarteko ikasleak, erretiratuak, eta 60-64 urte bitarteko \npertsona ez-aktiboak, horien etxeko diru-sarreren iturri nagusia pentsioak badira). Aldagai hori ez da aplikatzen \n65 urte edo gehiagoko pertsonen kasuan.\n\nAROPE pobreziaren eta bazterketaren adierazlearen 2022tik aurrerako datuak tasaren definizio berriarekin kalkulatzen \ndira (2021eko Eurostat metodologia); aurreko datuak, berriz, lehengo definizioari dagozkio. \n", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "indicator_sort_order"=>"OCECA-1-2-2-1A", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"OCECA.1.a.2.1", "slug"=>"OCECA-1-a-2-1", "name"=>"Proporción del gasto de las administraciones públicas autonómicas que se dedica a educación", "url"=>"/site/es/OCECA-1-a-2-1/", "sort"=>"OCECA01aa0201", "goal_number"=>"OCECA", "target_number"=>"OCECA.1", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción del gasto de las administraciones públicas autonómicas que se dedica a educación", "indicator_number"=>"OCECA-1-a-2-1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"OCECA-1-a-2-1", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Bienal", "url"=>"https://www.eustat.eus/estadisticas/tema_302/opt_0/ti_cuenta-de-la-educacion/temas.html", "url_text"=>"Cuenta de la Educación", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_149/opt_1/ti_cuentas-de-las-administraciones-publicas/temas.html", "url_text"=>"Cuenta de las Administraciones Públicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_151/opt_1/ti_estadisticas-presupuestarias-del-sector-publico/temas.html", "url_text"=>"Estadística presupuestaria del sector público", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>true, "tags"=>[], "x_axis_label"=>"", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.a- Garantizar una movilización significativa de recursos procedentes de diversas fuentes, incluso mediante la mejora de la cooperación para el desarrollo, a fin de proporcionar medios suficientes y previsibles a los países en desarrollo, en particular los países menos adelantados, para que implementen programas y políticas encaminados a poner fin a la pobreza en todas sus dimensiones", "definicion"=>"Proporción del gasto de las administraciones públicas autonómicas que se dedica a educación (grupo funcional 09 de la Clasificación de las Funciones de las Administraciones Públicas (COFOG), utilizada a nivel internacional para clasificar los propósitos de las actividades gubernamentales)", "formula"=>"\n$$PG_{educación}^{t} = \\frac{G_{educación}^{t}}{G^{t}} \\cdot 100$$\n\ndonde:\n\n$G_{educación}^{t} =$ gasto de las administraciones públicas autonómicas que se dedica a educación (grupo funcional 09 de la Clasificación de las Funciones de las Administraciones Públicas -COFOG-) en el año $t$\n\n$G^{t} =$ gasto total de las administraciones públicas autonómicas en el año $t$\n", "desagregacion"=>"", "observaciones"=>"\nLa Clasificación de las Funciones de las Administraciones Públicas (COFOG), desarrollada por la \nOrganización para la Cooperación y el Desarrollo Económicos (OCDE) y publicada \npor la División de Estadística de las Naciones Unidas, estructura el gasto público\nen 10 grupos funcionales:\n\n- 01 Servicios públicos generales\n- 02 Defensa\n- 03 Orden público y seguridad\n- 04 Asuntos económicos\n- 05 Protección del medio ambiente\n- 06 Vivienda y servicios comunitarios\n- 07 Salud\n- 08 Ocio, cultura y religión\n- 09 Educación\n- 10 Protección social\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-03-15", "en"=>{"objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.a- Garantizar una movilización significativa de recursos procedentes de diversas fuentes, incluso mediante la mejora de la cooperación para el desarrollo, a fin de proporcionar medios suficientes y previsibles a los países en desarrollo, en particular los países menos adelantados, para que implementen programas y políticas encaminados a poner fin a la pobreza en todas sus dimensiones", "definicion"=>"Proportion of the expenditure of the autonomous administration that is dedicated to education (functional group 09 of the Classifications of the  Functions of Government (COFOG), used internationally to classify the purposes of government activities)", "formula"=>"\n$$PG_{education}^{t} = \\frac{G_{education}^{t}}{G^{t}} \\cdot 100$$\n\nwhere:\n\n$G_{educationn}^{t} =$ expenditure of the autonomous administration dedicated to education (functional group 09 of the Classifications of the Functions of Government (COFOG) in yea $t$\n\n$G^{t} =$ total expenditure of the autonomous public administration in yea $t$\n", "desagregacion"=>"", "observaciones"=>"\nThe Classifications of the Functions of Government (COFOG), developed by the Organization for Economic Cooperation and Development \n(OECD) and published by the United Nations Statistics Division, structures public spending into 10 functional groups: \n\n- 01 General public services\n- 02 Defense\n- 03 Public order and security\n- 04 Economic affairs\n- 05 Environmental protection\n- 06 Housing and community services\n- 07 Health\n- 08 Leisure, culture and religion\n- 09 Education\n- 10 Social protection\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.a- Garantizar una movilización significativa de recursos procedentes de diversas fuentes, incluso mediante la mejora de la cooperación para el desarrollo, a fin de proporcionar medios suficientes y previsibles a los países en desarrollo, en particular los países menos adelantados, para que implementen programas y políticas encaminados a poner fin a la pobreza en todas sus dimensiones", "definicion"=>"Hezkuntzara bideratutako herri-administrazio autonomikoen gastuaren proportzioa  (09 talde funtzionala, Herri-Administrazioen Funtzioen Sailkapenean (COFOG),  nazioartean gobernuaren jardueren helburuak sailkatzeko erabiltzen dena) ", "formula"=>"\n$$PG_{hezkuntza}^{t} = \\frac{G_{hezkuntza}^{t}}{G^{t}} \\cdot 100$$\n\nnon:\n\n$G_{hezkuntza}^{t} =$ hezkuntzara bideratutako herri-administrazio autonomikoen gastua  \n(09 talde funtzionala Herri-Administrazioen Funtzioen Sailkapenean (COFOG) $t$ urtean\n\n$G^{t} =$ herri-administrazio autonomikoen guztizko gastua $t$ urtean\n", "desagregacion"=>"", "observaciones"=>"\nHerri-Administrazioen Funtzioen Sailkapenak (COFOG), Ekonomia Lankidetza eta Garapenerako Erakundeak (ELGA) \ngaratu eta Nazio Batuen Estatistika Atalak argitaratutakoak, gastu publikoa 10 talde funtzionaletan \negituratzen du:\n\n- 01 Zerbitzu publiko orokorrak\n- 02 Defentsa\n- 03 Ordena publikoa eta segurtasuna\n- 04 Gai ekonomikoak\n- 05 Ingurumena babestea\n- 06 Etxebizitza eta zerbitzu komunitarioak\n- 07 Osasuna\n- 08 Aisia, kultura eta erlijioa\n- 09 Hezkuntza\n- 10 Gizarte-babesa\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "indicator_sort_order"=>"OCECA-1-a-2-1", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"OCECA.1.a.2.2", "slug"=>"OCECA-1-a-2-2", "name"=>"Proporción del gasto de las administraciones públicas autonómicas que se dedica a salud", "url"=>"/site/es/OCECA-1-a-2-2/", "sort"=>"OCECA01aa0202", "goal_number"=>"OCECA", "target_number"=>"OCECA.1", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción del gasto de las administraciones públicas autonómicas que se dedica a salud", "indicator_number"=>"OCECA-1-a-2-2", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"OCECA-1-a-2-2", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Bienal", "url"=>"https://www.eustat.eus/estadisticas/tema_121/opt_0/ti_cuenta-de-la-salud/temas.html", "url_text"=>"Cuenta de la Salud", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_149/opt_1/ti_cuentas-de-las-administraciones-publicas/temas.html", "url_text"=>"Cuenta de las Administraciones Públicas", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}, {"organisation"=>"Eustat-Instituto Vasco de Estadística", "periodicity"=>"Anual", "url"=>"https://www.eustat.eus/estadisticas/tema_151/opt_1/ti_estadisticas-presupuestarias-del-sector-publico/temas.html", "url_text"=>"Estadística presupuestaria del sector público", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Eustat.png?raw=true"}], "standalone"=>true, "tags"=>[], "x_axis_label"=>"", "objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.a- Garantizar una movilización significativa de recursos procedentes de diversas fuentes, incluso mediante la mejora de la cooperación para el desarrollo, a fin de proporcionar medios suficientes y previsibles a los países en desarrollo, en particular los países menos adelantados, para que implementen programas y políticas encaminados a poner fin a la pobreza en todas sus dimensiones", "definicion"=>"Proporción del gasto de las administraciones públicas autonómicas que se dedica a salud (grupo funcional 07 de la Clasificación de las Funciones de las Administraciones Públicas -COFOG-), utilizada a nivel internacional para clasificar los propósitos de las actividades gubernamentales)", "formula"=>"\n$$PG_{salud}^{t} = \\frac{G_{salud}^{t}}{G^{t}} \\cdot 100$$\n\ndonde:\n\n$G_{salud}^{t} =$ gasto de las administraciones públicas autonómicas que se dedica a salud (grupo funcional 07 de la Clasificación de las Funciones de las Administraciones Públicas -COFOG-) en el año $t$\n\n$G^{t} =$ gasto total de las administraciones públicas autonómicas en el año $t$\n", "desagregacion"=>"", "observaciones"=>"\nLa Clasificación de las Funciones de las Administraciones Públicas -COFOG-, desarrollada por la \nOrganización para la Cooperación y el Desarrollo Económicos (OCDE) y publicada \npor la División de Estadística de las Naciones Unidas, estructura el gasto público\nen 10 grupos funcionales:\n\n01 Servicios públicos generales\n02 Defensa\n03 Orden público y seguridad\n04 Asuntos económicos\n05 Protección del medio ambiente\n06 Vivienda y servicios comunitarios\n07 Salud\n08 Ocio, cultura y religión\n09 Educación\n10 Protección social\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-29", "en"=>{"objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.a- Garantizar una movilización significativa de recursos procedentes de diversas fuentes, incluso mediante la mejora de la cooperación para el desarrollo, a fin de proporcionar medios suficientes y previsibles a los países en desarrollo, en particular los países menos adelantados, para que implementen programas y políticas encaminados a poner fin a la pobreza en todas sus dimensiones", "definicion"=>"Proportion of the expenditure of the autonomous public administration that is dedicated to health (functional group 07 of the Classifications of the  Functions of Government (COFOG), used internationally to classify the purposes of government activities)", "formula"=>"\n$$PG_{health}^{t} = \\frac{G_{health}^{t}}{G^{t}} \\cdot 100$$\n\nwhere:\n\n$G_{salud}^{t} =$ expenditure of the autonomous public administration dedicated to health (functional group 07 of the Classifications of the Functions of Government (COFOG) in year $t$\n\n$G^{t} =$  total expenditure of the autonomous public administration in year $t$\n", "desagregacion"=>"", "observaciones"=>"\nThe Classifications of the Functions of Government (COFOG), developed by the Organization for Economic Cooperation and Development \n(OECD) and published by the United Nations Statistics Division, structures public spending into 10 functional groups: \n\n- 01 General public services\n- 02 Defense\n- 03 Public order and security\n- 04 Economic affairs\n- 05 Environmental protection\n- 06 Housing and community services\n- 07 Health\n- 08 Leisure, culture and religion\n- 09 Education\n- 10 Social protection\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "eu"=>{"objetivo_global"=>"1- Poner fin a la pobreza en todas sus formas y en todo el mundo", "meta_global"=>"1.a- Garantizar una movilización significativa de recursos procedentes de diversas fuentes, incluso mediante la mejora de la cooperación para el desarrollo, a fin de proporcionar medios suficientes y previsibles a los países en desarrollo, en particular los países menos adelantados, para que implementen programas y políticas encaminados a poner fin a la pobreza en todas sus dimensiones", "definicion"=>"Osasunera bideratutako herri-administrazio autonomikoen gastuaren proportzioa (07 talde funtzionala, Herri-Administrazioen Funtzioen Sailkapenean (COFOG),  nazioartean gobernuaren jardueren helburuak sailkatzeko erabiltzen dena) ", "formula"=>"\n$$PG_{osasuna}^{t} = \\frac{G_{osasuna}^{t}}{G^{t}} \\cdot 100$$\n\nnon:\n\n$G_{osasuna}^{t} =$ osasunera bideratutako herri-administrazio autonomikoen gastua  \n(07 talde funtzionala Herri-Administrazioen Funtzioen Sailkapenean (COFOG) $t$ urtean\n\n$G^{t} =$ herri-administrazio autonomikoen guztizko gastua $t$ urtean\n", "desagregacion"=>"", "observaciones"=>"\nHerri-Administrazioen Funtzioen Sailkapenak (COFOG), Ekonomia Lankidetza eta Garapenerako Erakundeak (ELGA) \ngaratu eta Nazio Batuen Estatistika Atalak argitaratutakoak, gastu publikoa 10 talde funtzionaletan \negituratzen du:\n\n- 01 Zerbitzu publiko orokorrak\n- 02 Defentsa\n- 03 Ordena publikoa eta segurtasuna\n- 04 Gai ekonomikoak\n- 05 Ingurumena babestea\n- 06 Etxebizitza eta zerbitzu komunitarioak\n- 07 Osasuna\n- 08 Aisia, kultura eta erlijioa\n- 09 Hezkuntza\n- 10 Gizarte-babesa\n", "periodicidad"=>"Anual", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "informacion_interes"=>nil, "national_data_updated_date"=>"2024-07-29", "national_metadata_updated_date"=>"2024-07-29"}, "indicator_sort_order"=>"OCECA-1-a-2-2", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"OCECA.6.1.1", "slug"=>"OCECA-6-1-1", "name"=>"Proporción de personas en zonas de abastecimiento que notifican al Sistema de Información Nacional de Agua de Consumo (SINAC)", "url"=>"/site/es/OCECA-6-1-1/", "sort"=>"OCECA060101", "goal_number"=>"OCECA", "target_number"=>"OCECA.6", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción de personas en zonas de abastecimiento que notifican al Sistema de Información Nacional de Agua de Consumo (SINAC)", "indicator_number"=>"OCECA-6-1-1", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"OCECA-6-1-1", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Ministerio de Sanidad", "periodicity"=>"Anual", "url"=>"https://www.miteco.gob.es/es/agua/temas/estado-y-calidad-de-las-aguas/aguas-superficiales/programas-seguimiento/control-adiciona-zonas-protegidas-abastecimientos.html", "url_text"=>"Calidad de las Aguas de Consumo Humano", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/GE.png?raw=true"}], "standalone"=>true, "tags"=>[], "x_axis_label"=>"", "objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.1- De aquí a 2030, lograr el acceso universal y equitativo al agua potable a un precio asequible para todos", "definicion"=>"Proporción de personas en zonas de abastecimiento que notifican al Sistema de Información Nacional de Agua de Consumo (SINAC)\n", "formula"=>"\n$$PPC^{t} = \\frac{PC^{t}}{P^{t}} \\cdot 100$$\n\ndonde:\n\n$PC^{t} =$ población en zonas de abastecimiento que notifican al Sistema Nacional de Información de Agua de Consumo en el año $t$\n\n$P{t} =$ población a 1 de enero del año $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"\nValores de 100 o próximos a esta cifra deben ser tomados con cautela, ya que se pueden deber a la doble contabilización de la población asociada a zonas que se han visto afectadas por fusiones de zonas de abastecimiento", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "informacion_interes"=>"", "national_data_updated_date"=>"2025-04-22", "national_metadata_updated_date"=>"2025-04-14", "en"=>{"objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.1- De aquí a 2030, lograr el acceso universal y equitativo al agua potable a un precio asequible para todos", "definicion"=>"Proportion of people in supply areas that report to the National Drinking Water Information System (SINAC)\n", "formula"=>"\n$$PPC^{t} = \\frac{PC^{t}}{P^{t}} \\cdot 100$$\n\nwhere:\n\n$PC^{t} =$ population in supply areas that report to the National Drinking Water Information System in year $t$\n\n$P{t} =$ population as of January 1 of year $t$\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"\nValues of 100 or close to this figure should be taken with caution, as they may be due to double counting of the population associated with areas that have been affected by mergers of supply zones.", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "eu"=>{"objetivo_global"=>"6- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos", "meta_global"=>"6.1- De aquí a 2030, lograr el acceso universal y equitativo al agua potable a un precio asequible para todos", "definicion"=>"Kontsumorako Uraren Informazio Sistema Nazionalari (SINAC) jakinarazitako hornidura-eremuetako pertsonen proportzioa \n", "formula"=>"\n$$PPC^{t} = \\frac{PC^{t}}{P^{t}} \\cdot 100$$\n\nnon:\n\n$PC^{t} =$ kontsumorako Uraren Informazio Sistema Nazionalari (SINAC) jakinarazitako hornidura-eremuetako pertsonak $t$ urtean\n\n$P{t} =$ biztanleria $t$ urteko urtarrilaren 1ean\n", "desagregacion"=>"", "periodicidad"=>"Anual", "observaciones"=>"\n100eko balioak edo kopuru horretatik hurbil daudenak zuhurtziaz hartu behar dira; izan ere, hornidura-eremuen  bat-egiteen eraginez eremu horietako biztanleria bi aldiz kontabilizatu izanaren ondorio izan daiteke. ", "texto_oceca"=>"<img src=\"/site/assets/img/oceca/ocecas_image.png\" style=\"width: 200px; float: left; margin-right: 10px;\" alt=\"Logotipo OCECAS\"> Indicador calculado utilizando una metodología armonizada entre los órganos centrales de estadística de las comunidades autónomas.", "informacion_interes"=>nil, "national_data_updated_date"=>"", "national_metadata_updated_date"=>""}, "indicator_sort_order"=>"OCECA-6-1-1", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"OCECA.9.4.1.5", "slug"=>"OCECA-9-4-1-5", "name"=>"Emisiones de CO2 respecto al año 1990", "url"=>"/site/es/OCECA-9-4-1-5/", "sort"=>"OCECA09040105", "goal_number"=>"OCECA", "target_number"=>"OCECA.9", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Emisiones de CO2 respecto al año 1990", "indicator_number"=>"OCECA-9-4-1-5", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"OCECA-9-4-1-5", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/inventario-de-gases-de-efecto-invernadero-090205/web01-a2ingair/es/", "url_text"=>"Inventario de gases de efecto invernadero", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>true, "tags"=>[], "x_axis_label"=>"", "indicator_sort_order"=>"OCECA-9-4-1-5", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"OCECA.9.4.1.6", "slug"=>"OCECA-9-4-1-6", "name"=>"Emisiones de CO2 respecto al año 2005", "url"=>"/site/es/OCECA-9-4-1-6/", "sort"=>"OCECA09040106", "goal_number"=>"OCECA", "target_number"=>"OCECA.9", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Emisiones de CO2 respecto al año 2005", "indicator_number"=>"OCECA-9-4-1-6", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"OCECA-9-4-1-6", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Industria, Transición Energética y Sostenibilidad", "periodicity"=>"Anual", "url"=>"https://www.euskadi.eus/informacion/inventario-de-gases-de-efecto-invernadero-090205/web01-a2ingair/es/", "url_text"=>"Inventario de gases de efecto invernadero", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}], "standalone"=>true, "tags"=>[], "x_axis_label"=>"", "indicator_sort_order"=>"OCECA-9-4-1-6", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}, {"number"=>"OCECA.10.2.1.1A", "slug"=>"OCECA-10-2-1-1A", "name"=>"Proporción de personas que viven por debajo del 50% de la mediana de los ingresos, considerando la mediana nacional", "url"=>"/site/es/OCECA-10-2-1-1A/", "sort"=>"OCECA1002011A", "goal_number"=>"OCECA", "target_number"=>"OCECA.10", "global"=>{}, "composite_breakdown_label"=>"", "computation_units"=>"", "copyright"=>"", "data_footnote"=>"", "data_non_statistical"=>false, "data_notice_class"=>"", "data_notice_heading"=>"", "data_notice_text"=>"", "data_show_map"=>false, "data_start_values"=>"", "embedded_feature_footer"=>"", "embedded_feature_html"=>"", "embedded_feature_tab_title"=>"", "embedded_feature_title"=>"", "embedded_feature_url"=>"", "expected_disaggregations"=>[], "footer_fields"=>[], "graph_annotations"=>[], "graph_limits"=>[], "graph_stacked_disaggregation"=>"", "graph_target_lines"=>[], "graph_title"=>"", "graph_titles"=>[], "graph_type"=>"line", "indicator_available"=>"", "indicator_name"=>"Proporción de personas que viven por debajo del 50% de la mediana de los ingresos, considerando la mediana nacional", "indicator_number"=>"OCECA-10-2-1-1A", "indicator_tabs"=>{"override"=>false, "tab_1"=>"", "tab_2"=>"", "tab_3"=>"", "tab_4"=>""}, "national_geographical_coverage"=>"", "page_content"=>"", "permalink"=>"OCECA-10-2-1-1A", "placeholder"=>"", "precision"=>[], "proxy"=>"", "proxy_series"=>[], "publications"=>[], "related_indicators"=>[], "reporting_status"=>"complete", "sources"=>[{"organisation"=>"Departamento de Bienestar, Juventud y Reto Demográfico", "periodicity"=>"Bienal", "url"=>"https://www.euskadi.eus/encuesta-de-pobreza-y-desigualdades-sociales-epds/web01-s2enple/es/", "url_text"=>"Encuesta de pobreza y desigualdades sociales (EPDS)", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/Euskadi.png?raw=true"}, {"organisation"=>"Instituto Nacional de Estadística (INE)", "periodicity"=>"Anual", "url"=>"https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176807&menu=ultiDatos&idp=1254735976608", "url_text"=>"Encuesta de condiciones de vida", "logo"=>"https://github.com/EUSTAT-DES/site/blob/develop/assets/img/fuentes/INE.png?raw=true"}], "standalone"=>true, "tags"=>[], "x_axis_label"=>"", "indicator_sort_order"=>"OCECA-10-2-1-1A", "indicator_tabs_list"=>[{"type"=>"chart", "label"=>"indicator.chart"}, {"type"=>"table", "label"=>"indicator.table"}]}]
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Indicadores desagregados por territorio histórico

Objetivo OCECA
